@misc{10216, abstract = {{Wet granulation is a frequent process in the pharmaceutical industry. As a starting point for numerous dosage forms, the quality of the granulation not only affects subsequent production steps but also impacts the quality of the final product. It is thus crucial and economical to monitor this operation thoroughly. Here, we report on identifying different phases of a granulation process using a machine learning approach. The phases reflect the water content which, in turn, influences the processability and quality of the granule mass. We used two kinds of microphones and an acceleration sensor to capture acoustic emissions and vibrations. We trained convolutional neural networks (CNNs) to classify the different phases using transformed sound recordings as the input. We achieved a classification accuracy of up to 90% using vibrational data and an accuracy of up to 97% using the audible microphone data. Our results indicate the suitability of using audible sound and machine learning to monitor pharmaceutical processes. Moreover, since recording acoustic emissions is contactless, it readily complies with legal regulations and presents Good Manufacturing Practices.}}, author = {{Fulek, Ruwen and Ramm, Selina and Kiera, Christian and Pein-Hackelbusch, Miriam and Odefey, Ulrich}}, booktitle = {{Pharmaceutics}}, issn = {{1999-4923 }}, keywords = {{wet granulation, acoustic classification, machine learning, convolutional neural networks}}, number = {{8}}, publisher = {{MDPI}}, title = {{{A machine learning approach to qualitatively evaluate different granulation phases by acoustic emissions}}}, doi = {{https://doi.org/10.3390/pharmaceutics15082153}}, volume = {{15}}, year = {{2023}}, } @misc{12785, abstract = {{Due to the demographic aging of society, the demand for skilled caregiving is increasing. However, the already existing shortage of professional caregivers will exacerbate in the future. As a result, family caregivers must shoulder a heavier share of the care burden. To ease the burden and promote a better work-life balance, we developed the Digital Case Manager. This tool uses machine learning algorithms to learn the relationship between a care situation and the next care steps and helps family caregivers balance their professional and private lives so that they are able to continue caring for their family members without sacrificing their own jobs and personal ambitions. The data for the machine learning model are generated by means of a questionnaire based on professional assessment instruments. We implemented a proof-of-concept of the Digital Case Manager and initial tests show promising results. It offers a quick and easy-to-use tool for family caregivers in the early stages of a care situation.}}, author = {{Wunderlich, Paul and Wiegräbe, Frauke and Dörksen, Helene}}, booktitle = {{INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH}}, issn = {{1660-4601}}, keywords = {{machine learning, healthcare, case management, caring, multi-label classification}}, number = {{2}}, publisher = {{MDPI}}, title = {{{Digital Case Manager-A Data-Driven Tool to Support Family Caregivers with Initial Guidance}}}, doi = {{10.3390/ijerph20021215}}, volume = {{20}}, year = {{2023}}, } @misc{12806, abstract = {{Cyber-Physical Systems (CPS) play an essential role in today’s production processes, leveraging Artificial Intelligence (AI) to enhance operations such as optimization, anomaly detection, and predictive maintenance. This article reviews a cognitive architecture for Artificial Intelligence, which has been developed to establish a standard framework for integrating AI solutions into existing production processes. Given that machines in these processes continuously generate large streams of data, Online Machine Learning (OML) is identified as a crucial extension to the existing architecture. To substantiate this claim, real-world experiments using a slitting machine are conducted, to compare the performance of OML to traditional Batch Machine Learning. The assessment of contemporary OML algorithms using a real production system is a fundamental innovation in this research. The evaluations clearly indicate that OML adds significant value to CPS, and it is strongly recommended as an extension of related architectures, such as the cognitive architecture for AI discussed in this article. Additionally, surrogate-model-based optimization is employed, to determine the optimal hyperparameter settings for the corresponding OML algorithms, aiming to achieve peak performance in their respective tasks.}}, author = {{Hinterleitner, Alexander and Schulz, Richard and Hans, Lukas and Subbotin, Aleksandr and Barthel, Nils and Pütz, Noah and Rosellen, Martin and Bartz-Beielstein, Thomas and Geng, Christoph and Priss, Phillip}}, booktitle = {{ Applied Sciences : open access journal}}, issn = {{2076-3417}}, keywords = {{machine learning, online algorithms, cyber-physical production systems, surrogate-based optimization}}, number = {{20}}, publisher = {{MDPI AG}}, title = {{{Online Machine Learning and Surrogate-Model-Based Optimization for Improved Production Processes Using a Cognitive Architecture}}}, doi = {{10.3390/app132011506}}, volume = {{13}}, year = {{2023}}, } @misc{12843, abstract = {{This article contributes to the ongoing dialogue regarding the future application of renewable e‐fuels as part of a holistic solution to the energy crisis. In order to be able to continue using internal combustion engines in a sustainable manner, it must be ensured that these engines are operated exclusively with renewable, CO2‐neutral fuels. One way to achieve this is the use of a fluorescence sensor in the vehicle in combination with fuels that are labeled with a fluorescence marker. This study presents an investigation into the use of the benzophenoxazine dye Nile red as a fluorescent marker for distinguishing fossil from renewable fuels. In addition to assessing the stability of the fluorescent marker against thermo‐oxidative aging, the study probes its antioxidative impact on fuel aging, by comparing unlabeled and with Nile red labeled aged fuels. Furthermore, an examination of fuel‐specific parameters underscores the positive effect of Nile red on fuel stability. A comparison with the antioxidant butylated hydroxytoluene confirms the antioxidant effect of Nile red.}}, author = {{Lichtinger, Anne and Poller, Maximilian J. and Türck, Julian and Schröder, Olaf and Garbe, Thomas and Krahl, Jürgen and Singer, Anja and Jakob, Markus and Albert, Jakob}}, booktitle = {{Energy technology : generation, conversion, storage, distribution}}, issn = {{2194-4296}}, keywords = {{antioxidants, climate policy, climate-neutral, e-fuels, fluorescence markers, oxidation}}, number = {{11}}, publisher = {{Wiley}}, title = {{{Nile Red as a Fluorescence Marker and Antioxidant for Regenerative Fuels}}}, doi = {{10.1002/ente.202300260}}, volume = {{11}}, year = {{2023}}, } @misc{13017, abstract = {{The article presents the potentials and capacities of extracurricular activities such as student workshops for strengthening existing curricula and introducing emerging specialised areas, topics, and challenges into architectural higher education. The specific objective of this study is to enhance and test different pedagogical models for learning on the sustainable rehabilitation of mass housing neighbourhoods (MHN), as a specific type of modern heritage, through innovative extracurricular teaching practices based on interdisciplinarity, flexibility, and adaptability. This research presents three student workshops focusing on the rehabilitation of mass housing neighbourhoods (MHN), involving students, academics, and professionals from the field, organised in Germany, Serbia, and North Macedonia in 2022. Moreover, it engages a comparative analysis of the learning formats and approaches developed within this discipline-specific cross-border collaboration. The study provides (1) an insight into the comparative analysis of learning capabilities and (2) the formulation of workshop models supported by diagramming of the workshop structure. The conclusion of the article summarises the findings and highlights the essential aspects for engaging student workshops, as an instrument for generating operational knowledge in the field of mass housing rehabilitation.