Enabling rich human-agent interaction for a calendar scheduling agent
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CHI '05 Extended Abstracts on Human Factors in Computing Systems
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Interactive Intelligent Systems (TIIS)
publishes papers on research concerning the design, realization, or evaluation of interactive systems that incorporate some form of machine intelligence.
Latest Articles
Katrien Verbert, Denis Parra, Peter Brusilovsky
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Forthcoming Articles
In addition to social and behavioral deficits, individuals with ASD often struggle to develop the adaptive skills necessary to achieve independence. Essential daily living tasks such as driving a vehicle or preparing a meal which are extremely important, can prove very challenging to these individuals. Driving in particular has become the focus of a growing body of research aimed at developing intervention programs to teach proper driving skills in individuals with ASD. We present the development and preliminary assessment of a gaze-sensitive adaptive VR 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 study of 12 adolescents with ASD using our system: 6 with the adaptive gaze-sensitive version of the system and 6 in a purely performance-based version. Preliminary results suggest that a gaze-contingent method of intervention may be more effective in teaching driving skills to individuals with ASD than one based solely on performance.
This paper 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 operators 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. Physical expressions have an effect in the quality of the solutions to spatial navigation problems. The combination of feet gestures with visual feedback provides the best task performance.
Human visual attention is a key factor for context-aware computing. We can already see a number of applications that take human visual attention into account in order to provide the user with adequate services or information in a particular scenario such as office work, museum guidance and others. However, it is still a challenging task for computers to identify content that the user is attending to in a complex daily environment because various types of visual content such as text, objects, faces, and others are present in many scenes. In this paper, we present a framework for recognition of user-attended content. In particular, eye gaze and image analysis are combined to measure user attention in complex environments. Eye gaze is detected by a wearable eye tracker. The data is used to recognize the users cognitive state, i.e. a certain type of attention, and to identify the focus of attention in the scene. This information is used to run respective image analysis modules specialized on different types of relevant resources, such as a controlled set of objects, or a relevant set of text labels, warnings, or written instructions, or even people whose faces are known to the system. For our experiments we considered three different daily scenarios capturing different types of visual content. Our findings underline the feasibility of the proposed framework for user-attended content recognition. Furthermore, combination with augmented reality glasses facilitates information presentation of the user-attended content.
Given the rapid technological advancements 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 import extreme values and the stimulus space between those extremes. We use this proposed framework to identify the impact of a number of 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 in order 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, while more predictable environments and less reliable advisors led to underutilization. Additionally, 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 when the advice was not consistent with the environmental cues. These results contribute to a more complete understanding of advice utilization and trust in advisors.
Exploiting Social Tags for Cross-Domain Collaborative Filtering
Shi, Y Larson, M Hanjalic, Alan
In this paper, 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, e.g. by suggesting relevant information sources, or give an overview of how he spent his time during the day.
We present a conceptual 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.
The increasing complexity and "intelligence" of technical systems we use in our everyday lives are quite often diametrically opposed to their lack of user-friendliness and ease of operation. Companion-Technology aims to bridge this gap. It complements the functional intelligence of technical systems with equivalent intelligence in interacting with the user and integrates the two. This way, so-called Companion-Systems emerge. They assist users by providing to-the-point instructions and explanations in a completely individualized way: they adapt to the users knowledge of the application at hand, to his or her capabilities, and to the current situation. In this article, we show how techniques from various research areas are integrated to synergetically implement Companion-Systems. As an example, we present a prototype that assists users in the task of setting up a home entertainment system.
Human Decision Making and Recommender Systems
Chen, L de Gemmis, M Felfernig, A Lops, P Ricci, F Semeraro, Giovanni
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 framework 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 on-line segment and recognize the observed human activities with better accuracy when using both input sources, for example, the activity recognition of reaching increases from 23% (only using external cameras) to 84% by including the gaze information.
Virtual environments offer an ideal setting to develop intelligent t yet, their ability to support complex procedures depends on the appropriate integration of knowledge-based techniques and natural interaction.
In this paper, 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 both 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.
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 users 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 RP3? that re-ranks items based on 3-hop random walk transition probabilities. We show empirically, that RP3? provides accurate recommendations with high long-tail item frequency at the top of the recommendation list. We also present approximate versions of RP3? and the two most accurate previously published vertex ranking algorithms based on random walk transition probabilities and show that these approximations converge with increasing number of samples.
To obtain interactively updatable recommendations, we additionally show how our algorithm can be extended for on-line updates at interactive speeds. The underlying random walk sampling technique makes it possible to perform the updates without having to re-compute the values for the entire dataset.
