The 3rd Conference on Creativity in Intelligent Technologies & Data Science September 16-19, 2019

Volgograd, Russia

THE CONFERENCE PROGRAM

http://citds2019.ru/

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Welcome to CIT&DS 2019 In the era of digitalization and raise

of artificial intelligence, creativity is

still a crucial and essential ability for

humankind. Creativity is the process

of breaking out of established

patterns; it is the reorganization,

recombination or reinterpretation of

concepts and ideas for getting

something unique and previously

unknown. Creating intelligent systems

and artificial intelligence approaches

is one of the most creative activities

that humans undertake. It is

developed for people and by people

and people’s creativity can be a good

source to improvise solutions to

problems for dominating complex

systems such as intelligent system

design and development. These

actions merge science and art.

The third Conference on Creativity

in Intelligent Technologies and Data

Science (CIT&DS 2019) continues the

successful series of previous

conferences took place in 2017 and

2015. The main objective of the

CIT&DS 2019 is to bring together

researchers and practitioners to share

ideas in using creativity to theory and

practice in software and intelligent

systems engineering as well as data

science and decision-making support

solutions. Participants will find results

of creating cutting-edge intelligent

technologies based on state-of-the-

art research.

The conference focuses on research

around the following topics. A.

Artificial intelligence & Deep Learning

Technologies for Creative Tasks: (A.i)

Knowledge Discovery in Patent and

Open Sources; (A.ii) Open Science

Semantic Technologies; (A.iii)

Computer Vision and Knowledge-

Based Control. B. Cyber-Physical

Systems (CPS) & Big Data-driven

world: (B.i) Proactive Modeling in

Intelligent Decision Making Support;

(B.ii) Design Creativity in

CASE/CAI/CAD/PDM; (B.iii) Intelligent

Internet of Services and Internet of

Things. C. Intelligent Technologies in

Social Engineering: (C.i) Data Science

in Social Networks Analysis and

cybersecurity; (C.ii) Creativity &

Game-Based Learning; (C.iii)

Intelligent Assistive Technologies:

Software Design and Application.

CIT&DS 2019 Proceedings are

published by Springer in their

Communications in Computer and

Information Science series, Volumes

1083, 1084.

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Proceedings All papers published in Two volume proceedings are available online. This two-volume set constitutes the proceedings of the Third Conference on Creativity in Intellectual Technologies and Data Science, CIT&DS 2019, held in Volgograd, Russia, in September 2019. The 67 full papers, 1 short paper and 3 keynote papers presented were carefully reviewed and selected from 231 submissions. The papers are organized in topical sections in the two volumes. Part I: cyber-physical systems and Big Data-driven world. Part II: artificial intelligence and deep learning technologies for creative tasks; intelligent technologies in social engineering. Proceedings, Part I https://www.springer.com/gp/book/9783030297497

Proceedings, Part II https://www.springer.com/gp/book/9783030297497

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Venue The conference on Creativity in Intelligent Technologies & Data Science 2019 is

held in Volgograd State Technical University. The University is located in Lenin

avenue, 28. In Russian it pronounces “prospect Lenina”.

The Main Building.

The “B” Building. All rooms in schedule start from “B-” are located in “B” Building

extension. This time the Conference Hall is located here.

Our volunteers will help you in any cases. You can recognize them by white T-shirt

with label of our Conference

If you have an urgent question, do not hesitate to contact organizing committee

phone: +7 917 337 61 52

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Conference General Co-chairs

Vladimir Lysak

Academician of

Russian Academy of Science, scientific

supervisor of Volgograd State

Technical University

Igor Kalyaev

Academician of Russian

Academy of Science, scientific supervisor of Research Institute of Computing Systems

Dmitriy Novikov

corresponding member of

Russian Academy of Science, director of Institute of Control Sciences, Russian

Academy of Science

The Program Committee Co-chairs

prof. Alla Kravets, Volgograd State Technical University, Russia

prof. Peter Groumpos, University of Patras, Greece

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The keynote speakers

Prof. Peter P. Groumpos, Professor and Director of the

Laboratory for Automation and

Robotics, Department of Electrical and

Computer Engineering Technology,

University of Patras, Greece

groumpos@ece.upatras.gr

ARTIFICIAL INTELLIGENCE: ISSUES,

CHALLENGES, OPPORTUNITIES AND

THREATS

Today the whole world is phasing with an

unprecedented set of problems never

had before. Challenging and difficult

problems relating to the Energy and

Environment, the Health and Ecology, the

business and Economics and the ongoing

process of Spiritual Decline are part of

our everyday life. Catastrophic physical

phenomena are on the rise, lately and

many say that this is due to human

disrespect to the environment. Urgently

valuable and sustainable solutions are

need-ed. One scientific approach to

address these challenging questions is

Artificial Intelligence (AI). Artificial

Intelligence (AI) is the mantra of the

current era. Technologists, academicians,

researchers, journalists and venture

capitalists alike, use the phrase-term (AI).

As with many phrases that cross over

from technical academic fields into

general circulation, there is significant

misunderstanding accompanying the use

of the phrase. In the beginning AI was

only a branch of computer science that

aimed to create intelligent machines.

Today AI is found in all scientific fields.In

this plenary talk, the basic and

fundamental aspects of AI are briefly

reviewed. From its early introduction to

the scientific world, in the late 1950s until

today. It will address the phenomenon;

how in the beginning scientists saw

artificial intelligence as the scientific

apotheosis of one of the most enduring,

glorious, promising often amusing, and

sometimes alarming, traditions of human

culture: the endless fascination with

artifacts that think. AI and intelligent

machines would solve ALL our problems.

This theme became an international myth

and travelled all around the world. It

stayed in a high respected position for

almost 30 years. Then it had a slowdown

and AI was almost vanished. However, AI

has come back again. In this plenary talk,

we will also visit AI from last 20 years

perspectives. Now Artificial intelligence

(AI) makes it possible for machines to

learn from experience, adjust to new

inputs and perform human-like tasks.