}}, author = {{Dragutinovic, Anica and Milovanovic, Aleksandra and Stojanovski, Mihajlo and Damjanovska, Tea and Đorđevic, Aleksandra and Nikezic, Ana and Pottgiesser, Uta and Ivanovska Deskova, Ana and Ivanovski, Jovan}}, booktitle = {{Sustainability}}, issn = {{2071-1050}}, keywords = {{extracurricular activities, extracurricular learning formats, student workshops, workshop models, pedagogical models, architectural higher education, mass housing neighbourhoods, sustainable rehabilitation}}, number = {{3}}, publisher = {{MDPI }}, title = {{{Approaching Extracurricular Activities for Teaching and Learning on Sustainable Rehabilitation of Mass Housing: Reporting from the Arena of Architectural Higher Education}}}, doi = {{10.3390/su15032476}}, volume = {{15}}, year = {{2023}}, } @misc{13019, abstract = {{The digital transformation of manufacturing companies is a huge driver of complexity in organizational structures and processes. Challenges such as an increasing number of variants, rapid changes in technology, and a multitude of interfaces between IT systems within companies require changed qualifications in the workforce. Employees lack a profound understanding of the added value that digitalization can bring to the company and themselves. To address these challenges, simulation games are a suitable approach. Simulation games are active learning methods that simulate real systems in an artificial environment. The goal is to give employees the opportunity to gain experience and make decisions without creating a pressure situation or endangering the real production system. This enables them to better understand, evaluate and design real systems. In order to make optimal use of simulation games in manufacturing companies, they should be customized to the company and its employees due to individual processes and structures. This paper presents a procedure model for designing a concept of individualized simulation games for manufacturing companies in the context of digitalization. It starts with the identification of requirements. Subsequently, the requirements of the individual elements are combined into a holistic simulation game. The piloting of the framework is presented using an example from industrial practice.}}, author = {{Machon, Fabian and Gabriel, Stefan and Latos, Benedikt and Holtkötter, Christoph and Lütkehoff, Ben and Asmar, Laban and Kühn, Dr. Arno and Dumitrescu, Prof. Dr. Roman}}, booktitle = {{Procedia CIRP}}, issn = {{2212-8271}}, keywords = {{industry 4.0, digitalization, digital transformation, simulation games, game-based learning, education, employee education, qualification}}, pages = {{1017--1022}}, publisher = {{Elsevier BV}}, title = {{{Design of individual simulation games in manufacturing companies for game-based learning}}}, doi = {{10.1016/j.procir.2023.03.145}}, volume = {{119}}, year = {{2023}}, } @misc{8888, abstract = {{Diese Arbeit handelt von der Frage, wie Tonaufnahmen-basierte Lernprozesse im Learning Management System der Hochschule für Musik Detmold, Moodle, erweitert werden können. Dazu werden LMS zunächst definiert und anschließend in die Bildungslandschaft eingeordnet. Daraufhin wird der Status Quo betrachtet mit der Feststellung, dass ein Bedarf an Werkzeugen besteht. Dieser Bedarf wurde durch die Programmierung zweier Anwendungen adressiert, die eine Integration im LMS ermöglichen und damit zu einer erhöhten Nutzbarkeit von Tonaufnahmen und musikalischen Inhalten führen sollen. Zum einen ist das eine Implementation des DTW Algorithmus, mittels welchem sich Synchronisationsdaten zwischen zwei verschiedenen Musikdarstellungen desselben Stückes berechnen lassen. Damit ließe sich bspw. ein Interface erstellen, auf dem die Anzeige der Musikwiedergabe mit der Anzeige einer Notenpartitur synchronisiert wird. Die zweite Anwendung fällt in den Bereich des maschinellen Lernens – es wurde ein automatischer Instrumentenklassifizierer geschrieben. Dieser eignet sich zur Erstellung von automatischen Taggings, zwecks Organisation von Daten und Gehörübungen. Die Nutzung einer CNN-Architektur hat sich dabei als effektiv erwiesen: Nach insgesamt 39 Lernepochen und knapp 7 Millionen gelernten Parametern konnte eine Genauigkeit von 95% erzielt werden. Als Datensatz diente die frei verfügbare Aufnahmensammlung des britischen Philharmonia Orchesters (vgl. Thorben Dittes). Im zweiten Kapitel soll ein Abstecken der Zwecke der einzelnen Programme die Designentscheidungen informieren, welche daraufhin erläutert werden. Im dritten Teil wird anschließend mit ScoreTube eine DTW Implementation von Berndt et al. zum Vergleich herangezogen, um die vorliegende Arbeit in den aktuellen Diskurs einzuordnen. Der Beitrag endet mit einer Evaluation der Ergebnisse und einem Ausblick auf potenzielle zukünftige Arbeiten.}}, author = {{Treiber, Dennis}}, keywords = {{learning management system, dynamic time warping, deep learning, convolutional neural network}}, pages = {{53}}, publisher = {{Technische Hochschule Ostwestfalen-Lippe}}, title = {{{Die Verwendung von Tonaufnahmen im LMS : Entwicklung spezifischer digitaler Werkzeuge an Hochschulen.}}}, year = {{2022}}, } @misc{9161, abstract = {{Employees in household-related services have so far been neglected in research and practice. The overall goal of our project is to identify work-related stress of this special target group, develop recommendations, and disseminate them using low-threshold, attractive edutainment offers. In this context, this contribution presents a learning platform design for the special target group of domestic workers, such as gardeners or cleaners. The design is based on a requirements analysis with respect to this special target group, which we as well outline in this contribution.}}, author = {{Grimm, Valentin and Geiger, Laura and Rubart, Jessica and Faller, Gudrun}}, booktitle = {{DELFI 2022 : die 20. Fachtagung Bildungstechnologien der Gesellschaft für Informatik e.V., 12.-14. September 2022, Karlsruhe}}, editor = {{Henning, Peter A. and Striewe, Michael and Wölfel, Matthias}}, isbn = {{978-3-88579-716-6}}, issn = {{1617-5468}}, keywords = {{E-Learning, Minority Group, Gameful Design, Gamification}}, location = {{Karlsruhe, DE}}, pages = {{213--214}}, publisher = {{Gesellschaft für Informatik e.V.}}, title = {{{Requirements and Design of a Training System for Domestic Workers}}}, doi = {{10.18420/delfi2022-037}}, volume = {{P-322}}, year = {{2022}}, } @misc{7578, abstract = {{In recent years considerable research efforts have been made to provide evidence for a nexus be-tween game design elements in non-game contexts. Our research presents a new approach to bridge game design elements and educational theory: defining a set of motivational “patterns” used for peda-gogical purposes in university teaching scenarios. To this end, we will build upon preliminary empirical results from a research project called EMPAMOS®. It derived a set of motivational elements frequently used in social game designs. Our hypothesis is that these elements resemble on a structural level and are directly transferable to motivational factors in online education contexts. Focused on cooperative teaching and learning, we develop a curriculum to enable educators to im-plement motivational molecules from game design in their learning settings. The paper presents basic premises and a preliminary structure of the curriculum. By examining educational settings in terms of a “broken game”, we provide a new perspective on the prerequisites for learning at the university level.