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
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".