Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
With the rapid rise of information technology in Indonesia, the risks associated with it, such as the leakage or theft of sensitive data, are increasingly apparent. Among the most frequently shared file formats, Microsoft Word documents are a crucial factor. Therefore, this study aims to implement and evaluate the performance of the RSA cryptographic algorithm to protect these types of documents. The applied methodology includes the design and implementation of a system using PHP, a data farm using MySQL, and an Xampp-based test environment. We utilized RSA to encrypt and, conversely, decrypt the Word files, with the main indicator being the processing time for each type of process. Theoretical analysis and manual calculations confirmed that RSA operates through the risk inherent in the difficulty of factoring large prime numbers. Manual simulations of encryption and decryption times verified that the RSA algorithm, when inverted, produces data in the correct format. Based on the results obtained, we conclude that the use of RSA in securing Word is feasible and appropriate, and the degree of protection can now be evaluated through the time required for each encryption and decryption operation.
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.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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