Introduction: Development of post-quantum digital signature standards represents a current challenge in the area of cryptography. Recently, the signature schemes based on the hidden discrete logarithm problem had been proposed. Further development of this approach represents significant practical interest, since it provides possibility of designing practical signature schemes possessing small size of public key and signature. Purpose: Development of the method for designing post-quantum signature schemes and new forms of the hidden discrete logarithm problem, corresponding to the method. Results: A method for designing post-quantum signature schemes is proposed. The method consists in setting the dependence of the publickey elements on masking multipliers that eliminates the periodicity connected with the value of discrete logarithm of periodic functions constructed on the base of the public parameters of the cryptoscheme. Two novel forms for defining the hidden discrete logarithm problem in finite associative algebras are proposed. The first (second) form has allowed to use the finite commutative (non-commutative) algebra as algebraic support of the developed signature schemes. Practical relevance: Due to significantly smaller size of public key and signature and approximately equal performance in comparison with the known analogues, the developed signature algorithms represent interest as candidates for practical post-quantum cryptoschemes.
Introduction: Effective and prompt formulation of diagnostic conclusions about the presence of anxiety-phobic disorders requires the improvement of existing and the development of new methods for diagnosing and treating patients, including the use of virtual reality technology. Purpose: To analyze a reaction of an individual to a stimulus that triggers a fear response to virtual reality scenes (height exposure). To identify electroencephalographic (EEG) signal markers related to the level of anxiety and virtual reality environment susceptibility of an individual. Methods: A group of nine conditionally healthy males aged 23 to 26 years old who reported neither history of somatic symptoms nor organic brain disorders was formed to conduct the research. The immersion into virtual reality was accompanied by the registration of EEG signals and subsequent completion of a self-assessment questionnaire by the subjects. Results: The state of rest (a reference value) and the state of high emotional stress experience (at the height of a skyscraper) in the virtual reality environment were compared. The results obtained allow to make a conclusion that the simulated situation of being at a height causes a decrease in the indices of alpha, theta, beta rhythms, and an increase in the delta rhythm index of the EEG signal relative to the state of rest in various subjects, regardless of the intensity of fear manifestation. Practical relevance: The conducted research is among the pioneering studies in assessing the effect of virtual reality technologies on human phobic anxiety state. Some objective electrophysiological markers related to the level of anxiety were determined to confirm the presence of patterns in the functional state of the cerebral cortex with a sense of anxiety in individuals immersed in a virtual reality environment.
Introduction: Achievement of specified qualitative indicators in machine learning solutions depends not only on the efficiency of algorithms, but also on data properties. One of the lines for the development of classification and regression models is the specification of local properties of data. Purpose: To improve the qualitative predictors when solving classification and regression problems based on the adaptive selection of various machine learning models on separate local segments of data sample. Results: We propose a method that uses a combination of different models and machine learning algorithms on subsamples in regression and classification problems. The method is based on the calculation of qualitative predictors and the selection of the best models on the local segments of data sample. The finding of transformations of data and time series allows to create sample sets, with the data having different properties (for example, variance, sampling fraction, data range, etc.). We consider the data segmentation based on the change point detection algorithm in time series trends and on analytical information. On the example of the real dataset, we show the experimental values of the loss function for the proposed method with different classifiers on separate segments and on the whole sample. Practical relevance: The results can be used in classification and regression problems for the development of machine learning models and methods. The proposed method allows to improve classification and regression qualitative predictors by assigning models that have the best performance on separate segments.
Introduction: Orthogonal Hadamard matrices consisting of elements 1 and –1 (real number) exist for orders that are multiples of 4. The study considers the product of an orthogonal Hadamard matrix and its core, which is called the Scarpis product, and is similar in meaning to the Kronecker product. Purpose: To show by revealing the symmetries of the block Hadamard matrices that their observance contributes to a product that generalizes the Scarpis method to the nonexistence of a finite field. Results: The study demonstrates that orthogonality is an invariant of the product under discussion, subject to the two conditions: one of the multipliers is inserted into the other one, the sign of the elements of the second multiplier taken into account (the Kronecker product), but with a selective action of the sign on the elements and, most importantly, with the cyclic permutation of the core which depends on the insertion location. The paper shows that such shifts can be completely avoided by using symmetries that are characteristic of the universal forms of Hadamard matrices. In addition, this technique is common for many varieties of adjustable Kronecker products. Practical relevance: Orthogonal sequences and effective methods for their finding by the theory of finite fields and groups are of direct practical importance for the problems of noiseless coding, video compression and visual masking.
