Window segmentation and vectorization remains a significant challenge, particularly in the absence of clean facade images. To extract complete window segments from building façade images with occlusions, this article proposes an occlusion-aware window segmentation (OA-WinSeg) network with conditional adversarial training guided by prior structural information. This architecture combines the power of image segmentation and generative capabilities to handle occlusions. First, OA-WinSeg automatically detects occlusions and generates a rectangular boundary guidance from a coarse window segmentation, which incorporates structural information about the building layout into the process. Subsequently, the network refines the coarse segmentation and generates window segments in the missing regions by attending to contextual information of the nonoccluded parts of the façade. Finally, our approach generates accurate vector representations, information needed for building modeling systems. Experimental results demonstrate the effectiveness of our model with simulated and occluded real-world datasets. In addition, we evaluate our model on various ablation studies to explore the contribution of the different modules. Finally, we have analyzed the potential applications of the proposed segmentation network and the completed window segments, including building façade inpainting.

Reliable routing in multimodal transport networks requires more than minimizing travel time: it demands accounting for the risk of delays and disruptions. This paper presents ROMY (Risk-Optimized Mobility), a decision support system that integrates heterogeneous data sources, advanced feature engineering, and a graph neural network (GNN)–based predictive core to deliver personalized, risk-aware route recommendations. ROMY models the transport network as a directed, attributed graph, combining spatial, temporal, modal, and semantic attributes to capture complex dependencies between network segments. The predictive core employs edgeconditioned message passing to incorporate contextual information such as travel mode, time-of-day, and navigation instructions into segment-level risk estimation. A pilot implementation demonstrates the system’s ability to identify high-risk segments, outperform baseline models, and offer alternative routes that reduce risk with minimal impact on travel time. The results highlight ROMY’s potential to enhance the reliability of multimodal mobility services and support data-driven transport planning.

Artificial intelligence (AI) is rapidly transforming software development. At the same time, approaches that align with the intellectual tradition of agile, including code-first methods, rapid prototyping, and vibe coding, have become more prominent, due to their speed and flexibility. The role of conceptual modeling has become less clear in this emerging environment. However, danger exists in discarding modeling, as it has traditionally shielded software development from risks and vulnerabilities of developing a solution without careful and systematic deliberation. These risks are now dramatically amplified due to the speed and scale of AI. This manifesto finds a new place for conceptual modeling in software development with AI, and as a result, proposes a new way to conduct software development more broadly. The manifesto comprises core principles, termed SAFE-AI, and the process of implementing the principles, MADE with AI. Together, they pave the way for a synergistic collaboration between AI and human developers with the aim of producing effective and responsible software.

Large language models (LLMs) like GPT-4 and LLaMA-3 offer transformative potential across industries, e.g., enhancing customer service, revolutionizing medical diagnostics, or identifying crises in news articles. However, deploying LLMs faces challenges such as limited training data, high computational costs, and issues with transparency and explainability. Our research focuses on distilling compact, parameter-efficient tailored language models (TLMs) from LLMs for domain-specific tasks with comparable performance. Current approaches like knowledge distillation, fine-tuning, and model parallelism address computational efficiency but lack hybrid strategies to balance efficiency, adaptability, and accuracy. We present ANON - an adaptive knowledge distillation framework integrating knowledge distillation with adapters to generate computationally efficient TLMs without relying on labeled datasets. ANON uses cross-entropy loss to transfer knowledge from the teacher's outputs and internal representations while employing adaptive prompt engineering and a progressive distillation strategy for phased knowledge transfer. We evaluated ANON's performance in the crisis domain, where accuracy is critical and labeled data is scarce. Experiments showed that ANON outperforms recent approaches of knowledge distillation, both in terms of the resulting TLM performance and in reducing the computational costs for training and maintaining accuracy compared to LLMs for domain-specific applications.

Energy storage operation strategies depend on forecasts of the expected prices in the dispatch period. The quality of the forecast influences the economic success of the dispatch strategy. Nevertheless, current literature only evaluates the fore-cast with performance metrics on the time series, not with realized revenue. In addition, dispatch strategies are mostly evaluated with perfect foresight, which is not available in real life applications, or synthetically generated price forecasts, that do not precisely reflect shortcomings of real forecasting models. To examine the influence of forecasting performance on the realized revenue and the robustness against imprecise forecasts, we examine the use case of energy arbitrage in the day-ahead energy market with a battery storage. Different forecasting models are implemented to predict the resulting clearing price of the day-ahead auction before its closure based on the historic realized price of the past days. The price forecast is then used in a mixed integer linear programming-based dispatch model for the battery storage to derive the optimal schedule for market participation. To benchmark the realized revenue, we use the actual realized price to compute the optimal dispatch decision, resulting in the theoretical maximal revenue under perfect foresight. The impact of different forecasting models is evaluated by comparing the resulting revenues. Furthermore, we study the impact of including the ageing costs of the battery in the dispatch decision-making, which affects the forecast-based dispatch. The results show significantly higher missed revenue of 84.84 % compared to the theoretical maximum revenue when ageing costs are included and only 28.27 % missed revenue when ageing costs are neglected.

