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检索条件"机构=Cluster and Grid Computing Laboratory School of Computer Science and Technology"
608 条 记 录,以下是361-370 订阅
SKVM: Scaling In-Memory Key-Value Store on Multicore
SKVM: Scaling In-Memory Key-Value Store on Multicore
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IEEE Symposium on computers and Communication
作者: Ran Zheng Wenjin Wang Hai Jin Qin Zhang Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology
SKVM is a high performance in-memory Key-Value (KV) store for multicore, which is designed for high concurrent data access. There are some problems of existing systems dealing with high concurrent data processing on m... 详细信息
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Fine-grained and heterogeneous proxy re-encryption for secure cloud storage
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Chinese science Bulletin 2014年 第32期59卷 4201-4209页
作者: Peng Xu Hongwu Chen Deqing Zou Hai Jin Services Computing Technology and System Laboratory Cluster and Grid Computing Laboratory School of Computer Science and Technology Huazhong University of Science and Technology
Cloud is an emerging computing *** has drawn extensive attention from both academia and *** its security issues have been considered as a critical obstacle in its rapid *** data owners store their data as plaintext in... 详细信息
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Cost Minimization for Code Offloading with Cellular Traffic Aggregation
Cost Minimization for Code Offloading with Cellular Traffic ...
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IEEE Symposium on computers and Communication
作者: Jiao Song Feng Lu Hai Jin Zaiyang Tang Peng Li Xiaofei Liao Xia Xie Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology China
Code offloading has been proposed to improve the performance and energy-efficiency of mobile devices by sending heavy computation tasks to resourceful cloud, instead of executing all tasks on local mobile devices. Unf... 详细信息
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Erratum to: Search for supersymmetry in final states with two same-sign or three leptons and jets using 36 fb−1 of = 13 TeV pp collision data with the ATLAS detector
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Journal of High Energy Physics 2019年 第8期2019卷 1-19页
作者: Aaboud, M. Aad, G. Abbott, B. Abdinov, O. Abeloos, B. Abidi, S. H. AbouZeid, O. S. Abraham, N. L. Abramowicz, H. Abreu, H. Abreu, R. Abulaiti, Y. Acharya, B. S. Adachi, S. Adamczyk, L. Adelman, J. Adersberger, M. Adye, T. Affolder, A. A. Afik, Y. Agatonovic-Jovin, T. Agheorghiesei, C. Aguilar-Saavedra, J. A. Ahlen, S. P. Ahmadov, F. Aielli, G. Akatsuka, S. Akerstedt, H. Åkesson, T. P. A. Akilli, E. Akimov, A. V. Alberghi, G. L. Albert, J. Albicocco, P. Alconada Verzini, M. J. Alderweireldt, S. C. Aleksa, M. Aleksandrov, I. N. Alexa, C. Alexander, G. Alexopoulos, T. Alhroob, M. Ali, B. Aliev, M. Alimonti, G. Alison, J. Alkire, S. P. Allbrooke, B. M. M. Allen, B. W. Allport, P. P. Aloisio, A. Alonso, A. Alonso, F. Alpigiani, C. Alshehri, A. A. Alstaty, M. I. Alvarez Gonzalez, B. Álvarez Piqueras, D. Alviggi, M. G. Amadio, B. T. Amaral Coutinho, Y. Amelung, C. Amidei, D. Amor Dos Santos, S. P. Amoroso, S. Amundsen, G. Anastopoulos, C. Ancu, L. S. Andari, N. Andeen, T. Anders, C. F. Anders, J. K. Anderson, K. J. Andreazza, A. Andrei, V. Angelidakis, S. Angelozzi, I. Angerami, A. Anisenkov, A. V. Anjos, N. Annovi, A. Antel, C. Antonelli, M. Antonov, A. Antrim, D. J. Anulli, F. Aoki, M. Aperio Bella, L. Arabidze, G. Arai, Y. Araque, J. P. Araujo Ferraz, V. Arce, A. T. H. Ardell, R. E. Arduh, F. A. Arguin, J-F. Argyropoulos, S. Arik, M. Armbruster, A. J. Armitage, L. J. Arnaez, O. Arnold, H. Arratia, M. Arslan, O. Artamonov, A. Artoni, G. Artz, S. Asai, S. Asbah, N. Ashkenazi, A. Asquith, L. Assamagan, K. Astalos, R. Atkinson, M. Atlay, N. B. Augsten, K. Avolio, G. Axen, B. Ayoub, M. K. Azuelos, G. Baas, A. E. Baca, M. J. Bachacou, H. Bachas, K. Backes, M. Bagnaia, P. Bahmani, M. Bahrasemani, H. Baines, J. T. Bajic, M. Baker, O. K. Baldin, E. M. Balek, P. Balli, F. Balunas, W. K. Banas, E. Bandyopadhyay, A. Banerjee, Sw. Bannoura, A. A. E. Barak, L. Barberio, E. L. Barberis, D. Barbero, M. Barillari, T. Barisits, M-S Barkeloo, J. T. Barklow, T. Barlow, N. Barnes, S. L. Barnett, B. M. Barnett, R. M. Barnovska-Blenessy, Z Faculté des Sciences Université Mohamed Premier and LPTPM Oujda Morocco CPPM Aix-Marseille Université and CNRS/IN2P3 Marseille France Homer L. Dodge Department of Physics and Astronomy University of Oklahoma Norman U.S.A. Institute of Physics Azerbaijan Academy of Sciences Baku Azerbaijan LAL Univ. Paris-Sud CNRS/IN2P3 Université Paris-Saclay Orsay France Department of Physics University of Toronto Toronto Canada Santa Cruz Institute for Particle Physics University of California Santa Cruz Santa Cruz U.S.A. Department of Physics and Astronomy University of Sussex Brighton United Kingdom Raymond and Beverly Sackler School of Physics and Astronomy Tel Aviv University Tel Aviv Israel