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

Washington • Tokyo

Copyright © 2014 by The Institute of Electrical and Electronics Engineers, Inc.

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2014 Second InternationalConference on ArtificialIntelligence, Modelling

and Simulation

AIMS 2014Table 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 AddressKeynote 1: Feature Selection in Data-Driven Systems Modelling ................................................................1

Qiang Shen

Keynote 2: Challenges in Handling and Processing Huge Data ..................................................................2Hermann Hessling

Track 01.A Artificial IntelligenceStudy of Performance of Several Techniques of Fault Diagnosis for InductionMotors 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 ........................................................................9Ana Pérez, Eduardo Cermeño, and Juan Alberto Sigüenza

Expert Diagnosis Systems for Network Connection Problems ...................................................................15RajaaAldeen Khalid and Rafah Jassim

Topology-Aware Simulated Annealing ........................................................................................................19Said Kerrache and Hafida Benhidour

Skinning Analysis of a Mapping Algorithm in Higher Dimensions ..............................................................25Mustafa Youldash and John Rankin

v

Track 02.B. Neural Networks and Fuzzy SystemsConsolidation of the IFM with the JSSP through Neural Networks as Modelfor Software Projects ..................................................................................................................................33

Pandelis Ipsilandis, Dimitrios Tselios, and Vassilis C. Gerogiannis

Classification of Working Memory Impairment in Children UsingElectroencephalograph Signal at the Prefrontal Cortex ..............................................................................39

S.Z. Mohd Tumari and R. Sudirman

Designing ANFIS with Self-Extraction of Rules ..........................................................................................44Lamine Thiaw, Gustave Sow, Oumar Ba, and Salif Fall

An Approach to Represent Time Series Forecasting via Fuzzy Numbers .................................................51Atakan Sahin, Tufan Kumbasar, Engin Yesil, M. Furkan Doýdurka,and Onur Karasakal

Track 03.C Evolutionary ComputationTowards Deterministic Network Coding in Hierarchical Networks ..............................................................57

Oana Graur and Werner Henkel

Steps Towards Decentralized Deterministic Network Coding ....................................................................63Oana Graur and Werner Henkel

On the Improvement of Elite Swimmers Velocity Identification by Using NeuralNetwork 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 ContinuousOptimization ................................................................................................................................................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 .............................................81Mitsukuni Matayoshi

A Semi-Supervised Multi-view Genetic Algorithm ......................................................................................87Gergana Lazarova and Ivan Koychev

Track 06.F Bioinformatics and BioengineeringMoments Invariant for Expression Invariant Thermal Human Recognition ................................................92

Naser Zaeri

Movement Analysis for Surgical Skill Assessment and Measurementof Ergonomic Conditions .............................................................................................................................97

O. Weede, F. Möhrle, H. Wörn, M. Falkinger, and H. Feussner

vi

Track 11.K Intelligent Systems and ApplicationsExploring Experts Decisions in Concrete Delivery Dispatching Systems UsingBayesian Network Learning Techniques ..................................................................................................103

Mojtaba Maghrebi and S. Travis Waller

A Comparative Analysis on Home Automation Techniques .....................................................................109Mirza Qutab Baig, Junaid Maqsood, Muhammad Haris Bin Tariq Alvi,and Tamim Ahmed Khan

Spreading Activation Approach for Social Recommendations: The Caseof Microblogging Services .........................................................................................................................115

Xi Kong, Lennart Weller, Susanne Boll, and Wilko Heuten

System Failure Prediction through Rare-Events Elastic-Net LogisticRegression ................................................................................................................................................120

José M. Navarro, G. Hugo A. Parada, and Juan C. Dueñas

Eye-Gaze Tracking Method Driven by Raspberry PI Applicable in AutomotiveTraffic Safety .............................................................................................................................................126

Ovidiu Stan, Liviu Miclea, and Ana Centea

Parameter-Based Mechanism for Unifying User Interaction, Applicationsand 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 IntelligenceBalancing Control of Robot Gymnast Based on Discrete-Time LinearQuadratic Regulator Technique ................................................................................................................137

