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Pacific-Asia Workshop on Computational Intelligence and Industrial Application, PACIIA

作者： Xianjin Fang Longshu Li Key Lab of Intelligent Computing & Signal Processing of Ministry of Education Anhui University Hefei China Computer School Anhui University Hefei China

This paperpsilas purpose is to design a novel artificial immune model for network intrusion detection. This novel model can satisfy three main requirements of an efficient network intrusion detection system, namely, distributed, lightweight and self-organizing, can also quicken the process of affinity maturation of detector population and improve the efficiency of anomaly detection. The algorithms of adaptive extracting vaccines and vaccine operator are given in detail. And then, on the basis of Kimpsilas conceptual model for network intrusion detection, a novel artificial immune model and relevant algorithm for real-time network intrusion detection is proposed, which integrates vaccine operator with negative selection algorithm and clonal selection algorithm.

关键词： Vaccines Intrusion detection Detectors Computer networks Distributed computing Conferences Computational intelligence Computer industry Application software Computational and artificial intelligence

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A compression algorithm for multi-streams based on GEP

A compression algorithm for multi-streams based on GEP

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3rd International Conference on Genetic and Evolutionary computing, WGEC 2009

作者： Ding, Chao Yuan, Chang-An Qin, Xiao Peng, Yu-Zhong Computer and Information Engineering College Guangxi Teachers Education University Nanning 530001 China First Middle School of Mengcheng Anhui Mengcheng233500 China Key Lab. of Scientific Computing and Intelligent Information Processing in Universities of Guangxi Guangxi Teachers Education University Nanning China

ISBN: (纸本)9780769538990

This paper applied the Methods which based on GEP in compress multi-streams. The contributions of this paper include: 1) giving an introduction to data function finding based on GEP(DFF-GEP), defining the main conception of Multi-Streams, and revealing the map relation in it;2) putting forward the Compression Algorithm for Multi-Streams according to map relation lied in data between data streams;and 3)providing an experience with the real data and find that (3.1) the compression ratio of the new methods is 120-150 times as the traditional wavelets method, and 35-70 times as the wavelets and coincidence method;(3.2) the relative error of the new method is about 3??,yet maximum relative error is 0.01 by using the traditional relative error standard, the precision is improved from 7% to 15% as compared with the traditional method. © 2009 IEEE.

关键词： Errors

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Effective approach to improve the solving precision of electric field integral equation

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Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics 2009年第11期31卷 2553-2555页

作者： Song, Kai-Hong Zhang, Qing-Hua Wu, Xian-Liang Key Lab. of Intelligent Computing and Signal Processing Anhui Univ. Hefei 230039 China

To improve the accuracy and efficiency for solving the EFIE (electric field integral equation) by the method of moment, a novel scheme is put forward to improve the calculation precision based on the EFIE. Firstly, the inaccurate induced current is obtained by solving the EFIE under coarse mesh grids. Secondly, the scattered magnetic field at any given point on the surface of the object is calculated by the inaccurate induced current. Finally, the accurate induced current at any given point is determined through total magnetic field. The proposed approach is applied to the case of infinitely perfectly electric conducting circular cylinder and square cylinder. The numerical results are presented to demonstrate the high precision and efficiency when the size of mesh grid is large.

关键词： Method of moments

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Exploring multi-loss function based attention framework for vehicle re-identification 10

Exploring multi-loss function based attention framework for ...

