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.

Parkinson’s Disease is the second most prevalent neurodegenerative disorder, currently affecting as high as 3% of the global population. Research suggests that up to 80% of patients manifest phonatory symptoms as early signs of the disease. In this respect, various systems have been developed that identify high risk patients by analyzing their speech using recordings obtained from natural dialogues and reading tasks conducted in clinical settings. However, most of them are centralized models, where training and inference take place on a single machine, raising concerns about data privacy and scalability. To address these issues, the current study migrates an existing, state-of-the-art centralized approach to the concept of federated learning, where the model is trained in multiple independent sessions on different machines, each with its own dataset. Therefore, the main objective is to establish a proof of concept for federated learning in this domain, demonstrating its effectiveness and viability. Moreover, the study aims to overcome challenges associated with centralized machine learning models while promoting collaborative and privacy-preserving model training.

Medical imaging modalities, like magnetic resonance imaging (MRI), have enabled efficient diagnosis of various conditions, including cancer, lung disease, and brain tumors. With the advancements in machine learning, AI-based medical image segmentation and classification systems have emerged, potentially replacing human diagnosis. However, the security and robustness of these systems are crucial, as they are vulnerable to adversarial attacks, as demonstrated in previous studies. In this respect, the current work explores the one-pixel attack’s impact on the reliable VGG16 model, the effectiveness of combining the one-pixel attack with the FGSM attack, the potential of using the squeezing color bits detector to counter the one-pixel attack, and the possibility of using a combination of the squeezing color bits and PCA whitening detectors to protect against the aforementioned attacks.

Tourism is an important economic activity for many countries and the ability of understanding visitor needs as they evolve over time is a priority for all involved stakeholders. The analysis of textual reviews written by travelers on various online platforms may be a valuable tool in this direction. In this work, we showcase the potential of this idea by examining 8 well-known attractions in the City of Athens, Greece. After retrieving the relevant data from two popular online services, we employ a state-of-the-art transformer-based language model for two tasks; the extraction of distinctive keywords and phrases out of the free-text reviews and the assignment of a sentiment score to each review. Based on this information, we can associate certain keywords and phrases with specific sentiment values and monitor their evolution over time, in the context of specific touristic {&} cultural places. The analysis that follows explores the potential of this idea in more detail.

Open laboratories (OpenLabs) in Cultural Heritage institutions are an effective way to provide visibility into the behind-the-scenes processes and promote documentation data collected and produced by domain specialists. However, presenting these processes without proper explanation or communication with specialists may cause issues in terms of visitors’ understanding. To support OpenLabs and disseminate information, digital media and efficient data management can be utilized. The CAnTi (Conservation of Ancient Tiryns) project seeks to design and implement virtual and mixed reality applications that visualize conservation and restoration data, supporting OpenLab operations at the Acropolis of Ancient Tiryns. Semantic Web technologies will be used to model the digital content, facilitating organization and interoperability with external sources in the future. These applications will be part of the OpenLab activities on the site, enhancing visitors’ experiences and understanding of current and past conservation and restoration practices.

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.

Importance sampling, a variant of online sampling, is often used in neural network training to improve the learning process, and, in particular, the convergence speed of the model. We study, here, the performance of a set of batch selection algorithms, namely, online sampling algorithms that process small parts of the dataset at each iteration. Convergence is accelerated through the creation of a bias towards the learning of hard samples. We first consider the baseline algorithm and investigate its performance in terms of convergence speed and generalization efficiency. The latter, however, is limited in case of poor balancing of data sets. To alleviate this shortcoming, we propose two variations of the algorithm that achieve better generalization and also manage to not undermine the convergence speed boost offered by the original algorithm. Various data transformation techniques were tested in conjunction with the proposed scheme to develop an overall training method of the model and to ensure robustness in different training environments. An experimental framework was constructed using three naturally imbalanced datasets and one artificially imbalanced one. The results assess the advantage in convergence of the extended algorithm over the vanilla one, but, mostly, show better generalization performance in imbalanced data environments.