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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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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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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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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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Less Is More: Token Context-Aware Learning for Object Tracking 39

Less Is More: Token Context-Aware Learning for Object Tracki...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Xu, Chenlong Zhong, Bineng Liang, Qihua Zheng, Yaozong Li, Guorong Song, Shuxiang Key Laboratory of Education Blockchain and Intelligent Technology Ministry of Education Guangxi Normal University Guilin541004 China Key Laboratory of Big Data Mining and Knowledge Management University of Chinese Academy of Sciences China

ISBN: (纸本)157735897X

Recently, several studies have shown that utilizing contextual information to perceive target states is crucial for object tracking. They typically capture context by incorporating multiple video frames. However, these naive frame-context methods fail to consider the importance of each patch within a reference frame, making them susceptible to noise and redundant tokens, which deteriorates tracking performance. To address this challenge, we propose a new token context-aware tracking pipeline named LMTrack, designed to automatically learn high-quality reference tokens for efficient visual tracking. Embracing the principle of Less is More, the core idea of LMTrack is to analyze the importance distribution of all reference tokens, where important tokens are collected, continually attended to, and updated. Specifically, a novel Token Context Memory module is designed to dynamically collect high-quality spatio-temporal information of a target in an autoregressive manner, eliminating redundant background tokens from the reference frames. Furthermore, an effective Unidirectional Token Attention mechanism is designed to establish dependencies between reference tokens and search frame, enabling robust cross-frame association and target localization. Extensive experiments demonstrate the superiority of our tracker, achieving state-of-the-art results on tracking benchmarks such as GOT-10K, TrackingNet, and LaSOT. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Aircraft detection

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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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Hash Function Based on Quantum Walks with Two-Step Memory

Hash Function Based on Quantum Walks with Two-Step Memory

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2024 International Conference on Computing, Machine Learning and data Science, CMLDS 2024

作者： Zhou, Qing Lu, Songfeng Yang, Hao Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Hubei Wuhan China Shenzhen Huazhong University of Science and Technology Research Institute Guangdong Shenzhen China

ISBN: (纸本)9798400716393

We propose a new quantum-walk-based hash function QHF2M by combining two types of quantum walks with two-step memory and numerically test its statistical performance. The test result shows that QHF2M is on a par with the state-of-the-art hash functions based on discrete quantum walks in terms of sensitivity of hash value to message, diffusion and confusion properties, uniform distribution property, and collision resistance property. Moreover, both unequal and equivalent coin angles used by the underlying quantum walks can lead to near-ideal statistical performance. This result indicates that neither using quantum walks with different memory length nor adopting distinct coin angles is a necessary condition for constructing good quantum-walk-with-memory-based hash functions. © 2024 ACM.

关键词： Hash functions

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Advance of Thyroid Nodule Ultrasound Diagnosis Based on Deep Learning 29th

Advance of Thyroid Nodule Ultrasound Diagnosis Based on Deep...

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29th International Conference on Computational and Experimental engineering and Sciences, ICCES 2023

作者： Wan, Huiling Chen, Shuwen Ni, Yiyang Qi, Shaojia Qu, Hui School of Physics and Information Engineering Jiangsu Second Normal University Nanjing211200 China State Key Laboratory of Millimeter Waves Southeast University Nanjing210096 China Jiangsu Province Engineering Research Center of Basic Education Big Data Application Nanjing211200 China

ISBN: (纸本)9783031449468

The incidence of thyroid nodules is increasing rapidly worldwide. In order to reduce the burden on doctors and improve the accuracy of diagnosis, ultrasound imaging plays an important role in the diagnosis and follow-up of thyroid nodules. In recent years, artificial intelligence (AI) technology based on machine learning and deep learning has improved significantly. With the help of medical imaging screening tools, physicians can make clinical decisions about thyroid nodules more efficiently and provide the best protection for patients as early as possible. This paper mainly discusses the research progress of computer vision and related algorithms in ultrasonic imaging of thyroid nodules in recent years from the aspects of image preprocessing, detection and segmentation of focal areas, feature extraction, and identification of benign/malignant nodules. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Diagnosis

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REQUIRED NUMBER OF ITERATIONS FOR SPARSE SIGNAL RECOVERY VIA ORTHOGONAL LEAST SQUARES

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Journal of Computational Mathematics 2023年第1期41卷 1-17页

作者： Haifeng Li Jing Zhang Jinming Wen Dongfang Li Henan Engineering Laboratory for Big Data Statistical Analysis and Optimal Control College of Mathematics and Information ScienceHenan Normal UniversityXinxiang 453007China College of Information Science and Technology Jinan UniversityGuangzhou 510632China School of Mathematics and Statistics Huazhong University of Science and TechnologyWuhan 430074China Hubei Key Laboratory of Engineering Modeling and Scientific Computing Huazhong University of Science and TechnologyWuhan 430074China

In countless applications,we need to reconstruct a K-sparse signal x∈R n from noisy measurements y=Φx+v,whereΦ∈R^(m×n)is a sensing matrix and v∈R m is a noise *** least squares(OLS),which selects at each step the column that results in the most significant decrease in the residual power,is one of the most popular sparse recovery *** this paper,we investigate the number of iterations required for recovering x with the OLS *** show that OLS provides a stable reconstruction of all K-sparse signals x in[2.8K]iterations provided thatΦsatisfies the restricted isometry property(RIP).Our result provides a better recovery bound and fewer number of required iterations than those proposed by Foucart in 2013.

关键词： Sparse signal recovery Orthogonal least squares(OLS) Restricted isometry property(RIP)

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