Zobrazeno 1 - 10
of 103
pro vyhledávání: '"Koh, Eunyee"'
Sound plays a crucial role in enhancing user experience and immersiveness in Augmented Reality (AR). However, current platforms lack support for AR sound authoring due to limited interaction types, challenges in collecting and specifying context info
Externí odkaz:
http://arxiv.org/abs/2405.07089
The ubiquity and on-the-go availability of mobile devices makes them central to many tasks such as interpersonal communication and media consumption. However, despite the potential of mobile devices for on-demand exploratory data visualization, exist
Externí odkaz:
http://arxiv.org/abs/2404.11602
Autor:
Chen, Chen, Hoffswell, Jane, Guo, Shunan, Rossi, Ryan, Chan, Yeuk-Yin, Du, Fan, Koh, Eunyee, Liu, Zhicheng
Computational notebooks such as Jupyter are popular for exploratory data analysis and insight finding. Despite the module-based structure, notebooks visually appear as a single thread of interleaved cells containing text, code, visualizations, and ta
Externí odkaz:
http://arxiv.org/abs/2308.09802
Autor:
Narechania, Arpit, Du, Fan, Sinha, Atanu R, Rossi, Ryan A., Hoffswell, Jane, Guo, Shunan, Koh, Eunyee, Navathe, Shamkant B., Endert, Alex
Selecting relevant data subsets from large, unfamiliar datasets can be difficult. We address this challenge by modeling and visualizing two kinds of auxiliary information: (1) quality - the validity and appropriateness of data required to perform cer
Externí odkaz:
http://arxiv.org/abs/2303.01575
Autor:
Huang, Chieh-Yang, Hsu, Ting-Yao, Rossi, Ryan, Nenkova, Ani, Kim, Sungchul, Chan, Gromit Yeuk-Yin, Koh, Eunyee, Giles, Clyde Lee, Huang, Ting-Hao 'Kenneth'
Good figure captions help paper readers understand complex scientific figures. Unfortunately, even published papers often have poorly written captions. Automatic caption generation could aid paper writers by providing good starting captions that can
Externí odkaz:
http://arxiv.org/abs/2302.12324
Autor:
Aponte, Ryan, Rossi, Ryan A., Guo, Shunan, Hoffswell, Jane, Lipka, Nedim, Xiao, Chang, Chan, Gromit, Koh, Eunyee, Ahmed, Nesreen
In this work, we introduce a hypergraph representation learning framework called Hypergraph Neural Networks (HNN) that jointly learns hyperedge embeddings along with a set of hyperedge-dependent embeddings for each node in the hypergraph. HNN derives
Externí odkaz:
http://arxiv.org/abs/2212.14077
Graph Neural Networks (GNNs) have become increasingly important in recent years due to their state-of-the-art performance on many important downstream applications. Existing GNNs have mostly focused on learning a single node representation, despite t
Externí odkaz:
http://arxiv.org/abs/2212.13709
Autor:
Chen, April, Rossi, Ryan, Lipka, Nedim, Hoffswell, Jane, Chan, Gromit, Guo, Shunan, Koh, Eunyee, Kim, Sungchul, Ahmed, Nesreen K.
Learning fair graph representations for downstream applications is becoming increasingly important, but existing work has mostly focused on improving fairness at the global level by either modifying the graph structure or objective function without t
Externí odkaz:
http://arxiv.org/abs/2212.12040
Autor:
Koh, Eunyee
The importance of digital information collections is growing. Collections are typically represented with text-only, in a linear list format, which turns out to be a weak representation for cognition. We learned this from empirical research in cogniti
Externí odkaz:
http://hdl.handle.net/1969.1/ETD-TAMU-2912
ARShopping: In-Store Shopping Decision Support Through Augmented Reality and Immersive Visualization
Online shopping gives customers boundless options to choose from, backed by extensive product details and customer reviews, all from the comfort of home; yet, no amount of detailed, online information can outweigh the instant gratification and hands-
Externí odkaz:
http://arxiv.org/abs/2207.07643