Badania faktorów wpływających na wskaźniki efektywności transportu miejskiego

Intercity transportation load is described in the article. Reasons for development of additional loads in transportation are researched. Possible solutions for elimination of existing problem are discussed and a general model of City Transport Infrastructure (CTI) is proposed

Situations created in city transport infrastructure by drivers depending on their awareness level

The article deals with the influence of drivers transportation load depending on the drivers’ information they created in transportation some of the situations are analyzed in the city infrastructure

AN IMPROVED ENSEMBLE APPROACH FOR DOS ATTACKS DETECTION

Context. The task of using the ensemble of classifiers to detect DoS attacks in large arrays of network traffic data is solved to withstandattacks on the network.Objective of this paper is to build an ensemble of classifiers that surpasses single classifiers in terms of accuracy.Method. To achieve the formulated goal an algorithm, that indicates the probability of belonging to certain classes, which return avector of classification scores for each point, is proposed. The peculiarity of the proposed approach is that for each point from the dataset,the predicted class label corresponds to the maximum value among all scores obtained by classification methods for a given point. Asclassifiers, decision trees, k-nearest neighbors algorithm, support vector machines with various kernel functions, and naпve Bayes areconsidered. A comparative analysis of the proposed approach with single classifiers is considered using the following metrics: accuracy,precision, recall, and F-measure.Results. The experiments have been performed in R 3.4.1 on the NSL-KDD dataset of network attacks, which was divided into threeclasses (DoS, normal network behavior and other types of attack).Conclusions. The conducted experiments have confirmed the efficiency of the proposed approach. The most accurate result showedan ensemble of five classifiers. The development of techniques for attacks detection based on an ensemble of classifiers avoids the problems inherent in most approaches since it is capable of detecting both known and new attacks with high accuracy. It can be concluded that the proposed approach for network attacks detection is of practical significance. In order to further study the attacks detection in networktraffic, studies will be performed on real Big data sets

A linguistic treatment for automatic external plagiarism detection

Plagiarism is the unauthorized use of the ideas, presentation of someone else's words or work as your own. This paper presents an External Plagiarism Detection System (EPDS), which employs a combination of the Semantic Role Labeling (SRL) technique, the semantic and syntactic information. Most of the available methods fail to capture the meaning in the comparison between a source document sentence and a suspicious document sentence when two sentences have same surface text. Therefore, it leads to incorrect or even unnecessary matching results. However, the proposed method is able to avoid selecting the source text sentence whose similarity with suspicious text sentence is high but its meaning is different. On the other hand, an author may change the sentence from: active to passive and vice versa; hence, the method also employed the SRL technique to tackle the aforementioned challenge. Furthermore, the method used the content word expansion approach to bridge the lexical gaps and identify the similar ideas that are expressed using different wording. The proposed method is able to detect different types of plagiarism such as the exact verbatim copying, paraphrasing, transformation of sentences, changing of word structure. As a result, the experimental results have displayed that the proposed method is able to improve the performance compared with the participating systems in PAN-PC-11 and other existing techniques