%%% -*-BibTeX-*- %%% ==================================================================== %%% BibTeX-file{ %%% author = "Nelson H. F. Beebe", %%% version = "1.41", %%% date = "05 March 2026", %%% time = "11:49:39 MDT", %%% filename = "tiis.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 = "34890 16734 88471 839238", %%% email = "beebe at math.utah.edu, beebe at acm.org, %%% beebe at computer.org (Internet)", %%% codetable = "ISO/ASCII", %%% keywords = "bibliography; BibTeX; ACM Transactions on %%% Interactive Intelligent Systems (TIIS)", %%% license = "public domain", %%% supported = "yes", %%% docstring = "This is a COMPLETE BibTeX bibliography for %%% ACM Transactions on Interactive Intelligent %%% Systems (TIIS) (CODEN ????, ISSN 2160-6455 %%% (print), 2160-6463 (electronic)), covering %%% all journal issues from 2011 -- date. %%% %%% At version 1.41, the COMPLETE journal %%% coverage looked like this: %%% %%% 2011 ( 6) 2017 ( 19) 2023 ( 30) %%% 2012 ( 24) 2018 ( 32) 2024 ( 30) %%% 2013 ( 20) 2019 ( 24) 2025 ( 27) %%% 2014 ( 20) 2020 ( 34) 2026 ( 9) %%% 2015 ( 24) 2021 ( 32) %%% 2016 ( 36) 2022 ( 36) %%% %%% Article: 403 %%% %%% Total entries: 403 %%% %%% The journal Web site can be found at: %%% %%% http://tiis.acm.org/ %%% http://www.acm.org/tiis/ %%% %%% The journal table of contents page is at: %%% %%% http://dl.acm.org/pub.cfm?id=J1341 %%% http://portal.acm.org/browse_dl.cfm?idx=J1341 %%% %%% 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. %%% %%% bibsource keys in the bibliography entries %%% below indicate the entry originally came %%% from the computer science bibliography %%% archive, even though it has likely since %%% been corrected and updated. %%% %%% 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" # "\def \TM {${}^{\sc TM}$}" # "\ifx \undefined \booktitle \def \booktitle #1{{{\em #1}}} \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, 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-TIIS = "ACM Transactions on Interactive Intelligent Systems (TIIS)"} %%% ==================================================================== %%% Bibliography entries: @Article{Jameson:2011:ITI, author = "Anthony Jameson and John Riedl", title = "Introduction to the {Transactions on Interactive Intelligent Systems}", journal = j-TIIS, volume = "1", number = "1", pages = "1:1--1:??", month = oct, year = "2011", CODEN = "????", DOI = "https://doi.org/10.1145/2030365.2030366", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Thu Nov 3 17:51:10 MDT 2011", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tiis.bib", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "1", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Kulesza:2011:WOE, author = "Todd Kulesza and Simone Stumpf and Weng-Keen Wong and Margaret M. Burnett and Stephen Perona and Andrew Ko and Ian Oberst", title = "Why-oriented end-user debugging of naive {Bayes} text classification", journal = j-TIIS, volume = "1", number = "1", pages = "2:1--2:??", month = oct, year = "2011", CODEN = "????", DOI = "https://doi.org/10.1145/2030365.2030367", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Thu Nov 3 17:51:10 MDT 2011", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tiis.bib", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "2", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Hoi:2011:AMK, author = "Steven C. H. Hoi and Rong Jin", title = "Active multiple kernel learning for interactive {$3$D} object retrieval systems", journal = j-TIIS, volume = "1", number = "1", pages = "3:1--3:??", month = oct, year = "2011", CODEN = "????", DOI = "https://doi.org/10.1145/2030365.2030368", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Thu Nov 3 17:51:10 MDT 2011", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tiis.bib", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "3", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Hammond:2011:RSM, author = "Tracy Hammond and Brandon Paulson", title = "Recognizing sketched multistroke primitives", journal = j-TIIS, volume = "1", number = "1", pages = "4:1--4:??", month = oct, year = "2011", CODEN = "????", DOI = "https://doi.org/10.1145/2030365.2030369", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Thu Nov 3 17:51:10 MDT 2011", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tiis.bib", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "4", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Okita:2011:MAA, author = "Sandra Y. Okita and Victor Ng-Thow-Hing and Ravi K. Sarvadevabhatla", title = "Multimodal approach to affective human-robot interaction design with children", journal = j-TIIS, volume = "1", number = "1", pages = "5:1--5:??", month = oct, year = "2011", CODEN = "????", DOI = "https://doi.org/10.1145/2030365.2030370", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Thu Nov 3 17:51:10 MDT 2011", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tiis.bib", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "5", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Gibet:2011:SSD, author = "Sylvie Gibet and Nicolas Courty and Kyle Duarte and Thibaut Le Naour", title = "The {SignCom} system for data-driven animation of interactive virtual signers: Methodology and Evaluation", journal = j-TIIS, volume = "1", number = "1", pages = "6:1--6:??", month = oct, year = "2011", CODEN = "????", DOI = "https://doi.org/10.1145/2030365.2030371", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Thu Nov 3 17:51:10 MDT 2011", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tiis.bib", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "6", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Castellano:2012:ISI, author = "Ginevra Castellano and Laurel D. Riek and Christopher Peters and Kostas Karpouzis and Jean-Claude Martin and Louis-Philippe Morency", title = "Introduction to the special issue on affective interaction in natural environments", journal = j-TIIS, volume = "2", number = "1", pages = "1:1--1:??", month = mar, year = "2012", CODEN = "????", DOI = "https://doi.org/10.1145/2133366.2133367", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Fri Mar 16 12:34:07 MDT 2012", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "Affect-sensitive systems such as social robots and virtual agents are increasingly being investigated in real-world settings. In order to work effectively in natural environments, these systems require the ability to infer the affective and mental states of humans and to provide appropriate timely output that helps to sustain long-term interactions. This special issue, which appears in two parts, includes articles on the design of socio-emotional behaviors and expressions in robots and virtual agents and on computational approaches for the automatic recognition of social signals and affective states.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "1", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Beck:2012:EBL, author = "Aryel Beck and Brett Stevens and Kim A. Bard and Lola Ca{\~n}amero", title = "Emotional body language displayed by artificial agents", journal = j-TIIS, volume = "2", number = "1", pages = "2:1--2:??", month = mar, year = "2012", CODEN = "????", DOI = "https://doi.org/10.1145/2133366.2133368", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Fri Mar 16 12:34:07 MDT 2012", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "Complex and natural social interaction between artificial agents (computer-generated or robotic) and humans necessitates the display of rich emotions in order to be believable, socially relevant, and accepted, and to generate the natural emotional responses that humans show in the context of social interaction, such as engagement or empathy. Whereas some robots use faces to display (simplified) emotional expressions, for other robots such as Nao, body language is the best medium available given their inability to convey facial expressions. Displaying emotional body language that can be interpreted whilst interacting with the robot should significantly improve naturalness. This research investigates the creation of an affect space for the generation of emotional body language to be displayed by humanoid robots. To do so, three experiments investigating how emotional body language displayed by agents is interpreted were conducted. The first experiment compared the interpretation of emotional body language displayed by humans and agents. The results showed that emotional body language displayed by an agent or a human is interpreted in a similar way in terms of recognition. Following these results, emotional key poses were extracted from an actor's performances and implemented in a Nao robot. The interpretation of these key poses was validated in a second study where it was found that participants were better than chance at interpreting the key poses displayed. Finally, an affect space was generated by blending key poses and validated in a third study. Overall, these experiments confirmed that body language is an appropriate medium for robots to display emotions and suggest that an affect space for body expressions can be used to improve the expressiveness of humanoid robots.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "2", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Hiolle:2012:ECB, author = "Antoine Hiolle and Lola Ca{\~n}amero and Marina Davila-Ross and Kim A. Bard", title = "Eliciting caregiving behavior in dyadic human-robot attachment-like interactions", journal = j-TIIS, volume = "2", number = "1", pages = "3:1--3:??", month = mar, year = "2012", CODEN = "????", DOI = "https://doi.org/10.1145/2133366.2133369", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Fri Mar 16 12:34:07 MDT 2012", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "We present here the design and applications of an arousal-based model controlling the behavior of a Sony AIBO robot during the exploration of a novel environment: a children's play mat. When the robot experiences too many new perceptions, the increase of arousal triggers calls for attention towards its human caregiver. The caregiver can choose to either calm the robot down by providing it with comfort, or to leave the robot coping with the situation on its own. When the arousal of the robot has decreased, the robot moves on to further explore the play mat. We gathered results from two experiments using this arousal-driven control architecture. In the first setting, we show that such a robotic architecture allows the human caregiver to influence greatly the learning outcomes of the exploration episode, with some similarities to a primary caregiver during early childhood. In a second experiment, we tested how human adults behaved in a similar setup with two different robots: one `needy', often demanding attention, and one more independent, requesting far less care or assistance. Our results show that human adults recognise each profile of the robot for what they have been designed, and behave accordingly to what would be expected, caring more for the needy robot than for the other. Additionally, the subjects exhibited a preference and more positive affect whilst interacting and rating the robot we designed as needy. This experiment leads us to the conclusion that our architecture and setup succeeded in eliciting positive and caregiving behavior from adults of different age groups and technological background. Finally, the consistency and reactivity of the robot during this dyadic interaction appeared crucial for the enjoyment and engagement of the human partner.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "3", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Scherer:2012:SLN, author = "Stefan Scherer and Michael Glodek and Friedhelm Schwenker and Nick Campbell and G{\"u}nther Palm", title = "Spotting laughter in natural multiparty conversations: a comparison of automatic online and offline approaches using audiovisual data", journal = j-TIIS, volume = "2", number = "1", pages = "4:1--4:??", month = mar, year = "2012", CODEN = "????", DOI = "https://doi.org/10.1145/2133366.2133370", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Fri Mar 16 12:34:07 MDT 2012", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "It is essential for the advancement of human-centered multimodal interfaces to be able to infer the current user's state or communication state. In order to enable a system to do that, the recognition and interpretation of multimodal social signals (i.e., paralinguistic and nonverbal behavior) in real-time applications is required. Since we believe that laughs are one of the most important and widely understood social nonverbal signals indicating affect and discourse quality, we focus in this work on the detection of laughter in natural multiparty discourses. The conversations are recorded in a natural environment without any specific constraint on the discourses using unobtrusive recording devices. This setup ensures natural and unbiased behavior, which is one of the main foci of this work. To compare results of methods, namely Gaussian Mixture Model (GMM) supervectors as input to a Support Vector Machine (SVM), so-called Echo State Networks (ESN), and a Hidden Markov Model (HMM) approach, are utilized in online and offline detection experiments. The SVM approach proves very accurate in the offline classification task, but is outperformed by the ESN and HMM approach in the online detection (F 1 scores: GMM SVM 0.45, ESN 0.63, HMM 0.72). Further, we were able to utilize the proposed HMM approach in a cross-corpus experiment without any retraining with respectable generalization capability (F 1 score: 0.49). The results and possible reasons for these outcomes are shown and discussed in the article. The proposed methods may be directly utilized in practical tasks such as the labeling or the online detection of laughter in conversational data and affect-aware applications.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "4", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Song:2012:CBH, author = "Yale Song and David Demirdjian and Randall Davis", title = "Continuous body and hand gesture recognition for natural human-computer interaction", journal = j-TIIS, volume = "2", number = "1", pages = "5:1--5:??", month = mar, year = "2012", CODEN = "????", DOI = "https://doi.org/10.1145/2133366.2133371", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Fri Mar 16 12:34:07 MDT 2012", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "Intelligent gesture recognition systems open a new era of natural human-computer interaction: Gesturing is instinctive and a skill we all have, so it requires little or no thought, leaving the focus on the task itself, as it should be, not on the interaction modality. We present a new approach to gesture recognition that attends to both body and hands, and interprets gestures continuously from an unsegmented and unbounded input stream. This article describes the whole procedure of continuous body and hand gesture recognition, from the signal acquisition to processing, to the interpretation of the processed signals. Our system takes a vision-based approach, tracking body and hands using a single stereo camera. Body postures are reconstructed in 3D space using a generative model-based approach with a particle filter, combining both static and dynamic attributes of motion as the input feature to make tracking robust to self-occlusion. The reconstructed body postures guide searching for hands. Hand shapes are classified into one of several canonical hand shapes using an appearance-based approach with a multiclass support vector machine. Finally, the extracted body and hand features are combined and used as the input feature for gesture recognition. We consider our task as an online sequence labeling and segmentation problem. A latent-dynamic conditional random field is used with a temporal sliding window to perform the task continuously. We augment this with a novel technique called multilayered filtering, which performs filtering both on the input layer and the prediction layer. Filtering on the input layer allows capturing long-range temporal dependencies and reducing input signal noise; filtering on the prediction layer allows taking weighted votes of multiple overlapping prediction results as well as reducing estimation noise. We tested our system in a scenario of real-world gestural interaction using the NATOPS dataset, an official vocabulary of aircraft handling gestures. Our experimental results show that: (1) the use of both static and dynamic attributes of motion in body tracking allows statistically significant improvement of the recognition performance over using static attributes of motion alone; and (2) the multilayered filtering statistically significantly improves recognition performance over the nonfiltering method. We also show that, on a set of twenty-four NATOPS gestures, our system achieves a recognition accuracy of 75.37\%.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "5", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Eyben:2012:MAC, author = "Florian Eyben and Martin W{\"o}llmer and Bj{\"o}rn Schuller", title = "A multitask approach to continuous five-dimensional affect sensing in natural speech", journal = j-TIIS, volume = "2", number = "1", pages = "6:1--6:??", month = mar, year = "2012", CODEN = "????", DOI = "https://doi.org/10.1145/2133366.2133372", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Fri Mar 16 12:34:07 MDT 2012", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "Automatic affect recognition is important for the ability of future technical systems to interact with us socially in an intelligent way by understanding our current affective state. In recent years there has been a shift in the field of affect recognition from `in the lab' experiments with acted data to `in the wild' experiments with spontaneous and naturalistic data. Two major issues thereby are the proper segmentation of the input and adequate description and modeling of affective states. The first issue is crucial for responsive, real-time systems such as virtual agents and robots, where the latency of the analysis must be as small as possible. To address this issue we introduce a novel method of incremental segmentation to be used in combination with supra-segmental modeling. For modeling of continuous affective states we use Long Short-Term Memory Recurrent Neural Networks, with which we can show an improvement in performance over standard recurrent neural networks and feed-forward neural networks as well as Support Vector Regression. For experiments we use the SEMAINE database, which contains recordings of spontaneous and natural human to Wizard-of-Oz conversations. The recordings are annotated continuously in time and magnitude with FeelTrace for five affective dimensions, namely activation, expectation, intensity, power/dominance, and valence. To exploit dependencies between the five affective dimensions we investigate multitask learning of all five dimensions augmented with inter-rater standard deviation. We can show improvements for multitask over single-task modeling. Correlation coefficients of up to 0.81 are obtained for the activation dimension and up to 0.58 for the valence dimension. The performance for the remaining dimensions were found to be in between that for activation and valence.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "6", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Yazdani:2012:ARB, author = "Ashkan Yazdani and Jong-Seok Lee and Jean-Marc Vesin and Touradj Ebrahimi", title = "Affect recognition based on physiological changes during the watching of music videos", journal = j-TIIS, volume = "2", number = "1", pages = "7:1--7:??", month = mar, year = "2012", CODEN = "????", DOI = "https://doi.org/10.1145/2133366.2133373", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Fri Mar 16 12:34:07 MDT 2012", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "Assessing emotional states of users evoked during their multimedia consumption has received a great deal of attention with recent advances in multimedia content distribution technologies and increasing interest in personalized content delivery. Physiological signals such as the electroencephalogram (EEG) and peripheral physiological signals have been less considered for emotion recognition in comparison to other modalities such as facial expression and speech, although they have a potential interest as alternative or supplementary channels. This article presents our work on: (1) constructing a dataset containing EEG and peripheral physiological signals acquired during presentation of music video clips, which is made publicly available, and (2) conducting binary classification of induced positive/negative valence, high/low arousal, and like/dislike by using the aforementioned signals. The procedure for the dataset acquisition, including stimuli selection, signal acquisition, self-assessment, and signal processing is described in detail. Especially, we propose a novel asymmetry index based on relative wavelet entropy for measuring the asymmetry in the energy distribution of EEG signals, which is used for EEG feature extraction. Then, the classification systems based on EEG and peripheral physiological signals are presented. Single-trial and single-run classification results indicate that, on average, the performance of the EEG-based classification outperforms that of the peripheral physiological signals. However, the peripheral physiological signals can be considered as a good alternative to EEG signals in the case of assessing a user's preference for a given music video clip (like/dislike) since they have a comparable performance to EEG signals while being more easily measured.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "7", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Park:2012:CFM, author = "Souneil Park and Seungwoo Kang and Sangyoung Chung and Junehwa Song", title = "A Computational Framework for Media Bias Mitigation", journal = j-TIIS, volume = "2", number = "2", pages = "8:1--8:??", month = jun, year = "2012", CODEN = "????", DOI = "https://doi.org/10.1145/2209310.2209311", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Tue Nov 6 19:14:39 MST 2012", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "Bias in the news media is an inherent flaw of the news production process. The bias often causes a sharp increase in political polarization and in the cost of conflict on social issues such as the Iraq war. This article presents NewsCube, a novel Internet news service which aims to mitigate the effect of media bias. NewsCube automatically creates and promptly provides readers with multiple classified views on a news event. As such, it helps readers understand the event from a plurality of views and to formulate their own, more balanced, viewpoints. The media bias problem has been studied extensively in mass communications and social science. This article reviews related mass communication and journalism studies and provides a structured view of the media bias problem and its solution. We propose media bias mitigation as a practical solution and demonstrate it through NewsCube. We evaluate and discuss the effectiveness of NewsCube through various performance studies.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "8", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Berkovsky:2012:IIF, author = "Shlomo Berkovsky and Jill Freyne and Harri Oinas-Kukkonen", title = "Influencing Individually: Fusing Personalization and Persuasion", journal = j-TIIS, volume = "2", number = "2", pages = "9:1--9:??", month = jun, year = "2012", CODEN = "????", DOI = "https://doi.org/10.1145/2209310.2209312", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Tue Nov 6 19:14:39 MST 2012", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "Personalized technologies aim to enhance user experience by taking into account users' interests, preferences, and other relevant information. Persuasive technologies aim to modify user attitudes, intentions, or behavior through computer-human dialogue and social influence. While both personalized and persuasive technologies influence user interaction and behavior, we posit that this influence could be significantly increased if the two technologies were combined to create personalized and persuasive systems. For example, the persuasive power of a one-size-fits-all persuasive intervention could be enhanced by considering the users being influenced and their susceptibility to the persuasion being offered. Likewise, personalized technologies could cash in on increased success, in terms of user satisfaction, revenue, and user experience, if their services used persuasive techniques. Hence, the coupling of personalization and persuasion has the potential to enhance the impact of both technologies. This new, developing area clearly offers mutual benefits to both research areas, as we illustrate in this special issue.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "9", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Kaptein:2012:APS, author = "Maurits Kaptein and Boris {De Ruyter} and Panos Markopoulos and Emile Aarts", title = "Adaptive Persuasive Systems: a Study of Tailored Persuasive Text Messages to Reduce Snacking", journal = j-TIIS, volume = "2", number = "2", pages = "10:1--10:??", month = jun, year = "2012", CODEN = "????", DOI = "https://doi.org/10.1145/2209310.2209313", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Tue Nov 6 19:14:39 MST 2012", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "This article describes the use of personalized short text messages (SMS) to reduce snacking. First, we describe the development and validation ( N = 215) of a questionnaire to measure individual susceptibility to different social influence strategies. To evaluate the external validity of this Susceptibility to Persuasion Scale (STPS) we set up a two week text-messaging intervention that used text messages implementing social influence strategies as prompts to reduce snacking behavior. In this experiment ( N = 73) we show that messages that are personalized (tailored) to the individual based on their scores on the STPS, lead to a higher decrease in snacking consumption than randomized messages or messages that are not tailored (contra-tailored) to the individual. We discuss the importance of this finding for the design of persuasive systems and detail how designers can use tailoring at the level of social influence strategies to increase the effects of their persuasive technologies.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "10", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Cremonesi:2012:IPP, author = "Paolo Cremonesi and Franca Garzotto and Roberto Turrin", title = "Investigating the Persuasion Potential of Recommender Systems from a Quality Perspective: an Empirical Study", journal = j-TIIS, volume = "2", number = "2", pages = "11:1--11:??", month = jun, year = "2012", CODEN = "????", DOI = "https://doi.org/10.1145/2209310.2209314", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Tue Nov 6 19:14:39 MST 2012", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "Recommender Systems (RSs) help users search large amounts of digital contents and services by allowing them to identify the items that are likely to be more attractive or useful. RSs play an important persuasion role, as they can potentially augment the users' trust towards in an application and orient their decisions or actions towards specific directions. This article explores the persuasiveness of RSs, presenting two vast empirical studies that address a number of research questions. First, we investigate if a design property of RSs, defined by the statistically measured quality of algorithms, is a reliable predictor of their potential for persuasion. This factor is measured in terms of perceived quality, defined by the overall satisfaction, as well as by how users judge the accuracy and novelty of recommendations. For our purposes, we designed an empirical study involving 210 subjects and implemented seven full-sized versions of a commercial RS, each one using the same interface and dataset (a subset of Netflix), but each with a different recommender algorithm. In each experimental configuration we computed the statistical quality (recall and F-measures) and collected data regarding the quality perceived by 30 users. The results show us that algorithmic attributes are less crucial than we might expect in determining the user's perception of an RS's quality, and suggest that the user's judgment and attitude towards a recommender are likely to be more affected by factors related to the user experience. Second, we explore the persuasiveness of RSs in the context of large interactive TV services. We report a study aimed at assessing whether measurable persuasion effects (e.g., changes of shopping behavior) can be achieved through the introduction of a recommender. Our data, collected for more than one year, allow us to conclude that, (1) the adoption of an RS can affect both the lift factor and the conversion rate, determining an increased volume of sales and influencing the user's decision to actually buy one of the recommended products, (2) the introduction of an RS tends to diversify purchases and orient users towards less obvious choices (the long tail), and (3) the perceived novelty of recommendations is likely to be more influential than their perceived accuracy. Overall, the results of these studies improve our understanding of the persuasion phenomena induced by RSs, and have implications that can be of interest to academic scholars, designers, and adopters of this class of systems.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "11", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Andrews:2012:SPP, author = "Pierre Y. Andrews", title = "System Personality and Persuasion in Human-Computer Dialogue", journal = j-TIIS, volume = "2", number = "2", pages = "12:1--12:??", month = jun, year = "2012", CODEN = "????", DOI = "https://doi.org/10.1145/2209310.2209315", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Tue Nov 6 19:14:39 MST 2012", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "The human-computer dialogue research field has been studying interaction with computers since the early stage of Artificial Intelligence, however, research has often focused on very practical tasks to be completed with the dialogues. A new trend in the field tries to implement persuasive techniques with automated interactive agents; unlike booking a train ticket, for example, such dialogues require the system to show more anthropomorphic qualities. The influences of such qualities in the effectiveness of persuasive dialogue is only starting to be studied. In this article we focus on one important perceived trait of the system: personality, and explore how it influences the persuasiveness of a dialogue system. We introduce a new persuasive dialogue system and combine it with a state of the art personality utterance generator. By doing so, we can control the system's extraversion personality trait and observe its influence on the user's perception of the dialogue and its output. In particular, we observe that the user's extraversion influences their perception of the dialogue and its persuasiveness, and that the perceived personality of the system can affect its trustworthiness and persuasiveness. We believe that theses observations will help to set up guidelines to tailor dialogue systems to the user's interaction expectations and improve the persuasive interventions.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "12", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Vig:2012:TGE, author = "Jesse Vig and Shilad Sen and John Riedl", title = "The Tag Genome: Encoding Community Knowledge to Support Novel Interaction", journal = j-TIIS, volume = "2", number = "3", pages = "13:1--13:??", month = sep, year = "2012", CODEN = "????", DOI = "https://doi.org/10.1145/2362394.2362395", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Tue Nov 6 19:14:40 MST 2012", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "This article introduces the tag genome, a data structure that extends the traditional tagging model to provide enhanced forms of user interaction. Just as a biological genome encodes an organism based on a sequence of genes, the tag genome encodes an item in an information space based on its relationship to a common set of tags. We present a machine learning approach for computing the tag genome, and we evaluate several learning models on a ground truth dataset provided by users. We describe an application of the tag genome called Movie Tuner which enables users to navigate from one item to nearby items along dimensions represented by tags. We present the results of a 7-week field trial of 2,531 users of Movie Tuner and a survey evaluating users' subjective experience. Finally, we outline the broader space of applications of the tag genome.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "13", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Lieberman:2012:ISI, author = "Henry Lieberman and Catherine Havasi", title = "Introduction to the {Special Issue on Common Sense for Interactive Systems}", journal = j-TIIS, volume = "2", number = "3", pages = "14:1--14:??", month = sep, year = "2012", CODEN = "????", DOI = "https://doi.org/10.1145/2362394.2362396", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Tue Nov 6 19:14:40 MST 2012", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "This editorial introduction describes the aims and scope of the special issue on Common Sense for Interactive Systems of the ACM Transactions on Interactive Intelligent Systems. It explains why the common sense knowledge problem is crucial for both artificial intelligence and human-computer interaction, and it shows how the four articles selected for this issue fit into the theme.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "14", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Gil:2012:CCK, author = "Yolanda Gil and Varun Ratnakar and Timothy Chklovski and Paul Groth and Denny Vrandecic", title = "Capturing Common Knowledge about Tasks: Intelligent Assistance for To-Do Lists", journal = j-TIIS, volume = "2", number = "3", pages = "15:1--15:??", month = sep, year = "2012", CODEN = "????", DOI = "https://doi.org/10.1145/2362394.2362397", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Tue Nov 6 19:14:40 MST 2012", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "Although to-do lists are a ubiquitous form of personal task management, there has been no work on intelligent assistance to automate, elaborate, or coordinate a user's to-dos. Our research focuses on three aspects of intelligent assistance for to-dos. We investigated the use of intelligent agents to automate to-dos in an office setting. We collected a large corpus from users and developed a paraphrase-based approach to matching agent capabilities with to-dos. We also investigated to-dos for personal tasks and the kinds of assistance that can be offered to users by elaborating on them on the basis of substep knowledge extracted from the Web. Finally, we explored coordination of user tasks with other users through a to-do management application deployed in a popular social networking site. We discuss the emergence of Social Task Networks, which link users` tasks to their social network as well as to relevant resources on the Web. We show the benefits of using common sense knowledge to interpret and elaborate to-dos. Conversely, we also show that to-do lists are a valuable way to create repositories of common sense knowledge about tasks.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "15", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Swanson:2012:SAU, author = "Reid Swanson and Andrew S. Gordon", title = "Say Anything: Using Textual Case-Based Reasoning to Enable Open-Domain Interactive Storytelling", journal = j-TIIS, volume = "2", number = "3", pages = "16:1--16:??", month = sep, year = "2012", CODEN = "????", DOI = "https://doi.org/10.1145/2362394.2362398", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Tue Nov 6 19:14:40 MST 2012", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "We describe Say Anything, a new interactive storytelling system that collaboratively writes textual narratives with human users. Unlike previous attempts, this interactive storytelling system places no restrictions on the content or direction of the user's contribution to the emerging storyline. In response to these contributions, the computer continues the storyline with narration that is both coherent and entertaining. This capacity for open-domain interactive storytelling is enabled by an extremely large repository of nonfiction personal stories, which is used as a knowledge base in a case-based reasoning architecture. In this article, we describe the three main components of our case-based reasoning approach: a million-item corpus of personal stories mined from internet weblogs, a case retrieval strategy that is optimized for narrative coherence, and an adaptation strategy that ensures that repurposed sentences from the case base are appropriate for the user's emerging fiction. We describe a series of evaluations of the system's ability to produce coherent and entertaining stories, and we compare these narratives with single-author stories posted to internet weblogs.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "16", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Kuo:2012:PRM, author = "Yen-Ling Kuo and Jane Yung-Jen Hsu", title = "Planning for Reasoning with Multiple Common Sense Knowledge Bases", journal = j-TIIS, volume = "2", number = "3", pages = "17:1--17:??", month = sep, year = "2012", CODEN = "????", DOI = "https://doi.org/10.1145/2362394.2362399", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Tue Nov 6 19:14:40 MST 2012", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "Intelligent user interfaces require common sense knowledge to bridge the gap between the functionality of applications and the user's goals. While current reasoning methods have been used to provide contextual information for interface agents, the quality of their reasoning results is limited by the coverage of their underlying knowledge bases. This article presents reasoning composition, a planning-based approach to integrating reasoning methods from multiple common sense knowledge bases to answer queries. The reasoning results of one reasoning method are passed to other reasoning methods to form a reasoning chain to the target context of a query. By leveraging different weak reasoning methods, we are able to find answers to queries that cannot be directly answered by querying a single common sense knowledge base. By conducting experiments on ConceptNet and WordNet, we compare the reasoning results of reasoning composition, directly querying merged knowledge bases, and spreading activation. The results show an 11.03\% improvement in coverage over directly querying merged knowledge bases and a 49.7\% improvement in accuracy over spreading activation. Two case studies are presented, showing how reasoning composition can improve performance of retrieval in a video editing system and a dialogue assistant.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "17", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Dinakar:2012:CSR, author = "Karthik Dinakar and Birago Jones and Catherine Havasi and Henry Lieberman and Rosalind Picard", title = "Common Sense Reasoning for Detection, Prevention, and Mitigation of Cyberbullying", journal = j-TIIS, volume = "2", number = "3", pages = "18:1--18:??", month = sep, year = "2012", CODEN = "????", DOI = "https://doi.org/10.1145/2362394.2362400", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Tue Nov 6 19:14:40 MST 2012", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "Cyberbullying (harassment on social networks) is widely recognized as a serious social problem, especially for adolescents. It is as much a threat to the viability of online social networks for youth today as spam once was to email in the early days of the Internet. Current work to tackle this problem has involved social and psychological studies on its prevalence as well as its negative effects on adolescents. While true solutions rest on teaching youth to have healthy personal relationships, few have considered innovative design of social network software as a tool for mitigating this problem. Mitigating cyberbullying involves two key components: robust techniques for effective detection and reflective user interfaces that encourage users to reflect upon their behavior and their choices. Spam filters have been successful by applying statistical approaches like Bayesian networks and hidden Markov models. They can, like Google's GMail, aggregate human spam judgments because spam is sent nearly identically to many people. Bullying is more personalized, varied, and contextual. In this work, we present an approach for bullying detection based on state-of-the-art natural language processing and a common sense knowledge base, which permits recognition over a broad spectrum of topics in everyday life. We analyze a more narrow range of particular subject matter associated with bullying (e.g. appearance, intelligence, racial and ethnic slurs, social acceptance, and rejection), and construct BullySpace, a common sense knowledge base that encodes particular knowledge about bullying situations. We then perform joint reasoning with common sense knowledge about a wide range of everyday life topics. We analyze messages using our novel AnalogySpace common sense reasoning technique. We also take into account social network analysis and other factors. We evaluate the model on real-world instances that have been reported by users on Formspring, a social networking website that is popular with teenagers. On the intervention side, we explore a set of reflective user-interaction paradigms with the goal of promoting empathy among social network participants. We propose an ``air traffic control''-like dashboard, which alerts moderators to large-scale outbreaks that appear to be escalating or spreading and helps them prioritize the current deluge of user complaints. For potential victims, we provide educational material that informs them about how to cope with the situation, and connects them with emotional support from others. A user evaluation shows that in-context, targeted, and dynamic help during cyberbullying situations fosters end-user reflection that promotes better coping strategies.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "18", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Jameson:2012:ISI, author = "Anthony Jameson and John Riedl", title = "Introduction to the special issue on highlights of the decade in interactive intelligent systems", journal = j-TIIS, volume = "2", number = "4", pages = "19:1--19:??", month = dec, year = "2012", CODEN = "????", DOI = "https://doi.org/10.1145/2395123.2395124", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Tue Apr 30 18:37:15 MDT 2013", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "This editorial introduction explains the motivation and origin of the TiiS special issue on Highlights of the Decade in Interactive Intelligent Systems and shows how its five articles exemplify the types of research contribution that TiiS aims to encourage and publish.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "19", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Hoey:2012:PSD, author = "Jesse Hoey and Craig Boutilier and Pascal Poupart and Patrick Olivier and Andrew Monk and Alex Mihailidis", title = "People, sensors, decisions: Customizable and adaptive technologies for assistance in healthcare", journal = j-TIIS, volume = "2", number = "4", pages = "20:1--20:??", month = dec, year = "2012", CODEN = "????", DOI = "https://doi.org/10.1145/2395123.2395125", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Tue Apr 30 18:37:15 MDT 2013", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "The ratio of healthcare professionals to care recipients is dropping at an alarming rate, particularly for the older population. It is estimated that the number of persons with Alzheimer's disease, for example, will top 100 million worldwide by the year 2050 [Alzheimer's Disease International 2009]. It will become harder and harder to provide needed health services to this population of older adults. Further, patients are becoming more aware and involved in their own healthcare decisions. This is creating a void in which technology has an increasingly important role to play as a tool to connect providers with recipients. Examples of interactive technologies range from telecare for remote regions to computer games promoting fitness in the home. Currently, such technologies are developed for specific applications and are difficult to modify to suit individual user needs. The future potential economic and social impact of technology in the healthcare field therefore lies in our ability to make intelligent devices that are customizable by healthcare professionals and their clients, that are adaptive to users over time, and that generalize across tasks and environments. A wide application area for technology in healthcare is for assistance and monitoring in the home. As the population ages, it becomes increasingly dependent on chronic healthcare, such as assistance for tasks of everyday life (washing, cooking, dressing), medication taking, nutrition, and fitness. This article will present a summary of work over the past decade on the development of intelligent systems that provide assistance to persons with cognitive disabilities. These systems are unique in that they are all built using a common framework, a decision-theoretic model for general-purpose assistance in the home. In this article, we will show how this type of general model can be applied to a range of assistance tasks, including prompting for activities of daily living, assistance for art therapists, and stroke rehabilitation. This model is a Partially Observable Markov Decision Process (POMDP) that can be customized by end-users, that can integrate complex sensor information, and that can adapt over time. These three characteristics of the POMDP model will allow for increasing uptake and long-term efficiency and robustness of technology for assistance.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "20", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Carberry:2012:AMA, author = "Sandra Carberry and Stephanie Elzer Schwartz and Kathleen Mccoy and Seniz Demir and Peng Wu and Charles Greenbacker and Daniel Chester and Edward Schwartz and David Oliver and Priscilla Moraes", title = "Access to multimodal articles for individuals with sight impairments", journal = j-TIIS, volume = "2", number = "4", pages = "21:1--21:??", month = dec, year = "2012", CODEN = "????", DOI = "https://doi.org/10.1145/2395123.2395126", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Tue Apr 30 18:37:15 MDT 2013", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "Although intelligent interactive systems have been the focus of many research efforts, very few have addressed systems for individuals with disabilities. This article presents our methodology for an intelligent interactive system that provides individuals with sight impairments with access to the content of information graphics (such as bar charts and line graphs) in popular media. The article describes the methodology underlying the system's intelligent behavior, its interface for interacting with users, examples processed by the implemented system, and evaluation studies both of the methodology and the effectiveness of the overall system. This research advances universal access to electronic documents.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "21", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Chen:2012:MBI, author = "Fang Chen and Natalie Ruiz and Eric Choi and Julien Epps and M. Asif Khawaja and Ronnie Taib and Bo Yin and Yang Wang", title = "Multimodal behavior and interaction as indicators of cognitive load", journal = j-TIIS, volume = "2", number = "4", pages = "22:1--22:??", month = dec, year = "2012", CODEN = "????", DOI = "https://doi.org/10.1145/2395123.2395127", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Tue Apr 30 18:37:15 MDT 2013", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "High cognitive load arises from complex time and safety-critical tasks, for example, mapping out flight paths, monitoring traffic, or even managing nuclear reactors, causing stress, errors, and lowered performance. Over the last five years, our research has focused on using the multimodal interaction paradigm to detect fluctuations in cognitive load in user behavior during system interaction. Cognitive load variations have been found to impact interactive behavior: by monitoring variations in specific modal input features executed in tasks of varying complexity, we gain an understanding of the communicative changes that occur when cognitive load is high. So far, we have identified specific changes in: speech, namely acoustic, prosodic, and linguistic changes; interactive gesture; and digital pen input, both interactive and freeform. As ground-truth measurements, galvanic skin response, subjective, and performance ratings have been used to verify task complexity. The data suggest that it is feasible to use features extracted from behavioral changes in multiple modal inputs as indices of cognitive load. The speech-based indicators of load, based on data collected from user studies in a variety of domains, have shown considerable promise. Scenarios include single-user and team-based tasks; think-aloud and interactive speech; and single-word, reading, and conversational speech, among others. Pen-based cognitive load indices have also been tested with some success, specifically with pen-gesture, handwriting, and freeform pen input, including diagraming. After examining some of the properties of these measurements, we present a multimodal fusion model, which is illustrated with quantitative examples from a case study. The feasibility of employing user input and behavior patterns as indices of cognitive load is supported by experimental evidence. Moreover, symptomatic cues of cognitive load derived from user behavior such as acoustic speech signals, transcribed text, digital pen trajectories of handwriting, and shapes pen, can be supported by well-established theoretical frameworks, including O'Donnell and Eggemeier's workload measurement [1986] Sweller's Cognitive Load Theory [Chandler and Sweller 1991], and Baddeley's model of modal working memory [1992] as well as McKinstry et al.'s [2008] and Rosenbaum's [2005] action dynamics work. The benefit of using this approach to determine the user's cognitive load in real time is that the data can be collected implicitly that is, during day-to-day use of intelligent interactive systems, thus overcomes problems of intrusiveness and increases applicability in real-world environments, while adapting information selection and presentation in a dynamic computer interface with reference to load.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "22", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Dmello:2012:AAA, author = "Sidney D'mello and Art Graesser", title = "{AutoTutor} and {Affective Autotutor}: Learning by talking with cognitively and emotionally intelligent computers that talk back", journal = j-TIIS, volume = "2", number = "4", pages = "23:1--23:??", month = dec, year = "2012", CODEN = "????", DOI = "https://doi.org/10.1145/2395123.2395128", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Tue Apr 30 18:37:15 MDT 2013", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "We present AutoTutor and Affective AutoTutor as examples of innovative 21$^{st}$ century interactive intelligent systems that promote learning and engagement. AutoTutor is an intelligent tutoring system that helps students compose explanations of difficult concepts in Newtonian physics and enhances computer literacy and critical thinking by interacting with them in natural language with adaptive dialog moves similar to those of human tutors. AutoTutor constructs a cognitive model of students' knowledge levels by analyzing the text of their typed or spoken responses to its questions. The model is used to dynamically tailor the interaction toward individual students' zones of proximal development. Affective AutoTutor takes the individualized instruction and human-like interactivity to a new level by automatically detecting and responding to students' emotional states in addition to their cognitive states. Over 20 controlled experiments comparing AutoTutor with ecological and experimental controls such reading a textbook have consistently yielded learning improvements of approximately one letter grade after brief 30--60-minute interactions. Furthermore, Affective AutoTutor shows even more dramatic improvements in learning than the original AutoTutor system, particularly for struggling students with low domain knowledge. In addition to providing a detailed description of the implementation and evaluation of AutoTutor and Affective AutoTutor, we also discuss new and exciting technologies motivated by AutoTutor such as AutoTutor-Lite, Operation ARIES, GuruTutor, DeepTutor, MetaTutor, and AutoMentor. We conclude this article with our vision for future work on interactive and engaging intelligent tutoring systems.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "23", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Kay:2012:CPS, author = "Judy Kay and Bob Kummerfeld", title = "Creating personalized systems that people can scrutinize and control: Drivers, principles and experience", journal = j-TIIS, volume = "2", number = "4", pages = "24:1--24:??", month = dec, year = "2012", CODEN = "????", DOI = "https://doi.org/10.1145/2395123.2395129", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Tue Apr 30 18:37:15 MDT 2013", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "Widespread personalized computing systems play an already important and fast-growing role in diverse contexts, such as location-based services, recommenders, commercial Web-based services, and teaching systems. The personalization in these systems is driven by information about the user, a user model. Moreover, as computers become both ubiquitous and pervasive, personalization operates across the many devices and information stores that constitute the user's personal digital ecosystem. This enables personalization, and the user models driving it, to play an increasing role in people's everyday lives. This makes it critical to establish ways to address key problems of personalization related to privacy, invisibility of personalization, errors in user models, wasted user models, and the broad issue of enabling people to control their user models and associated personalization. We offer scrutable user models as a foundation for tackling these problems. This article argues the importance of scrutable user modeling and personalization, illustrating key elements in case studies from our work. We then identify the broad roles for scrutable user models. The article describes how to tackle the technical and interface challenges of designing and building scrutable user modeling systems, presenting design principles and showing how they were established over our twenty years of work on the Personis software framework. Our contributions are the set of principles for scrutable personalization linked to our experience from creating and evaluating frameworks and associated applications built upon them. These constitute a general approach to tackling problems of personalization by enabling users to scrutinize their user models as a basis for understanding and controlling personalization.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "24", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Giunchiglia:2013:ISS, author = "Fausto Giunchiglia and David Robertson", title = "Introduction to the special section on {Internet}-scale human problem solving", journal = j-TIIS, volume = "3", number = "1", pages = "1:1--1:??", month = apr, year = "2013", CODEN = "????", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Tue Apr 30 18:37:17 MDT 2013", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "This editorial introduction first outlines some of the research challenges raised by the emerging forms of internet-scale human problem solving. It then explains how the two articles in this special section can serve as illuminating complementary case studies, providing concrete examples embedded in general conceptual frameworks.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "1", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Yu:2013:ISI, author = "Lixiu Yu and Jeffrey V. Nickerson", title = "An {Internet}-scale idea generation system", journal = j-TIIS, volume = "3", number = "1", pages = "2:1--2:??", month = apr, year = "2013", CODEN = "????", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Tue Apr 30 18:37:17 MDT 2013", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "A method of organizing the crowd to generate ideas is described. It integrates crowds using evolutionary algorithms. The method increases the creativity of ideas across generations, and it works better than greenfield idea generation. Specifically, a design space of internet-scale idea generation systems is defined, and one instance is tested: a crowd idea generation system that uses combination to improve previous designs. The key process of the system is the following: A crowd generates designs, then another crowd combines the designs of the previous crowd. In an experiment with 540 participants, the combined designs were compared to the initial designs and to the designs produced by a greenfield idea generation system. The results show that the sequential combination system produced more creative ideas in the last generation and outperformed the greenfield idea generation system. The design space of crowdsourced idea generation developed here may be used to instantiate systems that can be applied to a wide range of design problems. The work has both pragmatic and theoretical implications: New forms of coordination are now possible, and, using the crowd, it is possible to test existing and emerging theories of coordination and participatory design. Moreover, it may be possible for human designers, organized as a crowd, to codesign with each other and with automated algorithms.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "2", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Poesio:2013:PDU, author = "Massimo Poesio and Jon Chamberlain and Udo Kruschwitz and Livio Robaldo and Luca Ducceschi", title = "Phrase detectives: Utilizing collective intelligence for {Internet}-scale language resource creation", journal = j-TIIS, volume = "3", number = "1", pages = "3:1--3:??", month = apr, year = "2013", CODEN = "????", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Tue Apr 30 18:37:17 MDT 2013", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "We are witnessing a paradigm shift in Human Language Technology (HLT) that may well have an impact on the field comparable to the statistical revolution: acquiring large-scale resources by exploiting collective intelligence. An illustration of this new approach is Phrase Detectives, an interactive online game with a purpose for creating anaphorically annotated resources that makes use of a highly distributed population of contributors with different levels of expertise. The purpose of this article is to first of all give an overview of all aspects of Phrase Detectives, from the design of the game and the HLT methods we used to the results we have obtained so far. It furthermore summarizes the lessons that we have learned in developing this game which should help other researchers to design and implement similar games.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "3", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Console:2013:ISN, author = "Luca Console and Fabrizio Antonelli and Giulia Biamino and Francesca Carmagnola and Federica Cena and Elisa Chiabrando and Vincenzo Cuciti and Matteo Demichelis and Franco Fassio and Fabrizio Franceschi and Roberto Furnari and Cristina Gena and Marina Geymonat and Piercarlo Grimaldi and Pierluige Grillo and Silvia Likavec and Ilaria Lombardi and Dario Mana and Alessandro Marcengo and Michele Mioli and Mario Mirabelli and Monica Perrero and Claudia Picardi and Federica Protti and Amon Rapp and Rossana Simeoni and Daniele Theseider Dupr{\'e} and Ilaria Torre and Andrea Toso and Fabio Torta and Fabiana Vernero", title = "Interacting with social networks of intelligent things and people in the world of gastronomy", journal = j-TIIS, volume = "3", number = "1", pages = "4:1--4:??", month = apr, year = "2013", CODEN = "????", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Tue Apr 30 18:37:17 MDT 2013", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "This article introduces a framework for creating rich augmented environments based on a social web of intelligent things and people. We target outdoor environments, aiming to transform a region into a smart environment that can share its cultural heritage with people, promoting itself and its special qualities. Using the applications developed in the framework, people can interact with things, listen to the stories that these things tell them, and make their own contributions. The things are intelligent in the sense that they aggregate information provided by users and behave in a socially active way. They can autonomously establish social relationships on the basis of their properties and their interaction with users. Hence when a user gets in touch with a thing, she is also introduced to its social network consisting of other things and of users; she can navigate this network to discover and explore the world around the thing itself. Thus the system supports serendipitous navigation in a network of things and people that evolves according to the behavior of users. An innovative interaction model was defined that allows users to interact with objects in a natural, playful way using smartphones without the need for a specially created infrastructure. The framework was instantiated into a suite of applications called WantEat, in which objects from the domain of tourism and gastronomy (such as cheese wheels or bottles of wine) are taken as testimonials of the cultural roots of a region. WantEat includes an application that allows the definition and registration of things, a mobile application that allows users to interact with things, and an application that supports stakeholders in getting feedback about the things that they have registered in the system. WantEat was developed and tested in a real-world context which involved a region and gastronomy-related items from it (such as products, shops, restaurants, and recipes), through an early evaluation with stakeholders and a final evaluation with hundreds of users.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "4", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Song:2013:PII, author = "Wei Song and Andrew Finch and Kumiko Tanaka-Ishii and Keiji Yasuda and Eiichiro Sumita", title = "{picoTrans}: an intelligent icon-driven interface for cross-lingual communication", journal = j-TIIS, volume = "3", number = "1", pages = "5:1--5:??", month = apr, year = "2013", CODEN = "????", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Tue Apr 30 18:37:17 MDT 2013", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "picoTrans is a prototype system that introduces a novel icon-based paradigm for cross-lingual communication on mobile devices. Our approach marries a machine translation system with the popular picture book. Users interact with picoTrans by pointing at pictures as if it were a picture book; the system generates natural language from these icons and the user is able to interact with the icon sequence to refine the meaning of the words that are generated. When users are satisfied that the sentence generated represents what they wish to express, they tap a translate button and picoTrans displays the translation. Structuring the process of communication in this way has many advantages. First, tapping icons is a very natural method of user input on mobile devices; typing is cumbersome and speech input errorful. Second, the sequence of icons which is annotated both with pictures and bilingually with words is meaningful to both users, and it opens up a second channel of communication between them that conveys the gist of what is being expressed. We performed a number of evaluations of picoTrans to determine: its coverage of a corpus of in-domain sentences; the input efficiency in terms of the number of key presses required relative to text entry; and users' overall impressions of using the system compared to using a picture book. Our results show that we are able to cover 74\% of the expressions in our test corpus using a 2000-icon set; we believe that this icon set size is realistic for a mobile device. We also found that picoTrans requires fewer key presses than typing the input and that the system is able to predict the correct, intended natural language sentence from the icon sequence most of the time, making user interaction with the icon sequence often unnecessary. In the user evaluation, we found that in general users prefer using picoTrans and are able to communicate more rapidly and expressively. Furthermore, users had more confidence that they were able to communicate effectively using picoTrans.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "5", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Schreiber:2013:ISI, author = "Daniel Schreiber and Kris Luyten and Max M{\"u}hlh{\"a}user and Oliver Brdiczka and Melanie Hartman", title = "Introduction to the special issue on interaction with smart objects", journal = j-TIIS, volume = "3", number = "2", pages = "6:1--6:??", month = jul, year = "2013", CODEN = "????", DOI = "https://doi.org/10.1145/2499474.2499475", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Thu Mar 13 06:46:45 MDT 2014", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "Smart objects can be smart because of the information and communication technology that is added to human-made artifacts. It is not, however, the technology itself that makes them smart but rather the way in which the technology is integrated, and their smartness surfaces through how people are able to interact with these objects. Hence, the key challenge for making smart objects successful is to design usable and useful interactions with them. We list five features that can contribute to the smartness of an object, and we discuss how smart objects can help resolve the simplicity-featurism paradox. We conclude by introducing the three articles in this special issue, which dive into various aspects of smart object interaction: augmenting objects with projection, service-oriented interaction with smart objects via a mobile portal, and an analysis of input-output relations in interaction with tangible smart objects.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "6", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Molyneaux:2013:CAM, author = "David Molyneaux and Hans Gellersen and Joe Finney", title = "Cooperative augmentation of mobile smart objects with projected displays", journal = j-TIIS, volume = "3", number = "2", pages = "7:1--7:??", month = jul, year = "2013", CODEN = "????", DOI = "https://doi.org/10.1145/2499474.2499476", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Thu Mar 13 06:46:45 MDT 2014", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "Sensors, processors, and radios can be integrated invisibly into objects to make them smart and sensitive to user interaction, but feedback is often limited to beeps, blinks, or buzzes. We propose to redress this input-output imbalance by augmentation of smart objects with projected displays, that-unlike physical displays-allow seamless integration with the natural appearance of an object. In this article, we investigate how, in a ubiquitous computing world, smart objects can acquire and control a projection. We consider that projectors and cameras are ubiquitous in the environment, and we develop a novel conception and system that enables smart objects to spontaneously associate with projector-camera systems for cooperative augmentation. Projector-camera systems are conceived as generic, supporting standard computer vision methods for different appearance cues, and smart objects provide a model of their appearance for method selection at runtime, as well as sensor observations to constrain the visual detection process. Cooperative detection results in accurate location and pose of the object, which is then tracked for visual augmentation in response to display requests by the smart object. In this article, we define the conceptual framework underlying our approach; report on computer vision experiments that give original insight into natural appearance-based detection of everyday objects; show how object sensing can be used to increase speed and robustness of visual detection; describe and evaluate a fully implemented system; and describe two smart object applications to illustrate the system's cooperative augmentation process and the embodied interactions it enables with smart objects.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "7", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Thebault:2013:ESP, author = "Pierrick Thebault and Dominique Decotter and Mathieu Boussard and Monique Lu", title = "Embodying services into physical places: Toward the design of a mobile environment browser", journal = j-TIIS, volume = "3", number = "2", pages = "8:1--8:??", month = jul, year = "2013", CODEN = "????", DOI = "https://doi.org/10.1145/2499474.2499477", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Thu Mar 13 06:46:45 MDT 2014", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "The tremendous developments in mobile computing and handheld devices have allowed for an increasing usage of the resources of the World Wide Web. People today consume information and services on the go, through smart phones applications capable of exploiting their location in order to adapt the content according to the context of use. As location-based services gain traction and reveal their limitations, we argue there is a need for intelligent systems to be created to better support people's activities in their experience of the city, especially regarding their decision-making processes. In this article, we explore the opportunity to move closer to the realization of the ubiquitous computing vision by turning physical places into smart environments capable of cooperatively and autonomously collecting, processing, and transporting information about their characteristics (e.g., practical information, presence of people, and ambience). Following a multidisciplinary approach which leverages psychology, design, and computer science, we propose to investigate the potential of building communication and interaction spaces, called information spheres, on top of physical places such as businesses, homes, and institutions. We argue that, if the latter are exposed on the Web, they can act as a platform delivering information and services and mediating interactions with smart objects without requiring too much effort for the deployment of the architecture. After presenting the inherent challenges of our vision, we go through the protocol of two preliminary experiments that aim to evaluate users' perception of different types of information (i.e., reviews, check-in information, video streams, and real-time representations) and their influence on the decision-making process. Results of this study lead us to elaborate the design considerations that must be taken into account to ensure the intelligibility and user acceptance of information spheres. We finally describe a research prototype application called Environment Browser (Env-B) and present the underlying smart space middleware, before evaluating the user experience with our system through quantitative and qualitative methods.