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Improving Federated Learning through Abnormal Client Detection and Incentive

Computer Modeling in Engineering & Sciences 2024年第4期139卷 383-403页

作者： Hongle Guo Yingchi Mao Xiaoming He Benteng Zhang Tianfu Pang Ping Ping College of Computer and Information Hohai UniversityNanjing211100China Key Laboratory ofWater Big data Technology of Ministry ofWater Resources Hohai UniversityNanjing211100China

data sharing and privacy protection are made possible by federated learning,which allows for continuous model parameter sharing between several clients and a central *** reliable and high-quality clients must participate in practical applications for the federated learning global model to be accurate,but because the clients are independent,the central server cannot fully control their *** central server has no way of knowing the correctness of the model parameters provided by each client in this round,so clients may purposefully or unwittingly submit anomalous data,leading to abnormal behavior,such as becoming malicious attackers or defective *** reduce their negative consequences,it is crucial to quickly detect these abnormalities and incentivize *** this paper,we propose a Federated Learning framework for Detecting and Incentivizing Abnormal Clients(FL-DIAC)to accomplish efficient and security federated *** build a detector that introduces an auto-encoder for anomaly detection and use it to perform anomaly identification and prevent the involvement of abnormal clients,in particular for the anomaly client detection *** them,before the model parameters are input to the detector,we propose a Fourier transform-based anomaly data detectionmethod for dimensionality reduction in order to reduce the computational ***,we create a credit scorebased incentive structure to encourage clients to participate in training in order tomake clients actively *** training models(CNN,MLP,and ResNet-18)and three datasets(MNIST,Fashion MNIST,and CIFAR-10)have been used in *** to theoretical analysis and experimental findings,the FL-DIAC is superior to other federated learning schemes of the same type in terms of effectiveness.

关键词： Federated learning abnormal clients incentive credit score abnormal score detection

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Automated Machine Learning Algorithm Using Recurrent Neural Network to Perform Long-Term Time Series Forecasting

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Computers, Materials & Continua 2024年第3期78卷 3529-3549页

作者： Ying Su Morgan C.Wang Shuai Liu Department of Statistics and data Science University of Central FloridaOrlandoFL32816-2370USA School of Mathematics and Statistics Beijing Technology and Business UniversityBeijing100048China

Long-term time series forecasting stands as a crucial research domain within the realm of automated machine learning(AutoML).At present,forecasting,whether rooted in machine learning or statistical learning,typically relies on expert input and necessitates substantial manual *** manual effort spans model development,feature engineering,hyper-parameter tuning,and the intricate construction of time series *** complexity of these tasks renders complete automation unfeasible,as they inherently demand human intervention at multiple *** surmount these challenges,this article proposes leveraging Long Short-Term Memory,which is the variant of Recurrent Neural Networks,harnessing memory cells and gating mechanisms to facilitate long-term time series ***,forecasting accuracy by particular neural network and traditional models can degrade significantly,when addressing long-term time-series ***,our research demonstrates that this innovative approach outperforms the traditional Autoregressive Integrated Moving Average(ARIMA)method in forecasting long-term univariate time *** is a high-quality and competitive model in time series prediction,and yet it requires significant preprocessing *** multiple accuracy metrics,we have evaluated both ARIMA and proposed method on the simulated time-series data and real data in both short and long ***,our findings indicate its superiority over alternative network architectures,including Fully Connected Neural Networks,Convolutional Neural Networks,and Nonpooling Convolutional Neural *** AutoML approach enables non-professional to attain highly accurate and effective time series forecasting,and can be widely applied to various domains,particularly in business and finance.

