EMC: Emotion-aware mobile cloud computing in 5G
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
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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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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