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In this paper we describe a class of cryptographic guess-and-determine attacks which is based on the notion of a linearizing set. A linearizing set-based attack is applied to a system of Multivariate Quadratic equations (MQ) over $GF(2)$ field, which encodes how a considered cryptographic function works. By substituting into such MQ system a random (in some strict sense) assignment of variables from a linearizing set we aim to transform the system into a linear one. We introduce a probability of such an event and call it a probability of linearization. Then we describe a guess-and-determine attack, the hardness of which can be expressed via a probability of linearization. To estimate the latter it is possible to use a simple Monte Carlo algorithm. Also we describe a technique that allows to augment a considered MQ system by new linear equations and to construct a new MQ system, for which the probability of linearization is usually larger than that for an original one. For this purpose we apply a SAT oracle to a Boolean formula that is naturally associated with a considered MQ system. Finally, we reduce the problem of searching for a linearizing set that yields the best effectiveness of a constructed guess-and-determine attack to a pseudo-Boolean optimization problem, which can be solved using metaheuristic optimization algorithms. The important consequence of this is that this way we can construct guess-and-determine attacks automatically by solving the corresponding optimization problem. In the computational experiments we used the proposed methodology to construct attacks on several well-known stream ciphers. The runtime estimations of some of the attacks make it possible to implement them in reasonable time.

According to recent estimates, one bitcoin transaction consumes as much energy as 1.5 million Visa transactions. Why is bitcoin using so much energy? Most of the energy is used during the bitcoin mining process, which serves at least two significant purposes: a) distributing new cryptocurrency coins to the cryptoeconomy and b) securing the Bitcoin blockchain ledger. In reality, the comparison of bitcoin transactions to Visa transactions is not that simple. The amount of transactions in the Bitcoin network is not directly connected to the amount of bitcoin mining power nor the energy consumption of those mining devices; for example, it is possible to multiply the number of bitcoin transactions per second without increasing the mining power and the energy consumption. Bitcoin is not only “digital money for hackers”. It has very promising future potential as a global reserve currency and a method to make the World Wide Web (WWW) immune to cyberattacks such as the Distributed Denial-of-Service attacks. This survey approached cryptocurrencies’ various technological and environmental issues from many different perspectives. To make various cryptocurrencies, including bitcoin (BTC) and ether (ETH), greener and more justified, what technological solutions do we have? We found that cryptocurrency mining might be cleaner than is generally expected. There is also a plan to make a vast renewable energy source available by combining Ocean Thermal Energy Conversion and Bitcoin mining. There are plans to use unconventional computing methods (quantum computing, reversible computing, ternary computing, optical computing, analog computing) to solve some of the issues regarding the vast energy consumption of conventional computing (including cryptocurrency mining). We think using spare computing cycles for grid computing efforts is justified. For example, there are billions of smartphones in the world. Many smartphones are being recharged every day. If this daily recharging period of twenty to sixty minutes would be used for grid computing, for example, finding new cures to cancer, it would probably be a significant breakthrough for medical research simulations. We call on the cryptocurrency communities to research and develop grid computing and unconventional computing methods for the most significant cryptocurrencies: bitcoin (BTC)and ether (ETH).

The process of the devitrification of copper nanoparticles in vacuum and in a silver shell during heating was studied using a molecular dynamics simulation. The results show that there is an inverse relationship between the particle diameter and devitrification temperature. As the size of the particles decreases, the temperature at which devitrification occurs increases due to a higher fraction of atoms near the interface. The presence of a silver shell leads to a significant increase in the devitrification temperature of the copper nanoparticles. For the considered particle sizes, the difference between the devitrification temperatures without a shell and with a shell ranged from 130 K for copper particles with a diameter of 11 nm to 250 K for 3 nm particles. The mechanisms of the nucleation of a crystalline phase in particles in vacuum and in a silver shell are significantly different. In the first case, crystalline nuclei are predominantly formed near the surface, while in the second case, on the contrary, they are formed within the particle’s volume.

