Hochschule Ruhr West
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Ansatzpunkte der zirkulären Wertschöpfung in der Haushaltsgeräteindustrie
No full textAnsatzpunkte der zirkulären Wertschöpfung in der Haushaltsgeräteindustri
Learning About Catcalling: An Interactive Virtual Gallery Concept Raising Awareness for Street Harassment
No full textIn this paper, we describe a virtual reality (VR) application that educates and sensitizes visitors for street harassment, a globally prevalent form of violence predominantly targeted against women. The phenomenon, also known as Catcalling, recently gained renewed attention in public discussions initiated by social media activists. Combining VR with interactive instruction settings known from museums might be a promising way to effectively reach victims, bystanders, and aggressors alike and obtain a lasting attitude and behavior change. We present a virtual gallery where visitors can explore a variety of curated interactive multimedia material intended to inform, raise awareness, inspire empathy, perspective-taking, and behavioral change. With interactions such as a self-assessment test, as well as opportunities for visitors to leave feedback and thoughts in the gallery, this virtual world can be used as a sensitization tool. Based on a first evaluation with experts (N=16) , the original proof-of-concept prototype was extended and evaluated by a larger group including students (N=50)
Der Einfluss von Herkunftsangaben auf die Kaufabsicht und die Zahlungsbereitschaft deutscher Kunden. Eine empirische Analyse der Wirkung von "Made in Germany" am Beispiel der Bekleidungsindustrie
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Die Entwicklung einer Markenidentität für die blockchain-basierten Bildungszertifikate von Digicerts
Get PDFZiel dieser Arbeit ist es, eine deutlich definierte Markenidentität für DigiCerts zu konzipieren. Zu diesem Zweck wird das Markensteuerrad von Esch (2018, S.98) angewandt, um in detaillierten Schritten eine nützliche Markenidentität aufzubauen. Mithilfe dieser soll anschließend folgende Fragestellung beantwortet werden: Wie kann sich DigiCerts in der Hochschullandschaft positionieren? Zur Beantwortung der zugrunde liegenden Frage, wird die Positionierungspyramide von Esch(2009, S. 163)genutzt.
Insgesamt soll mithilfe dieser Arbeit eine für DigiCerts anwendbare Identität aufgebaut werden
Artificial Muscle Signal Generation using Generative Adversarial Networks
Get PDFThis work aims to generate synthetic electromyographic (EMG) signals using Generative Adversarial Network (GAN). GANs are considered as one of the most exciting and promising approaches in deep learning [6], offering the possibility to generate artificial data based on real data. GAN consists of two main parts, a discriminator that attempts to differentiate between the generated data and the original data, and a generator that tries to fool the discriminator by generating data which looks like real data, the GAN works by staging a two-player
minimax game between generator and discriminator networks. To achieve the objective of generating realistic artificial electromyographic signals, two different architectures are considered for the generator and the discriminator networks of the GAN model: Long short-term memory (LSTM), which can avoid the long-term dependency problem and remembers information over a long period of time, and convolutional neural network (CNN), which is a powerful tool at automatic feature extraction. Different combinations of CNN and LSTM including hybrid model are experimented within the GAN using the same training data-set. The results and performances of each combination are compared and reviewed. The generated artificial EMG signals can be used to
simulate real muscle activity situations to for example improve muscle signal controlled prostheses using artificial data that may include conditions that does not exist in real data. This method of artificial data generation is not limited to EMG signals, the network can also be used to generate other synthetic biomedical signals such as electroencephalogram (EEG) or electrocardiogram (ECG) that can be practically used for testing algorithms and classifiers
On the influence of autonomy and transparency on blame and credit in flawed human-robot collaboration
No full textVorhersage von Aktienkursbewegungen der Energiebranche mithilfe maschinellen Lernens und Stimmungserkennung von Beiträgen aus sozialen Medien
Get PDFThe goal of this empirical study is to answer whether predictions about stock price movements can be made with the use of machine learning in the energy sector and what influence contributions from social media have on its development. To answer the research
question, the social media platforms Twitter and Reddit, in terms of the suitability of the information, were studied and evaluated. Then, the sentiments of the posts from social media were collected and used in machine learning models. The models include the Gradient Boosted Regression Tree, Multilayer Perceptron, and Long Short-Term
i Memory, which predict a subsequent day's closing stock price. The study showed that deviations from predictions of stock price movements of 1.05 % are possible and further sentiment values do not show significant positive effect on reducing the error value. The
result shows that the collected sentiments from the social media platform Twitter have no positive effect on the stock price movements within the energy industry.
Keywords: stock market, stock prediction, artificial neural networks, machine learning,
energy market, sentiment analysi
Development and Implementation of a Streaming Protocol for Reliable and Efficient Data Transfer via a Lossy and Narrow Radio Channel
Get PDFIn this document a reliable data streaming mechanism for a TDMA LPWAN application is developed by adapting a link layer solution for power line communication, published at the International Symposium on Power Line Communications and its Applications (ISPLC) 2015. A C++ implementation of the services link layer is provided and demonstrated
working at a packet error rate of 50%