ACM Transactions on

Interactive Intelligent Systems (TIIS)

Latest Articles

Chronodes: Interactive Multifocus Exploration of Event Sequences

VisForum: A Visual Analysis System for Exploring User Groups in Online Forums

A Visual Analytics Framework for Exploring Theme Park Dynamics

A Visual Approach for Interactive Keyterm-Based Clustering


TiiS 2017 Best Paper Award winners are Marius Kaminskas and Derek Bridge for Diversity, Serendipity, Novelty, and Coverage: A Survey and Empirical Analysis of Beyond-Accuracy Objectives in Recommender Systems from TiiS 7(1)! 

TiiS 2016 Best Paper Award is granted to Weike Pan, Qiang Yang, Yuchao Duan, and Zhong Ming, for their article "Transfer Learning for Semi-Supervised Collaborative Recommendation", appeared in TiiS 6(2). Congratulations to all the authors! 

Forthcoming Articles
Bi-Level Thresholding: Analyzing the Effect of Repeated Errors in Gesture Input

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 paper, we examine bi-level thresholding, a recognition strategy that uses two threshold: 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 lead 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 significant 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 data set 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.

Using Machine Learning to Support Qualitative Coding in Social Science: Shifting The Focus to Ambiguity

Machine learning (ML) has become increasingly inuential 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 elds. Social scientistsexperts trained to examine and explain the complexity of human behavior and interactions in the worldhave considerable expertise to contribute to the development of ML applications for human-generated data, and their analytic practices could benet from more human- centered ML methods. In this work, we highlight some of the gaps in applying ML to social science research. Building upon content analysis of social media papers, a survey study, and interviews, we summarize the current use and challenges of ML in social sciences. Additionally, we utilize our experience designing a visual analytics tool for collaborative qualitative coding as a case study to illustrate how we might re-imagine the way ML could support workows for social scientists. Finally, we propose three research directions to ground ML applications for social science with the ultimate goal of achieving truly human-centered machine learning.

Crowdsourcing Ground Truth for Medical Relation Extraction

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, that 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, that account for ambiguity in both human and machine performance on this task.

Evaluation and Refinement of Clustered Search Results with the Crowd

When searching on the web, 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. As a result, 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 work, 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 in order to find the best one. Finally, the workflow has the crowd spot and fix problems in the best result in order 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.

An Active Sleep Monitoring Framework Using Wearables

Sleep is the most important aspect of healthy and active living. Right amount of sleep at the right time helps an individual to protect his physical, mental, cognitive health and maintain his quality of life. The most durative of the Activities of Daily Living (ADL), sleep, has a major synergic influence on a persons 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 paper, we propose an intelligent algorithm to detect the microscopic states of the sleep, which fundamentally constitute the components of a good and bad sleeping behavior 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 the 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.

A Review of User Interface Design for Interactive Machine Learning

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. Design of the interface 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 characterization of Interactive Machine Learning from an interactive systems perspective. We propose and describe a structural and behavioural model of a generalized IML system and identify solution principles for building effective interfaces for IML. Where possible, these emergent solution principles are contextualized 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.

Its Not Just About Accuracy: Metrics that Matter when Modeling Expert Sketching Ability

Design sketching is an important tool for designers and creative professionals 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 sometimes 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 PerSketchTivity, we aim to mimic the feedback given by an instructor and assess the student drawn sketches to give them insight into the areas they need to improve on. In order to provide effective feedback to users, it is important to identify what features of their sketches they should work on to improve their sketching ability. After consulting with several domain experts in sketching, we came up with an initial list of 22 different metrics that could potentially differentiate expert and novice sketches. We gathered over 2000 sketches from 20 novices and four experts for analysis. Seven metrics were shown to significantly correlate with the quality of expert sketches and provided insight into providing intelligent user feedback as well as an overall model of expert sketching ability.

Toward an Understanding of Trust Repair in Human-Robot Interaction: Current Research and Future Directions

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 much 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. As HRI becomes widespread, so will trust violations; 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 trust is violated. 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. As such, the purpose of this paper is to provide a foundation for exploring human-robot trust repair by drawing upon prior work in the human-robot and human-human trust literature, concluding with recommendations for advancing this body of work.

