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5th International Seminar on Artificial intelligence, Networking and Information Technology, AINIT 2024

作者： Yao, Yahu Lu, Rong Wu, Jun Tao, Zaiyang Qu, Lei Key Laboratory of Computational Intelligence and Signal Processing Ministry of Education Hefei China Hefei China

ISBN: (纸本)9798350385557

We can focus on the challenges posed by traditional registration algorithms and the limitations of U-Net-based architectures in capturing sufficient semantic information due to their shallow coding layers with small receptive fields. We want to explore how newer architectures or modifications to existing ones could address these limitations. Inspired by introduced deformable convolutional networks, we plan to fuse deformable convolution into our proposed network structure. Building on its success in 2D target detection, we apply deformable convolution to 3D image registration networks. The Deform U-Net proposed in this paper can compensate for the shortcomings of existing registration networks by obtaining more semantic information with unsupervised training without Introduction of additional features. We conducted evaluations on two publicly available human brain MRI, specifically LPBA40 and IBSR18. On the LPBA40 and ISBR18 datasets, the DSC reached 68.82, 62.30, respectively. The experimental results show that the Deform U-N et image registration method outperforms the VoxelMorph image registration method. © 2024 IEEE.

关键词： Convolution

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A Comprehensive Overview of CFN From a Commonsense Perspective

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Machine intelligence Research 2024年第2期21卷 239-256页

作者： Ru Li Yunxiao Zhao Zhiqiang Wang Xuefeng Su Shaoru Guo Yong Guan Xiaoqi Han Hongyan Zhao Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education School of Computer and Information TechnologyShanxi UniversityTaiyuan 030006China

Chinese FrameNet(CFN)is a scenario commonsense knowledge base(CKB)that plays an important role in research on Chinese language *** is based on the theory of frame semantics and English FrameNet(FN).The CFN knowledge base contains a wealth of scenario commonsense knowledge,including frames,frame elements,and frame relations,as well as annotated instances with rich scenario-related labels on Chinese sentences and *** this paper,we conduct a comprehensive overview of CFN from a commonsense perspective,covering topics such as scenario commonsense representation,CFN resources,and its *** also summarize recent breakthroughs and identify future research ***,we introduce the concept of scenario commonsense,including its definitions,examples,and representation methods,with a focus on the relationship between scenario commonsense and the frame concept in *** addition,we provide a comprehensive overview of CFN resources and their applications,highlighting the newly proposed frame-based discourse representation and a human-machine collaboration framework for expanding the CFN ***,we explore emerging topics such as expanding the CFN resource,improving the interpretability of machine reading comprehension,and using scenario CKBs for text generation.

关键词： Chinese FrameNet(CFN) commonsense scenario commonsense frame knowledge

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Structure-to-word dynamic interaction model for abstractive sentence summarization

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Neural Computing and Applications 2025年第9期37卷 6567-6581页

作者： Guan, Yong Guo, Shaoru Li, Ru School of Computer & Information Technology Shanxi University Shanxi China Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education Shanxi University Shanxi China

Abstractive text summarization aims to capture important information from text and integrate contextual information to guide the summary generation. However, effective integration of important and relevant information remains a challenging problem. Existing graph-based methods only consider either word relations or structure information, but neglect the correlation between them. To simultaneously capture the word relations and structure information from sentences, we propose a novel Structure-to-Word dynamic interaction model for Abstractive Sentence Summarization (SWSum). Specifically, we first represent structure and word relation information of sentences by constructing semantic scenario graph and semantic word relation graph based on FrameNet. We subsequently stack multiple graph-based dynamic interaction layers that iteratively enhance their correlation to learn node representations. Finally, a graph fusion module is designed to obtain better overall graph representations, which provide an attention-based context vector for the decoder to generate summary. Experimental results demonstrate our model outperforms existing state-of-the-art methods on two popular benchmark datasets, i.e., Gigaword and DUC 2004. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Semantics

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SURE: Mutually Visible Objects and Self-generated Candidate Labels For Relation Extraction 31

SURE: Mutually Visible Objects and Self-generated Candidate ...

