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Toward an Effective Locality-Sensitive Hashing Search for WMSNs Based on the Neighborhood Rough Set Approach

IEEE INTERNET OF THINGS JOURNAL 2020年第11期7卷 10985-10995页

作者： Xiao, Ruliang Liu, Shirong Li, Yiqi Ni, Youcong Du, Xin Fujian Normal Univ Coll Math & Informat & Digit Fujian Internet OF T Fuzhou 350117 Peoples R China

With the advent of the 5G era, wireless multimedia sensor networks (WMSNs) will be more widely used in security monitoring, environmental monitoring, intelligent transportation, and other applications of the Internet of Things (IoT). Data collected by WMSNs, part of IoT systems, are high-dimensional, multilevel, unstructured, and large-scale complex data. Effective nearest neighbor searching faces the "dimension curse" problem. To this end, by incorporating the neighborhood rough set (NRS) approach, this article proposes a novel and effective locality-sensitive hashing (LSH) method for high-dimensional WMSN data based on the neighborhood (NLSH). This method innovatively combines the neighborhood knowledge representation method with the LSH mechanism and extends the indexing ability of LSH. Moreover, this method does not require any prior knowledge and has good universality. The indexing method of neighborhood bucket building can reduce the number of searches and improve the response speed of the WMSN system. Extensive experiments have been carried out on several real-world multimedia data sets. The results show that a large-scale high-dimensional NLSH search based on the NRS approach can efficiently query the target point and produce very accurate results. The NLSH algorithm based on the NRS approach outperforms other advanced mainstream LSH algorithms.

关键词： Internet of Things Rough sets Hash functions partitioning algorithms Environmental monitoring Search problems Approximate nearest neighbor high-dimensional wireless multimedia sensor network (WMSN) search locality-sensitive hashing (LSH) neighborhood rough set (NRS)

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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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Fusion of Centroid-Based Clustering With Graph Clustering: An Expectation-Maximization-Based Hybrid Clustering

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2023年第8期34卷 4068-4082页

作者： Uykan, Zekeriya Amer Univ Middle East Coll Engn & Technol Egaila Kuwait

This article extends the expectation-maximization (EM) formulation for the Gaussian mixture model (GMM) with a novel weighted dissimilarity loss. This extension results in the fusion of two different clustering methods, namely, centroid-based clustering and graph clustering in the same framework in order to leverage their advantages. The fusion of centroid-based clustering and graph clustering results in a simple ``soft'' asynchronous hybrid clustering method. The proposed algorithm may start as a pure centroid-based clustering algorithm (e.g., k-means), and as the time evolves, it may eventually and gradually turn into a pure graph clustering algorithm [e.g., basic greedy asynchronous distributed interference avoidance (GADIA) (Babadi and Tarokh, 2010)] as the algorithm converges and vice versa. The ``hard'' version of the proposed hybrid algorithm includes the standard Hopfield neural networks (and, thus, Bruck's Ln algorithm by (Bruck, 1990) and the Ising model in statistical mechanics), Babadi and Tarokh's basic GADIA in 2010, and the standard k-means (Steinhaus, 1956), (MacQueen, 1967) [i.e., the Lloyd algorithm (Lloyd, 1957, 1982)] as its special cases. We call the ``hard version'' of the proposed clustering as ``hybrid-nongreedy asynchronous clustering (H-NAC).'' We apply the H-NAC to various clustering problems using well-known benchmark datasets. The computer simulations confirm the superior performance of the H-NAC compared to the k-means clustering, k-GADIA, spectral clustering, and a very recent clustering algorithm structured graph learning (SGL) by Kang et al. (2021), which represents one of the state-of-the-art clustering algorithms.

关键词： Clustering algorithms Clustering methods partitioning algorithms Wireless communication Standards Principal component analysis Image segmentation Bruck's Ln algorithm centroid-based clustering expectation-maximization (EM) greedy asynchronous distributed interference avoidance (GADIA) graph clustering hybrid-nongreedy asynchronous clustering (H-NAC) k-means clustering

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A Service-Defined Approach for Orchestration of Heterogeneous Applications in Cloud/Edge Platforms

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IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT 2019年第4期16卷 1404-1418页

作者： Castellano, Gabriele Esposito, Flavio Risso, Fulvio Politecn Torino Dept Control & Comp Engn I-10129 Turin Italy St Louis Univ Comp Sci Dept St Louis MO 63103 USA