Most AI examples that we hear about

today – from chess-playing computers to

self-driving cars – rely heavily on deep

learning, knowledge search, data mining

and natural language processing. Using

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these technologies, computers can be

trained to accomplish specific tasks by

processing large amounts of data and

recognizing patterns in the data. It has

become an essential part of the

technology industry and to most

everyday tasks. We will see how from a

slightly dubious fringe science, artificial

intelligence has moved slowly after

recovering from the down path, but

faster the last 10 years to a central place

in our everyday lives, and how it will be

even more crucial as the World Wide

Web moves into its next generation. All

scientific fields related to AI will be

considered: Machine learning, perception

and Knowledge engineering are core part

of AI research. Robotics and mechatronics

is also a major field related to AI. Lately AI

research is strongly related human brain

cognition and neuroscience. Artificial

Intelligence (AI) and Deep Learning (DL)

are relying mainly on data analysis

without taking into consideration the

human nature. Theories of Fuzzy

Cognitive Maps (FCM) seem to provide a

useful tool in developing new AI theories

answering this problem.

However all these fast and sometimes

unpredictable changes, generate not only

a lot of challenges and opportunities but

many threats to the human kind. In the

plenary talk, the last issue of threats will

be reviewed in more details. The

understanding of the AI threats is a very

serious problem and is related directly to

human brain cognition and intelligence.

Number of examples from our real life

will be provided.

Short Bio. Prof. Peter P. Groumpos was

born in Greece at the small town of

Xylocastron. At the age of 18 years old, he

went to USA with the primary goal to do

his University studies. He did his

undergraduate and graduate studies at

the Department of Electrical and

Computer Engineering at the SUNYAB. In

1980, he moved as an Assistant Professor

at Cleveland State University and in 1985,

was promoted to Associate professor. In

1990, he returned to his motherland

Greece, as a full Professor at the

Department of Electrical and Computer

Engineering of the University of Patras. In

1991 he established the Laboratory for

Automation and Robotics to which has

been since then, its Director. A Fulbright

Scholar one year award by the State

Department of USA .Chairman of the

Dept. of Electrical and Computer

Engineering, University of Patras (1999-

2003). Academic Honorary Member of the

Russian Academic Council of

Mechatronics and Robotics since 2002

and Honorary Invited Professor of the

University of Science and Technology of

the Eastern China of Shanghai since 2013.

President and CEO of Patras Science Park,

Patras, Greece (2004–2010). The Greek

Representative to the High Level Group

(HLG) of EUREKA for 10 years (reporting

directly to the Minister of Research and

Technology). The Greek NMO

representative to the Council of the

International Federation of Automatic

Control (reporting directly to the Minister

of Development).Vice-President of the

Technical Advisory Board, National

Institute for Research (2005-10). The

Greek National representative to a

number of high positions at European

Management Committees (ESPRIT, ICT,

IMS, INCO). Vice-President of the

Mediterranean Control Association (MCA)

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1990-Present. Included in a) Who’s Who

in Frontiers of Science and Technology

and b) Men of Achievement. General

Chairman of IFAC Conferences, LSS ’98

and MIM 2000, and of the IEEE

Conferences, ISIC 2000 and MED 1994,

2000 and 2016.Chairman of the TC 9.5 of

Large Scale Systems of IFAC (1996-2001).

He has been teaching undergraduate and

graduate courses in the thematic areas of

automatic control, signals and systems,

stochastic processes, intelligent control,

Fuzzy systems, Robotics, modeling

Complex Dynamic Systems and

Bioinformatics. His research interests

covers the broad thematic areas of

modeling and control of large Complex

Dynamic Systems, Intelligent control,

Artificial Intelligence (AI), fuzzy systems,

Fuzzy Cognitive Maps, Dynamic Neural

Networks, Hybrid Energy Systems

(HES),Hierarchical Systems, Intelligent

Manufacturing systems, Renewable

Energy Sources (RES), Decision Support

Systems (DSS), Knowledge Management,

Creative software Computing, Simulation

Methods, Technology Transfer and

Innovation Systems.

He especially has conducted funded

research using advanced new intelligent

and fuzzy techniques in many applications

especially in Manufacturing, Health,

Energy, Environment, Agriculture and

Transportation. He has been the principal

investigator and/or participated as a

partner in many R&D projects funded by

the EC, the Greek Government and/or the

private sector. He has published 3 books,

edited 7 books, 10 invited chapters in

books and over 300 papers in journals

and/or in International conferences. He

has an h-index 32 the highest on his

department and more than 5200

citations. Prof. Groumpos has been the

Reviewer for a number of International

Journals and for many International

Conferences. Has organized many invited

special sessions on Conferences and has

been Keynote Plenary Invited Speaker in

more than 20 International conferences.

Member of IPC on more than 40

International conferences. Prof.

Groumpos has supervised more than 25

Doctoral Thesis and more than 200

Master Thesis on his life time (including

USA).

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Karamjit S Gill, Editor, AI&Society, Professor Emeritus,

University of Brighton, UK,

editoraisoc@yahoo.co.uk

DESIGNING AI FUTURES: A SYMBIOTIC

VISION

In seeking insight into ‘creativity in

intelligent technologies’, the talk explores

the potential and challenge of AI to the

design creative intelligent tools that move

beyond the notion of intelligence

embodied into the individual to collective

intelligence embedded in the shared

collective. It comments upon the two

thought tradition of AI, “being-in-the

world” and the representational

perspective, and the dominance of this

perspective on the design of AI or ‘smart,

machines to either mimic “Turing Test” or

even represent human intelligence.

However, Once Pandora’s box of the

technology of ‘Reckoning’ and

instrumental reason is opened, we

require creative intelligence and

judgment to harness its potential, and

wisdom to shape it for broader societal

purposes. This then poses a challenge to

the design of intelligent interactive and

collaborative tools that facilitate the

ongoing conversation between

technology and society. In doing so, it

raises questions such as can we transcend

the instrumental reason of ‘machine

thinking’ to mould technological futures

for common good? And further can we

harness collective intelligence as a

transforming tool for addressing

unpredictable problems of complex social

systems? Ultimately the AI machine

raises the question of values we hold and

judgements we make about shaping the

nature and path of technology. This talk

will explore whether reframing the ideas

of ‘causality’ and ‘alignment’ can

overcome the unpredictability of black

box architectures and the limit of

‘instrumental’ approach to socio-

technical solutions. The talk will draw

upon current AI narratives of the

relations between society and the

scientific project of AI and the challenges

it poses for us to come up with possible

symbiotic AI futures. When seen through

a lens of a creative mind, it can either be

seen as a ‘marvel of the human mind’,

and when seen through cognitive mind, it

can be seen in terms of ‘machine

creativity’ – or rather machine ‘creativity’.

The talk argues that the human centred

paradigm, rooted in the notions of

purpose, tacit dimension and symbiosis

provides a framework for designing

intelligent system for facilitating creativity

and judgment.

