Proceedings of the fifth ACM workshop on Scalable trusted computing
Bibliographic record
Abstract
The fifth ACM workshop on Scalable Trusted Computing (STC) continues in the footsteps of past STC workshops. It focuses on fundamental technologies of trusted computing in a broad sense and its applications in large-scale systems -- those involving large number of users and parties with varying degrees of trust. STC is intended to serve as a forum for researchers as well as practitioners to disseminate and discuss recent advances and emerging issues. The program committee accepted five full papers (29% acceptance) and four short papers covering a variety of topics ranging from hardware security and mobile trusted computing to trusted virtual domains. The proceedings include two invited papers by leading experts in related fields: Gernot Heiser on trustworthy systems and David Lie on virtualization. In addition, the program features two wellestablished and well-known keynote addresses: Paul van Oorschot (Carleton University, Canada) delivers the academic keynote, while Michael Waidner (IBM Chief Technology Officer for Security) delivers the industrial keynote.
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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.037 | 0.015 |
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".