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Enhancing Entity Relationship Extraction in Dialogue Texts Using Hypergraph and Heterogeneous Graph

Chinese Journal of Electronics 2025年第1期34卷 295-308页

作者： Shunmiao Zhang Siyuan Zheng Degen Huang Dan Li School of Computer Science and Mathematics Fujian University of Technology Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology School of Computer Science and Technology Dalian University of Technology Elsevier

Dialogue-based relation extraction(DialogRE) aims to predict relationships between two entities in dialogue. Current approaches to dialogue relationship extraction grapple with long-distance entity relationships in dialogue data as well as complex entity relationships, such as a single entity with multiple types of connections. To address these issues, this paper presents a novel approach for dialogue relationship extraction termed the hypergraphs and heterogeneous graphs model(HG2G). This model introduces a two-tiered structure, comprising dialogue hypergraphs and dialogue heterogeneous graphs, to address the shortcomings of existing methods. The dialogue hypergraph establishes connections between similar nodes using hyper-edges and utilizes hypergraph convolution to capture multi-level features. Simultaneously, the dialogue heterogeneous graph connects nodes and edges of different types, employing heterogeneous graph convolution to aggregate cross-sentence information. Ultimately, the integrated nodes from both graphs capture the semantic nuances inherent in dialogue. Experimental results on the DialogRE dataset demonstrate that the HG2G model outperforms existing state-of-the-art methods.

关键词： Convolution Aggregates Semantics Feature extraction Data mining

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Privacy-preserving explainable AI: a survey

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science China(Information sciences) 2025年第1期68卷 23-56页

作者： Thanh Tam NGUYEN Thanh Trung HUYNH Zhao REN Thanh Toan NGUYEN Phi Le NGUYEN Hongzhi YIN Quoc Viet Hung NGUYEN School of Information and Communication Technology Griffith University School of Computer and Communication Sciences Ecole Polytechnique Federale de Lausanne Faculty of Mathematics and Computer Science University of Bremen Faculty of Information Technology HUTECH University Department of Computer Science Hanoi University of Science and Technology School of Electrical Engineering and Computer Science The University of Queensland

As the adoption of explainable AI(XAI) continues to expand, the urgency to address its privacy implications intensifies. Despite a growing corpus of research in AI privacy and explainability, there is little attention on privacy-preserving model explanations. This article presents the first thorough survey about privacy attacks on model explanations and their countermeasures. Our contribution to this field comprises a thorough analysis of research papers with a connected taxonomy that facilitates the categorization of privacy attacks and countermeasures based on the targeted explanations. This work also includes an initial investigation into the causes of privacy leaks. Finally, we discuss unresolved issues and prospective research directions uncovered in our analysis. This survey aims to be a valuable resource for the research community and offers clear insights for those new to this domain. To support ongoing research, we have established an online resource repository, which will be continuously updated with new and relevant findings.

关键词： privacy-preserving explainable AI privacy attacks privacy defences PrivEx PPXAI

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Aligning enhanced feature representation for generalized zero-shot learning

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science China(Information sciences) 2025年第2期68卷 74-88页

作者： Zhiyu FANG Xiaobin ZHU Chun YANG Hongyang ZHOU Jingyan QIN Xu-Cheng YIN School of Computer & Communication Engineering University of Science and Technology Beijing

Constructing an effective common latent embedding by aligning the latent spaces of cross-modal variational autoencoders（VAEs） is a popular strategy for generalized zero-shot learning（GZSL）. However, due to the lack of fine-grained instance-wise annotations, existing VAE methods can easily suffer from the posterior collapse problem. In this paper, we propose an innovative asymmetric VAE network by aligning enhanced feature representation（AEFR） for GZSL. Distinguished from general VAE structures, we designed two asymmetric encoders for visual and semantic observations and one decoder for visual reconstruction. Specifically, we propose a simple yet effective gated attention mechanism（GAM） in the visual encoder for enhancing the information interaction between observations and latent variables, alleviating the possible posterior collapse problem effectively. In addition, we propose a novel distributional decoupling-based contrastive learning（D2-CL） to guide learning classification-relevant information while aligning the representations at the taxonomy level in the latent representation space. Extensive experiments on publicly available datasets demonstrate the state-of-the-art performance of our method. The source code is available at https://***/seeyourmind/AEFR.

