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International Conference on parallel and distributed Systems (ICPADS)

作者： Saiqin Long Zhetao Li Zihao Liu Qingyong Deng Sangyoon Oh Nobuyoshi Komuro Key Laboratory of Hunan Province for Internet of Things and Information Security Hunan International Scientific and Technological Cooperation Base of Intelligent network School of Computer Science Xiangtan University Xiangtan China Science and Technology on Parallel and Distributed Processing Laboratory (PDL) National University of Defense Technology Changsha China Ajou University Suwon South Korea Graduate School of Advanced Integration Science Chiba University Chiba Japan

ISBN: (数字)9781728190747

ISBN: (纸本)9781728183824

Deduplication is a data redundancy elimination technique, designed to save system storage resources by reducing redundant data in cloud storage systems. With the development of cloud computing technology, deduplication has been increasingly applied to cloud data centers. However, traditional technologies face great challenges in big data deduplication to properly weigh the two conflicting goals of deduplication throughput and high duplicate elimination ratio. This paper proposes a similarity clustering-based deduplication strategy (named SCDS), which aims to delete more duplicate data without significantly increasing system overhead. The main idea of SCDS is to narrow the query range of fingerprint index by data partitioning and similarity clustering algorithms. In the data preprocessing stage, SCDS uses data partitioning algorithm to classify similar data together. In the data deletion stage, the similarity clustering algorithm is used to divide the similar data fingerprint superblock into the same cluster. Repetitive fingerprints are detected in the same cluster to speed up the retrieval of duplicate fingerprints. Experiments show that the deduplication ratio of SCDS is better than some existing similarity deduplication algorithms, but the overhead is only slightly higher than some high throughput but low deduplication ratio methods.

关键词： Cloud computing Clustering algorithms Estimation Fingerprint recognition Throughput Partitioning algorithms Classification algorithms

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Weakly hard real-time schedule algorithm based on cutting down

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Ruan Jian Xue Bao/Journal of Software 2008年第7期19卷 1837-1846页

作者： Wu, Tong Jin, Shi-Yao Liu, Hua-Feng Chen, Ji-Ming National Laboratory of Parallel and Distributed Processing National University of Defense Technology Changsha 410073 China National Laboratory of Industrial Control Technology Zhejiang University Hangzhou 310027 China

Nowadays, weakly hard real-time is attracted in the research of real-time system. Existing weakly hard real-time scheduling algorithms cannot guarantee the meeting ratio of executing sequence of which the length is larger than fixed window-size. Therefore, this paper, based on the (m, p) constraint, proposes an algorithm which is named as CDBS (cut-down based scheduling). Since the discrimination of the satisfiability of (m, p) constraint needs to go over the whole executing sequence of the task, it's very difficult and infeasible. For this reason, this paper brings an efficient algorithm of cutting down the sequence, proves the correctness of the algorithm. We use proper data structures so that the complexity of judgment is not relevant to the length of sequence. Experiments show the efficiency. Furthermore, we compare CDBS with other classical algorithms, such as EDF, DBP, DWCS, and the results show its competence.

关键词： Real time systems

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Collaborative deep learning across multiple data centers

arXiv

引用

arXiv 2018年

作者： Xu, Kele Mi, Haibo Feng, Dawei Wang, Huaimin Chen, Chuan Zheng, Zibin Lan, Xu National Key Laboratory of Parallel and Distributed Processing Changsha China College of Computer National University of Defense Technology Changsha China School of Data and Computer Science Sun Yat-Sen University Guangzhou China Queen Mary University of London London United Kingdom

Valuable training data is often owned by independent organizations and located in multiple data centers. Most deep learning approaches require to centralize the multi-datacenter data for performance purpose. In practice, however, it is often infeasible to transfer all data to a centralized data center due to not only bandwidth limitation but also the constraints of privacy regulations. Model averaging is a conventional choice for data parallelized training, but its ineffectiveness is claimed by previous studies as deep neural networks are often non-convex. In this paper, we argue that model averaging can be effective in the decentralized environment by using two strategies, namely, the cyclical learning rate and the increased number of epochs for local model training. With the two strategies, we show that model averaging can provide competitive performance in the decentralized mode compared to the data-centralized one. In a practical environment with multiple data centers, we conduct extensive experiments using state-of-the-art deep network architectures on different types of data. Results demonstrate the effectiveness and robustness of the proposed method. Copyright © 2018, The Authors. All rights reserved.

