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CloudTech’20
The 5th International Conference on Cloud
Computing and Artificial Intelligence:
Technologies and Applications
CloudTech’20
November 24-25, 2020, Morocco www.macc.ma/cloudtech20
IEEE Catalog number: CFP20B66-ART ISBN: 978-1-7281-6175-4
© 2020 IEEE
No part of this book may be reproduced or transmitted in any form or any means, electronic,
recording or otherwise, without consent from IEEE.
Conference venue: Morocco
Co - Editors
Mohamed Essaaidi / Mostapha Zbakh
ENSIAS, Mohamed V University, Morocco
This conference is co-sponsored by the University Mohammed V of Rabat and ENSIAS in partnership with IEEE.
http://www.macc.ma/cloudtech20
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PREFACE
This is one of the most successful IEEE technically sponsored events on Cloud computing and Artificial Intelligence: technologies and applications, organized remotely from Rabat, Morocco.
Cloud computing has gained great and increasing attention from academia, government and industry as a new IT infrastructure requiring smaller investments in hardware platform, staff training, or licensing new software tools. Cloud computing can be seen as any subscription-based or pay-per-use service that, extends the Internet existing capabilities.
CloudTech´20 is organized by Higher National School of Computer Science and Systems Analysis - ENSIAS, Mohamed V University of Rabat and Moroccan Association of Cloud Computing in partnership with IEEE Morocco Section.
CloudTech´20 will address topics related to cloud technologies and Artificial Intelligence; architecture, applications and services including distributed computing and data centers, cloud infrastructure and its security, end-user services. This conference will provide a high-level international forum for scientists, researchers, professionals and students who will have the opportunity to present and to attend talks and papers on the state-of-the-art research results addresing new challenges, and discussing the new trends in Cloud Technology and Artificial Intelligence, technologies and applications.
CloudTech’20 received 160 submissions from 22 countries, which is really a great challenge we have won with such a level of submissions and international participations. From these, the Technical Program Committee selected 64 papers based on their originality, innovative relevance, and clarity of presentation, to be presented and included in this volume of the conference proceedings. CloudTech´20 technical program includes besides the accepted papers, 8 keynote talks covering different hot topics related with Cloud Computing and Artificial Intelligence: technologies, applications and services. These talks’ abstracts and short Biographies of their speakers are published in this proceeding.
We seize this opportunity to express our deepest thanks and gratitude to all the TPC chairs and members for the wonderful reviewing process which enabled us to come up with a high quality technical program.
M. Essaaidi and M. Zbakh
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HONORARY CHAIRS
Mohammed RHACHI, President of Mohammed V University, Rabat, Morocco
GENERAL CHAIRS
General Co-Chairs
An Breaken, Sidi Ahmed Mahmoudi VUB, Brussels, Belgium UMONS, Mons, Belgium
KEYNOTES
Chunming Rong, University of Stavanger, Norway
Maria Fasli, University of Essex, UK
Panda Dhabaleswar, The Ohio State University, USA
Helen Karatza, Aristotle University of Thessaloniki, Greece
Othmane Bouhali, Texas A&M University, Qatar
Abdelmounaam Rezgui, Illinois State University, USA
TPC CHAIRS
Pierre Manneback, University of Mons, Mons, Belgium
Claude Tadonki, Mines ParisTech Paris, France
Mohamed ESSAAIDI National Higher School of IT (ENSIAS), Professor
IEEE Morocco Section Chair Rabat, Morocco essaaidi@ieee.org
+ 212 (0) 661 725 992
Pierre Manneback UMONS, Mons, Belgium
Abdellah Touhafi
VUB, Brussels, Belgium
Claude TADONKI MINES ParisTech, France
Mostafa Daoudi,
Mohammed I University, Oujda, Morocco
Mostapha ZBAKH National Higher School of IT (ENSIAS),
Professor Rabat, Morocco
m.zbakh@um5s.net.ma + 212 (0) 666 904 940
http://www.chunming.net/http://www.mines-paristech.fr/Accueil/mailto:essaaidi@ieee.org
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ORGANIZING COMMITTEE
Abdellatif Afia
Ali Choukri,
Mohamed El Ghmary
Brahim Flilih
Fatima-Zohra Mhada
Hasnae Alaoui Lamrani
Rim Doukha
Abderrahmane Ez-zahout
Mahmoud Hamlaoui
Ali Ouacha
Laila Bouhouch
Abdellatif El Ghazi
Bouchaib Ferrahi
Najlae Kasmi
Hicham Touil
TECHNICAL PROGRAM COMMITTEE Karthik Gomadam, Accenture Technology Labs - Silicon Valley, USA
Marcel Kunze, Karlsruhe Institute of Technology (KIT), Germany
Pierre Manneback, Faculty of Engineering, University of Mons, Belgium
Abdulrahman A. Azab, University of Stavanger, Norway
Alexander Lazovik, The University of Groningen (RuG), Netherlands
Amac M. Guvensan, Yildiz Technical University, Turkey
Andy Ju An Wang, Southern Illinois University, USA
Bilal Kashif, COMSATS Institute of Information Technology, Pakistan
Bouchra Asri, ENSIAS, Rabat, Morocco
Chen Gao, Tufts University, USA
Ching-Hsien Hsu, Chung Hua University, Taiwan
Claude Tadonki, Mines ParisTech, France
Claudio Fiandrino, University of Luxembourg, Luxemburg
Claudio Rossi, Universidad Politécnica de Madrid - CSIC, Madrid, Spain
Dan Grigoras, UCC, Ireland
Dana Petcu, West University of Timisoara, Romania
Daniel Dïaz-Sanchez, Universidad Carlos III de Madrid, Spain
Deepal Jayasinghe, Microsoft, USA
Dongfang Zhao, Illinois Institute of Technology, Chicago IL, USA
Dzmitry Kliazovich, University of Luxembourg, Luxembourg
Eddy Caron, ENS Lyon, Inria CNRS, France
Fawaz Saleem Bokhari, PUCIT - University of the Punjab, Pakistan
Frederico Alvares, University of Nantes, France
Gianluigi Zavattaro, University of Bologna, Italy
Grégoire Danoy, University of Luxembourg, Luxembourg
Hai Jin, School of Computer Science and Technology, Huazhong University of Science and
Technology, Wuhan, China
Hajar Mousannif, Cadi Ayyad University, Marrakesh, Morocco
Hamid Harroud, AUI, Ifrane, Morocco
Helen Karatza, Aristotle University of Thessaloniki, Greece
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Na Gong, University of South Alabama, USA
Abdelhak Lakhouaja, Faculty of Sciences, Mohammed First University, Oujda, Morocco
Abdelmounaam Rezgui, Illinois State University, Normal, Illinois, USA
Alberto Cano, Virginia Commonwealth University, Richmond, USA
Mawloud Omar, Research Unity LaMOS Faculty of Exact Sciences, University of Bejaia, Algeria
Hassina Aliane, University of Sciences and Technology, Bejaia, Algeria
Héctor Menéndez, University College, London, United Kingdom
Ismael Bouassida Rodriguez, Faculty of Computer Science, Complutense University, Madrid, Spain
Jung-Chun Liu, Tunghai university, Taiwan
Juraj Machaj, University of Zilina, Slovakia
Kun Ma, Institute of Technological Sciences, Wuhan University, Wuhan, China
Hanan El Bakkali, ENSIAS, Mohammed V University, Rabat, Morocco
Bouchaib Ferrahi, Faculty of Sciences, Abdelmalek Essaadi University, Tetouan, Morocco
Ningfang Mi, Northeastern University, Boston, USA
Imed Kacem, University of Lorraine, France
Irena Bojanova, University of Maryland University College, USA
Ismail Khalil, Institute of Telecooperation Johannes Kepler University Linz, Austria
Iyer Vasanth, New College of Florida, USA
Jaime Lloret, Polytechnic University of Valencia, Spain
Javier Garcia Blas, Carlos III University of Madrid, Spain
Jérémie LEGUAY, Thales Group, Colombes, France
Jiannong Cao, Hong Kong Polytechnic University, Hong Kong
Jose Maria Alcaraz Calero, University of the West of Scotland, UK
Juan Durillo, University of Innsbruck, Austria
Abdelkrim Haqiq, Hassan 1st University, Settat, Morocco
Kashif Munir, KFUPM, Dhahran, KSA
Ke Wang, Illinois Institute of Technology, Chicago, USA
Hanan El Bakkali, ENSIAS, Mohammed V University, Rabat, Morocco
Khalid Osman, COMSATS Institute of Information Technology, Pakistan
Laurent Lefevre, INRIA, France
Omar Alfandi, University of Goettingen, Germany
Leonel Sousa, IST, Technical University of Lisbon, Portugal
Lukas Kencl, Czech Technical University in Prague, Czech Republic
Abdellatif Afia, ENSIAS, Mohammed V University, Rabat, Morocco
Marco Aiello, University of Groningen, Netherlands
Masaru Kitsuregawa, University of Tokyo, Japan
Mohammed Boulmalf, IUR, Rabat, Morocco
Mohammed Essaaidi, IEEE, ENSIAS, Mohammed V University, Rabat, Morocco
Mohand Mezmaz, Faculty of Engineering, University of Mons, Belgium
Mohsine Eleuldj, EMI, Mohammed V University, Rabat, Morocco
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Mostafa Daoudi, Mohammed I University, Oujda, Morocco
Mahdi Zargayouna, Université Paris Est, Ifsttar, France
Mahmoud Nassar, ENSIAS, Mohammed V University, Rabat, Morocco
Ali Ouacha, Faculty of Sciences, Mohammed V University, Rabat, Morocco
Amal Alhosban, University of Michigan-Flint, Flint, Michigan, USA