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "8", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{vandeGarde-Perik:2013:AIO, author = "Evelien van de Garde-Perik and Serge Offermans and Koen van Boerdonk and Kars-Michiel Lenssen and Elise van den Hoven", title = "An analysis of input-output relations in interaction with smart tangible objects", journal = j-TIIS, volume = "3", number = "2", pages = "9:1--9:??", month = jul, year = "2013", CODEN = "????", DOI = "https://doi.org/10.1145/2499474.2499478", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Thu Mar 13 06:46:45 MDT 2014", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "This article focuses on the conceptual relation between the user's input and a system's output in interaction with smart tangible objects. Understanding this input-output relation (IO relation) is a prerequisite for the design of meaningful interaction. A meaningful IO relation allows the user to know what to do with a system to achieve a certain goal and to evaluate the outcome. The work discussed in this article followed a design research process in which four concepts were developed and prototyped. An evaluation was performed using these prototypes to investigate the effect of highly different IO relations on the user's understanding of the interaction. The evaluation revealed two types of IO relations differing in functionality and the number of mappings between the user and system actions. These two types of relations are described by two IO models that provide an overview of these mappings. Furthermore, they illustrate the role of the user and the influence of the system in the process of understanding the interaction. The analysis of the two types of IO models illustrates the value of understanding IO relations for the design of smart tangible objects.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "9", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Andre:2013:ISS, author = "Elisabeth Andr{\'e} and Joyce Chai", title = "Introduction to the special section on eye gaze and conversation", journal = j-TIIS, volume = "3", number = "2", pages = "10:1--10:??", month = jul, year = "2013", CODEN = "????", DOI = "https://doi.org/10.1145/2499474.2499479", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Thu Mar 13 06:46:45 MDT 2014", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "This editorial introduction first explains the origin of this special section. It then outlines how each of the two articles included sheds light on possibilities for conversational dialog systems to use eye gaze as a signal that reflects aspects of participation in the dialog: degree of engagement and turn taking behavior, respectively.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "10", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Ishii:2013:GAC, author = "Ryo Ishii and Yukiko I. Nakano and Toyoaki Nishida", title = "Gaze awareness in conversational agents: Estimating a user's conversational engagement from eye gaze", journal = j-TIIS, volume = "3", number = "2", pages = "11:1--11:??", month = jul, year = "2013", CODEN = "????", DOI = "https://doi.org/10.1145/2499474.2499480", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Thu Mar 13 06:46:45 MDT 2014", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "In face-to-face conversations, speakers are continuously checking whether the listener is engaged in the conversation, and they change their conversational strategy if the listener is not fully engaged. With the goal of building a conversational agent that can adaptively control conversations, in this study we analyze listener gaze behaviors and develop a method for estimating whether a listener is engaged in the conversation on the basis of these behaviors. First, we conduct a Wizard-of-Oz study to collect information on a user's gaze behaviors. We then investigate how conversational disengagement, as annotated by human judges, correlates with gaze transition, mutual gaze (eye contact) occurrence, gaze duration, and eye movement distance. On the basis of the results of these analyses, we identify useful information for estimating a user's disengagement and establish an engagement estimation method using a decision tree technique. The results of these analyses show that a model using the features of gaze transition, mutual gaze occurrence, gaze duration, and eye movement distance provides the best performance and can estimate the user's conversational engagement accurately. The estimation model is then implemented as a real-time disengagement judgment mechanism and incorporated into a multimodal dialog manager in an animated conversational agent. This agent is designed to estimate the user's conversational engagement and generate probing questions when the user is distracted from the conversation. Finally, we evaluate the engagement-sensitive agent and find that asking probing questions at the proper times has the expected effects on the user's verbal/nonverbal behaviors during communication with the agent. We also find that our agent system improves the user's impression of the agent in terms of its engagement awareness, behavior appropriateness, conversation smoothness, favorability, and intelligence.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "11", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Jokinen:2013:GTT, author = "Kristiina Jokinen and Hirohisa Furukawa and Masafumi Nishida and Seiichi Yamamoto", title = "Gaze and turn-taking behavior in casual conversational interactions", journal = j-TIIS, volume = "3", number = "2", pages = "12:1--12:??", month = jul, year = "2013", CODEN = "????", DOI = "https://doi.org/10.1145/2499474.2499481", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Thu Mar 13 06:46:45 MDT 2014", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "Eye gaze is an important means for controlling interaction and coordinating the participants' turns smoothly. We have studied how eye gaze correlates with spoken interaction and especially focused on the combined effect of the speech signal and gazing to predict turn taking possibilities. It is well known that mutual gaze is important in the coordination of turn taking in two-party dialogs, and in this article, we investigate whether this fact also holds for three-party conversations. In group interactions, it may be that different features are used for managing turn taking than in two-party dialogs. We collected casual conversational data and used an eye tracker to systematically observe a participant's gaze in the interactions. By studying the combined effect of speech and gaze on turn taking, we aimed to answer our main questions: How well can eye gaze help in predicting turn taking? What is the role of eye gaze when the speaker holds the turn? Is the role of eye gaze as important in three-party dialogs as in two-party dialogue? We used Support Vector Machines (SVMs) to classify turn taking events with respect to speech and gaze features, so as to estimate how well the features signal a change of the speaker or a continuation of the same speaker. The results confirm the earlier hypothesis that eye gaze significantly helps in predicting the partner's turn taking activity, and we also get supporting evidence for our hypothesis that the speaker is a prominent coordinator of the interaction space. Such a turn taking model could be used in interactive applications to improve the system's conversational performance.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "12", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Jameson:2013:MJR, author = "Anthony Jameson", title = "In Memoriam: {John Riedl}", journal = j-TIIS, volume = "3", number = "3", pages = "13:1--13:??", month = oct, year = "2013", CODEN = "????", DOI = "https://doi.org/10.1145/2533670.2533671", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Thu Mar 13 06:46:47 MDT 2014", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "This recollection of John Riedl, founding coeditor-in-chief of the ACM Transactions on Interactive Intelligent Systems, presents a picture by editors of the journal of what it was like to collaborate and interact with him.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "13", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Amershi:2013:LAW, author = "Saleema Amershi and Jalal Mahmud and Jeffrey Nichols and Tessa Lau and German Attanasio Ruiz", title = "{LiveAction}: Automating {Web} Task Model Generation", journal = j-TIIS, volume = "3", number = "3", pages = "14:1--14:??", month = oct, year = "2013", CODEN = "????", DOI = "https://doi.org/10.1145/2533670.2533672", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Thu Mar 13 06:46:47 MDT 2014", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "Task automation systems promise to increase human productivity by assisting us with our mundane and difficult tasks. These systems often rely on people to (1) identify the tasks they want automated and (2) specify the procedural steps necessary to accomplish those tasks (i.e., to create task models). However, our interviews with users of a Web task automation system reveal that people find it difficult to identify tasks to automate and most do not even believe they perform repetitive tasks worthy of automation. Furthermore, even when automatable tasks are identified, the well-recognized difficulties of specifying task steps often prevent people from taking advantage of these automation systems. In this research, we analyze real Web usage data and find that people do in fact repeat behaviors on the Web and that automating these behaviors, regardless of their complexity, would reduce the overall number of actions people need to perform when completing their tasks, potentially saving time. Motivated by these findings, we developed LiveAction, a fully-automated approach to generating task models from Web usage data. LiveAction models can be used to populate the task model repositories required by many automation systems, helping us take advantage of automation in our everyday lives.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "14", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Wetzler:2013:CPM, author = "Philipp Wetzler and Steven Bethard and Heather Leary and Kirsten Butcher and Soheil Danesh Bahreini and Jin Zhao and James H. Martin and Tamara Sumner", title = "Characterizing and Predicting the Multifaceted Nature of Quality in Educational {Web} Resources", journal = j-TIIS, volume = "3", number = "3", pages = "15:1--15:??", month = oct, year = "2013", CODEN = "????", DOI = "https://doi.org/10.1145/2533670.2533673", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Thu Mar 13 06:46:47 MDT 2014", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "Efficient learning from Web resources can depend on accurately assessing the quality of each resource. We present a methodology for developing computational models of quality that can assist users in assessing Web resources. The methodology consists of four steps: (1) a meta-analysis of previous studies to decompose quality into high-level dimensions and low-level indicators, (2) an expert study to identify the key low-level indicators of quality in the target domain, (3) human annotation to provide a collection of example resources where the presence or absence of quality indicators has been tagged, and (4) training of a machine learning model to predict quality indicators based on content and link features of Web resources. We find that quality is a multifaceted construct, with different aspects that may be important to different users at different times. We show that machine learning models can predict this multifaceted nature of quality, both in the context of aiding curators as they evaluate resources submitted to digital libraries, and in the context of aiding teachers as they develop online educational resources. Finally, we demonstrate how computational models of quality can be provided as a service, and embedded into applications such as Web search.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "15", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Amir:2013:PRV, author = "Ofra Amir and Ya'akov (Kobi) Gal", title = "Plan Recognition and Visualization in Exploratory Learning Environments", journal = j-TIIS, volume = "3", number = "3", pages = "16:1--16:??", month = oct, year = "2013", CODEN = "????", DOI = "https://doi.org/10.1145/2533670.2533674", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Thu Mar 13 06:46:47 MDT 2014", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "Modern pedagogical software is open-ended and flexible, allowing students to solve problems through exploration and trial-and-error. Such exploratory settings provide for a rich educational environment for students, but they challenge teachers to keep track of students' progress and to assess their performance. This article presents techniques for recognizing students' activities in such pedagogical software and visualizing these activities to teachers. It describes a new plan recognition algorithm that uses a recursive grammar that takes into account repetition and interleaving of activities. This algorithm was evaluated empirically using an exploratory environment for teaching chemistry used by thousands of students in several countries. It was always able to correctly infer students' plans when the appropriate grammar was available. We designed two methods for visualizing students' activities for teachers: one that visualizes students' inferred plans, and one that visualizes students' interactions over a timeline. Both of these visualization methods were preferred to and found more helpful than a baseline method which showed a movie of students' interactions. These results demonstrate the benefit of combining novel AI techniques and visualization methods for the purpose of designing collaborative systems that support students in their problem solving and teachers in their understanding of students' performance.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "16", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Chen:2013:HDM, author = "Li Chen and Marco de Gemmis and Alexander Felfernig and Pasquale Lops and Francesco Ricci and Giovanni Semeraro", title = "Human Decision Making and Recommender Systems", journal = j-TIIS, volume = "3", number = "3", pages = "17:1--17:??", month = oct, year = "2013", CODEN = "????", DOI = "https://doi.org/10.1145/2533670.2533675", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Thu Mar 13 06:46:47 MDT 2014", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "Recommender systems have already proved to be valuable for coping with the information overload problem in several application domains. They provide people with suggestions for items which are likely to be of interest for them; hence, a primary function of recommender systems is to help people make good choices and decisions. However, most previous research has focused on recommendation techniques and algorithms, and less attention has been devoted to the decision making processes adopted by the users and possibly supported by the system. There is still a gap between the importance that the community gives to the assessment of recommendation algorithms and the current range of ongoing research activities concerning human decision making. Different decision-psychological phenomena can influence the decision making of users of recommender systems, and research along these lines is becoming increasingly important and popular. This special issue highlights how the coupling of recommendation algorithms with the understanding of human choice and decision making theory has the potential to benefit research and practice on recommender systems and to enable users to achieve a good balance between decision accuracy and decision effort.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "17", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Dodson:2013:ELA, author = "Thomas Dodson and Nicholas Mattei and Joshua T. Guerin and Judy Goldsmith", title = "An {English}-Language Argumentation Interface for Explanation Generation with {Markov} Decision Processes in the Domain of Academic Advising", journal = j-TIIS, volume = "3", number = "3", pages = "18:1--18:??", month = oct, year = "2013", CODEN = "????", DOI = "https://doi.org/10.1145/2513564", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Thu Mar 13 06:46:47 MDT 2014", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "A Markov Decision Process (MDP) policy presents, for each state, an action, which preferably maximizes the expected utility accrual over time. In this article, we present a novel explanation system for MDP policies. The system interactively generates conversational English-language explanations of the actions suggested by an optimal policy, and does so in real time. We rely on natural language explanations in order to build trust between the user and the explanation system, leveraging existing research in psychology in order to generate salient explanations. Our explanation system is designed for portability between domains and uses a combination of domain-specific and domain-independent techniques. The system automatically extracts implicit knowledge from an MDP model and accompanying policy. This MDP-based explanation system can be ported between applications without additional effort by knowledge engineers or model builders. Our system separates domain-specific data from the explanation logic, allowing for a robust system capable of incremental upgrades. Domain-specific explanations are generated through case-based explanation techniques specific to the domain and a knowledge base of concept mappings used to generate English-language explanations.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "18", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Freyne:2013:RBP, author = "Jill Freyne and Shlomo Berkovsky and Gregory Smith", title = "Rating Bias and Preference Acquisition", journal = j-TIIS, volume = "3", number = "3", pages = "19:1--19:??", month = oct, year = "2013", CODEN = "????", DOI = "https://doi.org/10.1145/2499673", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Thu Mar 13 06:46:47 MDT 2014", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "Personalized systems and recommender systems exploit implicitly and explicitly provided user information to address the needs and requirements of those using their services. User preference information, often in the form of interaction logs and ratings data, is used to identify similar users, whose opinions are leveraged to inform recommendations or to filter information. In this work we explore a different dimension of information trends in user bias and reasoning learned from ratings provided by users to a recommender system. Our work examines the characteristics of a dataset of 100,000 user ratings on a corpus of recipes, which illustrates stable user bias towards certain features of the recipes (cuisine type, key ingredient, and complexity). We exploit this knowledge to design and evaluate a personalized rating acquisition tool based on active learning, which leverages user biases in order to obtain ratings bearing high-value information and to reduce prediction errors with new users.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "19", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Knijnenburg:2013:MDA, author = "Bart P. Knijnenburg and Alfred Kobsa", title = "Making Decisions about Privacy: Information Disclosure in Context-Aware Recommender Systems", journal = j-TIIS, volume = "3", number = "3", pages = "20:1--20:??", month = oct, year = "2013", CODEN = "????", DOI = "https://doi.org/10.1145/2499670", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Thu Mar 13 06:46:47 MDT 2014", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "Recommender systems increasingly use contextual and demographical data as a basis for recommendations. Users, however, often feel uncomfortable providing such information. In a privacy-minded design of recommenders, users are free to decide for themselves what data they want to disclose about themselves. But this decision is often complex and burdensome, because the consequences of disclosing personal information are uncertain or even unknown. Although a number of researchers have tried to analyze and facilitate such information disclosure decisions, their research results are fragmented, and they often do not hold up well across studies. This article describes a unified approach to privacy decision research that describes the cognitive processes involved in users' ``privacy calculus'' in terms of system-related perceptions and experiences that act as mediating factors to information disclosure. The approach is applied in an online experiment with 493 participants using a mock-up of a context-aware recommender system. Analyzing the results with a structural linear model, we demonstrate that personal privacy concerns and disclosure justification messages affect the perception of and experience with a system, which in turn drive information disclosure decisions. Overall, disclosure justification messages do not increase disclosure. Although they are perceived to be valuable, they decrease users' trust and satisfaction. Another result is that manipulating the order of the requests increases the disclosure of items requested early but decreases the disclosure of items requested later.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "20", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Apostolopoulos:2014:IOL, author = "Ilias Apostolopoulos and Navid Fallah and Eelke Folmer and Kostas E. Bekris", title = "Integrated online localization and navigation for people with visual impairments using smart phones", journal = j-TIIS, volume = "3", number = "4", pages = "21:1--21:??", month = jan, year = "2014", CODEN = "????", DOI = "https://doi.org/10.1145/2499669", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Thu Mar 13 06:46:49 MDT 2014", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "Indoor localization and navigation systems for individuals with Visual Impairments (VIs) typically rely upon extensive augmentation of the physical space, significant computational resources, or heavy and expensive sensors; thus, few systems have been implemented on a large scale. This work describes a system able to guide people with VIs through indoor environments using inexpensive sensors, such as accelerometers and compasses, which are available in portable devices like smart phones. The method takes advantage of feedback from the human user, who confirms the presence of landmarks, something that users with VIs already do when navigating in a building. The system calculates the user's location in real time and uses it to provide audio instructions on how to reach the desired destination. Initial early experiments suggested that the accuracy of the localization depends on the type of directions and the availability of an appropriate transition model for the user. A critical parameter for the transition model is the user's step length. Consequently, this work also investigates different schemes for automatically computing the user's step length and reducing the dependence of the approach on the definition of an accurate transition model. In this way, the direction provision method is able to use the localization estimate and adapt to failed executions of paths by the users. Experiments are presented that evaluate the accuracy of the overall integrated system, which is executed online on a smart phone. Both people with VIs and blindfolded sighted people participated in the experiments, which included paths along multiple floors that required the use of stairs and elevators.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "21", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Zamborlin:2014:FGI, author = "Bruno Zamborlin and Frederic Bevilacqua and Marco Gillies and Mark D'inverno", title = "Fluid gesture interaction design: Applications of continuous recognition for the design of modern gestural interfaces", journal = j-TIIS, volume = "3", number = "4", pages = "22:1--22:??", month = jan, year = "2014", CODEN = "????", DOI = "https://doi.org/10.1145/2543921", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Thu Mar 13 06:46:49 MDT 2014", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "This article presents Gesture Interaction DEsigner (GIDE), an innovative application for gesture recognition. Instead of recognizing gestures only after they have been entirely completed, as happens in classic gesture recognition systems, GIDE exploits the full potential of gestural interaction by tracking gestures continuously and synchronously, allowing users to both control the target application moment to moment and also receive immediate and synchronous feedback about system recognition states. By this means, they quickly learn how to interact with the system in order to develop better performances. Furthermore, rather than learning the predefined gestures of others, GIDE allows users to design their own gestures, making interaction more natural and also allowing the applications to be tailored by users' specific needs. We describe our system that demonstrates these new qualities-that combine to provide fluid gesture interaction design-through evaluations with a range of performers and artists.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "22", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Young:2014:DET, author = "James E. Young and Takeo Igarashi and Ehud Sharlin and Daisuke Sakamoto and Jeffrey Allen", title = "Design and evaluation techniques for authoring interactive and stylistic behaviors", journal = j-TIIS, volume = "3", number = "4", pages = "23:1--23:??", month = jan, year = "2014", CODEN = "????", DOI = "https://doi.org/10.1145/2499671", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Thu Mar 13 06:46:49 MDT 2014", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "We present a series of projects for end-user authoring of interactive robotic behaviors, with a particular focus on the style of those behaviors: we call this approach Style-by-Demonstration (SBD). We provide an overview introduction of three different SBD platforms: SBD for animated character interactive locomotion paths, SBD for interactive robot locomotion paths, and SBD for interactive robot dance. The primary contribution of this article is a detailed cross-project SBD analysis of the interaction designs and evaluation approaches employed, with the goal of providing general guidelines stemming from our experiences, for both developing and evaluating SBD systems. In addition, we provide the first full account of our Puppet Master SBD algorithm, with an explanation of how it evolved through the projects.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "23", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Kumar:2014:TES, author = "Rohit Kumar and Carolyn P. Ros{\'e}", title = "Triggering effective social support for online groups", journal = j-TIIS, volume = "3", number = "4", pages = "24:1--24:??", month = jan, year = "2014", CODEN = "????", DOI = "https://doi.org/10.1145/2499672", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Thu Mar 13 06:46:49 MDT 2014", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "Conversational agent technology is an emerging paradigm for creating a social environment in online groups that is conducive to effective teamwork. Prior work has demonstrated advantages in terms of learning gains and satisfaction scores when groups learning together online have been supported by conversational agents that employ Balesian social strategies. This prior work raises two important questions that are addressed in this article. The first question is one of generality. Specifically, are the positive effects of the designed support specific to learning contexts? Or are they in evidence in other collaborative task domains as well? We present a study conducted within a collaborative decision-making task where we see that the positive effects of the Balesian social strategies extend to this new context. The second question is whether it is possible to increase the effectiveness of the Balesian social strategies by increasing the context sensitivity with which the social strategies are triggered. To this end, we present technical work that increases the sensitivity of the triggering. Next, we present a user study that demonstrates an improvement in performance of the support agent with the new, more sensitive triggering policy over the baseline approach from prior work. The technical contribution of this article is that we extend prior work where such support agents were modeled using a composition of conversational behaviors integrated within an event-driven framework. Within the present approach, conversation is orchestrated through context-sensitive triggering of the composed behaviors. The core effort involved in applying this approach involves building a set of triggering policies that achieve this orchestration in a time-sensitive and coherent manner. In line with recent developments in data-driven approaches for building dialog systems, we present a novel technique for learning behavior-specific triggering policies, deploying it as part of our efforts to improve a socially capable conversational tutor agent that supports collaborative learning.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "24", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Kritikos:2014:TMD, author = "K. Kritikos and D. Plexousakis and F. Patern{\`o}", title = "Task model-driven realization of interactive application functionality through services", journal = j-TIIS, volume = "3", number = "4", pages = "25:1--25:??", month = jan, year = "2014", CODEN = "????", DOI = "https://doi.org/10.1145/2559979", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Thu Mar 13 06:46:49 MDT 2014", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "The Service-Oriented Computing (SOC) paradigm is currently being adopted by many developers, as it promises the construction of applications through reuse of existing Web Services (WSs). However, current SOC tools produce applications that interact with users in a limited way. This limitation is overcome by model-based Human-Computer Interaction (HCI) approaches that support the development of applications whose functionality is realized with WSs and whose User Interface (UI) is adapted to the user's context. Typically, such approaches do not consider various functional issues, such as the applications' semantics and their syntactic robustness in terms of the WSs selected to implement their functionality and the automation of the service discovery and selection processes. To this end, we propose a model-driven design method for interactive service-based applications that is able to consider the functional issues and their implications for the UI. This method is realized by a semiautomatic environment that can be integrated into current model-based HCI tools to complete the development of interactive service front-ends. The proposed method takes as input an HCI task model, which includes the user's view of the interactive system, and produces a concrete service model that describes how existing services can be combined to realize the application's functionality. To achieve its goal, our method first transforms system tasks into semantic service queries by mapping the task objects onto domain ontology concepts; then it sends each resulting query to a semantic service engine so as to discover the corresponding services. In the end, only one service from those associated with a system task is selected, through the execution of a novel service concretization algorithm that ensures message compatibility between the selected services.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "25", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Rafailidis:2014:CBT, author = "Dimitrios Rafailidis and Apostolos Axenopoulos and Jonas Etzold and Stavroula Manolopoulou and Petros Daras", title = "Content-based tag propagation and tensor factorization for personalized item recommendation based on social tagging", journal = j-TIIS, volume = "3", number = "4", pages = "26:1--26:??", month = jan, year = "2014", CODEN = "????", DOI = "https://doi.org/10.1145/2487164", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Thu Mar 13 06:46:49 MDT 2014", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "In this article, a novel method for personalized item recommendation based on social tagging is presented. The proposed approach comprises a content-based tag propagation method to address the sparsity and ``cold start'' problems, which often occur in social tagging systems and decrease the quality of recommendations. The proposed method exploits (a) the content of items and (b) users' tag assignments through a relevance feedback mechanism in order to automatically identify the optimal number of content-based and conceptually similar items. The relevance degrees between users, tags, and conceptually similar items are calculated in order to ensure accurate tag propagation and consequently to address the issue of ``learning tag relevance.'' Moreover, the ternary relation among users, tags, and items is preserved by performing tag propagation in the form of triplets based on users' personal preferences and ``cold start'' degree. The latent associations among users, tags, and items are revealed based on a tensor factorization model in order to build personalized item recommendations. In our experiments with real-world social data, we show the superiority of the proposed approach over other state-of-the-art methods, since several problems in social tagging systems are successfully tackled. Finally, we present the recommendation methodology in the multimodal engine of I-SEARCH, where users' interaction capabilities are demonstrated.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "26", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Callaway:2014:EMD, author = "Charles Callaway and Oliviero Stock and Elyon Dekoven", title = "Experiments with Mobile Drama in an Instrumented Museum for Inducing Conversation in Small Groups", journal = j-TIIS, volume = "4", number = "1", pages = "2:1--2:??", month = apr, year = "2014", CODEN = "????", DOI = "https://doi.org/10.1145/2584250", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Sat Apr 12 11:14:27 MDT 2014", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "Small groups can have a better museum visit when that visit is both a social and an educational occasion. The unmediated discussion that often ensues during a shared cultural experience, especially when it is with a small group whose members already know each other, has been shown by ethnographers to be important for a more enriching experience. We present DRAMATRIC, a mobile presentation system that delivers hour-long dramas to small groups of museum visitors. DRAMATRIC continuously receives sensor data from the museum environment during a museum visit and analyzes group behavior from that data. On the basis of that analysis, DRAMATRIC delivers a series of dynamically coordinated dramatic scenes about exhibits that the group walks near, each designed to stimulate group discussion. Each drama presentation contains small, complementary differences in the narrative content heard by the different members of the group, leveraging the tension/release cycle of narrative to naturally lead visitors to fill in missing pieces in their own drama by interacting with their fellow group members. Using four specific techniques to produce these coordinated narrative variations, we describe two experiments: one in a neutral, nonmobile environment, and the other a controlled experiment with a full-scale drama in an actual museum. The first experiment tests the hypothesis that narrative differences will lead to increased conversation compared to hearing identical narratives, whereas the second experiment tests whether switching from presenting a drama using one technique to using another technique for the subsequent drama will result in increased conversation. The first experiment shows that hearing coordinated narrative variations can in fact lead to significantly increased conversation. The second experiment also serves as a framework for future studies that evaluate strategies for similar adaptive systems.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "2", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Martens:2014:ISI, author = "Jean-Bernard Martens", title = "Interactive Statistics with {Illmo}", journal = j-TIIS, volume = "4", number = "1", pages = "4:1--4:??", month = apr, year = "2014", CODEN = "????", DOI = "https://doi.org/10.1145/2509108", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Sat Apr 12 11:14:27 MDT 2014", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "Progress in empirical research relies on adequate statistical analysis and reporting. This article proposes an alternative approach to statistical modeling that is based on an old but mostly forgotten idea, namely Thurstone modeling. Traditional statistical methods assume that either the measured data, in the case of parametric statistics, or the rank-order transformed data, in the case of nonparametric statistics, are samples from a specific (usually Gaussian) distribution with unknown parameters. Consequently, such methods should not be applied when this assumption is not valid. Thurstone modeling similarly assumes the existence of an underlying process that obeys an a priori assumed distribution with unknown parameters, but combines this underlying process with a flexible response mechanism that can be either continuous or discrete and either linear or nonlinear. One important advantage of Thurstone modeling is that traditional statistical methods can still be applied on the underlying process, irrespective of the nature of the measured data itself. Another advantage is that Thurstone models can be graphically represented, which helps to communicate them to a broad audience. A new interactive statistical package, Interactive Log Likelihood MOdeling ( Illmo ), was specifically designed for estimating and rendering Thurstone models and is intended to bring Thurstone modeling within the reach of persons who are not experts in statistics. Illmo is unique in the sense that it provides not only extensive graphical renderings of the data analysis results but also an interface for navigating between different model options. In this way, users can interactively explore different models and decide on an adequate balance between model complexity and agreement with the experimental data. Hypothesis testing on model parameters is also made intuitive and is supported by both textual and graphical feedback. The flexibility and ease of use of Illmo means that it is also potentially useful as a didactic tool for teaching statistics.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "4", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Riveiro:2014:ENM, author = "Maria Riveiro", title = "Evaluation of Normal Model Visualization for Anomaly Detection in Maritime Traffic", journal = j-TIIS, volume = "4", number = "1", pages = "5:1--5:??", month = apr, year = "2014", CODEN = "????", DOI = "https://doi.org/10.1145/2591511", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Sat Apr 12 11:14:27 MDT 2014", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "Monitoring dynamic objects in surveillance applications is normally a demanding activity for operators, not only because of the complexity and high dimensionality of the data but also because of other factors like time constraints and uncertainty. Timely detection of anomalous objects or situations that need further investigation may reduce operators' cognitive load. Surveillance applications may include anomaly detection capabilities, but their use is not widespread, as they usually generate a high number of false alarms, they do not provide appropriate cognitive support for operators, and their outcomes can be difficult to comprehend and trust. Visual analytics can bridge the gap between computational and human approaches to detecting anomalous behavior in traffic data, making this process more transparent. As a step toward this goal of transparency, this article presents an evaluation that assesses whether visualizations of normal behavioral models of vessel traffic support two of the main analytical tasks specified during our field work in maritime control centers. The evaluation combines quantitative and qualitative usability assessments. The quantitative evaluation, which was carried out with a proof-of-concept prototype, reveals that participants who used the visualization of normal behavioral models outperformed the group that did not do so. The qualitative assessment shows that domain experts have a positive attitude toward the provision of automatic support and the visualization of normal behavioral models, as these aids may reduce reaction time and increase trust in and comprehensibility of the system.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "5", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Chen:2014:EPM, author = "Yingjie Victor Chen and Zhenyu Cheryl Qian and Robert Woodbury and John Dill and Chris D. Shaw", title = "Employing a Parametric Model for Analytic Provenance", journal = j-TIIS, volume = "4", number = "1", pages = "6:1--6:??", month = apr, year = "2014", CODEN = "????", DOI = "https://doi.org/10.1145/2591510", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Sat Apr 12 11:14:27 MDT 2014", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "We introduce a propagation-based parametric symbolic model approach to supporting analytic provenance. This approach combines a script language to capture and encode the analytic process and a parametrically controlled symbolic model to represent and reuse the logic of the analysis process. Our approach first appeared in a visual analytics system called CZSaw. Using a script to capture the analyst's interactions at a meaningful system action level allows the creation of a parametrically controlled symbolic model in the form of a Directed Acyclic Graph (DAG). Using the DAG allows propagating changes. Graph nodes correspond to variables in CZSaw scripts, which are results (data and data visualizations) generated from user interactions. The user interacts with variables representing entities or relations to create the next step's results. Graph edges represent dependency relationships among nodes. Any change to a variable triggers the propagation mechanism to update downstream dependent variables and in turn updates data views to reflect the change. The analyst can reuse parts of the analysis process by assigning new values to a node in the graph. We evaluated this symbolic model approach by solving three IEEE VAST Challenge contest problems (from IEEE VAST 2008, 2009, and 2010). In each of these challenges, the analyst first created a symbolic model to explore, understand, analyze, and solve a particular subproblem and then reused the model via its dependency graph propagation mechanism to solve similar subproblems. With the script and model, CZSaw supports the analytic provenance by capturing, encoding, and reusing the analysis process. The analyst can recall the chronological states of the analysis process with the CZSaw script and may interpret the underlying rationale of the analysis with the symbolic model.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "6", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Chan:2014:RCT, author = "Yu-Hsuan Chan and Carlos D. Correa and Kwan-Liu Ma", title = "{Regression Cube}: a Technique for Multidimensional Visual Exploration and Interactive Pattern Finding", journal = j-TIIS, volume = "4", number = "1", pages = "7:1--7:??", month = apr, year = "2014", CODEN = "????", DOI = "https://doi.org/10.1145/2590349", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Sat Sep 13 13:17:36 MDT 2014", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "Scatterplots are commonly used to visualize multidimensional data; however, 2D projections of data offer limited understanding of the high-dimensional interactions between data points. We introduce an interactive 3D extension of scatterplots called the Regression Cube (RC), which augments a 3D scatterplot with three facets on which the correlations between the two variables are revealed by sensitivity lines and sensitivity streamlines. The sensitivity visualization of local regression on the 2D projections provides insights about the shape of the data through its orientation and continuity cues. We also introduce a series of visual operations such as clustering, brushing, and selection supported in RC. By iteratively refining the selection of data points of interest, RC is able to reveal salient local correlation patterns that may otherwise remain hidden with a global analysis. We have demonstrated our system with two examples and a user-oriented evaluation, and we show how RCs enable interactive visual exploration of multidimensional datasets via a variety of classification and information retrieval tasks. A video demo of RC is available.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "7", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Jawaheer:2014:MUP, author = "Gawesh Jawaheer and Peter Weller and Patty Kostkova", title = "Modeling User Preferences in Recommender Systems: a Classification Framework for Explicit and Implicit User Feedback", journal = j-TIIS, volume = "4", number = "2", pages = "8:1--8:??", month = jul, year = "2014", CODEN = "????", DOI = "https://doi.org/10.1145/2512208", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Sat Sep 13 13:15:34 MDT 2014", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "Recommender systems are firmly established as a standard technology for assisting users with their choices; however, little attention has been paid to the application of the user model in recommender systems, particularly the variability and noise that are an intrinsic part of human behavior and activity. To enable recommender systems to suggest items that are useful to a particular user, it can be essential to understand the user and his or her interactions with the system. These interactions typically manifest themselves as explicit and implicit user feedback that provides the key indicators for modeling users' preferences for items and essential information for personalizing recommendations. In this article, we propose a classification framework for the use of explicit and implicit user feedback in recommender systems based on a set of distinct properties that include Cognitive Effort, User Model, Scale of Measurement, and Domain Relevance. We develop a set of comparison criteria for explicit and implicit user feedback to emphasize the key properties. Using our framework, we provide a classification of recommender systems that have addressed questions about user feedback, and we review state-of-the-art techniques to improve such user feedback and thereby improve the performance of the recommender system. Finally, we formulate challenges for future research on improvement of user feedback.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "8", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Fang:2014:CLM, author = "Yi Fang and Ziad Al Bawab and Jean-Fran{\c{c}}ois Crespo", title = "Collaborative Language Models for Localized Query Prediction", journal = j-TIIS, volume = "4", number = "2", pages = "9:1--9:??", month = jul, year = "2014", CODEN = "????", DOI = "https://doi.org/10.1145/2622617", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Sat Sep 13 13:15:34 MDT 2014", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "Localized query prediction (LQP) is the task of estimating web query trends for a specific location. This problem subsumes many interesting personalized web applications such as personalization for buzz query detection, for query expansion, and for query recommendation. These personalized applications can greatly enhance user interaction with web search engines by providing more customized information discovered from user input (i.e., queries), but the LQP task has rarely been investigated in the literature. Although exist abundant work on estimating global web search trends does exist, it often encounters the big challenge of data sparsity when personalization comes into play. In this article, we tackle the LQP task by proposing a series of collaborative language models (CLMs). CLMs alleviate the data sparsity issue by collaboratively collecting queries and trend information from the other locations. The traditional statistical language models assume a fixed background language model, which loses the taste of personalization. In contrast, CLMs are personalized language models with flexible background language models customized to various locations. The most sophisticated CLM enables the collaboration to adapt to specific query topics, which further advances the personalization level. An extensive set of experiments have been conducted on a large-scale web query log to demonstrate the effectiveness of the proposed models.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "9", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Castellano:2014:CSA, author = "Ginevra Castellano and Iolanda Leite and Andr{\'e} Pereira and Carlos Martinho and Ana Paiva and Peter W. Mcowan", title = "Context-Sensitive Affect Recognition for a Robotic Game Companion", journal = j-TIIS, volume = "4", number = "2", pages = "10:1--10:??", month = jul, year = "2014", CODEN = "????", DOI = "https://doi.org/10.1145/2622615", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Sat Sep 13 13:15:34 MDT 2014", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "Social perception abilities are among the most important skills necessary for robots to engage humans in natural forms of interaction. Affect-sensitive robots are more likely to be able to establish and maintain believable interactions over extended periods of time. Nevertheless, the integration of affect recognition frameworks in real-time human-robot interaction scenarios is still underexplored. In this article, we propose and evaluate a context-sensitive affect recognition framework for a robotic game companion for children. The robot can automatically detect affective states experienced by children in an interactive chess game scenario. The affect recognition framework is based on the automatic extraction of task features and social interaction-based features. Vision-based indicators of the children's nonverbal behaviour are merged with contextual features related to the game and the interaction and given as input to support vector machines to create a context-sensitive multimodal system for affect recognition. The affect recognition framework is fully integrated in an architecture for adaptive human-robot interaction. Experimental evaluation showed that children's affect can be successfully predicted using a combination of behavioural and contextual data related to the game and the interaction with the robot. It was found that contextual data alone can be used to successfully predict a subset of affective dimensions, such as interest toward the robot. Experiments also showed that engagement with the robot can be predicted using information about the user's valence, interest and anticipatory behaviour. These results provide evidence that social engagement can be modelled as a state consisting of affect and attention components in the context of the interaction.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "10", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Steichen:2014:IVT, author = "Ben Steichen and Cristina Conati and Giuseppe Carenini", title = "Inferring Visualization Task Properties, User Performance, and User Cognitive Abilities from Eye Gaze Data", journal = j-TIIS, volume = "4", number = "2", pages = "11:1--11:??", month = jul, year = "2014", CODEN = "????", DOI = "https://doi.org/10.1145/2633043", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Sat Sep 13 13:15:34 MDT 2014", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "Information visualization systems have traditionally followed a one-size-fits-all model, typically ignoring an individual user's needs, abilities, and preferences. However, recent research has indicated that visualization performance could be improved by adapting aspects of the visualization to the individual user. To this end, this article presents research aimed at supporting the design of novel user-adaptive visualization systems. In particular, we discuss results on using information on user eye gaze patterns while interacting with a given visualization to predict properties of the user's visualization task; the user's performance (in terms of predicted task completion time); and the user's individual cognitive abilities, such as perceptual speed, visual working memory, and verbal working memory. We provide a detailed analysis of different eye gaze feature sets, as well as over-time accuracies. We show that these predictions are significantly better than a baseline classifier even during the early stages of visualization usage. These findings are then discussed with a view to designing visualization systems that can adapt to the individual user in real time.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "11", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Cuayahuitl:2014:ISI, author = "Heriberto Cuay{\'a}huitl and Lutz Frommberger and Nina Dethlefs and Antoine Raux and Mathew Marge and Hendrik Zender", title = "Introduction to the Special Issue on Machine Learning for Multiple Modalities in Interactive Systems and Robots", journal = j-TIIS, volume = "4", number = "3", pages = "12e:1--12e:??", month = oct, year = "2014", CODEN = "????", DOI = "https://doi.org/10.1145/2670539", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Tue Oct 14 17:38:05 MDT 2014", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "This special issue highlights research articles that apply machine learning to robots and other systems that interact with users through more than one modality, such as speech, gestures, and vision. For example, a robot may coordinate its speech with its actions, taking into account (audio-)visual feedback during their execution. Machine learning provides interactive systems with opportunities to improve performance not only of individual components but also of the system as a whole. However, machine learning methods that encompass multiple modalities of an interactive system are still relatively hard to find. The articles in this special issue represent examples that contribute to filling this gap.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "12e", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Ngo:2014:EIM, author = "Hung Ngo and Matthew Luciw and Jawas Nagi and Alexander Forster and J{\"u}rgen Schmidhuber and Ngo Anh Vien", title = "Efficient Interactive Multiclass Learning from Binary Feedback", journal = j-TIIS, volume = "4", number = "3", pages = "12:1--12:??", month = aug, year = "2014", CODEN = "????", DOI = "https://doi.org/10.1145/2629631", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Sat Sep 13 13:15:36 MDT 2014", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "We introduce a novel algorithm called upper confidence --- weighted learning (UCWL) for online multiclass learning from binary feedback (e.g., feedback that indicates whether the prediction was right or wrong). UCWL combines the upper confidence bound (UCB) framework with the soft confidence-weighted (SCW) online learning scheme. In UCB, each instance is classified using both score and uncertainty. For a given instance in the sequence, the algorithm might guess its class label primarily to reduce the class uncertainty. This is a form of informed exploration, which enables the performance to improve with lower sample complexity compared to the case without exploration. Combining UCB with SCW leads to the ability to deal well with noisy and nonseparable data, and state-of-the-art performance is achieved without increasing the computational cost. A potential application setting is human-robot interaction (HRI), where the robot is learning to classify some set of inputs while the human teaches it by providing only binary feedback-or sometimes even the wrong answer entirely. Experimental results in the HRI setting and with two benchmark datasets from other settings show that UCWL outperforms other state-of-the-art algorithms in the online binary feedback setting-and surprisingly even sometimes outperforms state-of-the-art algorithms that get full feedback (e.g., the true class label), whereas UCWL gets only binary feedback on the same data sequence.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "12", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Benotti:2014:INL, author = "Luciana Benotti and Tessa Lau and Mart{\'\i}n Villalba", title = "Interpreting Natural Language Instructions Using Language, Vision, and Behavior", journal = j-TIIS, volume = "4", number = "3", pages = "13:1--13:??", month = aug, year = "2014", CODEN = "????", DOI = "https://doi.org/10.1145/2629632", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Sat Sep 13 13:15:36 MDT 2014", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "We define the problem of automatic instruction interpretation as follows. Given a natural language instruction, can we automatically predict what an instruction follower, such as a robot, should do in the environment to follow that instruction? Previous approaches to automatic instruction interpretation have required either extensive domain-dependent rule writing or extensive manually annotated corpora. This article presents a novel approach that leverages a large amount of unannotated, easy-to-collect data from humans interacting in a game-like environment. Our approach uses an automatic annotation phase based on artificial intelligence planning, for which two different annotation strategies are compared: one based on behavioral information and the other based on visibility information. The resulting annotations are used as training data for different automatic classifiers. This algorithm is based on the intuition that the problem of interpreting a situated instruction can be cast as a classification problem of choosing among the actions that are possible in the situation. Classification is done by combining language, vision, and behavior information. Our empirical analysis shows that machine learning classifiers achieve 77\% accuracy on this task on available English corpora and 74\% on similar German corpora. Finally, the inclusion of human feedback in the interpretation process is shown to boost performance to 92\% for the English corpus and 90\% for the German corpus.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "13", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Keizer:2014:MLS, author = "Simon Keizer and Mary Ellen Foster and Zhuoran Wang and Oliver Lemon", title = "Machine Learning for Social Multiparty Human--Robot Interaction", journal = j-TIIS, volume = "4", number = "3", pages = "14:1--14:??", month = oct, year = "2014", CODEN = "????", DOI = "https://doi.org/10.1145/2600021", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Tue Oct 14 17:38:05 MDT 2014", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "We describe a variety of machine-learning techniques that are being applied to social multiuser human--robot interaction using a robot bartender in our scenario. We first present a data-driven approach to social state recognition based on supervised learning. We then describe an approach to social skills execution-that is, action selection for generating socially appropriate robot behavior-which is based on reinforcement learning, using a data-driven simulation of multiple users to train execution policies for social skills. Next, we describe how these components for social state recognition and skills execution have been integrated into an end-to-end robot bartender system, and we discuss the results of a user evaluation. Finally, we present an alternative unsupervised learning framework that combines social state recognition and social skills execution based on hierarchical Dirichlet processes and an infinite POMDP interaction manager. The models make use of data from both human--human interactions collected in a number of German bars and human--robot interactions recorded in the evaluation of an initial version of the system.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "14", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Cuayahuitl:2014:NHR, author = "Heriberto Cuay{\'a}huitl and Ivana Kruijff-Korbayov{\'a} and Nina Dethlefs", title = "Nonstrict Hierarchical Reinforcement Learning for Interactive Systems and Robots", journal = j-TIIS, volume = "4", number = "3", pages = "15:1--15:??", month = oct, year = "2014", CODEN = "????", DOI = "https://doi.org/10.1145/2659003", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Tue Oct 14 17:38:05 MDT 2014", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "Conversational systems and robots that use reinforcement learning for policy optimization in large domains often face the problem of limited scalability. This problem has been addressed either by using function approximation techniques that estimate the approximate true value function of a policy or by using a hierarchical decomposition of a learning task into subtasks. We present a novel approach for dialogue policy optimization that combines the benefits of both hierarchical control and function approximation and that allows flexible transitions between dialogue subtasks to give human users more control over the dialogue. To this end, each reinforcement learning agent in the hierarchy is extended with a subtask transition function and a dynamic state space to allow flexible switching between subdialogues. In addition, the subtask policies are represented with linear function approximation in order to generalize the decision making to situations unseen in training. Our proposed approach is evaluated in an interactive conversational robot that learns to play quiz games. Experimental results, using simulation and real users, provide evidence that our proposed approach can lead to more flexible (natural) interactions than strict hierarchical control and that it is preferred by human users.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "15", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Bulling:2015:ISI, author = "Andreas Bulling and Ulf Blanke and Desney Tan and Jun Rekimoto and Gregory Abowd", title = "Introduction to the Special Issue on Activity Recognition for Interaction", journal = j-TIIS, volume = "4", number = "4", pages = "16:1--16:??", month = jan, year = "2015", CODEN = "????", DOI = "https://doi.org/10.1145/2694858", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Thu Jan 29 10:52:31 MST 2015", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "This editorial introduction describes the aims and scope of the ACM Transactions on Interactive Intelligent Systems special issue on Activity Recognition for Interaction. It explains why activity recognition is becoming crucial as part of the cycle of interaction between users and computing systems, and it shows how the five articles selected for this special issue reflect this theme.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "16e", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Ye:2015:UUS, author = "Juan Ye and Graeme Stevenson and Simon Dobson", title = "{USMART}: an Unsupervised Semantic Mining Activity Recognition Technique", journal = j-TIIS, volume = "4", number = "4", pages = "16:1--16:??", month = jan, year = "2015", CODEN = "????", DOI = "https://doi.org/10.1145/2662870", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Thu Jan 29 10:52:31 MST 2015", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "Recognising high-level human activities from low-level sensor data is a crucial driver for pervasive systems that wish to provide seamless and distraction-free support for users engaged in normal activities. Research in this area has grown alongside advances in sensing and communications, and experiments have yielded sensor traces coupled with ground truth annotations about the underlying environmental conditions and user actions. Traditional machine learning has had some success in recognising human activities; but the need for large volumes of annotated data and the danger of overfitting to specific conditions represent challenges in connection with the building of models applicable to a wide range of users, activities, and environments. We present USMART, a novel unsupervised technique that combines data- and knowledge-driven techniques. USMART uses a general ontology model to represent domain knowledge that can be reused across different environments and users, and we augment a range of learning techniques with ontological semantics to facilitate the unsupervised discovery of patterns in how each user performs daily activities. We evaluate our approach against four real-world third-party datasets featuring different user populations and sensor configurations, and we find that USMART achieves up to 97.5\% accuracy in recognising daily activities.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "16", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Dim:2015:ADS, author = "Eyal Dim and Tsvi Kuflik", title = "Automatic Detection of Social Behavior of Museum Visitor Pairs", journal = j-TIIS, volume = "4", number = "4", pages = "17:1--17:??", month = jan, year = "2015", CODEN = "????", DOI = "https://doi.org/10.1145/2662869", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Thu Jan 29 10:52:31 MST 2015", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "In many cases, visitors come to a museum in small groups. In such cases, the visitors' social context has an impact on their museum visit experience. Knowing the social context may allow a system to provide socially aware services to the visitors. Evidence of the social context can be gained from observing/monitoring the visitors' social behavior. However, automatic identification of a social context requires, on the one hand, identifying typical social behavior patterns and, on the other, using relevant sensors that measure various signals and reason about them to detect the visitors' social behavior. We present such typical social behavior patterns of visitor pairs, identified by observations, and then the instrumentation, detection process, reasoning, and analysis of measured signals that enable us to detect the visitors' social behavior. Simple sensors' data, such as proximity to other visitors, proximity to museum points of interest, and visitor orientation are used to detect social synchronization, attention to the social companion, and interest in museum exhibits. The presented approach may allow future research to offer adaptive services to museum visitors based on their social context to support their group visit experience better.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "17", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Caramiaux:2015:AGR, author = "Baptiste Caramiaux and Nicola Montecchio and Atau Tanaka and Fr{\'e}d{\'e}ric Bevilacqua", title = "Adaptive Gesture Recognition with Variation Estimation for Interactive Systems", journal = j-TIIS, volume = "4", number = "4", pages = "18:1--18:??", month = jan, year = "2015", CODEN = "????", DOI = "https://doi.org/10.1145/2643204", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Thu Jan 29 10:52:31 MST 2015", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "This article presents a gesture recognition/adaptation system for human--computer interaction applications that goes beyond activity classification and that, as a complement to gesture labeling, characterizes the movement execution. We describe a template-based recognition method that simultaneously aligns the input gesture to the templates using a Sequential Monte Carlo inference technique. Contrary to standard template-based methods based on dynamic programming, such as Dynamic Time Warping, the algorithm has an adaptation process that tracks gesture variation in real time. The method continuously updates, during execution of the gesture, the estimated parameters and recognition results, which offers key advantages for continuous human--machine interaction. The technique is evaluated in several different ways: Recognition and early recognition are evaluated on 2D onscreen pen gestures; adaptation is assessed on synthetic data; and both early recognition and adaptation are evaluated in a user study involving 3D free-space gestures. The method is robust to noise, and successfully adapts to parameter variation. Moreover, it performs recognition as well as or better than nonadapting offline template-based methods.