Predicting User's Confidence During Visual Decision Making

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 healthcare. 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 ``gazemaps" -- 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 H0.7 in both cases. Capturing just a simple subset of non-task-specific numerical features gave slightly worse, but still quite high accuracy (eg. MAE H1.0). Results obtained with gazemaps 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 gazemap-based approach forms a viable, transferable, alternative to hand-crafting 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

Observation-Level and Parametric Interaction for High-Dimensional Data Analysis

Exploring high-dimensional data is challenging. Dimension reduction algorithms, such as weighted multi- dimensional scaling, support data explorations 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 paper, 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 techniques affects 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.

Cues of Violent Intergroup Conflict Diminish Perceptions of Robotic Personhood

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 this shift was mediated by parallel reductions in emotional sympathy with the robot (with no significant effects of threat on attributions of intelligence). 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 between threat, coalitional bias and human-robot interaction.

Visualizing Ubiquitously Sensed Measures of Motor Ability in Multiple Sclerosis: Reflections on communicating machine learning in practice

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 algorithms decision-making process in a way that allows clinicians to integrate the algorithms result into their own decision process. To this end, we present an iterative design research study that draws out challenges of supporting interpretability in a real-world system. The key contribution of this paper 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.

Modeling the Human-Robot Trust Phenomenon: A Conceptual Framework based on Risk

This paper presents a conceptual framework for human-robot trust which uses 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 paper 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 paper concludes with a description of the important areas for future research on the topic of human-robot trust.


Publication Years 2011-2018
Publication Count 163
Citation Count 718
Available for Download 163
Downloads (6 weeks) 1551
Downloads (12 Months) 12505
Downloads (cumulative) 61661
Average downloads per article 378
Average citations per article 4
First Name Last Name Award
Gregory Abowd ACM Eugene L. Lawler Award for Humanitarian Contributions within Computer Science and Informatics (2009)
ACM Fellows (2008)
ACM Senior Member (2008)
Craig Boutilier ACM Fellows (2012)
Oliver Brdiczka ACM Senior Member (2015)
Peter Brusilovsky ACM Senior Member (2008)
Margaret Burnett ACM Fellows (2017)
ACM Distinguished Member (2015)
Yolanda Gil ACM Fellows (2016)
Michael L Gleicher ACM Distinguished Member (2011)
Tracy Anne Hammond ACM Senior Member (2015)
Andreas Kerren ACM Senior Member (2013)
Joseph A Konstan ACM Software System Award (2010)
ACM Fellows (2008)
ACM Distinguished Member (2006)
Wessel Kraaij ACM Distinguished Member (2017)
ACM Senior Member (2007)
Sarit Kraus ACM Fellows (2014)
Tsvi Kuflik ACM Distinguished Member (2013)
ACM Senior Member (2012)
Robin R Murphy ACM Eugene L. Lawler Award for Humanitarian Contributions within Computer Science and Informatics (2014)
Jeffrey Nichols ACM Senior Member (2013)
Fabio Paterno ACM Distinguished Member (2009)
Stefano Piana ACM Gordon Bell Prize
Special Category (2009) ACM Gordon Bell Prize
Special Category (2009)
John T Riedl ACM Software System Award (2010)
ACM Fellows (2009)
ACM Distinguished Member (2007)
Tom Rodden ACM Fellows (2014)
Domenico Sacca ACM Senior Member (2007)
Ben Shneiderman ACM Fellows (1997)
Matthew A Turk ACM Senior Member (2007)
Qiang Yang ACM Fellows (2017)
ACM Distinguished Member (2011)
Qiang Yang ACM Fellows (2017)
ACM Distinguished Member (2011)

