%%% -*-BibTeX-*- %%% ==================================================================== %%% BibTeX-file{ %%% author = "Nelson H. F. Beebe", %%% version = "1.53", %%% date = "02 February 2026", %%% time = "08:25:58 MDT", %%% filename = "tist.bib", %%% address = "University of Utah %%% Department of Mathematics, 110 LCB %%% 155 S 1400 E RM 233 %%% Salt Lake City, UT 84112-0090 %%% USA", %%% telephone = "+1 801 581 5254", %%% URL = "https://www.math.utah.edu/~beebe", %%% checksum = "19009 51694 266922 2530409", %%% email = "beebe at math.utah.edu, beebe at acm.org, %%% beebe at computer.org (Internet)", %%% codetable = "ISO/ASCII", %%% keywords = "bibliography; BibTeX; ACM Transactions on %%% Intelligent Systems and Technology (TIST)", %%% license = "public domain", %%% supported = "yes", %%% docstring = "This is a COMPLETE BibTeX bibliography for %%% the journal ACM Transactions on Intelligent %%% Systems and Technology (TIST) (CODEN ????, %%% ISSN 2157-6904 (print), 2157-6912 %%% (electronic)), covering all journal issues from %%% 2010 -- date. %%% %%% At version 1.53, the COMPLETE journal %%% coverage looked like this: %%% %%% 2010 ( 15) 2016 ( 68) 2022 ( 105) %%% 2011 ( 60) 2017 ( 82) 2023 ( 114) %%% 2012 ( 59) 2018 ( 59) 2024 ( 135) %%% 2013 ( 95) 2019 ( 65) 2025 ( 147) %%% 2014 ( 30) 2020 ( 73) 2026 ( 25) %%% 2015 ( 86) 2021 ( 82) %%% %%% Article: 1300 %%% %%% Total entries: 1300 %%% %%% The journal Web page can be found at: %%% %%% http://www.acm.org/pubs/tist %%% http://portal.acm.org/citation.cfm?id=J1318 %%% %%% The journal table of contents page is at: %%% %%% http://www.acm.org/pubs/contents/journals/tist/ %%% %%% The initial draft was extracted from the %%% journal Web site. %%% %%% ACM copyrights explicitly permit abstracting %%% with credit, so article abstracts, keywords, %%% and subject classifications have been %%% included in this bibliography wherever %%% available. Article reviews have been %%% omitted, until their copyright status has %%% been clarified. %%% %%% URL keys in the bibliography point to %%% World Wide Web locations of additional %%% information about the entry. %%% %%% Numerous errors in the sources noted above %%% have been corrected. Spelling has been %%% verified with the UNIX spell and GNU ispell %%% programs using the exception dictionary %%% stored in the companion file with extension %%% .sok. %%% %%% BibTeX citation tags are uniformly chosen %%% as name:year:abbrev, where name is the %%% family name of the first author or editor, %%% year is a 4-digit number, and abbrev is a %%% 3-letter condensation of important title %%% words. Citation tags were automatically %%% generated by software developed for the %%% BibNet Project. %%% %%% In this bibliography, entries are sorted in %%% publication order, using ``bibsort -byvolume.'' %%% %%% The checksum field above contains a CRC-16 %%% checksum as the first value, followed by the %%% equivalent of the standard UNIX wc (word %%% count) utility output of lines, words, and %%% characters. This is produced by Robert %%% Solovay's checksum utility.", %%% } %%% ==================================================================== @Preamble{"\input bibnames.sty"} %%% ==================================================================== %%% Acknowledgement abbreviations: @String{ack-nhfb = "Nelson H. F. Beebe, University of Utah, Department of Mathematics, 110 LCB, 155 S 1400 E RM 233, Salt Lake City, UT 84112-0090, USA, Tel: +1 801 581 5254, e-mail: \path|beebe@math.utah.edu|, \path|beebe@acm.org|, \path|beebe@computer.org| (Internet), URL: \path|https://www.math.utah.edu/~beebe/|"} %%% ==================================================================== %%% Journal abbreviations: @String{j-TIST = "ACM Transactions on Intelligent Systems and Technology (TIST)"} %%% ==================================================================== %%% Bibliography entries: @Article{Yang:2010:IAT, author = "Qiang Yang", title = "Introduction to {ACM TIST}", journal = j-TIST, volume = "1", number = "1", pages = "1:1--1:??", month = oct, year = "2010", CODEN = "????", DOI = "https://doi.org/10.1145/1858948.1858949", ISSN = "2157-6904 (print), 2157-6912 (electronic)", bibdate = "Tue Nov 23 12:18:28 MST 2010", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "1", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Liu:2010:IAT, author = "Huan Liu and Dana Nau", title = "Introduction to the {ACM TIST} special issue {AI} in social computing and cultural modeling", journal = j-TIST, volume = "1", number = "1", pages = "2:1--2:??", month = oct, year = "2010", CODEN = "????", DOI = "https://doi.org/10.1145/1858948.1858950", ISSN = "2157-6904 (print), 2157-6912 (electronic)", bibdate = "Tue Nov 23 12:18:28 MST 2010", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "2", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Bainbridge:2010:VWC, author = "William Sims Bainbridge", title = "Virtual worlds as cultural models", journal = j-TIST, volume = "1", number = "1", pages = "3:1--3:??", month = oct, year = "2010", CODEN = "????", DOI = "https://doi.org/10.1145/1858948.1858951", ISSN = "2157-6904 (print), 2157-6912 (electronic)", bibdate = "Tue Nov 23 12:18:28 MST 2010", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "3", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Feldman:2010:SCR, author = "Michal Feldman and Moshe Tennenholtz", title = "Structured coalitions in resource selection games", journal = j-TIST, volume = "1", number = "1", pages = "4:1--4:??", month = oct, year = "2010", CODEN = "????", DOI = "https://doi.org/10.1145/1858948.1858952", ISSN = "2157-6904 (print), 2157-6912 (electronic)", bibdate = "Tue Nov 23 12:18:28 MST 2010", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "4", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Wu:2010:OFU, author = "Fang Wu and Bernardo A. Huberman", title = "Opinion formation under costly expression", journal = j-TIST, volume = "1", number = "1", pages = "5:1--5:??", month = oct, year = "2010", CODEN = "????", DOI = "https://doi.org/10.1145/1858948.1858953", ISSN = "2157-6904 (print), 2157-6912 (electronic)", bibdate = "Tue Nov 23 12:18:28 MST 2010", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "5", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Roos:2010:ESD, author = "Patrick Roos and J. Ryan Carr and Dana S. Nau", title = "Evolution of state-dependent risk preferences", journal = j-TIST, volume = "1", number = "1", pages = "6:1--6:??", month = oct, year = "2010", CODEN = "????", DOI = "https://doi.org/10.1145/1858948.1858954", ISSN = "2157-6904 (print), 2157-6912 (electronic)", bibdate = "Tue Nov 23 12:18:28 MST 2010", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "6", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Goolsby:2010:SMC, author = "Rebecca Goolsby", title = "Social media as crisis platform: The future of community maps\slash crisis maps", journal = j-TIST, volume = "1", number = "1", pages = "7:1--7:??", month = oct, year = "2010", CODEN = "????", DOI = "https://doi.org/10.1145/1858948.1858955", ISSN = "2157-6904 (print), 2157-6912 (electronic)", bibdate = "Tue Nov 23 12:18:28 MST 2010", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "7", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Wang:2010:AIS, author = "Meng Wang and Bo Liu and Xian-Sheng Hua", title = "Accessible image search for colorblindness", journal = j-TIST, volume = "1", number = "1", pages = "8:1--8:??", month = oct, year = "2010", CODEN = "????", DOI = "https://doi.org/10.1145/1858948.1858956", ISSN = "2157-6904 (print), 2157-6912 (electronic)", bibdate = "Tue Nov 23 12:18:28 MST 2010", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "8", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Chen:2010:PSI, author = "Yixin Chen", title = "Preface to special issue on applications of automated planning", journal = j-TIST, volume = "1", number = "2", pages = "9:1--9:??", month = nov, year = "2010", CODEN = "????", DOI = "https://doi.org/10.1145/1869397.1869398", ISSN = "2157-6904 (print), 2157-6912 (electronic)", bibdate = "Wed Jan 26 14:40:50 MST 2011", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "9", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Porteous:2010:API, author = "Julie Porteous and Marc Cavazza and Fred Charles", title = "Applying planning to interactive storytelling: Narrative control using state constraints", journal = j-TIST, volume = "1", number = "2", pages = "10:1--10:??", month = nov, year = "2010", CODEN = "????", DOI = "https://doi.org/10.1145/1869397.1869399", ISSN = "2157-6904 (print), 2157-6912 (electronic)", bibdate = "Wed Jan 26 14:40:50 MST 2011", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "10", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Bryce:2010:PIB, author = "Daniel Bryce and Michael Verdicchio and Seungchan Kim", title = "Planning interventions in biological networks", journal = j-TIST, volume = "1", number = "2", pages = "11:1--11:??", month = nov, year = "2010", CODEN = "????", DOI = "https://doi.org/10.1145/1869397.1869400", ISSN = "2157-6904 (print), 2157-6912 (electronic)", bibdate = "Wed Jan 26 14:40:50 MST 2011", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "11", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Refanidis:2010:CBA, author = "Ioannis Refanidis and Neil Yorke-Smith", title = "A constraint-based approach to scheduling an individual's activities", journal = j-TIST, volume = "1", number = "2", pages = "12:1--12:??", month = nov, year = "2010", CODEN = "????", DOI = "https://doi.org/10.1145/1869397.1869401", ISSN = "2157-6904 (print), 2157-6912 (electronic)", bibdate = "Wed Jan 26 14:40:50 MST 2011", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "12", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Benaskeur:2010:CRT, author = "Abder Rezak Benaskeur and Froduald Kabanza and Eric Beaudry", title = "{CORALS}: a real-time planner for anti-air defense operations", journal = j-TIST, volume = "1", number = "2", pages = "13:1--13:??", month = nov, year = "2010", CODEN = "????", DOI = "https://doi.org/10.1145/1869397.1869402", ISSN = "2157-6904 (print), 2157-6912 (electronic)", bibdate = "Wed Jan 26 14:40:50 MST 2011", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "13", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Talamadupula:2010:PHR, author = "Kartik Talamadupula and J. Benton and Subbarao Kambhampati and Paul Schermerhorn and Matthias Scheutz", title = "Planning for human-robot teaming in open worlds", journal = j-TIST, volume = "1", number = "2", pages = "14:1--14:??", month = nov, year = "2010", CODEN = "????", DOI = "https://doi.org/10.1145/1869397.1869403", ISSN = "2157-6904 (print), 2157-6912 (electronic)", bibdate = "Wed Jan 26 14:40:50 MST 2011", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "14", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Cirillo:2010:HAT, author = "Marcello Cirillo and Lars Karlsson and Alessandro Saffiotti", title = "Human-aware task planning: An application to mobile robots", journal = j-TIST, volume = "1", number = "2", pages = "15:1--15:??", month = nov, year = "2010", CODEN = "????", DOI = "https://doi.org/10.1145/1869397.1869404", ISSN = "2157-6904 (print), 2157-6912 (electronic)", bibdate = "Wed Jan 26 14:40:50 MST 2011", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "15", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Zhang:2011:ISI, author = "Daqing Zhang and Matthai Philipose and Qiang Yang", title = "Introduction to the special issue on intelligent systems for activity recognition", journal = j-TIST, volume = "2", number = "1", pages = "1:1--1:??", month = jan, year = "2011", CODEN = "????", DOI = "https://doi.org/10.1145/1889681.1889682", ISSN = "2157-6904 (print), 2157-6912 (electronic)", bibdate = "Wed Jan 26 14:40:51 MST 2011", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "1", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Zheng:2011:LTR, author = "Yu Zheng and Xing Xie", title = "Learning travel recommendations from user-generated {GPS} traces", journal = j-TIST, volume = "2", number = "1", pages = "2:1--2:??", month = jan, year = "2011", CODEN = "????", DOI = "https://doi.org/10.1145/1889681.1889683", ISSN = "2157-6904 (print), 2157-6912 (electronic)", bibdate = "Wed Jan 26 14:40:51 MST 2011", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "2", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Farrahi:2011:DRL, author = "Katayoun Farrahi and Daniel Gatica-Perez", title = "Discovering routines from large-scale human locations using probabilistic topic models", journal = j-TIST, volume = "2", number = "1", pages = "3:1--3:??", month = jan, year = "2011", CODEN = "????", DOI = "https://doi.org/10.1145/1889681.1889684", ISSN = "2157-6904 (print), 2157-6912 (electronic)", bibdate = "Wed Jan 26 14:40:51 MST 2011", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "3", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Hsu:2011:PMC, author = "Jane Yung-Jen Hsu and Chia-Chun Lian and Wan-Rong Jih", title = "Probabilistic models for concurrent chatting activity recognition", journal = j-TIST, volume = "2", number = "1", pages = "4:1--4:??", month = jan, year = "2011", CODEN = "????", DOI = "https://doi.org/10.1145/1889681.1889685", ISSN = "2157-6904 (print), 2157-6912 (electronic)", bibdate = "Wed Jan 26 14:40:51 MST 2011", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "4", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Zhou:2011:RPA, author = "Yue Zhou and Bingbing Ni and Shuicheng Yan and Thomas S. Huang", title = "Recognizing pair-activities by causality analysis", journal = j-TIST, volume = "2", number = "1", pages = "5:1--5:??", month = jan, year = "2011", CODEN = "????", DOI = "https://doi.org/10.1145/1889681.1889686", ISSN = "2157-6904 (print), 2157-6912 (electronic)", bibdate = "Wed Jan 26 14:40:51 MST 2011", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "5", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Ward:2011:PMA, author = "Jamie A. Ward and Paul Lukowicz and Hans W. Gellersen", title = "Performance metrics for activity recognition", journal = j-TIST, volume = "2", number = "1", pages = "6:1--6:??", month = jan, year = "2011", CODEN = "????", DOI = "https://doi.org/10.1145/1889681.1889687", ISSN = "2157-6904 (print), 2157-6912 (electronic)", bibdate = "Wed Jan 26 14:40:51 MST 2011", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "6", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Wyatt:2011:ICC, author = "Danny Wyatt and Tanzeem Choudhury and Jeff Bilmes and James A. Kitts", title = "Inferring colocation and conversation networks from privacy-sensitive audio with implications for computational social science", journal = j-TIST, volume = "2", number = "1", pages = "7:1--7:??", month = jan, year = "2011", CODEN = "????", DOI = "https://doi.org/10.1145/1889681.1889688", ISSN = "2157-6904 (print), 2157-6912 (electronic)", bibdate = "Wed Jan 26 14:40:51 MST 2011", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "7", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Bao:2011:FRC, author = "Xinlong Bao and Thomas G. Dietterich", title = "{FolderPredictor}: Reducing the cost of reaching the right folder", journal = j-TIST, volume = "2", number = "1", pages = "8:1--8:??", month = jan, year = "2011", CODEN = "????", DOI = "https://doi.org/10.1145/1889681.1889689", ISSN = "2157-6904 (print), 2157-6912 (electronic)", bibdate = "Wed Jan 26 14:40:51 MST 2011", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "8", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Hua:2011:ISI, author = "Xian-Sheng Hua and Qi Tian and Alberto {Del Bimbo} and Ramesh Jain", title = "Introduction to the special issue on intelligent multimedia systems and technology", journal = j-TIST, volume = "2", number = "2", pages = "9:1--9:??", month = feb, year = "2011", CODEN = "????", DOI = "https://doi.org/10.1145/1899412.1899413", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Oct 1 16:23:55 MDT 2015", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "9", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Wang:2011:ALM, author = "Meng Wang and Xian-Sheng Hua", title = "Active learning in multimedia annotation and retrieval: a survey", journal = j-TIST, volume = "2", number = "2", pages = "10:1--10:??", month = feb, year = "2011", CODEN = "????", DOI = "https://doi.org/10.1145/1899412.1899414", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Oct 1 16:23:55 MDT 2015", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Active learning is a machine learning technique that selects the most informative samples for labeling and uses them as training data. It has been widely explored in multimedia research community for its capability of reducing human annotation effort. In this article, we provide a survey on the efforts of leveraging active learning in multimedia annotation and retrieval. We mainly focus on two application domains: image/video annotation and content-based image retrieval. We first briefly introduce the principle of active learning and then we analyze the sample selection criteria. We categorize the existing sample selection strategies used in multimedia annotation and retrieval into five criteria: risk reduction, uncertainty, diversity, density and relevance. We then introduce several classification models used in active learning-based multimedia annotation and retrieval, including semi-supervised learning, multilabel learning and multiple instance learning. We also provide a discussion on several future trends in this research direction. In particular, we discuss cost analysis of human annotation and large-scale interactive multimedia annotation.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "10", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Shao:2011:VIG, author = "Yuanlong Shao and Yuan Zhou and Deng Cai", title = "Variational inference with graph regularization for image annotation", journal = j-TIST, volume = "2", number = "2", pages = "11:1--11:??", month = feb, year = "2011", CODEN = "????", DOI = "https://doi.org/10.1145/1899412.1899415", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Oct 1 16:23:55 MDT 2015", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Image annotation is a typical area where there are multiple types of attributes associated with each individual image. In order to achieve better performance, it is important to develop effective modeling by utilizing prior knowledge. In this article, we extend the graph regularization approaches to a more general case where the regularization is imposed on the factorized variational distributions, instead of posterior distributions implicitly involved in EM-like algorithms. In this way, the problem modeling can be more flexible, and we can choose any factor in the problem domain to impose graph regularization wherever there are similarity constraints among the instances. We formulate the problem formally and show its geometrical background in manifold learning. We also design two practically effective algorithms and analyze their properties such as the convergence. Finally, we apply our approach to image annotation and show the performance improvement of our algorithm.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "11", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Yu:2011:CBS, author = "Jie Yu and Xin Jin and Jiawei Han and Jiebo Luo", title = "Collection-based sparse label propagation and its application on social group suggestion from photos", journal = j-TIST, volume = "2", number = "2", pages = "12:1--12:??", month = feb, year = "2011", CODEN = "????", DOI = "https://doi.org/10.1145/1899412.1899416", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Oct 1 16:23:55 MDT 2015", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Online social network services pose great opportunities and challenges for many research areas. In multimedia content analysis, automatic social group recommendation for images holds the promise to expand one's social network through media sharing. However, most existing techniques cannot generate satisfactory social group suggestions when the images are classified independently. In this article, we present novel methods to produce accurate suggestions of suitable social groups from a user's personal photo collection. First, an automatic clustering process is designed to estimate the group similarities, select the optimal number of clusters and categorize the social groups. Both visual content and textual annotations are integrated to generate initial predictions of the group categories for the images. Next, the relationship among images in a user's collection is modeled as a sparse graph. A collection-based sparse label propagation method is proposed to improve the group suggestions. Furthermore, the sparse graph-based collection model can be readily exploited to select the most influential and informative samples for active relevance feedback, which can be integrated with the label propagation process without the need for classifier retraining. The proposed methods have been tested on group suggestion tasks for real user collections and demonstrated superior performance over the state-of-the-art techniques.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "12", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Wu:2011:DML, author = "Lei Wu and Steven C. H. Hoi and Rong Jin and Jianke Zhu and Nenghai Yu", title = "Distance metric learning from uncertain side information for automated photo tagging", journal = j-TIST, volume = "2", number = "2", pages = "13:1--13:??", month = feb, year = "2011", CODEN = "????", DOI = "https://doi.org/10.1145/1899412.1899417", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Oct 1 16:23:55 MDT 2015", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Automated photo tagging is an important technique for many intelligent multimedia information systems, for example, smart photo management system and intelligent digital media library. To attack the challenge, several machine learning techniques have been developed and applied for automated photo tagging. For example, supervised learning techniques have been applied to automated photo tagging by training statistical classifiers from a collection of manually labeled examples. Although the existing approaches work well for small testbeds with relatively small number of annotation words, due to the long-standing challenge of object recognition, they often perform poorly in large-scale problems. Another limitation of the existing approaches is that they require a set of high-quality labeled data, which is not only expensive to collect but also time consuming. In this article, we investigate a social image based annotation scheme by exploiting implicit side information that is available for a large number of social photos from the social web sites. The key challenge of our intelligent annotation scheme is how to learn an effective distance metric based on implicit side information (visual or textual) of social photos. To this end, we present a novel ``Probabilistic Distance Metric Learning'' (PDML) framework, which can learn optimized metrics by effectively exploiting the implicit side information vastly available on the social web. We apply the proposed technique to photo annotation tasks based on a large social image testbed with over 1 million tagged photos crawled from a social photo sharing portal. Encouraging results show that the proposed technique is effective and promising for social photo based annotation tasks.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "13", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Tang:2011:IAK, author = "Jinhui Tang and Richang Hong and Shuicheng Yan and Tat-Seng Chua and Guo-Jun Qi and Ramesh Jain", title = "Image annotation by {$k$NN}-sparse graph-based label propagation over noisily tagged web images", journal = j-TIST, volume = "2", number = "2", pages = "14:1--14:??", month = feb, year = "2011", CODEN = "????", DOI = "https://doi.org/10.1145/1899412.1899418", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Oct 1 16:23:55 MDT 2015", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "In this article, we exploit the problem of annotating a large-scale image corpus by label propagation over noisily tagged web images. To annotate the images more accurately, we propose a novel k NN-sparse graph-based semi-supervised learning approach for harnessing the labeled and unlabeled data simultaneously. The sparse graph constructed by datum-wise one-vs- k NN sparse reconstructions of all samples can remove most of the semantically unrelated links among the data, and thus it is more robust and discriminative than the conventional graphs. Meanwhile, we apply the approximate k nearest neighbors to accelerate the sparse graph construction without loosing its effectiveness. More importantly, we propose an effective training label refinement strategy within this graph-based learning framework to handle the noise in the training labels, by bringing in a dual regularization for both the quantity and sparsity of the noise. We conduct extensive experiments on a real-world image database consisting of 55,615 Flickr images and noisily tagged training labels. The results demonstrate both the effectiveness and efficiency of the proposed approach and its capability to deal with the noise in the training labels.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "14", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Tong:2011:APL, author = "Xiaofeng Tong and Jia Liu and Tao Wang and Yimin Zhang", title = "Automatic player labeling, tracking and field registration and trajectory mapping in broadcast soccer video", journal = j-TIST, volume = "2", number = "2", pages = "15:1--15:??", month = feb, year = "2011", CODEN = "????", DOI = "https://doi.org/10.1145/1899412.1899419", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Oct 1 16:23:55 MDT 2015", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "In this article, we present a method to perform automatic player trajectories mapping based on player detection, unsupervised labeling, efficient multi-object tracking, and playfield registration in broadcast soccer videos. Player detector determines the players' positions and scales by combining the ability of dominant color based background subtraction and a boosting detector with Haar features. We first learn the dominant color with accumulate color histogram at the beginning of processing, then use the player detector to collect hundreds of player samples, and learn player appearance codebook by unsupervised clustering. In a soccer game, a player can be labeled as one of four categories: two teams, referee or outlier. The learning capability enables the method to be generalized well to different videos without any manual initialization. With the dominant color and player appearance model, we can locate and label each player. After that, we perform multi-object tracking by using Markov Chain Monte Carlo (MCMC) data association to generate player trajectories. Some data driven dynamics are proposed to improve the Markov chain's efficiency, such as label consistency, motion consistency, and track length, etc. Finally, we extract key-points and find the mapping from an image plane to the standard field model, and then map players' position and trajectories to the field. A large quantity of experimental results on FIFA World Cup 2006 videos demonstrate that this method can reach high detection and labeling precision, reliably tracking in scenes of player occlusion, moderate camera motion and pose variation, and yield promising field registration results.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "15", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Liu:2011:NJD, author = "Qingzhong Liu and Andrew H. Sung and Mengyu Qiao", title = "Neighboring joint density-based {JPEG} steganalysis", journal = j-TIST, volume = "2", number = "2", pages = "16:1--16:??", month = feb, year = "2011", CODEN = "????", DOI = "https://doi.org/10.1145/1899412.1899420", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Oct 1 16:23:55 MDT 2015", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/cryptography2010.bib; https://www.math.utah.edu/pub/tex/bib/tist.bib/", abstract = "The threat posed by hackers, spies, terrorists, and criminals, etc. using steganography for stealthy communications and other illegal purposes is a serious concern of cyber security. Several steganographic systems that have been developed and made readily available utilize JPEG images as carriers. Due to the popularity of JPEG images on the Internet, effective steganalysis techniques are called for to counter the threat of JPEG steganography. In this article, we propose a new approach based on feature mining on the discrete cosine transform (DCT) domain and machine learning for steganalysis of JPEG images. First, neighboring joint density features on both intra-block and inter-block are extracted from the DCT coefficient array and the absolute array, respectively; then a support vector machine (SVM) is applied to the features for detection. An evolving neural-fuzzy inference system is employed to predict the hiding amount in JPEG steganograms. We also adopt a feature selection method of support vector machine recursive feature elimination to reduce the number of features. Experimental results show that, in detecting several JPEG-based steganographic systems, our method prominently outperforms the well-known Markov-process based approach.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "16", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Bhatt:2011:PTM, author = "Chidansh Bhatt and Mohan Kankanhalli", title = "Probabilistic temporal multimedia data mining", journal = j-TIST, volume = "2", number = "2", pages = "17:1--17:??", month = feb, year = "2011", CODEN = "????", DOI = "https://doi.org/10.1145/1899412.1899421", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Oct 1 16:23:55 MDT 2015", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Existing sequence pattern mining techniques assume that the obtained events from event detectors are accurate. However, in reality, event detectors label the events from different modalities with a certain probability over a time-interval. In this article, we consider for the first time Probabilistic Temporal Multimedia (PTM) Event data to discover accurate sequence patterns. PTM event data considers the start time, end time, event label and associated probability for the sequence pattern discovery. As the existing sequence pattern mining techniques cannot work on such realistic data, we have developed a novel framework for performing sequence pattern mining on probabilistic temporal multimedia event data. We perform probability fusion to resolve the redundancy among detected events from different modalities, considering their cross-modal correlation. We propose a novel sequence pattern mining algorithm called Probabilistic Interval based Event Miner (PIE-Miner) for discovering frequent sequence patterns from interval based events. PIE-Miner has a new support counting mechanism developed for PTM data. Existing sequence pattern mining algorithms have event label level support counting mechanism, whereas we have developed event cluster level support counting mechanism. We discover the complete set of all possible temporal relationships based on Allen's interval algebra. The experimental results showed that the discovered sequence patterns are more useful than the patterns discovered with state-of-the-art sequence pattern mining algorithms.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "17", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Ling:2011:ISI, author = "Charles X. Ling", title = "Introduction to special issue on machine learning for business applications", journal = j-TIST, volume = "2", number = "3", pages = "18:1--18:??", month = apr, year = "2011", CODEN = "????", DOI = "https://doi.org/10.1145/1961189.1961190", ISSN = "2157-6904 (print), 2157-6912 (electronic)", bibdate = "Fri May 13 11:20:03 MDT 2011", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "18", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Dhar:2011:PFM, author = "Vasant Dhar", title = "Prediction in financial markets: The case for small disjuncts", journal = j-TIST, volume = "2", number = "3", pages = "19:1--19:??", month = apr, year = "2011", CODEN = "????", DOI = "https://doi.org/10.1145/1961189.1961191", ISSN = "2157-6904 (print), 2157-6912 (electronic)", bibdate = "Fri May 13 11:20:03 MDT 2011", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "19", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Huang:2011:LBC, author = "Szu-Hao Huang and Shang-Hong Lai and Shih-Hsien Tai", title = "A learning-based contrarian trading strategy via a dual-classifier model", journal = j-TIST, volume = "2", number = "3", pages = "20:1--20:??", month = apr, year = "2011", CODEN = "????", DOI = "https://doi.org/10.1145/1961189.1961192", ISSN = "2157-6904 (print), 2157-6912 (electronic)", bibdate = "Fri May 13 11:20:03 MDT 2011", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "20", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Li:2011:CCD, author = "Bin Li and Steven C. H. Hoi and Vivekanand Gopalkrishnan", title = "{CORN}: Correlation-driven nonparametric learning approach for portfolio selection", journal = j-TIST, volume = "2", number = "3", pages = "21:1--21:??", month = apr, year = "2011", CODEN = "????", DOI = "https://doi.org/10.1145/1961189.1961193", ISSN = "2157-6904 (print), 2157-6912 (electronic)", bibdate = "Fri May 13 11:20:03 MDT 2011", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "21", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Bonchi:2011:SNA, author = "Francesco Bonchi and Carlos Castillo and Aristides Gionis and Alejandro Jaimes", title = "Social Network Analysis and Mining for Business Applications", journal = j-TIST, volume = "2", number = "3", pages = "22:1--22:??", month = apr, year = "2011", CODEN = "????", DOI = "https://doi.org/10.1145/1961189.1961194", ISSN = "2157-6904 (print), 2157-6912 (electronic)", bibdate = "Fri May 13 11:20:03 MDT 2011", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "22", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Zhang:2011:HMF, author = "Richong Zhang and Thomas Tran", title = "A helpfulness modeling framework for electronic word-of-mouth on consumer opinion platforms", journal = j-TIST, volume = "2", number = "3", pages = "23:1--23:??", month = apr, year = "2011", CODEN = "????", DOI = "https://doi.org/10.1145/1961189.1961195", ISSN = "2157-6904 (print), 2157-6912 (electronic)", bibdate = "Fri May 13 11:20:03 MDT 2011", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "23", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Ge:2011:MLC, author = "Yong Ge and Hui Xiong and Wenjun Zhou and Siming Li and Ramendra Sahoo", title = "Multifocal learning for customer problem analysis", journal = j-TIST, volume = "2", number = "3", pages = "24:1--24:??", month = apr, year = "2011", CODEN = "????", DOI = "https://doi.org/10.1145/1961189.1961196", ISSN = "2157-6904 (print), 2157-6912 (electronic)", bibdate = "Fri May 13 11:20:03 MDT 2011", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "24", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Hsu:2011:ISI, author = "Chun-Nan Hsu", title = "Introduction to special issue on large-scale machine learning", journal = j-TIST, volume = "2", number = "3", pages = "25:1--25:??", month = apr, year = "2011", CODEN = "????", DOI = "https://doi.org/10.1145/1961189.1961197", ISSN = "2157-6904 (print), 2157-6912 (electronic)", bibdate = "Fri May 13 11:20:03 MDT 2011", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "25", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Liu:2011:PPL, author = "Zhiyuan Liu and Yuzhou Zhang and Edward Y. Chang and Maosong Sun", title = "{PLDA+}: Parallel latent {Dirichlet} allocation with data placement and pipeline processing", journal = j-TIST, volume = "2", number = "3", pages = "26:1--26:??", month = apr, year = "2011", CODEN = "????", DOI = "https://doi.org/10.1145/1961189.1961198", ISSN = "2157-6904 (print), 2157-6912 (electronic)", bibdate = "Fri May 13 11:20:03 MDT 2011", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "26", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Chang:2011:LLS, author = "Chih-Chung Chang and Chih-Jen Lin", title = "{LIBSVM}: a library for support vector machines", journal = j-TIST, volume = "2", number = "3", pages = "27:1--27:??", month = apr, year = "2011", CODEN = "????", DOI = "https://doi.org/10.1145/1961189.1961199", ISSN = "2157-6904 (print), 2157-6912 (electronic)", bibdate = "Fri May 13 11:20:03 MDT 2011", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "LIBSVM is a library for Support Vector Machines (SVMs). We have been actively developing this package since the year 2000. The goal is to help users to easily apply SVM to their applications. LIBSVM has gained wide popularity in machine learning and many other areas. In this article, we present all implementation details of LIBSVM. Issues such as solving SVM optimization problems theoretical convergence multiclass classification probability estimates and parameter selection are discussed in detail.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "27", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Gasso:2011:BOL, author = "Gilles Gasso and Aristidis Pappaioannou and Marina Spivak and L{\'e}on Bottou", title = "Batch and online learning algorithms for nonconvex {Neyman--Pearson} classification", journal = j-TIST, volume = "2", number = "3", pages = "28:1--28:??", month = apr, year = "2011", CODEN = "????", DOI = "https://doi.org/10.1145/1961189.1961200", ISSN = "2157-6904 (print), 2157-6912 (electronic)", bibdate = "Fri May 13 11:20:03 MDT 2011", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "28", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Ma:2011:LRE, author = "Hao Ma and Irwin King and Michael R. Lyu", title = "Learning to recommend with explicit and implicit social relations", journal = j-TIST, volume = "2", number = "3", pages = "29:1--29:??", month = apr, year = "2011", CODEN = "????", DOI = "https://doi.org/10.1145/1961189.1961201", ISSN = "2157-6904 (print), 2157-6912 (electronic)", bibdate = "Fri May 13 11:20:03 MDT 2011", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "29", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Ma:2011:LDM, author = "Justin Ma and Lawrence K. Saul and Stefan Savage and Geoffrey M. Voelker", title = "Learning to detect malicious {URLs}", journal = j-TIST, volume = "2", number = "3", pages = "30:1--30:??", month = apr, year = "2011", CODEN = "????", DOI = "https://doi.org/10.1145/1961189.1961202", ISSN = "2157-6904 (print), 2157-6912 (electronic)", bibdate = "Fri May 13 11:20:03 MDT 2011", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Malicious Web sites are a cornerstone of Internet criminal activities. The dangers of these sites have created a demand for safeguards that protect end-users from visiting them. This article explores how to detect malicious Web sites from the lexical and host-based features of their URLs. We show that this problem lends itself naturally to modern algorithms for online learning. Online algorithms not only process large numbers of URLs more efficiently than batch algorithms, they also adapt more quickly to new features in the continuously evolving distribution of malicious URLs. We develop a real-time system for gathering URL features and pair it with a real-time feed of labeled URLs from a large Web mail provider. From these features and labels, we are able to train an online classifier that detects malicious Web sites with 99\% accuracy over a balanced dataset.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "30", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Gomes:2011:ISI, author = "Carla Gomes and Qiang Yang", title = "Introduction to special issue on computational sustainability", journal = j-TIST, volume = "2", number = "4", pages = "31:1--31:??", month = jul, year = "2011", CODEN = "????", DOI = "https://doi.org/10.1145/1989734.1989735", ISSN = "2157-6904 (print), 2157-6912 (electronic)", bibdate = "Fri Jul 22 08:50:59 MDT 2011", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "31", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Krause:2011:SAO, author = "Andreas Krause and Carlos Guestrin", title = "Submodularity and its applications in optimized information gathering", journal = j-TIST, volume = "2", number = "4", pages = "32:1--32:??", month = jul, year = "2011", CODEN = "????", DOI = "https://doi.org/10.1145/1989734.1989736", ISSN = "2157-6904 (print), 2157-6912 (electronic)", bibdate = "Fri Jul 22 08:50:59 MDT 2011", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "32", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Cattafi:2011:SBP, author = "Massimiliano Cattafi and Marco Gavanelli and Michela Milano and Paolo Cagnoli", title = "Sustainable biomass power plant location in the {Italian Emilia-Romagna} region", journal = j-TIST, volume = "2", number = "4", pages = "33:1--33:??", month = jul, year = "2011", CODEN = "????", DOI = "https://doi.org/10.1145/1989734.1989737", ISSN = "2157-6904 (print), 2157-6912 (electronic)", bibdate = "Fri Jul 22 08:50:59 MDT 2011", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "33", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Patnaik:2011:TDM, author = "Debprakash Patnaik and Manish Marwah and Ratnesh K. Sharma and Naren Ramakrishnan", title = "Temporal data mining approaches for sustainable chiller management in data centers", journal = j-TIST, volume = "2", number = "4", pages = "34:1--34:??", month = jul, year = "2011", CODEN = "????", DOI = "https://doi.org/10.1145/1989734.1989738", ISSN = "2157-6904 (print), 2157-6912 (electronic)", bibdate = "Fri Jul 22 08:50:59 MDT 2011", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "34", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Ramchurn:2011:ABH, author = "Sarvapali D. Ramchurn and Perukrishnen Vytelingum and Alex Rogers and Nicholas R. Jennings", title = "Agent-based homeostatic control for green energy in the smart grid", journal = j-TIST, volume = "2", number = "4", pages = "35:1--35:??", month = jul, year = "2011", CODEN = "????", DOI = "https://doi.org/10.1145/1989734.1989739", ISSN = "2157-6904 (print), 2157-6912 (electronic)", bibdate = "Fri Jul 22 08:50:59 MDT 2011", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "35", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Mithal:2011:MGF, author = "Varun Mithal and Ashish Garg and Shyam Boriah and Michael Steinbach and Vipin Kumar and Christopher Potter and Steven Klooster and Juan Carlos Castilla-Rubio", title = "Monitoring global forest cover using data mining", journal = j-TIST, volume = "2", number = "4", pages = "36:1--36:??", month = jul, year = "2011", CODEN = "????", DOI = "https://doi.org/10.1145/1989734.1989740", ISSN = "2157-6904 (print), 2157-6912 (electronic)", bibdate = "Fri Jul 22 08:50:59 MDT 2011", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "36", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Li:2011:MMM, author = "Zhenhui Li and Jiawei Han and Ming Ji and Lu-An Tang and Yintao Yu and Bolin Ding and Jae-Gil Lee and Roland Kays", title = "{MoveMine}: Mining moving object data for discovery of animal movement patterns", journal = j-TIST, volume = "2", number = "4", pages = "37:1--37:??", month = jul, year = "2011", CODEN = "????", DOI = "https://doi.org/10.1145/1989734.1989741", ISSN = "2157-6904 (print), 2157-6912 (electronic)", bibdate = "Fri Jul 22 08:50:59 MDT 2011", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "37", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Toole:2011:SCC, author = "Jameson L. Toole and Nathan Eagle and Joshua B. Plotkin", title = "Spatiotemporal correlations in criminal offense records", journal = j-TIST, volume = "2", number = "4", pages = "38:1--38:??", month = jul, year = "2011", CODEN = "????", DOI = "https://doi.org/10.1145/1989734.1989742", ISSN = "2157-6904 (print), 2157-6912 (electronic)", bibdate = "Fri Jul 22 08:50:59 MDT 2011", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "38", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Ding:2011:SCD, author = "Wei Ding and Tomasz F. Stepinski and Yang Mu and Lourenco Bandeira and Ricardo Ricardo and Youxi Wu and Zhenyu Lu and Tianyu Cao and Xindong Wu", title = "Subkilometer crater discovery with boosting and transfer learning", journal = j-TIST, volume = "2", number = "4", pages = "39:1--39:??", month = jul, year = "2011", CODEN = "????", DOI = "https://doi.org/10.1145/1989734.1989743", ISSN = "2157-6904 (print), 2157-6912 (electronic)", bibdate = "Fri Jul 22 08:50:59 MDT 2011", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "39", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Berry:2011:PPA, author = "Pauline M. Berry and Melinda Gervasio and Bart Peintner and Neil Yorke-Smith", title = "{PTIME}: Personalized assistance for calendaring", journal = j-TIST, volume = "2", number = "4", pages = "40:1--40:??", month = jul, year = "2011", CODEN = "????", DOI = "https://doi.org/10.1145/1989734.1989744", ISSN = "2157-6904 (print), 2157-6912 (electronic)", bibdate = "Fri Jul 22 08:50:59 MDT 2011", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "40", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Reddy:2011:PSA, author = "Sudhakar Y. Reddy and Jeremy D. Frank and Michael J. Iatauro and Matthew E. Boyce and Elif K{\"u}rkl{\"u} and Mitchell Ai-Chang and Ari K. J{\'o}nsson", title = "Planning solar array operations on the {International Space Station}", journal = j-TIST, volume = "2", number = "4", pages = "41:1--41:??", month = jul, year = "2011", CODEN = "????", DOI = "https://doi.org/10.1145/1989734.1989745", ISSN = "2157-6904 (print), 2157-6912 (electronic)", bibdate = "Fri Jul 22 08:50:59 MDT 2011", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "41", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Haigh:2011:RLL, author = "Karen Zita Haigh and Fusun Yaman", title = "{RECYCLE}: Learning looping workflows from annotated traces", journal = j-TIST, volume = "2", number = "4", pages = "42:1--42:??", month = jul, year = "2011", CODEN = "????", DOI = "https://doi.org/10.1145/1989734.1989746", ISSN = "2157-6904 (print), 2157-6912 (electronic)", bibdate = "Fri Jul 22 08:50:59 MDT 2011", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "42", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Guy:2011:I, author = "Ido Guy and Li Chen and Michelle X. Zhou", title = "Introduction", journal = j-TIST, volume = "3", number = "1", pages = "1:1--1:??", month = oct, year = "2011", CODEN = "????", DOI = "https://doi.org/10.1145/2036264.2036265", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sun Nov 6 07:22:40 MST 2011", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "1", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Lipczak:2011:ETR, author = "Marek Lipczak and Evangelos Milios", title = "Efficient Tag Recommendation for Real-Life Data", journal = j-TIST, volume = "3", number = "1", pages = "2:1--2:??", month = oct, year = "2011", CODEN = "????", DOI = "https://doi.org/10.1145/2036264.2036266", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sun Nov 6 07:22:40 MST 2011", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "2", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Vasuki:2011:SAR, author = "Vishvas Vasuki and Nagarajan Natarajan and Zhengdong Lu and Berkant Savas and Inderjit Dhillon", title = "Scalable Affiliation Recommendation using Auxiliary Networks", journal = j-TIST, volume = "3", number = "1", pages = "3:1--3:??", month = oct, year = "2011", CODEN = "????", DOI = "https://doi.org/10.1145/2036264.2036267", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sun Nov 6 07:22:40 MST 2011", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "3", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{McNally:2011:CSC, author = "Kevin McNally and Michael P. O'Mahony and Maurice Coyle and Peter Briggs and Barry Smyth", title = "A Case Study of Collaboration and Reputation in Social {Web} Search", journal = j-TIST, volume = "3", number = "1", pages = "4:1--4:??", month = oct, year = "2011", CODEN = "????", DOI = "https://doi.org/10.1145/2036264.2036268", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sun Nov 6 07:22:40 MST 2011", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "4", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Zhao:2011:WDW, author = "Shiwan Zhao and Michelle X. Zhou and Xiatian Zhang and Quan Yuan and Wentao Zheng and Rongyao Fu", title = "Who is Doing What and When: Social Map-Based Recommendation for Content-Centric Social {Web} Sites", journal = j-TIST, volume = "3", number = "1", pages = "5:1--5:??", month = oct, year = "2011", CODEN = "????", DOI = "https://doi.org/10.1145/2036264.2036269", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sun Nov 6 07:22:40 MST 2011", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "5", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Liu:2011:I, author = "Huan Liu and Dana Nau", title = "Introduction", journal = j-TIST, volume = "3", number = "1", pages = "6:1--6:??", month = oct, year = "2011", CODEN = "????", DOI = "https://doi.org/10.1145/2036264.2036270", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sun Nov 6 07:22:40 MST 2011", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "6", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Shakarian:2011:GGA, author = "Paulo Shakarian and V. S. Subrahmanian and Maria Luisa Sapino", title = "{GAPs}: Geospatial Abduction Problems", journal = j-TIST, volume = "3", number = "1", pages = "7:1--7:??", month = oct, year = "2011", CODEN = "????", DOI = "https://doi.org/10.1145/2036264.2036271", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sun Nov 6 07:22:40 MST 2011", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "7", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Gal:2011:AAN, author = "Ya'akov Gal and Sarit Kraus and Michele Gelfand and Hilal Khashan and Elizabeth Salmon", title = "An Adaptive Agent for Negotiating with People in Different Cultures", journal = j-TIST, volume = "3", number = "1", pages = "8:1--8:??", month = oct, year = "2011", CODEN = "????", DOI = "https://doi.org/10.1145/2036264.2036272", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sun Nov 6 07:22:40 MST 2011", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "8", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Vu:2011:FSK, author = "Thuc Vu and Yoav Shoham", title = "Fair Seeding in Knockout Tournaments", journal = j-TIST, volume = "3", number = "1", pages = "9:1--9:??", month = oct, year = "2011", CODEN = "????", DOI = "https://doi.org/10.1145/2036264.2036273", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sun Nov 6 07:22:40 MST 2011", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "9", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Cioffi-Revilla:2011:GIS, author = "Claudio Cioffi-Revilla and J. Daniel Rogers and Atesmachew Hailegiorgis", title = "Geographic Information Systems and Spatial Agent-Based Model Simulations for Sustainable Development", journal = j-TIST, volume = "3", number = "1", pages = "10:1--10:??", month = oct, year = "2011", CODEN = "????", DOI = "https://doi.org/10.1145/2036264.2036274", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sun Nov 6 07:22:40 MST 2011", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "10", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Jiang:2011:UMS, author = "Yingying Jiang and Feng Tian and Xiaolong (Luke) Zhang and Guozhong Dai and Hongan Wang", title = "Understanding, Manipulating and Searching Hand-Drawn Concept Maps", journal = j-TIST, volume = "3", number = "1", pages = "11:1--11:??", month = oct, year = "2011", CODEN = "????", DOI = "https://doi.org/10.1145/2036264.2036275", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sun Nov 6 07:22:40 MST 2011", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "11", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Wang:2011:IIS, author = "Jingdong Wang and Xian-Sheng Hua", title = "Interactive Image Search by Color Map", journal = j-TIST, volume = "3", number = "1", pages = "12:1--12:??", month = oct, year = "2011", CODEN = "????", DOI = "https://doi.org/10.1145/2036264.2036276", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sun Nov 6 07:22:40 MST 2011", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "12", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Prettenhofer:2011:CLA, author = "Peter Prettenhofer and Benno Stein", title = "Cross-Lingual Adaptation Using Structural Correspondence Learning", journal = j-TIST, volume = "3", number = "1", pages = "13:1--13:??", month = oct, year = "2011", CODEN = "????", DOI = "https://doi.org/10.1145/2036264.2036277", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sun Nov 6 07:22:40 MST 2011", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "13", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Anagnostopoulos:2011:WPS, author = "Aris Anagnostopoulos and Andrei Z. Broder and Evgeniy Gabrilovich and Vanja Josifovski and Lance Riedel", title = "{Web} Page Summarization for Just-in-Time Contextual Advertising", journal = j-TIST, volume = "3", number = "1", pages = "14:1--14:??", month = oct, year = "2011", CODEN = "????", DOI = "https://doi.org/10.1145/2036264.2036278", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sun Nov 6 07:22:40 MST 2011", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "14", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Tang:2011:GPU, author = "Lei Tang and Xufei Wang and Huan Liu", title = "Group Profiling for Understanding Social Structures", journal = j-TIST, volume = "3", number = "1", pages = "15:1--15:??", month = oct, year = "2011", CODEN = "????", DOI = "https://doi.org/10.1145/2036264.2036279", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sun Nov 6 07:22:40 MST 2011", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "15", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Liu:2011:TWC, author = "Zhanyi Liu and Haifeng Wang and Hua Wu and Sheng Li", title = "Two-Word Collocation Extraction Using Monolingual Word Alignment Method", journal = j-TIST, volume = "3", number = "1", pages = "16:1--16:??", month = oct, year = "2011", CODEN = "????", DOI = "https://doi.org/10.1145/2036264.2036280", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sun Nov 6 07:22:40 MST 2011", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "16", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Liao:2011:MCS, author = "Zhen Liao and Daxin Jiang and Enhong Chen and Jian Pei and Huanhuan Cao and Hang Li", title = "Mining Concept Sequences from Large-Scale Search Logs for Context-Aware Query Suggestion", journal = j-TIST, volume = "3", number = "1", pages = "17:1--17:??", month = oct, year = "2011", CODEN = "????", DOI = "https://doi.org/10.1145/2036264.2036281", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sun Nov 6 07:22:40 MST 2011", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "17", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Sukthankar:2011:ARD, author = "Gita Sukthankar and Katia Sycara", title = "Activity Recognition for Dynamic Multi-Agent Teams", journal = j-TIST, volume = "3", number = "1", pages = "18:1--18:??", month = oct, year = "2011", CODEN = "????", DOI = "https://doi.org/10.1145/2036264.2036282", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sun Nov 6 07:22:40 MST 2011", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "18", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Liu:2012:ISS, author = "Shixia Liu and Michelle X. Zhou and Giuseppe Carenini and Huamin Qu", title = "Introduction to the Special Section on Intelligent Visual Interfaces for Text Analysis", journal = j-TIST, volume = "3", number = "2", pages = "19:1--19:??", month = feb, year = "2012", CODEN = "????", DOI = "https://doi.org/10.1145/2089094.2089095", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Fri Mar 16 15:10:10 MDT 2012", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "19", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Cui:2012:WSU, author = "Weiwei Cui and Huamin Qu and Hong Zhou and Wenbin Zhang and Steve Skiena", title = "Watch the Story Unfold with {TextWheel}: Visualization of Large-Scale News Streams", journal = j-TIST, volume = "3", number = "2", pages = "20:1--20:??", month = feb, year = "2012", CODEN = "????", DOI = "https://doi.org/10.1145/2089094.2089096", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Fri Mar 16 15:10:10 MDT 2012", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Keyword-based searching and clustering of news articles have been widely used for news analysis. However, news articles usually have other attributes such as source, author, date and time, length, and sentiment which should be taken into account. In addition, news articles and keywords have complicated macro/micro relations, which include relations between news articles (i.e., macro relation), relations between keywords (i.e., micro relation), and relations between news articles and keywords (i.e., macro-micro relation). These macro/micro relations are time varying and pose special challenges for news analysis. In this article we present a visual analytics system for news streams which can bring multiple attributes of the news articles and the macro/micro relations between news streams and keywords into one coherent analytical context, all the while conveying the dynamic natures of news streams. We introduce a new visualization primitive called TextWheel which consists of one or multiple keyword wheels, a document transportation belt, and a dynamic system which connects the wheels and belt. By observing the TextWheel and its content changes, some interesting patterns can be detected. We use our system to analyze several news corpora related to some major companies and the results demonstrate the high potential of our method.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "20", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Thai:2012:VAO, author = "Vinhtuan Thai and Pierre-Yves Rouille and Siegfried Handschuh", title = "Visual Abstraction and Ordering in Faceted Browsing of Text Collections", journal = j-TIST, volume = "3", number = "2", pages = "21:1--21:??", month = feb, year = "2012", CODEN = "????", DOI = "https://doi.org/10.1145/2089094.2089097", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Fri Mar 16 15:10:10 MDT 2012", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Faceted navigation is a technique for the exploration and discovery of a collection of resources, which can be of various types including text documents. While being information-rich resources, documents are usually not treated as content-bearing items in faceted browsing interfaces, and yet the required clean metadata is not always available or matches users' interest. In addition, the existing linear listing paradigm for representing result items from the faceted filtering process makes it difficult for users to traverse or compare across facet values in different orders of importance to them. In this context, we report in this article a visual support toward faceted browsing of a collection of documents based on a set of entities of interest to users. Our proposed approach involves using a multi-dimensional visualization as an alternative to the linear listing of focus items. In this visualization, visual abstraction based on a combination of a conceptual structure and the structural equivalence of documents can be simultaneously used to deal with a large number of items. Furthermore, the approach also enables visual ordering based on the importance of facet values to support prioritized, cross-facet comparisons of focus items. A user study was conducted and the results suggest that interfaces using the proposed approach can support users better in exploratory tasks and were also well-liked by the participants of the study, with the hybrid interface combining the multi-dimensional visualization with the linear listing receiving the most favorable ratings.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "21", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Candan:2012:PMV, author = "K. Sel{\c{c}}uk Candan and Luigi {Di Caro} and Maria Luisa Sapino", title = "{PhC}: Multiresolution Visualization and Exploration of Text Corpora with Parallel Hierarchical Coordinates", journal = j-TIST, volume = "3", number = "2", pages = "22:1--22:??", month = feb, year = "2012", CODEN = "????", DOI = "https://doi.org/10.1145/2089094.2089098", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Fri Mar 16 15:10:10 MDT 2012", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "The high-dimensional nature of the textual data complicates the design of visualization tools to support exploration of large document corpora. In this article, we first argue that the Parallel Coordinates (PC) technique, which can map multidimensional vectors onto a 2D space in such a way that elements with similar values are represented as similar poly-lines or curves in the visualization space, can be used to help users discern patterns in document collections. The inherent reduction in dimensionality during the mapping from multidimensional points to 2D lines, however, may result in visual complications. For instance, the lines that correspond to clusters of objects that are separate in the multidimensional space may overlap each other in the 2D space; the resulting increase in the number of crossings would make it hard to distinguish the individual document clusters. Such crossings of lines and overly dense regions are significant sources of visual clutter, thus avoiding them may help interpret the visualization. In this article, we note that visual clutter can be significantly reduced by adjusting the resolution of the individual term coordinates by clustering the corresponding values. Such reductions in the resolution of the individual term-coordinates, however, will lead to a certain degree of information loss and thus the appropriate resolution for the term-coordinates has to be selected carefully. Thus, in this article we propose a controlled clutter reduction approach, called Parallel hierarchical Coordinates (or PhC ), for reducing the visual clutter in PC-based visualizations of text corpora. We define visual clutter and information loss measures and provide extensive evaluations that show that the proposed PhC provides significant visual gains (i.e., multiple orders of reductions in visual clutter) with small information loss during visualization and exploration of document collections.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "22", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Gretarsson:2012:TVA, author = "Brynjar Gretarsson and John O'Donovan and Svetlin Bostandjiev and Tobias H{\"o}llerer and Arthur Asuncion and David Newman and Padhraic Smyth", title = "{TopicNets}: Visual Analysis of Large Text Corpora with Topic Modeling", journal = j-TIST, volume = "3", number = "2", pages = "23:1--23:??", month = feb, year = "2012", CODEN = "????", DOI = "https://doi.org/10.1145/2089094.2089099", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Fri Mar 16 15:10:10 MDT 2012", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "We present TopicNets, a Web-based system for visual and interactive analysis of large sets of documents using statistical topic models. A range of visualization types and control mechanisms to support knowledge discovery are presented. These include corpus- and document-specific views, iterative topic modeling, search, and visual filtering. Drill-down functionality is provided to allow analysts to visualize individual document sections and their relations within the global topic space. Analysts can search across a dataset through a set of expansion techniques on selected document and topic nodes. Furthermore, analysts can select relevant subsets of documents and perform real-time topic modeling on these subsets to interactively visualize topics at various levels of granularity, allowing for a better understanding of the documents. A discussion of the design and implementation choices for each visual analysis technique is presented. This is followed by a discussion of three diverse use cases in which TopicNets enables fast discovery of information that is otherwise hard to find. These include a corpus of 50,000 successful NSF grant proposals, 10,000 publications from a large research center, and single documents including a grant proposal and a PhD thesis.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "23", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Zhang:2012:DFE, author = "Yi Zhang and Tao Li", title = "{DClusterE}: a Framework for Evaluating and Understanding Document Clustering Using Visualization", journal = j-TIST, volume = "3", number = "2", pages = "24:1--24:??", month = feb, year = "2012", CODEN = "????", DOI = "https://doi.org/10.1145/2089094.2089100", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Fri Mar 16 15:10:10 MDT 2012", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Over the last decade, document clustering, as one of the key tasks in information organization and navigation, has been widely studied. Many algorithms have been developed for addressing various challenges in document clustering and for improving clustering performance. However, relatively few research efforts have been reported on evaluating and understanding document clustering results. In this article, we present DClusterE, a comprehensive and effective framework for document clustering evaluation and understanding using information visualization. DClusterE integrates cluster validation with user interactions and offers rich visualization tools for users to examine document clustering results from multiple perspectives. In particular, through informative views including force-directed layout view, matrix view, and cluster view, DClusterE provides not only different aspects of document inter/intra-clustering structures, but also the corresponding relationship between clustering results and the ground truth. Additionally, DClusterE supports general user interactions such as zoom in/out, browsing, and interactive access of the documents at different levels. Two new techniques are proposed to implement DClusterE: (1) A novel multiplicative update algorithm (MUA) for matrix reordering to generate narrow-banded (or clustered) nonzero patterns from documents. Combined with coarse seriation, MUA is able to provide better visualization of the cluster structures. (2) A Mallows-distance-based algorithm for establishing the relationship between the clustering results and the ground truth, which serves as the basis for coloring schemes. Experiments and user studies are conducted to demonstrate the effectiveness and efficiency of DClusterE.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "24", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Liu:2012:TIT, author = "Shixia Liu and Michelle X. Zhou and Shimei Pan and Yangqiu Song and Weihong Qian and Weijia Cai and Xiaoxiao Lian", title = "{TIARA}: Interactive, Topic-Based Visual Text Summarization and Analysis", journal = j-TIST, volume = "3", number = "2", pages = "25:1--25:??", month = feb, year = "2012", CODEN = "????", DOI = "https://doi.org/10.1145/2089094.2089101", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Fri Mar 16 15:10:10 MDT 2012", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "We are building an interactive visual text analysis tool that aids users in analyzing large collections of text. Unlike existing work in visual text analytics, which focuses either on developing sophisticated text analytic techniques or inventing novel text visualization metaphors, ours tightly integrates state-of-the-art text analytics with interactive visualization to maximize the value of both. In this article, we present our work from two aspects. We first introduce an enhanced, LDA-based topic analysis technique that automatically derives a set of topics to summarize a collection of documents and their content evolution over time. To help users understand the complex summarization results produced by our topic analysis technique, we then present the design and development of a time-based visualization of the results. Furthermore, we provide users with a set of rich interaction tools that help them further interpret the visualized results in context and examine the text collection from multiple perspectives. As a result, our work offers three unique contributions. First, we present an enhanced topic modeling technique to provide users with a time-sensitive and more meaningful text summary. Second, we develop an effective visual metaphor to transform abstract and often complex text summarization results into a comprehensible visual representation. Third, we offer users flexible visual interaction tools as alternatives to compensate for the deficiencies of current text summarization techniques. We have applied our work to a number of text corpora and our evaluation shows promise, especially in support of complex text analyses.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "25", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Rohrdantz:2012:FBV, author = "Christian Rohrdantz and Ming C. Hao and Umeshwar Dayal and Lars-Erik Haug and Daniel A. Keim", title = "Feature-Based Visual Sentiment Analysis of Text Document Streams", journal = j-TIST, volume = "3", number = "2", pages = "26:1--26:??", month = feb, year = "2012", CODEN = "????", DOI = "https://doi.org/10.1145/2089094.2089102", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Fri Mar 16 15:10:10 MDT 2012", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "This article describes automatic methods and interactive visualizations that are tightly coupled with the goal to enable users to detect interesting portions of text document streams. In this scenario the interestingness is derived from the sentiment, temporal density, and context coherence that comments about features for different targets (e.g., persons, institutions, product attributes, topics, etc.) have. Contributions are made at different stages of the visual analytics pipeline, including novel ways to visualize salient temporal accumulations for further exploration. Moreover, based on the visualization, an automatic algorithm aims to detect and preselect interesting time interval patterns for different features in order to guide analysts. The main target group for the suggested methods are business analysts who want to explore time-stamped customer feedback to detect critical issues. Finally, application case studies on two different datasets and scenarios are conducted and an extensive evaluation is provided for the presented intelligent visual interface for feature-based sentiment exploration over time.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "26", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Sugiyama:2012:ISS, author = "Masashi Sugiyama and Qiang Yang", title = "Introduction to the Special Section on the {2nd Asia Conference on Machine Learning (ACML 2010)}", journal = j-TIST, volume = "3", number = "2", pages = "27:1--27:??", month = feb, year = "2012", CODEN = "????", DOI = "https://doi.org/10.1145/2089094.2089103", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Fri Mar 16 15:10:10 MDT 2012", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "27", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Hajimirsadeghi:2012:CIL, author = "Hossein Hajimirsadeghi and Majid Nili Ahmadabadi and Babak Nadjar Araabi and Hadi Moradi", title = "Conceptual Imitation Learning in a Human-Robot Interaction Paradigm", journal = j-TIST, volume = "3", number = "2", pages = "28:1--28:??", month = feb, year = "2012", CODEN = "????", DOI = "https://doi.org/10.1145/2089094.2089104", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Fri Mar 16 15:10:10 MDT 2012", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "In general, imitation is imprecisely used to address different levels of social learning from high-level knowledge transfer to low-level regeneration of motor commands. However, true imitation is based on abstraction and conceptualization. This article presents a model for conceptual imitation through interaction with the teacher to abstract spatio-temporal demonstrations based on their functional meaning. Abstraction, concept acquisition, and self-organization of proto-symbols are performed through an incremental and gradual learning algorithm. In this algorithm, Hidden Markov Models (HMMs) are used to abstract perceptually similar demonstrations. However, abstract (relational) concepts emerge as a collection of HMMs irregularly scattered in the perceptual space but showing the same functionality. Performance of the proposed algorithm is evaluated in two experimental scenarios. The first one is a human-robot interaction task of imitating signs produced by hand movements. The second one is a simulated interactive task of imitating whole body motion patterns of a humanoid model. Experimental results show efficiency of our model for concept extraction, proto-symbol emergence, motion pattern recognition, prediction, and generation.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "28", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Li:2012:MRC, author = "Peipei Li and Xindong Wu and Xuegang Hu", title = "Mining Recurring Concept Drifts with Limited Labeled Streaming Data", journal = j-TIST, volume = "3", number = "2", pages = "29:1--29:??", month = feb, year = "2012", CODEN = "????", DOI = "https://doi.org/10.1145/2089094.2089105", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Fri Mar 16 15:10:10 MDT 2012", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Tracking recurring concept drifts is a significant issue for machine learning and data mining that frequently appears in real-world stream classification problems. It is a challenge for many streaming classification algorithms to learn recurring concepts in a data stream environment with unlabeled data, and this challenge has received little attention from the research community. Motivated by this challenge, this article focuses on the problem of recurring contexts in streaming environments with limited labeled data. We propose a semi-supervised classification algorithm for data streams with REcurring concept Drifts and Limited LAbeled data, called REDLLA, in which a decision tree is adopted as the classification model. When growing a tree, a clustering algorithm based on k -means is installed to produce concept clusters and unlabeled data are labeled in the method of majority-class at leaves. In view of deviations between history and new concept clusters, potential concept drifts are distinguished and recurring concepts are maintained. Extensive studies on both synthetic and real-world data confirm the advantages of our REDLLA algorithm over three state-of-the-art online classification algorithms of CVFDT, DWCDS, and CDRDT and several known online semi-supervised algorithms, even in the case with more than 90\% unlabeled data.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "29", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Bifet:2012:ERH, author = "Albert Bifet and Eibe Frank and Geoff Holmes and Bernhard Pfahringer", title = "Ensembles of Restricted {Hoeffding} Trees", journal = j-TIST, volume = "3", number = "2", pages = "30:1--30:??", month = feb, year = "2012", CODEN = "????", DOI = "https://doi.org/10.1145/2089094.2089106", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Fri Mar 16 15:10:10 MDT 2012", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "The success of simple methods for classification shows that it is often not necessary to model complex attribute interactions to obtain good classification accuracy on practical problems. In this article, we propose to exploit this phenomenon in the data stream context by building an ensemble of Hoeffding trees that are each limited to a small subset of attributes. In this way, each tree is restricted to model interactions between attributes in its corresponding subset. Because it is not known a priori which attribute subsets are relevant for prediction, we build exhaustive ensembles that consider all possible attribute subsets of a given size. As the resulting Hoeffding trees are not all equally important, we weigh them in a suitable manner to obtain accurate classifications. This is done by combining the log-odds of their probability estimates using sigmoid perceptrons, with one perceptron per class. We propose a mechanism for setting the perceptrons' learning rate using the change detection method for data streams, and also use to reset ensemble members (i.e., Hoeffding trees) when they no longer perform well. Our experiments show that the resulting ensemble classifier outperforms bagging for data streams in terms of accuracy when both are used in conjunction with adaptive naive Bayes Hoeffding trees, at the expense of runtime and memory consumption. We also show that our stacking method can improve the performance of a bagged ensemble.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "30", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Ma:2012:RPC, author = "Huadong Ma and Chengbin Zeng and Charles X. Ling", title = "A Reliable People Counting System via Multiple Cameras", journal = j-TIST, volume = "3", number = "2", pages = "31:1--31:??", month = feb, year = "2012", CODEN = "????", DOI = "https://doi.org/10.1145/2089094.2089107", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Fri Mar 16 15:10:10 MDT 2012", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Reliable and real-time people counting is crucial in many applications. Most previous works can only count moving people from a single camera, which cannot count still people or can fail badly when there is a crowd (i.e., heavy occlusion occurs). In this article, we build a system for robust and fast people counting under occlusion through multiple cameras. To improve the reliability of human detection from a single camera, we use a dimensionality reduction method on the multilevel edge and texture features to handle the large variations in human appearance and poses. To accelerate the detection speed, we propose a novel two-stage cascade-of-rejectors method. To handle the heavy occlusion in crowded scenes, we present a fusion method with error tolerance to combine human detection from multiple cameras. To improve the speed and accuracy of moving people counting, we combine our multiview fusion detection method with particle tracking to count the number of people moving in/out the camera view (`border control'). Extensive experiments and analyses show that our method outperforms state-of-the-art techniques in single- and multicamera datasets for both speed and reliability. We also design a deployed system for fast and reliable people (still or moving) counting by using multiple cameras.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "31", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Kolomvatsos:2012:FLS, author = "Kostas Kolomvatsos and Christos Anagnostopoulos and Stathes Hadjiefthymiades", title = "A Fuzzy Logic System for Bargaining in Information Markets", journal = j-TIST, volume = "3", number = "2", pages = "32:1--32:??", month = feb, year = "2012", CODEN = "????", DOI = "https://doi.org/10.1145/2089094.2089108", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Fri Mar 16 15:10:10 MDT 2012", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Future Web business models involve virtual environments where entities interact in order to sell or buy information goods. Such environments are known as Information Markets (IMs). Intelligent agents are used in IMs for representing buyers or information providers (sellers). We focus on the decisions taken by the buyer in the purchase negotiation process with sellers. We propose a reasoning mechanism on the offers (prices of information goods) issued by sellers based on fuzzy logic. The buyer's knowledge on the negotiation process is modeled through fuzzy sets. We propose a fuzzy inference engine dealing with the decisions that the buyer takes on each stage of the negotiation process. The outcome of the proposed reasoning method indicates whether the buyer should accept or reject the sellers' offers. Our findings are very promising for the efficiency of automated transactions undertaken by intelligent agents.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "32", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Shi:2012:BMA, author = "Lixin Shi and Yuhang Zhao and Jie Tang", title = "Batch Mode Active Learning for Networked Data", journal = j-TIST, volume = "3", number = "2", pages = "33:1--33:??", month = feb, year = "2012", CODEN = "????", DOI = "https://doi.org/10.1145/2089094.2089109", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Fri Mar 16 15:10:10 MDT 2012", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "We study a novel problem of batch mode active learning for networked data. In this problem, data instances are connected with links and their labels are correlated with each other, and the goal of batch mode active learning is to exploit the link-based dependencies and node-specific content information to actively select a batch of instances to query the user for learning an accurate model to label unknown instances in the network. We present three criteria (i.e., minimum redundancy, maximum uncertainty, and maximum impact) to quantify the informativeness of a set of instances, and formalize the batch mode active learning problem as selecting a set of instances by maximizing an objective function which combines both link and content information. As solving the objective function is NP-hard, we present an efficient algorithm to optimize the objective function with a bounded approximation rate. To scale to real large networks, we develop a parallel implementation of the algorithm. Experimental results on both synthetic datasets and real-world datasets demonstrate the effectiveness and efficiency of our approach.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "33", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Shakarian:2012:AGA, author = "Paulo Shakarian and John P. Dickerson and V. S. Subrahmanian", title = "Adversarial Geospatial Abduction Problems", journal = j-TIST, volume = "3", number = "2", pages = "34:1--34:??", month = feb, year = "2012", CODEN = "????", DOI = "https://doi.org/10.1145/2089094.2089110", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Fri Mar 16 15:10:10 MDT 2012", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Geospatial Abduction Problems (GAPs) involve the inference of a set of locations that `best explain' a given set of locations of observations. For example, the observations might include locations where a serial killer committed murders or where insurgents carried out Improvised Explosive Device (IED) attacks. In both these cases, we would like to infer a set of locations that explain the observations, for example, the set of locations where the serial killer lives/works, and the set of locations where insurgents locate weapons caches. However, unlike all past work on abduction, there is a strong adversarial component to this; an adversary actively attempts to prevent us from discovering such locations. We formalize such abduction problems as a two-player game where both players (an `agent' and an `adversary') use a probabilistic model of their opponent (i.e., a mixed strategy). There is asymmetry as the adversary can choose both the locations of the observations and the locations of the explanation, while the agent (i.e., us) tries to discover these. In this article, we study the problem from the point of view of both players. We define reward functions axiomatically to capture the similarity between two sets of explanations (one corresponding to the locations chosen by the adversary, one guessed by the agent). Many different reward functions can satisfy our axioms. We then formalize the Optimal Adversary Strategy (OAS) problem and the Maximal Counter-Adversary strategy (MCA) and show that both are NP-hard, that their associated counting complexity problems are \#P-hard, and that MCA has no fully polynomial approximation scheme unless P=NP. We show that approximation guarantees are possible for MCA when the reward function satisfies two simple properties (zero-starting and monotonicity) which many natural reward functions satisfy. We develop a mixed integer linear programming algorithm to solve OAS and two algorithms to (approximately) compute MCA; the algorithms yield different approximation guarantees and one algorithm assumes a monotonic reward function. Our experiments use real data about IED attacks over a 21-month period in Baghdad. We are able to show that both the MCA algorithms work well in practice; while MCA-GREEDY-MONO is both highly accurate and slightly faster than MCA-LS, MCA-LS (to our surprise) always completely and correctly maximized the expected benefit to the agent while running in an acceptable time period.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "34", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Li:2012:LIS, author = "Xueying Li and Huanhuan Cao and Enhong Chen and Jilei Tian", title = "Learning to Infer the Status of Heavy-Duty Sensors for Energy-Efficient Context-Sensing", journal = j-TIST, volume = "3", number = "2", pages = "35:1--35:??", month = feb, year = "2012", CODEN = "????", DOI = "https://doi.org/10.1145/2089094.2089111", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Fri Mar 16 15:10:10 MDT 2012", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "With the prevalence of smart mobile devices with multiple sensors, the commercial application of intelligent context-aware services becomes more and more attractive. However, limited by the battery capacity, the energy efficiency of context-sensing is the bottleneck for the success of context-aware applications. Though several previous studies for energy-efficient context-sensing have been reported, none of them can be applied to multiple types of high-energy-consuming sensors. Moreover, applying machine learning technologies to energy-efficient context-sensing is underexplored too. In this article, we propose to leverage machine learning technologies for improving the energy efficiency of multiple high-energy-consuming context sensors by trading off the sensing accuracy. To be specific, we try to infer the status of high-energy-consuming sensors according to the outputs of software-based sensors and the physical sensors that are necessary to work all the time for supporting the basic functions of mobile devices. If the inference indicates the high-energy-consuming sensor is in a stable status, we avoid the unnecessary invocation and instead use the latest invoked value as the estimation. The experimental results on real datasets show that the energy efficiency of GPS sensing and audio-level sensing are significantly improved by the proposed approach while the sensing accuracy is over 90\%.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "35", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Zhang:2012:AKR, author = "Weinan Zhang and Dingquan Wang and Gui-Rong Xue and Hongyuan Zha", title = "Advertising Keywords Recommendation for Short-Text {Web} Pages Using {Wikipedia}", journal = j-TIST, volume = "3", number = "2", pages = "36:1--36:??", month = feb, year = "2012", CODEN = "????", DOI = "https://doi.org/10.1145/2089094.2089112", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Fri Mar 16 15:10:10 MDT 2012", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Advertising keywords recommendation is an indispensable component for online advertising with the keywords selected from the target Web pages used for contextual advertising or sponsored search. Several ranking-based algorithms have been proposed for recommending advertising keywords. However, for most of them performance is still lacking, especially when dealing with short-text target Web pages, that is, those containing insufficient textual information for ranking. In some cases, short-text Web pages may not even contain enough keywords for selection. A natural alternative is then to recommend relevant keywords not present in the target Web pages. In this article, we propose a novel algorithm for advertising keywords recommendation for short-text Web pages by leveraging the contents of Wikipedia, a user-contributed online encyclopedia. Wikipedia contains numerous entities with related entities on a topic linked to each other. Given a target Web page, we propose to use a content-biased PageRank on the Wikipedia graph to rank the related entities. Furthermore, in order to recommend high-quality advertising keywords, we also add an advertisement-biased factor into our model. With these two biases, advertising keywords that are both relevant to a target Web page and valuable for advertising are recommended. In our experiments, several state-of-the-art approaches for keyword recommendation are compared. The experimental results demonstrate that our proposed approach produces substantial improvement in the precision of the top 20 recommended keywords on short-text Web pages over existing approaches.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "36", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Zhou:2012:LAD, author = "Ke Zhou and Jing Bai and Hongyuan Zha and Gui-Rong Xue", title = "Leveraging Auxiliary Data for Learning to Rank", journal = j-TIST, volume = "3", number = "2", pages = "37:1--37:??", month = feb, year = "2012", CODEN = "????", DOI = "https://doi.org/10.1145/2089094.2089113", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Fri Mar 16 15:10:10 MDT 2012", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "In learning to rank, both the quality and quantity of the training data have significant impacts on the performance of the learned ranking functions. However, in many applications, there are usually not sufficient labeled training data for the construction of an accurate ranking model. It is therefore desirable to leverage existing training data from other tasks when learning the ranking function for a particular task, an important problem which we tackle in this article utilizing a boosting framework with transfer learning. In particular, we propose to adaptively learn transferable representations called super-features from the training data of both the target task and the auxiliary task. Those super-features and the coefficients for combining them are learned in an iterative stage-wise fashion. Unlike previous transfer learning methods, the super-features can be adaptively learned by weak learners from the data. Therefore, the proposed framework is sufficiently flexible to deal with complicated common structures among different learning tasks. We evaluate the performance of the proposed transfer learning method for two datasets from the Letor collection and one dataset collected from a commercial search engine, and we also compare our methods with several existing transfer learning methods. Our results demonstrate that the proposed method can enhance the ranking functions of the target tasks utilizing the training data from the auxiliary tasks.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "37", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Peng:2012:MVC, author = "Wei Peng and Tong Sun and Shriram Revankar and Tao Li", title = "Mining the {``Voice} of the Customer'' for Business Prioritization", journal = j-TIST, volume = "3", number = "2", pages = "38:1--38:??", month = feb, year = "2012", CODEN = "????", DOI = "https://doi.org/10.1145/2089094.2089114", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Fri Mar 16 15:10:10 MDT 2012", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "To gain competitiveness and sustained growth in the 21st century, most businesses are on a mission to become more customer-centric. In order to succeed in this endeavor, it is crucial not only to synthesize and analyze the VOC (the VOice of the Customer) data (i.e., the feedbacks or requirements raised by customers), but also to quickly turn these data into actionable knowledge. Although there are many technologies being developed in this complex problem space, most existing approaches in analyzing customer requests are ad hoc, time-consuming, error-prone, people-based processes which hardly scale well as the quantity of customer information explodes. This often results in the slow response to customer requests. In this article, in order to mine VOC to extract useful knowledge for the best product or service quality, we develop a hybrid framework that integrates domain knowledge with data-driven approaches to analyze the semi-structured customer requests. The framework consists of capturing functional features, discovering the overlap or correlation among the features, and identifying the evolving feature trend by using the knowledge transformation model. In addition, since understanding the relative importance of the individual customer request is very critical and has a direct impact on the effective prioritization in the development process, we develop a novel semantic enhanced link-based ranking (SELRank) algorithm for relatively rating/ranking both customer requests and products. The framework has been successfully applied on Xerox Office Group Feature Enhancement Requirements (XOG FER) datasets to analyze customer requests.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "38", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Hua:2012:ISS, author = "Xian-Sheng Hua and Qi Tian and Alberto {Del Bimbo} and Ramesh Jain", title = "Introduction to the {Special Section on Intelligent Multimedia Systems and Technology Part II}", journal = j-TIST, volume = "3", number = "3", pages = "39:1--39:??", month = may, year = "2012", CODEN = "????", DOI = "https://doi.org/10.1145/2168752.2168753", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Nov 6 18:47:23 MST 2012", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "39", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Yang:2012:MRM, author = "Yi-Hsuan Yang and Homer H. Chen", title = "Machine Recognition of Music Emotion: a Review", journal = j-TIST, volume = "3", number = "3", pages = "40:1--40:??", month = may, year = "2012", CODEN = "????", DOI = "https://doi.org/10.1145/2168752.2168754", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Nov 6 18:47:23 MST 2012", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "The proliferation of MP3 players and the exploding amount of digital music content call for novel ways of music organization and retrieval to meet the ever-increasing demand for easy and effective information access. As almost every music piece is created to convey emotion, music organization and retrieval by emotion is a reasonable way of accessing music information. A good deal of effort has been made in the music information retrieval community to train a machine to automatically recognize the emotion of a music signal. A central issue of machine recognition of music emotion is the conceptualization of emotion and the associated emotion taxonomy. Different viewpoints on this issue have led to the proposal of different ways of emotion annotation, model training, and result visualization. This article provides a comprehensive review of the methods that have been proposed for music emotion recognition. Moreover, as music emotion recognition is still in its infancy, there are many open issues. We review the solutions that have been proposed to address these issues and conclude with suggestions for further research.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "40", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Ewerth:2012:RVC, author = "Ralph Ewerth and Markus M{\"u}hling and Bernd Freisleben", title = "Robust Video Content Analysis via Transductive Learning", journal = j-TIST, volume = "3", number = "3", pages = "41:1--41:??", month = may, year = "2012", CODEN = "????", DOI = "https://doi.org/10.1145/2168752.2168755", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Nov 6 18:47:23 MST 2012", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Reliable video content analysis is an essential prerequisite for effective video search. An important current research question is how to develop robust video content analysis methods that produce satisfactory results for a large variety of video sources, distribution platforms, genres, and content. The work presented in this article exploits the observation that the appearance of objects and events is often related to a particular video sequence, episode, program, or broadcast. This motivates our idea of considering the content analysis task for a single video or episode as a transductive setting: the final classification model must be optimal for the given video only, and not in general, as expected for inductive learning. For this purpose, the unlabeled video test data have to be used in the learning process. In this article, a transductive learning framework for robust video content analysis based on feature selection and ensemble classification is presented. In contrast to related transductive approaches for video analysis (e.g., for concept detection), the framework is designed in a general manner and not only for a single task. The proposed framework is applied to the following video analysis tasks: shot boundary detection, face recognition, semantic video retrieval, and semantic indexing of computer game sequences. Experimental results for diverse video analysis tasks and large test sets demonstrate that the proposed transductive framework improves the robustness of the underlying state-of-the-art approaches, whereas transductive support vector machines do not solve particular tasks in a satisfactory manner.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "41", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Suk:2012:VHM, author = "Myunghoon Suk and Ashok Ramadass and Yohan Jin and B. Prabhakaran", title = "Video Human Motion Recognition Using a Knowledge-Based Hybrid Method Based on a Hidden {Markov} Model", journal = j-TIST, volume = "3", number = "3", pages = "42:1--42:??", month = may, year = "2012", CODEN = "????", DOI = "https://doi.org/10.1145/2168752.2168756", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Nov 6 18:47:23 MST 2012", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Human motion recognition in video data has several interesting applications in fields such as gaming, senior/assisted-living environments, and surveillance. In these scenarios, we may have to consider adding new motion classes (i.e., new types of human motions to be recognized), as well as new training data (e.g., for handling different type of subjects). Hence, both the accuracy of classification and training time for the machine learning algorithms become important performance parameters in these cases. In this article, we propose a knowledge-based hybrid (KBH) method that can compute the probabilities for hidden Markov models (HMMs) associated with different human motion classes. This computation is facilitated by appropriately mixing features from two different media types (3D motion capture and 2D video). We conducted a variety of experiments comparing the proposed KBH for HMMs and the traditional Baum-Welch algorithms. With the advantage of computing the HMM parameter in a noniterative manner, the KBH method outperforms the Baum-Welch algorithm both in terms of accuracy as well as in reduced training time. Moreover, we show in additional experiments that the KBH method also outperforms the linear support vector machine (SVM).", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "42", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Zhang:2012:RVT, author = "Shengping Zhang and Hongxun Yao and Xin Sun and Shaohui Liu", title = "Robust Visual Tracking Using an Effective Appearance Model Based on Sparse Coding", journal = j-TIST, volume = "3", number = "3", pages = "43:1--43:??", month = may, year = "2012", CODEN = "????", DOI = "https://doi.org/10.1145/2168752.2168757", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Nov 6 18:47:23 MST 2012", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Intelligent video surveillance is currently one of the most active research topics in computer vision, especially when facing the explosion of video data captured by a large number of surveillance cameras. As a key step of an intelligent surveillance system, robust visual tracking is very challenging for computer vision. However, it is a basic functionality of the human visual system (HVS). Psychophysical findings have shown that the receptive fields of simple cells in the visual cortex can be characterized as being spatially localized, oriented, and bandpass, and it forms a sparse, distributed representation of natural images. In this article, motivated by these findings, we propose an effective appearance model based on sparse coding and apply it in visual tracking. Specifically, we consider the responses of general basis functions extracted by independent component analysis on a large set of natural image patches as features and model the appearance of the tracked target as the probability distribution of these features. In order to make the tracker more robust to partial occlusion, camouflage environments, pose changes, and illumination changes, we further select features that are related to the target based on an entropy-gain criterion and ignore those that are not. The target is finally represented by the probability distribution of those related features. The target search is performed by minimizing the Matusita distance between the distributions of the target model and a candidate using Newton-style iterations. The experimental results validate that the proposed method is more robust and effective than three state-of-the-art methods.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "43", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Ji:2012:CAS, author = "Rongrong Ji and Hongxun Yao and Qi Tian and Pengfei Xu and Xiaoshuai Sun and Xianming Liu", title = "Context-Aware Semi-Local Feature Detector", journal = j-TIST, volume = "3", number = "3", pages = "44:1--44:??", month = may, year = "2012", CODEN = "????", DOI = "https://doi.org/10.1145/2168752.2168758", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Nov 6 18:47:23 MST 2012", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "How can interest point detectors benefit from contextual cues? In this articles, we introduce a context-aware semi-local detector (CASL) framework to give a systematic answer with three contributions: (1) We integrate the context of interest points to recurrently refine their detections. (2) This integration boosts interest point detectors from the traditionally local scale to a semi-local scale to discover more discriminative salient regions. (3) Such context-aware structure further enables us to bring forward category learning (usually in the subsequent recognition phase) into interest point detection to locate category-aware, meaningful salient regions. Our CASL detector consists of two phases. The first phase accumulates multiscale spatial correlations of local features into a difference of contextual Gaussians (DoCG) field. DoCG quantizes detector context to highlight contextually salient regions at a semi-local scale, which also reveals visual attentions to a certain extent. The second phase locates contextual peaks by mean shift search over the DoCG field, which subsequently integrates contextual cues into feature description. This phase enables us to integrate category learning into mean shift search kernels. This learning-based CASL mechanism produces more category-aware features, which substantially benefits the subsequent visual categorization process. We conducted experiments in image search, object characterization, and feature detector repeatability evaluations, which reported superior discriminability and comparable repeatability to state-of-the-art works.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "44", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Berretti:2012:DFF, author = "Stefano Berretti and Alberto {Del Bimbo} and Pietro Pala", title = "Distinguishing Facial Features for Ethnicity-Based {$3$D} Face Recognition", journal = j-TIST, volume = "3", number = "3", pages = "45:1--45:??", month = may, year = "2012", CODEN = "????", DOI = "https://doi.org/10.1145/2168752.2168759", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Nov 6 18:47:23 MST 2012", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Among different approaches for 3D face recognition, solutions based on local facial characteristics are very promising, mainly because they can manage facial expression variations by assigning different weights to different parts of the face. However, so far, a few works have investigated the individual relevance that local features play in 3D face recognition with very simple solutions applied in the practice. In this article, a local approach to 3D face recognition is combined with a feature selection model to study the relative relevance of different regions of the face for the purpose of discriminating between different subjects. The proposed solution is experimented using facial scans of the Face Recognition Grand Challenge dataset. Results of the experimentation are two-fold: they quantitatively demonstrate the assumption that different regions of the face have different relevance for face discrimination and also show that the relevance of facial regions changes for different ethnic groups.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "45", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Zhang:2012:GAS, author = "Ning Zhang and Ling-Yu Duan and Lingfang Li and Qingming Huang and Jun Du and Wen Gao and Ling Guan", title = "A Generic Approach for Systematic Analysis of Sports Videos", journal = j-TIST, volume = "3", number = "3", pages = "46:1--46:??", month = may, year = "2012", CODEN = "????", DOI = "https://doi.org/10.1145/2168752.2168760", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Nov 6 18:47:23 MST 2012", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Various innovative and original works have been applied and proposed in the field of sports video analysis. However, individual works have focused on sophisticated methodologies with particular sport types and there has been a lack of scalable and holistic frameworks in this field. This article proposes a solution and presents a systematic and generic approach which is experimented on a relatively large-scale sports consortia. The system aims at the event detection scenario of an input video with an orderly sequential process. Initially, domain knowledge-independent local descriptors are extracted homogeneously from the input video sequence. Then the video representation is created by adopting a bag-of-visual-words (BoW) model. The video's genre is first identified by applying the k-nearest neighbor (k-NN) classifiers on the initially obtained video representation, and various dissimilarity measures are assessed and evaluated analytically. Subsequently, an unsupervised probabilistic latent semantic analysis (PLSA)-based approach is employed at the same histogram-based video representation, characterizing each frame of video sequence into one of four view groups, namely closed-up-view, mid-view, long-view, and outer-field-view. Finally, a hidden conditional random field (HCRF) structured prediction model is utilized for interesting event detection. From experimental results, k-NN classifier using KL-divergence measurement demonstrates the best accuracy at 82.16\% for genre categorization. Supervised SVM and unsupervised PLSA have average classification accuracies at 82.86\% and 68.13\%, respectively. The HCRF model achieves 92.31\% accuracy using the unsupervised PLSA based label input, which is comparable with the supervised SVM based input at an accuracy of 93.08\%. In general, such a systematic approach can be widely applied in processing massive videos generically.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "46", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Leung:2012:ISM, author = "Clement H. C. Leung and Alice W. S. Chan and Alfredo Milani and Jiming Liu and Yuanxi Li", title = "Intelligent Social Media Indexing and Sharing Using an Adaptive Indexing Search Engine", journal = j-TIST, volume = "3", number = "3", pages = "47:1--47:??", month = may, year = "2012", CODEN = "????", DOI = "https://doi.org/10.1145/2168752.2168761", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Nov 6 18:47:23 MST 2012", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Effective sharing of diverse social media is often inhibited by limitations in their search and discovery mechanisms, which are particularly restrictive for media that do not lend themselves to automatic processing or indexing. Here, we present the structure and mechanism of an adaptive search engine which is designed to overcome such limitations. The basic framework of the adaptive search engine is to capture human judgment in the course of normal usage from user queries in order to develop semantic indexes which link search terms to media objects semantics. This approach is particularly effective for the retrieval of multimedia objects, such as images, sounds, and videos, where a direct analysis of the object features does not allow them to be linked to search terms, for example, nontextual/icon-based search, deep semantic search, or when search terms are unknown at the time the media repository is built. An adaptive search architecture is presented to enable the index to evolve with respect to user feedback, while a randomized query-processing technique guarantees avoiding local minima and allows the meaningful indexing of new media objects and new terms. The present adaptive search engine allows for the efficient community creation and updating of social media indexes, which is able to instill and propagate deep knowledge into social media concerning the advanced search and usage of media resources. Experiments with various relevance distribution settings have shown efficient convergence of such indexes, which enable intelligent search and sharing of social media resources that are otherwise hard to discover.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "47", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Chien:2012:ISS, author = "Steve Chien and Amedeo Cesta", title = "Introduction to the Special Section on Artificial Intelligence in Space", journal = j-TIST, volume = "3", number = "3", pages = "48:1--48:??", month = may, year = "2012", CODEN = "????", DOI = "https://doi.org/10.1145/2168752.2168762", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Nov 6 18:47:23 MST 2012", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "48", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Wagstaff:2012:DLS, author = "Kiri L. Wagstaff and Julian Panetta and Adnan Ansar and Ronald Greeley and Mary Pendleton Hoffer and Melissa Bunte and Norbert Sch{\"o}rghofer", title = "Dynamic Landmarking for Surface Feature Identification and Change Detection", journal = j-TIST, volume = "3", number = "3", pages = "49:1--49:??", month = may, year = "2012", CODEN = "????", DOI = "https://doi.org/10.1145/2168752.2168763", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Nov 6 18:47:23 MST 2012", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Given the large volume of images being sent back from remote spacecraft, there is a need for automated analysis techniques that can quickly identify interesting features in those images. Feature identification in individual images and automated change detection in multiple images of the same target are valuable for scientific studies and can inform subsequent target selection. We introduce a new approach to orbital image analysis called dynamic landmarking. It focuses on the identification and comparison of visually salient features in images. We have evaluated this approach on images collected by five Mars orbiters. These evaluations were motivated by three scientific goals: to study fresh impact craters, dust devil tracks, and dark slope streaks on Mars. In the process we also detected a different kind of surface change that may indicate seasonally exposed bedforms. These experiences also point the way to how this approach could be used in an onboard setting to analyze and prioritize data as it is collected.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "49", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Estlin:2012:AAS, author = "Tara A. Estlin and Benjamin J. Bornstein and Daniel M. Gaines and Robert C. Anderson and David R. Thompson and Michael Burl and Rebecca Casta{\~n}o and Michele Judd", title = "{AEGIS} Automated Science Targeting for the {MER Opportunity Rover}", journal = j-TIST, volume = "3", number = "3", pages = "50:1--50:??", month = may, year = "2012", CODEN = "????", DOI = "https://doi.org/10.1145/2168752.2168764", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Nov 6 18:47:23 MST 2012", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "The Autonomous Exploration for Gathering Increased Science (AEGIS) system enables automated data collection by planetary rovers. AEGIS software was uploaded to the Mars Exploration Rover (MER) mission's Opportunity rover in December 2009 and has successfully demonstrated automated onboard targeting based on scientist-specified objectives. Prior to AEGIS, images were transmitted from the rover to the operations team on Earth; scientists manually analyzed the images, selected geological targets for the rover's remote-sensing instruments, and then generated a command sequence to execute the new measurements. AEGIS represents a significant paradigm shift---by using onboard data analysis techniques, the AEGIS software uses scientist input to select high-quality science targets with no human in the loop. This approach allows the rover to autonomously select and sequence targeted observations in an opportunistic fashion, which is particularly applicable for narrow field-of-view instruments (such as the MER Mini-TES spectrometer, the MER Panoramic camera, and the 2011 Mars Science Laboratory (MSL) ChemCam spectrometer). This article provides an overview of the AEGIS automated targeting capability and describes how it is currently being used onboard the MER mission Opportunity rover.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "50", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Hayden:2012:UCM, author = "David S. Hayden and Steve Chien and David R. Thompson and Rebecca Casta{\~n}o", title = "Using Clustering and Metric Learning to Improve Science Return of Remote Sensed Imagery", journal = j-TIST, volume = "3", number = "3", pages = "51:1--51:??", month = may, year = "2012", CODEN = "????", DOI = "https://doi.org/10.1145/2168752.2168765", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Nov 6 18:47:23 MST 2012", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Current and proposed remote space missions, such as the proposed aerial exploration of Titan by an aerobot, often can collect more data than can be communicated back to Earth. Autonomous selective downlink algorithms can choose informative subsets of data to improve the science value of these bandwidth-limited transmissions. This requires statistical descriptors of the data that reflect very abstract and subtle distinctions in science content. We propose a metric learning strategy that teaches algorithms how best to cluster new data based on training examples supplied by domain scientists. We demonstrate that clustering informed by metric learning produces results that more closely match multiple scientists' labelings of aerial data than do clusterings based on random or periodic sampling. A new metric-learning strategy accommodates training sets produced by multiple scientists with different and potentially inconsistent mission objectives. Our methods are fit for current spacecraft processors (e.g., RAD750) and would further benefit from more advanced spacecraft processor architectures, such as OPERA.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "51", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Hoi:2012:ISS, author = "Steven C. H. Hoi and Rong Jin and Jinhui Tang and Zhi-Hua Zhou", title = "Introduction to the Special Section on Distance Metric Learning in Intelligent Systems", journal = j-TIST, volume = "3", number = "3", pages = "52:1--52:??", month = may, year = "2012", CODEN = "????", DOI = "https://doi.org/10.1145/2168752.2168766", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Nov 6 18:47:23 MST 2012", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "52", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Zhai:2012:MML, author = "Deming Zhai and Hong Chang and Shiguang Shan and Xilin Chen and Wen Gao", title = "Multiview Metric Learning with Global Consistency and Local Smoothness", journal = j-TIST, volume = "3", number = "3", pages = "53:1--53:??", month = may, year = "2012", CODEN = "????", DOI = "https://doi.org/10.1145/2168752.2168767", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Nov 6 18:47:23 MST 2012", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "In many real-world applications, the same object may have different observations (or descriptions) from multiview observation spaces, which are highly related but sometimes look different from each other. Conventional metric-learning methods achieve satisfactory performance on distance metric computation of data in a single-view observation space, but fail to handle well data sampled from multiview observation spaces, especially those with highly nonlinear structure. To tackle this problem, we propose a new method called Multiview Metric Learning with Global consistency and Local smoothness (MVML-GL) under a semisupervised learning setting, which jointly considers global consistency and local smoothness. The basic idea is to reveal the shared latent feature space of the multiview observations by embodying global consistency constraints and preserving local geometric structures. Specifically, this framework is composed of two main steps. In the first step, we seek a global consistent shared latent feature space, which not only preserves the local geometric structure in each space but also makes those labeled corresponding instances as close as possible. In the second step, the explicit mapping functions between the input spaces and the shared latent space are learned via regularized locally linear regression. Furthermore, these two steps both can be solved by convex optimizations in closed form. Experimental results with application to manifold alignment on real-world datasets of pose and facial expression demonstrate the effectiveness of the proposed method.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "53", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Zhang:2012:TML, author = "Yu Zhang and Dit-Yan Yeung", title = "Transfer Metric Learning with Semi-Supervised Extension", journal = j-TIST, volume = "3", number = "3", pages = "54:1--54:??", month = may, year = "2012", CODEN = "????", DOI = "https://doi.org/10.1145/2168752.2168768", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Nov 6 18:47:23 MST 2012", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Distance metric learning plays a very crucial role in many data mining algorithms because the performance of an algorithm relies heavily on choosing a good metric. However, the labeled data available in many applications is scarce, and hence the metrics learned are often unsatisfactory. In this article, we consider a transfer-learning setting in which some related source tasks with labeled data are available to help the learning of the target task. We first propose a convex formulation for multitask metric learning by modeling the task relationships in the form of a task covariance matrix. Then we regard transfer learning as a special case of multitask learning and adapt the formulation of multitask metric learning to the transfer-learning setting for our method, called transfer metric learning (TML). In TML, we learn the metric and the task covariances between the source tasks and the target task under a unified convex formulation. To solve the convex optimization problem, we use an alternating method in which each subproblem has an efficient solution. Moreover, in many applications, some unlabeled data is also available in the target task, and so we propose a semi-supervised extension of TML called STML to further improve the generalization performance by exploiting the unlabeled data based on the manifold assumption. Experimental results on some commonly used transfer-learning applications demonstrate the effectiveness of our method.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "54", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Xu:2012:MLE, author = "Jun-Ming Xu and Xiaojin Zhu and Timothy T. Rogers", title = "Metric Learning for Estimating Psychological Similarities", journal = j-TIST, volume = "3", number = "3", pages = "55:1--55:??", month = may, year = "2012", CODEN = "????", DOI = "https://doi.org/10.1145/2168752.2168769", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Nov 6 18:47:23 MST 2012", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "An important problem in cognitive psychology is to quantify the perceived similarities between stimuli. Previous work attempted to address this problem with multidimensional scaling (MDS) and its variants. However, there are several shortcomings of the MDS approaches. We propose Yada, a novel general metric-learning procedure based on two-alternative forced-choice behavioral experiments. Our method learns forward and backward nonlinear mappings between an objective space in which the stimuli are defined by the standard feature vector representation and a subjective space in which the distance between a pair of stimuli corresponds to their perceived similarity. We conduct experiments on both synthetic and real human behavioral datasets to assess the effectiveness of Yada. The results show that Yada outperforms several standard embedding and metric-learning algorithms, both in terms of likelihood and recovery error.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "55", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Zheng:2012:MTP, author = "Yan-Tao Zheng and Zheng-Jun Zha and Tat-Seng Chua", title = "Mining Travel Patterns from Geotagged Photos", journal = j-TIST, volume = "3", number = "3", pages = "56:1--56:??", month = may, year = "2012", CODEN = "????", DOI = "https://doi.org/10.1145/2168752.2168770", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Nov 6 18:47:23 MST 2012", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Recently, the phenomenal advent of photo-sharing services, such as Flickr and Panoramio, have led to voluminous community-contributed photos with text tags, timestamps, and geographic references on the Internet. The photos, together with their time- and geo-references, become the digital footprints of photo takers and implicitly document their spatiotemporal movements. This study aims to leverage the wealth of these enriched online photos to analyze people's travel patterns at the local level of a tour destination. Specifically, we focus our analysis on two aspects: (1) tourist movement patterns in relation to the regions of attractions (RoA), and (2) topological characteristics of travel routes by different tourists. To do so, we first build a statistically reliable database of travel paths from a noisy pool of community-contributed geotagged photos on the Internet. We then investigate the tourist traffic flow among different RoAs by exploiting the Markov chain model. Finally, the topological characteristics of travel routes are analyzed by performing a sequence clustering on tour routes. Testings on four major cities demonstrate promising results of the proposed system.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "56", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Rendle:2012:FML, author = "Steffen Rendle", title = "Factorization Machines with {libFM}", journal = j-TIST, volume = "3", number = "3", pages = "57:1--57:??", month = may, year = "2012", CODEN = "????", DOI = "https://doi.org/10.1145/2168752.2168771", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Nov 6 18:47:23 MST 2012", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Factorization approaches provide high accuracy in several important prediction problems, for example, recommender systems. However, applying factorization approaches to a new prediction problem is a nontrivial task and requires a lot of expert knowledge. Typically, a new model is developed, a learning algorithm is derived, and the approach has to be implemented. Factorization machines (FM) are a generic approach since they can mimic most factorization models just by feature engineering. This way, factorization machines combine the generality of feature engineering with the superiority of factorization models in estimating interactions between categorical variables of large domain. libFM is a software implementation for factorization machines that features stochastic gradient descent (SGD) and alternating least-squares (ALS) optimization, as well as Bayesian inference using Markov Chain Monto Carlo (MCMC). This article summarizes the recent research on factorization machines both in terms of modeling and learning, provides extensions for the ALS and MCMC algorithms, and describes the software tool libFM.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "57", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Gabrilovich:2012:ISS, author = "Evgeniy Gabrilovich and Zhong Su and Jie Tang", title = "Introduction to the {Special Section on Computational Models of Collective Intelligence in the Social Web}", journal = j-TIST, volume = "3", number = "4", pages = "58:1--58:??", month = sep, year = "2012", CODEN = "????", DOI = "https://doi.org/10.1145/2337542.2337543", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Nov 6 18:47:26 MST 2012", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "58", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Herdagdelen:2012:BGP, author = "Ama{\c{c}} Herdagdelen and Marco Baroni", title = "Bootstrapping a Game with a Purpose for Commonsense Collection", journal = j-TIST, volume = "3", number = "4", pages = "59:1--59:??", month = sep, year = "2012", CODEN = "????", DOI = "https://doi.org/10.1145/2337542.2337544", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Nov 6 18:47:26 MST 2012", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Text mining has been very successful in extracting huge amounts of commonsense knowledge from data, but the extracted knowledge tends to be extremely noisy. Manual construction of knowledge repositories, on the other hand, tends to produce high-quality data in very small amounts. We propose an architecture to combine the best of both worlds: A game with a purpose that induces humans to clean up data automatically extracted by text mining. First, a text miner trained on a set of known commonsense facts harvests many more candidate facts from corpora. Then, a simple slot-machine-with-a-purpose game presents these candidate facts to the players for verification by playing. As a result, a new dataset of high precision commonsense knowledge is created. This combined architecture is able to produce significantly better commonsense facts than the state-of-the-art text miner alone. Furthermore, we report that bootstrapping (i.e., training the text miner on the output of the game) improves the subsequent performance of the text miner.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "59", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Carmel:2012:FBT, author = "David Carmel and Erel Uziel and Ido Guy and Yosi Mass and Haggai Roitman", title = "Folksonomy-Based Term Extraction for Word Cloud Generation", journal = j-TIST, volume = "3", number = "4", pages = "60:1--60:??", month = sep, year = "2012", CODEN = "????", DOI = "https://doi.org/10.1145/2337542.2337545", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Nov 6 18:47:26 MST 2012", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "In this work we study the task of term extraction for word cloud generation in sparsely tagged domains, in which manual tags are scarce. We present a folksonomy-based term extraction method, called tag-boost, which boosts terms that are frequently used by the public to tag content. Our experiments with tag-boost based term extraction over different domains demonstrate tremendous improvement in word cloud quality, as reflected by the agreement between manual tags of the testing items and the cloud's terms extracted from the items' content. Moreover, our results demonstrate the high robustness of this approach, as compared to alternative cloud generation methods that exhibit a high sensitivity to data sparseness. Additionally, we show that tag-boost can be effectively applied even in nontagged domains, by using an external rich folksonomy borrowed from a well-tagged domain.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "60", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Wang:2012:IOS, author = "Guan Wang and Sihong Xie and Bing Liu and Philip S. Yu", title = "Identify Online Store Review Spammers via Social Review Graph", journal = j-TIST, volume = "3", number = "4", pages = "61:1--61:??", month = sep, year = "2012", CODEN = "????", DOI = "https://doi.org/10.1145/2337542.2337546", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Nov 6 18:47:26 MST 2012", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Online shopping reviews provide valuable information for customers to compare the quality of products, store services, and many other aspects of future purchases. However, spammers are joining this community trying to mislead consumers by writing fake or unfair reviews to confuse the consumers. Previous attempts have used reviewers' behaviors such as text similarity and rating patterns, to detect spammers. These studies are able to identify certain types of spammers, for instance, those who post many similar reviews about one target. However, in reality, there are other kinds of spammers who can manipulate their behaviors to act just like normal reviewers, and thus cannot be detected by the available techniques. In this article, we propose a novel concept of review graph to capture the relationships among all reviewers, reviews and stores that the reviewers have reviewed as a heterogeneous graph. We explore how interactions between nodes in this graph could reveal the cause of spam and propose an iterative computation model to identify suspicious reviewers. In the review graph, we have three kinds of nodes, namely, reviewer, review, and store. We capture their relationships by introducing three fundamental concepts, the trustiness of reviewers, the honesty of reviews, and the reliability of stores, and identifying their interrelationships: a reviewer is more trustworthy if the person has written more honesty reviews; a store is more reliable if it has more positive reviews from trustworthy reviewers; and a review is more honest if many other honest reviews support it. This is the first time such intricate relationships have been identified for spam detection and captured in a graph model. We further develop an effective computation method based on the proposed graph model. Different from any existing approaches, we do not use an review text information. Our model is thus complementary to existing approaches and able to find more difficult and subtle spamming activities, which are agreed upon by human judges after they evaluate our results.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "61", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Lerman:2012:USM, author = "Kristina Lerman and Tad Hogg", title = "Using Stochastic Models to Describe and Predict Social Dynamics of {Web} Users", journal = j-TIST, volume = "3", number = "4", pages = "62:1--62:??", month = sep, year = "2012", CODEN = "????", DOI = "https://doi.org/10.1145/2337542.2337547", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Nov 6 18:47:26 MST 2012", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "The popularity of content in social media is unequally distributed, with some items receiving a disproportionate share of attention from users. Predicting which newly-submitted items will become popular is critically important for both the hosts of social media content and its consumers. Accurate and timely prediction would enable hosts to maximize revenue through differential pricing for access to content or ad placement. Prediction would also give consumers an important tool for filtering the content. Predicting the popularity of content in social media is challenging due to the complex interactions between content quality and how the social media site highlights its content. Moreover, most social media sites selectively present content that has been highly rated by similar users, whose similarity is indicated implicitly by their behavior or explicitly by links in a social network. While these factors make it difficult to predict popularity a priori, stochastic models of user behavior on these sites can allow predicting popularity based on early user reactions to new content. By incorporating the various mechanisms through which web sites display content, such models improve on predictions that are based on simply extrapolating from the early votes. Specifically, for one such site, the news aggregator Digg, we show how a stochastic model distinguishes the effect of the increased visibility due to the network from how interested users are in the content. We find a wide range of interest, distinguishing stories primarily of interest to users in the network (``niche interests'') from those of more general interest to the user community. This distinction is useful for predicting a story's eventual popularity from users' early reactions to the story.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "62", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Yin:2012:LCT, author = "Zhijun Yin and Liangliang Cao and Quanquan Gu and Jiawei Han", title = "Latent Community Topic Analysis: Integration of Community Discovery with Topic Modeling", journal = j-TIST, volume = "3", number = "4", pages = "63:1--63:??", month = sep, year = "2012", CODEN = "????", DOI = "https://doi.org/10.1145/2337542.2337548", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Nov 6 18:47:26 MST 2012", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "This article studies the problem of latent community topic analysis in text-associated graphs. With the development of social media, a lot of user-generated content is available with user networks. Along with rich information in networks, user graphs can be extended with text information associated with nodes. Topic modeling is a classic problem in text mining and it is interesting to discover the latent topics in text-associated graphs. Different from traditional topic modeling methods considering links, we incorporate community discovery into topic analysis in text-associated graphs to guarantee the topical coherence in the communities so that users in the same community are closely linked to each other and share common latent topics. We handle topic modeling and community discovery in the same framework. In our model we separate the concepts of community and topic, so one community can correspond to multiple topics and multiple communities can share the same topic. We compare different methods and perform extensive experiments on two real datasets. The results confirm our hypothesis that topics could help understand community structure, while community structure could help model topics.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "63", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Sizov:2012:LGS, author = "Sergej Sizov", title = "Latent Geospatial Semantics of Social Media", journal = j-TIST, volume = "3", number = "4", pages = "64:1--64:??", month = sep, year = "2012", CODEN = "????", DOI = "https://doi.org/10.1145/2337542.2337549", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Nov 6 18:47:26 MST 2012", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Multimodal understanding of shared content is an important success factor for many Web 2.0 applications and platforms. This article addresses the fundamental question of geo-spatial awareness in social media applications. In this context, we introduce an approach for improved characterization of social media by combining text features (e.g., tags as a prominent example of short, unstructured text labels) with spatial knowledge (e.g., geotags, coordinates of images, and videos). Our model-based framework GeoFolk combines these two aspects in order to construct better algorithms for content management, retrieval, and sharing. We demonstrate in systematic studies the benefits of this combination for a broad spectrum of scenarios related to social media: recommender systems, automatic content organization and filtering, and event detection. Furthermore, we establish a simple and technically sound model that can be seen as a reference baseline for future research in the field of geotagged social media.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "64", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Cortizo:2012:ISS, author = "Jos{\'e} Carlos Cortizo and Francisco Carrero and Iv{\'a}n Cantador and Jos{\'e} Antonio Troyano and Paolo Rosso", title = "Introduction to the Special Section on Search and Mining User-Generated Content", journal = j-TIST, volume = "3", number = "4", pages = "65:1--65:??", month = sep, year = "2012", CODEN = "????", DOI = "https://doi.org/10.1145/2337542.2337550", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Nov 6 18:47:26 MST 2012", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "The primary goal of this special section of ACM Transactions on Intelligent Systems and Technology is to foster research in the interplay between Social Media, Data/Opinion Mining and Search, aiming to reflect the actual developments in technologies that exploit user-generated content.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "65", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Paltoglou:2012:TMD, author = "Georgios Paltoglou and Mike Thelwall", title = "{Twitter}, {MySpace}, {Digg}: Unsupervised Sentiment Analysis in Social Media", journal = j-TIST, volume = "3", number = "4", pages = "66:1--66:??", month = sep, year = "2012", CODEN = "????", DOI = "https://doi.org/10.1145/2337542.2337551", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Nov 6 18:47:26 MST 2012", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Sentiment analysis is a growing area of research with significant applications in both industry and academia. Most of the proposed solutions are centered around supervised, machine learning approaches and review-oriented datasets. In this article, we focus on the more common informal textual communication on the Web, such as online discussions, tweets and social network comments and propose an intuitive, less domain-specific, unsupervised, lexicon-based approach that estimates the level of emotional intensity contained in text in order to make a prediction. Our approach can be applied to, and is tested in, two different but complementary contexts: subjectivity detection and polarity classification. Extensive experiments were carried on three real-world datasets, extracted from online social Web sites and annotated by human evaluators, against state-of-the-art supervised approaches. The results demonstrate that the proposed algorithm, even though unsupervised, outperforms machine learning solutions in the majority of cases, overall presenting a very robust and reliable solution for sentiment analysis of informal communication on the Web.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "66", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Trivedi:2012:LSB, author = "Anusua Trivedi and Piyush Rai and Hal {Daum{\'e} III} and Scott L. Duvall", title = "Leveraging Social Bookmarks from Partially Tagged Corpus for Improved {Web} Page Clustering", journal = j-TIST, volume = "3", number = "4", pages = "67:1--67:??", month = sep, year = "2012", CODEN = "????", DOI = "https://doi.org/10.1145/2337542.2337552", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Nov 6 18:47:26 MST 2012", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Automatic clustering of Web pages helps a number of information retrieval tasks, such as improving user interfaces, collection clustering, introducing diversity in search results, etc. Typically, Web page clustering algorithms use only features extracted from the page-text. However, the advent of social-bookmarking Web sites, such as StumbleUpon.com and Delicious.com, has led to a huge amount of user-generated content such as the social tag information that is associated with the Web pages. In this article, we present a subspace based feature extraction approach that leverages the social tag information to complement the page-contents of a Web page for extracting beter features, with the goal of improved clustering performance. In our approach, we consider page-text and tags as two separate views of the data, and learn a shared subspace that maximizes the correlation between the two views. Any clustering algorithm can then be applied in this subspace. We then present an extension that allows our approach to be applicable even if the Web page corpus is only partially tagged, that is, when the social tags are present for not all, but only for a small number of Web pages. We compare our subspace based approach with a number of baselines that use tag information in various other ways, and show that the subspace based approach leads to improved performance on the Web page clustering task. We also discuss some possible future work including an active learning extension that can help in choosing which Web pages to get tags for, if we only can get the social tags for only a small number of Web pages.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "67", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Potthast:2012:IRC, author = "Martin Potthast and Benno Stein and Fabian Loose and Steffen Becker", title = "Information Retrieval in the {Commentsphere}", journal = j-TIST, volume = "3", number = "4", pages = "68:1--68:??", month = sep, year = "2012", CODEN = "????", DOI = "https://doi.org/10.1145/2337542.2337553", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Nov 6 18:47:26 MST 2012", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "This article studies information retrieval tasks related to Web comments. Prerequisite of such a study and a main contribution of the article is a unifying survey of the research field. We identify the most important retrieval tasks related to comments, namely filtering, ranking, and summarization. Within these tasks, we distinguish two paradigms according to which comments are utilized and which we designate as comment-targeting and comment-exploiting. Within the first paradigm, the comments themselves form the retrieval targets. Within the second paradigm, the commented items form the retrieval targets (i.e., comments are used as an additional information source to improve the retrieval performance for the commented items). We report on four case studies to demonstrate the exploration of the commentsphere under information retrieval aspects: comment filtering, comment ranking, comment summarization and cross-media retrieval. The first three studies deal primarily with comment-targeting retrieval, while the last one deals with comment-exploiting retrieval. Throughout the article, connections to information retrieval research are pointed out.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "68", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Carmel:2012:RBN, author = "David Carmel and Haggai Roitman and Elad Yom-Tov", title = "On the Relationship between Novelty and Popularity of User-Generated Content", journal = j-TIST, volume = "3", number = "4", pages = "69:1--69:??", month = sep, year = "2012", CODEN = "????", DOI = "https://doi.org/10.1145/2337542.2337554", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Nov 6 18:47:26 MST 2012", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "This work deals with the task of predicting the popularity of user-generated content. We demonstrate how the novelty of newly published content plays an important role in affecting its popularity. More specifically, we study three dimensions of novelty. The first one, termed contemporaneous novelty, models the relative novelty embedded in a new post with respect to contemporary content that was generated by others. The second type of novelty, termed self novelty, models the relative novelty with respect to the user's own contribution history. The third type of novelty, termed discussion novelty, relates to the novelty of the comments associated by readers with respect to the post content. We demonstrate the contribution of the new novelty measures to estimating blog-post popularity by predicting the number of comments expected for a fresh post. We further demonstrate how novelty based measures can be utilized for predicting the citation volume of academic papers.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "69", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Li:2012:ERQ, author = "Xiaonan Li and Chengkai Li and Cong Yu", title = "Entity-Relationship Queries over {Wikipedia}", journal = j-TIST, volume = "3", number = "4", pages = "70:1--70:??", month = sep, year = "2012", CODEN = "????", DOI = "https://doi.org/10.1145/2337542.2337555", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Nov 6 18:47:26 MST 2012", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Wikipedia is the largest user-generated knowledge base. We propose a structured query mechanism, entity-relationship query, for searching entities in the Wikipedia corpus by their properties and interrelationships. An entity-relationship query consists of multiple predicates on desired entities. The semantics of each predicate is specified with keywords. Entity-relationship query searches entities directly over text instead of preextracted structured data stores. This characteristic brings two benefits: (1) Query semantics can be intuitively expressed by keywords; (2) It only requires rudimentary entity annotation, which is simpler than explicitly extracting and reasoning about complex semantic information before query-time. We present a ranking framework for general entity-relationship queries and a position-based Bounded Cumulative Model (BCM) for accurate ranking of query answers. We also explore various weighting schemes for further improving the accuracy of BCM. We test our ideas on a 2008 version of Wikipedia using a collection of 45 queries pooled from INEX entity ranking track and our own crafted queries. Experiments show that the ranking and weighting schemes are both effective, particularly on multipredicate queries.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "70", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Wang:2012:EFW, author = "Haofen Wang and Linyun Fu and Wei Jin and Yong Yu", title = "{EachWiki}: Facilitating Wiki Authoring by Annotation Suggestion", journal = j-TIST, volume = "3", number = "4", pages = "71:1--71:??", month = sep, year = "2012", CODEN = "????", DOI = "https://doi.org/10.1145/2337542.2337556", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Nov 6 18:47:26 MST 2012", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Wikipedia, one of the best-known wikis and the world's largest free online encyclopedia, has embraced the power of collaborative editing to harness collective intelligence. However, using such a wiki to create high-quality articles is not as easy as people imagine, given for instance the difficulty of reusing knowledge already available in Wikipedia. As a result, the heavy burden of upbuilding and maintaining the ever-growing online encyclopedia still rests on a small group of people. In this article, we aim at facilitating wiki authoring by providing annotation recommendations, thus lightening the burden of both contributors and administrators. We leverage the collective wisdom of the users by exploiting Semantic Web technologies with Wikipedia data and adopt a unified algorithm to support link, category, and semantic relation recommendation. A prototype system named EachWiki is proposed and evaluated. The experimental results show that it has achieved considerable improvements in terms of effectiveness, efficiency and usability. The proposed approach can also be applied to other wiki-based collaborative editing systems.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "71", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Lampos:2012:NES, author = "Vasileios Lampos and Nello Cristianini", title = "Nowcasting Events from the Social {Web} with Statistical Learning", journal = j-TIST, volume = "3", number = "4", pages = "72:1--72:??", month = sep, year = "2012", CODEN = "????", DOI = "https://doi.org/10.1145/2337542.2337557", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Nov 6 18:47:26 MST 2012", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "We present a general methodology for inferring the occurrence and magnitude of an event or phenomenon by exploring the rich amount of unstructured textual information on the social part of the Web. Having geo-tagged user posts on the microblogging service of Twitter as our input data, we investigate two case studies. The first consists of a benchmark problem, where actual levels of rainfall in a given location and time are inferred from the content of tweets. The second one is a real-life task, where we infer regional Influenza-like Illness rates in the effort of detecting timely an emerging epidemic disease. Our analysis builds on a statistical learning framework, which performs sparse learning via the bootstrapped version of LASSO to select a consistent subset of textual features from a large amount of candidates. In both case studies, selected features indicate close semantic correlation with the target topics and inference, conducted by regression, has a significant performance, especially given the short length --approximately one year-- of Twitter's data time series.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "72", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Tang:2012:RUI, author = "Xuning Tang and Christopher C. Yang", title = "Ranking User Influence in Healthcare Social Media", journal = j-TIST, volume = "3", number = "4", pages = "73:1--73:??", month = sep, year = "2012", CODEN = "????", DOI = "https://doi.org/10.1145/2337542.2337558", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Nov 6 18:47:26 MST 2012", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Due to the revolutionary development of Web 2.0 technology, individual users have become major contributors of Web content in online social media. In light of the growing activities, how to measure a user's influence to other users in online social media becomes increasingly important. This research need is urgent especially in the online healthcare community since positive influence can be beneficial while negative influence may cause-negative impact on other users of the same community. In this article, a research framework was proposed to study user influence within the online healthcare community. We proposed a new approach to incorporate users' reply relationship, conversation content and response immediacy which capture both explicit and implicit interaction between users to identify influential users of online healthcare community. A weighted social network is developed to represent the influence between users. We tested our proposed techniques thoroughly on two medical support forums. Two algorithms UserRank and Weighted in-degree are benchmarked with PageRank and in-degree. Experiment results demonstrated the validity and effectiveness of our proposed approaches.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "73", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Strohmaier:2012:EFI, author = "Markus Strohmaier and Denis Helic and Dominik Benz and Christian K{\"o}rner and Roman Kern", title = "Evaluation of Folksonomy Induction Algorithms", journal = j-TIST, volume = "3", number = "4", pages = "74:1--74:??", month = sep, year = "2012", CODEN = "????", DOI = "https://doi.org/10.1145/2337542.2337559", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Nov 6 18:47:26 MST 2012", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Algorithms for constructing hierarchical structures from user-generated metadata have caught the interest of the academic community in recent years. In social tagging systems, the output of these algorithms is usually referred to as folksonomies (from folk-generated taxonomies). Evaluation of folksonomies and folksonomy induction algorithms is a challenging issue complicated by the lack of golden standards, lack of comprehensive methods and tools as well as a lack of research and empirical/simulation studies applying these methods. In this article, we report results from a broad comparative study of state-of-the-art folksonomy induction algorithms that we have applied and evaluated in the context of five social tagging systems. In addition to adopting semantic evaluation techniques, we present and adopt a new technique that can be used to evaluate the usefulness of folksonomies for navigation. Our work sheds new light on the properties and characteristics of state-of-the-art folksonomy induction algorithms and introduces a new pragmatic approach to folksonomy evaluation, while at the same time identifying some important limitations and challenges of folksonomy evaluation. Our results show that folksonomy induction algorithms specifically developed to capture intuitions of social tagging systems outperform traditional hierarchical clustering techniques. To the best of our knowledge, this work represents the largest and most comprehensive evaluation study of state-of-the-art folksonomy induction algorithms to date.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "74", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Zhang:2012:EAL, author = "Xiaoqin Shelley Zhang and Bhavesh Shrestha and Sungwook Yoon and Subbarao Kambhampati and Phillip DiBona and Jinhong K. Guo and Daniel McFarlane and Martin O. Hofmann and Kenneth Whitebread and Darren Scott Appling and Elizabeth T. Whitaker and Ethan B. Trewhitt and Li Ding and James R. Michaelis and Deborah L. McGuinness and James A. Hendler and Janardhan Rao Doppa and Charles Parker and Thomas G. Dietterich and Prasad Tadepalli and Weng-Keen Wong and Derek Green and Anton Rebguns and Diana Spears and Ugur Kuter and Geoff Levine and Gerald DeJong and Reid L. MacTavish and Santiago Onta{\~n}{\'o}n and Jainarayan Radhakrishnan and Ashwin Ram and Hala Mostafa and Huzaifa Zafar and Chongjie Zhang and Daniel Corkill and Victor Lesser and Zhexuan Song", title = "An Ensemble Architecture for Learning Complex Problem-Solving Techniques from Demonstration", journal = j-TIST, volume = "3", number = "4", pages = "75:1--75:??", month = sep, year = "2012", CODEN = "????", DOI = "https://doi.org/10.1145/2337542.2337560", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Nov 6 18:47:26 MST 2012", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "We present a novel ensemble architecture for learning problem-solving techniques from a very small number of expert solutions and demonstrate its effectiveness in a complex real-world domain. The key feature of our ``Generalized Integrated Learning Architecture'' (GILA) is a set of heterogeneous independent learning and reasoning (ILR) components, coordinated by a central meta-reasoning executive (MRE). The ILRs are weakly coupled in the sense that all coordination during learning and performance happens through the MRE. Each ILR learns independently from a small number of expert demonstrations of a complex task. During performance, each ILR proposes partial solutions to subproblems posed by the MRE, which are then selected from and pieced together by the MRE to produce a complete solution. The heterogeneity of the learner-reasoners allows both learning and problem solving to be more effective because their abilities and biases are complementary and synergistic. We describe the application of this novel learning and problem solving architecture to the domain of airspace management, where multiple requests for the use of airspaces need to be deconflicted, reconciled, and managed automatically. Formal evaluations show that our system performs as well as or better than humans after learning from the same training data. Furthermore, GILA outperforms any individual ILR run in isolation, thus demonstrating the power of the ensemble architecture for learning and problem solving.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "75", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Wang:2012:LCR, author = "Zhenxing Wang and Laiwan Chan", title = "Learning Causal Relations in Multivariate Time Series Data", journal = j-TIST, volume = "3", number = "4", pages = "76:1--76:??", month = sep, year = "2012", CODEN = "????", DOI = "https://doi.org/10.1145/2337542.2337561", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Nov 6 18:47:26 MST 2012", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Many applications naturally involve time series data and the vector autoregression (VAR), and the structural VAR (SVAR) are dominant tools to investigate relations between variables in time series. In the first part of this work, we show that the SVAR method is incapable of identifying contemporaneous causal relations for Gaussian process. In addition, least squares estimators become unreliable when the scales of the problems are large and observations are limited. In the remaining part, we propose an approach to apply Bayesian network learning algorithms to identify SVARs from time series data in order to capture both temporal and contemporaneous causal relations, and avoid high-order statistical tests. The difficulty of applying Bayesian network learning algorithms to time series is that the sizes of the networks corresponding to time series tend to be large, and high-order statistical tests are required by Bayesian network learning algorithms in this case. To overcome the difficulty, we show that the search space of conditioning sets d-separating two vertices should be a subset of the Markov blankets. Based on this fact, we propose an algorithm enabling us to learn Bayesian networks locally, and make the largest order of statistical tests independent of the scales of the problems. Empirical results show that our algorithm outperforms existing methods in terms of both efficiency and accuracy.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "76", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Mandrake:2012:SSD, author = "Lukas Mandrake and Umaa Rebbapragada and Kiri L. Wagstaff and David Thompson and Steve Chien and Daniel Tran and Robert T. Pappalardo and Damhnait Gleeson and Rebecca Casta{\~n}o", title = "Surface Sulfur Detection via Remote Sensing and Onboard Classification", journal = j-TIST, volume = "3", number = "4", pages = "77:1--77:??", month = sep, year = "2012", CODEN = "????", DOI = "https://doi.org/10.1145/2337542.2337562", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Nov 6 18:47:26 MST 2012", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Orbital remote sensing provides a powerful way to efficiently survey targets such as the Earth and other planets and moons for features of interest. One such feature of astrobiological relevance is the presence of surface sulfur deposits. These deposits have been observed to be associated with microbial activity at the Borup Fiord glacial springs in Canada, a location that may provide an analogue to other icy environments such as Europa. This article evaluates automated classifiers for detecting sulfur in remote sensing observations by the hyperion spectrometer on the EO-1 spacecraft. We determined that a data-driven machine learning solution was needed because the sulfur could not be detected by simply matching observations to sulfur lab spectra. We also evaluated several methods (manual and automated) for identifying the most relevant attributes (spectral wavelengths) needed for successful sulfur detection. Our findings include (1) the Borup Fiord sulfur deposits were best modeled as containing two sub-populations: sulfur on ice and sulfur on rock; (2) as expected, classifiers using Gaussian kernels outperformed those based on linear kernels, and should be adopted when onboard computational constraints permit; and (3) Recursive Feature Elimination selected sensible and effective features for use in the computationally constrained environment onboard EO-1. This study helped guide the selection of algorithm parameters and configuration for the classification system currently operational on EO-1. Finally, we discuss implications for a similar onboard classification system for a future Europa orbiter.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "77", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{King:2013:ISS, author = "Irwin King and Wolfgang Nejdl", title = "Introduction to the special section on {Twitter} and microblogging services", journal = j-TIST, volume = "4", number = "1", pages = "1:1--1:??", month = jan, year = "2013", CODEN = "????", DOI = "https://doi.org/10.1145/2414425.2414426", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sun May 5 09:06:55 MDT 2013", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "1", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Cheng:2013:CDF, author = "Zhiyuan Cheng and James Caverlee and Kyumin Lee", title = "A content-driven framework for geolocating microblog users", journal = j-TIST, volume = "4", number = "1", pages = "2:1--2:??", month = jan, year = "2013", CODEN = "????", DOI = "https://doi.org/10.1145/2414425.2414427", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sun May 5 09:06:55 MDT 2013", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Highly dynamic real-time microblog systems have already published petabytes of real-time human sensor data in the form of status updates. However, the lack of user adoption of geo-based features per user or per post signals that the promise of microblog services as location-based sensing systems may have only limited reach and impact. Thus, in this article, we propose and evaluate a probabilistic framework for estimating a microblog user's location based purely on the content of the user's posts. Our framework can overcome the sparsity of geo-enabled features in these services and bring augmented scope and breadth to emerging location-based personalized information services. Three of the key features of the proposed approach are: (i) its reliance purely on publicly available content; (ii) a classification component for automatically identifying words in posts with a strong local geo-scope; and (iii) a lattice-based neighborhood smoothing model for refining a user's location estimate. On average we find that the location estimates converge quickly, placing 51\% of users within 100 miles of their actual location.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "2", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Liu:2013:NER, author = "Xiaohua Liu and Furu Wei and Shaodian Zhang and Ming Zhou", title = "Named entity recognition for tweets", journal = j-TIST, volume = "4", number = "1", pages = "3:1--3:??", month = jan, year = "2013", CODEN = "????", DOI = "https://doi.org/10.1145/2414425.2414428", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sun May 5 09:06:55 MDT 2013", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Two main challenges of Named Entity Recognition (NER) for tweets are the insufficient information in a tweet and the lack of training data. We propose a novel method consisting of three core elements: (1) normalization of tweets; (2) combination of a K-Nearest Neighbors (KNN) classifier with a linear Conditional Random Fields (CRF) model; and (3) semisupervised learning framework. The tweet normalization preprocessing corrects common ill-formed words using a global linear model. The KNN-based classifier conducts prelabeling to collect global coarse evidence across tweets while the CRF model conducts sequential labeling to capture fine-grained information encoded in a tweet. The semisupervised learning plus the gazetteers alleviate the lack of training data. Extensive experiments show the advantages of our method over the baselines as well as the effectiveness of normalization, KNN, and semisupervised learning.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "3", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Chang:2013:IRR, author = "Yi Chang and Anlei Dong and Pranam Kolari and Ruiqiang Zhang and Yoshiyuki Inagaki and Fernanodo Diaz and Hongyuan Zha and Yan Liu", title = "Improving recency ranking using {Twitter} data", journal = j-TIST, volume = "4", number = "1", pages = "4:1--4:??", month = jan, year = "2013", CODEN = "????", DOI = "https://doi.org/10.1145/2414425.2414429", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sun May 5 09:06:55 MDT 2013", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "In Web search and vertical search, recency ranking refers to retrieving and ranking documents by both relevance and freshness. As impoverished in-links and click information is the biggest challenge for recency ranking, we advocate the use of Twitter data to address the challenge in this article. We propose a method to utilize Twitter TinyURL to detect fresh and high-quality documents, and leverage Twitter data to generate novel and effective features for ranking. The empirical experiments demonstrate that the proposed approach effectively improves a commercial search engine for both Web search ranking and tweet vertical ranking.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "4", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Han:2013:LNS, author = "Bo Han and Paul Cook and Timothy Baldwin", title = "Lexical normalization for social media text", journal = j-TIST, volume = "4", number = "1", pages = "5:1--5:??", month = jan, year = "2013", CODEN = "????", DOI = "https://doi.org/10.1145/2414425.2414430", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sun May 5 09:06:55 MDT 2013", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Twitter provides access to large volumes of data in real time, but is notoriously noisy, hampering its utility for NLP. In this article, we target out-of-vocabulary words in short text messages and propose a method for identifying and normalizing lexical variants. Our method uses a classifier to detect lexical variants, and generates correction candidates based on morphophonemic similarity. Both word similarity and context are then exploited to select the most probable correction candidate for the word. The proposed method doesn't require any annotations, and achieves state-of-the-art performance over an SMS corpus and a novel dataset based on Twitter.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "5", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Shen:2013:RUT, author = "Keyi Shen and Jianmin Wu and Ya Zhang and Yiping Han and Xiaokang Yang and Li Song and Xiao Gu", title = "Reorder user's tweets", journal = j-TIST, volume = "4", number = "1", pages = "6:1--6:??", month = jan, year = "2013", CODEN = "????", DOI = "https://doi.org/10.1145/2414425.2414431", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sun May 5 09:06:55 MDT 2013", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Twitter displays the tweets a user received in a reversed chronological order, which is not always the best choice. As Twitter is full of messages of very different qualities, many informative or relevant tweets might be flooded or displayed at the bottom while some nonsense buzzes might be ranked higher. In this work, we present a supervised learning method for personalized tweets reordering based on user interests. User activities on Twitter, in terms of tweeting, retweeting, and replying, are leveraged to obtain the training data for reordering models. Through exploring a rich set of social and personalized features, we model the relevance of tweets by minimizing the pairwise loss of relevant and irrelevant tweets. The tweets are then reordered according to the predicted relevance scores. Experimental results with real Twitter user activities demonstrated the effectiveness of our method. The new method achieved above 30\% accuracy gain compared with the default ordering in Twitter based on time.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "6", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Guy:2013:ISS, author = "Ido Guy and Li Chen and Michelle X. Zhou", title = "Introduction to the special section on social recommender systems", journal = j-TIST, volume = "4", number = "1", pages = "7:1--7:??", month = jan, year = "2013", CODEN = "????", DOI = "https://doi.org/10.1145/2414425.2414432", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sun May 5 09:06:55 MDT 2013", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "7", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Quijano-Sanchez:2013:SFG, author = "Lara Quijano-Sanchez and Juan A. Recio-Garcia and Belen Diaz-Agudo and Guillermo Jimenez-Diaz", title = "Social factors in group recommender systems", journal = j-TIST, volume = "4", number = "1", pages = "8:1--8:??", month = jan, year = "2013", CODEN = "????", DOI = "https://doi.org/10.1145/2414425.2414433", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sun May 5 09:06:55 MDT 2013", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "In this article we review the existing techniques in group recommender systems and we propose some improvement based on the study of the different individual behaviors when carrying out a decision-making process. Our method includes an analysis of group personality composition and trust between each group member to improve the accuracy of group recommenders. This way we simulate the argumentation process followed by groups of people when agreeing on a common activity in a more realistic way. Moreover, we reflect how they expect the system to behave in a long term recommendation process. This is achieved by including a memory of past recommendations that increases the satisfaction of users whose preferences have not been taken into account in previous recommendations.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "8", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Zhang:2013:GVR, author = "Weishi Zhang and Guiguang Ding and Li Chen and Chunping Li and Chengbo Zhang", title = "Generating virtual ratings from {Chinese} reviews to augment online recommendations", journal = j-TIST, volume = "4", number = "1", pages = "9:1--9:??", month = jan, year = "2013", CODEN = "????", DOI = "https://doi.org/10.1145/2414425.2414434", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sun May 5 09:06:55 MDT 2013", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Collaborative filtering (CF) recommenders based on User-Item rating matrix as explicitly obtained from end users have recently appeared promising in recommender systems. However, User-Item rating matrix is not always available or very sparse in some web applications, which has critical impact to the application of CF recommenders. In this article we aim to enhance the online recommender system by fusing virtual ratings as derived from user reviews. Specifically, taking into account of Chinese reviews' characteristics, we propose to fuse the self-supervised emotion-integrated sentiment classification results into CF recommenders, by which the User-Item Rating Matrix can be inferred by decomposing item reviews that users gave to the items. The main advantage of this approach is that it can extend CF recommenders to some web applications without user rating information. In the experiments, we have first identified the self-supervised sentiment classification's higher precision and recall by comparing it with traditional classification methods. Furthermore, the classification results, as behaving as virtual ratings, were incorporated into both user-based and item-based CF algorithms. We have also conducted an experiment to evaluate the proximity between the virtual and real ratings and clarified the effectiveness of the virtual ratings. The experimental results demonstrated the significant impact of virtual ratings on increasing system's recommendation accuracy in different data conditions (i.e., conditions with real ratings and without).", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "9", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Biancalana:2013:ASR, author = "Claudio Biancalana and Fabio Gasparetti and Alessandro Micarelli and Giuseppe Sansonetti", title = "An approach to social recommendation for context-aware mobile services", journal = j-TIST, volume = "4", number = "1", pages = "10:1--10:??", month = jan, year = "2013", CODEN = "????", DOI = "https://doi.org/10.1145/2414425.2414435", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sun May 5 09:06:55 MDT 2013", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Nowadays, several location-based services (LBSs) allow their users to take advantage of information from the Web about points of interest (POIs) such as cultural events or restaurants. To the best of our knowledge, however, none of these provides information taking into account user preferences, or other elements, in addition to location, that contribute to define the context of use. The provided suggestions do not consider, for example, time, day of week, weather, user activity or means of transport. This article describes a social recommender system able to identify user preferences and information needs, thus suggesting personalized recommendations related to POIs in the surroundings of the user's current location. The proposed approach achieves the following goals: (i) to supply, unlike the current LBSs, a methodology for identifying user preferences and needs to be used in the information filtering process; (ii) to exploit the ever-growing amount of information from social networking, user reviews, and local search Web sites; (iii) to establish procedures for defining the context of use to be employed in the recommendation of POIs with low effort. The flexibility of the architecture is such that our approach can be easily extended to any category of POI. Experimental tests carried out on real users enabled us to quantify the benefits of the proposed approach in terms of performance improvement.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "10", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Gedikli:2013:IRA, author = "Fatih Gedikli and Dietmar Jannach", title = "Improving recommendation accuracy based on item-specific tag preferences", journal = j-TIST, volume = "4", number = "1", pages = "11:1--11:??", month = jan, year = "2013", CODEN = "????", DOI = "https://doi.org/10.1145/2414425.2414436", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sun May 5 09:06:55 MDT 2013", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "In recent years, different proposals have been made to exploit Social Web tagging information to build more effective recommender systems. The tagging data, for example, were used to identify similar users or were viewed as additional information about the recommendable items. Recent research has indicated that ``attaching feelings to tags'' is experienced by users as a valuable means to express which features of an item they particularly like or dislike. When following such an approach, users would therefore not only add tags to an item as in usual Web 2.0 applications, but also attach a preference ( affect ) to the tag itself, expressing, for example, whether or not they liked a certain actor in a given movie. In this work, we show how this additional preference data can be exploited by a recommender system to make more accurate predictions. In contrast to previous work, which also relied on so-called tag preferences to enhance the predictive accuracy of recommender systems, we argue that tag preferences should be considered in the context of an item. We therefore propose new schemes to infer and exploit context-specific tag preferences in the recommendation process. An evaluation on two different datasets reveals that our approach is capable of providing more accurate recommendations than previous tag-based recommender algorithms and recent tag-agnostic matrix factorization techniques.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "11", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Chen:2013:MRW, author = "Yu-Chih Chen and Yu-Shi Lin and Yu-Chun Shen and Shou-De Lin", title = "A modified random walk framework for handling negative ratings and generating explanations", journal = j-TIST, volume = "4", number = "1", pages = "12:1--12:??", month = jan, year = "2013", CODEN = "????", DOI = "https://doi.org/10.1145/2414425.2414437", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sun May 5 09:06:55 MDT 2013", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "The concept of random walk (RW) has been widely applied in the design of recommendation systems. RW-based approaches are effective in handling locality problem and taking extra information, such as the relationships between items or users, into consideration. However, the traditional RW-based approach has a serious limitation in handling bidirectional opinions. The propagation of positive and negative information simultaneously in a graph is nontrivial using random walk. To address the problem, this article presents a novel and efficient RW-based model that can handle both positive and negative comments with the guarantee of convergence. Furthermore, we argue that a good recommendation system should provide users not only a list of recommended items but also reasonable explanations for the decisions. Therefore, we propose a technique that generates explanations by backtracking the influential paths and subgraphs. The results of experiments on the MovieLens and Netflix datasets show that our model significantly outperforms state-of-the-art RW-based algorithms, and is capable of improving the overall performance in the ensemble with other models.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "12", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Said:2013:MRC, author = "Alan Said and Shlomo Berkovsky and Ernesto W. {De Luca}", title = "Movie recommendation in context", journal = j-TIST, volume = "4", number = "1", pages = "13:1--13:??", month = jan, year = "2013", CODEN = "????", DOI = "https://doi.org/10.1145/2414425.2414438", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sun May 5 09:06:55 MDT 2013", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "The challenge and workshop on Context-Aware Movie Recommendation (CAMRa2010) were conducted jointly in 2010 with the Recommender Systems conference. The challenge focused on three context-aware recommendation scenarios: time-based, mood-based, and social recommendation. The participants were provided with anonymized datasets from two real-world online movie recommendation communities and competed against each other for obtaining the highest accuracy of recommendations. The datasets contained contextual features, such as tags, annotation, social relationsips, and comments, normally not available in public recommendation datasets. More than 40 teams from 21 countries participated in the challenge. Their participation was summarized by 10 papers published by the workshop, which have been extended and revised for this special section. In this preface we overview the challenge datasets, tasks, evaluation metrics, and the obtained outcomes.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "13", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Bellogin:2013:ECS, author = "Alejandro Bellog{\'\i}n and Iv{\'a}n Cantador and Fernando D{\'\i}ez and Pablo Castells and Enrique Chavarriaga", title = "An empirical comparison of social, collaborative filtering, and hybrid recommenders", journal = j-TIST, volume = "4", number = "1", pages = "14:1--14:??", month = jan, year = "2013", CODEN = "????", DOI = "https://doi.org/10.1145/2414425.2414439", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sun May 5 09:06:55 MDT 2013", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "In the Social Web, a number of diverse recommendation approaches have been proposed to exploit the user generated contents available in the Web, such as rating, tagging, and social networking information. In general, these approaches naturally require the availability of a wide amount of these user preferences. This may represent an important limitation for real applications, and may be somewhat unnoticed in studies focusing on overall precision, in which a failure to produce recommendations gets blurred when averaging the obtained results or, even worse, is just not accounted for, as users with no recommendations are typically excluded from the performance calculations. In this article, we propose a coverage metric that uncovers and compensates for the incompleteness of performance evaluations based only on precision. We use this metric together with precision metrics in an empirical comparison of several social, collaborative filtering, and hybrid recommenders. The obtained results show that a better balance between precision and coverage can be achieved by combining social-based filtering (high accuracy, low coverage) and collaborative filtering (low accuracy, high coverage) recommendation techniques. We thus explore several hybrid recommendation approaches to balance this trade-off. In particular, we compare, on the one hand, techniques integrating collaborative and social information into a single model, and on the other, linear combinations of recommenders. For the last approach, we also propose a novel strategy to dynamically adjust the weight of each recommender on a user-basis, utilizing graph measures as indicators of the target user's connectedness and relevance in a social network.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "14", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Liu:2013:STC, author = "Nathan N. Liu and Luheng He and Min Zhao", title = "Social temporal collaborative ranking for context aware movie recommendation", journal = j-TIST, volume = "4", number = "1", pages = "15:1--15:??", month = jan, year = "2013", CODEN = "????", DOI = "https://doi.org/10.1145/2414425.2414440", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sun May 5 09:06:55 MDT 2013", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Most existing collaborative filtering models only consider the use of user feedback (e.g., ratings) and meta data (e.g., content, demographics). However, in most real world recommender systems, context information, such as time and social networks, are also very important factors that could be considered in order to produce more accurate recommendations. In this work, we address several challenges for the context aware movie recommendation tasks in CAMRa 2010: (1) how to combine multiple heterogeneous forms of user feedback? (2) how to cope with dynamic user and item characteristics? (3) how to capture and utilize social connections among users? For the first challenge, we propose a novel ranking based matrix factorization model to aggregate explicit and implicit user feedback. For the second challenge, we extend this model to a sequential matrix factorization model to enable time-aware parametrization. Finally, we introduce a network regularization function to constrain user parameters based on social connections. To the best of our knowledge, this is the first study that investigates the collective modeling of social and temporal dynamics. Experiments on the CAMRa 2010 dataset demonstrated clear improvements over many baselines.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "15", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Shi:2013:MCM, author = "Yue Shi and Martha Larson and Alan Hanjalic", title = "Mining contextual movie similarity with matrix factorization for context-aware recommendation", journal = j-TIST, volume = "4", number = "1", pages = "16:1--16:??", month = jan, year = "2013", CODEN = "????", DOI = "https://doi.org/10.1145/2414425.2414441", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sun May 5 09:06:55 MDT 2013", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Context-aware recommendation seeks to improve recommendation performance by exploiting various information sources in addition to the conventional user-item matrix used by recommender systems. We propose a novel context-aware movie recommendation algorithm based on joint matrix factorization (JMF). We jointly factorize the user-item matrix containing general movie ratings and other contextual movie similarity matrices to integrate contextual information into the recommendation process. The algorithm was developed within the scope of the mood-aware recommendation task that was offered by the Moviepilot mood track of the 2010 context-aware movie recommendation (CAMRa) challenge. Although the algorithm could generalize to other types of contextual information, in this work, we focus on two: movie mood tags and movie plot keywords. Since the objective in this challenge track is to recommend movies for a user given a specified mood, we devise a novel mood-specific movie similarity measure for this purpose. We enhance the recommendation based on this measure by also deploying the second movie similarity measure proposed in this article that takes into account the movie plot keywords. We validate the effectiveness of the proposed JMF algorithm with respect to the recommendation performance by carrying out experiments on the Moviepilot challenge dataset. We demonstrate that exploiting contextual information in JMF leads to significant improvement over several state-of-the-art approaches that generate movie recommendations without using contextual information. We also demonstrate that our proposed mood-specific movie similarity is better suited for the task than the conventional mood-based movie similarity measures. Finally, we show that the enhancement provided by the movie similarity capturing the plot keywords is particularly helpful in improving the recommendation to those users who are significantly more active in rating the movies than other users.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "16", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Okada:2013:MDA, author = "Isamu Okada and Hitoshi Yamamoto", title = "Mathematical description and analysis of adaptive risk choice behavior", journal = j-TIST, volume = "4", number = "1", pages = "17:1--17:??", month = jan, year = "2013", CODEN = "????", DOI = "https://doi.org/10.1145/2414425.2414442", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sun May 5 09:06:55 MDT 2013", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Which risk should one choose when facing alternatives with different levels of risk? We discuss here adaptive processes in such risk choice behavior by generalizing the study of Roos et al. [2010]. We deal with an n -choice game in which every player sequentially chooses n times of lotteries of which there are two types: a safe lottery and a risky lottery. We analyze this model in more detail by elaborating the game. Based on the results of mathematical analysis, replicator dynamics analysis, and numerical simulations, we derived some salient features of risk choice behavior. We show that all the risk strategies can be divided into two groups: persistence and nonpersistence. We also proved that the dynamics with perturbation in which a mutation is installed is globally asymptotically stable to a unique equilibrium point for any initial population. The numerical simulations clarify that the number of persistent strategies seldom increases regardless of the increase in n, and suggest that a rarity of dominant choice strategies is widely observed in many social contexts. These facts not only go hand-in-hand with some well-known insights from prospect theory, but may also provide some theoretical hypotheses for various fields such as behavioral economics, ecology, sociology, and consumer behavioral theory.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "17", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Song:2013:OSM, author = "Xuan Song and Huijing Zhao and Jinshi Cui and Xiaowei Shao and Ryosuke Shibasaki and Hongbin Zha", title = "An online system for multiple interacting targets tracking: Fusion of laser and vision, tracking and learning", journal = j-TIST, volume = "4", number = "1", pages = "18:1--18:??", month = jan, year = "2013", CODEN = "????", DOI = "https://doi.org/10.1145/2414425.2414443", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sun May 5 09:06:55 MDT 2013", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Multitarget tracking becomes significantly more challenging when the targets are in close proximity or frequently interact with each other. This article presents a promising online system to deal with these problems. The novelty of this system is that laser and vision are integrated with tracking and online learning to complement each other in one framework: when the targets do not interact with each other, the laser-based independent trackers are employed and the visual information is extracted simultaneously to train some classifiers online for ``possible interacting targets''. When the targets are in close proximity, the classifiers learned online are used alongside visual information to assist in tracking. Therefore, this mode of cooperation not only deals with various tough problems encountered in tracking, but also ensures that the entire process can be completely online and automatic. Experimental results demonstrate that laser and vision fully display their respective advantages in our system, and it is easy for us to obtain a good trade-off between tracking accuracy and the time-cost factor.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "18", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Chopra:2013:ISS, author = "Amit K. Chopra and Alexander Artikis and Jamal Bentahar and Frank Dignum", title = "Introduction to the special section on agent communication", journal = j-TIST, volume = "4", number = "2", pages = "19:1--19:??", month = mar, year = "2013", CODEN = "????", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sun May 5 09:06:55 MDT 2013", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "19", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Chopra:2013:RDA, author = "Amit K. Chopra and Alexander Artikis and Jamal Bentahar and Marco Colombetti and Frank Dignum and Nicoletta Fornara and Andrew J. I. Jones and Munindar P. Singh and Pinar Yolum", title = "Research directions in agent communication", journal = j-TIST, volume = "4", number = "2", pages = "20:1--20:??", month = mar, year = "2013", CODEN = "????", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sun May 5 09:06:55 MDT 2013", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Increasingly, software engineering involves open systems consisting of autonomous and heterogeneous participants or agents who carry out loosely coupled interactions. Accordingly, understanding and specifying communications among agents is a key concern. A focus on ways to formalize meaning distinguishes agent communication from traditional distributed computing: meaning provides a basis for flexible interactions and compliance checking. Over the years, a number of approaches have emerged with some essential and some irrelevant distinctions drawn among them. As agent abstractions gain increasing traction in the software engineering of open systems, it is important to resolve the irrelevant and highlight the essential distinctions, so that future research can be focused in the most productive directions. This article is an outcome of extensive discussions among agent communication researchers, aimed at taking stock of the field and at developing, criticizing, and refining their positions on specific approaches and future challenges. This article serves some important purposes, including identifying (1) points of broad consensus; (2) points where substantive differences remain; and (3) interesting directions of future work.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "20", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Gerard:2013:FVP, author = "Scott N. Gerard and Munindar P. Singh", title = "Formalizing and verifying protocol refinements", journal = j-TIST, volume = "4", number = "2", pages = "21:1--21:??", month = mar, year = "2013", CODEN = "????", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sun May 5 09:06:55 MDT 2013", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "A (business) protocol describes, in high-level terms, a pattern of communication between two or more participants, specifically via the creation and manipulation of the commitments between them. In this manner, a protocol offers both flexibility and rigor: a participant may communicate in any way it chooses as long as it discharges all of its activated commitments. Protocols thus promise benefits in engineering cross-organizational business processes. However, software engineering using protocols presupposes a formalization of protocols and a notion of the refinement of one protocol by another. Refinement for protocols is both intuitively obvious (e.g., PayViaCheck is clearly a kind of Pay ) and technically nontrivial (e.g., compared to Pay, PayViaCheck involves different participants exchanging different messages). This article formalizes protocols and their refinement. It develops Proton, an analysis tool for protocol specifications that overlays a model checker to compute whether one protocol refines another with respect to a stated mapping. Proton and its underlying theory are evaluated by formalizing several protocols from the literature and verifying all and only the expected refinements.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "21", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Baldoni:2013:CRS, author = "Matteo Baldoni and Cristina Baroglio and Elisa Marengo and Viviana Patti", title = "Constitutive and regulative specifications of commitment protocols: a decoupled approach", journal = j-TIST, volume = "4", number = "2", pages = "22:1--22:??", month = mar, year = "2013", CODEN = "????", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sun May 5 09:06:55 MDT 2013", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Interaction protocols play a fundamental role in multiagent systems. In this work, after analyzing the trends that are emerging not only from research on multiagent interaction protocols but also from neighboring fields, like research on workflows and business processes, we propose a novel definition of commitment-based interaction protocols, that is characterized by the decoupling of the constitutive and the regulative specifications and that explicitly foresees a representation of the latter based on constraints among commitments. A clear distinction between the two representations has many advantages, mainly residing in a greater openness of multiagent systems, and an easier reuse of protocols and of action definitions. A language, named 2CL, for writing regulative specifications is also given together with a designer-oriented graphical notation.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "22", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Falcone:2013:ISS, author = "Rino Falcone and Munindar P. Singh", title = "Introduction to special section on trust in multiagent systems", journal = j-TIST, volume = "4", number = "2", pages = "23:1--23:??", month = mar, year = "2013", CODEN = "????", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sun May 5 09:06:55 MDT 2013", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "23", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Zhang:2013:FTM, author = "Jie Zhang and Robin Cohen", title = "A framework for trust modeling in multiagent electronic marketplaces with buying advisors to consider varying seller behavior and the limiting of seller bids", journal = j-TIST, volume = "4", number = "2", pages = "24:1--24:??", month = mar, year = "2013", CODEN = "????", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sun May 5 09:06:55 MDT 2013", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "In this article, we present a framework of use in electronic marketplaces that allows buying agents to model the trustworthiness of selling agents in an effective way, making use of seller ratings provided by other buying agents known as advisors. The trustworthiness of the advisors is also modeled, using an approach that combines both personal and public knowledge and allows the relative weighting to be adjusted over time. Through a series of experiments that simulate e-marketplaces, including ones where sellers may vary their behavior over time, we are able to demonstrate that our proposed framework delivers effective seller recommendations to buyers, resulting in important buyer profit. We also propose limiting seller bids as a method for promoting seller honesty, thus facilitating successful selection of sellers by buyers, and demonstrate the value of this approach through experimental results. Overall, this research is focused on the technological aspects of electronic commerce and specifically on technology that would be used to manage trust.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "24", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Erriquez:2013:BUS, author = "Elisabetta Erriquez and Wiebe van der Hoek and Michael Wooldridge", title = "Building and using social structures: a case study using the agent {ART} testbed", journal = j-TIST, volume = "4", number = "2", pages = "25:1--25:??", month = mar, year = "2013", CODEN = "????", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sun May 5 09:06:55 MDT 2013", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "This article investigates the conjecture that agents who make decisions in scenarios where trust is important can benefit from the use of a social structure, representing the social relationships that exist between agents. We propose techniques that can be used by agents to initially build and then progressively update such a structure in the light of experience. We describe an implementation of our techniques in the domain of the Agent ART testbed: we take two existing agents for this domain (``Simplet'' and ``Connected'') and compare their performance with versions that use our social structure (``SocialSimplet'' and ``SocialConnected''). We show that SocialSimplet and SocialConnected outperform their counterparts with respect to the quality of the interactions, the number of rounds won in a competition, and the total utility gained.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "25", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Burnett:2013:STB, author = "Chris Burnett and Timothy J. Norman and Katia Sycara", title = "Stereotypical trust and bias in dynamic multiagent systems", journal = j-TIST, volume = "4", number = "2", pages = "26:1--26:??", month = mar, year = "2013", CODEN = "????", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sun May 5 09:06:55 MDT 2013", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Large-scale multiagent systems have the potential to be highly dynamic. Trust and reputation are crucial concepts in these environments, as it may be necessary for agents to rely on their peers to perform as expected, and learn to avoid untrustworthy partners. However, aspects of highly dynamic systems introduce issues which make the formation of trust relationships difficult. For example, they may be short-lived, precluding agents from gaining the necessary experiences to make an accurate trust evaluation. This article describes a new approach, inspired by theories of human organizational behavior, whereby agents generalize their experiences with previously encountered partners as stereotypes, based on the observable features of those partners and their behaviors. Subsequently, these stereotypes are applied when evaluating new and unknown partners. Furthermore, these stereotypical opinions can be communicated within the society, resulting in the notion of stereotypical reputation. We show how this approach can complement existing state-of-the-art trust models, and enhance the confidence in the evaluations that can be made about trustees when direct and reputational information is lacking or limited. Furthermore, we show how a stereotyping approach can help agents detect unwanted biases in the reputational opinions they receive from others in the society.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "26", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Falcone:2013:MKR, author = "Rino Falcone and Michele Piunti and Matteo Venanzi and Cristiano Castelfranchi", title = "From manifesta to krypta: The relevance of categories for trusting others", journal = j-TIST, volume = "4", number = "2", pages = "27:1--27:??", month = mar, year = "2013", CODEN = "????", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sun May 5 09:06:55 MDT 2013", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "In this article we consider the special abilities needed by agents for assessing trust based on inference and reasoning. We analyze the case in which it is possible to infer trust towards unknown counterparts by reasoning on abstract classes or categories of agents shaped in a concrete application domain. We present a scenario of interacting agents providing a computational model implementing different strategies to assess trust. Assuming a medical domain, categories, including both competencies and dispositions of possible trustees, are exploited to infer trust towards possibly unknown counterparts. The proposed approach for the cognitive assessment of trust relies on agents' abilities to analyze heterogeneous information sources along different dimensions. Trust is inferred based on specific observable properties (manifesta), namely explicitly readable signals indicating internal features (krypta) regulating agents' behavior and effectiveness on specific tasks. Simulative experiments evaluate the performance of trusting agents adopting different strategies to delegate tasks to possibly unknown trustees, while experimental results show the relevance of this kind of cognitive ability in the case of open multiagent systems.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "27", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Li:2013:ISS, author = "Qing Li and Xiangfeng Luo and Liu Wenyin and Cristina Conati", title = "Introduction to the special section on intelligent tutoring and coaching systems", journal = j-TIST, volume = "4", number = "2", pages = "28:1--28:??", month = mar, year = "2013", CODEN = "????", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sun May 5 09:06:55 MDT 2013", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "28", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Folsom-Kovarik:2013:TPR, author = "Jeremiah T. Folsom-Kovarik and Gita Sukthankar and Sae Schatz", title = "Tractable {POMDP} representations for intelligent tutoring systems", journal = j-TIST, volume = "4", number = "2", pages = "29:1--29:??", month = mar, year = "2013", CODEN = "????", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sun May 5 09:06:55 MDT 2013", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "With Partially Observable Markov Decision Processes (POMDPs), Intelligent Tutoring Systems (ITSs) can model individual learners from limited evidence and plan ahead despite uncertainty. However, POMDPs need appropriate representations to become tractable in ITSs that model many learner features, such as mastery of individual skills or the presence of specific misconceptions. This article describes two POMDP representations- state queues and observation chains -that take advantage of ITS task properties and let POMDPs scale to represent over 100 independent learner features. A real-world military training problem is given as one example. A human study ( n = 14) provides initial validation for the model construction. Finally, evaluating the experimental representations with simulated students helps predict their impact on ITS performance. The compressed representations can model a wide range of simulated problems with instructional efficacy equal to lossless representations. With improved tractability, POMDP ITSs can accommodate more numerous or more detailed learner states and inputs.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "29", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Yen:2013:LIS, author = "Neil Y. Yen and Timothy K. Shih and Qun Jin", title = "{LONET}: an interactive search network for intelligent lecture path generation", journal = j-TIST, volume = "4", number = "2", pages = "30:1--30:??", month = mar, year = "2013", CODEN = "????", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sun May 5 09:06:55 MDT 2013", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Sharing resources and information on the Internet has become an important activity for education. In distance learning, instructors can benefit from resources, also known as Learning Objects (LOs), to create plenteous materials for specific learning purposes. Our repository (called the MINE Registry) has been developed for storing and sharing learning objects, around 22,000 in total, in the past few years. To enhance reusability, one significant concept named Reusability Tree was implemented to trace the process of changes. Also, weighting and ranking metrics have been proposed to enhance the searchability in the repository. Following the successful implementation, this study goes further to investigate the relationships between LOs from a perspective of social networks. The LONET (Learning Object Network), as an extension of Reusability Tree, is newly proposed and constructed to clarify the vague reuse scenario in the past, and to summarize collaborative intelligence through past interactive usage experiences. We define a social structure in our repository based on past usage experiences from instructors, by proposing a set of metrics to evaluate the interdependency such as prerequisites and references. The structure identifies usage experiences and can be graphed in terms of implicit and explicit relations among learning objects. As a practical contribution, an adaptive algorithm is proposed to mine the social structure in our repository. The algorithm generates adaptive routes, based on past usage experiences, by computing possible interactive input, such as search criteria and feedback from instructors, and assists them in generating specific lectures.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "30", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Ehara:2013:PRS, author = "Yo Ehara and Nobuyuki Shimizu and Takashi Ninomiya and Hiroshi Nakagawa", title = "Personalized reading support for second-language {Web} documents", journal = j-TIST, volume = "4", number = "2", pages = "31:1--31:??", month = mar, year = "2013", CODEN = "????", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sun May 5 09:06:55 MDT 2013", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "A novel intelligent interface eases the browsing of Web documents written in the second languages of users. It automatically predicts words unfamiliar to the user by a collective intelligence method and glosses them with their meaning in advance. If the prediction succeeds, the user does not need to consult a dictionary; even if it fails, the user can correct the prediction. The correction data are collected and used to improve the accuracy of further predictions. The prediction is personalized in that every user's language ability is estimated by a state-of-the-art language testing model, which is trained in a practical response time with only a small sacrifice of prediction accuracy. The system was evaluated in terms of prediction accuracy and reading simulation. The reading simulation results show that this system can reduce the number of clicks for most readers with insufficient vocabulary to read documents and can significantly reduce the remaining number of unfamiliar words after the prediction and glossing for all users.