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2014 IEEE 5th International Conference on Software engineering and Service Science

作者： Chao Qi Hongchang Chen Chenglong He Yan Yu National Digital Switching System Engineering & Technological R&D Center

ISBN: (纸本)9781479932801

The role division on social networks is an important technology,for its potential values in analyzing social communities,exploring information dissemination rules and improving directional *** paper is focused on the research of role division from the perspective of information *** propose to measure users' secondary ability of information dissemination by weighting their neighborhoods' capability of information dissemination based on existing user characterization *** initial cluster centers in k-means are optimized by considering the characteristics of information sharing and dissemination on social ***,users are classified into three categories: ordinary users,bridge users and core *** results on Sina micro-blog dataset show that the proposed algorithm can better differentiate users according to information dissemination.

关键词： Social Network role division Neighborhood dissemination Capability K-Means

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Modeling internet traffic with decomposition

Modeling internet traffic with decomposition

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2014 International Conference on Industrial Electronics and engineering (ICIEE 2014)

作者： Xiaohui Zhang Liang Zhao Juan Shen National Digital Switching System Engineering & Technological R&D Center

An appropriate traffic model is the fundamental of many research topics on Internet. But the flourish of Internet applications results in more and more traffic generating mechanisms and the complexity of the traffic transmitting makes the problem worse. Thus it is a key issue to keep the traffic model simple and easy to be adjusted. This paper proposes a new traffic model based on decomposition. In this model, the traffic is decomposed into two components and each component is modeled with an L-system. Analysis shows that the model can reserve the inherent structure and dynamics introduced in traffic generating and transmitting progress and is easy to be tuned. Simulations on real-world traces demonstrate the performance.

关键词： internet traffic model decomposition traffic structure

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An Improved recurrent Neural Network Language Model with Context Vector Features

An Improved Recurrent Neural Network Language Model with Con...

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2014 IEEE 5th International Conference on Software engineering and Service Science

作者： Jian Zhang dan Qu Zhen Li National Digital Switching System Engineering & Technological R&D Center

recurrent neural network language models have solved the problems of data sparseness and dimensionality disaster which exist in traditional N-gram *** have recently demonstrated state-of-the-art performance in speech recognition,machine translation and other *** this paper,we improve the model performance by providing contextual word vectors in association with *** method can reinforce the ability of learning long-distance information using vectors training from Skip-gram *** experimental results show that the proposed method can improve the perplexity performance significantly on Penn Treebank *** we further apply the models to speech recognition task on the Wall Street Journal corpora,where we achieve obvious improvements in word-error-rate.

关键词： Speech recognition Language Model recurrent Neural Network Skip-gram

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An indexed hash bucket structure

An indexed hash bucket structure

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2014 International Conference on Industrial Electronics and engineering (ICIEE 2014)

作者： Huiqun Huang Le Tian JulongLan National Digital Switching System Engineering Technological R&D Center

Hash tables are widely used in networking. Traditional hash bucket has to read out all the items in the bucket to confirm a false positive, which is inefficient. In this paper, an indexed hash bucket structure is proposed. In the structure, all items in a bucket would be indexed with unique identifiers using a Perfect Hash Function. When accessing a bucket to check a possible match, the identifiers were read out instead of the entire items. Then, according to the identifiers, we can calculate where the only possible match item locates in the bucket, which is the only item we need to read out then, no matter it’s a match or not, no matter how many items are in the bucket. Thus, at most two memory accesses are needed for every search. Analysis and simulations show that the proposed method can enhance the detecting efficiency and greatly improve the performance in the worst case.

关键词： indexed hash bloom filter CCN

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Cooperative Spectrum Sensing with energy-efficient Sequential decision Fusion rule

Cooperative Spectrum Sensing with energy-efficient Sequentia...

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International Conference on Wireless and Optical Communications ( WOCC)

作者： Hongzhi Luan Ou Li Xiaoyi Zhang National Digital Switching System Engineering & Technology R&D Center P.R. China

Energy efficiency is an important issue in Cooperative Spectrum Sensing (CSS). In this paper, an energy-efficient Sequential decision Fusion (SdF) scheme based on K-out-of-N rule is proposed. By fusing the received decisions sequentially, Fusion center (FC) may achieve a global decision before receiving all individual decisions. We verify that the performance of CSS using the proposed scheme is completely equal to the conventional CSS where FC adopts all decisions. Average number of decisions required at FC is also investigated. Numerical results show that SdF reduces the average number of decisions required dramatically without any loss in sensing performance.

