Machine learning, optimization, and big data 2017

LNCS 10710

Giuseppe Nicosia · Panos Pardalos
Giovanni Giuffrida · Renato Umeton (Eds.)

Machine Learning,
Optimization,
and Big Data
Third International Conference, MOD 2017
Volterra, Italy, September 14–17, 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

10710


More information about this series at />

Giuseppe Nicosia Panos Pardalos
Giovanni Giuffrida Renato Umeton (Eds.)
•

•

Machine Learning,
Optimization,
and Big Data
Third International Conference, MOD 2017

Volterra, Italy, September 14–17, 2017
Revised Selected Papers

123


Editors
Giuseppe Nicosia
University of Catania
Catania
Italy

Giovanni Giuffrida
University of Catania
Catania
Italy

Panos Pardalos
University of Florida
Gainesville, FL
USA

Renato Umeton
Harvard University
Cambridge, MA
USA

ISSN 0302-9743
ISSN 1611-3349 (electronic)
Lecture Notes in Computer Science

ISBN 978-3-319-72925-1
ISBN 978-3-319-72926-8 (eBook)
/>Library of Congress Control Number: 2017962876
LNCS Sublibrary: SL3 – Information Systems and Applications, incl. Internet/Web, and HCI
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Preface

MOD is an international conference embracing the fields of machine learning, optimization, and data science. The third edition, MOD 2017, was organized during
September 14–17, 2017 in Volterra (Pisa, Italy), a stunning medieval town dominating
the picturesque countryside of Tuscany.
The key role of machine learning, reinforcement learning, artificial intelligence,

large-scale optimization, and big data for developing solutions to some of the greatest
challenges we are facing is undeniable. MOD 2017 attracted leading experts from the
academic world and industry with the aim of strengthening the connection between these
institutions. The 2017 edition of MOD represented a great opportunity for professors,
scientists, industry experts, and postgraduate students to learn about recent developments
in their own research areas and to learn about research in contiguous research areas, with
the aim of creating an environment to share ideas and trigger new collaborations.
As chairs, it was an honor to organize a premiere conference in these areas and to
have received a large variety of innovative and original scientific contributions.
During this edition, six plenary lectures were presented:
Yi-Ke Guo, Department of Computing, Faculty of Engineering, Imperial College
London, UK. Founding Director of Data Science Institute
Panos Pardalos, Department of Systems Engineering, University of Florida, USA.
Director of the Center for Applied Optimization
Ruslan Salakhutdinov, Machine Learning Department, School of Computer Science
at Carnegie Mellon University, USA. Director of AI Research at Apple
My Thai, Department of Computer and Information Science and Engineering,
University of Florida, USA
Jun Pei, Hefei University of Technology, China
Vincenzo Sciacca, Cloud and Cognitive Division – IBM Rome, Italy
There were also two tutorial speakers:
Domenico Talia, Dipartimento di Ingegneria Informatica, Modellistica, Elettronica
e Sistemistica Università della Calabria, Italy
Xin–She Yang, School of Science and Technology – Middlesex University London,
UK
Moreover, the conference hosted the second edition of the industrial session on
“Machine Learning, Optimization and Data Science for Real-World Applications”:
Luca Maria Aiello, Nokia Bell Labs, UK
Pierpaolo Basile, University of Bari, Italy

VI

Preface

Carlos Castillo, Universitat Pompeu Fabra in Barcelona, Spain
Moderator: Aris Anagnostopoulos, Sapienza University of Rome, Italy
We received 126 submissions from 46 countries and five continents; each manuscript was independently reviewed by a committee formed by at least five members
through a blind review process. These proceedings contain 49 research articles written
by leading scientists in the fields of machine learning, artificial intelligence, reinforcement learning, computational optimization, and data science presenting a substantial array of ideas, technologies, algorithms, methods, and applications.
For MOD 2017, Springer generously sponsored the MOD Best Paper Award. This
year, the paper by Khaled Sayed, Cheryl Telmer, Adam Butchy, and Natasa
Miskov-Zivanov titled “Recipes for Translating Big Data Machine Reading to Executable Cellular Signaling Models” received the MOD Best Paper Award.
This conference could not have been organized without the contributions of these
researchers, and so we thank them all for participating. A sincere thank you also goes to
all the Program Committee, formed by more than 300 scientists from academia and
industry, for their valuable work of selecting the scientific contributions.
Finally, we would like to express our appreciation to the keynote speakers, tutorial
speakers, and the industrial panel who accepted our invitation, and to all the authors
who submitted their research papers to MOD 2017.
September 2017

Giuseppe Nicosia
Panos Pardalos
Giovanni Giuffrida
Renato Umeton


Organization

General Chair
Renato Umeton

Harvard University, USA

Conference and Technical Program Committee Co-chairs
Giuseppe Nicosia
Panos Pardalos
Giovanni Giuffrida

University of Catania, Italy and University of Reading,
UK
University of Florida, USA
University of Catania, Italy

Tutorial Chair
Giuseppe Narzisi

New York University Tandon School of Engineering,
USA

Industrial Session Chairs
Ilaria Bordino
Marco Firrincieli
Fabio Fumarola
Francesco Gullo

UniCredit
UniCredit
UniCredit

UniCredit

R&D,
R&D,
R&D,
R&D,

Italy
Italy
Italy
Italy

Organizing Committee
Piero Conca
Jole Costanza
Giorgio Jansen
Giuseppe Narzisi
Andrea Patane’
Andrea Santoro
Renato Umeton

