Scientific Journal of KubSAU

The article briefly discusses the following questions. The classic definition of virtual reality systems. Effects of virtual reality: effects of the reality, presence, depersonalization (modification of consciousness), a modification of the consciousness of the user, virtualization, interests, goals, values, and motivations ("reals and virtuals"). The criteria of reality in various forms of consciousness and their application in virtual reality. Virtual reality systems and criteria of reality, the principles of equivalence (relativity) of Galileo and Einstein and the criteria for virtual reality. The virtual device I / o. The author's definition of virtual reality systems. Dreaming, hypnagogic state, and virtual reality. Augmented reality and augmented virtuality. The modification of consciousness and the consciousness of the user in virtual reality. Consideration of future and pathological changed forms of consciousness that arise in systems with intelligent interfaces. Observance of moral norms in virtual reality and the consequences of failure. The risk of effects of virtual reality and the need for serious scientific study. The transfer of knowledge and skills from virtual reality to true. The transfer of knowledge and skills from virtual reality to true. Mechanisms of formation of models of the true and the virtual reality of man and the principles of their correct and meaningful interpretation. Principles and perspectives correct meaningful interpretation of the subjective (virtual) models of the physical and social reality formed by the human consciousness. The application of virtual reality systems. There is also a test for understanding of virtual reality

The creation of artificial intelligence systems is one of important and perspective directions of development of modern information technology. As there are many alternatives to artificial intelligence systems, there is a need to evaluate mathematical models of these systems. In this work, we consider a solution of the problem of identifying classes of levels of pay of employees on their characteristics. To achieve this goal, it requires free access to test the source data and methodology, which will help to convert the data into the form needed for work in artificial intelligence systems. A good choice is the databases from the site: http://allexcel.ru/gotovyetablitsy-excel-besplatno. In this work, we have used the database called "The database table of employees, payments calculation". The most reliable in this application was the model of the INF4 based on semantic appropriate measure of information of A. Kharkevich with integral criteria of "Amount of knowledge". The accuracy of the model is 0.960, which is much higher than the reliability of expert evaluations, which is equal to about 70%. To assess the reliability of the models in the ACS-analysis and the system called "Eidos" we have used F-criterion of van Ritbergen and fuzzy multiclass generalization proposed by Professor E. V. Lutsenko

The creation of artificial intelligence systems is one of important and perspective directions of development of modern information technology. As there are many alternatives to artificial intelligence systems, there is a need to evaluate mathematical models of these systems. In this article, we consider a solution of the problem of identifying classes of levels of pay to employees on their characteristics. To achieve this goal it requires free access to test the source data and methodology, which will help to convert the data into the form needed for work in artificial intelligence systems. A good choice is a database of test problems for systems of UCI artificial intelligence repository. In this work we have used data base on teaching effectiveness for three regular semesters and two summer semesters of 151 teaching assistant (TA) assignments at the statistics Department of the University of Wisconsin-Madison. The most reliable in this application was the model of the INF4. The accuracy of the model in accordance with Lmeasure made up 0,809, which is much higher than the reliability of expert evaluations, which is equal to about 70%. To assess the reliability of the models in the ASC-analysis and in the system of "Eidos" we use F-criterion of van Ritbergen and its fuzzy multiclass generalization proposed by Professor E. V. Lutsenko

Creation of artificial intelligence systems is one of important and perspective directions of development of modern information technology. As there are many alternatives to artificial intelligence systems, there is a need to evaluate mathematical models of these systems. In this work, we present a solution of the problem of identifying classes of salary levels of employees depending on their characteristics. To achieve this goal it requires free access to test the source data and methodology, which will help to convert the data into the form needed for work in artificial intelligence systems. A good choice is a database of test problems for systems of artificial intelligence of UCI repository. In this work we used the database called "Wine Data Set" from the Bank's original task of artificial intelligence from repository UCI. The most reliable in this application was the model of the INF4 based on semantic, according to A. Kharkevich, integral criteria of "Amount of knowledge". The accuracy of the model is 0,916, which is much higher than the reliability of expert evaluations, which is equal to about 70%. To assess the reliability of the models in the ASC-analysis and the system of "Eidos" we used the F-criterion of van Ritbergen and fuzzy multiCLASS generalization proposed by Professor E. V. Lutsenko (L-measure)

Classic quantitative measure of the reliability of the models: F-measure by van Rijsbergen is based on counting the total number of correctly and incorrectly classified and not classified objects in the training sample. In multiclass classification systems, the facility can simultaneously apply to multiple classes. Accordingly, when the synthesis of the model description is used for formation of generalized images of many of the classes it belongs to. When using the model for classification, it is determined by the degree of similarity or divergence of the object with all classes, and a true-positive decision may be the membership of the object to several classes. The result of this classification may be that the object is not just rightly or wrongly relates or does not relate to different classes, both in the classical F-measure, but rightly or wrongly relates or does not relate to them in varying degrees. However, the classic F-measure does not count the fact that the object may in fact simultaneously belongs to multiple classes (multicrossover) and the fact that the classification result can be obtained with a different degree of similarity-differences of object classes (blurring). In the numerical example, the author states that with true-positive and true-negative decisions, the module similarities-differences of the object classes are much higher than for false-positive and false-negative decisions. It would therefore be rational to the extent that the reliability of the model to take into account not just the fact of true or false positive or negative decisions, but also to take into account the degree of confidence of the classifier in these decisions. In the intellectual system called "Eidos", which is a software toolkit for the automated system-cognitive analysis (ASC-analysis), we use initially proposed by its developers measure of the reliability of the models, which is essentially a fuzzy multiclass generalization of the classical F-measure (it is proposed to call it the L-measure). In this article, L-measure is mathematically described and its application is demonstrated on a simple numerical example

