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The objective of this paper is to examine how a firm’s participation in government export promotion programs (epps) may lead to a better export performance. Based on dynamic capabilities perspective, a mediated moderation model of epps on export performance was proposed and tested in Chinese manufacturing smes. The results show marketing implementation capabilities mediate the effect of information-related programs on export performance, and the financial aid-related epps moderate the process. The results contribute to the studies relating epps and export performance, as the results confirm the instrumental role of epps in enhancing export performance and examine the interplay between different types of epps. This study provides a guideline for managers as to how they can benefit from government epps. The findings also imply that policy makers should develop epps with a specific emphasis rather than a general goal of export performance. This study develops new insights on how export ventures exploit epps to develop useful capabilities. Also, the study expands current thinking on exporting by recognizing that different types of epps affect exporting.

Spontaneous speech emotion recognition is a new and challenging research topic. In this paper, we propose a new method of spontaneous speech emotion recognition on the basis of binaural representations and deep convolutional neural networks (CNNs). The proposed method initially employs multiple CNNs to learn deep segment-level binaural representations such as Left-Right and Mid-Side pairs from the extracted image-like Mel-spectrograms. These CNNs are fine-tuned on target emotional speech datasets from a pre-trained image CNN model. Then, a new feature pooling strategy, called block-based temporal feature pooling, is proposed to aggregate the learned segment-level features for producing fixedlength utterance-level features. Based on the utterance-level features, linear support vector machines (SVM) is adopted for emotion classification. Finally, a two-stage score-level fusion strategy is used to integrate the obtained results from Left-Right and Mid-Side pairs. Extensive experiments on two challenging spontaneous emotional speech datasets, including the AFEW5.0 and BAUM-1s databases, demonstrate the effectiveness of our proposed method.INDEX TERMS Spontaneous speech emotion recognition, binaural representations, deep convolutional neural networks, temporal feature pooling.

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This study examined the effects of integrating writing strategy training into EFL writing instruction on learners’ strategy use and writing performance. Two classes of EFL adult learners participated in this study. The experimental group were instructed with the writing strategy training in their EFL writing class for 14 weeks, whereas the control group received the same EFL writing program without any explicit strategy training.  Mixed methods were applied in collecting data. The quantitative instruments included the writing strategy questionnaires and writing performance tests, which were both pre-tested and post-tested in the both groups. The qualitative instruments of reflective journals were also conducted in the experimental group to probe deeper insights into learners’ strategy changes. The findings showed that there were significantly positive differences in learners’ using writing strategies and in writing proficiency favoring the experimental group.  The findings of this study indicate that writing strategy training can be integrated in the EFL writing instruction, and can bring to positive impacts for learners’ strategic awareness and writing strategy use as well as their writing performance. The paper suggested the strong need to the process-based writing instruction, and writing strategy training holds promise in this regard.

Compared with ordinary single exposure images, multi-exposure fusion (MEF) images are prone to color imbalance, detail information loss and abnormal exposure in the process of combining multiple images with different exposure levels. In this paper, we proposed a human visual perception-based multi-exposure fusion image quality assessment method by considering the related perceptual features (i.e., color, dense scale invariant feature transform (DSIFT) and exposure) to measure the quality degradation accurately, which is closely related to the symmetry principle in human eyes. Firstly, the L1 norm of chrominance components between fused images and the designed pseudo images with the most severe color attenuation is calculated to measure the global color degradation, and the color saturation similarity is added to eliminate the influence of color over-saturation. Secondly, a set of distorted images under different exposure levels with strong edge information of fused image is constructed through the structural transfer, thus DSIFT similarity and DSIFT saturation are computed to measure the local detail loss and enhancement, respectively. Thirdly, Gauss exposure function is used to detect the over-exposure or under-exposure areas, and the above perceptual features are aggregated with random forest to predict the final quality of fused image. Experimental results on a public MEF subjective assessment database show the superiority of the proposed method with the state-of-the-art image quality assessment models.

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