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Enhancing Graph Structure Learning via Motif-Driven Hypergraph Construction

IEEE Transactions on Network Science and Engineering 2025年第3期12卷 2333-2344页

作者： Zhao, Jia-Le Zhang, Xian-Jie Ding, Xiao Zhang, Xingyi Zhang, Hai-Feng Anhui University Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Mathematical Science Hefei230601 China Anhui University Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Hefei230601 China

Graph neural networks (GNNs), as a cutting-edge technology in deep learning, perform particularly well in various tasks that process graph structure data. However, their foundation on pairwise graphs often limits their capacity to capture latent higher-order topological semantic information. Thus, it is crucial to find a way to extract the latent higher-order information of graphs without missing the lower-order information of the original graph. To address this issue, we here develop a method to construct hypergraph based on motifs, and then a novel neural network framework, named MD-HGNN, is proposed for enhanced graph learning. Specifically, we first utilize motifs of the original graph to construct the hypergraph and eliminate nested structures within the hypergraph to prevent information redundancy. Subsequently, GNNs and hypergraph neural networks (HGNNs) are employed separately to extract the lower-order and higher-order topological semantic information of the graph. Finally, the lower-order and higher-order information are integrated to obtain an embedded representation of graph. Extensive experimental results demonstrate that MD-HGNN preserves the original lower-order graph structure information while effectively extracting higher-order features. Moreover, its performance and robustness are validated across different downstream tasks. © 2013 IEEE.

关键词： Graph neural networks

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RECONSTRUCTION-BASED A POSTERIORI ERROR ESTIMATES FOR THE L1 METHOD FOR TIME FRACTIONAL PARABOLIC PROBLEMS

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Journal of Computational Mathematics 2025年第2期43卷 345-368页

作者： Jiliang Cao Aiguo Xiao Wansheng Wang Department of Mathematics Shanghai Normal UniversityShanghai 200234China Hunan Key Laboratory for Computation and Simulation in Science and Engineering&Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan UniversityXiangtan 411105China

In this paper,we study a posteriori error estimates of the L1 scheme for time discretizations of time fractional parabolic differential equations,whose solutions have generally the initial *** derive optimal order a posteriori error estimates,the quadratic reconstruction for the L1 method and the necessary fractional integral reconstruction for the first-step integration are *** using these continuous,piecewise time reconstructions,the upper and lower error bounds depending only on the discretization parameters and the data of the problems are *** numerical experiments for the one-dimensional linear fractional parabolic equations with smooth or nonsmooth exact solution are used to verify and complement our theoretical results,with the convergence ofαorder for the nonsmooth case on a uniform *** recover the optimal convergence order 2-αon a nonuniform mesh,we further develop a time adaptive algorithm by means of barrier function recently *** numerical implementations are performed on nonsmooth case again and verify that the true error and a posteriori error can achieve the optimal convergence order in adaptive mesh.

关键词： Time fractional parabolic differential equations A posteriori error estimates Ll method Fractional integral reconstruction Quadratic reconstruction

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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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Dual Averaging for Distributed Unbalanced Optimization with Delayed information

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IEEE Transactions on Signal and information processing over Networks 2025年 11卷 366-377页

作者： Huang, Qing Fan, Yuan Cheng, Songsong Anhui University Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Electrical Engineering and Automation Anhui Hefei230601 China

In this paper, we study a category of distributed constrained optimization problems where each agent has access to local information, communicates with its neighbors, and cooperatively minimizes the aggregated cost functions over time-varying unbalanced graphs. To address the considered problems, we propose a distributed dual averaging algorithm based on a row-stochastic weighted matrix (DDAR), which improves the robustness of network topology compared to conventional push-sum algorithms. Moreover, we develop a modified version of DDAR with delayed information (DDARD), which considers the delays of both network communication and gradient calculation, enhancing the algorithm's flexibility in communication and iteration. Our analysis demonstrates that the DDAR and DDARD achieve the optimal value at rates of O(N/(1-λ)√T) and O(τ2N/(1-λ}})√T}), respectively. Finally, the theoretical results are confirmed by simulation on a logistic regression problem. © 2015 IEEE.

关键词： Cost functions

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An Improved Compressive Sensing-Based Model for Efficient Analysis of Monostatic Electromagnetic Scattering Problems

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IEEE Microwave and Wireless Technology Letters 2025年第4期35卷 388-391页

作者： Qi, Qi Fan, Yunuo Cao, Xinyuan Liu, Yi Kong, Meng Huang, Zhixiang Wu, Xianliang Hefei Normal University Anhui Province Key Laboratory of Simulation Calculation and Design for Electronic Information System Hefei230601 China Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Hefei230601 China

To further accelerate the analysis of monostatic electromagnetic (EM) scattering problems of objects, an improved compressive sensing (CS)-based model is proposed. First, an orthogonal subspace spanned by the wide-angle induced currents is identified by means of the wide-angle incident sources. Then, the sparse transform of the induced currents is replaced by the subspace projection. In addition, an optimal number of measurements is determined, only slightly larger than the dimension of the subspace. Finally, the recovery algorithm is simplified to the least-squares method to solve the resulting overdetermined system. Compared with the traditional CS-based model, the proposed model significantly improves the efficiency of the recovery step and optimizes both the requirement for and the way of determining the number of measurements. © 2023 IEEE.

