Abstract
Fake news detection (FND) tools are essential to increase the reliability of information in social media. FND can be approached as a machine learning classification problem so that discriminative features can be automatically extracted. However, this requires a large news set, which in turn implies a considerable amount of human experts' effort for labeling. In this paper, we explore Positive and Unlabeled Learning (PUL) to reduce the labeling cost. In particular, we improve PUL with the network-based Label Propagation (PU-LP) algorithm. PU-LP achieved competitive results in FND exploiting relations between news and terms and using few labeled fake news. We propose integrating an attention mechanism in PU-LP that can define which terms in the network are more relevant for detecting fake news. We use GNEE, a state-of-the-art algorithm based on graph attention networks. Our proposal outperforms state-of-the-art methods, improving F-1 in 2% to 10%, especially when only 10% labeled fake news are available. It is competitive with the binary baseline, even when nearly half of the data is labeled. Discrimination ability is also visualized through t-SNE. We also present an analysis of the limitations of our approach according to the type of text found in each dataset.

Abstract
The scoping review reported by this paper aimed to analyze and synthesize state-of-the-art studies focused on the application of machine learning methods to enhance the cyber resilience of cyber-physical systems. An electronic search was conducted, and 24 studies were included in this review after the selection process. The most representative application domains were computer networks and power systems, while in terms of cyber resilience functions, risk identification, risk mitigation or protection, and detection of anomalous situations were the most implemented functions. Moreover, the results of this scoping review show that the interest in the topic of cyber resilience and machine learning is quite recent, which justifies the heterogeneity of the included studies in terms of machine learning methods and datasets being used for the experimental validations, as well as in terms of outcomes being measured. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

Abstract
This systematic review explores the role of remote sensing technology in addressing the requirements of sustainable olive growing, set against the backdrop of growing global food demands and contemporary environmental constraints in agriculture. The critical analysis presented in this document assesses different remote sensing platforms (satellites, manned aircraft vehicles, unmanned aerial vehicles and terrestrial equipment) and sensors (RGB, multispectral, thermal, hyperspectral and LiDAR), emphasizing their strategic selection based on specific study aims and geographical scales. Focusing on olive growing, particularly prominent in the Mediterranean region, this article analyzes the diverse applications of remote sensing, including the management of inventory and irrigation; detection/monitoring of diseases and phenology; and estimation of crucial parameters regarding biophysical parameters, water stress indicators, crop evapotranspiration and yield. Through a global perspective and insights from studies conducted in diverse olive-growing regions, this review underscores the potential benefits of remote sensing in shaping and improving sustainable agricultural practices, mitigating environmental impacts and ensuring the economic viability of olive trees.

Abstract
Formal contracts and assertions are effective methods to enhance software quality by enforcing preconditions, postconditions, and invariants. However, the adoption and impact of contracts in the context of mobile application development, particularly of Android applications, remain unexplored. We present the first large-scale empirical study on the presence and use of contracts in Android applications, written in Java or Kotlin. We consider 2,390 applications and five categories of contract elements: conditional runtime exceptions, APIs, annotations, assertions, and other. We show that most contracts are annotation-based and are concentrated in a small number of applications. © 2024 IEEE Computer Society. All rights reserved.

Abstract
The power transformer is one of the key components of any electrical grid, and, as such, modern day industrialization activities require constant usage of the asset. This increases the possibility of failures and can potentially diminish the lifespan of a power transformer. Dissolved gas analysis (DGA) is a technique developed to quantify the existence of hydrocarbon gases in the content of the power transformer oil, which in turn can indicate the presence of faults. Since this process requires different chemical analysis for each type of gas, the overall cost of the operation increases with number of gases. Thus said, a machine learning methodology was defined to meet two simultaneous objectives, identify gas subsets, and predict the remaining gases, thus restoring them. Two subsets of equal or smaller size to those used by traditional methods (Duval's triangle, Roger's ratio, IEC table) were identified, while showing potentially superior performance. The models restored the discarded gases, and the restored set was compared with the original set in a variety of validation tasks.

Abstract
This article aimed to analyse state-of-the-art empirical evidence of randomized controlled trials designed to assess preventive cognitive training interventions based on virtual reality for older adults without cognitive impairment, by identifying virtual reality setups and tasks, clinical outcomes and respective measurement instruments, and positive effects on outcome parameters. A systematic electronic search was performed, and six randomized controlled trials were included in the systematic review. In terms of results, the included studies pointed to significant positive impact of virtual reality-based cognitive training interventions on global cognition, memory, attention, information processing speed, walking variability, balance, muscle strength, and falls. However, further research is required to evaluate the adequacy of the virtual reality setups and tasks, to study the impact of the interventions’ duration and intensity, to understand how to tailor the interventions to the characteristics and needs of the individuals, and to compare face-to-face to remote interventions. © 2024 by SCITEPRESS – Science and Technology Publications, Lda.