Abstract

Abstract
Experience in teaching functional programming (FP) on a relational basis has led the author to focus on a graphical style of expression and reasoning in which a geometric construct shines: the (semi) commutative square. In the classroom this is termed the magic square (MS), since virtually everything that we do in logic, FP, database modeling, formal semantics and so on fits in some MS geometry. The sides of each magic square are binary relations and the square itself is a comparison of two paths, each involving two sides. MSs compose and have a number of useful properties. Among several examples given in the paper ranging over different application domains, free-theorem MSs are shown to be particularly elegant and productive. Helped by a little bit of Galois connections, a generic, induction-free theory for ${\mathsf{foldr}}$ and $\mathsf{foldl}$ is given, showing in particular that ${\mathsf{foldl} \, {{s}}{}\mathrel{=}\mathsf{foldr}{({flip} \unicode{x005F}{s})}{}}$ holds under conditions milder than usually advocated.

Abstract
Quasi-identifiers (QIDs) are attributes in a dataset that are not directly unique identifiers of the users/entities themselves but can be used, often in conjunction with other datasets or information, to identify individuals and thus present a privacy risk in data sharing and analysis. Identifying QIDs is important in developing proper strategies for anonymization and data sanitization. This paper proposes QIDLEARNINGLIB, a Python library that offers a set of metrics and tools to measure the qualities of QIDs and identify them in data sets. It incorporates metrics from different domains-causality, privacy, data utility, and performance-to offer a holistic assessment of the properties of attributes in a given tabular dataset. Furthermore, QIDLEARNINGLIB offers visual analysis tools to present how these metrics shift over a dataset and implements an extensible framework that employs multiple optimization algorithms such as an evolutionary algorithm, simulated annealing, and greedy search using these metrics to identify a meaningful set of QIDs.

Abstract
In recent years, companies have faced increasing pressure from globalization, requiring them to adapt not only to survive but also to thrive in a highly competitive environment. This adaptation has been facilitated by the efficient integration of technology, achieved through digital processes and collaboration tools. Digital transformation has emerged as a critical element for maintaining competitiveness as economies become increasingly digital. To succeed in this ever-evolving environment, companies must balance leveraging existing strengths with seeking new organizational agility. Integrating advanced technologies like Artificial Intelligence (AI) and Web Technologies into education and professional training is a strategic response to the challenges posed by the current digital landscape. AI, with its adaptability and automation capabilities, offers benefits such as increased efficiency, personalized learning, and streamlined administrative processes. Continuous evaluation of teaching and learning, along with data extraction and predictive analysis, enhances e-learning quality and informs organizational decisions. This research aims to investigate how advanced technologies can predict and adapt organizational training needs to improve competency development and overall effectiveness. The research adopts a Design Science Research (DSR) methodology, focusing on the development and implementation of an AI-based framework for personalized training recommendations. Expected outcomes include integrating AI-driven predictive models with existing Human Resources Management Systems to identify and address training needs, fostering employee skill development, organizational agility, and competitiveness in a rapidly changing market. Additionally, addressing this issue promotes a more inclusive and empowering work environment, enabling employees to thrive in an increasingly digital world. © 2025 Elsevier B.V., All rights reserved.

Abstract
This paper presents an interactive game designed to improve users' experience related to driving behaviour, as well as to provide decision support in this context. This paper explores machine learning (ML) methods to enhance the decision-making and automation in a gaming environment. It examines various ML strategies, including supervised, unsupervised, and Reinforcement Learning (RL), emphasizing RL's effectiveness in interactive environments and its combination with Deep Learning, culminating in Deep Reinforcement Learning (DRL) for intricate decision-making processes. By leveraging these concepts, a practical application considering a gaming scenario is presented, which replicates vehicle behaviour simulations from real-world driving scenarios. Ultimately, the objective of this research is to contribute to the ML and artificial intelligence (AI) fields by introducing methods that could transform the way player agents adapt and interact with the environment and other agents decisions, leading to more authentic and fluid gaming experiences. Additionally, by considering recreational and serious games as case studies, this work aims to demonstrate the versatility of these methods, providing a rich, dynamic environment for testing the adaptability and responsiveness, while can also offer a context for applying these advancements to simulate and solve real-world problems in the complex and dynamic domain of mobility.

Abstract
JavaScript is a widely used programming language initially designed to make the Web more dynamic in the 1990s. In the last decade, though, its scope has extended far beyond the Web, finding utility in backend development, desktop applications, and even IoT devices. To circumvent the needs of modern programming, JavaScript has undergone a remarkable evolution since its inception, with the groundbreaking release of its sixth version in 2015 (ECMAScript 6 standard). While adopting modern JavaScript features promises several benefits (such as improved code comprehension and maintenance), little is known about which modern features of the language have been used in practice (or even ignored by the community). To fill this gap, in this paper, we report the results of an empirical study that aims to understand the adoption trends of modern JavaScript features, and whether or not developers conduct rejuvenation efforts to replace legacy JavaScript constructs and idioms with modern ones in legacy systems. To this end, we mined the source code history of 158 JavaScript open-source projects, identified contributions to rejuvenate legacy code, and used time series to characterize the adoption trends of modern JavaScript features. The results of our study reveal extensive use of JavaScript modern features which are present in more than 80% of the analyzed projects. Our findings also reveal that (a) the widespread adoption of modern features happened between one and two years after the release of ES6 and, (b) a consistent trend toward increasing the adoption of modern JavaScript language features in open-source projects and (c) large efforts to rejuvenate the source code of their programs.