Physics-Informed Neural Networks (PINNs) have emerged as a robust framework for solving Partial Differential Equations (PDEs) by approximating their solutions via neural networks and imposing physics-based constraints on the loss function. Traditionally, Multilayer Perceptrons (MLPs) have been the neural network of choice, with significant progress made in optimizing their training. Recently, Kolmogorov-Arnold Networks (KANs) were introduced as a viable alternative, with the potential of offering better interpretability and efficiency while requiring fewer parameters. In this paper, we present a fast JAX-based implementation of grid-dependent Physics-Informed Kolmogorov-Arnold Networks (PIKANs) for solving PDEs, achieving up to 84 times faster training times than the original KAN implementation. We propose an adaptive training scheme for PIKANs, introducing an adaptive state transition technique to avoid loss function peaks between grid extensions, and a methodology for designing PIKANs with alternative basis functions. Through comparative experiments, we demonstrate that the adaptive features significantly enhance solution accuracy, decreasing the L2 error relative to the reference solution by up to 43.02%. For the studied forward PDE problems, our methodology approaches or surpasses the results obtained from architectures that utilize up to 8.5 times more parameters, highlighting the potential of adaptive, grid-dependent PIKANs as a superior alternative in scientific and engineering applications.

This paper proposes the utilization of large language models as recommendation systems for museum visitors. Since the aforementioned models lack the notion of context, they cannot work with temporal information that is often present in recommendations for cultural environments (e.g., special exhibitions or events). In this respect, the current work aims to enhance the capabilities of large language models through a fine-tuning process that incorporates contextual information and user instructions. The resulting models are expected to be capable of providing personalized recommendations that are aligned with user preferences and desires. More specifically, Generative Pre-trained Transformer 4, a knowledge-based large language model is fine-tuned and turned into a context-aware recommendation system, adapting its suggestions based on user input and specific contextual factors such as location, time of visit, and other relevant parameters. The effectiveness of the proposed approach is evaluated through certain user studies, which ensure an improved user experience and engagement within the museum environment.

As the amount of content that is created on social media is constantly increasing, more and more opinions and sentiments are expressed by people in various subjects. In this respect, sentiment analysis and opinion mining techniques can be valuable for the automatic analysis of huge textual corpora (comments, reviews, tweets etc.). Despite the advances in text mining algorithms, deep learning techniques, and text representation models, the results in such tasks are very good for only a few high-density languages (e.g., English) that possess large training corpora and rich linguistic resources; nevertheless, there is still room for improvement for the other lower-density languages as well. In this direction, the current work employs various language models for representing social media texts and text classifiers in the Greek language, for detecting the polarity of opinions expressed on social media. The experimental results on a related dataset collected by the authors of the current work are promising, since various classifiers based on the language models (naive bayesian, random forests, support vector machines, logistic regression, deep feed-forward neural networks) outperform those of word or sentence-based embeddings (word2vec, GloVe), achieving a classification accuracy of more than 80%. Additionally, a new language model for Greek social media has also been trained on the aforementioned dataset, proving that language models based on domain specific corpora can improve the performance of generic language models by a margin of $2\%$. Finally, the resulting models are made freely available to the research community.

The research field of digital storytelling is cross-disciplinary and extremely wide. In this paper, methods, frameworks, and tools that have been created for authoring and presenting digital narratives, are selected and examined among hundreds of works. The basic criterion for selecting these works has been their ability to create content by computational, emergent methods. By delving into the work of many researchers, the objective is to study current trends in this research field and discuss possible future directions. Most of the relevant tools and methods have been designed with a specific purpose in mind, but their use could be expanded to other areas of interest or could at least be the steppingstone for other ideas. Therefore, the following works show elements of computational and emergent narrative creation and a classification is proposed according to their purpose of existence. Finally, new potential research directions in the field are identified and possible future research steps are discussed.

The maritime industry has a significant influence on the global economy, underscoring the need for operational availability and safety through effective maintenance practices. Predictive maintenance emerges as a promising solution compared to conventional maintenance schemes currently employed by the industry, offering proactive failure predictions, reduced downtime events, and extended machinery lifespan. This paper addresses a critical gap in the existing literature by providing a comprehensive overview of the main data-driven PdM systems. Specifically, the review explores common issues found in vessel components (i.e., propulsion, auxiliary, electric, hull), examining how different state-of-the-art PdM architectures, ranging from basic machine learning models to advanced deep learning techniques aim to address them. Additionally, the concepts of centralized machine learning, federated, and transfer learning are also discussed, demonstrating their potential to enhance PdM systems as well as their limitations. Finally, the current challenges hindering adoption are discussed, together with the future directions to advance implementation in the field.

Recent developments in digital technologies regarding the cultural heritage domain have driven technological trends in comfortable and convenient traveling, by offering interactive and personalized user experiences. The emergence of big data analytics, recommendation systems and personalization techniques have created a smart research field, augmenting cultural heritage visitor’s experience. In this work, a novel, hybrid recommender system for cultural places is proposed, that combines user preference with cultural tourist typologies. Starting with the McKercher typology as a user classification research base, which extracts five categories of heritage tourists out of two variables (cultural centrality and depth of user experience) and using a questionnaire, an enriched cultural tourist typology is developed, where three additional variables governing cultural visitor types are also proposed (frequency of visits, visiting knowledge and duration of the visit). The extracted categories per user are fused in a robust collaborative filtering, matrix factorization-based recommendation algorithm as extra user features. The obtained results on reference data collected from eight cities exhibit an improvement in system performance, thereby indicating the robustness of the presented approach.