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

Agent Environments for Multi-agent Systems – A Research Roadmap

Ten years ago, researchers in multi-agent systems became more and more aware that agent systems consist of more than only agents. The series of workshops on Environments for Multi-Agent Systems (E4MAS 2004-2006) emerged from this awareness. One of the primary outcomes of this endeavor was a principled understanding that the agent environment should be considered as a primary design abstraction, equally important as the agents. A special issue in JAAMAS 2007 contributed a set of influential papers that define the role of agent environments, describe their engineering, and outline challenges in the field that have been the drivers for numerous follow up research efforts. The goal of this paper is to wrap up what has been achieved in the past 10 years and identify challenges for future research on agent environments. Instead of taking a broad perspective, we focus on three particularly relevant topics of modern software intensive systems: large scale, openness, and humans in the loop. For each topic, we reflect on the challenges outlined 10 years ago, present an example application that highlights the current trends, and from that outline challenges for the future. We conclude with a roadmap on how the different challenges could be tackled. © Springer International Publishing Switzerland 2015.Peer reviewe

eMailMe: A Method to Build Datasets of Corporate Emails in Portuguese

One of the areas in which knowledge management has application is in companies that are concerned with maintaining and disseminating their practices among their members. However, studies involving these two domains may end up suffering from the issue of data confidentiality. Furthermore, it is difficult to find data regarding organizations processes and associated knowledge. Therefore, this paper presents a method to support the generation of a labeled dataset composed of texts that simulate corporate emails containing sensitive information regarding disclosure, written in Portuguese. The method begins with the definition of the dataset’s size and content distribution; the structure of its emails’ texts; and the guidelines for specialists to build the emails’ texts. It aims to create datasets that can be used in the validation of a tacit knowledge extraction process considering the 5W1H approach for the resulting base. The method was applied to create a dataset with content related to several domains, such as Federal Court and Registry Office and Marketing, giving it diversity and realism, while simulating real-world situations in the specialists’ professional life. The dataset generated is available in an open-access repository so that it can be downloaded and, eventually, expanded

On the inclusion of learners with visual impairment in computing education programs in Brazil: practices of educators and perceptions of visually impaired learners

Abstract Background Individuals with visual impairment face varied challenges when attending education programs in many fields and levels. In computing education programs, the frequent required use of charts, graphs, and diagrams poses additional challenges to their inclusion. In order to inform and to establish appropriate action plans for a more inclusive scenario, it is important to gather information from the parties involved. In this context, this work presents the practices of educators and the perceptions of visually impaired learners regarding such inclusion. Methods The practice and perceptions were gathered from a survey with 56 computing educators and with 19 visually impaired learners who have attended computing education programs. Results and discussion The results suggest educators have limited access to knowledge related to the inclusion of visually impaired learners in lectures and feel unprepared to this scenario. On the other hand, visually impaired learners do not feel included in computing education programs

Model-Driven Integration of Organizational Models

16 pagesInternational audienceCurrently, the design and running of a Multi-Agent System (MAS) mobilize several models. Besides agents' architectures, high level agent communication languages and domain ontologies, explicit organization specifications (written in some organizational model) are more and more used to structure and constrain the behavior of MASs. In the case of open MASs, one important requirement is interoperability w.r.t. these models. Focusing organizational model interoperability, in this paper, we propose an integration process for organizational models based on concepts (models, mappings, transformations, etc.) and techniques (Match, Merge, TransfGen, Compose, etc.) from Model-Driven Engineering (MDE). The process is concretely used to integrate five organizational models: AGR, ${\mathcal M}$ oise+, TÆMS, ISLANDER and OperA