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2020 IEEE International Conference on Information Technology, Big Data and Artificial Intelligence, ICIBA 2020

作者： Yu, Xiaoran Tang, Huanrong Zou, Juan Yu, Fan College of Computer Science and College of Cyberspace Security Xiangtan University Xiangtan China Key Laboratory of Intelligent Computing and Information Processing Xiangtan University Ministry of Education Xiangtan China

ISBN: (纸本)9781728152240

The Spectrum-based Software Fault Localization (SBFL) method has been proved to be effective. The SBFL mainly analyzes the execution information of the test case set on each program entity and counts the suspiciousness value of each program entity through the suspiciousness formula. Different measurement methods of program entities have a significant influence on the accuracy of software faults localization because the effectiveness of the SBFL method depends on the suspiciousness calculation formula used. This paper proposes a new suspiciousness formula, fully considered the coverage information of different dimensions of the program spectrum. The suspiciousness formula can be widely used in a variety of faults localization methods extended on the SBFL method. The experimental results show that this method has advantages in effectiveness and efficiency of faults localization. © 2020 IEEE.

关键词： Software testing

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Data gravitation-based three-way sampling method for imbalanced data classification

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Information Sciences 2025年 718卷

作者： Yuanting Yan Weiqing Wang Yingao Ma Yang Tian Peng Zhou Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Anhui University Hefei Anhui Province 230601 PR China School of Computer Science and Technology Anhui University Hefei Anhui Province 230601 PR China

Data resampling is widely used for addressing the class imbalance problem, with numerous methods developed over recent decades. Among these, the K-nearest neighbor (KNN) technique is widely adopted for leveraging local neighborhood information in rebalancing imbalanced data. However, KNN struggles to accurately characterize local data distribution, as samples with identical KNNs may also exhibit divergent local structures, which may further hamper imbalanced learning performance. We propose a hybrid sampling method called data gravitation-based three-way sampling (DGS) to improve classification performance on imbalanced data. DGS introduces the concept of data gravitation into KNN to evaluate the local importance of samples, and combines it with three-way decision theory to selectively oversample minority-class samples and remove risky majority-class samples. DGS introduces a cost-sensitive sampling mechanism and adaptive threshold estimation using a Gaussian-based model, reducing reliance on manually tuned parameters. Extensive experiments on 38 datasets and seven classifiers demonstrate the superiority of DGS over 18 resampling methods across three evaluation metrics. Our code is available at https://***/ytyancp/DGS .

关键词： Class imbalance Data gravitation Hybrid sampling Three-way decision

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A Novel Preference-Driven Dynamic Multi-Objective Evolutionary Algorithm for Solving Dynamic Multi-Objective Problems

SSRN

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SSRN 2024年

作者： Wang, Xueqing Zheng, Jinhua Hou, Zhanglu Liu, Yuan Zou, Juan Xia, Yizhang Yang, Shengxiang Key Laboratory of Intelligent Computing and Information Processing Ministry of Education School of Computer Science School of Cyberspace Science of Xiangtan University Hunan Province Xiangtan China Faculty of School of Computer Science School of Cyberspace Science of Xiangtan University Xiangtan411105 China School of Computer Science and Informatics De Montfort University LeicesterLE1 9BH United Kingdom

Most studies in dynamic multi-objective optimization have predominantly focused on rapidly and accurately tracking changes in the Pareto optimal front (POF) and Pareto optimal set (POS) when the environment undergoes changes. However, there are real-world scenarios where it is necessary to simultaneously solve changing objective functions and satisfy the preference of Decision Makers (DMs). In particular, the DMs may be only interested in a partial region of the POF, known as the region of interest (ROI), rather than requiring the entire POF. To meet the challenge of simultaneously predicting a changing POF and/or POS and dynamic ROI, this paper proposes a new dynamic multi-objective evolutionary algorithm (DMOEAs) based on the preference. The proposed algorithm consists of three key components: an evolutionary direction adjustment strategy based on changing reference points to accommodate shifts in preferences, an angle-based search strategy for tracking the varying ROI, and a hybrid prediction strategy that combines linear prediction models and population manifold estimation within the ROI to ensure convergence and distribution in scenarios where preferences remain unchanged. Experimental studies conducted on 16 widely used benchmark problems and empirical results demonstrate the significant advantages of the proposed algorithm over existing state-of-the-art DMOEAs. © 2024, The Authors. All rights reserved.

