Computer vision is the study of automatic methods to interpret visual data. The visual data could be single images, multiple images, video, or even the product of exotic imaging systems (thermal images and suchlike). Computer vision in general has been growing recently, because it has been producing practical solutions to quite important problems. Visual data is now so prevalent that people who work in other areas often find themselves doing computer vision by accident. I will give a broad overview of the ideas and methods of computer vision, accessible to a general computer science audience.
I will discuss the two core intellectual problems in computer vision: reconstruction and recognition, both very broadly interpreted. In reconstruction, one attempts to build representations of the geometric and photometric properties of the world from visual data. These representations vary from computer graphics models to complex data structures linking images so that they can be presented to viewers in a way that give a strong sense of movement and layout in space.
In recognition, one attempts to attach semantics to visual data like images or video. One might mark particular instances of objects --- for example, that image is a picture of my fluffy cat. Alternatively, one might want to mark categories --- for example, that image is a picture of a cat. Finally, one might want to mark attributes of objects --- for example, that image contains a fluffy thing. Successful recognition methods could help address large social needs: for example, how to purge web sites of stolen material; how to ensure users of photo and video sites get relevant results for a keyword search; how to ensure that advertisers and users need not fear objectionable content.
Over 500 million host computers, three billion PCs and mobile devices consume over a billion kilowatts of electricity.As part of this "system" computer networks consume an increasing amount of energy, and help reduce energy expenditure from other sources through E-Work, E-Commerce and E-Learning. Traditionally, network design seeks to minimise network cost and maximise quality of service (QoS). This paper examines some approaches for dynamically managing wired packet networks to minimise energy consumption while meeting users' QoS needs, by automatically turning link drivers and/or routers on/off in response to changes in network load.
Prof. Erol Gelenbe studied at the Middle East Technical University in Ankara. He is a Member of the Turkish Academy of Sciences and of the French National Academy of Engineering, and holds the "Dennis Gabor Chair" in the Electrical and Electronic Engineering Department at Imperial College, where he conducts research on computer systems and networks. Author of over 140 journal papers, and of several books published in English, French, Japanese and Korean, he won numerous major international awards and honours, including the ACM SIGMETRICS Lifetime Achievement Award, and the Honoris Causa Doctorate from three universities: Liege in Belgium (2006), Bogazici in Istanbul (2004) and Rome in Italy (1996). His recent work includes path finding algorithms in noisy and uncertain conditions, adaptive routing in computer networks, decision making based on market based techniques, and modeling in the basic sciences including neuronal networks, gene regulatory networks, and chemical reactions. His research is currently funded by EPSRC with significant industry participation (BT, BAE Systems and QinetiQ), and by the EU FP7 Programme. Appointed to his first chair at the age of 27 at the University of Liège in Belgium, he served as a research director at INRIA (France), with successive professorial posts at the University of Paris, Duke University where he was Department Head, and the University of Central Florida where he held a distinguished chair and was Associate Dean of Engineering. He is the Editor in Chief of The Computer Journal, and editor of the Proc. of the Royal Society and of several other journals. He has graduated over 50 PhDs, an.d is a Fellow of IEEE, ACM and of IET (London).
We are entering the mobile Internet era where people, vehicles, and hand-held devices are connected at all times. Location becomes a piece of important information for real-time information access, on-demand service discovery and delivery, as well as continuous and personalized service provision. In location-aware computing, there are conceivably two types of location privacy - personal subscriber level privacy and corporate enterprise-level privacy. Companies need enterprise-level privacy to preserve corporate secrets and maintain competitive edge.
Location privacy has attracted attention in mobile computing, mobile data management, and wireless communication research over the past few years. Most of the location privacy solutions try to prevent disclosure of unauthorized location information by techniques that explicitly or implicitly control what and how location information is given to whom and when. In this talk, I will give a brief overview of location privacy research and discuss three classes of location privacy protection techniques: (1) Location protection through user-defined or system-supplied privacy policies; (2) Location protection through location anonymization, a system capability to obfuscate the location information such that a state of a subject is not identifiable within the anonymity set; and (3) Location protection through pseudonymity of user identities, which uses an internal pseudonym rather than the user's actual identity. I will also describe the intrinsic relationships among location privacy, location utility, and personalization.
