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In this article, we present the Brazilian Portuguese Lexicon, a new word-based corpus for psycholinguistic and computational linguistic research in Brazilian Portuguese. We describe the corpus development, the specific characteristics on the internet site and database for user access. We also perform distributional analyses of the corpus and comparisons to other current databases. Our main objective was to provide a large, reliable, and useful word-based corpus with a dynamic, easy-to-use, and intuitive interface with free internet access for word and word-criteria searches. We used the Núcleo Interinstitucional de Linguística Computacional’s corpus as the basic data source and developed the Brazilian Portuguese Lexicon by deriving and adding metalinguistic and psycholinguistic information about Brazilian Portuguese words. We obtained a final corpus with more than 30 million word tokens, 215 thousand word types and 25 categories of information about each word. This corpus was made available on the internet via a free-access site with two search engines: a simple search and a complex search. The simple engine basically searches for a list of words, while the complex engine accepts all types of criteria in the corpus categories. The output result presents all entries found in the corpus with the criteria specified in the input search and can be downloaded as a.csv file. We created a module in the results that delivers basic statistics about each search. The Brazilian Portuguese Lexicon also provides a pseudoword engine and specific tools for linguistic and statistical analysis. Therefore, the Brazilian Portuguese Lexicon is a convenient instrument for stimulus search, selection, control, and manipulation in psycholinguistic experiments, as also it is a powerful database for computational linguistics research and language modeling related to lexicon distribution, functioning, and behavior. [ABSTRACT FROM AUTHOR]

Governments are issuing regulations and laws demanding that companies protect collected and processed personal data. In Brazil, the federal government sanctioned the General Personal Data Protection law, which defines personal and sensitive data associated with Brazilian citizens. One existing barrier for companies to comply with the law is identifying where personal data is stored inside their infrastructure, mainly concerning personal data inserted into unstructured documents. Named Entity Recognition and Classification (NERC) can support companies in this task by implementing supervised learning models to identify personal data. In this study, we designed an experiment to evaluate machine learning-based NERC using BERT and LSTM approaches to recognize personal data related to Brazil's context. We established a generic training corpus based on online documents and trained two models for each approach, one considering the original corpus and another after lowercasing it. The study also assessed relation extraction to differentiate personal entities from others. For NERC and relation extraction evaluations, we established a training corpus comprising documents from two organizations related to the education and health sectors. BERT fine-tuned with uncased corpus scored an F1 measure of 0.8 and achieved the best performance in recognizing entities, followed by LSTM based on the same corpus. After applying relation extraction, BERT models achieved better F1 scores than LSTM models. The uncased BERT model achieved an F1 score of 0.85, which was the best. Experiment results also indicated that relation extraction improves the performance of BERT models to discover personal entities. [ABSTRACT FROM AUTHOR]

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This work develops a mathematical model for designing bikeway networks that integrate into public bus transport services in urban areas using concepts from network multiobjective optimization and mixed-integer linear programming (MILP). The proposed model maximizes 14 objectives and 14 constraints by referring in a generalized way to lane comfort, safety, path objectivity, network connectivity, intermodality with bus service, and monetary budgets. In the context of a developing country, we believe that this model can address the transit network design problem (TNDP) as an innovative proposal for integration between alternative modes of transport and buses. A case study in Sao Jose dos Campos city, Sao Paulo state, Brazil, is conducted, and exact solutions are obtained with partially connected networks (first approach) and fully connected networks (second approach). A scenario analysis enables verification of the integrated transportation system performance and, for the case study, prioritizes the least expensive bikeway type. Therefore, the results from the proposed model can contribute to urban planning in testing alternative scenarios for bike–bus networks. [ABSTRACT FROM AUTHOR]

The effectiveness of using Augmentative and Alternative Communication (AAC) with individuals with Autism spectrum disorder (ASD) is well documented in descriptive reviews and meta-analytic investigations published in international journals in the last decade. Most of these analyses include researches conducted in North-American or European countries, where AAC has become an evidence-based practice. Little is known, however, about the use of AAC for individuals with ASD in developing countries. The purpose of this descriptive review was to elucidate how AAC has been used with individuals with ASD in Brazil. Nineteen studies, including dissertations, theses and journal articles, published in the last five years, were analysed. Results revealed the prevalence of low-technology AAC, with a significant portion using picture-exchange communication methods. The use of higher-technology devices was limited and none of the investigations used manual signs. Most were intervention studies that used primarily quasi-experimental designs to evaluate the effectiveness of intervention packages on school-aged children in natural settings. Strengths/weaknesses of the studies, overall limitations, and recommendations for future research are discussed. [ABSTRACT FROM AUTHOR]