Proceedings of the Seventh International Workshop on Principles of Engineering Service-Oriented and Cloud Systems
Bibliographic record
Abstract
Welcome to the 7th International Workshop on of Engineering Service-Oriented and Cloud Systems (PESOS 2015). This year, PESOS was held in Florence, Italy on May 23rd, 2015, in conjunction with ICSE 2015. Continuing the special theme at PESOS last year, the 7th edition of the PESOS workshop focuses on Principles and Practices for Engineering Collaborative Services in the The Cloud computing paradigm is having a significant impact on the way modern software is designed, developed, deployed and governed. In particular, the scale and readily accessible nature of the Cloud opens new opportunities for not only individual applications, but also complete processes that require collaboration among such systems. Even though cloud platforms and infrastructures are typically designed to scale on demand, the questions are (i) whether this automatic elasticity translates to all services deployed on them, and (ii) whether collaboration amongst the services on (multiple) Cloud be managed elastically. Other qualities of concern and interest in this environment include monitorability, manageability, privacy, security, availability and reliability. Collaborative services in the Cloud will have to be better engineered, to either take advantage of the qualities offered by cloud platforms and infrastructures or to account for lack of full control over important quality attributes. There are therefore a number of open research challenges related to design, development, deployment, use, and integration of software, human and collaborative services in the Cloud.
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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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.034 | 0.010 |
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".