004 Datenverarbeitung; Informatik
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In this thesis the possibilities for real-time visualization of OpenVDB
files are investigated. The basics of OpenVDB, its possibilities, as well
as NanoVDB and its GPU port, were studied. A system was developed
using PNanoVDB, the graphics API port of OpenVDB. Techniques were
explored to improve and accelerate a single ray approach of ray tracing.
To prove real-time capability, two single scattering approaches were
also implemented. One of these was selected, further investigated and
optimized to achieve interactive real-time rendering.
It is important to give artists immediate feedback on their adjustments, as
well as the possibility to change all parameters to ensure a user friendly
creation process.
In addition to the optical rendering, corresponding benchmarks were
collected to compare different improvement approaches and to prove
their relevance. Attention was paid to the rendering times and memory
consumption on the GPU to ensure optimal use. A special focus, when
rendering OpenVDB files, was put on the integrability and extensibility of
the program to allow easy integration into an existing real-time renderer
like U-Render.
Current political issues are often reflected in social media discussions, gathering politicians and voters on common platforms. As these can affect the public perception of politics, the inner dynamics and backgrounds of such debates are of great scientific interest. This thesis takes user generated messages from an up-to-date dataset of considerable relevance as Time Series, and applies a topic-based analysis of inspiration and agenda setting to it. The Institute for Web Science and Technologies of the University Koblenz-Landau has collected Twitter data generated beforehand by candidates of the European Parliament Election 2019. This work processes and analyzes the dataset for various properties, while focusing on the influence of politicians and media on online debates. An algorithm to cluster tweets into topical threads is introduced. Subsequently, Sequential Association Rules are mined, yielding wide array of potential influence relations between both actors and topics. The elaborated methodology can be configured with different parameters and is extensible in functionality and scope of application.
The content aggregator platform Reddit has established itself as one of the most popular websites in the world. However, scientific research on Reddit is hindered as Reddit allows (and even encourages) user anonymity, i.e., user profiles do not contain personal information such as the gender. Inferring the gender of users in large-scale could enable the analysis of gender-specific areas of interest, reactions to events, and behavioral patterns. In this direction, this thesis suggests a machine learning approach of estimating the gender of Reddit users. By exploiting specific conventions in parts of the website, we obtain a ground truth for more than 190 million comments of labeled users. This data is then used to train machine learning classifiers to use them to gain insights about the gender balance of particular subreddits and the platform in general. By comparing a variety of different approaches for classification algorithm, we find that character-level convolutional neural network achieves performance with an 82.3% F1 score on a task of predicting a gender of a user based on his/her comments. The score surpasses 85% mark for frequent users with more than 50 comments. Furthermore, we discover that female users are less active on Reddit platform, they write fewer comments and post in fewer subreddits on average, when compared to male users.
“Did I say something wrong?” A word-level analysis of Wikipedia articles for deletion discussions
(2016)
This thesis focuses on gaining linguistic insights into textual discussions on a word level. It was of special interest to distinguish messages that constructively contribute to a discussion from those that are detrimental to them. Thereby, we wanted to determine whether “I”- and “You”-messages are indicators for either of the two discussion styles. These messages are nowadays often used in guidelines for successful communication. Although their effects have been successfully evaluated multiple times, a large-scale analysis has never been conducted. Thus, we used Wikipedia Articles for Deletion (short: AfD) discussions together with the records of blocked users and developed a fully automated creation of an annotated data set. In this data set, messages were labelled either constructive or disruptive. We applied binary classifiers to the data to determine characteristic words for both discussion styles. Thereby, we also investigated whether function words like pronouns and conjunctions play an important role in distinguishing the two. We found that “You”-messages were a strong indicator for disruptive messages which matches their attributed effects on communication. However, we found “I”-messages to be indicative for disruptive messages as well which is contrary to their attributed effects. The importance of function words could neither be confirmed nor refuted. Other characteristic words for either communication style were not found. Yet, the results suggest that a different model might represent disruptive and constructive messages in textual discussions better.
The mitral valve is one of the four valves in the human heart. It is located in the left heart chamber and its function is to control the blood flow from the left atrium to the left ventricle. Pathologies can lead to malfunctions of the valve so that blood can flow back to the atrium. Patients with a faulty mitral valve function may suffer from fatigue and chest pain. The functionality can be surgically restored, which is often a long and exhaustive intervention. Thorough planning is necessary to ensure a safe and effective surgery. This can be supported by creating pre-operative segmentations of the mitral valve. A post-operative analysis can determine the success of an intervention. This work will combine existing and new ideas to propose a new approach to (semi-)automatically create such valve models. The manual part can guarantee a high quality model and reliability, whereas the automatic part contributes to saving valuable labour time.
