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The paper deals with a problem of the obfuscated JavaScript code detection and classification based on Abstract Syntax Trees (AST) coloring. Colors of the AST vertexes and edges are assigned with regard to the types of the AST vertexes specified by the program lexical and syntax structure and the programming language standard. Research involved a few stages. First of the all, a non-obfuscated JavaScript programs dataset was collected by the public repositories evaluation. Secondly, obfuscated samples were created using eight open-source obfuscators. Classifier models were built using an algorithm of gradient boosting on the decision trees (GBDT). We built two types of the classifiers. The first one is the model that classifies the program according to the type of the obfuscator used, i.e. based on what obfuscator created the sample. The second one tries to detect samples obfuscated by the obfuscator whose samples are not observed during training. The quality of the obtained models is on par with the known published results. The feature engineering method proposed in the paper does not require a preliminary analysis of the obfuscators and obfuscating transformations. In the final part of the paper we analyze a quality of models estimated, discussing the certain statistical properties of the obfuscated and non-obfuscated samples obtained and corresponding colored ASTs. Analysis of generated samples of obfuscated programs has shown that the method proposed in the paper has some limitations. In particular, it is difficult to recognize minifiers or other obfuscating programs, which change the lexical structure to a greater extent and the syntax to a lesser extent. To improve the quality of detection of this kind of obscuring transformations, one can built combined classifiers using both the method based on the AST coloring and the additional information about lexemes and punctuation, for example, entropy of identifiers and strings, proportion of characters in upper and lower case, usage frequency of certain characters etc.

The paper analyses the known available method to search for the author of the text in the natural language base of knowledge proposed by O. Khrulev in which the minimum distance between the frequency dictionaries of the presumed authors and the text under analysis is accepted as a criterion for the successful identification of the author. The patterns and drawbacks of the method are revealed.The paper suggested that since the distance value is based on the average values of this lexeme-usage in all papers of the author on the basis of which frequency dictionaries are created, such leaps will show up when the specific value of the lexeme-usage frequency stands in stark difference to the average one.To test this hypothesis, the paper determines a variation coefficient of each lexeme-usage frequency in the texts under analysis.The analysis of frequency dictionaries of Russian canonical writers conducted in the paper has shown that on average about 90% of the authors' frequency dictionaries contain lexemes whose frequencies of usage are inhomogeneous.The author of the paper suggested that the coefficient of variation shows increase in author's word-hoard, i.e. the larger the vocabulary size, the richer the speech, and, therefore, the less frequently the author uses the same lexemes.In the paper there is a hypothesis that it is wrong to reduce the analysed size of the authors' frequency dictionaries only by critical boundaries: it is necessary to analyse lexemes with a variation coefficient over 33%, which illustrate rich word-hoard.The paper also proposes to define only one specific critical boundary of 10 thousand lexemes, since the indefinite boundary of 5 - 10 thousand lexemes offered by O. Khrulev makes it difficult to identify the author of unknown text. In this case, the lexemes with a variation coefficient over 33% of the total vocabulary size of the studied authors beyond the critical boundary are subjected to analysis.To test this hypothesis, a numerical experiment was carried out. The main point of the experiment was to identify the authors of unknown texts based on the authors' frequency dictionaries. At the same time, there were no unknown texts in the data compilation of the frequency dictionaries. The identification was based on the calculation of distance from the unknown text to the authors' frequency dictionaries, i.e. according to O. Khrulev’ s technique. In calculation different critical boundaries were specified.A numerical experiment has shown that the method proposed in the paper increases the successful identification percent for the larger size texts (more than 5,000 word forms) by 12.5%, and for texts of small size (less than 5,000 word forms) by 15.2%.

The paper explores the key problems of establishing the type of negative information and differentiating between facts and opinions. This is one of the most challenging problems of forensic linguistics that requires a robust methodological framework to be tackled successfully. When faced with this problem, the expert has to deal with the ambiguity of terminology and wording of questions, as well as the complexity of the object of study – the semantics of statements encountered in acts of speech. The paper analyzes the content of key terms traditionally used to address this forensic problem, and offers a possible solution. The so-called “factual square” is a method based on analyzing a statement against four semantic parameters: presence of statement of past action; verifiability of this statement on the basis of specificity of description of the action and its potential observability; lack of alternative, i.e. indication of a different possibility; the purpose of conveying negative information. The paper also analyzes the basic types of factual information and offers specific examples of semantic analysis of statements using the proposed approach.