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "18", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Cooney:2015:AIS, author = "Martin Cooney and Shuichi Nishio and Hiroshi Ishiguro", title = "Affectionate Interaction with a Small Humanoid Robot Capable of Recognizing Social Touch Behavior", journal = j-TIIS, volume = "4", number = "4", pages = "19:1--19:??", month = jan, year = "2015", CODEN = "????", DOI = "https://doi.org/10.1145/2685395", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Thu Jan 29 10:52:31 MST 2015", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "Activity recognition, involving a capability to recognize people's behavior and its underlying significance, will play a crucial role in facilitating the integration of interactive robotic artifacts into everyday human environments. In particular, social intelligence in recognizing affectionate behavior will offer value by allowing companion robots to bond meaningfully with interacting persons. The current article addresses the issue of designing an affectionate haptic interaction between a person and a companion robot by exploring how a small humanoid robot can behave to elicit affection while recognizing touches. We report on an experiment conducted to gain insight into how people perceive three fundamental interactive strategies in which a robot is either always highly affectionate, appropriately affectionate, or superficially unaffectionate (emphasizing positivity, contingency, and challenge, respectively). Results provide insight into the structure of affectionate interaction between humans and humanoid robots-underlining the importance of an interaction design expressing sincere liking, stability and variation-and suggest the usefulness of novel modalities such as warmth and cold.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "19", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{DeCarolis:2015:ILD, author = "Berardina {De Carolis} and Stefano Ferilli and Domenico Redavid", title = "Incremental Learning of Daily Routines as Workflows in a {Smart} Home Environment", journal = j-TIIS, volume = "4", number = "4", pages = "20:1--20:??", month = jan, year = "2015", CODEN = "????", DOI = "https://doi.org/10.1145/2675063", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Thu Jan 29 10:52:31 MST 2015", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "Smart home environments should proactively support users in their activities, anticipating their needs according to their preferences. Understanding what the user is doing in the environment is important for adapting the environment's behavior, as well as for identifying situations that could be problematic for the user. Enabling the environment to exploit models of the user's most common behaviors is an important step toward this objective. In particular, models of the daily routines of a user can be exploited not only for predicting his/her needs, but also for comparing the actual situation at a given moment with the expected one, in order to detect anomalies in his/her behavior. While manually setting up process models in business and factory environments may be cost-effective, building models of the processes involved in people's everyday life is infeasible. This fact fully justifies the interest of the Ambient Intelligence community in automatically learning such models from examples of actual behavior. Incremental adaptation of the models and the ability to express/learn complex conditions on the involved tasks are also desirable. This article describes how process mining can be used for learning users' daily routines from a dataset of annotated sensor data. The solution that we propose relies on a First-Order Logic learning approach. Indeed, First-Order Logic provides a single, comprehensive and powerful framework for supporting all the previously mentioned features. Our experiments, performed both on a proprietary toy dataset and on publicly available real-world ones, indicate that this approach is efficient and effective for learning and modeling daily routines in Smart Home Environments.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "20", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Gianni:2015:SRF, author = "Mario Gianni and Geert-Jan M. Kruijff and Fiora Pirri", title = "A Stimulus-Response Framework for Robot Control", journal = j-TIIS, volume = "4", number = "4", pages = "21:1--21:??", month = jan, year = "2015", CODEN = "????", DOI = "https://doi.org/10.1145/2677198", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Thu Jan 29 10:52:31 MST 2015", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "We propose in this article a new approach to robot cognitive control based on a stimulus-response framework that models both a robot's stimuli and the robot's decision to switch tasks in response to or inhibit the stimuli. In an autonomous system, we expect a robot to be able to deal with the whole system of stimuli and to use them to regulate its behavior in real-world applications. The proposed framework contributes to the state of the art of robot planning and high-level control in that it provides a novel perspective on the interaction between robot and environment. Our approach is inspired by Gibson's constructive view of the concept of a stimulus and by the cognitive control paradigm of task switching. We model the robot's response to a stimulus in three stages. We start by defining the stimuli as perceptual functions yielded by the active robot processes and learned via an informed logistic regression. Then we model the stimulus-response relationship by estimating a score matrix that leads to the selection of a single response task for each stimulus, basing the estimation on low-rank matrix factorization. The decision about switching takes into account both an interference cost and a reconfiguration cost. The interference cost weighs the effort of discontinuing the current robot mental state to switch to a new state, whereas the reconfiguration cost weighs the effort of activating the response task. A choice is finally made based on the payoff of switching. Because processes play such a crucial role both in the stimulus model and in the stimulus-response model, and because processes are activated by actions, we address also the process model, which is built on a theory of action. The framework is validated by several experiments that exploit a full implementation on an advanced robotic platform and is compared with two known approaches to replanning. Results demonstrate the practical value of the system in terms of robot autonomy, flexibility, and usability.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "21", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Taranta:2015:EBC, author = "Eugene M. {Taranta II} and Thaddeus K. Simons and Rahul Sukthankar and Joseph J. {Laviola, Jr.}", title = "Exploring the Benefits of Context in {$3$D} Gesture Recognition for Game-Based Virtual Environments", journal = j-TIIS, volume = "5", number = "1", pages = "1:1--1:??", month = mar, year = "2015", CODEN = "????", DOI = "https://doi.org/10.1145/2656345", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Thu Mar 26 05:43:35 MDT 2015", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "We present a systematic exploration of how to utilize video game context (e.g., player and environmental state) to modify and augment existing 3D gesture recognizers to improve accuracy for large gesture sets. Specifically, our work develops and evaluates three strategies for incorporating context into 3D gesture recognizers. These strategies include modifying the well-known Rubine linear classifier to handle unsegmented input streams and per-frame retraining using contextual information (CA-Linear); a GPU implementation of dynamic time warping (DTW) that reduces the overhead of traditional DTW by utilizing context to evaluate only relevant time sequences inside of a multithreaded kernel (CA-DTW); and a multiclass SVM with per-class probability estimation that is combined with a contextually based prior probability distribution (CA-SVM). We evaluate each strategy using a Kinect-based third-person perspective VE game prototype that combines parkour-style navigation with hand-to-hand combat. Using a simple gesture collection application to collect a set of 57 gestures and the game prototype that implements 37 of these gestures, we conduct three experiments. In the first experiment, we evaluate the effectiveness of several established classifiers on our gesture set and demonstrate state-of-the-art results using our proposed method. In our second experiment, we generate 500 random scenarios having between 5 and 19 of the 57 gestures in context. We show that the contextually aware classifiers CA-Linear, CA-DTW, and CA-SVM significantly outperform their non--contextually aware counterparts by 37.74\%, 36.04\%, and 20.81\%, respectively. On the basis of the results of the second experiment, we derive upper-bound expectations for in-game performance for the three CA classifiers: 96.61\%, 86.79\%, and 96.86\%, respectively. Finally, our third experiment is an in-game evaluation of the three CA classifiers with and without context. Our results show that through the use of context, we are able to achieve an average in-game recognition accuracy of 89.67\% with CA-Linear compared to 65.10\% without context, 79.04\% for CA-DTW compared to 58.1\% without context, and 90.85\% with CA-SVM compared to 75.2\% without context.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "1", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Gil:2015:HTI, author = "Yolanda Gil", title = "Human Tutorial Instruction in the Raw", journal = j-TIIS, volume = "5", number = "1", pages = "2:1--2:??", month = mar, year = "2015", CODEN = "????", DOI = "https://doi.org/10.1145/2531920", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Thu Mar 26 05:43:35 MDT 2015", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "Humans learn procedures from one another through a variety of methods, such as observing someone do the task, practicing by themselves, reading manuals or textbooks, or getting instruction from a teacher. Some of these methods generate examples that require the learner to generalize appropriately. When procedures are complex, however, it becomes unmanageable to induce the procedures from examples alone. An alternative and very common method for teaching procedures is tutorial instruction, where a teacher describes in general terms what actions to perform and possibly includes explanations of the rationale for the actions. This article provides an overview of the challenges in using human tutorial instruction for teaching procedures to computers. First, procedures can be very complex and can involve many different types of interrelated information, including (1) situating the instruction in the context of relevant objects and their properties, (2) describing the steps involved, (3) specifying the organization of the procedure in terms of relationships among steps and substeps, and (4) conveying control structures. Second, human tutorial instruction is naturally plagued with omissions, oversights, unintentional inconsistencies, errors, and simply poor design. The article presents a survey of work from the literature that highlights the nature of these challenges and illustrates them with numerous examples of instruction in many domains. Major research challenges in this area are highlighted, including the difficulty of the learning task when procedures are complex, the need to overcome omissions and errors in the instruction, the design of a natural user interface to specify procedures, the management of the interaction of a human with a learning system, and the combination of tutorial instruction with other teaching modalities.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "2", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Pejsa:2015:GAM, author = "Tomislav Pejsa and Sean Andrist and Michael Gleicher and Bilge Mutlu", title = "Gaze and Attention Management for Embodied Conversational Agents", journal = j-TIIS, volume = "5", number = "1", pages = "3:1--3:??", month = mar, year = "2015", CODEN = "????", DOI = "https://doi.org/10.1145/2724731", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Thu Mar 26 05:43:35 MDT 2015", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "To facilitate natural interactions between humans and embodied conversational agents (ECAs), we need to endow the latter with the same nonverbal cues that humans use to communicate. Gaze cues in particular are integral in mechanisms for communication and management of attention in social interactions, which can trigger important social and cognitive processes, such as establishment of affiliation between people or learning new information. The fundamental building blocks of gaze behaviors are gaze shifts: coordinated movements of the eyes, head, and body toward objects and information in the environment. In this article, we present a novel computational model for gaze shift synthesis for ECAs that supports parametric control over coordinated eye, head, and upper body movements. We employed the model in three studies with human participants. In the first study, we validated the model by showing that participants are able to interpret the agent's gaze direction accurately. In the second and third studies, we showed that by adjusting the participation of the head and upper body in gaze shifts, we can control the strength of the attention signals conveyed, thereby strengthening or weakening their social and cognitive effects. The second study shows that manipulation of eye--head coordination in gaze enables an agent to convey more information or establish stronger affiliation with participants in a teaching task, while the third study demonstrates how manipulation of upper body coordination enables the agent to communicate increased interest in objects in the environment.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "3", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Deng:2015:ESA, author = "James J. Deng and Clement H. C. Leung and Alfredo Milani and Li Chen", title = "Emotional States Associated with Music: Classification, Prediction of Changes, and Consideration in Recommendation", journal = j-TIIS, volume = "5", number = "1", pages = "4:1--4:??", month = mar, year = "2015", CODEN = "????", DOI = "https://doi.org/10.1145/2723575", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Thu Mar 26 05:43:35 MDT 2015", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "We present several interrelated technical and empirical contributions to the problem of emotion-based music recommendation and show how they can be applied in a possible usage scenario. The contributions are (1) a new three-dimensional resonance-arousal-valence model for the representation of emotion expressed in music, together with methods for automatically classifying a piece of music in terms of this model, using robust regression methods applied to musical/acoustic features; (2) methods for predicting a listener's emotional state on the assumption that the emotional state has been determined entirely by a sequence of pieces of music recently listened to, using conditional random fields and taking into account the decay of emotion intensity over time; and (3) a method for selecting a ranked list of pieces of music that match a particular emotional state, using a minimization iteration method. A series of experiments yield information about the validity of our operationalizations of these contributions. Throughout the article, we refer to an illustrative usage scenario in which all of these contributions can be exploited, where it is assumed that (1) a listener's emotional state is being determined entirely by the music that he or she has been listening to and (2) the listener wants to hear additional music that matches his or her current emotional state. The contributions are intended to be useful in a variety of other scenarios as well.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "4", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Mazilu:2015:WAG, author = "Sinziana Mazilu and Ulf Blanke and Moran Dorfman and Eran Gazit and Anat Mirelman and Jeffrey M. Hausdorff and Gerhard Tr{\"o}ster", title = "A Wearable Assistant for Gait Training for {Parkinson}'s Disease with Freezing of Gait in Out-of-the-Lab Environments", journal = j-TIIS, volume = "5", number = "1", pages = "5:1--5:??", month = mar, year = "2015", CODEN = "????", DOI = "https://doi.org/10.1145/2701431", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Thu Mar 26 05:43:35 MDT 2015", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "People with Parkinson's disease (PD) suffer from declining mobility capabilities, which cause a prevalent risk of falling. Commonly, short periods of motor blocks occur during walking, known as freezing of gait (FoG). To slow the progressive decline of motor abilities, people with PD usually undertake stationary motor-training exercises in the clinics or supervised by physiotherapists. We present a wearable system for the support of people with PD and FoG. The system is designed for independent use. It enables motor training and gait assistance at home and other unsupervised environments. The system consists of three components. First, FoG episodes are detected in real time using wearable inertial sensors and a smartphone as the processing unit. Second, a feedback mechanism triggers a rhythmic auditory signal to the user to alleviate freeze episodes in an assistive mode. Third, the smartphone-based application features support for training exercises. Moreover, the system allows unobtrusive and long-term monitoring of the user's clinical condition by transmitting sensing data and statistics to a telemedicine service. We investigate the at-home acceptance of the wearable system in a study with nine PD subjects. Participants deployed and used the system on their own, without any clinical support, at their homes during three protocol sessions in 1 week. Users' feedback suggests an overall positive attitude toward adopting and using the system in their daily life, indicating that the system supports them in improving their gait. Further, in a data-driven analysis with sensing data from five participants, we study whether there is an observable effect on the gait during use of the system. In three out of five subjects, we observed a decrease in FoG duration distributions over the protocol days during gait-training exercises. Moreover, sensing data-driven analysis shows a decrease in FoG duration and FoG number in four out of five participants when they use the system as a gait-assistive tool during normal daily life activities at home.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "5", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Salah:2015:BIS, author = "Albert Ali Salah and Hayley Hung and Oya Aran and Hatice Gunes and Matthew Turk", title = "Brief Introduction to the Special Issue on Behavior Understanding for Arts and Entertainment", journal = j-TIIS, volume = "5", number = "2", pages = "6:1--6:??", month = jul, year = "2015", CODEN = "????", DOI = "https://doi.org/10.1145/2786762", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Fri Aug 7 09:18:56 MDT 2015", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "This editorial introduction describes the aims and scope of the special issue of the ACM Transactions on Interactive Intelligent Systems on Behavior Understanding for Arts and Entertainment, which is being published in issues 2 and 3 of volume 5 of the journal. Here we offer a brief introduction to the use of behavior analysis for interactive systems that involve creativity in either the creator or the consumer of a work of art. We then characterize each of the five articles included in this first part of the special issue, which span a wide range of applications.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "6", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Grenader:2015:VIA, author = "Emily Grenader and Danilo Gasques Rodrigues and Fernando Nos and Nadir Weibel", title = "The {VideoMob} Interactive Art Installation Connecting Strangers through Inclusive Digital Crowds", journal = j-TIIS, volume = "5", number = "2", pages = "7:1--7:??", month = jul, year = "2015", CODEN = "????", DOI = "https://doi.org/10.1145/2768208", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Fri Aug 7 09:18:56 MDT 2015", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "VideoMob is an interactive video platform and an artwork that enables strangers visiting different installation locations to interact across time and space through a computer interface that detects their presence, video-records their actions while automatically removing the video background through computer vision, and co-situates visitors as part of the same digital environment. Through the combination of individual user videos to form a digital crowd, strangers are connected through the graphic display. Our work is inspired by the way distant people can interact with each other through technology and influenced by artists working in the realm of interactive art. We deployed VideoMob in a variety of settings, locations, and contexts to observe hundreds of visitors' reactions. By analyzing behavioral data collected through depth cameras from our 1,068 recordings across eight venues, we studied how participants behave when given the opportunity to record their own video portrait into the artwork. We report the specific activity performed in front of the camera and the influences that existing crowds impose on new participants. Our analysis informs the integration of a series of possible novel interaction paradigms based on real-time analysis of the visitors' behavior through specific computer vision and machine learning techniques that have the potential to increase the engagement of the artwork's visitors and to impact user experience.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "7", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Sartori:2015:AAP, author = "Andreza Sartori and Victoria Yanulevskaya and Almila Akdag Salah and Jasper Uijlings and Elia Bruni and Nicu Sebe", title = "Affective Analysis of Professional and Amateur Abstract Paintings Using Statistical Analysis and Art Theory", journal = j-TIIS, volume = "5", number = "2", pages = "8:1--8:??", month = jul, year = "2015", CODEN = "????", DOI = "https://doi.org/10.1145/2768209", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Fri Aug 7 09:18:56 MDT 2015", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "When artists express their feelings through the artworks they create, it is believed that the resulting works transform into objects with ``emotions'' capable of conveying the artists' mood to the audience. There is little to no dispute about this belief: Regardless of the artwork, genre, time, and origin of creation, people from different backgrounds are able to read the emotional messages. This holds true even for the most abstract paintings. Could this idea be applied to machines as well? Can machines learn what makes a work of art ``emotional''? In this work, we employ a state-of-the-art recognition system to learn which statistical patterns are associated with positive and negative emotions on two different datasets that comprise professional and amateur abstract artworks. Moreover, we analyze and compare two different annotation methods in order to establish the ground truth of positive and negative emotions in abstract art. Additionally, we use computer vision techniques to quantify which parts of a painting evoke positive and negative emotions. We also demonstrate how the quantification of evidence for positive and negative emotions can be used to predict which parts of a painting people prefer to focus on. This method opens new opportunities of research on why a specific painting is perceived as emotional at global and local scales.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "8", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Sanchez-Cortes:2015:MVM, author = "Dairazalia Sanchez-Cortes and Shiro Kumano and Kazuhiro Otsuka and Daniel Gatica-Perez", title = "In the Mood for Vlog: Multimodal Inference in Conversational Social Video", journal = j-TIIS, volume = "5", number = "2", pages = "9:1--9:??", month = jul, year = "2015", CODEN = "????", DOI = "https://doi.org/10.1145/2641577", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Fri Aug 7 09:18:56 MDT 2015", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "The prevalent ``share what's on your mind'' paradigm of social media can be examined from the perspective of mood: short-term affective states revealed by the shared data. This view takes on new relevance given the emergence of conversational social video as a popular genre among viewers looking for entertainment and among video contributors as a channel for debate, expertise sharing, and artistic expression. From the perspective of human behavior understanding, in conversational social video both verbal and nonverbal information is conveyed by speakers and decoded by viewers. We present a systematic study of classification and ranking of mood impressions in social video, using vlogs from YouTube. Our approach considers eleven natural mood categories labeled through crowdsourcing by external observers on a diverse set of conversational vlogs. We extract a comprehensive number of nonverbal and verbal behavioral cues from the audio and video channels to characterize the mood of vloggers. Then we implement and validate vlog classification and vlog ranking tasks using supervised learning methods. Following a reliability and correlation analysis of the mood impression data, our study demonstrates that, while the problem is challenging, several mood categories can be inferred with promising performance. Furthermore, multimodal features perform consistently better than single-channel features. Finally, we show that addressing mood as a ranking problem is a promising practical direction for several of the mood categories studied.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "9", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Vezzani:2015:GPS, author = "Roberto Vezzani and Martino Lombardi and Augusto Pieracci and Paolo Santinelli and Rita Cucchiara", title = "A General-Purpose Sensing Floor Architecture for Human-Environment Interaction", journal = j-TIIS, volume = "5", number = "2", pages = "10:1--10:??", month = jul, year = "2015", CODEN = "????", DOI = "https://doi.org/10.1145/2751566", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Fri Aug 7 09:18:56 MDT 2015", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "Smart environments are now designed as natural interfaces to capture and understand human behavior without a need for explicit human-computer interaction. In this article, we present a general-purpose architecture that acquires and understands human behaviors through a sensing floor. The pressure field generated by moving people is captured and analyzed. Specific actions and events are then detected by a low-level processing engine and sent to high-level interfaces providing different functions. The proposed architecture and sensors are modular, general-purpose, cheap, and suitable for both small- and large-area coverage. Some sample entertainment and virtual reality applications that we developed to test the platform are presented.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "10", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Baur:2015:CAA, author = "Tobias Baur and Gregor Mehlmann and Ionut Damian and Florian Lingenfelser and Johannes Wagner and Birgit Lugrin and Elisabeth Andr{\'e} and Patrick Gebhard", title = "Context-Aware Automated Analysis and Annotation of Social Human--Agent Interactions", journal = j-TIIS, volume = "5", number = "2", pages = "11:1--11:??", month = jul, year = "2015", CODEN = "????", DOI = "https://doi.org/10.1145/2764921", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Fri Aug 7 09:18:56 MDT 2015", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "The outcome of interpersonal interactions depends not only on the contents that we communicate verbally, but also on nonverbal social signals. Because a lack of social skills is a common problem for a significant number of people, serious games and other training environments have recently become the focus of research. In this work, we present NovA ( No n v erbal behavior A nalyzer), a system that analyzes and facilitates the interpretation of social signals automatically in a bidirectional interaction with a conversational agent. It records data of interactions, detects relevant social cues, and creates descriptive statistics for the recorded data with respect to the agent's behavior and the context of the situation. This enhances the possibilities for researchers to automatically label corpora of human--agent interactions and to give users feedback on strengths and weaknesses of their social behavior.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "11", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Salah:2015:BUA, author = "Albert Ali Salah and Hayley Hung and Oya Aran and Hatice Gunes and Matthew Turk", title = "Behavior Understanding for Arts and Entertainment", journal = j-TIIS, volume = "5", number = "3", pages = "12:1--12:??", month = oct, year = "2015", CODEN = "????", DOI = "https://doi.org/10.1145/2817208", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Sat Oct 17 18:18:51 MDT 2015", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "This editorial introduction complements the shorter introduction to the first part of the two-part special issue on Behavior Understanding for Arts and Entertainment. It offers a more expansive discussion of the use of behavior analysis for interactive systems that involve creativity, either for the producer or the consumer of such a system. We first summarise the two articles that appear in this second part of the special issue. We then discuss general questions and challenges in this domain that were suggested by the entire set of seven articles of the special issue and by the comments of the reviewers of these articles.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "12", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Alaoui:2015:IVM, author = "Sarah Fdili Alaoui and Frederic Bevilacqua and Christian Jacquemin", title = "Interactive Visuals as Metaphors for Dance Movement Qualities", journal = j-TIIS, volume = "5", number = "3", pages = "13:1--13:??", month = oct, year = "2015", CODEN = "????", DOI = "https://doi.org/10.1145/2738219", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Sat Oct 17 18:18:51 MDT 2015", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "The notion of ``movement qualities'' is central in contemporary dance; it describes the manner in which a movement is executed. Movement qualities convey information revealing movement expressiveness; their use has strong potential for movement-based interaction with applications in arts, entertainment, education, or rehabilitation. The purpose of our research is to design and evaluate interactive reflexive visuals for movement qualities. The theoretical basis for this research is drawn from a collaboration with the members of the international dance company Emio Greco|PC to study their formalization of movement qualities. We designed a pedagogical interactive installation called Double Skin/Double Mind (DS/DM) for the analysis and visualization of movement qualities through physical model-based interactive renderings. In this article, we first evaluate dancers' perception of the visuals as metaphors for movement qualities. This evaluation shows that, depending on the physical model parameterization, the visuals are capable of generating dynamic behaviors that the dancers associate with DS/DM movement qualities. Moreover, we evaluate dance students' and professionals' experience of the interactive visuals in the context of a dance pedagogical workshop and a professional dance training. The results of these evaluations show that the dancers consider the interactive visuals to be a reflexive system that encourages them to perform, improves their experience, and contributes to a better understanding of movement qualities. Our findings support research on interactive systems for real-time analysis and visualization of movement qualities, which open new perspectives in movement-based interaction design.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "13", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Yang:2015:QSM, author = "Yi-Hsuan Yang and Yuan-Ching Teng", title = "Quantitative Study of Music Listening Behavior in a {Smartphone} Context", journal = j-TIIS, volume = "5", number = "3", pages = "14:1--14:??", month = oct, year = "2015", CODEN = "????", DOI = "https://doi.org/10.1145/2738220", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Sat Oct 17 18:18:51 MDT 2015", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "Context-based services have attracted increasing attention because of the prevalence of sensor-rich mobile devices such as smartphones. The idea is to recommend information that a user would be interested in according to the user's surrounding context. Although remarkable progress has been made to contextualize music playback, relatively little research has been made using a large collection of real-life listening records collected in situ. In light of this fact, we present in this article a quantitative study of the personal, situational, and musical factors of musical preference in a smartphone context, using a new dataset comprising the listening records and self-report context annotation of 48 participants collected over 3wk via an Android app. Although the number of participants is limited and the population is biased towards students, the dataset is unique in that it is collected in a daily context, with sensor data and music listening profiles recorded at the same time. We investigate 3 core research questions evaluating the strength of a rich set of low-level and high-level audio features for music usage auto-tagging (i.e., music preference in different user activities), the strength of time-domain and frequency-domain sensor features for user activity classification, and how user factors such as personality traits are correlated with the predictability of music usage and user activity, using a closed set of 8 activity classes. We provide an in-depth discussion of the main findings of this study and their implications for the development of context-based music services for smartphones.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "14", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Bott:2015:WRW, author = "Jared N. Bott and Joseph J. {Laviola Jr.}", title = "The {WOZ Recognizer}: a {Wizard of Oz} Sketch Recognition System", journal = j-TIIS, volume = "5", number = "3", pages = "15:1--15:??", month = oct, year = "2015", CODEN = "????", DOI = "https://doi.org/10.1145/2743029", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Sat Oct 17 18:18:51 MDT 2015", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "Sketch recognition has the potential to be an important input method for computers in the coming years, particularly for STEM (science, technology, engineering, and math) education. However, designing and building an accurate and sophisticated sketch recognition system is a time-consuming and daunting task. Since sketch recognition mistakes are still common, it is important to understand how users perceive and tolerate recognition errors and other user interface elements with these imperfect systems. In order to solve this problem, we developed a Wizard of Oz sketch recognition tool, the WOZ Recognizer, that supports controlled recognition accuracy, multiple recognition modes, and multiple sketching domains for performing controlled experiments. We present the design of the WOZ Recognizer and our process for representing recognition domains using graphs and symbol alphabets. In addition, we discuss how sketches are altered, how to control the WOZ Recognizer, and how users interact with it. Finally, we present an expert user case study that examines the WOZ Recognizer's usability.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "15", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Soto:2015:EVA, author = "Axel J. Soto and Ryan Kiros and Vlado Keselj and Evangelos Milios", title = "Exploratory Visual Analysis and Interactive Pattern Extraction from Semi-Structured Data", journal = j-TIIS, volume = "5", number = "3", pages = "16:1--16:??", month = oct, year = "2015", CODEN = "????", DOI = "https://doi.org/10.1145/2812115", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Sat Oct 17 18:18:51 MDT 2015", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "Semi-structured documents are a common type of data containing free text in natural language (unstructured data) as well as additional information about the document, or meta-data, typically following a schema or controlled vocabulary (structured data). Simultaneous analysis of unstructured and structured data enables the discovery of hidden relationships that cannot be identified from either of these sources when analyzed independently of each other. In this work, we present a visual text analytics tool for semi-structured documents (ViTA-SSD), that aims to support the user in the exploration and finding of insightful patterns in a visual and interactive manner in a semi-structured collection of documents. It achieves this goal by presenting to the user a set of coordinated visualizations that allows the linking of the metadata with interactively generated clusters of documents in such a way that relevant patterns can be easily spotted. The system contains two novel approaches in its back end: a feature-learning method to learn a compact representation of the corpus and a fast-clustering approach that has been redesigned to allow user supervision. These novel contributions make it possible for the user to interact with a large and dynamic document collection and to perform several text analytical tasks more efficiently. Finally, we present two use cases that illustrate the suitability of the system for in-depth interactive exploration of semi-structured document collections, two user studies, and results of several evaluations of our text-mining components.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "16", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Meignan:2015:RTI, author = "David Meignan and Sigrid Knust and Jean-Marc Frayret and Gilles Pesant and Nicolas Gaud", title = "A Review and Taxonomy of Interactive Optimization Methods in Operations Research", journal = j-TIIS, volume = "5", number = "3", pages = "17:1--17:??", month = oct, year = "2015", CODEN = "????", DOI = "https://doi.org/10.1145/2808234", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Sat Oct 17 18:18:51 MDT 2015", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "This article presents a review and a classification of interactive optimization methods. These interactive methods are used for solving optimization problems. The interaction with an end user or decision maker aims at improving the efficiency of the optimization procedure, enriching the optimization model, or informing the user regarding the solutions proposed by the optimization system. First, we present the challenges of using optimization methods as a tool for supporting decision making, and we justify the integration of the user in the optimization process. This integration is generally achieved via a dynamic interaction between the user and the system. Next, the different classes of interactive optimization approaches are presented. This detailed review includes trial and error, interactive reoptimization, interactive multiobjective optimization, interactive evolutionary algorithms, human-guided search, and other approaches that are less well covered in the research literature. On the basis of this review, we propose a classification that aims to better describe and compare interaction mechanisms. This classification offers two complementary views on interactive optimization methods. The first perspective focuses on the user's contribution to the optimization process, and the second concerns the components of interactive optimization systems. Finally, on the basis of this review and classification, we identify some open issues and potential perspectives for interactive optimization methods.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "17", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Wang:2016:ART, author = "Weiyi Wang and Valentin Enescu and Hichem Sahli", title = "Adaptive Real-Time Emotion Recognition from Body Movements", journal = j-TIIS, volume = "5", number = "4", pages = "18:1--18:??", month = jan, year = "2016", CODEN = "????", DOI = "https://doi.org/10.1145/2738221", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Thu Jan 7 16:06:24 MST 2016", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "We propose a real-time system that continuously recognizes emotions from body movements. The combined low-level 3D postural features and high-level kinematic and geometrical features are fed to a Random Forests classifier through summarization (statistical values) or aggregation (bag of features). In order to improve the generalization capability and the robustness of the system, a novel semisupervised adaptive algorithm is built on top of the conventional Random Forests classifier. The MoCap UCLIC affective gesture database (labeled with four emotions) was used to train the Random Forests classifier, which led to an overall recognition rate of 78\% using a 10-fold cross-validation. Subsequently, the trained classifier was used in a stream-based semisupervised Adaptive Random Forests method for continuous unlabeled Kinect data classification. The very low update cost of our adaptive classifier makes it highly suitable for data stream applications. Tests performed on the publicly available emotion datasets (body gestures and facial expressions) indicate that our new classifier outperforms existing algorithms for data streams in terms of accuracy and computational costs.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "18", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Harper:2016:MDH, author = "F. Maxwell Harper and Joseph A. Konstan", title = "The {MovieLens} Datasets: History and Context", journal = j-TIIS, volume = "5", number = "4", pages = "19:1--19:??", month = jan, year = "2016", CODEN = "????", DOI = "https://doi.org/10.1145/2827872", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Thu Jan 7 16:06:24 MST 2016", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "The MovieLens datasets are widely used in education, research, and industry. They are downloaded hundreds of thousands of times each year, reflecting their use in popular press programming books, traditional and online courses, and software. These datasets are a product of member activity in the MovieLens movie recommendation system, an active research platform that has hosted many experiments since its launch in 1997. This article documents the history of MovieLens and the MovieLens datasets. We include a discussion of lessons learned from running a long-standing, live research platform from the perspective of a research organization. We document best practices and limitations of using the MovieLens datasets in new research.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "19", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Yordanova:2016:PSD, author = "Kristina Yordanova and Thomas Kirste", title = "A Process for Systematic Development of Symbolic Models for Activity Recognition", journal = j-TIIS, volume = "5", number = "4", pages = "20:1--20:??", month = jan, year = "2016", CODEN = "????", DOI = "https://doi.org/10.1145/2806893", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Thu Jan 7 16:06:24 MST 2016", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "Several emerging approaches to activity recognition (AR) combine symbolic representation of user actions with probabilistic elements for reasoning under uncertainty. These approaches provide promising results in terms of recognition performance, coping with the uncertainty of observations, and model size explosion when complex problems are modelled. But experience has shown that it is not always intuitive to model even seemingly simple problems. To date, there are no guidelines for developing such models. To address this problem, in this work we present a development process for building symbolic models that is based on experience acquired so far as well as on existing engineering and data analysis workflows. The proposed process is a first attempt at providing structured guidelines and practices for designing, modelling, and evaluating human behaviour in the form of symbolic models for AR. As an illustration of the process, a simple example from the office domain was developed. The process was evaluated in a comparative study of an intuitive process and the proposed process. The results showed a significant improvement over the intuitive process. Furthermore, the study participants reported greater ease of use and perceived effectiveness when following the proposed process. To evaluate the applicability of the process to more complex AR problems, it was applied to a problem from the kitchen domain. The results showed that following the proposed process yielded an average accuracy of 78\%. The developed model outperformed state-of-the-art methods applied to the same dataset in previous work, and it performed comparably to a symbolic model developed by a model expert without following the proposed development process.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "20", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Yamazaki:2016:ITN, author = "Keiichi Yamazaki and Akiko Yamazaki and Keiko Ikeda and Chen Liu and Mihoko Fukushima and Yoshinori Kobayashi and Yoshinori Kuno", title = "{``I'll Be There Next''}: a Multiplex Care Robot System that Conveys Service Order Using Gaze Gestures", journal = j-TIIS, volume = "5", number = "4", pages = "21:1--21:??", month = jan, year = "2016", CODEN = "????", DOI = "https://doi.org/10.1145/2844542", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Thu Jan 7 16:06:24 MST 2016", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "In this article, we discuss our findings from an ethnographic study at an elderly care center where we observed the utilization of two different functions of human gaze to convey service order (i.e., ``who is served first and who is served next''). In one case, when an elderly person requested assistance, the gaze of the care worker communicated that he/she would serve that client next in turn. In the other case, the gaze conveyed a request to the service seeker to wait until the care worker finished attending the current client. Each gaze function depended on the care worker's current engagement and other behaviors. We sought to integrate these findings into the development of a robot that might function more effectively in multiple human-robot party settings. We focused on the multiple functions of gaze and bodily actions, implementing those functions into our robot. We conducted three experiments to gauge a combination of gestures and gazes performed by our robot. This article demonstrates that the employment of gaze is an important consideration when developing robots that can interact effectively in multiple human-robot party settings.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "21", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Nakano:2016:GRG, author = "Yukiko I. Nakano and Takashi Yoshino and Misato Yatsushiro and Yutaka Takase", title = "Generating Robot Gaze on the Basis of Participation Roles and Dominance Estimation in Multiparty Interaction", journal = j-TIIS, volume = "5", number = "4", pages = "22:1--22:??", month = jan, year = "2016", CODEN = "????", DOI = "https://doi.org/10.1145/2743028", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Thu Jan 7 16:06:24 MST 2016", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "Gaze is an important nonverbal feedback signal in multiparty face-to-face conversations. It is well known that gaze behaviors differ depending on participation role: speaker, addressee, or side participant. In this study, we focus on dominance as another factor that affects gaze. First, we conducted an empirical study and analyzed its results that showed how gaze behaviors are affected by both dominance and participation roles. Then, using speech and gaze information that was statistically significant for distinguishing the more dominant and less dominant person in an empirical study, we established a regression-based model for estimating conversational dominance. On the basis of the model, we implemented a dominance estimation mechanism that processes online speech and head direction data. Then we applied our findings to human-robot interaction. To design robot gaze behaviors, we analyzed gaze transitions with respect to participation roles and dominance and implemented gaze-transition models as robot gaze behavior generation rules. Finally, we evaluated a humanoid robot that has dominance estimation functionality and determines its gaze based on the gaze models, and we found that dominant participants had a better impression of less dominant robot gaze behaviors. This suggests that a robot using our gaze models was preferred to a robot that was simply looking at the speaker. We have demonstrated the importance of considering dominance in human-robot multiparty interaction.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "22", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Nakano:2016:ISI, author = "Yukiko I. Nakano and Roman Bednarik and Hung-Hsuan Huang and Kristiina Jokinen", title = "Introduction to the Special Issue on New Directions in Eye Gaze for Interactive Intelligent Systems", journal = j-TIIS, volume = "6", number = "1", pages = "1:1--1:??", month = may, year = "2016", CODEN = "????", DOI = "https://doi.org/10.1145/2893485", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Sat May 21 08:06:01 MDT 2016", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "Eye gaze has been used broadly in interactive intelligent systems. The research area has grown in recent years to cover emerging topics that go beyond the traditional focus on interaction between a single user and an interactive system. This special issue presents five articles that explore new directions of gaze-based interactive intelligent systems, ranging from communication robots in dyadic and multiparty conversations to a driving simulator that uses eye gaze evidence to critique learners' behavior.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "1", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Xu:2016:SYS, author = "Tian (Linger) Xu and Hui Zhang and Chen Yu", title = "See You See Me: The Role of Eye Contact in Multimodal Human-Robot Interaction", journal = j-TIIS, volume = "6", number = "1", pages = "2:1--2:??", month = may, year = "2016", CODEN = "????", DOI = "https://doi.org/10.1145/2882970", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Sat May 21 08:06:01 MDT 2016", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "We focus on a fundamental looking behavior in human-robot interactions-gazing at each other's face. Eye contact and mutual gaze between two social partners are critical in smooth human-human interactions. Therefore, investigating at what moments and in what ways a robot should look at a human user's face as a response to the human's gaze behavior is an important topic. Toward this goal, we developed a gaze-contingent human-robot interaction system, which relied on momentary gaze behaviors from a human user to control an interacting robot in real time. Using this system, we conducted an experiment in which human participants interacted with the robot in a joint-attention task. In the experiment, we systematically manipulated the robot's gaze toward the human partner's face in real time and then analyzed the human's gaze behavior as a response to the robot's gaze behavior. We found that more face looks from the robot led to more look-backs (to the robot's face) from human participants, and consequently, created more mutual gaze and eye contact between the two. Moreover, participants demonstrated more coordinated and synchronized multimodal behaviors between speech and gaze when more eye contact was successfully established and maintained.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "2", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Wade:2016:GCA, author = "Joshua Wade and Lian Zhang and Dayi Bian and Jing Fan and Amy Swanson and Amy Weitlauf and Medha Sarkar and Zachary Warren and Nilanjan Sarkar", title = "A Gaze-Contingent Adaptive Virtual Reality Driving Environment for Intervention in Individuals with Autism Spectrum Disorders", journal = j-TIIS, volume = "6", number = "1", pages = "3:1--3:??", month = may, year = "2016", CODEN = "????", DOI = "https://doi.org/10.1145/2892636", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Sat May 21 08:06:01 MDT 2016", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "In addition to social and behavioral deficits, individuals with Autism Spectrum Disorder (ASD) often struggle to develop the adaptive skills necessary to achieve independence. Driving intervention in individuals with ASD is a growing area of study, but it is still widely under-researched. We present the development and preliminary assessment of a gaze-contingent adaptive virtual reality driving simulator that uses real-time gaze information to adapt the driving environment with the aim of providing a more individualized method of driving intervention. We conducted a small pilot study of 20 adolescents with ASD using our system: 10 with the adaptive gaze-contingent version of the system and 10 in a purely performance-based version. Preliminary results suggest that the novel intervention system may be beneficial in teaching driving skills to individuals with ASD.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "3", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Ishii:2016:PWW, author = "Ryo Ishii and Kazuhiro Otsuka and Shiro Kumano and Junji Yamato", title = "Prediction of Who Will Be the Next Speaker and When Using Gaze Behavior in Multiparty Meetings", journal = j-TIIS, volume = "6", number = "1", pages = "4:1--4:??", month = may, year = "2016", CODEN = "????", DOI = "https://doi.org/10.1145/2757284", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Sat May 21 08:06:01 MDT 2016", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "In multiparty meetings, participants need to predict the end of the speaker's utterance and who will start speaking next, as well as consider a strategy for good timing to speak next. Gaze behavior plays an important role in smooth turn-changing. This article proposes a prediction model that features three processing steps to predict (I) whether turn-changing or turn-keeping will occur, (II) who will be the next speaker in turn-changing, and (III) the timing of the start of the next speaker's utterance. For the feature values of the model, we focused on gaze transition patterns and the timing structure of eye contact between a speaker and a listener near the end of the speaker's utterance. Gaze transition patterns provide information about the order in which gaze behavior changes. The timing structure of eye contact is defined as who looks at whom and who looks away first, the speaker or listener, when eye contact between the speaker and a listener occurs. We collected corpus data of multiparty meetings, using the data to demonstrate relationships between gaze transition patterns and timing structure and situations (I), (II), and (III). The results of our analyses indicate that the gaze transition pattern of the speaker and listener and the timing structure of eye contact have a strong association with turn-changing, the next speaker in turn-changing, and the start time of the next utterance. On the basis of the results, we constructed prediction models using the gaze transition patterns and timing structure. The gaze transition patterns were found to be useful in predicting turn-changing, the next speaker in turn-changing, and the start time of the next utterance. Contrary to expectations, we did not find that the timing structure is useful for predicting the next speaker and the start time. This study opens up new possibilities for predicting the next speaker and the timing of the next utterance using gaze transition patterns in multiparty meetings.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "4", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Dardard:2016:ACL, author = "Floriane Dardard and Giorgio Gnecco and Donald Glowinski", title = "Automatic Classification of Leading Interactions in a String Quartet", journal = j-TIIS, volume = "6", number = "1", pages = "5:1--5:??", month = may, year = "2016", CODEN = "????", DOI = "https://doi.org/10.1145/2818739", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Sat May 21 08:06:01 MDT 2016", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "The aim of the present work is to analyze automatically the leading interactions between the musicians of a string quartet, using machine-learning techniques applied to nonverbal features of the musicians' behavior, which are detected through the help of a motion-capture system. We represent these interactions by a graph of ``influence'' of the musicians, which displays the relations ``is following'' and ``is not following'' with weighted directed arcs. The goal of the machine-learning problem investigated is to assign weights to these arcs in an optimal way. Since only a subset of the available training examples are labeled, a semisupervised support vector machine is used, which is based on a linear kernel to limit its model complexity. Specific potential applications within the field of human-computer interaction are also discussed, such as e-learning, networked music performance, and social active listening.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "5", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Piana:2016:ABG, author = "Stefano Piana and Alessandra Staglian{\`o} and Francesca Odone and Antonio Camurri", title = "Adaptive Body Gesture Representation for Automatic Emotion Recognition", journal = j-TIIS, volume = "6", number = "1", pages = "6:1--6:??", month = may, year = "2016", CODEN = "????", DOI = "https://doi.org/10.1145/2818740", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Sat May 21 08:06:01 MDT 2016", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "We present a computational model and a system for the automated recognition of emotions starting from full-body movement. Three-dimensional motion data of full-body movements are obtained either from professional optical motion-capture systems (Qualisys) or from low-cost RGB-D sensors (Kinect and Kinect2). A number of features are then automatically extracted at different levels, from kinematics of a single joint to more global expressive features inspired by psychology and humanistic theories (e.g., contraction index, fluidity, and impulsiveness). An abstraction layer based on dictionary learning further processes these movement features to increase the model generality and to deal with intraclass variability, noise, and incomplete information characterizing emotion expression in human movement. The resulting feature vector is the input for a classifier performing real-time automatic emotion recognition based on linear support vector machines. The recognition performance of the proposed model is presented and discussed, including the tradeoff between precision of the tracking measures (we compare the Kinect RGB-D sensor and the Qualisys motion-capture system) versus dimension of the training dataset. The resulting model and system have been successfully applied in the development of serious games for helping autistic children learn to recognize and express emotions by means of their full-body movement.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "6", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Hoque:2016:ITM, author = "Enamul Hoque and Giuseppe Carenini", title = "Interactive Topic Modeling for Exploring Asynchronous Online Conversations: Design and Evaluation of {ConVisIT}", journal = j-TIIS, volume = "6", number = "1", pages = "7:1--7:??", month = may, year = "2016", CODEN = "????", DOI = "https://doi.org/10.1145/2854158", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Sat May 21 08:06:01 MDT 2016", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "Since the mid-2000s, there has been exponential growth of asynchronous online conversations, thanks to the rise of social media. Analyzing and gaining insights from such conversations can be quite challenging for a user, especially when the discussion becomes very long. A promising solution to this problem is topic modeling, since it may help the user to understand quickly what was discussed in a long conversation and to explore the comments of interest. However, the results of topic modeling can be noisy, and they may not match the user's current information needs. To address this problem, we propose a novel topic modeling system for asynchronous conversations that revises the model on the fly on the basis of users' feedback. We then integrate this system with interactive visualization techniques to support the user in exploring long conversations, as well as in revising the topic model when the current results are not adequate to fulfill the user's information needs. Finally, we report on an evaluation with real users that compared the resulting system with both a traditional interface and an interactive visual interface that does not support human-in-the-loop topic modeling. Both the quantitative results and the subjective feedback from the participants illustrate the potential benefits of our interactive topic modeling approach for exploring conversations, relative to its counterparts.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "7", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Jannach:2016:SDM, author = "Dietmar Jannach and Michael Jugovac and Lukas Lerche", title = "Supporting the Design of Machine Learning Workflows with a Recommendation System", journal = j-TIIS, volume = "6", number = "1", pages = "8:1--8:??", month = may, year = "2016", CODEN = "????", DOI = "https://doi.org/10.1145/2852082", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Sat May 21 08:06:01 MDT 2016", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "Machine learning and data analytics tasks in practice require several consecutive processing steps. RapidMiner is a widely used software tool for the development and execution of such analytics workflows. Unlike many other algorithm toolkits, it comprises a visual editor that allows the user to design processes on a conceptual level. This conceptual and visual approach helps the user to abstract from the technical details during the development phase and to retain a focus on the core modeling task. The large set of preimplemented data analysis and machine learning operations available in the tool, as well as their logical dependencies, can, however, be overwhelming in particular for novice users. In this work, we present an add-on to the RapidMiner framework that supports the user during the modeling phase by recommending additional operations to insert into the currently developed machine learning workflow. First, we propose different recommendation techniques and evaluate them in an offline setting using a pool of several thousand existing workflows. Second, we present the results of a laboratory study, which show that our tool helps users to significantly increase the efficiency of the modeling process. Finally, we report on analyses using data that were collected during the real-world deployment of the plug-in component and compare the results of the live deployment of the tool with the results obtained through an offline analysis and a replay simulation.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "8", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Malik:2016:HVH, author = "Sana Malik and Ben Shneiderman and Fan Du and Catherine Plaisant and Margret Bjarnadottir", title = "High-Volume Hypothesis Testing: Systematic Exploration of Event Sequence Comparisons", journal = j-TIIS, volume = "6", number = "1", pages = "9:1--9:??", month = may, year = "2016", CODEN = "????", DOI = "https://doi.org/10.1145/2890478", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Sat May 21 08:06:01 MDT 2016", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "Cohort comparison studies have traditionally been hypothesis driven and conducted in carefully controlled environments (such as clinical trials). Given two groups of event sequence data, researchers test a single hypothesis (e.g., does the group taking Medication A exhibit more deaths than the group taking Medication B?). Recently, however, researchers have been moving toward more exploratory methods of retrospective analysis with existing data. In this article, we begin by showing that the task of cohort comparison is specific enough to support automatic computation against a bounded set of potential questions and objectives, a method that we refer to as High-Volume Hypothesis Testing (HVHT). From this starting point, we demonstrate that the diversity of these objectives, both across and within different domains, as well as the inherent complexities of real-world datasets, still requires human involvement to determine meaningful insights. We explore how visualization and interaction better support the task of exploratory data analysis and the understanding of HVHT results (how significant they are, why they are meaningful, and whether the entire dataset has been exhaustively explored). Through interviews and case studies with domain experts, we iteratively design and implement visualization and interaction techniques in a visual analytics tool, CoCo. As a result of our evaluation, we propose six design guidelines for enabling users to explore large result sets of HVHT systematically and flexibly in order to glean meaningful insights more quickly. Finally, we illustrate the utility of this method with three case studies in the medical domain.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "9", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Pan:2016:TLS, author = "Weike Pan and Qiang Yang and Yuchao Duan and Zhong Ming", title = "Transfer Learning for Semisupervised Collaborative Recommendation", journal = j-TIIS, volume = "6", number = "2", pages = "10:1--10:??", month = aug, year = "2016", CODEN = "????", DOI = "https://doi.org/10.1145/2835497", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Tue Oct 18 11:51:13 MDT 2016", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "Users' online behaviors such as ratings and examination of items are recognized as one of the most valuable sources of information for learning users' preferences in order to make personalized recommendations. But most previous works focus on modeling only one type of users' behaviors such as numerical ratings or browsing records, which are referred to as explicit feedback and implicit feedback, respectively. In this article, we study a Semisupervised Collaborative Recommendation (SSCR) problem with labeled feedback (for explicit feedback) and unlabeled feedback (for implicit feedback), in analogy to the well-known Semisupervised Learning (SSL) setting with labeled instances and unlabeled instances. SSCR is associated with two fundamental challenges, that is, heterogeneity of two types of users' feedback and uncertainty of the unlabeled feedback. As a response, we design a novel Self-Transfer Learning (sTL) algorithm to iteratively identify and integrate likely positive unlabeled feedback, which is inspired by the general forward/backward process in machine learning. The merit of sTL is its ability to learn users' preferences from heterogeneous behaviors in a joint and selective manner. We conduct extensive empirical studies of sTL and several very competitive baselines on three large datasets. The experimental results show that our sTL is significantly better than the state-of-the-art methods.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "10", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Verbert:2016:AVU, author = "Katrien Verbert and Denis Parra and Peter Brusilovsky", title = "Agents Vs. Users: Visual Recommendation of Research Talks with Multiple Dimension of Relevance", journal = j-TIIS, volume = "6", number = "2", pages = "11:1--11:??", month = aug, year = "2016", CODEN = "????", DOI = "https://doi.org/10.1145/2946794", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Tue Oct 18 11:51:13 MDT 2016", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "Several approaches have been researched to help people deal with abundance of information. An important feature pioneered by social tagging systems and later used in other kinds of social systems is the ability to explore different community relevance prospects by examining items bookmarked by a specific user or items associated by various users with a specific tag. A ranked list of recommended items offered by a specific recommender engine can be considered as another relevance prospect. The problem that we address is that existing personalized social systems do not allow their users to explore and combine multiple relevance prospects. Only one prospect can be explored at any given time-a list of recommended items, a list of items bookmarked by a specific user, or a list of items marked with a specific tag. In this article, we explore the notion of combining multiple relevance prospects as a way to increase effectiveness and trust. We used a visual approach to recommend articles at a conference by explicitly presenting multiple dimensions of relevance. Suggestions offered by different recommendation techniques were embodied as recommender agents to put them on the same ground as users and tags. The results of two user studies performed at academic conferences allowed us to obtain interesting insights to enhance user interfaces of personalized social systems. More specifically, effectiveness and probability of item selection increase when users are able to explore and interrelate prospects of items relevance-that is, items bookmarked by users, recommendations and tags. Nevertheless, a less-technical audience may require guidance to understand the rationale of such intersections.