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "31", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Wang:2013:RCI, author = "Fei-Yue Wang and Pak Kin Wong", title = "Research commentary: Intelligent systems and technology for integrative and predictive medicine: an {ACP} approach", journal = j-TIST, volume = "4", number = "2", pages = "32:1--32:??", month = mar, year = "2013", CODEN = "????", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sun May 5 09:06:55 MDT 2013", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "One of the principal goals in medicine is to determine and implement the best treatment for patients through fastidious estimation of the effects and benefits of therapeutic procedures. The inherent complexities of physiological and pathological networks that span across orders of magnitude in time and length scales, however, represent fundamental hurdles in determining effective treatments for patients. Here we argue for a new approach, called the ACP-based approach, that combines artificial (societies), computational (experiments), and parallel (execution) methods in intelligent systems and technology for integrative and predictive medicine, or more generally, precision medicine and smart health management. The advent of artificial societies that collect the clinically relevant information in prognostics and therapeutics provides a promising platform for organizing and experimenting complex physiological systems toward integrative medicine. The ability of computational experiments to analyze distinct, interactive systems such as the host mechanisms, pathological pathways, and therapeutic strategies, as well as other factors using the artificial systems, will enable control and management through parallel execution of real and artificial systems concurrently within the integrative medicine context. The development of this framework in integrative medicine, fueled by close collaborations between physicians, engineers, and scientists, will result in preventive and predictive practices of a personal, proactive, and precise nature, including rational combinatorial treatments, adaptive therapeutics, and patient-oriented disease management.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "32", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Tabia:2013:PBA, author = "Hedi Tabia and Mohamed Daoudi and Jean-Philippe Vandeborre and Olivier Colot", title = "A parts-based approach for automatic {$3$D} shape categorization using belief functions", journal = j-TIST, volume = "4", number = "2", pages = "33:1--33:??", month = mar, year = "2013", CODEN = "????", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sun May 5 09:06:55 MDT 2013", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Grouping 3D objects into (semantically) meaningful categories is a challenging and important problem in 3D mining and shape processing. Here, we present a novel approach to categorize 3D objects. The method described in this article, is a belief-function-based approach and consists of two stages: the training stage, where 3D objects in the same category are processed and a set of representative parts is constructed, and the labeling stage, where unknown objects are categorized. The experimental results obtained on the Tosca-Sumner and the Shrec07 datasets show that the system efficiently performs in categorizing 3D models.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "33", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Wang:2013:LIC, author = "Zhengxiang Wang and Yiqun Hu and Liang-Tien Chia", title = "Learning image-to-class distance metric for image classification", journal = j-TIST, volume = "4", number = "2", pages = "34:1--34:??", month = mar, year = "2013", CODEN = "????", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sun May 5 09:06:55 MDT 2013", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Image-To-Class (I2C) distance is a novel distance used for image classification and has successfully handled datasets with large intra-class variances. However, it uses Euclidean distance for measuring the distance between local features in different classes, which may not be the optimal distance metric in real image classification problems. In this article, we propose a distance metric learning method to improve the performance of I2C distance by learning per-class Mahalanobis metrics in a large margin framework. Our I2C distance is adaptive to different classes by combining with the learned metric for each class. These multiple per-class metrics are learned simultaneously by forming a convex optimization problem with the constraints that the I2C distance from each training image to its belonging class should be less than the distances to other classes by a large margin. A subgradient descent method is applied to efficiently solve this optimization problem. For efficiency and scalability to large-scale problems, we also show how to simplify the method to learn a diagonal matrix for each class. We show in experiments that our learned Mahalanobis I2C distance can significantly outperform the original Euclidean I2C distance as well as other distance metric learning methods in several prevalent image datasets, and our simplified diagonal matrices can preserve the performance but significantly speed up the metric learning procedure for large-scale datasets. We also show in experiment that our method is able to correct the class imbalance problem, which usually leads the NN-based methods toward classes containing more training images.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "34", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Song:2013:FOU, author = "Xuan Song and Xiaowei Shao and Quanshi Zhang and Ryosuke Shibasaki and Huijing Zhao and Jinshi Cui and Hongbin Zha", title = "A fully online and unsupervised system for large and high-density area surveillance: Tracking, semantic scene learning and abnormality detection", journal = j-TIST, volume = "4", number = "2", pages = "35:1--35:??", month = mar, year = "2013", CODEN = "????", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sun May 5 09:06:55 MDT 2013", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "For reasons of public security, an intelligent surveillance system that can cover a large, crowded public area has become an urgent need. In this article, we propose a novel laser-based system that can simultaneously perform tracking, semantic scene learning, and abnormality detection in a fully online and unsupervised way. Furthermore, these three tasks cooperate with each other in one framework to improve their respective performances. The proposed system has the following key advantages over previous ones: (1) It can cover quite a large area (more than 60$ \times $35m), and simultaneously perform robust tracking, semantic scene learning, and abnormality detection in a high-density situation. (2) The overall system can vary with time, incrementally learn the structure of the scene, and perform fully online abnormal activity detection and tracking. This feature makes our system suitable for real-time applications. (3) The surveillance tasks are carried out in a fully unsupervised manner, so that there is no need for manual labeling and the construction of huge training datasets. We successfully apply the proposed system to the JR subway station in Tokyo, and demonstrate that it can cover an area of 60$ \times $35m, robustly track more than 150 targets at the same time, and simultaneously perform online semantic scene learning and abnormality detection with no human intervention.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "35", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Tran:2013:CPB, author = "Vien Tran and Khoi Nguyen and Tran Cao Son and Enrico Pontelli", title = "A conformant planner based on approximation: {CpA(H)}", journal = j-TIST, volume = "4", number = "2", pages = "36:1--36:??", month = mar, year = "2013", CODEN = "????", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sun May 5 09:06:55 MDT 2013", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "This article describes the planner C pA( H ), the recipient of the Best Nonobservable Nondeterministic Planner Award in the ``Uncertainty Track'' of the 6 $^{th}$ International Planning Competition (IPC), 2008. The article presents the various techniques that help CpA( H ) to achieve the level of performance and scalability exhibited in the competition. The article also presents experimental results comparing CpA( H ) with state-of-the-art conformant planners.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "36", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Wang:2013:ISS, author = "Haifeng Wang and Bill Dolan and Idan Szpektor and Shiqi Zhao", title = "Introduction to special section on paraphrasing", journal = j-TIST, volume = "4", number = "3", pages = "37:1--37:??", month = jun, year = "2013", CODEN = "????", DOI = "https://doi.org/10.1145/2483669.2483670", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Mar 13 07:29:09 MDT 2014", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "37", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Resnik:2013:UTP, author = "Philip Resnik and Olivia Buzek and Yakov Kronrod and Chang Hu and Alexander J. Quinn and Benjamin B. Bederson", title = "Using targeted paraphrasing and monolingual crowdsourcing to improve translation", journal = j-TIST, volume = "4", number = "3", pages = "38:1--38:??", month = jun, year = "2013", CODEN = "????", DOI = "https://doi.org/10.1145/2483669.2483671", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Mar 13 07:29:09 MDT 2014", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Targeted paraphrasing is a new approach to the problem of obtaining cost-effective, reasonable quality translation, which makes use of simple and inexpensive human computations by monolingual speakers in combination with machine translation. The key insight behind the process is that it is possible to spot likely translation errors with only monolingual knowledge of the target language, and it is possible to generate alternative ways to say the same thing (i.e., paraphrases) with only monolingual knowledge of the source language. Formal evaluation demonstrates that this approach can yield substantial improvements in translation quality, and the idea has been integrated into a broader framework for monolingual collaborative translation that produces fully accurate, fully fluent translations for a majority of sentences in a real-world translation task, with no involvement of human bilingual speakers.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "38", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Marton:2013:DPP, author = "Yuval Marton", title = "Distributional phrasal paraphrase generation for statistical machine translation", journal = j-TIST, volume = "4", number = "3", pages = "39:1--39:??", month = jun, year = "2013", CODEN = "????", DOI = "https://doi.org/10.1145/2483669.2483672", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Mar 13 07:29:09 MDT 2014", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Paraphrase generation has been shown useful for various natural language processing tasks, including statistical machine translation. A commonly used method for paraphrase generation is pivoting [Callison-Burch et al. 2006], which benefits from linguistic knowledge implicit in the sentence alignment of parallel texts, but has limited applicability due to its reliance on parallel texts. Distributional paraphrasing [Marton et al. 2009a] has wider applicability, is more language-independent, but doesn't benefit from any linguistic knowledge. Nevertheless, we show that using distributional paraphrasing can yield greater gains in translation tasks. We report method improvements leading to higher gains than previously published, of almost 2 B leu points, and provide implementation details, complexity analysis, and further insight into this method.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "39", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Madnani:2013:GTP, author = "Nitin Madnani and Bonnie J. Dorr", title = "Generating targeted paraphrases for improved translation", journal = j-TIST, volume = "4", number = "3", pages = "40:1--40:??", month = jun, year = "2013", CODEN = "????", DOI = "https://doi.org/10.1145/2483669.2483673", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Mar 13 07:29:09 MDT 2014", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Today's Statistical Machine Translation (SMT) systems require high-quality human translations for parameter tuning, in addition to large bitexts for learning the translation units. This parameter tuning usually involves generating translations at different points in the parameter space and obtaining feedback against human-authored reference translations as to how good the translations. This feedback then dictates what point in the parameter space should be explored next. To measure this feedback, it is generally considered wise to have multiple (usually 4) reference translations to avoid unfair penalization of translation hypotheses which could easily happen given the large number of ways in which a sentence can be translated from one language to another. However, this reliance on multiple reference translations creates a problem since they are labor intensive and expensive to obtain. Therefore, most current MT datasets only contain a single reference. This leads to the problem of reference sparsity. In our previously published research, we had proposed the first paraphrase-based solution to this problem and evaluated its effect on Chinese--English translation. In this article, we first present extended results for that solution on additional source languages. More importantly, we present a novel way to generate ``targeted'' paraphrases that yields substantially larger gains (up to 2.7 BLEU points) in translation quality when compared to our previous solution (up to 1.6 BLEU points). In addition, we further validate these improvements by supplementing with human preference judgments obtained via Amazon Mechanical Turk.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "40", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Cohn:2013:AAS, author = "Trevor Cohn and Mirella Lapata", title = "An abstractive approach to sentence compression", journal = j-TIST, volume = "4", number = "3", pages = "41:1--41:??", month = jun, year = "2013", CODEN = "????", DOI = "https://doi.org/10.1145/2483669.2483674", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Mar 13 07:29:09 MDT 2014", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "In this article we generalize the sentence compression task. Rather than simply shorten a sentence by deleting words or constituents, as in previous work, we rewrite it using additional operations such as substitution, reordering, and insertion. We present an experimental study showing that humans can naturally create abstractive sentences using a variety of rewrite operations, not just deletion. We next create a new corpus that is suited to the abstractive compression task and formulate a discriminative tree-to-tree transduction model that can account for structural and lexical mismatches. The model incorporates a grammar extraction method, uses a language model for coherent output, and can be easily tuned to a wide range of compression-specific loss functions.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "41", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Moon:2013:IBM, author = "Taesun Moon and Katrin Erk", title = "An inference-based model of word meaning in context as a paraphrase distribution", journal = j-TIST, volume = "4", number = "3", pages = "42:1--42:??", month = jun, year = "2013", CODEN = "????", DOI = "https://doi.org/10.1145/2483669.2483675", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Mar 13 07:29:09 MDT 2014", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Graded models of word meaning in context characterize the meaning of individual usages (occurrences) without reference to dictionary senses. We introduce a novel approach that frames the task of computing word meaning in context as a probabilistic inference problem. The model represents the meaning of a word as a probability distribution over potential paraphrases, inferred using an undirected graphical model. Evaluated on paraphrasing tasks, the model achieves state-of-the-art performance.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "42", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Burrows:2013:PAC, author = "Steven Burrows and Martin Potthast and Benno Stein", title = "Paraphrase acquisition via crowdsourcing and machine learning", journal = j-TIST, volume = "4", number = "3", pages = "43:1--43:??", month = jun, year = "2013", CODEN = "????", DOI = "https://doi.org/10.1145/2483669.2483676", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Mar 13 07:29:09 MDT 2014", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "To paraphrase means to rewrite content while preserving the original meaning. Paraphrasing is important in fields such as text reuse in journalism, anonymizing work, and improving the quality of customer-written reviews. This article contributes to paraphrase acquisition and focuses on two aspects that are not addressed by current research: (1) acquisition via crowdsourcing, and (2) acquisition of passage-level samples. The challenge of the first aspect is automatic quality assurance; without such a means the crowdsourcing paradigm is not effective, and without crowdsourcing the creation of test corpora is unacceptably expensive for realistic order of magnitudes. The second aspect addresses the deficit that most of the previous work in generating and evaluating paraphrases has been conducted using sentence-level paraphrases or shorter; these short-sample analyses are limited in terms of application to plagiarism detection, for example. We present the Webis Crowd Paraphrase Corpus 2011 (Webis-CPC-11), which recently formed part of the PAN 2010 international plagiarism detection competition. This corpus comprises passage-level paraphrases with 4067 positive samples and 3792 negative samples that failed our criteria, using Amazon's Mechanical Turk for crowdsourcing. In this article, we review the lessons learned at PAN 2010, and explain in detail the method used to construct the corpus. The empirical contributions include machine learning experiments to explore if passage-level paraphrases can be identified in a two-class classification problem using paraphrase similarity features, and we find that a k-nearest-neighbor classifier can correctly distinguish between paraphrased and nonparaphrased samples with 0.980 precision at 0.523 recall. This result implies that just under half of our samples must be discarded (remaining 0.477 fraction), but our cost analysis shows that the automation we introduce results in a 18\% financial saving and over 100 hours of time returned to the researchers when repeating a similar corpus design. On the other hand, when building an unrelated corpus requiring, say, 25\% training data for the automated component, we show that the financial outcome is cost neutral, while still returning over 70 hours of time to the researchers. The work presented here is the first to join the paraphrasing and plagiarism communities.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "43", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Bouamor:2013:MPA, author = "Houda Bouamor and Aur{\'e}elien Max and Anne Vilnat", title = "Multitechnique paraphrase alignment: a contribution to pinpointing sub-sentential paraphrases", journal = j-TIST, volume = "4", number = "3", pages = "44:1--44:??", month = jun, year = "2013", CODEN = "????", DOI = "https://doi.org/10.1145/2483669.2483677", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Mar 13 07:29:09 MDT 2014", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "This work uses parallel monolingual corpora for a detailed study of the task of sub-sentential paraphrase acquisition. We argue that the scarcity of this type of resource is compensated by the fact that it is the most suited type for studies on paraphrasing. We propose a large exploration of this task with experiments on two languages with five different acquisition techniques, selected for their complementarity, their combinations, as well as four monolingual corpus types of varying comparability. We report, under all conditions, a significant improvement over all techniques by validating candidate paraphrases using a maximum entropy classifier. An important result of our study is the identification of difficult-to-acquire paraphrase pairs, which are classified and quantified in a bilingual typology.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "44", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Yu:2013:ISS, author = "Zhiwen Yu and Daqing Zhang and Nathan Eagle and Diane Cook", title = "Introduction to the special section on intelligent systems for socially aware computing", journal = j-TIST, volume = "4", number = "3", pages = "45:1--45:??", month = jun, year = "2013", CODEN = "????", DOI = "https://doi.org/10.1145/2483669.2483678", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Mar 13 07:29:09 MDT 2014", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "45", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Schuster:2013:PSC, author = "Daniel Schuster and Alberto Rosi and Marco Mamei and Thomas Springer and Markus Endler and Franco Zambonelli", title = "Pervasive social context: Taxonomy and survey", journal = j-TIST, volume = "4", number = "3", pages = "46:1--46:??", month = jun, year = "2013", CODEN = "????", DOI = "https://doi.org/10.1145/2483669.2483679", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Mar 13 07:29:09 MDT 2014", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "As pervasive computing meets social networks, there is a fast growing research field called pervasive social computing. Applications in this area exploit the richness of information arising out of people using sensor-equipped pervasive devices in their everyday life combined with intense use of different social networking services. We call this set of information pervasive social context. We provide a taxonomy to classify pervasive social context along the dimensions space, time, people, and information source (STiPI) as well as commenting on the type and reason for creating such context. A survey of recent research shows the applicability and usefulness of the taxonomy in classifying and assessing applications and systems in the area of pervasive social computing. Finally, we present some research challenges in this area and illustrate how they affect the systems being surveyed.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "46", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Shi:2013:NLR, author = "Yue Shi and Pavel Serdyukov and Alan Hanjalic and Martha Larson", title = "Nontrivial landmark recommendation using geotagged photos", journal = j-TIST, volume = "4", number = "3", pages = "47:1--47:??", month = jun, year = "2013", CODEN = "????", DOI = "https://doi.org/10.1145/2483669.2483680", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Mar 13 07:29:09 MDT 2014", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Online photo-sharing sites provide a wealth of information about user behavior and their potential is increasing as it becomes ever-more common for images to be associated with location information in the form of geotags. In this article, we propose a novel approach that exploits geotagged images from an online community for the purpose of personalized landmark recommendation. Under our formulation of the task, recommended landmarks should be relevant to user interests and additionally they should constitute nontrivial recommendations. In other words, recommendations of landmarks that are highly popular and frequently visited and can be easily discovered through other information sources such as travel guides should be avoided in favor of recommendations that relate to users' personal interests. We propose a collaborative filtering approach to the personalized landmark recommendation task within a matrix factorization framework. Our approach, WMF-CR, combines weighted matrix factorization and category-based regularization. The integrated weights emphasize the contribution of nontrivial landmarks in order to focus the recommendation model specifically on the generation of nontrivial recommendations. They support the judicious elimination of trivial landmarks from consideration without also discarding information valuable for recommendation. Category-based regularization addresses the sparse data problem, which is arguably even greater in the case of our landmark recommendation task than in other recommendation scenarios due to the limited amount of travel experience recorded in the online image set of any given user. We use category information extracted from Wikipedia in order to provide the system with a method to generalize the semantics of landmarks and allow the model to relate them not only on the basis of identity, but also on the basis of topical commonality. The proposed approach is computational scalable, that is, its complexity is linear with the number of observed preferences in the user-landmark preference matrix and the number of nonzero similarities in the category-based landmark similarity matrix. We evaluate the approach on a large collection of geotagged photos gathered from Flickr. Our experimental results demonstrate that WMF-CR outperforms several state-of-the-art baseline approaches in recommending nontrivial landmarks. Additionally, they demonstrate that the approach is well suited for addressing data sparseness and provides particular performance improvement in the case of users who have limited travel experience, that is, have visited only few cities or few landmarks.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "47", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Wei:2013:EPA, author = "Ling-Yin Wei and Wen-Chih Peng and Wang-Chien Lee", title = "Exploring pattern-aware travel routes for trajectory search", journal = j-TIST, volume = "4", number = "3", pages = "48:1--48:??", month = jun, year = "2013", CODEN = "????", DOI = "https://doi.org/10.1145/2483669.2483681", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Mar 13 07:29:09 MDT 2014", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "With the popularity of positioning devices, Web 2.0 technology, and trip sharing services, many users are willing to log and share their trips on the Web. Thus, trip planning Web sites are able to provide some new services by inferring Regions-Of-Interest (ROIs) and recommending popular travel routes from trip trajectories. We argue that simply providing some travel routes consisting of popular ROIs to users is not sufficient. To tour around a wide geographical area, for example, a city, some users may prefer a trip to visit as many ROIs as possible, while others may like to stop by only a few ROIs for an in-depth visit. We refer to a trip fitting the former user group as an in-breadth trip and a trip suitable for the latter user group as an in-depth trip. Prior studies on trip planning have focused on mining ROIs and travel routes without considering these different preferences. In this article, given a spatial range and a user preference of depth/breadth specified by a user, we develop a Pattern-Aware Trajectory Search (PATS) framework to retrieve the top K trajectories passing through popular ROIs. PATS is novel because the returned travel trajectories, discovered from travel patterns hidden in trip trajectories, may represent the most valuable travel experiences of other travelers fitting the user's trip preference in terms of depth or breadth. The PATS framework comprises two components: travel behavior exploration and trajectory search. The travel behavior exploration component determines a set of ROIs along with their attractive scores by considering not only the popularity of the ROIs but also the travel sequential relationships among the ROIs. To capture the travel sequential relationships among ROIs and to derive their attractive scores, a user movement graph is constructed. For the trajectory search component of PATS, we formulate two trajectory score functions, the depth-trip score function and the breadth-trip score function, by taking into account the number of ROIs in a trajectory and their attractive scores. Accordingly, we propose an algorithm, namely, Bounded Trajectory Search (BTS), to efficiently retrieve the top K trajectories based on the two trajectory scores. The PATS framework is evaluated by experiments and user studies using a real dataset. The experimental results demonstrate the effectiveness and the efficiency of the proposed PATS framework.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "48", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Yan:2013:STM, author = "Zhixian Yan and Dipanjan Chakraborty and Christine Parent and Stefano Spaccapietra and Karl Aberer", title = "Semantic trajectories: Mobility data computation and annotation", journal = j-TIST, volume = "4", number = "3", pages = "49:1--49:??", month = jun, year = "2013", CODEN = "????", DOI = "https://doi.org/10.1145/2483669.2483682", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Mar 13 07:29:09 MDT 2014", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "With the large-scale adoption of GPS equipped mobile sensing devices, positional data generated by moving objects (e.g., vehicles, people, animals) are being easily collected. Such data are typically modeled as streams of spatio-temporal (x,y,t) points, called trajectories. In recent years trajectory management research has progressed significantly towards efficient storage and indexing techniques, as well as suitable knowledge discovery. These works focused on the geometric aspect of the raw mobility data. We are now witnessing a growing demand in several application sectors (e.g., from shipment tracking to geo-social networks) on understanding the semantic behavior of moving objects. Semantic behavior refers to the use of semantic abstractions of the raw mobility data, including not only geometric patterns but also knowledge extracted jointly from the mobility data and the underlying geographic and application domains information. The core contribution of this article lies in a semantic model and a computation and annotation platform for developing a semantic approach that progressively transforms the raw mobility data into semantic trajectories enriched with segmentations and annotations. We also analyze a number of experiments we did with semantic trajectories in different domains.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "49", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Chin:2013:CPT, author = "Alvin Chin and Bin Xu and Hao Wang and Lele Chang and Hao Wang and Lijun Zhu", title = "Connecting people through physical proximity and physical resources at a conference", journal = j-TIST, volume = "4", number = "3", pages = "50:1--50:??", month = jun, year = "2013", CODEN = "????", DOI = "https://doi.org/10.1145/2483669.2483683", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Mar 13 07:29:09 MDT 2014", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "This work investigates how to bridge the gap between offline and online behaviors at a conference and how the physical resources in the conference (the physical objects used in the conference for gathering attendees together in engaging an activity such as rooms, sessions, and papers) can be used to help facilitate social networking. We build Find and Connect, a system that integrates offline activities and interactions captured in real time with online connections in a conference environment, to provide a list of potential people one should connect to for forming an ephemeral social network. We investigate how social connections can be established and integrated with physical resources through positioning technology, and the relationship between physical proximity encounters and online social connections. Results from our two datasets of two trials, one at the UIC/ATC 2010 conference and GCJK internal marketing event, show that social connections that are reciprocal in relationship, such as friendship and exchanged contacts, have tighter, denser, and highly clustered networks compared to unidirectional relationships such as follow. We discover that there is a positive relationship between physical proximity encounters and online social connections before the social connection is made for friends, but a negative relationship for after the social connection is made. The first indicates social selection is strong, and the second indicates social influence is weak. Even though our dataset is sparse, nonetheless we believe our work is promising and novel which is worthy of future research.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "50", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Yang:2013:ISS, author = "Shanchieh Jay Yang and Dana Nau and John Salerno", title = "Introduction to the special section on social computing, behavioral-cultural modeling, and prediction", journal = j-TIST, volume = "4", number = "3", pages = "51:1--51:??", month = jun, year = "2013", CODEN = "????", DOI = "https://doi.org/10.1145/2483669.2483684", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Mar 13 07:29:09 MDT 2014", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "51", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Hung:2013:OBI, author = "Benjamin W. K. Hung and Stephan E. Kolitz and Asuman Ozdaglar", title = "Optimization-based influencing of village social networks in a counterinsurgency", journal = j-TIST, volume = "4", number = "3", pages = "52:1--52:??", month = jun, year = "2013", CODEN = "????", DOI = "https://doi.org/10.1145/2483669.2483685", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Mar 13 07:29:09 MDT 2014", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "This article considers the nonlethal targeting assignment problem in the counterinsurgency in Afghanistan, the problem of deciding on the people whom U.S. forces should engage through outreach, negotiations, meetings, and other interactions in order to ultimately win the support of the population in their area of operations. We propose two models: (1) the Afghan counterinsurgency (COIN) social influence model, to represent how attitudes of local leaders are affected by repeated interactions with other local leaders, insurgents, and counterinsurgents, and (2) the nonlethal targeting model, a NonLinear Programming (NLP) optimization formulation that identifies a strategy for assigning k U.S. agents to produce the greatest arithmetic mean of the expected long-term attitude of the population. We demonstrate in an experiment the merits of the optimization model in nonlethal targeting, which performs significantly better than both doctrine-based and random methods of assignment in a large network.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "52", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Gintis:2013:MMS, author = "Herbert Gintis", title = "{Markov} models of social dynamics: Theory and applications", journal = j-TIST, volume = "4", number = "3", pages = "53:1--53:??", month = jun, year = "2013", CODEN = "????", DOI = "https://doi.org/10.1145/2483669.2483686", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Mar 13 07:29:09 MDT 2014", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "This article shows how agent-based models of social dynamics can be treated rigorously and analytically as finite Markov processes, and their long-run properties are then given by an expanded version of the ergodic theorem for Markov processes. A Markov process model of a simplified market economy shows the fruitfulness of this approach.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "53", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Fridman:2013:UQR, author = "Natalie Fridman and Gal A. Kaminka", title = "Using qualitative reasoning for social simulation of crowds", journal = j-TIST, volume = "4", number = "3", pages = "54:1--54:??", month = jun, year = "2013", CODEN = "????", DOI = "https://doi.org/10.1145/2483669.2483687", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Mar 13 07:29:09 MDT 2014", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "The ability to model and reason about the potential violence level of a demonstration is important to the police decision making process. Unfortunately, existing knowledge regarding demonstrations is composed of partial qualitative descriptions without complete and precise numerical information. In this article we describe a first attempt to use qualitative reasoning techniques to model demonstrations. To our knowledge, such techniques have never been applied to modeling and reasoning regarding crowd behaviors, nor in particular demonstrations. We develop qualitative models consistent with the partial, qualitative social science literature, allowing us to model the interactions between different factors that influence violence in demonstrations. We then utilize qualitative simulation to predict the potential eruption of violence, at various levels, based on a description of the demographics, environmental settings, and police responses. We incrementally present and compare three such qualitative models. The results show that while two of these models fail to predict the outcomes of real-world events reported and analyzed in the literature, one model provides good results. We also examine whether a popular machine learning algorithm (decision tree learning) can be used. While the results show that the decision trees provide improved predictions, we show that the QR models can be more sensitive to changes, and can account for what-if scenarios, in contrast to decision trees. Moreover, we introduce a novel analysis algorithm that analyzes the QR simulations, to automatically determine the factors that are most important in influencing the outcome in specific real-world demonstrations. We show that the algorithm identifies factors that correspond to experts' analysis of these events.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "54", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Saito:2013:DCI, author = "Kazumi Saito and Masahiro Kimura and Kouzou Ohara and Hiroshi Motoda", title = "Detecting changes in information diffusion patterns over social networks", journal = j-TIST, volume = "4", number = "3", pages = "55:1--55:??", month = jun, year = "2013", CODEN = "????", DOI = "https://doi.org/10.1145/2483669.2483688", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Mar 13 07:29:09 MDT 2014", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "We addressed the problem of detecting the change in behavior of information diffusion over a social network which is caused by an unknown external situation change using a small amount of observation data in a retrospective setting. The unknown change is assumed effectively reflected in changes in the parameter values in the probabilistic information diffusion model, and the problem is reduced to detecting where in time and how long this change persisted and how big this change is. We solved this problem by searching the change pattern that maximizes the likelihood of generating the observed information diffusion sequences, and in doing so we devised a very efficient general iterative search algorithm using the derivative of the likelihood which avoids parameter value optimization during each search step. This is in contrast to the naive learning algorithm in that it has to iteratively update the pattern boundaries, each requiring the parameter value optimization and thus is very inefficient. We tested this algorithm for two instances of the probabilistic information diffusion model which has different characteristics. One is of information push style and the other is of information pull style. We chose Asynchronous Independent Cascade (AsIC) model as the former and Value-weighted Voter (VwV) model as the latter. The AsIC is the model for general information diffusion with binary states and the parameter to detect its change is diffusion probability and the VwV is the model for opinion formation with multiple states and the parameter to detect its change is opinion value. The results tested on these two models using four real-world network structures confirm that the algorithm is robust enough and can efficiently identify the correct change pattern of the parameter values. Comparison with the naive method that finds the best combination of change boundaries by an exhaustive search through a set of randomly selected boundary candidates shows that the proposed algorithm far outperforms the native method both in terms of accuracy and computation time.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "55", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Marathe:2013:AFN, author = "Achla Marathe and Zhengzheng Pan and Andrea Apolloni", title = "Analysis of friendship network and its role in explaining obesity", journal = j-TIST, volume = "4", number = "3", pages = "56:1--56:??", month = jun, year = "2013", CODEN = "????", DOI = "https://doi.org/10.1145/2483669.2483689", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Mar 13 07:29:09 MDT 2014", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "We employ Add Health data to show that friendship networks, constructed from mutual friendship nominations, are important in building weight perception, setting weight goals, and measuring social marginalization among adolescents and young adults. We study the relationship between individuals' perceived weight status, actual weight status, weight status relative to friends' weight status, and weight goals. This analysis helps us understand how individual weight perceptions might be formed, what these perceptions do to the weight goals, and how friends' relative weight affects weight perception and weight goals. Combining this information with individuals' friendship network helps determine the influence of social relationships on weight-related variables. Multinomial logistic regression results indicate that relative status is indeed a significant predictor of perceived status, and perceived status is a significant predictor of weight goals. We also address the issue of causality between actual weight status and social marginalization (as measured by the number of friends) and show that obesity precedes social marginalization in time rather than the other way around. This lends credence to the hypothesis that obesity leads to social marginalization not vice versa. Attributes of the friendship network can provide new insights into effective interventions for combating obesity since adolescent friendships provide an important social context for weight-related behaviors.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "56", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Jiang:2013:MSB, author = "Daxin Jiang and Jian Pei and Hang Li", title = "Mining search and browse logs for {Web} search: a survey", journal = j-TIST, volume = "4", number = "4", pages = "57:1--57:??", month = sep, year = "2013", CODEN = "????", DOI = "https://doi.org/10.1145/2508037.2508038", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Mar 13 07:29:13 MDT 2014", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Huge amounts of search log data have been accumulated at Web search engines. Currently, a popular Web search engine may receive billions of queries and collect terabytes of records about user search behavior daily. Beside search log data, huge amounts of browse log data have also been collected through client-side browser plugins. Such massive amounts of search and browse log data provide great opportunities for mining the wisdom of crowds and improving Web search. At the same time, designing effective and efficient methods to clean, process, and model log data also presents great challenges. In this survey, we focus on mining search and browse log data for Web search. We start with an introduction to search and browse log data and an overview of frequently-used data summarizations in log mining. We then elaborate how log mining applications enhance the five major components of a search engine, namely, query understanding, document understanding, document ranking, user understanding, and monitoring and feedback. For each aspect, we survey the major tasks, fundamental principles, and state-of-the-art methods.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "57", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Li:2013:SAM, author = "Xi Li and Weiming Hu and Chunhua Shen and Zhongfei Zhang and Anthony Dick and Anton {Van Den Hengel}", title = "A survey of appearance models in visual object tracking", journal = j-TIST, volume = "4", number = "4", pages = "58:1--58:??", month = sep, year = "2013", CODEN = "????", DOI = "https://doi.org/10.1145/2508037.2508039", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Mar 13 07:29:13 MDT 2014", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Visual object tracking is a significant computer vision task which can be applied to many domains, such as visual surveillance, human computer interaction, and video compression. Despite extensive research on this topic, it still suffers from difficulties in handling complex object appearance changes caused by factors such as illumination variation, partial occlusion, shape deformation, and camera motion. Therefore, effective modeling of the 2D appearance of tracked objects is a key issue for the success of a visual tracker. In the literature, researchers have proposed a variety of 2D appearance models. To help readers swiftly learn the recent advances in 2D appearance models for visual object tracking, we contribute this survey, which provides a detailed review of the existing 2D appearance models. In particular, this survey takes a module-based architecture that enables readers to easily grasp the key points of visual object tracking. In this survey, we first decompose the problem of appearance modeling into two different processing stages: visual representation and statistical modeling. Then, different 2D appearance models are categorized and discussed with respect to their composition modules. Finally, we address several issues of interest as well as the remaining challenges for future research on this topic. The contributions of this survey are fourfold. First, we review the literature of visual representations according to their feature-construction mechanisms (i.e., local and global). Second, the existing statistical modeling schemes for tracking-by-detection are reviewed according to their model-construction mechanisms: generative, discriminative, and hybrid generative-discriminative. Third, each type of visual representations or statistical modeling techniques is analyzed and discussed from a theoretical or practical viewpoint. Fourth, the existing benchmark resources (e.g., source codes and video datasets) are examined in this survey.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "58", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Cena:2013:PSA, author = "Federica Cena and Antonina Dattolo and Pasquale Lops and Julita Vassileva", title = "Perspectives in {Semantic Adaptive Social Web}", journal = j-TIST, volume = "4", number = "4", pages = "59:1--59:??", month = sep, year = "2013", CODEN = "????", DOI = "https://doi.org/10.1145/2501603", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Mar 13 07:29:13 MDT 2014", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "The Social Web is now a successful reality with its quickly growing number of users and applications. Also the Semantic Web, which started with the objective of describing Web resources in a machine-processable way, is now outgrowing the research labs and is being massively exploited in many websites, incorporating high-quality user-generated content and semantic annotations. The primary goal of this special section is to showcase some recent research at the intersection of the Social Web and the Semantic Web that explores the benefits that adaptation and personalization have to offer in the Web of the future, the so-called Social Adaptive Semantic Web. We have selected two articles out of fourteen submissions based on the quality of the articles and we present the main lessons learned from the overall analysis of these submissions.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "59", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Biancalana:2013:SSQ, author = "Claudio Biancalana and Fabio Gasparetti and Alessandro Micarelli and Giuseppe Sansonetti", title = "Social semantic query expansion", journal = j-TIST, volume = "4", number = "4", pages = "60:1--60:??", month = sep, year = "2013", CODEN = "????", DOI = "https://doi.org/10.1145/2508037.2508041", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Mar 13 07:29:13 MDT 2014", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Weak semantic techniques rely on the integration of Semantic Web techniques with social annotations and aim to embrace the strengths of both. In this article, we propose a novel weak semantic technique for query expansion. Traditional query expansion techniques are based on the computation of two-dimensional co-occurrence matrices. Our approach proposes the use of three-dimensional matrices, where the added dimension is represented by semantic classes (i.e., categories comprising all the terms that share a semantic property) related to the folksonomy extracted from social bookmarking services, such as delicious and StumbleUpon. The results of an indepth experimental evaluation performed on both artificial datasets and real users show that our approach outperforms traditional techniques, such as relevance feedback and personalized PageRank, so confirming the validity and usefulness of the categorization of the user needs and preferences in semantic classes. We also present the results of a questionnaire aimed to know the users opinion regarding the system. As one drawback of several query expansion techniques is their high computational costs, we also provide a complexity analysis of our system, in order to show its capability of operating in real time.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "60", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Chen:2013:WMS, author = "Chao Chen and Qiusha Zhu and Lin Lin and Mei-Ling Shyu", title = "{Web} media semantic concept retrieval via tag removal and model fusion", journal = j-TIST, volume = "4", number = "4", pages = "61:1--61:??", month = sep, year = "2013", CODEN = "????", DOI = "https://doi.org/10.1145/2508037.2508042", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Mar 13 07:29:13 MDT 2014", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Multimedia data on social websites contain rich semantics and are often accompanied with user-defined tags. To enhance Web media semantic concept retrieval, the fusion of tag-based and content-based models can be used, though it is very challenging. In this article, a novel semantic concept retrieval framework that incorporates tag removal and model fusion is proposed to tackle such a challenge. Tags with useful information can facilitate media search, but they are often imprecise, which makes it important to apply noisy tag removal (by deleting uncorrelated tags) to improve the performance of semantic concept retrieval. Therefore, a multiple correspondence analysis (MCA)-based tag removal algorithm is proposed, which utilizes MCA's ability to capture the relationships among nominal features and identify representative and discriminative tags holding strong correlations with the target semantic concepts. To further improve the retrieval performance, a novel model fusion method is also proposed to combine ranking scores from both tag-based and content-based models, where the adjustment of ranking scores, the reliability of models, and the correlations between the intervals divided on the ranking scores and the semantic concepts are all considered. Comparative results with extensive experiments on the NUS-WIDE-LITE as well as the NUS-WIDE-270K benchmark datasets with 81 semantic concepts show that the proposed framework outperforms baseline results and the other comparison methods with each component being evaluated separately.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "61", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Reddy:2013:ISS, author = "Chandan K. Reddy and Cristopher C. Yang", title = "Introduction to the special section on intelligent systems for health informatics", journal = j-TIST, volume = "4", number = "4", pages = "62:1--62:??", month = sep, year = "2013", CODEN = "????", DOI = "https://doi.org/10.1145/2508037.2508043", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Mar 13 07:29:13 MDT 2014", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "62", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Batal:2013:TPM, author = "Iyad Batal and Hamed Valizadegan and Gregory F. Cooper and Milos Hauskrecht", title = "A temporal pattern mining approach for classifying electronic health record data", journal = j-TIST, volume = "4", number = "4", pages = "63:1--63:??", month = sep, year = "2013", CODEN = "????", DOI = "https://doi.org/10.1145/2508037.2508044", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Mar 13 07:29:13 MDT 2014", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "We study the problem of learning classification models from complex multivariate temporal data encountered in electronic health record systems. The challenge is to define a good set of features that are able to represent well the temporal aspect of the data. Our method relies on temporal abstractions and temporal pattern mining to extract the classification features. Temporal pattern mining usually returns a large number of temporal patterns, most of which may be irrelevant to the classification task. To address this problem, we present the Minimal Predictive Temporal Patterns framework to generate a small set of predictive and nonspurious patterns. We apply our approach to the real-world clinical task of predicting patients who are at risk of developing heparin-induced thrombocytopenia. The results demonstrate the benefit of our approach in efficiently learning accurate classifiers, which is a key step for developing intelligent clinical monitoring systems.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "63", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Rashidi:2013:CMM, author = "Parisa Rashidi and Diane J. Cook", title = "{COM}: a method for mining and monitoring human activity patterns in home-based health monitoring systems", journal = j-TIST, volume = "4", number = "4", pages = "64:1--64:??", month = sep, year = "2013", CODEN = "????", DOI = "https://doi.org/10.1145/2508037.2508045", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Mar 13 07:29:13 MDT 2014", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "The increasing aging population in the coming decades will result in many complications for society and in particular for the healthcare system due to the shortage of healthcare professionals and healthcare facilities. To remedy this problem, researchers have pursued developing remote monitoring systems and assisted living technologies by utilizing recent advances in sensor and networking technology, as well as in the data mining and machine learning fields. In this article, we report on our fully automated approach for discovering and monitoring patterns of daily activities. Discovering and tracking patterns of daily activities can provide unprecedented opportunities for health monitoring and assisted living applications, especially for older adults and individuals with mental disabilities. Previous approaches usually rely on preselected activities or labeled data to track and monitor daily activities. In this article, we present a fully automated approach by discovering natural activity patterns and their variations in real-life data. We will show how our activity discovery component can be integrated with an activity recognition component to track and monitor various daily activity patterns. We also provide an activity visualization component to allow caregivers to visually observe and examine the activity patterns using a user-friendly interface. We validate our algorithms using real-life data obtained from two apartments during a three-month period.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "64", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Wolf:2013:DUR, author = "Hannes Wolf and Klaus Herrmann and Kurt Rothermel", title = "Dealing with uncertainty: Robust workflow navigation in the healthcare domain", journal = j-TIST, volume = "4", number = "4", pages = "65:1--65:??", month = sep, year = "2013", CODEN = "????", DOI = "https://doi.org/10.1145/2508037.2508046", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Mar 13 07:29:13 MDT 2014", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Processes in the healthcare domain are characterized by coarsely predefined recurring procedures that are flexibly adapted by the personnel to suite-specific situations. In this setting, a workflow management system that gives guidance and documents the personnel's actions can lead to a higher quality of care, fewer mistakes, and higher efficiency. However, most existing workflow management systems enforce rigid inflexible workflows and rely on direct manual input. Both are inadequate for healthcare processes. In particular, direct manual input is not possible in most cases since (1) it would distract the personnel even in critical situations and (2) it would violate fundamental hygiene principles by requiring disinfected doctors and nurses to touch input devices. The solution could be activity recognition systems that use sensor data (e.g., audio and acceleration data) to infer the current activities by the personnel and provide input to a workflow (e.g., informing it that a certain activity is finished now). However, state-of-the-art activity recognition technologies have difficulties in providing reliable information. We describe a comprehensive framework tailored for flexible human-centric healthcare processes that improves the reliability of activity recognition data. We present a set of mechanisms that exploit the application knowledge encoded in workflows in order to reduce the uncertainty of this data, thus enabling unobtrusive robust healthcare workflows. We evaluate our work based on a real-world case study and show that the robustness of unobtrusive healthcare workflows can be increased to an absolute value of up to 91\% (compared to only 12\% with a classical workflow system). This is a major breakthrough that paves the way towards future IT-enabled healthcare systems.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "65", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Park:2013:CPC, author = "Yubin Park and Joydeep Ghosh", title = "{CUDIA}: Probabilistic cross-level imputation using individual auxiliary information", journal = j-TIST, volume = "4", number = "4", pages = "66:1--66:??", month = sep, year = "2013", CODEN = "????", DOI = "https://doi.org/10.1145/2508037.2508047", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Mar 13 07:29:13 MDT 2014", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "In healthcare-related studies, individual patient or hospital data are not often publicly available due to privacy restrictions, legal issues, or reporting norms. However, such measures may be provided at a higher or more aggregated level, such as state-level, county-level summaries or averages over health zones, such as hospital referral regions (HRR) or hospital service areas (HSA). Such levels constitute partitions over the underlying individual level data, which may not match the groupings that would have been obtained if one clustered the data based on individual-level attributes. Moreover, treating aggregated values as representatives for the individuals can result in the ecological fallacy. How can one run data mining procedures on such data where different variables are available at different levels of aggregation or granularity? In this article, we seek a better utilization of variably aggregated datasets, which are possibly assembled from different sources. We propose a novel cross-level imputation technique that models the generative process of such datasets using a Bayesian directed graphical model. The imputation is based on the underlying data distribution and is shown to be unbiased. This imputation can be further utilized in a subsequent predictive modeling, yielding improved accuracies. The experimental results using a simulated dataset and the Behavioral Risk Factor Surveillance System (BRFSS) dataset are provided to illustrate the generality and capabilities of the proposed framework.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "66", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Hoens:2013:RMR, author = "T. Ryan Hoens and Marina Blanton and Aaron Steele and Nitesh V. Chawla", title = "Reliable medical recommendation systems with patient privacy", journal = j-TIST, volume = "4", number = "4", pages = "67:1--67:??", month = sep, year = "2013", CODEN = "????", DOI = "https://doi.org/10.1145/2508037.2508048", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Mar 13 07:29:13 MDT 2014", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "One of the concerns patients have when confronted with a medical condition is which physician to trust. Any recommendation system that seeks to answer this question must ensure that any sensitive medical information collected by the system is properly secured. In this article, we codify these privacy concerns in a privacy-friendly framework and present two architectures that realize it: the Secure Processing Architecture (SPA) and the Anonymous Contributions Architecture (ACA). In SPA, patients submit their ratings in a protected form without revealing any information about their data and the computation of recommendations proceeds over the protected data using secure multiparty computation techniques. In ACA, patients submit their ratings in the clear, but no link between a submission and patient data can be made. We discuss various aspects of both architectures, including techniques for ensuring reliability of computed recommendations and system performance, and provide their comparison.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "67", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Khan:2013:VOM, author = "Atif Khan and John A. Doucette and Robin Cohen", title = "Validation of an ontological medical decision support system for patient treatment using a repository of patient data: Insights into the value of machine learning", journal = j-TIST, volume = "4", number = "4", pages = "68:1--68:??", month = sep, year = "2013", CODEN = "????", DOI = "https://doi.org/10.1145/2508037.2508049", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Mar 13 07:29:13 MDT 2014", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "In this article, we begin by presenting OMeD, a medical decision support system, and argue for its value over purely probabilistic approaches that reason about patients for time-critical decision scenarios. We then progress to present Holmes, a Hybrid Ontological and Learning MEdical System which supports decision making about patient treatment. This system is introduced in order to cope with the case of missing data. We demonstrate its effectiveness by operating on an extensive set of real-world patient health data from the CDC, applied to the decision-making scenario of administering sleeping pills. In particular, we clarify how the combination of semantic, ontological representations, and probabilistic reasoning together enable the proposal of effective patient treatments. Our focus is thus on presenting an approach for interpreting medical data in the context of real-time decision making. This constitutes a comprehensive framework for the design of medical recommendation systems for potential use by medical professionals and patients both, with the end result being personalized patient treatment. We conclude with a discussion of the value of our particular approach for such diverse considerations as coping with misinformation provided by patients, performing effectively in time-critical environments where real-time decisions are necessary, and potential applications facilitating patient information gathering.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "68", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Lee:2013:CPR, author = "Suk Jin Lee and Yuichi Motai and Elisabeth Weiss and Shumei S. Sun", title = "Customized prediction of respiratory motion with clustering from multiple patient interaction", journal = j-TIST, volume = "4", number = "4", pages = "69:1--69:??", month = sep, year = "2013", CODEN = "????", DOI = "https://doi.org/10.1145/2508037.2508050", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Mar 13 07:29:13 MDT 2014", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Information processing of radiotherapy systems has become an important research area for sophisticated radiation treatment methodology. Geometrically precise delivery of radiotherapy in the thorax and upper abdomen is compromised by respiratory motion during treatment. Accurate prediction of the respiratory motion would be beneficial for improving tumor targeting. However, a wide variety of breathing patterns can make it difficult to predict the breathing motion with explicit models. We proposed a respiratory motion predictor, that is, customized prediction with multiple patient interactions using neural network (CNN). For the preprocedure of prediction for individual patient, we construct the clustering based on breathing patterns of multiple patients using the feature selection metrics that are composed of a variety of breathing features. In the intraprocedure, the proposed CNN used neural networks (NN) for a part of the prediction and the extended Kalman filter (EKF) for a part of the correction. The prediction accuracy of the proposed method was investigated with a variety of prediction time horizons using normalized root mean squared error (NRMSE) values in comparison with the alternate recurrent neural network (RNN). We have also evaluated the prediction accuracy using the marginal value that can be used as the reference value to judge how many signals lie outside the confidence level. The experimental results showed that the proposed CNN can outperform RNN with respect to the prediction accuracy with an improvement of 50\%.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "69", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Baralis:2013:EPH, author = "Elena Baralis and Tania Cerquitelli and Silvia Chiusano and Vincenzo D'Elia and Riccardo Molinari and Davide Susta", title = "Early prediction of the highest workload in incremental cardiopulmonary tests", journal = j-TIST, volume = "4", number = "4", pages = "70:1--70:??", month = sep, year = "2013", CODEN = "????", DOI = "https://doi.org/10.1145/2508037.2508051", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Mar 13 07:29:13 MDT 2014", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Incremental tests are widely used in cardiopulmonary exercise testing, both in the clinical domain and in sport sciences. The highest workload (denoted W$_{peak}$ ) reached in the test is key information for assessing the individual body response to the test and for analyzing possible cardiac failures and planning rehabilitation, and training sessions. Being physically very demanding, incremental tests can significantly increase the body stress on monitored individuals and may cause cardiopulmonary overload. This article presents a new approach to cardiopulmonary testing that addresses these drawbacks. During the test, our approach analyzes the individual body response to the exercise and predicts the W$_{peak}$ value that will be reached in the test and an evaluation of its accuracy. When the accuracy of the prediction becomes satisfactory, the test can be prematurely stopped, thus avoiding its entire execution. To predict W$_{peak}$, we introduce a new index, the CardioPulmonary Efficiency Index (CPE), summarizing the cardiopulmonary response of the individual to the test. Our approach analyzes the CPE trend during the test, together with the characteristics of the individual, and predicts W$_{peak}$. A K-nearest-neighbor-based classifier and an ANN-based classier are exploited for the prediction. The experimental evaluation showed that the W$_{peak}$ value can be predicted with a limited error from the first steps of the test.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "70", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Lee:2013:SFI, author = "Yugyung Lee and Saranya Krishnamoorthy and Deendayal Dinakarpandian", title = "A semantic framework for intelligent matchmaking for clinical trial eligibility criteria", journal = j-TIST, volume = "4", number = "4", pages = "71:1--71:??", month = sep, year = "2013", CODEN = "????", DOI = "https://doi.org/10.1145/2508037.2508052", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Mar 13 07:29:13 MDT 2014", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "An integral step in the discovery of new treatments for medical conditions is the matching of potential subjects with appropriate clinical trials. Eligibility criteria for clinical trials are typically specified as inclusion and exclusion criteria for each study in freetext form. While this is sufficient for a human to guide a recruitment interview, it cannot be reliably and computationally construed to identify potential subjects. Standardization of the representation of eligibility criteria can enhance the efficiency and accuracy of this process. This article presents a semantic framework that facilitates intelligent matchmaking by identifying a minimal set of eligibility criteria with maximal coverage of clinical trials. In contrast to existing top-down manual standardization efforts, a bottom-up data driven approach is presented to find a canonical nonredundant representation of an arbitrary collection of clinical trial criteria. The methodology has been validated with a corpus of 709 clinical trials related to Generalized Anxiety Disorder containing 2,760 inclusion and 4,871 exclusion eligibility criteria. This corpus is well represented by a relatively small number of 126 inclusion clusters and 175 exclusion clusters, each of which corresponds to a semantically distinct criterion. Internal and external validation measures provide an objective evaluation of the method. An eligibility criteria ontology has been constructed based on the clustering. The resulting model has been incorporated into the development of the MindTrial clinical trial recruiting system. The prototype for clinical trial recruitment illustrates the effectiveness of the methodology in characterizing clinical trials and subjects and accurate matching between them.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "71", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Bi:2013:MLA, author = "Jinbo Bi and Jiangwen Sun and Yu Wu and Howard Tennen and Stephen Armeli", title = "A machine learning approach to college drinking prediction and risk factor identification", journal = j-TIST, volume = "4", number = "4", pages = "72:1--72:??", month = sep, year = "2013", CODEN = "????", DOI = "https://doi.org/10.1145/2508037.2508053", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Mar 13 07:29:13 MDT 2014", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Alcohol misuse is one of the most serious public health problems facing adolescents and young adults in the United States. National statistics shows that nearly 90\% of alcohol consumed by youth under 21 years of age involves binge drinking and 44\% of college students engage in high-risk drinking activities. Conventional alcohol intervention programs, which aim at installing either an alcohol reduction norm or prohibition against underage drinking, have yielded little progress in controlling college binge drinking over the years. Existing alcohol studies are deductive where data are collected to investigate a psychological/behavioral hypothesis, and statistical analysis is applied to the data to confirm the hypothesis. Due to this confirmatory manner of analysis, the resulting statistical models are cohort-specific and typically fail to replicate on a different sample. This article presents two machine learning approaches for a secondary analysis of longitudinal data collected in college alcohol studies sponsored by the National Institute on Alcohol Abuse and Alcoholism. Our approach aims to discover knowledge, from multiwave cohort-sequential daily data, which may or may not align with the original hypothesis but quantifies predictive models with higher likelihood to generalize to new samples. We first propose a so-called temporally-correlated support vector machine to construct a classifier as a function of daily moods, stress, and drinking expectancies to distinguish days with nighttime binge drinking from days without for individual students. We then propose a combination of cluster analysis and feature selection, where cluster analysis is used to identify drinking patterns based on averaged daily drinking behavior and feature selection is used to identify risk factors associated with each pattern. We evaluate our methods on two cohorts of 530 total college students recruited during the Spring and Fall semesters, respectively. Cross validation on these two cohorts and further on 100 random partitions of the total students demonstrate that our methods improve the model generalizability in comparison with traditional multilevel logistic regression. The discovered risk factors and the interaction of these factors delineated in our models can set a potential basis and offer insights to a new design of more effective college alcohol interventions.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "72", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Subbu:2013:LMF, author = "Kalyan Pathapati Subbu and Brandon Gozick and Ram Dantu", title = "{LocateMe}: Magnetic-fields-based indoor localization using smartphones", journal = j-TIST, volume = "4", number = "4", pages = "73:1--73:??", month = sep, year = "2013", CODEN = "????", DOI = "https://doi.org/10.1145/2508037.2508054", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Mar 13 07:29:13 MDT 2014", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Fine-grained localization is extremely important to accurately locate a user indoors. Although innovative solutions have already been proposed, there is no solution that is universally accepted, easily implemented, user centric, and, most importantly, works in the absence of GSM coverage or WiFi availability. The advent of sensor rich smartphones has paved a way to develop a solution that can cater to these requirements. By employing a smartphone's built-in magnetic field sensor, magnetic signatures were collected inside buildings. These signatures displayed a uniqueness in their patterns due to the presence of different kinds of pillars, doors, elevators, etc., that consist of ferromagnetic materials like steel or iron. We theoretically analyze the cause of this uniqueness and then present an indoor localization solution by classifying signatures based on their patterns. However, to account for user walking speed variations so as to provide an application usable to a variety of users, we follow a dynamic time-warping-based approach that is known to work on similar signals irrespective of their variations in the time axis. Our approach resulted in localization distances of approximately 2m--6m with accuracies between 80--100\% implying that it is sufficient to walk short distances across hallways to be located by the smartphone. The implementation of the application on different smartphones yielded response times of less than five secs, thereby validating the feasibility of our approach and making it a viable solution.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "73", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Chen:2013:RWM, author = "Bin Chen and Jian Su and Chew Lim Tan", title = "Random walks down the mention graphs for event coreference resolution", journal = j-TIST, volume = "4", number = "4", pages = "74:1--74:??", month = sep, year = "2013", CODEN = "????", DOI = "https://doi.org/10.1145/2508037.2508055", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Mar 13 07:29:13 MDT 2014", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Event coreference is an important task in event extraction and other natural language processing tasks. Despite its importance, it was merely discussed in previous studies. In this article, we present a global coreference resolution system dedicated to various sophisticated event coreference phenomena. First, seven resolvers are utilized to resolve different event and object coreference mention pairs with a new instance selection strategy and new linguistic features. Second, a global solution-a modified random walk partitioning-is employed for the chain formation. Being the first attempt to apply the random walk model for coreference resolution, the revised model utilizes a sampling method, termination criterion, and stopping probability to greatly improve the effectiveness of random walk model for event coreference resolution. Last but not least, the new model facilitates a convenient way to incorporate sophisticated linguistic constraints and preferences, the related object mention graph, as well as pronoun coreference information not used in previous studies for effective chain formation. In total, these techniques impose more than 20\% F-score improvement over the baseline system.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "74", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Editors:2013:ISS, author = "Editors", title = "Introduction to special section on intelligent mobile knowledge discovery and management systems", journal = j-TIST, volume = "5", number = "1", pages = "1:1--1:??", month = dec, year = "2013", CODEN = "????", DOI = "https://doi.org/10.1145/2542182.2542183", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Mar 13 07:29:16 MDT 2014", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "1", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Ying:2013:MGT, author = "Josh Jia-Ching Ying and Wang-Chien Lee and Vincent S. Tseng", title = "Mining geographic-temporal-semantic patterns in trajectories for location prediction", journal = j-TIST, volume = "5", number = "1", pages = "2:1--2:??", month = dec, year = "2013", CODEN = "????", DOI = "https://doi.org/10.1145/2542182.2542184", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Mar 13 07:29:16 MDT 2014", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "In recent years, research on location predictions by mining trajectories of users has attracted a lot of attention. Existing studies on this topic mostly treat such predictions as just a type of location recommendation, that is, they predict the next location of a user using location recommenders. However, an user usually visits somewhere for reasons other than interestingness. In this article, we propose a novel mining-based location prediction approach called Geographic-Temporal-Semantic-based Location Prediction (GTS-LP), which takes into account a user's geographic-triggered intentions, temporal-triggered intentions, and semantic-triggered intentions, to estimate the probability of the user in visiting a location. The core idea underlying our proposal is the discovery of trajectory patterns of users, namely GTS patterns, to capture frequent movements triggered by the three kinds of intentions. To achieve this goal, we define a new trajectory pattern to capture the key properties of the behaviors that are motivated by the three kinds of intentions from trajectories of users. In our GTS-LP approach, we propose a series of novel matching strategies to calculate the similarity between the current movement of a user and discovered GTS patterns based on various moving intentions. On the basis of similitude, we make an online prediction as to the location the user intends to visit. To the best of our knowledge, this is the first work on location prediction based on trajectory pattern mining that explores the geographic, temporal, and semantic properties simultaneously. By means of a comprehensive evaluation using various real trajectory datasets, we show that our proposed GTS-LP approach delivers excellent performance and significantly outperforms existing state-of-the-art location prediction methods.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "2", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Tang:2013:FTC, author = "Lu-An Tang and Yu Zheng and Jing Yuan and Jiawei Han and Alice Leung and Wen-Chih Peng and Thomas {La Porta}", title = "A framework of traveling companion discovery on trajectory data streams", journal = j-TIST, volume = "5", number = "1", pages = "3:1--3:??", month = dec, year = "2013", CODEN = "????", DOI = "https://doi.org/10.1145/2542182.2542185", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Mar 13 07:29:16 MDT 2014", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "The advance of mobile technologies leads to huge volumes of spatio-temporal data collected in the form of trajectory data streams. In this study, we investigate the problem of discovering object groups that travel together (i.e., traveling companions ) from trajectory data streams. Such technique has broad applications in the areas of scientific study, transportation management, and military surveillance. To discover traveling companions, the monitoring system should cluster the objects of each snapshot and intersect the clustering results to retrieve moving-together objects. Since both clustering and intersection steps involve high computational overhead, the key issue of companion discovery is to improve the efficiency of algorithms. We propose the models of closed companion candidates and smart intersection to accelerate data processing. A data structure termed traveling buddy is designed to facilitate scalable and flexible companion discovery from trajectory streams. The traveling buddies are microgroups of objects that are tightly bound together. By only storing the object relationships rather than their spatial coordinates, the buddies can be dynamically maintained along the trajectory stream with low cost. Based on traveling buddies, the system can discover companions without accessing the object details. In addition, we extend the proposed framework to discover companions on more complicated scenarios with spatial and temporal constraints, such as on the road network and battlefield. The proposed methods are evaluated with extensive experiments on both real and synthetic datasets. Experimental results show that our proposed buddy-based approach is an order of magnitude faster than the baselines and achieves higher accuracy in companion discovery.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "3", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Doo:2013:MTF, author = "Myungcheol Doo and Ling Liu", title = "{Mondrian} tree: a fast index for spatial alarm processing", journal = j-TIST, volume = "5", number = "1", pages = "4:1--4:??", month = dec, year = "2013", CODEN = "????", DOI = "https://doi.org/10.1145/2542182.2542186", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Mar 13 07:29:16 MDT 2014", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "With ubiquitous wireless connectivity and technological advances in mobile devices, we witness the growing demands and increasing market shares of mobile intelligent systems and technologies for real-time decision making and location-based knowledge discovery. Spatial alarms are considered as one of the fundamental capabilities for intelligent mobile location-based systems. Like time-based alarms that remind us the arrival of a future time point, spatial alarms remind us the arrival of a future spatial point. Existing approaches for scaling spatial alarm processing are focused on computing Alarm-Free Regions (A fr) and Alarm-Free Period (Afp) such that mobile objects traveling within an Afr can safely hibernate the alarm evaluation process for the computed Afp, to save battery power, until approaching the nearest alarm of interest. A key technical challenge in scaling spatial alarm processing is to efficiently compute Afr and Afp such that mobile objects traveling within an Afr can safely hibernate the alarm evaluation process during the computed Afp, while maintaining high accuracy. In this article we argue that on-demand computation of Afr is expensive and may not scale well for dense populations of mobile objects. Instead, we propose to maintain an index for both spatial alarms and empty regions (Afr) such that for a given mobile user's location, we can find relevant spatial alarms and whether it is in an alarm-free region more efficiently. We also show that conventional spatial indexing methods, such as R-tree family, k -d tree, Quadtree, and Grid, are by design not well suited to index empty regions. We present Mondrian Tree --- a region partitioning tree for indexing both spatial alarms and alarm-free regions. We first introduce the Mondrian Tree indexing algorithms, including index construction, search, and maintenance. Then we describe a suite of Mondrian Tree optimizations to further enhance the performance of spatial alarm processing. Our experimental evaluation shows that the Mondrian Tree index, as an intelligent technology for mobile systems, outperforms traditional index methods, such as R-tree, Quadtree, and k -d tree, for spatial alarm processing.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "4", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Bonchi:2013:ISI, author = "Francesco Bonchi and Wray Buntine and Ricard Gavald{\'a} and Shengbo Guo", title = "Introduction to the special issue on {Social Web} mining", journal = j-TIST, volume = "5", number = "1", pages = "5:1--5:??", month = dec, year = "2013", CODEN = "????", DOI = "https://doi.org/10.1145/2542182.2542187", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Mar 13 07:29:16 MDT 2014", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "5", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{He:2013:DJS, author = "Yulan He and Chenghua Lin and Wei Gao and Kam-Fai Wong", title = "Dynamic joint sentiment-topic model", journal = j-TIST, volume = "5", number = "1", pages = "6:1--6:??", month = dec, year = "2013", CODEN = "????", DOI = "https://doi.org/10.1145/2542182.2542188", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Mar 13 07:29:16 MDT 2014", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Social media data are produced continuously by a large and uncontrolled number of users. The dynamic nature of such data requires the sentiment and topic analysis model to be also dynamically updated, capturing the most recent language use of sentiments and topics in text. We propose a dynamic Joint Sentiment-Topic model (dJST) which allows the detection and tracking of views of current and recurrent interests and shifts in topic and sentiment. Both topic and sentiment dynamics are captured by assuming that the current sentiment-topic-specific word distributions are generated according to the word distributions at previous epochs. We study three different ways of accounting for such dependency information: (1) sliding window where the current sentiment-topic word distributions are dependent on the previous sentiment-topic-specific word distributions in the last S epochs; (2) skip model where history sentiment topic word distributions are considered by skipping some epochs in between; and (3) multiscale model where previous long- and short- timescale distributions are taken into consideration. We derive efficient online inference procedures to sequentially update the model with newly arrived data and show the effectiveness of our proposed model on the Mozilla add-on reviews crawled between 2007 and 2011.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "6", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Cataldi:2013:PET, author = "Mario Cataldi and Luigi {Di Caro} and Claudio Schifanella", title = "Personalized emerging topic detection based on a term aging model", journal = j-TIST, volume = "5", number = "1", pages = "7:1--7:??", month = dec, year = "2013", CODEN = "????", DOI = "https://doi.org/10.1145/2542182.2542189", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Mar 13 07:29:16 MDT 2014", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Twitter is a popular microblogging service that acts as a ground-level information news flashes portal where people with different background, age, and social condition provide information about what is happening in front of their eyes. This characteristic makes Twitter probably the fastest information service in the world. In this article, we recognize this role of Twitter and propose a novel, user-aware topic detection technique that permits to retrieve, in real time, the most emerging topics of discussion expressed by the community within the interests of specific users. First, we analyze the topology of Twitter looking at how the information spreads over the network, taking into account the authority/influence of each active user. Then, we make use of a novel term aging model to compute the burstiness of each term, and provide a graph-based method to retrieve the minimal set of terms that can represent the corresponding topic. Finally, since any user can have topic preferences inferable from the shared content, we leverage such knowledge to highlight the most emerging topics within her foci of interest. As evaluation we then provide several experiments together with a user study proving the validity and reliability of the proposed approach.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "7", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Arias:2013:FTD, author = "Marta Arias and Argimiro Arratia and Ramon Xuriguera", title = "Forecasting with {Twitter} data", journal = j-TIST, volume = "5", number = "1", pages = "8:1--8:??", month = dec, year = "2013", CODEN = "????", DOI = "https://doi.org/10.1145/2542182.2542190", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Mar 13 07:29:16 MDT 2014", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "The dramatic rise in the use of social network platforms such as Facebook or Twitter has resulted in the availability of vast and growing user-contributed repositories of data. Exploiting this data by extracting useful information from it has become a great challenge in data mining and knowledge discovery. A recently popular way of extracting useful information from social network platforms is to build indicators, often in the form of a time series, of general public mood by means of sentiment analysis. Such indicators have been shown to correlate with a diverse variety of phenomena. In this article we follow this line of work and set out to assess, in a rigorous manner, whether a public sentiment indicator extracted from daily Twitter messages can indeed improve the forecasting of social, economic, or commercial indicators. To this end we have collected and processed a large amount of Twitter posts from March 2011 to the present date for two very different domains: stock market and movie box office revenue. For each of these domains, we build and evaluate forecasting models for several target time series both using and ignoring the Twitter-related data. If Twitter does help, then this should be reflected in the fact that the predictions of models that use Twitter-related data are better than the models that do not use this data. By systematically varying the models that we use and their parameters, together with other tuning factors such as lag or the way in which we build our Twitter sentiment index, we obtain a large dataset that allows us to test our hypothesis under different experimental conditions. Using a novel decision-tree-based technique that we call summary tree we are able to mine this large dataset and obtain automatically those configurations that lead to an improvement in the prediction power of our forecasting models. As a general result, we have seen that nonlinear models do take advantage of Twitter data when forecasting trends in volatility indices, while linear ones fail systematically when forecasting any kind of financial time series. In the case of predicting box office revenue trend, it is support vector machines that make best use of Twitter data. In addition, we conduct statistical tests to determine the relation between our Twitter time series and the different target time series.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "8", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Lee:2013:CES, author = "Kyumin Lee and James Caverlee and Zhiyuan Cheng and Daniel Z. Sui", title = "Campaign extraction from social media", journal = j-TIST, volume = "5", number = "1", pages = "9:1--9:??", month = dec, year = "2013", CODEN = "????", DOI = "https://doi.org/10.1145/2542182.2542191", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Mar 13 07:29:16 MDT 2014", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "In this manuscript, we study the problem of detecting coordinated free text campaigns in large-scale social media. These campaigns-ranging from coordinated spam messages to promotional and advertising campaigns to political astro-turfing-are growing in significance and reach with the commensurate rise in massive-scale social systems. Specifically, we propose and evaluate a content-driven framework for effectively linking free text posts with common ``talking points'' and extracting campaigns from large-scale social media. Three of the salient features of the campaign extraction framework are: (i) first, we investigate graph mining techniques for isolating coherent campaigns from large message-based graphs; (ii) second, we conduct a comprehensive comparative study of text-based message correlation in message and user levels; and (iii) finally, we analyze temporal behaviors of various campaign types. Through an experimental study over millions of Twitter messages we identify five major types of campaigns-namely Spam, Promotion, Template, News, and Celebrity campaigns-and we show how these campaigns may be extracted with high precision and recall.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "9", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Fire:2013:CEL, author = "Michael Fire and Lena Tenenboim-Chekina and Rami Puzis and Ofrit Lesser and Lior Rokach and Yuval Elovici", title = "Computationally efficient link prediction in a variety of social networks", journal = j-TIST, volume = "5", number = "1", pages = "10:1--10:??", month = dec, year = "2013", CODEN = "????", DOI = "https://doi.org/10.1145/2542182.2542192", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Mar 13 07:29:16 MDT 2014", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Online social networking sites have become increasingly popular over the last few years. As a result, new interdisciplinary research directions have emerged in which social network analysis methods are applied to networks containing hundreds of millions of users. Unfortunately, links between individuals may be missing either due to an imperfect acquirement process or because they are not yet reflected in the online network (i.e., friends in the real world did not form a virtual connection). The primary bottleneck in link prediction techniques is extracting the structural features required for classifying links. In this article, we propose a set of simple, easy-to-compute structural features that can be analyzed to identify missing links. We show that by using simple structural features, a machine learning classifier can successfully identify missing links, even when applied to a predicament of classifying links between individuals with at least one common friend. We also present a method for calculating the amount of data needed in order to build more accurate classifiers. The new Friends measure and Same community features we developed are shown to be good predictors for missing links. An evaluation experiment was performed on ten large social networks datasets: Academia.edu, DBLP, Facebook, Flickr, Flixster, Google+, Gowalla, TheMarker, Twitter, and YouTube. Our methods can provide social network site operators with the capability of helping users to find known, offline contacts and to discover new friends online. They may also be used for exposing hidden links in online social networks.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "10", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Cruz:2013:CDV, author = "Juan David Cruz and C{\'e}cile Bothorel and Fran{\c{c}}ois Poulet", title = "Community detection and visualization in social networks: Integrating structural and semantic information", journal = j-TIST, volume = "5", number = "1", pages = "11:1--11:??", month = dec, year = "2013", CODEN = "????", DOI = "https://doi.org/10.1145/2542182.2542193", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Mar 13 07:29:16 MDT 2014", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Due to the explosion of social networking and the information sharing among their users, the interest in analyzing social networks has increased over the recent years. Two general interests in this kind of studies are community detection and visualization. In the first case, most of the classic algorithms for community detection use only the structural information to identify groups, that is, how clusters are formed according to the topology of the relationships. However, these methods do not take into account any semantic information which could guide the clustering process, and which may add elements to conduct further analyses. In the second case most of the layout algorithms for clustered graphs have been designed to differentiate the groups within the graph, but they are not designed to analyze the interactions between such groups. Identifying these interactions gives an insight into the way different communities exchange messages or information, and allows the social network researcher to identify key actors, roles, and paths from one community to another. This article presents a novel model to use, in a conjoint way, the semantic information from the social network and its structural information to, first, find structurally and semantically related groups of nodes, and second, a layout algorithm for clustered graphs which divides the nodes into two types, one for nodes with edges connecting other communities and another with nodes connecting nodes only within their own community. With this division the visualization tool focuses on the connections between groups facilitating deep studies of augmented social networks.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "11", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Cagliero:2013:PTR, author = "Luca Cagliero and Alessandro Fiori and Luigi Grimaudo", title = "Personalized tag recommendation based on generalized rules", journal = j-TIST, volume = "5", number = "1", pages = "12:1--12:??", month = dec, year = "2013", CODEN = "????", DOI = "https://doi.org/10.1145/2542182.2542194", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Mar 13 07:29:16 MDT 2014", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Tag recommendation is focused on recommending useful tags to a user who is annotating a Web resource. A relevant research issue is the recommendation of additional tags to partially annotated resources, which may be based on either personalized or collective knowledge. However, since the annotation process is usually not driven by any controlled vocabulary, the collections of user-specific and collective annotations are often very sparse. Indeed, the discovery of the most significant associations among tags becomes a challenging task. This article presents a novel personalized tag recommendation system that discovers and exploits generalized association rules, that is, tag correlations holding at different abstraction levels, to identify additional pertinent tags to suggest. The use of generalized rules relevantly improves the effectiveness of traditional rule-based systems in coping with sparse tag collections, because: (i) correlations hidden at the level of individual tags may be anyhow figured out at higher abstraction levels and (ii) low-level tag associations discovered from collective data may be exploited to specialize high-level associations discovered in the user-specific context. The effectiveness of the proposed system has been validated against other personalized approaches on real-life and benchmark collections retrieved from the popular photo-sharing system Flickr.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "12", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Elahi:2013:ALS, author = "Mehdi Elahi and Francesco Ricci and Neil Rubens", title = "Active learning strategies for rating elicitation in collaborative filtering: a system-wide perspective", journal = j-TIST, volume = "5", number = "1", pages = "13:1--13:??", month = dec, year = "2013", CODEN = "????", DOI = "https://doi.org/10.1145/2542182.2542195", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Mar 13 07:29:16 MDT 2014", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "The accuracy of collaborative-filtering recommender systems largely depends on three factors: the quality of the rating prediction algorithm, and the quantity and quality of available ratings. While research in the field of recommender systems often concentrates on improving prediction algorithms, even the best algorithms will fail if they are fed poor-quality data during training, that is, garbage in, garbage out. Active learning aims to remedy this problem by focusing on obtaining better-quality data that more aptly reflects a user's preferences. However, traditional evaluation of active learning strategies has two major flaws, which have significant negative ramifications on accurately evaluating the system's performance (prediction error, precision, and quantity of elicited ratings). (1) Performance has been evaluated for each user independently (ignoring system-wide improvements). (2) Active learning strategies have been evaluated in isolation from unsolicited user ratings (natural acquisition). In this article we show that an elicited rating has effects across the system, so a typical user-centric evaluation which ignores any changes of rating prediction of other users also ignores these cumulative effects, which may be more influential on the performance of the system as a whole (system centric). We propose a new evaluation methodology and use it to evaluate some novel and state-of-the-art rating elicitation strategies. We found that the system-wide effectiveness of a rating elicitation strategy depends on the stage of the rating elicitation process, and on the evaluation measures (MAE, NDCG, and Precision). In particular, we show that using some common user-centric strategies may actually degrade the overall performance of a system. Finally, we show that the performance of many common active learning strategies changes significantly when evaluated concurrently with the natural acquisition of ratings in recommender systems.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "13", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{deMeo:2013:AUB, author = "Pasquale de Meo and Emilio Ferrara and Fabian Abel and Lora Aroyo and Geert-Jan Houben", title = "Analyzing user behavior across social sharing environments", journal = j-TIST, volume = "5", number = "1", pages = "14:1--14:??", month = dec, year = "2013", CODEN = "????", DOI = "https://doi.org/10.1145/2535526", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Mar 13 07:29:16 MDT 2014", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "In this work we present an in-depth analysis of the user behaviors on different Social Sharing systems. We consider three popular platforms, Flickr, Delicious and StumbleUpon, and, by combining techniques from social network analysis with techniques from semantic analysis, we characterize the tagging behavior as well as the tendency to create friendship relationships of the users of these platforms. The aim of our investigation is to see if (and how) the features and goals of a given Social Sharing system reflect on the behavior of its users and, moreover, if there exists a correlation between the social and tagging behavior of the users. We report our findings in terms of the characteristics of user profiles according to three different dimensions: (i) intensity of user activities, (ii) tag-based characteristics of user profiles, and (iii) semantic characteristics of user profiles.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "14", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Shi:2013:ACL, author = "Ziqiang Shi and Jiqing Han and Tieran Zheng", title = "Audio classification with low-rank matrix representation features", journal = j-TIST, volume = "5", number = "1", pages = "15:1--15:??", month = dec, year = "2013", CODEN = "????", DOI = "https://doi.org/10.1145/2542182.2542197", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Mar 13 07:29:16 MDT 2014", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "In this article, a novel framework based on trace norm minimization for audio classification is proposed. In this framework, both the feature extraction and classification are obtained by solving corresponding convex optimization problem with trace norm regularization. For feature extraction, robust principle component analysis (robust PCA) via minimization a combination of the nuclear norm and the l$_1$ -norm is used to extract low-rank matrix features which are robust to white noise and gross corruption for audio signal. These low-rank matrix features are fed to a linear classifier where the weight and bias are learned by solving similar trace norm constrained problems. For this linear classifier, most methods find the parameters, that is the weight matrix and bias in batch-mode, which makes it inefficient for large scale problems. In this article, we propose a parallel online framework using accelerated proximal gradient method. This framework has advantages in processing speed and memory cost. In addition, as a result of the regularization formulation of matrix classification, the Lipschitz constant was given explicitly, and hence the step size estimation of the general proximal gradient method was omitted, and this part of computing burden is saved in our approach. Extensive experiments on real data sets for laugh/non-laugh and applause/non-applause classification indicate that this novel framework is effective and noise robust.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "15", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Osman:2013:TMA, author = "Nardine Osman and Carles Sierra and Fiona Mcneill and Juan Pane and John Debenham", title = "Trust and matching algorithms for selecting suitable agents", journal = j-TIST, volume = "5", number = "1", pages = "16:1--16:??", month = dec, year = "2013", CODEN = "????", DOI = "https://doi.org/10.1145/2542182.2542198", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Mar 13 07:29:16 MDT 2014", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "This article addresses the problem of finding suitable agents to collaborate with for a given interaction in distributed open systems, such as multiagent and P2P systems. The agent in question is given the chance to describe its confidence in its own capabilities. However, since agents may be malicious, misinformed, suffer from miscommunication, and so on, one also needs to calculate how much trusted is that agent. This article proposes a novel trust model that calculates the expectation about an agent's future performance in a given context by assessing both the agent's willingness and capability through the semantic comparison of the current context in question with the agent's performance in past similar experiences. The proposed mechanism for assessing trust may be applied to any real world application where past commitments are recorded and observations are made that assess these commitments, and the model can then calculate one's trust in another with respect to a future commitment by assessing the other's past performance.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "16", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Montali:2013:MBC, author = "Marco Montali and Fabrizio M. Maggi and Federico Chesani and Paola Mello and Wil M. P. van der Aalst", title = "Monitoring business constraints with the event calculus", journal = j-TIST, volume = "5", number = "1", pages = "17:1--17:??", month = dec, year = "2013", CODEN = "????", DOI = "https://doi.org/10.1145/2542182.2542199", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Mar 13 07:29:16 MDT 2014", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Today, large business processes are composed of smaller, autonomous, interconnected subsystems, achieving modularity and robustness. Quite often, these large processes comprise software components as well as human actors, they face highly dynamic environments and their subsystems are updated and evolve independently of each other. Due to their dynamic nature and complexity, it might be difficult, if not impossible, to ensure at design-time that such systems will always exhibit the desired/expected behaviors. This, in turn, triggers the need for runtime verification and monitoring facilities. These are needed to check whether the actual behavior complies with expected business constraints, internal/external regulations and desired best practices. In this work, we present Mobucon EC, a novel monitoring framework that tracks streams of events and continuously determines the state of business constraints. In Mobucon EC, business constraints are defined using the declarative language Declare. For the purpose of this work, Declare has been suitably extended to support quantitative time constraints and non-atomic, durative activities. The logic-based language Event Calculus (EC) has been adopted to provide a formal specification and semantics to Declare constraints, while a light-weight, logic programming-based EC tool supports dynamically reasoning about partial, evolving execution traces. To demonstrate the applicability of our approach, we describe a case study about maritime safety and security and provide a synthetic benchmark to evaluate its scalability.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "17", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Lu:2013:SBA, author = "Qiang Lu and Ruoyun Huang and Yixin Chen and You Xu and Weixiong Zhang and Guoliang Chen", title = "A {SAT-based} approach to cost-sensitive temporally expressive planning", journal = j-TIST, volume = "5", number = "1", pages = "18:1--18:??", month = dec, year = "2013", CODEN = "????", DOI = "https://doi.org/10.1145/2542182.2542200", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Mar 13 07:29:16 MDT 2014", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Complex features, such as temporal dependencies and numerical cost constraints, are hallmarks of real-world planning problems. In this article, we consider the challenging problem of cost-sensitive temporally expressive (CSTE) planning, which requires concurrency of durative actions and optimization of action costs. We first propose a scheme to translate a CSTE planning problem to a minimum cost (MinCost) satisfiability (SAT) problem and to integrate with a relaxed parallel planning semantics for handling true temporal expressiveness. Our scheme finds solution plans that optimize temporal makespan, and also minimize total action costs at the optimal makespan. We propose two approaches for solving MinCost SAT. The first is based on a transformation of a MinCost SAT problem to a weighted partial Max-SAT (WPMax-SAT), and the second, called BB-CDCL, is an integration of the branch-and-bound technique and the conflict driven clause learning (CDCL) method. We also develop a CSTE customized variable branching scheme for BB-CDCL which can significantly improve the search efficiency. Our experiments on the existing CSTE benchmark domains show that our planner compares favorably to the state-of-the-art temporally expressive planners in both efficiency and quality.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "18", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Shieh:2013:RTS, author = "Jyh-Ren Shieh and Ching-Yung Lin and Shun-Xuan Wang and Ja-Ling Wu", title = "Relational term-suggestion graphs incorporating multipartite concept and expertise networks", journal = j-TIST, volume = "5", number = "1", pages = "19:1--19:??", month = dec, year = "2013", CODEN = "????", DOI = "https://doi.org/10.1145/2542182.2542201", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Mar 13 07:29:16 MDT 2014", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Term suggestions recommend query terms to a user based on his initial query. Suggesting adequate terms is a challenging issue. Most existing commercial search engines suggest search terms based on the frequency of prior used terms that match the leading alphabets the user types. In this article, we present a novel mechanism to construct semantic term-relation graphs to suggest relevant search terms in the semantic level. We built term-relation graphs based on multipartite networks of existing social media, especially from Wikipedia. The multipartite linkage networks of contributor-term, term-category, and term-term are extracted from Wikipedia to eventually form term relation graphs. For fusing these multipartite linkage networks, we propose to incorporate the contributor-category networks to model the expertise of the contributors. Based on our experiments, this step has demonstrated clear enhancement on the accuracy of the inferred relatedness of the term-semantic graphs. Experiments on keyword-expanded search based on 200 TREC-5 ad-hoc topics showed obvious advantage of our algorithms over existing approaches.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "19", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Chen:2013:EEM, author = "Tianshi Chen and Yunji Chen and Qi Guo and Zhi-Hua Zhou and Ling Li and Zhiwei Xu", title = "Effective and efficient microprocessor design space exploration using unlabeled design configurations", journal = j-TIST, volume = "5", number = "1", pages = "20:1--20:??", month = dec, year = "2013", CODEN = "????", DOI = "https://doi.org/10.1145/2542182.2542202", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Mar 13 07:29:16 MDT 2014", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Ever-increasing design complexity and advances of technology impose great challenges on the design of modern microprocessors. One such challenge is to determine promising microprocessor configurations to meet specific design constraints, which is called Design Space Exploration (DSE). In the computer architecture community, supervised learning techniques have been applied to DSE to build regression models for predicting the qualities of design configurations. For supervised learning, however, considerable simulation costs are required for attaining the labeled design configurations. Given limited resources, it is difficult to achieve high accuracy. In this article, inspired by recent advances in semisupervised learning and active learning, we propose the COAL approach which can exploit unlabeled design configurations to significantly improve the models. Empirical study demonstrates that COAL significantly outperforms a state-of-the-art DSE technique by reducing mean squared error by 35\% to 95\%, and thus, promising architectures can be attained more efficiently.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "20", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Singh:2013:NBG, author = "Munindar P. Singh", title = "Norms as a basis for governing sociotechnical systems", journal = j-TIST, volume = "5", number = "1", pages = "21:1--21:??", month = dec, year = "2013", CODEN = "????", DOI = "https://doi.org/10.1145/2542182.2542203", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Mar 13 07:29:16 MDT 2014", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "We understand a sociotechnical system as a multistakeholder cyber-physical system. We introduce governance as the administration of such a system by the stakeholders themselves. In this regard, governance is a peer-to-peer notion and contrasts with traditional management, which is a top-down hierarchical notion. Traditionally, there is no computational support for governance and it is achieved through out-of-band interactions among system administrators. Not surprisingly, traditional approaches simply do not scale up to large sociotechnical systems. We develop an approach for governance based on a computational representation of norms in organizations. Our approach is motivated by the Ocean Observatory Initiative, a thirty-year \$400 million project, which supports a variety of resources dealing with monitoring and studying the world's oceans. These resources include autonomous underwater vehicles, ocean gliders, buoys, and other instrumentation as well as more traditional computational resources. Our approach has the benefit of directly reflecting stakeholder needs and assuring stakeholders of the correctness of the resulting governance decisions while yielding adaptive resource allocation in the face of changes in both stakeholder needs and physical circumstances.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "21", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{He:2014:ISI, author = "Qi He and Juanzi Li and Rong Yan and John Yen and Haizheng Zhang", title = "Introduction to the {Special Issue on Linking Social Granularity and Functions}", journal = j-TIST, volume = "5", number = "2", pages = "22:1--22:??", month = apr, year = "2014", CODEN = "????", DOI = "https://doi.org/10.1145/2594452", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Apr 24 16:09:50 MDT 2014", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "22", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Wang:2014:IUI, author = "Jinpeng Wang and Wayne Xin Zhao and Yulan He and Xiaoming Li", title = "Infer User Interests via Link Structure Regularization", journal = j-TIST, volume = "5", number = "2", pages = "23:1--23:??", month = apr, year = "2014", CODEN = "????", DOI = "https://doi.org/10.1145/2499380", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Apr 24 16:09:50 MDT 2014", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Learning user interests from online social networks helps to better understand user behaviors and provides useful guidance to design user-centric applications. Apart from analyzing users' online content, it is also important to consider users' social connections in the social Web. Graph regularization methods have been widely used in various text mining tasks, which can leverage the graph structure information extracted from data. Previously, graph regularization methods operate under the cluster assumption that nearby nodes are more similar and nodes on the same structure (typically referred to as a cluster or a manifold) are likely to be similar. We argue that learning user interests from complex, sparse, and dynamic social networks should be based on the link structure assumption under which node similarities are evaluated based on the local link structures instead of explicit links between two nodes. We propose a regularization framework based on the relation bipartite graph, which can be constructed from any type of relations. Using Twitter as our case study, we evaluate our proposed framework from social networks built from retweet relations. Both quantitative and qualitative experiments show that our proposed method outperforms a few competitive baselines in learning user interests over a set of predefined topics. It also gives superior results compared to the baselines on retweet prediction and topical authority identification.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "23", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Javari:2014:CBC, author = "Amin Javari and Mahdi Jalili", title = "Cluster-Based Collaborative Filtering for Sign Prediction in Social Networks with Positive and Negative Links", journal = j-TIST, volume = "5", number = "2", pages = "24:1--24:??", month = apr, year = "2014", CODEN = "????", DOI = "https://doi.org/10.1145/2501977", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Apr 24 16:09:50 MDT 2014", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Social network analysis and mining get ever-increasingly important in recent years, which is mainly due to the availability of large datasets and advances in computing systems. A class of social networks is those with positive and negative links. In such networks, a positive link indicates friendship (or trust), whereas links with a negative sign correspond to enmity (or distrust). Predicting the sign of the links in these networks is an important issue and has many applications, such as friendship recommendation and identifying malicious nodes in the network. In this manuscript, we proposed a new method for sign prediction in networks with positive and negative links. Our algorithm is based first on clustering the network into a number of clusters and then applying a collaborative filtering algorithm. The clusters are such that the number of intra-cluster negative links and inter-cluster positive links are minimal, that is, the clusters are socially balanced as much as possible (a signed graph is socially balanced if it can be divided into clusters with all positive links inside the clusters and all negative links between them). We then used similarity between the clusters (based on the links between them) in a collaborative filtering algorithm. Our experiments on a number of real datasets showed that the proposed method outperformed previous methods, including those based on social balance and status theories and one based on a machine learning framework (logistic regression in this work).", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "24", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Chen:2014:CCB, author = "Yi-Cheng Chen and Wen-Yuan Zhu and Wen-Chih Peng and Wang-Chien Lee and Suh-Yin Lee", title = "{CIM}: Community-Based Influence Maximization in Social Networks", journal = j-TIST, volume = "5", number = "2", pages = "25:1--25:??", month = apr, year = "2014", CODEN = "????", DOI = "https://doi.org/10.1145/2532549", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Apr 24 16:09:50 MDT 2014", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Given a social graph, the problem of influence maximization is to determine a set of nodes that maximizes the spread of influences. While some recent research has studied the problem of influence maximization, these works are generally too time consuming for practical use in a large-scale social network. In this article, we develop a new framework, community-based influence maximization (CIM), to tackle the influence maximization problem with an emphasis on the time efficiency issue. Our proposed framework, CIM, comprises three phases: (i) community detection, (ii) candidate generation, and (iii) seed selection. Specifically, phase (i) discovers the community structure of the network; phase (ii) uses the information of communities to narrow down the possible seed candidates; and phase (iii) finalizes the seed nodes from the candidate set. By exploiting the properties of the community structures, we are able to avoid overlapped information and thus efficiently select the number of seeds to maximize information spreads. The experimental results on both synthetic and real datasets show that the proposed CIM algorithm significantly outperforms the state-of-the-art algorithms in terms of efficiency and scalability, with almost no compromise of effectiveness.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "25", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Yang:2014:SOG, author = "Jaewon Yang and Jure Leskovec", title = "Structure and Overlaps of Ground-Truth Communities in Networks", journal = j-TIST, volume = "5", number = "2", pages = "26:1--26:??", month = apr, year = "2014", CODEN = "????", DOI = "https://doi.org/10.1145/2594454", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Apr 24 16:09:50 MDT 2014", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "One of the main organizing principles in real-world networks is that of network communities, where sets of nodes organize into densely linked clusters. Even though detection of such communities is of great interest, understanding the structure communities in large networks remains relatively limited. In particular, due to the unavailability of labeled ground-truth data, it was traditionally very hard to develop accurate models of network community structure. Here we use six large social, collaboration, and information networks where nodes explicitly state their ground-truth community memberships. For example, nodes in social networks join into explicitly defined interest based groups, and we use such groups as explicitly labeled ground-truth communities. We use such ground-truth communities to study their structural signatures by analyzing how ground-truth communities emerge in networks and how they overlap. We observe some surprising phenomena. First, ground-truth communities contain high-degree hub nodes that reside in community overlaps and link to most of the members of the community. Second, the overlaps of communities are more densely connected than the non-overlapping parts of communities. We show that this in contrast to the conventional wisdom that community overlaps are more sparsely connected than the non-overlapping parts themselves. We then show that many existing models of network communities do not capture dense community overlaps. This in turn means that most present models and community detection methods confuse overlaps as separate communities. In contrast, we present the community-affiliation graph model (AGM), a conceptual model of network community structure. We demonstrate that AGM reliably captures the overall structure of networks as well as the overlapping and hierarchical nature of network communities.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "26", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Gong:2014:JLP, author = "Neil Zhenqiang Gong and Ameet Talwalkar and Lester Mackey and Ling Huang and Eui Chul Richard Shin and Emil Stefanov and Elaine (Runting) Shi and Dawn Song", title = "Joint Link Prediction and Attribute Inference Using a Social-Attribute Network", journal = j-TIST, volume = "5", number = "2", pages = "27:1--27:??", month = apr, year = "2014", CODEN = "????", DOI = "https://doi.org/10.1145/2594455", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Apr 24 16:09:50 MDT 2014", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "The effects of social influence and homophily suggest that both network structure and node-attribute information should inform the tasks of link prediction and node-attribute inference. Recently, Yin et al. [2010a, 2010b] proposed an attribute-augmented social network model, which we call Social-Attribute Network (SAN), to integrate network structure and node attributes to perform both link prediction and attribute inference. They focused on generalizing the random walk with a restart algorithm to the SAN framework and showed improved performance. In this article, we extend the SAN framework with several leading supervised and unsupervised link-prediction algorithms and demonstrate performance improvement for each algorithm on both link prediction and attribute inference. Moreover, we make the novel observation that attribute inference can help inform link prediction, that is, link-prediction accuracy is further improved by first inferring missing attributes. We comprehensively evaluate these algorithms and compare them with other existing algorithms using a novel, large-scale Google+ dataset, which we make publicly available (http://www.cs.berkeley.edu/~stevgong/gplus.html).", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "27", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Pool:2014:DDC, author = "Simon Pool and Francesco Bonchi and Matthijs van Leeuwen", title = "Description-Driven Community Detection", journal = j-TIST, volume = "5", number = "2", pages = "28:1--28:??", month = apr, year = "2014", CODEN = "????", DOI = "https://doi.org/10.1145/2517088", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Apr 24 16:09:50 MDT 2014", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Traditional approaches to community detection, as studied by physicists, sociologists, and more recently computer scientists, aim at simply partitioning the social network graph. However, with the advent of online social networking sites, richer data has become available: beyond the link information, each user in the network is annotated with additional information, for example, demographics, shopping behavior, or interests. In this context, it is therefore important to develop mining methods which can take advantage of all available information. In the case of community detection, this means finding good communities (a set of nodes cohesive in the social graph) which are associated with good descriptions in terms of user information (node attributes). Having good descriptions associated to our models make them understandable by domain experts and thus more useful in real-world applications. Another requirement dictated by real-world applications, is to develop methods that can use, when available, any domain-specific background knowledge. In the case of community detection the background knowledge could be a vague description of the communities sought in a specific application, or some prototypical nodes (e.g., good customers in the past), that represent what the analyst is looking for (a community of similar users). Towards this goal, in this article, we define and study the problem of finding a diverse set of cohesive communities with concise descriptions. We propose an effective algorithm that alternates between two phases: a hill-climbing phase producing (possibly overlapping) communities, and a description induction phase which uses techniques from supervised pattern set mining. Our framework has the nice feature of being able to build well-described cohesive communities starting from any given description or seed set of nodes, which makes it very flexible and easily applicable in real-world applications. Our experimental evaluation confirms that the proposed method discovers cohesive communities with concise descriptions in realistic and large online social networks such as Delicious, Flickr, and LastFM.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "28", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Li:2014:LPH, author = "Nan Li and William Cushing and Subbarao Kambhampati and Sungwook Yoon", title = "Learning Probabilistic Hierarchical Task Networks as Probabilistic Context-Free Grammars to Capture User Preferences", journal = j-TIST, volume = "5", number = "2", pages = "29:1--29:??", month = apr, year = "2014", CODEN = "????", DOI = "https://doi.org/10.1145/2589481", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Apr 24 16:09:50 MDT 2014", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "We introduce an algorithm to automatically learn probabilistic hierarchical task networks (pHTNs) that capture a user's preferences on plans by observing only the user's behavior. HTNs are a common choice of representation for a variety of purposes in planning, including work on learning in planning. Our contributions are twofold. First, in contrast with prior work, which employs HTNs to represent domain physics or search control knowledge, we use HTNs to model user preferences. Second, while most prior work on HTN learning requires additional information (e.g., annotated traces or tasks) to assist the learning process, our system only takes plan traces as input. Initially, we will assume that users carry out preferred plans more frequently, and thus the observed distribution of plans is an accurate representation of user preference. We then generalize to the situation where feasibility constraints frequently prevent the execution of preferred plans. Taking the prevalent perspective of viewing HTNs as grammars over primitive actions, we adapt an expectation-maximization (EM) technique from the discipline of probabilistic grammar induction to acquire probabilistic context-free grammars (pCFG) that capture the distribution on plans. To account for the difference between the distributions of possible and preferred plans, we subsequently modify this core EM technique by rescaling its input. We empirically demonstrate that the proposed approaches are able to learn HTNs representing user preferences better than the inside-outside algorithm. Furthermore, when feasibility constraints are obfuscated, the algorithm with rescaled input performs better than the algorithm with the original input.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "29", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Reches:2014:FEC, author = "Shulamit Reches and Meir Kalech and Philip Hendrix", title = "A Framework for Effectively Choosing between Alternative Candidate Partners", journal = j-TIST, volume = "5", number = "2", pages = "30:1--30:??", month = apr, year = "2014", CODEN = "????", DOI = "https://doi.org/10.1145/2589482", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Apr 24 16:09:50 MDT 2014", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Many multi-agent settings require that agents identify appropriate partners or teammates with whom to work on tasks. When selecting potential partners, agents may benefit from obtaining information about the alternatives, for instance, through gossip (i.e., by consulting others) or reputation systems. When information is uncertain and associated with cost, deciding on the amount of information needed is a hard optimization problem. This article defines a statistical model, the Information-Acquisition Source Utility model (IASU), by which agents, operating in an uncertain world, can determine (1) which information sources they should request for information, and (2) the amount of information to collect about potential partners from each source. To maximize the expected gain from the choice, IASU computes the utility of choosing a partner by estimating the benefit of additional information. The article presents empirical studies through a simulation domain as well as a real-world domain of restaurants. We compare the IASU model to other relevant models and show that the use of the IASU model significantly increases agents' overall utility.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "30", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Heath:2014:CST, author = "Derrall Heath and David Norton and Dan Ventura", title = "Conveying Semantics through Visual Metaphor", journal = j-TIST, volume = "5", number = "2", pages = "31:1--31:??", month = apr, year = "2014", CODEN = "????", DOI = "https://doi.org/10.1145/2589483", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Apr 24 16:09:50 MDT 2014", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "In the field of visual art, metaphor is a way to communicate meaning to the viewer. We present a computational system for communicating visual metaphor that can identify adjectives for describing an image based on a low-level visual feature representation of the image. We show that the system can use this visual-linguistic association to render source images that convey the meaning of adjectives in a way consistent with human understanding. Our conclusions are based on a detailed analysis of how the system's artifacts cluster, how these clusters correspond to the semantic relationships of adjectives as documented in WordNet, and how these clusters correspond to human opinion.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "31", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Lian:2014:MCH, author = "Defu Lian and Xing Xie", title = "Mining Check-In History for Personalized Location Naming", journal = j-TIST, volume = "5", number = "2", pages = "32:1--32:??", month = apr, year = "2014", CODEN = "????", DOI = "https://doi.org/10.1145/2490890", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Apr 24 16:09:50 MDT 2014", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Many innovative location-based services have been established to offer users greater convenience in their everyday lives. These services usually cannot map user's physical locations into semantic names automatically. The semantic names of locations provide important context for mobile recommendations and advertisements. In this article, we proposed a novel location naming approach which can automatically provide semantic names for users given their locations and time. In particular, when a user opens a GPS device and submits a query with her physical location and time, she will be returned the most appropriate semantic name. In our approach, we drew an analogy between location naming and local search, and designed a local search framework to propose a spatiotemporal and user preference (STUP) model for location naming. STUP combined three components, user preference (UP), spatial preference (SP), and temporal preference (TP), by leveraging learning-to-rank techniques. We evaluated STUP on 466,190 check-ins of 5,805 users from Shanghai and 135,052 check-ins of 1,361 users from Beijing. The results showed that SP was most effective among three components and that UP can provide personalized semantic names, and thus it was a necessity for location naming. Although TP was not as discriminative as the others, it can still be beneficial when integrated with SP and UP. Finally, according to the experimental results, STUP outperformed the proposed baselines and returned accurate semantic names for 23.6\% and 26.6\% of the testing queries from Beijing and Shanghai, respectively.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "32", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Bian:2014:EUP, author = "Jiang Bian and Bo Long and Lihong Li and Taesup Moon and Anlei Dong and Yi Chang", title = "Exploiting User Preference for Online Learning in {Web} Content Optimization Systems", journal = j-TIST, volume = "5", number = "2", pages = "33:1--33:??", month = apr, year = "2014", CODEN = "????", DOI = "https://doi.org/10.1145/2493259", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Apr 24 16:09:50 MDT 2014", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Web portal services have become an important medium to deliver digital content (e.g. news, advertisements, etc.) to Web users in a timely fashion. To attract more users to various content modules on the Web portal, it is necessary to design a recommender system that can effectively achieve Web portal content optimization by automatically estimating content item attractiveness and relevance to user interests. The state-of-the-art online learning methodology adapts dedicated pointwise models to independently estimate the attractiveness score for each candidate content item. Although such pointwise models can be easily adapted for online recommendation, there still remain a few critical problems. First, this pointwise methodology fails to use invaluable user preferences between content items. Moreover, the performance of pointwise models decreases drastically when facing the problem of sparse learning samples. To address these problems, we propose exploring a new dynamic pairwise learning methodology for Web portal content optimization in which we exploit dynamic user preferences extracted based on users' actions on portal services to compute the attractiveness scores of content items. In this article, we introduce two specific pairwise learning algorithms, a straightforward graph-based algorithm and a formalized Bayesian modeling one. Experiments on large-scale data from a commercial Web portal demonstrate the significant improvement of pairwise methodologies over the baseline pointwise models. Further analysis illustrates that our new pairwise learning approaches can benefit personalized recommendation more than pointwise models, since the data sparsity is more critical for personalized content optimization.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "33", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Hossain:2014:AFS, author = "M. Shahriar Hossain and Manish Marwah and Amip Shah and Layne T. Watson and Naren Ramakrishnan", title = "{AutoLCA}: a Framework for Sustainable Redesign and Assessment of Products", journal = j-TIST, volume = "5", number = "2", pages = "34:1--34:??", month = apr, year = "2014", CODEN = "????", DOI = "https://doi.org/10.1145/2505270", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Apr 24 16:09:50 MDT 2014", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "With increasing public consciousness regarding sustainability, companies are ever more eager to introduce eco-friendly products and services. Assessing environmental footprints and designing sustainable products are challenging tasks since they require analysis of each component of a product through their life cycle. To achieve sustainable design of products, companies need to evaluate the environmental impact of their system, identify the major contributors to the footprint, and select the design alternative with the lowest environmental footprint. In this article, we formulate sustainable design as a series of clustering and classification problems, and propose a framework called AutoLCA that simplifies the effort of estimating the environmental footprint of a product bill of materials by more than an order of magnitude over current methods, which are mostly labor intensive. We apply AutoLCA to real data from a large computer manufacturer. We conduct a case study on bill of materials of four different products, perform a ``hotspot'' assessment analysis to identify major contributors to carbon footprint, and determine design alternatives that can reduce the carbon footprint from 1\% to 36\%.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "34", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Shi:2014:MLC, author = "Chuan Shi and Xiangnan Kong and Di Fu and Philip S. Yu and Bin Wu", title = "Multi-Label Classification Based on Multi-Objective Optimization", journal = j-TIST, volume = "5", number = "2", pages = "35:1--35:??", month = apr, year = "2014", CODEN = "????", DOI = "https://doi.org/10.1145/2505272", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Apr 24 16:09:50 MDT 2014", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Multi-label classification refers to the task of predicting potentially multiple labels for a given instance. Conventional multi-label classification approaches focus on single objective setting, where the learning algorithm optimizes over a single performance criterion (e.g., Ranking Loss ) or a heuristic function. The basic assumption is that the optimization over one single objective can improve the overall performance of multi-label classification and meet the requirements of various applications. However, in many real applications, an optimal multi-label classifier may need to consider the trade-offs among multiple inconsistent objectives, such as minimizing Hamming Loss while maximizing Micro F1. In this article, we study the problem of multi-objective multi-label classification and propose a novel solution (called Moml) to optimize over multiple objectives simultaneously. Note that optimization objectives may be inconsistent, even conflicting, thus one cannot identify a single solution that is optimal on all objectives. Our Moml algorithm finds a set of non-dominated solutions which are optimal according to different trade-offs among multiple objectives. So users can flexibly construct various predictive models from the solution set, which provides more meaningful classification results in different application scenarios. Empirical studies on real-world tasks demonstrate that the Moml can effectively boost the overall performance of multi-label classification by optimizing over multiple objectives simultaneously.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "35", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Tang:2014:DSM, author = "Xuning Tang and Christopher C. Yang", title = "Detecting Social Media Hidden Communities Using Dynamic Stochastic Blockmodel with Temporal {Dirichlet} Process", journal = j-TIST, volume = "5", number = "2", pages = "36:1--36:??", month = apr, year = "2014", CODEN = "????", DOI = "https://doi.org/10.1145/2517085", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Apr 24 16:09:50 MDT 2014", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Detecting evolving hidden communities within dynamic social networks has attracted significant attention recently due to its broad applications in e-commerce, online social media, security intelligence, public health, and other areas. Many community network detection techniques employ a two-stage approach to identify and detect evolutionary relationships between communities of two adjacent time epochs. These techniques often identify communities with high temporal variation, since the two-stage approach detects communities of each epoch independently without considering the continuity of communities across two time epochs. Other techniques require identification of a predefined number of hidden communities which is not realistic in many applications. To overcome these limitations, we propose the Dynamic Stochastic Blockmodel with Temporal Dirichlet Process, which enables the detection of hidden communities and tracks their evolution simultaneously from a network stream. The number of hidden communities is automatically determined by a temporal Dirichlet process without human intervention. We tested our proposed technique on three different testbeds with results identifying a high performance level when compared to the baseline algorithm.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "36", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Zheng:2014:ISS, author = "Yu Zheng and Licia Capra and Ouri Wolfson and Hai Yang", title = "Introduction to the Special Section on Urban Computing", journal = j-TIST, volume = "5", number = "3", pages = "37:1--37:??", month = sep, year = "2014", CODEN = "????", DOI = "https://doi.org/10.1145/2642650", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Wed Feb 11 12:29:08 MST 2015", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "37", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Zheng:2014:UCC, author = "Yu Zheng and Licia Capra and Ouri Wolfson and Hai Yang", title = "Urban Computing: Concepts, Methodologies, and Applications", journal = j-TIST, volume = "5", number = "3", pages = "38:1--38:??", month = sep, year = "2014", CODEN = "????", DOI = "https://doi.org/10.1145/2629592", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Wed Feb 11 12:29:08 MST 2015", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Urbanization's rapid progress has modernized many people's lives but also engendered big issues, such as traffic congestion, energy consumption, and pollution. Urban computing aims to tackle these issues by using the data that has been generated in cities (e.g., traffic flow, human mobility, and geographical data). Urban computing connects urban sensing, data management, data analytics, and service providing into a recurrent process for an unobtrusive and continuous improvement of people's lives, city operation systems, and the environment. Urban computing is an interdisciplinary field where computer sciences meet conventional city-related fields, like transportation, civil engineering, environment, economy, ecology, and sociology in the context of urban spaces. This article first introduces the concept of urban computing, discussing its general framework and key challenges from the perspective of computer sciences. Second, we classify the applications of urban computing into seven categories, consisting of urban planning, transportation, the environment, energy, social, economy, and public safety and security, presenting representative scenarios in each category. Third, we summarize the typical technologies that are needed in urban computing into four folds, which are about urban sensing, urban data management, knowledge fusion across heterogeneous data, and urban data visualization. Finally, we give an outlook on the future of urban computing, suggesting a few research topics that are somehow missing in the community.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "38", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Etienne:2014:MBC, author = "C{\^o}me Etienne and Oukhellou Latifa", title = "Model-Based Count Series Clustering for Bike Sharing System Usage Mining: a Case Study with the {V{\'e}lib'} System of {Paris}", journal = j-TIST, volume = "5", number = "3", pages = "39:1--39:??", month = sep, year = "2014", CODEN = "????", DOI = "https://doi.org/10.1145/2560188", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Wed Feb 11 12:29:08 MST 2015", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Today, more and more bicycle sharing systems (BSSs) are being introduced in big cities. These transportation systems generate sizable transportation data, the mining of which can reveal the underlying urban phenomenon linked to city dynamics. This article presents a statistical model to automatically analyze the trip data of a bike sharing system. The proposed solution partitions (i.e., clusters) the stations according to their usage profiles. To do so, count series describing the stations's usage through departure/arrival counts per hour throughout the day are built and analyzed. The model for processing these count series is based on Poisson mixtures and introduces a station scaling factor that handles the differences between the stations's global usage. Differences between weekday and weekend usage are also taken into account. This model identifies the latent factors that shape the geography of trips, and the results may thus offer insights into the relationships between station neighborhood type (its amenities, its demographics, etc.) and the generated mobility patterns. In other words, the proposed method brings to light the different functions in different areas that induce specific patterns in BSS data. These potentials are demonstrated through an in-depth analysis of the results obtained on the Paris V{\'e}lib' large-scale bike sharing system.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "39", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Ying:2014:MUC, author = "Josh Jia-Ching Ying and Wen-Ning Kuo and Vincent S. Tseng and Eric Hsueh-Chan Lu", title = "Mining User Check-In Behavior with a Random Walk for Urban Point-of-Interest Recommendations", journal = j-TIST, volume = "5", number = "3", pages = "40:1--40:??", month = sep, year = "2014", CODEN = "????", DOI = "https://doi.org/10.1145/2523068", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Wed Feb 11 12:29:08 MST 2015", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "In recent years, research into the mining of user check-in behavior for point-of-interest (POI) recommendations has attracted a lot of attention. Existing studies on this topic mainly treat such recommendations in a traditional manner-that is, they treat POIs as items and check-ins as ratings. However, users usually visit a place for reasons other than to simply say that they have visited. In this article, we propose an approach referred to as Urban POI-Walk (UPOI-Walk), which takes into account a user's social-triggered intentions (SI), preference-triggered intentions (PreI), and popularity-triggered intentions (PopI), to estimate the probability of a user checking-in to a POI. The core idea of UPOI-Walk involves building a HITS-based random walk on the normalized check-in network, thus supporting the prediction of POI properties related to each user's preferences. To achieve this goal, we define several user--POI graphs to capture the key properties of the check-in behavior motivated by user intentions. In our UPOI-Walk approach, we propose a new kind of random walk model-Dynamic HITS-based Random Walk-which comprehensively considers the relevance between POIs and users from different aspects. On the basis of similitude, we make an online recommendation as to the POI the user intends to visit. To the best of our knowledge, this is the first work on urban POI recommendations that considers user check-in behavior motivated by SI, PreI, and PopI in location-based social network data. Through comprehensive experimental evaluations on two real datasets, the proposed UPOI-Walk is shown to deliver excellent performance.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "40", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Mcardle:2014:UDF, author = "Gavin Mcardle and Eoghan Furey and Aonghus Lawlor and Alexei Pozdnoukhov", title = "Using Digital Footprints for a City-Scale Traffic Simulation", journal = j-TIST, volume = "5", number = "3", pages = "41:1--41:??", month = sep, year = "2014", CODEN = "????", DOI = "https://doi.org/10.1145/2517028", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Wed Feb 11 12:29:08 MST 2015", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "This article introduces a microsimulation of urban traffic flows within a large-scale scenario implemented for the Greater Dublin region in Ireland. Traditionally, the data available for traffic simulations come from a population census and dedicated road surveys that only partly cover shopping, leisure, or recreational trips. To account for the latter, the presented traffic modeling framework exploits the digital footprints of city inhabitants on services such as Twitter and Foursquare. We enriched the model with findings from our previous studies on geographical layout of communities in a country-wide mobile phone network to account for socially related journeys. These datasets were used to calibrate a variant of a radiation model of spatial choice, which we introduced in order to drive individuals' decisions on trip destinations within an assigned daily activity plan. We observed that given the distribution of population, the workplace locations, a comprehensive set of urban facilities, and a list of typical activity sequences of city dwellers collected within a national travel survey, the developed microsimulation reproduces not only the journey statistics such as peak travel periods but also the traffic volumes at main road segments with surprising accuracy.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "41", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Momtazpour:2014:CSI, author = "Marjan Momtazpour and Patrick Butler and Naren Ramakrishnan and M. Shahriar Hossain and Mohammad C. Bozchalui and Ratnesh Sharma", title = "Charging and Storage Infrastructure Design for Electric Vehicles", journal = j-TIST, volume = "5", number = "3", pages = "42:1--42:??", month = sep, year = "2014", CODEN = "????", DOI = "https://doi.org/10.1145/2513567", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Wed Feb 11 12:29:08 MST 2015", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Ushered by recent developments in various areas of science and technology, modern energy systems are going to be an inevitable part of our societies. Smart grids are one of these modern systems that have attracted many research activities in recent years. Before utilizing the next generation of smart grids, we should have a comprehensive understanding of the interdependent energy networks and processes. Next-generation energy systems networks cannot be effectively designed, analyzed, and controlled in isolation from the social, economic, sensing, and control contexts in which they operate. In this article, we present a novel framework to support charging and storage infrastructure design for electric vehicles. We develop coordinated clustering techniques to work with network models of urban environments to aid in placement of charging stations for an electrical vehicle deployment scenario. Furthermore, we evaluate the network before and after the deployment of charging stations, to recommend the installation of appropriate storage units to overcome the extra load imposed on the network by the charging stations. We demonstrate the multiple factors that can be simultaneously leveraged in our framework to achieve practical urban deployment. Our ultimate goal is to help realize sustainable energy system management in urban electrical infrastructure by modeling and analyzing networks of interactions between electric systems and urban populations.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "42", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Tan:2014:OOT, author = "Chang Tan and Qi Liu and Enhong Chen and Hui Xiong and Xiang Wu", title = "Object-Oriented Travel Package Recommendation", journal = j-TIST, volume = "5", number = "3", pages = "43:1--43:??", month = sep, year = "2014", CODEN = "????", DOI = "https://doi.org/10.1145/2542665", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Wed Feb 11 12:29:08 MST 2015", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Providing better travel services for tourists is one of the important applications in urban computing. Though many recommender systems have been developed for enhancing the quality of travel service, most of them lack a systematic and open framework to dynamically incorporate multiple types of additional context information existing in the tourism domain, such as the travel area, season, and price of travel packages. To that end, in this article, we propose an open framework, the Objected-Oriented Recommender System (ORS), for the developers performing personalized travel package recommendations to tourists. This framework has the ability to import all the available additional context information to the travel package recommendation process in a cost-effective way. Specifically, the different types of additional information are extracted and uniformly represented as feature--value pairs. Then, we define the Object, which is the collection of the feature--value pairs. We propose two models that can be used in the ORS framework for extracting the implicit relationships among Objects. The Objected-Oriented Topic Model (OTM) can extract the topics conditioned on the intrinsic feature--value pairs of the Objects. The Objected-Oriented Bayesian Network (OBN) can effectively infer the cotravel probability of two tourists by calculating the co-occurrence time of feature--value pairs belonging to different kinds of Objects. Based on the relationships mined by OTM or OBN, the recommendation list is generated by the collaborative filtering method. Finally, we evaluate these two models and the ORS framework on real-world travel package data, and the experimental results show that the ORS framework is more flexible in terms of incorporating additional context information, and thus leads to better performances for travel package recommendations. Meanwhile, for feature selection in ORS, we define the feature information entropy, and the experimental results demonstrate that using features with lower entropies usually leads to better recommendation results.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "43", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Gurung:2014:TIP, author = "Sashi Gurung and Dan Lin and Wei Jiang and Ali Hurson and Rui Zhang", title = "Traffic Information Publication with Privacy Preservation", journal = j-TIST, volume = "5", number = "3", pages = "44:1--44:??", month = sep, year = "2014", CODEN = "????", DOI = "https://doi.org/10.1145/2542666", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Wed Feb 11 12:29:08 MST 2015", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "We are experiencing the expanding use of location-based services such as AT\&T's TeleNav GPS Navigator and Intel's Thing Finder. Existing location-based services have collected a large amount of location data, which has great potential for statistical usage in applications like traffic flow analysis, infrastructure planning, and advertisement dissemination. The key challenge is how to wisely use the data without violating each user's location privacy concerns. In this article, we first identify a new privacy problem, namely, the inference-route problem, and then present our anonymization algorithms for privacy-preserving trajectory publishing. The experimental results have demonstrated that our approach outperforms the latest related work in terms of both efficiency and effectiveness.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "44", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Hsieh:2014:MRT, author = "Hsun-Ping Hsieh and Cheng-Te Li and Shou-De Lin", title = "Measuring and Recommending Time-Sensitive Routes from Location-Based Data", journal = j-TIST, volume = "5", number = "3", pages = "45:1--45:??", month = sep, year = "2014", CODEN = "????", DOI = "https://doi.org/10.1145/2542668", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Wed Feb 11 12:29:08 MST 2015", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Location-based services allow users to perform geospatial recording actions, which facilitates the mining of the moving activities of human beings. This article proposes to recommend time-sensitive trip routes consisting of a sequence of locations with associated timestamps based on knowledge extracted from large-scale timestamped location sequence data (e.g., check-ins and GPS traces). We argue that a good route should consider (a) the popularity of places, (b) the visiting order of places, (c) the proper visiting time of each place, and (d) the proper transit time from one place to another. By devising a statistical model, we integrate these four factors into a route goodness function that aims to measure the quality of a route. Equipped with the route goodness, we recommend time-sensitive routes for two scenarios. The first is about constructing the route based on the user-specified source location with the starting time. The second is about composing the route between the specified source location and the destination location given a starting time. To handle these queries, we propose a search method, Guidance Search, which consists of a novel heuristic satisfaction function that guides the search toward the destination location and a backward checking mechanism to boost the effectiveness of the constructed route. Experiments on the Gowalla check-in datasets demonstrate the effectiveness of our model on detecting real routes and performing cloze test of routes, comparing with other baseline methods. We also develop a system TripRouter as a real-time demo platform.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "45", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Joseph:2014:CIB, author = "Kenneth Joseph and Kathleen M. Carley and Jason I. Hong", title = "Check-ins in {``Blau Space''}: Applying {Blau}'s Macrosociological Theory to Foursquare Check-ins from New {York} City", journal = j-TIST, volume = "5", number = "3", pages = "46:1--46:??", month = sep, year = "2014", CODEN = "????", DOI = "https://doi.org/10.1145/2566617", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Wed Feb 11 12:29:08 MST 2015", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Peter Blau was one of the first to define a latent social space and utilize it to provide concrete hypotheses. Blau defines social structure via social ``parameters'' (constraints). Actors that are closer together (more homogeneous) in this social parameter space are more likely to interact. One of Blau's most important hypotheses resulting from this work was that the consolidation of parameters could lead to isolated social groups. For example, the consolidation of race and income might lead to segregation. In the present work, we use Foursquare data from New York City to explore evidence of homogeneity along certain social parameters and consolidation that breeds social isolation in communities of locations checked in to by similar users. More specifically, we first test the extent to which communities detected via Latent Dirichlet Allocation are homogeneous across a set of four social constraints-racial homophily, income homophily, personal interest homophily and physical space. Using a bootstrapping approach, we find that 14 (of 20) communities are statistically, and all but one qualitatively, homogeneous along one of these social constraints, showing the relevance of Blau's latent space model in venue communities determined via user check-in behavior. We then consider the extent to which communities with consolidated parameters, those homogeneous on more than one parameter, represent socially isolated populations. We find communities homogeneous on multiple parameters, including a homosexual community and a ``hipster'' community, that show support for Blau's hypothesis that consolidation breeds social isolation. We consider these results in the context of mediated communication, in particular in the context of self-representation on social media.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "46", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Mahmud:2014:HLI, author = "Jalal Mahmud and Jeffrey Nichols and Clemens Drews", title = "Home Location Identification of {Twitter} Users", journal = j-TIST, volume = "5", number = "3", pages = "47:1--47:??", month = sep, year = "2014", CODEN = "????", DOI = "https://doi.org/10.1145/2528548", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Wed Feb 11 12:29:08 MST 2015", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "We present a new algorithm for inferring the home location of Twitter users at different granularities, including city, state, time zone, or geographic region, using the content of users' tweets and their tweeting behavior. Unlike existing approaches, our algorithm uses an ensemble of statistical and heuristic classifiers to predict locations and makes use of a geographic gazetteer dictionary to identify place-name entities. We find that a hierarchical classification approach, where time zone, state, or geographic region is predicted first and city is predicted next, can improve prediction accuracy. We have also analyzed movement variations of Twitter users, built a classifier to predict whether a user was travelling in a certain period of time, and use that to further improve the location detection accuracy. Experimental evidence suggests that our algorithm works well in practice and outperforms the best existing algorithms for predicting the home location of Twitter users.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "47", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Neviarouskaya:2014:IIT, author = "Alena Neviarouskaya and Masaki Aono and Helmut Prendinger and Mitsuru Ishizuka", title = "Intelligent Interface for Textual Attitude Analysis", journal = j-TIST, volume = "5", number = "3", pages = "48:1--48:??", month = sep, year = "2014", CODEN = "????", DOI = "https://doi.org/10.1145/2535912", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Wed Feb 11 12:29:08 MST 2015", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "This article describes a novel intelligent interface for attitude sensing in text driven by a robust computational tool for the analysis of fine-grained attitudes (emotions, judgments, and appreciations) expressed in text. The module responsible for textual attitude analysis was developed using a compositional linguistic approach based on the attitude-conveying lexicon, the analysis of syntactic and dependency relations between words in a sentence, the compositionality principle applied at various grammatical levels, the rules elaborated for semantically distinct verb classes, and a method considering the hierarchy of concepts. The performance of this module was evaluated on sentences from personal stories about life experiences. The developed web-based interface supports recognition of nine emotions, positive and negative judgments, and positive and negative appreciations conveyed in text. It allows users to adjust parameters, to enable or disable various functionality components of the algorithm, and to select the format of text annotation and attitude statistics visualization.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "48", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Song:2014:UGF, author = "Yicheng Song and Yongdong Zhang and Juan Cao and Jinhui Tang and Xingyu Gao and Jintao Li", title = "A Unified Geolocation Framework for {Web} Videos", journal = j-TIST, volume = "5", number = "3", pages = "49:1--49:??", month = jul, year = "2014", CODEN = "????", DOI = "https://doi.org/10.1145/2533989", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Fri Jul 18 14:11:13 MDT 2014", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "In this article, we propose a unified geolocation framework to automatically determine where on the earth a web video was shot. We analyze different social, visual, and textual relationships from a real-world dataset and find four relationships with apparent geography clues that can be used for web video geolocation. Then, the geolocation process is formulated as an optimization problem that simultaneously takes the social, visual, and textual relationships into consideration. The optimization problem is solved by an iterative procedure, which can be interpreted as a propagation of the geography information among the web video social network. Extensive experiments on a real-world dataset clearly demonstrate the effectiveness of our proposed framework, with the geolocation accuracy higher than state-of-the-art approaches.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "49", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Zhao:2014:PRL, author = "Yi-Liang Zhao and Liqiang Nie and Xiangyu Wang and Tat-Seng Chua", title = "Personalized Recommendations of Locally Interesting Venues to Tourists via Cross-Region Community Matching", journal = j-TIST, volume = "5", number = "3", pages = "50:1--50:??", month = jul, year = "2014", CODEN = "????", DOI = "https://doi.org/10.1145/2532439", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Fri Jul 18 14:11:13 MDT 2014", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "You are in a new city. You are not familiar with the places and neighborhoods. You want to know all about the exciting sights, food outlets, and cultural venues that the locals frequent, in particular those that suit your personal interests. Even though there exist many mapping, local search, and travel assistance sites, they mostly provide popular and famous listings such as Statue of Liberty and Eiffel Tower, which are well-known places but may not suit your personal needs or interests. Therefore, there is a gap between what tourists want and what dominant tourism resources are providing. In this work, we seek to provide a solution to bridge this gap by exploiting the rich user-generated location contents in location-based social networks in order to offer tourists the most relevant and personalized local venue recommendations. In particular, we first propose a novel Bayesian approach to extract the social dimensions of people at different geographical regions to capture their latent local interests. We next mine the local interest communities in each geographical region. We then represent each local community using aggregated behaviors of community members. Finally, we correlate communities across different regions and generate venue recommendations to tourists via cross-region community matching. We have sampled a representative subset of check-ins from Foursquare and experimentally verified the effectiveness of our proposed approaches.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "50", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Wang:2014:VNF, author = "Shuaiqiang Wang and Jiankai Sun and Byron J. Gao and Jun Ma", title = "{VSRank}: a Novel Framework for Ranking-Based Collaborative Filtering", journal = j-TIST, volume = "5", number = "3", pages = "51:1--51:??", month = jul, year = "2014", CODEN = "????", DOI = "https://doi.org/10.1145/2542048", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Fri Jul 18 14:11:13 MDT 2014", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Collaborative filtering (CF) is an effective technique addressing the information overload problem. CF approaches generally fall into two categories: rating based and ranking based. The former makes recommendations based on historical rating scores of items and the latter based on their rankings. Ranking-based CF has demonstrated advantages in recommendation accuracy, being able to capture the preference similarity between users even if their rating scores differ significantly. In this study, we propose VSRank, a novel framework that seeks accuracy improvement of ranking-based CF through adaptation of the vector space model. In VSRank, we consider each user as a document and his or her pairwise relative preferences as terms. We then use a novel degree-specialty weighting scheme resembling TF-IDF to weight the terms. Extensive experiments on benchmarks in comparison with the state-of-the-art approaches demonstrate the promise of our approach.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "51", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Castells:2015:ISI, author = "Pablo Castells and Jun Wang and Rub{\'e}n Lara and Dell Zhang", title = "Introduction to the Special Issue on Diversity and Discovery in Recommender Systems", journal = j-TIST, volume = "5", number = "4", pages = "52:1--52:??", month = jan, year = "2015", CODEN = "????", DOI = "https://doi.org/10.1145/2668113", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Wed Feb 11 12:29:09 MST 2015", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "52", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Ribeiro:2015:MPE, author = "Marco Tulio Ribeiro and Nivio Ziviani and Edleno {Silva De Moura} and Itamar Hata and Anisio Lacerda and Adriano Veloso", title = "Multiobjective {Pareto}-Efficient Approaches for Recommender Systems", journal = j-TIST, volume = "5", number = "4", pages = "53:1--53:??", month = jan, year = "2015", CODEN = "????", DOI = "https://doi.org/10.1145/2629350", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Wed Feb 11 12:29:09 MST 2015", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Recommender systems are quickly becoming ubiquitous in applications such as e-commerce, social media channels, and content providers, among others, acting as an enabling mechanism designed to overcome the information overload problem by improving browsing and consumption experience. A typical task in many recommender systems is to output a ranked list of items, so that items placed higher in the rank are more likely to be interesting to the users. Interestingness measures include how accurate, novel, and diverse are the suggested items, and the objective is usually to produce ranked lists optimizing one of these measures. Suggesting items that are simultaneously accurate, novel, and diverse is much more challenging, since this may lead to a conflicting-objective problem, in which the attempt to improve a measure further may result in worsening other measures. In this article, we propose new approaches for multiobjective recommender systems based on the concept of Pareto efficiency-a state achieved when the system is devised in the most efficient manner in the sense that there is no way to improve one of the objectives without making any other objective worse off. Given that existing multiobjective recommendation algorithms differ in their level of accuracy, diversity, and novelty, we exploit the Pareto-efficiency concept in two distinct manners: (i) the aggregation of ranked lists produced by existing algorithms into a single one, which we call Pareto-efficient ranking, and (ii) the weighted combination of existing algorithms resulting in a hybrid one, which we call Pareto-efficient hybridization. Our evaluation involves two real application scenarios: music recommendation with implicit feedback (i.e., Last.fm) and movie recommendation with explicit feedback (i.e., MovieLens). We show that the proposed Pareto-efficient approaches are effective in suggesting items that are likely to be simultaneously accurate, diverse, and novel. We discuss scenarios where the system achieves high levels of diversity and novelty without compromising its accuracy. Further, comparison against multiobjective baselines reveals improvements in terms of accuracy (from 10.4\% to 10.9\%), novelty (from 5.7\% to 7.5\%), and diversity (from 1.6\% to 4.2\%).", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "53", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Adamopoulos:2015:URS, author = "Panagiotis Adamopoulos and Alexander Tuzhilin", title = "On Unexpectedness in Recommender Systems: Or How to Better Expect the Unexpected", journal = j-TIST, volume = "5", number = "4", pages = "54:1--54:??", month = jan, year = "2015", CODEN = "????", DOI = "https://doi.org/10.1145/2559952", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Wed Feb 11 12:29:09 MST 2015", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Although the broad social and business success of recommender systems has been achieved across several domains, there is still a long way to go in terms of user satisfaction. One of the key dimensions for significant improvement is the concept of unexpectedness. In this article, we propose a method to improve user satisfaction by generating unexpected recommendations based on the utility theory of economics. In particular, we propose a new concept of unexpectedness as recommending to users those items that depart from what they would expect from the system --- the consideration set of each user. We define and formalize the concept of unexpectedness and discuss how it differs from the related notions of novelty, serendipity, and diversity. In addition, we suggest several mechanisms for specifying the users' expectations and propose specific performance metrics to measure the unexpectedness of recommendation lists. We also take into consideration the quality of recommendations using certain utility functions and present an algorithm for providing users with unexpected recommendations of high quality that are hard to discover but fairly match their interests. Finally, we conduct several experiments on ``real-world'' datasets and compare our recommendation results with other methods. The proposed approach outperforms these baseline methods in terms of unexpectedness and other important metrics, such as coverage, aggregate diversity and dispersion, while avoiding any accuracy loss.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "54", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Kucuktunc:2015:DCR, author = "Onur K{\"u}{\c{c}}{\"u}ktun{\c{c}} and Erik Saule and Kamer Kaya and {\"U}mit V. {\c{C}}ataly{\"u}rek", title = "Diversifying Citation Recommendations", journal = j-TIST, volume = "5", number = "4", pages = "55:1--55:??", month = jan, year = "2015", CODEN = "????", DOI = "https://doi.org/10.1145/2668106", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Wed Feb 11 12:29:09 MST 2015", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Literature search is one of the most important steps of academic research. With more than 100,000 papers published each year just in computer science, performing a complete literature search becomes a Herculean task. Some of the existing approaches and tools for literature search cannot compete with the characteristics of today's literature, and they suffer from ambiguity and homonymy. Techniques based on citation information are more robust to the mentioned issues. Thus, we recently built a Web service called the advisor, which provides personalized recommendations to researchers based on their papers of interest. Since most recommendation methods may return redundant results, diversifying the results of the search process is necessary to increase the amount of information that one can reach via an automated search. This article targets the problem of result diversification in citation-based bibliographic search, assuming that the citation graph itself is the only information available and no categories or intents are known. The contribution of this work is threefold. We survey various random walk--based diversification methods and enhance them with the direction awareness property to allow users to reach either old, foundational (possibly well-cited and well-known) research papers or recent (most likely less-known) ones. Next, we propose a set of novel algorithms based on vertex selection and query refinement. A set of experiments with various evaluation criteria shows that the proposed $ \gamma $-RLM algorithm performs better than the existing approaches and is suitable for real-time bibliographic search in practice.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "55", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Javari:2015:ANR, author = "Amin Javari and Mahdi Jalili", title = "Accurate and Novel Recommendations: an Algorithm Based on Popularity Forecasting", journal = j-TIST, volume = "5", number = "4", pages = "56:1--56:??", month = jan, year = "2015", CODEN = "????", DOI = "https://doi.org/10.1145/2668107", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Wed Feb 11 12:29:09 MST 2015", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Recommender systems are in the center of network science, and they are becoming increasingly important in individual businesses for providing efficient, personalized services and products to users. Previous research in the field of recommendation systems focused on improving the precision of the system through designing more accurate recommendation lists. Recently, the community has been paying attention to diversity and novelty of recommendation lists as key characteristics of modern recommender systems. In many cases, novelty and precision do not go hand in hand, and the accuracy--novelty dilemma is one of the challenging problems in recommender systems, which needs efforts in making a trade-off between them. In this work, we propose an algorithm for providing novel and accurate recommendation to users. We consider the standard definition of accuracy and an effective self-information--based measure to assess novelty of the recommendation list. The proposed algorithm is based on item popularity, which is defined as the number of votes received in a certain time interval. Wavelet transform is used for analyzing popularity time series and forecasting their trend in future timesteps. We introduce two filtering algorithms based on the information extracted from analyzing popularity time series of the items. The popularity-based filtering algorithm gives a higher chance to items that are predicted to be popular in future timesteps. The other algorithm, denoted as a novelty and population-based filtering algorithm, is to move toward items with low popularity in past timesteps that are predicted to become popular in the future. The introduced filters can be applied as adds-on to any recommendation algorithm. In this article, we use the proposed algorithms to improve the performance of classic recommenders, including item-based collaborative filtering and Markov-based recommender systems. The experiments show that the algorithms could significantly improve both the accuracy and effective novelty of the classic recommenders.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "56", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Shen:2015:ISI, author = "Dou Shen and Deepak Agarwal", title = "Introduction to the Special Issue on Online Advertising", journal = j-TIST, volume = "5", number = "4", pages = "57:1--57:??", month = jan, year = "2015", CODEN = "????", DOI = "https://doi.org/10.1145/2668123", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Wed Feb 11 12:29:09 MST 2015", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "57", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Zhu:2015:MMU, author = "Hengshu Zhu and Enhong Chen and Hui Xiong and Kuifei Yu and Huanhuan Cao and Jilei Tian", title = "Mining Mobile User Preferences for Personalized Context-Aware Recommendation", journal = j-TIST, volume = "5", number = "4", pages = "58:1--58:??", month = jan, year = "2015", CODEN = "????", DOI = "https://doi.org/10.1145/2532515", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Wed Feb 11 12:29:09 MST 2015", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Recent advances in mobile devices and their sensing capabilities have enabled the collection of rich contextual information and mobile device usage records through the device logs. These context-rich logs open a venue for mining the personal preferences of mobile users under varying contexts and thus enabling the development of personalized context-aware recommendation and other related services, such as mobile online advertising. In this article, we illustrate how to extract personal context-aware preferences from the context-rich device logs, or context logs for short, and exploit these identified preferences for building personalized context-aware recommender systems. A critical challenge along this line is that the context log of each individual user may not contain sufficient data for mining his or her context-aware preferences. Therefore, we propose to first learn common context-aware preferences from the context logs of many users. Then, the preference of each user can be represented as a distribution of these common context-aware preferences. Specifically, we develop two approaches for mining common context-aware preferences based on two different assumptions, namely, context-independent and context-dependent assumptions, which can fit into different application scenarios. Finally, extensive experiments on a real-world dataset show that both approaches are effective and outperform baselines with respect to mining personal context-aware preferences for mobile users.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "58", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Ashkan:2015:LQA, author = "Azin Ashkan and Charles L. A. Clarke", title = "Location- and Query-Aware Modeling of Browsing and Click Behavior in Sponsored Search", journal = j-TIST, volume = "5", number = "4", pages = "59:1--59:??", month = jan, year = "2015", CODEN = "????", DOI = "https://doi.org/10.1145/2534398", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Wed Feb 11 12:29:09 MST 2015", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "An online advertisement's clickthrough rate provides a fundamental measure of its quality, which is widely used in ad selection strategies. Unfortunately, ads placed in contexts where they are rarely viewed-or where users are unlikely to be interested in commercial results-may receive few clicks regardless of their quality. In this article, we model the variability of a user's browsing behavior for the purpose of click analysis and prediction in sponsored search. Our model incorporates several important contextual factors that influence ad clickthrough rates, including the user's query and ad placement on search engine result pages. We formally model these factors with respect to the list of ads displayed on a result page, the probability that the user will initiate browsing of this list, and the persistence of the user in browsing the list. We incorporate these factors into existing click models by augmenting them with appropriate query and location biases. Using expectation maximization, we learn the parameters of these augmented models from click signals recorded in the logs of a commercial search engine. To evaluate the performance of the models and to compare them with state-of-the-art performance, we apply standard evaluation metrics, including log-likelihood and perplexity. Our evaluation results indicate that, through the incorporation of query and location biases, significant improvements can be achieved in predicting browsing and click behavior in sponsored search. In addition, we explore the extent to which these biases actually reflect varying behavioral patterns. Our observations confirm that correlations exist between the biases and user search behavior.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "59", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Qin:2015:SSA, author = "Tao Qin and Wei Chen and Tie-Yan Liu", title = "Sponsored Search Auctions: Recent Advances and Future Directions", journal = j-TIST, volume = "5", number = "4", pages = "60:1--60:??", month = jan, year = "2015", CODEN = "????", DOI = "https://doi.org/10.1145/2668108", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Wed Feb 11 12:29:09 MST 2015", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Sponsored search has been proven to be a successful business model, and sponsored search auctions have become a hot research direction. There have been many exciting advances in this field, especially in recent years, while at the same time, there are also many open problems waiting for us to resolve. In this article, we provide a comprehensive review of sponsored search auctions in hopes of helping both industry practitioners and academic researchers to become familiar with this field, to know the state of the art, and to identify future research topics. Specifically, we organize the article into two parts. In the first part, we review research works on sponsored search auctions with basic settings, where fully rational advertisers without budget constraints, preknown click-through rates (CTRs) without interdependence, and exact match between queries and keywords are assumed. Under these assumptions, we first introduce the generalized second price (GSP) auction, which is the most popularly used auction mechanism in the industry. Then we give the definitions of several well-studied equilibria and review the latest results on GSP's efficiency and revenue in these equilibria. In the second part, we introduce some advanced topics on sponsored search auctions. In these advanced topics, one or more assumptions made in the basic settings are relaxed. For example, the CTR of an ad could be unknown and dependent on other ads; keywords could be broadly matched to queries before auctions are executed; and advertisers are not necessarily fully rational, could have budget constraints, and may prefer rich bidding languages. Given that the research on these advanced topics is still immature, in each section of the second part, we provide our opinions on how to make further advances, in addition to describing what has been done by researchers in the corresponding direction.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "60", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Chapelle:2015:SSR, author = "Olivier Chapelle and Eren Manavoglu and Romer Rosales", title = "Simple and Scalable Response Prediction for Display Advertising", journal = j-TIST, volume = "5", number = "4", pages = "61:1--61:??", month = jan, year = "2015", CODEN = "????", DOI = "https://doi.org/10.1145/2532128", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Wed Feb 11 12:29:09 MST 2015", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Clickthrough and conversation rates estimation are two core predictions tasks in display advertising. We present in this article a machine learning framework based on logistic regression that is specifically designed to tackle the specifics of display advertising. The resulting system has the following characteristics: It is easy to implement and deploy, it is highly scalable (we have trained it on terabytes of data), and it provides models with state-of-the-art accuracy.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "61", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Balakrishnan:2015:RTB, author = "Raju Balakrishnan and Rushi P. Bhatt", title = "Real-Time Bid Optimization for Group-Buying Ads", journal = j-TIST, volume = "5", number = "4", pages = "62:1--62:??", month = jan, year = "2015", CODEN = "????", DOI = "https://doi.org/10.1145/2532441", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Wed Feb 11 12:29:09 MST 2015", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Group-buying ads seeking a minimum number of customers before the deal expiry are increasingly used by daily-deal providers. Unlike traditional web ads, the advertiser's profits for group-buying ads depend on the time to expiry and additional customers needed to satisfy the minimum group size. Since both these quantities are time-dependent, optimal bid amounts to maximize profits change with every impression. Consequently, traditional static bidding strategies are far from optimal. Instead, bid values need to be optimized in real-time to maximize expected bidder profits. This online optimization of deal profits is made possible by the advent of ad exchanges offering real-time (spot) bidding. To this end, we propose a real-time bidding strategy for group-buying deals based on the online optimization of bid values. We derive the expected bidder profit of deals as a function of the bid amounts and dynamically vary the bids to maximize profits. Furthermore, to satisfy time constraints of the online bidding, we present methods of minimizing computation timings. Subsequently, we derive the real-time ad selection, admissibility, and real-time bidding of the traditional ads as the special cases of the proposed method. We evaluate the proposed bidding, selection, and admission strategies on a multimillion click stream of 935 ads. The proposed real-time bidding, selection, and admissibility show significant profit increases over the existing strategies. Further experiments illustrate the robustness of the bidding and acceptable computation timings.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "62", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Liu:2015:IAC, author = "Qingzhong Liu and Zhongxue Chen", title = "Improved Approaches with Calibrated Neighboring Joint Density to Steganalysis and Seam-Carved Forgery Detection in {JPEG} Images", journal = j-TIST, volume = "5", number = "4", pages = "63:1--63:??", month = jan, year = "2015", CODEN = "????", DOI = "https://doi.org/10.1145/2560365", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Wed Feb 11 12:29:09 MST 2015", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/cryptography2010.bib; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Steganalysis and forgery detection in image forensics are generally investigated separately. We have designed a method targeting the detection of both steganography and seam-carved forgery in JPEG images. We analyze the neighboring joint density of the DCT coefficients and reveal the difference between the untouched image and the modified version. In realistic detection, the untouched image and the modified version may not be obtained at the same time, and different JPEG images may have different neighboring joint density features. By exploring the self-calibration under different shift recompressions, we propose calibrated neighboring joint density-based approaches with a simple feature set to distinguish steganograms and tampered images from untouched ones. Our study shows that this approach has multiple promising applications in image forensics. Compared to the state-of-the-art steganalysis detectors, our approach delivers better or comparable detection performances with a much smaller feature set while detecting several JPEG-based steganographic systems including DCT-embedding-based adaptive steganography and Yet Another Steganographic Scheme (YASS). Our approach is also effective in detecting seam-carved forgery in JPEG images. By integrating calibrated neighboring density with spatial domain rich models that were originally designed for steganalysis, the hybrid approach obtains the best detection accuracy to discriminate seam-carved forgery from an untouched image. Our study also offers a promising manner to explore steganalysis and forgery detection together.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "63", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Azaria:2015:SID, author = "Amos Azaria and Zinovi Rabinovich and Claudia V. Goldman and Sarit Kraus", title = "Strategic Information Disclosure to People with Multiple Alternatives", journal = j-TIST, volume = "5", number = "4", pages = "64:1--64:??", month = jan, year = "2015", CODEN = "????", DOI = "https://doi.org/10.1145/2558397", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Wed Feb 11 12:29:09 MST 2015", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "In this article, we study automated agents that are designed to encourage humans to take some actions over others by strategically disclosing key pieces of information. To this end, we utilize the framework of persuasion games-a branch of game theory that deals with asymmetric interactions where one player (Sender) possesses more information about the world, but it is only the other player (Receiver) who can take an action. In particular, we use an extended persuasion model, where the Sender's information is imperfect and the Receiver has more than two alternative actions available. We design a computational algorithm that, from the Sender's standpoint, calculates the optimal information disclosure rule. The algorithm is parameterized by the Receiver's decision model (i.e., what choice he will make based on the information disclosed by the Sender) and can be retuned accordingly. We then provide an extensive experimental study of the algorithm's performance in interactions with human Receivers. First, we consider a fully rational (in the Bayesian sense) Receiver decision model and experimentally show the efficacy of the resulting Sender's solution in a routing domain. Despite the discrepancy in the Sender's and the Receiver's utilities from each of the Receiver's choices, our Sender agent successfully persuaded human Receivers to select an option more beneficial for the agent. Dropping the Receiver's rationality assumption, we introduce a machine learning procedure that generates a more realistic human Receiver model. We then show its significant benefit to the Sender solution by repeating our routing experiment. To complete our study, we introduce a second (supply--demand) experimental domain and, by contrasting it with the routing domain, obtain general guidelines for a Sender on how to construct a Receiver model.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "64", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Liu:2015:SPA, author = "Si Liu and Qiang Chen and Shuicheng Yan and Changsheng Xu and Hanqing Lu", title = "{Snap \& Play}: Auto-Generated Personalized Find-the-Difference Game", journal = j-TIST, volume = "5", number = "4", pages = "65:1--65:??", month = jan, year = "2015", CODEN = "????", DOI = "https://doi.org/10.1145/2668109", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Wed Feb 11 12:29:09 MST 2015", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "In this article, by taking a popular game, the Find-the-Difference (FiDi) game, as a concrete example, we explore how state-of-the-art image processing techniques can assist in developing a personalized, automatic, and dynamic game. Unlike the traditional FiDi game, where image pairs (source image and target image) with five different patches are manually produced by professional game developers, the proposed Personalized FiDi (P-FiDi) electronic game can be played in a fully automatic Snap \& Play mode. Snap means that players first take photos with their digital cameras. The newly captured photos are used as source images and fed into the P-FiDi system to autogenerate the counterpart target images for users to play. Four steps are adopted to autogenerate target images: enhancing the visual quality of source images, extracting some changeable patches from the source image, selecting the most suitable combination of changeable patches and difference styles for the image, and generating the differences on the target image with state-of-the-art image processing techniques. In addition, the P-FiDi game can be easily redesigned for the im-game advertising. Extensive experiments show that the P-FiDi electronic game is satisfying in terms of player experience, seamless advertisement, and technical feasibility.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "65", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Reches:2015:CCU, author = "Shulamit Reches and Meir Kalech", title = "Choosing a Candidate Using Efficient Allocation of Biased Information", journal = j-TIST, volume = "5", number = "4", pages = "66:1--66:??", month = jan, year = "2015", CODEN = "????", DOI = "https://doi.org/10.1145/2558327", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Wed Feb 11 12:29:09 MST 2015", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "This article deals with a decision-making problem concerning an agent who wants to choose a partner from multiple candidates for long-term collaboration. To choose the best partner, the agent can rely on prior information he knows about the candidates. However, to improve his decision, he can request additional information from information sources. Nonetheless, acquiring information from external information sources about candidates may be biased due to different personalities of the agent searching for a partner and the information source. In addition, information may be costly. Considering the bias and the cost of the information sources, the optimization problem addressed in this article is threefold: (1) determining the necessary amount of additional information, (2) selecting information sources from which to request the information, and (3) choosing the candidates on whom to request the additional information. We propose a heuristic to solve this optimization problem. The results of experiments on simulated and real-world domains demonstrate the efficiency of our algorithm.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "66", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Zhuang:2015:CDS, author = "Jinfeng Zhuang and Tao Mei and Steven C. H. Hoi and Xian-Sheng Hua and Yongdong Zhang", title = "Community Discovery from Social Media by Low-Rank Matrix Recovery", journal = j-TIST, volume = "5", number = "4", pages = "67:1--67:??", month = jan, year = "2015", CODEN = "????", DOI = "https://doi.org/10.1145/2668110", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Wed Feb 11 12:29:09 MST 2015", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "The pervasive usage and reach of social media have attracted a surge of attention in the multimedia research community. Community discovery from social media has therefore become an important yet challenging issue. However, due to the subjective generating process, the explicitly observed communities (e.g., group-user and user-user relationship) are often noisy and incomplete in nature. This paper presents a novel approach to discovering communities from social media, including the group membership and user friend structure, by exploring a low-rank matrix recovery technique. In particular, we take Flickr as one exemplary social media platform. We first model the observed indicator matrix of the Flickr community as a summation of a low-rank true matrix and a sparse error matrix. We then formulate an optimization problem by regularizing the true matrix to coincide with the available rich context and content (i.e., photos and their associated tags). An iterative algorithm is developed to recover the true community indicator matrix. The proposed approach leads to a variety of social applications, including community visualization, interest group refinement, friend suggestion, and influential user identification. The evaluations on a large-scale testbed, consisting of 4,919 Flickr users, 1,467 interest groups, and over five million photos, show that our approach opens a new yet effective perspective to solve social network problems with sparse learning technique. Despite being focused on Flickr, our technique can be applied in any other social media community.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "67", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Yang:2015:IPI, author = "Yiyang Yang and Zhiguo Gong and Leong Hou U.", title = "Identifying Points of Interest Using Heterogeneous Features", journal = j-TIST, volume = "5", number = "4", pages = "68:1--68:??", month = jan, year = "2015", CODEN = "????", DOI = "https://doi.org/10.1145/2668111", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Wed Feb 11 12:29:09 MST 2015", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Deducing trip-related information from web-scale datasets has received large amounts of attention recently. Identifying points of interest (POIs) in geo-tagged photos is one of these problems. The problem can be viewed as a standard clustering problem of partitioning two-dimensional objects. In this work, we study spectral clustering, which is the first attempt for the identification of POIs. However, there is no unified approach to assigning the subjective clustering parameters, and these parameters vary immensely in different metropolitans and locations. To address this issue, we study a self-tuning technique that can properly determine the parameters for the clustering needed. Besides geographical information, web photos inherently store other rich information. Such heterogeneous information can be used to enhance the identification accuracy. Thereby, we study a novel refinement framework that is based on the tightness and cohesion degree of the additional information. We thoroughly demonstrate our findings by web-scale datasets collected from Flickr.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "68", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Ji:2015:WLM, author = "Rongrong Ji and Yue Gao and Wei Liu and Xing Xie and Qi Tian and Xuelong Li", title = "When Location Meets Social Multimedia: a Survey on Vision-Based Recognition and Mining for Geo-Social Multimedia Analytics", journal = j-TIST, volume = "6", number = "1", pages = "1:1--1:??", month = mar, year = "2015", CODEN = "????", DOI = "https://doi.org/10.1145/2597181", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Fri Mar 27 18:08:08 MDT 2015", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Coming with the popularity of multimedia sharing platforms such as Facebook and Flickr, recent years have witnessed an explosive growth of geographical tags on social multimedia content. This trend enables a wide variety of emerging applications, for example, mobile location search, landmark recognition, scene reconstruction, and touristic recommendation, which range from purely research prototype to commercial systems. In this article, we give a comprehensive survey on these applications, covering recent advances in recognition and mining of geographical-aware social multimedia. We review related work in the past decade regarding to location recognition, scene summarization, tourism suggestion, 3D building modeling, mobile visual search and city navigation. At the end, we further discuss potential challenges, future topics, as well as open issues related to geo-social multimedia computing, recognition, mining, and analytics.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "1", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Chin:2015:FPS, author = "Wei-Sheng Chin and Yong Zhuang and Yu-Chin Juan and Chih-Jen Lin", title = "A Fast Parallel Stochastic Gradient Method for Matrix Factorization in Shared Memory Systems", journal = j-TIST, volume = "6", number = "1", pages = "2:1--2:??", month = mar, year = "2015", CODEN = "????", DOI = "https://doi.org/10.1145/2668133", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Fri Mar 27 18:08:08 MDT 2015", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Matrix factorization is known to be an effective method for recommender systems that are given only the ratings from users to items. Currently, stochastic gradient (SG) method is one of the most popular algorithms for matrix factorization. However, as a sequential approach, SG is difficult to be parallelized for handling web-scale problems. In this article, we develop a fast parallel SG method, FPSG, for shared memory systems. By dramatically reducing the cache-miss rate and carefully addressing the load balance of threads, FPSG is more efficient than state-of-the-art parallel algorithms for matrix factorization.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "2", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Feuz:2015:TLA, author = "Kyle D. Feuz and Diane J. Cook", title = "Transfer Learning across Feature-Rich Heterogeneous Feature Spaces via {Feature-Space Remapping (FSR)}", journal = j-TIST, volume = "6", number = "1", pages = "3:1--3:??", month = mar, year = "2015", CODEN = "????", DOI = "https://doi.org/10.1145/2629528", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Fri Mar 27 18:08:08 MDT 2015", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Transfer learning aims to improve performance on a target task by utilizing previous knowledge learned from source tasks. In this paper we introduce a novel heterogeneous transfer learning technique, Feature-Space Remapping (FSR), which transfers knowledge between domains with different feature spaces. This is accomplished without requiring typical feature-feature, feature instance, or instance-instance co-occurrence data. Instead we relate features in different feature-spaces through the construction of metafeatures. We show how these techniques can utilize multiple source datasets to construct an ensemble learner which further improves performance. We apply FSR to an activity recognition problem and a document classification problem. The ensemble technique is able to outperform all other baselines and even performs better than a classifier trained using a large amount of labeled data in the target domain. These problems are especially difficult because, in addition to having different feature-spaces, the marginal probability distributions and the class labels are also different. This work extends the state of the art in transfer learning by considering large transfer across dramatically different spaces.