20 citations · 58 across the 9 of their papers we have counts for
12 papers
VAINE: Visualization and AI for Natural Experiments
Grace Guo, Maria Glenski, ZhuanYi Shaw +4
Natural experiments are observational studies where the assignment of treatment conditions to different populations occurs by chance "in the wild". Researchers from fields such as…
Towards Trustworthy Deception Detection: Benchmarking Model Robustness across Domains, Modalities, and Languages
Maria Glenski, Ellyn Ayton, Robin Cosbey +2
Evaluating model robustness is critical when developing trustworthy models not only to gain deeper understanding of model behavior, strengths, and weaknesses, but also to develop f…
Evaluating Deception Detection Model Robustness To Linguistic Variation
Maria Glenski, Ellyn Ayton, Robin Cosbey +2
With the increasing use of machine-learning driven algorithmic judgements, it is critical to develop models that are robust to evolving or manipulated inputs. We propose an extensi…
Measure Utility, Gain Trust: Practical Advice for XAI Researcher
Brittany Davis, Maria Glenski, William Sealy +1
Research into the explanation of machine learning models, i.e., explainable AI (XAI), has seen a commensurate exponential growth alongside deep artificial neural networks throughou…
Evaluating Neural Machine Comprehension Model Robustness to Noisy Inputs and Adversarial Attacks
Winston Wu, Dustin Arendt, Svitlana Volkova
We evaluate machine comprehension models' robustness to noise and adversarial attacks by performing novel perturbations at the character, word, and sentence level. We experiment wi…
CrossCheck: Rapid, Reproducible, and Interpretable Model Evaluation
Dustin Arendt, Zhuanyi Huang, Prasha Shrestha +3
Evaluation beyond aggregate performance metrics, e.g. F1-score, is crucial to both establish an appropriate level of trust in machine learning models and identify future model impr…
Steinhaus Filtration and Stable Paths in the Mapper
Dustin L. Arendt, Matthew Broussard, Bala Krishnamoorthy +2
We define a new filtration called the Steinhaus filtration built from a single cover based on a generalized Steinhaus distance, a generalization of Jaccard distance. The homology p…
Vulnerable to Misinformation? Verifi!
Alireza Karduni, Isaac Cho, Ryan Wesslen +5
We present Verifi2, a visual analytic system to support the investigation of misinformation on social media. On the one hand, social media platforms empower individuals and organiz…
Fishing for Clickbaits in Social Images and Texts with Linguistically-Infused Neural Network Models
Maria Glenski, Ellyn Ayton, Dustin Arendt +1
This paper presents the results and conclusions of our participation in the Clickbait Challenge 2017 on automatic clickbait detection in social media. We first describe linguistica…
Understanding Cognitive Depletion in Novice NMR Analysts
Lyndsey Franklin, Kyungsik Han, Zhuanyi Huang +2
We present the results of a user study with novice NMR analysts (N=19) involving a gamified simulation of the NMR analysis process. Participants solved randomly generated spectrum…
Measuring, Predicting and Visualizing Short-Term Change in Word Representation and Usage in VKontakte Social Network
Ian Stewart, Dustin Arendt, Eric Bell +1
Language in social media is extremely dynamic: new words emerge, trend and disappear, while the meaning of existing words can fluctuate over time. Such dynamics are especially nota…
SVEN: Informative Visual Representation of Complex Dynamic Structure
Dustin L. Arendt, Leslie M. Blaha
Graphs change over time, and typically variations on the small multiples or animation pattern is used to convey this dynamism visually. However, both of these classical techniques…