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "11", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Cafaro:2016:EIA, author = "Angelo Cafaro and Brian Ravenet and Magalie Ochs and Hannes H{\"o}gni Vilhj{\'a}lmsson and Catherine Pelachaud", title = "The Effects of Interpersonal Attitude of a Group of Agents on User's Presence and Proxemics Behavior", journal = j-TIIS, volume = "6", number = "2", pages = "12:1--12:??", month = aug, year = "2016", CODEN = "????", DOI = "https://doi.org/10.1145/2914796", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Tue Oct 18 11:51:13 MDT 2016", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "In the everyday world people form small conversing groups where social interaction takes place, and much of the social behavior takes place through managing interpersonal space (i.e., proxemics) and group formation, signaling their attention to others (i.e., through gaze behavior), and expressing certain attitudes, for example, friendliness, by smiling, getting close through increased engagement and intimacy, and welcoming newcomers. Many real-time interactive systems feature virtual anthropomorphic characters in order to simulate conversing groups and add plausibility and believability to the simulated environments. However, only a few have dealt with autonomous behavior generation, and in those cases, the agents' exhibited behavior should be evaluated by users in terms of appropriateness, believability, and conveyed meaning (e.g., attitudes). In this article we present an integrated intelligent interactive system for generating believable nonverbal behavior exhibited by virtual agents in small simulated group conversations. The produced behavior supports group formation management and the expression of interpersonal attitudes (friendly vs. unfriendly) both among the agents in the group (i.e., in-group attitude) and towards an approaching user in an avatar-based interaction (out-group attitude). A user study investigating the effects of these attitudes on users' social presence evaluation and proxemics behavior (with their avatar) in a three-dimensional virtual city environment is presented. We divided the study into two trials according to the task assigned to users, that is, joining a conversing group and reaching a target destination behind the group. Results showed that the out-group attitude had a major impact on social presence evaluations in both trials, whereby friendly groups were perceived as more socially rich. The user's proxemics behavior depended on both out-group and in-group attitudes expressed by the agents. Implications of these results for the design and implementation of similar intelligent interactive systems for the autonomous generation of agents' multimodal behavior are briefly discussed.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "12", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Taranta:2016:DPB, author = "Eugene M. {Taranta II} and Andr{\'e}s N. Vargas and Spencer P. Compton and Joseph J. {Laviola, Jr.}", title = "A Dynamic Pen-Based Interface for Writing and Editing Complex Mathematical Expressions With Math Boxes", journal = j-TIIS, volume = "6", number = "2", pages = "13:1--13:??", month = aug, year = "2016", CODEN = "????", DOI = "https://doi.org/10.1145/2946795", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Tue Oct 18 11:51:13 MDT 2016", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "Math boxes is a recently introduced pen-based user interface for simplifying the task of hand writing difficult mathematical expressions. Visible bounding boxes around subexpressions are automatically generated as the system detects relevant spatial relationships between symbols including superscripts, subscripts, and fractions. Subexpressions contained in a math box can then be extended by adding new terms directly into its given bounds. When new characters are accepted, box boundaries are dynamically resized and neighboring terms are translated to make room for the larger box. Feedback on structural recognition is given via the boxes themselves. In this work, we extend the math boxes interface to include support for subexpression modifications via a new set of pen-based interactions. Specifically, techniques to expand and rearrange terms in a given expression are introduced. To evaluate the usefulness of our proposed methods, we first conducted a user study in which participants wrote a variety of equations ranging in complexity from a simple polynomial to the more difficult expected value of the logistic distribution. The math boxes interface is compared against the commonly used offset typeset (small) method, where recognized expressions are typeset in a system font near the user's unmodified ink. In this initial study, we find that the fluidness of the offset method is preferred for simple expressions but that, as difficulty increases, our math boxes method is overwhelmingly preferred. We then conducted a second user study that focused only on modifying various mathematical expressions. In general, participants worked faster with the math boxes interface, and most new techniques were well received. On the basis of the two user studies, we discuss the implications of the math boxes interface and identify areas where improvements are possible.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "13", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Yang:2016:SUS, author = "Yi Yang and Shimei Pan and Jie Lu and Mercan Topkara and Yangqiu Song", title = "The Stability and Usability of Statistical Topic Models", journal = j-TIIS, volume = "6", number = "2", pages = "14:1--14:??", month = aug, year = "2016", CODEN = "????", DOI = "https://doi.org/10.1145/2954002", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Tue Oct 18 11:51:13 MDT 2016", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "Statistical topic models have become a useful and ubiquitous tool for analyzing large text corpora. One common application of statistical topic models is to support topic-centric navigation and exploration of document collections. Existing work on topic modeling focuses on the inference of model parameters so the resulting model fits the input data. Since the exact inference is intractable, statistical inference methods, such as Gibbs Sampling, are commonly used to solve the problem. However, most of the existing work ignores an important aspect that is closely related to the end user experience: topic model stability. When the model is either re-trained with the same input data or updated with new documents, the topic previously assigned to a document may change under the new model, which may result in a disruption of end users' mental maps about the relations between documents and topics, thus undermining the usability of the applications. In this article, we propose a novel user-directed non-disruptive topic model update method that balances the tradeoff between finding the model that fits the data and maintaining the stability of the model from end users' perspective. It employs a novel constrained LDA algorithm to incorporate pairwise document constraints, which are converted from user feedback about topics, to achieve topic model stability. Evaluation results demonstrate the advantages of our approach over previous methods.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "14", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Kveton:2016:MIC, author = "Branislav Kveton and Shlomo Berkovsky", title = "Minimal Interaction Content Discovery in Recommender Systems", journal = j-TIIS, volume = "6", number = "2", pages = "15:1--15:??", month = aug, year = "2016", CODEN = "????", DOI = "https://doi.org/10.1145/2845090", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Tue Oct 18 11:51:13 MDT 2016", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "Many prior works in recommender systems focus on improving the accuracy of item rating predictions. In comparison, the areas of recommendation interfaces and user-recommender interaction remain underexplored. In this work, we look into the interaction of users with the recommendation list, aiming to devise a method that simplifies content discovery and minimizes the cost of reaching an item of interest. We quantify this cost by the number of user interactions (clicks and scrolls) with the recommendation list. To this end, we propose generalized linear search (GLS), an adaptive combination of the established linear and generalized search (GS) approaches. GLS leverages the advantages of these two approaches, and we prove formally that it performs at least as well as GS. We also conduct a thorough experimental evaluation of GLS and compare it to several baselines and heuristic approaches in both an offline and live evaluation. The results of the evaluation show that GLS consistently outperforms the baseline approaches and is also preferred by users. In summary, GLS offers an efficient and easy-to-use means for content discovery in recommender systems.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "15", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Zhang:2016:BTE, author = "Cheng Zhang and Anhong Guo and Dingtian Zhang and Yang Li and Caleb Southern and Rosa I. Arriaga and Gregory D. Abowd", title = "Beyond the Touchscreen: an Exploration of Extending Interactions on Commodity {Smartphones}", journal = j-TIIS, volume = "6", number = "2", pages = "16:1--16:??", month = aug, year = "2016", CODEN = "????", DOI = "https://doi.org/10.1145/2954003", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Tue Oct 18 11:51:13 MDT 2016", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "Most smartphones today have a rich set of sensors that could be used to infer input (e.g., accelerometer, gyroscope, microphone); however, the primary mode of interaction is still limited to the front-facing touchscreen and several physical buttons on the case. To investigate the potential opportunities for interactions supported by built-in sensors, we present the implementation and evaluation of BeyondTouch, a family of interactions to extend and enrich the input experience of a smartphone. Using only existing sensing capabilities on a commodity smartphone, we offer the user a wide variety of additional inputs on the case and the surface adjacent to the smartphone. Although most of these interactions are implemented with machine learning methods, compact and robust rule-based detection methods can also be applied for recognizing some interactions by analyzing physical characteristics of tapping events on the phone. This article is an extended version of Zhang et al. [2015], which solely covered gestures implemented by machine learning methods. We extended our previous work by adding gestures implemented with rule-based methods, which works well with different users across devices without collecting any training data. We outline the implementation of both machine learning and rule-based methods for these interaction techniques and demonstrate empirical evidence of their effectiveness and usability. We also discuss the practicality of BeyondTouch for a variety of application scenarios and compare the two different implementation methods.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "16", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Bosch:2016:UVA, author = "Nigel Bosch and Sidney K. D'mello and Jaclyn Ocumpaugh and Ryan S. Baker and Valerie Shute", title = "Using Video to Automatically Detect Learner Affect in Computer-Enabled Classrooms", journal = j-TIIS, volume = "6", number = "2", pages = "17:1--17:??", month = aug, year = "2016", CODEN = "????", DOI = "https://doi.org/10.1145/2946837", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Tue Oct 18 11:51:13 MDT 2016", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "Affect detection is a key component in intelligent educational interfaces that respond to students' affective states. We use computer vision and machine-learning techniques to detect students' affect from facial expressions (primary channel) and gross body movements (secondary channel) during interactions with an educational physics game. We collected data in the real-world environment of a school computer lab with up to 30 students simultaneously playing the game while moving around, gesturing, and talking to each other. The results were cross-validated at the student level to ensure generalization to new students. Classification accuracies, quantified as area under the receiver operating characteristic curve (AUC), were above chance (AUC of 0.5) for all the affective states observed, namely, boredom (AUC = .610), confusion (AUC = .649), delight (AUC = .867), engagement (AUC = .679), frustration (AUC = .631), and for off-task behavior (AUC = .816). Furthermore, the detectors showed temporal generalizability in that there was less than a 2\% decrease in accuracy when tested on data collected from different times of the day and from different days. There was also some evidence of generalizability across ethnicity (as perceived by human coders) and gender, although with a higher degree of variability attributable to differences in affect base rates across subpopulations. In summary, our results demonstrate the feasibility of generalizable video-based detectors of naturalistic affect in a real-world setting, suggesting that the time is ripe for affect-sensitive interventions in educational games and other intelligent interfaces.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "17", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Tanaka:2016:TSC, author = "Hiroki Tanaka and Sakti Sakriani and Graham Neubig and Tomoki Toda and Hideki Negoro and Hidemi Iwasaka and Satoshi Nakamura", title = "Teaching Social Communication Skills Through Human-Agent Interaction", journal = j-TIIS, volume = "6", number = "2", pages = "18:1--18:??", month = aug, year = "2016", CODEN = "????", DOI = "https://doi.org/10.1145/2937757", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Tue Oct 18 11:51:13 MDT 2016", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "There are a large number of computer-based systems that aim to train and improve social skills. However, most of these do not resemble the training regimens used by human instructors. In this article, we propose a computer-based training system that follows the procedure of social skills training (SST), a well-established method to decrease human anxiety and discomfort in social interaction, and acquire social skills. We attempt to automate the process of SST by developing a dialogue system named the automated social skills trainer, which teaches social communication skills through human-agent interaction. The system includes a virtual avatar that recognizes user speech and language information and gives feedback to users. Its design is based on conventional SST performed by human participants, including defining target skills, modeling, role-play, feedback, reinforcement, and homework. We performed a series of three experiments investigating (1) the advantages of using computer-based training systems compared to human-human interaction (HHI) by subjectively evaluating nervousness, ease of talking, and ability to talk well; (2) the relationship between speech language features and human social skills; and (3) the effect of computer-based training using our proposed system. Results of our first experiment show that interaction with an avatar decreases nervousness and increases the user's subjective impression of his or her ability to talk well compared to interaction with an unfamiliar person. The experimental evaluation measuring the relationship between social skill and speech and language features shows that these features have a relationship with social skills. Finally, experiments measuring the effect of performing SST with the proposed application show that participants significantly improve their skill, as assessed by separate evaluators, by using the system for 50 minutes. A user survey also shows that the users thought our system is useful and easy to use, and that interaction with the avatar felt similar to HHI.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "18", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Mahmoud:2016:AAN, author = "Marwa Mahmoud and Tadas Baltrusaitis and Peter Robinson", title = "Automatic Analysis of Naturalistic Hand-Over-Face Gestures", journal = j-TIIS, volume = "6", number = "2", pages = "19:1--19:??", month = aug, year = "2016", CODEN = "????", DOI = "https://doi.org/10.1145/2946796", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Tue Oct 18 11:51:13 MDT 2016", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "One of the main factors that limit the accuracy of facial analysis systems is hand occlusion. As the face becomes occluded, facial features are lost, corrupted, or erroneously detected. Hand-over-face occlusions are considered not only very common but also very challenging to handle. However, there is empirical evidence that some of these hand-over-face gestures serve as cues for recognition of cognitive mental states. In this article, we present an analysis of automatic detection and classification of hand-over-face gestures. We detect hand-over-face occlusions and classify hand-over-face gesture descriptors in videos of natural expressions using multi-modal fusion of different state-of-the-art spatial and spatio-temporal features. We show experimentally that we can successfully detect face occlusions with an accuracy of 83\%. We also demonstrate that we can classify gesture descriptors ( hand shape, hand action, and facial region occluded ) significantly better than a na{\"\i}ve baseline. Our detailed quantitative analysis sheds some light on the challenges of automatic classification of hand-over-face gestures in natural expressions.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "19", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Ishii:2016:URP, author = "Ryo Ishii and Kazuhiro Otsuka and Shiro Kumano and Junji Yamato", title = "Using Respiration to Predict Who Will Speak Next and When in Multiparty Meetings", journal = j-TIIS, volume = "6", number = "2", pages = "20:1--20:??", month = aug, year = "2016", CODEN = "????", DOI = "https://doi.org/10.1145/2946838", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Tue Oct 18 11:51:13 MDT 2016", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "Techniques that use nonverbal behaviors to predict turn-changing situations-such as, in multiparty meetings, who the next speaker will be and when the next utterance will occur-have been receiving a lot of attention in recent research. To build a model for predicting these behaviors we conducted a research study to determine whether respiration could be effectively used as a basis for the prediction. Results of analyses of utterance and respiration data collected from participants in multiparty meetings reveal that the speaker takes a breath more quickly and deeply after the end of an utterance in turn-keeping than in turn-changing. They also indicate that the listener who will be the next speaker takes a bigger breath more quickly and deeply in turn-changing than the other listeners. On the basis of these results, we constructed and evaluated models for predicting the next speaker and the time of the next utterance in multiparty meetings. The results of the evaluation suggest that the characteristics of the speaker's inhalation right after an utterance unit-the points in time at which the inhalation starts and ends after the end of the utterance unit and the amplitude, slope, and duration of the inhalation phase-are effective for predicting the next speaker in multiparty meetings. They further suggest that the characteristics of listeners' inhalation-the points in time at which the inhalation starts and ends after the end of the utterance unit and the minimum and maximum inspiration, amplitude, and slope of the inhalation phase-are effective for predicting the next speaker. The start time and end time of the next speaker's inhalation are also useful for predicting the time of the next utterance in turn-changing.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "20", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Prendinger:2016:IBT, author = "Helmut Prendinger and Nahum Alvarez and Antonio Sanchez-Ruiz and Marc Cavazza and jo{\~a}o Catarino and Jo{\~a}o Oliveira and Rui Prada and Shuji Fujimoto and Mika Shigematsu", title = "Intelligent Biohazard Training Based on Real-Time Task Recognition", journal = j-TIIS, volume = "6", number = "3", pages = "21:1--21:??", month = oct, year = "2016", CODEN = "????", DOI = "https://doi.org/10.1145/2883617", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Tue Oct 18 11:51:14 MDT 2016", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "Virtual environments offer an ideal setting to develop intelligent training applications. Yet, their ability to support complex procedures depends on the appropriate integration of knowledge-based techniques and natural interaction. In this article, we describe the implementation of an intelligent rehearsal system for biohazard laboratory procedures, based on the real-time instantiation of task models from the trainee's actions. A virtual biohazard laboratory has been recreated using the Unity3D engine, in which users interact with laboratory objects using keyboard/mouse input or hand gestures through a Kinect device. Realistic behavior for objects is supported by the implementation of a relevant subset of common sense and physics knowledge. User interaction with objects leads to the recognition of specific actions, which are used to progressively instantiate a task-based representation of biohazard procedures. The dynamics of this instantiation process supports trainee evaluation as well as real-time assistance. This system is designed primarily as a rehearsal system providing real-time advice and supporting user performance evaluation. We provide detailed examples illustrating error detection and recovery, and results from on-site testing with students from the Faculty of Medical Sciences at Kyushu University. In the study, we investigate the usability aspect by comparing interaction with mouse and Kinect devices and the effect of real-time task recognition on recovery time after user mistakes.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "21", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Sappelli:2016:AIA, author = "Maya Sappelli and Suzan Verberne and Wessel Kraaij", title = "Adapting the Interactive Activation Model for Context Recognition and Identification", journal = j-TIIS, volume = "6", number = "3", pages = "22:1--22:??", month = oct, year = "2016", CODEN = "????", DOI = "https://doi.org/10.1145/2873067", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Tue Oct 18 11:51:14 MDT 2016", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "In this article, we propose and implement a new model for context recognition and identification. Our work is motivated by the importance of ``working in context'' for knowledge workers to stay focused and productive. A computer application that can identify the current context in which the knowledge worker is working can (among other things) provide the worker with contextual support, for example, by suggesting relevant information sources, or give an overview of how he or she spent his or her time during the day. We present a descriptive model for the context of a knowledge worker. This model describes the contextual elements in the work environment of the knowledge worker and how these elements relate to each other. This model is operationalized in an algorithm, the contextual interactive activation model (CIA), which is based on the interactive activation model by Rumelhart and McClelland. It consists of a layered connected network through which activation flows. We have tested CIA in a context identification setting. In this case, the data that we use as input is low-level computer interaction logging data. We found that topical information and entities were the most relevant types of information for context identification. Overall the proposed CIA model is more effective than traditional supervised methods in identifying the active context from sparse input data, with less labelled training data.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "22", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Aslan:2016:DEM, author = "Ilhan Aslan and Andreas Uhl and Alexander Meschtscherjakov and Manfred Tscheligi", title = "Design and Exploration of Mid-Air Authentication Gestures", journal = j-TIIS, volume = "6", number = "3", pages = "23:1--23:??", month = oct, year = "2016", CODEN = "????", DOI = "https://doi.org/10.1145/2832919", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Tue Oct 18 11:51:14 MDT 2016", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "Authentication based on touchless mid-air gestures would benefit a multitude of ubiquitous computing applications, especially those that are used in clean environments (e.g., medical environments or clean rooms). In order to explore the potential of mid-air gestures for novel authentication approaches, we performed a series of studies and design experiments. First, we collected data from more then 200 users during a 3-day science event organized within a shopping mall. These data were used to investigate capabilities of the Leap Motion sensor, observe interaction in the wild, and to formulate an initial design problem. The design problem, as well as the design of mid-air gestures for authentication purposes, were iterated in subsequent design activities. In a final study with 13 participants, we evaluated two mid-air gestures for authentication purposes in different situations, including different body positions. Our results highlight a need for different mid-air gestures for differing situations and carefully chosen constraints for mid-air gestures. We conclude by proposing an exemplary system, which aims to provide tool-support for designers and engineers, allowing them to explore authentication gestures in the original context of use and thus support them with the design of contextual mid-air authentication gestures.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "23", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{El-Glaly:2016:RWY, author = "Yasmine N. El-Glaly and Francis Quek", title = "Read What You Touch with Intelligent Audio System for Non-Visual Interaction", journal = j-TIIS, volume = "6", number = "3", pages = "24:1--24:??", month = oct, year = "2016", CODEN = "????", DOI = "https://doi.org/10.1145/2822908", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Tue Oct 18 11:51:14 MDT 2016", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "Slate-type devices allow Individuals with Blindness or Severe Visual Impairment (IBSVI) to read in place with the touch of their fingertip by audio-rendering the words they touch. Such technologies are helpful for spatial cognition while reading. However, users have to move their fingers slowly, or they may lose their place on screen. Also, IBSVI may wander between lines without realizing they did. We addressed these two interaction problems by introducing a dynamic speech-touch interaction model and an intelligent reading support system. With this model, the speed of the speech will dynamically change with the user's finger speed. The proposed model is composed of (1) an Audio Dynamics Model and (2) an Off-line Speech Synthesis Technique. The intelligent reading support system predicts the direction of reading, corrects the reading word if the user drifts, and notifies the user using a sonic gutter to help him/her from straying off the reading line. We tested the new audio dynamics model, the sonic gutter, and the reading support model in two user studies. The participants' feedback helped us fine-tune the parameters of the two models. A decomposition study was conducted to evaluate the main components of the system. The results showed that both intelligent reading support with tactile feedback are required to achieve the best performance in terms of efficiency and effectiveness. Finally, we ran an evaluation study where the reading support system is compared to other VoiceOver technologies. The results showed preponderance to the reading support system with its audio dynamics and intelligent reading support components.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "24", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Park:2016:MAP, author = "Sunghyun Park and Han Suk Shim and Moitreya Chatterjee and Kenji Sagae and Louis-Philippe Morency", title = "Multimodal Analysis and Prediction of Persuasiveness in Online Social Multimedia", journal = j-TIIS, volume = "6", number = "3", pages = "25:1--25:??", month = oct, year = "2016", CODEN = "????", DOI = "https://doi.org/10.1145/2897739", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Tue Oct 18 11:51:14 MDT 2016", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "Our lives are heavily influenced by persuasive communication, and it is essential in almost any type of social interaction from business negotiation to conversation with our friends and family. With the rapid growth of social multimedia websites, it is becoming ever more important and useful to understand persuasiveness in the context of social multimedia content online. In this article, we introduce a newly created multimedia corpus of 1,000 movie review videos with subjective annotations of persuasiveness and related high-level characteristics or attributes (e.g., confidence). This dataset will be made freely available to the research community. We designed our experiments around the following five main research hypotheses. First, we study if computational descriptors derived from verbal and nonverbal behavior can be predictive of persuasiveness. We further explore combining descriptors from multiple communication modalities (acoustic, verbal, para-verbal, and visual) for predicting persuasiveness and compare with using a single modality alone. Second, we investigate how certain high-level attributes, such as credibility or expertise, are related to persuasiveness and how the information can be used in modeling and predicting persuasiveness. Third, we investigate differences when speakers are expressing a positive or negative opinion and if the opinion polarity has any influence in the persuasiveness prediction. Fourth, we further study if gender has any influence in the prediction performance. Last, we test if it is possible to make comparable predictions of persuasiveness by only looking at thin slices (i.e., shorter time windows) of a speaker's behavior.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "25", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Tintarev:2016:ISI, author = "Nava Tintarev and John O'donovan and Alexander Felfernig", title = "Introduction to the Special Issue on Human Interaction with Artificial Advice Givers", journal = j-TIIS, volume = "6", number = "4", pages = "26:1--26:??", month = dec, year = "2016", CODEN = "????", DOI = "https://doi.org/10.1145/3014432", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Sat Mar 25 07:51:07 MDT 2017", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "Many interactive systems in today's world can be viewed as providing advice to their users. Commercial examples include recommender systems, satellite navigation systems, intelligent personal assistants on smartphones, and automated checkout systems in supermarkets. We will call these systems that support people in making choices and decisions artificial advice givers (AAGs): They propose and evaluate options while involving their human users in the decision-making process. This special issue addresses the challenge of improving the interaction between artificial and human agents. It answers the question of how an agent of each type (human and artificial) can influence and understand the reasoning, working models, and conclusions of the other agent by means of novel forms of interaction. To address this challenge, the articles in the special issue are organized around three themes: (a) human factors to consider when designing interactions with AAGs (e.g., over- and under-reliance, overestimation of the system's capabilities), (b) methods for supporting interaction with AAGs (e.g., natural language, visualization, and argumentation), and (c) considerations for evaluating AAGs (both criteria and methodology for applying them).", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "26", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Sutherland:2016:EAE, author = "Steven C. Sutherland and Casper Harteveld and Michael E. Young", title = "Effects of the Advisor and Environment on Requesting and Complying With Automated Advice", journal = j-TIIS, volume = "6", number = "4", pages = "27:1--27:??", month = dec, year = "2016", CODEN = "????", DOI = "https://doi.org/10.1145/2905370", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Sat Mar 25 07:51:07 MDT 2017", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "Given the rapid technological advances in our society and the increase in artificial and automated advisors with whom we interact on a daily basis, it is becoming increasingly necessary to understand how users interact with and why they choose to request and follow advice from these types of advisors. More specifically, it is necessary to understand errors in advice utilization. In the present study, we propose a methodological framework for studying interactions between users and automated or other artificial advisors. Specifically, we propose the use of virtual environments and the tarp technique for stimulus sampling, ensuring sufficient sampling of important extreme values and the stimulus space between those extremes. We use this proposed framework to identify the impact of several factors on when and how advice is used. Additionally, because these interactions take place in different environments, we explore the impact of where the interaction takes place on the decision to interact. We varied the cost of advice, the reliability of the advisor, and the predictability of the environment to better understand the impact of these factors on the overutilization of suboptimal advisors and underutilization of optimal advisors. We found that less predictable environments, more reliable advisors, and lower costs for advice led to overutilization, whereas more predictable environments and less reliable advisors led to underutilization. Moreover, once advice was received, users took longer to make a final decision, suggesting less confidence and trust in the advisor when the reliability of the advisor was lower, the environment was less predictable, and the advice was not consistent with the environmental cues. These results contribute to a more complete understanding of advice utilization and trust in advisors.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "27", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Knijnenburg:2016:ICI, author = "Bart P. Knijnenburg and Martijn C. Willemsen", title = "Inferring Capabilities of Intelligent Agents from Their External Traits", journal = j-TIIS, volume = "6", number = "4", pages = "28:1--28:??", month = dec, year = "2016", CODEN = "????", DOI = "https://doi.org/10.1145/2963106", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Sat Mar 25 07:51:07 MDT 2017", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "We investigate the usability of humanlike agent-based interfaces for interactive advice-giving systems. In an experiment with a travel advisory system, we manipulate the ``humanlikeness'' of the agent interface. We demonstrate that users of the more humanlike agents try to exploit capabilities that were not signaled by the system. This severely reduces the usability of systems that look human but lack humanlikehumanlike capabilities (overestimation effect). We explain this effect by showing that users of humanlike agents form anthropomorphic beliefs (a user's ``mental model'') about the system: They act humanlike towards the system and try to exploit typical humanlike capabilities they believe the system possesses. Furthermore, we demonstrate that the mental model users form of an agent-based system is inherently integrated (as opposed to the compositional mental model they form of conventional interfaces): Cues provided by the system do not instill user responses in a one-to-one matter but are instead integrated into a single mental model.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "28", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Clark:2016:MAA, author = "Leigh Clark and Abdulmalik Ofemile and Svenja Adolphs and Tom Rodden", title = "A Multimodal Approach to Assessing User Experiences with Agent Helpers", journal = j-TIIS, volume = "6", number = "4", pages = "29:1--29:??", month = dec, year = "2016", CODEN = "????", DOI = "https://doi.org/10.1145/2983926", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Sat Mar 25 07:51:07 MDT 2017", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "The study of agent helpers using linguistic strategies such as vague language and politeness has often come across obstacles. One of these is the quality of the agent's voice and its lack of appropriate fit for using these strategies. The first approach of this article compares human vs. synthesised voices in agents using vague language. This approach analyses the 60,000-word text corpus of participant interviews to investigate the differences of user attitudes towards the agents, their voices and their use of vague language. It discovers that while the acceptance of vague language is still met with resistance in agent instructors, using a human voice yields more positive results than the synthesised alternatives. The second approach in this article discusses the development of a novel multimodal corpus of video and text data to create multiple analyses of human-agent interaction in agent-instructed assembly tasks. The second approach analyses user spontaneous facial actions and gestures during their interaction in the tasks. It found that agents are able to elicit these facial actions and gestures and posits that further analysis of this nonverbal feedback may help to create a more adaptive agent. Finally, the approaches used in this article suggest these can contribute to furthering the understanding of what it means to interact with software agents.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "29", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Rosenfeld:2016:PAD, author = "Ariel Rosenfeld and Sarit Kraus", title = "Providing Arguments in Discussions on the Basis of the Prediction of Human Argumentative Behavior", journal = j-TIIS, volume = "6", number = "4", pages = "30:1--30:??", month = dec, year = "2016", CODEN = "????", DOI = "https://doi.org/10.1145/2983925", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Sat Mar 25 07:51:07 MDT 2017", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "Argumentative discussion is a highly demanding task. In order to help people in such discussions, this article provides an innovative methodology for developing agents that can support people in argumentative discussions by proposing possible arguments. By gathering and analyzing human argumentative behavior from more than 1000 human study participants, we show that the prediction of human argumentative behavior using Machine Learning (ML) is possible and useful in designing argument provision agents. This paper first demonstrates that ML techniques can achieve up to 76\% accuracy when predicting people's top three argument choices given a partial discussion. We further show that well-established Argumentation Theory is not a good predictor of people's choice of arguments. Then, we present 9 argument provision agents, which we empirically evaluate using hundreds of human study participants. We show that the Predictive and Relevance-Based Heuristic agent (PRH), which uses ML prediction with a heuristic that estimates the relevance of possible arguments to the current state of the discussion, results in significantly higher levels of satisfaction among study participants compared with the other evaluated agents. These other agents propose arguments based on Argumentation Theory; propose predicted arguments without the heuristics or with only the heuristics; or use Transfer Learning methods. Our findings also show that people use the PRH agents proposed arguments significantly more often than those proposed by the other agents.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "30", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Mutlu:2016:VRP, author = "Belgin Mutlu and Eduardo Veas and Christoph Trattner", title = "{VizRec}: Recommending Personalized Visualizations", journal = j-TIIS, volume = "6", number = "4", pages = "31:1--31:??", month = dec, year = "2016", CODEN = "????", DOI = "https://doi.org/10.1145/2983923", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Sat Mar 25 07:51:07 MDT 2017", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "Visualizations have a distinctive advantage when dealing with the information overload problem: Because they are grounded in basic visual cognition, many people understand them. However, creating proper visualizations requires specific expertise of the domain and underlying data. Our quest in this article is to study methods to suggest appropriate visualizations autonomously. To be appropriate, a visualization has to follow known guidelines to find and distinguish patterns visually and encode data therein. A visualization tells a story of the underlying data; yet, to be appropriate, it has to clearly represent those aspects of the data the viewer is interested in. Which aspects of a visualization are important to the viewer? Can we capture and use those aspects to recommend visualizations? This article investigates strategies to recommend visualizations considering different aspects of user preferences. A multi-dimensional scale is used to estimate aspects of quality for visualizations for collaborative filtering. Alternatively, tag vectors describing visualizations are used to recommend potentially interesting visualizations based on content. Finally, a hybrid approach combines information on what a visualization is about (tags) and how good it is (ratings). We present the design principles behind VizRec, our visual recommender. We describe its architecture, the data acquisition approach with a crowd sourced study, and the analysis of strategies for visualization recommendation.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "31", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Paudel:2017:UAD, author = "Bibek Paudel and Fabian Christoffel and Chris Newell and Abraham Bernstein", title = "Updatable, Accurate, Diverse, and Scalable Recommendations for Interactive Applications", journal = j-TIIS, volume = "7", number = "1", pages = "1:1--1:??", month = mar, year = "2017", CODEN = "????", DOI = "https://doi.org/10.1145/2955101", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Sat Mar 25 07:51:07 MDT 2017", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "Recommender systems form the backbone of many interactive systems. They incorporate user feedback to personalize the user experience typically via personalized recommendation lists. As users interact with a system, an increasing amount of data about a user's preferences becomes available, which can be leveraged for improving the systems' performance. Incorporating these new data into the underlying recommendation model is, however, not always straightforward. Many models used by recommender systems are computationally expensive and, therefore, have to perform offline computations to compile the recommendation lists. For interactive applications, it is desirable to be able to update the computed values as soon as new user interaction data is available: updating recommendations in interactive time using new feedback data leads to better accuracy and increases the attraction of the system to the users. Additionally, there is a growing consensus that accuracy alone is not enough and user satisfaction is also dependent on diverse recommendations. In this work, we tackle this problem of updating personalized recommendation lists for interactive applications in order to provide both accurate and diverse recommendations. To that end, we explore algorithms that exploit random walks as a sampling technique to obtain diverse recommendations without compromising on efficiency and accuracy. Specifically, we present a novel graph vertex ranking recommendation algorithm called RP$^3_\beta $ that reranks items based on three-hop random walk transition probabilities. We show empirically that RP$^3_\beta $ provides accurate recommendations with high long-tail item frequency at the top of the recommendation list. We also present approximate versions of RP$^3_\beta $ and the two most accurate previously published vertex ranking algorithms based on random walk transition probabilities and show that these approximations converge with an increasing number of samples. To obtain interactively updatable recommendations, we additionally show how our algorithm can be extended for online updates at interactive speeds. The underlying random walk sampling technique makes it possible to perform the updates without having to recompute the values for the entire dataset. In an empirical evaluation with three real-world datasets, we show that RP$^3_\beta $ provides highly accurate and diverse recommendations that can easily be updated with newly gathered information at interactive speeds ($ \ll $ 100 ms ).", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "1", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Kaminskas:2017:DSN, author = "Marius Kaminskas and Derek Bridge", title = "Diversity, Serendipity, Novelty, and Coverage: a Survey and Empirical Analysis of Beyond-Accuracy Objectives in Recommender Systems", journal = j-TIIS, volume = "7", number = "1", pages = "2:1--2:??", month = mar, year = "2017", CODEN = "????", DOI = "https://doi.org/10.1145/2926720", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Sat Mar 25 07:51:07 MDT 2017", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "What makes a good recommendation or good list of recommendations? Research into recommender systems has traditionally focused on accuracy, in particular how closely the recommender's predicted ratings are to the users' true ratings. However, it has been recognized that other recommendation qualities-such as whether the list of recommendations is diverse and whether it contains novel items-may have a significant impact on the overall quality of a recommender system. Consequently, in recent years, the focus of recommender systems research has shifted to include a wider range of ``beyond accuracy'' objectives. In this article, we present a survey of the most discussed beyond-accuracy objectives in recommender systems research: diversity, serendipity, novelty, and coverage. We review the definitions of these objectives and corresponding metrics found in the literature. We also review works that propose optimization strategies for these beyond-accuracy objectives. Since the majority of works focus on one specific objective, we find that it is not clear how the different objectives relate to each other. Hence, we conduct a set of offline experiments aimed at comparing the performance of different optimization approaches with a view to seeing how they affect objectives other than the ones they are optimizing. We use a set of state-of-the-art recommendation algorithms optimized for recall along with a number of reranking strategies for optimizing the diversity, novelty, and serendipity of the generated recommendations. For each reranking strategy, we measure the effects on the other beyond-accuracy objectives and demonstrate important insights into the correlations between the discussed objectives. For instance, we find that rating-based diversity is positively correlated with novelty, and we demonstrate the positive influence of novelty on recommendation coverage.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "2", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Zhang:2017:EEI, author = "Ting Zhang and Yu-Ting Li and Juan P. Wachs", title = "The Effect of Embodied Interaction in Visual-Spatial Navigation", journal = j-TIIS, volume = "7", number = "1", pages = "3:1--3:??", month = mar, year = "2017", CODEN = "????", DOI = "https://doi.org/10.1145/2953887", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Sat Mar 25 07:51:07 MDT 2017", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "This article aims to assess the effect of embodied interaction on attention during the process of solving spatio-visual navigation problems. It presents a method that links operator's physical interaction, feedback, and attention. Attention is inferred through networks called Bayesian Attentional Networks (BANs). BANs are structures that describe cause-effect relationship between attention and physical action. Then, a utility function is used to determine the best combination of interaction modalities and feedback. Experiments involving five physical interaction modalities (vision-based gesture interaction, glove-based gesture interaction, speech, feet, and body stance) and two feedback modalities (visual and sound) are described. The main findings are: (i) physical expressions have an effect in the quality of the solutions to spatial navigation problems; (ii) the combination of feet gestures with visual feedback provides the best task performance.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "3", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Ochs:2017:UPB, author = "Magalie Ochs and Catherine Pelachaud and Gary Mckeown", title = "A User Perception--Based Approach to Create Smiling Embodied Conversational Agents", journal = j-TIIS, volume = "7", number = "1", pages = "4:1--4:??", month = mar, year = "2017", CODEN = "????", DOI = "https://doi.org/10.1145/2925993", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Sat Mar 25 07:51:07 MDT 2017", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "In order to improve the social capabilities of embodied conversational agents, we propose a computational model to enable agents to automatically select and display appropriate smiling behavior during human--machine interaction. A smile may convey different communicative intentions depending on subtle characteristics of the facial expression and contextual cues. To construct such a model, as a first step, we explore the morphological and dynamic characteristics of different types of smiles (polite, amused, and embarrassed smiles) that an embodied conversational agent may display. The resulting lexicon of smiles is based on a corpus of virtual agents' smiles directly created by users and analyzed through a machine-learning technique. Moreover, during an interaction, a smiling expression impacts on the observer's perception of the interpersonal stance of the speaker. As a second step, we propose a probabilistic model to automatically compute the user's potential perception of the embodied conversational agent's social stance depending on its smiling behavior and on its physical appearance. This model, based on a corpus of users' perceptions of smiling and nonsmiling virtual agents, enables a virtual agent to determine the appropriate smiling behavior to adopt given the interpersonal stance it wants to express. An experiment using real human--virtual agent interaction provided some validation of the proposed model.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "4", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Ramirez-Amaro:2017:AVG, author = "Karinne Ramirez-Amaro and Humera Noor Minhas and Michael Zehetleitner and Michael Beetz and Gordon Cheng", title = "Added Value of Gaze-Exploiting Semantic Representation to Allow Robots Inferring Human Behaviors", journal = j-TIIS, volume = "7", number = "1", pages = "5:1--5:??", month = mar, year = "2017", CODEN = "????", DOI = "https://doi.org/10.1145/2939381", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Sat Mar 25 07:51:07 MDT 2017", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "Neuroscience studies have shown that incorporating gaze view with third view perspective has a great influence to correctly infer human behaviors. Given the importance of both first and third person observations for the recognition of human behaviors, we propose a method that incorporates these observations in a technical system to enhance the recognition of human behaviors, thus improving beyond third person observations in a more robust human activity recognition system. First, we present the extension of our proposed semantic reasoning method by including gaze data and external observations as inputs to segment and infer human behaviors in complex real-world scenarios. Then, from the obtained results we demonstrate that the combination of gaze and external input sources greatly enhance the recognition of human behaviors. Our findings have been applied to a humanoid robot to online segment and recognize the observed human activities with better accuracy when using both input sources; for example, the activity recognition increases from 77\% to 82\% in our proposed pancake-making dataset. To provide completeness of our system, we have evaluated our approach with another dataset with a similar setup as the one proposed in this work, that is, the CMU-MMAC dataset. In this case, we improved the recognition of the activities for the egg scrambling scenario from 54\% to 86\% by combining the external views with the gaze information, thus showing the benefit of incorporating gaze information to infer human behaviors across different datasets.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "5", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Cena:2017:ISI, author = "F. Cena and C. Gena and G. J. Houben and M. Strohmaier", title = "Introduction to the {Special Issue on Big Personal Data in Interactive Intelligent Systems}", journal = j-TIIS, volume = "7", number = "2", pages = "6:1--6:??", month = jul, year = "2017", CODEN = "????", DOI = "https://doi.org/10.1145/3101102", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Fri Sep 8 08:41:25 MDT 2017", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "This brief introduction begins with an overview of the types of research that are relevant to the special issue on Big Personal Data in Interactive Intelligent Systems. The overarching question is: How can big personal data be collected, analyzed, and exploited so as to provide new or improved forms of interaction with intelligent systems, and what new issues have to be taken into account? The three articles accepted for the special issue are then characterized in terms of the concepts of this overview.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "6", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Cassavia:2017:DUB, author = "Nunziato Cassavia and Elio Masciari and Chiara Pulice and Domenico Sacc{\`a}", title = "Discovering User Behavioral Features to Enhance Information Search on Big Data", journal = j-TIIS, volume = "7", number = "2", pages = "7:1--7:??", month = jul, year = "2017", CODEN = "????", DOI = "https://doi.org/10.1145/2856059", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Fri Sep 8 08:41:25 MDT 2017", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "Due to the emerging Big Data paradigm, driven by the increasing availability of intelligent services easily accessible by a large number of users (e.g., social networks), traditional data management techniques are inadequate in many real-life scenarios. In particular, the availability of huge amounts of data pertaining to user social interactions, user preferences, and opinions calls for advanced analysis strategies to understand potentially interesting social dynamics. Furthermore, heterogeneity and high speed of user-generated data require suitable data storage and management tools to be designed from scratch. This article presents a framework tailored for analyzing user interactions with intelligent systems while seeking some domain-specific information (e.g., choosing a good restaurant in a visited area). The framework enhances a user's quest for information by exploiting previous knowledge about their social environment, the extent of influence the users are potentially subject to, and the influence they may exert on other users. User influence spread across the network is dynamically computed as well to improve user search strategy by providing specific suggestions, represented as tailored faceted features. Such features are the result of data exchange activity (called data posting) that enriches information sources with additional background information and knowledge derived from experiences and behavioral properties of domain experts and users. The approach is tested in an important application scenario such as tourist recommendation, but it can be profitably exploited in several other contexts, for example, viral marketing and food education.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "7", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Kim:2017:MML, author = "Seungjun Kim and Dan Tasse and Anind K. Dey", title = "Making Machine-Learning Applications for Time-Series Sensor Data Graphical and Interactive", journal = j-TIIS, volume = "7", number = "2", pages = "8:1--8:??", month = jul, year = "2017", CODEN = "????", DOI = "https://doi.org/10.1145/2983924", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Fri Sep 8 08:41:25 MDT 2017", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "The recent profusion of sensors has given consumers and researchers the ability to collect significant amounts of data. However, understanding sensor data can be a challenge, because it is voluminous, multi-sourced, and unintelligible. Nonetheless, intelligent systems, such as activity recognition, require pattern analysis of sensor data streams to produce compelling results; machine learning (ML) applications enable this type of analysis. However, the number of ML experts able to proficiently classify sensor data is limited, and there remains a lack of interactive, usable tools to help intermediate users perform this type of analysis. To learn which features these tools must support, we conducted interviews with intermediate users of ML and conducted two probe-based studies with a prototype ML and visual analytics system, Gimlets. Our system implements ML applications for sensor-based time-series data as a novel domain-specific prototype that integrates interactive visual analytic features into the ML pipeline. We identify future directions for usable ML systems based on sensor data that will enable intermediate users to build systems that have been prohibitively difficult.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "8", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Zanzotto:2017:YLT, author = "Fabio Massimo Zanzotto and Lorenzo Ferrone", title = "Have You Lost the Thread? {Discovering} Ongoing Conversations in Scattered Dialog Blocks", journal = j-TIIS, volume = "7", number = "2", pages = "9:1--9:??", month = jul, year = "2017", CODEN = "????", DOI = "https://doi.org/10.1145/2885501", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Fri Sep 8 08:41:25 MDT 2017", bibsource = "http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "Finding threads in textual dialogs is emerging as a need to better organize stored knowledge. We capture this need by introducing the novel task of discovering ongoing conversations in scattered dialog blocks. Our aim in this article is twofold. First, we propose a publicly available testbed for the task by solving the insurmountable problem of privacy of Big Personal Data. In fact, we showed that personal dialogs can be surrogated with theatrical plays. Second, we propose a suite of computationally light learning models that can use syntactic and semantic features. With this suite, we showed that models for this challenging task should include features capturing shifts in language use and, possibly, modeling underlying scripts.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "9", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Jugovac:2017:IRO, author = "Michael Jugovac and Dietmar Jannach", title = "Interacting with Recommenders-Overview and Research Directions", journal = j-TIIS, volume = "7", number = "3", pages = "10:1--10:??", month = oct, year = "2017", CODEN = "????", DOI = "https://doi.org/10.1145/3001837", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Mon Jan 22 17:18:51 MST 2018", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "Automated recommendations have become a ubiquitous part of today's online user experience. These systems point us to additional items to purchase in online shops, they make suggestions to us on movies to watch, or recommend us people to connect with on social websites. In many of today's applications, however, the only way for users to interact with the system is to inspect the recommended items. Often, no mechanisms are implemented for users to give the system feedback on the recommendations or to explicitly specify preferences, which can limit the potential overall value of the system for its users. Academic research in recommender systems is largely focused on algorithmic approaches for item selection and ranking. Nonetheless, over the years a variety of proposals were made on how to design more interactive recommenders. This work provides a comprehensive overview on the existing literature on user interaction aspects in recommender systems. We cover existing approaches for preference elicitation and result presentation, as well as proposals that consider recommendation as an interactive process. Throughout the work, we furthermore discuss examples of real-world systems and outline possible directions for future works.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "10", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Paiva:2017:EVA, author = "Ana Paiva and Iolanda Leite and Hana Boukricha and Ipke Wachsmuth", title = "Empathy in Virtual Agents and Robots: a Survey", journal = j-TIIS, volume = "7", number = "3", pages = "11:1--11:??", month = oct, year = "2017", CODEN = "????", DOI = "https://doi.org/10.1145/2912150", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Mon Jan 22 17:18:51 MST 2018", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "This article surveys the area of computational empathy, analysing different ways by which artificial agents can simulate and trigger empathy in their interactions with humans. Empathic agents can be seen as agents that have the capacity to place themselves into the position of a user's or another agent's emotional situation and respond appropriately. We also survey artificial agents that, by their design and behaviour, can lead users to respond emotionally as if they were experiencing the agent's situation. In the course of this survey, we present the research conducted to date on empathic agents in light of the principles and mechanisms of empathy found in humans. We end by discussing some of the main challenges that this exciting area will be facing in the future.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "11", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Nourbakhsh:2017:DUC, author = "Nargess Nourbakhsh and Fang Chen and Yang Wang and Rafael A. Calvo", title = "Detecting Users' Cognitive Load by Galvanic Skin Response with Affective Interference", journal = j-TIIS, volume = "7", number = "3", pages = "12:1--12:??", month = oct, year = "2017", CODEN = "????", DOI = "https://doi.org/10.1145/2960413", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Mon Jan 22 17:18:51 MST 2018", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "Experiencing high cognitive load during complex and demanding tasks results in performance reduction, stress, and errors. However, these could be prevented by a system capable of constantly monitoring users' cognitive load fluctuations and adjusting its interactions accordingly. Physiological data and behaviors have been found to be suitable measures of cognitive load and are now available in many consumer devices. An advantage of these measures over subjective and performance-based methods is that they are captured in real time and implicitly while the user interacts with the system, which makes them suitable for real-world applications. On the other hand, emotion interference can change physiological responses and make accurate cognitive load measurement more challenging. In this work, we have studied six galvanic skin response (GSR) features in detection of four cognitive load levels with the interference of emotions. The data was derived from two arithmetic experiments and emotions were induced by displaying pleasant and unpleasant pictures in the background. Two types of classifiers were applied to detect cognitive load levels. Results from both studies indicate that the features explored can detect four and two cognitive load levels with high accuracy even under emotional changes. More specifically, rise duration and accumulative GSR are the common best features in all situations, having the highest accuracy especially in the presence of emotions.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "12", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Duncan:2017:ESC, author = "Brittany A. Duncan and Robin R. Murphy", title = "Effects of Speed, Cyclicity, and Dimensionality on Distancing, Time, and Preference in Human--Aerial Vehicle Interactions", journal = j-TIIS, volume = "7", number = "3", pages = "13:1--13:??", month = oct, year = "2017", CODEN = "????", DOI = "https://doi.org/10.1145/2983927", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Mon Jan 22 17:18:51 MST 2018", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "This article will present a simulation-based approach to testing multiple variables in the behavior of a small Unmanned Aerial Vehicle (sUAV), inspired by insect and animal motions, to understand how these variables impact time of interaction, preference for interaction, and distancing in Human-Robot Interaction (HRI). Previous work has focused on communicating directionality of flight, intentionality of the robot, and perception of motion in sUAVs, while interactions involving direct distancing from these vehicles have been limited to a single study (likely due to safety concerns). This study takes place in a Cave Automatic Virtual Environment (CAVE) to maintain a sense of scale and immersion with the users, while also allowing for safe interaction. Additionally, the two-alternative forced-choice method is employed as a unique methodology to the study of collocated HRI in order to both study the impact of these variables on preference and allow participants to choose whether or not to interact with a specific robot. This article will be of interest to end-users of sUAV technologies to encourage appropriate distancing based on their application, practitioners in HRI to understand the use of this new methodology, and human-aerial vehicle researchers to understand the perception of these vehicles by 64 naive users. Results suggest that low speed (by 0.27m, $ p < 0.02$) and high cyclicity (by 0.28m, $ p < 0.01$) expressions can be used to increase distancing; that low speed (by 4.4s, $ p < 0.01$) and three-dimensional (by 2.6s, $ p < 0.01$) expressions can be used to decrease time of interaction; and low speed (by 10.4\%, $ p < 0.01$) expressions are less preferred for passability in human-aerial vehicle interactions.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "13", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Kucher:2017:ALV, author = "Kostiantyn Kucher and Carita Paradis and Magnus Sahlgren and Andreas Kerren", title = "Active Learning and Visual Analytics for Stance Classification with {ALVA}", journal = j-TIIS, volume = "7", number = "3", pages = "14:1--14:??", month = oct, year = "2017", CODEN = "????", DOI = "https://doi.org/10.1145/3132169", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Mon Jan 22 17:18:51 MST 2018", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "The automatic detection and classification of stance (e.g., certainty or agreement) in text data using natural language processing and machine-learning methods creates an opportunity to gain insight into the speakers' attitudes toward their own and other people's utterances. However, identifying stance in text presents many challenges related to training data collection and classifier training. To facilitate the entire process of training a stance classifier, we propose a visual analytics approach, called ALVA, for text data annotation and visualization. ALVA's interplay with the stance classifier follows an active learning strategy to select suitable candidate utterances for manual annotaion. Our approach supports annotation process management and provides the annotators with a clean user interface for labeling utterances with multiple stance categories. ALVA also contains a visualization method to help analysts of the annotation and training process gain a better understanding of the categories used by the annotators. The visualization uses a novel visual representation, called CatCombos, which groups individual annotation items by the combination of stance categories. Additionally, our system makes a visualization of a vector space model available that is itself based on utterances. ALVA is already being used by our domain experts in linguistics and computational linguistics to improve the understanding of stance phenomena and to build a stance classifier for applications such as social media monitoring.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "14", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Masai:2017:EFE, author = "Katsutoshi Masai and Kai Kunze and Yuta Sugiura and Masa Ogata and Masahiko Inami and Maki Sugimoto", title = "Evaluation of Facial Expression Recognition by a Smart Eyewear for Facial Direction Changes, Repeatability, and Positional Drift", journal = j-TIIS, volume = "7", number = "4", pages = "15:1--15:??", month = dec, year = "2017", CODEN = "????", DOI = "https://doi.org/10.1145/3012941", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Mon Jan 22 17:18:51 MST 2018", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "This article presents a novel smart eyewear that recognizes the wearer's facial expressions in daily scenarios. Our device uses embedded photo-reflective sensors and machine learning to recognize the wearer's facial expressions. Our approach focuses on skin deformations around the eyes that occur when the wearer changes his or her facial expressions. With small photo-reflective sensors, we measure the distances between the skin surface on the face and the 17 sensors embedded in the eyewear frame. A Support Vector Machine (SVM) algorithm is then applied to the information collected by the sensors. The sensors can cover various facial muscle movements. In addition, they are small and light enough to be integrated into daily-use glasses. Our evaluation of the device shows the robustness to the noises from the wearer's facial direction changes and the slight changes in the glasses' position, as well as the reliability of the device's recognition capacity. The main contributions of our work are as follows: (1) We evaluated the recognition accuracy in daily scenes, showing 92.8\% accuracy regardless of facial direction and removal/remount. Our device can recognize facial expressions with 78.1\% accuracy for repeatability and 87.7\% accuracy in case of its positional drift. (2) We designed and implemented the device by taking usability and social acceptability into account. The device looks like a conventional eyewear so that users can wear it anytime, anywhere. (3) Initial field trials in a daily life setting were undertaken to test the usability of the device. Our work is one of the first attempts to recognize and evaluate a variety of facial expressions with an unobtrusive wearable device.