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   https://doi.org/10.1111/cdev.13987  

   Fish, Lesenia_R; Hildebrand, Lindsey; Chernyak, Nadia; Cordes, Sara (August 2023, Child Development)

   Abstract Games are frequently used to promote math learning, yet the competitive and collaborative contexts introduced by games may exacerbate gender differences. In this study, 1st and 2nd grade children in the U.S. (ages 5–8;N = 274; 70% White, 15% Asian, 2% Black, 1% Native American, 14% mixed or other race; 17% Hispanic) played either a competitive, collaborative, or solo game to learn about a challenging novel math concept: proportion. Overall, both social contexts boosted perseverance and task attitudes. However, analyses revealed the competitive condition yielded gender differences in attention to proportion in the presence of competing cues, with older boys underperforming in the competition condition. Potential explanations for these findings, as well as implications for classroom math learning, are discussed. 

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2. Who's Waldo? Linking People Across Text and Images

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3. Who's Waldo? Linking People Across Text and Images

   Cui, Claire Yuqing; Khandelwal, Apoorv; Artzi, Yoav; Snavely, Noah; Averbuch-Elor, Hadar (January 2021, Proceedings)

   null (Ed.)
4. Who's In Control? On Security Risks of Disjointed IoT Device Management Channels

   https://doi.org/10.1145/3460120.3484592  

   Jia, Yan; Yuan, Bin; Xing, Luyi; Zhao, Dongfang; Zhang, Yifan; Wang, XiaoFeng; Liu, Yijing; Zheng, Kaimin; Crnjak, Peyton; Zhang, Yuqing; et al (November 2021, Proceedings of the 2021 ACM SIGSAC Conference on Computer and Communications Security)
5. Who's Calling? Characterizing Robocalls through Audio and Metadata Analysis.

   Prasad, Sathvik; Bouma-Sims, Elijah; Kiran Mylappan, Athishay; Reaves, Bradley (August 2020, Proceedings of the 2020 USENIX Security Symposium)

   null (Ed.)

   Unsolicited calls are one of the most prominent security issues facing individuals today. Despite wide-spread anecdotal discussion of the problem, many important questions remain unanswered. In this paper, we present the first large-scale, longitudinal analysis of unsolicited calls to a honeypot of up to 66,606 lines over 11 months. From call metadata we characterize the long-term trends of unsolicited calls, develop the first techniques to measure voicemail spam, wangiri attacks, and identify unexplained high-volume call incidences. Additionally, we mechanically answer a subset of the call attempts we receive to cluster related calls into operational campaigns, allowing us to characterize how these campaigns use telephone numbers. Critically, we find no evidence that answering unsolicited calls increases the amount of unsolicited calls received, overturning popular wisdom. We also find that we can reliably isolate individual call campaigns, in the process revealing the extent of two distinct Social Security scams while empirically demonstrating the majority of campaigns rarely reuse phone numbers. These analyses comprise powerful new tools and perspectives for researchers, investigators, and a beleaguered public. 

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