Machine learning is applied in every field of human activity using digital data. In recent years, many papers have been published concerning artificial intelligence use in classification, regression and segmentation purposes in medicine and in ophthalmology, in particular. Artificial intelligence is a subsection of computer science and its principles, and concepts are often incomprehensible or used and interpreted by doctors incorrectly. Diagnostics of ophthalmology patients is associated with a significant amount of medical data that can be used for further software processing. By using of machine learning methods, it’s possible to find out, identify and count almost any pathological signs of diseases by analyzing medical images, clinical and laboratory data. Machine learning includes models and algorithms that mimic the architecture of biological neural networks. The greatest interest in the field is represented by artificial neural networks, in particular, networks based on deep learning due to the ability of the latter to work effectively with complex and multidimensional databases, coupled with the increasing availability of databases and performance of graphics processors. Artificial neural networks have the potential to be used in automated screening, determining the stage of diseases, predicting the therapeutic effect of treatment and the diseases outcome in the analysis of clinical data in patients with diabetic retinopathy, age-related macular degeneration, glaucoma, cataracts, ocular tumors and concomitant pathology. The main characteristics were the size of the training and validation datasets, accuracy, sensitivity, specificity, AUROC (Area Under Receiver Operating Characteristic Curve). A number of studies investigate the comparative characteristics of algorithms. Many of the articles presented in the review have shown the results in accuracy, sensitivity, specificity, AUROC, error values that exceed the corresponding indicators of an average ophthalmologist. Their introduction into routine clinical practice will increase the diagnostic, therapeutic and professional capabilities of a clinicians, which is especially important in the field of ophthalmic oncology, where there is a patient survival matter.

Рассмотрена задача прогнозирования временных рядов в ее одно- и многомерной постановках. Описан метод прогнозирования, в основе которого лежат алгоритмы сжатия данных и искусственного интеллекта. В качестве примера практического применения этого метода приведены вычисления для некоторых социальных, демографических и экономических показателей Новосибирской области. Наряду с широко используемыми на практике программами для сжатия данных, использованы реализации относительно менее известных моделей на основе формальных грамматик, а также метода, основанного на конечных автоматах. Для прогнозных значений построены доверительные интервалы., This paper describes and experimentally investigates a time series forecasting method based on data compression and artificial intelligence techniques. Its basic idea is to combine various algorithms that can estimate the compressed size of a sequence of discrete values into a single method of forecasting. Generalizations of the method to continuous and multivariate cases are described. We use several popular data compression libraries (zlib, bzip2, ppmd), as well as applications of relatively lesser-known algorithms based on formal grammars (re-pair) along with our implementation of an algorithm based on finite automata with multiple heads. All these are employed to make forecasts for some social, economic and demographic indicators of the Novosibirsk region. The article elaborates both our methodology and the methods used for data preprocessing. Confidence intervals with a confidence level of 0.95 are plotted for all predicted values; the average relative errors calculated from the results of predicting the already fixed values are given. Cases in which multivariate forecasting turned out to be more accurate than univariate forecasting are: 1. Average annual population and natural population growth in the Novosibirsk region. Figure 1 shows their graphs along with multivariate predictions, while confidence intervals and mean relative errors are presented in tables 1, 2. 2. Life expectancy and number of deaths in the Novosibirsk region. Figure 2 shows their graphs along with multivariate predictions, confidence intervals and mean relative errors are presented in tables 3, 4. Table 5 gives univariate forecasts of some other indicators of the Novosibirsk region. We think that the results of computations show that the proposed method is capable of finding non-trivial patterns in the data and can be used in practice., Вычислительные технологии, Выпуск 5 (25) 2020, Pages 80-90

