Browsing by Author "Wang, Chen"

Now showing 1 - 6 of 6
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    Clinical, immunological features, treatments, and outcomes of autoimmune hemolytic anemia in patients with RAG deficiency
    (American Society of Hematology, 2024) Wang, Chen; Sun, Bijun; Wu, Kevin; Farmer, Jocelyn R.; Ujhazi, Boglarka; Geier, Christoph B.; Gordon, Sumai; Westermann-Clark, Emma; Savic, Sinisa; Secord, Elizabeth; Sargur, Ravishankar; Chen, Karin; Jin, Jay J.; Dutmer, Cullen M.; Kanariou, Maria G.; Adeli, Mehdi; Palma, Paolo; Bonfim, Carmem; Lycopoulou, Evangelia; Wolska-Kusnierz, Beata; Dbaibo, Ghassan; Bleesing, Jack; Moshous, Despina; Neven, Benedicte; Schuetz, Catharina; Geha, Raif S.; Notarangelo, Luigi D.; Miano, Maurizio; Buchbinder, David K.; Csomos, Krisztian; Wang, Wenjie; Wang, Ji-Yang; Wang, Xiaochuan; Walter, Jolan E.; Pediatrics, School of Medicine
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    Inferring Mobile Payment Passcodes Leveraging Wearable Devices
    (ACM, 2018-10) Wang, Chen; Liu, Jian; Guo, Xiaonan; Wang, Yan; Chen, Yingying; Computer and Information Science, School of Science
    Mobile payment has drawn considerable attention due to its convenience of paying via personal mobile devices at anytime and anywhere, and passcodes (i.e., PINs) are the first choice of most consumers to authorize the payment. This work demonstrates a serious security breach and aims to raise the awareness of the public that the passcodes for authorizing transactions in mobile payments can be leaked by exploiting the embedded sensors in wearable devices (e.g., smartwatches). We present a passcode inference system, which examines to what extent the user's PIN during mobile payment could be revealed from a single wrist-worn wearable device under different input scenarios involving either two hands or a single hand. Extensive experiments with 15 volunteers demonstrate that an adversary is able to recover a user's PIN with high success rate within 5 tries under various input scenarios.
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    Periodontal health: A national cross‐sectional study of knowledge, attitudes and practices for the public oral health strategy in China
    (Wiley, 2019-04) Zhao, Qian; Wang, Shi-Bin; Xu, Guodong; Song, Yiqing; Han, Xiaozhe; Liu, Zhiqiang; Zhou, Xuan; Zhang, Tianyi; Huang, Kewu; Yang, Ting; Lin, Yingxiang; Wu, Sinan; Wang, Zuomin; Wang, Chen; Epidemiology, School of Public Health
    Aim To assess the status of periodontal health knowledge, attitudes and practices (KAP) among Chinese adults. Materials and Methods A cross‐sectional study was conducted in a nationally representative sample of adults (N = 50,991) aged 20 years or older from ten provinces, autonomous regions, and municipalities. Percentages of Chinese adults with correct periodontal knowledge, positive periodontal attitudes, and practices were estimated. Multiple logistic regression analyses were used to examine the related factors. Results Less than 20% of Chinese adults were knowledgeable about periodontal disease. Very few (2.6%) of Chinese adults use dental floss ≥once a day and undergo scaling ≥once a year and visit a dentist (6.4%) in the case of gingival bleeding. Periodontal health KAP was associated with gender, age, body mass index, marital status, place of residence, education level, income, smoking status, and history of periodontal disease. Conclusions Periodontal health KAP are generally poor among the Chinese adult population. Community‐based health strategies to improve periodontal health KAP need to be implemented. Increasing knowledge of periodontal disease, the cultivation of correct practices in response to gingival bleeding, and the development of good habits concerning the use of dental floss and regular scaling should be public oral health priorities.