H.G. Kamil, E.E. Eldukhri, and M.S. Packianather

Track 16.P Robotics, Cybernetics, Engineering, Manufacturingand ControlValidating the Camera and Light Simulation of a Virtual Reality Testbedby Means of Physical Mockup Data ..........................................................................................................143

Thomas Steil and Jürgen Roßmann

Mobile Robot Performance in Robotics Challenges: Analyzing a SimulatedIndoor 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 ............................................................................................155Gennady Veselov, Andrey Sklyrov, Alexey Mushenko, and Sergey Sklyrov

vii

Track 19.S Image, Speech, and Signal ProcessingModified 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 .....................................166Hyunduk Kim, Myoung-Kyu Sohn, Dong-Ju Kim, and Nuri Ryu

Geometric Feature-Based Face Normalization for Facial ExpressionRecognition ...............................................................................................................................................172

Dong-Ju Kim, Myoung-Kyu Sohn, Hyunduk Kim, and Nuri Ryu

Qualitative Evaluation of Full Body Movements with Gesture DescriptionLanguage ..................................................................................................................................................176

Tomasz Hachaj and Marek R. Ogiela

Scene Text Recognition Based on Positional Relation between Closed Curves .....................................182Yuji Waizumi and Kazuyuki Tanaka

Studying the Effects of 2D and 3D Educational Contents on Memory RecallUsing 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 AssessmentBased on Psychovisual Characteristics ....................................................................................................193

J.P. López, J.A. Rodrigo, D. Jiménez, and J. M. Menéndez

Feature Based Encryption Technique for Securing Forensic Biometric ImageData 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 TechnologiesSpecification Model of Paragraph Summarization by Verbal Relationships:Objective, Cause, Consequence, Concurrence ........................................................................................205

Trung Tran and Dang Tuan Nguyen

viii

Track 20.T Industry, Business, Management, Human Factors,and Social IssuesA Modeling Approach for IT Governance Basics Application on IT Projectsand IT Goals .............................................................................................................................................211

Rabii El Ghorfi, Mohamed Ouadou, Driss Aboutajdine, and Mohamed El Aroussi

Human Resource Assessment in Software Development Projects Using FuzzyLinguistic 2-Tuples ....................................................................................................................................217

Vassilis C. Gerogiannis, Elli Rapti, Anthony Karageorgos, and Panos Fitsilis

Track 21.U Energy, Power, Transport, Logistics, Harbour, Shippingand Marine SimulationA 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 ...................................................228Varun Goyal, Rajasekhar Dondapati, Rakesh Dang, and S.K. Mangal

Design and Comparison of Feasible Control Systems for VSC-HVDCTransmission System ...............................................................................................................................234

Boyang Shen, Sheng Wang, Lin Fu, and Jun Liang

Verification of a Synchronous Machine Model for Stator Ground FaultSimulation 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 AutonomousUnderwater Vehicles: Evaluating Position Controllers in Changing UnderwaterCurrents ....................................................................................................................................................246

Javier Pérez, Jorge Sales, Raúl Marín, and Pedro J. Sanz

Communality Performance Assessment of Electricity Load ManagementModel for Namibia .....................................................................................................................................252

Godwin Norense Osarumwense Asemota

Track 22.V Parallel, Distributed, and Software Architecturesand SystemsArchitecture of Real-Time Database in Cloud Environment for DistributedSystems ....................................................................................................................................................258

Sebastijan Stoja, Srđjan Vukmirović, Bojan Jelačić, Darko Čapko,and Nikola Dalčeković

Simulating a Multi-core x8664 Architecture with Hardware ISA ExtensionSupporting a Data-Flow Execution Model ................................................................................................264

Nam Ho, Antoni Portero, Marcos Solinas, Alberto Scionti, Andrea Mondelli,Paolo Faraboschi, and Roberto Giorgi

ix

Attack Prediction Models for Cloud Intrusion Detection Systems .............................................................270Hisham A. Kholidy, Abdelkarim Erradi, and Sherif Abdelwahed

