Semantic techniques for reconfiguring and adapting networks in pervasive environments
Bibliographic record
Abstract
Computing and networking technologies are becoming ever more pervasive in order to support their users' increasingly dynamic lifestyles. These technologies aim to increase the networks' awareness of their users' requirements. At the same time, they strive to reduce the amount of manual reconfiguration and explicit interaction between the users and the network resources. The main problem is the extreme difficulty in reconfiguring the network's resources at runtime and in adapting the network's behaviour automatically according to the changes happening in the surroundings. To address these issues, we investigated the use of the emerging semantic Web technologies, policies, and context information. We also developed an ontology-based reasoning machinery to address the problem of automated adaptability. In this paper, we provide an analysis of the ontologies we developed and the reasoning machinery required increasing the network's awareness of its context. We report on the experiments we conducted to evaluate the techniques we are proposing.
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How this classification was reachedexpand
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".