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Makoto Itoh

Publications and source records attributed to Makoto Itoh.

12 recordsLinked to original sources
arXiv2026

CoPlan: A Trustworthy Co-Intelligence Interface for Care Planning through Role-Based Contestable Argument Graphs

AI-supported care planning can help clinicians, patients, caregivers, and care teams coordinate complex decisions across clinical, functional, psychosocial, and environmental needs. However, many AI systems present recommendations as fixed outputs, limiting stakeholders' ability to inspect, challenge, and revise plans when they conflict with clinical judgment, patient values, or real-world feasibility. We present CoPlan - a Co-Intelligent and Contestable Interface for Human-AI Care Planning. CoPlan uses a multi-agent workflow in which specialized AI agents generate candidate interventions and supporting or challenging arguments, while human care planners can accept, reject, modify, or add arguments before final plan generation. Through this design, CoPlan combines co-intelligence, in which humans and AI agents contribute complementary expertise, with contestability, where recommendations remain open to inspection, revision, and justification. We demonstrate CoPlan in an aging-in-place care planning scenario. The system supports adaptive care team recruitment, role-based argument review, final care plan generation, and practical follow-up through scheduling agents. This work contributes a contestable care planning interface and a design framing for trustworthy human-AI care planning that preserves human agency and clinical accountability.

cs.AI↗
arXiv2026

Hanger Reflex Based Driving Assistance for Drivers with Peripheral Visual Field Defects

Drivers with peripheral visual field defects may fail to notice pedestrians in their peripheral visual field, leading to delayed hazard awareness and increased collision risk. This study explores hanger reflex cue (HRC) as a driving assistance method for drivers with peripheral visual field defects, in which mechanical pressure is applied to specific regions of the head to facilitate anticipatory orientation toward potentially risky pedestrians and support safer driving. In a driving simulator experiment with 15 participants, we compared driving behavior with and without HRC during pedestrian encounters under simulated peripheral visual field defect. The results showed that HRC significantly shifted drivers' modal head rotation angle toward the risky pedestrian and significantly increased gaze duration toward that pedestrian. Collision occurrence was lower in the w/ HRC condition than in the w/o HRC condition, although the direct effect of HRC on collision occurrence showed only a marginal trend. A piecewise structural equation modeling analysis further suggested that HRC may contribute to collision reduction through a sequential pathway from head rotation to gaze allocation and then to collision occurrence. These findings provide preliminary evidence that HRC can support anticipatory attention allocation toward peripheral hazards and may offer a promising driving assistance method for drivers with visual field impairment.

cs.HC↗
arXiv2026

Position: Multi-Agent Algorithmic Care Systems Demand Contestability for Trustworthy AI

Multi-agent systems (MAS) are increasingly used in healthcare to support complex decision-making through collaboration among specialized agents. Because these systems act as collective decision-makers, they raise challenges for trust, accountability, and human oversight. Existing approaches to trustworthy AI largely rely on explainability, but explainability alone is insufficient in multi-agent settings, as it does not enable care partners to challenge or correct system outputs. To address this limitation, Contestable AI (CAI) characterizes systems that support effective human challenge throughout the decision-making lifecycle by providing transparency, structured opportunities for intervention, and mechanisms for review, correction, or override. This position paper argues that contestability is a necessary design requirement for trustworthy multi-agent algorithmic care systems. We identify key limitations in current MAS and Explainable AI (XAI) research and present a human-in-the-loop framework that integrates structured argumentation and role-based contestation to preserve human agency, clinical responsibility, and trust in high-stakes care contexts.

cs.AI↗
arXiv2025

Data-driven simulator of multi-animal behavior with unknown dynamics via offline and online reinforcement learning

Simulators of animal movements play a valuable role in studying behavior. Advances in imitation learning for robotics have expanded possibilities for reproducing human and animal movements. A key challenge for realistic multi-animal simulation in biology is bridging the gap between unknown real-world transition models and their simulated counterparts. Because locomotion dynamics are seldom known, relying solely on mathematical models is insufficient; constructing a simulator that both reproduces real trajectories and supports reward-driven optimization remains an open problem. We introduce a data-driven simulator for multi-animal behavior based on deep reinforcement learning and counterfactual simulation. We address the ill-posed nature of the problem caused by high degrees of freedom in locomotion by estimating movement variables of an incomplete transition model as actions within an RL framework. We also employ a distance-based pseudo-reward to align and compare states between cyber and physical spaces. Validated on artificial agents, flies, newts, and silkmoth, our approach achieves higher reproducibility of species-specific behaviors and improved reward acquisition compared with standard imitation and RL methods. Moreover, it enables counterfactual behavior prediction in novel experimental settings and supports multi-individual modeling for flexible what-if trajectory generation, suggesting its potential to simulate and elucidate complex multi-animal behaviors.

cs.LG↗
arXiv2025

Breaking New Ground in Software Defect Prediction: Introducing Practical and Actionable Metrics with Superior Predictive Power for Enhanced Decision-Making

Software defect prediction using code metrics has been extensively researched over the past five decades. However, prediction harnessing non-software metrics is under-researched. Considering that the root cause of software defects is often attributed to human error, human factors theory might offer key forecasting metrics for actionable insights. This paper explores automated software defect prediction at the method level based on the developers' coding habits. First, we propose a framework for deciding the metrics to conduct predictions. Next, we compare the performance of our metrics to that of the code and commit history metrics shown by research to achieve the highest performance to date. Finally, we analyze the prediction importance of each metric. As a result of our analyses of twenty-one critical infrastructure large-scale open-source software projects, we have presented: (1) a human error-based framework with metrics useful for defect prediction at method level; (2) models using our proposed metrics achieve better average prediction performance than the state-of-the-art code metrics and history measures; (3) the prediction importance of all metrics distributes differently with each of the novel metrics having better average importance than code and history metrics; (4) the novel metrics dramatically enhance the explainability, practicality, and actionability of software defect prediction models, significantly advancing the field. We present a systematic approach to forecasting defect-prone software methods via a human error framework. This work empowers practitioners to act on predictions, empirically demonstrating how developer coding habits contribute to defects in software systems.

