Report of the 2013 IEEE 7th international symposium on the maintenance and evolution of service-oriented and cloud-based systems (MESOCA 2013)
Bibliographic record
Abstract
The 2013 IEEE 7th International Symposium on the Maintenance and Evolution of Service-Oriented and Cloud-Based Systems (MESOCA 2013) took place in Eindhoven, The Netherlands, on September 24, 2013, as a co-located event of the 29th IEEE International Conference on Software Maintenance (ICSM 2013). MESOCA 2013 covered a wide range of academic and industrial experiences, brought together through one keynote, two invited presentations and eleven paper presentations, which triggered lively discussions. They approached aspects related to the entire software maintenance process, from requirements to testing, with specific solutions for Service-Oriented Architecture and Cloud Computing environments. Technical and business perspectives were discussed, including issues about optimization techniques, pre-migration evaluation of legacy software, decision analysis, energy efficiency, multi-cloud architectures and adaptability. It thus confirmed MESOCA as an ongoing forum for researchers and practitioners to identify and address the increasing challenges related to the evolution of service-provisioning systems.
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 | 0.013 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.028 | 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".