The aim of this work is to analyze reports of migraine attacks collected online in the citizen science project CLUE with respect to gender- and migraine type-specific differences in drug effectiveness and pain perception. Citizen science project data collection opens the possibility to examine these differences based on a large number of individual attacks instead of a simple survey of patients.

To provide robots for a wide range of users, there needs to be an easy and intuitive way to program them. This issue is addressed by the robot programming by demonstration or imitation learning paradigm, where the user demonstrates the task to the robot by teleoperation. Although single-shot approaches could save a lot of time and effort, they are still a niche due to some drawbacks, like ambiguities in selecting the relevant features.In this work we try to enhance a single shot programming by demonstration approach on sub-symbolic level by extending it to a multi modal input. While most approaches mainly focus on the trajectories and visual detection of objects, we combine speech and kinestethic teaching in order to resolve ambiguities and to rise the level of transferred information.

Die Informationen unserer DNA sind die persönlichsten Daten, die wir kennen. Kaum eine andere Datenkategorie erlaubt ähnlich tiefe Einblicke in die Kundenbedürfnisse. Die individuelle Zuschneidung von Angeboten kann demnach heute auch auf Basis unseres Erbgutes erfolgen, und zwar im Rahmen eines „DNA-Targetings“. Auf der Grundlage einer detaillierten Analyse des Erbgutes – vermarktet als eigenständiges Produkt, mit dem sich etwa ethnische Herkunft oder Krankheitsrisiken bestimmen ­lassen – eröffnen sich zahlreiche Einsatzmöglichkeiten, von der, „streuverlustfreien“ Kommunikation über ein DNA-basiertes Kunden-Clustering bis hin zu personalisierten Ernährungsprodukten und Medikamenten. Im internationalen Kontext spielen diese Anwendungsfälle eine noch ungleich größere Rolle als in Europa, ungeachtet der auch andernorts durchaus erkannten besonderen Datenschutzproblematik. Eine Auseinandersetzung mit der Thematik ist daher auch dringend im akademischen Marketing hierzulande dringend geboten.

While many speech synthesis systems based on deep neural networks are thoroughly evaluated and released for free use in English, models for languages with far less active speakers like German are scarcely trained and most often not published for common use. This work covers specific challenges in training text to speech models for the German language, including dataset selection and data preprocessing, and presents the training process for multiple models of an end-to-end text to speech system based on a combination of Tacotron 2 and Multi- Band MelGAN. All model compositions were evaluated against the mean opinion score, which revealed comparable results to models in literature that are trained and evaluated on English datasets. In addition, empirical analyses identified distinct aspects influencing the quality of such systems, based on subjective user experience. All trained models are released for public use.

Kaum ein Aspekt der digitalen Transformation wird bis heute so intensiv diskutiert, wie das Phänomen der „Plattformökonomie“, der Wirkungsweise der Geschäftsmodelle der großen Tech-Unternehmen wie Google, Facebook oder Amazon. Die daraus erwachsenen Konsequenzen greifen tief in die wirtschaftlichen Ökosphären ein, Plattformmechanismen haben sich inzwischen aber auch als gesellschaftspolitischer Faktor etabliert, insbesondere bei der Nachrichtenverbreitung und der politischen Meinungsbildung. Plattformen sorgen für mehr Effizienz in der Distribution, auf den Produktmärkten wie auch bei der Informationsbereitstellung, aber gleichzeitig eben auch für mehr Konzentration und weniger Ausgewogenheit. Sie haben die Eigenschaft, den Wettbewerb in einem Angebots-Monopol zu absorbieren. Ihr Handlungsprinzip lässt sich dabei durchaus als parasitär beschreiben, mit weitreichenden Folgen für Wirtschaft und Gesellschaft.

