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Record W2014668993 · doi:10.5539/jmr.v4n1p67

Analysis and Design of Affine and Hill Cipher

2012· article· en· W2014668993 on OpenAlex

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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJournal of Mathematics Research · 2012
Typearticle
Languageen
FieldComputer Science
TopicChaos-based Image/Signal Encryption
Canadian institutionsnot available
FundersIslamic Azad University
KeywordsCryptographyCryptanalysisComputer securityCipherComputer scienceMathematicsRunning key cipherTheoretical computer scienceEncryption

Abstract

fetched live from OpenAlex

Cryptography is the study of mathematical techniques for all aspects of informationsecurity. Cryptanalysis is the complementary science concerned with the methods todefeat these techniques. Cryptology is the study of cryptography and cryptanalysis. Thesecurity of information encompasses the following aspects:\begin{asparaitem} \item confidentiality or privacy, \item data integrity, \item authentication, \item nonrepudiation.\end{asparaitem}Each of these aspects of message security can addressed by standard methods in cryptography.Besides exchange of messages, tools from cryptography can be applied to sharingan access key between multiple parties so that no one person can gain access to a vaultby any two of them can. Another role is in the design of electronic forms of cash.In this paper, we study affine and Hill cipher in cryptography.

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 imitation

Not 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.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.854
Threshold uncertainty score0.294

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.133
GPT teacher head0.389
Teacher spread0.256 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it