Proceedings
Second International Conference
on
Artificial Intelligence, Modelling, and Simulation
Madrid, Spain 18–20 November 2014
Technical Sponsors, Patrons, Promoters, and Supporters
IEEE Region 8
IEEE Spain Section
Asia Modelling and Simulation Section
UK Simulation Society
European Simulation Council (EUROSIM)
European Council for Modelling and Simulation (ECMS)
University Polytechnic of Madrid (UPM)
University of Kingston, UK
University of Liverpool, UK
University of Malaysia in Sabah (UMS)
University of Malaysia in Pahang (UMP)
University of Malaysia in Perlis (UniMaP)
University of Technology Malaysia (UTM)
University of Technology Mara (UiTM)
Institute of Technology Bandung (ITB)
University of Science Malaysia (USM)
Machine Intelligence Research Labs (MIR Labs)
Nottingham Trent University, UK
Los Alamitos, California
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I E E E C o m p u t e r S o c i e t y
2014 Second International
Conference on Artificial
Intelligence, Modelling
and Simulation
AIMS 2014
Table of Contents
Welcome Message from the Chairs
...xii
Conference Organization
...xiii
International Program Committee
...xiv
International Reviewers
...xv
Technical Sponsors, Patrons, Promoters,
and Supporters
...xvi
Keynote Address
Keynote 1: Feature Selection in Data-Driven Systems Modelling ...1
Qiang Shen
Keynote 2: Challenges in Handling and Processing Huge Data ...2
Hermann Hessling
Track 01.A Artificial Intelligence
Study of Performance of Several Techniques of Fault Diagnosis for Induction
Motors in Steady-State with SVM Learning Algorithms ...3
J. Burriel Valencia, M. Pineda Sanchez, J. Martinez Roman,
R. Puche Panadero, and A. Sapena Baño
Simulation of Human Opinions about Calligraphy Aesthetic ...9
Ana Pérez, Eduardo Cermeño, and Juan Alberto Sigüenza
Expert Diagnosis Systems for Network Connection Problems ...15
RajaaAldeen Khalid and Rafah Jassim
Topology-Aware Simulated Annealing ...19
Said Kerrache and Hafida Benhidour
Skinning Analysis of a Mapping Algorithm in Higher Dimensions ...25
Mustafa Youldash and John Rankin
Track 02.B. Neural Networks and Fuzzy Systems
Consolidation of the IFM with the JSSP through Neural Networks as Model
for Software Projects ...33
Pandelis Ipsilandis, Dimitrios Tselios, and Vassilis C. Gerogiannis
Classification of Working Memory Impairment in Children Using
Electroencephalograph Signal at the Prefrontal Cortex ...39
S.Z. Mohd Tumari and R. Sudirman
Designing ANFIS with Self-Extraction of Rules ...44
Lamine Thiaw, Gustave Sow, Oumar Ba, and Salif Fall
An Approach to Represent Time Series Forecasting via Fuzzy Numbers ...51
Atakan Sahin, Tufan Kumbasar, Engin Yesil, M. Furkan Doýdurka,
and Onur Karasakal
Track 03.C Evolutionary Computation
Towards Deterministic Network Coding in Hierarchical Networks ...57
Oana Graur and Werner Henkel
Steps Towards Decentralized Deterministic Network Coding ...63
Oana Graur and Werner Henkel
On the Improvement of Elite Swimmers Velocity Identification by Using Neural
Network Associated to Multiobjective Optimization ...69
Elcio A. Bardeli Jr., Luciano F. da Cruz, Helon V.H. Ayala, Roberto Z. Freire,
and Leandro dos S. Coelho
A Wind Driven Approach Using Lévy Flights for Global Continuous
Optimization ...75
Emerson Hochsteiner de Vasconcelos Segundo, Anderson Levati Amoroso,
Viviana Cocco Mariani, and Leandro dos Santos Coelho
Shapes Extraction Method by Genetic Algorithm with Local Search Method ...81
Mitsukuni Matayoshi
A Semi-Supervised Multi-view Genetic Algorithm ...87
Gergana Lazarova and Ivan Koychev
Track 06.F Bioinformatics and Bioengineering
Moments Invariant for Expression Invariant Thermal Human Recognition ...92
Naser Zaeri
Movement Analysis for Surgical Skill Assessment and Measurement
of Ergonomic Conditions ...97
O. Weede, F. Möhrle, H. Wörn, M. Falkinger, and H. Feussner
Track 11.K Intelligent Systems and Applications
Exploring Experts Decisions in Concrete Delivery Dispatching Systems Using
Bayesian Network Learning Techniques ...103
Mojtaba Maghrebi and S. Travis Waller
A Comparative Analysis on Home Automation Techniques ...109
Mirza Qutab Baig, Junaid Maqsood, Muhammad Haris Bin Tariq Alvi,
and Tamim Ahmed Khan
Spreading Activation Approach for Social Recommendations: The Case
of Microblogging Services ...115
Xi Kong, Lennart Weller, Susanne Boll, and Wilko Heuten
System Failure Prediction through Rare-Events Elastic-Net Logistic
Regression ...120
José M. Navarro, G. Hugo A. Parada, and Juan C. Dueñas
Eye-Gaze Tracking Method Driven by Raspberry PI Applicable in Automotive
Traffic Safety ...126
Ovidiu Stan, Liviu Miclea, and Ana Centea
Parameter-Based Mechanism for Unifying User Interaction, Applications
and Communication Protocols ...131
Jie Song, Silvia Calatrava Sierra, Jaime Caffarel Rodríguez,
Jorge Martín Perandones, Guillermo del Campo Jiménez, Jorge Olloqui Buján,
Rocío Martínez García, and Asunción Santamaría Galdón
Track 14.N Control of Intelligent Systems and Control Intelligence
Balancing Control of Robot Gymnast Based on Discrete-Time Linear
Quadratic Regulator Technique ...137
H.G. Kamil, E.E. Eldukhri, and M.S. Packianather
Track 16.P Robotics, Cybernetics, Engineering, Manufacturing
and Control
Validating the Camera and Light Simulation of a Virtual Reality Testbed
by Means of Physical Mockup Data ...143
Thomas Steil and Jürgen Roßmann
