LNCS 10716

Wil M. P. van der Aalst et al. (Eds.)

Analysis of Images,
Social Networks and Texts
6th International Conference, AIST 2017
Moscow, Russia, July 27–29, 2017
Revised Selected Papers

123


Lecture Notes in Computer Science
Commenced Publication in 1973
Founding and Former Series Editors:
Gerhard Goos, Juris Hartmanis, and Jan van Leeuwen

Editorial Board
David Hutchison
Lancaster University, Lancaster, UK
Takeo Kanade
Carnegie Mellon University, Pittsburgh, PA, USA
Josef Kittler
University of Surrey, Guildford, UK
Jon M. Kleinberg
Cornell University, Ithaca, NY, USA
Friedemann Mattern
ETH Zurich, Zurich, Switzerland
John C. Mitchell
Stanford University, Stanford, CA, USA

Moni Naor
Weizmann Institute of Science, Rehovot, Israel
C. Pandu Rangan
Indian Institute of Technology, Madras, India
Bernhard Steffen
TU Dortmund University, Dortmund, Germany
Demetri Terzopoulos
University of California, Los Angeles, CA, USA
Doug Tygar
University of California, Berkeley, CA, USA
Gerhard Weikum
Max Planck Institute for Informatics, Saarbrücken, Germany

10716


More information about this series at />

Wil M. P. van der Aalst Dmitry I. Ignatov
Michael Khachay Sergei O. Kuznetsov
Victor Lempitsky Irina A. Lomazova
Natalia Loukachevitch Amedeo Napoli
Alexander Panchenko Panos M. Pardalos
Andrey V. Savchenko Stanley Wasserman (Eds.)
•

•

•


•

•

•

Analysis of Images,
Social Networks and Texts
6th International Conference, AIST 2017
Moscow, Russia, July 27–29, 2017
Revised Selected Papers

123


Editors
Wil M. P. van der Aalst
Eindhoven University of Technology
Eindhoven, The Netherlands
Dmitry I. Ignatov
National Research University Higher School
of Economics
Moscow, Russia
Michael Khachay
Krasovsky Institute of Mathematics
and Mechanics
Ekaterinburg, Russia
Sergei O. Kuznetsov
National Research University Higher School
of Economics

Moscow, Russia
Victor Lempitsky
Skolkovo Institute of Science
and Technology
Moscow, Russia

Natalia Loukachevitch
Moscow State University
Moscow, Russia
Amedeo Napoli
LORIA, Campus Scientifique
Vandœuvre lès Nancy, France
Alexander Panchenko
University of Hamburg
Hamburg, Germany
Panos M. Pardalos
University of Florida
Gainesville, FL, USA
Andrey V. Savchenko
National Research University Higher School
of Economics
Nizhny Novgorod, Russia
Stanley Wasserman
Indiana University
Bloomington, IN, USA

Irina A. Lomazova
National Research University Higher School
of Economics
Moscow, Russia


ISSN 0302-9743
ISSN 1611-3349 (electronic)
Lecture Notes in Computer Science
ISBN 978-3-319-73012-7
ISBN 978-3-319-73013-4 (eBook)
/>Library of Congress Control Number: 2017961808
LNCS Sublibrary: SL3 – Information Systems and Applications, incl. Internet/Web, and HCI
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Preface


This volume contains the refereed proceedings of the 6th International Conference on
Analysis of Images, Social Networks, and Texts (AIST 2017)1. The previous conferences during 2012–2016 attracted a significant number of students, researchers, academics, and engineers working on interdisciplinary data analysis of images, texts, and
social networks.
The broad scope of AIST made it an event where researchers from different
domains, such as image and text processing, exploiting various data analysis techniques, can meet and exchange ideas. We strongly believe that this may lead to cross
fertilisation of ideas between researchers relying on modern data analysis machinery.
Therefore, AIST brought together all kinds of applications of data mining and machine
learning techniques. The conference allowed specialists from different fields to meet
each other, present their work, and discuss both theoretical and practical aspects of their
data analysis problems. Another important aim of the conference was to stimulate
scientists and people from industry to benefit from the knowledge exchange and
identify possible grounds for fruitful collaboration.
The conference was held during July 27–29, 2017. The conference was organised in
Moscow, the capital of Russia, on the campus of Moscow Polytechnic University2.
This year, the key topics of AIST were grouped into six tracks:
1. General topics of data analysis chaired by Sergei Kuznetsov (Higher School of
Economics, Russia) and Amedeo Napoli (LORIA, France)
2. Natural language processing chaired by Natalia Loukachevitch (Lomonosov
Moscow State University, Russia) and Alexander Panchenko (University of
Hamburg, Germany)
3. Social network analysis chaired by Stanley Wasserman (Indiana University, USA)
4. Analysis of images and video chaired by Victor Lempitsky (Skolkovo Institute of
Science and Technology, Russia) and Andrey Savchenko (Higher School of Economics, Russia)
5. Optimisation problems on graphs and network structures chaired by Panos Pardalos
(University of Florida, USA) and Michael Khachay (IMM UB RAS and Ural
Federal University, Russia)
6. Analysis of dynamic behaviour through event data chaired by Wil van der Aalst
(Eindhoven University of Technology, The Netherlands) and Irina Lomazova
(Higher School of Economics, Russia)
One of the novelties this year was the introduction of a new specialised track on

process mining (Track 6).

