Please find the full list of my publications from Google Scholar
2024
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Sensor2Scene: Foundation Model-driven Interactive Realities
Yunqi Guo, Kaiyuan Hou, Zhenyu Yan, and 3 more authors
FMSys, 2024
Augmented Reality (AR) is acclaimed for its potential to bridge the physical and virtual worlds. Yet, current integration between these realms often lacks a deep understanding of the physical environment and the subsequent scene generation that reflects this understanding. This research introduces Sensor2Scene, a novel system framework designed to enhance user interactions with sensor data through AR. At its core, an AI agent leverages large language models (LLMs) to decode subtle information from sensor data, constructing detailed scene descriptions for visualization. To enable these scenes to be rendered in AR, we decompose the scene creation process into tasks of text-to-3D model generation and spatial composition, allowing new AR scenes to be sketched from the descriptions. We evaluated our framework using an LLM evaluator based on five metrics on various datasets to examine the correlation between sensor readings and corresponding visualizations, and demonstrated the system’s effectiveness with scenes generated from end-to-end. The results highlight the potential of LLMs to understand IoT sensor data. Furthermore, generative models can aid in transforming these interpretations into visual formats, thereby enhancing user interaction. This work not only displays the capabilities of Sensor2Scene but also lays a foundation for advancing AR with the goal of creating more immersive and contextually rich experiences.
2023
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Sign-to-911: Emergency Call Service for Sign Language Users with Assistive AR Glasses
Yunqi Guo, Jinghao Zhao, Boyan Ding, and 6 more authors
MobiCom, 2023
Sign-to-911 offers a compact mobile system solution to fast and runtime American Sign Language (ASL) and English translations. It is designated as 911 call services for ASL users with hearing disabilities upon emergencies. It enables bidirectional translations of ASL-to-English and English-to-ASL. The signer wears the AR glasses, runs Sign-to-911 on his/her smartphone and glasses, and interacts with a 911 operator. The design of Sign-to-911 departs from the popular deep learning based solution paradigm, and adopts simpler traditional AI/machine learning (ML) models. The key is to exploit ASL linguistic features to simplify the model structures and improve accuracy and speed. It further leverages recent component solutions from graphics, vision, natural language processing, and AI/ML. Our evaluation with six ASL signers and 911 call records has confirmed its viability.
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LDRP: Device-Centric Latency Diagnostic and Reduction for Cellular Networks without Root
Zhaowei Tan, Jinghao Zhao, Yuanjie Li, and 3 more authors
IEEE TMC, 2023
2021
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A Model Obfuscation Approach to IoT Security
Yunqi Guo, Zhaowei Tan, Kaiyuan Chen, and 2 more authors
IEEE CNS, 2021
Co-first author
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On Key Reinstallation Attacks over 4G LTE Control-Plane: Feasibility and Negative Impact
Muhammad Taqi Raza, Yunqi Guo, Songwu Lu, and 1 more author
ACM ACSAC, 2021
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SecureSIM: Rethinking Authentication and Access Control for SIM/eSIM
Jinghao Zhao, Boyan Ding, Yunqi Guo, and 2 more authors
AMC MobiCom, 2021
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Data-Plane Signaling in Cellular IoT: Attacks and Defense
Zhaowei Tan, Boyan Ding, Jinghao Zhao, and 2 more authors
ACM MobiCom, 2021
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Experience: a Five-Year Retrospective of MobileInsight
Yuanjie Li, Chunyi Peng, Zhehui Zhang, and 9 more authors
ACM MobiCom, 2021
2020
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Towards Model-Centric Security for IoT Systems
Yunqi Guo, Zhaowei Tan, and Songwu Lu
IEEE ICCCN, 2020
Invited paper