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.
The volume consists of scientifi c and research papers of the Sixth International Conference “Actual Problems of System and Software Engineering” (APSSE-2019). The Conference was held at the National Research University “Higher School of Economics” from November 12 to November 14, 2019 in Moscow, Russia. The conference was devoted to the analysis of the status, contemporary trends, research issues and practical results obtained by national and foreign scientists and experts in the system and software engineering area, as well as information and analytical systems development area using Big Data technologies. The target audience of the conference came to be the experts, students and postgraduates working in the area of ordering, designing, development, implementation, operation, and maintenance of information and analytical systems for various applications 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. Submitted articles were selected for publication. All the submitted articles were reviewed by the members of the Program Committee as well as by the independent reviewers.
Social network analysis (SNA) is a multidisciplinary research area that has attracted many researchers from different disciplines such as Physics, Mathematics, Sociology, Biology and Computer Science, and has been studied according to different approaches and techniques. A social network is a dynamic structure (generally represented as a graph) of a set of entities/actors (nodes) together with links (edges) between them. The explosive growth of online social media has provided users with the opportunity to create and share digital content on a range hardly imaginable a few years ago. Indeed, massive participation has transformed online social networks into cores of social activity and a critical information vehicle. This is reflected by the number of news, opinions, and reviews that are constantly posted and discussed on these networks. The size and diversity of user generated content create an opportunity for identifying central and influential players, behavioral trends and user communities.
As the number of digital texts increases rapidly, there is a pressing need for more advanced and diverse tools of natural language processing. While purely statistical approaches proved powerful and efficient for many NLP tasks, there are many applications that would benefit from the formal models and approaches traditional language science has to offer. With hopes to facilitate this interaction between theory and practical implementation, we are pleased to announce the workshop on Computational Linguistics and Language Science to be held in Moscow, Russia on April 25, 2016 (11 AM to 6 PM).
Formal Concept Analysis (FCA) is a mathematically well-founded theory aimed at data analysis and classication, introduced and detailed in the book of Bernhard Ganter and Rudolf Wille, \Formal Concept Analysis", Springer 1999. The area came into being in the early 1980s and has since then spawned over 10000 scientic publications and a variety of practically deployed tools. FCA allows one to build from a data table with objects in rows and attributes in columns a taxonomic data structure called concept lattice, which can be used for many purposes, especially for Knowledge Discovery and Information Retrieval. The \Formal Concept Analysis Meets Information Retrieval" (FCAIR) workshop collocated with the 35th European Conference on Information Retrieval (ECIR 2013) was intended, on the one hand, to attract researchers from FCA community to a broad discussion of FCA-based research on information retrieval, and, on the other hand, to promote ideas, models, and methods of FCA in the community of Information Retrieval. This volume contains 11 contributions to FCAIR workshop (including 3 abstracts for invited talks and tutorial) held in Moscow, on March 24, 2013. All submissions were assessed by at least two reviewers from the program committee of the workshop to which we express our gratitude. We would also like to thank the co-organizers and sponsors of the FCAIR workshop: Russian Foundation for Basic Research, National Research University Higher School of Economics, and Yandex.
The The workshop first took place in 2015 as a succession of the Applications of Region Theory (ART) workshop series. After the success of the initial workshop, it is only natural to bring together researchers working on region-based synthesis and process mining again. The ATAED'2016 workshop took place in Torun on June 20-21, 2016 and was a satellite event of both the 37th International Conference on Application and Theory of Petri Nets and Concurrency (Petri Nets 2016) and the 16th International Conference on Application of Concurrency to System Design (ACSD 2016). Papers related to process mining, region theory and other synthesis techniques were presented at ATAED'2016.
Workshop concentrates on an interdisciplinary approach to modeling human behavior incorporating data mining and/or expert knowledge from behavioral sciences. Data analysis results extracted from clean data of laboratory experiments can be compared with noisy industrial data-sets 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.
