## Computer Science

The article suggests the integration of a neural network as a parallel element base in a telecommunication system. In this case, the ability to learn or adapt to external conditions is applied as the main advantage. For telecommunication systems in conditions when it is possible, this ability will improve noise immunity, reliability, operability, etc. The article considers an example of the integration of a neural network into a discrete matched signal filter. It is noted that the use of parallel mathematical methods in signal processing leads to the maximum effect of increasing the quality parameters of such telecommunication elements

Artificial intelligence and machine learning helps to improve the quality of customer service and change the methods of companies’ activities. For this reason, enterprises should consider integrating these technologies into digital transformation plans to remain competitive. Low-code machine learning platforms allow companies and business professionals with minimal coding experience to create applications and fill in the gaps of the personnel in their organization. Automated machine leaning (AutoML) technology represents the next step in the evolution of machine learning, providing non-technical companies with the ability to create machine learning applications quickly and cheaply

The article discusses the possibilities of studying the state of the social sphere according to the repository of the Moscow Government open data portal by administrative districts and city districts using Business Intelligence Platforms and Data Science and Machine Learning Platforms intellectual technologies. Opportunities are presented for using machine learning technologies for business analytics platforms to identify hidden patterns in order to make informed management decisions

Logic has found application in virtually all aspects of Information Technology, from software engineering and hardware to programming and artificial intelligence. Indeed, logic, artificial intelligence and theoretical computing are influencing each other to the extent that a new interdisciplinary area of Logic and Computation is emerging.

The * Journal of Logic and Computation * aims to promote the growth of logic and computing, including, among others, the following areas of interest: Logical Systems, such as classical and non-classical logic, constructive logic, categorical logic, modal logic, type theory, feasible maths.... Logical issues in logic programming, knowledge-based systems and automated reasoning; logical issues in knowledge representation, such as non-monotonic reasoning and systems of knowledge and belief; logics and semantics of programming; specification and verification of programs and systems; applications of logic in hardware and VLSI, natural language, concurrent computation, planning, and databases.

This book focuses on crisis management in software development which includes forecasting, responding and adaptive engineering models, methods, patterns and practices. It helps the stakeholders in understanding and identifying the key technology, business and human factors that may result in a software production crisis. These factors are particularly important for the enterprise-scale applications, typically considered very complex in managerial and technological aspects and therefore, specifically addressed by the discipline of software engineering. Therefore, this book throws light on the crisis responsive, resilient methodologies and practices; therewith, it also focuses on their evolutionary changes and the resulting benefits.

Proceedings of Machine Learning Research: Volume 119: International Conference on Machine Learning, 12-18 July 2020

The 24th European Conference on Advances in Databases and Information Systems (ADBIS 2020) was set to be held in Lyon, France, during August 25–28, 2020, in conjunction with the 24th International Conference on Theory and Practice of Digital Libraries (TPDL 2020) and the 16th EDA days on Business Intelligence & Big Data (EDA 2020). However, because of the worldwide COVID-19 crisis, ADBIS, TPDL, and EDA had to take place online during August 25–27, 2020. Yet, the three con- ferences joined their forces to propose common keynotes, workshops, and a Doctoral Consortium.

The 24th European Conference on Advances in Databases and Information Systems (ADBIS 2020) was set to be held in Lyon, France, during August 25–28, 2020, in conjunction with the 24th International Conference on Theory and Practice of Digital Libraries (TPDL 2020) and the 16th EDA days on Business Intelligence & Big Data (EDA 2020). However, because of the worldwide COVID-19 crisis, ADBIS, TPDL, and EDA had to take place online during August 25–27, 2020. Yet, the three con- ferences joined their forces to propose common keynotes, workshops, and a Doctoral Consortium.

This CCIS volume published by Springer contains the post-proceedings of the XXI International Conference on Data Analytics and Management in Data Intensive Domains (DAMDID/RCDL 2019) that took place during October 15–18 at the Kazan Federal University, Russia.