Edge Computing is moving resources toward the network borders, thus enabling the deployment of a pool of new applications that benefit from the new distributed infrastructure. However, due to the heterogeneity of such applications, specific orchestration strategies need to be adopted for each deployment request. Each application can potentially require different optimization criteria and may prefer particular reactions upon the occurrence of the same event. This paper presents a Service-Defined approach for orchestrating cloud/edge services in a distributed fashion, where each application can define its own orchestration strategy by means of declarative statements, which are parsed into a Service-Defined Orchestrator (SDO). Moreover, to coordinate the coexistence of a variety of SDOs on the same infrastructure while preserving the resource assignment optimality, we present DRAGON, a Distributed Resource AssiGnment and OrchestratioN algorithm that seeks optimal partitioning of shared resources between different actors. We evaluate the advantages of our novel Service-Defined orchestration approach over some representative edge use cases, as well as measure convergence and performance of DRAGON on a prototype implementation, assessing the benefits compared to conventional orchestration approaches.

关键词： Optimization Streaming media Computer architecture Resource management Cloud computing partitioning algorithms Convergence Orchestration mathematical optimization distributed algorithms distributed management

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Hierarchical Dirichlet Multinomial Allocation Model for Multi-Source Document Clustering

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IEEE ACCESS 2020年 8卷 109917-109927页

作者： Huang, Ruizhang Xu, Weijia Qin, Yongbin Chen, Yanping Guizhou Univ Coll Comp Sci & Technol Guiyang 550025 Peoples R China Guizhou Intelligent Human Comp Interact Engn Tech Guiyang 550025 Peoples R China Guizhou Univ Guizhou Prov Key Lab Publ Big Data Guiyang 550025 Peoples R China

Mining a document structure from multiple data sources in terms of their underlying topics has become an important task of document clustering. The traditional document clustering approach cannot be applied directly to the multi-source document clustering problem. There are three typical difficulties: 1) The topics of different data sources are related but not the same. 2) Usually, each data source has its own focus on topics. 3) The number of clusters of the data sources are not necessarily the same and are not known beforehand. In this paper, based on our previous research, we design a novel multi-source document clustering model, namely, the hierarchical Dirichlet multinomial allocation (HDMA) model, to solve all the above problems. The HDMA model is investigated with a two-step hierarchical topic generation process. Topics are learnt to share their general characteristics across data source, while at the same time preserve the local characteristics of the data source. Each data source is applied with an exclusive topic partition to learn the source-level topic emphasis. A Gibbs sampling algorithm is then used to learn the number of clusters for each data source as well as the parameters of the HDMA model at the same time. Experimental results demonstrate that the HDMA model is effective.

关键词： Clustering algorithms Data models Resource management partitioning algorithms Clustering methods Social networking (online) Licenses Document clustering multi-source document clustering Dirichlet distribution Gibbs sampling

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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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Low Complexity Decision Algorithm for CU Partition Based on Image Texture Information and LGBM

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IEEE ACCESS 2024年 12卷 111422-111432页

作者： Tian, Erlin Qian, Xiaowei Zhang, Qiuwen Zhengzhou Univ Light Ind Coll Comp Sci & Technol Zhengzhou 450002 Peoples R China

In order to solve the problem of insufficient compression efficiency of High-Efficiency Video coding (HEVC) in the current video market, a new generation of Versatile Video Coding (VVC) has been proposed. However, the newly added Multi-Type Tree (MTT) in VVC leads to an increase in coding complexity, so this paper proposes a fast decision algorithm for Coding Unit (CU) based on image information and using Light Gradient Boosting Machine (LGBM) as the classifier for CU fast decision algorithm. This algorithm utilizes the pixel information and gradient information of the encoding unit to make decision and skip unnecessary partitioning methods. Meanwhile, efficient classifiers are utilized for partitioning decisions. And relevant features are extracted and trained for different partitioning decision problems, so as to obtain higher accuracy partitioning methods. The method balances the issues of coding efficiency and coding time. Compared to the VVC reference software (VTM), the method saves an average of 52.99% of coding time and increases BDBR by only 1.50%.

