Bahia Federal Institute of Education, Science and Technology

About: Bahia Federal Institute of Education, Science and Technology is a education organization based out in Salvador, Brazil. It is known for research contribution in the topics: Computer science & Context (language use). The organization has 787 authors who have published 999 publications receiving 4463 citations.

Identification and management of technical debt

Abstract: ContextThe technical debt metaphor describes the effect of immature artifacts on software maintenance that bring a short-term benefit to the project in terms of increased productivity and lower cost, but that may have to be paid off with interest later. Much research has been performed to propose mechanisms to identify debt and decide the most appropriate moment to pay it off. It is important to investigate the current state of the art in order to provide both researchers and practitioners with information that enables further research activities as well as technical debt management in practice. ObjectiveThis paper has the following goals: to characterize the types of technical debt, identify indicators that can be used to find technical debt, identify management strategies, understand the maturity level of each proposal, and identify what visualization techniques have been proposed to support technical debt identification and management activities. MethodA systematic mapping study was performed based on a set of three research questions. In total, 100 studies, dated from 2010 to 2014, were evaluated. ResultsWe proposed an initial taxonomy of technical debt types, created a list of indicators that have been proposed to identify technical debt, identified the existing management strategies, and analyzed the current state of art on technical debt, identifying topics where new research efforts can be invested. ConclusionThe results of this mapping study can help to identify points that still require further investigation in technical debt research.

Review of the genus Ipomoea: traditional uses, chemistry and biological activities

Abstract: Approximately 600-700 species of Ipomoea, Convolvulaceae, are found throughout tropical and subtropical regions of the world. Several of those species have been used as ornamental plants, food, medicines or in religious ritual. The present work reviews the traditional uses, chemistry and biological activities of Ipomoea species and illustrates the potential of the genus as a source of therapeutic agents. These species are used in different parts of the world for the treatment of several diseases, such as, diabetes, hypertension, dysentery, constipation, fatigue, arthritis, rheumatism, hydrocephaly, meningitis, kidney ailments and inflammations. Some of these species showed antimicrobial, analgesic, spasmolitic, spasmogenic, hypoglycemic, hypotensive, anticoagulant, anti-inflammatory, psychotomimetic and anticancer activities. Alkaloids, phenolics compounds and glycolipids are the most common biologically active constituents from these plant extracts.

Deep Instance Segmentation of Teeth in Panoramic X-Ray Images

Abstract: In dentistry, radiological examinations help specialists by showing structure of the tooth bones with the goal of screening embedded teeth, bone abnormalities, cysts, tumors, infections, fractures, problems in the temporomandibular regions, just to cite a few. Sometimes, relying solely in the specialist's opinion can bring differences in the diagnoses, which can ultimately hinder the treatment. Although tools for complete automatic diagnosis are no yet expected, image pattern recognition has evolved towards decision support, mainly starting with the detection of teeth and their components in X-ray images. Tooth detection has been object of research during at least the last two decades, mainly relying in threshold and region-based methods. Following a different direction, this paper proposes to explore a deep learning method for instance segmentation of the teeth. To the best of our knowledge, it is the first system that detects and segment each tooth in panoramic X-ray images. It is noteworthy that this image type is the most challenging one to isolate teeth, since it shows other parts of patient's body (e.g., chin, spine and jaws). We propose a segmentation system based on mask region-based convolutional neural network to accomplish an instance segmentation. Performance was thoroughly assessed from a 1500 challenging image data set, with high variation and containing 10 categories of different types of buccal image. By training the proposed system with only 193 images of mouth containing 32 teeth in average, using transfer learning strategies, we achieved 98% of accuracy, 88% of F1-score, 94% of precision, 84% of recall and 99% of specificity over 1224 unseen images, results very superior than other 10 unsupervised methods.