CNR, Italy
Italian Institute of Technology, Milan, Italy
University of Catania, Italy
New York University Tandon School of Engineering,
USA
University of Oxford, UK
Queen Mary University London, UK
Harvard University, USA

Technical Program Committee
Agostinho Agra
Kerem Akartunali
Richard Allmendinger
Aris Anagnostopoulos
Davide Anguita

Universidade de Aveiro, Portugal
University of Strathclyde, UK
The University of Manchester, UK
Università di Roma La Sapienza, Italy
University of Genoa, Italy


VIII

Organization

Takaya Arita
Jason Atkin
Chloe-Agathe Azencott
Jaume Bacardit
James Bailey
Baski Balasundaram
Elena Baralis
Xabier E. Barandiaran
Cristobal Barba-Gonzalez
Helio J. C. Barbosa
Roberto Battiti
Lucia Beccai

Aurelien Bellet
Gerardo Beni
Khaled Benkrid
Peter Bentley
Katie Bentley
Heder Bernardino
Daniel Berrar
Adam Berry
Luc Berthouze
Martin Berzins
Mauro Birattari
Leonidas Bleris
Christian Blum
Paul Bourgine
Anthony Brabazon
Paulo Branco
Juergen Branke
Larry Bull
Tadeusz Burczynski
Robert Busa-Fekete
Sergiy I Butenko
Stefano Cagnoni
Yizhi Cai
Guido Caldarelli
Alexandre Campo
Angelo Cangelosi
Salvador Eugenio Caoili
Timoteo Carletti
Jonathan Carlson
Celso Carneiro Ribeiro

Michelangelo Ceci
Adelaide Cerveira
Uday Chakraborty

Nagoya University, Japan
The University of Nottingham, UK
Institut Curie Research Centre, Paris, France
Newcastle University, UK
University of Melbourne, Australia
Oklahoma State University, USA
Politecnico di Torino, Italy
University of the Basque Country, Spain
University of Malaga, Spain
Laboratório Nacional de Computacao Cientifica, Brazil
University of Trento, Italy
Istituto Italiano di Tecnologia, Italy
Inria Lille, France
University of California at Riverside, USA
The University of Edinburgh, UK
University College London, UK
Harvard Medical School, USA
Universidade Federal de Juiz de Fora, Brazil
Tokyo Institute of Technology, Japan
CSIRO, Australia
University of Sussex, UK
SCI Institute, University of Utah, USA
IRIDIA, Université Libre de Bruxelles, Belgium
University of Texas at Dallas, USA
Spanish National Research Council, Spain
École Polytechnique Paris, France

University College Dublin, Ireland
Instituto Superior Tecnico, Portugal
University of Warwick, UK
University of the West of England, UK
Polish Academy of Sciences, Poland
Yahoo! Research, NY, USA
Texas A&M University, USA
University of Parma, Italy
University of Edinburgh, UK
IMT Lucca, Italy
Université Libre de Bruxelles, Belgium
University of Plymouth, UK
University of the Philippines Manila, Philippines
University of Namur, Belgium
Microsoft Research, USA
Universidade Federal Fluminense, Brazil
University of Bari, Italy
Universidade de Tras-os-Montes e Alto Douro,
Portugal
University of Missouri – St. Louis, USA


Organization

Xu Chang
W. Art Chaovalitwongse
Antonio Chella
Ying-Ping Chen
Haifeng Chen
Keke Chen

Gregory Chirikjian
Silvia Chiusano
Miroslav Chlebik
Sung-Bae Cho Yonsei
Anders Christensen
Dominique Chu
Philippe Codognet
Carlos Coello Coello
George Coghill
Pietro Colombo
David Cornforth
Luís Correia
Chiara Damiani
Thomas Dandekar
Ivan Luciano Danesi
Christian Darabos
Kalyanmoy Deb
Nicoletta Del Buono
Jordi Delgado
Ralf Der
Clarisse Dhaenens
Barbara Di Camillo
Gianni Di Caro
Luigi Di Caro
Luca Di Gaspero
Peter Dittrich
Federico Divina
Stephan Doerfel
Devdatt Dubhashi
George Dulikravich

Juan J. Durillo
Omer Dushek
Marc Ebner
Pascale Ehrenfreund
Gusz Eiben
Aniko Ekart
Talbi El-Ghazali
Michael Elberfeld
Michael T. M. Emmerich
Andries Engelbrecht

IX

University of Sydney, Australia
University of Washington, USA
Università di Palermo, Italy
National Chiao Tung University, Taiwan
NEC Labs, USA
Wright State University, USA
Johns Hopkins University, USA
Politecnico di Torino, Italy
University of Sussex, UK
University, South Korea
Lisbon University Institute, Portugal
University of Kent, UK
University Pierre and Marie Curie – Paris 6, France
CINVESTAV-IPN, Mexico
University of Aberdeen, UK
University of Insubria, Italy
University of Newcastle, UK

