A context-aware service provision system for smart environments based on the user interaction modalities
Bibliographic record
Abstract
Ambient and pervasive technologies provide several ways to assist people with special needs in smart environments. However, the system's complexity and the size of the contextual information of these environments lead to several difficulties in deploying and providing the assistance services. A service provision mechanism which is aware of the environment context can simplify the deployment of assistance services on environment devices, by taking care of the decision processes. Moreover, the integration of the interaction modalities in the decision processes of such mechanisms allows deliveries of services to users based on their capabilities and preferences. In this paper, we present a context-aware service provision system for smart environment, which takes into account a whole set of contextual information: user profiles, device profiles, software profiles and environment topology. In regards to our previous work, this paper focuses on the modeling of the user interaction capabilities, built around the notion of interaction modalities. We also detail the integration of the model to the service provision reasoning process, as well as its implementation. Finally, we demonstrate the functionalities of this system through technical validations and scenarios carried out in a real smart apartment.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".