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Harnessing Syntax GCN and Multi-view Interaction for Conversational Aspect-Based Quadruple Sentiment Analysis

IEEE Access 2025年 13卷 73332-73341页

作者： Kang, Houwei Wu, Chunling Chongqing Normal University School of Computer and Information Science Chongqing400000 China Chongqing Polytechnic University of Electronic Technology School of Artificial Intelligence and Big Data Chongqing400000 China

Conversational Aspect-Based Sentiment Analysis (DiaASQ) aims to extract fine-grained sentiment quadruples {target, aspect, opinion, polarity} from multiple segments of dialogue. The composition of these quadruples is often not limited to a single utterance but may span across the entire conversation. Therefore, the analysis needs to consider both the syntactic structure of individual utterances and the interactions between different views. However, previous studies often lack modeling of dialogue features and overlook the interdependence between utterances. To address this, this paper introduces the Syntax and Consecutive Multi-View Network. This model captures the syntactic dependencies among utterances using Graph Convolutional Networks (GCN) and employs multi-view interactions along with three consecutive multi-head attention modules to construct contextual connections within the dialogue. Finally, a triple scorer is used to decode the quadruples, thereby enhancing the weakly labeled relationships within the quadruples. Extensive experiments on standard datasets demonstrate that the proposed model significantly outperforms baseline methods across multiple dimensions. © 2013 IEEE.

关键词： Emotion Recognition

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Non-Reed-Solomon Type Cyclic MDS Codes

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IEEE Transactions on Information Theory 2025年第5期71卷 3489-3496页

作者： Li, Fagang Chen, Yangyang Chen, Hao Niu, Yongfeng Changzhou University School of Computer Science and Artificial Intelligence Aliyun School of Big Data Changzhou213164 China Jiangnan University School of Science Wuxi214122 China Jinan University College of Information Science and Technology Guangdong Guangzhou510632 China

As cyclic codes and maximum distance separable (MDS) codes, cyclic MDS codes have very nice structures and properties, which have been intensively investigated in literature due to their theoretical interest and practical importance. Particularly, abundant cyclic MDS codes have been determined and constructed for many parameters and most of them were proved to be equivalent to generalized Reed-Solomon (GRS) codes. Hence it is a challenging task to construct non-Reed-Solomon type cyclic MDS codes. In this work, we obtain many new cyclic MDS codes for certain parameters by determining the solutions of the system of polynomial equations. Moreover, by determining the dimension of the Schur square of an MDS code, we can easily show that all of our constructed codes are not equivalent to GRS codes. © 1963-2012 IEEE.

关键词： Reed-Solomon codes

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An electron-transfer-tuning strategy at the graphene/metal interface for improving acidic water electrolysis in proton exchange membrane electrolyzers

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Journal of Energy Chemistry 2025年第4期103卷 344-352页

作者： Yue Xu Shuaidong Li Yingjian He Fumiya Shiokawa Samuel Jeong Aimi Asilah Haji Tajuddin Zeyu Xi Yoshikazu Ito Jingzi Zhang Zeyun Cai Xi Lin Kailong Hu School of Materials Science and Engineering and Institute of Materials Genome & Big Data Harbin Institute of Technology Institute of Applied Physics Graduate School of Pure and Applied Sciences University of Tsukuba Institute of Special Environments Physical Sciences Harbin Institute of Technology School of Computer Science and Technology Harbin Institute of Technology

Graphene encapsulation has been shown to be an effective technique for improving the corrosion resistance of non-noble metal catalysts for the acidic water *** key challenge lies in enhancing the electrocatalytic activity of graphene-encapsulated metals while maintaining their durability in acidic ***,an electron-transfer-tuning strategy is investigated at the graphene/NiMo interface,aiming to improve the hydrogen evolution reaction（HER） performance of graphene-encapsulated NiMo *** doping of Ti,a low electronegativity element,into NiMo substrate was confirmed to increase electron transfer from the metal core toward the *** electron-rich state on graphene facilitates the adsorption of positively charged protons on graphene,thereby enabling a Pt/C-comparable performance in 0.5 M H2SO4,with only a 3.8% degradation in performance over a 120-h continuous *** proton exchange membrane（PEM） water electrolyzer assembled by the N-doped grapheneencapsulated Ti-doped NiMo exhibits a smaller cell voltage to achieve a current density of 2.0 A cm-2,in comparison to the Pt/C based *** study proposes a novel electron-transfer-tuning strategy to improve the HER activity of graphene-encapsulated non-noble metal catalysts without sacrificing durability in acidic electrolytes.

