The Measurement of Popularity and Prevalence of Software Vulnerability

Prioritizing bug fixes becomes a daunting task due to the increasing number of vulnerability disclosure programs. When making a decision, not only the Common Vulnerability Scoring System (CVSS) but also the probability of exploitation, the trend of particular security issues should be taken into account. This paper aims to discuss the sources and approaches for measuring degree of interest in a specific vulnerability at a particular point in real-time. This research presents а new metric and estimation model which is based on vulnerability assessment. We compared several techniques to determine the most suitable approach and relevant sources for improving vulnerability management and prioritization problems. We chose the Google Trend analytics tool to gather trend data, distinguish main features and build data set. The result of this study is the regression equation which helps efficiently prioritize vulnerabilities considering the public interest in the particular security issue. The proposed method provides the popularity estimation of Common Vulnerabilities and Exposures (CVE) using public resources.

The Parametric Optimization of Voltage Multiplier

The evolutionary model of voltage multiplier parametric optimization which includes 5 diodes and 5 capacitors is reviewed. It executes the transformation of alternating into constant voltage using a five times larger amplitude. The valve work is modelled according to the scheme of an ideal key. The original mathematical model of voltage multiplier which includes additional logical variables is deducted. It aссepts binary meanings 0 and 1, where 0 corresponds to closed valve status and 1 corresponds to open. In order to receive such a model, it is necessary to indicate the amount of open and closed valve combinations. Then for each of them, it is necessary to write the system of differential equations. Comparing them with each other the single differential equation system with additional logical variables is written as a generalization. The evolutional model is used in order to select the capacitor volume meaning. The goal function forecasts two conditions: maximum meaning of output voltage 1 kV and its minimal fluctuations in the stable regime.

Trilateral Location based Maximum Weighted Directive Spanning Tree for Optimal Routing in IoV

Internet of Vehicles (IoV) is one of the developing models in the Vehicular adhoc networks (VANETs) with the vast improvement of communication technologies. In order to improve data transmission among the multiple communities without link breakage, a novel Trilateral Location Identified Maximum Weighted Directive Spanning Tree (TLIMWDST) technique is introduced. The proposed TLIMWDST technique consists of two major phases, namely location identification and optimal path identification to improve the reliability of data transmission from source vehicle to destination vehicle. In the first phase, the location of the neighboring vehicles is identified by applying a trilateration technique. After the location identification, an optimal route path between the source and destination is identified using Maximum Weighted Directive Spanning Tree (MWDST) through the intermediate nodes. The performance of the TLIMWDST technique is assessed through simulation as compared to the previous path selection techniques in terms of different routing metrics such as packet delivery ratio, packet loss rate, end-to-end delay and throughput with respect to the number of data packets.

Earth’s Stratospheric Aerosol Parameters Reconstruction from Polarimetric Measurements of the Sky

Earth’s climate changes are the result of natural changes in the energy balance of Sun irradiation and influence of anthropogenic factors on the variations of ozone layer thickness and stratospheric aerosol abundance. It is developed a miniature polarimeter for satellite polarimetric experiments in the ultraviolet region of the sunlight spectrum. The main task of this device is to the obtain an information on the stratospheric aerosol physical properties. We tested this polarimeter on a bench specially designed and manufactured as well. It is possible to measure by it the phase dependences of the degree of linear polarization (DLP) of solar radiation scattered by the Earth’s atmosphere. A set of special computer programs was developed for comparing the spectral polarimetric measurements DLP data of cloudless sky with model calculations of DLP for the artificial gas-aerosol medium. Thus, the prototype of satellite polarimeter as well as special computer programs make it possible to study the Earth’s atmosphere aerosol physical characteristics.

A Robust User Authentication Technique in Online Examination

User authentication in the online environment is promoting a hugely challenging issue. This has contributed to the realization of a user authentication where the exams can be performed over the Internet at any time and from any place and by using any digital device. Consequently, further investigations are required to focus on improving user authentication methods to enhance online security mechanisms, especially in the field of e-exams. This research proposes a new user authentication technique based on the user interface (UI). The novel idea is created based on the design preferences of candidates who are taking the e-exams. Several design features are used to design a special user interface for e-exams, for example, the font attributes, back colour, number of questions per page, group categories for questions based on difficulties, and timer setting. The introduced technique can be used to support the user authentication process in the e-exams environment. Furthermore, the proposed technique provides the ability to login to the e-exam without the need to remember the login information, but to select what the student prefers according to his/her personal information. Based on the literature review, a primary evaluation claiming that the students have differences in their preferences and that each user has stable design preferences within different sessions is revealed. In regard to these facts, they become the resource and essence of this research. The security performance of the proposed method is evaluated. The results of the experiments show a false positive (FP) rate of 0.416% and a false negative (FN) rate of 0%.

