arXiv subjects

Shoya Ishimaru

Publications and source records attributed to Shoya Ishimaru.

15 recordsLinked to original sources

arXiv2026

Can AI Guess What You Know? Performance Comparison of Large Language Models for Human Domain Knowledge Estimation From Communication Logs

Employees often struggle to identify ``who knows what,'' leading to organizational productivity losses. We investigate whether Large Language Models (LLMs) can infer individual domain knowledge directly from long-term Slack logs. Analyzing 27,188 messages from 43 users, we evaluated seven models (including Gemini, Claude, and GPT families) by comparing their zero-shot estimates against self-reported skill ratings from 27 participants. Gemini 2.5 Flash achieved the lowest error (MAE 21.13%), while GPT models showed significantly larger discrepancies. Notably, estimation accuracy depended only weakly on message volume, indicating that more text alone does not guarantee better inference. These findings demonstrate the feasibility and current limits of automated expertise mapping, highlighting the need for privacy-preserving deployments and richer, structure-aware representations of human knowledge.

cs.CL↗

arXiv2026

EyeBrain: Left and Right Brain Lateralization Activity Classification Through Pupil Diameter and Fixation Duration

The relationship between brain lateralization and cognitive functions is well-documented. The left hemisphere primarily handles tasks such as language and arithmetic, while the right hemisphere is involved in creative activities like drawing and music perception. Eye-tracking technology has shown the potential to reveal cognitive states by measuring ocular metrics such as pupil diameter and fixation duration. However, the ability to distinguish lateralized brain activity using these ocular metrics remains underexplored. Here, we demonstrate that pupil diameter and fixation duration can effectively classify left and right brain hemisphere activities. We obtained a considerably high classification performance, with an F1 score of 0.894. The results suggest that ocular metrics are robust indicators of lateralized brain activity and can be applied in cognitive monitoring and neurorehabilitation. Our future work expands on this by integrating these methods into real-time applications EyeBrain, potentially broadening their use across various cognitive and neurological domains.

q-bio.NC↗

arXiv2026

Empowering Vocabulary Learning Through Teaching AI: Using LLMs as a Student to Perform Learning by Teaching in Vocabulary Acquisition

"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 reinforcing comprehension. However, existing systems for generating such questions often rely on rigid templates and are expensive to build. To overcome these limitations, we developed a system using Large Language Models (LLMs) to create dynamic, contextually relevant questions for LbT. In our English vocabulary learning study, we examined which learner characteristics best leverage the system's benefits. Our results showed improved memory retention over traditional methods at three and seven days of testing, with ten participants. Additionally, we identified traits linked to better learning outcomes, highlighting the potential for tailored approaches. These findings support the development of scalable, cost-effective solutions to enhance LbT methods across various fields.

cs.HC↗

arXiv2026

Estimating Treatment Effects in Panel Data Without Parallel Trends

This paper proposes a novel approach for estimating treatment effects in panel data settings, addressing key limitations of the standard difference-in-differences (DID) approach. The standard approach relies on the parallel trends assumption, implicitly requiring that unobservable factors correlated with treatment assignment be unidimensional, time-invariant, and affect untreated potential outcomes in an additively separable manner. This paper introduces a more flexible framework that allows for multidimensional unobservables and non-additive separability, and provides sufficient conditions for identifying the average treatment effect on the treated. An empirical application to job displacement reveals substantially smaller long-run earnings losses compared to the standard DID approach, demonstrating the framework's ability to account for unobserved heterogeneity that manifests as differential outcome trajectories between treated and control groups.

econ.EM↗

arXiv2025

HandyLabel: Towards Post-Processing to Real-Time Annotation Using Skeleton Based Hand Gesture Recognition

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-prone process. Traditional annotation tools, such as Label Studio, often require post-processing, where users label data after it has been recorded. Post-processing is highly time-consuming and labor-intensive, especially with large datasets, and may lead to erroneous annotations due to the difficulty of subjects' memory tasks when labeling cognitive activities such as emotions or comprehension levels. In this work, we introduce HandyLabel, a real-time annotation tool that leverages hand gesture recognition to map hand signs for labeling. The application enables users to customize gesture mappings through a web-based interface, allowing for real-time annotations. To ensure the performance of HandyLabel, we evaluate several hand gesture recognition models on an open-source hand sign (HaGRID) dataset, with and without skeleton-based preprocessing. We discovered that ResNet50 with preprocessed skeleton-based images performs an F1-score of 0.923. To validate the usability of HandyLabel, a user study was conducted with 46 participants. The results suggest that 88.9% of participants preferred HandyLabel over traditional annotation tools.

