Proceedings of the 2013 international workshop on Security in cloud computing
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
It is our great pleasure to welcome you to the 2014 International Workshop on Security in Cloud Computing (SCC). Cloud computing has emerged as today's most exciting computing paradigm shift in information technology, since it promises numerous benefits, including lower costs, rapid scaling, easier maintenance, and ubiquitous availability. Meanwhile, cloud computing also raises many security and privacy challenges such as data protection, recovery, privacy, access control, trusted computing, as well as legal issues in areas such as regulatory compliance, auditing, and many others. This workshop aims to bring together the research efforts from both the academia and industry in all security aspects related to cloud computing. This is the second year for our SCC workshop. We received 21 submissions from nine countries covering four continents, including Canada, China, Egypt, France, India, Oman, Sudan, Sweden, and United States. The submissions were reviewed by a technical program committee of 48 experts. The final program contains 9 papers, representing an acceptance rate of 43%. Our program also features a keynote speech On the Security of Cloud Data Storage and Sharing by Dr. Jianying Zhou from Infocomm Security Department at Institute for Infocomm Research, Singapore.
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 | 0.000 |
| 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 | 0.001 | 0.002 |
| Open science | 0.006 | 0.005 |
| 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