Business Communication in the People's Republic of China

This is a seminal statement on the pre-eminent position held by business communication in China's largest business school—specializing in interna tional trade—the Beijing Institute of Foreign Trade. The authors provide some historical background, review three courses in business communica tion in China, summarize the method of instruction, and end with con cludions and opportunities for closer academic ties with China in making business communication a truly international discipline.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/68578/2/10.1177_002194368302000103.pd

Communication in Foreign Trade: A Broader Concept for Business Communication in China

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/67435/2/10.1177_108056998604900211.pd

Cross-database micro-expression recognition based on a dual-stream convolutional neural network

Abstract Cross-database micro-expression recognition (CDMER) is a difficult task, where the target (testing) and source (training) samples come from different micro-expression (ME) databases, resulting in the inconsistency of the feature distributions between each other, and hence affecting the performance of many existing MER methods. To address this problem, we propose a dual-stream convolutional neural network (DSCNN) for dealing with CDMER tasks. In the DSCNN, two stream branches are designed to study temporal and facial region cues in ME samples with the goal of recognizing MEs. In addition, in the training process, the domain discrepancy loss is used to enforce the target and source samples to have similar feature distributions in some layers of the DSCNN. Extensive CDMER experiments are conducted to evaluate the DSCNN. The results show that our proposed DSCNN model achieves a higher recognition accuracy when compared with some representative CDMER methods

Recognizing spontaneous micro-expression using a three-stream convolutional neural network

Abstract Micro-expression recognition (MER) has attracted much attention with various practical applications, particularly in clinical diagnosis and interrogations. In this paper, we propose a three-stream convolutional neural network (TSCNN) to recognize MEs by learning ME-discriminative features in three key frames of ME videos. We design a dynamic-temporal stream, static-spatial stream, and local-spatial stream module for the TSCNN that respectively attempt to learn and integrate temporal, entire facial region, and facial local region cues in ME videos with the goal of recognizing MEs. In addition, to allow the TSCNN to recognize MEs without using the index values of apex frames, we design a reliable apex frame detection algorithm. Extensive experiments are conducted with five public ME databases: CASME II, SMIC-HS, SAMM, CAS(ME) 2, and CASME. Our proposed TSCNN is shown to achieve more promising recognition results when compared with many other methods