Contrary to the traditional concept of artificial intelligence, explainable artificial intelligence (XAI) aims to provide explanations for the prediction results and make users perceive the system as being reliable. However, despite its importance, only a few studies have investigated how the explanations of an XAI system should be designed. This study investigates how people attribute the perceived ability of XAI systems based on perceived attributional qualities and how the power status of the XAI and anthropomorphism affect the attribution process. In a laboratory experiment, participants (N = 500) read a scenarios of using an XAI system with either lower or higher power status and reported their perceptions of the system. Results indicated that an XAI system with a higher power status caused users to perceive the outputs of the XAI system to be more controllable by intention, and higher perceived stability and uncontrollability resulted in greater confidence in the system’s ability. The effect of perceived controllability on perceived ability was moderated by the extent to which participants anthropomorphised the system. Several design implications for XAI systems are suggested based on our findings.

The edge platform has evolved to become a part of a distributed computing environment. While typical edges do not have enough processing power to train machine learning models in real time, it is common to generate models in the cloud for use on the edge. The pattern of heterogeneous Internet of Things (IoT) data is dependent on individual circumstances. It is not easy to guarantee prediction performance when a monolithic model is used without considering the spatial characteristics of the space generating those data. In this paper, we propose a collaborative framework using a new method to select the best model for the edge from candidate models of cloud based on sample data correlation. This method lets the edge use the most suitable model without any training tasks on the edge side, and it also minimizes privacy issues. We apply the proposed method to predict future fine particulate matter concentration in an individual space. The results suggest that our method can provide better performance than the previous method.

OBJECTIVES: This study explored predictors of coronavirus disease 2019 (COVID-19) booster hesitancy among fully vaccinated young adults and parental COVID-19 vaccine hesitancy for their children.METHODS: This cross-sectional study administered an online survey from December 2 to December 20, 2021. We enrolled participants aged 18-49 years, for whom ≥2 weeks had passed after their initial COVID-19 vaccination. We estimated odds ratios (ORs) with 95% confidence intervals (CIs) using multivariate logistic regression to evaluate factors associated with booster/vaccine hesitancy.RESULTS: Among the 2,993 participants, 48.8% showed hesitancy (wait and see: 40.2%; definitely not: 8.7%). Booster hesitancy was more common among women (OR, 1.25; 95% CI, 1.05 to 1.50), younger people (OR, 1.44; 95% CI, 1.17 to 1.77), those with a lower education level (OR, 2.05; 95% CI, 1.10 to 3.82), those who received the mRNA-1273 vaccine type (OR, 2.01; 95% CI, 1.65 to 2.45), and those who experienced serious adverse events following previous COVID-19 vaccination (OR, 2.03; 95% CI, 1.47 to 2.80). The main reasons for booster hesitancy were concerns about safety (54.1%) and doubts about efficacy (29.8%). Among the 1,020 respondents with children aged <18 years, 65.8% were hesitant to vaccinate their children against COVID-19; hesitancy was associated with younger parental age, education level, the type of vaccine the parent received, and a history of COVID-19 infection.CONCLUSIONS: Concerns about the efficacy and safety of COVID-19 vaccines were the major barrier to booster acceptance. The initial COVID-19 vaccine type (mRNA-1273), young age, gender (women), a low education level, and adverse events after the first COVID-19 vaccine were key predictors of booster hesitancy.

Sensorimotor rhythm (SMR)-based brain–computer interface (BCI) controlled Functional Electrical Stimulation (FES) has gained importance in recent years for the rehabilitation of motor deficits. However, there still remain many research questions to be addressed, such as unstructured Motor Imagery (MI) training procedures; a lack of methods to classify different MI tasks in a single hand, such as grasping and opening; and difficulty in decoding voluntary MI-evoked SMRs compared to FES-driven passive-movement-evoked SMRs. To address these issues, a study that is composed of two phases was conducted to develop and validate an SMR-based BCI-FES system with 2-class MI tasks in a single hand (Phase 1), and investigate the feasibility of the system with stroke and traumatic brain injury (TBI) patients (Phase 2). The results of Phase 1 showed that the accuracy of classifying 2-class MIs (approximately 71.25%) was significantly higher than the true chance level, while that of distinguishing voluntary and passive SMRs was not. In Phase 2, where the patients performed goal-oriented tasks in a semi-asynchronous mode, the effects of the FES existence type and adaptive learning on task performance were evaluated. The results showed that adaptive learning significantly increased the accuracy, and the accuracy after applying adaptive learning under the No-FES condition (61.9%) was significantly higher than the true chance level. The outcomes of the present research would provide insight into SMR-based BCI-controlled FES systems that can connect those with motor disabilities (e.g., stroke and TBI patients) to other people by greatly improving their quality of life. Recommendations for future work with a larger sample size and kinesthetic MI were also presented.

In perceiving a virtual environment (VE), distance perception is important in that it affects users’ behaviors and interactions. This study addresses an on-going issue of virtual reality (VR), distance underestimation within a VE. For this purpose, a 2x2x2x5 mixed design was used with two between-subject variables [visual cue for perception (provided and non-provided) and visual cue for action (provided and non-provided)] and two within-subject variables [response measure (verbal estimation and triangulated blind walking) and distance (1 m, 5 m, 10 m, 20 m, and 35 m)]. Sixty-four undergraduate or graduate students participated in the experiment, and a cube was chosen as the target object. The experimental results showed that visual cues for action mitigated distance underestimation and improved the accuracy of perceived distances. In general, distance underestimation was more obvious in the triangulated blind walking than the verbal estimation, and in the action/vista space than personal space, with relatively lower accuracies. Meanwhile, when visual cue for action were provided, the degree of underestimation decreased significantly with higher accuracy particularly in the triangulated blind walking. Although overestimation was found across all the distances in the verbal estimation, overestimation was found only for the personal distance, and underestimation was found for the action/vista distances in the triangulated blind walking. In both response measures, the accuracy of distance perceptions were the worst in the 10 m condition. These findings can be expected to provide insight into the problem of distance underestimation and can help guide design of VEs.