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With the appearance of modern virtual reality (VR) headsets on the consumer market, there has been the biggest boom in the history of VR technology. Naturally, this was accompanied by an increasing focus on the problems of current VR hardware. Especially the control in VR has always been a complex topic.
One possible solution is the Leap Motion, a hand tracking device that was initially developed for desktop use, but with the last major software update it can be attached to standard VR headsets. This device allows very precise tracking of the user’s hands and fingers and their replication in the virtual world.
The aim of this work is to design virtual user interfaces that can be operated with the Leap Motion to provide a natural method of interaction between the user and the VR environment. After that, subject tests are performed to evaluate their performance and compare them to traditional VR controllers.
The purpose of this thesis is to explore the sentiment distributions of Wikipedia concepts.
We analyse the sentiment of the entire English Wikipedia corpus, which includes 5,669,867 articles and 1,906,375 talks, by using a lexicon-based method with four different lexicons.
Also, we explore the sentiment distributions from a time perspective using the sentiment scores obtained from our selected corpus. The results obtained have been compared not only between articles and talks but also among four lexicons: OL, MPQA, LIWC, and ANEW.
Our findings show that among the four lexicons, MPQA has the highest sensitivity and ANEW has the lowest sensitivity to emotional expressions. Wikipedia articles show more sentiments than talks according to OL, MPQA, and LIWC, whereas Wikipedia talks show more sentiments than articles according to ANEW. Besides, the sentiment has a trend regarding time series, and each lexicon has its own bias regarding text describing different things.
Moreover, our research provides three interactive widgets for visualising sentiment distributions for Wikipedia concepts regarding the time and geolocation attributes of concepts.
To construct a business process model manually is a highly complex and error-prone task which takes a lot of time and deep insights into the organizational structure, its operations and business rules. To improve the output of business analysts dealing with this process, different techniques have been introduced by researchers to support them during construction with helpful recommendations. These supporting recommendation systems vary in their way of what to recommend in the first place as well as their calculations taking place under the hood to recommend the most fitting element to the user. After a broad introduction into the field of business process modeling and its basic recommendation structures, this work will take a closer look at diverse proposals and descriptions published in current literature regarding implementation strategies to effectively and efficiently assist modelers during their business process model creation. A critical analysis of presentations in the selected literature will point out strengths and weaknesses of their approaches, studies and descriptions of those. As a result, the final concept matrix in this work will give a precise and helpful overview about the key features and recommendation methods used and implemented in previous research studies to pinpoint an entry into future works without the downsides already spotted by fellow researchers.
The thesis develops and evaluates a hypothetical model of the factors that influence user acceptance of weblog technology. Previous acceptance studies are reviewed, and the various models employed are discussed. The eventual model is based on the technology acceptance model (TAM) by Davis et al. It conceptualizes and operationalizes a quantitative survey conducted by means of an online questionnaire, strictly from a user perspective. Finally, it is tested and validated by applying methods of data analysis.
This Master Thesis is an exploratory research to determine whether it is feasible to construct a subjectivity lexicon using Wikipedia. The key hypothesis is that that all quotes in Wikipedia are subjective and all regular text are objective. The degree of subjectivity of a word, also known as ''Quote Score'' is determined based on the ratio of word frequency in quotations to its frequency outside quotations. The proportion of words in the English Wikipedia which are within quotations is found to be much smaller as compared to those which are not in quotes, resulting in a right-skewed distribution and low mean value of Quote Scores.
The methodology used to generate the subjectivity lexicon from text corpus in English Wikipedia is designed in such a way that it can be scaled and reused to produce similar subjectivity lexica of other languages. This is achieved by abstaining from domain and language-specific methods, apart from using only readily-available English dictionary packages to detect and exclude stopwords and non-English words in the Wikipedia text corpus.
The subjectivity lexicon generated from English Wikipedia is compared against other lexica; namely MPQA and SentiWordNet. It is found that words which are strongly subjective tend to have high Quote Scores in the subjectivity lexicon generated from English Wikipedia. There is a large observable difference between distribution of Quote Scores for words classified as strongly subjective versus distribution of Quote Scores for words classified as weakly subjective and objective. However, weakly subjective and objective words cannot be differentiated clearly based on Quote Score. In addition to that, a questionnaire is commissioned as an exploratory approach to investigate whether subjectivity lexicon generated from Wikipedia could be used to extend the coverage of words of existing lexica.
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.
Since the invention of U-net architecture in 2015, convolutional networks based on its encoder-decoder approach significantly improved results in image analysis challenges. It has been proven that such architectures can also be successfully applied in different domains by winning numerous championships in recent years. Also, the transfer learning technique created an opportunity to push state-of-the-art benchmarks to a higher level. Using this approach is beneficial for the medical domain, as collecting datasets is generally a difficult and expensive process.
In this thesis, we address the task of semantic segmentation with Deep Learning and make three main contributions and release experimental results that have practical value for medical imaging.
First, we evaluate the performance of four neural network architectures on the dataset of the cervical spine MRI scans. Second, we use transfer learning from models trained on the Imagenet dataset and compare it to randomly initialized networks. Third, we evaluate models trained on the bias field corrected and raw MRI data. All code to reproduce results is publicly available online.
