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National audience; The mathematical model and method for the problem of optimization of aggregation and of sequential-parallel execution modes of intersecting operation sets are proposed. The proposed method is based on the two-level decomposition scheme. At the top level the variant of aggregation for groups of operations is selected, and at the lower level the intensities of operations execution are optimized for a fixed version of aggregation.

The quantitative characteristics of information resources for automated information systems (AIS) are proposed in the paper. The entities represented in the AIS by the information object mappings and their components (information unit and information element) are considered. The composition of the system information space is substantiated, which represents the whole amount of information being processed in the system, auxiliary information resources and all data of the system and its users. Application of the quantitative values to AIS information resources characteristics, taking into account the sense properties of information objects being mapped in AIS, will make it possible to raise the design decision making quality of AIS and its performance within the lifecycle.

The paper considers a local wavelet transform with a singular basis wavelet. The problem of nonparametric approximation of a function is solved by the use of the sequence of local wavelet transforms. Traditionally believed that the wavelet should have an average equal to zero. Earlier, the author considered singular wavelets when the average value is not equal to zero. As an example, the delta-shaped functions, participated in the estimates of Parzen – Rosenblatt and Nadara – Watson, were used as a wavelet. Previously, a sequence of wavelet transforms for the entire numerical axis and finite interval was constructed for singular wavelets. The paper proposes a sequence of local wavelet transforms, a local wavelet transform is defined, the theorems that formulate the properties of a local wavelet transform are proved. To confirm the effectiveness of the algorithm an example of approximating the function by use of the sum of discrete local wavelet transforms is given. 

The comparative study of two types of voice signal representation for larynx pathology detection is presented. Parameters obtained in clinical system lingWaves compared to parameters obtained by mel-frequency cepstral analysis. The classifier based on the probabilistic model (logistic regression) was designed to determine the suitability of given parameters for the larynx pathology detection problem. To train the classifier, the base of voice samples of 60 persons was recorded, 30 of which constitute the control group, and the other 30 had various diseases of the larynx (nodules of the vocal folds, laryngeal paralysis, or functional dysphonia). The results show that the classifier based on mel-frequency cepstral parameters (83,8 %) higher than the classifier based on parameters obtained in lingWaves (60,4 %).

A problem of design of a flow line composed of stationary machines for sequential batch machining is considered. A MIP model for synthesis of manufacturing process structure is proposed. The model takes into account main technological and structural constraints with the criterion of the minimal line equipment cost. Results of solving a real industrial example are also presented.

The article is an essay on the development of technologies for natural language processing, which formed the basis of BERT (Bidirectional Encoder Representations from Transformers), a language model from Google, showing high results on the whole class of problems associated with the understanding of natural language. Two key ideas implemented in BERT are knowledge transfer and attention mechanism. The model is designed to solve two problems on a large unlabeled data set and can reuse the identified language patterns for effective learning for a specific text processing problem. Architecture Transformer is based on the attention mechanism, i.e. it involves evaluation of relationships between input data tokens. In addition, the article notes strengths and weaknesses of BERT and the directions for further model improvement.

In the paper the algorithm for object detection in high resolution images is proposed. The approach uses multiscale image representation followed by block processing with the overlapping value. For each block the object detection with convolutional neural network was performed. Number of pyramid layers is limited by the Convolutional Neural Network layer size and input image resolution. Overlapping blocks splitting to improve the classification and detection accuracy is performed on each layer of pyramid except the highest one. Detected areas are merged into one if they have high overlapping value and the same class. Experimental results for the algorithm are presented in the paper.

Time-management refers to planning the available time in correspondence with personal goals and preferences of the employee. The criterion for efficient time-management lies in achieving definite goals in minimum possible time. This paper presents a short review of the literature on time-management. It is shown how scheduling theory may be used for optimal planning in time-management. The problem of minimizing the total (average) weighted completion time of the planned jobs by an employee is considered provided that only lower and upper bounds of the possible processing time of each job are known before scheduling. Software for constructing a permutation of the given jobs with the largest relative semi-perimeter of the optimality parallelepiped has been developed. Computational experiment on the computer showed the effectiveness of the developed algorithms for constructing schedules for time-management.

In the problems of data flows analysis, the problems of statistical decision making on parameters of observed data flows are important. For their solution it is proposed to use sequential statistical decision rules. The rules are constructed for three models of observation flows: sequence of independent homogeneous observations; sequence of observations forming a time series with a trend; sequence of dependent observations forming a homogeneous Markov chain. For each case the situation is considered, where the model describes the observed stochastic data with a distortion. "Outliers" ("contamination") are used as the admissible distortions that adequately describe the majority of situations appear in practice. For such situations the families of sequential decision rules are proposed, and robust decision rules are constructed that allow to reduce influence of distortion to the efficiency characteristics. The results of computer experiments are given to illustrate the constructed decision rules.

The main options for the formation of a shared secret using synchronized artificial neural networks and possible patterns of behavior of a cryptanalyst are considered. To solve the problem of increasing the confidentiality of the generated shared secret, if it is used as a cryptographic key, it is proposed to use the mixing a certain number of results of individual synchronizations (convolution). As a mixing function, we consider the convolution of the vectors of network weights by bitwise addition modulo 2 of all the results of individual synchronizations. It is shown that the probability of success of a cryptanalyst is reduced exponentially with an increase of the number of terms in the convolution and can be chosen arbitrarily small. Moreover, the distribution law of the generated key after convolution is close to uniform and the uniformity increases with the number of terms in the convolution.