Complexity-Aware Assignment of Latent Values in Discriminative Models for Accurate Gesture Recognition

Many of the state-of-the-art algorithms for gesture recognition are based on Conditional Random Fields (CRFs). Successful approaches, such as the Latent-Dynamic CRFs, extend the CRF by incorporating latent variables, whose values are mapped to the values of the labels. In this paper we propose a novel methodology to set the latent values according to the gesture complexity. We use an heuristic that iterates through the samples associated with each label value, stimating their complexity. We then use it to assign the latent values to the label values. We evaluate our method on the task of recognizing human gestures from video streams. The experiments were performed in binary datasets, generated by grouping different labels. Our results demonstrate that our approach outperforms the arbitrary one in many cases, increasing the accuracy by up to 10%.Comment: Conference paper published at 2016 29th SIBGRAPI, Conference on Graphics, Patterns and Images (SIBGRAPI). 8 pages, 7 figure

Portinari: A Data Exploration Tool to Personalize Cervical Cancer Screening

Socio-technical systems play an important role in public health screening programs to prevent cancer. Cervical cancer incidence has significantly decreased in countries that developed systems for organized screening engaging medical practitioners, laboratories and patients. The system automatically identifies individuals at risk of developing the disease and invites them for a screening exam or a follow-up exam conducted by medical professionals. A triage algorithm in the system aims to reduce unnecessary screening exams for individuals at low-risk while detecting and treating individuals at high-risk. Despite the general success of screening, the triage algorithm is a one-size-fits all approach that is not personalized to a patient. This can easily be observed in historical data from screening exams. Often patients rely on personal factors to determine that they are either at high risk or not at risk at all and take action at their own discretion. Can exploring patient trajectories help hypothesize personal factors leading to their decisions? We present Portinari, a data exploration tool to query and visualize future trajectories of patients who have undergone a specific sequence of screening exams. The web-based tool contains (a) a visual query interface (b) a backend graph database of events in patients' lives (c) trajectory visualization using sankey diagrams. We use Portinari to explore diverse trajectories of patients following the Norwegian triage algorithm. The trajectories demonstrated variable degrees of adherence to the triage algorithm and allowed epidemiologists to hypothesize about the possible causes.Comment: Conference paper published at ICSE 2017 Buenos Aires, at the Software Engineering in Society Track. 10 pages, 5 figure

Characterizing and Detecting Hateful Users on Twitter

Most current approaches to characterize and detect hate speech focus on \textit{content} posted in Online Social Networks. They face shortcomings to collect and annotate hateful speech due to the incompleteness and noisiness of OSN text and the subjectivity of hate speech. These limitations are often aided with constraints that oversimplify the problem, such as considering only tweets containing hate-related words. In this work we partially address these issues by shifting the focus towards \textit{users}. We develop and employ a robust methodology to collect and annotate hateful users which does not depend directly on lexicon and where the users are annotated given their entire profile. This results in a sample of Twitter's retweet graph containing $100,386$ users, out of which $4,972$ were annotated. We also collect the users who were banned in the three months that followed the data collection. We show that hateful users differ from normal ones in terms of their activity patterns, word usage and as well as network structure. We obtain similar results comparing the neighbors of hateful vs. neighbors of normal users and also suspended users vs. active users, increasing the robustness of our analysis. We observe that hateful users are densely connected, and thus formulate the hate speech detection problem as a task of semi-supervised learning over a graph, exploiting the network of connections on Twitter. We find that a node embedding algorithm, which exploits the graph structure, outperforms content-based approaches for the detection of both hateful ($95\%$ AUC vs $88\%$ AUC) and suspended users ($93\%$ AUC vs $88\%$ AUC). Altogether, we present a user-centric view of hate speech, paving the way for better detection and understanding of this relevant and challenging issue.Comment: This is an extended version of the homonymous short paper to be presented at ICWSM-18. arXiv admin note: text overlap with arXiv:1801.0031

Paraparesia espástica como manifestação inicial da ataxia espinocerebelar do tipo 7

Conselho Nacional de Pesquisa (CNPq)(FAEPA) Fundação de Apoio ao Ensino, Pesquisa e Assistência do Hospital das Clínicas da Faculdade de Medicina de Ribeirão Pret

A review of common parameters and descriptors used in studies of the impacts of heavy metal pollution on marine macroalgae: identification of knowledge gaps and future needs

This study presents a systematic review to assess the main similarities and gaps in efforts to evaluate the impacts of heavy metals on benthic marine seaweeds. A total of 91 studies were compiled, the main parameters (abiotic, biological, ecotoxicological, and heavy metals) and descriptors of which were evaluated by quantitative and qualitative analyses. Our results indicate the importance of diversifying searches by including different languages (i.e. English, Portuguese and Spanish). Most of the studies were field characterizations, with few abiotic parameters and/or seasonality evaluations being employed. In contrast, the assessment of ecotoxicological parameters was highly frequent, which seems incoherent considering the absence of data to support the use of these results in biomonitoring applications. The genera Sargassum, Ulva and Enteromorpha were widely studied worldwide, apart from a small fraction of studies assessing higher levels of biological organization. Moreover, the use of different parameters and descriptors by the evaluated studies precludes making conclusive or reliable comparisons. These findings highlight the importance of greater efforts to construct a concise baseline of knowledge using similar parameters so that global evaluations of the impacts of heavy metals on photosynthetic organisms can be undertaken.info:eu-repo/semantics/publishedVersio

Cultivo de Saccharomyces cerevisiae adaptada em D-xilulose sob condições aeróbias e anaeróbias

O desenvolvimento de um processo para produção de etanol com uma alta produtividade a partir de D-xilulose é de grande interesse econômico. Esse processo pode agregar maior valor aos resíduos lignocelulósicos, além de promover um aproveitamento completo da biomassa, utilizando-se suas frações celulósica e hemicelulósica para a obtenção de etanol. O objetivo do presente trabalho foi estudar a assimilação de D-xilulose, o crescimento e a produção de etanol e xilitol em cultivo de levedura de panificação de Saccharomyces cerevisiae em condições aeróbias e anaeróbis. Os experimentos foram conduzidos em biorreator de bancada de 2L, utilizando meio mínimo contendo a mistura xilose-xilulose. Os cultivos foram realizados com colônia de levedura previamente selecionada a partir de experimentos de screening com mais de 20 colônias de isoladas de levedura comercial que apresentaram crescimento em meio mínimo contendo a mistura xilose-xilulose em condições anaeróbias. A fermentação da D-xilulose pela levedura na ausência de oxigênio resultou na produção de 4,2 g/L de etanol e 3,7 de xilitol. Já o crescimento da levedura em condições aeróbias forneceu como produto principal a biomassa, com formação de 8 g/L e como subproduto o xilitol, com concentração máxima de 2,0 g/L