%%% -*-BibTeX-*- %%% ==================================================================== %%% BibTeX-file{ %%% author = "Nelson H. F. Beebe", %%% version = "1.03", %%% date = "25 August 2023", %%% time = "12:27:25 MDT", %%% filename = "tds.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", %%% FAX = "+1 801 581 4148", %%% URL = "http://www.math.utah.edu/~beebe", %%% checksum = "59852 2121 9256 88051", %%% email = "beebe at math.utah.edu, beebe at acm.org, %%% beebe at computer.org (Internet)", %%% codetable = "ISO/ASCII", %%% keywords = "ACM/IMS Transactions on Data Science %%% (TDS); bibliography; BibTeX", %%% license = "public domain", %%% supported = "yes", %%% docstring = "This is a COMPLETE BibTeX bibliography for %%% ACM/IMS Transactions on Data Science (TDS) %%% (CODEN ????, ISSN 2691-1922). The journal %%% appears quarterly, and publication began with %%% volume 1, number 1, in February 2020. %%% %%% Publication terminated with volume 2, number %%% 4, in November 2021. The publisher's %%% announcement says: %%% %%% ``ACM/IMS Transactions on Data Science %%% will close to submissions on June 1, %%% 2021. The journal will re-launch under %%% the name ACM/IMS Journal of Data %%% Science.'' %%% %%% At version 1.03, the COMPLETE journal %%% coverage looked like this: %%% %%% 2020 ( 30) 2021 ( 39) %%% %%% Article: 69 %%% %%% Total entries: 69 %%% %%% The journal Web page can be found at: %%% %%% http://tds.acm.org/ %%% https://dl.acm.org/journal/tds %%% %%% The journal table of contents page is at: %%% %%% https://dl.acm.org/loi/tds %%% %%% Qualified subscribers can retrieve the full %%% text of recent articles in PDF form. %%% %%% The initial draft was extracted from the ACM %%% Web pages. %%% %%% 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. %%% %%% 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" # "\ifx \undefined \booktitle \def \booktitle #1{{{\em #1}}} \fi" # "\ifx \undefined \TM \def \TM {${}^{\sc TM}$} \fi" } %%% ==================================================================== %%% 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, FAX: +1 801 581 4148, e-mail: \path|beebe@math.utah.edu|, \path|beebe@acm.org|, \path|beebe@computer.org| (Internet), URL: \path|http://www.math.utah.edu/~beebe/|"} %%% ==================================================================== %%% Journal abbreviations: @String{j-TDS = "ACM Transactions on Data Science (TDS)"} %%% ==================================================================== %%% Bibliography entries: @Article{Ooi:2020:IIE, author = "Beng Chin Ooi", title = "Inaugural Issue Editorial", journal = j-TDS, volume = "1", number = "1", pages = "1:1--1:2", month = mar, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3368254", ISSN = "2691-1922", ISSN-L = "2691-1922", bibdate = "Tue Apr 7 15:14:47 MDT 2020", bibsource = "http://www.math.utah.edu/pub/tex/bib/tds.bib", URL = "https://dl.acm.org/doi/abs/10.1145/3368254", acknowledgement = ack-nhfb, articleno = "1", fjournal = "ACM Transactions on Data Science", journal-URL = "https://dl.acm.org/loi/tds", } @Article{Chakraborty:2020:EDA, author = "Tanmoy Chakraborty and Noseong Park and Ayush Agarwal and V. S. Subrahmanian", title = "Ensemble Detection and Analysis of Communities in Complex Networks", journal = j-TDS, volume = "1", number = "1", pages = "2:1--2:34", month = mar, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3313374", ISSN = "2691-1922", ISSN-L = "2691-1922", bibdate = "Tue Apr 7 15:14:47 MDT 2020", bibsource = "http://www.math.utah.edu/pub/tex/bib/tds.bib", URL = "https://dl.acm.org/doi/abs/10.1145/3313374", abstract = "Though much work has been done on ensemble clustering in data mining, the application of ensemble methods to community detection in networks is in its infancy. In this article, we propose MeDOF, an ensemble method which performs disjoint, overlapping, \ldots{}", acknowledgement = ack-nhfb, articleno = "2", fjournal = "ACM Transactions on Data Science", journal-URL = "https://dl.acm.org/loi/tds", } @Article{Li:2020:GBR, author = "Guohui Li and Qi Chen and Bolong Zheng and Hongzhi Yin and Quoc Viet Hung Nguyen and Xiaofang Zhou", title = "Group-Based Recurrent Neural Networks for {POI} Recommendation", journal = j-TDS, volume = "1", number = "1", pages = "3:1--3:18", month = mar, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3343037", ISSN = "2691-1922", ISSN-L = "2691-1922", bibdate = "Tue Apr 7 15:14:47 MDT 2020", bibsource = "http://www.math.utah.edu/pub/tex/bib/tds.bib", URL = "https://dl.acm.org/doi/abs/10.1145/3343037", abstract = "With the development of mobile Internet, many location-based services have accumulated a large amount of data that can be used for point-of-interest (POI) recommendation. However, there are still challenges in developing an unified framework to \ldots{}", acknowledgement = ack-nhfb, articleno = "3", fjournal = "ACM Transactions on Data Science", journal-URL = "https://dl.acm.org/loi/tds", } @Article{Li:2020:MAF, author = "Xiangyang Li and Luis Herranz and Shuqiang Jiang", title = "Multifaceted Analysis of Fine-Tuning in a Deep Model for Visual Recognition", journal = j-TDS, volume = "1", number = "1", pages = "4:1--4:22", month = mar, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3319500", ISSN = "2691-1922", ISSN-L = "2691-1922", bibdate = "Tue Apr 7 15:14:47 MDT 2020", bibsource = "http://www.math.utah.edu/pub/tex/bib/tds.bib", URL = "https://dl.acm.org/doi/abs/10.1145/3319500", abstract = "In recent years, convolutional neural networks (CNNs) have achieved impressive performance for various visual recognition scenarios. CNNs trained on large labeled datasets not only obtain significant performance on most challenging benchmarks but also \ldots{}", acknowledgement = ack-nhfb, articleno = "4", fjournal = "ACM Transactions on Data Science", journal-URL = "https://dl.acm.org/loi/tds", } @Article{Ward:2020:PAA, author = "Katrina Ward and Dan Lin and Sanjay Madria", title = "A Parallel Algorithm For Anonymizing Large-scale Trajectory Data", journal = j-TDS, volume = "1", number = "1", pages = "5:1--5:26", month = mar, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3368639", ISSN = "2691-1922", ISSN-L = "2691-1922", bibdate = "Tue Apr 7 15:14:47 MDT 2020", bibsource = "http://www.math.utah.edu/pub/tex/bib/tds.bib", URL = "https://dl.acm.org/doi/abs/10.1145/3368639", abstract = "With the proliferation of location-based services enabled by a large number of mobile devices and applications, the quantity of location data, such as trajectories collected by service providers, is gigantic. If these datasets could be published, then \ldots{}", acknowledgement = ack-nhfb, articleno = "5", fjournal = "ACM Transactions on Data Science", journal-URL = "https://dl.acm.org/loi/tds", } @Article{Theil:2020:EFU, author = "Christoph Kilian Theil and Sanja Stajner and Heiner Stuckenschmidt", title = "Explaining Financial Uncertainty through Specialized Word Embeddings", journal = j-TDS, volume = "1", number = "1", pages = "6:1--6:19", month = mar, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3343039", ISSN = "2691-1922", ISSN-L = "2691-1922", bibdate = "Tue Apr 7 15:14:47 MDT 2020", bibsource = "http://www.math.utah.edu/pub/tex/bib/tds.bib", URL = "https://dl.acm.org/doi/abs/10.1145/3343039", abstract = "The detection of vague, speculative, or otherwise uncertain language has been performed in the encyclopedic, political, and scientific domains yet left relatively untouched in finance. However, the latter benefits from public sources of big financial \ldots{}", acknowledgement = ack-nhfb, articleno = "6", fjournal = "ACM Transactions on Data Science", journal-URL = "https://dl.acm.org/loi/tds", } @Article{Beigi:2020:SPS, author = "Ghazaleh Beigi and Huan Liu", title = "A Survey on Privacy in Social Media: Identification, Mitigation, and Applications", journal = j-TDS, volume = "1", number = "1", pages = "7:1--7:38", month = mar, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3343038", ISSN = "2691-1922", ISSN-L = "2691-1922", bibdate = "Tue Apr 7 15:14:47 MDT 2020", bibsource = "http://www.math.utah.edu/pub/tex/bib/tds.bib", URL = "https://dl.acm.org/doi/abs/10.1145/3343038", abstract = "The increasing popularity of social media has attracted a huge number of people to