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Large-scale computing platforms, like the IBM System z mainframe, are often administrated in an out-of-band manner, with a large portion of the systems management software running on dedicated servers which cause extra hardware costs. Splitting up systems management applications into smaller services and spreading them over the platform itself likewise is an approach that potentially helps with increasing the utilization of platform-internal resources, while at the same time lowering the need for external server hardware, which would reduce the extra costs significantly. However, with regard to IBM System z, this raises the general question how a great number of critical services can be run and managed reliably on a heterogeneous computing landscape, as out-of-band servers and internal processor modules do not share the same processor architecture.
In this thesis, we introduce our prototypical design of a microservice infrastructure for multi-architecture environments, which we completely built upon preexisting open source projects and features they already bring along. We present how scheduling of services according to application-specific requirements and particularities can be achieved in a way that offers maximum transparency and comfort for platform operators and users.
Video games have a significant influence on our time. However, lack of accessibility makes it hard for disabled gamers to play most of them. Virtual reality offers new possibilities to include people with disabilities and enable them to play games. Additionally, serious VR games provide educational benefits, such as improved memory and engagement.
In this work, the accessibility problems in video games and VR applications are explored with an emphasis on serious games as well as a general lack of guidelines. An overview of existing guidelines is given. From this, a set of guidelines is derived that summarizes the relevant rules for accessible VR games.
New ways to interact with VR environments come with both opportunities and challenges. This work investigates the applicability of different hands-free input methods to play a VR game. Using a serious game five focus and three activation methods were implemented exemplary with the Oculus Go. The suitability of these methods was analyzed in a pre-study that excluded head movements for controlling the game. The remaining input methods were evaluated in an explorative user study in terms of operability and ease of use.In summary, all tested methods can be used to control the game. The evaluation shows head-tracking as the preferred input method, while scanning eye-tracking and voice control were rated mediocre.
In addition, the correlation between input methods and different menu types was examined, but the influence turned out to be negligible.
In recent years new trends such as industry 4.0 boosted the research and
development in the field of autonomous systems and robotics. Robots collaborate and
even take over complete tasks of humans. But the high degree of automation requires
high reliability even in complex and changing environments. Those challenging
conditions make it hard to rely on static models of the real world. In addition to
adaptable maps, mobile robots require a local and current understanding of the scene.
The Bosch Start-Up Company is developing robots for intra-logistic systems, which
could highly benefit from such a detailed scene understanding. The aim of this work
is to research and develop such a system for warehouse environments. While the
possible field of application is in general very broad, this work will focus on the
detection and localization of warehouse specific objects such as palettes.
In order to provide a meaningful perception of the surrounding a RGB-D camera is
used. A pre-trained convolutional network extracts scene understanding in the form
of pixelwise class labels. As this convolutional network is the core of the application,
this work focuses on different network set-ups and learning strategies. One difficulty
was the lack of annotated training data. Since the creation of densely labeled images
is a very time consuming process it was important to elaborate on good alternatives.
One interesting finding was that it’s possible to transfer learning to a high extent from
similar models pre-trained on thousands of RGB-images. This is done by selective
interventions on the net parameters. By ensuring a good initialization it’s possible
to train towards a well performing model within few iterations. In this way it’s
possible to train even branched nets at once. This can also be achieved by including
certain normalization steps. Another important aspect was to find a suitable way
to incorporate depth-information. How to fuse depth into the existing model? By
providing the height over ground as an additional feature the segmentation accuracy
was further improved while keeping the extra computational costs low.
Finally the segmentation maps are refined by a conditional random field. The joint
training of both parts results in accurate object segmentations comparable to recently
published state-of-the-art models.
Password-based authentication is widely used online, despite its numerous shortcomings, enabling attackers to take over users’ accounts. Phishing-resistant Fast IDentity Online (FIDO) credentials have therefore been proposed to improve account security and authentication user experience. With the recent introduction of FIDO-based passkeys, industry-leading corporations aim to drive widespread adoption of passwordless authentication to eliminate some of the most common account takeover attacks their users are exposed to. This thesis presents the first iteration of a distributed web crawler measuring the adoption of FIDO-based authentication methods on the web to observe ongoing developments and assess the viability of the promised passwordless future. The feasibility of automatically detecting authentication methods is investigated by analyzing crawled web content. Because today’s web is increasingly client-side rendered, capturing relevant data with traditional scraping methods is challenging. Thus, the traditional approach is compared to the browser-based crawling of dynamic content to optimize the detection rate. The results show that authentication method detection is possible, although there are some limitations regarding accuracy and coverage. Moreover, browser-based crawling is found to significantly increase detection rate.