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "3", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Patel:2015:DSI, author = "Dhaval Patel", title = "On Discovery of Spatiotemporal Influence-Based Moving Clusters", journal = j-TIST, volume = "6", number = "1", pages = "4:1--4:??", month = mar, year = "2015", CODEN = "????", DOI = "https://doi.org/10.1145/2631926", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Fri Mar 27 18:08:08 MDT 2015", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "A moving object cluster is a set of objects that move close to each other for a long time interval. Existing works have utilized object trajectories to discover moving object clusters efficiently. In this article, we define a spatiotemporal influence-based moving cluster that captures spatiotemporal influence spread over a set of spatial objects. A spatiotemporal influence-based moving cluster is a sequence of spatial clusters, where each cluster is a set of nearby objects, such that each object in a cluster influences at least one object in the next immediate cluster and is also influenced by an object from the immediate preceding cluster. Real-life examples of spatiotemporal influence-based moving clusters include diffusion of infectious diseases and spread of innovative ideas. We study the discovery of spatiotemporal influence-based moving clusters in a database of spatiotemporal events. While the search space for discovering all spatiotemporal influence-based moving clusters is prohibitively huge, we design a method, STIMer, to efficiently retrieve the maximal answer. The algorithm STIMer adopts a top-down recursive refinement method to generate the maximal spatiotemporal influence-based moving clusters directly. Empirical studies on the real data as well as large synthetic data demonstrate the effectiveness and efficiency of our method.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "4", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Sepehri-Rad:2015:ICW, author = "Hoda Sepehri-Rad and Denilson Barbosa", title = "Identifying Controversial {Wikipedia} Articles Using Editor Collaboration Networks", journal = j-TIST, volume = "6", number = "1", pages = "5:1--5:??", month = mar, year = "2015", CODEN = "????", DOI = "https://doi.org/10.1145/2630075", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Fri Mar 27 18:08:08 MDT 2015", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Wikipedia is probably the most commonly used knowledge reference nowadays, and the high quality of its articles is widely acknowledged. Nevertheless, disagreement among editors often causes some articles to become controversial over time. These articles span thousands of popular topics, including religion, history, and politics, to name a few, and are manually tagged as controversial by the editors, which is clearly suboptimal. Moreover, disagreement, bias, and conflict are expressed quite differently in Wikipedia compared to other social media, rendering previous approaches ineffective. On the other hand, the social process of editing Wikipedia is partially captured in the edit history of the articles, opening the door for novel approaches. This article describes a novel controversy model that builds on the interaction history of the editors and not only predicts controversy but also sheds light on the process that leads to controversy. The model considers the collaboration history of pairs of editors to predict their attitude toward one another. This is done in a supervised way, where the votes of Wikipedia administrator elections are used as labels indicating agreement (i.e., support vote) or disagreement (i.e., oppose vote). From each article, a collaboration network is built, capturing the pairwise attitude among editors, allowing the accurate detection of controversy. Extensive experimental results establish the superiority of this approach compared to previous work and very competitive baselines on a wide range of settings.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "5", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Changuel:2015:RSU, author = "Sahar Changuel and Nicolas Labroche and Bernadette Bouchon-Meunier", title = "Resources Sequencing Using Automatic Prerequisite--Outcome Annotation", journal = j-TIST, volume = "6", number = "1", pages = "6:1--6:??", month = mar, year = "2015", CODEN = "????", DOI = "https://doi.org/10.1145/2505349", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Fri Mar 27 18:08:08 MDT 2015", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "The objective of any tutoring system is to provide resources to learners that are adapted to their current state of knowledge. With the availability of a large variety of online content and the disjunctive nature of results provided by traditional search engines, it becomes crucial to provide learners with adapted learning paths that propose a sequence of resources that match their learning objectives. In an ideal case, the sequence of documents provided to the learner should be such that each new document relies on concepts that have been already defined in previous documents. Thus, the problem of determining an effective learning path from a corpus of web documents depends on the accurate identification of outcome and prerequisite concepts in these documents and on their ordering according to this information. Until now, only a few works have been proposed to distinguish between prerequisite and outcome concepts, and to the best of our knowledge, no method has been introduced so far to benefit from this information to produce a meaningful learning path. To this aim, this article first describes a concept annotation method that relies on machine-learning techniques to predict the class of each concept-prerequisite or outcome-on the basis of contextual and local features. Then, this categorization is exploited to produce an automatic resource sequencing on the basis of different representations and scoring functions that transcribe the precedence relation between learning resources. Experiments conducted on a real dataset built from online resources show that our concept annotation approach outperforms the baseline method and that the learning paths automatically generated are consistent with the ground truth provided by the author of the online content.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "6", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Ghosh:2015:MTD, author = "Siddhartha Ghosh and Steve Reece and Alex Rogers and Stephen Roberts and Areej Malibari and Nicholas R. Jennings", title = "Modeling the Thermal Dynamics of Buildings: a Latent-Force- Model-Based Approach", journal = j-TIST, volume = "6", number = "1", pages = "7:1--7:??", month = mar, year = "2015", CODEN = "????", DOI = "https://doi.org/10.1145/2629674", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Fri Mar 27 18:08:08 MDT 2015", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Minimizing the energy consumed by heating, ventilation, and air conditioning (HVAC) systems of residential buildings without impacting occupants' comfort has been highlighted as an important artificial intelligence (AI) challenge. Typically, approaches that seek to address this challenge use a model that captures the thermal dynamics within a building, also referred to as a thermal model. Among thermal models, gray-box models are a popular choice for modeling the thermal dynamics of buildings. They combine knowledge of the physical structure of a building with various data-driven inputs and are accurate estimators of the state (internal temperature). However, existing gray-box models require a detailed specification of all the physical elements that can affect the thermal dynamics of a building a priori. This limits their applicability, particularly in residential buildings, where additional dynamics can be induced by human activities such as cooking, which contributes additional heat, or opening of windows, which leads to additional leakage of heat. Since the incidence of these additional dynamics is rarely known, their combined effects cannot readily be accommodated within existing models. To overcome this limitation and improve the general applicability of gray-box models, we introduce a novel model, which we refer to as a latent force thermal model of the thermal dynamics of a building, or LFM-TM. Our model is derived from an existing gray-box thermal model, which is augmented with an extra term referred to as the learned residual. This term is capable of modeling the effect of any a priori unknown additional dynamic, which, if not captured, appears as a structure in a thermal model's residual (the error induced by the model). More importantly, the learned residual can also capture the effects of physical elements such as a building's envelope or the lags in a heating system, leading to a significant reduction in complexity compared to existing models. To evaluate the performance of LFM-TM, we apply it to two independent data sources. The first is an established dataset, referred to as the FlexHouse data, which was previously used for evaluating the efficacy of existing gray-box models [Bacher and Madsen 2011]. The second dataset consists of heating data logged within homes located on the University of Southampton campus, which were specifically instrumented to collect data for our thermal modeling experiments. On both datasets, we show that LFM-TM outperforms existing models in its ability to accurately fit the observed data, generate accurate day-ahead internal temperature predictions, and explain a large amount of the variability in the future observations. This, along with the fact that we also use a corresponding efficient sequential inference scheme for LFM-TM, makes it an ideal candidate for model-based predictive control, where having accurate online predictions of internal temperatures is essential for high-quality solutions.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "7", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Zhang:2015:SPL, author = "Zhao Zhang and Cheng-Lin Liu and Ming-Bo Zhao", title = "A Sparse Projection and Low-Rank Recovery Framework for Handwriting Representation and Salient Stroke Feature Extraction", journal = j-TIST, volume = "6", number = "1", pages = "9:1--9:??", month = mar, year = "2015", CODEN = "????", DOI = "https://doi.org/10.1145/2601408", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Fri Mar 27 18:08:08 MDT 2015", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "In this article, we consider the problem of simultaneous low-rank recovery and sparse projection. More specifically, a new Robust Principal Component Analysis (RPCA)-based framework called Sparse Projection and Low-Rank Recovery (SPLRR) is proposed for handwriting representation and salient stroke feature extraction. In addition to achieving a low-rank component encoding principal features and identify errors or missing values from a given data matrix as RPCA, SPLRR also learns a similarity-preserving sparse projection for extracting salient stroke features and embedding new inputs for classification. These properties make SPLRR applicable for handwriting recognition and stroke correction and enable online computation. A cosine-similarity-style regularization term is incorporated into the SPLRR formulation for encoding the similarities of local handwriting features. The sparse projection and low-rank recovery are calculated from a convex minimization problem that can be efficiently solved in polynomial time. Besides, the supervised extension of SPLRR is also elaborated. The effectiveness of our SPLRR is examined by extensive handwritten digital repairing, stroke correction, and recognition based on benchmark problems. Compared with other related techniques, SPLRR delivers strong generalization capability and state-of-the-art performance for handwriting representation and recognition.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "9", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Stapleton:2015:CST, author = "Gem Stapleton and Beryl Plimmer and Aidan Delaney and Peter Rodgers", title = "Combining Sketching and Traditional Diagram Editing Tools", journal = j-TIST, volume = "6", number = "1", pages = "10:1--10:??", month = mar, year = "2015", CODEN = "????", DOI = "https://doi.org/10.1145/2631925", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Fri Mar 27 18:08:08 MDT 2015", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "The least cognitively demanding way to create a diagram is to draw it with a pen. Yet there is also a need for more formal visualizations, that is, diagrams created using both traditional keyboard and mouse interaction. Our objective is to allow the creation of diagrams using traditional and stylus-based input. Having two diagram creation interfaces requires that changes to a diagram should be automatically rendered in the other visualization. Because sketches are imprecise, there is always the possibility that conversion between visualizations results in a lack of syntactic consistency between the two visualizations. We propose methods for converting diagrams between forms, checking them for equivalence, and rectifying inconsistencies. As a result of our theoretical contributions, we present an intelligent software system allowing users to create and edit diagrams in sketch or formal mode. Our proof-of-concept tool supports diagrams with connected and spatial syntactic elements. Two user studies show that this approach is viable and participants found the software easy to use. We conclude that supporting such diagram creation is now possible in practice.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "10", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Hong:2015:VUR, author = "Richang Hong and Shuicheng Yan and Zhengyou Zhang", title = "Visual Understanding with {RGB-D} Sensors: an Introduction to the Special Issue", journal = j-TIST, volume = "6", number = "2", pages = "11:1--11:??", month = apr, year = "2015", CODEN = "????", DOI = "https://doi.org/10.1145/2732265", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Apr 21 11:29:25 MDT 2015", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "11", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Chen:2015:KDR, author = "Chongyu Chen and Jianfei Cai and Jianmin Zheng and Tat Jen Cham and Guangming Shi", title = "{Kinect} Depth Recovery Using a Color-Guided, Region-Adaptive, and Depth-Selective Framework", journal = j-TIST, volume = "6", number = "2", pages = "12:1--12:??", month = apr, year = "2015", CODEN = "????", DOI = "https://doi.org/10.1145/2700475", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Apr 21 11:29:25 MDT 2015", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Considering that the existing depth recovery approaches have different limitations when applied to Kinect depth data, in this article, we propose to integrate their effective features including adaptive support region selection, reliable depth selection, and color guidance together under an optimization framework for Kinect depth recovery. In particular, we formulate our depth recovery as an energy minimization problem, which solves the depth hole filling and denoising simultaneously. The energy function consists of a fidelity term and a regularization term, which are designed according to the Kinect characteristics. Our framework inherits and improves the idea of guided filtering by incorporating structure information and prior knowledge of the Kinect noise model. Through analyzing the solution to the optimization framework, we also derive a local filtering version that provides an efficient and effective way of improving the existing filtering techniques. Quantitative evaluations on our developed synthesized dataset and experiments on real Kinect data show that the proposed method achieves superior performance in terms of recovery accuracy and visual quality.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "12", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Figueroa:2015:CAT, author = "Nadia Figueroa and Haiwei Dong and Abdulmotaleb {El Saddik}", title = "A Combined Approach Toward Consistent Reconstructions of Indoor Spaces Based on {$6$D RGB-D} Odometry and {KinectFusion}", journal = j-TIST, volume = "6", number = "2", pages = "14:1--14:??", month = apr, year = "2015", CODEN = "????", DOI = "https://doi.org/10.1145/2629673", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Apr 21 11:29:25 MDT 2015", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "We propose a 6D RGB-D odometry approach that finds the relative camera pose between consecutive RGB-D frames by keypoint extraction and feature matching both on the RGB and depth image planes. Furthermore, we feed the estimated pose to the highly accurate KinectFusion algorithm, which uses a fast ICP (Iterative Closest Point) to fine-tune the frame-to-frame relative pose and fuse the depth data into a global implicit surface. We evaluate our method on a publicly available RGB-D SLAM benchmark dataset by Sturm et al. The experimental results show that our proposed reconstruction method solely based on visual odometry and KinectFusion outperforms the state-of-the-art RGB-D SLAM system accuracy. Moreover, our algorithm outputs a ready-to-use polygon mesh (highly suitable for creating 3D virtual worlds) without any postprocessing steps.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "14", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Zha:2015:RMF, author = "Zheng-Jun Zha and Yang Yang and Jinhui Tang and Meng Wang and Tat-Seng Chua", title = "Robust Multiview Feature Learning for {RGB-D} Image Understanding", journal = j-TIST, volume = "6", number = "2", pages = "15:1--15:??", month = apr, year = "2015", CODEN = "????", DOI = "https://doi.org/10.1145/2735521", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Apr 21 11:29:25 MDT 2015", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "The availability of massive RGB-depth (RGB-D) images poses a compelling need for effective RGB-D content understanding techniques. RGB-D images provide synchronized information from multiple views (e.g., color and depth) of real-world objects and scenes. This work proposes learning compact and discriminative features from the multiple views of RGB-D content toward effective feature representation for RGB-D image understanding. In particular, a robust multiview feature learning approach is developed, which exploits the intrinsic relations among multiple views. The feature learning in multiple views is jointly optimized in an integrated formulation. The joint optimization essentially exploits the intrinsic relations among the views, leading to effective features and making the learning process robust to noises. The feature learning function is formulated as a robust nonnegative graph embedding function over multiple graphs in various views. The graphs characterize the local geometric and discriminating structure of the multiview data. The joint sparsity in $ l_1$-norm graph embedding and $ l_{21}$-norm data factorization further enhances the robustness of feature learning. We derive an efficient computational solution for the proposed approach and provide rigorous theoretical proof with regard to its convergence. We apply the proposed approach to two RGB-D image understanding tasks: RGB-D object classification and RGB-D scene categorization. We conduct extensive experiments on two real-world RGB-D image datasets. The experimental results have demonstrated the effectiveness of the proposed approach.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "15", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Zhang:2015:RDI, author = "Quanshi Zhang and Xuan Song and Xiaowei Shao and Huijing Zhao and Ryosuke Shibasaki", title = "From {RGB-D} Images to {RGB} Images: Single Labeling for Mining Visual Models", journal = j-TIST, volume = "6", number = "2", pages = "16:1--16:??", month = apr, year = "2015", CODEN = "????", DOI = "https://doi.org/10.1145/2629701", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Apr 21 11:29:25 MDT 2015", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Mining object-level knowledge, that is, building a comprehensive category model base, from a large set of cluttered scenes presents a considerable challenge to the field of artificial intelligence. How to initiate model learning with the least human supervision (i.e., manual labeling) and how to encode the structural knowledge are two elements of this challenge, as they largely determine the scalability and applicability of any solution. In this article, we propose a model-learning method that starts from a single-labeled object for each category, and mines further model knowledge from a number of informally captured, cluttered scenes. However, in these scenes, target objects are relatively small and have large variations in texture, scale, and rotation. Thus, to reduce the model bias normally associated with less supervised learning methods, we use the robust 3D shape in RGB-D images to guide our model learning, then apply the properly trained category models to both object detection and recognition in more conventional RGB images. In addition to model training for their own categories, the knowledge extracted from the RGB-D images can also be transferred to guide model learning for a new category, in which only RGB images without depth information in the new category are provided for training. Preliminary testing shows that the proposed method performs as well as fully supervised learning methods.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "16", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Huang:2015:ARM, author = "Meiyu Huang and Yiqiang Chen and Wen Ji and Chunyan Miao", title = "Accurate and Robust Moving-Object Segmentation for Telepresence Systems", journal = j-TIST, volume = "6", number = "2", pages = "17:1--17:??", month = apr, year = "2015", CODEN = "????", DOI = "https://doi.org/10.1145/2629480", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Apr 21 11:29:25 MDT 2015", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Moving-object segmentation is the key issue of Telepresence systems. With monocular camera--based segmentation methods, desirable segmentation results are hard to obtain in challenging scenes with ambiguous color, illumination changes, and shadows. Approaches based on depth sensors often cause holes inside the object and missegmentations on the object boundary due to inaccurate and unstable estimation of depth data. This work proposes an adaptive multi-cue decision fusion method based on Kinect (which integrates a depth sensor with an RGB camera). First, the algorithm obtains an initial foreground mask based on the depth cue. Second, the algorithm introduces a postprocessing framework to refine the segmentation results, which consists of two main steps: (1) automatically adjusting the weight of two weak decisions to identify foreground holes based on the color and contrast cue separately; and (2) refining the object boundary by integrating the motion probability weighted temporal prior, color likelihood, and smoothness constraint. The extensive experiments we conducted demonstrate that our method can segment moving objects accurately and robustly in various situations in real time.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "17", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Zhu:2015:FMF, author = "Yu Zhu and Wenbin Chen and Guodong Guo", title = "Fusing Multiple Features for Depth-Based Action Recognition", journal = j-TIST, volume = "6", number = "2", pages = "18:1--18:??", month = apr, year = "2015", CODEN = "????", DOI = "https://doi.org/10.1145/2629483", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Apr 21 11:29:25 MDT 2015", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Human action recognition is a very active research topic in computer vision and pattern recognition. Recently, it has shown a great potential for human action recognition using the three-dimensional (3D) depth data captured by the emerging RGB-D sensors. Several features and/or algorithms have been proposed for depth-based action recognition. A question is raised: Can we find some complementary features and combine them to improve the accuracy significantly for depth-based action recognition? To address the question and have a better understanding of the problem, we study the fusion of different features for depth-based action recognition. Although data fusion has shown great success in other areas, it has not been well studied yet on 3D action recognition. Some issues need to be addressed, for example, whether the fusion is helpful or not for depth-based action recognition, and how to do the fusion properly. In this article, we study different fusion schemes comprehensively, using diverse features for action characterization in depth videos. Two different levels of fusion schemes are investigated, that is, feature level and decision level. Various methods are explored at each fusion level. Four different features are considered to characterize the depth action patterns from different aspects. The experiments are conducted on four challenging depth action databases, in order to evaluate and find the best fusion methods generally. Our experimental results show that the four different features investigated in the article can complement each other, and appropriate fusion methods can improve the recognition accuracies significantly over each individual feature. More importantly, our fusion-based action recognition outperforms the state-of-the-art approaches on these challenging databases.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "18", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Spurlock:2015:EGD, author = "Scott Spurlock and Richard Souvenir", title = "An Evaluation of Gamesourced Data for Human Pose Estimation", journal = j-TIST, volume = "6", number = "2", pages = "19:1--19:??", month = apr, year = "2015", CODEN = "????", DOI = "https://doi.org/10.1145/2629465", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Apr 21 11:29:25 MDT 2015", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Gamesourcing has emerged as an approach for rapidly acquiring labeled data for learning-based, computer vision recognition algorithms. In this article, we present an approach for using RGB-D sensors to acquire annotated training data for human pose estimation from 2D images. Unlike other gamesourcing approaches, our method does not require a specific game, but runs alongside any gesture-based game using RGB-D sensors. The automatically generated datasets resulting from this approach contain joint estimates within a few pixel units of manually labeled data, and a gamesourced dataset created using a relatively small number of players, games, and locations performs as well as large-scale, manually annotated datasets when used as training data with recent learning-based human pose estimation methods for 2D images.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "19", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Sun:2015:LSV, author = "Chao Sun and Tianzhu Zhang and Changsheng Xu", title = "Latent Support Vector Machine Modeling for Sign Language Recognition with {Kinect}", journal = j-TIST, volume = "6", number = "2", pages = "20:1--20:??", month = apr, year = "2015", CODEN = "????", DOI = "https://doi.org/10.1145/2629481", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Apr 21 11:29:25 MDT 2015", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Vision-based sign language recognition has attracted more and more interest from researchers in the computer vision field. In this article, we propose a novel algorithm to model and recognize sign language performed in front of a Microsoft Kinect sensor. Under the assumption that some frames are expected to be both discriminative and representative in a sign language video, we first assign a binary latent variable to each frame in training videos for indicating its discriminative capability, then develop a latent support vector machine model to classify the signs, as well as localize the discriminative and representative frames in each video. In addition, we utilize the depth map together with the color image captured by the Kinect sensor to obtain a more effective and accurate feature to enhance the recognition accuracy. To evaluate our approach, we conducted experiments on both word-level sign language and sentence-level sign language. An American Sign Language dataset including approximately 2,000 word-level sign language phrases and 2,000 sentence-level sign language phrases was collected using the Kinect sensor, and each phrase contains color, depth, and skeleton information. Experiments on our dataset demonstrate the effectiveness of the proposed method for sign language recognition.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "20", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Tang:2015:RTH, author = "Ao Tang and Ke Lu and Yufei Wang and Jie Huang and Houqiang Li", title = "A Real-Time Hand Posture Recognition System Using Deep Neural Networks", journal = j-TIST, volume = "6", number = "2", pages = "21:1--21:??", month = apr, year = "2015", CODEN = "????", DOI = "https://doi.org/10.1145/2735952", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Apr 21 11:29:25 MDT 2015", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Hand posture recognition (HPR) is quite a challenging task, due to both the difficulty in detecting and tracking hands with normal cameras and the limitations of traditional manually selected features. In this article, we propose a two-stage HPR system for Sign Language Recognition using a Kinect sensor. In the first stage, we propose an effective algorithm to implement hand detection and tracking. The algorithm incorporates both color and depth information, without specific requirements on uniform-colored or stable background. It can handle the situations in which hands are very close to other parts of the body or hands are not the nearest objects to the camera and allows for occlusion of hands caused by faces or other hands. In the second stage, we apply deep neural networks (DNNs) to automatically learn features from hand posture images that are insensitive to movement, scaling, and rotation. Experiments verify that the proposed system works quickly and accurately and achieves a recognition accuracy as high as 98.12\%.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "21", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Zhang:2015:RTS, author = "Liyan Zhang and Fan Liu and Jinhui Tang", title = "Real-Time System for Driver Fatigue Detection by {RGB-D} Camera", journal = j-TIST, volume = "6", number = "2", pages = "22:1--22:??", month = apr, year = "2015", CODEN = "????", DOI = "https://doi.org/10.1145/2629482", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Apr 21 11:29:25 MDT 2015", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Drowsy driving is one of the major causes of fatal traffic accidents. In this article, we propose a real-time system that utilizes RGB-D cameras to automatically detect driver fatigue and generate alerts to drivers. By introducing RGB-D cameras, the depth data can be obtained, which provides extra evidence to benefit the task of head detection and head pose estimation. In this system, two important visual cues (head pose and eye state) for driver fatigue detection are extracted and leveraged simultaneously. We first present a real-time 3D head pose estimation method by leveraging RGB and depth data. Then we introduce a novel method to predict eye states employing the WLBP feature, which is a powerful local image descriptor that is robust to noise and illumination variations. Finally, we integrate the results from both head pose and eye states to generate the overall conclusion. The combination and collaboration of the two types of visual cues can reduce the uncertainties and resolve the ambiguity that a single cue may induce. The experiments were performed using an inside-car environment during the day and night, and they fully demonstrate the effectiveness and robustness of our system as well as the proposed methods of predicting head pose and eye states.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "22", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Kyan:2015:ABD, author = "Matthew Kyan and Guoyu Sun and Haiyan Li and Ling Zhong and Paisarn Muneesawang and Nan Dong and Bruce Elder and Ling Guan", title = "An Approach to Ballet Dance Training through {MS Kinect} and Visualization in a {CAVE} Virtual Reality Environment", journal = j-TIST, volume = "6", number = "2", pages = "23:1--23:??", month = apr, year = "2015", CODEN = "????", DOI = "https://doi.org/10.1145/2735951", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Apr 21 11:29:25 MDT 2015", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "This article proposes a novel framework for the real-time capture, assessment, and visualization of ballet dance movements as performed by a student in an instructional, virtual reality (VR) setting. The acquisition of human movement data is facilitated by skeletal joint tracking captured using the popular Microsoft (MS) Kinect camera system, while instruction and performance evaluation are provided in the form of 3D visualizations and feedback through a CAVE virtual environment, in which the student is fully immersed. The proposed framework is based on the unsupervised parsing of ballet dance movement into a structured posture space using the spherical self-organizing map (SSOM). A unique feature descriptor is proposed to more appropriately reflect the subtleties of ballet dance movements, which are represented as gesture trajectories through posture space on the SSOM. This recognition subsystem is used to identify the category of movement the student is attempting when prompted (by a virtual instructor) to perform a particular dance sequence. The dance sequence is then segmented and cross-referenced against a library of gestural components performed by the teacher. This facilitates alignment and score-based assessment of individual movements within the context of the dance sequence. An immersive interface enables the student to review his or her performance from a number of vantage points, each providing a unique perspective and spatial context suggestive of how the student might make improvements in training. An evaluation of the recognition and virtual feedback systems is presented.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "23", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Shi:2015:ESC, author = "Miaojing Shi and Xinghai Sun and Dacheng Tao and Chao Xu and George Baciu and Hong Liu", title = "Exploring Spatial Correlation for Visual Object Retrieval", journal = j-TIST, volume = "6", number = "2", pages = "24:1--24:??", month = apr, year = "2015", CODEN = "????", DOI = "https://doi.org/10.1145/2641576", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Apr 21 11:29:25 MDT 2015", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Bag-of-visual-words (BOVW)-based image representation has received intense attention in recent years and has improved content-based image retrieval (CBIR) significantly. BOVW does not consider the spatial correlation between visual words in natural images and thus biases the generated visual words toward noise when the corresponding visual features are not stable. This article outlines the construction of a visual word co-occurrence matrix by exploring visual word co-occurrence extracted from small affine-invariant regions in a large collection of natural images. Based on this co-occurrence matrix, we first present a novel high-order predictor to accelerate the generation of spatially correlated visual words and a penalty tree (PTree) to continue generating the words after the prediction. Subsequently, we propose two methods of co-occurrence weighting similarity measure for image ranking: Co-Cosine and Co-TFIDF. These two new schemes down-weight the contributions of the words that are less discriminative because of frequent co-occurrences with other words. We conduct experiments on Oxford and Paris Building datasets, in which the ImageNet dataset is used to implement a large-scale evaluation. Cross-dataset evaluations between the Oxford and Paris datasets and Oxford and Holidays datasets are also provided. Thorough experimental results suggest that our method outperforms the state of the art without adding much additional cost to the BOVW model.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "24", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Doherty:2015:PMT, author = "Jonathan Doherty and Kevin Curran and Paul McKevitt", title = "Pattern Matching Techniques for Replacing Missing Sections of Audio Streamed across Wireless Networks", journal = j-TIST, volume = "6", number = "2", pages = "25:1--25:??", month = apr, year = "2015", CODEN = "????", DOI = "https://doi.org/10.1145/2663358", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Apr 21 11:29:25 MDT 2015", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Streaming media on the Internet can be unreliable. Services such as audio-on-demand drastically increase the loads on networks; therefore, new, robust, and highly efficient coding algorithms are necessary. One method overlooked to date, which can work alongside existing audio compression schemes, is that which takes into account the semantics and natural repetition of music. Similarity detection within polyphonic audio has presented problematic challenges within the field of music information retrieval. One approach to deal with bursty errors is to use self-similarity to replace missing segments. Many existing systems exist based on packet loss and replacement on a network level, but none attempt repairs of large dropouts of 5 seconds or more. Music exhibits standard structures that can be used as a forward error correction (FEC) mechanism. FEC is an area that addresses the issue of packet loss with the onus of repair placed as much as possible on the listener's device. We have developed a server--client-based framework (SoFI) for automatic detection and replacement of large packet losses on wireless networks when receiving time-dependent streamed audio. Whenever dropouts occur, SoFI swaps audio presented to the listener between a live stream and previous sections of the audio stored locally. Objective and subjective evaluations of SoFI where subjects were presented with other simulated approaches to audio repair together with simulations of replacements including varying lengths of time in the repair give positive results.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "25", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Hai:2015:ABU, author = "Zhen Hai and Kuiyu Chang and Gao Cong and Christopher C. Yang", title = "An Association-Based Unified Framework for Mining Features and Opinion Words", journal = j-TIST, volume = "6", number = "2", pages = "26:1--26:??", month = apr, year = "2015", CODEN = "????", DOI = "https://doi.org/10.1145/2663359", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Apr 21 11:29:25 MDT 2015", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Mining features and opinion words is essential for fine-grained opinion analysis of customer reviews. It is observed that semantic dependencies naturally exist between features and opinion words, even among features or opinion words themselves. In this article, we employ a corpus statistics association measure to quantify the pairwise word dependencies and propose a generalized association-based unified framework to identify features, including explicit and implicit features, and opinion words from reviews. We first extract explicit features and opinion words via an association-based bootstrapping method (ABOOT). ABOOT starts with a small list of annotated feature seeds and then iteratively recognizes a large number of domain-specific features and opinion words by discovering the corpus statistics association between each pair of words on a given review domain. Two instances of this ABOOT method are evaluated based on two particular association models, likelihood ratio tests (LRTs) and latent semantic analysis (LSA). Next, we introduce a natural extension to identify implicit features by employing the recognized known semantic correlations between features and opinion words. Experimental results illustrate the benefits of the proposed association-based methods for identifying features and opinion words versus benchmark methods.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "26", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Huang:2015:HMC, author = "Shanshan Huang and Jun Ma and Peizhe Cheng and Shuaiqiang Wang", title = "A {Hybrid Multigroup CoClustering} Recommendation Framework Based on Information Fusion", journal = j-TIST, volume = "6", number = "2", pages = "27:1--27:??", month = apr, year = "2015", CODEN = "????", DOI = "https://doi.org/10.1145/2700465", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Apr 21 11:29:25 MDT 2015", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Collaborative Filtering (CF) is one of the most successful algorithms in recommender systems. However, it suffers from data sparsity and scalability problems. Although many clustering techniques have been incorporated to alleviate these two problems, most of them fail to achieve further significant improvement in recommendation accuracy. First of all, most of them assume each user or item belongs to a single cluster. Since usually users can hold multiple interests and items may belong to multiple categories, it is more reasonable to assume that users and items can join multiple clusters (groups), where each cluster is a subset of like-minded users and items they prefer. Furthermore, most of the clustering-based CF models only utilize historical rating information in the clustering procedure but ignore other data resources in recommender systems such as the social connections of users and the correlations between items. In this article, we propose HMCoC, a Hybrid Multigroup CoClustering recommendation framework, which can cluster users and items into multiple groups simultaneously with different information resources. In our framework, we first integrate information of user--item rating records, user social networks, and item features extracted from the DBpedia knowledge base. We then use an optimization method to mine meaningful user--item groups with all the information. Finally, we apply the conventional CF method in each cluster to make predictions. By merging the predictions from each cluster, we generate the top-n recommendations to the target users for return. Extensive experimental results demonstrate the superior performance of our approach in top-n recommendation in terms of MAP, NDCG, and F1 compared with other clustering-based CF models.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "27", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Fire:2015:DMO, author = "Michael Fire and Yuval Elovici", title = "Data Mining of Online Genealogy Datasets for Revealing Lifespan Patterns in Human Population", journal = j-TIST, volume = "6", number = "2", pages = "28:1--28:??", month = apr, year = "2015", CODEN = "????", DOI = "https://doi.org/10.1145/2700464", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Apr 21 11:29:25 MDT 2015", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Online genealogy datasets contain extensive information about millions of people and their past and present family connections. This vast amount of data can help identify various patterns in the human population. In this study, we present methods and algorithms that can assist in identifying variations in lifespan distributions of the human population in the past centuries, in detecting social and genetic features that correlate with the human lifespan, and in constructing predictive models of human lifespan based on various features that can easily be extracted from genealogy datasets. We have evaluated the presented methods and algorithms on a large online genealogy dataset with over a million profiles and over 9 million connections, all of which were collected from the WikiTree website. Our findings indicate that significant but small positive correlations exist between the parents' lifespan and their children's lifespan. Additionally, we found slightly higher and significant correlations between the lifespans of spouses. We also discovered a very small positive and significant correlation between longevity and reproductive success in males, and a small and significant negative correlation between longevity and reproductive success in females. Moreover, our predictive models presented results with a Mean Absolute Error as low as 13.18 in predicting the lifespans of individuals who outlived the age of 10, and our classification models presented better than random classification results in predicting which people who outlive the age of 50 will also outlive the age of 80. We believe that this study will be the first of many studies to utilize the wealth of data on human populations, existing in online genealogy datasets, to better understand factors that influence the human lifespan. Understanding these factors can assist scientists in providing solutions for successful aging.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "28", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Zheng:2015:TDM, author = "Yu Zheng", title = "Trajectory Data Mining: an Overview", journal = j-TIST, volume = "6", number = "3", pages = "29:1--29:??", month = may, year = "2015", CODEN = "????", DOI = "https://doi.org/10.1145/2743025", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu May 21 15:49:31 MDT 2015", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "The advances in location-acquisition and mobile computing techniques have generated massive spatial trajectory data, which represent the mobility of a diversity of moving objects, such as people, vehicles, and animals. Many techniques have been proposed for processing, managing, and mining trajectory data in the past decade, fostering a broad range of applications. In this article, we conduct a systematic survey on the major research into trajectory data mining, providing a panorama of the field as well as the scope of its research topics. Following a road map from the derivation of trajectory data, to trajectory data preprocessing, to trajectory data management, and to a variety of mining tasks (such as trajectory pattern mining, outlier detection, and trajectory classification), the survey explores the connections, correlations, and differences among these existing techniques. This survey also introduces the methods that transform trajectories into other data formats, such as graphs, matrices, and tensors, to which more data mining and machine learning techniques can be applied. Finally, some public trajectory datasets are presented. This survey can help shape the field of trajectory data mining, providing a quick understanding of this field to the community.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "29", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Bouguessa:2015:IAO, author = "Mohamed Bouguessa and Lotfi Ben Romdhane", title = "Identifying Authorities in Online Communities", journal = j-TIST, volume = "6", number = "3", pages = "30:1--30:??", month = may, year = "2015", CODEN = "????", DOI = "https://doi.org/10.1145/2700481", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu May 21 15:49:31 MDT 2015", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Several approaches have been proposed for the problem of identifying authoritative actors in online communities. However, the majority of existing methods suffer from one or more of the following limitations: (1) There is a lack of an automatic mechanism to formally discriminate between authoritative and nonauthoritative users. In fact, a common approach to authoritative user identification is to provide a ranked list of users expecting authorities to come first. A major problem of such an approach is the question of where to stop reading the ranked list of users. How many users should be chosen as authoritative? (2) Supervised learning approaches for authoritative user identification suffer from their dependency on the training data. The problem here is that labeled samples are more difficult, expensive, and time consuming to obtain than unlabeled ones. (3) Several approaches rely on some user parameters to estimate an authority score. Detection accuracy of authoritative users can be seriously affected if incorrect values are used. In this article, we propose a parameterless mixture model-based approach that is capable of addressing the three aforementioned issues in a single framework. In our approach, we first represent each user with a feature vector composed of information related to its social behavior and activity in an online community. Next, we propose a statistical framework, based on the multivariate beta mixtures, in order to model the estimated set of feature vectors. The probability density function is therefore estimated and the beta component that corresponds to the most authoritative users is identified. The suitability of the proposed approach is illustrated on real data extracted from the Stack Exchange question-answering network and Twitter.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "30", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Lee:2015:WWR, author = "Kyumin Lee and Jalal Mahmud and Jilin Chen and Michelle Zhou and Jeffrey Nichols", title = "Who Will Retweet This? {Detecting} Strangers from {Twitter} to Retweet Information", journal = j-TIST, volume = "6", number = "3", pages = "31:1--31:??", month = may, year = "2015", CODEN = "????", DOI = "https://doi.org/10.1145/2700466", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu May 21 15:49:31 MDT 2015", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "There has been much effort on studying how social media sites, such as Twitter, help propagate information in different situations, including spreading alerts and SOS messages in an emergency. However, existing work has not addressed how to actively identify and engage the right strangers at the right time on social media to help effectively propagate intended information within a desired time frame. To address this problem, we have developed three models: (1) a feature-based model that leverages people's exhibited social behavior, including the content of their tweets and social interactions, to characterize their willingness and readiness to propagate information on Twitter via the act of retweeting; (2) a wait-time model based on a user's previous retweeting wait times to predict his or her next retweeting time when asked; and (3) a subset selection model that automatically selects a subset of people from a set of available people using probabilities predicted by the feature-based model and maximizes retweeting rate. Based on these three models, we build a recommender system that predicts the likelihood of a stranger to retweet information when asked, within a specific time window, and recommends the top-N qualified strangers to engage with. Our experiments, including live studies in the real world, demonstrate the effectiveness of our work.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "31", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Hirschprung:2015:SDD, author = "Ron Hirschprung and Eran Toch and Oded Maimon", title = "Simplifying Data Disclosure Configurations in a Cloud Computing Environment", journal = j-TIST, volume = "6", number = "3", pages = "32:1--32:??", month = may, year = "2015", CODEN = "????", DOI = "https://doi.org/10.1145/2700472", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu May 21 15:49:31 MDT 2015", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Cloud computing offers a compelling vision of computation, enabling an unprecedented level of data distribution and sharing. Beyond improving the computing infrastructure, cloud computing enables a higher level of interoperability between information systems, simplifying tasks such as sharing documents between coworkers or enabling collaboration between an organization and its suppliers. While these abilities may result in significant benefits to users and organizations, they also present privacy challenges due to unwanted exposure of sensitive information. As information-sharing processes in cloud computing are complex and domain specific, configuring these processes can be an overwhelming and burdensome task for users. This article investigates the feasibility of configuring sharing processes through a small and representative set of canonical configuration options. For this purpose, we present a generic method, named SCON-UP (Simplified CON-figuration of User Preferences). SCON-UP simplifies configuration interfaces by using a clustering algorithm that analyzes a massive set of sharing preferences and condenses them into a small number of discrete disclosure levels. Thus, the user is provided with a usable configuration model while guaranteeing adequate privacy control. We describe the algorithm and empirically evaluate our model using data collected in two user studies (n = 121 and n = 352). Our results show that when provided with three canonical configuration options, on average, 82\% of the population can be covered by at least one option. We exemplify the feasibility of discretizing sharing levels and discuss the tradeoff between coverage and simplicity in discrete configuration options.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "32", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Elbadrawy:2015:USF, author = "Asmaa Elbadrawy and George Karypis", title = "User-Specific Feature-Based Similarity Models for Top-$n$ Recommendation of New Items", journal = j-TIST, volume = "6", number = "3", pages = "33:1--33:??", month = may, year = "2015", CODEN = "????", DOI = "https://doi.org/10.1145/2700495", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu May 21 15:49:31 MDT 2015", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Recommending new items for suitable users is an important yet challenging problem due to the lack of preference history for the new items. Noncollaborative user modeling techniques that rely on the item features can be used to recommend new items. However, they only use the past preferences of each user to provide recommendations for that user. They do not utilize information from the past preferences of other users, which can potentially be ignoring useful information. More recent factor models transfer knowledge across users using their preference information in order to provide more accurate recommendations. These methods learn a low-rank approximation for the preference matrix, which can lead to loss of information. Moreover, they might not be able to learn useful patterns given very sparse datasets. In this work, we present {{\sc UFSM}, a method for top-$n$ recommendation of new items given binary user preferences. {\sc UFSM} learns {{\bf U}ser}-specific {\bf F}eature}-based item-{\bf S}imilarity {\bf M}odels, and its strength lies in combining two points: (1) exploiting preference information across all users to learn multiple global item similarity functions and (2) learning user-specific weights that determine the contribution of each global similarity function in generating recommendations for each user. {\sc UFSM} can be considered as a sparse high-dimensional factor model where the previous preferences of each user are incorporated within his or her latent representation. This way, {\sc UFSM} combines the merits of item similarity models that capture local relations among items and factor models that learn global preference patterns. A comprehensive set of experiments was conduced to compare {\sc UFSM} against state-of-the-art collaborative factor models and noncollaborative user modeling techniques. Results show that {\sc UFSM} outperforms other techniques in terms of recommendation quality. {\sc UFSM} manages to yield better recommendations even with very sparse datasets. Results also show that {\sc UFSM} can efficiently handle high-dimensional as well as low-dimensional item feature spaces.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "33", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Zhang:2015:TGO, author = "Mingjin Zhang and Huibo Wang and Yun Lu and Tao Li and Yudong Guang and Chang Liu and Erik Edrosa and Hongtai Li and Naphtali Rishe", title = "{TerraFly GeoCloud}: an Online Spatial Data Analysis and Visualization System", journal = j-TIST, volume = "6", number = "3", pages = "34:1--34:??", month = may, year = "2015", CODEN = "????", DOI = "https://doi.org/10.1145/2700494", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu May 21 15:49:31 MDT 2015", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "With the exponential growth of the usage of web map services, geo-data analysis has become more and more popular. This article develops an online spatial data analysis and visualization system, TerraFly GeoCloud, which helps end-users visualize and analyze spatial data and share the analysis results. Built on the TerraFly Geo spatial database, TerraFly GeoCloud is an extra layer running upon the TerraFly map and can efficiently support many different visualization functions and spatial data analysis models. Furthermore, users can create unique URLs to visualize and share the analysis results. TerraFly GeoCloud also enables the MapQL technology to customize map visualization using SQL-like statements. The system is available at http://terrafly.fiu.edu/GeoCloud/.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "34", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Chen:2015:SCP, author = "Yi-Cheng Chen and Wen-Chih Peng and Jiun-Long Huang and Wang-Chien Lee", title = "Significant Correlation Pattern Mining in Smart Homes", journal = j-TIST, volume = "6", number = "3", pages = "35:1--35:??", month = may, year = "2015", CODEN = "????", DOI = "https://doi.org/10.1145/2700484", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu May 21 15:49:31 MDT 2015", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Owing to the great advent of sensor technology, the usage data of appliances in a house can be logged and collected easily today. However, it is a challenge for the residents to visualize how these appliances are used. Thus, mining algorithms are much needed to discover appliance usage patterns. Most previous studies on usage pattern discovery are mainly focused on analyzing the patterns of single appliance rather than mining the usage correlation among appliances. In this article, a novel algorithm, namely Correlation Pattern Miner (CoPMiner), is developed to capture the usage patterns and correlations among appliances probabilistically. CoPMiner also employs four pruning techniques and a statistical model to reduce the search space and filter out insignificant patterns, respectively. Furthermore, the proposed algorithm is applied on a real-world dataset to show the practicability of correlation pattern mining.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "35", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Guo:2015:ISI, author = "Bin Guo and Alvin Chin and Zhiwen Yu and Runhe Huang and Daqing Zhang", title = "An Introduction to the Special Issue on Participatory Sensing and Crowd Intelligence", journal = j-TIST, volume = "6", number = "3", pages = "36:1--36:??", month = may, year = "2015", CODEN = "????", DOI = "https://doi.org/10.1145/2745712", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu May 21 15:49:31 MDT 2015", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "36", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Zhang:2015:SPU, author = "Fuzheng Zhang and Nicholas Jing Yuan and David Wilkie and Yu Zheng and Xing Xie", title = "Sensing the Pulse of Urban Refueling Behavior: a Perspective from Taxi Mobility", journal = j-TIST, volume = "6", number = "3", pages = "37:1--37:??", month = may, year = "2015", CODEN = "????", DOI = "https://doi.org/10.1145/2644828", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu May 21 15:49:31 MDT 2015", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Urban transportation is an important factor in energy consumption and pollution, and is of increasing concern due to its complexity and economic significance. Its importance will only increase as urbanization continues around the world. In this article, we explore drivers' refueling behavior in urban areas. Compared to questionnaire-based methods of the past, we propose a complete data-driven system that pushes towards real-time sensing of individual refueling behavior and citywide petrol consumption. Our system provides the following: detection of individual refueling events (REs) from which refueling preference can be analyzed; estimates of gas station wait times from which recommendations can be made; an indication of overall fuel demand from which macroscale economic decisions can be made, and a spatial, temporal, and economic view of urban refueling characteristics. For individual behavior, we use reported trajectories from a fleet of GPS-equipped taxicabs to detect gas station visits. For time spent estimates, to solve the sparsity issue along time and stations, we propose context-aware tensor factorization (CATF), a factorization model that considers a variety of contextual factors (e.g., price, brand, and weather condition) that affect consumers' refueling decision. For fuel demand estimates, we apply a queue model to calculate the overall visits based on the time spent inside the station. We evaluated our system on large-scale and real-world datasets, which contain 4-month trajectories of 32,476 taxicabs, 689 gas stations, and the self-reported refueling details of 8,326 online users. The results show that our system can determine REs with an accuracy of more than 90\%, estimate time spent with less than 2 minutes of error, and measure overall visits in the same order of magnitude with the records in the field study.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "37", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Tangmunarunkit:2015:OGE, author = "H. Tangmunarunkit and C. K. Hsieh and B. Longstaff and S. Nolen and J. Jenkins and C. Ketcham and J. Selsky and F. Alquaddoomi and D. George and J. Kang and Z. Khalapyan and J. Ooms and N. Ramanathan and D. Estrin", title = "{Ohmage}: a General and Extensible End-to-End Participatory Sensing Platform", journal = j-TIST, volume = "6", number = "3", pages = "38:1--38:??", month = may, year = "2015", CODEN = "????", DOI = "https://doi.org/10.1145/2717318", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu May 21 15:49:31 MDT 2015", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Participatory sensing (PS) is a distributed data collection and analysis approach where individuals, acting alone or in groups, use their personal mobile devices to systematically explore interesting aspects of their lives and communities [Burke et al. 2006]. These mobile devices can be used to capture diverse spatiotemporal data through both intermittent self-report and continuous recording from on-board sensors and applications. Ohmage (http://ohmage.org) is a modular and extensible open-source, mobile to Web PS platform that records, stores, analyzes, and visualizes data from both prompted self-report and continuous data streams. These data streams are authorable and can dynamically be deployed in diverse settings. Feedback from hundreds of behavioral and technology researchers, focus group participants, and end users has been integrated into ohmage through an iterative participatory design process. Ohmage has been used as an enabling platform in more than 20 independent projects in many disciplines. We summarize the PS requirements, challenges and key design objectives learned through our design process, and ohmage system architecture to achieve those objectives. The flexibility, modularity, and extensibility of ohmage in supporting diverse deployment settings are presented through three distinct case studies in education, health, and clinical research.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "38", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Xiong:2015:EEE, author = "Haoyi Xiong and Daqing Zhang and Leye Wang and J. Paul Gibson and Jie Zhu", title = "{EEMC}: Enabling Energy-Efficient Mobile Crowdsensing with Anonymous Participants", journal = j-TIST, volume = "6", number = "3", pages = "39:1--39:??", month = may, year = "2015", CODEN = "????", DOI = "https://doi.org/10.1145/2644827", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu May 21 15:49:31 MDT 2015", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Mobile Crowdsensing (MCS) requires users to be motivated to participate. However, concerns regarding energy consumption and privacy-among other things-may compromise their willingness to join such a crowd. Our preliminary observations and analysis of common MCS applications have shown that the data transfer in MCS applications may incur significant energy consumption due to the 3G connection setup. However, if data are transferred in parallel with a traditional phone call, then such transfer can be done almost ``for free'': with only an insignificant additional amount of energy required to piggy-back the data-usually incoming task assignments and outgoing sensor results-on top of the call. Here, we present an {\em Energy-Efficient Mobile Crowdsensing\/} (EEMC) framework where task assignments and sensing results are transferred in parallel with phone calls. The main objective, and the principal contribution of this article, is an MCS task assignment scheme that guarantees that a minimum number of anonymous participants return sensor results within a specified time frame, while also minimizing the waste of energy due to redundant task assignments and considering privacy concerns of participants. Evaluations with a large-scale real-world phone call dataset show that our proposed {EEMC} framework outperforms the baseline approaches, and it can reduce overall energy consumption in data transfer by 54--66\% when compared to the 3G-based solution.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "39", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Zhang:2015:CSS, author = "Wangsheng Zhang and Guande Qi and Gang Pan and Hua Lu and Shijian Li and Zhaohui Wu", title = "City-Scale Social Event Detection and Evaluation with Taxi Traces", journal = j-TIST, volume = "6", number = "3", pages = "40:1--40:??", month = may, year = "2015", CODEN = "????", DOI = "https://doi.org/10.1145/2700478", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu May 21 15:49:31 MDT 2015", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "A social event is an occurrence that involves lots of people and is accompanied by an obvious rise in human flow. Analysis of social events has real-world importance because events bring about impacts on many aspects of city life. Traditionally, detection and impact measurement of social events rely on social investigation, which involves considerable human effort. Recently, by analyzing messages in social networks, researchers can also detect and evaluate country-scale events. Nevertheless, the analysis of city-scale events has not been explored. In this article, we use human flow dynamics, which reflect the social activeness of a region, to detect social events and measure their impacts. We first extract human flow dynamics from taxi traces. Second, we propose a method that can not only discover the happening time and venue of events from abnormal social activeness, but also measure the scale of events through changes in such activeness. Third, we extract traffic congestion information from traces and use its change during social events to measure their impact. The results of experiments validate the effectiveness of both the event detection and impact measurement methods.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "40", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Sang:2015:ASC, author = "Jitao Sang and Tao Mei and Changsheng Xu", title = "Activity Sensor: Check-In Usage Mining for Local Recommendation", journal = j-TIST, volume = "6", number = "3", pages = "41:1--41:??", month = may, year = "2015", CODEN = "????", DOI = "https://doi.org/10.1145/2700468", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu May 21 15:49:31 MDT 2015", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "While on the go, people are using their phones as a personal concierge discovering what is around and deciding what to do. Mobile phone has become a recommendation terminal customized for individuals-capable of recommending activities and simplifying the accomplishment of related tasks. In this article, we conduct usage mining on the check-in data, with summarized statistics identifying the local recommendation challenges of huge solution space, sparse available data, and complicated user intent, and discovered observations to motivate the hierarchical, contextual, and sequential solution. We present a point-of-interest (POI) category-transition--based approach, with a goal of estimating the visiting probability of a series of successive POIs conditioned on current user context and sensor context. A mobile local recommendation demo application is deployed. The objective and subjective evaluations validate the effectiveness in providing mobile users both accurate recommendation and favorable user experience.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "41", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Zhang:2015:EDQ, author = "Bo Zhang and Zheng Song and Chi Harold Liu and Jian Ma and Wendong Wang", title = "An Event-Driven {QoI}-Aware Participatory Sensing Framework with Energy and Budget Constraints", journal = j-TIST, volume = "6", number = "3", pages = "42:1--42:??", month = may, year = "2015", CODEN = "????", DOI = "https://doi.org/10.1145/2630074", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu May 21 15:49:31 MDT 2015", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Participatory sensing systems can be used for concurrent event monitoring applications, like noise levels, fire, and pollutant concentrations. However, they are facing new challenges as to how to accurately detect the exact boundaries of these events, and further, to select the most appropriate participants to collect the sensing data. On the one hand, participants' handheld smart devices are constrained with different energy conditions and sensing capabilities, and they move around with uncontrollable mobility patterns in their daily life. On the other hand, these sensing tasks are within time-varying quality-of-information (QoI) requirements and budget to afford the users' incentive expectations. Toward this end, this article proposes an event-driven QoI-aware participatory sensing framework with energy and budget constraints. The main method of this framework is event boundary detection. For the former, a two-step heuristic solution is proposed where the coarse-grained detection step finds its approximation and the fine-grained detection step identifies the exact location. Participants are selected by explicitly considering their mobility pattern, required QoI of multiple tasks, and users' incentive requirements, under the constraint of an aggregated task budget. Extensive experimental results, based on a real trace in Beijing, show the effectiveness and robustness of our approach, while comparing with existing schemes.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "42", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Anantharam:2015:ECT, author = "Pramod Anantharam and Payam Barnaghi and Krishnaprasad Thirunarayan and Amit Sheth", title = "Extracting City Traffic Events from Social Streams", journal = j-TIST, volume = "6", number = "4", pages = "43:1--43:??", month = aug, year = "2015", CODEN = "????", DOI = "https://doi.org/10.1145/2717317", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Aug 13 17:37:43 MDT 2015", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Cities are composed of complex systems with physical, cyber, and social components. Current works on extracting and understanding city events mainly rely on technology-enabled infrastructure to observe and record events. In this work, we propose an approach to leverage citizen observations of various city systems and services, such as traffic, public transport, water supply, weather, sewage, and public safety, as a source of city events. We investigate the feasibility of using such textual streams for extracting city events from annotated text. We formalize the problem of annotating social streams such as microblogs as a sequence labeling problem. We present a novel training data creation process for training sequence labeling models. Our automatic training data creation process utilizes instance-level domain knowledge (e.g., locations in a city, possible event terms). We compare this automated annotation process to a state-of-the-art tool that needs manually created training data and show that it has comparable performance in annotation tasks. An aggregation algorithm is then presented for event extraction from annotated text. We carry out a comprehensive evaluation of the event annotation and event extraction on a real-world dataset consisting of event reports and tweets collected over 4 months from the San Francisco Bay Area. The evaluation results are promising and provide insights into the utility of social stream for extracting city events.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "43", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Sawant:2015:AGC, author = "Anshul Sawant and John P. Dickerson and Mohammad T. Hajiaghayi and V. S. Subrahmanian", title = "Automated Generation of Counterterrorism Policies Using Multiexpert Input", journal = j-TIST, volume = "6", number = "4", pages = "44:1--44:??", month = aug, year = "2015", CODEN = "????", DOI = "https://doi.org/10.1145/2716328", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Aug 13 17:37:43 MDT 2015", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "The use of game theory to model conflict has been studied by several researchers, spearheaded by Schelling. Most of these efforts assume a single payoff matrix that captures players' utilities under different assumptions about what the players will do. Our experience in counterterrorism applications is that experts disagree on these payoffs. We leverage Shapley's notion of vector equilibria, which formulates games where there are multiple payoff matrices, but note that they are very hard to compute in practice. To effectively enumerate large numbers of equilibria with payoffs provided by multiple experts, we propose a novel combination of vector payoffs and well-supported $ \epsilon $-approximate equilibria. We develop bounds related to computation of these equilibria for some special cases and give a quasipolynomial time approximation scheme (QPTAS) for the general case when the number of players is small (which is true in many real-world applications). Leveraging this QPTAS, we give efficient algorithms to find such equilibria and experimental results showing that they work well on simulated data. We then built a policy recommendation engine based on vector equilibria, called PREVE. We use PREVE to model the terrorist group Lashkar-e-Taiba (LeT), responsible for the 2008 Mumbai attacks, as a five-player game. Specifically, we apply it to three payoff matrices provided by experts in India--Pakistan relations, analyze the equilibria generated by PREVE, and suggest counterterrorism policies that may reduce attacks by LeT. We briefly discuss these results and identify their strengths and weaknesses from a policy point of view.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "44", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Bai:2015:OPL, author = "Aijun Bai and Feng Wu and Xiaoping Chen", title = "Online Planning for Large {Markov} Decision Processes with Hierarchical Decomposition", journal = j-TIST, volume = "6", number = "4", pages = "45:1--45:??", month = aug, year = "2015", CODEN = "????", DOI = "https://doi.org/10.1145/2717316", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Aug 13 17:37:43 MDT 2015", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Markov decision processes (MDPs) provide a rich framework for planning under uncertainty. However, exactly solving a large MDP is usually intractable due to the ``curse of dimensionality''- the state space grows exponentially with the number of state variables. Online algorithms tackle this problem by avoiding computing a policy for the entire state space. On the other hand, since online algorithm has to find a near-optimal action online in almost real time, the computation time is often very limited. In the context of reinforcement learning, MAXQ is a value function decomposition method that exploits the underlying structure of the original MDP and decomposes it into a combination of smaller subproblems arranged over a task hierarchy. In this article, we present MAXQ-OP-a novel online planning algorithm for large MDPs that utilizes MAXQ hierarchical decomposition in online settings. Compared to traditional online planning algorithms, MAXQ-OP is able to reach much more deeper states in the search tree with relatively less computation time by exploiting MAXQ hierarchical decomposition online. We empirically evaluate our algorithm in the standard Taxi domain-a common benchmark for MDPs-to show the effectiveness of our approach. We have also conducted a long-term case study in a highly complex simulated soccer domain and developed a team named WrightEagle that has won five world champions and five runners-up in the recent 10 years of RoboCup Soccer Simulation 2D annual competitions. The results in the RoboCup domain confirm the scalability of MAXQ-OP to very large domains.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "45", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Ye:2015:SSB, author = "Yanfang Ye and Tao Li and Haiyin Shen", title = "{Soter}: Smart Bracelets for Children's Safety", journal = j-TIST, volume = "6", number = "4", pages = "46:1--46:??", month = aug, year = "2015", CODEN = "????", DOI = "https://doi.org/10.1145/2700483", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Aug 13 17:37:43 MDT 2015", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "In recent years, crimes against children and cases of missing children have increased at a high rate. Therefore, there is an urgent need for safety support systems to prevent crimes against children or for antiloss, especially when parents are not with their children, such as to and from school. However, existing children's tracking systems are not smart enough to provide the safety supports, as they simply locate the children's positions without offering any notification to parents that their children may be in danger. In addition, there is limited research on children's tracking and their antiloss. In this article, based on location histories, we introduce novel notions of children's life patterns that capture their general lifestyles and regularities, and develop an intelligent data mining framework to learn the safe regions and safe routes of children on the cloud side. When the children may be in danger, their parents will receive automatic notifications from the cloud. We also propose an effective energy-efficient positioning scheme that leverages the location tracking accuracy of the children while keeping energy overhead low by using a hybrid global positioning system and a global system for mobile communications. To the best of our knowledge, this is the first attempt in applying data mining techniques to applications designed for children's safety. Our proposed techniques have been incorporated into Soter, a children's safeguard system that is used to provide cloud service for smart bracelets produced by Qihoo. The case studies on real smart bracelet users of Qihoo demonstrate the effectiveness of our proposed methods and Soter for children's safety.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "46", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Wang:2015:PLL, author = "Yi Wang and Xuemin Zhao and Zhenlong Sun and Hao Yan and Lifeng Wang and Zhihui Jin and Liubin Wang and Yang Gao and Ching Law and Jia Zeng", title = "{Peacock}: Learning Long-Tail Topic Features for Industrial Applications", journal = j-TIST, volume = "6", number = "4", pages = "47:1--47:??", month = aug, year = "2015", CODEN = "????", DOI = "https://doi.org/10.1145/2700497", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Aug 13 17:37:43 MDT 2015", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Latent Dirichlet allocation (LDA) is a popular topic modeling technique in academia but less so in industry, especially in large-scale applications involving search engine and online advertising systems. A main underlying reason is that the topic models used have been too small in scale to be useful; for example, some of the largest LDA models reported in literature have up to 10$^3$ topics, which difficultly cover the long-tail semantic word sets. In this article, we show that the number of topics is a key factor that can significantly boost the utility of topic-modeling systems. In particular, we show that a ``big'' LDA model with at least 10$^5$ topics inferred from 10$^9$ search queries can achieve a significant improvement on industrial search engine and online advertising systems, both of which serve hundreds of millions of users. We develop a novel distributed system called Peacock to learn big LDA models from big data. The main features of Peacock include hierarchical distributed architecture, real-time prediction, and topic de-duplication. We empirically demonstrate that the Peacock system is capable of providing significant benefits via highly scalable LDA topic models for several industrial applications.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "47", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Jumadinova:2015:APM, author = "Janyl Jumadinova and Prithviraj Dasgupta", title = "Automated Pricing in a Multiagent Prediction Market Using a Partially Observable Stochastic Game", journal = j-TIST, volume = "6", number = "4", pages = "48:1--48:??", month = aug, year = "2015", CODEN = "????", DOI = "https://doi.org/10.1145/2700488", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Aug 13 17:37:43 MDT 2015", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Prediction markets offer an efficient market-based mechanism to aggregate large amounts of dispersed or distributed information from different people to predict the possible outcome of future events. Recently, automated prediction markets where software trading agents perform market operations such as trading and updating beliefs on behalf of humans have been proposed. A challenging aspect in automated prediction markets is to develop suitable techniques that can be used by automated trading agents to update the price at which they should trade securities related to an event so that they can increase their profit. This problem is nontrivial, as the decision to trade and the price at which trading should occur depends on several dynamic factors, such as incoming information related to the event for which the security is being traded, the belief-update mechanism and risk attitude of the trading agent, and the trading decision and trading prices of other agents. To address this problem, we have proposed a new behavior model for trading agents based on a game-theoretic framework called partially observable stochastic game with information (POSGI). We propose a correlated equilibrium (CE)-based solution strategy for this game that allows each agent to dynamically choose an action (to buy or sell or hold) in the prediction market. We have also performed extensive simulation experiments using the data obtained from the Intrade prediction market for four different prediction markets. Our results show that our POSGI model and CE strategy produces prices that are strongly correlated with the prices of the real prediction markets. Results comparing our CE strategy with five other strategies commonly used in similar market show that our CE strategy improves price predictions and provides higher utilities to the agents compared to other existing strategies.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "48", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Fu:2015:ESG, author = "Hao Fu and Aston Zhang and Xing Xie", title = "Effective Social Graph Deanonymization Based on Graph Structure and Descriptive Information", journal = j-TIST, volume = "6", number = "4", pages = "49:1--49:??", month = aug, year = "2015", CODEN = "????", DOI = "https://doi.org/10.1145/2700836", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Aug 13 17:37:43 MDT 2015", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "The study of online social networks has attracted increasing interest. However, concerns are raised for the privacy risks of user data since they have been frequently shared among researchers, advertisers, and application developers. To solve this problem, a number of anonymization algorithms have been recently developed for protecting the privacy of social graphs. In this article, we proposed a graph node similarity measurement in consideration with both graph structure and descriptive information, and a deanonymization algorithm based on the measurement. Using the proposed algorithm, we evaluated the privacy risks of several typical anonymization algorithms on social graphs with thousands of nodes from Microsoft Academic Search, LiveJournal, and the Enron email dataset, and a social graph with millions of nodes from Tencent Weibo. Our results showed that the proposed algorithm was efficient and effective to deanonymize social graphs without any initial seed mappings. Based on the experiments, we also pointed out suggestions on how to better maintain the data utility while preserving privacy.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "49", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Chen:2015:HIR, author = "Bo-Hao Chen and Shih-Chia Huang and Jian Hui Ye", title = "Hazy Image Restoration by Bi-Histogram Modification", journal = j-TIST, volume = "6", number = "4", pages = "50:1--50:??", month = aug, year = "2015", CODEN = "????", DOI = "https://doi.org/10.1145/2710024", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Aug 13 17:37:43 MDT 2015", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Visibility restoration techniques are widely used for information recovery of hazy images in many computer vision applications. Estimation of haze density is an essential task of visibility restoration techniques. However, conventional visibility restoration techniques often suffer from either the generation of serious artifacts or the loss of object information in the restored images due to uneven haze density, which usually means that the images contain heavy haze formation within their background regions and little haze formation within their foreground regions. This frequently occurs when the images feature real-world scenes with a deep depth of field. How to effectively and accurately estimate the haze density in the transmission map for these images is the most challenging aspect of the traditional state-of-the-art techniques. In response to this problem, this work proposes a novel visibility restoration approach that is based on Bi-Histogram modification, and which integrates a haze density estimation module and a haze formation removal module for effective and accurate estimation of haze density in the transmission map. As our experimental results demonstrate, the proposed approach achieves superior visibility restoration efficacy in comparison with the other state-of-the-art approaches based on both qualitative and quantitative evaluations. The proposed approach proves effective and accurate in terms of both background and foreground restoration of various hazy scenarios.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "50", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Combi:2015:IAT, author = "Carlo Combi and Jiming Liu", title = "Introduction to the {ACM TIST} Special Issue on Intelligent Healthcare Informatics", journal = j-TIST, volume = "6", number = "4", pages = "51:1--51:??", month = aug, year = "2015", CODEN = "????", DOI = "https://doi.org/10.1145/2791398", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Aug 13 17:37:43 MDT 2015", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "51", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Kim:2015:AAR, author = "Eunju Kim and Sumi Helal and Chris Nugent and Mark Beattie", title = "Analyzing Activity Recognition Uncertainties in Smart Home Environments", journal = j-TIST, volume = "6", number = "4", pages = "52:1--52:??", month = aug, year = "2015", CODEN = "????", DOI = "https://doi.org/10.1145/2651445", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Aug 13 17:37:43 MDT 2015", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "In spite of the importance of activity recognition (AR) for intelligent human-computer interaction in emerging smart space applications, state-of-the-art AR technology is not ready or adequate for real-world deployments due to its insufficient accuracy. The accuracy limitation is directly attributed to uncertainties stemming from multiple sources in the AR system. Hence, one of the major goals of AR research is to improve system accuracy by minimizing or managing the uncertainties encountered throughout the AR process. As we cannot manage uncertainties well without measuring them, we must first quantify their impact. Nevertheless, such a quantification process is very challenging given that uncertainties come from diverse and heterogeneous sources. In this article, we propose an approach, which can account for multiple uncertainty sources and assess their impact on AR systems. We introduce several metrics to quantify the various uncertainties and their impact. We then conduct a quantitative impact analysis of uncertainties utilizing data collected from actual smart spaces that we have instrumented. The analysis is intended to serve as groundwork for developing ``diagnostic'' accuracy measures of AR systems capable of pinpointing the sources of accuracy loss. This is to be contrasted with the currently used accuracy measures.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "52", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Soto-Mendoza:2015:DPS, author = "Valeria Soto-Mendoza and J. Antonio Garc{\'\i}a-Mac{\'\i}as and Edgar Ch{\'a}vez and Ana I. Mart{\'\i}nez-Garc{\'\i}a and Jes{\'u}s Favela and Patricia Serrano-Alvarado and Mayth{\'e} R. Z{\'u}{\~n}iga Rojas", title = "Design of a Predictive Scheduling System to Improve Assisted Living Services for Elders", journal = j-TIST, volume = "6", number = "4", pages = "53:1--53:??", month = aug, year = "2015", CODEN = "????", DOI = "https://doi.org/10.1145/2736700", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Aug 13 17:37:43 MDT 2015", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "As the number of older adults increases, and with it the demand for dedicated care, geriatric residences face a shortage of caregivers, who themselves experience work overload, stress, and burden. We conducted a long-term field study in three geriatric residences to understand the work conditions of caregivers with the aim of developing technologies to assist them in their work and help them deal with their burdens. From this study, we obtained relevant requirements and insights to design, implement, and evaluate two prototypes for supporting caregivers' tasks (e.g., electronic recording and automatic notifications) in order to validate the feasibility of their implementation in situ and their technical requirements. The evaluation in situ of the prototypes was conducted for a period of 4 weeks. The results of the evaluation, together with the data collected from 6 months of use, motivated the design of a predictive schedule, which was iteratively improved and evaluated in participative sessions with caregivers. PRESENCE, the predictive schedule we propose, triggers real-time alerts of risky situations (e.g., falls, entering off-limits areas such as the infirmary or the kitchen) and informs caregivers of routine tasks that need to be performed (e.g., medication administration, diaper change, etc.). Moreover, PRESENCE helps caregivers to record caring tasks (such as diaper changes or medication) and well-being assessments (such as the mood) that are difficult to automate. This facilitates caregiver's shift handover and can help to train new caregivers by suggesting routine tasks and by sending reminders and timely information about residents. It can be seen as a tool to reduce the workload of caregivers and medical staff. Instead of trying to substitute the caregiver with an automatic caring system, as proposed by others, we propose our predictive schedule system that blends caregiver assessments and measurements from sensors. We show the feasibility of predicting caregiver tasks and a formative evaluation with caregivers that provides preliminary evidence of its utility.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "53", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Champaign:2015:EPC, author = "John Champaign and Robin Cohen and Disney Yan Lam", title = "Empowering Patients and Caregivers to Manage Healthcare Via Streamlined Presentation of {Web} Objects Selected by Modeling Learning Benefits Obtained by Similar Peers", journal = j-TIST, volume = "6", number = "4", pages = "54:1--54:??", month = aug, year = "2015", CODEN = "????", DOI = "https://doi.org/10.1145/2700480", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Aug 13 17:37:43 MDT 2015", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "In this article, we introduce a framework for selecting web objects (texts, videos, simulations) from a large online repository to present to patients and caregivers, in order to assist in their healthcare. Motivated by the paradigm of peer-based intelligent tutoring, we model the learning gains achieved by users when exposed to specific web objects in order to recommend those objects most likely to deliver benefit to new users. We are able to show that this streamlined presentation leads to effective knowledge gains, both through a process of simulated learning and through a user study, for the specific application of caring for children with autism. The value of our framework for peer-driven content selection of health information is emphasized through two additional roles for peers: attaching commentary to web objects and proposing subdivided objects for presentation, both of which are demonstrated to deliver effective learning gains, in simulations. In all, we are offering an opportunity for patients to navigate the deep waters of excessive online information towards effective management of healthcare, through content selection influenced by previous peer experiences.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "54", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Yang:2015:UHC, author = "Haodong Yang and Christopher C. Yang", title = "Using Health-Consumer-Contributed Data to Detect Adverse Drug Reactions by Association Mining with Temporal Analysis", journal = j-TIST, volume = "6", number = "4", pages = "55:1--55:??", month = aug, year = "2015", CODEN = "????", DOI = "https://doi.org/10.1145/2700482", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Aug 13 17:37:43 MDT 2015", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Since adverse drug reactions (ADRs) represent a significant health problem all over the world, ADR detection has become an important research topic in drug safety surveillance. As many potential ADRs cannot be detected though premarketing review, drug safety currently depends heavily on postmarketing surveillance. Particularly, current postmarketing surveillance in the United States primarily relies on the FDA Adverse Event Reporting System (FAERS). However, the effectiveness of such spontaneous reporting systems for ADR detection is not as good as expected because of the extremely high underreporting ratio of ADRs. Moreover, it often takes the FDA years to complete the whole process of collecting reports, investigating cases, and releasing alerts. Given the prosperity of social media, many online health communities are publicly available for health consumers to share and discuss any healthcare experience such as ADRs they are suffering. Such health-consumer-contributed content is timely and informative, but this data source still remains untapped for postmarketing drug safety surveillance. In this study, we propose to use (1) association mining to identify the relations between a drug and an ADR and (2) temporal analysis to detect drug safety signals at the early stage. We collect data from MedHelp and use the FDA's alerts and information of drug labeling revision as the gold standard to evaluate the effectiveness of our approach. The experiment results show that health-related social media is a promising source for ADR detection, and our proposed techniques are effective to identify early ADR signals.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "55", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Ullah:2015:ERL, author = "Md Zia Ullah and Masaki Aono and Md Hanif Seddiqui", title = "Estimating a Ranked List of Human Genetic Diseases by Associating Phenotype-Gene with Gene-Disease Bipartite Graphs", journal = j-TIST, volume = "6", number = "4", pages = "56:1--56:??", month = aug, year = "2015", CODEN = "????", DOI = "https://doi.org/10.1145/2700487", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Aug 13 17:37:43 MDT 2015", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "With vast amounts of medical knowledge available on the Internet, it is becoming increasingly practical to help doctors in clinical diagnostics by suggesting plausible diseases predicted by applying data and text mining technologies. Recently, Genome-Wide Association Studies ( GWAS ) have proved useful as a method for exploring phenotypic associations with diseases. However, since genetic diseases are difficult to diagnose because of their low prevalence, large number, and broad diversity of symptoms, genetic disease patients are often misdiagnosed or experience long diagnostic delays. In this article, we propose a method for ranking genetic diseases for a set of clinical phenotypes. In this regard, we associate a phenotype-gene bipartite graph ( PGBG ) with a gene-disease bipartite graph ( GDBG ) by producing a phenotype-disease bipartite graph ( PDBG ), and we estimate the candidate weights of diseases. In our approach, all paths from a phenotype to a disease are explored by considering causative genes to assign a weight based on path frequency, and the phenotype is linked to the disease in a new PDBG. We introduce the Bidirectionally induced Importance Weight ( BIW ) prediction method to PDBG for approximating the weights of the edges of diseases with phenotypes by considering link information from both sides of the bipartite graph. The performance of our system is compared to that of other known related systems by estimating Normalized Discounted Cumulative Gain ( NDCG ), Mean Average Precision ( MAP ), and Kendall's tau metrics. Further experiments are conducted with well-known TF $ \cdot $ IDF, BM25, and Jenson-Shannon divergence as baselines. The result shows that our proposed method outperforms the known related tool Phenomizer in terms of NDCG@10, NDCG@20, MAP@10, and MAP@20; however, it performs worse than Phenomizer in terms of Kendall's tau-b metric at the top-10 ranks. It also turns out that our proposed method has overall better performance than the baseline methods.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "56", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Antonelli:2015:MCM, author = "Dario Antonelli and Elena Baralis and Giulia Bruno and Luca Cagliero and Tania Cerquitelli and Silvia Chiusano and Paolo Garza and Naeem A. Mahoto", title = "{MeTA}: Characterization of Medical Treatments at Different Abstraction Levels", journal = j-TIST, volume = "6", number = "4", pages = "57:1--57:??", month = aug, year = "2015", CODEN = "????", DOI = "https://doi.org/10.1145/2700479", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Aug 13 17:37:43 MDT 2015", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Physicians and health care organizations always collect large amounts of data during patient care. These large and high-dimensional datasets are usually characterized by an inherent sparseness. Hence, analyzing these datasets to figure out interesting and hidden knowledge is a challenging task. This article proposes a new data mining framework based on generalized association rules to discover multiple-level correlations among patient data. Specifically, correlations among prescribed examinations, drugs, and patient profiles are discovered and analyzed at different abstraction levels. The rule extraction process is driven by a taxonomy to generalize examinations and drugs into their corresponding categories. To ease the manual inspection of the result, a worthwhile subset of rules (i.e., nonredundant generalized rules) is considered. Furthermore, rules are classified according to the involved data features (medical treatments or patient profiles) and then explored in a top-down fashion: from the small subset of high-level rules, a drill-down is performed to target more specific rules. The experiments, performed on a real diabetic patient dataset, demonstrate the effectiveness of the proposed approach in discovering interesting rule groups at different abstraction levels.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "57", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Motai:2015:SCD, author = "Yuichi Motai and Dingkun Ma and Alen Docef and Hiroyuki Yoshida", title = "Smart Colonography for Distributed Medical Databases with Group Kernel Feature Analysis", journal = j-TIST, volume = "6", number = "4", pages = "58:1--58:??", month = aug, year = "2015", CODEN = "????", DOI = "https://doi.org/10.1145/2668136", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Aug 13 17:37:43 MDT 2015", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Computer-Aided Detection (CAD) of polyps in Computed Tomographic (CT) colonography is currently very limited since a single database at each hospital/institution doesn't provide sufficient data for training the CAD system's classification algorithm. To address this limitation, we propose to use multiple databases, (e.g., big data studies) to create multiple institution-wide databases using distributed computing technologies, which we call smart colonography. Smart colonography may be built by a larger colonography database networked through the participation of multiple institutions via distributed computing. The motivation herein is to create a distributed database that increases the detection accuracy of CAD diagnosis by covering many true-positive cases. Colonography data analysis is mutually accessible to increase the availability of resources so that the knowledge of radiologists is enhanced. In this article, we propose a scalable and efficient algorithm called Group Kernel Feature Analysis (GKFA), which can be applied to multiple cancer databases so that the overall performance of CAD is improved. The key idea behind the proposed GKFA method is to allow the feature space to be updated as the training proceeds with more data being fed from other institutions into the algorithm. Experimental results show that GKFA achieves very good classification accuracy.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "58", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Kim:2015:RPR, author = "Mi-Young Kim and Ying Xu and Osmar R. Zaiane and Randy Goebel", title = "Recognition of Patient-Related Named Entities in Noisy Tele-Health Texts", journal = j-TIST, volume = "6", number = "4", pages = "59:1--59:??", month = aug, year = "2015", CODEN = "????", DOI = "https://doi.org/10.1145/2651444", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Aug 13 17:37:43 MDT 2015", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "We explore methods for effectively extracting information from clinical narratives that are captured in a public health consulting phone service called HealthLink. Our research investigates the application of state-of-the-art natural language processing and machine learning to clinical narratives to extract information of interest. The currently available data consist of dialogues constructed by nurses while consulting patients by phone. Since the data are interviews transcribed by nurses during phone conversations, they include a significant volume and variety of noise. When we extract the patient-related information from the noisy data, we have to remove or correct at least two kinds of noise: explicit noise, which includes spelling errors, unfinished sentences, omission of sentence delimiters, and variants of terms, and implicit noise, which includes non-patient information and patient's untrustworthy information. To filter explicit noise, we propose our own biomedical term detection/normalization method: it resolves misspelling, term variations, and arbitrary abbreviation of terms by nurses. In detecting temporal terms, temperature, and other types of named entities (which show patients' personal information such as age and sex), we propose a bootstrapping-based pattern learning process to detect a variety of arbitrary variations of named entities. To address implicit noise, we propose a dependency path-based filtering method. The result of our denoising is the extraction of normalized patient information, and we visualize the named entities by constructing a graph that shows the relations between named entities. The objective of this knowledge discovery task is to identify associations between biomedical terms and to clearly expose the trends of patients' symptoms and concern; the experimental results show that we achieve reasonable performance with our noise reduction methods.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "59", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Ding:2015:LRN, author = "Wenkui Ding and Xiubo Geng and Xu-Dong Zhang", title = "Learning to Rank from Noisy Data", journal = j-TIST, volume = "7", number = "1", pages = "1:1--1:??", month = oct, year = "2015", CODEN = "????", DOI = "https://doi.org/10.1145/2576230", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Jan 25 06:10:36 MST 2016", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Learning to rank, which learns the ranking function from training data, has become an emerging research area in information retrieval and machine learning. Most existing work on learning to rank assumes that the training data is clean, which is not always true, however. The ambiguity of query intent, the lack of domain knowledge, and the vague definition of relevance levels all make it difficult for common annotators to give reliable relevance labels to some documents. As a result, the relevance labels in the training data of learning to rank usually contain noise. If we ignore this fact, the performance of learning-to-rank algorithms will be damaged. In this article, we propose considering the labeling noise in the process of learning to rank and using a two-step approach to extend existing algorithms to handle noisy training data. In the first step, we estimate the degree of labeling noise for a training document. To this end, we assume that the majority of the relevance labels in the training data are reliable and we use a graphical model to describe the generative process of a training query, the feature vectors of its associated documents, and the relevance labels of these documents. The parameters in the graphical model are learned by means of maximum likelihood estimation. Then the conditional probability of the relevance label given the feature vector of a document is computed. If the probability is large, we regard the degree of labeling noise for this document as small; otherwise, we regard the degree as large. In the second step, we extend existing learning-to-rank algorithms by incorporating the estimated degree of labeling noise into their loss functions. Specifically, we give larger weights to those training documents with smaller degrees of labeling noise and smaller weights to those with larger degrees of labeling noise. As examples, we demonstrate the extensions for McRank, RankSVM, RankBoost, and RankNet. Empirical results on benchmark datasets show that the proposed approach can effectively distinguish noisy documents from clean ones, and the extended learning-to-rank algorithms can achieve better performances than baselines.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "1", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Liu:2015:LSB, author = "Fan Liu and Jinhui Tang and Yan Song and Liyan Zhang and Zhenmin Tang", title = "Local Structure-Based Sparse Representation for Face Recognition", journal = j-TIST, volume = "7", number = "1", pages = "2:1--2:??", month = oct, year = "2015", CODEN = "????", DOI = "https://doi.org/10.1145/2733383", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Jan 25 06:10:36 MST 2016", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "This article presents a simple yet effective face recognition method, called local structure-based sparse representation classification (LS\_SRC). Motivated by the ``divide-and-conquer'' strategy, we first divide the face into local blocks and classify each local block, then integrate all the classification results to make the final decision. To classify each local block, we further divide each block into several overlapped local patches and assume that these local patches lie in a linear subspace. This subspace assumption reflects the local structure relationship of the overlapped patches, making sparse representation-based classification (SRC) feasible even when encountering the single-sample-per-person (SSPP) problem. To lighten the computing burden of LS\_SRC, we further propose the local structure-based collaborative representation classification (LS\_CRC). Moreover, the performance of LS\_SRC and LS\_CRC can be further improved by using the confusion matrix of the classifier. Experimental results on four public face databases show that our methods not only generalize well to SSPP problem but also have strong robustness to occlusion; little pose variation; and the variations of expression, illumination, and time.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "2", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Groves:2015:OAT, author = "William Groves and Maria Gini", title = "On Optimizing Airline Ticket Purchase Timing", journal = j-TIST, volume = "7", number = "1", pages = "3:1--3:??", month = oct, year = "2015", CODEN = "????", DOI = "https://doi.org/10.1145/2733384", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Jan 25 06:10:36 MST 2016", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Proper timing of the purchase of airline tickets is difficult even when historical ticket prices and some domain knowledge are available. To address this problem, we introduce an algorithm that optimizes purchase timing on behalf of customers and provides performance estimates of its computed action policy. Given a desired flight route and travel date, the algorithm uses machine-learning methods on recent ticket price quotes from many competing airlines to predict the future expected minimum price of all available flights. The main novelty of our algorithm lies in using a systematic feature-selection technique, which captures time dependencies in the data by using time-delayed features, and reduces the number of features by imposing a class hierarchy among the raw features and pruning the features based on in-situ performance. Our algorithm achieves much closer to the optimal purchase policy than other existing decision theoretic approaches for this domain, and meets or exceeds the performance of existing feature-selection methods from the literature. Applications of our feature-selection process to other domains are also discussed.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "3", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Dong:2015:NMR, author = "Yongsheng Dong and Dacheng Tao and Xuelong Li", title = "Nonnegative Multiresolution Representation-Based Texture Image Classification", journal = j-TIST, volume = "7", number = "1", pages = "4:1--4:??", month = oct, year = "2015", CODEN = "????", DOI = "https://doi.org/10.1145/2738050", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Jan 25 06:10:36 MST 2016", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Effective representation of image texture is important for an image-classification task. Statistical modelling in wavelet domains has been widely used to image texture representation. However, due to the intraclass complexity and interclass diversity of textures, it is hard to use a predefined probability distribution function to fit adaptively all wavelet subband coefficients of different textures. In this article, we propose a novel modelling approach, Heterogeneous and Incrementally Generated Histogram (HIGH), to indirectly model the wavelet coefficients by use of four local features in wavelet subbands. By concatenating all the HIGHs in all wavelet subbands of a texture, we can construct a nonnegative multiresolution vector (NMV) to represent a texture image. Considering the NMV's high dimensionality and nonnegativity, we further propose a Hessian regularized discriminative nonnegative matrix factorization to compute a low-dimensional basis of the linear subspace of NMVs. Finally, we present a texture classification approach by projecting NMVs on the low-dimensional basis. Experimental results show that our proposed texture classification method outperforms seven representative approaches.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "4", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Chen:2015:MKM, author = "Bowei Chen and Jun Wang and Ingemar J. Cox and Mohan S. Kankanhalli", title = "Multi-Keyword Multi-Click Advertisement Option Contracts for Sponsored Search", journal = j-TIST, volume = "7", number = "1", pages = "5:1--5:??", month = oct, year = "2015", CODEN = "????", DOI = "https://doi.org/10.1145/2743027", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Jan 25 06:10:36 MST 2016", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "In sponsored search, advertisement (abbreviated ad) slots are usually sold by a search engine to an advertiser through an auction mechanism in which advertisers bid on keywords. In theory, auction mechanisms have many desirable economic properties. However, keyword auctions have a number of limitations including: the uncertainty in payment prices for advertisers; the volatility in the search engine's revenue; and the weak loyalty between advertiser and search engine. In this article, we propose a special ad option that alleviates these problems. In our proposal, an advertiser can purchase an option from a search engine in advance by paying an upfront fee, known as the option price. The advertiser then has the right, but no obligation, to purchase among the prespecified set of keywords at the fixed cost-per-clicks (CPCs) for a specified number of clicks in a specified period of time. The proposed option is closely related to a special exotic option in finance that contains multiple underlying assets (multi-keyword) and is also multi-exercisable (multi-click). This novel structure has many benefits: advertisers can have reduced uncertainty in advertising; the search engine can improve the advertisers' loyalty as well as obtain a stable and increased expected revenue over time. Since the proposed ad option can be implemented in conjunction with the existing keyword auctions, the option price and corresponding fixed CPCs must be set such that there is no arbitrage between the two markets. Option pricing methods are discussed and our experimental results validate the development. Compared to keyword auctions, a search engine can have an increased expected revenue by selling an ad option.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "5", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Font:2015:AIT, author = "Frederic Font and Joan Serr{\`a} and Xavier Serra", title = "Analysis of the Impact of a Tag Recommendation System in a Real-World Folksonomy", journal = j-TIST, volume = "7", number = "1", pages = "6:1--6:??", month = oct, year = "2015", CODEN = "????", DOI = "https://doi.org/10.1145/2743026", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Jan 25 06:10:36 MST 2016", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Collaborative tagging systems have emerged as a successful solution for annotating contributed resources to online sharing platforms, facilitating searching, browsing, and organizing their contents. To aid users in the annotation process, several tag recommendation methods have been proposed. It has been repeatedly hypothesized that these methods should contribute to improving annotation quality and reducing the cost of the annotation process. It has been also hypothesized that these methods should contribute to the consolidation of the vocabulary of collaborative tagging systems. However, to date, no empirical and quantitative result supports these hypotheses. In this work, we deeply analyze the impact of a tag recommendation system in the folksonomy of Freesound, a real-world and large-scale online sound sharing platform. Our results suggest that tag recommendation effectively increases vocabulary sharing among users of the platform. In addition, tag recommendation is shown to contribute to the convergence of the vocabulary as well as to a partial increase in the quality of annotations. However, according to our analysis, the cost of the annotation process does not seem to be effectively reduced. Our work is relevant to increase our understanding about the nature of tag recommendation systems and points to future directions for the further development of those systems and their analysis.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "6", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Cheng:2015:HBS, author = "Fan-Chieh Cheng and Bo-Hao Chen and Shih-Chia Huang", title = "A Hybrid Background Subtraction Method with Background and Foreground Candidates Detection", journal = j-TIST, volume = "7", number = "1", pages = "7:1--7:??", month = oct, year = "2015", CODEN = "????", DOI = "https://doi.org/10.1145/2746409", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Jan 25 06:10:36 MST 2016", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Background subtraction for motion detection is often used in video surveillance systems. However, difficulties in bootstrapping restrict its development. This article proposes a novel hybrid background subtraction technique to solve this problem. For performance improvement of background subtraction, the proposed technique not only quickly initializes the background model but also eliminates unnecessary regions containing only background pixels in the object detection process. Furthermore, an embodiment based on the proposed technique is also presented. Experimental results verify that the proposed technique allows for reduced execution time as well as improvement of performance as evaluated by Recall, Precision, F1, and Similarity metrics when used with state-of-the-art background subtraction methods.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "7", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Muntean:2015:LPM, author = "Cristina Ioana Muntean and Franco Maria Nardini and Fabrizio Silvestri and Ranieri Baraglia", title = "On Learning Prediction Models for Tourists Paths", journal = j-TIST, volume = "7", number = "1", pages = "8:1--8:??", month = oct, year = "2015", CODEN = "????", DOI = "https://doi.org/10.1145/2766459", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Jan 25 06:10:36 MST 2016", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "In this article, we tackle the problem of predicting the ``next'' geographical position of a tourist, given her history (i.e., the prediction is done accordingly to the tourist's current trail) by means of supervised learning techniques, namely Gradient Boosted Regression Trees and Ranking SVM. The learning is done on the basis of an object space represented by a 68-dimension feature vector specifically designed for tourism-related data. Furthermore, we propose a thorough comparison of several methods that are considered state-of-the-art in recommender and trail prediction systems for tourism, as well as a popularity baseline. Experiments show that the methods we propose consistently outperform the baselines and provide strong evidence of the performance and robustness of our solutions.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "8", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Wang:2015:WHP, author = "Yinting Wang and Mingli Song and Dacheng Tao and Yong Rui and Jiajun Bu and Ah Chung Tsoi and Shaojie Zhuo and Ping Tan", title = "{Where2Stand}: a Human Position Recommendation System for Souvenir Photography", journal = j-TIST, volume = "7", number = "1", pages = "9:1--9:??", month = oct, year = "2015", CODEN = "????", DOI = "https://doi.org/10.1145/2770879", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Jan 25 06:10:36 MST 2016", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "People often take photographs at tourist sites and these pictures usually have two main elements: a person in the foreground and scenery in the background. This type of ``souvenir photo'' is one of the most common photos clicked by tourists. Although algorithms that aid a user-photographer in taking a well-composed picture of a scene exist [Ni et al. 2013], few studies have addressed the issue of properly positioning human subjects in photographs. In photography, the common guidelines of composing portrait images exist. However, these rules usually do not consider the background scene. Therefore, in this article, we investigate human-scenery positional relationships and construct a photographic assistance system to optimize the position of human subjects in a given background scene, thereby assisting the user in capturing high-quality souvenir photos. We collect thousands of well-composed portrait photographs to learn human-scenery aesthetic composition rules. In addition, we define a set of negative rules to exclude undesirable compositions. Recommendation results are achieved by combining the first learned positive rule with our proposed negative rules. We implement the proposed system on an Android platform in a smartphone. The system demonstrates its efficacy by producing well-composed souvenir photos.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "9", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Hennes:2015:MLS, author = "Daniel Hennes and Steven {De Jong} and Karl Tuyls and Ya'akov (Kobi) Gal", title = "Metastrategies in Large-Scale Bargaining Settings", journal = j-TIST, volume = "7", number = "1", pages = "10:1--10:??", month = oct, year = "2015", CODEN = "????", DOI = "https://doi.org/10.1145/2774224", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Jan 25 06:10:36 MST 2016", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "This article presents novel methods for representing and analyzing a special class of multiagent bargaining settings that feature multiple players, large action spaces, and a relationship among players' goals, tasks, and resources. We show how to reduce these interactions to a set of bilateral normal-form games in which the strategy space is significantly smaller than the original settings while still preserving much of their structural relationship. The method is demonstrated using the Colored Trails (CT) framework, which encompasses a broad family of games and has been used in many past studies. We define a set of heuristics (metastrategies) in multiplayer CT games that make varying assumptions about players' strategies, such as boundedly rational play and social preferences. We show how these CT settings can be decomposed into canonical bilateral games such as the Prisoners' Dilemma, Stag Hunt, and Ultimatum games in a way that significantly facilitates their analysis. We demonstrate the feasibility of this approach in separate CT settings involving one-shot and repeated bargaining scenarios, which are subsequently analyzed using evolutionary game-theoretic techniques. We provide a set of necessary conditions for CT games for allowing this decomposition. Our results have significance for multiagent systems researchers in mapping large multiplayer CT task settings to smaller, well-known bilateral normal-form games while preserving some of the structure of the original setting.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "10", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Zhang:2015:SSI, author = "Jia-Dong Zhang and Chi-Yin Chow", title = "Spatiotemporal Sequential Influence Modeling for Location Recommendations: a Gravity-based Approach", journal = j-TIST, volume = "7", number = "1", pages = "11:1--11:??", month = oct, year = "2015", CODEN = "????", DOI = "https://doi.org/10.1145/2786761", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Jan 25 06:10:36 MST 2016", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Recommending to users personalized locations is an important feature of Location-Based Social Networks (LBSNs), which benefits users who wish to explore new places and businesses to discover potential customers. In LBSNs, social and geographical influences have been intensively used in location recommendations. However, human movement also exhibits spatiotemporal sequential patterns, but only a few current studies consider the spatiotemporal sequential influence of locations on users' check-in behaviors. In this article, we propose a new gravity model for location recommendations, called LORE, to exploit the spatiotemporal sequential influence on location recommendations. First, LORE extracts sequential patterns from historical check-in location sequences of all users as a Location-Location Transition Graph (L$^2$ TG), and utilizes the L$^2$ TG to predict the probability of a user visiting a new location through the developed additive Markov chain that considers the effect of all visited locations in the check-in history of the user on the new location. Furthermore, LORE applies our contrived gravity model to weigh the effect of each visited location on the new location derived from the personalized attractive force (i.e., the weight) between the visited location and the new location. The gravity model effectively integrates the spatiotemporal, social, and popularity influences by estimating a power-law distribution based on (i) the spatial distance and temporal difference between two consecutive check-in locations of the same user, (ii) the check-in frequency of social friends, and (iii) the popularity of locations from all users. Finally, we conduct a comprehensive performance evaluation for LORE using three large-scale real-world datasets collected from Foursquare, Gowalla, and Brightkite. Experimental results show that LORE achieves significantly superior location recommendations compared to other state-of-the-art location recommendation techniques.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "11", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Guan:2015:DML, author = "Tao Guan and Yuesong Wang and Liya Duan and Rongrong Ji", title = "On-Device Mobile Landmark Recognition Using Binarized Descriptor with Multifeature Fusion", journal = j-TIST, volume = "7", number = "1", pages = "12:1--12:??", month = oct, year = "2015", CODEN = "????", DOI = "https://doi.org/10.1145/2795234", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Jan 25 06:10:36 MST 2016", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Along with the exponential growth of high-performance mobile devices, on-device Mobile Landmark Recognition (MLR) has recently attracted increasing research attention. However, the latency and accuracy of automatic recognition remain as bottlenecks against its real-world usage. In this article, we introduce a novel framework that combines interactive image segmentation with multifeature fusion to achieve improved MLR with high accuracy. First, we propose an effective vector binarization method to reduce the memory usage of image descriptors extracted on-device, which maintains comparable recognition accuracy to the original descriptors. Second, we design a location-aware fusion algorithm that can fuse multiple visual features into a compact yet discriminative image descriptor to improve on-device efficiency. Third, a user-friendly interaction scheme is developed that enables interactive foreground/background segmentation to largely improve recognition accuracy. Experimental results demonstrate the effectiveness of the proposed algorithms for on-device MLR applications.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "12", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Zhang:2016:EFC, author = "Kun Zhang and Zhikun Wang and Jiji Zhang and Bernhard Sch{\"o}lkopf", title = "On Estimation of Functional Causal Models: General Results and Application to the Post-Nonlinear Causal Model", journal = j-TIST, volume = "7", number = "2", pages = "13:1--13:??", month = jan, year = "2016", CODEN = "????", DOI = "https://doi.org/10.1145/2700476", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Jan 25 06:10:36 MST 2016", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Compared to constraint-based causal discovery, causal discovery based on functional causal models is able to identify the whole causal model under appropriate assumptions [Shimizu et al. 2006; Hoyer et al. 2009; Zhang and Hyv{\"a}rinen 2009b]. Functional causal models represent the effect as a function of the direct causes together with an independent noise term. Examples include the linear non-Gaussian acyclic model (LiNGAM), nonlinear additive noise model, and post-nonlinear (PNL) model. Currently, there are two ways to estimate the parameters in the models: dependence minimization and maximum likelihood. In this article, we show that for any acyclic functional causal model, minimizing the mutual information between the hypothetical cause and the noise term is equivalent to maximizing the data likelihood with a flexible model for the distribution of the noise term. We then focus on estimation of the PNL causal model and propose to estimate it with the warped Gaussian process with the noise modeled by the mixture of Gaussians. As a Bayesian nonparametric approach, it outperforms the previous one based on mutual information minimization with nonlinear functions represented by multilayer perceptrons; we also show that unlike the ordinary regression, estimation results of the PNL causal model are sensitive to the assumption on the noise distribution. Experimental results on both synthetic and real data support our theoretical claims.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "13", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Li:2016:OSC, author = "Jiuyong Li and Thuc Duy Le and Lin Liu and Jixue Liu and Zhou Jin and Bingyu Sun and Saisai Ma", title = "From Observational Studies to Causal Rule Mining", journal = j-TIST, volume = "7", number = "2", pages = "14:1--14:??", month = jan, year = "2016", CODEN = "????", DOI = "https://doi.org/10.1145/2746410", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Jan 25 06:10:36 MST 2016", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Randomised controlled trials (RCTs) are the most effective approach to causal discovery, but in many circumstances it is impossible to conduct RCTs. Therefore, observational studies based on passively observed data are widely accepted as an alternative to RCTs. However, in observational studies, prior knowledge is required to generate the hypotheses about the cause-effect relationships to be tested, and hence they can only be applied to problems with available domain knowledge and a handful of variables. In practice, many datasets are of high dimensionality, which leaves observational studies out of the opportunities for causal discovery from such a wealth of data sources. In another direction, many efficient data mining methods have been developed to identify associations among variables in large datasets. The problem is that causal relationships imply associations, but the reverse is not always true. However, we can see the synergy between the two paradigms here. Specifically, association rule mining can be used to deal with the high-dimensionality problem, whereas observational studies can be utilised to eliminate noncausal associations. In this article, we propose the concept of causal rules (CRs) and develop an algorithm for mining CRs in large datasets. We use the idea of retrospective cohort studies to detect CRs based on the results of association rule mining. Experiments with both synthetic and real-world datasets have demonstrated the effectiveness and efficiency of CR mining. In comparison with the commonly used causal discovery methods, the proposed approach generally is faster and has better or competitive performance in finding correct or sensible causes. It is also capable of finding a cause consisting of multiple variables-a feature that other causal discovery methods do not possess.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "14", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Leiva:2016:GGG, author = "Luis A. Leiva and Daniel Mart{\'\i}n-Albo and R{\'e}jean Plamondon", title = "Gestures {\`a} Go Go: Authoring Synthetic Human-Like Stroke Gestures Using the Kinematic Theory of Rapid Movements", journal = j-TIST, volume = "7", number = "2", pages = "15:1--15:??", month = jan, year = "2016", CODEN = "????", DOI = "https://doi.org/10.1145/2799648", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Jan 25 06:10:36 MST 2016", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Training a high-quality gesture recognizer requires providing a large number of examples to enable good performance on unseen, future data. However, recruiting participants, data collection, and labeling, etc., necessary for achieving this goal are usually time consuming and expensive. Thus, it is important to investigate how to empower developers to quickly collect gesture samples for improving UI usage and user experience. In response to this need, we introduce Gestures {\`a} Go Go ( g3), a web service plus an accompanying web application for bootstrapping stroke gesture samples based on the kinematic theory of rapid human movements. The user only has to provide a gesture example once, and g3 will create a model of that gesture. Then, by introducing local and global perturbations to the model parameters, g3 generates from tens to thousands of synthetic human-like samples. Through a comprehensive evaluation, we show that synthesized gestures perform equally similar to gestures generated by human users. Ultimately, this work informs our understanding of designing better user interfaces that are driven by gestures.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "15", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Minkov:2016:EEU, author = "Einat Minkov", title = "Event Extraction using Structured Learning and Rich Domain Knowledge: Application across Domains and Data Sources", journal = j-TIST, volume = "7", number = "2", pages = "16:1--16:??", month = jan, year = "2016", CODEN = "????", DOI = "https://doi.org/10.1145/2801131", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Jan 25 06:10:36 MST 2016", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "We consider the task of record extraction from text documents, where the goal is to automatically populate the fields of target relations, such as scientific seminars or corporate acquisition events. There are various inferences involved in the record-extraction process, including mention detection, unification, and field assignments. We use structured learning to find the appropriate field-value assignments. Unlike previous works, the proposed approach generates feature-rich models that enable the modeling of domain semantics and structural coherence at all levels and across fields. Given labeled examples, such an approach can, for instance, learn likely event durations and the fact that start times should come before end times. While the inference space is large, effective learning is achieved using a perceptron-style method and simple, greedy beam decoding. A main focus of this article is on practical aspects involved in implementing the proposed framework for real-world applications. We argue and demonstrate that this approach is favorable in conditions of data shift, a real-world setting in which models learned using a limited set of labeled examples are applied to examples drawn from a different data distribution. Much of the framework's robustness is attributed to the modeling of domain knowledge. We describe design and implementation details for the case study of seminar event extraction from email announcements, and discuss design adaptations across different domains and text genres.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "16", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Zhang:2016:PAT, author = "Kun Zhang and Jiuyong Li and Elias Bareinboim and Bernhard Sch{\"o}lkopf and Judea Pearl", title = "Preface to the {ACM TIST} Special Issue on Causal Discovery and Inference", journal = j-TIST, volume = "7", number = "2", pages = "17:1--17:??", month = jan, year = "2016", CODEN = "????", DOI = "https://doi.org/10.1145/2840720", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Jan 25 06:10:36 MST 2016", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "17", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Shan:2016:SBS, author = "Na Shan and Xiaogang Dong and Pingfeng Xu and Jianhua Guo", title = "Sharp Bounds on Survivor Average Causal Effects When the Outcome Is Binary and Truncated by Death", journal = j-TIST, volume = "7", number = "2", pages = "18:1--18:??", month = jan, year = "2016", CODEN = "????", DOI = "https://doi.org/10.1145/2700498", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Jan 25 06:10:36 MST 2016", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "In randomized trials with follow-up, outcomes may be undefined for individuals who die before the follow-up is complete. In such settings, Frangakis and Rubin [2002] proposed the ``principal stratum effect'' or ``Survivor Average Causal Effect'' (SACE), which is a fair treatment comparison in the subpopulation that would have survived under either treatment arm. Many of the existing results for estimating the SACE are difficult to carry out in practice. In this article, when the outcome is binary, we apply the symbolic Balke-Pearl linear programming method to derive simple formulas for the sharp bounds on the SACE under the monotonicity assumption commonly used by many researchers.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "18", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Chen:2016:SIC, author = "Hua Chen and Peng Ding and Zhi Geng and Xiao-Hua Zhou", title = "Semiparametric Inference of the Complier Average Causal Effect with Nonignorable Missing Outcomes", journal = j-TIST, volume = "7", number = "2", pages = "19:1--19:??", month = jan, year = "2016", CODEN = "????", DOI = "https://doi.org/10.1145/2668135", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Jan 25 06:10:36 MST 2016", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Noncompliance and missing data often occur in randomized trials, which complicate the inference of causal effects. When both noncompliance and missing data are present, previous papers proposed moment and maximum likelihood estimators for binary and normally distributed continuous outcomes under the latent ignorable missing data mechanism. However, the latent ignorable missing data mechanism may be violated in practice, because the missing data mechanism may depend directly on the missing outcome itself. Under noncompliance and an outcome-dependent nonignorable missing data mechanism, previous studies showed the identifiability of complier average causal effect for discrete outcomes. In this article, we study the semiparametric identifiability and estimation of complier average causal effect in randomized clinical trials with both all-or-none noncompliance and outcome-dependent nonignorable missing continuous outcomes, and propose a two-step maximum likelihood estimator in order to eliminate the infinite dimensional nuisance parameter. Our method does not need to specify a parametric form for the missing data mechanism. We also evaluate the finite sample property of our method via extensive simulation studies and sensitivity analysis, with an application to a double-blinded psychiatric clinical trial.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "19", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Luo:2016:BDI, author = "Peng Luo and Zhi Geng", title = "Bounds on Direct and Indirect Effects of Treatment on a Continuous Endpoint", journal = j-TIST, volume = "7", number = "2", pages = "20:1--20:??", month = jan, year = "2016", CODEN = "????", DOI = "https://doi.org/10.1145/2668134", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Jan 25 06:10:36 MST 2016", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Direct effect of a treatment variable on an endpoint variable and indirect effect through a mediate variable are important concepts for understanding a causal mechanism. However, the randomized assignment of treatment is not sufficient for identifying the direct and indirect effects, and extra assumptions and conditions are required, such as the sequential ignorability assumption without unobserved confounders or the sequential potential ignorability assumption. But these assumptions may not be credible in many applications. In this article, we consider the bounds on controlled direct effect, natural direct effect, and natural indirect effect without these extra assumptions. Cai et al. [2008] presented the bounds for the case of a binary endpoint, and we extend their results to the general case for an arbitrary endpoint.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "20", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Liu:2016:CDD, author = "Furui Liu and Laiwan Chan", title = "Causal Discovery on Discrete Data with Extensions to Mixture Model", journal = j-TIST, volume = "7", number = "2", pages = "21:1--21:??", month = jan, year = "2016", CODEN = "????", DOI = "https://doi.org/10.1145/2700477", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Jan 25 06:10:36 MST 2016", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "In this article, we deal with the causal discovery problem on discrete data. First, we present a causal discovery method for traditional additive noise models that identifies the causal direction by analyzing the supports of the conditional distributions. Then, we present a causal mixture model to address the problem that the function transforming cause to effect varies across the observations. We propose a novel method called Support Analysis (SA) for causal discovery with the mixture model. Experiments using synthetic and real data are presented to demonstrate the performance of our proposed algorithm.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "21", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Flaxman:2016:GPI, author = "Seth R. Flaxman and Daniel B. Neill and Alexander J. Smola", title = "{Gaussian} Processes for Independence Tests with Non-iid Data in Causal Inference", journal = j-TIST, volume = "7", number = "2", pages = "22:1--22:??", month = jan, year = "2016", CODEN = "????", DOI = "https://doi.org/10.1145/2806892", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Jan 25 06:10:36 MST 2016", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "In applied fields, practitioners hoping to apply causal structure learning or causal orientation algorithms face an important question: which independence test is appropriate for my data? In the case of real-valued iid data, linear dependencies, and Gaussian error terms, partial correlation is sufficient. But once any of these assumptions is modified, the situation becomes more complex. Kernel-based tests of independence have gained popularity to deal with nonlinear dependencies in recent years, but testing for conditional independence remains a challenging problem. We highlight the important issue of non-iid observations: when data are observed in space, time, or on a network, ``nearby'' observations are likely to be similar. This fact biases estimates of dependence between variables. Inspired by the success of Gaussian process regression for handling non-iid observations in a wide variety of areas and by the usefulness of the Hilbert--Schmidt Independence Criterion (HSIC), a kernel-based independence test, we propose a simple framework to address all of these issues: first, use Gaussian process regression to control for certain variables and to obtain residuals. Second, use HSIC to test for independence. We illustrate this on two classic datasets, one spatial, the other temporal, that are usually treated as iid. We show how properly accounting for spatial and temporal variation can lead to more reasonable causal graphs. We also show how highly structured data, like images and text, can be used in a causal inference framework using a novel structured input/output Gaussian process formulation. We demonstrate this idea on a dataset of translated sentences, trying to predict the source language.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "22", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Fire:2016:LPC, author = "Amy Fire and Song-Chun Zhu", title = "Learning Perceptual Causality from Video", journal = j-TIST, volume = "7", number = "2", pages = "23:1--23:??", month = jan, year = "2016", CODEN = "????", DOI = "https://doi.org/10.1145/2809782", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Jan 25 06:10:36 MST 2016", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Perceptual causality is the perception of causal relationships from observation. Humans, even as infants, form such models from observation of the world around them [Saxe and Carey 2006]. For a deeper understanding, the computer must make similar models through the analogous form of observation: video. In this article, we provide a framework for the unsupervised learning of this perceptual causal structure from video. Our method takes action and object status detections as input and uses heuristics suggested by cognitive science research to produce the causal links perceived between them. We greedily modify an initial distribution featuring independence between potential causes and effects by adding dependencies that maximize information gain. We compile the learned causal relationships into a Causal And-Or Graph, a probabilistic and-or representation of causality that adds a prior to causality. Validated against human perception, experiments show that our method correctly learns causal relations, attributing status changes of objects to causing actions amid irrelevant actions. Our method outperforms Hellinger's $ \chi^2$-statistic by considering hierarchical action selection, and outperforms the treatment effect by discounting coincidental relationships.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "23", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Demeshko:2016:NCS, author = "Marina Demeshko and Takashi Washio and Yoshinobu Kawahara and Yuriy Pepyolyshev", title = "A Novel Continuous and Structural {VAR} Modeling Approach and Its Application to Reactor Noise Analysis", journal = j-TIST, volume = "7", number = "2", pages = "24:1--24:??", month = jan, year = "2016", CODEN = "????", DOI = "https://doi.org/10.1145/2710025", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Jan 25 06:10:36 MST 2016", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "A vector autoregressive model in discrete time domain (DVAR) is often used to analyze continuous time, multivariate, linear Markov systems through their observed time series data sampled at discrete timesteps. Based on previous studies, the DVAR model is supposed to be a noncanonical representation of the system, that is, it does not correspond to a unique system bijectively. However, in this article, we characterize the relations of the DVAR model with its corresponding Structural Vector AR (SVAR) and Continuous Time Vector AR (CTVAR) models through a finite difference method across continuous and discrete time domain. We further clarify that the DVAR model of a continuous time, multivariate, linear Markov system is canonical under a highly generic condition. Our analysis shows that we can uniquely reproduce its SVAR and CTVAR models from the DVAR model. Based on these results, we propose a novel Continuous and Structural Vector Autoregressive (CSVAR) modeling approach to derive the SVAR and the CTVAR models from their DVAR model empirically derived from the observed time series of continuous time linear Markov systems. We demonstrate its superior performance through some numerical experiments on both artificial and real-world data.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "24", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Hours:2016:CAS, author = "Hadrien Hours and Ernst Biersack and Patrick Loiseau", title = "A Causal Approach to the Study of {TCP} Performance", journal = j-TIST, volume = "7", number = "2", pages = "25:1--25:??", month = jan, year = "2016", CODEN = "????", DOI = "https://doi.org/10.1145/2770878", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Jan 25 06:10:36 MST 2016", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Communication networks are complex systems whose operation relies on a large number of components that work together to provide services to end users. As the quality of these services depends on different parameters, understanding how each of them impacts the final performance of a service is a challenging but important problem. However, intervening on individual factors to evaluate the impact of the different parameters is often impractical due to the high cost of intervention in a network. It is, therefore, desirable to adopt a formal approach to understand the role of the different parameters and to predict how a change in any of these parameters will impact performance. The approach of causality pioneered by J. Pearl provides a powerful framework to investigate these questions. Most of the existing theory is non-parametric and does not make any assumption on the nature of the system under study. However, most of the implementations of causal model inference algorithms and most of the examples of usage of a causal model to predict intervention rely on assumptions such linearity, normality, or discrete data. In this article, we present a methodology to overcome the challenges of working with real-world data and extend the application of causality to complex systems in the area of telecommunication networks, for which assumptions of normality, linearity and discrete data do no hold. Specifically, we study the performance of TCP, which is the prevalent protocol for reliable end-to-end transfer in the Internet. Analytical models of the performance of TCP exist, but they take into account the state of network only and disregard the impact of the application at the sender and the receiver, which often influences TCP performance. To address this point, we take as application the file transfer protocol (FTP), which uses TCP for reliable transfer. Studying a well-understood protocol such as TCP allows us to validate our approach and compare its results to previous studies. We first present and evaluate our methodology using TCP traffic obtained via network emulation, which allows us to experimentally validate the prediction of an intervention. We then apply the methodology to real-world TCP traffic sent over the Internet. Throughout the article, we compare the causal approach for studying TCP performance to other approaches such as analytical modeling or simulation and and show how they can complement each other.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "25", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Belem:2016:BRE, author = "Fabiano M. Bel{\'e}m and Carolina S. Batista and Rodrygo L. T. Santos and Jussara M. Almeida and Marcos A. Gon{\c{c}}alves", title = "Beyond Relevance: Explicitly Promoting Novelty and Diversity in Tag Recommendation", journal = j-TIST, volume = "7", number = "3", pages = "26:1--26:??", month = apr, year = "2016", CODEN = "????", DOI = "https://doi.org/10.1145/2801130", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Jun 20 11:24:25 MDT 2016", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "The design and evaluation of tag recommendation methods has historically focused on maximizing the relevance of the suggested tags for a given object, such as a movie or a song. However, relevance by itself may not be enough to guarantee recommendation usefulness. Promoting novelty and diversity in tag recommendation not only increases the chances that the user will select ``some'' of the recommended tags but also promotes complementary information (i.e., tags), which helps to cover multiple aspects or topics related to the target object. Previous work has addressed the tag recommendation problem by exploiting at most two of the following aspects: (1) relevance, (2) explicit topic diversity, and (3) novelty. In contrast, here we tackle these three aspects conjointly, by introducing two new tag recommendation methods that cover all three aspects of the problem at different levels. Our first method, called Random Forest with topic-related attributes, or RF$_t$, extends a relevance-driven tag recommender based on the Random Forest ( RF ) learning-to-rank method by including new tag attributes to capture the extent to which a candidate tag is related to the topics of the target object. This solution captures topic diversity as well as novelty at the attribute level while aiming at maximizing relevance in its objective function. Our second method, called Explicit Tag Recommendation Diversifier with Novelty Promotion, or xTReND, reranks the recommendations provided by any tag recommender to jointly promote relevance, novelty, and topic diversity. We use RF$_t$ as a basic recommender applied before the reranking, thus building a solution that addresses the problem at both attribute and objective levels. Furthermore, to enable the use of our solutions on applications in which category information is unavailable, we investigate the suitability of using latent Dirichlet allocation (LDA) to automatically generate topics for objects. We evaluate all tag recommendation approaches using real data from five popular Web 2.0 applications. Our results show that RF$_t$ greatly outperforms the relevance-driven RF baseline in diversity while producing gains in relevance as well. We also find that our new xTReND reranker obtains considerable gains in both novelty and relevance when compared to that same baseline while keeping the same relevance levels. Furthermore, compared to our previous reranker method, xTReD, which does not consider novelty, xTReND is also quite effective, improving the novelty of the recommended tags while keeping similar relevance and diversity levels in most datasets and scenarios. Comparing our two new proposals, we find that xTReND considerably outperforms RF$_t$ in terms of novelty and diversity with only small losses (under 4\%) in relevance. Overall, considering the trade-off among relevance, novelty, and diversity, our results demonstrate the superiority of xTReND over the baselines and the proposed alternative, RF$_t$. Finally, the use of automatically generated latent topics as an alternative to manually labeled categories also provides significant improvements, which greatly enhances the applicability of our solutions to applications where the latter is not available.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "26", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Paik:2016:PDM, author = "Jiaul H. Paik", title = "Parameterized Decay Model for Information Retrieval", journal = j-TIST, volume = "7", number = "3", pages = "27:1--27:??", month = apr, year = "2016", CODEN = "????", DOI = "https://doi.org/10.1145/2800794", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Jun 20 11:24:25 MDT 2016", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "This article proposes a term weighting scheme for measuring query-document similarity that attempts to explicitly model the dependency between separate occurrences of a term in a document. The assumption is that, if a term appears once in a document, it is more likely to appear again in the same document. Thus, as the term appears again and again, the information content of the subsequent occurrences decreases gradually, since they are more predictable. We introduce a parameterized decay function to model this assumption, where the initial contribution of the term can be determined using any reasonable term discrimination factor. The effectiveness of the proposed model is evaluated on a number of recent web test collections of varying nature. The experimental results show that the proposed model significantly outperforms a number of well known retrieval models including a recently proposed strong Term Frequency and Inverse Document Frequency (TF-IDF) model.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "27", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Li:2016:MCA, author = "Zhifeng Li and Dihong Gong and Qiang Li and Dacheng Tao and Xuelong Li", title = "Mutual Component Analysis for Heterogeneous Face Recognition", journal = j-TIST, volume = "7", number = "3", pages = "28:1--28:??", month = apr, year = "2016", CODEN = "????", DOI = "https://doi.org/10.1145/2807705", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Jun 20 11:24:25 MDT 2016", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Heterogeneous face recognition, also known as cross-modality face recognition or intermodality face recognition, refers to matching two face images from alternative image modalities. Since face images from different image modalities of the same person are associated with the same face object, there should be mutual components that reflect those intrinsic face characteristics that are invariant to the image modalities. Motivated by this rationality, we propose a novel approach called Mutual Component Analysis (MCA) to infer the mutual components for robust heterogeneous face recognition. In the MCA approach, a generative model is first proposed to model the process of generating face images in different modalities, and then an Expectation Maximization (EM) algorithm is designed to iteratively learn the model parameters. The learned generative model is able to infer the mutual components (which we call the hidden factor, where hidden means the factor is unreachable and invisible, and can only be inferred from observations) that are associated with the person's identity, thus enabling fast and effective matching for cross-modality face recognition. To enhance recognition performance, we propose an MCA-based multiclassifier framework using multiple local features. Experimental results show that our new approach significantly outperforms the state-of-the-art results on two typical application scenarios: sketch-to-photo and infrared-to-visible face recognition.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "28", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Ye:2016:GIL, author = "Jintao Ye and Zhao Yan Ming and Tat Seng Chua", title = "Generating Incremental Length Summary Based on Hierarchical Topic Coverage Maximization", journal = j-TIST, volume = "7", number = "3", pages = "29:1--29:??", month = apr, year = "2016", CODEN = "????", DOI = "https://doi.org/10.1145/2809433", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Jun 20 11:24:25 MDT 2016", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Document summarization is playing an important role in coping with information overload on the Web. Many summarization models have been proposed recently, but few try to adjust the summary length and sentence order according to application scenarios. With the popularity of handheld devices, presenting key information first in summaries of flexible length is of great convenience in terms of faster reading and decision-making and network consumption reduction. Targeting this problem, we introduce a novel task of generating summaries of incremental length. In particular, we require that the summaries should have the ability to automatically adjust the coverage of general-detailed information when the summary length varies. We propose a novel summarization model that incrementally maximizes topic coverage based on the document's hierarchical topic model. In addition to the standard Rouge-1 measure, we define a new evaluation metric based on the similarity of the summaries' topic coverage distribution in order to account for sentence order and summary length. Extensive experiments on Wikipedia pages, DUC 2007, and general noninverted writing style documents from multiple sources show the effectiveness of our proposed approach. Moreover, we carry out a user study on a mobile application scenario to show the usability of the produced summary in terms of improving judgment accuracy and speed, as well as reducing the reading burden and network traffic.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "29", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Yang:2016:PCM, author = "Dingqi Yang and Daqing Zhang and Bingqing Qu", title = "Participatory Cultural Mapping Based on Collective Behavior Data in Location-Based Social Networks", journal = j-TIST, volume = "7", number = "3", pages = "30:1--30:??", month = apr, year = "2016", CODEN = "????", DOI = "https://doi.org/10.1145/2814575", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Jun 20 11:24:25 MDT 2016", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Culture has been recognized as a driving impetus for human development. It co-evolves with both human belief and behavior. When studying culture, Cultural Mapping is a crucial tool to visualize different aspects of culture (e.g., religions and languages) from the perspectives of indigenous and local people. Existing cultural mapping approaches usually rely on large-scale survey data with respect to human beliefs, such as moral values. However, such a data collection method not only incurs a significant cost of both human resources and time, but also fails to capture human behavior, which massively reflects cultural information. In addition, it is practically difficult to collect large-scale human behavior data. Fortunately, with the recent boom in Location-Based Social Networks (LBSNs), a considerable number of users report their activities in LBSNs in a participatory manner, which provides us with an unprecedented opportunity to study large-scale user behavioral data. In this article, we propose a participatory cultural mapping approach based on collective behavior in LBSNs. First, we collect the participatory sensed user behavioral data from LBSNs. Second, since only local users are eligible for cultural mapping, we propose a progressive ``home'' location identification method to filter out ineligible users. Third, by extracting three key cultural features from daily activity, mobility, and linguistic perspectives, respectively, we propose a cultural clustering method to discover cultural clusters. Finally, we visualize the cultural clusters on the world map. Based on a real-world LBSN dataset, we experimentally validate our approach by conducting both qualitative and quantitative analysis on the generated cultural maps. The results show that our approach can subtly capture cultural features and generate representative cultural maps that correspond well with traditional cultural maps based on survey data.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "30", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Jia:2016:LPT, author = "Yantao Jia and Yuanzhuo Wang and Xiaolong Jin and Xueqi Cheng", title = "Location Prediction: a Temporal-Spatial {Bayesian} Model", journal = j-TIST, volume = "7", number = "3", pages = "31:1--31:??", month = apr, year = "2016", CODEN = "????", DOI = "https://doi.org/10.1145/2816824", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Jun 20 11:24:25 MDT 2016", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "In social networks, predicting a user's location mainly depends on those of his/her friends, where the key lies in how to select his/her most influential friends. In this article, we analyze the theoretically maximal accuracy of location prediction based on friends' locations and compare it with the practical accuracy obtained by the state-of-the-art location prediction methods. Upon observing a big gap between the theoretical and practical accuracy, we propose a new strategy for selecting influential friends in order to improve the practical location prediction accuracy. Specifically, several features are defined to measure the influence of the friends on a user's location, based on which we put forth a sequential random-walk-with-restart procedure to rank the friends of the user in terms of their influence. By dynamically selecting the top N most influential friends of the user per time slice, we develop a temporal-spatial Bayesian model to characterize the dynamics of friends' influence for location prediction. Finally, extensive experimental results on datasets of real social networks demonstrate that the proposed influential friend selection method and temporal-spatial Bayesian model can significantly improve the accuracy of location prediction.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "31", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Li:2016:VFE, author = "Xiaoyan Li and Tongliang Liu and Jiankang Deng and Dacheng Tao", title = "Video Face Editing Using Temporal-Spatial-Smooth Warping", journal = j-TIST, volume = "7", number = "3", pages = "32:1--32:??", month = apr, year = "2016", CODEN = "????", DOI = "https://doi.org/10.1145/2819000", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Jun 20 11:24:25 MDT 2016", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Editing faces in videos is a popular yet challenging task in computer vision and graphics that encompasses various applications, including facial attractiveness enhancement, makeup transfer, face replacement, and expression manipulation. Directly applying the existing warping methods to video face editing has the major problem of temporal incoherence in the synthesized videos, which cannot be addressed by simply employing face tracking techniques or manual interventions, as it is difficult to eliminate the subtly temporal incoherence of the facial feature point localizations in a video sequence. In this article, we propose a temporal-spatial-smooth warping (TSSW) method to achieve a high temporal coherence for video face editing. TSSW is based on two observations: (1) the control lattices are critical for generating warping surfaces and achieving the temporal coherence between consecutive video frames, and (2) the temporal coherence and spatial smoothness of the control lattices can be simultaneously and effectively preserved. Based upon these observations, we impose the temporal coherence constraint on the control lattices on two consecutive frames, as well as the spatial smoothness constraint on the control lattice on the current frame. TSSW calculates the control lattice (in either the horizontal or vertical direction) by updating the control lattice (in the corresponding direction) on its preceding frame, i.e., minimizing a novel energy function that unifies a data-driven term, a smoothness term, and feature point constraints. The contributions of this article are twofold: (1) we develop TSSW, which is robust to the subtly temporal incoherence of the facial feature point localizations and is effective to preserve the temporal coherence and spatial smoothness of the control lattices for editing faces in videos, and (2) we present a new unified video face editing framework that is capable for improving the performances of facial attractiveness enhancement, makeup transfer, face replacement, and expression manipulation.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "32", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Li:2016:MNS, author = "Zechao Li and Jinhui Tang and Xueming Wang and Jing Liu and Hanqing Lu", title = "Multimedia News Summarization in Search", journal = j-TIST, volume = "7", number = "3", pages = "33:1--33:??", month = apr, year = "2016", CODEN = "????", DOI = "https://doi.org/10.1145/2822907", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Jun 20 11:24:25 MDT 2016", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "It is a necessary but challenging task to relieve users from the proliferative news information and allow them to quickly and comprehensively master the information of the whats and hows that are happening in the world every day. In this article, we develop a novel approach of multimedia news summarization for searching results on the Internet, which uncovers the underlying topics among query-related news information and threads the news events within each topic to generate a query-related brief overview. First, the hierarchical latent Dirichlet allocation (hLDA) model is introduced to discover the hierarchical topic structure from query-related news documents, and a new approach based on the weighted aggregation and max pooling is proposed to identify one representative news article for each topic. One representative image is also selected to visualize each topic as a complement to the text information. Given the representative documents selected for each topic, a time-bias maximum spanning tree (MST) algorithm is proposed to thread them into a coherent and compact summary of their parent topic. Finally, we design a friendly interface to present users with the hierarchical summarization of their required news information. Extensive experiments conducted on a large-scale news dataset collected from multiple news Web sites demonstrate the encouraging performance of the proposed solution for news summarization in news retrieval.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "33", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Hardegger:2016:SUB, author = "Michael Hardegger and Daniel Roggen and Alberto Calatroni and Gerhard Tr{\"o}ster", title = "{S-SMART}: a Unified {Bayesian} Framework for Simultaneous Semantic Mapping, Activity Recognition, and Tracking", journal = j-TIST, volume = "7", number = "3", pages = "34:1--34:??", month = apr, year = "2016", CODEN = "????", DOI = "https://doi.org/10.1145/2824286", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Jun 20 11:24:25 MDT 2016", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "The machine recognition of user trajectories and activities is fundamental to devise context-aware applications for support and monitoring in daily life. So far, tracking and activity recognition were mostly considered as orthogonal problems, which limits the richness of possible context inference. In this work, we introduce the novel unified computational and representational framework S-SMART that simultaneously models the environment state (semantic mapping), localizes the user within this map (tracking), and recognizes interactions with the environment (activity recognition). Thus, S-SMART identifies which activities the user executes where (e.g., turning a handle next to a window ), and reflects the outcome of these actions by updating the world model (e.g., the window is now open ). This in turn conditions the future possibility of executing actions at specific places (e.g., closing the window is likely to be the next action at this location). S-SMART works in a self-contained manner and iteratively builds the semantic map from wearable sensors only. This enables the seamless deployment to new environments. We characterize S-SMART in an experimental dataset with people performing hand actions as part of their usual routines at home and in office buildings. The framework combines dead reckoning from a foot-worn motion sensor with template-matching-based action recognition, identifying objects in the environment (windows, doors, water taps, phones, etc.) and tracking their state (open/closed, etc.). In real-life recordings with up to 23 action classes, S-SMART consistently outperforms independent systems for positioning and activity recognition, and constructs accurate semantic maps. This environment representation enables novel applications that build upon information about the arrangement and state of the user's surroundings. For example, it may be possible to remind elderly people of a window that they left open before leaving the house, or of a plant they did not water yet, using solely wearable sensors.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "34", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Luo:2016:IMD, author = "Tie Luo and Sajal K. Das and Hwee Pink Tan and Lirong Xia", title = "Incentive Mechanism Design for Crowdsourcing: an All-Pay Auction Approach", journal = j-TIST, volume = "7", number = "3", pages = "35:1--35:??", month = apr, year = "2016", CODEN = "????", DOI = "https://doi.org/10.1145/2837029", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Jun 20 11:24:25 MDT 2016", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Crowdsourcing can be modeled as a principal-agent problem in which the principal (crowdsourcer) desires to solicit a maximal contribution from a group of agents (participants) while agents are only motivated to act according to their own respective advantages. To reconcile this tension, we propose an all-pay auction approach to incentivize agents to act in the principal's interest, i.e., maximizing profit, while allowing agents to reap strictly positive utility. Our rationale for advocating all-pay auctions is based on two merits that we identify, namely all-pay auctions (i) compress the common, two-stage ``bid-contribute'' crowdsourcing process into a single ``bid-cum-contribute'' stage, and (ii) eliminate the risk of task nonfulfillment. In our proposed approach, we enhance all-pay auctions with two additional features: an adaptive prize and a general crowdsourcing environment. The prize or reward adapts itself as per a function of the unknown winning agent's contribution, and the environment or setting generally accommodates incomplete and asymmetric information, risk-averse (and risk-neutral) agents, and a stochastic (and deterministic) population. We analytically derive this all-pay auction-based mechanism and extensively evaluate it in comparison to classic and optimized mechanisms. The results demonstrate that our proposed approach remarkably outperforms its counterparts in terms of the principal's profit, agent's utility, and social welfare.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "35", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Ibrahim:2016:IEM, author = "Azhar Mohd Ibrahim and Ibrahim Venkat and K. G. Subramanian and Ahamad Tajudin Khader and Philippe {De Wilde}", title = "Intelligent Evacuation Management Systems: a Review", journal = j-TIST, volume = "7", number = "3", pages = "36:1--36:??", month = apr, year = "2016", CODEN = "????", DOI = "https://doi.org/10.1145/2842630", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Jun 20 11:24:25 MDT 2016", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Crowd and evacuation management have been active areas of research and study in the recent past. Various developments continue to take place in the process of efficient evacuation of crowds in mass gatherings. This article is intended to provide a review of intelligent evacuation management systems covering the aspects of crowd monitoring, crowd disaster prediction, evacuation modelling, and evacuation path guidelines. Soft computing approaches play a vital role in the design and deployment of intelligent evacuation applications pertaining to crowd control management. While the review deals with video and nonvideo based aspects of crowd monitoring and crowd disaster prediction, evacuation techniques are reviewed via the theme of soft computing, along with a brief review on the evacuation navigation path. We believe that this review will assist researchers in developing reliable automated evacuation systems that will help in ensuring the safety of the evacuees especially during emergency evacuation scenarios.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "36", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Ding:2016:CSP, author = "Changxing Ding and Dacheng Tao", title = "A Comprehensive Survey on Pose-Invariant Face Recognition", journal = j-TIST, volume = "7", number = "3", pages = "37:1--37:??", month = apr, year = "2016", CODEN = "????", DOI = "https://doi.org/10.1145/2845089", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Jun 20 11:24:25 MDT 2016", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "The capacity to recognize faces under varied poses is a fundamental human ability that presents a unique challenge for computer vision systems. Compared to frontal face recognition, which has been intensively studied and has gradually matured in the past few decades, Pose-Invariant Face Recognition (PIFR) remains a largely unsolved problem. However, PIFR is crucial to realizing the full potential of face recognition for real-world applications, since face recognition is intrinsically a passive biometric technology for recognizing uncooperative subjects. In this article, we discuss the inherent difficulties in PIFR and present a comprehensive review of established techniques. Existing PIFR methods can be grouped into four categories, that is, pose-robust feature extraction approaches, multiview subspace learning approaches, face synthesis approaches, and hybrid approaches. The motivations, strategies, pros/cons, and performance of representative approaches are described and compared. Moreover, promising directions for future research are discussed.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "37", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Cremonesi:2016:ISI, author = "Paolo Cremonesi and Alan Said and Domonkos Tikk and Michelle X. Zhou", title = "Introduction to the Special Issue on Recommender System Benchmarking", journal = j-TIST, volume = "7", number = "3", pages = "38:1--38:??", month = apr, year = "2016", CODEN = "????", DOI = "https://doi.org/10.1145/2870627", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Jun 20 11:24:25 MDT 2016", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "38", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Wu:2016:RMC, author = "Le Wu and Qi Liu and Enhong Chen and Nicholas Jing Yuan and Guangming Guo and Xing Xie", title = "Relevance Meets Coverage: a Unified Framework to Generate Diversified Recommendations", journal = j-TIST, volume = "7", number = "3", pages = "39:1--39:??", month = apr, year = "2016", CODEN = "????", DOI = "https://doi.org/10.1145/2700496", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Jun 20 11:24:25 MDT 2016", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Collaborative filtering (CF) models offer users personalized recommendations by measuring the relevance between the active user and each individual candidate item. Following this idea, user-based collaborative filtering (UCF) usually selects the local popular items from the like-minded neighbor users. However, these traditional relevance-based models only consider the individuals (i.e., each neighbor user and candidate item) separately during neighbor set selection and recommendation set generation, thus usually incurring highly similar recommendations that lack diversity. While many researchers have recognized the importance of diversified recommendations, the proposed solutions either needed additional semantic information of items or decreased accuracy in this process. In this article, we describe how to generate both accurate and diversified recommendations from a new perspective. Along this line, we first introduce a simple measure of coverage that quantifies the usefulness of the whole set, that is, the neighbor userset and the recommended itemset as a complete entity. Then we propose a recommendation framework named REC that considers both traditional relevance-based scores and the new coverage measure based on UCF. Under REC, we further prove that the goals of maximizing relevance and coverage measures simultaneously in both the neighbor set selection step and the recommendation set generation step are NP-hard. Luckily, we can solve them effectively and efficiently by exploiting the inherent submodular property. Furthermore, we generalize the coverage notion and the REC framework from both a data perspective and an algorithm perspective. Finally, extensive experimental results on three real-world datasets show that the REC-based recommendation models can naturally generate more diversified recommendations without decreasing accuracy compared to some state-of-the-art models.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "39", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Doerfel:2016:RCR, author = "Stephan Doerfel and Robert J{\"a}schke and Gerd Stumme", title = "The Role of Cores in Recommender Benchmarking for Social Bookmarking Systems", journal = j-TIST, volume = "7", number = "3", pages = "40:1--40:??", month = apr, year = "2016", CODEN = "????", DOI = "https://doi.org/10.1145/2700485", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Jun 20 11:24:25 MDT 2016", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Social bookmarking systems have established themselves as an important part in today's Web. In such systems, tag recommender systems support users during the posting of a resource by suggesting suitable tags. Tag recommender algorithms have often been evaluated in offline benchmarking experiments. Yet, the particular setup of such experiments has rarely been analyzed. In particular, since the recommendation quality usually suffers from difficulties such as the sparsity of the data or the cold-start problem for new resources or users, datasets have often been pruned to so-called cores (specific subsets of the original datasets), without much consideration of the implications on the benchmarking results. In this article, we generalize the notion of a core by introducing the new notion of a set-core, which is independent of any graph structure, to overcome a structural drawback in the previous constructions of cores on tagging data. We show that problems caused by some types of cores can be eliminated using set-cores. Further, we present a thorough analysis of tag recommender benchmarking setups using cores. To that end, we conduct a large-scale experiment on four real-world datasets, in which we analyze the influence of different cores on the evaluation of recommendation algorithms. We can show that the results of the comparison of different recommendation approaches depends on the selection of core type and level. For the benchmarking of tag recommender algorithms, our results suggest that the evaluation must be set up more carefully and should not be based on one arbitrarily chosen core type and level.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "40", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Dooms:2016:FDB, author = "Simon Dooms and Alejandro Bellog{\'\i}n and Toon {De Pessemier} and Luc Martens", title = "A Framework for Dataset Benchmarking and Its Application to a New Movie Rating Dataset", journal = j-TIST, volume = "7", number = "3", pages = "41:1--41:??", month = apr, year = "2016", CODEN = "????", DOI = "https://doi.org/10.1145/2751565", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Jun 20 11:24:25 MDT 2016", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Rating datasets are of paramount importance in recommender systems research. They serve as input for recommendation algorithms, as simulation data, or for evaluation purposes. In the past, public accessible rating datasets were not abundantly available, leaving researchers no choice but to work with old and static datasets like MovieLens and Netflix. More recently, however, emerging trends as social media and smartphones are found to provide rich data sources which can be turned into valuable research datasets. While dataset availability is growing, a structured way for introducing and comparing new datasets is currently still lacking. In this work, we propose a five-step framework to introduce and benchmark new datasets in the recommender systems domain. We illustrate our framework on a new movie rating dataset-called MovieTweetings-collected from Twitter. Following our framework, we detail the origin of the dataset, provide basic descriptive statistics, investigate external validity, report the results of a number of reproducible benchmarks, and conclude by discussing some interesting advantages and appropriate research use cases.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "41", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Moody:2016:NCF, author = "Jennifer Moody and David H. Glass", title = "A Novel Classification Framework for Evaluating Individual and Aggregate Diversity in Top-{$N$} Recommendations", journal = j-TIST, volume = "7", number = "3", pages = "42:1--42:??", month = apr, year = "2016", CODEN = "????", DOI = "https://doi.org/10.1145/2700491", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Jun 20 11:24:25 MDT 2016", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "The primary goal of a recommender system is to generate high quality user-centred recommendations. However, the traditional evaluation methods and metrics were developed before researchers understood all the factors that increase user satisfaction. This study is an introduction to a novel user and item classification framework. It is proposed that this framework should be used during user-centred evaluation of recommender systems and the need for this framework is justified through experiments. User profiles are constructed and matched against other users' profiles to formulate neighbourhoods and generate top-N recommendations. The recommendations are evaluated to measure the success of the process. In conjunction with the framework, a new diversity metric is presented and explained. The accuracy, coverage, and diversity of top-N recommendations is illustrated and discussed for groups of users. It is found that in contradiction to common assumptions, not all users suffer as expected from the data sparsity problem. In fact, the group of users that receive the most accurate recommendations do not belong to the least sparse area of the dataset.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "42", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Ben-Shimon:2016:AAR, author = "David Ben-Shimon and Lior Rokach and Guy Shani and Bracha Shapira", title = "Anytime Algorithms for Recommendation Service Providers", journal = j-TIST, volume = "7", number = "3", pages = "43:1--43:??", month = apr, year = "2016", CODEN = "????", DOI = "https://doi.org/10.1145/2835496", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Jun 20 11:24:25 MDT 2016", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Recommender systems (RS) can now be found in many commercial Web sites, often presenting customers with a short list of additional products that they might purchase. Many commercial sites do not typically have the ability and resources to develop their own system and may outsource the RS to a third party. This had led to the growth of a recommendation as a service industry, where companies, referred to as RS providers, provide recommendation services. These companies must carefully balance the cost of building recommendation models and the payment received from the e-business, as these payments are expected to be low. In such a setting, restricting the computational time required for model building is critical for the RS provider to be profitable. In this article, we propose anytime algorithms as an attractive method for balancing computational time and the recommendation model performance, thus tackling the RS provider problem. In an anytime setting, an algorithm can be stopped after any amount of computational time, always ensuring that a valid, although suboptimal, solution will be returned. Given sufficient time, however, the algorithm should converge to an optimal solution. In this setting, it is important to evaluate the quality of the returned solution over time, monitoring quality improvement. This is significantly different from traditional evaluation methods, which mostly estimate the performance of the algorithm only after its convergence is given sufficient time. We show that the popular item-item top-N recommendation approach can be brought into the anytime framework by smartly considering the order by which item pairs are being evaluated. We experimentally show that the time-accuracy trade-off can be significantly improved for this specific problem.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "43", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Chen:2016:ISI, author = "Kuan-Ta Chen and Omar Alonso and Martha Larson and Irwin King", title = "Introduction to the Special Issue on Crowd in Intelligent Systems", journal = j-TIST, volume = "7", number = "4", pages = "44:1--44:??", month = jul, year = "2016", CODEN = "????", DOI = "https://doi.org/10.1145/2920522", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Apr 3 11:19:56 MDT 2017", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "44", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Siddharthan:2016:CCR, author = "Advaith Siddharthan and Christopher Lambin and Anne-Marie Robinson and Nirwan Sharma and Richard Comont and Elaine O'Mahony and Chris Mellish and Ren{\'e} {Van Der Wal}", title = "Crowdsourcing Without a Crowd: Reliable Online Species Identification Using {Bayesian} Models to Minimize Crowd Size", journal = j-TIST, volume = "7", number = "4", pages = "45:1--45:??", month = jul, year = "2016", CODEN = "????", DOI = "https://doi.org/10.1145/2776896", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Apr 3 11:19:56 MDT 2017", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "We present an incremental Bayesian model that resolves key issues of crowd size and data quality for consensus labeling. We evaluate our method using data collected from a real-world citizen science program, BeeWatch, which invites members of the public in the United Kingdom to classify (label) photographs of bumblebees as one of 22 possible species. The biological recording domain poses two key and hitherto unaddressed challenges for consensus models of crowdsourcing: (1) the large number of potential species makes classification difficult, and (2) this is compounded by limited crowd availability, stemming from both the inherent difficulty of the task and the lack of relevant skills among the general public. We demonstrate that consensus labels can be reliably found in such circumstances with very small crowd sizes of around three to five users (i.e., through group sourcing). Our incremental Bayesian model, which minimizes crowd size by re-evaluating the quality of the consensus label following each species identification solicited from the crowd, is competitive with a Bayesian approach that uses a larger but fixed crowd size and outperforms majority voting. These results have important ecological applicability: biological recording programs such as BeeWatch can sustain themselves when resources such as taxonomic experts to confirm identifications by photo submitters are scarce (as is typically the case), and feedback can be provided to submitters in a timely fashion. More generally, our model provides benefits to any crowdsourced consensus labeling task where there is a cost (financial or otherwise) associated with soliciting a label.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "45", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Semertzidis:2016:CPS, author = "Theodoros Semertzidis and Jasminko Novak and Michalis Lazaridis and Mark Melenhorst and Isabel Micheel and Dimitrios Michalopoulos and Martin B{\"o}ckle and Michael G. Strintzis and Petros Daras", title = "A Crowd-Powered System for Fashion Similarity Search", journal = j-TIST, volume = "7", number = "4", pages = "46:1--46:??", month = jul, year = "2016", CODEN = "????", DOI = "https://doi.org/10.1145/2897365", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Apr 3 11:19:56 MDT 2017", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Driven by the needs of customers and industry, online fashion search and analytics are recently gaining much attention. As fashion is mostly expressed by visual content, the analysis of fashion images in online social networks is a rich source of possible insights on evolving trends and customer preferences. Although a plethora of visual content is available, the modeling of clothes' physics and movement, the implicit semantics in fashion designs, and the subjectivity of their interpretation pose difficulties to fully automated solutions for fashion search and analysis. In this article, we present the design and evaluation of a crowd-powered system for fashion similarity search from Twitter, supporting trend analysis for fashion professionals. The system enables fashion similarity search based on specific human-based similarity criteria. This is achieved by implementing a novel machine--crowd workflow that supports complex tasks requiring highly subjective judgments where multiple true solutions may coexist. We discuss how this leads to a novel class of crowd-powered systems for which the output of the crowd is not used to verify the automatic analysis but is the desired outcome. Finally, we show how this kind of crowd involvement enables a novel kind of similarity search and represents a crucial factor for the acceptance of system results by the end user.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "46", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Borish:2016:RLC, author = "Michael Borish and Benjamin Lok", title = "Rapid Low-Cost Virtual Human Bootstrapping via the Crowd", journal = j-TIST, volume = "7", number = "4", pages = "47:1--47:??", month = jul, year = "2016", CODEN = "????", DOI = "https://doi.org/10.1145/2897366", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Apr 3 11:19:56 MDT 2017", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Virtual human interactions provide an important avenue for training as emergent opportunities arise. In response to a new training need, we propose a framework to rapidly create experiential learning opportunities in the form of a question--answer chat interaction with virtual humans. This framework takes quickly generated case documents and breaks down the case into small tasks that can be crowdsourced by nonexperts. This framework can serve as a first step to rapidly bootstrapping new virtual humans. We have applied our framework to the task of preparing health care students and professionals to infrequent, but high-stakes, situations such as infectious diseases, cranial nerve disorders, and stroke. Our framework was utilized by medical professionals interested in providing new training experiences to students and colleagues. Over the course of two months, these professionals created seven scenarios on a diverse range of topics that included Ebola, cancer, and neurological disorders. These scenarios were developed for multiple target audiences such as medical students, residents, and fellows. As a first step, each scenario utilized our framework and crowdsourced workers to create an initial corpus over the course of two days. From these seven cases, we selected two to evaluate the quality of the resulting virtual-human corpuses. The two scenarios were compared to preexisting reference scenarios that have been in curricular use for several years. We found a reduction in author time commitment of at least 92\% while creating a character that was at least 75\% as accurate as its reference counterparts. The commitment reduction and accuracy achieved by our framework represents a first step towards rapid development of a virtual human. Our framework can then be combined with other creation processes for further virtual-human development in order to create a mature virtual human. As part of a virtual-human development process, our framework can help to rapidly develop new scenarios in response to emergent training opportunities.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "47", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Radanovic:2016:IEC, author = "Goran Radanovic and Boi Faltings and Radu Jurca", title = "Incentives for Effort in Crowdsourcing Using the Peer Truth Serum", journal = j-TIST, volume = "7", number = "4", pages = "48:1--48:??", month = jul, year = "2016", CODEN = "????", DOI = "https://doi.org/10.1145/2856102", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Apr 3 11:19:56 MDT 2017", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Crowdsourcing is widely proposed as a method to solve a large variety of judgment tasks, such as classifying website content, peer grading in online courses, or collecting real-world data. As the data reported by workers cannot be verified, there is a tendency to report random data without actually solving the task. This can be countered by making the reward for an answer depend on its consistency with answers given by other workers, an approach called peer consistency. However, it is obvious that the best strategy in such schemes is for all workers to report the same answer without solving the task. Dasgupta and Ghosh [2013] show that, in some cases, exerting high effort can be encouraged in the highest-paying equilibrium. In this article, we present a general mechanism that implements this idea and is applicable to most crowdsourcing settings. Furthermore, we experimentally test the novel mechanism, and validate its theoretical properties.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "48", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{DeBoer:2016:PTA, author = "Patrick M. {De Boer} and Abraham Bernstein", title = "{PPLib}: Toward the Automated Generation of Crowd Computing Programs Using Process Recombination and Auto-Experimentation", journal = j-TIST, volume = "7", number = "4", pages = "49:1--49:??", month = jul, year = "2016", CODEN = "????", DOI = "https://doi.org/10.1145/2897367", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Apr 3 11:19:56 MDT 2017", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Crowdsourcing is increasingly being adopted to solve simple tasks such as image labeling and object tagging, as well as more complex tasks, where crowd workers collaborate in processes with interdependent steps. For the whole range of complexity, research has yielded numerous patterns for coordinating crowd workers in order to optimize crowd accuracy, efficiency, and cost. Process designers, however, often don't know which pattern to apply to a problem at hand when designing new applications for crowdsourcing. In this article, we propose to solve this problem by systematically exploring the design space of complex crowdsourced tasks via automated recombination and auto-experimentation for an issue at hand. Specifically, we propose an approach to finding the optimal process for a given problem by defining the deep structure of the problem in terms of its abstract operators, generating all possible alternatives via the (re)combination of the abstract deep structure with concrete implementations from a Process Repository, and then establishing the best alternative via auto-experimentation. To evaluate our approach, we implemented PPLib (pronounced ``People Lib''), a program library that allows for the automated recombination of known processes stored in an easily extensible Process Repository. We evaluated our work by generating and running a plethora of process candidates in two scenarios on Amazon's Mechanical Turk followed by a meta-evaluation, where we looked at the differences between the two evaluations. Our first scenario addressed the problem of text translation, where our automatic recombination produced multiple processes whose performance almost matched the benchmark established by an expert translation. In our second evaluation, we focused on text shortening; we automatically generated 41 crowd process candidates, among them variations of the well-established Find-Fix-Verify process. While Find-Fix-Verify performed well in this setting, our recombination engine produced five processes that repeatedly yielded better results. We close the article by comparing the two settings where the Recombinator was used, and empirically show that the individual processes performed differently in the two settings, which led us to contend that there is no unifying formula, hence emphasizing the necessity for recombination.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "49", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Kim:2016:UCI, author = "Yubin Kim and Kevyn Collins-Thompson and Jaime Teevan", title = "Using the Crowd to Improve Search Result Ranking and the Search Experience", journal = j-TIST, volume = "7", number = "4", pages = "50:1--50:??", month = jul, year = "2016", CODEN = "????", DOI = "https://doi.org/10.1145/2897368", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Apr 3 11:19:56 MDT 2017", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Despite technological advances, algorithmic search systems still have difficulty with complex or subtle information needs. For example, scenarios requiring deep semantic interpretation are a challenge for computers. People, on the other hand, are well suited to solving such problems. As a result, there is an opportunity for humans and computers to collaborate during the course of a search in a way that takes advantage of the unique abilities of each. While search tools that rely on human intervention will never be able to respond as quickly as current search engines do, recent research suggests that there are scenarios where a search engine could take more time if it resulted in a much better experience. This article explores how crowdsourcing can be used at query time to augment key stages of the search pipeline. We first explore the use of crowdsourcing to improve search result ranking. When the crowd is used to replace or augment traditional retrieval components such as query expansion and relevance scoring, we find that we can increase robustness against failure for query expansion and improve overall precision for results filtering. However, the gains that we observe are limited and unlikely to make up for the extra cost and time that the crowd requires. We then explore ways to incorporate the crowd into the search process that more drastically alter the overall experience. We find that using crowd workers to support rich query understanding and result processing appears to be a more worthwhile way to make use of the crowd during search. Our results confirm that crowdsourcing can positively impact the search experience but suggest that significant changes to the search process may be required for crowdsourcing to fulfill its potential in search systems.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "50", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Katsimerou:2016:CEI, author = "Christina Katsimerou and Joris Albeda and Alina Huldtgren and Ingrid Heynderickx and Judith A. Redi", title = "Crowdsourcing Empathetic Intelligence: The Case of the Annotation of {EMMA} Database for Emotion and Mood Recognition", journal = j-TIST, volume = "7", number = "4", pages = "51:1--51:??", month = jul, year = "2016", CODEN = "????", DOI = "https://doi.org/10.1145/2897369", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Apr 3 11:19:56 MDT 2017", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Unobtrusive recognition of the user's mood is an essential capability for affect-adaptive systems. Mood is a subtle, long-term affective state, often misrecognized even by humans. The challenge to train a machine to recognize it from, for example, a video of the user, is significant, and already begins with the lack of ground truth for supervised learning. Existing affective databases consist mainly of short videos, annotated in terms of expressed emotions rather than mood. In very few cases, we encounter perceived mood annotations, of questionable reliability, however, due to the subjectivity of mood estimation and the small number of coders involved. In this work, we introduce a new database for mood recognition from video. Our database contains 180 long, acted videos, depicting typical daily scenarios, and subtle facial and bodily expressions. The videos cover three visual modalities (face, body, Kinect data), and are annotated in terms of emotions (via G-trace) and mood (via the Self-Assessment Manikin and the AffectButton). To annotate the database exhaustively, we exploit crowdsourcing to reach out to an extensive number of nonexpert coders. We validate the reliability of our crowdsourced annotations by (1) adopting a number of criteria to filter out unreliable coders, and (2) comparing the annotations of a subset of our videos with those collected in a controlled lab setting.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "51", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Chen:2016:UCS, author = "Chen Chen and Pawe{\l} W. Wo{\'z}niak and Andrzej Romanowski and Mohammad Obaid and Tomasz Jaworski and Jacek Kucharski and Krzysztof Grudzie{\'n} and Shengdong Zhao and Morten Fjeld", title = "Using Crowdsourcing for Scientific Analysis of Industrial Tomographic Images", journal = j-TIST, volume = "7", number = "4", pages = "52:1--52:??", month = jul, year = "2016", CODEN = "????", DOI = "https://doi.org/10.1145/2897370", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Mar 22 09:59:46 2018", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "In this article, we present a novel application domain for human computation, specifically for crowdsourcing, which can help in understanding particle-tracking problems. Through an interdisciplinary inquiry, we built a crowdsourcing system designed to detect tracer particles in industrial tomographic images, and applied it to the problem of bulk solid flow in silos. As images from silo-sensing systems cannot be adequately analyzed using the currently available computational methods, human intelligence is required. However, limited availability of experts, as well as their high cost, motivates employing additional nonexperts. We report on the results of a study that assesses the task completion time and accuracy of employing nonexpert workers to process large datasets of images in order to generate data for bulk flow research. We prove the feasibility of this approach by comparing results from a user study with data generated from a computational algorithm. The study shows that the crowd is more scalable and more economical than an automatic solution. The system can help analyze and understand the physics of flow phenomena to better inform the future design of silos, and is generalized enough to be applicable to other domains.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "52", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{You:2016:CFP, author = "Linlin You and Gianmario Motta and Kaixu Liu and Tianyi Ma", title = "{CITY FEED}: a Pilot System of Citizen-Sourcing for City Issue Management", journal = j-TIST, volume = "7", number = "4", pages = "53:1--53:??", month = jul, year = "2016", CODEN = "????", DOI = "https://doi.org/10.1145/2873064", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Apr 3 11:19:56 MDT 2017", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Crowdsourcing implies user collaboration and engagement, which fosters a renewal of city governance processes. In this article, we address a subset of crowdsourcing, named citizen-sourcing, where citizens interact with authorities collaboratively and actively. Many systems have experimented citizen-sourcing in city governance processes; however, their maturity levels are mixed. In order to focus on the service maturity, we introduce a city service maturity framework that contains five levels of service support and two levels of information integration. As an example, we introduce CITY FEED, which implements citizen-sourcing in city issue management process. In order to support such process, CITY FEED supports all levels of the maturity framework (publishing, transacting, interacting, collaborating, and evaluating) and integrates related information relationally and heterogeneously. In order to integrate heterogeneous information, it implements a threefold feed deduplication mechanism based on the geographic, text semantic, and image similarities of feeds. Currently, CITY FEED is in a pilot stage.