Abstract
This study introduces the development of a client-based software layer within the FGPE project, aimed at enhancing the usability of the FGPE programming learning environment through client-side processing. The primary goal is to enable the evaluation of programming exercises and the application of gamification rules directly on the client-side, thereby facilitating offline functionality. This approach is particularly beneficial in regions with unreliable internet connectivity, as it allows continuous student interaction and feedback without the need for a constant server connection. The implementation promises to reduce server load significantly by shifting the evaluation workload to the client-side. This not only improves response times but also alleviates the burden on server resources, enhancing overall system efficiency. Two main strategies are explored: 1) caching the gamification service interface on the client-side, and 2) implementing a complete client-side gamification service that synchronizes with the server when online. Each approach is evaluated in terms of its impact on user experience, system performance, and potential security concerns. The findings suggest that while client-side processing offers considerable benefits in terms of scalability and user engagement, it also introduces challenges such as increased system complexity and potential data synchronization issues. The study concludes with recommendations for balancing these factors to optimize the design and implementation of client-based systems for educational environments. © Ricardo Queirós, Robertas Damaševicius, Rytis Maskeliunas, and Jakub Swacha;

Abstract
DeepAR is a popular probabilistic time series forecasting algorithm. According to the authors, DeepAR is particularly suitable to build global models using hundreds of related time series. For this reason, it is a common expectation that DeepAR obtains poor results in univariate forecasting [10]. However, there are no empirical studies that clearly support this. Here, we compare the performance of DeepAR with standard forecasting models to assess its performance regarding 1 step-ahead forecasts. We use 100 time series from the M4 competition to compare univariate DeepAR with univariate LSTM and SARIMAX models, both for point and quantile forecasts. Results show that DeepAR obtains good results, which contradicts common perception. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

Abstract
Wikipedia is the world's largest online encyclopedia, but maintaining article quality through collaboration is challenging. Wikipedia designed a quality scale, but with such a manual assessment process, many articles remain unassessed. We review existing methods for automatically measuring the quality of Wikipedia articles, identifying and comparing machine learning algorithms, article features, quality metrics, and used datasets, examining 149 distinct studies, and exploring commonalities and gaps in them. The literature is extensive, and the approaches follow past technological trends. However, machine learning is still not widely used by Wikipedia, and we hope that our analysis helps future researchers change that reality.

Abstract
Local electricity markets are emerging as a viable solution to overcome the challenges brought by the large penetration of distributed renewable generation and the need to put consumers as central players in the system, with an active and dynamic role. Although there is significant literature addressing the topic of local electricity markets, this is still a rather new and emerging topic. Hence, this study provides an overall view of this domain and a perspective on future needs and challenges that must be addressed. This review introduces the most important concepts in the local electricity market domain, provides an analysis on the different policy and regulatory framework, exposes the most relevant worldwide initiatives related to the field implementation, and scrutinizes the alternative local market models proposed in the literature. The discussion puts forth the main benefits and barriers of the currently proposed local market models, and the expected impact of their widespread implementation. The review is concluded with the wrap-up and discussion on the most relevant paths for future research and development in this field of study.

Abstract
Ensuring the reproducibility of computational scientific experiments is crucial for advancing research and fostering scientific integrity. However, achieving reproducibility poses significant challenges, particularly in the absence of appropriate software tools to help. This paper addresses this issue by comparing existing tools designed to assist researchers across various fields in achieving reproducibility in their work. We were able to successfully run eight tools and execute them to reproduce three existing experiments from different domains. Our findings show the critical role of technical choices in shaping the capabilities of these tools for reproducibility efforts. By evaluating these tools for replicating experiments, we contribute insights into the current landscape of reproducibility support in scientific research. Our analysis offers guidance for researchers seeking appropriate tools to enhance the reproducibility of their experiments, highlighting the importance of informed technical decisions in facilitating reproducibility across diverse domains.

Abstract
A growing concern with current teaching approaches underscores the need for innovative paradigms and tools in computer programming education, aiming to address disparate user profiles, enhance engagement, and cultivate deeper understanding among learners This article proposes an innovative approach to teaching programming, where students are challenged to write statements for solutions automatically generated. With this approach, rather than simply solving exercises, students are encouraged to develop code analysis and problem formulation skills. For this purpose, a Web application was developed to materialize these ideas, using the OpenAI API to generate exercises and evaluate statements written by the students. The transformation of this application in H5P and its integration in a LMS gamified workflow is explored for wider and more effective adoption. © Ricardo Queirós;