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arXiv 2021年

作者： Shi, Minjia Krotov, Denis Li, Xiaoxiao Solé, Patrick Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Mathematics Sciences Anhui University HefeiAnhui China Sobolev Institute of Mathematics Novosibirsk630090 Russia I2M CNRS Centrale Marseille University of Aix-Marseille Marseilles France

The distribution of cardinalities of zero-sum sets in abelian groups is completely determined. A summation involving the Möbius function is given for the general abelian group, while in many special cases, including the case of elementary abelian groups, solved earlier by Li and Wan, it has a compact form. The proof involves two different Möbius transforms, on positive integers and on set *** Codes 05A18, 05E40, 68P27 Copyright © 2021, The Authors. All rights reserved.

关键词： Group theory

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Community-Grouping Based Particle Swarm optimisation Algorithm for Feature Selection

Community-Grouping Based Particle Swarm optimisation Algorit...

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Congress on Evolutionary Computation

作者： Jianfeng Qiu Jiangchuan Wan Lei Zhang Fan Cheng Yongkang Luo Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Anhui University China School of Computer Science and Technology Anhui University Hefei China

ISBN: (数字)9781728169293

ISBN: (纸本)9781728169309

As a frequently-used dimensionality reduction technique in machine learning, feature selection has attracted interests in the last decade. Since feature selection is essentially a combinatorial optimization problem, how to search the valuable feature subset is a challenging optimization task. Particle swarm optimization (PSO) algorithm and its variations have shown their competitiveness in solving feature selection problem. However, they have been proven to be easily trapped into the local optimal in high-dimensional space due to their intrinsic characteristic of quick convergence. To this end, an effective binary particle swarm optimization algorithm, named CBPSOFS, is proposed for feature selection, where a community-grouping based adaptive updating strategy is designed to avoid trapping into the local optimum and enhance the performance of PSO algorithm in feature selection. To be specific, the correlationship among features is used to construct the feature network, where multiple feature groups are obtained by dividing the achieved feature network. Considering that a community usually contains multiple similar features, the proposed adaptive updating strategy utilizes these feature groups to make the similar features not be included in the same particle so as to maintain the diversity of the population in the evolution. In addition, an information gain based initialization strategy and a history information based resetting strategy are also developed to improve the quality of obtained feature subset. Experimental results on several real world datasets have demonstrated the effectiveness of CBPSOFS in feature selection when compared with the several state-of-the-art baselines.

关键词： Feature extraction Sociology Statistics Particle swarm optimization Optimization History signal processing algorithms

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Development of Fast Periodic Characteristic Mode Analysis (PCMA) Tool

Development of Fast Periodic Characteristic Mode Analysis (P...

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IEEE International Conference on Computational Electromagnetics (ICCEM)

作者： G. S. Cheng Chao-Fu Wang Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Auhui University Hefei China Temasek Laboratories National University of Singapore Singapore Singapore

ISBN: (数字)9781728134482

ISBN: (纸本)9781728134499

This paper addresses how to develop theory of characteristic modes (TCM) for finite periodic arrays or structures with fully using their periodic property. For ease of reference, the developed method is called periodic characteristic mode analysis (PCMA) method. Its basic idea is that we adopt a small set of specially selected characteristic modes (CMs) of a reference cell (element) of the structure to build up entire-domain basis functions (EDBFs) defined on each cell of the array structure and apply them to represent the characteristic currents (CCs) of the whole structure. Making use of periodicity of the array can greatly reduce the matrix size of the resultant generalized eigenvalue equation and speed up its solution process.

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Challenge-aware U-net for breast lesion segmentation in ultrasound images

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Pattern Recognition 2025年 168卷

作者： Dengdi Sun Changxu Dong Yuchen Yan Bo Jiang Yayang Duan Zhengzheng Tu Chaoxue Zhang Key Laboratory of Intelligent Computing & Signal Processing (ICSP) Ministry of Education School of Artificial Intelligence Anhui University China Institute of Artificial Intelligence Hefei Comprehensive National Science Center China Anhui Provincial Key Laboratory of Multimodal Cognitive Computing School of Computer Science and Technology Anhui University China Department of Ultrasound the First Affiliated Hospital of Anhui Medical University China

Deep learning methods can enhance the efficiency of tumor segmentation in breast ultrasound (BUS) images. However, noise interference, small tumors, and blurred boundaries can reduce segmentation accuracy. We design a three-branch challenge-aware U-net (CAU-net) to address these main challenges in BUS images. Our CAU-net extracts the features from three challenge-aware encoders in parallel first. Secondly, we propose an adaptive aggregation layer (AAL) to merge the multi-scale features of three challenging branches, enabling the network to adaptively handle different breast lesion samples with these main challenges. To further enhance the accuracy of segmentation, we introduce the graph reasoning module (GRM) to the network to model the correlation between the channels of the features and acquire the global information in the features. The result of our experiment on two datasets demonstrates the superiority of CAU-net over the advanced medical image segmentation methods. Our code can be downloaded from https://***/tzz-ahu .

