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IUI Tables of Contents: 9798990001020304050607080910111213-113-214-114-215-1

Proceedings of the 2007 International Conference on Intelligent User Interfaces

Fullname:International Conference on Intelligent User Interfaces
Editors:Tessa Lau; Angel Puerta
Location:Honolulu, Hawaii
Dates:2007-Jan-28 to 2007-Jan-31
Publisher:ACM
Standard No:ISBN 1-59593-481-2; ACM Order Number: 608070; ACM DL: Table of Contents hcibib: IUI07
Papers:61
Pages:374
Links:Conference Home Page
  1. Keynotes
  2. Workshops
  3. Tutorials
  4. Recommender systems
  5. Social software
  6. User modeling
  7. Information retrieval
  8. Personal assistants
  9. Demonstration based interfaces
  10. Natural language interfaces
  11. Multi-modal interfaces
  12. Gesture- and sketch-based interfaces
  13. Short papers

Keynotes

Information retrieval in context BIBAFull-Text 2
  Susan Dumais
Today's search engines return a wide range of information from diverse sources with lightening speed. The information that is returned, however, is independent of who asked the question or the context in which the information need arose. Next generation search engines will make increasing use of context about the searcher, domain and tasks to dramatically change the search landscape.
Image-text interaction BIBAFull-Text 3
  Thomas Strothotte
In most interactive systems, the handling of text and images are still quite separate matters. Rendering pipelines handle 2D and 3D graphics, while text tends to be an after-thought which is hardly integrated at the system level, let alone at the level of user interaction.
   This talk will survey recent developments in the area of image-text interaction from both the system architecture point of view and with respect to user interfaces. The talk will emphasize interactive labelling of 2D and 3D graphics and outline new real-time algorithms for label placement. Labelled images will be discussed as an interface to methods and tools for data mining. Finally, challenges for future developments in the area of image-text interaction will be outlined.

Workshops

Tangible play: research and design for tangible and tabletop games BIBAFull-Text 6
  Elise van den Hoven; Ali Mazalek
This workshop addresses questions related to the areas of tangible interaction, game design and emerging technologies for tangible and tabletop games. We bring together researchers and practitioners from diverse fields related to these topics, such as HCI, computer science, interaction design and game design. We seek collaborative ways to move forward the field of tangible and tabletop games.
Common sense and intelligent user interfaces BIBAFull-Text 7
  Catherine Havasi; Henry Lieberman
There is a mutually beneficial relationship between user interfaces and common sense reasoning and acquisition. Common sense knowledge enables interfaces to better understand and to be more grounded in the world of the user, thus improving the user's overall experience with the interface. This would not be possible without large sources of common sense knowledge, which likewise benefit from intelligent interfaces designed to make the knowledge acquisition processes more productive and enjoyable for the contributor. These two complementary interface types and their interaction are explored in this workshop.

Tutorials

Building ubiquitous and robust speech and natural language interfaces BIBKFull-Text 10
  Gary Geunbae Lee; Shimei Pan
Keywords: adaptive user interfaces, dialogue system, error detection and correction, multimodal, natural language, speech, tutorial
Advanced topics in recommendation BIBAFull-Text 11
  John Riedl; Anthony Jameson
This full-day tutorial is designed to convey an up-to-date, active understanding of a representative set of current developments in recommender systems that will help the participants to conduct cutting-edge research and/or to work more effectively with the currently widespread recommendation technology.
Designing intelligent user interface for ubiquitous computing environments BIBAFull-Text 12
  Anxo Cereijo Roibas; Antonio Krüger
This full-day tutorial will provide an overview on Intelligent User Interfaces in the context of the technological development associated with the Ubiquitous Computing paradigm. Based on a technological assessment of the main technological areas involved (miniaturization, communication and network structures, advanced display and speech technologies, as well as sensor integration and deployment) this tutorial will discuss the main approaches in context and user modeling, as well as present new interaction paradigms for ubiquitous computing environments. Examples of past and current research projects of the field will be used to explain and discuss the intersection of UbiComp and IUIs. Having discussed the technical foundations, the tutorial aims to explore the design and development of new pervasive applications for mobile devices and other distributed interfaces. It will explore how handhelds can interact with other surrounding devices, intelligent environments and with the social context. The tutorial will also look at how Participatory Design approaches and advanced in-situ evaluation techniques and ethno-methodologies such as living labs, on-the-field data collection, enactments, and 'Cultural Probes', can lead to the representation of significant communication systems.

