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IEEE Transactions on Artificial Intelligence 2025年第3期6卷 518-532页

作者： Tian, Ye Ji, Luke Hu, Yiwei Ma, Haiping Wu, Le Zhang, Xingyi Anhui University Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Hefei230601 China Anhui University Institutes of Physical Science and Information Technology Hefei230601 China Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data Hefei230029 China

Represented by evolutionary algorithms and swarm intelligence algorithms, nature-inspired metaheuristics have been successfully applied to recommender systems and amply demonstrated effectiveness, in particular, for multiobjective recommendation. Owing to the population-based search paradigm, these algorithms can produce a number of recommendation lists, making diverse tradeoffs between multiple metrics and meeting the requirements of accuracy, novelty, diversity, and other user preferences. However, these algorithms are criticized for the low efficiency of the optimization process, especially when the number of users is large. To address this issue, this article proposes an evolutionary multitasking-based recommendation method, where each task corresponds to a user and all the tasks are optimized simultaneously, thus highly improving the efficiency of recommendation. To enhance the convergence speed, all the users are divided into multiple populations according to the similarity between their preferences, where each population evolves with internal knowledge transfer between users, and all the populations evolve with external knowledge transfer between populations. Experimental results on various datasets verify that the proposed method can better balance between multiple metrics than classical and deep neural network-based recommendation methods and exhibits significantly higher efficiency than evolutionary multiobjective optimization-based recommendation methods. © 2024 IEEE. All rights reserved.

关键词： Multiobjective optimization

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MSMAE-Net: multi-semantic and multi-attention enhanced network for image forgery localization

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signal, Image and Video processing 2025年第7期19卷 1-11页

作者： Liao, Jianjun Su, Lichao Lu, Menghan Engineering Research Center of Big Data Intelligence Ministry of Education Fuzhou China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China

The popularization of modern digital image technology has brought convenience to us, but it also poses many risks. The advancement of image editing software allows anyone to modify image content effortlessly. If these modified images are abused, they can severely impact societal safety and stability. To address these risks, we propose an end-to-end multi-scale collaborative enhancement image forgery localization network, termed MSMAE-Net. The method first employs a multi-branch feature extractor to initially capture both global and local information, with each branch optimized for different contextual information and features of the image. Next, to enhance the model’s ability to capture forged traces at various levels while maintaining the correlation between different spatial hierarchies, a boundary information aggregation module is designed. By constructing multiple branches with different receptive fields and collaborative learning with each other, a nested collaborative enhancement branch is designed to make the backbone network pay more attention to key features, so as to obtain better feature representation capability. Furthermore, to further fuse different semantic information, a novel strongly compatible semantic attention fusion module is proposed in this paper. Finally, an attention enhancement module is introduced in the localization of forgery regions to adaptively concentrate on key forged areas in the image and effectively capture the relationships between different pixels. In the experiments, we demonstrate that MSMAE-Net exhibits significant advantages in both localization accuracy and robustness in complex forgery scenarios when compared to other SOTA image forgery detection methods. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Photointerpretation

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JDC-GCN: joint diversity and consistency graph convolutional network

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Neural computing and Applications 2025年 1-17页

作者： Lin, Renjie Yao, Jie Wang, Shiping Guo, Wenzhong College of Computer and Data Science Fuzhou University Fuzhou350116 China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350116 China

In real-world scenarios, multi-view data comprises heterogeneous features, with each feature corresponding to a specific view. The objective of multi-view semi-supervised classification is to enhance classification performance by leveraging the inherent complementary and consistent information present within diverse views. Nevertheless, many existing frameworks primarily focus on assigning suitable weights to different views while neglecting the importance of consistent information. In this paper, a multi-view semi-supervised classification framework called joint diversity and consistency graph convolutional network (JDC-GCN) is proposed. Firstly, the structure of graph convolutional network is introduced to the multi-view semi-supervised classification, capable of propagating the label information over the topological structure of multi-view data. Secondly, the proposed JDC-GCN captures the complementary and consistent information from multiple views through two indispensable sub-modules, Diversity-GCN and Consistency-GCN, respectively. Finally, the attention mechanism is leveraged to dynamically adjust the weights of various views, allowing us to measure the significance of heterogeneous features and the consistent graph without introducing additional parameters. Comprehensive experiments on eight multi-view datasets are conducted to validate the effectiveness of the JDC-GCN algorithm. The results show that the proposed method exhibits superior classification performance compared to other state-of-the-art methods. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Self-supervised learning

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PRPFD: Polynomial Resampling Particle Filter Based Deconvolution Approach for Resting-State BOLD-fMRI Data Analysis 24

PRPFD: Polynomial Resampling Particle Filter Based Deconvolu...

