Abstract
Malicious actors can cause severe damage by exploiting software vulnerabilities. In industrial settings, where critical infrastructures rely on software, handling these vulnerabilities with utmost care is crucial to prevent catastrophic consequences. For this purpose, a cybersecurity awareness platform called Sifu was created. This platform automatically assesses challenges to verify its compliance to secure coding guidelines. Using an artificial intelligence method, an interactive component provides players with solution-guiding hints. This paper presents an improved version of the Sifu platform, which evolves the tool in the following aspects: architecture, data model and user interface. The new platform separates the server and client-side using a REST API architecture. It also accommodates an intrinsic and richer layer of gamification, which explores the concept of game rooms at an organizational and gamification level. Finally, it offers an improved interactive training experience for individuals and organizations through a responsive and intuitive single-page web application. © José Carlos Paiva, Ricardo Queirós, and Tiago Gasiba; licensed under Creative Commons License CC-BY 4.0.

Abstract
Fostering Pedagogy Through Micro and Adaptive Learning in Higher Education: Trends, Tools, and Applications is a timely and groundbreaking book that addresses the challenges of engaging the digital generations in the teaching-learning process, intensified by the pandemic. Written by Ricardo Queirós, a renowned researcher in e-learning interoperability and programming languages, the book offers a unique perspective on using micro and adaptive learning approaches to create immersive and personalized environments that cater to the learning styles and paces of diverse students. The book covers innovative trends, tools, and applications that enable educators to implement pedagogical practices that enhance the teaching-learning experience. It explores topics such as artificial intelligence in education, adaptive hypermedia, differentiated instruction, and micro-gamification design, providing readers with practical tools to create personalized and immersive learning environments. This book is a valuable resource for professors of any domain, practitioners, and students pursuing education, as well as research scholars looking to expand their understanding of e-learning and pedagogical innovation. It is a must-read for anyone interested in the future of education and how digital technologies can be leveraged to create engaging and immersive learning environments. © 2023 by IGI Global. All rights reserved.

Abstract
Escape rooms offer an immersive and engaging learning experience that encourages critical thinking, problem solving and teamwork. Although they have shown promising results in promoting student engagement in the teaching-learning process, they continue to operate as independent systems that are not fully integrated into educational environments. This work aims to detail the integration of educational escape rooms, based on international standards, with the typical central component of an educational setting - the learning management system (LMS). In order to proof this concept, we present the integration of a math escape room with the Moodle LMS using the Learning Tools Interoperability (LTI) specification. Currently, this specification comprises a set of Web services that enable seamless integration between learning platforms and external tools and is not limited to any specific LMS which fosters learning interoperability. With this implementation, a single sign-on ecosystem is created, where teachers and students can interact in a simple and immersive way. The major contribution of this work is to serve as an integration guide for other applications and in different domains. © Ricardo Queirós, Carla Pinto, Mário Cruz, and Daniela Mascarenhas;

Abstract
We show that recent approaches to static analysis based on quantitative typing systems can be extended to programming languages with global state. More precisely, we define a call-by-value language equipped with operations to access a global memory, together with a semantic model based on a (tight) multi-type system that captures exact measures of time and space related to evaluation of programs. We show that the type system is quantitatively sound and complete with respect to the operational semantics of the language.

Abstract
Smart cities, leveraging IoT technologies, are revolutionizing the quality of life for citizens. However, the massive data generated in these cities also poses significant privacy risks, particularly in de-anonymization and re-identification. This survey focuses on the privacy concerns and commonly used techniques for data protection in smart cities, specifically addressing geolocation data and video surveillance. We categorize the attacks into linking, predictive and inference, and side-channel attacks. Furthermore, we examine the most widely employed de-identification and anonymization techniques, highlighting privacy-preserving techniques and anonymization tools; while these methods can reduce the privacy risks, they are not enough to address all the challenges. In addition, we argue that de-identification must involve properties such as unlikability, selective disclosure and self-sovereignty. This paper concludes by outlining future research challenges in achieving complete de-identification in smart cities.

Abstract
As the digital world grows, data is being collected at high speed on a continuous and real-time scale. Hence, the imposed imbalanced and evolving scenario that introduces learning from streaming data remains a challenge. As the research field is still open to consistent strategies that assess continuous and evolving data properties, this paper proposes an unsupervised, online, and incremental anomaly detection ensemble of influence trees that implement adaptive mechanisms to deal with inactive or saturated leaves. This proposal features the fourth standardized moment, also known as kurtosis, as the splitting criteria and the isolation score, Shannon's information content, and the influence function of an instance as the anomaly score. In addition to improving interpretability, this proposal is also evaluated on publicly available datasets, providing a detailed discussion of the results.