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Best Practices in Chatbot Coaching—Insights into Research and Development of the StudiCoachBot at TH Köln and into the Coaching Chatbot Platform Evoach
Get PDFDer Beitrag stellt zwei Best Practices im Chatbot Coaching vor. Der erste Teil gibt Einblicke in die Entwicklung des StudiCoachBots der TH Köln, der Reflexionsprozesse bei Studierenden zu Prüfungsangst anregt. Er fasst zentrale Forschungsergebnisse zur Beziehungsgestaltung zusammen, die Hinweise auf beziehungsbildende Faktoren im Chatbot Coaching geben (technische Funktionalität, Disclosure-Verhalten, Interaktionsmethode). Der zweite Teil stellt das Startup evoach vor – eine Coaching Plattform, die hybrides Coaching mit maßgeschneiderten Chatbots anbietet – und beschreibt anhand konkreter Anwendungsfälle, wie Chatbots in Kombination mit persönlichem Coaching den Coachingprozess bereichern.This paper presents two best practices in chatbot coaching. The first part provides insights into the development of the StudiCoachBot at TH Köln/University of Applied Sciences, which stimulates reflection processes among students on the topic of exam anxiety. It summarizes research results on relationship building, which give hints on relationship building factors in chatbot coaching (technical functionality, disclosure behavior, interaction method). The second part introduces the startup evoach—a coaching platform offering hybrid coaching with customized chatbots—and describes for specific use cases how chatbots in combination with personal coaching enrich the coaching process
Analysis of Heat Transfer Enhancement due to Helical Static Mixing Elements Inside Cooling Channels in Machine Tools
Get PDFIn this contribution, the effectiveness of helical static mixers in different arrangements and flow configurations/regimes is explored. By means of a thorough numerical analysis, the application limits of helical static mixers for the heat transfer enhancement inside cooling channels of machine tools are provided. The numerical simulations were processed with the commercial finite volume Computational Fluid Dynamics (CFD) code, ANSYS Fluent 2020 R2. This study shows that there exists an optimal range of application for static mixers as heat exchange intensifier depending on the flow speed, the transmitted heat flow and the thermal conductivity of the tool. The investigations of this contribution are restricted to single-phase flow in circular cross-sections and straight channel geometries. As a representative application example for a machine tooling, the cooling of a simple injection mold is investigated. The research carried out reveals that the application of static mixing elements for enhancement of heat transfer is very effective, particularly for fluid flow with low to medium Reynolds numbers, close-contour cooling, high values of heat fluxes, and high thermal conductivity of the tooling material
The Wisdom of Crowds for Improved Disaster Resilience: A Near-Real-Time Analysis of Crowdsourced Social Media Data on the 2021 Flood in Germany
Get PDFTransformative disaster resilience in times of climate change underscores the importance of reflexive governance, facilitation of socio-technical advancement, co-creation of knowledge, and innovative and bottom-up approaches. However, implementing these capacity-building processes by relying on census-based datasets and nomothetic (or top-down) approaches remains challenging for many jurisdictions. Web 2.0 knowledge sharing via online social networks, whereas, provides a unique opportunity and valuable data sources to complement existing approaches, understand dynamics within large communities of individuals, and incorporate collective intelligence into disaster resilience studies. Using Twitter data (passive crowdsourcing) and an online survey, this study draws on the wisdom of crowds and public judgment in near-real-time disaster phases when the flood disaster hit Germany in July 2021. Latent Dirichlet Allocation, an unsupervised machine learning technique for Topic Modeling, was applied to the corpora of two data sources to identify topics associated with different disaster phases. In addition to semantic (textual) analysis, spatiotemporal patterns of online disaster communication were analyzed to determine the contribution patterns associated with the affected areas. Finally, the extracted topics discussed online were compiled into five themes related to disaster resilience capacities (preventive, anticipative, absorptive, adaptive, and transformative). The near-real-time collective sensing approach reflected optimized diversity and a spectrum of people’s experiences and knowledge regarding flooding disasters and highlighted communities’ sociocultural characteristics. This bottom-up approach could be an innovative alternative to traditional participatory techniques of organizing meetings and workshops for situational analysis and timely unfolding of such events at a fraction of the cost to inform disaster resilience initiatives
A Comparative Study on Recent Progress of Machine Learning-Based Human Activity Recognition with Radar