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "53", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Rao:2016:LHC, author = "Huaming Rao and Shih-Wen Huang and Wai-Tat Fu", title = "Leveraging Human Computations to Improve Schematization of Spatial Relations from Imagery", journal = j-TIST, volume = "7", number = "4", pages = "54:1--54:??", month = jul, year = "2016", CODEN = "????", DOI = "https://doi.org/10.1145/2873065", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Apr 3 11:19:56 MDT 2017", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "The process of generating schematic maps of salient objects from a set of pictures of an indoor environment is challenging. It has been an active area of research as it is crucial to a wide range of context- and location-aware services, as well as for general scene understanding. Although many automated systems have been developed to solve the problem, most of them either require predefining labels or expensive equipment, such as RGBD sensors or lasers, to scan the environment. In this article, we introduce a prototype system to show how human computations can be utilized to generate schematic maps from a set of pictures, without making strong assumptions or demanding extra devices. The system requires humans (crowd workers from Amazon Mechanical Turks) to do simple spatial mapping tasks in various conditions, and their data are aggregated by filtering and clustering techniques that allow salient cues to be identified in the pictures and their spatial relations to be inferred and projected on a two-dimensional map. In particular, we tested and demonstrated the effectiveness of two methods that improved the quality of the generated schematic map: (1) We encouraged humans to adopt an allocentric representations of salient objects by guiding them to perform mental rotations of these objects and (2) we sensitized human perception by guided arrows superimposed on the imagery to improve the accuracy of depth and width estimation. We demonstrated the feasibility of our system by evaluating the results of schematic maps generated from indoor pictures taken from an office building. By calculating Riemannian shape distances between the generated maps to the ground truth, we found that the generated schematic maps captured the spatial relations well. Our results showed that the combination of human computations and machine clustering could lead to more-accurate schematized maps from imagery. We also discuss how our approach may have important insights on methods that leverage human computations in other areas.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "54", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Moshfeghi:2016:GTA, author = "Yashar Moshfeghi and Alvaro Francisco Huertas Rosero and Joemon M. Jose", title = "A Game-Theory Approach for Effective Crowdsource-Based Relevance Assessment", journal = j-TIST, volume = "7", number = "4", pages = "55:1--55:??", month = jul, year = "2016", CODEN = "????", DOI = "https://doi.org/10.1145/2873063", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Apr 3 11:19:56 MDT 2017", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Despite the ever-increasing popularity of crowdsourcing (CS) in both industry and academia, procedures that ensure quality in its results are still elusive. We hypothesise that a CS design based on game theory can persuade workers to perform their tasks as quickly as possible with the highest quality. In order to do so, in this article we propose a CS framework inspired by the n -person Chicken game. Our aim is to address the problem of CS quality without compromising on CS benefits such as low monetary cost and high task completion speed. With that goal in mind, we study the effects of knowledge updates as well as incentives for good workers to continue playing. We define a general task with the characteristics of relevance assessment as a case study, because it has been widely explored in the past with CS due to its potential cost and complexity. In order to investigate our hypotheses, we conduct a simulation where we study the effect of the proposed framework on data accuracy, task completion time, and total monetary rewards. Based on a game-theoretical analysis, we study how different types of individuals would behave under a particular game scenario. In particular, we simulate a population comprised of different types of workers with varying ability to formulate optimal strategies and learn from their experiences. A simulation of the proposed framework produced results that support our hypothesis.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "55", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Han:2016:CHA, author = "Shuguang Han and Peng Dai and Praveen Paritosh and David Huynh", title = "Crowdsourcing Human Annotation on {Web} Page Structure: Infrastructure Design and Behavior-Based Quality Control", journal = j-TIST, volume = "7", number = "4", pages = "56:1--56:??", month = jul, year = "2016", CODEN = "????", DOI = "https://doi.org/10.1145/2870649", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Apr 3 11:19:56 MDT 2017", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Parsing the semantic structure of a web page is a key component of web information extraction. Successful extraction algorithms usually require large-scale training and evaluation datasets, which are difficult to acquire. Recently, crowdsourcing has proven to be an effective method of collecting large-scale training data in domains that do not require much domain knowledge. For more complex domains, researchers have proposed sophisticated quality control mechanisms to replicate tasks in parallel or sequential ways and then aggregate responses from multiple workers. Conventional annotation integration methods often put more trust in the workers with high historical performance; thus, they are called performance-based methods. Recently, Rzeszotarski and Kittur have demonstrated that behavioral features are also highly correlated with annotation quality in several crowdsourcing applications. In this article, we present a new crowdsourcing system, called Wernicke, to provide annotations for web information extraction. Wernicke collects a wide set of behavioral features and, based on these features, predicts annotation quality for a challenging task domain: annotating web page structure. We evaluate the effectiveness of quality control using behavioral features through a case study where 32 workers annotate 200 Q\&A web pages from five popular websites. In doing so, we discover several things: (1) Many behavioral features are significant predictors for crowdsourcing quality. (2) The behavioral-feature-based method outperforms performance-based methods in recall prediction, while performing equally with precision prediction. In addition, using behavioral features is less vulnerable to the cold-start problem, and the corresponding prediction model is more generalizable for predicting recall than precision for cross-website quality analysis. (3) One can effectively combine workers' behavioral information and historical performance information to further reduce prediction errors.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "56", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Wei:2016:MDC, author = "Yunchao Wei and Yao Zhao and Zhenfeng Zhu and Shikui Wei and Yanhui Xiao and Jiashi Feng and Shuicheng Yan", title = "Modality-Dependent Cross-Media Retrieval", journal = j-TIST, volume = "7", number = "4", pages = "57:1--57:??", month = jul, year = "2016", CODEN = "????", DOI = "https://doi.org/10.1145/2775109", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Apr 3 11:19:56 MDT 2017", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "In this article, we investigate the cross-media retrieval between images and text, that is, using image to search text (I2T) and using text to search images (T2I). Existing cross-media retrieval methods usually learn one couple of projections, by which the original features of images and text can be projected into a common latent space to measure the content similarity. However, using the same projections for the two different retrieval tasks (I2T and T2I) may lead to a tradeoff between their respective performances, rather than their best performances. Different from previous works, we propose a modality-dependent cross-media retrieval (MDCR) model, where two couples of projections are learned for different cross-media retrieval tasks instead of one couple of projections. Specifically, by jointly optimizing the correlation between images and text and the linear regression from one modal space (image or text) to the semantic space, two couples of mappings are learned to project images and text from their original feature spaces into two common latent subspaces (one for I2T and the other for T2I). Extensive experiments show the superiority of the proposed MDCR compared with other methods. In particular, based on the 4,096-dimensional convolutional neural network (CNN) visual feature and 100-dimensional Latent Dirichlet Allocation (LDA) textual feature, the mAP of the proposed method achieves the mAP score of 41.5\%, which is a new state-of-the-art performance on the Wikipedia dataset.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "57", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Morris:2016:DNM, author = "Robert Morris and Matthew Johnson and K. Brent Venable and James Lindsey", title = "Designing Noise-Minimal Rotorcraft Approach Trajectories", journal = j-TIST, volume = "7", number = "4", pages = "58:1--58:??", month = jul, year = "2016", CODEN = "????", DOI = "https://doi.org/10.1145/2838738", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Apr 3 11:19:56 MDT 2017", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "NASA and the international aviation community are investing in the development of a commercial transportation infrastructure that includes the increased use of rotorcraft, specifically helicopters and civil tilt rotors. However, there is significant concern over the impact of noise on the communities surrounding the transportation facilities. One way to address the rotorcraft noise problem is by exploiting powerful search techniques coming from artificial intelligence to design low-noise flight profiles that can be then validated though field tests. This article investigates the use of discrete heuristic search methods to design low-noise approach trajectories for rotorcraft. Our work builds on a long research tradition in trajectory optimization using either numerical methods or discrete search. Novel features of our approach include the use of a discrete search space with a resolution that can be varied, and the coupling of search with a robust simulator to evaluate candidates. The article includes a systematic comparison of different search techniques; in particular, in the experiments, we are able to do a trade study that compares complete search algorithms such as A$^*$ with faster but approximate methods such as local search.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "58", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Fang:2016:SST, author = "Quan Fang and Changsheng Xu and M. Shamim Hossain and G. Muhammad", title = "{STCAPLRS}: a Spatial-Temporal Context-Aware Personalized Location Recommendation System", journal = j-TIST, volume = "7", number = "4", pages = "59:1--59:??", month = jul, year = "2016", CODEN = "????", DOI = "https://doi.org/10.1145/2842631", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Apr 3 11:19:56 MDT 2017", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Newly emerging location-based social media network services (LBSMNS) provide valuable resources to understand users' behaviors based on their location histories. The location-based behaviors of a user are generally influenced by both user intrinsic interest and the location preference, and moreover are spatial-temporal context dependent. In this article, we propose a spatial-temporal context-aware personalized location recommendation system (STCAPLRS), which offers a particular user a set of location items such as points of interest or venues (e.g., restaurants and shopping malls) within a geospatial range by considering personal interest, local preference, and spatial-temporal context influence. STCAPLRS can make accurate recommendation and facilitate people's local visiting and new location exploration by exploiting the context information of user behavior, associations between users and location items, and the location and content information of location items. Specifically, STCAPLRS consists of two components: offline modeling and online recommendation. The core module of the offline modeling part is a context-aware regression mixture model that is designed to model the location-based user behaviors in LBSMNS to learn the interest of each individual user, the local preference of each individual location, and the context-aware influence factors. The online recommendation part takes a querying user along with the corresponding querying spatial-temporal context as input and automatically combines the learned interest of the querying user, the local preference of the querying location, and the context-aware influence factor to produce the top- k recommendations. We evaluate the performance of STCAPLRS on two real-world datasets: Dianping and Foursquare. The results demonstrate the superiority of STCAPLRS in recommending location items for users in terms of both effectiveness and efficiency. Moreover, the experimental analysis results also illustrate the excellent interpretability of STCAPLRS.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "59", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Xin:2016:EGF, author = "Bo Xin and Yoshinobu Kawahara and Yizhou Wang and Lingjing Hu and Wen Gao", title = "Efficient Generalized Fused Lasso and Its Applications", journal = j-TIST, volume = "7", number = "4", pages = "60:1--60:??", month = jul, year = "2016", CODEN = "????", DOI = "https://doi.org/10.1145/2847421", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Apr 3 11:19:56 MDT 2017", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Generalized fused lasso (GFL) penalizes variables with l$^1$ norms based both on the variables and their pairwise differences. GFL is useful when applied to data where prior information is expressed using a graph over the variables. However, the existing GFL algorithms incur high computational costs and do not scale to high-dimensional problems. In this study, we propose a fast and scalable algorithm for GFL. Based on the fact that fusion penalty is the Lov{\'a}sz extension of a cut function, we show that the key building block of the optimization is equivalent to recursively solving graph-cut problems. Thus, we use a parametric flow algorithm to solve GFL in an efficient manner. Runtime comparisons demonstrate a significant speedup compared to existing GFL algorithms. Moreover, the proposed optimization framework is very general; by designing different cut functions, we also discuss the extension of GFL to directed graphs. Exploiting the scalability of the proposed algorithm, we demonstrate the applications of our algorithm to the diagnosis of Alzheimer's disease (AD) and video background subtraction (BS). In the AD problem, we formulated the diagnosis of AD as a GFL regularized classification. Our experimental evaluations demonstrated that the diagnosis performance was promising. We observed that the selected critical voxels were well structured, i.e., connected, consistent according to cross validation, and in agreement with prior pathological knowledge. In the BS problem, GFL naturally models arbitrary foregrounds without predefined grouping of the pixels. Even by applying simple background models, e.g., a sparse linear combination of former frames, we achieved state-of-the-art performance on several public datasets.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "60", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Schulz:2016:MTN, author = "Sarah Schulz and Guy {De Pauw} and Orph{\'e}e {De Clercq} and Bart Desmet and V{\'e}ronique Hoste and Walter Daelemans and Lieve Macken", title = "Multimodular Text Normalization of {Dutch} User-Generated Content", journal = j-TIST, volume = "7", number = "4", pages = "61:1--61:??", month = jul, year = "2016", CODEN = "????", DOI = "https://doi.org/10.1145/2850422", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Apr 3 11:19:56 MDT 2017", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "As social media constitutes a valuable source for data analysis for a wide range of applications, the need for handling such data arises. However, the nonstandard language used on social media poses problems for natural language processing (NLP) tools, as these are typically trained on standard language material. We propose a text normalization approach to tackle this problem. More specifically, we investigate the usefulness of a multimodular approach to account for the diversity of normalization issues encountered in user-generated content (UGC). We consider three different types of UGC written in Dutch (SNS, SMS, and tweets) and provide a detailed analysis of the performance of the different modules and the overall system. We also apply an extrinsic evaluation by evaluating the performance of a part-of-speech tagger, lemmatizer, and named-entity recognizer before and after normalization.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "61", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Hegedus:2016:RDL, author = "Istv{\'a}n Heged{\H{u}}s and {\'A}rp{\'a}d Berta and Levente Kocsis and Andr{\'a}s A. Bencz{\'u}r and M{\'a}rk Jelasity", title = "Robust Decentralized Low-Rank Matrix Decomposition", journal = j-TIST, volume = "7", number = "4", pages = "62:1--62:??", month = jul, year = "2016", CODEN = "????", DOI = "https://doi.org/10.1145/2854157", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Apr 3 11:19:56 MDT 2017", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Low-rank matrix approximation is an important tool in data mining with a wide range of applications, including recommender systems, clustering, and identifying topics in documents. When the matrix to be approximated originates from a large distributed system, such as a network of mobile phones or smart meters, a challenging problem arises due to the strongly conflicting yet essential requirements of efficiency, robustness, and privacy preservation. We argue that although collecting sensitive data in a centralized fashion may be efficient, it is not an option when considering privacy and efficiency at the same time. Thus, we do not allow any sensitive data to leave the nodes of the network. The local information at each node (personal attributes, documents, media ratings, etc.) defines one row in the matrix. This means that all computations have to be performed at the edge of the network. Known parallel methods that respect the locality constraint, such as synchronized parallel gradient search or distributed iterative methods, require synchronized rounds or have inherent issues with load balancing, and thus they are not robust to failure. Our distributed stochastic gradient descent algorithm overcomes these limitations. During the execution, any sensitive information remains local, whereas the global features (e.g., the factor model of movies) converge to the correct value at all nodes. We present a theoretical derivation and a thorough experimental evaluation of our algorithm. We demonstrate that the convergence speed of our method is competitive while not relying on synchronization and being robust to extreme and realistic failure scenarios. To demonstrate the feasibility of our approach, we present trace-based simulations, real smartphone user behavior analysis, and tests over real movie recommender system data.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "62", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Luo:2016:TUA, author = "Chen Luo and Jia Zeng and Mingxuan Yuan and Wenyuan Dai and Qiang Yang", title = "Telco User Activity Level Prediction with Massive Mobile Broadband Data", journal = j-TIST, volume = "7", number = "4", pages = "63:1--63:??", month = jul, year = "2016", CODEN = "????", DOI = "https://doi.org/10.1145/2856057", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Apr 3 11:19:56 MDT 2017", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Telecommunication (telco) operators aim to provide users with optimized services and bandwidth in a timely manner. The goal is to increase user experience while retaining profit. To do this, knowing the changing behavior patterns of users through their activity levels in advance can be a great help for operators to adjust their management strategies and reduce operational risk. To achieve this goal, the operators can make use of knowledge discovered from telco's historical mobile broadband (MBB) records to predict mobile access activity level at an early stage. In this article, we report our research in a real-world telco setting involving more than one million telco users. Our novel contribution includes representing users as documents containing a collection of changing spatiotemporal ``words'' that express user behavior. By extracting users' space-time access records in MBB data, we use latent Dirichlet allocation (LDA) to learn user-specific compact topic features for user activity level prediction. We propose a scalable online expectation-maximization (OEM) algorithm that can scale LDA to massive MBB data, which is significantly faster than several state-of-the-art online LDA algorithms. Using these real-world MBB data, we confirm high performance in user activity level prediction. In addition, we show that the inferred topics indicate that future activity level anomalies correlate highly with early skewed bandwidth supply and demand relations. Thus, our prediction system can also guide the telco operators to balance the telecommunication network in terms of supply-demand relations, saving deployment costs and energy of cell towers in the future.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "63", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Wang:2016:CFI, author = "Senzhang Wang and Sihong Xie and Xiaoming Zhang and Zhoujun Li and Philip S. Yu and Yueying He", title = "Coranking the Future Influence of Multiobjects in Bibliographic Network Through Mutual Reinforcement", journal = j-TIST, volume = "7", number = "4", pages = "64:1--64:??", month = jul, year = "2016", CODEN = "????", DOI = "https://doi.org/10.1145/2897371", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Apr 3 11:19:56 MDT 2017", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Scientific literature ranking is essential to help researchers find valuable publications from a large literature collection. Recently, with the prevalence of webpage ranking algorithms such as PageRank and HITS, graph-based algorithms have been widely used to iteratively rank papers and researchers through the networks formed by citation and coauthor relationships. However, existing graph-based ranking algorithms mostly focus on ranking the current importance of literature. For researchers who enter an emerging research area, they might be more interested in new papers and young researchers that are likely to become influential in the future, since such papers and researchers are more helpful in letting them quickly catch up on the most recent advances and find valuable research directions. Meanwhile, although some works have been proposed to rank the prestige of a certain type of objects with the help of multiple networks formed of multiobjects, there still lacks a unified framework to rank multiple types of objects in the bibliographic network simultaneously. In this article, we propose a unified ranking framework MRCoRank to corank the future popularity of four types of objects: papers, authors, terms, and venues through mutual reinforcement. Specifically, because the citation data of new publications are sparse and not efficient to characterize their innovativeness, we make the first attempt to extract the text features to help characterize innovative papers and authors. With the observation that the current trend is more indicative of the future trend of citation and coauthor relationships, we then construct time-aware weighted graphs to quantify the importance of links established at different times on both citation and coauthor graphs. By leveraging both the constructed text features and time-aware graphs, we finally fuse the rich information in a mutual reinforcement ranking framework to rank the future importance of multiobjects simultaneously. We evaluate the proposed model through extensive experiments on the ArnetMiner dataset containing more than 1,500,000 papers. Experimental results verify the effectiveness of MRCoRank in coranking the future influence of multiobjects in a bibliographic network.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "64", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Li:2016:MLR, author = "Teng Li and Bin Cheng and Bingbing Ni and Guangchan Liu and Shuicheng Yan", title = "Multitask Low-Rank Affinity Graph for Image Segmentation and Image Annotation", journal = j-TIST, volume = "7", number = "4", pages = "65:1--65:??", month = jul, year = "2016", CODEN = "????", DOI = "https://doi.org/10.1145/2856058", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Apr 3 11:19:56 MDT 2017", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "This article investigates a low-rank representation--based graph, which can used in graph-based vision tasks including image segmentation and image annotation. It naturally fuses multiple types of image features in a framework named multitask low-rank affinity pursuit. Given the image patches described with multiple types of features, we aim at inferring a unified affinity matrix that implicitly encodes the relations among these patches. This is achieved by seeking the sparsity-consistent low-rank affinities from the joint decompositions of multiple feature matrices into pairs of sparse and low-rank matrices, the latter of which is expressed as the production of the image feature matrix and its corresponding image affinity matrix. The inference process is formulated as a minimization problem and solved efficiently with the augmented Lagrange multiplier method. Considering image patches as vertices, a graph can be built based on the resulted affinity matrix. Compared to previous methods, which are usually based on a single type of feature, the proposed method seamlessly integrates multiple types of features to jointly produce the affinity matrix in a single inference step. The proposed method is applied to graph-based image segmentation and graph-based image annotation. Experiments on benchmark datasets well validate the superiority of using multiple features over single feature and also the superiority of our method over conventional methods for feature fusion.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "65", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Leskovec:2016:SGP, author = "Jure Leskovec and Rok Sosic", title = "{SNAP}: a General-Purpose Network Analysis and Graph-Mining Library", journal = j-TIST, volume = "8", number = "1", pages = "1:1--1:??", month = oct, year = "2016", CODEN = "????", DOI = "https://doi.org/10.1145/2898361", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Apr 3 11:19:57 MDT 2017", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Large networks are becoming a widely used abstraction for studying complex systems in a broad set of disciplines, ranging from social-network analysis to molecular biology and neuroscience. Despite an increasing need to analyze and manipulate large networks, only a limited number of tools are available for this task. Here, we describe the Stanford Network Analysis Platform (SNAP), a general-purpose, high-performance system that provides easy-to-use, high-level operations for analysis and manipulation of large networks. We present SNAP functionality, describe its implementational details, and give performance benchmarks. SNAP has been developed for single big-memory machines, and it balances the trade-off between maximum performance, compact in-memory graph representation, and the ability to handle dynamic graphs in which nodes and edges are being added or removed over time. SNAP can process massive networks with hundreds of millions of nodes and billions of edges. SNAP offers over 140 different graph algorithms that can efficiently manipulate large graphs, calculate structural properties, generate regular and random graphs, and handle attributes and metadata on nodes and edges. Besides being able to handle large graphs, an additional strength of SNAP is that networks and their attributes are fully dynamic; they can be modified during the computation at low cost. SNAP is provided as an open-source library in C++ as well as a module in Python. We also describe the Stanford Large Network Dataset, a set of social and information real-world networks and datasets, which we make publicly available. The collection is a complementary resource to our SNAP software and is widely used for development and benchmarking of graph analytics algorithms.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "1", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Phan:2016:TAP, author = "Nhathai Phan and Javid Ebrahimi and David Kil and Brigitte Piniewski and Dejing Dou", title = "Topic-Aware Physical Activity Propagation with Temporal Dynamics in a Health Social Network", journal = j-TIST, volume = "8", number = "1", pages = "2:1--2:??", month = oct, year = "2016", CODEN = "????", DOI = "https://doi.org/10.1145/2873066", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Apr 3 11:19:57 MDT 2017", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Modeling physical activity propagation, such as activity level and intensity, is a key to preventing obesity from cascading through communities, and to helping spread wellness and healthy behavior in a social network. However, there have not been enough scientific and quantitative studies to elucidate how social communication may deliver physical activity interventions. In this work, we introduce a novel model named Topic-aware Community-level Physical Activity Propagation with Temporal Dynamics (TCPT) to analyze physical activity propagation and social influence at different granularities (i.e., individual level and community level). Given a social network, the TCPT model first integrates the correlations between the content of social communication, social influences, and temporal dynamics. Then, a hierarchical approach is utilized to detect a set of communities and their reciprocal influence strength of physical activities. The experimental evaluation shows not only the effectiveness of our approach but also the correlation of the detected communities with various health outcome measures. Our promising results pave a way for knowledge discovery in health social networks.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "2", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Doucette:2016:MRA, author = "John A. Doucette and Graham Pinhey and Robin Cohen", title = "Multiagent Resource Allocation for Dynamic Task Arrivals with Preemption", journal = j-TIST, volume = "8", number = "1", pages = "3:1--3:??", month = oct, year = "2016", CODEN = "????", DOI = "https://doi.org/10.1145/2875441", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Apr 3 11:19:57 MDT 2017", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "In this article, we present a distributed algorithm for allocating resources to tasks in multiagent systems, one that adapts well to dynamic task arrivals where new work arises at short notice. Our algorithm is designed to leverage preemption if it is available, revoking resource allocations to tasks in progress if new opportunities arise that those resources are better suited to handle. Our multiagent model assigns a task agent to each task that must be completed and a proxy agent to each resource that is available. Preemption occurs when a task agent approaches a proxy agent with a sufficiently compelling need that the proxy agent determines the newcomer derives more benefit from the proxy agent's resource than the task agent currently using that resource. Task agents reason about which resources to request based on a learning of churn and congestion. We compare to a well-established multiagent resource allocation framework that permits preemption under more conservative assumptions and show through simulation that our model allows for improved allocations through more permissive preemption. In all, we offer a novel approach for multiagent resource allocation that is able to cope well with dynamic task arrivals.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "3", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Ganesan:2016:DSC, author = "Rajesh Ganesan and Sushil Jajodia and Ankit Shah and Hasan Cam", title = "Dynamic Scheduling of Cybersecurity Analysts for Minimizing Risk Using Reinforcement Learning", journal = j-TIST, volume = "8", number = "1", pages = "4:1--4:??", month = oct, year = "2016", CODEN = "????", DOI = "https://doi.org/10.1145/2882969", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Apr 3 11:19:57 MDT 2017", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "An important component of the cyber-defense mechanism is the adequate staffing levels of its cybersecurity analyst workforce and their optimal assignment to sensors for investigating the dynamic alert traffic. The ever-increasing cybersecurity threats faced by today's digital systems require a strong cyber-defense mechanism that is both reactive in its response to mitigate the known risk and proactive in being prepared for handling the unknown risks. In order to be proactive for handling the unknown risks, the above workforce must be scheduled dynamically so the system is adaptive to meet the day-to-day stochastic demands on its workforce (both size and expertise mix). The stochastic demands on the workforce stem from the varying alert generation and their significance rate, which causes an uncertainty for the cybersecurity analyst scheduler that is attempting to schedule analysts for work and allocate sensors to analysts. Sensor data are analyzed by automatic processing systems, and alerts are generated. A portion of these alerts is categorized to be significant, which requires thorough examination by a cybersecurity analyst. Risk, in this article, is defined as the percentage of significant alerts that are not thoroughly analyzed by analysts. In order to minimize risk, it is imperative that the cyber-defense system accurately estimates the future significant alert generation rate and dynamically schedules its workforce to meet the stochastic workload demand to analyze them. The article presents a reinforcement learning-based stochastic dynamic programming optimization model that incorporates the above estimates of future alert rates and responds by dynamically scheduling cybersecurity analysts to minimize risk (i.e., maximize significant alert coverage by analysts) and maintain the risk under a pre-determined upper bound. The article tests the dynamic optimization model and compares the results to an integer programming model that optimizes the static staffing needs based on a daily-average alert generation rate with no estimation of future alert rates (static workforce model). Results indicate that over a finite planning horizon, the learning-based optimization model, through a dynamic (on-call) workforce in addition to the static workforce, (a) is capable of balancing risk between days and reducing overall risk better than the static model, (b) is scalable and capable of identifying the quantity and the right mix of analyst expertise in an organization, and (c) is able to determine their dynamic (on-call) schedule and their sensor-to-analyst allocation in order to maintain risk below a given upper bound. Several meta-principles are presented, which are derived from the optimization model, and they further serve as guiding principles for hiring and scheduling cybersecurity analysts. Days-off scheduling was performed to determine analyst weekly work schedules that met the cybersecurity system's workforce constraints and requirements.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "4", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Belcastro:2016:USD, author = "Loris Belcastro and Fabrizio Marozzo and Domenico Talia and Paolo Trunfio", title = "Using Scalable Data Mining for Predicting Flight Delays", journal = j-TIST, volume = "8", number = "1", pages = "5:1--5:??", month = oct, year = "2016", CODEN = "????", DOI = "https://doi.org/10.1145/2888402", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Apr 3 11:19:57 MDT 2017", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Flight delays are frequent all over the world (about 20\% of airline flights arrive more than 15min late) and they are estimated to have an annual cost of billions of dollars. This scenario makes the prediction of flight delays a primary issue for airlines and travelers. The main goal of this work is to implement a predictor of the arrival delay of a scheduled flight due to weather conditions. The predicted arrival delay takes into consideration both flight information (origin airport, destination airport, scheduled departure and arrival time) and weather conditions at origin airport and destination airport according to the flight timetable. Airline flight and weather observation datasets have been analyzed and mined using parallel algorithms implemented as MapReduce programs executed on a Cloud platform. The results show a high accuracy in predicting delays above a given threshold. For instance, with a delay threshold of 15min, we achieve an accuracy of 74.2\% and 71.8\% recall on delayed flights, while with a threshold of 60min, the accuracy is 85.8\% and the delay recall is 86.9\%. Furthermore, the experimental results demonstrate the predictor scalability that can be achieved performing data preparation and mining tasks as MapReduce applications on the Cloud.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "5", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Wang:2016:RPG, author = "Tianben Wang and Zhu Wang and Daqing Zhang and Tao Gu and Hongbo Ni and Jiangbo Jia and Xingshe Zhou and Jing Lv", title = "Recognizing {Parkinsonian} Gait Pattern by Exploiting Fine-Grained Movement Function Features", journal = j-TIST, volume = "8", number = "1", pages = "6:1--6:??", month = oct, year = "2016", CODEN = "????", DOI = "https://doi.org/10.1145/2890511", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Apr 3 11:19:57 MDT 2017", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Parkinson's disease (PD) is one of the typical movement disorder diseases among elderly people, which has a serious impact on their daily lives. In this article, we propose a novel computation framework to recognize gait patterns in patients with PD. The key idea of our approach is to distinguish gait patterns in PD patients from healthy individuals by accurately extracting gait features that capture all three aspects of movement functions, that is, stability, symmetry, and harmony. The proposed framework contains three steps: gait phase discrimination, feature extraction and selection, and pattern classification. In the first step, we put forward a sliding window--based method to discriminate four gait phases from plantar pressure data. Based on the gait phases, we extract and select gait features that characterize stability, symmetry, and harmony of movement functions. Finally, we recognize PD gait patterns by applying a hybrid classification model. We evaluate the framework using an open dataset that contains real plantar pressure data of 93 PD patients and 72 healthy individuals. Experimental results demonstrate that our framework significantly outperforms the four baseline approaches.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "6", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Towne:2016:MSS, author = "W. Ben Towne and Carolyn P. Ros{\'e} and James D. Herbsleb", title = "Measuring Similarity Similarly: {LDA} and Human Perception", journal = j-TIST, volume = "8", number = "1", pages = "7:1--7:??", month = oct, year = "2016", CODEN = "????", DOI = "https://doi.org/10.1145/2890510", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Apr 3 11:19:57 MDT 2017", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Several intelligent technologies designed to improve navigability in and digestibility of text corpora use topic modeling such as the state-of-the-art Latent Dirichlet Allocation (LDA). This model and variants on it provide lower-dimensional document representations used in visualizations and in computing similarity between documents. This article contributes a method for validating such algorithms against human perceptions of similarity, especially applicable to contexts in which the algorithm is intended to support navigability between similar documents via dynamically generated hyperlinks. Such validation enables researchers to ground their methods in context of intended use instead of relying on assumptions of fit. In addition to the methodology, this article presents the results of an evaluation using a corpus of short documents and the LDA algorithm. We also present some analysis of potential causes of differences between cases in which this model matches human perceptions of similarity more or less well.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "7", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Jiang:2016:CCS, author = "Yexi Jiang and Chang-Shing Perng and Anca Sailer and Ignacio Silva-Lepe and Yang Zhou and Tao Li", title = "{CSM}: a Cloud Service Marketplace for Complex Service Acquisition", journal = j-TIST, volume = "8", number = "1", pages = "8:1--8:??", month = oct, year = "2016", CODEN = "????", DOI = "https://doi.org/10.1145/2894759", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Apr 3 11:19:57 MDT 2017", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "The cloud service marketplace (CSM) is an exploratory project aiming to provide ``an AppStore for Services.'' It is an intelligent online marketplace that facilitates service discovery and acquisition for enterprise customers. Traditional service discovery and acquisition are time-consuming. In the era of OneClick Checkout and pay-as-you-go service plans, users expect services to be purchased online efficiently and conveniently. However, as services are complex and different from software apps, the currently prevailing App Store based on keyword search is inadequate for services. In CSM, exploring and configuring services are an iterative process. Customers provide their requirements in natural language and interact with the system through questioning and answering. Learning from the input, the system can incrementally clarify users' intention, narrow down the candidate services, and profile the configuration information for the candidates at the same time. CSM's back end is built around the Services Knowledge Graph (SKG) and leverages data mining technologies to enable the semantic understanding of customers' requirements. To quantitatively assess the value of CSM, empirical evaluation on real and synthetic datasets and case studies are given to demonstrate the efficacy and effectiveness of the proposed system.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "8", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{DiNoia:2016:SSP, author = "Tommaso {Di Noia} and Vito Claudio Ostuni and Paolo Tomeo and Eugenio {Di Sciascio}", title = "{SPrank}: Semantic Path-Based Ranking for Top-{$N$} Recommendations Using Linked Open Data", journal = j-TIST, volume = "8", number = "1", pages = "9:1--9:??", month = oct, year = "2016", CODEN = "????", DOI = "https://doi.org/10.1145/2899005", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Apr 3 11:19:57 MDT 2017", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "In most real-world scenarios, the ultimate goal of recommender system applications is to suggest a short ranked list of items, namely top- N recommendations, that will appeal to the end user. Often, the problem of computing top- N recommendations is mainly tackled with a two-step approach. The system focuses first on predicting the unknown ratings, which are eventually used to generate a ranked recommendation list. Actually, the top- N recommendation task can be directly seen as a ranking problem where the main goal is not to accurately predict ratings but to directly find the best-ranked list of items to recommend. In this article we present SPrank, a novel hybrid recommendation algorithm able to compute top- N recommendations exploiting freely available knowledge in the Web of Data. In particular, we employ DBpedia, a well-known encyclopedic knowledge base in the Linked Open Data cloud, to extract semantic path-based features and to eventually compute top- N recommendations in a learning-to-rank fashion. Experiments with three datasets related to different domains (books, music, and movies) prove the effectiveness of our approach compared to state-of-the-art recommendation algorithms.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "9", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Cheng:2016:UPI, author = "Chen Cheng and Haiqin Yang and Irwin King and Michael R. Lyu", title = "A Unified Point-of-Interest Recommendation Framework in Location-Based Social Networks", journal = j-TIST, volume = "8", number = "1", pages = "10:1--10:??", month = oct, year = "2016", CODEN = "????", DOI = "https://doi.org/10.1145/2901299", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Apr 3 11:19:57 MDT 2017", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Location-based social networks (LBSNs), such as Gowalla, Facebook, Foursquare, Brightkite, and so on, have attracted millions of users to share their social friendship and their locations via check-ins in the past few years. Plenty of valuable information is accumulated based on the check-in behaviors, which makes it possible to learn users' moving patterns as well as their preferences. In LBSNs, point-of-interest (POI) recommendation is one of the most significant tasks because it can help targeted users explore their surroundings as well as help third-party developers provide personalized services. Matrix factorization is a promising method for this task because it can capture users' preferences to locations and is widely adopted in traditional recommender systems such as movie recommendation. However, the sparsity of the check-in data makes it difficult to capture users' preferences accurately. Geographical influence can help alleviate this problem and have a large impact on the final recommendation result. By studying users' moving patterns, we find that users tend to check in around several centers and different users have different numbers of centers. Based on this, we propose a Multi-center Gaussian Model (MGM) to capture this pattern via modeling the probability of a user's check-in on a location. Moreover, users are usually more interested in the top 20 or even top 10 recommended POIs, which makes personalized ranking important in this task. From previous work, directly optimizing for pairwise ranking like Bayesian Personalized Ranking (BPR) achieves better performance in the top- k recommendation than directly using matrix matrix factorization that aims to minimize the point-wise rating error. To consider users' preferences, geographical influence and personalized ranking, we propose a unified POI recommendation framework, which unifies all of them together. Specifically, we first fuse MGM with matrix factorization methods and further with BPR using two different approaches. We conduct experiments on Gowalla and Foursquare datasets, which are two large-scale real-world LBSN datasets publicly available online. The results on both datasets show that our unified POI recommendation framework can produce better performance.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "10", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Deng:2016:EKL, author = "Zhaohong Deng and Yizhang Jiang and Hisao Ishibuchi and Kup-Sze Choi and Shitong Wang", title = "Enhanced Knowledge-Leverage-Based {TSK} Fuzzy System Modeling for Inductive Transfer Learning", journal = j-TIST, volume = "8", number = "1", pages = "11:1--11:??", month = oct, year = "2016", CODEN = "????", DOI = "https://doi.org/10.1145/2903725", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Apr 3 11:19:57 MDT 2017", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "The knowledge-leverage-based Takagi--Sugeno--Kang fuzzy system (KL-TSK-FS) modeling method has shown promising performance for fuzzy modeling tasks where transfer learning is required. However, the knowledge-leverage mechanism of the KL-TSK-FS can be further improved. This is because available training data in the target domain are not utilized for the learning of antecedents and the knowledge transfer mechanism from a source domain to the target domain is still too simple for the learning of consequents when a Takagi--Sugeno--Kang fuzzy system (TSK-FS) model is trained in the target domain. The proposed method, that is, the enhanced KL-TSK-FS (EKL-TSK-FS), has two knowledge-leverage strategies for enhancing the parameter learning of the TSK-FS model for the target domain using available information from the source domain. One strategy is used for the learning of antecedent parameters, while the other is for consequent parameters. It is demonstrated that the proposed EKL-TSK-FS has higher transfer learning abilities than the KL-TSK-FS. In addition, the EKL-TSK-FS has been further extended for the scene of the multisource domain.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "11", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{He:2016:STT, author = "Tieke He and Hongzhi Yin and Zhenyu Chen and Xiaofang Zhou and Shazia Sadiq and Bin Luo", title = "A Spatial-Temporal Topic Model for the Semantic Annotation of {POIs} in {LBSNs}", journal = j-TIST, volume = "8", number = "1", pages = "12:1--12:??", month = oct, year = "2016", CODEN = "????", DOI = "https://doi.org/10.1145/2905373", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Apr 3 11:19:57 MDT 2017", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Semantic tags of points of interest (POIs) are a crucial prerequisite for location search, recommendation services, and data cleaning. However, most POIs in location-based social networks (LBSNs) are either tag-missing or tag-incomplete. This article aims to develop semantic annotation techniques to automatically infer tags for POIs. We first analyze two LBSN datasets and observe that there are two types of tags, category-related ones and sentimental ones, which have unique characteristics. Category-related tags are hierarchical, whereas sentimental ones are category-aware. All existing related work has adopted classification methods to predict high-level category-related tags in the hierarchy, but they cannot apply to infer either low-level category tags or sentimental ones. In light of this, we propose a latent-class probabilistic generative model, namely the spatial-temporal topic model (STM), to infer personal interests, the temporal and spatial patterns of topics/semantics embedded in users' check-in activities, the interdependence between category-topic and sentiment-topic, and the correlation between sentimental tags and rating scores from users' check-in and rating behaviors. Then, this learned knowledge is utilized to automatically annotate all POIs with both category-related and sentimental tags in a unified way. We conduct extensive experiments to evaluate the performance of the proposed STM on a real large-scale dataset. The experimental results show the superiority of our proposed STM, and we also observe that the real challenge of inferring category-related tags for POIs lies in the low-level ones of the hierarchy and that the challenge of predicting sentimental tags are those with neutral ratings.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "12", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Sintsova:2016:DDS, author = "Valentina Sintsova and Pearl Pu", title = "Dystemo: Distant Supervision Method for Multi-Category Emotion Recognition in Tweets", journal = j-TIST, volume = "8", number = "1", pages = "13:1--13:??", month = oct, year = "2016", CODEN = "????", DOI = "https://doi.org/10.1145/2912147", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Apr 3 11:19:57 MDT 2017", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Emotion recognition in text has become an important research objective. It involves building classifiers capable of detecting human emotions for a specific application, for example, analyzing reactions to product launches, monitoring emotions at sports events, or discerning opinions in political debates. Most successful approaches rely heavily on costly manual annotation. To alleviate this burden, we propose a distant supervision method-Dystemo-for automatically producing emotion classifiers from tweets labeled using existing or easy-to-produce emotion lexicons. The goal is to obtain emotion classifiers that work more accurately for specific applications than available emotion lexicons. The success of this method depends mainly on a novel classifier-Balanced Weighted Voting (BWV)-designed to overcome the imbalance in emotion distribution in the initial dataset, and on novel heuristics for detecting neutral tweets. We demonstrate how Dystemo works using Twitter data about sports events, a fine-grained 20-category emotion model, and three different initial emotion lexicons. Through a series of carefully designed experiments, we confirm that Dystemo is effective both in extending initial emotion lexicons of small coverage to find correctly more emotional tweets and in correcting emotion lexicons of low accuracy to perform more accurately.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "13", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Nanni:2016:DPC, author = "Mirco Nanni and Roberto Trasarti and Anna Monreale and Valerio Grossi and Dino Pedreschi", title = "Driving Profiles Computation and Monitoring for Car Insurance {CRM}", journal = j-TIST, volume = "8", number = "1", pages = "14:1--14:??", month = oct, year = "2016", CODEN = "????", DOI = "https://doi.org/10.1145/2912148", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Apr 3 11:19:57 MDT 2017", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Customer segmentation is one of the most traditional and valued tasks in customer relationship management (CRM). In this article, we explore the problem in the context of the car insurance industry, where the mobility behavior of customers plays a key role: Different mobility needs, driving habits, and skills imply also different requirements (level of coverage provided by the insurance) and risks (of accidents). In the present work, we describe a methodology to extract several indicators describing the driving profile of customers, and we provide a clustering-oriented instantiation of the segmentation problem based on such indicators. Then, we consider the availability of a continuous flow of fresh mobility data sent by the circulating vehicles, aiming at keeping our segments constantly up to date. We tackle a major scalability issue that emerges in this context when the number of customers is large-namely, the communication bottleneck-by proposing and implementing a sophisticated distributed monitoring solution that reduces communications between vehicles and company servers to the essential. We validate the framework on a large database of real mobility data coming from GPS devices on private cars. Finally, we analyze the privacy risks that the proposed approach might involve for the users, providing and evaluating a countermeasure based on data perturbation.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "14", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Wang:2016:SCW, author = "Jialei Wang and Peilin Zhao and Steven C. H. Hoi", title = "Soft Confidence-Weighted Learning", journal = j-TIST, volume = "8", number = "1", pages = "15:1--15:??", month = oct, year = "2016", CODEN = "????", DOI = "https://doi.org/10.1145/2932193", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Apr 3 11:19:57 MDT 2017", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Online learning plays an important role in many big data mining problems because of its high efficiency and scalability. In the literature, many online learning algorithms using gradient information have been applied to solve online classification problems. Recently, more effective second-order algorithms have been proposed, where the correlation between the features is utilized to improve the learning efficiency. Among them, Confidence-Weighted (CW) learning algorithms are very effective, which assume that the classification model is drawn from a Gaussian distribution, which enables the model to be effectively updated with the second-order information of the data stream. Despite being studied actively, these CW algorithms cannot handle nonseparable datasets and noisy datasets very well. In this article, we propose a family of Soft Confidence-Weighted (SCW) learning algorithms for both binary classification and multiclass classification tasks, which is the first family of online classification algorithms that enjoys four salient properties simultaneously: (1) large margin training, (2) confidence weighting, (3) capability to handle nonseparable data, and (4) adaptive margin. Our experimental results show that the proposed SCW algorithms significantly outperform the original CW algorithm. When comparing with a variety of state-of-the-art algorithms (including AROW, NAROW, and NHERD), we found that SCW in general achieves better or at least comparable predictive performance, but enjoys considerably better efficiency advantage (i.e., using a smaller number of updates and lower time cost). To facilitate future research, we release all the datasets and source code to the public at http://libol.stevenhoi.org/.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "15", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Papalexakis:2017:TDM, author = "Evangelos E. Papalexakis and Christos Faloutsos and Nicholas D. Sidiropoulos", title = "Tensors for Data Mining and Data Fusion: Models, Applications, and Scalable Algorithms", journal = j-TIST, volume = "8", number = "2", pages = "16:1--16:??", month = jan, year = "2017", CODEN = "????", DOI = "https://doi.org/10.1145/2915921", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Apr 3 11:19:57 MDT 2017", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Tensors and tensor decompositions are very powerful and versatile tools that can model a wide variety of heterogeneous, multiaspect data. As a result, tensor decompositions, which extract useful latent information out of multiaspect data tensors, have witnessed increasing popularity and adoption by the data mining community. In this survey, we present some of the most widely used tensor decompositions, providing the key insights behind them, and summarizing them from a practitioner's point of view. We then provide an overview of a very broad spectrum of applications where tensors have been instrumental in achieving state-of-the-art performance, ranging from social network analysis to brain data analysis, and from web mining to healthcare. Subsequently, we present recent algorithmic advances in scaling tensor decompositions up to today's big data, outlining the existing systems and summarizing the key ideas behind them. Finally, we conclude with a list of challenges and open problems that outline exciting future research directions.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "16", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Schedl:2017:IIM, author = "Markus Schedl and Yi-Hsuan Yang and Perfecto Herrera-Boyer", title = "Introduction to Intelligent Music Systems and Applications", journal = j-TIST, volume = "8", number = "2", pages = "17:1--17:??", month = jan, year = "2017", CODEN = "????", DOI = "https://doi.org/10.1145/2991468", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Apr 3 11:19:57 MDT 2017", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Intelligent technologies have become an essential part of music systems and applications. This is evidenced by today's omnipresence of digital online music stores and streaming services, which rely on music recommenders, automatic playlist generators, and music browsing interfaces. A large amount of research leading to intelligent music applications deals with the extraction of musical and acoustic information directly from the audio signal using signal processing techniques. Other strategies exploit contextual aspects of music, not present in the signal, for example, community meta-data and trails of user interaction, as found, for instance, on social media platforms. In this editorial, we discuss the notion of ``intelligent music system'' and give an overview of the papers selected to this special issue.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "17", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Pachet:2017:JOA, author = "Fran{\c{c}}ois Pachet", title = "A Joyful Ode to Automatic Orchestration", journal = j-TIST, volume = "8", number = "2", pages = "18:1--18:??", month = jan, year = "2017", CODEN = "????", DOI = "https://doi.org/10.1145/2897738", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Apr 3 11:19:57 MDT 2017", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Most works in automatic music generation have addressed so far specific tasks. Such a reductionist approach has been extremely successful and some of these tasks have been solved once and for all. However, few works have addressed the issue of generating automatically fully fledged music material, of human-level quality. In this article, we report on a specific experiment in holistic music generation: the reorchestration of Beethoven's Ode to Joy, the European anthem, in seven styles. These reorchestrations were produced with algorithms developed in the Flow Machines project and within a short time frame. We stress the benefits of having had such a challenging and unifying goal, and the interesting problems and challenges it raised along the way.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "18", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Widmer:2017:GCE, author = "Gerhard Widmer", title = "Getting Closer to the Essence of Music: The Con Espressione Manifesto", journal = j-TIST, volume = "8", number = "2", pages = "19:1--19:??", month = jan, year = "2017", CODEN = "????", DOI = "https://doi.org/10.1145/2899004", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Apr 3 11:19:57 MDT 2017", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "This text offers a personal and very subjective view on the current situation of Music Information Research (MIR). Motivated by the desire to build systems with a somewhat deeper understanding of music than the ones we currently have, I try to sketch a number of challenges for the next decade of MIR research, grouped around six simple truths about music that are probably generally agreed on but often ignored in everyday research.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "19", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Schindler:2017:HMR, author = "Alexander Schindler and Andreas Rauber", title = "Harnessing Music-Related Visual Stereotypes for Music Information Retrieval", journal = j-TIST, volume = "8", number = "2", pages = "20:1--20:??", month = jan, year = "2017", CODEN = "????", DOI = "https://doi.org/10.1145/2926719", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Apr 3 11:19:57 MDT 2017", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Over decades, music labels have shaped easily identifiable genres to improve recognition value and subsequently market sales of new music acts. Referring to print magazines and later to music television as important distribution channels, the visual representation thus played and still plays a significant role in music marketing. Visual stereotypes developed over decades that enable us to quickly identify referenced music only by sight without listening. Despite the richness of music-related visual information provided by music videos and album covers as well as T-shirts, advertisements, and magazines, research towards harnessing this information to advance existing or approach new problems of music retrieval or recommendation is scarce or missing. In this article, we present our research on visual music computing that aims to extract stereotypical music-related visual information from music videos. To provide comprehensive and reproducible results, we present the Music Video Dataset, a thoroughly assembled suite of datasets with dedicated evaluation tasks that are aligned to current Music Information Retrieval tasks. Based on this dataset, we provide evaluations of conventional low-level image processing and affect-related features to provide an overview of the expressiveness of fundamental visual properties such as color, illumination, and contrasts. Further, we introduce a high-level approach based on visual concept detection to facilitate visual stereotypes. This approach decomposes the semantic content of music video frames into concrete concepts such as vehicles, tools, and so on, defined in a wide visual vocabulary. Concepts are detected using convolutional neural networks and their frequency distributions as semantic descriptions for a music video. Evaluations showed that these descriptions show good performance in predicting the music genre of a video and even outperform audio-content descriptors on cross-genre thematic tags. Further, highly significant performance improvements were observed by augmenting audio-based approaches through the introduced visual approach.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "20", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Oramas:2017:SMR, author = "Sergio Oramas and Vito Claudio Ostuni and Tommaso {Di Noia} and Xavier Serra and Eugenio {Di Sciascio}", title = "Sound and Music Recommendation with Knowledge Graphs", journal = j-TIST, volume = "8", number = "2", pages = "21:1--21:??", month = jan, year = "2017", CODEN = "????", DOI = "https://doi.org/10.1145/2926718", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Apr 3 11:19:57 MDT 2017", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "The Web has moved, slowly but steadily, from a collection of documents towards a collection of structured data. Knowledge graphs have then emerged as a way of representing the knowledge encoded in such data as well as a tool to reason on them in order to extract new and implicit information. Knowledge graphs are currently used, for example, to explain search results, to explore knowledge spaces, to semantically enrich textual documents, or to feed knowledge-intensive applications such as recommender systems. In this work, we describe how to create and exploit a knowledge graph to supply a hybrid recommendation engine with information that builds on top of a collections of documents describing musical and sound items. Tags and textual descriptions are exploited to extract and link entities to external graphs such as WordNet and DBpedia, which are in turn used to semantically enrich the initial data. By means of the knowledge graph we build, recommendations are computed using a feature combination hybrid approach. Two explicit graph feature mappings are formulated to obtain meaningful item feature representations able to catch the knowledge embedded in the graph. Those content features are further combined with additional collaborative information deriving from implicit user feedback. An extensive evaluation on historical data is performed over two different datasets: a dataset of sounds composed of tags, textual descriptions, and user's download information gathered from Freesound.org and a dataset of songs that mixes song textual descriptions with tags and user's listening habits extracted from Songfacts.com and Last.fm, respectively. Results show significant improvements with respect to state-of-the-art collaborative algorithms in both datasets. In addition, we show how the semantic expansion of the initial descriptions helps in achieving much better recommendation quality in terms of aggregated diversity and novelty.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "21", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Rodriguez-Serrano:2017:TDA, author = "Francisco Jose Rodriguez-Serrano and Julio Jose Carabias-Orti and Pedro Vera-Candeas and Damian Martinez-Munoz", title = "Tempo Driven Audio-to-Score Alignment Using Spectral Decomposition and Online Dynamic Time Warping", journal = j-TIST, volume = "8", number = "2", pages = "22:1--22:??", month = jan, year = "2017", CODEN = "????", DOI = "https://doi.org/10.1145/2926717", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Apr 3 11:19:57 MDT 2017", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "In this article, we present an online score following framework designed to deal with automatic accompaniment. The proposed framework is based on spectral factorization and online Dynamic Time Warping (DTW) and has two separated stages: preprocessing and alignment. In the first one, we convert the score into a reference audio signal using a MIDI synthesizer software and we analyze the provided information in order to obtain the spectral patterns (i.e., basis functions) associated to each score unit. In this work, a score unit represents the occurrence of concurrent or isolated notes in the score. These spectral patterns are learned from the synthetic MIDI signal using a method based on Non-negative Matrix Factorization (NMF) with Beta-divergence, where the gains are initialized as the ground-truth transcription inferred from the MIDI. On the second stage, a non-iterative signal decomposition method with fixed spectral patterns per score unit is used over the magnitude spectrogram of the input signal resulting in a distortion matrix that can be interpreted as the cost of the matching for each score unit at each frame. Finally, the relation between the performance and the musical score times is obtained using a strategy based on online DTW, where the optimal path is biased by the speed of interpretation. Our system has been evaluated and compared to other systems, yielding reliable results and performance.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "22", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Tian:2017:TMS, author = "Mi Tian and Mark B. Sandler", title = "Towards Music Structural Segmentation across Genres: Features, Structural Hypotheses, and Annotation Principles", journal = j-TIST, volume = "8", number = "2", pages = "23:1--23:??", month = jan, year = "2017", CODEN = "????", DOI = "https://doi.org/10.1145/2950066", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Apr 3 11:19:57 MDT 2017", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "This article faces the problem of how different audio features and segmentation methods work with different music genres. A new annotated corpus of Chinese traditional Jingju music is presented. We incorporate this dataset with two existing music datasets from the literature in an integrated retrieval system to evaluate existing features, structural hypotheses, and segmentation algorithms outside a Western bias. A harmonic-percussive source separation technique is introduced to the feature extraction process and brings significant improvement to the segmentation. Results show that different features capture the structural patterns of different music genres in different ways. Novelty- or homogeneity-based segmentation algorithms and timbre features can surpass the investigated alternatives for the structure analysis of Jingju due to their lack of harmonic repetition patterns. Findings indicate that the design of audio features and segmentation algorithms as well as the consideration of contextual information related to the music corpora should be accounted dependently in an effective segmentation system.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "23", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Sandouk:2017:LCM, author = "Ubai Sandouk and Ke Chen", title = "Learning Contextualized Music Semantics from Tags Via a {Siamese} Neural Network", journal = j-TIST, volume = "8", number = "2", pages = "24:1--24:??", month = jan, year = "2017", CODEN = "????", DOI = "https://doi.org/10.1145/2953886", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Apr 3 11:19:57 MDT 2017", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Music information retrieval faces a challenge in modeling contextualized musical concepts formulated by a set of co-occurring tags. In this article, we investigate the suitability of our recently proposed approach based on a Siamese neural network in fighting off this challenge. By means of tag features and probabilistic topic models, the network captures contextualized semantics from tags via unsupervised learning. This leads to a distributed semantics space and a potential solution to the out of vocabulary problem, which has yet to be sufficiently addressed. We explore the nature of the resultant music-based semantics and address computational needs. We conduct experiments on three public music tag collections-namely, CAL500, MagTag5K and Million Song Dataset-and compare our approach to a number of state-of-the-art semantics learning approaches. Comparative results suggest that this approach outperforms previous approaches in terms of semantic priming and music tag completion.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "24", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Marrella:2017:IPA, author = "Andrea Marrella and Massimo Mecella and Sebastian Sardina", title = "Intelligent Process Adaptation in the {SmartPM} System", journal = j-TIST, volume = "8", number = "2", pages = "25:1--25:??", month = jan, year = "2017", CODEN = "????", DOI = "https://doi.org/10.1145/2948071", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Apr 3 11:19:57 MDT 2017", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "The increasing application of process-oriented approaches in new challenging dynamic domains beyond business computing (e.g., healthcare, emergency management, factories of the future, home automation, etc.) has led to reconsider the level of flexibility and support required to manage complex knowledge-intensive processes in such domains. A knowledge-intensive process is influenced by user decision making and coupled with contextual data and knowledge production, and involves performing complex tasks in the ``physical'' real world to achieve a common goal. The physical world, however, is not entirely predictable, and knowledge-intensive processes must be robust to unexpected conditions and adaptable to unanticipated exceptions, recognizing that in real-world environments it is not adequate to assume that all possible recovery activities can be predefined for dealing with the exceptions that can ensue. To tackle this issue, in this paper we present SmartPM, a model and a prototype Process Management System featuring a set of techniques providing support for automated adaptation of knowledge-intensive processes at runtime. Such techniques are able to automatically adapt process instances when unanticipated exceptions occur, without explicitly defining policies to recover from exceptions and without the intervention of domain experts at runtime, aiming at reducing error-prone and costly manual ad-hoc changes, and thus at relieving users from complex adaptations tasks. To accomplish this, we make use of well-established techniques and frameworks from Artificial Intelligence, such as situation calculus, IndiGolog and classical planning. The approach, which is backed by a formal model, has been implemented and validated with a case study based on real knowledge-intensive processes coming from an emergency management domain.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "25", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Ramamohanarao:2017:SSM, author = "Kotagiri Ramamohanarao and Hairuo Xie and Lars Kulik and Shanika Karunasekera and Egemen Tanin and Rui Zhang and Eman Bin Khunayn", title = "{SMARTS}: Scalable Microscopic Adaptive Road Traffic Simulator", journal = j-TIST, volume = "8", number = "2", pages = "26:1--26:??", month = jan, year = "2017", CODEN = "????", DOI = "https://doi.org/10.1145/2898363", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Apr 3 11:19:57 MDT 2017", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Microscopic traffic simulators are important tools for studying transportation systems as they describe the evolution of traffic to the highest level of detail. A major challenge to microscopic simulators is the slow simulation speed due to the complexity of traffic models. We have developed the Scalable Microscopic Adaptive Road Traffic Simulator (SMARTS), a distributed microscopic traffic simulator that can utilize multiple independent processes in parallel. SMARTS can perform fast large-scale simulations. For example, when simulating 1 million vehicles in an area the size of Melbourne, the system runs 1.14 times faster than real time with 30 computing nodes and 0.2s simulation timestep. SMARTS supports various driver models and traffic rules, such as the car-following model and lane-changing model, which can be driver dependent. It can simulate multiple vehicle types, including bus and tram. The simulator is equipped with a wide range of features that help to customize, calibrate, and monitor simulations. Simulations are accurate and confirm with real traffic behaviours. For example, it achieves 79.1\% accuracy in predicting traffic on a 10km freeway 90 minutes into the future. The simulator can be used for predictive traffic advisories as well as traffic management decisions as simulations complete well ahead of real time. SMARTS can be easily deployed to different operating systems as it is developed with the standard Java libraries.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "26", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Kim:2017:DFN, author = "Jungeun Kim and Jae-Gil Lee and Sungsu Lim", title = "Differential Flattening: a Novel Framework for Community Detection in Multi-Layer Graphs", journal = j-TIST, volume = "8", number = "2", pages = "27:1--27:??", month = jan, year = "2017", CODEN = "????", DOI = "https://doi.org/10.1145/2898362", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Apr 3 11:19:57 MDT 2017", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "A multi-layer graph consists of multiple layers of weighted graphs, where the multiple layers represent the different aspects of relationships. Considering multiple aspects (i.e., layers) together is essential to achieve a comprehensive and consolidated view. In this article, we propose a novel framework of differential flattening, which facilitates the analysis of multi-layer graphs, and apply this framework to community detection. Differential flattening merges multiple graphs into a single graph such that the graph structure with the maximum clustering coefficient is obtained from the single graph. It has two distinct features compared with existing approaches. First, dealing with multiple layers is done independently of a specific community detection algorithm, whereas previous approaches rely on a specific algorithm. Thus, any algorithm for a single graph becomes applicable to multi-layer graphs. Second, the contribution of each layer to the single graph is determined automatically for the maximum clustering coefficient. Since differential flattening is formulated by an optimization problem, the optimal solution is easily obtained by well-known algorithms such as interior point methods. Extensive experiments were conducted using the Lancichinetti-Fortunato-Radicchi (LFR) benchmark networks as well as the DBLP, 20 Newsgroups, and MIT Reality Mining networks. The results show that our approach of differential flattening leads to discovery of higher-quality communities than baseline approaches and the state-of-the-art algorithms.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "27", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Xie:2017:JSS, author = "Liping Xie and Dacheng Tao and Haikun Wei", title = "Joint Structured Sparsity Regularized Multiview Dimension Reduction for Video-Based Facial Expression Recognition", journal = j-TIST, volume = "8", number = "2", pages = "28:1--28:??", month = jan, year = "2017", CODEN = "????", DOI = "https://doi.org/10.1145/2956556", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Apr 3 11:19:57 MDT 2017", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Video-based facial expression recognition (FER) has recently received increased attention as a result of its widespread application. Using only one type of feature to describe facial expression in video sequences is often inadequate, because the information available is very complex. With the emergence of different features to represent different properties of facial expressions in videos, an appropriate combination of these features becomes an important, yet challenging, problem. Considering that the dimensionality of these features is usually high, we thus introduce multiview dimension reduction (MVDR) into video-based FER. In MVDR, it is critical to explore the relationships between and within different feature views. To achieve this goal, we propose a novel framework of MVDR by enforcing joint structured sparsity at both inter- and intraview levels. In this way, correlations on and between the feature spaces of different views tend to be well-exploited. In addition, a transformation matrix is learned for each view to discover the patterns contained in the original features, so that the different views are comparable in finding a common representation. The model can be not only performed in an unsupervised manner, but also easily extended to a semisupervised setting by incorporating some domain knowledge. An alternating algorithm is developed for problem optimization, and each subproblem can be efficiently solved. Experiments on two challenging video-based FER datasets demonstrate the effectiveness of the proposed framework.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "28", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Song:2017:PSH, author = "Xuan Song and Quanshi Zhang and Yoshihide Sekimoto and Ryosuke Shibasaki and Nicholas Jing Yuan and Xing Xie", title = "Prediction and Simulation of Human Mobility Following Natural Disasters", journal = j-TIST, volume = "8", number = "2", pages = "29:1--29:??", month = jan, year = "2017", CODEN = "????", DOI = "https://doi.org/10.1145/2970819", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Apr 3 11:19:57 MDT 2017", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "In recent decades, the frequency and intensity of natural disasters has increased significantly, and this trend is expected to continue. Therefore, understanding and predicting human behavior and mobility during a disaster will play a vital role in planning effective humanitarian relief, disaster management, and long-term societal reconstruction. However, such research is very difficult to perform owing to the uniqueness of various disasters and the unavailability of reliable and large-scale human mobility data. In this study, we collect big and heterogeneous data (e.g., GPS records of 1.6 million users$^1$ over 3 years, data on earthquakes that have occurred in Japan over 4 years, news report data, and transportation network data) to study human mobility following natural disasters. An empirical analysis is conducted to explore the basic laws governing human mobility following disasters, and an effective human mobility model is developed to predict and simulate population movements. The experimental results demonstrate the efficiency of our model, and they suggest that human mobility following disasters can be significantly more predictable and be more easily simulated than previously thought.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "29", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Peng:2017:SLM, author = "Chong Peng and Jie Cheng and Qiang Cheng", title = "A Supervised Learning Model for High-Dimensional and Large-Scale Data", journal = j-TIST, volume = "8", number = "2", pages = "30:1--30:??", month = jan, year = "2017", CODEN = "????", DOI = "https://doi.org/10.1145/2972957", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Apr 3 11:19:57 MDT 2017", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "We introduce a new supervised learning model using a discriminative regression approach. This new model estimates a regression vector to represent the similarity between a test example and training examples while seamlessly integrating the class information in the similarity estimation. This distinguishes our model from usual regression models and locally linear embedding approaches, rendering our method suitable for supervised learning problems in high-dimensional settings. Our model is easily extensible to account for nonlinear relationship and applicable to general data, including both high- and low-dimensional data. The objective function of the model is convex, for which two optimization algorithms are provided. These two optimization approaches induce two scalable solvers that are of mathematically provable, linear time complexity. Experimental results verify the effectiveness of the proposed method on various kinds of data. For example, our method shows comparable performance on low-dimensional data and superior performance on high-dimensional data to several widely used classifiers; also, the linear solvers obtain promising performance on large-scale classification.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "30", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Liu:2017:IVL, author = "Yan Liu and Yang Liu and Shenghua Zhong and Songtao Wu", title = "Implicit Visual Learning: Image Recognition via Dissipative Learning Model", journal = j-TIST, volume = "8", number = "2", pages = "31:1--31:??", month = jan, year = "2017", CODEN = "????", DOI = "https://doi.org/10.1145/2974024", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Apr 3 11:19:57 MDT 2017", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "According to consciousness involvement, human's learning can be roughly classified into explicit learning and implicit learning. Contrasting strongly to explicit learning with clear targets and rules, such as our school study of mathematics, learning is implicit when we acquire new information without intending to do so. Research from psychology indicates that implicit learning is ubiquitous in our daily life. Moreover, implicit learning plays an important role in human visual perception. But in the past 60 years, most of the well-known machine-learning models aimed to simulate explicit learning while the work of modeling implicit learning was relatively limited, especially for computer vision applications. This article proposes a novel unsupervised computational model for implicit visual learning by exploring dissipative system, which provides a unifying macroscopic theory to connect biology with physics. We test the proposed Dissipative Implicit Learning Model (DILM) on various datasets. The experiments show that DILM not only provides a good match to human behavior but also improves the explicit machine-learning performance obviously on image classification tasks.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "31", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Barbieri:2017:EMI, author = "Nicola Barbieri and Francesco Bonchi and Giuseppe Manco", title = "Efficient Methods for Influence-Based Network-Oblivious Community Detection", journal = j-TIST, volume = "8", number = "2", pages = "32:1--32:??", month = jan, year = "2017", CODEN = "????", DOI = "https://doi.org/10.1145/2979682", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Apr 3 11:19:57 MDT 2017", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "We study the problem of detecting social communities when the social graph is not available but instead we have access to a log of user activity, that is, a dataset of tuples ( u, i, t ) recording the fact that user u ``adopted'' item i at time t. We propose a stochastic framework that assumes that the adoption of items is governed by an underlying diffusion process over the unobserved social network and that such a diffusion model is based on community-level influence. That is, we aim at modeling communities through the lenses of social contagion. By fitting the model parameters to the user activity log, we learn the community membership and the level of influence of each user in each community. The general framework is instantiated with two different diffusion models, one with discrete time and one with continuous time, and we show that the computational complexity of both approaches is linear in the number of users and in the size of the propagation log. Experiments on synthetic data with planted community structure show that our methods outperform non-trivial baselines. The effectiveness of the proposed techniques is further validated on real-word data, on which our methods are able to detect high-quality communities.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "32", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Wu:2017:CDT, author = "Zhonggang Wu and Zhao Lu and Shan-Yuan Ho", title = "Community Detection with Topological Structure and Attributes in Information Networks", journal = j-TIST, volume = "8", number = "2", pages = "33:1--33:??", month = jan, year = "2017", CODEN = "????", DOI = "https://doi.org/10.1145/2979681", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Apr 3 11:19:57 MDT 2017", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Information networks contain objects connected by multiple links and described by rich attributes. Detecting community for these networks is a challenging research problem, because there is a scarcity of effective approaches that balance the features of the network structure and the characteristics of the nodes. Some methods detect communities by considering topological structures while ignoring the contributions of attributes. Other methods have considered both topological structure and attributes but pay a high price in time complexity. We establish a new community detection algorithm which explores both topological Structure and Attributes using Global structure and Local neighborhood features (SAGL) which also has low computational complexity. The first step of SAGL evaluates the global importance of every node and calculates the similarity of each node pair by combining edge strength and node attribute similarity. The second step of SAGL uses a clustering algorithm that identifies communities by measuring the similarity of two nodes, calculated by the distance of their neighbors. Experimental results on three real-world datasets show the effectiveness of SAGL, particularly its fast convergence compared to current state-of-the-art attributed graph clustering methods.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "33", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Ji:2017:MSM, author = "Rongrong Ji and Wei Liu and Xing Xie and Yiqiang Chen and Jiebo Luo", title = "Mobile Social Multimedia Analytics in the Big Data Era: an Introduction to the Special Issue", journal = j-TIST, volume = "8", number = "3", pages = "34:1--34:??", month = apr, year = "2017", CODEN = "????", DOI = "https://doi.org/10.1145/3040934", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Dec 23 10:12:41 MST 2017", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "34", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Gao:2017:ECM, author = "Yue Gao and Hanwang Zhang and Xibin Zhao and Shuicheng Yan", title = "Event Classification in Microblogs via Social Tracking", journal = j-TIST, volume = "8", number = "3", pages = "35:1--35:??", month = apr, year = "2017", CODEN = "????", DOI = "https://doi.org/10.1145/2967502", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Dec 23 10:12:41 MST 2017", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Social media websites have become important information sharing platforms. The rapid development of social media platforms has led to increasingly large-scale social media data, which has shown remarkable societal and marketing values. There are needs to extract important events in live social media streams. However, microblogs event classification is challenging due to two facts, i.e., the short/conversational nature and the incompatible meanings between the text and the corresponding image in social posts, and the rapidly evolving contents. In this article, we propose to conduct event classification via deep learning and social tracking. First, we introduce a Multi-modal Multi-instance Deep Network (M$^2$ DN) for microblogs classification, which is able to handle the weakly labeled microblogs data oriented from the incompatible meanings inside microblogs. Besides predicting each microblogs as predefined events, we propose to employ social tracking to extract social-related auxiliary information to enrich the testing samples. We extract a set of candidate-relevant microblogs in a short time window by using social connections, such as related users and geographical locations. All these selected microblogs and the testing data are formulated in a Markov Random Field model. The inference on the Markov Random Field is conducted to update the classification results of the testing microblogs. This method is evaluated on the Brand-Social-Net dataset for classification of 20 events. Experimental results and comparison with the state of the arts show that the proposed method can achieve better performance for the event classification task.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "35", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Nie:2017:LUA, author = "Liqiang Nie and Luming Zhang and Meng Wang and Richang Hong and Aleksandr Farseev and Tat-Seng Chua", title = "Learning User Attributes via Mobile Social Multimedia Analytics", journal = j-TIST, volume = "8", number = "3", pages = "36:1--36:??", month = apr, year = "2017", CODEN = "????", DOI = "https://doi.org/10.1145/2963105", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Dec 23 10:12:41 MST 2017", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Learning user attributes from mobile social media is a fundamental basis for many applications, such as personalized and targeting services. A large and growing body of literature has investigated the user attributes learning problem. However, far too little attention has been paid to jointly consider the dual heterogeneities of user attributes learning by harvesting multiple social media sources. In particular, user attributes are complementarily and comprehensively characterized by multiple social media sources, including footprints from Foursqare, daily updates from Twitter, professional careers from Linkedin, and photo posts from Instagram. On the other hand, attributes are inter-correlated in a complex way rather than independent to each other, and highly related attributes may share similar feature sets. Towards this end, we proposed a unified model to jointly regularize the source consistency and graph-constrained relatedness among tasks. As a byproduct, it is able to learn the attribute-specific and attribute-sharing features via graph-guided fused lasso penalty. Besides, we have theoretically demonstrated its optimization. Extensive evaluations on a real-world dataset thoroughly demonstrated the effectiveness of our proposed model.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "36", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Tao:2017:LSC, author = "Dapeng Tao and Dacheng Tao and Xuelong Li and Xinbo Gao", title = "Large Sparse Cone Non-negative Matrix Factorization for Image Annotation", journal = j-TIST, volume = "8", number = "3", pages = "37:1--37:??", month = apr, year = "2017", CODEN = "????", DOI = "https://doi.org/10.1145/2987379", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Dec 23 10:12:41 MST 2017", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Image annotation assigns relevant tags to query images based on their semantic contents. Since Non-negative Matrix Factorization (NMF) has the strong ability to learn parts-based representations, recently, a number of algorithms based on NMF have been proposed for image annotation and have achieved good performance. However, most of the efforts have focused on the representations of images and annotations. The properties of the semantic parts have not been well studied. In this article, we revisit the sparseness-constrained NMF (sNMF) proposed by Hoyer [2004]. By endowing the sparseness constraint with a geometric interpretation and sNMF with theoretical analyses of the generalization ability, we show that NMF with such a sparseness constraint has three advantages for image annotation tasks: (i) The sparseness constraint is more l$_0$ -norm oriented than the l$_1$ -norm-based sparseness, which significantly enhances the ability of NMF to robustly learn semantic parts. (ii) The sparseness constraint has a large cone interpretation and thus allows the reconstruction error of NMF to be smaller, which means that the learned semantic parts are more powerful to represent images for tagging. (iii) The learned semantic parts are less correlated, which increases the discriminative ability for annotating images. Moreover, we present a new efficient large sparse cone NMF (LsCNMF) algorithm to optimize the sNMF problem by employing the Nesterov's optimal gradient method. We conducted experiments on the PASCAL VOC07 dataset and demonstrated the effectiveness of LsCNMF for image annotation.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "37", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Zhang:2017:LBP, author = "Jiaming Zhang and Shuhui Wang and Qingming Huang", title = "Location-Based Parallel Tag Completion for Geo-Tagged Social Image Retrieval", journal = j-TIST, volume = "8", number = "3", pages = "38:1--38:??", month = apr, year = "2017", CODEN = "????", DOI = "https://doi.org/10.1145/3001593", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Dec 23 10:12:41 MST 2017", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Having benefited from tremendous growth of user-generated content, social annotated tags get higher importance in the organization and retrieval of large-scale image databases on Online Sharing Websites (OSW). To obtain high-quality tags from existing community contributed tags with missing information and noise, tag-based annotation or recommendation methods have been proposed for performance promotion of tag prediction. While images from OSW contain rich social attributes, they have not taken full advantage of rich social attributes and auxiliary information associated with social images to construct global information completion models. In this article, beyond the image-tag relation, we take full advantage of the ubiquitous GPS locations and image-user relationship to enhance the accuracy of tag prediction and improve the computational efficiency. For GPS locations, we define the popular geo-locations where people tend to take more images as Points of Interests (POI), which are discovered by mean shift approach. For image-user relationship, we integrate a localized prior constraint, expecting the completed tag sub-matrix in each POI to maintain consistency with users' tagging behaviors. Based on these two key issues, we propose a unified tag matrix completion framework, which learns the image-tag relation within each POI. To solve the optimization problem, an efficient proximal sub-gradient descent algorithm is designed. The model optimization can be easily parallelized and distributed to learn the tag sub-matrix for each POI. Extensive experimental results reveal that the learned tag sub-matrix of each POI reflects the major trend of users' tagging results with respect to different POIs and users, and the parallel learning process provides strong support for processing large-scale online image databases. To fit the response time requirement and storage limitations of Tag-based Image Retrieval (TBIR) on mobile devices, we introduce Asymmetric Locality Sensitive Hashing (ALSH) to reduce the time cost and meanwhile improve the efficiency of retrieval.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "38", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Sang:2017:ESM, author = "Jitao Sang and Quan Fang and Changsheng Xu", title = "Exploiting Social-Mobile Information for Location Visualization", journal = j-TIST, volume = "8", number = "3", pages = "39:1--39:??", month = apr, year = "2017", CODEN = "????", DOI = "https://doi.org/10.1145/3001594", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Dec 23 10:12:41 MST 2017", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "With a smart phone at hand, it becomes easy now to snap pictures and publish them online with few lines of texts. The GPS coordinates and User-Generated Content (UGC) data embedded in the shared photos provide opportunities to exploit important knowledge to tackle interesting tasks like geographically organizing photos and location visualization. In this work, we propose to organize photos both geographically and semantically, and investigate the problem of location visualization from multiple semantic themes. The novel visualization scheme provides a rich display landscape for geographical exploration from versatile views. A two-level solution is presented, where we first identify the highly photographed places of interest (POI) and discover their focused themes, and then aggregate the lower-level POI themes to generate the higher-level city themes for location visualization. We have conducted experiments on crawled Flickr and Instagram data and exhibited the visualization for the cities of Singapore and Sydney. The experimental results have validated the proposed method and demonstrated the potentials of location visualization from multiple themes.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "39", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Hu:2017:COM, author = "Han Hu and Yonggang Wen and Tat-Seng Chua and Xuelong Li", title = "Cost-Optimized Microblog Distribution over Geo-Distributed Data Centers: Insights from Cross-Media Analysis", journal = j-TIST, volume = "8", number = "3", pages = "40:1--40:??", month = apr, year = "2017", CODEN = "????", DOI = "https://doi.org/10.1145/3014431", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Dec 23 10:12:41 MST 2017", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "The unprecedent growth of microblog services poses significant challenges on network traffic and service latency to the underlay infrastructure (i.e., geo-distributed data centers). Furthermore, the dynamic evolution in microblog status generates a huge workload on data consistence maintenance. In this article, motivated by insights of cross-media analysis-based propagation patterns, we propose a novel cache strategy for microblog service systems to reduce the inter-data center traffic and consistence maintenance cost, while achieving low service latency. Specifically, we first present a microblog classification method, which utilizes the external knowledge from correlated domains, to categorize microblogs. Then we conduct a large-scale measurement on a representative online social network system to study the category-based propagation diversity on region and time scales. These insights illustrate social common habits on creating and consuming microblogs and further motivate our architecture design. Finally, we formulate the content cache problem as a constrained optimization problem. By jointly using the Lyapunov optimization framework and simplex gradient method, we find the optimal online control strategy. Extensive trace-driven experiments further demonstrate that our algorithm reduces the system cost by 24.5\% against traditional approaches with the same service latency.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "40", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Xu:2017:DOD, author = "Jun Xu and Long Xia and Yanyan Lan and Jiafeng Guo and Xueqi Cheng", title = "Directly Optimize Diversity Evaluation Measures: a New Approach to Search Result Diversification", journal = j-TIST, volume = "8", number = "3", pages = "41:1--41:??", month = apr, year = "2017", CODEN = "????", DOI = "https://doi.org/10.1145/2983921", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Dec 23 10:12:41 MST 2017", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "The queries issued to search engines are often ambiguous or multifaceted, which requires search engines to return diverse results that can fulfill as many different information needs as possible; this is called search result diversification. Recently, the relational learning to rank model, which designs a learnable ranking function following the criterion of maximal marginal relevance, has shown effectiveness in search result diversification [Zhu et al. 2014]. The goodness of a diverse ranking model is usually evaluated with diversity evaluation measures such as $ \alpha $-NDCG [Clarke et al. 2008], ERR-IA [Chapelle et al. 2009], and D\#-NDCG [Sakai and Song 2011]. Ideally the learning algorithm would train a ranking model that could directly optimize the diversity evaluation measures with respect to the training data. Existing relational learning to rank algorithms, however, only train the ranking models by optimizing loss functions that loosely relate to the evaluation measures. To deal with the problem, we propose a general framework for learning relational ranking models via directly optimizing any diversity evaluation measure. In learning, the loss function upper-bounding the basic loss function defined on a diverse ranking measure is minimized. We can derive new diverse ranking algorithms under the framework, and several diverse ranking algorithms are created based on different upper bounds over the basic loss function. We conducted comparisons between the proposed algorithms with conventional diverse ranking methods using the TREC benchmark datasets. Experimental results show that the algorithms derived under the diverse learning to rank framework always significantly outperform the state-of-the-art baselines.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "41", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Peng:2017:NMF, author = "Chong Peng and Zhao Kang and Yunhong Hu and Jie Cheng and Qiang Cheng", title = "Nonnegative Matrix Factorization with Integrated Graph and Feature Learning", journal = j-TIST, volume = "8", number = "3", pages = "42:1--42:??", month = apr, year = "2017", CODEN = "????", DOI = "https://doi.org/10.1145/2987378", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Dec 23 10:12:41 MST 2017", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Matrix factorization is a useful technique for data representation in many data mining and machine learning tasks. Particularly, for data sets with all nonnegative entries, matrix factorization often requires that factor matrices be nonnegative, leading to nonnegative matrix factorization (NMF). One important application of NMF is for clustering with reduced dimensions of the data represented in the new feature space. In this paper, we propose a new graph regularized NMF method capable of feature learning and apply it to clustering. Unlike existing NMF methods that treat all features in the original feature space equally, our method distinguishes features by incorporating a feature-wise sparse approximation error matrix in the formulation. It enables important features to be more closely approximated by the factor matrices. Meanwhile, the graph of the data is constructed using cleaner features in the feature learning process, which integrates feature learning and manifold learning procedures into a unified NMF model. This distinctly differs from applying the existing graph-based NMF models after feature selection in that, when these two procedures are independently used, they often fail to align themselves toward obtaining a compact and most expressive data representation. Comprehensive experimental results demonstrate the effectiveness of the proposed method, which outperforms state-of-the-art algorithms when applied to clustering.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "42", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Zhang:2017:LKK, author = "Shichao Zhang and Xuelong Li and Ming Zong and Xiaofeng Zhu and Debo Cheng", title = "Learning $k$ for {$k$NN} Classification", journal = j-TIST, volume = "8", number = "3", pages = "43:1--43:??", month = apr, year = "2017", CODEN = "????", DOI = "https://doi.org/10.1145/2990508", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Dec 23 10:12:41 MST 2017", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "The K Nearest Neighbor (kNN) method has widely been used in the applications of data mining and machine learning due to its simple implementation and distinguished performance. However, setting all test data with the same k value in the previous kNN methods has been proven to make these methods impractical in real applications. This article proposes to learn a correlation matrix to reconstruct test data points by training data to assign different k values to different test data points, referred to as the Correlation Matrix kNN (CM-kNN for short) classification. Specifically, the least-squares loss function is employed to minimize the reconstruction error to reconstruct each test data point by all training data points. Then, a graph Laplacian regularizer is advocated to preserve the local structure of the data in the reconstruction process. Moreover, an l$_1$ -norm regularizer and an l$_{2, 1}$ -norm regularizer are applied to learn different k values for different test data and to result in low sparsity to remove the redundant/noisy feature from the reconstruction process, respectively. Besides for classification tasks, the kNN methods (including our proposed CM-kNN method) are further utilized to regression and missing data imputation. We conducted sets of experiments for illustrating the efficiency, and experimental results showed that the proposed method was more accurate and efficient than existing kNN methods in data-mining applications, such as classification, regression, and missing data imputation.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "43", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Hoang:2017:MTB, author = "Tuan-Anh Hoang and Ee-Peng Lim", title = "Modeling Topics and Behavior of Microbloggers: an Integrated Approach", journal = j-TIST, volume = "8", number = "3", pages = "44:1--44:??", month = apr, year = "2017", CODEN = "????", DOI = "https://doi.org/10.1145/2990507", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Dec 23 10:12:41 MST 2017", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Microblogging encompasses both user-generated content and behavior. When modeling microblogging data, one has to consider personal and background topics, as well as how these topics generate the observed content and behavior. In this article, we propose the Generalized Behavior-Topic (GBT) model for simultaneously modeling background topics and users' topical interest in microblogging data. GBT considers multiple topical communities (or realms) with different background topical interests while learning the personal topics of each user and the user's dependence on realms to generate both content and behavior. This differentiates GBT from other previous works that consider either one realm only or content data only. By associating user behavior with the latent background and personal topics, GBT helps to model user behavior by the two types of topics. GBT also distinguishes itself from other earlier works by modeling multiple types of behavior together. Our experiments on two Twitter datasets show that GBT can effectively mine the representative topics for each realm. We also demonstrate that GBT significantly outperforms other state-of-the-art models in modeling content topics and user profiling.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "44", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Mirsky:2017:COP, author = "Reuth Mirsky and Ya'akov (Kobi) Gal and Stuart M. Shieber", title = "{CRADLE}: an Online Plan Recognition Algorithm for Exploratory Domains", journal = j-TIST, volume = "8", number = "3", pages = "45:1--45:??", month = apr, year = "2017", CODEN = "????", DOI = "https://doi.org/10.1145/2996200", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Dec 23 10:12:41 MST 2017", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "In exploratory domains, agents' behaviors include switching between activities, extraneous actions, and mistakes. Such settings are prevalent in real world applications such as interaction with open-ended software, collaborative office assistants, and integrated development environments. Despite the prevalence of such settings in the real world, there is scarce work in formalizing the connection between high-level goals and low-level behavior and inferring the former from the latter in these settings. We present a formal grammar for describing users' activities in such domains. We describe a new top-down plan recognition algorithm called CRADLE (Cumulative Recognition of Activities and Decreasing Load of Explanations) that uses this grammar to recognize agents' interactions in exploratory domains. We compare the performance of CRADLE with state-of-the-art plan recognition algorithms in several experimental settings consisting of real and simulated data. Our results show that CRADLE was able to output plans exponentially more quickly than the state-of-the-art without compromising its correctness, as determined by domain experts. Our approach can form the basis of future systems that use plan recognition to provide real-time support to users in a growing class of interesting and challenging domains.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "45", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Zhang:2017:DSM, author = "Peng Zhang and Qian Yu and Yuexian Hou and Dawei Song and Jingfei Li and Bin Hu", title = "A Distribution Separation Method Using Irrelevance Feedback Data for Information Retrieval", journal = j-TIST, volume = "8", number = "3", pages = "46:1--46:??", month = apr, year = "2017", CODEN = "????", DOI = "https://doi.org/10.1145/2994608", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Dec 23 10:12:41 MST 2017", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "In many research and application areas, such as information retrieval and machine learning, we often encounter dealing with a probability distribution that is mixed by one distribution that is relevant to our task in hand and the other that is irrelevant and that we want to get rid of. Thus, it is an essential problem to separate the irrelevant distribution from the mixture distribution. This article is focused on the application in Information Retrieval, where relevance feedback is a widely used technique to build a refined query model based on a set of feedback documents. However, in practice, the relevance feedback set, even provided by users explicitly or implicitly, is often a mixture of relevant and irrelevant documents. Consequently, the resultant query model (typically a term distribution) is often a mixture rather than a true relevance term distribution, leading to a negative impact on the retrieval performance. To tackle this problem, we recently proposed a Distribution Separation Method (DSM), which aims to approximate the true relevance distribution by separating a seed irrelevance distribution from the mixture one. While it achieved a promising performance in an empirical evaluation with simulated explicit irrelevance feedback data, it has not been deployed in the scenario where one should automatically obtain the irrelevance feedback data. In this article, we propose a substantial extension of the basic DSM from two perspectives: developing a further regularization framework and deploying DSM in the automatic irrelevance feedback scenario. Specifically, in order to avoid the output distribution of DSM drifting away from the true relevance distribution when the quality of seed irrelevant distribution (as the input to DSM) is not guaranteed, we propose a DSM regularization framework to constrain the estimation for the relevance distribution. This regularization framework includes three algorithms, each corresponding to a regularization strategy incorporated in the objective function of DSM. In addition, we exploit DSM in automatic (i.e., pseudo) irrelevance feedback, by automatically detecting the seed irrelevant documents via three different document reranking methods. We have carried out extensive experiments based on various TREC datasets, in order to systematically evaluate the proposed methods. The experimental results demonstrate the effectiveness of our proposed approaches in comparison with various strong baselines.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "46", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Xiong:2017:DDA, author = "Haoyi Xiong and Jinghe Zhang and Yu Huang and Kevin Leach and Laura E. Barnes", title = "{Daehr}: a Discriminant Analysis Framework for Electronic Health Record Data and an Application to Early Detection of Mental Health Disorders", journal = j-TIST, volume = "8", number = "3", pages = "47:1--47:??", month = apr, year = "2017", CODEN = "????", DOI = "https://doi.org/10.1145/3007195", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Dec 23 10:12:41 MST 2017", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Electronic health records (EHR) provide a rich source of temporal data that present a unique opportunity to characterize disease patterns and risk of imminent disease. While many data-mining tools have been adopted for EHR-based disease early detection, linear discriminant analysis (LDA) is one of the most commonly used statistical methods. However, it is difficult to train an accurate LDA model for early disease diagnosis when too few patients are known to have the target disease. Furthermore, EHR data are heterogeneous with significant noise. In such cases, the covariance matrices used in LDA are usually singular and estimated with a large variance. This article presents Daehr, an extension of the LDA framework using electronic health record data to address these issues. Beyond existing LDA analyzers, we propose Daehr to (1) eliminate the data noise caused by the manual encoding of EHR data and (2) lower the variance of parameter (covariance matrices) estimation for LDA models when only a few patients' EHR are available for training. To achieve these two goals, we designed an iterative algorithm to improve the covariance matrix estimation with embedded data-noise/parameter-variance reduction for LDA. We evaluated Daehr extensively using the College Health Surveillance Network, a large, real-world EHR dataset. Specifically, our experiments compared the performance of LDA to three baselines (i.e., LDA and its derivatives) in identifying college students at high risk for mental health disorders from 23 U.S. universities. Experimental results demonstrate Daehr significantly outperforms the three baselines by achieving 1.4\%--19.4\% higher accuracy and a 7.5\%--43.5\% higher F1-score.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "47", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Wang:2017:SSS, author = "Weiqing Wang and Hongzhi Yin and Ling Chen and Yizhou Sun and Shazia Sadiq and Xiaofang Zhou", title = "{ST-SAGE}: a Spatial-Temporal Sparse Additive Generative Model for Spatial Item Recommendation", journal = j-TIST, volume = "8", number = "3", pages = "48:1--48:??", month = apr, year = "2017", CODEN = "????", DOI = "https://doi.org/10.1145/3011019", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Dec 23 10:12:41 MST 2017", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "With the rapid development of location-based social networks (LBSNs), spatial item recommendation has become an important mobile application, especially when users travel away from home. However, this type of recommendation is very challenging compared to traditional recommender systems. A user may visit only a limited number of spatial items, leading to a very sparse user-item matrix. This matrix becomes even sparser when the user travels to a distant place, as most of the items visited by a user are usually located within a short distance from the user's home. Moreover, user interests and behavior patterns may vary dramatically across different time and geographical regions. In light of this, we propose ST-SAGE, a spatial-temporal sparse additive generative model for spatial item recommendation in this article. ST-SAGE considers both personal interests of the users and the preferences of the crowd in the target region at the given time by exploiting both the co-occurrence patterns and content of spatial items. To further alleviate the data-sparsity issue, ST-SAGE exploits the geographical correlation by smoothing the crowd's preferences over a well-designed spatial index structure called the spatial pyramid. To speed up the training process of ST-SAGE, we implement a parallel version of the model inference algorithm on the GraphLab framework. We conduct extensive experiments; the experimental results clearly demonstrate that ST-SAGE outperforms the state-of-the-art recommender systems in terms of recommendation effectiveness, model training efficiency, and online recommendation efficiency.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "48", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Ben-Israel:2017:LPM, author = "Isaac Ben-Israel", title = "The Letter from {Prof. Maj. Gen. (Ret.) Isaac Ben-Israel}", journal = j-TIST, volume = "8", number = "4", pages = "49:1--49:??", month = jul, year = "2017", CODEN = "????", DOI = "https://doi.org/10.1145/3057727", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Dec 23 10:12:41 MST 2017", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "49e", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Harel:2017:CSR, author = "Yaniv Harel and Irad Ben Gal and Yuval Elovici", title = "Cyber Security and the Role of Intelligent Systems in Addressing its Challenges", journal = j-TIST, volume = "8", number = "4", pages = "49:1--49:??", month = jul, year = "2017", CODEN = "????", DOI = "https://doi.org/10.1145/3057729", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Dec 23 10:12:41 MST 2017", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "49", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Guri:2017:BAG, author = "Mordechai Guri and Matan Monitz and Yuval Elovici", title = "Bridging the Air Gap between Isolated Networks and Mobile Phones in a Practical Cyber-Attack", journal = j-TIST, volume = "8", number = "4", pages = "50:1--50:??", month = jul, year = "2017", CODEN = "????", DOI = "https://doi.org/10.1145/2870641", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Dec 23 10:12:41 MST 2017", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Information is the most critical asset of modern organizations, and accordingly it is one of the resources most coveted by adversaries. When highly sensitive data is involved, an organization may resort to air gap isolation in which there is no networking connection between the inner network and the external world. While infiltrating an air-gapped network has been proven feasible in recent years, data exfiltration from an air-gapped network is still considered one of the most challenging phases of an advanced cyber-attack. In this article, we present ``AirHopper,'' a bifurcated malware that bridges the air gap between an isolated network and nearby infected mobile phones using FM signals. While it is known that software can intentionally create radio emissions from a video card, this is the first time that mobile phones serve as the intended receivers of the maliciously crafted electromagnetic signals. We examine the attack model and its limitations and discuss implementation considerations such as modulation methods, signal collision, and signal reconstruction. We test AirHopper in an existing workplace at a typical office building and demonstrate how valuable data such as keylogging and files can be exfiltrated from physically isolated computers to mobile phones at a distance of 1--7 meters, with an effective bandwidth of 13--60 bytes per second.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "50", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Ovelgonne:2017:URB, author = "Michael Ovelg{\"o}nne and Tudor Dumitras and B. Aditya Prakash and V. S. Subrahmanian and Benjamin Wang", title = "Understanding the Relationship between Human Behavior and Susceptibility to Cyber Attacks: a Data-Driven Approach", journal = j-TIST, volume = "8", number = "4", pages = "51:1--51:??", month = jul, year = "2017", CODEN = "????", DOI = "https://doi.org/10.1145/2890509", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Dec 23 10:12:41 MST 2017", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Despite growing speculation about the role of human behavior in cyber-security of machines, concrete data-driven analysis and evidence have been lacking. Using Symantec's WINE platform, we conduct a detailed study of 1.6 million machines over an 8-month period in order to learn the relationship between user behavior and cyber attacks against their personal computers. We classify users into 4 categories (gamers, professionals, software developers, and others, plus a fifth category comprising everyone) and identify a total of 7 features that act as proxies for human behavior. For each of the 35 possible combinations (5 categories times 7 features), we studied the relationship between each of these seven features and one dependent variable, namely the number of attempted malware attacks detected by Symantec on the machine. Our results show that there is a strong relationship between several features and the number of attempted malware attacks. Had these hosts not been protected by Symantec's anti-virus product or a similar product, they would likely have been infected. Surprisingly, our results show that software developers are more at risk of engaging in risky cyber-behavior than other categories.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "51", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Ganesan:2017:OSC, author = "Rajesh Ganesan and Sushil Jajodia and Hasan Cam", title = "Optimal Scheduling of Cybersecurity Analysts for Minimizing Risk", journal = j-TIST, volume = "8", number = "4", pages = "52:1--52:??", month = jul, year = "2017", CODEN = "????", DOI = "https://doi.org/10.1145/2914795", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Dec 23 10:12:41 MST 2017", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Cybersecurity threats are on the rise with evermore digitization of the information that many day-to-day systems depend upon. The demand for cybersecurity analysts outpaces supply, which calls for optimal management of the analyst resource. Therefore, a key component of the cybersecurity defense system is the optimal scheduling of its analysts. Sensor data is analyzed by automatic processing systems, and alerts are generated. A portion of these alerts is considered to be significant, which requires thorough examination by a cybersecurity analyst. Risk, in this article, is defined as the percentage of unanalyzed or not thoroughly analyzed alerts among the significant alerts by analysts. The article presents a generalized optimization model for scheduling cybersecurity analysts to minimize risk (a.k.a., maximize significant alert coverage by analysts) and maintain risk under a pre-determined upper bound. The article tests the optimization model and its scalability on a set of given sensors with varying analyst experiences, alert generation rates, system constraints, and system requirements. Results indicate that the optimization model is scalable and is capable of identifying both the right mix of analyst expertise in an organization and the sensor-to-analyst allocation in order to maintain risk below a given upper bound. Several meta-principles are presented, which are derived from the optimization model, and they further serve as guiding principles for hiring and scheduling cybersecurity analysts. The simulation studies (validation) of the optimization model outputs indicate that risk varies non-linearly with an analyst/sensor ratio, and for a given analyst/sensor ratio, the risk is independent of the number of sensors in the system.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "52", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Neria:2017:RSF, author = "Michal Ben Neria and Nancy-Sarah Yacovzada and Irad Ben-Gal", title = "A Risk-Scoring Feedback Model for {Webpages} and {Web} Users Based on Browsing Behavior", journal = j-TIST, volume = "8", number = "4", pages = "53:1--53:??", month = jul, year = "2017", CODEN = "????", DOI = "https://doi.org/10.1145/2928274", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Dec 23 10:12:41 MST 2017", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "It has been claimed that many security breaches are often caused by vulnerable (na{\"\i}ve) employees within the organization [Ponemon Institute LLC 2015a]. Thus, the weakest link in security is often not the technology itself but rather the people who use it [Schneier 2003]. In this article, we propose a machine learning scheme for detecting risky webpages and risky browsing behavior, performed by na{\"\i}ve users in the organization. The scheme analyzes the interaction between two modules: one represents na{\"\i}ve users, while the other represents risky webpages. It implements a feedback loop between these modules such that if a webpage is exposed to a lot of traffic from risky users, its ``risk score'' increases, while in a similar manner, as the user is exposed to risky webpages (with a high ``risk score''), his own ``risk score'' increases. The proposed scheme is tested on a real-world dataset of HTTP logs provided by a large American toolbar company. The results suggest that a feedback learning process involving webpages and users can improve the scoring accuracy and lead to the detection of unknown malicious webpages.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "53", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Kolman:2017:SCG, author = "Eyal Kolman and Benny Pinkas", title = "Securely Computing a Ground Speed Model", journal = j-TIST, volume = "8", number = "4", pages = "54:1--54:??", month = jul, year = "2017", CODEN = "????", DOI = "https://doi.org/10.1145/2998550", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Dec 23 10:12:41 MST 2017", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Consider a server offering risk assessment services and potential clients of these services. The risk assessment model that is run by the server is based on current and historical data of the clients. However, the clients might prefer not sharing such sensitive data with external parties such as the server, and the server might consider the possession of this data as a liability rather than an asset. Secure multi-party computation (MPC) enables one, in principle, to compute any function while hiding the inputs to the function, and would thus enable the computation of the risk assessment model while hiding the client's data from the server. However, a direct application of a generic MPC solution to this problem is rather inefficient due to the large scale of the data and the complexity of the function. We examine a specific case of risk assessment-the ground speed model. In this model, the geographical locations of successive user-authentication attempts are compared, and a warning flag is raised if the physical speed required to move between these locations is greater than some threshold, and some other conditions, such as authentication from two related networks, do not hold. We describe a very efficient secure computation solution that is tailored for this problem. This solution demonstrates that a risk model can be applied over encrypted data with sufficient efficiency to fit the requirements of commercial systems.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "54", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Kleinmann:2017:ACS, author = "Amit Kleinmann and Avishai Wool", title = "Automatic Construction of Statechart-Based Anomaly Detection Models for Multi-Threaded Industrial Control Systems", journal = j-TIST, volume = "8", number = "4", pages = "55:1--55:??", month = jul, year = "2017", CODEN = "????", DOI = "https://doi.org/10.1145/3011018", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Dec 23 10:12:41 MST 2017", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Traffic of Industrial Control System (ICS) between the Human Machine Interface (HMI) and the Programmable Logic Controller (PLC) is known to be highly periodic. However, it is sometimes multiplexed, due to asynchronous scheduling. Modeling the network traffic patterns of multiplexed ICS streams using Deterministic Finite Automata (DFA) for anomaly detection typically produces a very large DFA and a high false-alarm rate. In this article, we introduce a new modeling approach that addresses this gap. Our Statechart DFA modeling includes multiple DFAs, one per cyclic pattern, together with a DFA-selector that de-multiplexes the incoming traffic into sub-channels and sends them to their respective DFAs. We demonstrate how to automatically construct the statechart from a captured traffic stream. Our unsupervised learning algorithms first build a Discrete-Time Markov Chain (DTMC) from the stream. Next, we split the symbols into sets, one per multiplexed cycle, based on symbol frequencies and node degrees in the DTMC graph. Then, we create a sub-graph for each cycle and extract Euler cycles for each sub-graph. The final statechart is comprised of one DFA per Euler cycle. The algorithms allow for non-unique symbols, which appear in more than one cycle, and also for symbols that appear more than once in a cycle. We evaluated our solution on traces from a production ICS using the Siemens S7-0x72 protocol. We also stress-tested our algorithms on a collection of synthetically-generated traces that simulated multiplexed ICS traces with varying levels of symbol uniqueness and time overlap. The algorithms were able to split the symbols into sets with 99.6\% accuracy. The resulting statechart modeled the traces with a median false-alarm rate of as low as 0.483\%. In all but the most extreme scenarios, the Statechart model drastically reduced both the false-alarm rate and the learned model size in comparison with the naive single-DFA model.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "55", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Maltinsky:2017:NNM, author = "Alex Maltinsky and Ran Giladi and Yuval Shavitt", title = "On Network Neutrality Measurements", journal = j-TIST, volume = "8", number = "4", pages = "56:1--56:??", month = jul, year = "2017", CODEN = "????", DOI = "https://doi.org/10.1145/3040966", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Dec 23 10:12:41 MST 2017", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Network level surveillance, censorship, and various man-in-the-middle attacks target only specific types of network traffic (e.g., HTTP, HTTPS, VoIP, or Email). Therefore, packets of these types will likely receive ``special'' treatment by a transit network or a man-in-the-middle attacker. A transit Internet Service Provider (ISP) or an attacker may pass the targeted traffic through special software or equipment to gather data or perform an attack. This creates a measurable difference between the performance of the targeted traffic versus the general case. In networking terms, it violates the principle of ``network neutrality,'' which states that all traffic should be treated equally. Many techniques were designed to detect network neutrality violations, and some have naturally suggested using them to detect surveillance and censorship. In this article, we show that the existing network neutrality measurement techniques can be easily detected and therefore circumvented. We then briefly propose a new approach to overcome the drawbacks of current measurement techniques.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "56", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Hirschprung:2017:AOA, author = "Ron Hirschprung and Eran Toch and Hadas Schwartz-Chassidim and Tamir Mendel and Oded Maimon", title = "Analyzing and Optimizing Access Control Choice Architectures in Online Social Networks", journal = j-TIST, volume = "8", number = "4", pages = "57:1--57:??", month = jul, year = "2017", CODEN = "????", DOI = "https://doi.org/10.1145/3046676", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Dec 23 10:12:41 MST 2017", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "The way users manage access to their information and computers has a tremendous effect on the overall security and privacy of individuals and organizations. Usually, access management is conducted using a choice architecture, a behavioral economics concept that describes the way decisions are framed to users. Studies have consistently shown that the design of choice architectures, mainly the selection of default options, has a strong effect on the final decisions users make by nudging them toward certain behaviors. In this article, we propose a method for optimizing access control choice architectures in online social networks. We empirically evaluate the methodology on Facebook, the world's largest online social network, by measuring how well the default options cover the existing user choices and preferences and toward which outcome the choice architecture nudges users. The evaluation includes two parts: (a) collecting access control decisions made by 266 users of Facebook for a period of 3 months; and (b) surveying 533 participants who were asked to express their preferences regarding default options. We demonstrate how optimal defaults can be algorithmically identified from users' decisions and preferences, and we measure how existing defaults address users' preferences compared with the optimal ones. We analyze how access control defaults can better serve existing users, and we discuss how our method can be used to establish a common measuring tool when examining the effects of default options.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "57", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Yang:2017:TID, author = "Xitong Yang and Jiebo Luo", title = "Tracking Illicit Drug Dealing and Abuse on {Instagram} Using Multimodal Analysis", journal = j-TIST, volume = "8", number = "4", pages = "58:1--58:??", month = jul, year = "2017", CODEN = "????", DOI = "https://doi.org/10.1145/3011871", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Dec 23 10:12:41 MST 2017", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Illicit drug trade via social media sites, especially photo-oriented Instagram, has become a severe problem in recent years. As a result, tracking drug dealing and abuse on Instagram is of interest to law enforcement agencies and public health agencies. However, traditional approaches are based on manual search and browsing by trained domain experts, which suffers from the problem of poor scalability and reproducibility. In this article, we propose a novel approach to detecting drug abuse and dealing automatically by utilizing multimodal data on social media. This approach also enables us to identify drug-related posts and analyze the behavior patterns of drug-related user accounts. To better utilize multimodal data on social media, multimodal analysis methods including multi-task learning and decision-level fusion are employed in our framework. We collect three datasets using Instagram and web search engine for training and testing our models. Experiment results on expertly labeled data have demonstrated the effectiveness of our approach, as well as its scalability and reproducibility over labor-intensive conventional approaches.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "58", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Panagopoulos:2017:AEC, author = "Athanasios Aris Panagopoulos and Sasan Maleki and Alex Rogers and Matteo Venanzi and Nicholas R. Jennings", title = "Advanced Economic Control of Electricity-Based Space Heating Systems in Domestic Coalitions with Shared Intermittent Energy Resources", journal = j-TIST, volume = "8", number = "4", pages = "59:1--59:??", month = jul, year = "2017", CODEN = "????", DOI = "https://doi.org/10.1145/3041216", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Dec 23 10:12:41 MST 2017", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Over the past few years, Domestic Heating Automation Systems (DHASs) that optimize the domestic space heating control process with minimum user input, utilizing appropriate occupancy prediction technology, have emerged as commercial products (e.g., the smart thermostats from Nest and Honeywell). At the same time, many houses are being equipped with, potentially grid-connected, Intermittent Energy Resources (IERs), such as rooftop photovoltaic systems and/or small wind turbine generators. Now, in many regions of the world, such houses can sell energy to the grid but at a lower price than the price of buying it. In this context, and given the anticipated increase in electrification of heating, the next generation DHASs need to incorporate Advanced Economic Control (AEC). Such AEC can exploit the energy buffer that heating loads provide, in order to shift the consumption of electricity-based heating systems to follow the intermittent energy generation of the house. By so doing, the energy imported from the grid can be minimized and considerable monetary gains for the household can be achieved, without affecting the occupants' schedule. These benefits can be amplified still further in domestic coalitions, where a number of houses come together and share their IER generation to minimize their cumulative grid energy import. Given the above, in this work we extend a state-of-the-art DHAS, to propose AdaHeat+, a practical DHAS, that, for the first time, incorporates AEC. Our work is applicable to both individual houses and domestic coalitions and comes complete with an allocation mechanism to share the coalition gains. Importantly, we propose an effective heuristic heating schedule planning approach for collective AEC that (i) has a complexity that scales in a linear and parallelizable manner with the coalition size, and (ii) enables AdaHeat+ to handle the distinct preferences, in balancing heating cost and thermal discomfort, of the households. Our approach relies on stochastic IER power output predictions. In this context, we propose a simple and effective formulation for the site-specific calibration of such predictions based on adaptive Gaussian process modeling. Finally, we demonstrate the effectiveness of AdaHeat+ through real data evaluation, to show that collective AEC can improve heating cost-efficiency by up to 60\%, compared to independent AEC (and even more when compared to no-AEC).", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "59", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Bistaffa:2017:AGC, author = "Filippo Bistaffa and Alessandro Farinelli and Jes{\'u}s Cerquides and Juan Rodr{\'\i}guez-Aguilar and Sarvapali D. Ramchurn", title = "Algorithms for Graph-Constrained Coalition Formation in the Real World", journal = j-TIST, volume = "8", number = "4", pages = "60:1--60:??", month = jul, year = "2017", CODEN = "????", DOI = "https://doi.org/10.1145/3040967", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Dec 23 10:12:41 MST 2017", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Coalition formation typically involves the coming together of multiple, heterogeneous, agents to achieve both their individual and collective goals. In this article, we focus on a special case of coalition formation known as Graph-Constrained Coalition Formation (GCCF) whereby a network connecting the agents constrains the formation of coalitions. We focus on this type of problem given that in many real-world applications, agents may be connected by a communication network or only trust certain peers in their social network. We propose a novel representation of this problem based on the concept of edge contraction, which allows us to model the search space induced by the GCCF problem as a rooted tree. Then, we propose an anytime solution algorithm (Coalition Formation for Sparse Synergies (CFSS)), which is particularly efficient when applied to a general class of characteristic functions called m + a functions. Moreover, we show how CFSS can be efficiently parallelised to solve GCCF using a nonredundant partition of the search space. We benchmark CFSS on both synthetic and realistic scenarios, using a real-world dataset consisting of the energy consumption of a large number of households in the UK. Our results show that, in the best case, the serial version of CFSS is four orders of magnitude faster than the state of the art, while the parallel version is 9.44 times faster than the serial version on a 12-core machine. Moreover, CFSS is the first approach to provide anytime approximate solutions with quality guarantees for very large systems of agents (i.e., with more than 2,700 agents).", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "60", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{An:2017:DDF, author = "Bo An and Haipeng Chen and Noseong Park and V. S. Subrahmanian", title = "Data-Driven Frequency-Based Airline Profit Maximization", journal = j-TIST, volume = "8", number = "4", pages = "61:1--61:??", month = jul, year = "2017", CODEN = "????", DOI = "https://doi.org/10.1145/3041217", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Dec 23 10:12:41 MST 2017", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Although numerous traditional models predict market share and demand along airline routes, the prediction of existing models is not precise enough, and to the best of our knowledge, there is no use of data mining--based forecasting techniques for improving airline profitability. We propose the maximizing airline profits (MAP) architecture designed to help airlines and make two key contributions in airline market share and route demand prediction and prediction-based airline profit optimization. Compared to past methods used to forecast market share and demand along airline routes, we introduce a novel ensemble forecasting (MAP-EF) approach considering two new classes of features: (i) features derived from clusters of similar routes and (ii) features based on equilibrium pricing. We show that MAP-EF achieves much better Pearson correlation coefficients (greater than 0.95 vs. 0.82 for market share, 0.98 vs. 0.77 for demand) and R$^2$ -values compared to three state-of-the-art works for forecasting market share and demand while showing much lower variance. Using the results of MAP-EF, we develop MAP--bilevel branch and bound (MAP-BBB) and MAP-greedy (MAP-G) algorithms to optimally allocate flight frequencies over multiple routes to maximize an airline's profit. We also study two extensions of the profit maximization problem considering frequency constraints and long-term profits. Furthermore, we develop algorithms for computing Nash equilibrium frequencies when there are multiple strategic airlines. Experimental results show that airlines can increase profits by a significant margin. All experiments were conducted with data aggregated from four sources: the U.S. Bureau of Transportation Statistics (BTS), the U.S. Bureau of Economic Analysis (BEA), the National Transportation Safety Board (NTSB), and the U.S. Census Bureau (CB).", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "61", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Yao:2017:UCM, author = "Lina Yao and Quan Z. Sheng and Anne H. H. Ngu and Xue Li and Boualem Benattalah", title = "Unveiling Correlations via Mining Human-Thing Interactions in the {Web of Things}", journal = j-TIST, volume = "8", number = "5", pages = "62:1--62:??", month = sep, year = "2017", CODEN = "????", DOI = "https://doi.org/10.1145/3035967", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Dec 23 10:12:42 MST 2017", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "With recent advances in radio-frequency identification (RFID), wireless sensor networks, and Web services, physical things are becoming an integral part of the emerging ubiquitous Web. Finding correlations among ubiquitous things is a crucial prerequisite for many important applications such as things search, discovery, classification, recommendation, and composition. This article presents DisCor-T, a novel graph-based approach for discovering underlying connections of things via mining the rich content embodied in the human-thing interactions in terms of user, temporal, and spatial information. We model this various information using two graphs, namely a spatio-temporal graph and a social graph. Then, random walk with restart (RWR) is applied to find proximities among things, and a relational graph of things (RGT) indicating implicit correlations of things is learned. The correlation analysis lays a solid foundation contributing to improved effectiveness in things management and analytics. To demonstrate the utility of the proposed approach, we develop a flexible feature-based classification framework on top of RGT and perform a systematic case study. Our evaluation exhibits the strength and feasibility of the proposed approach.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "62", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Lagree:2017:YTS, author = "Paul Lagr{\'e}e and Bogdan Cautis and Hossein Vahabi", title = "As-You-Type Social Aware Search", journal = j-TIST, volume = "8", number = "5", pages = "63:1--63:??", month = sep, year = "2017", CODEN = "????", DOI = "https://doi.org/10.1145/3035969", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Dec 23 10:12:42 MST 2017", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Modern search applications feature real-time as-you-type query search. In its elementary form, the problem consists in retrieving a set of k search results, that is, performing a search with a given prefix, and showing the top-ranked results. In this article, we focus on as-you-type keyword search over social media, that is, data published by users who are interconnected through a social network. We adopt a ``network-aware'' interpretation for information relevance, by which information produced by users who are closer to the user issuing a request is considered more relevant. This query model raises new challenges for effectiveness and efficiency in online search, even when the intent of the user is fully specified, as a complete query given as input in one keystroke. This is mainly because it requires a joint exploration of the social space and traditional IR indexes, such as inverted lists. We describe a memory-efficient and incremental prefix-based retrieval algorithm, which also exhibits an anytime behavior, allowing output of the most likely answer within any chosen runtime limit. We evaluate our approach through extensive experiments for several applications and search scenarios. We consider searching for posts in microblogging (Twitter and Tumblr), for businesses (Yelp), as well as for movies (Amazon) based on reviews. We also conduct a series of experiments comparing our algorithm with baselines using state-of-the-art techniques and measuring the improvements brought by several key optimizations. They show that our solution is effective in answering real-time as-you-type searches over social media.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "63", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Gao:2017:SOL, author = "Xingyu Gao and Steven C. H. Hoi and Yongdong Zhang and Jianshe Zhou and Ji Wan and Zhenyu Chen and Jintao Li and Jianke Zhu", title = "Sparse Online Learning of Image Similarity", journal = j-TIST, volume = "8", number = "5", pages = "64:1--64:??", month = sep, year = "2017", CODEN = "????", DOI = "https://doi.org/10.1145/3065950", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Dec 23 10:12:42 MST 2017", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Learning image similarity plays a critical role in real-world multimedia information retrieval applications, especially in Content-Based Image Retrieval (CBIR) tasks, in which an accurate retrieval of visually similar objects largely relies on an effective image similarity function. Crafting a good similarity function is very challenging because visual contents of images are often represented as feature vectors in high-dimensional spaces, for example, via bag-of-words (BoW) representations, and traditional rigid similarity functions, for example, cosine similarity, are often suboptimal for CBIR tasks. In this article, we address this fundamental problem, that is, learning to optimize image similarity with sparse and high-dimensional representations from large-scale training data, and propose a novel scheme of Sparse Online Learning of Image Similarity (SOLIS). In contrast to many existing image-similarity learning algorithms that are designed to work with low-dimensional data, SOLIS is able to learn image similarity from large-scale image data in sparse and high-dimensional spaces. Our encouraging results showed that the proposed new technique achieves highly competitive accuracy as compared to the state-of-the-art approaches but enjoys significant advantages in computational efficiency, model sparsity, and retrieval scalability, making it more practical for real-world multimedia retrieval applications.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "64", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Pan:2017:TLB, author = "Weike Pan and Qiang Yang and Yuchao Duan and Ben Tan and Zhong Ming", title = "Transfer Learning for Behavior Ranking", journal = j-TIST, volume = "8", number = "5", pages = "65:1--65:??", month = sep, year = "2017", CODEN = "????", DOI = "https://doi.org/10.1145/3057732", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Dec 23 10:12:42 MST 2017", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Intelligent recommendation has been well recognized as one of the major approaches to address the information overload problem in the big data era. A typical intelligent recommendation engine usually consists of three major components, that is, data as the main input, algorithms for preference learning, and system for user interaction and high-performance computation. We observe that the data (e.g., users' behavior) are usually in different forms, such as examinations (e.g., browse and collection) and ratings, where the former are often much more abundant than the latter. Although the data are in different representations, they are both related to users' true preferences and are also deemed complementary to each other for preference learning. However, very few ranking or recommendation algorithms have been developed to exploit such two types of user behavior. In this article, we focus on jointly modeling the examination behavior and rating behavior and develop a novel and efficient ranking-oriented recommendation algorithm accordingly. First, we formally define a new recommendation problem termed behavior ranking, which aims to build a ranking-oriented model by exploiting both the examination behavior and rating behavior. Second, we develop a simple and generic transfer to rank (ToR) algorithm for behavior ranking, which transfers knowledge of candidate items from a global preference learning task to a local preference learning task. Compared with the previous work on integrating heterogeneous user behavior, our ToR algorithm is the first ranking-oriented solution, which can effectively generate recommendations in a more direct manner than those regression-oriented methods. Extensive empirical studies show that our ToR algorithm performs significantly more accurately than the state-of-the-art methods in most cases. Furthermore, our ToR algorithm is very efficient in terms of the time complexity, which is similar to those for homogeneous user behavior alone.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "65", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Agrawal:2017:HWS, author = "Rakesh Agrawal and Behzad Golshan and Evangelos E. Papalexakis", title = "Homogeneity in {Web} Search Results: Diagnosis and Mitigation", journal = j-TIST, volume = "8", number = "5", pages = "66:1--66:??", month = sep, year = "2017", CODEN = "????", DOI = "https://doi.org/10.1145/3057731", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Dec 23 10:12:42 MST 2017", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Access to diverse perspectives nurtures an informed citizenry. Google and Bing have emerged as the duopoly that largely arbitrates which English-language documents are seen by web searchers. We present our empirical study over the search results produced by Google and Bing that shows a large overlap. Thus, citizens may not gain different perspectives by simultaneously probing them for the same query. Fortunately, our study also shows that by mining Twitter data, one can obtain search results that are quite distinct from those produced by Google, Bing, and Bing News. Additionally, the users found those results to be quite informative. We also present two novel tools we designed for this study. One uses tensor analysis to derive low-dimensional compact representation of search results and study their behavior over time. The other uses machine learning and quantifies the similarity of results between two search engines by framing it as a prediction problem. Although these tools have different underpinnings, the analytical results obtained using them corroborate each other, which reinforces the confidence one can place in them for finding meaningful insights from big data.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "66", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Hu:2017:VCF, author = "Zhenhen Hu and Yonggang Wen and Luoqi Liu and Jianguo Jiang and Richang Hong and Meng Wang and Shuicheng Yan", title = "Visual Classification of Furniture Styles", journal = j-TIST, volume = "8", number = "5", pages = "67:1--67:??", month = sep, year = "2017", CODEN = "????", DOI = "https://doi.org/10.1145/3065951", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Dec 23 10:12:42 MST 2017", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Furniture style describes the discriminative appearance characteristics of furniture. It plays an important role in real-world indoor decoration. In this article, we explore the furniture style features and study the problem of furniture style classification. Differing from traditional object classification, furniture style classification aims at classifying different furniture in terms of the ``style'' that describes its appearance (e.g., American style, Gothic style, Rococo style, etc.) rather than the ``kind'' that is more related to its functional structure (e.g., bed, desk, etc.). To pursue efficient furniture style features, we construct a novel dataset of furniture styles that contains 16 common style categories and implement three strategies with respect to two categories of classification, that is, handcrafted classification and learning-based classification. First, we follow the typical image classification pipeline to extract the handcrafted features and train the classifier by support vector machine. Then we use the convolutional neural network to extract learning-based features from training images. To obtain comprehensive furniture style features, we finally combine the handcrafted image classification pipeline and the learning-based network. We experimentally evaluate the performances of handcrafted features and learning-based features of each strategy, and the results show the superiority of learning-based features and also the comprehensiveness of handcrafted features.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "67", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Ou:2017:AIV, author = "Xinyu Ou and Hefei Ling and Han Yu and Ping Li and Fuhao Zou and Si Liu", title = "Adult Image and Video Recognition by a Deep Multicontext Network and Fine-to-Coarse Strategy", journal = j-TIST, volume = "8", number = "5", pages = "68:1--68:??", month = sep, year = "2017", CODEN = "????", DOI = "https://doi.org/10.1145/3057733", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Dec 23 10:12:42 MST 2017", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Adult image and video recognition is an important and challenging problem in the real world. Low-level feature cues do not produce good enough information, especially when the dataset is very large and has various data distributions. This issue raises a serious problem for conventional approaches. In this article, we tackle this problem by proposing a deep multicontext network with fine-to-coarse strategy for adult image and video recognition. We employ a deep convolution networks to model fusion features of sensitive objects in images. Global contexts and local contexts are both taken into consideration and are jointly modeled in a unified multicontext deep learning framework. To make the model more discriminative for diverse target objects, we investigate a novel hierarchical method, and a task-specific fine-to-coarse strategy is designed to make the multicontext modeling more suitable for adult object recognition. Furthermore, some recently proposed deep models are investigated. Our approach is extensively evaluated on four different datasets. One dataset is used for ablation experiments, whereas others are used for generalization experiments. Results show significant and consistent improvements over the state-of-the-art methods.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "68", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Ottens:2017:DUC, author = "Brammert Ottens and Christos Dimitrakakis and Boi Faltings", title = "{DUCT}: an Upper Confidence Bound Approach to Distributed Constraint Optimization Problems", journal = j-TIST, volume = "8", number = "5", pages = "69:1--69:??", month = sep, year = "2017", CODEN = "????", DOI = "https://doi.org/10.1145/3066156", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Dec 23 10:12:42 MST 2017", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "We propose a distributed upper confidence bound approach, DUCT, for solving distributed constraint optimization problems. We compare four variants of this approach with a baseline random sampling algorithm, as well as other complete and incomplete algorithms for DCOPs. Under general assumptions, we theoretically show that the solution found by DUCT after T steps is approximately T$^{-1}$ -close to the optimal. Experimentally, we show that DUCT matches the optimal solution found by the well-known DPOP and O-DPOP algorithms on moderate-size problems, while always requiring less agent communication. For larger problems, where DPOP fails, we show that DUCT produces significantly better solutions than local, incomplete algorithms. Overall, we believe that DUCT is a practical, scalable algorithm for complex DCOPs.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "69", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Assem:2017:RRC, author = "Haytham Assem and Teodora Sandra Buda and Declan O'Sullivan", title = "{RCMC}: Recognizing Crowd-Mobility Patterns in Cities Based on Location Based Social Networks Data", journal = j-TIST, volume = "8", number = "5", pages = "70:1--70:??", month = sep, year = "2017", CODEN = "????", DOI = "https://doi.org/10.1145/3086636", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Dec 23 10:12:42 MST 2017", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "During the past few years, the analysis of data generated from Location-Based Social Networks (LBSNs) have aided in the identification of urban patterns, understanding activity behaviours in urban areas, as well as producing novel recommender systems that facilitate users' choices. Recognizing crowd-mobility patterns in cities is very important for public safety, traffic managment, disaster management, and urban planning. In this article, we propose a framework for Recognizing the Crowd Mobility Patterns in Cities using LBSN data. Our proposed framework comprises four main components: data gathering, recurrent crowd-mobility patterns extraction, temporal functional regions detection, and visualization component. More specifically, we employ a novel approach based on Non-negative Matrix Factorization and Gaussian Kernel Density Estimation for extracting the recurrent crowd-mobility patterns in cities illustrating how crowd shifts from one area to another during each day across various time slots. Moreover, the framework employs a hierarchical clustering-based algorithm for identifying what we refer to as temporal functional regions by modeling functional areas taking into account temporal variation by means of check-ins' categories. We build the framework using a spatial-temporal dataset crawled from Twitter for two entire years (2013 and 2014) for the area of Manhattan in New York City. We perform a detailed analysis of the extracted crowd patterns with an exploratory visualization showing that our proposed approach can identify clearly obvious mobility patterns that recur over time and location in the urban scenario. Using same time interval, we show that correlating the temporal functional regions with the recognized recurrent crowd-mobility patterns can yield to a deeper understanding of city dynamics and the motivation behind the crowd mobility. We are confident that our proposed framework not only can help in managing complex city environments and better allocation of resources based on the expected crowd mobility and temporal functional regions but also can have a direct implication on a variety of applications such as personalized recommender systems, anomalous event detection, disaster resilience management systems, and others.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "70", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Cui:2017:ACF, author = "Chaoran Cui and Jialie Shen and Liqiang Nie and Richang Hong and Jun Ma", title = "Augmented Collaborative Filtering for Sparseness Reduction in Personalized {POI} Recommendation", journal = j-TIST, volume = "8", number = "5", pages = "71:1--71:??", month = sep, year = "2017", CODEN = "????", DOI = "https://doi.org/10.1145/3086635", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Dec 23 10:12:42 MST 2017", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "As mobile device penetration increases, it has become pervasive for images to be associated with locations in the form of geotags. Geotags bridge the gap between the physical world and the cyberspace, giving rise to new opportunities to extract further insights into user preferences and behaviors. In this article, we aim to exploit geotagged photos from online photo-sharing sites for the purpose of personalized Point-of-Interest (POI) recommendation. Owing to the fact that most users have only very limited travel experiences, data sparseness poses a formidable challenge to personalized POI recommendation. To alleviate data sparseness, we propose to augment current collaborative filtering algorithms along from multiple perspectives. Specifically, hybrid preference cues comprising user-uploaded and user-favored photos are harvested to study users' tastes. Moreover, heterogeneous high-order relationship information is jointly captured from user social networks and POI multimodal contents with hypergraph models. We also build upon the matrix factorization algorithm to integrate the disparate sources of preference and relationship information, and apply our approach to directly optimize user preference rankings. Extensive experiments on a large and publicly accessible dataset well verified the potential of our approach for addressing data sparseness and offering quality recommendations to users, especially for those who have only limited travel experiences.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "71", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Zhao:2017:PLS, author = "Hongke Zhao and Yong Ge and Qi Liu and Guifeng Wang and Enhong Chen and Hefu Zhang", title = "{P2P} Lending Survey: Platforms, Recent Advances and Prospects", journal = j-TIST, volume = "8", number = "6", pages = "72:1--72:??", month = sep, year = "2017", CODEN = "????", DOI = "https://doi.org/10.1145/3078848", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Dec 23 10:12:42 MST 2017", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "P2P lending is an emerging Internet-based application where individuals can directly borrow money from each other. The past decade has witnessed the rapid development and prevalence of online P2P lending platforms, examples of which include Prosper, LendingClub, and Kiva. Meanwhile, extensive research has been done that mainly focuses on the studies of platform mechanisms and transaction data. In this article, we provide a comprehensive survey on the research about P2P lending, which, to the best of our knowledge, is the first focused effort in this field. Specifically, we first provide a systematic taxonomy for P2P lending by summarizing different types of mainstream platforms and comparing their working mechanisms in detail. Then, we review and organize the recent advances on P2P lending from various perspectives (e.g., economics and sociology perspective, and data-driven perspective). Finally, we propose our opinions on the prospects of P2P lending and suggest some future research directions in this field. Meanwhile, throughout this paper, some analysis on real-world data collected from Prosper and Kiva are also conducted.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "72", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Feyisetan:2017:SIP, author = "Oluwaseyi Feyisetan and Elena Simperl", title = "Social Incentives in Paid Collaborative Crowdsourcing", journal = j-TIST, volume = "8", number = "6", pages = "73:1--73:??", month = sep, year = "2017", CODEN = "????", DOI = "https://doi.org/10.1145/3078852", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Dec 23 10:12:42 MST 2017", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Paid microtask crowdsourcing has traditionally been approached as an individual activity, with units of work created and completed independently by the members of the crowd. Other forms of crowdsourcing have, however, embraced more varied models, which allow for a greater level of participant interaction and collaboration. This article studies the feasibility and uptake of such an approach in the context of paid microtasks. Specifically, we compare engagement, task output, and task accuracy in a paired-worker model with the traditional, single-worker version. Our experiments indicate that collaboration leads to better accuracy and more output, which, in turn, translates into lower costs. We then explore the role of the social flow and social pressure generated by collaborating partners as sources of incentives for improved performance. We utilise a Bayesian method in conjunction with interface interaction behaviours to detect when one of the workers in a pair tries to exit the task. Upon this realisation, the other worker is presented with the opportunity to contact the exiting partner to stay: either for personal financial reasons (i.e., they have not completed enough tasks to qualify for a payment) or for fun (i.e., they are enjoying the task). The findings reveal that: (1) these socially motivated incentives can act as furtherance mechanisms to help workers attain and exceed their task requirements and produce better results than baseline collaborations; (2) microtask crowd workers are empathic (as opposed to selfish) agents, willing to go the extra mile to help their partners get paid; and, (3) social furtherance incentives create a win-win scenario for the requester and for the workers by helping more workers get paid by re-engaging them before they drop out.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "73", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Khezerlou:2017:TFA, author = "Amin Vahedian Khezerlou and Xun Zhou and Lufan Li and Zubair Shafiq and Alex X. Liu and Fan Zhang", title = "A Traffic Flow Approach to Early Detection of Gathering Events: Comprehensive Results", journal = j-TIST, volume = "8", number = "6", pages = "74:1--74:??", month = sep, year = "2017", CODEN = "????", DOI = "https://doi.org/10.1145/3078850", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Dec 23 10:12:42 MST 2017", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Given a spatial field and the traffic flow between neighboring locations, the early detection of gathering events ( edge) problem aims to discover and localize a set of most likely gathering events. It is important for city planners to identify emerging gathering events that might cause public safety or sustainability concerns. However, it is challenging to solve the edge problem due to numerous candidate gathering footprints in a spatial field and the nontrivial task of balancing pattern quality and computational efficiency. Prior solutions to model the edge problem lack the ability to describe the dynamic flow of traffic and the potential gathering destinations because they rely on static or undirected footprints. In our recent work, we modeled the footprint of a gathering event as a Gathering Graph (G-Graph), where the root of the directed acyclic G-Graph is the potential destination and the directed edges represent the most likely paths traffic takes to move toward the destination. We also proposed an efficient algorithm called SmartEdge to discover the most likely nonoverlapping G-Graphs in the given spatial field. However, it is challenging to perform a systematic performance study of the proposed algorithm, due to unavailability of the ground truth of gathering events. In this article, we introduce an event simulation mechanism, which makes it possible to conduct a comprehensive performance study of the SmartEdge algorithm. We measure the quality of the detected patterns, in a systematic way, in terms of timeliness and location accuracy. The results show that, on average, the SmartEdge algorithm is able to detect patterns within a grid cell away (less than 500 meters) of the simulated events and detect patterns of the simulated events as early as 10 minutes prior to the first arrival to the gathering event.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "74", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Feng:2017:MHC, author = "Xiaodong Feng and Sen Wu and Wenjun Zhou", title = "Multi-Hypergraph Consistent Sparse Coding", journal = j-TIST, volume = "8", number = "6", pages = "75:1--75:??", month = sep, year = "2017", CODEN = "????", DOI = "https://doi.org/10.1145/3078846", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Dec 23 10:12:42 MST 2017", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Sparse representation has been a powerful technique for modeling high-dimensional data. As an unsupervised technique to extract sparse representations, sparse coding encodes the original data into a new sparse code space and simultaneously learns a dictionary representing high-level semantics. Existing methods have considered local manifold within high-dimensional data using graph/hypergraph Laplacian regularization, and more from the manifold could be utilized to improve the performance. In this article, we propose to further regulate the sparse coding so that the learned sparse codes can well reconstruct the hypergraph structure. In particular, we add a novel hypergraph consistency regularization term (HC) by minimizing the reconstruction error of the hypergraph incidence or weight matrix. Moreover, we extend the HC term to multi-hypergraph consistent sparse coding (MultiCSC) and automatically select the optimal manifold structure under the multi-hypergraph learning framework. We show that the optimization of MultiCSC can be solved efficiently, and that several existing sparse coding methods can fit into the general framework of MultiCSC as special cases. As a case study, hypergraph incidence consistent sparse coding is applied to perform semi-auto image tagging, demonstrating the effectiveness of hypergraph consistency regulation. We perform further experiments using MultiCSC for image clustering, which outperforms a number of baselines.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "75", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Burt:2017:ISI, author = "Ronald Burt and Jie Tang and Michalis Vazirgiannis and Shuang Yang", title = "Introduction to Special Issue on Social Media Processing ({TIST --- SMP})", journal = j-TIST, volume = "8", number = "6", pages = "76:1--76:??", month = sep, year = "2017", CODEN = "????", DOI = "https://doi.org/10.1145/3110318", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Dec 23 10:12:42 MST 2017", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "76", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Li:2017:PMR, author = "Yang Li and Jing Jiang and Ting Liu and Minghui Qiu and Xiaofei Sun", title = "Personalized Microtopic Recommendation on Microblogs", journal = j-TIST, volume = "8", number = "6", pages = "77:1--77:??", month = sep, year = "2017", CODEN = "????", DOI = "https://doi.org/10.1145/2932192", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Dec 23 10:12:42 MST 2017", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Microblogging services such as Sina Weibo and Twitter allow users to create tags explicitly indicated by the \# symbol. In Sina Weibo, these tags are called microtopics, and in Twitter, they are called hashtags. In Sina Weibo, each microtopic has a designate page and can be directly visited or commented on. Recommending these microtopics to users based on their interests can help users efficiently acquire information. However, it is non-trivial to recommend microtopics to users to satisfy their information needs. In this article, we investigate the task of personalized microtopic recommendation, which exhibits two challenges. First, users usually do not give explicit ratings to microtopics. Second, there exists rich information about users and microtopics, for example, users' published content and biographical information, but it is not clear how to best utilize such information. To address the above two challenges, we propose a joint probabilistic latent factor model to integrate rich information into a matrix factorization-based solution to microtopic recommendation. Our model builds on top of collaborative filtering, content analysis, and feature regression. Using two real-world datasets, we evaluate our model with different kinds of content and contextual information. Experimental results show that our model significantly outperforms a few competitive baseline methods, especially in the circumstance where users have few adoption behaviors.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "77", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Glenski:2017:RES, author = "Maria Glenski and Tim Weninger", title = "Rating Effects on Social News Posts and Comments", journal = j-TIST, volume = "8", number = "6", pages = "78:1--78:??", month = sep, year = "2017", CODEN = "????", DOI = "https://doi.org/10.1145/2963104", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Dec 23 10:12:42 MST 2017", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "At a time when information seekers first turn to digital sources for news and opinion, it is critical that we understand the role that social media plays in human behavior. This is especially true when information consumers also act as information producers and editors through their online activity. In order to better understand the effects that editorial ratings have on online human behavior, we report the results of a two large-scale in vivo experiments in social media. We find that small, random rating manipulations on social media posts and comments created significant changes in downstream ratings, resulting in significantly different final outcomes. We found positive herding effects for positive treatments on posts, increasing the final rating by 11.02\% on average, but not for positive treatments on comments. Contrary to the results of related work, we found negative herding effects for negative treatments on posts and comments, decreasing the final ratings, on average, of posts by 5.15\% and of comments by 37.4\%. Compared to the control group, the probability of reaching a high rating ($ \geq 2000$) for posts is increased by 24.6\% when posts receive the positive treatment and for comments it is decreased by 46.6\% when comments receive the negative treatment.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "78", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Chen:2017:ECB, author = "Chien-Cheng Chen and Kuo-Wei Hsu and Wen-Chih Peng", title = "Exploring Communication Behaviors of Users to Target Potential Users in Mobile Social Networks", journal = j-TIST, volume = "8", number = "6", pages = "79:1--79:??", month = sep, year = "2017", CODEN = "????", DOI = "https://doi.org/10.1145/3022472", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Dec 23 10:12:42 MST 2017", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "In mobile communication services, users can communicate with each other over different telecommunication carriers. For telecom operators, how to acquire and retain users is a significant and practical task. Note that telecom operators only have their own customer profiles. For the users from other telecom operators, their information is sparse. Thus, given a set of communication logs, the main theme of our work is to identify the potential users who will possibly join the target services in the near future. Since only a limited amount of information is available, one challenging issue is how to extract features from the communication logs. In this article, we propose a Communication-Based Feature Generation (CBFG) framework that extracts features and builds models to infer the potential users. Explicitly, we construct a heterogeneous information network from the communication logs of users. Then, we extract the explicit features, which refer to those calling features of users, from the potential users' interaction behaviors in the heterogeneous information network. Moreover, from the calling behaviors of users, one could extract the possible community structures of users. Based on the community structures, we further extract the implicit features of users. In light of both explicit and implicit features, we propose an information-gain-based method to select the effective features. According to the features selected, we utilize three popular classifiers (i.e., AdaBoost, Random Forest, and SVM) to build models to target the potential users. In addition, we have designed a sampling approach to extract training data for classifiers. To evaluate our methods, we have conducted experiments on a real dataset. The results of our experiments show that the features extracted by our proposed method can be effective for targeting the potential users.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "79", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Huang:2017:UAI, author = "Chao Huang and Dong Wang and Jun Tao", title = "An Unsupervised Approach to Inferring the Localness of People Using Incomplete Geotemporal Online Check-In Data", journal = j-TIST, volume = "8", number = "6", pages = "80:1--80:??", month = sep, year = "2017", CODEN = "????", DOI = "https://doi.org/10.1145/3022471", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Dec 23 10:12:42 MST 2017", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Inferring the localness of people is to classify people who are local residents in a city from people who visit the city by analyzing online check-in points that are contributed by online users. This information is critical for the urban planning, user profiling, and localized recommendation systems. Supervised learning approaches have been developed to infer the location of people in a city by assuming the availability of high-quality training datasets with complete geotemporal information. In this article, we develop an unsupervised model to accurately identify local people in a city by using the incomplete online check-in data that are publicly available. In particular, we develop an incomplete geotemporal expectation maximization (IGT-EM) scheme, which incorporates a set of hidden variables to represent the localness of people and a set of estimation parameters to represent the likelihood of venues to attract local and nonlocal people, respectively. Our solution can accurately classify local people from nonlocal nones without requiring any training data. We also implement a parallel IGT-EM algorithm by leveraging the computing power of a graphic processing unit (GPU) that consists of 2,496 cores. In the evaluation, we compare our new approach with the existing solutions through four real-world case studies using data from the New York City, Chicago, Boston, and Washington, DC. The results show that our approach can identify the local people and significantly outperform the compared baselines in estimation accuracy and execution time.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "80", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Tu:2017:PPI, author = "Cunchao Tu and Zhiyuan Liu and Huanbo Luan and Maosong Sun", title = "{PRISM}: Profession Identification in Social Media", journal = j-TIST, volume = "8", number = "6", pages = "81:1--81:??", month = sep, year = "2017", CODEN = "????", DOI = "https://doi.org/10.1145/3070665", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Dec 23 10:12:42 MST 2017", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Profession is an important social attribute of people. It plays a crucial role in commercial services such as personalized recommendation and targeted advertising. In practice, profession information is usually unavailable due to privacy and other reasons. In this article, we explore the task of identifying user professions according to their behaviors in social media. The task confronts the following challenges that make it non-trivial: how to incorporate heterogeneous information of user behaviors, how to effectively utilize both labeled and unlabeled data, and how to exploit community structure. To address these challenges, we present a framework called Profession Identification in Social Media. It takes advantage of both personal information and community structure of users in the following aspects: (1) We present a cascaded two-level classifier with heterogeneous personal features to measure the confidence of users belonging to different professions. (2) We present a multi-training process to take advantages of both labeled and unlabeled data to enhance classification performance. (3) We design a profession identification method synthetically considering the confidences from personal features and community structure. We collect a real-world dataset to conduct experiments, and experimental results demonstrate the significant effectiveness of our method compared with other baseline methods. By applying prediction on large-scale users, we also analyze characteristics of microblog users, finding that there are significant diversities among users of different professions in demographics, social network structures, and linguistic styles.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "81", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Chikhaoui:2017:DCA, author = "Belkacem Chikhaoui and Mauricio Chiazzaro and Shengrui Wang and Martin Sotir", title = "Detecting Communities of Authority and Analyzing Their Influence in Dynamic Social Networks", journal = j-TIST, volume = "8", number = "6", pages = "82:1--82:??", month = sep, year = "2017", CODEN = "????", DOI = "https://doi.org/10.1145/3070658", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Dec 23 10:12:42 MST 2017", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Users in real-world social networks are organized into communities that differ from each other in terms of influence, authority, interest, size, etc. This article addresses the problems of detecting communities of authority and of estimating the influence of such communities in dynamic social networks. These are new issues that have not yet been addressed in the literature, and they are important in applications such as marketing and recommender systems. To facilitate the identification of communities of authority, our approach first detects communities sharing common interests, which we call ``meta-communities,'' by incorporating topic modeling based on users' community memberships. Then, communities of authority are extracted with respect to each meta-community, using a new measure based on the betweenness centrality. To assess the influence between communities over time, we propose a new model based on the Granger causality method. Through extensive experiments on a variety of social network datasets, we empirically demonstrate the suitability of our approach for community-of-authority detection and assessment of the influence between communities over time.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "82", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Fu:2017:RSD, author = "Hao Fu and Xing Xie and Yong Rui and Neil Zhenqiang Gong and Guangzhong Sun and Enhong Chen", title = "Robust Spammer Detection in Microblogs: Leveraging User Carefulness", journal = j-TIST, volume = "8", number = "6", pages = "83:1--83:??", month = sep, year = "2017", CODEN = "????", DOI = "https://doi.org/10.1145/3086637", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Dec 23 10:12:42 MST 2017", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Microblogging Web sites, such as Twitter and Sina Weibo, have become popular platforms for socializing and sharing information in recent years. Spammers have also discovered this new opportunity to unfairly overpower normal users with unsolicited content, namely social spams. Although it is intuitive for everyone to follow legitimate users, recent studies show that both legitimate users and spammers follow spammers for different reasons. Evidence of users seeking spammers on purpose is also observed. We regard this behavior as useful information for spammer detection. In this article, we approach the problem of spammer detection by leveraging the ``carefulness'' of users, which indicates how careful a user is when she is about to follow a potential spammer. We propose a framework to measure the carefulness and develop a supervised learning algorithm to estimate it based on known spammers and legitimate users. We illustrate how the robustness of the detection algorithms can be improved with aid of the proposed measure. Evaluation on two real datasets from Sina Weibo and Twitter with millions of users are performed, as well as an online test on Sina Weibo. The results show that our approach indeed captures the carefulness, and it is effective for detecting spammers. In addition, we find that our measure is also beneficial for other applications, such as link prediction.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "83", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Li:2017:RGR, author = "Xuelong Li and Guosheng Cui and Yongsheng Dong", title = "Refined-Graph Regularization-Based Nonnegative Matrix Factorization", journal = j-TIST, volume = "9", number = "1", pages = "1:1--1:??", month = oct, year = "2017", CODEN = "????", DOI = "https://doi.org/10.1145/3090312", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Dec 23 10:12:42 MST 2017", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Nonnegative matrix factorization (NMF) is one of the most popular data representation methods in the field of computer vision and pattern recognition. High-dimension data are usually assumed to be sampled from the submanifold embedded in the original high-dimension space. To preserve the locality geometric structure of the data, $k$-nearest neighbor ($k$-NN) graph is often constructed to encode the near-neighbor layout structure. However, $k$-NN graph is based on Euclidean distance, which is sensitive to noise and outliers. In this article, we propose a refined-graph regularized nonnegative matrix factorization by employing a manifold regularized least-squares regression (MRLSR) method to compute the refined graph. In particular, each sample is represented by the whole dataset regularized with $ l_2$-norm and Laplacian regularizer. Then a MRLSR graph is constructed based on the representative coefficients of each sample. Moreover, we present two optimization schemes to generate refined-graphs by employing a hard-thresholding technique. We further propose two refined-graph regularized nonnegative matrix factorization methods and use them to perform image clustering. Experimental results on several image datasets reveal that they outperform 11 representative methods.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "1", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Li:2017:MAO, author = "Zhifeng Li and Dihong Gong and Kai Zhu and Dacheng Tao and Xuelong Li", title = "Multifeature Anisotropic Orthogonal {Gaussian} Process for Automatic Age Estimation", journal = j-TIST, volume = "9", number = "1", pages = "2:1--2:??", month = oct, year = "2017", CODEN = "????", DOI = "https://doi.org/10.1145/3090311", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Dec 23 10:12:42 MST 2017", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Automatic age estimation is an important yet challenging problem. It has many promising applications in social media. Of the existing age estimation algorithms, the personalized approaches are among the most popular ones. However, most person-specific approaches rely heavily on the availability of training images across different ages for a single subject, which is usually difficult to satisfy in practical application of age estimation. To address this limitation, we first propose a new model called Orthogonal Gaussian Process (OGP), which is not restricted by the number of training samples per person. In addition, without sacrifice of discriminative power, OGP is much more computationally efficient than the standard Gaussian Process. Based on OGP, we then develop an effective age estimation approach, namely anisotropic OGP (A-OGP), to further reduce the estimation error. A-OGP is based on an anisotropic noise level learning scheme that contributes to better age estimation performance. To finally optimize the performance of age estimation, we propose a multifeature A-OGP fusion framework that uses multiple features combined with a random sampling method in the feature space. Extensive experiments on several public domain face aging datasets (FG-NET, MORPH Album1, and MORPH Album 2) are conducted to demonstrate the state-of-the-art estimation accuracy of our new algorithms.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "2", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Gao:2017:FSV, author = "Yang Gao and Yuefeng Li and Raymond Y. K. Lau and Yue Xu and Md Abul Bashar", title = "Finding Semantically Valid and Relevant Topics by Association-Based Topic Selection Model", journal = j-TIST, volume = "9", number = "1", pages = "3:1--3:??", month = oct, year = "2017", CODEN = "????", DOI = "https://doi.org/10.1145/3094786", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Dec 23 10:12:42 MST 2017", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Topic modelling methods such as Latent Dirichlet Allocation (LDA) have been successfully applied to various fields, since these methods can effectively characterize document collections by using a mixture of semantically rich topics. So far, many models have been proposed. However, the existing models typically outperform on full analysis on the whole collection to find all topics but difficult to capture coherent and specifically meaningful topic representations. Furthermore, it is very challenging to incorporate user preferences into existing topic modelling methods to extract relevant topics. To address these problems, we develop a novel personalized Association-based Topic Selection (ATS) model, which can identify semantically valid and relevant topics from a set of raw topics based on the semantical relatedness between users' preferences and the structured patterns captured in topics. The advantage of the proposed ATS model is that it enables an interactive topic modelling process driven by users' specific interests. Based on three benchmark datasets, namely, RCV1, R8, and WT10G under the context of information filtering (IF) and information retrieval (IR), our rigorous experiments show that the proposed ATS model can effectively identify relevant topics with respect to users' specific interests, and hence to improve the performance of IF and IR.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "3", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Zhu:2017:ATS, author = "Wenwu Zhu and Jean Walrand and Yike Guo and Zhi Wang", title = "{ACM TIST} Special Issue on Data-Driven Intelligence for Wireless Networking", journal = j-TIST, volume = "9", number = "1", pages = "4:1--4:??", month = oct, year = "2017", CODEN = "????", DOI = "https://doi.org/10.1145/3104984", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Dec 23 10:12:42 MST 2017", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "4", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Fan:2017:RMA, author = "Xiaoyi Fan and Wei Gong and Jiangchuan Liu", title = "{i$^2$ tag}: {RFID} Mobility and Activity Identification Through Intelligent Profiling", journal = j-TIST, volume = "9", number = "1", pages = "5:1--5:??", month = oct, year = "2017", CODEN = "????", DOI = "https://doi.org/10.1145/3035968", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Dec 23 10:12:42 MST 2017", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Many radio frequency identification (RFID) applications, such as virtual shopping cart and tag-assisted gaming, involve sensing and recognizing tag mobility. However, existing RFID localization methods are mostly designed for static or slowly moving targets (less than 0.3m/sec). More importantly, we observe that prior methods suffer from serious performance degradation for detecting real-world moving tags in typical indoor environments with multipath interference. In this article, we present i$^2$ tag, an intelligent mobility-aware activity identification system for RFID tags in multipath-rich environments (e.g., indoors). i$^2$ tag employs a supervised learning framework based on our novel fine-grain mobility profile, which can quantify different levels of mobility. Unlike previous methods that mostly rely on phase measurement, i$^2$ tag takes into account various measurements, including RSSI variance, packet loss rate, and our novel relative phase--based fingerprint. Additionally, we design a multidimensional dynamic time warping--based algorithm to robustly detect mobility and the associated activities. We show that i$^2$ tag is readily deployable using off-the-shelf RFID devices. A prototype has been implemented using a ThingMagic reader and standard-compatible tags. Experimental results demonstrate its superiority in mobility detection and activity identification in various indoor environments.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "5", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Zhang:2017:EEM, author = "Wei Zhang and Rui Fan and Yonggang Wen and Fang Liu", title = "Energy-Efficient Mobile Video Streaming: a Location-Aware Approach", journal = j-TIST, volume = "9", number = "1", pages = "6:1--6:??", month = oct, year = "2017", CODEN = "????", DOI = "https://doi.org/10.1145/3102301", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Dec 23 10:12:42 MST 2017", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Video streaming is one of the most widely used mobile applications today, and it also accounts for a large fraction of mobile battery usage. Much of the energy consumption is for wireless data transmission and is highly correlated to network bandwidth conditions. In periods of poor connectivity, up to 90\% of mobile energy can be used for wireless data transfer. In this article, we study the problem of energy-efficient mobile video streaming. We make use of the observed correlation between bandwidth and user location, and also observe that a user's location is predictable in many situations, such as when commuting to a known destination. Based on the user's predicted locations and bandwidth conditions, we optimize wireless transmission times to achieve high quality video playback while minimizing energy use. We propose an optimal offline algorithm for this problem, which runs in O ( Tk ) time, where T is the duration of the video and k is the size of the video buffer. We also propose LAWS, a Location AWare Streaming algorithm. LAWS learns from historical location-aware bandwidth conditions and predicts future bandwidths along a planned route to make online wireless download decisions. We evaluate LAWS using real bandwidth traces, and show that LAWS closely approximates the performance of the optimal offline algorithm, achieving 90.6\% of the optimal performance on average, and 97\% in certain cases. LAWS also outperforms three popular strategies used in practice by, on average, 69\%, 63\%, and 38\%, respectively. Lastly, we show that LAWS is able to deal with noisy data and can attain the stated performance after sampling bandwidth conditions only five times.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "6", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Yin:2017:UUI, author = "Hao Yin and Wei Wang and Xu Zhang and Yongqiang Lyu and Geyong Min and Dongchao Guo", title = "{UMCR}: User Interaction-Driven Mobile Content Retrieval", journal = j-TIST, volume = "9", number = "1", pages = "7:1--7:??", month = oct, year = "2017", CODEN = "????", DOI = "https://doi.org/10.1145/3102292", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Dec 23 10:12:42 MST 2017", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Although mobile application ecosystems have experienced tremendous growth in recent years, retrieving content of mobile applications that serves a key to mobile content search engines still faces grand challenges. Compared to web content retrieval, it is much more difficult to capture content in mobile applications due to the diversity of applications and the lack of Uniform Resource Locator indices. In this study, we propose and implement a user interaction-driven mobile content retrieval (UMCR) system to address such issues, which is the first mobile content crawler in the current literature. UMCR is a distributed system that contains many measurement nodes, each of which combines the user interaction path traversing (UIPT) and Deep Package Inspection (DPI) together to obtain mobile content. UIPT determines the events of user interactions in various applications to capture the static content such as text and images, in which a traversal depth termination scheme and an optional cut-off component are adopted to balance the content coverage and traversing efficiency. Meanwhile, the analysis based on DPI is responsible for extracting the videos as well as digging the infrastructural information and performance metrics. In addition, a distributed traversal scheduling method is designed for UIPT tasks to improve the throughput and scalability in large-scale content retrieval. Experiments on retrieving content of 64 real mobile applications demonstrate that UMCR can handle diverse mobile applications efficiently. The scheduler can improve throughput by 3 times compared to the legacy arbitrary task assignment strategy.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "7", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Wang:2017:TTD, author = "Xuyu Wang and Chao Yang and Shiwen Mao", title = "{TensorBeat}: Tensor Decomposition for Monitoring Multiperson Breathing Beats with Commodity {WiFi}", journal = j-TIST, volume = "9", number = "1", pages = "8:1--8:??", month = oct, year = "2017", CODEN = "????", DOI = "https://doi.org/10.1145/3078855", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Dec 23 10:12:42 MST 2017", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Breathing signal monitoring can provide important clues for health problems. Compared to existing techniques that require wearable devices and special equipment, a more desirable approach is to provide contact-free and long-term breathing rate monitoring by exploiting wireless signals. In this article, we propose TensorBeat, a system to employ channel state information (CSI) phase difference data to intelligently estimate breathing rates for multiple persons with commodity WiFi devices. The main idea is to leverage the tensor decomposition technique to handle the CSI phase difference data. The proposed TensorBeat scheme first obtains CSI phase difference data between pairs of antennas at the WiFi receiver to create CSI tensors. Then canonical polyadic (CP) decomposition is applied to obtain the desired breathing signals. A stable signal matching algorithm is developed to identify the decomposed signal pairs, and a peak detection method is applied to estimate the breathing rates for multiple persons. Our experimental study shows that TensorBeat can achieve high accuracy under different environments for multiperson breathing rate monitoring.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "8", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Ying:2017:EIW, author = "Xuhang Ying and Jincheng Zhang and Lichao Yan and Yu Chen and Guanglin Zhang and Minghua Chen and Ranveer Chandra", title = "Exploring Indoor White Spaces in Metropolises", journal = j-TIST, volume = "9", number = "1", pages = "9:1--9:??", month = oct, year = "2017", CODEN = "????", DOI = "https://doi.org/10.1145/3059149", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Dec 23 10:12:42 MST 2017", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "It is a promising vision to exploit white spaces, that is, vacant VHF and UHF TV channels, to meet the rapidly growing demand for wireless data services in both outdoor and indoor scenarios. While most prior works have focused on outdoor white space, the indoor story is largely open for investigation. Motivated by this observation and discovering that 70\% of the spectrum demand comes from indoor environment, we carry out a comprehensive study to explore indoor white spaces. We first conduct a large-scale measurement study and compare outdoor and indoor TV spectrum occupancy at 30+ diverse locations in a typical metropolis-Hong Kong. Our results show that abundant white spaces are available in different areas in Hong Kong, which account for more than 50\% and 70\% of the entire TV spectrum in outdoor and indoor scenarios, respectively. Although there are substantially more white spaces indoors than outdoors, there have been very few solutions for identifying indoor white space. To fill in this gap, we develop the first data-driven, low-cost indoor white space identification system for White-space Indoor Spectrum EnhanceR (WISER), to allow secondary users to identify white spaces for communication without sensing the spectrum themselves. We design the architecture and algorithms to address the inherent challenges. We build a WISER prototype and carry out real-world experiments to evaluate its performance. Our results show that WISER can identify 30\%--40\% more indoor white spaces with negligible false alarms, as compared to alternative baseline approaches.