We investigate the effectiveness of anti-phishing support systems through a quantitative study involving 453 participants. To this end, we developed a tool that allows participants to immerse themselves in a realistic setting, tasked with classifying emails as either phishing or legitimate, while being assisted by support systems. Despite the prevalence of support systems in webmailers and email clients, our results indicate no significant difference in correctly assessing emails of varying difficulty between these systems and the control group. We found a minor negative effect of the support system that uses tooltips compared to other support systems. In the subsequent survey, we found that the support systems are appreciated and considered helpful by users, as supported by the results of the UEQ-S, even if they have no observable effect. Email context, such as the contact list, as well as hovering over the links, had stronger effects on the classification than the tested support systems. 

Täglich hört man von der angeblich unausweichlichen Notwendigkeit eines Social-Media-Verbotes für Minderjährige. Das scheint weitgehender gesellschaftlicher Konsens zu sein. Einige wichtige Fragen werden dazu aber nicht gestellt: Was ist mit den Eltern?  Und warum eigentlich nur für Minderjährige?

Accurate and timely forecasts of road surface conditions are crucial for efficient winter maintenance, enhanced traffic safety, and the optimized use of de-icing agents. Road surface phenomena, in complex fields present challenges to traditional forecasting methods due to their nonlinear and localized nature. This study presents a machine learning framework predicting real-time road states (dry, wet, icy, snowy) across Bavaria, Germany. It integrates data from over 516 Road Weather Stations (RWS), thermal measurements from winter maintenance vehicles, and elevation data from the Open Elevation API. Data undergoes temporal alignment, spatial interpolation, and missing-value imputation. Decision Trees form the core model for interpretability and nonlinear pattern handling. Each RWS employs a localized model, while a generalized version covers unmonitored roads via spatial adjustments. With over 85% accuracy, the system facilitates dynamic winter maintenance and minimizes resource waste. Cyber-physical in smart mobility and transportation networks support improved real-time hazard responses. This approach shows how scalable infrastructure can be made resilient using machine learning.

This paper presents a low-complexity, open-source platform designed to empower small and medium-sized enterprises (SMEs) in the premium business to business (B2B) packaging industry with advanced digital capabilities for product personalization and rapid design visualization. Addressing the sector’s persistent barriers such as limited IT resources, manual workflows, and lack of structured supplier data access, the proposed system integrates dynamic web scraping for automated supplier data acquisition with real-time image processing for printable area detection on packaging components, particularly bottles. Leveraging open-source tools like Beautiful Soup, OpenCV, and Shapely, the platform eliminates reliance on time-intensive manual integration and supports agile, data-driven design workflows. The development process is guided by human-centered design principles to ensure usability and alignment with SME operational realities. Results demonstrate that this approach significantly streamlines catalog management and design preparation, offering a scalable pathway for SMEs to achieve digital transformation and maintain competitive differentiation in an increasingly digitalized packaging market.

Täglich hört man von der angeblich unausweichlichen Notwendigkeit eines Social-Media-Verbotes für Minderjährige. Das scheint weitgehender gesellschaftlicher Konsens zu sein. Einige wichtige Fragen werden dazu aber nicht gestellt: Was ist mit den Eltern?  Und warum eigentlich nur für Minderjährige?

We present SITUATE, a novel dataset designed for training and evaluating Vision Language Models on counting tasks with spatial constraints. The dataset bridges the gap between simple 2D datasets like VLMCountBench and often ambiguous real-life datasets like TallyQA, which lack control over occlusions and spatial composition. Experiments show that our dataset helps to improve generalization for out-of-distribution images, since a finetune of Qwen VL 2.5 7B on SITUATE improves accuracy on the Pixmo count test data, but not vice versa. We cross validate this by comparing the model performance across established other counting benchmarks and against an equally sized fine-tuning set derived from Pixmo count.

Digital transformation in health and social care extends far beyond the adoption of new technologies and requires coordinated organizational change. The projectpulsnetz –Mensch und Technik im Gemeinwesen (MuTiG)addresses this challenge with an Integrative Model for Leadership and Employee Development that links individual upskilling with strategic organizational transformation to advance digital maturity.Led by an interdisciplinary consortium, the project began with a comprehensive needs assessment and subsequently developed modular training programs, tailored organizational consulting, and a digital knowledge platform to foster long-term learning and peer exchange. Implementation is continuously evaluated using standardized online surveys and qualitative interviews. To date, more than 3,700 professionals from health and social care organizations have participated. Survey response rates have been moderate to high, and feedback is largely positive, with mean ratings typicallyabove 3.5 (0–4 Likert-scale); trainer performance and support receive the highest scores. Participants in leadership roles reported slightly lower levels of new learning, likely due to their higher prior knowledge.Preliminary findings suggest that sustainable digital transformation requires a combined focus on individual skill development, organizational learning, and structural adaptation. The MuTiG model provides a scalable, practice-oriented, and transferable framework that can guide health and social care organizations in their digital transformation journeys. While long-term impact cannot yet be fully assessed due to the project’s ongoing nature, early results underline its potential to support lasting digital transformation.

Triple Graph Grammars (TGGs) are a visual, intuitive approach for specifying model transformations, allowing the automatic derivation of model management operations including forward/backward transformations and incremental synchronisation with guaranteed, desirable properties.
The conceptual simplicity of TGGs comes at a price, however, as all TGG tools impose substantial limits on practical expressiveness (measured by ease of specification, size, and readability in this paper), rendering TGGs unsuitable for real-world transformations and representing a major barrier to their mainstream adoption.
This paper discusses excerpts of model transformations that are exceedingly difficult (and perhaps even impossible) to specify using TGGs, analyses the underlying causes, and suggests suitable extensions of existing language features.
Our goal is to inspire research that improves the practical expressiveness of TGGs and facilitates applications of the approach.