Abstract
Social media platforms, increasingly used as news sources for varied data analytics, have transformed how information is generated and disseminated. However, the unverified nature of this content raises concerns about trustworthiness and accuracy, potentially negatively impacting readers' critical judgment due to disinformation. This work aims to contribute to the automatic data quality validation field, addressing the rapid growth of online content on wiki pages. Our scalable solution includes stream-based data processing with feature engineering, feature analysis and selection, stream-based classification, and real-time explanation of prediction outcomes. The explainability dashboard is designed for the general public, who may need more specialized knowledge to interpret the model's prediction. Experimental results on two datasets attain approximately 90% values across all evaluation metrics, demonstrating robust and competitive performance compared to works in the literature. In summary, the system assists editors by reducing their effort and time in detecting disinformation.

Abstract
Load forecasting is an asset for sustainable building energy management, as accurate predictions enable efficient energy consumption and con- tribute to decarbonisation efforts. However, data-driven models are often limited by dataset length and quality. This study investigates the effectiveness of transfer learning (TL) for load forecasting in office buildings, with the aim of addressing data scarcity issues and improving forecasting accuracy. The case study consists in a group of eight virtual buildings (VB) located in Porto, Portugal. VB A2 serves as pre-trained base model to transfer knowledge to the remaining VBs, which are analysed in varying degrees of data availability. Our findings indicate that TL can significantly reduce training time, for up to 87%, while maintaining accuracy levels comparable to those of models trained with full dataset, and exhibiting superior performance when com- pared to models trained with scarce data, with average RMSE reduction of 42.76%.

Abstract
This paper presents a Local Energy Market (LEM) model based on Walrasian Auctions for near real-time energy trading among peers in an Energy Community. The market operates with minimal information exchange, where peers only indicate trade decisions and quantities. The auctioneer updates prices iteratively to balance supply and demand. Two core algorithms support the LEM: (1) the Auctioneer Price Decision Algorithm, which adjusts prices based on past imbalances, and (2) a real-time bidding optimization algorithm, which optimizes peers' energy dispatch and local energy trading decisions based on expected demand, generation, storage, and opportunity costs of external trading. This work details the design and implementation of the bidding optimization algorithm and evaluates its performance through simulations. The results compare the LEM to a centralized pool-based market and individual optimizations, assessing its efficiency and imbalance control. The findings support the development of innovative and decentralized energy markets and smart grid applications.

Abstract
Featured Application This review highlights interoperability, automation, and decision-making as critical requirements for context-aware systems in the manufacturing domain that integrate the principles of Industry 4.0. It discusses relevant patterns and technologies, identifies context gaps, emphasises ontologies' importance, and proposes directions for future research.Abstract Technological evolution has driven the integration of computing devices in various domains, giving rise to heterogeneous and dynamic intelligent environments; together with market pressure, these pose challenges in formulating an architecture that takes advantage of contextual knowledge. In terms of architectural design, we are witnessing a transition from a centralised, monolithic view of systems to a decentralised view that incorporates the vertical and horizontal dimensions of the production environment. Therefore, this review aimed to (i) identify the requirements, (ii) find out about the representation models and context inference techniques, and (iii) identify architectural technologies, norms, models, and standards. The results observed in 25 articles made it possible to identify interoperability, automation, and decision-making as convergence points and observe the adoption of ontologies as a research area for context representation. In contrast, the discussion of context inference techniques remains open. Finally, this study presents recommendations for the design of a context-aware systems architecture that incorporates the principles of Industry 4.0 and facilitates the development of applications.

Abstract

Abstract
Mobile devices have become integral to our everyday lives, yet their utility hinges on their battery life. In Android apps, resource leaks caused by inefficient resource management are a significant contributor to battery drain and poor user experience. Our work introduces Alpakka, a source-to-source compiler for Android's Smali syntax. To showcase Alpakka's capabilities, we developed an Alpakka library capable of detecting and automatically correcting resource leaks in Android APK files. We demonstrate Alpakka's effectiveness through empirical testing on 124 APK files from 31 real-world Android apps in the DroidLeaks [12] dataset. In our analysis, Alpakka identified 93 unique resource leaks, of which we estimate 15% are false positives. From these, we successfully applied automatic corrections to 45 of the detected resource leaks.