University of Maribor

About: University of Maribor is a education organization based out in Maribor, Slovenia. It is known for research contribution in the topics: Population & KEKB. The organization has 3987 authors who have published 13077 publications receiving 258339 citations. The organization is also known as: Univerza v Mariboru.

Defection and extortion as unexpected catalysts of unconditional cooperation in structured populations

Abstract: We study the evolution of cooperation in the spatial prisoner's dilemma game, where besides unconditional cooperation and defection, tit-for-tat, win-stay-lose-shift and extortion are the five competing strategies. While pairwise imitation fails to sustain unconditional cooperation and extortion regardless of game parametrization, myopic updating gives rise to the coexistence of all five strategies if the temptation to defect is sufficiently large or if the degree distribution of the interaction network is heterogeneous. This counterintuitive evolutionary outcome emerges as a result of an unexpected chain of strategy invasions. Firstly, defectors emerge and coarsen spontaneously among players adopting win-stay-lose-shift. Secondly, extortioners and players adopting tit-for-tat emerge and spread via neutral drift among the emerged defectors. And lastly, among the extortioners, cooperators become viable too. These recurrent evolutionary invasions yield a five-strategy phase that is stable irrespective of the system size and the structure of the interaction network, and they reveal the most unexpected mechanism that stabilizes extortion and cooperation in an evolutionary setting.

Explaining machine learning models in sales predictions

Abstract: Uniform and comprehensive explanations for an arbitrary black-box prediction model.Interactive what-if analysis for the evaluation of decision options.Support for validation and updates of decision makers' mental models.Real-world application on a difficult business problem - sales forecasting.The real-world business-to-business sales data set used is publicly available. A complexity of business dynamics often forces decision-makers to make decisions based on subjective mental models, reflecting their experience. However, research has shown that companies perform better when they apply data-driven decision-making. This creates an incentive to introduce intelligent, data-based decision models, which are comprehensive and support the interactive evaluation of decision options necessary for the business environment.Recently, a new general explanation methodology has been proposed, which supports the explanation of state-of-the-art black-box prediction models. Uniform explanations are generated on the level of model/individual instance and support what-if analysis. We present a novel use of this methodology inside an intelligent system in a real-world case of business-to-business (B2B) sales forecasting, a complex task frequently done judgmentally. Users can validate their assumptions with the presented explanations and test their hypotheses using the presented what-if parallel graph representation. The results demonstrate effectiveness and usability of the methodology. A significant advantage of the presented method is the possibility to evaluate seller's actions and to outline general recommendations in sales strategy.This flexibility of the approach and easy-to-follow explanations are suitable for many different applications. Our well-documented real-world case shows how to solve a decision support problem, namely that the best performing black-box models are inaccessible to human interaction and analysis. This could extend the use of the intelligent systems to areas where they were so far neglected due to their insistence on comprehensible models. A separation of the machine learning model selection from model explanation is another significant benefit for expert and intelligent systems. Explanations unconnected to a particular prediction model positively influence acceptance of new and complex models in the business environment through their easy assessment and switching.

Vaccination and epidemics in networked populations—An introduction

Abstract: This is an introduction to the special issue titled “Vaccination and epidemics in networked populations” that is in the making at Chaos, Solitons & Fractals . While vaccination is undoubtedly one of the most important preventive measures of modern times, epidemics are feared as one of the most damaging phenomena in human societies. Recent research has explored the pivotal implications of individual behavior and heterogeneous contact patterns in networked populations, as well as the many feedback loops that exist between vaccinating behavior and disease propagation [1, 2]. Interdisciplinary explorations in the realm of statistical physics, network science, nonlinear dynamics, and data analysis have given rise to theoretical epidemiology, as well as to the theory of epidemic processes in complex networks. From classical models assuming well-mixed populations and ignoring human behavior, to recent models that account for behavioral feedback and population structure, we have come a long way in understanding disease transmission and disease dynamics, and in using this knowledge to devise effective prevention strategies. This special issue is aimed at helping the further development of these synergies. We hope that it contributes to enhance our understanding of vaccination and epidemics in networked populations, by featuring works related to vaccination and epidemics using techniques ranging from complex and temporal networks to network of networks and show-casing the possibilities of interdisciplinarity via complex systems science to tackle the challenges in our quest for a healthier future. Topics of interest include but are not limited to epidemiological modeling and vaccination, behavior-vaccination dynamics, reaction-diffusion processes and metapopulation models, evolutionary and game theoretical models in epidemiology, as well as to influence maximization and digital epidemiology.

A hybrid differential evolution for optimal multilevel image thresholding

Abstract: Real test images were used to perform thresholding using Otsu's method with a high level of thresholds.The proposed hybrid is compared with DE, jDE, PSO, ABC, and CS.Algorithms are compared based on PSNR and SSIM metrics.Friedman and Wilcoxon statistical tests are used to show the performances.Proposed hjDE shows superior performance in the quality of the results. Image thresholding is a process for separating interesting objects within an image from their background. An optimal threshold's selection can be regarded as a single objective optimization problem, where obtaining a solution can be computationally expensive and time-consuming, especially when the number of thresholds increases greatly. This paper proposes a novel hybrid differential evolution algorithm for selecting the optimal threshold values for a given gray-level input image, using the criterion defined by Otsu. The hybridization is done by adding a reset strategy, adopted from the Cuckoo Search, within the evolutionary loop of differential evolution. Additionally a study of different evolutionary or swarm-based intelligence algorithms for the purpose of thresholding, with a higher number of thresholds was performed, since many real-world applications require more than just a few thresholds for further processing. Experiments were performed on eleven real world images. The efficiency of the hybrid was compared to the cuckoo search and self-adaptive differential evolution, the original differential evolution, particle swarm optimization, and artificial bee colony where the results showed the superiority of the hybrid in terms of better segmentation results with the increased number of thresholds. Since the proposed method needs only two parameters adjusted, it is by far a better choice for real-life applications.