1
2

/> />

VI

Preface

The Programme Committee and the reviewers of the conference included 167
well-known experts in data mining and machine learning, natural language processing,
image processing, social network analysis, and related areas from leading institutions of
30 countries including Argentina, Australia, Austria, Belgium, Brazil, Canada, China,
Croatia, Czech Republic, Denmark, Egypt, Finland, France, Germany, Greece,
Hungary, India, Ireland, Japan, Lithuania, Norway, Portugal, Qatar, Romania, Russia,
Spain, Ukraine, The Netherlands, UK, and USA. This year we received 127 submissions mostly from Russia but also from Algeria, Australia, Brazil, China, Finland,
Germany, India, Iran, Kazakhstan, Latvia, Mexico, The Netherlands, Norway, Turkey,
USA, and Vietnam.
Out of 127 submissions only 37 papers were accepted as regular oral papers. Thus,
the acceptance rate of this volume was around 29%. In order to encourage young
practitioners and researchers, we included 34 papers to the supplementary proceedings
after their poster presentation at the conference. Each submission was reviewed by at
least three reviewers, experts in their fields, in order to supply detailed and helpful
comments.
The conference featured several invited talks and an industry session dedicated to
current trends and challenges.
The keynote talk was presented by Andrzej Cichocki on “Bridge Between Tensor
Networks and Deep Neural Networks: From Fundamentals to Real Applications.”

The invited talks were:
– Stanley Wasserman (Indiana University, USA), “Sensitivity Analysis of p* and
SAOM: The Effects of Missing Data on Parameter Estimates”
– Dirk Fahland (Eindhoven University of Technology, The Netherlands), “Process
Mining: Past, Present, and Open Challenges”
Sergey Nikolenko from the St. Petersburg Department of the Steklov Mathematical
Institute presented a tutorial on “Deep Learning for Natural Language Processing.”
The business speakers also covered a wide variety of topics3. We list those invited
talks below:
– Iosif Itkin (Exactpro Systems), Industrial day opening. Keynote on “Exactpro and
Data Analysis in London Stock Exchange: Capital Markets, Post Trade and
Information Services”
– Dmitry Bugaychenko (OK.ru), “Odnoklassniki Data Science Research Initiative”
– Olga Megorskaya (Yandex), “Yandex Toloka: Crowdsource Your Data”
– Artur Kuzin (Avito), “AvitoNet: Computer Vision Service in Avito”
– Alexander Zhebrak, Arthur Kadurin, Daniil Polykovskiy (Insilico Medicine),
“Artificial Intelligence for Drug Discovery”
We would like to thank the authors for submitting their papers and the members
of the Programme Committee for their efforts in providing exhaustive reviews.

3

A detailed program of AIST 2017 Business Day can be found on a separate website: .


Preface

VII

We would also like to express our special gratitude to all the invited speakers and

industry representatives.
We deeply thank all the partners and sponsors. Our golden sponsors is Exactpro.
Exactpro, a fully owned subsidiary of the London Stock Exchange Group, specialises
in quality assurance for exchanges, investment banks, brokers, and other financial
sector organisations. Our special thanks goes to Springer for their help, starting from
the first conference call to the final version of the proceedings. Last but not least, we are
grateful to all the organisers, especially to Marina Danshina, and the volunteers, whose
endless energy saved us at the most critical stages of the conference preparation.
Here, we would like to mention the Russian word “aist” is more than just a simple
abbreviation (in Cyrillic) — it means a “stork.” Since it is a wonderful free bird, a
symbol of happiness and peace, this stork gave us the inspiration to organise the AIST
conference. So we believe that this young and rapidly growing conference will likewise
be bringing inspiration to data scientists around the world!
October 2017

Wil van der Aalst
Dmitry Ignatov
Michael Khachay
Sergei Kuznetsov
Victor Lempitsky
Irina Lomazova
Natalia Loukachevitch
Amedeo Napoli
Alexander Panchenko
Panos Pardalos
Andrey Savchenko
Stanley Wasserman


Organisation


Programme Committee Chairs
Wil van der Aalst
Michael Khachay

Sergei Kuznetsov
Amedeo Napoli
Victor Lempitsky
Irina Lomazova
Natalia Loukachevitch
Alexander Panchenko
Panos Pardalos
Andrey Savchenko
Stanley Wasserman

