从无结构化自然语言文本中抽取实体关系三元组是构建大型知识图谱中最为关键的一步,但现有研究仍存在3方面问题:1)忽略文本中因多个三元组共享同一实体而产生的实体关系重叠问题;2)当前以编码器-解码器为基础的联合抽取模型未充分考虑...从无结构化自然语言文本中抽取实体关系三元组是构建大型知识图谱中最为关键的一步,但现有研究仍存在3方面问题:1)忽略文本中因多个三元组共享同一实体而产生的实体关系重叠问题;2)当前以编码器-解码器为基础的联合抽取模型未充分考虑文本语句词之间的依赖关系;3)部分三元组序列过长导致误差累积与传播,影响实体关系抽取的精度和效率.基于此,提出基于图卷积增强多路解码的实体关系联合抽取模型(graph convolution-enhanced multi-channel decoding joint entity and relation extraction model,GMCD-JERE).首先,基于BiLSTM作为模型编码器,强化文本中词的双向特征融合;其次,通过图卷积多跳特征融合句中词之间的依赖关系,提高关系抽取准确性;此外,改进传统模型按三元组先后顺序的解码机制,通过多路解码三元组机制,解决实体关系重叠问题,同时缓解三元组序列过长造成误差累积、传播的影响;最后,实验选用当前3个主流模型进行性能验证,在NYT(New York times)数据集上结果表明在精确率、召回率和F1这3个指标上分别提升了4.3%,5.1%,4.8%,同时在WebNLG(Web natural language generation)数据集上验证以关系为开始的抽取顺序.展开更多
The fusion of infrared and visible images should emphasize the salient targets in the infrared image while preserving the textural details of the visible images.To meet these requirements,an autoencoder-based method f...The fusion of infrared and visible images should emphasize the salient targets in the infrared image while preserving the textural details of the visible images.To meet these requirements,an autoencoder-based method for infrared and visible image fusion is proposed.The encoder designed according to the optimization objective consists of a base encoder and a detail encoder,which is used to extract low-frequency and high-frequency information from the image.This extraction may lead to some information not being captured,so a compensation encoder is proposed to supplement the missing information.Multi-scale decomposition is also employed to extract image features more comprehensively.The decoder combines low-frequency,high-frequency and supplementary information to obtain multi-scale features.Subsequently,the attention strategy and fusion module are introduced to perform multi-scale fusion for image reconstruction.Experimental results on three datasets show that the fused images generated by this network effectively retain salient targets while being more consistent with human visual perception.展开更多
Steganography based on bits-modification of speech frames is a kind of commonly used method, which targets at RTP payloads and offers covert communications over voice-over-IP(Vo IP). However, direct modification on fr...Steganography based on bits-modification of speech frames is a kind of commonly used method, which targets at RTP payloads and offers covert communications over voice-over-IP(Vo IP). However, direct modification on frames is often independent of the inherent speech features, which may lead to great degradation of speech quality. A novel frame-bitrate-change based steganography is proposed in this work, which discovers a novel covert channel for Vo IP and introduces less distortion. This method exploits the feature of multi-rate speech codecs that the practical bitrate of speech frame is identified only by speech decoder at receiving end. Based on this characteristic, two steganography strategies called bitrate downgrading(BD) and bitrate switching(BS)are provided. The first strategy substitutes high bit-rate speech frames with lower ones to embed secret message, which introduces very low distortion in practice, and much less than other bits-modification based methods with the same embedding capacity. The second one encodes secret message bits into different types of speech frames, which is an alternative choice for supplement. The two strategies are implemented and tested on our covert communication system Steg Vo IP. The experiment results show that our proposed method is effective and fulfills the real-time requirement of Vo IP communication.展开更多
文摘从无结构化自然语言文本中抽取实体关系三元组是构建大型知识图谱中最为关键的一步,但现有研究仍存在3方面问题:1)忽略文本中因多个三元组共享同一实体而产生的实体关系重叠问题;2)当前以编码器-解码器为基础的联合抽取模型未充分考虑文本语句词之间的依赖关系;3)部分三元组序列过长导致误差累积与传播,影响实体关系抽取的精度和效率.基于此,提出基于图卷积增强多路解码的实体关系联合抽取模型(graph convolution-enhanced multi-channel decoding joint entity and relation extraction model,GMCD-JERE).首先,基于BiLSTM作为模型编码器,强化文本中词的双向特征融合;其次,通过图卷积多跳特征融合句中词之间的依赖关系,提高关系抽取准确性;此外,改进传统模型按三元组先后顺序的解码机制,通过多路解码三元组机制,解决实体关系重叠问题,同时缓解三元组序列过长造成误差累积、传播的影响;最后,实验选用当前3个主流模型进行性能验证,在NYT(New York times)数据集上结果表明在精确率、召回率和F1这3个指标上分别提升了4.3%,5.1%,4.8%,同时在WebNLG(Web natural language generation)数据集上验证以关系为开始的抽取顺序.
基金Supported by the Henan Province Key Research and Development Project(231111211300)the Central Government of Henan Province Guides Local Science and Technology Development Funds(Z20231811005)+2 种基金Henan Province Key Research and Development Project(231111110100)Henan Provincial Outstanding Foreign Scientist Studio(GZS2024006)Henan Provincial Joint Fund for Scientific and Technological Research and Development Plan(Application and Overcoming Technical Barriers)(242103810028)。
文摘The fusion of infrared and visible images should emphasize the salient targets in the infrared image while preserving the textural details of the visible images.To meet these requirements,an autoencoder-based method for infrared and visible image fusion is proposed.The encoder designed according to the optimization objective consists of a base encoder and a detail encoder,which is used to extract low-frequency and high-frequency information from the image.This extraction may lead to some information not being captured,so a compensation encoder is proposed to supplement the missing information.Multi-scale decomposition is also employed to extract image features more comprehensively.The decoder combines low-frequency,high-frequency and supplementary information to obtain multi-scale features.Subsequently,the attention strategy and fusion module are introduced to perform multi-scale fusion for image reconstruction.Experimental results on three datasets show that the fused images generated by this network effectively retain salient targets while being more consistent with human visual perception.
基金Project(2011CB302305)supported by National Basic Research Program(973 Program)of ChinaProjects(61232004,61302094)supported by National Natural Science Foundation of China+2 种基金Project(ZQN-PY115)supported by Promotion Program for Young and Middle-aged Teacher in Science and Technology Research of Huaqiao University,ChinaProject(JA13012)supported by Education Science Research Program for Young and Middle-aged Teacher of Fujian Province of ChinaProject(2014J01238)supported by Natural Science Foundation of Fujian Province of China
文摘Steganography based on bits-modification of speech frames is a kind of commonly used method, which targets at RTP payloads and offers covert communications over voice-over-IP(Vo IP). However, direct modification on frames is often independent of the inherent speech features, which may lead to great degradation of speech quality. A novel frame-bitrate-change based steganography is proposed in this work, which discovers a novel covert channel for Vo IP and introduces less distortion. This method exploits the feature of multi-rate speech codecs that the practical bitrate of speech frame is identified only by speech decoder at receiving end. Based on this characteristic, two steganography strategies called bitrate downgrading(BD) and bitrate switching(BS)are provided. The first strategy substitutes high bit-rate speech frames with lower ones to embed secret message, which introduces very low distortion in practice, and much less than other bits-modification based methods with the same embedding capacity. The second one encodes secret message bits into different types of speech frames, which is an alternative choice for supplement. The two strategies are implemented and tested on our covert communication system Steg Vo IP. The experiment results show that our proposed method is effective and fulfills the real-time requirement of Vo IP communication.
