Month: November 2018

Rank-frequency distribution of natural languages: a difference of probabilities approach

The time variation of the rank k of words for six Indo-European languages is obtained using data from Google Books. For low ranks the distinct languages behave differently, maybe due to syntaxis rules, whereas for k>50 the law of large numbers predominates. The dynamics of k is described stochastically through a master equation governing the time evolution of its probability density, which is approximated by a Fokker-Planck equation that is solved analytically. The difference between the data and the asymptotic solution is identified with the transient solution, and good agreement is obtained.


Rank-frequency distribution of natural languages: a difference of probabilities approach
Germinal Cocho, R. F. Rodríguez, Sergio Sánchez, Jorge Flores, Carlos Pineda, Carlos Gershenson


Measurability of the epidemic reproduction number in data-driven contact networks

The analysis of real epidemiological data has raised issues of the adequacy of the classic homogeneous modeling framework and quantities, such as the basic reproduction number in real-world situations. Based on high-quality sociodemographic data, here we generate a multiplex network describing the contact pattern of the Italian and Dutch populations. By using a microsimulation approach, we show that, for epidemics spreading on realistic contact networks, it is not possible to define a steady exponential growth phase and a basic reproduction number. We show the operational use of the instantaneous reproduction rate as a good descriptor of the transmission dynamics.


Measurability of the epidemic reproduction number in data-driven contact networks
Quan-Hui Liu, Marco Ajelli, Alberto Aleta, Stefano Merler, Yamir Moreno, and Alessandro Vespignani


Bio-inspired computing: Algorithms review, deep analysis, and the scope of applications

Bio-inspired computing represents the umbrella of different studies of computer science, mathematics, and biology in the last years. Bio-inspired computing optimization algorithms is an emerging approach which is based on the principles and inspiration of the biological evolution of nature to develop new and robust competing techniques. In the last years, the bio-inspired optimization algorithms are recognized in machine learning to address the optimal solutions of complex problems in science and engineering. However, these problems are usually nonlinear and restricted to multiple nonlinear constraints which propose many problems such as time requirements and high dimensionality to find the optimal solution. To tackle the problems of the traditional optimization algorithms, the recent trends tend to apply bio-inspired optimization algorithms which represent a promising approach for solving complex optimization problems. This paper presents state-of-art of nine of recent bio-inspired algorithms, gap analysis, and its applications namely; Genetic Bee Colony (GBC) Algorithm, Fish Swarm Algorithm (FSA), Cat Swarm Optimization (CSO), Whale Optimization Algorithm (WOA), Artificial Algae Algorithm (AAA), Elephant Search Algorithm (ESA), Chicken Swarm Optimization Algorithm (CSOA), Moth flame optimization (MFO), and Grey Wolf Optimization (GWO) algorithm. The previous related works are collected from Scopus databases are presented. Also, we explore some key issues in optimization and some applications for further research. We also analyze in-depth discussions the essence of these algorithms and their connections to self-organization and its applications in different areas of research are presented. As a result, the proposed analysis of these algorithms leads to some key problems that have to be addressed in the future.


Bio-inspired computing: Algorithms review, deep analysis, and the scope of applications
Ashraf Darwish

Future Computing and Informatics Journal


Artificial Intelligence Hits the Barrier of Meaning

As someone who has worked in A.I. for decades, I’ve witnessed the failure of similar predictions of imminent human-level A.I., and I’m certain these latest forecasts will fall short as well. The challenge of creating humanlike intelligence in machines remains greatly underestimated. Today’s A.I. systems sorely lack the essence of human intelligence: understanding the situations we experience, being able to grasp their meaning. The mathematician and philosopher Gian-Carlo Rota famously asked, “I wonder whether or when A.I. will ever crash the barrier of meaning.” To me, this is still the most important question.


Credit Is About Perception More Than Performance

I first learned about Douglas Prasher three years ago, when an algorithm we’d just developed made an unpredictable prediction: He should have been a recipient of the 2008 Nobel Prize.

Instead, the award had been given to three other scientists. Even more surprising was our inability to find Prasher anywhere. He wasn’t on the faculty at any university. We couldn’t locate him at an industrial research lab. In fact, as we started digging for him, we realized that he hadn’t written a research paper in nearly a decade. It was truly puzzling. This fellow, who, according to our algorithm, deserved a Nobel Prize, had seemingly disappeared off the face of the Earth.