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Mobile Cloud Computing (MCC) enables mobile devices to use resource providers other than mobile devices themselves to host the execution of mobile applications. Recent research shows that it is more suitable for mobile devices to offload complex real-time applications to the cloud formed by nearby mobile devices, referred to as the local mobile cloud,(More)
Mobile Cloud Computing (MCC) enables mobile devices to use resource providers other than mobile devices themselves to host the execution of mobile applications. Various mobile cloud architectures and scheduling algorithms have been studied recently. However, how to utilize MCC to enable mobile devices to run complex real-time applications while keeping high(More)
Free and open in nature, open cloud platforms have enjoyed their wide acceptance in academic institutions and enterprises. Fair and insightful comparisons of these available open cloud platforms, however, can still be a challenging task, mainly due to lack of an appropriate evaluation methodology. In this paper, we thus attempt to perform a quantitative(More)
The majority of methods for recognizing human actions are based on single-view video or multi-camera data. In this paper, we propose a novel multi-surface video analysis strategy. The video can be expressed as three-surface motion feature (3SMF) and spatio-temporal interest feature. 3SMF is extracted from the motion history image in three different video(More)
This paper explores how to improve BOW model for human action recognition in real environment. Traditional codebook learning uses single appearance based local features, thus spatial and temporal correlations of local features are ignored. This leads to a considerable amount of mismatch between sample vectors and noisy visual words resulted from background(More)
Mobile Entertainment is increasing as recent advances in mobile terminal, mobile technologies and mobile commerce. Understanding the determinants of consumer adoption of Mobile Entertainment will provide important theoretical contributions to this field and lead to the development of Mobile Entertainment Services. This paper tries to explore the factors of(More)
Label imbalance and the insufficiency of labeled training samples are major obstacles in most methods for counting people in images or videos. In this work, a sparse representation-based semi-supervised regression method is proposed to count people in images with limited data. The basic idea is to predict the unlabeled training data, select reliable samples(More)