关键词： Evolutionary algorithms

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Dynamic Constrained Multi-Objective Optimization Algorithm Based on Co-Evolution and Diversity Enhancement

SSRN

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SSRN 2024年

作者： Che, Wang Zheng, Jinhua Hu, Yaru Zou, Juan Yang, Shengxiang Key Laboratory of Intelligent Computing and Information Processing Ministry of Education School of Computer Science School of Cyberspace Science of Xiangtan University Hunan Province Xiangtan China Faculty of School of Computer Science School of Cyberspace Science of Xiangtan University Xiangtan411105 China School of Computer Science and Informatics De Montfort University LeicesterLE1 9BH United Kingdom

Dynamic constrained multi-objective optimization problems (DCMOPs) involve objectives, constraints, and parameters that change over time. This kind of problem presents a greater challenge for evolutionary algorithms because it requires the population to quickly track the changing pareto-optimal set (PS) while being subject to constrained limitations and maintaining a good distribution of solutions. This paper proposes a dynamic constrained multi-objective optimization algorithm based on co-evolution and diversity enhancement (CEDE). The static optimization involves the co-evolution of three populations. We evolve the first population to prioritize the preservation of feasible individuals. The second population is evolved to assist the first population in finding better feasible regions, while the third population is evolved to aid the first population in exploring feasible regions comprehensively. At different stages, the co-evolution of these three populations will also be adjusted. Additionally, to prevent the elimination of potentially valuable infeasible individuals due to pareto domination, we employ an archive set to store and update these individuals, providing more useful information for both static and dynamic optimization. When the environment changes, we propose a diversity enhancement strategy, which includes a diversity maintenance strategy and a center point-based exploration strategy. The experimental results demonstrate that CEDE is highly competitive in handling DCMOPs compared to several state-of-the-art algorithms. © 2024, The Authors. All rights reserved.

关键词： Evolutionary algorithms

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A hybrid iterative neural solver based on spectral analysis for parametric PDEs

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Journal of Computational Physics 2025年 538卷

作者： Chen Cui Kai Jiang Yun Liu Shi Shu Hunan Key Laboratory for Computation and Simulation in Science and Engineering Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education School of Mathematics and Computational Science Xiangtan University Xiangtan 411105 Hunan China

Deep learning-based hybrid iterative methods (DL-HIM) have emerged as a promising approach for designing fast neural solvers to tackle large-scale sparse linear systems. DL-HIM combine the smoothing effect of simple iterative methods with the spectral bias of neural networks, which allows them to effectively eliminate both high-frequency and low-frequency error components. However, their efficiency may decrease if simple iterative methods can not provide effective smoothing, making it difficult for the neural network to learn mid-frequency and high-frequency components. This paper first conducts a convergence analysis for general DL-HIM from a spectral viewpoint, concluding that under reasonable assumptions, DL-HIM exhibit a convergence rate independent of grid size h and physical parameters μ . To meet these assumptions, we design a neural network from an eigen perspective, focusing on learning the eigenvalues and eigenvectors corresponding to error components that simple iterative methods struggle to eliminate. Specifically, the eigenvalues are learned by a meta subnet, while the eigenvectors are approximated using Fourier modes with a transition matrix provided by another meta subnet. The resulting DL-HIM, termed the Fourier Neural Solver (FNS), can be trained to achieve a convergence rate independent of PDE parameters and grid size within a local neighborhood of the training scale by designing a loss function that ensures the neural network complements the smoothing effect of the damped Jacobi iterative methods. We verify the performance of FNS on five types of linear parametric PDEs.

