International Speakers

Professor Hussain Mahdi

Director - Peer Supported Learning Centre (PSLC),
Joint Director - ICT Learning Centre (ICTLC),
Leader -Text Analytics & Knowledge Organisation (TAKO) Research Group,
Department of Electronic & Computer Engineering, Department of Electronic and Computer Engineering, University of Limerick (UL), Ireland.

TOPIC:
Peer-Supported Learning Groups for Engineering and ICT Courses: An Insight into Students’ Perceptions and Experience

Abstract:
In this talk, I will present a non-traditional tutoring programme, which is based on collaborative peer learning, and provide an insightful reflection on more than a decade of its implementation to specific subjects in electronic engineering and ICT based courses at the University of Limerick. The programme, known as Peer-Supported Learning Groups (PSLG), is an academic enrichment scheme, has been developed by adapting the American SI model to meet the needs of the students in Ireland and fits into the Irish third-level education system. PSLG is a proactive student-centred academic enrichment scheme run by the students for the students. It targets difficult modules/subjects, and fosters cross-year collaborative support between students on the same course under the guidance of student leaders from the year(s) above. The talk will first provide a rationale for the introduction of the PSLG to the targeted subjects, followed by descriptions of the characteristics and operational structure of the scheme. I will then presents the main findings of our latest study on the evaluation and effectiveness of the PSLG, in terms of developing students’ learning and transferrable skills, and creating a community of mutual support among the students, as well as enhancing their academic performance. The talk concludes with thoughts and reflection on the implications of above findings in terms of challenges and our plans going forward.

Biography:
Professor Hussain Mahdi (Abdulhussain E. Mahdi) is the Leader of the Text Analytics & Knowledge Organisation (TAKO) Research Group at the Department of Electronic and Computer Engineering, University of Limerick (UL), Ireland. He is Director of the Regional Peer-Supported Learning Centre – UL and Joint Director of the ICT Learning Centre – UL. He is a Chartered Engineer (CEng), Member of the Institution of Engineering and Technology and Member of the Engineering Council (1994-2017). Prof. Mahdi is a Graduate of Electrical Engineering from the University of Basrah (BSc 1st Class 1978) and earned his PhD in Electronic Engineering at the University of Wales – Bangor, UK in 1990. He is also a SEDA Accredited Teacher of Higher Education (SEDA & University of Plymouth, UK 1998). His current research focuses on text data mining & analytics, knowledge organisation & linked data, information retrieval & information extraction, as well as signal, speech, and NLP. His research interests also include learning analytics, inquiry-based learning, collaborative and student-centred active learning. He has authored and co-authored more than 150 peer- reviewed journal articles, book chapters, and conference papers, and has edited two books.

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Dr. Syed Saad Azhar Ali

Associate Professor,
Center for Intelligent Signal & Imaging Research Electrical & Electronic Engineering Department Universiti Teknologi PETRONAS Bandar Seri Iskandar 32610, Perak, Malaysia.

TOPIC:
Deep Learning based Real-Stress Assessment for Efficient Mental Stress Management

Abstract:

Biography:
(Senior Member IEEE) received the B.E. degree in electrical engineering from NED University, Pakistan, and the master’s and doctoral degrees in nonlinear control from the King Fahd University of Petroleum & Minerals, Saudi Arabia. He was with Air University and Iqra University prior to being engaged as Associate Professor with the Center of Intelligent Signal and Imaging Research, Universiti Teknologi PETRONAS, Malaysia. Recently, he has been involved in neurosignal processing. He has authored over 70 peer- reviewed publications, including four books/chapters. His research focus has been on neurosignal processing, intelligent control, signal processing, underwater robotics with the emphasis on image enhancement and 3-D scene reconstruction. He is the PI for several funded research projects.

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Dr. Helen Crompton

Professor of Instructional Technology,
Old Dominion University Virginia USA

TOPIC:
Moving Toward a Mobile Learning Landscape: Effective Device Integration

Abstract:
Teaching styles have emerged from a nebulous blend of rituals, traditions and beliefs built over time. Affordances of recent digital technologies have conflicted with those practices as 21st century tools are used with 20th century teaching methods. This problem is amplified with mobile technologies that push beyond the boundaries of traditional pedagogies. This requires educators to shed old beliefs and ideas to make way for a new contextualized, ubiquitous mobile learning landscape. The speaker presents frameworks for effective device integration that provide a lens for educators to structure new educational thinking and practice.

