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BVAGQ-AR for Fragmented Database Replication Management

IEEE ACCESS 2021年 9卷 56168-56177页

作者： Noraziah, A. Fauzi, Ainul Azila Che Ubaidillah, Sharifah Hafizah S. Y. Ahmad Alkazemi, Basem Odili, Julius Beneoluchi Univ Malaysia Pahang Fac Comp Pekan 26600 Malaysia Univ Malaysia Pahang Ctr Software Dev & Integrated Comp Pekan 26600 Malaysia Univ Teknol Mara Kelantan Fac Comp & Math Sci Machang 18500 Malaysia Umm Al Qura Univ Coll Comp & Informat Syst Dept Comp Sci Mecca 21955 Saudi Arabia Anchor Univ Lagos Dept Math Sci Lagos 234101 Nigeria

Large amounts of data have been produced at a rapid rate since the invention of computers. This condition is the key motivation for up-to-date and forthcoming research frontiers. Replication is one of the mechanisms for managing data, since it improves data accessibility and reliability in the distributed database environment. In recent years, the amount of various data grows rapidly with widely available low-cost technology. Although we have been packed with data, we still have lacked of knowledge. Nevertheless, if the impractical data is used in database replication, this will cause waste of data storage and the time taken for a replication process will be delayed. This paper proposes Binary Vote Assignment on Grid Quorum with Association Rule (BVAGQ-AR) algorithm in order to handle fragmented database synchronous replication. BVAGQ-AR algorithm is capable for partitioning the database into disjoint fragments. Fragmentation in distributed database is very useful in terms of usage, reliability and efficiency. Managing fragmented database replication becomes a concern for the administrator because the distributed database is disseminated into split replica partitions. The result from the experiment shows that handling fragmented database synchronous replication through proposed BVAGQ-AR algorithm able to preserve data consistency in distributed environment.

关键词： Distributed databases Databases Data mining Correlation partitioning algorithms Servers Reliability Replication algorithm fragmentation data mining computational intelligence distributed databases data grid

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A GGIW-PHD Filter for Multiple Non-Ellipsoidal Extended Targets Tracking With Varying Number of Sub-Objects

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IEEE ACCESS 2021年 9卷 64719-64731页

作者： Gong, Yang Cui, Chen Wu, Biao Natl Univ Def Technol Inst Elect Countermeasure Hefei 230037 Peoples R China Huayin Ordnance Test Ctr Weinan 714000 Peoples R China

When the extension state of the non-ellipsoidal extended target (NET) changes, the performance of traditional multiple target tracking algorithms based on the constant number of sub-objects will decrease. To solve this problem, this paper proposes a gamma Gaussian inverse Wishart probability hypothesis density filter for non-ellipsoidal extended targets with varying number of sub-objects, called VN-NET-GGIW-PHD filter. In the proposed filter, each NET is considered as a combination of multiple spatially close sub-objects, and the label management is introduced to realize the association between the NET and corresponding sub-objects. Then, by target spawning and combination, the number of sub-objects for approximating the extension state of each NET can be adjusted automatically. Furthermore, to obtain the partition of the measurement set, an approach based on the clustering by fast search and find of density peaks (CFSFDP) algorithm and expectation maximization (EM) algorithm is proposed. Simulation results show that the proposed filter can adaptively adjust the number of sub-objects and has better performance when the extension state of the NET changes.

关键词： Target tracking Information filters Filtering algorithms Sensors partitioning algorithms Kinematics Noise measurement Multi-target tracking non-ellipsoidal extended target target spawning target combination measurement set partition

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An Adaptive Resource Allocation Strategy for Objective Space Partition-Based Multiobjective Optimization

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2021年第3期51卷 1507-1522页

作者： Chen, Huangke Wu, Guohua Pedrycz, Witold Suganthan, Ponnuthurai Nagaratnam Xing, Lining Zhu, Xiaomin Natl Univ Def Technol Coll Syst Engn Changsha 410073 Peoples R China Univ Alberta Dept Elect & Comp Engn Edmonton AB T6G 2V4 Canada Cent South Univ Sch Traff & Transportat Engn Changsha 410075 Peoples R China King Abdulaziz Univ Dept Elect & Comp Engn Fac Engn Jeddah 21589 Saudi Arabia Polish Acad Sci Syst Res Inst PL-01447 Warsaw Poland Nanyang Technol Univ Sch Elect Elect Engn Singapore 639798 Singapore