An Braeken, Vrije Universiteit Brussel (VUB), Brussels, Belgium
Meriem Thabet, University of Abdelhamid Mehri Constantine 2, Constantine, Algeria
Muhammad Younas, Oxford Brookes University
Mustafa Rafique, Rochester Institute of Technology, New York, USA
Okba Kazar, University of Biskra, Biskra, Algeria
Pardeep Kumar, University of Oxford
Paulo Quaresma, University of Evora, Portugal
Qanita Bani Baker, University of science and technology, jordan
Saqib Munawwar, Nazeer Hussain University, Karachi, Pakistan
Driss Olfa Belkahla, University of Manouba, Tunisia
Mostafa Bellafkih, INPT, Mohammed V University, Rabat, Morocco
Mostafa Ezziyyani, Faculty of Sciences and Techniques, Abdelmalek Essaâdi University, Tangier,
Morocco
Amine Roukh, University of Mons, Belgium
Nouredine Melab, LIFL, Lille, France
Paolo Bellavista, University of Bologna, Italy
Pascal Bouvry, University of Luxembourg, Luxembourg
Pascal Lorenz, University of Haute Alsace, France
Abdellatif El Ghazi, IUR, Rabat, Morocco
Rahal Romadi, ENSIAS, Mohammed V University, Rabat, Morocco
Rajdeep Bhowmik, Cisco Systems, Inc., USA
Saif Malik, COMSATS Institute of Information Technology, Pakistan
Abdellah Touhafi, VUB, Brussels, Belgium
Sajjad Ahmad Madani, COMSATS Institute of Information Technology, Pakistan
Samee U. Khan, North Dakota State University, Fargo, ND, USA
Samir Youcef, University of Lorraine LORIA-INRIA-UMR, Vandoeuvre-les-Nancy, France
Santiago Iturriaga, Universidad de la República, Uruguay
Sherif Khattab, Faculty of Computers and Information, Cairo University, Egypt
Sidi Ahmed Mahmoudi, Faculty of Engineering, University of Mons, Belgium
Song Fu, University of North Texas, USA
Stefano Giordano, University of Pisa, Italy
Steve Crago, Southern California University, USA
Tarek El-Ghazawi, The George Washington University, USA
Thomas Magedanz, TU Berlin
Fraunhofer FOKUS, Germany
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Tomas Margalef, Autonomous University of Barcelona Barcelona, Spain
Karim Baina, ENSIAS, Mohammed V University, Rabat, Morocco
Tonglin Li, Illinois Institute of Technology, Chicago, USA
Toyotaro Suzumura, IBM Tokyo Research Laboratory, Japan
Veena B. Mendiratta, Bell Labs, Alcatel-Lucent, USA
Wei Jie, West London University, UK
Weiwei Chen, University of Southern California, USA
Yeh-Ching Chung, National Tsing Hua University, Taiwan
Atilla Elci, Aksaray University, Isparta, Turkey
Azahara Camacho, Universidad Complutense de Madrid, Spain
Chau Yuen, Singapore University of Technology and Design, Singapore
Raluca Maria Aileni, Politehnica University of Bucharest, Faculty of Electronics Telecommunication
and Information Technology, Romania
Saïd Mahmoudi, University of Mons - FPMs Mons, Belgium
Said Tazi, University of Pau et Pays de l’Adour, Anglet, FRANCE
Stanimir Stoyanov, University of Plovdiv "Paisii Hilendarski", Plovdiv, Bulgaria
Tzung-Pei Hong, National University of Kaohsiung, Kaohsiung, Taiwan
Mohammed Benjelloun, Faculte Polytehnique Mons, UMONS, Mons, Belgium
Zafeirios Papazachos, Aristotle University of Thessaloniki, Thessaloniki, Greece
Meriem Belguidoum, University of Constantine 2, Algeria
Ahmed Zerouali, University of Mons, Mons, Belgium
Nicola Capuano, University of Salerno, Fisciano, Italy
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Contents
Keynotes
Secure Blockchain Ecosystem – The Network is my Computer _____________________________ 13
Chunming Rong, University of Stavanger, Norway
Cloud and Fog Computing Resource Allocation and Scheduling of Complex Real-Time
Applications______________________________________________________________________14
Helen Karatza, Aristotle University of Thessaloniki, Greece
Cloud-based Intelligent Remote Patient Monitoring_____________________________________ 15
Abdelmounaam Rezgui, Illinois State University, USA
Data Science and AI: Trends, Myths and Challenges______________________________________16
Maria Fasli, University of ESSEX, UK
Artificial intelligence and modeling activities in imaging and medical physics application_______ 17
Othmane Bouhali, Texas A&M University, Qatar
HPC and AI Meet Cloud: Opportunities and Challenges in Designing High-Performance MPI and Deep
Learning Libraries for Public Cloud____________________________________________________18
Panda Dhabaleswar, The Ohio State University, USA
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Session 1
Security and Privacy
Efficient Mobile User Authentication Service Based on User's Memories with Privacy Preservation
and User Untraceability (ID-14) ______________________________________________________19
An Braeken and Abdellah Touhafi
Secure, Efficient and Dynamic Data Search using Searchable Symmetric Encryption (ID-74) ______20
Mohammed Saad Shaikh, Jatna Bavishi and Reema Patel
Cloud computing security classifications and taxonomies: a comprehensive study and comparison
(ID-78) __________________________________________________________________________21
Najat Tissir, Said El Kafhali and Nourddine Aboutabit
ESSMAR: Edge Supportive Secure Mobile Augmented Reality Architecture for Healthcare (ID-15)
________________________________________________________________________________22
An Braeken, Pawani Porambage, Amirthan Puvaneswaran and Madhusanka Liyanage
Towards Smart Blockchain-Based Framework for Privacy Preserving Data sharing in Smart Cities (ID-
39) _____________________________________________________________________________23
Driss El Majdoubi, Hanan El Bakkali and Souad Sadki
Session 2
Smart cities/Farming and Mobile Technologies
Big Data Storage and Analysis for Smart Farming (ID-58) _________________________________24
Fabrice Nolack Fote, Saïd Mahmoudi, Sidi Ahmed Mahmoudi and Amine Roukh
Performance Evaluation of Newly Implemented Resource Blocks (RB) Allocation Schemes on NS-3
simulator for mMTC 5G NR (New Radio) Femtocells (ID-70) ______________________________ 25
Ismail Angri, Abdellah Najid and Mohammed Mahfoudi
Improvements of Centroid Localization Algorithm for Wireless Sensor Networks (ID-96) ______ 26
Abdelali Hadir, Khalid Zine-Dine and Mohamed Bakhouya
An overview of recommender systems in the context of smart cities (ID-64) ________________ 27
Rabie Madani, Abderrahmane Ez-Zahout and Abdellah Idrissi
A re-engineering approach for extension of the Tourist Guide Knowledge Base (ID-40) _______ 28
Asya Stoyanova-Doycheva, Todorka Glushkova, Nevena Moraliyska and Emil Doychev
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Session 3
Cloud services
NFaaS: A new cloud service for water distribution network monitoring (ID-11)________________29
Rayane El Sibai, Jacques Bou Abdo, Chady Abou Jaoude, Jacques Demerjian, Yousra Chabchoub and
Raja Chiky
An overall statistical analysis of AI tools deployed in Cloud computing and networking systems (ID-
10)______________________________________________________________________________30
Ikhlasse Hamzaoui, Benjamin Duthil, Vincent Courboulay and Hicham Medromi
Multi-task Offloading and Computational Resources Management in a Mobile Edge Computing
Environment (ID-102) ______________________________________________________________31
Mohamed El Ghmary, Youssef Hmimz, Tarik Chanyour and Mohamed Ouçamah Cherkaoui Malki
Deployment of Containerized Deep Learning Applications in the cloud (ID-51) ________________32
Rim Doukha, Sidi Ahmed Mahmoudi, Mostapha Zbakh and Pierre Manneback
Autonomous Provisioning of Preemptive Instances in Google Cloud for Maximum Performance Per
Dollar (ID-87) _____________________________________________________________________33
Jonas Kruger Svensson, Hårek Haugerud and Anis Yazidi
Session 4
Macine Learning
A Review of Credit Card Fraud Detection Using Machine Learning Techniques (ID-53) __________34
Nadia Boutaher, Amina Elomri, Noreddine Abghour, Khalid Moussaid and Mohamed Rida
Machine Learning for Anomaly Detection. Performance Study considering Anomaly Distribution in
an Unbalanced Dataset (ID-37) ______________________________________________________35
Salma El Hajjami, Jamal Malki, Mohammed Berrada and Bouziane Fourka
Novel Convex Polyhedron Classifier for Sentiment Analysis (ID-73) _________________________36
Soufiane El Mrabti, Mohamed Lazaar, Mohammed Al Achhab and Hicham Omara
A Recommendation approach based on Correlation and Co-occurrence within social learning
network (ID-7) ___________________________________________________________________37
Sonia Souabi, Asmaâ Retbi, Mohammed Khalidi Idrissi and Samir Bennani
Graph-based Model for Negative e-WOM Influence in Social Media (ID-66) _________________ 38
Abderraouf Dembri and Mohamed Gharzouli
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Session 5
Big data
Big Data Architectures Benchmark for Forecasting Electricity Consumption (ID-52) ____________39
Houda Daki, Asmaa El Hannani and Hassan Ouahmane
A Big Data Placement Strategy in Geographically Distributed Datacenters (ID-59) _____________40
