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
This study investigates the impact of Research and Development (R&D) investment on the performance of Portuguese companies, specifically addressing the gap in understanding how R&D influences a company's value and performance. We employ a dynamic panel data model estimated using the Generalized Method of Moments (GMM) to account for potential endogeneity issues. This approach allows us to analyze the influence of R&D investment on the Return on Operating Assets (ROA) for Portuguese companies with significant R&D investments between 2012 and 2019. The analysis reveals that while R&D investment itself may not have a statistically significant short-term impact on ROA, lagged financial performance, leverage, asset turnover ratio, and accounts payable turnover all demonstrate a statistically significant relationship with the dependent variable.

Abstract
This study discusses the intricate relations between generative artificial intelligence (AI) and music composers. Based on a previous rapid review of recent literature, it reinforces a gap and suggests the need to develop human-centered generative AI design strategies prioritizing cultural artistic (and non-artistic) aspects. We posit that AI-based music generation solutions should resonate with the cultural diversity of stakeholders who are impacted by these systems in practice. The paper highlights the significance of metaphorical design as an effective method in human-AI music co-creation by leveraging familiar interfaces and features that are rooted in everyday objects and cognitive models derived from real-world settings. Our insights illustrate possible ways of (re)framing human-AI metaphorical design to shape perceptions and facilitate seamless interactions between humans and intelligent systems in music co-creativity, particularly at the compositional level. At the heart of this research is the alignment of AI-driven music creation systems with user needs, values, and expectations that vary from culture to culture and thus require a continuous and transparent adaptation of the technology in use to accommodate individual preferences and the socio-algorithmic specificities underlying musicians’ activities.

Abstract
The data exchange between different sectors of society has led to the development of electronic documents supported by different reading formats, namely portable PDF format. These documents have characteristics similar to those used in programming languages, allowing the incorporation of potentially malicious code, which makes them a vector for cyberattacks. Thus, detecting anomalies in digital documents, such as PDF files, has become crucial in several domains, such as finance, digital forensic analysis and law enforcement. Currently, detection methods are mostly based on machine learning and are characterised by being complex, slow and mainly inefficient in detecting zero-day attacks. This paper aims to propose a Benford Law (BL) based model to uncover manipulated PDF documents by analysing potential anomalies in the first digit extracted from the PDF document's characteristics. The proposed model was evaluated using the CIC Evasive PDFMAL-2022 dataset, consisting of 1191 documents (278 benign and 918 malicious). To classify the PDF documents, based on BL, into malicious or benign documents, three statistical models were used in conjunction with the mean absolute deviation: the parametric Pearson and the non-parametric Spearman and Cramer-Von Mises models. The results show a maximum F1 score of 87.63% in detecting malicious documents using Pearson's model, demonstrating the suitability and effectiveness of applying Benford's Law in detecting anomalies in digital documents to maintain the accuracy and integrity of information and promoting trust in systems and institutions.

Abstract

Abstract
This paper introduces a novel approach to autonomous vehicle control using an end-to-end learning framework. While existing solutions in the field often rely on computationally expensive architectures, our proposed lightweight model achieves comparable efficiency. We leveraged the Car Learning to Act (CARLA) simulator to generate training data by recording sensor inputs and corresponding control actions during simulated driving. The Mean Squared Error (MSE) loss function served as a performance metric during model training. Our end-to-end learning architecture demonstrates promising results in predicting steering angle and throttle, offering a practical and accessible solution for autonomous driving. Results of the experiment showed that our proposed network is approximate to 5.4 times lighter than Nvidia's PilotNet and had a slightly lower testing loss. We showed that our network is offering a balance between performance and computational efficiency. By eliminating the need for handcrafted feature engineering, our approach simplifies the control process and reduces computational demands. Experimental evaluation on a testing map showcases the model's effectiveness in real-world scenarios whilst being competitive with other existing models.