IUI 2010 Informal Summary<br />http://www.iuiconf.org/images/iui2010_banner.jpg<br />Highlights from the main track, poste...
About this Summary<br />Compilation of personal notes from the conference & proceedings, twitter (#iui2010), Ian Ozsvald's...
IUI General Information<br />IUI = Intelligent User Interfaces<br />Single track conference with corporate and univ. parti...
VISSW/UDISW Workshop<br />Visual Interfaces to the Social and Semantic Web<br />http://smart-ui.org/events/vissw2010/<br /...
Ontology Based Queries – Investigating a Natural Language InterfaceIelka van der Sluis et al., Trinity College Dublin, Ire...
 An Intelligent Query Interface Based on Ontology NavigationEnrico Franconi et al., Free University of Bozen-Bolzano, Ital...
 An Intelligent Query Interface Based on Ontology NavigationEnrico Franconi et al., Free University of Bozen-Bolzano, Ital...
Semantic Cloud: An Enhanced Browsing Interface for Exploring Resources in Folksonomy SystemsHidir Aras, Sandra Siegel, Rai...
http://semanticcloud.sandra-siegel.de/<br />
Designing Social Mobile Interfaces: Experiences with MobiMood, a Mobile Mood Sharing ApplicationKaren Church et al., Telef...
Designing Social Mobile Interfaces: Experiences with MobiMood, a Mobile Mood Sharing ApplicationKaren Church et al., Telef...
EGIHMI Workshop<br />International Workshop on Eye Gaze in Intelligent Human Machine Interaction<br />http://links.cse.msu...
The Text 2.0 Framework – Writing Web-Based Gaze-Controlled Realtime Applications Quickly and EasilyR. Biedert et al., DFKI...
IUI 2010 Conference<br />
Cortically-Coupled Computer VisionPaul Sajda et al., Columbia University, USA<br />Image recognition at the blink of an ey...
Cortically-Coupled Computer VisionPaul Sajda et al., Columbia University, USA<br />Sample subset of a large image db<br />...
Personalized News Recommendation Based on Click BehaviorJiahui Liu, Peter Dolan, Elin Rønby Pedersen, Google Inc., USA<br ...
http://twitpic.com/1216bu<br />
Personalized News Recommendation Based on Click BehaviorJiahui Liu, Peter Dolan, Elin Rønby Pedersen, Google Inc., USA<br ...
Aspect-Level News BrowsingS. Park, KAIST, Korea<br />Media bias comparison<br />Puts news snippets of different services s...
Aspect-Level News BrowsingS. Park, KAIST, Korea<br />Audience suggested a dynamic number of clusters…<br />http://nclab.ka...
Agent-Assisted Task Management that Reduces Email OverloadA. Faulring et al., CMU, USA<br />Offspring of RADAR project in ...
Agent-Assisted Task Management that Reduces Email OverloadA. Faulring et al., CMU, USA<br />http://portal.acm.org/ft_gatew...
Agent-Assisted Task Management that Reduces Email OverloadA. Faulring et al., CMU, USA<br />Simulated conference-planning ...
Tell Me More, Not Just More of the SameF. Lacobelli et al. , Northwestern Univ., USA<br />Most existing approaches are vec...
Tell Me More, Not Just More of the SameF. Lacobelli et al. , Northwestern Univ., USA<br />http://portal.acm.org/ft_gateway...
Tell Me More, Not Just More of the SameF. Lacobelli et al. , Northwestern Univ., USA<br />Evaluation suggested that users ...
Business Microscope: Interfacing with Organizational NetworksKazuo Yano, Hitachi, Japan<br />Analysis of business particip...
http://www.hitachi-hitec.com/global/business-microscope/solution/index.html<br />
Business Microscope: Interfacing with Organizational NetworksKazuo Yano, Hitachi, Japan<br />Organizational network visual...
Business Microscope: Interfacing with Organizational NetworksKazuo Yano, Hitachi, Japan<br />http://www.youtube.com/watch?...
