%0 Conference Proceedings
%4 sid.inpe.br/sibgrapi/2018/10.17.18.21
%2 sid.inpe.br/sibgrapi/2018/10.17.18.21.38
%T Finding Patterns and Exploiting Pseudo-randomness using Complex Systems
%D 2018
%A Machicao, Jeaneth,
%A Bruno, Odemir M.,
%@affiliation Instituto de Física de São Carlos
%@affiliation Instituto de Física de São Carlos
%E Ross, Arun,
%E Gastal, Eduardo S. L.,
%E Jorge, Joaquim A.,
%E Queiroz, Ricardo L. de,
%E Minetto, Rodrigo,
%E Sarkar, Sudeep,
%E Papa, João Paulo,
%E Oliveira, Manuel M.,
%E Arbeláez, Pablo,
%E Mery, Domingo,
%E Oliveira, Maria Cristina Ferreira de,
%E Spina, Thiago Vallin,
%E Mendes, Caroline Mazetto,
%E Costa, Henrique Sérgio Gutierrez,
%E Mejail, Marta Estela,
%E Geus, Klaus de,
%E Scheer, Sergio,
%B Conference on Graphics, Patterns and Images, 31 (SIBGRAPI)
%C Foz do Iguaçu, PR, Brazil
%8 29 Oct.-1 Nov. 2018
%I Sociedade Brasileira de Computação
%J Porto Alegre
%S Proceedings
%K patterns, pseudo-randomness, pattern recognition, complex systems, chaos theory.
%X 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.
%@language en
%3 camara-ready.pdf