Adjunct Proceedings of the 28th Annual ACM Symposium on User Interface Software & Technology
Bibliographic record
Abstract
We are very excited to welcome you to the 28th Annual ACM Symposium on User Interface Software and Technology (UIST), held from November 8-11th 2015, in Charlotte, North Carolina, USA. UIST is the premier forum for the presentation of research 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 including graphical & web user interfaces, tangible & ubiquitous computing, virtual & augmented reality, multimedia, new input & output devices, fabrication, wearable computing and CSCW. UIST 2015 received 297 technical paper submissions. After a thorough review process, the 39-member program committee accepted 70 papers (23.6%). Each anonymous submission that entered the full review process was first reviewed by three external reviewers, and a meta-review was provided by a program committee member. If, after these four reviews, the submission was deemed to pass a rebuttal threshold, we asked the authors to submit a short rebuttal addressing the reviewers' concerns. A second member of the program committee was then asked to examine the paper, rebuttal, and reviews, and to provide their own meta-review. The program committee met in person in Berkeley, California, USA on June 25th and 26th, 2015, to select which papers to invite for the program. Submissions were accepted only after the authors provided a final revision addressing the committee's comments. In addition to papers, our program includes two papers from the ACM Transactions on Computer-Human Interaction journal (TOCHI), as well as 22 posters, 45 demonstrations, and 8 student presentations in the eleventh annual Doctoral Symposium. Our program also features the seventh annual Student Innovation Contest. Teams from all over the world will compete in this year's contest, which focuses on blurring the lines between art and engineering and creating tools for robotic storytelling. UIST 2015 will feature two keynote presentations. The opening keynote will be given by Ramesh Raskar (MIT Media Lab) on extreme computational imaging. Blaise Aguera Y Arcas from Google will deliver the closing keynote on machine intelligence. We welcome you to Charlotte, a city full of southern hospitality. We hope that you will find the technical program interesting and thought-provoking. We also hope that UIST 2015 will provide you with enjoyable opportunities to engage with fellow researchers from both industry and academia, from institutions around the world.
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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 | 0.008 | 0.023 |
| 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 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.193 | 0.122 |
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".