Abstract
This paper shows the advantage of using different sensors such the Microsoft Kinect and Myo Armband to acquire movement description of the plantarflexion and dorsiflexion of the foot with the help of the quaternions and the EMG Myo sensor. For the integration of these devices, it was chosen Python to develop the algorithm and create an interface to aid the signal acquisition. This integration, enabling an accurate motion description as well as a scale of EMG signal, allow the possibility of quantifying the treatment of the people with equinus foot.

Abstract
Teaching complex assembly and maintenance skills to human operators usually requires extensive reading and the help of tutors. In order to reduce the training period and avoid the need for human supervision, an immersive teaching system using spatial augmented reality was developed for guiding inexperienced operators. The system provides textual and video instructions for each task while also allowing the operator to navigate between the teaching steps and control the video playback using a bare hands natural interaction interface that is projected into the workspace. Moreover, for helping the operator during the final validation and inspection phase, the system projects the expected 3D outline of the final product. The proposed teaching system was tested with the assembly of a starter motor and proved to be more intuitive than reading the traditional user manuals. This proof of concept use case served to validate the fundamental technologies and approaches that were proposed to achieve an intuitive and accurate augmented reality teaching application. Among the main challenges were the proper modeling and calibration of the sensing and projection hardware along with the 6 DoF pose estimation of objects for achieving precise overlap between the 3D rendered content and the physical world. On the other hand, the conceptualization of the information flow and how it can be conveyed on-demand to the operator was also of critical importance for ensuring a smooth and intuitive experience for the operator.

Abstract
Improving execution time and energy efficiency is needed for many applications and usually requires sophisticated code transformations and compiler optimizations. One of the optimization techniques is memoization, which saves the results of computations so that future computations with the same inputs can be avoided. In this article we present a framework that automatically applies memoization techniques to C/C++ applications. The framework is based on automatic code transformations using a source-to-source compiler and on a memoization library. With the framework users can select functions to memoize as long as they obey to certain restrictions imposed by our current memoization library. We show the use of the framework and associated memoization technique and the impact on reducing the execution time and energy consumption of four representative benchmarks. (C) 2019 The Authors. Published by Elsevier B.V.

Abstract
This paper describes the journey of a multinational and multidisciplinary team enrolled in the European Project Semester (EPS) at the Instituto Superior de Engenharia do Porto (ISEP) during the spring semester of 2019. The team embraced the idea of repurposing coffee leftovers to cultivate oyster mushrooms and benefited from the background diversity of the team members as well as from newly acquired marketing, sustainability and design ethics skills to consolidate and strengthen the overall feasibility of the project. The project was set to design, develop and test grey oyster mushroom growth kits with an automated monitoring system, using coffee grounds as growing substrate and complying with the applicable regulations and pre-defined requirements. The ulterior aims of the project were to reconnect people with the food they eat and to disseminate sustainable food production processes, which are not only healthy but environmentally friendly. To achieve these goals, the team developed a circular economy business model where grey oyster mushroom growth kits reuse coffee grounds as growing beds and food buckets as containers. The designed growth kits include a controlled fruiting chamber with an integrated monitoring system. This allows easy domestic cultivation, monitoring through a smart phone. Moreover, the proposed solution contemplates information sharing on the mushroom cultivation process, monitoring system and recipes as well as the maintenance of a dedicated discussion forum. Tests have been conducted to test the concept, cultivation process, monitoring system and fruiting chamber from the incubation of mycelium all the way to the harvesting. Results show the feasibility of creating a business based on the devised concept. © 2019 ACM.

Abstract
Together with the significant improvement in health and longevity came a number of health and economic concerns related to the demand for healthcare services and resources: changes in the patterns of health and illness, increasing amount and complexity of healthcare services demanded, rising health expenditures and uncertainty about whether there will be enough human, physical and financial resources to deliver the healthcare services needed. This paper aims to draw attention to the importance of planning the demand for healthcare in the aforementioned context, to create awareness of the need for a comprehensive study on the demand for healthcare services and resources and to propose an integrated approach for planning them, to inform managers and policy-makers on what can be the main challenges on assuring healthcare delivery in the future.

Abstract
Addressing the class imbalance problem is critical for several real world applications. The application of pre-processing methods is a popular way of dealing with this problem. These solutions increase the rare class examples and/or decrease the normal class cases. However, these procedures typically only take into account the characteristics of each individual class. This segmented view of the data can have a negative impact. We propose a new method that uses an integrated view of the data classes to generate new examples and remove cases. ClUstered REsampling (CURE) is a method based on a holistic view of the data that uses hierarchical clustering and a new distance measure to guide the sampling procedure. Clusters generated in this way take into account the structure of the data. This enables CURE to avoid common mistakes made by other resampling methods. In particular, CURE prevents the generation of synthetic examples in dangerous regions and undersamples safe, non-borderline, regions of the majority class. We show the effectiveness of CURE in an extensive set of experiments with benchmark domains. We also show that CURE is a user-friendly method that does not require extensive fine-tuning of hyper-parameters. © Springer Nature Switzerland AG 2019.