Abstract

Abstract
Software documentation is an important part of the captured knowledge of a software project and documentation patterns have often been used as a systematic way to describe good practices on software documentation. Still, many software teams are challenged by what to document, how to keep the documentation consistent and how to make their consumers aware of the relevant documents. A literature review was done over 14 publications and identified 16 quality attributes and 114 patterns about software documentation. This knowledge was analysed and classified and led to the proposal of new categories and relationships between the existing patterns. These are depicted as a new pattern map that provides a new perspective of documentation patterns and can be used to guide teams in adopting software documentation practices.

Abstract
The extraction of accurate geometric measurements from images normally requires the use of metric cameras and stereoscopic observations. However, as good-quality digital cameras are widely available in mobile devices (smartphones, tablets), there is great interest to develop alternative approaches, suitable for these devices. This paper presents a methodology to compute the surface area and volume of a spheroid-shaped object, such as many types of fruit, from a single image acquired by a standard (non-metric) camera and a basic calibration target. An iterative process is used to establish a 3D spheroid out of the observed 2D ellipse, after which auxiliary images of height, resolution and surface area of each pixel on the 3D object surface are created. The method was tested with a data set of 2400 images, of 10 different objects, 2 calibration targets, 2 cameras and 2 mark types. The average relative errors (< d>) in establishing the 3D object semi-diameters were 0.863% and 0.791%. The semi-diameters are used to compute the object’s surface area (< d> = 1.557%) and volume (< d> = 2.365%). The estimation of the sub-region (mark) surface area over the modelled 3D object resulted in < d> = 2.985%, much lower that what is obtained ignoring the fact that the mark is not on the reference (calibration) plane (< d> = 50.7%), thus proving the effectiveness of the proposed iterative process to model the 3D object (spheroid). © 2020, Springer Nature Singapore Pte Ltd.

Abstract
This paper presents an efficient, battery-powered, low-cost, and context-aware IoT edge computing solution tailored for monitoring a real enterprise cooking oil collecting infrastructure. The presented IoT solution allows the collecting enterprise to monitor the amount of oil deposited in specific barrels, deployed country-wide around several partner restaurants. The paper focuses on the specification, implementation, deployment and testing of ESP32/ESP8266-based end-node components deployed as an edge computing monitoring infrastructure. The achieved low-cost solution guarantees more than a year of battery life, reliable data communication, and enables automatic over-the-air end-node updates. The open-source software libraries developed for this project are shared with the community and may be applied in scenarios with similar requirements.

Abstract
The rapid expansion of Asian hornets poses a high threat for the honey bee survival, as these invaders pray on them. Furthermore, they also pose a threat to people who are allergic, whose sting can lead to death. This study proposes a Decision Support System that uses Computer Vision techniques to automatically detect signs of Vespa velutina through images from GPS equipped camera. The goal of the system is to provide timely information about the presence of these invaders, allowing park managers and beekeepers to act quickly in removing the Vespidae. The proposed methodology obtained an 85% accuracy in the detection of V. velutina using the Mask RCNN architecture, enabling the system to perform detection at 3 FPS. © 2020 MIR Labs.

Abstract
Objectives: Artificial intelligence (AI) is poised to transform breast cancer care. However, most scientists, engineers, and clinicians are not prepared to contribute to the AI revolution in healthcare. In this paper, we describe our experiences teaching a new undergraduate course for American students that aims to prepare the next generation for cross-cultural designthinking, which we believe is crucial for AI to achieve its full potential in breast cancer care. Materials and methods: The key course activities are planning, conducting, and interpreting interviews of healthcare professionals from both Portugal and the United States. Since the course is offered as a short-term faculty-led study abroad program in Portugal, students are able to explore the impact of culture on healthcare delivery and the design of healthcare technologies. Results: The learning assessments demonstrated student growth in several areas pertinent for future development of AI for breast cancer care. With respect to understanding breast cancer care, prior to taking this course, most students had underestimated the impact of cancer and its treatment on women's quality of life and most were unaware of the importance of multidisciplinary care teams. Regarding AI in medicine, students became more mindful of data privacy issues and the need to consider the effect of AI on healthcare professionals. Conclusion: This course illustrates the potential benefits for AI in medicine of introducing future scientists, engineers, and clinicians to cross cultural design-thinking early in their educational experiences. (C) 2020 The Author(s). Published by Elsevier Ltd.