IUI 2010: An Informal Summary of the International Conference on Intelligent User Interfaces
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IUI 2010: An Informal Summary of the International Conference on Intelligent User Interfaces

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Highlights from the main track, poster/demo-session & the VISSW/UDISW/EGIHMI workshops. This is an informal compilation of personal notes from the conference & proceedings, twitter (#iui2010), Ian ...

Highlights from the main track, poster/demo-session & the VISSW/UDISW/EGIHMI workshops. This is an informal compilation of personal notes from the conference & proceedings, twitter (#iui2010), Ian Ozsvald's blog (http://ianozsvald.com/), and other sources. Citations were not coherently possible, so I chose to stick with links instead. Please let me know if you'd like to see your work more thoroughly referenced.

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  • Requested QuickWoZ info!!!
  • Folksonomy: User generated vocabularies (e.g. tags)A combined initial tag + related tags does not typically help to filter results is determined!Semi-automatic clustering
  • The brain emits a signal as soon as it sees something interesting, and that "aha" signal can be detected by an electroencephalogram, or EEG cap. While users sift through streaming images or video footage, the technology tags the images that elicit a signal, and ranks them in order of the strength of the neural signatures. Afterwards, the user can examine only the information that their brains identified as important, instead of wading through thousands of images.Pasted from
  • Large workgroup including psychologists, economists, comp. sc., etc.WHAT IF WE APPLY OTHER FORCES TO THE NETWORK : PRESSURE / REMOVE NODES / OBSERVE SPREADMORE TIES SPRINGS -> MORE RIGID COMPANY NETWORKCONTRADICTION OF PHYS MODEL WITH PRESENTED CORRELATION OF PLASTICITY AND STABILITY OF COMPANY NETWORKS
  • The image on this slide is adapted for a hierarchical display … not the original springs model.
  • Andreas Butz‘s workgroup…
  • Such a lot of media to choose from that we could really use some good recommendations
  • Sometimes a lot of great different ingredients…
  • … just don‘t mix that well !
  • So the goal is to find a right combination and order.
  • Some systems out there try to achieve just that.
  • But more often than not they can‘t dynamically adjust to ever-changing human moods.
  • … and most-likely just recommend more of the same.
  • So how to adjust for the right order and the needs of the many?
  • [29],whichsupportstheexplorationanddiscoveryofinforma-tionthroughbothqueryingandbrowsingstrategies.Inthatregard,Marchionini[21]identifiedthreetypesofsearchac-tivities:(1)lookup,(2)learnand(3)investigate.Lookupsearchescanbethoughtofastraditionalsearch,whilelearnandinvestigatesearchesrelatetodiscovery-orientedtasks.GOOD SOURCE FOR CEION A MAP: WORLD LOCATIONS /// WHAT ABOUT OTHER TOPICSINTERFACE PRIMES FOR LOCATION QUERIES … WHAT ABOUT ALTERNATIVE INTERFACES?
  • Interesting for digital media confetti project…
  • works by fingerprinting the print pattern in a marked area … needs online db … not currently possible on purely white bgThey are investigating further codes to allow less distinct patterns
  • Interesting to CEI group…
  • KINDA IMPORTANT TO THE CEI!!! As described earlier, by changing the spread of prior distributions of words over all the available words, different knowledge representations of the user could be created. The smaller spread (i.e., lower s.d.) in the probability distribution of words within each topic implied that the words were more accurate in predicting the concepts in the document, such that the simulated user would be better able to interpret a tag and infer the topic as well as to assign a tag to represent the topic. We assumed that this reflected the performance of domain experts.  The most generally accepted property of social tagging systems is that the proportion of tags assigned to a document converges over time [12]. So, as the total number of tags increase in a system, the ratio of the frequency of a tag to the total number of tags remains fairly constant. This emergent property of tags, called convergence, was attributed to the social nature of the tagging process. In our previous simulations [7], we showed that the semantic imitation model produced not only the convergence, but also predicted how experts and novices could lead to different rates of convergence. http://portal.acm.org/ft_gateway.cfm?id=1719998&type=pdf&coll=ACM&dl=ACM&CFID=78317288&CFTOKEN=12971413
  • faster convergence in the expert network can be explained: tags assigned by experts were more predictive of the topics in the document and experts could extract these topics better than novices. Additionally, other experts tagging the same resource tended to choose the same higher quality tags.In contrast, novices were less knowledgeable about the contents of the document and consequently less effective in extracting the appropriate topics (and therefore tags) from the documents. Novices therefore selected tags that were more diverse than experts and hence the slower convergence. http://portal.acm.org/ft_gateway.cfm?id=1719998&type=pdf&coll=ACM&dl=ACM&CFID=78317288&CFTOKEN=12971413Exploratory information search by domain experts and novices POSTER ALSO INTERESTING
  • Nice video!