关键词： Acidic media Graphene encapsulation Hydrogen evolution Interface Proton exchange membrane

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Enhancing Adversarial Example Transferability via Regularized Constrained Feature Layer

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computers, Materials & Continua 2025年第4期83卷 157-175页

作者： Xiaoyin Yi Long Chen Jiacheng Huang Ning Yu Qian Huang School of Computer Science and Technology Chongqing University of Posts and TelecommunicationsChongqing400065China Chongqing Key Laboratory of Public Big Data Security Technology Chongqing401420China School of Cyber Security and Information Law Chongqing University of Posts and TelecommunicationsChongqing400065China Key Laboratory of Cyberspace Big Data Intelligent Security Ministry of EducationChongqing400065China Artificial Intelligence and Big Data College Chongqing Polytechnic University of Electronic TechnologyChongqing401331China

Transfer-based Adversarial Attacks(TAAs)can deceive a victim model even without prior *** is achieved by leveraging the property of adversarial *** is,when generated from a surrogate model,they retain their features if applied to other models due to their good ***,adversarial examples often exhibit overfitting,as they are tailored to exploit the particular architecture and feature representation of source ***,when attempting black-box transfer attacks on different target models,their effectiveness is *** solve this problem,this study proposes an approach based on a Regularized Constrained Feature Layer(RCFL).The proposed method first uses regularization constraints to attenuate the initial examples of low-frequency *** are then added to a pre-specified layer of the source model using the back-propagation technique,in order to modify the original adversarial ***,a regularized loss function is used to enhance the black-box transferability between different target *** proposed method is finally tested on the ImageNet,CIFAR-100,and Stanford Car datasets with various target models,The obtained results demonstrate that it achieves a significantly higher transfer-based adversarial attack success rate compared with baseline techniques.

关键词： Adversarial examples black-box transferability regularized constrained transfer-based adversarial attacks

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DRL-Enabled Computation Offloading for AIGC Services in IIoT-Assisted Edge Computing Networks

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IEEE Internet of Things Journal 2025年第9期12卷 12829-12844页

作者： Zhang, Xingxing Li, Shaobo Tang, Jianhang Zhu, Keyu Zhang, Yang Guizhou University State Key Laboratory of Public Big Data Guiyang550025 China National University of Singapore Department of Electrical and Computer Engineering Queenstown Singapore Guizhou Institute of Technology School of Big Data Guiyang550003 China Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology Nanjing210016 China

The widespread application of AI-generated content (AIGC) services has driven demand for efficient computational resources, making effective task scheduling and computation offloading in edge computing (EC) environments a critical research topic. However, the high computational requirements and low latency demands of AIGC services, combined with the limitations of EC, present challenges for existing offloading methods, such as unstable decision making in dynamic task environments and resource overloading. Here, we propose a decentralized AIGC task offloading architecture within an IoT-assisted EC network to optimize the quality of AIGC services. In this architecture, we define a multiobjective joint optimization problem for AIGC task offloading, aiming to simultaneously optimize key performance metrics, such as task latency, energy efficiency, and load balancing. To address this problem, we introduce an improved proximal policy optimization (PPO)-based deep reinforcement learning (DRL) algorithm, named TOPPO. By incorporating a policy update step size constraint and a clipping mechanism, TOPPO significantly enhances the stability of the training process and reduces fluctuations during policy updates. Additionally, the algorithm integrates an LSTM model to improve its ability to handle temporal dependencies. Through continuous interaction between the model and the environment, the offloading strategy is iteratively updated to ensure that diverse AIGC tasks are efficiently executed on IoT devices or edge servers. Extensive simulations and performance evaluations demonstrate that the proposed method achieves significant improvements in task latency, energy consumption, and load management during AIGC task processing. © 2014 IEEE.

