Bibliographic record
Abstract
Many of the User Behavioral Analytics (UBA) applications rely on the distributions and baselines of individual users and are sensitive to the changes in these patterns.These applications identify anomalies and threats by observing the deviation of user behavior from their baselines.Development and testing of these applications depend heavily on synthetic data as the availability of the real data is scarce in most of the cases.Synthetic data generated has to follow these patterns, or else it could result in noisy results.Through this work, we present a synthetic data generation technique, which could be utilized by UBA applications for their development and testing.The proposed system for data generation extracts the distribution of attributes, considering the dependencies between these attributes provided by the user.The extracted patterns are stored and can be used any number of time to generate synthetic data.Additionally, we also generate synthetic users, whose behaviors and baselines are similar to that of real users.The scalable system developed will help the development of UBA applications, especially during performance testing.The experiments conducted showed that the synthetic data captured the required patterns and relations from the real data.The experiments also showed that the process of data generation we follow can be scaled up linearly to the available number of processors.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".