First Name Last Name Paper Counts
Albert Salah 3
Elisabeth André 3
Joseph LaViola, 3
Kazuhiro Otsuka 3
Anthony Jameson 3
Shiro Kumano 3
Ryo Ishii 3
John Riedl 3
Frédéric Bevilacqua 3
Ana Paiva 2
Junji Yamato 2
Magalie Ochs 2
Bilge Mutlu 2
Yolanda Gil 2
Yukiko Nakano 2
Oya Aran 2
Catherine Pélachaud 2
Heriberto Cuayáhuitl 2
Nina Dethlefs 2
Louis Morency 2
Catherine Havasi 2
Federica Cena 2
Lola Cañamero 2
Giuseppe Carenini 2
Alexander Felfernig 2
Henry Lieberman 2
Cristina Gena 2
Ginevra Castellano 2
Shlomo Berkovsky 2
Iolanda Leite 2
Matthew Turk 2
Chen Yu 2
Ulf Blanke 2
Nan Cao 2
Michael Jugovac 2
Bart Knijnenburg 2
Kim Bard 2
Yang Wang 2
Hatice Gunes 2
Gregory Abowd 2
Dietmar Jannach 2
Hiroshi Ishiguro 2
Sidney D'mello 2
Eduardo Veas 2
Hayley Hung 2
Eugene Taranta 2
Kristiina Jokinen 2
Andrew Finch 1
Fabrizio Antonelli 1
Claudia Picardi 1
Daniele Dupré 1
Elisa Chiabrando 1
Matteo Demichelis 1
Günther Palm 1
Martin WöLlmer 1
Yale Song 1
Joyce Chai 1
Kris Luyten 1
Pierrick Thébault 1
Koen Van Boerdonk 1
Jens Edlund 1
Ralf Biedert 1
Geert Houben 1
Markus Strohmaier 1
Ben Steichen 1
Donald Glowinski 1
Javier San Agustin 1
Samer Al Moubayed 1
Georg Buscher 1
Fabio Zanzotto 1
Gawesh Jawaheer 1
Zachary Warren 1
Roman Bednarik 1
Hui Zhang 1
Michael Zehetleitner 1
Anne Meyer 1
Ningxiao Sun 1
Chihpin Hsiao 1
Gilles Pesant 1
Ryan Kiros 1
Leigh Clark 1
Carita Paradis 1
Mercan Topkara 1
Hidemi Iwasaka 1
Peter Robinson 1
Peter Brusilovsky 1
Weike Pan 1
Andrés Vargas 1
Dingtian Zhang 1
Stefano Ferilli 1
Eelke Folmer 1
Jeffrey Allen 1
Sangyoung Chung 1
Martin Cooney 1
Marco Gillies 1
Franca Garzotto 1
Roberto Turrin 1
Boris De Ruyter 1
Andreas Bulling 1
Kirsten Butcher 1
Tamara Sumner 1
Hung Ngo 1
Matthew Luciw 1
Antoine Raux 1
Zhuoran Wang 1
James Deng 1
Tomislav Pejša 1
Nahum Álvarez 1
Manfred Tscheligi 1
Sunghyun Park 1
Andrew Gordon 1
Roberto Vezzani 1
Paolo Santinelli 1
Gregor Mehlmann 1
Florian Lingenfelser 1
Andreas Forsblom 1
Kwanliu Ma 1
David Ebert 1
Nadir Weibel 1
Kathleen McCoy 1
Edward Schwartz 1
Alex Mihailidis 1
Bob Kummerfeld 1
Luca Ducceschi 1
Piercarlo Grimaldi 1
Marina Geymonat 1
Vincenzo Cuciti 1
Fausto Giunchiglia 1
Kostas Karpouzis 1
Ashkan Yazdani 1
Andreas Dengel 1
Seungjun Kim 1
Jean Crespo 1
André Pereira 1
Floriane Dardard 1
Giorgio Gnecco 1
Bibek Paudel 1
Karinne Ramirez-Amaro 1
Humera Minhas 1
Ting Zhang 1
Yuting Li 1
Marius Kaminskas 1
Shuo Yan 1
