Abstract
Polar codes have been shown to provide an effective mechanism for achieving physical-layer security over various wiretap channels. A majority of these schemes require channel state information (CSI) at the encoder for both intended receivers and eavesdroppers. In this paper, we consider a polar coding scheme for secrecy over a Gaussian wiretap channel when no CSI is available. We show that the availability of a shared keystream between friendly parties allows polar codes to be used for both secure and reliable communications, even when the eavesdropper knows a large fraction of the keystream. The scheme relies on a predetermined strategy for partitioning the bits to be encoded into a set of frozen bits and a set of information bits. The frozen bits are filled with bits from the keystream, and we evaluate the security gap when the cyclic redundancy check-aided successive cancellation list decoder is used at both receivers in the wiretap channel model.

Abstract
This article documents some experiments on teaching a class on a Master Degree Program using a different perspective on gamification. Instead of winning badges or getting achievements, students earn classification points. This allows them to work as hard as they are willing, having in mind their current classification and how far they can reach. In the specific experiment being reported, students can earn final grade points with pull requests to a shared class project. The article describes the details of the experiment, extrapolates on different ideas for implementing this in other classes, and concludes with the pros and cons of such approach for student evaluation.

Abstract

Abstract
The European Structural and Investment Funds (ESIF) are the main instrument of the European Union (EU) Cohesion Policy to promote convergence, economic growth and reduce imbalances between EU members. The objectives of the 2014-2020 programming period follow the agenda of the Europe 2020 Strategy to promote smart, sustainable and inclusive growth of EU members. Since before joining the EU, in 1986, until the end of the Portugal 2020 Partnership Agreement (PT2020), Portugal will have received more than EUR 130 billion. Have the subsidies that Portugal has received been well applied? Our study fills a gap in the literature by portraying citizens' perceptions about the effectiveness of EU funds for the development of the country and its regions. The study is quantitative in nature, and a non-probabilistic sample of 1119 participants answered our survey. A high proportion (76%) of the respondents considered that EU funds contributed to the development of the region where they live, although a significant percentage of the respondents (more than half) considered that there may be corruption in Portugal. The Portuguese also mentioned the existence of practices such as favouritism and lobbying regarding the approval of projects. Our findings are supported by the literature, which refers to "lost opportunities" in the inefficient application of ESIF, while recognising that EU funds have played a significant role in Portugal's development over the last three decades.

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.