Although widely used in many applications, accurate and efficient human action recognition remains a challenging area of research in the field of computer vision. Most recent surveys have focused on narrow problems such as human action recognition methods using depth data, 3D-skeleton data, still image data, spatiotemporal interest point-based methods, and human walking motion recognition. However, there has been no systematic survey of human action recognition. To this end, we present a thorough review of human action recognition methods and provide a comprehensive overview of recent approaches in human action recognition research, including progress in hand-designed action features in RGB and depth data, current deep learning-based action feature representation methods, advances in human–object interaction recognition methods, and the current prominent research topic of action detection methods. Finally, we present several analysis recommendations for researchers. This survey paper provides an essential reference for those interested in further research on human action recognition.

The fields of human activity analysis have recently begun to diversify. Many researchers have taken much interest in developing action recognition or action prediction methods. The research on human action evaluation differs by aiming to design computation models and evaluation approaches for automatically assessing the quality of human actions. This line of study has become popular because of its explosively emerging real-world applications, such as physical rehabilitation, assistive living for elderly people, skill training on self-learning platforms, and sports activity scoring. This paper presents a comprehensive survey of approaches and techniques in action evaluation research, including motion detection and preprocessing using skeleton data, handcrafted feature representation methods, and deep learning-based feature representation methods. The benchmark datasets from this research field and some evaluation criteria employed to validate the algorithms’ performance are introduced. Finally, the authors present several promising future directions for further studies.

An energy-efficient scheduling scheme for time-constrained tasks in local mobile clouds, Pervasive and Mobile Computing (2015), http://dx. ABSTRACTMobile 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 energy efficiency remains a challenge. In this paper, firstly, we introduce the local mobile clouds formed by nearby mobile devices and give the mathematical models of the mobile devices and their applications. Secondly, we formulate the scheduling problem in local mobile clouds. After describing the resource discovery scheme and the adaptive, probabilistic scheduling algorithm, we finally validate the performance of the proposed algorithm by simulation experiments.

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The immunologic interaction between parenchyma cells and encircling inflammatory cells is thought to be the most important mechanism of biliary damage and repair in primary sclerosing cholangitis (PSC). Monocytes/macrophages as master regulators of hepatic inflammation have been demonstrated to contribute to PSC pathogenesis. Macrophages coordinate with liver regeneration, and multiple phenotypes have been identified with diverse expressions of surface proteins and cytokine productions. We analyzed the expression of Notch ligand Jagged1 in polarized macrophages and investigated the relevance of Notch signalling activation in liver regeneration. M1 or M2 macrophages were generated from mouse bone marrow-derived macrophages (BMDMs) by classical or alternative activation, respectively. Then, the expression levels of Jagged1 (Jag1) of each phenotype were measured. The effects of polarized BMDMs on the expression of hepatic progenitor cell- (HPC-) specific markers and hairy and enhancer of split-1 (HES1) in HPCs in coculture were also analyzed. Monocyte-macrophage and Notch signalling-associated gene signatures were evaluated in the GEO database (access ID: GSE61260) by gene set enrichment analysis (GSEA). M1 macrophages were found associated with elevated Jag1 expression, which increased the fraction of HPC with self-renewing phenotypes (CD326+CD44+ or CD324+CD44+) and HES1 expression level in cocultured HPC. Blocking Jagged1 by siRNA or antibody in the coculture system attenuates HPC self-renewing phenotypes as well as HES1 expression in HPC. GSEA data show that macrophage activation and Notch signalling-associated gene signatures are enriched in PSC patients. These findings suggest that M1 macrophages promote an HPC self-renewing phenotype which is associated with Notch signalling activation within HPC. In the liver of PSC patients, the prevalence of activated macrophages, with M1 polarized accounting for the main part, is associated with increment of Notch signalling and enhancement of HPC self-renewal.