  • Hello, my name is Jan Smeddinck from the Digital Media workgroup at the University of Bremen, Often times we would like to test certain aspects of an embodied conversational agent before natural language processing is solved. Thus we do WoZ experiments where the user is tricked to believe to be interacting with a real functioning artificial agent. These experiments are slow and complicated to setup, especially when researching 3D agents. That’s why we developed the QuickWoZ framework where scenes with agents are constructed using traditional 3d modeling software including animations, foliage, etc. and then exported to our system that allows a wizard operator to easily steer the interaction with experiment participants.
  • Professor Tracy HammondThe system performs facial recognition on the user’s sketch and compares it to the target image so it can give feedback on areas that are wrong.
  • Henry Lieberman’s poster

IUI 2010: An Informal Summary of the International Conference on Intelligent User Interfaces IUI 2010: An Informal Summary of the International Conference on Intelligent User Interfaces Presentation Transcript

  • IUI 2010 Informal Summary
    http://www.iuiconf.org/images/iui2010_banner.jpg
    Highlights from the main track, poster/demo-session & the VISSW/UDISW/EGIHMI workshops
    Jan Smeddinck & Hidir Aras
    jan83(at)tzi(dot)de | aras(at)tzi(dot)de
    Digital Media, FB 3, University of Bremen, Germany
  • About this Summary
    Compilation of personal notes from the conference & proceedings, twitter (#iui2010), Ian Ozsvald's blog (http://ianozsvald.com/), and more…
    Biased for the digital media workgroup … had to skip many interesting pieces of work 
    Sloppy references – lack of time – but all links!
    Will be on slideshare:
    http://www.slideshare.net/Sanook/presentations
  • IUI General Information
    IUI = Intelligent User Interfaces
    Single track conference with corporate and univ. participation
    Formerly workshop, yearly conference since 1997
    ACM sponsored
    HCI meets AI and related fields…
    ~ 30 % paper acceptance rate
    Website: http://www.iuiconf.org/
    Proceedings: http://portal.acm.org/toc.cfm?id=1719970&idx=SERIES823&type=proceeding&coll=ACM&dl=ACM&part=series&WantType=Journals&title=Proceeding%20of%20the%2014th%20international%20conference%20on%20Intelligent%20user%20interfaces&CFID=78317288&CFTOKEN=12971413
  • VISSW/UDISW Workshop
    Visual Interfaces to the Social and Semantic Web
    http://smart-ui.org/events/vissw2010/
    User Data Interoperability in the Social Web
    http://www.wis.ewi.tudelft.nl/UDISW2010/
  • Ontology Based Queries – Investigating a Natural Language InterfaceIelka van der Sluis et al., Trinity College Dublin, Ireland
    Qualitative comparison study between the written interface semantic web browser "Longwell" and the natural language query interface "LIBER"
    Test was done with queries about US geograpy posed by untrained users
    Complex tasks (e.g. How many lakes are there in a certain state?)
    “From the experimental data, it is clear that subjects preferred Longwell over LIBER and they performed better with Longwell than with LIBER in almost all respects. It should be noted, however, that subjects felt that both interfaces were needlessly complicated.”
    http://www.smart-ui.org/events/vissw2010/papers/VISSW2010_Sluis.pdf
  • An Intelligent Query Interface Based on Ontology NavigationEnrico Franconi et al., Free University of Bozen-Bolzano, Italy
    Ontology based data access:
    How to formulate queries?
