Power Conservation in Cloud-Assisted Real-Time Context Learning System
Bibliographic record
Abstract
Contextual information can be learned at the mobile devices, such as smartphones, in real-time from the sensors to provide better services to the user. The sensor data collection process, where the data is collected by various internal sensors or autonomous external sensors, incurs greater power consumption, depending upon the type of sensor and data capturing rate in the mobile devices. On the other hand, the learning process itself drains the battery and at the same time affects the accuracy of the learning due to the limited computational power of the mobile devices. These problems can be addressed by shifting the learning process to the cloud, which is however achieved at the cost of reducing the accuracy of the real-time solutions and incurs heavy bandwidth usage depending upon the context of the user. Therefore, we propose a cloud-based real-time context-learning system where the user of the system will get the predetermined service in real-time according to the userdetermined context, which is learned from the related sensors while conserving a maximum amount of power compared to the standalone system or the cloud-based system. We have produced experimental results using a smartphone that illustrates that our system conserves 96.36% of power compared to the mobilelearning system while at the same time, the network data usage is 80% lower when compared to the cloud-based system. We have also showed that the proposed system works 76.17% and 94.81% faster compared to the mobile-learning and cloud-based system respectively.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 | 0.000 |
| 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".