Abstract
In this work, we consider the pros and cons of using various layers of keyless coding to achieve secure and reliable communication over the Gaussian wiretap channel. We define a new approach to information theoretic security, called practical secrecy and the secrecy benefit, to be used over real-world channels and finite blocklength instantiations of coding layers, and use this new approach to show the fundamental reliability and security implications of several coding mechanisms that have traditionally been used for physical-layer security. We perform a systematic/structured analysis of the effect of error-control coding, scrambling, interleaving, and coset coding, as coding layers of a secrecy system. Using this new approach, scrambling and interleaving are shown to be of no effect in increasing information theoretic security, even when measuring the effect at the output of the eavesdropper's decoder. Error control coding is shown to present a trade-off between secrecy and reliability that is dictated by the chosen code and the signal-to-noise ratios at the legitimate and eavesdropping receivers. Finally, the benefits of secrecy coding are highlighted, and it is shown how one can shape the secrecy benefit according to system specifications using combinations of different layers of coding to achieve both reliable and secure throughput.

Abstract
Motivation: Network alignment (NA) finds conserved regions between two networks. NA methods optimize node conservation (NC) and edge conservation. Dynamic graphlet degree vectors are a state-of-the-art dynamic NC measure, used within the fastest and most accurate NA method for temporal networks: DynaWAVE. Here, we use graphlet-orbit transitions (GoTs), a different graphlet-based measure of temporal node similarity, as a new dynamic NC measure within DynaWAVE, resulting in GoT-WAVE. Results: On synthetic networks, GoT-WAVE improves DynaWAVE's accuracy by 30% and speed by 64%. On real networks, when optimizing only dynamic NC, the methods are complementary. Furthermore, only GoT-WAVE supports directed edges. Hence, GoT-WAVE is a promising new temporal NA algorithm, which efficiently optimizes dynamic NC. We provide a user-friendly user interface and source code for GoT-WAVE.

Abstract
This paper presents an innovative and motivating methodology to learn vision systems using a humanoid robot, NAO robot. Vision systems are an area of growing development and interest of engineering students. This approach to learning was applied in students of Master of Electrical Engineering. The goal is to introduce students the main approaches of visual object recognition and human face recognition using computer vision techniques to be embedded in a social robot and therefore he is able to iteract with human beings. NAO robot as an educational platform easy to learn how to program, and it has a high sensory ability and two cameras that can capture the images for processing.

Abstract
A computational analysis of physiological systems has been used to support the understanding of how these systems work, and in the case of foetal heart rate, many different approaches have been developed in the last decades. Our objective was to apply a new method of classification, which is based on spectral analysis, in foetal heart rate (FHR) traces to predict foetal acidosis diagnosed with umbilical arterial blood pH <= 7.05. Fast Fourier transform was applied to a real database for the classification approach. To evaluate the models, sensitivity, specificity, and area under the receiver operating characteristic (ROC) curve were used. Sensitivity equal to 1, specificity equal to 0.85 and an area under the ROC curve of 0.94 were found. In addition, when the definition of metabolic acidosis of umbilical arterial blood pH <= 7.05 and base excess <= -10 mmol/L was used, the proposed methodology obtained sensitivity = 1, specificity = 0.97 and area under the ROC curve = 0.98. The proposed methodology relies exclusively on the spectral frequency decomposition of the FHR signal. After further successful validation in more datasets, this approach can be incorporated easily in clinical practice due to its simple implementation. Likewise, the incorporation of this novel technique in an intrapartum monitoring station should be straightforward, thus enabling the assistance of labour professionals in the anticipated detection of acidaemia.

Abstract
The role of transportation in the overall emissions in light of the increasing environmental awareness has led to a rapid transition to the use of electric vehicles (EVs), especially in the last decade. The EVs have seminal advantages in terms of different point of views; however, they may pose vital challenges for the electric power system operation due to their stochastic characteristics as an electrical load. Several industrial and academic research studies have already been and are still conducted in this respect. Specifically, the development of combined technical and business-oriented operational models is extremely significant for sustainable penetration of EVs. In this study, an interoperability platform is proposed for EV charging service taking dual sides of the mentioned service as system operator and EV owner into account, being proposed as a new perspective in this area, also compared to industrial software platforms for EV charging service by service providers rather than power system operators. The developed software platforms are demonstrated and case study based analyses are conducted to present the applicability of the proposed concept. © 2019 IEEE.

Abstract
Crowdsourcing has shown to be a valuable problem-solving approach to handle the increasing complexity and scale of tasks for which the current AI algorithms are still struggling. Crowd intelligence can be particularly useful to train and supervise AI systems in a symbiotic, co-evolutionary relationship that raises long-term research challenges to the hybrid, crowd-computing design space. With the increase in the scale of mixed-initiative approaches, we need to gain a better understanding of the implications of crowd-powered systems as a scaffold for AI through the study of massive crowd-machine interactions. In this paper, we identify some open challenges and design implications for future crowd-AI hybrid systems. A framework is also proposed based on the practical challenges of addressing human-centered AI methods and processes.