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arXiv 2021年

作者： Yang, Shangshang Tian, Ye Xiang, Xiaoshu Peng, Shichen Zhang, Xingyi Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Artificial Intelligence Anhui University Hefei230039 China Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Institutes of Physical Science and Information Technology Anhui University Hefei230601 China

Evolutionary neural architecture search (ENAS) has recently received increasing attention by effectively finding high-quality neural architectures, which however consumes high computational cost by training the architecture encoded by each individual for complete epochs in individual evaluation. Numerous ENAS approaches have been developed to reduce the evaluation cost, but it is often difficult for most of these approaches to achieve high evaluation accuracy. To address this issue, in this paper we propose an accelerated ENAS via multifidelity evaluation termed MFENAS, where the individual evaluation cost is significantly reduced by training the architecture encoded by each individual for only a small number of epochs. The balance between evaluation cost and evaluation accuracy is well maintained by suggesting a multi-fidelity evaluation, which identifies the potentially good individuals that cannot survive from previous generations by integrating multiple evaluations under different numbers of training epochs. For high diversity of neural architectures, a population initialization strategy is devised to produce different neural architectures varying from ResNetlike architectures to Inception-like ones. Experimental results on CIFAR-10 show that the architecture obtained by the proposed MFENAS achieves a 2.39% test error rate at the cost of only 0.6 GPU days on one NVIDIA 2080TI GPU, demonstrating the superiority of the proposed MFENAS over state-of-the-art NAS approaches in terms of both computational cost and architecture quality. The architecture obtained by the proposed MFENAS is then transferred to CIFAR-100 and ImageNet, which also exhibits competitive performance to the architectures obtained by existing NAS approaches. The source code of the proposed MFENAS is available at https://***/DevilYangS/MFENAS/.MSC Codes 68W50, 68T07 © 2021, CC BY.

关键词： Convolutional neural networks

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Design of Filtering Power Divider with Wide Stopband Based on QMSIW 2

Design of Filtering Power Divider with Wide Stopband Based o...

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2020 2nd International Conference on Artificial Intelligence Technologies and Application, ICAITA 2020

作者： Cheng, Yi Huang, Zhixiang Wang, Zhongmiao Wang, Chao Li, Xuemei Key Lab of Ministry of Education of Intelligent Computing and Signal Processing Anhui University Hefei Anhui230601 China

A miniaturized filtering power divider with wide stopband based on quarter-mode substrate integrated waveguide (QMSIW) is proposed. The physical size of the QMSIW is reduced by 3/4 comparing with the traditional SIW structure, which can achieve miniaturization, and its electromagnetic field distribution has little effect. Using the metal interference column and the etched U-shaped groove to adjust the resonance frequency, so that the main mode and the first higher mode form a passband, meanwhile, suppressing the second higher mode. The simulation results show that the center frequency is 4.42 GHz with insertion loss of 0.45 dB, the 3-dB bandwidth is 4.12-4.63 GHz with a fractional bandwidths of 9.7%, and there is a great stopband up to 11 GHz with 15 dB rejection level. Besides, the return loss of passband is better than 17.5 dB. To verify the validity of this design, a prototype is fabricated and measured. The measured results demonstrate that it has a good agreement with the design simulations. © 2020 Published under licence by IOP Publishing Ltd.

关键词： Substrate integrated waveguides

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A Circularly Polarized Antenna for GPS L1 band with U-Shaped-Like Strips

A Circularly Polarized Antenna for GPS L1 band with U-Shaped...

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International Applied Computational Electromagnetics Society Symposium (ACES)

作者： Lulu Meng Zhixiang Huang Yingsong Li School of Electronic and Information Engineering Anhui University Hefei China Anhui Province Key Laboratory of Target Recognition and Feature Extraction LuAn China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University China

A U-shaped-like parasitic strip loading antenna is proposed and designed, which can work in L1 band of GPS with circular polarization (CP) characteristics. The antenna consists of ground plane, dielectric, radiation patch and U-shaped-like parasitic strips using a simple coaxial feed probe. The asymmetric U-shaped-like strips are to realize CP. The simulation and optimization results for the modeled directional CP antenna show that the axial ratio bandwidth is 10MHz and the impedance bandwidth is 30MHz, which can meet the requirements for GPS L1 band.

关键词： Polarization Loading Bandwidth Directive antennas Probes Loaded antennas Optimization

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A Novel DDoS Detection Model for SDN Using Single-Class Cluster Oversampling and Weighted Ensemble Method

A Novel DDoS Detection Model for SDN Using Single-Class Clus...

