Using MVCA to Improve Architecture Modularity of Smart Spaces
Bibliographic record
Abstract
There has been increasing interest in the use of context awareness, as a technique for designing architectures dedicated to smart spaces in order to adapt and produce suitable services according to user context. In recent years, various architectures have been developed to support context-aware systems. The major challenge with these systems is decomposing the entire architecture into smaller, modular components that facilitate the comprehension and modification of the architecture. In this study, we propose the Model View Controller Adapter (MVCA) architecture, derived from the model-view-controller pattern, which is modular, flexible and capable of adapting services autonomously on behalf of users. The main concept of MVCA architecture is that it decomposes the overall functionalities into modular components with high cohesion and low coupling, which facilitates reusability and maintainability of the system. The MVCA architecture is essentially composed of four components that are responsible for sensing and managing the environmental context in order to adapt and produce services proactively according to user context. To clarify and show the usability of our architecture, we present a scenario-based simulation of MVCA architecture using the Java Agent Development Framework platform.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".