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1st International Conference on Big data and security, ICBDS 2019

作者： Wang, Li Shu, Xin Zhang, Wei Chen, Yunfang Nanjing University of Posts and Telecommunications NanjingJiangsu210023 China Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing University of Posts and Telecommunications NanjingJiangsu210023 China

ISBN: (纸本)9789811575297

The combination of Unmanned Aerial Vehicle (UAV) technology and computer vision has become popular in a wide range of applications, such as surveillance and reconnaissance, while popular evaluation measures are sometimes not applicable for specific tasks. In order to evaluate visual object detection and tracking algorithms of low-altitude aerial video properly, we first summarize the evaluation basis of computer vision tasks, including ground truth, prediction-to-ground truth assignment strategy and distance measures between prediction and ground truth. Then, we analyze the advantages and disadvantages of visual object detection and tracking performance measures, including average precision (AP), F-measure, and accuracy. Finally, for the low-altitude (nearly 100 m) surveillance mission of small unmanned aerial vehicles, we discuss the threshold optimization method of popular measures and the design strategy of application measures. Our work provides a reference in the aspect of performance measures design for researchers of UAV vision. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Object detection

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Smart surgical telementoring system with patch-to-pixel point tracking method

EngMedicine

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EngMedicine 2025年第1期2卷

作者： Yanwei Qu Ling Li Tongtong Li Jieyu Zheng Wentao Tang Xiaojian Li School of Management Hefei University of Technology Hefei 230009 Anhui China Philosophy and Social Sciences Laboratory of Data Science and Smart Society Governance Ministry of Education Hefei 230009 Anhui China Key Laboratory of Process Optimization and Intelligent Decision-making Ministry of Education Hefei 230009 Anhui China Hefei Haochuan Information Technology Co. Ltd Hefei 230088 Anhui China Ministry of Education Engineering Research Center for Intelligent Decision-Making Information System Technologies Hefei 230009 Anhui China

The utilization of surgical telementoring has become increasingly prevalent in enhancing the surgical standards of grassroots hospitals in contemporary times. In the traditional framework of surgical telementoring, remote doctors guide grassroots doctors via video and audio communication, which may encounter obstacles such as inadequate information transfer and challenges in accurately pinpointing the surgical site and path. To mitigate these issues, this study introduces an intelligent surgical telementoring system based on edge computing. This system enables the transmission of points marked by remote doctors to grassroots doctors, updating these points’ coordinates in real-time on the local endoscopic video. In this system, a novel method named P2PTracking (Patch-to-Pixel Point Tracking) is implemented. This process begins with the tracking of a square patch surrounding the point marked by the remote doctor using SiameseFC. Following this, feature matching is performed on the tracked patch in the current frame and the square patch in the labelled frame or template frame. The affine transformation is then calculated based on the feature matching results. Lastly, the point tracking result is derived using the computed affine transformation. Experimental results indicate that the proposed system has a transmission speed of 1.99M/s and a transmission latency of 171 ms when transmitting video at a resolution of 1920 × 1080px, while the proposed method can achieve an accuracy of 96.6 % when the pixel error is 4. The code and data are available at https://***/hfut66/*** .

关键词： Surgical telementoring Patch tracking Point tracking Feature matching Template update mechanism

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Differentially Private and Heterogeneity-Robust Federated Learning With Theoretical Guarantee

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第12期5卷 6369-6384页

作者： Wang, Xiuhua Wang, Shuai Li, Yiwei Fan, Fengrui Li, Shikang Lin, Xiaodong Huazhong University of Science and Technology Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Wuhan430074 China University of Electronic Science and Technology of China National Key Laboratory of Wireless Communications Chengdu611731 China Xiamen University of Technology Fujian Key Laboratory of Communication Network and Information Processing Xiamen361024 China University of Guelph School of Computer Science GuelphONN1G 2W1 Canada

