The aim of this paper is to categorify the n-th tensor power of the vector representation of U（ ο（7,C））. The main tools are certain singular blocks and projective functors of the BGG category of the complex Lie a...The aim of this paper is to categorify the n-th tensor power of the vector representation of U（ ο（7,C））. The main tools are certain singular blocks and projective functors of the BGG category of the complex Lie algebra gln.展开更多
Action model learning has become a hot topic in knowledge engineering for automated planning.A key problem for learning action models is to analyze state changes before and after action executions from observed"p...Action model learning has become a hot topic in knowledge engineering for automated planning.A key problem for learning action models is to analyze state changes before and after action executions from observed"plan traces".To support such an analysis,a new approach is proposed to partition propositions of plan traces into states.First,vector representations of propositions and actions are obtained by training a neural network called Skip-Gram borrowed from the area of natural language processing(NLP).Then,a type of semantic distance among propositions and actions is defined based on their similarity measures in the vector space.Finally,k-means and k-nearest neighbor(kNN)algorithms are exploited to map propositions to states.This approach is called state partition by word vector(SPWV),which is implemented on top of a recent action model learning framework by Rao et al.Experimental results on the benchmark domains show that SPWV leads to a lower error rate of the learnt action model,compared to the probability based approach for state partition that was developed by Rao et al.展开更多
Linxia brick carving is an artistic carrier of multi-ethnic cultural intermingling,but its symbolic abstraction and diversity make digital conservation challenging.Currently,the traditional qualitative recording metho...Linxia brick carving is an artistic carrier of multi-ethnic cultural intermingling,but its symbolic abstraction and diversity make digital conservation challenging.Currently,the traditional qualitative recording methods are unable to realize dynamic analysis and innovative applications.This study builds a framework for the integration of vector representation and multimodal semantic mapping,and uses that framework to quantify the historical semantics,artistic fusion,and technological features of Linxia brick carving cultural heritage by constructing a 26-dimensional vector space.This approach allowed us to solve the semantic heterogeneity of the textual-image data through the help of structured descriptive templates.The results show that this framework can support the systematic analysis and innovation of Linxia brick carving cultural symbols with high classification accuracy and reveal the structured semantic association of patterns.This study realizes the transformation of abstract symbols to computable values through the generalized 26-dimensional vectors,and can use standardized templates to regulate their digital expressions,depending on multimodal data sets that establish the multidimensional innovation of artificial intelligence-driven protection mechanisms.The results can provide methodological support for the shift in cultural heritage from static records to living inheritance,and demonstrate potential transferability to analogous heritage contexts through dimensional remapping and template localization strategies.These advances can promote the deep integration of artificial intelligence and traditional art symbols,and thus support research on the protection strategies for traditional cultural heritage in the era of digitalization.展开更多
Text analysis is a popular technique for finding the most significant information from texts including semantic,emotional,and other hidden features,which became a research hotspot in the last few years.Specially,there...Text analysis is a popular technique for finding the most significant information from texts including semantic,emotional,and other hidden features,which became a research hotspot in the last few years.Specially,there are some text analysis tasks with judgment reports,such as analyzing the criminal process and predicting prison terms.Traditional researches on text analysis are generally based on special feature selection and ontology model generation or require legal experts to provide external knowledge.All these methods require a lot of time and labor costs.Therefore,in this paper,we use textual data such as judgment reports creatively to perform prison term prediction without external legal knowledge.We propose a framework that combines value-based rules and a fuzzy text to predict the target prison term.The procedure in our framework includes information extraction,term fuzzification,and document vector regression.We carry out experiments with real-world judgment reports and compare our model’s performance with those of ten traditional classification and regression models and two deep learning models.The results show that our model achieves competitive results compared with other models as evaluated by the RMSE and R-squared metrics.Finally,we implement a prototype system with a user-friendly GUI that can be used to predict prison terms according to the legal text inputted by the user.展开更多
With the growing popularity of somatosensory interaction devices,human action recognition is becoming attractive in many application scenarios.Skeleton-based action recognition is effective because the skeleton can re...With the growing popularity of somatosensory interaction devices,human action recognition is becoming attractive in many application scenarios.Skeleton-based action recognition is effective because the skeleton can represent the position and the structure of key points of the human body.In this paper,we leverage spatiotemporal vectors between skeleton sequences as input feature representation of the network,which is more sensitive to changes of the human skeleton compared with representations based on distance and angle features.In addition,we redesign residual blocks that have different strides in the depth of the network to improve the processing ability of the temporal convolutional networks(TCNs)for long time dependent actions.In this work,we propose the two-stream temporal convolutional networks(TSTCNs)that take full advantage of the inter-frame vector feature and the intra-frame vector feature of skeleton sequences in the spatiotemporal representations.The framework can integrate different feature representations of skeleton sequences so that the two feature representations can make up for each other’s shortcomings.The fusion loss function is used to supervise the training parameters of the two branch networks.Experiments on public datasets show that our network achieves superior performance and attains an improvement of 1.2%over the recent GCN-based(BGC-LSTM)method on the NTU RGB+D dataset.展开更多
基金Supported by the Natural Science Foundation of Beijing(Grant No.1122006)the Specialized Research Fund for the Doctoral Program of Higher Education of China(Grant No.201111103110011)Science and Technology Foundation of BJUT(Grant No.ykj-4787)
文摘The aim of this paper is to categorify the n-th tensor power of the vector representation of U（ ο（7,C））. The main tools are certain singular blocks and projective functors of the BGG category of the complex Lie algebra gln.
