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1. Endorsement of inconsistent imperatives

   https://doi.org/10.3765/plsa.v4i1.4562  

   Condoravdi, Cleo; Jarvis, Rebecca; Jeong, Sunwoo (March 2019, Proceedings of the Linguistic Society of America)

   There is an ongoing debate regarding how imperatives convey speaker endorsement. One line of approach builds it into the imperative meaning. Another posits weaker meanings. Indifference uses, like 'Go right! Go left! I don't care!', pose a challenge to the endorsement account. We reconcile the endorsement approach with such uses and argue that they can reduce to the speaker endorsing disjunctive prejacents, which results from one imperative operator taking a list of prejacents under its scope. This analysis predicts that intonational patterns that signal lists will facilitate disjunctive interpretations. We test and confirm this prediction in an experimental study. 

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2. A Usability Study of Five Two-Factor Authentication Methods

   Reese, K.; Smith, T.; Dutson, J.; Armknecht, J.; Cameron, J.; Seamons, K. (July 2019, Proceedings of the Fifteenth Symposium on Usable Privacy and Security)

   Two-factor authentication (2FA) defends against account compromise. An account secured with 2FA typically requires an individual to authenticate using something they know—typically a password—as well as something they have, such as a cell phone or hardware token. Many 2FA methods in widespread use today have not been subjected to adequate usability testing. Furthermore, previous 2FA usability research is difficult to compare due to widely-varying contexts across different studies. We conducted a two-week, between-subjects usability study of five common 2FA methods with 72 participants, collecting both quantitative and qualitative data. Participants logged into a simulated banking website nearly every day using 2FA and completed an assigned task. Participants generally gave high marks to the methods studied, and many expressed an interest in using 2FA to provide more security for their sensitive online accounts. We also conducted a within-subjects laboratory study with 30 participants to assess the general usability of the setup procedure for the five methods. While a few participants experienced difficulty setting up a hardware token and a one-time password, in general, users found the methods easy to set up. 

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3. Investigating the role of group-based morality in extreme behavioral expressions of prejudice

   https://doi.org/10.1038/s41467-021-24786-2  

   Hoover, Joe; Atari, Mohammad; Mostafazadeh Davani, Aida; Kennedy, Brendan; Portillo-Wightman, Gwenyth; Yeh, Leigh; Dehghani, Morteza (July 2021, Nature Communications)

   Abstract Understanding motivations underlying acts of hatred are essential for developing strategies to prevent such extreme behavioral expressions of prejudice (EBEPs) against marginalized groups. In this work, we investigate the motivations underlying EBEPs as a function of moral values. Specifically, we propose EBEPs may often be best understood as morally motivated behaviors grounded in people’s moral values and perceptions of moral violations. As evidence, we report five studies that integrate spatial modeling and experimental methods to investigate the relationship between moral values and EBEPs. Our results, from these U.S. based studies, suggest that moral values oriented around group preservation are predictive of the county-level prevalence of hate groups and associated with the belief that extreme behavioral expressions of prejudice against marginalized groups are justified. Additional analyses suggest that the association between group-based moral values and EBEPs against outgroups can be partly explained by the belief that these groups have done something morally wrong. 

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4. The ethics of diversity, equity, inclusion, and justice in the earth system sciences

   https://doi.org/10.3389/fphy.2022.1085789  

   Burt, Melissa A.; Haacker, Rebecca; Montaño, Patricia; Vara, Marissa; Sloan, Valerie (December 2022, Frontiers in Physics)

   Institutions’ motivations for pursuing diversity, equity, inclusion and justice (DEIJ) often center on the benefits to the organization, an argument known as the business case for diversity in which diverse teams are more creative, set high bars for research, and produce ideas that are more innovative than those produced by homogeneous groups. As the sole motivation for DEIJ efforts, the business case is flawed and does not address the harmful workplaces many marginalized scholars encounter. Institutions can make more progress towards diversifying the STEM workforce by acknowledging the ethical responsibilities for doing so and transitioning to an equity-centered approach. Emphasizing personal motivations to actively engage in DEIJ work resonates with individuals more, rather than engaging with DEIJ to benefit an institution’s goals. Two recent studies support this argument. The first is an alumni survey and focus groups of postdoctoral fellows in the Advanced Studies Program at the National Center for Atmospheric Research to explore alumni efforts and motivations for engaging in DEIJ work. The second study surveyed attitudes towards DEIJ efforts among STEM graduate students at Colorado State University who took a course on social responsibility in science. Both studies show the motivations for scientists to support and get involved in these efforts and indicate that the business case is misaligned with the motivations of students and professionals in STEM. Understanding the attitudes and motivations that individuals have for DEIJ in STEM presents an opportunity for how institutions can best learn from and support these motivations for systemic change. 

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5. SoK: The Faults in our ASRs: An Overview of Attacks against Automatic Speech Recognition and Speaker Identification Systems

   https://doi.org/10.1109/SP40001.2021.00014  

   Abdullah, Hadi; Warren, Kevin; Bindschaedler, Vincent; Papernot, Nicholas; Traynor, Patrick (April 2021, Proceedings of the IEEE Symposium on Security and Privacy)

   null (Ed.)

   Speech and speaker recognition systems are employed in a variety of applications, from personal assistants to telephony surveillance and biometric authentication. The wide deployment of these systems has been made possible by the improved accuracy in neural networks. Like other systems based on neural networks, recent research has demonstrated that speech and speaker recognition systems are vulnerable to attacks using manipulated inputs. However, as we demonstrate in this paper, the end-to-end architecture of speech and speaker systems and the nature of their inputs make attacks and defenses against them substantially different than those in the image space. We demonstrate this first by systematizing existing research in this space and providing a taxonomy through which the community can evaluate future work. We then demonstrate experimentally that attacks against these models almost universally fail to transfer. In so doing, we argue that substantial additional work is required to provide adequate mitigations in this space. 

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