Federated learning (FL) is a popular distributed paradigm where enormous clients collaboratively train a machine learning (ML) model under the orchestration of a central server without knowing the clients' private raw data. The development of effective FL algorithms faces multiple practical challenges including data heterogeneity and clients' privacy protection. Despite that numerous attempts have been made to deal with data heterogeneity or rigorous privacy protection, none have effectively tackled both issues simultaneously. In this article, we propose a differentially private and heterogeneity-robust FL algorithm, named DP-FedCVR to mitigate the data heterogeneity by following the client-variance-reduction strategy. Besides, it adopts a sophisticated differential privacy (DP) mechanism where the privacy-amplified strategy is applied, to achieve a rigorous privacy protection guarantee. We show that the proposed DP-FedCVR algorithm maintains its heterogeneity-robustness though DP noises are incorporated, while achieving a sublinear convergence rate for a nonconvex FL problem. Numerical experiments based on image classification tasks are presented to demonstrate that DP-FedCVR provides superior performance over the benchmark algorithms in the presence of data heterogeneity and various DP privacy budgets. © 2020 IEEE.

关键词： Federated learning

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Discrimination-Aware Domain Adversarial Neural Network

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Journal of Computer Science & Technology 2020年第2期35卷 259-267页

作者： Yun-Yun Wang Jian-Min Gu Chao Wang Song-Can Chen Hui Xue College of Computer Science and Engineering Nanjing University of Posts and Telecommunications Nanjing 210046China Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing 210046China College of Computer Science and Technology/College of Artificial Intelligence Nanjing University of Aeronautics and AstronauticsNanjing 210023China Key Laboratory of Pattern Analysis and Machine Intelligence Ministry of Industry and Information Technology Nanjing 210023China School of Computer Science and Engineering Southeast UniversityNanjing 210096China

The domain adversarial neural network(DANN)methods have been successfully proposed and attracted much attention *** DANNs,a discriminator is trained to discriminate the domain labels of features generated by a generator,whereas the generator attempts to confuse it such that the distributions between domains are *** a result,it actually encourages the whole alignment or transfer between domains,while the inter-class discriminative information across domains is not *** this paper,we present a Discrimination-Aware Domain Adversarial Neural Network(DA2NN)method to introduce the discriminative information or the discrepancy of inter-class instances across domains into deep domain ***2NN considers both the alignment within the same class and the separation among different classes across domains in knowledge transfer via multiple *** results show that DA2NN can achieve better classification performance compared with the DANN methods.

关键词： adversarial learning inter-class separation deep neural network discrimination-aware domain adaptation

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Community Detection Based on DeepWalk in Large Scale Networks 1st

Community Detection Based on DeepWalk in Large Scale Network...

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1st International Conference on Big data and security, ICBDS 2019

作者： Chen, Yunfang Wang, Li Qi, Dehao Zhang, Wei Nanjing University of Posts and Telecommunications NanjingJiangsu210023 China Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing University of Posts and Telecommunications NanjingJiangsu210023 China

ISBN: (纸本)9789811575297

The large scale and complex structure of real networks bring enormous challenges to traditional community detection methods. In order to detect community structure in large scale networks more accurately and efficiently, we propose a community detection algorithm based on network embedding representation method. This algorithm first selects the DeepWalk network embedding method to represent the vertices in the network graph as low-dimensional vector representations which reflect the topology in the network. Then this algorithm treats each vertex as a sample, and each dimension of the vector representation as a feature. Finally, the sample data are input into the variational Gaussian mixture clustering model to obtain the clusters in the network. Experimental results on the DBLP dataset show that the model method of this paper can more effectively discover the communities in large scale networks. Through further analysis of the excavated community structure, the organizational characteristics within the community are better revealed. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Network embeddings

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Anatomical Prior Guided Spatial Contrastive Learning for Few-Shot Medical Image Segmentation 24

Anatomical Prior Guided Spatial Contrastive Learning for Few...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Huang, Wendong Hu, Jinwu Bi, Xiuli Xiao, Bin Chongqing Key Laboratory of Image Cognition Key Laboratory of Cyberspace Big Data Intelligent Security Ministry of Education Chongqing University of Post and Telecommunications Chongqing China Pazhou Lab School of Software Engineering South China University of Technology Guangzhou China