In an empirical evaluation with three real-world datasets we show that RP3? provides highly accurate and diverse recommendations that can easily be updated with newly gathered information at interactive speeds (j 100ms).
Our lives are heavily influenced by persuasive communication, and it is essential in almost any types of social interactions from business negotiation to conversation with our friends and family. With a 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 paper, 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. Firstly, 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. Secondly, 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. Thirdly, we investigate differences when speakers are expressing a positive or negative opinion and if the opinion polarity has any influence in the persuasiveness prediction. Fourthly, we further study if gender has any influence in the prediction performance. Lastly, 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 speakers behavior.
This paper surveys the area of Computational empathy,
analysing different ways by which agents can simulate and trigger
empathy in their interactions with humans.
In the course of this survey, we analyse the research
conducted to date on computational empathy in virtual agents
and social robots in light of the principles and mechanisms of empathy found in humans.
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. So, to construct such a model, as a first step, we explore the morphological and dynamic characteristics of different types of smile (polite, amused and embarrassed smiles) that an embodied conversational agent may display. The resulting lexicon of smiles is based on a corpus of virtual agent's smiles directly created by users and analyzed through a machine learning technique. Moreover, during an interaction, the expression of smile 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' perception of smiling and non-smiling 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.
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 behaviours 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 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.
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 agents voice and its lack of appropriate fit for using these strategies. Part of this paper compares human vs. synthesized voices in agents using vague language. Another main focus is the use of qualitative measures in analyzing human-agent interaction. More specifically this paper 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. Two approaches were used in assessing this corpus. The first analysed the 60,000-word text corpus of participant interviews to investigate the differences 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 synthesized alternatives. 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, it is suggest that a qualitative and corpus approach to analyzing human-agent interaction may be one step forward in create richer datasets that allow for multiple analyses, and to complement the quantitative methods.
Today's Internet users are increasingly relying on user provided content from social media streams for news updates. In many cases, there is a limited window of information upon which an information consumer can make a credibility assessment about piece of information or its source. To better understand the dynamics of credibility assessment in social media streams, we describe series of experiments to identify and evaluate key factors that influence credibility perception in microblogs. Specifically, we describe a user experiment (N=646) that builds on a previous study of credibility perception in order to answer the following research questions: (1) What are the important cues that contribute to information being perceived as credible. (2) Can we separate these cues from the content and quantify their influence on credibility perception?, and (3) To what extent is such a quantification portable across different microblogging platforms? To answer the third question, we study two popular microblogs, Reddit and Twitter. Key results include that significant effects of individual factors can be isolated, are portable, and that links, profile pictures and image content are the strongest influencing factors in credibility assessments.
Submission for consideration for the special issue on Human Interaction With Artificial Advice Givers.
Argumentative discussion is a highly demanding task. In order to help people in such discussions, this paper 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 a 1000 human subjects, 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 peoples 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 subjects. 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 subjects compared to the other evaluated agents. The presented agents propose arguments based on the Argumentation Theory, predicted arguments without the heuristics or only the heuristics, or use Transfer Learning methods. Our findings also show that people use PRH agent's proposed arguments significantly more often than those proposed by the other agents.
A Review and Taxonomy of Interactive Optimization Methods in Operations Research
Meignan, D Knust, S Frayret, Jean-M Pesant, G Gaud, Nicolas
Due to the emerging Big Data paradigm, traditional data management techniques result inadequate in many real life scenarios. In particular, the availability of huge amounts of data pertaining to social interactions among users calls for advanced analysis strategies. Furthermore, heterogeneity and high speed of this data require suitable data storage and management tools to be designed from scratch. In this paper, we describe a framework tailored for analysing user searches when they are connected to a social network in order to quickly identify users able to spread their influence across the network. It is worth noting that, gathering information about user preferences is crucial in several scenarios like viral marketing, tourism promotion and food education.
Introduction to the Special Issue on Interaction with Smart Objects
Schreiber, D Luyten, K Mühlh?user, M Brdiczka, O Hartmann, Melanie
Topic modeling is a common tool for understanding large bodies of text, but is typically provided as a take it or leave it proposition. Incorporating human knowledge in unsupervised learning is a promising approach to create high-quality topic models. Existing interactive systems and modeling algorithms support a wide range of refinement operations to express feedback. However, these systems interactions are primarily driven by algorithmic convenience, ignoring users who may not have expertise in topic modeling. To better understand how non-expert users understand, assess, and refine topics, we conduct two user studiesan in-person interview study and an online crowdsourced study. These studies demonstrate a disconnect between what non-expert users want and what current interactive systems support. In particular, our findings include: (1) analysis of how non-expert users p (2) six primary refinement operations ordered by (3) further evidence of the benefits of supporting users in directly re (4) design implications for future human-in-the-loop topic modeling interfaces.