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5th International Symposium on Artificial Intelligence for Medicine Science, ISAIMS 2024

作者： Luo, Yunying Zeng, Weiming Key Laboratory for Digital Imaging Processing and Intelligent Computing School of Information Engineering Shanghai Maritime University Shanghai China

ISBN: (纸本)9798400717826

Functional Magnetic Resonance Imaging (fMRI) stands out in brain science research due to its non-invasive, non-intrusive, radiation-free, high spatial resolution, and precise localization advantages. However, the BOLD-fMRI signals it generates exhibit a several-second delay compared to neural activity, making it unable to reflect brain neural activity directly. This discrepancy may lead to differences from the actual situation. Therefore, this study aims to enhance the deconvolution of fMRI by refining existing methods to reconstruct the brain’s neural signals more faithfully. Building upon the foundation of particle filtering deconvolution, this paper introduced a novel approach to deconvolving resting-state fMRI data: the polynomial resampling particle filtering deconvolution (PRPFD). They adapted the particle distribution better to match the actual state through weight computation and resampling. We performed a simulation-based experiment to validate the feasibility of our approach. Besides, we validated the performance of our method using group data, focusing on both effective connectivity and functional connectivity, providing strong evidence for the usability and stability of our approach, and compared our method with other deconvolution techniques, demonstrating improvements in accuracy and computational efficiency. © 2024 Copyright held by the owner/author(s).

关键词： Polynomials

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Angle measurement using the integrated amplitude of difference beam from an optical phased array

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Optics Express 2025年第6期33卷 13785-13795页

作者： Yuan, Jing Wu, Guihan Yang, Shichong Yu, Fuhao Xin, Yu Cao, Haijiang Luo, Quan Luo, Hongsheng Su, Jinshan Jiang, Wei College of Engineering and Applied Sciences Nanjing University Nanjing210023 China Key Laboratory of Intelligent Optical Sensing and Manipulation Ministry of Education Nanjing University Nanjing210023 China Jiangsu Key Laboratory of Artificial Functional Materials Nanjing University Nanjing210023 China Yili Normal University Key Laboratory of Vibration Signal Capture and Intelligent Processing ILI835000 China

We propose an angle measurement method by comparing the integrated amplitude of the difference beam (AMCIAD) generated by an optical phase array (OPA), which can be used to improve the precision of angle measurements. The AMCIAD utilizes the beam-forming capability of an OPA to improve upon the traditional method of angle measurement by comparing amplitude (AMCA). In the experiment, an on-chip OPA is used to generate the difference beams and scan the object. An algorithm is developed to calculate the angular position of the object from the reflected light. The experimental results demonstrate that AMCIAD improves the accuracy of angle measurement by about four times compared to AMCA (0.13° of AMCIAD and 0.60° of AMCA). Angular accuracy equivalent to ∼4% of the beam width is demonstrated. © 2025 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement.

关键词： Angle measurement

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Online Streaming Feature Selection Using Bidirectional Complementarity Based on Fuzzy Gini Entropy

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IEEE Transactions on Fuzzy Systems 2025年第5期33卷 1592-1604页

作者： Zhang, Chucai Lu, Zhengxiang Zhang, Yongkang Dai, Jianhua Hunan Normal University Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing College of Information Science and Engineering Changsha410081 China School of Mathematics and Statistics Changsha410081 China

Online streaming feature selection has garnered widespread attention due to its efficiency and adaptability in dynamic data environments. However, existing methods primarily focus on the correlation and redundancy among features, often overlooking the complementarity between candidate and selected features. In this article, we address this gap by introducing three key innovations. First, we construct a novel metric, fuzzy Gini entropy (FGE), to measure feature uncertainty within datasets. Unlike traditional information entropy, fuzzy Gini entropy inherits the advantages of the Gini index, effectively measuring the impurity of datasets, while also being capable of handling common fuzzy environments. Accordingly, related metrics such as fuzzy joint Gini entropy, fuzzy conditional Gini entropy, and fuzzy mutual Gini information are developed. Second, we innovatively propose the concept of the bidirectional complementarity ratio, which captures the relationship between candidate features and previously selected features in online streaming feature selection. This mitigates the unfairness associated with the late arrival of features, ensuring that candidate features with a bidirectional complementary effect that outweighs their redundancy effect with the selected features are chosen. Third, we design an online streaming feature selection method named FGE-OSFS. The method evaluates streaming features through three steps: Online relevance analysis, online bidirectional complementarity analysis, and online redundancy analysis. Finally, we compare the proposed method with five state-of-the-art online streaming feature selection methods, demonstrating the effectiveness of our new approach. © 1993-2012 IEEE.