关键词： Image segmentation

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Iterated Problem Reformulation for Evolutionary Large-Scale Multiobjective Optimization

Iterated Problem Reformulation for Evolutionary Large-Scale ...

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Congress on Evolutionary Computation

作者： Cheng He Ran Cheng Ye Tian Xingyi Zhang Department of Computer Science and Engineering University Key Laboratory of Evolving Intelligent Systems of Guangdong Province Southern University of Science and Technology Shenzhen China Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Institutes of Physical Science and Information Technology Anhui University Hefei China Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei China

ISBN: (数字)9781728169293

ISBN: (纸本)9781728169309

Due to the curse of dimensionality, two main issues remain challenging for applying evolutionary algorithms (EAs) to large-scale multiobjective optimization. The first issue is how to improve the efficiency of EAs for reducing computation cost. The second one is how to improve the diversity maintenance of EAs to avoid local optima. Nevertheless, these two issues are somehow conflicting with each other, and thus it is crucial to strike a balance between them in practice. Thereby, we propose an iterated problem reformulation based EA for large-scale multiobjective optimization, where the problem reformulation based method and the decomposition based method are used iteratively to address the aforementioned issues. The proposed method is compared with several state-of-the-art EAs on a variety of large-scale multiobjective optimization problems. Experimental results demonstrate the effectiveness of our proposed iterated method in large-scale multiobjective optimization.

关键词： Sociology Convergence Pareto optimization Indexes Computer science

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The Graph Structure of Chebyshev Permutation Polynomials over Ring pk

arXiv

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arXiv 2022年

作者： Li, Chengqing Lu, Xiaoxiong Tan, Kai Chen, Guanrong The School of Computer Science Xiangtan University Hunan Xiangtan411105 China The School of Mathematics and Computational Science Xiangtan University Xiangtan411105 China The Key Laboratory of Intelligent Computing & Information Processing Ministry of Education Xiangtan University Hunan Xiangtan411105 China The Department of Electrical Engineering City University of Hong Kong Hong Kong

Understanding the underlying graph structure of a nonlinear map over a particular domain is essential in evaluating its potential for real applications. In this paper, we investigate the structure of the associated functional graph of Chebyshev permutation polynomials over a ring pk, with p being a prime number greater than three, where every number in the ring is considered as a vertex and the existing mapping relation between two vertices is regarded as a directed edge. Based on some new properties of Chebyshev polynomials and their derivatives, we disclose how the basic structure of the functional graph evolves with respect to parameter k. First, we present a complete and explicit form of the length of a path starting from any given vertex. Then, we show that the strong patterns of the functional graph that the number of cycles of any given length always remains constant as k increases. Moreover, we rigorously prove the rules on the elegant structure of the functional graph and verify them experimentally. Our results could be useful for studying the emergence of the complexity of a nonlinear map in digital computers and security analysis of its cryptographic *** Codes 94A55 Copyright © 2022, The Authors. All rights reserved.

关键词： Chebyshev polynomials

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Error Analysis of Virtual Element Methods for the Time-dependent Poisson-Nernst-Planck Equations

arXiv

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arXiv 2022年

作者： Yang, Ying Liu, Ya Shu, Shi School of Mathematics and Computational Science Guangxi Colleges Universities Key Laboratory of Data Analysis and Computation Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Guilin University of Electronic Technology Guangxi Guilin541004 China School of Mathematics and Computational Science Guilin University of Electronic Technology Guangxi Guilin541004 China School of Mathematics and Computational Science Xiangtan University Xiangtan411105 China

We discuss and analyze the virtual element method on general polygonal meshes for the time-dependent Poisson-Nernst-Planck equations, which are a nonlinear coupled system widely used in semiconductors and ion channels. The spatial discretization is based on the elliptic projection and the L2 projection operator, and for the temporal discretization, the backward Euler scheme is employed. After presenting the semi and fully discrete schemes, we derive the a priori error estimates in the L2 and H1 norms. Finally, a numerical experiment verifies the theoretical convergence results. Copyright © 2022, The Authors. All rights reserved.

