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Note: During this session, system demonstrations will be given by some of the short paper presenters, as well as by presenters of some of the long papers.
Affective Computing
Person-Independent Estimation of Emotional Experiences From Facial Expressions (ACM Digital Library Link)
Timo Partala, Veikko Surakka, and Toni Vanhala (University of Tampere)
Abstract
The aim of this research was to develop methods for the automatic person-independent estimation of experienced emotions from facial expressions. Ten subjects watched series of emotionally arousing pictures and videos, while the electromyographic (EMG) activity of two facial muscles: zygomaticus major (activated in smiling) and corrugator supercilii (activated in frowning) was registered. Based on the changes in the activity of these two facial muscles, it was possible to distinguish between ratings of positive and negative emotional experiences at a rate of almost 70% for pictures and over 80% for videos. Using these methods, the computer could adapt its behavior according to the user
s emotions during humancomputer interaction.
Pedagogical Agent Image Matters (ACM Digital Library Link)
Amy Baylor (Florida State University)
Abstract
Pedagogical agent image is a key feature for animated interface agents. Experimental research indicates that agent interface images should be carefully designed, considering both the relevant outcomes (learning or motivational) together with student characteristics. This paper summarizes empirically-derived design guidelines for pedagogical agent image.
Emotive Alert: HMM-Based Emotion Detection In Voicemail Messages (ACM Digital Library Link)
Zeynep Inanoglu and Ron Caneel (MIT Media Laboratory)
Abstract
Voicemail has become an integral part of our personal and professional communication. The number of messages that accumulate in our voice mailboxes necessitate new ways of prioritizing them. Currently, we are forced to actively listen to all messages in order to find out which ones are important and which ones can be attended to later on. In this paper, we describe Emotive Alert, a system that can detect some of the significant emotions in a new message and notify the account owner along various affective axes, including ururgency, formality, valence (happy vs. sad) and arousal (calm vs. excited). We have used a purely acoustic, HMM-based approach for identifying the emotions, which allows application of this system to all messages independent of language.
Human-Robot Interaction
Vision-Based GUI for Interactive Mobile Robots (ACM Digital Library Link)
Randeep Singh, Bhartendu Seth, and Uday B. Desai (Indian Institute of Technology)
Abstract
Interactive mobile robots are an active area of research. This paper presents a framework for designing a real-time vision based hand-body gesture user interface for such robots. The said framework works in real world lighting conditions, with complex background, and can handle intermittent motion of the camera. The input signal is captured
by using a singular monocular color camera. Vision is the only feedback sensor being used. It is assumed that the gesturer is wearing clothes that are slightly different from the background. We have tested this framework on a gesture database consisting of 11 hand-body gestures and have recorded recognition accuracy up to 90%.
User Intentions Funneled Through a Human-Robot Interface (ACM Digital Library Link)
Michael T. Rosenstein, Andrew H. Fagg, Shichao Ou, and Roderic A. Grupen (University of Massachusetts Amherst)
Abstract
We describe a method for predicting user intentions as part of a human-robot interface. In particular, we show that funnels, i.e., geometric objects that partition an input space, provide a convenient means for discriminating individual objects and for clustering sets of objects for hierarchical tasks. One advantage of the proposed implementation is that a simple parametric model can be used to specify the shape of a funnel, and a straightforward heuristic for setting initial parameter values appears promising. We discuss the possibility of adapting the user interface with machine learning techniques, and we illustrate the approach with a humanoid robot performing a variation of a standard peginsertion task.
Personal Assistants
Context-Based Similar Words Detection and Its Application in Specialized Search Engines (ACM Digital Library Link)
Hisham Al-Mubaid and Ping Chen (University of Houston)
Abstract
This paper presents a new context-based method for automatic detection and extraction of similar and related words from texts. Finding similar words is a very important task for many NLP applications including anaphora resolution, document retrieval, text segmentation, and text summarization. Here we use word similarity to improve search quality for search engines in (general and) specific domains. Our method is based on rules for extracting the words in the neighborhood of a target word, then connecting this with the surroundings of other occurrences of the same word in the (training) text corpus. This is an ongoing work, and is still under extensive testing. The preliminary results, however, are promising and encouraging more work in this direction.