}}, author = {{Bröker, Thomas and Schmulius, Nina and Schmohl, Tobias and Dulisch, Fabian and Marquardt, Sabrina and Höllen, Max and Voit, Thomas and Zinger, Benjamin}}, booktitle = {{New Perspectives in Science Education}}, keywords = {{cooperative learning, gamification, motivation, train-the-trainer, curriculum}}, location = {{Florenz}}, pages = {{22--26}}, publisher = {{Libreriauniversitaria.it}}, title = {{{What Can Educators Learn from Social Game Design in University Online Teaching?}}}, volume = {{11}}, year = {{2022}}, } @misc{7734, abstract = {{ Der Konferenzbeitrag zeigt den Forschungs- und Technikstand bezüglich des Griff-in-die-Kiste auf. Basierend auf einer Literaturrecherche werden Beispiele für regelbasierte und lernende Verfahren vorgestellt. Anschließend erfolgt eine systematische Gegenüberstellung der Verfahren. Hierfür werden die Anforderungen, die ein Griff-in-die-Kiste-System zu erfüllen hat, dargelegt. Die Kriterien resultieren aus einer Expertenbefragung des produktionstechnischen Umfelds der Weidmüller Gruppe. Neben den Anforderungen werden die Gewichtungen zur Bildung einer Rangfolge ermittelt. Die erarbeiteten Anforderungen dienen anschließend zur Bewertung der regelbasierten und lernenden Verfahren. Die Analyse mündet in einer methodischen Lücke zwischen beiden Paradigmen und stellt die Ausgangsbasis für die weitere Arbeit zur Entwicklung des industriellen Griff-in-die-Kiste dar. Abschließend werden erste Arbeitsergebnisse zur Objekterkennung von Reihenklemmen veröffentlicht. In einer Untersuchung werden die Zuverlässigkeit, die Robustheit sowie die Einrichtdauer einer Objekterkennung mithilfe von Deep Learning ermittelt. Das angestrebte Forschungsergebnis stellt einen Entwicklungsschritt von automatisierten Systemen, die in einem definierten Wirkbereich eigenständig arbeiten, zu autonomen Systemen, die selbstständig auf zeitvariante Größen reagieren, dar.}}, author = {{Stuke, Tobias and Bartsch, Thomas and Rauschenbach, Thomas}}, booktitle = {{Tagungsband AALE 2022: Wissenstransfer im Spannungsfeld von Autonomisierung und Fachkräftemangel}}, editor = {{Härle, Christian and Jäkel, Jens and Sand, Guido}}, keywords = {{Griff-in-die-Kiste, Bildverarbeitung, Robotik, Deep Learning, lernende Verfahren, regelbasierte Verfahren}}, location = {{Pforzheim}}, pages = {{145 – 154}}, publisher = {{Open Access}}, title = {{{Adaptiver Griff-in-die-Kiste – Die methodische Lücke zwischen Forschung und Industrie}}}, doi = {{https://doi.org/10.33968/2022.14}}, year = {{2022}}, } @misc{12817, abstract = {{Sub-optimal control policies in intersection traffic signal controllers (TSC) contribute to congestion and lead to negative effects on human health and the environment. Reinforcement learning (RL) for traffic signal control is a promising approach to design better control policies and has attracted considerable research interest in recent years. However, most work done in this area used simplified simulation environments of traffic scenarios to train RL-based TSC. To deploy RL in real-world traffic systems, the gap between simplified simulation environments and real-world applications has to be closed. Therefore, we propose LemgoRL, a benchmark tool to train RL agents as TSC in a realistic simulation environment of Lemgo, a medium-sized town in Germany. In addition to the realistic simulation model, LemgoRL encompasses a traffic signal logic unit that ensures compliance with all regulatory and safety requirements. LemgoRL offers the same interface as the well-known OpenAI gym toolkit to enable easy deployment in existing research work. To demonstrate the functionality and applicability of LemgoRL, we train a state-of-the-art Deep RL algorithm on a CPU cluster utilizing a framework for distributed and parallel RL and compare its performance with other methods. Our benchmark tool drives the development of RL algorithms towards real-world applications.}}, author = {{Müller, Arthur and Rangras, Vishal and Ferfers, Tobias and Hufen, Florian and Schreckenberg, Lukas and Jasperneite, Jürgen and Schnittker, Georg and Waldmann, Michael and Friesen, Maxim and Wiering, Marco}}, booktitle = {{20th IEEE International Conference on Machine Learning and Applications (ICMLA)}}, editor = {{Wani, M. Arif and Sethi, Ishwar and Shi, Weisong and Qu, Guangzhi and Stan Raicu, Daniela and Jin, Ruoming }}, isbn = {{978-1-6654-4337-1}}, keywords = {{deep reinforcement learning, traffic signal control, intelligent transportation system, traffic simulation}}, location = {{Online}}, pages = {{507--514}}, publisher = {{IEEE}}, title = {{{Towards Real-World Deployment of Reinforcement Learning for Traffic Signal Control}}}, doi = {{10.1109/icmla52953.2021.00085}}, year = {{2022}}, } @misc{11803, abstract = {{Sub-optimal control policies in intersection traffic signal controllers (TSC) contribute to congestion and lead to negative effects on human health and the environment. Reinforcement learning (RL) for traffic signal control is a promising approach to design better control policies and has attracted considerable research interest in recent years. However, most work done in this area used simplified simulation environments of traffic scenarios to train RL-based TSC. To deploy RL in real-world traffic systems, the gap between simplified simulation environments and real-world applications has to be closed. Therefore, we propose LemgoRL, a benchmark tool to train RL agents as TSC in a realistic simulation environment of Lemgo, a medium-sized town in Germany. In addition to the realistic simulation model, LemgoRL encompasses a traffic signal logic unit that ensures compliance with all regulatory and safety requirements. LemgoRL offers the same interface as the well-known OpenAI gym toolkit to enable easy deployment in existing research work. To demonstrate the functionality and applicability of LemgoRL, we train a state-of-the-art Deep RL algorithm on a CPU cluster utilizing a framework for distributed and parallel RL and compare its performance with other methods. Our benchmark tool drives the development of RL algorithms towards real-world applications.