The growth in both frequency and severity of gun violence in the United States has necessitated increased research into prevention, despite the lack of funding. Comprising more than 60k gun violence media articles with a total data size of 520 MB, the gun violence database (GVDB) was developed to assist natural language processing researchers in developing and testing prevention methods. Original research based on the GVDB utilized a span-selection model to extract shooter and victim information, but their works might potentially trim out important span candidates. We proposed a new approach to improve identification accuracy and recognize every token in a sentence using a sequence tagging technique. We implemented a BIO sequence tagging model at the token-level using BERT, then further classified each token using LSTM, BiLSTM, and CRF. We found that utilizing BERT as an embedding layer, and decoding word representation as a sequence tagging task, improved shooter/victim identification compared to a span-selection model. We believe that if this improved model is combined with gun violence related keywords, automated techniques could be implemented to identify precursors/risks to gun violence on social media, allowing for intervention by law enforcement or community agencies before escalation to deaths.

Wearable devices have evolved towards intrinsic human augmentation, unlocking the human skin as an interface for seamless interaction. However, the non-traditional form factor of these on-skin interfaces, as well as the gestural interactions performed on them may raise concerns for public wear. These perceptions will influence whether a new form of technology will eventually be accepted, or rejected by society. Therefore, it is essential for researchers to consider the societal implications of device design. In this paper, we investigate the third person perceptions of a user's interactions with an on-skin touch sensor. Specifically, we examine social perceptions towards the placement of the on-skin interface in different body locations, as well as gestural interactions performed on the device. The study was conducted in the United States and Taiwan to examine cross-cultural attitudes towards device usage. The results of this structured examination offer insight into the design of on-skin interfaces for public use.

Our current project involves the development of a wristband-mounted sensor that is meant to function as an alcohol use monitoring system. This paper focuses on the degree to which physical activity influences ethanol concentrations in the vapor secreted from the skin through collecting data from seven recruited participants when they conducting one designated activity, which could presumably affect the accuracy of detection results. We proposes a preliminary design of building a personal alcohol tracking system that can improve the reliability and affordability of current transdermal ethanol tracking devices to accommodate potential interferences presented in daily life and be intuitive to be used to raise the awareness of alcohol use.