On the Fly Service Composition for Local Interaction Environments
Bibliographic record
Abstract
Dynamically creating new, composite services "on the fly" using existing ones in a local interaction environment (e.g. a home, meeting room, airport lounge, etc.) presents challenging problems. In this paper, we describe a middleware model for such service composition targeted, initially, to a home area network (HAN) scenario. Being able to automatically synthesize new and useful services in a HAN (or other environment) without user intervention makes the use of the network simpler and more attractive for non-expert users (e.g. home owners). We propose a service composition model which involves third-party "service providers" (SPs) in the composition process thereby allowing the discovery of services without direction from the users as to what type of service is desired. We also discuss our experiences with our initial prototype system where ontology based matching is done by the service providers and the resulting composite service is deployed as a workflow using available in-home protocols (e.g. UPnP, Jini, etc.)
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".