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31st International Conference on computational Linguistics, COLING 2025

作者： Feng, Yuxuan Chen, Qian Wu, Qianyou Guo, Xin Wang, Suge School of Computer and Information Technology Shanxi University China Key Laboratory of Computational Intelligence and Chinese Information Processing China

ISBN: (纸本)9798891761964

Joint relation extraction models effectively mitigate the error propagation problem inherently present in pipeline models. Nevertheless, joint models face challenges including high computational complexity, complex network architectures, difficult parameter tuning, and notably, limited interpretability. In contrast, recent advances in pipeline relation extraction models (PURE, PL-Marker) have attracted considerable attention due to their lightweight design and high extraction accuracy. A key advancement is the introduction of a marker mechanism, which enhances relation extraction (RE) process by highlighting entities. However, these models primarily focus on generating correct labels. In doing so, they neglect the label selection process. Moreover, they fail to adequately capture the intricate interactions between entity pairs. To overcome these limitations, we develop a Candidate Label Markers (CLMs) mechanism that prioritizes strategic label selection over simple label generation. Furthermore, we facilitate interactions among diverse relation pairs, enabling the identification of more intricate relational patterns. Experimental results show that we achieve a new SOTA performance. Specifically, based on the same Named Entity Recognition (NER) results as theirs, we improve the SOTA methods by 2.5%, 1.9%, 1.3% in terms of strict F1 scores on SciERC, ACE05 and ACE04. © 2025 Association for computational Linguistics.

关键词： computational linguistics

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DMFVAE:miRNA-disease associations prediction based on deep matrix factorization method with variational autoencoder

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Frontiers of Computer Science 2024年第6期18卷 259-270页

作者： Pijing WEI Qianqian WANG Zhen GAO Ruifen CAO Chunhou ZHENG Information Materials and Intelligent Sensing Laboratory of Anhui Province Institutes of Physical Science and Information TechnologyAnhui UniversityHefei 230601China Key Lab of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and TechnologyAnhui UniversityHefei 230601China Key Lab of Intelligent Computing and Signal Processing of Ministry of Education School of Artificial IntelligenceAnhui UniversityHefei 230601China

MicroRNAs(miRNAs)are closely related to numerous complex human diseases,therefore,exploring miRNA-disease associations(MDAs)can help people gain a better understanding of complex disease *** increasing number of computational methods have been developed to predict ***,the sparsity of the MDAs may hinder the performance of many *** addition,many methods fail to capture the nonlinear relationships of miRNA-disease network and inadequately leverage the features of network and neighbor *** this study,we propose a deep matrix factorization model with variational autoencoder(DMFVAE)to predict potential *** first decomposes the original association matrix and the enhanced association matrix,in which the enhanced association matrix is enhanced by self-adjusting the nearest neighbor method,to obtain sparse vectors and dense vectors,***,the variational encoder is employed to obtain the nonlinear latent vectors of miRNA and disease for the sparse vectors,and meanwhile,node2vec is used to obtain the network structure embedding vectors of miRNA and disease for the dense ***,sample features are acquired by combining the latent vectors and network structure embedding vectors,and the final prediction is implemented by convolutional neural network with channel *** evaluate the performance of DMFVAE,we conduct five-fold cross validation on the HMDD v2.0 and HMDD v3.2 datasets and the results show that DMFVAE performs ***,case studies on lung neoplasms,colon neoplasms,and esophageal neoplasms confirm the ability of DMFVAE in identifying potential miRNAs for human diseases.

关键词： miRNA-disease association deep matrix factorization self-adjusted nearest neighbor variational encoder network structure

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Fast construction and exploration of performance-cost design space for belief propagation polar decoders

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Science China(Information Sciences) 2025年第3期68卷 401-402页

作者： Chao JI You YOU Weikang QIAN Yongming HUANG Chuan ZHANG Laboratory of Efficient Architectures for Digital-communication and Signal-processing (LEADS) National Mobile Communications Research Laboratory Southeast University Purple Mountain Laboratories University of Michigan-Shanghai Jiao Tong University Joint Institute Ministry of Education Key Laboratory of Artificial Intelligence Shanghai Jiao Tong University

Considering polar codes in the channel coding field, belief propagation(BP) polar decoding stands out as a potential solution for high-throughput applications due to its inherent advantage on high parallelism, and also emerging other improved BP-based decoding such as BP flip(BPF) [1, 2] and BP list(BPL) [3,4] polar decoders.

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A Privacy Preservation Method for Attributed Social Network Based on Negative Representation of Information

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Computer Modeling in Engineering & Sciences 2024年第7期140卷 1045-1075页

作者： Hao Jiang Yuerong Liao Dongdong Zhao Wenjian Luo Xingyi Zhang Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and TechnologyAnhui UniversityHefei230601China Chongqing Research Institute School of Computer Science and Artificial IntelligenceWuhan University of TechnologyWuhan430070China Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies School of Computer Science and TechnologyHarbin Institute of TechnologyShenzhen518055China

Due to the presence of a large amount of personal sensitive information in social networks,privacy preservation issues in social networks have attracted the attention of many *** by the self-nonself discrimination paradigmin the biological immune system,the negative representation of information indicates features such as simplicity and efficiency,which is very suitable for preserving social network ***,we suggest a method to preserve the topology privacy and node attribute privacy of attribute social networks,called ***,a negative survey-based method is developed to disturb the relationship between nodes in the social network so that the topology structure can be kept ***,a negative database-based method is proposed to hide node attributes,so that the privacy of node attributes can be preserved while supporting the similarity estimation between different node attributes,which is crucial to the analysis of social *** evaluate the performance of the AttNetNRI,empirical studies have been conducted on various attribute social networks and compared with several state-of-the-art methods tailored to preserve the privacy of social *** experimental results show the superiority of the developed method in preserving the privacy of attribute social networks and demonstrate the effectiveness of the topology disturbing and attribute hiding *** experimental results show the superiority of the developed methods in preserving the privacy of attribute social networks and demonstrate the effectiveness of the topological interference and attribute-hiding components.

