Abstract
Fog/Edge computing improves the latency and security of data by keeping storage and computation close to the data source. Nevertheless, this raises other security challenges against malicious, a.k.a, Byzantine, attacks that can exploit the isolation of nodes, or when access to distributed data is required in untrusted environments. In this work, we study the feasibility of deploying Byzantine Agreement protocols to improve the security of fog/edge systems in untrusted environments. In particular, we explore existing Byzantine Agreement protocols, heavily developed in the Blockchain area, emphasizing the Consistency, Availability, and Partition-Tolerance tradeoffs in a geo-replicated system. Our work identifies and discusses three different approaches that follow the Strong Consistency, Eventual Consistency, and Strong Eventual Consistency models. Our conclusions show that Byzantine Agreement protocols are still immature to be used by fog/edge computing in untrusted environment due to their high finality latency; however, they are promising candidates that encourage further research in this direction. © 2021, Springer Nature Switzerland AG.

Abstract
The harsh impacts of extreme weather events like cyclones or precipitation extremes are increasingly being felt with hazardous consequences. These extreme events are exceptions to well-known weather patterns and therefore are not forecasted with current automatic computational methods. In this context, the use of human computation to annotate extreme atmospheric phenomena could provide novel insights for computational forecasting algorithms and a step forward in climate change research by enabling the early detection of abnormal weather conditions. However, existing crowd computing solutions have technological limitations and show several gaps when involving expert crowds. This paper presents a research approach to fulfill some of the technological and knowledge gaps for expert crowds’ participation. A case study on expert annotation of extreme atmospheric phenomena is used as a baseline for an innovative architecture able to support expert crowdsourcing. The full stack service-oriented architecture ensures interoperability and provides an end-to-end approach able to fetch weather data from international databases, generating experts’ visualizations (weather maps), annotating data by expert crowds, and delivering annotated data for processing weather forecasts. An implementation of the architecture suggests that it can deliver an effective mechanism for expert crowd work while solving some of the identified issues with extant platforms. Therefore, we conclude that the proposed architecture has the potential to contribute as an effective annotation solution for extreme weather events. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract
Deepfake and manipulated digital photos and videos are being increasingly used in a myriad of cybercrimes. Ransomware, the dissemination of fake news, and digital kidnapping-related crimes are the most recurrent, in which tampered multimedia content has been the primordial disseminating vehicle. Digital forensic analysis tools are being widely used by criminal investigations to automate the identification of digital evidence in seized electronic equipment. The number of files to be processed and the complexity of the crimes under analysis have highlighted the need to employ efficient digital forensics techniques grounded on state-of-the-art technologies. Machine Learning (ML) researchers have been challenged to apply techniques and methods to improve the automatic detection of manipulated multimedia content. However, the implementation of such methods have not yet been massively incorporated into digital forensic tools, mostly due to the lack of realistic and well-structured datasets of photos and videos. The diversity and richness of the datasets are crucial to benchmark the ML models and to evaluate their appropriateness to be applied in real-world digital forensics applications. An example is the development of third-party modules for the widely used Autopsy digital forensic application. This paper presents a dataset obtained by extracting a set of simple features from genuine and manipulated photos and videos, which are part of state-of-the-art existing datasets. The resulting dataset is balanced, and each entry comprises a label and a vector of numeric values corresponding to the features extracted through a Discrete Fourier Transform (DFT). The dataset is available in a GitHub repository, and the total amount of photos and video frames is 40,588 and 12,400, respectively. The dataset was validated and benchmarked with deep learning Convolutional Neural Networks (CNN) and Support Vector Machines (SVM) methods; however, a plethora of other existing ones can be applied. Generically, the results show a better F1-score for CNN when comparing with SVM, both for photos and videos processing. CNN achieved an F1-score of 0.9968 and 0.8415 for photos and videos, respectively. Regarding SVM, the results obtained with 5-fold cross-validation are 0.9953 and 0.7955, respectively, for photos and videos processing. A set of methods written in Python is available for the researchers, namely to preprocess and extract the features from the original photos and videos files and to build the training and testing sets. Additional methods are also available to convert the original PKL files into CSV and TXT, which gives more flexibility for the ML researchers to use the dataset on existing ML frameworks and tools.

Abstract
In this paper, a numerical study is conducted to investigate the influence of imposed inlet velocity profile on the hemodynamics in the region of the abdominal aorta bifurcation, for a patient specific. The influences of two different inflow velocity profiles on the hemodynamics of the abdominal aorta bifurcation have been investigated. The simulations were carried out under the same conditions changing only the shape of the inlet velocity profile in abdominal aorta. The simulations were performed with a parabolic profile (PP) and a uniform profile (UP) to quantify the hemodynamic differences between them, in the arterial regions, that is, in the upstream bifurcation, in the bifurcation and downstream bifurcation in each of the common iliac arteries. The results reported provide fundamental knowledge to a better understand of the inflow velocity profiles influence in the hemodynamics of the abdominal aorta bifurcation, such as the distribution of the velocity, pressure and wall shear stress (WSS), as well as the distribution of the stress hemodynamic descriptors on the artery wall. The results highlighted that the influence of the inlet velocity profiles in the time-averaged wall shear stress (AWSS) and relative residence time (RRT) is not significant after the abdominal aorta bifurcation. In general, for the hemodynamic descriptors studied, the correlation between the results obtained with the two velocity profiles reaches values close to 1 in the iliac arteries, in contrast to the abdominal region, where the correlation is less than 0.6.

Abstract
Long non-coding RNA (lncRNA) genes have well-established and important impacts on molecular and cellular functions. However, among the thousands of lncRNA genes, it is still a major challenge to identify the subset with disease or trait relevance. To systematically characterize these lncRNA genes, we used Genotype Tissue Expression (GTEx) project v8 genetic and multi-tissue transcriptomic data to profile the expression, genetic regulation, cellular contexts, and trait associations of 14,100 lncRNA genes across 49 tissues for 101 distinct complex genetic traits. Using these approaches, we identified 1,432 lncRNA gene-trait associations, 800 of which were not explained by stronger effects of neighboring protein-coding genes. This included associations between lncRNA quantitative trait loci and inflammatory bowel disease, type 1 and type 2 diabetes, and coronary artery disease, as well as rare variant associations to body mass index.

Abstract
The oceans have the capability to support the current transitions occurring within our societies, including the implementation of clean energy production and storage technologies and new paths for sustainable food production. These transitions are, nonetheless, many times dependent on innovative technologies which require long paths of technology maturation before they can fit the existing ecosystems and markets. One critical step for technology validation is the demonstration stage in real offshore conditions, which is necessary to validate the performance of the proposed technologies, as well as their reliability and economic viability. In this respect, Portugal has been the testbed of several ocean-based technologies, including the Windfloat device, and possesses the necessary infrastructures to implement and test further innovative concepts and designs. Still, these infrastructures are currently underutilized, which means more technology developers could be testing and implementing their technologies in the country. This paper presents the OceanACT initiative, which is being led by five partners, + ATLANTIC, CEIIA, Fórum Oceano, INESC TEC and WavEC, aiming to promote and manage the existing offshore testing infrastructures in the country. The vision and the strategic path for the initiative, as well as the available infrastructures, and its respective metocean conditions, are presented here. This initiative intends to attract new technology developers to the country, and consequently generate relevant socioeconomic benefits, such as the attraction of investment, the inclusion of the national industry into the supply chain of these innovative projects, and the creation of highly qualified jobs.