Intention as a Context: An Activity Intention Model for Adaptable Development of Applications in the Internet of Things
Bibliographic record
Abstract
End-users, such as aging people, look for dynamic applications to help them in their daily activities while usually assisted by domain experts (e.g., physicians) in performing these activities. Nowadays, the Internet of Things (IoT) augments applications with a changing context based on the activities, environments, and services. Unfortunately, most IoT application development tools are restricted to specific scenarios or involve technical challenges. We propose an activity intention model for quick application development, targeting domain experts who are not traditional software developers. Our model is based on the ContextAA micro context-awareness approach with autonomic computing-based components providing pervasive adaptations. Unlike other Internet of Things application definitions, our model promotes elicitation of the activity semantics and provides mechanisms to compute the semantics. We then generate intention as a context (IaaC), which contains a self-described activity intention context with compiled knowledge ready to be assessed in pervasive smart environments for augmented adaptations. Experimental results show a potential resource optimization for dynamic Internet of Things applications in smart homes and smart cities.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| 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".