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Global sensitivity analysis to improve geomechanical stress characterizations using physics-based machine learning models
No full textAddressing Multiple Responsibilities in the Early Stages of R&D with Provenance Assessment
No full textA wealth of literature and best practices on Responsible Research and Innovation (RRI) document how it can be implemented in projects. However, each project is too specific to simply replicate existing patterns. Especially in early projects with a high degree of uncertainty, where indicators and measures cannot be applied, the so-called provenance assessment as a methodological change of perspective makes it possible to assess the procedural quality of research by means of narratives. A clear picture of the challenges for European bio-economy projects is sought by mapping the broader debate on "RRI in practice" in the context of biotechnology. The SUSPHIRE project is used as a case study to show how project-specific narratives integrate and signify RRI. By unpacking various concepts of "responsibility" that are already present in the project narrative at an early stage, I will show how this assessment differs significantly from other attempts to "do RRI". It is precisely in the absence of other criteria that the assessment of provenance can bring to the fore the specific form(s) of responsibility inherent in the development of projects
Interacting with Machines: Can an Artificially Intelligent Agent Be a Partner?
No full textIn the past decade, the fields of machine learning and artificial intelligence (AI) have seen unprecedented developments that raise human-machine interactions (HMI) to the next level. Smart machines, i.e., machines endowed with artificially intelligent systems, have lost their character as mere instruments. This, at least, seems to be the case if one considers how humans experience their interactions with them. Smart machines are construed to serve complex functions involving increasing degrees of freedom, and they generate solutions not fully anticipated by humans. Consequently, their performances show a touch of action and even autonomy. HMI is therefore often described as a sort of "cooperation" rather than as a mere application of a tool. Some authors even go as far as subsuming cooperation with smart machines under the label of partnership, akin to cooperation between human agents sharing a common goal. In this paper, we explore how far the notion of shared agency and partnership can take us in our understanding of human interaction with smart machines. Discussing different topoi related to partnerships in general, we suggest that different kinds of "partnership" depending on the form of interaction between agents need to be kept apart. Building upon these discussions, we propose a tentative taxonomy of different kinds of HMI distinguishing coordination, collaboration, cooperation, and social partnership
A short note on coproducts of Abelian pro-Lie groups
No full textThe notion of conditional coproduct of a family of abelian pro-Lie groups in the category of abelian pro-Lie groups is introduced. It is shown that the cartesian product of an arbitrary family of abelian pro-Lie groups can be characterized by the universal property of the conditional coproduct
Einsatzszenarien und mögliche Effekte STACK-basierter Mathematikaufgaben im Ingenieurstudium des ersten Studienjahres
No full textDie vorliegende Arbeit beschreibt die Untersuchung von möglichen Effekten STACK-basierter Mathematikaufgaben auf das Lernverhalten und den Lernerfolg von Studierenden im Ingenieurstudium des ersten Studienjahres. Dazu werden im Sinne des Design-Based Research Ansatzes zunächst der Übergang von Lernenden von der Schule zur Universität beschrieben und Probleme sowie bereits bestehende Interventionsmöglichkeiten in der Studieneingangsphase in Bezug auf das mathematikhaltige Studium benannt.
