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Grid and Cooperative Computing - GCC 2004 Workshops 1

引用

丛书名： Lecture Notes in Computer science

1000年

作者： Hai Jin Yi Pan Nong Xiao Jianhua Sun

ISBN: (数字)9783540302070

ISBN: (纸本)9783540235781

Welcome to the proceedings of GCC2004 and the city of Wuhan. Grid computing has become a mainstream research area in computer science and the GCC conference has become one of the premier forums for presentation of new and exciting research in all aspectsofgridandcooperativecomputing. Theprogramcommitteeispleasedtopresent the proceedings of the 3rd International Conference on Grid and Cooperative Comp- ing (GCC2004), which comprises a collection of excellent technical papers, posters, workshops, and keynote speeches. The papers accepted cover a wide range of exciting topics, including resource grid and service grid, information grid and knowledge grid, grid monitoring,managementand organizationtools, grid portal, grid service, Web s- vices and their QoS, service orchestration, grid middleware and toolkits, software glue technologies, grid security, innovative grid applications, advanced resource reservation andscheduling,performanceevaluationandmodeling,computer-supportedcooperative work, P2P computing, automatic computing, and meta-information management. The conference continues to grow and this year a record total of 581 manuscripts (including workshop submissions) were submitted for consideration. Expecting this growth, the size of the program committee was increased from 50 members for GCC 2003 for 70 in GCC 2004. Relevant differences from previous editions of the conf- ence: it is worth mentioning a signi?cant increase in the number of papers submitted by authors from outside China; and the acceptance rate was much lower than for p- vious GCC conferences. From the 427 papers submitted to the main conference, the program committee selected only 96 regular papers for oral presentation and 62 short papers for poster presentation in the program.

关键词： Theory of Computation Computer Communication Networks Operating Systems Information Systems Applications (incl. Internet) Information Storage and Retrieval User Interfaces and Human Computer Interaction

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Research on Integrated Detection of SQL Injection Behavior Based on Text Features and Traffic Features

Research on Integrated Detection of SQL Injection Behavior B...

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作者： Ming Li Bo Liu Guangsheng Xing Xiaodong Wang Zhihui Wang College of Intelligence Science and Technology National University of Defence Technology National Key Laboratory of Parallel and Distributed Processing College of Computer Science and Technology National University of Defence Technology

With the rapid development of Internet technology, various network attack methods come out one after the other. SQL injection has become one of the most severe threats to Web applications and seriously threatens various Web application services and users' data security. There are both traditional detection methods and emerging methods based on deep learning technology with higher detection accuracy for the detection of SQL injection. However, they are all for detecting a single statement and cannot determine the stage of the attack. To further improve the effect of SQL injection detection, this paper proposes an integrated detection framework for SQL injection behavior based on both text features and traffic features. We propose a SQL-LSTM model based on deep learning technology as the detection model at the text features level. Meanwhile,the features of the data traffic are merged. By this integrated method, the detection effect of SQL injection is further improved.

关键词： SQL injection Integrated method SQL-LSTM Traffic features

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Implementation of an Accurate and Efficient Compensated DGEMM for 64-bit ARMv8 Multi-Core Processors

Implementation of an Accurate and Efficient Compensated DGEM...

引用

International Conference on parallel and Distributed Systems (ICPADS)

作者： Hao Jiang Feng Wang Kuan Li Canqun Yang Kejia Zhao Chun Huang College of Computer Science National University of Defense Technology Changsha China Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha China

ISBN: (纸本)9781467386692

This paper presents an implementation of an accurate and efficient compensated Double-precision General Matrix Multiplication (DGEMM) based on OpenBLAS for 64-bit ARMv8 multi-core processors. Due to cancellation phenomena in floating point arithmetic, the results of DGEMM may not be as accurate as expected. In order to increase the accuracy of DGEMM, we compensate the error introduced by its dot product kernel (GEBP) by applying an error-free transformation to rewrite the kernel in assembly language. We optimize the computations in the inner kernel through exploiting loop unrolling, instruction scheduling and software-implemented register rotation to exploit instruction level parallelism (ILP). We also conduct a priori error analysis of the derived CompDGEMM. Our compensated DGEMM is as accurate as the existing quadruple precision GEMM using MBLAS, but is up to 6.4x faster. Our parallel implementation achieves good performance and scalability under varying thread counts across a range of matrix sizes evaluated.

关键词： Multicore processing Kernel Registers Libraries Error analysis Algorithm design and analysis

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A Class of Fast and Accurate Multi-layer Block Summation and Dot Product Algorithms 18th

A Class of Fast and Accurate Multi-layer Block Summation a...

