Additional file 1 of The effect of psychological contract on turnover intention among nurses: a meta-analytic review

Supplementary Material

Parallelizing convolutional neural networks for action event recognition in surveillance videos

In order to deal with action recognition for large scale video data, this paper presents a MapReduce based parallel algorithm for SASTCNN, a sparse auto-combination spatio-temporal convolutional neural network. We design and implement a parallel matrix multiplication algorithm based on MapReduce. We use the MapReduce programming model to parallelize SASTCNN on a Hadoop platform. In order to take advantage of the computing power of multi-core CPU, the Map and Reduce processes of MapReduce are implemented using a multi-thread technique. A series of experiments on both WEIZMAN and KTH data sets are carried out. Compared with traditional serial algorithms, the feasibility, stability and correctness of the parallel SASTCNN are validated and a speedup in computation is obtained. Experimental results also show that the proposed method could provide more competitive results on the two data sets than other benchmark methods.This work was partially supported by NSFC under contract No. 61501451, by the scholarship from China Scholarship Council (CSC) under the Grant CSC No. 201606315022, and by the XMU-NU Joint Strategic Partnership Fund