关键词： generalized zero-shot learning gated attention mechanism contrastive learning multi-modal alignment

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Short-Term Rolling Prediction of Tropical Cyclone Intensity Based on Multi-Task Learning with Fusion of Deviation-Angle Variance and Satellite Imagery

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Advances in Atmospheric sciences 2025年第1期42卷 111-128页

作者： Wei TIAN Ping SONG Yuanyuan CHEN Yonghong ZHANG Liguang WU Haikun ZHAO Kenny Thiam Choy LIM KAM SIAN Chunyi XIANG School of Software Nanjing University of Information Science and TechnologyNanjing 210044China School of Computer Nanjing University of Information Science and TechnologyNanjing 210044China School of Automation Nanjing University of Information Science and TechnologyNanjing 210044China Fudan University Shanghai 200433China Pacific Typhoon Research Center Nanjing University of Information Science and TechnologyNanjing 210044China Wuxi University Wuxi 214100China National Meteorological Center Beijing 100081China

Tropical cyclones(TCs)are one of the most serious types of natural disasters,and accurate TC activity predictions are key to disaster prevention and ***,TC track predictions have made significant progress,but the ability to predict their intensity is obviously lagging *** present,research on TC intensity prediction takes atmospheric reanalysis data as the research object and mines the relationship between TC-related environmental factors and intensity through deep ***,reanalysis data are non-real-time in nature,which does not meet the requirements for operational forecasting ***,a TC intensity prediction model named TC-Rolling is proposed,which can simultaneously extract the degree of symmetry for strong TC convective cloud and convection intensity,and fuse the deviation-angle variance with satellite images to construct the correlation between TC convection structure and *** TCs'complex dynamic processes,a convolutional neural network(CNN)is used to learn their temporal and spatial *** real-time intensity estimation,multi-task learning acts as an implicit time-series *** model is designed with a rolling strategy that aims to moderate the long-term dependent decay problem and improve accuracy for short-term intensity *** multiple tasks are correlated,the loss function of 12 h and 24 h are *** testing on a sample of TCs in the Northwest Pacific,with a 4.48 kt root-mean-square error(RMSE)of 6 h intensity prediction,5.78 kt for 12 h,and 13.94 kt for 24 h,TC records from official agencies are used to assess the validity of TC-Rolling.

关键词： tropical cyclone intensity structure rolling prediction multi-task

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Magnetic Fe_(3)O_(4)nanoparticles supported on carbonized corncob as heterogeneous Fenton catalyst for efficient degradation of methyl orange

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Chinese Journal of Chemical Engineering 2025年第1期77卷 144-155页

作者： Chen Chen Zeming Yan Zhuoli Ma Dianjie Ma Shijun Xing Wenping Li Jiazhi Yang Qiaofeng Han School of Chemistry and Chemical Engineering Nanjing University of Science and TechnologyNanjing 210094China

The pollution especially organic dyes pollution of water resources is an urgent issue to be *** is crucial to develop highly efficient,low cost and recyclable heterogeneous catalysts for wastewater *** this study,a heterogeneous Fenton catalyst loaded with Fe_(3)O_(4)nanoparticles was prepared by one step pyrolysis using natural crop waste corncob as carbon *** prepared porous carbon catalyst can effectively degrade methyl orange(MO,25 mg·L^(-1))at room temperature,and the degradation rate is 99.7%.In addition to high catalytic degradation activity,the layered porous carbon structure of the catalyst also provides high stability and *** degradation rate can be maintained above 93%after 10 ***,the prepared catalyst is magnetic,which makes the catalyst easy to recycle in practical *** addition,the prepared Fe3O4/RCC catalyst has efficient Fenton degradation activity for bisphenol A(BPA)(96.9%)and antibiotic tetracycline hydrochloride(TC-HCl)(95.5%),which proves that it has universal applicability for the degradation of most organic *** study provides a feasible and scalable strategy to prepare a heterogeneous Fenton catalyst treating wastewater and high-value utilization of biomass waste.

