Publishing and discovering context-dependent services
Bibliographic record
Abstract
Abstract In service oriented computing, service providers and service requesters are main interacting entities. A service provider publishes the services it wishes to make public using service registries. A service requester initiates a discovery process to find the service that meets its requirements using the service registries. Current approaches for the publication and discovery do not realize the essential relationship between the service contract and the conditions in which the service can guarantee its contract. Moreover, they do not use any formal methods for specifying services, contracts, and compositions. Without a formal basis it is not possible to justify through a rigorous verification the correctness conditions for service compositions and the satisfaction of contractual obligations in service provisions. In our recent works, we have identified the role of contextual information, trustworthiness information and legal rules in service provision. This paper focuses on the publication and discovery of trustworthy context-dependent services as supported by the novel framework FrSeC . It introduces a novel ranking algorithm that ranks trustworthy context-dependent services according to the degree they match service requesters requirements. Finally, this paper introduces a prototype implementation for the matching and ranking of services as supported by FrSeC .
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.011 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".