Zentral ist die theoretisch fundierte Entwicklung einer digitalen Aufgabendatenbank mathematischer Übungsaufgaben mit Hilfe des Computer-Algebra-Systems STACK und deren Einsatzmöglichkeiten in universitären Lehrveranstaltungen. Zudem stellt die empirische Untersuchung der Effektivität dieser digitalen Übungsaufgaben auf das Lernverhalten und den Lernerfolg von Studierenden sowie auf die Arbeitsbelastung von Übungsleitenden in ingenieurwissenschaftlichen Mathematiklehrveranstaltungen im ersten Studienjahr einen Fokus dieser Arbeit dar. Die Begriffe Lernstrategien und Lernverhalten werden auf Grundlage kognitionspsychologischer Theorien konzeptualisiert und durch die Entwicklung eines Lernstrategiefragebogens operationalisiert. Die Operationalisierung wird im Rahmen einer nicht-randomisierten kontrollierten Längsschnittstudie über einem Zeitraum von fünf Semestern erprobt und evaluiert. Anhand der Ergebnisse dieser empirischen Untersuchungen können positive Effekte auf das Anwenden von ausgewählten Lernstrategien, insbesondere von metakognitiven Lernstrategien und auf den Lernerfolg von Studierenden sowie eine geringere Arbeitsbelastung von Übungsleitenden beobachtet werden
A Holistic Framework for Developing Expert Systems to Improve Energy Efficiency in Manufacturing
No full textAmid growing environmental and societal concerns about energy use, companies face increasing pressure to adopt sustainable manufacturing practices. The European Union’s guiding principles, aimed in part at achieving climate neutrality and fostering green growth, underscore the need for systematic, data-driven approaches to energy efficiency. This involves the measurement, monitoring, and analysis of energy data. However, identifying efficiency potentials often relies on expert knowledge, which is becoming increasingly scarce due to skilled labor shortages. Expert systems offer a solution by consolidating and analyzing data to automatically identify energy-saving opportunities. These systems leverage stored expertise, applying it to measurement data to generate actionable insights, while their explicit knowledge representation and transparent reasoning facilitate knowledge transfer. Despite their potential, most expert systems are developed intuitively and tailored to specific applications, limiting their broader adoption. To address this, we propose a holistic framework for systematic expert system development, supported by defined personas and an expert system shell serving as a software template. The framework is demonstrated and evaluated through its application in a metalworking process chain
Cyber hate awareness: information types and technologies relevant to the law enforcement and reporting center domain
No full textIn Germany, both law enforcement agencies (LEAs) and dedicated reporting centers (RCs) engage in various activities to counter illegal online hate speech (HS). Due to the high volume of such content and against the background of limited resources, their personnel can be confronted with the issue of information overload. To mitigate this issue, information filtering, classification, prioritization, and visualization technologies offer great potential. However, a nuanced understanding of situational awareness is required to inform the domain-sensitive implementation of supportive technology and adequate decision-making. Although previous research has explored the concept of situational awareness in policing, it has not been studied in relation to online HS. Based on a qualitative research design employing a thematic analysis of qualitative expert interviews with practitioners from German LEAs and RCs (N = 29), we will contribute to the state of research in human-computer interaction with a systematization of 23 information types of relevance for situational awareness of online HS in the law enforcement and RC domain. On that basis, we identify victim, perpetrator, context, evidence, legal, and threat awareness as domain-specific situational awareness sub-types and formulate ten implications for designing reporting, open-source intelligence, classification, and visual analytics tools
Functional paper for paper-based microsampling and analysis techniques
No full textRecent advances in the sensitivity of chemical analytical instruments, as well as their miniaturization, have led to increasing interest in the use of micro samples, both for research purposes, as well as for sample collection at home or in medical facilities. Paper is by far the most commonly used substrates for micro collection of biofluids such as whole blood samples. MS is the preferred analytical method for analyzing DBS samples. With the development of novel paper-based ionization methods for MS, such as Paper-Spray MS, the evolution of these micro sampling devices into sophisticated sample preparation chips is becoming increasingly the subject of ongoing research.
The aim of the project presented in this thesis is the development and investigation of functional paper for the simplification of sample preparation steps targeting the analysis of micro samples
SSMSPC: self-supervised multivariate statistical in-process control in discrete manufacturing processes
No full textSelf-supervised learning has demonstrated state-of-the-art performance on various anomaly detection tasks. Learning effective representations by solving a supervised pretext task with pseudo-labels generated from unlabeled data provides a promising concept for industrial downstream tasks such as process monitoring. In this paper, we present SSMSPC a novel approach for multivariate statistical in-process control (MSPC) based on self-supervised learning. Our motivation for SSMSPC is to leverage the potential of unsupervised representation learning by incorporating self-supervised learning into the general statistical process control (SPC) framework to develop a holistic approach for the detection and localization of anomalous process behavior in discrete manufacturing processes. We propose a pretext task called Location + Transformation prediction, where the objective is to classify both, the type and the location of a randomly applied augmentation on a given time series input. In the downstream task, we follow the one-class classification setting and apply the Hotelling’s T² statistic on the learned representations. We further propose an extension to the control chart view that combines metadata with the learned representations to visualize the anomalous time steps in the process data which supports a machine operator in the root cause analysis. We evaluate the effectiveness of SSMSPC with two real-world CNC-milling datasets and show that it outperforms state-of-the-art anomaly detection approaches, achieving 100% and 99.6% AUROC, respectively. Lastly, we deploy SSMSPC at a CNC-milling machine to demonstrate its practical applicability when used as a process monitoring tool in a running process