Recommender systems

Lies and propaganda: detecting spam users in collaborative filtering BIBAFull-Text 14-21
  Bhaskar Mehta; Thomas Hofmann; Peter Fankhauser
Collaborative Filtering systems are essentially social systems which base their recommendation on the judgment of a large number of people. However, like other social systems, they are also vulnerable to manipulation by malicious social elements. Lies and Propaganda may be spread by a malicious user who may have an interest in promoting an item, or downplaying the popularity of another one. By doing this systematically, with either multiple identities, or by involving more people, a few malicious user votes and profiles can be injected into a collaborative recommender system. This can significantly affect the robustness of a system or algorithm, as has been studied in recent work [5, 7]. While current detection algorithms are able to use certain characteristics of spam profiles to detect them, they suffer from low precision, and require a large amount of training data. In this work, we provide a simple unsupervised algorithm, which exploits statistical properties of effective spam profiles to provide a highly accurate and fast algorithm for detecting spam.
Hybrid critiquing-based recommender systems BIBAFull-Text 22-31
  Li Chen; Pearl Pu
We propose a novel critiquing-based recommender interface, the hybrid critiquing interface that integrates the user self-motivated critiquing facility to compensate for the limitations of system-proposed critiques. The results from our user study show that the integration of such self-motivated critiquing support enables users to achieve a higher level of decision accuracy while consuming less cognitive effort. In addition, users expressed higher subjective opinions of the hybrid critiquing interface than the interface simply providing system-proposed critiques, and they would more likely return to it for future use.
SuggestBot: using intelligent task routing to help people find work in wikipedia BIBAFull-Text 32-41
  Dan Cosley; Dan Frankowski; Loren Terveen; John Riedl
Member-maintained communities ask their users to perform tasks the community needs. From Slashdot, to IMDb, to Wikipedia, groups with diverse interests create community-maintained artifacts of lasting value (CALV) that support the group's main purpose and provide value to others. Said communities don't help members find work to do, or do so without regard to individual preferences, such as Slashdot assigning meta-moderation randomly. Yet social science theory suggests that reducing the cost and increasing the personal value of contribution would motivate members to participate more.
   We present SuggestBot, software that performs intelligent task routing (matching people with tasks) in Wikipedia. SuggestBot uses broadly applicable strategies of text analysis, collaborative filtering, and hyperlink following to recommend tasks. SuggestBot's intelligent task routing increases the number of edits by roughly four times compared to suggesting random articles. Our contributions are: 1) demonstrating the value of intelligent task routing in a real deployment; 2) showing how to do intelligent task routing; and 3) sharing our experience of deploying a tool in Wikipedia, which offered both challenges and opportunities for research.

Social software

From social bookmarking to social summarization: an experiment in community-based summary generation BIBAFull-Text 42-51
  Oisin Boydell; Barry Smyth
We describe a novel document summarization technique that uses informational cues, such as social bookmarks or search queries, as the basis for summary construction by leveraging the snippet-generation capabilities of standard search engines. A comprehensive evaluation demonstrates how the social summarization technique can generate summaries that are of significantly higher quality that those produced by a number of leading alternatives.
Collecting community wisdom: integrating social search & social navigation BIBAFull-Text 52-61
  Jill Freyne; Rosta Farzan; Peter Brusilovsky; Barry Smyth; Maurice Coyle
The goal of this paper is to detail the integration of two "social Web" technologies -- social search and social navigation -- and to highlight the benefits of such integration on two levels. Firstly, both technologies harvest and harness "community wisdom" and in an integrated system each of the search and navigation components can benefit from the additional community wisdom gathered by the other when assisting users to locate relevant information. Secondly, by integrating search and browsing we facilitate the development of a unique interface that effectively blends search and browsing functionality as part of a seamless social information access service. This service allows users to effectively combine their search and browsing behaviors. In this paper we will argue that this integration provides significantly more than the simple sum of the parts.
Talk amongst yourselves: inviting users to participate in online conversations BIBAFull-Text 62-71
  F. Maxwell Harper; Dan Frankowski; Sara Drenner; Yuqing Ren; Sara Kiesler; Loren Terveen; Robert Kraut; John Riedl
Many small online communities would benefit from increased diversity or activity in their membership. Some communities run the risk of dying out due to lack of participation. Others struggle to achieve the critical mass necessary for diverse and engaging conversation. But what tools are available to these communities to increase participation? Our goal in this research was to spark contributions to the movielens.org discussion forum, where only 2% of the members write posts. We developed personalized invitations, messages designed to entice users to visit or contribute to the forum. In two field experiments, we ask (1) if personalized invitations increase activity in a discussion forum, (2) how the choice of algorithm for intelligently choosing content to emphasize in the invitation affects participation, and (3) how the suggestion made to the user affects their willingness to act. We find that invitations lead to increased participation, as measured by levels of reading and posting. More surprisingly, we find that invitations emphasizing the social nature of the discussion forum increase user activity, while invitations emphasizing other details of the discussion are less successful.

User modeling

Unsupervised and supervised machine learning in user modeling for intelligent learning environments BIBAFull-Text 72-81
  Saleema Amershi; Cristina Conati
In this research, we outline a user modeling framework that uses both unsupervised and supervised machine learning in order to reduce development costs of building user models, and facilitate transferability. We apply the framework to model student learning during interaction with the Adaptive Coach for Exploration (ACE) learning environment (using both interface and eye-tracking data). In addition to demonstrating framework effectiveness, we also compare results from previous research on applying the framework to a different learning environment and data type. Our results also confirm previous research on the value of using eye-tracking data to assess student learning.
Toward harnessing user feedback for machine learning BIBAFull-Text 82-91
  Simone Stumpf; Vidya Rajaram; Lida Li; Margaret Burnett; Thomas Dietterich; Erin Sullivan; Russell Drummond; Jonathan Herlocker
There has been little research into how end users might be able to communicate advice to machine learning systems. If this resource -- the users themselves -- could somehow work hand-in-hand with machine learning systems, the accuracy of learning systems could be improved and the users' understanding and trust of the system could improve as well. We conducted a think-aloud study to see how willing users were to provide feedback and to understand what kinds of feedback users could give. Users were shown explanations of machine learning predictions and asked to provide feedback to improve the predictions. We found that users had no difficulty providing generous amounts of feedback. The kinds of feedback ranged from suggestions for reweighting of features to proposals for new features, feature combinations, relational features, and wholesale changes to the learning algorithm. The results show that user feedback has the potential to significantly improve machine learning systems, but that learning algorithms need to be extended in several ways to be able to assimilate this feedback.
Supporting interface customization using a mixed-initiative approach BIBAFull-Text 92-101
  Andrea Bunt; Cristina Conati; Joanna McGrenere
We describe a mixed-initiative framework designed to support the customization of complex graphical user interfaces. The framework uses an innovative form of online GOMS analysis to provide the user with tailored customization suggestions aimed at maximizing the user's performance with the interface. The suggestions are presented non-intrusively, minimizing disruption and allowing the user to maintain full control. The framework has been applied to a general user-productivity application. A formal user evaluation of the system provides encouraging evidence that this mixed-initiative approach is preferred to a purely adaptable alternative and that the system's suggestions help improve task performance.

