Alberto H. F. Laender

A pandemia da COVID-19 teve um impacto social e econômico global, inclusive no desenvolvimento de pesquisas científicas, devido às medidas de distanciamento social e da redução de recursos financeiros. Usando dados da Plataforma Lattes, este trabalho compara a produção acadêmica de pós-graduandos brasileiros antes, durante e após a pandemia, analisando a diminuição expressiva nas conclusões de mestrados e doutorados. Os resultados ressaltam a extensão do impacto nas trajetórias acadêmicas, inclusive indicando o diferencial de impacto em áreas específicas do conhecimento.

Transformer architectures have become the main component of various state-of-the-art methods for natural language processing tasks, such as Named Entity Recognition and Relation Extraction (NER+RE). As these architectures rely on semantic (contextual) aspects of word sequences, they may fail to accurately identify and delimit entity spans when there is little semantic context surrounding the named entities. This is the case of entities composed only by digits and punctuation, such as IDs and phone numbers, as well as long composed names. In this article, we propose new techniques for contextual reinforcement and entity delimitation based on pre- and post-processing techniques to provide a richer semantic context, improving SpERT, a state-of-the-art Span-based Entity and Relation Transformer. To provide further context to the training process of NER+RE, we propose a data augmentation technique based on Generative Pretrained Transformers (GPT). We evaluate our strategies using real data from public administration documents (official gazettes and biddings) and court lawsuits. Our results show that our pre- and post-processing strategies, when used co-jointly, allows significant improvements on NER+ER effectiveness, while we also show the benefits of using GPT for training data augmentation.

Based on openness and transparency for good governance, unimpeded and verifiable access to legal and regulatory information is essential. With such access, we can monitor government actions to ensure that public financial resources are not improperly or inconsistently used. This facilitates, for example, the detection of unlawful behavior in public actions, such as bidding processes and auctions. However, different public agencies have their own criteria for standardizing the models and formats used to make information available, as exemplified in the varying styles observed in municipal, state, and union (federal) documents. In this context, we aim to minimize the effort to deal with public documents, notably official gazettes. For this, we propose a structure-oriented heuristic for extracting relevant excerpts from their texts. We then characterize these excerpts through morphosyntactic analysis and entity recognition. Subsequently, we semantically classify the extracted fragments into "sections of interest" (e.g., bids, laws, personnel, budget) using an active learning strategy to reduce the manual labeling effort. We also improve the classification process by incorporating transformers, stacking, and by combining different types of representations (e.g., frequentist, static, and contextual semantic embeddings). Furthermore, we exploit oversampling based on semi-supervised learning to deal with (labeled) data scarceness and skewness. Finally, we combine all these contributions in a real-time annotation tool with active learning support that achieves 100% accuracy in extraction and an overall accuracy of 85% in classification with very little labeling effort.

This tutorial is based on our book “Automatic Disambiguation of Author Names in Bibliographic Repositories” and aims to spread the problem and its challenges among the SBBD community. Author name ambiguity problem occurs when an author publishes works under distinct names or distinct authors publish works under similar names. This problem may be caused by a number of reasons, including the lack of standards and common practices, and the decentralized generation of bibliographic content. In this tutorial, we intend to present an ample view on the automatic disambiguation of author names. We start by discussing its motivational issues, defining the author name disambiguation task and presenting its foundations. Next, we describe some methods proposed by our research group, as well as some recent approaches to author name disambiguation. Finally, we discuss open issues. This tutorial is based on our book “Automatic Disambiguation of Author Names in Bibliographic Repositories” and aims to spread the problem and its challenges among the SBBD community. Author name ambiguity problem occurs when an author publishes works under distinct names or distinct authors publish works under similar names. This problem may be caused by a number of reasons, including the lack of standards and common practices, and the decentralized generation of bibliographic content. In this tutorial, we intend to present an ample view on the automatic disambiguation of author names. We start by discussing its motivational issues, defining the author name disambiguation task and presenting its foundations. Next, we describe some methods proposed by our research group, as well as some recent approaches to author name disambiguation. Finally, we discuss open issues.

Interdisciplinary collaborations have recently attracted the attention of scholars, since they help bridging academic relationships and contribute to make scientific collaboration networks even stronger. However, previous works on this subject have mainly focused on characterizing such interdisciplinary collaborations in specific research groups or scientific communities. In this article, we start from a previous work in which we characterized the interdisciplinary collaborations within the entire Brazilian scientific community, as defined according to the upper level of the knowledge area classification scheme proposed by CNPq, the Brazilian National Council for Scientific and Technological Development, considering the following eight major areas: Agrarian Sciences, Applied and Social Sciences, Biological Sciences, Engineering, Exact and Earth Sciences, Health Sciences, Humanities, and Linguistics, Letters and Arts. Based on this interdisciplinary collaboration network, we conducted a geographic analysis that characterizes how these collaborations have been spread across the Brazilian geographic regions. Overall, our results show strong collaborative ties involving the triad formed by the three main Brazilian geographic regions (Southeast, South and Northeast) for all major areas. Besides, three of the eight major areas (Agrarian Sciences, Biological Sciences, and Health Sciences) show a massive participation in interdisciplinary collaborations across all regions. Despite that, geographic proximity is an important factor, since the proportion of interdisciplinary collaborations involving researchers from the same region is high. Finally, we analyze the patterns of interdisciplinary collaboration by regions and by major areas, thus showing that the Brazilian interdisciplinary network is highly connected.

Identifying and studying the formation of researchers over the years is a challenging task, since the current repositories of theses and dissertations are cataloged in a decentralized manner in different digital libraries, many of them with limited scope. In this article, we report our efforts towards building a large repository to record the Brazilian academic genealogy. For this, we collected data from the Lattes platform, an internationally recognized initiative that provides a repository of researchers’ curricula maintained by the Brazilian National Council for Scientific and Technological Development (CNPq), and developed a user-oriented platform, named Science Tree, to generate the academic genealogy trees of Brazilian researchers from them, also providing additional data resulting from a series of analyses regarding the main properties of such trees. In order to assess the facilities provided by the Science Tree platform, we conducted an experimental evaluation of it with two groups of users, the first one consisting of 286 researchers who answered an evaluation questionnaire and the second one involving seven researchers with large academic experience who agreed to participate in a face-to-face assessment conducted through a personal interview, during which they performed some pre-defined tasks. The results of these two evaluations with typical users enabled us not only to validate the main features offered by the platform, but also to identify new ones that could be added to it in the future. Overall, our effort has allowed us to identify interesting aspects related to the academic career of the Brazilian researchers, thus highlighting the importance of generating and cataloging their academic genealogy trees.

Cloud computing is a general term that involves delivering hosted services over the Internet. With the accelerated growth of the volume of data used by applications, many organizations have moved their data into cloud servers to provide scalable, reliable and highly available services. A particularly challenging issue that arises in the context of cloud storage systems with geographically-distributed data replication is how to reach a consistent state for all replicas. This survey reviews major aspects related to consistency issues in cloud data storage systems, categorizing recently proposed methods into three categories: (1) fixed consistency methods, (2) configurable consistency methods and (3) consistency monitoring methods.