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Adaptive Resonance Theory-Based Topological Clustering With a Divisive Hierarchical Structure Capable of Continual Learning

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IEEE ACCESS 2022年 10卷 68042-68056页

作者： Masuyama, Naoki Amako, Narito Yamada, Yuna Nojima, Yusuke Ishibuchi, Hisao Osaka Metropolitan Univ Grad Sch Informat Sakai Osaka 5998531 Japan Osaka Prefecture Univ Grad Sch Engn Sakai Osaka 5998531 Japan Southern Univ Sci & Technol Dept Comp Sci & Engn Guangdong Prov Key Lab Brain Inspired Intelligent Shenzhen 518055 Peoples R China

Adaptive Resonance Theory (ART) is considered as an effective approach for realizing continual learning thanks to its ability to handle the plasticity-stability dilemma. In general, however, the clustering performance of ART-based algorithms strongly depends on the specification of a similarity threshold, i.e., a vigilance parameter, which is data-dependent and specified by hand. This paper proposes an ART-based topological clustering algorithm with a mechanism that automatically estimates a similarity threshold from the distribution of data points. In addition, for improving information extraction performance, a divisive hierarchical clustering algorithm capable of continual learning is proposed by introducing a hierarchical structure to the proposed algorithm. Experimental results demonstrate that the proposed algorithm has high clustering performance comparable with recently-proposed state-of-the-art hierarchical clustering algorithms.

关键词： Clustering algorithms Kernel Subspace constraints partitioning algorithms Data mining Big Data Training data Adaptive resonance theory topological clustering hierarchical clustering continual learning

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Approximating Region Boundaries Based on Qualitative and Quantitative Information

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IEEE INTELLIGENT SYSTEMS 2022年第6期37卷 38-47页

作者： Long, Zhiguo Sioutis, Michael Li, Qingqian Meng, Hua Li, Heng-Chao Southwest Jiaotong Univ Chengdu 611756 Peoples R China Univ Bamberg D-96047 Bamberg Germany Southwest Jiaotong Univ Sch Comp & Artificial Intelligence Chengdu Peoples R China Southwest Jiaotong Univ Sch Math Chengdu Peoples R China Southwest Jiaotong Univ Sch Informat Sci & Technol Chengdu Peoples R China

Approximating regions is a topic that can have important applications in artificial intelligence whenever uncertain, incomplete, or inconsistent/contradictory spatial information is involved. This article devises a new method to generate region approximations based on rough qualitative direction and distance information. The main idea is first to give a suitability score to smaller regions, cells, that are obtained by partitioning the area of interest, and then to identify candidates to form an approximation by evaluating score contribution ratios under a certain threshold. This article designs a novel mechanism that compares the actual information of cells with the provided rough information in order to calculate suitability scores, and proposes to exploit a regressor model that can predict a threshold given certain suitability scores. Experimental results show that, given a good threshold, this new method can approximate target regions effectively, and that good thresholds can be reliably obtained through a trained regressor.

关键词： Intelligent systems Social networking (online) Shape Roads Natural languages Probabilistic logic partitioning algorithms

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COSINE: Compressive Network Embedding on Large-Scale Information Networks

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2022年第8期34卷 3655-3668页

作者： Zhang, Zhengyan Yang, Cheng Liu, Zhiyuan Sun, Maosong Fang, Zhichong Zhang, Bo Lin, Leyu Tsinghua Univ Dept Comp Sci & Technol Beijing 100084 Peoples R China Beijing Univ Posts & Telecommun Sch Comp Sci Beijing 100876 Peoples R China Cornell Univ Dept Comp Sci Ithaca NY 14850 USA Tencent Search Prod Ctr WeChat Search Applicat Dept Beijing 100080 Peoples R China

There is recently a surge in approaches that learn low-dimensional embeddings of nodes in networks. However, for large-scale real-world networks, it's inefficient for existing approaches to store amounts of parameters in memory and update them edge by edge. With the knowledge that nodes having similar neighborhoods will be close to each other in the embedding space, we propose COSINE (COmpresSIve Network Embedding) algorithm, which reduces the memory footprint and accelerates the training process by parameter sharing among similar nodes. COSINE applies graph partitioning algorithms to networks and builds parameter sharing dependency of nodes based on the results of partitioning. In this way, COSINE injects prior knowledge about high-order structural information into models, which makes network embedding more efficient and effective. COSINE can be applied to any embedding lookup method and learn high-quality embeddings with limited memory and less training time. We conduct experiments on multi-label classification and link prediction, where baselines and our model have the same memory usage. Experimental results show that COSINE improves baselines by up to 23 percent on classification and 25 percent on link prediction. Moreover, the training time of all representation learning methods using COSINE decreases by 30 to 70 percent.

