## Computer Science

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.

The International Workshop on Enterprise and Organizational Modeling and Simulation (EOMAS) represents a forum where researchers and practitioners exchange and mutually enrich their views, approaches, and obtain results in the field of enterprise engineering and enterprise architecture. The most valuable asset of every conference and workshop is its community. The community of EOMAS is small, but it consists of founding members, long-term contributors, and every year it attracts new innovative participants. This year, EOMAS reached its 15th edition and took place in Rome, Italy, during June 3–4, 2019. Traditionally, we can offer a balanced assortment of papers addressing formal foundations of enterprise modeling and simulation, conceptual modeling approaches, higher-level insights and applications bringing novel ideas to traditional approaches, as well as new emerging trends. Out of 24 submitted papers, 12 were accepted for publication as full papers and for oral presentation, and each paper was carefully selected, reviewed, and revised. In additional to this we reflected on the interest of last year’s invited workshop on usability and invited the experts to make a sequel. You can find a short report in this issue. This year, we included a novel outlet of Master and Doctoral Consortium, which attracted young talent to present their work. The presented work was then discussed, and feedback, advice, and encouragement was given. We were really surprised by the relevance, methodological quality, and results of their work – you may find their contributions on our website https://eomas-workshop.org. We would like to express our sincere thanks to the entire EOMAS community: the authors, the Program Committee and the CAiSE organizers, the chairs for their enthusiasm and devotion, as well as all participants for their contributions. We look forward to the 16th edition of EOMAS!

Workshop concentrates on an interdisciplinary approach to modelling human behavior incorporating data mining and expert knowledge from behavioral sciences. Data analysis results extracted from clean data of laboratory experiments will be compared with noisy industrial datasets from the web e.g. Insights from behavioral sciences will help data scientists. Behavior scientists will see new inspirations to research from industrial data science. Market leaders in Big Data, as Microsoft, Facebook, and Google, have already realized the importance of experimental economics know-how for their business.

In Experimental Economics, although financial rewards restrict subjects preferences in experiments, exclusive application of analytical game theory is not enough to explain the collected data. It calls for the development and evaluation of more sophisticated models. The more data is used for evaluation, the more statistical significance can be achieved. Since large amounts of behavioral data are required to scan for regularities, along with automated agents needed to simulate and intervene in human interactions, Machine Learning is the tool of choice for research in Experimental Economics. This workshop is aimed at bringing together researchers from both Data Analysis and Economics in order to achieve mutually beneficial results.

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.

This two volume constitutes the refereed proceedings of the Fourth International Conference on Digital Transformation and Global Society, DTGS 2019, held in St. Petersburg, Russia, in June 2019.

This edition of Procedia Computer Science represents the proceedings of the 23rd International Conference on Knowledge - Based and Intelligent Information & Engineering Systems (KES 2019), organised by KES International and held at the Danubius Health Spa Resort, Budapest over 4-6 September 2019. KES 2019 was the 23rd event in a series of broad-spectrum intelligent systems conferences first held in Adelaide, Australia in 1997. The main aim of this KES conference series is to provide an internationally respected forum for the dissemination of research results and the discussion of issues relating to the theory, technologies and applications of intelligent engineering and information systems. This truly international conference attracted submissions from a substantial number of researchers and practitioners from all over the world, who submitted their papers to three general tracks, one thematic track and 34 special sessions on specific topics. A large number of submissions was received and each paper was peer reviewed by at least two members of the International Program Committee. From them, 274 high-quality papers were accepted for oral presentation and publication in Procedia Computer Science, submitted for indexing in Conference Proceedings Citation Index (CPCI) and Scopus. The conference chairs would like to express their gratitude to the Keynote Speakers: Prof Dana Barry, Clarkson University, USA, title of talk: 'STEM and ICT Education in Intelligent Environments'; Dr Carlos Toro, ARTC (Advanced Remanufacturing and Technology Centre) - A*Star, Singapore, title of talk: 'Smart Manufacturing coming of age'; Prof Katsutoshi Yada, Kansai University, Japan, title of talk: 'Sensor Marketing and Data Mining'; Prof Cecilia Zanni-Merk, INSA Rouen Normandie / LITIS Laboratory, France, title of talk 'On the need of an Explainable Artificial Intelligence'; and Prof Sergey Zykov, National Research University Higher School of Economics, Russia, title of talk: 'IT Crisisology: the New Discipline for Managing Software Development in Crisis'. We would like to acknowledge also the Programme Co-Chairs, the General Track Chairs, the International Programme Committee members and reviewers for their valuable efforts in the review process, helping us to guarantee the highest quality possible for the conference. We would also like to thank the organisers and chairs of the special sessions which make an essential contribution to the success of the conference. Lastly, we would like to thank all the authors, presenters and delegates for their valuable contribution in making this an extraordinary event. KES International hopes and intends that KES2019 will make a significant contribution to international research collaboration and understanding, an essential task for the promotion of scientific joint work and excellence.

