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FL-EASGD:Federated Learning Privacy Security Method Based on Homomorphic Encryption

Computers, Materials & Continua 2024年第5期79卷 2361-2373页

作者： Hao Sun Xiubo Chen Kaiguo yuan Information Security Center State Key Laboratory ofNetworking and Switching TechnologyBeijing University of Posts and TelecommunicationsBeijing100876China

Federated learning ensures data privacy and security by sharing models among multiple computing nodes instead of plaintext ***,there is still a potential risk of privacy leakage,for example,attackers can obtain the original data through model inference ***,safeguarding the privacy of model parameters becomes *** proposed solution involves incorporating homomorphic encryption algorithms into the federated learning ***,the existing federated learning privacy protection scheme based on homomorphic encryption will greatly reduce the efficiency and robustness when there are performance differences between parties or abnormal *** solve the above problems,this paper proposes a privacy protection scheme named Federated Learning-Elastic Averaging Stochastic Gradient Descent(FL-EASGD)based on a fully homomorphic encryption ***,this paper introduces the homomorphic encryption algorithm into the FL-EASGD scheme to preventmodel plaintext leakage and realize privacy security in the process ofmodel ***,this paper designs a robust model aggregation algorithm by adding time variables and constraint coefficients,which ensures the accuracy of model prediction while solving performance differences such as computation speed and node anomalies such as downtime of each *** addition,the scheme in this paper preserves the independent exploration of the local model by the nodes of each party,making the model more applicable to the local data ***,experimental analysis shows that when there are abnormalities in the participants,the efficiency and accuracy of the whole protocol are not significantly affected.

关键词： Federated learning homomorphic encryption privacy security stochastic gradient descent

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Interleukins in Epilepsy:Friend or Foe

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Neuroscience Bulletin 2024年第5期40卷 635-657页

作者： yuan Dong Xia Zhang Ying Wang Neuropsychiatry Research Institute The Affiliated Hospital of Qingdao UniversityQingdao UniversityQingdao266000China Program in Cellular and Molecular Medicine Boston Children’s HospitalBostonMA02115USA

Epilepsy is a chronic neurological disorder with recurrent unprovoked seizures,affecting~65 million *** in patients with epilepsy and animal models suggests a contribution of neuroinflammation to epileptogenesis and the development of ***(ILs),as one of the major contributors to neuroinflammation,are intensively studied for their association and modulatory effects on ictogenesis and *** are commonly divided into pro-and anti-inflammatory cytokines and therefore are expected to be pathogenic or neuroprotective in ***,both protective and destructive effects have been reported for many *** may be due to the complex nature of ILs,and also possibly due to the different disease courses that those ILs are involved *** this review,we summarize the contributions of different ILs in those processes and provide a current overview of recent research advances,as well as preclinical and clinical studies targeting ILs in the treatment of epilepsy.

关键词： Epilepsy Interleukins Neuroinflammation

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Boundary points, minimal L^(2) integrals and concavity property Ⅱ: Weakly pseudoconvex Kähler manifolds

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Science China Mathematics 2024年第7期67卷 1665-1718页

作者： Qi’an Guan Zhitong Mi Zheng yuan School of Mathematical Sciences Peking UniversityBeijing100871China School of Mathematics and Statistics Beijing Jiaotong UniversityBeijing100044China Institute of Mathematics Academy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing100190China

In this paper, we consider minimal L^(2) integrals on the sublevel sets of plurisubharmonic functions on weakly pseudoconvex K?hler manifolds with Lebesgue measurable gain related to modules at boundary points of the sublevel sets, and establish a concavity property of the minimal L^(2) integrals. As applications, we present a necessary condition for the concavity degenerating to linearity, a concavity property related to modules at inner points of the sublevel sets, an optimal support function related to modules, a strong openness property of modules and a twisted version, and an effectiveness result of the strong openness property of modules.

关键词： minimal L^(2)integrals plurisubharmonic functions boundary points weakly pseudoconvex K?hler manifold

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A Lightweight Network with Dual Encoder and Cross Feature Fusion for Cement Pavement Crack Detection

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Computer Modeling in Engineering & Sciences 2024年第7期140卷 255-273页

作者： Zhong Qu Guoqing Mu Bin yuan School of Computer Science and Technology Chongqing University of Posts and TelecommunicationsChongqing400065China School of Software Engineering Chongqing University of Posts and TelecommunicationsChongqing400065China