Eindhoven University of Technology, The Netherlands
Krasovsky Institute of Mathematics and Mechanics
of RAS, Russia and Ural Federal University,
Ekaterinburg, Russia
National Research University Higher School
of Economics, Moscow, Russia
LORIA CNRS, University of Lorraine, and Inria,
Nancy, France
Skolkovo Institute of Science and Technology, Russia
National Research University Higher School
of Economics, Moscow, Russia
Computing Centre of Lomonosov Moscow State
University, Russia
University of Hamburg, Germany
and Université catholique de Louvain, Belgium

University of Florida, USA
National Research University Higher School
of Economics, Nizhny Novgorod, Russia
Indiana University, USA

Proceedings Chair
Dmitry Ignatov

National Research University Higher School
of Economics, Moscow, Russia

Business Day Chair
Rostislav Yavorskiy

National Research University Higher School
of Economics, Moscow, Russia

Steering Committee
Dmitry Ignatov
Michael Khachay

National Research University Higher School
of Economics, Moscow, Russia
Krasovsky Institute of Mathematics and Mechanics
of RAS, Russia and Ural Federal University,
Ekaterinburg, Russia


X


Organisation

Alexander Panchenko
Rostislav Yavorskiy

University of Hamburg, Germany
National Research University Higher School
of Economics, Russia

Programme Committee
Mehwish Alam
Gabriela Arevalo
Artem Babenko
Jaume Baixeries
Artem Baklanov
Sergey Bartunov
Timo Baumann
Darina Benikova
Malay Bhattacharyya
Chris Biemann
Elena Bolshakova

Anastasia
Bonch-Osmolovskaya
Aurélien Bossard
Jean-Leon Bouraoui
Joos Buijs
Andrea Burattin
Evgeny Burnaev
Aleksey Buzmakov


Ignacio Cassol
Artem Chernodub
Vladimir Chernov
Ekaterina Chernyak
Marina Chicheva
Bonaventura Coppola
Hernani Costa
Massimiliano de Leoni
Boris Dobrov

Université Paris 13, France
Universidad Austral, Argentina
Yandex, Russia
Universitat Politècnica de Catalunya, Spain
International Institute for Applied Systems Analysis,
Austria
National Research University Higher School
of Economics, Moscow, Russia and DeepMind, UK
Universität Hamburg, Germany
University of Duisburg-Essen, Germany
Indian Institute of Engineering Science and
Technology, India
University of Hamburg, Germany
Moscow State Lomonosov University, Russia and
National Research University Higher School
of Economics, Russia
National Research University Higher School
of Economics, Russia
Université Paris 8, France

CENTAL (Université Catholique de Louvain),
Belgium
Eindhoven University of Technology, The Netherlands
Technical University of Denmark, Denmark
Institute for Information Transmission Problems
of RAS, Russia
Inria, LORIA (CNRS, Université de Lorraine), Nancy,
France and National Research University Higher
School of Economics, Perm, Russia
Universidad Austral, Argentina
Institute of Mathematical Machines and Systems
of NASU, Ukraine
Institute for Image Processing of RAS, Russia
National Research University Higher School
of Economics, Moscow, Russia
Samara State Aerospace University, Russia
Technische Universität Darmstadt, Germany
University of Malaga, Spain
Eindhoven University of Technology, The Netherlands
Lomonosov Moscow State University, Russia


Organisation

Sofia Dokuka
Florent Domenach
Alexey Drutsa
Mirela-Stefania Duma
Richard Eckart de Castilho
Judith Eckle-Kohler

Maria Eskevich
Dirk Fahland
Stefano Faralli
Victor Fedoseev
Michael Figurnov
Elena Filatova
Kerstin Fischer
Fedor Fomin
Thomas Francois
Oleksandr Frei
Edward K. Gimadi
Ivan Gostev
Natalia Grabar
Dmitry Granovsky
Alexey Gruzdev
Ivan Habernal
Mena Habib
Marianne Huchard
Dmitry Ignatov
Dmitry Ilvovsky
Vladimir Ivanov
Pei Jun
Anna Kalenkova
Nikolay Karpov
Egor Kashkin
Mehdi Kaytoue
Alexander Kelmanov
Oleg Khamisov
Andrey Kibzun
Edward Klyshinsky

