小枝模式查询是XML查询中重要的操作,已经有许多种算法提出,如TwigStack和TJFast算法等,但是他们都是基于归并思想的,不能避免大量的不必要的路径归并.本文提出的TwigWM(Twig Without Merging)算法使用部分栈与链表的结构来实现非归并查...小枝模式查询是XML查询中重要的操作,已经有许多种算法提出,如TwigStack和TJFast算法等,但是他们都是基于归并思想的,不能避免大量的不必要的路径归并.本文提出的TwigWM(Twig Without Merging)算法使用部分栈与链表的结构来实现非归并查询,由于从扩展Dewey编码中能够直接得到祖先元素结点的编码,所以TwigWM算法采用扩展Dewey编码.实验结果表明,TwigWM算法要优于TJFast、Twig2Stack等算法.展开更多
John Dewey was a pragmatic philosopher,psychologist,and educator commonly regarded as the founder of the pro gressive education movement.His philosophy of pragmatic education not only exerts great influence on Western...John Dewey was a pragmatic philosopher,psychologist,and educator commonly regarded as the founder of the pro gressive education movement.His philosophy of pragmatic education not only exerts great influence on Western education,but also on China's education.This paper attempts to make a detailed analysis of Dewey's philosophy of education and its value.展开更多
Purpose:With more and more digital collections of various information resources becoming available,also increasing is the challenge of assigning subject index terms and classes from quality knowledge organization syst...Purpose:With more and more digital collections of various information resources becoming available,also increasing is the challenge of assigning subject index terms and classes from quality knowledge organization systems.While the ultimate purpose is to understand the value of automatically produced Dewey Decimal Classification(DDC)classes for Swedish digital collections,the paper aims to evaluate the performance of six machine learning algorithms as well as a string-matching algorithm based on characteristics of DDC.Design/methodology/approach:State-of-the-art machine learning algorithms require at least 1,000 training examples per class.The complete data set at the time of research involved 143,838 records which had to be reduced to top three hierarchical levels of DDC in order to provide sufficient training data(totaling 802 classes in the training and testing sample,out of 14,413 classes at all levels).Findings:Evaluation shows that Support Vector Machine with linear kernel outperforms other machine learning algorithms as well as the string-matching algorithm on average;the string-matching algorithm outperforms machine learning for specific classes when characteristics of DDC are most suitable for the task.Word embeddings combined with different types of neural networks(simple linear network,standard neural network,1 D convolutional neural network,and recurrent neural network)produced worse results than Support Vector Machine,but reach close results,with the benefit of a smaller representation size.Impact of features in machine learning shows that using keywords or combining titles and keywords gives better results than using only titles as input.Stemming only marginally improves the results.Removed stop-words reduced accuracy in most cases,while removing less frequent words increased it marginally.The greatest impact is produced by the number of training examples:81.90%accuracy on the training set is achieved when at least 1,000 records per class are available in the training set,and 66.13%when too few records(often less than A Comparison of Approaches100 per class)on which to train are available—and these hold only for top 3 hierarchical levels(803 instead of 14,413 classes).Research limitations:Having to reduce the number of hierarchical levels to top three levels of DDC because of the lack of training data for all classes,skews the results so that they work in experimental conditions but barely for end users in operational retrieval systems.Practical implications:In conclusion,for operative information retrieval systems applying purely automatic DDC does not work,either using machine learning(because of the lack of training data for the large number of DDC classes)or using string-matching algorithm(because DDC characteristics perform well for automatic classification only in a small number of classes).Over time,more training examples may become available,and DDC may be enriched with synonyms in order to enhance accuracy of automatic classification which may also benefit information retrieval performance based on DDC.In order for quality information services to reach the objective of highest possible precision and recall,automatic classification should never be implemented on its own;instead,machine-aided indexing that combines the efficiency of automatic suggestions with quality of human decisions at the final stage should be the way for the future.Originality/value:The study explored machine learning on a large classification system of over 14,000 classes which is used in operational information retrieval systems.Due to lack of sufficient training data across the entire set of classes,an approach complementing machine learning,that of string matching,was applied.This combination should be explored further since it provides the potential for real-life applications with large target classification systems.展开更多
The idea of Philosophy for Children （P4C） initiated by Matthew Lipman aims to foster critical and creative thinking in children through the pedagogy of a community of inquiry. In his formulation of P4C, Lipman empha...The idea of Philosophy for Children （P4C） initiated by Matthew Lipman aims to foster critical and creative thinking in children through the pedagogy of a community of inquiry. In his formulation of P4C, Lipman emphasizes the role of logical reasoning in thinking and assumes a mutually reinforcing relationship between critical and creative thinking. In this paper, I present an example of a real classroom dialogue which illustrates the inherent tension between logical and creative thinking, as well as the need to go beyond critical thinking. I then proceed to argue for the importance of communication in creating and sustaining a genuine community of inquiry. In conclusion, I suggest that John Dewey＇s view of communication as essentially transformative, aesthetic, educative, and moral can be made the basis for envisioning an alternative focus of P4C--namely, the ideal of artful communication, which has far-reaching implications for realizing the democratic idea of ＂community＂ in a community of inquiry.展开更多
文摘小枝模式查询是XML查询中重要的操作,已经有许多种算法提出,如TwigStack和TJFast算法等,但是他们都是基于归并思想的,不能避免大量的不必要的路径归并.本文提出的TwigWM(Twig Without Merging)算法使用部分栈与链表的结构来实现非归并查询,由于从扩展Dewey编码中能够直接得到祖先元素结点的编码,所以TwigWM算法采用扩展Dewey编码.实验结果表明,TwigWM算法要优于TJFast、Twig2Stack等算法.