Short Bio. Karamjit S Gill is Professor

Emeritus, University of Brighton (UK),

Founding Editor of AI&Society Journal

(Springer), Visiting Professor at the

universities of Wales (UK), Urbino (Italy),

Waterford Institute of Technology

(Ireland), Beijing Academy of Soft

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Technology (China), and Symbiotic

Network- IIT Mumbai, Delhi University,

Arizona State University and UCLA (USA),

Universities at Rio de Janeiro (Brazil),

Cybernetics Network, Santiago (Chile) and

ATA Lima (Peru). Karamjit is the Founding

Chair of the International INSYTE-CRL

laboratory at Waterford Institute of

Technology, Republic of Ireland, and is a

member of the Advisory Panel of the

Artificial Intelligence for Societal Good

Challenge of Science Foundation Ireland

(SFI). Over the years he has directed cross-

cultural research networks, including EU-

India cross-cultural innovation network

(EU); Europe-Japan network on human-

centred systems; European postgraduate

and doctoral research network in human

centred systems (EU), Knowledge, culture

and artificial intelligence network (EU);

New Technology and Adult Literacy (EU);

Computer Aided Animated Arts Theatre

(CAAAT) Project and the Europe-Japan

human centred systems (NTT Data,

Japan- 1990s); Culture, Language and

Artificial Intelligence (COST- EC/Sweden).

He has been the founding Series Editor of

the Human Centred Systems Society Book

Series (Springer) He is also actively

involved in the Community-University

Partnership in social mentoring

encompassing art, music and craft

therapeutic environment and co-

production. At Cambridge, he is involved

with the Interdisciplinary Performance

Research Network, The 'Re-'

Interdisciplinary Network, Cambridge

Digital Humanities Network, AI

Community, and Cambridge Community

Arts. At the European level, he is

collaborating with PROMISE.eu, a

European enterprise in Compliance.

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Takako Nakatani, PhD, Professor, The Open University of

Japan,

nakatani@gssm.otsuka.tsukuba.ac.jp

LEARN FROM THE PAST AND CREATE A

NEW PROCESS OF REQUIREMENTS

ELICITATION

Over the past decade, the situation of

software development has been changed.

In the past, most managers of software

development projects said that the water

fall process is mandatory. Further, they

also said “requirements must not be

changed after the contract with the

customer has been finalized.” Now, such

managers have started to accept an agile

development process as the business

environment surrounding software has

become more volatile. From the

requirements engineering view point,

researchers and practitioners know that

we cannot avoid requirements changes.

According to the Cynefin framework, we

are now facing a situation in which we

have to solve complex problems that

depend on each other, and where there

is a trade-off relationship. Therefore, we

must apply our solutions to the real

world, observe the results, discover other

problems and, find new solutions for

them. In these situations, the analysis of

requirements stability and volatility is still

important, since we can apply the water

fall process to satisfy stable

requirements; and furthermore, the agile

process can be applied only to cope with

unstable requirements. However, how

can we know the stability of

requirements? We know that

requirements on user interfaces are

changed frequently. How about other

requirements? In order to clarify the

differences between the stability and the

volatility of requirements, we can learn

from past project data. The data that

consists of the “when”, “what”, and

“why” aspect of each changed

requirement is available in projects

meeting minutes. In this instance, the

data provided us a lot of information to

plan the requirements elicitation process

throughout the project.

Short Bio. Prof. Nakatani graduated from

Tokyo University of Science. After her

graduation, she started her career as a

software engineer with Japan Information

Processing Service Co., Ltd. After she

moved to Fuji Xerox Information Systems,

Co., Ltd., she joined an object-oriented

research and development team and

developed modeling education

courseware. She managed her own

company for ten years and provided

modeling consulting services. She received

a master’s degree in systems

management from the University of

Tsukuba in 1994. She also received a

Ph.D. from the University of Tokyo in

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1998. She is a professor of the Open

University of Japan.

Her major research area is requirements

engineering. She has focused on

techniques and tools for requirements

elicitation and the process of

requirements elicitation in real projects.

Her research has been supported by

Grant-in-Aid for Scientific Research of

Japan Society for the Promotion of

Science.

During the past few years, she has

focused on conceptual modeling and the

development of tools to derive

requirements from the models. She was

the chief of Requirements Engineering

Working Group (REWG) of Information

Processing Society of Japan from 2008 to

2015. REWG was published in 1998 and

she is active as a leader of REWG for

researchers and practitioners in Japan.

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Alla G. Kravets, Professor of CAD Department, Volgograd

State Technical University, Volgograd,

Russia, agk@gde.ru

PATENTS ANALYSIS FORMAL METRIC

At the current stage of technology and

education technical progress, the

problem of targeted innovative creativity

training became most urgent. Evaluation

of cross-thematic relationships between

the domains in relation to the

development of engineering and

technology at the global level requires

the formalization of the subjective

conclusions of the patent office expert,

who is considering applications for

inventions. Assessing the novelty, the

expert compares the patent application

with the already existing patents for their

differences from each other. At the same

time, IPC of inventions is used to identify

analogs on the world level. If there are

few differences or the application repeats

already existing patents in its content, it

will be rejected at the “substantive

consideration” stage. Industrial

applicability is the set of factors that

allow using the invention in the near

future to establish mass production of

products or to modernize technological

processes. At the same time, the

possibilities of implementing the

invention for the current level of scientific

and technological development are

assessed. Assessing the inventive step,

the expert makes a conclusion about how

obvious and intuitive the intended

invention was - from the standpoint of

technological and structural complexity.

To automate a patent examiner, each of

these three areas of work needs to be

formalized. Despite the fact that for

expert assessments there are specialized

guidelines (prescriptive criteria for

assessments), this activity is time-

consuming, and it requires significant

intellectual labor. Moreover, its results

have a large share of subjectivity.

Automation of this activity will

significantly reduce the time and

economic costs; greatly reduce the

influence of the human factor in the

processing of patent applications.

However, to automate the activities of a

patent examiner, a three-step approach is

required - so that each of the three points

listed above can be formalized. Existing

developments in this area are mainly

based on the analysis of the text of a

patent or patent application. Therefore,

the solution is to create (develop) more

advanced methods using other

approaches to this problem, namely,

formal metrics suitable for evaluating the

invention at the initial stages of the

examination.