We study the Maximum Happy Vertices and Maximum Happy Edges problems. The former problem is a variant of clusterization, where some vertices have already been assigned to clusters. The second problem gives a natural generalization of Multiway Uncut, which is the complement of the classical Multiway Cut problem. Due to their fundamental role in theory and practice, clusterization and cut problems has always attracted a lot of attention. We establish a new connection between these two classes of problems by providing a reduction between Maximum Happy Vertices and Node Multiway Cut. Moreover, we study structural and distance to triviality parameterizations of Maximum Happy Vertices and Maximum Happy Edges. Obtained results in these directions answer questions explicitly asked in four works: Agrawal ’17, Aravind et al. ’16, Choudhari and Reddy ’18, Misra and Reddy ’17.

The Third Workshop on Computer Modelling in Decision Making (CMDM 2018) was held in Saratov State University (Saratov, Russia) within the VII International Youth Research and Practice Conference ‘Mathematical and Computer Modelling in Economics, Insurance and Risk Management’. The workshop 's main topic is computer and mathematical modeling in decision making in finance, insurance, banking, economic forecasting, investment and financial analysis. Researchers, postgraduate students, academics as well as financial, bank, insurance and government workers participated in the Workshop.

ICUMT is an IEEE premier an annual international congress providing an open forum for researchers, engineers, network planners and service providers targeted on newly emerging algorithms, systems, standards, services, and applications, bringing together leading international players in telecommunications, control systems, automation and robotics. The event is positioned as a major international annual congress for the presentation of original results achieved from fundamental as well as applied research and engineering works.

We study synchronization aspects in parallel discrete event simulation (PDES) algorithms. Our analysis is based on the recently introduced model of virtual times evolution in an optimistic synchronization algorithm. This model connects synchronization aspects with the properties of the profile of the local virtual times. The main parameter of the model is a “growth rate” q = 1/(1 + b), where b is a mean rollback length. We measure the average utilization of events and the desynchronization between logical processes as functions of the parameter q. We found that there is a phase transition between an “active phase”, i.e. when the utilization of the average processing time is finite, and an “absorbing state” with zero utilization, vanishing at a critical point qc ≈ 0.136. The average desynchronization degree (i.e. the vari- ance of local virtual times) grows with the parameter q. We also investi- gate the influence of the sparse distant communications between logical processes and found that they do not change drastically the synchronization properties in the optimistic synchronization algorithm, which is the sharp contrast with the conservative algorithm [1]. Finally, we compare our results with the existing case-study simulations.

This paper provides a comprehensive overview of the gapping dataset for Russian that consists of 7.5k sentences with gapping (as well as 15k relevant negative sentences) and comprises data from various genres: news, fiction, social media and technical texts. The dataset was prepared for the Automatic Gapping Resolution Shared Task for Russian (AGRR-2019) - a competition aimed at stimulating the development of NLP tools and methods for processing of ellipsis. In this paper, we pay special attention to the gapping resolution methods that were introduced within the shared task as well as an alternative test set that illustrates that our corpus is a diverse and representative subset of Russian language gapping sufficient for effective utilization of machine learning techniques.

