Proceedings of the 2010 ACM workshop on Cloud computing security workshop
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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) aims 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 34 submissions from Asia, Canada, Europe, and the United States. The program committee accepted 5 full and 9 short papers. Additionally, four distinguished speakers were invited: Leendert van Doorn, AMD Senior Fellow, Eric Grosse, the Google Security Engineering Director, Steve Riley, Amazon Web Services' Sr. Technical Program Manager, and Michael Waidner, IBM Security CTO.
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.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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 it