Track 23.W Internet Modelling, Semantic Web, and OntologiesUsing Ontology for Providing Content Recommendation Based on LearningStyles inside E-learning ............................................................................................................................276

Sri Suning Kusumawardani, Robertus Sonny Prakoso, and Paulus Insap Santosa

Track 24.X Mobile/Ad Hoc Wireless Networks, Mobicast, SensorPlacement, Target TrackingNovel Location Tracking Energy Efficient Model for Robust Routingover Wireless Sensor Networks ................................................................................................................282

Fatma Almajadub and Khaled Elleithy

Prevention of Wormhole Attacks in Wireless Sensor Networks ...............................................................287Dema Aldhobaiban, Khaled Elleithy, and Laiali Almazaydeh

A Fully Functional Shopping Mall Application—SHOPPING EYE ............................................................292K.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 RoutingAlgorithm for MANET ................................................................................................................................297

Abderezak Touzene and Ishaq Al-Yahyai

“Smart Ships”: Mobile Applications, Cloud and Bigdata on Marine Trafficfor Increased Safety and Optimized Costs Operations .............................................................................303

Alejandro García Dominguez

A Potential Game Approach Towards Distributive Interference Managementin OFDMA-Based Femtocell Networks .....................................................................................................309

Adnan Shahid, Saleem Aslam, Hyung Seok Kim, and Kyung Geun Lee

Location-Based Services with iBeacon Technology .................................................................................315Markus Koühne and Jürgen Sieck

Track 25.Y Performance Engineering of Computer and CommunicationSystemsStudy of Energy Saving in Carrier-Ethernet Network ...............................................................................322

Rihab Maaloul, Lamia Chaari, and Bernard Cousin

Achieving Better Performance Using a New Variable LMS Algorithm Equalizerfor Systems-Based OFDM ........................................................................................................................329

Ziba Reza-zadeh Razlighi and Saeed Ghazi-Maghrebi

Ultra-Wideband Antenna for RFID Underground Oil Industry Application ................................................333Maged Aldhaeebi, Khalid Jamil, and Abdel R. Sebak

x

Analysis of VoIP over LTE End-to-End Performances in Congested Scenarios ......................................339Alessandro Vizzarri

Track 26.Z Circuits, Sensors, and DevicesDetecting and Minimizing Bad Posture Using Postuino among EngineeringStudents ....................................................................................................................................................344

Reem Alattas and Khaled Elleithy

A New Approach for the Differential Spectrum Using the Frobenius Norm ..............................................350Gelson Cruz, Jonas Kunzler, Rodrigo Lemos, Diego Burgos, Hugo Silva,and Yroá Ferreira

Author Index ............................................................................................................................................355

xi

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

xii

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

xiii

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 Eduard Babulak Arijit Bhattacharya Leon Bobrowski Irfan Syamsuddin Vesna Bosilj-Vuksic Fabian Böttinger Jadranka Bozikov Felix Breitenecker Agostino Bruzzon Piers Campbell Theodoros Kostis Hüseyin K. Çakmak Richard Cant Andrejs Romanovs Vlatko Ceric Sanjay Chaudhary Yuehui Chen Russell Cheng Sung-Bae Cho 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 Min-Shiang Hwang Zuwairie Ibrahim Kunio Igusa Hisao Ishibuchi Gerrit Janssens Andras Javor Er Meng Joo Kai Juslin Esko Juuso Nikolaos Karadimas Helen Karatza Arpad Kelemen Marzuki Khalid Dong-hwa Kim Mario Koeppen Issakki Kosonen Kambiz Badie Vincent Lee Hongbo Liu Xiangrong Liu Franco Maceri Emelio Jimenez Macias Mahdi Mahfouf Rashid Mehmood Yuri Merkuryev Galina Merkuryeva Zhou Mingtao Farshad Moradi Gaius Mulley Atulya Nagar Gaby Neumann Leonid Novitski Osamu Ono