Rush: Repeated Recommendations on Mobile DevicesD. Baur et. al., Univ. of Munich, Germany<br />Interesting approach for re...
http://www.slideshare.net/dominikus/rush-repeated-recommendations-on-mobile-devices-iui10-3119488<br />
http://www.slideshare.net/dominikus/rush-repeated-recommendations-on-mobile-devices-iui10-3119488<br />
http://www.slideshare.net/dominikus/rush-repeated-recommendations-on-mobile-devices-iui10-3119488<br />
http://www.slideshare.net/dominikus/rush-repeated-recommendations-on-mobile-devices-iui10-3119488<br />
http://www.slideshare.net/dominikus/rush-repeated-recommendations-on-mobile-devices-iui10-3119488<br />
http://www.slideshare.net/dominikus/rush-repeated-recommendations-on-mobile-devices-iui10-3119488<br />
http://www.slideshare.net/dominikus/rush-repeated-recommendations-on-mobile-devices-iui10-3119488<br />
http://www.slideshare.net/dominikus/rush-repeated-recommendations-on-mobile-devices-iui10-3119488<br />
Rush: Repeated Recommendations on Mobile DevicesD. Baur et. al., Univ. of Munich, Germany<br />http://portal.acm.org/ft_ga...
Rush: Repeated Recommendations on Mobile DevicesD. Baur et. al., Univ. of Munich, Germany<br />http://www.youtube.com/watc...
Social Search BrowserK. Chruch et. al. Telefonica Research, Spain<br />Questions of mobile phone users placed on map locat...
Social Search BrowserK. Chruch et. al. Telefonica Research, Spain<br />http://portal.acm.org/ft_gateway.cfm?id=1719985&typ...
Estimating User's Acute Engagement from Eye-gaze Behaviors in Human-Agent ConversationsY. Nakano & R. Yukiko, Seikei Uni.,...
Embedded Media Markers: Marks on Paper that Signify Associated MediaQ. Liu et al., FXPAL, USA<br />Markers both human and ...
Lowering the Barriers to Website Testing with CoScripterM. Jalal & T. Lau, IBM Research, USA<br />Nice FF plugin to record...
Facilitating Exploratory Search by Model-Based Navigational CuesW. Fu et al., UIUC, USA<br />Background knowledge of peopl...
Facilitating Exploratory Search by Model-Based Navigational CuesW. Fu et al., UIUC, USA<br />http://portal.acm.org/ft_gate...
Facilitating Exploratory Search by Model-Based Navigational CuesW. Fu et al., UIUC, USA<br />http://portal.acm.org/ft_gate...
Towards a Reputation-based Model of Social Web SearchK. McNally, et al., Univ. College Dublin, Ireland<br />HeyStacks: Sup...
A Code Reuse Interface for Non-Programmer Middle School StudentsP. Gross et al., Washington Univ., USA<br />Based on „Look...
Speeding Pointing in Tiled WidgetsJ. Ruiz & E. Lank, Univ. of Waterloo, Canada<br />Based on using Fitt's Law to predict m...
QuickWoZ: A Multi-purpose Wizard-of-Oz Framework for Experiments with Embodied Conversational Agents<br />Jan Smeddinck, K...
Using Sketch Recognition to Teach DrawingTracy Hammond, Texas A&M Univ., USA<br />Sketch recognition<br />Tool uses an off...
Why UI: Using Goal Networks to Improve User InterfacesD. A. Smith & H. Lieberman, MIT, USA<br /><ul><li>Matching Users Pro...
Other Notable Posters / Demos<br />Avara: a system to improve user experience in web and virtual world<br />Important for ...