}}, author = {{Müller, Arthur and Rangras, Vishal and Schnittker, Georg and Waldmann, Michael and Friesen, Maxim and Ferfers, Tobias and Schreckenberg, Lukas and Hufen, Florian and Jasperneite, Jürgen and Wiering, Marco}}, booktitle = {{20th IEEE International Conference on Machine Learning and Applications (ICMLA)}}, editor = {{Wani, M. Arif}}, keywords = {{deep reinforcement learning, traffic signal control, intelligent transportation system, traffic simulation}}, location = {{Pasadena, CA, USA }}, publisher = {{IEEE}}, title = {{{Towards Real-World Deployment of Reinforcement Learning for Traffic Signal Control}}}, doi = {{10.1109/ICMLA52953.2021.00085}}, year = {{2021}}, } @article{6689, abstract = {{Free amino nitrogen (FAN) concentrations in beer mash can be determined with machine learning algorithms from near-infrared (NIR) spectra. NIR spectroscopy is an alternative to a classical chemical analysis and allows for the application of inline process quality control. This study investigates the capabilities of different machine learning techniques such as Ordinary Least Squares (OLS) regression, Decision Tree Regressor (DTR), Bayesian Ridge Regression (BRR), Ridge Regression (RR), K-nearest neighbours (KNN) regression as well as Support Vector Regression (SVR) to predict the FAN content in beer mash from NIR spectra. Various pre-processing strategies such as principal component analysis (PCA) and data standardization were used to process NIR data that were used to train the machine learning algorithms. Algorithm training was conducted with NIR data obtained from 16 beer mashes with varying FAN concentrations. The trained models were then validated with 4 beer mashes that were not used for model training. Machine learning algorithms based on linear regression showed the highest prediction accuracy on unpre-processed data. BRR reached a root mean square error of calibration (RMSEC) of 2.58 mg/L (R2 = 0.96) and a prediction accuracy (RMSEP) of 2.81 mg/L (R2 = 0.96). The FAN concentration range of the investigated samples was between approx. 180 and 220 mg/L. Machine learning based NIR spectra analysis is an alternative to classical chemical FAN level determination methods and can also be used as inline sensor system.}}, author = {{Wefing, Patrick and Conradi, Florian and Rämisch, Johannes and Neubauer, Peter and Schneider, Jan}}, issn = {{0723-1520}}, journal = {{Brewing science }}, keywords = {{mashing, NIR, machine learning, FAN}}, number = {{9/10}}, pages = {{107 -- 121}}, publisher = {{Carl}}, title = {{{Determination of free amino nitrogen in beer mash with an inline NIR transflectance probe and data evaluation by machine learning algorithms}}}, doi = {{https://doi.org/10.23763/BrSc21-10wefing}}, volume = {{74}}, year = {{2021}}, } @misc{7519, abstract = {{Increasing consumer engagement is a cornerstone of companies' social media efforts. However, how social media brand engagement behavior affects brand performance remains largely unexplored. We capture engagement along two dimensions - volume and variety - and measure brand performance using consumers' brand attachment, attitudes, and purchase intentions. Based on the power law of practice and combining survey measures with social media data, our analyses reveal a diminishing marginal utility of engagement volume, as the positive impact of engagement behavior on brand outcomes declines at higher engagement levels. However, the variation across performed activities attenuates these diminishing returns on engagement volume. We find consistent evidence for these effects across two studies with 1347 consumers who interacted with different brands. The results question companies' often unidimensional focus on increasing engagement volume. Instead, our findings suggest that to maximize brand performance on social media platforms, companies should also encourage engagement variety.}}, author = {{Schäfers, Tobias and Falk, Tomas and Kumar, Ashish and Schamari, Julia}}, booktitle = {{Journal of Business Research}}, issn = {{1873-7978}}, keywords = {{Social media, Brand engagement, Diminishing marginal utility, Learning curve}}, pages = {{282--294}}, publisher = {{Elsevier}}, title = {{{More of the same? Effects of volume and variety of social media brand engagement behavior}}}, doi = {{10.1016/j.jbusres.2021.06.033}}, volume = {{135}}, year = {{2021}}, } @misc{12800, abstract = {{his paper presents the cognitive module of the Cognitive Architecture for Artificial Intelligence (CAAI) in cyber-physical production systems (CPPS). The goal of this architecture is to reduce the implementation effort of artificial intelligence (AI) algorithms in CPPS. Declarative user goals and the provided algorithm-knowledge base allow the dynamic pipeline orchestration and configuration. A big data platform (BDP) instantiates the pipelines and monitors the CPPS performance for further evaluation through the cognitive module. Thus, the cognitive module is able to select feasible and robust configurations for process pipelines in varying use cases. Furthermore, it automatically adapts the models and algorithms based on model quality and resource consumption. The cognitive module also instantiates additional pipelines to evaluate algorithms from different classes on test functions. CAAI relies on well-defined interfaces to enable the integration of additional modules and reduce implementation effort. Finally, an implementation based on Docker, Kubernetes, and Kafka for the virtualization and orchestration of the individual modules and as messaging technology for module communication is used to evaluate a real-world use case.}}, author = {{Strohschein, Jan and Fischbach, Andreas and Bunte, Andreas and Faeskorn-Woyke, Heide and Moriz, Natalia and Bartz-Beielstein, Thomas}}, booktitle = {{The International Journal of Advanced Manufacturing Technology}}, issn = {{1433-3015}}, keywords = {{Cognition, Industry 40, Big data platform, Machine learning, CPPS, Optimization, Algorithm selection, Simulation}}, number = {{11-12}}, pages = {{3513--3532}}, publisher = {{Springer }}, title = {{{Cognitive capabilities for the CAAI in cyber-physical production systems}}}, doi = {{10.1007/s00170-021-07248-3}}, volume = {{115}}, year = {{2021}}, } @inbook{11883, abstract = {{Zur Professionalisierung von Lehrkräften innerhalb der Lehramtsausbildung bedarf es neben fachlicher und didaktischer Elemente auch Wege, die Persönlichkeitsentwicklung der Studierenden zu fördern und die eigene Lehr-Lern-Haltung zu reflektieren (KMK, 2004). Der Einsatz von reflexiven (E-)Portfolios hat das Potenzial, diese Professionalisierung zu begleiten. Allerdings kann es hierbei für Lehrende zu einem Konflikt zwischen der Rolle als Praxisanleiter*in und der Rolle als Prüfer*in kommen, wenn das (E-)Portfolio sowohl Reflexions- als auch Dokumentationszwecken dient. An der Technischen Hochschule Ostwestfalen-Lippe, vormals Hochschule Ostwestfalen-Lippe, wird ein Konzept pilotiert, das diesen Zwiespalt löst, indem der Reflexionsteil innerhalb der Lehramtsausbildung durch Coaching begleitet wird. Der Beitrag soll das Konzept und die derzeitige Umsetzung vorstellen, einen Überblick über die Potenziale und Schwierigkeiten geben sowie den wichtigen Input diskutieren, den wir innerhalb unseres Workshops, aber auch im Nachgang bekommen haben. }}, author = {{Claes, Svenja and Fischer, Yvonne and Mertens, Claudia}}, booktitle = {{Hochschuldidaktik als Akteurin der Hochschulentwicklung}}, editor = {{Heuchemer, Sylvia and Szczyrba, Birgit and Treeck, Timo van}}, isbn = {{978-3-7639-6103-0}}, keywords = {{(E)Portfolio, Lehramtsausbildung, Professionalisierung, Rolle, Coaching}}, pages = {{207--213}}, publisher = {{wbv }}, title = {{{Das eCoFolio - ein reflexives E-Portfolio in der Lehramtsausbildung für Berufsschulen}}}, volume = {{136}}, year = {{2020}}, } @inproceedings{4097, abstract = {{The capabilities of object detection are well known, but many projects don’t use them, despite potential benefit. Even though the use of object detection algorithms is facilitated through frameworks and publications, a big issue is the creation of the necessary training data. To tackle this issue, this work shows the design and evaluation of a prototype, which allows users to create synthetic datasets for object detection in images. The prototype is evaluated using YOLOv3 as the underlying detector and shows that the generated datasets are equally good in quality as manually created data. This encourages a wide adoption of object detection algorithms in different areas, since image creation and labeling is often the most time consuming step.