Mobile Robot Performance in Robotics Challenges: Analyzing a Simulated
Indoor Scenario and Its Translation to Real-World ...149
Francisco Rodríguez Lera, Fernando Casado García, Gonzalo Esteban,
and Vicente Matellán
Synergetic Control of a Mobile Robot Group ...155
Gennady Veselov, Andrey Sklyrov, Alexey Mushenko, and Sergey Sklyrov
Track 19.S Image, Speech, and Signal Processing
Modified Back Projection Kernel Based Image Super Resolution ...161
Pejman Rasti, Iiris Lüsi, Armen Sahakyan, Andres Traumann,
Anastasia Bolotnikova, Morteza Daneshmand, Rudolf Kiefer, Alvo Aabloo,
Gholamreza Anbarjafar, Hasan Demirel, and Cagri Ozcinar
User’s Gaze Tracking System and Its Application Using Head Pose Estimation ...166
Hyunduk Kim, Myoung-Kyu Sohn, Dong-Ju Kim, and Nuri Ryu
Geometric Feature-Based Face Normalization for Facial Expression
Recognition ...172
Dong-Ju Kim, Myoung-Kyu Sohn, Hyunduk Kim, and Nuri Ryu
Qualitative Evaluation of Full Body Movements with Gesture Description
Language ...176
Tomasz Hachaj and Marek R. Ogiela
Scene Text Recognition Based on Positional Relation between Closed Curves ...182
Yuji Waizumi and Kazuyuki Tanaka
Studying the Effects of 2D and 3D Educational Contents on Memory Recall
Using EEG Signals, PCA and Statistical Features ...187
Saeed Bamatraf, Hatim Aboalsamh, Muhammad Hussain, Hassan Mathkour,
Emad-Ul-Haq Qazi, Aamir Malik, and Hafeezullah Amin
Insertion of Impairments in Test Video Sequences for Quality Assessment
Based on Psychovisual Characteristics ...193
J.P. López, J.A. Rodrigo, D. Jiménez, and J. M. Menéndez
Feature Based Encryption Technique for Securing Forensic Biometric Image
Data Using AES and Visual Cryptography ...199
Quist-Aphetsi Kester, Laurent Nana, Anca Christine Pascu, Sophie Gire,
J.M. Eghan, Nii Narku Quaynor, Robert A. Baffour,
Daniel Michael Okwabi Adjin, Yeboah-Boateng Eo, Isaac Hanson,
and Osei K. Darkwa
Track 19.S1 Natural Language Processing/Language Technologies
Specification Model of Paragraph Summarization by Verbal Relationships:
Objective, Cause, Consequence, Concurrence ...205
Trung Tran and Dang Tuan Nguyen
Track 20.T Industry, Business, Management, Human Factors,
and Social Issues
A Modeling Approach for IT Governance Basics Application on IT Projects
and IT Goals ...211
Rabii El Ghorfi, Mohamed Ouadou, Driss Aboutajdine, and Mohamed El Aroussi
Human Resource Assessment in Software Development Projects Using Fuzzy
Linguistic 2-Tuples ...217
Vassilis C. Gerogiannis, Elli Rapti, Anthony Karageorgos, and Panos Fitsilis
Track 21.U Energy, Power, Transport, Logistics, Harbour, Shipping
and Marine Simulation
A Simulation Study of the Hamada to Zawiyah Crude Oil Pipeline in Libya ...223
Awad Shamekh, Jonathan Theakston, and Salah Masheiti
Exergy Analysis of a 210 MW Unit at 1260 MW Thermal Plant in India ...228
Varun Goyal, Rajasekhar Dondapati, Rakesh Dang, and S.K. Mangal
Design and Comparison of Feasible Control Systems for VSC-HVDC
Transmission System ...234
Boyang Shen, Sheng Wang, Lin Fu, and Jun Liang
Verification of a Synchronous Machine Model for Stator Ground Fault
Simulation Through Measurements in a Large Generator ...240
A. Bermejo, C.A. Platero, F. Blázquez, F. Blánquez, and E. Rebollo
Online Tool for Benchmarking of Simulated Intervention Autonomous
Underwater Vehicles: Evaluating Position Controllers in Changing Underwater
Currents ...246
Javier Pérez, Jorge Sales, Raúl Marín, and Pedro J. Sanz
Communality Performance Assessment of Electricity Load Management
Model for Namibia ...252
Godwin Norense Osarumwense Asemota
Track 22.V Parallel, Distributed, and Software Architectures
and Systems
Architecture of Real-Time Database in Cloud Environment for Distributed
Systems ...258
Sebastijan Stoja, Srđjan Vukmirović, Bojan Jelačić, Darko Čapko,
and Nikola Dalčeković
Simulating a Multi-core x8664 Architecture with Hardware ISA Extension
Supporting a Data-Flow Execution Model ...264
Nam Ho, Antoni Portero, Marcos Solinas, Alberto Scionti, Andrea Mondelli,
Paolo Faraboschi, and Roberto Giorgi
Attack Prediction Models for Cloud Intrusion Detection Systems ...270
Hisham A. Kholidy, Abdelkarim Erradi, and Sherif Abdelwahed
Track 23.W Internet Modelling, Semantic Web, and Ontologies
Using Ontology for Providing Content Recommendation Based on Learning
Styles inside E-learning ...276
Sri Suning Kusumawardani, Robertus Sonny Prakoso, and Paulus Insap Santosa
Track 24.X Mobile/Ad Hoc Wireless Networks, Mobicast, Sensor
Placement, Target Tracking
Novel Location Tracking Energy Efficient Model for Robust Routing
over Wireless Sensor Networks ...282
Fatma Almajadub and Khaled Elleithy
Prevention of Wormhole Attacks in Wireless Sensor Networks ...287
Dema Aldhobaiban, Khaled Elleithy, and Laiali Almazaydeh
A Fully Functional Shopping Mall Application—SHOPPING EYE ...292
K.M.D.M. Karunarathna, H.M.D.A. Weerasingha, M.M Rumy, M.M Rajapaksha,
D.I De Silva, and N. Kodagoda
Performance Analysis of a Grid Based Route Discovery in AODV Routing
Algorithm for MANET ...297
Abderezak Touzene and Ishaq Al-Yahyai
“Smart Ships”: Mobile Applications, Cloud and Bigdata on Marine Traffic
for Increased Safety and Optimized Costs Operations ...303
Alejandro García Dominguez
A Potential Game Approach Towards Distributive Interference Management
in OFDMA-Based Femtocell Networks ...309
Adnan Shahid, Saleem Aslam, Hyung Seok Kim, and Kyung Geun Lee