Short Bio. Alla G. Kravets was graduated

from the Volgograd State Polytechnic

Institute in 1993 with diploma of engineer

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in CAD systems development. She got

Diploma in Business Translation (English)

in 1997. Kravets A.G. works at VSTU since

1993. as a teacher in the Department of

Descriptive Geometry and Engineering

Graphics. In 1993-1996 - study in post-

graduate courses, which since 1994 was

combined with teaching at the CAD

Department as an assistant, and then in

1999 - 2000 as a senior lecturer. In 1993-

1997 - Head of the Training Center in the

first joint Russian-Singaporean enterprise

“Automated Banking Systems”. In the late

90s - one of the organizers of the

Computer Center at the CAD Department.

In 2000-2001 Kravets A.G. began her

activities related to innovation as an

executive director of the Volgograd

Regional Agency for the Support of Small

and Medium Enterprises. Since 2003 -

work at the CAD Department from the

leading programmer to professor.

In-depth 20+ years expertise in DBMS,

data bases and knowledge bases

development, knowledge discovery,

analytics including predictive analytics,

data mining. Key scientific interests: social

networks analysis, coordinated

management in complex systems, IT in

intellectual capital management, big data

analysis, knowledge discovery. Scientific

supervisor and/or owner of 10+ small

innovative enterprises in IT.

Conducts research on automated control

in complex systems. Academic degree -

Doctor of Technical Sciences since July 11,

2008. Member of the dissertation councils

D 212.028.08 at the Volgograd State

Technical University, D 307.001.10 at the

Astrakhan State Technical University.

Lecturing and presentations in: KH Leuven

(Belgium), Università degli Studi di

Genova (Italy), Lusófona University Of

Humanities And Technologies (Portugal),

Information Systems Management

Institute (ISMA) (Latvia), Prague High

School of Economics (Czech Republic),

University of North Texas (USA). Member

of the International Association of

Development of Information Society

(IADIS).

Kravets A.G. is the director of the Center

for Support of Innovation and

International Projects at the CAD

Department and since 2019 the head of

Project Laboratory of Cyber-Physical

Systems.

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Adriaan Brebels, PhD, KU Leuven, campus Geel, Belgium,

Adriaan.brebels@kuleuven.be

DATA DRIVEN ENERGY MANAGEMENT:

CLOUD COMPUTING AND BLOCKCHAIN

FOR LOCAL ENERGY COMMUNITIES

An important factor in climate change is

the way we produce, distribute and

consume utilities as electricity, gas and

water.

Traditionally we buy utilities them when

we need them and we expect the

distributed is able to deliver when we

wish. The billing is done by utility meters

from the distribution companies. For

years those resources where produced

centrally and distributed to the

communities, offices, factories and

homes and the cost was fairly cheap for

the consumers. The only use of the data

was for the billing and the statistics.

A first step toward using data for energy

management is taken with the ‘Trias

Energ(et)ica’. In this concept you first try

to use as less energy as possible, second

you use ‘green energy’ and third you use

the fossil energy as efficient as possible

(for instance by using heat pumps).

Around 2010 norms as ISO50001

(currently 2018) and the IPMVP

International Performance Measurement

and Verification Protocol), make it

possible to measure progress compared

to a reference year.

Recently the technology shifts to local

energy production (by solar panels, wind,

Combined Heat Power systems,…) and

storage (batteries, (hot) water, hydrogen,

ammonia ….). Additionally local data from

indoor comfort (thermal, air quality) and

the building usage contributes to a

flexible consumption of local energy

consumption. The local control strategy

cannot do without the available

information in the cloud as the weather

forecast (which has a serious impact on

the consumption) but also the varying

cost of the energy prices per kWh and the

kg of CO2 per kWh produced. Not only to

stay within local constraints as the

maximal connection (as in kW) but also to

get the lowest possible price all over a

year. The current contracts with (nearly)

fixed high and low tariffs can be replaced

by other contracts based on the real cost

of electricity that is changing all the time

(as on the one day ahead market). The

information that comes from the cloud

enhances the local strategy to avoid

peaks by using the flexibility and to use

the utilities at the lowest cost.

But one building is part of a network. So

the local units need to be able to deliver

and get information about the conditions

(availability and prices) of this utility as a

function of time. Together with buildings

‘around’ a ‘microgrid’ or a ‘local energy

community \’ can be created. Therefore

good forecast algorithms of the local

production (as solar panels) and

consumption are needed to balance

production and demand. Continuously

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the forecasts need to be evaluated by the

actual demand and the (local) flexibility

can be used in the local control strategy

to avoid unbalances.

For the Distribution System Operators

(DSOs) who are responsible for the low,

medium and high voltage networks for

regional distribution of electricity it is

important that the local production and

demand doesn’t generate any

congestion. The valorization of the

production/consumption/storage into a

community can be tracked by blockchain

technology.

Moreover the local communities can use

their local capacity to contribute into the

Frequency Containment Reserves (FCR)

which is a concern on transmission

system operator (TSO) level. Here you

have Continuous monitoring up to mHz

accuracy is required to react on seconds

level to stabilize the frequency of the

network. This way the payback of the

local investments can be improved too.

Short Bio. Adriaan Brebels has a mixed

academic and industrial background. He

got a master in physics at KU Leuven and

he got a master in electrotechnical

engineering by the Belgian state

examination committee.

After being a few years docent for

engineering students he was a researcher

and he started up business units for

Umicore. He got business degrees from

the KU Leuven and the Vlerick Business

School.

He was (co-)founder of several high-tech

companies mainly in the test- and

measurement and in material domain. In

was a technical expert and docent for

Philips and wrote books and articles

around measurement technologies and

techniques. From 2010 on he specialized

in data usage for energy management

and he got a PhD from Volgograd State

Technical University in 2013.

Partly based on this he was co-architect of

EcoSCADA (Ecology/Economy Supervisory

Control and Data Acquisition) which is

used as the basis for the new startup

i.Leco (based in Belgium and Poland).

He is part-time guest professor at KU

Leuven campus Geel and contributed to

many seminaries and lectures all over the

world.

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Dr. Bal Krishna Bal, Associate Professor & Head, Department

of Computer Science & Engineering,

Kathmandu University, Dhulikhel, Kavre

Nepal, bal@ku.edu.np

MACHINE LEARNING APPROACH TO

SOLVING NATURAL LANGUAGE

PROCESSING PROBLEMS

Texts represent a rich source of data for

processing and analysis, particularly in the

context of their abundant usage on a

daily basis in the web and other media.

With the advent of advanced

technologies like Machine Learning and

Machine Intelligence, there is a growing

trend towards applying the Machine

Learning approaches to different Natural

Language Processing (NLP) problems. In

the first part of the talk, I will introduce

Natural Language Processing as a domain

and the standard steps applied to solve

generic problems in NLP. Then in the

second part of the talk, I will discuss the

relevance and applicability of the

Machine Learning approach to solving

NLP problems focusing on classification

and clustering problems in text

processing.

