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DreamStruct: Understanding Slides and User Interfaces via Synthetic Data Generation

Autor: Peng, Yi-Hao, Huq, Faria, Jiang, Yue, Wu, Jason, Li, Amanda Xin Yue, Bigham, Jeffrey, Pavel, Amy

Enabling machines to understand structured visuals like slides and user interfaces is essential for making them accessible to people with disabilities. However, achieving such understanding computationally has required manual data collection and anno

Externí odkaz: http://arxiv.org/abs/2410.00201

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NoTeeline: Supporting Real-Time, Personalized Notetaking with LLM-Enhanced Micronotes

Autor: Huq, Faria, Samee, Abdus, Lin, David Chuan-en, Tang, Xiaodi Alice, Bigham, Jeffrey P.

Taking notes quickly while effectively capturing key information can be challenging, especially when watching videos that present simultaneous visual and auditory streams. Manually taken notes often miss crucial details due to the fast-paced nature o

Externí odkaz: http://arxiv.org/abs/2409.16493

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'This really lets us see the entire world:' Designing a conversational telepresence robot for homebound older adults

Autor: Hu, Yaxin, Stegner, Laura, Kotturi, Yasmine, Zhang, Caroline, Peng, Yi-Hao, Huq, Faria, Zhao, Yuhang, Bigham, Jeffrey P., Mutlu, Bilge

In this paper, we explore the design and use of conversational telepresence robots to help homebound older adults interact with the external world. An initial needfinding study (N=8) using video vignettes revealed older adults' experiential needs for

Externí odkaz: http://arxiv.org/abs/2405.15037

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'What's important here?': Opportunities and Challenges of Using LLMs in Retrieving Information from Web Interfaces

Autor: Huq, Faria, Bigham, Jeffrey P., Martelaro, Nikolas

Large language models (LLMs) that have been trained on a corpus that includes large amount of code exhibit a remarkable ability to understand HTML code. As web interfaces are primarily constructed using HTML, we design an in-depth study to see how LL

Externí odkaz: http://arxiv.org/abs/2312.06147

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Riemannian Functional Map Synchronization for Probabilistic Partial Correspondence in Shape Networks

Autor: Huq, Faria, Dey, Adrish, Yusuf, Sahra, Bazazian, Dena, Birdal, Tolga, Miolane, Nina

We consider the problem of graph-matching on a network of 3D shapes with uncertainty quantification. We assume that the pairwise shape correspondences are efficiently represented as \emph{functional maps}, that match real-valued functions defined ove

Externí odkaz: http://arxiv.org/abs/2111.14762

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Static and Animated 3D Scene Generation from Free-form Text Descriptions

Autor: Huq, Faria, Ahmed, Nafees, Iqbal, Anindya

Generating coherent and useful image/video scenes from a free-form textual description is technically a very difficult problem to handle. Textual description of the same scene can vary greatly from person to person, or sometimes even for the same per

Externí odkaz: http://arxiv.org/abs/2010.01549

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Review4Repair: Code Review Aided Automatic Program Repairing

Autor: Huq, Faria, Hasan, Masum, Pantho, Mahim Anzum Haque, Mahbub, Sazan, Iqbal, Anindya, Ahmed, Toufique

Context: Learning-based automatic program repair techniques are showing promise to provide quality fix suggestions for detected bugs in the source code of the software. These tools mostly exploit historical data of buggy and fixed code changes and ar

Externí odkaz: http://arxiv.org/abs/2010.01544

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