In distributed fusion,when one or more sensors are disturbed by faults,a common problem is that their local estimations are inconsistent with those of other fault-free sensors.Most of the existing fault-tolerant distr...In distributed fusion,when one or more sensors are disturbed by faults,a common problem is that their local estimations are inconsistent with those of other fault-free sensors.Most of the existing fault-tolerant distributed fusion algorithms,such as the Covariance Union(CU)and Faulttolerant Generalized Convex Combination(FGCC),are only used for the point estimation case where local estimates and their associated error covariances are provided.A treatment with focus on the fault-tolerant distributed fusions of arbitrary local Probability Density Functions(PDFs)is lacking.For this problem,we first propose Kullback–Leibler Divergence(KLD)and reversed KLD induced functional Fuzzy c-Means(FCM)clustering algorithms to soft cluster all local PDFs,respectively.On this basis,two fault-tolerant distributed fusion algorithms of arbitrary local PDFs are then developed.They select the representing PDF of the cluster with the largest sum of memberships as the fused PDF.Numerical examples verify the better fault tolerance of the developed two distributed fusion algorithms.展开更多
为了研究LHCb 7 TeV W+、W-和Z实验数据对CT14HERA2部分子分布函数(Parton Distribution Functions,PDFs)的影响,首先将收集到的数据进行了理论预测并将其和实验测量结果进行了比较,在误差允许的范围内理论和实验符合的很好。其次,用误...为了研究LHCb 7 TeV W+、W-和Z实验数据对CT14HERA2部分子分布函数(Parton Distribution Functions,PDFs)的影响,首先将收集到的数据进行了理论预测并将其和实验测量结果进行了比较,在误差允许的范围内理论和实验符合的很好。其次,用误差PDFs更新软件包(Error PDFs Updated Method Package,EPUMP)更新了CT14HERA2 PDFs,并和全局拟合的PDFs进行了比较。最后,加入协方差矩阵后的实验数据可以在较大和较小的x区域减少d(x,Q)/u(x,Q)误差,同时也对CT14HERA2 PDFs进行了优化。验证结果表明,LHCb 7 TeV W+、W-和Z产生的实验数据在较大的x区域对g(x,Q)、d(x,Q)、d(x,Q)/u(x,Q)、d(x,Q)/u(x,Q)、u(x,Q)、d(x,Q)和u(x,Q)PDFs的中心值约束较大,可以用前4个误差PDFs代替原来全局拟合或优化后得到的56个误差集。展开更多
This paper describes a new method to generate discrete signals with arbitrary power spectral density (PSD) and first order probability density function (PDF) without any limitation on PDFs and PSDs. The first approxim...This paper describes a new method to generate discrete signals with arbitrary power spectral density (PSD) and first order probability density function (PDF) without any limitation on PDFs and PSDs. The first approximation has been achieved by using a nonlinear transform function. At the second stage the desired PDF was approximated by a number of symmetric PDFs with defined variance. Each one provides a part of energy from total signal with different ratios of remained desired PSD. These symmetric PDFs defined by sinusoidal components with random amplitude, frequency and phase variables. Both analytic results and examples are included. The proposed scheme has been proved to be useful in simulations involving non-Gaussian processes with specific PSDs and PDFs.展开更多
The growing use of Portable Document Format(PDF)files across various sectors such as education,government,and business has inadvertently turned them into a major target for cyberattacks.Cybercriminals take advantage o...The growing use of Portable Document Format(PDF)files across various sectors such as education,government,and business has inadvertently turned them into a major target for cyberattacks.Cybercriminals take advantage of the inherent flexibility and layered structure ofPDFs to inject malicious content,often employing advanced obfuscation techniques to evade detection by traditional signature-based security systems.These conventional methods are no longer adequate,especially against sophisticated threats like zero-day exploits and polymorphic malware.In response to these challenges,this study introduces a machine learning-based detection framework specifically designed to combat such threats.Central to the proposed solution is a stacked ensemble learning model that combines the strengths of four high-performing classifiers:Random Forest(RF),Extreme Gradient Boosting(XGB),LightGBM(LGBM),and CatBoost(CB).These models operate in parallel as base learners,each capturing different aspects of the data.Their outputs are then refined by a Gradient