ISBN: (纸本)9798400706868

Few-shot semantic segmentation has considerable potential for low-data scenarios, especially for medical images that require expert-level dense annotations. Existing few-shot medical image segmentation methods strive to deal with the task by means of prototype learning. However, this scheme relies on support prototypes to guide the segmentation of query images, ignoring the rich anatomical prior knowledge in medical images, which hinders effective feature enhancement for medical images. In this paper, we propose an anatomical prior guided spatial contrastive learning, called APSCL, which exploits anatomical prior knowledge derived from medical images to construct contrastive learning from a spatial perspective for few-shot medical image segmentation. The new framework forces the model to learn the features in line with the embedded anatomical representations. Besides, to fully exploit the guidance information of the support samples, we design a mutual guidance decoder to predict the label of each pixel in the query image. Furthermore, our APSCL can be trained end-to-end in the form of episodic training. Comprehensive experiments on three challenging medical image datasets, i.e., CHAOS-T2, MS-CMRSeg, and Synapse, prove that our method significantly surpasses state-of-the-art few-shot medical segmentation methods, with a mean improvement of 3.61%, 2.30%, and 6.38% on the Dice score, respectively. © 2024 ACM.

关键词： Contrastive Learning

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Facial expression recognition under occlusion conditions based on multi-feature cross-attention

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Journal of intelligent and Fuzzy systems 2024年第4期46卷 9841-9856页

作者： Guo, Sheng Tan, Mian Cai, Shan Zhang, Zaijun Liang, Yihui Feng, Hongxi Zou, Xue Wang, Lin School of Data Science and Information Engineering Guizhou Key Laboratory of Pattern Recognition and Intelligent System Guizhou Minzu University Guiyang China School of Mathematics and Statistics Qiannan Normal University for Nationalities Key Laboratory of Complex Systems and Intelligent Optimization of Guizhou Province Duyun China Zhongshan Institute University of Electronic Science and Technology of China Zhongshan China Guizhou Aerospace Tianma Electromechanical Technology Co LTD Zunyi China

Although facial expression recognition (FER) has a wide range of applications, it may be difficult to achieve under local occlusion conditions which may result in the loss of valuable expression features. This issue has motivated the present study, as a part of which an effective multi-feature cross-attention network (MFCA-Net) is proposed. The MFCA-Net consists of a two-branch network comprising a multi-feature convolution module and a local cross-attention module. Thus, it enables decomposition of facial features into multiple sub-features by the multi-feature convolution module to reduce the impact of local occlusion on facial expression feature extraction. In the next step, the local cross-attention module distinguishes between occluded and unoccluded sub-features and focuses on the latter to facilitate FER. When the MFCA-Net performance is evaluated by applying it to three public large-scale datasets (RAF-DB, FERPlus, and AffectNet), the experimental results confirm its good robustness. Further validation is performed on a real FER dataset with local occlusion of the face. © 2024 – IOS Press.

关键词： Convolution

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Adaptive Uniform Performance Control of Strict-Feedback Nonlinear systems With Time-Varying Control Gain

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IEEE/CAA Journal of Automatica Sinica 2023年第2期10卷 451-461页

作者： Kai Zhao Changyun Wen Yongduan Song Frank L.Lewis IEEE the Department of Electrical and Computer Engineering National University of SingaporeSingapore 119077Singapore the School of Electrical and Electronic Engineering Nanyang Technological UniversitySingapore 639798Singapore the State Key Laboratory of Power Transmission Equipment&System Security and New Technology Chongqing Key Laboratory of Intelligent Unmanned SystemsSchool of AutomationChongqing UniversityChongqing 400044China the UTA Research Institute University of Texas at ArlingtonFort WorthTX 76118 USA

In this paper,we present a novel adaptive performance control approach for strict-feedback nonparametric systems with unknown time-varying control coefficients,which mainly includes the following ***,by introducing several key transformation functions and selecting the initial value of the time-varying scaling function,the symmetric prescribed performance with global and semi-global properties can be handled uniformly,without the need for control ***,to handle the problem of unknown time-varying control coefficient with an unknown sign,we propose an enhanced Nussbaum function(ENF)bearing some unique properties and characteristics,with which the complex stability analysis based on specific Nussbaum functions as commonly used is no longer ***,by utilizing the core-function information technique,the nonparametric uncertainties in the system are gracefully handled so that no approximator is ***,simulation results verify the effectiveness and benefits of the approach.

