Real-time captions presented through AR glasses hold great promise for enhancing accessibility for Deaf and Hard of Hearing (DHH) individuals. However, conventional approaches often overlook the diversity of user preferences and perceptual characteristics. In this study, we explore how spatial personalization—adjustments in display method, position, and depth—can improve the user experience of AR captions.We conducted two experiments. Experiment 1 focused on 2D position personalization, revealing that user-controlled positioning enhanced visibility and comfort, though comprehension gains were modest. Experiment 2 introduced a novel concept, Simulated Depth Personalization, which emulates different viewing distances despite a fixed AR focal plane (4 m). This revealed clear individual differences: participants varied in whether their preferences aligned more with the presentation method (e.g., monocular vs. binocular) or viewing distance.Our findings emphasize that effective AR captioning is not one-size-fits-all but must flexibly adapt to individual spatial preferences. We argue for incorporating spatial personalization as a core design principle for accessible AR systems.

Auditory directionality remains challenging for d/Deaf and hard of hearing (DHH) people. We evaluated a shoulder-mounted tactile system that vibrates bilaterally to signal sound direction and urgency in multi-person meetings. Eight DHH participants watched subtitled VR meetings with and without the device, pressing a trigger when their name or an alarm appeared and selecting the speaker afterward. The device increased reaction count for name calls and shortened reaction time for both name and alarm alerts (all p < 0.05), while speaker identification accuracy stayed high and unchanged. Post-experiment surveys confirmed strong demand for directional cues (p < 0.01) and alarm sound alerts (p < 0.05). Reflective Thematic Analysis of open-ended responses highlighted (i) the need to customize target sounds, (ii) meeting-focused use cases, and (iii) requests for refined vibration patterns and lighter design. These findings demonstrate the system’s value for timely notifications and emphasize directional feedback and customizability as critical to practical adoption.

Japanese Sign Language (JSL) and Manually Coded Japanese (MCJ) are the two main forms of sign language used in Japan. The former differs significantly from spoken Japanese, while the latter closely aligns with spoken syntax. Pidgin Sign Japanese (PSJ) is an intermediate form that heavily relies on nonmanual signals such as facial expressions and head movements for grammatical nuances. Current sign language recognition (SLR) systems predominantly focus on MCJ, neglecting the challenging properties of PSJ. This paper proposes an annotation assistance tool designed to automate the annotation of non-manual and spatial elements in PSJ. Our tool significantly reduces the manual effort required for annotation by using state-of-the-art methods for tracking human pose, hand, and face landmarks, along with recognizing facial action units (FAUs). Validation on a preliminary dataset of 30 videos containing over 90 instances of nonmanual elements demonstrated a $\mathbf{4 0 \%}$ reduction in annotation time, highlighting our proposal’s efficiency and effectiveness in handling the complexities of PSJ.

Abstract This paper addresses the issue in live events, especially in sports viewing, where individuals who are deaf, hard of hearing (DHH), blind, or have low vision (BLV) struggle to access sufficient information. Traditionally, information accessibility has relied on specific professionals or volunteers, which is often inadequate. To tackle this challenge, we propose a mechanism that facilitates information sharing for all individuals, regardless of their abilities. We have developed an inclusive web application tailored to address these needs. This application is beneficial not only for DHH and BLV individuals but also for all audiences. Additionally, the system’s applicability extends to other domains, such as museum visits. This paper details the newly developed web application and presents the outcomes of pilot studies conducted in sports viewing and museum settings with DHH and BLV participants. The results of these experiments are analyzed to assess the system’s effectiveness and to identify future improvement areas.

Some deaf and hard-of-hearing (DHH) individuals like to “listen to” music. For them to access a wider range of music, both the preference for and the audibility of music are important. As a starting point for understanding music accessibility, we investigated the audibility and preference for 19 musical instruments using five pitches totaling 95 timbres. To examine how people listen to music daily, owing to the difficulty of having participants come to the laboratory due to the spread of COVID-19, we conducted the experiment online. Participants listened to short clips of 95 sounds and subjectively answered questions on their audibility and preferences. An analysis of their subjective answers revealed the audibility and preference differences among instruments, specifically that the recorder lacks the same audibility and preference as some of the other instruments at certain pitches. However, there was little correlation between audibility and timbre preferences. The study findings will be used in music classes in which DHH students participate and indicate that timbre audibility is necessary for building music recommendation systems for DHH individuals. We also discuss the limitations and possibilities of online experiments using sound. Music, timbre, deaf and hard of hearing, audibility, preference, accessibility

Task assignment is one of the important issues in crowdsourcing. Most existing schemes consider both of task-centric measures (e.g., required abilities) and worker-centric measures (e.g., worker preference) at local (each task) assignment level, but consider only task-centric measures at the global (the whole workflow) assignment level, such as productivity and throughput. This paper proposes to introduce Inclusiveness and Activity Degree as the worker-centric measures at the global level. It is not trivial whether we can find assignment that are good in terms of all of the two task-centric measures (productivity and throughput) and the two worker-centric measures (Inclusiveness and Activity Degree) at the global level. This paper explains five assignments that are expected to increase some measures and shows Activity Degree Conscious Assignment can produce high Inclusiveness and Activity Degree and not significantly reduce the productivity and throughput assignment through a simulation.

