Proceedings of the 2009 ACM workshop on Cloud computing security
Bibliographic record
Abstract
Notwithstanding the latest buzzword (grid, cloud, utility computing, SaaS, IaaS, KaaS, PaaS, etc.), large-scale computing and cloud-like infrastructures are here to stay. How exactly they will look like tomorrow is still for the markets to decide, yet one thing is certain: clouds bring with them new untested deployment and associated adversarial models and vulnerabilities. Thus, it is essential that our community becomes involved at this early stage. The Cloud Computing Security Workshop (CCSW) was started with this purpose in mind: to bring together researchers and practitioners in all security aspects of cloud-centric and outsourced computing. The call for papers attracted overwhelming interest from the community with over 30 submissions from Asia, Canada, Europe, and the United States. The program committee accepted 11 full and 3 short papers. Additionally, we felt that in this first instance of the workshop it is essential to bootstrap the dialogue by inviting distinguished speakers such as Whitfield Diffie, Sun's Chief Security Officer and one of the fathers of public key cryptography, Ian Foster, one of the founders of the international Grid community, as well as Peter Mell and Tim Grance from the Computer Security Division of the National Institute of Standards and Technology (NIST) who are initiating important government efforts to shape essential components in the broader cloud arena.
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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.038 | 0.013 |
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".