关键词： Redundancy

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Glare-SNet: Unsupervised Glare Suppression Balance Network 27th

Glare-SNet: Unsupervised Glare Suppression Balance Network

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Li, Pei Zuo, Chengyu Wei, Wangjuan Pan, Xiaoying Wang, Zhanhao School of Computer Science and Technology Xi’an University of Posts and Telecommunications Xi’an710121 China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi’an China Shaanxi Key Laboratory of Intelligent Media Computing and interaction Xi’an China Xi’an Key Laboratory of Big Data and Intelligent Computing Xi’an China

ISBN: (纸本)9783031781681

In light of the problems associated with glare and halo effects in low-light images, as well as the inadequacy of existing processing algorithms in handling details, a glare suppression balance network based on unsupervised learning has been designed and implemented in this study. A network that perceives brightness through light decomposition has been proposed, and an illumination learning network has been designed. The idea of integration has been employed to form an unsupervised glare suppression balance network, aiming to tackle the issues of glare and the accompanying halo effects. Through objective and subjective evaluations of real images, it has been proven that the method introduced in this paper surpasses other methods in suppressing glare and enhancing the overall quality of low-light images. Furthermore, it has been shown to exhibit good performance and effectiveness in target detection. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Image enhancement

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Identifying important nodes of hypergraph:An improved PageRank algorithm

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Chinese Physics B 2025年第4期34卷 162-171页

作者： Yu-Hao Piao Jun-Yi Wang Ke-Zan Li School of Mathematics and Computing Science Guilin University of Electronic TechnologyGuilin 541004China School of Information and Communication Guilin University of Electronic TechnologyGuilin 541004China Guangxi Wireless Broadband Communication and Signal Processing Key Laboratory Guilin University of Electronic TechnologyGuilin 541004China

Hypergraphs can accurately capture complex higher-order relationships,but it is challenging to identify their important *** this paper,an improved PageRank(ImPageRank)algorithm is designed to identify important nodes in a directed *** algorithm introduces the Jaccard similarity of directed *** comparing the numbers of common neighbors between nodes with the total number of their neighbors,the Jaccard similarity measure takes into account the similarity between nodes that are not directly connected,and can reflect the potential correlation between *** improved susceptible–infected(SI)model in directed hypergraph is proposed,which considers nonlinear propagation mode and more realistic propagation *** addition,some important node evaluation methods are transferred from undirected hypergraphs and applied to directed ***,the ImPageRank algorithm is used to evaluate the performance of the SI model,network robustness and *** of real networks demonstrate the excellent performance of the proposed algorithm and provide a powerful framework for identifying important nodes in directed hypergraphs.

关键词： hypergraph important node PageRank susceptible-infected(SI)model centrality index

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Determining the Covering Radius of All Generalized Zetterberg Codes in Odd Characteristic

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IEEE Transactions on Information Theory 2025年第5期71卷 3602-3613页

作者： Shi, Minjia Li, Shitao Helleseth, Tor Ozbudak, Ferruh Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education School of Mathematical Sciences Hefei230601 China Anhui University School of Mathematical Sciences Anhui Hefei230601 China University of Bergen Department of Informatics Bergen5007 Norway Sabancı University Faculty of Engineering and Natural Sciences İstanbul34956 Turkey

For an integer s ≥ 1, let Cs(q0) be the generalized Zetterberg code of length q0s + 1 over the finite field Fq0 of odd characteristic. Recently, Shi et al. 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 q0s ≡ 7 (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. © 1963-2012 IEEE.

关键词： Turbo codes

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Relaxing Distillation Constraints for Improved New Class Learning in Continual Semantic Segmentation

Relaxing Distillation Constraints for Improved New Class Lea...

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2025 IEEE International Conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Ti, Zheng Hao, Xuze Chen, Renhai College of Intelligence and Computing Tianjin University Tianjin China School of Computer Science Shanghai Key Laboratory of Intelligent Information Processing Fudan University Shanghai China

ISBN: (纸本)9798350368741

Continual Semantic Segmentation (CSS) aims to continuously learn new classes while mitigating catastrophic forgetting. Existing CSS methods primarily address this challenge through knowledge distillation. While they focus on maintaining stability for old classes, this emphasis often restricts plasticity in learning new ones. In this work, we propose two plug-and-play methods to better balance stability and plasticity. First, for feature map distillation, we replace pixel-level constraints with statistical information extraction within a window, improving the learning of new classes while preserving structural integrity to overcome forgetting. Second, we use relative ranks of predictions to replace exact probabilistic values to loosen the constraints in the logit distribution of old classes. Extensive experiments demonstrate that our methods outperform state-of-the-art methods when integrating into several baselines across various CSS scenarios. © 2025 IEEE.

关键词： Continual Learning Knowledge Distillation Semantic Segmentation

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