关键词： Network architecture

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Single image defogging with single and multiple hybrid scattering model

Single image defogging with single and multiple hybrid scatt...

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International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)

作者： Weijiang Feng Naiyang Guan Xiang Zhang Xuhui Huang Zhigang Luo Science and Technology on Parallel and Distributed Processing Laboratory College of Computer National University of Defense Technology Changsha Hunan P.R. China Department of Computer Science and Technology College of Computer National University of Defense Technology Changsha Hunan P.R. China

Image defogging (IDF) removes influences of fogs from an image to improve its quality. Since defogged images can significantly boost the performance of subsequent processing, IDF has attracted many attentions from the computer vision community. However, existing IDF algorithms are built on the assumption that light is scattered once by a grain. Since such assumption is violated if images are contaminated by dense haze or heavy fog, traditional IDF algorithms often fail in this situation. In this paper, we propose a hybrid image defogging (HIDF) algorithm to overcome this deficiency. In particular, HIDF applies the single scattering physics model (SSPM) to pixels dominated by single scattering of light, and applies the multiple scattering physics model (MSPM) to remaining pixels. To distinguish two types of pixels, HIDF utilizes the optical thickness of corresponding pixels. If optical thickness is smaller than a threshold that determines whether the single scattering or the multiple scattering dominates, HIDF applies the SSPM, and HIDF applies the MSPM otherwise. Experimental results on several popular foggy images demonstrate that HIDF competes with the state-of-the-art algorithms, and show the promise of HIDF for defogging heavily foggy images.

关键词： Meteorology Optical imaging Optical scattering Atmospheric modeling Image color analysis

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RLLL: Accurate Relative Localization of RFID Tags with Low Latency

RLLL: Accurate Relative Localization of RFID Tags with Low L...

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International Workshop on Quality of Service

作者： Xuan Liu Quan Yang Shigeng Zhang Bin Xiao The Science and Technology on Parallel and Distributed Processing Laboratory (PDL) P. R. China College of Computer Science and Electronic Engineering Hunan University P. R. China School of Computer Science and Engineering Central South University P. R. China The Hong Kong Polytechnic University Hong Kong

ISBN: (数字)9781728168876

ISBN: (纸本)9781728168883

Radio frequency identification (RFID) has been widely used in many smart applications. In many scenarios, it is essential to know the ordering of a set of RFID tags. For example, to quickly detect misplaced books in smart libraries, we need to know the relative ordering of the tags attached to the books. Although several relative RFID localization algorithms have been proposed, they usually suffer from large localization latency and cannot support applications that require real-time detection of tag (product) positions like automatic manufacturing on an assembly line. Moreover, existing approaches face significant degradation in ordering accuracy when the tags are close to each other. In this paper, we propose RLLL, an accurate Relative Localization algorithm for RFID tags with Low Latency. RLLL reduces localization latency by proposing a novel geometry-based approach to identifying the V-zone in the phase reading sequence of each tag. Moreover, RLLL uses only the data in the V-zone to calculate relative positions of tags and thus avoids the negative effects of low-quality data collected when the tag is far from the antenna. Experimental results with commercial RFID devices show that RLLL achieves an ordering accuracy of higher than 0.986 with latency less than 0.8 seconds even when the tags are spaced only 7 mm from adjacent tags, in which case the state-of-the-art solutions only achieve ordering accuracy of lower than 0.8 with localization latency larger than 3 seconds.

关键词： RFID tags Manufacturing Antenna arrays Quality of service Libraries

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PPSS: A Privacy-Preserving Supervision Scheme Based on Blockchain

PPSS: A Privacy-Preserving Supervision Scheme Based on Block...