This paper presents the calculations and construction of load diagrams and the static characteristics of individual mechanisms of a mining roadheader, which form a multimotor electric drive system, using the methods of equivalent forces and reduced moments. The productivity of the mine and the roadheader are calculated. The operating forces of idle and on-load mechanisms are calculated, and operation time intervals and a complete working cycle are determined. The load diagrams of mechanisms are constructed and their kinematic diagrams are designed. Based on the calculations performed, asynchronous motors with a short-circuit rotor, which have the closest power values and are suitable in terms of voltage and mode of operation, were selected from the catalogue. The gear reduction ratio is calculated, static resistance moments, and operation speeds to the motor shaft are specified. The static characteristics of the most powerful electric motor of a mining roadheader – or cutter – are formulated. The high accuracy of the calculations was confirmed by computer simulation, which showed minimum errors.

The paper focuses on econometrically justified robust analysis of the effects of the COVID-19 pandemic on financial markets in different countries across the World. It provides the results of robust estimation and inference on predictive regressions for returns on major stock indexes in 23 countries in North and South America, Europe, and Asia incorporating the time series of reported infections and deaths from COVID-19. We also present a detailed study of persistence, heavy-tailedness and tail risk properties of the time series of the COVID-19 infections and death rates that motivate the necessity in applications of robust inference methods in the analysis. Econometrically justified analysis is based on heteroskedasticity and autocorrelation consistent (HAC) inference methods, recently developed robust t-statistic inference approaches and robust tail index estimation.

BackgroundThere is a limited amount of data on the safety profile of the COVID-19 vector vaccine Gam-COVID-Vac (Sputnik V). Previous infodemiology studies showed that social media discourse could be analyzed to assess the most concerning adverse events (AE) caused by drugs. ObjectiveWe aimed to investigate mild AEs of Sputnik V based on a participatory trial conducted on Telegram in the Russian language. We compared AEs extracted from Telegram with other limited databases on Sputnik V and other COVID-19 vaccines. We explored symptom co-occurrence patterns and determined how counts of administered doses, age, gender, and sequence of shots could confound the reporting of AEs. MethodsWe collected a unique dataset consisting of 11,515 self-reported Sputnik V vaccine AEs posted on the Telegram group, and we utilized natural language processing methods to extract AEs. Specifically, we performed multilabel classifications using the deep neural language model Bidirectional Encoder Representations from Transformers (BERT) “DeepPavlov,” which was pretrained on a Russian language corpus and applied to the Telegram messages. The resulting area under the curve score was 0.991. We chose symptom classes that represented the following AEs: fever, pain, chills, fatigue, nausea/vomiting, headache, insomnia, lymph node enlargement, erythema, pruritus, swelling, and diarrhea. ResultsTelegram users complained mostly about pain (5461/11,515, 47.43%), fever (5363/11,515, 46.57%), fatigue (3862/11,515, 33.54%), and headache (2855/11,515, 24.79%). Women reported more AEs than men (1.2-fold, P

A total of 381 hepatitis B virus (HBV) DNA sequences collected from nine groups of Siberian native populations were phylogenetically analyzed along with 179 HBV strains sampled in different urban populations of former western USSR republics and 50 strains from Central Asian republics and Mongolia. Different HBV subgenotypes predominated in various native Siberian populations. Subgenotype D1 was dominant in Altaian Kazakhs (100%), Tuvans (100%), and Teleuts (100%) of southern Siberia as well as in Dolgans and Nganasans (69%), who inhabit the polar Taimyr Peninsula. D2 was the most prevalent subgenotype in the combined group of Nenets, Komi, and Khants of the northern Yamalo-Nenets Autonomous Region (71%) and in Yakuts (36%) from northeastern Siberia. D3 was the main subgenotype in South Altaians (76%) and Buryats (40%) of southeastern Siberia, and in Chukchi (51%) of the Russian Far East. Subgenotype C2 was found in Taimyr (19%) and Chukchi (27%), while subgenotype A2 was common in Yakuts (33%). In contrast, D2 was dominant (56%) in urban populations of the former western USSR, and D1 (62%) in Central Asian republics and Mongolia. Statistical analysis demonstrated that the studied groups are epidemiologically isolated from each other and might have contracted HBV from different sources during the settlement of Siberia.

