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   Beginning the path to a bachelor’s degree in community college has the potential to be a more cost-effective higher education option. Previous research on transfer students has focused broadly on curriculum alignment, articulation policies, and academic advising in efforts to reduce credit loss. Credit loss can significantly impact transfer students and result in unnecessary time and costs for them. Minimal research quantifies and visualizes credit loss or explains in detail how and why it occurs throughout students’ entire education trajectories. This study visualizes credit loss for bachelor’s programs seeking engineering transfer students who began at in-state community colleges using data from the sending and receiving institutions. Findings revealed that credit loss can occur throughout the entire degree pathway, including high school dual enrollment and advanced placement credits to community college credits. This work has implications for informing degree pathways and policies that promote successful transfer and degree completion. 

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5. Boxer: Preventing fraud by scanning credit cards

   Abi Din, Zainul; Venugopalan, Hari; Park, Jaime; Li, Andy; Yin, Weisu; Mai, Haohui; Lee, Yong Jae; Liu, Steven; King, Samuel (January 2020, USENIX Security Symposium)

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   Card-not-present credit card fraud costs businesses billions of dollars a year. In this paper, we present Boxer, a mobile SDK and server that enables apps to combat card-not-present fraud by scanning cards and verifying that they are genuine. Boxer analyzes the images from these scans, looking for telltale signs of attacks, and introduces a novel abstraction on top of modern security hardware for complementary protection. Currently, 323 apps have integrated Boxer, and tens of them have deployed it to production, including some large, popular, and international apps, resulting in Boxer scanning over 10 million real cards already. Our evaluation of Boxer from one of these deployments shows ten cases of real attacks that our novel hardware-based abstraction detects. Additionally, from the same deployment, without letting in any fraud, Boxer’s card scanning recovers 89% of the good users whom the app would have blocked. In another evaluation of Boxer, we run our image analysis models against images from real users and show an accuracy of 96% and 100% on the two models that we use. 

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