Dr. rer. nat.

Rüping, Stefan

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  • Publication
    Symptom diaries as a digital tool to detect SARS-CoV-2 infections and differentiate between prevalent variants
    ( 2022-11-14)
    Grüne, Barbara
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    Kugler, Sabine
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    Ginzel, Sebastian
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    Wolff, Anna
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    Buess, Michael
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    Kossow, Annelene
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    Küfer-Weiß, Annika
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    Rüping, Stefan
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    Neuhann, Florian
    The COVID-19 pandemic and the high numbers of infected individuals pose major challenges for public health departments. To overcome these challenges, the health department in Cologne has developed a software called DiKoMa. This software offers the possibility to track contact and index persons, but also provides a digital symptom diary. In this work, the question of whether these can also be used for diagnostic purposes will be investigated. Machine learning makes it possible to identify infections based on early symptom profiles and to distinguish between the predominant dominant variants. Focusing on the occurrence of the symptoms in the first week, a decision tree is trained for the differentiation between contact and index persons and the prevailing dominant variants (Wildtype, Alpha, Delta, and Omicron). The model is evaluated, using sex- and age-stratified cross-validation and validated by symptom profiles of the first 6 days. The variants achieve an AUC-ROC from 0.89 for Omicron and 0.6 for Alpha. No significant differences are observed for the results of the validation set (Alpha 0.63 and Omicron 0.87). The evaluation of symptom combinations using artificial intelligence can determine the individual risk for the presence of a COVID-19 infection, allows assignment to virus variants, and can contribute to the management of epidemics and pandemics on a national and international level. It can help to reduce the number of specific tests in times of low labor capacity and could help to early identify new virus variants.
  • Publication
    Inspect, Understand, Overcome: A Survey of Practical Methods for AI Safety
    ( 2021)
    Houben, Sebastian
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    Abrecht, Stephanie
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    Akila, Maram
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    Bär, Andreas
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    Brockherde, Felix
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    Feifel, Patrick
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    Fingscheidt, Tim
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    Gannamaneni, Sujan Sai
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    Ghobadi, Seyed Eghbal
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    Hammam, Ahmed
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    Haselhoff, Anselm
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    Hauser, Felix
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    Heinzemann, Christian
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    Hoffmann, Marco
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    Kapoor, Nikhil
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    Kappel, Falk
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    Klingner, Marvin
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    Kronenberger, Jan
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    Küppers, Fabian
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    Löhdefink, Jonas
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    Mlynarski, Michael
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    Mock, Michael
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    Mualla, Firas
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    Pavlitskaya, Svetlana
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    Poretschkin, Maximilian
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    Pohl, Alexander
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    Ravi-Kumar, Varun
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    Rosenzweig, Julia
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    Rottmann, Matthias
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    Rüping, Stefan
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    Sämann, Timo
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    Schneider, Jan David
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    Schulz, Elena
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    Schwalbe, Gesina
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    Sicking, Joachim
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    Srivastava, Toshika
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    Varghese, Serin
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    Weber, Michael
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    Wirkert, Sebastian
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    Wirtz, Tim
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    Woehrle, Matthias
    The use of deep neural networks (DNNs) in safety-critical applications like mobile health and autonomous driving is challenging due to numerous model-inherent shortcomings. These shortcomings are diverse and range from a lack of generalization over insufficient interpretability to problems with malicious inputs. Cyber-physical systems employing DNNs are therefore likely to suffer from safety concerns. In recent years, a zoo of state-of-the-art techniques aiming to address these safety concerns has emerged. This work provides a structured and broad overview of them. We first identify categories of insufficiencies to then describe research activities aiming at their detection, quantification, or mitigation. Our paper addresses both machine learning experts and safety engineers: The former ones might profit from the broad range of machine learning (ML) topics covered and discussions on limitations of recent methods. The latter ones might gain insights into the specifics of modern ML methods. We moreover hope that our contribution fuels discussions on desiderata for ML systems and strategies on how to propel existing approaches accordingly.
  • Publication
    A review of machine learning for the optimization of production processes
    ( 2019)
    Weichert, Dorina
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    Link, Patrick
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    Stoll, Anke
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    Rüping, Stefan
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    Ihlenfeldt, Steffen
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    Wrobel, Stefan
    Due to the advances in the digitalization process of the manufacturing industry and the resulting available data, there is tremendous progress and large interest in integrating machine learning and optimization methods on the shop floor in order to improve production processes. Additionally, a shortage of resources leads to increasing acceptance of new approaches, such as machine learning to save energy, time, and resources, and avoid waste. After describing possible occurring data types in the manufacturing world, this study covers the majority of relevant literature from 2008 to 2018 dealing with machine learning and optimization approaches for product quality or process improvement in the manufacturing industry. The review shows that there is hardly any correlation between the used data, the amount of data, the machine learning algorithms, the used optimizers, and the respective problem from the production. The detailed correlations between these criteria and the recent progress made in this area as well as the issues that are still unsolved are discussed in this paper.
  • Publication
    E2mC: Improving Emergency Management Service Practice through Social Media and Crowdsourcing Analysis in Near Real Time
    ( 2017)
    Havas, C.
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    Resch, B.
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    Francalanci, C.
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    Pernici, B.
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    Scalia, G.
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    Fernandez-Marquez, J.L.
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    Achte, T. Van
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    Zeug, G.
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    Mondardini, M.R.R.
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    Grandoni, D.
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    Kirsch, Birgit
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    Kalas, M.
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    Lorini, V.
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    Rüping, Stefan
    In the first hours of a disaster, up-to-date information about the area of interest is crucial for effective disaster management. However, due to the delay induced by collecting and analysing satellite imagery, disaster management systems like the Copernicus Emergency Management Service (EMS) are currently not able to provide information products until up to 48-72 h after a disaster event has occurred. While satellite imagery is still a valuable source for disaster management, information products can be improved through complementing them with user-generated data like social media posts or crowdsourced data. The advantage of these new kinds of data is that they are continuously produced in a timely fashion because users actively participate throughout an event and share related information. The research project Evolution of Emergency Copernicus services (E2mC) aims to integrate these novel data into a new EMS service component called Witness, which is presented in this paper. Like this, the timeliness and accuracy of geospatial information products provided to civil protection authorities can be improved through leveraging user-generated data. This paper sketches the developed system architecture, describes applicable scenarios and presents several preliminary case studies, providing evidence that the scientific and operational goals have been achieved.