Mohd Shafry Mohd Rahim
University Technology Malaysia, Malaysia
Mohd Shafry Mohd Rahim is currently a Professor and Chair, Institute for Life Ready Graduate, Universiti Teknologi Malaysia (UTM). He is also a professor at School of Computing, UTM and Senior Research Fellow at Media and Game Innovation Centre of Excellence (MaGICX), Institute of. Human Centred Enggineering (iHumEn), UTM. Prof. Shafry has received a B.Sc. (Hons.) Computer Science and M.Sc. in Computer Science from Universiti Teknologi Malaysia (UTM), Malaysia in 1999 and 2002, respectively. He has received a PhD in Spatial Modelling from Universiti Putra Malaysia (UPM), Malaysia in 2008. His research interests in image processing, image data analytics, computer graphics and medical imaging.
Speech Title: Medical Image Segmentation Approaches for Diagnostic Analytics
Abstract: Humans have a strong ability to process millions of data and information to assist in the decision-making process. With new disruptive technology, a trillion of medical data has been flooded into cloud computing and require an analytical process to produce valuable diagnosis information. Images are one of the data collected using a variety of sensors that carry a lot of valuable information for the diagnosis process. Therefore, Medical Image Diagnostic Analytics is a very significant research area to be strengthened in the new era of Big Data to improve healthcare industries by providing reliable information. The most important process in Medical Image Analytics is image segmentation. Image segmentation is to extract clinically relevant information with intelligent insight. There are several methods can be used in the segmentation process. In this discussion, several image segmentation methods will be presented. The advantages and disadvantages of each method are described besides examination of each algorithm with its application in Magnetic Resonance Imaging and Computed Tomography image analysis. Each algorithm is explained separately with its ability and features for the analysis of grey-level images. In order to evaluate the segmentation results, some popular benchmark measurements also presented in the final section. In this keynote, the discussion also focuses on experiences in Medical Image Diagnostic Analytics and discussing key challenges in various types of data for further research including semantic gaps.
Prof. Lisa Gandy
Central Michigan University, USA
Dr. Gandy is a professor of Computer Science at Central Michigan University, USA. She got his phd degree in Ph.D., Northwestern University, Evanston Illinois, USA. Her research fields are in the areas of natural language processing and text informatics, Currently helping biologists annotate research results automatically, Investigating how the content of legislation in the U.S. Senate affects legislator's voting behavior.
Prof. Roberto Montemanni
University of Modena and Reggio Emilia, Italy
Roberto Montemanni is full professor of operations research at the University of Modena and Reggio Emilia, Italy. He also acts as an external research advisor at the Dalle Molle Institute for Artificial Intelligence, University of Lugano, Switzerland. He obtained a Laurea degree in Computer Science from the University of Bologna, Italy and a Ph.D. in Applied Mathematics from the University of Glamorgan, UK. He has been leading basic and applied research projects both at national and international levels, administrating more more than one million of Euros. His main research interests are in the fields of algorithms, mathematical modeling and machine learning. Recently he is focussing on the interactions of optimization and machine learning.
Speech Title: Algorithms for Travelings Salesman Problems with Drones
Abstract: The use of drones in logistics is gaining more and more interest. In this presentation we consider the flying sidekick traveling salesman problem, where a set of customers requires a delivery and deliveries can be served either by a truck or by a drone. The aim is minimizing the total time required to service all the customers. In this talk we discuss some algorithms based on branch and bound for the problem. A first contribution is an exact algorithm characterized by not completely specified solutions in the search tree, that are later fully determined by solving an Assignment Problem. Such a choice limits the size of the search tree, but on the other hand tends to weaken lower bounds. Experimental results show that the choice pays off for instances of limited size, leading to very good and consistent results in terms of speed for instances up to 10-15 customers. Note that these results are in line with the current literature. The same branch and bound algorithm is also effectively used as a subroutine for a heuristic algorithm, which is the second contribution of this work. The idea is to iteratively optimize overlapping chunks of a solution with the exact algorithms. Again, experimental results prove that such a heuristic approach is extremely competitive on large instances, being able to effectively deal with instances with up to 229 customers in a operatively short time, representing the current state-of-the-art.
