Abstract
The Locarno Film Festival (LFF) archives represent a valuable collection of cinematic history, providing essential resources for research, education, and the promotion of international film culture. To ensure these resources are easily accessible, it is crucial to develop advanced methods for managing and linking the information they contain. This work focuses on creating a shared way for organizing information, transforming the LFF archives into dynamic, interconnected resources. This transformation is essential for preserving cinematic heritage, improving discoverability, promoting digital transformation, and efficiently managing archives. Using an interdisciplinary approach, we developed the OntoFest following the Linked Open Terms (LOT) Methodology. Significant outcomes of this project include the successful reuse of existing ontologies to manage heterogeneous information, which has improved our ability to understand and retrieve relevant data. This work demonstrates the potential of digital archives in the cinematic field and provides a foundation for future initiatives in digitizing cinematic heritage archives. OntoFest not only contributes to preserving the cinematic cultural heritage of the LFF but also lays the groundwork for new research and creative applications in the digital transformation of film festival archives. © 2024 by SCITEPRESS – Science and Technology Publications, Lda.

Abstract
Context: Mutation testing is a rigorous approach for assessing the quality of test suites by injecting faults (i.e., mutants) into software under test. Tools, such as CosmicRay and Mutpy, are examples of Mutation Testing tools for Python software programs. Problem: With different Python mutation testing tools, comparative analysis is lacking to evaluate their effectiveness in different usage scenarios. Furthermore, the evolution of these tools makes continuous evaluation of their functionalities and characteristics necessary. Method: In this work, we evaluate (statically and dynamically) four Python mutation testing tools, namely CosmicRay, MutPy, MutMut, and Mutatest. In static evaluation, we introduce a comparison framework, adapted from one previously applied to Java tools, and collected information from tool documentation and developer surveys. For dynamic evaluation, we use tests built based on those produced by Pynguin, which are improved through the application of Large Language Models (LLMs) and manual analyses. Then, the adequate test suites were cross-tested among different tools to evaluate their effectiveness in killing mutants each other. Results: Our findings reveal that CosmicRay offers superior functionalities and customization options for mutant generation compared to its counterparts. Although CosmicRay’s performance was slightly lower than MutPy in the dynamic tests, its recent updates and active community support highlight its potential for future enhancements. Cross-examination of the test suites further shows that mutation scores varied narrowly among tools, with a slight emphasis on MutPy as the most effective mutant fault model. © 2024 Copyright held by the owner/author(s).

Abstract

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.