Yufeng Wu 1
Kunwar Singh 1
Yihsuan Yang 1
Yuanching Teng 1
Jean Frayret 1
Nicolas Gaud 1
Jared Bott 1
Axel Soto 1
Franklin Harper 1
Martijn Willemsen 1
Kyle Duarte 1
Margaret Burnett 1
Rafael Calvo 1
Victor Ng-Thow-Hing 1
Denis Parra 1
Anhong Guo 1
Atau Tanaka 1
Navid Fallah 1
Kostas Bekris 1
Mario Gianni 1
Apostolos Axenopoulos 1
Stavroula Manolopoulou 1
Thomas Dodson 1
Jeffrey Nichols 1
Ofra Amir 1
Luciana Benotti 1
Martin Villalba 1
Alfredo Milani 1
Rahul Sukthankar 1
Wessel Kraaij 1
Marc Cavazza 1
Joao Catarino 1
Hansuk Shim 1
Yenling Kuo 1
Birago Jones 1
Jesse Vig 1
Denny Vrandečić 1
Zhenyucheryl Qian 1
Ionut Damian 1
Elyon DeKoven 1
Udo Kruschwitz 1
Roberto Furnari 1
Ilaria Lombardi 1
Dario Mana 1
Friedhelm Schwenker 1
Masafumi Nishida 1
Melanie Hartman 1
Elio Masciari 1
Erhardt Barth 1
Pablo Varona 1
Lorenzo Ferrone 1
Antonio Camurri 1
Lian Zhang 1
Dayi Bian 1
Medha Sarkar 1
Sana Malik 1
Fan Du 1
Juan Wachs 1
Katsutoshi Masai 1
Takashi Yoshino 1
Yutaka Takase 1
Kristina Yordanova 1
Valentin Enescu 1
Chen Liu 1
Christoph Trattner 1
Sarit Kraus 1
Michael Young 1
Nava Tintarev 1
Nicolas Courty 1
Tracy Hammond 1
Shimei Pan 1
Graham Neubig 1
Tadas Baltrušaitis 1
Zhong Ming 1
Ryan Baker 1
Spencer Compton 1
Moushumi Sharmin 1
Baptiste Caramiaux 1
Ilias Apostolopoulos 1
Seungwoo Kang 1
Junehwa Song 1
Shuichi Nishio 1
Carolyn Rosé 1
Bruno Zamborlin 1
Pierre Andrews 1
Joshua Guerin 1
Pasquale Lops 1
Jürgen Schmidhuber 1
Alexander Förster 1
Sigrid Knust 1
Thomas Kirste 1
Akiko Yamazaki 1
Keiko Ikeda 1
Steven Hoi 1
Todd Kulesza 1
Ian Oberst 1
Sandra Okita 1
Brittany Duncan 1
Branislav Kveton 1
Jaclyn Ocumpaugh 1
Caleb Southern 1
Minsuk Kahng 1
Duenhorng Chau 1
Yuru Lin 1
Takeo Igarashi 1
Kyriakos Kritikos 1
Fiora Pirri 1
Hendrik Zender 1
Oliver Lemon 1
Clement Leung 1
Li Chen 1
Moran Dorfman 1
Eran Gazit 1
Suzan Verberne 1
Jeffrey Hausdorff 1
Rui Prada 1
Shuji Fujimoto 1
Andreas Uhl 1
Francis Quek 1
Shilad Sen 1
Reid Swanson 1
Martino Lombardi 1
Johannes Wagner 1
Yuhsuan Chan 1
Natalie Ruiz 1
Seniz Demir 1
Craig Boutilier 1
Andrea Toso 1
Fabiana Vernero 1
Francesca Carmagnola 1
David Robertson 1
Stefan Scherer 1
Aryel Beck 1
Antoine Hiolle 1
Marina Davila-Ross 1
Jean Vesin 1
David Demirdjian 1
Randall Davis 1
Oliver Brdiczka 1
Max Mühlhäuser 1
Daniel Schreiber 1
Kars Lenssen 1
Jessica Hodgins 1
Anind Dey 1
Nunziato Cassavia 1
Yi Fang 1
Cristina Conati 1
Joyce Chai 1
Carlos Martinho 1