The creation of artificial intelligence systems is one of important and perspective directions of development of modern information technology. Since there are many alternatives of mathematical models of systems of artificial intelligence, there is a need to assess the quality of these models, which requires their comparison. To achieve this goal we require free access to the source data and methodology, which allows to convert these data into a form needed for processing in artificial intelligence. A good choice for these purposes is a database of test problems for systems of artificial intelligence of repository of UCI. In this work we used the database "Iris Data Set" from the bank's original task of artificial intelligence – UCI repository, which solved the problem of formalization of the subject area (development of classification and descriptive dials and graduations and the encoding of the source data, resulting training sample, essentially representing a normalized source data), synthesis and verification statistical and system-cognitive models of the subject area, identify colors with classes, which serve varieties of Iris, as well as studies of the subject area by studying its model. To solve these problems we used the automated system-cognitive analysis (ASC-analysis) and its programmatic Toolkit – intellectual system called "Eidos"

In this article we consider application of the automated systemic and cognitive analysis (ASK-analysis), its mathematical model – a systemic information theory and the program tools realizing them – the intellectual Eidos system, for input (digitization) of images from graphic files, synthesis of the generalized images of classes, their abstraction, classification of the generalized images of classes (clusters and constructs), comparison of concrete images with the generalized images (identification) of classes, comparisons of classes with each other and creations of the generalized images of genus of ground beetles on the basis of images of the types. The new approach to digitization of images of ground beetles based on use of a polar frame, the center of weight of the image and its external contour is offered. Before digitization of images, their transformations standardizing the provision of images, their sizes and an angle of rotation can be applied. Therefore, the results of digitization and the ASK-analysis of images can be invariant (are independent) concerning their situation, the sizes and turn. There is a successful experience of the solution of similar tasks in other subject domains. This article can be considered as a continuation of series of the works devoted to application of the automated systemic and cognitive analysis (ASK-analysis) and its program tools – the Eidos system

Adequate and effective assessment of the efficiency, effectiveness and the quality of scientific activities of specific scientists and research teams is crucial for any information society and a society based on knowledge. The solution to this problem is the subject of scientometrics and its purpose. The current stage of development scientometrics differs greatly from his previous appearance in the open as well as paid on-line access to huge amount of detailed data on a large number of indicators on individual authors and on scientific organizations and universities. The world has well-known bibliographic databases: Web of Science, Scopus, Astrophysics Data System, PubMed, MathSciNet, zbMATH, Chemical Abstracts, Springer, Agris, or GeoRef. In Russia, it is primarily the Russian scientific citing index (RSCI). RSCI is a national information-analytical system, accumulating more than 9 million publications of Russian scientists, as well as the information about citation of these publications from more than 6,000 Russian journals. There is too much information; it is so-called "Big data". But the problem is how to make sense of these large data, more precisely, to identify the meaning of scientometric indicators) and thus to convert them into great information ("great information"), and then apply this information to achieve the objective of scientometrics, i.e. to transform it into a lot of knowledge ("great knowledge") about the specific scientists and research teams. The solution to this problem is creating a "Scientific smart metering system" based on the use of the automated system-cognitive analysis and its software tools – an intellectual system called "Eidos". The article provides a numerical example of the creation and application of Scientometric intelligent measurement system based on a small amount of real scientific data that are publicly available using free on-line access to the RSCI

To increase the validity of conclusions about the impact of environment on quality of life we need to move from generalities to the application of quantitative modeling techniques. This requires the joint processing environmental databases and databases depicting various aspects of quality of life. These databases need to be handled not just together, but in a comparable form approach, technology and methodology; and we need to implement them in one software system. For the first time in the environmental studies it has been planned to be done with the application of the ASK- analysis and the system called "Eidos". In this work, we set the goals and the objectives of the application of the ASK-analysis to study the effect of environmental factors on the quality of life of the population of the region. The article reveals the urgency of this study; the requirements for the method of conducting the study, the choice of research method, the contents of the objectives of the study. The proposed work is at the edge of mathematical ecology and mathematical modeling of quality of life (which refers to mathematical and instrumental methods of Economics), resulting from expected synergies, consists in obtaining of new knowledge in these fields, that is relevant to both ecology and economy. This knowledge will make it more meaningful and justified for the application of environmental criteria and concepts in the economy

To increase the validity of conclusions about the impact of the environment on the quality of life we need to move from generalities to the application of quantitative modeling techniques. This requires the joint processing environmental databases and databases depicting various aspects of quality of life. These databases are needed to be handled not just together, but in a comparable form approach, including technology and methodology, and to be implemented in one software system. For the first time in the environmental studies, it has been planned to be done with the application of the ASK-analysis and the system called "Eidos". Previously, the authors have set the goals and the objectives of the application of the ASK-analysis to study the effect of environmental factors on the quality of life of the population of the region. The article reveals the urgency of this study; the requirements for the method of conducting the study, the choice of a research method; as well as the contents of the objectives of the study. The proposed work is at the edge of mathematical ecology and mathematical modeling of quality of life (which refers to mathematical and instrumental methods of Economics), resulting from expected synergies, consists in obtaining of new knowledge in these fields that is relevant to both ecology and economy. This knowledge will make it more meaningful and justified for the application of environmental criteria and concepts in the economy. This work contains a description of the basic data sources for the study of the impact of environmental factors on various aspects of quality of life of the region's population, the source data for this study, the characteristics of the original data, substantiation of requirements to the method of research, choosing research methods appropriate to requirements; the development of steps to achieve the objectives of the study