关键词： Automated machine learning autoregressive integrated moving average neural networks time series analysis

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Asymptotic Inferences in the Random Coefficient Autoregressive Model with Time-functional Variance Noises

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Acta Mathematicae Applicatae Sinica 2024年

作者： En-wen ZHU Zi-wei DENG Han-jun ZHANG Jun CAO Xiao-hui LIU Department of Mathematics and Statistics Changsha University of Science and Technology School of Statistics and data Science and Key Laboratory of Data Science in Finance and Economics Jiangxi University of Finance and Economics School of Mathematics and Computational Science Xiangtan University

This paper considers the random coefficient autoregressive model with time-functional variance noises, hereafter the RCA-TFV model. We first establish the consistency and asymptotic normality of the conditional least squares estimator for the constant coefficient. The semiparametric least squares estimator for the variance of the random coefficient and the nonparametric estimator for the variance function are constructed,and their asymptotic results are reported. A simulation study is presented along with an analysis of real data to assess the performance of our method in finite samples.

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On convergence of covariance matrix of empirical Bayes hyper-parameter estimator

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Control Theory and Technology 2024年第2期22卷 149-162页

作者： Yue Ju Biqiang Mu Tianshi Chen School of data Science and Shenzhen Research Institute of Big data The Chinese University of Hong KongShenzhenShenzhen518172GuangdongChina Division of Decision and Control Systems School of Electrical Engineering and Computer ScienceKTH Royal Institute of Technology10044StockholmSweden Key Laboratory of Systems and Control Institute of Systems ScienceAcademy of Mathematics and System ScienceChinese Academy of SciencesBeijing100190China

Regularized system identification has become the research frontier of system identification in the past *** related core subject is to study the convergence properties of various hyper-parameter estimators as the sample size goes to *** this paper,we consider one commonly used hyper-parameter estimator,the empirical Bayes(EB).Its convergence in distribution has been studied,and the explicit expression of the covariance matrix of its limiting distribution has been ***,what we are truly interested in are factors contained in the covariance matrix of the EB hyper-parameter estimator,and then,the convergence of its covariance matrix to that of its limiting distribution is *** general,the convergence in distribution of a sequence of random variables does not necessarily guarantee the convergence of its covariance ***,the derivation of such convergence is a necessary complement to our theoretical analysis about factors that influence the convergence properties of the EB hyper-parameter *** this paper,we consider the regularized finite impulse response(FIR)model estimation with deterministic inputs,and show that the covariance matrix of the EB hyper-parameter estimator converges to that of its limiting ***,we run numerical simulations to demonstrate the efficacy of ourtheoretical results.

关键词： Regularized system identification Hyper-parameter estimator Empirical Bayes Convergence of covariance matrix

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Applying Hybrid Clustering in Pulsar Candidate Sifting with Multi-modality for FAST Survey

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Research in Astronomy and Astrophysics 2024年第3期24卷 283-296页

作者： Zi-Yi You Yun-Rong Pan Zhi Ma Li Zhang Shuo Xiao Dan-Dan Zhang Shi-Jun Dang Ru-Shuang Zhao Pei Wang Ai-Jun Dong Jia-Tao Jiang Ji-Bing Leng Wei-An Li Si-Yao Li School of Physics and Electronic Science Guizhou Normal UniversityGuiyang 550025China Guizhou Provincial Key Laboratory of Radio Astronomy and data Processing Guizhou Normal UniversityGuiyang 550025China College of Big data and Information Engineering Guizhou UniversityGuiyang 550025China CAS Key Laboratory of FAST National Astronomical ObservatoriesChinese Academy of SciencesBeijing 100101China Key Laboratory of Information and Computing Guizhou Province Guizhou Normal UniversityGuiyang 550001China School of Big data and Computer Science Guizhou Normal UniversityGuiyang 550025China Guizhou Software Engineering Research Center Guiyang 550000China