Interactively Building Agents for Consumer-Side Data Mining (ACM Digital Library Link)
Rattapoom Tuchinda and Craig A. Knoblock (University of Southern California)
Abstract
Integrating and mining data from different web sources can make end-users well-informed when they make decisions. One of many limitations that bars end-users from taking advantages of such process is the complexity in each of the steps
required to gather, integrate, monitor, and mine data from different websites. We present the idea of combining the data integration, monitoring, and mining as one single process in the form of an intelligent assistant that guides end-users to specify their mining tasks by just answering questions. This easy-to-use approach, which trades off complexity in terms of available operations with the ease of use, has the ability to provide interesting insight into the data that would requires days of human effort to gather, combine, and mine manually from the web.
Adaptive Teaching Strategy for Online Learning (ACM Digital Library Link)
Jungsoon Yoo, Cen Li, and Chrisila Pettey (Middle Tennessee State University)
Abstract
Finding the optimal teaching strategy for an individual student is difficult even for an experienced teacher.
Identifying and incorporating multiple optimal teaching strategies for different students in a class is even harder. This paper presents an Adaptive tutor for online Learning, AtoL, for Computer Science laboratories that identifies
and applies the appropriate teaching strategies for students on an individual basis. The optimal strategy for a student is identified in two steps. First, a basic strategy for a student is identified using rules learned from a supervised learning system. Then the basic strategy is refined to better fit the student using models learned using
an unsupervised learning system that takes into account the temporal nature of the problem solving process. The learning algorithms as well as the initial experimental results are presented.
Providing Intelligent Help Across Applications in Dynamic User and Environment Contexts (ACM Digital Library Link)
Ashwin Ramachandran and R. Michael Young (North Carolina State University)
Abstract
The problem of providing help for complex application interfaces has been a source of interest for a number of researcher efforts. As the computational power of computers increases, typical applications not only increase in functionality but also in the degree of interaction with the computational environment in which they reside. This paper describes an ongoing project to design an Intelligent Help System (IHS) that provides context-sensitivity not only through its modeling of application states but also its modeling of the interaction between applications and between an application and the environment in which it resides.
Visualization and Presentation
ScentHighlights: Highlighting Conceptually Related Sentences During Reading (ACM Digital Library Link)
Ed Chi, Lichan Hong, Michelle Gumbrecht, and Stuart Card (Palo Alto Research Center)
Abstract
Researchers have noticed that readers are increasingly skimming instead of reading in depth. Skimming also occur in re-reading activities, where the goal is to recall specific topical facts. Bookmarks and highlighters were invented precisely to achieve this goal. For skimming activities, readers need effective ways to direct their attention toward
the most relevant passages within text. We describe how we have enhanced skimming activity by conceptually highlighting sentences within electronic text that relate to search keywords. We perform the conceptual highlighting by computing what conceptual keywords are related to each other via word co-occurrence and spreading activation. Spreading activation is a cognitive model developed in psychology to simulate how memory chunks and conceptual items
are retrieved in our brain. We describe the method used, and illustrate the idea with realistic scenarios
using our system.
Personal Reporting of a Museum Visit as an Entry Point to Future Cultural Experience (ACM Digital Library Link)
Charles Callaway, Tsvi Kuflik, Elena Not, Alessandra Novello, Oliviero Stock, and Massimo Zancanaro (ITC-irst)
Abstract
Museum visitors can continue interacting with museum exhibits even after they have left the museum. We can help them do this by creating a report that includes a basic, personalized narration of their visit, the items and relationships they found most interesting, pointers to additional related online information, and suggestions for future visits to the current and other museums. In this work we describe the automatic generation of personalized natural language reports to help create one episode in an ongoing coherent sequence of cultural activities.
