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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".