In the recent Smart Home(SH)research work,intelligent service recommendation technique based on behavior recognition,it has been extensively preferred by researchers.However,most current research uses the Semantic rec...In the recent Smart Home(SH)research work,intelligent service recommendation technique based on behavior recognition,it has been extensively preferred by researchers.However,most current research uses the Semantic recognition to construct the user’s basic behavior model.This method is usually restricted by environmental factors,the way these models are built makes it impossible for them to dynamically match the services that might be provided in the user environment.To solve this problem,this paper proposes a Semantic behavior assistance(Semantic behavior assistance,SBA).By joining the semantic model on the intelligent gateway,building an SA model,in this way,a logical Internet networks for smart home is established.At the same time,a behavior assistant method based on SBA model is proposed,among them,the user environment-related entities,sensors,devices,and user-related knowledge models exist in the logical interconnection network of the SH system through the semantic model.In this paper,the data simulation experiment is carried out on the method.The experimental results show that the SBA model is better than the knowledge-based pre-defined model.展开更多
传统范畴论与共代数等方法在分析语义行为与描述共归纳规则方面存在不足,应用Fibrations理论对程序语言中索引共归纳数据类型(indexed co-inductive data type,ICDT)进行了研究。通过基变换构造索引Fibration,建立索引Fibration的等式...传统范畴论与共代数等方法在分析语义行为与描述共归纳规则方面存在不足,应用Fibrations理论对程序语言中索引共归纳数据类型(indexed co-inductive data type,ICDT)进行了研究。通过基变换构造索引Fibration,建立索引Fibration的等式函子与商函子等工具,应用伴随性质与保持等式的提升深入分析ICDT的语义行为;以此为基础,构造ICDT上参数化的共递归操作,在Fibrations理论框架内抽象描述具有普适意义的共归纳规则,并以实例分析简要介绍Fibrations理论在ICDT中的应用。与传统研究方法相比,Fibrations理论具有简洁的描述性与灵活的扩展性,可以精确分析ICDT的语义行为,具有高度的抽象性且不依赖特定的计算环境,描述了ICDT具有普适意义的共归纳规则。展开更多
Current search engines in most geospatial data portals tend to induce users to focus on one single-data characteristic dimension(e.g.popularity and release date).This approach largely fails to take account of users’m...Current search engines in most geospatial data portals tend to induce users to focus on one single-data characteristic dimension(e.g.popularity and release date).This approach largely fails to take account of users’multidimensional preferences for geospatial data,and hence may likely result in a less than optimal user experience in discovering the most applicable dataset.This study reports a machine learning framework to address the ranking challenge,the fundamental obstacle in geospatial data discovery,by(1)identifying a number of ranking features of geospatial data to represent users’multidimensional preferences by considering semantics,user behavior,spatial similarity,and static dataset metadata attributes;(2)applying a machine learning method to automatically learn a ranking function;and(3)proposing a system architecture to combine existing search-oriented open source software,semantic knowledge base,ranking feature extraction,and machine learning algorithm.Results show that the machine learning approach outperforms other methods,in terms of both precision at K and normalized discounted cumulative gain.As an early attempt of utilizing machine learning to improve the search ranking in the geospatial domain,we expect this work to set an example for further research and open the door towards intelligent geospatial data discovery.展开更多
基金supported by the National Natural Science Foundation of China(61772196,61472136)the Hunan Provincial Focus Social Science Fund(2016ZDB006)+2 种基金Hunan Provincial Social Science Achievement Review Committee results in appraisal identification project(Xiang social assessment 2016JD05)Key Project of Hunan Provincial Social Science Achievement Review Committee(XSP 19ZD1005)financial support provided by the Key Laboratory of Hunan Province for New Retail Virtual Reality Technology(2017TP1026)。
文摘In the recent Smart Home(SH)research work,intelligent service recommendation technique based on behavior recognition,it has been extensively preferred by researchers.However,most current research uses the Semantic recognition to construct the user’s basic behavior model.This method is usually restricted by environmental factors,the way these models are built makes it impossible for them to dynamically match the services that might be provided in the user environment.To solve this problem,this paper proposes a Semantic behavior assistance(Semantic behavior assistance,SBA).By joining the semantic model on the intelligent gateway,building an SA model,in this way,a logical Internet networks for smart home is established.At the same time,a behavior assistant method based on SBA model is proposed,among them,the user environment-related entities,sensors,devices,and user-related knowledge models exist in the logical interconnection network of the SH system through the semantic model.In this paper,the data simulation experiment is carried out on the method.The experimental results show that the SBA model is better than the knowledge-based pre-defined model.
文摘传统范畴论与共代数等方法在分析语义行为与描述共归纳规则方面存在不足,应用Fibrations理论对程序语言中索引共归纳数据类型(indexed co-inductive data type,ICDT)进行了研究。通过基变换构造索引Fibration,建立索引Fibration的等式函子与商函子等工具,应用伴随性质与保持等式的提升深入分析ICDT的语义行为;以此为基础,构造ICDT上参数化的共递归操作,在Fibrations理论框架内抽象描述具有普适意义的共归纳规则,并以实例分析简要介绍Fibrations理论在ICDT中的应用。与传统研究方法相比,Fibrations理论具有简洁的描述性与灵活的扩展性,可以精确分析ICDT的语义行为,具有高度的抽象性且不依赖特定的计算环境,描述了ICDT具有普适意义的共归纳规则。
基金NSF I/UCRC:[Grant Number IIP-1338925]NSF EarthCube:[Grant Number ICER-1540998]NASA AIST Program:[Grant Number NNX15AM85G].
文摘Current search engines in most geospatial data portals tend to induce users to focus on one single-data characteristic dimension(e.g.popularity and release date).This approach largely fails to take account of users’multidimensional preferences for geospatial data,and hence may likely result in a less than optimal user experience in discovering the most applicable dataset.This study reports a machine learning framework to address the ranking challenge,the fundamental obstacle in geospatial data discovery,by(1)identifying a number of ranking features of geospatial data to represent users’multidimensional preferences by considering semantics,user behavior,spatial similarity,and static dataset metadata attributes;(2)applying a machine learning method to automatically learn a ranking function;and(3)proposing a system architecture to combine existing search-oriented open source software,semantic knowledge base,ranking feature extraction,and machine learning algorithm.Results show that the machine learning approach outperforms other methods,in terms of both precision at K and normalized discounted cumulative gain.As an early attempt of utilizing machine learning to improve the search ranking in the geospatial domain,we expect this work to set an example for further research and open the door towards intelligent geospatial data discovery.
