Computer vision has achieved impressive progress in recent years. Meanwhile, mobile phones have become the primary computing platforms for millions of people. In addition to mobile phones, many autonomous systems rely on visual data for making decisions and some of these systems have limited energy (such as unmanned aerial vehicles also called drones and mobile robots). These systems rely on batteries and energy efficiency is critical. This article serves two main purposes: (1) Examine the state-of-the-art for low-power solutions to detect objects in images. Since 2015, the IEEE Annual International Low-Power Image Recognition Challenge (LPIRC) has been held to identify the most energy-efficient computer vision solutions. This article summarizes 2018 winners' solutions. (2) Suggest directions for research as well as opportunities for low-power computer vision.
As Cross-Site Scripting (XSS) remains one of the top web security risks, people keep exploring ways to detect such attacks efficiently. So far, existing solutions only focus on the payload in a web request or a response, a single stage of a web transaction. This work proposes a new approach that integrates evidences from both a web request and its response in order to better characterize XSS attacks and separate them from normal web transactions. We first collect complete payloads of XSS and normal web transactions from two databases and extract features from them using the Word2vec technique. Next, we train two Gaussian mixture models (GMM) with these features, one for XSS transaction and one for normal web transactions. These two models can generate two probability scores for a new web transaction, which indicate how similar this web transaction is to XSS and normal traffics respectively. Finally, we put together these two GMM models in classification by combining these two probabilities to further improve detection accuracy.
Alcoholic fatty liver disease (AFLD) is caused by long-term heavy alcohol consumption; therefore, useful and practical methods for the prevention of AFLD are urgently needed. The edible flower of Dendrobium officinale contains diverse flavonoids, and has shown antioxidant activity as well as antihypertensive and anti-inflammatory effects. In this study, an AFLD model was established, the protective effect of D. officinale flower (DOF) ethanol extract on AFLD was evaluated, and its mechanisms were investigated by analyzing gut microbiota and short-chain fatty acids (SCFAs). DOF extract (DOFE) supplementation promoted alcohol metabolism, restored hepatic antioxidant capacity, alleviated oxidative stress, reduced inflammatory factor levels, and inhibited dyslipidemia induced by alcohol intake in chronic alcohol-exposed mice, especially in the high DOFE group. Moreover, DOFE supplementation increased the diversity, structure, and composition of the gut microbiota in mice, restored some of the abnormal SCFA levels caused by AFLD, and helped restore intestinal function. DOFE supplementation significantly increased the relative abundance of Akkermansia, suggesting that Akkermansia may be a potential target of the protective effect of DOFE. Therefore, DOFE supplementation to improve the composition of the gut microbiota may be an effective therapeutic strategy for the prevention of AFLD.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.