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "15", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Yan:2017:EAR, author = "Shuo Yan and Gangyi Ding and Hongsong Li and Ningxiao Sun and Zheng Guan and Yufeng Wu and Longfei Zhang and Tianyu Huang", title = "Exploring Audience Response in Performing Arts with a Brain-Adaptive Digital Performance System", journal = j-TIIS, volume = "7", number = "4", pages = "16:1--16:??", month = dec, year = "2017", CODEN = "????", DOI = "https://doi.org/10.1145/3009974", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Mon Jan 22 17:18:51 MST 2018", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "Audience response is an important indicator of the quality of performing arts. Psychophysiological measurements enable researchers to perceive and understand audience response by collecting their bio-signals during a live performance. However, how the audience respond and how the performance is affected by these responses are the key elements but are hard to implement. To address this issue, we designed a brain-computer interactive system called Brain-Adaptive Digital Performance ( BADP ) for the measurement and analysis of audience engagement level through an interactive three-dimensional virtual theater. The BADP system monitors audience engagement in real time using electroencephalography (EEG) measurement and tries to improve it by applying content-related performing cues when the engagement level decreased. In this article, we generate EEG-based engagement level and build thresholds to determine the decrease and re-engage moments. In the experiment, we simulated two types of theatre performance to provide participants a high-fidelity virtual environment using the BADP system. We also create content-related performing cues for each performance under three different conditions. The results of these evaluations show that our algorithm could accurately detect the engagement status and the performing cues have a positive impact on regaining audience engagement across different performance types. Our findings open new perspectives in audience-based theatre performance design.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "16", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Gotz:2017:ACM, author = "David Gotz and Shun Sun and Nan Cao and Rita Kundu and Anne-Marie Meyer", title = "Adaptive Contextualization Methods for Combating Selection Bias during High-Dimensional Visualization", journal = j-TIIS, volume = "7", number = "4", pages = "17:1--17:??", month = dec, year = "2017", CODEN = "????", DOI = "https://doi.org/10.1145/3009973", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Mon Jan 22 17:18:51 MST 2018", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "Large and high-dimensional real-world datasets are being gathered across a wide range of application disciplines to enable data-driven decision making. Interactive data visualization can play a critical role in allowing domain experts to select and analyze data from these large collections. However, there is a critical mismatch between the very large number of dimensions in complex real-world datasets and the much smaller number of dimensions that can be concurrently visualized using modern techniques. This gap in dimensionality can result in high levels of selection bias that go unnoticed by users. The bias can in turn threaten the very validity of any subsequent insights. This article describes Adaptive Contextualization (AC), a novel approach to interactive visual data selection that is specifically designed to combat the invisible introduction of selection bias. The AC approach (1) monitors and models a user's visual data selection activity, (2) computes metrics over that model to quantify the amount of selection bias after each step, (3) visualizes the metric results, and (4) provides interactive tools that help users assess and avoid bias-related problems. This article expands on an earlier article presented at ACM IUI 2016 [16] by providing a more detailed review of the AC methodology and additional evaluation results.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "17", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{DiSciascio:2017:SES, author = "Cecilia {Di Sciascio} and Vedran Sabol and Eduardo Veas", title = "Supporting Exploratory Search with a Visual User-Driven Approach", journal = j-TIIS, volume = "7", number = "4", pages = "18:1--18:??", month = dec, year = "2017", CODEN = "????", DOI = "https://doi.org/10.1145/3009976", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Mon Jan 22 17:18:51 MST 2018", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "Whenever users engage in gathering and organizing new information, searching and browsing activities emerge at the core of the exploration process. As the process unfolds and new knowledge is acquired, interest drifts occur inevitably and need to be accounted for. Despite the advances in retrieval and recommender algorithms, real-world interfaces have remained largely unchanged: results are delivered in a relevance-ranked list. However, it quickly becomes cumbersome to reorganize resources along new interests, as any new search brings new results. We introduce an interactive user-driven tool that aims at supporting users in understanding, refining, and reorganizing documents on the fly as information needs evolve. Decisions regarding visual and interactive design aspects are tightly grounded on a conceptual model for exploratory search. In other words, the different views in the user interface address stages of awareness, exploration, and explanation unfolding along the discovery process, supported by a set of text-mining methods. A formal evaluation showed that gathering items relevant to a particular topic of interest with our tool incurs in a lower cognitive load compared to a traditional ranked list. A second study reports on usage patterns and usability of the various interaction techniques within a free, unsupervised setting.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "18", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Davis:2017:QCC, author = "N. Davis and C. Hsiao and K. Y. Singh and B. Lin and B. Magerko", title = "Quantifying Collaboration with a Co-Creative Drawing Agent", journal = j-TIIS, volume = "7", number = "4", pages = "19:1--19:??", month = dec, year = "2017", CODEN = "????", DOI = "https://doi.org/10.1145/3009981", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Mon Jan 22 17:18:51 MST 2018", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "This article describes a new technique for quantifying creative collaboration and applies it to the user study evaluation of a co-creative drawing agent. We present a cognitive framework called creative sense-making that provides a new method to visualize and quantify the interaction dynamics of creative collaboration, for example, the rhythm of interaction, style of turn taking, and the manner in which participants are mutually making sense of a situation. The creative sense-making framework includes a qualitative coding technique, interaction coding software, an analysis method, and the cognitive theory behind these applications. This framework and analysis method are applied to empirical studies of the Drawing Apprentice collaborative sketching system to compare human collaboration with a co-creative AI agent vs. a Wizard of Oz setup. The analysis demonstrates how the proposed technique can be used to analyze interaction data using continuous functions (e.g., integrations and moving averages) to measure and evaluate how collaborations unfold through time.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "19", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Lin:2018:GES, author = "Yu-Ru Lin and Nan Cao", title = "Guest Editorial: Special Issue on Interactive Visual Analysis of Human and Crowd Behaviors", journal = j-TIIS, volume = "8", number = "1", pages = "1:1--1:??", month = mar, year = "2018", CODEN = "????", DOI = "https://doi.org/10.1145/3178569", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Mon Mar 4 08:29:40 MST 2019", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "The analysis of human behaviors has impacted many social and commercial domains. How could interactive visual analytic systems be used to further provide behavioral insights? This editorial introduction features emerging research trend related to this question. The four articles accepted for this special issue represent recent progress: they identify research challenges arising from analysis of human and crowd behaviors, and present novel methods in visual analysis to address those challenges and help make behavioral data more useful.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "1", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Polack:2018:CIM, author = "Peter J. {Polack Jr.} and Shang-Tse Chen and Minsuk Kahng and Kaya {De Barbaro} and Rahul Basole and Moushumi Sharmin and Duen Horng Chau", title = "Chronodes: Interactive Multifocus Exploration of Event Sequences", journal = j-TIIS, volume = "8", number = "1", pages = "2:1--2:??", month = mar, year = "2018", CODEN = "????", DOI = "https://doi.org/10.1145/3152888", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Mon Mar 4 08:29:40 MST 2019", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "The advent of mobile health (mHealth) technologies challenges the capabilities of current visualizations, interactive tools, and algorithms. We present Chronodes, an interactive system that unifies data mining and human-centric visualization techniques to support explorative analysis of longitudinal mHealth data. Chronodes extracts and visualizes frequent event sequences that reveal chronological patterns across multiple participant timelines of mHealth data. It then combines novel interaction and visualization techniques to enable multifocus event sequence analysis, which allows health researchers to interactively define, explore, and compare groups of participant behaviors using event sequence combinations. Through summarizing insights gained from a pilot study with 20 behavioral and biomedical health experts, we discuss Chronodes's efficacy and potential impact in the mHealth domain. Ultimately, we outline important open challenges in mHealth, and offer recommendations and design guidelines for future research.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "2", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Fu:2018:VVA, author = "Siwei Fu and Yong Wang and Yi Yang and Qingqing Bi and Fangzhou Guo and Huamin Qu", title = "{VisForum}: a Visual Analysis System for Exploring User Groups in Online Forums", journal = j-TIIS, volume = "8", number = "1", pages = "3:1--3:??", month = mar, year = "2018", CODEN = "????", DOI = "https://doi.org/10.1145/3162075", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Mon Mar 4 08:29:40 MST 2019", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "User grouping in asynchronous online forums is a common phenomenon nowadays. People with similar backgrounds or shared interests like to get together in group discussions. As tens of thousands of archived conversational posts accumulate, challenges emerge for forum administrators and analysts to effectively explore user groups in large-volume threads and gain meaningful insights into the hierarchical discussions. Identifying and comparing groups in discussion threads are nontrivial, since the number of users and posts increases with time and noises may hamper the detection of user groups. Researchers in data mining fields have proposed a large body of algorithms to explore user grouping. However, the mining result is not intuitive to understand and difficult for users to explore the details. To address these issues, we present VisForum, a visual analytic system allowing people to interactively explore user groups in a forum. We work closely with two educators who have released courses in Massive Open Online Courses (MOOC) platforms to compile a list of design goals to guide our design. Then, we design and implement a multi-coordinated interface as well as several novel glyphs, i.e., group glyph, user glyph, and set glyph, with different granularities. Accordingly, we propose the group Detecting 8 Sorting Algorithm to reduce noises in a collection of posts, and employ the concept of ``forum-index'' for users to identify high-impact forum members. Two case studies using real-world datasets demonstrate the usefulness of the system and the effectiveness of novel glyph designs. Furthermore, we conduct an in-lab user study to present the usability of VisForum.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "3", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Steptoe:2018:VAF, author = "Michael Steptoe and Robert Kr{\"u}ger and Rolando Garcia and Xing Liang and Ross Maciejewski", title = "A Visual Analytics Framework for Exploring Theme Park Dynamics", journal = j-TIIS, volume = "8", number = "1", pages = "4:1--4:??", month = mar, year = "2018", CODEN = "????", DOI = "https://doi.org/10.1145/3162076", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Mon Mar 4 08:29:40 MST 2019", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "In 2015, the top 10 largest amusement park corporations saw a combined annual attendance of over 400 million visitors. Daily average attendance in some of the most popular theme parks in the world can average 44,000 visitors per day. These visitors ride attractions, shop for souvenirs, and dine at local establishments; however, a critical component of their visit is the overall park experience. This experience depends on the wait time for rides, the crowd flow in the park, and various other factors linked to the crowd dynamics and human behavior. As such, better insight into visitor behavior can help theme parks devise competitive strategies for improved customer experience. Research into the use of attractions, facilities, and exhibits can be studied, and as behavior profiles emerge, park operators can also identify anomalous behaviors of visitors which can improve safety and operations. In this article, we present a visual analytics framework for analyzing crowd dynamics in theme parks. Our proposed framework is designed to support behavioral analysis by summarizing patterns and detecting anomalies. We provide methodologies to link visitor movement data, communication data, and park infrastructure data. This combination of data sources enables a semantic analysis of who, what, when, and where, enabling analysts to explore visitor-visitor interactions and visitor-infrastructure interactions. Analysts can identify behaviors at the macro level through semantic trajectory clustering views for group behavior dynamics, as well as at the micro level using trajectory traces and a novel visitor network analysis view. We demonstrate the efficacy of our framework through two case studies of simulated theme park visitors.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "4", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Wang:2018:VRI, author = "Yong Wang and Conglei Shi and Liangyue Li and Hanghang Tong and Huamin Qu", title = "Visualizing Research Impact through Citation Data", journal = j-TIIS, volume = "8", number = "1", pages = "5:1--5:??", month = mar, year = "2018", CODEN = "????", DOI = "https://doi.org/10.1145/3132744", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Mon Mar 4 08:29:40 MST 2019", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "Research impact plays a critical role in evaluating the research quality and influence of a scholar, a journal, or a conference. Many researchers have attempted to quantify research impact by introducing different types of metrics based on citation data, such as h -index, citation count, and impact factor. These metrics are widely used in the academic community. However, quantitative metrics are highly aggregated in most cases and sometimes biased, which probably results in the loss of impact details that are important for comprehensively understanding research impact. For example, which research area does a researcher have great research impact on? How does the research impact change over time? How do the collaborators take effect on the research impact of an individual? Simple quantitative metrics can hardly help answer such kind of questions, since more detailed exploration of the citation data is needed. Previous work on visualizing citation data usually only shows limited aspects of research impact and may suffer from other problems including visual clutter and scalability issues. To fill this gap, we propose an interactive visualization tool, ImpactVis, for better exploration of research impact through citation data. Case studies and in-depth expert interviews are conducted to demonstrate the effectiveness of ImpactVis.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "5", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Nourashrafeddin:2018:VAI, author = "Seyednaser Nourashrafeddin and Ehsan Sherkat and Rosane Minghim and Evangelos E. Milios", title = "A Visual Approach for Interactive Keyterm-Based Clustering", journal = j-TIIS, volume = "8", number = "1", pages = "6:1--6:??", month = mar, year = "2018", CODEN = "????", DOI = "https://doi.org/10.1145/3181669", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Mon Mar 4 08:29:40 MST 2019", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "The keyterm-based approach is arguably intuitive for users to direct text-clustering processes and adapt results to various applications in text analysis. Its way of markedly influencing the results, for instance, by expressing important terms in relevance order, requires little knowledge of the algorithm and has predictable effect, speeding up the task. This article first presents a text-clustering algorithm that can easily be extended into an interactive algorithm. We evaluate its performance against state-of-the-art clustering algorithms in unsupervised mode. Next, we propose three interactive versions of the algorithm based on keyterm labeling, document labeling, and hybrid labeling. We then demonstrate that keyterm labeling is more effective than document labeling in text clustering. Finally, we propose a visual approach to support the keyterm-based version of the algorithm. Visualizations are provided for the whole collection as well as for detailed views of document and cluster relationships. We show the effectiveness and flexibility of our framework, Vis-Kt, by presenting typical clustering cases on real text document collections. A user study is also reported that reveals overwhelmingly positive acceptance toward keyterm-based clustering.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "6", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Fiebrink:2018:ISI, author = "Rebecca Fiebrink and Marco Gillies", title = "Introduction to the Special Issue on Human-Centered Machine Learning", journal = j-TIIS, volume = "8", number = "2", pages = "7:1--7:??", month = jul, year = "2018", CODEN = "????", DOI = "https://doi.org/10.1145/3205942", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Mon Mar 4 08:29:40 MST 2019", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "Machine learning is one of the most important and successful techniques in contemporary computer science. Although it can be applied to myriad problems of human interest, research in machine learning is often framed in an impersonal way, as merely algorithms being applied to model data. However, this viewpoint hides considerable human work of tuning the algorithms, gathering the data, deciding what should be modeled in the first place, and using the outcomes of machine learning in the real world. Examining machine learning from a human-centered perspective includes explicitly recognizing human work, as well as reframing machine learning workflows based on situated human working practices, and exploring the co-adaptation of humans and intelligent systems. A human-centered understanding of machine learning in human contexts can lead not only to more usable machine learning tools, but to new ways of understanding what machine learning is good for and how to make it more useful. This special issue brings together nine articles that present different ways to frame machine learning in a human context. They represent very different application areas (from medicine to audio) and methodologies (including machine learning methods, human-computer interaction methods, and hybrids), but they all explore the human contexts in which machine learning is used. This introduction summarizes the articles in this issue and draws out some common themes.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "7", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Dudley:2018:RUI, author = "John J. Dudley and Per Ola Kristensson", title = "A Review of User Interface Design for Interactive Machine Learning", journal = j-TIIS, volume = "8", number = "2", pages = "8:1--8:??", month = jul, year = "2018", CODEN = "????", DOI = "https://doi.org/10.1145/3185517", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Mon Mar 4 08:29:40 MST 2019", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "Interactive Machine Learning (IML) seeks to complement human perception and intelligence by tightly integrating these strengths with the computational power and speed of computers. The interactive process is designed to involve input from the user but does not require the background knowledge or experience that might be necessary to work with more traditional machine learning techniques. Under the IML process, non-experts can apply their domain knowledge and insight over otherwise unwieldy datasets to find patterns of interest or develop complex data-driven applications. This process is co-adaptive in nature and relies on careful management of the interaction between human and machine. User interface design is fundamental to the success of this approach, yet there is a lack of consolidated principles on how such an interface should be implemented. This article presents a detailed review and characterisation of Interactive Machine Learning from an interactive systems perspective. We propose and describe a structural and behavioural model of a generalised IML system and identify solution principles for building effective interfaces for IML. Where possible, these emergent solution principles are contextualised by reference to the broader human-computer interaction literature. Finally, we identify strands of user interface research key to unlocking more efficient and productive non-expert interactive machine learning applications.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "8", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Chen:2018:UML, author = "Nan-Chen Chen and Margaret Drouhard and Rafal Kocielnik and Jina Suh and Cecilia R. Aragon", title = "Using Machine Learning to Support Qualitative Coding in Social Science: Shifting the Focus to Ambiguity", journal = j-TIIS, volume = "8", number = "2", pages = "9:1--9:??", month = jul, year = "2018", CODEN = "????", DOI = "https://doi.org/10.1145/3185515", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Mon Mar 4 08:29:40 MST 2019", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "Machine learning (ML) has become increasingly influential to human society, yet the primary advancements and applications of ML are driven by research in only a few computational disciplines. Even applications that affect or analyze human behaviors and social structures are often developed with limited input from experts outside of computational fields. Social scientists-experts trained to examine and explain the complexity of human behavior and interactions in the world-have considerable expertise to contribute to the development of ML applications for human-generated data, and their analytic practices could benefit from more human-centered ML methods. Although a few researchers have highlighted some gaps between ML and social sciences [51, 57, 70], most discussions only focus on quantitative methods. Yet many social science disciplines rely heavily on qualitative methods to distill patterns that are challenging to discover through quantitative data. One common analysis method for qualitative data is qualitative coding. In this article, we highlight three challenges of applying ML to qualitative coding. Additionally, we utilize our experience of designing a visual analytics tool for collaborative qualitative coding to demonstrate the potential in using ML to support qualitative coding by shifting the focus to identifying ambiguity. We illustrate dimensions of ambiguity and discuss the relationship between disagreement and ambiguity. Finally, we propose three research directions to ground ML applications for social science as part of the progression toward human-centered machine learning.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "9", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Smith:2018:PUC, author = "Jim Smith and Phil Legg and Milos Matovic and Kristofer Kinsey", title = "Predicting User Confidence During Visual Decision Making", journal = j-TIIS, volume = "8", number = "2", pages = "10:1--10:??", month = jul, year = "2018", CODEN = "????", DOI = "https://doi.org/10.1145/3185524", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Mon Mar 4 08:29:40 MST 2019", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "People are not infallible consistent ``oracles'': their confidence in decision-making may vary significantly between tasks and over time. We have previously reported the benefits of using an interface and algorithms that explicitly captured and exploited users' confidence: error rates were reduced by up to 50\% for an industrial multi-class learning problem; and the number of interactions required in a design-optimisation context was reduced by 33\%. Having access to users' confidence judgements could significantly benefit intelligent interactive systems in industry, in areas such as intelligent tutoring systems and in health care. There are many reasons for wanting to capture information about confidence implicitly. Some are ergonomic, but others are more ``social''-such as wishing to understand (and possibly take account of) users' cognitive state without interrupting them. We investigate the hypothesis that users' confidence can be accurately predicted from measurements of their behaviour. Eye-tracking systems were used to capture users' gaze patterns as they undertook a series of visual decision tasks, after each of which they reported their confidence on a 5-point Likert scale. Subsequently, predictive models were built using ``conventional'' machine learning approaches for numerical summary features derived from users' behaviour. We also investigate the extent to which the deep learning paradigm can reduce the need to design features specific to each application by creating ``gaze maps''-visual representations of the trajectories and durations of users' gaze fixations-and then training deep convolutional networks on these images. Treating the prediction of user confidence as a two-class problem (confident/not confident), we attained classification accuracy of 88\% for the scenario of new users on known tasks, and 87\% for known users on new tasks. Considering the confidence as an ordinal variable, we produced regression models with a mean absolute error of \approx 0.7 in both cases. Capturing just a simple subset of non-task-specific numerical features gave slightly worse, but still quite high accuracy (e.g., MAE \approx 1.0). Results obtained with gaze maps and convolutional networks are competitive, despite not having access to longer-term information about users and tasks, which was vital for the ``summary'' feature sets. This suggests that the gaze-map-based approach forms a viable, transferable alternative to handcrafting features for each different application. These results provide significant evidence to confirm our hypothesis, and offer a way of substantially improving many interactive artificial intelligence applications via the addition of cheap non-intrusive hardware and computationally cheap prediction algorithms.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "10", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Dumitrache:2018:CGT, author = "Anca Dumitrache and Lora Aroyo and Chris Welty", title = "Crowdsourcing Ground Truth for Medical Relation Extraction", journal = j-TIIS, volume = "8", number = "2", pages = "11:1--11:??", month = jul, year = "2018", CODEN = "????", DOI = "https://doi.org/10.1145/3152889", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Mon Mar 4 08:29:40 MST 2019", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "Cognitive computing systems require human labeled data for evaluation and often for training. The standard practice used in gathering this data minimizes disagreement between annotators, and we have found this results in data that fails to account for the ambiguity inherent in language. We have proposed the CrowdTruth method for collecting ground truth through crowdsourcing, which reconsiders the role of people in machine learning based on the observation that disagreement between annotators provides a useful signal for phenomena such as ambiguity in the text. We report on using this method to build an annotated data set for medical relation extraction for the cause and treat relations, and how this data performed in a supervised training experiment. We demonstrate that by modeling ambiguity, labeled data gathered from crowd workers can (1) reach the level of quality of domain experts for this task while reducing the cost, and (2) provide better training data at scale than distant supervision. We further propose and validate new weighted measures for precision, recall, and F-measure, which account for ambiguity in both human and machine performance on this task.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "11", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Morrison:2018:VUS, author = "Cecily Morrison and Kit Huckvale and Bob Corish and Richard Banks and Martin Grayson and Jonas Dorn and Abigail Sellen and S{\^a}n Lindley", title = "Visualizing Ubiquitously Sensed Measures of Motor Ability in Multiple Sclerosis: Reflections on Communicating Machine Learning in Practice", journal = j-TIIS, volume = "8", number = "2", pages = "12:1--12:??", month = jul, year = "2018", CODEN = "????", DOI = "https://doi.org/10.1145/3181670", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Mon Mar 4 08:29:40 MST 2019", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "Sophisticated ubiquitous sensing systems are being used to measure motor ability in clinical settings. Intended to augment clinical decision-making, the interpretability of the machine-learning measurements underneath becomes critical to their use. We explore how visualization can support the interpretability of machine-learning measures through the case of Assess MS, a system to support the clinical assessment of Multiple Sclerosis. A substantial design challenge is to make visible the algorithm's decision-making process in a way that allows clinicians to integrate the algorithm's result into their own decision process. To this end, we present a series of design iterations that probe the challenges in supporting interpretability in a real-world system. The key contribution of this article is to illustrate that simply making visible the algorithmic decision-making process is not helpful in supporting clinicians in their own decision-making process. It disregards that people and algorithms make decisions in different ways. Instead, we propose that visualisation can provide context to algorithmic decision-making, rendering observable a range of internal workings of the algorithm from data quality issues to the web of relationships generated in the machine-learning process.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "12", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Kim:2018:HLS, author = "Bongjun Kim and Bryan Pardo", title = "A Human-in-the-Loop System for Sound Event Detection and Annotation", journal = j-TIIS, volume = "8", number = "2", pages = "13:1--13:??", month = jul, year = "2018", CODEN = "????", DOI = "https://doi.org/10.1145/3214366", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Mon Mar 4 08:29:40 MST 2019", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "Labeling of audio events is essential for many tasks. However, finding sound events and labeling them within a long audio file is tedious and time-consuming. In cases where there is very little labeled data (e.g., a single labeled example), it is often not feasible to train an automatic labeler because many techniques (e.g., deep learning) require a large number of human-labeled training examples. Also, fully automated labeling may not show sufficient agreement with human labeling for many uses. To solve this issue, we present a human-in-the-loop sound labeling system that helps a user quickly label target sound events in a long audio. It lets a user reduce the time required to label a long audio file (e.g., 20 hours) containing target sounds that are sparsely distributed throughout the recording (10\% or less of the audio contains the target) when there are too few labeled examples (e.g., one) to train a state-of-the-art machine audio labeling system. To evaluate the effectiveness of our tool, we performed a human-subject study. The results show that it helped participants label target sound events twice as fast as labeling them manually. In addition to measuring the overall performance of the proposed system, we also measure interaction overhead and machine accuracy, which are two key factors that determine the overall performance. The analysis shows that an ideal interface that does not have interaction overhead at all could speed labeling by as much as a factor of four.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "13", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Zhang:2018:ERC, author = "Amy X. Zhang and Jilin Chen and Wei Chai and Jinjun Xu and Lichan Hong and Ed Chi", title = "Evaluation and Refinement of Clustered Search Results with the Crowd", journal = j-TIIS, volume = "8", number = "2", pages = "14:1--14:??", month = jul, year = "2018", CODEN = "????", DOI = "https://doi.org/10.1145/3158226", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Mon Mar 4 08:29:40 MST 2019", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "When searching on the web or in an app, results are often returned as lists of hundreds to thousands of items, making it difficult for users to understand or navigate the space of results. Research has demonstrated that using clustering to partition search results into coherent, topical clusters can aid in both exploration and discovery. Yet clusters generated by an algorithm for this purpose are often of poor quality and do not satisfy users. To achieve acceptable clustered search results, experts must manually evaluate and refine the clustered results for each search query, a process that does not scale to large numbers of search queries. In this article, we investigate using crowd-based human evaluation to inspect, evaluate, and improve clusters to create high-quality clustered search results at scale. We introduce a workflow that begins by using a collection of well-known clustering algorithms to produce a set of clustered search results for a given query. Then, we use crowd workers to holistically assess the quality of each clustered search result to find the best one. Finally, the workflow has the crowd spot and fix problems in the best result to produce a final output. We evaluate this workflow on 120 top search queries from the Google Play Store, some of whom have clustered search results as a result of evaluations and refinements by experts. Our evaluations demonstrate that the workflow is effective at reproducing the evaluation of expert judges and also improves clusters in a way that agrees with experts and crowds alike.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "14", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Self:2018:OLP, author = "Jessica Zeitz Self and Michelle Dowling and John Wenskovitch and Ian Crandell and Ming Wang and Leanna House and Scotland Leman and Chris North", title = "Observation-Level and Parametric Interaction for High-Dimensional Data Analysis", journal = j-TIIS, volume = "8", number = "2", pages = "15:1--15:??", month = jul, year = "2018", CODEN = "????", DOI = "https://doi.org/10.1145/3158230", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Mon Mar 4 08:29:40 MST 2019", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "Exploring high-dimensional data is challenging. Dimension reduction algorithms, such as weighted multidimensional scaling, support data exploration by projecting datasets to two dimensions for visualization. These projections can be explored through parametric interaction, tweaking underlying parameterizations, and observation-level interaction, directly interacting with the points within the projection. In this article, we present the results of a controlled usability study determining the differences, advantages, and drawbacks among parametric interaction, observation-level interaction, and their combination. The study assesses both interaction technique effects on domain-specific high-dimensional data analyses performed by non-experts of statistical algorithms. This study is performed using Andromeda, a tool that enables both parametric and observation-level interaction to provide in-depth data exploration. The results indicate that the two forms of interaction serve different, but complementary, purposes in gaining insight through steerable dimension reduction algorithms.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "15", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Francoise:2018:MSM, author = "Jules Fran{\c{c}}oise and Fr{\'e}d{\'e}ric Bevilacqua", title = "Motion-Sound Mapping through Interaction: an Approach to User-Centered Design of Auditory Feedback Using Machine Learning", journal = j-TIIS, volume = "8", number = "2", pages = "16:1--16:??", month = jul, year = "2018", CODEN = "????", DOI = "https://doi.org/10.1145/3211826", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Mon Mar 4 08:29:40 MST 2019", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "Technologies for sensing movement are expanding toward everyday use in virtual reality, gaming, and artistic practices. In this context, there is a need for methodologies to help designers and users create meaningful movement experiences. This article discusses a user-centered approach for the design of interactive auditory feedback using interactive machine learning. We discuss Mapping through Interaction, a method for crafting sonic interactions from corporeal demonstrations of embodied associations between motion and sound. It uses an interactive machine learning approach to build the mapping from user demonstrations, emphasizing an iterative design process that integrates acted and interactive experiences of the relationships between movement and sound. We examine Gaussian Mixture Regression and Hidden Markov Regression for continuous movement recognition and real-time sound parameter generation. We illustrate and evaluate this approach through an application in which novice users can create interactive sound feedback based on coproduced gestures and vocalizations. Results indicate that Gaussian Mixture Regression and Hidden Markov Regression can efficiently learn complex motion-sound mappings from few examples.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "16", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Sidner:2018:CNT, author = "Candace L. Sidner and Timothy Bickmore and Bahador Nooraie and Charles Rich and Lazlo Ring and Mahni Shayganfar and Laura Vardoulakis", title = "Creating New Technologies for Companionable Agents to Support Isolated Older Adults", journal = j-TIIS, volume = "8", number = "3", pages = "17:1--17:??", month = aug, year = "2018", CODEN = "????", DOI = "https://doi.org/10.1145/3213050", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Mon Mar 4 08:29:41 MST 2019", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "This article reports on the development of capabilities for (on-screen) virtual agents and robots to support isolated older adults in their homes. A real-time architecture was developed to use a virtual agent or a robot interchangeably to interact via dialog and gesture with a human user. Users could interact with either agent on 12 different activities, some of which included on-screen games, and forms to complete. The article reports on a pre-study that guided the choice of interaction activities. A month-long study with 44 adults between the ages of 55 and 91 assessed differences in the use of the robot and virtual agent.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "17", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Oviatt:2018:DHS, author = "S. Oviatt and K. Hang and J. Zhou and K. Yu and F. Chen", title = "Dynamic Handwriting Signal Features Predict Domain Expertise", journal = j-TIIS, volume = "8", number = "3", pages = "18:1--18:??", month = aug, year = "2018", CODEN = "????", DOI = "https://doi.org/10.1145/3213309", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Mon Mar 4 08:29:41 MST 2019", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "As commercial pen-centric systems proliferate, they create a parallel need for analytic techniques based on dynamic writing. Within educational applications, recent empirical research has shown that signal-level features of students' writing, such as stroke distance, pressure and duration, are adapted to conserve total energy expenditure as they consolidate expertise in a domain. The present research examined how accurately three different machine-learning algorithms could automatically classify users' domain expertise based on signal features of their writing, without any content analysis. Compared with an unguided machine-learning classification accuracy of 71\%, hybrid methods using empirical-statistical guidance correctly classified 79-92\% of students by their domain expertise level. In addition to improved accuracy, the hybrid approach contributed a causal understanding of prediction success and generalization to new data. These novel findings open up opportunities to design new automated learning analytic systems and student-adaptive educational technologies for the rapidly expanding sector of commercial pen systems.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "18", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Hammond:2018:JAA, author = "Tracy Hammond and Shalini Priya Ashok Kumar and Matthew Runyon and Josh Cherian and Blake Williford and Swarna Keshavabhotla and Stephanie Valentine and Wayne Li and Julie Linsey", title = "It's Not Just about Accuracy: Metrics That Matter When Modeling Expert Sketching Ability", journal = j-TIIS, volume = "8", number = "3", pages = "19:1--19:??", month = aug, year = "2018", CODEN = "????", DOI = "https://doi.org/10.1145/3181673", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Mon Mar 4 08:29:41 MST 2019", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "Design sketching is an important skill for designers, engineers, and creative professionals, as it allows them to express their ideas and concepts in a visual medium. Being a critical and versatile skill for many different disciplines, courses on design sketching are often taught in universities. Courses today predominately rely on pen and paper; however, this traditional pedagogy is limited by the availability of human instructors, who can provide personalized feedback. Using a stylus-based intelligent tutoring system called SketchTivity, we aim to eventually mimic the feedback given by an instructor and assess student-drawn sketches to give students insight into areas for improvement. To provide effective feedback to users, it is important to identify what aspects of their sketches they should work on to improve their sketching ability. After consulting with several domain experts in sketching, we came up with several classes of features that could potentially differentiate expert and novice sketches. Because improvement on one metric, such as speed, may result in a decrease in another metric, such as accuracy, the creation of a single score may not mean much to the user. We attempted to create a single internal score that represents overall drawing skill so that the system can track improvement over time and found that this score correlates highly with expert rankings. We gathered over 2,000 sketches from 20 novices and four experts for analysis. We identified key metrics for quality assessment that were shown to significantly correlate with the quality of expert sketches and provide insight into providing intelligent user feedback in the future.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "19", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Koskela:2018:PIR, author = "Markus Koskela and Petri Luukkonen and Tuukka Ruotsalo and Mats Sj{\"O}berg and Patrik Flor{\'e}en", title = "Proactive Information Retrieval by Capturing Search Intent from Primary Task Context", journal = j-TIIS, volume = "8", number = "3", pages = "20:1--20:??", month = aug, year = "2018", CODEN = "????", DOI = "https://doi.org/10.1145/3150975", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Mon Mar 4 08:29:41 MST 2019", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "A significant fraction of information searches are motivated by the user's primary task. An ideal search engine would be able to use information captured from the primary task to proactively retrieve useful information. Previous work has shown that many information retrieval activities depend on the primary task in which the retrieved information is to be used, but fairly little research has been focusing on methods that automatically learn the informational intents from the primary task context. We study how the implicit primary task context can be used to model the user's search intent and to proactively retrieve relevant and useful information. Data comprising of logs from a user study, in which users are writing an essay, demonstrate that users' search intents can be captured from the task and relevant and useful information can be proactively retrieved. Data from simulations with several datasets of different complexity show that the proposed approach of using primary task context generalizes to a variety of data. Our findings have implications for the design of proactive search systems that can infer users' search intent implicitly by monitoring users' primary task activities.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "20", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Narzt:2018:ECA, author = "Wolfgang Narzt and Otto Weichselbaum and Gustav Pomberger and Markus Hofmarcher and Michael Strauss and Peter Holzkorn and Roland Haring and Monika Sturm", title = "Estimating Collective Attention toward a Public Display", journal = j-TIIS, volume = "8", number = "3", pages = "21:1--21:??", month = aug, year = "2018", CODEN = "????", DOI = "https://doi.org/10.1145/3230715", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Mon Mar 4 08:29:41 MST 2019", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "Enticing groups of passers-by to focused interaction with a public display requires the display system to take appropriate action that depends on how much attention the group is already paying to the display. In the design of such a system, we might want to present the content so that it indicates that a part of the group that is looking head-on at the display has already been registered and is addressed individually, whereas it simultaneously emits a strong audio signal that makes the inattentive rest of the group turn toward it. The challenge here is to define and delimit adequate mixed attention states for groups of people, allowing for classifying collective attention based on inhomogeneous variants of individual attention, i.e., where some group members might be highly attentive, others even interacting with the public display, and some unperceptive. In this article, we present a model for estimating collective human attention toward a public display and investigate technical methods for practical implementation that employs measurement of physical expressive features of people appearing within the display's field of view (i.e., the basis for deriving a person's attention). We delineate strengths and weaknesses and prove the potentials of our model by experimentally exerting influence on the attention of groups of passers-by in a public gaming scenario.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "21", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Hossain:2018:ASM, author = "H. M. Sajjad Hossain and Sreenivasan R. Ramamurthy and Md Abdullah {Al Hafiz Khan} and Nirmalya Roy", title = "An Active Sleep Monitoring Framework Using Wearables", journal = j-TIIS, volume = "8", number = "3", pages = "22:1--22:??", month = aug, year = "2018", CODEN = "????", DOI = "https://doi.org/10.1145/3185516", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Mon Mar 4 08:29:41 MST 2019", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "Sleep is the most important aspect of healthy and active living. The right amount of sleep at the right time helps an individual to protect his or her physical, mental, and cognitive health and maintain his or her quality of life. The most durative of the Activities of Daily Living (ADL), sleep has a major synergic influence on a person's fuctional, behavioral, and cognitive health. A deep understanding of sleep behavior and its relationship with its physiological signals, and contexts (such as eye or body movements), is necessary to design and develop a robust intelligent sleep monitoring system. In this article, we propose an intelligent algorithm to detect the microscopic states of sleep that fundamentally constitute the components of good and bad sleeping behaviors and thus help shape the formative assessment of sleep quality. Our initial analysis includes the investigation of several classification techniques to identify and correlate the relationship of microscopic sleep states with overall sleep behavior. Subsequently, we also propose an online algorithm based on change point detection to process and classify the microscopic sleep states. We also develop a lightweight version of the proposed algorithm for real-time sleep monitoring, recognition, and assessment at scale. For a larger deployment of our proposed model across a community of individuals, we propose an active-learning-based methodology to reduce the effort of ground-truth data collection and labeling. Finally, we evaluate the performance of our proposed algorithms on real data traces and demonstrate the efficacy of our models for detecting and assessing the fine-grained sleep states beyond an individual.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "22", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Park:2018:MFU, author = "Souneil Park and Joan Serr{\`a} and Enrique Frias Martinez and Nuria Oliver", title = "{MobInsight}: a Framework Using Semantic Neighborhood Features for Localized Interpretations of Urban Mobility", journal = j-TIIS, volume = "8", number = "3", pages = "23:1--23:??", month = aug, year = "2018", CODEN = "????", DOI = "https://doi.org/10.1145/3158433", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Mon Mar 4 08:29:41 MST 2019", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "Collective urban mobility embodies the residents' local insights on the city. Mobility practices of the residents are produced from their spatial choices, which involve various considerations such as the atmosphere of destinations, distance, past experiences, and preferences. The advances in mobile computing and the rise of geo-social platforms have provided the means for capturing the mobility practices; however, interpreting the residents' insights is challenging due to the scale and complexity of an urban environment and its unique context. In this article, we present MobInsight, a framework for making localized interpretations of urban mobility that reflect various aspects of the urbanism. MobInsight extracts a rich set of neighborhood features through holistic semantic aggregation, and models the mobility between all-pairs of neighborhoods. We evaluate MobInsight with the mobility data of Barcelona and demonstrate diverse localized and semantically rich interpretations.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "23", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Carreno-Medrano:2018:PVG, author = "Pamela Carreno-Medrano and Sylvie Gibet and Pierre-Fran{\c{C}}ois Marteau", title = "Perceptual Validation for the Generation of Expressive Movements from End-Effector Trajectories", journal = j-TIIS, volume = "8", number = "3", pages = "24:1--24:??", month = aug, year = "2018", CODEN = "????", DOI = "https://doi.org/10.1145/3150976", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Mon Mar 4 08:29:41 MST 2019", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "Endowing animated virtual characters with emotionally expressive behaviors is paramount to improving the quality of the interactions between humans and virtual characters. Full-body motion, in particular, with its subtle kinematic variations, represents an effective way of conveying emotionally expressive content. However, before synthesizing expressive full-body movements, it is necessary to identify and understand what qualities of human motion are salient to the perception of emotions and how these qualities can be exploited to generate novel and equally expressive full-body movements. Based on previous studies, we argue that it is possible to perceive and generate expressive full-body movements from a limited set of joint trajectories, including end-effector trajectories and additional constraints such as pelvis and elbow trajectories. Hence, these selected trajectories define a significant and reduced motion space, which is adequate for the characterization of the expressive qualities of human motion and that is both suitable for the analysis and generation of emotionally expressive full-body movements. The purpose and main contribution of this work is the methodological framework we defined and used to assess the validity and applicability of the selected trajectories for the perception and generation of expressive full-body movements. This framework consists of the creation of a motion capture database of expressive theatrical movements, the development of a motion synthesis system based on trajectories re-played or re-sampled and inverse kinematics, and two perceptual studies.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "24", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Knott:2018:ATI, author = "Benjamin A. Knott and Jonathan Gratch and Angelo Cangelosi and James Caverlee", title = "{{\booktitle{ACM Transactions on Interactive Intelligent Systems (TiiS)}}} Special Issue on Trust and Influence in Intelligent Human-Machine Interaction", journal = j-TIIS, volume = "8", number = "4", pages = "25:1--25:??", month = nov, year = "2018", CODEN = "????", DOI = "https://doi.org/10.1145/3281451", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Mon Mar 4 08:29:41 MST 2019", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/ft_gateway.cfm?id=3281451", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "25", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Wagner:2018:MHR, author = "Alan R. Wagner and Paul Robinette and Ayanna Howard", title = "Modeling the Human-Robot Trust Phenomenon: a Conceptual Framework based on Risk", journal = j-TIIS, volume = "8", number = "4", pages = "26:1--26:??", month = nov, year = "2018", CODEN = "????", DOI = "https://doi.org/10.1145/3152890", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Mon Mar 4 08:29:41 MST 2019", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/ft_gateway.cfm?id=3152890", abstract = "This article presents a conceptual framework for human-robot trust which uses computational representations inspired by game theory to represent a definition of trust, derived from social psychology. This conceptual framework generates several testable hypotheses related to human-robot trust. This article examines these hypotheses and a series of experiments we have conducted which both provide support for and also conflict with our framework for trust. We also discuss the methodological challenges associated with investigating trust. The article concludes with a description of the important areas for future research on the topic of human-robot trust.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "26", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Akash:2018:CMS, author = "Kumar Akash and Wan-Lin Hu and Neera Jain and Tahira Reid", title = "A Classification Model for Sensing Human Trust in Machines Using {EEG} and {GSR}", journal = j-TIIS, volume = "8", number = "4", pages = "27:1--27:??", month = nov, year = "2018", CODEN = "????", DOI = "https://doi.org/10.1145/3132743", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Mon Mar 4 08:29:41 MST 2019", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/ft_gateway.cfm?id=3132743", abstract = "Today, intelligent machines interact and collaborate with humans in a way that demands a greater level of trust between human and machine. A first step toward building intelligent machines that are capable of building and maintaining trust with humans is the design of a sensor that will enable machines to estimate human trust level in real time. In this article, two approaches for developing classifier-based empirical trust-sensor models are presented that specifically use electroencephalography and galvanic skin response measurements. Human subject data collected from 45 participants is used for feature extraction, feature selection, classifier training, and model validation. The first approach considers a general set of psychophysiological features across all participants as the input variables and trains a classifier-based model for each participant, resulting in a trust-sensor model based on the general feature set (i.e., a ``general trust-sensor model''). The second approach considers a customized feature set for each individual and trains a classifier-based model using that feature set, resulting in improved mean accuracy but at the expense of an increase in training time. This work represents the first use of real-time psychophysiological measurements for the development of a human trust sensor. Implications of the work, in the context of trust management algorithm design for intelligent machines, are also discussed.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "27", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Holbrook:2018:CVI, author = "Colin Holbrook", title = "Cues of Violent Intergroup Conflict Diminish Perceptions of Robotic Personhood", journal = j-TIIS, volume = "8", number = "4", pages = "28:1--28:??", month = nov, year = "2018", CODEN = "????", DOI = "https://doi.org/10.1145/3181674", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Mon Mar 4 08:29:41 MST 2019", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/ft_gateway.cfm?id=3181674", abstract = "Convergent lines of evidence indicate that anthropomorphic robots are represented using neurocognitive mechanisms typically employed in social reasoning about other people. Relatedly, a growing literature documents that contexts of threat can exacerbate coalitional biases in social perceptions. Integrating these research programs, the present studies test whether cues of violent intergroup conflict modulate perceptions of the intelligence, emotional experience, or overall personhood of robots. In Studies 1 and 2, participants evaluated a large, bipedal all-terrain robot; in Study 3, participants evaluated a small, social robot with humanlike facial and vocal characteristics. Across all studies, cues of violent conflict caused significant decreases in perceived robotic personhood, and these shifts were mediated by parallel reductions in emotional connection with the robot (with no significant effects of threat on attributions of intelligence/skill). In addition, in Study 2, participants in the conflict condition estimated the large bipedal robot to be less effective in military combat, and this difference was mediated by the reduction in perceived robotic personhood. These results are discussed as they motivate future investigation into the links among threat, coalitional bias and human-robot interaction.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "28", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Chien:2018:ECT, author = "Shih-Yi Chien and Michael Lewis and Katia Sycara and Jyi-Shane Liu and Asiye Kumru", title = "The Effect of Culture on Trust in Automation: Reliability and Workload", journal = j-TIIS, volume = "8", number = "4", pages = "29:1--29:??", month = nov, year = "2018", CODEN = "????", DOI = "https://doi.org/10.1145/3230736", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Mon Mar 4 08:29:41 MST 2019", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/ft_gateway.cfm?id=3230736", abstract = "Trust in automation has become a topic of intensive study since the late 1990s and is of increasing importance with the advent of intelligent interacting systems. While the earliest trust experiments involved human interventions to correct failures/errors in automated control systems, a majority of subsequent studies have investigated information acquisition and analysis decision aiding tasks such as target detection for which automation reliability is more easily manipulated. Despite the high level of international dependence on automation in industry, almost all current studies have employed Western samples primarily from the U.S. The present study addresses these gaps by running a large sample experiment in three (U.S., Taiwan, and Turkey) diverse cultures using a ``trust sensitive task'' consisting of both automated control and target detection subtasks. This article presents results for the target detection subtask for which reliability and task load were manipulated. The current experiments allow us to determine whether reported effects are universal or specific to Western culture, vary in baseline or magnitude, or differ across cultures. Results generally confirm consistent effects of manipulations across the three cultures as well as cultural differences in initial trust and variation in effects of manipulations consistent with 10 cultural hypotheses based on Hofstede's Cultural Dimensions and Leung and Cohen's theory of Cultural Syndromes. These results provide critical implications and insights for correct trust calibration and to enhance human trust in intelligent automation systems across cultures. Additionally, our results would be useful in designing intelligent systems for users of different cultures. Our article presents the following contributions: First, to the best of our knowledge, this is the first set of studies that deal with cultural factors across all the cultural syndromes identified in the literature by comparing trust in the Honor, Face, Dignity cultures. Second, this is the first set of studies that uses a validated cross-cultural trust measure for measuring trust in automation. Third, our experiments are the first to study the dynamics of trust across cultures.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "29", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Baker:2018:TUT, author = "Anthony L. Baker and Elizabeth K. Phillips and Daniel Ullman and Joseph R. Keebler", title = "Toward an Understanding of Trust Repair in Human-Robot Interaction: Current Research and Future Directions", journal = j-TIIS, volume = "8", number = "4", pages = "30:1--30:??", month = nov, year = "2018", CODEN = "????", DOI = "https://doi.org/10.1145/3181671", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Mon Mar 4 08:29:41 MST 2019", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/ft_gateway.cfm?id=3181671", abstract = "Gone are the days of robots solely operating in isolation, without direct interaction with people. Rather, robots are increasingly being deployed in environments and roles that require complex social interaction with humans. The implementation of human-robot teams continues to increase as technology develops in tandem with the state of human-robot interaction (HRI) research. Trust, a major component of human interaction, is an important facet of HRI. However, the ideas of trust repair and trust violations are understudied in the HRI literature. Trust repair is the activity of rebuilding trust after one party breaks the trust of another. These trust breaks are referred to as trust violations. Just as with humans, trust violations with robots are inevitable; as a result, a clear understanding of the process of HRI trust repair must be developed in order to ensure that a human-robot team can continue to perform well after a trust violation. Previous research on human-automation trust and human-human trust can serve as starting places for exploring trust repair in HRI. Although existing models of human-automation and human-human trust are helpful, they do not account for some of the complexities of building and maintaining trust in unique relationships between humans and robots. The purpose of this article is to provide a foundation for exploring human-robot trust repair by drawing upon prior work in the human-robot, human-automation, and human-human trust literature, concluding with recommendations for advancing this body of work.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "30", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Wang:2018:TBM, author = "Yue Wang and Laura R. Humphrey and Zhanrui Liao and Huanfei Zheng", title = "Trust-Based Multi-Robot Symbolic Motion Planning with a Human-in-the-Loop", journal = j-TIIS, volume = "8", number = "4", pages = "31:1--31:??", month = nov, year = "2018", CODEN = "????", DOI = "https://doi.org/10.1145/3213013", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Mon Mar 4 08:29:41 MST 2019", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/ft_gateway.cfm?id=3213013", abstract = "Symbolic motion planning for robots is the process of specifying and planning robot tasks in a discrete space, then carrying them out in a continuous space in a manner that preserves the discrete-level task specifications. Despite progress in symbolic motion planning, many challenges remain, including addressing scalability for multi-robot systems and improving solutions by incorporating human intelligence. In this article, distributed symbolic motion planning for multi-robot systems is developed to address scalability. More specifically, compositional reasoning approaches are developed to decompose the global planning problem, and atomic propositions for observation, communication, and control are proposed to address inter-robot collision avoidance. To improve solution quality and adaptability, a hypothetical dynamic, quantitative, and probabilistic human-to-robot trust model is developed to aid this decomposition. Furthermore, a trust-based real-time switching framework is proposed to switch between autonomous and manual motion planning for tradeoffs between task safety and efficiency. Deadlock- and livelock-free algorithms are designed to guarantee reachability of goals with a human-in-the-loop. A set of nontrivial multi-robot simulations with direct human inputs and trust evaluation is provided, demonstrating the successful implementation of the trust-based multi-robot symbolic motion planning methods.