关键词： Convergence analysis Hybrid iterative method Neural solver Preconditioning Spectral bias

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Size-invariance matters: rethinking metrics and losses for imbalanced multi-object salient object detection 24

Size-invariance matters: rethinking metrics and losses for i...

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Proceedings of the 41st International Conference on Machine Learning

作者： Feiran Li Qianqian Xu Shilong Bao Zhiyong Yang Runmin Cong Xiaochun Cao Qingming Huang Institute of Information Engineering Chinese Academy of Sciences Beijing China and School of Cyber Security University of Chinese Academy of Sciences Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China Institute of Information Science Beijing Jiaotong University Beijing China and School of Control Science and Engineering Shandong University Jinan China and Key Laboratory of Machine Intelligence and System Control Ministry of Education Jinan China School of Cyber Science and Tech. Shenzhen Campus Sun Yat-sen University School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China and Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China and Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing China

This paper explores the size-invariance of evaluation metrics in Salient Object Detection (SOD), especially when multiple targets of diverse sizes co-exist in the same image. We observe that current metrics are size-sensitive, where larger objects are focused, and smaller ones tend to be ignored. We argue that the evaluation should be size-invariant because bias based on size is unjustified without additional semantic information. In pursuit of this, we propose a generic approach that evaluates each salient object separately and then combines the results, effectively alleviating the imbalance. We further develop an optimization framework tailored to this goal, achieving considerable improvements in detecting objects of different sizes. Theoretically, we provide evidence supporting the validity of our new metrics and present the generalization analysis of SOD. Extensive experiments demonstrate the effectiveness of our method. The code is available at https://***/Ferry-Li/SI-SOD.

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SSD Real-Time Illegal Parking Detection Based on Contextual Information Transmission

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Computers, Materials & Continua 2020年第1期62卷 293-307页

作者： Huanrong Tang Aoming Peng Dongming Zhang Tianming Liu Jianquan Ouyang Key Laboratory of Intelligent Computing and Information Processing Ministry of EducationCollege of Information EngineeringXiangtan UniversityXiangtan411105China National Computer Network Emergency Response Technical Team/Coordination Center of China Beijing100029China Department of Computer Science the University of GeorgiaAthensGeorgiaUSA

With the improvement of the national economic level,the number of vehicles is still increasing year by *** to the statistics of National Bureau of Statics,the number is approximately up to 327 million in China by the end of 2018,which makes urban traffic pressure continues to rise so that the negative impact of urban traffic order is *** parking-the common problem in the field of transportation security is urgent to be solved and traditional methods to address it are mainly based on ground loop and manual supervision,which may miss detection and cost much *** to the rapidly developing deep learning sweeping the world in recent years,object detection methods relying on background segmentation cannot meet the requirements of complex and various scenes on speed and ***,an improved Single Shot MultiBox Detector(SSD)based on deep learning is proposed in our study,we introduce attention mechanism by spatial transformer module which gives neural networks the ability to actively spatially transform feature maps and add contextual information transmission in specified ***,we found out the best connection layer in the detection model by repeated experiments especially for small objects and increased the precision by 1.5%than the baseline SSD without extra training ***,we designed an illegal parking vehicle detection method by the improved SSD,reaching a high precision up to 97.3%and achieving a speed of 40FPS,superior to most of vehicle detection methods,will make contributions to relieving the negative impact of illegal parking.

关键词： Contextual information transmission illegal parking detection spatial attention mechanism deep learning

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An enumeration of 1-perfect ternary codes

arXiv

引用

arXiv 2021年

作者： Shi, Minjia Krotov, Denis S. Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education School of Mathematical Sciences Anhui University Anhui Hefei China State Key Laboratory of Integrated Service Networks Xidian University Xi’an710071 China Sobolev Institute of Mathematics Novosibirsk630090 Russia

MSC Codes 94B25We study codes with parameters of the ternary Hamming (n=(3m−1)/2, 3n−m, 3) code, i.e., ternary 1-perfect codes. The rank of the code is defined to be the dimension of its affine span. We characterize ternary 1-perfect codes of rank n − m + 1, count their number, and prove that all such codes can be obtained from each other by a sequence of two-coordinate switchings. We enumerate ternary 1-perfect codes of length 13 obtained by concatenation from codes of lengths 9 and 4;we find that there are 93241327 equivalence classes of such codes. Copyright © 2021, The Authors. All rights reserved.

