Publications

@inproceedings{qin_utilizing_2024,

title = {Utilizing Generative AI for VR Exploration Testing: A Case Study},

author = {X. Qin and G. Weaver},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85213332710&doi=10.1145%2f3691621.3694955&partnerID=40&md5=8f3dc03520214cd2e270ed41a0fc0e19},

doi = {10.1145/3691621.3694955},

isbn = {979-840071249-4 (ISBN)},

year  = {2024},

date = {2024-01-01},

booktitle = {Proc. - ACM/IEEE Int. Conf. Autom. Softw. Eng. Workshops, ASEW},

pages = {228–232},

publisher = {Institute of Electrical and Electronics Engineers Inc.},

abstract = {As the virtual reality (VR) industry expands, the need for automated GUI testing for applications is growing rapidly. With its long-term memory and ability to process mixed data, including images and text, Generative AI (GenAI) shows the potential to understand complex user interfaces. In this paper, we conduct a case study to investigate the potential of using GenAI for field of view (FOV) analysis in VR exploration testing. Specifically, we examine how the model can assist in test entity selection and test action suggestions. Our experiments demonstrate that while GPT-4o achieves a 63% accuracy rate in object identification within an arbitrary FOV, it struggles with object organization and localization. We also identify critical contexts that can improve the accuracy of suggested actions across multiple FOVs. Finally, we discuss the limitations found during the experiment and offer insights into future research directions. © 2024 ACM.},

keywords = {Ability testing, Accuracy rate, Case Study, Case-studies, Entity selections, Field of views, Generative adversarial networks, GUI Exploration Testing, GUI testing, Localisation, Long term memory, Mixed data, Object identification, Object recognition, Virtual environments, Virtual Reality},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{li_pixelgen_2024,

title = {PixelGen: Rethinking Embedded Cameras for Mixed-Reality},

author = {K. Li and M. Gulati and D. Shah and S. Waskito and S. Chakrabarty and A. Varshney},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105002721208&doi=10.1145%2f3636534.3696216&partnerID=40&md5=97ee680318c72552b3e642aa57aaeca5},

doi = {10.1145/3636534.3696216},

isbn = {979-840070489-5 (ISBN)},

year  = {2024},

date = {2024-01-01},

booktitle = {ACM MobiCom - Proc. Int. Conf. Mob. Comput. Netw.},

pages = {2128–2135},

publisher = {Association for Computing Machinery, Inc},

abstract = {Mixed-reality headsets offer new ways to perceive our environment. They employ visible spectrum cameras to capture and display the environment on screens in front of the user's eyes. However, these cameras lead to limitations. Firstly, they capture only a partial view of the environment. They are positioned to capture whatever is in front of the user, thus creating blind spots during complete immersion and failing to detect events outside the restricted field of view. Secondly, they capture only visible light fields, ignoring other fields like acoustics and radio that are also present in the environment. Finally, these power-hungry cameras rapidly deplete the mixed-reality headset's battery. We introduce PixelGen to rethink embedded cameras for mixed-reality headsets. PixelGen proposes to decouple cameras from the mixed-reality headset and balance resolution and fidelity to minimize the power consumption. It employs low-resolution, monochrome image sensors and environmental sensors to capture the surroundings around the headset. This approach reduces the system's communication bandwidth and power consumption. A transformer-based language and image model process this information to overcome resolution trade-offs, thus generating a higher-resolution representation of the environment. We present initial experiments that show PixelGen's viability. © 2024 Copyright is held by the owner/author(s). Publication rights licensed to ACM.},

keywords = {Blind spots, embedded systems, Embedded-system, Field of views, Language Model, Large language model, large language models, Mixed reality, Networking, Partial views, Pixels, Power, Visible spectrums},

pubstate = {published},

tppubtype = {inproceedings}

}