Location-Based Services with iBeacon Technology ...315
Markus Koühne and Jürgen Sieck
Track 25.Y Performance Engineering of Computer and Communication
Systems
Study of Energy Saving in Carrier-Ethernet Network ...322
Rihab Maaloul, Lamia Chaari, and Bernard Cousin
Achieving Better Performance Using a New Variable LMS Algorithm Equalizer
for Systems-Based OFDM ...329
Ziba Reza-zadeh Razlighi and Saeed Ghazi-Maghrebi
Ultra-Wideband Antenna for RFID Underground Oil Industry Application ...333
Maged Aldhaeebi, Khalid Jamil, and Abdel R. Sebak
Analysis of VoIP over LTE End-to-End Performances in Congested Scenarios ...339
Alessandro Vizzarri
Track 26.Z Circuits, Sensors, and Devices
Detecting and Minimizing Bad Posture Using Postuino among Engineering
Students ...344
Reem Alattas and Khaled Elleithy
A New Approach for the Differential Spectrum Using the Frobenius Norm ...350
Gelson Cruz, Jonas Kunzler, Rodrigo Lemos, Diego Burgos, Hugo Silva,
and Yroá Ferreira
Author Index
...355
Welcome Message from the Chairs
We are very pleased to welcome our colleagues from Europe, Asia and other parts of the world to our second international
conference on Artificial Intelligence, Modelling and Simulation 2014 (AIMS2014), held in Madrid, Spain. It follows last
year’s outstandingly successful (with 83 published papers) first international conference held in Kota Kinabalu, Sabah,
Malaysia, 3 – 5 December 2013. The second such event internationally we are hopeful that its outstanding technical content
contributed by leading researchers in the field from numerous countries and research laboratories in both university and
industry worldwide will ensure its continued success. The conference Program Committee has organized an exciting and
balanced program comprising presentations from distinguished experts in the field, and important and wide-ranging
contributions on state-of-the-art research that provides new insights into the latest innovations in computational intelligence,
mathematical and analytical modelling and computer simulation of a diverse range of topics in science, engineering and
technology. No plans have yet been finalized for the location of next year’s event, but it would be appropriate to choose
another interesting location in suitable location in either South East Asia or Europe.
The main themes addressed by this conference are:
•
Artificial Intelligence
•
Neural Networks & Fuzzy Systems
•
Evolutionary Computation
•
Bioinformatics and Bioengineering
•
Intelligent Systems and Applicaitons
•
Control of Intelligent Systems and Control Intelligence
•
Robotics, Cybernetics, Engineering, Manufacturing and Control
•
Image, Speech and Signal Processing
•
Natural Language Processing/Language Technologies
•
Industry, Business, Management, Human Factors and Social Issues
•
Energy, Power, Transport, Logistics, Harbour, Shipping and Marine Simulation
•
Parallel, Distributed and Software Architectures and Systems
•
Internet Modelling, Semantic Web and Ontologies
•
Mobile/Ad hoc wireless networks, mobicast, sensor placement, target tracking
•
Performance Engineering of Computer & Communication Systems
•
Circuits, Sensors and Devices
AIMS 2014 is technically co-sponsored with patrons, promoters and supports including IEEE Region 8, Asia Modelling and
Simulation Section, UK Simulation Society, European Simulation Council (EUROSIM), European Council for Modelling
and Simulation (ECMS), University Polytechnic of Madrid (UPM), University of Kingston, UK, University of Liverpool,
UK, University of Malaysia in Sabah (UMS), University of Malaysia in Pahang (UMP), University of Malaysia in Perlis
(UniMaP), University of Technology Malaysia (UTM), University of Technology Mara (UiTM), Institute of Technology
Bandung (ITB), University of Science Malaysia (USM), Machine Intelligence Research Labs (MIR Labs) and Nottingham
Trent University, UK. AIMS2014 proved to be very popular and received submissions from over 20 countries. The
conference program committee had a very challenging task of choosing high quality submissions. Each paper was peer
reviewed by several independent referees of the program committee and, based on the recommendation of the reviewers, 61
papers were finally accepted for publication. The papers offer stimulating insights into emerging modelling and simulation
techniques for intelligent and hybrid intelligent systems and systems that employ intelligent methodologies. We express our
sincere thanks to the keynote speakers, authors, track chairs, program committee members, and additional reviewers who
have made this conference a success. Finally, we hope that you will find the conference to be a valuable resource in your
professional, research, and educational activities whether you are a student, academic, researcher, or a practicing
professional. Enjoy!
David Al-Dabass, Gregorio Romero, Emilio Corchado, Ismail Saad, Alessandra Orsoni, Athanasios Pantelous
General, Conference and Program Chairs
Conference Organization
Conference Chair
Gregorio Romero,
University Polytechnic of Madrid, Spain
Honorary Conference Co-Chairs
Emilio Corchado,
University of Burgos, Spain
Ismail Saad,
University of Malaysia in Sabah, Malaysia
Alessandra Orsoni
, University of Kingston, UK
Program Chairs and Honorary Program Co-Chairs
Athanasios Pantelous,
University of Liverpool
Zuwairie Ibrahim,
University of Malaysia in Pahang (UMP)
