Original signé par Frédéric Painchaud
Bibliographic record
Abstract
Chef de Section, Gestion de l’Information et de la Connaissance Ce travail a été effectué à RDDC – Valcartier en collaboration avec l’Université Laval entre mai et août 2002. © Her Majesty the Queen as represented by the Minister of National Defence, 2002 © Sa majesté la reine, représentée par le ministre de la Défense nationale, 2002 In this document, three recently published papers on different aspects of Java Security are summarized. These papers appeared in international conference proceedings and computer science journals. The first paper presents an approach aiming at replacing the dataflow analysis carried out by the Bytecode Verifier in the Java Virtual Machine. This approach is based on model checking. The second paper presents an approach aiming at instrumenting Java bytecode to ensure better security of applets and Jini services. This new approach makes it possible to detect and stop certain types of denial of service, to ensure integrity of critical data and confidentiality of the system and even to thwart certain types of spoofing. Finally, the third and last paper presents a security
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.079 | 0.047 |
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".