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "9", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Yeh:2017:SIB, author = "Lo-Yao Yeh and Woei-Jiunn Tsaur and Hsin-Han Huang", title = "Secure {IoT}-Based, Incentive-Aware Emergency Personnel Dispatching Scheme with Weighted Fine-Grained Access Control", journal = j-TIST, volume = "9", number = "1", pages = "10:1--10:??", month = oct, year = "2017", CODEN = "????", DOI = "https://doi.org/10.1145/3063716", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Dec 23 10:12:42 MST 2017", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Emergency response times following a traffic accident are extremely crucial in reducing the number of traffic-related deaths. Existing emergency vehicle dispatching systems rely heavily on manual assignments. Although some technology-assisted emergency systems engage in emergency message dissemination and path planning, efficient emergency response is one of the main factors that can decrease traffic-related deaths. Obviously, effective emergency response often plays a far more important role in a successful rescue. In this article, we propose a secure IoT-based and incentive-aware emergency personnel dispatching scheme (EPDS) with weighted fine-grained access control. Our EPDS can recruit available medical personnel on-the-fly, such as physicians driving in the vicinity of the accident scene. An appropriate incentive, such as paid leave, can be offered to encourage medical personnel to join rescue missions. Furthermore, IoT-based devices are installed in vehicles or wearable on drivers to gather biometric signals from the driver, which can be used to decide precisely which divisions or physicians are needed to administer the appropriate remedy. Additionally, our scheme can cryptographically authorize the assigned rescue vehicle to control traffic to increase rescue efficacy. Our scheme also takes advantage of adjacent roadside units to organize the appropriate rescue personnel without requiring long-distance communication with a trusted traffic authority. Proof of security is provided and extensive analyses, including qualitative and quantitative analyses and simulations, show that the proposed scheme can significantly improve rescue response time and effectiveness. To the best of our knowledge, this is the first work to make use of medical personnel that are close by in emergency rescue missions.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "10", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Shen:2017:DDD, author = "Jiaxing Shen and Jiannong Cao and Xuefeng Liu and Chisheng Zhang", title = "{DMAD}: Data-Driven Measuring of {Wi-Fi} Access Point Deployment in Urban Spaces", journal = j-TIST, volume = "9", number = "1", pages = "11:1--11:??", month = oct, year = "2017", CODEN = "????", DOI = "https://doi.org/10.1145/3065949", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Dec 23 10:12:42 MST 2017", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Wireless networks offer many advantages over wired local area networks such as scalability and mobility. Strategically deployed wireless networks can achieve multiple objectives like traffic offloading, network coverage, and indoor localization. To this end, various mathematical models and optimization algorithms have been proposed to find optimal deployments of access points (APs). However, wireless signals can be blocked by the human body, especially in crowded urban spaces. As a result, the real coverage of an on-site AP deployment may shrink to some degree and lead to unexpected dead spots (areas without wireless coverage). Dead spots are undesirable, since they degrade the user experience in network service continuity, on one hand, and, on the other hand paralyze some applications and services like tracking and monitoring when users are in these areas. Nevertheless, it is nontrivial for existing methods to analyze the impact of human beings on wireless coverage. Site surveys are too time consuming and labor intensive to conduct. It is also infeasible for simulation methods to predict the number of on-site people. In this article, we propose DMAD, a Data-driven Measuring of Wi-Fi Access point Deployment, which not only estimates potential dead spots of an on-site AP deployment but also quantifies their severity, using simple Wi-Fi data collected from the on-site deployment and shop profiles from the Internet. DMAD first classifies static devices and mobile devices with a decision-tree classifier. Then it locates mobile devices to grid-level locations based on shop popularities, wireless signal, and visit duration. Last, DMAD estimates the probability of dead spots for each grid during different time slots and derives their severity considering the probability and the number of potential users. The analysis of Wi-Fi data from static devices indicates that the Pearson Correlation Coefficient of wireless coverage status and the number of on-site people is over 0.7, which confirms that human beings may have a significant impact on wireless coverage. We also conduct extensive experiments in a large shopping mall in Shenzhen. The evaluation results demonstrate that DMAD can find around 70\% of dead spots with a precision of over 70\%.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "11", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Wu:2017:TPU, author = "Yanqiu Wu and Tehila Minkus and Keith W. Ross", title = "Taking the Pulse of {US} College Campuses with Location-Based Anonymous Mobile Apps", journal = j-TIST, volume = "9", number = "1", pages = "12:1--12:??", month = oct, year = "2017", CODEN = "????", DOI = "https://doi.org/10.1145/3078843", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Dec 23 10:12:42 MST 2017", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "We deploy GPS hacking in conjunction with location-based mobile apps to passively survey users in targeted geographical regions. Specifically, we investigate surveying students at different college campuses with Yik Yak, an anonymous mobile app that is popular on US college campuses. In addition to being campus centric, Yik Yak's anonymity allows students to express themselves candidly without self-censorship. We collect nearly 1.6 million Yik Yak messages (``yaks'') from a diverse set of 45 college campuses in the United States. We use natural language processing to determine the sentiment (positive, negative, or neutral) of all of the yaks. We employ supervised machine learning to predict the gender of the authors of the yaks and then analyze how sentiment differs among the two genders on college campuses. We also use supervised machine learning to classify all the yaks into nine topics and then investigate which topics are most popular throughout the US and how topic popularity varies on the different campuses. The results in this article provide significant insight into how campus culture and student's thinking varies among US colleges and universities.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "12", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Zhang:2017:EPE, author = "Ruide Zhang and Ning Zhang and Changlai Du and Wenjing Lou and Y. Thomas Hou and Yuichi Kawamoto", title = "From Electromyogram to Password: Exploring the Privacy Impact of Wearables in Augmented Reality", journal = j-TIST, volume = "9", number = "1", pages = "13:1--13:??", month = oct, year = "2017", CODEN = "????", DOI = "https://doi.org/10.1145/3078844", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Dec 23 10:12:42 MST 2017", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "With the increasing popularity of augmented reality (AR) services, providing seamless human-computer interactions in the AR setting has received notable attention in the industry. Gesture control devices have recently emerged to be the next great gadgets for AR due to their unique ability to enable computer interaction with day-to-day gestures. While these AR devices are bringing revolutions to our interaction with the cyber world, it is also important to consider potential privacy leakages from these always-on wearable devices. Specifically, the coarse access control on current AR systems could lead to possible abuse of sensor data. Although the always-on gesture sensors are frequently quoted as a privacy concern, there has not been any study on information leakage of these devices. In this article, we present our study on side-channel information leakage of the most popular gesture control device, Myo. Using signals recorded from the electromyography (EMG) sensor and accelerometers on Myo, we can recover sensitive information such as passwords typed on a keyboard and PIN sequence entered through a touchscreen. EMG signal records subtle electric currents of muscle contractions. We design novel algorithms based on dynamic cumulative sum and wavelet transform to determine the exact time of finger movements. Furthermore, we adopt the Hudgins feature set in a support vector machine to classify recorded signal segments into individual fingers or numbers. We also apply coordinate transformation techniques to recover fine-grained spatial information with low-fidelity outputs from the sensor in keystroke recovery. We evaluated the information leakage using data collected from a group of volunteers. Our results show that there is severe privacy leakage from these commodity wearable sensors. Our system recovers complex passwords constructed with lowercase letters, uppercase letters, numbers, and symbols with a mean success rate of 91\%.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "13", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Goodwin:2018:KRI, author = "Travis R. Goodwin and Sanda M. Harabagiu", title = "Knowledge Representations and Inference Techniques for Medical Question Answering", journal = j-TIST, volume = "9", number = "2", pages = "14:1--14:??", month = jan, year = "2018", CODEN = "????", DOI = "https://doi.org/10.1145/3106745", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Mar 22 10:01:53 MDT 2018", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Answering medical questions related to complex medical cases, as required in modern Clinical Decision Support (CDS) systems, imposes (1) access to vast medical knowledge and (2) sophisticated inference techniques. In this article, we examine the representation and role of combining medical knowledge automatically derived from (a) clinical practice and (b) research findings for inferring answers to medical questions. Knowledge from medical practice was distilled from a vast Electronic Medical Record (EMR) system, while research knowledge was processed from biomedical articles available in PubMed Central. The knowledge automatically acquired from the EMR system took into account the clinical picture and therapy recognized from each medical record to generate a probabilistic Markov network denoted as a Clinical Picture and Therapy Graph (CPTG). Moreover, we represented the background of medical questions available from the description of each complex medical case as a medical knowledge sketch. We considered three possible representations of medical knowledge sketches that were used by four different probabilistic inference methods to pinpoint the answers from the CPTG. In addition, several answer-informed relevance models were developed to provide a ranked list of biomedical articles containing the answers. Evaluations on the TREC-CDS data show which of the medical knowledge representations and inference methods perform optimally. The experiments indicate an improvement of biomedical article ranking by 49\% over state-of-the-art results.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "14", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Sun:2018:SVA, author = "Guodao Sun and Tan Tang and Tai-Quan Peng and Ronghua Liang and Yingcai Wu", title = "{SocialWave}: Visual Analysis of Spatio-temporal Diffusion of Information on Social Media", journal = j-TIST, volume = "9", number = "2", pages = "15:1--15:??", month = jan, year = "2018", CODEN = "????", DOI = "https://doi.org/10.1145/3106775", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Mar 22 10:01:53 MDT 2018", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Rapid advancement of social media tremendously facilitates and accelerates the information diffusion among users around the world. How and to what extent will the information on social media achieve widespread diffusion across the world? How can we quantify the interaction between users from different geolocations in the diffusion process? How will the spatial patterns of information diffusion change over time? To address these questions, a dynamic social gravity model (SGM) is proposed to quantify the dynamic spatial interaction behavior among social media users in information diffusion. The dynamic SGM includes three factors that are theoretically significant to the spatial diffusion of information: geographic distance, cultural proximity, and linguistic similarity. Temporal dimension is also taken into account to help detect recency effect, and ground-truth data is integrated into the model to help measure the diffusion power. Furthermore, SocialWave, a visual analytic system, is developed to support both spatial and temporal investigative tasks. SocialWave provides a temporal visualization that allows users to quickly identify the overall temporal diffusion patterns, which reflect the spatial characteristics of the diffusion network. When a meaningful temporal pattern is identified, SocialWave utilizes a new occlusion-free spatial visualization, which integrates a node-link diagram into a circular cartogram for further analysis. Moreover, we propose a set of rich user interactions that enable in-depth, multi-faceted analysis of the diffusion on social media. The effectiveness and efficiency of the mathematical model and visualization system are evaluated with two datasets on social media, namely, Ebola Epidemics and Ferguson Unrest.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "15", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Zhuang:2018:SRL, author = "Fuzhen Zhuang and Xiaohu Cheng and Ping Luo and Sinno Jialin Pan and Qing He", title = "Supervised Representation Learning with Double Encoding-Layer Autoencoder for Transfer Learning", journal = j-TIST, volume = "9", number = "2", pages = "16:1--16:??", month = jan, year = "2018", CODEN = "????", DOI = "https://doi.org/10.1145/3108257", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Mar 22 10:01:53 MDT 2018", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Transfer learning has gained a lot of attention and interest in the past decade. One crucial research issue in transfer learning is how to find a good representation for instances of different domains such that the divergence between domains can be reduced with the new representation. Recently, deep learning has been proposed to learn more robust or higher-level features for transfer learning. In this article, we adapt the autoencoder technique to transfer learning and propose a supervised representation learning method based on double encoding-layer autoencoder. The proposed framework consists of two encoding layers: one for embedding and the other one for label encoding. In the embedding layer, the distribution distance of the embedded instances between the source and target domains is minimized in terms of KL-Divergence. In the label encoding layer, label information of the source domain is encoded using a softmax regression model. Moreover, to empirically explore why the proposed framework can work well for transfer learning, we propose a new effective measure based on autoencoder to compute the distribution distance between different domains. Experimental results show that the proposed new measure can better reflect the degree of transfer difficulty and has stronger correlation with the performance from supervised learning algorithms (e.g., Logistic Regression), compared with previous ones, such as KL-Divergence and Maximum Mean Discrepancy. Therefore, in our model, we have incorporated two distribution distance measures to minimize the difference between source and target domains in the embedding representations. Extensive experiments conducted on three real-world image datasets and one text data demonstrate the effectiveness of our proposed method compared with several state-of-the-art baseline methods.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "16", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Ranganath:2018:UIR, author = "Suhas Ranganath and Xia Hu and Jiliang Tang and Suhang Wang and Huan Liu", title = "Understanding and Identifying Rhetorical Questions in Social Media", journal = j-TIST, volume = "9", number = "2", pages = "17:1--17:??", month = jan, year = "2018", CODEN = "????", DOI = "https://doi.org/10.1145/3108364", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Mar 22 10:01:53 MDT 2018", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Social media provides a platform for seeking information from a large user base. Information seeking in social media, however, occurs simultaneously with users expressing their viewpoints by making statements. Rhetorical questions have the form of a question but serve the function of a statement and are an important tool employed by users to express their viewpoints. Therefore, rhetorical questions might mislead platforms assisting information seeking in social media. It becomes difficult to identify rhetorical questions as they are not syntactically different from other questions. In this article, we develop a framework to identify rhetorical questions by modeling some motivations of the users to post them. We focus on two motivations of the users drawing from linguistic theories to implicitly convey a message and to modify the strength of a statement previously made. We develop a quantitative framework from these motivations to identify rhetorical questions in social media. We evaluate the framework using two datasets of questions posted on a social media platform Twitter and demonstrate its effectiveness in identifying rhetorical questions. This is the first framework, to the best of our knowledge, to model the possible motivations for posting rhetorical questions to identify them on social media platforms.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "17", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Wu:2018:IDC, author = "Ou Wu and Xue Mao and Weiming Hu", title = "Iteratively Divide-and-Conquer Learning for Nonlinear Classification and Ranking", journal = j-TIST, volume = "9", number = "2", pages = "18:1--18:??", month = jan, year = "2018", CODEN = "????", DOI = "https://doi.org/10.1145/3122802", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Mar 22 10:01:53 MDT 2018", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Nonlinear classifiers (i.e., kernel support vector machines (SVMs)) are effective for nonlinear data classification. However, nonlinear classifiers are usually prohibitively expensive when dealing with large nonlinear data. Ensembles of linear classifiers have been proposed to address this inefficiency, which is called the ensemble linear classifiers for nonlinear data problem. In this article, a new iterative learning approach is introduced that involves two steps at each iteration: partitioning the data into clusters according to Gaussian mixture models with local consistency and then training basic classifiers (i.e., linear SVMs) for each cluster. The two divide-and-conquer steps are combined into a graphical model. Meanwhile, with training, each classifier is regarded as a task; clustered multitask learning is employed to capture the relatedness among different tasks and avoid overfitting in each task. In addition, two novel extensions are introduced based on the proposed approach. First, the approach is extended for quality-aware web data classification. In this problem, the types of web data vary in terms of information quality. The ignorance of the variations of information quality of web data leads to poor classification models. The proposed approach can effectively integrate quality-aware factors into web data classification. Second, the approach is extended for listwise learning to rank to construct an ensemble of linear ranking models, whereas most existing listwise ranking methods construct a solely linear ranking model. Experimental results on benchmark datasets show that our approach outperforms state-of-the-art algorithms. During prediction for nonlinear classification, it also obtains comparable classification performance to kernel SVMs, with much higher efficiency.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "18", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Zhang:2018:SCA, author = "Yexun Zhang and Wenbin Cai and Wenquan Wang and Ya Zhang", title = "Stopping Criterion for Active Learning with Model Stability", journal = j-TIST, volume = "9", number = "2", pages = "19:1--19:??", month = jan, year = "2018", CODEN = "????", DOI = "https://doi.org/10.1145/3125645", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Mar 22 10:01:53 MDT 2018", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Active learning selectively labels the most informative instances, aiming to reduce the cost of data annotation. While much effort has been devoted to active sampling functions, relatively limited attention has been paid to when the learning process should stop. In this article, we focus on the stopping criterion of active learning and propose a model stability--based criterion, that is, when a model does not change with inclusion of additional training instances. The challenge lies in how to measure the model change without labeling additional instances and training new models. Inspired by the stochastic gradient update rule, we use the gradient of the loss function at each candidate example to measure its effect on model change. We propose to stop active learning when the model change brought by any of the remaining unlabeled examples is lower than a given threshold. We apply the proposed stopping criterion to two popular classifiers: logistic regression (LR) and support vector machines (SVMs). In addition, we theoretically analyze the stability and generalization ability of the model obtained by our stopping criterion. Substantial experiments on various UCI benchmark datasets and ImageNet datasets have demonstrated that the proposed approach is highly effective.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "19", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Wang:2018:SCE, author = "Leye Wang and Daqing Zhang and Dingqi Yang and Animesh Pathak and Chao Chen and Xiao Han and Haoyi Xiong and Yasha Wang", title = "{SPACE-TA}: Cost-Effective Task Allocation Exploiting Intradata and Interdata Correlations in Sparse Crowdsensing", journal = j-TIST, volume = "9", number = "2", pages = "20:1--20:??", month = jan, year = "2018", CODEN = "????", DOI = "https://doi.org/10.1145/3131671", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Mar 22 10:01:53 MDT 2018", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Data quality and budget are two primary concerns in urban-scale mobile crowdsensing. Traditional research on mobile crowdsensing mainly takes sensing coverage ratio as the data quality metric rather than the overall sensed data error in the target-sensing area. In this article, we propose to leverage spatiotemporal correlations among the sensed data in the target-sensing area to significantly reduce the number of sensing task assignments. In particular, we exploit both intradata correlations within the same type of sensed data and interdata correlations among different types of sensed data in the sensing task. We propose a novel crowdsensing task allocation framework called SPACE-TA (SPArse Cost-Effective Task Allocation), combining compressive sensing, statistical analysis, active learning, and transfer learning, to dynamically select a small set of subareas for sensing in each timeslot (cycle), while inferring the data of unsensed subareas under a probabilistic data quality guarantee. Evaluations on real-life temperature, humidity, air quality, and traffic monitoring datasets verify the effectiveness of SPACE-TA. In the temperature-monitoring task leveraging intradata correlations, SPACE-TA requires data from only 15.5\% of the subareas while keeping the inference error below 0.25${}^\circ $C in 95\% of the cycles, reducing the number of sensed subareas by 18.0\% to 26.5\% compared to baselines. When multiple tasks run simultaneously, for example, for temperature and humidity monitoring, SPACE-TA can further reduce $ \approx $10\% of the sensed subareas by exploiting interdata correlations.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "20", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Katz:2018:VEC, author = "Gilad Katz and Cornelia Caragea and Asaf Shabtai", title = "Vertical Ensemble Co-Training for Text Classification", journal = j-TIST, volume = "9", number = "2", pages = "21:1--21:??", month = jan, year = "2018", CODEN = "????", DOI = "https://doi.org/10.1145/3137114", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Mar 22 10:01:53 MDT 2018", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "High-quality, labeled data is essential for successfully applying machine learning methods to real-world text classification problems. However, in many cases, the amount of labeled data is very small compared to that of the unlabeled, and labeling additional samples could be expensive and time consuming. Co-training algorithms, which make use of unlabeled data to improve classification, have proven to be very effective in such cases. Generally, co-training algorithms work by using two classifiers, trained on two different views of the data, to label large amounts of unlabeled data. Doing so can help minimize the human effort required for labeling new data, as well as improve classification performance. In this article, we propose an ensemble-based co-training approach that uses an ensemble of classifiers from different training iterations to improve labeling accuracy. This approach, which we call vertical ensemble, incurs almost no additional computational cost. Experiments conducted on six textual datasets show a significant improvement of over 45\% in AUC compared with the original co-training algorithm.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "21", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Zhang:2018:RTH, author = "Desheng Zhang and Tian He and Fan Zhang", title = "Real-Time Human Mobility Modeling with Multi-View Learning", journal = j-TIST, volume = "9", number = "3", pages = "22:1--22:??", month = feb, year = "2018", CODEN = "????", DOI = "https://doi.org/10.1145/3092692", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Mar 22 10:01:53 MDT 2018", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Real-time human mobility modeling is essential to various urban applications. To model such human mobility, numerous data-driven techniques have been proposed. However, existing techniques are mostly driven by data from a single view, for example, a transportation view or a cellphone view, which leads to over-fitting of these single-view models. To address this issue, we propose a human mobility modeling technique based on a generic multi-view learning framework called coMobile. In coMobile, we first improve the performance of single-view models based on tensor decomposition with correlated contexts, and then we integrate these improved single-view models together for multi-view learning to iteratively obtain mutually reinforced knowledge for real-time human mobility at urban scale. We implement coMobile based on an extremely large dataset in the Chinese city Shenzhen, including data about taxi, bus, and subway passengers along with cellphone users, capturing more than 27 thousand vehicles and 10 million urban residents. The evaluation results show that our approach outperforms a single-view model by 51\% on average. More importantly, we design a novel application where urban taxis are dispatched based on unaccounted mobility demand inferred by coMobile.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "22", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{An:2018:ATS, author = "Bo An and Nick Jennings and Zhenhui Jessie Li", title = "{ACM TIST} Special Issue on Urban Intelligence", journal = j-TIST, volume = "9", number = "3", pages = "23:1--23:??", month = feb, year = "2018", CODEN = "????", DOI = "https://doi.org/10.1145/3154942", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Mar 22 10:01:53 MDT 2018", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "23", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Varakantham:2018:RSS, author = "Pradeep Varakantham and Akshat Kumar and Hoong Chuin Lau and William Yeoh", title = "Risk-Sensitive Stochastic Orienteering Problems for Trip Optimization in Urban Environments", journal = j-TIST, volume = "9", number = "3", pages = "24:1--24:??", month = feb, year = "2018", CODEN = "????", DOI = "https://doi.org/10.1145/3080575", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Mar 22 10:01:53 MDT 2018", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Orienteering Problems (OPs) are used to model many routing and trip planning problems. OPs are a variant of the well-known traveling salesman problem where the goal is to compute the highest reward path that includes a subset of vertices and has an overall travel time less than a specified deadline. However, the applicability of OPs is limited due to the assumption of deterministic and static travel times. To that end, Campbell et al. extended OPs to Stochastic OPs (SOPs) to represent uncertain travel times (Campbell et al. 2011). In this article, we make the following key contributions: (1) We extend SOPs to Dynamic SOPs (DSOPs), which allow for time-dependent travel times; (2) we introduce a new objective criterion for SOPs and DSOPs to represent a percentile measure of risk; (3) we provide non-linear optimization formulations along with their linear equivalents for solving the risk-sensitive SOPs and DSOPs; (4) we provide a local search mechanism for solving the risk-sensitive SOPs and DSOPs; and (5) we provide results on existing benchmark problems and a real-world theme park trip planning problem.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "24", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Cao:2018:MBA, author = "Zhiguang Cao and Hongliang Guo and Jie Zhang", title = "A Multiagent-Based Approach for Vehicle Routing by Considering Both Arriving on Time and Total Travel Time", journal = j-TIST, volume = "9", number = "3", pages = "25:1--25:??", month = feb, year = "2018", CODEN = "????", DOI = "https://doi.org/10.1145/3078847", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Mar 22 10:01:53 MDT 2018", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Arriving on time and total travel time are two important properties for vehicle routing. Existing route guidance approaches always consider them independently, because they may conflict with each other. In this article, we develop a semi-decentralized multiagent-based vehicle routing approach where vehicle agents follow the local route guidance by infrastructure agents at each intersection, and infrastructure agents perform the route guidance by solving a route assignment problem. It integrates the two properties by expressing them as two objective terms of the route assignment problem. Regarding arriving on time, it is formulated based on the probability tail model, which aims to maximize the probability of reaching destination before deadline. Regarding total travel time, it is formulated as a weighted quadratic term, which aims to minimize the expected travel time from the current location to the destination based on the potential route assignment. The weight for total travel time is designed to be comparatively large if the deadline is loose. Additionally, we improve the proposed approach in two aspects, including travel time prediction and computational efficiency. Experimental results on real road networks justify its ability to increase the average probability of arriving on time, reduce total travel time, and enhance the overall routing performance.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "25", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Cheng:2018:SUM, author = "Shih-Fen Cheng and Cen Chen and Thivya Kandappu and Hoong Chuin Lau and Archan Misra and Nikita Jaiman and Randy Tandriansyah and Desmond Koh", title = "Scalable Urban Mobile Crowdsourcing: Handling Uncertainty in Worker Movement", journal = j-TIST, volume = "9", number = "3", pages = "26:1--26:??", month = feb, year = "2018", CODEN = "????", DOI = "https://doi.org/10.1145/3078842", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Mar 22 10:01:53 MDT 2018", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "In this article, we investigate effective ways of utilizing crowdworkers in providing various urban services. The task recommendation platform that we design can match tasks to crowdworkers based on workers' historical trajectories and time budget limits, thus making recommendations personal and efficient. One major challenge we manage to address is the handling of crowdworker's trajectory uncertainties. In this article, we explicitly allow multiple routine routes to be probabilistically associated with each worker. We formulate this problem as an integer linear program whose goal is to maximize the expected total utility achieved by all workers. We further exploit the separable structures of the formulation and apply the Lagrangian relaxation technique to scale up computation. Numerical experiments have been performed over the instances generated using the realistic public transit dataset in Singapore. The results show that we can find significantly better solutions than the deterministic formulation, and in most cases we can find solutions that are very close to the theoretical performance limit. To demonstrate the practicality of our approach, we deployed our recommendation engine to a campus-scale field trial, and we demonstrate that workers receiving our recommendations incur fewer detours and complete more tasks, and are more efficient against workers relying on their own planning (25\% more for top workers who receive recommendations). This is achieved despite having highly uncertain worker trajectories. We also demonstrate how to further improve the robustness of the system by using a simple multi-coverage mechanism.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "26", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Kaminka:2018:SUP, author = "Gal A. Kaminka and Natalie Fridman", title = "Simulating Urban Pedestrian Crowds of Different Cultures", journal = j-TIST, volume = "9", number = "3", pages = "27:1--27:??", month = feb, year = "2018", CODEN = "????", DOI = "https://doi.org/10.1145/3102302", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Mar 22 10:01:53 MDT 2018", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Models of crowd dynamics are critically important for urban planning and management. They support analysis, facilitate qualitative and quantitative predictions, and synthesize behaviors for simulations. One promising approach to crowd modeling relies on micro-level agent-based simulations, where the interactions of simulated individual agents in the crowd result in macro-level crowd dynamics which are the object of study. This article reports on an agent-based model of urban pedestrian crowds, where culture is explicitly modeled. We extend an established agent-based social agent model, inspired by social psychology, to account for individual cultural attributes discussed in social science literature. We then embed the model in a simulation of pedestrians and explore the resulting macro-level crowd behaviors, such as pedestrian flow, lane changes rate, and so on. We validate the model by quantitatively comparing the simulation results to the pedestrian dynamics in movies of human crowds in five different countries: Iraq, Israel, England, Canada, and France. We conclude that the model can faithfully replicate urban pedestrians in different cultures. Encouraged by these results, we explore simulations of mixed-culture pedestrian crowds.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "27", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Auffenberg:2018:CBA, author = "Frederik Auffenberg and Stephen Snow and Sebastian Stein and Alex Rogers", title = "A Comfort-Based Approach to Smart Heating and Air Conditioning", journal = j-TIST, volume = "9", number = "3", pages = "28:1--28:??", month = feb, year = "2018", CODEN = "????", DOI = "https://doi.org/10.1145/3057730", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Mar 22 10:01:53 MDT 2018", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "In this article, we address the interrelated challenges of predicting user comfort and using this to reduce energy consumption in smart heating, ventilation, and air conditioning (HVAC) systems. At present, such systems use simple models of user comfort when deciding on a set-point temperature. Being built using broad population statistics, these models generally fail to represent individual users' preferences, resulting in poor estimates of the users' preferred temperatures. To address this issue, we propose the Bayesian Comfort Model (BCM). This personalised thermal comfort model uses a Bayesian network to learn from a user's feedback, allowing it to adapt to the users' individual preferences over time. We further propose an alternative to the ASHRAE 7-point scale used to assess user comfort. Using this model, we create an optimal HVAC control algorithm that minimizes energy consumption while preserving user comfort. Through an empirical evaluation based on the ASHRAE RP-884 dataset and data collected in a separate deployment by us, we show that our model is consistently 13.2\% to 25.8\% more accurate than current models and how using our alternative comfort scale can increase our model's accuracy. Through simulations we show that using this model, our HVAC control algorithm can reduce energy consumption by 7.3\% to 13.5\% while decreasing user discomfort by 24.8\% simultaneously.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "28", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Wang:2018:STP, author = "Pengfei Wang and Guannan Liu and Yanjie Fu and Yuanchun Zhou and Jianhui Li", title = "Spotting Trip Purposes from Taxi Trajectories: a General Probabilistic Model", journal = j-TIST, volume = "9", number = "3", pages = "29:1--29:??", month = feb, year = "2018", CODEN = "????", DOI = "https://doi.org/10.1145/3078849", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Mar 22 10:01:53 MDT 2018", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "What is the purpose of a trip? What are the unique human mobility patterns and spatial contexts in or near the pickup points and delivery points of trajectories for a specific trip purpose? Many prior studies have modeled human mobility patterns in urban regions; however, these analytics mainly focus on interpreting the semantic meanings of geographic topics at an aggregate level. Given the lack of information about human activities at pick-up and dropoff points, it is challenging to convert the prior studies into effective tools for inferring trip purposes. To address this challenge, in this article, we study large-scale taxi trajectories from an unsupervised perspective in light of the following observations. First, the POI configurations of origin and destination regions closely relate to the urban functionality of these regions and further indicate various human activities. Second, with respect to the functionality of neighborhood environments, trip purposes can be discerned from the transitions between regions with different functionality at particular time periods. Along these lines, we develop a general probabilistic framework for spotting trip purposes from massive taxi GPS trajectories. Specifically, we first augment the origin and destination regions of trajectories by attaching neighborhood POIs. Then, we introduce a latent factor, POI Topic, to represent the mixed functionality of the regions, such that each origin or destination point in the city can be modeled as a mixture over POI Topics. In addition, considering the transitions from origins to destinations at specific time periods, the trip time is generated collaboratively from the pairwise POI Topics at both ends of the O-D pairs, constituting POI Links, and hence the trip purpose can be explained semantically by the POI Links. Finally, we present extensive experiments with the real-world data of New York City to demonstrate the effectiveness of our proposed method for spotting trip purposes, and moreover, the model is validated to perform well in predicting the destinations and trip time among all the baseline methods.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "29", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Liu:2018:PAT, author = "Jie Liu and Bin Liu and Yanchi Liu and Huipeng Chen and Lina Feng and Hui Xiong and Yalou Huang", title = "Personalized Air Travel Prediction: a Multi-factor Perspective", journal = j-TIST, volume = "9", number = "3", pages = "30:1--30:??", month = feb, year = "2018", CODEN = "????", DOI = "https://doi.org/10.1145/3078845", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Mar 22 10:01:53 MDT 2018", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Human mobility analysis is one of the most important research problems in the field of urban computing. Existing research mainly focuses on the intra-city ground travel behavior modeling, while the inter-city air travel behavior modeling has been largely ignored. Actually, the inter-city travel analysis can be of equivalent importance and complementary to the intra-city travel analysis. Understanding massive passenger-air-travel behavior delivers intelligence for airlines' precision marketing and related socioeconomic activities, such as airport planning, emergency management, local transportation planning, and tourism-related businesses. Moreover, it provides opportunities to study the characteristics of cities and the mutual relationships between them. However, modeling and predicting air traveler behavior is challenging due to the complex factors of the market situation and individual characteristics of customers (e.g., airlines' market share, customer membership, and travelers' intrinsic interests on destinations). To this end, in this article, we present a systematic study on the personalized air travel prediction problem, namely where a customer will fly to and which airline carrier to fly with, by leveraging real-world anonymized Passenger Name Record (PNR) data. Specifically, we first propose a relational travel topic model, which combines the merits of latent factor model with a neighborhood-based method, to uncover the personal travel preferences of aviation customers and the latent travel topics of air routes and airline carriers simultaneously. Then we present a multi-factor travel prediction framework, which fuses complex factors of the market situation and individual characteristics of customers, to predict airline customers' personalized travel demands. Experimental results on two real-world PNR datasets demonstrate the effectiveness of our approach on both travel topic discovery and customer travel prediction.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "30", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Pellungrini:2018:DMA, author = "Roberto Pellungrini and Luca Pappalardo and Francesca Pratesi and Anna Monreale", title = "A Data Mining Approach to Assess Privacy Risk in Human Mobility Data", journal = j-TIST, volume = "9", number = "3", pages = "31:1--31:??", month = feb, year = "2018", CODEN = "????", DOI = "https://doi.org/10.1145/3106774", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Mar 22 10:01:53 MDT 2018", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Human mobility data are an important proxy to understand human mobility dynamics, develop analytical services, and design mathematical models for simulation and what-if analysis. Unfortunately mobility data are very sensitive since they may enable the re-identification of individuals in a database. Existing frameworks for privacy risk assessment provide data providers with tools to control and mitigate privacy risks, but they suffer two main shortcomings: (i) they have a high computational complexity; (ii) the privacy risk must be recomputed every time new data records become available and for every selection of individuals, geographic areas, or time windows. In this article, we propose a fast and flexible approach to estimate privacy risk in human mobility data. The idea is to train classifiers to capture the relation between individual mobility patterns and the level of privacy risk of individuals. We show the effectiveness of our approach by an extensive experiment on real-world GPS data in two urban areas and investigate the relations between human mobility patterns and the privacy risk of individuals.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "31", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Zhang:2018:UOG, author = "Yingjie Zhang and Beibei Li and Jason Hong", title = "Using Online Geotagged and Crowdsourced Data to Understand Human Offline Behavior in the City: an Economic Perspective", journal = j-TIST, volume = "9", number = "3", pages = "32:1--32:??", month = feb, year = "2018", CODEN = "????", DOI = "https://doi.org/10.1145/3078851", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Mar 22 10:01:53 MDT 2018", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "The pervasiveness of mobile technologies today has facilitated the creation of massive online crowdsourced and geotagged data from individual users at different locations in a city. Such ubiquitous user-generated data allow us to study the social and behavioral trajectories of individuals across both digital and physical environments. This information, combined with traditional economic and behavioral indicators in the city (e.g., store purchases, restaurant visits, parking), can help us better understand human behavior and interactions with cities. In this study, we take an economic perspective and focus on understanding human economic behavior in the city by examining the performance of local businesses based on the values learned from crowsourced and geotagged data. Specifically, we extract multiple traffic and human mobility features from publicly available data source geomapping and geo-social-tagging techniques and examine the effects of both static and dynamic features on booking volume of local restaurants. Our study is instantiated on a unique dataset of restaurant bookings from OpenTable for 3,187 restaurants in New York City from November 2013 to March 2014. Our results suggest that foot traffic can increase local popularity and business performance, while mobility and traffic from automobiles may hurt local businesses, especially the well-established chains and high-end restaurants. We also find that, on average, one or more street closure (caused by events or construction projects) nearby leads to a 4.7\% decrease in the probability of a restaurant being fully booked during the dinner peak. Our study demonstrates the potential to best make use of the large volumes and diverse sources of crowdsourced and geotagged user-generated data to create matrices to predict local economic demand in a manner that is fast, cheap, accurate, and meaningful.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "32", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Dong:2018:SBU, author = "Xiaowen Dong and Yoshihiko Suhara and Bur{\c{c}}in Bozkaya and Vivek K. Singh and Bruno Lepri and Alex `Sandy' Pentland", title = "Social Bridges in Urban Purchase Behavior", journal = j-TIST, volume = "9", number = "3", pages = "33:1--33:??", month = feb, year = "2018", CODEN = "????", DOI = "https://doi.org/10.1145/3149409", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Mar 22 10:01:53 MDT 2018", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "The understanding and modeling of human purchase behavior in city environment can have important implications in the study of urban economy and in the design and organization of cities. In this article, we study human purchase behavior at the community level and argue that people who live in different communities but work at close-by locations could act as ``social bridges'' between the respective communities and that they are correlated with similarity in community purchase behavior. We provide empirical evidence by studying millions of credit card transaction records for tens of thousands of individuals in a city environment during a period of three months. More specifically, we show that the number of social bridges between communities is a much stronger indicator of similarity in their purchase behavior than traditionally considered factors such as income and sociodemographic variables. Our findings also suggest that such an effect varies across different merchant categories, that the presence of female customers in social bridges is a stronger indicator compared to that of their male counterparts, and that there seems to be a geographical constraint for this effect, all of which may have implications in the studies of urban economy and data-driven urban planning.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "33", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Zhang:2018:GER, author = "Chao Zhang and Dongming Lei and Quan Yuan and Honglei Zhuang and Lance Kaplan and Shaowen Wang and Jiawei Han", title = "{GeoBurst+}: Effective and Real-Time Local Event Detection in Geo-Tagged Tweet Streams", journal = j-TIST, volume = "9", number = "3", pages = "34:1--34:??", month = feb, year = "2018", CODEN = "????", DOI = "https://doi.org/10.1145/3066166", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Mar 22 10:01:53 MDT 2018", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "The real-time discovery of local events (e.g., protests, disasters) has been widely recognized as a fundamental socioeconomic task. Recent studies have demonstrated that the geo-tagged tweet stream serves as an unprecedentedly valuable source for local event detection. Nevertheless, how to effectively extract local events from massive geo-tagged tweet streams in real time remains challenging. To bridge the gap, we propose a method for effective and real-time local event detection from geo-tagged tweet streams. Our method, named GeoBurst+, first leverages a novel cross-modal authority measure to identify several pivots in the query window. Such pivots reveal different geo-topical activities and naturally attract similar tweets to form candidate events. GeoBurst+ further summarizes the continuous stream and compares the candidates against the historical summaries to pinpoint truly interesting local events. Better still, as the query window shifts, GeoBurst+ is capable of updating the event list with little time cost, thus achieving continuous monitoring of the stream. We used crowdsourcing to evaluate GeoBurst+ on two million-scale datasets and found it significantly more effective than existing methods while being orders of magnitude faster.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "34", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Muralidhar:2018:III, author = "Nikhil Muralidhar and Chen Wang and Nathan Self and Marjan Momtazpour and Kiyoshi Nakayama and Ratnesh Sharma and Naren Ramakrishnan", title = "{\tt illiad}: {InteLLigent} Invariant and Anomaly Detection in Cyber-Physical Systems", journal = j-TIST, volume = "9", number = "3", pages = "35:1--35:??", month = feb, year = "2018", CODEN = "????", DOI = "https://doi.org/10.1145/3066167", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Mar 22 10:01:53 MDT 2018", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Cyber-physical systems (CPSs) are today ubiquitous in urban environments. Such systems now serve as the backbone to numerous critical infrastructure applications, from smart grids to IoT installations. Scalable and seamless operation of such CPSs requires sophisticated tools for monitoring the time series progression of the system, dynamically tracking relationships, and issuing alerts about anomalies to operators. We present an online monitoring system ( illiad ) that models the state of the CPS as a function of its relationships between constituent components, using a combination of model-based and data-driven strategies. In addition to accurate inference for state estimation and anomaly tracking, illiad also exploits the underlying network structure of the CPS (wired or wireless) for state estimation purposes. We demonstrate the application of illiad to two diverse settings: a wireless sensor motes application and an IEEE 33-bus microgrid.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "35", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Li:2018:EUB, author = "Liangda Li and Hongyuan Zha", title = "Energy Usage Behavior Modeling in Energy Disaggregation via {Hawkes} Processes", journal = j-TIST, volume = "9", number = "3", pages = "36:1--36:??", month = feb, year = "2018", CODEN = "????", DOI = "https://doi.org/10.1145/3108413", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Mar 22 10:01:53 MDT 2018", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Energy disaggregation, the task of taking a whole home electricity signal and decomposing it into its component appliances, has been proved to be essential in energy conservation research. One powerful cue for breaking down the entire household's energy consumption is user's daily energy usage behavior, which has so far received little attention: existing works on energy disaggregation mostly ignored the relationship between the energy usages of various appliances by householders across different time slots. The major challenge in modeling such a relationship in that, with ambiguous appliance usage membership of householders, we find it difficult to appropriately model the influence between appliances, since such influence is determined by human behaviors in energy usage. To address this problem, we propose to model the influence between householders' energy usage behaviors directly through a novel probabilistic model, which combines topic models with the Hawkes processes. The proposed model simultaneously disaggregates the whole home electricity signal into each component appliance and infers the appliance usage membership of household members and enables those two tasks to mutually benefit each other. Experimental results on both synthetic data and four real-world data sets demonstrate the effectiveness of our model, which outperforms state-of-the-art approaches in not only decomposing the entire consumed energy to each appliance in houses but also the inference of household structures. We further analyze the inferred appliance-householder assignment and the corresponding influence within the appliance usage of each householder and across different householders, which provides insight into appealing human behavior patterns in appliance usage.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "36", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Tran:2018:RTF, author = "Luan Tran and Hien To and Liyue Fan and Cyrus Shahabi", title = "A Real-Time Framework for Task Assignment in Hyperlocal Spatial Crowdsourcing", journal = j-TIST, volume = "9", number = "3", pages = "37:1--37:??", month = feb, year = "2018", CODEN = "????", DOI = "https://doi.org/10.1145/3078853", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Mar 22 10:01:53 MDT 2018", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Spatial Crowdsourcing (SC) is a novel platform that engages individuals in the act of collecting various types of spatial data. This method of data collection can significantly reduce cost and turnover time and is particularly useful in urban environmental sensing, where traditional means fail to provide fine-grained field data. In this study, we introduce hyperlocal spatial crowdsourcing, where all workers who are located within the spatiotemporal vicinity of a task are eligible to perform the task (e.g., reporting the precipitation level at their area and time). In this setting, there is often a budget constraint, either for every time period or for the entire campaign, on the number of workers to activate to perform tasks. The challenge is thus to maximize the number of assigned tasks under the budget constraint despite the dynamic arrivals of workers and tasks. We introduce a taxonomy of several problem variants, such as budget-per-time-period vs. budget-per-campaign and binary-utility vs. distance-based-utility. We study the hardness of the task assignment problem in the offline setting and propose online heuristics which exploit the spatial and temporal knowledge acquired over time. Our experiments are conducted with spatial crowdsourcing workloads generated by the SCAWG tool, and extensive results show the effectiveness and efficiency of our proposed solutions.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "37", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Zhang:2018:RCS, author = "Dingwen Zhang and Huazhu Fu and Junwei Han and Ali Borji and Xuelong Li", title = "A Review of Co-Saliency Detection Algorithms: Fundamentals, Applications, and Challenges", journal = j-TIST, volume = "9", number = "4", pages = "38:1--38:??", month = feb, year = "2018", CODEN = "????", DOI = "https://doi.org/10.1145/3158674", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Mar 22 10:01:54 MDT 2018", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Co-saliency detection is a newly emerging and rapidly growing research area in the computer vision community. As a novel branch of visual saliency, co-saliency detection refers to the discovery of common and salient foregrounds from two or more relevant images, and it can be widely used in many computer vision tasks. The existing co-saliency detection algorithms mainly consist of three components: extracting effective features to represent the image regions, exploring the informative cues or factors to characterize co-saliency, and designing effective computational frameworks to formulate co-saliency. Although numerous methods have been developed, the literature is still lacking a deep review and evaluation of co-saliency detection techniques. In this article, we aim at providing a comprehensive review of the fundamentals, challenges, and applications of co-saliency detection. Specifically, we provide an overview of some related computer vision works, review the history of co-saliency detection, summarize and categorize the major algorithms in this research area, discuss some open issues in this area, present the potential applications of co-saliency detection, and finally point out some unsolved challenges and promising future works. We expect this review to be beneficial to both fresh and senior researchers in this field and to give insights to researchers in other related areas regarding the utility of co-saliency detection algorithms.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "38", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Wang:2018:VME, author = "Bingsheng Wang and Zhiqian Chen and Arnold P. Boedihardjo and Chang-Tien Lu", title = "Virtual Metering: an Efficient Water Disaggregation Algorithm via Nonintrusive Load Monitoring", journal = j-TIST, volume = "9", number = "4", pages = "39:1--39:??", month = feb, year = "2018", CODEN = "????", DOI = "https://doi.org/10.1145/3141770", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Mar 22 10:01:54 MDT 2018", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "The scarcity of potable water is a critical challenge in many regions around the world. Previous studies have shown that knowledge of device-level water usage can lead to significant conservation. Although there is considerable interest in determining discriminative features via sparse coding for water disaggregation to separate whole-house consumption into its component appliances, existing methods lack a mechanism for fitting coefficient distributions and are thus unable to accurately discriminate parallel devices' consumption. This article proposes a Bayesian discriminative sparse coding model, referred to as Virtual Metering (VM), for this disaggregation task. Mixture-of-Gammas is employed for the prior distribution of coefficients, contributing two benefits: (i) guaranteeing the coefficients' sparseness and non-negativity, and (ii) capturing the distribution of active coefficients. The resulting method effectively adapts the bases to aggregated consumption to facilitate discriminative learning in the proposed model, and devices' shape features are formalized and incorporated into Bayesian sparse coding to direct the learning of basis functions. Compact Gibbs Sampling (CGS) is developed to accelerate the inference process by utilizing the sparse structure of coefficients. The empirical results obtained from applying the new model to large-scale real and synthetic datasets revealed that VM significantly outperformed the benchmark methods.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "39", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Fu:2018:MLM, author = "Yanjie Fu and Junming Liu and Xiaolin Li and Hui Xiong", title = "A Multi-Label Multi-View Learning Framework for In-App Service Usage Analysis", journal = j-TIST, volume = "9", number = "4", pages = "40:1--40:??", month = feb, year = "2018", CODEN = "????", DOI = "https://doi.org/10.1145/3151937", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Mar 22 10:01:54 MDT 2018", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "The service usage analysis, aiming at identifying customers' messaging behaviors based on encrypted App traffic flows, has become a challenging and emergent task for service providers. Prior literature usually starts from segmenting a traffic sequence into single-usage subsequences, and then classify the subsequences into different usage types. However, they could suffer from inaccurate traffic segmentations and mixed-usage subsequences. To address this challenge, we exploit a multi-label multi-view learning strategy and develop an enhanced framework for in-App usage analytics. Specifically, we first devise an enhanced traffic segmentation method to reduce mixed-usage subsequences. Besides, we develop a multi-label multi-view logistic classification method, which comprises two alignments. The first alignment is to make use of the classification consistency between packet-length view and time-delay view of traffic subsequences and improve classification accuracy. The second alignment is to combine the classification of single-usage subsequence and the post-classification of mixed-usage subsequences into a unified multi-label logistic classification problem. Finally, we present extensive experiments with real-world datasets to demonstrate the effectiveness of our approach. We find that the proposed multi-label multi-view framework can help overcome the pain of mixed-usage subsequences and can be generalized to latent activity analysis in sequential data, beyond in-App usage analytics.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "40", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Wang:2018:CAB, author = "Pengwei Wang and Lei Ji and Jun Yan and Dejing Dou and Nisansa {De Silva} and Yong Zhang and Lianwen Jin", title = "Concept and Attention-Based {CNN} for Question Retrieval in Multi-View Learning", journal = j-TIST, volume = "9", number = "4", pages = "41:1--41:??", month = feb, year = "2018", CODEN = "????", DOI = "https://doi.org/10.1145/3151957", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Mar 22 10:01:54 MDT 2018", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Question retrieval, which aims to find similar versions of a given question, is playing a pivotal role in various question answering (QA) systems. This task is quite challenging, mainly in regard to five aspects: synonymy, polysemy, word order, question length, and data sparsity. In this article, we propose a unified framework to simultaneously handle these five problems. We use the word combined with corresponding concept information to handle the synonymy problem and the polysemous problem. Concept embedding and word embedding are learned at the same time from both the context-dependent and context-independent views. To handle the word-order problem, we propose a high-level feature-embedded convolutional semantic model to learn question embedding by inputting concept embedding and word embedding. Due to the fact that the lengths of some questions are long, we propose a value-based convolutional attentional method to enhance the proposed high-level feature-embedded convolutional semantic model in learning the key parts of the question and the answer. The proposed high-level feature-embedded convolutional semantic model nicely represents the hierarchical structures of word information and concept information in sentences with their layer-by-layer convolution and pooling. Finally, to resolve data sparsity, we propose using the multi-view learning method to train the attention-based convolutional semantic model on question-answer pairs. To the best of our knowledge, we are the first to propose simultaneously handling the above five problems in question retrieval using one framework. Experiments on three real question-answering datasets show that the proposed framework significantly outperforms the state-of-the-art solutions.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "41", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Khan:2018:NIC, author = "Naimul Mefraz Khan and Riadh Ksantini and Ling Guan", title = "A Novel Image-Centric Approach Toward Direct Volume Rendering", journal = j-TIST, volume = "9", number = "4", pages = "42:1--42:??", month = feb, year = "2018", CODEN = "????", DOI = "https://doi.org/10.1145/3152875", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Mar 22 10:01:54 MDT 2018", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Transfer function (TF) generation is a fundamental problem in direct volume rendering (DVR). A TF maps voxels to color and opacity values to reveal inner structures. Existing TF tools are complex and unintuitive for the users who are more likely to be medical professionals than computer scientists. In this article, we propose a novel image-centric method for TF generation where instead of complex tools, the user directly manipulates volume data to generate DVR. The user's work is further simplified by presenting only the most informative volume slices for selection. Based on the selected parts, the voxels are classified using our novel sparse nonparametric support vector machine classifier, which combines both local and near-global distributional information of the training data. The voxel classes are mapped to aesthetically pleasing and distinguishable color and opacity values using harmonic colors. Experimental results on several benchmark datasets and a detailed user survey show the effectiveness of the proposed method.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "42", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Huang:2018:QBP, author = "Michael Xuelin Huang and Jiajia Li and Grace Ngai and Hong Va Leong", title = "Quick Bootstrapping of a Personalized Gaze Model from Real-Use Interactions", journal = j-TIST, volume = "9", number = "4", pages = "43:1--43:??", month = feb, year = "2018", CODEN = "????", DOI = "https://doi.org/10.1145/3156682", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Mar 22 10:01:54 MDT 2018", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Understanding human visual attention is essential for understanding human cognition, which in turn benefits human--computer interaction. Recent work has demonstrated a Personalized, Auto-Calibrating Eye-tracking (PACE) system, which makes it possible to achieve accurate gaze estimation using only an off-the-shelf webcam by identifying and collecting data implicitly from user interaction events. However, this method is constrained by the need for large amounts of well-annotated data. We thus present fast-PACE, an adaptation to PACE that exploits knowledge from existing data from different users to accelerate the learning speed of the personalized model. The result is an adaptive, data-driven approach that continuously ``learns'' its user and recalibrates, adapts, and improves with additional usage by a user. Experimental evaluations of fast-PACE demonstrate its competitive accuracy in iris localization, validity of alignment identification between gaze and interactions, and effectiveness of gaze transfer. In general, fast-PACE achieves an initial visual error of 3.98 degrees and then steadily improves to 2.52 degrees given incremental interaction-informed data. Our performance is comparable to state-of-the-art, but without the need for explicit training or calibration. Our technique addresses the data quality and quantity problems. It therefore has the potential to enable comprehensive gaze-aware applications in the wild.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "43", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Kulev:2018:BAI, author = "Igor Kulev and Pearl Pu and Boi Faltings", title = "A {Bayesian} Approach to Intervention-Based Clustering", journal = j-TIST, volume = "9", number = "4", pages = "44:1--44:??", month = feb, year = "2018", CODEN = "????", DOI = "https://doi.org/10.1145/3156683", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Mar 22 10:01:54 MDT 2018", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "An important task for intelligent healthcare systems is to predict the effect of a new intervention on individuals. This is especially true for medical treatments. For example, consider patients who do not respond well to a new drug or have adversary reactions. Predicting the likelihood of positive or negative response before trying the drug on the patient can potentially save his or her life. We are therefore interested in identifying distinctive subpopulations that respond differently to a given intervention. For this purpose, we have developed a novel technique, Intervention-based Clustering, based on a Bayesian mixture model. Compared to the baseline techniques, the novelty of our approach lies in its ability to model complex decision boundaries by using soft clustering, thus predicting the effect for individuals more accurately. It can also incorporate prior knowledge, making the method useful even for smaller datasets. We demonstrate how our method works by applying it to both simulated and real data. Results of our evaluation show that our model has strong predictive power and is capable of producing high-quality clusters compared to the baseline methods.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "44", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Lu:2018:SPA, author = "Jing Lu and Doyen Sahoo and Peilin Zhao and Steven C. H. Hoi", title = "Sparse Passive-Aggressive Learning for Bounded Online Kernel Methods", journal = j-TIST, volume = "9", number = "4", pages = "45:1--45:??", month = feb, year = "2018", CODEN = "????", DOI = "https://doi.org/10.1145/3156684", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Mar 22 10:01:54 MDT 2018", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "One critical deficiency of traditional online kernel learning methods is their unbounded and growing number of support vectors in the online learning process, making them inefficient and non-scalable for large-scale applications. Recent studies on scalable online kernel learning have attempted to overcome this shortcoming, e.g., by imposing a constant budget on the number of support vectors. Although they attempt to bound the number of support vectors at each online learning iteration, most of them fail to bound the number of support vectors for the final output hypothesis, which is often obtained by averaging the series of hypotheses over all the iterations. In this article, we propose a novel framework for bounded online kernel methods, named ``Sparse Passive-Aggressive (SPA)'' learning, which is able to yield a final output kernel-based hypothesis with a bounded number of support vectors. Unlike the common budget maintenance strategy used by many existing budget online kernel learning approaches, the idea of our approach is to attain the bounded number of support vectors using an efficient stochastic sampling strategy that samples an incoming training example as a new support vector with a probability proportional to its loss suffered. We theoretically prove that SPA achieves an optimal mistake bound in expectation, and we empirically show that it outperforms various budget online kernel learning algorithms. Finally, in addition to general online kernel learning tasks, we also apply SPA to derive bounded online multiple-kernel learning algorithms, which can significantly improve the scalability of traditional Online Multiple-Kernel Classification (OMKC) algorithms while achieving satisfactory learning accuracy as compared with the existing unbounded OMKC algorithms.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "45", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Reyes:2018:ESP, author = "Oscar Reyes and Sebasti{\'a}n Ventura", title = "Evolutionary Strategy to Perform Batch-Mode Active Learning on Multi-Label Data", journal = j-TIST, volume = "9", number = "4", pages = "46:1--46:??", month = feb, year = "2018", CODEN = "????", DOI = "https://doi.org/10.1145/3161606", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Mar 22 10:01:54 MDT 2018", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Multi-label learning has become an important area of research owing to the increasing number of real-world problems that contain multi-label data. Data labeling is an expensive process that requires expert handling. The annotation of multi-label data is laborious since a human expert needs to consider the presence/absence of each possible label. Consequently, numerous modern multi-label problems may involve a small number of labeled examples and plentiful unlabeled examples simultaneously. Active learning methods allow us to induce better classifiers by selecting the most useful unlabeled data, thus considerably reducing the labeling effort and the cost of training an accurate model. Batch-mode active learning methods focus on selecting a set of unlabeled examples in each iteration in such a way that the selected examples are informative and as diverse as possible. This article presents a strategy to perform batch-mode active learning on multi-label data. The batch-mode active learning is formulated as a multi-objective problem, and it is solved by means of an evolutionary algorithm. Extensive experiments were conducted in a large collection of datasets, and the experimental results confirmed the effectiveness of our proposal for better batch-mode multi-label active learning.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "46", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Chen:2018:MQC, author = "Qin Chen and Qinmin Hu and Jimmy Xiangji Huang and Liang He", title = "Modeling Queries with Contextual Snippets for Information Retrieval", journal = j-TIST, volume = "9", number = "4", pages = "47:1--47:??", month = feb, year = "2018", CODEN = "????", DOI = "https://doi.org/10.1145/3161607", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Mar 22 10:01:54 MDT 2018", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Query expansion under the pseudo-relevance feedback (PRF) framework has been extensively studied in information retrieval. However, most expansion methods are mainly based on the statistics of single terms, which can generate plenty of irrelevant query terms and decrease retrieval performance. To alleviate this problem, we propose an approach that adapts the PRF-based contextual snippets into a context-aware topic model to enhance query representations. Specifically, instead of selecting a series of independent terms, we make full use of the query contextual information and focus on the snippets with the length of n in the PRF documents. Furthermore, we propose a context-aware topic (CAT) model to mine the topic distributions of the query-relevant snippets, namely, fine contextual snippets. In contrast to the traditional topic models that infer the topics from the whole corpus, we establish a bridge between the snippets and the corresponding PRF documents, which can be used for modeling the topics more precisely and efficiently. Finally, the topic distributions of the fine snippets are used for context-aware and topic-sensitive query representations. To evaluate the performance of our approach, we integrate the obtained queries into a topic-based hybrid retrieval model and conduct extensive experiments on various TREC collections. The experimental results show that our query-modeling approach is more effective in boosting retrieval performance compared with the state-of-the-art methods.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "47", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Liu:2018:FCD, author = "Qi Liu and Runze Wu and Enhong Chen and Guandong Xu and Yu Su and Zhigang Chen and Guoping Hu", title = "Fuzzy Cognitive Diagnosis for Modelling Examinee Performance", journal = j-TIST, volume = "9", number = "4", pages = "48:1--48:??", month = feb, year = "2018", CODEN = "????", DOI = "https://doi.org/10.1145/3168361", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Mar 22 10:01:54 MDT 2018", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Recent decades have witnessed the rapid growth of educational data mining (EDM), which aims at automatically extracting valuable information from large repositories of data generated by or related to people's learning activities in educational settings. One of the key EDM tasks is cognitive modelling with examination data, and cognitive modelling tries to profile examinees by discovering their latent knowledge state and cognitive level (e.g. the proficiency of specific skills). However, to the best of our knowledge, the problem of extracting information from both objective and subjective examination problems to achieve more precise and interpretable cognitive analysis remains underexplored. To this end, we propose a fuzzy cognitive diagnosis framework (FuzzyCDF) for examinees' cognitive modelling with both objective and subjective problems. Specifically, to handle the partially correct responses on subjective problems, we first fuzzify the skill proficiency of examinees. Then we combine fuzzy set theory and educational hypotheses to model the examinees' mastery on the problems based on their skill proficiency. Finally, we simulate the generation of examination score on each problem by considering slip and guess factors. In this way, the whole diagnosis framework is built. For further comprehensive verification, we apply our FuzzyCDF to three classical cognitive assessment tasks, i.e., predicting examinee performance, slip and guess detection, and cognitive diagnosis visualization. Extensive experiments on three real-world datasets for these assessment tasks prove that FuzzyCDF can reveal the knowledge states and cognitive level of the examinees effectively and interpretatively.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "48", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Zhang:2018:DLE, author = "Zixing Zhang and J{\"u}rgen Geiger and Jouni Pohjalainen and Amr El-Desoky Mousa and Wenyu Jin and Bj{\"o}rn Schuller", title = "Deep Learning for Environmentally Robust Speech Recognition: an Overview of Recent Developments", journal = j-TIST, volume = "9", number = "5", pages = "49:1--49:??", month = jul, year = "2018", CODEN = "????", DOI = "https://doi.org/10.1145/3178115", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Nov 15 16:23:08 MST 2018", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Eliminating the negative effect of non-stationary environmental noise is a long-standing research topic for automatic speech recognition but still remains an important challenge. Data-driven supervised approaches, especially the ones based on deep neural networks, have recently emerged as potential alternatives to traditional unsupervised approaches and with sufficient training, can alleviate the shortcomings of the unsupervised methods in various real-life acoustic environments. In this light, we review recently developed, representative deep learning approaches for tackling non-stationary additive and convolutional degradation of speech with the aim of providing guidelines for those involved in the development of environmentally robust speech recognition systems. We separately discuss single- and multi-channel techniques developed for the front-end and back-end of speech recognition systems, as well as joint front-end and back-end training frameworks. In the meanwhile, we discuss the pros and cons of these approaches and provide their experimental results on benchmark databases. We expect that this overview can facilitate the development of the robustness of speech recognition systems in acoustic noisy environments.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "49", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Liu:2018:MSM, author = "Qiang Liu and Feng Yu and Shu Wu and Liang Wang", title = "Mining Significant Microblogs for Misinformation Identification: an Attention-Based Approach", journal = j-TIST, volume = "9", number = "5", pages = "50:1--50:??", month = jul, year = "2018", CODEN = "????", DOI = "https://doi.org/10.1145/3173458", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Nov 15 16:23:08 MST 2018", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "With the rapid growth of social media, massive misinformation is also spreading widely on social media, e.g., Weibo and Twitter, and brings negative effects to human life. Today, automatic misinformation identification has drawn attention from academic and industrial communities. Whereas an event on social media usually consists of multiple microblogs, current methods are mainly constructed based on global statistical features. However, information on social media is full of noise, which should be alleviated. Moreover, most of the microblogs about an event have little contribution to the identification of misinformation, where useful information can be easily overwhelmed by useless information. Thus, it is important to mine significant microblogs for constructing a reliable misinformation identification method. In this article, we propose an attention-based approach for identification of misinformation (AIM). Based on the attention mechanism, AIM can select microblogs with the largest attention values for misinformation identification. The attention mechanism in AIM contains two parts: content attention and dynamic attention. Content attention is the calculated-based textual features of each microblog. Dynamic attention is related to the time interval between the posting time of a microblog and the beginning of the event. To evaluate AIM, we conduct a series of experiments on the Weibo and Twitter datasets, and the experimental results show that the proposed AIM model outperforms the state-of-the-art methods.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "50", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Shah:2018:DOL, author = "Ankit Shah and Rajesh Ganesan and Sushil Jajodia and Hasan Cam", title = "Dynamic Optimization of the Level of Operational Effectiveness of a {CSOC} Under Adverse Conditions", journal = j-TIST, volume = "9", number = "5", pages = "51:1--51:??", month = jul, year = "2018", CODEN = "????", DOI = "https://doi.org/10.1145/3173457", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Nov 15 16:23:08 MST 2018", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "The analysts at a cybersecurity operations center (CSOC) analyze the alerts that are generated by intrusion detection systems (IDSs). Under normal operating conditions, sufficient numbers of analysts are available to analyze the alert workload. For the purpose of this article, this means that the cybersecurity analysts in each shift can fully investigate each and every alert that is generated by the IDSs in a reasonable amount of time and perform their normal tasks in a shift. Normal tasks include analysis time, time to attend training programs, report writing time, personal break time, and time to update the signatures on new patterns in alerts as detected by the IDS. There are several disruptive factors that occur randomly and can adversely impact the normal operating condition of a CSOC, such as (1) higher alert generation rates from a few IDSs, (2) new alert patterns that decrease the throughput of the alert analysis process, and (3) analyst absenteeism. The impact of the preceding factors is that the alerts wait for a long duration before being analyzed, which impacts the level of operational effectiveness (LOE) of the CSOC. To return the CSOC to normal operating conditions, the manager of a CSOC can take several actions, such as increasing the alert analysis time spent by analysts in a shift by canceling a training program, spending some of his own time to assist the analysts in alert investigation, and calling upon the on-call analyst workforce to boost the service rate of alerts. However, additional resources are limited in quantity over a 14-day work cycle, and the CSOC manager must determine when and how much action to take in the face of uncertainty, which arises from both the intensity and the random occurrences of the disruptive factors. The preceding decision by the CSOC manager is nontrivial and is often made in an ad hoc manner using prior experiences. This work develops a reinforcement learning (RL) model for optimizing the LOE throughout the entire 14-day work cycle of a CSOC in the face of uncertainties due to disruptive events. Results indicate that the RL model is able to assist the CSOC manager with a decision support tool to make better decisions than current practices in determining when and how much resource to allocate when the LOE of a CSOC deviates from the normal operating condition.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "51", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Jian:2018:EMI, author = "Ling Jian and Jundong Li and Huan Liu", title = "Exploiting Multilabel Information for Noise-Resilient Feature Selection", journal = j-TIST, volume = "9", number = "5", pages = "52:1--52:??", month = jul, year = "2018", CODEN = "????", DOI = "https://doi.org/10.1145/3158675", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Nov 15 16:23:08 MST 2018", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "In a conventional supervised learning paradigm, each data instance is associated with one single class label. Multilabel learning differs in the way that data instances may belong to multiple concepts simultaneously, which naturally appear in a variety of high impact domains, ranging from bioinformatics and information retrieval to multimedia analysis. It targets leveraging the multiple label information of data instances to build a predictive learning model that can classify unlabeled instances into one or multiple predefined target classes. In multilabel learning, even though each instance is associated with a rich set of class labels, the label information could be noisy and incomplete as the labeling process is both time consuming and labor expensive, leading to potential missing annotations or even erroneous annotations. The existence of noisy and missing labels could negatively affect the performance of underlying learning algorithms. More often than not, multilabeled data often has noisy, irrelevant, and redundant features of high dimensionality. The existence of these uninformative features may also deteriorate the predictive power of the learning model due to the curse of dimensionality. Feature selection, as an effective dimensionality reduction technique, has shown to be powerful in preparing high-dimensional data for numerous data mining and machine-learning tasks. However, a vast majority of existing multilabel feature selection algorithms either boil down to solving multiple single-labeled feature selection problems or directly make use of the imperfect labels to guide the selection of representative features. As a result, they may not be able to obtain discriminative features shared across multiple labels. In this article, to bridge the gap between a rich source of multilabel information and its blemish in practical usage, we propose a novel noise-resilient multilabel informed feature selection framework (MIFS) by exploiting the correlations among different labels. In particular, to reduce the negative effects of imperfect label information in obtaining label correlations, we decompose the multilabel information of data instances into a low-dimensional space and then employ the reduced label representation to guide the feature selection phase via a joint sparse regression framework. Empirical studies on both synthetic and real-world datasets demonstrate the effectiveness and efficiency of the proposed MIFS framework.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "52", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Shen:2018:MDH, author = "Xiaobo Shen and Fumin Shen and Li Liu and Yun-Hao Yuan and Weiwei Liu and Quan-Sen Sun", title = "Multiview Discrete Hashing for Scalable Multimedia Search", journal = j-TIST, volume = "9", number = "5", pages = "53:1--53:??", month = jul, year = "2018", CODEN = "????", DOI = "https://doi.org/10.1145/3178119", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Nov 15 16:23:08 MST 2018", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Hashing techniques have recently gained increasing research interest in multimedia studies. Most existing hashing methods only employ single features for hash code learning. Multiview data with each view corresponding to a type of feature generally provides more comprehensive information. How to efficiently integrate multiple views for learning compact hash codes still remains challenging. In this article, we propose a novel unsupervised hashing method, dubbed multiview discrete hashing (MvDH), by effectively exploring multiview data. Specifically, MvDH performs matrix factorization to generate the hash codes as the latent representations shared by multiple views, during which spectral clustering is performed simultaneously. The joint learning of hash codes and cluster labels enables that MvDH can generate more discriminative hash codes, which are optimal for classification. An efficient alternating algorithm is developed to solve the proposed optimization problem with guaranteed convergence and low computational complexity. The binary codes are optimized via the discrete cyclic coordinate descent (DCC) method to reduce the quantization errors. Extensive experimental results on three large-scale benchmark datasets demonstrate the superiority of the proposed method over several state-of-the-art methods in terms of both accuracy and scalability.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "53", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Li:2018:AEB, author = "Chen Li and William K. Cheung and Jiming Liu and Joseph K. Ng", title = "Automatic Extraction of Behavioral Patterns for Elderly Mobility and Daily Routine Analysis", journal = j-TIST, volume = "9", number = "5", pages = "54:1--54:??", month = jul, year = "2018", CODEN = "????", DOI = "https://doi.org/10.1145/3178116", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Nov 15 16:23:08 MST 2018", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "The elderly living in smart homes can have their daily movement recorded and analyzed. As different elders can have their own living habits, a methodology that can automatically identify their daily activities and discover their daily routines will be useful for better elderly care and support. In this article, we focus on automatic detection of behavioral patterns from the trajectory data of an individual for activity identification as well as daily routine discovery. The underlying challenges lie in the need to consider longer-range dependency of the sensor triggering events and spatiotemporal variations of the behavioral patterns exhibited by humans. We propose to represent the trajectory data using a behavior-aware flow graph that is a probabilistic finite state automaton with its nodes and edges attributed with some local behavior-aware features. We identify the underlying subflows as the behavioral patterns using the kernel k -means algorithm. Given the identified activities, we propose a novel nominal matrix factorization method under a Bayesian framework with Lasso to extract highly interpretable daily routines. For empirical evaluation, the proposed methodology has been compared with a number of existing methods based on both synthetic and publicly available real smart home datasets with promising results obtained. We also discuss how the proposed unsupervised methodology can be used to support exploratory behavior analysis for elderly care.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "54", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Wang:2018:IHU, author = "Jun-Zhe Wang and Jiun-Long Huang", title = "On Incremental High Utility Sequential Pattern Mining", journal = j-TIST, volume = "9", number = "5", pages = "55:1--55:??", month = jul, year = "2018", CODEN = "????", DOI = "https://doi.org/10.1145/3178114", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Nov 15 16:23:08 MST 2018", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "High utility sequential pattern (HUSP) mining is an emerging topic in pattern mining, and only a few algorithms have been proposed to address it. In practice, most sequence databases usually grow over time, and it is inefficient for existing algorithms to mine HUSPs from scratch when databases grow with a small portion of updates. In view of this, we propose the IncUSP-Miner$^+$ algorithm to mine HUSPs incrementally. Specifically, to avoid redundant re-computations, we propose a tighter upper bound of the utility of a sequence, called Tight Sequence Utility (TSU), and then we design a novel data structure, called the candidate pattern tree, to buffer the sequences whose TSU values are greater than or equal to the minimum utility threshold in the original database. Accordingly, to avoid keeping a huge amount of utility information for each sequence, a set of concise utility information is designed to be stored in each tree node. To improve the mining efficiency, several strategies are proposed to reduce the amount of computation for utility update and the scopes of database scans. Moreover, several strategies are also proposed to properly adjust the candidate pattern tree for the support of multiple database updates. Experimental results on some real and synthetic datasets show that IncUSP-Miner$^+$ is able to efficiently mine HUSPs incrementally.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "55", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Zdesar:2018:OVP, author = "Andrej Zdesar and Igor Skrjanc", title = "Optimum Velocity Profile of Multiple {Bernstein--B{\'e}zier} Curves Subject to Constraints for Mobile Robots", journal = j-TIST, volume = "9", number = "5", pages = "56:1--56:??", month = jul, year = "2018", CODEN = "????", DOI = "https://doi.org/10.1145/3183891", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Nov 15 16:23:08 MST 2018", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "This article deals with trajectory planning that is suitable for nonholonomic differentially driven wheeled mobile robots. The path is approximated with a spline that consists of multiple Bernstein-B{\'e}zier curves that are merged together in a way that continuous curvature of the spline is achieved. The article presents the approach for optimization of velocity profile of Bernstein-B{\'e}zier spline subject to velocity and acceleration constraints. For the purpose of optimization, velocity and turning points are introduced. Based on these singularity points, local segments are defined where local velocity profiles are optimized independently of each other. From the locally optimum velocity profiles, the global optimum velocity profile is determined. Since each local velocity profile can be evaluated independently, the algorithm is suitable for concurrent implementation and modification of one part of the curve does not require recalculation of all local velocity profiles. These properties enable efficient implementation of the optimization algorithm. The optimization algorithm is also suitable for the splines that consist of Bernstein-B{\'e}zier curves that have substantially different lengths. The proposed optimization approach was experimentally evaluated and validated in simulation environment and on real mobile robots.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "56", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Peng:2018:ICD, author = "Chong Peng and Zhao Kang and Shuting Cai and Qiang Cheng", title = "Integrate and Conquer: Double-Sided Two-Dimensional $k$-Means Via Integrating of Projection and Manifold Construction", journal = j-TIST, volume = "9", number = "5", pages = "57:1--57:??", month = jul, year = "2018", CODEN = "????", DOI = "https://doi.org/10.1145/3200488", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Nov 15 16:23:08 MST 2018", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "In this article, we introduce a novel, general methodology, called integrate and conquer, for simultaneously accomplishing the tasks of feature extraction, manifold construction, and clustering, which is taken to be superior to building a clustering method as a single task. When the proposed novel methodology is used on two-dimensional (2D) data, it naturally induces a new clustering method highly effective on 2D data. Existing clustering algorithms usually need to convert 2D data to vectors in a preprocessing step, which, unfortunately, severely damages 2D spatial information and omits inherent structures and correlations in the original data. The induced new clustering method can overcome the matrix-vectorization-related issues to enhance the clustering performance on 2D matrices. More specifically, the proposed methodology mutually enhances three tasks of finding subspaces, learning manifolds, and constructing data representation in a seamlessly integrated fashion. When used on 2D data, we seek two projection matrices with optimal numbers of directions to project the data into low-rank, noise-mitigated, and the most expressive subspaces, in which manifolds are adaptively updated according to the projections, and new data representation is built with respect to the projected data by accounting for nonlinearity via adaptive manifolds. Consequently, the learned subspaces and manifolds are clean and intrinsic, and the new data representation is discriminative and robust. Extensive experiments have been conducted and the results confirm the effectiveness of the proposed methodology and algorithm.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "57", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Huang:2018:CFR, author = "Dingjiang Huang and Shunchang Yu and Bin Li and Steven C. H. Hoi and Shuigeng Zhou", title = "Combination Forecasting Reversion Strategy for Online Portfolio Selection", journal = j-TIST, volume = "9", number = "5", pages = "58:1--58:??", month = jul, year = "2018", CODEN = "????", DOI = "https://doi.org/10.1145/3200692", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Nov 15 16:23:08 MST 2018", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Machine learning and artificial intelligence techniques have been applied to construct online portfolio selection strategies recently. A popular and state-of-the-art family of strategies is to explore the reversion phenomenon through online learning algorithms and statistical prediction models. Despite gaining promising results on some benchmark datasets, these strategies often adopt a single model based on a selection criterion (e.g., breakdown point) for predicting future price. However, such model selection is often unstable and may cause unnecessarily high variability in the final estimation, leading to poor prediction performance in real datasets and thus non-optimal portfolios. To overcome the drawbacks, in this article, we propose to exploit the reversion phenomenon by using combination forecasting estimators and design a novel online portfolio selection strategy, named Combination Forecasting Reversion (CFR), which outputs optimal portfolios based on the improved reversion estimator. We further present two efficient CFR implementations based on online Newton step (ONS) and online gradient descent (OGD) algorithms, respectively, and theoretically analyze their regret bounds, which guarantee that the online CFR model performs as well as the best CFR model in hindsight. We evaluate the proposed algorithms on various real markets with extensive experiments. Empirical results show that CFR can effectively overcome the drawbacks of existing reversion strategies and achieve the state-of-the-art performance.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "58", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Rossi:2018:IVG, author = "Ryan A. Rossi and Nesreen K. Ahmed and Rong Zhou and Hoda Eldardiry", title = "Interactive Visual Graph Mining and Learning", journal = j-TIST, volume = "9", number = "5", pages = "59:1--59:??", month = jul, year = "2018", CODEN = "????", DOI = "https://doi.org/10.1145/3200764", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Nov 15 16:23:08 MST 2018", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "This article presents a platform for interactive graph mining and relational machine learning called GraphVis. The platform combines interactive visual representations with state-of-the-art graph mining and relational machine learning techniques to aid in revealing important insights quickly as well as learning an appropriate and highly predictive model for a particular task (e.g., classification, link prediction, discovering the roles of nodes, and finding influential nodes). Visual representations and interaction techniques and tools are developed for simple, fast, and intuitive real-time interactive exploration, mining, and modeling of graph data. In particular, we propose techniques for interactive relational learning (e.g., node/link classification), interactive link prediction and weighting, role discovery and community detection, higher-order network analysis (via graphlets, network motifs), among others. GraphVis also allows for the refinement and tuning of graph mining and relational learning methods for specific application domains and constraints via an end-to-end interactive visual analytic pipeline that learns, infers, and provides rapid interactive visualization with immediate feedback at each change/prediction in real-time. Other key aspects include interactive filtering, querying, ranking, manipulating, exporting, as well as tools for dynamic network analysis and visualization, interactive graph generators (including new block model approaches), and a variety of multi-level network analysis techniques.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "59", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Peng:2018:EPH, author = "Xuefeng Peng and Li-Kai Chi and Jiebo Luo", title = "The Effect of Pets on Happiness: a Large-Scale Multi-Factor Analysis Using Social Multimedia", journal = j-TIST, volume = "9", number = "5", pages = "60:1--60:??", month = jul, year = "2018", CODEN = "????", DOI = "https://doi.org/10.1145/3200751", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Nov 15 16:23:08 MST 2018", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "From reducing stress and loneliness, to boosting productivity and overall well-being, pets are believed to play a significant role in people's daily lives. Many traditional studies have identified that frequent interactions with pets could make individuals become healthier and more optimistic, and ultimately enjoy a happier life. However, most of those studies are not only restricted in scale, but also may carry biases by using subjective self-reports, interviews, and questionnaires as the major approaches. In this article, we leverage large-scale data collected from social media and the state-of-the-art deep learning technologies to study this phenomenon in depth and breadth. Our study includes five major steps: (1) collecting timeline posts from around 20,000 Instagram users; (2) using face detection and recognition on 2 million photos to infer users' demographics, relationship status, and whether having children, (3) analyzing a user's degree of happiness based on images and captions via smiling classification and textual sentiment analysis; (4) applying transfer learning techniques to retrain the final layer of the Inception v3 model for pet classification; and (5) analyzing the effects of pets on happiness in terms of multiple factors of user demographics. Our main results have demonstrated the efficacy of our proposed method with many new insights. We believe this method is also applicable to other domains as a scalable, efficient, and effective methodology for modeling and analyzing social behaviors and psychological well-being. In addition, to facilitate the research involving human faces, we also release our dataset of 700K analyzed faces.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "60", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Wang:2018:TTP, author = "Weiqing Wang and Hongzhi Yin and Xingzhong Du and Quoc Viet Hung Nguyen and Xiaofang Zhou", title = "{TPM}: a Temporal Personalized Model for Spatial Item Recommendation", journal = j-TIST, volume = "9", number = "6", pages = "61:1--61:??", month = nov, year = "2018", CODEN = "????", DOI = "https://doi.org/10.1145/3230706", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Nov 15 16:23:08 MST 2018", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/ft_gateway.cfm?id=3230706", abstract = "With the rapid development of location-based social networks (LBSNs), spatial item recommendation has become an important way of helping users discover interesting locations to increase their engagement with location-based services. The availability of spatial, temporal, and social information in LBSNs offers an unprecedented opportunity to enhance the spatial item recommendation. Many previous works studied spatial and social influences on spatial item recommendation in LBSNs. Due to the strong correlations between a user's check-in time and the corresponding check-in location, which include the sequential influence and temporal cyclic effect, it is essential for spatial item recommender system to exploit the temporal effect to improve the recommendation accuracy. Leveraging temporal information in spatial item recommendation is, however, very challenging, considering (1) when integrating sequential influences, users' check-in data in LBSNs has a low sampling rate in both space and time, which renders existing location prediction techniques on GPS trajectories ineffective, and the prediction space is extremely large, with millions of distinct locations as the next prediction target, which impedes the application of classical Markov chain models; (2) there are various temporal cyclic patterns (i.e., daily, weekly, and monthly) in LBSNs, but existing work is limited to one specific pattern; and (3) there is no existing framework that unifies users' personal interests, temporal cyclic patterns, and the sequential influence of recently visited locations in a principled manner. In light of the above challenges, we propose a Temporal Personalized Model ( TPM ), which introduces a novel latent variable topic-region to model and fuse sequential influence, cyclic patterns with personal interests in the latent and exponential space. The advantages of modeling the temporal effect at the topic-region level include a significantly reduced prediction space, an effective alleviation of data sparsity, and a direct expression of the semantic meaning of users' spatial activities. Moreover, we introduce two methods to model the effect of various cyclic patterns. The first method is a time indexing scheme that encodes the effect of various cyclic patterns into a binary code. However, the indexing scheme faces the data sparsity problem in each time slice. To deal with this data sparsity problem, the second method slices the time according to each cyclic pattern separately and explores these patterns in a joint additive model. Furthermore, we design an asymmetric Locality Sensitive Hashing (ALSH) technique to speed up the online top- k recommendation process by extending the traditional LSH. We evaluate the performance of TPM on two real datasets and one large-scale synthetic dataset. The performance of TPM in recommending cold-start items is also evaluated. The results demonstrate a significant improvement in TPM's ability to recommend spatial items, in terms of both effectiveness and efficiency, compared with the state-of-the-art methods.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "61", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Lucchese:2018:XCL, author = "Claudio Lucchese and Franco Maria Nardini and Salvatore Orlando and Raffaele Perego and Fabrizio Silvestri and Salvatore Trani", title = "{X-CLEaVER}: Learning Ranking Ensembles by Growing and Pruning Trees", journal = j-TIST, volume = "9", number = "6", pages = "62:1--62:??", month = nov, year = "2018", CODEN = "????", DOI = "https://doi.org/10.1145/3205453", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Nov 15 16:23:08 MST 2018", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/ft_gateway.cfm?id=3205453", abstract = "Learning-to-Rank (LtR) solutions are commonly used in large-scale information retrieval systems such as Web search engines, which have to return highly relevant documents in response to user query within fractions of seconds. The most effective LtR algorithms adopt a gradient boosting approach to build additive ensembles of weighted regression trees. Since the required ranking effectiveness is achieved with very large ensembles, the impact on response time and query throughput of these solutions is not negligible. In this article, we propose X-CLE aVER, an iterative meta-algorithm able to build more efficient and effective ranking ensembles. X-CLEaVER interleaves the iterations of a given gradient boosting learning algorithm with pruning and re-weighting phases. First, redundant trees are removed from the given ensemble, then the weights of the remaining trees are fine-tuned by optimizing the desired ranking quality metric. We propose and analyze several pruning strategies and we assess their benefits showing that interleaving pruning and re-weighting phases during learning is more effective than applying a single post-learning optimization step. Experiments conducted using two publicly available LtR datasets show that X-CLEaVER can be successfully exploited on top of several LtR algorithms as it is effective in optimizing the effectiveness of the learnt ensembles, thus obtaining more compact forests that hence are much more efficient at scoring time.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "62", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Wang:2018:LUC, author = "Pengyang Wang and Yanjie Fu and Jiawei Zhang and Xiaolin Li and Dan Lin", title = "Learning Urban Community Structures: a Collective Embedding Perspective with Periodic Spatial-temporal Mobility Graphs", journal = j-TIST, volume = "9", number = "6", pages = "63:1--63:??", month = nov, year = "2018", CODEN = "????", DOI = "https://doi.org/10.1145/3209686", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Nov 15 16:23:08 MST 2018", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Learning urban community structures refers to the efforts of quantifying, summarizing, and representing an urban community's (i) static structures, e.g., Point-Of-Interests (POIs) buildings and corresponding geographic allocations, and (ii) dynamic structures, e.g., human mobility patterns among POIs. By learning the community structures, we can better quantitatively represent urban communities and understand their evolutions in the development of cities. This can help us boost commercial activities, enhance public security, foster social interactions, and, ultimately, yield livable, sustainable, and viable environments. However, due to the complex nature of urban systems, it is traditionally challenging to learn the structures of urban communities. To address this problem, in this article, we propose a collective embedding framework to learn the community structure from multiple periodic spatial-temporal graphs of human mobility. Specifically, we first exploit a probabilistic propagation-based approach to create a set of mobility graphs from periodic human mobility records. In these mobility graphs, the static POIs are regarded as vertexes, the dynamic mobility connectivities between POI pairs are regarded as edges, and the edge weights periodically evolve over time. A collective deep auto-encoder method is then developed to collaboratively learn the embeddings of POIs from multiple spatial-temporal mobility graphs. In addition, we develop a Unsupervised Graph based Weighted Aggregation method to align and aggregate the POI embeddings into the representation of the community structures. We apply the proposed embedding framework to two applications (i.e., spotting vibrant communities and predicting housing price return rates) to evaluate the performance of our proposed method. Extensive experimental results on real-world urban communities and human mobility data demonstrate the effectiveness of the proposed collective embedding framework.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "63", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Anaissi:2018:AOO, author = "Ali Anaissi and Nguyen Lu Dang Khoa and Thierry Rakotoarivelo and Mehrisadat Makki Alamdari and Yang Wang", title = "Adaptive Online One-Class Support Vector Machines with Applications in Structural Health Monitoring", journal = j-TIST, volume = "9", number = "6", pages = "64:1--64:??", month = nov, year = "2018", CODEN = "????", DOI = "https://doi.org/10.1145/3230708", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Nov 15 16:23:08 MST 2018", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "One-class support vector machine (OCSVM) has been widely used in the area of structural health monitoring, where only data from one class (i.e., healthy) are available. Incremental learning of OCSVM is critical for online applications in which huge data streams continuously arrive and the healthy data distribution may vary over time. This article proposes a novel adaptive self-advised online OCSVM that incrementally tunes the kernel parameter and decides whether a model update is required or not. As opposed to existing methods, this novel online algorithm does not rely on any fixed threshold, but it uses the slack variables in the OCSVM to determine which new data points should be included in the training set and trigger a model update. The algorithm also incrementally tunes the kernel parameter of OCSVM automatically based on the spatial locations of the edge and interior samples in the training data with respect to the constructed hyperplane of OCSVM. This new online OCSVM algorithm was extensively evaluated using synthetic data and real data from case studies in structural health monitoring. The results showed that the proposed method significantly improved the classification error rates, was able to assimilate the changes in the positive data distribution over time, and maintained a high damage detection accuracy in all case studies.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "64", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Li:2018:DOS, author = "Xuelong Li and Guosheng Cui and Yongsheng Dong", title = "Discriminative and Orthogonal Subspace Constraints-Based Nonnegative Matrix Factorization", journal = j-TIST, volume = "9", number = "6", pages = "65:1--65:??", month = nov, year = "2018", CODEN = "????", DOI = "https://doi.org/10.1145/3229051", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Nov 15 16:23:08 MST 2018", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/ft_gateway.cfm?id=3229051", abstract = "Nonnegative matrix factorization (NMF) is one widely used feature extraction technology in the tasks of image clustering and image classification. For the former task, various unsupervised NMF methods based on the data distribution structure information have been proposed. While for the latter task, the label information of the dataset is one very important guiding. However, most previous proposed supervised NMF methods emphasis on imposing the discriminant constraints on the coefficient matrix. When dealing with new coming samples, the transpose or the pseudoinverse of the basis matrix is used to project these samples to the low dimension space. In this way, the label influence to the basis matrix is indirect. Although, there are also some methods trying to constrain the basis matrix in NMF framework, either they only restrict within-class samples or impose improper constraint on the basis matrix. To address these problems, in this article a novel NMF framework named discriminative and orthogonal subspace constraints-based nonnegative matrix factorization (DOSNMF) is proposed. In DOSNMF, the discriminative constraints are imposed on the projected subspace instead of the directly learned representation. In this manner, the discriminative information is directly connected with the projected subspace. At the same time, an orthogonal term is incorporated in DOSNMF to adjust the orthogonality of the learned basis matrix, which can ensure the orthogonality of the learned subspace and improve the sparseness of the basis matrix at the same time. This framework can be implemented in two ways. The first way is based on the manifold learning theory. In this way, two graphs, i.e., the intrinsic graph and the penalty graph, are constructed to capture the intra-class structure and the inter-class distinctness. With this design, both the manifold structure information and the discriminative information of the dataset are utilized. For convenience, we name this method as the name of the framework, i.e., DOSNMF. The second way is based on the Fisher's criterion, we name it Fisher's criterion-based DOSNMF (FDOSNMF). The objective functions of DOSNMF and FDOSNMF can be easily optimized using multiplicative update (MU) rules. The new methods are tested on five datasets and compared with several supervised and unsupervised variants of NMF. The experimental results reveal the effectiveness of the proposed methods.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "65", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Liu:2018:HPC, author = "Xiaobai Liu and Qian Xu and Yadong Mu and Jiadi Yang and Liang Lin and Shuicheng Yan", title = "High-Precision Camera Localization in Scenes with Repetitive Patterns", journal = j-TIST, volume = "9", number = "6", pages = "66:1--66:??", month = nov, year = "2018", CODEN = "????", DOI = "https://doi.org/10.1145/3226111", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Nov 15 16:23:08 MST 2018", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "This article presents a high-precision multi-modal approach for localizing moving cameras with monocular videos, which has wide potentials in many intelligent applications, including robotics, autonomous vehicles, and so on. Existing visual odometry methods often suffer from symmetric or repetitive scene patterns, e.g., windows on buildings or parking stalls. To address this issue, we introduce a robust camera localization method that contributes in two aspects. First, we formulate feature tracking, the critical step of visual odometry, as a hierarchical min-cost network flow optimization task, and we regularize the formula with flow constraints, cross-scale consistencies, and motion heuristics. The proposed regularized formula is capable of adaptively selecting distinctive features or feature combinations, which is more effective than traditional methods that detect and group repetitive patterns in a separate step. Second, we develop a joint formula for integrating dense visual odometry and sparse GPS readings in a common reference coordinate. The fusion process is guided with high-order statistics knowledge to suppress the impacts of noises, clusters, and model drifting. We evaluate the proposed camera localization method on both public video datasets and a newly created dataset that includes scenes full of repetitive patterns. Results with comparisons show that our method can achieve comparable performance to state-of-the-art methods and is particularly effective for addressing repetitive pattern issues.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "66", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Li:2018:CDR, author = "Cheng-Te Li and Chia-Tai Hsu and Man-Kwan Shan", title = "A Cross-Domain Recommendation Mechanism for Cold-Start Users Based on Partial Least Squares Regression", journal = j-TIST, volume = "9", number = "6", pages = "67:1--67:??", month = nov, year = "2018", CODEN = "????", DOI = "https://doi.org/10.1145/3231601", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Nov 15 16:23:08 MST 2018", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/ft_gateway.cfm?id=3231601", abstract = "Recommender systems are common in e-commerce platforms in recent years. Recommender systems are able to help users find preferential items among a large amount of products so that users' time is saved and sellers' profits are increased. Cross-domain recommender systems aim to recommend items based on users' different tastes across domains. While recommender systems usually suffer from the user cold-start problem that leads to unsatisfying recommendation performance, cross-domain recommendation can remedy such a problem. This article proposes a novel cross-domain recommendation model based on regression analysis, partial least squares regression (PLSR). The proposed recommendation models, PLSR-CrossRec and PLSR-Latent, are able to purely use source-domain ratings to predict the ratings for cold-start users who never rated items in the target domains. Experiments conducted on the Epinions dataset with ten various domains' rating records demonstrate that PLSR-Latent can outperform several matrix factorization-based competing methods under a variety of cross-domain settings. The time efficiency of PLSR-Latent is also satisfactory.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "67", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Yang:2018:CUS, author = "Longqi Yang and Chen Fang and Hailin Jin and Matthew D. Hoffman and Deborah Estrin", title = "Characterizing User Skills from Application Usage Traces with Hierarchical Attention Recurrent Networks", journal = j-TIST, volume = "9", number = "6", pages = "68:1--68:??", month = nov, year = "2018", CODEN = "????", DOI = "https://doi.org/10.1145/3232231", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Nov 15 16:23:08 MST 2018", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/ft_gateway.cfm?id=3232231", abstract = "Predicting users' proficiencies is a critical component of AI-powered personal assistants. This article introduces a novel approach for the prediction based on users' diverse, noisy, and passively generated application usage histories. We propose a novel bi-directional recurrent neural network with hierarchical attention mechanism to extract sequential patterns and distinguish informative traces from noise. Our model is able to attend to the most discriminative actions and sessions to make more accurate and directly interpretable predictions while requiring 50$ \times $ less training data than the state-of-the-art sequential learning approach. We evaluate our model with two large scale datasets collected from 68K Photoshop users: a digital design skill dataset where the user skill is determined by the quality of the end products and a software skill dataset where users self-disclose their software usage skill levels. The empirical results demonstrate our model's superior performance compared to existing user representation learning techniques that leverage action frequencies and sequential patterns. In addition, we qualitatively illustrate the model's significant interpretative power. The proposed approach is broadly relevant to applications that generate user time-series analytics.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "68", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Wang:2018:RFI, author = "Suhang Wang and Charu Aggarwal and Huan Liu", title = "Random-Forest-Inspired Neural Networks", journal = j-TIST, volume = "9", number = "6", pages = "69:1--69:??", month = nov, year = "2018", CODEN = "????", DOI = "https://doi.org/10.1145/3232230", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Nov 15 16:23:08 MST 2018", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/ft_gateway.cfm?id=3232230", abstract = "Neural networks have become very popular in recent years, because of the astonishing success of deep learning in various domains such as image and speech recognition. In many of these domains, specific architectures of neural networks, such as convolutional networks, seem to fit the particular structure of the problem domain very well and can therefore perform in an astonishingly effective way. However, the success of neural networks is not universal across all domains. Indeed, for learning problems without any special structure, or in cases where the data are somewhat limited, neural networks are known not to perform well with respect to traditional machine-learning methods such as random forests. In this article, we show that a carefully designed neural network with random forest structure can have better generalization ability. In fact, this architecture is more powerful than random forests, because the back-propagation algorithm reduces to a more powerful and generalized way of constructing a decision tree. Furthermore, the approach is efficient to train and requires a small constant factor of the number of training examples. This efficiency allows the training of multiple neural networks to improve the generalization accuracy. Experimental results on real-world benchmark datasets demonstrate the effectiveness of the proposed enhancements for classification and regression.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "69", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Du:2018:SMS, author = "Bowen Du and Yifeng Cui and Yanjie Fu and Runxing Zhong and Hui Xiong", title = "{SmartTransfer}: Modeling the Spatiotemporal Dynamics of Passenger Transfers for Crowdedness-Aware Route Recommendations", journal = j-TIST, volume = "9", number = "6", pages = "70:1--70:??", month = nov, year = "2018", CODEN = "????", DOI = "https://doi.org/10.1145/3232229", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Nov 15 16:23:08 MST 2018", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/ft_gateway.cfm?id=3232229", abstract = "In urban transportation systems, transfer stations refer to hubs connecting a variety of bus and subway lines and, thus, are the most important nodes in transportation networks. The pervasive availability of large-scale travel traces of passengers, collected from automated fare collection (AFC) systems, has provided unprecedented opportunities for understanding citywide transfer patterns, which can benefit smart transportation, such as smart route recommendation to avoid crowded lines, and dynamic bus scheduling to enhance transportation efficiency. To this end, in this article, we provide a systematic study of the measurement, patterns, and modeling of spatiotemporal dynamics of passenger transfers. Along this line, we develop a data-driven analytical system for modeling the transfer volumes of each transfer station. More specifically, we first identify and quantify the discriminative patterns of spatiotemporal dynamics of passenger transfers by utilizing heterogeneous sources of transfer related data for each station. Also, we develop a multi-task spatiotemporal learning model for predicting the transfer volumes of a specific station at a specific time period. Moreover, we further leverage the predictive model of passenger transfers to provide crowdedness-aware route recommendations. Finally, we conduct the extensive evaluations with a variety of real-world data. Experimental results demonstrate the effectiveness of our proposed modeling method and its applications for smart transportation.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "70", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Liu:2018:FST, author = "Wenhe Liu and Xiaojun Chang and Yan Yan and Yi Yang and Alexander G. Hauptmann", title = "Few-Shot Text and Image Classification via Analogical Transfer Learning", journal = j-TIST, volume = "9", number = "6", pages = "71:1--71:??", month = nov, year = "2018", CODEN = "????", DOI = "https://doi.org/10.1145/3230709", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Nov 15 16:23:08 MST 2018", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/ft_gateway.cfm?id=3230709", abstract = "Learning from very few samples is a challenge for machine learning tasks, such as text and image classification. Performance of such task can be enhanced via transfer of helpful knowledge from related domains, which is referred to as transfer learning. In previous transfer learning works, instance transfer learning algorithms mostly focus on selecting the source domain instances similar to the target domain instances for transfer. However, the selected instances usually do not directly contribute to the learning performance in the target domain. Hypothesis transfer learning algorithms focus on the model/parameter level transfer. They treat the source hypotheses as well-trained and transfer their knowledge in terms of parameters to learn the target hypothesis. Such algorithms directly optimize the target hypothesis by the observable performance improvements. However, they fail to consider the problem that instances that contribute to the source hypotheses may be harmful for the target hypothesis, as instance transfer learning analyzed. To relieve the aforementioned problems, we propose a novel transfer learning algorithm, which follows an analogical strategy. Particularly, the proposed algorithm first learns a revised source hypothesis with only instances contributing to the target hypothesis. Then, the proposed algorithm transfers both the revised source hypothesis and the target hypothesis (only trained with a few samples) to learn an analogical hypothesis. We denote our algorithm as Analogical Transfer Learning. Extensive experiments on one synthetic dataset and three real-world benchmark datasets demonstrate the superior performance of the proposed algorithm.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "71", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Chin:2018:EAN, author = "Wei-Sheng Chin and Bo-Wen Yuan and Meng-Yuan Yang and Chih-Jen Lin", title = "An Efficient Alternating {Newton} Method for Learning Factorization Machines", journal = j-TIST, volume = "9", number = "6", pages = "72:1--72:??", month = nov, year = "2018", CODEN = "????", DOI = "https://doi.org/10.1145/3230710", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Nov 15 16:23:08 MST 2018", bibsource = "https://www.math.utah.edu/pub/tex/bib/multithreading.bib; https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/ft_gateway.cfm?id=3230710", abstract = "To date, factorization machines (FMs) have emerged as a powerful model in many applications. In this work, we study the training of FM with the logistic loss for binary classification, which is a nonlinear extension of the linear model with the logistic loss (i.e., logistic regression). For the training of large-scale logistic regression, Newton methods have been shown to be an effective approach, but it is difficult to apply such methods to FM because of the nonconvexity. We consider a modification of FM that is multiblock convex and propose an alternating minimization algorithm based on Newton methods. Some novel optimization techniques are introduced to reduce the running time. Our experiments demonstrate that the proposed algorithm is more efficient than stochastic gradient algorithms and coordinate descent methods. The parallelism of our method is also investigated for the acceleration in multithreading environments.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "72", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Cao:2019:ATS, author = "Nan Cao and Steffen Koch and David Gotz / Yingcai Wu", title = "{ACM TIST} Special Issue on Visual Analytics", journal = j-TIST, volume = "10", number = "1", pages = "1:1--1:??", month = jan, year = "2019", CODEN = "????", DOI = "https://doi.org/10.1145/3277019", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Oct 22 11:55:44 MDT 2019", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/ft_gateway.cfm?id=3277019", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "1", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Chen:2019:RVR, author = "Wei Chen and Jing Xia and Xumeng Wang and Yi Wang and Jun Chen and Liang Chang", title = "{RelationLines}: Visual Reasoning of Egocentric Relations from Heterogeneous Urban Data", journal = j-TIST, volume = "10", number = "1", pages = "2:1--2:??", month = jan, year = "2019", CODEN = "????", DOI = "https://doi.org/10.1145/3200766", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Oct 22 11:55:44 MDT 2019", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/ft_gateway.cfm?id=3200766", abstract = "The increased accessibility of urban sensor data and the popularity of social network applications is enabling the discovery of crowd mobility and personal communication patterns. However, studying the egocentric relationships of an individual can be very challenging because available data may refer to direct contacts, such as phone calls between individuals, or indirect contacts, such as paired location presence. In this article, we develop methods to integrate three facets extracted from heterogeneous urban data (timelines, calls, and locations) through a progressive visual reasoning and inspection scheme. Our approach uses a detect-and-filter scheme such that, prior to visual refinement and analysis, a coarse detection is performed to extract the target individual and construct the timeline of the target. It then detects spatio-temporal co-occurrences or call-based contacts to develop the egocentric network of the individual. The filtering stage is enhanced with a line-based visual reasoning interface that facilitates a flexible and comprehensive investigation of egocentric relationships and connections in terms of time, space, and social networks. The integrated system, RelationLines, is demonstrated using a dataset that contains taxi GPS data, cell-base mobility data, mobile calling data, microblog data, and point-of-interest (POI) data from a city with millions of citizens. We examine the effectiveness and efficiency of our system with three case studies and user review.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "2", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Xu:2019:TSV, author = "Mingliang Xu and Hua Wang and Shili Chu and Yong Gan and Xiaoheng Jiang and Yafei Li and Bing Zhou", title = "Traffic Simulation and Visual Verification in Smog", journal = j-TIST, volume = "10", number = "1", pages = "3:1--3:??", month = jan, year = "2019", CODEN = "????", DOI = "https://doi.org/10.1145/3200491", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Oct 22 11:55:44 MDT 2019", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/ft_gateway.cfm?id=3200491", abstract = "Smog causes low visibility on the road and it can impact the safety of traffic. Modeling traffic in smog will have a significant impact on realistic traffic simulations. Most existing traffic models assume that drivers have optimal vision in the simulations, making these simulations are not suitable for modeling smog weather conditions. In this article, we introduce the Smog Full Velocity Difference Model (SMOG-FVDM) for a realistic simulation of traffic in smog weather conditions. In this model, we present a stadia model for drivers in smog conditions. We introduce it into a car-following traffic model using both psychological force and body force concepts, and then we introduce the SMOG-FVDM. Considering that there are lots of parameters in the SMOG-FVDM, we design a visual verification system based on SMOG-FVDM to arrive at an adequate solution which can show visual simulation results under different road scenarios and different degrees of smog by reconciling the parameters. Experimental results show that our model can give a realistic and efficient traffic simulation of smog weather conditions.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "3", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Xie:2019:VAH, author = "Cong Xie and Wen Zhong and Wei Xu and Klaus Mueller", title = "Visual Analytics of Heterogeneous Data Using Hypergraph Learning", journal = j-TIST, volume = "10", number = "1", pages = "4:1--4:??", month = jan, year = "2019", CODEN = "????", DOI = "https://doi.org/10.1145/3200765", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Oct 22 11:55:44 MDT 2019", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/ft_gateway.cfm?id=3200765", abstract = "For real-world learning tasks (e.g., classification), graph-based models are commonly used to fuse the information distributed in diverse data sources, which can be heterogeneous, redundant, and incomplete. These models represent the relations in different datasets as pairwise links. However, these links cannot deal with high-order relations which connect multiple objects (e.g., in public health datasets, more than two patient groups admitted by the same hospital in 2014). In this article, we propose a visual analytics approach for the classification on heterogeneous datasets using the hypergraph model. The hypergraph is an extension to traditional graphs in which a hyperedge connects multiple vertices instead of just two. We model various high-order relations in heterogeneous datasets as hyperedges and fuse different datasets with a unified hypergraph structure. We use the hypergraph learning algorithm for predicting missing labels in the datasets. To allow users to inject their domain knowledge into the model-learning process, we augment the traditional learning algorithm in a number of ways. In addition, we also propose a set of visualizations which enable the user to construct the hypergraph structure and the parameters of the learning model interactively during the analysis. We demonstrate the capability of our approach via two real-world cases.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "4", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Vogogias:2019:BVS, author = "Athanasios Vogogias and Jessie Kennedy and Daniel Archambault and Benjamin Bach and V. Anne Smith and Hannah Currant", title = "{BayesPiles}: Visualisation Support for {Bayesian} Network Structure Learning", journal = j-TIST, volume = "10", number = "1", pages = "5:1--5:??", month = jan, year = "2019", CODEN = "????", DOI = "https://doi.org/10.1145/3230623", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Oct 22 11:55:44 MDT 2019", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/ft_gateway.cfm?id=3230623", abstract = "We address the problem of exploring, combining, and comparing large collections of scored, directed networks for understanding inferred Bayesian networks used in biology. In this field, heuristic algorithms explore the space of possible network solutions, sampling this space based on algorithm parameters and a network score that encodes the statistical fit to the data. The goal of the analyst is to guide the heuristic search and decide how to determine a final consensus network structure, usually by selecting the top-scoring network or constructing the consensus network from a collection of high-scoring networks. BayesPiles, our visualisation tool, helps with understanding the structure of the solution space and supporting the construction of a final consensus network that is representative of the underlying dataset. BayesPiles builds upon and extends MultiPiles to meet our domain requirements. We developed BayesPiles in conjunction with computational biologists who have used this tool on datasets used in their research. The biologists found our solution provides them with new insights and helps them achieve results that are representative of the underlying data.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "5", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Liu:2019:DVT, author = "Dongyu Liu and Weiwei Cui and Kai Jin and Yuxiao Guo and Huamin Qu", title = "{DeepTracker}: Visualizing the Training Process of Convolutional Neural Networks", journal = j-TIST, volume = "10", number = "1", pages = "6:1--6:??", month = jan, year = "2019", CODEN = "????", DOI = "https://doi.org/10.1145/3200489", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Oct 22 11:55:44 MDT 2019", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/ft_gateway.cfm?id=3200489", abstract = "Deep Convolutional Neural Networks (CNNs) have achieved remarkable success in various fields. However, training an excellent CNN is practically a trial-and-error process that consumes a tremendous amount of time and computer resources. To accelerate the training process and reduce the number of trials, experts need to understand what has occurred in the training process and why the resulting CNN behaves as it does. However, current popular training platforms, such as TensorFlow, only provide very little and general information, such as training/validation errors, which is far from enough to serve this purpose. To bridge this gap and help domain experts with their training tasks in a practical environment, we propose a visual analytics system, DeepTracker, to facilitate the exploration of the rich dynamics of CNN training processes and to identify the unusual patterns that are hidden behind the huge amount of information in training log. Specifically, we combine a hierarchical index mechanism and a set of hierarchical small multiples to help experts explore the entire training log from different levels of detail. We also introduce a novel cube-style visualization to reveal the complex correlations among multiple types of heterogeneous training data, including neuron weights, validation images, and training iterations. Three case studies are conducted to demonstrate how DeepTracker provides its users with valuable knowledge in an industry-level CNN training process; namely, in our case, training ResNet-50 on the ImageNet dataset. We show that our method can be easily applied to other state-of-the-art ``very deep'' CNN models.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "6", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Jin:2019:LFE, author = "Hai Jin and Yuanfeng Lian and Jing Hua", title = "Learning Facial Expressions with {$3$D} Mesh Convolutional Neural Network", journal = j-TIST, volume = "10", number = "1", pages = "7:1--7:??", month = jan, year = "2019", CODEN = "????", DOI = "https://doi.org/10.1145/3200572", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Oct 22 11:55:44 MDT 2019", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/ft_gateway.cfm?id=3200572", abstract = "Making machines understand human expressions enables various useful applications in human-machine interaction. In this article, we present a novel facial expression recognition approach with 3D Mesh Convolutional Neural Networks (3DMCNN) and a visual analytics-guided 3DMCNN design and optimization scheme. From an RGBD camera, we first reconstruct a 3D face model of a subject with facial expressions and then compute the geometric properties of the surface. Instead of using regular Convolutional Neural Networks (CNNs) to learn intensities of the facial images, we convolve the geometric properties on the surface of the 3D model using 3DMCNN. We design a geodesic distance-based convolution method to overcome the difficulties raised from the irregular sampling of the face surface mesh. We further present interactive visual analytics for the purpose of designing and modifying the networks to analyze the learned features and cluster similar nodes in 3DMCNN. By removing low-activity nodes in the network, the performance of the network is greatly improved. We compare our method with the regular CNN-based method by interactively visualizing each layer of the networks and analyze the effectiveness of our method by studying representative cases. Testing on public datasets, our method achieves a higher recognition accuracy than traditional image-based CNN and other 3D CNNs. The proposed framework, including 3DMCNN and interactive visual analytics of the CNN, can be extended to other applications.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "7", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Zhang:2019:RVA, author = "Chen Zhang and Hao Wang", title = "{ResumeVis}: a Visual Analytics System to Discover Semantic Information in Semi-structured Resume Data", journal = j-TIST, volume = "10", number = "1", pages = "8:1--8:??", month = jan, year = "2019", CODEN = "????", DOI = "https://doi.org/10.1145/3230707", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Oct 22 11:55:44 MDT 2019", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/ft_gateway.cfm?id=3230707", abstract = "Massive public resume data emerging on the internet indicates individual-related characteristics in terms of profile and career experiences. Resume Analysis (RA) provides opportunities for many applications, such as recruitment trend predict, talent seeking and evaluation. Existing RA studies either largely rely on the knowledge of domain experts, or leverage classic statistical or data mining models to identify and filter explicit attributes based on pre-defined rules. However, they fail to discover the latent semantic information from semi-structured resume text, i.e., individual career progress trajectory and social-relations, which are otherwise vital to comprehensive understanding of people's career evolving patterns. Besides, when dealing with large numbers of resumes, how to properly visualize such semantic information to reduce the information load and to support better human cognition is also challenging. To tackle these issues, we propose a visual analytics system called ResumeVis to mine and visualize resume data. First, a text mining-based approach is presented to extract semantic information. Then, a set of visualizations are devised to represent the semantic information in multiple perspectives. Through interactive exploration on ResumeVis performed by domain experts, the following tasks can be accomplished: to trace individual career evolving trajectory; to mine latent social-relations among individuals; and to hold the full picture of massive resumes' collective mobility. Case studies with over 2,500 government officer resumes demonstrate the effectiveness of our system.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "8", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Du:2019:VIR, author = "Fan Du and Catherine Plaisant and Neil Spring and Ben Shneiderman", title = "Visual Interfaces for Recommendation Systems: Finding Similar and Dissimilar Peers", journal = j-TIST, volume = "10", number = "1", pages = "9:1--9:??", month = jan, year = "2019", CODEN = "????", DOI = "https://doi.org/10.1145/3200490", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Oct 22 11:55:44 MDT 2019", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/ft_gateway.cfm?id=3200490", abstract = "Recommendation applications can guide users in making important life choices by referring to the activities of similar peers. For example, students making academic plans may learn from the data of similar students, while patients and their physicians may explore data from similar patients to select the best treatment. Selecting an appropriate peer group has a strong impact on the value of the guidance that can result from analyzing the peer group data. In this article, we describe a visual interface that helps users review the similarity and differences between a seed record and a group of similar records and refine the selection. We introduce the LikeMeDonuts, Ranking Glyph, and History Heatmap visualizations. The interface was refined through three rounds of formative usability evaluation with 12 target users, and its usefulness was evaluated by a case study with a student review manager using real student data. We describe three analytic workflows observed during use and summarize how users' input shaped the final design.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "9", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Liang:2019:CTB, author = "Haoran Liang and Ming Jiang and Ronghua Liang and Qi Zhao", title = "{CapVis}: Toward Better Understanding of Visual-Verbal Saliency Consistency", journal = j-TIST, volume = "10", number = "1", pages = "10:1--10:??", month = jan, year = "2019", CODEN = "????", DOI = "https://doi.org/10.1145/3200767", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Oct 22 11:55:44 MDT 2019", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/ft_gateway.cfm?id=3200767", abstract = "When looking at an image, humans shift their attention toward interesting regions, making sequences of eye fixations. When describing an image, they also come up with simple sentences that highlight the key elements in the scene. What is the correlation between where people look and what they describe in an image? To investigate this problem intuitively, we develop a visual analytics system, CapVis, to look into visual attention and image captioning, two types of subjective annotations that are relatively task-free and natural. Using these annotations, we propose a word-weighting scheme to extract visual and verbal saliency ranks to compare against each other. In our approach, a number of low-level and semantic-level features relevant to visual-verbal saliency consistency are proposed and visualized for a better understanding of image content. Our method also shows the different ways that a human and a computational model look at and describe images, which provides reliable information for a captioning model. Experiment also shows that the visualized feature can be integrated into a computational model to effectively predict the consistency between the two modalities on an image dataset with both types of annotations.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "10", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Chen:2019:DMI, author = "Siming Chen and Shuai Chen and Zhenhuang Wang and Jie Liang and Yadong Wu and Xiaoru Yuan", title = "{D-Map+}: Interactive Visual Analysis and Exploration of Ego-centric and Event-centric Information Diffusion Patterns in Social Media", journal = j-TIST, volume = "10", number = "1", pages = "11:1--11:??", month = jan, year = "2019", CODEN = "????", DOI = "https://doi.org/10.1145/3183347", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Oct 22 11:55:44 MDT 2019", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/ft_gateway.cfm?id=3183347", abstract = "Information diffusion analysis is important in social media. In this work, we present a coherent ego-centric and event-centric model to investigate diffusion patterns and user behaviors. Applying the model, we propose Diffusion Map+ (D-Maps+), a novel visualization method to support exploration and analysis of user behaviors and diffusion patterns through a map metaphor. For ego-centric analysis, users who participated in reposting (i.e., resending a message initially posted by others) one central user's posts (i.e., a series of original tweets) are collected. Event-centric analysis focuses on multiple central users discussing a specific event, with all the people participating and reposting messages about it. Social media users are mapped to a hexagonal grid based on their behavior similarities and in the chronological order of repostings. With the additional interactions and linkings, D-Map+ is capable of providing visual profiling of influential users, describing their social behaviors and analyzing the evolution of significant events in social media. A comprehensive visual analysis system is developed to support interactive exploration with D-Map+. We evaluate our work with real-world social media data and find interesting patterns among users and events. We also perform evaluations including user studies and expert feedback to certify the capabilities of our method.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "11", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Yang:2019:FML, author = "Qiang Yang and Yang Liu and Tianjian Chen and Yongxin Tong", title = "Federated Machine Learning: Concept and Applications", journal = j-TIST, volume = "10", number = "2", pages = "12:1--12:??", month = feb, year = "2019", CODEN = "????", DOI = "https://doi.org/10.1145/3298981", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Oct 22 11:55:44 MDT 2019", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/ft_gateway.cfm?id=3298981", abstract = "Today's artificial intelligence still faces two major challenges. One is that, in most industries, data exists in the form of isolated islands. The other is the strengthening of data privacy and security. We propose a possible solution to these challenges: secure federated learning. Beyond the federated-learning framework first proposed by Google in 2016, we introduce a comprehensive secure federated-learning framework, which includes horizontal federated learning, vertical federated learning, and federated transfer learning. We provide definitions, architectures, and applications for the federated-learning framework, and provide a comprehensive survey of existing works on this subject. In addition, we propose building data networks among organizations based on federated mechanisms as an effective solution to allowing knowledge to be shared without compromising user privacy.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "12", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Wang:2019:SZS, author = "Wei Wang and Vincent W. Zheng and Han Yu and Chunyan Miao", title = "A Survey of Zero-Shot Learning: Settings, Methods, and Applications", journal = j-TIST, volume = "10", number = "2", pages = "13:1--13:??", month = feb, year = "2019", CODEN = "????", DOI = "https://doi.org/10.1145/3293318", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Oct 22 11:55:44 MDT 2019", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/ft_gateway.cfm?id=3293318", abstract = "Most machine-learning methods focus on classifying instances whose classes have already been seen in training. In practice, many applications require classifying instances whose classes have not been seen previously. Zero-shot learning is a powerful and promising learning paradigm, in which the classes covered by training instances and the classes we aim to classify are disjoint. In this paper, we provide a comprehensive survey of zero-shot learning. First of all, we provide an overview of zero-shot learning. According to the data utilized in model optimization, we classify zero-shot learning into three learning settings. Second, we describe different semantic spaces adopted in existing zero-shot learning works. Third, we categorize existing zero-shot learning methods and introduce representative methods under each category. Fourth, we discuss different applications of zero-shot learning. Finally, we highlight promising future research directions of zero-shot learning.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "13", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Mirsky:2019:GPR, author = "Reuth Mirsky and Kobi Gal and Roni Stern and Meir Kalech", title = "Goal and Plan Recognition Design for Plan Libraries", journal = j-TIST, volume = "10", number = "2", pages = "14:1--14:??", month = feb, year = "2019", CODEN = "????", DOI = "https://doi.org/10.1145/3234464", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Oct 22 11:55:44 MDT 2019", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/ft_gateway.cfm?id=3234464", abstract = "This article provides new techniques for optimizing domain design for goal and plan recognition using plan libraries. We define two new problems: Goal Recognition Design for Plan Libraries (GRD-PL) and Plan Recognition Design (PRD). Solving the GRD-PL helps to infer which goal the agent is trying to achieve, while solving PRD can help to infer how the agent is going to achieve its goal. For each problem, we define a worst-case distinctiveness measure that is an upper bound on the number of observations that are necessary to unambiguously recognize the agent's goal or plan. This article studies the relationship between these measures, showing that the worst-case distinctiveness of GRD-PL is a lower bound of the worst-case plan distinctiveness of PRD and that they are equal under certain conditions. We provide two complete algorithms for minimizing the worst-case distinctiveness of plan libraries without reducing the agent's ability to complete its goals: One is a brute-force search over all possible plans and one is a constraint-based search that identifies plans that are most difficult to distinguish in the domain. These algorithms are evaluated in three hierarchical plan recognition settings from the literature. We were able to reduce the worst-case distinctiveness of the domains using our approach, in some cases reaching 100\% improvement within a predesignated time window. Our iterative algorithm outperforms the brute-force approach by an order of magnitude in terms of runtime.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "14", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Skibski:2019:ECS, author = "Oskar Skibski and Talal Rahwan and Tomasz P. Michalak and Michael Wooldridge", title = "Enumerating Connected Subgraphs and Computing the {Myerson} and {Shapley} Values in Graph-Restricted Games", journal = j-TIST, volume = "10", number = "2", pages = "15:1--15:??", month = feb, year = "2019", CODEN = "????", DOI = "https://doi.org/10.1145/3235026", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Oct 22 11:55:44 MDT 2019", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/ft_gateway.cfm?id=3235026", abstract = "At the heart of multi-agent systems is the ability to cooperate to improve the performance of individual agents and/or the system as a whole. While a widespread assumption in the literature is that such cooperation is essentially unrestricted, in many realistic settings this assumption does not hold. A highly influential approach for modelling such scenarios are graph-restricted games introduced by Myerson [36]. In this approach, agents are represented by nodes in a graph, edges represent communication channels, and a group can generate an arbitrary value only if there exists a direct or indirect communication channel between every pair of agents within the group. Two fundamental solution-concepts that were proposed for such games are the Myerson value and the Shapley value. While an algorithm has been developed to compute the Shapley value in arbitrary graph-restricted games, no such general-purpose algorithm has been developed for the Myerson value to date. With this in mind, we set out to develop for such games a general-purpose algorithm to compute the Myerson value, and a more efficient algorithm to compute the Shapley value. Since the computation of either value involves enumerating all connected induced subgraphs of the game's underlying graph, we start by developing an algorithm dedicated to this enumeration, and then we show empirically that it is faster than the state of the art in the literature. Finally, we present a sample application of both algorithms, in which we test the Myerson value and the Shapley value as advanced measures of node centrality in networks.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "15", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Zhang:2019:UEO, author = "Jason Shuo Zhang and Qin Lv", title = "Understanding Event Organization at Scale in Event-Based Social Networks", journal = j-TIST, volume = "10", number = "2", pages = "16:1--16:??", month = feb, year = "2019", CODEN = "????", DOI = "https://doi.org/10.1145/3243227", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Oct 22 11:55:44 MDT 2019", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/ft_gateway.cfm?id=3243227", abstract = "Understanding real-world event participation behavior has been a subject of active research and can offer valuable insights for event-related recommendation and advertisement. The emergence of event-based social networks (EBSNs), which attracts online users to host/attend offline events, has enabled exciting new research in this domain. However, most existing works focus on understanding or predicting individual users' event participation behavior or recommending events to individual users. Few studies have addressed the problem of event popularity from the event organizer's point of view. In this work, we study the latent factors for determining event popularity using large-scale datasets collected from the popular Meetup.com EBSN in five major cities around the world. We analyze and model four contextual factors: spatial factor using location convenience, quality, popularity density, and competitiveness; group factor using group member entropy and loyalty; temporal factor using temporal preference and weekly event patterns; and semantic factor using readability, sentiment, part of speech, and text novelty. In addition, we have developed a group-based social influence propagation network to model group-specific influences on events. By combining the COntextual features and Social Influence NEtwork, our integrated prediction framework COSINE can capture the diverse influential factors of event participation and can be used by event organizers to predict/improve the popularity of their events. Detailed evaluations demonstrate that our COSINE framework achieves high accuracy for event popularity prediction in all five cities with diverse cultures and user event behaviors.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "16", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Hsieh:2019:IOS, author = "Hsun-Ping Hsieh and Cheng-Te Li", title = "Inferring Online Social Ties from Offline Geographical Activities", journal = j-TIST, volume = "10", number = "2", pages = "17:1--17:??", month = feb, year = "2019", CODEN = "????", DOI = "https://doi.org/10.1145/3293319", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Oct 22 11:55:44 MDT 2019", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/ft_gateway.cfm?id=3293319", abstract = "As mobile devices are becoming ubiquitous nowadays, the geographical activities and interactions of human beings can be easily recorded and accessed. Each mobile individual can belong to an online social network. Unfortunately, the underlying online social relationships are hidden and only available to service providers. Acquiring the social network of mobile users would enrich lots of mobile applications, such as friend recommendation and energy-saving mobile database management. In this work, we propose to infer online social ties using purely offline geographical activities of users, such as check-in records and spatial meeting events. To tackle the problem, we devise a novel inference framework, O2O-I nf, which consists of two components, Feature Modeling and Link Inference. Feature modeling is to characterize both direct and indirect geographical interactions between nodes from co-location and graph features. Link inference aims to infer the social ties based on a small set of observed social links, and the idea is that pairs of nodes sharing similar geographical behaviors have the same tendency of linkage (i.e., either being friends or non-friends). Experiments conducted on a Gowalla location-based social network and a Meetup event-based social network exhibit a satisfying performance in comparison to state-of-the-art prediction methods under the settings of offline-to-online network inference and geo-link prediction.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "17", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Yu:2019:RHR, author = "Zeng Yu and Tianrui Li and Ning Yu and Yi Pan and Hongmei Chen and Bing Liu", title = "Reconstruction of Hidden Representation for Robust Feature Extraction", journal = j-TIST, volume = "10", number = "2", pages = "18:1--18:??", month = feb, year = "2019", CODEN = "????", DOI = "https://doi.org/10.1145/3284174", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Oct 22 11:55:44 MDT 2019", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/ft_gateway.cfm?id=3284174", abstract = "This article aims to develop a new and robust approach to feature representation. Motivated by the success of Auto-Encoders, we first theoretically analyze and summarize the general properties of all algorithms that are based on traditional Auto-Encoders: (1) The reconstruction error of the input cannot be lower than a lower bound, which can be viewed as a guiding principle for reconstructing the input. Additionally, when the input is corrupted with noises, the reconstruction error of the corrupted input also cannot be lower than a lower bound. (2) The reconstruction of a hidden representation achieving its ideal situation is the necessary condition for the reconstruction of the input to reach the ideal state. (3) Minimizing the Frobenius norm of the Jacobian matrix of the hidden representation has a deficiency and may result in a much worse local optimum value. We believe that minimizing the reconstruction error of the hidden representation is more robust than minimizing the Frobenius norm of the Jacobian matrix of the hidden representation. Based on the above analysis, we propose a new model termed Double Denoising Auto-Encoders (DDAEs), which uses corruption and reconstruction on both the input and the hidden representation. We demonstrate that the proposed model is highly flexible and extensible and has a potentially better capability to learn invariant and robust feature representations. We also show that our model is more robust than Denoising Auto-Encoders (DAEs) for dealing with noises or inessential features. Furthermore, we detail how to train DDAEs with two different pretraining methods by optimizing the objective function in a combined and separate manner, respectively. Comparative experiments illustrate that the proposed model is significantly better for representation learning than the state-of-the-art models.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "18", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Wang:2019:SBT, author = "Hongjian Wang and Xianfeng Tang and Yu-Hsuan Kuo and Daniel Kifer and Zhenhui Li", title = "A Simple Baseline for Travel Time Estimation using Large-scale Trip Data", journal = j-TIST, volume = "10", number = "2", pages = "19:1--19:??", month = feb, year = "2019", CODEN = "????", DOI = "https://doi.org/10.1145/3293317", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Oct 22 11:55:44 MDT 2019", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/ft_gateway.cfm?id=3293317", abstract = "The increased availability of large-scale trajectory data provides rich information for the study of urban dynamics. For example, New York City Taxi 8 Limousine Commission regularly releases source/destination information of taxi trips, where 173 million taxi trips released for Year 2013 [29]. Such a big dataset provides us potential new perspectives to address the traditional traffic problems. In this article, we study the travel time estimation problem. Instead of following the traditional route-based travel time estimation, we propose to simply use a large amount of taxi trips without using the intermediate trajectory points to estimate the travel time between source and destination. Our experiments show very promising results. The proposed big-data-driven approach significantly outperforms both state-of-the-art route-based method and online map services. Our study indicates that novel simple approaches could be empowered by big data and these approaches could serve as new baselines for some traditional computational problems.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "19", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Deng:2019:DMS, author = "Cheng Deng and Zhao Li and Xinbo Gao and Dacheng Tao", title = "Deep Multi-scale Discriminative Networks for Double {JPEG} Compression Forensics", journal = j-TIST, volume = "10", number = "2", pages = "20:1--20:??", month = feb, year = "2019", CODEN = "????", DOI = "https://doi.org/10.1145/3301274", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Oct 22 11:55:44 MDT 2019", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/ft_gateway.cfm?id=3301274", abstract = "As JPEG is the most widely used image format, the importance of tampering detection for JPEG images in blind forensics is self-evident. In this area, extracting effective statistical characteristics from a JPEG image for classification remains a challenge. Effective features are designed manually in traditional methods, suggesting that extensive labor-consuming research and derivation is required. In this article, we propose a novel image tampering detection method based on deep multi-scale discriminative networks (MSD-Nets). The multi-scale module is designed to automatically extract multiple features from the discrete cosine transform (DCT) coefficient histograms of the JPEG image. This module can capture the characteristic information in different scale spaces. In addition, a discriminative module is also utilized to improve the detection effect of the networks in those difficult situations when the first compression quality ( QF 1) is higher than the second one ( QF 2). A special network in this module is designed to distinguish the small statistical difference between authentic and tampered regions in these cases. Finally, a probability map can be obtained and the specific tampering area is located using the last classification results. Extensive experiments demonstrate the superiority of our proposed method in both quantitative and qualitative metrics when compared with state-of-the-art approaches.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "20", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Sharma:2019:CFN, author = "Karishma Sharma and Feng Qian and He Jiang and Natali Ruchansky and Ming Zhang and Yan Liu", title = "Combating Fake News: a Survey on Identification and Mitigation Techniques", journal = j-TIST, volume = "10", number = "3", pages = "21:1--21:??", month = may, year = "2019", CODEN = "????", DOI = "https://doi.org/10.1145/3305260", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Oct 22 11:55:44 MDT 2019", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/ft_gateway.cfm?id=3305260", abstract = "The proliferation of fake news on social media has opened up new directions of research for timely identification and containment of fake news and mitigation of its widespread impact on public opinion. While much of the earlier research was focused on identification of fake news based on its contents or by exploiting users' engagements with the news on social media, there has been a rising interest in proactive intervention strategies to counter the spread of misinformation and its impact on society. In this survey, we describe the modern-day problem of fake news and, in particular, highlight the technical challenges associated with it. We discuss existing methods and techniques applicable to both identification and mitigation, with a focus on the significant advances in each method and their advantages and limitations. In addition, research has often been limited by the quality of existing datasets and their specific application contexts. To alleviate this problem, we comprehensively compile and summarize characteristic features of available datasets. Furthermore, we outline new directions of research to facilitate future development of effective and interdisciplinary solutions.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "21", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Li:2019:CSD, author = "Zun Li and Congyan Lang and Jiashi Feng and Yidong Li and Tao Wang and Songhe Feng", title = "Co-saliency Detection with Graph Matching", journal = j-TIST, volume = "10", number = "3", pages = "22:1--22:??", month = may, year = "2019", CODEN = "????", DOI = "https://doi.org/10.1145/3313874", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Oct 22 11:55:44 MDT 2019", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/ft_gateway.cfm?id=3313874", abstract = "Recently, co-saliency detection, which aims to automatically discover common and salient objects appeared in several relevant images, has attracted increased interest in the computer vision community. In this article, we present a novel graph-matching based model for co-saliency detection in image pairs. A solution of graph matching is proposed to integrate the visual appearance, saliency coherence, and spatial structural continuity for detecting co-saliency collaboratively. Since the saliency and the visual similarity have been seamlessly integrated, such a joint inference schema is able to produce more accurate and reliable results. More concretely, the proposed model first computes the intra-saliency for each image by aggregating multiple saliency cues. The common and salient regions across multiple images are thus discovered via a graph matching procedure. Then, a graph reconstruction scheme is proposed to refine the intra-saliency iteratively. Compared to existing co-saliency detection methods that only utilize visual appearance cues, our proposed model can effectively exploit both visual appearance and structure information to better guide co-saliency detection. Extensive experiments on several challenging image pair databases demonstrate that our model outperforms state-of-the-art baselines significantly.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "22", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Likhyani:2019:LSI, author = "Ankita Likhyani and Srikanta Bedathur and Deepak P.", title = "Location-Specific Influence Quantification in Location-Based Social Networks", journal = j-TIST, volume = "10", number = "3", pages = "23:1--23:??", month = may, year = "2019", CODEN = "????", DOI = "https://doi.org/10.1145/3300199", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Oct 22 11:55:44 MDT 2019", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/ft_gateway.cfm?id=3300199", abstract = "Location-based social networks (LBSNs) such as Foursquare offer a platform for users to share and be aware of each other's physical movements. As a result of such a sharing of check-in information with each other, users can be influenced to visit (or check-in) at the locations visited by their friends. Quantifying such influences in these LBSNs is useful in various settings such as location promotion, personalized recommendations, mobility pattern prediction, and so forth. In this article, we develop a model to quantify the influence specific to a location between a pair of users. Specifically, we develop a framework called LoCaTe, that combines (a) a user mobility model based on kernel density estimates; (b) a model of the semantics of the location using topic models; and (c) a user correlation model that uses an exponential distribution. We further develop LoCaTe+, an advanced model within the same framework where user correlation is quantified using a Mutually Exciting Hawkes Process. We show the applicability of LoCaTe and LoCaTe+ for location promotion and location recommendation tasks using LBSNs. Our models are validated using a long-term crawl of Foursquare data collected between January 2015 and February 2016, as well as other publicly available LBSN datasets. Our experiments demonstrate the efficacy of the LoCaTe framework in capturing location-specific influence between users. We also show that our models improve over state-of-the-art models for the task of location promotion as well as location recommendation.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "23", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Yao:2019:PAP, author = "Huaxiu Yao and Defu Lian and Yi Cao and Yifan Wu and Tao Zhou", title = "Predicting Academic Performance for College Students: a Campus Behavior Perspective", journal = j-TIST, volume = "10", number = "3", pages = "24:1--24:??", month = may, year = "2019", CODEN = "????", DOI = "https://doi.org/10.1145/3299087", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Oct 22 11:55:44 MDT 2019", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/ft_gateway.cfm?id=3299087", abstract = "Detecting abnormal behaviors of students in time and providing personalized intervention and guidance at the early stage is important in educational management. Academic performance prediction is an important building block to enabling this pre-intervention and guidance. Most of the previous studies are based on questionnaire surveys and self-reports, which suffer from small sample size and social desirability bias. In this article, we collect longitudinal behavioral data from the smart cards of 6,597 students and propose three major types of discriminative behavioral factors, diligence, orderliness, and sleep patterns. Empirical analysis demonstrates these behavioral factors are strongly correlated with academic performance. Furthermore, motivated by the social influence theory, we analyze the correlation between each student's academic performance with his/her behaviorally similar students'. Statistical tests indicate this correlation is significant. Based on these factors, we further build a multi-task predictive framework based on a learning-to-rank algorithm for academic performance prediction. This framework captures inter-semester correlation, inter-major correlation, and integrates student similarity to predict students' academic performance. The experiments on a large-scale real-world dataset show the effectiveness of our methods for predicting academic performance and the effectiveness of proposed behavioral factors.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "24", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Yang:2019:MAC, author = "Bailin Yang and Luhong Zhang and Frederick W. B. Li and Xiaoheng Jiang and Zhigang Deng and Meng Wang and Mingliang Xu", title = "Motion-Aware Compression and Transmission of Mesh Animation Sequences", journal = j-TIST, volume = "10", number = "3", pages = "25:1--25:??", month = may, year = "2019", CODEN = "????", DOI = "https://doi.org/10.1145/3300198", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Oct 22 11:55:44 MDT 2019", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/ft_gateway.cfm?id=3300198", abstract = "With the increasing demand in using 3D mesh data over networks, supporting effective compression and efficient transmission of meshes has caught lots of attention in recent years. This article introduces a novel compression method for 3D mesh animation sequences, supporting user-defined and progressive transmissions over networks. Our motion-aware approach starts with clustering animation frames based on their motion similarities, dividing a mesh animation sequence into fragments of varying lengths. This is done by a novel temporal clustering algorithm, which measures motion similarity based on the curvature and torsion of a space curve formed by corresponding vertices along a series of animation frames. We further segment each cluster based on mesh vertex coherence, representing topological proximity within an object under certain motion. To produce a compact representation, we perform intra-cluster compression based on Graph Fourier Transform (GFT) and Set Partitioning In Hierarchical Trees (SPIHT) coding. Optimized compression results can be achieved by applying GFT due to the proximity in vertex position and motion. We adapt SPIHT to support progressive transmission and design a mechanism to transmit mesh animation sequences with user-defined quality. Experimental results show that our method can obtain a high compression ratio while maintaining a low reconstruction error.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "25", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Wu:2019:OHT, author = "Hanrui Wu and Yuguang Yan and Yuzhong Ye and Huaqing Min and Michael K. Ng and Qingyao Wu", title = "Online Heterogeneous Transfer Learning by Knowledge Transition", journal = j-TIST, volume = "10", number = "3", pages = "26:1--26:??", month = may, year = "2019", CODEN = "????", DOI = "https://doi.org/10.1145/3309537", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Oct 22 11:55:44 MDT 2019", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/ft_gateway.cfm?id=3309537", abstract = "In this article, we study the problem of online heterogeneous transfer learning, where the objective is to make predictions for a target data sequence arriving in an online fashion, and some offline labeled instances from a heterogeneous source domain are provided as auxiliary data. The feature spaces of the source and target domains are completely different, thus the source data cannot be used directly to assist the learning task in the target domain. To address this issue, we take advantage of unlabeled co-occurrence instances as intermediate supplementary data to connect the source and target domains, and perform knowledge transition from the source domain into the target domain. We propose a novel online heterogeneous transfer learning algorithm called Online Heterogeneous Knowledge Transition (OHKT) for this purpose. In OHKT, we first seek to generate pseudo labels for the co-occurrence data based on the labeled source data, and then develop an online learning algorithm to classify the target sequence by leveraging the co-occurrence data with pseudo labels. Experimental results on real-world data sets demonstrate the effectiveness and efficiency of the proposed algorithm.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "26", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Shi:2019:CBV, author = "Neng Shi and Yubo Tao", title = "{CNNs} Based Viewpoint Estimation for Volume Visualization", journal = j-TIST, volume = "10", number = "3", pages = "27:1--27:??", month = may, year = "2019", CODEN = "????", DOI = "https://doi.org/10.1145/3309993", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Oct 22 11:55:44 MDT 2019", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/ft_gateway.cfm?id=3309993", abstract = "Viewpoint estimation from 2D rendered images is helpful in understanding how users select viewpoints for volume visualization and guiding users to select better viewpoints based on previous visualizations. In this article, we propose a viewpoint estimation method based on Convolutional Neural Networks (CNNs) for volume visualization. We first design an overfit-resistant image rendering pipeline to generate the training images with accurate viewpoint annotations, and then train a category-specific viewpoint classification network to estimate the viewpoint for the given rendered image. Our method can achieve good performance on images rendered with different transfer functions and rendering parameters in several categories. We apply our model to recover the viewpoints of the rendered images in publications, and show how experts look at volumes. We also introduce a CNN feature-based image similarity measure for similarity voting based viewpoint selection, which can suggest semantically meaningful optimal viewpoints for different volumes and transfer functions.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "27", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Mikhail:2019:SBN, author = "Joseph W. Mikhail and John M. Fossaceca and Ronald Iammartino", title = "A Semi-Boosted Nested Model With Sensitivity-Based Weighted Binarization for Multi-Domain Network Intrusion Detection", journal = j-TIST, volume = "10", number = "3", pages = "28:1--28:??", month = may, year = "2019", CODEN = "????", DOI = "https://doi.org/10.1145/3313778", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Oct 22 11:55:44 MDT 2019", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/ft_gateway.cfm?id=3313778", abstract = "Effective network intrusion detection techniques are required to thwart evolving cybersecurity threats. Historically, traditional enterprise networks have been researched extensively in this regard. However, the cyber threat landscape has grown to include wireless networks. In this article, the authors present a novel model that can be trained on completely different feature sets and applied to two distinct intrusion detection applications: traditional enterprise networks and 802.11 wireless networks. This is the first method that demonstrates superior performance in both aforementioned applications. The model is based on a one-versus-all binary framework comprising multiple nested sub-ensembles. To provide good generalization ability, each sub-ensemble contains a collection of sub-learners, and only a portion of the sub-learners implement boosting. A class weight based on the sensitivity metric (true-positive rate), learned from the training data only, is assigned to the sub-ensembles of each class. The use of pruning to remove sub-learners that do not contribute to or have an adverse effect on overall system performance is investigated as well. The results demonstrate that the proposed system can achieve exceptional performance in applications to both traditional enterprise intrusion detection and 802.11 wireless intrusion detection.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "28", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Gou:2019:LMR, author = "Jianping Gou and Wenmo Qiu and Zhang Yi and Yong Xu and Qirong Mao and Yongzhao Zhan", title = "A Local Mean Representation-based {$K$}-Nearest Neighbor Classifier", journal = j-TIST, volume = "10", number = "3", pages = "29:1--29:??", month = may, year = "2019", CODEN = "????", DOI = "https://doi.org/10.1145/3319532", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Oct 22 11:55:44 MDT 2019", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/ft_gateway.cfm?id=3319532", abstract = "K -nearest neighbor classification method (KNN), as one of the top 10 algorithms in data mining, is a very simple and yet effective nonparametric technique for pattern recognition. However, due to the selective sensitiveness of the neighborhood size k, the simple majority vote, and the conventional metric measure, the KNN-based classification performance can be easily degraded, especially in the small training sample size cases. In this article, to further improve the classification performance and overcome the main issues in the KNN-based classification, we propose a local mean representation-based k -nearest neighbor classifier (LMRKNN). In the LMRKNN, the categorical k -nearest neighbors of a query sample are first chosen to calculate the corresponding categorical k -local mean vectors, and then the query sample is represented by the linear combination of the categorical k -local mean vectors; finally, the class-specific representation-based distances between the query sample and the categorical k -local mean vectors are adopted to determine the class of the query sample. Extensive experiments on many UCI and KEEL datasets and three popular face databases are carried out by comparing LMRKNN to the state-of-art KNN-based methods. The experimental results demonstrate that the proposed LMRKNN outperforms the related competitive KNN-based methods with more robustness and effectiveness.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "29", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Zhuo:2019:RMA, author = "Hankz Hankui Zhuo", title = "Recognizing Multi-Agent Plans When Action Models and Team Plans Are Both Incomplete", journal = j-TIST, volume = "10", number = "3", pages = "30:1--30:??", month = may, year = "2019", CODEN = "????", DOI = "https://doi.org/10.1145/3319403", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Oct 22 11:55:44 MDT 2019", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/ft_gateway.cfm?id=3319403", abstract = "Multi-Agent Plan Recognition (MAPR) aims to recognize team structures (which are composed of team plans) from the observed team traces (action sequences) of a set of intelligent agents. In this article, we introduce the problem formulation of MAPR based on partially observed team traces, and present a weighted MAX-SAT-based framework to recognize multi-agent plans from partially observed team traces with the help of two types of auxiliary knowledge to help recognize multi-agent plans, i.e., a library of incomplete team plans and a set of incomplete action models. Our framework functions with two phases. We first build a set of hard constraints that encode the correctness property of the team plans, and a set of soft constraints that encode the optimal utility property of team plans based on the input team trace, incomplete team plans, and incomplete action models. After that, we solve all of the constraints using a weighted MAX-SAT solver and convert the solution to a set of team plans that best explain the structure of the observed team trace. We empirically exhibit both effectiveness and efficiency of our framework in benchmark domains from International Planning Competition (IPC).", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "30", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Golpayegani:2019:USD, author = "Fatemeh Golpayegani and Ivana Dusparic and Siobhan Clarke", title = "Using Social Dependence to Enable Neighbourly Behaviour in Open Multi-Agent Systems", journal = j-TIST, volume = "10", number = "3", pages = "31:1--31:??", month = may, year = "2019", CODEN = "????", DOI = "https://doi.org/10.1145/3319402", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Oct 22 11:55:44 MDT 2019", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/ft_gateway.cfm?id=3319402", abstract = "Agents frequently collaborate to achieve a shared goal or to accomplish a task that they cannot do alone. However, collaboration is difficult in open multi-agent systems where agents share constrained resources to achieve both individual and shared goals. In current approaches to collaboration, agents are organised into disjoint groups and social reasoning is used to capture their capabilities when selecting a qualified set of collaborators. These approaches are not useful when agents are in multiple, overlapping groups; depend on each other when using shared resources; have multiple goals to achieve simultaneously; and have to share the overall costs and benefits. In this article, agents use social reasoning to enhance their understanding of other agents' goals and their dependencies, and self-adaptive techniques to adapt their level of self-interest in a collaborative process, with a view to contributing to lowering shared costs or increasing shared benefits. This model aims at improving the extent to which agents' goals are met while improving shared resource usage efficiency. For example, in a public transport system where each mode of transport has limited capacity, commuters will be enabled to make choices that avoid over-capacity in different modes, or in a smart energy grid with limited capacity, users can make choices as to when they increase their demand. The model simultaneously helps avoid overloading a shared resource while allowing users to achieve their own goals. The proposed model is evaluated in an open multi-agent system with 100 agents operating in multiple overlapping groups and sharing multiple constrained resources. The impact of agents' varying levels of social dependencies, mobility, and their groups' density on their individual and shared goal achievement is analysed.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "31", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Zhu:2019:EVC, author = "Chunbiao Zhu and Wenhao Zhang and Thomas H. Li and Shan Liu and Ge Li", title = "Exploiting the Value of the Center-dark Channel Prior for Salient Object Detection", journal = j-TIST, volume = "10", number = "3", pages = "32:1--32:??", month = may, year = "2019", CODEN = "????", DOI = "https://doi.org/10.1145/3319368", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Oct 22 11:55:44 MDT 2019", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/ft_gateway.cfm?id=3319368", abstract = "Saliency detection aims to detect the most attractive objects in images and is widely used as a foundation for various applications. In this article, we propose a novel salient object detection algorithm for RGB-D images using center-dark channel priors. First, we generate an initial saliency map based on a color saliency map and a depth saliency map of a given RGB-D image. Then, we generate a center-dark channel map based on center saliency and dark channel priors. Finally, we fuse the initial saliency map with the center dark channel map to generate the final saliency map. Extensive evaluations over four benchmark datasets demonstrate that our proposed method performs favorably against most of the state-of-the-art approaches. Besides, we further discuss the application of the proposed algorithm in small target detection and demonstrate the universal value of center-dark channel priors in the field of object detection.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "32", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Bian:2019:TDC, author = "Jiang Bian and Dayong Tian and Yuanyan Tang and Dacheng Tao", title = "Trajectory Data Classification: a Review", journal = j-TIST, volume = "10", number = "4", pages = "33:1--33:??", month = aug, year = "2019", CODEN = "????", DOI = "https://doi.org/10.1145/3330138", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Oct 22 11:55:45 MDT 2019", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/ft_gateway.cfm?id=3330138", abstract = "This article comprehensively surveys the development of trajectory data classification. Considering the critical role of trajectory data classification in modern intelligent systems for surveillance security, abnormal behavior detection, crowd behavior analysis, and traffic control, trajectory data classification has attracted growing attention. According to the availability of manual labels, which is critical to the classification performances, the methods can be classified into three categories, i.e., unsupervised, semi-supervised, and supervised. Furthermore, classification methods are divided into some sub-categories according to what extracted features are used. We provide a holistic understanding and deep insight into three types of trajectory data classification methods and present some promising future directions.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "33", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Verenich:2019:SCB, author = "Ilya Verenich and Marlon Dumas and Marcello {La Rosa} and Fabrizio Maria Maggi and Irene Teinemaa", title = "Survey and Cross-benchmark Comparison of Remaining Time Prediction Methods in Business Process Monitoring", journal = j-TIST, volume = "10", number = "4", pages = "34:1--34:??", month = aug, year = "2019", CODEN = "????", DOI = "https://doi.org/10.1145/3331449", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Oct 22 11:55:45 MDT 2019", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/ft_gateway.cfm?id=3331449", abstract = "Predictive business process monitoring methods exploit historical process execution logs to generate predictions about running instances (called cases) of a business process, such as the prediction of the outcome, next activity, or remaining cycle time of a given process case. These insights could be used to support operational managers in taking remedial actions as business processes unfold, e.g., shifting resources from one case onto another to ensure the latter is completed on time. A number of methods to tackle the remaining cycle time prediction problem have been proposed in the literature. However, due to differences in their experimental setup, choice of datasets, evaluation measures, and baselines, the relative merits of each method remain unclear. This article presents a systematic literature review and taxonomy of methods for remaining time prediction in the context of business processes, as well as a cross-benchmark comparison of 16 such methods based on 17 real-life datasets originating from different industry domains.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "34", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Gong:2019:MMC, author = "Chen Gong and Jian Yang and Dacheng Tao", title = "Multi-Modal Curriculum Learning over Graphs", journal = j-TIST, volume = "10", number = "4", pages = "35:1--35:??", month = aug, year = "2019", CODEN = "????", DOI = "https://doi.org/10.1145/3322122", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Oct 22 11:55:45 MDT 2019", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/ft_gateway.cfm?id=3322122", abstract = "Curriculum Learning (CL) is a recently proposed learning paradigm that aims to achieve satisfactory performance by properly organizing the learning sequence from simple curriculum examples to more difficult ones. Up to now, few works have been done to explore CL for the data with graph structure. Therefore, this article proposes a novel CL algorithm that can be utilized to guide the Label Propagation (LP) over graphs, of which the target is to ``learn'' the labels of unlabeled examples on the graphs. Specifically, we assume that different unlabeled examples have different levels of difficulty for propagation, and their label learning should follow a simple-to-difficult sequence with the updated curricula. Furthermore, considering that the practical data are often characterized by multiple modalities, every modality in our method is associated with a ``teacher'' that not only evaluates the difficulties of examples from its own viewpoint, but also cooperates with other teachers to generate the overall simplest curriculum examples for propagation. By taking the curriculums suggested by the teachers as a whole, the common preference (i.e., commonality) of teachers on selecting the simplest examples can be discovered by a row-sparse matrix, and their distinct opinions (i.e., individuality) are captured by a sparse noise matrix. As a result, an accurate curriculum sequence can be established and the propagation quality can thus be improved. Theoretically, we prove that the propagation risk bound is closely related to the examples' difficulty information, and empirically, we show that our method can generate higher accuracy than the state-of-the-art CL approach and LP algorithms on various multi-modal tasks.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "35", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Ao:2019:LSF, author = "Xiang Ao and Haoran Shi and Jin Wang and Luo Zuo and Hongwei Li and Qing He", title = "Large-Scale Frequent Episode Mining from Complex Event Sequences with Hierarchies", journal = j-TIST, volume = "10", number = "4", pages = "36:1--36:??", month = aug, year = "2019", CODEN = "????", DOI = "https://doi.org/10.1145/3326163", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Oct 22 11:55:45 MDT 2019", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/ft_gateway.cfm?id=3326163", abstract = "Frequent Episode Mining (FEM), which aims at mining frequent sub-sequences from a single long event sequence, is one of the essential building blocks for the sequence mining research field. Existing studies about FEM suffer from unsatisfied scalability when faced with complex sequences as it is an NP-complete problem for testing whether an episode occurs in a sequence. In this article, we propose a scalable, distributed framework to support FEM on ``big'' event sequences. As a rule of thumb, ``big'' illustrates an event sequence is either very long or with masses of simultaneous events. Meanwhile, the events in this article are arranged in a predefined hierarchy. It derives some abstractive events that can form episodes that may not directly appear in the input sequence. Specifically, we devise an event-centered and hierarchy-aware partitioning strategy to allocate events from different levels of the hierarchy into local processes. We then present an efficient special-purpose algorithm to improve the local mining performance. We also extend our framework to support maximal and closed episode mining in the context of event hierarchy, and to the best of our knowledge, we are the first attempt to define and discover hierarchy-aware maximal and closed episodes. We implement the proposed framework on Apache Spark and conduct experiments on both synthetic and real-world datasets. Experimental results demonstrate the efficiency and scalability of the proposed approach and show that we can find practical patterns when taking event hierarchies into account.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "36", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Cong:2019:EUG, author = "Phan Thanh Cong and Nguyen Thanh Tam and Hongzhi Yin and Bolong Zheng and Bela Stantic and Nguyen Quoc Viet Hung", title = "Efficient User Guidance for Validating Participatory Sensing Data", journal = j-TIST, volume = "10", number = "4", pages = "37:1--37:??", month = aug, year = "2019", CODEN = "????", DOI = "https://doi.org/10.1145/3326164", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Oct 22 11:55:45 MDT 2019", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/ft_gateway.cfm?id=3326164", abstract = "Participatory sensing has become a new data collection paradigm that leverages the wisdom of the crowd for big data applications without spending cost to buy dedicated sensors. It collects data from human sensors by using their own devices such as cell phone accelerometers, cameras, and GPS devices. This benefit comes with a drawback: human sensors are arbitrary and inherently uncertain due to the lack of quality guarantee. Moreover, participatory sensing data are time series that exhibit not only highly irregular dependencies on time but also high variance between sensors. To overcome these limitations, we formulate the problem of validating uncertain time series collected by participatory sensors. In this article, we approach the problem by an iterative validation process on top of a probabilistic time series model. First, we generate a series of probability distributions from raw data by tailoring a state-of-the-art dynamical model, namely Generalised Auto Regressive Conditional Heteroskedasticity (GARCH), for our joint time series setting. Second, we design a feedback process that consists of an adaptive aggregation model to unify the joint probabilistic time series and an efficient user guidance model to validate aggregated data with minimal effort. Through extensive experimentation, we demonstrate the efficiency and effectiveness of our approach on both real data and synthetic data. Highlights from our experiences include the fast running time of a probabilistic model, the robustness of an aggregation model to outliers, and the significant effort saving of a guidance model.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "37", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Cui:2019:STA, author = "Wanqiu Cui and Junping Du and Dawei Wang and Xunpu Yuan and Feifei Kou and Liyan Zhou and Nan Zhou", title = "Short Text Analysis Based on Dual Semantic Extension and Deep Hashing in Microblog", journal = j-TIST, volume = "10", number = "4", pages = "38:1--38:??", month = aug, year = "2019", CODEN = "????", DOI = "https://doi.org/10.1145/3326166", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Oct 22 11:55:45 MDT 2019", bibsource = "https://www.math.utah.edu/pub/tex/bib/hash.bib; https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/ft_gateway.cfm?id=3326166", abstract = "Short text analysis is a challenging task as far as the sparsity and limitation of semantics. The semantic extension approach learns the meaning of a short text by introducing external knowledge. However, for the randomness of short text descriptions in microblogs, traditional extension methods cannot accurately mine the semantics suitable for the microblog theme. Therefore, we use the prominent and refined hashtag information in microblogs as well as complex social relationships to provide implicit guidance for semantic extension of short text. Specifically, we design a deep hash model based on social and conceptual semantic extension, which consists of dual semantic extension and deep hashing representation. In the extension method, the short text is first conceptualized to achieve the construction of hashtag graph under conceptual space. Then, the associated hashtags are generated by correlation calculation based on the integration of social relationships and concepts to extend the short text. In the deep hash model, we use the semantic hashing model to encode the abundant semantic features and form a compact and meaningful binary encoding. Finally, extensive experiments demonstrate that our method can learn and represent the short texts well by using more meaningful semantic signal. It can effectively enhance and guide the semantic analysis and understanding of short text in microblogs.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "38", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{He:2019:STA, author = "Suining He and Kang G. Shin", title = "Spatio-temporal Adaptive Pricing for Balancing Mobility-on-Demand Networks", journal = j-TIST, volume = "10", number = "4", pages = "39:1--39:??", month = aug, year = "2019", CODEN = "????", DOI = "https://doi.org/10.1145/3331450", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Oct 22 11:55:45 MDT 2019", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/ft_gateway.cfm?id=3331450", abstract = "Pricing in mobility-on-demand (MOD) networks, such as Uber, Lyft, and connected taxicabs, is done adaptively by leveraging the price responsiveness of drivers (supplies) and passengers (demands) to achieve such goals as maximizing drivers' incomes, improving riders' experience, and sustaining platform operation. Existing pricing policies only respond to short-term demand fluctuations without accurate trip forecast and spatial demand-supply balancing, thus mismatching drivers to riders and resulting in loss of profit. We propose CAPrice, a novel adaptive pricing scheme for urban MOD networks. It uses a new spatio-temporal deep capsule network (STCapsNet) that accurately predicts ride demands and driver supplies with vectorized neuron capsules while accounting for comprehensive spatio-temporal and external factors. Given accurate perception of zone-to-zone traffic flows in a city, CAPrice formulates a joint optimization problem by considering spatial equilibrium to balance the platform, providing drivers and riders/passengers with proactive pricing ``signals.'' We have conducted an extensive experimental evaluation upon over 4.0$ \times $ 10$^8$ MOD trips (Uber, Didi Chuxing, and connected taxicabs) in New York City, Beijing, and Chengdu, validating the accuracy, effectiveness, and profitability (often 20\% ride prediction accuracy and 30\% profit improvements over the state-of-the-arts) of CAPrice in managing urban MOD networks.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "39", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Tonge:2019:PAT, author = "Ashwini Tonge and Cornelia Caragea", title = "Privacy-aware Tag Recommendation for Accurate Image Privacy Prediction", journal = j-TIST, volume = "10", number = "4", pages = "40:1--40:??", month = aug, year = "2019", CODEN = "????", DOI = "https://doi.org/10.1145/3335054", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Oct 22 11:55:45 MDT 2019", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/ft_gateway.cfm?id=3335054", abstract = "Online images' tags are very important for indexing, sharing, and searching of images, as well as surfacing images with private or sensitive content, which needs to be protected. Social media sites such as Flickr generate these metadata from user-contributed tags. However, as the tags are at the sole discretion of users, these tags tend to be noisy and incomplete. In this article, we present a privacy-aware approach to automatic image tagging, which aims at improving the quality of user annotations, while also preserving the images' original privacy sharing patterns. Precisely, we recommend potential tags for each target image by mining privacy-aware tags from the most similar images of the target image, which are obtained from a large collection. Experimental results show that, although the user-input tags compose noise, our privacy-aware approach is able to predict accurate tags that can improve the performance of a downstream application on image privacy prediction and outperforms an existing privacy-oblivious approach to image tagging. The results also show that, even for images that do not have any user tags, our proposed approach can recommend accurate tags. Crowd-sourcing the predicted tags exhibits the quality of our privacy-aware recommended tags. Our code, features, and the dataset used in experiments are available at: https://github.com/ashwinitonge/privacy-aware-tag-rec.git.