关键词： Encoding Complexity theory Image coding partitioning algorithms Accuracy Classification algorithms Streaming media Gradient methods Video coding Versatile video coding LGBM image information pixel information gradient information

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Variational Bayesian Inference for Robust Identification of PWARX Systems With Time-Varying Time-Delays

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IEEE TRANSACTIONS ON CYBERNETICS 2023年第6期53卷 3613-3623页

作者： Bai, Wentao Guo, Fan Chen, Lei Hao, Kuangrong Huang, Biao Donghua Univ Coll Informat Sci & Technol Engn Res Ctr Digitized Text & Apparel Technol Minist Educ Shanghai 201620 Peoples R China Univ Alberta Dept Chem & Mat Engn Edmonton AB T6G 2G6 Canada

This article presents a robust variational Bayesian (VB) algorithm for identifying piecewise autoregressive exogenous (PWARX) systems with time-varying time-delays. To alleviate the adverse effects caused by outliers, the probability distribution of noise is taken to follow a $t$ -distribution. Meanwhile, a solution strategy for more accurately classifying undecidable data points is proposed, and the hyperplanes used to split data are determined by a support vector machine (SVM). In addition, maximum-likelihood estimation (MLE) is adopted to re-estimate the unknown parameters through the classification results. The time-delay is regarded as a hidden variable and identified through the VB algorithm. The effectiveness of the proposed algorithm is illustrated by two simulation examples.

关键词： Time-varying systems Support vector machines partitioning algorithms Bayes methods Maximum likelihood estimation Delays Probability distribution Piecewise autoregressive exogenous (PWARX) robust identification support vector machine (SVM) t-distribution time-delay variational Bayesian (VB)

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Higher Order Fuzzy Membership in Motif Modularity Optimization

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IEEE TRANSACTIONS ON FUZZY SYSTEMS 2024年第12期32卷 7143-7156页

作者： Xiao, Jing Wei, Ya-Wei Cao, Jing Xu, Xiao-Ke Shenzhen Technol Univ Coll Big Data & Internet Shenzhen 518118 Peoples R China Dalian Minzu Univ Coll Informat & Commun Engn Dalian 116600 Peoples R China Beijing Normal Univ Sch Journalism & Commun Beijing 100875 Peoples R China

Higher order community detection (HCD) reveals both mesoscale structures and functional characteristics of real-world networks. Although many methods have been developed from diverse perspectives, to our knowledge, none can provide fine-grained higher order fuzzy community information. This study introduces a novel concept of higher order fuzzy memberships that quantify the membership grades of motifs to crisp higher order communities, thereby revealing partial community affiliations. Furthermore, we utilize higher order fuzzy memberships to enhance HCD via a general framework called fuzzy memberships-assisted motif-based evolutionary modularity. On the one hand, a fuzzy membership-based neighbor community modification strategy is designed to correct misassigned bridge nodes, thereby improving partition quality. On the other hand, a fuzzy membership-based local community merging strategy is proposed to combine excessively fragmented communities, enhancing local search ability. Experimental results indicate that the proposed framework outperforms state-of-the-art methods in both synthetic and real-world datasets, particularly in networks with ambiguous and complex structures.

关键词： Optimization Topology Network motifs Bridges Accuracy partitioning algorithms Image edge detection Fuzzy systems Clustering algorithms Space exploration Fuzzy membership higher order community detection (HCD) modularity optimization network motif

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Exploring the Design Space of Distributed Parallel Sparse Matrix-Multiple Vector Multiplication

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IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS 2024年第11期35卷 1977-1988页

作者： Huang, Hua Chow, Edmond Georgia Inst Technol Sch Computat Sci Engn Atlanta GA 30332 USA

We consider the distributed memory parallel multiplication of a sparse matrix by a dense matrix (SpMM). The dense matrix is often a collection of dense vectors. Standard implementations will multiply the sparse matrix by multiple dense vectors at the same time, to exploit the computational efficiencies therein. But such approaches generally utilize the same sparse matrix partitioning as if multiplying by a single vector. This article explores the design space of parallelizing SpMM and shows that a coarser grain partitioning of the matrix combined with a column-wise partitioning of the block of vectors can often require less communication volume and achieve higher SpMM performance. An algorithm is presented that chooses a process grid geometry for a given number of processes to optimize the performance of parallel SpMM. The algorithm can augment existing graph partitioners by utilizing the additional concurrency available when multiplying by multiple dense vectors to further reduce communication.

关键词： Sparse matrices partitioning algorithms Vectors Costs Three-dimensional displays Space exploration Optimization SpMM SpMV distributed-memory matrix multiplication communication optimization

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