В статье рассматривается апробация интегративной методики атрибуционного анализа текста на русском языке., This paper focuses on the approbation of an integrative method of attribution text analysis in Russian, based on a combination of the results of an interpretive study of the material and objectification of these results through mathematical statistics., Политическая лингвистика, Issue № 3 (81), Pages 215-224

В статье рассматриваются вопросы, посвященные графической форме существования языка, выраженной в шрифтовом начертании. Шрифтовой знак — буква — интересен как объект дизайна и как объект визуального искусства. Изучение его значения в визуальной системе коммуникаций определяет актуальность темы. Также в работе анализируется значимость изучения связи зрительного образа и его восприятия., The paper discusses the issues related to the graphical form of the language, expressed in the font style. Font sign, a letter, is interesting as an object of design and as an object of visual art. The study of its signifi cance in the visual communication system determines the relevance of the topic. Along with this, the work reinterprets its methodological signifi cance. Materials are proposed for theoretical and practical exercises., Университетский научный журнал, Выпуск 57 2020

In the last 10 years there has been a revolu on in the fi eld of computer image analysis and pa ern recogni on. Modern algorithms of computer vision equaled and even in some problems surpassed human capabili es. This jerk is largely due to the emergence and development of the technology of deep convolu onal neural networks.Recent developments in the fi eld of image processing and machine learning open up the prospect of crea ng systems based on ar fi cial neural convolu onal networks, superior to humans in problems of image classifi ca on, in par cular, in solving problems of analysis of various medical images. Among the most promising applica ons: automated recogni on and classifi ca on of skin diseases, detec on of pathologies on X-ray, CT, MRI, ultrasound imaging. In the proposed project, we will focusour a en on on the diagnosis of human skin diseases.At the moment, melanoma is one of the most dangerous types of malignant tumors of the skin with a lot of deaths due to rapid metastasis, which is difficult to treat. The development of computer vision technology has allowed the development of technical vision systems that allow detec on and classifi ca on of skin diseases with a quality that is comparable and in some cases exceeds the values a ained by man.In this paper, the authors propose an algorithm for the primary diagnosis of skin melanoma based on deep neural networks, achieving an accuracy of 91% for melanoma in dermatoscopic images. At the moment, the algorithm is implemented programma cally and is used in the test version of the online system for detec ng and classifying skin diseases, available at skincheckup.online.Thanks to this development, the prospect of a signifi cantincrease in the propor on of people subjected to preven ve examina on for the presence of skin diseases opens up. Along with this, an addi onal source of informa on for specialized professionals can also play a role in seng the right diagnosis.

Introduction. The study is concerned with revealing specific aspects of use of implicit knowledge when combined sequence is learned. The paper focuses on the problem of unconscious perception of multiple information. Unlike most of other researches in implicit learning, the present study probes into implicit knowledge usage under condition of conscious manipulation of one of several parameters in combined sequence. The aim of the study is to reveal aspects of implicit knowledge use in combined sequences learning. Materials and Methods. “Sequences learning” experimental technique was implemented to find out attributes of implicit knowledge gaining and usage in sensomotor tasks. 9 geometrical figures of different forms and colours were exposed 15 times sequentially in the certain order during the training phase of the study. Participants were asked to press certain key after a figure of relevant form appeared on the screen as quick as they can. During the test phase that followed, participants were asked to press a certain key after a figure of relevant colour appeared on the screen. Conditions varied depending on whether one or another pattern (by form or by color) was implemented in the sequence of stimulus. Time of sensorimotor reaction in serial reaction task was used as empirical marker of learning efficacy. The procedure was carried out on a typical serial laptop with screen resolution 1366x768 pixels running a program specially written for the present study. Results. We have found that a person is capable to learn implicitly several patterns in structured information presented during the “sequence learning” task. There is no interference between different rules that were learned. Implicit knowledge about the pattern of relevant parameter has a major impact on solving the task that needs conscious control. Thereafter, learning the pattern of irrelevant parameter has no significant effect. Discussion and Conclusion. Learning of combined sequences can lead to positive and negative effects on further actions. Positive effect occurs when implicitly learned sequence corresponds to conditions of the task (relevant parameter remains). Negative effect occurs when sequence does not correspond to conditions of the task (relevant parameter changes). Positive and negative aftereffects of combined sequences learning can be interpreted as an expression of complex type setting phenomena: these effects occur because of unconscious state of readiness to perceive an appearance of objects in a certain way. The results can be useful for educational programme developers, especially in trai ning to use different kind of interfaces.