In the evolving domain of prevention and rehabilitation, adaptive and personalized Artificial Intelligence (AI) systems are pivotal for delivering patient-centric care. This study investigates the application of Artificial Mental Models (AMM) within healthcare AI systems, specifically in knee rehabilitation, to address cognitive challenges that result in incomplete or biased data, impacting patient decision-making and communication. Leveraging Large Language Models (LLMs), we develop and fine-tune AMMs to accurately capture individual patients' mental models and enhance support during rehabilitation. Our research adopts a Design Science Research (DSR) methodology encompassing two phases: elicitation and individualization. In the elicitation phase, a domain-specific AMM is generated through a quantitative study involving 150 participants and indirect patient observations, ensuring a discrimination- and bias-free model. The individualization phase utilizes curated and non-curated patient data to fine-tune the AMM for individual patients. The effectiveness of these patient-specific AMMs is evaluated in real-world rehabilitation settings through A/B testing, comparing patient and AMM predictions of pain with actual pain assessments. The study's outcomes highlight the potential of AI systems to provide accurate, personalized, and bias-free patient care, significantly improving rehabilitation outcomes. The methodology and findings suggest broader applicability across various healthcare domains, enhancing the integration of AI in routine patient care and advancing the effectiveness of therapeutic interventions.

The following discussion paper summarizes the results of a panel discussion conducted on July 10, 2023 at the International Conference on Software Technologies (ICSOFT) in Rome, Italy. The panelists included Jordi Cabot from Luxembourg Institute of Science and Technology, Luxembourg, Wolfgang Maass from University of Saarland, Germany, and Marten van Sinderen from University of Twente, Netherlands. The panel was moderated by Hans-Georg Fill from University of Fribourg, Switzerland.

This paper explores the integration of graph knowledge from linguistic ontologies into multilingual Large Language Models (LLMs) using adapters to improve performance for low-resource languages (LRLs) in sentiment analysis (SA) and named entity recognition (NER). Building upon successful parameter-efficient fine-tuning techniques, such as K-ADAPTER and MAD-X, we propose a similar approach for incorporating knowledge from multilingual graphs, connecting concepts in various languages with each other through linguistic relationships, into multilingual LLMs for LRLs. Specifically, we focus on eight LRLs --- Maltese, Bulgarian, Indonesian, Nepali, Javanese, Uyghur, Tibetan, and Sinhala --- and employ language-specific adapters fine-tuned on data extracted from the language-specific section of ConceptNet, aiming to enable knowledge transfer across the languages covered by the knowledge graph. We compare various fine-tuning objectives, including standard Masked Language Modeling (MLM), MLM with full-word masking, and MLM with targeted masking, to analyze their effectiveness in learning and integrating the extracted graph data. Through empirical evaluation on language-specific tasks, we assess how structured graph knowledge affects the performance of multilingual LLMs for LRLs in SA and NER, providing insights into the potential benefits of adapting language models for low-resource scenarios.

In the field of prevention and rehabilitation, adaptive and personalized Artificial Intelligence (AI) systems play a vital role in enhancing patient-centric care. These AI systems analyze extensive data on user behavior and situational context to provide tailored support that meets unique needs of individuals, such as patients recovering from knee surgeries. Such patients often face cognitive challenges that interfere with their ability to process complex medical information, make informed decisions, and communicate effectively about their symptoms. These challenges may lead to the generation of incomplete, inaccurate, or biased data, significantly impacting the effectiveness of their treatment. This research introduces the concept of artificial mental models (AMM) integrated into healthcare AI systems. AMMs are cognitive frameworks that encapsulate patient's perceptions and expectations about their therapy and recovery journey. They are beneficial in scenarios requiring nuanced understanding and adaptation to patient's changing conditions, e.g., to assist an amateur soccer player recovering from knee surgery. Here, the AMM acts as a liaison between patient and therapist, helping to devise a personalized exercise regimen that adapts to patient's pain and progress. This work explores the generation of AMMs using Large Language Models instrumental in both eliciting and fine-tuning AMMs to individual patient needs. The research encompasses a prospective study with two phases: elicitation and individualization. The elicitation phase involves creating a bias-free, domain-specific basis AMM through extensive data collection from both quantitative studies and indirect observations, including data on personality traits and expected pain during specific exercises. This basis AMM is evaluated in a technical experiment to ensure it is free from bias and discrimination. In the individualization phase, the basis AMM is refined using direct observations of specific patients, incorporating curated data such as medication details, rehabilitation plans, and patient-reported outcomes, as well as non-curated data like movement patterns and fitness status. The final AMM tailored for individual patients is assessed through action research, involving real-world application to evaluate its impact on rehabilitation outcomes. The research questions focus on whether the predictions made by the AMM about pain are consistent with the patients' experiences and the assessments made by therapists. Results validate the effectiveness of AMMs in real-time clinical settings, demonstrating their potential to significantly enhance the personalization and effectiveness of patient care in knee rehabilitation. Broader implications suggest that AI-driven approaches could enhance patient care across various areas of healthcare, supporting the ability of systems to predict patient needs and improve overall outcomes by more targeted and effective interventions.