Biography:
Dr. Helen Crompton is a Professor of Instructional Technology at Old Dominion University Virginia USA. She is a highly experienced researcher, educator, author and presenter in the field of educational technology. She draws from over 20 years in education and a PhD in educational technology and mathematics education from the University of North Carolina at Chapel Hill. Dr. Crompton has won numerous awards in the USA and her home country England. Dr. Crompton has worked with UNESCO and ITU, two divisions of the United Nations, and the International Society for Technology in Education. Dr Crompton has presented at national and international conferences on the topic of educational technology and published over 100 articles, book chapters, and white papers in this field.

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Dr. Eranjan Padumadasa

Lecturer in Information Technology Management / Programme Director Degree Apprenticeships,
Queen Mary University of London

TOPIC:
Social Network of Influence: Regulating Online Social Media Influencers “How Twitter Network Analysis Helped in Legal Reforms”

Abstract:
This study was completed at the invitation of European Commission to investigate the enforcement measures of online gambling regulations in Europe. The regulators were particularly interested in the use of social media influencers used for gambling advertising. The case study explored the networks and subnetworks on twitter to identify the influencers emanating from these sub networks. Identification of these influencers helped to understand how to regulate them. Twitter has been identified as one of the social media networks (SMN) that help users exchange information, what some might call as “digital noise”, used in the context of online gambling to share betting odds, betting tips, deals with gambling enthusiast. Due to the nature of being able to share concise information with mass amounts of people have made twitter a popular social media network and have gained traction over some other social media outlets such as YouTube, Facebook for certain types of gambling.

Biography:
Dr. Eranjan Padumadasa, is an academic from EECS at Queen Mary University of London, who has over 10 years of academic experience in higher education. His field of expertise ranges from Computer Science to Law. He hold a Bachelor’s degree in Computer Science from which he moved on to Management and Economics and completed a PhD in Commercial Law. His area of expertise is in cyberspace law and is currently working on Artificial Intelligence and Machine Learning projects. Dr. Eranjan has suggested to deliver a talk which relates to Social Media and its Influence. The talk will focus on the context of social media and discuss the twitter network analysis that went in to identifying social media influencers. The discussion will be accessible to any audience as the technical details will be kept out to make it easy to understand.

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Dr. Sergio Pinna

Integrated Photonics Laboratory, Electrical and Computer Engineering, University of California Santa Barbara (UCSB), California, USA.

TOPIC:
Integrated Photonics for Free Space Optics Communications and Sensing.

Abstract:
Free space optical systems are becoming ubiquitous instruments in science, industry and consumer systems. Integrated photonics is an enabling technology capable of reducing system costs, size and power consumption, while improving system performance. In this talk we will discuss applications, challenges and development opportunities opened by integrated photonics for free space optical systems.

Biography:
Sergio Pinna is a project scientist at the Integrated Photonics Laboratory, University of California Santa Barbara, California, USA. His research focuses on Photonics for microwave and sensing applications, and on highly energy efficient optical transceivers for free space optics and data-centers applications.

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Dr. Manzoor Ahmed Hashmani

Biography:
Dr. Hashmani has more than 25 years of broad IT experience both in the fields of research and development. He has done both his M.E. and Ph.D. from Nara Institute of Science & Technology, Japan in a very short duration of four years. He did B.E. (Computer Systems Engineering) in 1991 from Mehran University of Engineering & Technology, Jamshoro, Pakistan. After Ph.D. he worked in NS Solutions Corporation, Japan for about three years. Here he supervised parts of a large governmental project (Japanese) and worked as a lead researcher and developer. He then worked for BBR (Broadband Research) Japan for about two years. Here he participated in a project which involved HDTV, VoIP and teleconferencing. As part of this project, Dr. Hashmani designed and developed a bandwidth broker to allocate and manage network bandwidth. Besides industry experience of 5 years, Dr. Hashmani has worked in academia for around 20 years. He has on his credit many funded research project. He successfully supervised 7 PhD projects and 50 MS/MPhil projects. He has more than 100 research papers (in journals and conferences of international repute) on his credit. His research areas of interest include: • Blockchain • Data Analytics • Deep Learning • Artificial Intelligence • Soft Computing • Software Engineering • High Speed Communication Networks