Get PDFThe importance of radar-based human activity recognition has increased significantly over the last two decades in safety and smart surveillance applications due to its superiority in vision-based sensing in the presence of poor environmental conditions like low illumination, increased radiative heat, occlusion, and fog. Increased public sensitivity to privacy protection and the progress of cost-effective manufacturing have led to higher acceptance and distribution of this technology. Deep learning approaches have proven that manual feature extraction that relies heavily on process knowledge can be avoided due to its hierarchical, non-descriptive nature. On the other hand, ML techniques based on manual feature extraction provide a robust, yet empirical-based approach, where the computational effort is comparatively low. This review outlines the basics of classical ML- and DL-based human activity recognition and its advances, taking the recent progress in both categories into account. For every category, state-of-the-art methods are introduced, briefly explained, and their related works summarized. A comparative study is performed to evaluate the performance and computational effort based on a benchmarking dataset to provide a common basis for the assessment of the techniques’ degrees of suitability
Agile Work Organisation in German Start-Ups: Exploration of Deregulation of Employment
Get PDFStart-ups operate in dynamic seed stage, start-up stage and growth stage in an uncertain and volatile environment. An analysis of 59 start-ups shows that companies have special characteristics in terms of the organisational characteristics of employer attractiveness and flexible work organisation. The effects of the two organisational characteristics on an agile workforce are proven by a literature study. The study concludes with a theoretical-conceptual model that illustrates the factors influencing employer attractiveness and flexible work organisation. The results of the survey are brought together with the current state of literature and an approach to organisational agility is developed that takes deregulation tendencies into account
Exploring the Influencing Factors on User Experience in Robot-Assisted Health Monitoring Systems Combining Subjective and Objective Health Data
Get PDFAs the population ages, the demand for care for older adults is increasing. To maintain their independence and autonomy, even with declining health, assistive technologies such as connected medical devices or social robots can be useful. In previous work, we introduced a novel health monitoring system that combines commercially available products with apps designed specifically for older adults. The system is intended for the long-term collection of subjective and objective health data. In this work, we present an exploratory user experience (UX) and usability study we conducted with older adults as the target group of the system and with younger expert users who tested our msystem. All participants interacted with a social robot conducting a health assessment and tested sensing devices and an app for data visualization. The UX and usability of the individual components of the system were rated highly in questionnaires in all sessions. All participants also said they would use such a system in their everyday lives, demonstrating the potential of these systems for self-managing users’ health. Finally, we found factors such as previous experience with social robots and technological expertise to have an influence on the reported UX of the users
Ground Tire Rubber Particles as Substitute for Calcium Carbonate in an EPDM Sealing Compound
Get PDFGround tire rubber (GTR) is a product obtained by grinding worn tire treads before retreading them or via the cryogenic or ambient temperature milling of end-of-life tires (ELTs). The aim of this study is to evaluate if calcium carbonate can be substituted by GTR and, if so, to what extent. Different types of ground tire rubber are incorporated in an EPDM (ethylene–propylene–diene–rubber) model compound as partial or complete substitutes of calcium carbonate. The raw compounds and the vulcanizates are characterized to identify the limits. In general, it is apparent that increasing amounts of GTR and larger particles degrade the mechanical properties. The GTR also influences the vulcanization kinetics by reducing the scorch time up to 50% and vulcanization time up to nearly 80%. This is significant for production processes. The compounds with one-third substitution with the smaller-particle-size GTR show mostly similar or even better properties than the reference
Evaluation künstlicher neuronaler Netze für eine Out-of-Stock-Erkennung
Get PDFAs a customer, it can be frustrating to face an empty shelf in a store. The market does not always realize that a product has been out of stock for a while, as the item is still listed as in stock in the inventory management system. To address this issue, a camera should be used to check for Out-of-Stock (OOS) situations.