Introduction: It is conjectured that the symmetric Hadamard matrices of order 4v exist for all odd integers v>0. In recent years, their existence has been proven for many new orders by using a special method known as the propus construction. This construction uses difference families Xk (k=1, 2, 3, 4) over the cyclic group Zv (integers mod v) with parameters (v; k1, k2, k3, k4; λ) where X1 is symmetric, X2=X3, and k1+2k2+k4=v+λ. It is also conjectured that such difference families (known as propus families) exist for all parameter sets mentioned above excluding the case when all the ki are equal. This new conjecture has been verified for all odd v≤53. Purpose: To construct many new symmetric Hadamard matrices by using the propus construction and to provide further support for the above-mentioned conjecture. Results: The first examples of symmetric Hadamard matrices of orders 4v are presented for v=127 and v=191. The systematic computer search for symmetric Hadamard matrices based on the propus construction has been extended to cover the cases v=55, 57, 59, 61, 63. Practical relevance: Hadamard matrices are used extensively in the problems of error-free coding, and compression and masking of video information.
Introduction: Under the conditions of imperfect methods and means of detection and response to computer attacks there is a constant growth of destructive impacts aimed at critical information systems. This generates a need to develop research methods for early warning systems to provide information security in case of malware attacks. One of the effective ways to solve this problem is to use the methods of the theory of stochastic indicators. Purpose: The development of a tool for evaluating the effectiveness of the information security system functioning. Results: We describe deterministic, random and indefinite components of the information security system functioning. Constant and functional indicators are constructed, their distinctive features are revealed. To solve the problem of evaluating the effectiveness of the process under consideration stochastic superindicators are constructed. We have also described the features of the construction of stochastic indicators of different ranks on the basis of the theory of the effectiveness of targeted processes and purposeful systems. Practical relevance: Through the developed stochastic time indicators, the probabilistic and temporal characteristics of the destructive impact are estimated, with the intervals and time points of its occurrence taken into account. This allows the system to be timely warned of a possible destructive impact scenario for the elements of critical information infrastructure.
Introduction: The modern approach to radio planning provides subway passengers with uninterrupted access to the Internet. This is achieved through the use of a special signal propagation model which calculates signal power loss during its propagation between a transmitter and a receiver on subway lines. The disadvantage of the model is the high computational complexity. Purpose: Using machine learning methods to develop an algorithm for predicting the signal power loss, the algorithm being characterized by high accuracy and low computational complexity. Results: The analysis of machine learning methods revealed that the maximum possible accuracy in solving the problem is provided by the random forest method. A data structure containing the parameters of a digital map of subway lines was developed to train the selected method and predict a signal power loss. While developing the final algorithm a number of assumptions were made, such as: the problem is solved as a classification problem, the predicted values are integers. A signal power loss prediction algorithm that does not directly use the propagation model was developed, which reduced the computational complexity and the execution time for solving radio planning problems, with high prediction accuracy maintained. Practical relevance: Due to the use of machine learning methods in developed algorithms the time for performing radio planning was reduced from several days to several hours, with accuracy preserved. This allows to process more radio planning orders or to reduce the working time for engineers to complete the same number of orders, which is a financial benefit.