Department of Physics Technion: Israel Institute of Technology Haifa Israel Center for High Energy Physics University of Oregon Eugene U.S.A. Department of Physics Stockholm University Stockholm Sweden The Oskar Klein Centre Stockholm Sweden INFN Gruppo Collegato di Udine Udine Italy ICTP Trieste Italy Department of Physics King’s College London London United Kingdom International Center for Elementary Particle Physics and Department of Physics The University of Tokyo Tokyo Japan AGH University of Science and Technology Faculty of Physics and Applied Computer Science Krakow Poland Department of Physics Northern Illinois University DeKalb U.S.A. Fakultät für Physik Ludwig-Maximilians-Universität München München Germany Particle Physics Department Rutherford Appleton Laboratory Didcot United Kingdom Institute of Physics University of Belgrade Belgrade Serbia Department of Physics Alexandru Ioan Cuza University of Iasi Iasi Romania Laboratório de Instrumentação e Física Experimental de Partículas – LIP Lisboa Portugal Departamento de Fisica Teorica y del Cosmos Universidad de Granada Granada Portugal Department of Physics Boston University Boston U.S.A. Joint Institute for Nuclear Research JINR Dubna Dubna Russia INFN Sezione di Roma Tor Vergata Roma Italy Dip
One correction is made to the figure 4e of the paper.
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A Novel Fast Approach for Convolutional Networks with Small Filters Based on GPU
A Novel Fast Approach for Convolutional Networks with Small ...
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IEEE International Conference on High Performance computing and Communications (HPCC)
作者: Wenbin Jiang Yiming Chen Hai Jin Bin Luo Ye Chi Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China
Recently, convolutional networks have achieved great successes in the field of computer vision. In order to improve the efficiency of convolutional networks, large amount of solutions focusing on training algorithms a... 详细信息
来源: 评论
Cost-Effective Hybrid Replacement Strategy for SSD in Web Cache
Cost-Effective Hybrid Replacement Strategy for SSD in Web Ca...
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IEEE International Conference on Ubiquitous computing and Communications
作者: Qu Li Xiaofei Liao Hai Jin Li Lin Xia Xie Qiongjie Yao Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan 430074 China
The increasing popularity of photo sharing in social networking service (SNS) complicates the challenge of storing and transmitting large photo data for SNS providers. Distributed web caches are generally used by SNS ... 详细信息
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PSG: a two-layer graph model for document summarization
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Frontiers of computer science 2014年 第1期8卷 119-130页
作者: Heng CHEN Hai JIN Feng ZHAO Service Computing Technology and System Lab & Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan 430074 China
Graph model has been widely applied in docu- ment summarization by using sentence as the graph node, and the similarity between sentences as the edge. In this paper, a novel graph model for document summarization is p... 详细信息
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A Map-reduce Method for Training Autoencoders on Xeon Phi
A Map-reduce Method for Training Autoencoders on Xeon Phi
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IEEE International Conference on Ubiquitous computing and Communications
作者: Qiongjie Yao Xiaofei Liao Hai Jin Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan 430074 China
The stacked autoencoder is a deep learning model that consists of multiple autoencoders. This model has been widely applied in numerous machine learning applications. A significant amount of effort has been made to in... 详细信息
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URMG: Enhanced CBMG-Based Method for Automatically Testing Web Applications in the Cloud
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Tsinghua science and technology 2014年 第1期19卷 65-75页
作者: Xiaolin Xu Hai Jin Song Wu Lixiang Tang Yihong Wang Services Computing Technology and System Lab and Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology
To satisfy the rapid growth of cloud technologies, a large number of web applications have been developed and deployed, and these applications are being run in clouds. Due to the scalability provided by clouds, a sing... 详细信息
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An adaptive switching scheme for iterative computing in the cloud
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Frontiers of computer science 2014年 第6期8卷 872-884页
作者: Yu ZHANG Xiaofei LIAO Hai JIN Li LIN Feng LU Service Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan 430074 China
Delta-based accumulative iterative computation (DAIC) model is currently proposed to support iterative algorithms in a synchronous or an asynchronous way. However, both the synchronous DAIC model and the asynchronou... 详细信息
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