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2021 International Conference on Signal Processing and Communication Technology, SPCT 2021

作者： Shen, Xiajiong Li, Xingchen Han, Daojun Zhang, Yanfeng Henan Key Laboratory of Big Data Analysis and Processing Henan Univ. HA Kaifeng475004 China School of Computer and Information Engineering Henan Univ. HA Kaifeng475004 China Institute of Data and Knowledge Engineering Henan Univ. HA Kaifeng475004 China

ISBN: (数字)9781510653344

ISBN: (纸本)9781510653337

With the continuous development of education, online education is becoming more and more popular with learners because of its convenience. However, online education has the problem of insufficient interaction with students. Students still need to rely on the students themselves to judge the lack of knowledge in practice tasks, which will greatly affect the learning efficiency. In this paper, we propose a method based on formal concept analysis to analyze the knowledge that may not be mastered, and a method of intensive practice for students. First of all, establishing the formal context of student error test questions, construct the concept lattice to calculate the degree of importance of the knowledge concept that may not be mastered, judge the student's mastery of the knowledge concept, and give feedback. Secondly, construct the concept lattice of the unpractised test set, and calculate the similarity of the concept extension set that meets the threshold, find similar topics and push them, and realize targeted training for students who have not mastered the knowledge concepts. © 2022 SPIE.

关键词： Students

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FedDGL: Federated Dynamic Graph Learning for Temporal Evolution and data Heterogeneity 16

FedDGL: Federated Dynamic Graph Learning for Temporal Evolut...

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16th Asian Conference on Machine Learning, ACML 2024

作者： Xie, Zaipeng Li, Likun Chen, Xiangbin Yu, Hao Huang, Qian Key Laboratory of Water Big Data Technology of Ministry of Water Resources Hohai University Nanjing China College of Computer Science and Software Engineering Hohai University Nanjing China College of Artificial Intelligence and Automation Hohai University Nanjing China

Federated graph learning enhances federated learning by enabling privacy-preserving collaborative training on distributed graph data. While traditional methods are effective in managing data heterogeneity, they typically assume static graph structures, overlooking the dynamic nature of real-world graphs. Integrating federated graph learning with dynamic graph neural networks addresses this issue but often fails to retain previously acquired knowledge, limiting generalization for both global and personalized models. This paper proposes FedDGL, a novel framework designed to address temporal evolution, and data heterogeneity in federated dynamic graph learning. Unlike conventional approaches, FedDGL captures temporal dynamics through a global knowledge distillation technique and manages client heterogeneity using a global prototype-based regularization method. The framework employs contrastive learning to generate global prototypes, enhancing feature representation while utilizing a prototype similarity-based personalized aggregation strategy for effective adaptation to local and global data distributions. Experiments on multiple benchmark datasets show that FedDGL achieves significant performance improvements over state-of-the-art methods, with up to 9.02% and 8.77% gains in local and global testing, respectively, compared to FedAvg. These results highlight FedDGL’s effectiveness in improving personalized and global model performance in dynamic, heterogeneous federated graph learning scenarios. © 2024 Z. Xie, L. Li, X. Chen, H. Yu & Q. Huang.

关键词： Dynamic Graph Learning Federated Graph Learning knowledge Distillation Personalized Aggregation Prototype Learning Temporal Evolution

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ISAR Imaging and Cross-Range Scaling Based on Image Rotation Correlation

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Journal of Beijing Institute of Technology 2022年第2期31卷 196-207页

作者： Fangming Liu Cong Du Yujiao Ding Minghai Wang Wei Dong State Key Laboratory on Integrated Optoelectronics College of Electronic Science&EngineeringJilin UniversityChangchun 130012China. Big Data and Network Management Center Jilin UniversityChangchun 130012China.

