We discuss a prediction of the solar activity on a short time-scale applying the method based on a combination of a nonlinear mean-field dynamo model and the artificial neural network. The artificial neural network which serves as a correction scheme for the forecast, uses the currently available observational data (e.g., the 13 month running average of the observed solar sunspot numbers) and the dynamo model output. The nonlinear mean-field α Ω dynamo produces the large-scale magnetic flux which is redistributed by negative effective magnetic pressure instability (NEMPI) producing sunspots and active regions. The nonlinear mean-field dynamo model includes algebraic nonlinearity (caused by the feedback of the growing magnetic field on the plasma motion) and dynamic nonlinearities (related to the dynamics of the magnetic helicity of small-scale magnetic field). We compare the forecast errors with a horizon of 1, 6, 12 and 18 months, for different forecast methods, with the same corrections on the current monthly observations. Our forecast is in good agreement with the observed solar activity, the forecast error is almost stably small over short-medium ranges of forecasting windows. Despite a strong level of chaotic component in the solar magnetic activity we present quantitative evidence that the solar activity on a short range can be stably well predicted, by the joint use of the physically based model with the neural network. This result may have an immediate practical implementation for predictions of various phenomena of solar activity and other astrophysical processes, so may be of interest to a broad community.

Optimization of open-pit mining is one of significant tasks to date, with the blasting quality estimation being a key factor. The blasting quality is determined through evaluating the number of fragments and block size distribution, the so-called fragmentation task. Currently, computer vision-based methods using instance or semantic segmentation approaches are most widely applied in the task. However, in practice, such approaches require a lot of computational resources. Because of this, the use of alternative techniques based on algorithms for the real-time object detection is highly relevant. The paper studies the use of YOLO family architectures for solving the task of the blasting quality assessment. Based on the research results, YOLOv7x architecture is proposed as a baseline model. The proposed neural network architecture was trained on a dataset selected by the present authors from digital images of blasted open-pit block fragments, which consisted of 220 images. The obtained results also allow one to suggest the geometrical size of rock chunks as a measure of blasting quality.

Currently, operating systems based on the Linux kernel and open-source software are widely used, including: Debian, Centos, openSUSE, RedHat, Slackware and others, all distributions of which include the kernel and open-source software (packages). This allows you to organize user interaction with various devices. The article presents the results of an analysis of the creation of user account files for operating systems based on the Linux kernel. We are considering using the grep utility to search for data on the hard drive. In operating system forensics, all possible behavior patterns of such source code are very important. Of particular interest are user account management processes and their traces in operating systems. The forensic application of the found methods for controlling user accounts is considered, which will increase the level of information security of the system. A theory will be put forward about the origin of the phenomenon of duplication of account file records on the hard drive, which assumes that duplication occurs due to the characteristics of the file system and the operation of the useradd utility. An experiment was conducted to confirm the theory and show the stages of creating duplicate account files, which allows you to track all changes in the account manipulation system, regardless of system logs.

For many years, the Microsoft Office package has actually been a standard tool for processing electronic documents. Its vast capabilities and rich functionality set it apart from existing competitors. However, from the point of view of information security, extensive features and functionality are not always a boon. MS Office documents, primarily MS Word text editor documents, are often used by attackers as the main, as well as an intermediate stage in many types of attacks on computer systems. Built-in support for powerful and functional programming languages in office packages creates additional opportunities for the violator. In this article, we have reviewed several well-known vulnerabilities, for the successful exploitation of which it is necessary to use MS Word at one of the stages of the attack. The paper analyzes the potential threats created by the use of MS Office and other popular office packages in organizations. The features and functionality of office packages are described, in terms of using them for malicious purposes. To neutralize such threats recommendations on the safe configuration of office packages in the organization are made.

Buffer overflow vulnerabilities have been known for a long time, are widespread and relevant today. To protect against buffer overflows, developers of operating systems usually use standard mechanisms of operating systems, but they are not very effective in attacks using return-oriented programming techniques. The article proposes a model for checking the legitimacy of program control flow transitions when executing a return instruction, based on an access graph. The article also provides a model for checking the legitimacy of program control flow transitions when executing a return instruction, based on the use of a shadow stack, a model for generating a program control flow. Modeling was carried out in the specification language based on set theory, first-order logic and temporal logic of actions, models were checked using the Model Checking method. The constructed models can be refined to the required level of abstraction, used to create mechanisms for protecting operating systems based on Linux from attacks using the return-oriented programming technique.