This master thesis evaluates different model configurations of Artificial Neural Networks (ANNs) to determine which one best detects OOS situations in the market using images. To create a dataset, 2,712 photos were taken in six stores. The photos clearly show whether there is a gap on the shelf or if the product is in stock. Based on the pre-trained VGG16 model from Keras, two fully connected layers were implemented, with 36 different ANNs differing in the optimization method and activation function pairings. In total, 216 models were generated in this thesis to investigate the effects of three different optimization methods combined with twelve different activation function pairings. An almost balanced ratio of OOS and in-stock data was used to generate these models.
The evaluation of the generated OOS models shows that the FTRL optimization method achieved the least favorable results and is therefore not suitable for this application. Model configurations using the Adam or SGD optimization methods achieve much better results. Of the top six model configurations, five use the Adam optimization method and one uses SGD. They all achieved an accuracy of at least 93% and were able to predict the Recall for the OOS class with at least 91%.
As the data ratio between OOS and in-stock data did not correspond to reality in the previously generated models, the in-stock images were augmented. Including the augmented images, new OOS models were generated for the top six model configurations. The results of these OOS models show no convergences. This suggests that more epochs in the training phase lead to better results. However, the results of the OOS model using the Adam optimization method and the Sigmoid and ReLU activation functions stand out positively. It achieved the best result with an accuracy of 97.91% and a Recall of the OOS class of 87.82%.
Overall, several OOS models have the potential to increase both market sales and customer satisfaction. In a future study, the OOS models should be installed in the market to evaluate their performance under real conditions. The resulting insights can be used for continuous optimization of the model
Captives: Bedeutung durch Marktverhärtung in der Rückversicherung
Get PDFDie Forschungsarbeit untersucht den sich verhärtenden Rückversicherungsmarkt und die Relevanz von Captives für Industrieunternehmen. Angesichts von Marktveränderungen wie steigender Inflation, Währungsschwankungen und höheren Schadensbelastungen überdenken Unternehmen ihre Risikomanagementansätze. Captives, insbesondere für mittelständische Unternehmen, gewinnen an Bedeutung. Sie bieten direkten Zugang zum Rückversicherungsmarkt, Unabhängigkeit und flexible Gestaltungsmöglichkeiten. Trotz Vorteilen wie optimierten Deckungsstrukturen und Umgehung von Marktveränderungen existieren Herausforderungen, darunter Kapitalanforderungen und Betriebskosten. Der Trend zu neuen Captives im harten Rückversicherungsmarkt ist nicht eindeutig, aber sie bleiben eine innovative Lösung für das betriebliche Risikomanagement. Traditionelle Rückversicherer bleiben entscheidend für die Branchenstabilität
Katastrophenversicherung ohne Prämienzahlung – Das Konzept der Eventualverpflichtung in der Schweiz
Get PDFSeit den Starkniederschlägen 2021 wird in Deutschland über die Einführung einer Pflichtversicherung für Naturgefahren diskutiert. In der Schweiz besteht bereits eine Pflichtversicherung, ausgenommen für Erdbeben. Ein neuer Vorstoß plant eine "Eventualverpflichtung" für Erdbeben, bei der keine laufende Prämienzahlung erforderlich ist. Im Schadensfall tragen alle Gebäudeeigentümer zur Deckung bei. Dieser Ansatz zielt darauf ab, 99,5 Prozent der Gebäude gegen Erdbeben zu versichern. Der Plan sieht eine Prämienrate von 0,7%, Selbstbehalt von 5%, und eine Deckung von Schäden, die nur alle 500 Jahre auftreten. Während das Konzept Vorteile bietet, wie breite Abdeckung und staatsunabhängige Finanzierung, gibt es auch Bedenken hinsichtlich finanzieller Mittel und individueller Risikobetrachtung