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IEEE International Conference on Big Data and Cloud Computing (BdCloud)

作者： Ming Chen Peichang Shi Xiang Fu Feng Jiang Fei Gao Penghui Ma Jinzhu Kong National Key Laboratory of Parallel and Distributed Processing College of Computer Science National University of Defense Technology Changsha China Key Laboratory of Software Engineering for Complex Systems College of Computer Science National University of Defense Technology Changsha China Kylin Software Co.LTD Changsha China Zhejiang Lab Hangzhou China

In JointCloud Computing, multi-party participation introduces complexity and uncertainty. For all participants in JointCloud Computing, both continuous supervision and necessary privacy protection are required. Traditional supervision methods usually adopt the centralized information interaction mode, which has such defects as collusion of interests, single point of failure, privacy disclosure, etc. Building a decentralized supervision mechanism has become a new research direction. In this paper, we propose PPSS, a privacy-preserving supervision scheme based on blockchain, which decentralizes the supervision of the participants in JointCloud Computing, and combines the “double encryptions” and “threshold encryption” technologies to provide privacy protection. While making full use of the decentralization of the blockchain, a committee is established to carry out the analysis and decision-making tasks in terms of supervision and privacy protection. Experimental results indicate that PPSS can balance performance and security by reasonably configuring the committee.

关键词： Privacy Uncertainty Decision making Buildings Reliability engineering Blockchains Complexity theory

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A Retina-LGN-V1 Structure-like Spiking Neuron Network for Image Feature Extraction 21

A Retina-LGN-V1 Structure-like Spiking Neuron Network for Im...

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Proceedings of the 2021 5th International Conference on Video and Image processing

作者： Sheng Ji Qijun Gu Ye Yuan Peng Zhao Tao Fang Hong Huo Xin Niu Department of Automation_School of Electronic Information and Electrical Engineering_Key Laboratory of System Control and Information Processing Ministry of Education Shanghai Jiao Tong University China College of Computer Science and Technology on Parallel and Distributed Laboratory National University of Defense Technology China

ISBN: (纸本)9781450385893

SNNs have achieved great attention in recent years as they contain neurons more like those in the brain and use spikes to encode and transmit information efficiently among neurons with lower energy consumption. A Retina-LGN-V1 structure-like spiking neuron network (RLVSL-SNN) is proposed in this paper. It is inspired by the structure of mammalian primary visual pathway, and simulates different biological structures of Retina, LGN and V1. Noise reduction circuit and light adaptation circuit are also simulated for enhancing the robustness of its extracted features. RLVSL-SNN is a bio-plausible neuron network as it has firing rates of neurons in each layer that are similar to those of biological experiments. Besides, a full-connected SNN (FC SNN) is implemented following RLVSL-SNN for classification to evaluate the extracted features. The additive spiking timing dependent plasticity (STDP) learning rules and the ANN-to-SNN conversion method are utilized to train RLVSL-SNN and FC SNN, respectively. The experiments on MNIST dataset have verified that RLVSL-SNN is comparable to AlexNet for classification by features from spikes.

关键词： Additive Spiking Timing Dependent Plasticity Learning Primary Visual Pathway Image Classification Spiking Neural Network

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Underwater acoustic classification using masked modeling-based swin transformer

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The Journal of the Acoustical Society of America 2022年第4_SUPPLEMENT期152卷 A296-A296页

作者： Kang you Kele Xu Ming Feng Boqing Zhu Tonguing Univ. Shanghai China National Key Lab. of Parallel and Distributed Processing (PDL) Changsha China 107 Yanwachi Changsha 410073 China kelele.xu@*** National Key Lab. of Parallel and Distributed Processing (PDL) Changsha China Changsha China

