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12th International Conference on Advanced Cloud and Big Data, CBD 2024

作者： Chen, Zining Wang, Can Zhang, Baisong Wang, Weiye Li, Wen sheng Zhang, Wei dong School of Computer Science and Technology Anhui University of Technology Ma'anshan China Anhui Provincial Key Laboratory of Digital Twin Technology in Metallurgical Industry Ma'anshan China

ISBN: (纸本)9798331511074

The field of federated learning faces a fundamental challenge posed by the non-independent and identically distributed (Non-IID) data among heterogeneous clients. Personalized federated learning (PFL) addresses this issue by providing customized models for each client. The existing PFL methods ignore the impact of redundant information brought by shared knowledge on processing local tasks of clients, and clients prefer to find the mapping of local data distribution from shared knowledge space. This paper proposes a personalized aggregation method, FedCGM, which customizes a clustering coordinator for each client. The method extracts core knowledge from the global model that aligns with the local data distribution and adapts it to the local client. The challenge of FedCGM lies in identifying the mapping relationship between local data distributions and shared knowledge. To address this challenge, we construct posterior probability indices of the global modules on the target data distribution among participating members. This approach establishes a mapping relationship between data distribution and shared knowledge. We conducted extensive experiments on real-world datasets and found that FedCGM achieved higher accuracy compared to the state-of-the-art PFL methods. © 2024 IEEE.

关键词： Federated learning

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Research on Classroom Behavior Target Detection Algorithm based on YOLOv7 6

Research on Classroom Behavior Target Detection Algorithm ba...

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6th IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2024

作者： Qiu, Wenke Wu, Xiaoming Liu, Xiangzhi Computer Science and Qilu University of Technology Shandong Academy of Sciences ShanDong Jinan China Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9798350316520

The integration of video information for the purpose of detecting students' behavior in the classroom, with the aim of enhancing teaching efficiency and maintaining a stable classroom environment, offers significant assistance. However, existing models usually struggle to achieve good accuracy due to the lack of datasets, dense targets, interclass occlusion, and other issues. To address these difficulties, the CD-YOLO classroom behavior detection algorithm is proposed in this paper. And the classroom behavior dataset (DSL-Dataset) is created. We propose a loss function named VAIoU, which can be combined with vector ratio to filter the prediction box. To further improve the detection accuracy of occluded targets, we propose an attention module CM module with command feature fusion. Moreover, in this paper, we improve the convolutional kernel to design a lightweight convolutional kernel FFconv. Experimental results on DSL-Dataset show that CD-YOLO outperforms existing one-stage detection models in terms of detection accuracy. © 2024 IEEE.

关键词： Convolution

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Label-Dependent Hypergraph Neural Network for Enhanced Multi-label Text Classification 24th

Label-Dependent Hypergraph Neural Network for Enhanced Mult...

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24th International Conference on Web Information Systems Engineering, WISE 2023

作者： Xue, Xuqiang Wu, Xiaoming Li, Shengnan Liu, Xiangzhi Li, Man Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China School of IT Deakin University GeelongVIC3220 Australia

ISBN: (纸本)9789819972531

Multi-label text classification (MLTC) is a challenging task in natural language processing. Improving the performance of MLTC through building label dependencies remains a focus of current research. Previous researches used label tree structure or label graph structure to build label dependencies. However, these label dependency structure building methods suffer from complexity and lack of interpretability of label relationships. To solve these problems, we propose a new model LHGN: Label-Dependent Hypergraph Neural Network for Enhanced Multi-label Text Classification, which introduces hypergraph structure to build label-dependent relationships, enhance the correlation between labels, reduce graph complexity and improve the interpretability of label relationships. In addition, we build hypergraph structures for each text instance to capture its structural information, and use the BERT model to capture the semantic information of texts. By integrating text information and combining the hypergraph label structure dependencies for multi-label text classification. Experimental results on three benchmark datasets demonstrate that the LHGN model outperforms state-of-the-art baseline models. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Trees (mathematics)

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Characterization and Properties of Bent-Negabent Functions

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Chinese Journal of Electronics 2022年第4期31卷 786-792页

作者： JIANG Niu ZHAO Min YANG Zhiyao ZHUO Zepeng CHEN Guolong School of Mathematical Sciences Huaibei Normal University Fujian Provincial Key Laboratory of Network Security and Cryptology Fujian Normal University School of Cyber Science University of Science and Technology of China School of Computer Engineering Bengbu University

A further characterization of the bentnegabent functions is presented. Based on the concept of complete mapping polynomial, we provide a necessary and sufficient condition for a class of quadratic Boolean functions to be bent-negabent. A new characterization of negabent functions can be described by using the parity of Hamming weight. We further generalize the classical convolution theorem and give the nega-Hadamard transform of the composition of a Boolean function and a vectorial Boolean function. The nega-Hadamard transform of a generalized indirect sum is calculated by this composition method.

