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Evaluating the Efficacy of latent variables in Mitigating Data Poisoning Attacks in the Context of Bayesian Networks:An Empirical Study

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Computer Modeling in Engineering & Sciences 2024年第5期139卷 1635-1654页

作者： Shahad Alzahrani Hatim Alsuwat Emad Alsuwat Independent Researcher Specializing in Data Security and Privacy Taif26571Saudi Arabia Department of Computer Science College of Computer and Information SystemsUmm Al-Qura UniversityMakkah24382Saudi Arabia Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif26571Saudi Arabia

Bayesian networks are a powerful class of graphical decision models used to represent causal relationships among ***,the reliability and integrity of learned Bayesian network models are highly dependent on the quality of incoming data *** of the primary challenges with Bayesian networks is their vulnerability to adversarial data poisoning attacks,wherein malicious data is injected into the training dataset to negatively influence the Bayesian network models and impair their *** this research paper,we propose an efficient framework for detecting data poisoning attacks against Bayesian network structure learning *** framework utilizes latent variables to quantify the amount of belief between every two nodes in each causal model over *** use our innovative methodology to tackle an important issue with data poisoning assaults in the context of Bayesian *** regard to four different forms of data poisoning attacks,we specifically aim to strengthen the security and dependability of Bayesian network structure learning techniques,such as the PC *** doing this,we explore the complexity of this area and offer workablemethods for identifying and reducing these sneaky ***,our research investigates one particular use case,the“Visit to Asia Network.”The practical consequences of using uncertainty as a way to spot cases of data poisoning are explored in this inquiry,which is of utmost *** results demonstrate the promising efficacy of latent variables in detecting and mitigating the threat of data poisoning ***,our proposed latent-based framework proves to be sensitive in detecting malicious data poisoning attacks in the context of stream data.

关键词： Bayesian networks data poisoning attacks latent variables structure learning algorithms adversarial attacks

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Frontiers of Information Technology & Electronic Engineering 2021年第9期22卷 1234-1246页

作者： Yuxue XU Yun WANG Tianhong YAN Yuchen HE Jun WANG De GU Haiping DU Weihua LI College of Mechanical and Electrical Engineering China JiliangUniversityHangzhou 310018China Mechanical and Electrical EngineeringDepartment Zhejiang Tongji Vocational College of Science andTechnologyHangzhou 311231China Key Laboratory of Advanced ProcessControl for Light Industry(Ministry of Education) Institute ofAutomationJiangnan UniversityWuxi 214122China School ofElectrical Computer and Telecommunications EngineeringUniversity ofWollongongWollongongNSW 2522Australia

Soft sensors are widely used to predict quality variables which are usually hard to *** is necessary to construct an adaptive model to cope with process *** this study,a novel quality-related locally weighted soft sensing method is designed for non-stationary processes based on a Bayesian network with latent ***,a supervised Bayesian network is proposed where quality-oriented latent variables are extracted and further applied to a double-layer similarity meas-urement *** proposed soft sensing method tries to find a general approach for non-stationary processes via quality-related information where the concepts of local similarities and window confidence are explained in *** performance of the developed method is demonstrated by application to a numerical example and a debutanizer *** is shown that the proposed method outperforms competitive methods in terms of the accuracy of predicting key quality variables.

关键词： Soft sensor Supervised Bayesian network latent variables Locally weighted modeling Quality prediction

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A Novel Step-by-Step High-Order Extended Kalman Filter Design for a Class of Complex Systems with Multiple Basic Multipliers

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Chinese Journal of Electronics 2021年第2期30卷 313-321页

作者： SUN Xiaohui WEN Chenglin WEN Tao School of Automation Hangzhou Dianzi University School of Automation Guangdong University of Petrochemical Technology School of Electronic and Information Engineering Beijing Jiaotong University

A novel step-by-step linearization high-order Extended Kalman filter SH-EKF is designed for a class of nonlinear systems composed of linear functions and the product of several separable basic functions. The basic functions in the state and measurement models are defined as latent variables; the state and measurement models are equivalently formulated into pseudo-linear models based on the combination of the original variable and the latent variables; latent variables are regarded as new variables,and a dynamic linear model between each latent variable and other latent variables with original state is established;the measurement model is rewritten into the first-order linear product form between the current state and each latent variable; latent variables are solved by Kalman filter step by step, and a stepwise linearized high-order extended Kalman filter is designed. Illustration examples are presented to demonstrate the effectiveness of the new algorithm.

关键词： Filtering methods in signal processing Signal processing theory High-order extended Kalman filter Nonlinear systems latent variables Pseudo-linear Basic functions

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潜变量交互效应结构方程：分布分析方法

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心理学 2014年第3期 409-414页

作者：温忠麟吴艳侯杰泰

对于潜变量交互效应结构方程分析，目前应用较多的是乘积指标方法。分布分析方法国内还罕有应用，包括潜调节结构方程(LMS)方法和准极大似然(QML)方法。该研究以乘积指标方法的模型假设为参照，介绍了分布分析方法的模型假设。并简要叙... 详细信息

对于潜变量交互效应结构方程分析，目前应用较多的是乘积指标方法。分布分析方法国内还罕有应用，包括潜调节结构方程(LMS)方法和准极大似然(QML)方法。该研究以乘积指标方法的模型假设为参照，介绍了分布分析方法的模型假设。并简要叙述了LMS方法及其Mplus程序，QML方法及其QML程序。综合现有研究结果，总结出LMS和QML方法、无约束和约束方法的特点，从中可以看出各方法的优缺点，推荐了不同条件下合适的分析方法。

关键词：潜变量交互效应潜调节结构方程准极大似然乘积指标 latent variables interaction effect latent moderated structural equations quasi-maximum likelihood product indicator

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