DUQ: Dual Uncertainty Quantification for Text-Video Retrieval
Bibliographic record
Abstract
As software development projects increasingly rely on open-source software, users face the risk of security vulnerabilities from third-party libraries. To address label and character noise in code changes, we present VF-Detector to automatically identifying bug-fix commits in actual noise development environment. VF-Detector consists of three componments: Data Pre-processing (DP), Vulnerability Confidence Computation (VCC) and Confidence Learning Denoising (CLD). The DP component is responsible for preprocessing code change data. The VCC component calculates code change confidence value for each bug-fix by extracting features at various granularity levels. The CLD component removes noise and enhances model robustness by pruning noisy data with confidence values and performing effort-aware adjustments. Experimental results demonstrate VF-Detector's superiority over state-of-the-art methods in EffortCost@L and Popt@L metrics on Java and Python datasets. The improvements were 6.5% and 5% for Java, and 23.4% and 17.8% for Python.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".