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Recommending mobile content using content diffusion and user preference transition

Recommending mobile content using content diffusion and user...

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2008 International Conference on e-Learning, e-Business, Enterprise Information systems, and e-Government, EEE 2008

作者： Lee, Tong Queue Lee, Eunchang Park, Young Park, Yongtae Department of Mobile Internet Dongyang Technical College Seoul Korea Republic of Graduate Program in Technology and Management Seoul National University Seoul Korea Republic of Department of Computer Science and Information Systems Bradley University Peoria IL United States Department of Industrial Engineering Seoul National University Seoul Korea Republic of

ISBN: (纸本)1601320639

Recommendation accuracy is especially important in mobile e-commerce environments due to the limited screen size of mobile devices and relatively expensive connection costs. Mobile content tends to be fashionable and are geared for young users. This paper presents a novel method of building a more accurate recommender system for mobile content in a mobile ecommerce environment. The method is based on collaborative filtering, and models content diffusion and user preference transition and incorporates them in constructing pseudo ratings from implicit feedback data. In a variety of experiments, recommender systems based on the method showed significantly better recommendation accuracy than a pure collaborative filtering-based recommender system.

关键词： Collaborative filtering

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Transactional WaveCache: Towards Speculative and Out-of-Order DataFlow Execution of Memory Operations

Transactional WaveCache: Towards Speculative and Out-of-Orde...

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International Symposium on computer Architecture and High Performance Computing (SBAC-PAD)

作者： Leandro A.J. Marzulo Felipe M.G. Franca Vítor Santos Costa Systems Engineering and Computer Science Program COPPE Universidade Federal do Rio de Janeiro Brazil Departmento de Ciência de Computadores Universidade do Porto Porto Portugal

The WaveScalar is the first dataflow architecture that can efficiently provide the sequential memory semantics required by imperative languages. This work presents a speculative memory disambiguation mechanism for this architecture, the transaction WaveCache. Our mechanism maintains the execution order of memory operations within blocks of code, called waves, but adds the ability to speculatively execute, out-of-order, operations from different waves. This mechanism is inspired by progress in supporting transactional memories. Waves are considered as atomic regions and executed as nested transactions. Wave that have finished the execution of all their memory operations are committed, as soon as the previous waves are also committed. If a hazard is detected in a speculative wave, all the following waves (children) are aborted and re-executed. We evaluated the transactional WaveCache on a set of benchmarks from Spec 2000, Mediabench and Mibench (telecomm). Speedups ranging from 1.31 to 2.24 (related to the original WaveScalar) where observed when the benchmark doesn't perform lots of emulated function calls or access memory very often. Low speedups of 1.1 to slowdowns of 0.96 were observed when the opposite happens or when the memory concurrency was high.

关键词： Out of order computer architecture Parallel processing High performance computing Telecommunications Concurrent computing Hardware Data engineering systems engineering and theory computer science

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DLM: A distributed Large Memory System using remote memory swapping over cluster nodes

DLM: A distributed Large Memory System using remote memory s...

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IEEE International Conference on Cluster Computing

作者： Hiroko Midorikawa Motoyoshi Kurokawa Ryutaro Himeno Mitsuhisa Sato Department of Computer and Information Science Seikei University Musashino Tokyo Japan Graduate School of Systems and Information Engineering University of Tsukuba Tennodai Tsukuba Ibaraki Japan Advanced Center of Computing and Communication Wako Saitama Japan Research Program for Computational Science RIKEN Wako Saitama Japan

Emerging 64 bitOSpsilas supply a huge amount of memory address space that is essential for new applications using very large data. It is expected that the memory in connected nodes can be used to store swapped pages efficiently, especially in a dedicated cluster which has a high-speed network such as 10 GbE and Infiniband. In this paper, we propose the distributed large memory system (DLM), which provides very large virtual memory by using remote memory distributed over the nodes in a cluster. The performance of DLM programs using remote memory is compared to ordinary programs using local memory. The results of STREAM, NPB and Himeno benchmarks show that the DLM achieves better performance than other remote paging schemes using a block swap device to access remote memory. In addition to performance, DLM offers the advantages of easy availability and high portability, because it is a user-level software without the need for special hardware. To obtain high performance, the DLM can tune its parameters independently from kernel swap parameters. We also found that DLMpsilas independence of kernel swapping provides more stable behavior.

