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		<identifier>8JMKD3MGPAW/3S3E6JL</identifier>
		<repository>sid.inpe.br/sibgrapi/2018/10.17.18.21</repository>
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		<site>sibgrapi.sid.inpe.br 802</site>
		<citationkey>MachicaoBrun:2018:FiPaEx</citationkey>
		<author>Machicao, Jeaneth,</author>
		<author>Bruno, Odemir M.,</author>
		<affiliation>Instituto de Física de São Carlos</affiliation>
		<affiliation>Instituto de Física de São Carlos</affiliation>
		<title>Finding Patterns and Exploiting Pseudo-randomness using Complex Systems</title>
		<conferencename>Conference on Graphics, Patterns and Images, 31 (SIBGRAPI)</conferencename>
		<year>2018</year>
		<editor>Ross, Arun,</editor>
		<editor>Gastal, Eduardo S. L.,</editor>
		<editor>Jorge, Joaquim A.,</editor>
		<editor>Queiroz, Ricardo L. de,</editor>
		<editor>Minetto, Rodrigo,</editor>
		<editor>Sarkar, Sudeep,</editor>
		<editor>Papa, João Paulo,</editor>
		<editor>Oliveira, Manuel M.,</editor>
		<editor>Arbeláez, Pablo,</editor>
		<editor>Mery, Domingo,</editor>
		<editor>Oliveira, Maria Cristina Ferreira de,</editor>
		<editor>Spina, Thiago Vallin,</editor>
		<editor>Mendes, Caroline Mazetto,</editor>
		<editor>Costa, Henrique Sérgio Gutierrez,</editor>
		<editor>Mejail, Marta Estela,</editor>
		<editor>Geus, Klaus de,</editor>
		<editor>Scheer, Sergio,</editor>
		<booktitle>Proceedings</booktitle>
		<date>Oct. 29 - Nov. 1, 2018</date>
		<publisheraddress>Porto Alegre</publisheraddress>
		<publisher>Sociedade Brasileira de Computação</publisher>
		<conferencelocation>Foz do Iguaçu, PR, Brazil</conferencelocation>
		<keywords>patterns, pseudo-randomness, pattern recognition, complex systems, chaos theory.</keywords>
		<abstract>In 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.</abstract>
		<language>en</language>
		<tertiarytype>Master's or Doctoral Work</tertiarytype>
		<format>On-line</format>
		<size>2991 KiB</size>
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		<e-mailaddress>mj.machicao@gmail.com</e-mailaddress>
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		<url>http://sibgrapi.sid.inpe.br/rep-/sid.inpe.br/sibgrapi/2018/10.17.18.21</url>
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