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991.
KangWoo Lee 《计算机科学技术学报》2008,23(5):874-884
A common assumption in visual attention is based on the rationale of "limited capacity of information processing". From this view point there is little consideration of how different information channels or modules are cooperating because cells in processing stages are forced to compete for the limited resource. To examine the mechanism behind the cooperative behavior of information channels, a computational model of selective attention is implemented based on two hypotheses. Unlike the traditional view of visual attention, the cooperative behavior is assumed to be a dynamic integration process between the bottom-up and top-down information. Furthermore, top-down information is assumed to provide a contextual cue during selection process and to guide the attentional allocation among many bottom-up candidates. The result from a series of simulation with still and video images showed some interesting properties that could not be explained by the competitive aspect of selective attention alone. 相似文献
992.
Yi-Song Wang 《计算机科学技术学报》2009,24(6):1125-1137
Logic programming under the stable model semantics is proposed as a non-monotonic language for knowledge representation and
reasoning in artificial intelligence. In this paper, we explore and extend the notion of compatibility and the Λ operator, which were first proposed by Zhang to characterize default theories. First, we present a new characterization
of stable models of a logic program and show that an extended notion of compatibility can characterize stable submodels. We further propose the notion of weak auto-compatibility which characterizes the Normal Forward Chaining Construction proposed by Marek, Nerode and Remmel. Previously, this construction was only known to construct the stable models of FC-normal
logic programs, which turn out to be a proper subclass of weakly auto-compatible logic programs. We investigate the properties
and complexity issues for weakly auto-compatible logic programs and compare them with some subclasses of logic programs. 相似文献
993.
Huanyu Zhao 《计算机科学技术学报》2009,24(5):833-843
Peer-to-Peer Desktop Grid (P2PDG) has emerged as a pervasive cyber-infrastructure tackling many large-scale applications with
high impacts. As a burgeoning research area, P2PDG can support numerous applications, including scientific computing, file
sharing, web services, and virtual organization for collaborative activities and projects. To handle trustworthiness issues
of these services, trust and reputation schemes are proposed to establish trust among peers in P2PDG. In this paper, we propose
a robust group trust management system, called H-Trust, inspired by the H-index aggregation technique. Leveraging the robustness
of the H-index algorithm under incomplete and uncertain circumstances, H-Trust offers a robust personalized reputation evaluation
mechanism for both individual and group trusts with minimal communication and computation overheads. We present the H-Trust
scheme in five phases, including trust recording, local trust evaluation, trust query phase, spatial-temporal update phase,
and group reputation evaluation phases. The rationale for its design, the analysis of the algorithm are further investigated.
To validate the performance of H-Trust scheme, we designed the H-Trust simulator HTrust-Sim to conduct multi-agent-based simulations.
Simulation results demonstrate that H-Trust is robust and can identify and isolate malicious peers in large scale systems
even when a large portion of peers are malicious. 相似文献
994.
Hua-Fu Li 《Multimedia Tools and Applications》2009,41(2):287-304
Mining of music data is one of the most important problems in multimedia data mining. In this paper, two research issues of
mining music data, i.e., online mining of music query streams and change detection of music query streams, are discussed.
First, we proposed an efficient online algorithm, FTP-stream (Frequent Temporal Pattern mining of streams), to mine all frequent melody structures over sliding windows of music melody sequence streams. An effective bit-sequence
representation is used in the proposed algorithm to reduce the time and memory needed to slide the windows. An effective list
structure is developed in the FTP-stream algorithm to overcome the performance bottleneck of 2-candidate generation. Experiments
show that the proposed algorithm FTP-stream only needs a half of memory requirement of original melody sequence data, and
just scans the music query stream once. After mining frequent melody structures, we developed a simple online algorithm, MQS-change
(changes of Music Query Streams), to detect the changes of frequent melody structures in current user-centered music query streams. Two music melody
structures (set of chord-sets and string of chord-sets) are maintained and four melody structure changes (positive burst,
negative burst, increasing change and decreasing change) are monitored in a new summary data structure, MSC-list (a list of Music Structure Changes). Experiments show that the MQS-change algorithm is an effective online method to detect the changes of music melody
structures over continuous music query streams.
相似文献
Hua-Fu LiEmail: |
995.
The content–user gap is the difference between the limited range of content-relevant preferences that may be expressed using
the MPEG-7 user interaction tools and the much wider range of metadata that may be represented using the MPEG-7 content tools.
One approach for closing this gap is to make the user and content metadata isomorphic by using the existing MPEG-7 content
tools to represent user (as well as content) metadata (Agius and Angelides 2006, 2007). Subsequently, user preferences may
be specified for all content, without omission. Since there is a wealth of user preference and history metadata within the
MPEG-7 user interaction tools that can usefully complement these specific content preferences, in this paper we develop a
method by which all user and content metadata may be bridged.
相似文献
Marios C. AngelidesEmail: |
996.
997.