Dr Adam Brentnall,
Queen Mary, London University, UK
Local Arrangements Chair
Gregorio Romero,
University Polytechnic of Madrid, Spain
General Chairs
David Al-Dabass,
Nottingham Trent University, UK
Ajith Abraham,
Norwegian University of Science and Technology, Norway
International Program Committee
Jasmy Yunus
Rosni Abdullah
Shamin Ahmad
Khalid Al-Begain
David Al-Dabass
Mikulas Alexik
Saleh Al-Jufout
Ferda Nur Alpaslan
Shamsudin Amin
Fabian Böttinger
Jadranka Bozikov
Felix Breitenecker
Agostino Bruzzon
Piers Campbell
Theodoros Kostis
Hüseyin K. Çakmak
Richard Cant
Emilio Corchado
Alan Crispin
Andrzej Dzielinski
Mohammad Essaaidi
Ford Lumban Gaol
Fengge Gao
Xiaohong Gao
Xiao-Zhi Gao
Crina Grosan
Antonio Guasch
Otávio Noura Teixeira
Sadiq Hussain
Arpad Kelemen
Marzuki Khalid
Franco Maceri
Emelio Jimenez Macias
Mahdi Mahfouf
Miguel Angel Piera
Henri Pierreval
D K Pra
Marius Radulescu
Fazal Rehman
Mojca Indihar Stemberger
K.G. Subramanian
R K Subramanian
Vassilis Tsoulkas
Pandian Vasant
Carlos Martin Vide
Siegfried Wassertheurer
Roland Wertz
Richard Zobel
Borut Zupancic
International Reviewers
Dayang Norhayati
Abg Jawawi
Ajith Abraham
Mohammad Nazir
Ahmad
David Al-Dabass
Giuseppe De
Francesco
Jiri Dvorsky
Andrzej Dzielinski
Muhammad H Fazli
Fauadi
G Ganesan
Ford Gaol
Ida Giriantari
Visvasuresh Victor
Govindaswamy
Sami Habib
Aboul Ella
Hassanien
JERBI Houssem
Emilio Jimenez
Macias
S. D.
Katebi
Dong-hwa Kim
Petia Koprinkova-Hristova
Ku Ruhana
Ku-Mahamud
Satya Kumara
Nooritawati Md
Tahir
Rashid Mehmood
Galina Merkuryeva
Durgesh Mishra
Veronica Moertini
Siti Zaiton
Mohd Hashim
Salwani Mohd.
Daud
Atulya Nagar
Atul Negi
Gaby Neumann
Alessandra Orsoni
Kama Azura
Othman
Athanasios Pantelous
Charles Patchett
Gillian Pearce
Mirjana Pejic-Bach
Evtim Peytchev
Raja Kamil
Raja Ahmad
Technical Sponsors, Patrons, Promoters and Supporters
IEEE Region 8
IEEE Sain Srection
Asia Modelling and Simulation Section
UK Simulation Society
European Simulation Council (EUROSIM)
European Council for Modelling and Simulation (ECMS)
University Polytechnic of Madrid (UPM)
University of Kingston, UK
University of Liverpool, UK
University of Malaysia in Sabah (UMS)
University of Malaysia in Pahang (UMP)
University of Malaysia in Perlis (UniMaP)
University of Technology Malaysia (UTM)
University of Technology Mara (UiTM)
Institute of Technology Bandung (ITB)
University of Science Malaysia (USM)
Machine Intelligence Research Labs (MIR Labs)
Nottingham Trent University, UK
Keynote Speaker-1
Feature Selection in Data-Driven Systems Modelling
Prof Qiang Shen
Director, Institute of Mathematics, Physics and Computer Science
Aberystwyth University, Wales, UK.
Email: qqs@aber.ac.uk
Feature selection (FS) addresses the problem of selecting those system descriptors that are most
predictive of a given outcome. Unlike other dimensionality reduction methods, with FS the original
meaning of the features is preserved. This has found application in tasks that involve datasets
containing very large numbers of features that might otherwise be impractical to model and process
(e.g., large-scale image analysis, text processing and Web content classification).
This talk will focus on the development and application of FS mechanisms based on rough and
fuzzy-rough theories. Such techniques provide a means by which data can be effectively reduced
without the need for user-supplied information. In particular, fuzzy-rough feature selection (FRFS)
works with discrete and real-valued noisy data (or a mixture of both). As such, it is suitable for
regression as well as for classification. The only additional information required is the fuzzy partition
for each feature, which can be automatically derived from the data. FRFS has been shown to be a
powerful technique for data dimensionality reduction. In introducing the general background of FS, this
talk will first cover the rough-set-based approach, before focusing on FRFS and its application to
real-world problems. The talk will conclude with an outline of opportunities for further development.
Speaker’s Biography
Professor Qiang Shen received a PhD in Knowledge-Based Systems and a
DSc in Computational Intelligence. He holds the Established Chair of
Computer Science and is Director of the Institute of Mathematics, Physics and
Computer Science at Aberystwyth University. He is a Fellow of the Learned
Society of Wales, a UK REF 2014 panel member for Computer Science and
Informatics, and a long-serving Associate Editor of two IEEE flagship
Journals (
IEEE Transactions on Cybernetics
and
IEEE Transactions on Fuzzy
Systems
). He has chaired and given keynotes at numerous international
conferences.
Professor Shen’s current research interests include: computational
intelligence, reasoning under uncertainty, pattern recognition, data mining, and their real-world
applications for intelligent decision support (e.g., crime detection, consumer profiling, systems
monitoring, and medical diagnosis). He has authored 2 research monographs and over 340
peer-reviewed papers, including an award-winning IEEE Outstanding Transactions paper. Qiang has served
as the first supervisor of over 40 PDRAs/PhDs, including one UK Distinguished Dissertation Award
winner.
2014 Second International Conference on Artificial Intelligence, Modelling and Simulation
978-1-4799-7600-3/14 $31.00 © 2014 IEEE DOI 10.1109/AIMS.2014.73
Keynote Speaker-2
Challenges in Handling and Processing Huge Data
Prof Hermann-Hessling
Hochschule für Technik und Wirtschaft Berlin
10313 Berlin
hessling@htw-berlin.de
Data-intensive computing is considered as the fourth paradigm in science. The term “data-intensive
computing” did not establish in other communities although they are also confronted with enormous
amounts of data. Nowadays, Big Data refers to data sets that are too large, too complex, too distributed
for analysing them by conventional methods. One strategy for handling Big Data is known as “software
to the data” which is applicable when it is more efficient to bring the analysis tools to the data than,
vice versa, to apply traditional methods where, for example, all data are collected at some place and
analysed there.
The data production rate is expected to increase exponentially for the time being. This is particularly
true in science where the resolution power of experiments is steadily improving. Sooner or later it has
to be taken into account that it is not feasible to store all data anymore. A new era is on the horizon:
Huge Data.
Huge Data have to be pre-analysed during the data-taking period in order to extract a sufficiently
small subset of data that is worth to be analysed in more detail later on. An effective and efficient
pre-selection in real-time or near-realtime is most critical for successfully handling Huge Data. This is
made more challenging if during the pre-analysis that has to be done in parallel, intermediate results
have to be exchanged.
The talk considers selected challenges of Huge Data. Some examples from different scientific
communities are presented.
Speaker’s Biography
Hermann Heßling studied Physics at the Universities of Münster, Göttingen and
Hamburg. He received the Ph.D. (Dr. rer. nat) in Theoretical Physics and was
appointed a postdoctoral research fellow at Deutsches Elektronen-Synchrotron
(DESY) Hamburg (1993-1996). Subsequently, he continued his work with a computer
communicaitons and networking company and accepted in 1999 an offer from the
University of Applied Sciences Hof as a Professor of Operating Systems. Since 2000
he has been professor of Applied Informatics at the University of Applied Sciences
HTW Berlin.
