Métricas de qualidade de vídeo com características top-down de atenção visual

Monografia (graduação)—Universidade de Brasília, Faculdade de Tecnologia, Departamento de Engenharia Elétrica, 2014.Propomos neste trabalho a criação de um modelo de saliência para vídeos baseado em um modelo de saliência para imagens com um dos melhores desempenhos na literatura. Os mapas do modelo de saliência ponderam os mapas de erro (ou de qualidade) de métricas objetivas de qualidade e mostramos que essa integração da atenção visual com a avaliação de qualidade traz melhora de desempenho na estimação da qualidade de vídeos percebida pelos seres humanos.We propose in this work the creation of a saliency model for videos based in a saliency model for images that has one of the best performances in the literature. The maps from the saliency model weigh the error maps (or quality maps) of objective quality metrics and we show that this integration of visual attention and quality assessment improves the prediction performance of video quality as perceived by human beings

On the optimal sampling design for the Graph Total Variation decoder: recovering piecewise-constant graph signals from a few vertex measurements

Compressed Sensing teaches us that measurements can be traded for offline computation if the signal being sensed has a simple enough representation. Proper decoders can exactly recover the high-dimensional signal of interest from a lower-dimensional vector of that signal's observations. In graph domains -- like social, similarity, or interaction networks -- the relevant signals often have to do with the network's cluster structure. Partitioning a graph into different communities induces a piecewise-constant signal, an object that can be decoded via Graph Total Variation (G-TV) minimization even if it is not fully observed. In fact, assume that such a signal can only be accessed by querying vertices at random. Then, we could sensibly ask: what are the sampling probabilities that minimize the number of queries required for a successful G-TV recovery? This thesis is an attempt to answer this question through the study of the success conditions in G-TV minimization programs. I show that the recovery error in these programs undergoes a phase transition in terms of the number of measurements, with a threshold that explicitly depends on the vertex-sampling probabilities. It suffices to minimize this threshold to obtain an optimal sampling design. Yet, sampling optimally in practice has problems of its own. While numerical experiments reveal that it is important to focus on the places of the graph where the signal varies, implementing the optimal design without actually knowing the signal-to-be-sampled remains an open issue

Source Localization on Graphs via l1 Recovery and Spectral Graph Theory

We cast the problem of source localization on graphs as the simultaneous problem of sparse recovery and diffusion ker- nel learning. An l1 regularization term enforces the sparsity constraint while we recover the sources of diffusion from a single snapshot of the diffusion process. The diffusion ker- nel is estimated by assuming the process to be as generic as the standard heat diffusion. We show with synthetic data that we can concomitantly learn the diffusion kernel and the sources, given an estimated initialization. We validate our model with cholera mortality and atmospheric tracer diffusion data, showing also that the accuracy of the solution depends on the construction of the graph from the data points

Predicting Openness of Communication in Families With Hereditary Breast and Ovarian Cancer Syndrome: Natural Language Processing Analysis

BackgroundIn health care research, patient-reported opinions are a critical element of personalized medicine and contribute to optimal health care delivery. The importance of integrating natural language processing (NLP) methods to extract patient-reported opinions has been gradually acknowledged over the past years. One form of NLP is sentiment analysis, which extracts and analyses information by detecting feelings (thoughts, emotions, attitudes, etc) behind words. Sentiment analysis has become particularly popular following the rise of digital interactions. However, NLP and sentiment analysis in the context of intrafamilial communication for genetic cancer risk is still unexplored. Due to privacy laws, intrafamilial communication is the main avenue to inform at-risk relatives about the pathogenic variant and the possibility of increased cancer risk. ObjectiveThe study examined the role of sentiment in predicting openness of intrafamilial communication about genetic cancer risk associated with hereditary breast and ovarian cancer (HBOC) syndrome. MethodsWe used narratives derived from 53 in-depth interviews with individuals from families that harbor pathogenic variants associated with HBOC: first, to quantify openness of communication about cancer risk, and second, to examine the role of sentiment in predicting openness of communication. The interviews were conducted between 2019 and 2021 in Switzerland and South Korea using the same interview guide. We used NLP to extract and quantify textual features to construct a handcrafted lexicon about interpersonal communication of genetic testing results and cancer risk associated with HBOC. Moreover, we examined the role of sentiment in predicting openness of communication using a stepwise linear regression model. To test model accuracy, we used a split-validation set. We measured the performance of the training and testing model using area under the curve, sensitivity, specificity, and root mean square error. ResultsHigher “openness of communication” scores were associated with higher overall net sentiment score of the narrative, higher fear, being single, having nonacademic education, and higher informational support within the family. Our results demonstrate that NLP was highly effective in analyzing unstructured texts from individuals of different cultural and linguistic backgrounds and could also reliably predict a measure of “openness of communication” (area under the curve=0.72) in the context of genetic cancer risk associated with HBOC. ConclusionsOur study showed that NLP can facilitate assessment of openness of communication in individuals carrying a pathogenic variant associated with HBOC. Findings provided promising evidence that various features from narratives such as sentiment and fear are important predictors of interpersonal communication and self-disclosure in this context. Our approach is promising and can be expanded in the field of personalized medicine and technology-mediated communication