We investigate the usability of human-like agent-based interfaces for interactive advice-giving systems. In an experiment with a travel advisory system, we manipulate the human-likeness of the agent interface. We demonstrate that users of the more human-like agents form an anthropomorphic use image of the system: they act human-like towards the system and try to exploit typical human-like capabilities they believe the system possesses. Unfortunately, this severely reduces the usability of systems that look human but lack human-like capabilities (overestimation effect). Furthermore, we demonstrate that the use image users form of an agent-based system is inherently integrated (as opposed to the compositional use image they form of conventional interfaces): feedforward cues provided by the system do not instill user responses in a one-to-one matter, but are instead integrated into a single use image. Consequently, users try to exploit capabilities that were not signaled by the system to begin with, thereby further exacerbating the overestimation effect.
What makes a good recommendation or good list of recommendations?
Early research into recommender systems focused on accuracy, in particular how closely the recommenders predicted ratings were to the users true ratings. More recently, the focus has shifted to a wider range of objectives  so-called beyond-accuracy objectives  such as whether the list of recommendations is diverse and whether it contains novel items.
In this paper, we analyze the most discussed beyond-accuracy objectives in recommender systems research: diversity, serendipity, novelty, and coverage. We review the definitions of these objectives found in the literature and a number of strategies for optimizing the objectives. We find that it is not clear how these objectives relate to each other.
Hence, we conduct a set of offline experiments aimed at comparing the performance of different optimization approaches with a particular view to seeing how they affect objectives other than the ones they are optimizing. We demonstrate important insights into the correlations between the discussed objectives, for instance, the positive correlation between rating-based diversity and novelty, and the influence of novelty on recommendation coverage.
Nowadays, social competition is gradually increasing and enterprise employees perceive stress of different degrees, affecting their health, decreasing job-control in organizational performance and reducing their quality of life in general.
Stress assessment is a complex issue and numerous studies have examined factors that influence stress in working environments.
Most of current approaches for detecting stress use facial and voice recognition algorithms, others have used physiological sensors.
However, research studies have shown that monitoring individuals' behaviour parameters during daily life can be a source in stress assessment. Moreover few of them have analyzed the benefits from using unobtrusive technology, for example by means of mobile devices.
In this study, we aimed to examine the effects of work-related stress events and other personality traits (e.g. behaviour and routine changes) on working environments, using features derived from smartphones. In particular, we use information from physical activity levels, location, social-interactions, social-activity (e.g., phone calls and SMS's) and application usage during the working days.
Participants in this study were 30 employees chosen from two different private companies, monitored over a period of 8 weeks in a real-work environments.
Our first contribution is to apply correlation analysis, hierarchical clustering and multi-linear regression analysis to find patterns, behaviors and features associated with stress. The findings suggest that information from phone usage shows important correlation with employees perceived stress level. Secondly, we used machine learning methods to classify perceived stress levels based on the analysis of the information provided by the smart phones, as indicated above. We used decision trees obtaining 67.57% accuracy and 71.73% after applying a semi-supervised method. Our results have shown that stress levels can be monitored in unobtrusive manner, through analysis of smartphone data.
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 on-going conversations in scattered dialog blocks. Our aim in this paper is twofold. First, we propose a publicly available testbed for the task by solving the insurmountable problem of privacy of personal dialogs. In fact, we showed that these 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.
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 place on screen. Also, IBSVI may wander between lines without realizing they did. In this paper, we address these two interaction problems by introducing dynamic speech-touch interaction model, and intelligent reading support system. With this model, the speed of the speech will dynamically change coping up with the users finger speed. The proposed model is composed of: 1-Audio Dynamics Model, and 2- 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 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. 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.
Introduction to the Special Issue on Interactive Computational Visual Analytics
Editors, ICVA
This paper will present a unique approach to testing variables in the behavior of a small Unmanned Aerial Vehicle (sUAV) to understand how these variables impact time of interaction, preference for interaction, and distancing in human-robot interaction. 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 human-robot interaction (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 paper 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 predictability (by 0.28 m, p <0.01) expressions can be used to that low speed (by 4.4 seconds, p <0.01) and three-dimensional (by 2.6 seconds, p <0.01) expressions can be used to decrease and low speed (by 10.4%, p <0.01) expressions are less preferred for passability in human-aerial vehicle interactions.
