Keynote Talks

Keynote 1: From Cloud to Fog and Mist Computing: Resource Allocation and Scheduling Issues

Prof. Helen Karatza

Aristotle University of Thessaloniki, Greece

Abstract: With the exponential growth of the Internet of Things (IoT) applications and the development of smart environments, cloud computing is insufficient for transferring the huge volume of data generated by IoT sensors and devices. As a result, Fog and Mist computing have been adopted as computing paradigms to cope with transmission latency issues. Fog computing complements and extends the cloud to the network edge, closer to where the IoT data are generated in an attempt to meet the requirements of low latency. Mist computing is a lightweight form of fog computing, that brings fog capabilities even closer to the IoT layer. Most of the IoT applications are delay-sensitive and the goal is to ensure that all deadlines are met. Therefore, in such computing environments it is crucial to adopt efficient resource allocation and scheduling schemes, in order to provide timeliness for the real-time workload and to fully exploit the potential of cloud, fog and mist computing systems. In this keynote we will talk about new trends driving the development of new computing paradigms. Furthermore, novel techniques to explore challenges in resource allocation and scheduling in cloud, fog and mist computing environments will be presented and discussed.

Biography: Helen D. Karatza (senior member of IEEE, ACM, SCS) is a Professor Emeritus in the Department of Informatics at the Aristotle University of Thessaloniki, Greece. Her research interests include cloud, fog and mist computing, energy efficiency, fault tolerance, resource allocation, scheduling algorithms and real-time distributed systems. Dr. Karatza has authored or co-authored over 250 technical papers and book chapters including seven papers that earned best paper awards at international conferences. 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 Senior Associate Editor of the Elsevier journal “Simulation Modelling Practice and Theory”, an Editor of “Future Generation Computer Systems” of Elsevier, an Associate Editor of IEEE Transactions on Services Computing and an Editorial Board member of Cluster Computing of Springer. She was Editor-in-Chief of the Elsevier journal “Simulation Modelling Practice and Theory”, Editor-in-Chief of “Simulation Transactions of The Society for Modeling and Simulation International”, Associate Editor of “ACM Transactions on Modeling and Computer Simulation” and Senior Associate Editor of the “Journal of Systems and Software” of Elsevier. She served as Guest Editor of Special Issues in several international journals. More info about her activities and publications can be found at: https://users.auth.gr/karatza/ .

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Keynote 2: Semi-Supervised Learning, Life Long Learning and Scalable Spatio-Temporal Graph Neural Networks for Social Good

Prof. Latifur Khan

University of Texas at Dallas (UT Dallas), USA

Abstract: With regard to semi-supervised learning, various efforts have been proposed for reducing the annotation cost when training Deep neural networks (DNN). Semi-Supervised Learning (SSL) is one of the solutions that has been provably handy in leveraging unlabeled instances to mitigate the efficacy of the DNN model’s performance and has been attracting an increasing amount of attention in recent times. In this work, our main insight is that semi-supervised learning can benefit from the recently proposed unsupervised contrastive learning approach, which aims to achieve the positive concentrated and negative separated representation in the unlabeled feature space. Herein, we introduce MultiCon, a semi-supervised learning paradigm that aims at learning data augmentation invariant based embedding. Experiments on multiple standard datasets including Covid19 Chest X-ray images, and CT Scans demonstrate that MultiCon achieves state-of-the-art performance across existing SSL benchmarks. In addition, we will demonstrate how semi-supervised learning can be used to identify Choroidal Tumors in Fundus Photographs and find vulnerable functions in application libraries.
With regard to lifelong learning, we will monitor conflicts and political violence around the world by analyzing volumes of continuous or stream specialized text on a global scale. To help advance research in political science, we introduce ConfliBERT, a domain-specific pre-trained language model for conflict and political violence. We first gather a large domain-specific text corpus for language modeling from various sources. We then build ConfliBERT using two approaches: pre-training from scratch and continual pre-training to facilitate lifelong learning. For incremental/continual learning, deep learning models should be able to learn new information while retaining previously learned skills or knowledge, but catastrophic forgetting does happen and we will address that in this talk.
Time series forecasting with additional spatial information has attracted a tremendous amount of attention in recent research, due to its importance in various real-world applications in social studies, such as conflict prediction and pandemic forecasting. Conventional machine learning methods either consider temporal dependencies only or treat spatial and temporal relations as two separate autoregressive models, namely, space-time autoregressive models. Such methods suffer when it comes to long-term forecasting or predictions for large-scale areas, due to the high nonlinearity and complexity of spatio-temporal data. In this talk, we describe how to address these challenges using spatio-temporal graph neural networks.

Biography: Dr. Latifur Khan is currently a Professor in the Computer Science department at the University of Texas at Dallas, USA where he has been teaching and conducting research since September 2000. He received his Ph.D. degree in Computer Science from the University of Southern California (USC) in August of 2000. In addition, he received his bachelor degree in Computer Science and Engineering (CSE) from Bangladesh University of Engineering and Technology (BUET) with first class honors (2nd position). Dr. Khan is a fellow of IEEE, IET, BCS, and an ACM Distinguished Scientist. He has received prestigious awards including the IEEE Technical Achievement Award for Intelligence and Security Informatics, IEEE Big Data Security Award, and IBM Faculty Award (research) 2016. Dr. Khan has published over 300 papers in premier journals and prestigious conferences. Currently, Dr. Khan’s research focuses on big data management and analytics, data mining and its application to cyber security, and complex data management including geospatial data and multimedia data. His research has been supported by grants from NSF, NIH, the Air Force Office of Scientific Research (AFOSR), DOE, NSA, IBM, and HPE. More details can be found at www.utdallas.edu/~lkhan.

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Keynote 3: Write-Optimized Location Data Systems: Challenges and Solutions

Prof. Walid G. Aref

Purdue University, USA

Abstract: Location data systems are write-heavy. As objects move in space and change their location, they need to write their new locations in continuous bases into the underlying location data system. Thus, location data systems need to be optimized for writes. These writes can be in the form of new inserts or updates to existing old locations. In this talk, I will highlight several challenges related to supporting write optimized location data systems and techniques to address these challenges. In specific, I will present techniques for (1) Indexing location data with frequent updates, (2) Addressing the problem of non-deterministic insert performance (the phenomenon of waves of misery in location data indexing), and (3) LSM-based techniques for handling location updates in big location data systems.

Biography: Walid G. Aref is a Professor of Computer Science at Purdue University, USA. His research interests are in extending the functionality of database systems in support of emerging applications, e.g., spatial, spatio-temporal, graph, biological, and sensor databases. He is also interested in query processing, indexing, data streaming, and geographic information systems (GIS). His research has been supported by the US National Science Foundation, the National Institute of Health, Purdue Research Foundation, CERIAS, Panasonic, and Microsoft Corp. In 2001, he received the CAREER Award from the National Science Foundation and in 2004, he received a Purdue University Faculty Scholar award. He is a member of Purdue's CERIAS. He has been the Editor-in-Chief of the ACM Transactions of Spatial Algorithms and Systems (ACM TSAS) since 2018, an editorial board member of the Journal of Spatial Information Science (JOSIS), and has served as an editor of the VLDB Journal and the ACM Transactions of Database Systems (ACM TODS). He has won several best paper awards including a VLDB ten-year best paper award. He is a Fellow of the IEEE, and a member of the ACM. Between 2011 and 2014, he has served as the chair of the ACM Special Interest Group on Spatial Information (SIGSPATIAL).