关键词： Least squares approximations

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HyFit: Hybrid Fine-Tuning With Diverse Sampling for Abstractive Summarization

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IEEE Transactions on Big Data 2025年第3期11卷 1054-1065页

作者： Zhao, Shu Cheng, Yuanfang Zhang, Yanping Chen, Jie Duan, Zhen Sun, Yang Wang, Xinyuan Anhui University Ministry of Education Key Laboratory of Intelligent Computing and Signal Processing Hefei230601 China Anhui University School of Computer Science and Technology Hefei230601 China Anhui University Information Materials and Intelligent Sensing Laboratory of Anhui Province Hefei230601 China

Abstractive summarization has made significant progress in recent years, which aims to generate a concise and coherent summary that contains the most important facts from the source document. Current fine-tuning approaches based on pre-training models typically rely on autoregressive and maximum likelihood estimation, which may result in inconsistent historical distributions generated during the training and inference stages, i.e., exposure bias problem. To alleviate this problem, we propose a hybrid fine-tuning model(HyFit), which combines contrastive learning and reinforcement learning in a diverse sampling space. Firstly, we introduce reparameterization and probability-based sampling methods to generate a set of summary candidates called candidates bank, which improves the diversity and quality of the decoding sampling space and incorporates the potential for uncertainty. Secondly, hybrid fine-tuning with sampled candidates bank, upweighting confident summaries and downweighting unconfident ones. Experiments demonstrate that HyFit significantly outperforms the state-of-the-art models on SAMSum and DialogSum. HyFit also shows good performance on low-resource summarization, on DialogSum dataset, using only approximate 8% of the examples exceed the performance of the base model trained on all examples. © 2015 IEEE.

关键词： Reinforcement learning

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Multi-objective Balanced Task Offloading in Vehicular Networks Based on Edge computing 27th

Multi-objective Balanced Task Offloading in Vehicular Networ...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Wang, Lingjiao Meng, Lingwei Guo, Hua Xiangtan University Hunan Xiangtan411105 China Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan University Hunan Xiangtan411105 China

ISBN: (纸本)9783031783821

The increasing number of vehicular networking devices and application demands has made the limited computing and communication resources a significant challenge. The heuristic task offloading strategy mechanism was proposed to improve the efficiency of task offloading in vehicular networks. This strategy mechanism utilized the Nondominated Sorting Genetic Fireworks Algorithm (NSGFA) based on the characteristics of the problem, integrated consideration of the multi-target balance between system offloading costs and load balancing. Experimental data results demonstrate that this method performs well in reducing latency, energy consumption, and load balancing, effectively enhancing the quality of service for vehicular network users. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Computation offloading

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An Evolutionary Multitasking Algorithm for Efficient Multiobjective Recommendations

IEEE Transactions on Artificial Intelligence

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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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A numerical magnitude aware multi-channel hierarchical encoding network for math word problem solving

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Neural computing and Applications 2025年第3期37卷 1651-1672页

作者： Xu, Junjie Chen, Yuzhong Xiao, Lingsheng Liao, Hongmiao Zhong, Jiayuan Dong, Chen College of Computer and Data Science Fuzhou University Fujian Province Fuzhou350108 China Engineering Research Center of Big Data Intelligence Ministry of Education Fuzhou China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fujian Province Fuzhou350108 China

Math word problem (MWP) represents a critical research area within reading comprehension, where accurate comprehension of math problem text is crucial for generating math expressions. However, current approaches still grapple with unresolved challenges in grasping the sensitivity of math problem text and delineating distinct roles across various clause types, and enhancing numerical representation. To address these challenges, this paper proposes a Numerical Magnitude Aware Multi-Channel Hierarchical Encoding Network (NMA-MHEA) for math expression generation. Firstly, NMA-MHEA implements a multi-channel hierarchical context encoding module to learn context representations at three different channels: intra-clause channel, inter-clause channel, and context-question interaction channel. NMA-MHEA constructs hierarchical constituent-dependency graphs for different levels of sentences and employs a Hierarchical Graph Attention Neural Network (HGAT) to learn syntactic and semantic information within these graphs at the intra-clause and inter-clause channels. NMA-MHEA then refines context clauses using question information at the context-question interaction channel. Secondly, NMA-MHEA designs a number encoding module to enhance the relative magnitude information among numerical values and type information of numerical values. Experimental results on two public benchmark datasets demonstrate that NMA-MHEA outperforms other state-of-the-art models. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Semantics

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A Novel Optimal Distributed Nonlinear Filter for Simultaneous State and Unknown Input Estimation in Multi-Sensor Networks

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IEEE Signal processing Letters 2025年 32卷 1745-1749页

作者： Ding, Bo Wei, Yuanchu Zhang, Enze Fang, Huajing Yangzhou University College of Information Engineering Yangzhou225127 China Huazhong University of Science and Technology School of Artificial Intelligence and Automation Key Laboratory of Image Processing and Intelligent Control Ministry of Education Wuhan430074 China

This letter investigates the problem of simultaneous state and unknown input estimation in a nonlinear system within multi-sensor networks. To avoid the linearization errors caused by existing methods, such as statistical linear regression techniques and first-order Taylor approximation, a novel optimal distributed nonlinear filter is proposed based on the unscented transformation and unbiased minimum variance criterion. First, the unscented transformation is applied to generate predicted estimates and the covariance matrix. Second, input and state estimation are carried out in an unbiased minimum variance manner using measurements from the nonlinear system. Third, a distributed strategy leveraging average consensus is employed to incorporate local estimates from neighboring sensors. Finally, simulation results confirm that the proposed method achieves substantial improvements in estimation accuracy compared to existing methods. © 1994-2012 IEEE.

关键词： Covariance matrix

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