Thanks! Questions?<br />http://www.youtube.com/watch?v=VdhGYn32ACg&feature=youtu.be&a<br />
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
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
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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 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 &lt;- too general and depends on how &quot;relation&quot; is determined!Semi-automatic clustering
  • The brain emits a signal as soon as it sees something interesting, and that &quot;aha&quot; 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 &lt;http://www.wired.com/medtech/health/news/2006/07/71364&gt;
  • Large workgroup including psychologists, economists, comp. sc., etc.WHAT IF WE APPLY OTHER FORCES TO THE NETWORK : PRESSURE / REMOVE NODES / OBSERVE SPREADMORE TIES SPRINGS -&gt; 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&amp;type=pdf&amp;coll=ACM&amp;dl=ACM&amp;CFID=78317288&amp;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&amp;type=pdf&amp;coll=ACM&amp;dl=ACM&amp;CFID=78317288&amp;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

    1. 1. IUI 2010 Informal Summary<br />http://www.iuiconf.org/images/iui2010_banner.jpg<br />Highlights from the main track, poster/demo-session & the VISSW/UDISW/EGIHMI workshops<br />Jan Smeddinck & Hidir Aras<br />jan83(at)tzi(dot)de | aras(at)tzi(dot)de<br />Digital Media, FB 3, University of Bremen, Germany<br />
    2. 2. About this Summary<br />Compilation of personal notes from the conference & proceedings, twitter (#iui2010), Ian Ozsvald's blog (http://ianozsvald.com/), and more…<br />Biased for the digital media workgroup … had to skip many interesting pieces of work <br />Sloppy references – lack of time – but all links!<br />Will be on slideshare:<br />http://www.slideshare.net/Sanook/presentations<br />
    3. 3. IUI General Information<br />IUI = Intelligent User Interfaces<br />Single track conference with corporate and univ. participation<br />Formerly workshop, yearly conference since 1997<br />ACM sponsored<br />HCI meets AI and related fields…<br />~ 30 % paper acceptance rate<br />Website: http://www.iuiconf.org/<br />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<br />
    4. 4.
    5. 5. VISSW/UDISW Workshop<br />Visual Interfaces to the Social and Semantic Web<br />http://smart-ui.org/events/vissw2010/<br />User Data Interoperability in the Social Web<br />http://www.wis.ewi.tudelft.nl/UDISW2010/<br />
    6. 6. Ontology Based Queries – Investigating a Natural Language InterfaceIelka van der Sluis et al., Trinity College Dublin, Ireland<br />Qualitative comparison study between the written interface semantic web browser "Longwell" and the natural language query interface "LIBER"<br />Test was done with queries about US geograpy posed by untrained users<br />Complex tasks (e.g. How many lakes are there in a certain state?)<br />“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.”<br />http://www.smart-ui.org/events/vissw2010/papers/VISSW2010_Sluis.pdf<br />
    7. 7. An Intelligent Query Interface Based on Ontology NavigationEnrico Franconi et al., Free University of Bozen-Bolzano, Italy<br />Ontology based data access:<br />How to formulate queries?<br />Ontology navigation / queries:<br />Queries as multi-labelled trees<br />Alternative: Written language<br />Lexicon derived from the ontology (engineers definition)<br />Problematic<br />70% success rate<br />http://www.smart-ui.org/events/vissw2010/papers/VISSW2010_Franconi.pdf<br />
    8. 8. An Intelligent Query Interface Based on Ontology NavigationEnrico Franconi et al., Free University of Bozen-Bolzano, Italy<br />http://www.smart-ui.org/events/vissw2010/papers/VISSW2010_Franconi.pdf<br />
    9. 9. Semantic Cloud: An Enhanced Browsing Interface for Exploring Resources in Folksonomy SystemsHidir Aras, Sandra Siegel, Rainer Malaka, University of Bremen, Germany<br />Innovative interface approach for browsing resources in folksonomy systems<br />based on a hierarchical semantic representation of the folksonomy space using tag co-occurrence analysis<br />Provides multiple topic clouds that can be explored hierarchically<br />Allows for the composition of queries from the tag cloud, while consulting results and refining the query afterwards<br />
