Little is known on how visual content affects the popularity on social networks, despite images being now ubiquitous on the Web, and currently accounting for a considerable fraction of all content shared. Existing art on image sharing focuses mainly on non-visual attributes. In this work we take a complementary approach, and investigate resharing from a mainly visual perspective. Two sets of visual features are proposed, encoding both aesthetical properties (brightness, contrast, sharpness, etc.), and semantical content (concepts represented by the images). We collected data from a large image-sharing service (Pinterest) and evaluated the predictive power of different features on popularity (number of reshares). We found that visual properties have low predictive power compared that of social cues. However, after factoring-out social influence, visual features show considerable predictive power, especially for images with higher exposure, with over 3:1 accuracy odds when classifying highly exposed images between very popular and unpopular.

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As Redes Veiculares (Vehicular Ad-hoc Networks-VANETs) são tecnologias que permitem a utilização de serviços de rede por motoristas e passageiros em veículos. A heterogeneidade das VANETs, que podem utilizar diferentes tecnologias sem-fio, representa um desafio para o desenvolvimento de protocolos de comunicação. Neste contexto, o paradigma de Redes Veiculares Definidas por Software (Software Defined Vehicular Network-SDVN) surge como uma alternativa promissora que permite a criação de protocolos flexíveis e adaptáveis. Portanto, este trabalho apresenta um protocolo de disseminação geocast que utiliza informações do ambiente e dos veículos para realizar a disseminação de mensagens de forma inteligente. Os resultados obtidos através de simulações mostram que o protocolo apresenta um comportamento mais eficiente que os adversários, tanto em ambientes que não consideram construções (prédios, casas, etc.) quanto em ambientes que construções são consideradas (realístico).

This paper introduces the first corpus for Automatic Post-Editing of English and a low-resource language, Brazilian Portuguese. The source English texts were extracted from the WebNLG corpus and automatically translated into Portuguese using a state-of-the-art industrial neural machine translator. Post-edits were then obtained in an experiment with native speakers of Brazilian Portuguese. To assess the quality of the corpus, we performed error analysis and computed complexity indicators measuring how difficult the APE task would be. We report preliminary results of Phrase-Based and Neural Machine Translation Models on this new corpus. Data and code publicly available in our repository. 1

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