Disaster preparedness, adaptive politics and lifelong learning: a case of Japan

Preparedness for disaster scenarios is progressively becoming an educational agenda for governments because of diversifying risks and threats worldwide. In disaster-prone Japan, disaster preparedness has been a prioritised national agenda, and preparedness education has been undertaken in both formal schooling and lifelong learning settings. This article examines the politics behind one prevailing policy discourse in the field of disaster preparedness referred to as ‘the four forms of aid’ – ‘kojo [public aid]’, ‘jijo [self-help]’, ‘gojo/kyojo [mutual aid]’. The study looks at the Japanese case, however, the significance is global, given that neo-liberal governments are increasingly having to deal with a range of disaster situations whether floods or terrorism, while implementing austerity measures. Drawing on the theory of the adaptiveness of neo-liberalism, the article sheds light on the hybridity of the current Abe government’s politics: a ‘dominant’ neo-liberal economic approach – public aid and self-help – and a ‘subordinate’ moral conservative agenda – mutual aid. It is argued that the four forms of aid are an effective ‘balancing act’, and that kyojo in particular is a powerful legitimator in the hybrid politics. The article concludes that a lifelong and life-wide preparedness model could be developed in Japan which has taken a social approach to lifelong learning. © 2016 Informa UK Limited, trading as Taylor & Francis Group

Recognition of Activities of Daily Living from Topic Model

Research in ubiquitous and pervasive technologies have made it possible to recognise activities of daily living through non-intrusive sensors. The data captured from these sensors are required to be classified using various machine learning or knowledge driven techniques to infer and recognise activities. The process of discovering the activities and activity-object patterns from the sensors tagged to objects as they are used is critical to recognising the activities. In this paper, we propose a topic model process of discovering activities and activity-object patterns from the interactions of low level state-change sensors. We also develop a recognition and segmentation algorithm to recognise activities and recognise activity boundaries. Experimental results we present validates our framework and shows it is comparable to existing approaches.