Alessandra Orsoni Jeng-Shyang Pan Athanasios Pantelous Charles Patchett P. Pichappan Miguel Angel Piera Henri Pierreval D K Pra Marius Radulescu Fazal Rehman Marco Remondino Olaf Ruhle Paramasivan Saratchandran Kazunori Sato Peter Schwartz Janos Janosy Rohit Sharma Igor Skrjanc Miroslav Snorek Mo Song Mojca Indihar Stemberger K.G. Subramanian R K Subramanian Vassilis Tsoulkas Pandian Vasant Carlos Martin Vide Siegfried Wassertheurer Roland Wertz Wolfgang Wiechert Edward Williams Zhang Yi Rubiyah Yusof Daniela Zaharie Richard Zobel Borut Zupancic

xiv

International Reviewers

Dayang Norhayati Abg Jawawi Ajith Abraham Mohammad Nazir Ahmad David Al-Dabass Dhiya Al-Jumeily David Aldabass Mikulas Alexik Belal Alhaija Konar Amit Bagus Arthaya Mohsen Askari Eduard Babulak Kambiz Badie Gurvinder-Singh Baicher Arijit Bhattacharya Nurmin Bolong Hueseyin Cakmak Richard Cant Andre Carvalho Brijesh Chaurasia Sung-Bae Cho Jamal Dargham 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 Elisati Hulu Min-Shiang Hwang Zuwairie Ibrahim Nauman Israr Gerrit Janssens 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 Arshin Rezazadeh Norlaili Safri Ignatius Sandy Ali Selamat Ajay Singh Fadzilah Siraj Mo Song Rubita Sudirman Dedy Suryadi Irfan Syamsuddin Otavio Teixeira Jason Teo Kenneth Teo Geetam Tomar Martin Tunnicliffe Ijaz Uddin Shekhar Verma Farrah Wong Jasmy Yunus Richard Zobel

xv

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

xvi

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

1

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

2

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-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.

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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 Moodle-based 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.

277

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.

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278

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-Silverman Learning Style Model

• Active-Reflective • Sensing-Intuitive • Visual-Verbal • Sequential-Global

• Category name • is same dimension as

Learning Style Questionnaire

Questionnaire based 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 Moodle-based 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 • Image file • Video file

• File ID • File name • MIME type

Student Student that use Moodle-based 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)

279

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)) � belong to category(?x, active)} (1)

∀(?x) {(Student(?x) active-reflective score(?x, <=5)) � 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) � 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/...")) � 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) � 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: asurvey,” 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.

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[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].

