Abstract
Machine Learning is a field in which significant steps forward have been taken in the last years, resulting in a wide variety of available algorithms, for many different problems. Nonetheless, most of these algorithms focus on the training of static models, in the sense that the model stops evolving after the training phase. This is increasingly becoming a limitation, especially in an era in which datasets are increasingly larger and may even arrive as sequential streams of data. Frequently retraining a model, in these scenarios, is not realistic. In this paper we propose evoRF: a combination of a Random Forest with an evolutionary approach. Its key innovative aspect is the evolution of the weights of the Random Forest over time, as new data arrives, thus making the forest's voting scheme adapt to the new data. Older trees can also be replaced by newly trained ones, according to their accuracy, ensuring that the ensemble remains up to date without requiring a whole retraining.

Abstract
EGFR and KRAS are the most frequently mutated genes in lung cancer, being active research topics in targeted therapy. The biopsy is the traditional method to genetically characterise a tumour. However, it is a risky procedure, painful for the patient, and, occasionally, the tumour might be inaccessible. This work aims to study and debate the nature of the relationships between imaging phenotypes and lung cancer-related mutation status. Until now, the literature has failed to point to new research directions, mainly consisting of results-oriented works in a field where there is still not enough available data to train clinically viable models. We intend to open a discussion about critical points and to present new possibilities for future radiogenomics studies. We conducted high-dimensional data visualisation and developed classifiers, which allowed us to analyse the results for EGFR and KRAS biological markers according to different combinations of input features. We show that EGFR mutation status might be correlated to CT scans imaging phenotypes; however, the same does not seem to hold for KRAS mutation status. Also, the experiments suggest that the best way to approach this problem is by combining nodule-related features with features from other lung structures.

Abstract
The breakdown of Dennard scaling has resulted in a decade-long stall of the maximum operating clock frequencies of processors. To mitigate this issue, computing shifted to multi-core devices. This introduced the need for programming flows and tools that facilitate the expression of workload parallelism at high abstraction levels. However, not all workloads are easily parallelizable, and the minor improvements to processor cores have not significantly increased single-threaded performance. Simultaneously, Instruction Level Parallelism in applications is considerably underexplored. This article reviews notable approaches that focus on exploiting this potential parallelism via automatic generation of specialized hardware from binary code. Although research on this topic spans over more than 20 years, automatic acceleration of software via translation to hardware has gained new importance with the recent trend toward reconfigurable heterogeneous platforms. We characterize this kind of binary acceleration approach and the accelerator architectures on which it relies. We summarize notable state-of-the-art approaches individually and present a taxonomy and comparison. Performance gains from 2.6x to 5.6x are reported, mostly considering bare-metal embedded applications, along with power consumption reductions between 1.3x and 3.9x. We believe the methodologies and results achievable by automatic hardware generation approaches are promising in the context of emergent reconfigurable devices.

Abstract

Abstract
The Information and Communication Technologies (ICT) plays a major role in tourism, travel and hospitality industry. The Integration of ICT in the tourism industry is essential for success of tourism enterprise, as such it is necessary to integrate ICT in higher education curricula. This paper analyzes the relevance given by the various Portuguese and Spanish institutions of higher education to ICT in their degrees. The analysis carried out was done in degree courses operating in the academic year 2018/2019, in Portuguese and Spanish universities and polytechnics. A comparison was also made with the reality of 2012/2013. Copyright © 2020. Elisabete PAULO MORAIS, Carlos R. CUNHA and João Pedro GOMES. Distributed under Creative Commons Attribution 4.0 International CC-BY 4.0.

Abstract
Diabetic Retinopathy (DR) is the most common cause of avoidable vision loss, predominantly affecting the working-age population across the globe. Screening for DR, coupled with timely consultation and treatment, is a globally trusted policy to avoid vision loss. However, implementation of DR screening programs is challenging due to the scarcity of medical professionals able to screen a growing global diabetic population at risk for DR. Computer-aided disease diagnosis in retinal image analysis could provide a sustainable approach for such large-scale screening effort. The recent scientific advances in computing capacity and machine learning approaches provide an avenue for biomedical scientists to reach this goal. Aiming to advance the state-of-the-art in automatic DR diagnosis, a grand challenge on "Diabetic Retinopathy - Segmentation and Grading" was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI-2018). In this paper, we report the set-up and results of this challenge that is primarily based on Indian Diabetic Retinopathy Image Dataset (IDRiD). There were three principal subchallenges: lesion segmentation, disease severity grading, and localization of retinal landmarks and segmentation. These multiple tasks in this challenge allow to test the generalizability of algorithms, and this is what makes it different from existing ones. It received a positive response from the scientific community with 148 submissions from 495 registrations effectively entered in this challenge. This paper outlines the challenge, its organization, the dataset used, evaluation methods and results of top-performing participating solutions. The top-performing approaches utilized a blend of clinical information, data augmentation, and an ensemble of models. These findings have the potential to enable new developments in retinal image analysis and image-based DR screening in particular.