Towards Enhancing Out-of-Distribution Detection with Adversarial Outliers

As machine learning classifiers tend to only work reliably for inputs from the distribution they have been trained on, Out-of-Distribution (OOD) detection is an important subject in safety-critical applications. Previous studies found that training with synthetically generated outliers can increase the ability of neural networks to recognize OOD inputs. In this paper, we present results of a preliminary study on the effects of Adversarial Attacks on such synthetic outlier examples during training. We find that, while any attack improves the results, the weakest adversary, FGSM, surprisingly works best. Furthermore, we study the influence of dynamic attack strength during fine-tuning of the model, showing that the manipulation of the attack strength can be beneficial for the robustness of the model

Workshop Summary

In this second edition of the workshop, we invite researchers from the classical software engineering community and from quantum sciences to shape the future of this new interdisciplinary research area. The recently accepted DFG priority programme 2514 Quantum Software, Algorithms and Systems Concepts, Methods and Tools for the Quantum Software Stack pushes quantum software engineering on top of the research agenda

History-Based Active Learning

In this demo, we will show how Active Learning (AL) can be used to establish and transfer classification information over partially/loosely related datasets, in particular fine-grained user roles on social media, with many and unbalanced classes, large number of data points as well as different internal structures or drifts over time. The key idea is to incorporate the history of learning steps into the tool, allowing us to analyze, restart, and modify the transfer. We also provide a rich visualization that allows the human oracle to interpret the most critical cases

Data-driven Database Engineering at Snowflake

Snowflake’s cloud-native data platform processes billions of queries daily and scans petabytes of data, creating unique database engineering challenges. As a service-oriented platform, we rely on detailed query telemetry, workload replay, and staged rollouts to drive continuous engine innovation. This talk highlights two projects from our Berlin engineering team that exemplify this data-driven approach to database engineering. First, we examine how query telemetry and workload analysis informed the transformation of Snowflake’s analytical execution engine to handle transactional-scale throughput. Second, we explore the redesign of Snowflake’s dynamic join strategy, replacing a static method with a holistic, adaptive algorithm. Through rigorous testing, workload replay, and incremental rollouts, we ensured a smooth production transition for customers. By examining these case studies, we provide practical insights into the intersection of database research and large-scale system engineering. The lessons learned highlight how data-driven methods can effectively address complex engineering challenges in modern database systems, offering perspectives relevant to both academic researchers and industry practitioners

Bereitstellung und Präsentation der Analysedaten zu Verdachtsfällen auf PSM-Vergiftung von Honigbienen in einem Multiakteur-Softwaresystem

Im Projekt Sen2Bee wird ein Softwaresystem zur Unterstützung der Analyse von Bienenvergiftungen entwickelt. Es integriert und visualisiert relevante Informationen wie Feldgeometrien, Kulturen, Phänologie und meteorologische Bedingungen. Die Akteure umfassen Imkernde, die Vorfälle melden, Amtspersonen, die Proben entnehmen, und Fachleute der UBieV, welche die Untersuchungen durchführen. Die Systementwicklung adressiert Herausforderungen wie Datenintegration, Interoperabilität und Benutzerfreundlichkeit. Für eine Reihe dieser Herausforderungen wurden Lösungen vorgeschlagen und diskutiert. Darunter die Auswahl der passenden Quelle für Wetterdaten zur qualitativen Beurteilung der Bienenflugwahrscheinlichkeit, Einbindung der Daten aus der JKI-Infrastruktur, Optimierungsstrategien für die Antwortzeiten der WCPS-Abfragen sowie Entwicklung einer App für bessere Benutzerakzeptanz

Total Recall? How Good Are Static Call Graphs Really?

Static call graphs are a fundamental building block of program analysis. However, differences in callgraph construction and the use of specific language features can yield unsoundness and imprecision. Call-graph analyses are evaluated using measures of precision and recall, but this is hard when a ground truth for real-world programs is generally unobtainable. We propose to use dynamic baselines based on fixed entry points and input corpora. The creation of this dynamic baseline is posed as an approximation of the ground truth—an optimization problem. We use manual extension and coverage-guided fuzzing for creating suitable input corpora. With these dynamic baselines, we study call-graph quality of multiple algorithms and implementations using four real-world Java programs. We find that our methodology provides insights into call-graph quality and how to measure it. We provide a novel methodology to advance the field of static program analysis as we assess the computation of one of its core data structures—the call graph

Real-world labs for digital transformation in forestry

Forests play a central role in our ecosystem and have a major influence on our climate, biodiversity, and quality of life. According to the Forest Condition Report, only one in five trees is currently considered healthy. Climate change and environmental stressors like heat and drought challenge forests globally, making protection and adaptation essential. Digital technologies offer new opportunities for sustainable forest management, with real-world labs (RWLs) playing a key role. RWLs allow to test innovative solutions in real environments and foster collaboration between scientists, policymakers, businesses and the public. In the form of a case study, we employ an RWL approach for digitization in forestry, which uses IoT sensors to monitor forests and collect data. Transdisciplinary workshops bring together diverse stakeholders to discuss these technologies and co-create solutions. This paper summarizes the RWL experiences in this context, summarizing results and experiences

Beyond Big Data — The Ocient Hyperscale Data Warehouse

The Ocient Hyperscale Data Warehouse is a massively parallel processing (MPP) system designed to efficiently store and analyze petabyte-scale datasets. Ocient utilizes a compute-adjacent storage architecture (CASA), where storage and compute resources are co-located to minimize data movement, thus enhancing query performance. We present the system architecture and dive deeper into data storage in segments, which do not only store columnar table data but also index information. This design, combined with parallel query processing, allows for high throughput and low-latency execution. Beyond that, the paper highlights OcientGeo – deeply integrated data types for geospatial analytics – as well as OcientML – a machine learning integration for running analytics and model training directly inside the database system. These features expand the system’s utility across diverse industries and applications

Workshop Summary

The main goal of the workshop GenSE’25 is to discuss the newest developments in the area of generative artificial intelligence in the context of software engineering and their practical applications. In order to successfully apply generative AI methods in software engineering, it is particularly important to analyze and critically reflect on issues about the trustworthiness and robustness of this technology. To overcome these issues, one of the main topics of the workshop was selected to be neurosymbolic methods, i.e. those methods that combine subsymbolic (machine learning) with symbolic approaches (e.g. knowledge representation and inference based on symbolic logic) to improve the reliability of generative AI methods