Abstract
Covid19 pandemic has stimulated both the discussion on the use of IT related teaching tools and the exposure of the student population to vulnerabilities linked to cybersecurity literacy. The study presented is based on the assumption that the use of gamification as an element or tool that promotes learning within digital environments may be feasible, and more specifically may function as a teaching element on issues related to cybersecurity for students, especially for higher education students. In order to quantify the openness of students to such a tool path, quantitative methodology was used, and a survey was carried out in two Polytechnic Institutions (PI), achieving a sample of 95 students, and seeking perceptions on positive impacts resulting from the creation of a game scenario for better learning. The statistical analysis conducted tested hypotheses regarding representations and practices about gamification and cybersecurity. Results show that students, regardless of their higher education course, clearly understand what Gamification is and its goals, and also that students adopt good cybersecurity practices according to their higher education course. This last result goes accordingly with the supposition that gamification can and should be used in cybersecurity literacy.

Abstract
The use of Generative Adversarial Networks is almost traditional in creating synthetic images for medical purposes. They are probably the best use of GANs until now, as their results can easily be checked by the eye of specialists. In fake news detection models, we have seen lately that neural models (and deep learning) can provide a considerable improvement from standard classifiers. Yet, the most problematic problem still is the lack of data, mostly fake news data to feed these models. In this paper, we address that by proposing the use of a GAN. Results show a better capacity to generalize when used for training an extended dataset based on synthetic samples created by this GAN.

Abstract
Natural language processing (NLP) refers to the field of study that focuses on the interactions between human language and computers. It has recently gained much attention for analyzing human language computationally and has spread its applications for various tasks such as machine translation, information extraction, summarization, question answering, and others. With the rapid growth of cloud computing services, merging NLP in the cloud is a significant benefit. It allows researchers to conduct NLP-related experiments on large amounts of data handled by big data techniques while harnessing the cloud's vast, on-demand computing power. However, it has not sufficiently spread its tools and applications as a service in the cloud and there is little literature available that discusses the scope of interdisciplinary work. NLP, cloud Computing, and big data are vast domains and contain their challenges and potentials. By overcoming those challenges and integrating these fields, great potential for NLP and its applications can be unleashed. This paper presents a survey of NLP in cloud computing with a key focus on the comparison of cloud-based NLP services, challenges of NLP and big data while emphasizing the necessity of viable cloud-based NLP services. In the first part of this paper, an overview of NLP is presented by discussing different levels of NLP and components of natural language generation (NLG), followed by the applications of NLP. In the second part, the concept of cloud computing is discussed that highlights the architectural layers and deployment models of cloud computing and cloud-hosted NLP services. In the third part, the field of big data in the cloud is discussed with an emphasis on NLP. Furthermore, information extraction via NLP techniques within big data is introduced.

Abstract
The application of machine learning to insurance risk prediction requires learning from sensitive data. This raises multiple ethical and legal issues. One of the most relevant ones is privacy. However, privacy-preserving methods can potentially hinder the predictive potential of machine learning models. In this paper, we present preliminary experiments with life insurance data using two privacy-preserving techniques: discretization and encryption. Our objective with this work is to assess the impact of such privacy preservation techniques in the accuracy of ML models. We instantiate the problem in three general, but plausible Use Cases involving the prediction of insurance claims within a 1-year horizon. Our preliminary experiments suggest that discretization and encryption have negligible impact in the accuracy of ML models. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract
Driven by the high fidelity and low cost of the latest head-mounted devices reaching the consumer market, Virtual Reality (VR) is a technology upon which rests increased expectations for improving education and training outcomes. The unique capacity of VR to produce experiences with high levels of immersion, presence, and interactivity, opens a series of prospects to improve the learning of declarative, procedural, and practical knowledge through a new modality of educational content. This paper explores some of the most promising opportunities of VR through the development and evaluation of Sea of Cells, an immersive VR interactive experience to enhance the learning of the prokaryotic cell. Methodologies to introduce the VR experience, both inside and outside classes, were also explored by analysing assessments from several Portuguese biology teachers. A test pilot made through video demonstration, shows a promising future for VR in education. Despite the physical limitations of the pilot study, due to Covid, after presenting the project to 7 10th grade Biology teachers, it was concluded that VR might be a relevant and innovative tool for educational settings. © 2022, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

Abstract