Information retrieval

What do people recall about their documents?: implications for desktop search tools BIBAFull-Text 102-111
  Tristan Blanc-Brude; Dominique L. Scapin
This study aims at finding out which attributes people actually recall about their own documents (electronic and paper), and what are the characteristics of their recall, in order to provide recommendations on how to improve tools allowing users to retrieve their electronic files more effectively and more easily. An experiment was conducted with fourteen participants at their workplace. They were asked first to recall features about one (or several) of their own work documents, and secondly to retrieve these documents. The difficulties encountered by the participants in retrieving their electronic documents support the need for better retrieval tools. More specifically, results of the recall task indicate which attributes are candidates for facilitating file retrieval and how search tools should use these attributes.
Mobile content enrichment BIBAFull-Text 112-121
  Karen Church; Barry Smyth
Delivering an effective mobile search service is challenging for many reasons. Certainly small-screen mobile handsets with limited text input capabilities do not make ideal search devices. In addition, the brevity of Mobile Internet content hampers effective indexing and limits retrieval opportunities. In this paper we focus on this indexing issue and describe an approach that leverages Web search engines as a source of content enrichment. We present an evaluation using a mobile news service that demonstrated significant improvements in search performance compared to a standard benchmark system.
Context-Aware, adaptive information retrieval for investigative tasks BIBAFull-Text 122-131
  Zhen Wen; Michelle X. Zhou; Vikram Aggarwal
We are building an intelligent information system to aid users in their investigative tasks, such as detecting fraud. In such a task, users must progressively search and analyze relevant information before drawing a conclusion. In this paper, we address how to help users find relevant information during an investigation. Specifically, we present a novel approach that can improve information retrieval by exploiting a user's investigative context. Compared to existing retrieval systems, which are either context insensitive or leverage only limited user context, our work offers two unique contributions. First, our system works with users cooperatively to build an investigative context, which is otherwise very difficult to capture by machine or human alone. Second, we develop a context-aware method that can adaptively retrieve and evaluate information relevant to an ongoing investigation. Experiments show that our approach can improve the relevance of retrieved information significantly. As a result, users can fulfill their investigative tasks more efficiently and effectively.

Personal assistants

Active EM to reduce noise in activity recognition BIBAFull-Text 132-140
  Jianqiang Shen; Thomas G. Dietterich
Intelligent desktop environments allow the desktop user to define a set of projects or activities that characterize the user's desktop work. These environments then attempt to identify the current activity of the user in order to provide various kinds of assistance. These systems take a hybrid approach in which they allow the user to declare their current activity but they also employ learned classifiers to predict the current activity to cover those cases where the user forgets to declare the current activity. The classifiers must be trained on the very noisy data obtained from the user's activity declarations. Instead of asking the user to review and relabel the data manually, we employ an active EM algorithm that combines the EM algorithm and active learning. EM can be viewed as retraining on its own predictions. To make it more robust, we only retrain on those predictions that are made with high confidence. For active learning, we make a small number of queries to the user based on the most uncertain instances. Experimental results on real users show this active EM algorithm can significantly improve the prediction precision, and that it performs better than either EM or active learning alone.
Entropy-Driven online active learning for interactive calendar management BIBAFull-Text 141-150
  Julie S. Weber; Martha E. Pollack
We present a new algorithm for active learning embedded within an interactive calendar management system that learns its users' scheduling preferences. When the system receives a meeting request, the active learner selects a set of alternative solutions to present to the user; learning is then achieved by noting the user's preferences for the selected schedule over the others presented. To achieve the goals of presenting solutions that meet the user's needs while enhancing the preference-learning process, we introduce a new approach to active learning that makes online decisions about the technique to use in selecting the schedules to present in response to each meeting request. The decision is based on the entropy of the available options: a highly diverse set of possible solutions calls for a selection technique that chooses instances that are different from one another, maximizing coarse-grained learning, whereas a set of possible solutions containing little diversity is met with a selection strategy that promotes fine-grained learning. We present experimental results that indicate that our entropy-driven approach provides a better balance between learning efficiency and user satisfaction than static selection techniques.
Segmenting meetings into agenda items by extracting implicit supervision from human note-taking BIBAFull-Text 151-159
  Satanjeev Banerjee; Alexander I. Rudnicky
Splitting a meeting into segments such that each segment contains discussions on exactly one agenda item is useful for tasks such as retrieval and summarization of agenda item discussions. However, accurate topic segmentation of meetings is a difficult task. In this paper, we investigate the idea of acquiring implicit supervision from human meeting participants to solve the segmentation problem. Specifically we have implemented and tested a note taking interface that gives value to users by helping them organize and retrieve their notes easily, but that also extracts a segmentation of the meeting based on note taking behavior. We show that the segmentation so obtained achieves a Pk value of 0.212 which improves upon an unsupervised baseline by 45% relative, and compares favorably with a current state-of-the-art algorithm. Most importantly, we achieve this performance without any features or algorithms in the classic sense.