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Author IndexAabloo, Alvo............................................................... 161 Dang, Rakesh............................................................ 228Abdelwahed, Sherif.................................................... 270 Darkwa, Osei K. ........................................................ 199Aboalsamh, Hatim...................................................... 187 De Silva, D.I............................................................... 292Aboutajdine, Driss...................................................... 211 Demirel, Hasan.......................................................... 161Adjin, Daniel Michael Okwabi..................................... 199 Dominguez, Alejandro García.................................... 303Alattas, Reem............................................................. 344 Dondapati, Rajasekhar............................................... 228Aldhaeebi, Maged...................................................... 333 Doýdurka, M. Furkan.................................................... 51Aldhobaiban, Dema.................................................... 287 Dueñas, Juan C. ....................................................... 120Almajadub, Fatma...................................................... 282 Eghan, J.M. ............................................................... 199Almazaydeh, Laiali..................................................... 287 El Aroussi, Mohamed................................................. 211Alvi, Muhammad Haris Bin Tariq................................ 109 El Ghorfi, Rabii........................................................... 211Al-Yahyai, Ishaq......................................................... 297 Eldukhri, E.E. ............................................................ 137Amin, Hafeezullah...................................................... 187 Elleithy, Khaled.......................................... 344, 282, 287Amoroso, Anderson Levati........................................... 75 Eo, Yeboah-Boateng.................................................. 199Anbarjafar, Gholamreza............................................. 161 Erradi, Abdelkarim...................................................... 270Asemota, Godwin Norense Osarumwense................ 252 Esteban, Gonzalo....................................................... 149Aslam, Saleem........................................................... 309 Falkinger, M. ............................................................... 97Ayala, Helon V.H. ........................................................ 69 Fall, Salif...................................................................... 44Ba, Oumar.................................................................... 44 Faraboschi, Paolo...................................................... 264Baffour, Robert A. ..................................................... 199 Ferreira, Yroá............................................................. 350Baig, Mirza Qutab...................................................... 109 Feussner, H. ................................................................ 97Bamatraf, Saeed........................................................ 187 Fitsilis, Panos............................................................. 217Baño, A. Sapena............................................................ 3 Freire, Roberto Z. ........................................................ 69Bardeli Jr., Elcio A. ...................................................... 69 Fu, Lin........................................................................ 234Benhidour, Hafida........................................................ 19 Galdón, Asunción Santamaría................................... 131Bermejo, A. ............................................................... 240 García, Fernando Casado.......................................... 149Blánquez, F. .............................................................. 240 García, Rocío Martínez.............................................. 131Blázquez, F. .............................................................. 240 Gerogiannis, Vassilis C. ...................................... 217, 33Boll, Susanne............................................................. 115 Ghazi-Maghrebi, Saeed............................................. 329Bolotnikova, Anastasia............................................... 161 Giorgi, Roberto........................................................... 264Buján, Jorge Olloqui................................................... 131 Gire, Sophie............................................................... 199Burgos, Diego............................................................ 350 Goyal, Varun.............................................................. 228Čapko, Darko............................................................. 258 Graur, Oana........................................................... 57, 63Centea, Ana............................................................... 126 Hachaj, Tomasz......................................................... 176Cermeño, Eduardo......................................................... 9 Hanson, Isaac............................................................ 199Chaari, Lamia............................................................. 322 Henkel, Werner...................................................... 57, 63Coelho, Leandro dos S. .............................................. 69 Hessling, Hermann......................................................... 2Coelho, Leandro dos Santos........................................ 75 Heuten, Wilko............................................................. 115Cousin, Bernard......................................................... 322 Ho, Nam..................................................................... 264Cruz, Gelson.............................................................. 350 Hussain, Muhammad................................................. 187Cruz, Luciano F. da...................................................... 69 Ipsilandis, Pandelis...................................................... 33Dalčeković, Nikola...................................................... 258 Jamil, Khalid............................................................... 333Daneshmand, Morteza............................................... 161 Jassim, Rafah.............................................................. 15