University of Lisbon, Portugal
University of Milan-Bicocca, Italy
University of Würzburg, Germany
Unicredit Bank, Italy
Dartmouth College, USA
Michigan State University, USA
University of Bari, Italy
Universitat Politecnica de Catalunya, Spain
MPG, Germany
Université Lille, France
University of Padua, Italy
IDSIA, Switzerland
University of Turin, Italy
University of Udine, Italy
Friedrich Schiller University of Jena, Germany
Pablo de Olavide University of Seville, Spain
Kassel University, Germany
Chalmers University, Sweden
Florida International University, USA
University of Innsbruck, Austria
University of Oxford, UK
Ernst-Moritz-Arndt-Universität Greifswald, Germany
The George Washington University, USA
VU Amsterdam, The Netherlands
Aston University, UK
University of Lille, France
RWTH Aachen University, Germany
Leiden University, The Netherlands
University of Pretoria, South Africa

X

Organization

Anton Eremeev
Harold Fellermann
Chrisantha Fernando
Cesar Ferri
Paola Festa
Jose Rui Figueira
Grazziela Figueredo
Alessandro Filisetti
Christoph Flamm
Enrico Formenti
Giuditta Franco
Piero Fraternali
Valerio Freschi
Enrique Frias Martinez
Walter Frisch
Rudolf M. Fuchslin
Claudio Gallicchio
Patrick Gallinari
Luca Gambardella
Jean-Gabriel Ganascia
Xavier Gandibleux
Alfredo G. Hernandez-Diaz
Jose Manuel Garcia Nieto
Paolo Garza
Romaric Gaudel

Nicholas Geard
Philip Gerlee
Mario Giacobini
Onofrio Gigliotta
Giovanni Giuffrida
Giorgio Stefano Gnecco
Christian Gogu
Faustino Gomez
Michael Granitzer
Alex Graudenzi
Julie Greensmith
Roderich Gross
Mario Guarracino
Francesco Gullo
Steven Gustafson
Jin-Kao Hao
Simon Harding
Richard Hartl
Inman Harvey
Jamil Hasan
Mohammad Hasan

Sobolev Institute of Mathematics, Russia
Newcastle University, UK
Queen Mary University, UK
Universidad Politecnica de Valencia, Spain
University of Naples Federico II, Italy
Instituto Superior Tecnico, Lisbon, Portugal
The University of Nottingham, UK
Explora Biotech Srl, Italy

University of Vienna, Austria
Nice Sophia Antipolis University, France
University of Verona, Italy
Politecnico di Milano, Italy
University of Urbino, Italy
Telefonica Research, Spain
University of Vienna, Austria
Zurich University of Applied Sciences, Switzerland
University of Pisa, Italy
LIP6 – University of Paris 6, France
IDSIA, Switzerland
Pierre and Marie Curie University – LIP6, France
Université de Nantes, France
Pablo de Olvide University – Seville, Spain
University of Malaga, Spain
Politecnico di Torino, Italy
Inria, France
University of Melbourne, Australia
Chalmers University, Sweden
University of Turin, Italy
University of Naples Federico II, Italy
University of Catania, Italy
University of Genoa, Italy
Université Toulouse III, France
IDSIA, Switzerland
University of Passau, Germany
University of Milan-Bicocca, Italy
University of Nottingham, UK
The University of Sheffield, UK
ICAR-CNR, Italy

Unicredit Bank, Italy
GE Global Research, USA
University of Angers, France
Machine Intelligence Ltd., Canada
University of Vienna, Austria
University of Sussex
University of Idaho, USA
Indiana University – Purdue University, USA


Organization

Geir Hasle
Carlos Henggeler Antunes
Francisco Herrera
Arjen Hommersom
Vasant Honavar
Fabrice Huet
Hiroyuki Iizuka
Takashi Ikegami
Bordino Ilaria
Hisao Ishibuchi
Peter Jacko
Christian Jacob
Yaochu Jin
Colin Johnson
Gareth Jones
Laetitia Jourdan
Narendra Jussien
Janusz Kacprzyk

Theodore Kalamboukis
George Kampis
Dervis Karaboga
George Karakostas
Istvan Karsai
Jozef Kelemen
Graham Kendall
Didier Keymeulen
Daeeun Kim
Zeynep Kiziltan
Georg Krempl
Erhun Kundakcioglu
Renaud Lambiotte
Doron Lancet
Pier Luca Lanzi
Sanja Lazarova-Molnar
Doheon Lee
Jay Lee
Eva K. Lee
Tom Lenaerts
Rafael Leon
Shuai Li
Lei Li
Xiaodong Li
Joseph Lizier
Giosue’ Lo Bosco
Daniel Lobo
Fernando Lobo

XI

SINTEF ICT, Norway
University of Coimbra, Portugal
University of Granada, Spain
Radboud University, The Netherlands
Pennsylvania State University, USA
University of Nice Sophia Antipolis, France
Hokkaido University, Japan
University of Tokyo, Japan
Unicredit Bank, Italy
Osaka Prefecture University, Japan
Lancaster University Management School, UK
University of Calgary, Canada
University of Surrey, UK
University of Kent, UK
Dublin City University, Ireland
Inria/LIFL/CNRS, France
Ecole des Mines de Nantes/LINA, France
Polish Academy of Sciences, Poland
Athens University of Economics and Business, Greece
Eotvos University, Hungary
Erciyes University, Turkey
McMaster University, Canada
ETSU, USA
Silesian University, Czech Republic
Nottingham University, UK
NASA – Jet Propulsion Laboratory, USA
Yonsei University, South Korea
University of Bologna, Italy
University of Magdeburg, Germany