Laila Bouhouch, Mostapha Zbakh and Claude Tadonki
New Approach for implementing big Datamart using NoSQL Key-Value stores (ID-41) __________41
Abdelhak Khalil and Mustapha Belaissaoui
Big data for sustainability: A qualitative analysis (ID-6) ___________________________________42
Wail El Hilali, Abdellah El Manouar and Mohammed Abdou Janati Idrissi
Bloom filter and its variants for the optimization of MapReduce’s algorithms: A review (ID-55) __43
Fatimazahra Ezzaki, Noreddine Abghour, Amina Elomri, Khalid Moussaid and Mohamed Rida
Session 6
Deep Learning
Deep Learning and Tensorflow for Tracking People’s Movements in a Video (ID-97) ___________44
Bornia Jemai, Ali Frihida and Olivier Debauche
SAHAR-LSTM: An enhanced Model for Sentiment Analysis of Hotels’Arabic Reviews based on LSTM
(ID-21) __________________________________________________________________________45
Manal Nejjari and Abdelouafi Meziane
Medical Image Registration via Similarity Measure based on Convolutional Neural Network
(ID-2)___________________________________________________________________________ 46
Li Dong, Yongzheng Lin and Yishen Pang
Survey of Automated Deep Neural Networks Implementation on FPGAs (ID-42) ______________47
El Hadrami Cheikh Tourad
Machine learning and datamining methods for hybrid Iot intrusion detection (ID-27) _________ 48
Rachid Ait Moulay, Abdellatif El Ghazi and Zine El Abidine Abdelali
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Session 7
High-performance computing
High-performance computing under availability constraints to solve dense triangular system
(ID-61)___________________________________________________________________________49
Mounira Belmabrouk and Mounir Marrakchi
Run Time Optimization using a novel implementation of Parallel-PSO for real-word applications (ID-
54) _____________________________________________________________________________50
Amine Chraibi, Said Ben Alla, Abdellah Touhafi and Abdellah Ezzati
Evaluation of NDVI and NDWI parameters in CPU-GPU Heterogeneous Platforms based CUDA (ID-
22) _____________________________________________________________________________51
Fatima Zahra Guerrouj, Rachid Latif and Amine Saddik
Better Edges not Bigger Graphs: An Interaction-Driven Friending Algorithm for the Next-Generation
Social Networks (ID-95) ____________________________________________________________52
Aadil Alshammari and Abdelmounaam Rezgui
A Cross-Layered Interference in Multichannel MAC of VANET (ID-77) _______________________53
Fadlallah Chbib, Khoukhi, Fahs, Haydar and Khatoun
Session 8
Image processing using Artificial Intelligence
Towards Breast Cancer Response Prediction using Artificial Intelligence and Radiomics
(ID-75) __________________________________________________________________________54
Yassine Amkrane, Mohammed El Adoui and Mohammed Benjelloun
An Image Processing Player Acquisition and Tracking System for E-sports (ID-88) _____________55
Nicolaas Luwes and Joaquim Vieira
Density Based Support Vector Machine (DBSVM) (ID-100) ________________________________56
Karim Elmoutaouakil, Abdellatif El Ouissari, Abdellah Touhafi and Nabil Aharrane
A Business Process Modeling Notation Extension for Real Time Handling - Application to Novel
Coronavirus (2019-nCoV) management process (ID-29) __________________________________57
Asma Ouarhim, Jihane Lakhrouit and Karim Baina
A Model of a Biometric Recognition System Based On the Hough Transform of Libor Masek and 1-D
Log-Gabor Filter (ID-81) ___________________________________________________________58
Djalal Rafik Hammou, Sidi Ahmed Mahmoudi, Reda Adjoudj and Boubaker Mechab
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Session 9
IoT
Internet of Things learning: a practical case for smart building automation (ID-33) ____________59
Olivier Debauche, Saïd Mahmoudi and Yahya Moussaoui
Open Phytotron: A New IoT Device for Home Gardening (ID-34) ___________________________60
Rachida Ait Abdelouahid, Olivier Debauche, Saïd Mahmoudi, Abdelaziz Marzak, Pierre Manneback
and Frédéric Lebeau
An IoT data logging instrument for monitor and early efficiency loss detection at a photovoltaic
generation plant (ID-90) ____________________________________________________________61
Nicolaas Luwes and Burger Lubbe
Formal Modeling and Validation of Micro Smart Grids Based on IoT ReDy Architecture
(ID-80) __________________________________________________________________________62
Kaoutar Hafdi and Abderahman Kriouile
The key layers of IoT architecture (ID-98) ______________________________________________63
Alae-Eddine Bouaouad, Adil Cherradi, Saliha Assoul and Nissrine Souissi
Modelling and simulation of photovoltaic modules using combined analytical - numerical methods
(ID-72) (Poster)___________________________________________________________________64
Khalid Chennofi and Mohammed Ferfra
Session 10
Computational Intelligence
A New Recurrent Neural Network Fuzzy Mean Square Clustering Method (ID-99) _____________65 Abdellah Touhafi and Karim El Moutaouakil
Learning Analytics based on Bayesian Optimization of Support Vector Machines with Application to Student Success Prediction in Mathematics Course (ID-13) ________________________________66 Salim Lahmiri, Raafat G. Saade, Danielle Morin and Fassil Nebebe
Block Sizes Control for an Efficient Real Time Record Linkage (ID-48) ________________________67
Hamid Naceur Benkhaled, Djamel Berrabah and Faouzi Boufares
An Artificial Neural Networks Based Ensemble System to Forecast Bitcoin Daily Trading Volume (ID-
12) _____________________________________________________________________________68
Salim Lahmiri, Raafat G. Saade, Danielle Morin and Fassil Nebebe
A distributed large graph coloring algorithm on Giraph (ID-18) ____________________________69
Assia Brighen, Hachem Slimani Slimani, Abdelmounaam Rezgui Rezgui, Hamamache Kheddouci
Kheddouci and Assia Brighen
Quality Approach to Analyze the Causes of Failures in MOOC (ID-67) (Poster)________________70
Soukaina Sraidi, Smaili El Miloud, Salma Azzouzi and My El Hassan Charaf
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Secure Blockchain Ecosystem – The Network is my Computer
Chunming Rong
University of Stavanger, Norway
Abstract:
Blockchain and other distributed ledger technologies (DLTs), through recent development, have not
only enabled simple transactions, but also complex computation on a network where parties are
geographically distant or have no particular trust in each other to interact and exchange value and
information on a fully distributed basis with fewer to non-existent central intermediaries. There are
obvious advantages associated with blockchain in areas such as, decentralization, disintermediation,
replication, timestamping, immutability, digital signatures, automation and smart contracts,
transparency and censorship resistance, especially in relation with the financial sector. However, there
are also noticeable challenges, for instances, scalability and performance, energy consumption,
security and privacy. Many popular applications are cloud related. These advances are now not just
limited to the financial sector, but also new internet applications can harness these building blocks to
empower users to take control of their online footprint, such as in healthcare, social media and other
digital services. We are building an open source hub for a secure and source-controlled blockchain
ecosystem, as the gathering place for academic researchers, practitioners and business innovators
alike, where they may meet and work together online to embrace, promote and enhance blockchain
technologies and their applications. This will bring us to new platform, new business model and new
ecosystem.
Biography:
Prof. Chunming Rong is the chair of IEEE CS STC on Blockchain, and served as co-chair of IEEE
Blockchain in 2018. He is also chair of IEEE Cloud Computing. He works as the head of the Center for
IP-based Service Innovation (CIPSI) at the University of Stavanger (UiS) and also as adjunct Senior
Scientist leading Big-Data Initiative at NORCE. He is also co-founder and CTO of two start-ups bitYoga
and Dataunitor in Norway, both received EU Seal of Excellence Award in 2018. He was the vice
president of CSA Norway Chapter (2016-2017). His research work focuses on data science, cloud
computing, security and privacy. He is an IEEE senior member and is honoured as member of the
Norwegian Academy of Technological Sciences (NTVA) since 2011. He has extensive contact network
and projects in both the industry and academic. He is also founder and Steering Chair of IEEE CloudCom
conference and workshop series. He is the steering chair and associate editor of the IEEE Transactions
on Cloud Computing (TCC), and co-Editors-in-Chief of the Journal of Cloud Computing (ISSN: 2192-
113X) by Springer. Prof. Rong has extensive experience in managing large-scale R&D projects funded
by both industry and funding agencies, both in Norway and EU.
