Abstract

Abstract
A non-parametric identification algorithm is proposed to identify Linear Time Periodic (LTP) systems. The period is unknown and can be any real positive number. The system is modelled as an ARX Linear Parameter Varying (LPV) system with a virtual scheduling signal consisting of two orthogonal sinusoids (a sine and a cosine) with a period equal to the system period. Hence, the system parameters are polynomial functions of the scheduling vector. As these polynomials may have infinite degree, a non-parametric model is adopted to describe the LPV system. This model is identified by a Gaussian Process Regression (GPR) algorithm where the system period is a hyperparameter. The performance of the proposed identification algorithm is illustrated through the identification of a simulated LTP continuous system described by a state-space model. The ARX-LTP discrete-time model estimated in the noiseless case was taken as the true model. Copyright (C) 2021 The Authors.

Abstract
As software becomes more complex and assumes an even greater role in our lives, formal verification is set to become the gold standard in securing software systems into the future, since it can guarantee the absence of errors and entire classes of attack. Recent advances in formal verification are being used to secure everything from unmanned drones to the internet. At the same time, the usable security research community has made huge progress in improving the usability of security products and end-users comprehension of security issues. However, there have been no human-centered studies focused on the impact of formal verification on the use and adoption of formally verified software products. We propose a research agenda to fill this gap and to contribute with the first collection of studies on people's mental models on formal verification and associated security and privacy guarantees and threats. The proposed research has the potential to increase the adoption of more secure products and it can be directly used by the security and formal methods communities to create more effective and secure software tools.

Abstract
This work addresses the use of hyperspectral imaging systems for remote detection of marine litter concentrations in oceanic environments. The work consisted on mounting an off-the-shelf hyperspectral imaging system (400-2500 nm) in two aerial platforms: manned and unmanned, and performing data acquisition to develop AI methods capable of detecting marine litter concentrations at the water surface. We performed the campaigns at Porto Pim Bay, Fail Island, Azores, resorting to artificial targets built using marine litter samples. During this work, we also developed a Convolutional Neural Network (CNN-3D), using spatial and spectral information to evaluate deep learning methods to detect marine litter in an automated manner. Results show over 84% overall accuracy (OA) in the detection and classification of the different types of marine litter samples present in the artificial targets.

Abstract
Traditional detection of aquatic invasive species, via morphological identification is often time-consuming and can require a high level of taxonomic expertise, leading to delayed mitigation responses. Environmental DNA (eDNA) detection approaches of multiple species using Illumina-based sequencing technology have been used to overcome these hindrances, but sample processing is often lengthy. More recently, portable nanopore sequencing technology has become available, which has the potential to make molecular detection of invasive species more widely accessible and to substantially decrease sample turnaround times. However, nanopore-sequenced reads have a much higher error rate than those produced by Illumina platforms, which has so far hindered the adoption of this technology. We provide a detailed laboratory protocol and bioinformatic tools to increase the reliability of nanopore sequencing to detect invasive species, and we test its application using invasive bivalves. We sampled water from sites with pre-existing bivalve occurrence and abundance data, and contrasting bivalve communities, in Italy and Portugal. We extracted, amplified and sequenced eDNA with a turnaround of 3.5 days. The majority of processed reads were = 99 % identical to reference sequences. There were no taxa detected other than those known to occur. The lack of detections of some species at some sites could be explained by their known low abundances. The approach is now being tested on other target taxa such as fish and other vertebrates.

Abstract
This study aims to identify and systematically compare the main large-scale agile frameworks that companies can adopt to manage the work of large-scale and distributed teams. Through this, companies can more consciously perform a better-informed decision on the choice of the framework that best fits the practices and challenges of their organizations. This work employs a qualitative approach supported by an exploratory analysis that identifies and explores the processes of migration to a large-scale agile. In the first phase, fifteen assessment criteria for scaling agile are discussed. In a second phase, these criteria are used to perform a comparative analysis of six large-scale agile frameworks (i.e., DAD, LeSS, Nexus, SAFe, Scrum at Scale, and Spotify). The findings reveal there isn't a dominant large-scale agile framework in all dimensions. However, it is possible to identify frameworks like Nexus and Spotify that target smaller teams and offer low technical complexity. These frameworks easily accommodate changes, while there are other frameworks like SAFe and DAD that offer high levels of scalability but require more demanding and deep efforts in changing work processes in an organization.