关键词： Boolean function Bent-negabent function Walsh-Hadamard transform Nega-Hadamard transform

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Scale‐wise interaction fusion and knowledge distillation network for aerial scene recognition

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CAAI Transactions on Intelligence technology 2023年第4期8卷 1178-1190页

作者： Hailong Ning Tao Lei Mengyuan An Hao Sun Zhanxuan Hu Asoke K.Nandi School of Computer Science and Technology Xi'an University of Posts and TelecommunicationsShaanxi Key Laboratory of Network Data Analysis and Intelligent ProcessingXi'anChina Xi'an Key Laboratory of Big Data and Intelligent Computing Xi'anChina School of Electronic Information and Artificial Intelligence Shaanxi University of Science and TechnologyXi'anChina School of Computer Central China Normal UniversityWuhanChina Department of Electronic and Electrical Engineering Brunel University LondonLondonUK Xi'an Jiaotong University Xi'anChina

Aerial scene recognition(ASR)has attracted great attention due to its increasingly essential *** of the ASR methods adopt the multi‐scale architecture because both global and local features play great roles in ***,the existing multi‐scale methods neglect the effective interactions among different scales and various spatial locations when fusing global and local features,leading to a limited ability to deal with challenges of large‐scale variation and complex background in aerial scene *** addition,existing methods may suffer from poor generalisations due to millions of to‐belearnt parameters and inconsistent predictions between global and local *** tackle these problems,this study proposes a scale‐wise interaction fusion and knowledge distillation(SIF‐KD)network for learning robust and discriminative features with scaleinvariance and background‐independent *** main highlights of this study include two *** the one hand,a global‐local features collaborative learning scheme is devised for extracting scale‐invariance features so as to tackle the large‐scale variation problem in aerial scene ***,a plug‐and‐play multi‐scale context attention fusion module is proposed for collaboratively fusing the context information between global and local *** the other hand,a scale‐wise knowledge distillation scheme is proposed to produce more consistent predictions by distilling the predictive distribution between different scales during *** experimental results show the proposed SIF‐KD network achieves the best overall accuracy with 99.68%,98.74%and 95.47%on the UCM,AID and NWPU‐RESISC45 datasets,respectively,compared with state of the arts.

关键词： deep learning image analysis image classification information fusion

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GS-Hider: Hiding Messages into 3D Gaussian Splatting 38

GS-Hider: Hiding Messages into 3D Gaussian Splatting

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Zhang, Xuanyu Meng, Jiarui Li, Runyi Xu, Zhipei Zhang, Yongbing Zhang, Jian School of Electronic and Computer Engineering Peking University China Guangdong Provincial Key Laboratory of Ultra High Definition Immersive Media Technology Peking University Shenzhen Graduate School China School of Computer Science and Technology Harbin Institute of Technology Shenzhen China

3D Gaussian Splatting (3DGS) has already become the emerging research focus in the fields of 3D scene reconstruction and novel view synthesis. Given that training a 3DGS requires a significant amount of time and computational cost, it is crucial to protect the copyright, integrity, and privacy of such 3D assets. Steganography, as a crucial technique for encrypted transmission and copyright protection, has been extensively studied. However, it still lacks profound exploration targeted at 3DGS. Unlike its predecessor NeRF, 3DGS possesses two distinct features: 1) explicit 3D representation;and 2) real-time rendering speeds. These characteristics result in the 3DGS point cloud files being public and transparent, with each Gaussian point having a clear physical significance. Therefore, ensuring the security and fidelity of the original 3D scene while embedding information into the 3DGS point cloud files is an extremely challenging task. To solve the above-mentioned issue, we first propose a steganography framework for 3DGS, dubbed GS-Hider, which can embed 3D scenes and images into original GS point clouds in an invisible manner and accurately extract the hidden messages. Specifically, we design a coupled secured feature attribute to replace the original 3DGS's spherical harmonics coefficients and then use a scene decoder and a message decoder to disentangle the original RGB scene and the hidden message. Extensive experiments demonstrated that the proposed GS-Hider can effectively conceal multimodal messages without compromising rendering quality and possesses exceptional security, robustness, capacity, and flexibility. Our project is available at: https://***/project/gshider/. © 2024 Neural information processing systems foundation. All rights reserved.

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GA-Net: Gated Attention Mechanism Based Global Refinement Network for Image Inpainting 29

GA-Net: Gated Attention Mechanism Based Global Refinement Ne...

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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Qiu, Ran Zhao, Shengrong Liang, Hu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9798350330717

Underwater images are often affected by problems such as light attenuation, color distortion, noise and scattering, resulting in image defects. A novel image inpainting method is proposed to intelligently predict and fill damaged areas for complete and continuous visualization of the image. First, in order to effectively solve the problem of color distortion caused by light refraction in underwater environments, the improved gated attention mechanism is used. This mechanism improves the local details by learning and weighting the important features of the image. Second, gated convolution automatically determines the degree of restoration for each pixel based on local features of the original image. It eliminates distractions such as low contrast and scattering, retaining more original detailed information. By doing so, image inpainting techniques improve the quality and visualization of underwater images. © 2023 IEEE.

