## Computer Science

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

Positive-Unlabeled (PU) learning is an analog to supervised binary classification for the case when only the positive sample is clean, while the negative sample is contaminated with latent instances of positive class and hence can be considered as an unlabeled mixture. The objectives are to classify the unlabeled sample and train an unbiased positive-negative classifier, which generally requires to identify the mixing proportions of positives and negatives first. Recently, unbiased risk estimation framework has achieved state-of-the-art performance in PU learning. This approach, however, exhibits two major bottlenecks. First, the mixing proportions are assumed to be identified, i.e. known in the domain or estimated with additional methods. Second, the approach relies on the classifier being a neural network. In this paper, we propose DEDPUL, a method that solves PU Learning without the aforementioned issues. The mechanism behind DEDPUL is to apply a computationally cheap post-processing procedure to the predictions of any classifier trained to distinguish positive and unlabeled data. Instead of assuming the proportions to be identified, DEDPUL estimates them alongside with classifying unlabeled sample. Experiments show that DEDPUL outperforms the current state-of-the-art in both proportion estimation and PU Classification and is flexible in the choice of the classifier.

Maps and diagrams have long been used by science and education. The results and achievements of geography, astronomy, biology, economics have always been presented in the form of maps. Modern methods and tools of network science allow to deeper understand collaboration because relations between agents of activity are represented as a map. For many collaborative educational systems maps of relations between agents and activity products are built automatically. However, these diagrams are not used in educational practice as tools for better learning. The paper provides examples of how the diagrams were used in educational practice in order to support a group reflection of collaborative activities.

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.

This volume contains the refereed proceedings of the 8th International Conference on Analysis of Images, Social Networks, and Texts (AIST 2019). The previous conferences during 2012–2018 attracted a significant number of data scientists – students, researchers, academics, and engineers working on interdisciplinary data analysis of images, texts, and social networks.

Proceedings of the international conference "Neural Information Processing Systems 2019." (NeurIPS 2019)

This book constitutes the refereed proceedings of the 11th International Conference on Intelligent Data Processing, IDP 2016, held in Barcelona, Spain, in October 2016.

The 11 revised full papers were carefully reviewed and selected from 52 submissions. The papers of this volume are organized in topical sections on machine learning theory with applications; intelligent data processing in life and social sciences; morphological and technological approaches to image analysis.

We propose a novel machine-learning-based approach to detect bid leakage in first-price sealed-bid auctions. We extract and analyze the data on more than 1.4 million Russian procurement auctions between 2014 and 2018. As bid leakage in each particular auction is tacit, the direct classification is impossible. Instead, we reduce the problem of bid leakage detection to Positive-Unlabeled Classification. The key idea is to regard the losing participants as fair and the winners as possibly corrupted. This allows us to estimate the prior probability of bid leakage in the sample, as well as the posterior probability of bid leakage for each specific auction. We find that at least 16% of auctions are exposed to bid leakage. Bid leakage is more likely in auctions with a higher reserve price, lower number of bidders and lower price fall, and where the winning bid is received in the last hour before the deadline.

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In this paper, we study network feature engineering for the problem of future co-author recommendation, also called collaborator recommender system. We present a system, which uses authors' research interests and existing collaboration information to predict missing and most probable in the future links in the co-authorship network. The recommender system is stated as a link prediction problem for the current network and for new edges that appear next year. From machine learning point of view, both problems are treated as binary classification. We evaluate our research on our University researchers co-authorship network, while also mentioning results on sub-network of publications indexed in Scopus. Our approach has high accuracy and provides scalable solution for any significantly large co-authorship network.

Autonomous driving highly depends on depth information for safe driving. Recently, major improvements have been taken towards improving both supervised and self-supervised methods for depth reconstruction. However, most of the current approaches focus on single frame depth estimation, where quality limit is hard to beat due to limitations of supervised learning of deep neural networks in general. One of the way to improve quality of existing methods is to utilize temporal information from frame sequences. In this paper, we study intelligent ways of integrating recurrent block in common supervised depth estimation pipeline. We propose a novel method, which takes advantage of the convolutional gated recurrent unit (convGRU) and convolutional long short-term memory (convLSTM). We compare use of convGRU and convLSTM blocks and determine the best model for real-time depth estimation task. We carefully study training strategy and provide new deep neural networks architectures for the task of depth estimation from monocular video using information from past frames based on attention mechanism. We demonstrate the efficiency of exploiting temporal information by comparing our best recurrent method with existing image-based and video-based solutions for monocular depth reconstruction.

