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pro vyhledávání: '"Tei, Kenji"'
People with color vision deficiency often face challenges in distinguishing colors such as red and green, which can complicate daily tasks and require the use of assistive tools or environmental adjustments. Current support tools mainly focus on pres
Externí odkaz:
http://arxiv.org/abs/2407.04362
Rule-based adaptation is a foundational approach to self-adaptation, characterized by its human readability and rapid response. However, building high-performance and robust adaptation rules is often a challenge because it essentially involves search
Externí odkaz:
http://arxiv.org/abs/2407.02203
Autor:
Xu, Jinglue, Li, Jialong, Liu, Zhen, Suryanarayanan, Nagar Anthel Venkatesh, Zhou, Guoyuan, Guo, Jia, Iba, Hitoshi, Tei, Kenji
Recently, program synthesis driven by large language models (LLMs) has become increasingly popular. However, program synthesis for machine learning (ML) tasks still poses significant challenges. This paper explores a novel form of program synthesis,
Externí odkaz:
http://arxiv.org/abs/2405.03727
Evolutionary computation (EC), as a powerful optimization algorithm, has been applied across various domains. However, as the complexity of problems increases, the limitations of EC have become more apparent. The advent of large language models (LLMs
Externí odkaz:
http://arxiv.org/abs/2405.02876
Social media platforms such as Twitter, Reddit, and Sina Weibo play a crucial role in global communication but often encounter strict regulations in geopolitically sensitive regions. This situation has prompted users to ingeniously modify their way o
Externí odkaz:
http://arxiv.org/abs/2405.02858
Recent advancements in large language models (LLMs) have highlighted the potential for vulnerability detection, a crucial component of software quality assurance. Despite this progress, most studies have been limited to the perspective of a single ro
Externí odkaz:
http://arxiv.org/abs/2403.14274
In the realm of autonomous vehicles, dynamic user preferences are critical yet challenging to accommodate. Existing methods often misrepresent these preferences, either by overlooking their dynamism or overburdening users as humans often find it chal
Externí odkaz:
http://arxiv.org/abs/2403.02928
In this paper, we address the challenges faced by Value Iteration Networks (VIN) in handling larger input maps and mitigating the impact of accumulated errors caused by increased iterations. We propose a novel approach, Value Iteration Networks with
Externí odkaz:
http://arxiv.org/abs/2305.07039
Autor:
Jiang, Pengcheng, Tei, Kenji
Users interacting with a system through UI are typically obliged to perform their actions in a pre-determined order, to successfully achieve certain functional goals. However, such obligations are often not followed strictly by users, which may lead
Externí odkaz:
http://arxiv.org/abs/2108.08282
A self-learning adaptive system (SLAS) uses machine learning to enable and enhance its adaptability. Such systems are expected to perform well in dynamic situations. For learning high-performance adaptation policy, some assumptions must be made on th
Externí odkaz:
http://arxiv.org/abs/2105.04986