Introduction: The solution of the task of the recognition and assessment of user engagement in the acts of human-machine interaction or telecommunication, achieved through the use of automatic means, is highly important in computer recognition of human psycho-emotional states. This is necessary for e-learning, business and entertainment applications design. Purpose: To conduct a comparative analysis of the current information support in the field of automatic recognition and assessment of user involvement in human-machine interaction or virtual communication, as well as to establish a methodology for building a data body based on the idea of multimodal communication. Results: The conducted analysis of research papers has shown that in most existing databases there is a substantial lack of data for natural online communication. Moreover, not all databases contain different modalities in “human-machine-human” communication system. Text and audio modalities turn out to be important for a multilevel engagement classification task, aimed at the determination of engagement intensity. It is also promising to take into account “body language” features, such as facial expressions, movements of the body and the head, gestures. For the correct assessment of involvement, an engagement database must contain meta-data on the psycho-emotional states of communicants. Neural network-based approaches to the automatic detection of user engagement show the best performance. Practical relevance: Based on the obtained analytical conclusions, the authors of the paper are going to elaborate an original software system for automatic recognition of user engagement, and to collect a data set for machine learning purposes. The presented review formulates basic requirements for such systems and contributes to the solution of the problem of automatic recognition of psycho-emotional states. Discussion: The survey leads to the conclusion that the notion of engagement as understood in studies on automatic emotion recognition differs from that used in psychology. User (or communicant) engagement in terms of info- and communicative sphere implies the manifestation of a person's mental activity level (emotional, cognitive, and behavioral components) changing dynamically while interacting with another person or computer system.
Introduction: Internet of Things devices are actively used within the framework of Massive Machine-Type Communication scenarios. The interaction of devices is carried out by random multiple-access algorithms with limited throughput. To improve throughput one can use orthogonal preambles in the ALOHA-type class of algorithms. Purpose: To analyze ALOHA-based algorithms using the exploration phase and to calculate the characteristics for the algorithm with and without losses with a finite number of channels. Results: We have described a system model that employs random access for data transmission over a common communication channel with the use of orthogonal preambles and exploration phase. We have obtained a formula for numerical calculation of the throughput of an algorithm channel with losses with an infinite number of preambles and a given finite number of channels. The calculation results for several values of the number of independent channels are presented. A modification of the algorithm using the exploration phase and repeated transmissions is proposed and described. The system in question can work without losses. For this system, we have given the analysis of the maximum input throughput up to which the system operates stably. Also, the average delay values for the algorithm that were obtained by simulation modeling are shown. By reducing the number of available preambles, the results obtained can be used as an upper bound on the system throughput. Practical relevance: The results obtained allow to assess the potential for improving the throughput of random multiple-access systems in 6G networks through the application of the exploration phase.
Introduction: Due to the growth in the number and variety of devices connected to the Internet, the requirements for network performance and data transmission security are increasing. Today, performance problems are usually solved through cloud, fog and edge computing, while the problem of data storage and transmission security remains relevant. One of the effective ways to solve this problem is to use blockchain technology. Purpose: Designing the architecture of a fog computing network based on blockchain technology. Results: Based on the research in the field of fog computing, the requirements for the fog computing architecture were determined, such as: autonomy, scalability, flexibility, hierarchy, security, reliability, availability, serviceability. The selected criteria for building an architecture led to the choice in favor of a private blockchain due to its higher performance compared to a public blockchain A comparative analysis of the consensus algorithms that are most often used in private blockchains was carried out and the most suitable one was chosen. Based on the requirements put forward and the results of the analysis, a fog computing architecture model based on a private blockchain was designed. The architecture consists of four elements: end devices, fog nodes, orchestration nodes, and cloud infrastructure. The blockchain includes fog nodes and orchestration nodes, which ensures the confidentiality, availability and integrity of data in the fog network. Practical relevance: Paper results can be used in the design of fog computing networks both separately and as part of 5G mobile networks.
Introduction: rapidly growing volumes of information pose new challenges to modern data analysis technologies. Currently, based on cost and performance considerations, data processing is usually performed in cluster systems. One of the most common related operations in analytics is the joins of datasets. Join is an extremely expensive operation that is difficult to scale and increase efficiency in distributed databases or systems based on the MapReduce paradigm. Despite the fact that a lot of effort has been put into improving the performance of this operation, often the proposed methods either require fundamental changes in the MapReduce structure, or are aimed at reducing the overhead of the operation, such as balancing the load on the network. Objective: to develop an algorithm to accelerate the integration of data sets in distributed systems. Results: a review of the Apache Spark architecture and the features of distributed computing based on MapReduce is performed, typical methods for combining datasets are analyzed, the main recommendations for optimizing the operation of combining data are presented, an algorithm that allows you to speed up the special case of combining implemented in Apache Spark is presented. This algorithm uses the methods of partitioning and partial transfer of sets to the computing nodes of the cluster, in such a way as to take advantage of the merge and broadcast associations. The experimental data presented demonstrate that the method is all the more effective the larger the volume of input data. So, for 2Tb compressed data, acceleration up to ~37% was obtained in comparison with standard Spark SQL.