Short Bio. Dr. Bal Krishna Bal is currently

Associate Professor and Head at the

Department of Computer Science &

Engineering, Kathmandu University (KU).

He joined the Department in 2009. His

primary research area is Natural

Language Processing (NLP) and he is in

the field since 2005. Dr. Bal Krishna Bal

graduated from Volgograd State

Technical University. He has worked both

in the industry and the academia

conducting various Research and

Development of NLP applications like the

Spellchecker, Grammar checker, Machine

Translation, Text-to-Speech, Optical

Character Recognition(OCR), Sentiment

Analysis etc. Besides NLP, he also has

significant industrial experiences on

Software Localization. He has been

involved with multi-national software

localization Projects like PAN Localization,

http://panl10n.net and am currently

providing consulting services to software

giants for developing localized software

and content.

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Uranchimeg Tudevdagva, Professor, PhD, Dr.h.s., Scientist and

Guest Professor of Faculty of Computer

Science of Chemnitz University of

Technology, Germany, Professor of Power

Engineering School of Mongolian

University of Science and Technology,

Mongolia,

uranchimeg.tudevdagva@informatik.tu-

chemnitz.de

TUTORING ROBOT WITH AI FOR LEARNER

CENTRED PLATFORM

Technological development extends

permanently educational resources of

students. Nowadays university students

can receive various type of information

from a plurality of information sources

beside Professors lectures. MOOCs,

eBooks, videos and social networks, for

instance, are producing a huge amount of

documents. How we can lead our

students in this technological era

correctly, how we can support them not

to lost in information ocean? Which kind

of tutoring supports students in their

learning most suitable? Some solution

can be to develop corresponding learning

platform, where the STUDENT will be in

centre. All kind of resources for study

should be available for the student round

the clock such that a kind of full time

support (24/7) becomes possible. How

we can implement such kind of tutoring?

Is this possible at all? Would be that

useful? Professors believe that fast and

cooperative feedback to students is

helpful keeping motivation to study and

finish study in time.

Short Bio. Uranchimeg Tudevdagva was

born in Ulaanbaatar, Mongolia. After high

school, in 1987 she moved for study of

informatics to Russia. She finished her

undergraduate study at the Faculty of

Automation and Computer Engineering at

Novosibirsk Institute for Electrical

Engineering. In 1992 she moved back to

home country and started to teach at

Power Engineering School of Mongolian

University of Science and Technology. In

1997 she finished her graduated study

and received a Magister degree in

Automation and system engineering.

From 1998 to 2001 she was granted a

scholarship from her home university for a

PhD study. In 2002 she moved back to

Novosibirsk State Technical University

where she finished her PhD thesis and in

2003 successful defended.

In 2004, she returned to home university,

as associate Professor at the Power

Engineering School. In 2007 she received

scholarship from Russia for study for

Doctor of Science. For family reason she

could not use this opportunity and

returned to home country where she

continued to work on her field. In 2011

she received Grant from Schlumberger

19

Foundation for Post-Doc study. With this

grant she moved to Germany, to

Chemnitz University of Technology. In

2014 she successful finished her Post-Doc

thesis and received Dr.-Ing.habil. degree

as Doctor of Science. Since 2008 she is

working as full professor in Power

Engineering School of MUST. Main

working areas are e-learning and virtual

laboratories.

In 2016 she received an invitation from

Chemnitz University of Technology to

teach as Professor of Computer

Architecture. Since 2017 she is working as

Scientist and Guest Professor at Faculty of

Computer Science of Chemnitz University

of Technology. She is main Editor of online

journal 'Embedded Selforganising Systems

(ESS)'. In 2017 she received a honorary

doctorate from the Novosibirsk State

Technical University, Russia.

She has been teaching undergraduate and

graduate courses in the thematic areas of

programming languages, databases,

artificial intelligence and theory of

compiler. Her research interests cover the

areas of artificial intelligence, decision

support systems, experts systems,

optimization, evaluation theory,

evaluation models and e-learning. Last

decade she is dealing with

multidimensional evaluation models for e-

learning and complex systems.

She leads and is included into active

running projects: Automated Power Line

Inspection (APOLI), DrIVE-MATH -

development of innovative mathematical

teaching strategies in European

engineering degree programs, Learner

Centred Learning. She has published 6

books, edited 3 books, 1 chapter in an

Elsevier book. She published over 150

papers in journals and/or in International

conferences. Prof. Uranchimeg

Tudevdagva has supervised 1 Doctoral

Thesis and around 30 Master Theses.

20

Sessions schedule 16 September

Registration Desk 8.00 – 16.00

1st Floor Hall («B» building)

Sessions Start Finish Room

Opening session. Alexandr Navrotskiy, rector of Volgograd State Technical University. Welcome speech. General Co-Chair Vladimir Lysak, academician of Russian Academy of Science, scientific supervisor of Volgograd State Technical University. Welcome speech. Evgeny Kharichkin, vice-governor of Volgograd region. Welcome speech. General Co-Chair Dmitriy Novikov, correspondence member of Russian Academy of Science, director of Institute of Control Sciences of Russian Academy of Sciences. Welcome speech. Artem Glotov, General Director of Mobile Gas Turbine Energy Stations JSC company. Welcome speech. Oleg Gorchatov, General Director of JSC VNIKTIneftekhimoborudovanie. Welcome speech.

09:00 10:30 Conference Hall («B» building)

Keynote speaker Peter Groumpos, University of Patras, Greece Artificial Intelligence: Issues, Challenges, Opportunities And Threats

10:30 11:10 Conference Hall («B» building)

Coffee-break 11:10 11:40 B -225

Keynote speaker Karamjit S Gill, AI&Society, University of Brighton, UK Designing AI Futures: A Symbiotic Vision

11:40 12:20 Conference Hall («B» building)

Keynote speaker Takako Nakatani, The Open University of Japan Learn From The Past And Create A New Process Of Requirements Elicitation

12:20 13:00 Conference Hall («B» building)

Lunch 13:15 14:15

Volgograd State Technical University overview 14:15 15:15

Conference tour 15:15 17:10

Conference dinner 17:10

Keynote speech 30 min+10 min discussion.

Presentation 15 min+5 min discussion.