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "31", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Afergan:2018:DR, author = "Daniel Afergan", title = "Distinguished Reviewers", journal = j-TIIS, volume = "8", number = "4", pages = "32:1--32:??", month = nov, year = "2018", CODEN = "????", DOI = "https://doi.org/10.1145/3283374", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Mon Mar 4 08:29:41 MST 2019", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/ft_gateway.cfm?id=3283374", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "32", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Niewiadomski:2019:AMQ, author = "Radoslaw Niewiadomski and Ksenia Kolykhalova and Stefano Piana and Paolo Alborno and Gualtiero Volpe and Antonio Camurri", title = "Analysis of Movement Quality in Full-Body Physical Activities", journal = j-TIIS, volume = "9", number = "1", pages = "1:1--1:??", month = feb, year = "2019", CODEN = "????", DOI = "https://doi.org/10.1145/3132369", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Mon Mar 4 08:29:41 MST 2019", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/ft_gateway.cfm?id=3132369", abstract = "Full-body human movement is characterized by fine-grain expressive qualities that humans are easily capable of exhibiting and recognizing in others' movement. In sports (e.g., martial arts) and performing arts (e.g., dance), the same sequence of movements can be performed in a wide range of ways characterized by different qualities, often in terms of subtle (spatial and temporal) perturbations of the movement. Even a non-expert observer can distinguish between a top-level and average performance by a dancer or martial artist. The difference is not in the performed movements--the same in both cases--but in the ``quality'' of their performance. In this article, we present a computational framework aimed at an automated approximate measure of movement quality in full-body physical activities. Starting from motion capture data, the framework computes low-level (e.g., a limb velocity) and high-level (e.g., synchronization between different limbs) movement features. Then, this vector of features is integrated to compute a value aimed at providing a quantitative assessment of movement quality approximating the evaluation that an external expert observer would give of the same sequence of movements. Next, a system representing a concrete implementation of the framework is proposed. Karate is adopted as a testbed. We selected two different katas (i.e., detailed choreographies of movements in karate) characterized by different overall attitudes and expressions (aggressiveness, meditation), and we asked seven athletes, having various levels of experience and age, to perform them. Motion capture data were collected from the performances and were analyzed with the system. The results of the automated analysis were compared with the scores given by 14 karate experts who rated the same performances. Results show that the movement-quality scores computed by the system and the ratings given by the human observers are highly correlated (Pearson's correlations r = 0.84, p = 0.001 and r = 0.75, p = 0.005).", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "1", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Ramachandran:2019:TER, author = "Aditi Ramachandran and Chien-Ming Huang and Brian Scassellati", title = "Toward Effective Robot--Child Tutoring: Internal Motivation, Behavioral Intervention, and Learning Outcomes", journal = j-TIIS, volume = "9", number = "1", pages = "2:1--2:??", month = feb, year = "2019", CODEN = "????", DOI = "https://doi.org/10.1145/3213768", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Mon Mar 4 08:29:41 MST 2019", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/ft_gateway.cfm?id=3213768", abstract = "Personalized learning environments have the potential to improve learning outcomes for children in a variety of educational domains, as they can tailor instruction based on the unique learning needs of individuals. Robot tutoring systems can further engage users by leveraging their potential for embodied social interaction and take into account crucial aspects of a learner, such as a student's motivation in learning. In this article, we demonstrate that motivation in young learners corresponds to observable behaviors when interacting with a robot tutoring system, which, in turn, impact learning outcomes. We first detail a user study involving children interacting one on one with a robot tutoring system over multiple sessions. Based on empirical data, we show that academic motivation stemming from one's own values or goals as assessed by the Academic Self-Regulation Questionnaire (SRQ-A) correlates to observed suboptimal help-seeking behavior during the initial tutoring session. We then show how an interactive robot that responds intelligently to these observed behaviors in subsequent tutoring sessions can positively impact both student behavior and learning outcomes over time. These results provide empirical evidence for the link between internal motivation, observable behavior, and learning outcomes in the context of robot--child tutoring. We also identified an additional suboptimal behavioral feature within our tutoring environment and demonstrated its relationship to internal factors of motivation, suggesting further opportunities to design robot intervention to enhance learning. We provide insights on the design of robot tutoring systems aimed to deliver effective behavioral intervention during learning interactions for children and present a discussion on the broader challenges currently faced by robot--child tutoring systems.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "2", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Marge:2019:MDR, author = "Matthew Marge and Alexander I. Rudnicky", title = "Miscommunication Detection and Recovery in Situated Human--Robot Dialogue", journal = j-TIIS, volume = "9", number = "1", pages = "3:1--3:??", month = feb, year = "2019", CODEN = "????", DOI = "https://doi.org/10.1145/3237189", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Mon Mar 4 08:29:41 MST 2019", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/ft_gateway.cfm?id=3237189", abstract = "Even without speech recognition errors, robots may face difficulties interpreting natural-language instructions. We present a method for robustly handling miscommunication between people and robots in task-oriented spoken dialogue. This capability is implemented in TeamTalk, a conversational interface to robots that supports detection and recovery from the situated grounding problems of referential ambiguity and impossible actions. We introduce a representation that detects these problems and a nearest-neighbor learning algorithm that selects recovery strategies for a virtual robot. When the robot encounters a grounding problem, it looks back on its interaction history to consider how it resolved similar situations. The learning method is trained initially on crowdsourced data but is then supplemented by interactions from a longitudinal user study in which six participants performed navigation tasks with the robot. We compare results collected using a general model to user-specific models and find that user-specific models perform best on measures of dialogue efficiency, while the general model yields the highest agreement with human judges. Our overall contribution is a novel approach to detecting and recovering from miscommunication in dialogue by including situated context, namely, information from a robot's path planner and surroundings.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "3", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Guo:2019:VEA, author = "Fangzhou Guo and Tianlong Gu and Wei Chen and Feiran Wu and Qi Wang and Lei Shi and Huamin Qu", title = "Visual Exploration of Air Quality Data with a Time-correlation-partitioning Tree Based on Information Theory", journal = j-TIIS, volume = "9", number = "1", pages = "4:1--4:??", month = feb, year = "2019", CODEN = "????", DOI = "https://doi.org/10.1145/3182187", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Mon Mar 4 08:29:41 MST 2019", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/ft_gateway.cfm?id=3182187", abstract = "Discovering the correlations among variables of air quality data is challenging, because the correlation time series are long-lasting, multi-faceted, and information-sparse. In this article, we propose a novel visual representation, called Time-correlation-partitioning (TCP) tree, that compactly characterizes correlations of multiple air quality variables and their evolutions. A TCP tree is generated by partitioning the information-theoretic correlation time series into pieces with respect to the variable hierarchy and temporal variations, and reorganizing these pieces into a hierarchically nested structure. The visual exploration of a TCP tree provides a sparse data traversal of the correlation variations and a situation-aware analysis of correlations among variables. This can help meteorologists understand the correlations among air quality variables better. We demonstrate the efficiency of our approach in a real-world air quality investigation scenario.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "4", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Krokos:2019:EDL, author = "Eric Krokos and Hsueh-Chen Cheng and Jessica Chang and Bohdan Nebesh and Celeste Lyn Paul and Kirsten Whitley and Amitabh Varshney", title = "Enhancing Deep Learning with Visual Interactions", journal = j-TIIS, volume = "9", number = "1", pages = "5:1--5:??", month = feb, year = "2019", CODEN = "????", DOI = "https://doi.org/10.1145/3150977", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Mon Mar 4 08:29:41 MST 2019", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "Deep learning has emerged as a powerful tool for feature-driven labeling of datasets. However, for it to be effective, it requires a large and finely labeled training dataset. Precisely labeling a large training dataset is expensive, time-consuming, and error prone. In this article, we present a visually driven deep-learning approach that starts with a coarsely labeled training dataset and iteratively refines the labeling through intuitive interactions that leverage the latent structures of the dataset. Our approach can be used to (a) alleviate the burden of intensive manual labeling that captures the fine nuances in a high-dimensional dataset by simple visual interactions, (b) replace a complicated (and therefore difficult to design) labeling algorithm by a simpler (but coarse) labeling algorithm supplemented by user interaction to refine the labeling, or (c) use low-dimensional features (such as the RGB colors) for coarse labeling and turn to higher-dimensional latent structures that are progressively revealed by deep learning, for fine labeling. We validate our approach through use cases on three high-dimensional datasets and a user study.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "5", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Koh:2019:DHG, author = "Jung In Koh and Josh Cherian and Paul Taele and Tracy Hammond", title = "Developing a Hand Gesture Recognition System for Mapping Symbolic Hand Gestures to Analogous Emojis in Computer-Mediated Communication", journal = j-TIIS, volume = "9", number = "1", pages = "6:1--6:??", month = feb, year = "2019", CODEN = "????", DOI = "https://doi.org/10.1145/3297277", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Mon Mar 4 08:29:41 MST 2019", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "Recent trends in computer-mediated communication (CMC) have not only led to expanded instant messaging through the use of images and videos but have also expanded traditional text messaging with richer content in the form of visual communication markers (VCMs) such as emoticons, emojis, and stickers. VCMs could prevent a potential loss of subtle emotional conversation in CMC, which is delivered by nonverbal cues that convey affective and emotional information. However, as the number of VCMs grows in the selection set, the problem of VCM entry needs to be addressed. Furthermore, conventional means of accessing VCMs continue to rely on input entry methods that are not directly and intimately tied to expressive nonverbal cues. In this work, we aim to address this issue by facilitating the use of an alternative form of VCM entry: hand gestures. To that end, we propose a user-defined hand gesture set that is highly representative of a number of VCMs and a two-stage hand gesture recognition system (trajectory-based, shape-based) that can identify these user-defined hand gestures with an accuracy of 82\%. By developing such a system, we aim to allow people using low-bandwidth forms of CMCs to still enjoy their convenient and discreet properties while also allowing them to experience more of the intimacy and expressiveness of higher-bandwidth online communication.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "6", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Chen:2019:SIH, author = "Fang Chen and Carlos Duarte and Wai-Tat Fu", title = "Special Issue on Highlights of {ACM Intelligent User Interface (IUI) 2017}", journal = j-TIIS, volume = "9", number = "2--3", pages = "7:1--7:??", month = apr, year = "2019", CODEN = "????", DOI = "https://doi.org/10.1145/3301292", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Wed Dec 11 06:36:19 MST 2019", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/ft_gateway.cfm?id=3301292", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "7", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Pham:2019:AMA, author = "Phuong Pham and Jingtao Wang", title = "{AttentiveVideo}: a Multimodal Approach to Quantify Emotional Responses to Mobile Advertisements", journal = j-TIIS, volume = "9", number = "2--3", pages = "8:1--8:??", month = apr, year = "2019", CODEN = "????", DOI = "https://doi.org/10.1145/3232233", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Wed Dec 11 06:36:19 MST 2019", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/ft_gateway.cfm?id=3232233", abstract = "Understanding a target audience's emotional responses to a video advertisement is crucial to evaluate the advertisement's effectiveness. However, traditional methods for collecting such information are slow, expensive, and coarse grained. We propose AttentiveVideo, a scalable intelligent mobile interface with corresponding inference algorithms to monitor and quantify the effects of mobile video advertising in real time. Without requiring additional sensors, AttentiveVideo employs a combination of implicit photoplethysmography (PPG) sensing and facial expression analysis (FEA) to detect the attention, engagement, and sentiment of viewers as they watch video advertisements on unmodified smartphones. In a 24-participant study, AttentiveVideo achieved good accuracy on a wide range of emotional measures (the best average accuracy = 82.6\% across nine measures). While feature fusion alone did not improve prediction accuracy with a single model, it significantly improved the accuracy when working together with model fusion. We also found that the PPG sensing channel and the FEA technique have different strength in data availability, latency detection, accuracy, and usage environment. These findings show the potential for both low-cost collection and deep understanding of emotional responses to mobile video advertisements.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "8", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Mihoub:2019:WSS, author = "Alaeddine Mihoub and Gr{\'e}goire Lefebvre", title = "Wearables and Social Signal Processing for Smarter Public Presentations", journal = j-TIIS, volume = "9", number = "2--3", pages = "9:1--9:??", month = apr, year = "2019", CODEN = "????", DOI = "https://doi.org/10.1145/3234507", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Wed Dec 11 06:36:19 MST 2019", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/ft_gateway.cfm?id=3234507", abstract = "Social Signal Processing techniques have given the opportunity to analyze in-depth human behavior in social face-to-face interactions. With recent advancements, it is henceforth possible to use these techniques to augment social interactions, especially human behavior in oral presentations. The goal of this study is to train a computational model able to provide a relevant feedback to a public speaker concerning his/her coverbal communication. Hence, the role of this model is to augment the social intelligence of the orator and then the relevance of his/her presentation. To this end, we present an original interaction setting in which the speaker is equipped with only wearable devices. Several coverbal modalities have been extracted and automatically annotated namely speech volume, intonation, speech rate, eye gaze, hand gestures, and body movements. In this article, which is an extension of our previous article published in IUI'17, we compare our Dynamic Bayesian Network design to classical J48/Multi-Layer Perceptron/Support Vector Machine classifiers, propose a subjective evaluation of presenter skills with a discussion in regards to our automatic evaluation, and we add a complementary study about using DBScan versus k -means algorithm in the design process of our Dynamic Bayesian Network.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "9", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Zhou:2019:TVA, author = "Michelle X. Zhou and Gloria Mark and Jingyi Li and Huahai Yang", title = "Trusting Virtual Agents: The Effect of Personality", journal = j-TIIS, volume = "9", number = "2--3", pages = "10:1--10:??", month = apr, year = "2019", CODEN = "????", DOI = "https://doi.org/10.1145/3232077", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Wed Dec 11 06:36:19 MST 2019", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/ft_gateway.cfm?id=3232077", abstract = "We present artificial intelligent (AI) agents that act as interviewers to engage with a user in a text-based conversation and automatically infer the user's personality traits. We investigate how the personality of an AI interviewer and the inferred personality of a user influences the user's trust in the AI interviewer from two perspectives: the user's willingness to confide in and listen to an AI interviewer. We have developed two AI interviewers with distinct personalities and deployed them in a series of real-world events. We present findings from four such deployments involving 1,280 users, including 606 actual job applicants. Notably, users are more willing to confide in and listen to an AI interviewer with a serious, assertive personality in a high-stakes job interview. Moreover, users' personality traits, inferred from their chat text, along with interview context, influence their perception of and their willingness to confide in and listen to an AI interviewer. Finally, we discuss the design implications of our work on building hyper-personalized, intelligent agents.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "10", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Santos:2019:PPT, author = "Carlos Pereira Santos and Kevin Hutchinson and Vassilis-Javed Khan and Panos Markopoulos", title = "Profiling Personality Traits with Games", journal = j-TIIS, volume = "9", number = "2--3", pages = "11:1--11:??", month = apr, year = "2019", CODEN = "????", DOI = "https://doi.org/10.1145/3230738", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Wed Dec 11 06:36:19 MST 2019", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/ft_gateway.cfm?id=3230738", abstract = "Trying to understand a player's characteristics with regards to a computer game is a major line of research known as player modeling. The purpose of player modeling is typically the adaptation of the game itself. We present two studies that extend player modeling into player profiling by trying to identify abstract personality traits, such as the need for cognition and self-esteem, through a player's in-game behavior. We present evidence that game mechanics that can be broadly adopted by several game genres, such as hints and a player's self-evaluation at the end of a level, correlate with the aforementioned personality traits. We conclude by presenting future directions for research regarding this topic, discuss the direct applications for the games industry, and explore how games can be developed as profiling tools with applications to other contexts.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "11", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Sen:2019:TUS, author = "Shilad Sen and Anja Beth Swoap and Qisheng Li and Ilse Dippenaar and Monica Ngo and Sarah Pujol and Rebecca Gold and Brooke Boatman and Brent Hecht and Bret Jackson", title = "Toward Universal Spatialization Through {Wikipedia}-Based Semantic Enhancement", journal = j-TIIS, volume = "9", number = "2--3", pages = "12:1--12:??", month = apr, year = "2019", CODEN = "????", DOI = "https://doi.org/10.1145/3213769", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Wed Dec 11 06:36:19 MST 2019", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/ft_gateway.cfm?id=3213769", abstract = "This article introduces Cartograph, a visualization system that harnesses the vast world knowledge encoded within Wikipedia to create thematic maps of almost any data. Cartograph extends previous systems that visualize non-spatial data using geographic approaches. Although these systems required data with an existing semantic structure, Cartograph unlocks spatial visualization for a much larger variety of datasets by enhancing input datasets with semantic information extracted from Wikipedia. Cartograph's map embeddings use neural networks trained on Wikipedia article content and user navigation behavior. Using these embeddings, the system can reveal connections between points that are unrelated in the original datasets but are related in meaning and therefore embedded close together on the map. We describe the design of the system and key challenges we encountered. We present findings from two user studies exploring design choices and use of the system.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "12", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{DiSciascio:2019:IQA, author = "Cecilia {Di Sciascio} and David Strohmaier and Marcelo Errecalde and Eduardo Veas", title = "Interactive Quality Analytics of User-generated Content: an Integrated Toolkit for the Case of {Wikipedia}", journal = j-TIIS, volume = "9", number = "2--3", pages = "13:1--13:??", month = apr, year = "2019", CODEN = "????", DOI = "https://doi.org/10.1145/3150973", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Wed Dec 11 06:36:19 MST 2019", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/ft_gateway.cfm?id=3150973", abstract = "Digital libraries and services enable users to access large amounts of data on demand. Yet, quality assessment of information encountered on the Internet remains an elusive open issue. For example, Wikipedia, one of the most visited platforms on the Web, hosts thousands of user-generated articles and undergoes 12 million edits/contributions per month. User-generated content is undoubtedly one of the keys to its success but also a hindrance to good quality. Although Wikipedia has established guidelines for the ``perfect article,'' authors find it difficult to assert whether their contributions comply with them and reviewers cannot cope with the ever-growing amount of articles pending review. Great efforts have been invested in algorithmic methods for automatic classification of Wikipedia articles (as featured or non-featured) and for quality flaw detection. Instead, our contribution is an interactive tool that combines automatic classification methods and human interaction in a toolkit, whereby experts can experiment with new quality metrics and share them with authors that need to identify weaknesses to improve a particular article. A design study shows that experts are able to effectively create complex quality metrics in a visual analytics environment. In turn, a user study evidences that regular users can identify flaws, as well as high-quality content based on the inspection of automatic quality scores.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "13", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Paudyal:2019:CTS, author = "Prajwal Paudyal and Junghyo Lee and Ayan Banerjee and Sandeep K. S. Gupta", title = "A Comparison of Techniques for Sign Language Alphabet Recognition Using Armband Wearables", journal = j-TIIS, volume = "9", number = "2--3", pages = "14:1--14:??", month = apr, year = "2019", CODEN = "????", DOI = "https://doi.org/10.1145/3150974", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Wed Dec 11 06:36:19 MST 2019", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/ft_gateway.cfm?id=3150974", abstract = "Recent research has shown that reliable recognition of sign language words and phrases using user-friendly and noninvasive armbands is feasible and desirable. This work provides an analysis and implementation of including fingerspelling recognition (FR) in such systems, which is a much harder problem due to lack of distinctive hand movements. A novel algorithm called DyFAV (Dynamic Feature Selection and Voting) is proposed for this purpose that exploits the fact that fingerspelling has a finite corpus (26 alphabets for the American Sign Language (ASL)). Detailed analysis of the algorithm used as well as comparisons with other traditional machine-learning algorithms is provided. The system uses an independent multiple-agent voting approach to identify letters with high accuracy. The independent voting of the agents ensures that the algorithm is highly parallelizable and thus recognition times can be kept low to suit real-time mobile applications. A thorough explanation and analysis is presented on results obtained on the ASL alphabet corpus for nine people with limited training. An average recognition accuracy of 95.36\% is reported and compared with recognition results from other machine-learning techniques. This result is extended by including six additional validation users with data collected under similar settings as the previous dataset. Furthermore, a feature selection schema using a subset of the sensors is proposed and the results are evaluated. The mobile, noninvasive, and real-time nature of the technology is demonstrated by evaluating performance on various types of Android phones and remote server configurations. A brief discussion of the user interface is provided along with guidelines for best practices.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "14", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Katsuragawa:2019:BLT, author = "Keiko Katsuragawa and Ankit Kamal and Qi Feng Liu and Matei Negulescu and Edward Lank", title = "Bi-Level Thresholding: Analyzing the Effect of Repeated Errors in Gesture Input", journal = j-TIIS, volume = "9", number = "2--3", pages = "15:1--15:??", month = apr, year = "2019", CODEN = "????", DOI = "https://doi.org/10.1145/3181672", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Wed Dec 11 06:36:19 MST 2019", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/ft_gateway.cfm?id=3181672", abstract = "In gesture recognition, one challenge that researchers and developers face is the need for recognition strategies that mediate between false positives and false negatives. In this article, we examine bi-level thresholding, a recognition strategy that uses two thresholds: a tighter threshold limits false positives and recognition errors, and a looser threshold prevents repeated errors (false negatives) by analyzing movements in sequence. We first describe early observations that led to the development of the bi-level thresholding algorithm. Next, using a Wizard-of-Oz recognizer, we hold recognition rates constant and adjust for fixed versus bi-level thresholding; we show that systems using bi-level thresholding result in significantly lower workload scores on the NASA-TLX and significantly lower accelerometer variance when performing gesture input. Finally, we examine the effect that bi-level thresholding has on a real-world dataset of wrist and finger gestures, showing an ability to significantly improve measures of precision and recall. Overall, these results argue for the viability of bi-level thresholding as an effective technique for balancing between false positives, recognition errors, and false negatives.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "15", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Intharah:2019:HDI, author = "Thanapong Intharah and Daniyar Turmukhambetov and Gabriel J. Brostow", title = "{HILC}: Domain-Independent {PbD} System Via Computer Vision and Follow-Up Questions", journal = j-TIIS, volume = "9", number = "2--3", pages = "16:1--16:??", month = apr, year = "2019", CODEN = "????", DOI = "https://doi.org/10.1145/3234508", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Wed Dec 11 06:36:19 MST 2019", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/ft_gateway.cfm?id=3234508", abstract = "Creating automation scripts for tasks involving Graphical User Interface (GUI) interactions is hard. It is challenging because not all software applications allow access to a program's internal state, nor do they all have accessibility APIs. Although much of the internal state is exposed to the user through the GUI, it is hard to programmatically operate the GUI's widgets. To that end, we developed a system prototype that learns by demonstration, called HILC (Help, It Looks Confusing). Users, both programmers and non-programmers, train HILC to synthesize a task script by demonstrating the task. A demonstration produces the needed screenshots and their corresponding mouse-keyboard signals. After the demonstration, the user answers follow-up questions. We propose a user-in-the-loop framework that learns to generate scripts of actions performed on visible elements of graphical applications. Although pure programming by demonstration is still unrealistic due to a computer's limited understanding of user intentions, we use quantitative and qualitative experiments to show that non-programming users are willing and effective at answering follow-up queries posed by our system, to help with confusing parts of the demonstrations. Our models of events and appearances are surprisingly simple but are combined effectively to cope with varying amounts of supervision. The best available baseline, Sikuli Slides, struggled to assist users in the majority of the tests in our user study experiments. The prototype with our proposed approach successfully helped users accomplish simple linear tasks, complicated tasks (monitoring, looping, and mixed), and tasks that span across multiple applications. Even when both systems could ultimately perform a task, ours was trained and refined by the user in less time.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "16", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Thomason:2019:CAV, author = "John Thomason and Photchara Ratsamee and Jason Orlosky and Kiyoshi Kiyokawa and Tomohiro Mashita and Yuki Uranishi and Haruo Takemura", title = "A Comparison of Adaptive View Techniques for Exploratory {$3$D} Drone Teleoperation", journal = j-TIIS, volume = "9", number = "2--3", pages = "17:1--17:??", month = apr, year = "2019", CODEN = "????", DOI = "https://doi.org/10.1145/3232232", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Wed Dec 11 06:36:19 MST 2019", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/ft_gateway.cfm?id=3232232", abstract = "Drone navigation in complex environments poses many problems to teleoperators. Especially in three dimensional (3D) structures such as buildings or tunnels, viewpoints are often limited to the drone's current camera view, nearby objects can be collision hazards, and frequent occlusion can hinder accurate manipulation. To address these issues, we have developed a novel interface for teleoperation that provides a user with environment-adaptive viewpoints that are automatically configured to improve safety and provide smooth operation. This real-time adaptive viewpoint system takes robot position, orientation, and 3D point-cloud information into account to modify the user's viewpoint to maximize visibility. Our prototype uses simultaneous localization and mapping (SLAM) based reconstruction with an omnidirectional camera, and we use the resulting models as well as simulations in a series of preliminary experiments testing navigation of various structures. Results suggest that automatic viewpoint generation can outperform first- and third-person view interfaces for virtual teleoperators in terms of ease of control and accuracy of robot operation.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "17", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Oraby:2019:MCC, author = "Shereen Oraby and Mansurul Bhuiyan and Pritam Gundecha and Jalal Mahmud and Rama Akkiraju", title = "Modeling and Computational Characterization of {Twitter} Customer Service Conversations", journal = j-TIIS, volume = "9", number = "2--3", pages = "18:1--18:??", month = apr, year = "2019", CODEN = "????", DOI = "https://doi.org/10.1145/3213014", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Wed Dec 11 06:36:19 MST 2019", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/ft_gateway.cfm?id=3213014", abstract = "Given the increasing popularity of customer service dialogue on Twitter, analysis of conversation data is essential to understanding trends in customer and agent behavior for the purpose of automating customer service interactions. In this work, we develop a novel taxonomy of fine-grained ``dialogue acts'' frequently observed in customer service, showcasing acts that are more suited to the domain than the more generic existing taxonomies. Using a sequential SVM-HMM model, we model conversation flow, predicting the dialogue act of a given turn in real time, and showcase this using our ``PredDial'' portal. We characterize differences between customer and agent behavior in Twitter customer service conversations and investigate the effect of testing our system on different customer service industries. Finally, we use a data-driven approach to predict important conversation outcomes: customer satisfaction, customer frustration, and overall problem resolution. We show that the type and location of certain dialogue acts in a conversation have a significant effect on the probability of desirable and undesirable outcomes and present actionable rules based on our findings. We explore the correlations between different dialogue acts and the outcome of the conversations in detail using an actionable-rule discovery task by leveraging a state-of-the-art sequential rule mining algorithm while modeling a set of conversations as a set of sequences. The patterns and rules we derive can be used as guidelines for outcome-driven automated customer service platforms.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "18", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Sharma:2019:LSI, author = "Mohit Sharma and F. Maxwell Harper and George Karypis", title = "Learning from Sets of Items in Recommender Systems", journal = j-TIIS, volume = "9", number = "4", pages = "19:1--19:??", month = dec, year = "2019", CODEN = "????", DOI = "https://doi.org/10.1145/3326128", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Wed Dec 11 06:36:20 MST 2019", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/ft_gateway.cfm?id=3326128", abstract = "Most of the existing recommender systems use the ratings provided by users on individual items. An additional source of preference information is to use the ratings that users provide on sets of items. The advantages of using preferences on sets are twofold. First, a rating provided on a set conveys some preference information about each of the set's items, which allows us to acquire a user's preferences for more items than the number of ratings that the user provided. Second, due to privacy concerns, users may not be willing to reveal their preferences on individual items explicitly but may be willing to provide a single rating to a set of items, since it provides some level of information hiding. This article investigates two questions related to using set-level ratings in recommender systems. First, how users' item-level ratings relate to their set-level ratings. Second, how collaborative filtering-based models for item-level rating prediction can take advantage of such set-level ratings. We have collected set-level ratings from active users of Movielens on sets of movies that they have rated in the past. Our analysis of these ratings shows that though the majority of the users provide the average of the ratings on a set's constituent items as the rating on the set, there exists a significant number of users that tend to consistently either under- or over-rate the sets. We have developed collaborative filtering-based methods to explicitly model these user behaviors that can be used to recommend items to users. Experiments on real data and on synthetic data that resembles the under- or over-rating behavior in the real data demonstrate that these models can recover the overall characteristics of the underlying data and predict the user's ratings on individual items.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "19", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Chen:2019:UES, author = "Li Chen and Dongning Yan and Feng Wang", title = "User Evaluations on Sentiment-based Recommendation Explanations", journal = j-TIIS, volume = "9", number = "4", pages = "20:1--20:??", month = dec, year = "2019", CODEN = "????", DOI = "https://doi.org/10.1145/3282878", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Wed Dec 11 06:36:20 MST 2019", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/ft_gateway.cfm?id=3282878", abstract = "The explanation interface has been recognized as important in recommender systems because it can allow users to better judge the relevance of recommendations to their preferences and, hence, make more informed decisions. In different product domains, the specific purpose of explanation can be different. For high-investment products (e.g., digital cameras, laptops), how to educate the typical type of new buyers about product knowledge and, consequently, improve their preference certainty and decision quality is essentially crucial. With this objective, we have developed a novel tradeoff-oriented explanation interface that particularly takes into account sentiment features as extracted from product reviews to generate recommendations and explanations in a category structure. In this manuscript, we first reported the results of an earlier user study (in both before-after and counter-balancing setups) that compared our prototype system with the traditional one that purely considers static specifications for explanations. This experiment revealed that adding sentiment-based explanations can significantly increase users' product knowledge, preference certainty, perceived information usefulness, perceived recommendation transparency and quality, and purchase intention. In order to further identify the reason behind users' perception improvements on the sentiment-based explanation interface, we performed a follow-up lab controlled eye-tracking experiment that investigated how users viewed information and compared products on the interface. This study shows that incorporating sentiment features into the tradeoff-oriented explanations can significantly affect users' eye-gaze pattern. They were stimulated to not only notice bottom categories of products, but also, more frequently, to compare products across categories. The results also disclose users' inherent information needs for sentiment-based explanations, as they allow users to better understand the recommended products and gain more knowledge about static specifications.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "20", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Du:2019:EVA, author = "Fan Du and Catherine Plaisant and Neil Spring and Kenyon Crowley and Ben Shneiderman", title = "{EventAction}: a Visual Analytics Approach to Explainable Recommendation for Event Sequences", journal = j-TIIS, volume = "9", number = "4", pages = "21:1--21:??", month = dec, year = "2019", CODEN = "????", DOI = "https://doi.org/10.1145/3301402", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Wed Dec 11 06:36:20 MST 2019", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/ft_gateway.cfm?id=3301402", abstract = "People use recommender systems to improve their decisions; for example, item recommender systems help them find films to watch or books to buy. Despite the ubiquity of item recommender systems, they can be improved by giving users greater transparency and control. This article develops and assesses interactive strategies for transparency and control, as applied to event sequence recommender systems, which provide guidance in critical life choices such as medical treatments, careers decisions, and educational course selections. This article's main contribution is the use of both record attributes and temporal event information as features to identify similar records and provide appropriate recommendations. While traditional item recommendations are based on choices by people with similar attributes, such as those who looked at this product or watched this movie, our event sequence recommendation approach allows users to select records that share similar attribute values and start with a similar event sequence. Then users see how different choices of actions and the orders and times between them might lead to users' desired outcomes. This paper applies a visual analytics approach to present and explain recommendations of event sequences. It presents a workflow for event sequence recommendation that is implemented in EventAction and reports on three case studies in two domains to illustrate the use of generating event sequence recommendations based on personal histories. It also offers design guidelines for the construction of user interfaces for event sequence recommendation and discusses ethical issues in dealing with personal histories. A demo video of EventAction is available at https://hcil.umd.edu/eventaction.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "21", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Lee:2019:UAM, author = "Junghyo Lee and Prajwal Paudyal and Ayan Banerjee and Sandeep K. S. Gupta", title = "A User-adaptive Modeling for Eating Action Identification from Wristband Time Series", journal = j-TIIS, volume = "9", number = "4", pages = "22:1--22:??", month = dec, year = "2019", CODEN = "????", DOI = "https://doi.org/10.1145/3300149", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Wed Dec 11 06:36:20 MST 2019", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/ft_gateway.cfm?id=3300149", abstract = "Eating activity monitoring using wearable sensors can potentially enable interventions based on eating speed to mitigate the risks of critical healthcare problems such as obesity or diabetes. Eating actions are poly-componential gestures composed of sequential arrangements of three distinct components interspersed with gestures that may be unrelated to eating. This makes it extremely challenging to accurately identify eating actions. The primary reasons for the lack of acceptance of state-of-the-art eating action monitoring techniques include the following: (i) the need to install wearable sensors that are cumbersome to wear or limit the mobility of the user, (ii) the need for manual input from the user, and (iii) poor accuracy in the absence of manual inputs. In this work, we propose a novel methodology, IDEA, that performs accurate eating action identification within eating episodes with an average F1 score of 0.92. This is an improvement of 0.11 for precision and 0.15 for recall for the worst-case users as compared to the state of the art. IDEA uses only a single wristband and provides feedback on eating speed every 2 min without obtaining any manual input from the user.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "22", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Hezarjaribi:2019:HLL, author = "Niloofar Hezarjaribi and Sepideh Mazrouee and Saied Hemati and Naomi S. Chaytor and Martine Perrigue and Hassan Ghasemzadeh", title = "Human-in-the-loop Learning for Personalized Diet Monitoring from Unstructured Mobile Data", journal = j-TIIS, volume = "9", number = "4", pages = "23:1--23:??", month = dec, year = "2019", CODEN = "????", DOI = "https://doi.org/10.1145/3319370", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Wed Dec 11 06:36:20 MST 2019", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/ft_gateway.cfm?id=3319370", abstract = "Lifestyle interventions with the focus on diet are crucial in self-management and prevention of many chronic conditions, such as obesity, cardiovascular disease, diabetes, and cancer. Such interventions require a diet monitoring approach to estimate overall dietary composition and energy intake. Although wearable sensors have been used to estimate eating context (e.g., food type and eating time), accurate monitoring of dietary intake has remained a challenging problem. In particular, because monitoring dietary intake is a self-administered task that involves the end-user to record or report their nutrition intake, current diet monitoring technologies are prone to measurement errors related to challenges of human memory, estimation, and bias. New approaches based on mobile devices have been proposed to facilitate the process of dietary intake recording. These technologies require individuals to use mobile devices such as smartphones to record nutrition intake by either entering text or taking images of the food. Such approaches, however, suffer from errors due to low adherence to technology adoption and time sensitivity to the dietary intake context. In this article, we introduce EZNutriPal, an interactive diet monitoring system that operates on unstructured mobile data such as speech and free-text to facilitate dietary recording, real-time prompting, and personalized nutrition monitoring. EZNutriPal features a natural language processing unit that learns incrementally to add user-specific nutrition data and rules to the system. To prevent missing data that are required for dietary monitoring (e.g., calorie intake estimation), EZNutriPal devises an interactive operating mode that prompts the end-user to complete missing data in real-time. Additionally, we propose a combinatorial optimization approach to identify the most appropriate pairs of food names and food quantities in complex input sentences. We evaluate the performance of EZNutriPal using real data collected from 23 human subjects who participated in two user studies conducted in 13 days each. The results demonstrate that EZNutriPal achieves an accuracy of 89.7\% in calorie intake estimation. We also assess the impacts of the incremental training and interactive prompting technologies on the accuracy of nutrient intake estimation and show that incremental training and interactive prompting improve the performance of diet monitoring by 49.6\% and 29.1\%, respectively, compared to a system without such computing units.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "23", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Juvina:2019:TUT, author = "Ion Juvina and Michael G. Collins and Othalia Larue and William G. Kennedy and Ewart {De Visser} and Celso {De Melo}", title = "Toward a Unified Theory of Learned Trust in Interpersonal and Human-Machine Interactions", journal = j-TIIS, volume = "9", number = "4", pages = "24:1--24:??", month = dec, year = "2019", CODEN = "????", DOI = "https://doi.org/10.1145/3230735", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Wed Dec 11 06:36:20 MST 2019", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/ft_gateway.cfm?id=3230735", abstract = "A proposal for a unified theory of learned trust implemented in a cognitive architecture is presented. The theory is instantiated as a computational cognitive model of learned trust that integrates several seemingly unrelated categories of findings from the literature on interpersonal and human-machine interactions and makes unintuitive predictions for future studies. The model relies on a combination of learning mechanisms to explain a variety of phenomena such as trust asymmetry, the higher impact of early trust breaches, the black-hat/white-hat effect, the correlation between trust and cognitive ability, and the higher resilience of interpersonal as compared to human-machine trust. In addition, the model predicts that trust decays in the absence of evidence of trustworthiness or untrustworthiness. The implications of the model for the advancement of the theory on trust are discussed. Specifically, this work suggests two more trust antecedents on the trustor's side: perceived trust necessity and cognitive ability to detect cues of trustworthiness.", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "24", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Billinghurst:2020:SIH, author = "Mark Billinghurst and Margaret Burnett and Aaron Quigley", title = "Special Issue on Highlights of {ACM Intelligent User Interface (IUI) 2018}", journal = j-TIIS, volume = "10", number = "1", pages = "1:1--1:3", month = jan, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3357206", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Sun Mar 28 07:49:20 MDT 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/10.1145/3357206", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "1", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Vanderdonckt:2020:EDS, author = "Jean Vanderdonckt and Sara Bouzit and Ga{\"e}lle Calvary and Denis Ch{\^e}ne", title = "Exploring a Design Space of Graphical Adaptive Menus: Normal vs. Small Screens", journal = j-TIIS, volume = "10", number = "1", pages = "2:1--2:40", month = jan, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3237190", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Sun Mar 28 07:49:20 MDT 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/10.1145/3237190", abstract = "Graphical Adaptive Menus are Graphical User Interface menus whose predicted items of immediate use can be automatically rendered in a prediction window. Rendering this prediction window is a key question for adaptivity to enable the end-user to \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "2", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Song:2020:FLT, author = "Jean Y. Song and Raymond Fok and Juho Kim and Walter S. Lasecki", title = "{FourEyes}: Leveraging Tool Diversity as a Means to Improve Aggregate Accuracy in Crowdsourcing", journal = j-TIIS, volume = "10", number = "1", pages = "3:1--3:30", month = jan, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3237188", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Sun Mar 28 07:49:20 MDT 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/10.1145/3237188", abstract = "Crowdsourcing is a common means of collecting image segmentation training data for use in a variety of computer vision applications. However, designing accurate crowd-powered image segmentation systems is challenging, because defining object boundaries \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "3", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Broden:2020:BBE, author = "Bj{\"o}rn Brod{\'e}n and Mikael Hammar and Bengt J. Nilsson and Dimitris Paraschakis", title = "A Bandit-Based Ensemble Framework for Exploration\slash Exploitation of Diverse Recommendation Components: an Experimental Study within E-Commerce", journal = j-TIIS, volume = "10", number = "1", pages = "4:1--4:32", month = jan, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3237187", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Sat Jan 11 08:20:51 MST 2020", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/abs/10.1145/3237187", abstract = "This work presents an extension of Thompson Sampling bandit policy for orchestrating the collection of base recommendation algorithms for e-commerce. We focus on the problem of item-to-item recommendations, for which multiple behavioral and attribute-\ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "4", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Tsai:2020:ESR, author = "Chun-Hua Tsai and Peter Brusilovsky", title = "Exploring Social Recommendations with Visual Diversity-Promoting Interfaces", journal = j-TIIS, volume = "10", number = "1", pages = "5:1--5:34", month = jan, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3231465", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Sun Mar 28 07:49:20 MDT 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/10.1145/3231465", abstract = "The beyond-relevance objectives of recommender systems have been drawing more and more attention. For example, a diversity-enhanced interface has been shown to associate positively with overall levels of user satisfaction. However, little is known about \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "5", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Sherkat:2020:VAA, author = "Ehsan Sherkat and Evangelos E. Milios and Rosane Minghim", title = "A Visual Analytics Approach for Interactive Document Clustering", journal = j-TIIS, volume = "10", number = "1", pages = "6:1--6:33", month = jan, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3241380", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Sun Mar 28 07:49:20 MDT 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/10.1145/3241380", abstract = "Document clustering is a necessary step in various analytical and automated activities. When guided by the user, algorithms are tailored to imprint a perspective on the clustering process that reflects the user's understanding of the dataset. More than \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "6", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Suh:2020:AFS, author = "Jina Suh and Soroush Ghorashi and Gonzalo Ramos and Nan-Chen Chen and Steven Drucker and Johan Verwey and Patrice Simard", title = "{AnchorViz}: Facilitating Semantic Data Exploration and Concept Discovery for Interactive Machine Learning", journal = j-TIIS, volume = "10", number = "1", pages = "7:1--7:38", month = jan, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3241379", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Sun Mar 28 07:49:20 MDT 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/10.1145/3241379", abstract = "When building a classifier in interactive machine learning (iML), human knowledge about the target class can be a powerful reference to make the classifier robust to unseen items. The main challenge lies in finding unlabeled items that can either help \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "7", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Sciascio:2020:RUC, author = "Cecilia {Di Sciascio} and Peter Brusilovsky and Christoph Trattner and Eduardo Veas", title = "A Roadmap to User-Controllable Social Exploratory Search", journal = j-TIIS, volume = "10", number = "1", pages = "8:1--8:38", month = jan, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3241382", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Sat Jan 11 08:20:51 MST 2020", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/abs/10.1145/3241382", abstract = "Information-seeking tasks with learning or investigative purposes are usually referred to as exploratory search. Exploratory search unfolds as a dynamic process where the user, amidst navigation, trial and error, and on-the-fly selections, gathers and \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "8", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Todi:2020:IGL, author = "Kashyap Todi and Jussi Jokinen and Kris Luyten and Antti Oulasvirta", title = "Individualising Graphical Layouts with Predictive Visual Search Models", journal = j-TIIS, volume = "10", number = "1", pages = "9:1--9:24", month = jan, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3241381", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Sun Mar 28 07:49:20 MDT 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/10.1145/3241381", abstract = "In domains where users are exposed to large variations in visuo-spatial features among designs, they often spend excess time searching for common elements (features) on an interface. This article contributes individualised predictive models of visual \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "9", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{He:2020:DDA, author = "Yangyang He and Paritosh Bahirat and Bart P. Knijnenburg and Abhilash Menon", title = "A Data-Driven Approach to Designing for Privacy in Household {IoT}", journal = j-TIIS, volume = "10", number = "1", pages = "10:1--10:47", month = jan, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3241378", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Sat Jan 11 08:20:51 MST 2020", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/abs/10.1145/3241378", abstract = "In this article, we extend and improve upon a previously developed data-driven approach to design privacy-setting interfaces for users of household IoT devices. The essence of this approach is to gather users' feedback on household IoT scenarios before\ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "10", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Avrahami:2020:UAR, author = "Daniel Avrahami and Mitesh Patel and Yusuke Yamaura and Sven Kratz and Matthew Cooper", title = "Unobtrusive Activity Recognition and Position Estimation for Work Surfaces Using {RF}-Radar Sensing", journal = j-TIIS, volume = "10", number = "1", pages = "11:1--11:28", month = jan, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3241383", ISSN = "2160-6455 (print), 2160-6463 (electronic)", bibdate = "Sat Jan 11 08:20:51 MST 2020", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/abs/10.1145/3241383", abstract = "Activity recognition is a core component of many intelligent and context-aware systems. We present a solution for discreetly and unobtrusively recognizing common work activities above a work surface without using cameras. We demonstrate our approach, \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "11", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J1341", } @Article{Conati:2020:CCI, author = "Cristina Conati and S{\'e}bastien Lall{\'e} and Md Abed Rahman and Dereck Toker", title = "Comparing and Combining Interaction Data and Eye-tracking Data for the Real-time Prediction of User Cognitive Abilities in Visualization Tasks", journal = j-TIIS, volume = "10", number = "2", pages = "12:1--12:41", month = jun, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3301400", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Sat Jun 27 14:42:35 MDT 2020", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/abs/10.1145/3301400", abstract = "Previous work has shown that some user cognitive abilities relevant for processing information visualizations can be predicted from eye-tracking data. Performing this type of user modeling is important for devising visualizations that can detect a user'. \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "12", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Ahn:2020:PIP, author = "Yongsu Ahn and Yu-Ru Lin", title = "{PolicyFlow}: Interpreting Policy Diffusion in Context", journal = j-TIIS, volume = "10", number = "2", pages = "13:1--13:23", month = jun, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3385729", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Sat Jun 27 14:42:35 MDT 2020", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/abs/10.1145/3385729", abstract = "Stability in social, technical, and financial systems, as well as the capacity of organizations to work across borders, requires consistency in public policy across jurisdictions. The diffusion of laws and regulations across political boundaries can \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "13", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Mohan:2020:DAH, author = "Shiwali Mohan and Anusha Venkatakrishnan and Andrea L. Hartzler", title = "Designing an {AI} Health Coach and Studying Its Utility in Promoting Regular Aerobic Exercise", journal = j-TIIS, volume = "10", number = "2", pages = "14:1--14:30", month = jun, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3366501", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Sat Jun 27 14:42:35 MDT 2020", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/abs/10.1145/3366501", abstract = "Our research aims to develop interactive, social agents that can coach people to learn new tasks, skills, and habits. In this article, we focus on coaching sedentary, overweight individuals (i.e., ``trainees'') to exercise regularly. We employ adaptive \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "14", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Anderson:2020:MMM, author = "Andrew Anderson and Jonathan Dodge and Amrita Sadarangani and Zoe Juozapaitis and Evan Newman and Jed Irvine and Souti Chattopadhyay and Matthew Olson and Alan Fern and Margaret Burnett", title = "Mental Models of Mere Mortals with Explanations of Reinforcement Learning", journal = j-TIIS, volume = "10", number = "2", pages = "15:1--15:37", month = jun, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3366485", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Sat Jun 27 14:42:35 MDT 2020", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/abs/10.1145/3366485", abstract = "How should reinforcement learning (RL) agents explain themselves to humans not trained in AI? To gain insights into this question, we conducted a 124-participant, four-treatment experiment to compare participants' mental models of an RL agent in the \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "15", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Fan:2020:ADU, author = "Mingming Fan and Yue Li and Khai N. Truong", title = "Automatic Detection of Usability Problem Encounters in Think-aloud Sessions", journal = j-TIIS, volume = "10", number = "2", pages = "16:1--16:24", month = jun, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3385732", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Sat Jun 27 14:42:35 MDT 2020", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/abs/10.1145/3385732", abstract = "Think-aloud protocols are a highly valued usability testing method for identifying usability problems. Despite the value of conducting think-aloud usability test sessions, analyzing think-aloud sessions is often time-consuming and labor-intensive. \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "16", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Pan:2020:SID, author = "Shimei Pan and Oliver Brdiczka and Andrea Kleinsmith and Yangqiu Song", title = "Special Issue on Data-Driven Personality Modeling for Intelligent Human-Computer Interaction", journal = j-TIIS, volume = "10", number = "3", pages = "17:1--17:3", month = nov, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3402522", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Sun Mar 28 07:49:21 MDT 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/10.1145/3402522", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "17", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Taib:2020:PSD, author = "Ronnie Taib and Shlomo Berkovsky and Irena Koprinska and Eileen Wang and Yucheng Zeng and Jingjie Li", title = "Personality Sensing: Detection of Personality Traits Using Physiological Responses to Image and Video Stimuli", journal = j-TIIS, volume = "10", number = "3", pages = "18:1--18:32", month = nov, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3357459", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Sun Mar 28 07:49:21 MDT 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/10.1145/3357459", abstract = "Personality detection is an important task in psychology, as different personality traits are linked to different behaviours and real-life outcomes. Traditionally it involves filling out lengthy questionnaires, which is time-consuming, and may also be \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "18", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Dotti:2020:BCA, author = "Dario Dotti and Mirela Popa and Stylianos Asteriadis", title = "Being the Center of Attention: a Person-Context {CNN} Framework for Personality Recognition", journal = j-TIIS, volume = "10", number = "3", pages = "19:1--19:20", month = nov, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3338245", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Sun Mar 28 07:49:21 MDT 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/10.1145/3338245", abstract = "This article proposes a novel study on personality recognition using video data from different scenarios. Our goal is to jointly model nonverbal behavioral cues with contextual information for a robust, multi-scenario, personality recognition system. \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "19", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Bagheri:2020:ACE, author = "Elahe Bagheri and Pablo G. Esteban and Hoang-Long Cao and Albert {De Beir} and Dirk Lefeber and Bram Vanderborght", title = "An Autonomous Cognitive Empathy Model Responsive to Users' Facial Emotion Expressions", journal = j-TIIS, volume = "10", number = "3", pages = "20:1--20:23", month = nov, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3341198", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Sun Mar 28 07:49:21 MDT 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/10.1145/3341198", abstract = "Successful social robot services depend on how robots can interact with users. The effective service can be obtained through smooth, engaged, and humanoid interactions in which robots react properly to a user's affective state. This article proposes a \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "20", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Wang:2020:MDS, author = "Ruijie Wang and Liming Chen and Ivar Solheim", title = "Modeling Dyslexic Students' Motivation for Enhanced Learning in E-learning Systems", journal = j-TIIS, volume = "10", number = "3", pages = "21:1--21:34", month = nov, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3341197", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Sun Mar 28 07:49:21 MDT 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/10.1145/3341197", abstract = "E-Learning systems can support real-time monitoring of learners' learning desires and effects, thus offering opportunities for enhanced personalized learning. Recognition of the determinants of dyslexic users' motivation to use e-learning systems is \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "21", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Khan:2020:PUM, author = "Euna Mehnaz Khan and Md. Saddam Hossain Mukta and Mohammed Eunus Ali and Jalal Mahmud", title = "Predicting Users' Movie Preference and Rating Behavior from Personality and Values", journal = j-TIIS, volume = "10", number = "3", pages = "22:1--22:25", month = nov, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3338244", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Sun Mar 28 07:49:21 MDT 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/10.1145/3338244", abstract = "In this article, we propose novel techniques to predict a user's movie genre preference and rating behavior from her psycholinguistic attributes obtained from the social media interactions. The motivation of this work comes from various psychological \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "22", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Higuchi:2020:LCD, author = "Keita Higuchi and Hiroki Tsuchida and Eshed Ohn-Bar and Yoichi Sato and Kris Kitani", title = "Learning Context-dependent Personal Preferences for Adaptive Recommendation", journal = j-TIIS, volume = "10", number = "3", pages = "23:1--23:26", month = nov, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3359755", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Sun Mar 28 07:49:21 MDT 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/10.1145/3359755", abstract = "We propose two online-learning algorithms for modeling the personal preferences of users of interactive systems. The proposed algorithms leverage user feedback to estimate user behavior and provide personalized adaptive recommendation for supporting \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "23", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Hailpern:2020:HIP, author = "Joshua Hailpern and Mark Huber and Ronald Calvo", title = "How Impactful Is Presentation in Email? {The} Effect of Avatars and Signatures", journal = j-TIIS, volume = "10", number = "3", pages = "24:1--24:26", month = nov, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3345641", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Sun Mar 28 07:49:21 MDT 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/10.1145/3345641", abstract = "A primary well-controlled study of 900 participants found that personal presentation choices in professional emails (non-content changes like Profile Avatar 8 Signature) impact the recipient's perception of the sender's personality and the quality of \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "24", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Zhou:2020:ITS, author = "Michele X. Zhou", title = "Introduction to the {TiiS} Special Column", journal = j-TIIS, volume = "10", number = "4", pages = "25:1--25:1", month = dec, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3427592", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Sun Mar 28 07:49:22 MDT 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/10.1145/3427592", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "25", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Shneiderman:2020:BGB, author = "Ben Shneiderman", title = "Bridging the Gap Between Ethics and Practice: Guidelines for Reliable, Safe, and Trustworthy Human-centered {AI} Systems", journal = j-TIIS, volume = "10", number = "4", pages = "26:1--26:31", month = dec, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3419764", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Sun Mar 28 07:49:22 MDT 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/10.1145/3419764", abstract = "This article attempts to bridge the gap between widely discussed ethical principles of Human-centered AI (HCAI) and practical steps for effective governance. Since HCAI systems are developed and implemented in multiple organizational structures, I \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "26", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Brdiczka:2020:ISI, author = "Oliver Brdiczka and Duen Horng Chau and Minsuk Kahng and Ga{\"e}lle Calvary", title = "Introduction to the Special Issue on Highlights of {ACM Intelligent User Interface (IUI) 2019}", journal = j-TIIS, volume = "10", number = "4", pages = "27:1--27:2", month = dec, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3429946", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Sun Mar 28 07:49:22 MDT 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/10.1145/3429946", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "27", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Eiband:2020:MAE, author = "Malin Eiband and Sarah Theres V{\"o}lkel and Daniel Buschek and Sophia Cook and Heinrich Hussmann", title = "A Method and Analysis to Elicit User-Reported Problems in Intelligent Everyday Applications", journal = j-TIIS, volume = "10", number = "4", pages = "28:1--28:27", month = dec, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3370927", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Sun Mar 28 07:49:22 MDT 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/10.1145/3370927", abstract = "The complex nature of intelligent systems motivates work on supporting users during interaction, for example, through explanations. However, as of yet, there is little empirical evidence in regard to specific problems users face when applying such \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "28", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Springer:2020:PDW, author = "Aaron Springer and Steve Whittaker", title = "Progressive Disclosure: When, Why, and How Do Users Want Algorithmic Transparency Information?", journal = j-TIIS, volume = "10", number = "4", pages = "29:1--29:32", month = dec, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3374218", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Sun Mar 28 07:49:22 MDT 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/10.1145/3374218", abstract = "It is essential that users understand how algorithmic decisions are made, as we increasingly delegate important decisions to intelligent systems. Prior work has often taken a techno-centric approach, focusing on new computational techniques to support \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "29", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Dominguez:2020:AHA, author = "Vicente Dominguez and Ivania Donoso-Guzm{\'a}n and Pablo