Quality of experience-enabled social networks
Bibliographic record
Abstract
Social Networks (SNs), such as Facebook, Twitter and LinkedIn, have become ubiquitous in our daily life. However, as the number of SN users grows there is higher demand for users' Quality of Experience (QoE). Some users may prefer to subscribe to a higher Quality of Service (QoS) level with their SN provider, e.g. to have higher priority on posting/retrieving, when for instance there are outages like the Twitter outage that happened during the Oscars 2014. In addition some users may wish to filter some posts, e.g. unwanted friendship requests. In this paper, we propose a novel architecture that enables differentiated QoS and information filtering in SNs to improve the users QoE. Our SN runs on top of 3GPP 4G Evolved Packet Core (EPC)-Based systems, and it uses EPC services to enable differentiated QoS. The components of our architecture interact through RESTful web services. Our architecture allows users to filter posts through their own criteria and have priority over other users in posting and/or retrieving.
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How this classification was reachedexpand
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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".