Blockchain to Disrupt Industries -- Can Developing Countries Leverage?
Every now and then a technology comes along that changes everything. Blockchain has the potential to disrupt many industries from currency to education. It would have an impact on effective healthcare programs, improve supply chain processes and, perhaps, clean up unethical behavior in high-value businesses such as diamond trading, Finance, Politics, Real Estate, Security, Education. Blockchain is like the Internet, which is an open, global infrastructure that allows companies and individuals making transactions to cut out the middleman, reducing the cost of transactions and the time-lapse of working through third parties. It is celebrated as General Purpose Technology (GPT) like the wheel, electricity, and the internet before it. The core capabilities of blockchain technology are beyond any that we have seen before. The only difference is that unlike previous GPTs, blockchains will spread far more rapidly throughout the world due to the global communication infrastructure even in developing countries like Malaysia, Pakistan, etc. In a World Economic Forum report released in September 2015, 58 percent of all survey respondents said that by the year 2025 it is expected that 10 percent of vital global gross domestic product (GDP) data will be stored using Blockchain technology. And according to a recent report by Santander, Oliver Wyman and venture capital investor Anthemis, it is estimated that the technology could cut banks’ infrastructure costs for cross-border payments, securities trading and regulatory compliance by $15bn-$20bn a year from 2022. A large portion of the population in the developing world can benefit from blockchain technologies. According to the ICT Facts and Figures 2017 report, 42.9 percent of households in developing countries have Internet access. It can be argued that in many ways, blockchain has a much higher value proposition for the developing world than for the developed world. And it has the potential to bridge the gap between the developed world and developing nations because blockchain has the potential to make up for a lack of effective formal institutions — rules, laws, regulations, and their enforcement. Blockchain is proliferating in the developing countries but at a relatively slower pace. The question before the policy makers in the developing countries is very simple, “Are they Willing and Aware-Enough to Leverage on Blockchain”. I will address this question in detail in my talk.

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National Speakers

Dr. Bhawani Shankar Chowdhry

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Dr. Muhammad Atif Tahir

Topic: Flood Classification using Text and Image Data

Abstract:

The design and implementation of effective flood classification system is a challenging problem. The recent trend of using social media during disastrous situations has opened the new pathways for the research. Additionally, satellites are significant source for collection of remotely sensed data related to flooding conditions. The methods for processing disastrous situations have been improved with the transformation of conventional algorithms to their learning counterparts. The research has various developments in recent past, but it still faces the challenges of processing text and images related to disastrous situation. This talk will discuss various states of art computer vision and machine learning methods and their respective use in different stages of flood classification.

BIOGRAPHY:

Dr Muhammad Atif Tahir received his PhD from School of Computer Science & Engineering at Queens University, Belfast, UK, MSc in Computer Engineering from King Fahd University, Dhahran, KSA, and BE in Computer Systems Engineering from NED University of Engg, and Tech., Karachi, Pakistan. He is also academic fellow of UK higher education. He is currently working as Professor in School of Computer Science, FAST University, Karachi Campus, Pakistan. Before joining FAST, he was working as Senior Lecturer at Northumbria University, United Kingdom. Dr Tahir also worked as Research Officer at the Centre for Vision, Speech and Signal Processing (CVSSP), University of Surrey on research projects involving interactive semantic video/audio search with a large thesaurus of machine learned audio-visual concepts and face recognition on uncontrolled environment. He has developed novel machine learning methods for concept detection/visual learning/face recognition. One of my methods has achieved the best performance and ranked first in prestigious international software competitions on visual category recognition (TrecVid 2009/2010, Pascal VOC 2010/2008 and ImageCLEF 2010). Dr Tahir also worked as Research Fellow in University of the West of England. His main research is in Machine Learning & Combinatorial Optimization Techniques with applications in image / video retrieval, cancer classification, surface inspection, bioinformatics, multi-label classification, and face recognition. He has authored and co-authored more than 60 publications in top quality journals including IEEE Transactions on Pattern Analysis and Machine Intelligence, Pattern Recognition, Journal of Machine Learning Research, IEEE Transactions on Multimedia.