The main contributions of the automatic algorithm are an estimated semantic separation of the two leaflets of the mitral valve and an optimization process that is capable of finding a coaptation-line and -area between the leaflets. The segmentation method can perform a fully automatic segmentation of the mitral leaflets if the annulus ring is already given. The intermediate steps of this process will be integrated into a manual segmentation method so a user can guide the whole procedure. The quality of the valve models generated by the method proposed in this work will be measured by comparing them to completely manually segmented models. This will show that commonly used methods to measure the quality of a segmentation are too general and do not suffice to reflect the real quality of a model. Consequently the work at hand will introduce a set of measurements that can qualify a mitral valve segmentation in more detail and with respect to anatomical landmarks. Besides the intra-operative support for a surgeon, a segmented mitral valve provides additional benefits. The ability to patient-specifically obtain and objectively describe the valve anatomy may be the base for future medical research in this field and automation allows to process large data sets with reduced expert dependency. Further, simulation methods that use the segmented models as input may predict the outcome of a surgery.
Web application testing is an active research area. Garousi et al. did a systematic mapping study and classified 79 papers published between 2000-2011. However, there seems to be a lack of information exchange between the scientific community and tool developers.
This thesis systematically analyzes the field of functional, system level web application testing tools. 194 candidate tools were collected in the tool search and screened, with 23 tools being selected as foundation of this thesis. These 23 tools were systematically used to generate a feature model of the domain. The methodology to support this is an additional contribution of this thesis. It processes end user documentation of tools belonging to an examined domain and creates a feature model. The feature model gives an overview over the existing features, their alternatives and their distribution. It can be used to identify trends and problems, extraordinary features, help decision making of tool purchase or guide scientists how to focus research.
One task of executives and project managers in IT companies or departments is to hire suitable developers and to assign them to suitable problems. In this paper, we propose a new technique that directly leverages previous work experience of developers in a systematic manner. Existing evidence for developer expertise based on the version history of existing projects is analyzed. More specifically, we analyze the commits to a repository in terms of affected API usage. On these grounds, we associate APIs with developers and thus we assess API experience of developers. In transitive closure, we also assess programming domain experience.
Geographic cluster based routing in ad-hoc wireless sensor networks is a current field of research. Various algorithms to route in wireless ad-hoc networks based on position information already exist. Among them algorithms that use the traditional beaconing approach as well as algorithms that work beaconless (no information about the environment is required besides the own position and the destination). Geographic cluster based routing with guaranteed message delivery can be carried out on overlay graphs as well. Until now the required planar overlay graphs are not being constructed reactively.
This thesis proposes a reactive algorithm, the Beaconless Cluster Based Planarization (BCBP) algorithm, which constructs a planar overlay graph and noticeably reduces the number of messages required for that. Based on an algorithm for cluster based planarization it beaconlessly constructs a planar overlay graph in an unit disk graph (UDG). An UDG is a model for a wireless network in which every participant has the same sending radius. Evaluation of the algorithm shows it to be more efficient than the non beaconless variant. Another result of this thesis is the Beaconless LLRAP (BLLRAP) algorithm, for which planarity but not continued connectivity could be proven.
In this work a framework is developed that is used to create an evaluation scheme for the evaluation of text processing tools. The evaluation scheme is developed using a model-dependent software evaluation approach and the focus of the model-dependent part is the text-processing process which is derived from the Conceptual Analysis Process developed in the GLODERS project. As input data a German court document is used containing two incidents of extortion racketeering which happened in 2011 and 2012. The evaluation of six different tools shows that one tool offers great results for the given dataset when it is compared to manual results. It is able to identify and visualize relations between concepts without any additional manual work. Other tools also offer good results with minor drawbacks. The biggest drawback for some tools is the unavailability of models for the German language. They can perform automated tasks only on English documents. Nonetheless some tools can be enhanced by self-written code which allows users with development experience to apply additional methods.
Code package managers like Cabal track dependencies between packages. But packages rarely use the functionality that their dependencies provide. This leads to unnecessary compilation of unused parts and to speculative conflicts between package versions where there are no conflicts. In two case studies we show how relevant these two problems are. We then describe how we could avoid them by tracking dependencies not between packages but between individual code fragments.