Demonstration based interfaces

Distributed augmentation-based learning: a learning algorithm for distributed collaborative programming-by-demonstration BIBAFull-Text 160-169
  Vittorio Castelli; Lawrence Bergman
The learning algorithms used in Programming-by-Demonstration (PBD) are either on-line and incremental or off-line and batch. Neither category is entirely suitable for capturing know-how from demonstrations in a distributed, collaborative environment, where multiple experts can independently provide examples to improve the model.
   In this paper we describe Distributed Augmentation-Based Learning (DABL), the first real-time PBD learning algorithm suited for distributed know-how acquisition. DABL is an incremental learning algorithm that uses a version-control-like paradigm to combine independently constructed procedure models. An expert can check out a procedure model from a repository and modify it by means of new demonstrations or by manually editing it. The expert then reconciles the changes with those concurrently made by other experts and checked into the repository.
   DABL automatically merges the two procedures, learns new decision points based on reconcilable differences, and identifies conflicts where there are multiple valid ways of combining the changes or where the combination produces an invalid model, that is, one that does not lie in the search space of the learning algorithm.
Building data integration queries by demonstration BIBAFull-Text 170-179
  Rattapoom Tuchinda; Pedro Szekely; Craig A. Knoblock
The magnitude of data available on the web prompts the need for an easy to use query interface that enables users to integrate data from multiple web sources in an intelligent fashion. Past work in the area of databases has resulted in different query interface systems that simplify query formulation. While these approaches reduce the user's effort to compose queries, the user is still required to pick data sources to use and the interaction is not guaranteed to yield a non-empty result set. We introduce a novel approach that exploits the structure of the relational data source(s) to formulate a set of constraints. These constraints are used in conjunction with partial plans to produce an intelligent query interface that (a) does not require the user to know details about data sources or existing values (b) suggests valid inputs to the user (c) creates consistent queries that always return values.

Natural language interfaces

Porting natural language interfaces between domains: an experimental user study with the ORAKEL system BIBAFull-Text 180-189
  Philipp Cimiano; Peter Haase; Jörg Heizmann
We present a user-centered model for porting natural language interfaces (NLIs) between domains efficiently. The model assumes that domain experts without any background knowledge about computational linguistics will perform the customization of the NLI to a specific domain. In fact, it merely requires familiarity with the underlying knowledge base as well as with a few basic subcategorization types. Our model is iterative in the sense that the adaption of the NLI is performed in several cycles on the basis of the questions which the NLI failed to answer, thus iteratively increasing the coverage of the system. We provide experimental evidence in form of a user study as well as a case study involving a real-world application corroborating that our model is indeed a feasible way of customizing the interface to a certain domain.
BuzzTrack: topic detection and tracking in email BIBAFull-Text 190-197
  Gabor Cselle; Keno Albrecht; Roger Wattenhofer
We present BuzzTrack, an email client extension that helps users deal with email overload. This plugin enhances the interface to present messages grouped by topic, instead of the traditional approach of organizing email in folders. We discuss a clustering algorithm that creates the topic-based grouping, and a heuristic for labeling the resulting clusters to summarize their contents. Lastly, we evaluate the clustering scheme in the context of existing work on topic detection and tracking (TDT) for news articles: Our algorithm exhibits similar performance on emails as current work on news text. We believe BuzzTrack's organization structure, which can be obtained at no cost to the end user, will be helpful for managing the massive amounts of email that land in the inbox every day.
Knowledge acquisition from simplified text BIBAFull-Text 198-205
  Kevin Livingston; Christopher K. Riesbeck
The problem of entering and integrating new knowledge into a logic-based knowledge base is substantial. Our solution is to provide a natural language interface, which reads simplified English, enabled by a knowledge-based memory-retrieval driven natural language understander. This paper presents a set of tools and interfaces for interacting with such a system, and a discussion of the underlying Reader system, the reading component of the Learning Reader project. The interfaces presented provide direct feedback about what portions of the text are understood, and what interpretations are being produced from it. In addition tools are presented to, among other things, provide example sentences, to facilitate users producing simplified English text suitable for the Reader.