participate in numerous activities on a daily basis. This results in tremendous amounts of rich user-generated data. These data provide opportunities for researchers and \ldots{}", acknowledgement = ack-nhfb, articleno = "7", fjournal = "ACM Transactions on Data Science", journal-URL = "https://dl.acm.org/loi/tds", } @Article{Gan:2020:UMA, author = "Wensheng Gan and Jerry Chun-Wei Lin and Jiexiong Zhang and Philip S. Yu", title = "Utility Mining across Multi-Sequences with Individualized Thresholds", journal = j-TDS, volume = "1", number = "2", pages = "8:1--8:29", month = jul, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3362070", ISSN = "2691-1922", ISSN-L = "2691-1922", bibdate = "Wed Mar 10 06:28:05 MST 2021", bibsource = "http://www.math.utah.edu/pub/tex/bib/tds.bib", URL = "https://dl.acm.org/doi/10.1145/3362070", abstract = "Utility-oriented pattern mining is an emerging topic, since it can reveal high-utility patterns from different types of data, which provides more information than the traditional frequency/confidence-based pattern mining models. The utilities of various \ldots{}", acknowledgement = ack-nhfb, articleno = "8", fjournal = "ACM Transactions on Data Science", journal-URL = "https://dl.acm.org/loi/tds", } @Article{Yu:2020:MBI, author = "Lei Yu and Guohui Li and Ling Yuan", title = "Maximizing Boosted Influence Spread with Edge Addition in Online Social Networks", journal = j-TDS, volume = "1", number = "2", pages = "9:1--9:21", month = jul, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3364993", ISSN = "2691-1922", ISSN-L = "2691-1922", bibdate = "Wed Mar 10 06:28:05 MST 2021", bibsource = "http://www.math.utah.edu/pub/tex/bib/tds.bib", URL = "https://dl.acm.org/doi/10.1145/3364993", abstract = "Influence maximization with application to viral marketing is a well-studied problem of finding a small number of influential users in a social network to maximize the spread of influence under certain influence cascade models. However, almost all \ldots{}", acknowledgement = ack-nhfb, articleno = "9", fjournal = "ACM Transactions on Data Science", journal-URL = "https://dl.acm.org/loi/tds", } @Article{Chowdhury:2020:RTP, author = "Ranak Roy Chowdhury and Muhammad Abdullah Adnan and Rajesh K. Gupta", title = "Real-Time Principal Component Analysis", journal = j-TDS, volume = "1", number = "2", pages = "10:1--10:36", month = jul, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3374750", ISSN = "2691-1922", ISSN-L = "2691-1922", bibdate = "Wed Mar 10 06:28:05 MST 2021", bibsource = "http://www.math.utah.edu/pub/tex/bib/tds.bib", URL = "https://dl.acm.org/doi/10.1145/3374750", abstract = "We propose a variant of Principal Component Analysis (PCA) that is suited for real-time applications. In the real-time version of the PCA problem, we maintain a window over the most recent data and project every incoming row of data into a lower-. \ldots{}", acknowledgement = ack-nhfb, articleno = "10", fjournal = "ACM Transactions on Data Science", journal-URL = "https://dl.acm.org/loi/tds", } @Article{Wu:2020:DST, author = "Gene P. K. Wu and Keith C. C. Chan", title = "Discovery of Spatio-Temporal Patterns in Multivariate Spatial Time Series", journal = j-TDS, volume = "1", number = "2", pages = "11:1--11:22", month = jul, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3374748", ISSN = "2691-1922", ISSN-L = "2691-1922", bibdate = "Wed Mar 10 06:28:05 MST 2021", bibsource = "http://www.math.utah.edu/pub/tex/bib/tds.bib", URL = "https://dl.acm.org/doi/10.1145/3374748", abstract = "With the advancement of the computing technology and its wide range of applications, collecting large sets of multivariate time series in multiple geographical locations introduces a problem of identifying interesting spatio-temporal patterns. We \ldots{}", acknowledgement = ack-nhfb, articleno = "11", fjournal = "ACM Transactions on Data Science", journal-URL = "https://dl.acm.org/loi/tds", } @Article{Bessa:2020:EDM, author = "Aline Bessa and Juliana Freire and Tamraparni Dasu and Divesh Srivastava", title = "Effective Discovery of Meaningful Outlier Relationships", journal = j-TDS, volume = "1", number = "2", pages = "12:1--12:33", month = jul, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3385192", ISSN = "2691-1922", ISSN-L = "2691-1922", bibdate = "Wed Mar 10 06:28:05 MST 2021", bibsource = "http://www.math.utah.edu/pub/tex/bib/tds.bib", URL = "https://dl.acm.org/doi/10.1145/3385192", abstract = "We propose Predictable Outliers in Data-trendS (PODS), a method that, given a collection of temporal datasets, derives data-driven explanations for outliers by identifying meaningful relationships between them. First, we formalize the notion of \ldots{}", acknowledgement = ack-nhfb, articleno = "12", fjournal = "ACM Transactions on Data Science", journal-URL = "https://dl.acm.org/loi/tds", } @Article{Nashaat:2020:AGL, author = "Mona Nashaat and Aindrila Ghosh and James Miller and Shaikh Quader", title = "{Asterisk}: Generating Large Training Datasets with Automatic Active Supervision", journal = j-TDS, volume = "1", number = "2", pages = "13:1--13:25", month = jul, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3385188", ISSN = "2691-1922", ISSN-L = "2691-1922", bibdate = "Wed Mar 10 06:28:05 MST 2021", bibsource = "http://www.math.utah.edu/pub/tex/bib/tds.bib", URL = "https://dl.acm.org/doi/10.1145/3385188", abstract = "Labeling datasets is one of the most expensive bottlenecks in data preprocessing tasks in machine learning. Therefore, organizations, in many domains, are applying weak supervision to produce noisy labels. However, since weak supervision relies on \ldots{}", acknowledgement = ack-nhfb, articleno = "13", fjournal = "ACM Transactions on Data Science", journal-URL = "https://dl.acm.org/loi/tds", } @Article{Li:2020:ISI, author = "Yanhua Li and Jie Bao and Zhi-Li Zhang and Saif Benjaafar", title = "Introduction to the Special Issue on Urban Computing and Smart Cities", journal = j-TDS, volume = "1", number = "3", pages = "14:1--14:2", month = oct, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3412392", ISSN = "2691-1922", ISSN-L = "2691-1922", bibdate = "Wed Mar 10 06:28:05 MST 2021", bibsource = "http://www.math.utah.edu/pub/tex/bib/tds.bib", URL = "https://dl.acm.org/doi/10.1145/3412392", acknowledgement = ack-nhfb, articleno = "14", fjournal = "ACM Transactions on Data Science", journal-URL = "https://dl.acm.org/loi/tds", } @Article{Sainju:2020:MRS, author = "Arpan Man Sainju and Zhe Jiang", title = "Mapping Road Safety Features from Streetview Imagery: a Deep Learning Approach", journal = j-TDS, volume = "1", number = "3", pages = "15:1--15:20", month = oct, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3362069", ISSN = "2691-1922", ISSN-L = "2691-1922", bibdate = "Wed Mar 10 06:28:05 MST 2021", bibsource = "http://www.math.utah.edu/pub/tex/bib/tds.bib", URL = "https://dl.acm.org/doi/10.1145/3362069", abstract = "Each year, an average of around 6 million car accidents occur in the United States. Road safety features (e.g., concrete barriers, metal crash barriers, rumble strips) play an important role in preventing or mitigating vehicle crashes. Accurate maps of \ldots{}", acknowledgement = ack-nhfb, articleno = "15", fjournal = "ACM Transactions on Data Science", journal-URL = "https://dl.acm.org/loi/tds", } @Article{Tian:2020:UEB, author = "Zhihong Tian and Chaochao Luo and Hui Lu and Shen Su and Yanbin Sun and Man Zhang", title = "User and Entity Behavior Analysis under Urban Big Data", journal = j-TDS, volume = "1", number = "3", pages = "16:1--16:19", month = oct, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3374749", ISSN = "2691-1922", ISSN-L = "2691-1922", bibdate = "Wed Mar 10 06:28:05 MST 2021", bibsource = "http://www.math.utah.edu/pub/tex/bib/tds.bib", URL = "https://dl.acm.org/doi/10.1145/3374749", abstract = "Recently, the urban network infrastructure has undergone a rapid expansion that is increasingly generating a large quantity of data and transforming our cities into smart cities. However, serious security problems arise with this development with more \ldots{}", acknowledgement = ack-nhfb, articleno = "16", fjournal = "ACM Transactions on Data Science", journal-URL = "https://dl.acm.org/loi/tds", } @Article{Xie:2020:UFR, author = "Yiqun Xie and Shashi Shekhar", title = "A Unified Framework for Robust and Efficient Hotspot Detection in Smart Cities", journal = j-TDS, volume = "1", number = "3", pages = "17:1--17:29", month = oct, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3379562", ISSN = "2691-1922", ISSN-L = "2691-1922", bibdate = "Wed Mar 10 06:28:05 MST 2021", bibsource = "http://www.math.utah.edu/pub/tex/bib/tds.bib", URL = "https://dl.acm.org/doi/10.1145/3379562", abstract = "Given N geo-located point instances (e.g., crime or disease cases) in a spatial domain, we aim to detect sub-regions (i.e., hotspots) that have a higher probability density of generating such instances than the others. Hotspot detection has been widely \ldots{}", acknowledgement = ack-nhfb, articleno = "17", fjournal = "ACM Transactions on Data Science", journal-URL = "https://dl.acm.org/loi/tds", } @Article{Ding:2020:TAH, author = "Weilong Ding and Zhuofeng Zhao and Jianwu Wang and Han Li", title = "Task Allocation in Hybrid Big Data Analytics for Urban {IoT} Applications", journal = j-TDS, volume = "1", number = "3", pages = "18:1--18:22", month = oct, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3374751", ISSN = "2691-1922", ISSN-L = "2691-1922", bibdate = "Wed Mar 10 06:28:05 MST 2021", bibsource = "http://www.math.utah.edu/pub/tex/bib/tds.bib", URL = "https://dl.acm.org/doi/10.1145/3374751", abstract = "In urban Internet of Things (IoT) environments, data generated in real time could be processed by analytical applications in online or offline mode. In the management perspective of runtime environments, such modes can hardly be supported in a unified \ldots{}", acknowledgement = ack-nhfb, articleno = "18", fjournal = "ACM Transactions on Data Science", journal-URL = "https://dl.acm.org/loi/tds", } @Article{Yang:2020:SAT, author = "Zhong Yang and Bolong Zheng and Guohui Li and Nguyen Quoc Viet Hung and Guanfeng Liu and Kai Zheng", title = "Searching Activity Trajectories by Exemplar", journal = j-TDS, volume = "1", number = "3", pages = "19:1--19:18", month = oct, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3379561", ISSN = "2691-1922", ISSN-L = "2691-1922", bibdate = "Wed Mar 10 06:28:05 MST 2021", bibsource = "http://www.math.utah.edu/pub/tex/bib/tds.bib", URL = "https://dl.acm.org/doi/10.1145/3379561", abstract = "The rapid explosion of urban cities has modernized the residents' lives and generated a large amount of data (e.g., human mobility data, traffic data, and geographical data), especially the activity trajectory data that contains spatial and temporal as \ldots{}", acknowledgement = ack-nhfb, articleno = "19", fjournal = "ACM Transactions on Data Science", journal-URL = "https://dl.acm.org/loi/tds", } @Article{Xu:2020:AWF, author = "Xin Xu and Yanjie Fu and Jingyi Wu and Yuqi Wang and Zeyu Huang and Zhiguo Fu and Minghao Yin", title = "Adaptive Weighted Finite Mixture Model: Identifying the Feature-Influence of Real Estate", journal = j-TDS, volume = "1", number = "3", pages = "20:1--20:16", month = oct, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3379560", ISSN = "2691-1922", ISSN-L = "2691-1922", bibdate = "Wed Mar 10 06:28:05 MST 2021", bibsource = "http://www.math.utah.edu/pub/tex/bib/tds.bib", URL = "https://dl.acm.org/doi/10.1145/3379560", abstract = "It is significant for real estate investors to understand how the construction environments and building characteristics impact the housing unit price. However, it is challenging for identifying the complex feature-influence from construction \ldots{}", acknowledgement = ack-nhfb, articleno = "20", fjournal = "ACM Transactions on Data Science", journal-URL = "https://dl.acm.org/loi/tds", } @Article{Zeidan:2020:GEL, author = "Ayman Zeidan and Eemil Lagerspetz and Kai Zhao and Petteri Nurmi and Sasu Tarkoma and Huy T. Vo", title = "{GeoMatch}: Efficient Large-scale Map Matching on {Apache Spark}", journal = j-TDS, volume = "1", number = "3", pages = "21:1--21:30", month = oct, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3402904", ISSN = "2691-1922", ISSN-L = "2691-1922", bibdate = "Wed Mar 10 06:28:05 MST 2021", bibsource = "http://www.math.utah.edu/pub/tex/bib/tds.bib", URL = "https://dl.acm.org/doi/10.1145/3402904", abstract = "We develop GeoMatch as a novel, scalable, and efficient big-data pipeline for large-scale map matching on Apache Spark. GeoMatch improves existing spatial big-data solutions by utilizing a novel spatial partitioning scheme inspired by Hilbert space-. \ldots{}", acknowledgement = ack-nhfb, articleno = "21", fjournal = "ACM Transactions on Data Science", journal-URL = "https://dl.acm.org/loi/tds", } @Article{Li:2020:PGE, author = "Yan Li and Pratik Kotwal and Pengyue Wang and Yiqun Xie and Shashi Shekhar and William Northrop", title = "Physics-guided Energy-efficient Path Selection Using On-board Diagnostics Data", journal = j-TDS, volume = "1", number = "3", pages = "22:1--22:28", month = oct, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3406596", ISSN = "2691-1922", ISSN-L = "2691-1922", bibdate = "Wed Mar 10 06:28:05 MST 2021", bibsource = "http://www.math.utah.edu/pub/tex/bib/tds.bib", URL = "https://dl.acm.org/doi/10.1145/3406596", abstract = "Given a spatial graph, an origin and a destination, and on-board diagnostics (OBD) data, the energy-efficient path selection problem aims to find the path with the least expected energy consumption (EEC). Two main objectives of smart cities are \ldots{}", acknowledgement = ack-nhfb, articleno = "22", fjournal = "ACM Transactions on Data Science", journal-URL = "https://dl.acm.org/loi/tds", } @Article{Arabghalizi:2020:DDB, author = "Tahereh Arabghalizi and Alexandros Labrinidis", title = "Data-driven Bus Crowding Prediction Models Using Context-specific Features", journal = j-TDS, volume = "1", number = "3", pages = "23:1--23:33", month = oct, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3406962", ISSN = "2691-1922", ISSN-L = "2691-1922", bibdate = "Wed Mar 10 06:28:05 MST 2021", bibsource = "http://www.math.utah.edu/pub/tex/bib/tds.bib", URL = "https://dl.acm.org/doi/10.1145/3406962", abstract = "Public transit is one of the first things that come to mind when someone talks about ``smart cities.'' As a result, many technologies, applications, and infrastructure have already been deployed to bring the promise of the smart city to public \ldots{}", acknowledgement = ack-nhfb, articleno = "23", fjournal = "ACM Transactions on Data Science", journal-URL = "https://dl.acm.org/loi/tds", } @Article{Hu:2020:ISI, author = "Haibo Hu and Rik Sarkar and Zhengzhang Chen", title = "Introduction to the Special Issue on Retrieving and Learning from {Internet of Things} Data", journal = j-TDS, volume = "1", number = "4", pages = "24:1--24:1", month = dec, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3426368", ISSN = "2691-1922", ISSN-L = "2691-1922", bibdate = "Wed Mar 10 06:28:06 MST 2021", bibsource = "http://www.math.utah.edu/pub/tex/bib/tds.bib", URL = "https://dl.acm.org/doi/10.1145/3426368", acknowledgement = ack-nhfb, articleno = "24", fjournal = "ACM Transactions on Data Science", journal-URL = "https://dl.acm.org/loi/tds", } @Article{Fang:2020:HHR, author = "Liming Fang and Hongwei Zhu and Boqing Lv and Zhe Liu and Weizhi Meng and Yu Yu and Shouling Ji and Zehong Cao", title = "{HandiText}: Handwriting Recognition Based on Dynamic Characteristics with Incremental {LSTM}", journal = j-TDS, volume = "1", number = "4", pages = "25:1--25:18", month = dec, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3385189", ISSN = "2691-1922", ISSN-L = "2691-1922", bibdate = "Wed Mar 10 06:28:06 MST 2021", bibsource = "http://www.math.utah.edu/pub/tex/bib/cryptography2020.bib; http://www.math.utah.edu/pub/tex/bib/tds.bib", URL = "https://dl.acm.org/doi/10.1145/3385189", abstract = "The Internet of Things (IoT) is a new manifestation of data science. To ensure the credibility of data about IoT devices, authentication has gradually become an important research topic in the IoT ecosystem. However, traditional graphical passwords and text passwords can cause user's serious memory burdens. Therefore, a convenient method for determining user identity is needed. In this article, we propose a handwriting recognition authentication scheme named