Research in the field ofmedical image is an important part of themedical robot to operate human organs.Amedical robot is the intersection ofmulti-disciplinary research fields,in whichmedical image is an important dire...Research in the field ofmedical image is an important part of themedical robot to operate human organs.Amedical robot is the intersection ofmulti-disciplinary research fields,in whichmedical image is an important direction and has achieved fruitful results.In this paper,amethodof soft tissue surface feature tracking basedonadepthmatching network is proposed.This method is described based on the triangular matching algorithm.First,we construct a self-made sample set for training the depth matching network from the first N frames of speckle matching data obtained by the triangle matching algorithm.The depth matching network is pre-trained on the ORL face data set and then trained on the self-made training set.After the training,the speckle matching is carried out in the subsequent frames to obtain the speckle matching matrix between the subsequent frames and the first frame.From this matrix,the inter-frame feature matching results can be obtained.In this way,the inter-frame speckle tracking is completed.On this basis,the results of this method are compared with the matching results based on the convolutional neural network.The experimental results show that the proposed method has higher matching accuracy.In particular,the accuracy of the MNIST handwritten data set has reached more than 90%.展开更多
To enhance network security,this study employs a deep graph matching model for vulnerability similarity detection.The model utilizes a Word Embedding layer to vectorize data words,an Image Embedding layer to vectorize...To enhance network security,this study employs a deep graph matching model for vulnerability similarity detection.The model utilizes a Word Embedding layer to vectorize data words,an Image Embedding layer to vectorize data graphs,and an LSTM layer to extract the associations between word and graph vectors.A Dropout layer is applied to randomly deactivate neurons in the LSTM layer,while a Softmax layer maps the LSTM analysis results.Finally,a fully connected layer outputs the detection results with a dimension of 1.Experimental results demonstrate that the AUC of the deep graph matching vulnerability similarity detection model is 0.9721,indicating good stability.The similarity scores for vulnerabilities such as memory leaks,buffer overflows,and targeted attacks are close to 1,showing significant similarity.In contrast,the similarity scores for vulnerabilities like out-of-bounds memory access and logical design flaws are less than 0.4,indicating good similarity detection performance.The model’s evaluation metrics are all above 97%,with high detection accuracy,which is beneficial for improving network security.展开更多
The traditional Chinese-English translation model tends to translate some source words repeatedly,while mistakenly ignoring some words.Therefore,we propose a novel English-Chinese neural machine translation based on s...The traditional Chinese-English translation model tends to translate some source words repeatedly,while mistakenly ignoring some words.Therefore,we propose a novel English-Chinese neural machine translation based on self-organizing mapping neural network and deep feature matching.In this model,word vector,two-way LSTM,2D neural network and other deep learning models are used to extract the semantic matching features of question-answer pairs.Self-organizing mapping(SOM)is used to classify and identify the sentence feature.The attention mechanism-based neural machine translation model is taken as the baseline system.The experimental results show that this framework significantly improves the adequacy of English-Chinese machine translation and achieves better results than the traditional attention mechanism-based English-Chinese machine translation model.展开更多
The rapid growth of scientific papers makes it difficult to query related papers efficiently,accurately and with high coverage.Traditional citation recommendation algorithms rely heavily on the metadata of query docum...The rapid growth of scientific papers makes it difficult to query related papers efficiently,accurately and with high coverage.Traditional citation recommendation algorithms rely heavily on the metadata of query documents,which leads to the low quality of recommendation results.In this paper,DeepCite,a content-based hybrid neural network citation recommendation method is proposed.First,the BERT model was used to extract the high-level semantic representation vectors in the text,then the multi-scale CNN model and BiLSTM model were used to obtain the local information and the sequence information of the context in the sentence,and the text vectors were matched in depth to generate candidate sets.Further,the depth neural network was used to rerank the candidate sets by combining the score of candidate sets and multisource features.In the reranking stage,a variety of Metapath features were extracted from the citation network,and added to the deep neural network to learn,and the ranking of recommendation results were optimized.Compared with PWFC,ClusCite,BM25,RW,NNRank models,the results of the Deepcite algorithm presented in the ANN datasets show that the precision(P@20),recall rate(R@20),MRR and MAP indexesrise by 2.3%,3.9%,2.4%and 2.1%respectively.Experimental results on DBLP datasets show that the improvement is 2.4%,4.3%,1.8%and 1.2%respectively.Therefore,the algorithm proposed in this paper effectively improves the quality of citation recommendation.展开更多
基金supported by the Sichuan Science and Technology Program (Grant:2021YFQ0003,Acquired by Wenfeng Zheng).