Polynomial completeness of an operation guarantees that deciding solvability of equations over this operation is an NP-complete problem. Thus this property is beneficial from the viewpoint of cryptographic applications. We propose an algorithm for verification of polynomial completeness of quasigroups and analyse efficiency of its serial and parallel implementations.

In “Lattice dynamics and structure of the new langasites Ln3CrGe3Be2O14 (Ln ¼ La, Pr, Nd): vibrational spectra and ab initio calculations” [1], experimental and calculated results on lattice dynamics of the recently discovered new compounds La3CrGe3- Be2O14, Pr3CrGe3Be2O14, and Nd3CrGe3Be2O14 are reported. These compounds belong to the langasite series and constitute a new class of low-dimensional antiferromagnets. The data presented in this article includes IR diffuse transmission spectra of powder samples of Ln3CrGe3Be2O14 (Ln ¼ La, Pr, Nd) registered at room temperature with a Bruker 125HR Fourier spectrometer, Raman spectra taken in the backscattering geometry (also at room temperature) with a triple monochromator using the line 514, 5 nm of an argon laser as an excitation, results of the DFT calculations with the B3LYP and PBE0 hybrid functionals on the optimized crystal structures, eigenfrequencies and eigenvectors of the normal vibrational modes. These data can be used to analyse electronphonon interaction and multiferroic properties of the new langasites and to compare the lattice dynamics of different langasites.

Currently advanced driver-assistance systems develop actively, which allow to signal to the driver about danger of collision of vehicles, necessary braking or changes of motion parameters of the car to prevent dangerous situations and accidents. The effectiveness of advanced driver-assistance systems from the parameters of their work. Therefore, the actual task is to set the optimal parameters of advanced driver-assistance systems. The values of such parameters in general can be both statistical and independent of the current road situation, and dynamic and depend on the current road situation. This problem cannot be solved without modeling dangerous situations, creating models of motion and collision risks, the results of which can be given recommendations for setting the parameters of the advanced driver-assistance systems. The aim of the work is statistical modeling collision vehicles on the unregulated crossroads and the evaluation dependency of probability collision from the parameters of the road situation. An analytical modeling of the movement of vehicles at the crossroads was used and a mathematical model of their collisions was built. With the help of statistical modeling a probability of their collision with different parameters road situation was rated. Received result can be used to configuration parameters management vehicle advanced driver-assistance systems, including dynamic.

In statistical classification and machine learning, as well as in social and other sciences, a number of measures of association have been proposed for assessing and comparing individual classifiers, raters, as well as their groups. In this paper, we introduce, justify, and explore several new measures of association, which we call CO-, ANTI-, and COANTI-correlation coefficients, that we demonstrate to be powerful tools for classifying confusion matrices. We illustrate the performance of these new coefficients using a number of examples, from which we also conclude that the coefficients are new objects in the sense that they differ from those already in the literature.

Control systems are exposed to unintentional errors, deliberate intrusions, false data injection attacks, and various other disruptions. In this paper we propose, justify, and illustrate a rule of thumb for detecting, or confirming the absence of, such disruptions. To facilitate the use of the rule, we rigorously discuss underlying results that delineate the boundaries of the rule’s applicability. We also discuss ways to further widen the applicability of the proposed intrusion-detection methodology.

Network interdiction problems by deleting critical edges have wide applicatio ns. However, in some practical applications, the goal of deleting edges is difficult to achieve. We consider the maximum shortest path interdiction problem by upgrading edges on trees (MSPIT) under unit/weighted l1l1 norm. We aim to maximize the the length of the shortest path from the root to all the leaves by increasing the weights of some edges such that the upgrade cost under unit/weighted l1l1 norm is upper-bounded by a given value. We construct their mathematical models and prove some properties. We propose a revised algorithm for the problem (MSPIT) under unit l1l1 norm with time complexity *O*(*n*), where *n* is the number of vertices in the tree. We put forward a primal dual algorithm in O(n2)O(n2) time to solve the problem (MSPIT) under weighted l1l1 norm, in which a minimum cost cut is found in each iteration. We also solve the problem to minimize the cost to upgrade edges such that the length of the shortest path is lower bounded by a value and present an O(n2)O(n2) algorithm. Finally, we perform some numerical experiments to compare the results obtained by these algorithms.