Search

Your search keyword '"Choi, Joonwon"' showing total 25 results
Start Over Author "Choi, Joonwon" Publication Year Range Last 10 years

Refine your results

25 results on '"Choi, Joonwon"'

2. Low-Shot Learning for Fictional Claim Verification

Author

Chadalapaka, Viswanath, Nguyen, Derek, Choi, JoonWon, Joshi, Shaunak, and Rostami, Mohammad

Subjects
Computer Science - Artificial Intelligence
Abstract

In this paper, we study the problem of claim verification in the context of claims about fictional stories in a low-shot learning setting. To this end, we generate two synthetic datasets and then develop an end-to-end pipeline and model that is tested on both benchmarks. To test the efficacy of our pipeline and the difficulty of benchmarks, we compare our models' results against human and random assignment results. Our code is available at https://github.com/Derposoft/plot_hole_detection., Comment: 6 pages

Published
2023

3. State Prediction of Human-in-the-Loop Multi-rotor System with Stochastic Human Behavior Model

Author

Choi, Joonwon, Byeon, Sooyung, and Hwang, Inseok

Subjects
Electrical Engineering and Systems Science - Systems and Control
Abstract

Reachability analysis is a widely used method to analyze the safety of a Human-in-the-Loop Cyber Physical System (HiLCPS). This strategy allows the HiLCPS to respond against an imminent threat in advance by predicting reachable states of the system. However, it could lead to an unnecessarily conservative reachable set if the prediction only relies on the system dynamics without explicitly considering human behavior, and thus the risk might be overestimated. To reduce the conservativeness of the reachability analysis, we present a state prediction method which takes into account a stochastic human behavior model represented as a Gaussian Mixture Model (GMM). In this paper, we focus on the multi-rotor in a near-collision situation. The stochastic human behavior model is trained using experimental data to represent human operators' evasive maneuver. Then, we can retrieve a human control input probability distribution from the trained stochastic human behavior model using the Gaussian Mixture Regression (GMR). The proposed algorithm predicts the probability distribution of the multi-rotor's future state based on the given dynamics and the retrieved human control input probability distribution. Besides, the proposed state prediction method considers the uncertainty of the initial state modeled as a GMM, which yields more robust performance. Human subject experiment results are provided to demonstrate the effectiveness of the proposed algorithm., Comment: This work has been submitted to IFAC for possible publication

Published
2022

6. Stochastic-skill-level-based Shared Control for Human Training in Urban Air Mobility Scenario.

Author

Byeon, Sooyung, Choi, Joonwon, Zhang, Yutong, and Hwang, Inseok

Subjects
RANDOM variables, LEARNING, USER experience, SOCIAL interaction, AIR pilots
Abstract

This article proposes a novel stochastic-skill-level-based shared control framework to assist human novices to emulate human experts in complex dynamic control tasks. The proposed framework aims to infer stochastic-skill-levels (SSLs) of the human novices and provide personalized assistance based on the inferred SSLs. SSL can be assessed as a stochastic variable that denotes the probability that the novice will behave similarly to experts. We propose a data-driven method that can characterize novice demonstrations as a novice model and expert demonstrations as an expert model, respectively. Then, our SSL inference approach utilizes the novice and expert models to assess the SSL of the novices in complex dynamic control tasks. The shared control scheme is designed to dynamically adjust the level of assistance based on the inferred SSL to prevent frustration or tedium during human training due to poorly imposed assistance. The proposed framework is demonstrated by a human subject experiment in a human training scenario for a remotely piloted urban air mobility vehicle. The results show that the proposed framework can assess the SSL and tailor the assistance for an individual in real time. The proposed framework is compared to practice-only training (no assistance) and a baseline shared control approach to test the human learning rates in the designed training scenario with human subjects. A subjective survey is also examined to monitor the user experience of the proposed framework. [ABSTRACT FROM AUTHOR]

Published
2024
Full Text
View/download PDF

13. Integration verification across software and hardware for a simple embedded system

Author

Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory, Erbsen, Andres, Gruetter, Samuel, Choi, Joonwon, Wood, Clark, Chlipala, Adam, Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory, Erbsen, Andres, Gruetter, Samuel, Choi, Joonwon, Wood, Clark, and Chlipala, Adam

Published
2022

17. Kami: a platform for high-level parametric hardware specification and its modular verification

Author

Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory, Choi, Joonwon, Vijayaraghavan, Muralidaran, Sherman, Benjamin, Chlipala, Adam, Arvind, Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory, Choi, Joonwon, Vijayaraghavan, Muralidaran, Sherman, Benjamin, Chlipala, Adam, and Arvind

Abstract

It has become fairly standard in the programming-languages research world to verify functional programs in proof assistants using induction, algebraic simplification, and rewriting. In this paper, we introduce Kami, a Coq library that enables similar expressive and modular reasoning for hardware designs expressed in the style of the Bluespec language. We can specify, implement, and verify realistic designs entirely within Coq, ending with automatic extraction into a pipeline that bottoms out in FPGAs. Our methodology, using labeled transition systems, has been evaluated in a case study verifying an infinite family of multicore systems, with cache-coherent shared memory and pipelined cores implementing (the base integer subset of) the RISC-V instruction set.

Published
2021

19. EverCrypt: A Fast, Verified, Cross-Platform Cryptographic Provider

Author

Protzenko, Jonathan, primary, Parno, Bryan, additional, Fromherz, Aymeric, additional, Hawblitzel, Chris, additional, Polubelova, Marina, additional, Bhargavan, Karthikeyan, additional, Beurdouche, Benjamin, additional, Choi, Joonwon, additional, Delignat-Lavaud, Antoine, additional, Fournet, Cedric, additional, Kulatova, Natalia, additional, Ramananandro, Tahina, additional, Rastogi, Aseem, additional, Swamy, Nikhil, additional, Wintersteiger, Christoph M., additional, and Zanella-Beguelin, Santiago, additional

Published
2020
Full Text
View/download PDF

23. Crellvm: verified credible compilation for LLVM

Author

Kang, Jeehoon, primary, Kim, Yoonseung, additional, Song, Youngju, additional, Lee, Juneyoung, additional, Park, Sanghoon, additional, Shin, Mark Dongyeon, additional, Kim, Yonghyun, additional, Cho, Sungkeun, additional, Choi, Joonwon, additional, Hur, Chung-Kil, additional, and Yi, Kwangkeun, additional

Published
2018
Full Text
View/download PDF

25. An inlining approach to formal hardware semantics

Author

Arvind., Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science., Choi, Joonwon, Arvind., Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science., and Choi, Joonwon

Abstract

Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2016., This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections., Cataloged from student-submitted PDF version of thesis., Includes bibliographical references (page 61)., Hardware components are extremely complex due to concurrency. Modularity has been considered as an effective way to design and understand such complex hardware components. Among various hardware description languages (HDLs), Bluespec allows designers to develop hardware not only based on modularity, but also based on the notion of guarded atomic actions (GAAs). Following the concepts of modularity and GAA, we have been defining a framework called Kami to specify, verify, and synthesize Bluespec-style hardware components. However, modular semantics has an inherent weakness in that it is hard to infer internal changes. In this thesis, I present a new semantic approach based on inlining. Inlining semantics is defined for open hardware systems and resolves the weakness by construction. An implication from modular semantics to inlining semantics is also formally proven; thus the inlining semantics can be used to efficiently prove hardware properties., by Joonwon Choi., S.M.

Published
2016

Catalog

Books, media, physical & digital resources
See catalog results