关键词： Training partitioning algorithms Predictive models Sparse matrices Knowledge engineering Computational modeling Memory management Node classification link prediction large-scale real-world network network embedding model compression

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A Quadratic Convergent Iteration-Variable-Correction-Based Method for Optimal Power Flow of Transmission-Distribution-Coupled Systems

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IEEE SYSTEMS JOURNAL 2022年第2期16卷 3360-3371页

作者： Tang, Kunjie Dong, Shufeng Song, Yonghua Zhejiang Univ Coll Elect Engn Hangzhou 310027 Peoples R China Univ Macau State Key Lab Internet Things Smart City Macau 999078 Peoples R China Univ Macau Dept Elect & Comp Engn Macau 999078 Peoples R China

An iteration-variable-correction (IVC) based method is proposed for solving optimal power flow (OPF) of transmission-distribution-coupled (TDC) systems. First, a TDC system can be divided into a single coordinator and multiple agents. Then, the basic formulation, subproblem construction, and selection of iteration variables are given based on the conventional heterogeneous decomposition algorithm (HGD). Considering the limitations of the conventional HGD, the IVC is proposed. According to the general theory of the IVC, when it is applied to solve TDC OPF, the voltage magnitude of boundary buses and the multipliers corresponding to boundary power injections will be corrected every four iterations with first-order and second-order differential operations on historical results in the previous four iterations. The quadratic convergence of the IVC is proved under several weak assumptions. The numerical experiments demonstrate that the proposed IVC has the same accuracy as the method based on the centralized OPF model. Also, better convergence and efficiency can be achieved under the IVC compared with some conventional decomposition methods, including alternating direction method of multipliers, auxiliary problem principle method, analytical target cascading method, and the HGD.

关键词： Convergence partitioning algorithms Optimization Numerical models Mathematical model Master-slave Linear programming Iteration-variable-correction (IVC) based method optimal power flow (OPF) quadratic convergence transmission-distribution-coupled (TDC) systems

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Towards Enhancing the Performance of Parallel FP-Growth on Spark

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IEEE ACCESS 2022年 10卷 286-296页

作者： Essam, Amr Abdel-Fattah, Manal A. Abdelhamid, Laila Helwan Univ Fac Comp & Artificial Intelligence Dept Informat Syst Cairo 12317 Egypt

Frequent itemset mining (FIM) is a crucial tool for identifying hidden patterns in information. FP-Growth is an FIM algorithm used to find associations. When the data size increases, the execution of FIM algorithms on a single machine suffers from computational problems, such as memory and time consumption. For these reasons, parallel and distributed processing on platforms such as Spark is essential. The parallel frequent pattern (PFP) is the implementation of FP-Growth in Spark. The main problem with PFP is that it does not consider the load balancing between cluster units. This research proposes an enhanced balanced parallel frequent pattern "EBPFP" algorithm to enhance and balance the PFP. The proposed algorithm (EBPFP) proposes two ideas. First, a strategy for load balancing between groups is proposed to ensure that the items are evenly divided between the nodes, and the cluster resources are used more effectively. Second, the improved conditional pattern base (ICPB) method aims to remove infrequent items from the conditional pattern base before constructing local FP-Trees. The experimental results show that the proposed EBPFP algorithm outperforms PFP, and the difference in running time between EBPFP and PFP was 21.56% and 39.72%, respectively.