Underwater acoustic classification is a challenging task due to complex background noise and complicated sound propagation patterns. How to represent the signals is important for the classification task. In this paper, we propose a novel representation learning method for the underwater acoustic signals, leveraging the mask modeling-based self-supervised learning paradigm. Specifically, we first explore modifying the Swin Transformer architecture to learn general representation for the audio signals, accompanied with random masking on the log-mel spectrogram. The main goal of the pretext task is to predict the masked parts of Log-mel spectrogram and the gamma-stone spectrogram, so that the model can not only learn the local and global features but also learn complementary information. For downstream task, we utilize the labelled datasets to fine-tune the pre-trained model. On DeepShip datasets which consist of 47 hand 4 minof ship sounds in four categories, our model achieves state-of-the-art performance compared with competitive approaches. Our method obtains a classification accuracy of 78.03%, which is better than the separable convolution autoencoder (SCAE) and using the constant-Q transform spectrogram. This work demonstrates the potential of the masked modeling based self-supervised learning for understanding and interpretation of underwater acoustic signals.

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Scheduling algorithm for long duration transaction based on cost of compensation

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Ruan Jian Xue Bao/Journal of Software 2009年第3期20卷 744-753页

作者： Zhu, Rui Guo, Chang-Guo Wang, Huai-Min School of Computer National University of Defense Technology Changsha 410073 China National Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha 410073 China

Transaction of service composition has long-lived feature which a global-transaction is divided into several distributed sub-transactions. Atomicity property is preserved by using compensating transactions, which semantically undo the effects of the completed sub-transactions, in case of global-transaction abort. However, the cost of compensation may be expensive and methods may be complex. To overcome this limitation, a novel scheduling algorithm named STCD (SubTransaction committing delay) is presented based on analysis of compensation. Different from traditional methods, sub-transactions determine the time of committing according to both the cost of compensation and the state of execution dynamically. The correctness of proposed algorithm is proved. Simulations show that STCD algorithm can confine the compensation sphere and reduce the cost of compensation. © by Institute of Software, the Chinese Academy of Sciences. All rights reserved.

关键词： Costs

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Multi-task Pre-training Language Model for Semantic Network Completion

arXiv

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arXiv 2022年

作者： Li, Da Yang, Sen Xu, Kele Yi, Ming He, Yukai Wang, Huaimin Tencent Beijing100084 China Beijing Institute of Microbiology and Epidemiology Beijing100071 China State Key Laboratory of Pathogen and Biosecurity Beijing100071 China National University of Defense Technology Changsha410073 China National Key Lab of Parallel and Distributed Processing Changsha410073 China

Semantic networks, such as the knowledge graph, can represent the knowledge leveraging the graph structure. Although the knowledge graph shows promising values in natural language processing, it suffers from incompleteness. This paper focuses on knowledge graph completion by predicting linkage between entities, which is a fundamental yet critical task. Semantic matching is a potential solution for link prediction as it can deal with unseen entities, while the translational distance based methods struggle with the unseen entities. However, to achieve competitive performance as translational distance based methods, semantic matching based methods require large-scale datasets for the training purpose, which are typically unavailable in practical settings. Therefore, we employ the language model and introduce a novel knowledge graph architecture named LP-BERT, which contains two main stages: multi-task pre-training and knowledge graph fine-tuning. In the pre-training phase, three tasks are taken to drive the model to learn the relationship information from triples by predicting either entities or relations. While in the fine-tuning phase, inspired by contrastive learning, we design a triple-style negative sampling in a batch, which greatly increases the proportion of negative sampling while keeping the training time almost unchanged. Furthermore, we propose a new data augmentation method utilizing the inverse relationship of triples to improve the performance and robustness of the model. To demonstrate the effectiveness of our proposed framework, we conduct extensive experiments on three widely-used knowledge graph datasets, WN18RR, FB15k-237, and UMLS. The experimental results demonstrate the superiority of our methods, and our approach achieves state-of-the-art results on the WN18RR and FB15k-237 datasets. Significantly, the Hits@10 indicator is improved by 5% from the previous state-of-the-art result on the WN18RR dataset while reaching 100% on the UMLS dataset. Copy

关键词： Inverse problems

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