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Enterprise Design, Operations, and Computing - 28th International Conference, EDOC 2024, Vienna, Austria, September 10–13, 2024, Revised Selected Papers
No full textThis publication presents the revised selected papers from the 28th International Conference on Enterprise Design, Operations, and Computing (EDOC 2024), held in Vienna, Austria. The conference focused on exploring the intersection of enterprise computing, business informatics, and sustainability, with a particular emphasis on digital twins, artificial intelligence, and data analytics. Through 18 research papers selected from 70 submissions, this publication showcases innovative contributions to the field, including novel methods for enhancing architectural frameworks, advancements in process mining and monitoring techniques, and design principles for improving sustainable practices within enterprises
KGPRUNE: A Web Application to Extract Subgraphs of Interest from Wikidata with Analogical Pruning
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Language as a predictor of multiple clinical chronic pain assessments
No full textClinical chronic pain assessment often relies on standardized self-report questionnaires, which constrain patients’ narratives and are frequently reported as difficult to interpret. Adapting these questionnaires across populations and cultures is costly and time-consuming, and minor ad-hoc adaptations can have major consequences in scoring validity. In this study, we investigated whether patients’ personal descriptions of their pain, i.e., their language of pain, can predict scores from standard clinical self-report measures, including assessments of pain intensity, positive and negative affect, catastrophizing, depression, and anxiety. These are crucial dimensions of the chronic pain experience. To model these predictive relations, we developed a suite of NLP pipelines, including psycholinguistic feature extraction, language model encoding, and large language model prompting, emphasizing locally hosted solutions addressing privacy concerns w.r.t. clinical data. We systematically evaluated the robustness and generalization of these pipelines across three datasets of language of pain, encompassing transcriptions of chronic pain interviews varying in topics, format, length, language, and chronic pain conditions, mirroring challenges in the clinical practice. Our results and analyses systematize the strengths and limitations of each pipeline and lay the foundation for selecting predictive models based on clinical targets and evaluation scenarios
Artificial intelligence in smartphone video analysis for equine asthma diagnostic support
No full textOn the Usage of Genetic Algorithms, Reinforcement Learning and Bayesian Optimisation for RF IC Design Automation
No full textThis research explores the application of three artificial intelligence-driven optimization techniques - Genetic Algorithms (GA), Reinforcement Learning (RL), and Bayesian Optimisation (BO) - to automate and enhance the Radio Frequency Integrated Circuit (RF IC) design process. The study demonstrates that these methods can significantly reduce design time while achieving superior performance compared to conventional methods, highlighting their potential in revolutionizing RF IC design automation. By leveraging these techniques, the work aims to accelerate time-to-market and deliver RF ICs with improved performance characteristics
