The learning Bayesian network （BN） structure from data is an NP-hard problem and still one of the most exciting chal- lenges in the machine learning. In this work, a novel algorithm is presented which combines ideas...The learning Bayesian network （BN） structure from data is an NP-hard problem and still one of the most exciting chal- lenges in the machine learning. In this work, a novel algorithm is presented which combines ideas from local learning, constraint- based, and search-and-score techniques in a principled and ef- fective way. It first reconstructs the junction tree of a BN and then performs a K2-scoring greedy search to orientate the local edges in the cliques of junction tree. Theoretical and experimental results show the proposed algorithm is capable of handling networks with a large number of variables. Its comparison with the well-known K2 algorithm is also presented.展开更多
Fault diagnosis occupies a pivotal position within the domain of machine and equipment management.Existing methods,however,often exhibit limitations in their scope of application,typically focusing on specific types o...Fault diagnosis occupies a pivotal position within the domain of machine and equipment management.Existing methods,however,often exhibit limitations in their scope of application,typically focusing on specific types of signals or faults in individual mechanical components while being constrained by data types and inherent characteristics.To address the limitations of existing methods,we propose a fault diagnosis method based on graph neural networks(GNNs)embedded with multirelationships of intrinsic mode functions(MIMF).The approach introduces a novel graph topological structure constructed from the features of intrinsic mode functions(IMFs)of monitored signals and their multirelationships.Additionally,a graph-level based fault diagnosis network model is designed to enhance feature learning capabilities for graph samples and enable flexible application across diverse signal sources and devices.Experimental validation with datasets including independent vibration signals for gear fault detection,mixed vibration signals for concurrent gear and bearing faults,and pressure signals for hydraulic cylinder leakage characterization demonstrates the model's adaptability and superior diagnostic accuracy across various types of signals and mechanical systems.展开更多
漏洞检测是软件系统安全领域的关键技术.近年来,深度学习凭借其代码特征提取的卓越能力,在漏洞检测领域取得了显著进展.然而,当前基于深度学习的方法仅关注于代码实例自身的独立结构特征,而忽视了不同漏洞代码间存在的结构特征相似关联...漏洞检测是软件系统安全领域的关键技术.近年来,深度学习凭借其代码特征提取的卓越能力,在漏洞检测领域取得了显著进展.然而,当前基于深度学习的方法仅关注于代码实例自身的独立结构特征,而忽视了不同漏洞代码间存在的结构特征相似关联,限制了漏洞检测技术的性能.针对这一问题,提出了一种基于函数间结构特征关联的软件漏洞检测方法(vulnerability detection method based on correlation of structural features between functions,CSFF-VD).该方法首先将函数解析为代码属性图,并通过门控图神经网络提取函数内的独立结构特征.在此基础之上,利用特征之间的相似性构建函数间的关联网络并构建基于图注意力网络进一步提取函数间关联信息,以此提升漏洞检测的性能.实验结果显示,CSFF-VD在3个公开的漏洞检测数据集上超过了当前基于深度学习的漏洞检测方法.此外,在函数内各独立特征提取的基础上,通过增加CSFF-VD中函数间关联特征提取方法的实验,证明了集成函数间关联信息的有效性.展开更多
基金supported by the National Natural Science Fundation of China (6097408261075055)the Fundamental Research Funds for the Central Universities (K50510700004)
文摘The learning Bayesian network （BN） structure from data is an NP-hard problem and still one of the most exciting chal- lenges in the machine learning. In this work, a novel algorithm is presented which combines ideas from local learning, constraint- based, and search-and-score techniques in a principled and ef- fective way. It first reconstructs the junction tree of a BN and then performs a K2-scoring greedy search to orientate the local edges in the cliques of junction tree. Theoretical and experimental results show the proposed algorithm is capable of handling networks with a large number of variables. Its comparison with the well-known K2 algorithm is also presented.
文摘Fault diagnosis occupies a pivotal position within the domain of machine and equipment management.Existing methods,however,often exhibit limitations in their scope of application,typically focusing on specific types of signals or faults in individual mechanical components while being constrained by data types and inherent characteristics.To address the limitations of existing methods,we propose a fault diagnosis method based on graph neural networks(GNNs)embedded with multirelationships of intrinsic mode functions(MIMF).The approach introduces a novel graph topological structure constructed from the features of intrinsic mode functions(IMFs)of monitored signals and their multirelationships.Additionally,a graph-level based fault diagnosis network model is designed to enhance feature learning capabilities for graph samples and enable flexible application across diverse signal sources and devices.Experimental validation with datasets including independent vibration signals for gear fault detection,mixed vibration signals for concurrent gear and bearing faults,and pressure signals for hydraulic cylinder leakage characterization demonstrates the model's adaptability and superior diagnostic accuracy across various types of signals and mechanical systems.
文摘漏洞检测是软件系统安全领域的关键技术.近年来,深度学习凭借其代码特征提取的卓越能力,在漏洞检测领域取得了显著进展.然而,当前基于深度学习的方法仅关注于代码实例自身的独立结构特征,而忽视了不同漏洞代码间存在的结构特征相似关联,限制了漏洞检测技术的性能.针对这一问题,提出了一种基于函数间结构特征关联的软件漏洞检测方法(vulnerability detection method based on correlation of structural features between functions,CSFF-VD).该方法首先将函数解析为代码属性图,并通过门控图神经网络提取函数内的独立结构特征.在此基础之上,利用特征之间的相似性构建函数间的关联网络并构建基于图注意力网络进一步提取函数间关联信息,以此提升漏洞检测的性能.实验结果显示,CSFF-VD在3个公开的漏洞检测数据集上超过了当前基于深度学习的漏洞检测方法.此外,在函数内各独立特征提取的基础上,通过增加CSFF-VD中函数间关联特征提取方法的实验,证明了集成函数间关联信息的有效性.