Our theoretical and numerical analysis have suggested that for low-mass main sequences stars (of the spectral classes from M5 to G0) rotating much faster than the Sun, the generated large-scale magnetic field is caused by the mean-field α2Ω dynamo, whereby the α2 dynamo is modified by a weak differential rotation. Even for a weak differential rotation, the behaviour of the magnetic activity is changed drastically from aperiodic regime to non-linear oscillations and appearance of a chaotic behaviour with increase of the differential rotation. Periods of the magnetic cycles decrease with increase of the differential rotation, and they vary from tens to thousand years. This long-term behaviour of the magnetic cycles may be related to the characteristic time of the evolution of the magnetic helicity density of the small-scale field. The performed analysis is based on the mean-field simulations (MFS) of the α2Ω and α2 dynamos and a developed non-linear theory of α2 dynamo. The applied MFS model was calibrated using turbulent parameters typical for the solar convective zone.

The paper is devoted to a complex analysis of the current system of regulations in the field of security of critical information infrastructure (CII) facilities of the Russian Federation from the point of view of the logic of formation of the legal basis and the chronology of their creation, the results of which have provided a systematic regulatory framework for the security of CII facilities. The main directions of legislative activity in the field of security CII of the Russian Federation have been highlighted and a classification of the current legal acts in terms of it’s requirements has been proposed..The evolution of the content of the regulatory system to ensure the security of significant CII facilities has been described. The results of the analysis led to the conclusion that the state and regulators in the field of IS has developed a sufficient regulatory framework that defines the basic rules, procedures and requirements for the process of categorization, monitoring of its results, as well as providing information security of significant CII facilities. At the same time, on the basis of the experience of categorization of significant objects of the gas industry by the heat and power complex of the Russian Federation, a hypothesis has been made that the establishment of the information security system at specific significant CII sites (e.g., a variety of types of CII objects and areas of activity of CII entities) will require not only the application of existing legal instruments, but also the development of existing sectoral methodical documents in the field of categorization of objects of CII and in the field of construction of the information security system, taking into account their sectoral characteristics.

The FAT 32 file system, despite its solid age, still, remains relevant and demanded by a large number of flash drives and other devices. First of all, it is caused by its simplicity and high-speed performance. At the same time, there is a number of problems revealed in the course of practical use of this file system, for example, it does not support files of 4 gigabytes in size and more. Besides, this file system has low fault tolerance and also there are essential difficulties at recovery of mistakenly or accidentally deleted files. First, it is caused by erasing of a chain of clusters in the file table during file deletion. That does not allow to discover which clusters and in what sequence the content of the deleted file was stored. Secondly, during file deletion in its file record two top bytes of the first cluster address are erased. That significantly complicates search of the first file cluster and the subsequent identification of its chain of clusters. For these problems' solution authors developed the algorithm of recovery of deleted files described earlier and received evaluation of efficient recovery of deleted files of different types by means of this algorithm.

The article presents the results of a comparative analysis of randomness testing using statistical tests in the Information Technology Laboratory of the National Institute of Standards and Technology (STS NIST) and Move-To-Front test in software implementations of floating-point random number generators and random integer numbers of the MATLAB package and the NumPy Python library. It was found thatin MATLAB package passing the randomness of Move-To-Front test of a sequence which was made up of random integer numbers calculated on the basis of the conversion from floating-point random number generators to random integer numbers depends on the sequence length and the size of the alphabet used to convert from floating-point random number generators to random integer numbers. According to the authors, as this effect is not detected in the NumPy Python library, the result obtained in the workindicates the presence of an internal defect in the algorithm for generating floating-point random number generators MATLAB.