Boosting Classifier(GBC),which serves as a meta-learner to enhance prediction accuracy.To ensure the model remains both efficient and effective,Principal Component Analysis(PCA)is applied to reduce feature dimensionality while preserving critical information necessary for malware classification.The model is trained and validated using the CIC-Evasive PDFMalware2022 dataset,which includes a wide range of both malicious and benign PDF samples.The results demonstrate that the framework achieves impressive performance,with 97.10% accuracy and a 97.39% F1-score,surpassing several existing techniques.To enhance trust and interpretability,the system incorporates Local Interpretable Model-agnostic Explanations(LIME),which provides user-friendly insights into the rationale behind each prediction.This research emphasizes how the integration of ensemble learning,feature reduction,and explainable AI can lead to a practical and scalable solution for detecting complex PDF-based threats.The proposed framework lays the foundation for the next generation of intelligent,resilient cybersecurity systems that can address ever-evolving attack strategies.展开更多
We now differentiate between the requirements for new and revised submissions.You may choose to submit your manuscript as a single Word or PDF file to be used in the refereeing process.Only when your paper is at the r...We now differentiate between the requirements for new and revised submissions.You may choose to submit your manuscript as a single Word or PDF file to be used in the refereeing process.Only when your paper is at the revision stage,will you be requested to put your paper into a'correct format'for acceptance and provide the items required for the publication of your article.展开更多
By applying the Error PDF Updating Method,we analyze the impact of the absolute and normalized single differential cross-sections for top-quark pair production data from the ATLAS and CMS experiments at the Large Hadr...By applying the Error PDF Updating Method,we analyze the impact of the absolute and normalized single differential cross-sections for top-quark pair production data from the ATLAS and CMS experiments at the Large Hadron Collider,at a center-of-mass energy of √s=8TeV,on the CT14HERA2 PDFs.We find that the top quark pair single differential distributions provide minor constraints on the CT14HERA2 gluon PDF when the nominal CT14HERA2 inclusive jet production data are included in the fit.Larger constraints on the gluon distribution are present when the jet data are removed(CT14HERA2mJ)and/or when increased weights are given to the top data in the CT14HERA2 fits.The weighted$t\bar t$data provide significant constraints on the CT14HERA2mJ gluon PDF,which are comparable to those obtained from inclusive jet production data.Furthermore,we examine the top quark mass sensitivity of the top-quark pair single differential distributions.展开更多
基金supported in part by the Open Fund of Intelligent Control Laboratory,China(No.ICL-2023–0202)in part by National Key R&D Program of China(Nos.2021YFC2202600,2021YFC2202603)。
文摘In distributed fusion,when one or more sensors are disturbed by faults,a common problem is that their local estimations are inconsistent with those of other fault-free sensors.Most of the existing fault-tolerant distributed fusion algorithms,such as the Covariance Union(CU)and Faulttolerant Generalized Convex Combination(FGCC),are only used for the point estimation case where local estimates and their associated error covariances are provided.A treatment with focus on the fault-tolerant distributed fusions of arbitrary local Probability Density Functions(PDFs)is lacking.For this problem,we first propose Kullback–Leibler Divergence(KLD)and reversed KLD induced functional Fuzzy c-Means(FCM)clustering algorithms to soft cluster all local PDFs,respectively.On this basis,two fault-tolerant distributed fusion algorithms of arbitrary local PDFs are then developed.They select the representing PDF of the cluster with the largest sum of memberships as the fused PDF.Numerical examples verify the better fault tolerance of the developed two distributed fusion algorithms.
