Benchmarks for Pir\'a 2.0, a Reading Comprehension Dataset about the Ocean, the Brazilian Coast, and Climate Change

Pir\'a is a reading comprehension dataset focused on the ocean, the Brazilian coast, and climate change, built from a collection of scientific abstracts and reports on these topics. This dataset represents a versatile language resource, particularly useful for testing the ability of current machine learning models to acquire expert scientific knowledge. Despite its potential, a detailed set of baselines has not yet been developed for Pir\'a. By creating these baselines, researchers can more easily utilize Pir\'a as a resource for testing machine learning models across a wide range of question answering tasks. In this paper, we define six benchmarks over the Pir\'a dataset, covering closed generative question answering, machine reading comprehension, information retrieval, open question answering, answer triggering, and multiple choice question answering. As part of this effort, we have also produced a curated version of the original dataset, where we fixed a number of grammar issues, repetitions, and other shortcomings. Furthermore, the dataset has been extended in several new directions, so as to face the aforementioned benchmarks: translation of supporting texts from English into Portuguese, classification labels for answerability, automatic paraphrases of questions and answers, and multiple choice candidates. The results described in this paper provide several points of reference for researchers interested in exploring the challenges provided by the Pir\'a dataset.Comment: Accepted at Data Intelligence. Online ISSN 2641-435

The BLue Amazon Brain (BLAB): A Modular Architecture of Services about the Brazilian Maritime Territory

We describe the first steps in the development of an artificial agent focused on the Brazilian maritime territory, a large region within the South Atlantic also known as the Blue Amazon. The "BLue Amazon Brain" (BLAB) integrates a number of services aimed at disseminating information about this region and its importance, functioning as a tool for environmental awareness. The main service provided by BLAB is a conversational facility that deals with complex questions about the Blue Amazon, called BLAB-Chat; its central component is a controller that manages several task-oriented natural language processing modules (e.g., question answering and summarizer systems). These modules have access to an internal data lake as well as to third-party databases. A news reporter (BLAB-Reporter) and a purposely-developed wiki (BLAB-Wiki) are also part of the BLAB service architecture. In this paper, we describe our current version of BLAB's architecture (interface, backend, web services, NLP modules, and resources) and comment on the challenges we have faced so far, such as the lack of training data and the scattered state of domain information. Solving these issues presents a considerable challenge in the development of artificial intelligence for technical domains

Enhancing Completion Time Prediction Through Attribute Selection

Approaches have been proposed in process mining to predict the completion time of process instances. However, the accuracy levels of the prediction models depend on how useful the log attributes used to build such models are. A canonical subset of attributes can also offer a better understanding of the underlying process. We describe the application of two automatic attribute selection methods to build prediction models for completion time. The filter was used with ranking whereas the wrapper was used with hill-climbing and best-first techniques. Annotated transition systems were used as the prediction model. Compared to decision-making by human experts, only the automatic attribute selectors using wrappers performed better. The filter-based attribute selector presented the lowest performance on generalization capacity. The semantic reasonability of the selected attributes in each case was analyzed in a real-world incident management process

Discovery of Unstructured Business Processes Through Genetic Algorithms Using Activity Transitions-Based Completeness and Precision

Process model discovery can be approached as an optimization problem, for which genetic algorithms have been used previously. However, the fitness functions used, which consider full log traces, have not been found adequate to discover unstructured processes. We propose a solution based on a local analysis of activity transitions, which proves effective for unstructured processes, most common in organizations. Our solution considers completeness and accuracy calculation for the fitness function