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    Towards In-baggage Suspicious Object Detection Using Commodity WiFi
    (IEEE, 2018) Wang, Chen; Liu, Jian; Chen, Yingying; Liu, Hongbo; Wang, Yan; Computer Information and Graphics Technology, School of Engineering and Technology
    The growing needs of public safety urgently require scalable and low-cost techniques on detecting dangerous objects (e.g., lethal weapons, homemade-bombs, explosive chemicals) hidden in baggage. Traditional baggage check involves either high manpower for manual examinations or expensive and specialized instruments, such as X-ray and CT. As such, many public places (i.e., museums and schools) that lack of strict security check are exposed to high risk. In this work, we propose to utilize the fine-grained channel state information (CSI) from off-the-shelf WiFi to detect suspicious objects that are suspected to be dangerous (i.e., defined as any metal and liquid object) without penetrating into the user's privacy through physically opening the baggage. Our suspicious object detection system significantly reduces the deployment cost and is easy to set up in public venues. Towards this end, our system is realized by two major components: it first detects the existence of suspicious objects and identifies the dangerous material type based on the reconstructed CSI complex value (including both amplitude and phase information); it then determines the risk level of the object by examining the object's dimension (i.e., liquid volume and metal object's shape) based on the reconstructed CSI complex of the signals reflected by the object. Extensive experiments are conducted with 15 metal and liquid objects and 6 types of bags in a 6-month period. The results show that our system can detect over 95% suspicious objects in different types of bags and successfully identify 90% dangerous material types. In addition, our system can achieve the average errors of 16ml and 0.5cm when estimating the volume of liquid and shape (i.e., width and height) of metal objects, respectively.
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    Wi-Fi-Enabled Automatic Eating Moment Monitoring Using Smartphones
    (Springer, 2020) Lin, Zhenzhe; Xie, Yucheng; Guo, Xiaonan; Wang, Chen; Ren, Yanzhi; Chen, Yingying; Computer Information and Graphics Technology, School of Engineering and Technology
    Dietary habits are closely correlated with people’s health. Study reveals that unhealthy eating habits may cause various diseases such as obesity, diabetes and anemia. To help users create good eating habits, eating moment monitoring plays a significant role. However, traditional methods mainly rely on manual self-report or wearable devices, which either require much user efforts or intrusive dedicated hardware. In this work, we propose a user effort-free eating moment monitoring system by leveraging the WiFi signals extracted from the commercial off-the-shelf (COTS) smartphones. In particular, our system captures the eating activities of users to determine the eating moments. The proposed system can further identify the fine-grained food intake gestures (e.g., eating with fork, knife, spoon, chopsticks and bard hand) to estimate the detailed eating episode for each food intake gesture. Utilizing the dietary information, our system shows the potential to infer the food category and food amount. Extensive experiments with 10 subjects over 400-min eating show that our system can recognize a user’s food intake gestures with up to 97.8% accuracy and estimate the dietary moment within 1.1-s error.
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    WristSpy: Snooping Passcodes in Mobile Payment Using Wrist-worn Wearables
    (IEEE, 2019-04) Wang, Chen; Liu, Jian; Guo, Xiaonan; Wang, Yan; Chen, Yingying; Computer Information and Graphics Technology, School of Engineering and Technology
    Mobile payment has drawn considerable attention due to its convenience of paying via personal mobile devices at anytime and anywhere, and passcodes (i.e., PINs or patterns) are the first choice of most consumers to authorize the payment. This paper demonstrates a serious security breach and aims to raise the awareness of the public that the passcodes for authorizing transactions in mobile payments can be leaked by exploiting the embedded sensors in wearable devices (e.g., smartwatches). We present a passcode inference system, WristSpy, which examines to what extent the user's PIN/pattern during the mobile payment could be revealed from a single wrist-worn wearable device under different passcode input scenarios involving either two hands or a single hand. In particular, WristSpy has the capability to accurately reconstruct fine-grained hand movement trajectories and infer PINs/patterns when mobile and wearable devices are on two hands through building a Euclidean distance-based model and developing a training-free parallel PIN/pattern inference algorithm. When both devices are on the same single hand, a highly challenging case, WristSpy extracts multi-dimensional features by capturing the dynamics of minute hand vibrations and performs machine-learning based classification to identify PIN entries. Extensive experiments with 15 volunteers and 1600 passcode inputs demonstrate that an adversary is able to recover a user's PIN/pattern with up to 92% success rate within 5 tries under various input scenarios.