Bibliographic record
Abstract
In large-scale infrastructure wireless networks several access points (APs) may be unusable at any given moment in time. Unlike completely failed APs, whose failure can be detected by probes to their wired interface, an AP with a faulty wireless interface or whose antenna has been accidentally shielded can only be diagnosed by the actual use of the wireless interface for data communication. We present several algorithms that detect such failed access points by online analysis of AP usage logs. In particular, we demonstrate that we can exploit device mobility to detect faulty APs. We also present efficient heuristics to select a path for a technician to repair failed access points. We evaluate our algorithms using actual log files from an infrastructure network at Dartmouth College. We find that our best algorithm is able to detect nearly 90% of failed access points simply by processing log files. Compared to a naive approach, our algorithm has more than six times fewer false positives. We are also able to construct tours that are up to an order of magnitude more effective that a straightforward greedy approach. Our algorithms require no modifications to either APs or devices. We believe that these properties make our work immediately applicable to real-world scenarios
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".