Messina and Denis Parra", title = "Algorithmic and {HCI} Aspects for Explaining Recommendations of Artistic Images", journal = j-TIIS, volume = "10", number = "4", pages = "30:1--30:31", month = dec, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3369396", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Sun Mar 28 07:49:22 MDT 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/10.1145/3369396", abstract = "Explaining suggestions made by recommendation systems is key to make users trust and accept these systems. This is specially critical in areas such as art image recommendation. Traditionally, artworks are sold in galleries where people can see them \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "30", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Kouki:2020:GUP, author = "Pigi Kouki and James Schaffer and Jay Pujara and John O'Donovan and Lise Getoor", title = "Generating and Understanding Personalized Explanations in Hybrid Recommender Systems", journal = j-TIIS, volume = "10", number = "4", pages = "31:1--31:40", month = dec, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3365843", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Sun Mar 28 07:49:22 MDT 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/10.1145/3365843", abstract = "Recommender systems are ubiquitous and shape the way users access information and make decisions. As these systems become more complex, there is a growing need for transparency and interpretability. In this article, we study the problem of generating \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "31", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Hsu:2020:SPE, author = "Yen-Chia Hsu and Jennifer Cross and Paul Dille and Michael Tasota and Beatrice Dias and Randy Sargent and Ting-Hao (Kenneth) Huang and Illah Nourbakhsh", title = "{Smell Pittsburgh}: Engaging Community Citizen Science for Air Quality", journal = j-TIIS, volume = "10", number = "4", pages = "32:1--32:49", month = dec, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3369397", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Sun Mar 28 07:49:22 MDT 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/10.1145/3369397", abstract = "Urban air pollution has been linked to various human health concerns, including cardiopulmonary diseases. Communities who suffer from poor air quality often rely on experts to identify pollution sources due to the lack of accessible tools. Taking this \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "32", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Mohanty:2020:PSI, author = "Vikram Mohanty and David Thames and Sneha Mehta and Kurt Luther", title = "{Photo Sleuth}: Identifying Historical Portraits with Face Recognition and Crowdsourced Human Expertise", journal = j-TIIS, volume = "10", number = "4", pages = "33:1--33:36", month = dec, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3365842", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Sun Mar 28 07:49:22 MDT 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/10.1145/3365842", abstract = "Identifying people in historical photographs is important for preserving material culture, correcting the historical record, and creating economic value, but it is also a complex and challenging task. In this article, we focus on identifying portraits \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "33", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Kulahcioglu:2020:AAW, author = "Tugba Kulahcioglu and Gerard {De Melo}", title = "Affect-Aware Word Clouds", journal = j-TIIS, volume = "10", number = "4", pages = "34:1--34:25", month = dec, year = "2020", CODEN = "????", DOI = "https://doi.org/10.1145/3370928", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Sun Mar 28 07:49:22 MDT 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/10.1145/3370928", abstract = "Word clouds are widely used for non-analytic purposes, such as introducing a topic to students, or creating a gift with personally meaningful text. Surveys show that users prefer tools that yield word clouds with a stronger emotional impact. Fonts and \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "34", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Mohan:2021:ERC, author = "Shiwali Mohan", title = "Exploring the Role of Common Model of Cognition in Designing Adaptive Coaching Interactions for Health Behavior Change", journal = j-TIIS, volume = "11", number = "1", pages = "1:1--1:30", month = apr, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3375790", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Tue Apr 27 08:00:40 MDT 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/10.1145/3375790", abstract = "Our research aims to develop intelligent collaborative agents that are human-aware: They can model, learn, and reason about their human partner's physiological, cognitive, and affective states. In this article, we study how adaptive coaching interactions \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "1", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Penney:2021:SGE, author = "Sean Penney and Jonathan Dodge and Andrew Anderson and Claudia Hilderbrand and Logan Simpson and Margaret Burnett", title = "The Shoutcasters, the Game Enthusiasts, and the {AI}: Foraging for Explanations of Real-time Strategy Players", journal = j-TIIS, volume = "11", number = "1", pages = "2:1--2:46", month = apr, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3396047", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Tue Apr 27 08:00:40 MDT 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/10.1145/3396047", abstract = "Assessing and understanding intelligent agents is a difficult task for users who lack an AI background. ``Explainable AI'' (XAI) aims to address this problem, but what should be in an explanation? One route toward answering this question is to turn to \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "2", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Bessghaier:2021:DSA, author = "Narjes Bessghaier and Makram Soui and Christophe Kolski and Mabrouka Chouchane", title = "On the Detection of Structural Aesthetic Defects of {Android} Mobile User Interfaces with a Metrics-based Tool", journal = j-TIIS, volume = "11", number = "1", pages = "3:1--3:27", month = apr, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3410468", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Tue Apr 27 08:00:40 MDT 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/10.1145/3410468", abstract = "Smartphone users are striving for easy-to-learn and use mobile apps user interfaces. Accomplishing these qualities demands an iterative evaluation of the Mobile User Interface (MUI). Several studies stress the value of providing a MUI with a pleasing look \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "3", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Oviatt:2021:KWY, author = "Sharon Oviatt and Jionghao Lin and Abishek Sriramulu", title = "{I} Know What You Know: What Hand Movements Reveal about Domain Expertise", journal = j-TIIS, volume = "11", number = "1", pages = "4:1--4:26", month = apr, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3423049", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Tue Apr 27 08:00:40 MDT 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/10.1145/3423049", abstract = "This research investigates whether students' level of domain expertise can be detected during authentic learning activities by analyzing their physical activity patterns. More expert students reduced their manual activity by a substantial 50\%, which was \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "4", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Douer:2021:TMS, author = "Nir Douer and Joachim Meyer", title = "Theoretical, Measured, and Subjective Responsibility in Aided Decision Making", journal = j-TIIS, volume = "11", number = "1", pages = "5:1--5:37", month = apr, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3425732", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Tue Apr 27 08:00:40 MDT 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/10.1145/3425732", abstract = "When humans interact with intelligent systems, their causal responsibility for outcomes becomes equivocal. We analyze the descriptive abilities of a newly developed responsibility quantification model (ResQu) to predict actual human responsibility and \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "5", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Bhattacharya:2021:RTI, author = "Samit Bhattacharya and Viral Bharat Shah and Krishna Kumar and Ujjwal Biswas", title = "A Real-time Interactive Visualizer for Large Classroom", journal = j-TIIS, volume = "11", number = "1", pages = "6:1--6:26", month = apr, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3418529", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Tue Apr 27 08:00:40 MDT 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/10.1145/3418529", abstract = "In improving the teaching and learning experience in a classroom environment, it is crucial for a teacher to have a fair idea about the students who need help during a lecture. However, teachers of large classes usually face difficulties in identifying \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "6", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Chen:2021:PPR, author = "Xiaoyu Chen and Nathan Lau and Ran Jin", title = "{PRIME}: a Personalized Recommender System for Information Visualization Methods via Extended Matrix Completion", journal = j-TIIS, volume = "11", number = "1", pages = "7:1--7:30", month = apr, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3366484", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Tue Apr 27 08:00:40 MDT 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/10.1145/3366484", abstract = "Adapting user interface designs for specific tasks performed by different users is a challenging yet important problem. Automatically adapting visualization designs to users and contexts (e.g., tasks, display devices, environments, etc.) can theoretically \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "7", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Ma:2021:HTR, author = "Wanqi Ma and Xiaoxiao Liao and Wei Dai and Weike Pan and Zhong Ming", title = "Holistic Transfer to Rank for Top-{$N$} Recommendation", journal = j-TIIS, volume = "11", number = "1", pages = "8:1--8:1", month = apr, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3434360", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Tue Apr 27 08:00:40 MDT 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/10.1145/3434360", abstract = "Recommender systems have been a valuable component in various online services such as e-commerce and entertainment. To provide an accurate top-N recommendation list of items for each target user, we have to answer a very basic question of how to model \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "8", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Tran:2021:HRS, author = "Thi Ngoc Trang Tran and Alexander Felfernig and Nava Tintarev", title = "Humanized Recommender Systems: State-of-the-art and Research Issues", journal = j-TIIS, volume = "11", number = "2", pages = "9:1--9:41", month = jul, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3446906", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Thu Jul 22 08:06:11 MDT 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/10.1145/3446906", abstract = "Psychological factors such as personality, emotions, social connections, and decision biases can significantly affect the outcome of a decision process. These factors are also prevalent in the existing literature related to the inclusion of psychological \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "9", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Shatilov:2021:EEB, author = "Kirill A. Shatilov and Dimitris Chatzopoulos and Lik-Hang Lee and Pan Hui", title = "Emerging {ExG}-based {NUI} Inputs in Extended Realities: a Bottom-up Survey", journal = j-TIIS, volume = "11", number = "2", pages = "10:1--10:49", month = jul, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3457950", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Thu Jul 22 08:06:11 MDT 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/10.1145/3457950", abstract = "Incremental and quantitative improvements of two-way interactions with e x tended realities (XR) are contributing toward a qualitative leap into a state of XR ecosystems being efficient, user-friendly, and widely adopted. However, there are multiple \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "10", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Gil:2021:AIM, author = "Yolanda Gil and Daniel Garijo and Deborah Khider and Craig A. Knoblock and Varun Ratnakar and Maximiliano Osorio and Hern{\'a}n Vargas and Minh Pham and Jay Pujara and Basel Shbita and Binh Vu and Yao-Yi Chiang and Dan Feldman and Yijun Lin and Hayley Song and Vipin Kumar and Ankush Khandelwal and Michael Steinbach and Kshitij Tayal and Shaoming Xu and Suzanne A. Pierce and Lissa Pearson and Daniel Hardesty-Lewis and Ewa Deelman and Rafael Ferreira {Da Silva} and Rajiv Mayani and Armen R. Kemanian and Yuning Shi and Lorne Leonard and Scott Peckham and Maria Stoica and Kelly Cobourn and Zeya Zhang and Christopher Duffy and Lele Shu", title = "Artificial Intelligence for Modeling Complex Systems: Taming the Complexity of Expert Models to Improve Decision Making", journal = j-TIIS, volume = "11", number = "2", pages = "11:1--11:49", month = jul, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3453172", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Thu Jul 22 08:06:11 MDT 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/10.1145/3453172", abstract = "Major societal and environmental challenges involve complex systems that have diverse multi-scale interacting processes. Consider, for example, how droughts and water reserves affect crop production and how agriculture and industrial needs affect water \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "11", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Rosenberg:2021:ECA, author = "Maor Rosenberg and Hae Won Park and Rinat Rosenberg-Kima and Safinah Ali and Anastasia K. Ostrowski and Cynthia Breazeal and Goren Gordon", title = "Expressive Cognitive Architecture for a Curious Social Robot", journal = j-TIIS, volume = "11", number = "2", pages = "12:1--12:25", month = jul, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3451531", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Thu Jul 22 08:06:11 MDT 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/10.1145/3451531", abstract = "Artificial curiosity, based on developmental psychology concepts wherein an agent attempts to maximize its learning progress, has gained much attention in recent years. Similarly, social robots are slowly integrating into our daily lives, in schools, \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "12", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Roffarello:2021:UDM, author = "Alberto Monge Roffarello and Luigi {De Russis}", title = "Understanding, Discovering, and Mitigating Habitual Smartphone Use in Young Adults", journal = j-TIIS, volume = "11", number = "2", pages = "13:1--13:34", month = jul, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3447991", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Thu Jul 22 08:06:11 MDT 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/10.1145/3447991", abstract = "People, especially young adults, often use their smartphones out of habit: They compulsively browse social networks, check emails, and play video-games with little or no awareness at all. While previous studies analyzed this phenomena qualitatively, e.g., \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "13", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Spiller:2021:PVS, author = "Moritz Spiller and Ying-Hsang Liu and Md Zakir Hossain and Tom Gedeon and Julia Geissler and Andreas N{\"u}rnberger", title = "Predicting Visual Search Task Success from Eye Gaze Data as a Basis for User-Adaptive Information Visualization Systems", journal = j-TIIS, volume = "11", number = "2", pages = "14:1--14:25", month = jul, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3446638", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Thu Jul 22 08:06:11 MDT 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/10.1145/3446638", abstract = "Information visualizations are an efficient means to support the users in understanding large amounts of complex, interconnected data; user comprehension, however, depends on individual factors such as their cognitive abilities. The research literature \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "14", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Banisetty:2021:SAN, author = "Santosh Balajee Banisetty and Scott Forer and Logan Yliniemi and Monica Nicolescu and David Feil-Seifer", title = "Socially Aware Navigation: a Non-linear Multi-objective Optimization Approach", journal = j-TIIS, volume = "11", number = "2", pages = "15:1--15:26", month = jul, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3453445", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Thu Jul 22 08:06:11 MDT 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/10.1145/3453445", abstract = "Mobile robots are increasingly populating homes, hospitals, shopping malls, factory floors, and other human environments. Human society has social norms that people mutually accept; obeying these norms is an essential signal that someone is participating \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "15", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Mousas:2021:PSV, author = "Christos Mousas and Claudia Krogmeier and Zhiquan Wang", title = "Photo Sequences of Varying Emotion: Optimization with a Valence-Arousal Annotated Dataset", journal = j-TIIS, volume = "11", number = "2", pages = "16:1--16:19", month = jul, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3458844", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Thu Jul 22 08:06:11 MDT 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/10.1145/3458844", abstract = "Synthesizing photo products such as photo strips and slideshows using a database of images is a time-consuming and tedious process that requires significant manual work. To overcome this limitation, we developed a method that automatically synthesizes \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "16", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Turkay:2021:SII, author = "Cagatay Turkay and Tatiana {Von Landesberger} and Daniel Archambault and Shixia Liu and Remco Chang", title = "Special Issue on Interactive Visual Analytics for Making Explainable and Accountable Decisions", journal = j-TIIS, volume = "11", number = "3--4", pages = "17:1--17:4", month = dec, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3471903", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Fri Dec 10 11:35:09 MST 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/10.1145/3471903", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "17", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Zhang:2021:MMI, author = "Yu Zhang and Bob Coecke and Min Chen", title = "{MI3}: Machine-initiated Intelligent Interaction for Interactive Classification and Data Reconstruction", journal = j-TIIS, volume = "11", number = "3--4", pages = "18:1--18:34", month = dec, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3412848", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Fri Dec 10 11:35:09 MST 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/10.1145/3412848", abstract = "In many applications, while machine learning (ML) can be used to derive algorithmic models to aid decision processes, it is often difficult to learn a precise model when the number of similar data points is limited. One example of such applications is \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "18", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Sevastjanova:2021:QGA, author = "Rita Sevastjanova and Wolfgang Jentner and Fabian Sperrle and Rebecca Kehlbeck and J{\"u}rgen Bernard and Mennatallah El-assady", title = "{QuestionComb}: a Gamification Approach for the Visual Explanation of Linguistic Phenomena through Interactive Labeling", journal = j-TIIS, volume = "11", number = "3--4", pages = "19:1--19:38", month = dec, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3429448", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Fri Dec 10 11:35:09 MST 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/10.1145/3429448", abstract = "Linguistic insight in the form of high-level relationships and rules in text builds the basis of our understanding of language. However, the data-driven generation of such structures often lacks labeled resources that can be used as training data for \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "19", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Bernard:2021:TPM, author = "J{\"u}rgen Bernard and Marco Hutter and Michael Sedlmair and Matthias Zeppelzauer and Tamara Munzner", title = "A Taxonomy of Property Measures to Unify Active Learning and Human-centered Approaches to Data Labeling", journal = j-TIIS, volume = "11", number = "3--4", pages = "20:1--20:42", month = dec, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3439333", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Fri Dec 10 11:35:09 MST 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/10.1145/3439333", abstract = "Strategies for selecting the next data instance to label, in service of generating labeled data for machine learning, have been considered separately in the machine learning literature on active learning and in the visual analytics literature on human-. \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "20", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Segura:2021:BBO, author = "Vin{\'i}cius Segura and Simone D. J. Barbosa", title = "{BONNIE}: Building Online Narratives from Noteworthy Interaction Events", journal = j-TIIS, volume = "11", number = "3--4", pages = "21:1--21:31", month = dec, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3423048", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Fri Dec 10 11:35:09 MST 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/10.1145/3423048", abstract = "Nowadays, we have access to data of unprecedented volume, high dimensionality, and complexity. To extract novel insights from such complex and dynamic data, we need effective and efficient strategies. One such strategy is to combine data analysis and \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "21", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Hinterreiter:2021:PPE, author = "Andreas Hinterreiter and Christian Steinparz and Moritz Sch{\"O}fl and Holger Stitz and Marc Streit", title = "Projection Path Explorer: Exploring Visual Patterns in Projected Decision-making Paths", journal = j-TIIS, volume = "11", number = "3--4", pages = "22:1--22:29", month = dec, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3387165", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Fri Dec 10 11:35:09 MST 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/10.1145/3387165", abstract = "In problem-solving, a path towards a solutions can be viewed as a sequence of decisions. The decisions, made by humans or computers, describe a trajectory through a high-dimensional representation space of the problem. By means of dimensionality reduction,. \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "22", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Kim:2021:LGR, author = "Chris Kim and Xiao Lin and Christopher Collins and Graham W. Taylor and Mohamed R. Amer", title = "Learn, Generate, Rank, Explain: a Case Study of Visual Explanation by Generative Machine Learning", journal = j-TIIS, volume = "11", number = "3--4", pages = "23:1--23:34", month = dec, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3465407", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Fri Dec 10 11:35:09 MST 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/10.1145/3465407", abstract = "While the computer vision problem of searching for activities in videos is usually addressed by using discriminative models, their decisions tend to be opaque and difficult for people to understand. We propose a case study of a novel machine learning \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "23", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Mohseni:2021:MSF, author = "Sina Mohseni and Niloofar Zarei and Eric D. Ragan", title = "A Multidisciplinary Survey and Framework for Design and Evaluation of Explainable {AI} Systems", journal = j-TIIS, volume = "11", number = "3--4", pages = "24:1--24:45", month = dec, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3387166", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Fri Dec 10 11:35:09 MST 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/10.1145/3387166", abstract = "The need for interpretable and accountable intelligent systems grows along with the prevalence of artificial intelligence (AI) applications used in everyday life. Explainable AI (XAI) systems are intended to self-explain the reasoning behind system \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "24", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Hepenstal:2021:DCA, author = "Sam Hepenstal and Leishi Zhang and Neesha Kodagoda and B. L. William Wong", title = "Developing Conversational Agents for Use in Criminal Investigations", journal = j-TIIS, volume = "11", number = "3--4", pages = "25:1--25:35", month = dec, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3444369", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Fri Dec 10 11:35:09 MST 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/10.1145/3444369", abstract = "The adoption of artificial intelligence (AI) systems in environments that involve high risk and high consequence decision-making is severely hampered by critical design issues. These issues include system transparency and brittleness, where transparency \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "25", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Meng:2021:VVA, author = "Linhao Meng and Yating Wei and Rusheng Pan and Shuyue Zhou and Jianwei Zhang and Wei Chen", title = "{VADAF}: Visualization for Abnormal Client Detection and Analysis in Federated Learning", journal = j-TIIS, volume = "11", number = "3--4", pages = "26:1--26:23", month = dec, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3426866", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Fri Dec 10 11:35:09 MST 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/10.1145/3426866", abstract = "Federated Learning (FL) provides a powerful solution to distributed machine learning on a large corpus of decentralized data. It ensures privacy and security by performing computation on devices (which we refer to as clients) based on local data to \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "26", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Li:2021:ASE, author = "Mingzhao Li and Zhifeng Bao and Farhana Choudhury and Hanan Samet and Matt Duckham and Timos Sellis", title = "{AOI}-shapes: an Efficient Footprint Algorithm to Support Visualization of User-defined Urban Areas of Interest", journal = j-TIIS, volume = "11", number = "3--4", pages = "27:1--27:32", month = dec, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3431817", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Fri Dec 10 11:35:09 MST 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/10.1145/3431817", abstract = "Understanding urban areas of interest (AOIs) is essential in many real-life scenarios, and such AOIs can be computed based on the geographic points that satisfy user queries. In this article, we study the problem of efficient and effective visualization \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "27", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Barral:2021:EAG, author = "Oswald Barral and S{\'e}bastien Lall{\'e} and Alireza Iranpour and Cristina Conati", title = "Effect of Adaptive Guidance and Visualization Literacy on Gaze Attentive Behaviors and Sequential Patterns on Magazine-Style Narrative Visualizations", journal = j-TIIS, volume = "11", number = "3--4", pages = "28:1--28:46", month = dec, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3447992", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Fri Dec 10 11:35:09 MST 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/10.1145/3447992", abstract = "We study the effectiveness of adaptive interventions at helping users process textual documents with embedded visualizations, a form of multimodal documents known as Magazine-Style Narrative Visualizations (MSNVs). The interventions are meant to \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "28", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Dodge:2021:AAR, author = "Jonathan Dodge and Roli Khanna and Jed Irvine and Kin-ho Lam and Theresa Mai and Zhengxian Lin and Nicholas Kiddle and Evan Newman and Andrew Anderson and Sai Raja and Caleb Matthews and Christopher Perdriau and Margaret Burnett and Alan Fern", title = "After-Action Review for {AI (AAR\slash AI)}", journal = j-TIIS, volume = "11", number = "3--4", pages = "29:1--29:35", month = dec, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3453173", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Fri Dec 10 11:35:09 MST 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/10.1145/3453173", abstract = "Explainable AI is growing in importance as AI pervades modern society, but few have studied how explainable AI can directly support people trying to assess an AI agent. Without a rigorous process, people may approach assessment in ad hoc ways-leading to \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "29", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Starke:2021:PEE, author = "Alain Starke and Martijn Willemsen and Chris Snijders", title = "Promoting Energy-Efficient Behavior by Depicting Social Norms in a Recommender Interface", journal = j-TIIS, volume = "11", number = "3--4", pages = "30:1--30:32", month = dec, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3460005", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Fri Dec 10 11:35:09 MST 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/10.1145/3460005", abstract = "How can recommender interfaces help users to adopt new behaviors? In the behavioral change literature, social norms and other nudges are studied to understand how people can be convinced to take action (e.g., towel re-use is boosted when stating that \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "30", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Zhou:2021:ISC, author = "Michelle X. Zhou", title = "Introduction to the Special Column for Human-Centered Artificial Intelligence", journal = j-TIIS, volume = "11", number = "3--4", pages = "31:1--31:1", month = dec, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3490553", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Fri Dec 10 11:35:09 MST 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/10.1145/3490553", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "31", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Yang:2021:TRA, author = "Qiang Yang", title = "Toward Responsible {AI}: an Overview of Federated Learning for User-centered Privacy-preserving Computing", journal = j-TIIS, volume = "11", number = "3--4", pages = "32:1--32:22", month = dec, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3485875", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Fri Dec 10 11:35:09 MST 2021", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/10.1145/3485875", abstract = "With the rapid advances of Artificial Intelligence (AI) technologies and applications, an increasing concern is on the development and application of responsible AI technologies. Building AI technologies or machine-learning models often requires massive \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "32", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Khanna:2022:FAF, author = "Roli Khanna and Jonathan Dodge and Andrew Anderson and Rupika Dikkala and Jed Irvine and Zeyad Shureih and Kin-Ho Lam and Caleb R. Matthews and Zhengxian Lin and Minsuk Kahng and Alan Fern and Margaret Burnett", title = "Finding {AI's} Faults with {AAR\slash AI}: an Empirical Study", journal = j-TIIS, volume = "12", number = "1", pages = "1:1--1:33", month = mar, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3487065", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Fri Mar 25 07:11:26 MDT 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/10.1145/3487065", abstract = "Would you allow an AI agent to make decisions on your behalf? If the answer is ``not always,'' the next question becomes ``in what circumstances''? Answering this question requires human users to be able to assess an AI agent-and not just with overall pass/. \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "1", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{VanBerkel:2022:IRF, author = "Niels {Van Berkel} and Jeremy Opie and Omer F. Ahmad and Laurence Lovat and Danail Stoyanov and Ann Blandford", title = "Initial Responses to False Positives in {AI}-Supported Continuous Interactions: a Colonoscopy Case Study", journal = j-TIIS, volume = "12", number = "1", pages = "2:1--2:18", month = mar, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3480247", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Fri Mar 25 07:11:26 MDT 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/10.1145/3480247", abstract = "The use of artificial intelligence (AI) in clinical support systems is increasing. In this article, we focus on AI support for continuous interaction scenarios. A thorough understanding of end-user behaviour during these continuous human-AI interactions, \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "2", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Zini:2022:ACT, author = "Floriano Zini and Fabio {Le Piane} and Mauro Gaspari", title = "Adaptive Cognitive Training with Reinforcement Learning", journal = j-TIIS, volume = "12", number = "1", pages = "3:1--3:29", month = mar, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3476777", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Fri Mar 25 07:11:26 MDT 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/10.1145/3476777", abstract = "Computer-assisted cognitive training can help patients affected by several illnesses alleviate their cognitive deficits or healthy people improve their mental performance. In most computer-based systems, training sessions consist of graded exercises, \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "3", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Tang:2022:SOA, author = "Tan Tang and Junxiu Tang and Jiewen Lai and Lu Ying and Yingcai Wu and Lingyun Yu and Peiran Ren", title = "{SmartShots}: an Optimization Approach for Generating Videos with Data Visualizations Embedded", journal = j-TIIS, volume = "12", number = "1", pages = "4:1--4:21", month = mar, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3484506", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Fri Mar 25 07:11:26 MDT 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/10.1145/3484506", abstract = "Videos are well-received methods for storytellers to communicate various narratives. To further engage viewers, we introduce a novel visual medium where data visualizations are embedded into videos to present data insights. However, creating such data-. \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "4", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Ahn:2022:TCI, author = "Yongsu Ahn and Muheng Yan and Yu-Ru Lin and Wen-Ting Chung and Rebecca Hwa", title = "Tribe or Not? {Critical} Inspection of Group Differences Using {TribalGram}", journal = j-TIIS, volume = "12", number = "1", pages = "5:1--5:34", month = mar, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3484509", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Fri Mar 25 07:11:26 MDT 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/10.1145/3484509", abstract = "With the rise of AI and data mining techniques, group profiling and group-level analysis have been increasingly used in many domains, including policy making and direct marketing. In some cases, the statistics extracted from data may provide insights to a \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "5", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Bruckner:2022:LGC, author = "Lukas Br{\"u}ckner and Luis A. Leiva and Antti Oulasvirta", title = "Learning {GUI} Completions with User-defined Constraints", journal = j-TIIS, volume = "12", number = "1", pages = "6:1--6:40", month = mar, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3490034", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Fri Mar 25 07:11:26 MDT 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/10.1145/3490034", abstract = "A key objective in the design of graphical user interfaces (GUIs) is to ensure consistency across screens of the same product. However, designing a compliant layout is time-consuming and can distract designers from creative thinking. This paper studies \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "6", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Zhou:2022:EIT, author = "Michelle X. Zhou", title = "Editorial Introduction to {TiiS} Special Category Article: Practitioners' Toolbox", journal = j-TIIS, volume = "12", number = "1", pages = "7:1--7:1", month = mar, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3519381", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Fri Mar 25 07:11:26 MDT 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/10.1145/3519381", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "7", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Mascarenhas:2022:FTT, author = "Samuel Mascarenhas and Manuel Guimar{\~a}es and Rui Prada and Pedro A. Santos and Jo{\~a}o Dias and Ana Paiva", title = "{FAtiMA} Toolkit: Toward an Accessible Tool for the Development of Socio-emotional Agents", journal = j-TIIS, volume = "12", number = "1", pages = "8:1--8:30", month = mar, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3510822", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Fri Mar 25 07:11:26 MDT 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/10.1145/3510822", abstract = "More than a decade has passed since the development of FearNot!, an application designed to help children deal with bullying through role-playing with virtual characters. It was also the application that led to the creation of FAtiMA, an affective agent \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "8", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Kocaballi:2022:SIC, author = "A. Baki Kocaballi and Liliana Laranjo and Leigh Clark and Rafa{\l} Kocielnik and Robert J. Moore and Q. Vera Liao and Timothy Bickmore", title = "Special Issue on Conversational Agents for Healthcare and Wellbeing", journal = j-TIIS, volume = "12", number = "2", pages = "9:1--9:3", month = jun, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3532860", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Mon Jul 25 09:40:04 MDT 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/10.1145/3532860", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "9", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Maharjan:2022:ESE, author = "Raju Maharjan and Kevin Doherty and Darius Adam Rohani and Per B{\ae}kgaard and Jakob E. Bardram", title = "Experiences of a Speech-enabled Conversational Agent for the Self-report of Well-being among People Living with Affective Disorders: an In-the-Wild Study", journal = j-TIIS, volume = "12", number = "2", pages = "10:1--10:29", month = jun, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3484508", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Mon Jul 25 09:40:04 MDT 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/10.1145/3484508", abstract = "The growing commercial success of smart speaker devices following recent advancements in speech recognition technology has surfaced new opportunities for collecting self-reported health and well-being data. Speech-enabled conversational agents (CAs) in \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "10", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Koulouri:2022:CSY, author = "Theodora Koulouri and Robert D. Macredie and David Olakitan", title = "Chatbots to Support Young Adults' Mental Health: an Exploratory Study of Acceptability", journal = j-TIIS, volume = "12", number = "2", pages = "11:1--11:39", month = jun, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3485874", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Mon Jul 25 09:40:04 MDT 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/10.1145/3485874", abstract = "Despite the prevalence of mental health conditions, stigma, lack of awareness, and limited resources impede access to care, creating a need to improve mental health support. The recent surge in scientific and commercial interest in conversational agents \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "11", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Eagle:2022:DKW, author = "Tessa Eagle and Conrad Blau and Sophie Bales and Noopur Desai and Victor Li and Steve Whittaker", title = "{``I don't know what you mean by `I am anxious'''}: a New Method for Evaluating Conversational Agent Responses to Standardized Mental Health Inputs for Anxiety and Depression", journal = j-TIIS, volume = "12", number = "2", pages = "12:1--12:23", month = jun, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3488057", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Mon Jul 25 09:40:04 MDT 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/10.1145/3488057", abstract = "Conversational agents (CAs) are increasingly ubiquitous and are now commonly used to access medical information. However, we lack systematic data about the quality of advice such agents provide. This paper evaluates CA advice for mental health (MH) \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "12", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Brewer:2022:ESO, author = "Robin Brewer and Casey Pierce and Pooja Upadhyay and Leeseul Park", title = "An Empirical Study of Older Adult's Voice Assistant Use for Health Information Seeking", journal = j-TIIS, volume = "12", number = "2", pages = "13:1--13:32", month = jun, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3484507", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Mon Jul 25 09:40:04 MDT 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/10.1145/3484507", abstract = "Although voice assistants are increasingly being adopted by older adults, we lack empirical research on how they interact with these devices for health information seeking. Also, prior work shows how voice assistant responses can provide misleading or \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "13", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Razavi:2022:DBO, author = "S. Zahra Razavi and Lenhart K. Schubert and Kimberly van Orden and Mohammad Rafayet Ali and Benjamin Kane and Ehsan Hoque", title = "Discourse Behavior of Older Adults Interacting with a Dialogue Agent Competent in Multiple Topics", journal = j-TIIS, volume = "12", number = "2", pages = "14:1--14:21", month = jun, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3484510", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Mon Jul 25 09:40:04 MDT 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/10.1145/3484510", abstract = "We present a conversational agent designed to provide realistic conversational practice to older adults at risk of isolation or social anxiety, and show the results of a content analysis on a corpus of data collected from experiments with elderly patients \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "14", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Jang:2022:RAH, author = "Yi Hyun Jang and Soo Han Im and Younah Kang and Joon Sang Baek", title = "Relational Agents for the Homeless with Tuberculosis Experience: Providing Social Support Through Human-agent Relationships", journal = j-TIIS, volume = "12", number = "2", pages = "15:1--15:22", month = jun, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3488056", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Mon Jul 25 09:40:04 MDT 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/10.1145/3488056", abstract = "In human-computer interaction (HCI) research, relational agents (RAs) are increasingly used to improve social support for vulnerable groups including people exposed to stigmas, alienation, and isolation. However, technical support for tuberculosis (TB) \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "15", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Zorrilla:2022:MNC, author = "Asier L{\'o}pez Zorrilla and M. In{\'e}s Torres", title = "A Multilingual Neural Coaching Model with Enhanced Long-term Dialogue Structure", journal = j-TIIS, volume = "12", number = "2", pages = "16:1--16:47", month = jun, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3487066", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Mon Jul 25 09:40:04 MDT 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/10.1145/3487066", abstract = "In this work we develop a fully data driven conversational agent capable of carrying out motivational coaching sessions in Spanish, French, Norwegian, and English. Unlike the majority of coaching, and in general well-being related conversational agents \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "16", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Caldwell:2022:ANR, author = "Sabrina Caldwell and Penny Sweetser and Nicholas O'Donnell and Matthew J. Knight and Matthew Aitchison and Tom Gedeon and Daniel Johnson and Margot Brereton and Marcus Gallagher and David Conroy", title = "An Agile New Research Framework for Hybrid Human-{AI} Teaming: Trust, Transparency, and Transferability", journal = j-TIIS, volume = "12", number = "3", pages = "17:1--17:36", month = sep, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3514257", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Tue Sep 20 09:43:15 MDT 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/10.1145/3514257", abstract = "We propose a new research framework by which the nascent discipline of human-AI teaming can be explored within experimental environments in preparation for transferal to real-world contexts. We examine the existing literature and unanswered research \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "17", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Nakao:2022:TIE, author = "Yuri Nakao and Simone Stumpf and Subeida Ahmed and Aisha Naseer and Lorenzo Strappelli", title = "Toward Involving End-users in Interactive Human-in-the-loop {AI} Fairness", journal = j-TIIS, volume = "12", number = "3", pages = "18:1--18:30", month = sep, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3514258", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Tue Sep 20 09:43:15 MDT 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/10.1145/3514258", abstract = "Ensuring fairness in artificial intelligence (AI) is important to counteract bias and discrimination in far-reaching applications. Recent work has started to investigate how humans judge fairness and how to support machine learning experts in making their \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "18", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Kruse:2022:EMA, author = "Jan Kruse and Andy M. Connor and Stefan Marks", title = "Evaluation of a Multi-agent {``Human-in-the-loop''} Game Design System", journal = j-TIIS, volume = "12", number = "3", pages = "19:1--19:26", month = sep, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3531009", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Tue Sep 20 09:43:15 MDT 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/10.1145/3531009", abstract = "Designing games is a complicated and time-consuming process, where developing new levels for existing games can take weeks. Procedural content generation offers the potential to shorten this timeframe, however, automated design tools are not adopted \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "19", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Alhejaili:2022:ELF, author = "Abdullah Alhejaili and Shaheen Fatima", title = "Expressive Latent Feature Modelling for Explainable Matrix Factorisation-based Recommender Systems", journal = j-TIIS, volume = "12", number = "3", pages = "20:1--20:30", month = sep, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3530299", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Tue Sep 20 09:43:15 MDT 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/10.1145/3530299", abstract = "The traditional matrix factorisation (MF)-based recommender system methods, despite their success in making the recommendation, lack explainable recommendations as the produced latent features are meaningless and cannot explain the recommendation. This \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "20", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Hsieh:2022:ADA, author = "Sheng-Jen Hsieh and Andy R. Wang and Anna Madison and Chad Tossell and Ewart de Visser", title = "Adaptive Driving Assistant Model {(ADAM)} for Advising Drivers of Autonomous Vehicles", journal = j-TIIS, volume = "12", number = "3", pages = "21:1--21:28", month = sep, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3545994", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Tue Sep 20 09:43:15 MDT 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/10.1145/3545994", abstract = "Fully autonomous driving is on the horizon; vehicles with advanced driver assistance systems (ADAS) such as Tesla's Autopilot are already available to consumers. However, all currently available ADAS applications require a human driver to be alert and \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "21", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Handler:2022:CIQ, author = "Abram Handler and Narges Mahyar and Brendan O'Connor", title = "{ClioQuery}: Interactive Query-oriented Text Analytics for Comprehensive Investigation of Historical News Archives", journal = j-TIIS, volume = "12", number = "3", pages = "22:1--22:49", month = sep, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3524025", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Tue Sep 20 09:43:15 MDT 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/10.1145/3524025", abstract = "Historians and archivists often find and analyze the occurrences of query words in newspaper archives to help answer fundamental questions about society. But much work in text analytics focuses on helping people investigate other textual units, such as \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "22", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Liu:2022:SSE, author = "Fang Liu and Xiaoming Deng and Jiancheng Song and Yu-Kun Lai and Yong-Jin Liu and Hao Wang and Cuixia Ma and Shengfeng Qin and Hongan Wang", title = "{SketchMaker}: Sketch Extraction and Reuse for Interactive Scene Sketch Composition", journal = j-TIIS, volume = "12", number = "3", pages = "23:1--23:26", month = sep, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3543956", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Tue Sep 20 09:43:15 MDT 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/10.1145/3543956", abstract = "Sketching is an intuitive and simple way to depict sciences with various object form and appearance characteristics. In the past few years, widely available touchscreen devices have increasingly made sketch-based human-AI co-creation applications popular. \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "23", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Kronenberg:2022:IOW, author = "Rotem Kronenberg and Tsvi Kuflik and Ilan Shimshoni", title = "Improving Office Workers' Workspace Using a Self-adjusting Computer Screen", journal = j-TIIS, volume = "12", number = "3", pages = "24:1--24:32", month = sep, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3545993", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Tue Sep 20 09:43:15 MDT 2022", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/10.1145/3545993", abstract = "With the rapid evolution of technology, computers and their users' workspaces have become an essential part of our life in general. Today, many people use computers both for work and for personal needs, spending long hours sitting at a desk in front of a \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "24", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Hammond:2022:SIH, author = "Tracy Hammond and Bart Knijnenburg and John O'Donovan and Paul Taele", title = "Special Issue on Highlights of {IUI} 2021: Introduction", journal = j-TIIS, volume = "12", number = "4", pages = "25:1--25:??", month = dec, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3561516", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Tue Mar 21 06:18:07 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/10.1145/3561516", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "25", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Sovrano:2022:GUC, author = "Francesco Sovrano and Fabio Vitali", title = "Generating User-Centred Explanations via Illocutionary Question Answering: From Philosophy to Interfaces", journal = j-TIIS, volume = "12", number = "4", pages = "26:1--26:??", month = dec, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3519265", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Tue Mar 21 06:18:07 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/10.1145/3519265", abstract = "We propose a new method for generating explanations with Artificial Intelligence (AI) and a tool to test its expressive power within a user interface. In order to bridge the gap between philosophy and human-computer interfaces, we show a new approach for \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "26", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Wang:2022:EEA, author = "Xinru Wang and Ming Yin", title = "Effects of Explanations in {AI}-Assisted Decision Making: Principles and Comparisons", journal = j-TIIS, volume = "12", number = "4", pages = "27:1--27:??", month = dec, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3519266", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Tue Mar 21 06:18:07 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/10.1145/3519266", abstract = "Recent years have witnessed the growing literature in empirical evaluation of explainable AI (XAI) methods. This study contributes to this ongoing conversation by presenting a comparison on the effects of a set of established XAI methods in AI-assisted \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "27", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Nourani:2022:IUB, author = "Mahsan Nourani and Chiradeep Roy and Jeremy E. Block and Donald R. Honeycutt and Tahrima Rahman and Eric D. Ragan and Vibhav Gogate", title = "On the Importance of User Backgrounds and Impressions: Lessons Learned from Interactive {AI} Applications", journal = j-TIIS, volume = "12", number = "4", pages = "28:1--28:??", month = dec, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3531066", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Tue Mar 21 06:18:07 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/10.1145/3531066", abstract = "While EXplainable Artificial Intelligence (XAI) approaches aim to improve human-AI collaborative decision-making by improving model transparency and mental model formations, experiential factors associated with human users can cause challenges in ways \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "28", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Buschek:2022:HSU, author = "Daniel Buschek and Malin Eiband and Heinrich Hussmann", title = "How to Support Users in Understanding Intelligent Systems? An Analysis and Conceptual Framework of User Questions Considering User Mindsets, Involvement, and Knowledge Outcomes", journal = j-TIIS, volume = "12", number = "4", pages = "29:1--29:??", month = dec, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3519264", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Tue Mar 21 06:18:07 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/10.1145/3519264", abstract = "The opaque nature of many intelligent systems violates established usability principles and thus presents a challenge for human-computer interaction. Research in the field therefore highlights the need for transparency, scrutability, intelligibility, \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "29", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Ramos:2022:FAO, author = "Gonzalo Ramos and Napol Rachatasumrit and Jina Suh and Rachel Ng and Christopher Meek", title = "{ForSense}: Accelerating Online Research Through Sensemaking Integration and Machine Research Support", journal = j-TIIS, volume = "12", number = "4", pages = "30:1--30:??", month = dec, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3532853", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Tue Mar 21 06:18:07 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/10.1145/3532853", abstract = "Online research is a frequent and important activity people perform on the Internet, yet current support for this task is basic, fragmented and not well integrated into web browser experiences. Guided by sensemaking theory, we present ForSense, a browser \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "30", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Karimi:2022:TTS, author = "Pegah Karimi and Emanuele Plebani and Aqueasha Martin-Hammond and Davide Bolchini", title = "Textflow: Toward Supporting Screen-free Manipulation of Situation-Relevant Smart Messages", journal = j-TIIS, volume = "12", number = "4", pages = "31:1--31:??", month = dec, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3519263", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Tue Mar 21 06:18:07 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/10.1145/3519263", abstract = "Texting relies on screen-centric prompts designed for sighted users, still posing significant barriers to people who are blind and visually impaired (BVI). Can we re-imagine texting untethered from a visual display? In an interview study, 20 BVI adults \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "31", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Svikhnushina:2022:PMK, author = "Ekaterina Svikhnushina and Pearl Pu", title = "{PEACE}: a Model of Key Social and Emotional Qualities of Conversational Chatbots", journal = j-TIIS, volume = "12", number = "4", pages = "32:1--32:??", month = dec, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3531064", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Tue Mar 21 06:18:07 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/10.1145/3531064", abstract = "Open-domain chatbots engage with users in natural conversations to socialize and establish bonds. However, designing and developing an effective open-domain chatbot is challenging. It is unclear what qualities of a chatbot most correspond to users' \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "32", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Das:2022:DRD, author = "Kapotaksha Das and Michalis Papakostas and Kais Riani and Andrew Gasiorowski and Mohamed Abouelenien and Mihai Burzo and Rada Mihalcea", title = "Detection and Recognition of Driver Distraction Using Multimodal Signals", journal = j-TIIS, volume = "12", number = "4", pages = "33:1--33:??", month = dec, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3519267", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Tue Mar 21 06:18:07 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/10.1145/3519267", abstract = "Distracted driving is a leading cause of accidents worldwide. The tasks of distraction detection and recognition have been traditionally addressed as computer vision problems. However, distracted behaviors are not always expressed in a visually observable \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "33", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Ang:2022:LSR, author = "Gary Ang and Ee-Peng Lim", title = "Learning Semantically Rich Network-based Multi-modal Mobile User Interface Embeddings", journal = j-TIIS, volume = "12", number = "4", pages = "34:1--34:??", month = dec, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3533856", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Tue Mar 21 06:18:07 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/10.1145/3533856", abstract = "Semantically rich information from multiple modalities-text, code, images, categorical and numerical data-co-exist in the user interface (UI) design of mobile applications. Moreover, each UI design is composed of inter-linked UI entities that support \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "34", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Yagi:2022:GFR, author = "Takuma Yagi and Takumi Nishiyasu and Kunimasa Kawasaki and Moe Matsuki and Yoichi Sato", title = "{GO-Finder}: a Registration-free Wearable System for Assisting Users in Finding Lost Hand-held Objects", journal = j-TIIS, volume = "12", number = "4", pages = "35:1--35:??", month = dec, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3519268", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Tue Mar 21 06:18:07 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/10.1145/3519268", abstract = "People spend an enormous amount of time and effort looking for lost objects. To help remind people of the location of lost objects, various computational systems that provide information on their locations have been developed. However, prior systems for \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "35", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Feng:2022:AIA, author = "Sidong Feng and Minmin Jiang and Tingting Zhou and Yankun Zhen and Chunyang Chen", title = "{Auto-Icon+}: an Automated End-to-End Code Generation Tool for Icon Designs in {UI} Development", journal = j-TIIS, volume = "12", number = "4", pages = "36:1--36:??", month = dec, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3531065", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Tue Mar 21 06:18:07 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/10.1145/3531065", abstract = "Approximately 50\% of development resources are devoted to user interface (UI) development tasks [ 9 ]. Occupying a large proportion of development resources, developing icons can be a time-consuming task, because developers need to consider not only \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "36", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Li:2023:ESE, author = "Xingjun Li and Yizhi Zhang and Justin Leung and Chengnian Sun and Jian Zhao", title = "{EDAssistant}: Supporting Exploratory Data Analysis in Computational Notebooks with In Situ Code Search and Recommendation", journal = j-TIIS, volume = "13", number = "1", pages = "1:1--1:??", month = mar, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3545995", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Tue Mar 21 06:18:08 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/10.1145/3545995", abstract = "Using computational notebooks (e.g., Jupyter Notebook), data scientists rationalize their exploratory data analysis (EDA) based on their prior experience and external knowledge, such as online examples. For novices or data scientists who lack specific \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "1", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Liu:2023:SGL, author = "Huimin Liu and Minsoo Choi and Dominic Kao and Christos Mousas", title = "Synthesizing Game Levels for Collaborative Gameplay in a Shared Virtual Environment", journal = j-TIIS, volume = "13", number = "1", pages = "2:1--2:??", month = mar, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3558773", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Tue Mar 21 06:18:08 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/10.1145/3558773", abstract = "We developed a method to synthesize game levels that accounts for the degree of collaboration required by two players to finish a given game level. We first asked a game level designer to create playable game level chunks. Then, two artificial \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "2", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Yan:2023:IPT, author = "Dongning Yan and Li Chen", title = "The Influence of Personality Traits on User Interaction with Recommendation Interfaces", journal = j-TIIS, volume = "13", number = "1", pages = "3:1--3:??", month = mar, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3558772", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Tue Mar 21 06:18:08 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/10.1145/3558772", abstract = "Users' personality traits can take an active role in affecting their behavior when they interact with a computer interface. However, in the area of recommender systems (RS), though personality-based RS has been extensively studied, most works focus on \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "3", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Lin:2023:PIM, author = "Yi-Ling Lin and Shao-Wei Lee", title = "A Personalized Interaction Mechanism Framework for Micro-moment Recommender Systems", journal = j-TIIS, volume = "13", number = "1", pages = "4:1--4:??", month = mar, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3569586", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Tue Mar 21 06:18:08 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/10.1145/3569586", abstract = "The emergence of the micro-moment concept highlights the influence of context; recommender system design should reflect this trend. In response to different contexts, a micro-moment recommender system (MMRS) requires an effective interaction mechanism \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "4", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Afzal:2023:VVA, author = "Shehzad Afzal and Sohaib Ghani and Mohamad Mazen Hittawe and Sheikh Faisal Rashid and Omar M. Knio and Markus Hadwiger and Ibrahim Hoteit", title = "Visualization and Visual Analytics Approaches for Image and Video Datasets: a Survey", journal = j-TIIS, volume = "13", number = "1", pages = "5:1--5:??", month = mar, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3576935", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Tue Mar 21 06:18:08 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/10.1145/3576935", abstract = "Image and video data analysis has become an increasingly important research area with applications in different domains such as security surveillance, healthcare, augmented and virtual reality, video and image editing, activity analysis and recognition, \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "5", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Hernandez-Bocanegra:2023:ERT, author = "Diana C. Hernandez-Bocanegra and J{\"u}rgen Ziegler", title = "Explaining Recommendations through Conversations: Dialog Model and the Effects of Interface Type and Degree of Interactivity", journal = j-TIIS, volume = "13", number = "2", pages = "6:1--6:??", month = jun, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3579541", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Mon Jul 3 06:48:34 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/10.1145/3579541", abstract = "Explaining system-generated recommendations based on user reviews can foster users' understanding and assessment of the recommended items and the recommender system (RS) as a whole. While up to now explanations have mostly been static, shown in a single \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "6", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Das:2023:EAR, author = "Devleena Das and Yasutaka Nishimura and Rajan P. Vivek and Naoto Takeda and Sean T. Fish and Thomas Pl{\"o}tz and Sonia Chernova", title = "Explainable Activity Recognition for Smart Home Systems", journal = j-TIIS, volume = "13", number = "2", pages = "7:1--7:??", month = jun, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3561533", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Mon Jul 3 06:48:34 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/10.1145/3561533", abstract = "Smart home environments are designed to provide services that help improve the quality of life for the occupant via a variety of sensors and actuators installed throughout the space. Many automated actions taken by a smart home are governed by the output \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "7", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Rudrauf:2023:CPC, author = "D. Rudrauf and G. Sergeant-Perhtuis and Y. Tisserand and T. Monnor and V. {De Gevigney} and O. Belli", title = "Combining the Projective Consciousness Model and Virtual Humans for Immersive Psychological Research: a Proof-of-concept Simulating a {ToM} Assessment", journal = j-TIIS, volume = "13", number = "2", pages = "8:1--8:??", month = jun, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3583886", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Mon Jul 3 06:48:34 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/10.1145/3583886", abstract = "Relating explicit psychological mechanisms and observable behaviours is a central aim of psychological and behavioural science. One of the challenges is to understand and model the role of consciousness and, in particular, its subjective perspective as an \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "8", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Guo:2023:GGF, author = "Mengtian Guo and Zhilan Zhou and David Gotz and Yue Wang", title = "{GRAFS}: Graphical Faceted Search System to Support Conceptual Understanding in Exploratory Search", journal = j-TIIS, volume = "13", number = "2", pages = "9:1--9:??", month = jun, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3588319", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Mon Jul 3 06:48:34 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/10.1145/3588319", abstract = "When people search for information about a new topic within large document collections, they implicitly construct a mental model of the unfamiliar information space to represent what they currently know and guide their exploration into the unknown. \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "9", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Jentner:2023:VAC, author = "Wolfgang Jentner and Giuliana Lindholz and Hanna Hauptmann and Mennatallah El-Assady and Kwan-Liu Ma and