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International Conference on Network Protocols

作者： Hao Zhang Shuqi Wu Jie Pan Zhi Wang Xiaolong Sun College of Computer and Data Science Fuzhou University Fuzhou China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China Engineering Research Center of Big Data Intelligence Chinese Ministry of Education Fuzhou China

ISBN: (数字)9798350351712

ISBN: (纸本)9798350351729

The centralized control plane characteristic of Software Defined Networking (SDN) makes it a prime target for Distributed Denial of Service (DDoS) attacks. A significant issue in network traffic data is the severe imbalance between background traffic and various types of DDoS attack traffic. To address this challenge, we propose a novel DDoS detection model based on single-class cluster oversampling and weighted ensemble method. Initially, we design a multi-strategy feature selection using variance inflation factor and recursive feature elimination. Next, we construct a single-class cluster model based on the spatial distribution of samples, and create minority class samples independently within each cluster. Finally, detection results are determined using the weight vector of each base classifier in ensemble learning. The proposed DDoS detection model's performance is validated through simulations of various DDoS attack scenarios in an SDN environment. Experimental results indicate that the proposed strategy exhibits superior performance, showing improvements in precision, recall, and F1 score compared to state-of-the-art techniques.

关键词： Interpolation Protocols Telecommunication traffic Denial-of-service attack Feature extraction Vectors Data models Ensemble learning Computer crime Software defined networking

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D-FGNAE: Decentralized Federated Graph Normalized AutoEncoder 19th

D-FGNAE: Decentralized Federated Graph Normalized AutoEncode...

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19th CCF Conference on Computer Supported Cooperative Work and Social computing, ChineseCSCW 2024

作者： Liang, Yuting Cai, Weixin Guo, Kun College of Computer and Data Science Fuzhou University Fuzhou350108 China Engineering Research Center of Big Data Intelligence Ministry of Education Fuzhou350108 China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350108 China

ISBN: (纸本)9789819623723

Graphs widely exist in real-world, and Graph Neural Networks (GNNs) have exhibited exceptional efficacy in graph learning in diverse fields. With the strengthening of data privacy protection worldwide in recent years, Federated graph neural networks (FedGNNs) have gained increasing attention in academia and industry owing to their ability to train the model in a collaborative manner while complying with the privacy protection regulations. However, in federated learning, the non-independent and identically distributed (non-IID) problem of local data possessed by multiple participants can significantly undermine model accuracy. We propose a new Decentralized Federated Graph Normalized AutoEncoder (D-FGNAE). First, the model is designed as a decentralized federated learning framework with dynamically assigned tripartite roles. This design eliminates the fixed server role found in traditional federated learning, enhances system fault tolerance, avoids single points of failure, and protects model privacy. Second, the splitting and correcting of calculation by layer in the model, along with the special design of the normalization layer, effectively tackle the non-IID problem in both the structural and attribute aspects. Experimental results on real-world networks demonstrate the effectiveness of D-FGNAE, which can achieve nearly the same accuracy as the centralized model. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Federated learning

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Community-Aware Heterogeneous Graph Contrastive Learning 19th

Community-Aware Heterogeneous Graph Contrastive Learning

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19th CCF Conference on Computer Supported Cooperative Work and Social computing, ChineseCSCW 2024

作者： Li, Xinying Wu, Ling Guo, Kun College of Computer and Data Science Fuzhou University Fuzhou350108 China Engineering Research Center of Big Data Intelligence Ministry of Education Fuzhou350108 China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350108 China

ISBN: (纸本)9789819623723

Recently, heterogeneous graph contrastive learning, which can mine supervision signals from the data, has attracted widespread attention. However, most existing methods employ random data augmentation strategies to construct contrastive views, which may destroy the semantic information in heterogeneous graphs. Moreover, they often select positive and negative samples based solely on node-level proximity and overlook hard samples that are difficult to distinguish from anchors. To solve the above problems, we propose a Community-Aware Heterogeneous Graph Contrastive Learning model called CAHGCL. In particular, we design an adaptive data augmentation strategy to construct views, including feature augmentation and topology augmentation. To improve the quality of samples, we propose a dynamic sample weighting strategy based on node similarity and community information, capable of identifying both hard positive samples and hard negative samples. Finally, we introduce community-level contrast to improve community cohesion. Extensive experiments and analyses demonstrate that CAHGCL consistently outperforms state-of-the-art baselines on three datasets. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive Learning

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UGCM-LU: A Unified Stream and Batch Graph computing Model with Local Update for Community Detection 19th

UGCM-LU: A Unified Stream and Batch Graph Computing Model w...