关键词： Computation offloading

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Aligning Instance-Semantic Sparse Representation towards Unsupervised Object Segmentation and Shape Abstraction with Repeatable Primitives

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IEEE Transactions on Visualization and computer Graphics 2025年第5期31卷 2579-2588页

作者： Li, Jiaxin Wang, Hongxing Tan, Jiawei Ou, Zhilong Yuan, Junsong Chongqing University School of Big Data and Software Engineering Chongqing401331 China State University of New York Department of Computer Science and Engineering BuffaloNY14260 United States

Understanding 3D object shapes necessitates shape representation by object parts abstracted from results of instance and semantic segmentation. Promising shape representations enable computers to interpret a shape with meaningful parts and identify their repeatability. However, supervised shape representations depend on costly annotation efforts, while current unsupervised methods work under strong semantic priors and involve multi-stage training, thereby limiting their generalization and deployment in shape reasoning and understanding. Driven by the tendency of high-dimensional semantically similar features to lie in or near low-dimensional subspaces, we introduce a one-stage, fully unsupervised framework towards semantic-aware shape representation. This framework produces joint instance segmentation, semantic segmentation, and shape abstraction through sparse representation and feature alignment of object parts in a high-dimensional space. For sparse representation, we devise a sparse latent membership pursuit method that models each object part feature as a sparse convex combination of point features at either the semantic or instance level, promoting part features in the same subspace to exhibit similar semantics. For feature alignment, we customize an attention-based strategy in the feature space to align instance- and semantic-level object part features and reconstruct the input shape using both of them, ensuring geometric reusability and semantic consistency of object parts. To firm up semantic disambiguation, we construct cascade unfrozen learning on geometric parameters of object parts. Experiments conducted on benchmark datasets confirm that our approach results in instance- and semantic-level joint segmentation and shape abstraction with repeatable primitives, providing coherent semantic interpretations of 3D object shapes across categories in a one-stage, fully unsupervised manner, without relying on annotations or heuristic semantic priors. Code will be

关键词： Semantic Segmentation

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A Novel Community Detection Algorithm Based on Node Propagation and Attraction

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IEEE Access 2025年 13卷 69161-69174页

作者： Fu, Lidong Xie, Xinxing Dou, Zengfa Bai, Yun Zhao, Xi Xi'an University of Science and Technology College of Computer Science and Technology Xi'an710600 China Qinghai Institute of Technology School of Computer and Information Science Xining810016 China Xi'an University School of Information Engineering Xi'an710065 China Xi'an Big Data Service Center Xi'an710007 China

High-quality community detection in complex networks typically relies on the similarity between nodes to select communities. However, when a node exhibits the same similarity with different communities, it becomes ambiguous to determining the optimal community assignment for that node, potentially leading to incorrect predictions and affecting the accuracy of community assignments for other nodes. To address this problem, this paper proposes a novel community detection algorithm based on node propagation and attraction. The algorithm first introduces a node propagation strategy, which identifies high-propagation nodes based on their connectivity and core attributes. These high-propagation nodes form the initial backbone and framework of communities, minimizing the interference of low-propagation nodes on community structure. Subsequently, an attraction definition is introduced, which starts from high-propagation nodes to select partitioning methods based on their relationships with neighboring nodes, using the attraction among these nodes facilitate community organization and propagation, aiming to enhance community quality. Finally, in the community optimization phase, the algorithm effectively improves the accuracy of partitions that result from similar attractions by considering the association degree between nodes and communities. Experimental results demonstrate that, on both synthetic and real networks, the proposed algorithm outperforms various existing community detection algorithms. © 2013 IEEE.

关键词： Optimization algorithms

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Underwater Image Restoration Through a Prior Guided Hybrid Sense Approach and Extensive Benchmark Analysis

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IEEE Transactions on Circuits and Systems for Video Technology 2025年第5期35卷 4784-4800页

作者： Guo, Xiaojiao Chen, Xuhang Wang, Shuqiang Pun, Chi-Man The Department of Computer and Information Science University of Macau Macao China Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China The School of Big Data Baoshan University Baoshan678000 China The School of Computer Science and Engineering Huizhou University Huizhou516007 China

