International students (IntlS) in the US refer to foreign students who acquire student visas to study in the US, primarily in higher education. As IntlS arrive in the US, they face several challenges, such as adjusting to a new country and culture, securing housing remotely, and arranging finances for tuition and personal expenses. These experiences, coupled with recent events such as visa revocations and the cessation of new visas, compound IntlS' risk of being targeted by and falling victim to online scams. While prior work has investigated IntlS' security and privacy, as well as general end users' reactions to online scams, research on how IntlS are uniquely impacted by scams remains largely absent. To address this gap, we conduct a two-phase user study comprising surveys (n=48) and semi-structured interviews (n=9). We investigate IntlS' exposure and interactions with scams, post-exposure actions such as reporting, and their perceptions of the usefulness of existing prevention resources and the barriers to following prevention advice. We find that IntlS are often targeted by scams (e.g., attackers impersonating government officials) and fear legal implications or deportation, which directly impacts their interactions with scams (e.g., they may prolong engagement with a scammer due to a sense of urgency). Interestingly, we also find that IntlS may lack awareness of - or access to - reliable resources that inform them about scams or guide them in reporting incidents to authorities. In fact, they may also face unique barriers in enacting scam prevention advice, such as avoiding reporting financial losses, since IntlS are required to demonstrate financial ability to stay in the US. The findings produced by our study help synthesize guidelines for stakeholders to better aid IntlS in reacting to scams.

Deploying AI models on low-power hardware remains limited by severe constraints in memory, compute throughput, and energy efficiency. This project explores whether the ESP32-S3, a low-cost microcontroller with SIMD acceleration, can run a lightweight AI model efficiently.