Automatic crack detection of cement pavement chiefly benefits from the rapid development of deep learning,with convolutional neural networks(CNN)playing an important role in this ***,as the performance of crack detection in cement pavement improves,the depth and width of the network structure are significantly increased,which necessitates more computing power and storage *** limitation hampers the practical implementation of crack detection models on various platforms,particularly portable devices like small mobile *** solve these problems,we propose a dual-encoder-based network architecture that focuses on extracting more comprehensive fracture feature information and combines cross-fusion modules and coordinated attention mechanisms formore efficient feature ***,we use small channel convolution to construct shallow feature extractionmodule(SFEM)to extract low-level feature information of cracks in cement pavement images,in order to obtainmore information about cracks in the shallowfeatures of *** addition,we construct large kernel atrous convolution(LKAC)to enhance crack information,which incorporates coordination attention mechanism for non-crack information filtering,and large kernel atrous convolution with different cores,using different receptive fields to extract more detailed edge and context ***,the three-stage feature map outputs from the shallow feature extraction module is cross-fused with the two-stage feature map outputs from the large kernel atrous convolution module,and the shallow feature and detailed edge feature are fully fused to obtain the final crack prediction *** evaluate our method on three public crack datasets:DeepCrack,CFD,and *** results on theDeepCrack dataset demonstrate the effectiveness of our proposed method compared to state-of-the-art crack detection methods,which achieves Precision(P)87.2%,Recall(R)87.7%,and F-score(F1)87.4%.Thanks to our lightweight cr

关键词： Shallow feature extraction module large kernel atrous convolution dual encoder lightweight network crack detection

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A New Method for Deriving High-Vertical-Resolution Wind Vector Data from the L-Band Radar Sounding System in China

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Advances in Atmospheric Sciences 2024年第11期41卷 2192-2202页

作者： Fang yuan Zijiang ZHOU Jie LIAO National Meteorological Information Center China Meteorological AdministrationBeijing 100081China

High-vertical-resolution radiosonde wind data are highly valuable for describing the dynamics of the meso-and microscale atmosphere. However, the current algorithm used in China's L-band radar sounding system for calculating highvertical-resolution wind vectors excessively smooths the data, resulting in significant underestimation of the calculated kinetic energy of gravity waves compared to similar products from other countries, which greatly limits the effective utilization of the data. To address this issue, this study proposes a novel method to calculate high-vertical-resolution wind vectors that utilizes the elevation angle, azimuth angle, and slant range from L-band radar. In order to obtain wind data with a stable quality, a two-step automatic quality control procedure, including the RMSE-F(root-mean-square error F) test and elemental consistency test are first applied to the slant range data, to eliminate continuous erroneous data caused by unstable signals or radar malfunctions. Then, a wind calculation scheme based on a sliding second-order polynomial fitting is utilized to derive the high-vertical-resolution radiosonde wind vectors. The evaluation results demonstrate that the wind data obtained through the proposed method show a high level of consistency with the high-resolution wind data observed using the Vaisala Global Positioning System and the data observed by the new Beidou Navigation Sounding System. The calculation of the kinetic energy of gravity waves in the recalculated wind data also reaches a level comparable to the Vaisala observations.

关键词： L-band radar sounding system upper air high-vertical-resolution radiosonde wind vectors quality control polynomial fitting

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Multi-Perspective Data Fusion Framework Based on Hierarchical BERT: Provide Visual Predictions of Business Processes

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Computers, Materials & Continua 2024年第1期78卷 1227-1252页

作者： Yongwang yuan Xiangwei Liu Ke Lu School of Mathematics and Big Data Anhui University of Science and TechnologyHuainan232001China School of Economics and Management Anhui University of Science and TechnologyHuainan232001China The Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji UniversityShanghai201804China

Predictive Business Process Monitoring(PBPM)is a significant research area in Business Process Management(BPM)aimed at accurately forecasting future behavioral *** present,deep learning methods are widely cited in PBPM research,but no method has been effective in fusing data information into the control flow for multi-perspective process ***,this paper proposes a process prediction method based on the hierarchical BERT and multi-perspective data ***,the first layer BERT network learns the correlations between different category attribute ***,the attribute data is integrated into a weighted event-level feature vector and input into the second layer BERT network to learn the impact and priority relationship of each event on future predicted ***,the multi-head attention mechanism within the framework is visualized for analysis,helping to understand the decision-making logic of the framework and providing visual ***,experimental results show that the predictive accuracy of the framework surpasses the current state-of-the-art research methods and significantly enhances the predictive performance of BPM.