2014 Second International Conference on Artificial Intelligence, Modelling and Simulation
978-1-4799-7600-3/14 $31.00 © 2014 IEEE DOI 10.1109/AIMS.2014.74
Using Ontology for Providing Content Recommendation Based on Learning Styles inside
E-Learning
Sri Suning Kusumawardani, Robertus Sonny Prakoso, Paulus Insap Santosa
Department of Electrical Engineering and Information Technology
Faculty of Engineering, Universitas Gadjah Mada Yogyakarta, Indonesia
suning@ieee.org, robertus.sonny.p@mail.ugm.ac.id, insap@jteti.gadjahmada.edu
Abstract—E-Learning as one of the learning support facilities provides various content types and interaction models inside. This wide range of content types inside e-Learning can be used for accommodating differences in learning styles among the students. In this research, we develop concept mapping between student characteristics and categories by Felder-Silverman Learning Style Model and appropriate content inside a Moodle-based e-Learning. This mapping is represented in the ontology and then implemented in Moodle-based e-Learning system for giving content recommendation to students based on their learning styles. There are some concepts that become basic definition of learning styles and e-Learning contents, and also some rules that is used for inferring content recommendation from the basic definition.
Keywords-ontology; e-Learning; learning style; Felder-Silverman Learning Style Model
I. INTRODUCTION
Every student has a preference in learning process. Learning style is used to classify students based their preference to receive and process information [1]. In conventional learning, the students should get different treatment that fits their learning style. However, it’s difficult for the teacher to teach in many ways and match all learning styles of the students because of their limitation and ability in teaching.
Today, there are many supporting facilities that can be used in learning and teaching. One of these facilities that begin to be widely used is e-Learning. E-Learning is one of the supporting facilities in learning that considered as an effective method for learning [2].
As the growth of information and communication technology, now there are many features and content types inside e-Learning that used as learning materials and communication models between teacher and students. This variety of features and content types can be used for accommodating many types of learning styles. It will be better for the students if they get a recommendation of contents and features in e-Learning that appropriate to their learning styles.
Refer to the above descriptions, a mapping from characteristics of each category on learning styles to the appropriate contents and features is needed. This mapping will be used for deciding in which learning style category a student fall, and which contents are appropriate to this student. This research is focused on developing knowledge
based of learning styles characteristics and appropriate contents on e-Learning.
Learning styles model that used in this research is Felder-Silverman Learning Style Model. This learning style model is more suitable for being implemented in adaptive e-Learning because it covers more psychological aspect than other models [3]. Moreover, kinds of e-Learning contents and features are picked from Moodle-based e-Learning, so this knowledge based will be implemented on Moodle-based e-Learning. This knowledge based will be represented as an ontology. We choose ontology as knowledge representation because it is readable and understandable, not only by human but also by machine [4].
II. THEORETICAL FOUNDATION
A. Related Works
There are some related works on e-Learning that giving recommendation function inside e-Learning based on students’ learning style. The developed system to detect students’ learning styles based on their choices inside a search engine in e-Learning, and then used this information to give a recommendation in search result [5]. Another work in personalized e-Learning is developed recommendation system inside e-Learning based on students’ learning style and record of the students’ activity in e-Learning [6]. Both works were using Felder-Silverman Learning Style Model as learning style model in the e-Learning.
The ontology for modelling learning tree inside e-Learning can be used for creating personalized e-e-Learning [7]. Another work is using ontology for generating student activity report inside from the activity log inside Moodle-based e-Learning [8]. This research combines these two concepts that is using ontology and giving recommendation inside e-Learning. More specific, this research use ontology as the main knowledge-based that the e-Learning used for giving content recommendation.
B. Felder-Silverman Learning Style Model
Felder-Silverman Learning Style Model (FSLSM) is learning style model that built and developed based on experience and environment in engineering education [1]. There are four dimensions in this model. Every student will have preference on each dimension. These four dimensions in Felder-Silverman Learning Style Model are.
2014 Second International Conference on Artificial Intelligence, Modelling and Simulation
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1) Active-Reflective: An active student tends to learn by directly try or discuss the lesson, while a reflective student tends to think and reflect the lesson first. An active student also prefers work in the group, while a reflective student prefers work individually.
2) Sensing-Intuitive: A sensing student prefers learn concrete material, such as facts and data, while an intuitive student prefers learn abstract material, such as a theory or mathematical model. A sensing student also likes practice with the standard method and repetition, while an intuitive student likes practice that involves creative solution or innovation.
3) Visual-Verbal: A visual student prefers visual information such as pictures and charts, while a verbal student prefers verbal information, both written information and spoken information.
4) Sequential-Global: A sequential student tends to learn in linear and systematic flow, starts from the first chapter to the last chapter. A global student tends to learn in a big leap, by learning the summary of each chapter first then jump to some chapters that the student needs to get more information about.
Refer to measure the FSLSM, there are questionnaire named Index of Learning Styles (ILS) [9]. This can be used for determining learning style category of a student, and this is also have been validated [10] [11]. This questionnaire consists of 44 questions with two options, A and B. Each question is related to exactly one dimension.
Each student will have their score on each dimension. Because there are 44 questions and each question is related to one dimension, there are 11 questions that related to each dimension. Each option is related to one category, for example in active-reflective. It means that the option A is related to active, and the option B is related to reflective. The score for each dimension is get by counting of sum of questions that the student chooses A. If the score for this dimension is from 0 to 5, it represents category that related to B, and if the score is from 6 to 11, it represents category that related to A. Thus, for example in active-reflective, score 0-5 represents reflective, and score 6-11 represents active [11].
C. Ontology
Ontology is a formal and systematic explanation about a domain. The purpose of the ontology is to capture consensual knowledge by general and standard method that it can be reused by other people and applications [12]. From the ontology, we can get knowledge structure of the domain and constraint of the terminology interpretation in this domain [7]. There are some components that could be included in the ontology, such as:
• Concept or Class, terms in the domain that have the broad sense,
• Relation, representation of the association between concepts in a domain,
• Function, a special case of relation,
• Formal axiom, statement that always true inside the ontology,
• Instance, representation of class individual,
• Rules, statement that used for inferring information,
• Attribute, relation that range is data type.
III. IMPLEMENTATION SCENARIO
This ontology can be implemented in Moodlbased e-Learning in many ways. We design some basic scenarios of ontology implementation on e-Learning. There are some core activities in e-Learning that can be modified with ontology implementation for giving content recommendation. These scenarios can be executed by using some Moodle plugin or creating new plugin to add functionality to e-Learning, in this case the functionality to interact with the ontology.