关键词： Kernel Arrays Memory management Servers Bandwidth Performance evaluation Benchmark testing

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Proceedings of the 2008 MidSouth Computational Biology and Bioinformatics Society (MCBIOS) Conference

Proceedings of the 2008 MidSouth Computational Biology and B...

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作者： Wren, Jonathan D. Wilkins, Dawn Fuscoe, James C. Bridges, Susan Winters-Hilt, Stephen Gusev, Yuriy Arthritis and Immunology Research Program Oklahoma Medical Research Foundation 825 N.E. 13th Street Oklahoma City OK 73104-5005 United States Computer and Information Science Department The University of Mississippi University MS 38677 United States Center for Functional Genomics Division of Systems Toxicology National Center for Toxicological Research Jefferson AR 72079 United States Department of Computer Science and Engineering Mississippi State University Box 9637 Mississippi State MS 39762 United States Department of Computer Science University of New Orleans The Research Institute for Children 200 Henry Clay Ave. New Orleans LA 70118 United States Department of Surgery Health Sciences Center The University of Oklahoma Oklahoma City OK 73104 United States

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Parallel self-organizing maps with application in clustering distributed data

Parallel self-organizing maps with application in clustering...

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International Joint Conference on Neural Networks (IJCNN)

作者： Flavius L. Gorgonio Jose Alfredo F. Costa Electrical Engineering and Computer Science Postgraduate Program Federal University of Rio Grande do Norte Natal Rio Grande do Norte Brazil Adaptive Systems Laboratory Electrical Engineering Department Federal University of Rio Grande do Norte Natal Rio Grande do Norte Brazil

Clustering is the process of discovering groups within multidimensional data, based on similarities, with a minimal, if any, knowledge of their structure. Distributed data clustering is a recent approach to deal with geographically distributed databases, since traditional clustering methods require centering all databases in a single dataset. Moreover, current privacy requirements in distributed databases demand algorithms with the ability to process clustering securely. Among the unsupervised neural network models, the self-organizing map (SOM) plays a major role. SOM features include information compression while trying to preserve the topological and metric relationship of the primary data space. This paper presents a strategy for efficient cluster analysis in geographically distributed databases using SOM networks. Local datasets relative to database vertical partitions are applied to distinct maps in order to obtain partial views of the existing clusters. Units of each local map are chosen to represent original data and are sent to a central site, which performs a fusion of the partial results. Experimental results are presented for different datasets.

关键词： Distributed databases Artificial neural networks Joints Conferences Iris Clustering methods Clustering algorithms

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Generalized evidence pre-propagated importance sampling for hybrid bayesian networks

Generalized evidence pre-propagated importance sampling for ...

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AAAI-07/IAAI-07 Proceedings: 22nd AAAI Conference on Artificial Intelligence and the 19th Innovative Applications of Artificial Intelligence Conference

作者： Yuan, Changhe Druzdzel, Marek J. Department of Computer Science and Engineering Mississippi State University Mississippi State MS 39762 Intelligent Systems Program School of Information Sciences University of Pittsburgh Pittsburgh PA 15260

ISBN: (纸本)1577353234

In this paper, we first provide a new theoretical understanding of the Evidence Pre-propagated Importance Sampling algorithm (EPIS-BN) (Yuan & Druzdzel 2003;2006b) and show that its importance function minimizes the KL-divergence between the function itself and the exact posterior probability distribution in Polytrees. We then generalize the method to deal with inference in general hybrid Bayesian networks consisting of deterministic equations and arbitrary probability distributions. Using a novel technique called soft arc reversal, the new algorithm can also handle evidential reasoning with observed deterministic variables. Copyright © 2007, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Bayesian networks

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Importance sampling for general hybrid Bayesian networks

Importance sampling for general hybrid Bayesian networks

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11th International Conference on Artificial Intelligence and Statistics, AISTATS 2007

作者： Yuan, Changhe Druzdzel, Marek J. Department of Computer Science and Engineering Mississippi State University Mississippi State MS 39762 United States Decision Systems Laboratory School of Information Sciences and Intelligent Systems Program University of Pittsburgh Pittsburgh PA 15260 United States