The article discusses the results of forecasting time series compiled based on the number of ships that passed through the Kerch Strait from the Black Sea to the Sea of Azov, as well as from the Sea of Azov to the Black Sea in one hour in the period from February 14 to 28, 2022. The information needed to compile the time series under discussion was extracted automatically using a software tool developed by the authors from publicly available online nautical charts posted on the Internet. It is demonstrated that the prediction accuracy based on the integration of formal time series forecasting methods and the method Data Assimilation turns out to be higher than a similar value in the case of forecasting based on an AR-model of a time series of any order.

The article presents the results of the analysis problems related to ensuring the reliability of exit poll data which has led to a reasonable question that the discussed task is isomorphic in its formulation to information security tasks. Therefore, it is advisable to use information protection methods to solve this question. The choice of appropriate methods is justified, as well as the need for additional legal regulation defining and specifying the procedure for conducting exit polls, rules of behaviour and limits of their compliance for participants. The models of security threats to the data of sociological polls at the exit of polling stations, violators models and protection models, development of optimal means and methods to ensure the reliability, integrity, availability of exit poll data for each stage of this sociological research are proposed. Both additional legal regulation of exit polls at the federal level and the development of rules for conducting such studies are necessary for the implementation of this protection system.

The paper discusses elements of logical models of graphical user interfaces used in both universal and specialized scientific visualization systems. Criteria of expressiveness of programming language that are discussed in “Structure and Interpretation of Computer Programs” book are applied to graphical interfaces. It is shown that graphical interfaces allow user to operate on same digital substance and with same logical approaches as in textual programming languages. Both use basic elements, allow their combination, and support the procedure of abstraction. Authors suggest considering this aspect when developing graphical interfaces. Then, idea of modifiers (known also as behaviors, effects, so on) is discussed. Idea of extensions (known also as plugins, modules, and applications) is also discussed. Some methods of programming of scene dynamics are presented. Also languages and ontologies of scientific visualization are discussed, e.g. models for editing visualization pipeline: adding data to projects, filtering of that data, and methods of description of data representation on screen.

The paper discusses elements of logical models of graphical user interfaces used in both universal and specialized scientific visualization systems.Criteria of expressiveness of programming language that are discussed in "Structure and Interpretation of Computer Programs" book are applied to graphical interfaces.It is shown that graphical interfaces allow users to operate on the same digital substance and with the same logical approaches as in textual programming languages.Both give basic elements, allow their combination, and support the procedures of abstraction.Authors suggest considering these aspects when developing graphical interfaces.This perspective is applied to the following presentation of the paper.The idea of modifiers (also known as behaviors or effects) and the idea of extensions (also known as plugins, modules, and applications) are discussed.Some methods of programming of scene dynamics are presented.Also languages and ontologies of scientific visualization are discussed, e.g.models for editing visualization pipeline: adding data to projects, filtering of that data, and methods of description of data representation on screen.Finally, we discuss additional ideas on systems analysis of visualization systems.

The article describes the method developed for integration of formal methods of time series (TS) forecasting (such as autoregressive integrated moving average (ARIMA), singular spectrum analysis (SSA), group method of data handling (GMDH), artificial recurrent neural network with long short-term memory (LSTM)) into the Data Assimilation (DA) technique. The method can be used in cases where mathematical model of the dynamic system generating the TS is not known (for example, TS consisting of economic indicators). The performance of the integration method is confirmed by a forecasting of a TS generated using AR(p) process of order p, where $p = \overline {1,10} $. The comparative analysis of the forecasting accuracy of the method against ARIMA method was carried out. The developed technique showed high accuracy, except for a small set of parameters that lead to an increase in the forecasting error. This, from our point of view, is due to the local features of the predicted TS. The analysis shows that the developed method shows higher forecasting accuracy in comparison with formal methods.

