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Virtual-reality (VR) is an immersive technology with a growing market and many applications for gesture recognition. This thesis presents a VR gesture recognition method using signal processing techniques. The core concept is based on the comparison of motion features in the form of signals between a runtime recording of users and a possible gesture set. This comparison yields a similarity score through which the most similar gesture can be recognized by a continuous recognition system. Some selected comparison methods are presented, evaluated and discussed. An example implementation is demonstrated. However, due to an introduced layer model parts of the method and its implementation are interchangeable.
Similar or even better performance is achieved compared to other related work. The comparison method Dynamic Time Warping (DTW) reaches an average positive recognitions rate of 98.18% with acceptable real-time application performance. Additionally, the method comes with some benefits: position and direction of users is irrelevant, body proportions have no significant negative impact on recognition rates, faster and slower gesture executions are possible, no user inputs are needed to communicate gesture start and end (continuous recognition), also continuous gestures can be recognized, and the recognition is fast enough to trigger gesture specific events already during the execution.
This study investigates the possibility of using Bartle’s player types for gamification
in the context of language learning apps. By taking user preferences into
account, this might assist in selecting the most suitable game elements. Learning
apps are gaining popularity as an innovative method for obtaining an independent
and flexible learning experience. Gamification keeps users motivated and involved
with the content.
After the research on the usage of gamification and its effects on the user, a language
learning app prototype was created. The evaluation consisted of a user test with
interview questions and the short User Experience Questionnaire (UEQ). The Bartle
test of gamer psychology was used to determine the player types of the participants.
The results show that, while player type and gamification preference can partially
coincide, there are too many deviations to confidently say it can be transferred into
gamification contexts. We conclude that game elements should not be chosen based
on a user’s Bartle player type and are more effectively used by incorporating a variety
of different gamification components.
Privacy in Social Networks
(2016)
Online Social Networks (OSNs) are heavily used today and despite of all privacy concerns found a way into our daily life. After showing how heavy data collection is a violation of the user's privacy, this thesis establishes mandatory and optional requirements for a Privacy orientated Online Social Network (POSN). It evaluates twelve existing POSNs in general and in regard to those requirements. The paper will find that none of these POSNs are able to fulfill the requirements and therefore proposes features and patterns as a reference architecture.
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.
The number of people with cognitive impairments increases together with the aging population. Thus, social robots are being researched to aid relieve the nursing
sector as well as to combat cognitive impairments. However, it raises concerns regarding how a social robot should relate to members of this group and what might
be appropriate. In this thesis, research about the current state of social robots has been conducted and focus groups with people from the nursing and medical field were held. To verify the credibility of the results and the scenario developed, final
user tests were conducted with representatives of the target group. When using a
social robot in an interaction with persons who have cognitive disabilities, the robot
should speak and behave more human-like and make use of its facial expressions,
stressing empathy and responding to the person accordingly. Though the situation
of interacting with a social robot may be more significant in future generations.
Deep learning methods have proven highly effective for object recognition tasks, especially
in the form of artificial neural networks. In this bachelor’s thesis, a way is shown to imple-
ment a ready-to-use object recognition implementation on the NAO robotic platform using
Convolutional Neural Networks based on pretrained models. Recognition of multiple objects
at once is realized with the help of the Multibox algorithm. The implementation’s object
recognition rates are evaluated and analyzed in several tests.
Furthermore, the implementation offers a graphical user interface with several options to
adjust the recognition process and for controlling movements of the robot’s head in order
to easier acquire objects in the field of view. Additionally, a dialogue system for querying
further results is presented.
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.
By now GPUs have become powerful general purpose processors that found their way not only into desktop systems but also supercomputers. To use GPUs efficiently one needs to understand their basic architecture and their limitations. We take a look at how GPUs evolved and how they differ from CPUs to gain a deeper understanding of the workloads well suited for GPUs.
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.
The increasing availability of online video content, partially fueled by the Covid-19 pandemic and the growing presence of social media, adds to the importance of providing audio descriptions as a media alternative to video content for blind and visually impaired people. In order to address concerns as to what can be sufficiently described and how such descriptions can be delivered to users, a concept has been developed providing audio descriptions in multiple levels of detail. Relevant information is incorporated into an XML-based data structure. The concept also includes a process to provide optional explanations to terms and abbreviations, helping users without specific knowledge or people with cognitive concerns in comprehending complex videos. These features are implemented into a prototype based on the Able Player software. By conducting a user test, the benefits of multi-layered audio descriptions and optional explanatory content are evaluated. Findings suggest that the choice of several levels of detail is received positively. Users acknowledged the concept of explanations played parallelly to the video and described further use cases for such a practice. Participants preferred a higher level of detail for a high-paced action video and a lower level for informative content. Possibilities to extend the data structure and features include multilanguage use cases and distributed systems.