关键词： Nonlinear equations

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GenFace: A Large-Scale Fine-Grained Face Forgery Benchmark and Cross Appearance-Edge Learning

arXiv

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arXiv 2024年

作者： Zhang, Yaning Yu, Zitong Wang, Tianyi Huang, Xiaobin Shen, Linlin Gao, Zan Ren, Jianfeng Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China School of Computing and Information Technology Great Bay University Dongguan523000 China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China Nanyang Technological University 50 Nanyang Ave Block N 4 639798 Singapore Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518129 China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University China Jinan250014 China Key Laboratory of Computer Vision and System Ministry of Education Tianjin University of Technology Tianjin300384 China School of Computer Science University of Nottingham Ningbo China

The rapid advancement of photorealistic generators has reached a critical juncture where the discrepancy between authentic and manipulated images is increasingly indistinguishable. Thus, benchmarking and advancing techniques detecting digital manipulation become an urgent issue. Although there have been a number of publicly available face forgery datasets, the forgery faces are mostly generated using GAN-based synthesis technology, which does not involve the most recent technologies like diffusion. The diversity and quality of images generated by diffusion models have been significantly improved and thus a much more challenging face forgery dataset shall be used to evaluate SOTA forgery detection literature. In this paper, we propose a large-scale, diverse, and fine-grained high-fidelity dataset, namely GenFace, to facilitate the advancement of deepfake detection, which contains a large number of forgery faces generated by advanced generators such as the diffusion-based model and more detailed labels about the manipulation approaches and adopted generators. In addition to evaluating SOTA approaches on our benchmark, we design an innovative Cross Appearance-Edge Learning (CAEL) detector to capture multi-grained appearance and edge global representations, and detect discriminative and general forgery traces. Moreover, we devise an Appearance-Edge Cross-Attention (AECA) module to explore the various integrations across two domains. Extensive experiment results and visualizations show that our detection model outperforms the state of the arts on different settings like cross-generator, cross-forgery, and cross-dataset evaluations. Code and datasets will be available at https://***/Jenine-321/GenFace. Copyright © 2024, The Authors. All rights reserved.

关键词： Image enhancement

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Improved Matrix-based Attribute Reduction Algorithm Based on Minimal Elements for Mobile Edge computing

Improved Matrix-based Attribute Reduction Algorithm Based on...

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IEEE/ACM International Symposium on Cluster computing and the Grid (CCGRID)

作者： Yi Xu Zhiqiang Zheng Yan Cheng Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei China School of Computer Science and Technology Anhui University Hefei China

ISBN: (数字)9781728160955

ISBN: (纸本)9781728196497

With the development of Internet of Thlngs(IoT) technology, the Mobile Edge computing (MEC) environment plays an important role in different scenarios. This paper focus on the "last-mile" delivery scenario in logistics. In this scenario, due to the limited computation ability of the unmanned aerial vehicle (UAV), high real-time and computational resource-intensive tasks such as obstacle avoidance, route planning, and face recognition are require the assistance by MEC computing resources. However, due to the limited computing power of edge servers, the computing tasks of neural networks has very high delay. It cause the deadline of neural networks computation task may not be satisfied. Besides with the users and environment changed continuously, the neural nework take a long time to update which may affect the work of UAV. Therefore, It is an critical problem to accelerate the calculation and update of neural network on edge server. This paper proposes an attribute reduction algorithm based on rough set, which can reduce redundant attributes and compress the dimensionality of data. The experimental results show that can accelerate the calculation and update of neural network significantly and save the energy consumption.

关键词： Servers Edge computing Task analysis Neural networks Energy consumption Information systems Rough sets

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PROM: A persistent routing optimization method based on supervised learning

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Journal of Network and Computer Applications 2025年 242卷

作者： Yingya Guo Zebo Huang Mingjie Ding Bin Lin Huan Luo College of Computer and Data Science Fuzhou University China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University China Engineering Research Center of Big Data Intelligence Ministry of Education China

Traditional Traffic Engineering (TE) typically improves network performance by formulating and solving routing optimization problems based on measured or predicted Traffic Matrices (TMs). However, since network traffic is dynamic, routing strategies optimized for the current measured TMs may not be suitable for future traffic patterns. To adapt to dynamic traffic, one possible solution is to perform more frequent routing updates, but this may lead to service disruptions. Another approach is the two-step method of prediction followed by optimization, which could lead to degraded performance of routing strategies due to inaccuracies in traffic prediction. Additionally, existing approaches focus solely on either distributed or centralized networks, lacking flexibility to adapt to different network architectures. To address these challenges, we propose PROM, a Machine Learning (ML)-based routing optimization model that directly predicts an effective routing strategy in an end-to-end manner based on historical traffic. This routing strategy is applicable across multiple future TMs, thereby reducing the need for frequent routing updates. Furthermore, PROM can be easily extended from a centralized implementation to a distributed one, adapting to different network architectures. To enhance model performance, we have also designed a custom loss function for routing optimization scenarios to avoid overfitting. Extensive simulation results demonstrate that PROM can predict high-quality routing strategies in future unknown traffic scenarios and is adaptable to both centralized and distributed network architectures.

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