Documents often have paragraphs packed with numbers that are difficult to extract, compare, and interpret. To help readers make sense of data in text, we introduce the concept of Charagraphs: dynamically generated interactive charts and annotations for in-situ visualization, comparison, and manipulation of numeric data included within text. Three Charagraph characteristics are defined: leveraging related textual information about data; integrating textual and graphical representations; and interacting at different contexts. We contribute a document viewer to select in-text data; generate and customize Charagraphs; merge and refine a Charagraph using other in-text data; and identify, filter, compare, and sort data synchronized between text and visualization. Results of a study show participants can easily create Charagraphs for diverse examples of data-rich text, and when answering questions about data in text, participants were more correct compared to only reading text.
https://doi.org/10.1145/3544548.3581091
Extracting underlying data from rasterized charts is tedious and inaccurate; values might be partially occluded or hard to distinguish, and the quality of the image limits the precision of the data being recovered. To address these issues, we introduce a semi-automatic system leveraging vector charts to extract the underlying data easily and accurately. The system is designed to make the most of vector information by relying on a drag-and-drop interface combined with selection, filtering, and previsualization features. A user study showed that participants spent less than 4 minutes to accurately recover data from charts published at CHI with diverse styles, thousands of data points, a combination of different encodings, and elements partially or completely occluded. Compared to other approaches relying on raster images, our tool successfully recovered all data, even when hidden, with a 78% lower relative error.
https://doi.org/10.1145/3544548.3581113
Multiverse analysis—a paradigm for statistical analysis that considers all combinations of reasonable analysis choices in parallel—promises to improve transparency and reproducibility. Although recent tools help analysts specify multiverse analyses, they remain difficult to use in practice. In this work, we identify debugging as a key barrier due to the latency from running analyses to detecting bugs and the scale of metadata processing needed to diagnose a bug. To address these challenges, we prototype a command-line interface tool, Multiverse Debugger, which helps diagnose bugs in the multiverse and propagate fixes. In a qualitative lab study (n=13), we use Multiverse Debugger as a probe to develop a model of debugging workflows and identify specific challenges, including difficulty in understanding the multiverse's composition. We conclude with design implications for future multiverse analysis authoring systems.
https://doi.org/10.1145/3544548.3581099
There are myriad ways to analyse a dataset. But which one to trust? In the face of such uncertainty, analysts may adopt multiverse analysis: running all reasonable analyses on the dataset. Yet this is cognitively and technically difficult with existing tools—how does one specify and execute all combinations of reasonable analyses of a dataset?—and often requires discarding existing workflows. We present multiverse, a tool for implementing multiverse analyses in R with expressive syntax supporting existing computational notebook workflows. multiverse supports building up a multiverse through local changes to a single analysis and optimises execution by pruning redundant computations. We evaluate how multiverse supports programming multiverse analyses using (a) principles of cognitive ergonomics to compare with two existing multiverse tools; and (b) case studies based on semi-structured interviews with researchers who have successfully implemented an end-to-end analysis using multiverse. We identify design tradeoffs (e.g. increased flexibility versus learnability), and suggest future directions for multiverse tool design.
https://doi.org/10.1145/3544548.3580726
We present Z-Ring, a wearable ring that enables gesture input, object detection, user identification, and interaction with passive user interface (UI) elements using a single sensing modality and a single point of instrumentation on the finger. Z-Ring uses active electrical field sensing to detect changes in the hand's electrical impedance caused by finger motions or contact with external surfaces. We develop a diverse set of interactions and evaluate them with 21 users. We demonstrate: (1) Single- and two-handed gesture recognition with up to 93\% accuracy (2) Tangible input with a set of passive touch UI elements, including buttons, a continuous 1D slider, and a continuous 2D trackpad with 91.8\% accuracy, <4.4 cm MAE, and <4.1cm MAE, respectively (3) Object recognition across six household objects with 94.5\% accuracy (4) User identification among 14 users with 99\% accuracy. Z-Ring's sensing methodology uses only a single co-located electrode pair for both receiving and sensing, lending itself well to future miniaturization for use in on-the-go scenarios.
https://doi.org/10.1145/3544548.3581422
Electromyography (EMG) has been explored as an HCI input modality following a long history of success for prosthesis control. While EMG has the potential to address a range of hands-free interaction needs, it has yet to be widely accepted outside of prosthetics due to a perceived lack of robustness and intuitiveness. To understand how EMG input systems can be better designed, we sampled the ACM digital library to identify limitations in the approaches taken. Leveraging these works in combination with our research group's extensive interdisciplinary experience in this field, four themes emerged (1) interaction design, (2) model design, (3) system evaluation, and (4) reproducibility. Using these themes, we provide a step-by-step framework for designing EMG-based input systems to strengthen the foundation on which EMG-based interactions are built. Additionally, we provide a call-to-action for researchers to unlock the hidden potential of EMG as a widely applicable and highly usable input modality.
https://doi.org/10.1145/3544548.3580962