Abstract The goal of this study is to demonstrate how network science and graph theory tools and concepts can be effectively used for exploring and comparing semantic spaces of word embeddings and lexical databases. Specifically, we construct semantic networks based on word2vec representation of words, which is “learnt” from large text corpora (Google news, Amazon reviews), and “human built” word networks derived from the well-known lexical databases: WordNet and Moby Thesaurus. We compare “global” (e.g., degrees, distances, clustering coefficients) and “local” (e.g., most central nodes and community-type dense clusters) characteristics of considered networks. Our observations suggest that human built networks possess more intuitive global connectivity patterns, whereas local characteristics (in particular, dense clusters) of the machine built networks provide much richer information on the contextual usage and perceived meanings of words, which reveals interesting structural differences between human built and machine built semantic networks. To our knowledge, this is the first study that uses graph theory and network science in the considered context; therefore, we also provide interesting examples and discuss potential research directions that may motivate further research on the synthesis of lexicographic and machine learning based tools and lead to new insights in this area.

Abstract The influence of the air atmosphere on the electrical conductivity of nanocrystalline thin SiC films obtained by method of direct ion deposition on sapphire was studied. Measurements were performed on two series of nc-SiC films with different structure: one series contained mainly 3C-SiC polytype nanocrystals and were denoted as monopolytypic, the second series were a nanoheterostructures based on a mixture of 3C-SiC and 21R-SiC nanocrystals. Gas sensitivity of the films appeared after annealing in vacuum at temperatures above 500 K. Measurements of the gas sensitivity of the films to the air atmosphere at a temperature of 700 K showed that the resistance increased for 12 times for the monopolytype film, while the heteropolytype film showed an increase of resistance for almost 16 times.

The “post-Soviet space” consists of countries with a substantial fraction of the world’s population; however, unlike many other regions, its social media network landscape is still somewhat under-explored. This paper aims at filling this gap. To this purpose, we use anonymized data on user friendships at VK.com (also known as VKontakte and, informally, as “Russian Facebook”), which is the largest and most popular social media portal in the post-Soviet space with hundreds of millions of user accounts. Using the VK network snapshots from October 2015 to December 2016, we conduct a “multiscale” empirical study of this network by considering connections among individual users, cities, and countries. Our findings indicate that the VK users form a small-world network with basic characteristics consistent with Facebook and other social media networks. In addition, the analysis of modularity-based communities within the user scale network reveals a pattern of geographical separation of the identified communities mostly along the borders between countries. However, the comparison of the two network snapshots suggests that some of these communities may be “blending” within the network, whereas other communities remain “self-contained.” Furthermore, the analysis of city scale and country scale networks identifies cities and countries that are most “central” (in the context of certain metrics) in the VK network.

The article analyzes the possibility and rationality of using proctoring technology in remote monitoring of the progress of university students as a tool for identifying a student. Proctoring technology includes face recognition technology. Face recognition belongs to the field of artificial intelligence and biometric recognition. It is a very successful application of image analysis and understanding. To implement the task of determining a person’s face in a video stream, the Python programming language was used with the OpenCV code. Mathematical models of face recognition are also described. These mathematical models are processed during data generation, face analysis and image classification. We considered methods that allow the processes of data generation, image analysis and image classification. We have presented algorithms for solving computer vision problems. We placed 400 photographs of 40 students on the base. The photographs were taken at different angles and used different lighting conditions; there were also interferences such as the presence of a beard, mustache, glasses, hats, etc. When analyzing certain cases of errors, it can be concluded that accuracy decreases primarily due to images with noise and poor lighting quality.