Worker-task assignments represent one of the critical issues in crowdsourcing, as they affect the quality of task results. This study addresses the problem of forming worker groups assigned to the same task in a task stream that requires more than one worker. We introduce a worker-group queue model that covers practical and common scenarios for task-stream crowdsourcing, and compare three strategies in terms of the skill balance among worker groups, the quality of the final outputs, the number of worker re-assignments of workers, and psychological stress felt by workers. We found that one of the compared strategies that employs multiple worker queues yields good results based on these measures.

We have promoted a project called "Information Support of Everyone, by Everyone, for Everyone (ISeee). "We have been developing a live timeline posting/reading system to use at sports games for audiences to share data consisting of photos and texts. We describe two experiments using the system for the purpose of information support for deaf and hard-of-hearing people (DHH). The results suggest that the data generated with the live timeline system is helpful as information support for DHH.

A common workflow to perform a continuous human task stream is to divide workers into groups, have one group perform the newly-arrived task, and rotate the groups. Usually, more than one worker belongs to each group for improving the quality of task results. We call this type of workflow the group rotation. This paper addresses the problem of how to manage Group Rotation Type Crowdsourcing, the group rotation in a crowdsourcing setting. In the group-rotation type crowdsourcing, we must change the group structure dynamically because workers come in and leave frequently. However, changing the group structure will give workers psychological stress, such as surprise, confusion or irritation. This paper explores a design space for group restructuring algorithms in the group rotation type crowdsourcing and compares implemented strategies in terms of the evaluation results on psychological stress with real-world crowd workers.

In order to realize smooth communication between people with or without difficulty in hearing, we aim to implement an information support system based on crowdsourcing, a problem-solving model, in which numerous people cooperate to accomplish a job. More specifically, we implement a crowdsourced captioning system that enables groups of deaf and hard-of-hearing people to collaboratively interpret a speech or lecture given by sign language to text in real-time. Deaf and hard-of-hearing people are generally considered as the ones who receive support from others, but our proposal helps them become the ones who offer support to others. In this paper, we explore the issues that need be resolved for the practical use of the proposed system through several experiments and succeed in verifying its feasibility.

We present a novel system that combines the advantages of social communication and Web search by simultaneously discovering important pages and users. First, the system provides a communication interface attached to pages, which allows users to talk with each other in real time while browsing the same page, i.e., page-centric communication. Then, the system can efficiently provide two ranking lists of pages and users by analyzing a hybrid structure of hyperlinks (page-page relationship) and social links (page-user relationship and user-user relationship). Thus, users can efficiently search for important pages as well as important users related to their queries through the ranking function, and immediately obtain useful information or knowledge from not only pages themselves but also from other users.

Universal learning networks with branch control (BrcULNs) are proposed, which consist of basic networks and branch control networks. The branch control network can be used to determine which branches of the basic network should be connected or disconnected. This determination depends on the inputs or the network flows of the basic network. Therefore, by using the BrcULNs, locally functions distributed networks can be realized depending on the values of the inputs of the network or the information of the network flows. The proposed network is applied to some function approximation problems. The simulation results show that the BrcULNs exhibit better performance than the conventional networks with comparable complexity.

S. A. Kauffman (1993) explored the law of self-organization in random Boolean networks, and K. Inagaki (1998) also did it in neural networks partially. The aim of the paper is to show that probabilistic neural networks (PNNs) hold the order, even though the weights, the thresholds, and the connections between neurons are determined randomly; PNNs are recurrent networks and controlled by a probabilistic transition rule based on a Boltzmann machine. In addition, the deterministic transient neural networks (DNNs) which are the special networks of PNNs are studied extensively. From simulations, it is shown that in DNNs the dynamics follow the square-root law and there is another new critical point for the distribution of the thresholds. In addition, it is shown that in PNNs the averages of the Hamming distance between the attractors of DNN and PNN stay around a certain value depending on the thresholds and the gradient of the Sigmoidal function. These results can be explained by the sensitivity to the initial conditions of DNNs.