This paper investigates the utility of open source Large Language Models for sentiment analysis in tourism reviews, with particular focus on the hospitality sector. By harnessing the power of Large Language Models and zero-shot classification techniques, we propose a resource-efficient solution that enables firms to analyse sentiments and extract keywords from reviews without the need for extensive model customisation. Through a comprehensive analysis of various open source models, experimentation, and validation on real-world tourism datasets, we demonstrate the viability and effectiveness of our approach. Our findings highlight the potential of these models as accessible tools for enhancing decision-making processes in the tourism sector, enabling firms in the hospitality domain to leverage cutting-edge technology for competitive advantage.

The popularity of neural networks has grown significantly in various domains, however their use in safety-critical areas has been restricted due to reliability concerns. The AIDOaRt project, an H2020-ECSEL European initiative, aims to develop dependable neural networks for safety-critical contexts. This work investigates the application of Satisfiability Modulo Theory technologies to verify neural networks with non-linear activation functions in computer vision tasks.

In the realm of predictive maintenance, the incorporation of artificial intelligence (AI) methods has revolutionized the field by empowering businesses to actively monitor and preemptively address equipment malfunctions. Detecting anomalies plays a crucial role in predictive maintenance as it serves as an early indicator of potential faults or failures. This paper introduces initial findings from the use of autoencoders and their associated vector reconstruction error within the context of the IMOCO4.E project.

In recent years, the integration of artificial intelligence (AI) techniques has significantly transformed the field of predictive maintenance, enabling businesses to proactively monitor and address potential equipment failures before they occur. One critical aspect of predictive maintenance is the detection of anomalies, which can serve as early warning signs for impending faults or failures. In this paper we present some preliminary results obtained by leveraging autoencoders and the related vector reconstruction error in the scope of the IMOCO4.E Project.

In recent years, there has been growing interest in machine learning and neural networks within research and industrial communities. While neural networks have shown impressive capabilities across various domains, their practical applications are still limited in safety-critical contexts due to a lack of formal guarantees regarding their reliability and behavior. This paper explores the latest advancements in Satisfiability Modulo Theory (SMT) technologies for verifying neural networks with piece-wise linear and transcendent activation functions. Through experimental analysis, we evaluate these technologies using neural networks trained on a real-world predictive maintenance dataset. This research contributes to the ongoing efforts to enhance the safety and reliability of neural networks through formal verification, enabling their deployment in safety-critical domains.

In the last couple of decades, the popularity of neural networks has soared and they have been successfully utilized in many different domains across computer science. However, their application in safety and security-critical domains has been limited due to concerns regarding their reliability. Traditional methods for verifying neural networks (NNs) often uses linear Satisfiability Modulo Theory (SMT) solvers. These solvers work well for simple and shallow NN architectures but face limitations regarding their inability to handle non-linear activations, pooling layers, and complex activation functions, commonly used in modern deep neural networks.In this paper, we explore the potential of non-linear SMT solvers to verify intricate neural network architectures. By leveraging non-linear SMT solvers, a wider range of activation functions can be considered, leading to more accurate reasoning about the behavior of complex deep neural networks. The focus is on using recent advancements in SMT solver development to verify NNs with non-linear activation functions, particularly in the context of Computer Vision tasks. To test this idea, we conducted an experimental analysis to assess whether current nonlinear SMT solvers can efficiently handle NNs with transcendent activation functions.

Using semantic web technologies is becoming an efficient way to overcome metadata storage and data integration problems in digital archives, thus enhancing the accuracy of the search process and leading to the retrieval of more relevant results. In this paper, the results of the implementation of the semantic layer of the Józef Piłsudski Institute of America digital archive are presented. In order to represent and integrate data about the archival collections housed by the institute, the authors developed arkivo, an ontology that accommodates the archival description of records but also provides a reference schema for publishing linked data. The authors describe the application of arkivo to the digitized archival collections of the institute, with emphasis on how these resources have been linked to external datasets in the linked data cloud. They also show the results of an experiment focused on the query answering task involving a state-of-the-art triple store system. The dataset related to the Piłsudski Institute archival collections has been made available for ontology benchmarking purposes.