    Ontology navigation / queries:
    Queries as multi-labelled trees
    Alternative: Written language
    Lexicon derived from the ontology (engineers definition)
    Problematic
    70% success rate
    http://www.smart-ui.org/events/vissw2010/papers/VISSW2010_Franconi.pdf
  • An Intelligent Query Interface Based on Ontology NavigationEnrico Franconi et al., Free University of Bozen-Bolzano, Italy
    http://www.smart-ui.org/events/vissw2010/papers/VISSW2010_Franconi.pdf
  • Semantic Cloud: An Enhanced Browsing Interface for Exploring Resources in Folksonomy SystemsHidir Aras, Sandra Siegel, Rainer Malaka, University of Bremen, Germany
    Innovative interface approach for browsing resources in folksonomy systems
    based on a hierarchical semantic representation of the folksonomy space using tag co-occurrence analysis
    Provides multiple topic clouds that can be explored hierarchically
    Allows for the composition of queries from the tag cloud, while consulting results and refining the query afterwards
  • http://semanticcloud.sandra-siegel.de/
  • Designing Social Mobile Interfaces: Experiences with MobiMood, a Mobile Mood Sharing ApplicationKaren Church et al., Telefonica Research, Spain
    http://www.smart-ui.org/events/vissw2010/papers/VISSW2010_Church.pdf
  • Designing Social Mobile Interfaces: Experiences with MobiMood, a Mobile Mood Sharing ApplicationKaren Church et al., Telefonica Research, Spain
    People were really interested in strangers moods
    The interface was often abused for status updates
    Concrete replies to moods not that frequent
    Most used: custom moods / positive presets
    Audience suggested to integrate the moods into the contact list … update frequency is a problem
    http://www.smart-ui.org/events/vissw2010/papers/VISSW2010_Church.pdf
  • EGIHMI Workshop
    International Workshop on Eye Gaze in Intelligent Human Machine Interaction
    http://links.cse.msu.edu:8000/iui/program.html
    Information from Ian Ozsvald‘s report:
    http://ianozsvald.com/2010/02/07/intelligent-user-interfaces-2010-conference/
  • The Text 2.0 Framework – Writing Web-Based Gaze-Controlled Realtime Applications Quickly and EasilyR. Biedert et al., DFKI, Germany
    http://www.youtube.com/watch?v=8QocWsWd7fc
    http://text20.net/
    Browser Plugin with mark-up for OnGazeOver, OnPersual, OnRead, etc.
    Exciting Technology, but expensive tracking hardware!
  • Robust Pupil Detection for Gaze-based User InterfacesW.H. Liao
    60 $, 40x40 pixel accuracy, IR based, 30fps on core2
    http://www.youtube.com/watch?v=WvWdwB6nTkk
  • IUI 2010 Conference
  • Cortically-Coupled Computer VisionPaul Sajda et al., Columbia University, USA
    Image recognition at the blink of an eye…
    System harnesses brain'sability to recognize an image much faster than the person can identifyit
    Neural activity recording of visual cortex activity while observing flashing images to score "gist" of the images
    Standard signal + target + novel items neural activity test
    Normally done with averaging many iterations
    How to achieve single test precision?
    Decode EEG signal over time (~ 800 ms per sample) and space (electrodes spread over the skull) 
    http://www.wired.com/medtech/health/news/2006/07/71364
    http://newton.bme.columbia.edu/publications/triage_ieee.pdf
    http://www.wired.com/news/images/full/brain1_f.jpg
  • Cortically-Coupled Computer VisionPaul Sajda et al., Columbia University, USA
    Sample subset of a large image db
    > feature abstraction of entire image db based on sample subset results
    C3 vision search: E.g. help with labeling in maps:
    vision module recognizes possibly interesting regions in huge maps
    Chips of possibly interesting images shown rapidly to the actual labeller (person)
    Then lead the labeler to image(-sections) of interest
  • Personalized News Recommendation Based on Click BehaviorJiahui Liu, Peter Dolan, Elin Rønby Pedersen, Google Inc., USA
    Web provides access to news articles from millions of sources around the world
    Help users find the articles that are interesting to read
    Recommendationsystem builds profiles of users’ news interests based on their past click behavior
    large-scale analysis of anonymized Google News users click logs
    Bayesian framework for predicting users’ current news interests from the activities of that particular user and the news trends demonstrated in the activity of all users
    Deployed in Google News
    Improves quality of news recommendation and increases traffic
    http://portal.acm.org/ft_gateway.cfm?id=1719976&type=pdf&coll=ACM&dl=ACM&CFID=78317288&CFTOKEN=12971413
  • http://twitpic.com/1216bu
  • Personalized News Recommendation Based on Click BehaviorJiahui Liu, Peter Dolan, Elin Rønby Pedersen, Google Inc., USA
    http://portal.acm.org/ft_gateway.cfm?id=1719976&type=pdf&coll=ACM&dl=ACM&CFID=78317288&CFTOKEN=12971413