355

Author IndexJelačić, Bojan............................................................. 258 Möhrle, F. .................................................................... 97Jiménez, D. ............................................................... 193 Mondelli, Andrea........................................................ 264Jiménez, Guillermo del Campo.................................. 131 Mushenko, Alexey...................................................... 155Kamil, H.G. ................................................................ 137 Nana, Laurent............................................................ 199Karageorgos, Anthony............................................... 217 Navarro, José M. ....................................................... 120Karasakal, Onur........................................................... 51 Nguyen, Dang Tuan................................................... 205Karunarathna, K.M.D.M. ........................................... 292 Ogiela, Marek R. ....................................................... 176Kerrache, Said............................................................. 19 Ouadou, Mohamed.................................................... 211Kester, Quist-Aphetsi................................................. 199 Ozcinar, Cagri............................................................ 161Khalid, RajaaAldeen..................................................... 15 Packianather, M.S. .................................................... 137Khan, Tamim Ahmed................................................. 109 Panadero, R. Puche....................................................... 3Kholidy, Hisham A. .................................................... 270 Parada, G. Hugo A. ................................................... 120Kiefer, Rudolf............................................................. 161 Pascu, Anca Christine................................................ 199Kim, Dong-Ju..................................................... 166, 172 Perandones, Jorge Martín.......................................... 131Kim, Hyunduk..................................................... 166, 172 Pérez, Ana..................................................................... 9Kim, Hyung Seok....................................................... 309 Pérez, Javier.............................................................. 246Kodagoda, N. ............................................................ 292 Platero, C.A. .............................................................. 240Kong, Xi...................................................................... 115 Portero, Antoni........................................................... 264Koühne, Markus......................................................... 315 Prakoso, Robertus Sonny.......................................... 276Koychev, Ivan............................................................... 87 Qazi, Emad-Ul-Haq.................................................... 187Kumbasar, Tufan.......................................................... 51 Quaynor, Nii Narku..................................................... 199Kunzler, Jonas........................................................... 350 Rajapaksha, M.M....................................................... 292Kusumawardani, Sri Suning....................................... 276 Rankin, John................................................................ 25Lazarova, Gergana...................................................... 87 Rapti, Elli.................................................................... 217Lee, Kyung Geun....................................................... 309 Rasti, Pejman............................................................. 161Lemos, Rodrigo.......................................................... 350 Razlighi, Ziba Reza-zadeh......................................... 329Lera, Francisco Rodríguez......................................... 149 Rebollo, E. ................................................................. 240Liang, Jun................................................................... 234 Rodrigo, J.A. ............................................................. 193López, J.P. ................................................................ 193 Rodríguez, Jaime Caffarel......................................... 131Lüsi, Iiris..................................................................... 161 Roman, J. Martinez........................................................ 3Maaloul, Rihab........................................................... 322 Roßmann, Jürgen...................................................... 143Maghrebi, Mojtaba..................................................... 103 Rumy, M.M................................................................. 292Malik, Aamir............................................................... 187 Ryu, Nuri............................................................ 166, 172Mangal, S.K. .............................................................. 228 Sahakyan, Armen....................................................... 161Maqsood, Junaid........................................................ 109 Sahin, Atakan............................................................... 51Mariani, Viviana Cocco................................................ 75 Sales, Jorge............................................................... 246Marín, Raúl................................................................. 246 Sanchez, M. Pineda....................................................... 3Masheiti, Salah........................................................... 223 Santosa, Paulus Insap............................................... 276Matayoshi, Mitsukuni.................................................... 81 Sanz, Pedro J. ........................................................... 246Matellán, Vicente........................................................ 149 Scionti, Alberto........................................................... 264Mathkour, Hassan...................................................... 187 Sebak, Abdel R. ........................................................ 333Menéndez, J. M. ........................................................ 193 Segundo, Emerson Hochsteiner de Vasconcelos........ 75Miclea, Liviu............................................................... 126 Shahid, Adnan............................................................ 309

356

Author IndexShamekh, Awad......................................................... 223 Touzene, Abderezak.................................................. 297Shen, Boyang............................................................. 234 Tran, Trung................................................................ 205Shen, Qiang................................................................... 1 Traumann, Andres..................................................... 161Sieck, Jürgen............................................................. 315 Tselios, Dimitrios.......................................................... 33Sierra, Silvia Calatrava............................................... 131 Tumari, S.Z. Mohd....................................................... 39Sigüenza, Juan Alberto.................................................. 9 Valencia, J. Burriel......................................................... 3Silva, Hugo................................................................. 350 Veselov, Gennady...................................................... 155Sklyrov, Andrey.......................................................... 155 Vizzarri, Alessandro................................................... 339Sklyrov, Sergey.......................................................... 155 Vukmirović, Srđjan..................................................... 258Sohn, Myoung-Kyu............................................. 166, 172 Waizumi, Yuji............................................................. 182Solinas, Marcos.......................................................... 264 Waller, S. Travis......................................................... 103Song, Jie.................................................................... 131 Wang, Sheng............................................................. 234Sow, Gustave............................................................... 44 Weede, O. ................................................................... 97Stan, Ovidiu................................................................ 126 Weerasingha, H.M.D.A. ............................................ 292Steil, Thomas............................................................. 143 Weller, Lennart........................................................... 115Stoja, Sebastijan........................................................ 258 Wörn, H. ...................................................................... 97Sudirman, R. ............................................................... 39 Yesil, Engin.................................................................. 51Tanaka, Kazuyuki....................................................... 182 Youldash, Mustafa....................................................... 25Theakston, Jonathan.................................................. 223 Zaeri, Naser................................................................. 92Thiaw, Lamine.............................................................. 44

357

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