The ability to predict the dynamics of financial instruments is an important topic for financial market players. In the context of large and heterogeneous information, there is a need to use effective methods to data processing for management decision-making. In particular, machine learning techniques are becoming very popular in financial modeling. The aim of this paper is to forecast the Russian stock price index by using machine learning methods such as neural network modeling and support vector machine and to examine their predictive power. For economic and mathematical modeling, we use statistical and analytical information on the dynamics of the MICEX stock price index, fundamental and technical indicators of the stock market for the period 2002–2016 years. For computational experiments, we use training, testing and validation datasets and software for machine learning in Python. The predictive power of the methods is estimated on the validation data with using both traditional indicators of mathematical statistics (such as absolute and relative prediction error) and count coefficient of determination. We found the use of a longer time period for the MICEX index that corresponds to large training data set in neural network modeling has led to training error reduction. The predictive power of support vector machine on validation data set is higher comparing with neural network modeling. However, that difference in prediction metrics is not significant. The development of a methodology for filtering input data and trading strategy based on machine learning algorithms are possible directions for further research.

The paper is devoted to the study of the verbal semantic parameters used in the Russian National Corpus (RNC). The author analyzes the lexical-semantic groups of verbs and the problems of creating filters for automatically searching for a selected group (so-called secondary imperfectives). A detailed analysis of the corpus results revealed the problem with the semantic annotation of the RNC and the need for its improvement. Настоящая статья посвящена семантическим параметрам, используемым при поиске глаголов в Национальном корпусе русского языка (НКРЯ). Автор анализирует лексикально-семантическую группу глаголов и проблемы образования фильтров, применяемых к автоматическому поиску особенной группы глаголов (так наз. вторичных имперфективов). Анализ их семантического описания указывает на существенные недостатки НКРЯ и необходимость усовершенствования семантической разметки.

The purpose of this paper is to present the basic principles of the construction and operation of monitoring systems of the tool condition, and the machine tooling backup man-agement in the computer-aided manufacturing. The proposed approach is focused on creating conditions for the effective tool state management. For this, the application of the intelligent host control module on the basis of the interactive electronic technical documentation of the mechatronic technological object is proposed. Valuation principles of the tool life fuzzy boundary are fully considered on the basis of the of fuzzy knowledge theory. Three methods for estimating the parameters of the resistance fuzzy boundary are presented. A neural network which operates on the basis of the developed fuzzy prediction rule base of the cutting tool wear depending on the instrument status and its impact on the part cutting accuracy is built. The results obtained are applicable in the sphere of the industrial engineering.

The research is devoted to teaching one of the basic mathematical concepts – the function – in the secondary school. Regarded as the key instrument of mathematics and experimental modeling, the notion of function including its perception, interpretation and application have always been under the scrutiny of Russian and foreign scientists. The authors focus their attention on specificity of students’ perception of the above concept, integrated in teaching process, and provide several examples of functions, applied in different spheres of everyday life, in order to develop students’ operational skills and competences related to mathematical functions. All the interrelated aspects of teaching methods and practices are considered on the basis of activity approach and information technologies. The paper recommends a series of particular exercises, based on the APOS theory (Action – Process – Object – Scheme), along with the Geogebra courseware to help students master their conceptual understanding of mathematical function, and its operational options in various mathematical contexts (e.g. calculating the roots, estimating the limits and derivatives, changing the parameters, solving practical problems, etc). The assignment samples demonstrate visibility of the courseware and effectiveness of its application in practical teaching.

The paper proposes the new approach to educational text selection for the humanities teaching. Its specifics is based on the wide interpretation of educational texts, as the culture texts involved in cognitive learning activity and fulfilling the multiple tasks of the related subjects and personal development. The educational text is taken as a generalized model, its content and structure determining the scheme of a speech cognitive activity, as well as the meaning formation mechanism responsible for understanding and personal development. The texts and tasks selection is oriented on schoolchildren’s understanding with the reference to the psycholinguistic, psychological and hermeneutic approaches; information extraction and interpretation level related to the author’s and student’s context. The given approach could provide the basis for developing and structuring the textbooks content for the humanities subjects.

