Security attributes are the premise and foundation for implementing Attribute-Based Access Control(ABAC)mechanisms.However,when dealing with massive volumes of unstructured text big data resources,the current attribut...Security attributes are the premise and foundation for implementing Attribute-Based Access Control(ABAC)mechanisms.However,when dealing with massive volumes of unstructured text big data resources,the current attribute management methods based on manual extraction face several issues,such as high costs for attribute extraction,long processing times,unstable accuracy,and poor scalability.To address these problems,this paper proposes an attribute mining technology for access control institutions based on hybrid capsule networks.This technology leverages transfer learning ideas,utilizing Bidirectional Encoder Representations from Transformers(BERT)pre-trained language models to achieve vectorization of unstructured text data resources.Furthermore,we have designed a novel end-to-end parallel hybrid network structure,where the parallel networks handle global and local information features of the text that they excel at,respectively.By employing techniques such as attention mechanisms,capsule networks,and dynamic routing,effective mining of security attributes for access control resources has been achieved.Finally,we evaluated the performance level of the proposed attribute mining method for access control institutions through experiments on the medical referral text resource dataset.The experimental results show that,compared with baseline algorithms,our method adopts a parallel network structure that can better balance global and local feature information,resulting in improved overall performance.Specifically,it achieves a comprehensive performance enhancement of 2.06%to 8.18%in the F1 score metric.Therefore,this technology can effectively provide attribute support for access control of unstructured text big data resources.展开更多
Turner syndrome(TS)is a chromosomal disorder disease that only affects the growth of female patients.Prompt diagnosis is of high significance for the patients.However,clinical screening methods are time-consuming and ...Turner syndrome(TS)is a chromosomal disorder disease that only affects the growth of female patients.Prompt diagnosis is of high significance for the patients.However,clinical screening methods are time-consuming and cost-expensive.Some researchers used machine learning-based methods to detect TS,the performance of which needed to be improved.Therefore,we propose an ensemble method of two-path capsule networks(CapsNets)for detecting TS based on global-local facial images.Specifically,the TS facial images are preprocessed and segmented into eight local parts under the direction of physicians;then,nine two-path CapsNets are respectively trained using the complete TS facial images and eight local images,in which the few-shot learning is utilized to solve the problem of limited data;finally,a probability-based ensemble method is exploited to combine nine classifiers for the classification of TS.By studying base classifiers,we find two meaningful facial areas are more related to TS patients,i.e.,the parts of eyes and nose.The results demonstrate that the proposed model is effective for the TS classification task,which achieves the highest accuracy of 0.9241.展开更多
社交文本的情感分析主要存在结构不规则、特征稀疏和分类效果不理想等问题。针对这些问题,提出了一种双向长短期记忆网络(Bi-directional long short-term memory,BiLSTM)和胶囊网络(Capsule network,CapsNet)混合模型(BiLSTM-CapsNet)...社交文本的情感分析主要存在结构不规则、特征稀疏和分类效果不理想等问题。针对这些问题,提出了一种双向长短期记忆网络(Bi-directional long short-term memory,BiLSTM)和胶囊网络(Capsule network,CapsNet)混合模型(BiLSTM-CapsNet)。该模型先使用胶囊网络提取单个特征词在整个句子中的位置语义信息,再使用双向长短期记忆网络提取社交文本的上下文词语之间的关系,最后通过softmax分类器,进行情感倾向的分类。试验结果表明,该模型在粗粒度和细粒度情感分类中都有优势。展开更多
基金supported by National Natural Science Foundation of China(No.62102449).
文摘Security attributes are the premise and foundation for implementing Attribute-Based Access Control(ABAC)mechanisms.However,when dealing with massive volumes of unstructured text big data resources,the current attribute management methods based on manual extraction face several issues,such as high costs for attribute extraction,long processing times,unstable accuracy,and poor scalability.To address these problems,this paper proposes an attribute mining technology for access control institutions based on hybrid capsule networks.This technology leverages transfer learning ideas,utilizing Bidirectional Encoder Representations from Transformers(BERT)pre-trained language models to achieve vectorization of unstructured text data resources.Furthermore,we have designed a novel end-to-end parallel hybrid network structure,where the parallel networks handle global and local information features of the text that they excel at,respectively.By employing techniques such as attention mechanisms,capsule networks,and dynamic routing,effective mining of security attributes for access control resources has been achieved.Finally,we evaluated the performance level of the proposed attribute mining method for access control institutions through experiments on the medical referral text resource dataset.The experimental results show that,compared with baseline algorithms,our method adopts a parallel network structure that can better balance global and local feature information,resulting in improved overall performance.Specifically,it achieves a comprehensive performance enhancement of 2.06%to 8.18%in the F1 score metric.Therefore,this technology can effectively provide attribute support for access control of unstructured text big data resources.
基金the National Key R&D Program of China(No.2020YFB2104402)。
文摘Turner syndrome(TS)is a chromosomal disorder disease that only affects the growth of female patients.Prompt diagnosis is of high significance for the patients.However,clinical screening methods are time-consuming and cost-expensive.Some researchers used machine learning-based methods to detect TS,the performance of which needed to be improved.Therefore,we propose an ensemble method of two-path capsule networks(CapsNets)for detecting TS based on global-local facial images.Specifically,the TS facial images are preprocessed and segmented into eight local parts under the direction of physicians;then,nine two-path CapsNets are respectively trained using the complete TS facial images and eight local images,in which the few-shot learning is utilized to solve the problem of limited data;finally,a probability-based ensemble method is exploited to combine nine classifiers for the classification of TS.By studying base classifiers,we find two meaningful facial areas are more related to TS patients,i.e.,the parts of eyes and nose.The results demonstrate that the proposed model is effective for the TS classification task,which achieves the highest accuracy of 0.9241.
文摘社交文本的情感分析主要存在结构不规则、特征稀疏和分类效果不理想等问题。针对这些问题,提出了一种双向长短期记忆网络(Bi-directional long short-term memory,BiLSTM)和胶囊网络(Capsule network,CapsNet)混合模型(BiLSTM-CapsNet)。该模型先使用胶囊网络提取单个特征词在整个句子中的位置语义信息,再使用双向长短期记忆网络提取社交文本的上下文词语之间的关系,最后通过softmax分类器,进行情感倾向的分类。试验结果表明,该模型在粗粒度和细粒度情感分类中都有优势。