Presents an adaptive knowledge distillation framework to create efficient domain-specific language models.

Despite the widespread application of artificial intelligence (AI) as universal solution for complex business problems, there remains a significant gap in design methods for AI systems, distinguishing them sharply from traditional software systems. This research aims to address the lack of standardized design methods tailored for AI projects, which are often impeded by unique challenges such as data sensitivity, model performance, and regulatory compliance. Through an exploratory case study of four AI projects, this paper investigates correlations between characteristics of AI projects and the design methods applied, introducing a set of If-This-Then-That (IFTTT) patterns. These patterns are intended to aid in selecting and combining design method components that align with the specific needs of AI projects. Results highlight the importance of understanding project-specific characteristics to enhance the effectiveness of design methods in AI engineering, offering practitioners actionable insights for improving quality and reliability of AI systems through tailored design approaches.

Proceedings of the 43rd International Conference on Conceptual Modeling, presenting the latest advancements in the field.

Analyzes digital resilience in manufacturing industries during crises.

Im Dezember 2019 wurden in Deutschland Digitale Gesundheitsanwendungen (DiGA) in die Regelversorgung aufgenommen und können somit durch die gesetzlichen Krankenkassen erstattet werden, um PatientInnen bei der Behandlung von Erkrankungen oder Beeinträchtigungen zu unterstützen. Inzwischen gibt es 48 DiGA (Stand: Oktober 2023) im Verzeichnis des Bundesinstituts für Arzneimittel und Medizinprodukte (BfArM), die vor allem in den Bereichen mentale Gesundheit, Hormone und Stoffwechsel sowie Muskeln, Knochen und Gelenke eingesetzt werden. In diesem Artikel beschreibt die Fachgruppe „Digital Health“ der Gesellschaft für Informatik e. V. (GI) die aktuellen Entwicklungen rund um die DiGA sowie das derzeitige Stimmungsbild zu Themen wie Nutzerzentrierung, Akzeptanz von PatientInnen und Behandelnden sowie Innovationspotenzial. Zusammenfassend haben DiGA in den letzten 3 Jahren eine positive Entwicklung in Form eines langsam steigenden Angebots verschiedener DiGA und Leistungsbereiche erfahren. Nichtsdestotrotz sind in einigen Bereichen noch erhebliche regulatorische Weichenstellungen notwendig, um DiGA langfristig in der Regelversorgung zu etablieren. Zentrale Herausforderungen bestehen u. a. in der Nutzerzentrierung oder in der nachhaltigen Verwendung der Anwendungen.

The power consumption of data centers has doubled in the last ten years and will account for 13% of global energy consumption by 2030. AI is one of the biggest drivers of data center power consumption: training a natural language processing model such as the GPT-3 model consumes 936,000 kWh and generates approximately 284 t of CO2. ESCADE's goal is to significantly reduce the energy requirements of data centers by using world-leading hardware and software technologies to improve the environmental footprint of AI applications. The focus will be on the use of neuromorphic chip technologies, as these promise efficiency gains of up to 50% in training and up to 80% in the inference of AI models. The SpiNNcloud data center at TU Dresden based on neuromorphic SpiNNaker2 chips will serve as prototype for evaluation of two use-cases: Visual computing for steel industry and efficient training of language models for digital industry. An AI sustainability framework will be developed to monitor the sustainability of AI systems going far beyond mere power measurement. Concepts for integrating neuromorphic chips into classic GPU-based data centers will help planning more sustainable AI data centers. This makes a concrete contribution to decoupling economic growth and prosperity from resource consumption.