Yury Kochetov

XI

National Research University Higher School
of Economics, Moscow, Russia
Akita International University, Japan
Lomonosov Moscow State University and Yandex,
Russia
University of Hamburg, Germany
Technische Universität Darmstadt, Germany
UKP Lab, Technische Universität Darmstadt, Germany
Radboud University Nijmegen, The Netherlands
Eindhoven University of Technology, The Netherlands
University of Mannheim, Germany
Samara National Research University, Russia
Skolkovo Institute of Science and Technology, Russia
City University of New York, USA
University of Southern Denmark, Denmark
University of Bergen, Norway
Université catholique de Louvain, Belgium
Universitetet i Oslo, Norway
Sobolev Institute of Mathematics of RAS, Russia
National Research University Higher School
of Economics, Moscow, Russia
Université Lille 3 and CNRS, France
Yandex, Russia
Intel, Russia
Technische Universität Darmstadt, Germany
Maastricht University, The Netherlands

Université Montpellier 2 and CNRS, France
National Research University Higher School
of Economics, Moscow, Russia
National Research University Higher School
of Economics, Moscow, Russia
Innopolis University, Russia
Hefei University of Technology, China
National Research University Higher School
of Economics, Moscow, Russia
National Research University Higher School
of Economics, Nizhniy Novgorod, Russia
V. V. Vinogradov Russian Language Institute of RAS,
Russia
LIRIS - INSA de Lyon, France
Sobolev Institute of Mathematics of RAS, Russia
Melentiev Institute of Energy Systems of RAS, Russia
Moscow Aviation Institute, Russia
HSE Moscow Institute of Electronics and Mathematics,
Russia
Sobolev Institute of Mathematics of RAS, Russia


XII

Organisation

Ekaterina Kochmar
Sergei Koltcov
Olessia Koltsova
Jan Konecny

Daniil Kononenko
Natalia Konstantinova
Andrey Kopylov
Mikhail Korobov
Anton Korshunov
Evgeny Kotelnikov
Ilias Kotsireas
Olga Krasotkina
Tomas Krilavicius
Victor Kulikov
Valentina Kuskova
Andrey Kutuzov
Andrey Kuzmin
Andrey Kuznetsov
Sergei Kuznetsov
Alexander Lazarev
Florence Le Ber
Vadim Lebedev
Victor Lempitsky
Alexander Lepskiy
Benjamin Lind
Irina Lomazova
Natalia Loukachevitch
Olga Lyashevskaya
Yury Malkov
Luis Marujo
Sérgio Matos
Yelena Mejova
Nizar Messai
Tristan Miller

Olga Mitrofanova
Evgeny Myasnikov
Sergey Nikolenko

University of Cambridge, UK
National Research University Higher School
of Economics, St. Petersburg, Russia
National Research University Higher School
of Economics, St. Petersburg, Russia
Palacky University, Czech Republic
Skolkovo Institute of Science and Technology, Russia
University of Wolverhampton, UK
Tula State University, Russia
ScrapingHub Inc., Ireland
Institute for System Programming of RAS, Russia
Vyatka State University, Russia
Wilfrid Laurier University, Canada
Lomonosov Moscow State University, Russia
Vytautas Magnus University, Lithuania
Institute of Automation and Electrometry of RAS,
Russia
National Research University Higher School
of Economics, Moscow, Russia
Universitetet i Oslo, Norway
Skolkovo Institute of Science and Technology, Russia
Samara State Aerospace University, Russia
National Research University Higher School
of Economics, Moscow, Russia
Institute of Control Sciences of RAS, Russia
Université de Strasbourg, France

Skolkovo Institute of Science and Technology, Russia
Skolkovo Institute of Science and Technology, Russia
National Research University Higher School
of Economics, Moscow, Russia
Anglo-American School of St. Petersburg, Russia
National Research University Higher School
of Economics, Moscow, Russia
Lomonosov Moscow State University, Russia
National Research University Higher School
of Economics, Moscow, Russia
Institute of Applied Physics of RAS, Russia
Carnegie Mellon University, USA, and Instituto
Superior Técnico, Portugal
Universidade de Aveiro, Portugal
Qatar Computing Research Institute, Qatar
Universitộ Franỗois Rabelais Tours, France
Technische Universität Darmstadt, Germany
St. Petersburg State University, Russia
Samara National Research University, Russia
Steklov Mathematical Institute, St. Petersburg, Russia


Organisation

Vassilina Nikoulina
Damien Nouvel
Dimitri Nowicki
Panos Pardalos
Georgios Petasis
Stefan Pickl

Lidia Pivovarova
Vladimir Pleshko
Hernan Ponce-De-Leon
Alexander Porshnev
Alexey Potapov
Surya Prasath
Uta Priss
Oleg Prokopyev
Artem Pyatkin
Carlos Ramisch
Alexandr Rassadin

Artem Revenko
Evgeniy Riabenko
Martin Riedl
Alexey Romanov
Andrey Ronzhin
Alexandra Roshchina
Eugen Ruppert
Christian Sacarea
Mohammed Abdel-Mgeed
M. Salem
Sheikh Muhammad Sarwar
Andrey Savchenko
Friedhelm Schwenker
Alexander Semenov
Oleg Seredin
Andrey Shcherbakov
Oleg Slavin
Jan Snajder


