Social media has been widely used among billions of people with dramatical participation of new users every day. Among them, social networks maintain the basic social characters and host huge amount of personal data. While protecting user sensitive data is obvious and demanding, information leakage due to adversarial attacks is somehow unavoidable, yet hard to detect. For example, implicit social relation such as family information may be simply exposed by network structure and hosted face images through off-the-shelf graph neural networks (GNN), which will be empirically proved in this paper. To address this issue, in this paper, we propose a novel adversarial attack algorithm for social good. First, we start from conventional visual family understanding problem, and demonstrate that familial information can easily be exposed to attackers by connecting sneak shots to social networks. Second, to protect family privacy on social networks, we propose a novel adversarial attack algorithm that produces both adversarial features and graph under a given budget. Specifically, both features on the node and edges between nodes will be perturbed gradually such that the probe images and its family information can not be identified correctly through conventional GNN. Extensive experiments on a popular visual social dataset have demonstrated that our defense strategy can significantly mitigate the impacts of family information leakage.
Problem
Although physical restraint practices and psychotropic/sedative pro re neta (PRN, as needed) medications have been commonly used for managing inpatient aggression, little is known about the characteristics of adolescents who receive them in psychiatric adolescent inpatient units. We aimed to determine the relationship between the use of physical restraints and psychotropic/sedative PRN medications, and to characterize individual attributes, substance use, clinical factors, and time of the first restraint episodes of the use of physical restraints and psychotropic/sedative PRN medications.
Methods
A retrospective case‐control study approach was used with the data from electronic health records at a pediatric psychiatric hospital in the United States. Descriptive statistics, χ2, multivariate logistic regression, and Cox proportional hazard model were used.
Findings
Participants of younger age and participants with a longer length of stay were significantly associated with the use of physical restraints and psychotropic/sedative PRN medications, although the substance‐related risks were not significantly associated with the use of restraints. Physical restraints were more likely to have occurred soon after the admission and tapered off as the length of stay increased.
Conclusions
This study provides important information in understanding the risk factors of the use of restraints and psychotropic/sedative PRN medications in psychiatric adolescent inpatient units.
Handwriting is considered as an input method through pen-based or touch-based mean. Consequently, it is an unique feature preserving users' individuality. Since, it is becoming more lively aspect of user interaction, it is a very facile and more theoretical measure to reproduce an individual's cursive and noncursive English handwriting from ASCII transcription. Special input arrangement is designed to collect user's natural handwriting. Then, the system depicts the individuality features and characteristics of anyone's handwriting that machine learns afterwards. And at last, at a given set of instructions, for any set of ASCII value, user natural handwriting is synthesized hierarchically.
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