Social engineering through email and phishing is an ever-present threat to users’ daily digital security. To combat the lack of accessible, adaptive training tools, we developed SEA-u-lator, a privacy-preserving Chrome extension that simulates phishing attacks directly within Gmail. The extension includes features such as real-time feedback, adjustable phishing frequency, and an optional post-detection survey. The system inserts realistic phishing emails and provides real-time, on-device feedback after user interaction without modifying actual mailbox data. All data are locally accessible in a JSON file for reflection or research use. This demo demonstrates how integrated simulations can support practical training while maintaining privacy.

This paper introduces AnVILMEDFORD, an R package that aims to enable MEDFORD metadata annotation from within the cloud based AnVIL platform for computational analysis. AnVILMEDFORD helps solve the problem of documentation within the AnVIL environment by providing bidirectional tools that both allow a user to generate MEDFORD documentation from a workspace, and also allow a user to update an AnVIL workspace starting with a MEDFORD meta-data file. AnVILMEDFORD accomplishes this by leveraging the Bioconductor AnVIL package and the AnVIL API to download and process AnVIL metadata, Tree-sitter-medford module along with the AnVIL API to update the workspace in the cloud. An implementation is available at https://github.com/bjstubbs/AnVILMEDFORD

As social media continues to grow as a space for emotional self-disclosure, it is important to understand whether self-disclosure acts as a causal factor impacting positive outcomes for users. Thus we developed Chirp, an anonymous social media sandbox space designed to explore the underlying effects of disclosure within online spaces. Users in Chirp are prompted to self-disclose moods and emotions using emojis. Through a between-subjects study among a cohort of first-year undergraduate student users on Chirp, we evaluate the effect of self-disclosure within semi-private online spaces on social support. While Chirp use does not show a significant increase in measured feelings of social support, user responses suggest that self-disclosure in Chirp may provide more social support than typical social media use or simple mood tracking over a two-week period. Our findings indicate that even in pseudo-anonymous, low-bandwidth communication platforms, self-disclosure may cause increased feelings of social support. This work highlights the impact of communication in semi-private online spaces on perceived social support.

Physical reading rulers are simple yet effective interventions that help readers with dyslexia. Digital reading rulers may offer similar benefits. Given their potential value, we provide the following contributions: (1) We host focus group sessions including people with dyslexia to build upon their lived experiences, (2) We provide evidence for designs that are effective and preferred, (3) We measure reading gains of rulers for readers with and without dyslexia. Using inclusive design principles, we arrive at four digital ruler designs - Grey Bar, Lightbox, Shade, and Underline. For the first time, we offer a comprehensive evaluation of digital ruler effectiveness on 91 crowdsourced readers with dyslexia and 86 without. Considering reading speed, comprehension, and preference, many readers benefit from these rulers, with the largest gains among readers with dyslexia. Rulers designed by readers with dyslexia increased the reading speeds of readers with dyslexia, supporting the need for inclusive design practices.

Reading large tables on small mobile screens presents serious usability challenges that can be addressed, in part, by better table formatting. However, there are few evidenced-based guidelines for formatting mobile tables to improve readability. For this work, we first conducted a survey to investigate how people interact with tables on mobile devices and conducted a study with designers to identify which design considerations are most critical. Based on these findings, we designed and conducted three large scale studies with remote crowdworker participants. Across the studies, we analyze over 14,000 trials from 590 participants who each viewed and answered questions about 28 diverse tables rendered in different formats. We find that smaller cell padding and frozen headers lead to faster task completion, and that while zebra striping and row borders do not speed up tasks, they are still subjectively preferred by participants.

The amount of text people need to read and understand grows daily. Software defaults, designers, or publishers often choose the fonts people read in. However, matching individuals with a faster font could help them cope with information overload. We collaborated with typographers to (1) select eight fonts designed for digital reading to systematically compare their effectiveness and to (2) understand how font and reader characteristics affect reading speed. We collected font preferences, reading speeds, and characteristics from 252 crowdsourced participants in a remote readability study. We use font and reader characteristics to train FontMART, a learning to rank model that automatically orders a set of eight fonts per participant by predicted reading speed. FontMART’s fastest font prediction shows an average increase of 14–25 WPM compared to other font defaults, without hindering comprehension. This encouraging evidence provides motivation for adding our personalized font recommendation to future interactive systems.