}}, author = {{Besginow, Andreas and Büttner, Sebastian and Röcker, Carsten}}, booktitle = {{22nd International Conference on Human-Computer Interaction}}, isbn = {{978-3-030-50343-7}}, keywords = {{Object detection, Synthetic datasets, Machine learning, Deep learning}}, location = {{Copenhagen, Denmark}}, pages = {{178--192}}, publisher = {{Springer}}, title = {{{Making Object Detection Available to Everyone - A Hardware Prototype for Semi-automatic Synthetic Data Generation}}}, doi = {{https://doi.org/10.1007/978-3-030-50344-4_14}}, volume = {{12203}}, year = {{2020}}, } @misc{4100, author = {{Schmohl, Tobias and Schwickert, Susanne and Glahn, Oliver}}, booktitle = {{The Future of Education}}, keywords = {{Artificial Intelligence, intelligent tutoring system, reflection, project-based learning, online-learning, interactive video}}, location = {{Florenz}}, pages = {{309--313}}, publisher = {{Libreriauniversitaria.it}}, title = {{{Conceptual Design of an AI-Based Learning Assistant }}}, doi = {{10.26352/E618_2384-9509}}, year = {{2020}}, } @misc{12807, abstract = {{Writing chorales in the style of Bach has been a music theory exercise for generations of music students. As such it is not surprising that automatic Bach chorale harmonization has been a topic in music technology for decades. We suggest several improvements to current neural network solutions based on musicological insights into human choral composition practices. Evaluations with expert listeners show that the generated chorales closely resemble Bach's harmonization style.}}, author = {{Leemhuis, Alexander and Waloschek, Simon and Hadjakos, Aristotelis}}, booktitle = {{Machine Learning and Knowledge Discovery in Databases : International Workshops of ECML PKDD 2019}}, editor = {{Cellier, Peggy and Driessens, Kurt}}, isbn = {{978-3-030-43886-9}}, issn = {{1865-0937}}, keywords = {{Bach chorale harmonization, Deep learning, Beam search}}, location = {{Würzburg}}, pages = {{462–469}}, publisher = {{Springer International Publishing}}, title = {{{Bacher than Bach? On Musicologically Informed AI-Based Bach Chorale Harmonization}}}, doi = {{10.1007/978-3-030-43887-6_39}}, volume = {{1168}}, year = {{2020}}, } @misc{12812, abstract = {{Discerning unexpected from expected data patterns is the key challenge of anomaly detection. Although a multitude of solutions has been applied to this modern Industry 4.0 problem, it remains an open research issue to identify the key characteristics subjacent to an anomaly, sc. generate hypothesis as to why they appear. In recent years, machine learning models have been regarded as universal solution for a wide range of problems. While most of them suffer from non-self-explanatory representations, Gaussian Processes (GPs) deliver interpretable and robust statistical data models, which are able to cope with unreliable, noisy, or partially missing data. Thus, we regard them as a suitable solution for detecting and appropriately representing anomalies and their respective characteristics. In this position paper, we discuss the problem of automatic and interpretable anomaly detection by means of GPs. That is, we elaborate on why GPs are well suited for anomaly detection and what the current challenges are when applying these probabilistic models to large-scale production data.}}, author = {{Berns, Fabian and Lange-Hegermann, Markus and Beecks, Christian}}, booktitle = {{ Proceedings of the International Conference on Innovative Intelligent Industrial Production and Logistics IN4PL - Volume 1}}, editor = {{Panetto, H. and Madani, K. and Smirnov, A.}}, isbn = {{978-989-758-476-3}}, keywords = {{Anomaly Detection, Gaussian Processes, Explainable Machine Learning, Industry 4.0}}, location = {{Budapest, HUNGARY}}, pages = {{87--92}}, publisher = {{SCITEPRESS - Science and Technology Publications}}, title = {{{Towards Gaussian Processes for Automatic and Interpretable Anomaly Detection in Industry 4.0}}}, doi = {{10.5220/0010130300870092}}, year = {{2020}}, } @misc{13641, abstract = {{The neuro-physiological response to stress has far-reaching implications for learning and memory processes. Here, we examined whether and how the stress-induced release of cortisol, following the socially-evaluated cold pressor test, influenced the acquisition of preferences in an evaluative conditioning (EC) procedure. We found that when the stressor preceded the evaluation phase, cortisol responders showed decreased evaluative conditioning effects. By contrast, impairing effects of a stressor-induced cortisol release before encoding were not found. Moreover, explicit memory was not found to be affected by the stressor or its timing. Implications of the timing-dependent effects of stress-induced cortisol release on EC and the relation between stress and associative memory are discussed.}}, author = {{Halbeisen, Georg and Buttlar, Benjamin and Kamp, Siri-Maria and Walther, Eva}}, booktitle = {{International Journal of Psychophysiology}}, issn = {{1872-7697}}, keywords = {{Affective learning, Socially-evaluated cold pressor test, Free salivary cortisol, Hypothalamus-pituitary-adrenal axis, Evaluative conditioning}}, pages = {{44--52}}, publisher = {{Elsevier BV}}, title = {{{The timing-dependent effects of stress-induced cortisol release on evaluative conditioning}}}, doi = {{10.1016/j.ijpsycho.2020.04.007}}, volume = {{152}}, year = {{2020}}, } @inproceedings{4102, abstract = {{Complexity is a fundamental part of product design and manufacturing today, owing to increased demands for customization and advances in digital design techniques. Assembling and repairing such an enormous variety of components means that workers are cognitively challenged, take longer to search for the relevant information and are prone to making mistakes. Although in recent years deep learning approaches to object recognition have seen rapid advances, the combined potential of deep learning and augmented reality in the industrial domain remains relatively under explored. In this paper we introduce AR-ProMO, a combined hardware/software solution that provides a generalizable assistance system for identifying mistakes during product assembly and repair.}}, author = {{Dhiman, Hitesh and Büttner, Sebastian and Röcker, Carsten and Reisch, Raphael}}, booktitle = {{Proceedings of the 31st Australian Conference on Human-Computer-Interaction (OzCHI'19) : 2nd Dec.-5th Dec. 2019, Perth/Fremantle, WA, Australia}}, isbn = {{978-1-4503-7696-9}}, keywords = {{Augmented Reality, Deep Learning}}, location = {{Perth/Fremantle, WA, Australia}}, pages = {{ 518–522}}, publisher = {{ACM}}, title = {{{Handling Work Complexity with AR/Deep Learning}}}, doi = {{10.1145/3369457.3370919}}, year = {{2019}}, } @inbook{4312, abstract = {{Computer-aided assistance systems are entering the world of work and production. Such systems utilize augmented- and virtual-reality for operator training and live guidance as well as mobile maintenance and support. This is particularly important in the modern production reality of ever-changing products and `lot size one' customization of production.This paper focuses on the application of machine learning approach to extend the functionality of assistance systems. Machine learning provides tools to analyse large amounts of data and extract meaningful information. The goal here is to recognize the movement of an operator which would enable automatic display of instructions relevant to them.We present the challenges facing machine learning applications in human-centered assistance systems and a framework to assess machine learning approaches feasible for this scenario. The approach is assessed on a historical data set and then deployed in a work station for live testing. The post-hoc, or historical, analysis yields promising results. The ad-hoc, or live, analysis is a complex task and the results are affected by multiple factors, most of which are introduced by the human influence.The contribution of this paper is an approach to adapt state- of-the-art machine learning to operator movement recognition with a special focus on approaches to spatial time series data pre-processing. Presented experiment results validate the approach and show that it performs well in a real-world scenario.