arXiv subjects

Kenta Yamamoto

Publications and source records attributed to Kenta Yamamoto.

10 recordsLinked to original sources

arXiv2026

Large Language Model Counterarguments in Older Adults: Cognitive Offloading or Susceptibility to Moral Persuasion?

This study examined whether counterarguments generated by large language models (LLMs) influence the moral judgments of younger and older adults, and whether these effects vary by dilemma type, cognitive functioning, trust in AI, and prior LLM experience. Using the switch and footbridge trolley dilemmas, 130 participants (56 younger adults and 74 older adults) were presented with ChatGPT-generated counterarguments that opposed their initial judgments. More than 30% of participants reversed their judgments in both dilemmas (32.31% in the switch dilemma and 36.92% in the footbridge dilemma). Older adults tended to be more likely than younger adults to reverse their judgments and showed a significantly greater degree of judgment change in the switch dilemma. In the emotionally aversive footbridge dilemma, older adults with lower cognitive functioning were significantly more likely to align with the LLM-generated counterargument. General trust in AI and prior LLM experience did not predict judgment reversal, whereas lower initial confidence and higher perceived task difficulty were associated with greater susceptibility to LLM influence. These findings suggest that LLMs may support cognitive offloading but increase susceptibility among individuals with limited cognitive resources. The ecological generalizability of these findings to everyday dilemma situations remains to be examined in future research.

cs.HC↗

arXiv2025

Preference-Aligned Options from Generative AI Compensates for Age-Related Cognitive Decline in Decision Making

Older adults often experience increased difficulty in decision making due to age-related declines particularly in contexts that require information search or the generation of alternatives from memory. This study examined whether using generative AI for information search enhances choice satisfaction and reduces choice difficulty among older adults. A total of 130 participants (younger, n = 56; older, n = 74) completed a music-selection task under AI-use and AI-nonuse conditions across two contexts: previously experienced (road trip) and not previously experienced (space travel). In the AI-nonuse condition, participants generated candidate options from memory; in the AI-use condition, GPT-4o presented options tailored to individual preferences. Cognitive functions, including working memory, processing speed, verbal comprehension, and perceptual reasoning, were assessed. Results showed that AI use significantly reduced perceived choice difficulty across age groups, with larger benefits in unfamiliar contexts. Regarding cognitive function, among older adults, lower cognitive function was associated with fewer recalled options, higher choice difficulty, and lower satisfaction in the AI-nonuse condition; these associations were substantially attenuated when AI was used. These results demonstrate that generative AI can mitigate age-related cognitive constraints by reducing the cognitive load associated with information search during decision making. While the use of AI reduced perceived difficulty, choice satisfaction remained unchanged, suggesting that autonomy in decision making was preserved. These findings indicate that generative AI can support everyday decision making by compensating for the constraints in information search that older adults face due to cognitive decline.

cs.HC↗

arXiv2025

Designing Reputation Systems for Manufacturing Data Trading Markets: A Multi-Agent Evaluation with Q-Learning and IRL-Estimated Utilities

Recent advances in machine learning and big data analytics have intensified the demand for high-quality cross-domain datasets and accelerated the growth of data trading across organizations. As data become increasingly recognized as an economic asset, data marketplaces have emerged as a key infrastructure for data-driven innovation. However, unlike mature product or service markets, data-trading environments remain nascent and suffer from pronounced information asymmetry. Buyers cannot verify the content or quality before purchasing data, making trust and quality assurance central challenges. To address these issues, this study develops a multi-agent data-market simulator that models participant behavior and evaluates the institutional mechanisms for trust formation. Focusing on the manufacturing sector, where initiatives such as GAIA-X and Catena-X are advancing, the simulator integrates reinforcement learning (RL) for adaptive agent behavior and inverse reinforcement learning (IRL) to estimate utility functions from empirical behavioral data. Using the simulator, we examine the market-level effects of five representative reputation systems-Time-decay, Bayesian-beta, PageRank, PowerTrust, and PeerTrust-and found that PeerTrust achieved the strongest alignment between data price and quality, while preventing monopolistic dominance. Building on these results, we develop a hybrid reputation mechanism that integrates the strengths of existing systems to achieve improved price-quality consistency and overall market stability. This study extends simulation-based data-market analysis by incorporating trust and reputation as endogenous mechanisms and offering methodological and institutional insights into the design of reliable and efficient data ecosystems.

