Keynote Speaker
Pingjia
n Zhang is a professor of software engineering of South China University of Technology. He is a member of China Simulation Committee and the director of the laboratory of Theory and Methods for Software Construction of SCUT. Dr. Zhang’s research interest covers Software Architecture, Data Warehouse and Data Mining, High Performance Computing and Knowledge Engineering. Dr. Zhang has published more than 30 papers, served as reviewer for several journals and conferences. Dr. Zhang has been authorized one patent and obtained more than 20 software copyrights.
n Zhang is a professor of software engineering of South China University of Technology. He is a member of China Simulation Committee and the director of the laboratory of Theory and Methods for Software Construction of SCUT. Dr. Zhang’s research interest covers Software Architecture, Data Warehouse and Data Mining, High Performance Computing and Knowledge Engineering. Dr. Zhang has published more than 30 papers, served as reviewer for several journals and conferences. Dr. Zhang has been authorized one patent and obtained more than 20 software copyrights.
Keynote : Speech recognition : methods and applications
Abstract : Speech recognition is an inter-disciplinary sub-field of computational linguistics that develops methodologies and technologies that enables the recognition and translation of spoken language into text by computers. It incorporates knowledge and research in the linguistics, computer science, and statistics.
Speech recognition applications include voice user interfaces such as voice dialing, domestic appliance control, search, simple data input, preparation of structured documents, speech-to-text processing, and driving.
Speech recognition has a long history with several waves of major innovations. Most recently, the field has benefited from advances in deep learning and big data. The advances are evidenced not only by the surge of academic papers published in the field, but more importantly by the worldwide industry adoption of a variety of deep learning methods in designing and deploying speech recognition systems.
Speech recognition applications include voice user interfaces such as voice dialing, domestic appliance control, search, simple data input, preparation of structured documents, speech-to-text processing, and driving.
Speech recognition has a long history with several waves of major innovations. Most recently, the field has benefited from advances in deep learning and big data. The advances are evidenced not only by the surge of academic papers published in the field, but more importantly by the worldwide industry adoption of a variety of deep learning methods in designing and deploying speech recognition systems.
Rui Li is currently a distinguished professor and Ph.D. Supervisor of Dongguan University of Technology. Prof Li received the Ph.D degree from Hunan University in 2012 and his research interests include design and analysis of advanced algorithms and network security, etc. Dr Li presides over and participates in a series of key research projects supported by the National Natural Science Foundation. Furthermore, he has about 20 peer-reviewed publications in renowned international books, journals and conferences. Dr Li has won Core Young Teacher of Hunan Province award in 2015. Distinguished Young Teacher Award by Luanxiong Liu Funding in Hunan University in 2010 and Outstanding Young Teacher of Hunan Province in 2009.Keynote : Adaptive Secure and Fast Processing of Conjunctive Queries over Encrypted Data
Abstract : Both enterprises and end users have been increasingly outsourcing their data and computing services to public clouds for lower cost, higher reliability, better performance, and faster deployment. However, privacy has become the key concern as data owner may not fully trust public clouds. We concerns the fundamental problem of processing conjunctive queries that contain both keyword conditions and range conditions on public clouds in a privacy preserving manner. No prior Searchable Symmetric Encryption (SSE) based privacy preserving conjunctive query processing scheme satisfies the three requirements of adaptive security, efficient query processing, and scalable index size. We propose the first privacy preserving conjunctive query processing scheme that satisfies the above requirements. To achieve adaptive security, we propose an Indistinguishable Bloom Filter (IBF) data structure for indexing. To achieve efficient query processing and structure indistinguishability, we propose a highly balanced binary tree data structure called Indistin- guishable Binary Tree (IBtree). To optimize searching efficiency, we propose a traversal width minimization algorithm and a traversal depth minimization algorithm. To achieve scalable and compact index size, we propose an IBtree space compression algorithm to remove redundant information in IBFs. We formally prove that our scheme is adaptive secure under IND-CKA secure model.
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