HandiText based on behavior and biometrics features. When people write a word by hand, HandiText captures their static biological features and dynamic behavior features during the writing process (writing speed, pressure, etc.). The features are related to habits, which make it difficult for attackers to imitate. We also carry out algorithms comparisons and experiments evaluation to prove the reliability of our scheme. The experiment results show that the Long Short-Term Memory has the best classification accuracy, reaching 99\% while keeping relatively low false-positive rate and false-negative rate. We also test other datasets, the average accuracy of HandiText reach 98\%, with strong generalization ability. Besides, the 324 users we investigated indicated that they are willing to use this scheme on IoT devices.", acknowledgement = ack-nhfb, articleno = "25", fjournal = "ACM Transactions on Data Science", journal-URL = "https://dl.acm.org/loi/tds", } @Article{Li:2020:TTR, author = "Huan Li and Hua Lu and Gang Chen and Ke Chen and Qinkuang Chen and Lidan Shou", title = "Toward Translating Raw Indoor Positioning Data into Mobility Semantics", journal = j-TDS, volume = "1", number = "4", pages = "26:1--26:37", month = dec, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3385190", ISSN = "2691-1922", ISSN-L = "2691-1922", bibdate = "Wed Mar 10 06:28:06 MST 2021", bibsource = "http://www.math.utah.edu/pub/tex/bib/tds.bib", URL = "https://dl.acm.org/doi/10.1145/3385190", abstract = "Indoor mobility analyses are increasingly interesting due to the rapid growth of raw indoor positioning data obtained from IoT infrastructure. However, high-level analyses are still in urgent need of a concise but semantics-oriented representation of \ldots{}", acknowledgement = ack-nhfb, articleno = "26", fjournal = "ACM Transactions on Data Science", journal-URL = "https://dl.acm.org/loi/tds", } @Article{Busany:2020:IBQ, author = "Nimrod Busany and Han {Van Der Aa} and Arik Senderovich and Avigdor Gal and Matthias Weidlich", title = "Interval-based Queries over Lossy {IoT} Event Streams", journal = j-TDS, volume = "1", number = "4", pages = "27:1--27:27", month = dec, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3385191", ISSN = "2691-1922", ISSN-L = "2691-1922", bibdate = "Wed Mar 10 06:28:06 MST 2021", bibsource = "http://www.math.utah.edu/pub/tex/bib/tds.bib", URL = "https://dl.acm.org/doi/10.1145/3385191", abstract = "Recognising patterns that correlate multiple events over time becomes increasingly important in applications that exploit the Internet of Things, reaching from urban transportation through surveillance monitoring to business workflows. In many real-. \ldots{}", acknowledgement = ack-nhfb, articleno = "27", fjournal = "ACM Transactions on Data Science", journal-URL = "https://dl.acm.org/loi/tds", } @Article{Schmeisser:2020:CCS, author = "Stephan Schmei{\ss}er and Gregor Schiele", title = "{coSense}: The Collaborative Sensing Middleware for the {Internet}-of-Things", journal = j-TDS, volume = "1", number = "4", pages = "28:1--28:21", month = dec, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3395233", ISSN = "2691-1922", ISSN-L = "2691-1922", bibdate = "Wed Mar 10 06:28:06 MST 2021", bibsource = "http://www.math.utah.edu/pub/tex/bib/tds.bib", URL = "https://dl.acm.org/doi/10.1145/3395233", abstract = "We present coSense -the collaborative, fault-tolerant, and adaptive sensing middleware for the Internet-of-Things (IoT). By actively harnessing the greatest asset of the IoT, the sheer number of devices, coSense is able to provide easy data acquisition \ldots{}", acknowledgement = ack-nhfb, articleno = "28", fjournal = "ACM Transactions on Data Science", journal-URL = "https://dl.acm.org/loi/tds", } @Article{Elbassuoni:2020:FSO, author = "Shady Elbassuoni and Sihem Amer-Yahia and Ahmad Ghizzawi", title = "Fairness of Scoring in Online Job Marketplaces", journal = j-TDS, volume = "1", number = "4", pages = "29:1--29:30", month = dec, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3402883", ISSN = "2691-1922", ISSN-L = "2691-1922", bibdate = "Wed Mar 10 06:28:06 MST 2021", bibsource = "http://www.math.utah.edu/pub/tex/bib/tds.bib", URL = "https://dl.acm.org/doi/10.1145/3402883", abstract = "We study fairness of scoring in online job marketplaces. We focus on group fairness and aim to algorithmically explore how a scoring function, through which individuals are ranked for jobs, treats different demographic groups. Previous work on group-. \ldots{}", acknowledgement = ack-nhfb, articleno = "29", fjournal = "ACM Transactions on Data Science", journal-URL = "https://dl.acm.org/loi/tds", } @Article{Yang:2020:MFR, author = "Luoying Yang and Zhou Xu and Jiebo Luo", title = "Measuring Female Representation and Impact in Films over Time", journal = j-TDS, volume = "1", number = "4", pages = "30:1--30:14", month = dec, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3411213", ISSN = "2691-1922", ISSN-L = "2691-1922", bibdate = "Wed Mar 10 06:28:06 MST 2021", bibsource = "http://www.math.utah.edu/pub/tex/bib/tds.bib", URL = "https://dl.acm.org/doi/10.1145/3411213", abstract = "Women have always been underrepresented in movies and not until recently has the representation of women in movies improved. To investigate the improvement of female representation and its relationship with a movie's success, we propose a new measure, \ldots{}", acknowledgement = ack-nhfb, articleno = "30", fjournal = "ACM Transactions on Data Science", journal-URL = "https://dl.acm.org/loi/tds", } @Article{Shi:2021:NAT, author = "Tian Shi and Yaser Keneshloo and Naren Ramakrishnan and Chandan K. Reddy", title = "Neural Abstractive Text Summarization with Sequence-to-Sequence Models", journal = j-TDS, volume = "2", number = "1", pages = "1:1--1:37", month = jan, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3419106", ISSN = "2691-1922", ISSN-L = "2691-1922", bibdate = "Wed Mar 10 06:28:07 MST 2021", bibsource = "http://www.math.utah.edu/pub/tex/bib/tds.bib", URL = "https://dl.acm.org/doi/10.1145/3419106", abstract = "In the past few years, neural abstractive text summarization with sequence-to-sequence (seq2seq) models have gained a lot of popularity. Many interesting techniques have been proposed to improve seq2seq models, making them capable of handling different \ldots{}", acknowledgement = ack-nhfb, articleno = "1", fjournal = "ACM Transactions on Data Science", journal-URL = "https://dl.acm.org/loi/tds", } @Article{Li:2021:ISI, author = "Yanhua Li and Jie Bao and Zhi-Li Zhang and Saif Benjaafar", title = "Introduction to the Special Issue on Urban Computing and Smart Cities", journal = j-TDS, volume = "2", number = "1", pages = "2e:1--2e:2", month = jan, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3441679", ISSN = "2691-1922", ISSN-L = "2691-1922", bibdate = "Wed Mar 10 06:28:07 MST 2021", bibsource = "http://www.math.utah.edu/pub/tex/bib/tds.bib", URL = "https://dl.acm.org/doi/10.1145/3441679", acknowledgement = ack-nhfb, articleno = "2e", fjournal = "ACM Transactions on Data Science", journal-URL = "https://dl.acm.org/loi/tds", } @Article{Liu:2021:SBU, author = "Xiuming Liu and Edith Ngai and Dave Zachariah", title = "Scalable Belief Updating for Urban Air Quality Modeling and Prediction", journal = j-TDS, volume = "2", number = "1", pages = "2:1--2:19", month = jan, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3402903", ISSN = "2691-1922", ISSN-L = "2691-1922", bibdate = "Wed Mar 10 06:28:07 MST 2021", bibsource = "http://www.math.utah.edu/pub/tex/bib/tds.bib", URL = "https://dl.acm.org/doi/10.1145/3402903", abstract = "Air pollution is one of the major concerns in global urbanization. Data science can help to understand the dynamics of air pollution and build reliable statistical models to forecast air pollution levels. To achieve these goals, one needs to learn the \ldots{}", acknowledgement = ack-nhfb, articleno = "2", fjournal = "ACM Transactions on Data Science", journal-URL = "https://dl.acm.org/loi/tds", } @Article{Iyengar:2021:WDD, author = "Srinivasan Iyengar and Stephen Lee and David Irwin and Prashant Shenoy and Benjamin Weil", title = "{WattScale}: a Data-driven Approach for Energy Efficiency Analytics of Buildings at Scale", journal = j-TDS, volume = "2", number = "1", pages = "3:1--3:25", month = jan, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3406961", ISSN = "2691-1922", ISSN-L = "2691-1922", bibdate = "Wed Mar 10 06:28:07 MST 2021", bibsource = "http://www.math.utah.edu/pub/tex/bib/tds.bib", URL = "https://dl.acm.org/doi/10.1145/3406961", abstract = "Buildings consume over 40\% of the total energy in modern societies, and improving their energy efficiency can significantly reduce our energy footprint. In this article, we present WattScale, a data-driven approach to identify the least energy-efficient \ldots{}", acknowledgement = ack-nhfb, articleno = "3", fjournal = "ACM Transactions on Data Science", journal-URL = "https://dl.acm.org/loi/tds", } @Article{Jiang:2021:TUH, author = "Renhe Jiang and Xuan Song and Zipei Fan and Tianqi Xia and Zhaonan Wang and Quanjun Chen and Zekun Cai and Ryosuke Shibasaki", title = "Transfer Urban Human Mobility via {POI} Embedding over Multiple Cities", journal = j-TDS, volume = "2", number = "1", pages = "4:1--4:26", month = jan, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3416914", ISSN = "2691-1922", ISSN-L = "2691-1922", bibdate = "Wed Mar 10 06:28:07 MST 2021", bibsource = "http://www.math.utah.edu/pub/tex/bib/tds.bib", URL = "https://dl.acm.org/doi/10.1145/3416914", abstract = "Rapidly developing location acquisition technologies provide a powerful tool for understanding and predicting human mobility in cities, which is very significant for urban planning, traffic regulation, and emergency management. However, with the \ldots{}", acknowledgement = ack-nhfb, articleno = "4", fjournal = "ACM Transactions on Data Science", journal-URL = "https://dl.acm.org/loi/tds", } @Article{Babicheva:2021:EVR, author = "Tatiana Babicheva and Matej Cebecauer and Dominique Barth and Wilco Burghout and Le{\"\i}la Kloul", title = "Empty Vehicle Redistribution with Time Windows in Autonomous Taxi Systems", journal = j-TDS, volume = "2", number = "1", pages = "5:1--5:22", month = jan, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3416915", ISSN = "2691-1922", ISSN-L = "2691-1922", bibdate = "Wed Mar 10 06:28:07 MST 2021", bibsource = "http://www.math.utah.edu/pub/tex/bib/tds.bib", URL = "https://dl.acm.org/doi/10.1145/3416915", abstract = "In this article, we investigate empty vehicle redistribution algorithms with time windows for personal rapid transit or autonomous station-based taxi services, from a passenger service perspective. We present an Index Based Redistribution Time Limited \ldots{}", acknowledgement = ack-nhfb, articleno = "5", fjournal = "ACM Transactions on Data Science", journal-URL = "https://dl.acm.org/loi/tds", } @Article{Molinaro:2021:SST, author = "Cristian Molinaro and Chiara Pulice and Anja Subasic and Abigail Bartolome and V. S. Subrahmanian", title = "{STAR: Summarizing Timed Association Rules}", journal = j-TDS, volume = "2", number = "1", pages = "6:1--6:36", month = jan, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3419107", ISSN = "2691-1922", ISSN-L = "2691-1922", bibdate = "Wed Mar 10 06:28:07 MST 2021", bibsource = "http://www.math.utah.edu/pub/tex/bib/tds.bib", URL = "https://dl.acm.org/doi/10.1145/3419107", abstract = "Timed association rules (TARs) generalize classical association rules (ARs) so that we can express temporal dependencies of the form ``If $X$ is true at time $t$, then $Y$ will likely be true at time $ (t + \tau)$.'' As with ARs, solving the TAR mining problem can \ldots{}", acknowledgement = ack-nhfb, articleno = "6", fjournal = "ACM Transactions on Data Science", journal-URL = "https://dl.acm.org/loi/tds", } @Article{Zoppi:2021:UAD, author = "Tommaso Zoppi and Andrea Ceccarelli and Tommaso Capecchi and Andrea Bondavalli", title = "Unsupervised Anomaly Detectors to Detect Intrusions in the Current Threat Landscape", journal = j-TDS, volume = "2", number = "2", pages = "7:1--7:26", month = may, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3441140", ISSN = "2691-1922", ISSN-L = "2691-1922", bibdate = "Thu Feb 17 07:13:14 MST 2022", bibsource = "http://www.math.utah.edu/pub/tex/bib/tds.bib", URL = "https://dl.acm.org/doi/10.1145/3441140", abstract = "Anomaly detection aims at identifying unexpected fluctuations in the expected behavior of a given system. It is acknowledged as a reliable answer to the identification of zero-day attacks to such extent, several ML algorithms that suit for binary \ldots{}", acknowledgement = ack-nhfb, articleno = "7", fjournal = "ACM Transactions on Data Science", journal-URL = "https://dl.acm.org/loi/tds", } @Article{Cheng:2021:MTP, author = "Lu Cheng and Ruocheng Guo and Yasin N. Silva and Deborah Hall and Huan Liu", title = "Modeling Temporal Patterns of Cyberbullying Detection with Hierarchical Attention Networks", journal = j-TDS, volume = "2", number = "2", pages = "8:1--8:23", month = may, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3441141", ISSN = "2691-1922", ISSN-L = "2691-1922", bibdate = "Thu Feb 17 07:13:14 MST 2022", bibsource = "http://www.math.utah.edu/pub/tex/bib/tds.bib", URL = "https://dl.acm.org/doi/10.1145/3441141", abstract = "Cyberbullying is rapidly becoming one of the most serious online risks for adolescents. This has motivated work on machine learning methods to automate the process of cyberbullying detection, which have so far mostly viewed cyberbullying as one-off \ldots{}", acknowledgement = ack-nhfb, articleno = "8", fjournal = "ACM Transactions on Data Science", journal-URL = "https://dl.acm.org/loi/tds", } @Article{Zhou:2021:ISI, author = "Ke Zhou and Jingkuan Song", title = "Introduction to the Special Issue on Learning-based Support for Data Science Applications", journal = j-TDS, volume = "2", number = "2", pages = "9:1--9:1", month = may, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3450751", ISSN = "2691-1922", ISSN-L = "2691-1922", bibdate = "Thu Feb 17 07:13:14 MST 2022", bibsource = "http://www.math.utah.edu/pub/tex/bib/tds.bib", URL = "https://dl.acm.org/doi/10.1145/3450751", acknowledgement = ack-nhfb, articleno = "9", fjournal = "ACM Transactions on Data Science", journal-URL = "https://dl.acm.org/loi/tds", } @Article{Li:2021:TAN, author = "Yamin Li and Jun Zhang and Zhongliang Yang and Ru Zhang", title = "Topic-aware Neural Linguistic Steganography Based on Knowledge Graphs", journal = j-TDS, volume = "2", number = "2", pages = "10:1--10:13", month = may, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3418598", ISSN = "2691-1922", ISSN-L = "2691-1922", bibdate = "Thu Feb 17 07:13:14 MST 2022", bibsource = "http://www.math.utah.edu/pub/tex/bib/tds.bib", URL = "https://dl.acm.org/doi/10.1145/3418598", abstract = "The core challenge of steganography is always how to improve the hidden capacity and the concealment. Most current generation-based linguistic steganography methods only consider the probability distribution between text characters, and the emotion and \ldots{}", acknowledgement = ack-nhfb, articleno = "10", fjournal = "ACM Transactions on Data Science", journal-URL = "https://dl.acm.org/loi/tds", } @Article{Liu:2021:DHB, author = "Yu Liu and Yangtao Wang and Lianli Gao and Chan Guo and Yanzhao Xie and Zhili Xiao", title = "Deep Hash-based Relevance-aware Data Quality Assessment for Image Dark Data", journal = j-TDS, volume = "2", number = "2", pages = "11:1--11:26", month = may, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3420038", ISSN = "2691-1922", ISSN-L = "2691-1922", bibdate = "Thu Feb 17 07:13:14 MST 2022", bibsource = "http://www.math.utah.edu/pub/tex/bib/tds.bib", URL = "https://dl.acm.org/doi/10.1145/3420038", abstract = "Data mining can hardly solve but always faces a problem that there is little meaningful information within the dataset serving a given requirement. Faced with multiple unknown datasets, to allocate data mining resources to acquire more desired data, it is \ldots{}", acknowledgement = ack-nhfb, articleno = "11", fjournal = "ACM Transactions on Data Science", journal-URL = "https://dl.acm.org/loi/tds", } @Article{Zhang:2021:SIR, author = "Xinyu Zhang and Xiaocui Li and Xiao-Yuan Jing and Li Cheng", title = "Simultaneous Image Reconstruction and Feature Learning with {$3$D-CNNs} for Image Set-Based Classification", journal = j-TDS, volume = "2", number = "2", pages = "12:1--12:13", month = may, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3420037", ISSN = "2691-1922", ISSN-L = "2691-1922", bibdate = "Thu Feb 17 07:13:14 MST 2022", bibsource = "http://www.math.utah.edu/pub/tex/bib/tds.bib", URL = "https://dl.acm.org/doi/10.1145/3420037", abstract = "Image set-based classification has attracted substantial research interest because of its broad applications. Recently, lots of methods based on feature learning or dictionary learning have been developed to solve this problem, and some of them have made \ldots{}", acknowledgement = ack-nhfb, articleno = "12", fjournal = "ACM Transactions on Data Science", journal-URL = "https://dl.acm.org/loi/tds", } @Article{Yang:2021:BRP, author = "Ru Yang and Yuhui Deng and Yi Zhou and Ping Huang", title = "Boosting the Restoring Performance of Deduplication Data by Classifying Backup Metadata", journal = j-TDS, volume = "2", number = "2", pages = "13:1--13:16", month = may, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3437261", ISSN = "2691-1922", ISSN-L = "2691-1922", bibdate = "Thu Feb 17 07:13:14 MST 2022", bibsource = "http://www.math.utah.edu/pub/tex/bib/tds.bib", URL = "https://dl.acm.org/doi/10.1145/3437261", abstract = "Restoring data is the main purpose of data backup in storage systems. The fragmentation issue, caused by physically scattering logically continuous data across a variety of disk locations, poses a negative impact on the restoring performance of a \ldots{}", acknowledgement = ack-nhfb, articleno = "13", fjournal = "ACM Transactions on Data Science", journal-URL = "https://dl.acm.org/loi/tds", } @Article{Zandavi:2021:MUR, author = "Seid Miad Zandavi and Vera Chung and Ali Anaissi", title = "Multi-user Remote Lab: Timetable Scheduling Using Simplex Nondominated Sorting Genetic Algorithm", journal = j-TDS, volume = "2", number = "2", pages = "14:1--14:13", month = may, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3437260", ISSN = "2691-1922", ISSN-L = "2691-1922", bibdate = "Thu Feb 17 07:13:14 MST 2022", bibsource = "http://www.math.utah.edu/pub/tex/bib/tds.bib", URL = "https://dl.acm.org/doi/10.1145/3437260", abstract = "The scheduling of multi-user remote laboratories is modeled as a multimodal function for the proposed optimization algorithm. The hybrid optimization algorithm, hybridization of the Nelder--Mead Simplex algorithm, and Non-dominated Sorting Genetic \ldots{}", acknowledgement = ack-nhfb, articleno = "14", fjournal = "ACM Transactions on Data Science", journal-URL = "https://dl.acm.org/loi/tds", } @Article{Gao:2021:MGD, author = "Hongchao Gao and Yujia Li and Jiao Dai and Xi Wang and Jizhong Han and Ruixuan Li", title = "Multi-granularity Deep Local Representations for Irregular Scene Text Recognition", journal = j-TDS, volume = "2", number = "2", pages = "15:1--15:18", month = may, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3446971", ISSN = "2691-1922", ISSN-L = "2691-1922", bibdate = "Thu Feb 17 07:13:14 MST 2022", bibsource = "http://www.math.utah.edu/pub/tex/bib/tds.bib", URL = "https://dl.acm.org/doi/10.1145/3446971", abstract = "Recognizing irregular text from natural scene images is challenging due to the unconstrained appearance of text, such as curvature, orientation, and distortion. Recent recognition networks regard this task as a text sequence labeling problem and most \ldots{}", acknowledgement = ack-nhfb, articleno = "15", fjournal = "ACM Transactions on Data Science", journal-URL = "https://dl.acm.org/loi/tds", } @Article{Tian:2021:TST, author = "Xiancai Tian and Baihua Zheng and Yazhe Wang and Hsiao-Ting Huang and Chih-Chieh Hung", title = "{TRIPDECODER}: Study Travel Time Attributes and Route Preferences of Metro Systems from Smart Card Data", journal = j-TDS, volume = "2", number = "3", pages = "16:1--16:21", month = aug, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3430768", ISSN = "2691-1922", ISSN-L = "2691-1922", bibdate = "Thu Feb 17 07:13:15 MST 2022", bibsource = "http://www.math.utah.edu/pub/tex/bib/tds.bib", URL = "https://dl.acm.org/doi/10.1145/3430768", abstract = "In this article, we target at recovering the exact routes taken by commuters inside a metro system that are not captured by an Automated Fare Collection (AFC) system and hence remain unknown. We strategically propose two inference tasks to handle the \ldots{}", acknowledgement = ack-nhfb, articleno = "16", fjournal = "ACM Transactions on Data Science", journal-URL = "https://dl.acm.org/loi/tds", } @Article{Steadman:2021:KDS, author = "Liam Steadman and Nathan Griffiths and Stephen Jarvis and Mark Bell and Shaun Helman and Caroline Wallbank", title = "{kD-STR}: a Method for Spatio-Temporal Data Reduction and Modelling", journal = j-TDS, volume = "2", number = "3", pages = "17:1--17:31", month = may, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3439334", ISSN = "2691-1922", ISSN-L = "2691-1922", bibdate = "Thu Feb 17 07:13:15 MST 2022", bibsource = "http://www.math.utah.edu/pub/tex/bib/tds.bib", URL = "https://dl.acm.org/doi/10.1145/3439334", abstract = "Analysing and learning from spatio-temporal datasets is an important process in many domains, including transportation, healthcare and meteorology. In particular, data collected by sensors in the environment allows us to understand and model the processes \ldots{}", acknowledgement = ack-nhfb, articleno = "17", fjournal = "ACM Transactions on Data Science", journal-URL = "https://dl.acm.org/loi/tds", } @Article{Nashaat:2021:TUR, author = "Mona Nashaat and Aindrila Ghosh and James Miller and Shaikh Quader", title = "{TabReformer}: Unsupervised Representation Learning for Erroneous Data Detection", journal = j-TDS, volume = "2", number = "3", pages = "18:1--18:29", month = may, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3447541", ISSN = "2691-1922", ISSN-L = "2691-1922", bibdate = "Thu Feb 17 07:13:15 MST 2022", bibsource = "http://www.math.utah.edu/pub/tex/bib/tds.bib", URL = "https://dl.acm.org/doi/10.1145/3447541", abstract = "Error detection is a crucial preliminary phase in any data analytics pipeline. Existing error detection techniques typically target specific types of errors. Moreover, most of these detection models either require user-defined rules or ample hand-labeled \ldots{}", acknowledgement = ack-nhfb, articleno = "18", fjournal = "ACM Transactions on Data Science", journal-URL = "https://dl.acm.org/loi/tds", } @Article{Behrens:2021:DCC, author = "Hans Walter Behrens and K. Sel{\c{c}}uk Candan and Xilun Chen and Yash Garg and Mao-Lin Li and Xinsheng Li and Sicong Liu and Maria Luisa Sapino and Md Shadab and Dalton Turner and Magesh Vijayakumaren", title = "{DataStorm}: Coupled, Continuous Simulations for Complex Urban Environments", journal = j-TDS, volume = "2", number = "3", pages = "19:1--19:37", month = aug, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3447572", ISSN = "2691-1922", ISSN-L = "2691-1922", bibdate = "Thu Feb 17 07:13:15 MST 2022", bibsource = "http://www.math.utah.edu/pub/tex/bib/tds.bib", URL = "https://dl.acm.org/doi/10.1145/3447572", abstract = "Urban systems are characterized by complexity and dynamicity. Data-driven simulations represent a promising approach in understanding and predicting complex dynamic processes in the presence of shifting demands of urban systems. Yet, today's silo-based, \ldots{}", acknowledgement = ack-nhfb, articleno = "19", fjournal = "ACM Transactions on Data Science", journal-URL = "https://dl.acm.org/loi/tds", } @Article{Jia:2021:PGM, author = "Xiaowei Jia and Jared Willard and Anuj Karpatne and Jordan S. Read and Jacob A. Zwart and Michael Steinbach and Vipin Kumar", title = "Physics-Guided Machine Learning for Scientific Discovery: an Application in Simulating Lake Temperature Profiles", journal = j-TDS, volume = "2", number = "3", pages = "20:1--20:26", month = may, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3447814", ISSN = "2691-1922", ISSN-L = "2691-1922", bibdate = "Thu Feb 17 07:13:15 MST 2022", bibsource = "http://www.math.utah.edu/pub/tex/bib/tds.bib", URL = "https://dl.acm.org/doi/10.1145/3447814", abstract = "Physics-based models are often used to study engineering and environmental systems. The ability to model these systems is the key to achieving our future environmental sustainability and improving