文摘Research in the field ofmedical image is an important part of themedical robot to operate human organs.Amedical robot is the intersection ofmulti-disciplinary research fields,in whichmedical image is an important direction and has achieved fruitful results.In this paper,amethodof soft tissue surface feature tracking basedonadepthmatching network is proposed.This method is described based on the triangular matching algorithm.First,we construct a self-made sample set for training the depth matching network from the first N frames of speckle matching data obtained by the triangle matching algorithm.The depth matching network is pre-trained on the ORL face data set and then trained on the self-made training set.After the training,the speckle matching is carried out in the subsequent frames to obtain the speckle matching matrix between the subsequent frames and the first frame.From this matrix,the inter-frame feature matching results can be obtained.In this way,the inter-frame speckle tracking is completed.On this basis,the results of this method are compared with the matching results based on the convolutional neural network.The experimental results show that the proposed method has higher matching accuracy.In particular,the accuracy of the MNIST handwritten data set has reached more than 90%.
基金Special Project Funded by Tsinghua University Press:“Engineering Drawing and CAD”Course Construction and Textbook Development。
文摘To enhance network security,this study employs a deep graph matching model for vulnerability similarity detection.The model utilizes a Word Embedding layer to vectorize data words,an Image Embedding layer to vectorize data graphs,and an LSTM layer to extract the associations between word and graph vectors.A Dropout layer is applied to randomly deactivate neurons in the LSTM layer,while a Softmax layer maps the LSTM analysis results.Finally,a fully connected layer outputs the detection results with a dimension of 1.Experimental results demonstrate that the AUC of the deep graph matching vulnerability similarity detection model is 0.9721,indicating good stability.The similarity scores for vulnerabilities such as memory leaks,buffer overflows,and targeted attacks are close to 1,showing significant similarity.In contrast,the similarity scores for vulnerabilities like out-of-bounds memory access and logical design flaws are less than 0.4,indicating good similarity detection performance.The model’s evaluation metrics are all above 97%,with high detection accuracy,which is beneficial for improving network security.
文摘The traditional Chinese-English translation model tends to translate some source words repeatedly,while mistakenly ignoring some words.Therefore,we propose a novel English-Chinese neural machine translation based on self-organizing mapping neural network and deep feature matching.In this model,word vector,two-way LSTM,2D neural network and other deep learning models are used to extract the semantic matching features of question-answer pairs.Self-organizing mapping(SOM)is used to classify and identify the sentence feature.The attention mechanism-based neural machine translation model is taken as the baseline system.The experimental results show that this framework significantly improves the adequacy of English-Chinese machine translation and achieves better results than the traditional attention mechanism-based English-Chinese machine translation model.
基金“Shenzhen Science and Technology Project”(JCYJ20180306170836595)“National key research and development program in China”(2019YFB2102300)+4 种基金“the World-Class Universities(Disciplines)and the Characteristic Development Guidance Funds for the Central Universities of China”(PY3A022)“Ministry of Education Fund Projects”(No.18JZD022 and 2017B00030)“Basic Scientific Research Operating Expenses of Central Universities”(No.ZDYF2017006)“Xi’an Navinfo Corp.&Engineering Center of Xi’an Intelligence Spatial-temporal Data Analysis Project”(C2020103)“Beilin District of Xi’an Science&Technology Project”(GX1803).
文摘The rapid growth of scientific papers makes it difficult to query related papers efficiently,accurately and with high coverage.Traditional citation recommendation algorithms rely heavily on the metadata of query documents,which leads to the low quality of recommendation results.In this paper,DeepCite,a content-based hybrid neural network citation recommendation method is proposed.First,the BERT model was used to extract the high-level semantic representation vectors in the text,then the multi-scale CNN model and BiLSTM model were used to obtain the local information and the sequence information of the context in the sentence,and the text vectors were matched in depth to generate candidate sets.Further,the depth neural network was used to rerank the candidate sets by combining the score of candidate sets and multisource features.In the reranking stage,a variety of Metapath features were extracted from the citation network,and added to the deep neural network to learn,and the ranking of recommendation results were optimized.Compared with PWFC,ClusCite,BM25,RW,NNRank models,the results of the Deepcite algorithm presented in the ANN datasets show that the precision(P@20),recall rate(R@20),MRR and MAP indexesrise by 2.3%,3.9%,2.4%and 2.1%respectively.Experimental results on DBLP datasets show that the improvement is 2.4%,4.3%,1.8%and 1.2%respectively.Therefore,the algorithm proposed in this paper effectively improves the quality of citation recommendation.
