Bibliographic record
Abstract
Microsoft Excel offers number of data analysis tools commonly known as what if analysis.These tools are extensively used to solve many business problems.However in Engineering and Computing Science these easy to use features are seldom used to solve appropriate problems.Instead most of the problems are solved using specific tools such as MATLAB or specific Computer programs.In situation where Microsoft Excel is used, only rudimentary features such as formula, mathematical functions are utilized.The Rivest-Shamir-Adleman (RSA) algorithm is one of the most well-known and secure public-key encryption methods used for secured data transmission.To determine the private key for RSA algorithm one has to use complex mathematical calculations.Many students of Introductory Computer Security courses without appropriate Mathematical background find it difficult to use RSA algorithm to determine appropriate solution for private decryption key.In this paper author attempts to show how RSA algorithm to determine private key can be modelled for Microsoft Excel Spread sheet, and how Excel Solver can easily and effectively be used subsequently to determine private keys for RSA algorithm.The author uses several examples to show viability of usage of EXCEL solver to quickly find private RSA keys.The author finds that the EXCEL Solver can be easily used in the class room to demonstrate how RSA algorithm can be used.The author also discusses the limitations and practical issues related to using EXCEL solver.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.026 | 0.010 |
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".