Proceedings of the ninth ACM symposium on Access control models and technologies
Bibliographic record
Abstract
It is our great pleasure to welcome you to the 9th ACM symposium on Access Control Models and Technologies - SACMAT 2004. This year's symposium continues its tradition of being the premier forum for presentation of research results and experience reports on leading edge issues of access control, including models, systems, applications, and theory. The mission of the symposium is to share novel access control solutions that fulfill the needs of heterogeneous applications and environments and identify new directions for future research and development. SACMAT gives researchers and practitioners a unique opportunity to share their perspectives with others interested in the various aspects of access control.The call for papers attracted 65 submissions from Asia, Canada, Europe, Africa, and the United States. The program committee accepted 18 papers that cover a variety of topics, including next generation access control models, engineering and analysis techniques for access control policies and models, and security administration. In addition, the program includes a panel on Security for Grid-based computing systems and a keynote speech by Bhavani Thuraisingham on Developments and Directions in Database Access Control. We hope that these proceedings will serve as a valuable reference for security researchers and developers.Putting together SACMAT 2004 was a team effort. First of all, we would like to thank the authors and panelists for providing the content of the program. We would like to express our gratitude to the program committee and external reviewers, who worked very hard in reviewing papers and providing suggestions for their improvements. We would also like to thank Elisa Bertino, this year's Panels Chair, Gail-Joon Ahn, our Proceedings Chair, and Charles Youman, our Registration Chair and Treasurer. Special thanks go to Konstantin Beznosov for maintaining the SACMAT 2004 web site and for his effort in advertising the symposium, to Barbara Carminati for her help in managing the review process, and to Reiner Sailer and Catherine Zhang of the Local Arrangements Committee for their work on the local arrangements. Finally, we would like to thank our sponsor, ACM SIGSAC, for their continued support of these successful meetings.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.068 | 0.022 |
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".