Stefano Piana 1
Joshua Wade 1
Amy Weitlauf 1
Hunghsuan Huang 1
Margrét Bjarnadóttir 1
Mark D'Inverno 1
Saleema Amershi 1
Jill Freyne 1
Jin Zhao 1
Ya'akov Gal 1
Marco De Gemmis 1
Matthew Marge 1
Lutz Frommberger 1
Tessa Lau 1
Anat Mirelman 1
Maya Sappelli 1
Joao Oliveira 1
Ilhan Aslan 1
Kenji Sagae 1
Augusto Pieracci 1
Jean Martens 1
Petteri Nurmi 1
Antti Salovaara 1
Fernando Nos 1
Elia Bruni 1
Nicu Sebe 1
Dairazalia Sanchez-Cortes 1
Ronnie Taib 1
Bo Yin 1
Priscilla Moraes 1
Pascal Poupart 1
Andrew Monk 1
Wei Song 1
Lixiu Yu 1
Luca Console 1
Federica Protti 1
Mario Mirabelli 1
Giulia Biamino 1
Franco Fassio 1
Christopher Peters 1
Florian Eyben 1
Laurel Riek 1
Brett Stevens 1
Hirohisa Furukawa 1
Evelien Van De Garde-Perik 1
Elise Hoven 1
Takayuki Kanda 1
Ludger Van Elst 1
Laura Pomarjanschi 1
David Rozado 1
Francisco Rodrıguez 1
Tian(Linger) Xu 1
David Gotz 1
Vedran Sabol 1
Maki Sugimoto 1
Vlado Kešelj 1
Hichem Sahli 1
Yoshinori Kuno 1
Svenja Adolphs 1
Casper Harteveld 1
Robin Murphy 1
Yangqiu Song 1
Tomoki Toda 1
Marwa Mahmoud 1
Brian Ravenet 1
Qiang Yang 1
Yang Li 1
Nicola Montecchio 1
Ehud Sharlin 1
Daisuke Sakamoto 1
Jalal Mahmud 1
German Ruiz 1
Gregory Smith 1
Francesco Ricci 1
Jawad Nagi 1
Mika Shigematsu 1
Moitreya Chatterjee 1
Patrick Gebhard 1
Robert Woodbury 1
Maria Riveiro 1
Tobias Baur 1
Fabian Bohnert 1
Daniel Keim 1
Emily Grenader 1
Stephanie Elzer 1
Judy Kay 1
Jeffrey Nickerson 1
Fabrizio Franceschi 1
Silvia Likavec 1
Björn Schuller 1
Touradj Ebrahimi 1
Yukiko Nakano 1
Dominique Decotter 1
David Molyneaux 1
Jonas Beskow 1
Domenico Saccà 1
Michael Dorr 1
Peter McOwan 1
Peter Weller 1
Alessandra Staglianò 1
Chris Newell 1
Enamul Hoque 1
Gordon Cheng 1
Gary McKeown 1
Cecilia Sciascio 1
Nicholas Davis 1
Brian Magerko 1
Kai Kunze 1
Sarah Fdili Alaoui 1
Misato Yatsushiro 1
Weiyi Wang 1
Yoshinori Kobayashi 1
Abdulmalik Ofemile 1
Sylvie Gibet 1
Brandon Paulson 1
Wengkeen Wong 1
Fang Chen 1
Ravi Sarvadevabhatla 1
Kostiantyn Kucher 1
Yi Yang 1
Sakti Sakriani 1
Hideki Negoro 1
Yuchao Duan 1
Cheng Zhang 1
Peter Polack 1
Berardina Carolis 1
Dimitris Plexousakis 1
Paolo Cremonesi 1
Simon Dobson 1
Desney Tan 1
Dimitrios Rafailidis 1
Steven Bethard 1
Soheil Bahreini 1
James Martin 1
Simon Keizer 1
Sean Andrist 1
Michael Gleicher 1
Jane Hsu 1
Rosalind Picard 1
John Dill 1
Chris Shaw 1
Danilo Rodrigues 1
Jasper Uijlings 1
Patty Kostkova 1
Jing Fan 1
Ben Shneiderman 1