Intoduction: the possibility of interaction with the physical world through the network infrastructures of spatially distributed nodes of Internet of Things, despite the undeniable advantages of the technology, produce significant loads on information consumers. In this regard, the current interest is the creation of methods that provide the reduction of transmitted data due to the adaptive synchronization of monitoring systems with the time of real processes. One effective way to solve this problem is to use the discrete Fourier transform to determine the sampling period of the observations. Purpose: to develop an approach to the formation of adaptive data broker subscriptions based on the study of the cyclicity of observations of Internet of Things devices. Methods: the discrete Fourier transform method was applied and, based on the calculated parameters of the harmonic series, a conclusion about the frequency characteristics of the data was made. The main peaks describing the periodicity of the data are selected, the fluctuation points are determined and, according to the Kotelnikov theorem (Nyquist-Kotelnikov-Shannon Sampling Theorem), a sampling frequency that provides a sufficient intensity of observations is chosen. Results: within the corporate network of the Krasnoyarsk Scientific Center, an infrastructure of devices and applications of the Internet of Things has been deployed to monitor temperature, humidity and PM2.5 in specialized technological rooms with telecommunications equipment. The analysis showed that for different rooms the data are periodic, but their harmonic profiles do not coincide. The choice of harmonic values, the fluctuation amplitude of which determines the dynamics of changes in the observed data, should be carried out periodically for each observed device. This approach is implemented in the broker software, which distributes data in subscriptions from each of the devices in accordance with the frequency of their changes. Practical relevance: the analysis of the frequency characteristics of the data determines the broker settings, which distributes the information flows, which is one of the aspects of reliability of the IoT infrastructure. In addition, observing data changes will allow us to identify malfunctions in the operation of cooling systems, which can lead to the failure of complex, expensive equipment with increased heat irradiation.
Introduction: Today sensor systems based on integrated photonics devices are the most important branch of embedded information and control systems for various functions. The output characteristics of a sensor system are significantly determined by the efficiency of the interrogator. The intensity interrogator based on a microring resonator can provide a high scanning rate and sensitivity that meets the requirements of a wide range of applications. Purpose: To develop an effective sensor system composed of a refractometric sensor and an interrogator located on the same photonic integrated circuit for marker-free determination of the concentration of substances in liquids. Methods: We use the numerical simulation of electromagnetic field propagation in a waveguide system (integrated silicon waveguides on a silicon dioxide substrate) in the research. The simulation has been carried out using the Ansys Lumerical environment, the FDTD (Finite Difference Time Domain) solver. The parameters of the microring resonators were optimized to obtain the coupling coefficients between the waveguides, providing the operation in the critical coupling mode. Results: We propose the concept of a fully integrated photonic sensor system based on micro-ring add-drop resonators. A sensor based on microring resonators has been developed, which consists of two half-rings with a radius of 18 μm, connected by sections of straight waveguides 3 μm long. An interrogator represented by a microring resonator with a radius of 10 µm has been developed. According to simulation results with a broadband source, the achieved sensor sensitivity was 110 nm per refractive index change, or 1350 dB per refractive index change. We propose a technique for choosing the optimal characteristics of the sensor and interrogator targeted to improve the complete system efficiency. Practical relevance: Sensor systems based on photonic integrated circuits can meet the demand for devices characterized by low power consumption, small size, immunity to electromagnetic interference and low cost.
