Philip Kortum

Philip Kortum, Michael D. Byrne, Julie Whitmore

The question of whether or not voters actually verify ballots produced by ballot marking devices (BMDs) is presently the subject of some controversy. Recent studies (e.g., Bernhard, et al. 2020) suggest the verification rate is low. What is not clear from previous research is whether this is more a result of voters being unable to do so accurately or whether this is because voters simply choose not to attempt verification in the first place. In order to understand this problem, we conducted an experiment in which 108 participants participated in a mock election where the BMD displayed the voters' true choices, but then changed a subset of those choices on the printed ballot. The design of the printed ballot, the length of the ballot, the number of changes that were made to the ballot, the location of those changes, and the instructions provided to the voters were manipulated as part of the experiment. Results indicated that of those voters who chose to examine the printed ballot, 76% detected anomalies, indicating that voters can reliably detect errors on their ballot if they will simply review it. This suggests that administrative remedies, rather than attempts to alter fundamental human perceptual capabilities, could be employed to encourage voters to check their ballots, which could prove as an effective countermeasure.

Ahmad Rahmati, Clayton Shepard, Chad Tossell et al.

Context information brings new opportunities for efficient and effective applications and services on mobile devices. A wide range of research has exploited context dependency, i.e., the relations between context(s) and the outcome, to achieve significant, quantified, performance gains for a variety of applications. These works often have to deal with the challenges of multiple sources of context that can lead to a sparse training data set, and the challenge of energy hungry context sensors. Often, they address these challenges in an application specific and ad-hoc manner. We liberate mobile application designers and researchers from these burdens by providing a methodical approach to these challenges. In particular, we 1) define and measure the context-dependency of three fundamental types of mobile usage in an application agnostic yet practical manner, which can provide clear insight into the performance of potential ap-plication. 2) Address the challenge of data sparseness when dealing with multiple and different sources of context in a systematic manner. 3) Present SmartContext to address the energy challenge by automatically selecting among context sources while ensuring the minimum accuracy for each estimation event is met. Our analysis and findings are based on usage and context traces collected in real-life settings from 24 iPhone users over a period of one year. We present findings regarding the context dependency of the three principal types of mobile usage; visited websites, phone calls, and app usage. Yet, our methodology and the lessons we learn can be readily extended to other context-dependent mobile usage and system resources as well. Our findings guide the development of context aware systems, and highlight the challenges and expectations regarding the context dependency of mobile usage.