The digitization of cultural heritage collections has opened new directions for research, yet the lack of enriched metadata poses a substantial challenge to accessibility, interoperability, and cross-institutional collaboration. In several past years neural networks models such as YOLOv11 and Detectron2 have revolutionized visual data analysis, but their application to domain-specific cultural artifacts - such as manuscripts and incunabula - remains limited by the absence of methodologies that address structural feature extraction and semantic interoperability. In this position paper, we argue, that the integration of neural networks with semantic technologies represents a paradigm shift in cultural heritage digitization processes. We present the Metadata Enrichment Model (MEM), a conceptual framework designed to enrich metadata for digitized collections by combining fine-tuned computer vision models, large language models (LLMs) and structured knowledge graphs. The Multilayer Vision Mechanism (MVM) appears as the key innovation of MEM. This iterative process improves visual analysis by dynamically detecting nested features, such as text within seals or images within stamps. To expose MEM’s potential, we apply it to a dataset of digitized incunabula from the Jagiellonian Digital Library and release a manually annotated dataset of 105 manuscript pages. We examine the practical challenges of MEM’s usage in real-world GLAM institutions, including the need for domain-specific fine-tuning, the adjustment of enriched metadata with Linked Data standards and computational costs. We present MEM as a flexible and extensible methodology. This paper contributes to the discussion on how artificial intelligence and semantic web technologies can advance cultural heritage research, and also use these technologies in practice.

Experiments in affective computing are based on stimulus datasets that, in the process of standardization, receive metadata describing which emotions each stimulus evokes. In this paper, we explore an approach to creating stimulus datasets for affective computing using generative adversarial networks (GANs). Traditional dataset preparation methods are costly and time consuming, prompting our investigation of alternatives. We conducted experiments with various GAN architectures, including Deep Convolutional GAN, Conditional GAN, Auxiliary Classifier GAN, Progressive Augmentation GAN, and Wasserstein GAN, alongside data augmentation and transfer learning techniques. Our findings highlight promising advances in the generation of emotionally evocative synthetic images, suggesting significant potential for future research and improvements in this domain.

Current review papers in the area of Affective Computing and Affective Gaming point to a number of issues with using their methods in out-of-the-lab scenarios, making them virtually impossible to be deployed. On the contrary, we present a game that serves as a proof-of-concept designed to demonstrate that—being aware of all the limitations and addressing them accordingly—it is possible to create a product that works in-the-wild. A key contribution is the development of a dynamic game adaptation algorithm based on the real-time analysis of emotions from facial expressions. The obtained results are promising, indicating the success in delivering a good game experience.

We present Loki, a semantic wiki designed to support the collaborative knowledge engineering process with the use of software engineering methods. Designed as a set of DokuWiki plug-ins, it provides a variety of knowledge representation methods, including semantic annotations, Prolog clauses, and business processes and rules oriented to specific tasks. Knowledge stored in Loki can be retrieved via SPARQL queries, in-line Semantic MediaWiki-like queries, or Prolog goals. Loki includes a number of useful features for a group of experts and knowledge engineers developing the wiki, such as knowledge visualization, ontology storage, or code hint and completion mechanism. Reasoning unit tests are also introduced to validate knowledge quality. The paper is complemented by the formulation of the collaborative knowledge engineering process and the description of experiments performed during Loki development to evaluate its functionality. Loki is available as free software at https://loki.re.

In the paper we present an original semantic data mining solution for Internet of Things. We designed an ontology for sensors used in the monitoring of goods and vehicles. Moreover, we developed a practical implementation for this solution based on the state of the art semantic tools, namely RDF triple stores with dedicated interfaces. However, due to number of undocumented limitations of these tools, this process turned out to require number of refactorizations to deliver a working solution. We describe these challenges and solutions we found. We believe our experiences are valuable for researchers and practitioners in the field, as production-ready development of such semantic solutions is far more complex than lab experiments. In fact, our work was developed for the deployment in a production environment, with a close cooperation with an IT company funding the project.

As technology becomes more ubiquitous and pervasive, special attention should be given to human-computer interaction, especially to the aspect related to the emotional states of the user. However, this approach assumes very specif c mode of data collection and storage. This data is used in the affective computing experiments for human emotion recognition. In the paper we describe a new software solution for mobile devices that allows for data acquisition from wristbands. The application reads physiological signals from wristbands and supports multiple recent devices. In our work we focus on the Heart Rate (HR) and Galvanic Skin Response (GSR) readings. The recorded data is conveniently stored in CSV files, ready for further interpretation. We provide the evaluation of our application with several experiments. The results indicate that the BandReader is a reliable software for data acquisition in affective computing scenarios.

Knowledge management in business information systems often requires a unified dictionary of business concepts, that allows for a transparent integration of such systems.Thanks to it sharing the conceptualization between users becomes possible, and better decision support facilities can be provided.The Prosecco project is a research and development project aims to address the needs and constraints of small and medium enterprises by designing methods that will significantly improve BPM systems.In this paper we focus on the development of ontology-based mechanisms allowing for creating taxonomies of business logic concepts unifying system objects.Building a taxonomy of business concepts shared by number of SMEs targeted in the project and then turning it into a formalized ontology integrating the software components is a major challenge.The paper demonstrates how this ontology is used to unify vocabulary of business processes and rules.The original contribution of this research discussed in the paper is the design and implementation of the ontology, and the demonstration of its practical use in the system.