Abstract
Memory safety issues in C are the origin of various vulnerabilities that can compromise a program’s correctness or safety from attacks. We propose an approach to tackle memory safety by replicating Rust’s Mid-level Intermediate Representation (MIR) Borrow Checker. Our solution uses static analysis and successive source-to-source code transformations to be composed upstream of the compiler, ensuring maximal compatibility with existing build systems. This allows us to apply the memory safety guarantees of the rustc compiler to C code with fewer changes than a rewrite in Rust. In this work, we present a comprehensive study of Rust’s efforts towards ensuring memory safety, and describe the theoretical basis for a C borrow checker, alongside a proof-of-concept that was developed to demonstrate its potential. We have evaluated the prototype on the CHStone and bzip2 benchmarks. This prototype correctly identified violations of the ownership and aliasing rules, and exposed incompatibilities between such rules and common C patterns, which can be addressed in future work.

Abstract
Online collaboration growing significantly in the development of open-source hardware and software has led to a surge of research interest. However, no comprehensive bibliometric review has investigated the management of digital communities in these ecosystems. In this study, academic contributions to the field of online community management in open-source hardware and software were mapped, highlighting influential research streams and trends. A bibliometric review was conducted based on a keyword search analysis of research databases (IEEExplore, Scopus, ScienceDirect, Web of Science), with a sample comprising an overall 399 papers. The study identifies the most impactful articles in the field, maps the diverse streams of research on online collaboration and community management, visualizes focus areas and trends, and pinpoints areas for further investigation. These findings will support future research within this rapidly evolving domain. © 2024 The Author(s). Published by Elsevier B.V.

Abstract
The exponential growth of data in the digital age has led to a significant demand for innovative approaches to assess data in a manner that is both effective and efficient. Machine Learning as a Service (MLaaS) is a category of services that offers considerable potential for organisations to extract valuable insights from their data while reducing the requirement for heavy technical expertise. This article explores the use of MLaaS within the realm of marketing applications. In this study, we provide a comprehensive analysis of MLaaS implementations and their benefits within the domain of marketing. Furthermore, we present a platform that possesses the capability to be customised and expanded to address marketing's unique requirements. Three modules are introduced: Churn Prediction, One-2-One Product Recommendation, and Send Frequency Prediction. When applied to marketing, the proposed MLaaS system exhibits considerable promise for use in applications such as automated detection of client churn prior to its occurrence, individualised product recommendations, and send time optimisation. Our study revealed that AI-driven campaigns can improve both the Open Rate and Click Rate. This approach has the potential to enhance customer engagement and retention for businesses while enabling well-informed decisions by leveraging insights derived from consumer data. This work contributes to the existing body of research on MLaaS in marketing and offers practical insights for businesses seeking to utilise this approach to enhance their competitive edge in the contemporary data-oriented marketplace.

Abstract
Hyper-parameter tuning of machine learning models has become a crucial task in achieving optimal results in terms of performance. Several researchers have explored the optimisation task during the last decades to reach a state-of-the-art method. However, most of them focus on batch or offline learning, where data distributions do not change arbitrarily over time. On the other hand, dealing with data streams and online learning is a challenging problem. In fact, the higher the technology goes, the greater the importance of sophisticated techniques to process these data streams. Thus, improving hyper-parameter self-tuning during online learning of these machine learning models is crucial. To this end, in this paper, we present MESSPT, an evolutionary algorithm for self-hyper-parameter tuning for data streams. We apply Differential Evolution to dynamically-sized samples, requiring a single pass-over of data to train and evaluate models and choose the best configurations. We take care of the number of configurations to be evaluated, which necessarily has to be reduced, thus making this evolutionary approach a micro-evolutionary one. Furthermore, we control how our evolutionary algorithm deals with concept drift. Experiments on different learning tasks and over well-known datasets show that our proposed MESSPT outperforms the state-of-the-art on hyper-parameter tuning for data streams.

Abstract

Abstract
Autism Spectrum Disorder (ASD) affects individuals in diverse ways, making personalized therapeutic approaches crucial. In this context, we propose a personalized mobile application designed for music-based therapy tailored to people with ASD. This adaptive piano app can be customized to suit the individual abilities of each user. The paper is structured as follows: The introduction provides context on autism and the importance of personalized therapy. The background section reviews related studies on music-based therapy. The methodology section introduces Professor Piano, our adaptive and adaptable music therapy application. The results and discussion section explores the challenges encountered during development and presents the findings from a heuristic evaluation conducted by experts. Finally, the conclusion summarizes the main insights and implications of the study.