Working vocabulary of features include most informative features of objects to be recognized. The aim is to develop a method of forming a working vocabulary of features for recognition algorithms based on Fourier-descriptors of the object image contours.To solve this problem the paper offers to use the method of functional maximization that is the ratio of the distance between the classes to the spread of objects within each of the classes represented in the feature space, which is formed on the basis of Fourier-descriptors.To check the effectiveness of the proposed method to form a working vocabulary of features the numerical experiments have been carried out. The experiments used two databases of reference images consisting of 10 and 13 reference images. Test images obtained by rotating the reference images, by zooming, as well as by adding the noise using the normal law of distribution have been created from these images. The proposed by the author algorithm, which uses the Prewitt operator, threshold segmentation, and morphological processing has marked the contours of images. The original vocabulary of features derived from the Fourier-descriptors has dimension of 98. The vocabularies of working features having the dimensions, respectively, 3 and 4 have been formed on the basis of functional maximization for both reference images. In the course of numerical experiments the frequency of correct decisions to recognise the features of reference bases of images for the original and working vocabularies has been evaluated. It has been proved that the algorithm of recognition with the formed working vocabularies of features provides a great efficiency of automatic recognition of objects.There are known publications, which use a similar method to form a working vocabulary of features in algorithms of human recognition by the image. But there are no publications on choosing the vocabulary of features for recognition algorithms based on the analysis of the image contours that can be used in computer vision systems of automated production lines.

In this paper, a frame-based description of verbal polysemy is used to answer some questions concerning syntactic behavior and argument structures associated with complex predicates in Persian. In Persian a number of CPs consisting of a light verb (LV) and a preverb (PV) participate in causative alternation. The causative variant is formed with the LV ændaxtæn 'cause to fall' and the anti-causative variant is formed with oftadæn 'fall'. In some contexts these verbs do not participate in causative alternatioin. In other words they do not have an anti-causative variant in such contexts. The peculiar behavior of these verbs in different contexts is explained in the framework of Frame Semantics [2]. I will argue that these verbs are associated with two semantic frames and just one of them is compatible with both causative and antiicausative form. The other frame is compatible with causative form but incompatibel with anticausative form hence no anticausative variant. As for the A-structure of CPs, using the notion of Frames [5] it is proposed that the whole construction including the CP determines its A-structure. It is not detrmined just by PV [9] or compositionally by PV and LV [6].

We present three generations of prototypes for a contactless admission control system that recognizes people from visual features while they walk towards the sensor. The system is meant to require as little interaction as possible to improve the aspect of comfort for its users. Especially for people with impairments, such a system can make a major difference. For data acquisition, we use the Microsoft Kinect 2, a low-cost depth sensor, and its SDK. We extract comprehensible geometric features and apply aggregation methods over a sequence of consecutive frames to obtain a compact and characteristic representation for each individual approaching the sensor. All three prototypes implement a data processing pipeline that transforms the acquired sensor data into a compact and characteristic representation through a sequence of small data transformations. Every single transformation takes one or more of the previously computed representations as input and computes a new representation from them. In the example models presented in this paper, we are focusing on the generation of frontal view images of peoples’ faces, which is part of the processing pipeline of our newest prototype. These frontal view images can be obtained from colour, infrared and depth data by rendering the scene from a changed viewport. This pipeline can be modelled considering the data flow between data transformations only. We show how the prototypes can be modelled using modelling frameworks and tools such as Cinco or the Cinco-Product Dime. The tools allow for modelling the data flow of the data processing pipeline in an intuitive way.

The paper addresses the issues of diachronic description genetically related vocabulary East Slavonic languages based on a study of its origin and semantics in accordance with the actual problems of the historical word formation lexicology and lexicography Slavonic languages. To study involved pre-Slavic vocabulary, of ancient and modern East languages, united in the genetic paradigm, which consists of etymological families with vertices *Dati, *Bьrati, *Imati//Imѣti, *Nesti. В статье затрагиваются вопросы диахронического описания генетически близкой лексики восточнославянских языков на основе исследования её происхождения и семантики в соответствии с актуальными проблемами исторической дериватологии, лексикологии и лексикографии славянских языков.