Introduction: For the detection of most dangerous artificial space objects (space junk) in near-earth space, it is planned to use specialized spacecraft equipped with optoelectronic devices. In relation to this a problem arose when selecting the most dangerous object from the observed multitude, based on the measurements of inhomogeneous selective features that characterize these objects. Purpose: To form a composite non-dimensional indicator that depends on the quantity and quality of measurement information about the observed space objects, and that determines the decision rule for selecting the most dangerous object to maximize the probability of making the right decision. Results: A method is proposed for selecting the most dangerous space object under the condition of the limited amount of measurement information about physically inhomogeneous selective features of space objects located in the area which is observed by a specialized spacecraft. It should be noted that the measurement data on individual selective features of space objects may be absent. The proposed decision rule for the selection of the most dangerous space object takes into account not only inaccuracies and errors, but also the number of measurements of the selective characteristics of each object. The efficiency of the method has been demonstrated on a relevant example. Practical relevance: The simplicity of the determination of composite indicators characterizing space objects that are located in the area observed by a specialized spacecraft, and of the decision rule for selecting the most dangerous object makes it possible to solve this problem on board a specialized spacecraft in real time.
Introduction: The lack of training data leads to low accuracy of visual pattern recognition. One way to solve this problem is to use real data in combination with synthetic data. Purpose: To improve the performance of pattern recognition systems in computer vision by mixing real and synthetic data for training, and to reduce the time needed for preparing training data. Results: We have built an intelligent information system on the basis of the proposed method which allows the generation of synthetic images. The system allows to generate large and representative samples of images for pattern recognition neural network training. We have also developed software for the synthetic image generator for neural network training. The generator has a modular architecture, which makes it easy to modify, remove or add individual stages to the synthetic image generation pipeline. One can adjust individual parameters (like lighting or blurring) for generated images. The experiment was aimed to compare the accuracy of pattern recognition for a neural network trained on different training samples. The combination of real and synthetic data in model training showed the best recognition performance. Artificially generated training samples, in which the scale of background objects is approximately equal to the scale of the object of interest, and the number of objects of interest in the frame is higher, turned out to be more efficient than other artificially constructed training samples. Changing focal length of the camera in the synthetic image generation scene had no effect on the recognition performance. Practical relevance: The proposed image generation method allows to create a large set of artificially constructed data for training neural networks in pattern recognition in less time than it would take to create the same set of real data.
Introduction: The use of linear programming methods in making decisions on hospitalization in a fragile epidemiological situation may be hampered by the necessity to take account of a large number of parameters and limitations of the participants. Purpose: Development of an approach to selecting effective action strategies for the participants in a hospitalization process, with social factors taken into consideration. The approach is based on the theory of cooperative games which are solved with the use of a genetic algorithm. Results: A cost function has been developed for evaluating the effectiveness of the hospitalization process on the basis of the selected strategies and in consideration of social factors. A genetic algorithm has been designed in which the proposed effectiveness evaluation function is used as a fitness function for a population, while to determine chromosomes of individuals in the population the set of selected strategies of the hospitalization process participants is used. The approach has been tested using the data on hospitalizations of patients with suspected COVID-19, that were provided by several ambulance stations in Saint-Petersburg, Russia. The study shows the superiority of the proposed approach over the previously developed one in terms of the speed of solving a cooperative game, the quality of the solution being maintained. Practical relevance: Some software which is based on the proposed approach can be integrated into an ambulance dispatcher’s automated workstation to support decision-making during the process of hospitalization in a fragile epidemiological situation.
Introduction: The analysis of interrelationships between the bioelectric activity of the brain and heart is one of the topical issues in modern neuroscience. Special attention of researchers in this area is attracted by the study of these interrelationships in cases of cerebral vascular pathology. Purpose: The use of synchrosqueezed wavelet transforms to measure the relationship between the rhythms of the brain and heart in cases of vascular pathology of varying severity before and during hyperventilation load. Results: The analysis of instantaneous frequencies has been carried out in the low-frequency components of an electroencephalogram and the RR interval time series extracted from the electrocardiogram of patients with vascular pathology of varying severity before and during hyperventilation. The research shows that the time when a correlation between instantaneous frequencies of the infra-slow oscillations of an electroencephalogram and the RR interval time series occurs is related to the degree of severity of cerebral vascular pathology. It has been found that the greater the severity of the vascular pathology of the brain, the faster a correlation occurs between instantaneous frequencies in the low-frequency components of an electroencephalogram and heart rate variability. Practical relevance: The discovered peculiarities of the frequency interrelationships between the rhythms of the brain and heart during hyperventilation may be useful for the search of neurophysiological correlates of the degree of severity of cerebral vascular pathology.