Prof. Dayang Norhayati Abang Jawawi
Universiti Teknologi Malaysia, Malaysia
She is a professor at the School of Computing, Faculty of Engineering, UTM. She received her B.Sc. in Software Engineering from Sheffield Hallam University, UK, and her M.Sc. and Ph.D. in the field of Software Engineering from UTM. She has served as an academic administrator at UTM, since 2009 and currently she is Associate Chair (Academic and Student Development) at School of Computing, Faculty of Engineering, UTM. Her research areas are software engineering and computing education. Most of her research projects are focused to the domain of educational robotics, computational thinking, healthcare system and real-time embedded system application.
Speech Title: Nurturing Computational Thinking Skills: An Adaptive Approach
Abstract: The fourth industrial revolution bring changes in terms of technologies and a major shift in the job environment. One of the important skills can be promoted with this revolution is computational thinking (CT) skills. CT is a vital and fundamental skill which involved solving problems activities, designing system, and also understanding human behavior by mapping the concepts into computer science discipline.
The Covid-19 pandemic have demanded the education system to utilize the digital platform in ensuring that the teaching-learning process can be conducted amid the pandemic. Thus, in this sense, ubiquitous learning is a learning environment to support digital learning paradigm. The overall purpose of this presentation is to share an adaptive approach to nurture students’ CT skills in ubiquitous learning environment. The approach enables students to have better opportunity in learning CT concepts and exploring various methods and technologies used in education including gamification and educational robotics.
Assoc. Prof. Zhen Liu
University of Electronic Science and Technology of China, China
Zhen Liu received his Ph.D. degree in computer application technology from the University of Electronic Science and Technology of China (UESTC) in 2007 and was a visiting scholar at the Data Mining Laboratory of the University of Minnesota from 2012 to 2013. Since 2011, he has been an Associate Professor with the School of Computer Science and Engineering at UESTC. In recent years, he has published more than forty academic papers on wide topics of data mining and presided over ten projects on data analysis and its engineering applications with cooperations with companies like Huawei, ZTE, and China UnionPay. His current research interests include Recommender systems, Anomaly detection, and Social network analysis.
Speech Title: Link Prediction: An Important Research Paradigm for Graph Mining
Abstract: Network-related graph mining has gained much attention in recent years. As an important computational tool, link prediction play a significant role in various graph mining tasks such as network community detection, network anomaly detection, and social recommendation, etc. There exists a wide range of link prediction techniques like similarity-based indices, probabilistic methods, dimensionality reduction approaches, and so on. In this presentation, I will briefly introduce the background and state of art of link prediction. Some interesting outlooks on the study of link prediction will be outlined as well.
Assoc. Prof. Alex Norta
Tallinn University of Technology, Estonia
Dr. Norta is currently an associate professor at the Department of Software Science where his recent research focus is on blockchain technology for enabling novel e-governance models that involve many collaborating organizations. His research background is interdisciplinary comprising cross-organizational business-process automation, multi-agent systems, security, agile software engineering, blockchain-system engineering, e-learning, the legal relevance of smart contracts. In conducting action-design research, he has been involved in numerous blockchain startup research. For example, he published the founding paper for qtum.org. He has also published many blockchain papers in diverse areas such as banking the unbanked, insurance issuance on blockchains, e-procurement on blockchains, multi-factor identity authentication with a blockchain-based distributed application. Recently he has developed a novel methodology for the design of distributed blockchain application Further pertaining to blockchain relevance, Alex has co-supervised an award-winning PhD thesis defended at the University of Goettingen about the novel machine-to-everything (M2X) economy in which it is assumed that self-driving cars participate in an open socio-technical ecosystem. His specific supply chain experience reaches back to his time as a PhD student at the TU-Eindhoven where he investigated the automation of cross-organizational business processes for truck production. As a post-doc at the University of Helsinki, he developed formalized lifecycle management for the setup, enactment, and orderly termination of such automated cross-organizational business-process collaboration. This keynote-speech paper summarizes conceptually the publication results that map into blockchain technology and have been further developed at the Department of Software Science. Alex was nominated as the most successful PhD supervisor of the IT School in 2020 and in 2021 he won also the best-paper award of the IT school in the category for social-science category for his journal paper about e-participatory budgeting.