With the rapid advancement of technology,not only do we need to acquire a clear in-verse synthetic aperture radar(ISAR)image,but also the real size of the target on the imaging plane,so it’s particularly important for the ISAR to rescale the *** is,the ISAR image which is in the range-Doppler domain is converted into the range-azimuth ***,the key point to solving the problem is to estimate the rotation *** this paper,a new scheme to rescale the images is *** the sake of solving the problem of two-dimensional image blur and target high-speed,the instantaneous range instantaneous Doppler(IRID)method is used to obtain ISAR images,and the rotation parameters are estimated by comparing the rotation correlation of the two *** this method,the error of the estimated rotation parameters is greatly reduced,so that the target can be rescaled *** simulation results verify the ef-fectiveness of the proposed algorithm.

关键词： cross-range scaling instantaneous range instantaneous Doppler(IRID) inverse synthet-ic aperture radar(ISAR) rotational correlation rotation parameters

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Dual decoder UNet with Contrastive learning for Brain Image Registration

Dual decoder UNet with Contrastive learning for Brain Image ...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Zhang, Ying Guo, Shuai Shi, Dantong Xiang, Jinhai Huazhong Agricultural University College of Informatics Wuhan430070 China Agricultural Bioinformatics Key Laboratory of Hubei Province China Key Laboratory of Smart Farming for Agricultural Animals Ministry of Agriculture Hubei Engineering Technology Research Center of Agricultural Big Data Engineering Research Center of Intelligent Technology for Agriculture Ministry of Education Huazhong Agricultural University Wuhan430070 China

ISBN: (纸本)9798350386226

The core of the image registration task is to accurately extract and compare the spatial feature information between moving and fixed images. The model must not only be able to capture features inside an image, but also be able to distinguish similarities and differences between moving image and fixed image. Although UNet and its variant networks are widely used in the field of registration, they still present challenges in terms of fine-grained and multi-level feature extraction. In order to address these issues, we propose a dual-decoder UNet network based on contrastive learning. For enhancing the discriminant ability of the network at the feature level, the proposed model applies contrastive learning to the encoder stage of UNet, which strengthens the differential learning in the process of feature extraction. In addition, a multi-level feature fusion method is introduced in the encoder to consider the feature differences at each level more comprehensively. Experimental results show that the proposed method has achieved a significant improvement in the registration accuracy. On the LPBA40 dataset, compared with Affine, VoxelMorph and CLMorph, the Dice scores were increased by 15.2%, 2.5% and 1.9%, respectively, which verified the effectiveness of the proposed method. Our code implementation is publicly available at https://***/LaneAzur/DD-CLUNet. © 2024 IEEE.

关键词： Contrastive Learning

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Discriminative latent semantics-preserving similarity embedding hashing for cross-modal retrieval

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Neural Computing and Applications 2024年第18期36卷 10655-10680页

作者： Chen, Yongfeng Tan, Junpeng Yang, Zhijing Cheng, Yongqiang Chen, Ruihan School of Information Engineering Guangdong University of Technology Guangdong Guangzhou510006 China School of Electronic and Information Engineering South China University of Technology Guangdong Guangzhou510641 China Guangdong Provincial Key Laboratory of Intellectual Property and Big Data Guangdong Guangzhou510665 China Department of Computer Science and Technology University of Hull HullHU6 7RX United Kingdom

Recently, there has been a significant increase in interest in cross-modal hashing technology. For hash code learning, most previous supervision methods use label information to create a similarity matrix in a straightforward manner. However, there are still the following challenges: (1) The asymmetric similarity matrix method only considers the similarity of labels, and the discriminant constraint in Hamming space is ignored;(2) there are optimization errors between the cross-modal semantic correlation of the Hamming space and the nonlinearity of the feature space;and (3) the cross-modal hash matrix is in a dynamic state during the optimization process, and the hash code is easily disturbed and there is bit uncertainty. To this end, we propose the Discriminative Latent Semantics-preserving Similarity Embedding Cross-modal Hashing (DLSSECH) method. Specifically, to reduce the quantization error, we introduce a non-asymmetric similarity decomposition based on orthogonal rotation bias. It can decompose the bitwise correlation of the learned hash code to capture more discriminative semantic information and compact hash code. In addition, to capture the cross-modal semantic correlation of nonlinear feature transformation and reduce quantization error, we propose the latent correlation error matrices based on orthogonal rotation decomposition. The model maintains the maximum difference in semantic dependencies between the projected data and its semantic representation. Finally, we use a sparse common hash matrix and non-asymmetric similarity decomposition factors to solve the uncertainty of the dynamic changes of the hash code. The effectiveness of DLSSECH has demonstrated through experiments on four cross-modal retrieval datasets, where it outperformed some state-of-the-art methods. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Semantics

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Learning Semantic-Rich Relation-Selective Entity Representation for knowledge Graph Completion 28th

Learning Semantic-Rich Relation-Selective Entity Representat...

