Subjects: Computer Science >> Integration Theory of Computer Science submitted time 2018-06-19 Cooperative journals: 《计算机应用研究》
Abstract： Aiming at the increasingly rich multi language information resources and multi-label data in news reports and scientific literatures, in order to mining the relevance between languages and the correlation between data, this paper proposed labeled bilingual topic model, applied on cross-lingual text classification and label recommendation. First of all, it could assume that the keywords in the scientific literature are relevant to the Abstract: in same article, then extracted the keywords and regarded it as labels, and aligned the labels with topics in topic model, instantiated the “latent” topic. Secondly, trained the Abstract: in article through the topic model proposed by this paper. Finally, classified the new documents by cross-lingual text classifier, also recommended the labels. The experiment result show that Micro-F1 measure reaches 94.81% in cross-lingual text classification task, and the recommended labels also reflects the sematic relevance with documents.
Subjects: Computer Science >> Integration Theory of Computer Science submitted time 2018-05-24 Cooperative journals: 《计算机应用研究》
Abstract： Pedestrian crossing delay in signal intersection is an important measure to evaluate traffic efficiency and service level. By analyzing the statistical features of the headway which change the traffic flow, this paper proposed a formula for calculating the delay of pedestrian crossing based on the headway distribution adaptive changing. First of all, according to the pedestrian crossing delay under the low traffic flow circumstance in which the headway obey negative exponential distribution, the paper derived the formulas for calculating the pedestrian crossing delay under the middle traffic flow circumstance and the high traffic flow circumstance. Secondly, this paper obtained the total delay calculation model of pedestrian crossing by combining with both signal phase delay and bidirectional pedestrian friction delay. Finally, the accuracy of the model was verified through the Vissim simulation experiment, and the deviation was less than 3%. In addition, this paper also compared the deviation between the pedestrian crossing delay model proposed in this paper and the model on the premise that the headway obeys negative exponential distribution , the results show that the deviation of the delay model proposed in this paper is smaller.
Subjects: Computer Science >> Integration Theory of Computer Science submitted time 2018-05-20 Cooperative journals: 《计算机应用研究》
Abstract： Aiming at the problem that the lack of accurate and efficient off-topic detection algorithm for the current English composition teaching system in China, proposed an off-topic detection algorithm of LDA and word2vec in this paper. The algorithm used LDA to model the documents and train it with word2vec, with obtained semantic relation between document's topic and words, calculated the probability weighted sum of each topic and its feature words in the document. Finally, by setting reasonable threshold, selected the off-topic essays. According to the different F values for the different number of topics in the document, determined the optimum number of topics in the experiment. The experimental results show that, compared to traditional vector space model, the proposed method can detect more off-topic essays with higher accuracy, and the F value is above 89%, which realizes the intelligent processing of off-topic essays detection, and may applies effectively in English essays teaching.
Subjects: Computer Science >> Integration Theory of Computer Science submitted time 2018-04-12 Cooperative journals: 《计算机应用研究》
Abstract： Aiming at the low efficiency caused by massive academic paper data, this paper proposed a recommendation algorithm method based on the hierarchical mixed model named WSVD++. According to the structural features of academic papers, the model constructs a weighted bipartite graph model. Firstly, this method extracted the features of each paper and constructs the composite relation graph according to the ratio of different features. Secondly, it uses an improved PPR algorithm on the graph to calculate the importance weight of each paper, and then weighs the relation between the user and the paper. Finally, it recommend on the weighted bipartite graph by using SVD++ graph algorithm. The result shows that the proposed algorithm improves the recommended accuracy. The whole process implemented in distributed graph calculation system, that means the method has good expansibility and is suitable for big data processing.
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