Pulsar search is always the basis of pulsar navigation,gravitational wave detection and other research ***,the volume of pulsar candidates collected by the Five-hundred-meter Aperture Spherical radio Telescope(FAST)shows an explosive growth rate that has brought challenges for its pulsar candidate filtering ***,the multi-view heterogeneous data and class imbalance between true pulsars and non-pulsar candidates have negative effects on traditional single-modal supervised classification *** this study,a multi-modal and semi-supervised learning based on a pulsar candidate sifting algorithm is presented,which adopts a hybrid ensemble clustering scheme of density-based and partition-based methods combined with a feature-level fusion strategy for input data and a data partition strategy for *** on both High Time Resolution Universe SurveyⅡ(HTRU2)and actual FAST observation data demonstrate that the proposed algorithm could excellently identify pulsars:On HTRU2,the precision and recall rates of its parallel mode reach0.981 and 0.988 *** FAST data,those of its parallel mode reach 0.891 and 0.961,meanwhile,the running time also significantly decreases with the increment of parallel nodes within ***,we can conclude that our algorithm could be a feasible idea for large scale pulsar candidate sifting for FAST drift scan observation.

关键词： methods data analysis-surveys-methods numerical

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Noise Effect on the 2D Stochastic Burgers Equation

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Acta Mathematica Sinica,English Series 2024年第9期40卷 2065-2090页

作者： Zhao DONG Jiang Lun WU Guo Li ZHOU RCSDS Academy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing 100190P.R.China Beijing Normal University-Hong Kong Baptist University United International College Zhuhai 519087P.R.China Guangdong Provincial Key Laboratory of Interdisciplinary Research and Application for data Science(IRADS) UICZhuhai 519087P.R.China School of Statistics and Mathematics Chongqing University Key Laboratory of Nonlinear analysis and Its Applications(Chongqing University) Ministry of EducationChongqing 400044P.R.China

By comprehensive utilizing of the geometry structure of 2D Burgers equation and the stochastic noise,we find the decay properties of the solution to the stochastic 2D Burgers equation with Dirichlet boundary ***,the e... 详细信息

关键词： 2D stochastic Burgers equation regularity

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Heat exposure and hospitalizations for chronic kidney disease in China:a nationwide time series study in 261 major Chinese cities

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Military Medical Research 2024年第4期 469-478页

作者： Fu-Lin Wang Wan-Zhou Wang Fei-Fei Zhang Su-Yuan Peng Huai-Yu Wang Rui Chen Jin-Wei Wang Peng-Fei Li Yang Wang Ming-Hui Zhao Chao Yang Lu-Xia Zhang Institute of Medical Technology Peking University Health Science Center National Institute of health data Science at Peking University Department of Occupational and Environmental health Sciences School of Public Health Peking University Renal Division Department of Medicine Peking University First Hospital Peking University Institute of Nephrology Research Units of Diagnosis and Treatment of Immune-Mediated Kidney Diseases Chinese Academy of Medical Sciences Advanced Institute of Information Technology Peking University National Climate Center China Meteorological Administration PekingTsinghua Center for Life Sciences

Background: Climate change profoundly shapes the population health at the global scale. However, there was still insufficient and inconsistent evidence for the association between heat exposure and chronic kidney disease(CKD).Methods: In the present study, we studied the association of heat exposure with hospitalizations for cause-specific CKD using a national inpatient database in China during the study period of hot season from 2015 to 2018. Standard time-series regression models and random-effects Meta-analysis were developed to estimate the city-specific and national averaged associations at a 7 lag-day span, ***: A total of 768,129 hospitalizations for CKD was recorded during the study period. The results showed that higher temperature was associated with elevated risk of hospitalizations for CKD, especially in sub-tropical cities. With a 1 °C increase in daily mean temperature, the cumulative relative risks(RR) over lag 0–7 d were 1.008 [95% confidence interval(CI) 1.003–1.012] for nationwide. The attributable fraction of CKD hospitalizations due to high temperatures was 5.50%. Stronger associations were observed among younger patients and those with obstructive *** study also found that exposure to heatwaves was associated with added risk of hospitalizations for CKD compared to non-heatwave days(RR=1.116, 95%CI 1.069–1.166) above the effect of daily mean ***: Short-term heat exposure may increase the risk of hospitalization for CKD. Our findings provide insights into the health effects of climate change and suggest the necessity of guided protection strategies against the adverse effects of high temperatures.