In evolutionary computation, balancing the diversity and convergence of the population for multiobjective evolutionary algorithms (MOEAs) is one of the most challenging topics. Decomposition-based MOEAs are efficient for population diversity, especially when the branch partitions the objective space of multiobjective optimization problem (MOP) into a series of subspaces, and each subspace retains a set of solutions. However, a persisting challenge is how to strengthen the population convergence while maintaining diversity for decomposition-based MOEAs. To address this issue, we first define a novel metric to measure the contributions of subspaces to the population convergence. Then, we develop an adaptive strategy that allocates computational resources to each subspace according to their contributions to the population. Based on the above two strategies, we design an objective space partition-based adaptive MOEA, called OPE-MOEA, to improve population convergence, while maintaining population diversity. Finally, 41 widely used MOP benchmarks are used to compare the performance of the proposed OPE-MOEA with other five representative algorithms. For the 41 MOP benchmarks, the OPE-MOEA significantly outperforms the five algorithms on 28 MOP benchmarks in terms of the metric hypervolume.

关键词： Sociology Statistics Optimization Convergence Measurement partitioning algorithms Sorting Adaptive strategy contribution metric evolutionary algorithm multiobjective objective space partition

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Information Sources Estimation in Time-Varying Networks

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IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY 2021年 16卷 2621-2636页

作者： Chai, Yun Wang, Youguo Zhu, Liang Nanjing Univ Posts & Telecommun Coll Telecommun & Informat Engn Nanjing 210003 Peoples R China

Identifying information sources plays a significant role in network science and engineering. However, existing source identification approaches generally focus on static networks without considering the temporal features of networks. To this end, we comprehensively study the problem of identifying single and multiple information sources in time-varying networks. Specifically, we first represent the time-varying networks by time aggregated graph (TAG), and employ a microcosmic susceptible-infected-recovered (SIR) model to characterize the diffusion dynamics of each node. Second, in the case of single-source, we exploit a TAG-based reverse infection (RI-TAG) algorithm to specify a set of suspect nodes, which not only reduces the scope of seeking the source but also ensures the feasibility of path calculation. Then, a novel computationally efficient algorithm is proposed to estimate the information source and diffusion time simultaneously. Subsequently, in the case of multi-source, we design a multi-source estimation algorithm, which divides the set of infected nodes into various partitions, and then runs our single-source estimation algorithm in each partition. Moreover, we present an effective algorithm to estimate the number of sources. Finally, experimental results on various synthetic and empirical time-varying networks demonstrate the effectiveness of the proposed algorithms.

关键词： partitioning algorithms Heuristic algorithms Maximum likelihood estimation Knowledge engineering Diffusion processes Computational modeling Time series analysis Information diffusion single-source estimation multi-source estimation time-varying networks

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Fast Texture Coding Based on Spatial, Temporal and Inter-View Correlations for 3D Video Coding

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IEEE ACCESS 2021年 9卷 100081-100095页

作者： Lin, Jie-Ru Chen, Mei-Juan Ciou, Yi-Sheng Yeh, Chia-Hung Lin, Min-Hui Kau, Lih-Jen Chang, Chuan-Yu Natl Dong Hwa Univ Dept Elect Engn Hualien 974301 Taiwan Natl Taiwan Normal Univ Dept Elect Engn Taipei 106308 Taiwan Natl Sun Yat Sen Univ Dept Elect Engn Kaohsiung 804201 Taiwan Natl Taipei Univ Technol Dept Elect Engn Taipei 106344 Taiwan Natl Yunlin Univ Sci & Technol Dept Comp Sci & Informat Engn Touliu 640301 Yunlin Taiwan

3D-HEVC (The 3D Extension of High Efficiency Video Coding), the latest international standard for 3D video coding, supports multiview plus depth 3D video format to enrich multimedia applications. For the texture coding, 3D-HEVC utilizes not only the information of temporal and spatial domains but also that of inter-view domain. However, the time consumption and complexity of 3D-HEVC also increase significantly. In this paper, fast texture coding algorithm for 3D-HEVC is proposed. We individually calculate the Pearson correlation coefficients by the rate-distortion costs (RD-costs) of coding tree units (CTUs) in the temporal, spatial and inter-view domains to analyze the correlations for independent view and dependent view. The proposed coding algorithm is based on the coding information of CTUs with higher correlations. The fast algorithm predicts and dynamically adjusts the depth range of the coding unit (CU). The prediction unit (PU) mode decision is proposed according to the complexity and partition direction of the best PU modes obtained from the highly correlated CTUs. The search range is adaptively adjusted for motion estimation. In addition, the RD-cost threshold is estimated to early terminate the CU split. Experimental results show that the proposed fast texture coding algorithm reduces the texture coding time significantly and outperforms numerous previous works.

