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Reference TypeConference Proceedings
Last Update2018: thales.korting
Metadata Last Update2020: administrator
Citation KeyMachicaoBrun:2018:FiPaEx
TitleFinding Patterns and Exploiting Pseudo-randomness using Complex Systems
DateOct. 29 - Nov. 1, 2018
Access Date2020, Nov. 29
Number of Files1
Size2991 KiB
Context area
Author1 Machicao, Jeaneth
2 Bruno, Odemir M.
Affiliation1 Instituto de Física de São Carlos
2 Instituto de Física de São Carlos
EditorRoss, Arun
Gastal, Eduardo S. L.
Jorge, Joaquim A.
Queiroz, Ricardo L. de
Minetto, Rodrigo
Sarkar, Sudeep
Papa, João Paulo
Oliveira, Manuel M.
Arbeláez, Pablo
Mery, Domingo
Oliveira, Maria Cristina Ferreira de
Spina, Thiago Vallin
Mendes, Caroline Mazetto
Costa, Henrique Sérgio Gutierrez
Mejail, Marta Estela
Geus, Klaus de
Scheer, Sergio
Conference NameConference on Graphics, Patterns and Images, 31 (SIBGRAPI)
Conference LocationFoz do Iguaçu, PR, Brazil
Book TitleProceedings
PublisherSociedade Brasileira de Computação
Publisher CityPorto Alegre
History2018-10-17 18:21:38 :: -> administrator ::
2018-11-06 12:26:56 :: administrator -> thales.korting :: 2018
2018-11-06 12:27:25 :: thales.korting -> administrator :: 2018
2020-02-20 22:06:50 :: administrator -> :: 2018
Content and structure area
Is the master or a copy?is the master
Document Stagecompleted
Document Stagenot transferred
Tertiary TypeMaster's or Doctoral Work
Keywordspatterns, pseudo-randomness, pattern recognition, complex systems, chaos theory.
AbstractIn this work, we present patterns and pseudo-randomness in an approach that relates both concepts, which traditionally are seen as opposites. This approach uses the mathematical basis of complex systems for two purposes: to exploit the spectrum of pseudo-randomness of chaotic systems in a quest to achieve true randomness and, the development of pattern recognition methods based on artificial life in complex networks that finally intertwined the search for patterns in pseudo-random sequences. In the first part, we developed a method to explore the depth properties of chaotic systems, specifically in the logistic map and tent map, as sources of pseudo-randomness. We observe that the patterns disappear and the pseudo-randomness is increased by removing k-digits to the right of the decimal separator of the chaotic orbits. Thus, a rapid transition from "weak to strong" randomness was evidenced as k tends to infinity, which allows a parametrically pseudo-randomness. In the second part, it was proposed the combination of cellular automata in the network topology (also called network-automata), to characterize networks in a pattern recognition context. Four problems were explored: identifying online social networks; identify organisms from different domains of life through their metabolic networks; the problem of authorship identification; and classifying stomatal distribution patterns varying according to different environmental conditions. Finally, this same approach was used to analyze the sequences of pseudo-random numbers generated by the gold standard k-logistic map PRNG in a context of pattern recognition. The proposed approach allowed to explore patterns and pseudo-randomness extracted from a myriad of systems with successful results in terms of accuracy and good pseudo-randomness. This work has brought significant advances in real-world pattern recognition tasks across a wide range of fields such as cryptography, cryptoanalysis, biology, and data science.
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Next Higher Units8JMKD3MGPAW/3RPADUS
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