关键词： Clustering algorithms Sparks Itemsets Task analysis Data mining Load management partitioning algorithms Big data data mining association rule analysis frequent pattern growth algorithm spark load balancing

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Survivable Routing, Spectrum, Core and Band Assignment in Multi-Band Space Division Multiplexing Elastic Optical Networks

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JOURNAL OF LIGHTWAVE TECHNOLOGY 2022年第11期40卷 3442-3455页

作者： Luo, Zhihuan Yin, Shan Zhao, Ligang Wang, Zhenhao Zhang, Wenchao Jiang, Liyou Huang, Shanguo Beijing Univ Posts & Telecommun State Key Lab Informat Photon & Opt Commun Beijing 100876 Peoples R China

The rapid growth in Internet traffic has contributed to the need to expand network transmission capacity. Multi-band (MB) using existing standard single-mode fibers (SMFs) in the free band is an ideal way to increase the capacity of the fiber. However, the introduction of MB has transferred space division multiplexing - elastic optical networks (SDM-EONs) to multi band-SDM-EONs (MB-SDM-EONs), and changed routing, spectrum and core assignment (RSCA) problem to the routing, spectrum, core and band assignment (RSCBA) problem. At the same time, it introduces a new issue named stimulated Raman scattering (SRS), which affects transmission quality. Although an expanded fiber can carry more services, a large number of services will be interrupted if a link fails, which will bring huge losses. Therefore, it is necessary to propose a reasonable protection strategy to ensure reliable transmission quality. In this paper, the survivable RSCBA problem in MB-SDM-EONs is studied, which copes with link failures and considers SRS in signal-to-noise ratio (SNR) analysis. In order to solve the problem, we propose a band partition protection scheme: working and protection resources are allocated in different frequency bands for increasing SNR in a fault-free network. We formulate this problem and band partition protection scheme as an integer linear programming (ILP) model, which takes into account inter-core crosstalk and SNR analysis. Furthermore, a heuristic algorithm based on genetic algorithm is proposed for large-scale networks. We evaluate the performance of the proposed approaches through experimental simulation. The results demonstrate that both proposed approaches are effective in finding the optimal solutions.

关键词： Optical fiber networks Signal to noise ratio Resource management C-band Routing partitioning algorithms Optical scattering Genetic algorithm (GA) multi band (MB) routing- spectrum- core-band assignment (RSCBA) signal-to-noise ratio (SNR) space division multiplexing (SDM) survivability

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A New Density Peak Clustering Algorithm Based on Cluster Fusion Strategy

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IEEE ACCESS 2022年 10卷 98034-98047页

作者： Li, Fuxiang Zhou, Ming Li, Shu Yang, Tianhao Harbin Univ Sci & Technol Sch Sci Harbin 150080 Peoples R China Harbin Univ Sci & Technol Sch Elect & Elect Engn Minist Educ Key Lab Engn Dielect & Applicat Harbin 150080 Peoples R China

When the density peak clustering algorithm deals with complex datasets and the problem of multiple density peaks in the same cluster, the subjectively selected cluster centers are not accurate enough, and the allocation of non-cluster centers is prone to joint and several errors. To solve the above problems, we propose a new density peak clustering algorithm based on cluster fusion strategy. First, the algorithm screens out the candidate cluster centers by setting two new thresholds to avoid the influence of noise points and outliers. Second, the remaining data points are allocated according to the density peak clustering algorithm to obtain the initial clusters. Third, considering the structural characteristics and spatial distribution of datasets, the new definitions of boundary points, inter-cluster intersection density and inter-cluster boundary density are provided. To correctly classify the clustering problems with multiple density peaks in the same cluster, a new cluster fusion strategy is proposed, which not only corrects the joint and several errors in the allocation of data points, but also correctly selects the cluster centers. Finally, to test the effectiveness of the proposed clustering algorithm, which is compared with DPC-KNN, DPC, K-means and DBSCAN on nine synthetic datasets and six real datasets. The experimental results demonstrate that the clustering performance of the proposed algorithm outperforms that of other algorithms.

关键词： Clustering algorithms Resource management partitioning algorithms Classification algorithms Solid modeling Image segmentation Complexity theory Density measurement Clustering density peaks candidate cluster center cluster fusion strategy

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A Compressed Data Partition and Loop Scheduling Scheme for Neural Networks

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IEEE ACCESS 2022年 10卷 95219-95228页

作者： Li, Dejian Fang, Rongqiang Wang, Jing Zhao, Dongyan Chong, Ting Ren, Zengmin Ma, Jun Beijing Smartchip Microelect Technol Co Ltd Beijing 100192 Peoples R China Beijing Jiaotong Univ Sch Comp & Informat Technol Beijing 100044 Peoples R China Renmin Univ China Sch Informat Beijing 100056 Peoples R China

