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.

This paper revisits the Families to Persons Case with a significant extension: concurrent model synchronization. Building on the original benchmark test cases, we introduce new tests for synchronizing models and resolving conflicts, thereby enhancing the framework's capability to benchmark bidirectional transformation tools under more realistic conditions. This advancement is crucial for assessing the tools' performance in concurrent engineering scenarios requiring data consistency across multiple models.

Fragt man, wie eine Umgebung auf Menschen wirkt, könnte man einerseits Eigenschaften dieser Umgebung beschreiben, oder andererseits untersuchen, wie Wahrnehmungseindrücke im Individuum entstehen. Bedeutung wird allerdings weder passiv registriert noch mental konstruiert. Nach Francisco Varela, Evan Thompson und Eleanor Rosch ist sie an tatsächliche und mögliche lebenserhaltende Aktivitäten eines Organismus gebunden. Lebewesen und Umwelt beziehen sich demnach wechselseitig aufeinander: Die Umwelt wird durch die Interaktionsmöglichkeiten und Bedürfnisse des Lebewesens bedingt; das Lebewesen wiederum organisiert sich durch die Begegnung mit dieser für ihn spezifischen Umwelt. Die Interaktion zwischen Mensch und Umwelt steht bei verschiedenen kognitionswissenschaftlichen und phänomenologischen Ansätzen im Fokus. Skizzenhaft betrachtet der folgende Text entsprechende Dynamiken. Einleitende Beispiele aus dem weit gefassten Bereich gestalteter Umgebungen dienen nicht nur als Einstiegspunkte, sondern bieten als gestalterische Erkundungen einen reichhaltigeren Zugang zu den verhandelten Phänomenen, als es dieser Text kann. Schließlich stellen sich Fragen – sowohl zur Erforschung als auch zur Gestaltung der Beziehung zwischen Mensch und Welt.

Rowhammer bit flips in DRAM enable software attackers to fully compromise a great variety of systems. Hardware mitigations can be precise and efficient but suffer from long deployment cycles and very limited or no update capabilities. Consequently, refined attack methods have repeatedly bypassed deployed hardware protections, repeatedly leaving commodity systems vulnerable to Rowhammer attacks.

In this paper, we present Memory Band-Aid, a principled defense-in-depth against Rowhammer. Memory Band-Aid is no replacement for long-term, efficient hardware mitigations but a defense-in-depth that is activated when hardware mitigations are discovered to be insufficient on a specific system generation. For this purpose, Memory Band-Aid introduces per-thread and per-bank rate limits for DRAM accesses in the memory controller, ensuring that the minimum number of row activations for Rowhammer bit flips cannot be reached. We implement a proof-of-concept of Memory Band-Aid on Ubuntu Linux and test it on 3 Intel and 3 AMD systems. In a micro-benchmark to cause DRAM pressure, we observe a slow down up to a factor of 5.2. In a collection of realistic Phoronix macro-benchmarks, we observe a low overhead of 0 % to 9.4 %. Both overheads only apply to untrusted throttled workloads, e.g., sandboxes, for instance in browsers. Especially as Memory Band-Aid can be enabled on demand, we conclude that Memory Band-Aid is an important defense-in-depth that should be deployed in practice as a second defense layer.

Rowhammer is a disturbance error in Dynamic Random-Access Memory (DRAM) that can be deliberately triggered from software by repeatedly reading, i. e., hammering, proximate memory locations in different DRAM rows. While numerous studies evaluated the Rowhammer effect, in particular how it can be triggered and how it can be exploited, most studies only use a small sample size of Dual In-line Memory Modules (DIMMs). Only few studies provided indication for the prevalence of the effect, with clear limitations to specific hardware configurations or FPGA-based experiments with precise control of the DIMM, limiting how far the results can be generalized.

