Keynotes

Image Quality Assessment

Prof. Xinbo Gao

Xinbo Gao

Xidian University,

Xi'an, China




Abstract:

The aim of the image quality assessment (IQA) is to find a computational model that can predict the visual perception quality automatically. This talk will focus on the image quality assessment based on machine learning. It is to simulate and describe the human visual system to find the optimal description of image feature for depicting the degree of image degradation, and then construct the best quality model for measuring visual quality of distorted image and the ability to provide information of the image. The available image quality assessment based on machine learning can be divided into 3 categories, IQA based on distortion classification, IQA based on feature representation and IQA based on feature mapping. By discovering the potential correlation between the cognitive model of human learning and the computational model of human perception, these IQA models are precisely established to describe the visual perception quality from different aspects. Finally, I will introduce some new research topics and applications of image quality assessment.

Short Bio:

Xinbo Gao received the B.E., M.Sc. and Ph.D. degrees in signal and information processing from Xidian University, China, in 1994, 1997 and 1999 respectively. From 1997 to 1998, he was a research fellow in the Department of Computer Science at Shizuoka University, Japan. From 2000 to 2001, he was a postdoctoral research fellow in the Department of Information Engineering at the Chinese University of Hong Kong. Since 2001, he joined the School of Electronic Engineering at Xidian University. Currently, he is a Cheung Kong Professor of Ministry of Education of China, and Director of the State Key Laboratory of Integrated Services Networks (ISN), Xidian University. His research interests include machine learning,visual information processing, and pattern recognition. In these areas, he has published 6 books and around 100 technical articles in refereed journals and proceedings including IEEE TIP, TCSVT, TNN, TSMC, Pattern Recognition etc. He is on the editorial boards of journals including EURASIP Signal Processing (Elsevier), and Neurocomputing (Elsevier). He served as general chair/co-chair or program committee chair/co-chair or PC member for around 30 major international conferences. He is a senior member of IEEE and the Membership Development Committee Chair of IEEE Xi'an Section, a Fellow of IET, and Vice Chairman of IET Xi'an Network.

Network design optimization: new trends and methods

Prof. Dritan Nace

Dritan Nace

University of Technology of Compiègne

Compiègne, France




Abstract:

Network optimization is a well identified research area in telecommunications. From network manager point of view Network Optimization is mainly concerned with the process to keep a network operating at high efficiency with a lower cost. From research methodology point of view Network Optimization includes mathematical modeling and optimization methods, with applications in network design, performance analysis, reliability, survivability, network restoration, routing, traffic analysis, wireless networks, etc. These last years there has been much progress in both methodology and operational tools development applied to Network Optimization. We can cite advances in robust optimization and mathematical programming solvers. During this talk we will present some related topics on network design with a special focus on resilient networks, evolving networks and robustness. For each case study, both the related mathematical problems and solving methodologies will be presented.

Short Bio:

Dritan Nace is a Professor at the University of Technology of Compiègne. He received the degrees of MSc in Computer Science in 1993 and PhD in Computer Science in 1997, both from University of Technology of Compiègne. From 1997 to 1998 he worked as research engineer at CNET (Centre National d'Etudes des Télécommunications), the France Telecom research center. From 1998, he is at the University of Technology of Compiègne, and since 2008 he holds a Full Professor position. His research interests include operations research and in particular network optimization and routing. He has lead several research projects for telecom industry in the field of networking.

Adaptable Resource Allocation in Cloud Computing Systems

Dr. Albert Y. Zomaya

Albert Y. Zomaya

University of Sydney

Sydney, Australia




Abstract:

Cloud Computing is among the fastest growing topics in computing research today. Although the idea of offering computational power as a service is not novel, it did not publically become available before Clouds. Clouds are fundamentally different from their predecessors (Grids/Clusters) and thus require specific expertise to be properly utilized. Today, there are literally hundreds of cloud providers in all forms and scales. Computing needs (applications) have also become increasingly diverse. In this talk, I will describe challenges with the dynamicity and heterogeneity of resources and the diversity of applications from resource management perspective. Then, I will discuss how we can turn these challenges into opportunities for cloud systems' efficiency.

Short Bio:

Albert Y. Zomaya is the Chair Professor of High Performance Computing & Networking and Australian Research Council Professorial Fellow in the School of Information Technologies, Sydney University. He is also the Director of the Centre for Distributed and High Performance Computing which was established in late 2009. Dr. Zomaya published more than 500 scientific papers and articles and is author, co-author or editor of more than 20 books. He is currently the Editor in Chief of the IEEE Transactions on Computers and Springer's Scalable Computing and serves as an associate editor for 22 leading journals. Dr. Zomaya is the Founding Editor of the Wiley Book Series on Parallel and Distributed Computing.

Dr. Zomaya was the Chair the IEEE Technical Committee on Parallel Processing (1999-2003) and currently serves on its executive committee. He is the Vice-Chair, IEEE Task Force on Computational Intelligence for Cloud Computing and serves on the advisory board of the IEEE Technical Committee on Scalable Computing and the steering committee of the IEEE Technical Area in Green Computing. Dr. Zomaya has delivered more than 130 keynote addresses, invited seminars, and media briefings and has been actively involved, in a variety of capacities, in the organization of more than 600 conferences.

Dr. Zomaya is a Fellow of the IEEE, the American Association for the Advancement of Science, the Institution of Engineering and Technology (UK). He received the 1997 Edgeworth David Medal from the Royal Society of New South Wales for outstanding contributions to Australian Science. He was the recipient of the IEEE Computer Society's Meritorious Service Award and Golden Core Recognition in 2000 and 2006, respectively. He received the IEEE TCPP Outstanding Service Award and the IEEE TCSC Medal for Excellence in Scalable Computing, both in 2011. His research interests span several areas in parallel and distributed computing.

Building Efficient Erasure-coded Clustered Storage Systems

Patrcik Lee

Patrick P. C. Lee

The Chinese University of Hong Kong

Hong Kong, China




Abstract:

Modern clustered storage systems increasingly adopt erasure coding to reduce the storage overhead of traditional 3-way replication. However, there remain challenging issues of maintaining high performance in erasure-coded clustered storage systems. In this talk, I will share our experiences of deploying erasure coding in Hadoop, a popular clustered storage platform for big data analytics. I will present two new designs: (1) CORE, which augments existing optimal regenerating codes for the recovery of a general number of failures including single and concurrent failures, and (2) Degraded-First Scheduling, which improves MapReduce performance in erasure-coded storage. I will present new analytical results, as well as experimental findings based on our prototypes in a Hadoop cluster.

Short Bio:

Patrick P. C. Lee received the Ph.D. degree in Computer Science from Columbia University in 2008. He is now an assistant professor of the Department of Computer Science and Engineering at the Chinese University of Hong Kong. He is interested in various applied/systems topics including cloud computing and storage, distributed systems and networks, operating systems, and security/resilience. His current research interests focus on building dependable storage systems, and in particular, improving the fault tolerance, recovery, security, and performance of different types of storage architectures including cloud file systems and SSDs.