Bibliographic record
Abstract
In the modern society of digitalization, integration, intelligence, and networking, while people enjoy the convenience of information technology to their production and lives, information security in the network, as the cornerstone of information communication, becomes more and more important. The research is to establish a new image encryption (IE) method based on LFSR state sequence (SS)s in VHDL language, and the stream cipher of LFSR is studied in detail to induce the idea of LFSR SSs based on VHDL language. The correlation coefficient (CC) of the original image (OI) and encrypted image (EI) pixel points (PP) are analyzed from horizontal direction, vertical direction and diagonal direction, and the results show that the CC of adjacent PP of the OI is large, which approaches 1. However, using the encryption algorithm proposed in this paper, the correlation coefficient of the PP of the EI is -0.0282 in the diagonal direction, and the highest correlation coefficient in the horizontal direction is only 0.0122, which indicates that the adjacent PP of the EI are almost uncorrelated with each other. Therefore, the encryption method can well resist statistical attacks, which illustrates the effectiveness, security, and reliability of this new IE method.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".