Zero-Crossing Analysis and Information Divergence of Lévy Walks for Real-Time Feature Extraction
Why this work is in the frame
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Bibliographic record
Abstract
A method, based on the Smirnov transform, for generating synthetic data with the statistical properties of Lévy-walks is presented. This method can be utilized for generating arbitrary prescribed probability density functions (pdf). A cybersecurity engineering problem associated with Internet traffic is addressed. The synthetic Lévy-walks process is intertwined with sections of distinct characteristics creating a composite signal that is analyzed through zero-crossing rate (ZCR) within a varying-size window to identify sections. The advantages of the ZCR computation directly in the time-domain are appealing for real-time implementations. Moreover, the characterization of the degree of closeness, via the Kullback-Leibler divergence (KLD), among the pdfs of arbitrary processes (focusing on Lévy walks) and model pdfs is presented. The results obtained from the KLD experiments provide a categorical determination of the closeness degree. These results, a remarkable achievement in this research, are also promising to be used as features for classification of complex signals in real-time.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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