Zobrazeno 1 - 10
of 1 832
pro vyhledávání: '"Yongsu An"'
Autor:
Yujing Ji, Jichuang Wu, Ha Eun Lee, Yongsu An, Duk-Young Jung, Chan Woo Lee, Young Dok Kim, Hyun Ook Seo
Publikováno v:
ACS Omega, Vol 9, Iss 49, Pp 48855-48866 (2024)
Externí odkaz:
https://doaj.org/article/e13f5daeef7f4101bc7935dc3231bd37
Autor:
Suryanto, Naufal, Adiputra, Andro Aprila, Kadiptya, Ahmada Yusril, Le, Thi-Thu-Huong, Pratama, Derry, Kim, Yongsu, Kim, Howon
Recent advancements in generative AI, particularly diffusion-based image editing, have enabled the transformation of images into highly realistic scenes using only text instructions. This technology offers significant potential for generating diverse
Externí odkaz:
http://arxiv.org/abs/2411.00425
LLMs have emerged as a promising tool for assisting individuals in diverse text-generation tasks, including job-related texts. However, LLM-generated answers have been increasingly found to exhibit gender bias. This study evaluates three LLMs (GPT-3.
Externí odkaz:
http://arxiv.org/abs/2410.20739
Recommender systems have become integral to digital experiences, shaping user interactions and preferences across various platforms. Despite their widespread use, these systems often suffer from algorithmic biases that can lead to unfair and unsatisf
Externí odkaz:
http://arxiv.org/abs/2409.06916
Monocular depth estimation (MDE) and semantic segmentation (SS) are crucial for the navigation and environmental interpretation of many autonomous driving systems. However, their vulnerability to practical adversarial attacks is a significant concern
Externí odkaz:
http://arxiv.org/abs/2408.14879
Autor:
Oh, Soon-young, Ahn, Yongsu
In schools, teachers play a multitude of roles, serving as educators, counselors, decision-makers, and members of the school community. With recent advances in artificial intelligence (AI), there is increasing discussion about how AI can assist, comp
Externí odkaz:
http://arxiv.org/abs/2405.13065
Autor:
Ahn, Yongsu, Lin, Yu-Ru
Despite the benefits of personalizing items and information tailored to users' needs, it has been found that recommender systems tend to introduce biases that favor popular items or certain categories of items, and dominant user groups. In this study
Externí odkaz:
http://arxiv.org/abs/2312.17443
The escalating food insecurity in Africa, caused by factors such as war, climate change, and poverty, demonstrates the critical need for advanced early warning systems. Traditional methodologies, relying on expert-curated data encompassing climate, g
Externí odkaz:
http://arxiv.org/abs/2311.10953
Autor:
Suryanto, Naufal, Kim, Yongsu, Larasati, Harashta Tatimma, Kang, Hyoeun, Le, Thi-Thu-Huong, Hong, Yoonyoung, Yang, Hunmin, Oh, Se-Yoon, Kim, Howon
Adversarial camouflage has garnered attention for its ability to attack object detectors from any viewpoint by covering the entire object's surface. However, universality and robustness in existing methods often fall short as the transferability aspe
Externí odkaz:
http://arxiv.org/abs/2308.07009
Big data and machine learning tools have jointly empowered humans in making data-driven decisions. However, many of them capture empirical associations that might be spurious due to confounding factors and subgroup heterogeneity. The famous Simpson's
Externí odkaz:
http://arxiv.org/abs/2307.14448