the quality of human life. This article focuses on \ldots{}", acknowledgement = ack-nhfb, articleno = "20", fjournal = "ACM Transactions on Data Science", journal-URL = "https://dl.acm.org/loi/tds", } @Article{Gramaglia:2021:GTP, author = "Marco Gramaglia and Marco Fiore and Angelo Furno and Razvan Stanica", title = "{GLOVE}: Towards Privacy-Preserving Publishing of Record-Level-Truthful Mobile Phone Trajectories", journal = j-TDS, volume = "2", number = "3", pages = "21:1--21:36", month = aug, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3451178", ISSN = "2691-1922", ISSN-L = "2691-1922", bibdate = "Thu Feb 17 07:13:15 MST 2022", bibsource = "http://www.math.utah.edu/pub/tex/bib/tds.bib", URL = "https://dl.acm.org/doi/10.1145/3451178", abstract = "Datasets of mobile phone trajectories collected by network operators offer an unprecedented opportunity to discover new knowledge from the activity of large populations of millions. However, publishing such trajectories also raises significant privacy \ldots{}", acknowledgement = ack-nhfb, articleno = "21", fjournal = "ACM Transactions on Data Science", journal-URL = "https://dl.acm.org/loi/tds", } @Article{Chakraborty:2021:ATN, author = "Vishal Chakraborty and Theo Delemazure and Benny Kimelfeld and Phokion G. Kolaitis and Kunal Relia and Julia Stoyanovich", title = "Algorithmic Techniques for Necessary and Possible Winners", journal = j-TDS, volume = "2", number = "3", pages = "22:1--22:23", month = aug, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3458472", ISSN = "2691-1922", ISSN-L = "2691-1922", bibdate = "Thu Feb 17 07:13:15 MST 2022", bibsource = "http://www.math.utah.edu/pub/tex/bib/tds.bib", URL = "https://dl.acm.org/doi/10.1145/3458472", abstract = "We investigate the practical aspects of computing the necessary and possible winners in elections over incomplete voter preferences. In the case of the necessary winners, we show how to implement and accelerate the polynomial-time algorithm of Xia and \ldots{}", acknowledgement = ack-nhfb, articleno = "22", fjournal = "ACM Transactions on Data Science", journal-URL = "https://dl.acm.org/loi/tds", } @Article{Song:2021:PPC, author = "Jie Song and Qiang He and Feifei Chen and Ye Yuan and Ge Yu", title = "{PoBery}: Possibly-complete Big Data Queries with Probabilistic Data Placement and Scanning", journal = j-TDS, volume = "2", number = "3", pages = "23:1--23:28", month = aug, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3465375", ISSN = "2691-1922", ISSN-L = "2691-1922", bibdate = "Thu Feb 17 07:13:15 MST 2022", bibsource = "http://www.math.utah.edu/pub/tex/bib/tds.bib", URL = "https://dl.acm.org/doi/10.1145/3465375", abstract = "In big data query processing, there is a trade-off between query accuracy and query efficiency, for example, sampling query approaches trade-off query completeness for efficiency. In this article, we argue that query performance can be significantly \ldots{}", acknowledgement = ack-nhfb, articleno = "23", fjournal = "ACM Transactions on Data Science", journal-URL = "https://dl.acm.org/loi/tds", } @Article{Dey:2021:SRC, author = "Paramita Dey and Subhayan Bhattacharya and Sarbani Roy", title = "A Survey on the Role of Centrality as Seed Nodes for Information Propagation in Large Scale Network", journal = j-TDS, volume = "2", number = "3", pages = "24:1--24:25", month = aug, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3465374", ISSN = "2691-1922", ISSN-L = "2691-1922", bibdate = "Thu Feb 17 07:13:15 MST 2022", bibsource = "http://www.math.utah.edu/pub/tex/bib/tds.bib", URL = "https://dl.acm.org/doi/10.1145/3465374", abstract = "From the popular concept of six-degree separation, social networks are generally analyzed in the perspective of small world networks where centrality of nodes play a pivotal role in information propagation. However, working with a large dataset of a scale-. \ldots{}", acknowledgement = ack-nhfb, articleno = "24", fjournal = "ACM Transactions on Data Science", journal-URL = "https://dl.acm.org/loi/tds", } @Article{Leng:2021:TEA, author = "Yan Leng and Alejandro Noriega and Alex Pentland", title = "Tourism Event Analytics with Mobile Phone Data", journal = j-TDS, volume = "2", number = "3", pages = "25:1--25:22", month = aug, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3479975", ISSN = "2691-1922", ISSN-L = "2691-1922", bibdate = "Thu Feb 17 07:13:15 MST 2022", bibsource = "http://www.math.utah.edu/pub/tex/bib/tds.bib", URL = "https://dl.acm.org/doi/10.1145/3479975", abstract = "Tourism has been an increasingly significant contributor to the economy, society, and environment. Policy-making and research on tourism traditionally rely on surveys and economic datasets, which are based on small samples and depict tourism dynamics at a \ldots{}", acknowledgement = ack-nhfb, articleno = "25", fjournal = "ACM Transactions on Data Science", journal-URL = "https://dl.acm.org/loi/tds", } @Article{Maji:2021:CUF, author = "Subhadip Maji and Smarajit Bose", title = "{CBIR} Using Features Derived by Deep Learning", journal = j-TDS, volume = "2", number = "3", pages = "26:1--26:24", month = aug, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3470568", ISSN = "2691-1922", ISSN-L = "2691-1922", bibdate = "Thu Feb 17 07:13:15 MST 2022", bibsource = "http://www.math.utah.edu/pub/tex/bib/tds.bib", URL = "https://dl.acm.org/doi/10.1145/3470568", abstract = "In a Content-based Image Retrieval (CBIR) System, the task is to retrieve similar images from a large database given a query image. The usual procedure is to extract some useful features from the query image and retrieve images that have a similar set of \ldots{}", acknowledgement = ack-nhfb, articleno = "26", fjournal = "ACM Transactions on Data Science", journal-URL = "https://dl.acm.org/loi/tds", } @Article{Du:2021:AED, author = "Zhekai Du and Jingjing Li and Lei Zhu and Ke Lu and Heng Tao Shen", title = "Adversarial Energy Disaggregation", journal = j-TDS, volume = "2", number = "4", pages = "27:1--27:16", month = nov, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3477301", ISSN = "2691-1922", ISSN-L = "2691-1922", bibdate = "Thu Feb 17 07:13:16 MST 2022", bibsource = "http://www.math.utah.edu/pub/tex/bib/tds.bib", URL = "https://dl.acm.org/doi/10.1145/3477301", abstract = "Energy disaggregation, also known as non-intrusive load monitoring (NILM), challenges the problem of separating the whole-home electricity usage into appliance-specific individual consumptions, which is a typical application of data analysis. NILM aims to \ldots{}", acknowledgement = ack-nhfb, articleno = "27", fjournal = "ACM Transactions on Data Science", journal-URL = "https://dl.acm.org/loi/tds", } @Article{Desmet:2021:RDP, author = "Chance Desmet and Diane J. Cook", title = "Recent Developments in Privacy-preserving Mining of Clinical Data", journal = j-TDS, volume = "2", number = "4", pages = "28:1--28:32", month = nov, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3447774", ISSN = "2691-1922", ISSN-L = "2691-1922", bibdate = "Thu Feb 17 07:13:16 MST 2022", bibsource = "http://www.math.utah.edu/pub/tex/bib/tds.bib", URL = "https://dl.acm.org/doi/10.1145/3447774", abstract = "With the dramatic improvements in both the capability to collect personal data and the capability to analyze large amounts of data, increasingly sophisticated and personal insights are being drawn. These insights are valuable for clinical applications but \ldots{}", acknowledgement = ack-nhfb, articleno = "28", fjournal = "ACM Transactions on Data Science", journal-URL = "https://dl.acm.org/loi/tds", } @Article{Zhang:2021:HSS, author = "Jiaru Zhang and Ruhui Ma and Tao Song and Yang Hua and Zhengui Xue and Chenyang Guan and Haibing Guan", title = "Hierarchical Satellite System Graph for Approximate Nearest Neighbor Search on Big Data", journal = j-TDS, volume = "2", number = "4", pages = "32:1--32:15", month = nov, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3488377", ISSN = "2691-1922", ISSN-L = "2691-1922", bibdate = "Thu Feb 17 07:13:16 MST 2022", bibsource = "http://www.math.utah.edu/pub/tex/bib/tds.bib", URL = "https://dl.acm.org/doi/10.1145/3488377", abstract = "Approximate nearest neighbor search is a classical problem in data science, which is widely applied in many fields. With