Owing to the continuous barrage of cyber threats,there is a massive amount of cyber threat intelligence.However,a great deal of cyber threat intelligence come from textual sources.For analysis of cyber threat intellig...Owing to the continuous barrage of cyber threats,there is a massive amount of cyber threat intelligence.However,a great deal of cyber threat intelligence come from textual sources.For analysis of cyber threat intelligence,many security analysts rely on cumbersome and time-consuming manual efforts.Cybersecurity knowledge graph plays a significant role in automatics analysis of cyber threat intelligence.As the foundation for constructing cybersecurity knowledge graph,named entity recognition(NER)is required for identifying critical threat-related elements from textual cyber threat intelligence.Recently,deep neural network-based models have attained very good results in NER.However,the performance of these models relies heavily on the amount of labeled data.Since labeled data in cybersecurity is scarce,in this paper,we propose an adversarial active learning framework to effectively select the informative samples for further annotation.In addition,leveraging the long short-term memory(LSTM)network and the bidirectional LSTM(BiLSTM)network,we propose a novel NER model by introducing a dynamic attention mechanism into the BiLSTM-LSTM encoderdecoder.With the selected informative samples annotated,the proposed NER model is retrained.As a result,the performance of the NER model is incrementally enhanced with low labeling cost.Experimental results show the effectiveness of the proposed method.展开更多
To the Editor:Delayed diagnosis is a challenge in ankylosing spondylitis(AS),a representative phenotype of axial spondylarthritis(ax-SpA).Such delay might be improving,^([1])as there have been several updates on the d...To the Editor:Delayed diagnosis is a challenge in ankylosing spondylitis(AS),a representative phenotype of axial spondylarthritis(ax-SpA).Such delay might be improving,^([1])as there have been several updates on the diagnosis criteria of AS and ax-SpA,especially the 2009 Assessment of SpondyloArthritis International Society(ASAS),^([2])which recognized MRI as a powerful approach to detect early-stage lesions.However,there is still limited knowledge of whether the diagnostic delay has been improved in China.Therefore,we performed a comparative study of two datasets from the same hospital collected over 14 years.展开更多
基金the National Natural Science Foundation of China undergrant 61501515.
文摘Owing to the continuous barrage of cyber threats,there is a massive amount of cyber threat intelligence.However,a great deal of cyber threat intelligence come from textual sources.For analysis of cyber threat intelligence,many security analysts rely on cumbersome and time-consuming manual efforts.Cybersecurity knowledge graph plays a significant role in automatics analysis of cyber threat intelligence.As the foundation for constructing cybersecurity knowledge graph,named entity recognition(NER)is required for identifying critical threat-related elements from textual cyber threat intelligence.Recently,deep neural network-based models have attained very good results in NER.However,the performance of these models relies heavily on the amount of labeled data.Since labeled data in cybersecurity is scarce,in this paper,we propose an adversarial active learning framework to effectively select the informative samples for further annotation.In addition,leveraging the long short-term memory(LSTM)network and the bidirectional LSTM(BiLSTM)network,we propose a novel NER model by introducing a dynamic attention mechanism into the BiLSTM-LSTM encoderdecoder.With the selected informative samples annotated,the proposed NER model is retrained.As a result,the performance of the NER model is incrementally enhanced with low labeling cost.Experimental results show the effectiveness of the proposed method.
文摘To the Editor:Delayed diagnosis is a challenge in ankylosing spondylitis(AS),a representative phenotype of axial spondylarthritis(ax-SpA).Such delay might be improving,^([1])as there have been several updates on the diagnosis criteria of AS and ax-SpA,especially the 2009 Assessment of SpondyloArthritis International Society(ASAS),^([2])which recognized MRI as a powerful approach to detect early-stage lesions.However,there is still limited knowledge of whether the diagnostic delay has been improved in China.Therefore,we performed a comparative study of two datasets from the same hospital collected over 14 years.