Multi-modal interfaces

A platform for output dialogic strategies in natural multimodal dialogue systems BIBAFull-Text 206-215
  Meriam Horchani; Laurence Nigay; Franck Panaget
The development of natural multimodal dialogue systems remains a very difficult task. The flexibility and naturalness they offer result in an increased complexity that current software tools do not address appropriately. One challenging issue we address here is the generation of cooperative responses in an appropriate multimodal form, highlighting the intertwined relation of content and presentation. We identify a key component, the dialogic strategy component, as a mediator between the natural dialogue management and the multimodal presentation. This component selects the semantic information content to be presented according to various presentation constraints. Constraints include inherent characteristics of modalities, the availability of a modality as well as preferences of the user. Thus the cooperative behaviour of the system could be adapted as could its multimodal behaviour. In this paper, we present the dialogic strategy component and an associated platform to quickly develop output multimodal cooperative responses in order to explore different dialogic strategies.
Music compositional intelligence with an affective flavor BIBAFull-Text 216-224
  Roberto Legaspi; Yuya Hashimoto; Koichi Moriyama; Satoshi Kurihara; Masayuki Numao
The consideration of human feelings in automated music generation by intelligent music systems, albeit a compelling theme, has received very little attention. This work aims to computationally specify a system's music compositional intelligence that tightly couples with the listener's affective perceptions. First, the system induces a model that describes the relationship between feelings and musical structures. The model is learned by applying the inductive logic programming paradigm of FOIL coupled with the Diverse Density weighting metric over a dataset that was constructed using musical score fragments that were hand-labeled by the listener according to a semantic differential scale that uses bipolar affective descriptor pairs. A genetic algorithm, whose fitness function is based on the acquired model and follows basic music theory, is then used to generate variants of the original musical structures. Lastly, the system creates chordal and non-chordal tones out of the GA-obtained variants. Empirical results show that the system is 80.6% accurate at the average in classifying the affective labels of the musical structures and that it is able to automatically generate musical pieces that stimulate four kinds of impressions, namely, favorable-unfavorable, bright-dark, happy-sad, and heartrending-not heartrending.
Making sense of virtual environments: action representation, grounding and common sense BIBAFull-Text 225-234
  Jean-Luc Lugrin; Marc Cavazza
The development of complex interactive 3D systems raises the need for representations supporting more abstract descriptions of world objects, their behaviour and the world dynamics. The inclusion of Artificial Intelligence representations and their use within 3D graphic worlds face both fundamental and technical issues due to the difference in representational logic between computer graphics and knowledge-based systems. We present a framework for such an integration illustrated by a first prototype.

Gesture- and sketch-based interfaces

Tracking of deformable human hand in real time as continuous input for gesture-based interaction BIBAFull-Text 235-242
  Xiying Wang; Xiwen Zhang; Guozhong Dai
Gesture input is a natural and effective interactive model. The tracking of deformable hand gesture is a very important task in gesture-based interaction. A novel real-time tracking approach is proposed to capture hand motion with single camera. It combines the characters of model-based and appearance-based method. The presented approach achieves auto-initialization by posture recognition and matching with image features. It solves the problem of interference among fingers successfully by the integration of K-Means clustering and Particle Filters. Moreover, tracking detection realizes resumption from tracking failure and automatic update of hand model. Experiments show that, the proposed method can achieve continuous real-time tracking of deformable hand gesture, and meets the requirements for gesture-based Human-Computer Interaction.
Crossmodal error correction of continuous handwriting recognition by speech BIBAFull-Text 243-250
  Xiang Ao; Xugang Wang; Feng Tian; Guozhong Dai; Hongan Wang
In recognition-based user interface, users' satisfaction is determined not only by recognition accuracy but also by effort to correct recognition errors. In this paper, we introduce a crossmodal error correction technique, which allows users to correct errors of Chinese handwriting recognition by speech. The focus of the paper is a multimodal fusion algorithm supporting the crossmodal error correction. By fusing handwriting and speech recognition, the algorithm can correct errors in both character extraction and recognition of handwriting. The experimental result indicates that the algorithm is effective and efficient. Moreover, the evaluation also shows the correction technique can help users to correct errors in handwriting recognition more efficiently than the other two error correction techniques.
NaturalDraw: interactive perception based drawing for everyone BIBAFull-Text 251-260
  Yasser F. O. Mohammad; Toyoaki Nishida
Drawing is a very natural activity of humans, and, despite the wide variety of drawing systems available on computers today, most of those systems lack the naturalness of the pencil-paper system. In this paper we present a new drawing interface that is easy for the human to use in a more natural way than the existing drawing interfaces. The proposed system is based on the Interactive Perception paradigm we developed for interfacing social robots to the humans.

