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Distributed Classification by Divide and Conquer Approach

Distributed Classification by Divide and Conquer Approach

引用

Computing and Machine Intelligence (ICMI), International Conference on

作者： Max Y. Chen Qiang Wu Illinois Mathematics and Science Academy Aurora IL USA Department of Mathematical Sciences Middle Tennessee State University Murfreesboro TN USA

ISBN: (数字)9798350372977

ISBN: (纸本)9798350372984

In this paper, we investigate the efficacy of the divide and conquer approach for implementing distributed lo-gistic regression and distributed support vector machine (SVM) algorithms for classification of large-scale datasets. This approach is designed to handle datasets that exceed the capacity of a single processor, necessitating the partitioning of data into multiple subsets. Logistic regression or SVM is then applied to each subset, yielding individual local classifiers. Subsequently, a global classifier is derived by aggregating these local classifiers to make the final decision. We propose three strategies for the aggregation stage: voting based on predicted labels, averaging of real-valued predictions, and averaging of posterior probabilities. Our analysis reveals that for distributed logistic regression, probability averaging is the most robust approach and is there-fore recommended. Conversely, in the context of distributed SVM, probability averaging requires additional modeling but has a minimal impact on the performance. Therefore, functional averaging is recommended instead.

关键词： Support vector machines Context Logistic regression Posterior probability Computational modeling Vectors partitioning algorithms

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P-TICER: An Effective Parallel TICER Acceleration Method for Model Order Reduction

P-TICER: An Effective Parallel TICER Acceleration Method for...

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Electronics Design Automation (ISEDA), International Symposium of

作者： Zijia Zhang Dan Niu Zhou Jin Pengju Chen Zhenya Zhou Changyin Sun School of Integrated Circuits Southeast University Nanjing China School of Automation Southeast University Nanjing China Super Scientific Software Laboratory University of Petroleum-Beijing Beijing China Huada Empyrean Software Co. Ltd Beijing China School of Artificial Intelligence Anhui University Hefei China

ISBN: (数字)9798350352030

ISBN: (纸本)9798350352047

In the field of post simulation verification in in-tegrated circuit design, Time-Constant Equilibration Reduction (TICER) is a well-known and widely used algorithm for model order reduction. However, the TICER for large-scale circuits is time-consuming. Moreover, there is a contradiction between the node compression ratio and circuit sparsity. In this paper, an effective TICER-based optimization parellel acceleration algorithm called P-TICER is proposed. An adaptive circuit judgment partitioning strategy based on graph partitioning is proposed to partition circuits and perform multi-threaded parallel acceleration. The compression ratio and sparsity of the circuit are optimized and balanced. Moreover, RC-In-RC-Out structure is designed, which is realizable and seamlessly integra table with other simulators as it takes circuit netlists as input and output. We apply P- TICER to the RC circuits with two different types and nodes scale ranging from 40k to 2 million. Experimental results show that compared with the conventional TICER, the proposed P- TICER is up to 24 times faster, while achieving a node compression ratio up to 50% with the relative error remained within 0.2%.

关键词： Adaptation models Design automation Accuracy Parallel processing Distance measurement Reduced order systems partitioning algorithms

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A Construction Method of a Network-Type Index System for Air Combat System Effectiveness Evaluation Based on Association Rules Mining

A Construction Method of a Network-Type Index System for Air...

引用

Chinese Automation Congress (CAC)

作者： Hao Chen Siyang Yu Shuaiwei Nan Wenhao Bi An Zhang School of Aeronautics Northwestern Polytechnical University Xi'an China National Key Laboratory of Aircraft Configuration Design Xi'an

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

Aiming at the problem that the traditional tree-type and cluster-type index systems do not fully meet the requirements of complex air combat effectiveness evaluation under the background of system combat, a method of constructing network-type index system is proposed. Firstly, through the analysis of the index hierarchies of typical complex air combat systems, the capability hierarchy indexes with network characteristics are selected as the research object. Then, FP-Growth algorithm is used to mine the association rules among indexes and compared with Apriori algorithm in the difference of run time. Then, the lift is introduced to quantitatively describe the influence relationship among the indexes and the air combat effectiveness evaluation network-type index system is constructed. The feasibility and effectiveness of the proposed method are verified by simulation.

关键词： Automation partitioning algorithms Association rule learning Indexes

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Integrating PAM Clustering and GTO-Based Cluster Head Selection in WSNs with Multi-Metric Optimization

Integrating PAM Clustering and GTO-Based Cluster Head Select...