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10th International Conference on Information Technology in Medicine and Education, ITME 2019

作者： Liu, Wei Zhang, Yi Luo, Zhiming Li, Shaozi East China Jiaotong University Jiangxi China Cognitive Science Department Xiamen University Fujian China Postdoc Center of Information and Communication Engineering Xiamen University China Key Lab. of Cogn. Computing and Intelligent Information Processing of Fujian Education Institutions Wuyishan China Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering Yi Chang China

ISBN: (纸本)9781728139173

Vehicle Re-identification(Re-ID) refers to the task of matching a specific vehicle across multiple non-overlapping cameras. Precise vehicle Re-ID suffers from two main challenges: (1) different vehicle identities with a similar appearance of the same type have a subtle inter-instance discrepancy, (2) one vehicle usually has large intra-instance differences due to viewpoint and illumination variations. To address these challenges, in this paper, we proposed an Attention-based Framework for Vehicle Re-ID named AFVR, which combines the benefits of both the cross-entropy loss function and the triplet loss function. Experimental results on two popular vehicle Re-ID benchmarks demonstrate the effectiveness of our proposed method, compared with the state of the arts. © 2019 IEEE.

关键词： Vehicles

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Parallel implementation of apriori algorithm based on MapReduce

Parallel implementation of apriori algorithm based on MapRed...

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13th ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed computing, SNPD 2012

作者： Li, Ning Zeng, Li He, Qing Shi, Zhongzhi Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China Graduate University Chinese Academy of Sciences Beijing 100139 China Key Lab. of Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding 071002 Hebei China

ISBN: (纸本)9780769547619

Searching frequent patterns in transactional databases is considered as one of the most important data mining problems and Apriori is one of the typical algorithms for this task. Developing fast and efficient algorithms that can handle large volumes of data becomes a challenging task due to the large databases. In this paper, we implement a parallel Apriori algorithm based on MapReduce, which is a framework for processing huge datasets on certain kinds of distributable problems using a large number of computers (nodes). The experimental results demonstrate that the proposed algorithm can scale well and efficiently process large datasets on commodity hardware. © 2012 IEEE.

关键词： MapReduce

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Strong Convergence of Empirical Distribution for a Class of Random Matrices

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Chinese Quarterly Journal of Mathematics 2006年第1期21卷 28-32页

作者： LIANG Qing-wen MIAO Bai-qi WANG Da-peng Department of Statistics and Finance University of Science and Technology of China Hefei 230026 China Key Lab of intelligent Computing and Signal Processing of the Ministry of Education AnhuiUniversity Hefei 230026 China

Let {vij}, i, j = 1, 2, …, be i.i.d, random variables with Ev11 = 0, Ev11^2 = 1 and a1 = （ai1,…, aiM） be random vectors with {aij} being i.i.d, random variables. Define XN =（x1,…, xk） and SN =XNXN^T,where xi=ai×si and si=1/√N（v1i,…, vN,i）^T. The spectral distribution of SN is proven to converge, with probability one, to a nonrandom distribution function under mild conditions.

关键词： empirical spectral distribution function sample covariance matrix Stieltjes transform strong convergence

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SGCN: A Scalab.e Graph Convolutional Network with Graph-Shaped Kernels and Multi-Channels

SSRN

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SSRN 2023年

作者： Huang, Zhenhua Zhou, Wenhao Li, Kunhao Jia, Zhaohong School of Internet Anhui University Feixi Road Hefei230039 China Key Lab of Intelligent Computing and Signal Processing of Ministry of Education Hefei China

Graph neural networks (GNNs) have shown great success in graph processing. However, current message-passing-based GNNs have limitations in feature aggregations and update mechanisms that rely on a fixed mode, resulting in inadequate representations of the neighborhood structure's richness. Furthermore, the convolution layer in most GNNs lacks flexibility and multi-channels compared to convolutional neural networks (CNNs). CNNs employ multiple channels, where different kernels capture shared patterns of images. To address the limitations, we propose a novel scalab.e graph convolution network (SGCN). SGCN enhances structural information learning by introducing an expressive feature aggregation mechanism. Drawing inspiration from the design of CNNs, SGCN designs graph-shaped kernels to perform multi-channel convolutions on the subgraph structure. A sampler based on degree centrality is employed to simplify the computation costs. Extensive experiments demonstrate that SGCN achieved state-of-the-art performances on various scales of graph datasets (accuracy improved up to 5.38%). © 2023, The Authors. All rights reserved.

