author = "Machicao, Jeaneth and Bruno, Odemir M.",
          affiliation = "{Instituto de F{\'{\i}}sica de S{\~a}o Carlos} and {Instituto 
                         de F{\'{\i}}sica de S{\~a}o Carlos}",
                title = "Finding Patterns and Exploiting Pseudo-randomness using Complex 
            booktitle = "Proceedings...",
                 year = "2018",
               editor = "Ross, Arun and Gastal, Eduardo S. L. and Jorge, Joaquim A. and 
                         Queiroz, Ricardo L. de and Minetto, Rodrigo and Sarkar, Sudeep and 
                         Papa, Jo{\~a}o Paulo and Oliveira, Manuel M. and Arbel{\'a}ez, 
                         Pablo and Mery, Domingo and Oliveira, Maria Cristina Ferreira de 
                         and Spina, Thiago Vallin and Mendes, Caroline Mazetto and Costa, 
                         Henrique S{\'e}rgio Gutierrez and Mejail, Marta Estela and Geus, 
                         Klaus de and Scheer, Sergio",
         organization = "Conference on Graphics, Patterns and Images, 31. (SIBGRAPI)",
            publisher = "Sociedade Brasileira de Computa{\c{c}}{\~a}o",
              address = "Porto Alegre",
             keywords = "patterns, pseudo-randomness, pattern recognition, complex systems, 
                         chaos theory.",
             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.",
  conference-location = "Foz do Igua{\c{c}}u, PR, Brazil",
      conference-year = "Oct. 29 - Nov. 1, 2018",
             language = "en",
                  ibi = "8JMKD3MGPAW/3S3E6JL",
                  url = "",
           targetfile = "camara-ready.pdf",
        urlaccessdate = "2020, Dec. 04"