XIII

Xerox Research Center Europe, France
Université Sorbonne, France
Institute of Mathematical Machines and Systems
of NASU, Ukraine
University of Florida, USA
National Centre for Scientific Research Demokritos,
Greece
Universität der Bundeswehr München, Germany
University of Helsinki, Finland
RCO, Russia
fortiss GmbH, Germany
National Research University Higher School
of Economics, Nizhniy Novgorod, Russia
AIDEUS, Russia
University of Missouri-Columbia, USA
Ostfalia Universiy of Applied Sciences, Germany
University of Pittsburgh, USA
Novosibirsk State University and Sobolev Institute
of Mathematics, Russia
Aix Marseille University, France
KPMG, Russia and National Research University
Higher School of Economics, Nizhniy Novgorod,
Russia
Semantic Web Company GmbH, Austria
National Research University Higher School
of Economics, Moscow, Russia
University of Hamburg, Germany

University of Massachusetts Lowell, USA
St. Petersburg Institute for Informatics and Automation
of Russian Academy of Sciences, Russia
Institute of Technology Tallaght, Ireland
Technische Universität Darmstadt, Germany
Babes-Bolyai University, Hungary
Ain Shams University, Cairo
University of Massachusetts Amherst, USA
National Research University Higher School
of Economics, Nizhniy Novgorod, Russia
Ulm University, Germany
National Research University Higher School
of Economics, Moscow, Russia
Tula State University, Russia
University of Melbourne, Australia
Institute for Systems Analysis of Russian Academy
of Sciences
University of Zagreb, Croatia


XIV

Organisation

Henry Soldano
Tobias Staron
Dmitry Stepanov
Vadim Strijov
Maria Sukhareva
Diana Sungatullina

Laszlo Szathmary
Irina Temnikova
Diana Troanca
Christos Tryfonopoulos
Denis Turdakov
Dmitry Ulyanov
Dmitry Ustalov

Evgeniya Ustinova
Alexander Vakhitov
Wil van der Aalst
Natalia Vassilieva
Dmitry Vetrov

Renato Vimieiro
Ekaterina Vylomova
Roman Yangarber
Rostislav Yavorsky
Marcos Zampieri
Nikolai Zolotykh
Olga Zvereva

Université Paris 13, France
Universität Hamburg, Germany
Program System Institute of RAS, Russia
Computing Center of RAS, Russia
Goethe University Frankfurt, Germany
Skolkovo Institute of Science and Technology, Russia
University of Debrecen, Hungary
Qatar Computing Research Institute, Qatar

Babes-Bolyai University, Hungary
University of the Peloponnese, Greece
Institute for System Programming of RAS, Russia
Skolkovo Institute of Science and Technology, Russia
Krasovskii Institute of Mathematics and Mechanics
of RAS, Russia and Ural Federal University,
Yekaterinburg, Russia
Skolkovo Institute of Science and Technology, Russia
St. Petersburg State University, Russia
Eindhoven University of Technology, The Netherlands
Hewlett Packard Enterprise, USA
Moscow State University and National Research
University Higher School of Economics, Moscow,
Russia
Universidade Federal de Pernambuco
The University of Melbourne, Australia
University of Helsinki, Finland
National Research University Higher School
of Economics, Moscow, Russia
University of Wolverhampton, UK
University of Nizhniy Novgorod, Russia
Ural Federal University, Russia

Additional Reviewers
Sujoy Chatterjee
Anton Eremeev
Aleksey Glebov
Sergey Khamidullin
Vladimir Khandeev


Sofya Kulikova
Abhishek Kumar
Alexander Plyasunov
Vladimir Servakh


Organisation

Organising Committee
Rostislav Yavorskiy
(Conference Chair)
Andrey Novikov
(Head of Organization)
Marina Danshina
(Venue Organization
and Management)
Anna Ukhanaeva
(Information Partners
and Communications)
Anna Kalenkova (Visa
Support and International
Communications)
Alexander Gnevshev
(Venue Organization
and Management)

National Research University Higher School
of Economics, Russia
National Research University Higher School
of Economics, Russia

Moscow Polytechnic University, Russia

National Research University Higher School
of Economics, Russia
National Research University Higher School
of Economics, Russia
Moscow Polytechnic University, Russia

Volunteers
Daniil Bannyh
Ksenia Belkova
Tatiana Mishina
Zahar Kuhtenkov
Maxim Pasynkov
Ivan Poylov

Sponsors
Golden sponsor
Exactpro
Bronze sponsor
Springer

Moscow Polytechnic University, Russia
Moscow Polytechnic University, Russia
Moscow Polytechnic University, Russia
Moscow Polytechnic University, Russia
Krasovsky Institute of Mathematics and Mechanics
of RAS, Ekaterinburg, Russia
Moscow Polytechnic University, Russia