In the present paper, we propose a technology for translating algorithmic descriptions of discrete functions to SAT. The proposed technology is aimed at applications in algebraic cryptanalysis. We describe how cryptanalysis problems are reduced to SAT in such a way that it should be perceived as natural by the cryptographic community. In~the theoretical part of the paper we justify the main principles of general reduction to SAT for discrete functions from a class containing the majority of functions employed in cryptography. Then, we describe the Transalg software tool developed based on these principles with SAT-based cryptanalysis specifics in mind. We demonstrate the results of applications of Transalg to construction of a number of attacks on various cryptographic functions. Some of the corresponding attacks are state of the art. We compare the functional capabilities of the proposed tool with that of other domain-specific software tools which can be used to reduce cryptanalysis problems to SAT, and also with the CBMC system widely employed in symbolic verification. The paper also presents vast experimental data, obtained using the SAT solvers that took first places at the SAT competitions in the recent several years.

The development of global and regional energy markets is one of the most important problems of the current state of global economic relations and is one of the highest-priority areas in the formation of national and regional strategies for the socio-economic development of any country. The role of energy supply is focused on monitoring and compliance with standards. However, in recent years, the importance of energy supply to the housing stock has increased significantly, which has led to an expansion of the role of management within the framework of the risk management function. Kazakhstan has a Unified electric power system, which is represented by a set of power stations, transmission lines, and substations that provide reliable and high-quality energy supply to consumers. Currently, the main task within the framework of the priority of the development of Kazakhstan’s energy sector is to build up the energy base and provide the growing needs of the population and the economy with the necessary energy resources based on the development of modern energy complexes and alternative energy sources in conjunction with the implemented and planned macroprojects. Since the current economic development of Kazakhstan is closely interrelated with the realization of energy resources and their effective use, it acts as one of the fundamental levers for the development of the national economy. According to our research goal, it is necessary to consider sustainable energy supply in the country based on the analysis and assessment of energy consumption volumes and the impact of the country’s regional and sectoral policies on their use. The author’s approach to the distribution of countries by MNC has been developed and estimates of regression parameters, correlation coefficients, and elasticity coefficients. When constructing the author’s approach, the countries of Europe and Central Asia were studied and a sample of estimates was carried out, which has statistically significant effects on the formation of energy consumption volumes in the Republic of Kazakhstan.

This study investigates the key determinants of the size of investment in Russian IT companies during follow-on rounds starting from the second round. On a sample of 55 deals (2010–2016), which represents the most important deals on Russian market, the research finds out two key factors positively influencing the size of investments: investments in the first round and presence of experienced investor among shareholders. The investor was considered as experienced if she has had success on VC market or has a totally accepted reputation on the same market. Results are consistent with the prior literature including the preliminary results of the authors done on lesser datasets. Additionally, effects of belonging to a particular industry or the time period of investments were proved to be insignificant for Russian VC market for IT companies. New hypothesis proposed by the authors (about impact of the number of founders on the investment in sub-sequent rounds) as well as hypothesis about the impact of target market growth on the investment size are also rejected. The latter is surprising as the target market growth is considered among the most important criteria for the investment decision. The result may be explained by the bias of the dataset as most of the early stage company data is confidential, and the target market growth plays the most significant role at the beginning of investment process.

Abstract While single photon detectors provide superior intensity sensitivity, spectral resolution is usually lost after the detection event. Yet for applications in low signal infrared spectroscopy recovering information about the photon’s frequency contributions is essential. Here we use highly efficient waveguide integrated superconducting single-photon detectors for on-chip coherent detection. In a single nanophotonic device, we demonstrate both single-photon counting with up to 86% on-chip detection efficiency, as well as heterodyne coherent detection with spectral resolution f/∆f exceeding 1011. By mixing a local oscillator with the single photon signal field, we observe frequency modulation at the intermediate frequency with ultra-low local oscillator power in the femto-Watt range. By optimizing the nanowire geometry and the working parameters of the detection scheme, we reach quantum-limited sensitivity. Our approach enables to realize matrix integrated heterodyne nanophotonic devices in the C-band wavelength range, for classical and quantum optics applications where single-photon counting as well as high spectral resolution are required simultaneously.