关键词： Attributed social network topology privacy node attribute privacy negative representation of information negative survey negative database

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Graph External Attention Enhanced Transformer 41

Graph External Attention Enhanced Transformer

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41st International Conference on Machine Learning, ICML 2024

作者： Liang, Jianqing Chen, Min Liang, Jiye Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education School of Computer and Information Technology Shanxi University Shanxi Taiyuan030006 China

The Transformer architecture has recently gained considerable attention in the field of graph representation learning, as it naturally overcomes several limitations of Graph Neural Networks (GNNs) with customized attention mechanisms or positional and structural encodings. Despite making some progress, existing works tend to overlook external information of graphs, specifically the correlation between graphs. Intuitively, graphs with similar structures should have similar representations. Therefore, we propose Graph External Attention (GEA) - a novel attention mechanism that leverages multiple external node/edge key-value units to capture inter-graph correlations implicitly. On this basis, we design an effective architecture called Graph External Attention Enhanced Transformer (GEAET), which integrates local structure and global interaction information for more comprehensive graph representations. Extensive experiments on benchmark datasets demonstrate that GEAET achieves state-of-the-art empirical performance. The source code is available for reproducibility at: https://***/icm1018/GEAET. Copyright 2024 by the author(s)

关键词： Graph neural networks

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A Bayesian matrix factorization model for dynamic user embedding in recommender system

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Frontiers of Computer Science 2022年第5期16卷 233-235页

作者： Kaihan ZHANG Zhiqiang WANG Jiye LIANG Xingwang ZHAO Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education School of Computer and Information TechnologyShanxi UniversityTaiyuan 030006China

1 Introduction The main idea of recommender system is how to learn accurate users’embeddings from behavior data[1].Each dimension of users’embeddings can reflect the interests of users in different potential *** real-world scenarios,users’interests are drifting over time,which brings a challenge to learn accurate dynamic users’***,various time-aware recommendation methods have been proposed to tackle this problem by modeling the evolution process of users’interests[2−4].However,they usually assume that users’embeddings drift with the same range on all *** practice,users’embeddings should change diversely on different dimensions over ***,for the rapidly changing interests of the users,the corresponding dimensions should change *** the contrary,the dimensions representing stable interests may change slightly.

关键词： dimensions system dynamic

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Linear Subspace Surrogate Modeling for Large-Scale Expensive Single/Multi-Objective Optimization

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IEEE Transactions on Evolutionary Computation 2023年第3期29卷 1-1页

作者： Si, Langchun Zhang, Xingyi Tian, Ye Yang, Shangshang Zhang, Limiao Jin, Yaochu School of Computer Science and Technology Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Anhui University Hefei China School of Artificial Intelligence Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Anhui University Hefei China Information Materials and Intelligent Sensing Laboratory of Anhui Province Anhui University Hefei China Faculty of Technology Bielefeld University Bielefeld Germany

Despite that the surrogate-assisted evolutionary algorithms have achieved great success in addressing expensive optimization problems, they still suffer from stiff challenges when the number of dimensions of problems becomes large. The primary reason lies in that it is very hard to build an acceptable surrogate model in the high-dimensional search space with small amounts of evaluated historical data in evolution. To tackle this issue, we suggest an effective surrogate modeling method for large-scale expensive optimization in this paper, where the models are built on a number of linear subspaces instead of the original search space. Specifically, a linear subspace is constructed by a pair of points/solutions which are generated based on the set of elite solutions. For each linear subspace, several historical solutions are first associated according to their distance to the linear subspace, and then a surrogate model is trained by the associated solutions and used to evaluate the offspring. To ensure the exploration and exploitation capacity of the proposed method, these linear subspaces and the surrogate models are updated after a few iterations. Experimental results on CEC’2010 and CEC’2013 single-objective optimization problems with up to 1500 decision variables show that the proposed algorithm is superior over six comparison algorithms. Moreover, we also extend the proposed algorithm to multi-objective optimization problems and verified its competitiveness on problems with up to 1500 decision variables. IEEE

关键词： Evolutionary algorithms

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