Ozyegin University, Turkey
University of Namur, Belgium
Weizmann Institute of Science, Israel
Politecnico di Milano, Italy
University of Southern Denmark, Denmark
KAIST, South Korea
Center for Intelligent Maintenance Systems – UC, USA
Georgia Tech, USA
Université Libre de Bruxelles, Belgium
Universidad Politecnica de Madrid, Spain
Cambridge University, UK
Florida International University, USA
RMIT University, Australia
The University of Sydney, Australia
Università di Palermo, Italy
University of Maryland Baltimore County, USA
University of Algarve, Portugal


XII

Organization

Daniele Loiacono
Jose A. Lozano
Paul Lu
Angelo Lucia
Dario Maggiorini
Gilvan Maia
Donato Malerba

Lina Mallozzi
Jacek Mandziuk
Vittorio Maniezzo
Marco Maratea
Elena Marchiori
Tiziana Margaria
Omer Markovitch
Carlos Martin-Vide
Dominique Martinez
Matteo Matteucci
Giancarlo Mauri
Mirjana Mazuran
Suzanne McIntosh
Peter Mcowan
Gabor Melli
Jose Fernando Mendes
David Merodio-Codinachs
Silja Meyer-Nieberg
Martin Middendorf
Taneli Mielikainen
Kaisa Miettinen
Orazio Miglino
Julian Miller
Marco Mirolli
Natasa Miskov-Zivanov
Carmen Molina-Paris
Sara Montagna
Marco Montes de Oca
Sanaz Mostaghim
Mohamed Nadif

Hidemoto Nakada
Amir Nakib
Mirco Nanni
Sriraam Natarajan
Chrystopher L. Nehaniv
Michael Newell
Giuseppe Nicosia
Xia Ning
Wieslaw Nowak

Politecnico di Milano, Italy
University of the Basque Country, Spain
University of Alberta, Canada
University of Rhode Island, USA
University of Milan, Italy
Universidade Federal do Cear, Brazil
University of Bari, Italy
University of Naples Federico II, Italy
Warsaw University of Technology, Poland
University of Bologna, Italy
University of Genoa, Italy
Radboud University, The Netherlands
University of Limerick and Lero, Ireland
University of Groningen, The Netherlands
Rovira i Virgili University, Spain
LORIA, France
Politecnico di Milano, Italy
University of Milan-Bicocca, Italy
Politecnico di Milano, Italy
NYU Courant Institute, and Cloudera Inc., USA

Queen Mary University, UK
Sony Interactive Entertainment Inc., Japan
University of Aveiro, Portugal
ESA, France
Universität der Bundeswehr München, Germany
University of Leipzig, Germany
Nokia, Finland
University of Jyvaskyla, Finland
University of Naples “Federico II”, Italy
University of York, UK
ISTC-CNR, Italy
University of Pittsburgh, USA
University of Leeds, UK
Università di Bologna, Italy
Clypd, Inc., USA
Otto von Guericke University Magdeburg, Germany
University of Paris Descartes, France
NIAIST, Japan
Università Paris EST Creteil, Laboratoire LISSI, France
CNR – ISTI, Italy
Indiana University, USA
University of Hertfordshire, UK
Athens Consulting, LLC
University of Catania, Italy
IUPUI, USA
N. Copernicus University, Poland


Organization

Eirini Ntoutsi
Michal Or-Guil
Mathias Pacher
Ping-Feng Pai
Wei Pang
George Papastefanatos
Luis Paquete
Panos Pardalos
Andrew J. Parkes
Andrea Patane’
Joshua Payne
Jun Pei
Nikos Pelekis
Dimitri Perrin
Koumoutsakos Petros
Juan Peypouquet
Andrew Philippides
Vincenzo Piuri
Alessio Plebe
Silvia Poles
Philippe Preux
Mikhail Prokopenko
Paolo Provero
Buyue Qian
Chao Qian
Gunther Raidl
Helena R. Dias Lourenco
Palaniappan Ramaswamy
Jan Ramon
Vitorino Ramos

Shoba Ranganathan
Cristina Requejo
John Rieffel
Laura Anna Ripamonti
Eduardo Rodriguez-Tello
Andrea Roli
Vittorio Romano
Andre Rosendo
Samuel Rota Bulo
Arnab Roy
Alessandro Rozza
Kepa Ruiz-Mirazo
Florin Rusu
Jakub Rydzewski
Nick Sahinidis
Lorenza Saitta

XIII

Leibniz University of Hanover, Germany
Humboldt University of Berlin, Germany
Goethe-Universität Frankfurt am Main, Germany
National Chi Nan University, Taiwan
University of Aberdeen, UK
IMIS/RC Athena, Greece
University of Coimbra, Portugal
University of Florida, USA
Nottingham University, UK
University of Oxford, UK
University of Zurich, Switzerland

University of Florida, USA
University of Piraeus, Greece
Queensland University of Technology, Australia
ETH, Switzerland
Universidad Tecnica Federico Santa Maria, Chile
University of Sussex, UK
University of Milan, Italy
University of Messina, Italy
Noesis Solutions NV
Inria, France
University of Sydney, Australia
University of Turin, Italy
IBM T. J. Watson, USA
University of Science and Technology of China, China
TU Wien, Austria
Pompeu Fabra University, Spain
University of Kent, UK
Inria, France
Technical University of Lisbon, Portugal
Macquarie University, Australia
Universidade de Aveiro, Portugal
Union College, USA
Università degli Studi di Milano, Italy
Cinvestav-Tamaulipas, Mexico
Università di Bologna, Italy
University of Catania, Italy
University of Cambridge, UK
Fondazione Bruno Kessler, Italy
Fujitsu Laboratories of America, USA
Parthenope University of Naples, Italy