Sessions schedule 17 September Keynote speaker Bal Krishna Bal, Kathmandu University, Dhulikhel, Kavre Nepal Machine Learning Approach To Solving Natural Language Processing Problems

09:00 09:40 B-209

Session 3. Computer Vision and Knowledge-Based Control. Chair: Danila Parygin

9:40 11:20 B-209

Session 4. Pro-active Modeling in Intelligent Decision Making Support. Chair: Peter Groumpos

09:40 11:20 B-208

Coffee-break 11:20 11:50 B -225

Session 4. Pro-active Modeling in Intelligent Decision Making Support. Chair: Peter Groumpos

11:50 12:50 B-208

Session 3. Computer Vision and Knowledge-Based Control. Chair: Danila Parygin

11:50 12:50 B-209

Lunch 13:00 14:00

Industrial Track. Anastasia Perdero, Internet of Energy Project Manager, SKOLKOVO Business School

14:00 14:40 B-208

Keynote speaker Alla Kravets, Volgograd State Technical University, Russia Patents Analysis Formal Metrics

14:40 15:20 B-208

Session 4. Pro-active Modeling in Intelligent Decision Making Support. Chair: Maxim Shcherbakov

15:20 16:00 B-208

Session 6. Intelligent Internet of Services and Internet of Things. Chair: Adriaan Brebels

15:20 16:00 B-209

Coffee-break 16:00 16:30 B -225

Session 4. Pro-active Modeling in Intelligent Decision Making Support. Chair: Maxim Shcherbakov

16:30 18:50 B-208

Session 6. Intelligent Internet of Services and Internet of Things. Chair: Adriaan Brebels

16:30 18:50 B-209

22

Sessions schedule 18 September Keynote speaker Adriaan Brebels, KU Leuven, campus Geel, Belgium Data Driven Energy Management: Cloud Computing And Blockchain For Local Energy Communities

09:00 09:40 B-209

Session 1. Knowledge Discovery in Patent and Open Sources for Creative Tasks. Chair: Alla Kravets

09:40 11:20 B-209

Session 7. Data Science in Social Networks Analysis and cybersecurity Chair: Natalia Sadovnikova

09:40 11:20 B-208

Coffee-break 11:20 11:50 B -225

Session 8. Creativity and Game-Based Learning. Chair: Olga Shabalina 11:50 12:50 B-209

Session 7. Data Science in Social Networks Analysis and cybersecurity Chair: Natalia Sadovnikova

11:50 13:00 B-208

Lunch 13:00 14:00

Industrial Track. Sergey Nikolaev, The Center for Design, Manufacturing, and Materials (CDMM), Skoltech

14:00 14:40 B-208

Session 2. Open Science Semantic Technologies. Chair: Bal Krishna Bal 14:00 15:40 B-209

VSTU Software& Robotics exhibition 14:00 16:00 2nd Floor

(«B» building)

Coffee-break 15:40 16:00 B -225

25 Anniversary of Faculty of Informatics and Computing Celebration 16:00

Sessions schedule 19 September Keynote speaker Uranchimeg Tudevdagva, Chemnitz University of Technology, Germany, Mongolian University of Science and Technology, Mongolia Tutoring Robot With AI For Learner Centred Platform

09:00 09:40 B-208

Session 5. Design Creativity in CASE/CAI/CAD/PDM. Chair: Alexey Kizim

09:40 11:20 B-209

Session 9. Intelligent Assistive Technologies: Software Design and Application. Chair: Marina Kultsova

09:40 11:20 B-208

Coffee-break 11:20 11:50 B -225

Session 5. Design Creativity in CASE/CAI/CAD/PDM. Chair: Alexey Kizim

11:50 12:30 B-209

Session 9. Intelligent Assistive Technologies: Software Design and Application. Chair: Marina Kultsova

11:50 12:30 B-208

Lunch 13:00 14:00

Poster session 14:00 15:30 B-209

Coffee-break 15:30 16:00 B -225

Final session. Best papers award. 16:00 17:30 B-208

23

# Topic Date Start Finish Room

Track1. Artificial intelligence & Deep Learning Technologies

1. Knowledge discovery in patent and open sources for creative tasks 18-Sep-

19 9:40 11:20 B-209

1.1.

Extraction and processing of knowledge from the patent array in natural

language to support the synthesis of new technical solutions based on

SAO structures

Dmitriy Korobkin, Sergey Fomenkov, Marina Fomenkova, Alla Kravets

1.2.

Architectural Approach to Ontological Maintenance of Solving the

Project Tasks in Conceptual Designing a Software Intensive System

Petr Sosnin, Anna Kulikova, Segei Shumilov

1.3.

The Study of Neural Networks Effective Architectures for Patents

Images Processing

Nataliya Salnikova, Alla Kravets, Sergey Kolesnikov, Mikhail Lempert,

Olga Poplavskaya

1.4.

The software and information complex which uses structured physical

knowledge for technical systems design

Ilya Vayngolts, Dmitriy Korobkin, Sergey Fomenkov, Sergey Kolesnikov

1.5.

Construction of a matrix "Physical Effects - Technical functions" on the

base of patent corpus analysis

Dmitry Shabanov, Dmitriy Korobkin, Sergey Fomenkov, Alexander

Golovanchikov

2. Open Science Semantic Technologies 18-Sep-

19 14:00 15:40 B-209

2.1. Named Entity Recognition (NER) for Nepali

Gopal Maharjan, Bal Krishna Bal, Santosh Regmi

2.2. Semantic Zooming Approach to Semantic Link Network Visualization

Dmitry Litovkin, Anton Anikin, Marina Kultsova

2.3.

Modified knowledge inference method based on fuzzy ontology and base

of cases

Vadim Moshkin, Nadezhda Yarushkina

2.4.

Cognitive Developing of Semiotic Data in Computer-Based

Communication (Signs, Concepts, Discourse)

Andrew Olyanitch, Zaineta Khachmafova, Susanna Makerova, Tatiana

Ostrovskaya, Marjet Akhidzhakova

2.5.

Methods of software self-adaptation based on monitoring the information

environment

Alexander Bozhday, Yulia Evseeva, Alexey Gudkov, Alexander

Bershadsky

3. Computer Vision and Knowledge-Based Control 17-Sep-

19 9:40 12:50 B-209

3.1.

The fuzzy rule base automatic optimization method of intelligent

controllers for technical objects using fuzzy clustering

Vladimir Ignatyev, Viktor Soloviev, Denis Beloglazov, Victor Kureychik,

Andrey Kovalev, Alexandra Ignatyeva

3.2.