}}, author = {{Fullen, Marta and Maier, Alexander and Nazarenko, Arthur and Jenderny, Sascha and Röcker, Carsten}}, booktitle = {{2019 IEEE 17th International Conference on Industrial Informatics (INDIN)}}, isbn = {{978-1-7281-2927-3}}, issn = {{2378-363X}}, keywords = {{augmented reality, computer based training, data handling, industrial training, learning (artificial intelligence), time series}}, location = {{Helsinki, Finland,}}, pages = {{296 -- 302}}, publisher = {{IEEE}}, title = {{{Machine Learning for Assistance Systems: Pattern-Based Approach to Online Step Recognition}}}, doi = {{10.1109/INDIN41052.2019.8972122}}, year = {{2019}}, } @inbook{4313, abstract = {{This paper reports on a study (N = 471) exploring the acceptance of video-based home monitoring systems as well as criteria influencing their acceptance. While most participants stated that they would home monitoring solutions under certain conditions, the majority of participants is rather reluctant to use systems that transmit visual and acoustical information to remote medical personnel. Besides age, most user characteristics, which played important roles in technology acceptance research for many years, do not appear to be decisive factors for the acceptance of electronic home-monitoring services.}}, author = {{Röcker, Carsten}}, booktitle = {{Intelligent Human Systems Integration 2019}}, editor = {{Karwowski, Waldemar and Ahram, Tareq}}, isbn = {{978-3-030-11050-5}}, keywords = {{Active assisted living, Electronic homecare, e-health : Video-based monitoring, Technology acceptance, User-centered design, Study}}, location = {{San Diego, California, USA}}, pages = {{551 -- 556}}, publisher = {{Springer}}, title = {{{Exploring the Acceptance of Video-Based Medical Support}}}, doi = {{10.1007/978-3-030-11051-2_83}}, volume = {{903}}, year = {{2019}}, } @inbook{6850, abstract = {{Dieser Beitrag betrachtet die Konzeption und den Einsatz von eTutorien im Rahmen der Hochschullehre. Dabei wird deutlich, dass eTutorien eine E-Learning-Maßnahme darstellen, die in einem bestimmten Kontext eingesetzt werden kann. Dozenten von digitalen Tutorien müssen sich dabei aber neuen Herausforderungen stellen. Das Fehlen von visueller oder akustischer Rückmeldung der Zuhörerschaft ist gewöhnungsbedürftig und muss über ein gut ausgewogenes akustisches Format mit visuellen Elementen kompensiert werden. eTutorien stellen damit eine sinnvolle Ergänzung des klassischen Tutoriums dar. Der Bedarf von nicht-digitalen Ergänzungsveranstaltungen wie z. B. Übungsgruppen und Präsenztutorien ist aber weiterhin gegeben. }}, author = {{von Blanckenburg, Korbinian and Knost, Eike}}, booktitle = {{Lehrexperimente der Hochschulbildung- Didaktische Innovationen aus den Fachdisziplinen}}, editor = {{Schmohl, Tobias and Schäffer, Dennis}}, isbn = {{978-3-7639-6114-6}}, keywords = {{E-Learning, Hochschule, Hochschullehre, Virtuelle Hochschule, Visuelles Medium, Lehrveranstaltung, Tutorium, Online-Angebot, Online-Kurs, Virtuelle Lehre, Digitale Medien, Interaktive Medien, Elektronische Medien, Ostwestfalen-Lippe, Deutschland}}, pages = {{41--46}}, publisher = {{wbv }}, title = {{{Einsatz von eTutorien als komplementäre Lehr- und Lernform}}}, doi = {{ 10.25656/01:18561}}, volume = {{2}}, year = {{2019}}, } @inproceedings{4327, abstract = {{In ever changing world, the industrial systems become more and more complex. Machine feedback in the form of alarms and notifications, due to its growing volume, becomes overwhelming for the operator. In addition, expectations in relation to system availability are growing as well. Therefore, there exists strong need for new solutions guaranteeing fast troubleshooting of problems that arise during system operation. The approach proposed in this study uses advantages of the Asset Administration Shell, machine learning, and human-machine interaction in order to create the assistance system which holistically addresses the issue of troubleshooting complex industrial systems.}}, author = {{Lang, Dorota and Wunderlich, Paul and Heinz, Mario and Wisniewski, Lukasz and Jasperneite, Jürgen and Niggemann, Oliver and Röcker, Carsten}}, booktitle = {{14th IEEE International Workshop on Factory Communication Systems (WFCS)}}, keywords = {{Maintenance engineering, Adaptation models, Machine learning, Data models, Standards, Software, Bayes methods}}, location = {{Imperia, Italy }}, publisher = {{IEEE}}, title = {{{Assistance System to Support Troubleshooting of Complex Industrial Systems}}}, doi = {{10.1109/WFCS.2018.8402380}}, year = {{2018}}, } @misc{9650, abstract = {{In Germany, there is much academic discourse on and scientific inquiry into pedagogical issues of science teaching and learning at the school level. Concepts like ‘Bildung’ (inquiry-based self-formation) or ‘Didaktik’ (instruction-based reflections on teaching) are almost directly associated with institutions or actors rooted in pedagogical departments. Unfortunately, those departments rarely focus on issues of science teaching and learning at the University level – and if they do so, they most often try to apply conceptions and models borrowed from upper or post-secondary education. The few research-based institutions that address specific issues of higher education are commonly fitted out so that they are nowhere near the impacts of research institutions covering teaching methodology in primary or secondary education, for example. Yet from an international perspective, the university as an institution does hold a great potential to improve educational practice in a systematic, cross-disciplinary and research-based way. Around the globe, more and more institutions rely on the notion of scholarship in this context: ‘The improvement of learning and teaching is dependent upon the development of scholarship and research in teaching’ (Prosser & Trigwell, 1999, p. 8). If incorporated at the heart of tertiary education, scholarship could contribute to develop new faculty in the German higher-educational sector. }}, author = {{Schmohl, Tobias}}, booktitle = {{ International Conference New Perspectives in Science Education }}, keywords = {{Scholarship of Teaching and Learning, Scholarship of Academic Development, Higher Education, community building}}, location = {{Florence, Italy}}, publisher = {{libreriauniversitaria.it edizioni}}, title = {{{Towards a New Scholarship of German Science Education}}}, year = {{2018}}, } @inproceedings{4254, abstract = {{The current trend of integrating machines and factories into cyber-physical systems (CPS) creates an enormous complexity for operators of such systems. Especially the search for the root cause of cascading failures becomes highly time-consuming. Within this paper, we address the question on how to help human users to better and faster understand root causes of such situations. We propose a concept of interactive alarm flood reduction and present the implementation of a first vertical prototype for such a system. We consider this prototype as a first artifact to be discussed by the research community and aim towards an incremental further development of the system in order to support humans in complex error situations.