关键词： Business process prediction monitoring deep learning attention mechanism BERT multi-perspective

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Biderivations of Hom-Lie Algebras and Superalgebras

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Acta Mathematica Sinica,English Series 2024年第10期40卷 2337-2358页

作者： La Mei yuan Jia Xin LI School of Mathematics Harbin Institute of Technology

On Hom-Lie algebras and superalgebras, we introduce the notions of biderivations and linear commuting maps, and compute them for some typical Hom-Lie algebras and superalgebras,including the q-deformed W(2, 2) algebra... 详细信息

关键词： 17A30 17A60 17B05 17B60 17B70 biderivations Hom-Lie algebras linear commuting maps q-deformed W(2 2) algebra q-deformed Witt algebra q-deformed Witt superalgebra

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Ghost Module Based Residual Mixture of Self-Attention and Convolution for Online Signature Verification

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Computers, Materials & Continua 2024年第4期79卷 695-712页

作者： Fangjun Luan Xuewen Mu Shuai yuan Department of Computer Technology School of Computer Science and EngineeringShenyang Jianzhu UniversityShenyang110168China Liaoning Province Big Data Management and Analysis Laboratory of Urban Construction Shenyang Jianzhu UniversityShenyang110168China Shenyang Branch of National Special Computer Engineering Technology Research Center Shenyang Jianzhu UniversityShenyang110168China

Online Signature Verification (OSV), as a personal identification technology, is widely used in various ***, it faces challenges, such as incomplete feature extraction, low accuracy, and computational heaviness. Toaddress these issues, we propose a novel approach for online signature verification, using a one-dimensionalGhost-ACmix Residual Network (1D-ACGRNet), which is a Ghost-ACmix Residual Network that combines convolutionwith a self-attention mechanism and performs improvement by using Ghost method. The Ghost-ACmix Residualstructure is introduced to leverage both self-attention and convolution mechanisms for capturing global featureinformation and extracting local information, effectively complementing whole and local signature features andmitigating the problem of insufficient feature extraction. Then, the Ghost-based Convolution and Self-Attention(ACG) block is proposed to simplify the common parts between convolution and self-attention using the Ghostmodule and employ feature transformation to obtain intermediate features, thus reducing computational ***, feature selection is performed using the random forestmethod, and the data is dimensionally reducedusing Principal Component Analysis (PCA). Finally, tests are implemented on the MCYT-100 datasets and theSVC-2004 Task2 datasets, and the equal error rates (EERs) for small-sample training using five genuine andforged signatures are 3.07% and 4.17%, respectively. The EERs for training with ten genuine and forged signaturesare 0.91% and 2.12% on the respective datasets. The experimental results illustrate that the proposed approacheffectively enhances the accuracy of online signature verification.

关键词： Online signature verification feature selection ACG block ghost-ACmix residual structure

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Current status of early gastric cancer screening research

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Cancer Biology & Medicine 2024年第3期21卷 218-222页

作者： Can Hu Li yuan Xiangdong Cheng Department of Gastric surgery Zhejiang Cancer HospitalHangzhou Institute of Medicine(HIM)Chinese Academy of SciencesHangzhouZhejiang 310022China Key Laboratory of Prevention Diagnosis and Therapy of Upper Gastrointestinal Cancer of Zhejiang ProvinceHangzhou 310022China Zhejiang Provincial Research Center for Upper Gastrointestinal Tract Cancer Zhejiang Cancer HospitalHangzhou 310022China

Gastric cancer is a predominant threat to the health and well-being of China's residents. Data from the World Health Organization(WHO) in 2020 revealed that gastric cancer in China notably accounted for 44.0% of new c... 详细信息

关键词： cancer Gastric gastric

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An Accelerated Stochastic Mirror Descent Method

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Journal of the Operations Research Society of China 2024年第3期12卷 549-571页

作者： Bo-Ou Jiang Ya-Xiang yuan LSEC ICMSECAMSSChinese Academy of SciencesBeijing100190China Department of Mathematics University of Chinese Academy of SciencesBeijing100049China

Driven by large-scale optimization problems arising from machine learning,the development of stochastic optimization methods has witnessed a huge *** types of methods have been developed based on vanilla stochastic gradient descent ***,for most algorithms,convergence rate in stochastic setting cannot simply match that in deterministic *** understanding the gap between deterministic and stochastic optimization is the main goal of this ***,we are interested in Nesterov acceleration of gradient-based *** our study,we focus on acceleration of stochastic mirror descent method with implicit regularization *** that the problem objective is smooth and convex or strongly convex,our analysis prescribes the method parameters which ensure fast convergence of the estimation error and satisfied numerical performance.

关键词： Large-scale optimization Variance reduction Mirror descent Acceleration Independent sampling Importance sampling

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