Short papers

iMime: an interactive character animation system for use in dementia care BIBAFull-Text 262-265
  Andreas Wiratanaya; Michael J. Lyons; Nicholas J. Butko; Shinji Abe
We describe the design and implementation of an interactive character animation interface. The system analyzes the attentive state and aspects of the affective behaviour of a viewer using input from a video camera and uses this to control the behaviour of a cartoon-like animated character. Using the interaction metaphor of a mime artist, we design the system to encourage viewer attention and interaction, with adaptation using an online reinforcement learning based on the viewer's attentive state. This work is ultimately aimed at developing a system to support the care of dementia sufferers.
Robotic telecommunication system based on facial information measurement BIBAFull-Text 266-269
  Junichi Ido; Etsuko Ueda; Yoshio Matsumoto; Tsukasa Ogasawara
This paper proposes a multi-modal telecommunication system using a facial expression robot. We developed a telecommunication system which projects the facial expression of an operator to a remote place using the facial expression robot "Infanoid2." The facial information of the operator is measured using a stereo camera system and projected through a robot in order to communicate with another person in a remote location. Impression evaluation experiment is performed using this system. This paper discusses the effectiveness of robots as a telecommunication medium based on the experimental results.
A data-oriented approach to integrate emotions in adaptive dialogue management BIBAFull-Text 270-273
  Johannes Pittermann; Angela Pittermann
During the past years the involvement of emotions in dialogue design has attracted much interest in current research on intelligent human-computer interfaces. We focus on the implementation of a flexible and robust dialogue system which integrates emotions and other influencing parameters in the dialogue flow. In order to achieve a higher degree of adaptability we propose a simplified stochastic approach to model the dialogue manager's behavior based on the user's input and dialogue-influencing parameters like emotions.
MathBox: interactive pen-based interface for inputting mathematical expressions BIBAFull-Text 274-277
  Yuji Kasuya; Hayato Yamana
Inputting mathematical expressions with a mouse and a keyboard is a troublesome task. Thus, a number of mathematical expression recognition systems capable of recognizing handwritten mathematical expressions to input them into computers have been proposed. Even with these systems, however, structure recognition of mathematical expressions is still difficult. This paper presents MathBox, a new pen-based interface for inputting mathematical expressions into computers. MathBox interactively shows "boxes" in which the user can write one symbol. The boxes are shown along with the user's writing. For example, when the user writes 'x,' the boxes for a power and an index of 'x' and for the next symbol are shown. When the user inputs a fraction line, boxes for the numerator, denominator, and the next symbol are shown. MathBox skips recognizing the structures of expressions, which enables users to write mathematical expressions with practical accuracy.
Analysis of affect expressed through the evolving language of online communication BIBAFull-Text 278-281
  Alena Neviarouskaya; Helmut Prendinger; Mitsuru Ishizuka
In this paper, we focus on affect recognition from text in order to facilitate sensitive and expressive communication in computer-mediated environments. Our model for analyzing affect conveyed by text is tailored to handle the style and specifics of informal online conversations. The motivation behind our approach is to improve social interactivity and emotional expressiveness of real-time messaging.
   In order to estimate affect in text, our model processes symbolic cues, such as emoticons, detects and transforms abbreviations, and employs natural language processing techniques for word/phrase/sentence-level analysis, e.g. by considering relations among words in a sentence. As a result of the analysis, text can be categorized into emotional states and communicative functions. A designed graphical representation of a user (avatar) displays emotions and social behaviour driven by text and performs natural idle movements. The proposed system shows promising results on affect recognition in real examples of online conversation.
On the community-based explanation of search results BIBAFull-Text 282-285
  Maurice Coyle; Barry Smyth
Collaborative Web search (CWS) is an approach to personalizing search results, returned by an underlying search engine(s), to the preferences of a community of like-minded searchers. In this paper we propose an alternative architecture that facilitates a more flexible integration between CWS and the underlying search engine(s) and evaluate how community behaviour can be used to annotate search results with explanatory information to facilitate relevancy judgments.
Social radio: a music-based approach to emotional awareness mediation BIBAFull-Text 286-289
  Carsten Röcker; Richard Etter
This paper presents a novel approach for mediating awareness in small intimate groups. Instead of traditional communication media, music is used to inform users about the presence and mood of multiple remote peers. Based on this conceptual idea, an awareness system called 'Social Radio' was developed. The system consists of several smart artifacts and an underlying multi-user communication infrastructure.
ONCOR: ontology- and evidence-based context reasoner BIBAFull-Text 290-293
  Judy Kay; William T. Niu; David J. Carmichael
In this paper, we describe ONCOR, our ontology- and evidence-based approach to reason about context. ONCOR tackles the critical problem of providing a flexible and pragmatic approach to building light-weight ontologies of places, devices and sensors in MyPlace, a ubiquitous computing application which provides personalised information about a building. Our contributions lie in constructing a general middle ontology for a building (MIBO) based on OpenCyc, combined with our own approach to evidence-based reasoning, (accretion and resolution). We describe the evaluation of ONCOR in terms of a comparison of its answers to core ontological questions about context with previous work.
Refining preference-based search results through Bayesian filtering BIBAFull-Text 294-297
  Jiyong Zhang; Pearl Pu
Preference-based search (PBS) is a popular approach for helping consumers find their desired items from online catalogs. Currently most PBS tools generate search results by a certain set of criteria based on preferences elicited from the current user during the interaction session. Due to the incompleteness and uncertainty of the user's preferences, the search results are often inaccurate and may contain items that the user has no desire to select. In this paper we develop an efficient Bayesian filter based on a group of users' past choice behavior and use it to refine the search results by filtering out items which are unlikely to be selected by the user. Our preliminary experiment shows that our approach is highly promising in generating more accurate search results and saving user's interaction effort.
Personalized ambient media experience: move.me case study BIBAFull-Text 298-301
