Online human gesture recognition has a wide range of applications in computer vision, especially in human-computer interaction applications. Recent introduction of cost-effective depth cameras brings on a new trend of research on bodymovement gesture recognition. However, there are two major challenges: i) how to continuously recognize gestures from unsegmented streams, and ii) how to differentiate different styles of a same gesture from other types of gestures. In this paper, we solve these two problems with a new effective and efficient feature extraction method that uses a dynamic matching approach to construct a feature vector for each frame and improves sensitivity to the features of different gestures and decreases sensitivity to the features of gestures within the same class. Our comprehensive experiments on MSRC-12 Kinect Gesture and MSR-Action3D datasets have demonstrated a superior performance than the stat-of-the-art approaches.
With the growing popularity of anomaly detection systems, which is due partly to the rise in zero-day attacks, a new class of threats have evolved where the attacker mimics legitimate activity to blend in and avoid detection. We propose a new system called Siren that injects crafted human input alongside legitimate user activity to thwart these mimicry attacks. The crafted input is specially designed to trigger a known sequence of network requests, which Siren compares to the actual traffic. It then flags unexpected messages as malicious. Using this method, we were able to detect ten spyware programs that we tested, many of which attempt to blend in with user activity. This paper presents the design, implementation, and evaluation of the Siren activity injection system, as well as a discussion of its potential limitations.
Healthy longevity has been an unremitting pursuit of human, but its genetic and the environment causes are still unclear. As longevity population is a good healthy aging model for understanding how the body begin aging and the process of aging, and plasma lipids metabolism and balance is a very important to life maintain and physiologic functional turnover. It is important to explore how the effect of genetic variants associated long-life individuals on lipids metabolism and balance. Therefore, we developed a comparative study based population which contains 2816 longevity and 2819 control. Through whole-exome sequencing and sanger sequencing genotypes, we identified four new single nucleotide polymorphisms of HLA-DQB1(major histocompatibility complex, class II, DQ beta 1), rs41542812 rs1049107 rs1049100 rs3891176(Prange=0.048-2.811×10−8 for allele frequencies), associated with longevity in Chinese Longevity Cohort. Further, by analysis of the longevity-variants linked to blood lipids, we identified HLA-DQB1 rs1049107, T-carriers (PHDL=0.006, OR: 11.277; PTG=9.095×10−7, OR: 0.025; PLDL/HDL=0.047, OR: 1.901) and HLA-DQB1 rs1049100, T-carriers (PTG=1.799×10-6, OR: 0.028) associated with lipid homeostasis in long lived individuals. Our finding showed that longevity and lipid homeostasis were associated with HLA-DQB1 and suggested that immune gene variants could act on both new function of maintaining the homeostasis and anti-aging in longevity.
Online human gesture recognition has a wide range of applications in computer vision, especially in humancomputer interaction applications. The recent introduction of cost-effective depth cameras brings a new trend of research on body-movement gesture recognition. However, there are two major challenges: (i) how to continuously detect gestures from unsegmented streams, and (ii) how to differentiate different styles of the same gesture from other types of gestures. In this article, we solve these two problems with a new effective and efficient feature extraction method-Structured Streaming Skeleton (SSS)-which uses a dynamic matching approach to construct a feature vector for each frame. Our comprehensive experiments on MSRC-12 Kinect Gesture, Huawei/3DLife-2013, and MSR-Action3D datasets have demonstrated superior performances than the state-of-the-art approaches. We also demonstrate model selection based on the proposed SSS feature, where the classifier of squared loss regression with l 2,1 norm regularization is a recommended classifier for best performance. . 2014. Structured Streaming Skeleton -A new feature for online human gesture recognition. ACM Trans. Multimedia Comput. Commun.
Uncovering the latent structure of various hotly discussed topics and the corresponding sentiments from different social media user groups (e.g., Twitter) is critical for helping organizations and governments understand how users feel about their services and facilities, along with the events happening around them.Although numerous research texts have explored sentiment analysis on the different aspects of a product, fewer works have focused on why users like or dislike those products. In this paper, a novel probabilistic model is proposed, namely, the Hierarchical User Sentiment Topic Model (HUSTM), to discover the hidden structure of topics and users while performing sentiment analysis in a unified way. The goal of the HUSTM is to hierarchically model the users attitudes (opinions ) using different topic and sentiment information, including the positive, negative, and neutral. The experiment results on real-world datasets show the high quality of the hierarchy obtained by the HUSTM in comparison to those discovered using other state-of-the-art techniques.
The findings suggest positive spillover effects of children's political status on parents' health. The benefits of having a cadre child are at least equivalent to the rural-urban gap in health and even stronger for the parents of higher ranking cadres. One potential explanation for such spillover effects is that a child's political status can improve parents' community involvement and social interactions.
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