Modeling of environment for technical vision: automatic detection of

dynamic objects in the data stream Korney Tertychny, Sergey Karpov,

Dmitry Krivolapov, Alexander Khoperskov

3.3.

The Introduction of Multi-Level Parallelism Solvers in Multibody

Dynamics

Andrey Andreev, Vitaly Egunov, Evgenija Movchan, Nikita Cherednikov,

Egor Kharkov, Natalia Kohtashvili

3.4.

Traffic Light Detection and Recognition using Image Processing and

Convolution Neural Networks

Peter Groumpos, George Symeonidis, Evangelos Dermatas

3.5.

Methods of increasing service minibots functional capabilities

Alexander Gorobtsov, Pavel Tarasov, Andrey Skorikov, Alexey Markov,

Andrey Andreev

3.6. Development the Online Operating System of Urban Infrastructure Data

24

Danila Parygin, Dmitriy Kozlov, Natalia Sadovnikova, Vladislav

Kvetkin, Ivan Soplyakov, Vitaliy Malikov

3.7.

Spherical Panoramic Photo Shooting and Virtual Reality Demonstration

of a Crime Scene

Vladimir Bulgakov, Igor Trushchenkov, Elena Bulgakova

3.8.

System for automatic adjustment of intelligent controller parameters

Vladimir Ignatyev, Viktor Soloviev, Denis Beloglazov, Victor Kureychik,

Alexandra Ignatyeva, Anastasiia Vorotova

Track 2. Cyber-Physical Systems & Big Data-driven world

4. Pro-active Modeling in Intelligent Decision Making Support 17-Sep-

19 9:40 17:10 B-208

4.1.

A New Approach to Reduce Time Consumption of Data Quality

Assessment in the Field of Energy Consumption

Alexander Sokolov, Maxim Shcherbakov, Anton Tyukov, Timur Janovsky

4.2.

Decision Support System for the Socio-Economic Development of the

Northern Part of the Volga-Akhtuba Floodplain (Russia)

Inessa Isaeva, Alexander Voronin, Alexander Khoperskov, Konstantin

Dubinko, Anna Klikunova

4.3.

Modeling And Optimization Of Proactive Management Of The

Production Pollutions In The Conditions Of In-Formation Asymmetry

Aleksey Rogachev, Maksim Lukashin

4.4.

Making a choice of resulting estimates of characteristics with multiple

options of their evaluation

Olga Zholobova, Georgi Popov, Irina Kvyatkovskaya, Anastasia

Kvyatkovskaya, Elena Chertina

4.5.

Adaptive Analysis of Merchant Big Data

Oleg Surnin, Anton Ivaschenko, Pavel Sitnikov, Anastasia Stolbova,

Mariia Sigova

4.6.

Big Data in the Stochastic Model of the Passengers Flow at the

Megalopolis Transport System Stops

Elena Krushel, Ilya Stepanchenko, Alexander Panfilov, Elena Berisheva

4.7.

Building a company’s maturity management trajectory based on the

methods of optimal control of Letov-Kalman

Mikhail Dorrer

4.8.

Networkalization of Network-Unlike Entities: how to preserve encoded

information

Olga Berestneva, Olga Marukhina, Alessandra Rossodivita, Alexei

Tikhomirov, Andrey Trufanov

4.9. HR Decision-making Support based on Natural Language Processing

Anton Ivaschenko, Michael Milutkin

4.10.

The algorithm for the classification of methods for processing

multidimensional heterogeneous data in application to designing of oil

fields development

Stepan Nebaba, Dmitry Zavyalov, Alena Zakharova

4.11.

Detection of the Patterns in the Daily Route Choices of the Urban Social

Transport System Clients Based on the Decoupling of Passengers’

Preferences between the Levels of Uncertainty

Elena Krushel, Ilya Stepanchenko, Alexander Panfilov, Tatyana Lyutaya

4.12.

Analysis of a short-term time series of crop sales based on machine

learning methods

Mohammed Al-Gunaid, Vladislav Trubitsin, Aleksandr Shumkin, Maxim

Shcherbakov, Kirill Dereguzov

4.13. A Multifactor Small Business Management Model

Andrey Vazhdaev, Artur Mitsel

4.14.

Tourism Cluster Enterprises Departments’ Resource Management Based

on Mobile Technologies

Aleksandr Morozov, Konstantin Zadiran, Gais Al-Merri, Ekaterina

Trishkina, Alla Kravets

4.15.

The information and analytical platform for the big data mining about

innovation in the region

Leyla Gamidullaeva, Alexey Finogeev, Serge Vasin, Mikhail Deev,

25

Anton Finogeev

5. Design Creativity in CASE/CAI/CAD/PDM 19-Sep-

19 9:40 12:30 B-209

5.1. Meta-Design of Processes Based on Visualization Tools

Vekhter Evgeniya, Aleksey Shklyar, Alena Zakharova, Anton Krysko

5.2.

Exploring design options of near-zero energy buildings using advanced

fuzzy cognitive maps

Theodor Panagiotakopoulos, Nikolaos Zafeirakis, Iliana-Vasiliki

Tsoulea, Peter Groumpos

5.3. The evaluation method of the design department’s information asset

Svetlana Kozunova, Natalia Solovieva, Alla Kravets

5.4. Technology Model to Support the Initiation of Innovation Artefacts

Maria-Iulianna Dascalu, Elisabeth Lazarou, Victor-Florin Constantin

5.5. Solving the Inverse Kinematics of a robotic arm using autoencoders

Konstantinos Polyzos, Peter Groumpos, Evangelos Dermatas

5.6.

Structural and Parametrical Model of the Physical Functional Principle of

the Microelectronic Capacitive Pressure Sensor

Mikhail Shikulskiy, Olga Shikulskaya, Irina Petrova, Gennady Popov,

Issa Bogatyrev, Victor Samsonov, Alla Kachalova

5.7.

Theoretical bases of the application of various-color graphs in the

solution of intellectual chemical tasks

Ilya Germashev, Evgeniya Derbisher, Vyacheslav Derbisher

6. Intelligent Internet of Services and Internet of Things 17-Sep-

19 14:00 17:30 B-209

6.1. Forecasting and optimization Internet of things system

Yakov Lvovich, Igor Lvovich, Andrey Preobrazhenskiy, Oleg Choporov

6.2.

Method of acquiring the video conference using the skill in investigative

actions

Evgeniy Kravets, Vladimir Shinkaruk, Svetlana Gladkova, Nikolai

Bukharov, Vladimir Ovchinnikov

6.3.

