Prof. Sergei Gorlatch
Universitaet Muenster, Institut fuer Informatik, Germany
Prof. Sergei Gorlatch is an internationally
acknowledged expert in the area of algorithms, architectures,
software and applications for modern and emerging computer and
networked systems. Sergei Gorlatch has been Full Professor of
Computer Science at the University of Muenster (Germany) since 2003.
Earlier he was Associate Professor at the Technical University of
Berlin, Assistant Professor at the University of Passau, and
Humboldt Research Fellow at the Technical University of Munich, all
Prof. Gorlatch has about 200 peer reviewed publications in renowned international books, journals and conferences. He is often delivering invited talks at international conferences and serves at their program committees. Prof. Gorlatch was principal investigator in several international research and development projects in the field of parallel, distributed, Grid and Cloud algorithms and computing, as well as e-Learning, funded by the European Commission and by German national bodies. Among his recent achievements in the area of data management, communications and future internet is the novel Real-Time Framework (www.real-time-framework.com) developed in his group as a platform for high-level development of real-time, highly interactive applications like multi-player online games, advanced e-Learning, crowd simulations, etc. In the area of high-performance computing, his group has been recently developing a high-level SkelCL library (skelcl.uni-muenster.de/) for efficient programming of parallel algorithms on emerging parallel and distributed many-core systems with accelerators.
"Distributed Applications Based on Mobile Cloud Computing and Software-Defined Networks"
Abstract: We consider an emerging class of challenging networked multimedia applications called Real-Time Online Interactive Applications (ROIA). ROIA are networked applications connecting a potentially very high number of users who interact with the application and with each other in real time, i.e., a response to a user’s action happens virtually immediately. Typical representatives of ROIA are multiplayer online computer games, advanced simulation-based e-learning and serious gaming. All these applications are characterized by high performance and QoS requirements, such as: short response times to user inputs (about 0.1-1.5 s); frequent state updates (up to 100 Hz); large and frequently changing numbers of users in a single application instance (up to tens of thousands simultaneous users). This talk will address two challenging aspects of future Internet-based ROIA applications: a) using Mobile Cloud Computing for allowing high application performance when a ROIA application is accessed from multiple mobile devices, and b) managing dynamic QoS requirements of ROIA applications by employing the emerging technology of Software-Defined Networking (SDN).
Assoc. Prof. P. W. T. Pong
The University of Hong Kong, Hong Kong
Philip W. T. Pong is a chartered physicist, a chartered electrical engineer, and a chartered energy engineer. He is a registered professional engineer in electrical, electronics, and energy. He is working on spintronic magnetic field sensors, smart grid, and nano-bio at the Department of Electrical and Electronic Engineering (EEE), the University of Hong Kong (HKU). He received a PhD in engineering from the University of Cambridge in 2005. After working as a postdoctoral researcher at the Magnetic Materials Group at the National Institute of Standards and Technology (NIST) in the United States for three years, he joined the HKU Faculty of Engineering where he is now an associate professor working on development and applications of spintronic sensors and magnetic nanoparticle technologies in smart grid and smart living. He is a Senior Member of IEEE and Corporate Member of HKIE in Electrical, Electronics, and Energy Divisions. He is an associate editor for two SCI journals, and he serves on the editorial review board of the IEEE Magnetics Letters. He published over 200 technical papers. He is a Fellow of the Institute of Materials, Minerals and Mining and also a Fellow of the NANOSMAT Society.
"Advancing New Frontiers of Autonomous Internet-of-Things (A-IoT) by Magnetic Sensors"
Abstract: Internet-of-Things (IoT)
will provide the framework for establishing a smarter and more
connected future for people. The exploratory IoT technologies
will enable scientists and engineers to build a better place for
everyone to work, live and enjoy life. Charting out the
evolution of IoT is the development of autonomous IoT (A-IoT)
whereby data and decision will both be actively managed by the
devices. A-IoT will carry out autonomous decision-making. Human
factor will be taken out of the equation, driving value and cost
efficiencies and possibly enhancing safety. Some examples of
future A-IoT applications include autonomous vehicles that
self-navigate to the destinations, smart switches that
automatically open or close to implement self-resilience in
power grids, and smart meters that form part of the home energy
management systems and shift the energy usage to an optimal time
for renewable energy generation. These new autonomous
applications will open up new frontiers in many aspects of smart
living, and their decision-making processes rely primarily on
perception with sensors.
Sensors are often the first thing people think of when talking about A-IoT. They carry out the critical work of monitoring processes, taking measurements and collecting data, functioning as the important enablers of A-IoT. A sensor is a device providing a usable output in response to a specific measurand. Sensor fusion adopting a combination of different sensors measuring various measurands is needed to monitor the state of the device and the ambient surrounding. Magnetic field, flux and permeability are one of the major categories of measurands. In this talk, we will talk about how magnetic sensors will advance the new frontiers of A-IoT and the associated challenges.
