3 citations · 9 across the 19 of their papers we have counts for
20 papers
Can AI Guess What You Know? Performance Comparison of Large Language Models for Human Domain Knowledge Estimation From Communication Logs
Ko Watanabe, Shoya Ishimaru
Employees often struggle to identify ``who knows what,'' leading to organizational productivity losses. We investigate whether Large Language Models (LLMs) can infer individual dom…
LimeCross: Context-Conditioned Layered Image Editing with Structural Consistency
Ryugo Morita, Stanislav Frolov, Brian Bernhard Moser +4
Layered image assets are widely used in real-world creative workflows, enabling non-destructive iteration and flexible re-composition. Recent advances in layered image generation a…
EyeBrain: Left and Right Brain Lateralization Activity Classification Through Pupil Diameter and Fixation Duration
Ko Watanabe, Pooja Pol, Nicolas Großmann +2
The relationship between brain lateralization and cognitive functions is well-documented. The left hemisphere primarily handles tasks such as language and arithmetic, while the rig…
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…
LGTM: Training-Free Light-Guided Text-to-Image Diffusion Model via Initial Noise Manipulation
Ryugo Morita, Stanislav Frolov, Brian Bernhard Moser +3
Diffusion models have demonstrated high-quality performance in conditional text-to-image generation, particularly with structural cues such as edges, layouts, and depth. However, l…
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…
Towards Facilitated Fairness Assessment of AI-based Skin Lesion Classifiers Through GenAI-based Image Synthesis
Ko Watanabe, Stanislav Frolov, Aya Hassan +3
Recent advances in deep learning and on-device inference could transform routine screening for skin cancers. Along with the anticipated benefits of this technology, potential dange…
From Neck to Head: Bio-Impedance Sensing for Head Pose Estimation
Mengxi Liu, Lala Shakti Swarup Ray, Sizhen Bian +6
We present NeckSense, a novel wearable system for head pose tracking that leverages multi-channel bio-impedance sensing with soft, dry electrodes embedded in a lightweight, necklac…
Unifying VXAI: A Systematic Review and Framework for the Evaluation of Explainable AI
David Dembinsky, Adriano Lucieri, Stanislav Frolov +3
Modern AI systems frequently rely on opaque black-box models, most notably Deep Neural Networks, whose performance stems from complex architectures with millions of learned paramet…
TrackThinkDashboard: Understanding Student Self-Regulated Learning in Programming Study
Ko Watanabe, Yuki Matsuda, Yugo Nakamura +2
In programming education, fostering self-regulated learning (SRL) skills is essential for both students and teachers. This paper introduces TrackThinkDashboard, an application desi…
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…
SensPS: Sensing Personal Space Comfortable Distance between Human-Human Using Multimodal Sensors
Ko Watanabe, Nico Förster, Shoya Ishimaru
Personal space, also known as peripersonal space, is crucial in human social interaction, influencing comfort, communication, and social stress. Estimating and respecting personal…
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…
TKG-DM: Training-free Chroma Key Content Generation Diffusion Model
Ryugo Morita, Stanislav Frolov, Brian Bernhard Moser +4
Diffusion models have enabled the generation of high-quality images with a strong focus on realism and textual fidelity. Yet, large-scale text-to-image models, such as Stable Diffu…
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…
Webcam-based Pupil Diameter Prediction Benefits from Upscaling
Vijul Shah, Brian B. Moser, Ko Watanabe +1
Capturing pupil diameter is essential for assessing psychological and physiological states such as stress levels and cognitive load. However, the low resolution of images in eye da…
PupilSense: A Novel Application for Webcam-Based Pupil Diameter Estimation
Vijul Shah, Ko Watanabe, Brian B. Moser +1
Measuring pupil diameter is vital for gaining insights into physiological and psychological states - traditionally captured by expensive, specialized equipment like Tobii eye-track…