the rapid growth of data in the real world, it becomes more and more important to speed up the nearest neighbor search process. \ldots{}", acknowledgement = ack-nhfb, articleno = "32", fjournal = "ACM Transactions on Data Science", journal-URL = "https://dl.acm.org/loi/tds", } @Article{Gao:2021:QTN, author = "Yuan Gao and Laurence T. Yang and Dehua Zheng and Jing Yang and Yaliang Zhao", title = "Quantized Tensor Neural Network", journal = j-TDS, volume = "2", number = "4", pages = "33:1--33:18", month = nov, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3491255", ISSN = "2691-1922", ISSN-L = "2691-1922", bibdate = "Thu Feb 17 07:13:16 MST 2022", bibsource = "http://www.math.utah.edu/pub/tex/bib/tds.bib", URL = "https://dl.acm.org/doi/10.1145/3491255", abstract = "Tensor network as an effective computing framework for efficient processing and analysis of high-dimensional data has been successfully applied in many fields. However, the performance of traditional tensor networks still cannot match the strong fitting \ldots{}", acknowledgement = ack-nhfb, articleno = "33", fjournal = "ACM Transactions on Data Science", journal-URL = "https://dl.acm.org/loi/tds", } @Article{Ding:2021:DPD, author = "Xiaofeng Ding and Lin Chen and Pan Zhou and Wenbin Jiang and Hai Jin", title = "Differentially Private Deep Learning with Iterative Gradient Descent Optimization", journal = j-TDS, volume = "2", number = "4", pages = "34:1--34:27", month = nov, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3491254", ISSN = "2691-1922", ISSN-L = "2691-1922", bibdate = "Thu Feb 17 07:13:16 MST 2022", bibsource = "http://www.math.utah.edu/pub/tex/bib/tds.bib", URL = "https://dl.acm.org/doi/10.1145/3491254", abstract = "Deep learning has achieved great success in various areas and its success is closely linked to the availability of massive data. But in general, a large dataset could include sensitive data and therefore the model should have the capability to avoid \ldots{}", acknowledgement = ack-nhfb, articleno = "34", fjournal = "ACM Transactions on Data Science", journal-URL = "https://dl.acm.org/loi/tds", } @Article{Agarwal:2021:STS, author = "Mridul Agarwal and Vaneet Aggarwal and Abhishek K. Umrawal and Christopher J. Quinn", title = "Stochastic Top {$K$}-Subset Bandits with Linear Space and Non-Linear Feedback with Applications to Social Influence Maximization", journal = j-TDS, volume = "2", number = "4", pages = "38:1--38:39", month = nov, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3507787", ISSN = "2691-1922", ISSN-L = "2691-1922", bibdate = "Thu Feb 17 07:13:16 MST 2022", bibsource = "http://www.math.utah.edu/pub/tex/bib/tds.bib", URL = "https://dl.acm.org/doi/10.1145/3507787", abstract = "There are numerous real-world problems where a user must make decisions under uncertainty. For the problem of influence maximization on a social network, for example, the user must select a set of K influencers who will jointly have a large influence on \ldots{}", acknowledgement = ack-nhfb, articleno = "38", fjournal = "ACM Transactions on Data Science", journal-URL = "https://dl.acm.org/loi/tds", } @Article{Xia:2021:SBA, author = "Feng Xia and Teng Guo and Xiaomei Bai and Adrian Shatte and Zitao Liu and Jiliang Tang", title = "{SUMMER}: Bias-aware Prediction of Graduate Employment Based on Educational Big Data", journal = j-TDS, volume = "2", number = "4", pages = "39:1--39:??", month = nov, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3510361", ISSN = "2691-1922", ISSN-L = "2691-1922", bibdate = "Fri Aug 25 12:23:02 MDT 2023", bibsource = "http://www.math.utah.edu/pub/tex/bib/tds.bib", URL = "https://dl.acm.org/doi/10.1145/3510361", abstract = "The failure of obtaining employment could lead to serious psychosocial outcomes such as depression and substance abuse, especially for college students who \ldots{}", acknowledgement = ack-nhfb, ajournal = "", articleno = "39", fjournal = "ACM Transactions on Data Science", journal-URL = "https://dl.acm.org/loi/tds", } @Article{Goodman:2021:DBP, author = "Joel Goodman and Sharham Sarkani and Thomas Mazzuchi", title = "Distance-based Probabilistic Data Augmentation for Synthetic Minority Oversampling", journal = j-TDS, volume = "2", number = "4", pages = "40:1--40:??", month = nov, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3510834", ISSN = "2691-1922", ISSN-L = "2691-1922", bibdate = "Fri Aug 25 12:23:02 MDT 2023", bibsource = "http://www.math.utah.edu/pub/tex/bib/tds.bib", URL = "https://dl.acm.org/doi/10.1145/3510834", abstract = "Class imbalance can adversely affect the performance of machine learning for prediction and classification. One approach to address the class imbalance \ldots{}", acknowledgement = ack-nhfb, ajournal = "", articleno = "40", fjournal = "ACM Transactions on Data Science", journal-URL = "https://dl.acm.org/loi/tds", } @Article{Ahmed:2021:SAP, author = "Shibbir Ahmed and Md Johirul Islam and Hridesh Rajan", title = "Semantics and Anomaly Preserving Sampling Strategy for Large-Scale Time Series Data", journal = j-TDS, volume = "2", number = "4", pages = "41:1--41:??", month = nov, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3511918", ISSN = "2691-1922", ISSN-L = "2691-1922", bibdate = "Fri Aug 25 12:23:02 MDT 2023", bibsource = "http://www.math.utah.edu/pub/tex/bib/tds.bib", URL = "https://dl.acm.org/doi/10.1145/3511918", abstract = "We propose PASS, a O ( n ) algorithm for data reduction that is specifically aimed at preserving the semantics of time series data visualization in the form of line \ldots{}", acknowledgement = ack-nhfb, ajournal = "", articleno = "41", fjournal = "ACM Transactions on Data Science", journal-URL = "https://dl.acm.org/loi/tds", } @Article{Gan:2021:TRM, author = "Wensheng Gan and Guoting Chen and Hongzhi Yin and Philippe Fournier-Viger and Chien-Ming Chen and Philip S. Yu", title = "Towards Revenue Maximization with Popular and Profitable Products", journal = j-TDS, volume = "2", number = "4", pages = "42:1--42:??", month = nov, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3488058", ISSN = "2691-1922", ISSN-L = "2691-1922", bibdate = "Fri Aug 25 12:23:02 MDT 2023", bibsource = "http://www.math.utah.edu/pub/tex/bib/tds.bib", URL = "https://dl.acm.org/doi/10.1145/3488058", abstract = "Economic-wise, a common goal for companies conducting marketing is to maximize the return revenue/profit by utilizing the various effective \ldots{}", acknowledgement = ack-nhfb, ajournal = "", articleno = "42", fjournal = "ACM Transactions on Data Science", journal-URL = "https://dl.acm.org/loi/tds", } @Article{Dutta:2021:BDC, author = "Hridoy Sankar Dutta and Tanmoy Chakraborty", title = "Blackmarket-Driven Collusion on Online Media: a Survey", journal = j-TDS, volume = "2", number = "4", pages = "43:1--43:??", month = nov, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3517931", ISSN = "2691-1922", ISSN-L = "2691-1922", bibdate = "Fri Aug 25 12:23:02 MDT 2023", bibsource = "http://www.math.utah.edu/pub/tex/bib/tds.bib", URL = "https://dl.acm.org/doi/10.1145/3517931", abstract = "Online media platforms have enabled users to connect with individuals and organizations, and share their thoughts. Other than connectivity, these \ldots{}", acknowledgement = ack-nhfb, ajournal = "", articleno = "43", fjournal = "ACM Transactions on Data Science", journal-URL = "https://dl.acm.org/loi/tds", } @Article{Wang:2021:TBD, author = "Chaoyang Wang and Zhiqiang Guo and Jianjun Li and Guohui Li and Peng Pan", title = "A Text-based Deep Reinforcement Learning Framework Using Self-supervised Graph Representation for Interactive Recommendation", journal = j-TDS, volume = "2", number = "4", pages = "44:1--44:??", month = nov, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3522596", ISSN = "2691-1922", ISSN-L = "2691-1922", bibdate = "Fri Aug 25 12:23:02 MDT 2023", bibsource = "http://www.math.utah.edu/pub/tex/bib/tds.bib", URL = "https://dl.acm.org/doi/10.1145/3522596", abstract = "Due to its nature of learning from dynamic interactions and planning for long-run performance, Reinforcement Learning (RL) has attracted much attention in \ldots{}", acknowledgement = ack-nhfb, ajournal = "", articleno = "44", fjournal = "ACM Transactions on Data Science", journal-URL = "https://dl.acm.org/loi/tds", }