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The capabilities of Artificial Intelligence (AI) are utilized increasingly
in today‘s world. The autonomous and adaptive characteristics
allow applications to be more effective and efficient. A certain
subfield of Artificial Intelligence, Machine Learning, is enabling
services to be tailored to a user‘s specific needs. This could prove to
be useful in an information-heavy field such as Statistics. As design
research from SPSS Statistics, a legacy statistical application, has
indicated, statistics beginners struggle to tackle the challenge of
preparing a statistical research study. They turn to several sources
of information in an attempt to find help and answers but are not
always successful. This leads to them being unconfident before
they have even started to execute the statistical study. The adaptive
features of Artificial Intelligence could help support students
in this case, if designed according to established principles. This
thesis investigated the question whether an AI-powered solution
could elevate the users‘ confidence in statistical research studies.
In order to find the answer, a prototype with exemplary User Experience
was designed and implemented. Preceding research determined
the domain and market offer. User research was conducted
to ensure a human-centered outcome. The prototype was evaluated
with real test users and the results answered the question in
the affirmative.
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.
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.
Today’s digital cameras use a mosaic of red, green, and blue color filters to capture images in three color channels on a single sensor plane. This thesis investigates the use of convolutional neural networks (CNNs) for demosaicing – the process of reconstructing full-color images from raw mosaic sensor data. While there are existing CNNs for demosaicing raw images from the well-established regular Bayer color filter array (CFA), this thesis focuses on how they perform on alternative non-regular sampling patterns that produce less aliasing artifacts, namely the stochastic Gaussian- and the RandomQuarter sampling pattern (Backes and Fröhlich, 2020).
A basic UNet (Ronneberger et al., 2015) and the spatially adaptive SANet (T. Zhang et al., 2022) are implemented in a supervised training pipeline based on the PixelShift200 image dataset (Qian et al., 2021) to investigate their suitability for the irregular demosaicing task. The experiments indicate that the basic UNet encounters difficulties in restoring the missing color values, whereas the spatially adaptive convolutional layers help in processing the irregularly sampled raw images.
In addition, this thesis enhances SANet effectiveness by employing an alternative residual branch based on a CFA-normalized Gaussian filter, as well as a tileable modification to the Gaussian CFA pattern. The modified SANet is shown to outperform the conventional dFSR algorithm (Backes & Fröhlich, 2020) in terms of peak signal to noise ratio (PSNR) and structural similarity index measure (SSIM).
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.
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.
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.