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A new chaotic map development through the composition of the MS Map and the Dyadic Transformation Map

2020· article· en· W3034988553 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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Physics Conference Series · 2020
Typearticle
Languageen
FieldComputer Science
TopicChaos-based Image/Signal Encryption
Canadian institutionsToronto Metropolitan University
FundersUniversitas Indonesia
KeywordsRandomnessChaoticLyapunov exponentNISTTransformation (genetics)Chaotic mapMathematicsBifurcation diagramStandard mapTent mapBifurcationDiagramStatistical physicsMathematical analysisComputer scienceStatisticsPhysicsArtificial intelligenceNonlinear system

Abstract

fetched live from OpenAlex

Abstract In this paper, a new chaotic map is proposed, that is obtained from the composition of two chaotic maps, that is, the MS Map and the Dyadic Transformation Map. The composition process starts from the MS Map, followed by the Dyadic Transformation Map. The resulting composition is a new chaotic function. This is shown by the bifurcation diagram analysis result, Lyapunov Exponents, and the NIST randomness test. The bifurcation diagram shows that the best densities occur at λ ∈ (0.3, 5) and r = 3.8. The Lyapunov Exponents has nonnegative values for r ∈ [1, 4]. The NIST randomness test with initial value and parameters x 0 = 0.6, r = 3.8, and λ = 3.5 shows that the new chaotic map passes 14 out of 16 NIST tests.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.980
Threshold uncertainty score0.272

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.024
GPT teacher head0.224
Teacher spread0.200 · 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