}}, author = {{Büttner, Sebastian and Wunderlich, Paul and Heinz, Mario and Niggemann, Oliver and Röcker, Carsten}}, booktitle = {{ Machine Learning and Knowledge Extraction : First IFIP TC 5, WG 8.4, 8.9, 12.9 International Cross-Domain Conference, CD-MAKE 2017, Reggio, Italy, August 29 – September 1, 2017, Proceedings}}, editor = {{Holzinger, Andreas}}, isbn = {{978-3-319-66807-9}}, keywords = {{Alarm flood reduction, Machine learning, Assistive system}}, location = {{Reggio, Italy}}, pages = {{69--82}}, publisher = {{Springer}}, title = {{{Managing Complexity: Towards Intelligent Error-Handling Assistance Trough Interactive Alarm Flood Reduction}}}, volume = {{10410}}, year = {{2017}}, } @misc{811, author = {{Böhl, Freda}}, keywords = {{E-Learning, eLearning}}, pages = {{60}}, publisher = {{Hochschule Ostwestfalen-Lippe}}, title = {{{eLearning in der Hochschullehre: Entwicklung eines Leitfadens für den Studiengang Medienproduktion}}}, year = {{2017}}, } @misc{7592, author = {{Schmohl, Tobias}}, booktitle = {{The Future of Education}}, isbn = {{ ‎ 978-8862928687}}, keywords = {{Scholarship of Academic Development, Scholarship of Teaching and Learning}}, location = {{Florenz}}, pages = {{317--321}}, publisher = {{Libreriauniversitaria.it}}, title = {{{The research—education nexus: Basic premises and practical application of the "Scholarship" movement}}}, volume = {{7}}, year = {{2017}}, } @inbook{4298, abstract = {{In this paper, we present the current state-of-the-art of decision making (DM) and machine learning (ML) and bridge the two research domains to create an integrated approach of complex problem solving based on human and computational agents. We present a novel classification of ML, emphasizing the human-in-the-loop in interactive ML (iML) and more specific on collaborative interactive ML (ciML), which we understand as a deep integrated version of iML, where humans and algorithms work hand in hand to solve complex problems. Both humans and computers have specific strengths and weaknesses and integrating humans into machine learning processes might be a very efficient way for tackling problems. This approach bears immense research potential for various domains, e.g., in health informatics or in industrial applications. We outline open questions and name future challenges that have to be addressed by the research community to enable the use of collaborative interactive machine learning for problem solving in a large scale.}}, author = {{Robert, Sebastian and Büttner, Sebastian and Röcker, Carsten and Holzinger, Andreas}}, booktitle = {{Machine Learning for Health Informatics : State-of-the-Art and Future Challenges }}, editor = {{Holzinger, Andreas}}, isbn = {{978-3-319-50477-3 }}, keywords = {{Decision making, Reasoning, Interactive machine learning, Collaborative interactive machine learning}}, pages = {{357--376}}, publisher = {{Springer}}, title = {{{Reasoning Under Uncertainty: Towards Collaborative Interactive Machine Learning}}}, doi = {{10.1007/978-3-319-50478-0_18}}, volume = {{9605}}, year = {{2016}}, } @book{4336, abstract = {{Prolonged life expectancy along with the increasing complexity of medicine and health services raises health costs worldwide dramatically. Whilst the smart health concept has much potential to support the concept of the emerging P4-medicine (preventive, participatory, predictive, and personalized), such high-tech medicine produces large amounts of high-dimensional, weakly-structured data sets and massive amounts of unstructured information. All these technological approaches along with “big data” are turning the medical sciences into a data-intensive science. To keep pace with the growing amounts of complex data, smart hospital approaches are a commandment of the future, necessitating context aware computing along with advanced interaction paradigms in new physical-digital ecosystems. The very successful synergistic combination of methodologies and approaches from Human-Computer Interaction (HCI) and Knowledge Discovery and Data Mining (KDD) offers ideal conditions for the vision to support human intelligence with machine learning. The papers selected for this volume focus on hot topics in smart health; they discuss open problems and future challenges in order to provide a research agenda to stimulate further research and progress.}}, editor = {{Holzinger, Andreas and Röcker, Carsten and Ziefle, Martina}}, isbn = {{978-3-319-16225-6}}, issn = {{1611-3349}}, keywords = {{HCI, ambient assisted living, big data, computational intelligence, context awareness, data centric medicine, decision support, interactive data mining, keyword detection, knoweldge bases, knoweldge discovery, machine learning, medical decision support, medical informatics, natural language processing, pervasive health, smart home, ubiquitous computing, visualization, wearable sensors}}, pages = {{275}}, publisher = {{Springer}}, title = {{{Smart Health: Open Problems and Future Challenges}}}, doi = {{10.1007/978-3-319-16226-3}}, volume = {{8700}}, year = {{2015}}, } @inproceedings{2167, abstract = {{Cyber-Physical Production Systems (CPPSs) are in the focus of research, industry and politics: By applying new IT and new computer science solutions, production systems will become more adaptable, more resource ef- ficient and more user friendly. The analysis and diagnosis of such systems is a major part of this trend: Plants should detect automatically wear, faults and suboptimal configurations. This paper reflects the current state-of- the-art in diagnosis against the requirements of CPPSs, identifies three main gaps and gives application scenarios to outline first ideas for potential solutions to close these gaps. }}, author = {{Niggemann, Oliver and Lohweg, Volker}}, booktitle = {{Twenty-Ninth Conference on Artificial Intelligence (AAAI-15)}}, keywords = {{Cyber-Physical Systems, Machine Learning, Diagnosis, Anomaly Detection}}, title = {{{On the Diagnosis of Cyber-Physical Production Systems - State-of-the-Art and Research Agenda}}}, year = {{2015}}, } @inbook{4375, abstract = {{This chapter starts with an overview of the technical innovations and societal transformation processes we have seen in the last decades and as well as the consequences those changes have for the design of pervasive healthcare systems. Based on this theoretical foundation, emerging design requirements and research challenges are outlined, which are crucial to be addressed when developing future health technologies.}}, author = {{Röcker, Carsten and Ziefle, Martina and Holzinger, Andreas}}, booktitle = {{Pervasive Health}}, editor = {{Holzinger, Andreas and Ziefle, Martina and Röcker, Carsten}}, isbn = {{978-1-4471-6412-8}}, issn = {{1571-5035}}, keywords = {{Pervasive health, Ambient assisted living, E-Health, Trends, Research challenges, Design requirements}}, pages = {{1 -- 17}}, publisher = {{Springer}}, title = {{{From Computer Innovation to Human Integration: Current Trends and Challenges for Pervasive Health Technologies}}}, doi = {{10.1007/978-1-4471-6413-5_1}}, year = {{2014}}, } @article{4377, abstract = {{Within the last years the concept of trust has attracted increased attention in the field of smart home environments. However, little is known about what determines trustworthiness in this context. For this reason the objective was to examine mental models in terms of anthropomorphic perception of smart home environments and its relation to trustworthiness. Two studies (N=36) were carried out in the Future Care Lab, a simulated intelligent home environment. We used the teach-back method to help participants to talk about the smart home environment technology and asked to generate a metaphor of an experienced home-monitoring scenario. Finally, we applied linguistic analysis of responses to detect anthropomorphic characteristics. In general, results demonstrate inspiring metaphors related to the personal assistance system, e.g. "like an airbag…" or "like a family member…", which might be useful for future interface designs and approaches of communication in the context of smart home environments. However, no relation of anthropomorphism and trustworthiness could be found. Therefore, we suggest an anthropomorphic threshold, which should be investigated by using an improved method and trust scale.