关键词： Equivalence classes

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A Flexible Two-Stage Constrained Multi-Objective Evolutionary Algorithm Based on Autonomic Regulation

SSRN

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SSRN 2022年

作者： Zou, Juan Luo, Jian Liu, Yuan Yang, Shengxiang Zheng, Jinhua Key Laboratory of Intelligent Computing and Information Processing Ministry of Education Computer science College of Xiangtan University Hunan Province Xiangtan China Faculty of Informational Engineering University of Xiangtan Xiangtan411105 China Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Hengyang421002 China School of Computer Science and Informatics De Montfort University LeicesterLE1 9BH United Kingdom

The core element in solving constrained multi-objective problems (CMOPs) with evolutionary algorithms is simultaneously balancing objective optimization and constraint satisfaction. The di ffi culty of maintaining this balance increases significantly for existing algorithms when dealing with complex CMOPs because various complex feasible regions often make CMOPs with very di ff erent characteristics. To address this issue, we propose a more flexible two-stage evolutionary algorithm based on autonomic regulation (ARCMO), which can e ff ectively control evolutionary trends to adapt to complex CMOPs. Specifically, the first stage performs a fast global search and passes the population information to the second stage. The second stage consists of two dynamically complementary sub-processes: named exploration subprocess and convergence subprocess. The ratio of these two sub-processes is dynamically adjusted according to the information from the first stage to adapt to CMOPs with di ff erent complexities. Experiments on several recently proposed benchmark suites and real-world application problems show that ARCMO is more adaptable than the contender algorithms when solving di ff erent complex CMOPs. © 2022, The Authors. All rights reserved.

关键词： Evolutionary algorithms

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SurgicalGaussian: Deformable 3D Gaussians for High-Fidelity Surgical Scene Reconstruction

arXiv

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arXiv 2024年

作者： Xie, Weixing Yao, Junfeng Cao, Xianpeng Lin, Qiqin Tang, Zerui Dong, Xiao Guo, Xiaohu Center for Digital Media Computing School of Film School of Informatics Xiamen University Xiamen China National Institute for Data Science in Health and Medicine Xiamen University China Key Laboratory of Digital Protection and Intelligent Processing of Intangible Cultural Heritage of Fujian and Taiwan Ministry of Culture and Tourism China Guangdong Provincial Key Laboratory IRADS Department of Computer Science BNU-HKBU United International College Zhuhai China Department of Computer Science The University of Texas at Dallas Dallas United States

Dynamic reconstruction of deformable tissues in endoscopic video is a key technology for robot-assisted surgery. Recent reconstruction methods based on neural radiance fields (NeRFs) have achieved remarkable results in the reconstruction of surgical scenes. However, based on implicit representation, NeRFs struggle to capture the intricate details of objects in the scene and cannot achieve real-time rendering. In addition, restricted single view perception and occluded instruments also propose special challenges in surgical scene reconstruction. To address these issues, we develop SurgicalGaussian, a deformable 3D Gaussian Splatting method to model dynamic surgical scenes. Our approach models the spatio-temporal features of soft tissues at each time stamp via a forward-mapping deformation MLP and regularization to constrain local 3D Gaussians to comply with consistent movement. With the depth initialization strategy and tool mask-guided training, our method can remove surgical instruments and reconstruct high-fidelity surgical scenes. Through experiments on various surgical videos, our network outperforms existing method on many aspects, including rendering quality, rendering speed and GPU usage. The project page can be found at https://***. © 2024, CC BY-SA.

关键词： Tissue

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