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A Spark-based distributed Dragonfly Algorithm for Feature Selection

A Spark-based Distributed Dragonfly Algorithm for Feature Se...

引用

international conference on Computer Science & Education (ICCSE)

作者： Hongwei Chen Dongqiang Liu Lin Han Shuyu Yao Congyuan Jin Xiang Hu School of Computer Science Hubei University of Technology Wuhan China Wuhan Fiberhome Technical ServicesCo. Ltd Wuhan China Fiberhome Telecommunication Technologies Co. Ltd Wuhan China

ISBN: (数字)9781728172675

ISBN: (纸本)9781728172682

Dragonfly algorithm is an intelligent group optimization algorithm. In this paper, dragonfly algorithm is well used in the field of feature selection. However, dragonfly algorithm has the problem of falling into local optimal solution, which reduces the performance of feature selection and classification. therefore, this paper proposes a binary Dragonfly optimization algorithm based on spark, which integrates the global optimization ability of dragonfly algorithm with the parallel computing ability of spark, greatly improving the performance of the algorithm. Experimental results show that binary Dragonfly algorithm has better performance than traditional particle swarm optimization algorithm and genetic algorithm. the binary Dragonfly algorithm based on spark solves the problem of falling into the local optimal solution, improves the running speed of the algorithm, can effectively deal with massive data, and greatly enhances the performance of the algorithm.

关键词： Sparks Feature extraction Optimization Sociology Statistics Clustering algorithms Classification algorithms

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Adaptive Gateway Traffic Scheduling Algorithm Based on Multidimensional Information

Adaptive Gateway Traffic Scheduling Algorithm Based on Multi...

引用

international conference on Computer and Communications (ICCC)

作者： Jinlong Li Junming Liu Jiaxin Lu Zhiyang Lu Shenyang Institute of Computing Technology Chinese Academy of Science University of Chinese Academy of Science Shenyang China

ISBN: (数字)9798331507077

ISBN: (纸本)9798331507084

In this paper, an adaptive gateway traffic scheduling algorithm based on multi-dimensional information is proposed to solve the load balancing problem in high concurrency scenarios. the algorithm considers the three dimensions of server performance, network status and traffic prediction, and dynamically adjusts the performance load weight, service access weight and traffic prediction weight to realize more intelligent traffic scheduling. By monitoring load pressure index, network pressure index and traffic prediction difference index in real time, the algorithm can flexibly deal with different network environments and traffic fluctuations. Experimental results show that compared with traditional load balancing algorithms, the proposed algorithm has a remarkable performance improvement in system throughput and response time. Finally, this paper looks forward to future research directions in introducing more dimensional information, optimizing traffic prediction models, extending to distributed environments, and improving real-time algorithms.

关键词： Scheduling algorithms Heuristic algorithms Logic gates Prediction algorithms Load management Dynamic scheduling Real-time systems Indexes Time factors Forecasting

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A DISCRIMINATIVE NULL SPACE BASED DEEP LEARNING APPROACH FOR PERSON RE-IDENTIFICATION

A DISCRIMINATIVE NULL SPACE BASED DEEP LEARNING APPROACH FOR...

引用

2016 4th IEEE international conference on Cloud computing and Intelligence Systems（IEEE CCIS2016）

作者： Shuangqun Li Xinchen Liu Wu Liu Huadong Ma Haitao Zhang Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia Beijing University of Posts and Telecommunications

ISBN: (纸本)9781509012572

Person re-identification across multiple camera views is a rather challenging task due to various view points, illuminations, backgrounds and poses. How to extract discriminative features is the most critical way to overcome these challenges. In this paper, we design a discriminative null space based deep learning approach for person re-identification. Firstly, a Siamese Convolutional Neural Network(SCNN) is designed to automatically learn effective semantic features for person re-identification in different camera views. Furthermore, to obtain better recognition performance, we adopt the Null Foley-Sammon Transform(NFST) metric learning approach to combine the low-level, mid-level features and high-level features learned by the SCNN in a new discriminative null space. In this null space, images of the same person are collapsed into a single point thus minimizing the within-class scatter to the extreme and maximizing the relative between-class separation simultaneously. Finally, the comprehensive evaluations demonstrate that our approach outperforms all state-of-the-art methods on the Market-1501, which is the world's largest person re-identification benchmark dataset.