文摘John Dewey was a pragmatic philosopher,psychologist,and educator commonly regarded as the founder of the pro gressive education movement.His philosophy of pragmatic education not only exerts great influence on Western education,but also on China's education.This paper attempts to make a detailed analysis of Dewey's philosophy of education and its value.
文摘Purpose:With more and more digital collections of various information resources becoming available,also increasing is the challenge of assigning subject index terms and classes from quality knowledge organization systems.While the ultimate purpose is to understand the value of automatically produced Dewey Decimal Classification(DDC)classes for Swedish digital collections,the paper aims to evaluate the performance of six machine learning algorithms as well as a string-matching algorithm based on characteristics of DDC.Design/methodology/approach:State-of-the-art machine learning algorithms require at least 1,000 training examples per class.The complete data set at the time of research involved 143,838 records which had to be reduced to top three hierarchical levels of DDC in order to provide sufficient training data(totaling 802 classes in the training and testing sample,out of 14,413 classes at all levels).Findings:Evaluation shows that Support Vector Machine with linear kernel outperforms other machine learning algorithms as well as the string-matching algorithm on average;the string-matching algorithm outperforms machine learning for specific classes when characteristics of DDC are most suitable for the task.Word embeddings combined with different types of neural networks(simple linear network,standard neural network,1 D convolutional neural network,and recurrent neural network)produced worse results than Support Vector Machine,but reach close results,with the benefit of a smaller representation size.Impact of features in machine learning shows that using keywords or combining titles and keywords gives better results than using only titles as input.Stemming only marginally improves the results.Removed stop-words reduced accuracy in most cases,while removing less frequent words increased it marginally.The greatest impact is produced by the number of training examples:81.90%accuracy on the training set is achieved when at least 1,000 records per class are available in the training set,and 66.13%when too few records(often less than A Comparison of Approaches100 per class)on which to train are available—and these hold only for top 3 hierarchical levels(803 instead of 14,413 classes).Research limitations:Having to reduce the number of hierarchical levels to top three levels of DDC because of the lack of training data for all classes,skews the results so that they work in experimental conditions but barely for end users in operational retrieval systems.Practical implications:In conclusion,for operative information retrieval systems applying purely automatic DDC does not work,either using machine learning(because of the lack of training data for the large number of DDC classes)or using string-matching algorithm(because DDC characteristics perform well for automatic classification only in a small number of classes).Over time,more training examples may become available,and DDC may be enriched with synonyms in order to enhance accuracy of automatic classification which may also benefit information retrieval performance based on DDC.In order for quality information services to reach the objective of highest possible precision and recall,automatic classification should never be implemented on its own;instead,machine-aided indexing that combines the efficiency of automatic suggestions with quality of human decisions at the final stage should be the way for the future.Originality/value:The study explored machine learning on a large classification system of over 14,000 classes which is used in operational information retrieval systems.Due to lack of sufficient training data across the entire set of classes,an approach complementing machine learning,that of string matching,was applied.This combination should be explored further since it provides the potential for real-life applications with large target classification systems.
文摘The idea of Philosophy for Children （P4C） initiated by Matthew Lipman aims to foster critical and creative thinking in children through the pedagogy of a community of inquiry. In his formulation of P4C, Lipman emphasizes the role of logical reasoning in thinking and assumes a mutually reinforcing relationship between critical and creative thinking. In this paper, I present an example of a real classroom dialogue which illustrates the inherent tension between logical and creative thinking, as well as the need to go beyond critical thinking. I then proceed to argue for the importance of communication in creating and sustaining a genuine community of inquiry. In conclusion, I suggest that John Dewey＇s view of communication as essentially transformative, aesthetic, educative, and moral can be made the basis for envisioning an alternative focus of P4C--namely, the ideal of artful communication, which has far-reaching implications for realizing the democratic idea of ＂community＂ in a community of inquiry.
