An Adaptable Context Management Framework for Pervasive Computing
Bibliographic record
Abstract
Pervasive computing presents an exciting realm where intelligent devices interact within the background of our environments to create a more intuitive experience for their human users. Context-awareness is a key requirement in a pervasive environment because it enables an application to adapt to the current situation. Context-awareness is best facilitated by a context management system that supports the automatic discovery, retrieval and exchange of context information by devices. Such a system must perform its functions in a pervasive computing environment that involves heterogeneous mobile devices which may experience intermittent connectivity and resource and power constraints. The objective of the chapter is to describe a robust and adaptable context management system. We achieve an adaptable context management system by adopting the autonomic computing paradigm, which supports systems that are aware of their surroundings and that can automatically react to changes in them. A robust context management system is achieved with an implementation based on widely accepted standards, specifically Web services and the Web Services Distributed Management (WSDM) standard.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| 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".