QUANTITATIVE EVALUATION OF THE LIVING STANDARDS OF THE RUSSIAN FEDERATION REGIONS
N.A. Shchukina, A.V. Golub
The study is devoted to the issue of assessing the living standards and quality of the Russian Federation regions in 2010-2018. An integral indicator is formed on the basis of the values of 33 socio-economic indicators and acts as an indicator of assessing the living standards. Selected for the study indicators are combined into seven groups: the income level of the population, the level of development of the consumer market, the standard of housing and quality of housing conditions, level of development and availability of health and education, demographic indicators, employment and unemployment, as well as the environment. The information base of the study consists of official statistics for 2010-2018. Based on the results of the integral indicator calculations the distribution of Russian regions by the living standards is obtained. The changes dynamics in the average Russian integral indicator indicates a decrease in the living standards of over the period under review. To conduct a comparative analysis of changes in the living standard in the Russian Federation regions for each region, the total increments of the integral indicator and its components were obtained. These values formed a feature space for identifying homogeneous groups of regions by the total increment of each of the seven indicators using cluster analysis methods. As a result of the stable classification procedure, the Russian Federation regions were divided into three homogeneous groups and 13 atypical regions were identified. Atypical regions differ in subindex increments that are not typical for the selected groups. A significant disparity in the rate of change in the living standard was revealed. This characterizes the lack of effectiveness of state planning and implementation of social programs at the regional level.
Keywords: living standards, integral indicator, ranking, classification of regions, cluster analysis.
DESTRUCTIVE INFORMATIONAL AND PSYCHOLOGICAL INFLUENCE IN SOCIAL NETWORKS
V.P. Okhapkin, E.P. Okhapkina, A.O. Iskhakova, A.Y. Iskhakov
The article discusses the problem of the destructive information influence in social networks revealing. It is noted that the tasks that are associated with the rapid detection of destructive information influence are prerequisites for the development and improvement of methods and means for identifying such influences in social networks. To understand the social dynamics of social networks groups we consider: the communication model proposed by Theodore Newcomb, Kurt Levin’s “planar map”, and Fritz Haider’s theory of cognitive balance. UN documents on the counteraction of the use of the Internet for the extremist purposes and radicalization were analyzed. The role of the cognitive approach to the analysis of social network messages and the main scenarios implemented by influence actors in texts aimed at different audiences are considered. The study presents a systematic approach to the task of designing a multi-agent platform. Special attention is paid to the block of pattern analysis of user’s messages in social networks both from the position of mathematical modeling and social dynamics. The article describes the architecture and methods of the multi-agent system for the destructive information and humanitarian impact detection. The system consists of the administration interface, subsystems for the multi-agent system administration and agents management, clustering agents, network messages analysis and dispersion analysis. The description of the main blocks of agents and subsystems is given.
Keywords:multi-agent technologies, cluster analysis, information security, aggression, radicalization, machine learning, personality, information and psychological impact destructive informational impact, socio-cyberphysical system.
FORECASTING TIME SERIES USING EVENT BINDING
This article discusses the concept of modification of the time series analysis method, focused on integration with clustering methods in real-time training mode. Various methods of forecasting time series and machine learning are analyzed. The method described in the article predicts the behavior of the time series based on large data obtained from various sources and associated with existing transactions in the time series. This approach makes it possible to find the dependence of changes in certain indicators of the considered systems depending on various events. The performed research offers the concept of automated system training in real time with the possibility of further software implementation. The concept under consideration allows you to build forecasts for any time series, depending on various events, news and data that are in the public domain. An approach is proposed that links events to a transaction chart. The advantage of this approach is the ability to find various dependencies between events and various changes in indicators, for example: prices on exchanges, values of social indicators and many others.
Keywords: data analysis, forecasting, time series, big data, cluster analysis, data mining.
FORECASTING OF MANIPULATIVE INFORMATION INFLUENCES
IN SOCIAL NETWORKS: TERRITORIAL ASPECT
V.A. Minaev, K.M. Bondar, E.V. Vaits, A.V. Kantysheva
Negative factors affecting information security of countries are described. Special attention is paid to the information-psychological effects highlighted in the Doctrine of information security of the Russian Federation. It is pointed to the expansion of the use of simulation methods for modeling information impacts on social groups and the corresponding information counteraction. The necessary definitions related to the use of the simulation approach proposed for the study of complex nonlinear systems to the modeling of information influences in social networks are given. The description of the system-dynamic model of information counteraction in the form of differential equations system is given. Simulation experiments were carried out with the model on the Anylogic software platform and analytical dependences of characteristic times reflecting the susceptibility of the population of the country’s settlements to influence through social networks, including mechanisms of negative influence, on the statistical characteristics of users were obtained. Typology of settlements of the Russian Federation on characteristics of information propagation in social networks of regions is carried out. It is concluded that the identified relationships can be used to predict manipulative information effects and planning information counteraction. In addition, it is emphasized that the simulation model allows, using statistically observed variables, to estimate parameters and variables characterizing the dynamics of information propagation in the population, which are statistically unobservable.