Speech- and Vision-Based Interfaces
How to Wreck a Nice Beach You Sing Calm Incense (ACM Digital Library Link)
Henry Lieberman, Alexander Faaborg, Waseem Daher, and José Espinosa (MIT Media Laboratory)
Abstract
A principal problem in speech recognition is distinguishing between words and phrases that sound similar but have different meanings. Speech recognition programs produce a list of weighted candidate hypotheses for a given audio segment, and choose the "best" candidate. If the choice is incorrect, the user must invoke a correction interface that displays a list of the hypotheses and choose the desired one. The correction interface is time-consuming, and accounts for much of the frustration of today's dictation systems. Conventional dictation systems prioritize hypotheses based on language models derived from statistical techniques such as n-grams and Hidden Markov Models. We propose a supplementary method for ordering hypotheses based on Commonsense Knowledge. We filter acoustical and word-frequency hypotheses by testing their plausibility with a semantic network derived from 700,000 statements about everyday life. This often filters out possibilities that "don't make sense" from the user's viewpoint, and leads to improved recognition. Reducing the hypothesis space in this way also makes possible streamlined correction interfaces that improve the overall throughput of dictation systems.
HMM-Based Efficient Sketch Recognition (ACM Digital Library Link)
Tevfik Metin Sezgin and Randall Davis (Massachusetts Institute of Technology)
Abstract
Current sketch recognition systems treat sketches as images or a collection of strokes, rather than viewing sketching as an interactive and incremental process. We show how viewing sketching as an interactive process allows us to recognize sketches using Hidden Markov Models. We report results of a user study indicating that in certain domains people draw objects using consistent stroke orderings. We show how this consistency, when present, can be used to perform sketch recognition efficiently. This novel approach enables us to have polynomial time algorithms for sketch recognition and segmentation, unlike conventional methods with exponential complexity.
Interaction Techniques Using Prosodic Features of Speech and Audio Localization (ACM Digital Library Link)
Alex Olwal (Royal Institute of Technology)
Steven Feiner (Columbia University)
Abstract
We describe several approaches for using prosodic features of speech and audio localization to control interactive applications. This information can be applied to parameter control, as well as to speech disambiguation. We discuss how characteristics of spoken sentences can be exploited in the user interface; for example, by considering the speed
with which a sentence is spoken and the presence of extraneous utterances. We also show how coarse audio localization can be used for low-fidelity gesture tracking, by inferring the speaker's head position.
Doubleshot: An Interactive User-Aided Segmentation Tool (ACM Digital Library Link)
Tom Yeh and Trevor Darrell (Massachusetts Institute of Technology)
Abstract
In this paper, we describe an intelligent user interface designed for camera phones to allow mobile users to specify the object of interest in the scene simply by taking two pictures: one with the object and one without the object. By
comparing these two images, the system can reliably extract the visual appearance of the object, which can be useful to a wide-range of applications such as content-based image retrieval and object recognition.
Generating Semantic Contexts from Spoken Conversation in Meetings (ACM Digital Library Link)
Jürgen Ziegler and Zoulfa El Jerrroudi (University of Duisburg-Essen)
Karsten Böhm (University of Leipzig)
Abstract
SemanticTalk is a tool for supporting face-to-face meetings and discussions by automatically generating a semantic context from spoken conversations. We use speech recognition and topic extraction from a large terminological database to create a network of discussion topics in real-time. This network includes concepts explicitly addressed in the discussion as well as semantically associated terms, and is visualized to increase conversational awareness and creativity in the group.
Conventions in Human-Human Multi-Threaded Dialogues: A Preliminary Study (ACM Digital Library Link)
Peter Heeman and Fan Yang (Oregon Health & Science University)
Andrew Kun and Alexander Shyrokov (University of New Hampshire)
Abstract
In this paper, we explore the conventions that people use in managing multiple dialogue threads. In particular, we focus on where in a thread people interrupt when switching to another thread. We nd that some subjects are able to vary where they switch depending on how urgent the interrupting task is. When time-allowed, they switched at the
end of a discourse segment, which we hypothesize is less disruptive to the interrupted task when it is later resumed.