    10. 10. http://semanticcloud.sandra-siegel.de/<br />
    11. 11. Designing Social Mobile Interfaces: Experiences with MobiMood, a Mobile Mood Sharing ApplicationKaren Church et al., Telefonica Research, Spain<br />http://www.smart-ui.org/events/vissw2010/papers/VISSW2010_Church.pdf<br />
    12. 12. Designing Social Mobile Interfaces: Experiences with MobiMood, a Mobile Mood Sharing ApplicationKaren Church et al., Telefonica Research, Spain<br />People were really interested in strangers moods<br />The interface was often abused for status updates<br />Concrete replies to moods not that frequent<br />Most used: custom moods / positive presets<br />Audience suggested to integrate the moods into the contact list … update frequency is a problem<br />http://www.smart-ui.org/events/vissw2010/papers/VISSW2010_Church.pdf<br />
    13. 13. EGIHMI Workshop<br />International Workshop on Eye Gaze in Intelligent Human Machine Interaction<br />http://links.cse.msu.edu:8000/iui/program.html<br />Information from Ian Ozsvald‘s report:<br />http://ianozsvald.com/2010/02/07/intelligent-user-interfaces-2010-conference/<br />
    14. 14. The Text 2.0 Framework – Writing Web-Based Gaze-Controlled Realtime Applications Quickly and EasilyR. Biedert et al., DFKI, Germany<br />http://www.youtube.com/watch?v=8QocWsWd7fc<br />http://text20.net/<br />Browser Plugin with mark-up for OnGazeOver, OnPersual, OnRead, etc.<br />Exciting Technology, but expensive tracking hardware!<br />
    15. 15. Robust Pupil Detection for Gaze-based User InterfacesW.H. Liao<br />60 $, 40x40 pixel accuracy, IR based, 30fps on core2<br />http://www.youtube.com/watch?v=WvWdwB6nTkk<br />
    16. 16. IUI 2010 Conference<br />
    17. 17. Cortically-Coupled Computer VisionPaul Sajda et al., Columbia University, USA<br />Image recognition at the blink of an eye…<br />System harnesses brain'sability to recognize an image much faster than the person can identifyit<br />Neural activity recording of visual cortex activity while observing flashing images to score "gist" of the images<br />Standard signal + target + novel items neural activity test<br />Normally done with averaging many iterations<br />How to achieve single test precision?<br />Decode EEG signal over time (~ 800 ms per sample) and space (electrodes spread over the skull) <br />http://www.wired.com/medtech/health/news/2006/07/71364<br />http://newton.bme.columbia.edu/publications/triage_ieee.pdf<br />http://www.wired.com/news/images/full/brain1_f.jpg<br />
    18. 18. Cortically-Coupled Computer VisionPaul Sajda et al., Columbia University, USA<br />Sample subset of a large image db<br />> feature abstraction of entire image db based on sample subset results<br />C3 vision search: E.g. help with labeling in maps:<br />vision module recognizes possibly interesting regions in huge maps<br />Chips of possibly interesting images shown rapidly to the actual labeller (person)<br />Then lead the labeler to image(-sections) of interest<br />
    19. 19. Personalized News Recommendation Based on Click BehaviorJiahui Liu, Peter Dolan, Elin Rønby Pedersen, Google Inc., USA<br />Web provides access to news articles from millions of sources around the world<br />Help users find the articles that are interesting to read<br />Recommendationsystem builds profiles of users’ news interests based on their past click behavior<br />large-scale analysis of anonymized Google News users click logs<br />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<br />Deployed in Google News<br />Improves quality of news recommendation and increases traffic<br />http://portal.acm.org/ft_gateway.cfm?id=1719976&type=pdf&coll=ACM&dl=ACM&CFID=78317288&CFTOKEN=12971413<br />
    20. 20. http://twitpic.com/1216bu<br />
    21. 21. Personalized News Recommendation Based on Click BehaviorJiahui Liu, Peter Dolan, Elin Rønby Pedersen, Google Inc., USA<br />http://portal.acm.org/ft_gateway.cfm?id=1719976&type=pdf&coll=ACM&dl=ACM&CFID=78317288&CFTOKEN=12971413<br />
