The coronavirus disease (COVID-19) pandemic has greatly altered peoples’ daily lives, and it continues spreading as a crucial concern globally. Knowledge, attitudes, and practices (KAP) toward COVID-19 are related to individuals’ adherence to government measures. This study evaluated KAP toward COVID-19 among university students in Japan between May 22 and July 16, 2020, via an online questionnaire, and it further investigated the associated determining KAP factors. Among the eligible respondents (n = 362), 52.8% were female, 79.0% were undergraduate students, 32.9% were students whose major university subjects were biology-related, 35.4% were from the capital region, and 83.7% were Japanese. The overall KAP of university students in Japan was high. All respondents (100%) showed they possessed knowledge on avoiding enclosed spaces, crowded areas, and close situations. Most respondents showed a moderate or higher frequency of washing their hands or wearing masks (both at 96.4%). In addition, 68.5% of respondents showed a positive attitude toward early drug administration. In the logistic regressions, gender, major subjects, education level, nationality, residence, and psychological factors (private self-consciousness and extroversion) were associated with knowledge or attitudes toward COVD-19 (p < 0.05). In the logistic and multiple linear regressions, capital regions, high basic knowledge, high information acquisition, correct information explanations contributed positively to preventative action (p < 0.05). Non-capital regions, male gender, non-bio-backgrounds, high public self-consciousness, high advanced knowledge, incorrect information explanations, and high extroversion contributed negatively to self-restraint (p < 0.05). Moreover, self-restraint was decreasing over time. These findings clarify the Japanese university students’ KAP and the related factors in the early period of the COVID-19 pandemic, and they may help university managers, experts, and policymakers control the future spread of COVID-19 and other emerging infections.

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Automatic vehicle re-identification (re-ID) is highly valuable and significant in public transportation systems, but has not achieved much progress since the visual appearances vary hugely across different viewpoints of a vehicle. Feature matching in this problem is extremely difficult, and traditional person re-ID algorithms cannot be suitably applied to vehicles. However, image generation by convolutional generative adversarial networks (GANs), which has obtained breakthrough progress, inspires us to generate vehicles in different viewpoints from only one visible view to tackle vehicle re-ID. In this work, we propose a new deep architecture, called Cross-View Generative Adversarial Network (XVGAN), to learn the features of vehicle images captured by cameras with disjoint views, and take the features as conditional variables to effectively infer cross-view images. Finally, the features of the original images are combined with the features of generated images in other views to learn distance metrics for vehicle re-ID. Our model can successfully generate realistic images in different views of the same vehicle, and contribute to re-ID on two public datasets: VeRi and VehicleID.

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