Towards Automatic Transcription of Expressive Oral Percussive Performances (ACM Digital Library Link)
Amaury Hazan and Rafael Ramirez (Pompeu Fabra University)
Abstract
We describe a tool for transcribing voice generated percussive rhythms. The system consists of: (a) a segmentation component which separates the monophonic input stream into percussive events (b) a descriptors generation component that computes a set of acoustic features from each of the extracted segments, (c) a machine learning component which assigns to each of the segmented sounds of the input stream a symbolic class. We describe each of these components and compare different machine learning strategies that can be used to obtain a symbolic representation of the oral percussive performance.
Communicating the User's Focus of Attention by Image Processing as Input for a Mobile Museum Guide (ACM Digital Library Link)
Adriano Albertini, Roberto Brunelli, Oliviero Stock, and Massimo Zancanaro (ITC-irst)
Abstract
The paper presents a first prototype of a handheld museum guide delivering contextualized information based on the recognition of drawing details selected by the user through the guide camera. The resulting interaction modality has been analyzed and compared to previous approaches. Finally, alternative, more scalable, solutions are presented that preserve the most interesting features of the system described.
Knowledge Acquisition and Knowledge-Based Design
ComicKit: Acquiring Story Scripts Using Common Sense Feedback (ACM Digital Library Link)
Ryan Williams, Barbara Barry, and Push Singh (MIT Media Laboratory)
Abstract
At the Media Lab we are developing a resource called StoryNet, a very-large database of story scripts that can be used for commonsense reasoning by computers. This paper introduces ComicKit, an interface for acquiring StoryNet scripts from casual internet users. The core element of the interface is its ability to dynamically make commonsense suggestions that guide user story construction. We describe the encouraging results of a preliminary user study, and discuss future directions for ComicKit.
Metafor: Visualizing Stories as Code (ACM Digital Library Link)
Hugo Liu and Henry Lieberman (MIT Media Laboratory)
Abstract
Every program tells a story. Programming, then, is the art of constructing a story about the objects in the program and what they do in various situations. So-called programming languages, while easy for the computer to accurately convert into code, are, unfortunately, difficult for people to write and understand. We explore the idea of using descriptions in a natural language as a representation for programs. While we cannot yet convert arbitrary English to fully specified code, we can use a reasonably expressive subset of English as a visualization tool. Simple descriptions of program objects and their behavior generate scaffolding (underspecified) code fragments, that can be used as feedback for the designer. Roughly speaking, noun phrases can be interpreted as program objects; verbs can be functions, adjectives can be properties. A surprising amount of what we call programmatic semantics can be inferred from linguistic structure. We present a program editor, Metafor, that dynamically converts a user's stories into program code, and in a user study, participants found it useful as a brainstorming tool.
Task-Aware Information Access for Diagnosis of Manufacturing Problems (ACM Digital Library Link)
Larry Birnbaum, Wallace Hopp, Seyed Iravani, Kevin Livingston, and Biying Shou (Northwestern University)
Abstract
Pinpoint is a promising first step towards using a rich model of task context in proactive and dynamic IR systems. Pinpoint allows a user to navigate decision tree representations of problem spaces, built by domain experts, while dynamically entering annotations specific to their problem. The system then automatically generates queries to information repositories based on both the userís annotations and location in the problem space, producing results that are both task focused and problem specific. Initial feedback from users and domain experts has been positive.
Designing Interfaces for Guided Incremental Collection of Knowledge About Everyday Objects from Volunteers (ACM Digital Library Link)
Timothy Chklovski (University of Southern California)
Abstract
A new generation of intelligent applications can be enabled by broad-coverage knowledge repositories about everyday objects. We distill lessons in design of intelligent user interfaces which collect such broad-coverage knowledge from untrained volunteers. We motivate the knowledge-driven template-based approach adopted in LEARNER2, a second generation proactive acquisition interface for eliciting such knowledge. We present volume, accuracy, and recall of knowledge collected by fielding the system for 5 months. LEARNER2 has so far acquired 99,018 general statements, emphasizing knowledge about parts of and typical uses of objects.