关键词： Hafnium compounds Sensors Probability radio access networks

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A novel mesh-based hierarchical topology for network-on-chip

A novel mesh-based hierarchical topology for network-on-chip

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IEEE International Conference on Software engineering and Service Sciences (ICSESS)

作者： Feng Kong Guo-dong Han Jian-liang Shen National Digital Switching System Engineering & Technological R&D Center Zhengzhou China

ISBN: (纸本)9781479932801

As the number of modules in system-on-Chip increases, the topology is more likely to suffer from excessive end-to-end hop-counts, causing an increase of power consumption and area overhead. Concerning this, a novel hierarchical topology called CHMesh is proposed, which is based on mesh and Cmesh. Correspondingly, this article elaborates on a shortest deadlock-free CHXY routing algorithm which has a low complexity. Experimental results demonstrate that, compared with traditional mesh and ref-mesh, the CHMesh can increase the average throughput by about 60% and 10% respectively under the nonuniform traffic patterns, presenting more advantages on large-scale NoC.

关键词： routing Topology Network topology Traffic control system recovery Throughput Algorithm design and analysis

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One Intelligent Algorithm for Estimation of TdOA and FdOA

One Intelligent Algorithm for Estimation of TDOA and FDOA

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2014 IEEE 7th Joint International Information technology and Artificial Intelligence Conference

作者： Zhi yu Lu Jian hui Wang da ming Wang Yue Wang China National Digital Switching System Engineering &Technological R&D Center NDSC

The calculation is large to estimate the TdOA and FdOA with cross ambiguity function. Existing algorithms which are based on the ergodic theory have poor real-time performance. To solve this problem, the genetic algorithm is proposed with improvements based on the characteristics of cross ambiguity function. With the self-adapting mutation probability by following the convergence extent of the population and multiple population initializations, the diversity of the population is effectively improved to prevent the algorithm into a local optimum. The simulation results show that the computational efficiency of the improved algorithm, compared with the existing algorithms, is greatly improved, and the TdOA/FdOA estimation results can quickly be obtained.

关键词： genetic algorithm cross ambiguity function TdOA FdOA passive location

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Sparse-view image reconstruction with nonlocal total variation

Sparse-view image reconstruction with nonlocal total variati...

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IEEE Symposium on Nuclear Science (NSS/MIC)

作者： Hanming Zhang Bin Yan Linyuan Wang Lei Li Xiaoqi Xi Guoen Hu National Digital Switching System Engineering and Technological Research Center Zhengzhou P.R.China National Digital Switching System Engineering and Technological Research Center Zhengzhou 450002 China

The concept of computed tomography (CT) reconstruction from sparse-view data has been a considerable area of much research over the last several years. With the famous piecewise constant assumption, total variation (TV) model has been shown that it could be successfully applied to sparse-view CT reconstruction for producing accurate reconstructions. However, the resulting images from the traditional TV model based on local operators always meet the problems of smeared edges or staircase effects. In this paper, the TV minimization reconstruction model is extanded to a nonlocal TV (NLTV) model, using auxiliary variables and efficient split Bregman iterartive scheme, a reconstruction algorithm based on NLTV minimization has been developed. The proposed method shows excellent properties of edge preserving and smoothness preserving by using the nonlocal operators. Experimental results indicate that the proposed method could solve the above mentioned effects and reconstruct more accurate than the popular split Bregman-TV algorithm when applied to a sparse-view problem.

关键词： Image reconstruction TV Computed tomography Minimization Gold detectors Biomedical imaging

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An improved recurrent neural network language model with context vector features

An improved recurrent neural network language model with con...

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IEEE International Conference on Software engineering and Service Sciences (ICSESS)

作者： Jian Zhang dan Qu Zhen Li National Digital Switching System Engineering & Technological R&D Center Zhengzhou Henan Province China

ISBN: (纸本)9781479932801

recurrent neural network language models have solved the problems of data sparseness and dimensionality disaster which exist in traditional N-gram models. rNNLMs have recently demonstrated state-of-the-art performance in speech recognition, machine translation and other tasks. In this paper, we improve the model performance by providing contextual word vectors in association with rNNLMs. This method can reinforce the ability of learning long-distance information using vectors training from Skip-gram model. The experimental results show that the proposed method can improve the perplexity performance significantly on Penn Treebank data. And we further apply the models to speech recognition task on the Wall Street Journal corpora, where we achieve obvious improvements in word-error-rate.