Abraham Bernstein 1
Michael Beetz 1
Rita Kundu 1
Shun Sun 1
Hongsong Li 1
Zheng Guan 1
Andreza Sartori 1
M Khawaja 1
Peng Wu 1
Charles Greenbacker 1
Daniel Chester 1
Jesse Hoey 1
Arthur Graesser 1
Keiji Yasuda 1
Kumiko Tanaka-Ishii 1
Livio Robaldo 1
Pierluigi Grillo 1
Michele Mioli 1
Rossana Simeoni 1
Michael Glodek 1
Jean Martin 1
Jongseok Lee 1
Monique Lu 1
Serge Offermans 1
Joe Finney 1
Dan Tasse 1
Paul Schermerhorn 1
Matthias Scheutz 1
Francesca Odone 1
Amy Swanson 1
Nilanjan Sarkar 1
Catherine Plaisant 1
Fabian Christoffel 1
Longfei Zhang 1
Masahiko Inami 1
Mihoko Fukushima 1
Joseph Konstan 1
Ariel Rosenfeld 1
Belgin Mutlu 1
John O'Donovan 1
Thibaut Naour 1
Simone Stumpf 1
Stephen Perona 1
Andrew Ko 1
Andreas Kerren 1
Jie Lu 1
Satoshi Nakamura 1
Hannes Vilhjálmsson 1
Angelo Cafaro 1
Nigel Bosch 1
Valerie Shute 1
Rosa Arriaga 1
Rahul Basole 1
Shangtse Chen 1
Kaya De Barbaro 1
James Young 1
Souneil Park 1
Emile Aarts 1
Rohit Kumar 1
Maurits Kaptein 1
Eyal Dim 1
Tsvi Kuflik 1
Petros Daras 1
Nicholas Mattei 1
Judy Goldsmith 1
Tessa Lau 1
Alfred Kobsa 1
Heather Leary 1
Giovanni Semeraro 1
Mary Ellen Foster 1
Thaddeus Simons 1
Helmut Prendinger 1
Antonio Sánchez-Ruiz 1
Alexander Meschtscherjakov 1
Yasmine El-Glaly 1
Karthik Dinakar 1
Varun Ratnakar 1
Paul Groth 1
Rita Cucchiara 1
Yingjievictor Chen 1
Birgit Lugrin 1
Patrik Floréen 1
Charles Callaway 1
Carlos Correa 1
Remco Chang 1
Eric Choi 1
Julien Epps 1
Patrick Olivier 1
Eiichiro Sumita 1
Massimo Poesio 1
Jon Chamberlain 1
Alessandro Marcengo 1
Monica Perrero 1
Amon Rapp 1
Ilaria Torre 1
Fabio Torta 1
Nick Campbell 1
Toyoaki Nishida 1
Seiichi Yamamoto 1
Mathieu Boussard 1
Hans Gellersen 1
Jodi Forlizzi 1
Chiara Pulice 1
Ziad Bawab 1
Lukas Lerche 1
Derek Bridge 1
Gangyi Ding 1
Tianyu Huang 1
Yuta Sugiura 1
Brenda Lin 1
Masa Ogata 1
Christian Jacquemin 1
David Meignan 1
Evangelos Milios 1
Keiichi Yamazaki 1
Tom Rodden 1
Steven Sutherland 1
Rong Jin 1
Nargess Nourbakhsh 1
Hana Boukricha 1
Magnus Sahlgren 1
Ipke Wachsmuth 1
Hiroki Tanaka 1
Katrien Verbert 1
Domenico Redavid 1
Fabio Paternò 1
Geert Kruijff 1
Panos Markopoulos 1
Juan Ye 1
Graeme Stevenson 1
Jun Rekimoto 1
Jonas Etzold 1
Philipp Wetzler 1
Li Chen 1
Ngoanh Vien 1
Ivana Kruijff-Korbayová 1
Sinziana Mazilu 1
Gerhard Tröster, 1
Timothy Chklovski 1
Oliviero Stock 1
Victoria Yanulevskaya 1
Daniel Gatica-Perez 1
Fang Chen 1
Sandra Carberry 1
David Oliver 1