Underwater imaging grapples with challenges from light-water interactions, leading to color distortions and reduced clarity. In response to these challenges, we propose a novel Color Balance Prior Guided Hybrid Sense Underwater Image Restoration framework (GuidedHybSensUIR). This framework operates on multiple scales, employing the proposed Detail Restorer module to restore low-level detailed features at finer scales and utilizing the proposed Feature Contextualizer module to capture long-range contextual relations of high-level general features at a broader scale. The hybridization of these different scales of sensing results effectively addresses color casts and restores blurry details. In order to effectively point out the evolutionary direction for the model, we propose a novel Color Balance Prior as a strong guide in the feature contextualization step and as a weak guide in the final decoding phase. We construct a comprehensive benchmark using paired training data from three real-world underwater datasets and evaluate on six test sets, including three paired and three unpaired, sourced from four real-world underwater datasets. Subsequently, we tested 14 traditional and retrained 23 deep learning existing underwater image restoration methods on this benchmark, obtaining metric results for each approach. This effort aims to furnish a valuable benchmarking dataset for standard basis for comparison. The extensive experiment results demonstrate that our method outperforms 37 other state-of-the-art methods overall on various benchmark datasets and metrics, despite not achieving the best results in certain individual cases. © 1991-2012 IEEE.

关键词： Underwater imaging

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AI-Enhanced Secure data Aggregation for Smart Grids with Privacy Preservation

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computers, Materials & Continua 2025年第1期82卷 799-816页

作者： Congcong Wang Chen Wang Wenying Zheng Wei Gu School of Software Nanjing University of Information Science and TechnologyNanjing210044China School of Information Science and Engineering Zhejiang Sci-Tech UniversityHangzhou310018China State Key Laboratory of Public Big Data Guizhou UniversityGuiyang550025China School of Computer Science and Technology(School of Artificial Intelligence) Zhejiang Sci-Tech UniversityHangzhou310018China School of Computer Science(School of Cyber Science and Engineering) Nanjing University of Information Science and TechnologyNanjing210044China

As smart grid technology rapidly advances,the vast amount of user data collected by smart meter presents significant challenges in data security and privacy *** research emphasizes data security and user privacy concerns within smart ***,existing methods struggle with efficiency and security when processing large-scale *** efficient data processing with stringent privacy protection during data aggregation in smart grids remains an urgent *** paper proposes an AI-based multi-type data aggregation method designed to enhance aggregation efficiency and security by standardizing and normalizing various data *** approach optimizes data preprocessing,integrates Long Short-Term Memory(LSTM)networks for handling time-series data,and employs homomorphic encryption to safeguard user *** also explores the application of Boneh Lynn Shacham(BLS)signatures for user *** proposed scheme’s efficiency,security,and privacy protection capabilities are validated through rigorous security proofs and experimental analysis.

关键词： Smart grid data security privacy protection artificial intelligence data aggregation

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Neighbor-relation aware low-rank multi-view subspace clustering

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Multimedia Systems 2025年第3期31卷 1-16页

作者： Jia, Hongjie Wang, Tengteng Song, Heping School of Computer Science and Communication Engineering Jiangsu University Zhenjiang212013 China Jiangsu Engineering Research Center of Big Data Ubiquitous Perception and Intelligent Agriculture Applications Zhenjiang212013 China

Low-rank multi-view subspace clustering seeks to uncover complex relationships in high-dimensional data by leveraging multiple views and low-rank representations. However, two key challenges persist: (1) Existing methods emphasize low-rank consistency through nuclear norm regularization but often overlook the unique low-rank features of individual views. (2) Constructing the affinity matrix directly from the coefficient matrix can introduce excessive connections, degrading clustering performance. To address these challenges, we propose neighbor-relation aware low-rank multi-view subspace clustering (NLMSC), a unified model that integrates view-specific low-rank structure learning with neighbor-aware affinity fusion. NLMSC decomposes the coefficient matrix into smaller factor matrices to capture the underlying low-rank structures of each view and constructs a sparser, more interpretable affinity matrix by reliable neighbor selection. These affinity matrices are then fused into a block-diagonal consensus graph through a self-weighting strategy for spectral clustering. Extensive experiments on diverse multi-view datasets demonstrate NLMSC’s superior performance and effectiveness in challenging clustering tasks. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Low-rank structure Multi-view clustering Neighbor-relation awareness Subspace clustering

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