Sebastiano Battiato Giovanni Maria Farinella Giovanni Giuffrida Catarina Sismeiro Giuseppe Tribulato 《Multimedia Tools and Applications》2009,42(1):5-30
Traditionally, direct marketing companies have relied on pre-testing to select the best offers to send to their audience.
Companies systematically dispatch the offers under consideration to a limited sample of potential buyers, rank them with respect
to their performance and, based on this ranking, decide which offers to send to the wider population. Though this pre-testing
process is simple and widely used, recently the industry has been under increased pressure to further optimize learning, in
particular when facing severe time and learning space constraints. The main contribution of the present work is to demonstrate
that direct marketing firms can exploit the information on visual content to optimize the learning phase. This paper proposes
a two-phase learning strategy based on a cascade of regression methods that takes advantage of the visual and text features
to improve and accelerate the learning process. Experiments in the domain of a commercial Multimedia Messaging Service (MMS)
show the effectiveness of the proposed methods and a significant improvement over traditional learning techniques. The proposed
approach can be used in any multimedia direct marketing domain in which offers comprise both a visual and text component.
Sebastiano Battiato was born in Catania, Italy, in 1972. He received the degree in Computer Science (summa cum laude) in 1995 and his Ph.D in Computer Science and Applied Mathematics in 1999. From 1999 to 2003 he has lead the “Imaging” team c/o STMicroelectronics in Catania. Since 2004 he works as a Researcher at Department of Mathematics and Computer Science of the University of Catania. His research interests include image enhancement and processing, image coding and camera imaging technology. He published more than 90 papers in international journals, conference proceedings and book chapters. He is co-inventor of about 15 international patents. He is reviewer for several international journals and he has been regularly a member of numerous international conference committees. He has participated in many international and national research projects. He is an Associate Editor of the SPIE Journal of Electronic Imaging (Specialty: digital photography and image compression). He is director of ICVSS (International Computer Vision Summer School). He is a Senior Member of the IEEE. Giovanni Maria Farinella is currently contract researcher at Dipartimento di Matematica e Informatica, University of Catania, Italy (IPLAB research group). He is also associate member of the Computer Vision and Robotics Research Group at University of Cambridge since 2006. His research interests lie in the fields of computer vision, pattern recognition and machine learning. In 2004 he received his degree in Computer Science (egregia cum laude) from University of Catania. He was awarded a Ph.D. (Computer Vision) from the University of Catania in 2008. He has co-authored several papers in international journals and conferences proceedings. He also serves as reviewer numerous international journals and conferences. He is currently the co-director of the International Summer School on Computer Vision (ICVSS). Giovanni Giuffrida is an assistant professor at University of Catania, Italy. He received a degree in Computer Science from the University of Pisa, Italy in 1988 (summa cum laude), a Master of Science in Computer Science from the University of Houston, Texas, in 1992, and a Ph.D. in Computer Science, from the University of California in Los Angeles (UCLA) in 2001. He has an extensive experience in both the industrial and academic world. He served as CTO and CEO in the industry and served as consultant for various organizations. His research interest is on optimizing content delivery on new media such as Internet, mobile phones, and digital tv. He published several papers on data mining and its applications. He is a member of ACM and IEEE. Catarina Sismeiro is a senior lecturer at Imperial College Business School, Imperial College London. She received her Ph.D. in Marketing from the University of California, Los Angeles, and her Licenciatura in Management from the University of Porto, Portugal. Before joining Imperial College Catarina had been and assistant professor at Marshall School of Business, University of Southern California. Her primary research interests include studying pharmaceutical markets, modeling consumer behavior in interactive environments, and modeling spatial dependencies. Other areas of interest are decision theory, econometric methods, and the use of image and text features to predict the effectiveness of marketing communications tools. Catarina’s work has appeared in innumerous marketing and management science conferences. Her research has also been published in the Journal of Marketing Research, Management Science, Marketing Letters, Journal of Interactive Marketing, and International Journal of Research in Marketing. She received the 2003 Paul Green Award and was the finalist of the 2007 and 2008 O’Dell Awards. Catarina was also a 2007 Marketing Science Institute Young Scholar, and she received the D. Antonia Adelaide Ferreira award and the ADMES/MARKTEST award for scientific excellence. Catarina is currently on the editorial boards of the Marketing Science journal and the International Journal of Research in Marketing. Giuseppe Tribulato was born in Messina, Italy, in 1979. He received the degree in Computer Science (summa cum laude) in 2004 and his Ph.D in Computer Science in 2008. From 2005 he has lead the research team at Neodata Group. His research interests include data mining techniques, recommendation systems and customer targeting. 相似文献
Giuseppe TribulatoEmail: |