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Dr. Salman A. Khan

Title: Application of multi-criteria decision-making techniques for wind turbine selection.

Abstract:

The recent revolution in the use renewable energy worldwide has opened many dimensions of research and development for sustainable energy. In this context, the use wind energy has received notable attention. One critical decision in the development of a wind farm is the selection of most appropriate turbine compatible with the characteristics of the geographical location under consideration in order to harness maximum energy. This selection process considers multiple decision criteria which are often in conflict with each other, as improving one criterion negatively affect one or more other criteria. Therefore, it is desired to find a tradeoff solution where all selection criteria are simultaneously optimized to the best possible level. This talk provides an overview of several multi-criteria decision making-techniques proposes that have been used for the turbine selection problem. A case study is shown on real data collected from a potential wind farm while considering variety of turbines from various manufacturers in the decision process.

BIOGRAPHY:

Salman A. Khan received BS and MS degrees in Computer Engineering from King Fahd University of Petroleum & Minerals (KFUPM), Saudi Arabia in 1997 and 1999 respectively. He received PhD in Computer Science from University of Pretoria in 2009. He has held academic and research positions at KFUPM, University of Bahrain, and University of Pretoria. Currently, he is a Professor at College of Computer and Information Sciences, Pakistan Air Force – Karachi Institute of Economics and Technology, Pakistan. He has published over 60 research papers in ISI and other indexed journals, and reputed conferences. He has supervised/ co-supervised 11 PhD and MSc thesis. In addition, he is an active reviewer for leading journals and conferences, and is ranked in the top 1% reviewers globally by Publons. In addition, he has completed several funded research projects as a PI/ consultant at KFUPM and University of Bahrain. His research interests are optimization, fuzzy logic, multi-criteria decision-making, nature-inspired algorithms, and applications of these concepts to several domains such as computer networks, network security, cloud computing, and renewable energy. He is a member of IEEE and IEEE Computer Society.

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Dr. Imran Naseem

Title : Machine Learning(Conventional and Modern Perspectives).

Abstract:

Machine learning (ML) can be considered as an application of artificial intelligence (AI) that provides machines the ability to automatically learn and improve from experience without being explicitly programmed. ML focuses on the development of computer programs that can access data and use it to learn for themselves and then take reasonable decisions on unseen examples. Conventionally, a model-based approach has been used in ML and a number of algorithms have been proposed. Main aim of conventional ML is to extract useful information from the data, called as feature extraction, and train a particular model for the given problem. A set of features or model suitable for one application might not be reasonable for the other. Recently, there has been a twist of modernity in the story in the form of deep learning (DL). With the advent of DL the focus has shifted more towards gathering more and more data rather than developing precise models. In fact researchers have been trying to develop one generic model to address a variety of problems in a particular domain. In this talk, we will try to critically compare the conventional and modern approaches in ML and how they have affected the field. We will evaluate the tall claims associated with the recent success of DL in various fields and will try to get our heads around the million dollar question “will machines surpass human intelligence?”

BIOGRAPHY:

Imran Naseem received his B.E. (Electrical Engineering) degree in 2002 from the NED University of Engineering and Technology, Pakistan. He did his M.S. (Electrical Engineering) in 2005 from the King Fahd University of Petroleum and Minerals (KFUPM), KSA and Ph.D in 2010 from The University of Western Australia. He did his post doctorate at the Institute for Multi-sensor Processing and Content Analysis, Curtin University of Technology, Australia. He joined the College of Engineering, Karachi Institute of Engineering and Technology (KIET) in 2011 where he is currently a full Professor. He is also an Adjunct Senior Research Fellow at the School of Electrical, Electronic and Computer Engineering, The University of Western Australia, since 2013. His research interests include pattern classification and machine learning with a special emphasis on biometrics and bioinformatics applications. He has authored several publications in top journals and conferences including IEEE Transactions on Pattern Analysis and Machine Intelligence, Pattern Recognition, IEEE Transactions on Computational Biology and Bioinformatics, IEEE International Conference on Image Processing etc. His benchmark work on face recognition has received more than 800 citations. He is also a reviewer of IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Image Processing and IEEE Signal Processing Letters.

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