This book concentrates on in-depth explanation of a few methods to address core issues, rather than presentation of a multitude of methods that are popular among the scientists. An added value of this edition is that I am trying to address two features of the brave new world that materialized after the first edition was written in 2010. These features are the emergence of “Data science” and changes in student cognitive skills in the process of global digitalization. The birth of Data science gives me more opportunities in delineating the field of data analysis. An overwhelming majority of both theoreticians and practition-ers are inclined to consider the notions of ‘data analysis” (DA) and “machine learning” (ML) as synonymous. There are, however, at least two differences between the two. First comes the difference in perspectives. ML is to equip computers with methods and rules to see through regularities of the environment - and behave accordingly. DA is to enhance conceptual understanding. These goals are not inconsistent indeed, which explains a huge overlap between DA and ML. However, there are situations in which these perspectives are not consistent. Regarding the current students’ cognitive habits, I came to the conclusion that they prefer to immediately get into the “thick of it”. Therefore, I streamlined the presentation of multidimensional methods. These methods are now organized in four Chapters, one of which presents correlation learning (Chapter 3). Three other Chapters present summarization methods both quantitative (Chapter 2) and categorical (Chapters 4 and 5). Chapter 4 relates to finding and characterizing partitions by using K-means clustering and its extensions. Chapter 5 relates to hierarchical and separative cluster structures. Using encoder-decoder data recovery approach brings forth a number of mathematically proven interrelations between methods that are used for addressing such practical issues as the analysis of mixed scale data, data standardization, the number of clusters, cluster interpretation, etc. An obvious bias towards summarization against correlation can be explained, first, by the fact that most texts in the field are biased in the opposite direction, and, second, by my personal preferences. Categorical summarization, that is, clustering is considered not just a method of DA but rather a model of classification as a concept in knowledge engineering. Also, in this edition, I somewhat relaxed the “presentation/formulation/computation” narrative struc-ture, which was omnipresent in the first edition, to be able do things in one go. Chapter 1 presents the author’s view on the DA mainstream, or core, as well as on a few Data science issues in general. Specifically, I bring forward novel material on the role of DA, including its successes and pitfalls (Section 1.4), and classification as a special form of knowledge (Section 1.5). Overall, my goal is to show the reader that Data science is not a well-formed part of knowledge yet but rather a piece of science-in-the-making.

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 volume collects the referred papers based on plenary, invited, and oral talks, as well on the posters presented at the Third International Conference on Computer Simulations in Physics and beyond (CSP2018), which took place September 24-27, 2018 in Moscow. The Conference continues the tradition started by an inaugural conference in 2015. It took place on the campus of A.N. Tikhonov Moscow Institute of Electronics and Mathematics in Strogino, was jointly organized by the National Research University Higher School of Economics, the Landau Institute for Theoretical Physics and Science Center in Chernogolovka.

The Conference is a multidisciplinary meeting, with a focus on computational physics and related subjects. Indeed, methods of computational physics prove useful in a broad spectrum of research in multiple branches of natural sciences, and this volume provides a sample.

We hope that this volume will interest readers, and we are already looking forward to the next conference in the series.

Moscow, Russia

November, 2018

CSP2018 Conference Chair and Volume Editor

Lev Shchur

2019 International Siberian Conference on Control and Communications (SIBCON). Proceedings

This book constitutes the refereed proceedings of the 9th International Conference on Optimization and Applications, OPTIMA 2018, held in Petrovac, Montenegro, in October 2018.The 35 revised full papers and the one short paper presented were carefully reviewed and selected from 103 submissions. The papers are organized in topical sections on mathematical programming; combinatorial and discrete optimization; optimal control; optimization in economy, finance and social sciences; applications.

This book covers the classical theory of Markov chains on general state-spaces as well as many recent developments. The theoretical results are illustrated by simple examples, many of which are taken from Markov Chain Monte Carlo methods. The book is self-contained, while all the results are carefully and concisely proven. Bibliographical notes are added at the end of each chapter to provide an overview of the literature.

We propose an accelerated gradient-free method with a non-Euclidean proximal operator associated with the *p*-norm (1 ⩽ *p* ⩽ 2). We obtain estimates for the rate of convergence of the method under low noise arising in the calculation of the function value. We present the results of computational experiments.

We consider a generalization of the classical game of Nim called hypergraph Nim. Given a hypergraph H on the ground set V={1,…,n} of *n* piles of stones, two players alternate in choosing a hyperedge H∈H and strictly decreasing all piles i∈H. The player who makes the last move is the winner. In this paper we give an explicit formula that describes the Sprague-Grundy function of hypergraph Nim for several classes of hypergraphs. In particular we characterize all 2-uniform hypergraphs (that is graphs) and all matroids for which the formula works. We show that all self-dual matroids are included in this class.

We consider two-person zero-sum stochastic mean payoff games with perfect information, or BWR-games, given by a digraph G=(V,E), with local rewards r:E→Z, and three types of positions: black VB, white VW, and random VR forming a partition of *V*. It is a long-standing open question whether a polynomial time algorithm for BWR-games exists, or not, even when |VR|=0. In fact, a pseudo-polynomial algorithm for BWR-games would already imply their polynomial solvability. In this paper,1 we show that BWR-games with a constant number of random positions can be solved in pseudo-polynomial time. More precisely, in any BWR-game with |VR|=O(1), a saddle point in uniformly optimal pure stationary strategies can be found in time polynomial in |VW|+|VB|, the maximum absolute local reward, and the common denominator of the transition probabilities.

