Christopher Warren

Gavin Eddington, Christopher Warren, Seth Poulsen et al.

Background and Context. Computer programming often involves extended periods of sustained activity and mobile phone notifications introduce frequent opportunities for interruption. Prior work demonstrates that suppressing phone notifications may reduce these disruptions. Objectives. Our primary research question is: How does suppressing phone notifications affect students' task engagement and productivity while programming? Method. We report on a replication and methodological extension study conducted in a CS1 course involving 22 students. Using a within-subject design, selected programming assignments were randomly designated for enabling notification suppression. Phone state logs were synchronized with millisecond-resolution IDE keystroke data to measure student attention and focus when in the control and notification-suppression conditions. Findings. Assignments completed with notification suppression enabled significantly lower break rates and longer intervals of focus compared to assignments completed in the control condition for many, but not all, students. This study provides evidence that notification suppression is associated with measurable differences in programming engagement and behavior. We also find a remarkable bimodality in the effect across students -- many students are positively affected, a small number are negatively affected, and very few experience little or no effect. This finding is consistent with other studies in diverse disciplines. Implications. Our results show that, for many students, phone notification suppression tools, such as Do Not Disturb, can improve attention and focus. Implications apply to educational settings (do-not-disturb as an intervention) and scholarship (understanding the effects of phone distraction).

Kartik Goyal, Chris Dyer, Christopher Warren et al.

We propose a deep and interpretable probabilistic generative model to analyze glyph shapes in printed Early Modern documents. We focus on clustering extracted glyph images into underlying templates in the presence of multiple confounding sources of variance. Our approach introduces a neural editor model that first generates well-understood printing phenomena like spatial perturbations from template parameters via interpertable latent variables, and then modifies the result by generating a non-interpretable latent vector responsible for inking variations, jitter, noise from the archiving process, and other unforeseen phenomena associated with Early Modern printing. Critically, by introducing an inference network whose input is restricted to the visual residual between the observation and the interpretably-modified template, we are able to control and isolate what the vector-valued latent variable captures. We show that our approach outperforms rigid interpretable clustering baselines (Ocular) and overly-flexible deep generative models (VAE) alike on the task of completely unsupervised discovery of typefaces in mixed-font documents.

Christopher Warren, Eman El-Sheikh, Nhien-An Le-Khac

With the advance of technology, Criminal Justice agencies are being confronted with an increased need to investigate crimes perpetuated partially or entirely over the Internet. These types of crime are known as cybercrimes. In order to conceal illegal online activity, criminals often use private browsing features or browsers designed to provide total browsing privacy. The use of private browsing is a common challenge faced in for example child exploitation investigations, which usually originate on the Internet. Although private browsing features are not designed specifically for criminal activity, they have become a valuable tool for criminals looking to conceal their online activity. As such, Technological Crime units often focus their forensic analysis on thoroughly examining the web history on a computer. Private browsing features and browsers often require a more in-depth, post mortem analysis. This often requires the use of multiple tools, as well as different forensic approaches to uncover incriminating evidence. This evidence may be required in a court of law, where analysts are often challenged both on their findings and on the tools and approaches used to recover evidence. However, there are very few research on evaluating of private browsing in terms of privacy preserving as well as forensic acquisition and analysis of privacy preserving internet browsers. Therefore in this chapter, we firstly review the private mode of popular internet browsers. Next, we describe the forensic acquisition and analysis of Browzar, a privacy preserving internet browser and compare it with other popular internet browsers