Parallel HAIFA Hashing Algorithm Based on Lorenz Chaos
Why this work is in the frame
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Bibliographic record
Abstract
Aiming at the inefficiency under parallel environment or large data computation, HAIFA hash function based on Lorenz chaos is constructed in parallel, and a parallel hash function based on Lorenz chaos is proposed. The algorithm compresses each message block independently and can be executed concurrently. After the hash value of each message block is obtained, every two hash values are combined. The odd-numbered rounds are combined with modular addition and right loop operation, while the even-numbered rounds are combined with XOR and left loop operation. The difference of each round of operation further enhances the anti-collision and anti-forgery attacks of the algorithm. The new parallel algorithm is tested for safety analysis and efficiency. The results show that the parallel modified algorithm has good performance and high efficiency, which has certain reference significance for the safety construction of parallel chaotic hash algorithm.
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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 | 0.000 |
| 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 | 0.001 | 0.001 |
| 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