Proceedings of the 2013 international workshop on Hot topics in cloud services
Bibliographic record
Abstract
It is our great pleasure to welcome you to the International Workshop on Hot Topics in Cloud Services (HotTopiCS 2013). The primary goal of Hot Topics in Cloud Services is providing a platform for academics and industrial practitioners to exchange novel research ideas and current problems from practice and to identify new and hot topics in the field. As indicated by the colocation with ICPE, one focus is on work tackling performance-related problems (understood in a very broad sense), but other work related to the creation and management of service-based cloud applications (e.g., from an economic perspective) are equally welcome. The call for papers attracted 15 paper submissions. Each paper went through a rigorous peer review process involving at least 3 program committee members. The program committee accepted 5 full research papers, and 5 position papers. The program is further enriched with a keynote from Alexandru Iosup on IaaS Cloud Benchmarking: Approaches, Challenges, and Experience and a presentation by Samuel Kounev on the RELATE EU FP7 Marie Curie ITN Project, as main supporter of the workshop.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.071 | 0.023 |
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".