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Cloud and Fog Computing Resource Allocation and Scheduling of Complex Real-Time
Applications
Helen Karatza
Aristotle University of Thessaloniki, Greece
Abstract:
Cloud computing provides a virtually unlimited pool of resources to the end-users.Due to the popularity of the cloud computing paradigm, new complex computationally intensive applications have emerged. However, many important issues such as resource allocation, job scheduling, cost, and quality of service must be addressed in order to exploit cloud’s full potential. Complex application scheduling in the cloud is a challenging problem. Furthermore, it is more difficult to schedule complex real-time applications, considering deadlines along with energy conservation. Therefore, energy efficient scheduling strategies are required ensuring timeliness. Due to the increasing number of Internet of Things (IoT) applications, fog computing has emerged as a new paradigm, beyond cloud computing. The fog extends the cloud to the network edge in order to reduce data transmission latency. Real-time applications should be appropriately assigned to resources in both the fog and cloud layers, taking into account the communication and computational characteristics of each application. In this keynote talk we will focus towards challenges on resource allocation and scheduling complex real-time applications in cloud and fog systems and we will provide future research directions in this emerging research area. Biography:
Prof. Helen Karatza is a Professor Emeritus in the Department of Informatics at the Aristotle University of Thessaloniki, Greece, where she teaches courses in the postgraduate and undergraduate level. Dr. Karatza's research interests include Fog and Cloud Computing, Energy Efficiency in Large Scale Distributed Systems, Resource Allocation and Scheduling and Real-time Distributed Systems. Dr. Karatza has authored or co-authored 225 technical papers and book chapters including five papers that earned best paper awards at international conferences. She is senior member of IEEE, ACM and SCS, and she served as an elected member of the Board of Directors at Large of the Society for Modeling and Simulation International. She served as Chair and Keynote Speaker in International Conferences. Dr. Karatza is the Editor-in-Chief of the Elsevier Journal “Simulation Modeling Practice and Theory” and Senior Associate Editor of the “Journal of Systems and Software” of Elsevier. She was Editor-in-Chief of “Simulation Transactions of The Society for Modeling and Simulation International” and Associate Editor of “ACM Transactions on Modeling and Computer Simulation”. She served as Guest Editor of Special Issues in International Journals. More info about her activities/publications can be found in http://agent.csd.auth.gr/~karatza/
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Cloud-based Intelligent Remote Patient Monitoring
Abdelmounaam Rezgui
Illinois State University, USA
Abstract:
The worldwide population growth has led to an unprecedented need for new sustainable approaches and technologies to provide health care. A health care system has three major stakeholders: (i) people, (ii) medical staff, and (iii) health administrators. People may want to monitor their health status while they conduct their daily activities. For this, they need solutions that perform continuous health monitoring. Health professionals (physicians, nurses, ICU specialists, etc.) need advanced accurate predictive capabilities to help make better decisions regarding their patients. Administrators at hospitals look for solutions that enable them to reduce waste and wait time, and increase service efficiency and productivity. In particular, administrators face significant challenges in terms of bed management, patient flow, and effective provision of medical supplies. In this talk, we present our recent research aimed at addressing the aforementioned challenges. To validate our research, we developed a cloud-based, intelligent remote patient monitoring system (called CIRPM) that can be used by people, medical professionals, and health administrators to improve the quality of health services.
Biography:
Prof. Abdelmounaam Rezgui is a faculty member in the School of Information Technology at Illinois State University. He received his PhD in Computer Science from Virginia Tech. His research interests include: networking, cloud computing, social networks, and big data. Abdelmounaam authored or coauthored over 90 papers in top journals and conferences including IEEE TBD, IEEE TKDE, ACM TOIT, IEEE TPDS, IEEE Internet Computing, IEEE Security and Privacy, IEEE ICDE, IEEE IC2E, and IEEE CLOUD. His research has been funded by NASA and Microsoft. He regularly serves on the program committees of several major conferences including IEEE Big Data, IEEE BigDataSE, IEEE Globecom, IEEE CloudNet, IEEE LCN, and IoTBDS. He has been a track chair, keynote speaker, or tutorial presenter at several international conferences. He also is on the editorial board of several journals including Springer’s Big Data Analytics. Research conducted by Abdelmounaam and his students has been invited for presentation at very selective events organized by major organizations including Google, NSF, and Siemens.
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16
Data Science and AI: Trends, Myths and Challenges
Maria Fasli
University of ESSEX, UK
Abstract:
The availability of cheap computational devices and the growth of the WWW and applications has led to the proliferation in the amount of data - both structured and unstructured. In turn, increased computational power, cheap storage and data proliferation have led to the development of new techniques in Data Science and unprecedented progress in Artificial Intelligence (AI). From powering recommendation engines to autonomous vehicles, AI has seen a reinvigoration in interest while industry and academia have been investing heavily to remain competitive and take advantage of the power of AI. But is AI a panacea and how much progress has really been made in the field? This talk will explore the trends, myths but also challenges that AI poses providing a critical perspective of the field and where the pitfalls may lie. Biography:
Prof. Maria Fasli is a Professor, Director of the ESRC Business and Local Government Data Research
Centre and Director of the Institute for Analytics and Data Science at the University of Essex. Educated
in Greece (BSc Informatics 1996) and the UK (PhD Computer Science 2000), she has held positions at
Essex since 1999. In 2005, she was awarded a National Teaching Fellowship by the HEA UK for her
innovations in education. Between 2009-2014, she was Head of School of Computer Science and
Electronic Engineering. In 2016, she was awarded a UNESCO Chair in Analytics and Data Science. Her
research interests lie in artificial intelligence techniques for complex systems and analysing and
modelling structured/unstructured data. Her research has been funded by Research Councils and
other organisations and she has worked with a range of companies in data analytics related projects.
She has published over 130 papers in the field of AI and data science and has delivered keynote talks
at conferences.
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17
Artificial intelligence and modeling activities in imaging and medical physics application
Othmane Bouhali
Texas A&M University, Qatar
Abstract
Artificial intelligence (AI) has captured the attention of the scientific and business communities for
decades. With its associated technologies, such as machine learning (ML) and deep Learning (DL), AI
has provided very efficient tools and platforms that help reducing cost, improving efficiency, and
increasing competitiveness. In this talk I will give an overview of our recent work on applying AI
techniques in several fields of health sectors including imaging and medical physics applications. Our I
will also review our activities in modeling medical and imaging devices.
Biography:
Prof. Othmane Bouhali is research professor and director of Research Computing at Texas A&M
University at Qatar. He is the founder and director of the TAMUQ Advanced Scientific Computing group
(TASC), a specialized group in computational modeling and simulation. His main work is on
computational physics, Monte Carlo simulation and high performance computing. Before he joined
TAMUQ, he was given the responsibility of the computing group at the High Energy Physics Institute
of Brussels. His research focused on the scientific grid computing, in particular the optimization and
implementation of the grid infrastructure for intensive scientific applications. Currently he is involved
in several projects on medical physics, 3D scientific visualization, distributed computing and image
analysis techniques.
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18
HPC and AI Meet Cloud: Opportunities and Challenges in Designing High-Performance MPI
and Deep Learning Libraries for Public Cloud
Panda Dhabaleswar
Ohio State University, USA
Abstract:
Significant growth has been witnessed during the last decade in dedicated HPC clusters with multi-/many-core processors, accelerators, and high-performance interconnects (such as InfiniBand, Omni-Path, iWARP, RoCE and EFA). Public cloud providers like Amazon-AWS and Microsoft-Azure are gradually aiming to provide solutions for High-Performance Computing (HPC) and Deep Learning (DL) users and applications. Microsoft-Azure has recently moved to bare-metal environment with InfiniBand networking technology. Amazon-AWS has moved to using a new network adapter, Elastic Fabric Adapter (EFA), for its HPC instances. These new networks and platforms provide a new set of opportunities and challenges to design high-performance MPI and DL libraries for these emerging public cloud platforms. The MVAPICH MPI library (http://mvapich.cse.ohio-state.edu) has been a significant middleware component in the HPC domain for the last 18 years. Recently, the MVAPICH library is also enabling high-performance and scalable DL applications. The MVAPICH team members are currently working closely with Amazon-AWS and Microsoft-Azure to design and deploy high-performance and scalable MPI library for their respective could infrastructures. This talk will provide details on these designs, their performance (micro-benchmarks and applications) and comparison with other MPI libraries. A set of results comparing the performance numbers of HPC and AI applications on cloud environments with dedicated (non-virtualized) clusters will be presented.
Biography:
Prof. Panda Dhabaleswar is a Professor and University Distinguished Scholar of Computer Science and Engineering at the Ohio State University. He has published over 450 papers in the area of high-end computing and networking. The MVAPICH2 (High Performance MPI and PGAS over InfiniBand, Omni-Path, iWARP and RoCE) libraries, designed and developed by his research group (http://mvapich.cse.ohio-state.edu), are currently being used by more than 3,025 organizations worldwide (in 89 countries). More than 589,000 downloads of this software have taken place from the project's site. This software is empowering several InfiniBand clusters (including the 3rd, 5th, 8th, 15th, 16th, 19th, and 31st ranked ones) in the TOP500 list. The RDMA packages for Apache Spark, Apache Hadoop and Memcached together with OSU HiBD benchmarks from his group (http://hibd.cse.ohio-state.edu) are also publicly available. These libraries are currently being used by more than 315 organizations in 35 countries. More than 31,100 downloads of these libraries have taken place. High-performance and scalable versions of the Caffe and TensorFlow framework are available from https://hidl.cse.ohio-state.edu. Prof. Panda is an IEEE Fellow. More details about Prof. Panda are available at http://www.cse.ohio-state.edu/~panda.
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19
Efficient Mobile User Authentication Service Based on User's Memories with Privacy
Preservation and User Untraceability
An Braeken, Abdellah Touhafi
INDI, Vrije Universiteit Brussel (VUB), Brussels, Belgium
Abstract— Security questions and answers for authentication are a common approach to enable the user to reset forgotten passwords. Moreover, they are also sometimes used as alternative for the classical username-password system, which fails in offering a good balance between user friendliness and security as long and complex passwords are required. However, in order to guarantee the privacy of the user as imposed by the new General Data Protection Regulation (GDPR), it should be impossible to derive the answer of the user by any other entity, including the server provider or the server managing the authentication. In this paper, we present an efficient mobile based security mechanism to realise this goal. The proposed scheme can be applied on top of any type of question-answer based authentication system. In addition, our solution also offers anonymity and untraceability of the user, such that no activity patterns can be drawn by simply eavesdropping on the communication channel to the service provider or the authentication server. We show that our proposed mechanism not only offers more security features compared to related work, but it is also significantly faster, in particular at the side of the user. Keywords— Authentication, Anonymity, Physical Unclonable Function.
