This volume contains the papers presented at YSIP-3: the 3rd International Workshop for Young Researchers working in Information Processing (YSIP), which was organized by the North-Caucasus Federal University and the International Center for Computational Logic of the Technische Universitat Dresden. This years YSIP was held in Stavropol and Arkhyz during September 17-20, 2019. YSIP aims at bringing together master and PhD students from Russia, Europe, and beyond to present and to discuss their new scienti c results in the area papers are welcome in, but are not limited to, the following areas of Semantic Methods, Semantic Web, Languages for Semantic Information Processing, Applications of Semantic Information Processing, Formal Concept Analysis, Big Data, Indexing in Big Data, Integrity and Quality of Data, Information Retrieval, Question-Answering Systems, Information Security, Problem Solving, Knowledge Representation and Reasoning, Common Sense Reasoning, Deep Learning, Explainable Arti cial Intelligence, Ethical Decision Making, Pattern Recognition, Data and Work ows, Cognitive Processes and Applications, Multiagent Systems, and Computer Simulation.
King Mongkut's Institute of Technology Ladkrabang, Bangkok, Thailand Novosibirsk State Technical University, Novosibirsk, Russia Orenburg State University, Orenburg, Russia Eastern Mediterranean University, Famagusta, Turkey Hanoi University of Science and Technology, Hanoi, Vietnam Technische Universitat Wien, Vienna, Austria University of Innsbruck, Innsbruck, Austria Kuban State Technological University, Krasnodar, Russia Universitat Koblenz-Landau, Koblenz, Germany Technische Universitat Dresden, Dresden, Germany (co-chair) University of Deusto, Bilbao, Spain University of Oslo and Bosch AI center, Oslo, Norway and Renningen, Germany Roskilde University, Roskilde, Denmark Universidade NOVA de Lisboa, Lisbon, Portugal Tomsk State University of Control Systems and Radioelectronics, Tomsk, Russia North-Caucasus Federal University, Stavropol, Russia North-Caucasus Federal University, Stavropol, Russia (co-chair) Sapienza { Universita di Roma, Rome, Italy North-Caucasus Federal University, Stavropol, Russia North-Caucasus Federal University, Stavropol, Russia KU Leuven, Leuven, Belgium Orenburg State University, Orenburg, Russia University of Ljubljana, Ljubljana, Slovenia North-Caucasus Federal University, Stavropol, Russia University of Applied Science Deggendorf, Deggendorf, Germany Siberian Branch of the Russian Academy of Sciences, Russia
Ufa State Aviation Technical University, Ufa, Russia
Information based Smart Decisions
Kyungsung University, Department of Industrial and Management Engineering, South Korea This keynote speech introduces diverse intelligent decisions made based on gathered information. Some examples are historically famous and others are contemporary. Florence Nightingale (1820 1910) is well known as \The Lady with the Lamp" and \The White Angel." Her rose diagram, a skillful infographic, was e ective in decreasing the death rate of soldiers. Abraham Wald's work on aircraft survivability was also a prominent study with intelligent decisions. Recent case studies of global companies would show how useful information creates value. This keynote address will broaden your perspective on data analysis and information gathering. Intelligent Analysis of Medical and Psychophysiological Data
The paper is dedicated to the application of Intelligent methods of data analysis on the examples of medical and psychophysiological tasks. Although there are pretty much research in this eld, uni ed complex methodology of medical data analysis does not exist. In this paper we present the short overview of using various means of data analysis in medical applications: big data, machine learning, text mining, multi-agent systems. We present wo cases of intelligent data analysis performed by the researchers from Ufa State Aviation Technical University in collaboration with experts and researchers from the medical institutions in the city of Ufa. First case consist in analysis of weak-structured data about acute poisonings in the Republic of Bashkortostan. The second case was connected to analysis of the results of psychophysiological diagnostics of students in order to determine recommendations for their physical activity.
This work describes a conversational agent, integrated into the Google Assistant platform, which is able to recommend personalised leisure activities for a user's free time. Smart Time is a context-aware Action on Google capable of extracting free slots from a user's calendar and recommend customised activities { which include book, restaurant or outdoor recommendations { in accordance to his/her speci c context computed with basis on data extracted from the smart device's various sensors. Additionally, the conversational agent integrates a constantly evolving recommendation engine, which is able to adapt in accordance to the user's choices.