Link prediction is used to complete the knowledge graph.Convolu-tional neural network models are commonly used for link prediction tasks,but they only consider the direct relations between entity pairs,ignoring the se...Link prediction is used to complete the knowledge graph.Convolu-tional neural network models are commonly used for link prediction tasks,but they only consider the direct relations between entity pairs,ignoring the semantic information contained in the relation paths.In addition,the embedding dimension of the relation is generally larger than that of the entity in the ConvR model,which blocks the progress of downstream tasks.If we reduce the embedding dimension of the relation,the performance will be greatly degraded.This paper proposes a convolutional model PITri-R-ConvR based on triangular structure relational infer-ence.The model uses relational path inference to capture semantic information,while using a triangular structure to ensure the reliability and computational effi-ciency of relational inference.In addition,the decoder R-ConvR improves the initial embedding of the ConvR model,which solves the problems of the ConvR model and significantly improves the prediction performance.Finally,this paper conducts sufficient experiments in multiple datasets to verify the superiority of the model and the rationality of each module.展开更多
The global Internet is a complex network of interconnected autonomous systems(ASes).Understanding Internet inter-domain path information is crucial for understanding,managing,and improving the Internet.The path inform...The global Internet is a complex network of interconnected autonomous systems(ASes).Understanding Internet inter-domain path information is crucial for understanding,managing,and improving the Internet.The path information can also help protect user privacy and security.However,due to the complicated and heterogeneous structure of the Internet,path information is not publicly available.Obtaining path information is challenging due to the limited measurement probes and collectors.Therefore,inferring Internet inter-domain paths from the limited data is a supplementary approach to measure Internet inter-domain paths.The purpose of this survey is to provide an overview of techniques that have been conducted to infer Internet inter-domain paths from 2005 to 2023 and present the main lessons from these studies.To this end,we summarize the inter-domain path inference techniques based on the granularity of the paths,for each method,we describe the data sources,the key ideas,the advantages,and the limitations.To help readers understand the path inference techniques,we also summarize the background techniques for path inference,such as techniques to measure the Internet,infer AS relationships,resolve aliases,and map IP addresses to ASes.A case study of the existing techniques is also presented to show the real-world applications of inter-domain path inference.Additionally,we discuss the challenges and opportunities in inferring Internet inter-domain paths,the drawbacks of the state-of-the-art techniques,and the future directions.展开更多
As one of the most valuable assets in China,traditional medicine has a long history and contains pieces of knowledge.The diagnosis and treatment of Traditional Chinese Medicine(TCM)has benefited from the natural langu...As one of the most valuable assets in China,traditional medicine has a long history and contains pieces of knowledge.The diagnosis and treatment of Traditional Chinese Medicine(TCM)has benefited from the natural language processing technology.This paper proposes a knowledge-based syndrome reasoning method in computer-assisted diagnosis.This method is based on the established knowledge graph of TCM and this paper introduces the reinforcement learning algorithm to mine the hidden relationship among the entities and obtain the reasoning path.According to this reasoning path,we could infer the path from the symptoms to the syndrome and get all possibilities via the relationship between symptoms and causes.Moreover,this study applies the Term Frequency-Inverse Document Frequency(TF-IDF)idea to the computer-assisted diagnosis of TCM for the score of syndrome calculation.Finally,combined with symptoms,syndrome,and causes,the disease could be confirmed comprehensively by voting,and the experiment shows that the system can help doctors and families to disease diagnosis effectively.展开更多
Learning and self-adaptation ability is highly required to be integrated in path planning algorithm for underwater robot during navigation through an unspecified underwater environment. High frequency oscillations dur...Learning and self-adaptation ability is highly required to be integrated in path planning algorithm for underwater robot during navigation through an unspecified underwater environment. High frequency oscillations during underwater motion are responsible for nonlinearities in dynamic behavior of underwater robot as well as uncertainties in hydrodynamic coefficients. Reactive behaviors of underwater robot are designed considering the position and orientation of both target and nearest obstacle from robot s current position. Human like reasoning power and approximation based learning skill of neural based adaptive fuzzy inference system（ANFIS）has been found to be effective for underwater multivariable motion control. More than one ANFIS models are used here for achieving goal and obstacle avoidance while avoiding local minima situation in both horizontal and vertical plane of three dimensional workspace.An error gradient approach based on input-output training patterns for learning purpose has been promoted to spawn trajectory of underwater robot optimizing path length as well as time taken. The simulation and experimental results endorse sturdiness and viability of the proposed method in comparison with other navigational methodologies to negotiate with hectic conditions during motion of underwater mobile robot.展开更多
基金This work was supported by the National Key R&D Program of China under Grant No.20201710200.