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "40", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Zhao:2019:PRG, author = "Guoshuai Zhao and Hao Fu and Ruihua Song and Tetsuya Sakai and Zhongxia Chen and Xing Xie and Xueming Qian", title = "Personalized Reason Generation for Explainable Song Recommendation", journal = j-TIST, volume = "10", number = "4", pages = "41:1--41:??", month = aug, year = "2019", CODEN = "????", DOI = "https://doi.org/10.1145/3337967", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Oct 22 11:55:45 MDT 2019", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/ft_gateway.cfm?id=3337967", abstract = "Personalized recommendation has received a lot of attention as a highly practical research topic. However, existing recommender systems provide the recommendations with a generic statement such as ``Customers who bought this item also bought \ldots{}''. Explainable recommendation, which makes a user aware of why such items are recommended, is in demand. The goal of our research is to make the users feel as if they are receiving recommendations from their friends. To this end, we formulate a new challenging problem called personalized reason generation for explainable recommendation for songs in conversation applications and propose a solution that generates a natural language explanation of the reason for recommending a song to that particular user. For example, if the user is a student, our method can generate an output such as ``Campus radio plays this song at noon every day, and I think it sounds wonderful,'' which the student may find easy to relate to. In the offline experiments, through manual assessments, the gain of our method is statistically significant on the relevance to songs and personalization to users comparing with baselines. Large-scale online experiments show that our method outperforms manually selected reasons by 8.2\% in terms of click-through rate. Evaluation results indicate that our generated reasons are relevant to songs and personalized to users, and they attract users to click the recommendations.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "41", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Thukral:2019:DER, author = "Deepak Thukral and Adesh Pandey and Rishabh Gupta and Vikram Goyal and Tanmoy Chakraborty", title = "{DiffQue}: Estimating Relative Difficulty of Questions in Community Question Answering Services", journal = j-TIST, volume = "10", number = "4", pages = "42:1--42:??", month = aug, year = "2019", CODEN = "????", DOI = "https://doi.org/10.1145/3337799", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Oct 22 11:55:45 MDT 2019", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/ft_gateway.cfm?id=3337799", abstract = "Automatic estimation of relative difficulty of a pair of questions is an important and challenging problem in community question answering (CQA) services. There are limited studies that addressed this problem. Past studies mostly leveraged expertise of users answering the questions and barely considered other properties of CQA services such as metadata of users and posts, temporal information, and textual content. In this article, we propose DiffQue, a novel system that maps this problem to a network-aided edge directionality prediction problem. DiffQue starts by constructing a novel network structure that captures different notions of difficulties among a pair of questions. It then measures the relative difficulty of two questions by predicting the direction of a (virtual) edge connecting these two questions in the network. It leverages features extracted from the network structure, metadata of users/posts, and textual description of questions and answers. Experiments on datasets obtained from two CQA sites (further divided into four datasets) with human annotated ground-truth show that DiffQue outperforms four state-of-the-art methods by a significant margin (28.77\% higher F$_1$ score and 28.72\% higher AUC than the best baseline). As opposed to the other baselines, (i) DiffQue appropriately responds to the training noise, (ii) DiffQue is capable of adapting multiple domains (CQA datasets), and (iii) DiffQue can efficiently handle the ``cold start'' problem that may arise due to the lack of information for newly posted questions or newly arrived users.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "42", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Jiang:2019:SEL, author = "Zhe Jiang and Arpan Man Sainju and Yan Li and Shashi Shekhar and Joseph Knight", title = "Spatial Ensemble Learning for Heterogeneous Geographic Data with Class Ambiguity", journal = j-TIST, volume = "10", number = "4", pages = "43:1--43:??", month = aug, year = "2019", CODEN = "????", DOI = "https://doi.org/10.1145/3337798", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Oct 22 11:55:45 MDT 2019", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/ft_gateway.cfm?id=3337798", abstract = "Class ambiguity refers to the phenomenon whereby similar features correspond to different classes at different locations. Given heterogeneous geographic data with class ambiguity, the spatial ensemble learning (SEL) problem aims to find a decomposition of the geographic area into disjoint zones such that class ambiguity is minimized and a local classifier can be learned in each zone. The problem is important for applications such as land cover mapping from heterogeneous earth observation data with spectral confusion. However, the problem is challenging due to its high computational cost. Related work in ensemble learning either assumes an identical sample distribution (e.g., bagging, boosting, random forest) or decomposes multi-modular input data in the feature vector space (e.g., mixture of experts, multimodal ensemble) and thus cannot effectively minimize class ambiguity. In contrast, we propose a spatial ensemble framework that explicitly partitions input data in geographic space. Our approach first preprocesses data into homogeneous spatial patches and uses a greedy heuristic to allocate pairs of patches with high class ambiguity into different zones. We further extend our spatial ensemble learning framework with spatial dependency between nearby zones based on the spatial autocorrelation effect. Both theoretical analysis and experimental evaluations on two real world wetland mapping datasets show the feasibility of the proposed approach.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "43", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Banerjee:2019:AAR, author = "Suvadeep Banerjee and Abhijit Chatterjee", title = "{ALERA}: Accelerated Reinforcement Learning Driven Adaptation to Electro-Mechanical Degradation in Nonlinear Control Systems Using Encoded State Space Error Signatures", journal = j-TIST, volume = "10", number = "4", pages = "44:1--44:??", month = aug, year = "2019", CODEN = "????", DOI = "https://doi.org/10.1145/3338123", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Oct 22 11:55:45 MDT 2019", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/ft_gateway.cfm?id=3338123", abstract = "The successful deployment of autonomous real-time systems is contingent on their ability to recover from performance degradation of sensors, actuators, and other electro-mechanical subsystems with low latency. In this article, we introduce ALERA, a novel framework for real-time control law adaptation in nonlinear control systems assisted by system state encodings that generate an error signal when the code properties are violated in the presence of failures. The fundamental contributions of this methodology are twofold-first, we show that the time-domain error signal contains perturbed system parameters' diagnostic information that can be used for quick control law adaptation to failure conditions and second, this quick adaptation is performed via reinforcement learning algorithms that relearn the control law of the perturbed system from a starting condition dictated by the diagnostic information, thus achieving significantly faster recovery. The fast (up to 80X faster than traditional reinforcement learning paradigms) performance recovery enabled by ALERA is demonstrated on an inverted pendulum balancing problem, a brake-by-wire system, and a self-balancing robot.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "44", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Strobl:2019:ECF, author = "Eric V. Strobl and Peter L. Spirtes and Shyam Visweswaran", title = "Estimating and Controlling the False Discovery Rate of the {PC} Algorithm Using Edge-specific {$P$}-Values", journal = j-TIST, volume = "10", number = "5", pages = "46:1--46:??", month = oct, year = "2019", CODEN = "????", DOI = "https://doi.org/10.1145/3351342", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Oct 22 11:55:45 MDT 2019", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Many causal discovery algorithms infer graphical structure from observational data. The PC algorithm in particular estimates a completed partially directed acyclic graph (CPDAG), or an acyclic graph containing directed edges identifiable with conditional independence testing. However, few groups have investigated strategies for estimating and controlling the false discovery rate (FDR) of the edges in the CPDAG. In this article, we introduce PC with p-values (PC-p), a fast algorithm that robustly computes edge-specific p-values and then estimates and controls the FDR across the edges. PC-p specifically uses the p-values returned by many conditional independence (CI) tests to upper bound the p-values of more complex edge-specific hypothesis tests. The algorithm then estimates and controls the FDR using the bounded p-values and the Benjamini-Yekutieli FDR procedure. Modifications to the original PC algorithm also help PC-p accurately compute the upper bounds despite non-zero Type II error rates. Experiments show that PC-p yields more accurate FDR estimation and control across the edges in a variety of CPDAGs compared to alternative methods.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "46", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Herd:2019:DCR, author = "Benjamin C. Herd and Simon Miles", title = "Detecting Causal Relationships in Simulation Models Using Intervention-based Counterfactual Analysis", journal = j-TIST, volume = "10", number = "5", pages = "47:1--47:??", month = oct, year = "2019", CODEN = "????", DOI = "https://doi.org/10.1145/3322123", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Oct 22 11:55:45 MDT 2019", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Central to explanatory simulation models is their capability to not just show that but also why particular things happen. Explanation is closely related with the detection of causal relationships and is, in a simulation context, typically done by means of controlled experiments. However, for complex simulation models, conventional ``blackbox'' experiments may be too coarse-grained to cope with spurious relationships. We present an intervention-based causal analysis methodology that exploits the manipulability of computational models, and detects and circumvents spurious effects. The core of the methodology is a formal model that maps basic causal assumptions to causal observations and allows for the identification of combinations of assumptions that have a negative impact on observability. First, experiments indicate that the methodology can successfully deal with notoriously tricky situations involving asymmetric and symmetric overdetermination and detect fine-grained causal relationships between events in the simulation. As illustrated in the article, the methodology can be easily integrated into an existing simulation environment.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "47", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Rahmadi:2019:SSS, author = "Ridho Rahmadi and Perry Groot and Tom Heskes", title = "Stable Specification Search in Structural Equation Models with Latent Variables", journal = j-TIST, volume = "10", number = "5", pages = "48:1--48:??", month = oct, year = "2019", CODEN = "????", DOI = "https://doi.org/10.1145/3341557", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Oct 22 11:55:45 MDT 2019", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "In our previous study, we introduced stable specification search for cross-sectional data (S3C). It is an exploratory causal method that combines the concept of stability selection and multi-objective optimization to search for stable and parsimonious causal structures across the entire range of model complexities. S3C, however, is designed to model causal relations among observed variables. In this study, we extended S3C to S3C-Latent, to model linear causal relations between latent variables that are measured through observed proxies. We evaluated S3C-Latent on simulated data and compared the results to those of PC-MIMBuild, an extension of the PC algorithm, the state-of-the-art causal discovery method. The comparison shows that S3C-Latent achieved better performance. We also applied S3C-Latent to real-world data of children with attention deficit/hyperactivity disorder and data about measuring mental abilities among pupils. The results are consistent with those of previous studies.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "48", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Liu:2019:LLA, author = "Yue Liu and Zheng Cai and Chunchen Liu and Zhi Geng", title = "Local Learning Approaches for Finding Effects of a Specified Cause and Their Causal Paths", journal = j-TIST, volume = "10", number = "5", pages = "49:1--49:??", month = oct, year = "2019", CODEN = "????", DOI = "https://doi.org/10.1145/3313147", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Oct 22 11:55:45 MDT 2019", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Causal networks are used to describe and to discover causal relationships among variables and data generating mechanisms. There have been many approaches for learning a global causal network of all observed variables. In many applications, we may be interested in finding what are the effects of a specified cause variable and what are the causal paths from the cause variable to its effects. Instead of learning a global causal network, we propose several local learning approaches for finding all effects (or descendants) of the specified cause variable and the causal paths from the cause variable to some effect variable of interest. We discuss the identifiability of the effects and the causal paths from observed data and prior knowledge. For the case that the causal paths are not identifiable, our approaches try to find a path set that contains the causal paths of interest.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "49", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Zhang:2019:MCI, author = "Hao Zhang and Shuigeng Zhou and Jihong Guan and Jun (Luke) Huan", title = "Measuring Conditional Independence by Independent Residuals for Causal Discovery", journal = j-TIST, volume = "10", number = "5", pages = "50:1--50:??", month = oct, year = "2019", CODEN = "????", DOI = "https://doi.org/10.1145/3325708", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Oct 22 11:55:45 MDT 2019", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "We investigate the relationship between conditional independence (CI) x \vdbar y | Z and the independence of two residuals x -E( x | Z )\vdbar y -E( y | Z ), where x and y are two random variables and Z is a set of random variables. We show that if x, y, and Z are generated by following linear structural equation models and all external influences follow joint Gaussian distribution, then x \vdbar y | Z if and only if x -E( x | Z )\vdbar y -E( y | Z ). That is, the test of x \vdbar y | Z can be relaxed to a simpler unconditional independence test of x -E( x | Z )\vdbar y -E( y | Z ). Furthermore, testing x -E( x | Z )\vdbar y -E( y | Z ) can be simplified by testing x -E( x | Z )\vdbar y or y -E( y | Z )\vdbar x. On the other side, if all these external influences follow non-Gaussian distributions and the model satisfies structural faithfulness condition, then we have x \vdbar y | Z {$<$}={$>$} x -E( x | Z )\vdbar y -E( y | Z ). We apply the results above to the causal discovery problem, where the causal directions are generally determined by a set of V -structures and their consistent propagations, so CI test-based methods can return a set of Markov equivalence classes. We show that in the linear non-Gaussian context, in many cases x -E( x | Z )\vdbar z or y -E( y | Z )\vdbar z ( \forall z \in Z and Z is a minimal d -separator) is satisfied when x E( x | Z )\vdbar y -E( y | Z ), which implies z causes x (or y ) if z directly connects to x (or y ). Therefore, we conclude that CIs have useful information for distinguishing Markov equivalence classes. In summary, comparing with the existing discretization-based and kernel-based CI testing methods, the proposed method provides a simpler way to measure CI, which needs only one unconditional independence test and two regression operations. When being applied to causal discovery, it can find more causal relationships, which is extensively validated by experiments.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "50", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", remark = "The symbol denoted \vdbar is a horizontal bar on the baseline, with two vertical bars extending upward; I cannot find it in TeX math font listings, or in Unicode 5.0.", } @Article{Heckerman:2019:TAH, author = "David Heckerman", title = "Toward Accounting for Hidden Common Causes When Inferring Cause and Effect from Observational Data", journal = j-TIST, volume = "10", number = "5", pages = "51:1--51:??", month = oct, year = "2019", CODEN = "????", DOI = "https://doi.org/10.1145/3309720", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Oct 22 11:55:45 MDT 2019", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Hidden common causes make it difficult to infer causal relationships from observational data. Here, we begin an investigation into a new method to account for a hidden common cause that infers its presence from the data. As with other approaches that can account for common causes, this approach is successful only in some cases. We describe such a case taken from the field of genomics, wherein one tries to identify which genomic markers causally influence a trait of interest.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "51", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Ling:2019:BBM, author = "Zhaolong Ling and Kui Yu and Hao Wang and Lin Liu and Wei Ding and Xindong Wu", title = "{BAMB}: a Balanced {Markov} Blanket Discovery Approach to Feature Selection", journal = j-TIST, volume = "10", number = "5", pages = "52:1--52:??", month = oct, year = "2019", CODEN = "????", DOI = "https://doi.org/10.1145/3335676", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Oct 22 11:55:45 MDT 2019", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "The discovery of Markov blanket (MB) for feature selection has attracted much attention in recent years, since the MB of the class attribute is the optimal feature subset for feature selection. However, almost all existing MB discovery algorithms focus on either improving computational efficiency or boosting learning accuracy, instead of both. In this article, we propose a novel MB discovery algorithm for balancing efficiency and accuracy, called BAlanced Markov Blanket (BAMB) discovery. To achieve this goal, given a class attribute of interest, BAMB finds candidate PC (parents and children) and spouses and removes false positives from the candidate MB set in one go. Specifically, once a feature is successfully added to the current PC set, BAMB finds the spouses with regard to this feature, then uses the updated PC and the spouse set to remove false positives from the current MB set. This makes the PC and spouses of the target as small as possible and thus achieves a trade-off between computational efficiency and learning accuracy. In the experiments, we first compare BAMB with 8 state-of-the-art MB discovery algorithms on 7 benchmark Bayesian networks, then we use 10 real-world datasets and compare BAMB with 12 feature selection algorithms, including 8 state-of-the-art MB discovery algorithms and 4 other well-established feature selection methods. On prediction accuracy, BAMB outperforms 12 feature selection algorithms compared. On computational efficiency, BAMB is close to the IAMB algorithm while it is much faster than the remaining seven MB discovery algorithms.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "52", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Zhang:2019:MVF, author = "Yongshan Zhang and Jia Wu and Chuan Zhou and Zhihua Cai and Jian Yang and Philip S. Yu", title = "Multi-View Fusion with Extreme Learning Machine for Clustering", journal = j-TIST, volume = "10", number = "5", pages = "53:1--53:??", month = oct, year = "2019", CODEN = "????", DOI = "https://doi.org/10.1145/3340268", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Oct 22 11:55:45 MDT 2019", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Unlabeled, multi-view data presents a considerable challenge in many real-world data analysis tasks. These data are worth exploring because they often contain complementary information that improves the quality of the analysis results. Clustering with multi-view data is a particularly challenging problem as revealing the complex data structures between many feature spaces demands discriminative features that are specific to the task and, when too few of these features are present, performance suffers. Extreme learning machines (ELMs) are an emerging form of learning model that have shown an outstanding representation ability and superior performance in a range of different learning tasks. Motivated by the promise of this advancement, we have developed a novel multi-view fusion clustering framework based on an ELM, called MVEC. MVEC learns the embeddings from each view of the data via the ELM network, then constructs a single unified embedding according to the correlations and dependencies between each embedding and automatically weighting the contribution of each. This process exposes the underlying clustering structures embedded within multi-view data with a high degree of accuracy. A simple yet efficient solution is also provided to solve the optimization problem within MVEC. Experiments and comparisons on eight different benchmarks from different domains confirm MVEC's clustering accuracy.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "53", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Law:2019:TLA, author = "Stephen Law and Brooks Paige and Chris Russell", title = "Take a Look Around: Using Street View and Satellite Images to Estimate House Prices", journal = j-TIST, volume = "10", number = "5", pages = "54:1--54:??", month = oct, year = "2019", CODEN = "????", DOI = "https://doi.org/10.1145/3342240", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Oct 22 11:55:45 MDT 2019", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "When an individual purchases a home, they simultaneously purchase its structural features, its accessibility to work, and the neighborhood amenities. Some amenities, such as air quality, are measurable while others, such as the prestige or the visual impression of a neighborhood, are difficult to quantify. Despite the well-known impacts intangible housing features have on house prices, limited attention has been given to systematically quantifying these difficult to measure amenities. Two issues have led to this neglect. Not only do few quantitative methods exist that can measure the urban environment, but that the collection of such data is both costly and subjective. We show that street image and satellite image data can capture these urban qualities and improve the estimation of house prices. We propose a pipeline that uses a deep neural network model to automatically extract visual features from images to estimate house prices in London, UK. We make use of traditional housing features such as age, size, and accessibility as well as visual features from Google Street View images and Bing aerial images in estimating the house price model. We find encouraging results where learning to characterize the urban quality of a neighborhood improves house price prediction, even when generalizing to previously unseen London boroughs. We explore the use of non-linear vs. linear methods to fuse these cues with conventional models of house pricing, and show how the interpretability of linear models allows us to directly extract proxy variables for visual desirability of neighborhoods that are both of interest in their own right, and could be used as inputs to other econometric methods. This is particularly valuable as once the network has been trained with the training data, it can be applied elsewhere, allowing us to generate vivid dense maps of the visual appeal of London streets.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "54", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Zhang:2019:DDF, author = "Ya-Lin Zhang and Jun Zhou and Wenhao Zheng and Ji Feng and Longfei Li and Ziqi Liu and Ming Li and Zhiqiang Zhang and Chaochao Chen and Xiaolong Li and Yuan (Alan) Qi and Zhi-Hua Zhou", title = "Distributed Deep Forest and its Application to Automatic Detection of Cash-Out Fraud", journal = j-TIST, volume = "10", number = "5", pages = "55:1--55:??", month = oct, year = "2019", CODEN = "????", DOI = "https://doi.org/10.1145/3342241", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Oct 22 11:55:45 MDT 2019", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Internet companies are facing the need for handling large-scale machine learning applications on a daily basis and distributed implementation of machine learning algorithms which can handle extra-large-scale tasks with great performance is widely needed. Deep forest is a recently proposed deep learning framework which uses tree ensembles as its building blocks and it has achieved highly competitive results on various domains of tasks. However, it has not been tested on extremely large-scale tasks. In this work, based on our parameter server system, we developed the distributed version of deep forest. To meet the need for real-world tasks, many improvements are introduced to the original deep forest model, including MART (Multiple Additive Regression Tree) as base learners for efficiency and effectiveness consideration, the cost-based method for handling prevalent class-imbalanced data, MART based feature selection for high dimension data, and different evaluation metrics for automatically determining the cascade level. We tested the deep forest model on an extra-large-scale task, i.e., automatic detection of cash-out fraud, with more than 100 million training samples. Experimental results showed that the deep forest model has the best performance according to the evaluation metrics from different perspectives even with very little effort for parameter tuning. This model can block fraud transactions in a large amount of money each day. Even compared with the best-deployed model, the deep forest model can additionally bring a significant decrease in economic loss each day.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "55", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Braytee:2019:CML, author = "Ali Braytee and Wei Liu and Ali Anaissi and Paul J. Kennedy", title = "Correlated Multi-label Classification with Incomplete Label Space and Class Imbalance", journal = j-TIST, volume = "10", number = "5", pages = "56:1--56:??", month = oct, year = "2019", CODEN = "????", DOI = "https://doi.org/10.1145/3342512", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Oct 22 11:55:45 MDT 2019", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Multi-label classification is defined as the problem of identifying the multiple labels or categories of new observations based on labeled training data. Multi-labeled data has several challenges, including class imbalance, label correlation, incomplete multi-label matrices, and noisy and irrelevant features. In this article, we propose an integrated multi-label classification approach with incomplete label space and class imbalance (ML-CIB) for simultaneously training the multi-label classification model and addressing the aforementioned challenges. The model learns a new label matrix and captures new label correlations, because it is difficult to find a complete label vector for each instance in real-world data. We also propose a label regularization to handle the imbalanced multi-labeled issue in the new label, and l$_1$ regularization norm is incorporated in the objective function to select the relevant sparse features. A multi-label feature selection (ML-CIB-FS) method is presented as a variant of the proposed ML-CIB to show the efficacy of the proposed method in selecting the relevant features. ML-CIB is formulated as a constrained objective function. We use the accelerated proximal gradient method to solve the proposed optimisation problem. Last, extensive experiments are conducted on 19 regular-scale and large-scale imbalanced multi-labeled datasets. The promising results show that our method significantly outperforms the state-of-the-art.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "56", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Zamani:2019:AAT, author = "Hamed Zamani and Markus Schedl and Paul Lamere and Ching-Wei Chen", title = "An Analysis of Approaches Taken in the {ACM RecSys Challenge 2018} for Automatic Music Playlist Continuation", journal = j-TIST, volume = "10", number = "5", pages = "57:1--57:??", month = oct, year = "2019", CODEN = "????", DOI = "https://doi.org/10.1145/3344257", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Oct 22 11:55:45 MDT 2019", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "The ACM Recommender Systems Challenge 2018 focused on the task of automatic music playlist continuation, which is a form of the more general task of sequential recommendation. Given a playlist of arbitrary length with some additional meta-data, the task was to recommend up to 500 tracks that fit the target characteristics of the original playlist. For the RecSys Challenge, Spotify released a dataset of one million user-generated playlists. Participants could compete in two tracks, i.e., main and creative tracks. Participants in the main track were only allowed to use the provided training set, however, in the creative track, the use of external public sources was permitted. In total, 113 teams submitted 1,228 runs to the main track; 33 teams submitted 239 runs to the creative track. The highest performing team in the main track achieved an R-precision of 0.2241, an NDCG of 0.3946, and an average number of recommended songs clicks of 1.784. In the creative track, an R-precision of 0.2233, an NDCG of 0.3939, and a click rate of 1.785 was obtained by the best team. This article provides an overview of the challenge, including motivation, task definition, dataset description, and evaluation. We further report and analyze the results obtained by the top-performing teams in each track and explore the approaches taken by the winners. We finally summarize our key findings, discuss generalizability of approaches and results to domains other than music, and list the open avenues and possible future directions in the area of automatic playlist continuation.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "57", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Corno:2019:RRI, author = "Fulvio Corno and Luigi {De Russis} and Alberto Monge Roffarello", title = "{RecRules}: Recommending {IF--THEN} Rules for End-User Development", journal = j-TIST, volume = "10", number = "5", pages = "58:1--58:??", month = oct, year = "2019", CODEN = "????", DOI = "https://doi.org/10.1145/3344211", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Oct 22 11:55:45 MDT 2019", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Nowadays, end users can personalize their smart devices and web applications by defining or reusing IF-THEN rules through dedicated End-User Development (EUD) tools. Despite apparent simplicity, such tools present their own set of issues. The emerging and increasing complexity of the Internet of Things, for example, is barely taken into account, and the number of possible combinations between triggers and actions of different smart devices and web applications is continuously growing. Such a large design space makes end-user personalization a complex task for non-programmers, and motivates the need of assisting users in easily discovering and managing rules and functionality, e.g., through recommendation techniques. In this article, we tackle the emerging problem of recommending IF-THEN rules to end users by presenting RecRules, a hybrid and semantic recommendation system. Through a mixed content and collaborative approach, the goal of RecRules is to recommend by functionality: it suggests rules based on their final purposes, thus overcoming details like manufacturers and brands. The algorithm uses a semantic reasoning process to enrich rules with semantic information, with the aim of uncovering hidden connections between rules in terms of shared functionality. Then, it builds a collaborative semantic graph, and it exploits different types of path-based features to train a learning to rank algorithm and compute top-N recommendations. We evaluate RecRules through different experiments on real user data extracted from IFTTT, one of the most popular EUD tools. Results are promising: they show the effectiveness of our approach with respect to other state-of-the-art algorithms and open the way for a new class of recommender systems for EUD that take into account the actual functionality needed by end users.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "58", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Pappalardo:2019:PDD, author = "Luca Pappalardo and Paolo Cintia and Paolo Ferragina and Emanuele Massucco and Dino Pedreschi and Fosca Giannotti", title = "{PlayeRank}: Data-driven Performance Evaluation and Player Ranking in Soccer via a Machine Learning Approach", journal = j-TIST, volume = "10", number = "5", pages = "59:1--59:??", month = oct, year = "2019", CODEN = "????", DOI = "https://doi.org/10.1145/3343172", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Oct 22 11:55:45 MDT 2019", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "The problem of evaluating the performance of soccer players is attracting the interest of many companies and the scientific community, thanks to the availability of massive data capturing all the events generated during a match (e.g., tackles, passes, shots, etc.). Unfortunately, there is no consolidated and widely accepted metric for measuring performance quality in all of its facets. In this article, we design and implement PlayeRank, a data-driven framework that offers a principled multi-dimensional and role-aware evaluation of the performance of soccer players. We build our framework by deploying a massive dataset of soccer-logs and consisting of millions of match events pertaining to four seasons of 18 prominent soccer competitions. By comparing PlayeRank to known algorithms for performance evaluation in soccer, and by exploiting a dataset of players' evaluations made by professional soccer scouts, we show that PlayeRank significantly outperforms the competitors. We also explore the ratings produced by PlayeRank and discover interesting patterns about the nature of excellent performances and what distinguishes the top players from the others. At the end, we explore some applications of PlayeRank-i.e. searching players and player versatility-showing its flexibility and efficiency, which makes it worth to be used in the design of a scalable platform for soccer analytics.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "59", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Ning:2019:DRL, author = "Zhaolong Ning and Peiran Dong and Xiaojie Wang and Joel J. P. C. Rodrigues and Feng Xia", title = "Deep Reinforcement Learning for Vehicular Edge Computing: an Intelligent Offloading System", journal = j-TIST, volume = "10", number = "6", pages = "60:1--60:??", month = oct, year = "2019", CODEN = "????", DOI = "https://doi.org/10.1145/3317572", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Oct 22 11:55:45 MDT 2019", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "The development of smart vehicles brings drivers and passengers a comfortable and safe environment. Various emerging applications are promising to enrich users' traveling experiences and daily life. However, how to execute computing-intensive applications on resource-constrained vehicles still faces huge challenges. In this article, we construct an intelligent offloading system for vehicular edge computing by leveraging deep reinforcement learning. First, both the communication and computation states are modelled by finite Markov chains. Moreover, the task scheduling and resource allocation strategy is formulated as a joint optimization problem to maximize users' Quality of Experience (QoE). Due to its complexity, the original problem is further divided into two sub-optimization problems. A two-sided matching scheme and a deep reinforcement learning approach are developed to schedule offloading requests and allocate network resources, respectively. Performance evaluations illustrate the effectiveness and superiority of our constructed system.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "60", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Tariq:2019:EES, author = "Umair Ullah Tariq and Haider Ali and Lu Liu and John Panneerselvam and Xiaojun Zhai", title = "Energy-efficient Static Task Scheduling on {VFI}-based {NoC--HMPSoCs} for Intelligent Edge Devices in Cyber-physical Systems", journal = j-TIST, volume = "10", number = "6", pages = "66:1--66:??", month = oct, year = "2019", CODEN = "????", DOI = "https://doi.org/10.1145/3336121", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Oct 22 11:55:45 MDT 2019", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "The interlinked processing units in modern Cyber-Physical Systems (CPS) creates a large network of connected computing embedded systems. Network-on-Chip (NoC)-based Multiprocessor System-on-Chip (MPSoC) architecture is becoming a de facto computing platform for real-time applications due to its higher performance and Quality-of-Service (QoS). The number of processors has increased significantly on the multiprocessor systems in CPS; therefore, Voltage Frequency Island (VFI) has been recently adopted for effective energy management mechanism in the large-scale multiprocessor chip designs. In this article, we investigated energy-efficient and contention-aware static scheduling for tasks with precedence and deadline constraints on intelligent edge devices deploying heterogeneous VFI-based NoC-MPSoCs (VFI-NoC-HMPSoC) with DVFS-enabled processors. Unlike the existing population-based optimization algorithms, we proposed a novel population-based algorithm called ARSH-FATI that can dynamically switch between explorative and exploitative search modes at run-time. Our static scheduler ARHS-FATI collectively performs task mapping, scheduling, and voltage scaling. Consequently, its performance is superior to the existing state-of-the-art approach proposed for homogeneous VFI-based NoC-MPSoCs. We also developed a communication contention-aware Earliest Edge Consistent Deadline First (EECDF) scheduling algorithm and gradient descent--inspired voltage scaling algorithm called Energy Gradient Decent (EGD). We introduced a notion of Energy Gradient (EG) that guides EGD in its search for island voltage settings and minimize the total energy consumption. We conducted the experiments on eight real benchmarks adopted from Embedded Systems Synthesis Benchmarks (E3S). Our static scheduling approach ARSH-FATI outperformed state-of-the-art technique and achieved an average energy-efficiency of $ \approx $24\% and $ \approx $30\% over CA-TMES-Search and CA-TMES-Quick, respectively.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "66", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Zhou:2019:LCN, author = "Junhao Zhou and Hong-Ning Dai and Hao Wang", title = "Lightweight Convolution Neural Networks for Mobile Edge Computing in Transportation Cyber Physical Systems", journal = j-TIST, volume = "10", number = "6", pages = "67:1--67:??", month = dec, year = "2019", CODEN = "????", DOI = "https://doi.org/10.1145/3339308", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Dec 16 07:23:45 MST 2019", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/ft_gateway.cfm?id=3339308", abstract = "Cloud computing extends Transportation Cyber-Physical Systems (T-CPS) with provision of enhanced computing and storage capability via offloading computing tasks to remote cloud servers. However, cloud computing cannot fulfill the requirements such as low latency and context awareness in T-CPS. The appearance of Mobile Edge Computing (MEC) can overcome the limitations of cloud computing via offloading the computing tasks at edge servers in approximation to users, consequently reducing the latency and improving the context awareness. Although MEC has the potential in improving T-CPS, it is incapable of processing computational-intensive tasks such as deep learning algorithms due to the intrinsic storage and computing-capability constraints. Therefore, we design and develop a lightweight deep learning model to support MEC applications in T-CPS. In particular, we put forth a stacked convolutional neural network (CNN) consisting of factorization convolutional layers alternating with compression layers (namely, lightweight CNN-FC). Extensive experimental results show that our proposed lightweight CNN-FC can greatly decrease the number of unnecessary parameters, thereby reducing the model size while maintaining the high accuracy in contrast to conventional CNN models. In addition, we also evaluate the performance of our proposed model via conducting experiments at a realistic MEC platform. Specifically, experimental results at this MEC platform show that our model can maintain the high accuracy while preserving the portable model size.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "67", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Tang:2019:EPP, author = "Wenjuan Tang and Ju Ren and Kuan Zhang and Deyu Zhang and Yaoxue Zhang and Xuemin (Sherman) Shen", title = "Efficient and Privacy-preserving Fog-assisted Health Data Sharing Scheme", journal = j-TIST, volume = "10", number = "6", pages = "68:1--68:??", month = dec, year = "2019", CODEN = "????", DOI = "https://doi.org/10.1145/3341104", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Dec 16 07:23:45 MST 2019", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/ft_gateway.cfm?id=3341104", abstract = "Pervasive data collected from e-healthcare devices possess significant medical value through data sharing with professional healthcare service providers. However, health data sharing poses several security issues, such as access control and privacy leakage, as well as faces critical challenges to obtain efficient data analysis and services. In this article, we propose an efficient and privacy-preserving fog-assisted health data sharing (PFHDS) scheme for e-healthcare systems. Specifically, we integrate the fog node to classify the shared data into different categories according to disease risks for efficient health data analysis. Meanwhile, we design an enhanced attribute-based encryption method through combination of a personal access policy on patients and a professional access policy on the fog node for effective medical service provision. Furthermore, we achieve significant encryption consumption reduction for patients by offloading a portion of the computation and storage burden from patients to the fog node. Security discussions show that PFHDS realizes data confidentiality and fine-grained access control with collusion resistance. Performance evaluations demonstrate cost-efficient encryption computation, storage and energy consumption.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "68", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Li:2019:SDS, author = "Jin Li and Tong Li and Zheli Liu and Xiaofeng Chen", title = "Secure Deduplication System with Active Key Update and Its Application in {IoT}", journal = j-TIST, volume = "10", number = "6", pages = "69:1--69:??", month = dec, year = "2019", CODEN = "????", DOI = "https://doi.org/10.1145/3356468", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Dec 16 07:23:45 MST 2019", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/ft_gateway.cfm?id=3356468", abstract = "The rich cloud services in the Internet of Things create certain needs for edge computing, in which devices should be able to handle storage tasks securely, reliably, and efficiently. When processing the storage requests from edge devices, each cloud server is supposed to eliminate duplicate copies of repeating data to reduce the amount of storage space and save on bandwidth. To protect data confidentiality while supporting deduplication, some convergent-encryption-based techniques have been proposed to encrypt the data before uploading. However, all these works cannot meet two requirements while preventing brute-force attacks: (i) power-constrained edge nodes should update encryption keys efficiently when an edge node is abandoned; and (ii) the access privacy of edge nodes should be guaranteed. In this article, we propose a novel encryption scheme for secure chunk-level deduplication. Based on this scheme, we present two constructions of the secure deduplication system that support an efficient key update protocol. The key update protocol does not involve any edge node in computational tasks, so that the deduplication system can adopt an active key update strategy. Moreover, one of our constructions, which is called advance construction, can provide access privacy assurances for edge nodes. The security analysis is given in terms of the proposed threat model. The experimental analysis demonstrates that the proposed deduplication system is practical.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "69", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Zhao:2019:VAA, author = "Ying Zhao and Lei Wang and Shijie Li and Fangfang Zhou and Xiaoru Lin and Qiang Lu and Lei Ren", title = "A Visual Analysis Approach for Understanding Durability Test Data of Automotive Products", journal = j-TIST, volume = "10", number = "6", pages = "70:1--70:??", month = dec, year = "2019", CODEN = "????", DOI = "https://doi.org/10.1145/3345640", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Dec 16 07:23:45 MST 2019", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "People face data-rich manufacturing environments in Industry 4.0. As an important technology for explaining and understanding complex data, visual analytics has been increasingly introduced into industrial data analysis scenarios. With the durability test of automotive starters as background, this study proposes a visual analysis approach for understanding large-scale and long-term durability test data. Guided by detailed scenario and requirement analyses, we first propose a migration-adapted clustering algorithm that utilizes a segmentation strategy and a group of matching-updating operations to achieve an efficient and accurate clustering analysis of the data for starting mode identification and abnormal test detection. We then design and implement a visual analysis system that provides a set of user-friendly visual designs and lightweight interactions to help people gain data insights into the test process overview, test data patterns, and durability performance dynamics. Finally, we conduct a quantitative algorithm evaluation, case study, and user interview by using real-world starter durability test datasets. The results demonstrate the effectiveness of the approach and its possible inspiration for the durability test data analysis of other similar industrial products.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "70", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Leyli-Abadi:2019:MJN, author = "Milad Leyli-Abadi and Allou sam{\'e} and Latifa Oukhellou and Nicolas Cheifetz and Pierre Mandel and C{\'e}dric F{\'e}liers and Olivier Chesneau", title = "Mixture of Joint Nonhomogeneous {Markov} Chains to Cluster and Model Water Consumption Behavior Sequences", journal = j-TIST, volume = "10", number = "6", pages = "71:1--71:??", month = dec, year = "2019", CODEN = "????", DOI = "https://doi.org/10.1145/3347452", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Dec 16 07:23:45 MST 2019", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/ft_gateway.cfm?id=3347452", abstract = "The emergence of smart meters has fostered the collection of massive data that support a better understanding of consumer behaviors and better management of water resources and networks. The main focus of this article is to analyze consumption behavior over time; thus, we first identify the main weekly consumption patterns. This approach allows each meter to be represented by a categorical series, where each category corresponds to a weekly consumption behavior. By considering the resulting consumption behavior sequences, we propose a new methodology based on a mixture of nonhomogeneous Markov models to cluster these categorical time series. Using this method, the meters are described by the Markovian dynamics of their cluster. The latent variable that controls cluster membership is estimated alongside the parameters of the Markov model using a novel classification expectation maximization algorithm. A specific entropy measure is formulated to evaluate the quality of the estimated partition by considering the joint Markovian dynamics. The proposed clustering model can also be used to predict future consumption behaviors within each cluster. Numerical experiments using real water consumption data provided by a water utility in France and gathered over 19 months are conducted to evaluate the performance of the proposed approach in terms of both clustering and prediction. The results demonstrate the effectiveness of the proposed method.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "71", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Zhou:2020:FPT, author = "Binbin Zhou and Sha Zhao and Longbiao Chen and Shijian Li and Zhaohui Wu and Gang Pan", title = "Forecasting Price Trend of Bulk Commodities Leveraging Cross-domain Open Data Fusion", journal = j-TIST, volume = "11", number = "1", pages = "1:1--1:26", month = feb, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3354287", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Feb 15 07:31:36 MST 2020", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/abs/10.1145/3354287", abstract = "Forecasting price trend of bulk commodities is important in international trade, not only for markets participants to schedule production and marketing plans but also for government administrators to adjust policies. Previous studies cannot support \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "1", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Xie:2020:DIS, author = "Yiqun Xie and Xun Zhou and Shashi Shekhar", title = "Discovering Interesting Subpaths with Statistical Significance from Spatiotemporal Datasets", journal = j-TIST, volume = "11", number = "1", pages = "2:1--2:24", month = feb, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3354189", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Feb 15 07:31:36 MST 2020", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/abs/10.1145/3354189", abstract = "Given a path in a spatial or temporal framework, we aim to find all contiguous subpaths that are both interesting (e.g., abrupt changes) and statistically significant (i.e., persistent trends rather than local fluctuations). Discovering interesting \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "2", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Lopes:2020:GBR, author = "Ramon Lopes and Renato Assun{\c{c}}{\~a}o and Rodrygo L. T. Santos", title = "Graph-based Recommendation Meets {Bayes} and Similarity Measures", journal = j-TIST, volume = "11", number = "1", pages = "3:1--3:26", month = feb, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3356882", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Feb 15 07:31:36 MST 2020", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/abs/10.1145/3356882", abstract = "Graph-based approaches provide an effective memory-based alternative to latent factor models for collaborative recommendation. Modern approaches rely on either sampling short walks or enumerating short paths starting from the target user in a user-item bipartite graph. While the effectiveness of random walk sampling heavily depends on the underlying path sampling strategy, path enumeration is sensitive to the strategy adopted for scoring each individual path. In this article, we demonstrate how both strategies can be improved through Bayesian reasoning. In particular, we propose to improve random walk sampling by exploiting distributional aspects of items' ratings on the sampled paths. Likewise, we extend existing path enumeration approaches to leverage categorical ratings and to scale the score of each path proportionally to the affinity of pairs of users and pairs of items on the path. Experiments on several publicly available datasets demonstrate the effectiveness of our proposed approaches compared to state-of-the-art graph-based recommenders.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "3", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Nelke:2020:MCB, author = "Sofia Amador Nelke and Steven Okamoto and Roie Zivan", title = "Market Clearing-based Dynamic Multi-agent Task Allocation", journal = j-TIST, volume = "11", number = "1", pages = "4:1--4:25", month = feb, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3356467", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Feb 15 07:31:36 MST 2020", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/abs/10.1145/3356467", abstract = "Realistic multi-agent team applications often feature dynamic environments with soft deadlines that penalize late execution of tasks. This puts a premium on quickly allocating tasks to agents. However, when such problems include temporal and spatial \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "4", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Waniek:2020:SAA, author = "Marcin Waniek and Tomasz P. Michalak and Aamena Alshamsi", title = "Strategic Attack \& Defense in Security Diffusion Games", journal = j-TIST, volume = "11", number = "1", pages = "5:1--5:35", month = feb, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3357605", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Feb 15 07:31:36 MST 2020", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/abs/10.1145/3357605", abstract = "Security games model the confrontation between a defender protecting a set of targets and an attacker who tries to capture them. A variant of these games assumes security interdependence between targets, facilitating contagion of an attack. So far, only stochastic spread of an attack has been considered. In this work, we introduce a version of security games, where the attacker strategically drives the entire spread of attack and where interconnections between nodes affect their susceptibility to be captured. We find that the strategies effective in the settings without contagion or with stochastic contagion are no longer feasible when spread of attack is strategic. While in the former settings it was possible to efficiently find optimal strategies of the attacker, doing so in the latter setting turns out to be an NP-complete problem for an arbitrary network. However, for some simpler network structures, such as cliques, stars, and trees, we show that it is possible to efficiently find optimal strategies of both players. For arbitrary networks, we study and compare the efficiency of various heuristic strategies. As opposed to previous works with no or stochastic contagion, we find that centrality-based defense is often effective when spread of attack is strategic, particularly for centrality measures based on the Shapley value.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "5", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Wang:2020:TLD, author = "Jindong Wang and Yiqiang Chen and Wenjie Feng and Han Yu and Meiyu Huang and Qiang Yang", title = "Transfer Learning with Dynamic Distribution Adaptation", journal = j-TIST, volume = "11", number = "1", pages = "6:1--6:25", month = feb, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3360309", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Feb 15 07:31:36 MST 2020", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/abs/10.1145/3360309", abstract = "Transfer learning aims to learn robust classifiers for the target domain by leveraging knowledge from a source domain. Since the source and the target domains are usually from different distributions, existing methods mainly focus on adapting the cross-. \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "6", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Horne:2020:RFN, author = "Benjamin D. Horne and Jeppe N{\o}rregaard and Sibel Adali", title = "Robust Fake News Detection Over Time and Attack", journal = j-TIST, volume = "11", number = "1", pages = "7:1--7:23", month = feb, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3363818", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Feb 15 07:31:36 MST 2020", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/abs/10.1145/3363818", abstract = "In this study, we examine the impact of time on state-of-the-art news veracity classifiers. We show that, as time progresses, classification performance for both unreliable and hyper-partisan news classification slowly degrade. While this degradation does happen, it happens slower than expected, illustrating that hand-crafted, content-based features, such as style of writing, are fairly robust to changes in the news cycle. We show that this small degradation can be mitigated using online learning. Last, we examine the impact of adversarial content manipulation by malicious news producers. Specifically, we test three types of attack based on changes in the input space and data availability. We show that static models are susceptible to content manipulation attacks, but online models can recover from such attacks.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "7", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Pan:2020:DDH, author = "Menghai Pan and Weixiao Huang and Yanhua Li and Xun Zhou and Zhenming Liu and Rui Song and Hui Lu and Zhihong Tian and Jun Luo", title = "{DHPA}: Dynamic Human Preference Analytics Framework: a Case Study on Taxi Drivers' Learning Curve Analysis", journal = j-TIST, volume = "11", number = "1", pages = "8:1--8:19", month = feb, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3360312", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Feb 15 07:31:36 MST 2020", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/abs/10.1145/3360312", abstract = "Many real-world human behaviors can be modeled and characterized as sequential decision-making processes, such as a taxi driver's choices of working regions and times. Each driver possesses unique preferences on the sequential choices over time and \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "8", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Wang:2020:FMH, author = "Meng Wang and Hui Li and Jiangtao Cui and Sourav S. Bhowmick and Ping Liu", title = "{FROST}: Movement History-Conscious Facility Relocation", journal = j-TIST, volume = "11", number = "1", pages = "9:1--9:26", month = feb, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3361740", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Feb 15 07:31:36 MST 2020", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/abs/10.1145/3361740", abstract = "The facility relocation (FR) problem, which aims to optimize the placement of facilities to accommodate the changes of users' locations, has a broad spectrum of applications. Despite the significant progress made by existing solutions to the FR problem, \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "9", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Fu:2020:TER, author = "Tao-Yang Fu and Wang-Chien Lee", title = "{Trembr}: Exploring Road Networks for Trajectory Representation Learning", journal = j-TIST, volume = "11", number = "1", pages = "10:1--10:25", month = feb, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3361741", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Feb 15 07:31:36 MST 2020", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/abs/10.1145/3361741", abstract = "In this article, we propose a novel representation learning framework, namely TRajectory EMBedding via Road networks (Trembr), to learn trajectory embeddings (low-dimensional feature vectors) for use in a variety of trajectory applications. The novelty \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "10", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Beigi:2020:SSG, author = "Ghazaleh Beigi and Jiliang Tang and Huan Liu", title = "Social Science-guided Feature Engineering: a Novel Approach to Signed Link Analysis", journal = j-TIST, volume = "11", number = "1", pages = "11:1--11:27", month = feb, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3364222", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Feb 15 07:31:36 MST 2020", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/abs/10.1145/3364222", abstract = "Many real-world relations can be represented by signed networks with positive links (e.g., friendships and trust) and negative links (e.g., foes and distrust). Link prediction helps advance tasks in social network analysis such as recommendation \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "11", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Luo:2020:ECN, author = "Ping Luo and Kai Shu and Junjie Wu and Li Wan and Yong Tan", title = "Exploring Correlation Network for Cheating Detection", journal = j-TIST, volume = "11", number = "1", pages = "12:1--12:23", month = feb, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3364221", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Feb 15 07:31:36 MST 2020", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/abs/10.1145/3364221", abstract = "The correlation network, typically formed by computing pairwise correlations between variables, has recently become a competitive paradigm to discover insights in various application domains, such as climate prediction, financial marketing, and \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "12", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Zhang:2020:WTE, author = "Shuo Zhang and Krisztian Balog", title = "{Web} Table Extraction, Retrieval, and Augmentation: a Survey", journal = j-TIST, volume = "11", number = "2", pages = "13:1--13:35", month = mar, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3372117", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Mar 3 09:15:47 MST 2020", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/abs/10.1145/3372117", abstract = "Tables are powerful and popular tools for organizing and manipulating data. A vast number of tables can be found on the Web, which represent a valuable knowledge resource. The objective of this survey is to synthesize and present two decades of research \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "13", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Zhu:2020:FMM, author = "Lei Zhu and Xu Lu and Zhiyong Cheng and Jingjing Li and Huaxiang Zhang", title = "Flexible Multi-modal Hashing for Scalable Multimedia Retrieval", journal = j-TIST, volume = "11", number = "2", pages = "14:1--14:20", month = mar, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3365841", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Mar 3 09:15:47 MST 2020", bibsource = "https://www.math.utah.edu/pub/tex/bib/hash.bib; https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/abs/10.1145/3365841", abstract = "Multi-modal hashing methods could support efficient multimedia retrieval by combining multi-modal features for binary hash learning at the both offline training and online query stages. However, existing multi-modal methods cannot binarize the queries, \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "14", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Shmueli:2020:MSM, author = "Erez Shmueli and Tamir Tassa", title = "Mediated Secure Multi-Party Protocols for Collaborative Filtering", journal = j-TIST, volume = "11", number = "2", pages = "15:1--15:25", month = mar, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3375402", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Mar 3 09:15:47 MST 2020", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/abs/10.1145/3375402", abstract = "Recommender systems have become extremely common in recent years and are utilized in a variety of domains such as movies, music, news, products, restaurants, and so on. While a typical recommender system bases its recommendations solely on users' \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "15", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Oliveira:2020:RAE, author = "Samuel E. L. Oliveira and Victor Diniz and Anisio Lacerda and Luiz Merschmanm and Gisele L. Pappa", title = "Is Rank Aggregation Effective in Recommender Systems? {An} Experimental Analysis", journal = j-TIST, volume = "11", number = "2", pages = "16:1--16:26", month = mar, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3365375", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Mar 3 09:15:47 MST 2020", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/abs/10.1145/3365375", abstract = "Recommender Systems are tools designed to help users find relevant information from the myriad of content available online. They work by actively suggesting items that are relevant to users according to their historical preferences or observed actions. \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "16", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Ye:2020:XLA, author = "Juan Ye and Simon Dobson and Franco Zambonelli", title = "{XLearn}: Learning Activity Labels across Heterogeneous Datasets", journal = j-TIST, volume = "11", number = "2", pages = "17:1--17:28", month = mar, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3368272", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Mar 3 09:15:47 MST 2020", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/abs/10.1145/3368272", abstract = "Sensor-driven systems often need to map sensed data into meaningfully labelled activities to classify the phenomena being observed. A motivating and challenging example comes from human activity recognition in which smart home and other datasets are \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "17", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Zhuo:2020:DUP, author = "Hankz Hankui Zhuo and Yantian Zha and Subbarao Kambhampati and Xin Tian", title = "Discovering Underlying Plans Based on Shallow Models", journal = j-TIST, volume = "11", number = "2", pages = "18:1--18:30", month = mar, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3368270", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Mar 3 09:15:47 MST 2020", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/abs/10.1145/3368270", abstract = "Plan recognition aims to discover target plans (i.e., sequences of actions) behind observed actions, with history plan libraries or action models in hand. Previous approaches either discover plans by maximally ``matching'' observed actions to plan \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "18", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Wang:2020:NMC, author = "Chien-Chih Wang and Kent Loong Tan and Chih-Jen Lin", title = "{Newton} Methods for Convolutional Neural Networks", journal = j-TIST, volume = "11", number = "2", pages = "19:1--19:30", month = mar, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3368271", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Mar 3 09:15:47 MST 2020", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/abs/10.1145/3368271", abstract = "Deep learning involves a difficult non-convex optimization problem, which is often solved by stochastic gradient (SG) methods. While SG is usually effective, it may not be robust in some situations. Recently, Newton methods have been investigated as an \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "19", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Huang:2020:SIS, author = "Shih-Chia Huang and Da-Wei Jaw and Bo-Hao Chen and Sy-Yen Kuo", title = "Single Image Snow Removal Using Sparse Representation and Particle Swarm Optimizer", journal = j-TIST, volume = "11", number = "2", pages = "20:1--20:15", month = mar, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3372116", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Mar 3 09:15:47 MST 2020", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/abs/10.1145/3372116", abstract = "Images are often corrupted by natural obscuration (e.g., snow, rain, and haze) during acquisition in bad weather conditions. The removal of snowflakes from only a single image is a challenging task due to situational variety and has been investigated \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "20", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Liu:2020:PBU, author = "Wenhe Liu and Xiaojun Chang and Ling Chen and Dinh Phung and Xiaoqin Zhang and Yi Yang and Alexander G. Hauptmann", title = "Pair-based Uncertainty and Diversity Promoting Early Active Learning for Person Re-identification", journal = j-TIST, volume = "11", number = "2", pages = "21:1--21:15", month = mar, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3372121", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Mar 3 09:15:47 MST 2020", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/abs/10.1145/3372121", abstract = "The effective training of supervised Person Re-identification (Re-ID) models requires sufficient pairwise labeled data. However, when there is limited annotation resource, it is difficult to collect pairwise labeled data. We consider a challenging and \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "21", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Chen:2020:TRF, author = "Lei Chen and Zhiang Wu and Jie Cao and Guixiang Zhu and Yong Ge", title = "Travel Recommendation via Fusing Multi-Auxiliary Information into Matrix Factorization", journal = j-TIST, volume = "11", number = "2", pages = "22:1--22:24", month = mar, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3372118", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Mar 3 09:15:47 MST 2020", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/abs/10.1145/3372118", abstract = "As an e-commerce feature, the personalized recommendation is invariably highly-valued by both consumers and merchants. The e-tourism has become one of the hottest industries with the adoption of recommendation systems. Several lines of evidence have \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "22", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Pereira:2020:USO, author = "Ramon Fraga Pereira and Nir Oren and Felipe Meneguzzi", title = "Using Sub-Optimal Plan Detection to Identify Commitment Abandonment in Discrete Environments", journal = j-TIST, volume = "11", number = "2", pages = "23:1--23:26", month = mar, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3372119", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Mar 3 09:15:47 MST 2020", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/abs/10.1145/3372119", abstract = "Assessing whether an agent has abandoned a goal or is actively pursuing it is important when multiple agents are trying to achieve joint goals, or when agents commit to achieving goals for each other. Making such a determination for a single goal by \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "23", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Zhang:2020:AAD, author = "Wei Emma Zhang and Quan Z. Sheng and Ahoud Alhazmi and Chenliang Li", title = "Adversarial Attacks on Deep-learning Models in Natural Language Processing: a Survey", journal = j-TIST, volume = "11", number = "3", pages = "24:1--24:41", month = may, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3374217", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue May 19 09:21:48 MDT 2020", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/abs/10.1145/3374217", abstract = "With the development of high computational devices, deep neural networks (DNNs), in recent years, have gained significant popularity in many Artificial Intelligence (AI) applications. However, previous efforts have shown that DNNs are vulnerable to \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "24", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Liu:2020:UGA, author = "Zhuang Liu and Keli Xiao and Bo Jin and Kaiyu Huang and Degen Huang and Yunxia Zhang", title = "Unified Generative Adversarial Networks for Multiple-Choice Oriented Machine Comprehension", journal = j-TIST, volume = "11", number = "3", pages = "25:1--25:20", month = may, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3372120", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue May 19 09:21:48 MDT 2020", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/abs/10.1145/3372120", abstract = "In this article, we address the multiple-choice machine comprehension (MC) problem in natural language processing. Existing approaches for MC are usually designed for general cases; however, we specially develop a novel method for solving the multiple-\ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "25", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Shah:2020:TCP, author = "Ankit Shah and Arunesh Sinha and Rajesh Ganesan and Sushil Jajodia and Hasan Cam", title = "Two Can Play That Game: an Adversarial Evaluation of a Cyber-Alert Inspection System", journal = j-TIST, volume = "11", number = "3", pages = "32:1--32:20", month = may, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3377554", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue May 19 09:21:48 MDT 2020", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/abs/10.1145/3377554", abstract = "Cyber-security is an important societal concern. Cyber-attacks have increased in numbers as well as in the extent of damage caused in every attack. Large organizations operate a Cyber Security Operation Center (CSOC), which forms the first line of cyber-\ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "32", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Lin:2020:CDM, author = "Adi Lin and Jie Lu and Junyu Xuan and Fujin Zhu and Guangquan Zhang", title = "A Causal {Dirichlet} Mixture Model for Causal Inference from Observational Data", journal = j-TIST, volume = "11", number = "3", pages = "33:1--33:29", month = may, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3379500", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue May 19 09:21:48 MDT 2020", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/abs/10.1145/3379500", abstract = "Estimating causal effects by making causal inferences from observational data is common practice in scientific studies, business decision-making, and daily life. In today's data-driven world, causal inference has become a key part of the evaluation \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "33", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Lyu:2020:HPL, author = "Gengyu Lyu and Songhe Feng and Yidong Li and Yi Jin and Guojun Dai and Congyan Lang", title = "{HERA}: Partial Label Learning by Combining Heterogeneous Loss with Sparse and Low-Rank Regularization", journal = j-TIST, volume = "11", number = "3", pages = "34:1--34:19", month = may, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3379501", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue May 19 09:21:48 MDT 2020", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/abs/10.1145/3379501", abstract = "Partial label learning (PLL) aims to learn from the data where each training instance is associated with a set of candidate labels, among which only one is correct. Most existing methods deal with this type of problem by either treating each candidate \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "34", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Horvath:2020:CBA, author = "G{\'a}bor Horv{\'a}th and Edith Kov{\'a}cs and Roland Molontay and Szabolcs Nov{\'a}czki", title = "Copula-Based Anomaly Scoring and Localization for Large-Scale, High-Dimensional Continuous Data", journal = j-TIST, volume = "11", number = "3", pages = "26:1--26:26", month = may, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3372274", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue May 19 09:21:48 MDT 2020", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/abs/10.1145/3372274", abstract = "The anomaly detection method presented by this article has a special feature: it not only indicates whether or not an observation is anomalous but also tells what exactly makes an anomalous observation unusual. Hence, it provides support to localize the \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "26", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Jin:2020:MNP, author = "Di Jin and Bingyi Li and Pengfei Jiao and Dongxiao He and Hongyu Shan and Weixiong Zhang", title = "Modeling with Node Popularities for Autonomous Overlapping Community Detection", journal = j-TIST, volume = "11", number = "3", pages = "27:1--27:23", month = may, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3373760", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue May 19 09:21:48 MDT 2020", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/abs/10.1145/3373760", abstract = "Overlapping community detection has triggered recent research in network analysis. One of the promising techniques for finding overlapping communities is the popular stochastic models, which, unfortunately, have some common drawbacks. They do not \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "27", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Zhao:2020:UDR, author = "Yawei Zhao and Qian Zhao and Xingxing Zhang and En Zhu and Xinwang Liu and Jianping Yin", title = "Understand Dynamic Regret with Switching Cost for Online Decision Making", journal = j-TIST, volume = "11", number = "3", pages = "28:1--28:21", month = may, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3375788", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue May 19 09:21:48 MDT 2020", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/abs/10.1145/3375788", abstract = "As a metric to measure the performance of an online method, dynamic regret with switching cost has drawn much attention for online decision making problems. Although the sublinear regret has been provided in much previous research, we still have little \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "28", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Liu:2020:DNC, author = "Xueliang Liu and Xun Yang and Meng Wang and Richang Hong", title = "Deep Neighborhood Component Analysis for Visual Similarity Modeling", journal = j-TIST, volume = "11", number = "3", pages = "29:1--29:15", month = may, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3375787", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue May 19 09:21:48 MDT 2020", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/abs/10.1145/3375787", abstract = "Learning effective visual similarity is an essential problem in multimedia research. Despite the promising progress made in recent years, most existing approaches learn visual features and similarities in two separate stages, which inevitably limits \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "29", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Brock:2020:LTD, author = "Heike Brock and Felix Law and Kazuhiro Nakadai and Yuji Nagashima", title = "Learning Three-dimensional Skeleton Data from Sign Language Video", journal = j-TIST, volume = "11", number = "3", pages = "30:1--30:24", month = may, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3377552", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue May 19 09:21:48 MDT 2020", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/abs/10.1145/3377552", abstract = "Data for sign language research is often difficult and costly to acquire. We therefore present a novel pipeline able to generate motion three-dimensional (3D) skeleton data from single-camera sign language videos only. First, three recurrent neural \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "30", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Zhang:2020:WUA, author = "Lei Zhang and Yixiang Zhang and Xiaolong Zheng", title = "{WiSign}: Ubiquitous {American Sign Language} Recognition Using Commercial {Wi-Fi} Devices", journal = j-TIST, volume = "11", number = "3", pages = "31:1--31:24", month = may, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3377553", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue May 19 09:21:48 MDT 2020", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/abs/10.1145/3377553", abstract = "In this article, we propose WiSign that recognizes the continuous sentences of American Sign Language (ASL) with existing WiFi infrastructure. Instead of identifying the individual ASL words from the manually segmented ASL sentence in existing works, \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "31", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Arora:2020:ADC, author = "Udit Arora and Hridoy Sankar Dutta and Brihi Joshi and Aditya Chetan and Tanmoy Chakraborty", title = "Analyzing and Detecting Collusive Users Involved in Blackmarket Retweeting Activities", journal = j-TIST, volume = "11", number = "3", pages = "35:1--35:24", month = may, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3380537", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue May 19 09:21:48 MDT 2020", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/abs/10.1145/3380537", abstract = "With the rise in popularity of social media platforms like Twitter, having higher influence on these platforms has a greater value attached to it, since it has the power to influence many decisions in the form of brand promotions and shaping opinions. \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "35", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Yao:2020:VOS, author = "Rui Yao and Guosheng Lin and Shixiong Xia and Jiaqi Zhao and Yong Zhou", title = "Video Object Segmentation and Tracking: a Survey", journal = j-TIST, volume = "11", number = "4", pages = "36:1--36:47", month = jul, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3391743", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Wed Jul 8 17:19:20 MDT 2020", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/abs/10.1145/3391743", abstract = "Object segmentation and object tracking are fundamental research areas in the computer vision community. These two topics are difficult to handle some common challenges, such as occlusion, deformation, motion blur, scale variation, and more. The former \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "36", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Zhang:2020:KAA, author = "Yingying Zhang and Quan Fang and Shengsheng Qian and Changsheng Xu", title = "Knowledge-aware Attentive {Wasserstein} Adversarial Dialogue Response Generation", journal = j-TIST, volume = "11", number = "4", pages = "37:1--37:20", month = jul, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3384675", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Wed Jul 8 17:19:20 MDT 2020", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/abs/10.1145/3384675", abstract = "Natural language generation has become a fundamental task in dialogue systems. RNN-based natural response generation methods encode the dialogue context and decode it into a response. However, they tend to generate dull and simple responses. In this \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "37", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Feygin:2020:BBI, author = "Sidney A. Feygin and Jessica R. Lazarus and Edward H. Forscher and Valentine Golfier-Vetterli and Jonathan W. Lee and Abhishek Gupta and Rashid A. Waraich and Colin J. R. Sheppard and Alexandre M. Bayen", title = "{BISTRO}: {Berkeley Integrated System for Transportation Optimization}", journal = j-TIST, volume = "11", number = "4", pages = "38:1--38:27", month = jul, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3384344", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Wed Jul 8 17:19:20 MDT 2020", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/abs/10.1145/3384344", abstract = "The current trend toward urbanization and adoption of flexible and innovative mobility technologies will have complex and difficult-to-predict effects on urban transportation systems. Comprehensive methodological frameworks that account for the \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "38", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Liu:2020:SRM, author = "Hui Liu and Haiou Wang and Yan Wu and Lei Xing", title = "Superpixel Region Merging Based on Deep Network for Medical Image Segmentation", journal = j-TIST, volume = "11", number = "4", pages = "39:1--39:22", month = jul, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3386090", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Wed Jul 8 17:19:20 MDT 2020", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/abs/10.1145/3386090", abstract = "Automatic and accurate semantic segmentation of pathological structures in medical images is challenging because of noisy disturbance, deformable shapes of pathology, and low contrast between soft tissues. Classical superpixel-based classification \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "39", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Singhal:2020:CBM, author = "Divya Singhal and Abhinav Gupta and Anurag Tripathi and Ravi Kothari", title = "{CNN}-based Multiple Manipulation Detector Using Frequency Domain Features of Image Residuals", journal = j-TIST, volume = "11", number = "4", pages = "40:1--40:26", month = jul, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3388634", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Wed Jul 8 17:19:20 MDT 2020", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/abs/10.1145/3388634", abstract = "Increasingly sophisticated image editing tools make it easy to modify images. Often these modifications are elaborate, convincing, and undetectable by even careful human inspection. These considerations have prompted the development of forensic \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "40", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Li:2020:CPC, author = "Lin Li and Weike Pan and Zhong Ming", title = "{CoFi}-points: Collaborative Filtering via Pointwise Preference Learning on User\slash Item-Set", journal = j-TIST, volume = "11", number = "4", pages = "41:1--41:24", month = jul, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3389127", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Wed Jul 8 17:19:20 MDT 2020", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/abs/10.1145/3389127", abstract = "With the explosive growth of web resources, an increasingly important task in recommender systems is to provide high-quality personalized services by learning users' preferences from historically observed information. As an effective preference learning \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "41", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Ma:2020:ABR, author = "Jing Ma and Wei Gao and Shafiq Joty and Kam-Fai Wong", title = "An Attention-based Rumor Detection Model with Tree-structured Recursive Neural Networks", journal = j-TIST, volume = "11", number = "4", pages = "42:1--42:28", month = jul, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3391250", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Wed Jul 8 17:19:20 MDT 2020", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/abs/10.1145/3391250", abstract = "Rumor spread in social media severely jeopardizes the credibility of online content. Thus, automatic debunking of rumors is of great importance to keep social media a healthy environment. While facing a dubious claim, people often dispute its \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "42", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Wang:2020:MHU, author = "Jun-Zhe Wang and Yi-Cheng Chen and Wen-Yueh Shih and Lin Yang and Yu-Shao Liu and Jiun-Long Huang", title = "Mining High-utility Temporal Patterns on Time Interval-based Data", journal = j-TIST, volume = "11", number = "4", pages = "43:1--43:31", month = jul, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3391230", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Wed Jul 8 17:19:20 MDT 2020", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/abs/10.1145/3391230", abstract = "In this article, we propose a novel temporal pattern mining problem, named high-utility temporal pattern mining, to fulfill the needs of various applications. Different from classical temporal pattern mining aimed at discovering frequent temporal \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "43", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Wu:2020:DAC, author = "Hanrui Wu and Yuguang Yan and Michael K. Ng and Qingyao Wu", title = "Domain-attention Conditional {Wasserstein} Distance for Multi-source Domain Adaptation", journal = j-TIST, volume = "11", number = "4", pages = "44:1--44:19", month = jul, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3391229", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Wed Jul 8 17:19:20 MDT 2020", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/abs/10.1145/3391229", abstract = "Multi-source domain adaptation has received considerable attention due to its effectiveness of leveraging the knowledge from multiple related sources with different distributions to enhance the learning performance. One of the fundamental challenges in \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "44", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Kim:2020:GCC, author = "Jungeun Kim and Jae-Gil Lee and Byung Suk Lee and Jiajun Liu", title = "Geosocial Co-Clustering: a Novel Framework for Geosocial Community Detection", journal = j-TIST, volume = "11", number = "4", pages = "45:1--45:26", month = jul, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3391708", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Wed Jul 8 17:19:20 MDT 2020", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/abs/10.1145/3391708", abstract = "As location-based services using mobile devices have become globally popular these days, social network analysis (especially, community detection) increasingly benefits from combining social relationships with geographic preferences. In this regard, \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "45", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Huang:2020:TDE, author = "Yapei Huang and Yun Tian and Zhijie Liu and Xiaowei Jin and Yanan Liu and Shifeng Zhao and Daxin Tian", title = "A Traffic Density Estimation Model Based on Crowdsourcing Privacy Protection", journal = j-TIST, volume = "11", number = "4", pages = "46:1--46:18", month = jul, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3391707", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Wed Jul 8 17:19:20 MDT 2020", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/abs/10.1145/3391707", abstract = "Acquiring traffic condition information is of great significance in transportation guidance, urban planning, and route recommendation. To date, traffic density data are generally acquired by road sound analysis, video data analysis, or in-vehicle \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "46", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Wang:2020:EET, author = "Min Wang and Congyan Lang and Liqian Liang and Songhe Feng and Tao Wang and Yutong Gao", title = "End-to-End Text-to-Image Synthesis with Spatial Constrains", journal = j-TIST, volume = "11", number = "4", pages = "47:1--47:19", month = jul, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3391709", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Wed Jul 8 17:19:20 MDT 2020", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/abs/10.1145/3391709", abstract = "Although the performance of automatically generating high-resolution realistic images from text descriptions has been significantly boosted, many challenging issues in image synthesis have not been fully investigated, due to shapes variations, viewpoint \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "47", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Wang:2020:ULT, author = "Guang Wang and Fan Zhang and Huijun Sun and Yang Wang and Desheng Zhang", title = "Understanding the Long-Term Evolution of Electric Taxi Networks: a Longitudinal Measurement Study on Mobility and Charging Patterns", journal = j-TIST, volume = "11", number = "4", pages = "48:1--48:27", month = jul, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3393671", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Wed Jul 8 17:19:20 MDT 2020", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/abs/10.1145/3393671", abstract = "Due to the ever-growing concerns over air pollution and energy security, more and more cities have started to replace their conventional taxi fleets with electric ones. Even though environmentally friendly, the rapid promotion of electric taxis raises \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "48", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Zhang:2020:DMB, author = "Xiang Zhang and Lina Yao and Chaoran Huang and Tao Gu and Zheng Yang and Yunhao Liu", title = "{DeepKey}: a Multimodal Biometric Authentication System via Deep Decoding Gaits and Brainwaves", journal = j-TIST, volume = "11", number = "4", pages = "49:1--49:24", month = jul, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3393619", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Wed Jul 8 17:19:20 MDT 2020", bibsource = "https://www.math.utah.edu/pub/tex/bib/cryptography2020.bib; https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/abs/10.1145/3393619", abstract = "Biometric authentication involves various technologies to identify individuals by exploiting their unique, measurable physiological and behavioral characteristics. However, traditional biometric authentication systems (e.g., face recognition, iris, \ldots{}).", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "49", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Wilson:2020:SUD, author = "Garrett Wilson and Diane J. Cook", title = "A Survey of Unsupervised Deep Domain Adaptation", journal = j-TIST, volume = "11", number = "5", pages = "51:1--51:46", month = sep, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3400066", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Sep 7 06:54:29 MDT 2020", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3400066", abstract = "Deep learning has produced state-of-the-art results for a variety of tasks. While such approaches for supervised learning have performed well, they assume that training and testing data are drawn from the same distribution, which may not always be the \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "51", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Chen:2020:PPP, author = "Chaochao Chen and Jun Zhou and Bingzhe Wu and Wenjing Fang and Li Wang and Yuan Qi and Xiaolin Zheng", title = "Practical Privacy Preserving {POI} Recommendation", journal = j-TIST, volume = "11", number = "5", pages = "52:1--52:20", month = sep, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3394138", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Sep 7 06:54:29 MDT 2020", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3394138", abstract = "Point-of-Interest (POI) recommendation has been extensively studied and successfully applied in industry recently. However, most existing approaches build centralized models on the basis of collecting users' data. Both private data and models are held \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "52", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Muralidhar:2020:CRT, author = "Nikhil Muralidhar and Anika Tabassum and Liangzhe Chen and Supriya Chinthavali and Naren Ramakrishnan and B. Aditya Prakash", title = "{Cut-n-Reveal}: Time Series Segmentations with Explanations", journal = j-TIST, volume = "11", number = "5", pages = "53:1--53:26", month = sep, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3394118", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Sep 7 06:54:29 MDT 2020", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3394118", abstract = "Recent hurricane events have caused unprecedented amounts of damage on critical infrastructure systems and have severely threatened our public safety and economic health. The most observable (and severe) impact of these hurricanes is the loss of \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "53", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Huang:2020:MTL, author = "Jizhou Huang and Haifeng Wang and Wei Zhang and Ting Liu", title = "Multi-Task Learning for Entity Recommendation and Document Ranking in {Web} Search", journal = j-TIST, volume = "11", number = "5", pages = "54:1--54:24", month = sep, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3396501", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Sep 7 06:54:29 MDT 2020", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3396501", abstract = "Entity recommendation, providing users with an improved search experience by proactively recommending related entities to a given query, has become an indispensable feature of today's Web search engine. Existing studies typically only consider the query \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "54", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Albaqsami:2020:AHM, author = "Ahmad Albaqsami and Maryam S. Hosseini and Masoomeh Jasemi and Nader Bagherzadeh", title = "Adaptive {HTF-MPR}: an Adaptive Heterogeneous {TensorFlow} Mapper Utilizing {Bayesian} Optimization and Genetic Algorithms", journal = j-TIST, volume = "11", number = "5", pages = "55:1--55:25", month = sep, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3396949", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Sep 7 06:54:29 MDT 2020", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3396949", abstract = "Deep neural networks are widely used in many artificial intelligence applications. They have demonstrated state-of-the-art accuracy on many artificial intelligence tasks. For this high accuracy to occur, deep neural networks require the right parameter \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "55", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Liu:2020:SDA, author = "Rui Liu and Runze Liu and Andrea Pugliese and V. S. Subrahmanian", title = "{STARS}: Defending against Sockpuppet-Based Targeted Attacks on Reviewing Systems", journal = j-TIST, volume = "11", number = "5", pages = "56:1--56:25", month = sep, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3397463", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Sep 7 06:54:29 MDT 2020", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3397463", abstract = "Customers of virtually all online marketplaces rely upon reviews in order to select the product or service they wish to buy. These marketplaces in turn deploy review fraud detection systems so that the integrity of reviews is preserved. A well-known \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "56", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Zhou:2020:DCN, author = "Yuxiang Zhou and Lejian Liao and Yang Gao and Heyan Huang and Xiaochi Wei", title = "A Discriminative Convolutional Neural Network with Context-aware Attention", journal = j-TIST, volume = "11", number = "5", pages = "57:1--57:21", month = sep, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3397464", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Sep 7 06:54:29 MDT 2020", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3397464", abstract = "Feature representation and feature extraction are two crucial procedures in text mining. Convolutional Neural Networks (CNN) have shown overwhelming success for text-mining tasks, since they are capable of efficiently extracting n -gram features from \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "57", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Dai:2020:STM, author = "Chenglong Dai and Dechang Pi and Stefanie I. Becker", title = "Shapelet-transformed Multi-channel {EEG} Channel Selection", journal = j-TIST, volume = "11", number = "5", pages = "58:1--58:27", month = sep, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3397850", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Sep 7 06:54:29 MDT 2020", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3397850", abstract = "This article proposes an approach to select EEG channels based on EEG shapelet transformation, aiming to reduce the setup time and inconvenience for subjects and to improve the applicable performance of Brain-Computer Interfaces (BCIs). In detail, the \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "58", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Yadamjav:2020:QRC, author = "Munkh-Erdene Yadamjav and Zhifeng Bao and Baihua Zheng and Farhana M. Choudhury and Hanan Samet", title = "Querying Recurrent Convoys over Trajectory Data", journal = j-TIST, volume = "11", number = "5", pages = "59:1--59:24", month = sep, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3400730", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Sep 7 06:54:29 MDT 2020", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3400730", abstract = "Moving objects equipped with location-positioning devices continuously generate a large amount of spatio-temporal trajectory data. An interesting finding over a trajectory stream is a group of objects that are travelling together for a certain period of \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "59", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Tu:2020:LGI, author = "Xiaoguang Tu and Zheng Ma and Jian Zhao and Guodong Du and Mei Xie and Jiashi Feng", title = "Learning Generalizable and Identity-Discriminative Representations for Face Anti-Spoofing", journal = j-TIST, volume = "11", number = "5", pages = "60:1--60:19", month = sep, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3402446", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Sep 7 06:54:29 MDT 2020", bibsource = "https://www.math.utah.edu/pub/tex/bib/cryptography2020.bib; https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3402446", abstract = "Face anti-spoofing aims to detect presentation attack to face recognition--based authentication systems. It has drawn growing attention due to the high security demand. The widely adopted CNN-based methods usually well recognize the spoofing faces when \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "60", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Tian:2020:MGD, author = "Qing Tian and Wenqiang Zhang and Meng Cao and Liping Wang and Songcan Chen and Hujun Yin", title = "Moment-Guided Discriminative Manifold Correlation Learning on Ordinal Data", journal = j-TIST, volume = "11", number = "5", pages = "61:1--61:18", month = sep, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3402445", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Sep 7 06:54:29 MDT 2020", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3402445", abstract = "Canonical correlation analysis (CCA) is a typical and useful learning paradigm in big data analysis for capturing correlation across multiple views of the same objects. When dealing with data with additional ordinal information, traditional CCA suffers \ldots{}.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "61", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Yuan:2020:DTS, author = "Kun Yuan and Guannan Liu and Junjie Wu and Hui Xiong", title = "Dancing with {Trump} in the Stock Market: a Deep Information Echoing Model", journal = j-TIST, volume = "11", number = "5", pages = "62:1--62:22", month = sep, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3403578", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Sep 7 06:54:29 MDT 2020", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3403578", abstract = "It is always deemed crucial to identify the key factors that could have significant impact on the stock market trend. Recently, an interesting phenomenon has emerged that some of President Trump's posts in Twitter can surge into a dominant role on the \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "62", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Maddalena:2020:MPI, author = "Eddy Maddalena and Luis-Daniel Ib{\'a}{\~n}ez and Elena Simperl", title = "Mapping Points of Interest Through Street View Imagery and Paid Crowdsourcing", journal = j-TIST, volume = "11", number = "5", pages = "63:1--63:28", month = sep, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3403931", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Sep 7 06:54:29 MDT 2020", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3403931", abstract = "We present the Virtual City Explorer (VCE), an online crowdsourcing platform for the collection of rich geotagged information in urban environments. Compared to other volunteered geographic information approaches, which are constrained by the number and \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "63", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Xia:2020:DPP, author = "Tong Xia and Yong Li and Jie Feng and Depeng Jin and Qing Zhang and Hengliang Luo and Qingmin Liao", title = "{DeepApp}: Predicting Personalized Smartphone App Usage via Context-Aware Multi-Task Learning", journal = j-TIST, volume = "11", number = "6", pages = "64:1--64:12", month = nov, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3408325", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Feb 23 10:41:28 MST 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3408325", abstract = "Smartphone mobile application (App) usage prediction, i.e., which Apps will be used next, is beneficial for user experience improvement. Through an in-depth analysis on a real-world dataset, we find that App usage is highly spatio-temporally correlated \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "64", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Zheng:2020:CAD, author = "Zimu Zheng and Jie Pu and Linghui Liu and Dan Wang and Xiangming Mei and Sen Zhang and Quanyu Dai", title = "Contextual Anomaly Detection in Solder Paste Inspection with Multi-Task Learning", journal = j-TIST, volume = "11", number = "6", pages = "65:1--65:17", month = nov, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3383261", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Feb 23 10:41:28 MST 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3383261", abstract = "In this article, we study solder paste inspection (SPI), an important stage that is used in the semiconductor manufacturing industry, where abnormal boards should be detected. A highly accurate SPI can substantially reduce human expert involvement, as \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "65", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Levy:2020:SLN, author = "Sharon Levy and Wenhan Xiong and Elizabeth Belding and William Yang Wang", title = "{SafeRoute}: Learning to Navigate Streets Safely in an Urban Environment", journal = j-TIST, volume = "11", number = "6", pages = "66:1--66:17", month = nov, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3402818", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Feb 23 10:41:28 MST 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3402818", abstract = "Recent studies show that 85\% of women have changed their traveled routes to avoid harassment and assault. Despite this, current mapping tools do not empower users with information to take charge of their personal safety. We propose SafeRoute, a novel \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "66", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Jan:2020:MEB, author = "Zohaib Md. Jan and Brijesh Verma", title = "Multiple Elimination of Base Classifiers in Ensemble Learning Using Accuracy and Diversity Comparisons", journal = j-TIST, volume = "11", number = "6", pages = "67:1--67:17", month = nov, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3405790", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Feb 23 10:41:28 MST 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3405790", abstract = "When generating ensemble classifiers, selecting the best set of classifiers from the base classifier pool is considered a combinatorial problem and an efficient classifier selection methodology must be utilized. Different researchers have used different \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "67", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Tama:2020:EID, author = "Bayu Adhi Tama and Marco Comuzzi and Jonghyeon Ko", title = "An Empirical Investigation of Different Classifiers, Encoding, and Ensemble Schemes for Next Event Prediction Using Business Process Event Logs", journal = j-TIST, volume = "11", number = "6", pages = "68:1--68:34", month = nov, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3406541", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Feb 23 10:41:28 MST 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3406541", abstract = "There is a growing need for empirical benchmarks that support researchers and practitioners in selecting the best machine learning technique for given prediction tasks. In this article, we consider the next event prediction task in business process \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "68", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Banerjee:2020:BRB, author = "Debopriyo Banerjee and Krothapalli Sreenivasa Rao and Shamik Sural and Niloy Ganguly", title = "{BOXREC}: Recommending a {Box} of Preferred Outfits in Online Shopping", journal = j-TIST, volume = "11", number = "6", pages = "69:1--69:28", month = nov, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3408890", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Feb 23 10:41:28 MST 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3408890", abstract = "Fashionable outfits are generally created by expert fashionistas, who use their creativity and in-depth understanding of fashion to make attractive outfits. Over the past few years, automation of outfit composition has gained much attention from the \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "69", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Li:2020:LUR, author = "Pan Li and Alexander Tuzhilin", title = "Latent Unexpected Recommendations", journal = j-TIST, volume = "11", number = "6", pages = "70:1--70:25", month = nov, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3404855", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Feb 23 10:41:28 MST 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3404855", abstract = "Unexpected recommender system constitutes an important tool to tackle the problem of filter bubbles and user boredom, which aims at providing unexpected and satisfying recommendations to target users at the same time. Previous unexpected recommendation \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "70", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Eiras-Franco:2020:FDN, author = "Carlos Eiras-Franco and David Mart{\'\i}nez-Rego and Leslie Kanthan and C{\'e}sar Pi{\~n}eiro and Antonio Bahamonde and Bertha Guijarro-Berdi{\~n}as and Amparo Alonso-Betanzos", title = "Fast Distributed $k$ {NN} Graph Construction Using Auto-tuned Locality-sensitive Hashing", journal = j-TIST, volume = "11", number = "6", pages = "71:1--71:18", month = nov, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3408889", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Feb 23 10:41:28 MST 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/hash.bib; https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3408889", abstract = "The k -nearest-neighbors ( k NN) graph is a popular and powerful data structure that is used in various areas of Data Science, but the high computational cost of obtaining it hinders its use on large datasets. Approximate solutions have been described in \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "71", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Li:2020:JNM, author = "Junwei Li and Le Wu and Richang Hong and Kun Zhang and Yong Ge and Yan Li", title = "A Joint Neural Model for User Behavior Prediction on Social Networking Platforms", journal = j-TIST, volume = "11", number = "6", pages = "72:1--72:25", month = nov, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3406540", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Feb 23 10:41:28 MST 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3406540", abstract = "Social networking services provide platforms for users to perform two kinds of behaviors: consumption behavior (e.g., recommending items of interest) and social link behavior (e.g., recommending potential social links). Accurately modeling and \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "72", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Mash:2020:HCC, author = "Moshe Mash and Roy Fairstein and Yoram Bachrach and Kobi Gal and Yair Zick", title = "Human-computer Coalition Formation in Weighted Voting Games", journal = j-TIST, volume = "11", number = "6", pages = "73:1--73:20", month = nov, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3408294", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Feb 23 10:41:28 MST 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3408294", abstract = "This article proposes a negotiation game, based on the weighted voting paradigm in cooperative game theory, where agents need to form coalitions and agree on how to share the gains. Despite the prevalence of weighted voting in the real world, there has \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "73", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Fang:2020:AEC, author = "Xiu Susie Fang and Quan Z. Sheng and Xianzhi Wang and Wei Emma Zhang and Anne H. H. Ngu and Jian Yang", title = "From Appearance to Essence: Comparing Truth Discovery Methods without Using Ground Truth", journal = j-TIST, volume = "11", number = "6", pages = "74:1--74:24", month = nov, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3411749", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Feb 23 10:41:28 MST 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3411749", abstract = "Truth discovery has been widely studied in recent years as a fundamental means for resolving the conflicts in multi-source data. Although many truth discovery methods have been proposed based on different considerations and intuitions, investigations \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "74", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Adamczak:2021:SBH, author = "Jens Adamczak and Yashar Deldjoo and Farshad Bakhshandegan Moghaddam and Peter Knees and Gerard-Paul Leyson and Philipp Monreal", title = "Session-based Hotel Recommendations Dataset: As part of the {ACM Recommender System Challenge 2019}", journal = j-TIST, volume = "12", number = "1", pages = "1:1--1:20", month = feb, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3412379", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Feb 23 10:41:29 MST 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3412379", abstract = "In 2019, the Recommender Systems Challenge [17] dealt for the first time with a real-world task from the area of e-tourism, namely the recommendation of hotels in booking sessions. In this context, we present the release of a new dataset that we believe \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "1", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Jiang:2021:IFT, author = "Di Jiang and Yongxin Tong and Yuanfeng Song and Xueyang Wu and Weiwei Zhao and Jinhua Peng and Rongzhong Lian and Qian Xu and Qiang Yang", title = "Industrial Federated Topic Modeling", journal = j-TIST, volume = "12", number = "1", pages = "2:1--2:22", month = feb, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3418283", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Feb 23 10:41:29 MST 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3418283", abstract = "Probabilistic topic modeling has been applied in a variety of industrial applications. Training a high-quality model usually requires a massive amount of data to provide comprehensive co-occurrence information for the model to learn. However, industrial \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "2", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Duan:2021:NMT, author = "Mingxing Duan and Kenli Li and Keqin Li and Qi Tian", title = "A Novel Multi-task Tensor Correlation Neural Network for Facial Attribute Prediction", journal = j-TIST, volume = "12", number = "1", pages = "3:1--3:22", month = feb, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3418285", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Feb 23 10:41:29 MST 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3418285", abstract = "Multi-task learning plays an important role in face multi-attribute prediction. At present, most researches excavate the shared information between attributes by sharing all convolutional layers. However, it is not appropriate to treat the low-level and \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "3", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Yan:2021:SWR, author = "Caixia Yan and Xiaojun Chang and Minnan Luo and Qinghua Zheng and Xiaoqin Zhang and Zhihui Li and Feiping Nie", title = "Self-weighted Robust {LDA} for Multiclass Classification with Edge Classes", journal = j-TIST, volume = "12", number = "1", pages = "4:1--4:19", month = feb, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3418284", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Feb 23 10:41:29 MST 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3418284", abstract = "Linear discriminant analysis (LDA) is a popular technique to learn the most discriminative features for multi-class classification. A vast majority of existing LDA algorithms are prone to be dominated by the class with very large deviation from the \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "4", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Xuan:2021:BNU, author = "Junyu Xuan and Jie Lu and Guangquan Zhang", title = "{Bayesian} Nonparametric Unsupervised Concept Drift Detection for Data Stream Mining", journal = j-TIST, volume = "12", number = "1", pages = "5:1--5:22", month = feb, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3420034", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Feb 23 10:41:29 MST 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3420034", abstract = "Online data stream mining is of great significance in practice because of its ubiquity in many real-world scenarios, especially in the big data era. Traditional data mining algorithms cannot be directly applied to data streams due to (1) the possible \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "5", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Bouguessa:2021:BBN, author = "Mohamed Bouguessa and Khaled Nouri", title = "{BiNeTClus}: Bipartite Network Community Detection Based on Transactional Clustering", journal = j-TIST, volume = "12", number = "1", pages = "6:1--6:26", month = feb, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3423067", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Feb 23 10:41:29 MST 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3423067", abstract = "We investigate the problem of community detection in bipartite networks that are characterized by the presence of two types of nodes such that connections exist only between nodes of different types. While some approaches have been proposed to identify \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "6", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Alim:2021:CSC, author = "Adil Alim and Jin-Hee Cho and Feng Chen", title = "{CSL+}: Scalable Collective Subjective Logic under Multidimensional Uncertainty", journal = j-TIST, volume = "12", number = "1", pages = "7:1--7:26", month = feb, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3426193", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Feb 23 10:41:29 MST 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3426193", abstract = "Using unreliable information sources generating conflicting evidence may lead to a large uncertainty, which significantly hurts the decision making process. Recently, many approaches have been taken to integrate conflicting data from multiple sources \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "7", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Lai:2021:DEF, author = "Chih-Te Lai and Cheng-Te Li and Shou-De Lin", title = "Deep Energy Factorization Model for Demographic Prediction", journal = j-TIST, volume = "12", number = "1", pages = "8:1--8:16", month = feb, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3426240", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Feb 23 10:41:29 MST 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3426240", abstract = "Demographic information is important for various commercial and academic proposes, but in reality, few of these data are accessible for analysis and research. To solve this problem, several studies predict demographic attributes from users' behavioral \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "8", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Liu:2021:DLT, author = "Shuo Liu and Mingliang Gao and Vijay John and Zheng Liu and Erik Blasch", title = "Deep Learning Thermal Image Translation for Night Vision Perception", journal = j-TIST, volume = "12", number = "1", pages = "9:1--9:18", month = feb, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3426239", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Feb 23 10:41:29 MST 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3426239", abstract = "Context enhancement is critical for the environmental perception in night vision applications, especially for the dark night situation without sufficient illumination. In this article, we propose a thermal image translation method, which can translate \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "9", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Wu:2021:TRL, author = "Wendi Wu and Yawei Zhao and En Zhu and Xinwang Liu and Xingxing Zhang and Lailong Luo and Shixiong Wang and Jianping Yin", title = "A Theoretical Revisit to Linear Convergence for Saddle Point Problems", journal = j-TIST, volume = "12", number = "1", pages = "10:1--10:17", month = feb, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3420035", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Feb 23 10:41:29 MST 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3420035", abstract = "Recently, convex-concave bilinear Saddle Point Problems (SPP) is widely used in lasso problems, Support Vector Machines, game theory, and so on. Previous researches have proposed many methods to solve SPP, and present their convergence rate \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "10", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Wang:2021:RLR, author = "Meng-Xiang Wang and Wang-Chien Lee and Tao-Yang Fu and Ge Yu", title = "On Representation Learning for Road Networks", journal = j-TIST, volume = "12", number = "1", pages = "11:1--11:27", month = feb, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3424346", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Feb 23 10:41:29 MST 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3424346", abstract = "Informative representation of road networks is essential to a wide variety of applications on intelligent transportation systems. In this article, we design a new learning framework, called Representation Learning for Road Networks (RLRN), which \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "11", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Zhou:2021:UMB, author = "Yiyi Zhou and Rongrong Ji and Jinsong Su and Jiaquan Yao", title = "Uncovering Media Bias via Social Network Learning", journal = j-TIST, volume = "12", number = "1", pages = "12:1--12:12", month = feb, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3422181", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Feb 23 10:41:29 MST 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3422181", abstract = "It is known that media outlets, such as CNN and FOX, have intrinsic political bias that is reflected in their news reports. The computational prediction of such bias has broad application prospects. However, the prediction is difficult via directly \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "12", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Wang:2021:PAR, author = "Guang Wang and Zhihan Fang and Xiaoyang Xie and Shuai Wang and Huijun Sun and Fan Zhang and Yunhuai Liu and Desheng Zhang", title = "Pricing-aware Real-time Charging Scheduling and Charging Station Expansion for Large-scale Electric Buses", journal = j-TIST, volume = "12", number = "1", pages = "13:1--13:26", month = feb, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3428080", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Feb 23 10:41:29 MST 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3428080", abstract = "We are witnessing a rapid growth of electrified vehicles due to the ever-increasing concerns on urban air quality and energy security. Compared to other types of electric vehicles, electric buses have not yet been prevailingly adopted worldwide due to \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "13", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Guo:2021:CTG, author = "Bin Guo and Hao Wang and Yasan Ding and Wei Wu and Shaoyang Hao and Yueqi Sun and Zhiwen Yu", title = "Conditional Text Generation for Harmonious Human-Machine Interaction", journal = j-TIST, volume = "12", number = "2", pages = "14:1--14:50", month = mar, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3439816", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Wed Mar 17 08:23:18 MDT 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3439816", abstract = "In recent years, with the development of deep learning, text-generation technology has undergone great changes and provided many kinds of services for human beings, such as restaurant reservation and daily communication. The automatically generated text \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "14", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Firdaus:2021:AAR, author = "Mauajama Firdaus and Nidhi Thakur and Asif Ekbal", title = "Aspect-Aware Response Generation for Multimodal Dialogue System", journal = j-TIST, volume = "12", number = "2", pages = "15:1--15:33", month = mar, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3430752", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Wed Mar 17 08:23:18 MDT 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3430752", abstract = "Multimodality in dialogue systems has opened up new frontiers for the creation of robust conversational agents. Any multimodal system aims at bridging the gap between language and vision by leveraging diverse and often complementary information from \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "15", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Baek:2021:RMR, author = "Yoonji Baek and Unil Yun and Heonho Kim and Hyoju Nam and Hyunsoo Kim and Jerry Chun-Wei Lin and Bay Vo and Witold Pedrycz", title = "{RHUPS}: Mining Recent High Utility Patterns with Sliding Window-based Arrival Time Control over Data Streams", journal = j-TIST, volume = "12", number = "2", pages = "16:1--16:27", month = mar, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3430767", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Wed Mar 17 08:23:18 MDT 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3430767", abstract = "Databases that deal with the real world have various characteristics. New data is continuously inserted over time without limiting the length of the database, and a variety of information about the items constituting the database is contained. Recently \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "16", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Winter:2021:CBS, author = "Felix Winter and Nysret Musliu", title = "Constraint-based Scheduling for Paint Shops in the Automotive Supply Industry", journal = j-TIST, volume = "12", number = "2", pages = "17:1--17:25", month = mar, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3430710", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Wed Mar 17 08:23:18 MDT 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3430710", abstract = "Factories in the automotive supply industry paint a large number of items requested by car manufacturing companies on a daily basis. As these factories face numerous constraints and optimization objectives, finding a good schedule becomes a challenging \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "17", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Dahmen:2021:ISA, author = "Jessamyn Dahmen and Diane J. Cook", title = "Indirectly Supervised Anomaly Detection of Clinically Meaningful Health Events from Smart Home Data", journal = j-TIST, volume = "12", number = "2", pages = "18:1--18:18", month = mar, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3439870", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Wed Mar 17 08:23:18 MDT 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3439870", abstract = "Anomaly detection techniques can extract a wealth of information about unusual events. Unfortunately, these methods yield an abundance of findings that are not of interest, obscuring relevant anomalies. In this work, we improve upon traditional anomaly \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "18", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Mansoury:2021:FBP, author = "Masoud Mansoury and Robin Burke and Bamshad Mobasher", title = "Flatter Is Better: Percentile Transformations for Recommender Systems", journal = j-TIST, volume = "12", number = "2", pages = "19:1--19:16", month = mar, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3437910", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Wed Mar 17 08:23:18 MDT 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3437910", abstract = "It is well known that explicit user ratings in recommender systems are biased toward high ratings and that users differ significantly in their usage of the rating scale. Implementers usually compensate for these issues through rating normalization or \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "19", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Cui:2021:DIR, author = "Zeyu Cui and Feng Yu and Shu Wu and Qiang Liu and Liang Wang", title = "Disentangled Item Representation for Recommender Systems", journal = j-TIST, volume = "12", number = "2", pages = "20:1--20:20", month = mar, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3445811", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Wed Mar 17 08:23:18 MDT 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3445811", abstract = "Item representations in recommendation systems are expected to reveal the properties of items. Collaborative recommender methods usually represent an item as one single latent vector. Nowadays the e-commercial platforms provide various kinds of \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "20", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Ali:2021:PAN, author = "Sarwan Ali and Muhammad Haroon Shakeel and Imdadullah Khan and Safiullah Faizullah and Muhammad Asad Khan", title = "Predicting Attributes of Nodes Using Network Structure", journal = j-TIST, volume = "12", number = "2", pages = "21:1--21:23", month = mar, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3442390", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Wed Mar 17 08:23:18 MDT 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3442390", abstract = "In many graphs such as social networks, nodes have associated attributes representing their behavior. Predicting node attributes in such graphs is an important task with applications in many domains like recommendation systems, privacy preservation, and \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "21", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Mao:2021:FGB, author = "Jiali Mao and Jiaye Liu and Cheqing Jin and Aoying Zhou", title = "Feature Grouping-based Trajectory Outlier Detection over Distributed Streams", journal = j-TIST, volume = "12", number = "2", pages = "22:1--22:23", month = mar, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3444753", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Wed Mar 17 08:23:18 MDT 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3444753", abstract = "Owing to a wide variety of deployment of GPS -enabled devices, tremendous amounts of trajectories have been generated in distributed stream manner. It opens up new opportunities to track and analyze the moving behaviors of the entities. In this work, we \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "22", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Li:2021:CMT, author = "Zijian Li and Ruichu Cai and Hong Wei Ng and Marianne Winslett and Tom Z. J. Fu and Boyan Xu and Xiaoyan Yang and Zhenjie Zhang", title = "Causal Mechanism Transfer Network for Time Series Domain Adaptation in Mechanical Systems", journal = j-TIST, volume = "12", number = "2", pages = "23:1--23:21", month = mar, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3445033", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Wed Mar 17 08:23:18 MDT 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3445033", abstract = "Data-driven models are becoming essential parts in modern mechanical systems, commonly used to capture the behavior of various equipment and varying environmental characteristics. Despite the advantages of these data-driven models on excellent \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "23", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Bashar:2021:ALE, author = "Md Abul Bashar and Richi Nayak", title = "Active Learning for Effectively Fine-Tuning Transfer Learning to Downstream Task", journal = j-TIST, volume = "12", number = "2", pages = "24:1--24:24", month = mar, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3446343", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Wed Mar 17 08:23:18 MDT 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3446343", abstract = "Language model (LM) has become a common method of transfer learning in Natural Language Processing (NLP) tasks when working with small labeled datasets. An LM is pretrained using an easily available large unlabelled text corpus and is fine-tuned with \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "24", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Chen:2021:DPB, author = "Jianguo Chen and Kenli Li and Keqin Li and Philip S. Yu and Zeng Zeng", title = "Dynamic Planning of Bicycle Stations in Dockless Public Bicycle-sharing System Using Gated Graph Neural Network", journal = j-TIST, volume = "12", number = "2", pages = "25:1--25:22", month = mar, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3446342", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Wed Mar 17 08:23:18 MDT 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3446342", abstract = "Benefiting from convenient cycling and flexible parking locations, the Dockless Public Bicycle-sharing (DL-PBS) network becomes increasingly popular in many countries. However, redundant and low-utility stations waste public urban space and maintenance \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "25", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Zhou:2021:AEA, author = "Qianli Zhou and Tianrui Hui and Rong Wang and Haimiao Hu and Si Liu", title = "Attentive Excitation and Aggregation for Bilingual Referring Image Segmentation", journal = j-TIST, volume = "12", number = "2", pages = "26:1--26:17", month = mar, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3446345", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Wed Mar 17 08:23:18 MDT 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3446345", abstract = "The goal of referring image segmentation is to identify the object matched with an input natural language expression. Previous methods only support English descriptions, whereas Chinese is also broadly used around the world, which limits the potential \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "26", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Cui:2021:MMV, author = "Wanqiu Cui and Junping Du and Dawei Wang and Feifei Kou and Zhe Xue", title = "{MVGAN}: Multi-View Graph Attention Network for Social Event Detection", journal = j-TIST, volume = "12", number = "3", pages = "27:1--27:24", month = jul, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3447270", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Jul 22 08:10:42 MDT 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3447270", abstract = "Social networks are critical sources for event detection thanks to the characteristics of publicity and dissemination. Unfortunately, the randomness and semantic sparsity of the social network text bring significant challenges to the event detection task. \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "27", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Liu:2021:MTA, author = "Yan Liu and Bin Guo and Daqing Zhang and Djamal Zeghlache and Jingmin Chen and Sizhe Zhang and Dan Zhou and Xinlei Shi and Zhiwen Yu", title = "{MetaStore}: a Task-adaptative Meta-learning Model for Optimal Store Placement with Multi-city Knowledge Transfer", journal = j-TIST, volume = "12", number = "3", pages = "28:1--28:23", month = jul, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3447271", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Jul 22 08:10:42 MDT 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3447271", abstract = "Optimal store placement aims to identify the optimal location for a new brick-and-mortar store that can maximize its sale by analyzing and mining users' preferences from large-scale urban data. In recent years, the expansion of chain enterprises in new \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "28", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Bhatia:2021:ISG, author = "Munish Bhatia", title = "Intelligent System of Game-Theory-Based Decision Making in Smart Sports Industry", journal = j-TIST, volume = "12", number = "3", pages = "29:1--29:23", month = jul, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3447986", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Jul 22 08:10:42 MDT 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3447986", abstract = "Internet of Things (IoT) technology backed by Artificial Intelligence (AI) techniques has been increasingly utilized for the realization of the Industry 4.0 vision. Conspicuously, this work provides a novel notion of the smart sports industry for \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "29", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Jiang:2021:GCE, author = "Di Jiang and Conghui Tan and Jinhua Peng and Chaotao Chen and Xueyang Wu and Weiwei Zhao and Yuanfeng Song and Yongxin Tong and Chang Liu and Qian Xu and Qiang Yang and Li Deng", title = "A {GDPR}-compliant Ecosystem for Speech Recognition with Transfer, Federated, and Evolutionary Learning", journal = j-TIST, volume = "12", number = "3", pages = "30:1--30:19", month = jul, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3447687", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Jul 22 08:10:42 MDT 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3447687", abstract = "Automatic Speech Recognition (ASR) is playing a vital role in a wide range of real-world applications. However, Commercial ASR solutions are typically ``one-size-fits-all'' products and clients are inevitably faced with the risk of severe performance \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "30", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Elmadany:2021:IAR, author = "Nour Eldin Elmadany and Yifeng He and Ling Guan", title = "Improving Action Recognition via Temporal and Complementary Learning", journal = j-TIST, volume = "12", number = "3", pages = "31:1--31:24", month = jul, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3447686", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Jul 22 08:10:42 MDT 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3447686", abstract = "In this article, we study the problem of video-based action recognition. We improve the action recognition performance by finding an effective temporal and appearance representation. For capturing the temporal representation, we introduce two temporal \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "31", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Shi:2021:GGT, author = "Yukai Shi and Sen Zhang and Chenxing Zhou and Xiaodan Liang and Xiaojun Yang and Liang Lin", title = "{GTAE}: Graph Transformer-Based Auto-Encoders for Linguistic-Constrained Text Style Transfer", journal = j-TIST, volume = "12", number = "3", pages = "32:1--32:16", month = jul, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3448733", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Jul 22 08:10:42 MDT 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3448733", abstract = "Non-parallel text style transfer has attracted increasing research interests in recent years. Despite successes in transferring the style based on the encoder-decoder framework, current approaches still lack the ability to preserve the content and even \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "32", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Yin:2021:IFR, author = "Chunyong Yin and Haoqi Cuan and Yuhang Zhu and Zhichao Yin", title = "Improved Fake Reviews Detection Model Based on Vertical Ensemble Tri-Training and Active Learning", journal = j-TIST, volume = "12", number = "3", pages = "33:1--33:19", month = jul, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3450285", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Jul 22 08:10:42 MDT 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3450285", abstract = "People's increasingly frequent online activity has generated a large number of reviews, whereas fake reviews can mislead users and harm their personal interests. In addition, it is not feasible to label reviews on a large scale because of the high cost of \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "33", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Gupta:2021:VQB, author = "Amulya Gupta and Zhu Zhang", title = "Vector-Quantization-Based Topic Modeling", journal = j-TIST, volume = "12", number = "3", pages = "34:1--34:30", month = jul, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3450946", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Jul 22 08:10:42 MDT 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3450946", abstract = "With the purpose of learning and utilizing explicit and dense topic embeddings, we propose three variations of novel vector-quantization-based topic models (VQ-TMs): (1) Hard VQ-TM, (2) Soft VQ-TM, and (3) Multi-View Soft VQ-TM. The model family \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "34", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Li:2021:PPC, author = "Shilei Li and Meng Li and Jiongming Su and Shaofei Chen and Zhimin Yuan and Qing Ye", title = "{PP-PG}: Combining Parameter Perturbation with Policy Gradient Methods for Effective and Efficient Explorations in Deep Reinforcement Learning", journal = j-TIST, volume = "12", number = "3", pages = "35:1--35:21", month = jul, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3452008", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Jul 22 08:10:42 MDT 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3452008", abstract = "Efficient and stable exploration remains a key challenge for deep reinforcement learning (DRL) operating in high-dimensional action and state spaces. Recently, a more promising approach by combining the exploration in the action space with the exploration \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "35", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Hong:2021:SRI, author = "Thanh Phuoc Hong and Ling Guan", title = "A Scale and Rotational Invariant Key-point Detector based on Sparse Coding", journal = j-TIST, volume = "12", number = "3", pages = "36:1--36:19", month = jul, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3452009", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Jul 22 08:10:42 MDT 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3452009", abstract = "Most popular hand-crafted key-point detectors such as Harris corner, SIFT, SURF aim to detect corners, blobs, junctions, or other human-defined structures in images. Though being robust with some geometric transformations, unintended scenarios or non-. \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "36", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Xia:2021:CSK, author = "Zhenchang Xia and Jia Wu and Libing Wu and Yanjiao Chen and Jian Yang and Philip S. Yu", title = "A Comprehensive Survey of the Key Technologies and Challenges Surrounding Vehicular Ad Hoc Networks", journal = j-TIST, volume = "12", number = "4", pages = "37:1--37:30", month = aug, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3451984", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Aug 28 07:23:27 MDT 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3451984", abstract = "Vehicular ad hoc networks (VANETs) and the services they support are an essential part of intelligent transportation. Through physical technologies, applications, protocols, and standards, they help to ensure traffic moves efficiently and vehicles operate \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "37", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Tian:2021:CIG, author = "Jiajie Tian and Qihao Tang and Rui Li and Zhu Teng and Baopeng Zhang and Jianping Fan", title = "A Camera Identity-guided Distribution Consistency Method for Unsupervised Multi-target Domain Person Re-identification", journal = j-TIST, volume = "12", number = "4", pages = "38:1--38:18", month = aug, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3454130", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Aug 28 07:23:27 MDT 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3454130", abstract = "Unsupervised domain adaptation (UDA) for person re-identification (re-ID) is a challenging task due to large variations in human classes, illuminations, camera views, and so on. Currently, existing UDA methods focus on two-domain adaptation and are \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "38", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Wang:2021:LMU, author = "Huandong Wang and Yong Li and Gang Wang and Depeng Jin", title = "Linking Multiple User Identities of Multiple Services from Massive Mobility Traces", journal = j-TIST, volume = "12", number = "4", pages = "39:1--39:28", month = aug, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3439817", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Aug 28 07:23:27 MDT 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3439817", abstract = "Understanding the linkability of online user identifiers (IDs) is critical to both service providers (for business intelligence) and individual users (for assessing privacy risks). Existing methods are designed to match IDs across two services but face \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "39", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Shen:2021:MMK, author = "Xiangjun Shen and Kou Lu and Sumet Mehta and Jianming Zhang and Weifeng Liu and Jianping Fan and Zhengjun Zha", title = "{MKEL}: Multiple Kernel Ensemble Learning via Unified Ensemble Loss for Image Classification", journal = j-TIST, volume = "12", number = "4", pages = "40:1--40:21", month = aug, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3457217", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Aug 28 07:23:27 MDT 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3457217", abstract = "In this article, a novel ensemble model, called Multiple Kernel Ensemble Learning (MKEL), is developed by introducing a unified ensemble loss. Different from the previous multiple kernel learning (MKL) methods, which attempt to seek a linear combination \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "40", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Sun:2021:VIV, author = "Guodao Sun and Hao Wu and Lin Zhu and Chaoqing Xu and Haoran Liang and Binwei Xu and Ronghua Liang", title = "{VSumVis}: Interactive Visual Understanding and Diagnosis of Video Summarization Model", journal = j-TIST, volume = "12", number = "4", pages = "41:1--41:28", month = aug, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3458928", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Aug 28 07:23:27 MDT 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3458928", abstract = "With the rapid development of mobile Internet, the popularity of video capture devices has brought a surge in multimedia video resources. Utilizing machine learning methods combined with well-designed features, we could automatically obtain video \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "41", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Wang:2021:MCB, author = "Daheng Wang and Qingkai Zeng and Nitesh V. Chawla and Meng Jiang", title = "Modeling Complementarity in Behavior Data with Multi-Type Itemset Embedding", journal = j-TIST, volume = "12", number = "4", pages = "42:1--42:25", month = aug, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3458724", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Aug 28 07:23:27 MDT 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3458724", abstract = "People are looking for complementary contexts, such as team members of complementary skills for project team building and/or reading materials of complementary knowledge for effective student learning, to make their behaviors more likely to be successful. \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "42", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Huang:2021:SHF, author = "Anbu Huang and Yang Liu and Tianjian Chen and Yongkai Zhou and Quan Sun and Hongfeng Chai and Qiang Yang", title = "{StarFL}: Hybrid Federated Learning Architecture for Smart Urban Computing", journal = j-TIST, volume = "12", number = "4", pages = "43:1--43:23", month = aug, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3467956", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Aug 28 07:23:27 MDT 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3467956", abstract = "From facial recognition to autonomous driving, Artificial Intelligence (AI) will transform the way we live and work over the next couple of decades. Existing AI approaches for urban computing suffer from various challenges, including dealing with \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "43", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Abhadiomhen:2021:MCS, author = "Stanley Ebhohimhen Abhadiomhen and Zhiyang Wang and Xiangjun Shen and Jianping Fan", title = "Multiview Common Subspace Clustering via Coupled Low Rank Representation", journal = j-TIST, volume = "12", number = "4", pages = "44:1--44:25", month = aug, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3465056", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Aug 28 07:23:27 MDT 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3465056", abstract = "Multi-view subspace clustering (MVSC) finds a shared structure in latent low-dimensional subspaces of multi-view data to enhance clustering performance. Nonetheless, we observe that most existing MVSC methods neglect the diversity in multi-view data by \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "44", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Aydogan:2021:NVB, author = "Reyhan Aydogan and {\"O}zg{\"u}r Kafali and Furkan Arslan and Catholijn M. Jonker and Munindar P. Singh", title = "Nova: Value-based Negotiation of Norms", journal = j-TIST, volume = "12", number = "4", pages = "45:1--45:29", month = aug, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3465054", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Aug 28 07:23:27 MDT 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3465054", abstract = "Specifying a normative multiagent system (nMAS) is challenging, because different agents often have conflicting requirements. Whereas existing approaches can resolve clear-cut conflicts, tradeoffs might occur in practice among alternative nMAS \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "45", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Bozzano:2021:CAB, author = "Marco Bozzano and Alessandro Cimatti and Marco Roveri", title = "A Comprehensive Approach to On-board Autonomy Verification and Validation", journal = j-TIST, volume = "12", number = "4", pages = "46:1--46:29", month = aug, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3472715", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Aug 28 07:23:27 MDT 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3472715", abstract = "Deep space missions are characterized by severely constrained communication links. To meet the needs of future missions and increase their scientific return, future space systems will require an increased level of autonomy on-board. In this work, we \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "46", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Tajeuna:2021:MCC, author = "Etienne Gael Tajeuna and Mohamed Bouguessa and Shengrui Wang", title = "Mining Customers' Changeable Electricity Consumption for Effective Load Forecasting", journal = j-TIST, volume = "12", number = "4", pages = "47:1--47:26", month = aug, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3466684", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Aug 28 07:23:27 MDT 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3466684", abstract = "Most existing approaches for electricity load forecasting perform the task based on overall electricity consumption. However, using such a global methodology can affect load forecasting accuracy, as it does not consider the possibility that customers' \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "47", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Fu:2021:MCE, author = "Teng Fu and Guido Zampieri and David Hodgson and Claudio Angione and Yifeng Zeng", title = "Modeling Customer Experience in a Contact Center through Process Log Mining", journal = j-TIST, volume = "12", number = "4", pages = "48:1--48:21", month = aug, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3468269", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Aug 28 07:23:27 MDT 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3468269", abstract = "The use of data mining and modeling methods in service industry is a promising avenue for optimizing current processes in a targeted manner, ultimately reducing costs and improving customer experience. However, the introduction of such tools in already \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "48", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Khezerlou:2021:DPU, author = "Amin Vahedian Khezerlou and Xun Zhou and Xinyi Li and W. Nick Street and Yanhua Li", title = "{DILSA+}: Predicting Urban Dispersal Events through Deep Survival Analysis with Enhanced Urban Features", journal = j-TIST, volume = "12", number = "4", pages = "49:1--49:25", month = aug, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3469085", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Aug 28 07:23:27 MDT 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3469085", abstract = "Urban dispersal events occur when an unexpectedly large number of people leave an area in a relatively short period of time. It is beneficial for the city authorities, such as law enforcement and city management, to have an advance knowledge of such \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "49", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Hou:2021:TTL, author = "Chenyu Hou and Bin Cao and Sijie Ruan and Jing Fan", title = "{TLDS}: a Transfer-Learning-Based Delivery Station Location Selection Pipeline", journal = j-TIST, volume = "12", number = "4", pages = "50:1--50:24", month = aug, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3469084", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Aug 28 07:23:27 MDT 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3469084", abstract = "Delivery stations play important roles in logistics systems. Well-designed delivery station planning can improve delivery efficiency significantly. However, existing delivery station locations are decided by experts, which requires much preliminary \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "50", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Zhao:2021:PCL, author = "Qi Zhao and Chuqiao Chen and Guangcan Liu and Qingshan Liu and Shengyong Chen", title = "Parallel Connected {LSTM} for Matrix Sequence Prediction with Elusive Correlations", journal = j-TIST, volume = "12", number = "4", pages = "51:1--51:16", month = aug, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3469437", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Aug 28 07:23:27 MDT 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3469437", abstract = "This article is about a challenging problem called matrix sequence prediction, which is motivated from the application of taxi order prediction. Remarkably, the problem differs greatly from previous sequence prediction tasks in the sense that the time-. \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "51", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Yuan:2021:MGC, author = "Changsen Yuan and Heyan Huang and Chong Feng", title = "Multi-Graph Cooperative Learning Towards Distant Supervised Relation Extraction", journal = j-TIST, volume = "12", number = "5", pages = "52:1--52:21", month = oct, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3466560", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Fri Dec 24 06:30:08 MST 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3466560", abstract = "The Graph Convolutional Network (GCN) is a universal relation extraction method that can predict relations of entity pairs by capturing sentences' syntactic features. However, existing GCN methods often use dependency parsing to generate graph matrices \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "52", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Chaudhari:2021:ASA, author = "Sneha Chaudhari and Varun Mithal and Gungor Polatkan and Rohan Ramanath", title = "An Attentive Survey of Attention Models", journal = j-TIST, volume = "12", number = "5", pages = "53:1--53:32", month = oct, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3465055", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Fri Dec 24 06:30:08 MST 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3465055", abstract = "Attention Model has now become an important concept in neural networks that has been researched within diverse application domains. This survey provides a structured and comprehensive overview of the developments in modeling attention. In particular, we \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "53", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Wu:2021:BSB, author = "Qiong Wu and Adam Hare and Sirui Wang and Yuwei Tu and Zhenming Liu and Christopher G. Brinton and Yanhua Li", title = "{BATS}: a Spectral Biclustering Approach to Single Document Topic Modeling and Segmentation", journal = j-TIST, volume = "12", number = "5", pages = "54:1--54:29", month = oct, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3468268", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Fri Dec 24 06:30:08 MST 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3468268", abstract = "Existing topic modeling and text segmentation methodologies generally require large datasets for training, limiting their capabilities when only small collections of text are available. In this work, we reexamine the inter-related problems of \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "54", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Zhu:2021:CLG, author = "Yisheng Zhu and Hu Han and Guangcan Liu and Qingshan Liu", title = "Collaborative Local-Global Learning for Temporal Action Proposal", journal = j-TIST, volume = "12", number = "5", pages = "55:1--55:14", month = oct, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3466181", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Fri Dec 24 06:30:08 MST 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3466181", abstract = "Temporal action proposal generation is an essential and challenging task in video understanding, which aims to locate the temporal intervals that likely contain the actions of interest. Although great progress has been made, the problem is still far from \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "55", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Chen:2021:QAE, author = "Congliang Chen and Li Shen and Haozhi Huang and Wei Liu", title = "Quantized {Adam} with Error Feedback", journal = j-TIST, volume = "12", number = "5", pages = "56:1--56:26", month = oct, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3470890", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Fri Dec 24 06:30:08 MST 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3470890", abstract = "In this article, we present a distributed variant of an adaptive stochastic gradient method for training deep neural networks in the parameter-server model. To reduce the communication cost among the workers and server, we incorporate two types of \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "56", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Guo:2021:DDH, author = "Jinjin Guo and Zhiguo Gong and Longbing Cao", title = "{dhCM}: Dynamic and Hierarchical Event Categorization and Discovery for Social Media Stream", journal = j-TIST, volume = "12", number = "5", pages = "57:1--57:25", month = oct, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3470888", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Fri Dec 24 06:30:08 MST 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3470888", abstract = "The online event discovery in social media based documents is useful, such as for disaster recognition and intervention. However, the diverse events incrementally identified from social media streams remain accumulated, ad hoc, and unstructured. They \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "57", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Cheng:2021:SNF, author = "Yuan Cheng and Yuchao Yang and Hai-Bao Chen and Ngai Wong and Hao Yu", title = "{S3-Net}: a Fast Scene Understanding Network by Single-Shot Segmentation for Autonomous Driving", journal = j-TIST, volume = "12", number = "5", pages = "58:1--58:19", month = oct, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3470660", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Fri Dec 24 06:30:08 MST 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3470660", abstract = "Real-time segmentation and understanding of driving scenes are crucial in autonomous driving. Traditional pixel-wise approaches extract scene information by segmenting all pixels in a frame, and hence are inefficient and slow. Proposal-wise approaches \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "58", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Hu:2021:IID, author = "Chuanbo Hu and Minglei Yin and Bin Liu and Xin Li and Yanfang Ye", title = "Identifying Illicit Drug Dealers on {Instagram} with Large-scale Multimodal Data Fusion", journal = j-TIST, volume = "12", number = "5", pages = "59:1--59:23", month = oct, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3472713", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Fri Dec 24 06:30:08 MST 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3472713", abstract = "Illicit drug trafficking via social media sites such as Instagram have become a severe problem, thus drawing a great deal of attention from law enforcement and public health agencies. How to identify illicit drug dealers from social media data has \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "59", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Wang:2021:FGS, author = "Min Wang and Congyan Lang and Liqian Liang and Songhe Feng and Tao Wang and Yutong Gao", title = "Fine-Grained Semantic Image Synthesis with Object-Attention Generative Adversarial Network", journal = j-TIST, volume = "12", number = "5", pages = "60:1--60:18", month = oct, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3470008", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Fri Dec 24 06:30:08 MST 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3470008", abstract = "Semantic image synthesis is a new rising and challenging vision problem accompanied by the recent promising advances in generative adversarial networks. The existing semantic image synthesis methods only consider the global information provided by the \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "60", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Ji:2021:LGE, author = "Shengwei Ji and Chenyang Bu and Lei Li and Xindong Wu", title = "Local Graph Edge Partitioning", journal = j-TIST, volume = "12", number = "5", pages = "61:1--61:25", month = oct, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3466685", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Fri Dec 24 06:30:08 MST 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3466685", abstract = "Graph edge partitioning, which is essential for the efficiency of distributed graph computation systems, divides a graph into several balanced partitions within a given size to minimize the number of vertices to be cut. Existing graph partitioning models \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "61", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Xie:2021:SDS, author = "Yiqun Xie and Xiaowei Jia and Shashi Shekhar and Han Bao and Xun Zhou", title = "Significant {DBSCAN+}: Statistically Robust Density-based Clustering", journal = j-TIST, volume = "12", number = "5", pages = "62:1--62:26", month = oct, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3474842", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Fri Dec 24 06:30:08 MST 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3474842", abstract = "Cluster detection is important and widely used in a variety of applications, including public health, public safety, transportation, and so on. Given a collection of data points, we aim to detect density-connected spatial clusters with varying geometric \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "62", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Li:2021:NED, author = "Xingjian Li and Haoyi Xiong and Zeyu Chen and Jun Huan and Cheng-Zhong Xu and Dejing Dou", title = "{``In-Network Ensemble''}: Deep Ensemble Learning with Diversified Knowledge Distillation", journal = j-TIST, volume = "12", number = "5", pages = "63:1--63:19", month = oct, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3473464", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Fri Dec 24 06:30:08 MST 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3473464", abstract = "Ensemble learning is a widely used technique to train deep convolutional neural networks (CNNs) for improved robustness and accuracy. While existing algorithms usually first train multiple diversified networks and then assemble these networks as an \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "63", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Dutta:2021:DAC, author = "Hridoy Sankar Dutta and Mayank Jobanputra and Himani Negi and Tanmoy Chakraborty", title = "Detecting and Analyzing Collusive Entities on {YouTube}", journal = j-TIST, volume = "12", number = "5", pages = "64:1--64:28", month = oct, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3477300", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Fri Dec 24 06:30:08 MST 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3477300", abstract = "YouTube sells advertisements on the posted videos, which in turn enables the content creators to monetize their videos. As an unintended consequence, this has proliferated various illegal activities such as artificial boosting of views, likes, comments, \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "64", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Wang:2021:CSG, author = "Yu Wang and Yuelin Wang and Kai Dang and Jie Liu and Zhuo Liu", title = "A Comprehensive Survey of Grammatical Error Correction", journal = j-TIST, volume = "12", number = "5", pages = "65:1--65:51", month = oct, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3474840", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Fri Dec 24 06:30:08 MST 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/spell.bib; https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3474840", abstract = "Grammatical error correction (GEC) is an important application aspect of natural language processing techniques, and GEC system is a kind of very important intelligent system that has long been explored both in academic and industrial communities. The past decade has witnessed significant progress achieved in GEC for the sake of increasing popularity of machine learning and deep learning. However, there is not a survey that untangles the large amount of research works and progress in this field. We present the first survey in GEC for a comprehensive retrospective of the literature in this area. We first give the definition of GEC task and introduce the public datasets and data annotation schema. After that, we discuss six kinds of basic approaches, six commonly applied performance boosting techniques for GEC systems, and three data augmentation methods. Since GEC is typically viewed as a sister task of Machine Translation (MT), we put more emphasis on the statistical machine translation (SMT)-based approaches and neural machine translation (NMT)-based approaches for the sake of their importance. Similarly, some performance-boosting techniques are adapted from MT and are successfully combined with GEC systems for enhancement on the final performance. More importantly, after the introduction of the evaluation in GEC, we make an in-depth analysis based on empirical results in aspects of GEC approaches and GEC systems for a clearer pattern of progress in GEC, where error type analysis and system recapitulation are clearly presented. Finally, we discuss five prospective directions for future GEC researches.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "65", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Koutroulis:2021:KCC, author = "Georgios Koutroulis and Leo Botler and Belgin Mutlu and Konrad Diwold and Kay R{\"o}mer and Roman Kern", title = "{KOMPOS}: Connecting Causal Knots in Large Nonlinear Time Series with Non-Parametric Regression Splines", journal = j-TIST, volume = "12", number = "5", pages = "66:1--66:27", month = oct, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3480971", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Fri Dec 24 06:30:08 MST 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3480971", abstract = "Recovering causality from copious time series data beyond mere correlations has been an important contributing factor in numerous scientific fields. Most existing works assume linearity in the data that may not comply with many real-world scenarios. \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "66", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Wang:2021:ATS, author = "Senzhang Wang and Junbo Zhang and Yanjie Fu and Yong Li", title = "{ACM TIST} Special Issue on Deep Learning for Spatio-Temporal Data: {Part 1}", journal = j-TIST, volume = "12", number = "6", pages = "67:1--67:3", month = dec, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3495188", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Fri Dec 24 06:30:09 MST 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3495188", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "67", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Huang:2021:THG, author = "Ling Huang and Xing-Xing Liu and Shu-Qiang Huang and Chang-Dong Wang and Wei Tu and Jia-Meng Xie and Shuai Tang and Wendi Xie", title = "Temporal Hierarchical Graph Attention Network for Traffic Prediction", journal = j-TIST, volume = "12", number = "6", pages = "68:1--68:21", month = dec, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3446430", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Fri Dec 24 06:30:09 MST 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3446430", abstract = "As a critical task in intelligent traffic systems, traffic prediction has received a large amount of attention in the past few decades. The early efforts mainly model traffic prediction as the time-series mining problem, in which the spatial dependence \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "68", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Zhou:2021:PEL, author = "Haoyi Zhou and Hao Peng and Jieqi Peng and Shuai Zhang and Jianxin Li", title = "{POLLA}: Enhancing the Local Structure Awareness in Long Sequence Spatial-temporal Modeling", journal = j-TIST, volume = "12", number = "6", pages = "69:1--69:24", month = dec, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3447987", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Fri Dec 24 06:30:09 MST 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3447987", abstract = "The spatial-temporal modeling on long sequences is of great importance in many real-world applications. Recent studies have shown the potential of applying the self-attention mechanism to improve capturing the complex spatial-temporal dependencies. \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "69", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Qiao:2021:DCN, author = "Shaojie Qiao and Nan Han and Jianbin Huang and Kun Yue and Rui Mao and Hongping Shu and Qiang He and Xindong Wu", title = "A Dynamic Convolutional Neural Network Based Shared-Bike Demand Forecasting Model", journal = j-TIST, volume = "12", number = "6", pages = "70:1--70:24", month = dec, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3447988", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Fri Dec 24 06:30:09 MST 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3447988", abstract = "Bike-sharing systems are becoming popular and generate a large volume of trajectory data. In a bike-sharing system, users can borrow and return bikes at different stations. In particular, a bike-sharing system will be affected by weather, the time period, \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "70", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Dharejo:2021:TGT, author = "Fayaz Ali Dharejo and Farah Deeba and Yuanchun Zhou and Bhagwan Das and Munsif Ali Jatoi and Muhammad Zawish and Yi Du and Xuezhi Wang", title = "{TWIST-GAN}: Towards Wavelet Transform and Transferred {GAN} for Spatio-Temporal Single Image Super Resolution", journal = j-TIST, volume = "12", number = "6", pages = "71:1--71:20", month = dec, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3456726", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Fri Dec 24 06:30:09 MST 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3456726", abstract = "Single Image Super-resolution (SISR) produces high-resolution images with fine spatial resolutions from a remotely sensed image with low spatial resolution. Recently, deep learning and generative adversarial networks (GANs) have made breakthroughs for the \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "71", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{He:2021:TNF, author = "Yifan He and Zhao Li and Lei Fu and Anhui Wang and Peng Zhang and Shuigeng Zhou and Ji Zhang and Ting Yu", title = "{TARA-Net}: a Fusion Network for Detecting Takeaway Rider Accidents", journal = j-TIST, volume = "12", number = "6", pages = "72:1--72:19", month = dec, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3457218", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Fri Dec 24 06:30:09 MST 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3457218", abstract = "In the emerging business of food delivery, rider traffic accidents raise financial cost and social traffic burden. Although there has been much effort on traffic accident forecasting using temporal-spatial prediction models, none of the existing work \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "72", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Dai:2021:PPT, author = "Tianlun Dai and Bohan Li and Ziqiang Yu and Xiangrong Tong and Meng Chen and Gang Chen", title = "{PARP}: a Parallel Traffic Condition Driven Route Planning Model on Dynamic Road Networks", journal = j-TIST, volume = "12", number = "6", pages = "73:1--73:24", month = dec, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3459099", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Fri Dec 24 06:30:09 MST 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3459099", abstract = "The problem of route planning on road network is essential to many Location-Based Services (LBSs). Road networks are dynamic in the sense that the weights of the edges in the corresponding graph constantly change over time, representing evolving traffic \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "73", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Guo:2021:ROE, author = "Pengzhan Guo and Keli Xiao and Zeyang Ye and Wei Zhu", title = "Route Optimization via Environment-Aware Deep Network and Reinforcement Learning", journal = j-TIST, volume = "12", number = "6", pages = "74:1--74:21", month = dec, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3461645", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Fri Dec 24 06:30:09 MST 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3461645", abstract = "Vehicle mobility optimization in urban areas is a long-standing problem in smart city and spatial data analysis. Given the complex urban scenario and unpredictable social events, our work focuses on developing a mobile sequential recommendation system to \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "74", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Xu:2021:TTA, author = "Jiajie Xu and Saijun Xu and Rui Zhou and Chengfei Liu and An Liu and Lei Zhao", title = "{TAML}: a Traffic-aware Multi-task Learning Model for Estimating Travel Time", journal = j-TIST, volume = "12", number = "6", pages = "75:1--75:14", month = dec, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3466686", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Fri Dec 24 06:30:09 MST 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3466686", abstract = "Travel time estimation has been recognized as an important research topic that can find broad applications. Existing approaches aim to explore mobility patterns via trajectory embedding for travel time estimation. Though state-of-the-art methods utilize \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "75", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Gupta:2021:SVA, author = "Jayant Gupta and Carl Molnar and Yiqun Xie and Joe Knight and Shashi Shekhar", title = "Spatial Variability Aware Deep Neural Networks {(SVANN)}: a General Approach", journal = j-TIST, volume = "12", number = "6", pages = "76:1--76:21", month = dec, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3466688", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Fri Dec 24 06:30:09 MST 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3466688", abstract = "Spatial variability is a prominent feature of various geographic phenomena such as climatic zones, USDA plant hardiness zones, and terrestrial habitat types (e.g., forest, grasslands, wetlands, and deserts). However, current deep learning methods follow a \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "76", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Tedjopurnomo:2021:STS, author = "David Alexander Tedjopurnomo and Xiucheng Li and Zhifeng Bao and Gao Cong and Farhana Choudhury and A. K. Qin", title = "Similar Trajectory Search with Spatio-Temporal Deep Representation Learning", journal = j-TIST, volume = "12", number = "6", pages = "77:1--77:26", month = dec, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3466687", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Fri Dec 24 06:30:09 MST 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3466687", abstract = "Similar trajectory search is a crucial task that facilitates many downstream spatial data analytic applications. Despite its importance, many of the current literature focus solely on the trajectory's spatial similarity while neglecting the temporal \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "77", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Tao:2021:PHM, author = "Shuo Tao and Jingang Jiang and Defu Lian and Kai Zheng and Enhong Chen", title = "Predicting Human Mobility with Reinforcement-Learning-Based Long-Term Periodicity Modeling", journal = j-TIST, volume = "12", number = "6", pages = "78:1--78:23", month = dec, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3469860", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Fri Dec 24 06:30:09 MST 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3469860", abstract = "Mobility prediction plays an important role in a wide range of location-based applications and services. However, there are three problems in the existing literature: (1) explicit high-order interactions of spatio-temporal features are not systemically \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "78", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Strohmeier:2021:CFI, author = "Martin Strohmeier and Matthew Smith and Vincent Lenders and Ivan Martinovic", title = "{Classi-Fly}: Inferring Aircraft Categories from Open Data", journal = j-TIST, volume = "12", number = "6", pages = "79:1--79:23", month = dec, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3480969", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Fri Dec 24 06:30:09 MST 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3480969", abstract = "In recent years, air traffic communication data has become easy to access, enabling novel research in many fields. Exploiting this new data source, a wide range of applications have emerged, from weather forecasting to stock market prediction, or the \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "79", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Qiao:2021:CDC, author = "Jie Qiao and Ruichu Cai and Kun Zhang and Zhenjie Zhang and Zhifeng Hao", title = "Causal Discovery with Confounding Cascade Nonlinear Additive Noise Models", journal = j-TIST, volume = "12", number = "6", pages = "80:1--80:28", month = dec, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3482879", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Fri Dec 24 06:30:09 MST 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3482879", abstract = "Identification of causal direction between a causal-effect pair from observed data has recently attracted much attention. Various methods based on functional causal models have been proposed to solve this problem, by assuming the causal process satisfies \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "80", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Notaro:2021:SAM, author = "Paolo Notaro and Jorge Cardoso and Michael Gerndt", title = "A Survey of {AIOps} Methods for Failure Management", journal = j-TIST, volume = "12", number = "6", pages = "81:1--81:45", month = dec, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3483424", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Fri Dec 24 06:30:09 MST 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3483424", abstract = "Modern society is increasingly moving toward complex and distributed computing systems. The increase in scale and complexity of these systems challenges O\&M teams that perform daily monitoring and repair operations, in contrast with the increasing demand \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "81", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Zhao:2021:MSF, author = "Jiaqi Zhao and Yong Zhou and Boyu Shi and Jingsong Yang and Di Zhang and Rui Yao", title = "Multi-Stage Fusion and Multi-Source Attention Network for Multi-Modal Remote Sensing Image Segmentation", journal = j-TIST, volume = "12", number = "6", pages = "82:1--82:20", month = dec, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3484440", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Fri Dec 24 06:30:09 MST 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3484440", abstract = "With the rapid development of sensor technology, lots of remote sensing data have been collected. It effectively obtains good semantic segmentation performance by extracting feature maps based on multi-modal remote sensing images since extra modal data \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "82", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Zheng:2022:ISIa, author = "Kai Zheng and Yong Li and Cyrus Shahabi and Hongzhi Yin", title = "Introduction to the Special Issue on Intelligent Trajectory Analytics: {Part I}", journal = j-TIST, volume = "13", number = "1", pages = "1:1--1:2", month = feb, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3495230", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Feb 17 07:52:04 MST 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3495230", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "1", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Wang:2022:PMP, author = "Yuandong Wang and Hongzhi Yin and Tong Chen and Chunyang Liu and Ben Wang and Tianyu Wo and Jie Xu", title = "Passenger Mobility Prediction via Representation Learning for Dynamic Directed and Weighted Graphs", journal = j-TIST, volume = "13", number = "1", pages = "2:1--2:25", month = feb, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3446344", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Feb 17 07:52:04 MST 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3446344", abstract = "In recent years, ride-hailing services have been increasingly prevalent, as they provide huge convenience for passengers. As a fundamental problem, the timely prediction of passenger demands in different regions is vital for effective traffic flow control \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "2", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Chen:2022:IBD, author = "Wen-Cheng Chen and Wan-Lun Tsai and Huan-Hua Chang and Min-Chun Hu and Wei-Ta Chu", title = "Instant Basketball Defensive Trajectory Generation", journal = j-TIST, volume = "13", number = "1", pages = "3:1--3:20", month = feb, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3460619", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Feb 17 07:52:04 MST 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3460619", abstract = "Tactic learning in virtual reality (VR) has been proven to be effective for basketball training. Endowed with the ability of generating virtual defenders in real time according to the movement of virtual offenders controlled by the user, a VR basketball \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "3", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Zhou:2022:CTL, author = "Fan Zhou and Pengyu Wang and Xovee Xu and Wenxin Tai and Goce Trajcevski", title = "Contrastive Trajectory Learning for Tour Recommendation", journal = j-TIST, volume = "13", number = "1", pages = "4:1--4:25", month = feb, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3462331", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Feb 17 07:52:04 MST 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3462331", abstract = "The main objective of Personalized Tour Recommendation (PTR) is to generate a sequence of point-of-interest (POIs) for a particular tourist, according to the user-specific constraints such as duration time, start and end points, the number of attractions \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "4", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Chen:2022:OAL, author = "Meng Chen and Qingjie Liu and Weiming Huang and Teng Zhang and Yixuan Zuo and Xiaohui Yu", title = "Origin-Aware Location Prediction Based on Historical Vehicle Trajectories", journal = j-TIST, volume = "13", number = "1", pages = "5:1--5:18", month = feb, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3462675", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Feb 17 07:52:04 MST 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3462675", abstract = "Next location prediction is of great importance for many location-based applications and provides essential intelligence to various businesses. In previous studies, a common approach to next location prediction is to learn the sequential transitions with \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "5", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Loffler:2022:DSM, author = "Christoffer L{\"o}ffler and Luca Reeb and Daniel Dzibela and Robert Marzilger and Nicolas Witt and Bj{\"o}rn M. Eskofier and Christopher Mutschler", title = "Deep {Siamese} Metric Learning: a Highly Scalable Approach to Searching Unordered Sets of Trajectories", journal = j-TIST, volume = "13", number = "1", pages = "6:1--6:23", month = feb, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3465057", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Feb 17 07:52:04 MST 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3465057", abstract = "This work proposes metric learning for fast similarity-based scene retrieval of unstructured ensembles of trajectory data from large databases. We present a novel representation learning approach using Siamese Metric Learning that approximates a distance \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "6", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Sun:2022:PFL, author = "Heli Sun and Xianglan Guo and Zhou Yang and Xuguang Chu and Xinwang Liu and Liang He", title = "Predicting Future Locations with Semantic Trajectories", journal = j-TIST, volume = "13", number = "1", pages = "7:1--7:20", month = feb, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3465060", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Feb 17 07:52:04 MST 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3465060", abstract = "Location prediction has attracted much attention due to its important role in many location-based services, including taxi services, route navigation, traffic planning, and location-based advertisements. Traditional methods only use spatial-temporal \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "7", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Luo:2022:LTS, author = "Hui Luo and Zhifeng Bao and Gao Cong and J. Shane Culpepper and Nguyen Lu Dang Khoa", title = "Let Trajectories Speak Out the Traffic Bottlenecks", journal = j-TIST, volume = "13", number = "1", pages = "8:1--8:21", month = feb, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3465058", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Feb 17 07:52:04 MST 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3465058", abstract = "Traffic bottlenecks are a set of road segments that have an unacceptable level of traffic caused by a poor balance between road capacity and traffic volume. A huge volume of trajectory data which captures realtime traffic conditions in road networks \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "8", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Niu:2022:ERT, author = "Hongting Niu and Hengshu Zhu and Ying Sun and Xinjiang Lu and Jing Sun and Zhiyuan Zhao and Hui Xiong and Bo Lang", title = "Exploring the Risky Travel Area and Behavior of Car-hailing Service", journal = j-TIST, volume = "13", number = "1", pages = "9:1--9:22", month = feb, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3465059", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Feb 17 07:52:04 MST 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3465059", abstract = "Recent years have witnessed the rapid development of car-hailing services, which provide a convenient approach for connecting passengers and local drivers using their personal vehicles. At the same time, the concern on passenger safety has gradually \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "9", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Zhu:2022:SPC, author = "Yanliang Zhu and Dongchun Ren and Yi Xu and Deheng Qian and Mingyu Fan and Xin Li and Huaxia Xia", title = "Simultaneous Past and Current Social Interaction-aware Trajectory Prediction for Multiple Intelligent Agents in Dynamic Scenes", journal = j-TIST, volume = "13", number = "1", pages = "10:1--10:16", month = feb, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3466182", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Feb 17 07:52:04 MST 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3466182", abstract = "Trajectory prediction of multiple agents in a crowded scene is an essential component in many applications, including intelligent monitoring, autonomous robotics, and self-driving cars. Accurate agent trajectory prediction remains a significant challenge \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "10", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Bi:2022:UBN, author = "Xin Bi and Chao Zhang and Fangtong Wang and Zhixun Liu and Xiangguo Zhao and Ye Yuan and Guoren Wang", title = "An Uncertainty-based Neural Network for Explainable Trajectory Segmentation", journal = j-TIST, volume = "13", number = "1", pages = "11:1--11:18", month = feb, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3467978", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Feb 17 07:52:04 MST 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3467978", abstract = "As a variant task of time-series segmentation, trajectory segmentation is a key task in the applications of transportation pattern recognition and traffic analysis. However, segmenting trajectory is faced with challenges of implicit patterns and sparse \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "11", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Waniek:2022:HMC, author = "Marcin Waniek and Tomasz P. Michalak and Michael Wooldridge and Talal Rahwan", title = "How Members of Covert Networks Conceal the Identities of Their Leaders", journal = j-TIST, volume = "13", number = "1", pages = "12:1--12:29", month = feb, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3490462", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Feb 17 07:52:04 MST 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3490462", abstract = "Centrality measures are the most commonly advocated social network analysis tools for identifying leaders of covert organizations. While the literature has predominantly focused on studying the effectiveness of existing centrality measures or developing \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "12", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Huang:2022:SAF, author = "Shih-Chia Huang and Quoc-Viet Hoang and Da-Wei Jaw", title = "Self-Adaptive Feature Transformation Networks for Object Detection in low luminance Images", journal = j-TIST, volume = "13", number = "1", pages = "13:1--13:11", month = feb, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3480973", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Feb 17 07:52:04 MST 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3480973", abstract = "Despite the recent improvement of object detection techniques, many of them fail to detect objects in low-luminance images. The blurry and dimmed nature of low-luminance images results in the extraction of vague features and failure to detect objects. In \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "13", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Wen:2022:MWP, author = "Yu-Ting Wen and Hui-Kuo Yang and Wen-Chih Peng", title = "Mining Willing-to-Pay Behavior Patterns from Payment Datasets", journal = j-TIST, volume = "13", number = "1", pages = "14:1--14:19", month = feb, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3485848", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Feb 17 07:52:04 MST 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3485848", abstract = "The customer base is the most valuable resource to E-commerce companies. A comprehensive understanding of customers' preferences and behavior is crucial to developing good marketing strategies, in order to achieve optimal customer lifetime values (CLVs). \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "14", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Zhou:2022:GNN, author = "Yu Zhou and Haixia Zheng and Xin Huang and Shufeng Hao and Dengao Li and Jumin Zhao", title = "Graph Neural Networks: Taxonomy, Advances, and Trends", journal = j-TIST, volume = "13", number = "1", pages = "15:1--15:54", month = feb, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3495161", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Feb 17 07:52:04 MST 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3495161", abstract = "Graph neural networks provide a powerful toolkit for embedding real-world graphs into low-dimensional spaces according to specific tasks. Up to now, there have been several surveys on this topic. However, they usually lay emphasis on different angles so \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "15", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Li:2022:FFA, author = "Cheng-Te Li and Cheng Hsu and Yang Zhang", title = "{FairSR}: Fairness-aware Sequential Recommendation through Multi-Task Learning with Preference Graph Embeddings", journal = j-TIST, volume = "13", number = "1", pages = "16:1--16:21", month = feb, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3495163", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Feb 17 07:52:04 MST 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3495163", abstract = "Sequential recommendation (SR) learns from the temporal dynamics of user-item interactions to predict the next ones. Fairness-aware recommendation mitigates a variety of algorithmic biases in the learning of user preferences. This article aims at bringing \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "16", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Wang:2022:ISI, author = "Senzhang Wang and Junbo Zhang and Yanjie Fu and Yong Li", title = "Introduction to the Special Issue on Deep Learning for Spatio-Temporal Data:{Part 2}", journal = j-TIST, volume = "13", number = "2", pages = "17:1--17:4", month = apr, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3510023", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Fri Apr 22 08:41:23 MDT 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3510023", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "17", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Saxena:2022:MST, author = "Divya Saxena and Jiannong Cao", title = "Multimodal Spatio-Temporal Prediction with Stochastic Adversarial Networks", journal = j-TIST, volume = "13", number = "2", pages = "18:1--18:23", month = apr, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3458025", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Fri Apr 22 08:41:23 MDT 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3458025", abstract = "Spatio-temporal (ST) data is a collection of multiple time series data with different spatial locations and is inherently stochastic and unpredictable. An accurate prediction over such data is an important building block for several urban applications, \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "18", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Li:2022:DST, author = "He Li and Xuejiao Li and Liangcai Su and Duo Jin and Jianbin Huang and Deshuang Huang", title = "Deep Spatio-temporal Adaptive {$3$D} Convolutional Neural Networks for Traffic Flow Prediction", journal = j-TIST, volume = "13", number = "2", pages = "19:1--19:21", month = apr, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3510829", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Fri Apr 22 08:41:23 MDT 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3510829", abstract = "Traffic flow prediction is the upstream problem of path planning, intelligent transportation system, and other tasks. Many studies have been carried out on the traffic flow prediction of the spatio-temporal network, but the effects of spatio-temporal \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "19", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Lu:2022:GSN, author = "Zhilong Lu and Weifeng Lv and Zhipu Xie and Bowen Du and Guixi Xiong and Leilei Sun and Haiquan Wang", title = "Graph Sequence Neural Network with an Attention Mechanism for Traffic Speed Prediction", journal = j-TIST, volume = "13", number = "2", pages = "20:1--20:24", month = apr, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3470889", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Fri Apr 22 08:41:23 MDT 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3470889", abstract = "Recent years have witnessed the emerging success of Graph Neural Networks (GNNs) for modeling graphical data. A GNN can model the spatial dependencies of nodes in a graph based on message passing through node aggregation. However, in many application \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "20", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Jiang:2022:PCC, author = "Renhe Jiang and Zekun Cai and Zhaonan Wang and Chuang Yang and Zipei Fan and Quanjun Chen and Xuan Song and Ryosuke Shibasaki", title = "Predicting Citywide Crowd Dynamics at Big Events: a Deep Learning System", journal = j-TIST, volume = "13", number = "2", pages = "21:1--21:24", month = apr, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3472300", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Fri Apr 22 08:41:23 MDT 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3472300", abstract = "Event crowd management has been a significant research topic with high social impact. When some big events happen such as an earthquake, typhoon, and national festival, crowd management becomes the first priority for governments (e.g., police) and public \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "21", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Gao:2022:GAN, author = "Nan Gao and Hao Xue and Wei Shao and Sichen Zhao and Kyle Kai Qin and Arian Prabowo and Mohammad Saiedur Rahaman and Flora D. Salim", title = "Generative Adversarial Networks for Spatio-temporal Data: a Survey", journal = j-TIST, volume = "13", number = "2", pages = "22:1--22:25", month = apr, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3474838", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Fri Apr 22 08:41:23 MDT 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3474838", abstract = "Generative Adversarial Networks (GANs) have shown remarkable success in producing realistic-looking images in the computer vision area. Recently, GAN-based techniques are shown to be promising for spatio-temporal-based applications such as trajectory \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "22", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Zhang:2022:UTD, author = "Yingxue Zhang and Yanhua Li and Xun Zhou and Jun Luo and Zhi-Li Zhang", title = "Urban Traffic Dynamics Prediction --- a Continuous Spatial-temporal Meta-learning Approach", journal = j-TIST, volume = "13", number = "2", pages = "23:1--23:19", month = apr, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3474837", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Fri Apr 22 08:41:23 MDT 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3474837", abstract = "Urban traffic status (e.g., traffic speed and volume) is highly dynamic in nature, namely, varying across space and evolving over time. Thus, predicting such traffic dynamics is of great importance to urban development and transportation management. \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "23", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Wen:2022:DMC, author = "Haomin Wen and Youfang Lin and Huaiyu Wan and Shengnan Guo and Fan Wu and Lixia Wu and Chao Song and Yinghui Xu", title = "{DeepRoute+}: Modeling Couriers' Spatial-temporal Behaviors and Decision Preferences for Package Pick-up Route Prediction", journal = j-TIST, volume = "13", number = "2", pages = "24:1--24:23", month = apr, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3481006", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Fri Apr 22 08:41:23 MDT 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3481006", abstract = "Over 10 billion packages are picked up every day in China. A fundamental task raised in the emerging intelligent logistics systems is the couriers' package pick-up route prediction, which is beneficial for package dispatching, arrival-time estimation and \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "24", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Jiang:2022:WSS, author = "Zhe Jiang and Wenchong He and Marcus Stephen Kirby and Arpan Man Sainju and Shaowen Wang and Lawrence V. Stanislawski and Ethan J. Shavers and E. Lynn Usery", title = "Weakly Supervised Spatial Deep Learning for {Earth} Image Segmentation Based on Imperfect Polyline Labels", journal = j-TIST, volume = "13", number = "2", pages = "25:1--25:20", month = apr, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3480970", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Fri Apr 22 08:41:23 MDT 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3480970", abstract = "In recent years, deep learning has achieved tremendous success in image segmentation for computer vision applications. The performance of these models heavily relies on the availability of large-scale high-quality training labels (e.g., PASCAL VOC 2012). \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "25", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{He:2022:EIS, author = "Wenchong He and Arpan Man Sainju and Zhe Jiang and Da Yan and Yang Zhou", title = "{Earth} Imagery Segmentation on Terrain Surface with Limited Training Labels: a Semi-supervised Approach based on Physics-Guided Graph Co-Training", journal = j-TIST, volume = "13", number = "2", pages = "26:1--26:22", month = apr, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3481043", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Fri Apr 22 08:41:23 MDT 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3481043", abstract = "Given earth imagery with spectral features on a terrain surface, this paper studies surface segmentation based on both explanatory features and surface topology. The problem is important in many spatial and spatiotemporal applications such as flood extent \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "26", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Bao:2022:CGE, author = "Han Bao and Xun Zhou and Yiqun Xie and Yingxue Zhang and Yanhua Li", title = "{COVID-GAN+}: Estimating Human Mobility Responses to {COVID-19} through Spatio-temporal Generative Adversarial Networks with Enhanced Features", journal = j-TIST, volume = "13", number = "2", pages = "27:1--27:23", month = apr, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3481617", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Fri Apr 22 08:41:23 MDT 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3481617", abstract = "Estimating human mobility responses to the large-scale spreading of the COVID-19 pandemic is crucial, since its significance guides policymakers to give Non-pharmaceutical Interventions, such as closure or reopening of businesses. It is challenging to \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "27", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Lu:2022:MMC, author = "Bin Lu and Xiaoying Gan and Haiming Jin and Luoyi Fu and Xinbing Wang and Haisong Zhang", title = "Make More Connections: Urban Traffic Flow Forecasting with Spatiotemporal Adaptive Gated Graph Convolution Network", journal = j-TIST, volume = "13", number = "2", pages = "28:1--28:25", month = apr, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3488902", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Fri Apr 22 08:41:23 MDT 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3488902", abstract = "Urban traffic flow forecasting is a critical issue in intelligent transportation systems. Due to the complexity and uncertainty of urban road conditions, how to capture the dynamic spatiotemporal correlation and make accurate predictions is very \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "28", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Wang:2022:DDT, author = "Liang Wang and Zhiwen Yu and Bin Guo and Dingqi Yang and Lianbo Ma and Zhidan Liu and Fei Xiong", title = "Data-driven Targeted