  Lora Aroyo; Frank Nack; Thecla Schiphorst; Hielke Schut; Michiel KauwATjoe
The move.me prototype illustrates a scenario for social interaction in which users can manipulate audio-visual sources presented on various screens through an interaction with a sensor-enhanced pillow. The technology developed for move.me uses the surface of a pillow as a tactile interface. We describe the underlying concepts of move.me and its motivations. We present a case study of the environment as the context of evaluating aspects of our approach and conclude with plans for future work.
Auditory perceptible landmarks in mobile navigation BIBAFull-Text 302-304
  Jörg Baus; Rainer Wasinger; Ilhan Aslan; Antonio Krüger; Andreas Maier; Tim Schwartz
Normally, mobile pedestrian navigation systems use visually perceptible landmarks to guide their users through the environment. In this article we introduce concepts for the use of auditory perceptible landmarks in route descriptions. Such auditory perceptible landmarks complement their visual counterparts and also stand to be beneficial for certain groups like the visually impaired and the elderly.
Supporting small groups in the museum by context-aware communication services BIBAFull-Text 305-308
  Tsvi Kuflik; Julia Sheidin; Sadek Jbara; Dina Goren-Bar; Pnina Soffer; Oliviero Stock; Massimo Zancanaro
Visitors often tend to visit museums in groups, mainly with family or friends, yet most of the today mobile museum guides focus on supporting the individual visitor. The technology described in this paper allows supporting groups of visitors in addition to individuals by providing context-aware services aimed at supporting the whole group. These include context-aware communication and alerting services that are provided by the museum visitor's guide system developed in the framework of the PIL (PEACH-Israel) project, as an example case of a larger variety of possible context-aware services.
Towards intelligent mapping applications: a study of elements found in cognitive maps BIBAFull-Text 309-312
  Gary Look; Howard Shrobe
This paper describes a study that examines common elements found in people's mental maps of the city of Boston. The intent of the study is to understand the mental model people have of a city. Understanding this mental model will provide insight into developing mapping applications that present location information in a way that makes it easier to conceptualize and situate new location information in terms of places a person already knows. An analysis of hand-annotated maps showing the locations of prominent places in a person's mental map of Boston suggests that prominent places can be characterized by a certain set of properties and that major transit points (subway stops) play an important role in framing a person's mental map.
Modelling personality in voices of talking products through prosodic parameters BIBAFull-Text 313-316
  Michael Schmitz; Antonio Krüger; Sarah Schmidt
In this paper we report preliminary findings from two user studies that on the one hand investigate how prosodic parameters of synthetic speech can influence the perceived impression of the speakers personality and on the other hand explores if and how people attribute personality to objects such as typical products of daily shopping. The results show that a) prosodic parameters have a strong influence on the perceived personality and can be partially used to achieve a desired impression and b) that subjects clearly attribute personalities to products. Both findings encourage us to continue our work on a dialogue shell for talking products.
A comparison of two compound critiquing systems BIBAFull-Text 317-320
  James Reilly; Jiyong Zhang; Lorraine McGinty; Pearl Pu; Barry Smyth
Compound critiques allow users to simultaneously express directional preferences over several product attributes. Presenting the user with compound critiques is not a new idea. The original Find-Me Systems (e.g., Car Navigator) showed static compound critiques; they didn't change irrespective of user preferences or the product availability. Recently, a number of techniques for dynamically generating compound critiques have been proposed. While these techniques have been evaluated in isolation, to date no direct comparison of these (in terms of their interfacing characteristics and recommendation performance) has been reported. Motivated by this, our research groups have come together to carry out this comparison for the approaches we each take. The user study platform that we have developed facilitates the comparison of various critiquing based recommenders. In this paper we report the first set of results from a comprehensive real-user evaluation of two dynamic compound critique systems using this evaluation platform.
User-context for adaptive user interfaces BIBAFull-Text 321-324
  Anil Shankar; Sushil J. Louis; Sergiu Dascalu; Linda J. Hayes; Ramona Houmanfar
We present results from an empirical user-study with ten users which investigates if information from a user's environment helps a user interface to personalize itself to individual users to better meet usability goals and improve user-experience. In our research we use a microphone and a web-camera to collect this information (user-context) from the vicinity of a subject's desktop computer. Sycophant, our context-aware calendaring application and research test-bed uses machine learning techniques to successfully predict a user-preferred alarm type. Discounting user identity and motion information significantly degrades Sycophant's performance on the alarm prediction task. Our user study emphasizes the need for user-context for personalizable user interfaces which can better meet effectiveness and utility usability goals. Results from our study further demonstrate that contextual information helps adaptive interfaces to improve user-experience.
Exploiting web browsing histories to identify user needs BIBAFull-Text 325-328
  Fabio Gasparetti; Alessandro Micarelli
Browsing activities are an important source of information to build profiles of the user interests and personalize the human-computer interaction during information seeking tasks. Visited pages are easily collectible, e.g., from browsers' histories and toolbars, or desktop search tools, and they often contain documents related to the current user needs. Nevertheless, menus, advertisements or pages that cover multiple topics affect negatively the advantages of an implicit feedback technique that exploits these data to build and keep updated user profiles. This work describes a technique to collect text relevant to the current needs from sequences of pages visited by the user. The evaluation shows how it outperforms other techniques that consider the whole page contents. We also introduce an improvement based on machine learning techniques that is currently under evaluation.
Emotionally reactive television BIBAFull-Text 329-332
  Chia-Hsun Jackie Lee; Chaochi Chang; Hyemin Chung; Connor Dickie; Ted Selker
When is an interface simple? Is it when it is invisible or very obvious, even intrusive? From the time TV was created, watching TV is considered as a static activity. TV audiences have very limited choices to interact with TV, such as turning on/off, increasing/decreasing volume, and traversing among different channels. This paper suggests that TV program should have social responses to people, such as affording and accepting audience's emotional feeling with the growth of technologies. This paper presents HiTV, an Emotionally-Reactive TV system using a digitally augmented soft ball as affect-input interfaces that can amplify TV program's video/audio signals. HiTV transforms the original video and audio into effects that intrigue and fulfill people's emotional expectation.
