Efficient comparison-based fault diagnosis of multiprocessor systems using an evolutionary approach
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In comparison models for system-level fault diagnosis pairs of units are given the same job and results are compared. The result of such a comparison test can be 0 (match) or 1 (mismatch) and diagnosis is based on the collection of test results. Two such models have been studied, among others: the symmetric model of Chwa and Hakimi and the asymmetric model of Malek. In this paper a novel approach is proposed for identifying faulty units, based on a well-known optimization procedure, as genetic algorithms, which have proven to be useful in various kinds of problems. Furthermore, a new problem-specific genetic mutation is presented and shown to be better than the standard one. A series of simulations was conducted to show the efficiency of the genetic-based approach.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it