Neural networks (NNs) have been widely adopted in various application domains. Deeper NNs greatly enhance the output accuracy, but complex NNs with more parameters incur intensive memory accesses, and the data usually need to be partitioned since it may exceed the on-chip storage. However, there is no research considering the partition and scheduling co-design of the NNs. In this paper, we propose a sparse NN data partition and loop scheduling scheme. We establish the compression efficiency model of the matrix sparse algorithm and design a partition selection method based on sparsity characteristics analyzed by the compression efficiency model. Further, we design a loop scheduling scheme based on the proper partition size. The experiment results show that the average memory access of each layer can be compressed to 68% of the original, and the throughput of the AlexNet, VGG and VGG19 is increased to an average of 1.66 times.

关键词： Sparse matrices Neural networks partitioning algorithms Data models Optimization Scheduling Sparse matrix neural networks loop scheduling compression partition

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Motion-Adaptive Detection of HEVC Double Compression With the Same Coding Parameters

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IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY 2022年 17卷 2015-2029页

作者： Xu, Qiang Jiang, Xinghao Sun, Tanfeng Kot, Alex C. City Univ Hong Kong Dept Elect Engn Hong Kong Peoples R China City Univ Hong Kong Ctr Intelligent Multidimens Data Anal Hong Kong Peoples R China Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn Natl Engn Lab Informat Content Anal Tech Shanghai 200240 Peoples R China Nanyang Technol Univ Singapore 637553 Singapore

High Efficiency Video Coding (HEVC) double compression detection is of prime significance in video forensics. However, double compression with the same parameters and video content with high motion displacement intensity have become two main factors that limit the performance of existing algorithms. To address these issues, a novel motion-adaptive algorithm is proposed in this paper. Firstly, the analysis of GOP structure in HEVC standard and the coding process of HEVC double compression are provided. Next, sub-features composed of fluctuation intensities of intra prediction modes and unstable Prediction Units (PUs) in normal Intra-Frames (I-frames) and optical flow in adaptive I-frames are exploited in our algorithm. Each sub-feature is extracted during the process of multiple decompression. We further combine these sub-features into a 27-dimensional detection feature, which is finally fed to the Support Vector Machine (SVM) classifier. By following a separation-fusion detection strategy, the experimental result shows that the proposed algorithm outperforms the existing state-of-the-art methods and demonstrates superior robustness to various motion displacement intensities and a wide variety of coding parameter settings.

关键词： Encoding Prediction algorithms Feature extraction Standards partitioning algorithms Detection algorithms Video sequences HEVC double compression detection motion displacement intensity intra prediction modes unstable prediction units optical flow

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Optimal Modification of Peak-Valley Period Under Multiple Time-of-Use Schemes Based on Dynamic Load Point Method Considering Reliability

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IEEE TRANSACTIONS ON POWER SYSTEMS 2022年第5期37卷 3889-3901页

作者： Yang, Hejun Gao, Yuan Ma, Yinghao Zhang, Dabo Hefei Univ Technol Anhui Prov Key Lab Renewable Energy Utilizat & En Hefei 230009 Peoples R China

Time-of-use (TOU) is an effective price-based demand response strategy. A reasonable design of TOU strategy can effectively reduce the peak-valley difference, and then produce a lot of benefits (such as delaying power grid investment, reducing interruption cost, and improving reliability). However, changing peak-valley period has a great influence on the peak-valley difference and power supply reliability of power system. Therefore, this paper aims to investigate the optimal modification of peak-valley period considering reliability loss under multiple TOU schemes. Firstly, this paper presents a clustering model and algorithm of optimal load curve based on a minimum error iteration method. Secondly, an optimal modification of peak-valley period based on a dynamic load point method is proposed, and the traditional peak-valley difference is replaced by the global peak-valley difference to calculate the objective function. Thirdly, this paper establishes a load-reliability relation fitting model based on the back propagation neural network. Finally, the effectiveness and correctness of the proposed method are investigated by the Roy Billinton test system and reliability test system.

关键词： Reliability Load modeling Power system reliability Clustering algorithms Optimization Heuristic algorithms partitioning algorithms Time-of-use load curve clustering period partition BP neural network reliability loss

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