Keywords: simulation model, information manipulative influence, forecasting, counteraction, social network, typology, cluster analysis.
ADAPTIVE AUTOMATED SYSTEM AS A TRAINING DIFFERENTIATION MEANS IN THE INFORMATION TECHNOLOGY SPECIALISTS EDUCATION PROCESS
UDC 004.9, 37.04
N.V. Datsenko, S.A. Gorbatenko, V.V. Gorbatenko
One of the most effective ways to implement a differentiated approach in the information technology (IT) specialists training which consists in the adaptive automated learning system use is proposed in the article. The system will allow to store a large amount of educational information, to modify it if necessary, to adapt it to different categories of users in accordance with their level of initial training, to check the formation of competencies and analysis of mistakes made by students in the control testing process. The system dataware includes a relational database (DB), which contains theoretical information of the discipline, exercises and control tasks on all topics, adapted to different categories of students, as well as a table in which all erroneous answers of users are entered during the control testing for further analysis. The software contains the users automatic classification module which is based on the cluster analysis method and allows at the input control stage to form four groups of students depending on the level of residual knowledge obtained in the previous disciplines study, corresponding to the marks as “excellent”, “good”, “satisfactory” and “unsatisfactory”, in order to differentiate the educational material. The training program module is designed to solve the problems of new knowledge acquiring by students of different groups, using the theoretical knowledge obtained in the practical tasks implementation and checking the competence formation level. In the event that the level has not reached the baseline, the error analysis module allows to determine which discipline subjects have caused the greatest difficulties for the student to re-study.
Keywords: :training differentiation, IT disciplines, improving the training quality, adaptive automated system, automatic classification, cluster analysis
MODELING MANIPULATIVE INFLUENCES IN SOCIAL NETWORKS
V. A. Minaev, M. P. Sychev, L.S. Kulikov, E.V. Vaitz
In the Doctrine of information security of the Russian Federation the main negative factors affecting the state of information security (IS), called informational and technical influences (ITI) and information and psychological influences (IPI). Therefore, modeling, evaluation and forecasting of information influences (II) on social groups and organizing of the corresponding information counteraction (ICA) are urgent tasks of management. The system-dynamic models of information influences in social networks and groups are considered. Their application for purposes of counteraction to information terrorism and extremism is proved. The description in the form of flowcharts is given. Systems of differential equations are presented. Experiments with models using the advanced simulation platform Anylogic were carried out. In a sample of Russian settlements based on cluster analysis found homogeneous typological groups that differ in the average time of transmission of information in social networks. Based on Gibbs ‘ postulate, the system-dynamic model of information influences among students has been successfully tested. The high consistency of simulation results with empirical data (determination coefficients of at least 90%) is shown. Models allow you to forecast the II and ICA and to play different scenarios of the dynamics of these processes.
Keywords: : simulation modeling, information influences, management, social network, topology, typology, cluster analysis.
APPLICATION OF A NEURO NETWORK APPROACH TO LOGICAL DATA PROBLEMS AND BUILDING INTELLIGENT DECISION-MAKING SYSTEMS
D.P. Dimitrichenko, R.A. Zhilov
The need to reduce the dimensionality of large data sets while maintaining the logical structure, as well as the detection of hidden patterns and the removal of information noise and redundancy in the description of diagnostic (recognition) objects leads to the need to construct an effective method for classifying objects in weakly formalized areas of knowledge. Logical functions that describe objects using variable-valued predicates allow us to reveal hidden regularities and eliminate redundancy in the description of objects. Ordered by means of variable-valued logical functions, object classes are the basis for the formation of the structure of cognitive maps. The purpose of this study is to create an algorithm for constructing a logical neural network based on the variable-valued logic function and justifying the possibility of applying the results obtained in the construction of cognitive maps. The theoretical possibility and algorithms allowing to make the transition from variable-valued logic functions to cognitive maps using the neural network approach are grounded. The result of this work is the procedure for constructing a cognitive map using logical neural networks built on the basis of variable-valued logical functions. The advantage of the obtained cognitive map is the possibility of functioning within the framework of fuzzy logic.
Keywords: predicate, predicate significance, variable-valued logical function, logical neural network, cognitive map, cluster analysis, neural network.
APPLYING DATA MINING TECHNIQUES AND MULTILEVEL MONITORING TO SOLVING A PROBLEM OF MEDICAL AID RATIONALIZATION
O.N.Choporov, S.V.Bolgov, I.I.Manakin
The question of using methods of the data intellectual analysis is considered through studies of medical systems of various levels. The authors formulate the problems arising while solving a question of medical aid rationalization. The role of multilevel monitoring while solving application tasks is defined and the review of recommended methods of prediction and classification models construction is presented. The necessity of using procedures of preliminary processing of the information is proved.
Keywords: : data intellectual analysis, multilevel monitoring, cluster analysis, disease forecasting.