    22. 22. Aspect-Level News BrowsingS. Park, KAIST, Korea<br />Media bias comparison<br />Puts news snippets of different services side-to-side<br />Splits articles in (1)first and mostly similar reports and (2) later, more and more diverse articles / comments<br />Scans title, subtitle and lead for keywords and clusters common and uncommon keywords<br />Uses uncommon keywords to make opinion opposites: <br />http://newscube.kr/<br />
    23. 23. Aspect-Level News BrowsingS. Park, KAIST, Korea<br />Audience suggested a dynamic number of clusters…<br />http://nclab.kaist.ac.kr/papers/Conference/NewsCube.pdf<br />
    24. 24. Agent-Assisted Task Management that Reduces Email OverloadA. Faulring et al., CMU, USA<br />Offspring of RADAR project in DARPAs PAL program<br />Turns inbox into action-list (task-based) by scanning emails for commonly mentioned tasks<br />Statistically significantly helped users organize their email and tasks<br />RADAR 2.0 system: task-centric workflow enabled by AI technologies helps users<br />User performance varied significantly<br />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)<br />
    25. 25. Agent-Assisted Task Management that Reduces Email OverloadA. Faulring et al., CMU, USA<br />http://portal.acm.org/ft_gateway.cfm?id=1719980&type=pdf&coll=ACM&dl=ACM&CFID=78317288&CFTOKEN=12971413<br />
    26. 26. Agent-Assisted Task Management that Reduces Email OverloadA. Faulring et al., CMU, USA<br />Simulated conference-planning scenario<br />Scheduling, website, informational requests, vendors, briefing<br />An evaluation score, designed by external program evaluators, summarized overall performance into a single objective score ( 0 – 1 )<br />http://portal.acm.org/ft_gateway.cfm?id=1719980&type=pdf&coll=ACM&dl=ACM&CFID=78317288&CFTOKEN=12971413<br />
    27. 27. Tell Me More, Not Just More of the SameF. Lacobelli et al. , Northwestern Univ., USA<br />Most existing approaches are vector-set analyses based on words, phrases or time<br />User picks a news article to read (full article on a specific news website) and "more" information is shown along-side from various sources<br />Paragraph analysis based on OpenCalais combined with WPED (checks if entities are present in wikipedia to normalize the results of OC)<br />To tackle problems like senator Kennedy != ted Kennedy<br />http://portal.acm.org/ft_gateway.cfm?id=1719982&type=pdf&coll=ACM&dl=ACM&CFID=78317288&CFTOKEN=12971413<br />
    28. 28. Tell Me More, Not Just More of the SameF. Lacobelli et al. , Northwestern Univ., USA<br />http://portal.acm.org/ft_gateway.cfm?id=1719982&type=pdf&coll=ACM&dl=ACM&CFID=78317288&CFTOKEN=12971413<br />
    29. 29. Tell Me More, Not Just More of the SameF. Lacobelli et al. , Northwestern Univ., USA<br />Evaluation suggested that users trust the new information presented<br />96% of participants read news online, 76% of them consult more than onesource<br />Respondents said TellMeMore contains relevant details and background information<br />They would like to see a similar interface in their news reading experience<br />http://infolab.northwestern.edu/projects/news-at-seven/<br />
    30. 30. Business Microscope: Interfacing with Organizational NetworksKazuo Yano, Hitachi, Japan<br />Analysis of business participants mimics, also tracking movement, activities of employees of companies and storing in huge db for analysis <-> Audience: Big Brother!<br />Organizational-behavior db with about 100.000 data sets<br />http://www.hitachi-hitec.com/global/business-microscope/solution/index.html<br />
    31. 31. http://www.hitachi-hitec.com/global/business-microscope/solution/index.html<br />
    32. 32. Business Microscope: Interfacing with Organizational NetworksKazuo Yano, Hitachi, Japan<br />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<br />~ most important persons at the center<br />Raises questions about the general applicability of laws of physics on social / organizational science<br />Allows for “biofeedback effects” if organization is seen as an organism<br />http://www.hitachi-hitec.com/global/business-microscope/solution/index.html<br />
    33. 33. Business Microscope: Interfacing with Organizational NetworksKazuo Yano, Hitachi, Japan<br />http://www.youtube.com/watch?v=mlGFzevfftk&feature=related<br />http://www.youtube.com/watch?v=cGW15V9Lt80&feature=related<br />