A. Student registration:
First, a student registers in Moodle-based e-Learning. While e-Learning stores student’s information in the database, it also sends some important data to the ontology to be stored there. After the registration, student is asked to complete ILS questionnaire. Then e-Learning stores student’s score in the database for processing the result and display it to the student. Meanwhile, e-Learning also sends these scores to ontology to be stored and then used for determining learning style category of the student. This scenario is more clearly shown in swim lane diagram as seen on Fig. 1.
B. Course and content creation:
When a teacher or administrator creates a new course, e-Learning stores the course data in the database and then sends course data to the ontology. The same process happens when a teacher or administrator adds some course contents such as activities, resources, or files. After the content data is stored in the ontology and known the learning style category, then the ontology classify this content to the suitable learning style. This scenario is shown in swim lane diagram on Fig. 2.
C. Giving content recommendation:
When a student joins (in Moodle-based environment, we use terms ‘enroll’) a course, e-Learning stores enrollment data in the database and then sends enrollment data to the ontology. After that, every time the student enters the enrolled course page, e-Learning asks for recommended course format and contents to the ontology. Ontology will give this information to e-Learning. Then, e-Learning shows the course page to the student with recommended course format and gives information of recommended contents in this course to the student. The swim lane diagram for this scenario is shown on Fig. 3.
IV. DESIGNING AND BUILDING ONTOLOGY
A. Learning style mapping
Some literatures explain detail characteristics for each category of learning styles and methods for addressing characteristics in the classroom [1] [6] [13]. We try to convert these methods to a Moodle-based e-Learning environment by looking for similar features available in Moodle. Moodle have 21 standard features consists of 14 activities [14] and seven resources [15]. We use these standard features to address the learning styles characteristics in the e-Learning environment. In addition, we also give recommendation based on file type and course format [16], which is the course layout in Moodle. Table I summarizes this information and shows mapping from learning style characteristics and appropriate features.
B. Designing ontology
After the concept mapping is created, the ontology can be started to build by listing classes, attributes, and relations. Some concepts that will be created inside the ontology are:
• Student,
• Categories in Felder-Silverman Learning Style Model,
• Questions in Index of Learning Styles,
• e-Learning courses,
• Modules (activities and resources),
• Course formats, and
• File classifications.
Lists of classes and its subclasses, attributes, and relations are shown in Table II. There might be additional classes, attributes, or relations depends on how this ontology being implemented in Moodle-based e-Learning. However, these lists have shown basic structure and mapping from learning style categories and appropriate content in Moodle-based e-Learning. Most of these classes getting their instance from e-Learning database because it is related on e-Learning users and contents, for example, student, course, and module.
Figure 1. Swim lane diagram of student registration and questionnaire completion scenario.
Figure 3. Swim lane diagram for course creation scenario.
Figure 2. Swim lane diagram for giving content recommendation scenario.
TABLE II. LIST OF CLASS, SUBCLASS, ATTRIBUTE, AND RELATIONS IN ONTOLOGY.
Class Representation of Subclass Attribute Relation
Learning Style Category
Type of learning styles based on Felder-on Index of Learning Styles
• Active-Reflective Questionnaire • Sensing-Intuitive Questionnaire • Visual-Verbal Questionnaire • Sequential-Global Questionnaire
• Number
• Question • Option A • Option B
-
Course Course inside
e-Learning
- • Course ID
• Course name
-
Course Format Course format available in Moodle-based e-Learning
- • Course format name
• Course format index -
Module Standard activities and
resources inside Moodlbased e-Learning
21 type of standard activities and
resources •
Module ID
• Module name
• Module type
• is inside a course • recommended for
File Learning materials in
forms of single file •
Document file • Presentation file • Audio file
Student Student that use
Moodlbased e-Learning
- • Student ID
• ILS score for each dimension
• belong to category • joining a course
• get recommended
course format TABLE I. MAPPING FROM LEARNING STYLE CHARACTERISTICS TO THE APPROPRIATE E-LEARNING CONTENT.
Dimension Category Characteristics Addressing method Appropriate features
Active-reflective
Active • Try it out
• Tends to discuss or apply materials or explain to others
• Like group work
• Provide group work • Provide time to discuss
materials
• Collaborative wiki • Single forum
Reflective • Think it through
• Tends to think about materials quite first
• Like individual work
• Provide individual work • Provide time to think and
reflect materials
• Individual wiki • Q&A forum
Sensing-intuitive
Sensing • Like learning facts
• Practical, prefer problem solving using standard method
• Provide additional
information (facts, data, etc.) • Provide standard exercise
• URL to external source • Quiz
Intuitive • Like learning theory and mathematical formulation
• Innovative, prefer innovation
• Provide theory and mathematical model
• Book
• Glossary
Visual-verbal
Visual • Prefer visual information • Provide visual materials • Visual file (picture,
presentation, video)
Verbal • Prefer verbal information • Provide verbal materials • Verbal file (document,
text, audio)
Sequential-global
Sequential • Learn in linear steps
• Finding solution by following logical steps
• Teach in linear steps • Course format ’all
section in one page’ (show all course content in a single page) Global • Learn randomly, with big jumps
• Finding solution by getting big picture first but may be hard to explaining this
• Provide syllabus or course summary
• Course format ’one section per page’ (show chapter summary and link to chapter page)
C. Defining rules
After getting all concepts and other components in ontology, we can now define some rules inside the ontology. These rules are used for inferring information based on student and content data on e-Learning. For the implementation of the ontology, we can use a reasoner tool to help infer information from the ontology.
We have two groups of rules in this ontology. Some rules are used to classify the student in learning style categories based on their answers on ILS. Some other rules are used to give recommended materials to students that belong to a certain category.
From the previous explanations, we get that if a student get active-reflective score in range 0-5 (or we can simplify this as less than or equal to 5), the student belongs to the reflective category. If the student get active-reflective score in range 6-11 (or we can simplify this as more than 5), the student belongs to the active category. Then we can create rules (1) and (2). These rules are examples that used to classify the student.
∀
(?x) {(Student(?x) active-reflective score(?x, >5)) Æbelong to category(?x, active)} (1)
∀(?x) {(Student(?x) active-reflective score(?x, <=5)) Æ
belong to category(?x, reflective)} (2)
Rules for giving recommended materials are straightforward. For example, from the mapping on Table I, we get that Quiz feature on Moodle are recommended for sensing students. Then we can create the rule (3).