cs.HC↗

arXiv2025

Push or Light: Nudging Standing to Break Prolonged Sitting

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 explicit prompts such as smartphone push notifications or light controls. However, comparisons of the effects of such interactions, discomfort, and user context have not yet been performed. The present study evaluated these methods in a mixed design experiment with 15 college students. Three intervention methods (none, push notifications, and light dimming) and three user task contexts (computer work, video calls, and reading) were tested. The frequency of standing up and comfort were assessed after each ten-minute session. Results showed that dimming resulted in slightly more breaks (1.4 \pm 1.55) than push notification (1.2 \pm 1.08), but caused discomfort for 66.7% of participants, compared to 20% for notification. The results were influenced by task context. Dimming was most effective during video calls and reading, while push notifications were more effective during computer work. These findings suggest adaptive nudging systems should tailor interventions based on context and individual preferences.

cs.HC↗

arXiv2025

TrackThinkDashboard: Understanding Student Self-Regulated Learning in Programming Study

In programming education, fostering self-regulated learning (SRL) skills is essential for both students and teachers. This paper introduces TrackThinkDashboard, an application designed to visualize the learning workflow by integrating web browsing and programming logs into one unified view. The system aims to (1) help students monitor and reflect on their problem-solving processes, identify knowledge gaps, and cultivate effective SRL strategies; and (2) enable teachers to identify at-risk learners more effectively and provide targeted, data-driven guidance. We conducted a study with 33 participants (32 male, 1 female) from Japanese universities, including individuals with and without prior programming experience, to explore differences in web browsing and coding patterns. The dashboards revealed multiple learning approaches, such as trial-and-error and trial-and-search methods, and highlighted how domain knowledge influenced the overall activity flow. We discuss how this visualization tool can be used continuously or in one-off experiments, consider associated privacy implications, and explore opportunities for expanding data sources to gain richer behavioral insights.

cs.HC↗

arXiv2025

GenAIReading: Augmenting Human Cognition with Interactive Digital Textbooks Using Large Language Models and Image Generation Models

Cognitive augmentation is a cornerstone in advancing education, particularly through personalized learning. However, personalizing extensive textual materials, such as narratives and academic textbooks, remains challenging due to their heavy use, which can hinder learner engagement and understanding. Building on cognitive theories like Dual Coding Theory -- which posits that combining textual and visual information enhances comprehension and memory -- this study explores the potential of Generative AI (GenAI) to enrich educational materials. We utilized large language models (LLMs) to generate concise text summaries and image generation models (IGMs) to create visually aligned content from textual inputs. After recruiting 24 participants, we verified that integrating AI-generated supplementary materials significantly improved learning outcomes, increasing post-reading test scores by 7.50%. These findings underscore GenAI's transformative potential in creating adaptive learning environments that enhance cognitive augmentation.

cs.HC↗

arXiv2025

Augmenting Online Meetings with Context-Aware Real-time Music Generation

As online communication continues to expand, participants often face cognitive fatigue and reduced engagement. Cognitive augmentation, which leverages technology to enhance human abilities, offers promising solutions to these challenges. In this study, we investigate the potential of generative artificial intelligence (GenAI) for real-time music generation to enrich online meetings. We introduce Discussion Jockey 2, a system that dynamically produces background music in response to live conversation transcripts. Through a user study involving 14 participants in an online interview setting, we examine the system's impact on relaxation, concentration, and overall user experience. The findings reveal that AI-generated background music significantly enhances user relaxation (average score: 5.75/9) and concentration (average score: 5.86/9). This research underscores the promise of context-aware music generation in improving the quality of online communication and points to future directions for optimizing its implementation across various virtual environments.

cs.HC↗

arXiv2025

SensPS: Sensing Personal Space Comfortable Distance between Human-Human Using Multimodal Sensors

Personal space, also known as peripersonal space, is crucial in human social interaction, influencing comfort, communication, and social stress. Estimating and respecting personal space is essential for enhancing human-computer interaction (HCI) and smart environments. Personal space preferences vary due to individual traits, cultural background, and contextual factors. Advanced multimodal sensing technologies, including eye-tracking and wristband sensors, offer opportunities to develop adaptive systems that dynamically adjust to user comfort levels. Integrating physiological and behavioral data enables a deeper understanding of spatial interactions. This study develops a sensor-based model to estimate comfortable personal space and identifies key features influencing spatial preferences. Our findings show that multimodal sensors, particularly eye-tracking and physiological wristband data, can effectively predict personal space preferences, with eye-tracking data playing a more significant role. An experimental study involving controlled human interactions demonstrates that a Transformer-based model achieves the highest predictive accuracy (F1 score: 0.87) for estimating personal space. Eye-tracking features, such as gaze point and pupil diameter, emerge as the most significant predictors, while physiological signals from wristband sensors contribute marginally. These results highlight the potential for AI-driven personalization of social space in adaptive environments, suggesting that multimodal sensing can be leveraged to develop intelligent systems that optimize spatial arrangements in workplaces, educational institutions, and public settings. Future work should explore larger datasets, real-world applications, and additional physiological markers to enhance model robustness.