  • Aspect-Level News BrowsingS. Park, KAIST, Korea
    Media bias comparison
    Puts news snippets of different services side-to-side
    Splits articles in (1)first and mostly similar reports and (2) later, more and more diverse articles / comments
    Scans title, subtitle and lead for keywords and clusters common and uncommon keywords
    Uses uncommon keywords to make opinion opposites:
    http://newscube.kr/
  • Aspect-Level News BrowsingS. Park, KAIST, Korea
    Audience suggested a dynamic number of clusters…
    http://nclab.kaist.ac.kr/papers/Conference/NewsCube.pdf
  • Agent-Assisted Task Management that Reduces Email OverloadA. Faulring et al., CMU, USA
    Offspring of RADAR project in DARPAs PAL program
    Turns inbox into action-list (task-based) by scanning emails for commonly mentioned tasks
    Statistically significantly helped users organize their email and tasks
    RADAR 2.0 system: task-centric workflow enabled by AI technologies helps users
    User performance varied significantly
    Hypothesis: Some users had difficulties finding a high-level strategy for completing the work (novice users lacked meta-knowledge about tasks such as task importance, expected task duration, and task ordering dependencies)
  • Agent-Assisted Task Management that Reduces Email OverloadA. Faulring et al., CMU, USA
    http://portal.acm.org/ft_gateway.cfm?id=1719980&type=pdf&coll=ACM&dl=ACM&CFID=78317288&CFTOKEN=12971413
  • Agent-Assisted Task Management that Reduces Email OverloadA. Faulring et al., CMU, USA
    Simulated conference-planning scenario
    Scheduling, website, informational requests, vendors, briefing
    An evaluation score, designed by external program evaluators, summarized overall performance into a single objective score ( 0 – 1 )
    http://portal.acm.org/ft_gateway.cfm?id=1719980&type=pdf&coll=ACM&dl=ACM&CFID=78317288&CFTOKEN=12971413
  • Tell Me More, Not Just More of the SameF. Lacobelli et al. , Northwestern Univ., USA
    Most existing approaches are vector-set analyses based on words, phrases or time
    User picks a news article to read (full article on a specific news website) and "more" information is shown along-side from various sources
    Paragraph analysis based on OpenCalais combined with WPED (checks if entities are present in wikipedia to normalize the results of OC)
    To tackle problems like senator Kennedy != ted Kennedy
    http://portal.acm.org/ft_gateway.cfm?id=1719982&type=pdf&coll=ACM&dl=ACM&CFID=78317288&CFTOKEN=12971413
  • Tell Me More, Not Just More of the SameF. Lacobelli et al. , Northwestern Univ., USA
    http://portal.acm.org/ft_gateway.cfm?id=1719982&type=pdf&coll=ACM&dl=ACM&CFID=78317288&CFTOKEN=12971413
  • Tell Me More, Not Just More of the SameF. Lacobelli et al. , Northwestern Univ., USA
    Evaluation suggested that users trust the new information presented
    96% of participants read news online, 76% of them consult more than onesource
    Respondents said TellMeMore contains relevant details and background information
    They would like to see a similar interface in their news reading experience
    http://infolab.northwestern.edu/projects/news-at-seven/
  • Business Microscope: Interfacing with Organizational NetworksKazuo Yano, Hitachi, Japan
    Analysis of business participants mimics, also tracking movement, activities of employees of companies and storing in huge db for analysis <-> Audience: Big Brother!
    Organizational-behavior db with about 100.000 data sets
    http://www.hitachi-hitec.com/global/business-microscope/solution/index.html
  • http://www.hitachi-hitec.com/global/business-microscope/solution/index.html
  • Business Microscope: Interfacing with Organizational NetworksKazuo Yano, Hitachi, Japan
    Organizational network visualization with: each person one node, connected to other persons that they are in contact with by springs: more dense interaction equals stronger springs
    ~ most important persons at the center
    Raises questions about the general applicability of laws of physics on social / organizational science
    Allows for “biofeedback effects” if organization is seen as an organism
    http://www.hitachi-hitec.com/global/business-microscope/solution/index.html
  • Business Microscope: Interfacing with Organizational NetworksKazuo Yano, Hitachi, Japan
    http://www.youtube.com/watch?v=mlGFzevfftk&feature=related
    http://www.youtube.com/watch?v=cGW15V9Lt80&feature=related
  • Rush: Repeated Recommendations on Mobile DevicesD. Baur et. al., Univ. of Munich, Germany
    Interesting approach for recommendation + interaction as opposed to limited recommendation systems
    Most motivating introduction of IUI 2010:
    http://www.slideshare.net/dominikus/rush-repeated-recommendations-on-mobile-devices-iui10-3119488
    Next 9 slides are a direct rip-off / re-enactment!
