Architecture de Sécurité pour les Grands
Bibliographic record
Abstract
At the outset, I would like to express my sincere gratitude to my thesis advisors. Most beneficial to my doctoral research was the vision, direction and significant feedback from my advisor, Professor Michel Riguidel; and the guidance and committed concentration towards technical quality from my co-advisor Professor Isabelle Demeure. I also thank the honorable members of the jury – Pascal Urien, Ken Chen, Ana Cavalli, Marcel Soberman, and André Cotton – for their attention and thoughtful comments. During my thesis work, I had the opportunity to work in the EU-funded project SEINIT (Security Expert Initiative). Most importantly, the scope of this project – trusted and dependable security framework, ubiquitous, working across multiple devices, heterogeneous networks, and organization independent (inter-operable) – largely influenced my research. I would like to thank all the participants of this project for their help especially the project coordinator, André Cotton, and technical coordinator, Sathya Rao. I would like to express my sincere gratitude to Professor Radha Poovendran of the University of Washington who provided me the opportunity to work with him in the Network Security Laboratory during the summer quarter of the year 2005. I learned a lot with him. I owe gratitude to the members of the networks and computer science department (INFRES) of ENST, notably Gwendal Legrand, with whom I frequently engaged in scientific and technical discussions. I am equally indebted to the members of Network
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.023 | 0.020 |
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".