1. Camera traps can generate huge amounts of images, and thus reliable methods for their automated processing are in high demand: in particular to find those images or image sequences that actually include animals. Automatically filtering out images that are empty or contain humans can be challenging, as images can be taken in different landscapes, habitats and light. Weather and seasonal conditions can vary greatly. Most of the images can be empty, because cameras using passive infrared sensors (PIR) trigger easily due to moving vegetation or rapidly varying shadows and sunny spots. Animals in images are often hiding behind vegetation, and camera traps will see them from previously unseen angles. Therefore, conventional animal image detection methods based on deep learning need huge training sets to achieve good accuracy. 2. We present a novel background removal approach based on movement masked images computed using sequences of images. Our deep vision classifier uses these movement images for classification instead of the original images. Additionally, we apply a deep active learning (active learning for deep models) for collecting training samples to reduce the number of annotations required from the user. 3. Our method performed well in singling out image sequences that actually include animals, thus filtering out the majority of images that were empty or contained humans. Most importantly, the method performed well also for backgrounds and animal species not seen in the training data. Active learning brought good separation between classes already with small training sets, without the need for laborious large-scale pre-annotation. 4. We present a reliable and efficient method for filtering out empty image sequences and sequences containing humans. This greatly facilitates camera trapping research by enabling researchers to restrict the task of animal classification to only those image sequences that actually contain animals.

This article examines how “Presence and Awareness Cues” (PAACs) such as read receipts, online status indicators, and typing notifications shape data disclosure in computer-mediated communication (CMC), with particular focus on emerging metaverse contexts. PAACs are often overlooked in current policy debates despite their potential to reveal sensitive behavioral, relational, and even physiological information. Drawing on a broad review of related literature, we propose a conceptual framework outlining four pillars of mediated presence (PAACs, content, aesthetics, and fidelity), offering policymakers a technology-agnostic lens for anticipating developments in augmented and virtual settings. We then present findings from a six-country survey (n = 18,358) examining whether and how users notice, interpret, and control PAACs, as well as their willingness to share additional cues in advanced AR/VR environments. Results indicate that most users recognize PAACs across diverse online services and adapt their behavior accordingly. These insights underscore potential policy gaps when biosignals such as heart rate and gaze become integral to projected availability or emotional states. We conclude that balancing consumer protection with user-friendly interfaces calls for more nuanced oversight, especially as the European AI Act and related legislation could inadvertently limit the adoption of intuitive PAACs. Future research should probe how users negotiate these cues in fully interoperable metaverse environments, particularly when multiple identities or cross-application interactions come into play.

Local disasters such as the Ahr Valley flood in Germany, the international backdrop of the Russo-Ukrainian War, or the global impact of the COVID-19 pandemic place high demands on the people and organisations that are involved in these situations and contexts to save lives, mitigate damage, provide comfort, or organise reconstruction. Novel technologies are constantly making their way into everyday life, such as artificial intelligence, big data, decentralised networks, internet of things, or virtual reality. Their adaptation, acceptance, usability, usefulness, and legal framework conditions for safety-critical systems must be researched and tested thoroughly. In this special issue, we investigate the use of computer-based solutions in areas and situations of direct relevance to people’s lives and well-being (Usable Safety), as well as contributions to user-oriented resilience concepts of sociotechnical systems concerning potential attacks (Usable Security) and data protection mechanisms (Usable Privacy).

Model transformations are central to model-driven software development. Applications of model transformations include creating models, handling model co-evolution, model merging, and understanding model evolution. In the past, various (semi-)automatic approaches to derive model transformations from meta-models or from examples have been proposed. These approaches require time-consuming handcrafting or the recording of concrete examples, or they are unable to derive complex transformations. We propose a novel unsupervised approach, called Ockham, which is able to learn edit operations from model histories in model repositories. Ockham is based on the idea that meaningful domain-specific edit operations are the ones that compress the model differences. It employs frequent subgraph mining to discover frequent structures in model difference graphs. We evaluate our approach in two controlled experiments and one real-world case study of a large-scale industrial model-driven architecture project in the railway domain. We found that our approach is able to discover frequent edit operations that have actually been applied before. Furthermore, Ockham is able to extract edit operations that are meaningful—in the sense of explaining model differences through the edit operations they comprise—to practitioners in an industrial setting. We also discuss use cases (i.e., semantic lifting of model differences and change profiles) for the discovered edit operations in this industrial setting. We find that the edit operations discovered by Ockham can be used to better understand and simulate the evolution of models.

The objective of the German non-profit association NFDI (German short form for ”National Research Data Infrastructure”) is to make the data stock of the entire German science system accessible to the public. To do so, it should involve all stakeholders. However, currently the Universities of Applied Sciences (UAS) are underrepresented in the NFDI, and there is a danger of neglecting their needs. Therefore, we present the project ”Research Data Management at Universities of Applied Sciences in the State of Rhineland-Palatinate” (FDM@HAW.rlp), which is funded by the German Federal Ministry of Education and Research (BMBF) and financed within the Recovery and Resilience Facility of the European Union. In the project, seven public UAS in Rhineland-Palatinate and the Catholic University of Applied Sciences (CUAS) Mainz follow a common goal: They intend to establish an institutional RDM within a period of three years by building up competencies at the UAS, setting up services for researchers and finding solutions for a common technical infrastructure.