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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.

International audience; The paper deals with the problem of identification of linearly varying frequency of sinusoidal signal with unknown amplitude and phase. Identification task for linearly varying frequency occurs, for example, during telescope operation control and it is of practical interest. Existing synthesis methods for identification algorithms of linearly varying frequency of sinusoidal signal use unlimited functions of time that is not attractive from a technical point of view, since the measurement noise multiplied by an unlimited function tends to give extremely poor estimates of the tunable parameter. This paper proposes a new approach for identification of linearly varying frequency comprising iterative filtering of measured sinusoidal signal (with the use of linear first order stable components), which in turn gives the possibility to obtain a simple linear regression model with one unknown constant parameter. We present computer simulation results, illustrating the performance of the proposed identification algorithm. Computer modeling was performed both in the presence and absence of the measurement noise. Also, comparative analysis of the proposed approach with the previously obtained methods was carried out within the framework of computer simulation. It was shown that the presented solution provides a significant improvement in the accuracy of an unknown frequency identification in the noise presence.; Рассмотрена задача идентификации линейно меняющейся частоты синусоидального сигнала, имеющего неизвестные амплитуду и фазу, возникающая, например, при управлении телескопом. Традиционно методы синтеза алгоритмов идентификации линейно меняющейся частоты синусоидального сигнала используют неограниченные функции времени, что вызывает технические проблемы, поскольку в этом случае наличие шумов при измерении может существенно исказить настраиваемый параметр. Предложен подход к идентификации линейно меняющейся частоты, предусматривающий итеративную фильтрацию (с использованием линейных устойчивых звеньев первого порядка) измеряемого синусоидального сигнала, которая позволяет получить простую линейную регрессионную модель с одним неизвестным постоянным параметром. Приведены результаты компьютерного моделирования, иллюстрирующие работоспособность представленного алгоритма идентификации. Компьютерное моделирование выполнено как при наличии шумов в измерениях, так и в их отсутствие. Путем компьютерного моделирования проведен сравнительный анализ предлагаемого подхода с полученными ранее методами, и показано, что представленное решение обеспечивает существенное повышение точности идентификации неизвестной частоты при наличии шумов

A review of robotic systems was carried out. The paper analyzes applied hardware and software solutions and summarizes the most common block diagrams of control systems. The analysis of approaches to control systems scaling, the use of intelligent control, the achievement of fault tolerance, and the reduction of the weight and size of control system elements belonging to various classes of robotic systems were carried out. The goal of the review is finding common approaches used in various areas of robotics to build on their basis a uniform methodology for designing scalable intelligent control systems for robots with a given level of fault tolerance on a unified component base. This part is dedicated to service robotics. The following conclusions are made on the basis of the review results: the key technology in service robotics from the point of view of scalability is the Robot Operating System (ROS); service robotics is today the main springboard for testing intelligent algorithms for the tactical and strategic control levels that are integrated into a common system based on ROS; the problem of ensuring fault tolerance in the service robotics is practically neglected, with the exception of the issue of increasing reliability by changing behavioral algorithms; in a number of areas of service robotics, in which the reduction of mass and dimensions is especially important, the robot control systems are implemented on a single computing device, in other cases a multi-level architecture implemented on Linux-based embedded computers with ROS are used.

A review of robotic systems is presented. The paper analyzes applied hardware and software solutions and summarizes the most common block diagrams of control systems. The analysis of approaches to control systems scaling, the use of intelligent control, achieving fault tolerance, reducing the weight and size of control system elements belonging to various classes of robotic systems is carried out. The goal of the review is finding common approaches used in various areas of robotics to build on their basis a uniform methodology for designing scalable intelligent control systems for robots with a given level of fault tolerance on a unified component base. This part is dedicated to industrial robotics. The following conclusions are made: scaling in industrial robotics is achieved through the use of the modular control systems and unification of main components; multiple industrial robot interaction is organized using centralized global planning or the use of previously simulated control programs, eliminating possible collisions in working area; intellectual technologies in industrial robotics are used primarily at the strategic level of the control system which is usually non-real time, and in some cases even implemented as a remote cloud service; from the point of view of ensuring fault tolerance, the industrial robots developers are primarily focused on the early prediction of faults and the planned decommissioning of the robots, and are not on highly-avaliability in case of failures; industrial robotics does not impose serious requirements on the dimensions and weight of the control devices.

The history of management of various social objects — family, private, public, church, regional, state — has been around for many millennia. And at the same time objectively constantly there were questions “What should heads (managers) of these objects do for ensuring their survival, growth and development?”, “What roles should leaders of social organizations play?”, “What competencies should a leader have in order to effectively and efficiently perform their roles?”. The evolution of views on the role of managers of social organizations from ancient times up today is stated in the paper. There are given results of scientific research on the subject “the role of managers”, which have been revealed the “national”, regional and industrial specific of managerial roles, a completely new role, and above all — the rating of managerial roles at various stages of the social organizations lifecycle.

One of the promising approaches of macroeconomic modeling and quantitative assessment of the impact of external and internal factors on macroeconomy of a country, which is actively used abroad, is a Bayesian approach to the description of macroeconomic processes. In this paper we examine Bayesian vector autoregression model (BVAR) to assess the impact of external shocks, such as the price of Brent crude oil, the volatility index VIX and the Shanghai Stock Exchange Composite index, on Russian macroeconomic indicators. The results allow us to estimate the contribution of external factors as a significant in the dynamics of Russia economic variables. This approach can be successfully applied for the analysis of Russian data, which was confirmed by the results presented in the article.