Portugal for Chinese Tourists, an e-guide

This project consists in an electronic guide (e-guide) dedicated to Chinese tourists who are interested and willing to travel to Portugal. This e-guide introduces Portugal to Chinese tourists, filling in an identified cultural gap, and enhancing Chinese tourists’ knowledge about this destination. In this project, Chinese tourists’ travel preferences and demands are analyzed based on two questionnaires (one a priori and one a posteriori). Based on these results, customized tourism information and travel plans were generated. The preparation and development processes of this e-guide are presented in detail in this project. This project is the final work of the Master’s degree in Tourism and Communication, leading to the achievement of the degree of Master by Escola Superior de Hotelaria e Turismo do Estoril, Faculdade de Letras e Instituto de Geografia e Ordenamento do Território.Neste projeto desenvolveu-se um guia eletrónico (e-guia), destinado a turistas chineses interessados e dispostos a viajar para Portugal. Este guia eletrónico também serve como um meio de apresentação de Portugal aos turistas chineses, preenchendo lacunas culturais identificadas e aumentando o conhecimento dos turistas chineses sobre este destino. Neste projeto, as preferências e exigências de viagem dos turistas chineses são analisadas com base em dois questionários (um a priori e outro a posterior). Com base nesses resultados, foram geradas informações personalizadas sobre turismo e desenvolvidos planos de viagem. Os processos de preparação e desenvolvimento deste guia eletrónico são apresentados em detalhe neste projeto. O projeto corresponde ao trabalho final do Mestrado em Turismo e Comunicação, levando à obtenção do grau de Mestre a atribuir pela Escola Superior de Hotelaria e Turismo do Estoril, pela Faculdade de Letras e pelo Instituto de Geografia e Ordenamento do Território

ASD Biomarker Detection on fMRI Images: Feature learning with Data Corruptions by Analyzing Deep Neural Network Classifier Outcomes

Autism spectrum disorder (ASD) is a complex neurological and developmental disorder. It emerges early in life and is generally associated with lifelong disability. Finding the biomarkers associated with ASD is extremely helpful to understand the underlying roots of the disorder and find more targeted treatment. Previous studies suggested brain activations are abnormal in ASDs, hence functional magnetic resonance imaging (fMRI) has been used to identify ASD. In this work we addressed the problem of interpreting reliable biomarkers in classifying ASD vs. control; therefore, we proposed a 2-step pipeline: 1) classifying ASD and control fMRI images by deep neural network, and 2) finding which brain regions are important for identifying ASD and control. Specifically, in step 2, we used the trained classifier to estimate the feature importance by measuring the prediction distribution change as a function of input image with the corrupted region. However, there is no certain way to corrupt the data without adding side effects. Thus, we aggregated two opposite corruption methods: a) blackout and b) add Gaussian noise. Biomarkers found by the 2-step pipeline were verified by Neurosynth brain function decoding. Several key innovations in our research include: i) we created an innovative pipeline for learning image data feature by analyzing the classifier outcomes with corruptions; ii) we proposed a deep learning strategy for classifying 4D data; iii) we aggregated different corruption methods for feature importance analysis, and iv) our neurological interpretation of the final results showed evidence that there were meaningful fMRI biomakers on fMRI for ASD

Semantic Image Segmentation via Deep Parsing Network

This paper addresses semantic image segmentation by incorporating rich information into Markov Random Field (MRF), including high-order relations and mixture of label contexts. Unlike previous works that optimized MRFs using iterative algorithm, we solve MRF by proposing a Convolutional Neural Network (CNN), namely Deep Parsing Network (DPN), which enables deterministic end-to-end computation in a single forward pass. Specifically, DPN extends a contemporary CNN architecture to model unary terms and additional layers are carefully devised to approximate the mean field algorithm (MF) for pairwise terms. It has several appealing properties. First, different from the recent works that combined CNN and MRF, where many iterations of MF were required for each training image during back-propagation, DPN is able to achieve high performance by approximating one iteration of MF. Second, DPN represents various types of pairwise terms, making many existing works as its special cases. Third, DPN makes MF easier to be parallelized and speeded up in Graphical Processing Unit (GPU). DPN is thoroughly evaluated on the PASCAL VOC 2012 dataset, where a single DPN model yields a new state-of-the-art segmentation accuracy.Comment: To appear in International Conference on Computer Vision (ICCV) 201