DAMDID is held as a multidisciplinary forum of researchers and practitioners from various domains of science and research, promoting cooperation and exchange of ideas in the area of data analysis and management in domains driven by data-intensive research. Approaches to data analysis and management being developed in specific data-intensive domains (DID) of X-informatics (such as X = astro, bio, chemo, geo, med, neuro, physics, chemistry, material science, etc.), social sciences, as well as in various branches of informatics, industry, new technologies, finance, and business are expected to contribute to the conference content.

This book studies complex systems with elements represented by random variables. Its main goal is to study and compare uncertainty of algorithms of network structure identification with applications to market network analysis. For this, a mathematical model of random variable network is introduced, uncertainty of identification procedure is defined through a risk function, random variables networks with different measures of similarity (dependence) are discussed, and general statistical properties of identification algorithms are studied. The volume also introduces a new class of identification algorithms based on a new measure of similarity and prove its robustness in a large class of distributions, and presents applications to social networks, power transmission grids, telecommunication networks, stock market networks, and brain networks through a theoretical analysis that identifies network structures. Both researchers and graduate students in computer science, mathematics, and optimization will find the applications and techniques presented useful.

The main target of the IEEE East-West Design & Test Symposium (EWDTS) is to exchange experiences between scientists and technologies from Eastern and Western Europe, as well as North America and other parts of the world, in the field of design, design automation and test of electronic circuits and systems. The symposium is typically held in countries around East Europe, the Black Sea, the Balkans and Central Asia region. We cordially invite you to participate and submit your contributions to EWDTS 2020 which covers (but is not limited to) the following topics. • Analog, Mixed-Signal and RF Test • ATPG and High-Level TPG • Automotive Reliability & Test • Built-In Self Test • Debug and Diagnosis • Defect/Fault Tolerance and Reliability • Design Verification and Validation • EDA Tools for Design and Test • Embedded Software • Failure Analysis & Fault Modeling • Functional Safely • High-level Synthesis • High-Performance Networks and Systems on a Chip • Internet of Things Design & Test • Low-power Design • Memory and Processor Test • Modeling & Fault Simulation • Network-on-Chip Design & Test • Flexible and Printed Electronics • Applied Electronics Automotive/Mechatronics • Algorithms • Object-Oriented System Specification and Design • On-Line Testing • Power Issues in Design & Test • Real Time Embedded Systems • Reliability of Digital Systems • Scan-Based Techniques • Self-Repair and Reconfigurable Architectures • Signal and Information Processing in Radio and Communication Engineering • System Level Modeling, Simulation & Test Generation • System-in-Package and 3D Design & Test • Using UML for Embedded System Specification • Optical signals in communication and Information Processing • CAD and EDA Tools, Methods and Algorithms • Hardware Security and Design for Security • Logic, Schematic and System Synthesis • Place and Route • Thermal and Electrostatic Analysis of SoCs • Wireless and RFID Systems Synthesis • Sensors and Transducers • Medical Electronics • Design of Integrated Passive Components

This book constitutes the proceedings of the 19th International Conference on Mathematical Optimization Theory and Operations Research, MOTOR 2020, held in Novosibirsk, Russia, in July 2020. The 31 full papers presented in this volume were carefully reviewed and selected from 102 submissions. The papers are grouped in these topical sections: discrete optimization; mathematical programming; game theory; scheduling problem; heuristics and metaheuristics; and operational research applications.

The materials of The International Scientific – Practical Conference is presented below. The Conference reflects the modern state of innovation in education, science, industry and social-economic sphere, from the standpoint of introducing new information technologies. It is interesting for a wide range of researchers, teachers, graduate students and professionals in the field of innovation and information technologies.

This concise book provides a survival toolkit for efficient, large-scale software development. Discussing a multi-contextual research framework that aims to harness human-related factors in order to improve flexibility, it includes a carefully selected blend of models, methods, practices, and case studies. To investigate mission-critical communication aspects in system engineering, it also examines diverse, i.e. cross-cultural and multinational, environments.