cs.HC↗

arXiv2025

Human-in-the-Loop Annotation for Image-Based Engagement Estimation: Assessing the Impact of Model Reliability on Annotation Accuracy

Human-in-the-loop (HITL) frameworks are increasingly recognized for their potential to improve annotation accuracy in emotion estimation systems by combining machine predictions with human expertise. This study focuses on integrating a high-performing image-based emotion model into a HITL annotation framework to evaluate the collaborative potential of human-machine interaction and identify the psychological and practical factors critical to successful collaboration. Specifically, we investigate how varying model reliability and cognitive framing influence human trust, cognitive load, and annotation behavior in HITL systems. We demonstrate that model reliability and psychological framing significantly impact annotators' trust, engagement, and consistency, offering insights into optimizing HITL frameworks. Through three experimental scenarios with 29 participants--baseline model reliability (S1), fabricated errors (S2), and cognitive bias introduced by negative framing (S3)--we analyzed behavioral and qualitative data. Reliable predictions in S1 yielded high trust and annotation consistency, while unreliable outputs in S2 led to increased critical evaluations but also heightened frustration and response variability. Negative framing in S3 revealed how cognitive bias influenced participants to perceive the model as more relatable and accurate, despite misinformation regarding its reliability. These findings highlight the importance of both reliable machine outputs and psychological factors in shaping effective human-machine collaboration. By leveraging the strengths of both human oversight and automated systems, this study establishes a scalable HITL framework for emotion annotation and lays the foundation for broader applications in adaptive learning and human-computer interaction.

cs.HC↗

arXiv2023

Skewness consistency relation in large-scale structure and test of gravity theory

We investigate the skewness of galaxy number density fluctuations as a possible probe to test gravity theories. We find that the specific linear combination of the skewness parameters corresponds to the coefficients of the second-order kernels of the density contrast, which can be regarded as the consistency relation and used as a test of general relativity and modified gravity theories. We also extend the analysis of the skewness parameters from real space to redshift space and derive the redshift-space skewness consistency relation.

astro-ph.CO↗

arXiv2022

Empirical Decomposition of the IV-OLS Gap with Heterogeneous and Nonlinear Effects

This study proposes an econometric framework to interpret and empirically decompose the difference between IV and OLS estimates given by a linear regression model when the true causal effects of the treatment are nonlinear in treatment levels and heterogeneous across covariates. I show that the IV-OLS coefficient gap consists of three estimable components: the difference in weights on the covariates, the difference in weights on the treatment levels, and the difference in identified marginal effects that arises from endogeneity bias. Applications of this framework to return-to-schooling estimates demonstrate the empirical relevance of this distinction in properly interpreting the IV-OLS gap.

econ.EM↗

arXiv2021

What Do We Get from Two-Way Fixed Effects Regressions? Implications from Numerical Equivalence

This paper develops numerical and causal interpretations of two-way fixed effects (TWFE) regressions in settings with nonbinary, nonstaggered treatments and time-varying covariates. Using the equivalence between TWFE and pooled first-difference (FD) regressions, I express the TWFE coefficient as a weighted average of FD coefficients across all horizons, clarifying how short- and long-run changes contribute to the estimate. Causal interpretation of the TWFE coefficient relies on common trends assumptions at all horizons simultaneously, whereas each FD coefficient relies on the assumption only at its own horizon. This structure opens the identifying assumptions to empirical scrutiny: I propose diagnostic procedures that assess common trends horizon by horizon, and illustrate them by reexamining TWFE estimates of minimum-wage effects on employment.

econ.EM↗

arXiv2021

Confidence-Aware Learning Assistant

Not only correctness but also self-confidence play an important role in improving the quality of knowledge. Undesirable situations such as confident incorrect and unconfident correct knowledge prevent learners from revising their knowledge because it is not always easy for them to perceive the situations. To solve this problem, we propose a system that estimates self-confidence while solving multiple-choice questions by eye tracking and gives feedback about which question should be reviewed carefully. We report the results of three studies measuring its effectiveness. (1) On a well-controlled dataset with 10 participants, our approach detected confidence and unconfidence with 81% and 79% average precision. (2) With the help of 20 participants, we observed that correct answer rates of questions were increased by 14% and 17% by giving feedback about correct answers without confidence and incorrect answers with confidence, respectively. (3) We conducted a large-scale data recording in a private school (72 high school students solved 14,302 questions) to investigate effective features and the number of required training samples.

cs.HC↗