关键词： Adaptive control enhanced Nussbaum function(ENF) strict-feedback systems unified prescribed performance

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Self-feedback LSTM regression model for real-time particle source apportionment

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Journal of Environmental Sciences 2022年第4期34卷 10-20页

作者： Wei Wang Weiman Xu Shuai Deng Yimeng Chai Ruoyu Ma Guoliang Shi Bo Xu Mei Li Yue Li Trusted AI System Laboratory College of Computer ScienceNankai UniversityTianjin 300350China KLMDASR Tianjin Key Laboratory of Network and Data Security TechnologyTianjin 300350China State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control College of Environmental Science and EngineeringNankai UniversityTianjin 300071China Institute of Mass Spectrometry and Atmospheric Environment Guangdong Provincial Engineering Research Center for on-line source apportionment system of air pollution Jinan UniversityGuangzhou 510632China Guangdong-Hongkong-Macao Joint Laboratory of Collaborative Innovation for Environmental Quality Guangzhou 510632China

Atmospheric particulate matter pollution has attracted much wider attention *** recent years,the development of atmospheric particle collection techniques has put forwards new demands on the real-time source apportionments *** demands are summarized,in this paper,as how to set up new restraints in apportionment and how to develop a non-linear regression model to process complicated circumstances,such as the existence of secondary source and similar *** this study,we firstly analyze the possible and potential restraints in single particle source apportionment,then propose a novel three-step self-feedback long short-term memory(SF-LSTM)network for approximating the source *** proposed deep learning neural network includes three modules,as generation,scoring and refining,and regeneration *** from the scoring modules,SF-LSTM implants four loss functions representing four restraints to be followed in the apportionment,meanwhile,the regeneration module calculates the source contribution in a non-linear *** results show that the model outperforms the conventional regression methods in the overall performance of the four evaluation indicators(residual sum of squares,stability,sparsity,negativity)for the ***,in short time-resolution analyzing,SF-LSTM provides better results under the restraint of stability.

关键词： Time series Regression Self-feedback LSTM network Particle source apportionment

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Cloud detection from visual band of satellite image based on variance of fractal dimension

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Journal of systems Engineering and Electronics 2019年第3期30卷 485-491页

作者： TIAN Pingfang GUANG Qiang LIU Xing School of Computer Science and Technology Wuhan University of Science and Technology Wuhan 430065 China Key Laboratory of Intelligent Information Processing and Real-Time Industrial System in Hubei Province Wuhan 430065 China Institute of Big Data Science and Engineering Wuhan University of Science and Technology Wuhan 430065 China Key Laboratory of Rich-Media Knowledge Organization and Service of Digital Publishing Content National Press and Publication Administration Beijing 100038 China

Cover ratio of cloud is a very important factor which affects the quality of a satellite image, therefore cloud detection from satellite images is a necessary step in assessing the image quality. The study on cloud detection from the visual band of a satellite image is developed. Firstly, we consider the differences between the cloud and ground including high grey level, good continuity of grey level, area of cloud region, and the variance of local fractal dimension (VLFD) of the cloud region. A single cloud region detection method is proposed. Secondly, by introducing a reference satellite image and by comparing the variance in the dimensions corresponding to the reference and the tested images, a method that detects multiple cloud regions and determines whether or not the cloud exists in an image is described. By using several Ikonos images, the performance of the proposed method is demonstrated.

关键词： cloud detection visual image satellite image variance of local fractal dimension (VLFD)

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