University of the Basque Country, Spain
University of California Merced, USA
N. Copernicus University, Poland
Carnegie Mellon University, USA
University of Piemonte Orientale, Italy


XIV

Organization

Francisco C. Santos
Claudio Sartori
Frederic Saubion
Andrea Schaerf
Oliver Schuetze
Luis Seabra Lopes
Roberto Serra
Marc Sevaux
Ruey-Lin Sheu
Hsu-Shih Shih
Patrick Siarry
Alkis Simitsis
Johannes Sollner
Ichoua Soumia
Giandomenico Spezzano
Antoine Spicher
Pasquale Stano
Thomas Stibor
Catalin Stoean

Reiji Suzuki
Domenico Talia
Kay Chen Tan
Letizia Tanca
Charles Taylor
Maguelonne Teisseire
Tzouramanis Theodoros
Jon Timmis
Gianna Toffolo
Joo Chuan Tong
Nickolay Trendafilov
Soichiro Tsuda
Shigeyoshi Tsutsui
Aditya Tulsyan
Ali Emre Turgut
Karl Tuyls
Jon Umerez
Renato Umeton
Ashish Umre
Olgierd Unold
Giorgio Valentini
Edgar Vallejo
Sergi Valverde
Werner Van Geit
Pascal Van Hentenryck
Ana Lucia Varbanescu

INESC-ID Instituto Superior Tecnico, Portugal
University of Bologna, Italy
Université d’Angers, France

University of Udine, Italy
CINVESTAV-IPN, Mexico
Universidade of Aveiro, Portugal
University of Modena and Reggio Emilia, Italy
Lab-STICC, Université de Bretagne-Sud, France
National Cheng Kung University, Taiwan
Tamkang University, Taiwan
Université de Paris 12, France
HP Labs, USA
Emergentec Biodevelopment GmbH, Germany
Embry-Riddle Aeronautical University, USA
CNR-ICAR, Italy
LACL University of Paris Est Creteil, France
University of Salento, Italy
GSI Helmholtz Centre for Heavy Ion Research,
Germany
University of Craiova, Romania
Nagoya University, Japan
University of Calabria, Italy
National University of Singapore, Singapore
Politecnico di Milano, Italy
UCLA, USA
Cemagref – UMR Tetis, France
University of the Aegean, Greece
University of York, UK
University of Padua, UK
Institute of HPC, Singapore
Open University, UK
University of Glasgow, UK
Hannan University, Japan

MIT, USA
IRIDIA-ULB, France
University of Liverpool, UK
University of the Basque Country, Spain
Harvard University, USA
University of Sussex, UK
Politechnika Wroclawska, Poland
Università degli Studi di Milano, Italy
ITESM Campus Estado de Mexico, Mexico
Pompeu Fabra University, Spain
EPFL, Switzerland
University of Michigan, USA
University of Amsterdam, The Netherlands


Organization

Carlos Varela
Eleni Vasilaki
Richard Vaughan
Kalyan Veeramachaneni
Vassilios Verykios
Mario Villalobos-Arias
Marco Villani
Katya Vladislavleva
Stefan Voss
Dean Vucinic
Markus Wagner
Toby Walsh
Lipo Wang

Liqiang Wang
Rainer Wansch
Syed Waziruddin
Janet Wiles
Man Leung Wong
Andrew Wuensche
Petros Xanthopoulos
Ning Xiong
Xin Xu
Gur Yaari
Larry Yaeger
Shengxiang Yang
Qi Yu
Zelda Zabinsky
Ras Zbyszek
Hector Zenil
Guang Lan Zhang
Qingfu Zhang
Rui Zhang
Zhi-Hua Zhou
Tom Ziemke
Antanas Zilinskas

Rensselaer Polytechnic Institute, USA
University of Sheffield, UK
Simon Fraser University, Canada
MIT, USA
Hellenic Open University, Greece
Univesidad de Costa Rica, Costa Rica
University of Modena and Reggio Emilia, Italy

Evolved Analytics LLC, Belgium
University of Hamburg, Germany
Vrije Universiteit Brussel, Belgium
The University of Adelaide, Australia
UNSW Sydney, Australia
Nanyang Technological University, Singapore
University of Central Florida, USA
Fraunhofer IIS, Germany
Kansas State University, USA
University of Queensland, Australia
Lingnan University, Hong Kong, SAR China
University of Sussex, UK
University of Central Florida, USA
Malardalen University, Sweden
George Washington University, USA
Yale University, USA
Indiana University, USA
De Montfort University, USA
Rochester Institute of Technology, USA
University of Washington, USA
University of North Carolina, USA
University of Oxford, UK
Boston University, USA
City University of Hong Kong, Hong Kong,
SAR China
IBM Research – Almaden, USA
Nanjing University, China
University of Skovde, Sweden
Vilnius University, Lithuania

