Proceedings of the ACM Symposium on Applied Perception
Bibliographic record
Abstract
It is our pleasure to present the proceedings of the Symposium on Applied Perception (ACM SAP) held in Vancouver, British Columbia, Canada, August 8-9, 2014. ACM SAP, formerly known as APGV, aims to advance and promote research that crosses the boundaries between perception and disciplines such as graphics, visualization, vision, haptics and acoustics. Our eleventh annual event includes exciting new research from all of these disciplines. We held the ACM SAP 2014 conference immediately prior to the SIGGRAPH conference as is customary for every even year of ACM SAP. By doing so, we hope to further promote communication between the core perception and computer graphics communities. The changing of the name to SAP was intended to broaden the scope of the conference and to encourage representation from all aspects of applied perception. We believe that this goal was again met this year. We were delighted to see a number of submissions that included auditory, haptic, and vestibular perception, in addition to the many papers investigating applied visual perception. We had 50 papers submitted for the SAP conference and 23 (16 long and 7 short) of those were accepted through reviews from at least three members of the International Program Committee. Bernhard Riecke also served as Poster's Chair and selected twelve submissions to be included in the meeting both as posters and flash forward 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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.187 | 0.059 |
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".