Behavioral traits play a major role in successful adaptation of wildlife to urban conditions. However, there are few studies showing how urban conditions affect the social behavior of urban animals during their direct encounters. It is generally believed that the higher density of urban populations translates into increased aggression between individuals. In this paper, using a camera-trap method, we compared the character of direct encounters in urban and non-urban populations of the striped field mouseApodemus agrarius(Pallas, 1771), a species known as an urban adapter. We confirmed the thesis that urbanization affects the social behavior and urban and rural populations differ from each other. Urban animals are less likely to avoid close contact with each other and are more likely to show tolerant behavior. They also have a lower tendency towards monopolization of food resources. The behavior of urban animals varies depending on the time of day: in the daytime, animals are more vigilant and less tolerant than at night. Our results indicate that, in the case of the species studied, behavioral adaptation to urban life is based on increasing tolerance rather than aggression in social relations. However, the studied urban adapter retains the high plasticity of social behavior revealed even in the circadian cycle. The observation that tolerance rather than aggression may predominate in urban populations is a new finding, while most studies suggest an increase in aggression in urban animals. This opens an avenue for formulating new hypotheses regarding the social behavior of urban adapters.

Anomaly (outlier) detection is one of the most important problems of modern data analysis. The sources of anomalies are varying. They can be the results of database users’ mistakes, operational errors, or just missing values. The problem is very important because of the fast growth of large datasets. Therefore, in this article, we present detailed results of work on the concept of granular computing-based approach to anomaly detection, classification, and gradation. The aim of the study is to introduce an innovative solution that allows the use of information granules to identify and classify anomalies. The novelty of the proposed solution consists in the use of fuzzy semantics implied by the statistical properties of the data considered. Moreover, instead of the classic approach to detecting anomalies in the data, it is proposed to determine the degree of anomaly for the data transformed to the new resulting state space. Thanks to the use of an innovative approach using the universal descriptor space, it is possible to determine the degree of anomaly, and by using various aggregation methods one can also specify its type.

Anomaly detection is one of the most important problems of modern data science due to the threat to the security of information systems as well as their users. This applies in particular to logistic data which is used to predict costs, times, organization of travel routes, etc. Data anomalies may endanger the welfare and safety of transport users, goods, handling companies, and consumers. Moreover, they contribute to the overexploitation of the natural environment. Therefore, it is extremely important to find methods that are responsible for their effective detection. The desired approach may be the Choquet integral and its extensions, which in various applications have proven that with their help it is possible to efficiently increase the quality of the classification measured, for example, with the help of the accuracy. Due to the fact that the Choquet integral is resistant to data fluctuations and takes into account the quality (significance) of the information source, it appears to be an effective proposition for the final determination of what data, or more precisely, which records can be considered anomalous. The innovative approach to analyze transport data has not been used before. This study considers four publicly available databases covering different fields of application of transport systems. In a series of comprehensive numerical experiments, the Choquet integral-based approach has proven high efficiency for each of them. Moreover, we made a comparative analysis of the solutions before applying the Choquet integral and the results after its application.