XV


Keynote and Invited Talks


Bridge Between Tensor Networks
and Deep Neural Networks: From Fundamentals
to Real Applications
Andrjei Cichocki1,2
2

1
RIKEN Brain Science Institute, Japan
Skolkovo Institute of Science and Technology, Russia


Abstract. Tensor decompositions (TD) and their generalizations tensor networks (TN) are promising, and emerging tools in Machine Learning (ML),
especially in Deep Learning (DL), since input/output data outputs in hidden
layers can be naturally represented and described as higher-order tensors and
most operations can be performed using optimized linear/multilinear algebra.
I will present a brief overview of tensor decomposition and tensor networks
architectures and associated learning algorithms. I will also discuss several
applications of tensor networks in Signal Processing, Machine Learning, both in
supervised and unsupervised learning and possibility of dramatic reduction of
set of parameters in state-of-the arts deep CNN, typically, from hundreds millions to tens of thousands of parameters. We focus on novel (Quantized) Tensor
Train-Tucker (QTT-Tucker) and Quantized Hierarchical Tucker (QHT) tensor
network models for higher order tensors (tensors of order at least four or higher).
Moreover, we present tensor sketching for efficient dimensionality reduction
which avoid curse of dimensionality.

Tensor Train-Tucker and HT models will be naturally extended to MERA
(Multiscale Entanglement Renormalization Ansatz) models, TTNS (Tree Tensor
Network States) and PEPS/PEPO and other 2D/3D tensor networks, with
improved expressive power of deep learning in convolutional neural networks
(DCNN) and inspiration to generate novel architectures of deep and
semi-shallow neural networks. Furthermore, we will be show how to apply
tensor networks to higher order multiway, partially restricted Boltzmann
Machine (RBM) with substantial reduction of set of learning parameters.
Keywords: Tensor networks Á Deep learning Á Tensor decompositions
Neural networks

References
1. Cichocki, A., Mandic, D.P., Lathauwer, L.D., Zhou, G., Zhao, Q., Caiafa, C.F., Phan, A.H.:
Tensor decompositions for signal processing applications: from two-way to multiway component analysis. IEEE Signal Process. Mag. 32(2), 145–163 (2015)


XX

Bridge Between Tensor Networks and Deep Neural Networks

2. Cichocki, A., Lee, N., Oseledets, I.V., Phan, A.H., Zhao, Q., Mandic, D.P.: Tensor networks
for dimensionality reduction and large-scale optimization: Part 1 low-rank tensor decompositions. Found. Trends Mach. Learn. 9(4–5), 249–429 (2016)
3. Cichocki, A., Phan, A.H., Zhao, Q., Lee, N., Oseledets, I.V., Sugiyama, M., Mandic, D.P.:
Tensor networks for dimensionality reduction and large-scale optimization: Part 2 applications
and future perspectives. Found. Trends Mach. Learn. 9(6), 431–673 (2017)


Process Mining:
Past, Present, and Open Challenges


Dirk Fahland
Eindhoven University of Technology, The Netherlands

Abstract. Since the first algorithms for automatically discovering process
models from event logs have been proposed in the late 1990ies the problem of
obtaining insights into processes by mining from event logs gained growing
attention. By now, the field has grown into a maturing discipline and industry
has begun adopting process mining in regular operations, supported by several
commercial process mining solutions that are available on the market.
In the early days of process mining, several algorithms for constructively
discovering a process model from an event log were proposed, each algorithm
pursuing unique principles for constructing a model. This first generation of
process discovery techniques, which includes, for instance, the alpha-algorithm,
paved the ground for process mining as a research discipline. As these algorithms were applied in practice, new research challenges showed up, sparking
new results in both pre-processing event data and evaluating process models on
event logs. In particular the latter deepened the understanding of the challenges
in process mining and established a reliable feedback mechanism in process
mining in the form of conformance checking. This feedback mechanism enabled
researching the second generation of process mining techniques addressing a
large variety of problems such as quality guarantees for discovered models,
including the data perspective in discovered models, or discovering temporal
logic constraints. In particular, the inductive miner family was seen as a new
milestone as it provided a systematic way to develop process discovery algorithms with reliable results. Yet again, as these more capable techniques are
being applied to the growing and more detailed event data recorded in practice,
further unsolved challenges arise.
In the first part of my talk I will draw an arc from the early days of process
mining to the current state of the art in process mining – highlighting central
techniques and their impact on later developments. In the second part of my talk,
I will then turn to what kinds of event data and challenges are being found in
practice today, how existing process mining techniques fail to address them, and

thus which open challenges and opportunities the process mining field offers
also for researchers from other domains.
Keywords: Process mining Á Information systems
Combined modeling paradigms Á Event logs


