Proceedings of the 18th ACM symposium on Virtual reality software and technology
Bibliographic record
Abstract
It is our great pleasure to welcome you to the 2012 ACM Symposium on Virtual Reality Software and Technology -- VRST'12. VRST has become one of the major scientific events in the area of virtual reality since its debut in 1994 in Singapore. The symposium continues its tradition as an international forum for the presentation of research results and experience reports on leading edge issues of software, hardware and systems for Virtual Reality. The mission of the symposium is to share novel technologies that fulfill the needs of Virtual Reality applications and environments and to identify new directions for future research and development. VRST gives researchers and practitioners a unique opportunity to share their perspectives with others interested in the various aspects of Virtual Reality and owes its existence to a vibrant and productive research community. This year, VRST was held December 10-12, 2012 in Toronto, Ontario, Canada. The call for papers attracted 88 submissions from Asia, Europe, Australia, and North and South America in all areas of Virtual Reality research. Particular attention was given to work on system with a special track focusing on architectures, frameworks, reusability, adaptivity, and performance testing and evaluation. An international program committee consisting of 16 experts in the topic areas and the three program chairs handled the highly competitive and selective review process. Almost every submission received four or more reviews, two from members of the international program committee and two from external reviewers. Reviewing was double-blind, where only the program chairs and the program committee member assigned to identify external reviewers knew the identity of the authors. In the end, the program committee was able to accept 25 out of 88 submissions, which corresponds to an acceptance rate of 28%. For posters, 15 out of 32 submissions will appear in the proceedings. The topics range from tracking, augmented and mixed reality, interaction, navigation and locomotion, collaboration, haptics, simulation, agents and behaviors to two sessions for a systems track. We hope that these proceedings will serve as a valuable reference for Virtual Reality researchers and developers.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.120 | 0.056 |
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".