Deep Learning, Ubiquitous and Toy Computing
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ItemPerceived Innovativeness and Privacy Risk of Smart Toys in Brazil and Argentina( 2018-01-03)A smart toy, such as "Hello Barbie," is a device consisting of a physical toy component that connects to a computing system with online services through networking to enhance the functionality of a traditional toy. Whilst these are new educational and entertaining values of smart toys, experts in western countries such as U.S. and Germany have warned consumers of the data security and privacy issues of these toys. In this preliminary research study, we particularly studied Brazilian and Argentinian consumers’ perceived innovativeness, risks and benefits of smart toys and their purchase intention toward such toys. Results indicate that Brazilian consumers have better perception and evaluation of the toy and thus higher purchase intention than Argentinian consumers do. Such difference may be explained by the cultural differences be-tween the two countries, such as relatively low vs. high uncertainty avoidance.
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ItemTwitter Connections Shaping New York City( 2018-01-03)Geo-tagged Twitter has been proven to be a useful proxy for urban mobility, this way helping to understand the structure of the city and the shape of its local neighborhoods. In the present work we approach this problem from another angle by leveraging additional information on Twitter customers mentioning each other, which might partially reveal their social relations. We propose a novel way of constructing a spatial social network based on such data, analyze its structure and evaluate its utility for delineating urban neighborhoods. This delineation happens to have substantial similarity to the earlier one based on the user mobility network. It leads to an assumption that the social connectivity between the users is strongly related with the similarity in their mobility patterns. We justify this hypothesis enabling extrapolation of the available user mobility patterns as a proxy for social connectivity and building a network of hidden ties based on the mobility pattern similarity. Finally, we evaluate the socio-economic characteristics of the partitions for all three networks of all mentioning, reciprocal mentioning and the hidden ties.
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