This book constitutes the refereed proceedings of the 14th International Workshop on Enterprise and Organizational Modeling and Simulation, EOMAS 2018, held in Tallinn, Estonia, in June 2018. The main focus of EOMAS is on the role, importance, and application of modeling and simulation within the extended organizational and enterprise context. The 11 full papers presented in this volume were carefully reviewed and selected from 22 submissions. They were organized in topical sections on conceptual modeling, enterprise engineering, and formal methods.
This state-of-the-art survey is dedicated to the memory of Emmanuil Markovich Braverman (1931-1977), a pioneer in developing the machine learning theory. The 12 revised full papers and 4 short papers included in this volume were presented at the conference "Braverman Readings in Machine Learning: Key Ideas from Inception to Current State" held in Boston, MA, USA, in April 2017, commemorating the 40th anniversary of Emmanuil Braverman's decease. The papers present an overview of some of Braverman's ideas and approaches. The collection is divided in three parts. The first part bridges the past and the present. Its main contents relate to the concept of kernel function and its application to signal and image analysis as well as clustering. The second part presents a set of extensions of Braverman's work to issues of current interest both in theory and applications of machine learning. The third part includes short essays by a friend, a student, and a colleague.
This book constitutes extended, revised and selected papers from the 7th International Conference on Optimization Problems and Their Applications, OPTA 2018, held in Omsk, Russia in July 2018. The 27 papers presented in this volume were carefully reviewed and selected from a total of 73 submissions. The papers are listed in thematic sections, namely location problems, scheduling and routing problems, optimization problems in data analysis, mathematical programming, game theory and economical applications, applied optimization problems and metaheuristics.
Information systems in different domains, such as healthcare, tourism, banking, government and others, record operational behavior in the form of event logs. The process mining discipline offers dozens of techniques to discover, analyze, and visualize processes running in information systems, based on their event logs. The representational bias (the language for processes representation) plays an important role in the process discovery. In this work BPMN (Business Process Model and Notation) language was chosen as a representational bias and as a starting point for the process discovery, analysis and enhancement. BPMN is a common process modeling language, widely used by consultants, managers, analysts, and software engineers in various application domains. This work aims to bridge the gap between process mining techniques and BPMN. Existing techniques are often limited to a single perspective, e.g., just the control flow, subprocesses, or just resources. The goal of this work is to fully support the BPMN specification in the context of process mining and suggest a unified and integrated approach allowing for the discovery, analysis and enhancement of hierarchical high-level BPMN models. The approach proposed in this thesis is supported by tools that enable users to analyze discovered processes in BPMN-compliant tools and even automate their executions, using existing BPMN engines.
This volume contains proceedings of the first Workshop on Data Analysis in Medicine held in May 2017 at the National Research University Higher School of Economics, Moscow. The volume contains one invited paper by Dr. Svetla Boytcheva, 6 regular contributions and 2 project proposals, carefully selected and reviewed by at least two reviewers from the international program commit- tee. The papers accepted for publication report on different aspects of analysis of medical data, among them treatment of data on particular diseases (Consoli- dated mathematical growth model of Breast Cancer CoMBreC, Artificial neural networks for prediction of final height in children with growth hormone deficiency), methods of data analysis (analysis of rare diseases, methods of machine learning and Big Data, subgroup discovery for treatment optimization), and instrumental tools (explanation-oriented methods of data analysis in medicine, information support features of the medical research process, modeling frame- work for medical data semantic transformations, radiology quality management and peer-review system). Organizers of the workshop would like to thank the reviewers for their careful work and all contributors and participants of the workshop.
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 book discusses smart, agile software development methods and their applications for enterprise crisis management, presenting a systematic approach that promotes agility and crisis management in software engineering. The key finding is that these crises are caused by both technology-based and human-related factors. Being mission-critical, human-related issues are often neglected. To manage the crises, the book suggests an efficient agile methodology including a set of models, methods, patterns, practices and tools. Together, these make a survival toolkit for large-scale software development in crises. Further, the book analyses lifecycles and methodologies focusing on their impact on the project timeline and budget, and incorporates a set of industry-based patterns, practices and case studies, combining academic concepts and practices of software engineering.
Sustaining a competitive edge in today’s business world requires innovative approaches to product, service, and management systems design and performance. Advances in computing technologies have presented managers with additional challenges as well as further opportunities to enhance their business models.
Software Engineering for Enterprise System Agility: Emerging Research and Opportunities is a collection of innovative research that identifies the critical technological and management factors in ensuring the agility of business systems and investigates process improvement and optimization through software development. Featuring coverage on a broad range of topics such as business architecture, cloud computing, and agility patterns, this publication is ideally designed for business managers, business professionals, software developers, academicians, researchers, and upper-level students interested in current research on strategies for improving the flexibility and agility of businesses and their systems.
This edited collection presents a range of methods that can be used to analyse linguistic data quantitatively. A series of case studies of Russian data spanning different aspects of modern linguistics serve as the basis for a discussion of methodological and theoretical issues in linguistic data analysis. The book presents current trends in quantitative linguistics, evaluates methods and presents the advantages and disadvantages of each. The chapters contain introductions to the methods and relevant references for further reading.