In this paper, we perform the frist large-scale study of the Rowhammer effect involving 1 006 data sets from 822 systems. We measure Rowhammer prevalence in a fully automated crossplatform framework, FLIPKIT, using the available state-of-theart software-based DRAM and Rowhammer tools. Our framework automatically gathers information about the DRAM and uses 5 tools to reverse-engineer the DRAM addressing functions, and based on the reverse-engineered functions uses 7 tools to mount Rowhammer. We distributed the framework online and via USB thumb drives to thousands of participants from December 30, 2024, to June 30, 2025. Overall, we collected 1 006 datasets from systems with various CPUs, DRAM generations, and vendors. Our study reveals that out of 1 006 datasets, 453 (371 of the 822 unique systems) succeeded in the first stage of reverseengineering the DRAM addressing functions, indicating that successfully and reliably recovering DRAM addressing functions remains a significant open problem. In the second stage, 126 (12.5 % of all datasets) exhibited bit flips in our fully automated Rowhammer attacks. Our results show that fully-automated, i. e., weaponizable, Rowhammer attacks work on a lower share of systems than FPGA-based and lab experiments indicated but with 12.5 % enough to be a practical vector for threat actors. Furthermore, our results highlight that the two most pressing research challenges around Rowhammer exploitability are more reliable reverse-engineering addressing functions, as 50 % of datasets without bit flips failed in the DRAM reverse-engineering stage, and reliable Rowhammer attacks across diverse processor microarchitectures1, as only 12.5 % of datasets contained bit flips. Addressing each of these challenges could double the number of systems susceptible to Rowhammer and make Rowhammer a more pressing threat in real-world scenarios. 

This paper introduces StoryMachine (AHRC grant ref. AH/Z507222/1, DFG project number 547532269), a newly funded transdisciplinary project (2025-2028) conducted jointly by six research groups in Germany and the UK, which aims to develop an innovative digital infrastructure to preserve, explore, and democratize access to folklore traditions and vernacular storytelling practices around the world, with a particular focus on German- and English-speaking communities. Recognizing folklore as a cornerstone of shared cultural identity, the project addresses critical challenges in archival practices, and in particular the lack of interactive, dynamic, accessible and inclusive tools for exploring both traditional and emerging folk narratives. Existing approaches to digital folklore archiving remain largely static, focusing on isolated collections without fostering meaningful exploration, collaboration, or analysis. StoryMachine redefines these paradigms by integrating spatial hypertext and recommender systems to create a visually dynamic, user-centered interface.

The transdisciplinary Digital Humanities project StoryMachine, conducted jointly (2025–2028) by six research groups in Germany and the UK, is developing an innovative digital infrastructure to preserve, explore, and democratize access to folklore traditions and vernacular storytelling practices around the world, with emphasis on German- and English-speaking communities. Recognizing folklore as a cornerstone of shared cultural identity, the project addresses critical challenges in archival practices, and particularly the lack of interactive, dynamic, accessible and inclusive tools for exploring both traditional and emerging folk narratives. Existing approaches to digital folklore archiving remain largely static, focusing on isolated collections without fostering meaningful exploration, collaboration, or analysis. StoryMachine redefines these paradigms by integrating spatial hypertext and recommender systems to create a visually dynamic, user-centered interface, which empowers users to actively participate in ongoing transregional, transcultural narrative practices while expanding previously dominant linear forms of storytelling to reflect the diversity and multifaceted nature of contemporary folklore.

The digital product passport is becoming an essential tool to increase transparency about the sustainability of products and to boost the circular economy in the European Union. With the revised Ecodesign for Sustainable Products Regulation having entered into force in July 2024, the digital product passport will become a necessary extension for many products placed on the European market. Industry practitioners are faced with the challenge of fulfilling content requirements while maintaining interoperability at the same time. Although industry practitioners face challenges in implementing digital product passports, existing research has largely focused on digital product passport systems, often overlooking the crucial aspect of digital product passport data. Work on this data typically falls short of addressing comprehensive interoperability. This research therefore proposes a minimal digital product passport data model that integrates current regulatory requirements while ensuring interoperability through the use of standardised submodel templates of the asset administration shell. We demonstrate that the current regulatory requirements can be met, but foreseeable extensions will require adjustments and therefore close synchronisation between standardisation initiatives and the institutions that further specify regulatory requirements.