cs.GT↗

arXiv2024

Conditional neural holography: a distance-adaptive CGH generator

A convolutional neural network (CNN) is useful for overcoming the trade-off between generation speed and accuracy in the process of synthesizing computer-generated holograms (CGHs). However, methods using a CNN have limited applicability as they cannot specify the propagation distance when synthesizing a hologram. We developed a distance-adaptive CGH generator that can generate CGHs by specifying the target image and propagation distance, which comprises a zone plate encoder stage and an augmented HoloNet stage. Our model is comparable to that of prior CNN methods, with a fixed distance, in terms of performance and achieves the generation accuracy and speed necessary for practical use.

physics.optics↗

arXiv2023

User-adaptive Tourist Information Dialogue System with Yes/No Classifier and Sentiment Estimator

We introduce our system developed for Dialogue Robot Competition 2023 (DRC2023). First, rule-based utterance selection and utterance generation using a large language model (LLM) are combined. We ensure the quality of system utterances while also being able to respond to unexpected user utterances. Second, dialogue flow is controlled by considering the results of the BERT-based yes/no classifier and sentiment estimator. These allow the system to adapt state transitions and sightseeing plans to the user.

cs.HC↗

arXiv2021

A Projector-Camera System Using Hybrid Pixels with Projection and Capturing Capabilities

We propose a novel projector-camera system (ProCams) in which each pixel has both projection and capturing capabilities. Our proposed ProCams solves the difficulty of obtaining precise pixel correspondence between the projector and the camera. We implemented a proof-of-concept ProCams prototype and demonstrated its applicability to a dynamic projection mapping.

cs.CV↗

arXiv2021

See-Through Captions: Real-Time Captioning on Transparent Display for Deaf and Hard-of-Hearing People

Real-time captioning is a useful technique for deaf and hard-of-hearing (DHH) people to talk to hearing people. With the improvement in device performance and the accuracy of automatic speech recognition (ASR), real-time captioning is becoming an important tool for helping DHH people in their daily lives. To realize higher-quality communication and overcome the limitations of mobile and augmented-reality devices, real-time captioning that can be used comfortably while maintaining nonverbal communication and preventing incorrect recognition is required. Therefore, we propose a real-time captioning system that uses a transparent display. In this system, the captions are presented on both sides of the display to address the problem of incorrect ASR, and the highly transparent display makes it possible to see both the body language and the captions.

cs.HC↗

arXiv2020

Acoustic Hologram Optimisation Using Automatic Differentiation

Acoustic holograms are the keystone of modern acoustics. It encodes three-dimensional acoustic fields in two dimensions, and its quality determine the performance of acoustic systems. Optimisation methods that control only the phase of an acoustic wave are considered inferior to methods that control both the amplitude and phase of the wave. In this paper, we present Diff-PAT, an acoustic hologram optimisation algorithm with automatic differentiation. We demonstrate that our method achieves superior accuracy than conventional methods. The performance of Diff-PAT was evaluated by randomly generating 1000 sets of up to 32 control points for single-sided arrays and single-axis arrays. The improved acoustic hologram can be used in wide range of applications of PATs without introducing any changes to existing systems that control the PATs. In addition, we applied Diff-PAT to acoustic metamaterial and achieved an >8 dB increase in the peak noise-to-signal ratio of acoustic hologram.

cs.SD↗

arXiv2020

A Preliminary Study for Identification of Additive Manufactured Objects with Transmitted Images

Additive manufacturing has the potential to become a standard method for manufacturing products, and product information is indispensable for the item distribution system. While most products are given barcodes to the exterior surfaces, research on embedding barcodes inside products is underway. This is because additive manufacturing makes it possible to carry out manufacturing and information adding at the same time, and embedding information inside does not impair the exterior appearance of the product. However, products that have not been embedded information can not be identified, and embedded information can not be rewritten later. In this study, we have developed a product identification system that does not require embedding barcodes inside. This system uses a transmission image of the product which contains information of each product such as different inner support structures and manufacturing errors. We have shown through experiments that if datasets of transmission images are available, objects can be identified with an accuracy of over 90%. This result suggests that our approach can be useful for identifying objects without embedded information.

eess.IV↗

arXiv2013

Quantization of hypercharge in gauge groups locally isomorphic but globally nonisomorphic to SU(3)_c X SU(2)_L X U(1)_Y

In the Standard Model the hypercharges of quarks and leptons are not determined by the gauge group itself. In a recent paper [C. Hattori et al. Phys. Rev. D83, 015009 (2011)] it is shown that, if the direct product gauge group G_SM is slightly modified to the semidirect product group G'_SM, hypercharges are restricted to quantized values as n/6 mod Z (n = 0,1,3,4). In this brief paper, we examine all of the compact Lie groups locally isomorphic to G_SM, and show that G'_SM (or its isomorphisms) is the unique possibility that yields the correct hypercharge quantization.

hep-ph↗