The Russian language, despite being one of the most studied in the world, until recently has been little explored quantitatively. After a burst of research activity in the years 1960-1980, quantitative studies of Russian vanished. They are now reappearing in an entirely different context. Today we have large and deeply annotated corpora available for extended quantitative research, such as the Russian National Corpus, ruWac, RuTenTen, to name just a few (websites for these and other resources will be found in a special section in the References). The present volume is intended to fill the lacuna between the available data and the methods that can be applied to studying them.
Our goal is to present current trends in researching Russian quantitative linguistics, to evaluate the research methods vis-à-vis Russian data, and to show both the advantages and the disadvantages of the methods. We especially encouraged our authors to focus on evaluating statistical methods and new models of analysis. New findings concern applicability, evaluation, and the challenges that arise from using quantitative approaches to Russian data.
Researchers and practitioners in computer science, optimization, operations research and mathematics will find this book useful as it illustrates optimization models and solution methods in discrete, non-differentiable, stochastic, and nonlinear optimization. Contributions from experts in optimization are showcased in this book showcase a broad range of applications and topics detailed in this volume, including pattern and image recognition, computer vision, robust network design, and process control in nonlinear distributed systems.
This book is dedicated to the 80th birthday of Ivan V. Sergienko, who is a member of the National Academy of Sciences (NAS) of Ukraine and the director of the V.M. Glushkov Institute of Cybernetics. His work has had a significant impact on several theoretical and applied aspects of discrete optimization, computational mathematics, systems analysis and mathematical modeling.
The volume consists of scientific and research papers of the Fifth International Con- ference “Actual Problems of System and Software Engineering” (APSSE-2017), which took place with the support of the Russian Foundation for Basic Research (RFBR) (Project No17-07-20565). The Conference was held at the National Research University “Higher School of Economics” from November 14 to November 16, 2017 in Moscow, Russia. The conference was devoted to the analysis of the status, contemporary trends, re- search issues and practical results obtained by national and foreign scientists and ex- perts in the system and software engineering area, as well as information and analyti- cal systems development area using Big Data technologies. The target audience of the conference came to be the experts, students and post- graduates working in the area of ordering, designing, development, implementation, operation, and maintenance of information and analytical systems for various applica- tions and their software, also working on custom software development. Plenary papers were delivered by the leading domestic and foreign specialists and were aimed at developing the views on the most important and fundamental aspects of the information technology development. The Conference hosted 13 invited reports. There were submitted 77 articles, 51 from which were selected for publication. All the submitted articles were reviewed by the members of the Program Committee as well as by the independent reviewers.
This book constitutes the refereed proceedings of the First International Conference on Digital Transformation and Global Society, DTGS 2017, held in St. Petersburg, Russia, in June 2017.
The 34 revised full papers and three revised short papers presented were carefully reviewed and selected from 134 submissions. The papers are organized in topical sections on eSociety: social media analysis; eSociety: ICTs in education and science; eSociety: legal, security and usability issues; ePolity: electronic governance and electronic participation; ePolity: politics of cyberspace; eCity: urban planning and smart cities; eHealth: ICTs in public health management; eEconomy and eFinance: finance and knowledge management.
The book prepared for the purposes of The 2nd World Congress on Logic and Religion, organised by the Institute of Philosophy of the University of Warsaw.
The book contains the final version of the abstracts submitted by majority of speakers.
Daily operation of a large-scale experiment is a resource consuming task, particularly from perspectives of routine data quality monitoring. Typically, data comes from different sub-detectors and the global quality of data depends on the combinatorial performance of each of them. In this paper, the problem of identifying channels in which anomalies occurred is considered. We introduce a generic deep learning model and prove that, under reasonable assumptions, the model learns to identify ’channels’ which are affected by an anomaly. Such model could be used for data quality manager cross-check and assistance and identifying good channels in anomalous data samples. The main novelty of the method is that the model does not require ground truth labels for each channel, only global flag is used. This effectively distinguishes the model from classical classification methods. Being applied to CMS data collected in the year 2010, this approach proves its ability to decompose anomaly by separate channels.
Reconstruction and identification of particles in calorimeters of modern High Energy Physics experiments is a complicated task. Solutions are usually driven by a priori knowledge about expected properties of reconstructed objects. Such an approach is also used to distinguish single photons in the electromagnetic calorimeter of the LHCb detector at the LHC from overlapping photons produced from decays of high momentum π 0. We studied an alternative solution based on first principles. This approach applies neural networks and classifier based on gradient boosting method to primary calorimeter information, that is energies collected in individual cells of the energy cluster. Mutial application of this methods allows to improve separation performance based on Monte Carlo data analysis. Receiver operating characteristic score of classifier increases from 0.81 to 0.95, that means reducing primary photons fake rate by factor of two or more.
t is known (from Counting curves and their projections by Joachim von zur Gathen, Marek Karpinski, Igor Shparlinski [1, part 4]) that counting the number of points on a curve where is a sparse polynomial over
is #P-complete under randomized reductions.
We give a simple proof of a stronger result: counting roots of a sparse univariate polynomial over
is #P-complete under deterministic reductions.