  • http://www.slideshare.net/dominikus/rush-repeated-recommendations-on-mobile-devices-iui10-3119488
  • http://www.slideshare.net/dominikus/rush-repeated-recommendations-on-mobile-devices-iui10-3119488
  • http://www.slideshare.net/dominikus/rush-repeated-recommendations-on-mobile-devices-iui10-3119488
  • http://www.slideshare.net/dominikus/rush-repeated-recommendations-on-mobile-devices-iui10-3119488
  • http://www.slideshare.net/dominikus/rush-repeated-recommendations-on-mobile-devices-iui10-3119488
  • http://www.slideshare.net/dominikus/rush-repeated-recommendations-on-mobile-devices-iui10-3119488
  • http://www.slideshare.net/dominikus/rush-repeated-recommendations-on-mobile-devices-iui10-3119488
  • http://www.slideshare.net/dominikus/rush-repeated-recommendations-on-mobile-devices-iui10-3119488
  • http://www.slideshare.net/dominikus/rush-repeated-recommendations-on-mobile-devices-iui10-3119488
  • Rush: Repeated Recommendations on Mobile DevicesD. Baur et. al., Univ. of Munich, Germany
    http://portal.acm.org/ft_gateway.cfm?id=1719984&type=pdf&coll=ACM&dl=ACM&CFID=78317288&CFTOKEN=12971413
  • Rush: Repeated Recommendations on Mobile DevicesD. Baur et. al., Univ. of Munich, Germany
    http://www.youtube.com/watch?v=2nGopSdD-hA
  • Social Search BrowserK. Chruch et. al. Telefonica Research, Spain
    Questions of mobile phone users placed on map locations, so people close-by can help
    Exploratory Search
    “In standard Web search, users submit a query via a searchbox and view a textual list of results. More recently, a newclass of search has emerged, called exploratory search…”
    Only SMS notifications really encouraged interaction
    http://portal.acm.org/ft_gateway.cfm?id=1719985&type=pdf&coll=ACM&dl=ACM&CFID=78317288&CFTOKEN=12971413
  • Social Search BrowserK. Chruch et. al. Telefonica Research, Spain
    http://portal.acm.org/ft_gateway.cfm?id=1719985&type=pdf&coll=ACM&dl=ACM&CFID=78317288&CFTOKEN=12971413
  • Estimating User's Acute Engagement from Eye-gaze Behaviors in Human-Agent ConversationsY. Nakano & R. Yukiko, Seikei Uni., Japan
    Eye-movement has large impact on dialogues (turn-taking, grounding, etc.)
    Tracking dialogues with a mobile phone sales agent
    Survey showed statistically significant increase in "natural feel" of the conversation as well as avoiding distraction
    http://portal.acm.org/ft_gateway.cfm?id=1719990&type=pdf&coll=ACM&dl=ACM&CFID=78317288&CFTOKEN=12971413
  • Estimating User's Acute Engagement from Eye-gaze Behaviors in Human-Agent ConversationsY. Nakano & R. Yukiko, Seikei Uni., Japan
    http://portal.acm.org/ft_gateway.cfm?id=1719990&type=pdf&coll=ACM&dl=ACM&CFID=78317288&CFTOKEN=12971413
  • Embedded Media Markers: Marks on Paper that Signify Associated MediaQ. Liu et al., FXPAL, USA
    Markers both human and machine readable
    http://www.youtube.com/watch?v=K-Qdap6h9TQ
    http://www.fxpal.com/?p=abstract&abstractID=551
    http://www.fxpal.com/publications/FXPAL-PR-10-551.pdf
  • Lowering the Barriers to Website Testing with CoScripterM. Jalal & T. Lau, IBM Research, USA
    Nice FF plugin to record and share java-script macros
    Lots of automation features and smart inter-exchangeable variables
    http://coscripter.researchlabs.ibm.com/
  • Facilitating Exploratory Search by Model-Based Navigational CuesW. Fu et al., UIUC, USA
    Background knowledge of people in the same culture tends to have shared structures
    using similar vocabularies and their corresponding meanings
    users of the same social tagging system may also share similar semantic representations of words and concepts
    For simple information retrieval expert networks serve better purpose
    For exploratory search a match of internal knowledge and external folksonomies is important (better for expert - expert and novice - novice)
