arXiv subjects

Nurit Kirshenbaum

Publications and source records attributed to Nurit Kirshenbaum.

4 recordsLinked to original sources

arXiv2024

Illustrating Transition Scenarios to Renewable Energy in Hawaii with ProjecTable

Creating engaging and immersive data visualization tools has become increasingly significant for a wide range of users who want to display their data in a meaningful way. However, this can be limiting for individuals with varying levels of coding expertise. There are specific needs, such as visualizing complex data in easily understandable ways, highlighting real-world problems, or telling a story with data. The Makawalu Visualization Environment (VE) package aims to address these needs through three distinct modular tools: Author, Presenter, and Editor. These tools work together to facilitate different use cases based on the user's requirements. This paper discusses the latest version of the ProjecTable and focuses on the design and usage of the Makawalu VE Author and Presenter tools.

cs.HC↗

arXiv2024

ArticulatePro: A Comparative Study on a Proactive and Non-Proactive Assistant in a Climate Data Exploration Task

Recent advances in Natural Language Interfaces (NLIs) and Large Language Models (LLMs) have transformed our approach to NLP tasks, shifting the focus towards a more Pragmatics-based approach. This shift enables more natural interactions between humans and voice assistants, which have been historically difficult to achieve. Pragmatics involves understanding how users often talk out of turn, interrupt one another, or provide relevant information without being explicitly asked (maxim of quantity). To explore this, we developed a digital assistant that continuously listens to conversations and proactively generates relevant visualizations during data exploration tasks. In a within-subject study, participants interacted with both proactive and non-proactive versions of a voice assistant while exploring the Hawaii Climate Data Portal (HCDP). Results suggest that the proactive assistant enhanced user engagement and facilitated quicker insights. Our study highlights the potential of Pragmatic, proactive AI in NLIs and identifies key challenges in its implementation, offering insights for future research.

cs.HC↗

arXiv2024

Space to Teach: Content-Rich Canvases for Visually-Intensive Education

With the decreasing cost of consumer display technologies making it easier for universities to have larger displays in classrooms, and the ubiquitous use of online tools such as collaborative whiteboards for remote learning during the COVID-19 pandemic, combining the two can be useful in higher education. This is especially true in visually intensive classes, such as data visualization courses, that can benefit from additional "space to teach," coined after the "space to think" sense-making idiom. In this paper, we reflect on our approach to using SAGE3, a collaborative whiteboard with advanced features, in higher education to teach visually intensive classes, provide examples of activities from our own visually-intensive courses, and present student feedback. We gather our observations into usage patterns for using content-rich canvases in education.

cs.HC↗

arXiv2024

Abstracted Trajectory Visualization for Explainability in Reinforcement Learning

Explainable AI (XAI) has demonstrated the potential to help reinforcement learning (RL) practitioners to understand how RL models work. However, XAI for users who do not have RL expertise (non-RL experts), has not been studied sufficiently. This results in a difficulty for the non-RL experts to participate in the fundamental discussion of how RL models should be designed for an incoming society where humans and AI coexist. Solving such a problem would enable RL experts to communicate with the non-RL experts in producing machine learning solutions that better fit our society. We argue that abstracted trajectories, that depicts transitions between the major states of the RL model, will be useful for non-RL experts to build a mental model of the agents. Our early results suggest that by leveraging a visualization of the abstracted trajectories, users without RL expertise are able to infer the behavior patterns of RL.

cs.HC↗