O terrível nascimento da beleza: a criação literária em diversos autores

De onde brota a inspiração para baladas de amor e canções de guerra, lendas e narrativas, tragédias e comédias? Ao longo de séculos, escritores e leitores interrogaram-se acerca do nascimento da beleza. Neste artigo, abordo essa questão intrigante, em quatro etapas: a) Examino algumas personificações criadas por Hesíodo, Homero, Luís de Camões e Federico García Lorca para descreverem a inspiração; b) Exploro as estratégias utilizadas por Samuel Coleridge, Salvador Dalí e William Burroughs, para penetrar no reino da fantasia, o inconsciente; c) Apresento as explicações científicas propostas por Sigmund Freud, Carl Jung e Robert Sperry para o impulso criativo; d) Para concluir, menciono as razões que levaram Fernando Pessoa, Eugénio de Andrade e Emily Dickinson a desconfiarem da musa inspiradora, preferindo o esforço que corrige a emoção e gera a obra de arte. Seguindo uma perspectiva comparada, o meu objectivo é mostrar diferentes formas de perceber a criatividade literária. Para tanto, recorro ao trabalho dos escritores e cientistas atrás mencionados e, naturalmente, à minha opinião.

Leis de Pareto para Guerras e Actos de Terrorismo

Mestrado em Engenharia Eletrotécnica e de Computadores - Área de Especialização de Sistemas e Planeamento Industrial

Tourism mobile and recommendation systems - a state of the art

Recommendation systems have been growing in number for the last fifteen years. To evolve and adapt to the demands of the actual society, many paradigms emerged giving birth to even more paradigms and hybrid approaches. Mobile devices have also been under an incredible growth rate in every business area, and there are already lots of mobile based systems to assist tourists. This explosive growth gave birth to different mobile applications, each having their own advantages and disadvantages. Since recommendation and mobile systems might as well be integrated, this work intends to present the current state of the art in tourism mobile and recommendation systems, as well as to state their advantages and disadvantages.

Recommendation & mobile systems - a state of the art for tourism

Recommendation systems have been growing in number over the last fifteen years. To evolve and adapt to the demands of the actual society, many paradigms emerged giving birth to even more paradigms and hybrid approaches. These approaches contain strengths and weaknesses that need to be evaluated according to the knowledge area in which the system is going to be implemented. Mobile devices have also been under an incredible growth rate in every business area, and there are already lots of mobile based systems to assist tourists. This explosive growth gave birth to different mobile applications, each having their own advantages and disadvantages. Since recommendation and mobile systems might as well be integrated, this work intends to present the current state of the art in tourism mobile and recommendation systems, as well as to state their advantages and disadvantages.

Recommendation of Tourism Resources Supported by Crowdsourcing

Context-aware recommendation of personalised tourism resources is possible because of personal mobile devices and powerful data filtering algorithms. The devices contribute with computing capabilities, on board sensors, ubiquitous Internet access and continuous user monitoring, whereas the filtering algorithms provide the ability to match the profile (interests and the context) of the tourist against a large knowledge bases of tourism resources. While, in terms of technology, personal mobile devices can gather user-related information, including the user context and access multiple data sources, the creation and maintenance of an updated knowledge base of tourism-related resources requires a collaborative approach due to the heterogeneity, volume and dynamic nature of the resources. The current PhD thesis aims to contribute to the solution of this problem by adopting a Crowdsourcing approach for the collaborative maintenance of the knowledge base of resources, Trust and Reputation for the validation of uploaded resources as well as publishers, Big Data for user profiling and context-aware filtering algorithms for the personalised recommendation of tourism resources.