    Results have significant implications on how social information systems should be designed to facilitate knowledge exchange among users with different background knowledge
    Social tags are more important in exploratory search
    http://portal.acm.org/ft_gateway.cfm?id=1719998&type=pdf&coll=ACM&dl=ACM&CFID=78317288&CFTOKEN=12971413
  • Facilitating Exploratory Search by Model-Based Navigational CuesW. Fu et al., UIUC, USA
    http://portal.acm.org/ft_gateway.cfm?id=1719998&type=pdf&coll=ACM&dl=ACM&CFID=78317288&CFTOKEN=12971413
  • Facilitating Exploratory Search by Model-Based Navigational CuesW. Fu et al., UIUC, USA
    http://portal.acm.org/ft_gateway.cfm?id=1719998&type=pdf&coll=ACM&dl=ACM&CFID=78317288&CFTOKEN=12971413
  • Facilitating Exploratory Search by Model-Based Navigational CuesW. Fu et al., UIUC, USA
    http://portal.acm.org/ft_gateway.cfm?id=1719998&type=pdf&coll=ACM&dl=ACM&CFID=78317288&CFTOKEN=12971413
  • Towards a Reputation-based Model of Social Web SearchK. McNally, et al., Univ. College Dublin, Ireland
    HeyStacks: Support collaboration on search
    Recommendations based on user experiences
    • Automatically calculates reputation scores for users to value their contributions (i.e. points per follow-up by other users)
    http://www.heystaks.com/
  • A Code Reuse Interface for Non-Programmer Middle School StudentsP. Gross et al., Washington Univ., USA
    Based on „Looking Glass Storytelling“ (programming) learning-tool for middleschoolers
    Correlates function calls to screenshots from storytelling action view
    Animations propagated through different working and presentation groups
    http://portal.acm.org/ft_gateway.cfm?id=1720001&type=pdf&coll=ACM&dl=ACM&CFID=78317288&CFTOKEN=12971413
  • Speeding Pointing in Tiled WidgetsJ. Ruiz & E. Lank, Univ. of Waterloo, Canada
    Based on using Fitt's Law to predict motion targets and then resizing them to allow for better performance
    Results not easily adaptable to different use cases
    http://portal.acm.org/ft_gateway.cfm?id=1720002&type=pdf&coll=ACM&dl=ACM&CFID=78317288&CFTOKEN=12971413
  • QuickWoZ: A Multi-purpose Wizard-of-Oz Framework for Experiments with Embodied Conversational Agents
    Jan Smeddinck, Kamila Wajda, et. al.
    Digital Media, FB 3, University of Bremen, Germany
  • Using Sketch Recognition to Teach DrawingTracy Hammond, Texas A&M Univ., USA
    Sketch recognition
    Tool uses an off-the-shelf face recognizer to help sketching students learn to draw better faces.
    Tracy is also the creator of the tech behind all the sketch-a-car-and-watch-it-move physics demos that appeared in the last year or so, see a video of her original approach here.
    http://faculty.cs.tamu.edu/hammond/
    Via: http://ianozsvald.com/2010/02/07/intelligent-user-interfaces-2010-conference/
    http://www.flickr.com/photos/54145418@N00/4343172889/
  • Why UI: Using Goal Networks to Improve User InterfacesD. A. Smith & H. Lieberman, MIT, USA
    • Matching Users Problems / Questions with typical solutions and embedding the results in a map-based interface
    Mapping how people solve tasks by performing natural language processing at 43Things to build networks of goals
    Automatically extract the steps required to solve goals by analyzing existing stories
    http://farm5.static.flickr.com/4055/4343923626_9c77b9e617_o.jpg
  • Other Notable Posters / Demos
    Avara: a system to improve user experience in web and virtual world
    Important for online games 
    An intuitive texture picker
    Similarities to HSBs Sound Torch
    Automatic configuration of spatially consistent mouse pointer navigation in multi-display environments
    Basic work towards future shared systems
    Understanding web documents using semantic overlays
    Relevance to CEI development
    NAO Demo:
    http://www.youtube.com/watch?v=VdhGYn32ACg&feature=youtu.be&a
  • Thanks! Questions?
    http://www.youtube.com/watch?v=VdhGYn32ACg&feature=youtu.be&a