X. Wang

China has a longstanding tradition of stressing the values of harmony and coherence, and Chinese society has often been portrayed as a culture in which conflict avoidance is viewed more positively than direct confrontation and argumentation. In order to evaluate the validity of this claim, this paper sketches Chinese people’s feelings and understandings about interpersonal arguing by reporting results of a data collection in China, using measures of argumentativeness, verbal aggressiveness, argument frames, and personalization of conflict. These results were compared to those from a US sample. Chinese and US data differed in complex ways, but did not show Chinese respondents to be more avoidant. The Chinese correlations among variables were a reasonable match to expectations based on Western argumentation theories, although they did not replicate the US results precisely. The paper offers evidence that Chinese respondents had a more sophisticated understanding of interpersonal arguing than their US counterparts, and were more sensitive to the constructive possibilities of face-to-face disagreement

This paper presents a theoretical framework of the circular shift network coding system through the study of nonmultiple clustered interval music performance style conversion and the analysis of music conversion by using circular shift topology, and a series of basic research results of circular shift network coding is obtained under this framework. It reveals the essential connection between scalar network coding based on finite domain and cyclic shift network coding, designs a solution construction algorithm for cyclic shift network coding under multicast network, and portrays the multicast capacity of cyclic shift network coding. It overcomes the problem that the piano roll-curtain representation cannot distinguish between a single long note and multiple consecutive notes of the same pitch, describes musical information more comprehensively, extracts musical implicit style from the note matrix based on autoencoder, and better eliminates the potential influence of musical content on musical performance style. A two-way recurrent neural network based on the gated recurrent unit is used to extract a sequence of note feature vectors of different styles, and a one-dimensional convolutional neural network is used to predict the intensity of the extracted note feature vector sequence for a specific style, which better learns the intensity variation of different styles of MIDI music.

Online social network services have brought a kind of new lifestyle to the world that is parallel to people’s daily offline activities. Social network analysis provides a useful perspective on a range of social computing applications. Social interaction on the Web includes both positive and negative relationships, which is certainly important to social networks. The authors of this article found that the accuracy of the signs of links in the underlying social networks can be predicted. The trust that other users impart on a node is an important attribute of networks. In this article, the authors present a model to compute the prestige of nodes in a trust-based network. The model is based on the idea that trustworthy nodes weigh more. To fulfill this task, the authors first attempt to infer the attitude of one user toward another by predicting signed edges in networks. Then, the authors propose an algorithm to compute the prestige and trustworthiness where the edge weight denotes the trust score. To prove the algorithm’s effectiveness, the authors conducted experiments on the public dataset. Theoretical analysis and experimental results show that this method is efficient and effective.