Some real problems are more naturally modeled by hybrid Bayesian networks that consist of mixtures of continuous and discrete variables with their interactions described by equations and continuous probability distributions. However, inference in such general hybrid models is hard. Therefore, existing approaches either only deal with special instances, such as Conditional Linear Gaussians (CLGs), or approximate a general model with a restricted version and then perform inference on the simpler model. However, results thus obtained highly depend on the quality of the approximations. This paper describes an importance sampling-based algorithm that directly deals with hybrid Bayesian networks constructed in the most general settings and guarantees to converge to the correct answers given enough time.

关键词： Importance sampling

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Implementing and Improving a method for non-invasive elicitation of probabilities for bayesian networks 07

Implementing and Improving a method for non-invasive elicita...

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International Conference on computer systems and Technologies and Workshop for PhD Students in Computing, CompSysTech '07

作者： De Jongh, Martinus Druzdzel, Marek Rothkrantz, Leon Faculty of Electrical Engineering Mathematics and Computer Science Man-Machine Interaction Group Delft University of Technology Delft Netherlands Decision Systems Laboratory School of Information Sciences and Intelligent Systems Program University of Pittsburgh Pittsburgh PA United States

ISBN: (纸本)9789549641509

Knowledge elicitation is difficult for expert systems that are based on probability theory. The elicitation of probabilities for a probabilistic model requires a lot of time and interaction between the knowledge engineer and the expert. Druzdzel and van der Gaag [5] have proposed a theoretical framework that would allow the use of multiple types of probabilistic information for elicitation of Bayesian networks. This framework has been used as a starting point for the implementation of a non-invasive elication method. © 2007 ACM.

关键词： Linear programming

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The Women in science and engineering (WiSE) program at the University of Southern California: Achievements and challenges of the first five years

The Women in Science and Engineering (WiSE) program at the U...

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114th Annual ASEE Conference and Exposition, 2007

作者： Hawkes, Nicole Morrison, Jean Raghavendra, Cauligi WiSE University of Southern California United States University of Southern California United States Program University of Southern California United States Department of Earth Sciences Program USC Viterbi School of Engineering United States Departments of Electrical Engineering-Systems and Computer Science University of Southern California United States

The establishment of the Women in science and engineering (WiSE) program represents the serious commitment of the University of Southern California to address the under-representation of women in science and engineering. Since the launch of the program in 2000, WiSE has helped to more than double the number of tenured and tenure-track women faculty at USC from 15 to 35, through its recruitment and retention programs. Its complimentary programs to support undergraduates, graduate students, and postdoctoral scholars through a range of financial awards and activities have also grown in scope and impact. This paper describes the programs developed and administered by WiSE, the perceived successes to date, the challenges that remain, and the future directions of the program to ensure continued progress toward gender equity in science and engineering at USC. Through an analysis of the WiSE program at USC, we hope to present a model from which others can draw on to tackle the same issues at their own institutions. © American Society for engineering Education, 2007.

关键词： engineering education

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Real-time recognition of traffic signs using opponent color filters

Real-time recognition of traffic signs using opponent color ...

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14th World Congress on Intelligent Transport systems, ITS 2007

作者： Baba, Kyoko Hirai, Yuzo Master's Program in Graduate School of Systems and Information Engineering University of Tsukuba 1-1-1 Ten-nodai Tsukuba Ibaraki 305-8573 Japan Department of Computer Science Graduate School of Systems and Information Engineering University of Tsukuba 1-1-1 Ten-nodai Tsukuba Ibaraki 305-8573 Japan

ISBN: (纸本)9781617387777

In order to assist driver's vision, a real-time recognition system for traffic signs is proposed. After detecting sign candidates, biologically inspired opponent-color filters are used to extract symbol parts of signs. After normalizing the size of symbol, structural features are calculated to identify the sign. 5572 segmented images are used to design the algorithm. In a real-time system, the same sign in a sequence of frames is tracked, and a majority vote is used to integrate the recognition results. For test data, 93.8% recall rate and 99.3% precision rate could be attained. In-vehicle experiment also showed high recall and precision rates.

关键词： Traffic signs

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