This book helps students better organize their knowledge bases, and presents conceptual frameworks, handy practices and case-based examples of agile development in diverse environments. Together with the authors’ previous books, "Crisis Management for Software Development and Knowledge Transfer" (2016) and "Managing Software Crisis: A Smart Way to Enterprise Agility" (2018), it constitutes a comprehensive reference resource that adds value to this book.

This book constitutes the proceedings of the 8th International Conference on Analysis of Images, Social Networks and Texts, AIST 2019, held in Kazan, Russia, in July 2019.

The 24 full papers and 10 short papers were carefully reviewed and selected from 134 submissions (of which 21 papers were rejected without being reviewed). The papers are organized in topical sections on general topics of data analysis; natural language processing; social network analysis; analysis of images and video; optimization problems on graphs and network structures; analysis of dynamic behaviour through event data.

Artists can represent a 3D object by using only contours in a 2D drawing. Prior studies have shown that people can use such drawings to perceive 3D shapes reliably, but it is not clear how useful this kind of contour information actually is in a real dynamical scene in which people interact with objects. To address this issue, we developed an Augmented Reality (AR) device that can show a participant a contour-drawing or a grayscale-image of a real dynamical scene in an immersive manner. We compared the performance of people in a variety of run-of-the-mill tasks with both contour-drawings and grayscale-images under natural viewing conditions in three behavioral experiments. The results of these experiments showed that the people could perform almost equally well with both types of images. This contour information may be sufficient to provide the basis for our visual system to obtain *much* of the 3D information needed for successful visuomotor interactions in our everyday life.

We consider a spatially distributed evolutionary game based on the Prisoner’s Dilemma with agents arranged on a three-dimensional simple cubic lattice. Comparing to two- dimensional arrangements, we find that the larger number of neighbors favors the formation of spatial chaos: the steady state of the game is chaotic unless the payoff parameter is small.

We consider a dynamic version of the HP model of a linear polymer: a self-avoiding walk on the square lattice, with monomers being either hydrophobic (H) or polar (P). We simulate the model in two dimensions in the grand canonical assemble via the Berretti-Sokal algorithm across the globule-coil transition and map out the phase diagram. Our results are consistent with the universality class of the transition being the same as the universality class of the theta-transition of an interacting self-avoiding walk.

Modern large-scale data-farms consist of hundreds of thousands of storage devices that span distributed infrastructure. Devices used in modern data centers (such as controllers, links, SSD- and HDD-disks) can fail due to hardware as well as software problems. Such failures or anomalies can be detected by monitoring the activity of components using machine learning techniques. In order to use these techniques, researchers need plenty of historical data of devices in normal and failure mode for training algorithms. In this work, we challenge two problems: 1) lack of storage data in the methods above by creating a simulator and 2) applying existing online algorithms that can faster detect a failure occurred in one of the components.

We created a Go-based (golang) package for simulating the behavior of modern storage infrastructure. The software is based on the discrete-event modeling paradigm and captures the structure and dynamics of high-level storage system building blocks. The package's exible structure allows us to create a model of a real-world storage system with a configurable number of components. The primary area of interest is exploring the storage machine's behavior under stress testing or exploitation in the medium-or long-term for observing failures of its components.

To discover failures in the time series distribution generated by the simulator, we modified a change point detection algorithm that works in online mode. The goal of the change-point detection is to discover differences in time series distribution. This work describes an approach for failure detection in time series data based on direct density ratio estimation via binary classifiers.