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18th IFIP WG 10.3 International Conference on Network and parallel Computing, NPC 2021

作者： He, Kang Barrio, Roberto Chen, Lin Jiang, Hao Liu, Jie Gu, Tongxiang Qi, Jin Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha410073 China Department of Applied Mathematics University of Zaragoza ZaragozaE50009 Spain College of Computer National University of Defense Technology Changsha410073 China Institute of Applied Physics and Computational Mathematics Beijing100000 China

ISBN: (纸本)9783030935702

Basic recursive summation and common dot product algorithm have a backward error bound that grows linearly with the vector dimension. Blanchard [1] proposed a class of fast and accurate summation and dot product algorithms respectively called FABsum and FABdot, which trades off the calculation accuracy and speed by the block size. Castaldo [2] proposed a multi-layer block summation and dot product algorithm called SuperBlocksum and SuperBlockdot that can increase the accuracy while adding almost no additional calculations. We combine the idea of [1] with the multi-layer block structure to propose SuperFABsum (for "super fast and accurate block summation") and SuperFABdot (for "super fast and accurate block dot product"). Our algorithms have two variants, one is SuperFAB(within), the other is SuperFAB(outside). Our algorithms further improve accuracy and speed compared with FAB and SuperBlock. We conducted accuracy and speed tests on the high-performance FT2000+ processor. Experimental results show that SuperFABdot(within) algorithm is more accurate than FABdot and SuperBlockdot. Compared with FABdot, SuperFABdot(outside) algorithm can achieve up to 1.2 × performance speedup while ensuring similar accuracy. © 2022, IFIP International Federation for Information processing.

关键词： Artificial intelligence

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Exploring Policy Diversity in parallel Actor-Critic Learning

Exploring Policy Diversity in Parallel Actor-Critic Learning

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International Conference on Tools for Artificial Intelligence (ICTAI)

作者： Yanqiang Zhang Yuanzhao Zhai Gongqian Zhou Bo Ding Dawei Feng Songwang Liu National Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha China Academy of Military Science Beijing China

Exploration is a critical challenge for deep reinforcement learning methods. Although existing works such as actor-critic algorithms have made much progress, most still suffer from the sample inefficiency problem in complex environments where rewards are sparse. parallel sampling, which uses multiple actors with the same policy interacting with the environment, is an effective approach to improve sample efficiency. However, parallel parameter-sharing actors collect similar samples, which generally hinders the improvement of the overall exploration process. In this paper, we propose a Policy Diversity enhanced approach for parallel Actor-Critic (PDAC). Specifically, we extend the parallel actor-critic architecture to the PDAC framework composed of a shared critic and parallel distinct actors. Then we introduce the KL-divergence of the action probability distribution between parallel actors as the intrinsic reward to encourage actors to explore diverse strategies. We evaluate our approach in multiple challenging procedurally-generated tasks and compare it with state-of-the-art algorithms. Experiments show that PDAC makes significant progress in the comparison, in terms of cumulative rewards and sample efficiency.

关键词： Deep learning Reinforcement learning Learning (artificial intelligence) Probability distribution Task analysis

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IWFR: Exploiting full-duplex in wireless relay networks

IWFR: Exploiting full-duplex in wireless relay networks

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International Conference on Information science and technology (ICIST)

作者： Yong Lu Shaohe Lv Xiaodong Wang Xingming Zhou National Key Laboratory for Parallel and Distributed Processing School of Computer Science National University of Defense Technology Changsha Hunan China

Traditional wireless relay networks have large end-to-end time delay and low throughput because of the limit that it can't receive and forward at the same time. In this paper, we proposed IWFR: Immediate Wireless Full-Duplex Relay which exploits the advantages of full-duplex to shorten the end-to-end time delay and improve the throughput. At the same time, we designed a new implicit acknowledgement mechanism, which can eliminate the ACK overheads and evidently improve the throughput of the relay. To implement IWFR, we also modified the full-duplex node architecture to make it support for immediate relay. Simulation shows that IWFR shortens the end-to-end time delay by 60% on average and improves the throughput to 240% of the original relay.

关键词： Wireless communication Throughput Delay effects Wireless sensor networks Interference Relay networks (telecommunications)

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Succinct Representations in Collaborative Filtering: A Case Study using Wavelet Tree on 1,000 Cores

Succinct Representations in Collaborative Filtering: A Case ...

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IEEE International Conference on parallel and Distributed Computing, Applications and Technologies (PDCAT)

作者： Xiangjun Peng Qingfeng Wang Xu Sun Chunye Gong Yaohua Wang User-Centric Computing Group University of Nottingham Ningbo China Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology

ISBN: (数字)9781728126166

ISBN: (纸本)9781728126173

User-Item (U-I) matrix has been used as the dominant data infrastructure of Collaborative Filtering (CF). To reduce space consumption in runtime and storage, caused by data sparsity and growing need to accommodate side information in CF design, one needs to go beyond the U-I Matrix. In this paper, we took a case study of Succinct Representations in Collaborative Filtering, rather than using a U-I Matrix. Our key insight is to introduce Succinct Data Structures as a new infrastructure of CF. Towards this, we implemented a User-based K-Nearest-Neighbor CF prototype via Wavelet Tree, by first designing a Accessible Compressed Documents (ACD) to compress U-I data in Wavelet Tree, which is efficient in both storage and runtime. Then, we showed that ACD can be applied to develop an efficient intersection algorithm without decompression, by taking advantage of ACD's characteristics. We evaluated our design on 1,000 cores of Tianhe-II supercomputer, with one of the largest public data set ml-20m. The results showed that our prototype could achieve 3.7 minutes on average to deliver the results.

