Proceedings of the 2013 international conference on Intelligent user interfaces
Bibliographic record
Abstract
It is our great pleasure to welcome you to the 2013 International Conference on Intelligent User Interfaces (IUI'13). This year marks the eighteenth meeting of this conference, continuing its tradition of being the principal international forum for reporting outstanding research at the intersection of Human-Computer Interaction (HCI) and Artificial Intelligence (AI). The work that appears at IUI bridges these two fields and also delves into related fields, such as psychology, cognitive science, computer graphics, the arts, and many others. Members of the IUI community are interested in improving the symbiosis between humans and computers, increasing the intelligence of both in the process. The call for papers attracted 195 submissions from Asia, Canada, Europe, Africa, and the United States. The program committee accepted 43 papers covering a diverse set of topics, including brain-computer interaction, social media analysis, automated design, and crowdsourcing. The program opens with a keynote by Professor Luis von Ahn on "Duolingo: Learn a Language for Free while Helping to Translate the Web," and closes with a keynote by Professor Monica S. Lam on "How Mobile Disrupts Social As We Know it." We also have an excellent poster and demonstration program consisting of 14 demos and 22 posters selected from a pool of 64 total submissions. In addition, the conference provides two exciting tutorials and four interesting workshops. The tutorials feature an introduction to Human Computation by Edith Law and an introduction to knowledge acquisition from the web and social media by Zornitsa Kozareva. The workshops cover topics ranging from interactive machine learning to IUI for developing worlds. No conference of this size could be organized without the help of a large number of individuals who volunteer an enormous amount of their own time. Their names can be found in the following pages and each and every one of these extraordinary volunteers deserve our thanks. We want to especially recognize all of the members of the organizing committee, who put in countless hours over nearly a year to make the conference happen. If you see one of them in the hotel bar at the conference, please buy them a beverage of their choice. We must also thank our senior program committee for coordinating the review process and all 654 members of the program committee for providing high quality reviews that exceeded even our lofty expectations. Last, but certainly not least, we must thank the authors for providing the content for the program that is the foundation of any successful conference. We look forward to your presentations!
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.146 | 0.075 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".