Assoc. Prof. Cagatay Catal
Department of Computer Engineering, Istanbul Kültür University, Turkey
Dr. Cagatay Catal is an Associate Professor and Head of Department at the Department of Computer Engineering in Istanbul Kültür University, Turkey. He received the BS & MSc degrees in Computer Engineering from Istanbul Technical University and the PhD degree in Computer Engineering from Yildiz Technical University, Istanbul. He worked 8 years at the Research Council of Turkey (TUBITAK), Information Technologies Institute as Senior Researcher and involved in the development of several large-scale software-intensive system projects. He’s been working in Istanbul Kültür University for 6 years. He has over 60 peer reviewed publications on software engineering and data science in international journals, books, and conferences. His research interests include softare engineering, data science, machine learning, big data, and software testing. He is external reviewer for Research Council of Canada, Research Council of Turkey (TUBITAK), and EUROSTARS program funded by European Union. For more information, please visit http://www.cagataycatal.net.
"Software Vulnerability Prediction in the Cloud: Machine Learning Perspective"
Abstract: Software security vulnerabilities are still very common and we encounter new alerts from several agencies day by day. For instance, on May 13, 2015 US Food and Drug Administration (FDA) published an alert about computerized infusion pumps which can be programmed remotely and malicious Internet users can modify the dosage of therapeutic drugs. Therefore, detecting vulnerable components of an application is a crucial activity to allocate verification resources effectively. Although there are several research papers on software vulnerability prediction, companies such as Microsoft still did not adopt Vulnerability Prediction Models although Software Fault Prediction models are applied. The reason is related with the low prediction performance of vulnerability prediction models on the source code level in terms of recall and precision parameters. In this talk, I will discuss a recent joint research project on software vulnerability prediction. We aimed to utilize from advanced machine learning techniques, specifically deep learning algorithms, to improve the performance of vulnerability prediction models in this project. Initial case study has been performed on Azure Machine Learning Studio to build a software vulnerability prediction web service and several machine learning algorithms have been investigated to build high-performance vulnerability predictors. A prediction web service has been deployed in Azure cloud computing platform and a client application has been implemented to utilize from this web service. Several challenging vulnerability prediction issues, which will be investigated in the project, will also be introduced in this talk.
Dr. Vincent Tam
The University of Hong Kong, Hong Kong
Dr. Tam completed his Ph.D. degree from the Department of Computer Science and Software Engineering in the University of Melbourne for his unprecedented work on integrating local search based constraint solvers into constraint logic programming. He is currently the Principal Lecturer and Honorary Associate Professor in the Department of Electrical and Electronic Engineering, HKU. He was the winner of the Faculty Best Teacher Award (2010) and the Faculty Outstanding Teaching Team Award (2013). Dr. Tam has over 100 internationally refereed publications, including 10 book chapters. His main research interests include big data analytics, computational finance, cloud/mobile computing, machine learning, and information visualization, in which he established collaborations with the Hong Kong Applied Science and Technology Research Institute (ASTRI) and various institutions in Australia, England, Japan and the U.S.A. Dr. Tam is active in participating in international conferences and reviewing for reputable journals. He was invited into the Program Committee of the IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2000 - 2007), and served as the Publication Chair of ICTAI 2005, the Publicity & Workshop Chairs of the 6th ACM/IEEE International Conference on Distributed Smart Cameras (ICDSC 2012). He is currently the Chairman of the IEEE (HK) Computational Intelligence Chapter, and an Executive Board member of the IEEE Technical Committee on Learning Technology (TCLT).
"Developing An Intelligent and Mobile Platform for Personalized Teaching and Learning Through Facial Analytics "
Abstract: Mobile computing devices drastically reshape many different aspects of our daily livings. In this talk, we consider the integration of web cameras as embedded image sensors available on most tablets or smartphones with a heuristic-based and intelligent tracking algorithm to continuously monitor and analyze the learners’ responses through their facial orientations and eye movements. The ultimate goal of the project is to carefully design and develop the PErsonalized Teaching And Learning, namely the PETAL, platform so as to nurture the academic development of our young learners while protecting their eyesight. Through using various Android programming toolkits with the Open Source Computer Vision library, the PETAL platform is able to detect the viewers’ responses to educational videos as a mean of self-learning or revisions. Besides, through notifying learners of their, possibly unconscious, reactions to such educational videos, the PETAL platform can help to promote a personalized teaching and learning environment through facial analytics for supporting any effective and open learning platform.
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