ID-14
Security and Privacy
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20
Secure, Efficient and Dynamic Data Search using Searchable Symmetric Encryption
Mohammed Saad Shaikh, Jatna Bavishi and Reema Patel
Department of Computer Science and Engineering
Pandit Deendayal Petroleum
University Gandhinagar, India
shaikhsaad133@gmail.com; jatnabavishi@gmail.com; reema.mtech@gmail.com
Abstract— Fog computing, which works complementary to cloud computing, is being developed to overcome the issue of cloud computing such as latency in the cases where the data is to be retrieved immediately. But, along with solving the problem of latency, fog computing brings along with it different set of security issues than cloud computing. The storage and processing capabilities of fog computing is limited and hence, the security issues must be solved with these constrained resources. One of the problems faced when the data is stored outside the internal network is loss of confidentiality. For this, the data must be encrypted. But, whenever document needs to be searched, all the related documents must be decrypted first and later the required document is to be fetched. Within this time frame, the document data can be accessed by an unauthorized person. So, in this paper, a searchable symmetric encryption scheme is proposed wherein the authorized members of an organization can search over the encrypted data and retrieve the required document in order to preserve the security and privacy of the data. Also, the searching complexity of the algorithm is much less so that it suitable to fog computing environment.
Keywords— Cloud Computing, Fog Computing, Secure Data Computation, Searchable Encryption, Searchable Symmetric Encryption.
ID-74 Security and Privacy
mailto:shaikhsaad133@gmail.commailto:jatnabavishi@gmail.commailto:reema.mtech@gmail.com
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21
Cloud computing security classifications and taxonomies: a comprehensive study and comparison
Najat Tissir
Process Engineering, Computer Science and Mathematics laboratory
National School of Applied Sciences Sultan Moulay Slimane University Khouribga, Morocco
tissir.najat@gmail.com
Said El Kafhali
Hassan First University of Settat,
Faculty of Sciences and Techniques,
Computer, Networks, Mobility and Modeling laboratory: IR2M, 26000-Settat, Morocco
said.elkafhali@uhp.ac.ma
Nourddine Aboutabit
Process Engineering, Computer Science and Mathematics laboratory
National School of Applied Sciences Sultan Moulay Slimane University Khouribga, Morocco
n.aboutabit@usms.ma
Abstract— Cloud Computing is an emerging paradigm that is based on the concept of distributed computing. As with any novel technology, Cloud Computing is subject to security threats, vulnerabilities, and attacks. Recently, various classifications and taxonomies have been proposed to describe and organize cloud security issues. Some of them are based on general security factors, such as the CIA triad (confidentiality, integrity, and availability), while others specify cloud security classes. Most of these classes are determined based on the cloud’s characteristics, such as Cloud service models, cloud deployment models, and cloud actors. In this paper, we explore the already existing criteria and dimensions considered in the development of cloud computing security classification/taxonomy. Then, we study and compare their strengths and characteristics. Thereafter, our objective is to provide and develop a comprehensive view of cloud security taxonomy and help researchers to better understand this evolving field.
Keywords— Cloud Computing, Taxonomy, Classification, Threat, Attacks.
ID-78 Security and Privacy
mailto:tissir.najat@gmail.commailto:said.elkafhali@uhp.ac.ma
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22
ESSMAR: Edge Supportive Secure Mobile Augmented Reality Architecture for Healthcare
An Braeken
Industrial Engineering INDI, Vrije Universiteit Brussel, Belgium
an.braeken@vub.be
Pawani Porambage, Amirthan Puvaneswaran
Centre for Wireless Communications, University of Oulu, Finland
pawani.porambage@oulu.fi, amirthan.puvaneswaran@student.oulu.fi
Madhusanka Liyanage
School of Computer Science, University College Dublin, Ireland
madhusanka@ucd.ie
Abstract— The recent advances in mobile devices and wireless communication sector transformed Mobile Augmented Reality (MAR) from science fiction to reality. Among the other MAR use cases, the incorporation of this MAR technology in the healthcare sector can elevate the quality of diagnosis and treatment for the patients. However, due to the highly sensitive nature of the data being circulated in this process, it is also highly vulnerable to security threats. In this paper, an edge-based secure architecture is presented for a MAR healthcare application. Based on the ESSMAR architecture, a secure key management scheme is proposed for registration and authentication phases. Then the security of the proposed scheme is validated using formal and informal verification methods.
ID-15 Security and Privacy
mailto:an.braeken@vub.bemailto:amirthan.puvaneswaran@student.oulu.fi
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23
Towards Smart Blockchain-Based Framework for Privacy Preserving Data sharing
in Smart Cities
Driss EL MAJDOUBI, Hanan EL BAKKALI, Souad SADKI Smart Systems Laboratory- Rabat IT Center
Mohammed V University in Rabat {Driss.elmajdoubi, h.elbakkali, souad.sadki}@um5s.net.ma
Abstract— Nowadays, the adoption of smart cities worldwide is accelerating the digital transformation of urban environments. Besides, a huge amount of data is being exchanged through the smart devices, networks, cloud infrastructure and Internet of Things (IoT) applications both in private and public sector. This seamless integration of the cyber capabilities of the corresponding devices with the physical world generates new opportunities in many areas but it also raises new challenges in terms of security and privacy due to the diversity of sources and stakeholders, the centralized data management and the resulting lack of trustworthiness and governance. Hence, we introduce “SmartPrivChain” a Smart Blockchain Based Framework for privacy-preserving data sharing in a smart city environment. The proposed approach is different from the existing schemes on many aspects. The data privacy is preserved by combining data access control and data usage auditing measures based on smart contracts. In addition, the proposed solution, is compliant with the main privacy laws and regulation especially the requirements of European Union General Data Protection Regulation (EU GDPR). Lastly, we propose an enhanced Proof of Reputation (PoR) consensus scheme using a multidimensional Trust model. Keywords— Blockchain; Smart cities; Privacy Laws; Consensus Protocol; Proof-of-Trustworthiness; Smart Contract.
ID- 39 Security and Privacy
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24
Big Data Storage and Analysis for Smart Farming
Fabrice Nolack Fote, Saïd Mahmoudi, Sidi Ahmed Mahmoudi and Amine Roukh Faculty of Engineering - ILIA / Infortech
University of Mons, Belgium
Abstract— Smart Farming has always been referred to as agriculture, but nowadays, that is no longer the case. Today, Smart farming is made up of Precision Agriculture (PA) and Precision Livestock Farming (PLF). Big Data technologies and algorithms can be relevant for managing and monitoring data related to any farm. Precision livestock farming concerns genetics, animal welfare, animal nutrition, reproduction, species protection and animal health. This paper presents a general overview of Big Data tools that can be applied in a smart farming application. New Technologies are offering many tools used to facilitate the management of data collection, risk minimization, climate change anticipation, secure storage and analysis, etc. The main purpose of Big Data tools is to increase productions in order to offer higher quantities while ensuring higher quality products. However, they remain some issues that need to be accomplished.
Keywords— Smart Farming, Big Data, Precision Agriculture, Precision Livestock Farming.
ID-58 Smart cities/Farming and Mobile Technologies
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25
Performance Evaluation of Newly Implemented Resource Blocks (RB) Allocation Schemes
on NS-3 simulator for mMTC 5G NR (New Radio) Femtocells
Ismail Angri, Abdellah Najid Telecommunication Systems, Networks and Services (STRS) laboratory
National Institute of Posts and Telecommunications (INPT) Rabat, Morocco
ismail.angri@gmail.com, najid_abdellah@yahoo.fr
Mohammed Mahfoudi Transmission and Data Processing Laboratory
(LTTI) Superior School of Technology (EST-Fez) Fez, Morocco
mahfoudi.mohammed@gmail.com
Abstract— The new standard of mobile technologies called 5G allows enormous improvements,
comparing to the previous telecommunication network system LTE, in terms of user requirements by
offering different use cases (eMBB, URLLC and mMTC). With the use of the Internet of Things (IoT) by
5G networks, the number of radio devices by each user will drop from 2 to around 7 to 10 devices.
Despite this, the saturation of the system does not arise, thanks to the connected equipment’s high
density, offered by Massive machine type communications (mMTC). A Radio Resource Management
RRM procedure for efficient distribution of available radio resources between those devices is essential
for 5G systems. In this article, we have studied the behavior of scheduling algorithms in a 5G
environment, for a large number of connected objects and for different types of data flows, while
limiting to small cells (Femtocells) with a speed of 3 km/h of the User Equipment (UE). In this objective,
we program in C++ two new scheduling algorithms at the base station gNb, namely Exponential PF
(EXP/PF) and Exponential Rule (EXP-rule), in addition to those already existing (Maximum-Weight
(MW) and Proportional Fair (PF)), using the mmWave model of the famous NS-3 simulator. The
performance comparison of the different 5G scheduler schemes was inspected via two important
parameters, which are the user throughput and the Signal-to-Interference-plus-Noise Ratio (SINR).
Consequently, we have demonstrated that the scheduling algorithms used by LTE networks can be
implemented at the 5G gNB level. The results of our simulations have shown that the EXP-rule
algorithm provides the best SINR and DataRate values for voice, video and data streams.
Keywords— 5G; New Radio; NR numerology; Scheduling; Radio Resource Management; NS-3; eMBB; mMTC; URLLC.