Serious Game Design by Unified Block Interactions to Support Educational Transformations

This paper introduces a generalized structure for the optimal development of an adventure serious game. Present pandemic conditions induce transformations in educational methods, of which games are an attractive option. A serious game is an interdisciplinary team project for which our structure allows all members to interact irrespective of computer science proficiency. A method of interaction between unified blocks is proposed, consisting of five blocks that cover all the characteristics necessary to describe a serious game. The unified blocks are: rules, characters, scenarios, communication and score. The blocks are divided into sub-blocks that detail the characteristics of the game. The “luck” sub-block allows the real-life non-predictability dimension to be included in the game. During the interaction of the sub-blocks the different contexts of the game are created. Each context has a specific educational content goal that the player must go through. The interactions between sub-blocks are described in an XML file, common working environment for all the interdisciplinary members of the design team involves graphic designers, programmers, game designers and experts in the content to be transmitted. The principle of unified blocks is applied to the fifteen contexts of an existing game, JUSEGU, for which five new contexts are included and implemented in this paper to increase its educational content.

Breast Tumors Diagnosis Using Fuzzy Inference System and Fuzzy C-Means Clustering

Many of the researches have been successful in the field of computer-aided diagnosis because of the important results the intelligent computing approaches have achieved in this field. In this paper the robust classification method is presented, that attempts to classify the tissue suspicion region as normal or not normal by using a Fuzzy Inference System (FIS) using the Fuzzy C-Mean (FCM) clustering for fuzzification of the Gray-Level Co-Occurrence Matrix (GLCM) feature and a match shape function for fuzzification of matrix shape, then by using (T-norm) generate 729 rules (243 rules based on normal DB case, 243 rules based on benign case, 243 rules based on malignant case), after that the best Eighteen rules are selected (best 6 rules based on normal DB case, best 6 rules based on benign DB case, best 6 rules based on malignant DB case) by using genetic algorithm, then make summation for each group if the summation of 6 rules based on normal DB is greater than other summation of two group (best 6 rules based on benign DB case and best 6 rules based on malignant DB case) that mean resulted of the classification step is normal. The model approved efficiency classification rate of 97.5% of input dataset image.

Aspects of the Study of Genetic Algorithms and Mechanisms for their Optimization for the Travelling Salesman Problem

Lately, artificial intelligence has become increasingly popular. Still, at the same time, a stereotype has been formed that AI is based solely on neural networks, even though a neural network is only one of the numerous directions of artificial intelligence. This paper aims to bring attention to other directions of AI, such as genetic algorithms. In this paper, we study the process of solving the travelling salesman problem (TSP) via genetic algorithms (GA) and consider the issues of this method. The genetic algorithm is a method for solving both constrained and unconstrained optimization problems that are based on natural selection, the process that drives biological evolution. One of the common problems in programming is the travelling salesman problem. Many methods can be used to solve it, but we are going consider genetic algorithms. This study aims at developing the most efficient application of genetic algorithms in the travelling salesman problem.

Using Latent Dirichlet Allocation and Text Mining Techniques for Understanding Medical Literature

Over the past few years, numerous studies and research articles have been published in the medical literature review domain. The topics covered by these researches included medical information retrieval, disease statistics, drug analysis, and many other fields and application domains. In this paper, we employ various text mining and data analysis techniques in an attempt to discover trending topics and topic concordance in the healthcare literature and data mining field. This analysis focuses on healthcare literature and bibliometric data and word association rules applied to 1945 research articles that had been published between the years 2006 and 2019. Our aim in this context is to assist saving time and effort required for manually summarizing large-scale amounts of information in such a broad and multi-disciplinary domain. To carry out this task, we employ topic modeling techniques through the utilization of Latent Dirichlet Allocation (LDA), in addition to various document and word embedding and clustering approaches. Findings reveal that since 2010 the interest in the healthcare big data analysis has increased significantly, as demonstrated by the five most commonly used topics in this domain.

Fast Algorithm for Calculating Transitive Closures of Binary Relations in the Structure of a Network Object

The article proposes a fast algorithm for constructing the transitive closures between all pairs of nodes in the structure of a network object, which can have both directional and non-directional links. The algorithm is based on the disjunctive addition of the elements of certain rows of the adjacency matrix, which models (describe) the structure of the original network object. The article formulates and proves a theorem that using such a procedure, the matrix of transitive closures of a network object can be obtained from the adjacency matrix in two iterations (traversal) on such an array. An estimate of the asymptotic computational complexity of the proposed algorithm is substantiated. The article presents the results of an experimental study of the execution time of such an algorithm on network structures of different dimensions and with different connection densities. For this indicator, the developed algorithm is compared with the well-known approaches of Bellman, Warshall-Floyd, Shimbel, which can also be used to determine the transitive closures of binary relations of network objects. The corresponding graphs of the obtained dependences are given. The proposed algorithm (the logic embedded in it) can become the basis for solving problems of monitoring the connectivity of various subscribers in data transmission networks in real time when managing the load in such networks, where the time spent on routing information flows directly depends on the execution time of control algorithms, as well as when solving other problems on the network structures.