The first and the second edition of the FCA4AI Workshop showed that many researchers working in Artificial Intelligence are indeed interested by a well-founded method for classi- fication and mining such as Formal Concept Analysis (see http://www.fca4ai.hse.ru/). The first edition of FCA4AI was co-located with ECAI 2012 in Montpellier and published as http://ceur-ws.org/Vol-939/ while the second edition was co-located with IJCAI 2013 in Beijing and published as http://ceur-ws.org/Vol-1058/. Based on that, we decided to continue the series and we took the chance to organize a new edition of the workshop in Prague at the ECAI 2014 Conference. This year, the workshop has again attracted many different researchers working on actual and important topics, e.g. recommendation, linked data, classification, biclustering, parallelization, and various applications. This shows the diversity and the richness of the relations between FCA and AI. Moreover, this is a good sign for the future and especially for young researchers that are at the moment working in this area or who will do.
This is the second edition of the FCA4AI workshop, the first edition being associated to the ECAI 2012 Conference, held in Montpellier, in August 2012 (see http://www.fca4ai.hse.ru/). In particular, the first edition of the workshop showed that there are many AI researchers interested in FCA. Based on that, the three co-editors decided to organize a second edition of the FCA4AI workshop at the IJCAI 2013 Conference in Beijing.
Formal Concept Analysis (FCA) is a mathematically well-founded theory aimed at data analysis and classification. FCA allows one to build a concept lattice and a system of dependencies (implications) which can be used for many AI needs, e.g. knowledge processing involving learning, knowledge discovery, knowledge representation and reasoning, ontology engineering, as well as information retrieval and text processing. Thus, there exist many “natural links” between FCA and AI.
Recent years have been witnessing increased scientific activity around FCA, in particular a strand of work emerged that is aimed at extending the possibilities of FCA w.r.t. knowledge processing, such as work on pattern structures and relational context analysis. These extensions are aimed at allowing FCA to deal with more complex than just binary data, both from the data analysis and knowledge discovery points of view and from the knowledge representation point of view, including, e.g., ontology engineering. All these works extend the capabilities of FCA and other new possibilities for AI activities in the framework of FCA. Accordingly, in this workshop, we are interested in two main issues:
- How can FCA support AI activities such as knowledge processing (knowledge discovery, knowledge representation and reasoning), learning (clustering, pattern and data mining), natural language processing, and information retrieval.
- How can FCA be extended in order to help AI researchers to solve new and complex problems in their domains.
The workshop is dedicated to discuss such issues.
The papers submitted to the workshop were carefully peer-reviewed by two members of the program committee and 11 papers with the highest scores were selected. We thank all the PC members for their reviews and all the authors for their contributions. We also thank the organizing committee of ECAI-2012 and especially workshop chairs Jerome Lang and Michele Sebag for the support of the workshop.
This volume contains the papers presented at the Third International Workshop on Experimental Economics and Machine Learning held on July 18, 2016 at the National Research University Higher School of Economics, Moscow. This proceedings concentrates on an interdisciplinary approach to modelling human behavior incorporating data mining and expert knowledge from behav- ioral sciences. Data analysis results extracted from clean data of laboratory ex- periments are of advantage if compared with noisy industrial datasets from the web and other sources. In their turn, insights from behavioral sciences help data scientists. Behavior scientists 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 fi nancial rewards restrict subjects pref- erences in experiments, the exclusive application of analytical game theory is not enough to explain the collected data. It calls for the development and evalua- tion of more sophisticated models. The more data is used for evaluation, the more statistical signi fi cance can be achieved. Since large amounts of behavioral data are required to scan for regularities, Machine Learning is the tool of choice for research in Experimental Economics. In some works, automated agents are needed to simulate and intervene in human interactions. This proceeding aims to create a forum, where researchers from both Data Analysis and Economics are brought together in order to achieve mutually-bene fi cial results. This year the workshop has hosted nine regular papers and two research proposals on a variety of topics related to di ff erent aspects of human behavior in games, demography, economy crises, stock markets, etc. Each paper has been reviewed by two PC members at least; all these papers rely on di ff erent data analysis techniques and the presented results are supported by data. The representatives of R&D department of Imhonet company, Vladimir Bo- brikov and Elena Nenova, have presented a keynote talk concerning how to consistently value recommendations produced by recommender systems. We would like to thank all the authors of submitted papers and the Pro- gram Committee members for their commitment. We are grateful to our invited speaker and our sponsors: National Research University Higher School of Eco- nomics (Moscow, Russia), Russian Foundation for Basic Research, and ExactPro. Finally, we would like to acknowledge the EasyChair system which helped us to manage the reviewing process.