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Sentiment Analysis and Deep Learning (ICSADL), International Conference on

作者： K. Hemalatha M. Amanulla Department of Information Technology Saveetha School of Engineering Saveetha Institute Of Medical and Technical Sciences Chennai India Department of Information Technology Saveetha School of Engineering Saveetha school Of Medical And Technical Sciences Chennai India

ISBN: (数字)9798350396157

ISBN: (纸本)9798350396164

Wireless Sensor Networks (WSNs) are a vital component of modern data-driven applications, ranging from environmental monitoring to industrial automation. Clustering plays a pivotal role in enhancing the efficiency and scalability of WSNs by organizing sensor nodes into clusters, where each cluster is led by a Cluster Head (CH). To optimize cluster formation and CH selection, the study proposes a novel approach that combines the partitioning Around Medoids (PAM) clustering algorithm with the Giant Trevally Optimizer (GTO) for cluster head selection. Our approach goes beyond traditional clustering methods by incorporating a comprehensive set of metrics to guide the clustering and CH selection processes. These metrics encompass Distance-Based Metrics, which consider proximity between nodes; Energy Metrics, accounting for energy availability and consumption; Energy Efficiency Metrics, aimed at prolonging network lifetime; Load Metrics, to balance the node workload; and Data Correlation Metrics, which capture data dependencies among nodes. The proposed clustering protocol implemented using MATLAB and verified for different network deployment conditions. Results shows that the proposed protocol outperforms all existing protocol in terms of lifetime and packet delivery ratio.

关键词： Measurement Wireless sensor networks Sentiment analysis Protocols Scalability Clustering algorithms partitioning algorithms

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Near-Field Beam Training with Analog Extremely Large True-Time-Delay Arrays: (Invited Paper)

Near-Field Beam Training with Analog Extremely Large True-Ti...

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Asilomar Conference on Signals, Systems & Computers

作者： Ibrahim Pehlivan Mehmet C. Ilter Mikko Valkama Danijela Cabric Electrical and Computer Engineering Department University of California Los Angeles Department of Electrical Engineering Tampere Wireless Research Center Tampere University Finland

ISBN: (数字)9798350354058

ISBN: (纸本)9798350354065

As wireless systems have started to use millimeter-wave (mmWave) frequencies to exploit the abundant bandwidth, large antenna arrays with high beamforming gain have become necessary for compensating severe path loss. However, deploying large number of antennas extends the near-field (NF) region where effective beamforming requires the knowledge of not only the user equipment (UE)'s direction, but also distance. In this paper, we present a fast NF beam training method for analog extremely large (XL) true-time-delay (TTD) arrays using only a single orthogonal frequency-division multiplexing (OFDM) pilot symbol. In the proposed virtual sub-array sparse rainbow (VSSR) algorithm, we virtually partition the antenna array into smaller aperture sub-arrays so that a UE falls in the far-field region of each sub-array. By exploiting properties of the rainbow beamforming, we obtain near-orthogonal sub-array measurements such that UE directions with respect to each sub-array can be estimated with a sparse recovery algorithm. Then, these sub-array referenced UE directions are combined to estimate UE's location. Our simulation results demonstrate that the proposed method enables near-optimal beam training using only a single OFDM symbol, achieving an average of at least the 95% of the optimal array gain under various channel conditions.

关键词： Training Wireless communication Array signal processing OFDM Simulation Symbols partitioning algorithms Noise measurement Antenna arrays Signal to noise ratio

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Efficient Approximate Range Aggregation over Large-scale Spatial Data Federation (Extended Abstract)

Efficient Approximate Range Aggregation over Large-scale Spa...

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38th IEEE International Conference on Data Engineering (ICDE)

作者： Shi, Yexuan Tong, Yongxin Zeng, Yuxiang Zhou, Zimu Ding, Bolin Chen, Lei Beihang Univ State Key Lab Software Dev Environm Beijing Peoples R China Hong Kong Univ Sci & Technol Dept Comp Sci & Engn Hong Kong Peoples R China Singapore Management Univ Sch Informat Syst Singapore Singapore

ISBN: (数字)9781665408837

ISBN: (纸本)9781665408837

Data federations notably increase the amount of data available for data-intensive applications such as smart mobility planning and public health emergency responses. Yet they also challenge the conventional implementation of range aggregation queries because the raw data cannot be shared within the federation and the data partition at each data silo is fixed during query processing. In this work, we propose the first-of-its-kind approximate algorithms for efficient range aggregation over spatial data federation. We devise novel singlesilo sampling algorithms that process queries in parallel and design a level sampling based algorithm which reduces the time complexity of local queries at each data silo to O(log 1 o), where o is the approximation ratio of the accuracy guarantee. Extensive experiments on real-world dataset validate the efficiency and effectiveness of the solutions.

关键词： Query processing Conferences Approximation algorithms Data engineering Spatial databases partitioning algorithms Planning

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Research on Optimization of Stream Data Processing Algorithm for Multi-Core Processor Based on Mapreduce

Research on Optimization of Stream Data Processing Algorithm...

