A Novel Model Based on Window-Pass Preferences for Data Emergency Aware Scheduling in Computer Networks
Bibliographic record
Abstract
The breakdown of vital communication infrastructures is one of the most common characteristics of all disasters. It can cause severe communication problems such as time delays and data loss, which cause deterioration in system performance. New techniques are needed to cope with such situations, many of which have been made possible due to the ongoing evolution of artificial intelligence technologies. In this study, we consider the case of a network consisting of several router allocation problems in situations of high priority and emergency data allocation. A novel network component called the scheduler is introduced and window constraints for routers are imposed. To solve the studied problem, four different algorithms are developed in this work. These algorithms were then applied in a particular scenario consisting of several routers and 2200 instances. In terms of the gap and running time, the proposed algorithms provide acceptable results. The best performances were achieved using the critical packet algorithm for 80% of instances with an average gap value of 0.009 and an average time of 0.209 s.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".