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- IBW - Internationale Betriebswirtschaft (89)
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- MBA - International Business Management (1)
This paper investigates the influence of manipulated attention on the perceived severity of financial risks. Participants undertake an online survey where they get exposed to images of various financial risk sources, while their attention is directed towards one of five available visual images of financial risk hazards.
Beforehand, they are instructed to remember and repeat the respective labeling of all pictures. It is ensured that the attended picture is randomly chosen, and the participants are aware of the corresponding names. Subsequently, participants are asked to rate the risk on four related characteristics.
Considering the responses of 292 people, only the numerical values indicate differences between attended and unattended risks, but the applied statistical measures provide no evidence for this hypothesis.
Eine Sonderpublikation von Bild der Wissenschaft in Kooperation mit der Hochschule Furtwangen.
Inhalt:
S. 4 Eine Hochschule auf Hochtouren
S. 8 Ziselieren mit Licht
S. 12 Bioaktiv und knochenhart
S. 16 "Im Fokus: die Megatrends der Medizintechnik"
S. 19 Smarter Schraubendreher
S. 20 Sensoren mit Gefühl
S. 24 Der vernetzte OP-Saal
S. 26 Die Augen der Künstlichen Intelligenz
S. 28 Kampf den Keimen
S. 32 Gedruckte Implantate
S. 35 Impressum/Bildnachweise
This paper reflects the topic of risk regarding an individual’s propensity to engage in risky behavior. Therefore, a survey was conducted among four different cultures to study cultural effects on risk-taking behavior. These contained Germany, France, China, and Latin America with a total sample size of 655. The applied questionnaire covered seven different domains regarding social, recreational, health/safety, ethical, investment, gambling, and business risk for which the participants had to assess the likelihood to engage in theoretical situations on a scale of 1 to 6. Significant cultural differences, as well as domain-specificity, could be confirmed using several one-way ANOVAs for the statistical evaluation. In this case, in comparison with the other cultures, Germany showed the highest risk propensity in the social domain, France for recreational and ethical risk, Latin America in the business as well as health/safety sector, and China in the investment and gambling domain.
One size fits all? A multicultural marketing perspective on the U.S. market for luxury watches
(2021)
The purchase of luxury goods like mechanical watches is experiencing steady growth and it is increasingly shifting to e-commerce platforms, also due to the COVID-19 pandemic.
Nevertheless, there are culture-related different motivations to purchase those items. One size fits all? According to this question it was investigated if there can be several cultures detected and if they show a different purchase behavior along the customer journey that can be explained with one of the six dimensions of Geert Hofstede (2001). Therefore, several hypotheses along the customer journey are set to find out if there are still ethnical backgrounds that have an influence on the buying process of the U.S. customers. The focus for this research lies on Asian Americans and Hispanics in comparison with the 'Rest of the USA', including ethnic groups like White Caucasians, Afro Americans, and many more.
In order to answer the research question, a quantitative study was deducted with the help of Google Analytics. It provided several Key Performance Indicators like for example sessions, bounce rates, session durations, purchased brands, and many more on Chrono24’s marketplace in the USA.
The study shows that the observed cultures in fact have different characteristics along the customer journey, but not every hypothesis could be explained conformingly with one of Hofstede’s dimensions. Consequently, a one size fits all customer journey does not exist. Additionally, the work suggests how further research and cultural adaptions can lead to optimized customer journeys on a marketplace.
Durch die Coronapandemie wurden die kleinen und mittelständischen Unternehmen (KMU) in eine schwere finanzielle Lage gebracht. Die von Bund beschlossenen Coronahilfen sollten den angeschlagenen Unternehmen helfen, ihre Umsatzeinbußen auszugleichen. Durch eine Umfrage bei KMU und Experteninterviews wurde die Situation der Unternehmen im Bezug auf die Hilfen betrachtet und neue Lösungsvorschläge erarbeitet.
Demografische Entwicklungen, wie der zukünftige Nachwuchskräftemangel aufgrund einer alternden Gesellschaft und dem Geburtenrückgang, stellen Unternehmen zunehmend vor neue Herausforderungen. Die Personalentwicklung im Unternehmen muss, auf die aus den verschiedenen Entwicklungen resultierenden Bedürfnissen der einzelnen Mitarbeitenden in einzelnen Lebensphasen angepasst werden.
Daraus ergibt sich folgende Forschungsfrage: Wie kann Maxon seine Personalentwicklung, unter Berücksichtigung der Ziele und Ressourcen des Unternehmens, anpassen, sodass die Bedürfnisse der Mitarbeitenden in ihren Lebensphasen befriedigt werden und gleichermaßen attraktive Angebote für jede Generation verfügbar sind?