关键词： Encoding Three-dimensional displays Correlation Video coding Correlation coefficient Prediction algorithms partitioning algorithms 3D-HEVC texture coding CU early termination fast PU decision search range adjustment

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ORP: An Online Rule Placement Scheme to Optimize the Traffic Overhead for Data Center Networks

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IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 2021年第3期8卷 2183-2197页

作者： Jiang, Wei Jiang, Wanchun Wang, Jianxin Gao, Jianliang Xiong, Neal Cent South Univ Sch Comp Sci & Engn Changsha 410083 Peoples R China Northeastern State Univ Dept Math & Comp Sci Tahlequah OK 74464 USA

In cloud data centers, the requirement for fine-grained policy leads to a large number of server rules. However, the servers cannot efficiently handle all of these rules due to limitations of the central processing unit (CPU) and memory resources. Thus, a rule partition and placement algorithms have been proposed to divide the original ruleset into several rule subsets and migrate some rule subsets to other devices. Correspondingly, the traffic, which is meant to be blocked by the original server's migratory rules, will also be redirected to the new device. The rule partition and placement algorithms should minimize this extra traffic overhead. The state-of-the-art algorithm, vCRIB, is offline, as its solution is calculated based on a static traffic matrix. In reality, when facing dynamic traffic, the vCRIB solution may actually be suboptimal in terms of traffic overhead. To reduce the traffic overhead, we propose the online rule replacement (ORP) algorithm. Specifically, ORP utilizes the difference between the old and new traffic matrices to discover the rule subsets matching the notably changed traffic overhead or blocked traffic. These rule subsets are then preferentially adjusted to their proper placement. In this manner, ORP can promptly make rule subset adjustments according to the newly updated traffic matrix. Additionally, ORP effectively avoids the invalid migrations existing in vCRIB. The simulation results confirm that ORP is capable of reducing traffic overhead by 26.67%-56.9% compared to vCRIB.

关键词： Servers partitioning algorithms IP networks Data centers Access control Heuristic algorithms Protocols Cloud data centers Fine-grained policy Traffic overhead Online rule placement

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A Hierarchical Event Detection Method Based on Spectral Theory of Multidimensional Matrix for Power System

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2021年第4期51卷 2173-2186页

作者： Ma, Dazhong Hu, Xuguang Zhang, Huaguang Sun, Qiuye Xie, Xiangpeng Northeastern Univ Coll Informat Sci & Engn Shenyang 110004 Peoples R China Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang 110004 Peoples R China Nanjing Univ Posts & Telecommun Inst Adv Technol Nanjing 210003 Peoples R China Linyi Univ Sch Automat & Elect Engn Linyi 276013 Shandong Peoples R China

This paper investigates the situation awareness issue of power system with massive measured data. To address this issue, first, a graph-theory-based network partitioning algorithm is proposed to realize decentralized detection in a faster response speed, while using power flow characteristics highlights the independency of different groups. Further, a hierarchical event detection method is proposed to judge voltage change and locate event position according to spectral distribution change of established multidimensional matrix. With the proposed method, the system situation can be assessed and the knowledge of the system model is not required. In addition, the accurate result of weak event happened in system could also be obtained. The simulation results are presented to illustrate the effectiveness of the proposed detection method.

关键词： Power system stability partitioning algorithms Event detection Network topology Clustering algorithms Stability analysis Event detection graph partition hierarchical detection power system random matrix theory (RMT)

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Graph-Induced Contrastive Learning for Intra-Camera Supervised Person Re-Identification

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IEEE ACCESS 2021年 9卷 20850-20860页

作者： Wang, Menglin Lai, Baisheng Huang, Jianqiang Gong, Xiaojin Hua, Xian-Sheng Zhejiang Univ Coll Informat Sci & Elect Engn Hangzhou 310027 Peoples R China Alibaba Grp Hangzhou 311121 Peoples R China

Intra-camera supervision (ICS) for person re-identification (Re-ID) assumes that identity labels are independently annotated within each camera view and no inter-camera identity association is labeled. It is a new setting proposed recently to reduce the burden of annotation while expect to maintain desirable Re-ID performance. However, the lack of inter-camera labels makes the ICS Re-ID problem much more challenging than the fully supervised counterpart. By investigating the characteristics of ICS, this article proposes a graph-induced contrastive learning (GCL) approach to address this issue. More specifically, we first formulate the cross-camera ID association task as a graph partitioning problem subjected to ICS-specific constraints and design a greedy agglomeration algorithm to solve it. Then, we propose a graph-induced contrastive loss that unifies both intra- and inter-camera learning into a contrastive learning framework to learn a Re-ID model. The cross-camera ID association step and the Re-ID model contrastive learning step are alternatively iterated, by which we progressively obtain a highly discriminative Re-ID model. Extensive experiments on three large-scale datasets show that our approach outperforms all previous ICS works. Especially, it gains 15.7% Rank-1 and 14.3% mAP improvements on the challenging MSMT17 dataset. Moreover, our approach performs even comparable to state-of-the-art fully supervised methods on all of the three datasets.