    34. 34. Rush: Repeated Recommendations on Mobile DevicesD. Baur et. al., Univ. of Munich, Germany<br />Interesting approach for recommendation + interaction as opposed to limited recommendation systems<br />Most motivating introduction of IUI 2010:<br />http://www.slideshare.net/dominikus/rush-repeated-recommendations-on-mobile-devices-iui10-3119488<br />Next 9 slides are a direct rip-off / re-enactment!<br />
    35. 35. http://www.slideshare.net/dominikus/rush-repeated-recommendations-on-mobile-devices-iui10-3119488<br />
    36. 36. http://www.slideshare.net/dominikus/rush-repeated-recommendations-on-mobile-devices-iui10-3119488<br />
    37. 37. http://www.slideshare.net/dominikus/rush-repeated-recommendations-on-mobile-devices-iui10-3119488<br />
    38. 38. http://www.slideshare.net/dominikus/rush-repeated-recommendations-on-mobile-devices-iui10-3119488<br />
    39. 39. http://www.slideshare.net/dominikus/rush-repeated-recommendations-on-mobile-devices-iui10-3119488<br />
    40. 40. http://www.slideshare.net/dominikus/rush-repeated-recommendations-on-mobile-devices-iui10-3119488<br />
    41. 41. http://www.slideshare.net/dominikus/rush-repeated-recommendations-on-mobile-devices-iui10-3119488<br />
    42. 42. http://www.slideshare.net/dominikus/rush-repeated-recommendations-on-mobile-devices-iui10-3119488<br />
    43. 43. http://www.slideshare.net/dominikus/rush-repeated-recommendations-on-mobile-devices-iui10-3119488<br />
    44. 44. Rush: Repeated Recommendations on Mobile DevicesD. Baur et. al., Univ. of Munich, Germany<br />http://portal.acm.org/ft_gateway.cfm?id=1719984&type=pdf&coll=ACM&dl=ACM&CFID=78317288&CFTOKEN=12971413<br />
    45. 45. Rush: Repeated Recommendations on Mobile DevicesD. Baur et. al., Univ. of Munich, Germany<br />http://www.youtube.com/watch?v=2nGopSdD-hA<br />
    46. 46. Social Search BrowserK. Chruch et. al. Telefonica Research, Spain<br />Questions of mobile phone users placed on map locations, so people close-by can help<br />Exploratory Search<br />“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…”<br />Only SMS notifications really encouraged interaction<br />http://portal.acm.org/ft_gateway.cfm?id=1719985&type=pdf&coll=ACM&dl=ACM&CFID=78317288&CFTOKEN=12971413<br />
    47. 47. Social Search BrowserK. Chruch et. al. Telefonica Research, Spain<br />http://portal.acm.org/ft_gateway.cfm?id=1719985&type=pdf&coll=ACM&dl=ACM&CFID=78317288&CFTOKEN=12971413<br />
    48. 48. Estimating User's Acute Engagement from Eye-gaze Behaviors in Human-Agent ConversationsY. Nakano & R. Yukiko, Seikei Uni., Japan<br />Eye-movement has large impact on dialogues (turn-taking, grounding, etc.)<br />Tracking dialogues with a mobile phone sales agent<br />Survey showed statistically significant increase in "natural feel" of the conversation as well as avoiding distraction<br />http://portal.acm.org/ft_gateway.cfm?id=1719990&type=pdf&coll=ACM&dl=ACM&CFID=78317288&CFTOKEN=12971413<br />
    49. 49. Estimating User's Acute Engagement from Eye-gaze Behaviors in Human-Agent ConversationsY. Nakano & R. Yukiko, Seikei Uni., Japan<br />http://portal.acm.org/ft_gateway.cfm?id=1719990&type=pdf&coll=ACM&dl=ACM&CFID=78317288&CFTOKEN=12971413<br />
    50. 50. Embedded Media Markers: Marks on Paper that Signify Associated MediaQ. Liu et al., FXPAL, USA<br />Markers both human and machine readable<br />http://www.youtube.com/watch?v=K-Qdap6h9TQ<br />http://www.fxpal.com/?p=abstract&abstractID=551<br />http://www.fxpal.com/publications/FXPAL-PR-10-551.pdf<br />
    51. 51. Lowering the Barriers to Website Testing with CoScripterM. Jalal & T. Lau, IBM Research, USA<br />Nice FF plugin to record and share java-script macros<br />Lots of automation features and smart inter-exchangeable variables<br />http://coscripter.researchlabs.ibm.com/<br />
    52. 52. Facilitating Exploratory Search by Model-Based Navigational CuesW. Fu et al., UIUC, USA<br />Background knowledge of people in the same culture tends to have shared structures<br />using similar vocabularies and their corresponding meanings<br />users of the same social tagging system may also share similar semantic representations of words and concepts<br />For simple information retrieval expert networks serve better purpose<br />For exploratory search a match of internal knowledge and external folksonomies is important (better for expert - expert and novice - novice)<br />Results have significant implications on how social information systems should be designed to facilitate knowledge exchange among users with different background knowledge<br />Social tags are more important in exploratory search<br />http://portal.acm.org/ft_gateway.cfm?id=1719998&type=pdf&coll=ACM&dl=ACM&CFID=78317288&CFTOKEN=12971413<br />