Daniel Keim", title = "Visual Analytics of Co-Occurrences to Discover Subspaces in Structured Data", journal = j-TIIS, volume = "13", number = "2", pages = "10:1--10:??", month = jun, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3579031", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Mon Jul 3 06:48:34 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/10.1145/3579031", abstract = "We present an approach that shows all relevant subspaces of categorical data condensed in a single picture. We model the categorical values of the attributes as co-occurrences with data partitions generated from structured data using pattern mining. We \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "10", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Yalcin:2023:IIP, author = "{\"O}zge Nilay Yal{\c{c}}{\i}n and S{\'e}bastien Lall{\'e} and Cristina Conati", title = "The Impact of Intelligent Pedagogical Agents' Interventions on Student Behavior and Performance in Open-Ended Game Design Environments", journal = j-TIIS, volume = "13", number = "3", pages = "11:1--11:??", month = sep, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3578523", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Wed Sep 13 06:40:19 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/10.1145/3578523", abstract = "Research has shown that free-form Game-Design (GD) environments can be very effective in fostering Computational Thinking (CT) skills at a young \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "11", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Ang:2023:LUU, author = "Gary Ang and Ee-Peng Lim", title = "Learning and Understanding User Interface Semantics from Heterogeneous Networks with Multimodal and Positional Attributes", journal = j-TIIS, volume = "13", number = "3", pages = "12:1--12:??", month = sep, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3578522", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Wed Sep 13 06:40:19 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/10.1145/3578522", abstract = "User interfaces (UI) of desktop, web, and mobile applications involve a hierarchy of objects (e.g., applications, screens, view class, and other types of design \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "12", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Ferdous:2023:EEW, author = "Javedul Ferdous and Hae-Na Lee and Sampath Jayarathna and Vikas Ashok", title = "Enabling Efficient {Web} Data-Record Interaction for People with Visual Impairments via Proxy Interfaces", journal = j-TIIS, volume = "13", number = "3", pages = "13:1--13:??", month = sep, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3579364", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Wed Sep 13 06:40:19 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/10.1145/3579364", abstract = "Web data records are usually accompanied by auxiliary webpage segments, such as filters, sort options, search form, and multi-page links, to enhance interaction \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "13", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Aguirre:2023:CTC, author = "Carlos Aguirre and Shiye Cao and Amama Mahmood and Chien-Ming Huang", title = "Crowdsourcing Thumbnail Captions: Data Collection and Validation", journal = j-TIIS, volume = "13", number = "3", pages = "14:1--14:??", month = sep, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3589346", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Wed Sep 13 06:40:19 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/10.1145/3589346", abstract = "Speech interfaces, such as personal assistants and screen readers, read image captions to users. Typically, however, only one caption is available per image, \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "14", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Shibata:2023:CCS, author = "Ryoichi Shibata and Shoya Matsumori and Yosuke Fukuchi and Tomoyuki Maekawa and Mitsuhiko Kimoto and Michita Imai", title = "Conversational Context-sensitive Ad Generation with a Few Core-Queries", journal = j-TIIS, volume = "13", number = "3", pages = "15:1--15:??", month = sep, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3588578", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Wed Sep 13 06:40:19 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/10.1145/3588578", abstract = "When people are talking together in front of digital signage, advertisements that are aware of the context of the dialogue will work the most effectively. \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "15", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Sluyters:2023:RAR, author = "Arthur Slu{\"y}ters and S{\'e}bastien Lambot and Jean Vanderdonckt and Radu-Daniel Vatavu", title = "{RadarSense}: Accurate Recognition of Mid-air Hand Gestures with Radar Sensing and Few Training Examples", journal = j-TIIS, volume = "13", number = "3", pages = "16:1--16:??", month = sep, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3589645", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Wed Sep 13 06:40:19 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/10.1145/3589645", abstract = "Microwave radars bring many benefits to mid-air gesture sensing due to their large field of view and independence from environmental conditions, such as \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "16", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Wang:2023:WBH, author = "Clarice Wang and Kathryn Wang and Andrew Y. Bian and Rashidul Islam and Kamrun Naher Keya and James Foulds and Shimei Pan", title = "When Biased Humans Meet Debiased {AI}: a Case Study in College Major Recommendation", journal = j-TIIS, volume = "13", number = "3", pages = "17:1--17:??", month = sep, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3611313", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Wed Sep 13 06:40:19 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/10.1145/3611313", abstract = "Currently, there is a surge of interest in fair Artificial Intelligence (AI) and Machine Learning (ML) research which aims to mitigate discriminatory bias in AI \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "17", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Smith:2023:GDB, author = "Ronnie Smith and Mauro Dragone", title = "Generalisable Dialogue-based Approach for Active Learning of Activities of Daily Living", journal = j-TIIS, volume = "13", number = "3", pages = "18:1--18:??", month = sep, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3616017", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Wed Sep 13 06:40:19 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/10.1145/3616017", abstract = "While Human Activity Recognition systems may benefit from Active Learning by allowing users to self-annotate their Activities of Daily Living (ADLs), \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "18", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Zhou:2023:TBP, author = "Michelle Zhou and Shlomo Berkovsky", title = "2022 {TiiS} Best Paper Announcement", journal = j-TIIS, volume = "13", number = "3", pages = "19:1--19:??", month = sep, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3615590", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Wed Sep 13 06:40:19 MDT 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/10.1145/3615590", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "19", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Li:2023:VAN, author = "Yiran Li and Junpeng Wang and Takanori Fujiwara and Kwan-Liu Ma", title = "Visual Analytics of Neuron Vulnerability to Adversarial Attacks on Convolutional Neural Networks", journal = j-TIIS, volume = "13", number = "4", pages = "20:1--20:??", month = dec, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3587470", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Thu Dec 21 10:44:24 MST 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/10.1145/3587470", abstract = "Adversarial attacks on a convolutional neural network (CNN)-injecting human-imperceptible perturbations into an input image-could fool a high-performance CNN into making incorrect predictions. The success of adversarial attacks raises serious concerns \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "20", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Panigutti:2023:CDH, author = "Cecilia Panigutti and Andrea Beretta and Daniele Fadda and Fosca Giannotti and Dino Pedreschi and Alan Perotti and Salvatore Rinzivillo", title = "Co-design of Human-centered, Explainable {AI} for Clinical Decision Support", journal = j-TIIS, volume = "13", number = "4", pages = "21:1--21:??", month = dec, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3587271", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Thu Dec 21 10:44:24 MST 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/10.1145/3587271", abstract = "eXplainable AI (XAI) involves two intertwined but separate challenges: the development of techniques to extract explanations from black-box AI models and the way such explanations are presented to users, i.e., the explanation user interface. Despite its \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "21", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Cau:2023:EAL, author = "Federico Maria Cau and Hanna Hauptmann and Lucio Davide Spano and Nava Tintarev", title = "Effects of {AI} and Logic-Style Explanations on Users' Decisions Under Different Levels of Uncertainty", journal = j-TIIS, volume = "13", number = "4", pages = "22:1--22:??", month = dec, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3588320", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Thu Dec 21 10:44:24 MST 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/10.1145/3588320", abstract = "Existing eXplainable Artificial Intelligence (XAI) techniques support people in interpreting AI advice. However, although previous work evaluates the users' understanding of explanations, factors influencing the decision support are largely overlooked in \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "22", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Singh:2023:DEA, author = "Ronal Singh and Tim Miller and Henrietta Lyons and Liz Sonenberg and Eduardo Velloso and Frank Vetere and Piers Howe and Paul Dourish", title = "Directive Explanations for Actionable Explainability in Machine Learning Applications", journal = j-TIIS, volume = "13", number = "4", pages = "23:1--23:??", month = dec, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3579363", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Thu Dec 21 10:44:24 MST 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/10.1145/3579363", abstract = "In this article, we show that explanations of decisions made by machine learning systems can be improved by not only explaining why a decision was made but also explaining how an individual could obtain their desired outcome. We formally define the \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "23", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Lee:2023:LAE, author = "Benjamin Charles Germain Lee and Doug Downey and Kyle Lo and Daniel S. Weld", title = "{LIMEADE}: From {AI} Explanations to Advice Taking", journal = j-TIIS, volume = "13", number = "4", pages = "24:1--24:??", month = dec, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3589345", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Thu Dec 21 10:44:24 MST 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/10.1145/3589345", abstract = "Research in human-centered AI has shown the benefits of systems that can explain their predictions. Methods that allow AI to take advice from humans in response to explanations are similarly useful. While both capabilities are well developed for ...$^$", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "24", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Schrills:2023:HDU, author = "Tim Schrills and Thomas Franke", title = "How Do Users Experience Traceability of {AI} Systems? {Examining} Subjective Information Processing Awareness in Automated Insulin Delivery {(AID)} Systems", journal = j-TIIS, volume = "13", number = "4", pages = "25:1--25:??", month = dec, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3588594", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Thu Dec 21 10:44:24 MST 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/10.1145/3588594", abstract = "When interacting with artificial intelligence (AI) in the medical domain, users frequently face automated information processing, which can remain opaque to them. For example, users with diabetes may interact daily with automated insulin delivery (AID). \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "25", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Vainio-Pekka:2023:REA, author = "Heidi Vainio-Pekka and Mamia Ori-Otse Agbese and Marianna Jantunen and Ville Vakkuri and Tommi Mikkonen and Rebekah Rousi and Pekka Abrahamsson", title = "The Role of Explainable {AI} in the Research Field of {AI} Ethics", journal = j-TIIS, volume = "13", number = "4", pages = "26:1--26:??", month = dec, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3599974", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Thu Dec 21 10:44:24 MST 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/10.1145/3599974", abstract = "Ethics of Artificial Intelligence (AI) is a growing research field that has emerged in response to the challenges related to AI. Transparency poses a key challenge for implementing AI ethics in practice. One solution to transparency issues is AI systems \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "26", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Martinez:2023:DEH, author = "Miguel Angel Meza Mart{\'\i}nez and Mario Nadj and Moritz Langner and Peyman Toreini and Alexander Maedche", title = "Does this Explanation Help? {Designing} Local Model-agnostic Explanation Representations and an Experimental Evaluation Using Eye-tracking Technology", journal = j-TIIS, volume = "13", number = "4", pages = "27:1--27:??", month = dec, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3607145", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Thu Dec 21 10:44:24 MST 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/10.1145/3607145", abstract = "In Explainable Artificial Intelligence (XAI) research, various local model-agnostic methods have been proposed to explain individual predictions to users in order to increase the transparency of the underlying Artificial Intelligence (AI) systems. However,. \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "27", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Zoller:2023:XVA, author = "Marc-Andr{\'e} Z{\"o}ller and Waldemar Titov and Thomas Schlegel and Marco F. Huber", title = "{XAutoML}: a Visual Analytics Tool for Understanding and Validating Automated Machine Learning", journal = j-TIIS, volume = "13", number = "4", pages = "28:1--28:??", month = dec, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3625240", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Thu Dec 21 10:44:24 MST 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/10.1145/3625240", abstract = "In the last 10 years, various automated machine learning (AutoML) systems have been proposed to build end-to-end machine learning (ML) pipelines with minimal human interaction. Even though such automatically synthesized ML pipelines are able to achieve \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "28", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Roy:2023:EAR, author = "Chiradeep Roy and Mahsan Nourani and Shivvrat Arya and Mahesh Shanbhag and Tahrima Rahman and Eric D. Ragan and Nicholas Ruozzi and Vibhav Gogate", title = "Explainable Activity Recognition in Videos using Deep Learning and Tractable Probabilistic Models", journal = j-TIIS, volume = "13", number = "4", pages = "29:1--29:??", month = dec, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3626961", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Thu Dec 21 10:44:24 MST 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/10.1145/3626961", abstract = "We consider the following video activity recognition (VAR) task: given a video, infer the set of activities being performed in the video and assign each frame to an activity. Although VAR can be solved accurately using existing deep learning techniques, \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "29", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Larasati:2023:MEE, author = "Retno Larasati and Anna {De Liddo} and Enrico Motta", title = "Meaningful Explanation Effect on {User}'s Trust in an {AI} Medical System: Designing Explanations for Non-Expert Users", journal = j-TIIS, volume = "13", number = "4", pages = "30:1--30:??", month = dec, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3631614", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Thu Dec 21 10:44:24 MST 2023", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/10.1145/3631614", abstract = "Whereas most research in AI system explanation for healthcare applications looks at developing algorithmic explanations targeted at AI experts or medical professionals, the question we raise is: How do we build meaningful explanations for laypeople? And \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "30", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Zhang:2024:SBO, author = "Yu Zhang and Martijn Tennekes and Tim {De Jong} and Lyana Curier and Bob Coecke and Min Chen", title = "Simulation-based Optimization of User Interfaces for Quality-assuring Machine Learning Model Predictions", journal = j-TIIS, volume = "14", number = "1", pages = "1:1--1:??", month = mar, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3594552", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Tue Jun 25 07:33:09 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/10.1145/3594552", abstract = "Quality-sensitive applications of machine learning (ML) require quality assurance (QA) by humans before the predictions of an ML model can be deployed. QA for ML (QA4ML) interfaces require users to view a large amount of data and perform many interactions \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "1", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Wenskovitch:2024:TAA, author = "John Wenskovitch and Michelle Dowling and Chris North", title = "Toward Addressing Ambiguous Interactions and Inferring User Intent with Dimension Reduction and Clustering Combinations in Visual Analytics", journal = j-TIIS, volume = "14", number = "1", pages = "2:1--2:??", month = mar, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3588565", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Tue Jun 25 07:33:09 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/10.1145/3588565", abstract = "Direct manipulation interactions on projections are often incorporated in visual analytics applications. These interactions enable analysts to provide incremental feedback to the system in a semi-supervised manner, demonstrating relationships that the \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "2", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Rathore:2024:VVI, author = "Archit Rathore and Sunipa Dev and Jeff M. Phillips and Vivek Srikumar and Yan Zheng and Chin-Chia Michael Yeh and Junpeng Wang and Wei Zhang and Bei Wang", title = "{VERB}: Visualizing and Interpreting Bias Mitigation Techniques Geometrically for Word Representations", journal = j-TIIS, volume = "14", number = "1", pages = "3:1--3:??", month = mar, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3604433", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Tue Jun 25 07:33:09 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/10.1145/3604433", abstract = "Word vector embeddings have been shown to contain and amplify biases in the data they are extracted from. Consequently, many techniques have been proposed to identify, mitigate, and attenuate these biases in word representations. In this article, we \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "3", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Mehrotra:2024:IBE, author = "Siddharth Mehrotra and Carolina Centeio Jorge and Catholijn M. Jonker and Myrthe L. Tielman", title = "Integrity-based Explanations for Fostering Appropriate Trust in {AI} Agents", journal = j-TIIS, volume = "14", number = "1", pages = "4:1--4:??", month = mar, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3610578", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Tue Jun 25 07:33:09 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/10.1145/3610578", abstract = "Appropriate trust is an important component of the interaction between people and AI systems, in that ``inappropriate'' trust can cause disuse, misuse, or abuse of AI. To foster appropriate trust in AI, we need to understand how AI systems can elicit \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "4", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Jorge:2024:HSA, author = "Carolina Centeio Jorge and Catholijn M. Jonker and Myrthe L. Tielman", title = "How Should an {AI} Trust its Human Teammates? {Exploring} Possible Cues of Artificial Trust", journal = j-TIIS, volume = "14", number = "1", pages = "5:1--5:??", month = mar, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3635475", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Tue Jun 25 07:33:09 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/10.1145/3635475", abstract = "In teams composed of humans, we use trust in others to make decisions, such as what to do next, who to help and who to ask for help. When a team member is artificial, they should also be able to assess whether a human teammate is trustworthy for a certain \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "5", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Zhang:2024:KLB, author = "Rui Zhang and Christopher Flathmann and Geoff Musick and Beau Schelble and Nathan J. McNeese and Bart Knijnenburg and Wen Duan", title = "{I} Know This Looks Bad, But {I} Can Explain: Understanding When {AI} Should Explain Actions In {Human--AI} Teams", journal = j-TIIS, volume = "14", number = "1", pages = "6:1--6:??", month = mar, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3635474", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Tue Jun 25 07:33:09 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/10.1145/3635474", abstract = "Explanation of artificial intelligence (AI) decision-making has become an important research area in human-computer interaction (HCI) and computer-supported teamwork research. While plenty of research has investigated AI explanations with an intent to \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "6", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Emamgholizadeh:2024:PGC, author = "Hanif Emamgholizadeh and Amra Deli{\'c} and Francesco Ricci", title = "Predicting Group Choices from Group Profiles", journal = j-TIIS, volume = "14", number = "1", pages = "7:1--7:??", month = mar, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3639710", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Tue Jun 25 07:33:09 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/10.1145/3639710", abstract = "Group recommender systems (GRSs) identify items to recommend to a group of people by aggregating group members' individual preferences into a group profile and selecting the items that have the largest score in the group profile. The GRS predicts that \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "7", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Guo:2024:TAN, author = "Yi Guo and Danqing Shi and Mingjuan Guo and Yanqiu Wu and Nan Cao and Qing Chen", title = "{Talk$2$Data}: a Natural Language Interface for Exploratory Visual Analysis via Question Decomposition", journal = j-TIIS, volume = "14", number = "2", pages = "8:1--8:??", month = jun, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3643894", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Tue Jun 25 07:33:10 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/10.1145/3643894", abstract = "Through a natural language interface (NLI) for exploratory visual analysis, users can directly ``ask'' analytical questions about the given tabular data. This process greatly improves user experience and lowers the technical barriers of data analysis. \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "8", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Yousefi:2024:EFM, author = "Zeinab R. Yousefi and Tung Vuong and Marie AlGhossein and Tuukka Ruotsalo and Giulio Jaccuci and Samuel Kaski", title = "Entity Footprinting: Modeling Contextual User States via Digital Activity Monitoring", journal = j-TIIS, volume = "14", number = "2", pages = "9:1--9:??", month = jun, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3643893", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Tue Jun 25 07:33:10 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/10.1145/3643893", abstract = "Our digital life consists of activities that are organized around tasks and exhibit different user states in the digital contexts around these activities. Previous works have shown that digital activity monitoring can be used to predict entities that \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "9", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Radeta:2024:MME, author = "Marko Radeta and Ruben Freitas and Claudio Rodrigues and Agustin Zuniga and Ngoc Thi Nguyen and Huber Flores and Petteri Nurmi", title = "Man and the Machine: Effects of {AI}-assisted Human Labeling on Interactive Annotation of Real-time Video Streams", journal = j-TIIS, volume = "14", number = "2", pages = "10:1--10:??", month = jun, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3649457", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Tue Jun 25 07:33:10 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/10.1145/3649457", abstract = "AI-assisted interactive annotation is a powerful way to facilitate data annotation-a prerequisite for constructing robust AI models. While AI-assisted interactive annotation has been extensively studied in static settings, less is known about its usage in \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "10", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Cheng:2024:IWW, author = "Ruijia Cheng and Ruotong Wang and Thomas Zimmermann and Denae Ford", title = "``{It} would work for me too'': How Online Communities Shape Software Developers' Trust in {AI}-Powered Code Generation Tools", journal = j-TIIS, volume = "14", number = "2", pages = "11:1--11:??", month = jun, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3651990", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Tue Jun 25 07:33:10 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/10.1145/3651990", abstract = "While revolutionary AI-powered code generation tools have been rising rapidly, we know little about how and how to help software developers form appropriate trust in those AI tools. Through a two-phase formative study, we investigate how online \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "11", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Liu:2024:SCM, author = "Can Liu and Yu Zhang and Cong Wu and Chen Li and Xiaoru Yuan", title = "A Spatial Constraint Model for Manipulating Static Visualizations", journal = j-TIIS, volume = "14", number = "2", pages = "12:1--12:??", month = jun, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3657642", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Tue Jun 25 07:33:10 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/10.1145/3657642", abstract = "We introduce a spatial constraint model to characterize the positioning and interactions in visualizations, thereby facilitating the activation of static visualizations. Our model provides users with the capability to manipulate visualizations through \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "12", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Mo:2024:CMO, author = "George Mo and John Dudley and Liwei Chan and Yi-Chi Liao and Antti Oulasvirta and Per Ola Kristensson", title = "Cooperative Multi-Objective {Bayesian} Design Optimization", journal = j-TIIS, volume = "14", number = "2", pages = "13:1--13:??", month = jun, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3657643", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Tue Jun 25 07:33:10 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/10.1145/3657643", abstract = "Computational methods can potentially facilitate user interface design by complementing designer intuition, prior experience, and personal preference. Framing a user interface design task as a multi-objective optimization problem can help with \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "13", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Spinner:2024:TEG, author = "Thilo Spinner and Rebecca Kehlbeck and Rita Sevastjanova and Tobias St{\"a}hle and Daniel A. Keim and Oliver Deussen and Mennatallah El-Assady", title = "{[tree-emoji]-generAItor}: Tree-in-the-loop Text Generation for Language Model Explainability and Adaptation", journal = j-TIIS, volume = "14", number = "2", pages = "14:1--14:??", month = jun, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3652028", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Tue Jun 25 07:33:10 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/10.1145/3652028", abstract = "Large language models (LLMs) are widely deployed in various downstream tasks, e.g., auto-completion, aided writing, or chat-based text generation. However, the considered output candidates of the underlying search algorithm are under-explored and under-. \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "14", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Li:2024:ISS, author = "Yante Li and Yang Liu and Andy Nguyen and Henglin Shi and Eija Vuorenmaa and Sanna J{\"a}rvel{\"a} and Guoying Zhao", title = "Interactions for Socially Shared Regulation in Collaborative Learning: an Interdisciplinary Multimodal Dataset", journal = j-TIIS, volume = "14", number = "3", pages = "15:1--15:??", month = sep, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3658376", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Wed Sep 25 11:23:38 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/10.1145/3658376", abstract = "Socially shared regulation plays a pivotal role in the success of collaborative learning. However, evaluating socially shared regulation of learning (SSRL) proves challenging due to the dynamic and infrequent cognitive and socio-emotional interactions, \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "15", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Humer:2024:RMD, author = "Christina Humer and Andreas Hinterreiter and Benedikt Leichtmann and Martina Mara and Marc Streit", title = "Reassuring, Misleading, Debunking: Comparing Effects of {XAI} Methods on Human Decisions", journal = j-TIIS, volume = "14", number = "3", pages = "16:1--16:??", month = sep, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3665647", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Wed Sep 25 11:23:38 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/10.1145/3665647", abstract = "Trust calibration is essential in AI-assisted decision-making. If human users understand the rationale on which an AI model has made a prediction, they can decide whether they consider this prediction reasonable. Especially in high-risk tasks such as \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "16", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Mcintosh:2024:RVA, author = "Timothy R. Mcintosh and Tong Liu and Teo Susnjak and Paul Watters and Malka N. Halgamuge", title = "A Reasoning and Value Alignment Test to Assess Advanced {GPT} Reasoning", journal = j-TIIS, volume = "14", number = "3", pages = "17:1--17:??", month = sep, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3670691", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Wed Sep 25 11:23:38 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/10.1145/3670691", abstract = "In response to diverse perspectives on artificial general intelligence (AGI), ranging from potential safety and ethical concerns to more extreme views about the threats it poses to humanity, this research presents a generic method to gauge the reasoning \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "17", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Tsiakas:2024:UHA, author = "Konstantinos Tsiakas and Dave Murray-Rust", title = "Unpacking Human-{AI} interactions: From Interaction Primitives to a Design Space", journal = j-TIIS, volume = "14", number = "3", pages = "18:1--18:??", month = sep, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3664522", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Wed Sep 25 11:23:38 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/10.1145/3664522", abstract = "This article aims to develop a semi-formal representation for Human-AI (HAI) interactions, by building a set of interaction primitives which can specify the information exchanges between users and AI systems during their interaction. We show how these \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "18", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Chatti:2024:VRE, author = "Mohamed Amine Chatti and Mouadh Guesmi and Arham Muslim", title = "Visualization for Recommendation Explainability: a Survey and New Perspectives", journal = j-TIIS, volume = "14", number = "3", pages = "19:1--19:??", month = sep, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3672276", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Wed Sep 25 11:23:38 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/10.1145/3672276", abstract = "Providing system-generated explanations for recommendations represents an important step toward transparent and trustworthy recommender systems. Explainable recommender systems provide a human-understandable rationale for their outputs. Over the past two \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "19", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Antony:2024:ICC, author = "Victor Nikhil Antony and Chien-Ming Huang", title = "{ID.8}: Co-Creating Visual Stories with Generative {AI}", journal = j-TIIS, volume = "14", number = "3", pages = "20:1--20:??", month = sep, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3672277", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Wed Sep 25 11:23:38 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/10.1145/3672277", abstract = "Storytelling is an integral part of human culture and significantly impacts cognitive and socio-emotional development and connection. Despite the importance of interactive visual storytelling, the process of creating such content requires specialized \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "20", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Anderson:2024:MUE, author = "Andrew Anderson and Jimena Noa Guevara and Fatima Moussaoui and Tianyi Li and Mihaela Vorvoreanu and Margaret Burnett", title = "Measuring User Experience Inclusivity in {Human-AI} Interaction via Five User Problem-Solving Styles", journal = j-TIIS, volume = "14", number = "3", pages = "21:1--21:??", month = sep, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3663740", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Wed Sep 25 11:23:38 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/10.1145/3663740", abstract = "Motivations: Recent research has emerged on generally how to improve AI products' human-AI interaction (HAI) user experience (UX), but relatively little is known about HAI-UX inclusivity. For example, what kinds of users are supported, and who are left \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "21", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Lawless:2024:WIW, author = "Connor Lawless and Jakob Schoeffer and Lindy Le and Kael Rowan and Shilad Sen and Cristina {St. Hill} and Jina Suh and Bahareh Sarrafzadeh", title = "{``I Want It That Way''}: Enabling Interactive Decision Support Using Large Language Models and Constraint Programming", journal = j-TIIS, volume = "14", number = "3", pages = "22:1--22:??", month = sep, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3685053", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Wed Sep 25 11:23:38 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/10.1145/3685053", abstract = "A critical factor in the success of many decision support systems is the accurate modeling of user preferences. Psychology research has demonstrated that users often develop their preferences during the elicitation process, highlighting the pivotal role \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "22", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Choi:2024:EES, author = "Minsoo Choi and Siqi Guo and Alexandros Koilias and Matias Volonte and Dominic Kao and Christos Mousas", title = "Exploring the Effects of Self-Correction Behavior of an Intelligent Virtual Character during a Jigsaw Puzzle Co-Solving Task", journal = j-TIIS, volume = "14", number = "3", pages = "23:1--23:??", month = sep, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3688006", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Wed Sep 25 11:23:38 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/10.1145/3688006", abstract = "Although researchers have explored how humans perceive the intelligence of virtual characters, few studies have focused on the ability of intelligent virtual characters to fix their mistakes. Thus, we explored the self-correction behavior of a virtual \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "23", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Berkovsky:2024:TBP, author = "Shlomo Berkovsky", title = "{2023 TiiS Best Paper} announcement", journal = j-TIIS, volume = "14", number = "3", pages = "24:1--24:??", month = sep, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3690000", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Wed Sep 25 11:23:38 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/10.1145/3690000", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "24", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Ning:2024:INL, author = "Zheng Ning and Yuan Tian and Zheng Zhang and Tianyi Zhang and Toby Jia-Jun Li", title = "Insights into Natural Language Database Query Errors: from Attention Misalignment to User Handling Strategies", journal = j-TIIS, volume = "14", number = "4", pages = "25:1--25:??", month = dec, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3650114", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Sat Dec 21 07:45:47 MST 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/10.1145/3650114", abstract = "Querying structured databases with natural language (NL2SQL) has remained a difficult problem for years. Recently, the advancement of machine learning (ML), natural language processing (NLP), and large language models (LLM) have led to significant \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "25", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Franke:2024:AXA, author = "Loraine Franke and Daniel Karl I. Weidele and Nima Dehmamy and Lipeng Ning and Daniel Haehn", title = "{AutoRL X}: Automated Reinforcement Learning on the {Web}", journal = j-TIIS, volume = "14", number = "4", pages = "26:1--26:??", month = dec, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3670692", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Sat Dec 21 07:45:47 MST 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/10.1145/3670692", abstract = "Reinforcement Learning (RL) is crucial in decision optimization, but its inherent complexity often presents challenges in interpretation and communication. Building upon AutoDOViz-an interface that pushed the boundaries of Automated RL for Decision \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "26", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Fok:2024:ASP, author = "Raymond Fok and Luca Soldaini and Cassidy Trier and Erin Bransom and Kelsey MacMillan and Evie Cheng and Hita Kambhamettu and Jonathan Bragg and Kyle Lo and Marti A. Hearst and Andrew Head and Daniel S. Weld", title = "Accelerating Scientific Paper Skimming with Augmented Intelligence Through Customizable Faceted Highlights", journal = j-TIIS, volume = "14", number = "4", pages = "27:1--27:??", month = dec, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3665648", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Sat Dec 21 07:45:47 MST 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/10.1145/3665648", abstract = "Scholars need to keep up with an exponentially increasing flood of scientific papers. To aid this challenge, we introduce Scim, a novel intelligent interface that helps scholars skim papers to rapidly review and gain a cursory understanding of its \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "27", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Warren:2024:CCF, author = "Greta Warren and Ruth M. J. Byrne and Mark T. Keane", title = "Categorical and Continuous Features in Counterfactual Explanations of {AI} Systems", journal = j-TIIS, volume = "14", number = "4", pages = "28:1--28:??", month = dec, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3673907", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Sat Dec 21 07:45:47 MST 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/10.1145/3673907", abstract = "Recently, eXplainable AI (XAI) research has focused on the use of counterfactual explanations to address interpretability, algorithmic recourse, and bias in AI system decision-making. The developers of these algorithms claim they meet user requirements in \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "28", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Kahr:2024:UTR, author = "Patricia K. Kahr and Gerrit Rooks and Martijn C. Willemsen and Chris C. P. Snijders", title = "Understanding Trust and Reliance Development in {AI} Advice: Assessing Model Accuracy, Model Explanations, and Experiences from Previous Interactions", journal = j-TIIS, volume = "14", number = "4", pages = "29:1--29:??", month = dec, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3686164", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Sat Dec 21 07:45:47 MST 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/10.1145/3686164", abstract = "People are increasingly interacting with AI systems, but successful interactions depend on people trusting these systems only when appropriate. Since neither gaining trust in AI advice nor restoring lost trust after AI mistakes is warranted, we seek to \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "29", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Kim:2024:DAB, author = "Jiwon Kim and Jiwon Kang and Migyeong Yang and Chaehee Park and Taeeun Kim and Hayeon Song and Jinyoung Han", title = "Developing an {AI}-based Explainable Expert Support System for Art Therapy", journal = j-TIIS, volume = "14", number = "4", pages = "30:1--30:??", month = dec, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3689649", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Sat Dec 21 07:45:47 MST 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/10.1145/3689649", abstract = "Sketch-based drawing assessments in art therapy are widely used to understand individuals' cognitive and psychological states, such as cognitive impairments or mental disorders. Along with self-reported measures based on questionnaires, psychological \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "30", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Wilchek:2025:AWS, author = "Matthew Wilchek and Kurt Luther and Feras A. Batarseh", title = "{Ajna}: a Wearable Shared Perception System for Extreme Sensemaking", journal = j-TIIS, volume = "15", number = "1", pages = "1:1--1:??", month = mar, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3690829", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Sat Mar 15 07:12:45 MDT 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/10.1145/3690829", abstract = "This article introduces the design and prototype of Ajna, a wearable shared perception system for supporting extreme sensemaking in emergency scenarios. Ajna addresses technical challenges in Augmented Reality (AR) devices, specifically the limitations of \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "1", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Dogru:2025:TAO, author = "Anil Dogru and Mehmet Onur Keskin and Reyhan Aydogan", title = "Taking into Account Opponent's Arguments in Human--Agent Negotiations", journal = j-TIIS, volume = "15", number = "1", pages = "2:1--2:??", month = mar, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3691643", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Sat Mar 15 07:12:45 MDT 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/10.1145/3691643", abstract = "Autonomous negotiating agents, which can interact with other agents, aim to solve decision-making problems involving participants with conflicting interests. Designing agents capable of negotiating with human partners requires considering some factors, \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "2", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Fujita:2025:CFV, author = "Kazuyuki Fujita and Keito Uwaseki and Hongyu Bu and Kazuki Takashima and Yoshifumi Kitamura", title = "{ConfusionLens}: {Focus+Context} Visualization Interface for Performance Analysis of Multiclass Image Classifiers", journal = j-TIIS, volume = "15", number = "1", pages = "3:1--3:??", month = mar, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3700139", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Sat Mar 15 07:12:45 MDT 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/10.1145/3700139", abstract = "Building higher-quality image classification models requires better performance analysis (PA) to help understand their behaviors. We propose ConfusionLens, a dynamic and interactive visualization interface that augments a conventional confusion matrix \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "3", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Schoenegger:2025:AAP, author = "Philipp Schoenegger and Peter S. Park and Ezra Karger and Sean Trott and Philip E. Tetlock", title = "{AI}-Augmented Predictions: {LLM} Assistants Improve Human Forecasting Accuracy", journal = j-TIIS, volume = "15", number = "1", pages = "4:1--4:??", month = mar, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3707649", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Sat Mar 15 07:12:45 MDT 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/10.1145/3707649", abstract = "Large language models (LLMs) match and sometimes exceed human performance in many domains. This study explores the potential of LLMs to augment human judgment in a forecasting task. We evaluate the effect on human forecasters of two LLM assistants: one \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "4", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Westphal:2025:TUA, author = "Monika Westphal and Patrick Hemmer and Michael V{\"o}ssing and Max Schemmer and Sebastian Vetter and Gerhard Satzger", title = "Towards Understanding {AI} Delegation: The Role of Self-Efficacy and Visual Processing Ability", journal = j-TIIS, volume = "15", number = "1", pages = "5:1--5:??", month = mar, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3696423", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Sat Mar 15 07:12:45 MDT 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/10.1145/3696423", abstract = "Recent work has proposed AI models that can learn to decide whether to make a prediction for a task instance or to delegate it to a human by considering both parties' capabilities. In simulations with synthetically generated or context-independent human \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "5", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Arakawa:2025:CSE, author = "Riku Arakawa and Kiyosu Maeda and Hiromu Yakura", title = "{ConverSearch}: Supporting Experts in Human Behavior Analysis of Conversational Videos with a Multimodal Scene Search Tool", journal = j-TIIS, volume = "15", number = "1", pages = "6:1--6:??", month = mar, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3709012", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Sat Mar 15 07:12:45 MDT 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", URL = "https://dl.acm.org/doi/10.1145/3709012", abstract = "Multimodal scene search of conversations is essential for unlocking valuable insights into social dynamics and enhancing our communication. While experts in conversational analysis have their own knowledge and skills to find key scenes, a lack of \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "6", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Ruoff:2025:MEU, author = "Marcel Ruoff and Brad A. Myers and Alexander Maedche", title = "{MALACHITE}-Enabling Users to Teach {GUI}-Aware Natural Language Interfaces", journal = j-TIIS, volume = "15", number = "2", pages = "7:1--7:??", month = jun, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3716141", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Sat Jun 14 15:24:28 MDT 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "Users can adapt contemporary natural language interfaces (NLIs) by teaching the NLIs how to handle new natural language (NL) inputs. One promising approach is interactive task learning (ITL), which enables users to teach new NL inputs for multi-modal \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "7", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Guimaraes:2025:AJU, author = "Manuel Guimar{\~a}es and Joana Campos and Pedro A. Santos and Jo{\~a}o Dias and Rui Prada", title = "The Author's Journey --- Understanding and Improving the Authoring Process of Theory-Driven Socially Intelligent Agents", journal = j-TIIS, volume = "15", number = "2", pages = "8:1--8:??", month = jun, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3711672", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Sat Jun 14 15:24:28 MDT 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "State-of-the-art agent-modelling tools support the creation of powerful Socially Intelligent Agents (SIAs) capable of engaging in social interactions with participants in various roles and environments. However, their deployment demands a labourious \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "8", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Alhamadi:2025:BIU, author = "Mohammed Alhamadi and Hatim Alsayahani and Sarah Clinch and Markel Vigo", title = "Behavioural Indicators of Usability in Visual Analytics Dashboards", journal = j-TIIS, volume = "15", number = "2", pages = "9:1--9:??", month = jun, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3715710", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Sat Jun 14 15:24:28 MDT 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "Information presentation problems on interactive dashboards are known to hinder decision-making. Since a traditional user-centred approach to designing usable dashboards cannot fully satisfy user demands, needs and skills, we isolate behavioural \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "9", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Strohm:2025:HHA, author = "Florian Strohm and Mihai B{\^a}ce and Andreas Bulling", title = "{HAIFAI}: Human--{AI} Interaction for Mental Face Reconstruction", journal = j-TIIS, volume = "15", number = "2", pages = "10:1--10:??", month = jun, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3725891", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Sat Jun 14 15:24:28 MDT 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "We present HAIFAI-a novel two-stage system where humans and AI interact to tackle the challenging task of reconstructing a visual representation of a face that exists only in a person's mind. In the first stage, users iteratively rank images our \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "10", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{You:2025:PPU, author = "Yuzhe You and Jarvis Tse and Jian Zhao", title = "{Panda} or Not {Panda}? {Understanding} Adversarial Attacks with Interactive Visualization", journal = j-TIIS, volume = "15", number = "2", pages = "11:1--11:??", month = jun, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3725739", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Sat Jun 14 15:24:28 MDT 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "Adversarial machine learning (AML) studies attacks that can fool machine learning algorithms into generating incorrect outcomes as well as the defenses against worst-case attacks to strengthen model robustness. Specifically for image classification, it \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "11", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Cinca:2025:PBM, author = "Robert Cinca and Enrico Costanza and Mirco Musolesi", title = "Practitioners and Bias in Machine Learning: a Study", journal = j-TIIS, volume = "15", number = "2", pages = "12:1--12:??", month = jun, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3733838", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Sat Jun 14 15:24:28 MDT 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "The increasing adoption of machine learning (ML) raises ethical concerns, particularly regarding bias. This study explores how ML practitioners with limited experience in bias understand and apply bias definitions, detection measures, and mitigation \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "12", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Xie:2025:CAP, author = "Jialan Xie and Ping Lan and Zhaonian Hu and Guangyuan Liu", title = "Comparative Analysis of Personality Recognition in Response to Virtual Reality and {2D} Emotional Stimulus Using {ECG} Signals", journal = j-TIIS, volume = "15", number = "3", pages = "13:1--13:19", month = sep, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3707648", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Thu Oct 2 12:36:02 MDT 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "Personality primarily refers to the unique and stable way of a person's thinking and behavior. A few studies have recently been conducted on personality recognition using \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "13", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Ferland:2025:WYL, author = "Libby Ferland and Risako Owan and Zachary Kunkel and Hannah Qu and Maria Gini and Wilma Koutstaal", title = "What Are You Looking Forward to? {Deliberate} Positivity as a Promising Strategy for Conversational Agents", journal = j-TIIS, volume = "15", number = "3", pages = "14:1--14:40", month = sep, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3725738", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Thu Oct 2 12:36:02 MDT 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "Conversational agents (CAs) are one of the most promising technologies for helping older adults maintain independence longer by augmenting their support and social networks. \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "14", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Chen:2025:MCI, author = "Chuer Chen and Shengqi Dang and Yuqi Liu and Nanxuan Zhao and Yang Shi and Nan Cao", title = "{MV-Crafter}: an Intelligent System for Music-Guided Video Generation", journal = j-TIIS, volume = "15", number = "3", pages = "15:1--15:27", month = sep, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3748515", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Thu Oct 2 12:36:02 MDT 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "Music videos, as a prevalent form of multimedia entertainment, deliver engaging audio-visual experiences to audiences and have gained immense popularity among singers and \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "15", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Lansman:2025:UED, author = "Lior Lansman and Osnat Mokryn and Lijie Guo and Mehtab Iqbal and Bart P. Knijnenburg", title = "Using Emotion Diversification Based on Movie Reviews to Improve the User Experience of Movie Recommender Systems", journal = j-TIIS, volume = "15", number = "3", pages = "16:1--16:27", month = sep, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3743147", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Thu Oct 2 12:36:02 MDT 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "Diversifying movie recommendations is an effective way to address choice overload, a phenomenon where recommenders generate lists with highly similar recommendations \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "16", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Spitzer:2025:IXP, author = "Philipp Spitzer and Katelyn Morrison and Violet Turri and Michelle Feng and Adam Perer and Niklas K{\"u}hl", title = "Imperfections of {XAI}: Phenomena Influencing {AI}-Assisted Decision-Making", journal = j-TIIS, volume = "15", number = "3", pages = "17:1--17:40", month = sep, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3750052", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Thu Oct 2 12:36:02 MDT 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "With the increasing use of AI, recent research in human-computer interaction explores Explainable AI (XAI) to make AI advice more interpretable. While research addresses \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "17", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Berkovsky:2025:TBP, author = "Shlomo Berkovsky", title = "2024 {TiiS} Best Paper Announcement", journal = j-TIIS, volume = "15", number = "3", pages = "18:1--18:??", month = sep, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3749645", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Thu Oct 2 12:36:02 MDT 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "18", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Li:2025:ISI, author = "Yunyao Li and Mary Lou Maher and Ziang Xiao", title = "Introduction to the Special Issue on Human-Centric Generative {AI}", journal = j-TIIS, volume = "15", number = "4", pages = "19:1--19:6", month = dec, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3779127", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Tue Dec 23 09:14:06 MST 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "19", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Govers:2025:FDI, author = "Jarod Govers and Saumya Pareek and Eduardo Velloso and Jorge Goncalves", title = "Feeds of Distrust: Investigating How {AI}-Powered News Chatbots Shape User Trust and Perceptions", journal = j-TIIS, volume = "15", number = "4", pages = "20:1--20:31", month = dec, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3722227", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Tue Dec 23 09:14:06 MST 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "20", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Hong:2025:DEC, author = "Matt-Heun Hong and Anamaria Crisan", title = "Data Has Entered the Chat: How Data Workers Conduct Exploratory Visual Analytic Conversations with {GenAI} Agents", journal = j-TIIS, volume = "15", number = "4", pages = "21:1--21:40", month = dec, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3744750", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Tue Dec 23 09:14:06 MST 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "21", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Mastrianni:2025:AES, author = "Angela Mastrianni and Hope Twede and Aleksandra Sarcevic and Jeremiah Wander and Christina Austin-Tse and Scott Saponas and Heidi Rehm and Ashley Mae Conard and Amanda K. Hall", title = "{AI}-Enhanced Sensemaking: Exploring the Design of a Generative {AI}-Based Assistant to Support Genetic Professionals", journal = j-TIIS, volume = "15", number = "4", pages = "22:1--22:30", month = dec, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3756326", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Tue Dec 23 09:14:06 MST 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "22", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Flathmann:2025:ETA, author = "Christopher Flathmann and Nathan J. McNeese and Subhasree Sengupta and Ethan Johnson", title = "Exploring Trust, Acceptance, and Behavioral Differences When Humans Collaborate with Large Language Models as Tools and Teammates", journal = j-TIIS, volume = "15", number = "4", pages = "23:1--23:33", month = dec, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3764591", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Tue Dec 23 09:14:06 MST 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "23", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Rezwana:2025:EMM, author = "Jeba Rezwana and Mary Lou Maher", title = "An Exploration of Mental Models of {AI} in Human--{AI} Co-Creativity: a Framework and Insights", journal = j-TIIS, volume = "15", number = "4", pages = "24:1--24:26", month = dec, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3769072", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Tue Dec 23 09:14:06 MST 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "24", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Bavaresco:2025:MHC, author = "Anna Bavaresco and Nhut Truong and Uri Hasson", title = "Modeling Human Concepts with Subspaces in Deep Vision Models", journal = j-TIIS, volume = "15", number = "4", pages = "25:1--25:25", month = dec, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3768340", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Tue Dec 23 09:14:06 MST 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "25", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Fowler:2025:EAM, author = "Max Fowler and Chinedu Emeka and Binglin Chen and David H. {Smith IV} and Matthew West and Craig Zilles", title = "Evaluating {AI} Models for Autograding Explain in Plain {English} Questions: Challenges and Considerations", journal = j-TIIS, volume = "15", number = "4", pages = "26:1--26:29", month = dec, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3774752", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Tue Dec 23 09:14:06 MST 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "26", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Kramer:2025:TTI, author = "Nicole C. Kr{\"a}mer and Ivana Lamia and Hanne Siegert and Florian Wenda and Lovis Suchmann", title = "Tricking into Trusting? {The} Influence of Social Cues of a Generative {AI} on Perceived Trust", journal = j-TIIS, volume = "15", number = "4", pages = "27:1--27:17", month = dec, year = "2025", CODEN = "????", DOI = "https://doi.org/10.1145/3771844", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Tue Dec 23 09:14:06 MST 2025", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "27", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Fan:2026:SHF, author = "Arlen Fan and Fan Lei and Steven R. Corman and Ross Maciejewski", title = "{Skeptik}: a Hybrid Framework for Combating Potential Misinformation in Journalism", journal = j-TIIS, volume = "16", number = "1", pages = "1:1--1:31", month = mar, year = "2026", CODEN = "????", DOI = "https://doi.org/10.1145/3766891", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "05 March 2026", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "The proliferation of misinformation in journalism, often stemming from flawed reasoning and logical fallacies, poses significant challenges to public understanding and trust in news media. Traditional fact-checking methods, while valuable, are \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "1", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Szymanski:2026:DPH, author = "Maxwell Szymanski and Stijn Keyaerts and Cristina Conati and Robin De Croon and Vero {Vanden Abeele} and Katrien Verbert", title = "Designing and Personalising Hybrid Health Explanations for Lay Users", journal = j-TIIS, volume = "16", number = "1", pages = "2:1--2:37", month = mar, year = "2026", CODEN = "????", DOI = "https://doi.org/10.1145/3772071", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Thu Mar 5 11:47:31 MST 2026", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "Recommender systems are increasingly used in mobile health interventions, such as managing Chronic Musculoskeletal Pain (CMP). While researchers have highlighted the importance of explaining health-related recommendations to lay users, with benefits such \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "2", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Walton:2026:MMT, author = "Sean P. Walton and Ben J. Evans and Alma A. M. Rahat and James Stovold and Jakub Vincalek", title = "From Metrics to Meaning: Time to Rethink Evaluation in Human--{AI} Collaborative Design", journal = j-TIIS, volume = "16", number = "1", pages = "3:1--3:31", month = mar, year = "2026", CODEN = "????", DOI = "https://doi.org/10.1145/3773292", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Thu Mar 5 11:47:31 MST 2026", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "As AI systems increasingly shape decision-making in creative design contexts, understanding how humans engage with these tools has become a critical challenge for interactive intelligent systems research. This article contributes a challenge to rethink \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "3", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Hughes:2026:DPH, author = "Nathan Hughes and Yan Jia and Mark Sujan and Tom Lawton and Ibrahim Habli and John McDermid", title = "Design Principles for Human-Centred Explainable {AI}: a Scoping Review", journal = j-TIIS, volume = "16", number = "1", pages = "4:1--4:26", month = mar, year = "2026", CODEN = "????", DOI = "https://doi.org/10.1145/3771720", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Thu Mar 5 11:47:31 MST 2026", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "The field of Human-Centred Explainable AI (HCXAI) has been rapidly expanding. In turn, there has been an increase in the number of papers suggesting design principles for HCXAI. However, it is unclear the extent to which design requirements overlap \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "4", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Jiao:2026:DDM, author = "Chuhan Jiao and Yao Wang and Guanhua Zhang and Mihai B{\^a}ce and Zhiming Hu and Andreas Bulling", title = "{DiffGaze}: a Diffusion Model for Modelling Fine-grained Human Gaze Behaviour on 360$^\circ$ Images", journal = j-TIIS, volume = "16", number = "1", pages = "5:1--5:23", month = mar, year = "2026", CODEN = "????", DOI = "https://doi.org/10.1145/3772075", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Thu Mar 5 11:47:31 MST 2026", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "Modelling human gaze behaviour on 360 ${}^{\circ } $ images is important for various human-computer interaction applications. However, existing methods are limited to predicting discrete fixation sequences or aggregated saliency maps, thereby neglecting \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "5", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Mahmoudi-Nejad:2026:SBA, author = "Athar Mahmoudi-Nejad and Matthew Guzdial and Pierre Boulanger", title = "Spiders Based on Anxiety: How Reinforcement Learning Can Deliver Desired User Experience in Virtual Reality Personalized Arachnophobia Treatment", journal = j-TIIS, volume = "16", number = "1", pages = "6:1--6:60", month = mar, year = "2026", CODEN = "????", DOI = "https://doi.org/10.1145/3766062", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Thu Mar 5 11:47:31 MST 2026", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "The need to generate a spider to provoke a desired anxiety response arises in the context of personalized virtual reality exposure therapy (VRET), a treatment approach for arachnophobia. This treatment involves patients observing virtual spiders in order \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "6", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Barradas:2026:ERP, author = "Isabel Barradas and Zartasha Naeem Khan and Angelika Peer", title = "Emotion Recognition from Peripheral Physiological Signals: a Systematic Review of Trends, Challenges and Opportunities", journal = j-TIIS, volume = "16", number = "1", pages = "7:1--7:41", month = mar, year = "2026", CODEN = "????", DOI = "https://doi.org/10.1145/3771719", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Thu Mar 5 11:47:31 MST 2026", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "The adaptability and intuitiveness of Human-Computer-Interaction systems are enhanced by emotion recognition capabilities, whose rapid advancement asks for updated and more complete surveys. In this comprehensive work, papers using at least one of three \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "7", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Hamid:2026:IDA, author = "Md Montaser Hamid and Fatima A. Moussaoui and Jimena Noa Guevara and Andrew Anderson and Puja Agarwal and Jonathan Dodge and Margaret Burnett", title = "Inclusive Design of {AI}'s Explanations: Just for Those Previously Left Out?", journal = j-TIIS, volume = "16", number = "1", pages = "8:1--8:44", month = mar, year = "2026", CODEN = "????", DOI = "https://doi.org/10.1145/3772074", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Thu Mar 5 11:47:31 MST 2026", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "Abstract Motivations. Explainable AI (XAI) systems aim to improve users' understanding of AI, but XAI research has shown that many XAI explanations serve some users well while failing others. In non-AI systems, software practitioners have used inclusive \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "8", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", } @Article{Guo:2026:UVD, author = "Yi Guo and Xiaoyu Qi and Haoyang Li and Jing Zhang and Danqing Shi and Qing Chen and Daniel Weiskopf and Nan Cao", title = "{Urania}: Visualizing Data Analysis Pipelines for Natural Language-Based Data Exploration", journal = j-TIIS, volume = "16", number = "1", pages = "9:1--9:26", month = mar, year = "2026", CODEN = "????", DOI = "https://doi.org/10.1145/3770071", ISSN = "2160-6455 (print), 2160-6463 (electronic)", ISSN-L = "2160-6455", bibdate = "Thu Mar 5 11:47:31 MST 2026", bibsource = "https://www.math.utah.edu/pub/tex/bib/tiis.bib", abstract = "Exploratory Data Analysis (EDA) is an essential yet tedious process for examining a new dataset. To facilitate it, Natural Language Interfaces (NLIs) can help people intuitively explore the dataset via data-oriented questions. However, existing NLIs \ldots{}", acknowledgement = ack-nhfb, ajournal = "ACM Trans. Interact. Intell. Syst.", articleno = "9", fjournal = "ACM Transactions on Interactive Intelligent Systems (TIIS)", journal-URL = "https://dl.acm.org/loi/tiis", }