Browsing by Author "Wang, Shiwei"

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    Psychological reactions to COVID-19: Survey data assessing perceived susceptibility, distress, mindfulness, and preventive health behaviors.
    (Data in brief, 2021-02) O'Brien, William H; Wang, Shan; Xu, Huanzhen; Wang, Shiwei; Yang, Zaiying; Yang, Joy Ting; Liu, Qinwanxian; Zhang, Xin; Tang, Lingli; Varga, Aniko V; Sims, Tracy; Lim, Chung Xiann; Jarukasemthawee, Somboon; Pisitsungkagarn, Kullaya
    The COVID-19 pandemic created a complex psychological environment for persons in America. A total of 450 USA MTurk workers completed measures of: (a) basic demographic characteristics; (b) health risk factors for COVID-19; (c) perceived susceptibility variables related to COVID-19; (d) COVID-19 preventive health behaviors; and (e) distress, physical symptoms, and quality of life measures. The surveys were completed between April 9, 2020 and April 18, 2020. This recruitment period corresponded to the first 2-3 weeks of lockdown in most of the USA. Follow-up surveys were completed by 151 of the USA participants between June 19, 2020 and July 11, 2020 (approximately 2 months after the first measurement). These data permit evaluation of relationships among demographic variables, COVID-19 stress and coping, COVID-19 preventive health behavior, and the role of mindfulness as a possible moderator of distress as well as a predictor of preventive health behavior. The availability of follow-up data permit longitudinal analyses that provide a stronger basis for causal inference.
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    Renovating the Search Experience of Art Image Databases
    (2022) Wang, Shiwei

    With the emergence of the computer, the digital image has become one of the most prevalent visual mediums in the 21st century. This paper aims at analyzing the current limitations in interacting with digital image databases in art historical research. In response to the limitations of current image database structures, this paper explores how emerging computer vision technologies can be applied to enrich the ways database users interact with art image databases. While current image databases primarily rely on manually-defined metadata and textual descriptions to associate art images, this digital project demonstrates how deep neural networks can add visual connections between art images through feature extraction algorithms. This thesis documents a digital product that demonstrates how deep neural network models can extract images’ visual features and connect art images by these visual features. Although it offers a new approach to the problem, this digital project is not intended to replace the existed metadata structure and text-based search in existing image database system. Metadata and text-based search have developed over time to assist people in managing data and navigating the digital world in the era of big data. As such, this digital project offers to overlay a visual-driven search path upon the existing database structure in order to provide a more diverse search environment.