Today art museum is facing the challenge of adapting it’s mechanisms of keeping and presenting the works of art to spectators belonging to the communication society. Therefore, a museum gets more and more engaged in the process of digitalization using such newer technologies as internet of things, virtual reality, artificial intelligence, bid data design etc. The aims of a museum are currently shifting from traditional keeping the art pieces and studying them to—developing a scientific networks, announcing the highlights in social media and creating platforms which present digitalized pieces online allowing a viewer to collect the information through the web, moreover, an offline visit could be guided by a specified application customized to fit the necessitates of each user. An art institution today is supposed to be flexible and democratic enough to create an engaging, immersive area for a visitor to interact with, in other words, we argue that a museum armed with newer technologies is supposed not only a to secure and present the works of art but also to incorporate these pieces into the bigger flux of information, make them visible and important to viewers, to create the conditions for a lasting dialogue. We argue that this process involves not only the technical development of a museum, but also a new approach no narration of art history.

An axisymmetric model is used to study changes in the shape of the Earth’s radiation belts and energy spectra of charged particles in them during a geomagnetic field reversal. Regions of stable existence of radiation belts are obtained analytically by generalizing the Størmer theory to the case of an axisymmetric quadrupole. An reversal scenario is proposed in which it is shown that a gradual reduction of radiation belts can occur with a decrease in the dipole component of the modern geomagnetic field. The spatial and energy distributions of the flux density of protons and electrons are obtained, which made it possible to determine the maximum dose rate of radiation on various magnetic shells during the reversal process.

We consider the maximum shortest path interdiction problem by upgrading edges on trees under Hamming distance (denoted by (MSPITH)), which has wide applications in transportation network, networkwar and terrorist network. The problem (MSPITH) aims to maximize the length of the shortest path from the root of a tree to all its leaves by upgrading edge weights such that the upgrade cost under sum-Hamming distance is upper-bounded by a given value. We show that the problem (MSPITH) under weighted sum-Hamming distance is NP-hard. We consider two cases of the problem (MSPITH) under unit sum-Hamming distance based on the number K of critical edges. We propose a greedy algorithm within O(n + l log l) time when K = 1 and a dynamic programming algorithm within O(n(log n + K3)) time when K > 1, where n and l are the numbers of nodes and leaves in a tree, respectively. Furthermore, we consider a minimum cost shortest path interdiction problem by upgrading edges on trees under unit Hamming distance, denoted by (MCSPITUH) and propose a binary search algorithm within O(n4 log n) time, where a dynamic programming algorithm is executed in each iteration to solve its corresponding problem (MSPITH). Finally, we design numerical experiments to show the effectiveness of the algorithms.

This paper discusses the development in the E-pulse technique, also known as the method of extinction pulse, which is an aspect-independent approach to ultra-wideband radar target discrimination in which each target can be characterized by the set of its natural resonances. It is shown that subsectional polynomial E-pulse can be constructed without composing a linear problem and further solution of the underlying matrix equation set. The key concept of the proposed algorithm consists of several steps, where the first one is building a skeleton E-pulse of an especial waveform, the second step is its extension, and the final step is the series of integration. The polynomial structure of the pulse allows above listed steps to be performed over the coefficients of basic functions rather than the functions themselves. As a result, the proposed solution could perform up to a thousand times faster than one based on direct matrix solution. It also provides the coefficients of the polynomial E-pulse sections without solving a linear problem associated with ill-conditioned sparse matrix in its left-hand side. The E-pulse signals synthesized by means of the fast algorithm are proven to be exactly the same as one synthesized by the direct approach. The numerical example given in the paper exposures the main features of the E-pulse technique. The discrimination scheme where two aircraft scaled model targets are involved is simulated. It was shown that the E-pulse discrimination number provides the effective tool for measuring the energy of the late-time part of the convolution as a measure of the difference of two pole sets belonging to the responses under comparison.

A new artificial neural network architecture that helps generating longer melodic patterns is introduced alongside with methods for post-generation filtering. The proposed approach, called variational autoencoder supported by history, is based on a recurrent highway gated network combined with a variational autoencoder. The combination of this architecture with filtering heuristics allows the generation of pseudo-live, acoustically pleasing, melodically diverse music.