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28th International Conference on database Systems for Advanced Applications, DASFAA 2023

作者： Xu, Zenan Qiu, Zexuan Su, Qinliang School of Computer Science and Engineering Sun Yat-sen University Guangzhou China The Chinese University of Hong Kong Hong Kong Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China

ISBN: (纸本)9783031306716

Many existing knowledge graph embedding methods learn semantic representations for entities by using graph neural networks (GNN) to harvest their intrinsic relevances. However, these methods mostly represent every entity with one coarse-grained representation, without considering the variation of the semantics of an entity under the context of different relations. To tackle this problem, we propose a method to learn multiple representations of an entity, with each emphasizing one specific aspect of information contained in the entity. During training and testing, only the representation that is most relevant to the considered relation is selected and shown to the model, leading to the relation-selective representations. To enable the selection of representations according to the relation, we first propose to incorporate a relation-controlled gating mechanism into the original GNN, which is used to decide which and how much information can flow into the next updating stage of the GNN. Then, a mixture of relation-level and entity-level negative sample generation methods is further developed to enhance semantic information contained in relation-selective entity representations under the framework of contrastive learning. Experiments on three benchmarks show that our proposed model outperforms all strong baselines. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： knowledge graph

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Research on WNN Greenhouse Temperature Prediction Method Based on GA

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Phyton-International Journal of Experimental Botany 2022年第10期91卷 2283-2296页

作者： Wenbin Dai Lina Wang Binrui Wang Xiaohong Cui Xue Li College of Mechanical and Electronic Engineering China Jiliang UniversityHangzhou310018China Key Laboratory of Intelligent Manufacturing Quality Big Data Tracing and Analysis of Zhejiang Province China Jiliang UniversityHangzhou310018China

Temperature in agricultural production has a direct impact on the growth of *** emergence of greenhouses has improved the impact of the original unpredictable changes in temperature,but the temperature modeling of greenhouses is still the main direction at *** network modeling relies on sufficient actual data to model greenhouses,but there is a widening gap in the application of different neural *** paper proposes a greenhouse temperature prediction model based on wavelet neural network with genetic algorithm(GA-WNN).With the simple network structure and the nonlinear adaptability of the wavelet basis function,wavelet neural network(WNN)improved model training speed and accuracy of prediction results compared with back propagation neural networks(BPNN),which was conducive to the prediction and control of short-term greenhouse temperature *** the same time,the genetic algorithm(GA)was introduced to globally optimize the initial weights of the original model,which improved the insensitivity of the model to the initial weights and thresholds,and improved the training speed and stability of the ***,simulation results for the greenhouse showed that the model training speed,prediction results accuracy and model stability of the GA-WNN in the greenhouse were improved in comparison to results obtained by the WNN and BPNN in the greenhouse.

关键词： Greenhouse temperature greenhouse modeling wavelet neural network genetic algorithm

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Multi-Server Cooperative Video Caching Strategy Based on Deep Reinforcement Learning in Cloud-Edge Computing 10

Multi-Server Cooperative Video Caching Strategy Based on Dee...