ID-70
Smart cities/Farming and Mobile Technologies
mailto:najid_abdellah@yahoo.fr
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26
Improvements of Centroid Localization Algorithm for Wireless Sensor Networks
Abdelali Hadir Hassan II University, National School of Business and Management, Casablanca, Morocco
a.hadir@encgcasa.ma
Khalid Zine-Dine Mohammed V University, Faculty of Sciences, Rabat, Morocco
khalid.zinedine@um5.ac.ma
Mohamed Bakhouya International University of Rabat, LERMA Lab., Sala Al Jadida, Morocco
mohamed.bakhouya@uir.ac.ma
Abstract— The accurate location of sensor nodes in Wireless Sensor Networks (WSNs) is considered as critical problem in many applications of IoT. In the last decades, many localization solutions have been proposed to provide location of sensor nodes, however a limited number of these solutions have been presented to accurately estimate the location of sensor nodes in the Internet of Things (IoT). In this paper we present three localization solutions, named Centroid + 4A, ICentroid, and ICentroid + 4A respectively, based on Centroid localization algorithm and a new weighted formula to calculate the position of unknown nodes. The performance of the proposed solutions was evaluated using the network simulator (OMNeT++) and compared to the basic Centroid localization algorithm. Results show that the proposed localization algorithms can significantly reduce the localization error of algorithms in Wireless Sensor Networks.
Keywords— Wireless Sensor Networks, Internet of Things Localization Algorithm, Centroid, ICentroid, Simula-tions and Performance Evaluation.
ID-96
Smart cities/Farming and Mobile Technologies
mailto:a.hadir@encgcasa.mamailto:khalid.zinedine@um5.ac.ma
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27
An overview of recommender systems in the context of smart cities
Rabie MADANI, Abderrahmane EZ-ZAHOUT and Abdellah IDRISSI1 Intelligent Processing and Security of Systems Team,
Computer Sciences Department, Faculty of Sciences, Mohammed V University of Rabat, Morocco
rabie.madani@um5.ac.ma; abderrahmane.ezzahout@um5s.net.ma; idriab@gmail.com
Abstract—The concept of smart city appeared following technological, societal and organizational changes. A smart city uses The Internet of things (IoT) which refers to billions of physical devices connected to internet, to collect data and use it to effectively manage resources and to improve the quality of urban services. The use of recommender systems in smart cities plays an important role in the process of guiding citizens to find services that match with their preferences. Recommendations provided allow users to satisfy their needs in an efficient and easy way and make their daily lifes less complicated. In this paper, we present an overview of recommender systems in the context of smart cities and describe real-world application of recommender systems in IoT and in smart cities.
Keywords— Smart cities, Recommender systems, Internet of things. ID-64
Smart cities/Farming and Mobile Technologies
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28
A re-engineering approach for extension of the Tourist Guide Knowledge Base
Asya Stoyanova-Doycheva, Todorka Glushkova, Emil Doychev Faculty of Mathematics and Informatics, University of Plovdiv “Paisii Hilendarski”
Plovdiv, Bulgaria astoyanova@uni-plovdiv.net, glushkova@uni-plovdiv.bg
Nevena Moraliyska
Department of Intelligent Systems Institute of Information and Communication Technologies (IICT), BAS
Sofia, Bulgaria nevena.uzunova@gmail.com
Abstract— The paper presents an extension of the knowledge base of the Tourist Guide with comprehensive information about Bulgarian cultural, historical, and natural sites, available in the databases created under the BECC project. To accomplish the task of enriching the knowledge base, the architecture of the Tourist Guide that was created as a reference architecture of the Virtual-Physical Space (ViPS) is presented and the restructuring process of the components in this architecture is described. In order to use the created databases in the BECC project, we had to re-engineer them on the basis of standards for the presentation of cultural and historical sites such as UNESCO and CCO (Cataloging Cultural Objects). Index Terms— Cataloging Cultural Objects (CCO), Virtual Physical Space (ViPS), Tourist Guide.
ID- 40
Smart cities/Farming and Mobile Technologies
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29
NFaaS: A new cloud service for water distribution network monitoring
Rayane El Sibai Faculty of Engineering, Al Maaref University, Beirut, Lebanon
rayane.elsibai@mu.edu.lb
Jacques Bou Abdo Faculty of Natural and Applied Sciences, Notre Dame University Deir El Kamar, Lebanon
jbouabdo@ndu.edu.lb
Chady Abou Jaoude Ticket Lab., Faculty of Engineering Antonine University Baabda, Lebanon
chady.aboujaoude@ua.edu.lb
Jacques Demerjian LaRRIS, Faculty of Sciences Lebanese University Fanar, Lebanon
jacques.demerjian@ul.edu.lb
Yousra Chabchoub LISITE ISEP Issy-les-Moulineaux, 92130, France
yousra.chabchoub@isep.fr
Raja Chiky LISITE ISEP Issy-les-Moulineaux, 92130, France
raja.chiky@isep.fr Abstract— Water monitoring is one of the critical battles of sustainability for a better future of humanity. 44 countries are considered at high risk of the water crisis, 28 of which are developing countries and have limited capacity to deploy a national scale solution. As a response to the United Nation’s sustainability goals and initiatives, this paper proposes an intelligent water monitoring service which acts as a foundational infrastructure for all future water management systems. It also provides municipalities, Non-Governmental Organization and other private initiatives with the tools needed to establish local water monitoring in the scale of villages or rural areas with a very small initial investment.
Index Terms— —Cloud computing, data streams, data quality, sampling techniques, Software as a Service (SaaS). ID-11
Cloud services
mailto:rayane.elsibai@mu.edu.lbmailto:jbouabdo@ndu.edu.lbmailto:chady.aboujaoude@ua.edu.lbmailto:jacques.demerjian@ul.edu.lbmailto:yousra.chabchoub@isep.fr
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30
An overall statistical analysis of AI tools deployed in Cloud computing and networking systems
Ikhlasse Hamzaoui
Research Foundation for Development and Innovation in Science and Engineering, Casablanca, Morocco
Engineering research laboratory, System Architecture Team, ENSEM, Hassan II University, 8118, Casablanca, Morocco
EIGSI, La Rochelle, France ikhlasse.h12@gmail.com
Benjamin Duthil, EIGSI, La Rochelle, France
L3i, La Rochelle University, La Rochelle, France duthil@eigsi.fr
Vincent Courboulay L3i, La Rochelle University, La Rochelle, France
vcourbou@univ-lr.fr
Hicham Medromi Research Foundation for Development and Innovation in Science and Engineering,
Casablanca, Morocco Engineering research laboratory, System Architecture Team, ENSEM, Hassan II University, 8118,
Casablanca, Morocco hmedromi@yahoo.fr
Abstract— As the vast amount of data destined to cloud systems never stop growing in seconds, minutes, hours and daily basis, the development of dynamic, autonomous and proactive architectures for cloud resources scheduling becomes a veritable prerequisite. This dominant trend is inciting to further seek for complete and accurate forecasting and predictive models to support decision making in several cloud-scheduling levels. In this perspective, this paper is a result of a meticulous statistical analysis of about five hundred relevant research articles dealing with proactive resources scheduling in cloud, fog, edge computing and networking systems, using a complete panoply of Artificial Intelligence (AI) predictive techniques. The first aim is to highlight for the first time current trends bridging the gap between cloud services management and AI tools.
Keywords— AI tools, Cloud Computing, Networking, Proactive scheduling ID-10
Cloud services
mailto:ikhlasse.h12@gmail.commailto:duthil@eigsi.frmailto:vcourbou@univ-lr.fr
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31
Multi-task Offloading and Computational Resources Management in a Mobile Edge
Computing Environment
Mohamed EL GHMARY FSDM, LIIAN Laboratory, Sidi Mohamed Ben Abdellah University
Fez, Morocco mohamed.elghmary@usmba.ac.ma
Youssef HMIMZ FSDM, LIIAN Laboratory, Sidi Mohamed Ben Abdellah University
Fez, Morocco youssef.hmimz@usmba.ac.ma
Tarik CHANYOUR FSDM, LIIAN Laboratory, Sidi Mohamed Ben Abdellah University
Fez, Morocco tarik.chanyour@usmba.ac.ma
Mohammed Ouc¸amah CHERKAOUI MALKI FSDM, LIIAN Laboratory, Sidi Mohamed Ben Abdellah University
Fez, Morocco oucamah.cherkaoui@usmba.ac.ma
Abstract— In Mobile Cloud Computing, Smart Mobile Devices (SMDs) and Cloud Computing are combined to create a new infrastructure that allows data processing and storage outside the device. The Internet of Things refers to the billions of physical devices that are connected to the Internet. With the rapid development of these, it is clear that the requirements are largely based on the need for autonomous devices to facilitate the services required by applications that require rapid response time and flexible mobility. In this article, we study the management of computational resources and the trade-off between the energy consumption of an SMD and the processing time of its tasks. For this, we define a system model, a problem formulation and offer heuristic solutions for offloading tasks in order to jointly optimize the allocation of computing resources under limited energy and sensitive latency. In addition, we introduce the residual energy of the SMD battery and the sensitive latency of its tasks in defining the weighting factor of energy consumption and processing time. Keywords-context— mobile edge computing, computation offloading, energy, processing time, bi-objective optimization, heuristic, multi-task.
