Federated learning (FL) enables privacy-preserving model training across distributed Electronic Health Records (EHRs), but its deployment remains limited by data-view heterogeneity, where institutions maintain incompatible local schemas. Most existing methods address this by enforcing flat, aligned data views, which require extensive cross-site preprocessing and manual harmonisation that often discards client-specific features, or by projecting inputs into a shared latent space, which sacrifices interpretability. We propose a modelling shift from conventional FL with vectorised inputs to a symbolic, relation-centric framework, where each client organises its EHR data as a structured, type-aware relational graph. This enables client-specific inference without requiring schema alignment and supports FL across heterogeneous data views. To model over these symbolic structures, we introduce an architecture that combines relation-aware message passing with a learnable feature relevance mechanism, jointly enabling accurate local predictions and client-specific interpretability while supporting parameter sharing across clients. Beyond strong performance on three real-world EHR datasets exhibiting data-view heterogeneity, we further show that our framework supports multimodal FL under modality-level heterogeneity. Using MC-MED, a publicly available multimodal emergency department dataset, we demonstrate that our method accommodates clients with partially missing modalities, highlighting its robustness and scalability in real-world clinical settings.

Quality journalism has become more important than ever due to the need for quality and trustworthy media outlets that can provide accurate information to the public and help to address and counterbalance the wide and rapid spread of disinformation. At the same time, quality journalism is under pressure due to loss of revenue and competition from alternative information providers. This vision paper discusses how recent advances in Artificial Intelligence (AI), and in Machine Learning (ML) in particular, can be harnessed to support efficient production of high-quality journalism. From a news consumer perspective, the key parameter here concerns the degree of trust that is engendered by quality news production. For this reason, the paper will discuss how AI techniques can be applied to all aspects of news, at all stages of its production cycle, to increase trust.

Newsrooms and journalists today rely on many different artificial-intelligence, big-data and knowledge-based systems to support efficient and high-quality journalism. However, making the different systems work together remains a challenge, calling for new unified journalistic knowledge platforms. A software reference architecture for journalistic knowledge platforms could help news organisations by capturing tried-and-tested best practices and providing a generic blueprint for how their IT infrastructure should evolve. To the best of our knowledge, no suitable architecture has been proposed in the literature. Therefore, this article proposes a software reference architecture for integrating artificial intelligence and knowledge bases to support journalists and newsrooms. The design of the proposed architecture is grounded on the research literature and on our experiences with developing a series of prototypes in collaboration with industry. Our aim is to make it easier for news organisations to evolve their existing independent systems for news production towards integrated knowledge platforms and to direct further research. Because journalists and newsrooms are early adopters of integrated knowledge platforms, our proposal can hopefully also inform architectures in other domains with similar needs.

Recent decades have seen a significant increase in the frequency, intensity, and impact of natural disasters and other emergencies, forcing the governments around the world to make emergency response and disaster management national priorities. The growth of extremely large and complex datasets—commonly referred to asbig data—and various advances in information and communications technology and computing now support more effective approaches to humanitarian relief, logistical coordination, overall disaster management, and long-term recovery in connection with natural disasters and emergency events. Leveraging big data and technological advances for emergency management has attracted considerable attention in the research community. However, the desired merging ofbig data and emergency management(BDEM) requires coordinated efforts to align and define interdisciplinary terminologies and methodologies. To date, the key concepts and technologies in this emerging research area have not been coherently discussed in a sufficiently broad and multidisciplinary manner. In this article, an international team presents an overview of the BDEM domain, highlighting a general framework and discussing key challenges from several perspectives. We introduce and summarize typical technologies and applications, organized into the six broad categories of remote sensing, resilient communication networks, mobile communication networks, human mobility and urban sensing, social network analysis, and knowledge graphs. Finally, we outline several directions of future research.

Abstract Journalism relies more and more on information and communication technology (ICT). ICT-basedjournalistic knowledge platformscontinuously harvest potentially news-relevant information from the Internet and make it useful for journalists. Because information about the same event is available from different sources and formats vary widely,knowledge graphsare emerging as a preferred technology for integrating, enriching, and preparing information for journalistic use. The paper explores how journalistic knowledge graphs can be augmented with support fornews angles, which can help journalists to detect newsworthy events and make them interesting for the intended audience. We argue that finding newsworthy angles on news-related information is an important example of a topical problem in information science: that of detecting interesting events and situations in big data sets and presenting those events and situations in interesting ways.