Um diese Forschungsfrage zu beantworten, wurden qualitative Interviews mit Mitarbeitenden des Unternehmens durchgeführt. 16 Mitarbeitende aus dem technischen und kaufmännischen Bereich wurden zu den Themen Lebens- und Berufsphasen, Personalentwicklung, sowie zwei weiteren Benefits, betriebliches Gesundheitsmanagement und Sozialleistungen, befragt. Die Interviews geben Aufschluss über die Bedürfnisse der befragten Personen in den unterschiedlichen Generationen und Lebensphasen. Es geht hervor, dass vor allem Flexibilität in der Einteilung der Arbeitszeit eine große Rolle spielt. Dies trifft vor allem auf die befragten Personen, in der Lebensphase Elternzeit, Pflege oder selbst initiierte Weiterbildungen, zu. Auch der Wunsch nach einer flexibleren Gestaltung der Personalentwicklung wird geäußert. Dies kann vor allem durch Online-Weiterbildungen realisiert werden, die rundum positiv bewertet werden. Persönlichkeitsentwicklung und mentale Gesundheit haben vor allem bei der Generation X und Y einen hohen Stellenwert. Diese Generationen äußern das Bedürfnis, diese Themen im Rahmen der Personalentwicklung intensiver zu behandeln, um Stress und Burnout vorzubeugen. Die befragten Personen der Generation 50+ wünschen sich mehr Personalentwicklungsangebote für ihre Altersgruppe. Bei dem Umgang mit digitalen Medien ist bei dieser Generation ebenso Entwicklungsbedarf festgestellt worden.
The Elephant in the Room - Educating Practitioners on Software Development for Sustainability
(2021)
Umsetzung eines Pay-as-you-Live Systems : Konzeptioneller Ansatz für ein eigenständiges PAYL-System
(2021)
Abrasive tools
(2021)
Abrasives
(2021)
In dem vorliegenden Beitrag wird der aktuelle Trend, Payas-you-live-Systeme (PAYL) in Verbindung mit Krankenversicherungen anzubieten, analysiert. PAYL-Systeme bedeutet konkret die kontinuierliche Erfassung von Gesundheitsdaten mithilfe technischer Geräte und Übermittlung dieser Daten an Versicherungen sowie die Auszahlung von Boni für erreichte Fitnessziele. Zunächst wird eine Definition von PAYL-Systemen dargelegt, dann werden die technischen Rahmenbedingungen erläutert, schließlich werden die Auswirkungen dieser soziotechnischen Systeme näher beleuchtet. Dies geschieht auf Grundlage unserer empirischen Untersuchung. Die drei identifizierten Hauptkonflikte betreffen die Genese von PAYL, seine Auswirkungen auf den Wert der Datensouveränität besonders für Versicherte und die Schwierigkeit, für den Anspruch auf Prävention und Kostenersparnis durch PAYL Evidenz zu erzeugen. Diese Konflikte werden diskutiert, um sowohl die direkten wie auch indirekten Auswirkungen der Digitalisierung und der Ökonomisierung des Sozialen durch PAYL zu beleuchten.
Nowadays, machine learning projects have become more and more relevant to various real-world use cases. The success of complex Neural Network models depends upon many factors, as the requirement for structured and machine learning-centric project development management arises. Due to the multitude of tools available for different operational phases, responsibilities and requirements become more and more unclear. In this work, Machine Learning Operations (MLOps) technologies and tools for every part of the overall project pipeline, as well as involved roles, are examined and clearly defined. With the focus on the inter-connectivity of specific tools and comparison by well-selected requirements of MLOps, model performance, input data, and system quality metrics are briefly discussed. By identifying aspects of machine learning, which can be reused from project to project, open-source tools which help in specific parts of the pipeline, and possible combinations, an overview of support in MLOps is given. Deep learning has revolutionized the field of Image processing, and building an automated machine learning workflow for object detection is of great interest for many organizations. For this, a simple MLOps workflow for object detection with images is portrayed.
While the number of devices connected together as the Internet of Things (IoT) is growing, the demand for an efficient and secure model of resource discovery in IoT is increasing. An efficient resource discovery model distributes the registration and discovery workload among many nodes and allow the resources to be discovered based on their attributes. In most cases this discovery ability should be restricted to a number of clients based on their attributes, otherwise, any client in the system can discover any registered resource. In a binary discovery policy, any client with the shared secret key can discover and decrypt the address data of a registered resource regardless of the attributes of the client. In this paper we propose Attred, a decentralized resource discovery model using the Region-based Distributed Hash Table (RDHT) that allows secure and location-aware discovery of the resources in IoT network. Using Attribute Based Encryption (ABE) and based on predefined discovery policies by the resources, Attred allows clients only by their inherent attributes, to discover the resources in the network. Attred distributes the workload of key generations and resource registration and reduces the risk of central authority management. In addition, some of the heavy computations in our proposed model can be securely distributed using secret sharing that allows a more efficient resource registration, without affecting the required security properties. The performance analysis results showed that the distributed computation can significantly reduce the computation cost while maintaining the functionality. The performance and security analysis results also showed that our model can efficiently provide the required security properties of discovery correctness, soundness, resource privacy and client privacy.