关键词： Person re-identification Metric learning SCNN Null foley-sammon transform Null space

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Convergent radio and fibre access architectures using low-energy systems

Convergent radio and fibre access architectures using low-en...

引用

international conference on Transparent Optical Networks

作者： Carlos Bock Sergi Figuerola Michael C. Parker Terence Quinlan Stuart D. Walker Distributed Applications and Networks Area Fundació i2CAT Barcelona Spain School of Computer Science and Electronic Engineering University of Essex Colchester UK

ISBN: (纸本)9781467322287

this paper presents an innovative high-bandwidth access network architecture that offers transparent transport services for fixed and mobile applications. Key to this is the development of a high-functionality, low-energy active remote node (ARN) offering high-speed switching and statistical-multiplexing features for maximal exploitation of available network resources, together with radio, wavelength conversion and legacy systems compatibility. the proposed architecture features an optimized and sophisticated management and control plane, allowing service providers to transparently monitor their connections, so simplifying network management and application interoperability with heterogeneous networks. Although inherently future-proofed, the proposed network platform is also intrinsically compatible with existing PON and wireless access solutions: fixed fibre and radio access, LTE and beyond, and legacy services (CATV and DSL technologies) can all be delivered and managed in an open access environment. In addition, its low-energy and low-cost design offers an environmentally sustainable technology solution, whilst acting as a critical enabler for new smart services, e-society initiatives, and intelligent lifestyle management.

关键词： Mobile communication Optical fibers Wavelength division multiplexing Mobile computing Wireless communication Optical fiber subscriber loops Passive optical networks

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A New-generation Nuclear Power Industrial Internet Platform Based on Cloud-Edge-Terminal Network Architecture

A New-generation Nuclear Power Industrial Internet Platform ...

引用

Digital Twins and Parallel Intelligence (DTPI), IEEE international conference on

作者： Ying Gang Jing Hao Liu Min Min Cheng Huajian Fang Kui Xu Yang Lan CNNC Key Laboratory on Nuclear Industry Simulation China Nuclear Power Operation Technology Corporation Ltd. Wuhan China School of Electrical Engineering and Automation Wuhan University Wuhan China CNNC Key Laboratory on Nuclear Industry Simulation China Nuclear Power Operation Technology Corporation Wuhan China

ISBN: (数字)9798350349252

ISBN: (纸本)9798350349269

To address the major challenges in the development of the nuclear power industrial internet—prominent industry barriers, inconsistent data standards, and high security and control risks, this paper proposes a solution guided by application needs. Leveraging new-generation technologies such as big data, edge computing, and artificial intelligence, we design a nuclear power industrial internet platform architecture that supports the development, operation, maintenance, and continuous upgrading of China’s nuclear power industrial information systems. Firstly, the overall architecture of the nuclear power industrial internet cloud-edge-terminal network is designed. this architecture addresses issues related to data, model, and application coordination within the platform by overcoming technological barriers such as multi-source heterogeneous data access, distributed data storage, and real-time intelligent computing through edge-cloud collaboration. It achieves the virtualization and containerization of edge nodes, network, and storage resources, meeting the requirements for elastic resource scheduling and cluster management. Subsequently, a layered security protection architecture is designed to address the platform’s security and privacy issues. Finally, the application of emerging technologies within the nuclear power platform is summarized and discussed, providing insights and prospects for future developments.

关键词： Cloud computing Privacy Biological system modeling Computer architecture Network architecture Big Data Security Protection Artificial intelligence Virtualization

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An Empirical Evaluation of Deep Neural Networks in Federated Learning

An Empirical Evaluation of Deep Neural Networks in Federated...