A security study of Bluetooth-powered robot toy

Aim: A smart toy robot has its intellect with circuits on board. It has a built-in microprocessor, sensors of one or more types, a mechanical system including moving parts, and some firmware to control and tie the parts together. The embedded sensors and devices help to create their functionality. These devices include wireless communication for data transfer. One such device for wireless communication is Bluetooth, which can be dangerous due to attack vulnerabilities, especially on Bluetooth Low Energy (BLE) devices.Methods: In addition to discovering vulnerabilities in Bluetooth communication, common issues have been identified, including related attacks, threats, malware, and vulnerabilities. To identify specific attacks for Bluetooth devices used in smart toys, this study adopted Qoopers, a robot capable of integrating different devices into its model. Qoopers was tested using security frameworks to simulate attacks.Results: We found that devices with BLE are more susceptible to attack. Qoopers was exposed to security frameworks used in restricted conditions, demonstrating that they can be hacked using a man-in-the-middle (MITM) attack and eavesdropping on data transfer. This paper also discusses solutions to prevent Bluetooth attacks.Conclusion: Bluetooth communication is vulnerable to different attacks, including MITM. This happens even with Qoopers robot when it is reprogrammed with customized applications with less security. These smart toy robots are used mainly by children under 16, who can make mistakes by ignoring security, focusing only on functionality, increasing the risk of personal information theft and other threats

Enhancing Completion Time Prediction Through Attribute Selection

Approaches have been proposed in process mining to predict the completion time of process instances. However, the accuracy levels of the prediction models depend on how useful the log attributes used to build such models are. A canonical subset of attributes can also offer a better understanding of the underlying process. We describe the application of two automatic attribute selection methods to build prediction models for completion time. The filter was used with ranking whereas the wrapper was used with hill-climbing and best-first techniques. Annotated transition systems were used as the prediction model. Compared to decision-making by human experts, only the automatic attribute selectors using wrappers performed better. The filter-based attribute selector presented the lowest performance on generalization capacity. The semantic reasonability of the selected attributes in each case was analyzed in a real-world incident management process

Software xLupa - um ampliador de tela para auxílio na educação de alunos com baixa visão Xlupa Software - a screen magnifier to aid in educating students with low vision

O modelo educacional brasileiro vem passando por muitas transformações. Parte delas está relacionada a, pelo menos, dois fatores: a necessidade de inclusão de alunos com necessidades especiais no sistema educacional e o uso dos computadores nos processos de ensino, como forma de potencializar os resultados da aprendizagem desses alunos. Apesar dos esforços realizados, tanto na questão da inclusão educacional quanto na informatização de atividades inerentes aos ambientes escolares, a grande dificuldade é saber como atender adequadamente a esses alunos que, atualmente, representam uma parcela significativa da população estudantil matriculada na rede de ensino pública e/ou particular. Considerando que dentre esses alunos estão aqueles que, embora não sejam cegos, apresentam um significativo grau de redução na acuidade visual, denominados "Baixa Visão", e sabendo que o uso do computador tem aumentado nas escolas brasileiras, há fortes motivos para acreditar que a lacuna hoje existente na formação desses indivíduos pode ser minimizada mediante a adequação de recursos computacionais ao processo de aprendizagem. Neste artigo, é apresentada a ferramenta computacional denominada xLupa, um ampliador de imagens e textos em telas de computador para alunos com baixa visão. Para tanto, os tópicos abordados nesse artigo são os seguintes: Descrição da especificação e desenvolvimento da ferramenta, o que envolveu um estudo das necessidades visuais do público alvo, bem como a interação contínua de desenvolvedores e usuários; Apresentação do software xLupa e de suas funcionalidades; e uma avaliação sobre como o seu uso pode auxiliar alunos e professores no dia-a-dia escolar.<br>The Brazilian education model has undergone many transformations. Some of these are related to at least two factors: the need for including students with special needs in the educational system, and the use of computers in learning-teaching processes in order to enhance such students' learning outcomes. Despite the efforts applied to both the issue of educational inclusion and to promoting computer access to educational activities in school environments, the major difficulty is how to adequately serve those students who currently represent a significant portion of the population enrolled in public, or private, school systems. Among the special needs students, there are those with a significant degree of reduction in visual acuity, called "Low Vision". Knowing that the use of computers in Brazilian schools has increased, there are strong reasons to believe that the gap currently inherent in the education of these individuals may be minimized by promoting access to computer resources in the learning-teaching process. In this paper, we describe the computer tool called xLupa, a screen magnifier, applied to images and texts, for students with low vision. To this end, the topics covered in this paper are: a) description of the tool specification and development process, which involved a study of the target users' visual needs, and the continuous interaction between developers and users, b) presentation of xLupa and its functionalities; c) assessment of how its use can help students and teachers in daily school activities