Coastal environments can be influenced by water body masses with particular physical, chemical, and biological properties that create favourable conditions for the development of unique planktonic communities. In this study, we investigated a continental shelf front at Ponza Island (Tyrrhenian Sea) and discussed its diversity and complexity in relation to major environmental parameters. Moon phase and current direction were found to play a significant role in shaping species abundance and behaviour. During in situ observations, we also provided the first data on the behaviour of juveniles of a rare mesopelagic species, Trachipterus trachypterus, suggesting the occurrence of Batesian mimicry.

Various temporal and spatial changes have manifested in Arctic storm activities, including the occurrence of the anomalously intense storms in the summers of 2012 and 2016, along with the amplified warming and rapidly decreased sea ice. To detect the variability of and changes in storm activity and understand its role in sea ice changes, we examined summer storm count and intensity year-by-year from ensemble hindcast simulations with an Arctic regional coupled climate model for the period of 1948–2008. The results indicated that the model realistically simulated the climatological spatial structure of the storm activity, characterized by the storm count and intensity. The simulated storm count captures the variability derived from the National Centers for Environmental Prediction-National Center for Atmospheric Research (NCEP–NCAR) reanalysis, though the simulated one is higher than that in the reanalysis. This could be attributed to the higher resolution of the model that may better represent smaller and shallower cyclones. The composite analysis shows that intense storms tend to form a low-pressure pattern with centers over the Kara Sea and Chukchi Sea, respectively, generating cyclonic circulation over the North Atlantic and North Pacific Arctic Ocean. The former drives intensification of the transpolar drift and Fram Strait sea ice export, and the latter suppresses thick ice transport from the Canada Basin to the Beaufort–Chukchi Seas, in spite of an increase in sea ice transport to the East Siberian Sea. Associated with these changes in sea ice transport, sea ice concentration and thickness show large decreases in the Barents–Kara Seas and the Chukchi–East-Siberian Seas, respectively. Energy budgets analysis suggests that more numerous intense storms substantially decrease the downward net sea ice heat fluxes, including net radiative fluxes, turbulent fluxes, and oceanic heat fluxes, compared with that when a lower number of intense storms occur. The decrease in the heat fluxes could be attributable to an increased cloudiness and the resultant reduction of downward shortwave radiation, as well as a destabilized boundary layer induced increase in upward turbulent fluxes.

In this paper, we propose an approach for modeling and analysis of a number of phenomena of collective behavior. By collectives we mean multi-agent systems that transition from one state to another at discrete moments of time. The behavior of a member of a collective (agent) is called conforming if the opinion of this agent at current time moment conforms to the opinion of some other agents at the previous time moment. We presume that at each moment of time every agent makes a decision by choosing from the set {0,1} (where 1-decision corresponds to action and 0-decision corresponds to inaction). In our approach we model collective behavior with synchronous Boolean networks. We presume that in a network there can be agents that act at every moment of time. Such agents are called instigators. Also there can be agents that never act. Such agents are called loyalists. Agents that are neither instigators nor loyalists are called simple agents. We study two combinatorial problems. The first problem is to find a disposition of instigators that in several time moments transforms a network from a state where the majority of simple agents are inactive to a state with the majority of active agents. The second problem is to find a disposition of loyalists that returns the network to a state with the majority of inactive agents. Similar problems are studied for networks in which simple agents demonstrate the contrary to conforming behavior that we call anticonforming. We obtained several theoretical results regarding the behavior of collectives of agents with conforming or anticonforming behavior. In computational experiments we solved the described problems for randomly generated networks with several hundred vertices. We reduced corresponding combinatorial problems to the Boolean satisfiability problem (SAT) and used modern SAT solvers to solve the instances obtained.

Many practically important combinatorial problems can be efficiently reducedto a problem of Boolean satisfiability (SAT). Therefore, the implementation ofdistributed algorithms for solving SAT problems is of great importance. In thisarticle we describe a technology for organizing desktop grid, which is meantfor solving SAT problems. This technology was implemented in the form ofa volunteer computing project SAT@home based on a popular BOINC platform.