引用

IEEE International Conference on Automation, Electronics and Electrical Engineering (AUTEEE)

作者： Yingmei Shang Genjie Wang Jing Ruan Anhui Sanlian University Hefei Anhui China

ISBN: (数字)9798350377033

ISBN: (纸本)9798350377040

With the rapid development of information technology and the popularization of the Internet, streaming data processing has become one of the core tasks of big data analysis. With their parallel computing capabilities, multi-core processors show great potential in efficiently processing massive streams of data. However, the traditional streaming data processing algorithm often faces the problems of uneven resource allocation, unbalanced load and high communication overhead when facing multi-core architecture, which limits the overall performance of the system. Based on MapReduce programming model, this paper discusses the optimization strategy of streaming data processing algorithm in multi-core processor environment. Firstly, this paper analyzes the limitations of the existing MapReduce framework in the stream data processing scenario, including the delay increase caused by batch processing tendency and the lack of adaptability to dynamic data flow. Then, an improved partitioning Strategy is proposed, which can dynamically adjust task allocation according to data flow characteristics, ensure load balancing among cores, and minimize cross-core communication costs. In order to further improve the processing efficiency, this paper also designs a kernel processor stream data processing algorithm based on MapReduce-partitioning strategy, which can dynamically adjust the task priority and optimize the execution order according to real-time performance monitoring information. The experimental results show that the proposed optimization method not only effectively improves the speed of upstream data processing, but also enhances the stability and reliability of the system, which provides theoretical support and technical reference for future large-scale streaming data analysis.

关键词： Multicore processing Heuristic algorithms Data processing Load management Throughput Stability analysis partitioning algorithms Resource management Streams Thermal stability

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An Overlapping Community Discovery Algorithm Based on Cycle Structure and Label Optimization

An Overlapping Community Discovery Algorithm Based on Cycle ...

引用

International Conference on Artificial Intelligence and Big Data (ICAIBD)

作者： Miaomiao Liu WenQing Zhang Jingfeng Guo Yuchen Liu School of Computer and Information Technology Northeast Petroleum University Daqing Heilongjiang China College of Information Science and Engineering Yanshan University Qinhuangdao Hebei China

ISBN: (数字)9798350385106

ISBN: (纸本)9798350385113

An overlapping community discovery algorithm integrating cycle structure and label optimization strategies is proposed. Initially, the node superiority index is defined, and the seed node set is selected based on a threshold. Then labels of non-seed nodes are updated through node similarity to decrease the number of initial labels, which helps enhance the stability of the algorithm and reduce computational complexity. Furthermore, based on cycle structure, an initial label updating sequence is generated from the descending order of node cycle ratios, subsequently combining node importance and node similarity to define label updating rules. Moreover, in cases where indicators are identical, the quantity of shared cycles is used to determine the category of labels, thereby increasing the accuracy of community partitioning. Experiments indicate that compared with five other algorithms, the proposed algorithm demonstrates highest up to 64.1% and 41.5% improvement in EQ and NMI.

关键词： Accuracy Stability criteria Stars Big Data partitioning algorithms Indexes Computational complexity

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From Multi -View Clustering to Ensemble Clustering

From Multi -View Clustering to Ensemble Clustering

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Intelligent Computing and Next Generation Networks（ICNGN), International Conference on

作者： Safa BETTOUMI Khadija SLIMANI Learning Data Robotics (LDR) Laboratory ESIEA Paris France

ISBN: (数字)9798331529222

ISBN: (纸本)9798331529239

Multi-view clustering and ensemble clustering are two main paradigms in machine learning that leverage a dataset to reach different perspectives. Although, they share some simi-larities in dataset management, they differ in their methodology and application. This paper conducts a study to demonstrate that exploiting different sources of information and choosing the right aggregation method is an asset to improve the quality of clustering compared to using classical methods. Different views were generated on two benchmark databases. Then, a clustering algorithm was applied separately on the different views. Subsequently, three well-known ensemble clustering approaches were studied, namely majority voting, meta-clustering and ensemble clustering based on a co-association matrix. Thus, an analysis emphasizes the role of the fusion method used to combine the obtained partitions and its effect on clustering during this fusion. It also highlights the advantages of multi-view clustering over classical classification. Moreover, it shows that the diversity of partitions from different views can have a great influence on the clustering performance.

关键词： Accuracy Databases Clustering methods Clustering algorithms Machine learning Benchmark testing Data structures partitioning algorithms Next generation networking

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A Distributed Storage Data Processing Method for Substation 3D Modeling

A Distributed Storage Data Processing Method for Substation ...

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Artificial Intelligence and Power Systems (AIPS), International Conference on

作者： Qifeng Luo Wentao Liu Lei Liu Yingying Tan Zhongshan Power Supply Bureau of Guangdong Power Grid Co. Ltd Zhongshan Guangdong China

ISBN: (数字)9798350392210

ISBN: (纸本)9798350392227

In view of the narrow scope of application of 3D reconstruction technology in the modeling of a single data source and the inability to deal with complex application scenarios, a distributed storage method is proposed to build device models according to the relationship between 3D model information and model reuse in this paper. The map array is assigned to the scene light map property to generate a device model for restoring light. The experimental results show that the proposed data processing method can effectively reduce the memory size of the model and save the time of loading the model. Compared with other distributed algorithms, the proposed method can take into account update cost and response speed, and improve data loading efficiency, which is more conducive to fast model rendering and storage.

关键词： Solid modeling Three-dimensional displays Substations Costs Loading Data processing Rendering (computer graphics) Data models partitioning algorithms Load modeling

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