EMC: Emotion-aware mobile cloud computing in 5G
Bibliographic record
Abstract
With the development of 5G, the wireless world will be interconnected without barriers. This new technology will enable many challenging applications, and more personalized and interactive services are expected to be available with resource-limited mobile terminals. Fortunately, mobile cloud computing (MCC) emerging in the context of 5G has the potential to overcome this bottleneck, which enables many resource-intensive services for mobile users with the support of mobile big data delivery and cloud-assisted computing. In this article we propose a novel framework named EMC in the context of 5G, which offers personalized emotion-aware services by MCC and affective computing. With the proposed framework, the traditional MCC architecture is modified to achieve the required Quality of Experience in emotion-aware applications. Furthermore, we design a partitioning solution corresponding to the fundamental trade-off between the communication and computation in EMC. The framework would be helpful to provide personalized, human-centric, intelligent emotion-aware services in 5G.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".