An algorithm based mesh check-sum fault tolerant scheme for stream ciphers
Bibliographic record
Abstract
To enhance the security and reliability of the widely-used stream ciphers, a novel mesh check-sum ABFT scheme for stream ciphers is developed. By utilising the ready-made arithmetic unit in stream ciphers, single and multiple errors can be detected and corrected in a cheap way. To meet different requirements in practical applications, 4D mesh check-sum ABFT scheme is proposed which can be applied to RC4 or other stream ciphers. The 2D mesh check-sum ABFT scheme is able to detect and correct single error with high efficiency. The 4D mesh check-sum ABFT scheme is capable of correcting up to three errors located randomly in an N-element matrix with acceptable computation and bandwidth overhead. The workload can be remarkably reduced when most communications are error-free. Our scheme also provides one-to-one mapping between index and check-sum, so that error can be located and recovered by easier logic and simpler operation.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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".