∀(?x) {Quiz(?x) Æ recommended for (?x, sensing)} (3)
However, it’s little different on giving recommended file. These files must first be classified as document, presentation, audio, image, video, or another kind of files. To perform this classification, we need to read value from MIME type attribute and to use this value for classification. For example, audio files always have a MIME type with prefix “audio/”, followed with its file-specific value. Thus, we can create the rule (4).
∀(?x) {(File(?x) MIME type(?x, "audio/...")) Æ Audio
file(?x)} (4)
Then, we create rules for giving recommended files based on each category. For example, audio files are recommended for verbal students. Thus, we can create the rule (5).
∀(?x) {Audio file(?x) Æ recommended for (?x, verbal)} (5)
V. CONCLUSION AND FUTURE WORKS
Many kinds of e-Learning contents can be used to accommodate students’ needs based on their learning styles. In the case of Felder-Silverman Learning Style Model and contents in Moodle-based e-Learning, there are some kinds of contents that can be used for accommodating characteristics on each dimension. Active-reflective and
sensing-intuitive dimension can be accommodated with different kinds of activities and resources in Moodle. Visual-verbal dimension can be accommodated with different kinds of course files. Sequential-global dimension can be accommodated by showing course in appropriate course format.
These mappings are clearer and more detail when it is represented in the form of ontology. The rules of giving recommendation are also clearer and can be used for generating information when it is represented as rules on ontology. Then, ontology can be used for represents a domain clearly and very detail.
The next work for this research is an implementation of the ontology inside a Moodle-based e-Learning. There are some mechanisms that must be used for this implementation because of platform difference between ontology development technology and Moodle-based e-Learning environment. This research also needs for validation and future research to know if the recommended content is appropriate to the student with the category of learning styles.
ACKNOWLEDGMENT
This work was supported in part by Semantic Web and Ontology Research Group (SWORG UGM).
REFERENCES
[1] R. M. Felder and L. K. Silverman, “Learning and Teaching Styles In Engineering Education,” Engineering Education, vol. 78, no. 7, pp. 674-681, 1988.
[2] A. L. F. Velazquez and S. Assar, “Using Learning Styles to Enhance an E-Learning System,” 2007. [Online]. Available: http://www-public.int-evry.fr/~assar/pdf/ECEL07_Franzoni.pdf. [Accessed 10 March 2014].
[3] L. J. Deborah, R. Baskaran and A. Kanaan, “Learning styles assessment and theoretical origin in an E-learning scenario: a survey,” Artificial Intelligent Review, 2012.
[4] R. Studer, R. Benjamin and D. Fensel, “Knowledge Engineering: Principles and methods,” Data and Knowledge Engineering, vol. 25, no. 1-2, pp. 161-197, 1998.
[5] E. Ozpolat and G. B. Akar, “Automatic detection of learning styles for an e-Learning system,” Computers & Education, vol. 53, pp. 355-367, 2009.
[6] A. Klasnja-Milicevic, B. Vesin, M. Ivanovic and Z. Budimac, “E-Learning personalization based on hybrid recommendation strategy and learning style identification,” Computers & Education, vol. 53, pp. 885-889, 2011.
[7] S. Cakula and M. Sedleniece, “Development of a personalized e-learning model using methods of ontology,” Procedia Computer Science, vol. 26, pp. 113-120, 2013.
[8] I. Diaconescu, S. Lukichev and A. Giurca, “Semantic Web and Rule Reasoning Inside of E-Learning System,” in International Symposium on Intelligent and Distributed Computing, Craiova, 2007. [9] R. M. Felder and B. A. Soloman, “Index of Learning Styles
Questionnaire,” 1997. [Online]. Available: http://www.engr.ncsu.edu/learningstyles/ilsweb.html. [Accessed 10 March 2014].
[10] T. A. Litzinger, S. H. Lee, J. C. Wise and R. M. Felder, “A Psychometric Study of the Index of Learning Styles,” Journal of Engineering Education, vol. 96, no. 4, pp. 309-319, 2007.
[11] R. M. Felder and J. Spurlin, “Applications, Reliability and Validity of the Index of Learning Styles,” Intl. Journal of Engineering Education, vol. 21, no. 1, pp. 103-112, 2005.
[12] A. Gomez-Perez, M. Fernandez-Lopez and O. Corcho, Ontological engineering : with examples from the areas of knowledge management, e-commerce and semantic web, London: Springer-Verlag, 2004.
[13] R. M. Felder and B. Soloman, “Learning Styles and Strategies,” [Online]. Available: http://www4.ncsu.edu/unity/lockers/users/f/ felder/public/ILSdir/styles.htm. [Accessed 08 October 2014]. [14] Moodle Community, “Activities,” 1 March 2014. [Online].
Available: http://docs.moodle.org/27/en/Activities. [Accessed 7 August 2014].
[15] Moodle Community, “Resources,” 25 June 2014. [Online]. Available: http://docs.moodle.org/27/en/Resources. [Accessed 7 August 2014]. [16] Moodle Community, “Course Formats,” 14 August 2014. [Online].
Available: https://docs.moodle.org/27/en/Course_formats. [Accessed 8 October 2014].
