Adaptive Context Dissemination in Heterogeneous Environments
Bibliographic record
Abstract
Developing and maintaining context-aware efficient systems in heterogeneous environments is a challenging task. In our research work, we enable context awareness in users, devices, and applications to enable context-based systems in Ambient Networks. We achieve this by proposing a context dissemination system which propagates the fast evolving context information from its sources (e.g., context sensors) to various interested information sinks (e.g., context sensitive clients). The proposed system implements a context aware overlay architecture composed of multi-level overlay networks. This overlay architecture acts as a base (middleware) for the development and maintenance of the application-layer context-specific dissemination protocol (the CSON-D protocol). The protocol's multi-level overlay structure and its intelligence, personalization, and fault tolerance features exhibit adaptive behavior with minimized casting functionality. Our conceptual model is reinforced by means of experimental evaluations. In general, the cost-based overhead for multi-level overlay formation and maintenance is minimal.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".