Machine Learning-Assisted Device Selection in a Context-Sensitive Ubiquitous Multimodal Multimedia Computing System
Bibliographic record
Abstract
In a computing system where a user moves from one environment to another, and as such the user's context and computing resources also change, a constant user intervention to enable/disable various devices to suit his needs is time-consuming and diminishes user productivity. Instead, a machine could be trained to acquire knowledge so that it would do the work (i.e. calculation and decision making) itself and leaves human do something else that is more important. In a ubiquitous multimodal multimedia (MM) computing system, the selection of appropriate media and modalities (i.e. devices) is based on user's context, user profile, and user's environment (a.k.a. pre-condition scenario). There are numerous possibilities of a pre-condition scenario and the available devices also changes depending on user's computing environment. Indeed, a machine learning (ML) component could be trained to "remember" all pre-condition scenarios, and each one's device selection (a.k.a. post-condition scenario). This ML component could also be trained to find a replacement to every missing or defective selected device. The ML component is integrated into a ubiquitous system making it available anytime, anywhere. This work is an original contribution in ML, one that permits automatic system adaptation based on user's environment
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".