关键词： Vegetation Collaboration Filtering Data structures Runtime Indexes Time complexity

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RZKPB: A Privacy-Preserving Blockchain-Based Fair Transaction Method for Sharing Economy

RZKPB: A Privacy-Preserving Blockchain-Based Fair Transactio...

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IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Bin Li Yijie Wang Science and Technology on Parallel and Distributed Processing Laboratory College of Computer National University of Defense Technology Hunan Changsha P.R. China

Emerging blockchain systems have been widely adopted in sharing economy, such as e-commerce, to allow mutually distrustful parties to transact fairly without trusted parties. Most blockchain systems, however, lack transactional privacy protection. All transactions, including trading relationship between pseudonyms and content transacted, are exposed on the blockchain. Although many existing privacy protection methods on the blockchain have been proposed, it is difficult to find a trade-off between keeping speed and protecting privacy of transactions. To address this limitation, we propose a novel privacy-preserving method RZKPB that does not store financial transactions in clear on the blockchain, thus retaining transactional privacy from the public's view. Meanwhile, these transactions are as proofs to solve disputes between trading partners. RZKPB ensures fairness and privacy of transactions between participants without adding a new trusted party and breaking the verifying protocol on the blockchain. We take the e-commerce as an example in sharing economy to introduce RZKPB in our paper. Our experimental results show that compared with existing privacy-preserving methods based on the blockchain, RZKPB is more efficient under different settings.

关键词： Privacy Protocols Encryption Distortion

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FPPB: A Fast and Privacy-Preserving Method Based on the Permissioned Blockchain for Fair Transactions in Sharing Economy

FPPB: A Fast and Privacy-Preserving Method Based on the Perm...

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IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Bin Li Yijie Wang Peichang Shi Huan Chen Li Cheng Science and Technology on Parallel and Distributed Processing Laboratory College of Computer National University of Defense Technology Changsha Hunan P.R. China

Blockchain is a distributed system with efficient transaction recording and has been widely adopted in sharing economy. Although many existing privacy-preserving methods on the blockchain have been proposed, finding a trade-off between keeping speed and preserving privacy of transactions remain challenging. To address this limitation, we propose a novel Fast and Privacy-preserving method based on the Permissioned Blockchain (FPPB) for fair transactions in sharing economy. Without breaking the verifying protocol and bringing additional off-blockchain interactive communication, FPPB protects the privacy and fairness of transactions. Additionally, experiments are implemented in EthereumJ (a Java implementation of the Ethereum protocol) to measure the performance of FPPB. Compared with normal transactions without cryptographic primitives, FPPB only slows down transactions slightly.

关键词： Privacy Cloud computing Encryption Registers Protocols

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NH-CIL: A Nested Hierarchy Algorithm for Class Incremental Learning

NH-CIL: A Nested Hierarchy Algorithm for Class Incremental L...

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International Conference on Computer Supported Cooperative Work in Design

作者： Mingyu Liu Yijie Wang Science and Technology on Parallel and Distributed Processing Laboratory College of Computer National University of Defense Technology Changsha Hunan P.R.China

ISBN: (纸本)9781728165981

Class incremental learning is widely applied in the classification scenarios as the number of classes is usually dynamically changing. However, the existing algorithms increase computational cost to implement class incremental learning in order to increase classification quality. In this paper, we propose a nested hierarchy algorithm based on OCSVM for class incremental learning, called NH-CIL. We reuse support vectors to eliminate redundant instances and catch the key ones to replace the whole model because of the generalization ability of OCSVM. When a new class arrives, NH-CIL adopts OCSVM on the new class and the old classes respectively to get the corresponding sketching supports vectors. Then NH-CIL reuses these two kinds of sketching support vectors to build a binary sub-classifier. These two steps are repeatedly nested to form a hierarchy classification model in a bottom-up manner while the number of classes increases. On the contrary, the testing phase is in a top-down manner. NH-CIL can be used as a flexible approach in the classification scenarios during the collaborative information processing. We conduct the experiments on 8 real-world benchmark datasets to compare NH-CIL with some other class incremental learning algorithms, e.g. SD-CIL, HS-CIL and OP-CIL. The experiment results show that NH-CIL averagely achieves more than 5.1%, 8.6% and 11.6% accuracy improvement and 39.8%, 24.7% and 12.6% efficiency improvement over SD-CIL, HS-CIL and OP-CIL, respectively.

关键词： Machine learning algorithms Computational modeling Heuristic algorithms Conferences Support vector machine classification Information processing Benchmark testing

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