The article describes a developed technology that allows you to extract information about the names of ships and their current coordinates from online nautical charts. This technology is implemented by the authors in the MATLAB package in the form of the ECS software tool, the table structure used to store this information is described. The analysis of information on the movement of sea vessels through the Kerch Strait in the period from 09/13/2020 to 10/13/2020, obtained from the corresponding electronic marine online maps, was carried out. It has been demonstrated that this information allows to divide the traffic of sea vessels into two separate flows: the flow of vessels moving from the Black Sea to the Sea of Azov (forward direction), and the flow of vessels moving in the opposite direction. It has been demonstrated that the availability of information on the names of ships and their coordinates allows a multidimensional analysis of sea traffic. Also count the number of ships that passed the Kerch Strait in forward and backward directions on each day of the week; to build the distribution of sea vessels: according to the time of their waiting for passage through the Kerch Strait, according to the time of day; calculate the dependencies of the number of ships passing in the forward and backward directions during the selected time interval, which represent a time series (TS) from a mathematical point of view. An express analysis of the TS data was carried out, which made it possible to conclude that they are some realizations of random processes. This allows the use of appropriate methods of applied mathematical statistics and TS analysis for their analysis. And then build mathematical models that describe the dynamics of sea traffic, based on which, potentially, it is possible to predict the workload of the Keren Strait.

The paper presents the results of study about the influence of the initial weights choice on the long-term forecast accuracy in the problem of multi-step forecast with a neural network based on long short-term memory (LSTM-net) for time series, composed of time-ordered samples of a harmonic, an amplitude-modulated signal and a frequency-modulated signal. The recursive forecast has been estimated on model time series data providing the means to measure the actual accuracy of the forecast while controlling influence of different characteristics. The analysis of the results show that a necessary condition for obtaining a high-quality long-term forecast is the correct choice of the LSTM-net parameters, especially the initialization of weights for neural network layers. At the same time the use of LSTM-net parameters, which provide high accuracy of short-term forecast, does not guarantee the accuracy of the long-term forecast, and vice-versa. In this regard it is concluded that the usage of other forecast methods and /or algorithms for correcting predicted values for mentioned types of time series is preferable.

The article discusses the approaches providing symmetric access of all industrial production services to the data of business processes of the enterprise by building a single warehouse of heterogeneous data of a metallurgical production. The warehouse is a part of an automated statistic quality control system for the products of a metallurgical enterprise. The article describes an ontological storage model of data coming from various sources of information in the production process. The concept of "a unit of production of metallurgical production" is introduced that is the connecting component of the entire production life cycle of a metallurgical production. The authors propose an ontological model of the production process, in terms of information flows which are formed in an enterprise at each stage of production. Based on the constructed ontological model, the structure of recording an array of information in the heterogeneous data warehouse is justified and formed. Heterogeneous data warehouse forms a single information space of the enterprise, which serves as the basis for analytical analysis throughout the production and decision-making process. For example, timely response to the deviation reasons from the given physical and chemical properties of the finished product.

Abstract To visualize any new entity, a visualization should be designed and programmed. Investigating approaches for programming new scientific visualizations, we come to the following idea: utilize CinemaScience format to describe 3D scenes. CinemaScience is developed for storing and visualizing supercomputer and physical modelling results, and differs with simplicity both for human and machine. It has a set of interesting features, for example it allows to specify dynamics in views dependent on parameters. However its current known applications are of 2D graphics, and in this paper we extend it for 3D. It’s main idea is to treat Cinema artifacts as visual objects of explicit type. We successfully used the suggested approach in various visualization tasks, examples are presented in the paper. We developed the open-source web application that implements the suggested approach.

The paper discusses the results of the first stage of research and development an innovative computer vision system for the automatic asbestos content control in stones veins at an asbestos processing factory. The discussed system is based on the applying of a semantic segmentation artificial neural networks, in particular U-Net based network architectures for solving both: the boundaries of stones segmentation and veins inside them. At the current stage, the following tasks were solved. 1. The discussed system prototype is developed. The system is allowing to takes images of the asbestos stones on the conveyor belt in the near-infrared range (NIR), avoiding the outer lighting influence, and processing the obtaining images. 2. The training, validation and test datasets were collected. 3. Substantiated the choice of the U-Net based neural network. 4. Proposed to estimate the resulted specific asbestos concentration as the average relation of all the veins square to all stones square on the image. 5. The resulted deviation between obtained and laboratory given results of the asbestos concentration is about 0.058 in the slope of graduation curve. The farther improvement recommendations for the developed system are given.