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10th International Conference on big data Computing and Communications, bigCOM 2024

作者： Li, Guanyu Mao, Yingchi Peng, Xinxin Zheng, Haotian Wang, Zibo College of Computer Science and Software Engineering Hohai University Nanjing China Hohai University Key Laboratory of Water Big Data Technology of Ministry of Water Resources Nanjing China Huaneng Hydro Lancang Xiaowan Hydropower Plant Dali China

ISBN: (纸本)9798331509538

The soaring number of mobile devices has led the innovation of the edge caching in edge computing, which relieves the heavy pressure of cloud computing. However, with the escalating popularity of short video services, the prevalence of video content in edge server caches has seen a marked increase. Previous work ignored the impact of edge server performance on video caching and failed to automatically adjust the optimal video bit rate in response to users' demand. To improve users' evaluation for watching videos on devices with different display sizes, we propose a novel edge caching strategy called Gate Self-attention Recurrent Network-Multi Agent Reinforcement Learning(GSRN-MARL). GSRN-MARL utilizes leverages Gate Recurrent Unit(GRU) and attention mechanisms to predict cache content based on user historical request information and device details, consequently improving edge server caching performance and users' evaluation of video. Experiment demonstrates that GSRN-MARL surpasses LRU, DRL, and MAAC across two critical metrics: video cache hit rate and users' QoE (Quality of Experience). © 2024 IEEE.

关键词： Deep reinforcement learning

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8th International Congress on Edge Computing, EDGE 2024, Held as Part of the Services Conference Federation, SCF 2024

作者： Tian, Yun Wu, Bin Shi, Jiaoli Zhang, Caicai Xu, Du School of Computer and Big Data Science Jiujiang University Jiujiang332005 China Jiujiang Key Laboratory of Network and Information Security Jiujiang332005 China School of Modern Information Technology Zhejiang Polytechnic University of Mechanical and Electrical Engineering Hangzhou310053 China

ISBN: (纸本)9783031770685

The development of cloud computing and the widespread application of cloud services have made outsourcing services more convenient. The need for individuals and businesses to store and manipulate the graph data they generate is growing rapidly. The unreliability and insecurity of cloud servers make outsourcing graph data a great risk of information leakage. To effectively protect data security, encrypting outsourced data is a useful method. The adjacent vertex query is a very commonly used and fundamental operation, and similarity search is a widely used and powerful tool to improve the scope and functionality of queries. After outsourcing encrypted sparse graph data to cloud servers, it becomes very inconvenient to use and manipulate the data. In this work, we present a scheme to realize the adjacent vertex query supporting similarity search on sparse graph data in cloud environment (SSAQ), which also protects the security of the information. This work uses edit distance and the searchable encryption principle to construct query index, and next implement the similar adjacent vertex query on cloud server. This work provides a formal security analysis, and also gives the experimental comparison and analysis. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Information leakage

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PEDTrans: A Fine-Grained Visual Classification Model for Self-attention Patch Enhancement and Dropout 16th

PEDTrans: A Fine-Grained Visual Classification Model for Se...

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16th Asian Conference on Computer Vision, ACCV 2022

作者： Lin, Xuhong Yan, Qian Wu, Caicong Chen, Yifei College of Information and Electrical Engineering China Agricultural University Beijing100083 China Key Laboratory of Agricultural Machinery Monitoring and Big Data Applications Ministry of Agriculture and Rural Affairs Beijing100083 China

ISBN: (纸本)9783031263507

Fine-grained visual classification (FGVC) is an essential and challenging classification task in computer visual classification, aiming to identify different cars and birds. Recently, most studies use a convolutional neural network combined with an attention mechanism to find discriminant regions to improve algorithm accuracy automatically. However, the discriminant regions selected by the convolutional neural network are extensive. Vision Transformer divides the image into patches and relies on self-attention to select more accurate discriminant regions. However, the Vision Transformer model ignores the response between local patches before patch embedding. In addition, patches usually have high similarity, and they are considered redundant. Therefore, we propose a PEDTrans model based on Vision Transformer. The model has a patch enhancement module based on attention mechanism and a random similar group patch discarding module based on similarity. These two modules can establish patch local feature relationships and select patches that are easier to distinguish between images. Combining these two modules with the Vision Transformer backbone network can improve the fine-grained visual classification accuracy. We employ commonly used fine-grained visual classification datasets CUB-200-2011, Stanford Cars, Stanford Dogs and NABirds to get advanced results. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Convolution

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