Author Index
Aabloo, Alvo... 161 Dang, Rakesh... 228
Abdelwahed, Sherif... 270 Darkwa, Osei K. ... 199
Aboalsamh, Hatim... 187 De Silva, D.I... 292
Aboutajdine, Driss... 211 Demirel, Hasan... 161
Adjin, Daniel Michael Okwabi... 199 Dominguez, Alejandro García... 303
Alattas, Reem... 344 Dondapati, Rajasekhar... 228
Aldhaeebi, Maged... 333 Doýdurka, M. Furkan... 51
Aldhobaiban, Dema... 287 Dueñas, Juan C. ... 120
Almajadub, Fatma... 282 Eghan, J.M. ... 199
Almazaydeh, Laiali... 287 El Aroussi, Mohamed... 211
Alvi, Muhammad Haris Bin Tariq... 109 El Ghorfi, Rabii... 211
Al-Yahyai, Ishaq... 297 Eldukhri, E.E. ... 137
Amin, Hafeezullah... 187 Elleithy, Khaled... 344, 282, 287 Amoroso, Anderson Levati... 75 Eo, Yeboah-Boateng... 199
Anbarjafar, Gholamreza... 161 Erradi, Abdelkarim... 270
Asemota, Godwin Norense Osarumwense... 252 Esteban, Gonzalo... 149
Aslam, Saleem... 309 Falkinger, M. ... 97
Ayala, Helon V.H. ... 69 Fall, Salif... 44
Ba, Oumar... 44 Faraboschi, Paolo... 264
Baffour, Robert A. ... 199 Ferreira, Yroá... 350
Baig, Mirza Qutab... 109 Feussner, H. ... 97
Bamatraf, Saeed... 187 Fitsilis, Panos... 217
Baño, A. Sapena... 3 Freire, Roberto Z. ... 69
Bardeli Jr., Elcio A. ... 69 Fu, Lin... 234
Benhidour, Hafida... 19 Galdón, Asunción Santamaría... 131
Bermejo, A. ... 240 García, Fernando Casado... 149
Blánquez, F. ... 240 García, Rocío Martínez... 131
Blázquez, F. ... 240 Gerogiannis, Vassilis C. ... 217, 33 Boll, Susanne... 115 Ghazi-Maghrebi, Saeed... 329
Bolotnikova, Anastasia... 161 Giorgi, Roberto... 264
Buján, Jorge Olloqui... 131 Gire, Sophie... 199
Burgos, Diego... 350 Goyal, Varun... 228
Čapko, Darko... 258 Graur, Oana... 57, 63 Centea, Ana... 126 Hachaj, Tomasz... 176
Cermeño, Eduardo... 9 Hanson, Isaac... 199
Chaari, Lamia... 322 Henkel, Werner... 57, 63 Coelho, Leandro dos S. ... 69 Hessling, Hermann... 2
Coelho, Leandro dos Santos... 75 Heuten, Wilko... 115
Cousin, Bernard... 322 Ho, Nam... 264
Cruz, Gelson... 350 Hussain, Muhammad... 187
Cruz, Luciano F. da... 69 Ipsilandis, Pandelis... 33
Dalčeković, Nikola... 258 Jamil, Khalid... 333
Daneshmand, Morteza... 161 Jassim, Rafah... 15
Author Index
Jelačić, Bojan... 258 Möhrle, F. ... 97
Jiménez, D. ... 193 Mondelli, Andrea... 264
Jiménez, Guillermo del Campo... 131 Mushenko, Alexey... 155
Kamil, H.G. ... 137 Nana, Laurent... 199
Karageorgos, Anthony... 217 Navarro, José M. ... 120
Karasakal, Onur... 51 Nguyen, Dang Tuan... 205
Karunarathna, K.M.D.M. ... 292 Ogiela, Marek R. ... 176
Kerrache, Said... 19 Ouadou, Mohamed... 211
Kester, Quist-Aphetsi... 199 Ozcinar, Cagri... 161
Khalid, RajaaAldeen... 15 Packianather, M.S. ... 137
Khan, Tamim Ahmed... 109 Panadero, R. Puche... 3
Kholidy, Hisham A. ... 270 Parada, G. Hugo A. ... 120
Kiefer, Rudolf... 161 Pascu, Anca Christine... 199
Kim, Dong-Ju... 166, 172 Perandones, Jorge Martín... 131
Kim, Hyunduk... 166, 172 Pérez, Ana... 9
Kim, Hyung Seok... 309 Pérez, Javier... 246
Kodagoda, N. ... 292 Platero, C.A. ... 240
Kong, Xi... 115 Portero, Antoni... 264
Koühne, Markus... 315 Prakoso, Robertus Sonny... 276
Koychev, Ivan... 87 Qazi, Emad-Ul-Haq... 187
Kumbasar, Tufan... 51 Quaynor, Nii Narku... 199
Kunzler, Jonas... 350 Rajapaksha, M.M... 292
Kusumawardani, Sri Suning... 276 Rankin, John... 25
Lazarova, Gergana... 87 Rapti, Elli... 217
Lee, Kyung Geun... 309 Rasti, Pejman... 161
Lemos, Rodrigo... 350 Razlighi, Ziba Reza-zadeh... 329
Lera, Francisco Rodríguez... 149 Rebollo, E. ... 240
Liang, Jun... 234 Rodrigo, J.A. ... 193
López, J.P. ... 193 Rodríguez, Jaime Caffarel... 131
Lüsi, Iiris... 161 Roman, J. Martinez... 3
Maaloul, Rihab... 322 Roßmann, Jürgen... 143
Maghrebi, Mojtaba... 103 Rumy, M.M... 292
Malik, Aamir... 187 Ryu, Nuri... 166, 172 Mangal, S.K. ... 228 Sahakyan, Armen... 161
Maqsood, Junaid... 109 Sahin, Atakan... 51
Mariani, Viviana Cocco... 75 Sales, Jorge... 246
Marín, Raúl... 246 Sanchez, M. Pineda... 3
Masheiti, Salah... 223 Santosa, Paulus Insap... 276
Matayoshi, Mitsukuni... 81 Sanz, Pedro J. ... 246
Matellán, Vicente... 149 Scionti, Alberto... 264
Mathkour, Hassan... 187 Sebak, Abdel R. ... 333
Menéndez, J. M. ... 193 Segundo, Emerson Hochsteiner de Vasconcelos... 75
Miclea, Liviu... 126 Shahid, Adnan... 309
Author Index
Shamekh, Awad... 223 Touzene, Abderezak... 297
Shen, Boyang... 234 Tran, Trung... 205
Shen, Qiang... 1 Traumann, Andres... 161
Sieck, Jürgen... 315 Tselios, Dimitrios... 33
Sierra, Silvia Calatrava... 131 Tumari, S.Z. Mohd... 39
Sigüenza, Juan Alberto... 9 Valencia, J. Burriel... 3
Silva, Hugo... 350 Veselov, Gennady... 155
Sklyrov, Andrey... 155 Vizzarri, Alessandro... 339
Sklyrov, Sergey... 155 Vukmirović, Srđjan... 258
Sohn, Myoung-Kyu... 166, 172 Waizumi, Yuji... 182
Solinas, Marcos... 264 Waller, S. Travis... 103
Song, Jie... 131 Wang, Sheng... 234
Sow, Gustave... 44 Weede, O. ... 97
Stan, Ovidiu... 126 Weerasingha, H.M.D.A. ... 292
Steil, Thomas... 143 Weller, Lennart... 115
Stoja, Sebastijan... 258 Wörn, H. ... 97
Sudirman, R. ... 39 Yesil, Engin... 51
Tanaka, Kazuyuki... 182 Youldash, Mustafa... 25
Theakston, Jonathan... 223 Zaeri, Naser... 92
Thiaw, Lamine... 44
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