News angles are approaches to content presentation in journalism, where the journalist chooses which facts of an event to present. The News Angler project investigates how to computationally support the creation and selection of original news angles for a news event based on information from big data sources. At least two creative approaches are possible. One is to maintain a library of well-known news angles represented in a suitable modeling language, matching published reports on a current event to news angles in order to identify possible angles that have not yet been used. A second approach is not to represent news angles explicitly, instead matching the current event with previous events, and transferring angles from past to present reports by similarity and analogy. Both approaches are described and technologies needed to proceed in either direction are discussed.

Paul Johannesson; Mong Li Lee; Liddle, S.; Opdahl, A.; Pastor López, O. (2017). Special issue on conceptual modeling - 34th International Conference on Conceptual Modeling (ER 2015). Data & Knowledge Engineering. 109:1-2. doi:10.1016/j.datak.2017.03.001

Model driven security has become an active area of research during the past decade. While many research works have contributed significantly to this objective by extending popular modeling notations to model security aspects, there has been little modeling support for state-based views of security issues. This paper undertakes a scientific approach to propose a new notational set that extends the UML (Unified Modeling Language) statecharts notation. An online industrial survey was conducted to measure the perceptions of the new notation with respect to its semantic transparency as well as its coverage of modeling state based security aspects. The survey results indicate that the new notation encompasses the set of semantics required in a state based security modeling language and was largely intuitive to use and understand provided very little training. A subject-based empirical evaluation using software engineering professionals was also conducted to evaluate the cognitive effectiveness of the proposed notation. The main finding was that the new notation is cognitively more effective than the original notational set of UML statecharts as it allowed the subjects to read models created using the new notation much quicker.

The information science study at the University of Bergen has human-computer interaction and semantic technologies as two of its focal points, with software development as a third focus that supports the two others. This talk will review the department's courses in semantic technologies at bachelor and graduate levels, with emphasis on the introductory course in advanced modelling and programming for the Web of Data (or Semantic Web).

Safety and security assessments aim to keep harm away from systems. Although they consider different causes of harm, the mitigations suggested by the assessments are often interrelated and affect each other, either by strengthening or weakening the other. Considering the relations and effects, a combined process for safety and security could save resources. It also improves the reliability of the system development when compared to having two independent processes whose results might contradict. This paper extends our previous research on a combined method for security and safety assessment, named CHASSIS, by detailing the process in a broader context of system development with the help of feedback from a safety expert. The enhanced CHASSIS method is discussed based on a case from the Air Traffic Management domain.

The idea of security aware system development from the start of the engineering process is generally accepted nowadays and is becoming applied in practice. Many recent initiatives support this idea with special focus on security requirements elicitation. However, there are so far no techniques that provide integrated overviews of security threats and system architecture. One way to achieve this is by combining misuse cases with use case maps into misuse case maps (MUCM). This paper presents an experimental evaluation of MUCM diagrams focusing on identification of vulnerabilities and mitigations. The controlled experiment with 33 IT students included a complex hacker intrusion from the literature, illustrated either with MUCM or with alternative diagrams. The results suggest that participants using MUCM found significantly more mitigations than participants using regular misuse cases combined with system architecture diagrams.

With the continuously developing technology and growing complexity of software and systems, new demands and challenges appear for security, calling for new techniques and methods in addition to the already existing ones. The variety of initiatives and the variations in the characterizations makes it hard for users to select the most appropriate one for their needs. We propose a set of uniform characterizing dimensions with sub-categories for security requirements initiatives. The set is derived by analyzing classifications and comparison frameworks from review papers on modelling techniques for security requirements engineering. The dimensions can be used to guide context-dependent choices of initiatives and further research of their combination and integration.

We are developing an approach to organizing bookmarks and other information resources by annotating them with metadata in the form of synsets taken from WordNet. This paper shows how a collection of annotated bookmarks can be semantically enriched by adding hyper-/hyponym relations from WordNet. It then illustrates how the semantically enriched bookmark collection can be browsed and visualized using the Longwell faceted browser. Our initial investigation suggests that the rich semantics of WordNet synsets combined with Longwell's faceted browsing provide users with a useful aid for navigating and comprehending complex annotated information resources.

The paper explains the need for a standard way of defining modelling constructs from different enterprise modelling languages and proposes a template for defining enterprise modelling constructs in a way that facilitates language integration. The template is based on the Bunge-Wand-Weber (BWW) representation model of information systems (IS) and has been used on several existing modelling languages and frameworks. It is illustrated with definitions of constructs from the Unified Modeling Language (UML). The paper focusses on modelling constructs that represent concrete problem domains, i.e., that represent materials rather than concepts, and thus focuses on the concrete parts and aspects of enterprises.