Abstract
In order to study the behavior and performance of a robot, building its simulation model is crucial. Realistic simulation tools using physics engines enable faster, more accurate and realistic testing conditions, without depending on the real vehicle. By combining legged and wheeled locomotion, hybrid vehicles are specially useful for operating in different types of terrains, both indoors and outdoors. They present increased mobility, versatility and adaptability, as well as easier maneuverability, when compared to vehicles using only one of the mechanisms. This paper presents the realistic simulation through the SimTwo simulator software of a hybrid legged-wheeled robot. It has four 3-DOF (degrees of freedom) legs combining rigid and non-rigid joints and has been fully designed, tested and validated in the simulated environment with incorporated dynamics.

Abstract
This paper presents a C++ application programming interface for a co-evolutionary algorithm for solution and scenario generation in stochastic problems. Based on a two-space biased random-key genetic algorithm, it involves two types of populations that are mutually impacted by the fitness calculations. In the solution population, high-quality solutions evolve, representing first-stage decisions evaluated by their performance in the face of the scenario population. The scenario population ultimately generates a diverse set of scenarios regarding their impact on the solutions. This application allows the straightforward implementation of this algorithm, where the user needs only to define the problem-dependent decoding procedure and may adjust the risk profile of the decision-maker. This paper presents the co-evolutionary algorithm and structures the interface. We also present some experiments that validate the impact of relevant features of the application.

Abstract
Synthetic data consists of artificially generated data. When data are scarce, or of poor quality, synthetic data can be used, for example, to improve the performance of machine learning models. Generative adversarial networks (GANs) are a state-of-the-art deep generative models that can generate novel synthetic samples that follow the underlying data distribution of the original dataset. Reviews on synthetic data generation and on GANs have already been written. However, none in the relevant literature, to the best of our knowledge, has explicitly combined these two topics. This survey aims to fill this gap and provide useful material to new researchers in this field. That is, we aim to provide a survey that combines synthetic data generation and GANs, and that can act as a good and strong starting point for new researchers in the field, so that they have a general overview of the key contributions and useful references. We have conducted a review of the state-of-the-art by querying four major databases: Web of Sciences (WoS), Scopus, IEEE Xplore, and ACM Digital Library. This allowed us to gain insights into the most relevant authors, the most relevant scientific journals in the area, the most cited papers, the most significant research areas, the most important institutions, and the most relevant GAN architectures. GANs were thoroughly reviewed, as well as their most common training problems, their most important breakthroughs, and a focus on GAN architectures for tabular data. Further, the main algorithms for generating synthetic data, their applications and our thoughts on these methods are also expressed. Finally, we reviewed the main techniques for evaluating the quality of synthetic data (especially tabular data) and provided a schematic overview of the information presented in this paper.

Abstract

Abstract
The complexity of systems continues to increase rapidly, especially due to the multi-level integration of subsystems from different domains into cyber-physical systems. This results in special challenges for the efficient verification and validation (V&V) of these systems with regard to their requirements and properties. In order to tackle the new challenges and improve the quality assurance processes, the V&V workflows have to be documented and analyzed. In this paper, a novel approach for the workflow modelling of V&V activities is presented. The generic approach is tailorable to different industrial domains and their specific constraints, V&V methods, and toolchains. The outcomes comprise a dedicated modelling notation (VVML) and tool-support using the modelling framework Enterprise Architect for the efficient documentation and implementation of workflows in the use cases. The solution enables the design of re-usable workflow assets such as V&V activities and artifacts that are exchanged between workflows. This work is part of the large scale European research project VALU3S that deals with the improvement and evaluation of V&V processes in different technical domains, focusing on safety, cybersecurity, and privacy properties.

Abstract
Premature ventricular contractions (PVC), or extrasystoles, represent a type of cardiac arrhythmia that is common among the general population and, notably, among athletes or individuals who exercise frequently. PVC may be asymptomatic and not clinically relevant when their rate is low, up to around 0.5%, or may be symptomatic and clinically relevant when it is high, in the order of or above 10%. ECG analysis in association with a cardiac stress test is important to detect and characterize PVC and to diagnose the heart condition and operation. In this paper, we describe and test a simple signal processing approach that can be used to effectively pinpoint subtle PVC occurrences in various physical effort conditions. In this regard, we discuss i) three important conditions to be met such that PVC are categorized as benign, ii) the design and implementation of a cardiac stress test and ECG data collection, iii) the algorithm analyzing and extracting information from the detected PVC occurrences, and iv) we present and discuss the obtained results, and conclude on their significance. © 2022 IEEE.