Sensitivity Analysis of p* and SAOM:
The Effects of Missing Data
on Parameter Estimates

Stanley Wasserman
Indiana University, USA

Abstract. Many studies use complicated network data sets. Take, for example,
the Framingham Heart Study, which was never intended for use in network
analyses, but whose family and friend contact information can be considered
relational data and thus can be massaged into a social network. Such data sets
are often sampled in various ways, but the effects of the inherent sampling
design on the findings of the study are unknown. Nevertheless, the sampling
design will certainly influence the results in some way. There is little published
research on the impact of network sampling on network structures and structural
measures. There is even less research that investigates how network sampling
impacts models that link network structure with behaviors and attitudes. We
need not only to study how sampling designs for social network studies impact
network measures, but also investigate how these sampling designs impact
models that include social influence effects. The old studies of measurement
error in network analysis (circa 1975) are woefully old. Our research contributes
much-needed, but rarely discussed, important information to a rapidly growing
field. Specifically, we study the following methodological questions:
1. How does eliminating links bias cross-sectional and longitudinal parameter

estimates and statistical models? Missing links approximate both fixed
choice designs where only a portion of links are provided and situations
where respondents do not report all relationships that exist.
2. How does eliminating alters and their links bias cross sectional and longitudinal parameter estimates and statistical models? Missing alters represent
panel-type loss of entire groups from a study as well as intermittent joiners
and leavers in studies (alters that appear at multiple, but not sequential time
points, as if a student were enrolled in a study and appeared at baseline but
then not again until the fourth and fifth waves of a five wave study).
3. How does the method of sampling bias estimates and models? That is, do
missing edges lead to greater problems than missing nodes? Does churn in
these models lead to greater problems than the loss of alters at baseline due
to sampling issues? Churn here refers to the presence of intermittent joiners
and leavers.
4. How do systematic sampling approaches such as snowball sampling or
link-tracing bias estimates and models? These approaches approximate
respondent-driven sampling approaches and other trace based designs.


Sensitivity Analysis of p* and SAOM
5. How does the information that is lost when one has personal (egocentered),
rather than complete, network information bias estimates and models?
6. Does missingness in alter attribute variables have the same effect as missingness in structural variables?
7. How much does measurement error (caused, for example, by forced
fixed-choice designs) affect statistical findings from these new models,
particularly for social influence parameters?
Keywords: Social networks Á Exponential-family random graph models
Stochastic actor-oriented models Á Sensitivity analysis Á Missing data

XXIII



Deep Learning for Natural Language
Processing (Tutorial)
Sergey Nikolenko1,2,3
1

St.-Petersburg Department of the Steklov Mathematical Institute, Russia
2
National Research University Higher School of Economics, Russia
3
Academic University, Russia


Abstract. Over the last decade, deep learning has revolutionized machine
learning. Neural network architectures have become the method of choice for
many different applications. In this tutorial, we survey the applications of deep
learning to natural language processing (NLP) problems.
We begin by briefly reviewing the basic notions and major architectures of
deep learning, including some recent advances that are especially important for
NLP.
Then we survey distributed representations of words, showing both how
word embeddings can be extended to sentences and paragraphs and how words
can be broken down further in character-level models.
Finally, the main part of the tutorial deals with various deep architectures that
have either arisen specifically for NLP tasks or have become a method of choice
for them; the tasks include sentiment analysis, dependency parsing, machine
translation, dialog and conversational models, question answering, and other
applications.
Keywords: Deep learning Á Natural language processing
Machine learning applications



Contents

Natural Language Processing
Automated Detection of Adverse Drug Reactions from Social Media Posts
with Machine Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Ilseyar Alimova and Elena Tutubalina

3

Automated Detection of Non-Relevant Posts on the Russian Imageboard
“2ch”: Importance of the Choice of Word Representations . . . . . . . . . . . . . .
Amir Bakarov and Olga Gureenkova

16

A Morphological Processor for Russian with Extended Functionality . . . . . . .
Elena I. Bolshakova and Alexander S. Sapin

22

SyntaxNet Errors from the Linguistic Point of View . . . . . . . . . . . . . . . . . .
Oleg Durandin, Alexey Malafeev, and Nikolai Zolotykh

34

Size vs. Structure in Training Corpora for Word Embedding Models:
Araneum Russicum Maximum and Russian National Corpus . . . . . . . . . . . .
Andrey Kutuzov and Maria Kunilovskaya

Combining Thesaurus Knowledge and Probabilistic Topic Models . . . . . . . .
Natalia Loukachevitch, Michael Nokel, and Kirill Ivanov

47
59

Russian-Language Question Classification: A New Typology
and First Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Kirill Nikolaev and Alexey Malafeev

