3 citations · 4 across the 10 of their papers we have counts for
10 papers
Empowering Vocabulary Learning Through Teaching AI: Using LLMs as a Student to Perform Learning by Teaching in Vocabulary Acquisition
Tokio Uchida, Ko Watanabe, Andrew Vargo +5
"Learning by Teaching (LbT)" helps learners deepen their understanding by explaining concepts to others, with questions playing a vital role in identifying knowledge gaps and reinf…
SensHRPS: Sensing Comfortable Human-Robot Proxemics and Personal Space With Eye-Tracking
Nadezhda Kushina, Ko Watanabe, Aarthi Kannan +3
Social robots must adjust to human proxemic norms to ensure user comfort and engagement. While prior research demonstrates that eye-tracking features reliably estimate comfort in h…
HandyLabel: Towards Post-Processing to Real-Time Annotation Using Skeleton Based Hand Gesture Recognition
Sachin Kumar Singh, Ko Watanabe, Brian Moser +2
The success of machine learning is deeply linked to the availability of high-quality training data, yet retrieving and manually labeling new data remains a time-consuming and error…
Push or Light: Nudging Standing to Break Prolonged Sitting
Sohshi Yoshida, Ko Watanabe, Andreas Dengel +3
Prolonged sitting is a health risk leading to metabolic and cardiovascular diseases. To combat this, various "nudging" strategies encourage stand-ups. Behavior change triggers use…
GenAIReading: Augmenting Human Cognition with Interactive Digital Textbooks Using Large Language Models and Image Generation Models
Ryugo Morita, Ko Watanabe, Jinjia Zhou +2
Cognitive augmentation is a cornerstone in advancing education, particularly through personalized learning. However, personalizing extensive textual materials, such as narratives a…
Augmenting Online Meetings with Context-Aware Real-time Music Generation
Haruki Suzawa, Ko Watanabe, Andreas Dengel +1
As online communication continues to expand, participants often face cognitive fatigue and reduced engagement. Cognitive augmentation, which leverages technology to enhance human a…
Human-in-the-Loop Annotation for Image-Based Engagement Estimation: Assessing the Impact of Model Reliability on Annotation Accuracy
Sahana Yadnakudige Subramanya, Ko Watanabe, Andreas Dengel +1
Human-in-the-loop (HITL) frameworks are increasingly recognized for their potential to improve annotation accuracy in emotion estimation systems by combining machine predictions wi…
Using Large Language Models to Generate Authentic Multi-agent Knowledge Work Datasets
Desiree Heim, Christian Jilek, Adrian Ulges +1
Current publicly available knowledge work data collections lack diversity, extensive annotations, and contextual information about the users and their documents. These issues hinde…
Feature Estimation of Global Language Processing in EEG Using Attention Maps
Dai Shimizu, Ko Watanabe, Andreas Dengel
Understanding the correlation between EEG features and cognitive tasks is crucial for elucidating brain function. Brain activity synchronizes during speaking and listening tasks. H…
Edge-based Denoising Image Compression
Ryugo Morita, Hitoshi Nishimura, Ko Watanabe +2
In recent years, deep learning-based image compression, particularly through generative models, has emerged as a pivotal area of research. Despite significant advancements, challen…