Guest Editorial: Special Section on Enforcement and Management in Services Computing
Bibliographic record
Abstract
SERVICE solutions are typically comprised of rather complex and continuously evolving service-oriented systems. While such systems may be under the sole control of an individual organizational unit, more often than not, they are deployed in interorganizational environments, must comply with various corporate and/or governmental governance regulations, and much more. Security and, in particular, privacy and trust are major concerns in almost all facets of such service solutions. In order to enable individual services or collections of services to easily interact, integrate, or be composed, it is often necessary to establish, manage, maintain, adapt, and enforce various service-level agreements, privacy policies, and/or rules across service and/or organizational boundaries. These tasks become only more challenging during the life-cycle of an evolving service solution. In this Special Issue on Enforcement and Management in Services Computing, we present seven high quality research articles on enforcement and management issues that are prevalent in current and emerging service solutions with a particular focus on privacy, security, trust, provenance, service solution (design and delivery) management, and service solution integration. Following the 2009 IEEE Asia-Pacific Services Computing Conference (APSCC) in Singapore, we launched an open call for submissions to this special issue of the IEEE Transactions on Services Computing. We received more than 30 submissions; the following seven articles were selected through a rigorous review process:
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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.009 | 0.012 |
| Insufficient payload (model declined to judge) | 0.012 | 0.008 |
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".