    53. 53. Facilitating Exploratory Search by Model-Based Navigational CuesW. Fu et al., UIUC, USA<br />http://portal.acm.org/ft_gateway.cfm?id=1719998&type=pdf&coll=ACM&dl=ACM&CFID=78317288&CFTOKEN=12971413<br />
    54. 54. Facilitating Exploratory Search by Model-Based Navigational CuesW. Fu et al., UIUC, USA<br />http://portal.acm.org/ft_gateway.cfm?id=1719998&type=pdf&coll=ACM&dl=ACM&CFID=78317288&CFTOKEN=12971413<br />
    55. 55. Facilitating Exploratory Search by Model-Based Navigational CuesW. Fu et al., UIUC, USA<br />http://portal.acm.org/ft_gateway.cfm?id=1719998&type=pdf&coll=ACM&dl=ACM&CFID=78317288&CFTOKEN=12971413<br />
    56. 56. Towards a Reputation-based Model of Social Web SearchK. McNally, et al., Univ. College Dublin, Ireland<br />HeyStacks: Support collaboration on search <br />Recommendations based on user experiences<br /><ul><li>Automatically calculates reputation scores for users to value their contributions (i.e. points per follow-up by other users)</li></ul>http://www.heystaks.com/<br />
    57. 57. A Code Reuse Interface for Non-Programmer Middle School StudentsP. Gross et al., Washington Univ., USA<br />Based on „Looking Glass Storytelling“ (programming) learning-tool for middleschoolers<br />Correlates function calls to screenshots from storytelling action view<br />Animations propagated through different working and presentation groups<br />http://portal.acm.org/ft_gateway.cfm?id=1720001&type=pdf&coll=ACM&dl=ACM&CFID=78317288&CFTOKEN=12971413<br />
    58. 58. Speeding Pointing in Tiled WidgetsJ. Ruiz & E. Lank, Univ. of Waterloo, Canada<br />Based on using Fitt's Law to predict motion targets and then resizing them to allow for better performance<br />Results not easily adaptable to different use cases<br />http://portal.acm.org/ft_gateway.cfm?id=1720002&type=pdf&coll=ACM&dl=ACM&CFID=78317288&CFTOKEN=12971413<br />
    59. 59. QuickWoZ: A Multi-purpose Wizard-of-Oz Framework for Experiments with Embodied Conversational Agents<br />Jan Smeddinck, Kamila Wajda, et. al.<br />Digital Media, FB 3, University of Bremen, Germany<br />
    60. 60. Using Sketch Recognition to Teach DrawingTracy Hammond, Texas A&M Univ., USA<br />Sketch recognition<br />Tool uses an off-the-shelf face recognizer to help sketching students learn to draw better faces. <br />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.<br />http://faculty.cs.tamu.edu/hammond/<br />Via: http://ianozsvald.com/2010/02/07/intelligent-user-interfaces-2010-conference/<br />http://www.flickr.com/photos/54145418@N00/4343172889/<br />
    61. 61. Why UI: Using Goal Networks to Improve User InterfacesD. A. Smith & H. Lieberman, MIT, USA<br /><ul><li>Matching Users Problems / Questions with typical solutions and embedding the results in a map-based interface</li></ul>Mapping how people solve tasks by performing natural language processing at 43Things to build networks of goals<br />Automatically extract the steps required to solve goals by analyzing existing stories<br />http://farm5.static.flickr.com/4055/4343923626_9c77b9e617_o.jpg<br />
    62. 62. Other Notable Posters / Demos<br />Avara: a system to improve user experience in web and virtual world<br />Important for online games <br />An intuitive texture picker<br />Similarities to HSBs Sound Torch<br />Automatic configuration of spatially consistent mouse pointer navigation in multi-display environments<br />Basic work towards future shared systems<br />Understanding web documents using semantic overlays<br />Relevance to CEI development<br />NAO Demo:<br />http://www.youtube.com/watch?v=VdhGYn32ACg&feature=youtu.be&a<br />
    63. 63. Thanks! Questions?<br />http://www.youtube.com/watch?v=VdhGYn32ACg&feature=youtu.be&a<br />

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