72

Domain Adaptation for Resume Classification Using Convolutional
Neural Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Luiza Sayfullina, Eric Malmi, Yiping Liao, and Alexander Jung

82

Fighting with the Sparsity of Synonymy Dictionaries for Automatic
Synset Induction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Dmitry Ustalov, Mikhail Chernoskutov, Chris Biemann,
and Alexander Panchenko
Men Are from Mars, Women Are from Venus: Evaluation and Modelling
of Verbal Associations. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Ekaterina Vylomova, Andrei Shcherbakov, Yuriy Philippovich,
and Galina Cherkasova

94

106



XXVIII

Contents

Rotations and Interpretability of Word Embeddings: The Case
of the Russian Language . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Alexey Zobnin

116

General Topics of Data Analysis
HuGaDB: Human Gait Database for Activity Recognition from Wearable
Inertial Sensor Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Roman Chereshnev and Attila Kertész-Farkas
On Finding Maximum Cardinality Subset of Vectors with a Constraint
on Normalized Squared Length of Vectors Sum . . . . . . . . . . . . . . . . . . . . .
Anton V. Eremeev, Alexander V. Kelmanov, Artem V. Pyatkin,
and Igor A. Ziegler
Using Cluster Analysis for Characteristics Detection in Software
Defect Reports . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Anna Gromova
A Machine Learning Approach to Enhanced Oil Recovery Prediction . . . . . .
Fedor Krasnov, Nikolay Glavnov, and Alexander Sitnikov

131

142


152
164

An Approach to Establishing the Correspondence of Spatial Objects
on Heterogeneous Maps Based on Methods of Computational Topology . . . .
Sergey Eremeev, Kirill Kuptsov, and Semyon Romanov

172

Predicting Winning Team and Probabilistic Ratings in “Dota 2”
and “Counter-Strike: Global Offensive” Video Games . . . . . . . . . . . . . . . . .
Ilya Makarov, Dmitry Savostyanov, Boris Litvyakov,
and Dmitry I. Ignatov

183

Bagging Prediction for Censored Data: Application for Theatre Demand . . . .
Evgeniy M. Ozhegov and Alina Ozhegova

197

Original Loop-Closure Detection Algorithm for Monocular vSLAM . . . . . . .
Andrey Bokovoy and Konstantin Yakovlev

210

Analysis of Images and Video
Organizing Multimedia Data in Video Surveillance Systems Based
on Face Verification with Convolutional Neural Networks . . . . . . . . . . . . . .
Anastasiia D. Sokolova, Angelina S. Kharchevnikova,

and Andrey V. Savchenko
Satellite Image Forgery Detection Based on Buildings Shadows Analysis. . . .
Andrey Kuznetsov and Vladislav Myasnikov

223

231


Contents

Nonlinear Dimensionality Reduction of Hyperspectral Data
Using Spectral Correlation as a Similarity Measure . . . . . . . . . . . . . . . . . . .
Evgeny Myasnikov
Large-Scale Shape Retrieval with Sparse 3D Convolutional
Neural Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Alexandr Notchenko, Yermek Kapushev,
and Evgeny Burnaev
Floor-Ladder Framework for Human Face Beautification . . . . . . . . . . . . . . .
Yulia Novskaya, Sun Ruoqi, Hengliang Zhu,
and Lizhuang Ma
Array DBMS and Satellite Imagery: Towards Big Raster Data
in the Cloud . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Ramon Antonio Rodriges Zalipynis, Evgeniy Pozdeev,
and Anton Bryukhov
Impulsive Noise Removal from Color Images
with Morphological Filtering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Alexey Ruchay and Vitaly Kober

XXIX


237

245

255

267

280

Optimization Problems on Graphs and Network Structures
An Exact Polynomial Algorithm for the Outerplanar Facility Location
Problem with Improved Time Complexity . . . . . . . . . . . . . . . . . . . . . . . . .
Edward Gimadi

295

Approximation Algorithms for the Maximum m-Peripatetic
Salesman Problem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Edward Kh. Gimadi and Oxana Yu. Tsidulko

304

A Randomized Algorithm for 2-Partition of a Sequence . . . . . . . . . . . . . . . .
Alexander Kel’manov, Sergey Khamidullin,
and Vladimir Khandeev

313


An Approximation Scheme for a Weighted Two-Cluster
Partition Problem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Alexander Kel’manov, Anna Motkova, and Vladimir Shenmaier

323

Hitting Set Problem for Axis-Parallel Squares Intersecting a Straight
Line Is Polynomially Solvable for Any Fixed Range of Square Sizes . . . . . .
Daniel Khachay, Michael Khachay, and Maria Poberiy

334

Polynomial Time Solvable Subclass of the Generalized Traveling Salesman
Problem on Grid Clusters. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Michael Khachay and Katherine Neznakhina

346