Представлены результаты экспериментов по добавлению биграмм в тематические модели и учету сходства между ними и униграммами. Предложен новый алгоритм PLSA-SIM, являющийся модификацией алгоритма построения тематических моделей PLSA (Probabilistic Latent Semantic Analysis). Предложенный алгоритм позволяет добавлять биграммы и учитывать сходство между ними и униграммными компонентами. Исследована возможность применения ассоциативных мер для выбора и последующего включения биграмм в тематические модели. В качестве текстовых коллекций взяты русскоязычная подборка статей из электронных банковских журналов, английские части корпусов параллельных текстов Europarl и JRC-Acquiz и англоязычный архив исследовательских работ по компьютерной лингвистике ACL Anthology. Выполненные эксперименты показывают, что существует подгруппа тестируемых мер, упорядочивающих биграммы таким образом, что при последующем их добавлении в предложенный алгоритм PLSA-SIM качество получающихся тематических моделей значительно повышается. Предложен новый итеративный алгоритм PLSA-ITER без учителя, позволяющий добавлять наиболее подходящие биграммы. Эксперименты показывают дальнейшее улучшение качества тематических моделей по сравнению с исходным алгоритмом PLSA., The results of experimental study of adding bigrams and taking account of the similarity between them and unigrams are discussed. A novel PLSA-SIM algorithm based on a modification of the original PLSA (Probabilistic Latent Semantic Analysis) algorithm is proposed. The proposed algorithm incorporates bigrams and takes into account the similarity between them and unigram components. Various word association measures are analyzed to integrate top-ranked bigrams into topic models. As target text collections, articles from various Russian electronic banking magazines, English parts of parallel corpora Europarl and JRC-Acquiz, and the English digital archive of research papers in computational linguistics (ACL Anthology) are chosen. The computational experiments show that there exists a subgroup of tested measures that produce top-ranked bigrams in such a way that their inclusion into the PLSA-SIM algorithm significantly improves the quality of topic models for all collections. A novel unsupervised iterative algorithm named PLSA-ITER is also proposed for adding the most relevant bigrams. The computational experiments show a further improvement in the quality of topic models compared to the PLSA algorithm., ВЫЧИСЛИТЕЛЬНЫЕ МЕТОДЫ И ПРОГРАММИРОВАНИЕ: НОВЫЕ ВЫЧИСЛИТЕЛЬНЫЕ ТЕХНОЛОГИИ, Выпуск 2 2015