Affiliation Paper Counts
Stevens Institute of Technology 1
Kansai University 1
Tokyo University of Technology 1
University of New South Wales 1
Palo Alto Research Center Incorporated 1
University of Memphis 1
Northeastern University 1
Macalester College 1
TELECOM ParisTech 1
Lund University 1
Bournemouth University 1
Pontificia Universidad Catolica de Chile 1
Queen's University Belfast 1
Rutgers, The State University of New Jersey 1
National Institute of Infectious Diseases 1
Swedish Institute of Computer Science 1
Ritsumeikan University 1
Laobratoire d'Informatique pour la Mecanique et les Sciences de l'Ingenieur 1
Fulda University of Applied Sciences 1
Reykjavik University 1
Laboratoire Traitement et Communication de l'Information 1
Harvard School of Engineering and Applied Sciences 1
Fondazione Bruno Kessler 1
Istituto di Scienza e Tecnologie dell'Informazione A. Faedo 1
British Broadcasting Corporation 1
Institutions Markets Technologies, Lucca 1
Karlsruhe Institute of Technology 1
University of Eastern Finland 1
IBM, Argentina 1
Max Planck Institute for Informatics 1
University of Padua 1
University of Illinois at Urbana-Champaign 1
University of Michigan 1
University of Miyazaki 1
Florida State University 1
University of Calgary 1
University of Amsterdam 1
Harvard Medical School 1
National Technical University of Athens 1
University of Perugia 1
Western Washington University 1
University of Eastern Piedmont Amedeo Avogadro 1
University of Geneva 1
Yale University 1
Nanyang Technological University 1
Newcastle University, United Kingdom 1
University of Pennsylvania 1
University of Koblenz-Landau 1
Hong Kong University of Science and Technology 1
Vrije Universiteit Amsterdam 1
University of Manitoba 1
University of Skovde 1
Hasselt University 1
Santa Clara University 1
University of Kent 1
University of Dublin, Trinity College 1
BBN Technologies 1
Norwegian University of Science and Technology 1
Institut de Recherche et Coordination Acoustique Musique 1
Beihang University 1
Federal University of Sao Carlos 1
University of Southern California, Information Sciences Institute 1
Japan Science and Technology Agency 1
University of Stuttgart 1
University of Kentucky 1
University of Tennessee at Martin 1
Yonsei University 1
National University of Singapore 1
Coventry University 1
IBM Thomas J. Watson Research Center 1
Middle Tennessee State University 1
IT University of Copenhagen 1
Monash University 1
University of Louisville 1
West Virginia University 1
Microsoft Research 1
Canon Inc. 1
University College London 1
Universite de Technologie Belfort-Montbeliard 1
Osaka University 1
National Institute of Advanced Industrial Science and Technology 1
Universite Paris-Sud XI 1
University of California, Santa Cruz 1
University of Maryland, Baltimore County 1
Catholic University of Leuven, Leuven 1
University of Utah 1
University of Konstanz 1
Lawrence Livermore National Laboratory 1
Microsoft Corporation 1
Clemson University 1
Nokia Corporation 1
Ben-Gurion University of the Negev 1
Complutense University of Madrid 1
National University of Cordoba 2
Millersville University 2
Bar-Ilan University 2
University of Helsinki 2
University of Haifa 2
University of Rostock 2
National Taiwan University 2
Bogazici University 2
Google Inc. 2
University of California, Irvine 2
Osnabruck University 2
Free University of Bozen-Bolzano 2
Kyoto University 2
Kyushu University 2
IBM Research 2
Technical University of Darmstadt 2
Lubeck University 2
Nara University of Education 2
University of Waterloo 2
University of California, Davis 2
University of Washington, Seattle 2
University of Pittsburgh 2
University of Bielefeld 2
University of York 2
Philips Research 2
Academia Sinica Taiwan 2
Polytechnic School of Montreal 2
Graz University of Technology 2
University of Birmingham 2
Texas A and M University 2
National University of Cuyo 2
Tufts University 2
Politecnico di Milano 2
University College Cork 2
University of Edinburgh 2
University of Roma Tor Vergata 2
Ludwig Maximilian University of Munich 2
Research Organization of Information and Systems National Institute of Informatics 2
University of Minnesota Twin Cities 2
Southern Illinois University at Carbondale 2
University of California, San Diego 2
Michigan State University 2
University of Roma La Sapienza 2
Institute of Computer Science Crete 2
Know-Center, Graz 2
Tongji University 2
University of Gastronomic Sciences 2
Laboratoire des sciences de l'information et des sytemes, Marseille 2
Linnaeus University, Vaxjo 2
Shenzhen University 3
University of Essex 3
University of Nevada, Reno 3
Delft University of Technology 3
CNRS Centre National de la Recherche Scientifique 3
Bremen University 3
Doshisha University 3
University of Zurich 3
Dalhousie University 3
Radboud University Nijmegen 3
University of Toronto 3
Texas A and M University System 3
Nokia Bell Labs 3
Queen Mary, University of London 3
University of St Andrews 3
Massachusetts Institute of Technology 3
Lancaster University 3
City University London 3
Universidad Autonoma de Madrid 3
Vrije Universiteit Brussel 3
University of California, Santa Barbara 3
Commonwealth Scientific and Industrial Research Organization 3
Institut Dalle Molle D'intelligence Artificielle Perceptive 3
Columbia University 3
University of Cambridge 3
Japan National Institute of Information and Communications Technology 3
Universite Paris Saclay 3
Helsinki Institute for Information Technology 4
Advanced Telecommunications Research Institute International (ATR) 4
Royal Institute of Technology 4
University of Salzburg 4
Universite de Bretagne-Sud 4
University of Minnesota System 4
Tel Aviv Sourasky Medical Center 4
University of Ulm 4
Goldsmiths, University of London 4
Hong Kong Baptist University 4
University of Nottingham 4
University of Trento 4
Simon Fraser University 4
Technical University of Munich 4
Indiana University 4
University of Notre Dame 4
The University of North Carolina at Chapel Hill 4
University of Genoa 4
University of Tokyo 4
University of Portsmouth 4
University of Sydney 4
Swiss Federal Institute of Technology, Zurich 4
Keio University 4
Swiss Federal Institute of Technology, Lausanne 4
Saitama University 4
Korea Advanced Institute of Science & Technology 4
University of Hertfordshire 4
University of Modena and Reggio Emilia 5
The University of British Columbia 5
Oregon State University 5
Dalle Molle Institute for Artificial Intelligence 5
University of Maryland 5
Nara Institute of Science and Technology 5
University of Wisconsin Madison 5
TU Dortmund University 5
University of Colorado at Boulder 6
Purdue University 6
Seikei University 6
University of Bari 6
University of Delaware 6
MIT Media Laboratory 7
Beijing Institute of Technology 7
Instituto Superior Tecnico 7
Vanderbilt University 8
University of Southern California 8
University of Central Florida 8
Heriot-Watt University, Edinburgh 8
German Research Center for Artificial Intelligence (DFKI) 9
Eindhoven University of Technology 9
University of Augsburg 9
Telecom Italia 10
Nippon Telegraph and Telephone Corporation 10
Carnegie Mellon University 11
CSIRO Data61 11
Georgia Institute of Technology 17
University of Turin 20