XV


XVI

Organization

Best Paper Awards
MOD 2017 Best Paper Award
“Recipes for Translating Big Data Machine Reading to Executable Cellular Signaling
Models”
Khaled Sayed*, Cheryl Telmer**, Adam Butchy*, and Natasa Miskov-Zivanov*
*University of Pittsburgh, USA
**Carnegie Mellon University, USA
Springer sponsored the MOD 2017 Best Paper Award with a cash prize of EUR 1,000.
MOD 2016 Best Paper Award
“Machine Learning: Multi-site Evidence-Based Best Practice Discovery”
Eva Lee, Yuanbo Wang and Matthew Hagen
Eva K. Lee, Professor Director, Center for Operations Research in Medicine and
HealthCare H. Milton Stewart School of Industrial and Systems Engineering, Georgia
Institute of Technology, Atlanta, GA, USA
MOD 2015 Best Paper Award
“Learning with Discrete Least Squares on Multivariate Polynomial Spaces Using
Evaluations at Random or Low-Discrepancy Point Sets”
Giovanni Migliorati
Ecole Polytechnique Federale de Lausanne – EPFL, Lausanne, Switzerland


Contents

Recipes for Translating Big Data Machine Reading to Executable
Cellular Signaling Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Khaled Sayed, Cheryl A. Telmer, Adam A. Butchy,
and Natasa Miskov-Zivanov

1

Improving Support Vector Machines Performance Using Local Search . . . . .
S. Consoli, J. Kustra, P. Vos, M. Hendriks, and D. Mavroeidis

16

Projective Approximation Based Quasi-Newton Methods . . . . . . . . . . . . . . .
Alexander Senov

29

Intra-feature Random Forest Clustering . . . . . . . . . . . . . . . . . . . . . . . . . . .
Michael Cohen

41

Dolphin Pod Optimization: A Nature-Inspired Deterministic
Algorithm for Simulation-Based Design . . . . . . . . . . . . . . . . . . . . . . . . . . .
Andrea Serani and Matteo Diez

50

Contraction Clustering (RASTER): A Big Data Algorithm
for Density-Based Clustering in Constant Memory and Linear Time . . . . . . .

Gregor Ulm, Emil Gustavsson, and Mats Jirstrand

63

Deep Statistical Comparison Applied on Quality Indicators
to Compare Multi-objective Stochastic Optimization Algorithms . . . . . . . . . .
Tome Eftimov, Peter Korošec, and Barbara Koroušić Seljak

76

On the Explicit Use of Enzyme-Substrate Reactions in Metabolic
Pathway Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Angelo Lucia, Edward Thomas, and Peter A. DiMaggio

88

A Comparative Study on Term Weighting Schemes
for Text Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Ahmad Mazyad, Fabien Teytaud, and Cyril Fonlupt

100

Dual Convergence Estimates for a Family of Greedy Algorithms
in Banach Spaces . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
S. P. Sidorov, S. V. Mironov, and M. G. Pleshakov

109

Nonlinear Methods for Design-Space Dimensionality Reduction
in Shape Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Danny D’Agostino, Andrea Serani, Emilio F. Campana,
and Matteo Diez

121


XVIII

Contents

A Differential Evolution Algorithm to Develop Strategies
for the Iterated Prisoner’s Dilemma . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Manousos Rigakis, Dimitra Trachanatzi, Magdalene Marinaki,
and Yannis Marinakis
Automatic Creation of a Large and Polished Training Set
for Sentiment Analysis on Twitter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Stefano Cagnoni, Paolo Fornacciari, Juxhino Kavaja,
Monica Mordonini, Agostino Poggi, Alex Solimeo,
and Michele Tomaiuolo

133

146

Forecasting Natural Gas Flows in Large Networks . . . . . . . . . . . . . . . . . . .
Mauro Dell’Amico, Natalia Selini Hadjidimitriou,
Thorsten Koch, and Milena Petkovic

158

A Differential Evolution Algorithm to Semivectorial Bilevel Problems. . . . . .
Maria João Alves and Carlos Henggeler Antunes

172

Evolving Training Sets for Improved Transfer Learning
in Brain Computer Interfaces . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Jason Adair, Alexander Brownlee, Fabio Daolio,
and Gabriela Ochoa
Hybrid Global/Local Derivative-Free Multi-objective Optimization
via Deterministic Particle Swarm with Local Linesearch. . . . . . . . . . . . . . . .
Riccardo Pellegrini, Andrea Serani, Giampaolo Liuzzi,
Francesco Rinaldi, Stefano Lucidi, Emilio F. Campana,
Umberto Iemma, and Matteo Diez
Artificial Bee Colony Optimization to Reallocate Personnel
to Tasks Improving Workplace Safety . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Beatrice Lazzerini and Francesco Pistolesi
Multi-objective Genetic Algorithm for Interior Lighting Design . . . . . . . . . .
Alice Plebe and Mario Pavone

186

198

210
222

An Elementary Approach to the Problem of Column Selection
in a Rectangular Matrix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Stéphane Chrétien and Sébastien Darses

234

A Simple and Effective Lagrangian-Based Combinatorial
Algorithm for S3 VMs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Francesco Bagattini, Paola Cappanera, and Fabio Schoen

244

A Heuristic Based on Fuzzy Inference Systems for Multiobjective
IMRT Treatment Planning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Joana Dias, Humberto Rocha, Tiago Ventura, Brígida Ferreira,
and Maria do Carmo Lopes

255


Contents

Data-Driven Machine Learning Approach for Predicting Missing Values
in Large Data Sets: A Comparison Study . . . . . . . . . . . . . . . . . . . . . . . . . .
Ogerta Elezaj, Sule Yildirim, and Edlira Kalemi

XIX

268

Mineral: Multi-modal Network Representation Learning. . . . . . . . . . . . . . . .
Zekarias T. Kefato, Nasrullah Sheikh, and Alberto Montresor