A markup language for describing interactive humanoid robot presentations BIBFull-Text 333-336
  Yoshitaka Nishimura; Shinichiro Minotsu; Hiroshi Dohi; Mitsuru Ishizuka; Mikio Nakano; Kotaro Funakoshi; Johane Takeuchi; Yuji Hasegawa; Hiroshi Tsujino
Trusted search communities BIBAFull-Text 337-340
  Peter Briggs; Barry Smyth
We describe a social search technique that harnesses the search experiences of a community of searchers to generate result recommendations, in a collaborative fashion, to complement results returned from some underlying search engine. We describe a dynamic model of trust as a way to coordinate collaboration, and provide experimental results to show that search performance improves as the network evolves.
Combating information overload in non-visual web access using context BIBAFull-Text 341-344
  Jalal Mahmud; Yevgen Borodin; Dipanjan Das; I. V. Ramakrishnan
Web sites are designed for graphical mode of interaction. Sighted users can visually segment Web pages and quickly identify relevant information. In contrast, visually-disabled individuals have to use screen readers to browse the Web. Screen readers process pages sequentially and read through everything, making Web browsing time-consuming and strenuous. The use of shortcut keys and searching offers some improvements, but the problem still remains. In this paper, we address this problem using the notion of context. When a user follows a link, we capture the context of the link, and use it to identify relevant information on the next page. The content of this page is rearranged, so that the relevant information is read out first. We conducted a series experiments to compare the performance of our prototype system with the state-of-the-art JAWS screen reader. Our results show that the use of context can potentially save browsing time as well as improve browsing experience of visually disabled individuals.
Temporal filtering system to reduce the risk of spoiling a user's enjoyment BIBAFull-Text 345-348
  Satoshi Nakamura; Katsumi Tanaka
This paper proposes a temporal filtering system called the Anti-Spoiler system. The system changes filters dynamically based on user-specified preferences and the user's timetable. The system then blocks contents that would spoil the user's enjoyment of a previously unwatched content. The system analyzes a user-requested Web content, and then uses filters to prevent portions of the content being displayed that might spoil user's enjoyment. For example, the system hides the final score of football from the Web content before watching it on TV.
Increasing web accessibility by automatically judging alternative text quality BIBAFull-Text 349-352
  Jeffrey P. Bigham
The lack of appropriate alternative text for web images remains a problem for blind users and others accessing the web with non-visual interfaces. The content contained within web images is vital for understanding many web sites but the majority are assigned either inaccurate alternative text or none at all. The capability to automatically judge the quality of alternative text has the promise to dramatically improve the accessibility of the web by bringing intelligence to three categories of interfaces: tools that help web authors verify that they have provided adequate alternative text for web images, systems that automatically produce and insert alternative text for web images, and screen reading software. In this paper we describe a classifier capable of measuring the quality of alternative text given only a few labeled training examples by automatically considering the image context.
Social robots as mediators between users and smart environments BIBAFull-Text 353-356
  Giovanni Cozzolongo; Berardina De Carolis; Sebastiano Pizzutilo
In this paper we propose the use of a social robot as mediator between the user and a smart environment. Since the speech is considered one of the more natural and immediate input channel in human-robot interaction we discuss the importance of recognizing both the linguistic content of the spoken sentence and the valence of the user tone of voice in order to infer properly the user's intent in communication during the interaction.
Modeling user behavior using a search-engine BIBAFull-Text 357-360
  Maeve O'Brien; Mark T. Keane
A model of user-search-engine interaction is developed using the ACT-R cognitive architecture. We test, using an empirical evaluation, the model across different result orderings and relevance distributions, demonstrating that across a number of trials, the model approximates the characteristics of large numbers of users interacting with search-engines. These results are discussed in terms of their practical implications for search interfaces and ranking algorithms.
Interactive visual clustering BIBAFull-Text 361-364
  Marie desJardins; James MacGlashan; Julia Ferraioli
Interactive Visual Clustering (IVC) is a novel method that allows a user to explore relational data sets interactively, in order to produce a clustering that satisfies their objectives. IVC combines spring-embedded graph layout with user interaction and constrained clustering. Experimental results on several synthetic and real-world data sets show that IVC yields better clustering performance than alternative methods.
What am I gonna wear?: scenario-oriented recommendation BIBAFull-Text 365-368
  Edward Shen; Henry Lieberman; Francis Lam
Electronic Commerce on the Web is thriving, but consumers still have trouble finding products that will meet their needs and desires. AI has offered many kinds of Recommender Systems [11], but they are all oriented toward searching based on concrete attributes of the product (e.g. price, color) or the user (as in Collaborative Filtering). Based on commonsense reasoning technology, we introduce a novel recommendation technique, Scenario-Oriented Recommendation, which helps users by mapping their daily scenarios to product attributes, and works even when users don't know exactly what products they are looking for.
VizScript: visualizing complex interactions in multi-agent systems BIBAFull-Text 369-372
  Jing Jin; Rajiv T. Maheswaran; Romeo Sanchez; Pedro Szekely
We address the problem of users creating visualizations to debug and understand multi-agent systems. The key challenges are that (1) needs arise dynamically, i.e., it is difficult to know a priori what visualizations one wants, (2) extensive expertise on the system, the algorithms and visualization tools are often needed for implementation, and (3) agents can be running in a distributed environment. We have developed VizScript, a collection of tools to expedite the process of creating visualizations. VizScript combines a generic application instrumentation, a knowledge base, and simple scene definition primitives with a reasoning system, to produce an easy to use visualization system. Using VizScript we were able to recreate the visualizations for a complex multi-agent system with an order-of-magnitude less effort than was required in a Java implementation.