Dynamic Context-Aware and Limited Resources-Aware Service Adaptation for Pervasive Computing
Why this work is in the frame
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Bibliographic record
Abstract
A pervasive computing system (PCS) requires that devices be context aware in order to provide proactively adapted services according to the current context. Because of the highly dynamic environment of a PCS, the service adaptation task must be performed during device operation. Most of the proposed approaches do not deal with the problem in depth, because they are either not really context aware or the problem itself is not thought to be dynamic. Devices in a PCS are generally hand-held, that is, they have limited resources, and so, in the effort to make them more reliable, the service adaptation must take into account this constraint. In this paper, we propose a dynamic service adaptation approach for a device operating in a PCS that is both context aware and limited resources aware. The approach is then modeled using colored Petri Nets and simulated using the CPN Tools, an important step toward its validation.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it