关键词： Cameras Integrated circuits Task analysis Reliability partitioning algorithms Feature extraction Annotations Intra-camera supervision person re-identification contrastive learning graph partition

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On the Convergence Behavior of Partitioned-Block Frequency-Domain Adaptive Filters

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2021年 69卷 4906-4920页

作者： Yang, Feiran Enzner, Gerald Yang, Jun Chinese Acad Sci Inst Acoust State Key Lab Acoust Beijing 100190 Peoples R China Carl von Ossietzky Univ Oldenburg Dept Med Phys & Acoust D-26111 Oldenburg Germany Chinese Acad Sci Inst Acoust Key & Aboratory Noise & Vibrat Res Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China

Partitioned-block frequency-domain adaptive filter (PBFDAF) algorithms have become very popular, in particular, for acoustic signal processing. However, the stochastic behavior of PBFDAFs has not been extensively examined. This paper presents a comprehensive statistical analysis for a family of the overlap-save PBFDAF algorithms with 50% overlap, including the transient and steady-state performance. The frequency-domain equations of the PBFDAFs are transformed into the time-domain counterparts, which allows us to carry out the analysis completely in the time domain. By means of the independence assumption and vectorization operation, theoretical models for the mean and mean-square behavior of the PBFDAF algorithms are established without restricting the distribution of the inputs to being Gaussian. Specifically, the mean weight behavior and corresponding steady-state solution are provided. Closed-form expressions for the mean-square deviation (MSD) and mean-square error (MSE) are derived. The upper bound on the step size for the mean and mean-square stability of the PBFDAFs is specified. The theoretical model presents new insights into the convergence properties of the PBFDAFs with a sufficient number of coefficients. It was revealed that both the constrained and unconstrained PBFDAFs converge to the Wiener solution. However, the mean weight vector of the unconstrained PBFDAF algorithms cannot converge to the true solution for any inputs, while that of the constrained version can. The presented theory explains why the steady-state MSD of the unconstrained PBFDAF algorithm is larger than that of the constrained version but their minimum MSE is the same. Monte Carlo simulations provide very good support for our theory.

关键词： Signal processing algorithms Steady-state Convergence Time-domain analysis partitioning algorithms Transient analysis Approximation algorithms Adaptive filtering frequency domain PBFDAF transient behavior steady-state solution

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Efficient Data Loader for Fast Sampling-Based GNN Training on Large Graphs

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IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS 2021年第10期32卷 2541-2556页

作者： Bai, Youhui Li, Cheng Lin, Zhiqi Wu, Yufei Miao, Youshan Liu, Yunxin Xu, Yinlong Univ Sci & Technol China Sch Comp Sci & Technol Hefei 230026 Anhui Peoples R China Microsoft Res Beijing 100080 Peoples R China Anhui Prov Key Lab High Performance Comp Hefei 230026 Anhui Peoples R China

Emerging graph neural networks (GNNs) have extended the successes of deep learning techniques against datasets like images and texts to more complex graph-structured data. By leveraging GPU accelerators, existing frameworks combine mini-batch and sampling for effective and efficient model training on large graphs. However, this setup faces a scalability issue since loading rich vertex features from CPU to GPU through a limited bandwidth link usually dominates the training cycle. In this article, we propose PaGraph, a novel, efficient data loader that supports general and efficient sampling-based GNN training on single-server with multi-GPU. PaGraph significantly reduces the data loading time by exploiting available GPU resources to keep frequently-accessed graph data with a cache. It also embodies a lightweight yet effective caching policy that takes into account graph structural information and data access patterns of sampling-based GNN training simultaneously. Furthermore, to scale out on multiple GPUs, PaGraph develops a fast GNN-computation-aware partition algorithm to avoid cross-partition access during data-parallel training and achieves better cache efficiency. Finally, it overlaps data loading and GNN computation for further hiding loading costs. Evaluations on two representative GNN models, GCN and GraphSAGE, using two sampling methods, Neighbor and Layer-wise, show that PaGraph could eliminate the data loading time from the GNN training pipeline, and achieve up to 4.8x performance speedup over the state-of-the-art baselines. Together with preprocessing optimization, PaGraph further delivers up to 16.0x end-to-end speedup.

关键词： Training Graphics processing units Loading Computational modeling Load modeling partitioning algorithms Deep learning Graph neural network cache large graph graph partition pipeline multi-GPU

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