Citizen science allows involving interested citizen in the entire research process in science. In the past, various citizen science projects have been performed in different research fields, especially in human medicine. We conducted a rapid scoping review to determine which citizen projects in human medicine already used software-based systems to engage citizens in the research process. Furthermore, we analysed which of the software-systems are publicly available, especially in the field of rare diseases, how citizens can participate using those tools and whether the usability was rated by the participants. To get insights for our project “SelEe (Seltene Erkrankungen bürgerwissenschaftlich erforschen)”, which is a citizen science project in rare diseases funded by the Federal Ministry of Education and Research (BMBF), we aimed to identify projects in this research area. We searched PubMed for articles between 2011 and 2021 and performed a title- and abstract screening, as well as a full-text screening. Finally, 12 studies were identified in different research areas like public health, genetic research and infectious diseases. We could not identify any study directly associated with rare diseases. None of the studies investigated usability of those systems. Furthermore, five publicly available citizen science software-systems were identified. Three of them are general systems that allow creating, operating, managing citizen science projects and including citizens in the research process. In further investigations, we will check and compare these systems, if they are appropriate for use in our SelEe-project.

This paper presents the design and the results of a novel approach to predict air pollutants in urban environments. The objective is to create an artificial intelligence (AI)-based system to support planning actors in taking effective and adequate short-term measures against unfavourable air quality situations. In general, air quality in European cities has improved over the past decades. Nevertheless, reductions of the air pollutants particulate matter (PM), nitrogen dioxide (NO2) and ground-level ozone (O3), in particular, are essential to ensure the quality of life and a healthy life in cities. To forecast these air pollutants for the next 48 hours, a sequence-to-sequence encoder-decoder model with a recurrent neural network (RNN) was implemented. The model was trained with historic in situ air pollutant measurements, traffic and meteorological data. An evaluation of the prediction results against historical data shows high accordance with in situ measurements and implicate the system’s applicability and its great potential for high quality forecasts of air pollutants in urban environments by including real time weather forecast data.

Digitalization and Industry 4.0 have led organizations to adopt technology-driven approaches in various business segments. Integrated environments in organizations mandate the requirement of having in-depth knowledge of process, data and technology integration. This is also a requirement for organizations to develop towards sustainability, as data-driven business models can help to reduce waste and increase efficiency. Hence, universities have to provide skills in Industry 4.0 and in developing integrated systems across companies. Furthermore, online learning methods are required to prepare students for distributed teams in global industries. Consequently, you need a holistic approach to teach integrated thinking and working in distributed teams at the same time. The problems addressed by this paper are how to teach complex and heterogeneous aspects of Industry 4.0 concepts and how to integrate data-driven business processes. The paper describes didactic solutions with reduced complexity in a fictitious company to understand interrelationships between business segments in end-to-end processes. A virtual teaching concept encourages the exchange between students in distributed teams. Empirical runs of the concept show that students develop own approaches, learn to estimate the complexity of integrated systems and develop practical skills to find suitable solutions in a value-adding network. 

This paper presents a way for the hypertext community to gain strength and contribute to other fields of research by joining forces. It discusses the challenges that need to be addressed with respect to geographically scattered students and scholars, interdisciplinary courses, and students with various foreknowledge. We propose the INTR/HT project, a platform that aims for bringing hypertext scholars and students together worldwide. The interdisciplinary approach fosters creativity in the context of hypertext and is valuable for educating and supporting the next generation of hypertext scholars and researchers.

Modern browsers, as we know them from the Web, are used to query and present a variety of different resources. This usually happens by traversing links (i.e., URIs) in hypertext documents. The creation of new links however, is impossible to ordinary users, because they usually are recipients, but not owners of the received resource. In this paper, we demonstrate a browser plugin called "Weblinks", which offers its users an additional and rich linking layer over the existing Web. This enhances the notion of links as strings (i.e., URIs) in today's Web context to links as rich objects (n-ary, unidirectional, or bidirectional), which can be created, traversed or shared by anyone using the Weblinks browser plugin.