Adaptive Composition of Distributed Pervasive Applications in Heterogeneous Environments
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Complex pervasive applications need to be distributed for two main reasons: due to the typical resource restrictions of mobile devices, and to use local services to interact with the immediate environment. To set up such an application, the distributed components require spontaneous composition. Since dynamics in the environment and device failures may imply the unavailability of components and devices at any time, finding, maintaining, and adapting such a composition is a nontrivial task. Moreover, the speed of such a configuration process directly influences the user since in the event of a configuration, the user has to wait. In this article, we introduce configuration algorithms for homogeneous and heterogeneous environments. We discuss a comprehensive approach to pervasive application configuration that adapts to the characteristics of the environment: It chooses the most efficient configuration method for the given environment to minimize the configuration latency. Moreover, we propose a new scheme for caching and reusing partial application configurations. This scheme reduces the configuration latency even further such that a configuration can be executed without notable disturbance of the user.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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 it