Advertising Recommendation System for Outdoor Billboard", journal = j-TIST, volume = "13", number = "2", pages = "29:1--29:23", month = apr, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3495159", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Fri Apr 22 08:41:23 MDT 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3495159", abstract = "In this article, we propose and study a novel data-driven framework for Targeted Outdoor Advertising Recommendation (TOAR) with a special consideration of user profiles and advertisement topics. Given an advertisement query and a set of outdoor billboards \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "29", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{He:2022:BAB, author = "Yulin He and Xuan Ye and Joshua Zhexue Huang and Philippe Fournier-Viger", title = "{Bayesian} Attribute Bagging-Based Extreme Learning Machine for High-Dimensional Classification and Regression", journal = j-TIST, volume = "13", number = "2", pages = "30:1--30:26", month = apr, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3495164", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Fri Apr 22 08:41:23 MDT 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3495164", abstract = "This article presents a Bayesian attribute bagging-based extreme learning machine (BAB-ELM) to handle high-dimensional classification and regression problems. First, the decision-making degree (DMD) of a condition attribute is calculated based on the \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "30", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Li:2022:STC, author = "Qian Li and Hao Peng and Jianxin Li and Congying Xia and Renyu Yang and Lichao Sun and Philip S. Yu and Lifang He", title = "A Survey on Text Classification: From Traditional to Deep Learning", journal = j-TIST, volume = "13", number = "2", pages = "31:1--31:41", month = apr, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3495162", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Fri Apr 22 08:41:23 MDT 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3495162", abstract = "Text classification is the most fundamental and essential task in natural language processing. The last decade has seen a surge of research in this area due to the unprecedented success of deep learning. Numerous methods, datasets, and evaluation metrics \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "31", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Gao:2022:LCH, author = "Guangliang Gao and Zhifeng Bao and Jie Cao and A. K. Qin and Timos Sellis", title = "Location-Centered House Price Prediction: a Multi-Task Learning Approach", journal = j-TIST, volume = "13", number = "2", pages = "32:1--32:25", month = apr, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3501806", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Fri Apr 22 08:41:23 MDT 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3501806", abstract = "Accurate house prediction is of great significance to various real estate stakeholders such as house owners, buyers, and investors. We propose a location-centered prediction framework that differs from existing work in terms of data profiling and \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "32", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Liang:2022:CFS, author = "Weichao Liang and Zhiang Wu and Zhe Li and Yong Ge", title = "{CrimeTensor}: Fine-Scale Crime Prediction via Tensor Learning with Spatiotemporal Consistency", journal = j-TIST, volume = "13", number = "2", pages = "33:1--33:24", month = apr, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3501807", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Fri Apr 22 08:41:23 MDT 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3501807", abstract = "Crime poses a major threat to human life and property, which has been recognized as one of the most crucial problems in our society. Predicting the number of crime incidents in each region of a city before they happen is of great importance to fight \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "33", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Zheng:2022:ISIb, author = "Kai Zheng and Yong Li and Cyrus Shahabi and Hongzhi Yin", title = "Introduction to the Special Issue on Intelligent Trajectory Analytics: {Part II}", journal = j-TIST, volume = "13", number = "3", pages = "34:1--34:2", month = jun, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3510021", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Wed May 25 07:55:15 MDT 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3510021", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "34", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Deng:2022:EES, author = "Liwei Deng and Hao Sun and Rui Sun and Yan Zhao and Han Su", title = "Efficient and Effective Similar Subtrajectory Search: a Spatial-aware Comprehension Approach", journal = j-TIST, volume = "13", number = "3", pages = "35:1--35:22", month = jun, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3456723", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Wed May 25 07:55:15 MDT 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3456723", abstract = "Although many applications take subtrajectories as basic units for analysis, there is little research on the similar subtrajectory search problem aiming to return a portion of a trajectory (i.e., subtrajectory), which is the most similar to a query \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "35", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Sharma:2022:ATG, author = "Arun Sharma and Shashi Shekhar", title = "Analyzing Trajectory Gaps to Find Possible Rendezvous Region", journal = j-TIST, volume = "13", number = "3", pages = "36:1--36:23", month = jun, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3467977", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Wed May 25 07:55:15 MDT 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3467977", abstract = "Given trajectory data with gaps, we investigate methods to identify possible rendezvous regions. The problem has societal applications such as improving maritime safety and regulatory enforcement. The challenges come from two aspects. First, gaps in \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "36", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Zheng:2022:SDA, author = "Bolong Zheng and Lingfeng Ming and Qi Hu and Zhipeng L{\"u} and Guanfeng Liu and Xiaofang Zhou", title = "Supply-Demand-aware Deep Reinforcement Learning for Dynamic Fleet Management", journal = j-TIST, volume = "13", number = "3", pages = "37:1--37:19", month = jun, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3467979", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Wed May 25 07:55:15 MDT 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3467979", abstract = "Online ride-hailing platforms have reduced significantly the amounts of the time that taxis are idle and that passengers spend on waiting. As a key component of these platforms, the fleet management problem can be naturally modeled as a Markov Decision \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "37", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Wang:2022:MCA, author = "Senzhang Wang and Meiyue Zhang and Hao Miao and Zhaohui Peng and Philip S. Yu", title = "Multivariate Correlation-aware Spatio-temporal Graph Convolutional Networks for Multi-scale Traffic Prediction", journal = j-TIST, volume = "13", number = "3", pages = "38:1--38:22", month = jun, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3469087", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Wed May 25 07:55:15 MDT 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3469087", abstract = "Traffic flow prediction based on vehicle trajectories collected from the installed GPS devices is critically important to Intelligent Transportation Systems (ITS). One limitation of existing traffic prediction models is that they mostly focus on \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "38", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Zhang:2022:IAS, author = "Yifan Zhang and Jinghuai Zhang and Jindi Zhang and Jianping Wang and Kejie Lu and Jeff Hong", title = "Integrating Algorithmic Sampling-Based Motion Planning with Learning in Autonomous Driving", journal = j-TIST, volume = "13", number = "3", pages = "39:1--39:27", month = jun, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3469086", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Wed May 25 07:55:15 MDT 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3469086", abstract = "Sampling-based motion planning (SBMP) is a major algorithmic trajectory planning approach in autonomous driving given its high efficiency and outstanding performance in practice. However, driving safety still calls for further refinement of SBMP. In this \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "39", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Fang:2022:GFC, author = "Chenglong Fang and Feng Wang and Bin Yao and Jianqiu Xu", title = "{GPSClean}: a Framework for Cleaning and Repairing {GPS} Data", journal = j-TIST, volume = "13", number = "3", pages = "40:1--40:22", month = jun, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3469088", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Wed May 25 07:55:15 MDT 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3469088", abstract = "The rise of GPS-equipped mobile devices has led to the emergence of big trajectory data. The collected raw data usually contain errors and anomalies information caused by device failure, sensor error, and environment influence. Low-quality data fails to \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "40", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Huang:2022:DRL, author = "Jianbin Huang and Longji Huang and Meijuan Liu and He Li and Qinglin Tan and Xiaoke Ma and Jiangtao Cui and De-Shuang Huang", title = "Deep Reinforcement Learning-based Trajectory Pricing on Ride-hailing Platforms", journal = j-TIST, volume = "13", number = "3", pages = "41:1--41:19", month = jun, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3474841", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Wed May 25 07:55:15 MDT 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3474841", abstract = "Dynamic pricing plays an important role in solving the problems such as traffic load reduction, congestion control, and revenue improvement. Efficient dynamic pricing strategies can increase capacity utilization, total revenue of service providers, and \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "41", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Yao:2022:PPT, author = "Lin Yao and Zhenyu Chen and Haibo Hu and Guowei Wu and Bin Wu", title = "Privacy Preservation for Trajectory Publication Based on Differential Privacy", journal = j-TIST, volume = "13", number = "3", pages = "42:1--42:21", month = jun, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3474839", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Wed May 25 07:55:15 MDT 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3474839", abstract = "With the proliferation of location-aware devices, trajectory data have been used widely in real-life applications. However, trajectory data are often associated with sensitive labels, such as users' purchase transactions and planned activities. As such, \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "42", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Han:2022:ATP, author = "Nan Han and Shaojie Qiao and Kun Yue and Jianbin Huang and Qiang He and Tingting Tang and Faliang Huang and Chunlin He and Chang-An Yuan", title = "Algorithms for Trajectory Points Clustering in Location-based Social Networks", journal = j-TIST, volume = "13", number = "3", pages = "43:1--43:29", month = jun, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3480972", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Wed May 25 07:55:15 MDT 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3480972", abstract = "Recent advances in localization techniques have fundamentally enhanced social networking services, allowing users to share their locations and location-related contents. This has further increased the popularity of location-based social networks (LBSNs) \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "43", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Zheng:2022:UAP, author = "Zhirun Zheng and Zhetao Li and Jie Li and Hongbo Jiang and Tong Li and Bin Guo", title = "Utility-aware and Privacy-preserving Trajectory Synthesis Model that Resists Social Relationship Privacy Attacks", journal = j-TIST, volume = "13", number = "3", pages = "44:1--44:28", month = jun, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3495160", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Wed May 25 07:55:15 MDT 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3495160", abstract = "For academic research and business intelligence, trajectory data has been widely collected and analyzed. Releasing trajectory data to a third party may lead to serious privacy leakage, which has spawned considerable researches on trajectory privacy \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "44", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Kim:2022:NNE, author = "Cheolhyeong Kim and Haeseong Moon and Hyung Ju Hwang", title = "{NEAR}: Neighborhood Edge {AggregatoR} for Graph Classification", journal = j-TIST, volume = "13", number = "3", pages = "45:1--45:17", month = jun, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3506714", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Wed May 25 07:55:15 MDT 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3506714", abstract = "Learning graph-structured data with graph neural networks (GNNs) has been recently emerging as an important field because of its wide applicability in bioinformatics, chemoinformatics, social network analysis, and data mining. Recent GNN algorithms are \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "45", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Wei:2022:WSV, author = "Lili Wei and Congyan Lang and Liqian Liang and Songhe Feng and Tao Wang and Shidi Chen", title = "Weakly Supervised Video Object Segmentation via Dual-attention Cross-branch Fusion", journal = j-TIST, volume = "13", number = "3", pages = "46:1--46:20", month = jun, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3506716", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Wed May 25 07:55:15 MDT 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3506716", abstract = "Recently, concerning the challenge of collecting large-scale explicitly annotated videos, weakly supervised video object segmentation (WSVOS) using video tags has attracted much attention. Existing WSVOS approaches follow a general pipeline including two \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "46", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Yan:2022:CFM, author = "Runze Yan and Xinwen Liu and Janine Dutcher and Michael Tumminia and Daniella Villalba and Sheldon Cohen and David Creswell and Kasey Creswell and Jennifer Mankoff and Anind Dey and Afsaneh Doryab", title = "A Computational Framework for Modeling Biobehavioral Rhythms from Mobile and Wearable Data Streams", journal = j-TIST, volume = "13", number = "3", pages = "47:1--47:27", month = jun, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3510029", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Wed May 25 07:55:15 MDT 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3510029", abstract = "This paper presents a computational framework for modeling biobehavioral rhythms --- the repeating cycles of physiological, psychological, social, and environmental events --- from mobile and wearable data streams. The framework incorporates four main \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "47", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Hu:2022:WCK, author = "Yang Hu and Adriane Chapman and Guihua Wen and Dame Wendy Hall", title = "What Can Knowledge Bring to Machine Learning? --- a Survey of Low-shot Learning for Structured Data", journal = j-TIST, volume = "13", number = "3", pages = "48:1--48:45", month = jun, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3510030", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Wed May 25 07:55:15 MDT 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3510030", abstract = "Supervised machine learning has several drawbacks that make it difficult to use in many situations. Drawbacks include heavy reliance on massive training data, limited generalizability, and poor expressiveness of high-level semantics. Low-shot Learning \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "48", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Lin:2022:TTP, author = "Fandel Lin and Hsun-Ping Hsieh", title = "Traveling Transporter Problem: Arranging a New Circular Route in a Public Transportation System Based on Heterogeneous Non-Monotonic Urban Data", journal = j-TIST, volume = "13", number = "3", pages = "49:1--49:25", month = jun, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3510034", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Wed May 25 07:55:15 MDT 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3510034", abstract = "Hybrid computational intelligent systems that synergize learning-based inference models and route planning strategies have thrived in recent years. In this article, we focus on the non-monotonicity originated from heterogeneous urban data, as well as \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "49", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Gupta:2022:DML, author = "Vinayak Gupta and Srikanta Bedathur", title = "Doing More with Less: Overcoming Data Scarcity for {POI} Recommendation via Cross-Region Transfer", journal = j-TIST, volume = "13", number = "3", pages = "50:1--50:24", month = jun, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3511711", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Wed May 25 07:55:15 MDT 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3511711", abstract = "Variability in social app usage across regions results in a high skew of the quantity and the quality of check-in data collected, which in turn is a challenge for effective location recommender systems. In this article, we present Axolotl (Automated cross. \ldots{})", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "50", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Choudhary:2022:SSS, author = "Nurendra Choudhary and Charu C. Aggarwal and Karthik Subbian and Chandan K. Reddy", title = "Self-supervised Short-text Modeling through Auxiliary Context Generation", journal = j-TIST, volume = "13", number = "3", pages = "51:1--51:21", month = jun, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3511712", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Wed May 25 07:55:15 MDT 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3511712", abstract = "Short text is ambiguous and often relies predominantly on the domain and context at hand in order to attain semantic relevance. Existing classification models perform poorly on short text due to data sparsity and inadequate context. Auxiliary context, \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "51", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Yang:2022:ISI, author = "Qiang Yang and Yongxin Tong and Yang Liu and Yangqiu Song and Hao Peng and Boi Faltings", title = "Introduction to the Special Issue on the Federated Learning: Algorithms, Systems, and Applications: {Part 1}", journal = j-TIST, volume = "13", number = "4", pages = "52:1--52:??", month = aug, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3514223", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Oct 29 07:22:17 MDT 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3514223", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "52", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Zhou:2022:TSP, author = "Jun Zhou and Longfei Zheng and Chaochao Chen and Yan Wang and Xiaolin Zheng and Bingzhe Wu and Cen Chen and Li Wang and Jianwei Yin", title = "Toward Scalable and Privacy-preserving Deep Neural Network via Algorithmic-Cryptographic Co-design", journal = j-TIST, volume = "13", number = "4", pages = "53:1--53:??", month = aug, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3501809", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Oct 29 07:22:17 MDT 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3501809", abstract = "Deep Neural Networks (DNNs) have achieved remarkable progress in various real-world applications, especially when abundant training data are provided. However, data isolation has become a serious problem currently. Existing works build privacy-preserving \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "53", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Antunes:2022:FLH, author = "Rodolfo Stoffel Antunes and Cristiano Andr{\'e} da Costa and Arne K{\"u}derle and Imrana Abdullahi Yari and Bj{\"o}rn Eskofier", title = "Federated Learning for Healthcare: Systematic Review and Architecture Proposal", journal = j-TIST, volume = "13", number = "4", pages = "54:1--54:??", month = aug, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3501813", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Oct 29 07:22:17 MDT 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3501813", abstract = "The use of machine learning (ML) with electronic health records (EHR) is growing in popularity as a means to extract knowledge that can improve the decision-making process in healthcare. Such methods require training of high-quality learning models based \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "54", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Liu:2022:FSR, author = "Zhiwei Liu and Liangwei Yang and Ziwei Fan and Hao Peng and Philip S. Yu", title = "Federated Social Recommendation with Graph Neural Network", journal = j-TIST, volume = "13", number = "4", pages = "55:1--55:??", month = aug, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3501815", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Oct 29 07:22:17 MDT 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3501815", abstract = "Recommender systems have become prosperous nowadays, designed to predict users' potential interests in items by learning embeddings. Recent developments of the Graph Neural Networks (GNNs) also provide recommender systems (RSs) with powerful backbones to \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "55", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Jiang:2022:FDG, author = "Meng Jiang and Taeho Jung and Ryan Karl and Tong Zhao", title = "Federated Dynamic Graph Neural Networks with Secure Aggregation for Video-based Distributed Surveillance", journal = j-TIST, volume = "13", number = "4", pages = "56:1--56:??", month = aug, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3501808", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Oct 29 07:22:17 MDT 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3501808", abstract = "Distributed surveillance systems have the ability to detect, track, and snapshot objects moving around in a certain space. The systems generate video data from multiple personal devices or street cameras. Intelligent video-analysis models are needed to \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "56", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Hu:2022:DAF, author = "Ziheng Hu and Hongtao Xie and Lingyun Yu and Xingyu Gao and Zhihua Shang and Yongdong Zhang", title = "Dynamic-Aware Federated Learning for Face Forgery Video Detection", journal = j-TIST, volume = "13", number = "4", pages = "57:1--57:??", month = aug, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3501814", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Oct 29 07:22:17 MDT 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3501814", abstract = "The spread of face forgery videos is a serious threat to information credibility, calling for effective detection algorithms to identify them. Most existing methods have assumed a shared or centralized training set. However, in practice, data may be \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "57", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Ren:2022:IAV, author = "Zhenghang Ren and Liu Yang and Kai Chen", title = "Improving Availability of Vertical Federated Learning: Relaxing Inference on Non-overlapping Data", journal = j-TIST, volume = "13", number = "4", pages = "58:1--58:??", month = aug, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3501817", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Oct 29 07:22:17 MDT 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3501817", abstract = "Vertical Federated Learning (VFL) enables multiple parties to collaboratively train a machine learning model over vertically distributed datasets without data privacy leakage. However, there is a limitation of the current VFL solutions: current VFL models \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "58", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Chai:2022:EFM, author = "Di Chai and Leye Wang and Kai Chen and Qiang Yang", title = "Efficient Federated Matrix Factorization Against Inference Attacks", journal = j-TIST, volume = "13", number = "4", pages = "59:1--59:??", month = aug, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3501812", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Oct 29 07:22:17 MDT 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3501812", abstract = "Recommender systems typically require the revelation of users' ratings to the recommender server, which will subsequently use these ratings to provide personalized services. However, such revelations make users vulnerable to a broader set of inference \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "59", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Liu:2022:GSE, author = "Zelei Liu and Yuanyuan Chen and Han Yu and Yang Liu and Lizhen Cui", title = "{GTG-Shapley}: Efficient and Accurate Participant Contribution Evaluation in Federated Learning", journal = j-TIST, volume = "13", number = "4", pages = "60:1--60:??", month = aug, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3501811", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Oct 29 07:22:17 MDT 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3501811", abstract = "Federated Learning (FL) bridges the gap between collaborative machine learning and preserving data privacy. To sustain the long-term operation of an FL ecosystem, it is important to attract high-quality data owners with appropriate incentive schemes. As \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "60", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Che:2022:FMV, author = "Sicong Che and Zhaoming Kong and Hao Peng and Lichao Sun and Alex Leow and Yong Chen and Lifang He", title = "Federated Multi-view Learning for Private Medical Data Integration and Analysis", journal = j-TIST, volume = "13", number = "4", pages = "61:1--61:??", month = aug, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3501816", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Oct 29 07:22:17 MDT 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3501816", abstract = "Along with the rapid expansion of information technology and digitalization of health data, there is an increasing concern on maintaining data privacy while garnering the benefits in the medical field. Two critical challenges are identified: First, \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "61", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Wu:2022:FFN, author = "Chuhan Wu and Fangzhao Wu and Lingjuan Lyu and Yongfeng Huang and Xing Xie", title = "{FedCTR}: Federated Native Ad {CTR} Prediction with Cross-platform User Behavior Data", journal = j-TIST, volume = "13", number = "4", pages = "62:1--62:??", month = aug, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3506715", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Oct 29 07:22:17 MDT 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3506715", abstract = "Native ad is a popular type of online advertisement that has similar forms with the native content displayed on websites. Native ad click-through rate (CTR) prediction is useful for improving user experience and platform revenue. However, it is \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "62", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Hu:2022:OBS, author = "Sixu Hu and Yuan Li and Xu Liu and Qinbin Li and Zhaomin Wu and Bingsheng He", title = "The {OARF} Benchmark Suite: Characterization and Implications for Federated Learning Systems", journal = j-TIST, volume = "13", number = "4", pages = "63:1--63:??", month = aug, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3510540", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Oct 29 07:22:17 MDT 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3510540", abstract = "This article presents and characterizes an Open Application Repository for Federated Learning (OARF), a benchmark suite for federated machine learning systems. Previously available benchmarks for federated learning (FL) have focused mainly on synthetic \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "63", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Kang:2022:FSS, author = "Yan Kang and Yang Liu and Xinle Liang", title = "{FedCVT}: Semi-supervised Vertical Federated Learning with Cross-view Training", journal = j-TIST, volume = "13", number = "4", pages = "64:1--64:??", month = aug, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3510031", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Oct 29 07:22:17 MDT 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3510031", abstract = "Federated learning allows multiple parties to build machine learning models collaboratively without exposing data. In particular, vertical federated learning (VFL) enables participating parties to build a joint machine learning model based upon \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "64", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Ren:2022:GGR, author = "Hanchi Ren and Jingjing Deng and Xianghua Xie", title = "{GRNN}: Generative Regression Neural Network --- a Data Leakage Attack for Federated Learning", journal = j-TIST, volume = "13", number = "4", pages = "65:1--65:??", month = aug, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3510032", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Oct 29 07:22:17 MDT 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3510032", abstract = "Data privacy has become an increasingly important issue in Machine Learning (ML), where many approaches have been developed to tackle this challenge, e.g., cryptography (Homomorphic Encryption (HE), Differential Privacy (DP)) and collaborative training \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "65", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Tian:2022:FWF, author = "Yuanyishu Tian and Yao Wan and Lingjuan Lyu and Dezhong Yao and Hai Jin and Lichao Sun", title = "{FedBERT}: When Federated Learning Meets Pre-training", journal = j-TIST, volume = "13", number = "4", pages = "66:1--66:??", month = aug, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3510033", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Oct 29 07:22:17 MDT 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3510033", abstract = "The fast growth of pre-trained models (PTMs) has brought natural language processing to a new era, which has become a dominant technique for various natural language processing (NLP) applications. Every user can download the weights of PTMs, then fine-. \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "66", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Mao:2022:CEF, author = "Yuzhu Mao and Zihao Zhao and Guangfeng Yan and Yang Liu and Tian Lan and Linqi Song and Wenbo Ding", title = "Communication-Efficient Federated Learning with Adaptive Quantization", journal = j-TIST, volume = "13", number = "4", pages = "67:1--67:??", month = aug, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3510587", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Oct 29 07:22:17 MDT 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3510587", abstract = "Federated learning (FL) has attracted tremendous attentions in recent years due to its privacy-preserving measures and great potential in some distributed but privacy-sensitive applications, such as finance and health. However, high communication \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "67", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Guo:2022:FLP, author = "Xu Guo and Han Yu and Boyang Li and Hao Wang and Pengwei Xing and Siwei Feng and Zaiqing Nie and Chunyan Miao", title = "Federated Learning for Personalized Humor Recognition", journal = j-TIST, volume = "13", number = "4", pages = "68:1--68:??", month = aug, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3511710", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Oct 29 07:22:17 MDT 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3511710", abstract = "Computational understanding of humor is an important topic under creative language understanding and modeling. It can play a key role in complex human-AI interactions. The challenge here is that human perception of humorous content is highly subjective. \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "68", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Yang:2022:PFL, author = "Qiang Yang and Yongxin Tong and Yang Liu and Yangqiu Song and Hao Peng and Boi Faltings", title = "Preface to Federated Learning: Algorithms, Systems, and Applications: {Part 2}", journal = j-TIST, volume = "13", number = "5", pages = "69:1--69:??", month = oct, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3536420", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Oct 29 07:22:19 MDT 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3536420", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "69", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Xu:2022:PPA, author = "Xiaolong Xu and Wentao Liu and Yulan Zhang and Xuyun Zhang and Wanchun Dou and Lianyong Qi and Md Zakirul Alam Bhuiyan", title = "{PSDF}: Privacy-aware {IoV} Service Deployment with Federated Learning in Cloud-Edge Computing", journal = j-TIST, volume = "13", number = "5", pages = "70:1--70:??", month = oct, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3501810", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Oct 29 07:22:19 MDT 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3501810", abstract = "Through the collaboration of cloud and edge, cloud-edge computing allows the edge that approximates end-users undertakes those non-computationally intensive service processing of the cloud, reducing the communication overhead and satisfying the low \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "70", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Zhong:2022:FHF, author = "Zhengyi Zhong and Weidong Bao and Ji Wang and Xiaomin Zhu and Xiongtao Zhang", title = "{FLEE}: a Hierarchical Federated Learning Framework for Distributed Deep Neural Network over Cloud, Edge, and End Device", journal = j-TIST, volume = "13", number = "5", pages = "71:1--71:??", month = oct, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3514501", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Oct 29 07:22:19 MDT 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3514501", abstract = "With the development of smart devices, the computing capabilities of portable end devices such as mobile phones have been greatly enhanced. Meanwhile, traditional cloud computing faces great challenges caused by privacy-leakage and time-delay problems, \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "71", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Dang:2022:FLE, author = "Trung Kien Dang and Xiang Lan and Jianshu Weng and Mengling Feng", title = "Federated Learning for Electronic Health Records", journal = j-TIST, volume = "13", number = "5", pages = "72:1--72:??", month = oct, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3514500", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Oct 29 07:22:19 MDT 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3514500", abstract = "In data-driven medical research, multi-center studies have long been preferred over single-center ones due to a single institute sometimes not having enough data to obtain sufficient statistical power for certain hypothesis testings as well as predictive \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "72", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Li:2022:AWR, author = "Shenghui Li and Edith Ngai and Fanghua Ye and Thiemo Voigt", title = "Auto-weighted Robust Federated Learning with Corrupted Data Sources", journal = j-TIST, volume = "13", number = "5", pages = "73:1--73:??", month = oct, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3517821", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Oct 29 07:22:19 MDT 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3517821", abstract = "Federated learning provides a communication-efficient and privacy-preserving training process by enabling learning statistical models with massive participants without accessing their local data. Standard federated learning techniques that naively \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "73", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Jiang:2022:SFL, author = "Xue Jiang and Xuebing Zhou and Jens Grossklags", title = "{SignDS-FL}: Local Differentially Private Federated Learning with Sign-based Dimension Selection", journal = j-TIST, volume = "13", number = "5", pages = "74:1--74:??", month = oct, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3517820", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Oct 29 07:22:19 MDT 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3517820", abstract = "Federated Learning (FL) [ 31 ] is a decentralized learning mechanism that has attracted increasing attention due to its achievements in computational efficiency and privacy preservation. However, recent research highlights that the original FL framework may \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "74", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Zeng:2022:CCB, author = "Bixiao Zeng and Xiaodong Yang and Yiqiang Chen and Hanchao Yu and Yingwei Zhang", title = "{CLC}: a Consensus-based Label Correction Approach in Federated Learning", journal = j-TIST, volume = "13", number = "5", pages = "75:1--75:??", month = oct, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3519311", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Oct 29 07:22:19 MDT 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3519311", abstract = "Federated learning (FL) is a novel distributed learning framework where multiple participants collaboratively train a global model without sharing any raw data to preserve privacy. However, data quality may vary among the participants, the most typical of \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "75", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Chen:2022:DAP, author = "Chien-Lun Chen and Sara Babakniya and Marco Paolieri and Leana Golubchik", title = "Defending against Poisoning Backdoor Attacks on Federated Meta-learning", journal = j-TIST, volume = "13", number = "5", pages = "76:1--76:??", month = oct, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3523062", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Oct 29 07:22:19 MDT 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3523062", abstract = "Federated learning allows multiple users to collaboratively train a shared classification model while preserving data privacy. This approach, where model updates are aggregated by a central server, was shown to be vulnerable to poisoning backdoor attacks: \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "76", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Xie:2022:ELF, author = "Lunchen Xie and Jiaqi Liu and Songtao Lu and Tsung-Hui Chang and Qingjiang Shi", title = "An Efficient Learning Framework for Federated {XGBoost} Using Secret Sharing and Distributed Optimization", journal = j-TIST, volume = "13", number = "5", pages = "77:1--77:??", month = oct, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3523061", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Oct 29 07:22:19 MDT 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3523061", abstract = "XGBoost is one of the most widely used machine learning models in the industry due to its superior learning accuracy and efficiency. Targeting at data isolation issues in the big data problems, it is crucial to deploy a secure and efficient federated \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "77", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Stripelis:2022:SSF, author = "Dimitris Stripelis and Paul M. Thompson and Jos{\'e} Luis Ambite", title = "Semi-Synchronous Federated Learning for Energy-Efficient Training and Accelerated Convergence in Cross-Silo Settings", journal = j-TIST, volume = "13", number = "5", pages = "78:1--78:??", month = oct, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3524885", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Oct 29 07:22:19 MDT 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3524885", abstract = "There are situations where data relevant to machine learning problems are distributed across multiple locations that cannot share the data due to regulatory, competitiveness, or privacy reasons. Machine learning approaches that require data to be copied \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "78", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Damaskinos:2022:FOF, author = "Georgios Damaskinos and Rachid Guerraoui and Anne-Marie Kermarrec and Vlad Nitu and Rhicheek Patra and Fran{\c{c}}ois Taiani", title = "{FLeet}: Online Federated Learning via Staleness Awareness and Performance Prediction", journal = j-TIST, volume = "13", number = "5", pages = "79:1--79:??", month = oct, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3527621", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Oct 29 07:22:19 MDT 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3527621", abstract = "Federated learning (FL) is very appealing for its privacy benefits: essentially, a global model is trained with updates computed on mobile devices while keeping the data of users local. Standard FL infrastructures are however designed to have no energy or \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "79", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Liu:2022:FMT, author = "Yijing Liu and Dongming Han and Jianwei Zhang and Haiyang Zhu and Mingliang Xu and Wei Chen", title = "Federated Multi-task Graph Learning", journal = j-TIST, volume = "13", number = "5", pages = "80:1--80:??", month = oct, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3527622", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Oct 29 07:22:19 MDT 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3527622", abstract = "Distributed processing and analysis of large-scale graph data remain challenging because of the high-level discrepancy among graphs. This study investigates a novel subproblem: the distributed multi-task learning on the graph, which jointly learns \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "80", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Li:2022:CFP, author = "Fuxian Li and Jie Feng and Huan Yan and Depeng Jin and Yong Li", title = "Crowd Flow Prediction for Irregular Regions with Semantic Graph Attention Network", journal = j-TIST, volume = "13", number = "5", pages = "81:1--81:??", month = oct, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3501805", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Oct 29 07:22:19 MDT 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3501805", abstract = "It is essential to predict crowd flow precisely in a city, which is practically partitioned into irregular regions based on road networks and functionality. However, prior works mainly focus on grid-based crowd flow prediction, where a city is divided \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "81", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Wang:2022:GBS, author = "Zongwei Wang and Min Gao and Jundong Li and Junwei Zhang and Jiang Zhong", title = "Gray-Box Shilling Attack: an Adversarial Learning Approach", journal = j-TIST, volume = "13", number = "5", pages = "82:1--82:??", month = oct, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3512352", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Oct 29 07:22:19 MDT 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3512352", abstract = "Recommender systems are essential components of many information services, which aim to find relevant items that match user preferences. Several studies have shown that shilling attacks can significantly weaken the robustness of recommender systems by \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "82", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Di:2022:FSR, author = "Kai Di and Yifeng Zhou and Fuhan Yan and Jiuchuan Jiang and Shaofu Yang and Yichuan Jiang", title = "A Foraging Strategy with Risk Response for Individual Robots in Adversarial Environments", journal = j-TIST, volume = "13", number = "5", pages = "83:1--83:??", month = oct, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3514499", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Oct 29 07:22:19 MDT 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3514499", abstract = "As an essential problem in robotics, foraging means that robots collect objects from a given environment and return them to a specified location. On many occasions, robots are required to perform foraging tasks in adversarial environments, such as \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "83", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Rai:2022:RSC, author = "Sawan Rai and Ramesh Chandra Belwal and Atul Gupta", title = "A Review on Source Code Documentation", journal = j-TIST, volume = "13", number = "5", pages = "84:1--84:??", month = oct, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3519312", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Oct 29 07:22:19 MDT 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3519312", abstract = "Context: Coding is an incremental activity where a developer may need to understand a code before making suitable changes in the code. Code documentation is considered one of the best practices in software development but requires significant efforts from \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "84", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Chen:2022:IIN, author = "Xiaoyu Chen and Yingyan Zeng and Sungku Kang and Ran Jin", title = "{INN}: an Interpretable Neural Network for {AI} Incubation in Manufacturing", journal = j-TIST, volume = "13", number = "5", pages = "85:1--85:??", month = oct, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3519313", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Oct 29 07:22:19 MDT 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3519313", abstract = "Both artificial intelligence (AI) and domain knowledge from human experts play an important role in manufacturing decision making. Smart manufacturing emphasizes a fully automated data-driven decision-making; however, the AI incubation process involves \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "85", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Li:2022:DPG, author = "Ke Li and Bin Guo and Jiaqi Liu and Jiangtao Wang and Haoyang Ren and Fei Yi and Zhiwen Yu", title = "Dynamic Probabilistic Graphical Model for Progressive Fake News Detection on Social Media Platform", journal = j-TIST, volume = "13", number = "5", pages = "86:1--86:??", month = oct, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3523060", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Oct 29 07:22:19 MDT 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3523060", abstract = "Recently, fake news has been readily spread by massive amounts of users in social media, and automatic fake news detection has become necessary. The existing works need to prepare the overall data to perform detection, losing important information about \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "86", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Verma:2022:IDB, author = "Rohit Verma and Sugandh Pargal and Debasree Das and Tanusree Parbat and Sai Shankar Kambalapalli and Bivas Mitra and Sandip Chakraborty", title = "Impact of Driving Behavior on {Commuter}'s Comfort During Cab Rides: Towards a New Perspective of Driver Rating", journal = j-TIST, volume = "13", number = "6", pages = "87:1--87:??", month = dec, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3523063", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Mar 11 08:47:22 MST 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3523063", abstract = "Commuter comfort in cab rides affects driver rating as well as the reputation of ride-hailing firms like Uber/Lyft. Existing research has revealed that commuter comfort not only varies at a personalized level but also is perceived differently on different \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "87", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Zhang:2022:IPI, author = "Lin Zhang and Lixin Fan and Yong Luo and Ling-Yu Duan", title = "Intrinsic Performance Influence-based Participant Contribution Estimation for Horizontal Federated Learning", journal = j-TIST, volume = "13", number = "6", pages = "88:1--88:??", month = dec, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3523059", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Mar 11 08:47:22 MST 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3523059", abstract = "The rapid development of modern artificial intelligence technique is mainly attributed to sufficient and high-quality data. However, in the data collection, personal privacy is at risk of being leaked. This issue can be addressed by federated learning, \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "88", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Ren:2022:DHC, author = "Siyuan Ren and Bin Guo and Longbing Cao and Ke Li and Jiaqi Liu and Zhiwen Yu", title = "{DeepExpress}: Heterogeneous and Coupled Sequence Modeling for Express Delivery Prediction", journal = j-TIST, volume = "13", number = "6", pages = "89:1--89:??", month = dec, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3526087", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Mar 11 08:47:22 MST 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3526087", abstract = "The prediction of express delivery sequence, i.e., modeling and estimating the volumes of daily incoming and outgoing parcels for delivery, is critical for online business, logistics, and positive customer experience, and specifically for resource \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "89", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Zheng:2022:JOE, author = "Qian Zheng and Yueming Wang and Zhenfang Hu and Xiaobo Zhang and Zhaohui Wu and Gang Pan", title = "Jointly Optimizing Expressional and Residual Models for {$3$D} Facial Expression Removal", journal = j-TIST, volume = "13", number = "6", pages = "90:1--90:??", month = dec, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3533312", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Mar 11 08:47:22 MST 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3533312", abstract = "This article proposes a facial expression removal method to recover a 3D neutral face from a single 3D expressional or non-neutral face. We treat a 3D non-neutral face as the sum of its neutral one and the residual. This can be satisfied if the \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "90", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Hammedi:2022:TLO, author = "Wided Hammedi and Sidi Mohammed Senouci and Philippe Brunet and Metzli Ramirez-Martinez", title = "Two-Level Optimization to Reduce Waiting Time at Locks in Inland Waterway Transportation", journal = j-TIST, volume = "13", number = "6", pages = "91:1--91:??", month = dec, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3527822", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Mar 11 08:47:22 MST 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3527822", abstract = "Inland vessels often have to cross numerous locks before reaching their final destination, which leads to a significant delay and sometimes represents as much as half of the total travel time. The delay affects shipment costs and can affect other parts of \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "91", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Elhamod:2022:CPL, author = "Mohannad Elhamod and Jie Bu and Christopher Singh and Matthew Redell and Abantika Ghosh and Viktor Podolskiy and Wei-Cheng Lee and Anuj Karpatne", title = "{CoPhy-PGNN}: Learning Physics-guided Neural Networks with Competing Loss Functions for Solving Eigenvalue Problems", journal = j-TIST, volume = "13", number = "6", pages = "92:1--92:??", month = dec, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3530911", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Mar 11 08:47:22 MST 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3530911", abstract = "Physics-guided Neural Networks (PGNNs) represent an emerging class of neural networks that are trained using physics-guided (PG) loss functions (capturing violations in network outputs with known physics), along with the supervision contained in data. \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "92", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Ding:2022:MME, author = "Yasan Ding and Bin Guo and Yan Liu and Yunji Liang and Haocheng Shen and Zhiwen Yu", title = "{MetaDetector}: Meta Event Knowledge Transfer for Fake News Detection", journal = j-TIST, volume = "13", number = "6", pages = "93:1--93:??", month = dec, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3532851", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Mar 11 08:47:22 MST 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3532851", abstract = "The blooming of fake news on social networks has devastating impacts on society, the economy, and public security. Although numerous studies are conducted for the automatic detection of fake news, the majority tend to utilize deep neural networks to learn \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "93", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Zhang:2022:CST, author = "Yao Zhang and Wenping Fan and Qichen Hao and Xinya Wu and Min-Ling Zhang", title = "{CAFE} and {SOUP}: Toward Adaptive {VDI} Workload Prediction", journal = j-TIST, volume = "13", number = "6", pages = "94:1--94:??", month = dec, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3529536", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Mar 11 08:47:22 MST 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3529536", abstract = "For Virtual Desktop Infrastructure (VDI) system, effective resource management is rather important where turning off spare virtual machines would help save running cost while maintaining sufficient virtual machines is essential to secure satisfactory user \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "94", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Dong:2022:HAR, author = "Junyi Dong and Qingze Huo and Silvia Ferrari", title = "A Holistic Approach for Role Inference and Action Anticipation in Human Teams", journal = j-TIST, volume = "13", number = "6", pages = "95:1--95:??", month = dec, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3531230", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Mar 11 08:47:22 MST 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3531230", abstract = "The ability to anticipate human actions is critical to many cyber-physical systems, such as robots and autonomous vehicles. Computer vision and sensing algorithms to date have focused on extracting and predicting visual features that are explicit in the \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "95", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Hografer:2022:SEP, author = "Marius Hogr{\"a}fer and Marco Angelini and Giuseppe Santucci and Hans-J{\"o}rg Schulz", title = "Steering-by-example for Progressive Visual Analytics", journal = j-TIST, volume = "13", number = "6", pages = "96:1--96:??", month = dec, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3531229", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Mar 11 08:47:22 MST 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3531229", abstract = "Progressive visual analytics allows users to interact with early, partial results of long-running computations on large datasets. In this context, computational steering is often brought up as a means to prioritize the progressive computation. This is \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "96", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Wu:2022:RLV, author = "Xian Wu and Chao Huang and Pablo Robles-Granda and Nitesh V. Chawla", title = "Representation Learning on Variable Length and Incomplete Wearable-Sensory Time Series", journal = j-TIST, volume = "13", number = "6", pages = "97:1--97:??", month = dec, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3531228", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Mar 11 08:47:22 MST 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3531228", abstract = "The prevalence of wearable sensors (e.g., smart wristband) is creating unprecedented opportunities to not only inform health and wellness states of individuals, but also assess and infer personal attributes, including demographic and personality \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "97", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Ceh-Varela:2022:PEA, author = "Edgar Ceh-Varela and Huiping Cao and Hady W. Lauw", title = "Performance Evaluation of Aggregation-based Group Recommender Systems for Ephemeral Groups", journal = j-TIST, volume = "13", number = "6", pages = "98:1--98:??", month = dec, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3542804", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Mar 11 08:47:22 MST 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3542804", abstract = "Recommender Systems ( RecSys ) provide suggestions in many decision-making processes. Given that groups of people can perform many real-world activities (e.g., a group of people attending a conference looking for a place to dine), the need for \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "98", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Das:2022:CSF, author = "Anirban Das and Timothy Castiglia and Shiqiang Wang and Stacy Patterson", title = "Cross-Silo Federated Learning for Multi-Tier Networks with Vertical and Horizontal Data Partitioning", journal = j-TIST, volume = "13", number = "6", pages = "99:1--99:??", month = dec, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3543433", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Mar 11 08:47:22 MST 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3543433", abstract = "We consider federated learning in tiered communication networks. Our network model consists of a set of silos, each holding a vertical partition of the data. Each silo contains a hub and a set of clients, with the silo's vertical data shard partitioned \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "99", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Navia-Vazquez:2022:BDS, author = "A. Navia-V{\'a}zquez and R. D{\'\i}az-Morales and M. Fern{\'a}ndez-D{\'\i}az", title = "Budget Distributed Support Vector Machine for Non-{ID} Federated Learning Scenarios", journal = j-TIST, volume = "13", number = "6", pages = "100:1--100:??", month = dec, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3539734", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Mar 11 08:47:22 MST 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3539734", abstract = "In recent years, there has been remarkable growth in Federated Learning (FL) approaches because they have proven to be very effective in training large Machine Learning (ML) models and also serve to preserve data confidentiality, as recommended by the \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "100", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Hu:2022:DET, author = "Yue Hu and Ao Qu and Dan Work", title = "Detecting Extreme Traffic Events Via a Context Augmented Graph Autoencoder", journal = j-TIST, volume = "13", number = "6", pages = "101:1--101:??", month = dec, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3539735", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Mar 11 08:47:22 MST 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3539735", abstract = "Accurate and timely detection of large events on urban transportation networks enables informed mobility management. This work tackles the problem of extreme event detection on large-scale transportation networks using origin-destination mobility data, \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "101", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Tassa:2022:PPC, author = "Tamir Tassa and Alon {Ben Horin}", title = "Privacy-preserving Collaborative Filtering by Distributed Mediation", journal = j-TIST, volume = "13", number = "6", pages = "102:1--102:??", month = dec, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3542950", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Mar 11 08:47:22 MST 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3542950", abstract = "Recommender systems have become very influential in our everyday decision making, e.g., helping us choose a movie from a content platform, or offering us suitable products on e-commerce websites. While most vendors who utilize recommender systems rely \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "102", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Gupta:2022:MCT, author = "Vinayak Gupta and Srikanta Bedathur and Sourangshu Bhattacharya and Abir De", title = "Modeling Continuous Time Sequences with Intermittent Observations using Marked Temporal Point Processes", journal = j-TIST, volume = "13", number = "6", pages = "103:1--103:??", month = dec, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3545118", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Mar 11 08:47:22 MST 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3545118", abstract = "A large fraction of data generated via human activities such as online purchases, health records, spatial mobility, etc. can be represented as a sequence of events over a continuous-time. Learning deep learning models over these continuous-time event \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "103", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Ou:2022:AAE, author = "Jinxiang Ou and Yunheng Shen and Feng Wang and Qiao Liu and Xuegong Zhang and Hairong Lv", title = "{AggEnhance}: Aggregation Enhancement by Class Interior Points in Federated Learning with Non-{IID} Data", journal = j-TIST, volume = "13", number = "6", pages = "104:1--104:??", month = dec, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3544495", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Mar 11 08:47:22 MST 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3544495", abstract = "Federated learning (FL) is a privacy-preserving paradigm for multi-institutional collaborations, where the aggregation is an essential procedure after training on the local datasets. Conventional aggregation algorithms often apply a weighted averaging of \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "104", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Costa:2022:SCN, author = "Miguel Costa and Diogo Costa and Tiago Gomes and Sandro Pinto", title = "Shifting Capsule Networks from the Cloud to the Deep Edge", journal = j-TIST, volume = "13", number = "6", pages = "105:1--105:??", month = dec, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3544562", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Mar 11 08:47:22 MST 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3544562", abstract = "Capsule networks (CapsNets) are an emerging trend in image processing. In contrast to a convolutional neural network, CapsNets are not vulnerable to object deformation, as the relative spatial information of the objects is preserved across the network. \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "105", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Zhang:2023:NFL, author = "Xiaojin Zhang and Hanlin Gu and Lixin Fan and Kai Chen and Qiang Yang", title = "No Free Lunch Theorem for Security and Utility in Federated Learning", journal = j-TIST, volume = "14", number = "1", pages = "1:1--1:??", month = feb, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3563219", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Mar 11 08:47:24 MST 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3563219", abstract = "In a federated learning scenario where multiple parties jointly learn a model from their respective data, there exist two conflicting goals for the choice of appropriate algorithms. On one hand, private and sensitive training data must be kept secure as \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "1", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Shao:2023:IIT, author = "Erzhuo Shao and Zhenyu Han and Yulai Xie and Yang Zhang and Lu Geng and Yong Li", title = "Interior Individual Trajectory Simulation with Population Distribution Constraint", journal = j-TIST, volume = "14", number = "1", pages = "2:1--2:??", month = feb, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3529108", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Mar 11 08:47:24 MST 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3529108", abstract = "Individual trajectory generation plays an important role in simulation tasks, reconstructing fine-grained mobility behaviors that can be used to evaluate epidemic risks, congestion risks, or commercial profit. Previous research works adopt the Newton's \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "2", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Danzinger:2023:SAI, author = "Philipp Danzinger and Tobias Geibinger and David Janneau and Florian Mischek and Nysret Musliu and Christian Poschalko", title = "A System for Automated Industrial Test Laboratory Scheduling", journal = j-TIST, volume = "14", number = "1", pages = "3:1--3:??", month = feb, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3546871", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Mar 11 08:47:24 MST 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3546871", abstract = "Automated scheduling solutions are tremendously important for the efficient operation of industrial laboratories. The Test Laboratory Scheduling Problem (TLSP) is an extension of the well-known Resource Constrained Project Scheduling Problem (RCPSP) and \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "3", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Liu:2023:TAC, author = "Haochen Liu and Yiqi Wang and Wenqi Fan and Xiaorui Liu and Yaxin Li and Shaili Jain and Yunhao Liu and Anil Jain and Jiliang Tang", title = "Trustworthy {AI}: a Computational Perspective", journal = j-TIST, volume = "14", number = "1", pages = "4:1--4:??", month = feb, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3546872", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Mar 11 08:47:24 MST 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3546872", abstract = "In the past few decades, artificial intelligence (AI) technology has experienced swift developments, changing everyone's daily life and profoundly altering the course of human society. The intention behind developing AI was and is to benefit humans by \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "4", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Li:2023:ALC, author = "Pan Li and Brian Brost and Alexander Tuzhilin", title = "Adversarial Learning for Cross Domain Recommendations", journal = j-TIST, volume = "14", number = "1", pages = "5:1--5:??", month = feb, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3548776", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Mar 11 08:47:24 MST 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3548776", abstract = "Existing cross domain recommender systems typically assume homogeneous user preferences across multiple domains to capture similarities of user-item interactions and to provide cross domain recommendations accordingly. Meanwhile, the heterogeneity of user \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "5", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Chen:2023:IDM, author = "Shaohan Chen and Chuanhou Gao and Ping Zhang", title = "Incorporation of Data-Mined Knowledge into Black-Box {SVM} for Interpretability", journal = j-TIST, volume = "14", number = "1", pages = "6:1--6:??", month = feb, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3548775", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Mar 11 08:47:24 MST 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3548775", abstract = "The lack of interpretability often makes black-box models challenging to be applied in many practical domains. For this reason, the current work, from the black-box model input port, proposes to incorporate data-mined knowledge into the black-box soft-. \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "6", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Wang:2023:HSI, author = "Wei-Yao Wang and Teng-Fong Chan and Wen-Chih Peng and Hui-Kuo Yang and Chih-Chuan Wang and Yao-Chung Fan", title = "How Is the Stroke? {Inferring} Shot Influence in Badminton Matches via Long Short-term Dependencies", journal = j-TIST, volume = "14", number = "1", pages = "7:1--7:??", month = feb, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3551391", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Mar 11 08:47:24 MST 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3551391", abstract = "Identifying significant shots in a rally is important for evaluating players' performance in badminton matches. While there are several studies that have quantified player performance in other sports, analyzing badminton data has remained untouched. In \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "7", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Hao:2023:HMA, author = "Qianyue Hao and Fengli Xu and Lin Chen and Pan Hui and Yong Li", title = "Hierarchical Multi-agent Model for Reinforced Medical Resource Allocation with Imperfect Information", journal = j-TIST, volume = "14", number = "1", pages = "8:1--8:??", month = feb, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3552436", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Mar 11 08:47:24 MST 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3552436", abstract = "With the advent of the COVID-19 pandemic, the shortage in medical resources became increasingly more evident. Therefore, efficient strategies for medical resource allocation are urgently needed. However, conventional rule-based methods employed by public \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "8", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Qin:2023:DGA, author = "Xin Qin and Jindong Wang and Yiqiang Chen and Wang Lu and Xinlong Jiang", title = "Domain Generalization for Activity Recognition via Adaptive Feature Fusion", journal = j-TIST, volume = "14", number = "1", pages = "9:1--9:??", month = feb, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3552434", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Mar 11 08:47:24 MST 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3552434", abstract = "Human activity recognition requires the efforts to build a generalizable model using the training datasets with the hope to achieve good performance in test datasets. However, in real applications, the training and testing datasets may have totally \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "9", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Freitas:2023:DLE, author = "Lucas Freitas and Valter Martins and Marilton de Aguiar and Lisane de Brisolara and Paulo Ferreira", title = "Deep Learning Embedded into Smart Traps for Fruit Insect Pests Detection", journal = j-TIST, volume = "14", number = "1", pages = "10:1--10:??", month = feb, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3552435", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Mar 11 08:47:24 MST 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3552435", abstract = "This article presents a novel approach to identify two species of fruit insect pests as part of a network of intelligent traps designed to monitor the population of these insects in a plantation. The proposed approach uses a simple Digital Image \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "10", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Yang:2023:SSD, author = "Wenlu Yang and Hongjun Wang and Yinghui Zhang and Zehao Liu and Tianrui Li", title = "Self-supervised Discriminative Representation Learning by Fuzzy Autoencoder", journal = j-TIST, volume = "14", number = "1", pages = "11:1--11:??", month = feb, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3555777", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Mar 11 08:47:24 MST 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3555777", abstract = "Representation learning based on autoencoders has received great concern for its potential ability to capture valuable latent information. Conventional autoencoders pursue minimal reconstruction error, but in most machine learning tasks such as \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "11", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Xu:2023:QOR, author = "Jianqiu Xu and Hua Lu and Zhifeng Bao", title = "A Query Optimizer for Range Queries over Multi-Attribute Trajectories", journal = j-TIST, volume = "14", number = "1", pages = "12:1--12:??", month = feb, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3555811", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Mar 11 08:47:24 MST 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3555811", abstract = "A multi-attribute trajectory consists of a spatio-temporal trajectory and a set of descriptive attributes. Such data enrich the representation of traditional spatio-temporal trajectories to have comprehensive knowledge of moving objects. Range query is a \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "12", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Wu:2023:DOL, author = "Wendi Wu and Zongren Li and Yawei Zhao and Chen Yu and Peilin Zhao and Ji Liu and Kunlun He", title = "Decentralized Online Learning: Take Benefits from Others' Data without Sharing Your Own to Track Global Trend", journal = j-TIST, volume = "14", number = "1", pages = "13:1--13:??", month = feb, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3559765", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Mar 11 08:47:24 MST 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3559765", abstract = "Decentralized online learning (online learning in decentralized networks) has been attracting more and more attention, since it is believed that decentralized online learning can help data providers cooperatively better solve their online problems without \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "13", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{He:2023:FFA, author = "Mingkai He and Jing Lin and Jinwei Luo and Weike Pan and Zhong Ming", title = "{FLAG}: a Feedback-aware Local and Global Model for Heterogeneous Sequential Recommendation", journal = j-TIST, volume = "14", number = "1", pages = "14:1--14:??", month = feb, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3557046", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Mar 11 08:47:24 MST 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3557046", abstract = "Heterogeneous sequential recommendation that models sequences of items associated with more than one type of feedback such as examinations and purchases is an emerging topic in the research community, which is also an important problem in many real-world \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "14", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Lyu:2023:RLL, author = "Gengyu Lyu and Songhe Feng and Wei Liu and Shuoyan Liu and Congyan Lang", title = "Redundant Label Learning via Subspace Representation and Global Disambiguation", journal = j-TIST, volume = "14", number = "1", pages = "15:1--15:??", month = feb, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3558547", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Mar 11 08:47:24 MST 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3558547", abstract = "Redundant Label Learning (RLL) aims at inducing a robust model from training data, where each example is associated with a set of candidate labels, among which some of them are incorrect. Most existing approaches deal with such problem by disambiguating \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "15", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Barros:2023:NSS, author = "Pedro Barros and Fabiane Queiroz and Fl{\'a}vio Figueiredo and Jefersson A. {Dos Santos} and Heitor Ramos", title = "A New Similarity Space Tailored for Supervised Deep Metric Learning", journal = j-TIST, volume = "14", number = "1", pages = "16:1--16:??", month = feb, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3559766", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Mar 11 08:47:24 MST 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3559766", abstract = "We propose a novel deep metric learning method. Differently from many works in this area, we define a novel latent space obtained through an autoencoder. The new space, namely S-space, is divided into different regions describing positions where pairs of \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "16", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Sun:2023:MBR, author = "Wei Sun and Shaoxiong Ji and Erik Cambria and Pekka Marttinen", title = "Multitask Balanced and Recalibrated Network for Medical Code Prediction", journal = j-TIST, volume = "14", number = "1", pages = "17:1--17:??", month = feb, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3563041", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Mar 11 08:47:24 MST 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3563041", abstract = "Human coders assign standardized medical codes to clinical documents generated during patients' hospitalization, which is error prone and labor intensive. Automated medical coding approaches have been developed using machine learning methods, such as deep \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "17", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Shi:2023:MIL, author = "Lei Shi and Yuankai Luo and Shuai Ma and Hanghang Tong and Zhetao Li and Xiatian Zhang and Zhiguang Shan", title = "Mobility Inference on Long-Tailed Sparse Trajectory", journal = j-TIST, volume = "14", number = "1", pages = "18:1--18:??", month = feb, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3563457", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Mar 11 08:47:24 MST 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3563457", abstract = "Analyzing the urban trajectory in cities has become an important topic in data mining. How can we model the human mobility consisting of stay and travel states from the raw trajectory data? How can we infer these mobility states from a single user's \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "18", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Leiva:2023:DUS, author = "Luis A. Leiva and Asutosh Hota and Antti Oulasvirta", title = "Describing {UI} Screenshots in Natural Language", journal = j-TIST, volume = "14", number = "1", pages = "19:1--19:??", month = feb, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3564702", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Mar 11 08:47:24 MST 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3564702", abstract = "Being able to describe any user interface (UI) screenshot in natural language can promote understanding of the main purpose of the UI, yet currently it cannot be accomplished with state-of-the-art captioning systems. We introduce XUI, a novel method \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "19", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Li:2023:SDH, author = "Jia Li and Dandan Song and Zhijing Wu", title = "A Semantically Driven Hybrid Network for Unsupervised Entity Alignment", journal = j-TIST, volume = "14", number = "2", pages = "20:1--20:??", month = apr, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3567829", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Mar 21 06:21:38 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3567829", abstract = "The major challenge in the task of entity alignment (EA) lies in the heterogeneity of the knowledge graph. The traditional solution to EA is to first map entities to the same space via knowledge embedding and then calculate the similarity between entities \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "20", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Li:2023:RBE, author = "Lei Li and Yongfeng Zhang and Li Chen", title = "On the Relationship between Explanation and Recommendation: Learning to Rank Explanations for Improved Performance", journal = j-TIST, volume = "14", number = "2", pages = "21:1--21:??", month = apr, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3569423", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Mar 21 06:21:38 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3569423", abstract = "Explaining to users why some items are recommended is critical, as it can help users to make better decisions, increase their satisfaction, and gain their trust in recommender systems (RS). However, existing explainable RS usually consider explanation as \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "21", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Wu:2023:SCL, author = "Yanzhao Wu and Ling Liu", title = "Selecting and Composing Learning Rate Policies for Deep Neural Networks", journal = j-TIST, volume = "14", number = "2", pages = "22:1--22:??", month = apr, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3570508", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Mar 21 06:21:38 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3570508", abstract = "The choice of learning rate (LR) functions and policies has evolved from a simple fixed LR to the decaying LR and the cyclic LR, aiming to improve the accuracy and reduce the training time of Deep Neural Networks (DNNs). This article presents a systematic \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "22", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Gupta:2023:NTM, author = "Amulya Gupta and Zhu Zhang", title = "Neural Topic Modeling via Discrete Variational Inference", journal = j-TIST, volume = "14", number = "2", pages = "23:1--23:??", month = apr, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3570509", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Mar 21 06:21:38 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3570509", abstract = "Topic models extract commonly occurring latent topics from textual data. Statistical models such as Latent Dirichlet Allocation do not produce dense topic embeddings readily integratable into neural architectures, whereas earlier neural topic models are \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "23", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Shen:2023:CST, author = "Ziyu Shen and Binghui Liu and Qing Zhou and Zheng Liu and Bin Xia and Yun Li", title = "Cost-sensitive Tensor-based Dual-stage Attention {LSTM} with Feature Selection for Data Center Server Power Forecasting", journal = j-TIST, volume = "14", number = "2", pages = "24:1--24:??", month = apr, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3569422", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Mar 21 06:21:38 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3569422", abstract = "Power forecasting has a guiding effect on power-aware scheduling strategies to reduce unnecessary power consumption in data centers. Many metrics related to power consumption can be collected in physical servers, such as the status of CPU, memory, and \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "24", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Lyu:2023:PKC, author = "Gengyu Lyu and Songhe Feng and Shaokai Wang and Zhen Yang", title = "Prior Knowledge Constrained Adaptive Graph Framework for Partial Label Learning", journal = j-TIST, volume = "14", number = "2", pages = "25:1--25:??", month = apr, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3569421", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Mar 21 06:21:38 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3569421", abstract = "Partial label learning (PLL) aims to learn a robust multi-class classifier from the ambiguous data, where each instance is given with several candidate labels, among which only one label is real. Most existing methods usually cope with such problem by \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "25", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Sarkar:2023:AHM, author = "Souvika Sarkar and Biddut Sarker Bijoy and Syeda Jannatus Saba and Dongji Feng and Yash Mahajan and Mohammad Ruhul Amin and Sheikh Rabiul Islam and Shubhra Kanti Karmaker (``Santu'')", title = "Ad-Hoc Monitoring of {COVID-19} Global Research Trends for Well-Informed Policy Making", journal = j-TIST, volume = "14", number = "2", pages = "26:1--26:??", month = apr, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3576901", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Mar 21 06:21:38 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3576901", abstract = "The COVID-19 pandemic has affected millions of people worldwide with severe health, economic, social, and political implications. Healthcare Policy Makers (HPMs) and medical experts are at the core of responding to this continuously evolving pandemic \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "26", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Zhang:2023:OOS, author = "Donglin Zhang and Xiao-Jun Wu and Guoqing Chen", title = "{ONION}: Online Semantic Autoencoder Hashing for Cross-Modal Retrieval", journal = j-TIST, volume = "14", number = "2", pages = "27:1--27:??", month = apr, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3572032", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Mar 21 06:21:38 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3572032", abstract = "Cross-modal hashing (CMH) has recently received increasing attention with the merit of speed and storage in performing large-scale cross-media similarity search. However, most existing cross-media approaches utilize the batch-based mode to update hash \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "27", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Ren:2023:GLA, author = "Jing Ren and Feng Xia and Ivan Lee and Azadeh Noori Hoshyar and Charu Aggarwal", title = "Graph Learning for Anomaly Analytics: Algorithms, Applications, and Challenges", journal = j-TIST, volume = "14", number = "2", pages = "28:1--28:??", month = apr, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3570906", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Mar 21 06:21:38 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3570906", abstract = "Anomaly analytics is a popular and vital task in various research contexts that has been studied for several decades. At the same time, deep learning has shown its capacity in solving many graph-based tasks, like node classification, link prediction, and \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "28", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Kang:2023:HET, author = "Jian Kang and Dan Lin", title = "Highly Efficient Traffic Planning for Autonomous Vehicles to Cross Intersections Without a Stop", journal = j-TIST, volume = "14", number = "2", pages = "29:1--29:??", month = apr, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3572034", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Mar 21 06:21:38 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3572034", abstract = "Waiting in a long queue at traffic lights not only wastes valuable time but also pollutes the environment. With the advances in autonomous vehicles and 5G networks, the previous jamming scenarios at intersections may be turned into non-stop weaving \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "29", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Tian:2023:SFU, author = "Qing Tian and Shun Peng and Tinghuai Ma", title = "Source-free Unsupervised Domain Adaptation with Trusted Pseudo Samples", journal = j-TIST, volume = "14", number = "2", pages = "30:1--30:??", month = apr, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3570510", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Mar 21 06:21:38 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3570510", abstract = "Source-free unsupervised domain adaptation (SFUDA) aims to accomplish the task of adaptation to the target domain by utilizing pre-trained source domain model and unlabeled target domain samples, without directly accessing any source domain data. Although \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "30", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Wu:2023:GFF, author = "Hao Wu and Jianyang Gu and Xiaojin Fan and He Li and Lidong Xie and Jian Zhao", title = "{$3$D}-Guided Frontal Face Generation for Pose-Invariant Recognition", journal = j-TIST, volume = "14", number = "2", pages = "31:1--31:??", month = apr, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3572035", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Mar 21 06:21:38 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3572035", abstract = "Although deep learning techniques have achieved extraordinary accuracy in recognizing human faces, the pose variances of images captured in real-world scenarios still hinder reliable model appliance. To mitigate this gap, we propose to recognize faces via \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "31", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Yao:2023:CCD, author = "Jing Yao and Zheng Liu and Junhan Yang and Zhicheng Dou and Xing Xie and Ji-Rong Wen", title = "{CDSM}: Cascaded Deep Semantic Matching on Textual Graphs Leveraging Ad-hoc Neighbor Selection", journal = j-TIST, volume = "14", number = "2", pages = "32:1--32:??", month = apr, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3573204", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Mar 21 06:21:38 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3573204", abstract = "Deep semantic matching aims at discriminating the relationship between documents based on deep neural networks. In recent years, it becomes increasingly popular to organize documents with a graph structure, then leverage both the intrinsic document \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "32", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Xu:2023:DRL, author = "Meng Xu and Jianping Wang", title = "Deep Reinforcement Learning for Parameter Tuning of Robot Visual Servoing", journal = j-TIST, volume = "14", number = "2", pages = "33:1--33:??", month = apr, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3579829", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Mar 21 06:21:38 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3579829", abstract = "Robot visual servoing controls the motion of a robot through real-time visual observations. Kinematics is a key approach to achieving visual servoing. One key challenge of kinematics-based visual servoing is that it requires time-varying parameter \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "33", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Tian:2023:DLD, author = "Jieru Tian and Yongxin Wang and Zhenduo Chen and Xin Luo and Xinshun Xu", title = "Diagnose Like Doctors: Weakly Supervised Fine-Grained Classification of Breast Cancer", journal = j-TIST, volume = "14", number = "2", pages = "34:1--34:??", month = apr, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3572033", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Mar 21 06:21:38 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3572033", abstract = "Breast cancer is the most common type of cancers in women. Therefore, how to accurately and timely diagnose it becomes very important. Some computer-aided diagnosis models based on pathological images have been proposed for this task. However, there are \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "34", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Zhang:2023:ESV, author = "Guozhen Zhang and Jinhui Yi and Jian Yuan and Yong Li and Depeng Jin", title = "{DAS}: Efficient Street View Image Sampling for Urban Prediction", journal = j-TIST, volume = "14", number = "2", pages = "35:1--35:??", month = apr, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3576902", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Mar 21 06:21:38 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3576902", abstract = "Street view data is one of the most common data sources for urban prediction tasks, such as estimating socioeconomic status, sensing physical urban changes, and identifying urban villages. Typical research in this field consists of two steps: acquiring a \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "35", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Yu:2023:STG, author = "Shuo Yu and Feng Xia and Shihao Li and Mingliang Hou and Quan Z. Sheng", title = "Spatio-temporal Graph Learning for Epidemic Prediction", journal = j-TIST, volume = "14", number = "2", pages = "36:1--36:??", month = apr, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3579815", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Mar 21 06:21:38 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3579815", abstract = "The COVID-19 pandemic has posed great challenges to public health services, government agencies, and policymakers, raising huge social conflicts between public health and economic resilience. Policies such as reopening or closure of business activities \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "36", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Fang:2023:RAG, author = "Yujie Fang and Xin Li and Rui Ye and Xiaoyan Tan and Peiyao Zhao and Mingzhong Wang", title = "Relation-aware Graph Convolutional Networks for Multi-relational Network Alignment", journal = j-TIST, volume = "14", number = "2", pages = "37:1--37:??", month = apr, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3579827", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Mar 21 06:21:38 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3579827", abstract = "The alignment of multiple multi-relational networks, such as knowledge graphs, is vital for many AI applications. In comparison with existing GCNs which cannot fully utilize relational information of multiple types, we propose a relation-aware graph \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "37", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Yin:2023:CBA, author = "Chunyong Yin and Shuangshuang Chen and Zhichao Yin", title = "Clustering-based Active Learning Classification towards Data Stream", journal = j-TIST, volume = "14", number = "2", pages = "38:1--38:??", month = apr, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3579830", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Mar 21 06:21:38 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3579830", abstract = "Many practical applications, such as social media and monitoring system, will constantly generate streaming data, which has problems of instability, lack of labels and multiclass imbalance. In order to solve these problems, a cluster-based active learning \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "38", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Zaji:2023:OBD, author = "Amirhossein Zaji and Zheng Liu and Takashi Bando and Lihua Zhao", title = "Ontology-Based Driving Simulation for Traffic Lights Optimization", journal = j-TIST, volume = "14", number = "3", pages = "39:1--39:??", month = jun, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3579839", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Jun 1 14:12:36 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3579839", abstract = "Traffic lights optimization is one of the principal components to lessen the traffic flow and travel time in an urban area. The present article seeks to introduce a novel procedure to design the traffic lights in a city using evolutionary-based \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "39", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Cai:2023:FLG, author = "Yaoming Cai and Zijia Zhang and Pedram Ghamisi and Zhihua Cai and Xiaobo Liu and Yao Ding", title = "Fully Linear Graph Convolutional Networks for Semi-Supervised and Unsupervised Classification", journal = j-TIST, volume = "14", number = "3", pages = "40:1--40:??", month = jun, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3579828", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Jun 1 14:12:36 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3579828", abstract = "This article presents FLGC, a simple yet effective fully linear graph convolutional network for semi-supervised and unsupervised learning. Instead of using gradient descent, we train FLGC based on computing a global optimal closed-form solution with a \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "40", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Manfredi:2023:TST, author = "Gilda Manfredi and Nicola Capece and Ugo Erra and Monica Gruosso", title = "{TreeSketchNet}: From Sketch to {$3$D} Tree Parameters Generation", journal = j-TIST, volume = "14", number = "3", pages = "41:1--41:??", month = jun, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3579831", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Jun 1 14:12:36 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3579831", abstract = "Three-dimensional (3D) modeling of non-linear objects from stylized sketches is a challenge even for computer graphics experts. The extrapolation of object parameters from a stylized sketch is a very complex and cumbersome task. In the present study, we \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "41", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Wang:2023:CVP, author = "Wenshan Wang and Su Yang and Weishan Zhang", title = "Customer Volume Prediction Using Fusion of Shared-private Dynamic Weighting over Multiple Modalities", journal = j-TIST, volume = "14", number = "3", pages = "42:1--42:??", month = jun, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3579826", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Jun 1 14:12:36 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3579826", abstract = "Customer volume prediction is crucial for a variety of urban applications, such as store location selection. So far, the key challenge lies in how to fuse multiple modalities from different data sources, on account of the massive amount of data accessible,. \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "42", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Jiang:2023:REK, author = "Lu Jiang and Kunpeng Liu and Yibin Wang and Dongjie Wang and Pengyang Wang and Yanjie Fu and Minghao Yin", title = "Reinforced Explainable Knowledge Concept Recommendation in {MOOCs}", journal = j-TIST, volume = "14", number = "3", pages = "43:1--43:??", month = jun, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3579991", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Jun 1 14:12:36 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3579991", abstract = "In this article, we study knowledge concept recommendation in Massive Open Online Courses (MOOCs) in an explainable manner. Knowledge concepts, composing course units (e.g., videos) in MOOCs, refer to topics and skills that students are expected to \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "43", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Sun:2023:RLQ, author = "Shuo Sun and Rundong Wang and Bo An", title = "Reinforcement Learning for Quantitative Trading", journal = j-TIST, volume = "14", number = "3", pages = "44:1--44:??", month = jun, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3582560", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Jun 1 14:12:36 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3582560", abstract = "Quantitative trading (QT), which refers to the usage of mathematical models and data-driven techniques in analyzing the financial market, has been a popular topic in both academia and financial industry since 1970s. In the last decade, reinforcement \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "44", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Dai:2023:SAT, author = "Zeyu Dai and Shengcai Liu and Qing Li and Ke Tang", title = "Saliency Attack: Towards Imperceptible Black-box Adversarial Attack", journal = j-TIST, volume = "14", number = "3", pages = "45:1--45:??", month = jun, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3582563", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Jun 1 14:12:36 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3582563", abstract = "Deep neural networks are vulnerable to adversarial examples, even in the black-box setting where the attacker is only accessible to the model output. Recent studies have devised effective black-box attacks with high query efficiency. However, such \ldots{}.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "45", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Li:2023:RLE, author = "Xing Li and Wei Wei and Ruizhi Zhang and Zhenyu Shi and Zhiming Zheng and Xiangnan Feng", title = "Representation Learning of Enhanced Graphs Using Random Walk Graph Convolutional Network", journal = j-TIST, volume = "14", number = "3", pages = "46:1--46:??", month = jun, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3582841", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Jun 1 14:12:36 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3582841", abstract = "Nowadays, graph structure data has played a key role in machine learning because of its simple topological structure, and therefore, the graph representation learning methods have attracted great attention. And it turns out that the low-dimensional \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "46", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Cai:2023:RDR, author = "Mingjian Cai and Xiangjun Shen and Stanley Ebhohimhen Abhadiomhen and Yingfeng Cai and Sirui Tian", title = "Robust Dimensionality Reduction via Low-rank {Laplacian} Graph Learning", journal = j-TIST, volume = "14", number = "3", pages = "47:1--47:??", month = jun, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3582698", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Jun 1 14:12:36 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3582698", abstract = "Manifold learning is a widely used technique for dimensionality reduction as it can reveal the intrinsic geometric structure of data. However, its performance decreases drastically when data samples are contaminated by heavy noise or occlusions, which \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "47", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Yin:2023:HRD, author = "Chunyong Yin and Sun Zhang and Qingkui Zeng", title = "Hybrid Representation and Decision Fusion towards Visual-textual Sentiment", journal = j-TIST, volume = "14", number = "3", pages = "48:1--48:??", month = jun, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3583076", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Jun 1 14:12:36 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3583076", abstract = "The rising use of online media has changed social customs of the public. Users have become gradually accustomed to sharing daily experiences and publishing personal opinions on social networks. Social data carrying with emotions and attitudes have \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "48", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Xu:2023:MWB, author = "En Xu and Zhiwen Yu and Zhuo Sun and Bin Guo and Lina Yao", title = "Modeling Within-Basket Auxiliary Item Recommendation with Matchability and Ubiquity", journal = j-TIST, volume = "14", number = "3", pages = "49:1--49:??", month = jun, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3574157", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Jun 1 14:12:36 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3574157", abstract = "Within-basket recommendation is to recommend suitable items for the current basket with some already known items. The within-basket auxiliary item recommendation ( WBAIR ) is to recommend auxiliary items based on the primary items in the basket. Such a task \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "49", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Wen:2023:EWC, author = "Haomin Wen and Youfang Lin and Fan Wu and Huaiyu Wan and Zhongxiang Sun and Tianyue Cai and Hongyu Liu and Shengnan Guo and Jianbin Zheng and Chao Song and Lixia Wu", title = "Enough Waiting for the Couriers: Learning to Estimate Package Pick-up Arrival Time from Couriers' Spatial-Temporal Behaviors", journal = j-TIST, volume = "14", number = "3", pages = "50:1--50:??", month = jun, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3582561", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Jun 1 14:12:36 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3582561", abstract = "In intelligent logistics systems, predicting the Estimated Time of Pick-up Arrival (ETPA) of packages is a crucial task, which aims to predict the courier's arrival time to all the unpicked-up packages at any time. Accurate prediction of ETPA can help \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "50", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Mei:2023:FRT, author = "Jianbiao Mei and Mengmeng Wang and Yu Yang and Yanjun Li and Yong Liu", title = "Fast Real-Time Video Object Segmentation with a Tangled Memory Network", journal = j-TIST, volume = "14", number = "3", pages = "51:1--51:??", month = jun, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3585076", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Jun 1 14:12:36 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3585076", abstract = "In this article, we present a fast real-time tangled memory network that segments the objects effectively and efficiently for semi-supervised video object segmentation (VOS). We propose a tangled reference encoder and a memory bank organization mechanism \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "51", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Wang:2023:TBE, author = "Hu Wang and Hui Li and Meng Wang and Jiangtao Cui", title = "Toward Balancing the Efficiency and Effectiveness in $k$-Facility Relocation Problem", journal = j-TIST, volume = "14", number = "3", pages = "52:1--52:??", month = jun, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3587039", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Jun 1 14:12:36 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3587039", abstract = "Facility Relocation (FR), which is an effort to reallocate the placement of facilities to adapt to the changes of urban planning, has remarkable impact on many areas. Existing solutions fail to guarantee the result quality on relocating k {$>$} 1 facilities. \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "52", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Luo:2023:HLR, author = "Qilun Luo and Ming Yang and Wen Li and Mingqing Xiao", title = "Hyper-{Laplacian} Regularized Multi-View Clustering with Exclusive {L21} Regularization and Tensor Log-Determinant Minimization Approach", journal = j-TIST, volume = "14", number = "3", pages = "53:1--53:??", month = jun, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3587034", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Jun 1 14:12:36 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3587034", abstract = "Multi-view clustering aims to capture the multiple views inherent information by identifying the data clustering that reflects distinct features of datasets. Since there is a consensus in literature that different views of a dataset share a common latent \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "53", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Preti:2023:MAM, author = "Giulia Preti and Gianmarco {De Francisci Morales} and Matteo Riondato", title = "{MaNIACS}: Approximate Mining of Frequent Subgraph Patterns through Sampling", journal = j-TIST, volume = "14", number = "3", pages = "54:1--54:??", month = jun, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3587254", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Jun 1 14:12:36 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3587254", abstract = "We present MaNIACS, a sampling-based randomized algorithm for computing high-quality approximations of the collection of the subgraph patterns that are frequent in a single, large, vertex-labeled graph, according to the Minimum Node Image-based (MNI) \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "54", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Gao:2023:DIT, author = "Lei Gao and Ling Guan", title = "A Discriminant Information Theoretic Learning Framework for Multi-modal Feature Representation", journal = j-TIST, volume = "14", number = "3", pages = "55:1--55:??", month = jun, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3587253", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Jun 1 14:12:36 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3587253", abstract = "As sensory and computing technology advances, multi-modal features have been playing a central role in ubiquitously representing patterns and phenomena for effective information analysis and recognition. As a result, multi-modal feature representation is \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "55", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Gupta:2023:ERV, author = "Trasha Gupta and Rajni Jindal and Indu Sreedevi", title = "Empirical Review of Various Thermography-based Computer-aided Diagnostic Systems for Multiple Diseases", journal = j-TIST, volume = "14", number = "3", pages = "56:1--56:??", month = jun, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3583778", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Jun 1 14:12:36 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3583778", abstract = "The lifestyle led by today's generation and its negligence towards health is highly susceptible to various diseases. Developing countries are at a higher risk of mortality due to late-stage presentation, inaccessible diagnosis, and high-cost treatment. \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "56", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Yang:2023:RPL, author = "Wun-Ting Yang and Chiao-Ting Chen and Chuan-Yun Sang and Szu-Hao Huang", title = "Reinforced {PU}-learning with Hybrid Negative Sampling Strategies for Recommendation", journal = j-TIST, volume = "14", number = "3", pages = "57:1--57:??", month = jun, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3582562", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Jun 1 14:12:36 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3582562", abstract = "The data of recommendation systems typically only contain the purchased item as positive data and other un-purchased items as unlabeled data. To train a good recommendation model, in addition to the known positive information, we also need high-quality \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "57", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Xiang:2023:TQE, author = "Tao Xiang and Hangcheng Liu and Shangwei Guo and Yan Gan and Wenjian He and Xiaofeng Liao", title = "Towards Query-Efficient Black-{Box} Attacks: a Universal Dual Transferability-Based Framework", journal = j-TIST, volume = "14", number = "4", pages = "58:1--58:??", month = aug, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3583777", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Aug 19 07:08:56 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3583777", abstract = "Adversarial attacks have threatened the application of deep neural networks in security-sensitive scenarios. Most existing black-box attacks fool the target model by interacting with it many times and producing global perturbations. However, all pixels \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "58", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Lin:2023:CCD, author = "Zhuoyi Lin and Lei Feng and Xingzhi Guo and Yu Zhang and Rui Yin and Chee Keong Kwoh and Chi Xu", title = "{COMET}: Convolutional Dimension Interaction for Collaborative Filtering", journal = j-TIST, volume = "14", number = "4", pages = "59:1--59:??", month = aug, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3588576", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Aug 19 07:08:56 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3588576", abstract = "Representation learning-based recommendation models play a dominant role among recommendation techniques. However, most of the existing methods assume both historical interactions and embedding dimensions are independent of each other, and thus \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "59", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Liu:2023:UUK, author = "Yu Liu and Jingtao Ding and Yanjie Fu and Yong Li", title = "{UrbanKG}: an Urban Knowledge Graph System", journal = j-TIST, volume = "14", number = "4", pages = "60:1--60:??", month = aug, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3588577", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Aug 19 07:08:56 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3588577", abstract = "Every day, our living city produces a tremendous amount of spatial-temporal data, involved with multiple sources from the individual scale to the city scale. Undoubtedly, such massive urban data can be explored for a better city and better life, as what \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "60", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Li:2023:LRS, author = "Tong Li and Yanxin Xi and Huandong Wang and Yong Li and Sasu Tarkoma and Pan Hui", title = "Learning Representations of Satellite Imagery by Leveraging Point-of-Interests", journal = j-TIST, volume = "14", number = "4", pages = "61:1--61:??", month = aug, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3589344", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Aug 19 07:08:56 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3589344", abstract = "Satellite imagery depicts the Earth's surface remotely and provides comprehensive information for many applications, such as land use monitoring and urban planning. Existing studies on unsupervised representation learning for satellite images only take \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "61", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Baradaaji:2023:JLS, author = "A. Baradaaji and F. Dornaika", title = "Joint Latent Space and Label Inference Estimation with Adaptive Fused Data and Label Graphs", journal = j-TIST, volume = "14", number = "4", pages = "62:1--62:??", month = aug, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3590172", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Aug 19 07:08:56 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3590172", abstract = "Recently, structured computing has become an interesting topic in the world of artificial intelligence, especially in the field of machine learning, as most researchers focus on the development of graph-based semi-supervised learning models. In this \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "62", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Gao:2023:CYF, author = "Yujia Gao and Pengfei Wang and Liang Liu and Chi Zhang and Huadong Ma", title = "Configure Your Federation: Hierarchical Attention-enhanced Meta-Learning Network for Personalized Federated Learning", journal = j-TIST, volume = "14", number = "4", pages = "63:1--63:??", month = aug, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3591362", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Aug 19 07:08:56 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3591362", abstract = "Federated learning, as a distributed machine learning framework, enables clients to conduct model training without transmitting their data to the server, which is used to solve the dilemma of data silos and data privacy. It can work well on clients having \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "63", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Jin:2023:DGC, author = "Guangyin Jin and Huan Yan and Fuxian Li and Yong Li and Jincai Huang", title = "Dual Graph Convolution Architecture Search for Travel Time Estimation", journal = j-TIST, volume = "14", number = "4", pages = "64:1--64:??", month = aug, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3591361", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Aug 19 07:08:56 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3591361", abstract = "Travel time estimation (TTE) is a crucial task in intelligent transportation systems, which has been widely used in navigation and route planning. In recent years, several deep learning frameworks have been proposed to capture the dynamic features of road \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "64", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Zhang:2023:NAL, author = "Jinghui Zhang and Dingyang Lv and Qiangsheng Dai and Fa Xin and Fang Dong", title = "Noise-aware Local Model Training Mechanism for Federated Learning", journal = j-TIST, volume = "14", number = "4", pages = "65:1--65:??", month = aug, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3591363", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Aug 19 07:08:56 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3591363", abstract = "As a new paradigm in training intelligent models, federated learning is widely used to train a global model without requiring local data to be uploaded from end devices. However, there are often mislabeled samples (i.e., noisy samples) in the dataset, \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "65", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Liu:2023:RFS, author = "Tianying Liu and Lu Zhang and Yang Wang and Jihong Guan and Yanwei Fu and Jiajia Zhao and Shuigeng Zhou", title = "Recent Few-shot Object Detection Algorithms: a Survey with Performance Comparison", journal = j-TIST, volume = "14", number = "4", pages = "66:1--66:??", month = aug, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3593588", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Aug 19 07:08:56 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3593588", abstract = "The generic object detection (GOD) task has been successfully tackled by recent deep neural networks, trained by an avalanche of annotated training samples from some common classes. However, it is still non-trivial to generalize these object detectors to \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "66", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Xu:2023:CLM, author = "Lingling Xu and Haoran Xie and Zongxi Li and Fu Lee Wang and Weiming Wang and Qing Li", title = "Contrastive Learning Models for Sentence Representations", journal = j-TIST, volume = "14", number = "4", pages = "67:1--67:??", month = aug, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3593590", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Aug 19 07:08:56 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3593590", abstract = "Sentence representation learning is a crucial task in natural language processing, as the quality of learned representations directly influences downstream tasks, such as sentence classification and sentiment analysis. Transformer-based pretrained \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "67", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Kljucaric:2023:DLI, author = "Luke Kljucaric and Alan D. George", title = "Deep Learning Inferencing with High-performance Hardware Accelerators", journal = j-TIST, volume = "14", number = "4", pages = "68:1--68:??", month = aug, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3594221", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Aug 19 07:08:56 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3594221", abstract = "As computer architectures continue to integrate application-specific hardware, it is critical to understand the relative performance of devices for maximum app acceleration. The goal of benchmarking suites, such as MLPerf for analyzing machine learning \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "68", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Shi:2023:SLI, author = "Yanhang Shi and Xue Li and Siguang Chen", title = "Skin Lesion Intelligent Diagnosis in Edge Computing Networks: an {FCL} Approach", journal = j-TIST, volume = "14", number = "4", pages = "69:1--69:??", month = aug, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3595186", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Aug 19 07:08:56 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3595186", abstract = "In recent years, automatic skin lesion diagnosis methods based on artificial intelligence have achieved great success. However, the lack of labeled data, visual similarity between skin diseases, and restriction on private data sharing remain the major \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "69", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Sun:2023:MUM, author = "Li Sun and Zhongbao Zhang and Gen Li and Pengxin Ji and Sen Su and Philip S. Yu", title = "{MC$^2$}: Unsupervised Multiple Social Network Alignment", journal = j-TIST, volume = "14", number = "4", pages = "70:1--70:??", month = aug, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3596514", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Aug 19 07:08:56 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3596514", abstract = "Social network alignment, identifying social accounts of the same individual across different social networks, shows fundamental importance in a wide spectrum of applications, such as link prediction and information diffusion. Individuals more often than \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "70", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Li:2023:YHY, author = "Tong Li and Yong Li and Mingyang Zhang and Sasu Tarkoma and Pan Hui", title = "You Are How You Use Apps: User Profiling Based on Spatiotemporal App Usage Behavior", journal = j-TIST, volume = "14", number = "4", pages = "71:1--71:??", month = aug, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3597212", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Aug 19 07:08:56 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3597212", abstract = "Mobile apps have become an indispensable part of people's daily lives. Users determine what apps to use and when and where to use them based on their tastes, interests, and personal demands, depending on their personality traits. This article aims to \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "71", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Gunarathna:2023:RTR, author = "Udesh Gunarathna and Hairuo Xie and Egemen Tanin and Shanika Karunasekera and Renata Borovica-Gajic", title = "Real-time Road Network Optimization with Coordinated Reinforcement Learning", journal = j-TIST, volume = "14", number = "4", pages = "72:1--72:??", month = aug, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3603379", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Aug 19 07:08:56 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3603379", abstract = "Dynamic road network optimization has been used for improving traffic flow in an infrequent and localized manner. The development of intelligent systems and technology provides an opportunity to improve the frequency and scale of dynamic road network \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "72", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Yang:2023:HHG, author = "Hanchen Yang and Wengen Li and Siyun Hou and Jihong Guan and Shuigeng Zhou", title = "{HiGRN}: a Hierarchical Graph Recurrent Network for Global Sea Surface Temperature Prediction", journal = j-TIST, volume = "14", number = "4", pages = "73:1--73:??", month = aug, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3597937", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Aug 19 07:08:56 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3597937", abstract = "Sea surface temperature (SST) is one critical parameter of global climate change, and accurate SST prediction is important to various applications, e.g., weather forecasting, fishing directions, and disaster warnings. The global ocean system is unified \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "73", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Choi:2023:GNR, author = "Jeongwhan Choi and Noseong Park", title = "Graph Neural Rough Differential Equations for Traffic Forecasting", journal = j-TIST, volume = "14", number = "4", pages = "74:1--74:??", month = aug, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3604808", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Aug 19 07:08:56 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3604808", abstract = "Traffic forecasting is one of the most popular spatio-temporal tasks in the field of machine learning. A prevalent approach in the field is to combine graph convolutional networks and recurrent neural networks for the spatio-temporal processing. There has \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "74", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Maggi:2023:DAD, author = "Fabrizio Maria Maggi and Andrea Marrella and Fabio Patrizi and Vasyl Skydanienko", title = "Data-Aware Declarative Process Mining with {SAT}", journal = j-TIST, volume = "14", number = "4", pages = "75:1--75:??", month = aug, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3600106", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Aug 19 07:08:56 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3600106", abstract = "Process Mining is a family of techniques for analyzing business process execution data recorded in event logs. Process models can be obtained as output of automated process discovery techniques or can be used as input of techniques for conformance \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "75", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Guo:2023:FCP, author = "Kun Guo and Wenzhong Guo and Enjie Ye and Yutong Fang and Jiachen Zheng and Ximeng Liu and Kai Chen", title = "Federated Clique Percolation for Privacy-preserving Overlapping Community Detection", journal = j-TIST, volume = "14", number = "4", pages = "76:1--76:??", month = aug, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3604807", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Aug 19 07:08:56 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3604807", abstract = "Community structure is a typical characteristic of complex networks. Finding communities in complex networks has many important applications, such as the advertisement and recommendation based on social networks and the discovery of new protein molecules \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "76", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Li:2023:JER, author = "Qibin Li and Nianmin Yao and Nai Zhou and Jian Zhao and Yanan Zhang", title = "A Joint Entity and Relation Extraction Model based on Efficient Sampling and Explicit Interaction", journal = j-TIST, volume = "14", number = "5", pages = "77:1--77:??", month = oct, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3604811", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Oct 17 05:58:14 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3604811", abstract = "Joint entity and relation extraction (RE) construct a framework for unifying entity recognition and relationship extraction, and the approach can exploit the dependencies between the two tasks to improve the performance of the task. However, the existing \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "77", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Yang:2023:CFS, author = "Shuai Yang and Xianjie Guo and Kui Yu and Xiaoling Huang and Tingting Jiang and Jin He and Lichuan Gu", title = "Causal Feature Selection in the Presence of Sample Selection Bias", journal = j-TIST, volume = "14", number = "5", pages = "78:1--78:??", month = oct, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3604809", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Oct 17 05:58:14 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3604809", abstract = "Almost all existing causal feature selection methods are proposed without considering the problem of sample selection bias. However, in practice, as data-gathering process cannot be fully controlled, sample selection bias often occurs, leading to spurious \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "78", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Wang:2023:DCT, author = "Mudan Wang and Yuan Yuan and Huan Yan and Hongjie Sui and Fan Zuo and Yue Liu and Yong Li and Depeng Jin", title = "Discovering Causes of Traffic Congestion via Deep Transfer Clustering", journal = j-TIST, volume = "14", number = "5", pages = "79:1--79:??", month = oct, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3604810", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Oct 17 05:58:14 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3604810", abstract = "Traffic congestion incurs long delay in travel time, which seriously affects our daily travel experiences. Exploring why traffic congestion occurs is significantly important to effectively address the problem of traffic congestion and improve user \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "79", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Mullner:2023:RNR, author = "Peter M{\"u}llner and Elisabeth Lex and Markus Schedl and Dominik Kowald", title = "{ReuseKNN}: Neighborhood Reuse for Differentially Private {KNN-Based} Recommendations", journal = j-TIST, volume = "14", number = "5", pages = "80:1--80:??", month = oct, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3608481", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Oct 17 05:58:14 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3608481", abstract = "User-based KNN recommender systems ( UserKNN ) utilize the rating data of a target user's k nearest neighbors in the recommendation process. This, however, increases the privacy risk of the neighbors, since the recommendations could expose the neighbors' \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "80", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Lu:2023:RLA, author = "Sidi Lu and Xin Yuan and Aggelos K. Katsaggelos and Weisong Shi", title = "Reinforcement Learning for Adaptive Video Compressive Sensing", journal = j-TIST, volume = "14", number = "5", pages = "81:1--81:??", month = oct, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3608479", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Oct 17 05:58:14 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3608479", abstract = "We apply reinforcement learning to video compressive sensing to adapt the compression ratio. Specifically, video snapshot compressive imaging (SCI), which captures high-speed video using a low-speed camera is considered in this work, in which multiple ( B ) \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "81", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Zhu:2023:UGR, author = "Yanqiao Zhu and Yichen Xu and Feng Yu and Qiang Liu and Shu Wu", title = "Unsupervised Graph Representation Learning with Cluster-aware Self-training and Refining", journal = j-TIST, volume = "14", number = "5", pages = "82:1--82:??", month = oct, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3608480", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Oct 17 05:58:14 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3608480", abstract = "Unsupervised graph representation learning aims to learn low-dimensional node embeddings without supervision while preserving graph topological structures and node attributive features. Previous Graph Neural Networks (GNN) require a large number of \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "82", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Goethals:2023:PIC, author = "Sofie Goethals and Kenneth S{\"o}rensen and David Martens", title = "The Privacy Issue of Counterfactual Explanations: Explanation Linkage Attacks", journal = j-TIST, volume = "14", number = "5", pages = "83:1--83:??", month = oct, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3608482", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Oct 17 05:58:14 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3608482", abstract = "Black-box machine learning models are used in an increasing number of high-stakes domains, and this creates a growing need for Explainable AI (XAI). However, the use of XAI in machine learning introduces privacy risks, which currently remain largely \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "83", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Han:2023:DSA, author = "Zhenyu Han and Siran Ma and Changzheng Gao and Erzhuo Shao and Yulai Xie and Yang Zhang and Lu Geng and Yong Li", title = "Disease Simulation in Airport Scenario Based on Individual Mobility Model", journal = j-TIST, volume = "14", number = "5", pages = "84:1--84:??", month = oct, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3593589", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Oct 17 05:58:14 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3593589", abstract = "As the rapid-spreading disease COVID-19 occupies the world, most governments adopt strict control policies to alleviate the impact of the virus. These policies successfully reduced the prevalence and delayed the epidemic peak, while they are also \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "84", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Yu:2023:ODI, author = "Guangsheng Yu and Xu Wang and Caijun Sun and Ping Yu and Wei Ni and Ren Ping Liu", title = "Obfuscating the Dataset: Impacts and Applications", journal = j-TIST, volume = "14", number = "5", pages = "85:1--85:??", month = oct, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3597936", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Oct 17 05:58:14 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3597936", abstract = "Obfuscating a dataset by adding random noises to protect the privacy of sensitive samples in the training dataset is crucial to prevent data leakage to untrusted parties when dataset sharing is essential. We conduct comprehensive experiments to \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "85", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Maddalena:2023:QEP, author = "Eddy Maddalena and Luis-Daniel Ib{\'a}{\~n}ez and Neal Reeves and Elena Simperl", title = "{Qrowdsmith}: Enhancing Paid Microtask Crowdsourcing with Gamification and Furtherance Incentives", journal = j-TIST, volume = "14", number = "5", pages = "86:1--86:??", month = oct, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3604940", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Oct 17 05:58:14 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3604940", abstract = "Microtask crowdsourcing platforms are social intelligence systems in which volunteers, called crowdworkers, complete small, repetitive tasks in return for a small fee. Beyond payments, task requesters are considering non-monetary incentives such as points,. \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "86", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Zhadan:2023:MAR, author = "Anastasia Zhadan and Alexander Allahverdyan and Ivan Kondratov and Vikenty Mikheev and Ovanes Petrosian and Aleksei Romanovskii and Vitaliy Kharin", title = "Multi-agent Reinforcement Learning-based Adaptive Heterogeneous {DAG} Scheduling", journal = j-TIST, volume = "14", number = "5", pages = "87:1--87:??", month = oct, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3610300", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Oct 17 05:58:14 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3610300", abstract = "Static scheduling of computational workflow represented by a directed acyclic graph (DAG) is an important problem in many areas of computer science. The main idea and novelty of the proposed algorithm is an adaptive heuristic or graph metric that uses a \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "87", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Chen:2023:DDD, author = "Kuan-Chun Chen and Cheng-Te Li and Kuo-Jung Lee", title = "{DDNAS}: Discretized Differentiable Neural Architecture Search for Text Classification", journal = j-TIST, volume = "14", number = "5", pages = "88:1--88:??", month = oct, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3610299", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Oct 17 05:58:14 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3610299", abstract = "Neural Architecture Search (NAS) has shown promising capability in learning text representation. However, existing text-based NAS neither performs a learnable fusion of neural operations to optimize the architecture nor encodes the latent hierarchical \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "88", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Mahesar:2023:ASD, author = "Quratul-Ain Mahesar and Simon Parsons", title = "Argument Schemes and a Dialogue System for Explainable Planning", journal = j-TIST, volume = "14", number = "5", pages = "89:1--89:??", month = oct, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3610301", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Oct 17 05:58:14 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3610301", abstract = "Artificial Intelligence (AI) is being increasingly deployed in practical applications. However, there is a major concern whether AI systems will be trusted by humans. To establish trust in AI systems, there is a need for users to understand the reasoning \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "89", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Deng:2023:RLP, author = "Bangchao Deng and Dingqi Yang and Bingqing Qu and Benjamin Fankhauser and Philippe Cudre-Mauroux", title = "Robust Location Prediction over Sparse Spatiotemporal Trajectory Data: Flashback to the Right Moment!", journal = j-TIST, volume = "14", number = "5", pages = "90:1--90:??", month = oct, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3616541", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Oct 17 05:58:14 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3616541", abstract = "As a fundamental problem in human mobility modeling, location prediction forecasts a user's next location based on historical user mobility trajectories. Recurrent neural networks (RNNs) have been widely used to capture sequential patterns of user visited \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "90", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Dominguez-Martin:2023:NAF, author = "Javier Dom{\'\i}nguez-Mart{\'\i}n and Mar{\'\i}a J. G{\'o}mez-Silva and Arturo {De la Escalera}", title = "Neural Architectures for Feature Embedding in Person Re-Identification: a Comparative View", journal = j-TIST, volume = "14", number = "5", pages = "91:1--91:??", month = oct, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3610298", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Oct 17 05:58:14 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3610298", abstract = "Solving Person Re-Identification (Re-Id) through Deep Convolutional Neural Networks is a daunting challenge due to the small size and variety of the training data, especially in Single-Shot Re-Id, where only two images per person are available. The lack \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "91", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Zhao:2023:FCG, author = "Tianxiang Zhao and Dongsheng Luo and Xiang Zhang and Suhang Wang", title = "Faithful and Consistent Graph Neural Network Explanations with Rationale Alignment", journal = j-TIST, volume = "14", number = "5", pages = "92:1--92:??", month = oct, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3616542", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Oct 17 05:58:14 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3616542", abstract = "Uncovering rationales behind predictions of graph neural networks (GNNs) has received increasing attention over recent years. Instance-level GNN explanation aims to discover critical input elements, such as nodes or edges, that the target GNN relies upon \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "92", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Zhu:2023:LSA, author = "Yupeng Zhu and Yanxiang Chen and Zuxing Zhao and Xueliang Liu and Jinlin Guo", title = "Local Self-attention-based Hybrid Multiple Instance Learning for Partial Spoof Speech Detection", journal = j-TIST, volume = "14", number = "5", pages = "93:1--93:??", month = oct, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3616540", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Oct 17 05:58:14 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3616540", abstract = "The development of speech synthesis technology has increased the attention toward the threat of spoofed speech. Although various high-performance spoofing countermeasures have been proposed in recent years, a particular scenario is overlooked: partially \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "93", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Moscato:2023:FSN, author = "Vincenzo Moscato and Marco Postiglione and Giancarlo Sperl{\'\i}", title = "Few-shot Named Entity Recognition: Definition, Taxonomy and Research Directions", journal = j-TIST, volume = "14", number = "5", pages = "94:1--94:??", month = oct, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3609483", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Oct 17 05:58:14 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3609483", abstract = "Recent years have seen an exponential growth (+98\% in 2022 w.r.t. the previous year) of the number of research articles in the few-shot learning field, which aims at training machine learning models with extremely limited available data. The research \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "94", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Li:2023:FRF, author = "Yunqi Li and Hanxiong Chen and Shuyuan Xu and Yingqiang Ge and Juntao Tan and Shuchang Liu and Yongfeng Zhang", title = "Fairness in Recommendation: Foundations, Methods, and Applications", journal = j-TIST, volume = "14", number = "5", pages = "95:1--95:??", month = oct, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3610302", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Oct 17 05:58:14 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3610302", abstract = "As one of the most pervasive applications of machine learning, recommender systems are playing an important role on assisting human decision-making. The satisfaction of users and the interests of platforms are closely related to the quality of the \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "95", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Teng:2023:IHF, author = "Shang-Hua Teng", title = "``{Intelligent} Heuristics Are the Future of Computing''", journal = j-TIST, volume = "14", number = "6", pages = "96:1--96:??", month = dec, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3627708", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Jun 4 05:57:07 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3627708", abstract = "Back in 1988, the partial game trees explored by computer chess programs were among the largest search structures in real-world computing. Because the game tree is too large to be fully evaluated, chess programs must make heuristic strategic decisions \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "96", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Rokh:2023:CSM, author = "Babak Rokh and Ali Azarpeyvand and Alireza Khanteymoori", title = "A Comprehensive Survey on Model Quantization for Deep Neural Networks in Image Classification", journal = j-TIST, volume = "14", number = "6", pages = "97:1--97:??", month = dec, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3623402", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Jun 4 05:57:07 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3623402", abstract = "Recent advancements in machine learning achieved by Deep Neural Networks (DNNs) have been significant. While demonstrating high accuracy, DNNs are associated with a huge number of parameters and computations, which leads to high memory usage and energy \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "97", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Zhang:2023:TPU, author = "Xiaojin Zhang and Yan Kang and Kai Chen and Lixin Fan and Qiang Yang", title = "Trading Off Privacy, Utility, and Efficiency in Federated Learning", journal = j-TIST, volume = "14", number = "6", pages = "98:1--98:??", month = dec, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3595185", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Jun 4 05:57:07 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3595185", abstract = "Federated learning (FL) enables participating parties to collaboratively build a global model with boosted utility without disclosing private data information. Appropriate protection mechanisms have to be adopted to fulfill the opposing requirements in \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "98", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Chen:2023:SHA, author = "Bolei Chen and Yongzheng Cui and Ping Zhong and Wang Yang and Yixiong Liang and Jianxin Wang", title = "{STExplorer}: a Hierarchical Autonomous Exploration Strategy with Spatio-temporal Awareness for Aerial Robots", journal = j-TIST, volume = "14", number = "6", pages = "99:1--99:??", month = dec, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3595184", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Jun 4 05:57:07 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3595184", abstract = "The autonomous exploration task we consider requires Unmanned Aerial Vehicles (UAVs) to actively navigate through unknown environments with the goal of fully perceiving and mapping the environments. Some existing exploration strategies suffer from rough \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "99", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Zhang:2023:AAN, author = "Yihao Zhang and Chu Zhao and Weiwen Liao and Wei Zhou and Meng Yuan", title = "Asymmetrical Attention Networks Fused Autoencoder for Debiased Recommendation", journal = j-TIST, volume = "14", number = "6", pages = "100:1--100:??", month = dec, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3596498", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Jun 4 05:57:07 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3596498", abstract = "Popularity bias is a massive challenge for autoencoder-based models, which decreases the level of personalization and hurts the fairness of recommendations. User reviews reflect their preferences and help mitigate bias or unfairness in the recommendation. \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "100", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Wu:2023:UGI, author = "Yingwen Wu and Sizhe Chen and Kun Fang and Xiaolin Huang", title = "Unifying Gradients to Improve Real-World Robustness for Deep Networks", journal = j-TIST, volume = "14", number = "6", pages = "101:1--101:??", month = dec, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3617895", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Jun 4 05:57:07 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3617895", abstract = "The wide application of deep neural networks (DNNs) demands an increasing amount of attention to their real-world robustness, i.e., whether a DNN resists black-box adversarial attacks, among which score-based query attacks (SQAs) are the most threatening \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "101", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Yao:2023:AGA, author = "Rui Yao and Ying Chen and Yong Zhou and Fuyuan Hu and Jiaqi Zhao and Bing Liu and Zhiwen Shao", title = "Attention-guided Adversarial Attack for Video Object Segmentation", journal = j-TIST, volume = "14", number = "6", pages = "102:1--102:??", month = dec, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3617067", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Jun 4 05:57:07 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3617067", abstract = "Video Object Segmentation (VOS) methods have made many breakthroughs with the help of the continuous development and advancement of deep learning. However, the deep learning model is vulnerable to malicious adversarial attacks, which mislead the model to \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "102", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Lin:2023:MAU, author = "Rui Lin and Jing Fan and Haifeng Wu", title = "Multi-aspect Understanding with Cooperative Graph Attention Networks for Medical Dialogue Information Extraction", journal = j-TIST, volume = "14", number = "6", pages = "103:1--103:??", month = dec, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3620675", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Jun 4 05:57:07 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3620675", abstract = "Medical dialogue information extraction is an important but challenging task for Electronic Medical Records. Existing medical information extraction methods ignore the crucial information of sentence and multi-level dependency in dialogue, which limits \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "103", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Kasthuriarachchy:2023:MST, author = "Buddhika Kasthuriarachchy and Madhu Chetty and Adrian Shatte and Darren Walls", title = "Meaning-Sensitive Text Data Augmentation with Intelligent Masking", journal = j-TIST, volume = "14", number = "6", pages = "104:1--104:??", month = dec, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3623403", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Jun 4 05:57:07 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3623403", abstract = "With the recent popularity of applying large-scale deep neural network-based models for natural language processing (NLP), attention to develop methods for text data augmentation is at its peak, since the limited size of training data tends to \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "104", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Li:2023:LPP, author = "Chu-Chen Li and Cheng-Te Li and Shou-De Lin", title = "Learning Privacy-Preserving Embeddings for Image Data to Be Published", journal = j-TIST, volume = "14", number = "6", pages = "105:1--105:??", month = dec, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3623404", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Jun 4 05:57:07 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3623404", abstract = "Deep learning shows superiority in learning feature representations that offer promising performance in various application domains. Recent advances have shown that privacy attributes of users and patients (e.g., identity, gender, and race) can be \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "105", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{u:2023:GBA, author = "Shuaiyi L(y)u and Kai Wang and Yuliang Wei and Hongri Liu and Qilin Fan and Bailing Wang", title = "{GNN}-based Advanced Feature Integration for {ICS} Anomaly Detection", journal = j-TIST, volume = "14", number = "6", pages = "106:1--106:??", month = dec, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3620676", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Jun 4 05:57:07 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3620676", abstract = "Recent adversaries targeting the Industrial Control Systems (ICSs) have started exploiting their sophisticated inherent contextual semantics such as the data associativity among heterogeneous field devices. In light of the subtlety rendered in these \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "106", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Xu:2023:DWP, author = "Meng Xu and Yechao She and Yang Jin and Jianping Wang", title = "Dynamic Weights and Prior Reward in Policy Fusion for Compound Agent Learning", journal = j-TIST, volume = "14", number = "6", pages = "107:1--107:??", month = dec, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3623405", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Jun 4 05:57:07 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3623405", abstract = "In Deep Reinforcement Learning (DRL) domain, a compound learning task is often decomposed into several sub-tasks in a divide-and-conquer manner, each trained separately and then fused concurrently to achieve the original task, referred to as policy \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "107", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Tran:2023:MNB, author = "Nhu-Thuat Tran and Hady W. Lauw", title = "Memory Network-Based Interpreter of User Preferences in Content-Aware Recommender Systems", journal = j-TIST, volume = "14", number = "6", pages = "108:1--108:??", month = dec, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3625239", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Jun 4 05:57:07 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3625239", abstract = "This article introduces a novel architecture for two objectives recommendation and interpretability in a unified model. We leverage textual content as a source of interpretability in content-aware recommender systems. The goal is to characterize user \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "108", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Zhou:2023:LEC, author = "Jianhang Zhou and Guancheng Wang and Shaoning Zeng and Bob Zhang", title = "Learning with {Euler} Collaborative Representation for Robust Pattern Analysis", journal = j-TIST, volume = "14", number = "6", pages = "109:1--109:??", month = dec, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3625235", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Jun 4 05:57:07 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3625235", abstract = "The Collaborative Representation (CR) framework has provided various effective and efficient solutions to pattern analysis. By leveraging between discriminative coefficient coding ($l_2$ regularization) and the best reconstruction quality (collaboration), \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "109", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Reyes:2023:PCD, author = "{\'O}scar Reyes and Eduardo P{\'e}rez", title = "Performing Cancer Diagnosis via an Isoform Expression Ranking-based {LSTM} Model", journal = j-TIST, volume = "14", number = "6", pages = "110:1--110:??", month = dec, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3625237", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Jun 4 05:57:07 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3625237", abstract = "The known set of genetic factors involved in the development of several types of cancer has considerably been expanded, thus easing to devise and implement better therapeutic strategies. The automatic diagnosis of cancer, however, remains as a complex \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "110", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Cheong:2023:AIC, author = "Chin Wang Cheong and Kejing Yin and William K. Cheung and Benjamin C. M. Fung and Jonathan Poon", title = "Adaptive Integration of Categorical and Multi-relational Ontologies with {EHR} Data for Medical Concept Embedding", journal = j-TIST, volume = "14", number = "6", pages = "111:1--111:??", month = dec, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3625224", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Jun 4 05:57:07 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3625224", abstract = "Representation learning has been applied to Electronic Health Records (EHR) for medical concept embedding and the downstream predictive analytics tasks with promising results. Medical ontologies can also be integrated to guide the learning so the \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "111", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Sun:2023:WYN, author = "Heli Sun and Chen Cao and Xuguang Chu and Tingting Hu and Junzhi Lu and Liang He and Zhi Wang and Hui He and Hui Xiong", title = "What Your Next Check-in Might Look Like: Next Check-in Behavior Prediction", journal = j-TIST, volume = "14", number = "6", pages = "112:1--112:??", month = dec, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3625234", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Jun 4 05:57:07 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3625234", abstract = "In recent years, the next-POI recommendation has become a trending research topic in the field of trajectory data mining. For protection of user privacy, users' complete GPS trajectories are difficult to obtain. The check-in information posted by users on \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "112", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Qu:2023:AAD, author = "Ao Qu and Yihong Tang and Wei Ma", title = "Adversarial Attacks on Deep Reinforcement Learning-based Traffic Signal Control Systems with Colluding Vehicles", journal = j-TIST, volume = "14", number = "6", pages = "113:1--113:??", month = dec, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3625236", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Jun 4 05:57:07 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3625236", abstract = "The rapid advancements of Internet of Things (IoT) and Artificial Intelligence (AI) have catalyzed the development of adaptive traffic control systems (ATCS) for smart cities. In particular, deep reinforcement learning (DRL) models produce state-of-the-. \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "113", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Xu:2023:SAR, author = "Ronghui Xu and Weiming Huang and Jun Zhao and Meng Chen and Liqiang Nie", title = "A Spatial and Adversarial Representation Learning Approach for Land Use Classification with {POIs}", journal = j-TIST, volume = "14", number = "6", pages = "114:1--114:25", month = dec, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3627824", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Jun 4 05:57:07 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3627824", abstract = "Points-of-interests (POIs) have been proven to be indicative for sensing urban land use in numerous studies. However, recent progress mainly relies on spatial co-occurrence patterns among POI categories, which falls short in utilizing the rich semantic \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "114", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Makhdomi:2024:TGF, author = "Aqsa Ashraf Makhdomi and Iqra Altaf Gillani", title = "Towards a Greener and Fairer Transportation System: a Survey of Route Recommendation Techniques", journal = j-TIST, volume = "15", number = "1", pages = "1:1--1:??", month = feb, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3627825", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Aug 29 08:03:38 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3627825", abstract = "In recent years, ride-hailing services have emerged as a popular means of transportation for the residents of urban areas. There is an inequality in the spatio-temporal distribution of demand and supply, which requires the proper recommendation of routes \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "1", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Lu:2024:ESR, author = "Wei-Qing Lu and Hai-Miao Hu and Jinzuo Yu and Shifeng Zhang and Hanzi Wang", title = "Explicit State Representation Guided Video-based Pedestrian Attribute Recognition", journal = j-TIST, volume = "15", number = "1", pages = "2:1--2:??", month = feb, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3626240", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Aug 29 08:03:38 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3626240", abstract = "The pedestrian attribute recognition aims to generate a structured description of pedestrians, which serves an important role in surveillance. Current works usually assume that the images and the specific pedestrian states, including pedestrian occlusion \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "2", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Wu:2024:HPT, author = "Kun Wu and Chengxiang Yin and Zhengping Che and Bo Jiang and Jian Tang and Zheng Guan and Gangyi Ding", title = "Human Pose Transfer with Augmented Disentangled Feature Consistency", journal = j-TIST, volume = "15", number = "1", pages = "3:1--3:??", month = feb, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3626241", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Aug 29 08:03:38 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3626241", abstract = "Deep generative models have made great progress in synthesizing images with arbitrary human poses and transferring the poses of one person to others. Though many different methods have been proposed to generate images with high visual fidelity, the main \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "3", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Bougie:2024:IIL, author = "Nicolas Bougie and Takashi Onishi and Yoshimasa Tsuruoka", title = "Interpretable Imitation Learning with Symbolic Rewards", journal = j-TIST, volume = "15", number = "1", pages = "4:1--4:??", month = feb, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3627822", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Aug 29 08:03:38 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3627822", abstract = "Sample inefficiency of deep reinforcement learning methods is a major obstacle for their use in real-world tasks as they naturally feature sparse rewards. In fact, this from-scratch approach is often impractical in environments where extreme negative \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "4", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Yang:2024:WSF, author = "Wenyuan Yang and Shuo Shao and Yue Yang and Xiyao Liu and Ximeng Liu and Zhihua Xia and Gerald Schaefer and Hui Fang", title = "Watermarking in Secure Federated Learning: a Verification Framework Based on Client-Side Backdooring", journal = j-TIST, volume = "15", number = "1", pages = "5:1--5:??", month = feb, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3630636", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Aug 29 08:03:38 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3630636", abstract = "Federated learning (FL) allows multiple participants to collaboratively build deep learning (DL) models without directly sharing data. Consequently, the issue of copyright protection in FL becomes important since unreliable participants may gain access to \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "5", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Dornaika:2024:OSM, author = "F. Dornaika", title = "One-step Multi-view Clustering with Consensus Graph and Data Representation Convolution", journal = j-TIST, volume = "15", number = "1", pages = "6:1--6:??", month = feb, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3630634", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Aug 29 08:03:38 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3630634", abstract = "Multi-view clustering aims to partition unlabeled patterns into disjoint clusters using consistent and complementary information derived from features of patterns in multiple views. Downstream methods perform this clustering sequentially: estimation of \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "6", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Zeng:2024:EAL, author = "Yingyan Zeng and Xiaoyu Chen and Ran Jin", title = "Ensemble Active Learning by Contextual Bandits for {AI} Incubation in Manufacturing", journal = j-TIST, volume = "15", number = "1", pages = "7:1--7:??", month = feb, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3627821", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Aug 29 08:03:38 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3627821", abstract = "An Industrial Cyber-physical System (ICPS) provides a digital foundation for data-driven decision-making by artificial intelligence (AI) models. However, the poor data quality (e.g., inconsistent distribution, imbalanced classes) of high-speed, large-. \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "7", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Belkhouja:2024:DDT, author = "Taha Belkhouja and Yan Yan and Janardhan Rao Doppa", title = "Out-of-distribution Detection in Time-series Domain: a Novel Seasonal Ratio Scoring Approach", journal = j-TIST, volume = "15", number = "1", pages = "8:1--8:??", month = feb, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3630633", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Aug 29 08:03:38 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3630633", abstract = "Safe deployment of time-series classifiers for real-world applications relies on the ability to detect the data that is not generated from the same distribution as training data. This task is referred to as out-of-distribution (OOD) detection. We consider \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "8", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Nguyen:2024:IGE, author = "Thanh Toan Nguyen and Thanh Tam Nguyen and Thanh Hung Nguyen and Hongzhi Yin and Thanh Thi Nguyen and Jun Jo and Quoc Viet Hung Nguyen", title = "Isomorphic Graph Embedding for Progressive Maximal Frequent Subgraph Mining", journal = j-TIST, volume = "15", number = "1", pages = "9:1--9:??", month = feb, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3630635", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Aug 29 08:03:38 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3630635", abstract = "Maximal frequent subgraph mining (MFSM) is the task of mining only maximal frequent subgraphs, i.e., subgraphs that are not a part of other frequent subgraphs. Although many intelligent systems require MFSM, MFSM is challenging compared to frequent \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "9", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Lv:2024:SOC, author = "Junwei Lv and Yuqi Chu and Jun Hu and Peipei Li and Xuegang Hu", title = "Second-order Confidence Network for Early Classification of Time Series", journal = j-TIST, volume = "15", number = "1", pages = "10:1--10:??", month = feb, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3631531", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Aug 29 08:03:38 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3631531", abstract = "Time series data are ubiquitous in a variety of disciplines. Early classification of time series, which aims to predict the class label of a time series as early and accurately as possible, is a significant but challenging task in many time-sensitive \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "10", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Saad:2024:QLI, author = "Yossef Saad and Joachim Meyer", title = "Quantifying Levels of Influence and Causal Responsibility in Dynamic Decision Making Events", journal = j-TIST, volume = "15", number = "1", pages = "11:1--11:??", month = feb, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3631611", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Aug 29 08:03:38 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3631611", abstract = "Intelligent systems support human operators' decision-making processes, many of which are dynamic and involve temporal changes in the decision-related parameters. As we increasingly depend on automation, it becomes imperative to understand and quantify \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "11", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Gao:2024:IRM, author = "Qiang Gao and Hongzhu Fu and Kunpeng Zhang and Goce Trajcevski and Xu Teng and Fan Zhou", title = "Inferring Real Mobility in Presence of Fake Check-ins Data", journal = j-TIST, volume = "15", number = "1", pages = "12:1--12:??", month = feb, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3604941", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Aug 29 08:03:38 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3604941", abstract = "Understanding human mobility has become an important aspect of location-based services in tasks such as personalized recommendation and individual moving pattern recognition, enabled by the large volumes of data from geo-tagged social media (GTSM). Prior \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "12", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Tang:2024:EGN, author = "Hao Tang and Cheng Wang and Jianguo Zheng and Changjun Jiang", title = "Enabling Graph Neural Networks for Semi-Supervised Risk Prediction in Online Credit Loan Services", journal = j-TIST, volume = "15", number = "1", pages = "13:1--13:??", month = feb, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3623401", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Aug 29 08:03:38 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3623401", abstract = "Graph neural networks (GNNs) are playing exciting roles in the application scenarios where features are hidden in information associations. Fraud prediction of online credit loan services (OCLSs) is such a typical scenario. But it has another rather \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "13", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Ma:2024:ESI, author = "Shuo Ma and Yingwei Zhang and Yiqiang Chen and Tao Xie and Shuchao Song and Ziyu Jia", title = "Exploring Structure Incentive Domain Adversarial Learning for Generalizable Sleep Stage Classification", journal = j-TIST, volume = "15", number = "1", pages = "14:1--14:??", month = feb, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3625238", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Aug 29 08:03:38 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3625238", abstract = "Sleep stage classification is crucial for sleep state monitoring and health interventions. In accordance with the standards prescribed by the American Academy of Sleep Medicine, a sleep episode follows a specific structure comprising five distinctive \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "14", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Wu:2024:HPD, author = "Yanzhao Wu and Ka-Ho Chow and Wenqi Wei and Ling Liu", title = "Hierarchical Pruning of Deep Ensembles with Focal Diversity", journal = j-TIST, volume = "15", number = "1", pages = "15:1--15:??", month = feb, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3633286", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Aug 29 08:03:38 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3633286", abstract = "Deep neural network ensembles combine the wisdom of multiple deep neural networks to improve the generalizability and robustness over individual networks. It has gained increasing popularity to study and apply deep ensemble techniques in the deep learning \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "15", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Wang:2024:VRT, author = "Yunchao Wang and Guodao Sun and Zihao Zhu and Tong Li and Ling Chen and Ronghua Liang", title = "{E$^2$Storyline}: Visualizing the Relationship with Triplet Entities and Event Discovery", journal = j-TIST, volume = "15", number = "1", pages = "16:1--16:??", month = feb, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3633519", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Aug 29 08:03:38 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3633519", abstract = "The narrative progression of events, evolving into a cohesive story, relies on the entity-entity relationships. Among the plethora of visualization techniques, storyline visualization has gained significant recognition for its effectiveness in offering an \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "16", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Chen:2024:RTF, author = "Shengyu Chen and Tianshu Bao and Peyman Givi and Can Zheng and Xiaowei Jia", title = "Reconstructing Turbulent Flows Using Spatio-temporal Physical Dynamics", journal = j-TIST, volume = "15", number = "1", pages = "17:1--17:??", month = feb, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3637491", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Aug 29 08:03:38 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3637491", abstract = "Accurate simulation of turbulent flows is of crucial importance in many branches of science and engineering. Direct numerical simulation (DNS) provides the highest fidelity means of capturing all intricate physics of turbulent transport. However, the \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "17", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Terroso-Saenz:2024:NAP, author = "Fernando Terroso-Saenz and Juan Morales-Garc{\'\i}a and Andres Mu{\~n}oz", title = "Nationwide Air Pollution Forecasting with Heterogeneous Graph Neural Networks", journal = j-TIST, volume = "15", number = "1", pages = "18:1--18:??", month = feb, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3637492", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Aug 29 08:03:38 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3637492", abstract = "Nowadays, air pollution is one of the most relevant environmental problems in most urban settings. Due to the utility in operational terms of anticipating certain pollution levels, several predictors based on Graph Neural Networks (GNN) have been proposed \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "18", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Khoshraftar:2024:SGR, author = "Shima Khoshraftar and Aijun An", title = "A Survey on Graph Representation Learning Methods", journal = j-TIST, volume = "15", number = "1", pages = "19:1--19:??", month = feb, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3633518", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Aug 29 08:03:38 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3633518", abstract = "Graph representation learning has been a very active research area in recent years. The goal of graph representation learning is to generate graph representation vectors that capture the structure and features of large graphs accurately. This is \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "19", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Zhao:2024:ELL, author = "Haiyan Zhao and Hanjie Chen and Fan Yang and Ninghao Liu and Huiqi Deng and Hengyi Cai and Shuaiqiang Wang and Dawei Yin and Mengnan Du", title = "Explainability for Large Language Models: a Survey", journal = j-TIST, volume = "15", number = "2", pages = "20:1--20:??", month = apr, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3639372", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Aug 29 08:03:40 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3639372", abstract = "Large language models (LLMs) have demonstrated impressive capabilities in natural language processing. However, their internal mechanisms are still unclear and this lack of transparency poses unwanted risks for downstream applications. Therefore, \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "20", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Zhang:2024:DDU, author = "Yunke Zhang and Tong Li and Yuan Yuan and Fengli Xu and Fan Yang and Funing Sun and Yong Li", title = "Demand-driven Urban Facility Visit Prediction", journal = j-TIST, volume = "15", number = "2", pages = "21:1--21:??", month = apr, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3625233", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Aug 29 08:03:40 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3625233", abstract = "Predicting citizens' visiting behaviors to urban facilities is instrumental for city governors and planners to detect inequalities in urban opportunities and optimize the distribution of facilities and resources. Previous works predict facility visits \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "21", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Tsouvalas:2024:LCL, author = "Vasileios Tsouvalas and Aaqib Saeed and Tanir Ozcelebi and Nirvana Meratnia", title = "Labeling Chaos to Learning Harmony: Federated Learning with Noisy Labels", journal = j-TIST, volume = "15", number = "2", pages = "22:1--22:??", month = apr, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3626242", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Aug 29 08:03:40 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3626242", abstract = "Federated Learning (FL) is a distributed machine learning paradigm that enables learning models from decentralized private datasets where the labeling effort is entrusted to the clients. While most existing FL approaches assume high-quality labels are \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "22", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Chen:2024:DSN, author = "Wei Chen and Hongjun Wang and Yinghui Zhang and Ping Deng and Zhipeng Luo and Tianrui Li", title = "{$T$}-Distributed Stochastic Neighbor Embedding for Co-Representation Learning", journal = j-TIST, volume = "15", number = "2", pages = "23:1--23:??", month = apr, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3627823", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Aug 29 08:03:40 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3627823", abstract = "Co-clustering is the simultaneous clustering of the samples and attributes of a data matrix that provides deeper insight into data than traditional clustering. However, there is a lack of representation learning algorithms that serve this mechanism of co-. \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "23", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Lee:2024:TFF, author = "Sangwon Lee and Junho Hong and Ling Liu and Wonik Choi", title = "{TS-Fastformer}: Fast Transformer for Time-series Forecasting", journal = j-TIST, volume = "15", number = "2", pages = "24:1--24:??", month = apr, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3630637", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Aug 29 08:03:40 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3630637", abstract = "Many real-world applications require precise and fast time-series forecasting. Recent trends in time-series forecasting models are shifting from LSTM-based models to Transformer-based models. However, the Transformer-based model has a limited ability to \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "24", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Lee:2024:EPC, author = "Eunji Lee and Sihyeon Kim and Sundong Kim and Soyeon Jung and Heeja Kim and Meeyoung Cha", title = "Explainable Product Classification for Customs", journal = j-TIST, volume = "15", number = "2", pages = "25:1--25:??", month = apr, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3635158", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Aug 29 08:03:40 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3635158", abstract = "The task of assigning internationally accepted commodity codes (aka HS codes) to traded goods is a critical function of customs offices. Like court decisions made by judges, this task follows the doctrine of precedent and can be nontrivial even for \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "25", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Neacsu:2024:EBA, author = "Ana Neacsu and Jean-Christophe Pesquet and Corneliu Burileanu", title = "{EMG}-Based Automatic Gesture Recognition Using {Lipschitz}-Regularized Neural Networks", journal = j-TIST, volume = "15", number = "2", pages = "26:1--26:??", month = apr, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3635159", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Aug 29 08:03:40 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3635159", abstract = "This article introduces a novel approach for building a robust Automatic Gesture Recognition system based on Surface Electromyographic (sEMG) signals, acquired at the forearm level. Our main contribution is to propose new constrained learning strategies \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "26", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Pessach:2024:FDP, author = "Dana Pessach and Tamir Tassa and Erez Shmueli", title = "Fairness-Driven Private Collaborative Machine Learning", journal = j-TIST, volume = "15", number = "2", pages = "27:1--27:??", month = apr, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3639368", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Aug 29 08:03:40 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3639368", abstract = "The performance of machine learning algorithms can be considerably improved when trained over larger datasets. In many domains, such as medicine and finance, larger datasets can be obtained if several parties, each having access to limited amounts of data,. \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "27", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Wu:2024:EDK, author = "Zhiyuan Wu and Sheng Sun and Yuwei Wang and Min Liu and Quyang Pan and Junbo Zhang and Zeju Li and Qingxiang Liu", title = "Exploring the Distributed Knowledge Congruence in Proxy-data-free Federated Distillation", journal = j-TIST, volume = "15", number = "2", pages = "28:1--28:??", month = apr, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3639369", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Aug 29 08:03:40 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3639369", abstract = "Federated learning (FL) is a privacy-preserving machine learning paradigm in which the server periodically aggregates local model parameters from cli ents without assembling their private data. Constrained communication and personalization requirements \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "28", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Yuan:2024:GDA, author = "Yuan Yuan and Jingtao Ding and Huandong Wang and Depeng Jin", title = "Generating Daily Activities with Need Dynamics", journal = j-TIST, volume = "15", number = "2", pages = "29:1--29:??", month = apr, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3637493", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Aug 29 08:03:40 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3637493", abstract = "Daily activity data recording individuals' various activities in daily life are widely used in many applications such as activity scheduling, activity recommendation, and policymaking. Though with high value, its accessibility is limited due to high \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "29", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Xu:2024:SCC, author = "Meng Xu and Xinhong Chen and Yechao She and Yang Jin and Guanyi Zhao and Jianping Wang", title = "Strengthening Cooperative Consensus in Multi-Robot Confrontation", journal = j-TIST, volume = "15", number = "2", pages = "30:1--30:??", month = apr, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3639371", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Aug 29 08:03:40 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3639371", abstract = "Multi-agent reinforcement learning (MARL) has proven effective in training multi-robot confrontation, such as StarCraft and robot soccer games. However, the current joint action policies utilized in MARL have been unsuccessful in recognizing and \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "30", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Han:2024:RND, author = "Jin Han and Yun-Feng Ren and Alessandro Brighente and Mauro Conti", title = "{RANGO}: a Novel Deep Learning Approach to Detect Drones Disguising from Video Surveillance Systems", journal = j-TIST, volume = "15", number = "2", pages = "31:1--31:??", month = apr, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3641282", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Aug 29 08:03:40 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3641282", abstract = "Video surveillance systems provide means to detect the presence of potentially malicious drones in the surroundings of critical infrastructures. In particular, these systems collect images and feed them to a deep-learning classifier able to detect the \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "31", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Zong:2024:RLS, author = "Zefang Zong and Xia Tong and Meng Zheng and Yong Li", title = "Reinforcement Learning for Solving Multiple Vehicle Routing Problem with Time Window", journal = j-TIST, volume = "15", number = "2", pages = "32:1--32:??", month = apr, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3625232", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Aug 29 08:03:40 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3625232", abstract = "Vehicle routing problem with time window (VRPTW) is of great importance for a wide spectrum of services and real-life applications, such as online take-out and car-hailing platforms. A promising method should generate high-qualified solutions within \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "32", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Liu:2024:EKG, author = "Jhih-Chen Liu and Chiao-Ting Chen and Chi Lee and Szu-Hao Huang", title = "Evolving Knowledge Graph Representation Learning with Multiple Attention Strategies for Citation Recommendation System", journal = j-TIST, volume = "15", number = "2", pages = "33:1--33:??", month = apr, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3635273", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Aug 29 08:03:40 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3635273", abstract = "The growing number of publications in the field of artificial intelligence highlights the need for researchers to enhance their efficiency in searching for relevant articles. Most paper recommendation models either rely on simplistic citation \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "33", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Jiang:2024:VVN, author = "Fenyu Jiang and Huandong Wang and Yong Li", title = "{VesNet}: a Vessel Network for Jointly Learning Route Pattern and Future Trajectory", journal = j-TIST, volume = "15", number = "2", pages = "34:1--34:??", month = apr, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3639370", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Aug 29 08:03:40 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3639370", abstract = "Vessel trajectory prediction is the key to maritime applications such as traffic surveillance, collision avoidance, anomaly detection, and so on. Making predictions more precisely requires a better understanding of the moving trend for a particular vessel \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "34", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Chen:2024:CCF, author = "Chiao-Ting Chen and Chi Lee and Szu-Hao Huang and Wen-Chih Peng", title = "Credit Card Fraud Detection via Intelligent Sampling and Self-supervised Learning", journal = j-TIST, volume = "15", number = "2", pages = "35:1--35:??", month = apr, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3641283", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Aug 29 08:03:40 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3641283", abstract = "The significant increase in credit card transactions can be attributed to the rapid growth of online shopping and digital payments, particularly during the COVID-19 pandemic. To safeguard cardholders, e-commerce companies, and financial institutions, the \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "35", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Job:2024:OTS, author = "Simi Job and Xiaohui Tao and Lin Li and Haoran Xie and Taotao Cai and Jianming Yong and Qing Li", title = "Optimal Treatment Strategies for Critical Patients with Deep Reinforcement Learning", journal = j-TIST, volume = "15", number = "2", pages = "36:1--36:??", month = apr, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3643856", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Aug 29 08:03:40 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3643856", abstract = "Personalized clinical decision support systems are increasingly being adopted due to the emergence of data-driven technologies, with this approach now gaining recognition in critical care. The task of incorporating diverse patient conditions and treatment \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "36", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Hao:2024:SSB, author = "Mai Hao and Ming Cai and Minghui Fang and Linlin You", title = "{SiG}: a {Siamese}-Based Graph Convolutional Network to Align Knowledge in Autonomous Transportation Systems", journal = j-TIST, volume = "15", number = "2", pages = "37:1--37:??", month = apr, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3643861", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Aug 29 08:03:40 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3643861", abstract = "Domain knowledge is gradually renovating its attributes to exhibit distinct features in autonomy, propelled by the shift of modern transportation systems (TS) toward autonomous TS (ATS) comprising three progressive generations. The knowledge graph (KG) \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "37", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Ang:2024:TIM, author = "Gary Ang and Ee-Peng Lim", title = "Temporal Implicit Multimodal Networks for Investment and Risk Management", journal = j-TIST, volume = "15", number = "2", pages = "38:1--38:??", month = apr, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3643855", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Aug 29 08:03:40 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3643855", abstract = "Many deep learning works on financial time-series forecasting focus on predicting future prices/returns of individual assets with numerical price-related information for trading, and hence propose models designed for univariate, single-task, and/or \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "38", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Chang:2024:SEL, author = "Yupeng Chang and Xu Wang and Jindong Wang and Yuan Wu and Linyi Yang and Kaijie Zhu and Hao Chen and Xiaoyuan Yi and Cunxiang Wang and Yidong Wang and Wei Ye and Yue Zhang and Yi Chang and Philip S. Yu and Qiang Yang and Xing Xie", title = "A Survey on Evaluation of Large Language Models", journal = j-TIST, volume = "15", number = "3", pages = "39:1--39:??", month = jun, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3641289", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Aug 29 08:03:41 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3641289", abstract = "Large language models (LLMs) are gaining increasing popularity in both academia and industry, owing to their unprecedented performance in various applications. As LLMs continue to play a vital role in both research and daily use, their evaluation becomes \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "39", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Molho:2024:DLS, author = "Dylan Molho and Jiayuan Ding and Wenzhuo Tang and Zhaoheng Li and Hongzhi Wen and Yixin Wang and Julian Venegas and Wei Jin and Renming Liu and Runze Su and Patrick Danaher and Robert Yang and Yu Leo Lei and Yuying Xie and Jiliang Tang", title = "Deep Learning in Single-cell Analysis", journal = j-TIST, volume = "15", number = "3", pages = "40:1--40:??", month = jun, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3641284", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Aug 29 08:03:41 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3641284", abstract = "Single-cell technologies are revolutionizing the entire field of biology. The large volumes of data generated by single-cell technologies are high dimensional, sparse, and heterogeneous and have complicated dependency structures, making analyses using \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "40", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Ge:2024:MNM, author = "Xuri Ge and Joemon M. Jose and Songpei Xu and Xiao Liu and Hu Han", title = "{MGRR-Net}: Multi-level Graph Relational Reasoning Network for Facial Action Unit Detection", journal = j-TIST, volume = "15", number = "3", pages = "41:1--41:??", month = jun, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3643863", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Aug 29 08:03:41 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3643863", abstract = "The Facial Action Coding System (FACS) encodes the action units (AUs) in facial images, which has attracted extensive research attention due to its wide use in facial expression analysis. Many methods that perform well on automatic facial action unit (AU) \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "41", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Yang:2024:BSN, author = "Qin Yang and Ramviyas Parasuraman", title = "{Bayesian} Strategy Networks Based Soft Actor-Critic Learning", journal = j-TIST, volume = "15", number = "3", pages = "42:1--42:??", month = jun, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3643862", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Aug 29 08:03:41 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3643862", abstract = "A strategy refers to the rules that the agent chooses the available actions to achieve goals. Adopting reasonable strategies is challenging but crucial for an intelligent agent with limited resources working in hazardous, unstructured, and dynamic \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "42", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Samarakoon:2024:IRR, author = "S. M. Bhagya P. Samarakoon and M. A. Viraj J. Muthugala and Mohan Rajesh Elara", title = "Internal Rehearsals for a Reconfigurable Robot to Improve Area Coverage Performance", journal = j-TIST, volume = "15", number = "3", pages = "43:1--43:??", month = jun, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3643854", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Aug 29 08:03:41 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3643854", abstract = "Reconfigurable robots are deployed for applications demanding area coverage, such as cleaning and inspections. Reconfiguration per context, considering beyond a small set of predefined shapes, is crucial for area coverage performance. However, the \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "43", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Kim:2024:GRG, author = "Bum Jun Kim and Hyeyeon Choi and Hyeonah Jang and Sang Woo Kim", title = "Guidelines for the Regularization of Gammas in Batch Normalization for Deep Residual Networks", journal = j-TIST, volume = "15", number = "3", pages = "44:1--44:??", month = jun, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3643860", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Aug 29 08:03:41 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3643860", abstract = "L$_2$ regularization for weights in neural networks is widely used as a standard training trick. In addition to weights, the use of batch normalization involves an additional trainable parameter $\gamma$, which acts as a scaling factor. However, L$_2$ regularization \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "44", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{He:2024:MDS, author = "Weidong He and Zhi Li and Hao Wang and Tong Xu and Zhefeng Wang and Baoxing Huai and Nicholas Jing Yuan and Enhong Chen", title = "Multimodal Dialogue Systems via Capturing Context-aware Dependencies and Ordinal Information of Semantic Elements", journal = j-TIST, volume = "15", number = "3", pages = "45:1--45:??", month = jun, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3645099", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Aug 29 08:03:41 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3645099", abstract = "The topic of multimodal conversation systems has recently garnered significant attention across various industries, including travel and retail, among others. While pioneering works in this field have shown promising performance, they often focus solely \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "45", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Gherardini:2024:CCA, author = "Luca Gherardini and Varun Ravi Varma and Karol Capa{\l}a and Roger Woods and Jose Sousa", title = "{CACTUS}: a Comprehensive Abstraction and Classification Tool for Uncovering Structures", journal = j-TIST, volume = "15", number = "3", pages = "46:1--46:??", month = jun, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3649459", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Aug 29 08:03:41 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3649459", abstract = "The availability of large datasets is providing the impetus for driving many current artificial intelligent developments. However, specific challenges arise in developing solutions that exploit small datasets, mainly due to practical and cost-effective \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "46", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Vukadin:2024:AAB, author = "Davor Vukadin and Petar Afri{\'c} and Marin Sili{\'c} and Goran Delac", title = "Advancing Attribution-Based Neural Network Explainability through Relative Absolute Magnitude Layer-Wise Relevance Propagation and Multi-Component Evaluation", journal = j-TIST, volume = "15", number = "3", pages = "47:1--47:??", month = jun, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3649458", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Aug 29 08:03:41 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3649458", abstract = "Recent advancement in deep-neural network performance led to the development of new state-of-the-art approaches in numerous areas. However, the black-box nature of neural networks often prohibits their use in areas where model explainability and model \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "47", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Liang:2024:LCM, author = "Yunji Liang and Nengzhen Chen and Zhiwen Yu and Lei Tang and Hongkai Yu and Bin Guo and Daniel Dajun Zeng", title = "Learning Cross-modality Interaction for Robust Depth Perception of Autonomous Driving", journal = j-TIST, volume = "15", number = "3", pages = "48:1--48:??", month = jun, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3650039", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Aug 29 08:03:41 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3650039", abstract = "As one of the fundamental tasks of autonomous driving, depth perception aims to perceive physical objects in three dimensions and to judge their distances away from the ego vehicle. Although great efforts have been made for depth perception, LiDAR-based \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "48", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Gupta:2024:TTA, author = "Vinayak Gupta and Srikanta Bedathur", title = "Tapestry of Time and Actions: Modeling Human Activity Sequences Using Temporal Point Process Flows", journal = j-TIST, volume = "15", number = "3", pages = "49:1--49:??", month = jun, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3650045", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Aug 29 08:03:41 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3650045", abstract = "Human beings always engage in a vast range of activities and tasks that demonstrate their ability to adapt to different scenarios. These activities can range from the simplest daily routines, like walking and sitting, to multi-level complex endeavors such \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "49", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Yi:2024:DCM, author = "Jing Yi and Zhenzhong Chen", title = "Deconfounded Cross-modal Matching for Content-based Micro-video Background Music Recommendation", journal = j-TIST, volume = "15", number = "3", pages = "50:1--50:??", month = jun, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3650042", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Aug 29 08:03:41 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3650042", abstract = "Object-oriented micro-video background music recommendation is a complicated task where the matching degree between videos and background music is a major issue. However, music selections in user-generated content (UGC) are prone to selection bias caused \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "50", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Fu:2024:MMH, author = "Chaofan Fu and Pengyang Yu and Yanwei Yu and Chao Huang and Zhongying Zhao and Junyu Dong", title = "{MHGCN+}: Multiplex Heterogeneous Graph Convolutional Network", journal = j-TIST, volume = "15", number = "3", pages = "51:1--51:??", month = jun, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3650046", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Aug 29 08:03:41 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3650046", abstract = "Heterogeneous graph convolutional networks have gained great popularity in tackling various network analytical tasks on heterogeneous graph data, ranging from link prediction to node classification. However, most existing works ignore the relation \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "51", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Zhang:2024:GTF, author = "Xiaojin Zhang and Lixin Fan and Siwei Wang and Wenjie Li and Kai Chen and Qiang Yang", title = "A Game-theoretic Framework for Privacy-preserving Federated Learning", journal = j-TIST, volume = "15", number = "3", pages = "52:1--52:??", month = jun, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3656049", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Aug 29 08:03:41 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3656049", abstract = "In federated learning, benign participants aim to optimize a global model collaboratively. However, the risk of privacy leakage cannot be ignored in the presence of semi-honest adversaries. Existing research has focused either on designing protection \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "52", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Zhong:2024:SSB, author = "Shenghai Zhong and Shu Guo and Jing Liu and Hongren Huang and Lihong Wang and Jianxin Li and Chen Li and Yiming Hei", title = "Self-supervised Bipartite Graph Representation Learning: a {Dirichlet} Max-margin Matrix Factorization Approach", journal = j-TIST, volume = "15", number = "3", pages = "53:1--53:??", month = jun, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3645098", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Aug 29 08:03:41 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3645098", abstract = "Bipartite graph representation learning aims to obtain node embeddings by compressing sparse vectorized representations of interactions between two types of nodes, e.g., users and items. Incorporating structural attributes among homogeneous nodes, such as \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "53", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Zeng:2024:EPM, author = "Jinwei Zeng and Guozhen Zhang and Jian Yuan and Yong Li and Depeng Jin", title = "Empowering Predictive Modeling by {GAN-based} Causal Information Learning", journal = j-TIST, volume = "15", number = "3", pages = "54:1--54:??", month = jun, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3652610", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Aug 29 08:03:41 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3652610", abstract = "Generally speaking, we can easily specify many causal relationships in the prediction tasks of ubiquitous computing, such as human activity prediction, mobility prediction, and health prediction. However, most of the existing methods in these fields \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "54", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Zhang:2024:MLF, author = "Xiaojin Zhang and Yan Kang and Lixin Fan and Kai Chen and Qiang Yang", title = "A Meta-Learning Framework for Tuning Parameters of Protection Mechanisms in Trustworthy Federated Learning", journal = j-TIST, volume = "15", number = "3", pages = "55:1--55:??", month = jun, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3652612", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Aug 29 08:03:41 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3652612", abstract = "Trustworthy federated learning typically leverages protection mechanisms to guarantee privacy. However, protection mechanisms inevitably introduce utility loss or efficiency reduction while protecting data privacy. Therefore, protection mechanisms and \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "55", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Lewis:2024:EFG, author = "Cody Lewis and Vijay Varadharajan and Nasimul Noman and Uday Tupakula", title = "Ensuring Fairness and Gradient Privacy in Personalized Heterogeneous Federated Learning", journal = j-TIST, volume = "15", number = "3", pages = "56:1--56:??", month = jun, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3652613", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Aug 29 08:03:41 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3652613", abstract = "With the increasing tension between conflicting requirements of the availability of large amounts of data for effective machine learning-based analysis, and for ensuring their privacy, the paradigm of federated learning has emerged, a distributed machine \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "56", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Bano:2024:FFC, author = "Saira Bano and Nicola Tonellotto and Pietro Cassar{\`a} and Alberto Gotta", title = "{FedCMD}: a Federated Cross-modal Knowledge Distillation for Drivers' Emotion Recognition", journal = j-TIST, volume = "15", number = "3", pages = "57:1--57:??", month = jun, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3650040", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Aug 29 08:03:41 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3650040", abstract = "Emotion recognition has attracted a lot of interest in recent years in various application areas such as healthcare and autonomous driving. Existing approaches to emotion recognition are based on visual, speech, or psychophysiological signals. However, \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "57", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Li:2024:PAT, author = "Rongchang Li and Tianyang Xu and Xiao-Jun Wu and Zhongwei Shen and Josef Kittler", title = "Perceiving Actions via Temporal Video Frame Pairs", journal = j-TIST, volume = "15", number = "3", pages = "58:1--58:??", month = jun, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3652611", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Aug 29 08:03:41 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3652611", abstract = "Video action recognition aims at classifying the action category in given videos. In general, semantic-relevant video frame pairs reflect significant action patterns such as object appearance variation and abstract temporal concepts like speed, rhythm, \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "58", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Wang:2024:SBG, author = "Pengyu Wang and Xuechen Luo and Wenxin Tai and Kunpeng Zhang and Goce Trajcevsky and Fan Zhou", title = "Score-based Graph Learning for Urban Flow Prediction", journal = j-TIST, volume = "15", number = "3", pages = "59:1--59:??", month = jun, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3655629", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Aug 29 08:03:41 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3655629", abstract = "Accurate urban flow prediction (UFP) is crucial for a range of smart city applications such as traffic management, urban planning, and risk assessment. To capture the intrinsic characteristics of urban flow, recent efforts have utilized spatial and \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "59", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{DeSmet:2024:HCA, author = "Chance DeSmet and Diane Cook", title = "{HydraGAN}: a Cooperative Agent Model for Multi-Objective Data Generation", journal = j-TIST, volume = "15", number = "3", pages = "60:1--60:??", month = jun, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3653982", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Aug 29 08:03:41 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3653982", abstract = "Generative adversarial networks have become a de facto approach to generate synthetic data points that resemble their real counterparts. We tackle the situation where the realism of individual samples is not the sole criterion for synthetic data \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "60", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Zhou:2024:QBR, author = "Cangqi Zhou and Hui Chen and Jing Zhang and Qianmu Li and Dianming Hu", title = "Quintuple-based Representation Learning for Bipartite Heterogeneous Networks", journal = j-TIST, volume = "15", number = "3", pages = "61:1--61:??", month = jun, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3653978", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Aug 29 08:03:41 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3653978", abstract = "Recent years have seen rapid progress in network representation learning, which removes the need for burdensome feature engineering and facilitates downstream network-based tasks. In reality, networks often exhibit heterogeneity, which means there may \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "61", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Sekulic:2024:AUL, author = "Ivan Sekuli{\'c} and Mohammad Alinannejadi and Fabio Crestani", title = "Analysing Utterances in {LLM-Based} User Simulation for Conversational Search", journal = j-TIST, volume = "15", number = "3", pages = "62:1--62:??", month = jun, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3650041", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Aug 29 08:03:41 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3650041", abstract = "Clarifying underlying user information needs by asking clarifying questions is an important feature of modern conversational search systems. However, evaluation of such systems through answering prompted clarifying questions requires significant human \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "62", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Miao:2024:FMC, author = "Runxuan Miao and Erdem Koyuncu", title = "Federated Momentum Contrastive Clustering", journal = j-TIST, volume = "15", number = "4", pages = "63:1--63:??", month = aug, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3653981", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Aug 29 08:03:44 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3653981", abstract = "Self-supervised representation learning and deep clustering are mutually beneficial to learn high-quality representations and cluster data simultaneously in centralized settings. However, it is not always feasible to gather large amounts of data at a \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "63", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Al-Bazzaz:2024:EFM, author = "Hussein Al-Bazzaz and Muhammad Azam and Manar Amayri and Nizar Bouguila", title = "Explainable finite mixture of mixtures of bounded asymmetric generalized {Gaussian} and Uniform distributions learning for energy demand management", journal = j-TIST, volume = "15", number = "4", pages = "64:1--64:??", month = aug, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3653980", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Aug 29 08:03:44 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3653980", abstract = "We introduce a mixture of mixtures of bounded asymmetric generalized Gaussian and uniform distributions. Based on this framework, we propose model-based classification and model-based clustering algorithms. We develop an objective function for the minimum \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "64", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Piao:2024:DEL, author = "Hai Yin Piao and Shengqi Yang and Hechang Chen and Junnan Li and Jin Yu and Xuanqi Peng and Xin Yang and Zhen Yang and Zhixiao Sun and Yi Chang", title = "Discovering Expert-Level Air Combat Knowledge via Deep Excitatory-Inhibitory Factorized Reinforcement Learning", journal = j-TIST, volume = "15", number = "4", pages = "65:1--65:??", month = aug, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3653979", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Aug 29 08:03:44 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3653979", abstract = "Artificial Intelligence (AI) has achieved a wide range of successes in autonomous air combat decision-making recently. Previous research demonstrated that AI-enabled air combat approaches could even acquire beyond human-level capabilities. However, there \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "65", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Chen:2024:RSA, author = "Xu Chen", title = "Robust Structure-Aware Graph-based Semi-Supervised Learning: Batch and Recursive Processing", journal = j-TIST, volume = "15", number = "4", pages = "66:1--66:??", month = aug, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3653986", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Aug 29 08:03:44 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3653986", abstract = "Graph-based semi-supervised learning plays an important role in large scale image classification tasks. However, the problem becomes very challenging in the presence of noisy labels and outliers. Moreover, traditional robust semi-supervised learning \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "66", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Jian:2024:CGC, author = "Meng Jian and Yulong Bai and Xusong Fu and Jingjing Guo and Ge Shi and Lifang Wu", title = "Counterfactual Graph Convolutional Learning for Personalized Recommendation", journal = j-TIST, volume = "15", number = "4", pages = "67:1--67:??", month = aug, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3655632", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Aug 29 08:03:44 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3655632", abstract = "Recently, recommender systems have witnessed the fast evolution of Internet services. However, it suffers hugely from inherent bias and sparsity issues in interactions. The conventional uniform embedding learning policies fail to utilize the imbalanced \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "67", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Zhu:2024:DCR, author = "Yaochen Zhu and Jing Yi and Jiayi Xie and Zhenzhong Chen", title = "Deep Causal Reasoning for Recommendations", journal = j-TIST, volume = "15", number = "4", pages = "68:1--68:??", month = aug, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3653985", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Aug 29 08:03:44 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3653985", abstract = "Traditional recommender systems aim to estimate a user's rating to an item based on observed ratings from the population. As with all observational studies, hidden confounders, which are factors that affect both item exposures and user ratings, lead to a \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "68", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Ting:2024:EEW, author = "Lo Pang-Yun Ting and Rong Chao and Chai-Shi Chang and Kun-Ta Chuang", title = "An Explore-Exploit Workload-Bounded Strategy for Rare Event Detection in Massive Energy Sensor Time Series", journal = j-TIST, volume = "15", number = "4", pages = "69:1--69:??", month = aug, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3657641", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Aug 29 08:03:44 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3657641", abstract = "With the rise of Internet-of-Things devices, the analysis of sensor-generated energy time series data has become increasingly important. This is especially crucial for detecting rare events like unusual electricity usage or water leakages in residential \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "69", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Zhao:2024:CCG, author = "Zhuo Zhao and Guangyou Zhou and Zhiwen Xie and Lingfei Wu and Jimmy Xiangji Huang", title = "{CGKPN}: Cross-Graph Knowledge Propagation Network with Adaptive Connection for Reasoning-Based Machine Reading Comprehension", journal = j-TIST, volume = "15", number = "4", pages = "70:1--70:??", month = aug, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3658673", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Aug 29 08:03:44 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3658673", abstract = "The task of machine reading comprehension (MRC) is to enable machine to read and understand a piece of text and then answer the corresponding question correctly. This task requires machine to not only be able to perform semantic understanding but also \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "70", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Pai:2024:IDD, author = "Yu-Tung Pai and Nien-En Sun and Cheng-Te Li and Shou-de Lin", title = "Incremental Data Drifting: Evaluation Metrics, Data Generation, and Approach Comparison", journal = j-TIST, volume = "15", number = "4", pages = "71:1--71:??", month = aug, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3655630", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Aug 29 08:03:44 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3655630", abstract = "Incremental data drifting is a common problem when employing a machine-learning model in industrial applications. The underlying data distribution evolves gradually, e.g., users change their buying preferences on an E-commerce website over time. The \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "71", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Yao:2024:SIR, author = "Lina Yao and Julian McAuley and Xianzhi Wang and Dietmar Jannach", title = "Special Issue on Responsible Recommender Systems {Part 1}", journal = j-TIST, volume = "15", number = "4", pages = "72:1--72:??", month = aug, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3663528", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Aug 29 08:03:44 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3663528", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "72", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Ahn:2024:BPU, author = "Yongsu Ahn and Yu-Ru Lin", title = "Break Out of a Pigeonhole: a Unified Framework for Examining Miscalibration, Bias, and Stereotype in Recommender Systems", journal = j-TIST, volume = "15", number = "4", pages = "73:1--73:??", month = aug, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3650044", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Aug 29 08:03:44 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3650044", abstract = "Despite the benefits of personalizing items and information tailored to users' needs, it has been found that recommender systems tend to introduce biases that favor popular items or certain categories of items and dominant user groups. In this study, we \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "73", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Coppolillo:2024:BQS, author = "Erica Coppolillo and Marco Minici and Ettore Ritacco and Luciano Caroprese and Francesco Pisani and Giuseppe Manco", title = "Balanced Quality Score: Measuring Popularity Debiasing in Recommendation", journal = j-TIST, volume = "15", number = "4", pages = "74:1--74:??", month = aug, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3650043", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Aug 29 08:03:44 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3650043", abstract = "Popularity bias is the tendency of recommender systems to further suggest popular items while disregarding niche ones, hence giving no chance for items with low popularity to emerge. Although the literature is rich in debiasing techniques, it still lacks \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "74", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Duran:2024:ODU, author = "Paula G. Duran and Pere Gilabert and Santi Segu{\'\i} and Jordi Vitri{\`a}", title = "Overcoming Diverse Undesired Effects in Recommender Systems: a Deontological Approach", journal = j-TIST, volume = "15", number = "4", pages = "75:1--75:??", month = aug, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3643857", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Aug 29 08:03:44 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3643857", abstract = "In today's digital landscape, recommender systems have gained ubiquity as a means of directing users toward personalized products, services, and content. However, despite their widespread adoption and a long track of research, these systems are not immune \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "75", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Liu:2024:PPP, author = "Yuwen Liu and Xiaokang Zhou and Huaizhen Kou and Yawu Zhao and Xiaolong Xu and Xuyun Zhang and Lianyong Qi", title = "Privacy-preserving Point-of-interest Recommendation based on Simplified Graph Convolutional Network for Geological Traveling", journal = j-TIST, volume = "15", number = "4", pages = "76:1--76:??", month = aug, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3620677", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Aug 29 08:03:44 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3620677", abstract = "The provision of privacy-preserving recommendations for geological tourist attractions is an important research area. The historical check-in data collected from location-based social networks (LBSNs) can be utilized to mine their preferences, thereby \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "76", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Li:2024:DFR, author = "Zhitao Li and Zhaohao Lin and Feng Liang and Weike Pan and Qiang Yang and Zhong Ming", title = "Decentralized Federated Recommendation with Privacy-aware Structured Client-level Graph", journal = j-TIST, volume = "15", number = "4", pages = "77:1--77:??", month = aug, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3641287", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Aug 29 08:03:44 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3641287", abstract = "Recommendation models are deployed in a variety of commercial applications to provide personalized services for users. However, most of them rely on the users' original rating records that are often collected by a centralized server for model training, \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "77", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Ali:2024:RRS, author = "Waqar Ali and Rajesh Kumar and Xiangmin Zhou and Jie Shao", title = "Responsible Recommendation Services with Blockchain Empowered Asynchronous Federated Learning", journal = j-TIST, volume = "15", number = "4", pages = "78:1--78:??", month = aug, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3633520", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Aug 29 08:03:44 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/bitcoin.bib; https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3633520", abstract = "Privacy and trust are highly demanding in practical recommendation engines. Although Federated Learning (FL) has significantly addressed privacy concerns, commercial operators are still worried about several technical challenges while bringing FL into \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "78", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Gao:2024:NBB, author = "Tieliang Gao and Li Duan and Lufeng Feng and Wei Ni and Quan Z. Sheng", title = "A Novel Blockchain-based Responsible Recommendation System for Service Process Creation and Recommendation", journal = j-TIST, volume = "15", number = "4", pages = "79:1--79:??", month = aug, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3643858", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Aug 29 08:03:44 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/bitcoin.bib; https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3643858", abstract = "Service composition platforms play a crucial role in creating personalized service processes. Challenges, including the risk of tampering with service data during service invocation and the potential single point of failure in centralized service \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "79", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Li:2024:FQR, author = "Nan Li and Bo Kang and Jefrey Lijffijt and Tijl {De Bie}", title = "{FEIR}: Quantifying and Reducing Envy and Inferiority for Fair Recommendation of Limited Resources", journal = j-TIST, volume = "15", number = "4", pages = "80:1--80:??", month = aug, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3643891", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Aug 29 08:03:44 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3643891", abstract = "Recommendation in settings such as e-recruitment and online dating involves distributing limited opportunities, which differs from recommending practically unlimited goods such as in e-commerce or music recommendation. This setting calls for novel \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "80", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Li:2024:BHE, author = "Ming Li and Lin Li and Xiaohui Tao and Zhongwei Xie and Qing Xie and Jingling Yuan", title = "Boosting Healthiness Exposure in Category-Constrained Meal Recommendation Using Nutritional Standards", journal = j-TIST, volume = "15", number = "4", pages = "81:1--81:??", month = aug, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3643859", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Aug 29 08:03:44 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3643859", abstract = "Food computing, a newly emerging topic, is closely linked to human life through computational methodologies. Meal recommendation, a food-related study about human health, aims to provide users a meal with courses constrained from specific categories (e.g.,. \ldots{})", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "81", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Ma:2024:PFR, author = "Jianghong Ma and Huiyue Sun and Dezhao Yang and Haijun Zhang", title = "Personalized Fashion Recommendations for Diverse Body Shapes with Contrastive Multimodal Cross-Attention Network", journal = j-TIST, volume = "15", number = "4", pages = "82:1--82:??", month = aug, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3637217", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Aug 29 08:03:44 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3637217", abstract = "Fashion recommendation has become a prominent focus in the realm of online shopping, with various tasks being explored to enhance the customer experience. Recent research has particularly emphasized fashion recommendation based on body shapes, yet a \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "82", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Elahi:2024:KGE, author = "Ehsan Elahi and Sajid Anwar and Babar Shah and Zahid Halim and Abrar Ullah and Imad Rida and Muhammad Waqas", title = "Knowledge Graph Enhanced Contextualized Attention-Based Network for Responsible User-Specific Recommendation", journal = j-TIST, volume = "15", number = "4", pages = "83:1--83:??", month = aug, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3641288", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Aug 29 08:03:44 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3641288", abstract = "With ever-increasing dataset size and data storage capacity, there is a strong need to build systems that can effectively utilize these vast datasets to extract valuable information. Large datasets often exhibit sparsity and pose cold start problems, \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "83", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Wang:2024:TRS, author = "Shoujin Wang and Xiuzhen Zhang and Yan Wang and Francesco Ricci", title = "Trustworthy Recommender Systems", journal = j-TIST, volume = "15", number = "4", pages = "84:1--84:??", month = aug, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3627826", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Aug 29 08:03:44 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3627826", abstract = "Recommender systems (RSs) aim at helping users to effectively retrieve items of their interests from a large catalogue. For a quite long time, researchers and practitioners have been focusing on developing accurate RSs. Recent years have witnessed an \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "84", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Yu:2024:MMS, author = "Dongjin Yu and Xingliang Wang and Yu Xiong and Xudong Shen and Runze Wu and Dongjing Wang and Zhene Zou and Guandong Xu", title = "{MHANER}: a Multi-source Heterogeneous Graph Attention Network for Explainable Recommendation in Online Games", journal = j-TIST, volume = "15", number = "4", pages = "85:1--85:??", month = aug, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3626243", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Aug 29 08:03:44 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3626243", abstract = "Recommender system helps address information overload problem and satisfy consumers' personalized requirement in many applications such as e-commerce, social networks, and in-game store. However, existing approaches mainly focus on improving the accuracy \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "85", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Ren:2024:EKG, author = "Xuhui Ren and Tong Chen and Quoc Viet Hung Nguyen and Lizhen Cui and Zi Huang and Hongzhi Yin", title = "Explicit Knowledge Graph Reasoning for Conversational Recommendation", journal = j-TIST, volume = "15", number = "4", pages = "86:1--86:??", month = aug, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3637216", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Aug 29 08:03:44 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3637216", abstract = "Traditional recommender systems estimate user preference on items purely based on historical interaction records, thus failing to capture fine-grained yet dynamic user interests and letting users receive recommendation only passively. Recent \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "86", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Lu:2024:ACD, author = "Kezhi Lu and Qian Zhang and Danny Hughes and Guangquan Zhang and Jie Lu", title = "{AMT-CDR}: a Deep Adversarial Multi-Channel Transfer Network for Cross-Domain Recommendation", journal = j-TIST, volume = "15", number = "4", pages = "87:1--87:??", month = aug, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3641286", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Aug 29 08:03:44 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3641286", abstract = "Recommender systems are one of the most successful applications of using AI for providing personalized e-services to customers. However, data sparsity is presenting enormous challenges that are hindering the further development of advanced recommender \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "87", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Gilman:2024:ADC, author = "Ekaterina Gilman and Francesca Bugiotti and Ahmed Khalid and Hassan Mehmood and Panos Kostakos and Lauri Tuovinen and Johanna Ylipulli and Xiang Su and Denzil Ferreira", title = "Addressing Data Challenges to Drive the Transformation of Smart Cities", journal = j-TIST, volume = "15", number = "5", pages = "88:1--88:??", month = oct, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3663482", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Nov 9 16:17:42 MST 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3663482", abstract = "Cities serve as vital hubs of economic activity and knowledge generation and dissemination. As such, cities bear a significant responsibility to uphold environmental protection measures while promoting the welfare and living comfort of their residents. \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "88", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Sharma:2024:NMD, author = "Mandar Sharma and Ajay Kumar Gogineni and Naren Ramakrishnan", title = "Neural Methods for Data-to-text Generation", journal = j-TIST, volume = "15", number = "5", pages = "89:1--89:??", month = oct, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3660639", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Nov 9 16:17:42 MST 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3660639", abstract = "The neural boom that has sparked natural language processing (NLP) research throughout the last decade has similarly led to significant innovations in data-to-text (D2T) generation. This survey offers a consolidated view into the neural D2T paradigm with \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "89", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Diffallah:2024:TSF, author = "Zhor Diffallah and Hadjer Ykhlef and Hafida Bouarfa", title = "Teacher--Student Framework for Polyphonic Semi-supervised Sound Event Detection: Survey and Empirical Analysis", journal = j-TIST, volume = "15", number = "5", pages = "90:1--90:??", month = oct, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3660641", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Nov 9 16:17:42 MST 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3660641", abstract = "Polyphonic sound event detection refers to the task of automatically identifying sound events occurring simultaneously in an auditory scene. Due to the inherent complexity and variability of real-world auditory scenes, building robust detectors for \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "90", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Fan:2024:BRL, author = "Lizhou Fan and Lingyao Li and Zihui Ma and Sanggyu Lee and Huizi Yu and Libby Hemphill", title = "A Bibliometric Review of Large Language Models Research from 2017 to 2023", journal = j-TIST, volume = "15", number = "5", pages = "91:1--91:??", month = oct, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3664930", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Nov 9 16:17:42 MST 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3664930", abstract = "Large language models (LLMs), such as OpenAI's Generative Pre-trained Transformer (GPT), are a class of language models that have demonstrated outstanding performance across a range of natural language processing (NLP) tasks. LLMs have become a highly \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "91", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Choudhary:2024:BTM, author = "Monika Choudhary and Satyendra Singh Chouhan and Santosh Singh Rathore", title = "Beyond Text: Multimodal Credibility Assessment Approaches for Online User-Generated Content", journal = j-TIST, volume = "15", number = "5", pages = "92:1--92:??", month = oct, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3673236", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Nov 9 16:17:42 MST 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3673236", abstract = "User-generated content (UGC) is increasingly becoming prevalent on various digital platforms. The content generated on social media, review forums, and question-answer platforms impacts a larger audience and influences their political, social, and other \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "92", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Layne:2024:ARA, author = "Janet Layne and Qudrat E. Alahy Ratul and Edoardo Serra and Sushil Jajodia", title = "Analyzing Robustness of Automatic Scientific Claim Verification Tools against Adversarial Rephrasing Attacks", journal = j-TIST, volume = "15", number = "5", pages = "93:1--93:??", month = oct, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3663481", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Nov 9 16:17:42 MST 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3663481", abstract = "The coronavirus pandemic has fostered an explosion of misinformation about the disease, including the risk and effectiveness of vaccination. AI tools for automatic Scientific Claim Verification (SCV) can be crucial to defeat misinformation campaigns \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "93", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Mahajan:2024:PPD, author = "Yash Mahajan and Jin-Hee Cho and Ing-Ray Chen", title = "Privacy-Preserving and Diversity-Aware Trust-based Team Formation in Online Social Networks", journal = j-TIST, volume = "15", number = "5", pages = "94:1--94:??", month = oct, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3670411", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Nov 9 16:17:42 MST 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3670411", abstract = "As online social networks (OSNs) become more prevalent, a new paradigm for problem-solving through crowd-sourcing has emerged. By leveraging the OSN platforms, users can post a problem to be solved and then form a team to collaborate and solve the \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "94", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Bi:2024:MAA, author = "Haoyang Bi and Qi Liu and Han Wu and Weidong He and Zhenya Huang and Yu Yin and Haiping Ma and Yu Su and Shijin Wang and Enhong Chen", title = "Model-Agnostic Adaptive Testing for Intelligent Education Systems via Meta-learned Gradient Embeddings", journal = j-TIST, volume = "15", number = "5", pages = "95:1--95:??", month = oct, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3660642", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Nov 9 16:17:42 MST 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3660642", abstract = "The field of education has undergone a significant revolution with the advent of intelligent systems and technology, which aim to personalize the learning experience, catering to the unique needs and abilities of individual learners. In this pursuit, a \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "95", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Yu:2024:FGC, author = "Fudan Yu and Guozhen Zhang and Haotian Wang and Depeng Jin and Yong Li", title = "Fine-grained {Courier} Delivery Behavior Recovery with a Digital Twin Based Iterative Calibration Framework", journal = j-TIST, volume = "15", number = "5", pages = "96:1--96:??", month = oct, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3663484", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Nov 9 16:17:42 MST 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3663484", abstract = "Recovering the fine-grained working process of couriers is becoming one of the essential problems for improving the express delivery systems because knowing the detailed process of how couriers accomplish their daily work facilitates the analyzing, \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "96", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Wu:2024:DDI, author = "Xuansheng Wu and Hanqin Wan and Qiaoyu Tan and Wenlin Yao and Ninghao Liu", title = "{DIRECT}: Dual Interpretable Recommendation with Multi-aspect Word Attribution", journal = j-TIST, volume = "15", number = "5", pages = "97:1--97:??", month = oct, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3663483", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Nov 9 16:17:42 MST 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3663483", abstract = "Recommending products to users with intuitive explanations helps improve the system in transparency, persuasiveness, and satisfaction. Existing interpretation techniques include post hoc methods and interpretable modeling. The former category could \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "97", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Luo:2024:PEP, author = "Sichun Luo and Yuanzhang Xiao and Xinyi Zhang and Yang Liu and Wenbo Ding and Linqi Song", title = "{PerFedRec++}: Enhancing Personalized Federated Recommendation with Self-Supervised Pre-Training", journal = j-TIST, volume = "15", number = "5", pages = "98:1--98:??", month = oct, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3664927", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Nov 9 16:17:42 MST 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3664927", abstract = "Federated recommendation systems employ federated learning techniques to safeguard user privacy by transmitting model parameters instead of raw user data between user devices and the central server. Nevertheless, the current federated recommender system \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "98", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Wang:2024:DDN, author = "Dexian Wang and Tianrui Li and Ping Deng and Zhipeng Luo and Pengfei Zhang and Keyu Liu and Wei Huang", title = "{DNSRF}: Deep Network-based {Semi-NMF} Representation Framework", journal = j-TIST, volume = "15", number = "5", pages = "99:1--99:??", month = oct, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3670408", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Nov 9 16:17:42 MST 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3670408", abstract = "Representation learning is an important topic in machine learning, pattern recognition, and data mining research. Among many representation learning approaches, semi-nonnegative matrix factorization (SNMF) is a frequently-used one. However, a typical \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "99", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Levy:2024:RTA, author = "Moshe Levy and Guy Amit and Yuval Elovici and Yisroel Mirsky", title = "Ranking the Transferability of Adversarial Examples", journal = j-TIST, volume = "15", number = "5", pages = "100:1--100:??", month = oct, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3670409", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Nov 9 16:17:42 MST 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3670409", abstract = "Adversarial transferability in blackbox scenarios presents a unique challenge: while attackers can employ surrogate models to craft adversarial examples, they lack assurance on whether these examples will successfully compromise the target model. Until \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "100", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Cai:2024:MRB, author = "Miaomiao Cai and Min Hou and Lei Chen and Le Wu and Haoyue Bai and Yong Li and Meng Wang", title = "Mitigating Recommendation Biases via Group-Alignment and Global-Uniformity in Representation Learning", journal = j-TIST, volume = "15", number = "5", pages = "101:1--101:??", month = oct, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3664931", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Nov 9 16:17:42 MST 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3664931", abstract = "Collaborative Filtering (CF) plays a crucial role in modern recommender systems, leveraging historical user-item interactions to provide personalized suggestions. However, CF-based methods often encounter biases due to imbalances in training data. This \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "101", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Baghersalimi:2024:MMS, author = "Saleh Baghersalimi and Alireza Amirshahi and Farnaz Forooghifar and Tomas Teijeiro and Amir Aminifar and David Atienza", title = "{M2SKD}: Multi-to-Single Knowledge Distillation of Real-Time Epileptic Seizure Detection for Low-Power Wearable Systems", journal = j-TIST, volume = "15", number = "5", pages = "102:1--102:??", month = oct, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3675402", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Nov 9 16:17:42 MST 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3675402", abstract = "Integrating low-power wearable systems into routine health monitoring is an ongoing challenge. Recent advances in the computation capabilities of wearables make it possible to target complex scenarios by exploiting multiple biosignals and using high-. \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "102", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Breve:2024:HPL, author = "Bernardo Breve and Gaetano Cimino and Vincenzo Deufemia", title = "Hybrid Prompt Learning for Generating Justifications of Security Risks in Automation Rules", journal = j-TIST, volume = "15", number = "5", pages = "103:1--103:??", month = oct, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3675401", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Nov 9 16:17:42 MST 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3675401", abstract = "Trigger-action platforms (TAPs) enable users without programming experience to personalize the behavior of Internet of Things applications and services through IF-THEN rules. Unfortunately, the arbitrary connection of smart devices and online services, \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "103", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Fu:2024:DOD, author = "Shucun Fu and Fang Dong and Dian Shen and Runze Chen and Jiangshan Hao", title = "{DESIGN}: Online Device Selection and Edge Association for Federated Synergy Learning-enabled {AIoT}", journal = j-TIST, volume = "15", number = "5", pages = "104:1--104:??", month = oct, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3673237", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Nov 9 16:17:42 MST 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", note = "See corrigendum \cite{Fu:2026:CDO}.", URL = "https://dl.acm.org/doi/10.1145/3673237", abstract = "The artificial intelligence of things (AIoT) is an emerging technology that enables numerous AIoT devices to participate in big data analytics and machine learning (ML) model training, providing various customized intelligent services for industry \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "104", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Makhdomi:2024:FER, author = "Aqsa Ashraf Makhdomi and Iqra Altaf Gillani", title = "Fair and Efficient Ridesharing: a Dynamic Programming-based Relocation Approach", journal = j-TIST, volume = "15", number = "5", pages = "105:1--105:??", month = oct, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3675403", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Nov 9 16:17:42 MST 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3675403", abstract = "Recommending routes by their probability of having a rider has long been the goal of conventional route recommendation systems. While this maximizes the platform-specific criteria of efficiency, it results in sub-optimal outcomes with the disparity among \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "105", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Sharma:2024:PBA, author = "Arun Sharma and Subhankar Ghosh and Shashi Shekhar", title = "Physics-Based Abnormal Trajectory Gap Detection", journal = j-TIST, volume = "15", number = "5", pages = "106:1--106:??", month = oct, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3673235", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Nov 9 16:17:42 MST 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3673235", abstract = "Given trajectories with gaps (i.e., missing data), we investigate algorithms to identify abnormal gaps in trajectories which occur when a given moving object did not report its location, but other moving objects in the same geographic region periodically \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "106", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Sghaier:2024:LNB, author = "Oussama Sghaier and Manar Amayri and Nizar Bouguila", title = "{Libby--Novick} Beta-{Liouville} Distribution for Enhanced Anomaly Detection in Proportional Data", journal = j-TIST, volume = "15", number = "5", pages = "107:1--107:??", month = oct, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3675405", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Nov 9 16:17:42 MST 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3675405", abstract = "We consider the problem of anomaly detection in proportional data by investigating the Libby-Novick Beta-Liouville distribution, a novel distribution merging the salient characteristics of Liouville and Libby-Novick Beta distributions. Its main benefit, \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "107", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Zhuang:2024:MEN, author = "Yan Zhuang and Junyan Zhang and Ruogu Lu and Kunlun He and Xiuxing Li", title = "{MedNER}: Enhanced Named Entity Recognition in Medical Corpus via Optimized Balanced and Deep Active Learning", journal = j-TIST, volume = "15", number = "5", pages = "108:1--108:??", month = oct, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3678178", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Nov 9 16:17:42 MST 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3678178", abstract = "Ever-growing electronic medical corpora provide unprecedented opportunities for researchers to analyze patient conditions and drug effects. Meanwhile, severe challenges emerged in the large-scale electronic medical records process phase. Primarily, \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "108", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Ting:2024:OST, author = "Lo Pang-Yun Ting and Huan-Yang Wang and Jhe-Yun Jhang and Kun-Ta Chuang", title = "Online Spatial-Temporal {EV} Charging Scheduling with Incentive Promotion", journal = j-TIST, volume = "15", number = "5", pages = "109:1--109:??", month = oct, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3678180", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Nov 9 16:17:42 MST 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3678180", abstract = "The growing adoption of electric vehicles (EVs) has resulted in an increased demand for public EV charging infrastructure. Currently, the collaboration between these stations has become vital for efficient charging scheduling and cost reduction. However, \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "109", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{LaQuatra:2024:SST, author = "Moreno {La Quatra} and Giuseppe Gallipoli and Luca Cagliero", title = "Self-supervised Text Style Transfer Using Cycle-Consistent Adversarial Networks", journal = j-TIST, volume = "15", number = "5", pages = "110:1--110:??", month = oct, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3678179", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Nov 9 16:17:42 MST 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3678179", abstract = "Text Style Transfer (TST) is a relevant branch of natural language processing that aims to control the style attributes of a piece of text while preserving its original content. To address TST in the absence of parallel data, Cycle-consistent Generative \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "110", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Chi:2024:WSZ, author = "Te-Yu Chi and Jyh-Shing Roger Jang", title = "{WC-SBERT}: Zero-Shot Topic Classification Using {SBERT} and Light Self-Training on {Wikipedia} Categories", journal = j-TIST, volume = "15", number = "5", pages = "111:1--111:??", month = oct, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3678183", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Nov 9 16:17:42 MST 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3678183", abstract = "In natural language processing (NLP), zero-shot topic classification requires machines to understand the contextual meanings of texts in a downstream task without using the corresponding labeled texts for training, which is highly desirable for various \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "111", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Zhang:2024:STF, author = "Yifei Zhang and Dun Zeng and Jinglong Luo and Xinyu Fu and Guanzhong Chen and Zenglin Xu and Irwin King", title = "A Survey of Trustworthy Federated Learning: Issues, Solutions, and Challenges", journal = j-TIST, volume = "15", number = "6", pages = "112:1--112:??", month = dec, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3678181", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Fri Dec 20 17:01:44 MST 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3678181", abstract = "Trustworthy artificial intelligence (TAI) has proven invaluable in curbing potential negative repercussions tied to AI applications. Within the TAI spectrum, federated learning (FL) emerges as a promising solution to safeguard personal information in \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "112", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Arbaoui:2024:FLS, author = "Meriem Arbaoui and Mohamed-el-Amine Brahmia and Abdellatif Rahmoun and Mourad Zghal", title = "Federated Learning Survey: a Multi-Level Taxonomy of Aggregation Techniques, Experimental Insights, and Future Frontiers", journal = j-TIST, volume = "15", number = "6", pages = "113:1--113:??", month = dec, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3678182", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Fri Dec 20 17:01:44 MST 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3678182", abstract = "The emerging integration of Internet of Things (IoT) and AI has unlocked numerous opportunities for innovation across diverse industries. However, growing privacy concerns and data isolation issues have inhibited this promising advancement. Unfortunately, \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "113", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Gabbolini:2024:SMT, author = "Giovanni Gabbolini and Derek Bridge", title = "Surveying More Than Two Decades of Music Information Retrieval Research on Playlists", journal = j-TIST, volume = "15", number = "6", pages = "114:1--114:??", month = dec, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3688398", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Fri Dec 20 17:01:44 MST 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3688398", abstract = "In this article, we present an extensive survey of music information retrieval (MIR) research into music playlists. Our survey spans more than 20 years, and includes around 300 papers about playlists, with over 70 supporting sources. It is the first \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "114", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Song:2024:EGP, author = "Peipei Song and Yuanen Zhou and Xun Yang and Daqing Liu and Zhenzhen Hu and Depeng Wang and Meng Wang", title = "Efficiently Gluing Pre-Trained Language and Vision Models for Image Captioning", journal = j-TIST, volume = "15", number = "6", pages = "115:1--115:??", month = dec, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3682067", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Fri Dec 20 17:01:44 MST 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3682067", abstract = "Vision-and-language pre-training models have achieved impressive performance for image captioning. But most of them are trained with millions of paired image-text data and require huge memory and computing overhead. To alleviate this, we try to stand on \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "115", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Wu:2024:IGB, author = "Jingjing Wu and Richang Hong and Shengeng Tang", title = "Intermediary-Generated Bridge Network for {RGB-D} Cross-Modal Re-Identification", journal = j-TIST, volume = "15", number = "6", pages = "116:1--116:??", month = dec, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3682066", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Fri Dec 20 17:01:44 MST 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3682066", abstract = "RGB-D cross-modal person re-identification (re-id) targets at retrieving the person of interest across RGB and depth image modalities. To cope with the modal discrepancy, some existing methods generate an auxiliary mode with either inherent properties of \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "116", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Huang:2024:TUI, author = "Huiqun Huang and Xi Yang and Suining He and Mahan Tabatabaie", title = "Toward Ubiquitous Interaction-Attentive and Extreme-Aware Crowd Activity Level Prediction", journal = j-TIST, volume = "15", number = "6", pages = "117:1--117:??", month = dec, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3682063", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Fri Dec 20 17:01:44 MST 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3682063", abstract = "Accurate prediction of citywide crowd activity levels (CALs), i.e., the numbers of participants of citywide crowd activities under different venue categories at certain time and locations, is essential for the city management, the personal service \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "117", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Prakash:2024:UFA, author = "V. Jothi Prakash and S. Arul Antran Vijay", title = "A Unified Framework for Analyzing Textual Context and Intent in Social Media", journal = j-TIST, volume = "15", number = "6", pages = "118:1--118:??", month = dec, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3682064", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Fri Dec 20 17:01:44 MST 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3682064", abstract = "In the realm of natural language processing, tasks like emotion recognition, irony detection, hate speech detection, offensive language identification, and stance detection are pivotal for understanding user-generated content. While several task-specific \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "118", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Koyuncu:2024:AMA, author = "Deniz Koyuncu and Alex Gittens and B{\"u}lent Yener and Moti Yung", title = "Adversarial Missingness Attacks on Causal Structure Learning", journal = j-TIST, volume = "15", number = "6", pages = "119:1--119:??", month = dec, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3682065", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Fri Dec 20 17:01:44 MST 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3682065", abstract = "Causality-informed machine learning has been proposed as an avenue for achieving many of the goals of modern machine learning, from ensuring generalization under domain shifts to attaining fairness, robustness, and interpretability. A key component of \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "119", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Meng:2024:GBA, author = "Kevin Meng and Damian Jimenez and Jacob Daniel Devasier and Sai Sandeep Naraparaju and Fatma Arslan and Daniel Obembe and Chengkai Li", title = "Gradient-Based Adversarial Training on Transformer Networks for Detecting Check-Worthy Factual Claims", journal = j-TIST, volume = "15", number = "6", pages = "120:1--120:??", month = dec, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3689212", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Fri Dec 20 17:01:44 MST 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3689212", abstract = "This article presents the latest developments to ClaimBuster's claim-spotting model, which tackles the critical task of identifying check-worthy claims from large streams of information. We introduce the first adversarially regularized, transformer-based \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "120", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Lian:2024:RCC, author = "Yuanfeng Lian and Shoushuang Pei and Mengqi Chen and Jing Hua", title = "Relation Constrained Capsule Graph Neural Networks for Non-Rigid Shape Correspondence", journal = j-TIST, volume = "15", number = "6", pages = "121:1--121:??", month = dec, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3688851", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Fri Dec 20 17:01:44 MST 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3688851", abstract = "Non-rigid 3D shape correspondence aims to establish dense correspondences between two non-rigidly deformed 3D shapes. However, the variability and symmetry of non-rigid shapes usually lead to mismatches due to shape deformation, topological changes, or \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "121", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Liu:2024:EFL, author = "Ji Liu and Juncheng Jia and Hong Zhang and Yuhui Yun and Leye Wang and Yang Zhou and Huaiyu Dai and Dejing Dou", title = "Efficient Federated Learning Using Dynamic Update and Adaptive Pruning with Momentum on Shared Server Data", journal = j-TIST, volume = "15", number = "6", pages = "122:1--122:??", month = dec, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3690648", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Fri Dec 20 17:01:44 MST 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3690648", abstract = "Despite achieving remarkable performance, Federated Learning (FL) encounters two important problems, i.e., low training efficiency and limited computational resources. In this article, we propose a new FL framework, i.e., FedDUMAP, with three original \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "122", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Gong:2024:KKG, author = "Jiahui Gong and Tong Li and Huandong Wang and Yu Liu and Xing Wang and Zhendong Wang and Chao Deng and Junlan Feng and Depeng Jin and Yong Li", title = "{KGDA}: a Knowledge Graph Driven Decomposition Approach for Cellular Traffic Prediction", journal = j-TIST, volume = "15", number = "6", pages = "123:1--123:??", month = dec, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3690650", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Fri Dec 20 17:01:44 MST 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3690650", abstract = "Understanding and accurately predicting cellular traffic data is vital for communication operators and device users, as it facilitates efficient resource allocation and ensures superior service quality. However, large-scale cellular traffic data \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "123", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Yi:2024:RST, author = "Jinhui Yi and Huan Yan and Haotian Wang and Jian Yuan and Yong Li", title = "{RCCNet}: a Spatial-Temporal Neural Network Model for Logistics Delivery Timely Rate Prediction", journal = j-TIST, volume = "15", number = "6", pages = "124:1--124:??", month = dec, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3690649", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Fri Dec 20 17:01:44 MST 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3690649", abstract = "In logistics service, the delivery timely rate is a key experience indicator, which is highly essential to the competitive advantage of express companies. Prediction on it enables intervention on couriers with low predicted results in advance, thus \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "124", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Maroto-Gomez:2024:AMU, author = "Marcos Maroto-G{\'o}mez and Matthew Lewis and {\'A}lvaro Castro-Gonz{\'a}lez and Mar{\'\i}a Malfaz and Miguel {\'A}ngel Salichs and Lola Ca{\~n}amero", title = "Adapting to My User, Engaging with My Robot: an Adaptive Affective Architecture for a Social Assistive Robot", journal = j-TIST, volume = "15", number = "6", pages = "125:1--125:??", month = dec, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3691348", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Fri Dec 20 17:01:44 MST 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3691348", abstract = "Affective feedback from social robots is a useful technique for communicating to people whether they are interacting ``well'' with the robot or not. However, some users, such as people with physical or cognitive difficulties, may not be able to interact in \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "125", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Ahanger:2024:QIA, author = "Tariq Ahamed Ahanger and Munish Bhatia and Abdulaziz Aldaej", title = "Quantum Informative Analysis in Smart Power Distribution", journal = j-TIST, volume = "15", number = "6", pages = "126:1--126:??", month = dec, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3691350", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Fri Dec 20 17:01:44 MST 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3691350", abstract = "Advancements in the Internet of Things (IoT) paradigm have greatly improved the quality of services in the electricity industry through the integration of smart energy distribution and dependable electric devices. Conspicuously, the current research \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "126", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Zhou:2024:MIL, author = "Shengjie Zhou and Senlin Shu and Haobo Wang and Hongxin Wei and Tao Xiang and Beibei Li", title = "Multiple-Instance Learning from Pairwise Comparison Bags", journal = j-TIST, volume = "15", number = "6", pages = "127:1--127:??", month = dec, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3696460", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Fri Dec 20 17:01:44 MST 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3696460", abstract = "Multiple-instance learning (MIL) is a significant weakly supervised learning problem, where the training data consist of bags containing multiple instances and bag-level labels. Most previous MIL research required fully labeled bags. However, collecting \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "127", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Phan:2024:FEF, author = "Nguyen Minh Thao Phan and Ling Chen and Chun-Hung Chen and Wen-Chih Peng", title = "{FastRx}: Exploring Fastformer and Memory-Augmented Graph Neural Networks for Personalized Medication Recommendations", journal = j-TIST, volume = "15", number = "6", pages = "128:1--128:??", month = dec, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3696111", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Fri Dec 20 17:01:44 MST 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3696111", abstract = "Personalized medication recommendations aim to suggest a set of medications based on the clinical conditions of a patient. Not only should the patient's diagnosis, procedure, and medication history be considered, but drug-drug interactions (DDIs) must \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "128", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Lee:2024:UOF, author = "Hyunho Lee and Younghoon Lee", title = "User Opinion-Focused Abstractive Summarization Using Explainable Artificial Intelligence", journal = j-TIST, volume = "15", number = "6", pages = "129:1--129:??", month = dec, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3696456", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Fri Dec 20 17:01:44 MST 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3696456", abstract = "Recent methodologies have achieved good performance in objectively summarizing important information from fact-based datasets such as Extreme Summarization and CNN Daily Mail. These methodologies involve abstractive summarization, extracting the core \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "129", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Cascavilla:2024:FIM, author = "Giuseppe Cascavilla and Gemma Catolino and Mauro Conti and Dimos Mellios and Damian Tamburri", title = "Few Images, Many Insights: Illicit Content Detection Using a Limited Number of Images", journal = j-TIST, volume = "15", number = "6", pages = "130:1--130:??", month = dec, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3696458", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Fri Dec 20 17:01:44 MST 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3696458", abstract = "The anonymity and untraceability benefits of the dark web increased its popularity exponentially. The cost of these technical benefits is that such anonymity has created a suitable womb for illicit activity. Hence-in collaboration with cybersecurity \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "130", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Hu:2024:URE, author = "Jiayu Hu and Senlin Shu and Beibei Li and Tao Xiang and Zhongshi He", title = "An Unbiased Risk Estimator for Partial Label Learning with Augmented Classes", journal = j-TIST, volume = "15", number = "6", pages = "131:1--131:??", month = dec, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3700137", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Fri Dec 20 17:01:44 MST 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", note = "See corrigendum \cite{Hu:2025:CUR}.", URL = "https://dl.acm.org/doi/10.1145/3700137", abstract = "Partial Label Learning (PLL) is a typical weakly supervised learning task, which assumes each training instance is annotated with a set of candidate labels containing the ground-truth label. Recent PLL methods adopt identification-based disambiguation to \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "131", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Le:2024:QAR, author = "Trung-Hoang Le and Hady W. Lauw", title = "Question-Attentive Review-Level Explanation for Neural Rating Regression", journal = j-TIST, volume = "15", number = "6", pages = "132:1--132:??", month = dec, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3699516", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Fri Dec 20 17:01:44 MST 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3699516", abstract = "Recommendation explanations help to improve their acceptance by end users. Explanations come in many different forms. One that is of interest here is presenting an existing review of the recommended item as the explanation. The challenge is in selecting a \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "132", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Hussain:2024:ONO, author = "Hanan Hussain and P. S. Tamizharasan and Praveen Kumar Yadav", title = "{OptiRet-Net}: an Optimized Low-Light Image Enhancement Technique for {CV}-Based Applications in Resource-Constrained Environments", journal = j-TIST, volume = "15", number = "6", pages = "133:1--133:??", month = dec, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3700136", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Fri Dec 20 17:01:44 MST 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3700136", abstract = "The illumination of images can significantly impact computer-vision applications such as image classification, multiple object detection, and tracking, leading to a significant decline in detection and tracking accuracy. Recent advancements in deep \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "133", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Kang:2024:OPU, author = "Yan Kang and Hanlin Gu and Xingxing Tang and Yuanqin He and Yuzhu Zhang and Jinnan He and Yuxing Han and Lixin Fan and Kai Chen and Qiang Yang", title = "Optimizing Privacy, Utility, and Efficiency in a Constrained Multi-Objective Federated Learning Framework", journal = j-TIST, volume = "15", number = "6", pages = "134:1--134:??", month = dec, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3701039", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Fri Dec 20 17:01:44 MST 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3701039", abstract = "Conventionally, federated learning aims to optimize a single objective, typically the utility. However, for a federated learning system to be trustworthy, it needs to simultaneously satisfy multiple objectives, such as maximizing model performance, \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "134", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Su:2024:PSR, author = "Shaowen Su and Yan Zhang and Minggang Gan", title = "Proposal Semantic Relationship Graph Network for Temporal Action Detection", journal = j-TIST, volume = "15", number = "6", pages = "135:1--135:??", month = dec, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3702233", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Fri Dec 20 17:01:44 MST 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3702233", abstract = "Temporal action detection, a critical task in video activity understanding, is typically divided into two stages: proposal generation and classification. However, most existing methods overlook the importance of information transfer among proposals during \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "135", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Yao:2025:SIR, author = "Lina Yao and Julian McAuley and Xianzhi Wang and Dietmar Jannach", title = "Special Issue on Responsible Recommender Systems {Part 2}", journal = j-TIST, volume = "16", number = "1", pages = "1:1--1:??", month = feb, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3689367", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Feb 13 06:01:52 MST 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3689367", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "1", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Zhao:2025:FDR, author = "Yuying Zhao and Yu Wang and Yunchao Liu and Xueqi Cheng and Charu C. Aggarwal and Tyler Derr", title = "Fairness and Diversity in Recommender Systems: a Survey", journal = j-TIST, volume = "16", number = "1", pages = "2:1--2:??", month = feb, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3664928", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Feb 13 06:01:52 MST 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3664928", abstract = "Recommender systems (RS) are effective tools for mitigating information overload and have seen extensive applications across various domains. However, the single focus on utility goals proves to be inadequate in addressing real-world concerns, leading to \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "2", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Anagnostopoulos:2025:FPM, author = "Aris Anagnostopoulos and Luca Becchetti and Matteo B{\"o}hm and Adriano Fazzone and Stefano Leonardi and Cristina Menghini and Chris Schwiegelshohn", title = "Fair Projections as a Means toward Balanced Recommendations", journal = j-TIST, volume = "16", number = "1", pages = "3:1--3:??", month = feb, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3664929", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Feb 13 06:01:52 MST 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3664929", abstract = "The goal of recommender systems is to provide to users suggestions that match their interests, with the eventual goal of increasing their satisfaction, as measured by the number of transactions (clicks, purchases, and so forth). Often, this leads to \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "3", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Yan:2025:PBC, author = "Chao Yan and Weiyi Zhong and Dengshuai Zhai and Arif Ali Khan and Wenwen Gong and Yanwei Xu and Baogui Xin", title = "Popularity Bias in Correlation Graph-based {API} Recommendation for Mashup Creation", journal = j-TIST, volume = "16", number = "1", pages = "4:1--4:??", month = feb, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3654445", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Feb 13 06:01:52 MST 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3654445", abstract = "The explosive growth of the Application Programming Interfaces (APIs) economy in recent years has led to a dramatic increase in available APIs. Mashup development, a dominant approach for creating data-centric applications based on APIs, has experienced a \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "4", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Zhang:2025:MII, author = "Chenhao Zhang and Weitong Chen and Wei Zhang and Miao Xu", title = "Mitigating the Impact of Inaccurate Feedback in Dynamic Learning-to-Rank: a Study of Overlooked Interesting Items", journal = j-TIST, volume = "16", number = "1", pages = "5:1--5:??", month = feb, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3653983", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Feb 13 06:01:52 MST 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3653983", abstract = "Dynamic Learning-to-Rank (DLTR) is a method of updating a ranking policy in real time based on user feedback, which may not always be accurate. Although previous DLTR work has achieved fair and unbiased DLTR under inaccurate feedback, they face the \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "5", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Medda:2025:GFE, author = "Giacomo Medda and Francesco Fabbri and Mirko Marras and Ludovico Boratto and Gianni Fenu", title = "{GNNUERS}: Fairness Explanation in {GNNs} for Recommendation via Counterfactual Reasoning", journal = j-TIST, volume = "16", number = "1", pages = "6:1--6:??", month = feb, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3655631", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Feb 13 06:01:52 MST 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3655631", abstract = "Nowadays, research into personalization has been focusing on explainability and fairness. Several approaches proposed in recent works are able to explain individual recommendations in a post-hoc manner or by explanation paths. However, explainability \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "6", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Moller:2025:ENN, author = "Lucas M{\"o}ller and Sebastian Pad{\'o}", title = "Explaining Neural News Recommendation with Attributions onto Reading Histories", journal = j-TIST, volume = "16", number = "1", pages = "7:1--7:??", month = feb, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3673233", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Feb 13 06:01:52 MST 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3673233", abstract = "An important aspect of responsible recommendation systems is the transparency of the prediction mechanisms. This is a general challenge for deep-learning-based systems such as the currently predominant neural news recommender architectures, which are \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "7", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Liao:2025:AEE, author = "Hao Liao and Shuo Wang and Hao Cheng and Wei Zhang and Jiwei Zhang and Mingyang Zhou and Kezhong Lu and Rui Mao and Xing Xie", title = "Aspect-Enhanced Explainable Recommendation with Multi-modal Contrastive Learning", journal = j-TIST, volume = "16", number = "1", pages = "8:1--8:??", month = feb, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3673234", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Feb 13 06:01:52 MST 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3673234", abstract = "Explainable recommender systems (ERS) aim to enhance users' trust in the systems by offering personalized recommendations with transparent explanations. This transparency provides users with a clear understanding of the rationale behind the \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "8", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Zhuang:2025:IFF, author = "Haojie Zhuang and Wei Zhang and Weitong Chen and Jian Yang and Quan Z. Sheng", title = "Improving Faithfulness and Factuality with Contrastive Learning in Explainable Recommendation", journal = j-TIST, volume = "16", number = "1", pages = "9:1--9:??", month = feb, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3653984", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Feb 13 06:01:52 MST 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3653984", abstract = "Recommender systems have become increasingly important in navigating the vast amount of information and options available in various domains. By tailoring and personalizing recommendations to user preferences and interests, these systems improve the user \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "9", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Carragher:2025:MRS, author = "Peter Carragher and Evan M. Williams and Kathleen M. Carley", title = "Misinformation Resilient Search Rankings with Webgraph-Based Interventions", journal = j-TIST, volume = "16", number = "1", pages = "10:1--10:??", month = feb, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3670410", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Feb 13 06:01:52 MST 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3670410", abstract = "The proliferation of unreliable news domains on the internet has had wide-reaching negative impacts on society. We introduce and evaluate interventions aimed at reducing traffic to unreliable news domains from search engines while maintaining traffic to \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "10", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Ye:2025:RRS, author = "Shanshan Ye and Jie Lu", title = "Robust Recommender Systems with Rating Flip Noise", journal = j-TIST, volume = "16", number = "1", pages = "11:1--11:??", month = feb, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3641285", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Feb 13 06:01:52 MST 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3641285", abstract = "Recommender systems have become important tools in the daily life of human beings since they are powerful to address information overload, and discover relevant and useful items for users. The success of recommender systems largely relies on the \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "11", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Uslu:2025:TRD, author = "Suleyman Uslu and Davinder Kaur and Samuel J. Rivera and Arjan Durresi and Meghna Babbar-Sebens and Jenna H. Tilt", title = "A Trustworthy and Responsible Decision-Making Framework for Resource Management in Food-Energy-Water Nexus: a Control-Theoretical Approach", journal = j-TIST, volume = "16", number = "1", pages = "12:1--12:??", month = feb, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3660640", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Feb 13 06:01:52 MST 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3660640", abstract = "This article introduces a hybrid framework for trustworthy and responsible natural resource management, aimed at building bottom-up trust to enhance cooperation among decision-makers in the food, energy, and water sectors. Cooperation is highly critical \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "12", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Sun:2025:FSR, author = "Hongliang Sun and Zhiying Tu and Dianbo Sui and Bolin Zhang and Xiaofei Xu", title = "A Federated Social Recommendation Approach with Enhanced Hypergraph Neural Network", journal = j-TIST, volume = "16", number = "1", pages = "13:1--13:??", month = feb, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3665931", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Feb 13 06:01:52 MST 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3665931", abstract = "In recent years, the development of online social network platforms has led to increased research efforts in social recommendation systems. Unlike traditional recommendation systems, social recommendation systems utilize both user-item interactions and \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "13", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Ariyarathna:2025:DUG, author = "Thirasara Ariyarathna and Meisam Mohommady and Hye-young Paik and Salil S. Kanhere", title = "{DeepSneak}: User {GPS} Trajectory Reconstruction from Federated Route Recommendation Models", journal = j-TIST, volume = "16", number = "1", pages = "14:1--14:??", month = feb, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3670412", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Feb 13 06:01:52 MST 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3670412", abstract = "Decentralized machine learning, such as Federated Learning (FL), is widely adopted in many application domains. Especially in domains like recommendation systems, sharing gradients instead of private data has recently caught the research community's \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "14", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Golbeck:2025:RSI, author = "Jennifer Golbeck", title = "Recommender System-Induced Eating Disorder Relapse: Harmful Content and the Challenges of Responsible Recommendation", journal = j-TIST, volume = "16", number = "1", pages = "15:1--15:??", month = feb, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3675404", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Feb 13 06:01:52 MST 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3675404", abstract = "As users' social media feeds have become increasingly driven by algorithmically recommended content, there is a need to understand the impact these recommendations have on users. People in recovery from eating disorders (ED) may try to avoid content that \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "15", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Fabris:2025:FBA, author = "Alessandro Fabris and Nina Baranowska and Matthew J. Dennis and David Graus and Philipp Hacker and Jorge Saldivar and Frederik Zuiderveen Borgesius and Asia J. Biega", title = "Fairness and Bias in Algorithmic Hiring: a Multidisciplinary Survey", journal = j-TIST, volume = "16", number = "1", pages = "16:1--16:??", month = feb, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3696457", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Feb 13 06:01:52 MST 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3696457", abstract = "Employers are adopting algorithmic hiring technology throughout the recruitment pipeline. Algorithmic fairness is especially applicable in this domain due to its high stakes and structural inequalities. Unfortunately, most work in this space provides \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "16", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Birla:2025:AND, author = "Lokendra Birla and Trishna Saikia and Puneet Gupta", title = "{AVENUE}: a Novel Deepfake Detection Method Based on Temporal Convolutional Network and {rPPG} Information", journal = j-TIST, volume = "16", number = "1", pages = "17:1--17:??", month = feb, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3702232", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Feb 13 06:01:52 MST 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3702232", abstract = "In Deep Learning (DL), an adversary creates Deepfakes by manipulating facial features to fool someone. The Deepfakes pose a security threat to anyone's privacy and a primary concern for our society. It can be detected by utilizing the texture and \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "17", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Zhang:2025:EPI, author = "Yihong Zhang and Takahiro Hara", title = "Extracting Political Interest Model from Interaction Data Based on Novel Word-level Bias Assignment", journal = j-TIST, volume = "16", number = "1", pages = "18:1--18:??", month = feb, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3702649", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Feb 13 06:01:52 MST 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3702649", abstract = "In democratic countries, political interest is deeply involved in people's daily lives. Research in political consumerism shows that product purchase decision is also influenced by the political orientation of the consumer. In traditional recommendation \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "18", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Ko:2025:BEB, author = "Jihoon Ko and Shinhwan Kang and Taehyung Kwon and Heechan Moon and Kijung Shin", title = "{BeGin}: Extensive Benchmark Scenarios and an Easy-to-use Framework for Graph Continual Learning", journal = j-TIST, volume = "16", number = "1", pages = "19:1--19:??", month = feb, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3702648", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Feb 13 06:01:52 MST 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3702648", abstract = "Continual Learning (CL) is the process of learning ceaselessly a sequence of tasks. Most existing CL methods deal with independent data (e.g., images and text) for which many benchmark frameworks and results under standard experimental settings are \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "19", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Fleischmann:2025:EDB, author = "Sophie Fleischmann and Simon Dietz and Julian Shanbhag and Annika Wuensch and Marlies Nitschke and J{\"o}rg Miehling and Sandro Wartzack and Sigrid Leyendecker and Bjoern M. Eskofier and Anne D. Koelewijn", title = "Exploring Dataset Bias and Scaling Techniques in Multi-Source Gait Biomechanics: an Explainable Machine Learning Approach", journal = j-TIST, volume = "16", number = "1", pages = "20:1--20:??", month = feb, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3702646", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Feb 13 06:01:52 MST 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3702646", abstract = "Machine learning has become increasingly important in biomechanics. It allows to unveil hidden patterns from large and complex data, which leads to a more comprehensive understanding of biomechanical processes and deeper insights into human movement. \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "20", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Auricchio:2025:EMM, author = "Gennaro Auricchio and Jun Liu and Qun Ma and Jie Zhang", title = "Edge Manipulations for the Maximum Vertex-Weighted Bipartite $b$-matching", journal = j-TIST, volume = "16", number = "1", pages = "21:1--21:??", month = feb, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3702650", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Feb 13 06:01:52 MST 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3702650", abstract = "In this article, we explore the Mechanism Design aspects of the Maximum Vertex-Weighted $b$-matching (MVbM) problem on bipartite graphs $ (A \cup T, E) $. The set $A$ comprises agents, while $T$ represents tasks. The set $E$, which \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "21", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Thukral:2025:CDH, author = "Megha Thukral and Harish Haresamudram and Thomas Pl{\"o}tz", title = "Cross-Domain {HAR}: Few-Shot Transfer Learning for Human Activity Recognition", journal = j-TIST, volume = "16", number = "1", pages = "22:1--22:??", month = feb, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3704921", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Feb 13 06:01:52 MST 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3704921", abstract = "The ubiquitous availability of smartphones and smartwatches with integrated inertial measurement units (IMUs) enables straightforward capturing of human activities through collecting movement data. For specific applications of sensor-based human activity \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "22", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Tokunaga:2025:FAE, author = "Junpei Tokunaga and Yuki Kikukawa and Hiroyuki Ebara and Naonori Ueda", title = "Fast and Accurate Evacuation Planning Algorithm with {Bayesian} Optimization", journal = j-TIST, volume = "16", number = "1", pages = "23:1--23:??", month = feb, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3704920", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Feb 13 06:01:52 MST 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3704920", abstract = "In this work, we propose a method for generating an evacuation plan at a high speed to realize safe and swift evacuation in the event of a large-scale disaster such as an earthquake and its accompanying tsunami. Existing conventional methods have several \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "23", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Huo:2025:HGN, author = "Cuiying Huo and Dongxiao He and Yawen Li and Di Jin and Jianwu Dang and Witold Pedrycz and Lingfei Wu and Weixiong Zhang", title = "Heterogeneous Graph Neural Networks using Self-supervised Reciprocally Contrastive Learning", journal = j-TIST, volume = "16", number = "1", pages = "24:1--24:??", month = feb, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3706115", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Feb 13 06:01:52 MST 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3706115", abstract = "Heterogeneous graph neural network (HGNN) is a popular technique for modeling and analyzing heterogeneous graphs. Most existing HGNN-based approaches are supervised or semi-supervised learning methods requiring graphs to be annotated, which is costly and \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "24", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Dai:2025:TDE, author = "Genan Dai and Weiyang Kong and Yubao Liu and Bowen Zhang and Xiaojiang Peng and Xiaomao Fan and Hu Huang", title = "{Tucker} Decomposition-Enhanced Dynamic Graph Convolutional Networks for Crowd Flows Prediction", journal = j-TIST, volume = "16", number = "1", pages = "25:1--25:??", month = feb, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3706116", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Feb 13 06:01:52 MST 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", URL = "https://dl.acm.org/doi/10.1145/3706116", abstract = "Crowd flows prediction is an important problem for traffic management and public safety. Graph Convolutional Network (GCN), known for its ability to effectively capture and utilize topological information, has demonstrated significant advancements in \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "25", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Taha:2025:EEI, author = "Kamal Taha", title = "Empirical and Experimental Insights into Data Mining Techniques for Crime Prediction: a Comprehensive Survey", journal = j-TIST, volume = "16", number = "2", pages = "26:1--26:??", month = apr, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3699515", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Wed Apr 23 07:20:52 MDT 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "This survey article presents a comprehensive analysis of crime prediction methodologies, exploring the various techniques and technologies utilized in this area. The article covers the statistical methods, machine learning algorithms, and deep learning \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "26", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Garcia:2025:CDA, author = "Cristiano Mesquita Garcia and Ramon Abilio and Alessandro Lameiras Koerich and Alceu de Souza Britto and Jean Paul Barddal", title = "Concept Drift Adaptation in Text Stream Mining Settings: a Systematic Review", journal = j-TIST, volume = "16", number = "2", pages = "27:1--27:??", month = apr, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3704922", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Wed Apr 23 07:20:52 MDT 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "The society produces textual data online in several ways, e.g., via reviews and social media posts. Therefore, numerous researchers have been working on discovering patterns in textual data that can indicate peoples' opinions, interests, and so on. Most \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "27", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Gupta:2025:RCT, author = "Vinayak Gupta and Srikanta Bedathur and Abir De", title = "Retrieving Continuous-Time Event Sequences Using Neural Temporal Point Processes with Learnable Hashing", journal = j-TIST, volume = "16", number = "2", pages = "28:1--28:??", month = apr, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3691349", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Wed Apr 23 07:20:52 MDT 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/hash.bib; https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Temporal sequences have become pervasive in various real-world applications such as finance, spatial mobility, health records, and so on. Consequently, the volume of data generated in the form of continuous-time event sequence(s) or CTES(s) has increased exponentially in the past few years. Thus, a significant fraction of the ongoing research on CTES datasets involves designing models to address downstream tasks such as next-event prediction, long-term forecasting, sequence classification, and so on. The recent developments in predictive modeling using marked temporal point processes (MTPP) have enabled an accurate characterization of several real-world applications involving the CTESs. However, due to the complex nature of these CTES datasets, the task of large-scale retrieval of temporal sequences has been overlooked by the past literature. In detail, by CTES retrieval we mean that for an input query sequence, a retrieval system must return a ranked list of relevant sequences from a large corpus. To tackle this, we propose NeuroSeqRet, a first-of-its-kind framework designed specifically for end-to-end CTES retrieval. Specifically, NeuroSeqRet introduces multiple enhancements over standard retrieval frameworks and first applies a trainable unwarping function on the query sequence which makes it comparable with corpus sequences, especially when a relevant query-corpus pair has individually different attributes. Next, it feeds the unwarped query sequence and the corpus sequence into MTPP-guided neural relevance models. We develop four variants of the relevance model for different kinds of applications based on the tradeoff between accuracy and efficiency. We also propose an optimization framework to learn binary sequence embeddings from the relevance scores, suitable for the locality-sensitive hashing leading to a significant speedup in returning top-K results for a given query sequence. Our experiments with several datasets show the significant accuracy boost of NeuroSeqRet beyond several baselines, as well as the efficacy of our hashing mechanism.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "28", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Zhang:2025:UOD, author = "Zhongping Zhang and Daoheng Liu and Jinwei Zhu and Youxi Wu", title = "Unsupervised Outlier Detection with Reinforced Noise Discriminator", journal = j-TIST, volume = "16", number = "2", pages = "29:1--29:??", month = apr, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3706117", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Wed Apr 23 07:20:52 MDT 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Outlier detection is one of the hot topics in the field of machine learning and data mining. At present, there are many kinds of outlier detection algorithms. The accuracies of traditional outlier detection algorithms are often affected by unique \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "29", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Schellaert:2025:APL, author = "Wout Schellaert and Fernando Mart{\'\i}nez-Plumed and Jos{\'e} Hern{\'a}ndez-Orallo", title = "Analysing the Predictability of Language Model Performance", journal = j-TIST, volume = "16", number = "2", pages = "30:1--30:??", month = apr, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3706118", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Wed Apr 23 07:20:52 MDT 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Can a language model predict for which questions another language model will answer successfully? We investigate the extent to which performance prediction is possible and dissect various factors that influence it. Our experimental setting fine-tunes \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "30", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Sun:2025:UIG, author = "Yujuan Sun and Xing Huang and Yanfang Cui and Junyu Dong and Xiaofeng Zhang and Tao Yao", title = "An Underwater Imaging Generative Adversarial Network by Simulating the Mechanism of Light Propagation in Water", journal = j-TIST, volume = "16", number = "2", pages = "31:1--31:??", month = apr, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3709003", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Wed Apr 23 07:20:52 MDT 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Since capturing underwater images without degradation is challenging, there are few real image datasets with paired ground truth for underwater image enhancement. In this article, we propose a generative adversarial network (UIGAN) for underwater imaging; \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "31", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Huh:2025:NIV, author = "Jungwoo Huh and Jiwoo Kang and Jongwook Woo and Sanghoon Lee", title = "A Novel Intelligent Video Surveillance System Using Low-Traffic Scene-Preserving Video Anonymization", journal = j-TIST, volume = "16", number = "2", pages = "32:1--32:??", month = apr, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3709001", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Wed Apr 23 07:20:52 MDT 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "With the development of computer vision technology, intelligent video surveillance systems have been developed for automatic monitoring. However, the problem of personal information protection has also emerged. Existing systems attempted to solve this \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "32", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Ghane:2025:MFD, author = "Sara Ghane and Stef Jacobs and Thomas Huybrechts and Peter Hellinckx and Siegfried Mercelis and Ivan Verhaert and Erik Mannens", title = "Model-Free Deep Reinforcement Learning for Adaptive Supply Temperature Control in Collective Space Heating Systems", journal = j-TIST, volume = "16", number = "2", pages = "33:1--33:??", month = apr, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3709010", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Wed Apr 23 07:20:52 MDT 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "The conventional approach for controlling the supply temperature in collective space heating networks relies on a predefined heating curve determined by outdoor temperature and heat emitter type. This prioritises thermal comfort but lacks energetic and \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "33", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Wei:2025:RLU, author = "Yang Wei and Shuo Chen and Shanshan Ye and Bo Han and Chen Gong", title = "Robust Learning under Hybrid Noise", journal = j-TIST, volume = "16", number = "2", pages = "34:1--34:??", month = apr, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3709149", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Wed Apr 23 07:20:52 MDT 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Feature noise and label noise are ubiquitous in practical scenarios, which pose great challenges for training a robust machine learning model. Most previous approaches usually deal with only a single problem of either feature noise or label noise. However,. \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "34", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Zhang:2025:AIL, author = "Qingbo Zhang and Xiaochun Yang and Hao Chen and Bin Wang and Zhu Sun and Xiangmin Zhou", title = "Adaptive Intention Learning for Session-Based Recommendation", journal = j-TIST, volume = "16", number = "2", pages = "35:1--35:??", month = apr, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3709004", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Wed Apr 23 07:20:52 MDT 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "In recent years, session-based recommender systems (SRSs) have emerged as a significant research focus within the recommendation field. Capturing user intentions to infer user interest accordingly has proven to be effective in enhancing the accuracy of \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "35", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Samadi:2025:CED, author = "Amir Samadi and Konstantinos Koufos and Kurt Debattista and Mehrdad Dianati", title = "Counterfactual Explainer for Deep Reinforcement Learning Models using Policy Distillation", journal = j-TIST, volume = "16", number = "2", pages = "36:1--36:??", month = apr, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3709146", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Wed Apr 23 07:20:52 MDT 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Deep Reinforcement Learning (DRL) has demonstrated promising capability in solving complex control problems. However, DRL applications in safety-critical systems are hindered by the inherent lack of robust validation techniques to assure their performance \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "36", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Gong:2025:NSE, author = "Nanxu Gong and Wangyang Ying and Dongjie Wang and Yanjie Fu", title = "Neuro-Symbolic Embedding for Short and Effective Feature Selection via Autoregressive Generation", journal = j-TIST, volume = "16", number = "2", pages = "37:1--37:??", month = apr, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3709011", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Wed Apr 23 07:20:52 MDT 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Feature selection aims to identify the optimal feature subset for enhancing downstream models. Effective feature selection can remove redundant features, save computational resources, accelerate the model learning process, and improve the model overall \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "37", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Ozcelik:2025:DMS, author = "Oguzhan Ozcelik and Cagri Toraman and Fazli Can", title = "Detecting Misinformation on Social Media using Community Insights and Contrastive Learning", journal = j-TIST, volume = "16", number = "2", pages = "38:1--38:??", month = apr, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3709009", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Wed Apr 23 07:20:52 MDT 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Social media users are more likely to be exposed to similar views and tend to avoid contrasting views, especially when they are part of a community of social media users. In this study, we investigate the presence of user communities and leverage them as \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "38", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Li:2025:POR, author = "Pei-En Li and Yao-Hua Ho", title = "Predicting the Occurrence of Respiratory Diseases Based on Campus Indoor Air Quality", journal = j-TIST, volume = "16", number = "2", pages = "39:1--39:??", month = apr, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3709008", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Wed Apr 23 07:20:52 MDT 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Air quality is known to be strongly correlated with respiratory diseases. Indoor air quality considerably affects human health, especially in spaces such as classrooms, where students gather and interact for long periods. Most schools are located in \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "39", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Bi:2025:JLJ, author = "Chunyang Bi and Mingnuo Teng and Tianhao Xie and Kun Li and Jingyu Yang", title = "{JASRNet}: Learning Joint Adaptive Sampling and Reconstruction for Depth Sensing", journal = j-TIST, volume = "16", number = "2", pages = "40:1--40:??", month = apr, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3716852", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Wed Apr 23 07:20:52 MDT 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Recent attempts to exploit irregular sampling strategies for depth sensing have shown prominent merits over the uniform rectangular sampling in terms of depth reconstruction quality, particularly at low sampling rates. However, the separate treatment of \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "40", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Wei:2025:MMV, author = "Chang Wei and Wengen Li and Yichao Zhang and Jihong Guan and Shuigeng Zhou", title = "{MVST}: a Multi-View Spatial-Temporal Model for Fine-Grained Crime Prediction", journal = j-TIST, volume = "16", number = "2", pages = "41:1--41:??", month = apr, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3712607", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Wed Apr 23 07:20:52 MDT 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Given a specific region, crime prediction aims to predict the occurrence of various crime events within a certain period of time in future, which is of high significance for guaranteeing urban safety. In practice, crime events are usually affected by a \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "41", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Qin:2025:CAB, author = "Zhida Qin and Wenhao Xue and Lu Zheng and Xiaoying Gan and Hongqiu Wu and Haiming Jin and Luoyi Fu", title = "Cost-aware Best Arm Identification in Stochastic Bandits", journal = j-TIST, volume = "16", number = "2", pages = "42:1--42:??", month = apr, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3712290", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Wed Apr 23 07:20:52 MDT 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "The best arm identification problem in multi-armed bandit model has been widely applied into many practical applications, such as spectrum sensing, online advertising, and cloud computing. Although lots of works have been devoted into this area, most of \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "42", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Chen:2025:SSI, author = "Ke-jia Chen and Wenhui Mu and Zulong Liu and Zheng Liu", title = "{SAug}: Structural Imbalance Aware Augmentation for Graph Neural Networks", journal = j-TIST, volume = "16", number = "2", pages = "43:1--43:??", month = apr, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3712699", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Wed Apr 23 07:20:52 MDT 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Graph machine learning (GML) has made great progress in node classification, link prediction, graph classification, and so on. However, graphs in reality are often structurally imbalanced, that is, only a few hub nodes have a denser local structure and \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "43", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Fan:2025:TPU, author = "Yu Fan and Xinjiang Lu and Hao Liu and Pengfei Wang and Liang Liu and Huadong Ma and Jingbo Zhou", title = "Towards Predicting Urban Land Use Changes: a Dynamic Graph Alignment Perspective", journal = j-TIST, volume = "16", number = "2", pages = "44:1--44:??", month = apr, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3712702", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Wed Apr 23 07:20:52 MDT 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Urban land use, intrinsically linked to people's daily activities, undergoes continuous evolution, presenting a complex interplay that remains partially understood. To bridge this gap, our study leverages fine-grained human mobility data to predict these \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "44", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Papicchio:2025:QAE, author = "Simone Papicchio and Paolo Papotti and Luca Cagliero", title = "{QATCH}: Automatic Evaluation of {SQL-Centric} Tasks on Proprietary Data", journal = j-TIST, volume = "16", number = "2", pages = "45:1--45:??", month = apr, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3712704", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Wed Apr 23 07:20:52 MDT 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Tabular Representation Learning (TRL) and Large Language Models (LLMs) have become established for tackling Question Answering (QA) and Semantic Parsing (SP) tasks on tabular data. State-of-the-art models are pre-trained and evaluated on large open-domain \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "45", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Wang:2025:IIT, author = "Ran Wang and Hua Zuo and Zhen Fang and Jie Lu", title = "Integrated Image-Text Augmentation for Few-Shot Learning in Vision-Language Models", journal = j-TIST, volume = "16", number = "2", pages = "46:1--46:??", month = apr, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3712700", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Wed Apr 23 07:20:52 MDT 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Vision-language models, such as the Contrastive Language-Image Pre-Training (CLIP) model, have achieved significant success in image classification tasks. CLIP demonstrates high expressive power in few-shot learning scenarios due to its pairing of text \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "46", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Le:2025:AKO, author = "Trung-Hieu Le and Shih-Chia Huang and Quoc-Viet Hoang and Zdenek Lokaj and Zhihui Lu", title = "Amalgamating Knowledge for Object Detection in Rainy Weather Conditions", journal = j-TIST, volume = "16", number = "2", pages = "47:1--47:??", month = apr, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3712703", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Wed Apr 23 07:20:52 MDT 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "In recent years, object detection has significantly advanced by using deep learning, especially convolutional neural networks. Most of the existing methods have focused on detecting objects under favorable weather conditions and achieved impressive \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "47", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Wei:2025:ERC, author = "Zhaohui Wei and Lizi Liao and Xinguang Xiang and Xiaoyu Du", title = "Enriching Responses with Crowd-Sourced Knowledge for Task-Oriented Conversational Agents", journal = j-TIST, volume = "16", number = "2", pages = "48:1--48:??", month = apr, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3714474", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Wed Apr 23 07:20:52 MDT 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Task-oriented conversational agents strive to aid users across various tasks by concentrating on generating suitable responses to guarantee successful task accomplishment. Nonetheless, several factors have a substantial influence on user contentment \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "48", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Chen:2025:MMG, author = "Meng Chen and Zechen Li and Hongwei Jia and Xin Shao and Jun Zhao and Qiang Gao and Min Yang and Yilong Yin", title = "{MGRL4RE}: a Multi-Graph Representation Learning Approach for Urban Region Embedding", journal = j-TIST, volume = "16", number = "2", pages = "49:1--49:??", month = apr, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3712698", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Wed Apr 23 07:20:52 MDT 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Using multi-modal data to learn region representations has gained popularity for its ability to reveal diverse socioeconomic features in cities. However, many studies focus solely on semantic features from points-of-interest (POIs), neglecting the issue \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "49", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Lyu:2025:GIS, author = "Hanjia Lyu and Jinfa Huang and Daoan Zhang and Yongsheng Yu and Xinyi Mou and Jinsheng Pan and Zhengyuan Yang and Zhongyu Wei and Jiebo Luo", title = "{GPT-4V(ision)} as A Social Media Analysis Engine", journal = j-TIST, volume = "16", number = "3", pages = "50:1--50:54", month = jun, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3709005", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Oct 25 07:04:51 MDT 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Recent research has shed light on the capabilities of Large Multimodal Models (LMMs) across various general vision and language tasks. The performance of LMMs in specialized domains, such as social media, which integrates text, images, videos, and \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "50", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Xu:2025:CIR, author = "Shuyuan Xu and Jianchao Ji and Yunqi Li and Yingqiang Ge and Juntao Tan and Yongfeng Zhang", title = "Causal Inference for Recommendation: Foundations, Methods, and Applications", journal = j-TIST, volume = "16", number = "3", pages = "51:1--51:51", month = jun, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3714430", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Oct 25 07:04:51 MDT 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Recommender systems are important and powerful tools for various personalized services. Traditionally, these systems use data mining and machine learning techniques to make recommendations based on correlations found in the data. However, relying solely \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "51", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Shin:2025:ISE, author = "Junho Shin and Younghoon Lee", title = "Improving the Summarization Effectiveness of Abstractive Datasets through Contrastive Learning", journal = j-TIST, volume = "16", number = "3", pages = "52:1--52:15", month = jun, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3716851", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Oct 25 07:04:51 MDT 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Most studies on abstractive summarization are conducted in a supervised learning framework, aiming to generate a golden summary from the original document. In this process, the model focuses on portions of the document that closely resemble the golden \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "52", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Chen:2025:DSA, author = "Na Chen and Ping Li and Jincheng Huang and Kai Zhang", title = "Denoising Structure against Adversarial Attacks on Graph Representation Learning", journal = j-TIST, volume = "16", number = "3", pages = "53:1--53:23", month = jun, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3714428", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Oct 25 07:04:51 MDT 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Despite their excellent performance in graph representation learning, graph convolutional networks have been proved to be vulnerable to adversarial perturbations on the connectivity between nodes in an unnoticed manner. In this work, by looking into the \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "53", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Li:2025:PSL, author = "Pei-Xuan Li and Hsun-Ping Hsieh", title = "Prediction for Sensor-Less Locations Using Multi-View Graph Fusion Approach with Approximation Module: a Case Study on Dengue Fever Risk Sensor", journal = j-TIST, volume = "16", number = "3", pages = "54:1--54:20", month = jun, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3718094", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Oct 25 07:04:51 MDT 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Dengue fever is an emergency disease spread by mosquitoes. The most direct way to prevent the disease is to predict risky areas and bolster mosquito preventive strategies. Risk is usually evaluated by monitoring the number of eggs in the ovitraps set up \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "54", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Mangal:2025:AFA, author = "Akshat Mangal and Santosh Singh Rathore", title = "{ATE-FS}: an Average Treatment Effect-Based Feature Selection Technique for Software Fault Prediction", journal = j-TIST, volume = "16", number = "3", pages = "55:1--55:28", month = jun, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3716857", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Oct 25 07:04:51 MDT 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "In software development, software fault prediction (SFP) models aim to identify code sections with a high likelihood of faults before the testing process. SFP models achieve this by analyzing data about the structural properties of the software's \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "55", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Jo:2025:EPO, author = "Ashly Ann Jo and Ebin Deni Raj and Jayakrushna Sahoo", title = "Efficiency and Performance Optimization in Large Language Models through {IB} Fine-Tuning", journal = j-TIST, volume = "16", number = "3", pages = "56:1--56:23", month = jun, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3718096", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Oct 25 07:04:51 MDT 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "In the rapidly evolving field of Natural Language Processing (NLP), optimizing methods for fine-tuning Large Language Models (LLMs) is increasingly critical for improving generalization and performance. Fine-tuning LLMs is challenging due to high costs, \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "56", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Vyas:2025:PMP, author = "Jayant Vyas and Jayesh Budhwani and Debasis Das", title = "{PRO-MTL}: Parameterized Route Optimization Using Multi-Task Learning", journal = j-TIST, volume = "16", number = "3", pages = "57:1--57:23", month = jun, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3718092", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Oct 25 07:04:51 MDT 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "In the current ridesharing scenario, finding a compatible passenger is highly challenging and largely dependent on chance. Existing algorithms prioritize the shortest route without considering future requests or traffic conditions, which reduces the \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "57", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Yang:2025:LEM, author = "Ruichao Yang and Jing Ma and Wei Gao and Hongzhan Lin", title = "{LLM}-Enhanced Multiple Instance Learning for Joint Rumor and Stance Detection with Social Context Information", journal = j-TIST, volume = "16", number = "3", pages = "58:1--58:27", month = jun, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3716856", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Oct 25 07:04:51 MDT 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "The proliferation of misinformation, such as rumors on social media, has drawn significant attention, prompting various expressions of stance among users. Although rumor detection and stance detection are distinct tasks, they can complement each other. \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "58", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Wang:2025:RDT, author = "Siyu Wang and Xiaocong Chen and Lina Yao", title = "Retentive Decision Transformer with Adaptive Masking for Reinforcement Learning-Based Recommendation Systems", journal = j-TIST, volume = "16", number = "3", pages = "59:1--59:20", month = jun, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3719208", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Oct 25 07:04:51 MDT 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Reinforcement Learning-Based Recommender Systems (RLRS) have shown promise across a spectrum of applications, from e-commerce platforms to streaming services. Yet, they grapple with challenges, notably in crafting reward functions and harnessing large \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "59", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Chang:2025:LAP, author = "Ching Chang and Wei-Yao Wang and Wen-Chih Peng and Tien-Fu Chen", title = "{LLM4TS}: Aligning Pre-Trained {LLMs} as Data-Efficient Time-Series Forecasters", journal = j-TIST, volume = "16", number = "3", pages = "60:1--60:20", month = jun, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3719207", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Oct 25 07:04:51 MDT 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Multivariate time-series forecasting is vital in various domains, e.g., economic planning and weather prediction. Deep train-from-scratch models have exhibited effective performance yet require large amounts of data, which limits real-world \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "60", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Liu:2025:MAR, author = "Yu Liu and Quanming Yao and Yong Li", title = "Modeling {$N$}-ary Relational Knowledge Bases with Tensor Decomposition", journal = j-TIST, volume = "16", number = "3", pages = "61:1--61:29", month = jun, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3709002", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Oct 25 07:04:51 MDT 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "The binary relational knowledge base (KB, a.k.a. knowledge graph), representing real-world knowledge with binary relations and entities, has been an important research topic in artificial intelligence, while, considerable knowledge also involves beyond-. \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "61", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{daSilva:2025:CTF, author = "Diego Corr{\^e}a da Silva and Dietmar Jannach and Frederico Ara{\'u}jo Dur{\~a}o", title = "Considering Time and Feature Entropy in Calibrated Recommendations", journal = j-TIST, volume = "16", number = "3", pages = "62:1--62:29", month = jun, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3716858", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Oct 25 07:04:51 MDT 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "The essence of calibration in recommender systems is to generate recommendations that match the distribution of a given user's past preferences regarding certain item features-e.g., in terms of preferred genres in the case of movies-while preserving \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "62", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Devgun:2025:PAL, author = "Tannishtha Devgun and Rahul Saha and Gulshan Kumar and Mauro Conti", title = "{PIN}: Application-Level Consensus for Blockchain-Based Artificial Intelligence Frameworks", journal = j-TIST, volume = "16", number = "3", pages = "63:1--63:25", month = jun, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3721845", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Oct 25 07:04:51 MDT 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Integrating AI into blockchain consensus, such as Proof-of-Learning and Proof of Useful Work, necessitates AI enablers. However, current consensus protocols cannot ensure AI enabler quality, crucial for AI-powered distributed blockchain and federated \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "63", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Meng:2025:DUI, author = "Wenhui Meng and Jiayi Xie and Jing Yi and Yaochen Zhu and Zhenzhong Chen", title = "Disentangling User Interest and Geographical Context for {POI} Recommendations", journal = j-TIST, volume = "16", number = "3", pages = "64:1--64:19", month = jun, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3723008", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Oct 25 07:04:51 MDT 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "POI recommendation plays an important role in many applications, such as mobility prediction and location-based advertisements. Existing POI recommendation methods mainly capture the observed patterns in user visits for recommendations, without a \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "64", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Hu:2025:MHG, author = "Zihan Hu and Jiuxiang You and Zhenguo Yang and Xiaoping Li and Haoran Xie and Qing Li and Wenyin Liu", title = "A Multi-Hop Graph Reasoning Network for Knowledge-Based {VQA}", journal = j-TIST, volume = "16", number = "3", pages = "65:1--65:23", month = jun, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3724125", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Oct 25 07:04:51 MDT 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Knowledge-based visual question answering (KB-VQA) requires reasoning about the visual grounding relations between the images and questions by incorporating external knowledge. Existing works typically retrieve knowledge from knowledge graphs by \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "65", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Sun:2025:EAS, author = "Xin Sun and Yongqing Mi and Hongao Li", title = "Enhancing Aspect Sentiment Classification with Dual-Channel Graph Convolutional Network", journal = j-TIST, volume = "16", number = "3", pages = "66:1--66:17", month = jun, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3721844", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Oct 25 07:04:51 MDT 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Aspect sentiment classification (ASC) constitutes a crucial research area within sentiment analysis tasks, aiming to predict sentiment polarity toward different aspects in given contexts. Identifying the relations between aspects and sentiments can be a \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "66", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Pietrantuono:2025:AGP, author = "Roberto Pietrantuono and Stefano Russo", title = "Automatic Generation of Plausible Co-Occurring Causes for Effects Explanation or Prediction", journal = j-TIST, volume = "16", number = "3", pages = "67:1--67:31", month = jun, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3725855", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Oct 25 07:04:51 MDT 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "In numerous contexts, ranging from systems safety assessment to finance and medical diagnosis, a relevant causal inference task is to predict unseen rare events-the so-called black swans. These are plausible, high-impact, but unexpected events for whose \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "67", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Marino:2025:GGN, author = "Antonio Marino and Claudio Pacchierotti and Paolo Robuffo Giordano", title = "A Gated Graph Neural Network Approach to Fast-Convergent Dynamic Average Estimation", journal = j-TIST, volume = "16", number = "3", pages = "68:1--68:18", month = jun, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3725857", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Oct 25 07:04:51 MDT 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Dynamic average estimation is a critical problem in multi-agent systems, enabling agents to collaboratively estimate time-varying signals using only local information exchange. Traditional model-based approaches often face challenges related to \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "68", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Peng:2025:JSF, author = "Ciyuan Peng and Huafei Huang and Tianqi Guo and Chengxuan Meng and Jingjing Zhou and Wenhong Zhao and Ruwan Tennakoon and Feng Xia", title = "Joint Structural-Functional Brain Graph Transformer", journal = j-TIST, volume = "16", number = "3", pages = "69:1--69:25", month = jun, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3729243", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Oct 25 07:04:51 MDT 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Multimodal brain graph transformers have become one of the foundational architectures of graph foundation models for brain science, relying on multimodal brain network fusion. However, most current multimodal brain network fusion methods primarily focus \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "69", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Ge:2025:HHM, author = "Xuri Ge and Fuhai Chen and Songpei Xu and Fuxiang Tao and Jie Wang and Joemon M. Jose", title = "{Hire}: Hybrid-Modal Interaction with Multiple Relational Enhancements for Image-Text Matching", journal = j-TIST, volume = "16", number = "3", pages = "70:1--70:23", month = jun, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3714431", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Oct 25 07:04:51 MDT 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Image-Text Matching (ITM) is a fundamental problem in computer vision. The key issue lies in jointly learning the visual and textual representation to estimate their similarity accurately. Most existing methods focus on feature enhancement within modality \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "70", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Lee:2025:SAT, author = "Seungyeon Lee and Ruoqi Liu and Wenyu Song and Lang Li and Ping Zhang", title = "{SubgroupTE}: Advancing Treatment Effect Estimation with Subgroup Identification", journal = j-TIST, volume = "16", number = "3", pages = "71:1--71:23", month = jun, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3718097", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Oct 25 07:04:51 MDT 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Precise estimation of treatment effects is crucial for accurately evaluating the intervention. While deep learning models have exhibited promising performance in learning counterfactual representations for Treatment Effect Estimation (TEE), a major \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "71", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Fan:2025:SOT, author = "Jinxiao Fan and Pengfei Wang and Liang Liu and Huadong Ma", title = "Self-Optimizing Teacher and Auto-Matching Student Framework for Change-Point Representation Learning in Time Series Forecasting", journal = j-TIST, volume = "16", number = "3", pages = "72:1--72:25", month = jun, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3718091", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Oct 25 07:04:51 MDT 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Real-world time series data is inherently complex, noisy, and exhibits abrupt changes, posing various challenges in data modeling. Given the ubiquity and importance of time series data, accurately forecasting change points, instead of the overall \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "72", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Chang:2025:LMT, author = "Li-Wei Chang and Cheng-Te Li and Chun-Pai Yang and Shou-de Lin", title = "Learning on Missing Tabular Data: Attention with Self-Supervision, Not Imputation, Is All You Need", journal = j-TIST, volume = "16", number = "3", pages = "73:1--73:24", month = jun, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3729241", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Oct 25 07:04:51 MDT 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Learning from data with missing values is a common challenge in real-world applications. Existing approaches for handling data incompleteness often involve imputation, which can introduce errors that propagate into downstream tasks or impose assumptions \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "73", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Shi:2025:GAF, author = "Wenda Shi and Waikeung Wong and Xingxing Zou", title = "Generative {AI} in Fashion: Overview", journal = j-TIST, volume = "16", number = "4", pages = "74:1--74:73", month = aug, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3718098", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Oct 25 07:04:53 MDT 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Generative Artificial Intelligence (GenAI) has recently gained immense popularity by offering various applications for generating high-quality and aesthetically pleasing content of image, 3D, and video data format. The innovative GenAI solutions have \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "74", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Hashemi:2025:UUP, author = "Maryam Hashemi and Ali Darejeh and Francisco Cruz", title = "Understanding User Preferences in Explainable Artificial Intelligence: a Mapping Function Proposal", journal = j-TIST, volume = "16", number = "4", pages = "75:1--75:37", month = aug, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3733837", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Oct 25 07:04:53 MDT 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "The increasing complexity of AI systems has led to the growth of the field of Explainable AI (XAI), which aims to provide explanations and justifications for the outputs of AI algorithms. While there is considerable demand for XAI, there remains a \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "75", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Xu:2025:ACB, author = "Chenglong Xu and Peipei Song and Shengeng Tang and Dan Guo and Xun Yang", title = "Alleviating Confirmation Bias in Learning with Noisy Labels via Two-Network Collaboration", journal = j-TIST, volume = "16", number = "4", pages = "76:1--76:21", month = aug, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3723009", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Oct 25 07:04:53 MDT 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Deep neural networks (DNNs) have achieved remarkable success in various computer vision tasks, e.g., image classification. However, most of the existing models depend heavily on annotated data, where label noise is inevitable. Training with such noisy \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "76", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Wu:2025:GCL, author = "Jiayang Wu and Wensheng Gan and Huashen Lu and Philip S. Yu", title = "Graph Contrastive Learning on Multi-label Classification for Recommendations", journal = j-TIST, volume = "16", number = "4", pages = "77:1--77:19", month = aug, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3725854", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Oct 25 07:04:53 MDT 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "In business analysis, providing effective recommendations is crucial for boosting company profits. Graph structures, especially bipartite graphs, are favored for analyzing complex data relationships. Link prediction is crucial for recommending specific \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "77", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Civitarese:2025:LLM, author = "Gabriele Civitarese and Michele Fiori and Priyankar Choudhary and Claudio Bettini", title = "Large Language Models Are Zero-Shot Recognizers for Activities of Daily Living", journal = j-TIST, volume = "16", number = "4", pages = "78:1--78:32", month = aug, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3725856", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Oct 25 07:04:53 MDT 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "The sensor-based recognition of Activities of Daily Living (ADLs) in smart home environments enables several applications in the areas of energy management, safety, well-being, and healthcare. ADL recognition is typically based on deep learning methods \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "78", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Greco:2025:TAA, author = "Salvatore Greco and Moreno {La Quatra} and Luca Cagliero and Tania Cerquitelli", title = "Towards {AI}-Assisted Inclusive Language Writing in {Italian} Formal Communications", journal = j-TIST, volume = "16", number = "4", pages = "79:1--79:24", month = aug, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3729237", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Oct 25 07:04:53 MDT 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Formal communications such as public calls, announcements, or regulations are supposed to exhibit respect for diversity in terms of gender, race, age, and disability. However, human writers often lack adequate inclusive writing skills. For instance, they \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "79", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Islam:2025:GGE, author = "Md Athikul Islam and Edoardo Serra and Sushil Jajodia", title = "{GenFighter}: a Generative and Evolutive Textual Attack Removal", journal = j-TIST, volume = "16", number = "4", pages = "80:1--80:23", month = aug, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3729240", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Oct 25 07:04:53 MDT 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Adversarial attacks pose significant challenges to deep neural networks (DNNs) such as Transformer models in natural language processing (NLP). This article introduces a novel defense strategy, called GenFighter, which enhances adversarial robustness by \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "80", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Rashidi:2025:IRI, author = "Shima Rashidi and Ruwan Tennakoon and Aref Miri Rekavandi and Papangkorn Jessadatavornwong and Amanda Freis and Garret Huff and Mark Easton and Adrian Mouritz and Reza Hoseinnezhad and Alireza Bab-Hadiashar", title = "{IT-RUDA}: Information Theory-Assisted Robust Unsupervised Domain Adaptation", journal = j-TIST, volume = "16", number = "4", pages = "81:1--81:17", month = aug, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3716853", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Oct 25 07:04:53 MDT 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Domain adaptation is a well-studied field in machine learning. Distribution shift between train (source) and test (target) datasets is a common problem encountered in machine learning applications. One approach to resolve this issue is to use the \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "81", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Chan:2025:ATO, author = "Sixian Chan and Xianpeng Zeng and Zhoujian Wu and Yu Wang and Xiaolong Zhou and Tinglong Tang and Jie Hu", title = "Adaptive Target-Oriented Tracking", journal = j-TIST, volume = "16", number = "4", pages = "82:1--82:15", month = aug, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3732785", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Oct 25 07:04:53 MDT 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "The current one-stream tracking pipelines are early relation modeling in feature extraction. However, insufficient discrimination may result in ambiguous relation modeling during early feature extraction. Moreover, the non-target information occupies \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "82", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Alhazmi:2025:CID, author = "Ahoud Alhazmi and Abdulwahab Aljubairy and Wei Emma Zhang and Quan Z. Sheng and Elaf Alhazmi", title = "Can Interpretability of Deep Learning Models Detect Textual Adversarial Distribution?", journal = j-TIST, volume = "16", number = "4", pages = "83:1--83:24", month = aug, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3729235", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Oct 25 07:04:53 MDT 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Deep Neural Networks (DNNs) are widely used in Natural Language Processing (NLP). However, adversarial samples attack benign inputs to readily fool the DNN models. The detection of these samples is a significant challenge that has received little \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "83", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Alqerm:2025:PPR, author = "Ismail Alqerm and Nuo Cheng and Jianli Pan", title = "{PREUS}: Proactive and Robust Edge-{UAV} Systems for Autonomous Monitoring in Dynamic Environments", journal = j-TIST, volume = "16", number = "4", pages = "84:1--84:20", month = aug, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3733836", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Oct 25 07:04:53 MDT 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Edge computing and AI can potentially empower Unmanned Aerial Vehicle (UAV) systems with automated decision-making and resource support for monitoring in future science tasks such as emergency response, search and rescue, inspections, and wildfires. \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "84", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Zhang:2025:LST, author = "Guang-Yu Zhang and Dong Huang and Chang-Dong Wang", title = "Large-Scale Tensorized Multi-View Kernel Subspace Clustering", journal = j-TIST, volume = "16", number = "4", pages = "85:1--85:21", month = aug, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3735644", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Oct 25 07:04:53 MDT 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "The anchor-based multi-view subspace clustering (AMSC) has turned into a favorable tool for large-scale multi-view clustering. However, there still exist some limitations to the current AMSC approaches. First, they typically recover anchor graph \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "85", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Peng:2025:SMS, author = "Kexing Peng and Shihao Zhu and Tinghuai Ma", title = "{STPE-MARL}: Spatio-Temporal Multi-Agent Population Evolution Reinforcement Learning", journal = j-TIST, volume = "16", number = "4", pages = "86:1--86:24", month = aug, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3742479", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Oct 25 07:04:53 MDT 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Achieving joint goals efficiently in complex real-world tasks demands effective collaboration among multiple agents. Multi-Agent Reinforcement Learning (MARL) faces two interrelated challenges: limited exploration leads to early convergence on suboptimal \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "86", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Peng:2025:CRG, author = "Han Peng and Wengen Li and Chang Jin and Yichao Zhang and Jihong Guan and Hanchen Yang and Shuigeng Zhou", title = "Cross-Region Graph Convolutional Network with Periodicity Shift Adaptation for Wide-Area {SST} Prediction", journal = j-TIST, volume = "16", number = "4", pages = "87:1--87:23", month = aug, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3735646", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Oct 25 07:04:53 MDT 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Accurate prediction of Sea Surface Temperature (SST) is of high importance in marine science, benefiting applications ranging from ecosystem protection to extreme weather forecasting and climate analysis. Wide-area SST usually shows diverse SST patterns \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "87", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Do:2025:FGN, author = "Pham Minh Thu Do and Jie Lu and Qian Zhang and Guangquan Zhang", title = "A Federated Graph Neural Network with Differential Privacy for Cross-domain Recommender Systems", journal = j-TIST, volume = "16", number = "4", pages = "88:1--88:25", month = aug, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3742791", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Oct 25 07:04:53 MDT 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Cross-domain recommender systems, which are designed to address issues with data sparsity, tend to suffer notable challenges with safeguarding user privacy. While existing cross-domain recommendation methods incorporate privacy mechanisms, they often \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "88", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Trad:2025:EEP, author = "Fouad Trad and Ali Chehab", title = "Evaluating the Efficacy of Prompt-Engineered Large Multimodal Models versus Fine-Tuned Vision Transformers in Image-Based Security Applications", journal = j-TIST, volume = "16", number = "4", pages = "89:1--89:22", month = aug, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3735648", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Oct 25 07:04:53 MDT 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "The success of Large Language Models (LLMs) has spurred the rise of Large Multimodal Models (LMMs), which integrate multiple modalities, such as text and images, to address complex data analysis tasks. As these black-box models gain popularity due to \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "89", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Xiao:2025:TDE, author = "Zhen Xiao and Xueliang Liu and Jinlin Guo and Jun He and Richang Hong and Meng Wang", title = "{Talking-DiSSM}: Enhancing Temporal Consistency in Talking Face Video Generation with Bidirectional {SSMs}", journal = j-TIST, volume = "16", number = "4", pages = "90:1--90:18", month = aug, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3742790", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Oct 25 07:04:53 MDT 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Generating temporally smooth and high-resolution videos is a crucial objective in talking face generation tasks. Diffusion-based generative models have emerged as a prime choice for these tasks due to their ability to produce high-quality outputs. To \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "90", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Zhu:2025:IHT, author = "Baofeng Zhu and Wanjun Cheng and Xia Zhang and Jiren Liu", title = "Integrated Hybrid Transformer and Multi-Receptive Feature Extraction Mechanism for Electrocardiogram Denoising Using Score-Based Diffusion Model", journal = j-TIST, volume = "16", number = "4", pages = "91:1--91:24", month = aug, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3744654", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Oct 25 07:04:53 MDT 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Electrocardiogram (ECG) is the foundation of the analysis of cardiac disease. In the hospital clinical ECG diagnostic scenarios, when doctors analyze ECG signals or when an ECG intelligent diagnostic system is used, there might be strong noises like \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "91", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Liang:2025:MMS, author = "Shichao Liang and Wen Wen and Yali Feng and Ruichu Cai and Zhifeng Hao", title = "Modeling Multi-Seasonal Multi-Behavior Dependency for Temporal Recommendation", journal = j-TIST, volume = "16", number = "4", pages = "92:1--92:23", month = aug, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3742793", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Oct 25 07:04:53 MDT 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Mining temporal patterns from user behaviors has long been investigated, but most of the existing work centers on single-type user-item interactions, such as purchase or click, which fails to take advantage of the user's diversified interests revealed by \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "92", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Martinez:2025:ORG, author = "Alex Mart{\'\i}nez and Federico Cinus and Francesco Bonchi and Jordi Vitri{\`a}", title = "Optimizing Reachability in Graph-Based Recommender Systems", journal = j-TIST, volume = "16", number = "4", pages = "93:1--93:23", month = aug, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3744658", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Oct 25 07:04:53 MDT 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "While accuracy has long been prioritized as the primary metric for Recommender Systems (RSs), it is increasingly accepted that the system's overall quality is not solely determined by this factor. Reachability, the ease with which users can navigate the \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "93", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Bokobza:2025:CSS, author = "Roey Bokobza and Yisroel Mirsky", title = "Counter-Samples: a Stateless Strategy to Neutralize Black-Box Adversarial Attacks", journal = j-TIST, volume = "16", number = "4", pages = "94:1--94:23", month = aug, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3744657", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Oct 25 07:04:53 MDT 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Our article introduces a novel defense mechanism against black-box attacks, where attackers exploit the victim model as an oracle to craft adversarial examples. Unlike traditional pre-processing defenses that rely on sanitizing input samples, our \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "94", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Sanchez:2025:SIB, author = "Pablo S{\'a}nchez and Alejandro Bellog{\'\i}n", title = "Smart Imputation, Better Recommendations: Improving Traditional Point-of-Interest Recommendation through Data Augmentation", journal = j-TIST, volume = "16", number = "4", pages = "95:1--95:35", month = aug, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3744347", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Oct 25 07:04:53 MDT 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Data sparsity is a persistent challenge in recommender systems, especially in specific domains like Point-of-Interest (POI) recommendation, where it significantly impacts model performance. While classical recommender systems have used various imputation \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "95", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Kang:2025:GFM, author = "Yan Kang and Tao Fan and Hanlin Gu and Xiaojin Zhang and Lixin Fan and Qiang Yang", title = "Grounding Foundation Models through Federated Transfer Learning: a General Framework", journal = j-TIST, volume = "16", number = "4", pages = "96:1--96:54", month = aug, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3742788", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Oct 25 07:04:53 MDT 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Foundation Models (FMs) such as GPT-4 encoded with vast knowledge and powerful emergent abilities have achieved remarkable success in various natural language processing and computer vision tasks. Grounding FMs by adapting them to domain-specific tasks \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "96", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Jin:2025:IAP, author = "Kebing Jin and Hankz Hankui Zhuo", title = "Integrating {AI} Planning with Natural Language Processing: a Combination of Explicit and Tacit Knowledge", journal = j-TIST, volume = "16", number = "4", pages = "97:1--97:37", month = aug, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3729236", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Oct 25 07:04:53 MDT 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Natural language processing (NLP) aims at investigating the interactions between agents and humans, which processes and analyzes large amounts of natural language data. Large-scale language models play an important role in current NLP. However, the \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "97", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Lauw:2025:IBP, author = "Hady W. Lauw and Marc Najork and Evimaria Terzi and Panayiotis Tsaparas", title = "Introduction to the {``Best Papers of WSDM 2023''} Special Issue", journal = j-TIST, volume = "16", number = "5", pages = "98:1--98:3", month = oct, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3736730", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Oct 25 07:04:55 MDT 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "98", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Kosan:2025:GGC, author = "Mert Kosan and Zexi Huang and Sourav Medya and Sayan Ranu and Ambuj Singh", title = "{GCFExplainer}: Global Counterfactual Explainer for Graph Neural Networks", journal = j-TIST, volume = "16", number = "5", pages = "99:1--99:23", month = oct, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3698108", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Oct 25 07:04:55 MDT 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Graph neural networks (GNNs) find applications in various domains such as computational biology, natural language processing, and computer security. Owing to their popularity, there is an increasing need to explain GNN predictions since GNNs are black-box \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "99", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Bhalla:2025:LED, author = "Nikita Bhalla and Adam Lechowicz and Cameron Musco", title = "Local Edge Dynamics and Opinion Polarization", journal = j-TIST, volume = "16", number = "5", pages = "100:1--100:27", month = oct, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3709006", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Oct 25 07:04:55 MDT 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "The proliferation of social media platforms, recommender systems, and their joint societal impacts have prompted significant interest in opinion formation and evolution within social networks. We study how local edge dynamics can drive opinion \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "100", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Zhang:2025:ASB, author = "Peiyan Zhang and Jiayan Guo and Chaozhuo Li and Liying Kang and Jaeboum Kim and Jie Xu and Xi Zhang and Yan Zhang and Haohan Wang and Sunghun Kim", title = "Advancing Session-Based Recommendations with Atten-Mixer+: Dynamic and Adaptive Multi-Level Intent Mining", journal = j-TIST, volume = "16", number = "5", pages = "101:1--101:26", month = oct, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3700445", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Oct 25 07:04:55 MDT 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Session-Based Recommendation (SBR) systems, traditionally reliant on complex Graph Neural Networks (GNNs), often face challenges with marginal performance improvements despite increased model complexity. In this article, we dissect the classical GNN-based \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "101", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Xuan:2025:KET, author = "Hongrui Xuan and Bohan Li and Wenlong Wu and Yi Liu and Hongzhi Yin", title = "Knowledge Enhancement and Temporal Aware for Multi-Behavior Contrastive Recommendation", journal = j-TIST, volume = "16", number = "5", pages = "102:1--102:23", month = oct, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3735512", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Oct 25 07:04:55 MDT 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "A well-designed recommender system can accurately learn the embeddings of users and items, reflecting the unique preferences of users. Traditional recommendation techniques usually focus on modeling the singular type of behaviors between users and items. \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "102", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Bera:2025:DCN, author = "Suman Bera and Jayesh Choudhari and Shahrzad Haddadan and Sara Ahmadian", title = "{DeMEtRIS}: Counting (near)-Cliques by Crawling", journal = j-TIST, volume = "16", number = "5", pages = "103:1--103:25", month = oct, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3699517", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Oct 25 07:04:55 MDT 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "We study the problem of approximately counting cliques and near-cliques in a graph, where the access to the graph is only available through crawling its vertices. This model has been introduced recently to capture real-life scenarios in which the entire \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "103", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Wang:2025:GML, author = "Shijie Wang and Jiani Huang and Zhikai Chen and Yu Song and Wenzhuo Tang and Haitao Mao and Wenqi Fan and Hui Liu and Xiaorui Liu and Dawei Yin and Qing Li", title = "Graph Machine Learning in the Era of Large Language Models ({LLMs})", journal = j-TIST, volume = "16", number = "5", pages = "104:1--104:40", month = oct, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3732786", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Oct 25 07:04:55 MDT 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Graphs play an important role in representing complex relationships in various domains like social networks, knowledge graphs, and molecular discovery. With the advent of deep learning, Graph Neural Networks (GNNs) have emerged as a cornerstone in Graph \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "104", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Nezhadettehad:2025:PNU, author = "Alireza Nezhadettehad and Arkady Zaslavsky and Abdur Rakib and Siraj Ahmed Shaikh and Seng W. Loke and Guang-Li Huang and Alireza Hassani", title = "Predicting Next Useful Location with Context-Awareness: The State-of-the-Art", journal = j-TIST, volume = "16", number = "5", pages = "105:1--105:35", month = oct, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3744653", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Oct 25 07:04:55 MDT 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Predicting the future location of mobile objects reinforces location-aware services with proactive intelligence and helps businesses and decision-makers with better planning and near real-time scheduling in different applications such as traffic \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "105", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Naveed:2025:COL, author = "Humza Naveed and Asad Ullah Khan and Shi Qiu and Muhammad Saqib and Saeed Anwar and Muhammad Usman and Naveed Akhtar and Nick Barnes and Ajmal Mian", title = "A Comprehensive Overview of Large Language Models", journal = j-TIST, volume = "16", number = "5", pages = "106:1--106:72", month = oct, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3744746", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Oct 25 07:04:55 MDT 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Large Language Models (LLMs) have recently demonstrated remarkable capabilities in natural language processing tasks and beyond. This success of LLMs has led to a large influx of research contributions in this direction. These works encompass diverse \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "106", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Rawat:2025:STF, author = "Mamta Rawat and Gaurav Singal", title = "Surveying Technology Fusion in {IoT} Networks for {IDS}: Exploring Datasets, Tools, Challenges, and Research Prospects", journal = j-TIST, volume = "16", number = "5", pages = "107:1--107:45", month = oct, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3744745", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Oct 25 07:04:55 MDT 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "The Internet of Things is quickly taking over the world. Nevertheless, security for the IoT is becoming a more important academic topic and commercial concern because of several factors including the diverse nature of devices, protocols in use, the \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "107", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Nguyen:2025:SMU, author = "Thanh Tam Nguyen and Thanh Trung Huynh and Zhao Ren and Phi Le Nguyen and Alan Wee-Chung Liew and Hongzhi Yin and Quoc Viet Hung Nguyen", title = "A Survey of Machine Unlearning", journal = j-TIST, volume = "16", number = "5", pages = "108:1--108:46", month = oct, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3749987", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Oct 25 07:04:55 MDT 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Today, computer systems hold large amounts of personal data. Yet while such an abundance of data allows breakthroughs in AI, and especially machine learning, its existence can be a threat to user privacy, and it can weaken the bonds of trust between \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "108", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Seo:2025:SMI, author = "Hoon Seo and Yuze Bai and Lodewijk Brand and Lucia Saldana Barco and Hua Wang", title = "Scalable Multi-Instance Multi-Shape Support Vector Machine for Whole Slide Breast Histopathology", journal = j-TIST, volume = "16", number = "5", pages = "109:1--109:26", month = oct, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3747593", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Oct 25 07:04:55 MDT 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Analysis of histopathological images is critical in cancer diagnosis and treatment. Due to the huge size of histopathological images and the varied number of imaging records per patient, many existing works analyze the Whole Slide Image (WSI) as a bag in \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "109", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Fu:2025:MDD, author = "Wenjie Fu and Huandong Wang and Chen Gao and Guanghua Liu and Yong Li and Tao Jiang", title = "Mobility Data-Driven Privacy-Preserving Model for Detecting High-Risk Infection Cases", journal = j-TIST, volume = "16", number = "5", pages = "110:1--110:29", month = oct, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3742789", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Oct 25 07:04:55 MDT 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "In the past few years, infectious diseases like COVID-19 have caused serious distress to the global society and the economy. To prevent its spread, the early detection and assessment of infectious diseases based on molecular tests or antigen testing of \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "110", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Tekin:2025:RFS, author = "Selim Furkan Tekin and Fatih Ilhan and Tiansheng Huang and Sihao Hu and Margaret Loper and Ling Liu", title = "Robust Few-Shot Ensemble Learning with Focal Diversity-Based Pruning", journal = j-TIST, volume = "16", number = "5", pages = "111:1--111:34", month = oct, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3746457", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Oct 25 07:04:55 MDT 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "This article presents FusionShot, a focal diversity-optimized few-shot ensemble learning approach for boosting the robustness and generalization performance of pre-trained few-shot models. The article makes three original contributions. First, we explore \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "111", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Jiang:2025:PLP, author = "Shantao Jiang and Yiping Wen and Jun Shen and Gaoxian Peng and Guosheng Kang and Jianxun Liu", title = "Personalized Learning Path Recommendation with Time-Aware Attention-Based Reinforcement Learning", journal = j-TIST, volume = "16", number = "5", pages = "112:1--112:24", month = oct, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3747594", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Oct 25 07:04:55 MDT 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Learning resources in online learning systems typically adhere to uniform formats and settings, lacking flexibility and personalization to meet diverse learning needs and preferences. This inability to meet individualized learning needs and preferences \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "112", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Geraghty:2025:LAM, author = "Jack Geraghty and Andrew Hines and Fatemeh Golpayegani", title = "Learning to Associate: Multimodal Inference with Fully Missing Modalities", journal = j-TIST, volume = "16", number = "5", pages = "113:1--113:48", month = oct, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3746456", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Oct 25 07:04:55 MDT 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "In this article, we propose Cross-Modal Association Models (C-MAMs), a novel approach for handling missing modalities during inference in multimodal learning. Unlike existing methods that modify the training process, C-MAMs generate missing modality \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "113", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Fan:2025:CFB, author = "Di Fan and Yannian Kou and Chuanhou Gao", title = "Causal Flow-Based Variational Auto-Encoder for Disentangled Causal Representation Learning", journal = j-TIST, volume = "16", number = "5", pages = "114:1--114:26", month = oct, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3748660", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Oct 25 07:04:55 MDT 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Disentangled representation learning aims to learn low-dimensional representations where each dimension corresponds to an underlying generative factor. While the Variational Auto-Encoder (VAE) is widely used for this purpose, most existing methods assume \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "114", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Peleska:2025:TTM, author = "Jakub Peleska and Gustav S{\'\i}r", title = "Tabular Transformers Meet Relational Databases", journal = j-TIST, volume = "16", number = "5", pages = "115:1--115:24", month = oct, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3749991", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Oct 25 07:04:55 MDT 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Transformer models have continuously expanded into all machine learning domains convertible to the underlying sequence-to-sequence representation, including tabular data. However, while ubiquitous, this representation restricts its extension to the more \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "115", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Wang:2025:BCC, author = "Liang Wang and Shan Su and Rongchang Cheng and Dingqi Yang and Lianbo Ma and Fei Xiong and Bin Guo and Zhiwen Yu", title = "Balancing Cooperation and Competition: Selfish Worker Coalition Formation in Spatial Crowdsourcing", journal = j-TIST, volume = "16", number = "5", pages = "116:1--116:24", month = oct, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3748661", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Oct 25 07:04:55 MDT 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Spatial Crowdsourcing (SC), which outsources location-dependent tasks to workers for physical completion, is gaining popularity. Recently, more complex tasks have emerged that require a group of workers collaborating in a coalition. Several pioneering \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "116", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Wang:2025:LEU, author = "Xinyuan Wang and Liang Wu and Liangjie Hong and Hao Liu and Yanjie Fu", title = "{LLM}-Enhanced User-Item Interactions: Leveraging Edge Information for Optimized Recommendations", journal = j-TIST, volume = "16", number = "5", pages = "117:1--117:24", month = oct, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3757925", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Oct 25 07:04:55 MDT 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Graph recommendation methods, representing a connected interaction perspective, reformulate user-item interactions as graphs to leverage graph structure and topology to recommend and have proved practical effectiveness at scale. Large language models \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "117", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Bhatia:2025:CDM, author = "Munish Bhatia", title = "Cognitive Decision Modeling for Quality of Service in Domestic Pipeline Network", journal = j-TIST, volume = "16", number = "5", pages = "118:1--118:23", month = oct, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3757926", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Oct 25 07:04:55 MDT 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "The Internet of Things (IoT) has transformed the industrial sector. This study presents a novel framework for real-time evaluation of service quality in residential gas pipeline networks. IoT devices collect critical operational and environmental data, \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "118", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Li:2025:CUF, author = "Yang Li and Enyue Yang and Weike Pan and Qiang Yang and Zhong Ming", title = "Cross-User Federated Recommendation Unlearning", journal = j-TIST, volume = "16", number = "5", pages = "119:1--119:24", month = oct, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3749990", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Oct 25 07:04:55 MDT 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Cross-user federated recommendation (CUFR) is a promising solution for providing personalized services without collecting users' raw data. However, most previous CUFR works mainly focus on providing accurate and privacy-preserving personalized \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "119", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Zhuang:2025:HFI, author = "Yong Zhuang and Matthew Almeida and Wei Ding and Shafiqul Islam and Zihan Li and Ping Chen", title = "Horizon Forcing: Improving the Recurrent Forecasting of Chaotic Systems", journal = j-TIST, volume = "16", number = "5", pages = "120:1--120:22", month = oct, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3718090", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Oct 25 07:04:55 MDT 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Chaotic dynamics are ubiquitous in many real-world systems, ranging from biological and industrial processes to climate dynamics and the spread of viruses. These systems are characterized by high sensitivity to initial conditions, making it challenging \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "120", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Shu:2025:EPF, author = "Yulou Shu and Wengen Li and Yu-Ping Ruan and Wuchao Liu and Yichao Zhang and Jihong Guan and Shuigeng Zhou", title = "Ensuring Pre-Fusion Modality Consistency: a New Approach to Multimodal Sentiment Detection", journal = j-TIST, volume = "16", number = "5", pages = "121:1--121:20", month = oct, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3748658", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Oct 25 07:04:55 MDT 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "With the growing diversity of data formats on social media, such as text, images, and videos, there is a growing need to analyze sentiment from multiple modalities. Multimodal sentiment detection, which aims to identify users' sentiment by jointly \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "121", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Jiang:2025:MAU, author = "Bin Jiang and Yining Wang and Fanhui Kong and Jian Wang", title = "Multi-Autonomous Underwater Vehicle Trajectory Planning in Ocean Current Based on Hierarchical Hunting and Evolutionary Learning", journal = j-TIST, volume = "16", number = "5", pages = "122:1--122:26", month = oct, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3757928", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Sat Oct 25 07:04:55 MDT 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "In the context of rising demands for marine resource exploitation and scientific research, collaborative trajectory planning for multiple Autonomous Underwater Vehicles (AUVs) in complex underwater environments-marked by obstacles, ocean currents, and \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "122", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Grabovski:2025:BTD, author = "Fred M. Grabovski and Lior Yasur and Guy Amit and Yisroel Mirsky", title = "Back-in-Time Diffusion: Unsupervised Detection of Medical Deepfakes", journal = j-TIST, volume = "16", number = "6", pages = "123:1--123:26", month = dec, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3744656", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Feb 2 08:20:50 MST 2026", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Recent progress in generative models has made it easier for a wide audience to edit and create image content, raising concerns about the proliferation of deepfakes, especially in healthcare. Despite the availability of numerous techniques for detecting \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "123", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Rostam:2025:APT, author = "Zhyar Rzgar K. Rostam and G{\'a}bor Kert{\'e}sz", title = "Advances in Pre-trained Language Models for Domain-Specific Text Classification: a Systematic Review", journal = j-TIST, volume = "16", number = "6", pages = "124:1--124:41", month = dec, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3763002", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Feb 2 08:20:50 MST 2026", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "The exponential increase in scientific literature and online information necessitates efficient methods for extracting knowledge from textual data. Natural language processing (NLP) plays a crucial role in addressing this challenge, particularly in text \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "124", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Febrinanto:2025:ECG, author = "Falih Gozi Febrinanto and Kristen Moore and Chandra Thapa and Mujie Liu and Vidya Saikrishna and Jiangang Ma and Feng Xia", title = "Entropy Causal Graphs for Multivariate Time Series Anomaly Detection", journal = j-TIST, volume = "16", number = "6", pages = "125:1--125:25", month = dec, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3757922", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Feb 2 08:20:50 MST 2026", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Many multivariate time series anomaly detection frameworks have been proposed and widely applied. However, most of these frameworks do not consider intrinsic relationships between variables in multivariate time series data, thus ignoring the causal \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "125", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Liu:2025:MCS, author = "Tong Liu and Timothy R. McIntosh and Teo Susnjak and Paul Watters and Malka N. Halgamuge", title = "Modeling the Chaotic Semantic States of Generative Artificial Intelligence ({AI}): a Quantum Mechanics Analogy Approach", journal = j-TIST, volume = "16", number = "6", pages = "126:1--126:36", month = dec, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3757927", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Feb 2 08:20:50 MST 2026", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Generative AI models have revolutionized intelligent systems by enabling machines to produce human-like content across diverse domains. However, their outputs often exhibit unpredictability due to complex and opaque internal semantic states, posing \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "126", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{He:2025:LHK, author = "Yeshuai He and Jianqiang Cheng and Yong Ge", title = "Learning to Hash Knowledge Graph: Element-wise Rotation", journal = j-TIST, volume = "16", number = "6", pages = "127:1--127:25", month = dec, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3763005", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Feb 2 08:20:50 MST 2026", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Knowledge graphs are vital for many tasks, including recommendation systems and node search. Learning to hash knowledge graph is to infer binary-vector representations of the graph. Compared with traditional knowledge graph embedding that learns \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "127", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Bhatia:2025:AII, author = "Munish Bhatia", title = "Artificial Intelligence-Inspired Anxiety Detection in Smart {Office}: Cyber Twin Perspective", journal = j-TIST, volume = "16", number = "6", pages = "128:1--128:26", month = dec, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3759253", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Feb 2 08:20:50 MST 2026", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Cyber twin technology, a successful branch of simulation modeling in business, is now being applied in the healthcare sector. An intelligent architecture inspired by cyber twins is proposed to explore the unique visual, behavioral, and physiological \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "128", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Lyu:2025:HMS, author = "Shiwei Lyu and Xiaofeng Li and Suting Hong and Qing Ke and Jinjie Gu and Kunpeng Zhang and Haipeng Zhang", title = "Help Me Screen: Analyzing and Predicting the Success of Start-ups in Dynamic Venture Capital Networks", journal = j-TIST, volume = "16", number = "6", pages = "129:1--129:26", month = dec, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3763001", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Feb 2 08:20:50 MST 2026", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Most start-ups fail, and early-stage ventures face even lower survival rates. Identifying high-potential start-ups remains a critical challenge for venture capital (VC) investors and policymakers. While predictive models exist, the evolving relationships \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "129", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Avraham:2025:PSC, author = "Bar Avraham and Yisroel Mirsky", title = "{PEAS}: a Strategy for Crafting Transferable Adversarial Examples", journal = j-TIST, volume = "16", number = "6", pages = "130:1--130:21", month = dec, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3763003", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Feb 2 08:20:50 MST 2026", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Black box attacks, where adversaries have limited knowledge of the target model, pose a significant threat to machine learning systems. Adversarial examples generated with a substitute model often suffer from limited transferability to the target model. \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "130", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Zhou:2025:ODH, author = "Peng Zhou and Huiqi Deng and Yunyun Zhang and Zhaolong Ling and Xindong Wu", title = "Online Distributed Heterogeneous Streaming Feature Selection", journal = j-TIST, volume = "16", number = "6", pages = "131:1--131:23", month = dec, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3767722", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Feb 2 08:20:50 MST 2026", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Data are exploding in many fields and may exist in the streaming mode. When the generation speed of massive streaming data far exceeds the processing speed of a single node and the generated data need to be processed in real time, traditional centralized \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "131", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Wang:2025:ISI, author = "Jindong Wang and Linyi Yang and Sunayana Sitaram and Qiang Yang and Bhiksha Raj", title = "Introduction to the Special Issue on Evaluations of Large Language Models: {Part 1}", journal = j-TIST, volume = "16", number = "6", pages = "132:1--132:3", month = dec, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3770500", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Feb 2 08:20:50 MST 2026", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "132", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Morales-Garcia:2025:DTS, author = "Juan Morales-Garc{\'\i}a and Antonio Llanes and Francisco Arcas-T{\'u}nez and Fernando Terroso-S{\'a}enz", title = "Developing Time Series Forecasting Models with Generative Large Language Models", journal = j-TIST, volume = "16", number = "6", pages = "133:1--133:12", month = dec, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3663485", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Feb 2 08:20:50 MST 2026", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Nowadays, Generative Large Language Models (GLLMs) have made a significant impact in the field of Artificial Intelligence (AI). One of the domains extensively explored for these models is their ability as generators of functional source code for software \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "133", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Hota:2025:ELL, author = "Aritra Hota and Soumyajit Chatterjee and Sandip Chakraborty", title = "Evaluating Large Language Models as Virtual Annotators for Time-Series Physical Sensing Data", journal = j-TIST, volume = "16", number = "6", pages = "134:1--134:25", month = dec, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3696461", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Feb 2 08:20:50 MST 2026", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Traditional human-in-the-loop-based annotation for time-series data like inertial data often requires access to alternate modalities like video or audio from the environment. These alternate sources provide the necessary information to the human annotator,. \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "134", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Ni:2025:SCA, author = "Qin Ni and Yangze Yu and Yiming Ma and Xin Lin and Ciping Deng and Tingjiang Wei and Mo Xuan", title = "The Social Cognition Ability Evaluation of {LLMs}: a Dynamic Gamified Assessment and Hierarchical Social Learning Measurement Approach", journal = j-TIST, volume = "16", number = "6", pages = "135:1--135:20", month = dec, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3673238", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Feb 2 08:20:50 MST 2026", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Large Language Model (LLM) has shown amazing abilities in reasoning tasks, theory of mind (ToM) has been tested in many studies as part of reasoning tasks, and social learning, which is closely related to ToM, is still lack of investigation. However, the \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "135", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Hrytsyna:2025:RRA, author = "Anastasiia Hrytsyna and Rodrigo Alves", title = "From Representation to Response: Assessing the Alignment of Large Language Models with Human Judgment Patterns", journal = j-TIST, volume = "16", number = "6", pages = "136:1--136:23", month = dec, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3709148", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Feb 2 08:20:50 MST 2026", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Large language models (LLMs) are sophisticated artificial intelligence systems designed to process and understand natural language at a complex level. The recent progress of these models, culminating in chat-based LLMs, has democratized the accessibility \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "136", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Lee:2025:RAL, author = "Seungpil Lee and Woochang Sim and Donghyeon Shin and Wongyu Seo and Jiwon Park and Seokki Lee and Sanha Hwang and Sejin Kim and Sundong Kim", title = "Reasoning Abilities of Large Language Models: In-Depth Analysis on the Abstraction and Reasoning Corpus", journal = j-TIST, volume = "16", number = "6", pages = "137:1--137:52", month = dec, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3712701", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Feb 2 08:20:50 MST 2026", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "The existing methods for evaluating the inference abilities of Large Language Models (LLMs) have been predominantly results-centric, making it challenging to assess the inference process comprehensively. We introduce a novel approach using the Abstraction \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "137", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Martinez:2025:BWE, author = "Gonzalo Mart{\'\i}nez and Jos{\'e} Alberto Hern{\'a}ndez and Javier Conde and Pedro Reviriego and Elena Merino-Gomez", title = "Beware of Words: Evaluating the Lexical Diversity of Conversational {LLMs} using {ChatGPT} as Case Study", journal = j-TIST, volume = "16", number = "6", pages = "138:1--138:15", month = dec, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3696459", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Feb 2 08:20:50 MST 2026", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "The performance of conversational Large Language Models (LLMs) in general, and of ChatGPT in particular, is currently being evaluated on many different tasks, from logical reasoning or math to answering questions on a myriad of topics. Instead, much less \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "138", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Mu:2025:MLL, author = "Jie Mu and Wei Wang and Wenqi Liu and Tiantian Yan and Guanglu Wang", title = "Multimodal Large Language Model with {LoRA} Fine-Tuning for Multimodal Sentiment Analysis", journal = j-TIST, volume = "16", number = "6", pages = "139:1--139:23", month = dec, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3709147", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Feb 2 08:20:50 MST 2026", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Multimodal sentiment analysis has become a popular research topic in recent years. However, existing methods have two unaddressed limitations: (1) they use limited supervised labels to train models, which makes it impossible for model to fully learn \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "139", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Liu:2025:EFB, author = "Shuo Liu and Lin Zhang and Weidong Liu and Jianfeng Zhang and Donghui Gao and Xiaofeng Jia", title = "The Evaluation Framework and Benchmark for Large Language Models in the Government Affairs Domain", journal = j-TIST, volume = "16", number = "6", pages = "140:1--140:24", month = dec, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3716854", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Feb 2 08:20:50 MST 2026", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "The rapid evolution of AI has driven advancements across numerous sectors. In the domain of government affairs, large language models (LLMs) hold significant potential for applications such as policy analysis, data processing, and decision support. \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "140", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Fang:2025:HDL, author = "Chen Fang and Yidong Wang and Yunze Song and Qingqing Long and Wang Lu and Linghui Chen and Guihai Feng and Yuanchun Zhou and Xin Li", title = "How Do Large Language Models Understand Genes and Cells", journal = j-TIST, volume = "16", number = "6", pages = "141:1--141:16", month = dec, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3702234", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Feb 2 08:20:50 MST 2026", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Researching genes and their interactions is crucial for deciphering the fundamental laws of cellular activity, advancing disease treatment, drug discovery, and more. Large language Models (LLMs), with their profound text comprehension and generation \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "141", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Deldjoo:2025:CCF, author = "Yashar Deldjoo and Tommaso {Di Noia}", title = "{CFaiRLLM}: Consumer Fairness Evaluation in Large-Language Model Recommender System", journal = j-TIST, volume = "16", number = "6", pages = "142:1--142:31", month = dec, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3725853", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Feb 2 08:20:50 MST 2026", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "This work takes a critical stance on previous studies concerning fairness evaluation in Large-Language Model (LLM)-based recommender systems, which have primarily assessed consumer fairness by comparing recommendation lists generated with and without \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "142", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Yang:2025:SKD, author = "Chuanpeng Yang and Yao Zhu and Wang Lu and Yidong Wang and Qian Chen and Chenlong Gao and Bingjie Yan and Yiqiang Chen", title = "Survey on Knowledge Distillation for Large Language Models: Methods, Evaluation, and Application", journal = j-TIST, volume = "16", number = "6", pages = "143:1--143:27", month = dec, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3699518", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Feb 2 08:20:50 MST 2026", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Large Language Models (LLMs) have showcased exceptional capabilities in various domains, attracting significant interest from both academia and industry. Despite their impressive performance, the substantial size and computational demands of LLMs pose \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "143", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Berahmand:2025:CSM, author = "Kamal Berahmand and Fatemeh Daneshfar and Maryam Rahmaninia and Maryam Haghighat and Mahdi Jalili", title = "A Comprehensive Survey on Multi-View Classification: Methods, Applications, and Challenges", journal = j-TIST, volume = "16", number = "6", pages = "144:1--144:34", month = dec, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3767728", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Feb 2 08:20:50 MST 2026", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Multi-view classification (MVC) has emerged as a promising approach in machine learning, aimed at enhancing classification accuracy by leveraging information from multiple perspectives. As the demand for more robust, interpretable, and effective machine \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "144", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Wang:2025:CSS, author = "Fali Wang and Zhiwei Zhang and Xianren Zhang and Zongyu Wu and TzuHao Mo and Qiuhao Lu and Wanjing Wang and Rui Li and Junjie Xu and Xianfeng Tang and Qi He and Yao Ma and Ming Huang and Suhang Wang", title = "A Comprehensive Survey of Small Language Models in the Era of Large Language Models: Techniques, Enhancements, Applications, Collaboration with {LLMs}, and Trustworthiness", journal = j-TIST, volume = "16", number = "6", pages = "145:1--145:87", month = dec, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3768165", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Feb 2 08:20:50 MST 2026", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Large language models (LLMs) have demonstrated emergent abilities in text generation, question answering, and reasoning, facilitating various tasks and domains. Despite their proficiency in various tasks, LLMs like PaLM 540B and Llama-3.1 405B face \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "145", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Li:2025:UCE, author = "Zhonghang Li and Lianghao Xia and Xubin Ren and Jiabin Tang and Tianyi Chen and Yong Xu and Chao Huang", title = "Urban Computing in the Era of Large Language Models", journal = j-TIST, volume = "16", number = "6", pages = "146:1--146:43", month = dec, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3768163", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Feb 2 08:20:50 MST 2026", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Urban computing has emerged as a multidisciplinary field that harnesses data-driven technologies to address challenges and improve urban living. Traditional approaches, while beneficial, often face challenges with generalization, scalability, and \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "146", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Hu:2025:CUR, author = "Jiayu Hu and Senlin Shu and Beibei Li and Tao Xiang and Zhongshi He", title = "Corrigendum: {An} Unbiased Risk Estimator for Partial Label Learning with Augmented Classes", journal = j-TIST, volume = "16", number = "6", pages = "1:1--1:??", month = dec, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3770856", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Feb 2 08:20:50 MST 2026", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", note = "See \cite{Hu:2024:URE}.", abstract = "This is a corrigendum for the article ``An Unbiased Risk Estimator for Partial Label Learning with Augmented Classes'' published in ACM Trans. Intell. Syst. Technol. 15(6): 131:1-131:22 (2024).", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "C1", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Zheng:2026:FCD, author = "Yu Zheng", title = "Fusing Cross-Domain Knowledge from Multimodal Data to Solve Problems in the Physical World", journal = j-TIST, volume = "17", number = "1", pages = "1:1--1:32", month = feb, year = "2026", CODEN = "????", DOI = "https://doi.org/10.1145/3768625", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Feb 2 08:20:53 MST 2026", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "The proliferation of artificial intelligence has enabled a diversity of applications that bridge the gap between digital and physical worlds. As physical environments are too complex to model through a single information acquisition approach, it is \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "1", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Bigdeli:2026:QPP, author = "Amin Bigdeli and Sajad Ebrahimi and Negar Arabzadeh and Sara Salamat and Shirin Seyedsalehi and Maryam Khodabakhsh and Fattane Zarrinkalam and Ebrahim Bagheri", title = "Query Performance Prediction Using Neural Query Space Proximity", journal = j-TIST, volume = "17", number = "1", pages = "2:1--2:25", month = feb, year = "2026", CODEN = "????", DOI = "https://doi.org/10.1145/3762197", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Feb 2 08:20:53 MST 2026", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "The varying performance of information retrieval (IR) methods, including state-of-the-art transformer-based neural retrievers, across diverse queries poses a significant challenge for achieving robust and reliable retrieval effectiveness. Query \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "2", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Marzari:2026:VOS, author = "Luca Marzari and Ferdinando Cicalese and Alessandro Farinelli and Christopher Amato and Enrico Marchesini", title = "Verifying Online Safety Properties for Safe Deep Reinforcement Learning", journal = j-TIST, volume = "17", number = "1", pages = "3:1--3:27", month = feb, year = "2026", CODEN = "????", DOI = "https://doi.org/10.1145/3770068", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Feb 2 08:20:53 MST 2026", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Ensuring safety in reinforcement learning (RL) is critical for deploying agents in real-world applications. During training, current safe RL approaches often rely on indicator cost functions that provide sparse feedback, resulting in two key limitations: \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "3", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Ma:2026:SSD, author = "Changyi Ma and Xuan Song", title = "Similarity Search with Data Missing", journal = j-TIST, volume = "17", number = "1", pages = "4:1--4:24", month = feb, year = "2026", CODEN = "????", DOI = "https://doi.org/10.1145/3724124", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Feb 2 08:20:53 MST 2026", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Similarity search is a fundamental research problem with broad applications in various research fields, including data mining, information retrieval, and machine learning. The core idea of similarity search is to find the most similar data sample of \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "4", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Li:2026:JSM, author = "Chunlin Li and Zihao Zhang and Bingxin Wang and Mengchao Lei and Sen Liu and Aoyong Li and Shaohua Wan", title = "Joint Service Migration and Resource Allocation for {DNN} Tasks using {SA-DDQN-DDPG} in Vehicular Edge Computing", journal = j-TIST, volume = "17", number = "1", pages = "5:1--5:25", month = feb, year = "2026", CODEN = "????", DOI = "https://doi.org/10.1145/3768152", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Feb 2 08:20:53 MST 2026", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "With the rapid development of vehicular edge computing (VEC) and artificial intelligence (AI), the emergence of vehicle edge intelligence meets the need for real-time vehicle intelligence applications. But the execution of deep neural networks (DNNs) \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "5", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Kundu:2026:RLB, author = "Debraj Kundu and Gadikoyila Satya Vamsi and Karnati Vivek Veman and Gurram Mahidhar and Sudip Roy", title = "Reinforcement Learning-based Reliable Synthesis of Bioassays on {MEDA} Digital Microfluidic Biochips", journal = j-TIST, volume = "17", number = "1", pages = "6:1--6:25", month = feb, year = "2026", CODEN = "????", DOI = "https://doi.org/10.1145/3773910", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Feb 2 08:20:53 MST 2026", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Microfluidic biochips play an important role in point-of-care diagnosis. Advanced microfluidic biochips can efficiently perform various fluidic operations and robustly execute bioassays like protein synthesis, drug discovery, and many others. Micro-. \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "6", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Zhao:2026:GSN, author = "Zicheng Zhao and Linhao Luo and Shirui Pan and Chengqi Zhang and Chen Gong", title = "Graph Stochastic Neural Process for Inductive Few-shot Knowledge Graph Completion", journal = j-TIST, volume = "17", number = "1", pages = "7:1--7:23", month = feb, year = "2026", CODEN = "????", DOI = "https://doi.org/10.1145/3773908", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Feb 2 08:20:53 MST 2026", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Knowledge graphs (KGs) store enormous facts as relationships between entities. Due to the long-tailed distribution of relations and the incompleteness of KGs, there is growing interest in few-shot knowledge graph completion (FKGC). Existing FKGC methods \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "7", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Wan:2026:SAF, author = "Shanshan Wan and Zebin Fu and Qiyi Zhou and Chuyuan Wei and Chang-Dong Wang", title = "A Structure-Aware Fair Recommendation Approach Based on Counterfactual Dynamic Hypergraphs", journal = j-TIST, volume = "17", number = "1", pages = "8:1--8:25", month = feb, year = "2026", CODEN = "????", DOI = "https://doi.org/10.1145/3773913", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Feb 2 08:20:53 MST 2026", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Unfair recommendations stem from user-sensitive attributes and information transmission biases. Graph-structured data can provide more balanced information for fair recommendations by capturing multidimensional user-item interactions. However, graph-. \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "8", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Ghannami:2026:GFM, author = "Aiman Ghannami", title = "Global Forecasting Model for {LED} Lumen Degradation: an Optimal Cluster Estimation Method", journal = j-TIST, volume = "17", number = "1", pages = "9:1--9:23", month = feb, year = "2026", CODEN = "????", DOI = "https://doi.org/10.1145/3776556", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Feb 2 08:20:53 MST 2026", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "The degradation process of Light-Emitting Diodes (LEDs) is considerably slow, making lifespan estimation through traditional testing impractical and cost-ineffective. Data-driven methods are also challenged by this slow degradation. Testing an LED for 10,. \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "9", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Kim:2026:PLC, author = "Yohan Kim and Sungyoung Yoon and Junho Shin and Younghoon Lee", title = "Patch-Level Contrastive Learning for Improved Time Series Classification with {Gramian} Angular Field", journal = j-TIST, volume = "17", number = "1", pages = "10:1--10:21", month = feb, year = "2026", CODEN = "????", DOI = "https://doi.org/10.1145/3776550", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Feb 2 08:20:53 MST 2026", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Time series classification (TSC) presents significant challenges in data analytics and plays an essential role in industries such as manufacturing, healthcare, and finance. Existing research has utilized sequence models, such as long short-term memory \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "10", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Yatbaz:2026:MLS, author = "Hakan Yekta Yatbaz and Konstantinos Koufos and Mehrdad Dianati and Roger Woodman", title = "Multi-Layer Self-Assessment with Filtering for {$3$D} Object Detection in Autonomous Vehicles", journal = j-TIST, volume = "17", number = "1", pages = "11:1--11:23", month = feb, year = "2026", CODEN = "????", DOI = "https://doi.org/10.1145/3776551", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Feb 2 08:20:53 MST 2026", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Reliable detection of road users is critical to the safety of automated driving systems. While object detectors based on deep neural networks are widely used for this purpose, they remain susceptible to errors that could compromise safety. A promising \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "11", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Le:2026:RCF, author = "Trung-Hieu Le and Shih-Chia Huang and Quoc-Viet Hoang and Zhihui Lu", title = "{RenHaze}: a Coarse-to-Fine Rendering Framework for Improving Robustness to Haze", journal = j-TIST, volume = "17", number = "1", pages = "12:1--12:22", month = feb, year = "2026", CODEN = "????", DOI = "https://doi.org/10.1145/3770745", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Feb 2 08:20:53 MST 2026", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Large-scale datasets centered on images have driven advancements in deep learning-based computer vision applications. While there is an abundance of datasets containing images depicting favorable weather scenes, datasets featuring images of adverse \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "12", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Nasehi:2026:OPO, author = "Saeed Nasehi and Farhana Choudhury and Egemen Tanin", title = "{OCP}: Proactive Optimal Charging Planning for Electric Vehicles", journal = j-TIST, volume = "17", number = "1", pages = "13:1--13:26", month = feb, year = "2026", CODEN = "????", DOI = "https://doi.org/10.1145/3771722", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Feb 2 08:20:53 MST 2026", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Due to the limited driving range, insufficient charging facilities, and time-consuming recharging, optimizing charging routes for electric vehicles (EVs) presents unique challenges compared to conventional vehicles. The time and location of EV charging \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "13", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Hu:2026:MAB, author = "Sihao Hu and Tiansheng Huang and Fatih Ilhan and Selim Furkan Tekin and Greg Eisenhauer and Margaret L. Loper and Ling Liu", title = "Matching Accounts on Blockchain via Pseudo Fine-tuning of Language Models", journal = j-TIST, volume = "17", number = "1", pages = "14:1--14:20", month = feb, year = "2026", CODEN = "????", DOI = "https://doi.org/10.1145/3773906", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Feb 2 08:20:53 MST 2026", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Web 3.0, built on blockchain technology, prioritizes user privacy and autonomy, presenting new opportunities for financial systems while also complicating the regulation of illicit activities. In this study, we present a novel infrastructure named Pseudo \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "14", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Geng:2026:MHA, author = "Meng Geng and Youxi Wu and Yan Li and Jing Liu and Lei Guo and Xingquan Zhu and Xindong Wu", title = "Mining High Average Utility Nonoverlapping Patterns from Sequential Database", journal = j-TIST, volume = "17", number = "1", pages = "15:1--15:24", month = feb, year = "2026", CODEN = "????", DOI = "https://doi.org/10.1145/3773899", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Feb 2 08:20:53 MST 2026", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "As a crucial aspect of data mining, high average utility sequential pattern mining (SPM) aims to discover low frequency and high average utility patterns (subsequences) in sequence data. Most existing high average utility SPM methods overlook the \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "15", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Zuo:2026:ADA, author = "Tian-Yu Zuo and Kai Di and Pan Li and Yichuan Jiang", title = "Autonomous Domain Adaptation Self-Optimization Approach for Cross-Domain Industrial Agents", journal = j-TIST, volume = "17", number = "1", pages = "16:1--16:25", month = feb, year = "2026", CODEN = "????", DOI = "https://doi.org/10.1145/3776560", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Feb 2 08:20:53 MST 2026", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "In the heterogeneous and dynamically evolving Industrial Internet, industrial agents are required to possess cross-domain adaptability and self-learning capabilities to facilitate task generalization and scalable deployment across diverse operational \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "16", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Badshah:2026:RLS, author = "Afzal Badshah and Ali Daud and Sakher Ghanem and Sami Alesawi and Mohammad D. Alahmadi and Ammar Almutawa", title = "Reinforcement Learning for Server-Aware Offloading in Multi-Tier Multi-Instance Computing Architecture", journal = j-TIST, volume = "17", number = "1", pages = "17:1--17:25", month = feb, year = "2026", CODEN = "????", DOI = "https://doi.org/10.1145/3776562", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Feb 2 08:20:53 MST 2026", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Task offloading in distributed computing involves complex tradeoffs among delay, scalability, cost, and resource utilization. Cloud platforms face long communication delays, while edge nodes have constrained capacity. Static, rule-based schedulers \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "17", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Qi:2026:RRB, author = "Hanwen Qi and Tinghuai Ma and Kexing Peng and Xin Yu", title = "{ROIS}: Role-Based Multi-Agent Collaboration by Context-Time-Aware Information Sharing", journal = j-TIST, volume = "17", number = "1", pages = "18:1--18:27", month = feb, year = "2026", CODEN = "????", DOI = "https://doi.org/10.1145/3779067", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Feb 2 08:20:53 MST 2026", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "In complex cooperative tasks, Multi-Agent Reinforcement Learning (MARL) faces the dual challenges of an exponentially growing joint action space and the constraints of partial observability. While the Centralized Training with Decentralized Execution \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "18", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Fan:2026:FPP, author = "Yuwei Fan and Yuan Yao and Wei Xi and Quan Zhao and Zelei Liu and Lixin Fan and Qiang Yang and Jian Jin", title = "{FedPRS}: a Privacy-preserving Representation Synthesis Framework for Federated Contribution Evaluation", journal = j-TIST, volume = "17", number = "1", pages = "19:1--19:24", month = feb, year = "2026", CODEN = "????", DOI = "https://doi.org/10.1145/3779055", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Feb 2 08:20:53 MST 2026", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Federated Learning (FL) enables the collaborative training of a global model while protecting participants' privacy. Evaluating each participant's contribution is essential to providing a high-quality model, ensuring fairness, and mitigating potential \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "19", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Varghese:2026:DEA, author = "Anish Samuel Varghese and Somasundaram G. and Athira Nambiar", title = "On Developing Explainable {AI} Evaluation Metrics for Image Classification Using {Borda} Count and Multiple Correlation Techniques", journal = j-TIST, volume = "17", number = "1", pages = "20:1--20:32", month = feb, year = "2026", CODEN = "????", DOI = "https://doi.org/10.1145/3779054", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Feb 2 08:20:53 MST 2026", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Deep learning models have achieved remarkable performance in various problems, including image classification. However, the complex and ``black-box'' nature of deep learning models leads to a lack of interpretability of model decisions and a reduced level \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "20", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Liu:2026:AFD, author = "Hui Liu and Ling Ding and Jiageng Chen and Jinghua Wang and Xu Du and Jiabao Guo", title = "Adversarial Face Database against Deep Learning-Enabled Reconstruction Attacks", journal = j-TIST, volume = "17", number = "1", pages = "21:1--21:19", month = feb, year = "2026", CODEN = "????", DOI = "https://doi.org/10.1145/3779060", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Feb 2 08:20:53 MST 2026", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Face recognition systems offer a range of applications that enhance security, efficiency, and personalization, e.g., access control, identity verification, and personalized services. Mainstream facial recognition systems employ the Edge-Cloud \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "21", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Pentsos:2026:IIC, author = "Vasilis Pentsos and Spyros Tragoudas and Kiriti Nagesh Gowda and Mike Schmit", title = "Improved Image Classification using Lightweight Deep Neural Network Enhancements", journal = j-TIST, volume = "17", number = "1", pages = "22:1--22:26", month = feb, year = "2026", CODEN = "????", DOI = "https://doi.org/10.1145/3779421", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Feb 2 08:20:53 MST 2026", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "In this article, a novel hierarchical deep neural network (DNN) is introduced that augments an input DNN to significantly enhance its image classification accuracy while reducing the inference time and the hardware overhead. The architecture comprises a \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "22", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Zhang:2026:BMB, author = "Zihan Zhang and Hongzhi Liu and Tianqi Sun and Xiaoshuang Guo and Zhonghai Wu", title = "{BH$^3$-MedRec}: Bilateral Hierarchical Heterogeneous Hypergraph Convolution Network for Medication Recommendation", journal = j-TIST, volume = "17", number = "1", pages = "23:1--23:25", month = feb, year = "2026", CODEN = "????", DOI = "https://doi.org/10.1145/3779446", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Feb 2 08:20:53 MST 2026", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "The development of artificial intelligence and medical informatics has empowered the medication recommendation systems with enhanced capabilities. However, existing methods struggle with the data imbalance problem in Electronic Health Records (EHRs), \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "23", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Zhong:2026:TDP, author = "Zhaofeng Zhong and Wei Yuan and Liang Qu and Tong Chen and Hao Wang and Xiangyu Zhao and Hongzhi Yin", title = "Towards On-device Personalization: Cloud-device Collaborative Data Augmentation for Efficient On-device Language Model", journal = j-TIST, volume = "17", number = "1", pages = "24:1--24:22", month = feb, year = "2026", CODEN = "????", DOI = "https://doi.org/10.1145/3779452", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Feb 2 08:20:53 MST 2026", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "With the advancement of large language models (LLMs), significant progress has been achieved in various natural language processing (NLP) tasks. However, existing LLMs still face two major challenges that hinder their broader adoption: (1) their \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "24", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", } @Article{Fu:2026:CDO, author = "Shucun Fu and Fang Dong and Dian Shen and Runze Chen and Jiangshan Hao", title = "Corrigendum: {DESIGN}: Online Device Selection and Edge Association for Federated Synergy Learning-enabled {AIoT}", journal = j-TIST, volume = "17", number = "1", pages = "1:1--1:??", month = feb, year = "2026", CODEN = "????", DOI = "https://doi.org/10.1145/3776570", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Mon Feb 2 08:20:53 MST 2026", bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib", note = "See \cite{Fu:2024:DOD}.", abstract = "This is a corrigendum for the article ``DESIGN: Online Device Selection and Edge Association for Federated Synergy Learning-enabled AIoT'' published in ACM Trans. Intell. Syst. Technol. 15, 5, Article 104 (November 2024), 28 pages.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Intell. Syst. Technol.", articleno = "C1", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "https://dl.acm.org/loi/tist", }