Search results for: Xiaoqi Jia

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Jia</title> <meta name="description" content="Search results for: Xiaoqi Jia"> <meta name="keywords" content="Xiaoqi Jia"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> <button class="d-block d-lg-none navbar-toggler ml-auto" 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id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="Xiaoqi Jia"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 2</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: Xiaoqi Jia</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2</span> Gender Recognition with Deep Belief Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Xiaoqi%20Jia">Xiaoqi Jia</a>, <a href="https://publications.waset.org/abstracts/search?q=Qing%20Zhu"> Qing Zhu</a>, <a href="https://publications.waset.org/abstracts/search?q=Hao%20Zhang"> Hao Zhang</a>, <a href="https://publications.waset.org/abstracts/search?q=Su%20Yang"> Su Yang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A gender recognition system is able to tell the gender of the given person through a few of frontal facial images. An effective gender recognition approach enables to improve the performance of many other applications, including security monitoring, human-computer interaction, image or video retrieval and so on. In this paper, we present an effective method for gender classification task in frontal facial images based on deep belief networks (DBNs), which can pre-train model and improve accuracy a little bit. Our experiments have shown that the pre-training method with DBNs for gender classification task is feasible and achieves a little improvement of accuracy on FERET and CAS-PEAL-R1 facial datasets. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=gender%20recognition" title="gender recognition">gender recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=beep%20belief%20net-works" title=" beep belief net-works"> beep belief net-works</a>, <a href="https://publications.waset.org/abstracts/search?q=semi-supervised%20learning" title=" semi-supervised learning"> semi-supervised learning</a>, <a href="https://publications.waset.org/abstracts/search?q=greedy-layer%20wise%20RBMs" title=" greedy-layer wise RBMs"> greedy-layer wise RBMs</a> </p> <a href="https://publications.waset.org/abstracts/56147/gender-recognition-with-deep-belief-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/56147.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">453</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1</span> Combined Mindfulness and Exercise Intervention for Depressive and Insomnia Symptoms in Chinese Students: A Pilot Randomized Controlled Trial</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Xinli%20Chi">Xinli Chi</a>, <a href="https://publications.waset.org/abstracts/search?q=Xiaoqi%20Wei"> Xiaoqi Wei</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Background: Body-mind theory refers to the concept that the mind and body are interconnected; in this case, combining aerobic exercise and mindfulness-based training may be beneficial for mind-body health; however, there is limited evidence regarding their effects and potential mechanisms among Chinese university students. Therefore, the current study aims to examine the preliminary effects and feasibility of the combined intervention on depressive and insomnia symptoms, as well as to explore the underlying mechanisms. Methods: This is a two-arm pilot study of a randomized, controlled trial. Sixty-one Chinese university students were randomly allocated to 8-week combined intervention group (aerobic exercise plus mindfulness, N = 36) or control group (N = 36). In addition, 8 participants in combined intervention group were later volunteer to engage in semi-structured interview. The Self-Rating Depression Scale (SDS) and the Youth Self-Rating Insomnia Scales (YSIS) were used to measure depressive and insomnia symptoms, respectively. The intervention outcome and feasibility were tested by repeated-measures ANOVA, mediation model, and qualitative analysis. Results: The study included 31 participants in the intervention group and 30 participants in the control group, all of whom completed pre-test and post-test questionnaires. The results of the repeated-measures ANOVA showed that the combined intervention was effective in reducing depressive and insomnia symptoms among university students. Moreover, the mediation analysis suggested that improvement in insomnia symptoms might be a significant mechanism for the combined intervention. Qualitative analysis identified two main themes: “Helpful aspects of mind-body state” (including 7 sub-themes) and “Factors that influence the training effects” (including 3 sub-themes). Conclusions: The study confirmed the preliminary effect and feasibility of the combined intervention of mindfulness and aerobic exercise, while also exploring the potential mechanisms underlying this effect. Additionally, qualitative data provided valuable insights for optimizing future protocols. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=combined%20intervention" title="combined intervention">combined intervention</a>, <a href="https://publications.waset.org/abstracts/search?q=mindfulness" title=" mindfulness"> mindfulness</a>, <a href="https://publications.waset.org/abstracts/search?q=aerobic%20exercise" title=" aerobic exercise"> aerobic exercise</a>, <a href="https://publications.waset.org/abstracts/search?q=depressive%20symptoms" title=" depressive symptoms"> depressive symptoms</a>, <a href="https://publications.waset.org/abstracts/search?q=insomnia%20symptoms" title=" insomnia symptoms"> insomnia symptoms</a> </p> <a href="https://publications.waset.org/abstracts/166259/combined-mindfulness-and-exercise-intervention-for-depressive-and-insomnia-symptoms-in-chinese-students-a-pilot-randomized-controlled-trial" class="btn 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Search results for: Xiaoqi Jia