文摘为了研究LHCb 7 TeV W+、W-和Z实验数据对CT14HERA2部分子分布函数(Parton Distribution Functions,PDFs)的影响,首先将收集到的数据进行了理论预测并将其和实验测量结果进行了比较,在误差允许的范围内理论和实验符合的很好。其次,用误差PDFs更新软件包(Error PDFs Updated Method Package,EPUMP)更新了CT14HERA2 PDFs,并和全局拟合的PDFs进行了比较。最后,加入协方差矩阵后的实验数据可以在较大和较小的x区域减少d(x,Q)/u(x,Q)误差,同时也对CT14HERA2 PDFs进行了优化。验证结果表明,LHCb 7 TeV W+、W-和Z产生的实验数据在较大的x区域对g(x,Q)、d(x,Q)、d(x,Q)/u(x,Q)、d(x,Q)/u(x,Q)、u(x,Q)、d(x,Q)和u(x,Q)PDFs的中心值约束较大,可以用前4个误差PDFs代替原来全局拟合或优化后得到的56个误差集。
文摘This paper describes a new method to generate discrete signals with arbitrary power spectral density (PSD) and first order probability density function (PDF) without any limitation on PDFs and PSDs. The first approximation has been achieved by using a nonlinear transform function. At the second stage the desired PDF was approximated by a number of symmetric PDFs with defined variance. Each one provides a part of energy from total signal with different ratios of remained desired PSD. These symmetric PDFs defined by sinusoidal components with random amplitude, frequency and phase variables. Both analytic results and examples are included. The proposed scheme has been proved to be useful in simulations involving non-Gaussian processes with specific PSDs and PDFs.
文摘The growing use of Portable Document Format(PDF)files across various sectors such as education,government,and business has inadvertently turned them into a major target for cyberattacks.Cybercriminals take advantage of the inherent flexibility and layered structure ofPDFs to inject malicious content,often employing advanced obfuscation techniques to evade detection by traditional signature-based security systems.These conventional methods are no longer adequate,especially against sophisticated threats like zero-day exploits and polymorphic malware.In response to these challenges,this study introduces a machine learning-based detection framework specifically designed to combat such threats.Central to the proposed solution is a stacked ensemble learning model that combines the strengths of four high-performing classifiers:Random Forest(RF),Extreme Gradient Boosting(XGB),LightGBM(LGBM),and CatBoost(CB).These models operate in parallel as base learners,each capturing different aspects of the data.Their outputs are then refined by a Gradient Boosting Classifier(GBC),which serves as a meta-learner to enhance prediction accuracy.To ensure the model remains both efficient and effective,Principal Component Analysis(PCA)is applied to reduce feature dimensionality while preserving critical information necessary for malware classification.The model is trained and validated using the CIC-Evasive PDFMalware2022 dataset,which includes a wide range of both malicious and benign PDF samples.The results demonstrate that the framework achieves impressive performance,with 97.10% accuracy and a 97.39% F1-score,surpassing several existing techniques.To enhance trust and interpretability,the system incorporates Local Interpretable Model-agnostic Explanations(LIME),which provides user-friendly insights into the rationale behind each prediction.This research emphasizes how the integration of ensemble learning,feature reduction,and explainable AI can lead to a practical and scalable solution for detecting complex PDF-based threats.The proposed framework lays the foundation for the next generation of intelligent,resilient cybersecurity systems that can address ever-evolving attack strategies.
文摘We now differentiate between the requirements for new and revised submissions.You may choose to submit your manuscript as a single Word or PDF file to be used in the refereeing process.Only when your paper is at the revision stage,will you be requested to put your paper into a'correct format'for acceptance and provide the items required for the publication of your article.
基金The work of S.Dulat was supported by the National Natural Science Foundation of China(11965020,11847160)。
文摘By applying the Error PDF Updating Method,we analyze the impact of the absolute and normalized single differential cross-sections for top-quark pair production data from the ATLAS and CMS experiments at the Large Hadron Collider,at a center-of-mass energy of √s=8TeV,on the CT14HERA2 PDFs.We find that the top quark pair single differential distributions provide minor constraints on the CT14HERA2 gluon PDF when the nominal CT14HERA2 inclusive jet production data are included in the fit.Larger constraints on the gluon distribution are present when the jet data are removed(CT14HERA2mJ)and/or when increased weights are given to the top data in the CT14HERA2 fits.The weighted$t\bar t$data provide significant constraints on the CT14HERA2mJ gluon PDF,which are comparable to those obtained from inclusive jet production data.Furthermore,we examine the top quark mass sensitivity of the top-quark pair single differential distributions.
