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
This paper sets the basis for a compositional and structured approach to the specification of paraconsistent transitions systems, framed as an institution. The latter and theirs logics were previously introduced in [CMB22] to deal with scenarios of inconsistency in which several requirements are on stake, either reinforcing or contradicting each other.

Abstract
The development of accurate methods for OCT image analysis is highly dependent on the availability of large annotated datasets. As such datasets are usually expensive and hard to obtain, novel approaches based on deep generative models have been proposed for data augmentation. In this work, a flow-based network (SRFlow) and a generative adversarial network (ESRGAN) are used for synthesizing high-resolution OCT B-scans from low-resolution versions of real OCT images. The quality of the images generated by the two models is assessed using two standard fidelity-oriented metrics and a learned perceptual quality metric. The performance of two classification models trained on real and synthetic images is also evaluated. The obtained results show that the images generated by SRFlow preserve higher fidelity to the ground truth, while the outputs of ESRGAN present, on average, better perceptual quality. Independently of the architecture of the network chosen to classify the OCT B-scans, the model's performance always improves when images generated by SRFlow are included in the training set.

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We extend and consolidate the security justification for the Dilithium signature scheme. In particular, we identify a subtle but crucial gap that appears in several ROM and QROM security proofs for signature schemes that are based on the Fiat-Shamir with aborts paradigm, including Dilithium. The gap lies in the CMA-to-NMA reduction and was uncovered when trying to formalize a variant of the QROM security proof by Kiltz, Lyubashevsky, and Schaffner (Eurocrypt 2018). The gap was confirmed by the authors, and there seems to be no simple patch for it. We provide new, fixed proofs for the affected CMA-to-NMA reduction, both for the ROM and the QROM, and we perform a concrete security analysis for the case of Dilithium to show that the claimed security level is still valid after addressing the gap. Furthermore, we offer a fully mechanized ROM proof for the CMA-security of Dilithium in the EasyCrypt proof assistant. Our formalization includes several new tools and techniques of independent interest for future formal verification results.