Abstract
Previous workshops have discussed a proposal to augment Ada with fine-grained parallelism, based on the notion of tasklets, a lightweight parallel entity. Recent works have shown the convergence of this model with the OpenMP tasking model and have proposed their coexistence. In this paper we provide a status of the existent works, and describe how these models could be combined.

Abstract
device to increase energy autonomy of moored oceanographic monitoring stations. Oscillations and currents through the sea or river are used to produce energy when the whole system is submerged to a depth between 3 to 10 meters. In order to have an inexpensive system, a buoy containing a Linear Electromagnetic Generator (LEG), is fabricated in a 3D printer, using PLA (polylactic acid) filament. Inside of the buoy, one cylinder shaped LEG (98mm length and 25mm of diameter) produces a maximum output power of 20 mW with a 4 Hz movement. To increase power output in larger systems, more LEGs can be added.

Abstract
With the recent technological developments new possibilities arise for the use of wearables and medical monitoring devices by patients and their respective integration into the digital health ecosystem. FHIRbox is a distributed system under development by the authors for integrating data from various diagnosis devices, complying with FHIR-the latest HL7 standard for exchanging clinical information. The innovative aspects of FHIRbox constitute a reference to drive a paradigm shift in terms of access to health information; as it is a solution that places the patient as the true owner of his clinical data. In this work the authors present the project requirements and the system architecture. (C) 2018 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Selection and peer-review under responsibility of the scientific committee of the CENTERIS-International Conference on ENTERprise Information Systems / ProjMAN-International Conference on Project MANagement / HCist-International Conference on Health and Social Care Information Systems and Technologies.

Abstract
Unit testing is an important component of software quality improvement. Several researchers proposed automated tools to improve this activity over the years. However, these research efforts have not been sufficient to help the practitioners to address some associated mental tasks. Motivated by this gap, we conducted a qualitative study of professionals with unit testing experience. The goal was to understand how to improve the cognitive support provided by the testing tools, by considering the practitioners’ perspective on their unit testing review practices. We obtained the responses from our volunteers through a questionnaire composed both of open-ended and closed questions. Our results revealed some primary tasks which require cognitive support, including monitoring of pending and executed unit testing tasks, and navigating across unit testing related artifacts. We summarize our results in a framework, and based on it, we develop a research agenda as an actionable instrument to the community. Our study's contributions comprise practical improvement suggestions for the current tools and describe further opportunities for research in software testing. Moreover, we comprehensively explain our qualitative methods.

Abstract
Diabetic retinopathy (DR) detection is a critical retinal image analysis task in the context of early blindness prevention. Unfortunately, in order to train a model to accurately detect DR based on the presence of different retinal lesions, typically a dataset with medical expert's annotations at the pixel level is needed. In this paper, a new methodology based on the multiple instance learning (MIL) framework is developed in order to overcome this necessity by leveraging the implicit information present on annotations made at the image level. Contrary to previous MIL-based DR detection systems, the main contribution of the proposed technique is the joint optimization of the instance encoding and the image classification stages. In this way, more useful mid-level representations of pathological images can be obtained. The explainability of the model decisions is further enhanced by means of a new loss function enforcing appropriate instance and mid-level representations. The proposed technique achieves comparable or better results than other recently proposed methods, with 90% area under the receiver operating characteristic curve (AUC) on Messidor, 93% AUC on DR1, and 96% AUC on DR2, while improving the interpretability of the produced decisions.

Abstract
We explore the feasibility of a database storage engine housing up to 307 billion genetic Single Nucleotide Polymorphisms (SNP) for online access. We evaluate database storage engines and implement a solution utilizing factors such as dataset size, information gain, cost and hardware constraints. Our solution provides a full feature functional model for scalable storage and query-ability for researchers exploring the SNP's in the human genome. We address the scalability problem by building physical infrastructure and comparing final costs to a major cloud provider.