关键词： Convolution

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A Wideband Millimeter Wave E-shape Antenna in Package with Embedded Wafer Level Ball Grid Array Technology

A Wideband Millimeter Wave E-shape Antenna in Package with E...

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International Conference on Numerical Electromagnetic Modeling and Optimization for RF, Microwave, and Terahertz Applications (NEMO)

作者： Bin Lu Haoran Zhu Zongming Duan Yuefei Dai Key Lab of Intelligent Computing & Signal Processing Ministry of Education Anhu University Hefei China Key Lab of Intelligent Computing & Signal Processing Ministry of Education Anhui University Hefei China Anhui Province Engineer Laboratory for Antennas and Microwave The 38th Research Institute of China Electronic Technology Group Corporationy Hefei China

ISBN: (数字)9781728169668

ISBN: (纸本)9781728169675

The feasibility of an antenna-in-package (AiP) concept is demonstrated for automotive radar sensors at the 77 GHz-band. Infineon's embedded wafer level ball grid array (eWLB) is employed as packaging technology. We developed a E-shape patch antenna in 6×6 mm 2 eWLB package that include 2×2 mm 2 dummy Si chips. From the simulated results of the proposed E-plane patch antenna, provides a wide bandwidth of 19.2% (|S 11 |

关键词： Patch antennas Radar antennas Sensors Broadband antennas Wideband Antenna arrays Electronics packaging

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Inter-piconet Interference Mitigation Schemes for Converged BTLE and Cellular Network

Inter-piconet Interference Mitigation Schemes for Converged ...

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2011年无线通信与信号处理国际会议(WCSP 2011)

作者： Ying Hu Guizhu Wang Yuling Ouyang Lianhai Shan Zhi Yuan Key laboratory of Intelligent Computing & Signal Processing Ministry of Education Anhui University Key laboratory of Intelligent Computing & SignalProcessing Ministry of Education Anhui UniversityHe Shanghai Research Center for Wireless CommunicationsKey Lab of Wireless Sensor Network & Communicati

In a Bluetooth Low Energy (BTLE) piconet, the master node controls the channel access by a simple polling scheme. Due to an absence of coordination among independent master nodes while accessing the wireless medium, devices will encounter high packet interference if many piconets are simultaneously operating in the same area. In this paper, we propose two schemes in the converged BTLE and cellular network, one is based on bad channel prediction for the BTLE piconets, and the other is based on frequency hopping sequences transformation. We compare the performance of the proposed algorithms with original scheme in the form of channel collision probability and new signaling overhead. The simulation results show that the proposed schemes can reduce channel collision probability significantly and simultaneously the new signaling overhead introduced to the downlink is not large.

关键词： Bluetooth Low Energy(BTLE) piconets cellular network interference channel collision probability

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A cascaded linear model for joint Chinese word segmentation and part-of-speech tagging

A cascaded linear model for joint Chinese word segmentation ...

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46th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, ACL-08: HLT

作者： Jiang, Wenbin Huang, Liang Liu, Qun Lü, Yajuan Key Lab. of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences P.O. Box 2704 Beijing 100190 China Department of Computer and Information Science Levine Hall University of Pennsylvania 3330 Walnut Street Philadelphia PA 19104 United States

ISBN: (纸本)9781932432046

We propose a cascaded linear model for joint Chinese word segmentation and partof- speech tagging. With a character-based perceptron as the core, combined with realvalued features such as language models, the cascaded model is able to efficiently utilize knowledge sources that are inconvenient to incorporate into the perceptron directly. Experiments show that the cascaded model achieves improved accuracies on both segmentation only and joint segmentation and part-of-speech tagging. On the Penn Chinese Treebank 5.0, we obtain an error reduction of 18.5% on segmentation and 12% on joint segmentation and part-of-speech tagging over the perceptron-only baseline. © 2008 Association for Computational Linguistics.

关键词： Computational linguistics

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