关键词： Vectors Context modeling recurrent neural networks Hidden Markov models Neurons Computational modeling Context

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Proceedings from the 9th annual conference on the science of dissemination and implementation: Washington, dC, USA. 14-15 december 2016

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Implement Sci 2017年第SUPPL 1期12卷 48-48页

作者： david Chambers Lisa Simpson Gila Neta Ulrica von Thiele Schwarz Antoinette Percy-Laurry Gregory A. Aarons ross Brownson Amanda Vogel Shannon Wiltsey Stirman Kenneth Sherr rachel Sturke Wynne E. Norton Allyson Varley Cynthia Vinson Lisa Klesges Suzanne Heurtin-roberts M. rashad Massoud Leighann Kimble Arne Beck Claire Neely Jennifer Boggs Carmel Nichols Wen Wan Erin Staab Neda Laiteerapong Nathalie Moise ravi Shah Susan Essock Margaret Handley Amy Jones Jay Carruthers Karina davidson Lauren Peccoralo Lloyd Sederer Todd Molfenter Ashley Scudder Sarah Taber-Thomas Kristen Schaffner Amy Herschell Eva Woodward Jeffery Pitcock Mona ritchie JoAnn Kirchner Julia E. Moore Sobia Khan Shusmita rashid Jamie Park Melissa Courvoisier Sharon Straus daniel Blonigen Allison rodriguez Luisa Manfredi Andrea Nevedal Joel rosenthal david Smelson Christine Timko Nicole Stadnick Jennifer regan Miya Barnett Anna Lau Lauren Brookman-Frazee Erick Guerrero Karissa Fenwick Yinfei Kong Gregory Aarons rebecca Lengnick-Hall Benjamin Henwood Nina Sayer Craig rosen robert Orazem Brandy Smith Lindsey Zimmerman david Lounsbury rachel Kimerling Jodie A. Trafton Steven Lindley rahul Bhargava Hal roberts Laura Gibson Gabriel J. Escobar Vincent Liu Benjamin Turk Arona ragins Patricia Kipnis Ashley Ketterer Gruszkowski Michael W. Kennedy Emily rentschler drobek Lior Turgeman Aleksandra Sasha Milicevic Terrence L. Hubert Larissa Myaskovsky Youxu C. Tjader robert J. Monte Kathryn G. Sapnas Edmond ramly diane r Lauver Christie M Bartels Shereef Elnahal Andrea Ippolito Hillary Peabody Carolyn Clancy randall Cebul Thomas Love douglas Einstadter Shari Bolen Brook Watts Vera Yakovchenko Angela Park William Lukesh donald r. Miller david Thornton Mari-Lynn drainoni Allen L. Gifford Shawna Smith Julia Kyle Mark S Bauer daniel Eisenberg Celeste Liebrecht Michelle Barbaresso Amy Kilbourne Elyse Park Giselle Perez Jamie Ostroff Sarah Greene Michael Parchman Brian Austin Eric Larson Stefanie Ferreri Chris Shea Megan Smith Kea Turner Jennifer Bacci Kyle Bigham Geoffr Division of Cancer Control and Population Sciences National Cancer Institute Rockville MD 20850 USA AcademyHealth Washington DC 20009 USA Department of Learning Informatics Management and Ethics Karolinska Institutet Stockholm 17177 Sweden Department of Psychiatry University of California San Diego San Diego CA 92093 USA George Warren Brown School of Social Work Washington University in St. Louis St. Louis MO 63130 USA Center for Global Health National Cancer Institute Rockville MD 20850 USA Dissemination and Training Division VA National Center for PTSD Palo Alto CA 94305 USA Department of Global Health University of Washington Seattle WA 98105 USA Center for Global Health Studies Fogarty International Center National Institutes of Health Bethesda MD 20912 USA School of Public Health University of Alabama at Birmingham Birmingham AL 35243 USA School of Public Health University of Memphis Memphis TN 38152 USA USAID Applying Science to Strengthen and Improve Systems (ASSIST) Project Bethesda Maryland 20814 USA Institute for Health Research Kaiser Permanente Denver CO 80231 USA ICSI Institute for Clinical Systems Improvement Bloomington MN 55425 USA Medicine University of Chicago Chicago IL 60637 USA Medicine Columbia University Medical Center New York NY 10032 USA Psychiatry Columbia University New York NY 10032 USA General Internal Medicine Epidemiology and Biostatistics University of California San Francisco San Francisco CA 94110 USA New York State Office of Mental Health New York NY 10001 USA Medicine Icahn School of Medicine at Mount Sinai New York NY 10029 USA Center for Health Enhancement System Studies University of Wisconsin Madison WI 53706 USA Department of Psychiatry University of Pittsburgh Pittsburgh PA 15213 USA Psychology University at Buffalo Buffalo NY 14260 USA Department of Psychology West Virginia University Morgantown WV 26506 USA MIRECC VA North Little Rock AR 72114 USA QUERI for Team-Based Behavioral Health Centr

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