关键词： Gated convolution Image inpainting Underwater images

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Excellent sensitivity and selectivity of g-C4N3 monolayer based gas senors for efficiently detecting nitrogen oxides: Spin-resolved quantum transport behaviors

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IEEE Sensors Journal 2025年第11期25卷 20687-20694页

作者： Zhou, Wensheng Luo, Cheng Tang, Peng Xiao, Xianbo Xionga, Songbo Chen, Tong Jiangxi University of Science and Technology School of Energy and Mechanical Engineering Jiangxi Provincial Key Laboratory of Multidimensional Intelligent Perception and Control Nanchang330013 China Jiangxi University of Chinese Medicine School of Computer Science Nanchang330004 China

NO and NO2 are important gases that can lead to the formation of photochemical smog and acid rain. Therefore, it is important to develop high-performance nitrogen oxide sensors. Here, the electronic structure, spin transport characteristics, and gas sensitivity of two-dimensional graphitic carbon nitride (2D g-C4N3) monolayers with doped magnetic metal atoms were studied via first-principles methods. A g-C4N3 monolayer exhibited ferromagnetic half-metallic and high-sensitivity properties for NO and NO2 detection. The monolayer nanodevices also exhibited perfect single-spin conduction with a spin-filtering efficiency of up to 100%. A designed g-C4N3 monolayer sensor strongly chemisorbed both NO and NO2, resulting in the formation of electrons in the local scattering region. This directly affected spin transport properties, resulting in NO and NO2 sensitivity up to 100%. A g-C4N3 sensor with transition metal doping (especially Fe) slightly improved NO detection and significantly reduced NO2 detection. Hence, a Fe-doped g-C4N3 sensor in a complex gas environment containing both NO and NO2 largely eliminated NO2 interference and increased NO detection. A g-C4N3 monolayer with excellent half-metal properties and high gas sensitivity will have potential applications in high-performance spin devices and spin-gas sensitive devices. © 2001-2012 IEEE.

关键词： Nitrogen oxides

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Pathological Image Segmentation of Gastric Cancer Based on Deep Learning 15

Pathological Image Segmentation of Gastric Cancer Based on D...

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15th International Conference on Graphics and Image Processing, ICGIP 2023

作者： Zhou, Hehu Pan, Jingshan Na, Li Jing, Ge Zhou, Chengjun Du, Wantong Qilu University of Technology Shandong Academy of Sciences Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Department of Pathology the Second Hospital of Shandong University China

ISBN: (纸本)9781510675018

Gastric cancer is a serious health threat and pathological images are an important criterion in its diagnosis. These images can help doctors accurately identify cancerous regions and provide important evidence for clinical decision-making. Thanks to the remarkable achievements of deep learning technology in the field of image processing, an increasing number of superior image segmentation models have emerged. The Swin-Unet model has achieved great success in the field of image segmentation. However, when applied to the image segmentation of gastric cancer pathological section data, the segmentation boundary appears jagged. We have put forth two potential solutions. Initially, we devised an attention connection module to supplant the skip connections within the model, thereby enhancing the model’s predictive precision. Subsequently, we engineered a prognostic processing unit that inputs the model’s predictive outcomes and employs a Conditional Random Field (CRF) for further predictive computations. The enhanced model increases the DSC by 2% and decreases the HD by 17%. Additionally, the issue of jagged boundaries in prediction results has been better optimized. We conducted comparative and ablation experiments, and the results showed that our improved method increased the accuracy of the model’s predictions and reduced the jaggedness of the results at the boundary. © 2024 SPIE. All rights reserved.

关键词： Image segmentation

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HyperplaneGAN: a unified consistent translation framework for facial attribute editing

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Multimedia Tools and Applications 2024年 1-25页

作者： Li, Defang Deng, Huiqi Qin, Peng Chen, Weifu Feng, Guocan College of General Education Guangzhou Vocational College of Technology and Business Guangzhou China Department of Computer Science Guangzhou Maritime University Guangzhou China School of Mathematics Sun Yat-sen University Guangzhou China Guangdong Province Key Laboratory Sun Yat-sen University Guangzhou China School of Computer Science and Technology Xi’an Jiaotong University Shaanxi Xi’an China

Facial attribute editing has been obtaining remarkable progress as the rapid development in deep generative models. Existing algorithms can be roughly grouped into two distinct categories: attribute-guided models and exemplar-guided models. These models achieve impressive facial attribute editing results, however, there are some limitations. For example, images generated by current attribute-guided models are lack of diversity and attribute styles are not controllable. For exemplar-guided models, low transfer precision and fidelity of generated images are commonly complained issues. In order to generate high-quality attribute-controllable facial images, we propose a novel unified translation framework called HyperplaneGAN which has following advantages: (1) the proposed model can do both attribute-guided facial editing and exemplar-guided facial editing;(2) by employing latent unit swapping and linear separation constraint for learning pair-wise linearly separable disentangled representations, the model can do flexible and controllable translation;(3) cycle-consistency loss and residual attribute vectors are used to guide the model to manipulate specific attributes precisely while other attributes are kept intact. Substantial experimental results demonstrate that HyperplaneGAN outperforms state-of-the-art models on both attribute-guided facial editing and exemplar-guided facial editing, in terms of quantitative evaluation and qualitative evaluation. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Generative adversarial networks

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