Additives and polymer composition influence the interaction of microplastics with xenobiotics
(2021)
In Industry 4.0 machine learning approaches are a state-of-the art for predictive maintenance, machine condition monitoring, and others. Distributed decision trees are one of the learning algorithms for such applications. A new approach of node based parallelization for the construction is presented and allows to classify data through a network of nodes. Attacks on the nodes are discussed based on different attack scenarios and attack classifications are presented. A thorough analysis of protection measurements is given, such that classification is not maliciously modified by an attacker. Different countermeasures are proposed and analyzed. A quorum-based system allows for a good balance between computational overhead and robustness of the algorithm.
Durch die zunehmende Intelligenz der Produkte und die Möglichkeiten der Vernetzung zu einem Internet-der-Dinge entstehen zusätzliche Potenziale durch die Verarbeitung von Daten und das Angebot von datenbasierten, smarten Services. In diesem Artikel wird ein Rahmen für die Gestaltung der Digitalen Transformation für Dienstleistungen anhand des Remote Services Continuums aufgezeigt, die digitale Wertschöpfungskette beschrieben und eine Roadmap zur Erschließung dieser Potenziale dargestellt.
Biosynthesis of iron oxide magnetic nanoparticles using clinically isolated Pseudomonas aeruginosa
(2021)
Distributed machine learning algorithms that employ Deep Neural Networks (DNNs) are widely used in Industry 4.0 applications, such as smart manufacturing. The layers of a DNN can be mapped onto different nodes located in the cloud, edge and shop floor for preserving privacy. The quality of the data that is fed into and processed through the DNN is of utmost importance for critical tasks, such as inspection and quality control. Distributed Data Validation Networks (DDVNs) are used to validate the quality of the data. However, they are prone to single points of failure when an attack occurs. This paper proposes QUDOS, an approach that enhances the security of a distributed DNN that is supported by DDVNs using quorums. The proposed approach allows individual nodes that are corrupted due to an attack to be detected or excluded when the DNN produces an output. Metrics such as corruption factor and success probability of an attack are considered for evaluating the security aspects of DNNs. A simulation study demonstrates that if the number of corrupted nodes is less than a given threshold for decision-making in a quorum, the QUDOS approach always prevents attacks. Furthermore, the study shows that increasing the size of the quorum has a better impact on security than increasing the number of layers. One merit of QUDOS is that it enhances the security of DNNs without requiring any modifications to the algorithm and can therefore be applied to other classes of problems.
Training of neural networks requires often high computational power and large memory on Graphics Processing Unit (GPU) hardware. Many cloud providers such as Amazon, Azure, Google, Siemens, etc, provide such infrastructure. However, should one choose a cloud infrastructure or an on premise system for a neural network application, how can these systems be compared with one another? This paper investigates seven prominent Machine Learning benchmarks, which are MLPerf, DAWNBench, DeepBench, DLBS, TBD, AIBench, and ADABench. The recent popularity and widespread use of Deep Learning in various applications have created a need for benchmarking in this field. This paper shows that these application domains need slightly different resources and argue that there is no standard benchmark suite available that addresses these different application needs. We compare these benchmarks and summarize benchmarkrelated datasets, domains, and metrics. Finally, a concept of an ideal benchmark is sketched.
Potentials of Semantic Image Segmentation Using Visual Attention Networks for People with Dementia
(2021)
Due to the increasing number of dementia patients, it is time to include the care sector in digitization as well. Digital media, for example, can be used on tablets in memory care and have considerable potential for reminiscence therapy for people with dementia. The time consuming assembly of digital media content has to be automated for the caretakers.
This work analyzes the potentials of semantic image segmentation with Visual Attention Networks for reminiscence therapy sessions. These approaches enable the selection of digital images to satisfy the patients individual experience and biographically. A detailed comparison of various Visual Attention Networks evaluated by the BLEU score is shown. The most promising networks for semantic image segmentation are VGG16 and VGG19.
In edge/fog computing infrastructures, the resources and services are offloaded to the edge and computations are distributed among different nodes instead of transmitting them to a centralized entity. Distributed Hash Table (DHT) systems provide a solution to organizing and distributing the computations and storage without involving a trusted third party. However, the physical locations of nodes are not considered during the creation of the overlay which causes some efficiency issues. In this paper, Locality aware Distributed Addressing (LADA) model is proposed that can be adopted in distributed infrastructures to create an overlay that considers the physical locations of participating nodes. LADA aims to address the efficiency issues during the store and lookup processes in DHT overlay. Additionally, it addresses the privacy issue in similar proposals and removes any possible set of fixed entities. Our studies showed that the proposed model is efficient, robust and is able to protect the privacy of the locations of the participating nodes.