Algorithmic statistics looks for models of observed data that are good in the following sense: a model is simple (i.e., has small Kolmogorov complexity) and captures all the algorithmically discoverable regularities in the data. However, this idea can not be used in practice as is because Kolmogorov complexity is not computable. In this paper we develop an algorithmic version of algorithmic statistics that uses space-bounded Kolmogorov complexity. We prove a space-bounded version of a basic result from “classical” algorithmic statistics, the connection between optimality and randomness deficiences. The main tool is the Nisan–Wigderson pseudo-random generator. An extended abstract of this paper was presented at the 12th International Computer Science Symposium in Russia (Milovanov 10).
Purpose – The purpose of this paper is to demonstrate that process mining techniques can help to discover process models from event logs, using conventional high-level process modeling languages, such as Business Process Model and Notation (BPMN), leveraging their representational bias. Design/methodology/approach – The integrated discovery approach presented in this work is aimed to mine: control, data and resource perspectives within one process diagram, and, if possible, construct a hierarchy of subprocesses improving the model readability. The proposed approach is defined as a sequence of steps, performed to discover a model, containing various perspectives and presenting a holistic view of a process. This approach was implemented within an open-source process mining framework called ProM and proved its applicability for the analysis of real-life event logs. Findings – This paper shows that the proposed integrated approach can be applied to real-life event logs of information systems from different domains. The multi-perspective process diagrams obtained within the approach are of good quality and better than models discovered using a technique that does not consider hierarchy. Moreover, due to the decomposition methods applied, the proposed approach can deal with large event logs, which cannot be handled by methods that do not use decomposition. Originality/value – The paper consolidates various process mining techniques, which were never integrated before and presents a novel approach for the discovery of multi-perspective hierarchical BPMN models. This approach bridges the gap between well-known process mining techniques and a wide range of BPMN-complaint tools.
This study proposes to minimize Rényi and Tsallis entropies for finding the optimal number of topics T in topic modeling (TM). A promising tool to obtain knowledge about large text collections, TM is a method whose properties are underresearched; in particular, parameter optimization in such models has been hindered by the use of monotonous quality functions with no clear thresholds. In this research, topic models obtained from large text collections are viewed as nonequilibrium complex systems where the number of topics is regarded as an equivalent of temperature. This allows calculating free energy of such systems—a value through which both Rényi and Tsallis entropies are easily expressed. Numerical experiments with four TM algorithms and two text collections show that both entropies as functions of the number of topics yield clear minima in the middle area of the range of T. On the marked-up dataset the minima of three algorithms correspond to the value of T detected by humans. It is concluded that Tsallis and especially Rényi entropy can be used for T optimization instead of Shannon entropy that decreases even when T becomes obviously excessive. Additionally, some algorithms are found to be better suited for revealing local entropy minima. Finally, we test whether the overall content of all topics taken together is resistant to the change of T and find out that this dependence has a quasi-periodic structure which demands further research.
Wi-Fi HaLow is an adaptation of the widespread Wi-Fi technology for the Internet of Things scenarios. Such scenarios often involve numerous wireless stations connected to a shared channel, and contention for the channel significantly affects the performance in such networks. Wi-Fi HaLow contains numerous solutions aimed at handling the contention between stations, two of which, namely, the Centralized Authentication Control (CAC) and the Distributed Authentication Control (DAC), address the contention reduction during the link set-up process. The link set-up process is special because the access point knows nothing of the connecting stations and its means of control of these stations are very limited. While DAC is self-adaptive, CAC does require an algorithm to dynamically control its parameters. Being just a framework, the Wi-Fi HaLow standard neither specifies such an algorithm nor recommends which protocol, CAC or DAC, is more suitable in a given situation. In this paper, we solve both issues by developing a novel robust close-to-optimal algorithm for CAC and compare CAC and DAC in a vast set of experiments.
Lemmatisation, which is one of the most important stages of text preprocessing, consists in grouping the inflected forms of a word together so they can be analysed as a single item. This task is often considered solved for most modern languages irregardless of their morphological type, but the situation is dramatically different for ancient languages. Rich inflectional system and high level of orthographic variation common to these languages together with lack of resources make lemmatising historical data a challenging task. It becomes more and more important as manuscripts are being extensively digitized now, but still remains poorly covered in literature. In this work, I compare a rule-based and a neural network based approach to lemmatisation in case of Early Irish data.
In this study, we examine the process of fitness landscape evolution of the hypercycle system. We assume that the parameters that define the fitness landscape change in order to maximize the mean fitness of the system. The environmental influence is expressed as resource limitation: we formalize it as a restriction on the fitness matrix coefficients. We show that this process of adaptation leads to a system that is sustainable in the presence of parasites. Even if the parasites are harmful to the classical hypercycle, the persistence of the population develops under fitness landscape evolution. One of the central results presented in this paper is the existence of a phase transition similar to the “error threshold” in the Eigen model: starting from some time moment the mean fitness value increase to a plateau and the hypercycle structure changes. The evolutionary parameters that define the fitness landscape and the mean fitness value tend to stabilize.