OS DOIS VALORES UTILIZADOS HOJE PELAS FESTAS TRADICIONAIS EM DUAS LOCALIDADES COREANAS

In the traditional Korean society, the Shamanism of the popular classes contrasted with the Confucianism of the upper ones. Feasts reflected this situation : they started with austere Confucian rituals before an explosion of joy of Shamanist and popular inspiration, which dominated them before the beginning of the Chosun Dynasty (AD 1392-1910). With the modernization and urbanization of the country, feasts evolved. In the suburbs of Seoul, only the Confucian component and the shared meal remain alive. In a faraway island of Southern Korea, the popular and shamanist component is still alive. In both cases, the sacred dimension of the feast reflects the longings of contemporary populations : the need for serenity and conviviality in suburban environments ; the permanency of the group in the island.Na sociedade coreana tradicional, o xamanismo das classes populares opunha-se ao confucionismo das classes superiores. As festas, que eram abertas com austeros rituais confucianos antes de conhecer a alegria da inspiração xamanista que as tinha dominado previamente ao começo da dinastia de Chosun (1392-1910), testemunham-no. Com a modernização e a urbanização do país,  as festas evoluíram. Na periferia de Seul, subsistem da festa apenas sua componente confuciana e o banquete comum que se segue. No sul do país, em uma ilha, a componente popular e xamanista permaneceu viva. Nos dois casos, a dimensão sagrada da festa adaptou-se às aspirações das populações atuais: necessidade de serenidade e convívio nos meios suburbanos, afirmação da presença do grupo na ilha

Mobility-Induced Graph Learning for WiFi Positioning

A smartphone-based user mobility tracking could be effective in finding his/her location, while the unpredictable error therein due to low specification of built-in inertial measurement units (IMUs) rejects its standalone usage but demands the integration to another positioning technique like WiFi positioning. This paper aims to propose a novel integration technique using a graph neural network called Mobility-INduced Graph LEarning (MINGLE), which is designed based on two types of graphs made by capturing different user mobility features. Specifically, considering sequential measurement points (MPs) as nodes, a user's regular mobility pattern allows us to connect neighbor MPs as edges, called time-driven mobility graph (TMG). Second, a user's relatively straight transition at a constant pace when moving from one position to another can be captured by connecting the nodes on each path, called a direction-driven mobility graph (DMG). Then, we can design graph convolution network (GCN)-based cross-graph learning, where two different GCN models for TMG and DMG are jointly trained by feeding different input features created by WiFi RTTs yet sharing their weights. Besides, the loss function includes a mobility regularization term such that the differences between adjacent location estimates should be less variant due to the user's stable moving pace. Noting that the regularization term does not require ground-truth location, MINGLE can be designed under semi- and self-supervised learning frameworks. The proposed MINGLE's effectiveness is extensively verified through field experiments, showing a better positioning accuracy than benchmarks, say root mean square errors (RMSEs) being 1.398 (m) and 1.073 (m) for self- and semi-supervised learning cases, respectively.Comment: submitted to a possible IEEE journa