In our age of ubiquitous digital displays, adults often read in short, opportunistic interludes. In this context of Interlude Reading , we consider if manipulating font choice can improve adult readers’ reading outcomes. Our studies normalize font size by human perception and use hundreds of crowdsourced participants to provide a foundation for understanding, which fonts people prefer and which fonts make them more effective readers. Participants’ reading speeds (measured in words-per-minute (WPM)) increased by 35% when comparing fastest and slowest fonts without affecting reading comprehension. High WPM variability across fonts suggests that one font does not fit all. We provide font recommendations related to higher reading speed and discuss the need for individuation, allowing digital devices to match their readers’ needs in the moment. We provide recommendations from one of the most significant online reading efforts to date. To complement this, we release our materials and tools with this article.

The life cycle of a peer-produced dataset follows the phases of growth, maturity, and decline. Paying crowdworkers is a proven method to collect and organize information into structured tables. However, these tabular representations may contain inaccuracies due to errors or data changing over time. Thus, the maturation phase of a dataset can benefit from the additional human examination. One method to improve accuracy is to recruit additional paid crowdworkers to verify and correct errors. An alternative method relies on unpaid contributors, collectively editing the dataset during regular use. We describe two case studies to examine different strategies for human verification and maintenance of in-flux tabular datasets. The first case study examines traditional micro-task verification strategies with paid crowdworkers, while the second examines long-term maintenance strategies with unpaid contributions from non-crowdworkers. Two paid verification strategies that produced more accurate corrections at a lower cost per accurate correction were redundant data collection followed by final verification from a trusted crowdworker and allowing crowdworkers to review any data freely. In the unpaid maintenance strategies, contributors provided more accurate corrections when asked to review data matching their interests. This research identifies considerations and future approaches to collectively improving information accuracy and longevity of tabular information.

In-person user studies show that designers draw inspiration by looking at their peers' work while sketching. To recreate this behavior in a virtual environment, we developed Sketchy, a web-based drawing application where users sketch in virtual rooms and use the "Peek'' functionality to gain ideas from their peers' sketches in real-time. To assess if "Peek'' supports individual creativity through finding inspiration, students from a Human-Computer Interaction class sketched user interface design tasks in two studies. Study 1 compares creativity measures with and without Peek between two groups of students, where self-reports reveal Peek increases satisfaction with their final sketch and better supports individual creativity. Study 2 took place in a large classroom, where 90 students, all with Peek enabled, completed different design tasks. Peeking led students to report an intention to change their sketch 18% of the time in Study 1 and 17% of the time in Study 2. Student designers were influenced by sketches that seem closer to completion, contain more details, and are carefully drawn. They were also about three times more likely to clear their canvas and start over if they found a sketch inspirational. Furthermore, sketches created by students with more sketching and design experience influence less experienced student designers. This work explores the directions and benefits of incorporating digital peeking to support individual creativity within a student designer's classroom experience to create more satisfactory final sketches.

Structured datasets are difficult to keep up-to-date since the underlying facts evolve over time; curated data about business financials, organizational hierarchies, or drug interactions are constantly changing. Drafty is a platform that enlists visitors of an editable dataset to become ``user-editors'' to help solve this problem. It records and analyzes user-editors' within-page interactions to construct user interest profiles, creating a cyclical feedback mechanism that enables Drafty to target requests for specific corrections from user-editors. To validate the automatically generated user interest profiles, we surveyed participants who performed self-created tasks with Drafty and found their user interest score was 3.2 higher on data they were interested in versus data they had no interest in. Next, a 7-month live experiment compared the efficacy of user-editor corrections depending on whether they were asked to review data that matched their interests. Our findings suggest that user-editors are approximately 3 times more likely to provide accurate corrections for data matching their interest profiles, and about 2 times more likely to provide corrections in the first place.