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19th CCF Conference on Computer Supported Cooperative Work and Social computing, ChineseCSCW 2024

作者： Li, Hong Wu, Ling Guo, Kun College of Computer and Data Science Fuzhou University Fuzhou350108 China Engineering Research Center of Big Data Intelligence Ministry of Education Fuzhou350108 China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350108 China

ISBN: (纸本)9789819623723

Unified stream and batch computing (USBC) aims to incorporate stream and batch computation into a unified framework, thereby enabling the development of a one-stop solution for stream and batch data processing and enhancing the generalization of the framework. However, research on unified graph computing models (UGCMs) faces several challenges. First, existing UGCMs need to consider all graph information in the cache during the incremental update phase, thus leading to decreased execution efficiency. Second, existing UGCMs use fixed bytes to store nodes without considering the actual space occupied by nodes resulting in wasted memory when dealing with large graphs. This paper proposes a UGCM with Local Updates for community detection (UGCM-LU). We first implement a local update strategy to consider partial information of the graph to achieve incremental updates. Secondly, we also designed a byte-compression-based module to store graph data according to the space occupied by nodes. The experimental results show the effectiveness and efficiency of the model in real-world and artificial networks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Lutetium alloys

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An Improved Differential Evolution Algorithm Integrating the Plant Evolutionary Strategy

SSRN

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SSRN 2024年

作者： Zhang, Xiaoming Cheng, Hongwei Li, Jun Li, Tingjuan Zhang, Panpan Tian, Ye Institutes of Physical Science and Information Technology Anhui University Hefei230601 China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei230601 China Qinghai Institute of Science and Technology Information Xining810008 China School of Computer Science and Technology Anhui University Hefei230601 China

Differential evolution algorithm (DE) and their variants often encounter a decrease in population diversity and convergence rate along with the evolution, resulting in local optima and premature convergence. To address this issue, this paper proposes an improved differential evolution integrating the plant evolutionary strategy, named DEPES, leveraging the exploitation capability of DE and the exploration capability of the plant population distribution evolutionary strategy. First, a tri-subpopulation optimization framework with a mutation strategy is introduced to cope with diversity maintenance, convergence enhancement, and local optima a Cauchy mutation operator based on plant population distribution evolutionary model is employed to enhance the exploration capability of the mutation strategy. Second, a parameter control strategy is performed on the superior and inferior solutions to balance the exploitation and exploration of the population. Third, a covariance matrix-based crossover strategy is designed to minimize interference from local population information, consequently generating high-quality solutions. The superior efficiency of DEPES is fully verified by comparing it with advanced DE-based variants and other algorithms on the IEEE CEC2020 test suite and engineering design problems. © 2024, The Authors. All rights reserved.

关键词： Covariance matrix

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Determining the covering radius of all generalized Zetterberg codes in odd characteristic

arXiv

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arXiv 2024年

作者： Shi, Minjia Li, Shitao Helleseth, Tor Özbudak, Ferruh Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education School of Mathematical Sciences Anhui University Hefei230601 China School of Mathematical Sciences Anhui University Anhui Hefei230601 China Department of Informatics University of Bergen Bergen Norway Faculty of Engineering and Natural Sciences Sabancı University Istanbul34956 Turkey

For an integer s ≥ 1, let Cs(q0) be the generalized Zetterberg code of length qs0 + 1 over the finite field Fq0 of odd characteristic. Recently, Shi, Helleseth, and Özbudak (IEEE Trans. Inf. Theory 69(11): 7025-7048, 2023) determined the covering radius of Cs(q0) for (formula presented) (mod 8), and left the remaining case as an open problem. In this paper, we develop a general technique involving arithmetic of finite fields and algebraic curves over finite fields to determine the covering radius of all generalized Zetterberg codes for (formula presented) (mod 8), which therefore solves this open problem. We also introduce the concept of twisted half generalized Zetterberg codes of length (formula presented) , and show the same results hold for them. As a result, we obtain some quasi-perfect codes. Copyright © 2024, The Authors. All rights reserved.

关键词： Turbo codes

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Community Evolution Tracking Based on High-Order Neighbor Consideration and Node Change Identification 19th

Community Evolution Tracking Based on High-Order Neighbor C...

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19th CCF Conference on Computer Supported Cooperative Work and Social computing, ChineseCSCW 2024

作者： Zhang, Yunan Wang, Chaohui Wu, Ling Guo, Kun College of Computer and Data Science Fuzhou University Fuzhou350108 China Engineering Research Center of Big Data Intelligence Ministry of Education Fuzhou350108 China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350108 China

ISBN: (纸本)9789819623723

Community evolution tracking is widely used in complex network analysis, which analyzes and identifies how communities evolve over time based on dynamic community detection. However, the current incremental dynamic community detection method has the phenomenon of ’concept drift’, which leads to inaccurate tracking of community evolution. Although some studies on dynamic community detection have proposed to perform global community detection on the network by timing or when the error information accumulates to a certain extent, this method will lead to a sudden change in community division, which will interrupt the evolution sequence of some communities and bring great difficulties to the continuous tracking of community evolution. In addition, the current community evolution tracking methods match the communities of two adjacent snapshots, and it is difficult to capture the fine-grained evolution process within the community. This paper proposes a community evolution tracking algorithm based on high-order neighbor consideration and node change identification. Firstly, based on an incremental update strategy that considers high-order neighbors, we adaptively expand the scope of incremental updates to reduce the accumulation of error information. Secondly, through the node change identification strategy to mine fine-grained community evolution events. Experiments show that our method can effectively track the evolution of communities in dynamic networks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Community evolution Complex network Dynamic network Evolution tracking

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