286

Visual Perception of Mixed Homogeneous Textures in Flying Pigeons . . . . .
Margarita Zaleshina, Alexander Zaleshin, and Adriana Galvani

299

Estimating Dynamics of Honeybee Population Densities
with Machine Learning Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Ziad Salem, Gerald Radspieler, Karlo Griparić,
and Thomas Schmickl

309

SQG-Differential Evolution for Difficult Optimization Problems
under a Tight Function Evaluation Budget . . . . . . . . . . . . . . . . . . . . . . . . .
Ramses Sala, Niccolò Baldanzini, and Marco Pierini

322

Age and Gender Classification of Tweets
Using Convolutional Neural Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Roy Khristopher Bayot and Teresa Gonçalves

337

Approximate Dynamic Programming with Combined Policy
Functions for Solving Multi-stage Nurse Rostering Problem . . . . . . . . . . . . .
Peng Shi and Dario Landa-Silva

349

A Data Mining Tool for Water Uses Classification
Based on Multiple Classifier Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Iván Darío López, Cristian Heidelberg Valencia,
and Juan Carlos Corrales
Parallelized Preconditioned Model Building Algorithm
for Matrix Factorization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Kamer Kaya, Ş. İlker Birbil, M. Kaan Öztürk,
and Amir Gohari
A Quantitative Analysis on Required Network Bandwidth
for Large-Scale Parallel Machine Learning . . . . . . . . . . . . . . . . . . . . . . . . .
Mingxi Li, Yusuke Tanimura, and Hidemoto Nakada
Can Differential Evolution Be an Efficient Engine
to Optimize Neural Networks? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Marco Baioletti, Gabriele Di Bari, Valentina Poggioni,
and Mirco Tracolli

362

376

389

401


XX

Contents

BRKGA-VNS for Parallel-Batching Scheduling on a Single Machine
with Step-Deteriorating Jobs and Release Times . . . . . . . . . . . . . . . . . . . . .
Chunfeng Ma, Min Kong, Jun Pei, and Panos M. Pardalos
Petersen Graph is Uniformly Most-Reliable . . . . . . . . . . . . . . . . . . . . . . . .
Guillermo Rela, Franco Robledo, and Pablo Romero
GRASP Heuristics for a Generalized Capacitated
Ring Tree Problem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Gabriel Bayá, Antonio Mauttone, Franco Robledo,
and Pablo Romero

414
426

436

Data-Driven Job Dispatching in HPC Systems . . . . . . . . . . . . . . . . . . . . . .
Cristian Galleguillos, Alina Sîrbu, Zeynep Kiziltan,
Ozalp Babaoglu, Andrea Borghesi, and Thomas Bridi

449

AbstractNet: A Generative Model for High Density Inputs . . . . . . . . . . . . . .
Boris Musarais

462

A Parallel Framework for Multi-Population Cultural
Algorithm and Its Applications in TSP . . . . . . . . . . . . . . . . . . . . . . . . . . .
Olgierd Unold and Radosław Tarnawski

470

Honey Yield Forecast Using Radial Basis Functions . . . . . . . . . . . . . . . . . .
Humberto Rocha and Joana Dias

483

Graph Fragmentation Problem for Natural Disaster Management . . . . . . . . . .
Natalia Castro, Graciela Ferreira, Franco Robledo,
and Pablo Romero

496

Job Sequencing with One Common and Multiple Secondary Resources:
A Problem Motivated from Particle Therapy for Cancer Treatment . . . . . . . .
Matthias Horn, Günther Raidl, and Christian Blum

506

Robust Reinforcement Learning with a Stochastic Value Function. . . . . . . . .
Reiji Hatsugai and Mary Inaba

519

Finding Smooth Graphs with Small Independence Numbers . . . . . . . . . . . . .
Benedikt Klocker, Herbert Fleischner, and Günther R. Raidl

527

BioHIPI: Biomedical Hadoop Image Processing Interface. . . . . . . . . . . . . . .
Francesco Calimeri, Mirco Caracciolo, Aldo Marzullo,
and Claudio Stamile

540

Evaluating the Dispatching Policies for a Regional Network
of Emergency Departments Exploiting Health Care Big Data . . . . . . . . . . . .
Roberto Aringhieri, Davide Dell’Anna, Davide Duma,
and Michele Sonnessa

549


Contents

Refining Partial Invalidations for Indexed Algebraic
Dynamic Programming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Christopher Bacher and Günther R. Raidl
Subject Recognition Using Wrist-Worn Triaxial Accelerometer Data . . . . . . .
Stefano Mauceri, Louis Smith, James Sweeney, and James McDermott

XXI

562
574

Detection of Age-Related Changes in Networks of B Cells
by Multivariate Time-Series Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Alberto Castellini and Giuditta Franco

586

Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

599


Recipes for Translating Big Data Machine
Reading to Executable Cellular
Signaling Models
Khaled Sayed1, Cheryl A. Telmer2, Adam A. Butchy3,
and Natasa Miskov-Zivanov1,3,4(&)
1

2

Department of Electrical and Computer Engineering,
University of Pittsburgh, Pittsburgh, PA, USA
{k.sayed,nmzivanov}@pitt.edu
Department of Biological Sciences, Carnegie Mellon University,
Pittsburgh, PA, USA