关键词： Water treatment Organic dyes Fe_(3)O_(4)nanoparticles Heterogeneous Fenton catalyst Corncob Porous carbon

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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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Deep learning-based software engineering: progress,challenges, and opportunities

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science China(Information sciences) 2025年第1期68卷 57-144页

作者： Xiangping CHEN Xing HU Yuan HUANG He JIANG Weixing JI Yanjie JIANG Yanyan JIANG Bo LIU Hui LIU Xiaochen LI Xiaoli LIAN Guozhu MENG Xin PENG Hailong SUN Lin SHI Bo WANG Chong WANG Jiayi WANG Tiantian WANG Jifeng XUAN Xin XIA Yibiao YANG Yixin YANG Li ZHANG Yuming ZHOU Lu ZHANG School of Journalism and Communication Sun Yat-sen University School of Software Technology Zhejiang University School of Software Engineering Sun Yat-sen University School of Software Dalian University of Technology School of Computer Science and Technology Beijing Institute of Technology Key Laboratory of High Confidence Software Technologies (Peking University) Ministry of EducationSchool of Computer Science Peking University State Key Laboratory for Novel Software Technology Nanjing University School of Computer Science and Engineering Beihang University Institute of Information Engineering Chinese Academy of Sciences School of Computer Science Fudan University State Key Laboratory of Complex & Critical Software Environment (CCSE) School of Software Beihang University School of Computer and Information Technology Beijing Jiaotong University School of Computer Science and Technology Harbin Institute of Technology School of Computer Science Wuhan University Huawei Technologies

Researchers have recently achieved significant advances in deep learning techniques, which in turn has substantially advanced other research disciplines, such as natural language processing, image processing, speech recognition, and software engineering. Various deep learning techniques have been successfully employed to facilitate software engineering tasks, including code generation, software refactoring, and fault localization. Many studies have also been presented in top conferences and journals, demonstrating the applications of deep learning techniques in resolving various software engineering tasks. However,although several surveys have provided overall pictures of the application of deep learning techniques in software engineering,they focus more on learning techniques, that is, what kind of deep learning techniques are employed and how deep models are trained or fine-tuned for software engineering tasks. We still lack surveys explaining the advances of subareas in software engineering driven by deep learning techniques, as well as challenges and opportunities in each subarea. To this end, in this study, we present the first task-oriented survey on deep learning-based software engineering. It covers twelve major software engineering subareas significantly impacted by deep learning techniques. Such subareas spread out through the whole lifecycle of software development and maintenance, including requirements engineering, software development, testing, maintenance, and developer collaboration. As we believe that deep learning may provide an opportunity to revolutionize the whole discipline of software engineering, providing one survey covering as many subareas as possible in software engineering can help future research push forward the frontier of deep learning-based software engineering more systematically. For each of the selected subareas,we highlight the major advances achieved by applying deep learning techniques with pointers to the available datasets i

关键词： deep learning software engineering software benchmark software artifact representation survey

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A Review on Air-Ground Coordination in Mobile Edge Computing:Key Technologies,Applications and Future Directions

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清华大学学报自然科学版（英文版） 2025年第3期30卷 1359-1386页

作者： Siqi Li Guoqiang Liu Li Li Zhongyuan Zhang Wenhao Fei Haolong Xiang School of Art Nanjing University of Information Science and TechnologyNanjing 210044China School of Software Nanjing University of Information Science and TechnologyNanjing 210044China College of Computer Science and Technology China University of Petroleum(East China)Qingdao 266555China School of Math and Applied Mathematics Nanjing University of Information Science and TechnologyNanjing 210044China