Introduction: Cretan matrices – orthogonal matrices, consisting of the elements 1 and –b (real number), are an ideal object for the visual application of finite-dimensional mathematics. These matrices include, in particular, the Hadamard matrices and, with the expansion of the number of elements, the conference matrices. The most convenient research apparatus is to use field theory and multiplicative Galois groups, which is especially important for new types of Cretan matrices. Purpose: To study the symmetries of the Cretan matrices and to investigate two new types of matrices of odd and even orders, distinguished by symmetries, respectively, which differ significantly from the previously known Mersenne, Euler and Fermat matrices. Results: Formulas for levels are given and symmetries of new Cretan matrices: Odin bicycles (with a border) of orders 4t – 1 and 4t – 3 and shadow matrices of orders 4t – 2 and 4t – 4 are described. For odd character sizes equal to prime numbers and powers of primes, the existence of matrix symmetries of special types, doubly symmetric, consisting of skew-symmetric (with respect to the signs of elements) and symmetric cyclic blocks, is proved. It is shown that the previously distinguished Cretan matrices are their special case: Mersenne matrices of orders 4t – 1 and Euler matrices of orders 4t – 2 existing in the absence of symmetry for all selected orders without exception. Practical relevance: Оrthogonal sequences and methods of their effective finding by the theory of finite fields and groups are of direct practical importance for the problems of noise-immune coding, compression and masking of video information.
Introduction: In the era of information technology almost all organizations face a wide range of automated and rapidly spreading cyber threats. This is due not only to the growing complexity, diversity and scale of digitalization, but also to the enlargement of cyber threats and the area of their possible implementation. Purpose: To compare possible ways of improving the effectiveness of attack detection for the objects of critical information infrastructure (CII): to detect a rare event, anomaly or novelty in the functions of the objects of CII. Results: The principle of operation of the proposed (effective) approach to cyberattack detection is to identify and separate anomalies from normal functioning of objects with the use of the concept of dynamic change of labels for a variable class over time. Dynamic novelty detection is compared to other approaches in terms of F1-score. For SWaT data, which is a layout of a critical information infrastructure object as an automated control system, it was determined that attack detection improved by up to 7% using the proposed approach. Practical relevance: The results of the research have shown a reduction in the risk of conducting (developing) a computer attack on critical information infrastructure objects. A possible targeted application of the dynamic novelty detection approach is to optimize the means of protecting information at critical information infrastructure facilities, as well as to integrate the proposed approach into the information security system as an intelligent detector.
Introduction: Radar information processing methods are used to identify targets at short observation intervals, based on the identification of marks of different radar stations and the parametric identification of a target. However, a mutual comparison of the effectiveness of using such methods of target selection in the conditions of relayed interference has not been carried out. Purpose: The comparison of the reliability of target selection in the conditions of relayed interference in a network of geographically separated radar stations for space surveillance with the implementation of correlation ellipsoid method, strobe method of target selection and spatial separation of measured target positions method. Results: We give the decision rules for dividing the space of coordinate difference of a target into subdomains to make a decision about the truth of the target. We have carried out simulation modeling of the target selection in the conditions of relayed interference and have obtained the dependencies for the change in the probability of erroneous selection of false marks on the normalized range which is measured from the middle of the spacing base of the two geographically separated radar stations. There are also quantitative estimates of the reliability of target selection for various conditions of radar surveillance by two geographically separated radar stations. The gain in increasing the normalized range with the use of the correlation ellipsoid method as compared to strobe methods of target selection and spatial separation of measured target positions method ranged from 36% to 46%. It is shown that in most practical situations one can use the simplest method, that is the spatial separation of measured target positions method, and when solving most important problems the correlation ellipsoid method can be used. Practical relevance: Research results can be used in the development of target selection algorithms in the conditions of relayed interference.
