Context-Aware VoIP Congestion Control Service
Bibliographic record
Abstract
IP networks can have difficulty coping with delay-sensitive VoIP traffics during emergency situations caused by fires and related disasters. During emergencies there is a huge increase in voice and video traffic, causing a huge strain on the network. The strain on the network is as a result of both essential and non-essential traffic. In such crisis situations, calls originating from or destined for rescue personnel, such as doctors and police, are considered essential. Any other calls from eyewitnesses and the public are considered non-essential, since they degrade the quality of service for the emergency response teams by consuming the scarce network resources. Providing the rescue team with the quality of service that they require necessitates network access restriction for non-essential traffic. In this paper, the authors present a voice and video service that uses Context-Awareness and Semantic Web technologies to restrict network access to privileged users during crisis situations. The service monitors the network for crisis conditions, enables the network to respond appropriately when a crisis occurs, detects the end of the crisis and reverts to its default state.
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 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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 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".