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The isolation measures adopted during the COVID-19 pandemic brought light to discussions related to the importance of meaningful social relationships as a basic need to human well-being. But even before the pandemic outbreak in the years 2020 and 2021, organizations and scholars were already drawing attention to the growing numbers related to lonely people in the world (World Economic Forum, 2019). Loneliness is an emotional distress caused by the lack of meaningful social connections, which affects people worldwide across all age groups, mainly young adults (Rook, 1984). The use of digital technologies has gained prominence as a means of alleviating the distress. As an example, studies have shown the benefits of using digital games both to stimulate social interactions (Steinfield, Ellison & Lampe, 2008) and to enhance the effects of digital interventions for mental health treatments, through gamification (Fleming et al., 2017). It is with these aspects in mind that the gamified app Noneliness was designed with the intention of reducing loneliness rates among young students at a German university. In addition to sharing the related works that supported the application development, this chapter also presents the aspects considered for the resource's design, its main functionalities, and the preliminary results related to the reduction of loneliness in the target audience.
We aim to debate and eventually be able to carefully judge how realistic the following statement of a young computer scientist is: “I would like to become an ethical correctly acting offensive cybersecurity expert”. The objective of this article is not to judge what is good and what is wrong behavior nor to present an overall solution to ethical dilemmas. Instead, the goal is to become aware of the various personal moral dilemmas a security expert may face during his work life. For this, a total of 14 cybersecurity students from HS Offenburg were asked to evaluate several case studies according to different ethical frameworks. The results and particularities are discussed, considering different ethical frameworks. We emphasize, that different ethical frameworks can lead to different preferred actions and that the moral understanding of the frameworks may differ even from student to student.
Sweaty has already participated several times in RoboCup soccer competitions (Adult Size). Now the work is focused on stabilizing the gait. Moreover, we would like to overcome the constraints of a ZMP-algorithm that has a horizontal footplate as precondition for the simplification of the equations. In addition we would like to switch between impedance and position control with a fuzzy-like algorithm that might help to minimize jerks when Sweaty’s feet touch the ground.
Editorial
(2022)
Editorial
(2022)
Editorial
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Editorial
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Editorial
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Editorial
(2022)
Publisher und Start-ups
(2022)
Mit dem Klimaschutzgesetz 2021 wurden von der Bundesregierung die Klimaschutzvorgaben verschärft und die Treibhausgasneutralität bis 2045 als Ziel verankert. Zur Erreichung dieses ambitionierten Ziels ist es notwendig, im Bereich der Mobilität weitgehend von Verbrennungsmotoren mit fossilen Kraftstoffen auf Elektromobilität mit regenerativ erzeugtem Strom umzusteigen. Dabei ist die zügige Bereitstellung einer ausreichenden Ladeinfrastruktur für die Elektrofahrzeuge eine große Herausforderung. Neben der Installation einer ausreichend großen Zahl von Ladepunkten selbst besteht die Herausforderung darin, diese in das bestehende Verteilungsnetz zu integrieren bzw. das Verteilungsnetz so auszubauen, dass weiter ein sicherer Netzbetrieb gewährleistet werden kann. Dabei sind insbesondere Lösungen gefragt, bei denen der Ausbau der Ladeinfrastruktur und der Netzbetriebsmittel durch intelligentes Management des Ladens so gering wie möglich gehalten wird, indem vorhandene oder neu zu installierender Hardware möglichst effizient genutzt wird.
Hier setzte das Projekt „Intelligente Ladeinfrastruktur für Elektrofahrzeuge auf dem Parkplatz der Hochschule Offenburg (INTLOG)“ (Projektlaufzeit 15.11.2020 – 30.09.2022) an. Inhalt des Projekts war es, einen Ladepark für den Parkplatz der Hochschule Offenburg mit 20 Ladepunkten à 11 kW und somit einer Gesamtladeleistung von 220 kW an einen vorhandenen Ortsnetztransformator mit 200 kW Nennleistung anzuschließen, der aber bereits von anderen Verbrauchern genutzt wurde. Das übergeordnete Ziel war es also, eine Ladeinfrastruktur von maßgeblichem Umfang in die bestehende Netzinfrastruktur ohne zusätzlichen Ausbau zu integrieren.
Dabei wurden zukunftsweisende Technologien genutzt und weiterentwickelt sowie teilweise in Praxis, im Labor und in der Computersimulation demonstriert.
Generative machine learning models for creative purposes play an increasingly prominent role in the field of dance and technology. A particularly popular approach is the use of such models for generating synthetic motions. Such motions can either serve as source of ideation for choreographers or control an artificial dancer that acts as improvisation partner for human dancers. Several examples employ autoencoder-based deep-learning architectures that have been trained on motion capture recordings of human dancers. Synthetic motions are then generated by navigating the autoencoder's latent space. This paper proposes an alternative approach of using an autoencoder for creating synthetic motions. This approach controls the generation of synthetic motions on the level of the motion itself rather than its encoding. Two different methods are presented that follow this principle. Both methods are based on the interactive control of a single joint of an artificial dancer while the other joints remain under the control of the autoencoder. The first method combines the control of the orientation of a joint with iterative autoencoding. The second method combines the control of the target position of a joint with forward kinematics and the application of latent difference vectors. As illustrative example of an artistic application, this latter method is used for an artificial dancer that plays a digital instrument. The paper presents the implementation of these two methods and provides some preliminary results.
Subspace clustering aims to find all clusters in all subspaces of a high-dimensional data space. We present a massively data-parallel approach that can be run on graphics processing units. It extends a previous density-based method that scales well with the number of dimensions. Its main computational bottleneck consists of (sequentially) generating a large number of minimal cluster candidates in each dimension and using hash collisions in order to find matches of such candidates across multiple dimensions. Our approach parallelizes this process by removing previous interdependencies between consecutive steps in the sequential generation process and by applying a very efficient parallel hashing scheme optimized for GPUs. This massive parallelization gives up to 70x speedup for
the bottleneck computation when it is replaced by our approach and run on current GPU hardware. We note that depending on data size and choice of parameters, the parallelized part of the algorithm can take different percentages of the overall runtime of the clustering process, and thus, the overall clustering speedup may vary significantly between different cases. However, even
in our ”worst-case” test, a small dataset where the computation makes up only a small fraction of the overall clustering time, our parallel approach still yields a speedup of more than 3x for the complete run of the clustering process. Our method could also be combined with parallelization of other parts of the clustering algorithm, with an even higher potential gain in processing speed.
Forschung im Fokus 2022
(2022)