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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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Deep Discriminative Clustering Network

Deep Discriminative Clustering Network

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International Joint Conference on Neural Networks

作者： Xuying Shao Keshi Ge Huayou Su Lei Luo Baoyun Peng Dongsheng Li Science and Technology on Parallel and Distributed Laboratory National University of Defense Technology Changsha China College of Computer National University of Defense Technology Changsha China

Deep clustering aims to cluster unlabeled data by embedding them into a subspace based on deep model. The key challenge of deep clustering is to learn discriminative representations for input data with high dimensions. In this paper, we present a deep discriminative clustering network for clustering the real-world images. We use a convolutional auto-encoder stacked with a softmax layer to predict clustering assignments. To learn a discriminative representations, the proposed approach adds discriminative loss as embedded regularization with relative entropy minimization. With the discriminative loss, the network can not only produce clustering assignments, but also learn discriminative features by reducing intra-cluster distance and increasing inter-cluster distance. We evaluate the proposed method on three datasets: MNIST-full, YTF and FRGC-v2.0. We outperform state-of-the-art results on MNIST-full and FRGC-v2.0 and achieve competitive result on YTF. The source code has been made publicly available at .

关键词： Machine learning Clustering algorithms Image reconstruction Linear programming Clustering methods Neural networks Task 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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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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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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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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FD-MOBILENET: IMPROVED MOBILENET WITH A FAST DOWNSAMPLING STRATEGY

FD-MOBILENET: IMPROVED MOBILENET WITH A FAST DOWNSAMPLING ST...

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IEEE International Conference on Image Processing

作者： Zheng Qin Zhaoning Zhang Xiaotao Chen Changjian Wang Yuxing Peng Science and Technology on Parallel and Distributed Laboratory National University of Defense Technology Changsha China College of Computer National University of Defense Technology Changsha China

We present Fast-Downsampling MobileNet (FD-MobileNet), an efficient and accurate network for very limited computational budgets (e.g., 10-140 MFLOPs). Our key idea is applying a fast down-sampling strategy to MobileNet framework. In FD-MobileNet, we perform 32× downsampling within 12 layers, only half the layers in the original MobileNet. This design brings three advantages: (i) It remarkably reduces the computational cost. (ii) It increases the information capacity and achieves significant performance improvements. (iii) It is engineering-friendly and provides fast actual inference speed. Experiments on ILSVRC 2012 and PASCAL VOC datasets demonstrate that FD-MobileNet consistently outperforms MobileNet and achieves comparable results with ShuffleNet under different computational budgets, for instance, surpassing Mobile-Net by 5.5% on the ILSVRC 2012 top-1 accuracy and 8.3% on the VOC 2007 mAP under a complexity of 12 MFLOPs. On an ARM-based device, FD-MobileNet achieves 1.11× inference speedup over MobileNet and 1.82× over ShuffleNet under the same complexity.

关键词： Computer architecture Complexity theory Neural networks Training Hardware Computational modeling Standards

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Accuracy evaluation of deep belief networks with fixed-point arithmetic

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Computer Modelling and New Technologies 2014年第6期18卷 7-14页

作者： Jiang, Jingfei Hu, Rongdong Mikel, Lujan Dou, Yong Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology ChangSha Hunan China University of Manchester Manchester United Kingdom

Deep Belief Networks (DBNs) are state-of-art Machine Learning techniques and one of the most important unsupervised learning algorithms. Training DBNs is computationally intensive which naturally leads to investigate FPGA acceleration. Fixed-point arithmetic can be used when implementing DBNs in FPGAs to reduce execution time, but it is not clear the implications for accuracy. Previous studies have focused only on accelerators using some fixed bit-widths. A contribution of this paper is to demonstrate the bit-width effect on various configurations of DBNs in a comprehensive way by experimental evaluation. Explicit performance changing points are found using various bit-widths. The impact of sigmoid function approximation, required part of DBNs, is evaluated. A solution of mixed bit-widths DBN is proposed, fitting the bit-widths of FPGA primitives and gaining similar performance to the software implementation. Our results provide a guide to inform the design choices on bit-widths when implementing DBNs in FPGAs documenting clearly the trade-off in accuracy.

关键词： Field programmable gate arrays (FPGA)

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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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A low-latency fine-grained dynamic shared cache management scheme for chip multi-processor

A low-latency fine-grained dynamic shared cache management s...

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IEEE International Conference on Performance, Computing and Communications (IPCCC)

作者： Jinbo Xu Weixia Xu Zhengbin Pang College of Computer National University of Defense Technology Changsha China Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha China

In order to utilize the shared last-level cache (LLC) in chip multi-processors (CMP) more efficiently, the partitioning of LLC resources among all cores should have the characteristics of low-latency for access, fine granularity for migration and simple hardware complexity for implementation. This paper proposes a dynamic LLC management scheme to achieve these goals. The proposed scheme migrates cache resources among different cores at the granularity of cache blocks, instead of ways. The quantity of victim cache blocks that each victim core can migrate to other target cores are related to an eviction probability, which are calculated according to the performance goal. Then the victim cache blocks for a target core is chosen from the nearest victim core who has non-zero eviction probability by introducing innovate E-Table structure in CMP. The eviction probabilities are updated periodically. With the help of E-Tables, the proposal achieves low-latency accesses by always keeping the required cache blocks near to the target cores. And fine granularity is guaranteed by maintaining an eviction probability for each core. In addition, only little additional hardware changes to traditional cache structure is required. Simulation results suggest significant performance improvements from 6.8% to 22.7% over related works.

关键词： Hardware Resource management Probability distribution Proposals Complexity theory Simulation

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