Computer Science

Crowdwork often entails tackling cognitively-demanding and time-consuming tasks. Crowdsourcing can be used for complex annotation tasks, from medical imaging to geospatial data, and such data powers sensitive applications, such as health diagnostics or autonomous driving. However, the existence and prevalence of underperforming crowdworkers is well-recognized, and can pose a threat to the validity of crowdsourcing. In this study, we propose the use of a computational framework to identify clusters of underperforming workers using clickstream trajectories. We focus on crowdsourced geopolitical forecasting. The framework can reveal different types of underperformers, such as workers with forecasts whose accuracy is far from the consensus of the crowd, those who provide low-quality explanations for their forecasts, and those who simply copy-paste their forecasts from other users. Our study suggests that clickstream clustering and analysis are fundamental tools to diagnose the performance of crowdworkers in platforms leveraging the wisdom of crowds.

In visualization education, both science and humanities, the literature is often divided into two parts: the design aspect and the analysis of the visualization. However, we find limited discussion on how to motivate and engage visualization students in the classroom. In the field of Writing Studies, researchers develop tools and frameworks for student peer review of writing. Based on the literature review from the field of Writing Studies, this paper proposes a new framework to implement visualization peer review in the classroom to engage today's students. This framework can be customized for incremental and double-blind review to inspire students and reinforce critical thinking about visualization.

The goal of this project is to create an inexpensive, lightweight, wearable assistive device that can measure hand or finger movements accurately enough to identify a range of hand gestures. One eventual application is to provide assistive technology and sign language detection for the hearing impaired. My system, called LiTe (Light-based Technology), uses optical fibers embedded into a wristband. The wrist is an optimal place for the band since the light propagation in the optical fibers is impacted even by the slight movements of the tendons in the wrist when gestures are performed. The prototype incorporates light dependent resistors to measure these light propagation changes. When creating LiTe, I considered a variety of fiber materials, light frequencies, and physical shapes to optimize the tendon movement detection so that it can be accurately correlated with different gestures. I implemented and evaluated two approaches for gesture recognition. The first uses an algorithm that combines moving averages of sensor readings with gesture sensor reading signatures to determine the current gesture. The second uses a neural network trained on a labelled set of gesture readings to recognize gestures. Using the signature-based approach, I was able to achieve a 99.8% accuracy at recognizing distinct gestures. Using the neural network the recognition accuracy was 98.8%. This shows that high accuracy is feasible using both approaches. The results indicate that this novel method of using fiber optics-based sensors is a promising first step to creating a gesture recognition system.

LinkRing is a novel wearable tactile display for providing multi-contact and multi-modal stimuli at the finger. The system of two five-bar linkage mechanisms is designed to operate with two independent contact points, which combined can provide such stimulation as shear force and twist stimuli, slippage, and pressure. The proposed display has a lightweight and easy to wear structure. Two experiments were carried out in order to determine the sensitivity of the finger surface, the first one aimed to determine the location of the contact points, and the other for discrimination the slippage with varying rates. The results of the experiments showed a high level of pattern recognition.

We introduce the concept of LabLinking: a technology-based interconnection of experimental laboratories across institutions, disciplines, cultures, languages, and time zones - in other words experiments without borders. In particular, we introduce LabLinking levels (LLL), which define the degree of tightness of empirical interconnection between labs. We describe the technological infrastructure in terms of hard- and software required for the respective LLLs and present examples of linked laboratories along with insights about the challenges and benefits. In sum, we argue that linked labs provide a unique platform for a continuous exchange between scientists and experimenters, thereby enabling a time synchronous execution of experiments performed with and by decentralized user and researchers, improving outreach and ease of subject recruitment, allowing to establish new experimental designs and to incorporate a panoply of complementary biosensors, devices, hard- and software solutions.

Digital humanities are rooted in text analysis. However, most visualization paradigms use only categoric, ordered or quantitative data. Literal text must be considered a base data type to encode into visualizations. Literal text offers functional, perceptual, cognitive, semantic and operational benefits. These are briefly illustrated with a subset of sample visualizations focused on semantic word sequences, indicating benefits over standard graphs, maps, treemaps, bar charts and narrative layouts.

Loneliness does not only have emotional implications on a person but also on his/her well-being. The study of loneliness has been challenging and largely inconclusive in findings because of the several factors that might correlate to the phenomenon. We present one approach to predicting this event by discovering patterns of language associated with loneliness. Our results show insights and promising directions for mining text from instant messaging to predict loneliness.

Recent graphical interfaces offer direct manipulation mechanisms for authoring visualizations, but are largely restricted to static output. To author interactive visualizations, users must instead turn to textual specification; but, such approaches impose a higher technical burden than their graphical counterparts. To bridge this gap, we introduce interaction design by demonstration: a novel method for authoring interaction techniques via direct manipulation. Users perform an interaction (e.g., button clicks, drags, or key presses) directly on the visualization they are editing. The system interprets this performance using a set of heuristics, and produces suggestions of possible interaction designs. Heuristics account for properties of the interaction (e.g., target and event type) as well as the visualization (e.g., mark and scale types, and multiple views). Interaction design suggestions are displayed as thumbnails; users can preview and test these suggestions, iteratively refine them through additional demonstrations, and finally apply and customize them via property inspectors. To evaluate our approach, we instantiate it in Lyra, an existing visualization design environment. We demonstrate its expressive extent with a gallery of diverse examples, and evaluate its usability through a first-use study and via an analysis of its cognitive dimensions. We find that, in Lyra, interaction design by demonstration enables users to rapidly express a wide range of interactive visualizations.

Design research is important for understanding and interrogating how emerging technologies shape human experience. However, design research with Machine Learning (ML) is relatively underdeveloped. Crucially, designers have not found a grasp on ML uncertainty as a design opportunity rather than an obstacle. The technical literature points to data and model uncertainties as two main properties of ML. Through post-phenomenology, we position uncertainty as one defining material attribute of ML processes which mediate human experience. To understand ML uncertainty as a design material, we investigate four design research case studies involving ML. We derive three provocative concepts: thingly uncertainty: ML-driven artefacts have uncertain, variable relations to their environments; pattern leakage: ML uncertainty can lead to patterns shaping the world they are meant to represent; and futures creep: ML technologies texture human relations to time with uncertainty. Finally, we outline design research trajectories and sketch a post-phenomenological approach to human-ML relations.

Choropleth maps are among the most common visualization techniques used to present geographical data. These maps require an equal-area projection but there are no clear criteria for selecting one. We collaborated with 20 social scientists researching on the Global South, interested in using choropleth maps, to investigate their design choices according to their research tasks. We asked them to design world choropleth maps through a survey, and analyzed their answers both qualitatively and quantitatively. The results suggest that the design choices of map projection, center, scale, and color scheme, were influenced by their personal research goals and the tasks. The projection was considered the most important choice and the Equal Earth projection was the most common projection used. Our study takes the first substantial step in investigating projection choices for world choropleth maps in applied visualization research.