ID -102
Cloud services
mailto:mohamed.elghmary@usmba.ac.mamailto:youssef.hmimz@usmba.ac.mamailto:tarik.chanyour@usmba.ac.ma
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32
Deployment of Containerized Deep Learning Applications in the cloud
Rim Doukha , Sidi Ahmed Mahmoudi, Mostapha Zbakh , Pierre Manneback Computer Science and Artificial Intelligence Department, Faculty of Engineering,
University of Mons Mons, Belgium
National School of Computer Science and Systems Analysis, Mohamed V University Rabat, Morocco
{rim.doukha, sidi.mahmoudi, pierre.manneback}@umons.ac.be m.zbakh@um5s.net.ma
Abstract— During the last years, the use of Cloud computing environment has increased as a result of the various services offered by Cloud providers (Amazon Web Services, Google Cloud, Microsoft Azure, etc.). Many companies are moving their data and applications to the Cloud in order to tackle the complex configuration effort, for having more flexibility, maintenance, and resource availability. However, it is important to mention the challenges that developers may face when using a Cloud solution such as the variation of applications requirements (in terms of computation, memory and energy consumption) over time, which makes the deployment and migration a hard process. In fact, the deployment will not depend only on the application, but it will also rely on the related services and hardware for the proper functioning of the application. In this paper, we propose a Cloud infrastructure for automatic deployment of applications using the services of Kubernetes, Docker, Ansible and Slurm. Our architecture includes a script to deploy the application depending of its requirement need. Experimentation are conducted with the analysis and the deployment of Deep Learning (DL) applications and more particularly images classification and object localization. Keywords— Cloud Computing, Application Deployment, Application Migration, Kubernetes, Docker, Ansible, Slurm, Deep Learning. ID- 51
Cloud services
mailto:pierre.manneback%7d@umons.ac.be
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33
Autonomous Provisioning of Preemptive Instances in Google Cloud for Maximum Performance Per Dollar
Harek Haugerud, Jonas Kruger Svensson and Anis Yazidi
Department of Computer Science OsloMet –– Oslo Metropolitan University
Oslo, Norway
Abstract— Cloud computing and its popularity has boomed over the last decade, enabling anyone to rent computing power on demand. Cloud providers such as Amazon and Google rent out surplus computing power for a discounted price according to demand in their data centers, but with the trade off that it is revocable and can only be rented for a short amount of time. This paper investigates the use of surplus computing power in order to reduce the cost of batch computing. We rely on a simple economical principle, the most cost-efficient Virtual Machine (VM) in a public cloud is the one that offers the highest performance per dollar. Therefore by rescheduling the workloads to the most cost-efficient location in terms of performance per dollar our solution dynamically provisions preemptible VMs in Google Cloud while continuously monitoring the performance per dollar of all available resources in every region. The algorithm automatically relocates workloads to a less expensive location if any appears and handles revoked access of the resources. Our algorithm views the cost reduction problem as a linear optimization problem with constraints and solves it using a greedy procedure. In the experiment we spawn Docker containers to mine cryptocurrency. The experimental results show that 67% of the cost is saved compared to renting on-demand VMs. The system can readily be extended to containers processing similar types of workloads and more generally to applications where the performance per dollar is easy to measure.
Keywords— Autonomous VM, performance per dollar, containers, VM migration, global cloud.
ID -87
Cloud services
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34
A Review of Credit Card Fraud Detection Using Machine Learning Techniques
Nadia Boutaher Laboratory of Computer Science, Systems
Modeling and Decision Support Faculty of Sciences AinChock, Hassan II University
Casablanca, Morocco nadia.boutaher1@gmail.com
Amina Elomri, Noreddine Abghour, Khalid Moussaid and Mohamed Rida Laboratory of Computer Science, Systems Modeling and Decision Support Faculty of Sciences
AinChock, Hassan II University Casablanca, Morocco {amina.elomri, noreddine.abghour, khalid.moussaid, mohamed.rida}@univh2c.ma
Abstract— Big Data technologies concern several critical areas such as Healthcare, Finance, Manufacturing, Transport, and ECommerce. Hence, they play an indispensable role in the financial sector, especially within the banking services which are impacted by the digitalization of services and the evolvement of ecommerce transactions. Therefore, there are various issues and challenges related to the banking sector and credit card transaction services generating by the emergence of the credit card use and the increasing number of fraudsters. Unfortunately, these issues obstruct the performance of Fraud Control Systems (Fraud Detection Systems & Fraud Prevention Systems) and abuse the transparency of online payments. Thus, financial institutions aim to secure credit card transactions and allow their customers to use e-banking services safely and efficiently. To reach this goal, they try to develop more relevant fraud detection techniques that can identify more fraudulent transactions and reduce the number of frauds and false alarms. This paper aims to present a comprehensive analysis of fundamental aspects of fraud detection, the current systems of fraud detection, the issues and challenges of frauds related to the banking sector, and the existing solutions based on machine learning techniques. Index Terms—big data; machine learning techniques; credit card fraud detection; legitimate transaction; financial institutions; digitalization; e-commerce. ID-53
Machine Learning
mailto:nadia.boutaher1@gmail.com
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35
Machine Learning for Anomaly Detection. Performance Study considering Anomaly Distribution in an Unbalanced Dataset
Salma El Hajjami
IASSE Laboratory, ENSA, USMBA, Fez, Morocco salma.elhajjami@usmba.ac.ma
Jamal Malki L3i Laboratory, La Rochelle University, La Rochelle, France
jmalki@univ-lr.fr
Mohammed Berrada IASSE Laboratory, ENSA, USMBA, Fez, Morocco
mohammed.berrada@gmail.com
Bouziane Fourka aYaline R&D Laboratory, aYaline, Poitiers-Futuroscope, France
bfourka@ayaline.com
Abstract— The continuous dematerialization of real-world data greatly contributes to the increase in the volume of data exchanged. In this case, anomaly detection is increasingly becoming an important task of data analysis in order to detect abnormal data, which is of particular interest and may require action. Recent advances in artificial intelligence approaches, such as machine learning, are making an important breakthrough in this area. Typically, these techniques have been designed for balanced data sets or that have certain assumptions about the distribution of data. However, the real applications are rather confronted with an imbalanced data distribution, where normal data are present in large quantities and abnormal cases are generally very few. This makes anomaly detection similar to looking for the needle in a haystack. In this article, we develop an experimental setup for comparative analysis of two types of machine learning techniques in their application to anomaly detection systems. We study their performance taking into account anomaly distribution in an imbalanced dataset. Keywords— Anomaly Detection, Data Analysis, Artificial Intelligence, Machine Learning, Imbalanced Data. ID-37
Machine Learning
mailto:salma.elhajjami@usmba.ac.mamailto:jmalki@univ-lr.frmailto:mohammed.berrada@gmail.commailto:bfourka@ayaline.com
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36
Novel Convex Polyhedron Classifier for Sentiment Analysis
EL MRABTI Soufiane, LAZAAR Mohamed ENSIAS, Mohammed V University
Rabat, Morocco elmrabtisouf@gmail.com; mohamed.lazaar@um5.ac.ma
AL ACHHAB Mohammed, OMARA Hicham ENSA, Abdelmalek Essaadi University
Tetuan, Morocco m.alachhab@uae.ac.ma; hichamomara@gmail.com
Abstract— In this paper, we propose a Novel Convex Polyhedron classifier (NCPC) based on the geometric concept convex hull. NCPC is essentially a piecewise linear classifier. It partitions linearly non-separable data into approximately linearly separable subset. Each subset of data is classified by a linear hyperplane. Experimental results on several real-world datasets indicate that the proposed classifier outperforms the other classifiers and achieved state-of-the-art performances. Keywords— Convex Polyhedron classifier; convex hull; Sentiment Analysis; Feature selection; Support vector machines. ID-73
Machine Learning
mailto:elmrabtisouf@gmail.commailto:mohamed.lazaar@um5.ac.mamailto:m.alachhab@uae.ac.mamailto:hichamomara@gmail.com
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37
A Recommendation approach based on Correlation and Co-occurrence within social learning network
Sonia Souabi, Asmaâ Retbi, Mohammed Khalidi Idrissi, Samir Bennani
RIME TEAM-Networking, Modeling and e-Learning Team- MASI Laboratory- ENGINEERING.3S Research centerMohammadia School of Engineers (EMI)
MOHAMMED V UNIVERSITY IN RABAT, Morocco
soniasouabi@research.emi.ac.ma, retbi@emi.ac.ma, khalidi@emi.ac.ma, sbennani@emi.ac.ma
Abstract— In the context of e-learning, social learning is viewed as an evolving educational practice, namely in social networks. It is extensively associated with new educational technologies and fosters collaborative learning between learners. To handle the various pedagogical resources, several recommendation systems were proposed, with considerable emphasis on interactions and social relationships, except that they did not raise a critical aspect, namely the underlying nature of the relationship between the learners' actions and recommendations. To support the current recommendation systems, we propose a recommendation system that can measure the influence of learners' actions on the calculated recommendations. We therefore seek to evaluate the connection and link between these actions, and thus to combine the two parameters: correlation and co-occurrence by estimating the similarity of occurrences on the one hand and the probability of two actions taking place on the other hand. Keywords-component— Social learning; recommendation systems; correlation; co-occurrence; actions; social networks ID-7
Machine Learning
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38
Graph-based Model for Negative e-WOM Influence in Social Media
Abderraouf DEMBRI and Mohamed GHARZOULI MISC Lab, University of Constantine 2 -Abdelhamid Mehri, Constantine, Algeria
abderraouf.dembri@univ-constantine2.dz, mohamed.gharzouli@univ-constantine2.dz
Abstract— Nowadays, several companies use social media marketing to increase profit and control the market. The customer’s feedback has a powerful influence on company reputation by conveying their experience in social media. Customers exchange their feedback about the services using electronic Word-of-Mouth (e-WOM). Negative feedback could help companies improve their service to increase profit. In this work, we propose an approach to determine the effect of negative e-WOM relating to a company’s products or services. Firstly, we apply a machine-learning algorithm called random forest to classify e-WOM on three classes based on polarity: Positive, negative, or neutral. Secondly, we group negative e-WOM into different clusters based on their topics using a similarity method named cosine similarity. Thirdly, we generate an influence graph of negative e-WOM based on time precedence and social ties. Finally, we analyze the resulted graph to identify risk patterns and convey useful information. The provided method is implemented using Python and is tested with collected data. Keywords— Social media marketing, Electronic word-of-mouth, Social commerce, Machine learning. ID-66