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Binary Hybrid Artificial Hummingbird with Flower Pollination Algorithm for Feature Selection in Parkinson’s Disease Diagnosis

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Journal of Bionic Engineering 2024年第2期21卷 1003-1021页

作者： Liuyan Feng Yongquan Zhou Qifang Luo College of Artificial Intelligence Guangxi University for NationalitiesNanning530006China Xiangsihu College of Guangxi University for Nationalities Nanning532100GuangxiChina Guangxi Key Laboratories of Hybrid Computation and IC Design analysis Nanning530006China

Parkinson’s disease is a neurodegenerative disorder that inflicts irreversible damage on *** experimental data regarding Parkinson’s patients are redundant and irrelevant,posing significant challenges for disease ***,there is a need to devise an effective method for the selective extraction of disease-specific information,ensuring both accuracy and the utilization of fewer *** this paper,a Binary Hybrid Artificial Hummingbird and Flower Pollination Algorithm(FPA),called BFAHA,is proposed to solve the problem of Parkinson’s disease diagnosis based on speech ***,combining FPA with Artificial Hummingbird Algorithm(AHA)can take advantage of the strong global exploration ability possessed by FPA to improve the disadvantages of AHA,such as premature convergence and easy falling into local ***,the Hemming distance is used to determine the difference between the other individuals in the population and the optimal individual after each iteration,if the difference is too significant,the cross-mutation strategy in the genetic algorithm(GA)is used to induce the population individuals to keep approaching the optimal individual in the random search process to speed up finding the optimal ***,an S-shaped function converts the improved algorithm into a binary version to suit the characteristics of the feature selection(FS)*** this paper,10 high-dimensional datasets from UCI and the ASU are used to test the performance of BFAHA and apply it to Parkinson’s disease *** with other state-of-the-art algorithms,BFAHA shows excellent competitiveness in both the test datasets and the classification problem,indicating that the algorithm proposed in this study has apparent advantages in the field of feature selection.

关键词： Artificial Hummingbird Algorithm Flower pollination algorithm Feature selection Parkinson’s disease Meta-heuristic

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analysis of Hot Topics Regarding Global Smart Elderly Care Research—1997-2021

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China CDC weekly 2024年第9期6卷 157-161页

作者： Hongman Wang Hong Chen Yuqi Wang School of health Humanities Peking UniversityBeijingChina Center for Global Public health Chinese Center for Disease Control and PreventionBeijingChina

Summary What is already known about this topic?With the assistance of the internet,big data,cloud computing,and other technologies,the concept of smart elderly care has *** is added by this report?This study presents information on the countries or regions that have conducted research on smart elderly care,as well as identifies global hotspots and development trends in this field.

关键词： Smart internet smart

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污水处理工程中的数字孪生技术（英文）

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Engineering 2024年第5期 21-35页

作者：王爱杰李贺文贺哲君陶彧王鸿程杨敏 Dragan Savic Glen T.Daigger 任南琪 School of Civil and Environmental Engineering Harbin Institute of Technology (Shenzhen) State Key Laboratory of Urban Water Resource and Environment Harbin Institute of Technology Department of Civil and Environmental Engineering Cornell University BIOMATH Department of Data Analysis and Mathematical Modelling Ghent University KWR Water Research Institute Centre for Water Systems University of Exeter Department of Civil and Environmental Engineering University of Michigan

The digital twins concept enhances modeling and simulation through the integration of real-time data and feedback. This review elucidates the foundational elements of digital twins, covering their concept,entities, domains, and key technologies. More specifically, we investigate the transformative potential of digital twins for the wastewater treatment engineering sector. Our discussion highlights the application of digital twins to wastewater treatment plants(WWTPs) and sewage networks, hardware(i.e., facilities and pipes, sensors for water quality and activated sludge, hydrodynamics, and power consumption), and software(i.e., knowledge-based and data-driven models, mechanistic models, hybrid twins, control methods, and the Internet of Things). Furthermore, two cases are provided, followed by an assessment of current challenges in and perspectives on the application of digital twins in WWTPs. This review serves as an essential primer for wastewater engineers navigating the digital paradigm shift.

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