A Feature-Based Machine Learning Approach for Mixed-Criticality Systems
Bibliographic record
Abstract
Driven by the recent technological advancements in the field of artificial intelligence, machine learning has emerged as a promising representation learning and decision-making method in many technological domains. Inspired by impressive these results, now machine learning techniques are also being applied to address the decision-making and control problems in the area of cyber-physical systems. For instance, some of these systems fall under the category of safety-critical systems such as chemical plants, autonomous vehicles, surgical robots, and modern medical equipment. One of the major performance issues related to the applicability of machine learning with safety-critical systems is related to the probability-based prediction nature of machine learning components used within such systems. This particular characteristic of machine learning makes it extremely difficult to guarantee safety as directed by standards such as ISO 26262. More importantly, the non-transparent and complex nature of machine learning algorithms make both the reasoning as well as formally establishing the safety aspects of the underlying system extremely difficult. The objective of this research work is to investigate on this key issue, and further on propose an efficient machine learning methodology based on the mixed-criticality approach feasible to safety-critical systems.
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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 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".