}}, author = {{Sack, Oliver and Röcker, Carsten}}, issn = {{2368-6103}}, journal = {{International Journal of Virtual Worlds and Human Computer Interaction}}, keywords = {{Smart environment, e-health, user study, mental model, anthropomorphism, metaphor, technology acceptance, trust, evaluation}}, number = {{1}}, pages = {{28 -- 36}}, publisher = {{ Avestia Publishing, International ASET Inc. }}, title = {{{“Like a Family Member Who Takes Care of Me” – Users’ Anthropomorphic Representations and Trustworthiness of Smart Home Environments}}}, doi = {{10.11159/vwhci.2014.004}}, volume = {{2}}, year = {{2014}}, } @article{4384, abstract = {{The number of elderly people requiring long-term care is rising every year. In this context, intelligent environments are often cited as a promising solution for providing personalized medical support in domestic spaces. This paper provides an overview over the most influential approaches in the area of intelligent environments and discusses the problems that might arise through computer-supported care concepts.}}, author = {{Röcker, Carsten}}, issn = {{2010-0248 }}, journal = {{International Journal of Innovation, Management and Technology : IJIMT}}, keywords = {{Intelligent environments, ambient assisted living, e-health, user-centered design.}}, number = {{1}}, pages = {{76 -- 79}}, publisher = {{International Association of Computer Science and Information Technology Press }}, title = {{{Intelligent Environments as a Promising Solution for Addressing Current Demographic Changes}}}, doi = {{10.7763/IJIMT.2013.V4.361 }}, volume = {{4}}, year = {{2013}}, } @article{4391, abstract = {{This paper presents a discussion of current developments in the field of smart medical services. Smart medical services are often cited as a promising solution to support elderly or disabled people. By providing a wide variety of services, they bear an immense potential for revolutionizing the way health services are provided in the future. In general, smart medical services can be clustered into three categories focusing on the detection and prevention of emergency situations, long-term treatment of chronic diseases, and the prevention and early-detection of illnesses. This paper provides an overview over the different types of applications and describes several research demonstrators and prototype systems for each category.}}, author = {{Röcker, Carsten}}, issn = {{2010-3700}}, journal = {{International Journal of Machine Learning and Computing : IJMLC}}, keywords = {{Smart medical services, ambient assisted living, E-healt, intelligent environments, ubiquitous and pervasive computing.}}, number = {{3}}, pages = {{226 -- 230}}, title = {{{Smart Medical Services: A Discussion of State-of-The-Art Approaches}}}, doi = {{10.7763/IJMLC.2012.V2.119 }}, volume = {{2}}, year = {{2012}}, } @inproceedings{4393, abstract = {{Research in the field of technology-supported personal care gained considerable momentum over the last 10 to 15 years. This paper provides a comprehensive overview over state-of-the-art research activities in this field by illustrating major projects and research initiatives as well as highlighting successful approaches to Ambient Assisted Living.}}, author = {{Röcker, Carsten and Ziefle, Martina}}, booktitle = {{2012 International Conference on Future Information Technology and Management Science & Engineering ; Lectute notes in information technology : (LNIT)}}, keywords = {{Smart medical services, ambient assisted living, E-healt, intelligent environments, ubiquitous and pervasive computing}}, location = {{Hong Kong}}, number = {{14}}, pages = {{6 -- 14}}, title = {{{Current Approaches to Ambient Assisted Living}}}, year = {{2012}}, } @inproceedings{4485, abstract = {{Research in the field of Ambient Assisted Living gained considerable momentum over the last decade and the diversity of existing applications is matched by a broad variety of implementation approaches. This paper takes a closer look at existing work in this field and provides a structured overview over state-of-the-art implementation concepts.}}, author = {{Röcker, Carsten}}, booktitle = {{Modeling, Simulation and Control}}, editor = {{Chunxiao, X.}}, keywords = {{Smart medical services, ambient assisted living, E-healt, intelligent environments, ubiquitous and pervasive computing}}, location = {{Singapore}}, pages = {{167--172}}, title = {{{Designing Ambient Assisted Living Applications: An Overview of State-of-the-Art Implementation Concepts}}}, year = {{2011}}, } @misc{9856, abstract = {{According to the Bologna Accord in 2006 the study courses for architecture, urban planning and landscape planning at Kassel university were reformed to a bachelor and master education programme. New courses – so called “modules” were found. One of them “Wahrnehmung und Analyse von Räumen” – “landscape perception and analysis” – is an interdisciplinary course teaching and comparing three different perspectives – those of ecology, social science and landscape planning – on landscape. To manage a high number of students the e-learning platform “Moodle” is used. Also giving an introduction into GIS is a major part of the course. This article – after “landscape perception and analysis” started four years ago – gives an overview of the recent and future development of the course from a teachers perspective.}}, author = {{Leiner, Claas and Stemmer, Boris}}, booktitle = {{gis.Science}}, issn = {{2698-4571}}, keywords = {{Universitarian teaching, GIS, e-learning, bologna process}}, number = {{4}}, pages = {{105–110}}, publisher = {{Wichmann}}, title = {{{Teaching Landscape Planning - Landscape Perception and Analysis}}}, year = {{2011}}, } @inproceedings{2087, abstract = {{It is likely in real-world applications that only little data isavailable for training a knowledge-based system. We present a method forautomatically training the knowledge-representing membership functionsof a Fuzzy-Pattern-Classification system that works also when only littledata is available and the universal set is described insufficiently. Actually,this paper presents how the Modified-Fuzzy-Pattern-Classifier’s member-ship functions are trained using probability distribution functions.}}, author = {{Mönks, Uwe and Lohweg, Volker and Petker, Denis}}, booktitle = {{IPMU 2010 - International Conference on Information Processing and Management of Uncertainty in Knowledge Based Systems}}, keywords = {{Fuzzy Logic, Probability Theory, Fuzzy-Pattern-Classification, Machine Learning, Artificial Intelligence, Pattern Recognition}}, publisher = {{28 Jun 2010 - 02 July 2010, Dortmund, Germany}}, title = {{{Fuzzy-Pattern-Classifier Training with Small Data Sets}}}, year = {{2010}}, } @inproceedings{6356, author = {{Czwalinna, R. and Wilhelm, Patrick and Lehre, Gerhard and Müller, Ulrich}}, keywords = {{GDL e. V., Bonn, (ISDN 3-931678-04-0)}}, location = {{Berlin}}, title = {{{Vergleich zweier Gefrierverfahren von Stutenmilch hinsichtlich der anschließenden Vakuumgefriertrocknung, Kurzfassung}}}, year = {{2001}}, }