引用

IEEE international conference on High Performance computing and Communications (HPCC)

作者： Xiaotong Wu Zilong Xue Lingling Shen School of Computer and Electronic Information Nanjing Normal University Nanjing China State Key Laboratory of Novel Software Technology Nanjing University China

With the fast development of artificial intelligence (e.g., deep learning), it brings the new opportunities and challenges for business management and service optimization of the business process management. However, there are few works to present the comprehensive analysis of neural network models in the new intelligent paradigm, especially of federated learning. In this paper, we attempt to give an empirical study of deep neural networks in federated learning, which is used for analyzing the possibility of applying federated learning to BPM. At first, we present an overview of the theoretical analysis of deep learning and its distributed extension, i.e., federated learning. then, We introduce three classic deep neural network models, including multi-layer perception, convolutional neural network, and residual network, and the corresponding structure and the key components. Next, we give the detailed experimental setting for the common test dataset called MNIST, including the structure of neural networks and the setting of the hyperparameters. Finally, extensive experiments are performed to evaluate the performance of the neural networks in federated learning.

关键词： Deep learning Training Analytical models Federated learning Neural networks Predictive models Data models

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High performance cache block replication using re-reference probability in CMPs

High performance cache block replication using re-reference ...

引用

international conference on High Performance computing

作者： Jinglei Wang Dongsheng Wang Haixia Wang Yibo Xue Tsinghua National Laboratory for Information Science and Technology Tsinghua University Beijing China

In a Chip Multiprocessor(CMP) with shared caches, the last level cache (LLC) is distributed across all the cores. this increases the on-chip communication delay and thus influence the pr ocessor's performance. the LLC is also quite inefficient due to plenty of dead blocks. Replication can be provided in shared caches by replicating cache blocks evicted from cores to the local LLC slices to minimize access latency through utilizing the cache space of dead blocks which will not be referenced again before they are evicted. However, naively allowing all evicted blocks to be replicated have limited performance benefit as such replicating does not take into account reuse probability of replicated blocks. this paper proposes Adaptive Probability Replication (APR), a mechanism that counts each block's accesses in L2 cache slices, and monitors the number of evicted blocks with different number of accesses, to estimate the Re-Reference Probability of blocks in their lifetime at runtime. Using predicted re-reference probability, APR adopts probability replication policy and probability insertion policy to replicate blocks at corresponding probabilities, and insert them at appropriate position, according to their re-reference probability. We evaluate APR for a 16-core tiled CMP using splash-2 and parsec benchmarks. APR improves performance by 21% on average compared to conventional shared cache design, by 17% over Victim Replication (VR), by 10% over Adaptive Selective Replication (ASR), and by 15% over Reactive NUCA (R-NUCA). the additional hardware cost of APR is well under 1% of L2 cache slice.

关键词： Radiation detectors Benchmark testing Tiles System-on-a-chip Monitoring Runtime Hardware

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Research on Security Assessment Model of Water Supply System Based on Leakage Control

Research on Security Assessment Model of Water Supply System...

引用

2011 2nd international conference on Challenges in Environmental Science and Computer Engineering(CESCE 2011)

作者： Li Hong wei Li Yan jing Wenyan Wu Lu Mou Department of Environmental and Municipal Engineering Qingdao Technological University Utility Department CPECC East-China Design Branch Faculty of Computing Engineering and Technology Staffordshire University

Based on the characteristics of urban water supply system, the urban pipe network security assessment system was established, and then AHP method combined with fuzzy evaluation was used to carry out a comprehensive security assessment for the leakage of the urban pipe network. By calculating a real case and analyzing various leakage causes and their impact, the assessment model was verified reasonable.

关键词： Water supply network Leakage control Security assessment AHP Fuzzy evaluation

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Research on Security Assessment Model of Water Supply System Based on Leakage Control

Research on Security Assessment Model of Water Supply System...

引用

2011 international conference on Energy and Environment(ICEE 2011)