My talk will end with a list of open issues and technical challenges in location privacy research. ----------------------------------------------------------------------------------------------------------------
Dr. Ling Liu is a Professor in the College of Computing at Georgia Institute of Technology. There she directs the research programs in Distributed Data Intensive Systems Lab (DiSL), examining performance, security, privacy, and data management issues in building large scale distributed computing systems. Dr. Liu and the DiSL research group have been working on various aspects of distributed data intensive systems, ranging from decentralized overlay networks, mobile computing and location based services, sensor network and event stream processing, to service oriented computing and architectures. She has published over 200 international journal and conference articles in the areas of Internet Computing systems, Internet data management, distributed systems, and information security. Her research group has produced a number of open source software systems, among which the most popular ones include WebCQ, XWRAPElite, PeerCrawl. Dr. Liu is the recipient of the best paper award of ICDCS 2003 and the best paper award of WWW 2004, and a recipient of 2005 Pat Goldberg Memorial Best Paper Award.
Dr. Ling Liu is an internationally recognized expert in the areas of Database Systems, Distributed Computing, Internet Systems, and Service oriented computing. She has chaired a number of conferences as a PC chair, vice PC chair, or a general chair, including IEEE International Conference on Data Engineering (ICDE 2004, ICDE 2006, ICDE 2007), IEEE International Conference on Distributed Computing (ICDCS 2006), IEEE International Conference on Web Services (ICWS 2004), CreateNet-ICST Collaborative Computing Conference (CollaborateCom 2005, 2006), ACM International Conference on Knowledge and Information Management (CIKM 2000). Dr. Liu is currently on the editorial board of several international journals, including IEEE Transactions on Knowledge and Data Engineering, International Journal of Peer-to-Peer Networking and Applications (Springer), International Journal of Very Large Database systems (Springer), International Journal of Web Services Research, Wireless Network Journal (WINET). Dr. Liu research is primarily sponsored by NSF, DoE, DARPA, IBM, and HP. Dr. Liu is a recipient of IBM Faculty Award (2003, 2006, 2007)
The demands of modern data management have recently stretched traditional relational database systems with increased focus on flexible, semi-structured data formats. These formats, such as XML, enable elegant representation of complex hierarchical structures. In this talk, we go beyond XML to focus on the challenges posed by a new generation of semi-structured data management problems, including genealogical data (pedigrees) used to track inherited diseases, ontologies, and more complex RDF data, such as longitudinal patient medical records used for clinical research. Specifically, we need new languages for querying, new models for storage, new types of indexes for efficient processing, and new techniques for query optimization. I will give a brief overview of types of queries on pedigree structures and methods for evaluating such queries efficiently. I will then overview the issues and challenges for semantic querying over very large patient records stored as RDF along with methods for evaluating semantic queries in the SPARQL query language efficiently utilizing relational database query optimization techniques
Z. Meral Özsoyoğlu received her PhD in Computer Science from University of Alberta, Edmonton, Canada in 1980. Her BSc. degree in Electrical Engineering and MSc. Degree in Computer Science are both from the Middle East Technical University, Ankara, Turkey. She is currently Andrew R. Jennings Professor of Computing and Department Chair of Electrical Engineering and Computer Science Department at Case Western Reserve University, where she has been a professor of Computer Science since 1980.
Professor Meral Özsoyoğlu is known for her research in the areas of Database Query Languages, Data Models, Query Optimization, Index Structures for high dimensional spaces, and Statistical Databases. More recently, her research focus is in Bioinformatics and biological data management including managing, visualizing and querying genomic pathways, efficient access methods and index structures for genomic sequences, and querying pedigree data. She has published over 90 papers in computer science journals including ACM TODS, IEEE TKDE, IEEE TSE, JCSS, and international conferences including ACM SIGMOD, ACM PODS, VLDB, ICDE and SSDBM. She has been the program chair of international conferences, IEEE ICDE 2004, ACM PODS'97, SSDBM'99 and ACM SIGMOD vice chair 1997-2001, associate editor of IEEE TKDE journal 1999-2002, IEEE DE Bulletin 1977-1991, 2004-2007, associate editor for ACM TODS journal 2001-2007. She is currently the Editor-in-Chief of ACM Transactions of Database Systems (TODS), and associate editor of WWW journal, and serves as a trustee of the VLDB Endowment. Professor Ozsoyoglu is a recipient of several awards including IBM Faculty Award, NSF Faculty Award for Women in Science and Technology, and Distinguished Alumni Award from University of Alberta, EECS Service Award, and a Research Spotlight Award from Case. ----------------------------------------------------------------------------------------------------------------