A novel service evolution approach for active services in ubiquitous computing
Bibliographic record
Abstract
Abstract With the emergence of more and more personalized service requirements, service customization has become a compelling problem in ubiquitous computing. As a new paradigm for service customization, theActive Servicesreuses existing services and obtains the user‐needed service evolved from them. In this paper, a novel service reuse approach is proposed for service evolution process in the active services paradigm. Besides the entire service reuse realized in existing works, our approach can also achieve partial reuse of existing services at functionality level. To generate a user‐needed service, the reusable parts of each existing service are extracted with an index strategy and utilized directly, and then the missing functionalities to satisfy the service requirement are further implemented. As quality of service (QoS) is very important for ubiquitous services, in this paper, based on the general service evolution process, two types of QoS‐aware service reuse methods are also introduced. The proposed methods have been implemented and extensive experiments were done to evaluate them. The experimental results demonstrate the superiority of our methods in several measurements of service reuse: computation cost, success rate, and the quality of generated services. Copyright © 2009 John Wiley & Sons, Ltd.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".