Цель. Целью выполненных исследований являлось построение модели обобщенного логистического отображения и оценка возможностей ее использования для формирования математического описания, а также проведение оперативных прогнозов параметров сложных динамических процессов, описываемых временными рядами. Методика. Результаты исследований получены на основе математического и имитационного моделирования нелинейных систем с использованием инструментария хаотической динамики. Результаты. Предложена модель обобщенного логистического отображения, которая применяется для интерпретации характеристик динамических процессов. Рассмотрены некоторые примеры представления процессов на основе обобщенного логистического отображения при вариациях величин параметров модели. Предложены процедуры моделирования и интерпретации данных об исследуемых процессах, представленных временными рядами, а также оперативного прогнозирования их параметров, использующие модели обобщенного логистического отображения. Научная новизна. В статье предложены усовершенствованная математическую модель и обобщенное логистическое отображение, предназначенные для исследования нелинейных дискретных динамических процессов. Практическая значимость. Проведенные с использованием обобщенного логистического отображения исследования процессов железнодорожного транспорта, в частности по оценке параметров вагонопотоков, свидетельствуют о значительных возможностях его применения на практике для решения задач анализа, моделирования и прогнозирования сложных нелинейных дискретных динамических процессов. Предложенная модель может использоваться при учете условий неопределенности, нерегулярности, проявления хаотической природы технических, экономических и других процессов, в том числе, железнодорожного транспорта., Purpose. The aim of the research is to build a model of the generalzed logistic mapping and assessment of the possibilities of its use for the formation of the mathematical description, as well as operational forecasts of parameters of complex dynamic processes described by the time series. Methodology. The research results are obtained on the basis of mathematical modeling and simulation of nonlinear systems using the tools of chaotic dynamics. Findings. A model of the generalized logistic mapping, which is used to interpret the characteristics of dynamic processes was proposed. We consider some examples of representations of processes based on enhanced logistic mapping varying the values of model parameters. The procedures of modeling and interpretation of the data on the investigated processes, represented by the time series, as well as the operational forecasting of parameters using the generalized model of logistic mapping were proposed. Originality. The paper proposes an improved mathematical model, generalized logistic mapping, designed for the study of nonlinear discrete dynamic processes. Practical value. The carried out research using the generalized logistic mapping of railway transport processes, in particular, according to assessment of the parameters of traffic volumes, indicate the great potential of its application in practice for solving problems of analysis, modeling and forecasting complex nonlinear discrete dynamical processes. The proposed model can be used, taking into account the conditions of uncertainty, irregularity, the manifestations of the chaotic nature of the technical, economic and other processes, including the railway ones., Мета. Метою виконаних досліджень була побудова моделі узагальненого логістичного відображення та оцінка можливостей її використання для формування математичного опису, а також проведення оперативних прогнозів параметрів складних динамічних процесів, описуваних часовими рядами. Методика. Результати досліджень отримані на основі математичного та імітаційного моделювання нелінійних систем із використанням інструментарію хаотичної динаміки. Результати. Запропоновано модель узагальненого логістичного відображення, яка застосовується для інтерпретації характеристик динамічних процесів. Розглянуто деякі приклади представлення процесів на основі узагальненого логістичного відображення при варіаціях величин параметрів моделі. Запропоновано процедури моделювання та інтерпретації даних про досліджувані процеси, представлені часовими рядами, а також оперативного прогнозування їх параметрів, що використовують моделі узагальненого логістичного відображення. Наукова новизна. У статті запропоновано вдосконалену математичну модель та узагальнене логістичне відображення, призначені для дослідження нелінійних дискретних динамічних процесів. Практична значимість. Проведені з використанням узагальненого логістичного відображення дослідження процесів залізничного транспорту, зокрема за оцінкою параметрів вагонопотоків, свідчать про значні можливості його застосування на практиці для вирішення завдань аналізу, моделювання та прогнозування складних нелінійних дискретних динамічних процесів. Запропонована модель може використовуватися при обліку умов невизначеності, нерегулярності, прояву хаотичної природи технічних, економічних та інших процесів, у тому числі, залізничного транспорту.

This paper describes our approach to document search based on the ontological resources and graph models. The approach is applicable in local networks and local computers. It can be useful for ontology engineering specialists or search specialists.

The paper represents main ideas of my theory “Relative verity logic” (RVL), that combines advantages of two contrary approaches to vagueness (many-valued theory and supervaluationism). RVL allows the fruitful treatment of borderline cases while saving all the classical tautologies and inference rules; and on top of all this, it also avoids the trap of the sorites paradoxes., В статье излагаются основные идеи разработанной автором теории относительной истинности для расплывчатых предикатов «Relative verity logic» (RVL), которая представляет попытку соединить преимущества двух противоположных подходов к проблеме расплывчатости (многозначного и сверхоценочного). RVL предлагает продуктивный анализ пограничных предложений, при этом сохраняет все классические тавтологии и правила вывода и позволяет избежать «парадокса кучи»., Осмислюються основні ідеї розробленої автором теорії відносної істинності для розмитих предикатів «Relative verity logic» (RVL). Теорія є спробою поєднати переваги двох протилежних підходів до проблеми розмитості (багатозначного і надоціночного). RVL пропонує продуктивний аналіз граничних речень, при цьому зберігає всі класичні тавтології і правила виведення, дозволяє уникнути «парадоксу купи».

The paper considers issues of formal methods in the tasks of knowledge representation including optimization of formal grammars by means of functional programming languages. One of the possible applications of the formal notation given is illustrated by the task of parallel natural texts analysis and comparison.