Data-driven framework for predictive maintenance in Industry 4.0

concept

Cuong Sai, Maxim Shcherbakov, Tran Van Phu

6.4.

Development the Methodology of Urban Area Transport Coherence

Assessment

Danila Parygin, Alexander Aleshkevich, Alexey Golubev, Natalia

Sadovnikova, Maxim Shcherbakov, Oksana Savina

6.5. Modeling a Microgrid using Fuzzy Cognitive Maps

Vassiliki Mpelogianni, George Kosmas, Peter Groumpos

6.6.

Cryptographic protection of data transmission channel

Arina Nikishova, Vitenburg Ekaterina, Mikhail Umnitsyn, Tatiana

Omelchenko

6.7.

Application of fuzzy neural networks for short-term prediction of

transport flow parameters

Dmitry Skorobogatchenko, Sergey Viselskiy

6.8.

Effective Quaternion and Octonion Cryptosystems and their FPGA

Implementation

Andrey Andreev, Vitaly Egunov, Evgeny Dukhnich, Mikhail Chalyshev,

Kristina Kuznetsova

6.9.

Smart contracts for multi-agent interaction of regional innovation

subjects

Leyla Gamidul laeva, Alexey Finogeev, Serge Vasin, Anton Finogeev,

Sergiy Shevchenko

Track 3. Intelligent Technologies in Social Engineering

7. Data science in social networks analysis 18-Sep-

19 9:40 12:50 B-208

7.1.

Error classification and Human performance level analysis of "Hiyari-

Hatto" incidents caused by maintenance engineers

Nobuhiko Mizusawa, Takako Nakatani

7.2. Assessing the response timeliness to threats as an important element of

cybersecurity: theoretical foundations and research model

26

Elena Smirnova, Sergey Skryl’, Mikhail Sychev, Tatyana

Meshcheryakova, Artem Sychev, Anna Ushakova, Elvira Abacharaeva

7.3. Intelligent Information Technologies in Social Safety

Vladimir Tsyganov

7.4.

Optimization of technical information protection system’s composition

Arina Nikishova, Yuriy Umnitsyn, Mikhail Umnitsyn, Tatiana

Omelchenko

7.5. Mass media as a data source on social preferences

Yaroslav Milchuk, Alla Kravets, Natalia Salnikova, Vladimir Shinkaruk

7.6.

Program modeling in the investigation of crimes against cybersecurity in

Russia

Natalia Solovieva, Evgeny Likholetov, Yuriy Naumov, Daniyar

Kairgaliev

7.7.

How Popular or Unpopular Have your Leaders been - Popularity

Tracking and Trend Analysis of Socio-Political Figures

Bal Krishna Bal, Santosh Regmi, Kamal Kafle

7.8.

Prevention of crimes made with the use of the Internet network as one of

the directions to ensure the cybersecurity of Russia

Ilyas Kasaev, Alexander Likholetov, Yuri Bokov, Tatiana Dugina,

Alexander Nemchenko

7.9.

Computer Tools Increasing the Quality of the Evidence Information

Received During the Investigation of Road-Transport Crimes

Taulan Boziev, Sergei Kolotushkin, Yuri Bokov, Dmitry Vasilev, Mikhail

Pavlik

8. Creativity and Game-Based Learning 18-Sep-

19 11:50 12:50 B-209

8.1.

Preparation of PhD students for engineering disciplines’ teaching

Elena Smirnova, Elisabeth Lazarou, Natalia Vatolkina, Maria-Iulianna

Dascalu

8.2.

Use Of Playing And Training Software Complexes In The Lawyers

Preparation

Elena Bulgakova, Vladimir Bulgakov, Igor Trushchenkov

8.3.

Method of preliminary computer evaluation of professional readiness of

the vehicle driver

Maksim Dyatlov, Olga Shabalina, Alexey Todorev, Rodion Kudrin,

Nikolaj Sentyabryov

9. Intelligent Assistive Technologies: Software Design and Application 19-Sep-

19 9:40 12:30 B-208

9.1.

Medical diagnostic expert system for training and decision support of

early-stage diagnoses

Yumchmaa Ayush, Uranchimeg Tudevdagva, Sharaf Asrorovich Boboev

9.2.

Using eye-tracking and emotional recognition as additional sources of

information for testing people with intellectual disabilities

Dmitriy Skvaznikov, Olga Shabalina, Jan Dekelver

9.3.

Ontology-Based Personalization of Mobile Interfaces for People with

Special Needs

Marina Kultsova, Anastasiya Potseluico, Alexander Dvoryankin

9.4. Proactive Control System of multicomponent General Anesthesia

Vitaliy Sokolskiy, Irina Petrova, M Sokolskiy, I Kitiashvili

9.5.

Verification and Validation of Computer Models for Diagnosing Breast

Cancer Based on Machine Learning for Medical Data Analysis

Vladislav Levshinskii, Maxim Polyakov, Alexander Losev, Alexander

Khoperskov

9.6.

Multi-Level Model for Structuring Heterogeneous Biomedical Data in

the Tasks of Socially Significant Diseases Risk Evaluation

Alena Zakharova, Dmitry Lagerev, Aleksandr Podvesovskii

9.7.

Adaptable Mobile Software For Supporting Daily Activities Of People

With Intellectual Disabilities

Olga Shabalina, Vladislav Guriev, Angelina Voronina, David Moffat,

Stanislav Kosyakov

27

Publishing This conference continues previous, the proceedings were published in Springer and indexed in Scopus

and Web of science Core collection (ISI). We are pleased to know that our proceedings book among the

25% most downloaded sources from Springer. You can find the book content using the links below.

1. Creativity in Intelligent Technologies and Data Science Second Conference, CIT&DS 2019,

Volgograd, Russia, September 16-19, 2019 (The current conference)

2. Creativity in Intelligent Technologies and Data Science Second Conference, CIT&DS 2017,

Volgograd, Russia, September 12-14, 2017, http://www.springer.com/cn/book/9783319655505

3. Creativity in Intelligent Technologies and Data Science First Conference, CIT&DS 2015, Volgograd,

Russia, September 15-17, 2015. http://www.springer.com/us/book/9783319237657

4. Knowledge-Based Software Engineering 11th Joint Conference, JCKBSE 2014, Volgograd, Russia,

September 17-20, 2014. Proceedings https://www.springer.com/us/book/9783319118536

Also we published the monography Big Data-driven World: Legislation Issues and Control Technologies

https://www.springer.com/gp/book/9783030013578

28

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