In recent years,Mobile Edge Computing(MEC)has received extensive research attention due to its characteristics,such as real-time data processing and flexible application ***,traditional MEC server deployment relies on the terrestrial Base Stations(BSs),resulting in high deployment costs and limited coverage *** response to these challenges,air-ground coordination has emerged,which effectively combines the advantages of edge computing and Unmanned Aerial Vehicles(UAVs),providing an effective architecture for edge *** utilizing the flexibility of UAVs and empowering them into edge nodes with computing resources,the coverage range of MEC can be expanded,thereby reducing the reliance of edge devices on terrestrial ***,leveraging terrestrial BSs as supplements to the computing power compensates for relatively limited computational capabilities of *** extensive studies have been conducted on air-ground coordination,there are few related summaries of application technologies and ***,the key technologies of air-ground coordination and applications are comprehensively reviewed in this ***,to provide guidance for interested researchers,the development trends and potential applications of air-ground coordination are explored.

关键词： air-ground coordination air-ground control air-ground perception emergency management resource management

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Risk diagnosis model for high-speed rail safety operation in big-data environment

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Journal of Traffic and Transportation Engineering(English Edition) 2025年第1期12卷 12-22页

作者： Qizhou Hu Xin Guan Xiaoyu Wu School of Automation Nanjing University of Science and TechnologyNanjing 210094China

Aiming at the risk issue of high-speed rail(HSR)safety operation,big data technology and uncertain mathematical method are adopted to study ***,from the perspective of system science,the risk diagnosis mode of HSR safety operation is put forward,which mainly includes the operation environment diagnosis mode based on multivariate product,high-speed train diagnosis mode based on failure influence,staff diagnosis mode based on management conditions,track diagnosis mode based on probability safety,*** based on comprehensive analysis,the conventional risk diagnosis index system is *** the dynamic diagnosis index system based on principal component analysis is proposed,and the risk diagnosis model of HSR safety operation is *** diagnosis model can quickly evaluate the operation situations of HSR,and the diagnosis results are conducive to grasping the situation of risk events quickly and accurately,so as to meet the timeliness requirements of emergency ***,to verify the effectiveness of this new model,the Beijing-Shanghai HSR is selected as a case *** analysis results show that the diagnosis model can quickly diagnose the safety operation situation of HSR,simplify the evaluation process and improve the efficiency of the comprehensive evaluation of emergencies.

关键词： High-speed rail Safety Big data Risk diagnosis Principal component analysis

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A novel wire arc additive and subtractive hybrid manufacturing process optimization method

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Journal of Southeast university (English Edition) 2025年第1期41卷 109-117页

作者： Guo, Yiming Zhang, Wanyuan Xiao, Mingkun Song, Shida Zhang, Xiaoyong School of Mechanical Engineering Nanjing University of Science and Technology Nanjing210094 China School of Materials Science and Engineering Nanjing University of Science and Technology Nanjing210094 China

A reasonable process plan is an important basis for implementing wire arc additive and subtractive hybrid manufacturing(ASHM), and a new optimization method is proposed. Firstly, the target parts and machining tools are modeled by level set functions. Secondly, the mathematical model of the additive direction optimization problem is established, and an improved particle swarm optimization algorithm is designed to decide the best additive direction. Then, the two-step strategy is used to plan the hybrid manufacturing alternating sequence. The target parts are directly divided into various processing regions;each processing region is optimized based on manufacturability and manufacturing efficiency, and the optimal hybrid manufacturing alternating sequence is obtained by merging some processing regions. Finally, the method is used to outline the process plan of the designed example model and applied to the actual hybrid manufacturing process of the model. The manufacturing result shows that the method can meet the main considerations in hybrid manufacturing. In addition, the degree of automation of process planning is high, and the dependence on manual intervention is low. © 2025 Southeast university. All rights reserved.

关键词： Particle swarm optimization (PSO)

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