基金Supported by the National Natural Science Foundation of China(61103136,61370156,61503074)Open Research Foundation of Science and Technology on Aerospace Flight Dynamics Laboratory(2014afdl002)
文摘Action model learning has become a hot topic in knowledge engineering for automated planning.A key problem for learning action models is to analyze state changes before and after action executions from observed"plan traces".To support such an analysis,a new approach is proposed to partition propositions of plan traces into states.First,vector representations of propositions and actions are obtained by training a neural network called Skip-Gram borrowed from the area of natural language processing(NLP).Then,a type of semantic distance among propositions and actions is defined based on their similarity measures in the vector space.Finally,k-means and k-nearest neighbor(kNN)algorithms are exploited to map propositions to states.This approach is called state partition by word vector(SPWV),which is implemented on top of a recent action model learning framework by Rao et al.Experimental results on the benchmark domains show that SPWV leads to a lower error rate of the learnt action model,compared to the probability based approach for state partition that was developed by Rao et al.
基金The 2022 General Project of Gansu Provincial Philosophy and Social Sciences Planning(2022YB034)。
文摘Linxia brick carving is an artistic carrier of multi-ethnic cultural intermingling,but its symbolic abstraction and diversity make digital conservation challenging.Currently,the traditional qualitative recording methods are unable to realize dynamic analysis and innovative applications.This study builds a framework for the integration of vector representation and multimodal semantic mapping,and uses that framework to quantify the historical semantics,artistic fusion,and technological features of Linxia brick carving cultural heritage by constructing a 26-dimensional vector space.This approach allowed us to solve the semantic heterogeneity of the textual-image data through the help of structured descriptive templates.The results show that this framework can support the systematic analysis and innovation of Linxia brick carving cultural symbols with high classification accuracy and reveal the structured semantic association of patterns.This study realizes the transformation of abstract symbols to computable values through the generalized 26-dimensional vectors,and can use standardized templates to regulate their digital expressions,depending on multimodal data sets that establish the multidimensional innovation of artificial intelligence-driven protection mechanisms.The results can provide methodological support for the shift in cultural heritage from static records to living inheritance,and demonstrate potential transferability to analogous heritage contexts through dimensional remapping and template localization strategies.These advances can promote the deep integration of artificial intelligence and traditional art symbols,and thus support research on the protection strategies for traditional cultural heritage in the era of digitalization.
基金support of the Science&Technology Development Project of Hangzhou Province,China(Grant No.20162013A08)the Research Project Support for Education of Zhejiang Province,China(Grant No.Y201941372)。
文摘Text analysis is a popular technique for finding the most significant information from texts including semantic,emotional,and other hidden features,which became a research hotspot in the last few years.Specially,there are some text analysis tasks with judgment reports,such as analyzing the criminal process and predicting prison terms.Traditional researches on text analysis are generally based on special feature selection and ontology model generation or require legal experts to provide external knowledge.All these methods require a lot of time and labor costs.Therefore,in this paper,we use textual data such as judgment reports creatively to perform prison term prediction without external legal knowledge.We propose a framework that combines value-based rules and a fuzzy text to predict the target prison term.The procedure in our framework includes information extraction,term fuzzification,and document vector regression.We carry out experiments with real-world judgment reports and compare our model’s performance with those of ten traditional classification and regression models and two deep learning models.The results show that our model achieves competitive results compared with other models as evaluated by the RMSE and R-squared metrics.Finally,we implement a prototype system with a user-friendly GUI that can be used to predict prison terms according to the legal text inputted by the user.
基金The work was supported by the National Natural Science Foundation(NSFC)-Zhejiang Joint Fund of the Integration of Informatization and Industrialization of China under Grant Nos.U1909210 and U1609218the National Natural Science Foundation of China under Grant No.61772312the Key Research and Development Project of Shandong Province of China under Grant No.2017GGX10110.
文摘With the growing popularity of somatosensory interaction devices,human action recognition is becoming attractive in many application scenarios.Skeleton-based action recognition is effective because the skeleton can represent the position and the structure of key points of the human body.In this paper,we leverage spatiotemporal vectors between skeleton sequences as input feature representation of the network,which is more sensitive to changes of the human skeleton compared with representations based on distance and angle features.In addition,we redesign residual blocks that have different strides in the depth of the network to improve the processing ability of the temporal convolutional networks(TCNs)for long time dependent actions.In this work,we propose the two-stream temporal convolutional networks(TSTCNs)that take full advantage of the inter-frame vector feature and the intra-frame vector feature of skeleton sequences in the spatiotemporal representations.The framework can integrate different feature representations of skeleton sequences so that the two feature representations can make up for each other’s shortcomings.The fusion loss function is used to supervise the training parameters of the two branch networks.Experiments on public datasets show that our network achieves superior performance and attains an improvement of 1.2%over the recent GCN-based(BGC-LSTM)method on the NTU RGB+D dataset.