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 produc machine learning applications enable this type of analysis. However, the number of machine learning 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 machine learning, and conducted two probe-based studies with a prototype machine learning and visual analytics system, Gimlets. Our system implements machine learning applications for sensor-based time series data as a novel domain-specific prototype that integrates interactive visual analytic features into the machine learning pipeline. We identify future directions for usable machine learning systems based on sensor data that will enable intermediate users to build systems that have been prohibitively difficult.
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 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 all decision-making to unseen situations during training. Our proposed approach is evaluated in an interactive conversational robot that learns to play Quiz games. Experimental results, using simulation and real users, show evidence that our proposed approach can lead to more flexible (natural) interactions than strict hierarchical control and is preferred by human users.
Since childhood we learn to graphically represent facts. Cognitive processes of a sophisticated human visual system that help us discriminate colors and shapes give way to artistic expresion and also to the visual encoding of information. Visualizations have a distinctive advantage when dealing with the information overload problem: since they are grounded in basic visual cognition, many people understand them. However, creating the appropriate representation requires specific expertise of the domain and underlying data. Our quest in this paper is to study methods to suggest appropriate visualizations autonomously. To be appropriate, a visualization has to follow studied guidelines to find and distinguish patterns visually, and encode data therein. But a visualization also tells a story of the underlying data, and, to be appropriate, it has to tell the story the viewer wants to hear. It has to clearly represent those aspects of the data the viewer is interested in. Our approach builds a multistage recommender system with two kinds of personalization empirically grounded on preference elicitation to identify which visualizations a user likes best: (i) based on user ratings, and (ii) based on user tags. We present the methodology to build a visualization recommender system and studies validating the approach.
Emotional States Associated With Music: Classification, Prediction of Changes, and Consideration in Recommendation
Deng, J Leung, Clement H. C.; Milani, A Chen, Li
In Memoriam: John Riedl
Clarification, Author
Editorial on Special Issue on Interactive Computational Visual Analytics
Keim, Daniel
Introduction to the Special Section on Eye Gaze and Conversation
André, E Chai, J Author, Clarification
Authentication based on touch-less mid-air gestures would benefit a multitude of ubicomp applications, especially those which 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 three-day science event organized within a shopping mall. This data was used to investigate capabilities of the Leap Motion sensor 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 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.
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ACM Senior Member (2015)
ACM Software System Award (2010)ACM Distinguished Member (2006)
ACM Distinguished Member (2013)ACM Senior Member (2012)
ACM Senior Member (2013)
ACM Distinguished Member (2009)
ACM Gordon Bell Prize Special Category (2009)
ACM Gordon Bell Prize Special Category (2009)
ACM Software System Award (2010)ACM Distinguished Member (2007)
ACM Senior Member (2007)
ACM Distinguished Member (2011)
First Name
Paper Counts
Fr&#x00e9;d&#x00e9;ric
Dairazalia
Alessandra
Christopher
Yingjievictor
Alessandro
G&#x00Nther
Ya&akov(Kobi)
Andr&#x00e9;s
Andr&#x00e9;
Zhenyucheryl
J&#x00rgen
Margr&#x00e9;t
Tian(Linger)
Bj&#x00f6;rn
Affiliation
Paper Counts
Kansai University
Tokyo University of Technology
University of New South Wales
Palo Alto Research Center
University of Memphis
Macalester College
TELECOM ParisTech
Pontificia Universidad Catolica de Chile
Rutgers, The State University of New Jersey
Ritsumeikan University
Laobratoire d'Informatique pour la Mecanique et les Sciences de l'Ingenieur
Fulda University of Applied Sciences
Reykjavik University
Harvard School of Engineering and Applied Sciences
Fondazione Bruno Kessler
Istituto di Scienza e Tecnologie dell'Informazione A. Faedo
Institutions Markets Technologies, Lucca
Aix Marseille Universite
Karlsruhe Institute of Technology
University of Eastern Finland
Max Planck Institute for Informatics
University of Padua
University of Illinois at Urbana-Champaign
University of Michigan
University of Miyazaki
Florida State University
University of Calgary
University of Amsterdam
Harvard Medical School
National Technical University of Athens
University of Perugia
University of Eastern Piedmont Amedeo Avogadro
University of Geneva
Yale University
Nanyang Technological University
Newcastle University, United Kingdom
University of Pennsylvania
Bremen University
Hong Kong University of Science and Technology
Vrije Universiteit Amsterdam
Alcatel-Lucent Bell Labs
University of Manitoba
University of Skovde
Hasselt University
Santa Clara University
Kyushu University
University of Dublin, Trinity College
BBN Technologies
Institut de Recherche et Coordination Acoustique Musique
Federal University of Sao Carlos
University of Southern California, Information Sciences Institute
University of Pittsburgh
Japan Science and Technology Agency
University of Stuttgart
University of Kentucky
University of Tennessee at Martin
Yonsei University
National University of Singapore
Coventry University
IBM Thomas J. Watson Research Center
Middle Tennessee State University
IT University of Copenhagen
Monash University
Georgia Institute of Technology
Graz University of Technology
University of Louisville
West Virginia University
Microsoft Research
University College London
UTBM Universite de Technologie Belfort-Montbeliard
University of Technology Sydney
Osaka University
Universite Paris-Sud XI
University of California, Santa Cruz
University of Maryland, Baltimore County
Catholic University of Leuven
University of Utah
University of Konstanz
Lawrence Livermore National Laboratory
Ben-Gurion University of the Negev
Stevens Institute of Technology
National University of Cordoba
Millersville University
University of Helsinki
University of Haifa
University of Rostock
National Taiwan University
Bogazici University
Google Inc.