An Ontology-Based Interface for Machine Learning (ACM Digital Library Link)
Mathias Bauer and Stephan Baldes (DFKI, German Research Center for Artificial Intelligence)
Abstract
Machine learning (ML) is a complex process that can hardly be carried out by non-expert users. Especially when using adaptive systems that interpret and exploit observations of the user to modify their behavior according to the user's perceived preferences, even naïve users may be confronted with learning systems. This paper presents an approach to make non-expert users understand and influence an ML system such as to improve trust and acceptance of the overall system behavior.
Smart Environments and Ubiquitous Computing
A Framework for Designing Intelligent Task-Oriented Augmented Reality User Interfaces (ACM Digital Library Link)
Leonardo Bonnani, Chia-Hsun Lee, and Ted Selker (MIT Media Laboratory)
Abstract
A task-oriented space can benefit from an augmented reality interface that layers the existing tools and surfaces with useful information to make cooking more easy, safe and efficient. To serve experienced users as well as novices, augmented reality interfaces need to adapt modalities to the user s expertise and allow for multiple ways to perform tasks. We present a framework for designing an intelligent user interface that informs and choreographs multiple tasks in a single space according to a model of tasks and users. A residential kitchen has been outfitted with systems to gather data from tools and surfaces and project multi-modal interfaces back onto the tools and surfaces themselves. Based on user evaluations of this augmented reality kitchen, we propose a system to tailor information modalities based on the spatial and temporal qualities of the task, and the expertise, location and progress of the user. The intelligent augmented reality user interface choreographs multiple tasks in the same space at the same time.
Seamless User Notification in Ambient Soundscapes (ACM Digital Library Link)
Andreas Butz (University of Munich)
Ralf Jung (Saarland University)
Abstract
We describe a method for notifying users through auditory cues embedded in an ambient soundscape in the environment. It uses pieces of music which are composed in such a way, that particular instruments or motifs can be added or omitted without losing the aesthetic quality of the overall composition. This allows for very subtle modifications in the soundscape which are only noticed by those users who have chosen this particular instrument or motif as their notification instrument before. As a side effect, the soundscape itself can be used to subtly influence the mood of users. The method has been implemented in a prototype, which we briefly discuss. The prototype is implemented using a spatial audio framework and can hence notify users from particular directions.
A Cart-Mounted Intelligent Shopping Assistant (ACM Digital Library Link)
Chad Cumby, Andrew Fano, Rayid Ghani, and Marko Krema (Accenture Technology Labs)
Abstract
This paper describes an Intelligent Shopping Assistant designed for a shopping cart mounted tablet PC that enables individual interactions with customers. We use machine learning algorithms to predict a shopping list for the customer's current trip and present this list on the device. As they navigate through the store, personalized promotions are presented using consumer models derived from loyalty card data for each inidvidual. In order for shopping assistant devices to be effective, we believe that they have to be powered by algorithms that are tuned for individual customers and can make accurate predictions about an individual's actions. We formally frame the shopping list prediction as a classification problem, describe the algorithms and methodology behind our system, and show that shopping list prediction can be done with high levels of accuracy, precision, and recall. Beyond the prediction of shopping lists we briefly introduce other aspects of the shopping assistant project, such as the use of consumer models to select appropriate promotional tactics, and the development of promotion planning simulation tools to enable retailers to plan personalized promotions delivered through such a shopping assistant.
Adaptive Navigation Support with Public Displays (ACM Digital Library Link)
Christian Kray and Gerd Kortuem (Lancaster University)
Antonio Krüger (University of Münster)
Abstract
In this paper, we describe a public navigation system which uses adaptive displays as directional signs. The displays are mounted to walls where they provide passersbys with directional information. Each sign is an autonomous, wirelessly networked digital displays connected to a central server. The signs are position-aware and able to adapt their display content in accordance with their current position. Advantages of such a navigation system include improved exibility, dynamic adaptation and ease of setup and maintenance.
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