Speech Title: Blockchain Technology for Secure IOT
Abstract: The keynote first introduces IoT, blockchain technology, smart contracts and security respectively. Next follows a pairwise investigation about the intersection of each elements, culminating in the lessons learned from this diagnosis. The current pros and cons are presented for applying blockchain technology for orchestrating and securing IoT systems. The presentation ends with giving several casestudies where blockchain is used in combination with IoT.
Assoc. Prof. Dong Hao
University of Electronic Science and Technology of China, China
Dong Hao is an associate professor in School of Computer Science and Engineering, University of Electronic Science and Technology of China (UESTC). Before working in UESTC, he obtained his Ph.D. in Informatics from Kyushu University, Japan under the supervision of Professor Makoto Yokoo. His research interests lies in the intersection of Artificial Intelligence and Economics, especially in Algorithmic Game Theory, Social and Economic Networks, and Multi-agent Learning and Planning. His research works are about understanding how people or intelligent agents think and behave when they interact with others, and (2) how they learn about others' behavior and how they adapt when they are in dynamic, uncertain or networked environments. His research contents are mainly about modeling, reasoning, prediction, optimization and algorithm design for these thinking and behavior.
Speech Title: Cooperation Enforcement and Collusion Resistance in Repeated Public Goods Games
Abstract: Enforcing cooperation among substantial agents is one of the main objectives for multi-agent systems. However, due to the existence of inherent social dilemmas in many scenarios, the free-rider problem may arise during agents' long-run interactions and things become even severer when self-interested agents work in collusion with each other to get extra benefits. It is commonly accepted that in such social dilemmas, there exists no simple strategy for an agent whereby she can simultaneously manipulate on the utility of each of her opponents and further promote mutual cooperation among all agents. Here, we show that such strategies do exist. Under the conventional repeated public goods game, we novelly identify them and find that, when confronted with such strategies, a single opponent can maximize his utility only via global cooperation and any colluding alliance cannot get the upper hand. Since a full cooperation is individually optimal for any single opponent, a stable cooperation among all players can be achieved. Moreover, we experimentally show that these strategies can still promote cooperation even when the opponents are both self-learning and collusive.
Assoc. Prof. Gabriela Mogos
Xi'an Jiaotong-Liverpool University, China
Dr. Gabriela Mogos is an Associate Professor in the Department of Computer Science and Software Engineering (CSSE) at the Xi'an Jiaotong-Liverpool University (XJTLU), Suzhou, China. She received her PhD in Computer Science from the Alexandru Ioan Cuza University of Iasi, Romania, in March 2010. She followed this with postdoctoral research positions at the University of Oradea, Romania.
Her interests and research activities are mainly centred around a quantum computing with an emphasis on design of new quantum algorithms and quantum cryptographic protocols. She has more than 100 academic publications, books, book chapters, and has acted as principal investigator and co-investigator in international and national research projects.
Speech Title: Quantum-based Technology in Cyber Defense
Abstract: With the rapid development of computer and network, new technologies and services are being generated, evolved, and promoted constantly, bringing great convenience and changes to people’s life. Over the next 5-10 years, we will see a flux of new possibilities, as quantum technologies become part of this mainstream computing and communicating landscape.
All security concepts, such as authentication, encryption but also more involved concepts as computation on encrypted data and secure multiparty computation, would need to be modified to apply to quantum information and quantum computation. Quantum technologies can offer advantages for cyber security research. To view this positive aspect, we should consider the possibility of including quantum steps in protocols with the aim of achieving certain improvement compared to the corresponding fully classical setting.
This presentation will explore quantum-technology solutions in the 21st century and their role in cyber security. The speaker will also share the most relevant results of the projects she has worked on.
Assoc. Prof. Burcu Erkmen
Yildiz Technical University, Turkey
Burcu ERKMEN is Associate Profesor in Department of Electronics and Communication Egineering from Yildiz Technical University. She received her M.S. and Ph. D. degrees from YTU in Istanbul with a focus on artificial intelligence and electronic circuit design. Burcu ERKMEN collaborates with scientists in many domains on artificial intelligence and digital technologies. Her current focus is on using for FPGA Based System Design, Optimization Techniques in Electronic Circuits, Artificial Neural Networks, Deep Learning and Artificial Intelligence in Power Converters.