3
Department of Bioengineering, University of Pittsburgh,
Pittsburgh, PA, USA

4
Department of Computational and Systems Biology,
University of Pittsburgh, Pittsburgh, PA, USA

Abstract. Biological literature is rich in mechanistic information that can be
utilized to construct executable models of complex systems to increase our
understanding of health and disease. However, the literature is vast and fragmented, and therefore, automation of information extraction from papers and of
model assembly from the extracted information is necessary. We describe here
our approach for translating machine reading outputs, obtained by reading
biological signaling literature, to discrete models of cellular networks. We use
outputs from three different reading engines, and demonstrate the translation of
different features using examples from cancer literature. We also outline several
issues that still arise when assembling cellular network models from
state-of-the-art reading engines. Finally, we illustrate the details of our approach
with a case study in pancreatic cancer.
Keywords: Machine reading Á Big data in literature Á Text mining
Cell signaling networks Á Automated model generation

1 Introduction
Biological knowledge is voluminous and fragmented; it is nearly impossible to read all
scientific papers on a single topic such as cancer. When building a model of a particular
biological system, one example being cancer microenvironment, researchers usually
start by searching for existing relevant models and by looking for information about
system components and their interactions in published literature.
Although there have been attempts to automate the process of model building
[1, 2], most often modelers conduct these steps manually, with multiple iterations
© Springer International Publishing AG 2018
G. Nicosia et al. (Eds.): MOD 2017, LNCS 10710, pp. 1–15, 2018.
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2

K. Sayed et al.

between (i) information extraction, (ii) model assembly, (iii) model analysis, and
(iv) model validation through comparison with most recently published results. To
allow for rapid modeling of complex diseases like cancer, and for efficiently using
ever-increasing amount of information in published work, we need representation
standards and interfaces such that these tasks can be automated. This, in turn, will allow
researchers to ask informed, interesting questions that can improve our understanding
of health and disease.
The systems biology community has designed and proposed a standardized format
for representing biological models called the systems biology markup language
(SBML). This language allows for using different software tools, without the need for
recreating models specific for each tool, as well as for sharing the built models between
different research groups [3]. However, the SBML standard is not easily understood by
biologists who create mechanistic models, and thus requires an interface that allows
biologists to focus on modeling tasks while hiding the details of the SBML language
[4–7].
To this end, the contributions of the work presented in this paper include:
• A representation format that is straightforward to use by both machines and
humans, and allows for efficient synthesis of models from big data in literature.
• An approach to effectively use state-of-the-art machine reading output to create
executable discrete models of cellular signaling.
• A proposal for directions to further improve automation of assembly of models
from big data in literature.
In Sect. 2, we briefly describe cellular networks, our modeling approach, and our
framework that integrates machine reading, model assembly and model analysis. In
Sect. 3, we present details of our model representation format, while Sect. 4 outlines
our approach to translate reading output to the model representation format. Section 5
discusses other issues that need to be taken into account when building interface
between big data reading and model assembly in biology. Section 6 describes a case
study that uses our translation methodology. Section 7 concludes the paper.

2 Background
2.1

Cellular Networks

Intra-cellular networks include signal transduction, gene regulation, and metabolic
networks [8]. Signaling networks are characterized by protein phosphorylation and
binding events, which transduce extracellular signals across the plasma membrane and
through the cytoplasm [9]. Gene regulatory networks involve translocation of signaling
proteins from the cytoplasm to the nucleus, where the integration of these protein
signals act on the genome, resulting in changes in gene expression and cellular processes [10]. The regulation of metabolic networks incorporates phosphorylation and
binding, as do signaling networks, and also integrates allosteric regulation, other
protein modifications, and subcellular compartmentalization [11].


Recipes for Translating Big Data Machine Reading

3

Inter-cellular networks assume interactions between cells of the same or different
types. These interactions occur via signaling molecules such as growth factors and
cytokines, synthesized and secreted by one cell, and bound to itself or other cells in its
surroundings, or via a cell-cell contact.
At all levels of signaling, there are feedforward and feedback loops and crosstalk
between signaling pathways to either maintain homeostasis or amplify changes initiated by extracellular signals [12].
2.2

Modeling Approach

When generating executable models, we use a discrete modeling approach previously
described in [13]. As illustrated in the example in Fig. 1, we represent system components as model elements (A, B, and C in the example), where each element is defined
as having a discrete number of levels of activity. Each element has a list of regulators
called influence set. In our example, A is a positive regulator of C, B and C are positive
regulators of A, and C activates itself while B inhibits itself. Additionally, each element
has a corresponding update rule, a discrete function of its regulators. In our example, A
is a conjunction of B and C, while C is a disjunction of A and C. Although the model
structure is fixed, the simulator that we use [14] is stochastic, and thus, allows for
closely recapitulating the behavior of biological pathways and networks.

Fig. 1. Toy example illustrating our modeling approach.

2.3

Framework Overview

To automatically incorporate new reading outputs into models, we have developed a
reading-modeling-explanation framework, called DySE (Dynamic System Explanation), outlined in Fig. 2. This framework allows for (i) expansion of existing models or
assembly of new models from machine reading output, (ii) analysis and explanation of
models, and (iii) generation of machine-readable feedback to reading engines. We
focus here on the front end of the framework, the translation from reading outputs to
the list of elements and their influence sets, with context information, where available.

3 Model Representation Format
To enable comprehensive translation from reading engine outputs to executable
models, the models are first represented in tabular format. It is important to note here
that the tabular representation does not include final update rules, that is, the tabular
version of the model is further translated into an executable model that can be