Proceedings of the 20th annual ACM symposium on User interface software and technology
Bibliographic record
Abstract
It is our pleasure to welcome you to Newport, Rhode Island, USA and to UIST 2007, the Twentieth Annual ACM Symposium on User Interface Software and Technology. The field has come a long way since our first conference twenty years ago, but UIST continues to be the premier forum for innovations in the software and technology of human-computer interfaces. Sponsored by ACM's special interest groups on computer-human interaction (SIGCHI) and computer graphics (SIGGRAPH), UIST brings together researchers and practitioners from diverse areas that include traditional graphical & web user interfaces, tangible & ubiquitous computing, virtual & augmented reality, multimedia, new input & output devices, and CSCW. The intimate size, the single track, and comfortable surroundings make this symposium an ideal opportunity to exchange research results and implementation experiences. The call for papers this year attracted a record 195 submissions (129 full papers and 66 tech notes), and the program committee accepted 24 full papers and 9 tech notes covering a wide range of topics. The technical program also includes posters, demos, and the doctoral symposium. In addition, this year's program includes an invited panel on Evaluating User Interface Systems Research anchored by an invited paper by Dan Olsen. We are pleased to have two invited keynotes: David Woods from the Ohio State University on Measuring How Design Changes Cognition at Work, and Jeremy Wolfe from Harvard Medical School on Capturing the User's Attention: Insights from the Study of Human Vision. In celebration of UIST's 20th Anniversary, we will unveil an interactive visualization of the past twenty years of UIST contributions and a commemorative video, and we have included the complete archive of UIST proceedings on the conference DVD. The cover of this year's proceedings features a mosaic created by designer Chris Harrison that is a composite of at least one image from every paper published in the first 20 years of UIST.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.162 | 0.098 |
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".