Informed SaaS pricing decision-making requires the involvement of different business units and integrated pricing approaches. Achieving both appears to be challenging for a lot of SaaS providers, and despite its declared importance, pricing is one of the most under-managed business processes. Small and medium-sized companies do not have the resources for or the understanding of how to make informed decisions on pricing strategy and tactics. Pricing is a topic of interest in several research domains including economics, management science, digital and service marketing, and, increasingly, in software engineering. Still, the lack of integration between studies creates inconsistency in research. A comprehensive SaaS pricing body of knowledge is missing, as is a coherent action-oriented “Cookbook”. This multi-vocal literature review both brings together results from these research domains and matches practitioner expertise with academic research outcomes to promote the advancement of SaaS pricing theory and practice.

The concept of random variables network used to model the complex system of random nature is discussed. The problem of threshold graph identication to network analysis of the complex system is considered as multiple decision statistical procedure. The properties of robustness of dierent tests for testing individual hypotheses for threshold graph identication are investigated by simulations.

The paper presents the results of the cognitive modeling of the COMPUTER SCIENCE terminological system in the form of a thesaurus. The thesaurus comprises over 3000 units, which are drawn from explanatory monolingual and bilingual dictionaries of computer science terms representing the basic phenomena and processes in the professional context. Methodologically, the analysis is based on the frame model and focuses on semantic relations specific to the sphere of computer science in terms of ontological and epistemological features. The thesaurus facilitates the detailed description and effective arrangement of the terminological system characterized by a complicated hierarchical structure, and thus plays a crucial role in forming and developing professional competencies.

Heaps are well-studied fundamental data structures, having myriads of applications, both theoretical and practical. We consider the problem of designing a heap with an “optimal” extract-min operation. Assuming an arbitrary linear ordering of keys, a heap with n elements typically takes O(log n) time to extract the minimum. Extracting all elements faster is impossible as this would violate the Ω (nlog n) bound for comparison-based sorting. It is known, however, that is takes only O(n+ klog k) time to sort just k smallest elements out of n given, which prompts that there might be a faster heap, whose extract-min performance depends on the number of elements extracted so far. In this paper we show that this is indeed the case. We present a version of heap that performs insert in O(1) time and takes only O(log ∗ n+ log k) time to carry out the k-th extraction (where log ∗ denotes the iterated logarithm). All the above bounds are worst-case. © 2018, Springer Science+Business Media, LLC, part of Springer Nature.

Two transforms of functions on a half-line are considered. It is proved that their composition gives a concave majorant for every non-negative function. In particular, this composition is an identical transform on the class of non-negative functions. Applications of this result in the operator theory of Hilbert space and in the theory of quantum systems are mentioned. Several open problems are formulated.

Applications that cater to the needs of disaster incident response generate large amount of data and demand large computational resource access. Such datasets are usually collected in real-time at the incident scenes using different Internet of Things (IoT) devices. Hierarchical clouds, *i.e.*, core and edge clouds, can help these applications’ real-time data orchestration challenges as well as with their IoT operations scalability, reliability and stability by overcoming infrastructure limitations at the ad-hoc wireless network edge. Routing is a crucial infrastructure management orchestration mechanism for such systems. Current geographic routing or greedy forwarding approaches designed for early wireless ad-hoc networks lack efficient solutions for disaster incident-supporting applications, given the high-speed and low-latency data delivery that edge cloud gateways impose. In this paper, we present a novel Artificial Intelligent (AI)-augmented geographic routing approach, that relies on an area knowledge obtained from the satellite imagery (available at the edge cloud) by applying deep learning. In particular, we propose a stateless greedy forwarding that uses such an environment learning to proactively avoid the local minimum problem by diverting traffic with an algorithm that emulates electrostatic repulsive forces. In our theoretical analysis, we show that our Greedy Forwarding achieves in the worst case a path stretch approximation bound with respect to the shortest path, without assuming presence of symmetrical links or unit disk graphs. We evaluate our approach with both numerical and event-driven simulations, and we establish the practicality of our approach in a real incident-supporting hierarchical cloud deployment to demonstrate improvement of application level throughput due to a reduced path stretch under severe node failures and high mobility challenges of disaster response scenarios.