This study investigates the performance of the DI-NOv2 pre-trained model within Federated Learning (FL) environments, focusing on its application to segmentation tasks across diverse domains. While DINOv2 has demonstrated high efficacy in centralized training scenarios, its capabilities under FL conditions—where data privacy and security are paramount—remain underexplored. Utilizing data sets spanning industrial, medical, and automotive sectors, we evaluated DINOv2’s accuracy and generalization in decentralized settings. Our findings reveal that federated DINOv2 performs comparably to centralized models, effectively segmenting objects despite the decentralized and heterogeneous nature of the data. However, inherent biases in the pre-trained model posed challenges, affecting performance across different domains. These results highlight the need for domain-specific fine-tuning and bias mitigation strategies to enhance the robustness of pre-trained models in FL contexts. Future work should address these challenges to maximize the potential of FL in privacy-sensitive applications, ensuring high performance while maintaining data confidentiality.

Real-time social media data can provide useful information on evolving hazards. Alongside traditional methods of disaster detection, the integration of social media data can considerably enhance disaster management. In this paper, we investigate the problem of detecting geolocation-content communities on Twitter and propose a novel distributed system that provides in near real-time information on hazard-related events and their evolution. We show that content-based community analysis can lead to better and faster dissemination of hazard-related reports than using only traditional methods, such as satellite or airborne sensing platforms. Our distributed disaster reporting system analyzes the social relationship among worldwide geolocated tweets and applies topic modeling to group tweets by topics. Considering for each tweet the following information: user, timestamp, geolocation, retweets, and replies, we create a publisher-subscriber distribution model for topics. We use content similarity and the proximity of nodes to create a new model for geolocation-content based communities. Users can subscribe to different topics in specific geographical areas or worldwide and receive real-time reports regarding these topics. As misinformation can lead to increased damage if propagated in hazards-related tweets, we propose a new deep learning model to detect fake news. The misinformed tweets are then removed from display. We also show empirically the scalability capabilities of the proposed system.

With the increase of digital communication, a surge in online hate speech can be witnessed. Recent studies have concentrated on automated supervised detection of hate speech. However, there remains limited understanding of an effective strategy for identifying multilingual hate speech in social media posts. This study introduces an innovate experimental design for multilingual hate speech detection. It compares different approaches to automatically detect multilingual hate speech through a series of experiments and creates a classification algorithm for hate speech in German, Italian and Spanish text-based social media content. The study creates monolingual, multilingual, and translated datasets specific to the language triplet. Subsequently, the research explores suitable models for multilingual hate speech detection, evaluating a total of seven transformer-based models along with corresponding SVM models on the constructed datasets. The findings indicate that all chosen transformer-based models outperform the baseline SVM models. The research highlights the superiority of a multilingual approach, utilizing XLM-RoBERTa as a classifier model, over monolingual, multilingual, and translation-based approaches. Furthermore, the study demonstrates that translation-based methods in connection to the model DistillBERT can serve as viable alternatives to the multilingual XLM-RoBERTa approach, particularly in scenarios where computational resources are restricted and processing speed is of importance.

Topic modeling is a well-known task in unsupervised machine learning, where clustering algorithms are used to find latent topics. Several algorithms are presented in the literature, but the best known of them suffer from the drawback of requiring a lot of hyperparameter tuning to achieve good results. Especially, the number of latent topics or clusters (k) needs to be known in advance. In view of this situation, this paper analyses objective functions that help to evaluate the models in order to determine optimal hyperparameters. An empirical qualitative study was conducted using the NMF algorithm on different datasets to experimentally determine numerical properties of topic models which indicate an optimal k. Based on this study, we propose objective functions to select optimal topic models and discuss their results on different datasets.

Aspect-Oriented Programming (AOP) is a technology for the decomposition of software systems based on cross-cutting concerns. As shown in our previous work, cross-cutting concerns are also present in ontologies, and Aspect-Oriented Ontology Development (AOOD) can be used for flexible and dynamic ontology modularization based on functional and non-functional requirements. When ontologies are used in applications, application and ontology-related requirements often coincide. In this paper, we show that aspects in ontologies can be expressed as software aspects and directly referred to from software code using the well-known AspectJ language and Java annotations. We present an extension of the well-known OWL API with aspect-oriented means that allow transparent access to and manipulation of ontology modules that are based on requirements.

Usage of background knowledge about events and their relations to other concepts in the application domain, can improve the quality of event processing. In this paper, we describe a system for knowledge-based event detection of complex stock market events based on available background knowledge about stock market companies. Our system profits from data fusion of live event stream and background knowledge about companies which is stored in a knowledge base. Users of our system can express their queries in a rule language which provides functionalities to specify semantic queries about companies in the SPARQL query language for querying the external knowledge base and combine it with event data stream. Background makes it possible to detect stock market events based on companies attributes and not only based on syntactic processing of stock price and volume.

The eight papers in this special section focus on the state-of-the-art approaches, solutions, and applications in the area of rule representation, reasoning, and interchange in the context of distributed, (partially) open, heterogeneous environments, such as the semantic Web, intelligent multiagent systems, event-driven architectures. and service-oriented computing.