Delft University of Technology
University of California, Irvine
University of Osnabruck
Free University of Bozen-Bolzano
Kyoto University
IBM Research
Technical University of Darmstadt
Lubeck University
University of Waterloo
University of California, Davis
University of Washington
University of York
Academia Sinica Taiwan
Polytechnic School of Montreal
University of Birmingham
Texas A and M University
Tufts University
Politecnico di Milano
University of Sydney
University of Edinburgh
University of California, Santa Barbara
University of Minnesota Twin Cities
University of California, San Diego
Michigan State University
University of Roma La Sapienza
Institute of Computer Science Crete
University of Gastronomic Sciences
Shenzhen University
University of Essex
University of Nevada, Reno
Royal Institute of Technology
Instituto Superior Tecnico
CNRS Centre National de la Recherche Scientifique
Purdue University
Doshisha University
Dalhousie University
University of Toronto
Queen Mary, University of London
University of St Andrews
Massachusetts Institute of Technology
Lancaster University
University of Tokyo
City University London
Alcatel-Lucent
Universidad Autonoma de Madrid
Vrije Universiteit Brussel
Commonwealth Scientific and Industrial Research Organization
Institut Dalle Molle D'intelligence Artificielle Perceptive
Columbia University
University of Cambridge
Japan National Institute of Information and Communications Technology
University of Dortmund
Universite Paris Saclay
University of Genoa
Helsinki Institute for Information Technology
Advanced Telecommunications Research Institute International (ATR)
University of Hertfordshire
University of Portsmouth
Swiss Federal Institute of Technology, Zurich
University of Notre Dame
Indiana University
Swiss Federal Institute of Technology, Lausanne
Saitama University
Universite de Bretagne-Sud
University of Minnesota System
Tel Aviv Sourasky Medical Center
University of Ulm
Goldsmiths, University of London
Korea Advanced Institute of Science & Technology
Hong Kong Baptist University
University of Trento
University of Southern California
Simon Fraser University
The University of British Columbia
Dalle Molle Institute for Artificial Intelligence
University of Maryland
University of Modena and Reggio Emilia
Oregon State University
University of Wisconsin Madison
University of Delaware
University of Bari
University of Colorado at Boulder
Seikei University
Carnegie Mellon University
Nippon Telegraph & Telephone
Eindhoven University of Technology
MIT Media Laboratory
Vanderbilt University
University of Central Florida
Heriot-Watt University
University of Augsburg
German Research Center for Artificial Intelligence (DFKI)
Telecom Italia
University of Turin
ACM Transactions on Interactive Intelligent Systems (TiiS) - 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) Archive
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)
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)
Regular Articles and Special issue on New Directions in Eye Gaze for Interactive Intelligent Systems (Part 1 of 2)
Special Issue on Behavior Understanding for Arts and Entertainment (Part 2 of 2) and Regular Articles
Special Issue on Behavior Understanding for Arts and Entertainment (Part 1 of 2)
Special Issue on Activity Recognition for Interaction and Regular Article
Special Issue on Multiple Modalities in Interactive Systems and Robots
Special Issue on Interactive Computational Visual Analytics
Special issue on interaction with smart objects, Special section on eye gaze and conversation
Special section on internet-scale human problem solving and regular papers
Special issue on highlights of the decade in interactive intelligent systems
Special Issue on Common Sense for Interactive Systems
Special Issue on Affective Interaction in Natural Environments
All ACM Journals
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Eugene M. Taranta, II, Andr&#233;s N. Vargas, Spencer P. Compton, Joseph J. Laviola, Jr.
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Nigel Bosch, Sidney K. D'mello, Jaclyn Ocumpaugh, Ryan S. Baker, Valerie Shute
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