文摘Link prediction is used to complete the knowledge graph.Convolu-tional neural network models are commonly used for link prediction tasks,but they only consider the direct relations between entity pairs,ignoring the semantic information contained in the relation paths.In addition,the embedding dimension of the relation is generally larger than that of the entity in the ConvR model,which blocks the progress of downstream tasks.If we reduce the embedding dimension of the relation,the performance will be greatly degraded.This paper proposes a convolutional model PITri-R-ConvR based on triangular structure relational infer-ence.The model uses relational path inference to capture semantic information,while using a triangular structure to ensure the reliability and computational effi-ciency of relational inference.In addition,the decoder R-ConvR improves the initial embedding of the ConvR model,which solves the problems of the ConvR model and significantly improves the prediction performance.Finally,this paper conducts sufficient experiments in multiple datasets to verify the superiority of the model and the rationality of each module.
基金the China Postdoctoral Science Foundation(2023TQ0089)the National Natural Science Foundation of China(Nos.62072465,62172155)the Science and Technology Innovation Program of Hunan Province(Nos.2022RC3061,2023RC3027).
文摘The global Internet is a complex network of interconnected autonomous systems(ASes).Understanding Internet inter-domain path information is crucial for understanding,managing,and improving the Internet.The path information can also help protect user privacy and security.However,due to the complicated and heterogeneous structure of the Internet,path information is not publicly available.Obtaining path information is challenging due to the limited measurement probes and collectors.Therefore,inferring Internet inter-domain paths from the limited data is a supplementary approach to measure Internet inter-domain paths.The purpose of this survey is to provide an overview of techniques that have been conducted to infer Internet inter-domain paths from 2005 to 2023 and present the main lessons from these studies.To this end,we summarize the inter-domain path inference techniques based on the granularity of the paths,for each method,we describe the data sources,the key ideas,the advantages,and the limitations.To help readers understand the path inference techniques,we also summarize the background techniques for path inference,such as techniques to measure the Internet,infer AS relationships,resolve aliases,and map IP addresses to ASes.A case study of the existing techniques is also presented to show the real-world applications of inter-domain path inference.Additionally,we discuss the challenges and opportunities in inferring Internet inter-domain paths,the drawbacks of the state-of-the-art techniques,and the future directions.
基金Supported by the National Key Research and Development Program of China under Grant 2017YFB1002304 and the National Natural Science Foundation of China(No.61672178)The author who received the grant is Azguri,and the official website of the funder is http://www.most.gov.cn/.
文摘As one of the most valuable assets in China,traditional medicine has a long history and contains pieces of knowledge.The diagnosis and treatment of Traditional Chinese Medicine(TCM)has benefited from the natural language processing technology.This paper proposes a knowledge-based syndrome reasoning method in computer-assisted diagnosis.This method is based on the established knowledge graph of TCM and this paper introduces the reinforcement learning algorithm to mine the hidden relationship among the entities and obtain the reasoning path.According to this reasoning path,we could infer the path from the symptoms to the syndrome and get all possibilities via the relationship between symptoms and causes.Moreover,this study applies the Term Frequency-Inverse Document Frequency(TF-IDF)idea to the computer-assisted diagnosis of TCM for the score of syndrome calculation.Finally,combined with symptoms,syndrome,and causes,the disease could be confirmed comprehensively by voting,and the experiment shows that the system can help doctors and families to disease diagnosis effectively.
文摘Learning and self-adaptation ability is highly required to be integrated in path planning algorithm for underwater robot during navigation through an unspecified underwater environment. High frequency oscillations during underwater motion are responsible for nonlinearities in dynamic behavior of underwater robot as well as uncertainties in hydrodynamic coefficients. Reactive behaviors of underwater robot are designed considering the position and orientation of both target and nearest obstacle from robot s current position. Human like reasoning power and approximation based learning skill of neural based adaptive fuzzy inference system（ANFIS）has been found to be effective for underwater multivariable motion control. More than one ANFIS models are used here for achieving goal and obstacle avoidance while avoiding local minima situation in both horizontal and vertical plane of three dimensional workspace.An error gradient approach based on input-output training patterns for learning purpose has been promoted to spawn trajectory of underwater robot optimizing path length as well as time taken. The simulation and experimental results endorse sturdiness and viability of the proposed method in comparison with other navigational methodologies to negotiate with hectic conditions during motion of underwater mobile robot.