cs.SE↗
arXiv2024

Development of a Chinese Human-Automation Trust Scale

The development of a reliable and valid assessment tool of human-automation trust is an important topic. This study aimed to develop a Chinese version of human-automation trust scale (C-HATS) with reasonable reliability and validity based on Lee and See (2004)'s trust model. After three phases of assessments including exploratory factor analysis, item analysis, and confirmatory factor analysis, different dimensions and items were considered for initial and posttask human-automation trust. For post-task trust, the scale had three dimensions and 11 items and reflected Lee and See (2004)'s model, whereas different from Lee and See (2004)'s model, the final scale had 14 items but only two dimensions for initial trust. Nevertheless, for both initial and post-task trust, reasonable reliability and validity of the scale were verified with various consumer automation products. Although further verification is still necessary, the developed C-HATS could be used to effectively assess human-automation trust in the Chinese context.

cs.HC↗
arXiv2019

Memristor Circuits for Simulating Nonlinear Dynamics and Their Periodic Forcing

In this paper, we show that the dynamics of a wide variety of nonlinear systems such as engineering, physical, chemical, biological, and ecological systems, can be simulated or modeled by the dynamics of memristor circuits. It has the advantage that we can apply nonlinear circuit theory to analyze the dynamics of memristor circuits. Applying an external source to these memristor circuits, they exhibit complex behavior, such as chaos and non-periodic oscillation. If the memristor circuits have an integral invariant, they can exhibit quasi-periodic or non-periodic behavior by the sinusoidal forcing. Their behavior greatly depends on the initial conditions, the parameters, and the maximum step size of the numerical integration. Furthermore, an overflow is likely to occur due to the numerical instability in long-time simulations. In order to generate a non-periodic oscillation, we have to choose the initial conditions, the parameters, and the maximum step size, carefully. We also show that we can reconstruct chaotic attractors by using the terminal voltage and current of the memristor. Furthermore, in many memristor circuits, the active memristor switches between passive and active modes of operation, depending on its terminal voltage. We can measure its complexity order by defining the binary coding for the operation modes. By using this coding, we show that in the forced memristor Toda lattice equations, the memristor's operation modes exhibit the higher complexity. Furthermore, in the memristor Chua circuit, the memristor has the special operation modes.

nlin.CD↗
arXiv2019

Some Interesting Features of Memristor CNN

In this paper, we introduce some interesting features of a memristor CNN (Cellular Neural Network). We first show that there is the similarity between the dynamics of memristors and neurons. That is, some kind of flux-controlled memristors can not respond to the sinusoidal voltage source quickly, namely, they can not switch `on' rapidly. Furthermore, these memristors have refractory period after switch `on', which means that it can not respond to further sinusoidal inputs until the flux is decreased. We next show that the memristor-coupled two-cell CNN can exhibit chaotic behavior. In this system, the memristors switch `off' and `on' at irregular intervals, and the two cells are connected when either or both of the memristors switches `on'. We then propose the modified CNN model, which can hold a binary output image, even if all cells are disconnected and no signal is supplied to the cell after a certain point of time. However, the modified CNN requires power to maintain the output image, that is, it is volatile. We next propose a new memristor CNN model. It can also hold a binary output state (image), even if all cells are disconnected, and no signal is supplied to the cell, by memristor's switching behavior. Furthermore, even if we turn off the power of the system during the computation, it can resume from the previous average output state, since the memristor CNN has functions of both short-term (volatile) memory and long-term (non-volatile) memory. The above suspend and resume feature are useful when we want to save the current state, and continue work later from the previous state. Finally, we show that the memristor CNN can exhibit interesting two-dimensional waves, if an inductor is connected to each memristor CNN cell.

cs.NE↗
arXiv1998

Monte Carlo study of Si(111) homoepitaxy

An attempt is made to simulate the homoepitaxial growth of a Si(111) surface by the kinetic Monte Carlo method in which the standard Solid-on-Solid model and the planar model of the (7x7) surface reconstruction are used in combination. By taking account of surface reconstructions as well as atomic deposition and migrations, it is shown that the effect of a coorparative stacking transformation is necessary for a layer growth.

cond-mat.stat-mech↗
arXiv1998

Hydrodynamical Simulation of Clusters of Galaxies in X-Ray, mm, and submm Bands: Determination of Peculiar Velocity and the Hubble Constant

We have performed a series of simulations of clusters of galaxies on the basis of the smoothed particle hydrodynamics technique in a spatially-flat cold dark matter universe with $Ω=0.3$, $λ=0.7$, and H_0=70 km/s/Mpc as one of the most successful representative cosmological scenarios. In particular, we focus on the Sunyaev--Zel'dovich effect in submm and mm bands, and estimate the reliability of the estimates of the global Hubble constant $H_0$ and the peculiar velocity of clusters $v_r$. Our simulations indicate that fractional uncertainties of the estimates of $H_0$ amount to $\sim 20%$ mainly due to the departure from the isothermal and spherical gas density distribution. We find a systematic underestimate bias of $H_0$ by $\sim 20%$ for clusters $z\approx 1$, but not at $z\approx 0$. The gas temperature drop in the central regions of our simulated clusters leads to the underestimate bias of $v_r$ by $\sim 5%$ at $z\approx 0$ and by $\sim 15%$ at $z\approx 1$ in addition to the statistical errors of the comparable amount due to the non-spherical gas profile.

astro-ph↗