Proceedings of the 1st ACM/USENIX international conference on Virtual execution environments
Bibliographic record
Abstract
It is our great pleasure to welcome you to the 1st International Conference on Virtual Execution Environments - VEE'05. Up to now, research results on virtual execution engines were scattered among a number of different venues in the language (VM, PLDI, OOPSLA, IVME, ICFP), operating system (SOSP, OSDI), and architecture (ASPLOS, CGO, PACT) communities. The organizers of the USENIX VM Symposium and the ACM SIGPLAN IVME Workshop felt the needs of the community would be better served by a single conference that could address the breadth of issues related to virtual execution environments. VEE is intended to be a unique forum that brings together practitioners and researchers working on interpreters, high-level language virtual machines, machine emulators, translators, and machine simulators. VEE'05 gives researchers and practitioners a unique opportunity to share their perspectives with others interested in the various aspects of virtual execution environments. This year's VEE is co-located with PLDI 2005 in Chicago, Illinois. Future instances are planned jointly with leading conferences in operating systems, programming languages, and architecture.The call for papers attracted 65 submissions from the USA (31), Canada (9), Austria (6), Switzerland (3), Japan (3), Ireland (2), Israel (2), Australia, Belgium, China, Finland, Germany, Hungary, Russia, Sweden, and the United Kingdom,. The submissions showed a healthy mix between academia (34), industry (22) and joint academia-industry projects (9). The program committee met at IBM Research in Hawthorne, NY on Friday, March 25, 2005. The committee accepted 19 excellent papers that cover a wide spectrum of topics related to virtual execution environments. On Saturday, March 26, members of the committee participated in an informal workshop that provided a forum for committee members to present their work and build collaborations. We hope this tradition will continue in future program committee meetings. In addition to the 19 accepted papers, the program includes keynote talks by James E. Smith and Martin Nally.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.123 | 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".