Machine Learning
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39
Big Data Architectures Benchmark for Forecasting Electricity Consumption
Houda Daki, Asmaa El Hannani and Hassan Ouahmane Laboratory of Information Technologies, National School of Applied Sciences
University of Chouaib Doukkali, El Jadida, Morocco {daki.h, elhannani.a, ouahmane.h}@ucd.ac.ma
Abstract— It is in the sector of the forecast of the electrical consumption of educational buildings that we will be interested in this paper. In fact, buildings (residential and tertiary) are key elements in the electricity grid because they present one of the highest power consuming sectors. And for the network operators, the forecasts play a crucial role for the stability of the network and for maintaining the balance between electricity supply and demand. Fortunately, this is possible by combining Big Data analytics with statistical and machine learning techniques, which have been proven in recent research to be very effective in predicting renewable energy production and consumption at various time scales. It is in this perspective that the National School of Applied Sciences of El Jadida (NSASE), a Moroccan engineering school has decided to deploy a Big Data architecture to implement a custom system to predict electrical energy consumption by analyzing all factors that influence its electrical energy use. In this paper, we propose a benchmark of the main Big Data architectures in the field and that will cover all electrical energy data processing from data collection, data storage, data analytic and data visualization. The aim of this benchmark is to choose the optimal architecture in term of fault tolerance, resource management, data storage and data modelling to forecast electricity consumption in the NSASE. Keyword— Big Data architecture, Lambda architecture, SMACK architecture, Electrical forecasting, Smart grid. ID-52
Big data
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40
A Big Data Placement Strategy in Geographically Distributed Datacenters
Laila Bouhouch, Mostapha Zbakh National School of Computer science and Systems Analysis,
Mohamed V University Rabat, Morocco
lailabouhouch94@gmail.com laila.bouhouch@mines-paristech.fr
m.zbakh@um5s.net.ma
Claude Tadonki Mines ParisTech-PSL, Centre de Recherche en Informatique (CRI)
Paris, France claude.tadonki@mines-paristech.fr
Abstract— With the pervasivness of the “Big Data” characteristic together with the expansion of geographically distributed datacenters in the Cloud computing context, processing largescale data applications has become a crucial issue. Indeed, the task of finding the most efficient way of storing massive data across distributed locations is increasingly complex. Furthermore, the execution time of a given task that requires several datasets might be dominated by the cost of data migrations/exchanges, which depends on the initial placement of the input datasets over the set of datacenters in the Cloud and also on the dynamic data management strategy. In this paper, we propose a data placement strategy to improve the workflow execution time through the reduction of the cost associated to data movements between geographically distributed datacenters, considering their characteristics such as storage capacity and read/write speeds. We formalize the overall problem and then propose a data placement algorithm structured into two phases. First, we compute the estimated transfer time to move all involved datasets from their respective locations to the one where the corresponding tasks are executed. Second, we apply a greedy algorithm in order to assign each dataset to the optimal datacenter w.r.t the overall cost of data migrations. The heterogeneity of the datacenters together with their caracteristics (storage and bandwith) are both taken into account. Our experiments are conducted using Cloudsim simulator. The obtained results show that our proposed strategy produces an efficient placement and actually reduces the overheads of the data movement compared to both a random assignment and a selected placement algorithm from the litterature. Keywords-component— Big Data, Cloud Computing, Data Placement, Greedy algorithm, Cloudsim. ID-59
Big data
mailto:laila.bouhouch@mines-paristech.frmailto:m.zbakh@um5s.net.ma
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41
New Approach for implementing big Datamart using NoSQL Key-Value stores
KHALIL Abdelhak SIAD Laboratory, Hassan First University of Settat
Settat, Morocco a.khalil@uhp.ac.ma
BELAISSAOUI Mustapha SIAD Laboratory, Hassan First University of Settat
Settat, Morocco mustapha.belaissaoui@uhp.ac.ma
Abstract— Nowadays, NoSQL technologies are gaining significant ground and considered as the future of data storage, especially when it comes to huge amount of data, which is the case of data warehouse solutions. NoSQL databases provide high scalability and good performance among relational ones, which are really time consuming and can’t handle large data volume. The growing popularity of the term NoSQL these days and vaguely related phrases like big data make us think about using this technology in decision support systems. The purpose of this paper is to investigate the possibility to instantiate a big data mart under one of the most popular and least complicated types of NoSQL databases; namely key-value store, the main challenge is to make a good correlation between the old-school approach of data warehousing based on traditional databases that favor data integrity, and interesting opportunities offered by new generation of database management systems. The paper describes the transformation process from multidimensional conceptual schema to the logical model following three approaches, and outlines a list of strengths and weaknesses for each one based on practical experience under Oracle NoSQL Database. Keywords— DataMart, NoSQL, Decision support systems, Big Data. ID-41
Big data
mailto:a.khalil@uhp.ac.ma
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42
Big data for sustainability: A qualitative analysis
Wail El Hilali, Abdellah El Manouar and Mohammed Abdou Janati Idrissi Mohammed V University
Rabat, Morocco wailelhilali@gmail.com; a.elmanouar@um5s.net.ma; a.janati@um5s.net.ma
Abstract— In this time and age, companies are facing difficult times to sustain their competitive advantages. The risk of disruption, the fierce and the bloody competition, the change in customer behaviors and the scarcity of resources, all of these are drivers that push companies to think out of the box and to change the way business is done. Digital capabilities, and especially big data, comes as a new tool to use in order to continue creating and capturing value in a sustainable manner. This paper is an attempt to enrich the literature about this subject. We conducted a qualitative analysis among three big size companies inside Morocco in order to assess the impact of using big data on these companies’ financial numbers, social footprints and environmental negative externalities. The finding showed that embracing this technology has helped these companies for their quest to reach sustainability.
Keywords- big data; sustainability; digital transformation; digital capabilities ID-6
Big data
mailto:wailelhilali@gmail.commailto:a.elmanouar@um5s.net.ma
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43
Bloom filter and its variants for the optimization of MapReduce’s algorithms: A review
F.EZZAKI Laboratoire Informatique, Modélisation des Systèmes et Aide à la Décision (LIMSAD)
Faculty of Sciences, University of Hassan II Department of Mathematics and Informatics
Casablanca, Morocco ezzakifati@gmail.com
N.Abghour, A.Elomri, K.Moussaid, M.Rida Laboratoire Informatique, Modélisation des Systèmes et Aide à la Décision (LIMSAD)
Faculty of Sciences, University of Hassan II Department of Mathematics and Informatics
Casablanca, Morocco {noreddine.abghour, amina.elomri, khalid.moussaid, mohamed.rida}@univh2c.ma
Abstract— The bloom filter is a probabilistic model used to represent a set in order to test the existence of an element in this set, i.e., for any given item, the bloom filter could test if this element is a member or not of the set represented by the bloom filter. The bloom filter has many advantages due to its simplicity and efficiency in highly solving the issue of data representation in many fields and to support membership queries, it has been known as space and time-efficient randomized data structure, by filtering out redundant data and optimizing the memory consumption. However, bloom filters are limited to membership tests and don’t support the deletion of elements. They also generate the false positive probability as they are based on a probabilistic model, this error rate is generated when an element that doesn’t belong to a set is considered as a member of this set by the bloom filter. Our goal is to compare a number of wellexisted algorithms related to the boom filter for future work on the optimization of the join’s algorithms in MapReduce. This paper provides an overview of the different variants of the bloom filter and analyses the studies that have been interested in this area of research.
Keywords— big data storage; optimization; bloom filter; cuckoo filter; membership; data structure; join algorithms; mapreduce. ID-55
Big data
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44
Deep Learning and Tensorflow for Tracking People’s Movements in a Video
Jemai Bornia Robotics, Computing and Complex Systems, National Engineering School of Tunis
University Tunis El Manar, Tunis, Tunisia bornia.jemai@gmail.com
Ali Frihida Robotics, Computing and Complex Systems, National Engineering School of Tunis
University Tunis El Manar, Tunis, Tunisia ali.frihida@enit.utm.tn
Olivier Debauche Faculty of Engineering – ILIA, Infortech Institut
University of Mons, Mons, Belgium
Sidi Ahmed Mahmoudi Faculty of Engineering – ILIA, Infortech Institut
University of Mons, Mons, Belgium
Pierre Manneback Faculty of Engineering – ILIA, Infortech Institut
University of Mons, Mons, Belgium
Abstract— With the advent of the digital age and more specifically videos, a huge amount of data is produced every day such as television archiving, video surveillance, etc. Faced with the need to keep control over this content, in terms of data analysis, classification, accurate AI (Artificial Intelligence) algorithms are required to perform this task efficiently and quickly. In this paper, we propose an approach for movement analysis from video sequences using deep learning technologies. The proposed approach splits video in set of images, detects objects/entities present in these images and stores their descriptions into a standard XML file. As result, we provide a Deep Learning algorithm using TensorFlow for tracking motion and animated entities in video sequences. Index Terms— Object tacking, motion tracking, TensorFlow, deep learning, video. ID-97
Deep Learning
mailto:bornia.jemai@gmail.commailto:ali.frihida@enit.utm.tn
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45
SAHAR-LSTM: An enhanced Model for Sentiment Analysis of Hotels’Arabic Reviews based on LSTM
Manal Nejjari and Abdelouafi Meziane
Department of Computer Science Science College, Mohammed 1st University Oujda, Morocco
manal.nejjari10@gmail.com; Abdelouafi_meziane@yahoo.fr
Abstract— Over the last few years, many scientists have paid special attention to the field of Opinion Mining (OM) or Sentiment analysis (SA), thanks to its interesting and useful applications in different domains. The most popular studies have tackled the issue of SA in the English language, however those dealing with SA in the Arabic language are, up to
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