Sebastiano Battiato was born in Catania, Italy, in 1972. He received the degree in Computer Science (summa cum laude) in 1995 and his Ph.D in Computer Science and Applied Mathematics in 1999. From 1999 to 2003 he has lead the “Imaging” team c/o STMicroelectronics in Catania. Since 2004 he works as a Researcher at Department of Mathematics and Computer Science of the University of Catania. His research interests include image enhancement and processing, image coding and camera imaging technology. He published more than 90 papers in international journals, conference proceedings and book chapters. He is co-inventor of about 15 international patents. He is reviewer for several international journals and he has been regularly a member of numerous international conference committees. He has participated in many international and national research projects. He is an Associate Editor of the SPIE Journal of Electronic Imaging (Specialty: digital photography and image compression). He is director of ICVSS (International Computer Vision Summer School). He is a Senior Member of the IEEE. Giovanni Maria Farinella is currently contract researcher at Dipartimento di Matematica e Informatica, University of Catania, Italy (IPLAB research group). He is also associate member of the Computer Vision and Robotics Research Group at University of Cambridge since 2006. His research interests lie in the fields of computer vision, pattern recognition and machine learning. In 2004 he received his degree in Computer Science (egregia cum laude) from University of Catania. He was awarded a Ph.D. (Computer Vision) from the University of Catania in 2008. He has co-authored several papers in international journals and conferences proceedings. He also serves as reviewer numerous international journals and conferences. He is currently the co-director of the International Summer School on Computer Vision (ICVSS). Giovanni Giuffrida is an assistant professor at University of Catania, Italy. He received a degree in Computer Science from the University of Pisa, Italy in 1988 (summa cum laude), a Master of Science in Computer Science from the University of Houston, Texas, in 1992, and a Ph.D. in Computer Science, from the University of California in Los Angeles (UCLA) in 2001. He has an extensive experience in both the industrial and academic world. He served as CTO and CEO in the industry and served as consultant for various organizations. His research interest is on optimizing content delivery on new media such as Internet, mobile phones, and digital tv. He published several papers on data mining and its applications. He is a member of ACM and IEEE. Catarina Sismeiro is a senior lecturer at Imperial College Business School, Imperial College London. She received her Ph.D. in Marketing from the University of California, Los Angeles, and her Licenciatura in Management from the University of Porto, Portugal. Before joining Imperial College Catarina had been and assistant professor at Marshall School of Business, University of Southern California. Her primary research interests include studying pharmaceutical markets, modeling consumer behavior in interactive environments, and modeling spatial dependencies. Other areas of interest are decision theory, econometric methods, and the use of image and text features to predict the effectiveness of marketing communications tools. Catarina’s work has appeared in innumerous marketing and management science conferences. Her research has also been published in the Journal of Marketing Research, Management Science, Marketing Letters, Journal of Interactive Marketing, and International Journal of Research in Marketing. She received the 2003 Paul Green Award and was the finalist of the 2007 and 2008 O’Dell Awards. Catarina was also a 2007 Marketing Science Institute Young Scholar, and she received the D. Antonia Adelaide Ferreira award and the ADMES/MARKTEST award for scientific excellence. Catarina is currently on the editorial boards of the Marketing Science journal and the International Journal of Research in Marketing. Giuseppe Tribulato was born in Messina, Italy, in 1979. He received the degree in Computer Science (summa cum laude) in 2004 and his Ph.D in Computer Science in 2008. From 2005 he has lead the research team at Neodata Group. His research interests include data mining techniques, recommendation systems and customer targeting. 相似文献
998.
In this paper, based on nonnegative matrix theory, the Halanay’s inequality and Lyapunov functional, some novel sufficient
conditions for global asymptotic robust stability and global exponential robust stability of neural networks with time-varying
delays are presented. It is shown that our results improve and generalize several previous results derived in the literatures.
From the obtained results, some linear matrix inequality criteria are derived. Finally, a simulation is given to show the
effectiveness of the results. 相似文献
999.
Broadcasting scheme with low client buffers and bandwidths for video-on-demand applications 总被引:1,自引:1,他引:0
Hsiang-Fu Yu Hung-Chang Yang Yao-Tien Wang Ping-Lin Fan Chu-Yi Chien 《Multimedia Tools and Applications》2009,42(3):295-316
Efficient data broadcasting is independent of request arrivals, and is thus highly promising when transmitting popular videos.
A conventionally adopted broadcasting method is periodic broadcasting, which divides a popular video into segments, which
are then simultaneously broadcast on different data channels. Once clients want to watch the video, they download the segments
from these channels. The skyscraper broadcasting (SkB) scheme supports clients with small bandwidths. An SkB client requires
only two-channel bandwidths to receive video segments. This work proposes a reverse SkB (RSkB) scheme, which extends SkB by
reducing buffering spaces. The RSkB is mathematically shown to achieve on-time video delivery and two-channel client bandwidths.
A formula for determining the maximum number of segments buffered by an RSkB client is presented. Finally, an analysis of
RSkB reveals that its client buffer requirements are usually 25–37% lower than SkB. Extensive simulations of RSkB further
demonstrate that RSkB yields lower client buffer demand than other proposed systems.
相似文献
Hsiang-Fu YuEmail: |
1000.