ACM Transactions on Interactive Intelligent Systems (TiiS)

Volume 8 Issue 1, February 2018  Issue-in-Progress

Volume 7 Issue 4, December 2017 Special Issue on IUI 2016 Highlights
Volume 7 Issue 3, October 2017
Volume 7 Issue 2, July 2017
Volume 7 Issue 1, March 2017

Volume 6 Issue 4, December 2016 Special Issue on Human Interaction with Artificial Advice Givers
Volume 6 Issue 3, October 2016 Regular Articles and Special Issue on Highlights of ICMI 2014 (Part 2 of 2)
Volume 6 Issue 2, August 2016 Regular Articles, Special Issue on Highlights of IUI 2015 (Part 2 of 2) and Special Issue on Highlights of ICMI 2014 (Part 1 of 2)
Volume 6 Issue 1, May 2016 Special Issue on New Directions in Eye Gaze for Interactive Intelligent Systems (Part 2 of 2), Regular Articles and Special Issue on Highlights of IUI 2015 (Part 1 of 2)
Volume 5 Issue 4, January 2016 Regular Articles and Special issue on New Directions in Eye Gaze for Interactive Intelligent Systems (Part 1 of 2)

Volume 5 Issue 3, October 2015 Special Issue on Behavior Understanding for Arts and Entertainment (Part 2 of 2) and Regular Articles
Volume 5 Issue 2, July 2015 Special Issue on Behavior Understanding for Arts and Entertainment (Part 1 of 2)
Volume 5 Issue 1, March 2015
Volume 4 Issue 4, January 2015 Special Issue on Activity Recognition for Interaction and Regular Article

Volume 4 Issue 3, October 2014 Special Issue on Multiple Modalities in Interactive Systems and Robots
Volume 4 Issue 2, July 2014
Volume 4 Issue 1, April 2014 Special Issue on Interactive Computational Visual Analytics
Volume 3 Issue 4, January 2014

Volume 3 Issue 3, October 2013
Volume 3 Issue 2, July 2013 Special issue on interaction with smart objects, Special section on eye gaze and conversation
Volume 3 Issue 1, April 2013 Special section on internet-scale human problem solving and regular papers

Volume 2 Issue 4, December 2012 Special issue on highlights of the decade in interactive intelligent systems
Volume 2 Issue 3, September 2012 Special Issue on Common Sense for Interactive Systems
Volume 2 Issue 2, June 2012
Volume 2 Issue 1, March 2012 Special Issue on Affective Interaction in Natural Environments
Volume 1 Issue 2, January 2012

Volume 1 Issue 1, October 2011
All ACM Journals | See Full Journal Index

Search TIIS
enter search term and/or author name