Despite the inception of new technologies at a breakneck pace, many analytics projects fail mainly due to the use of incompatible development methodologies. As big data analytics projects are different from software development projects, the methodologies used in software development projects could not be applied in the same fashion to analytics projects. The traditional agile project management approaches to the projects do not consider the complexities involved in the analytics. In this thesis, the challenges involved in generalizing the application of agile methodologies will be evaluated, and some suitable agile frameworks which are more compatible with the analytics project will be explored and recommended. The standard practices and approaches which are currently applied in the industry for analytics projects will be discussed concerning enablers and success factors for agile adaption. In the end, after the comprehensive discussion and analysis of the problem and complexities, a framework will be recommended that copes best with the discussed challenges and complexities and is generally well suited for the most data-intensive analytics projects.
Particle swarm optimization is an optimization technique based on simulation of the social behavior of swarms.
The goal of this thesis is to solve 6DOF local pose estimation using a modified particle swarm technique introduced by Khan et al. in 2010. Local pose estimation is achieved by using continuous depth and color data from a RGB-D sensor. Datasets are aquired from different camera poses and registered into a common model. Accuracy and computation time of the implementation is compared to state of the art algorithms and evaluated in different configurations.
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 output of eye tracking Web usability studies can be visualized to the analysts as screenshots of the Web pages with their gaze data. However, the screenshot visualizations are found to be corrupted whenever there are recorded fixations on fixed Web page elements on different scroll positions. The gaze data are not gathered on their fixated fixed elements; rather they are scattered on their recorded scroll positions. This problem has raised our attention to find an approach to link gaze data to their intended fixed elements and gather them in one position on the screenshot. The approach builds upon the concept of creating the screenshot during the recording session, where images of the viewport are captured on visited scroll positions and lastly stitched into one Web page screenshot. Additionally, the fixed elements in the Web page are identified and linked to their fixations. For the evaluation, we compared the interpretation of our enhanced screenshot against the video visualization, which overcomes the problem. The results revealed that both visualizations equally deliver accurate interpretations. However, interpreting the visualizations of eye tracking Web usability studies using the enhanced screenshots outperforms the video visualizations in terms of speed and it requires less temporal demands from the interpreters.
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.
This thesis focuses on approximate inference in assumption-based argumentation frameworks. Argumentation provides a significant idea in the computerization of theoretical and practical reasoning in AI. And it has a close connection with AI, engaging in arguments to perform scientific reasoning. The fundamental approach in this field is abstract argumentation frameworks developed by Dung. Assumption-based argumentation can be regarded as an instance of abstract argumentation with structured arguments. When facing a large scale of data, a challenge of reasoning in assumption-based argumentation is how to construct arguments and resolve attacks over a given claim with minimal cost of computation and acceptable accuracy at the same time. This thesis proposes and investigates approximate methods that randomly select and construct samples of frameworks based on graphical dispute derivations to solve this problem. The presented approach aims to improve reasoning performance and get an acceptable trade-off between computational time and accuracy. The evaluation shows that for reasoning in assumption-based argumentation, in general, the running time is reduced with the cost of slightly low accuracy by randomly sampling and constructing inference rules for potential arguments over a query.
Mobile payment has been a payment option in the market for a long time now and was predicted to become a widely used payment method. However, over the years, the market penetration rate of mPayments has been relatively low, despite it having all characteristics required of a convenient payment method. The primaryrnreason for this has been cited as a lack of customer acceptance mainly caused due to the lack of perceived security by the end-user. Although biometric authentication is not a new technology, it is experiencing a revival in the light of the present day terror threats and increased security requirements in various industries. The application of biometric authentication in mPayments is analysed here and a suitable biometric authentication method for use with mPayments is recommended. The issue of enrolment, human and technical factors to be considered are discussed and the STOF business model is applied to a BiMoP (biometric mPayment) application.
Currently more than 850 biological databases exist. The majority of biological knowledge is not in these databases but rather contained as free text in scientific literature. For systems biology tasks it is often necessary to integrate and extract data from heterogeneous databases and free text as well as to analyse the information in the context of experimental data. ONDEX is an integration framework which aims to address these challenges by combining features of database integration, text mining and sequence analysis with methods for graph-based data analysis and visualisation. The main topics of this diploma thesis are the redesign of the ONDEX backend, the development of a data exchange format, the development of a query environment and the allocation of Web services for data integration, data exchange and queries. These Web services allow backend workflow control from both local and remote workstations.
This thesis explores and examines the effectiveness and efficacy of traditional machine learning (ML), advanced neural networks (NN) and state-of-the-art deep learning (DL) models for identifying mental distress indicators from the social media discourses based on Reddit and Twitter as they are immensely used by teenagers. Different NLP vectorization techniques like TF-IDF, Word2Vec, GloVe, and BERT embeddings are employed with ML models such as Decision Tree (DT), Random Forest (RF), Logistic Regression (LR) and Support Vector Machine (SVM) followed by NN models such as Convolutional Neural Network (CNN), Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) to methodically analyse their impact as feature representation of models. DL models such as BERT, DistilBERT, MentalRoBERTa and MentalBERT are end-to-end fine tuned for classification task. This thesis also compares different text preprocessing techniques such as tokenization, stopword removal and lemmatization to assess their impact on model performance. Systematic experiments with different configuration of vectorization and preprocessing techniques in accordance with different model types and categories have been implemented to find the most effective configurations and to gauge the strengths, limitations, and capability to detect and interpret the mental distress indicators from the text. The results analysis reveals that MentalBERT DL model significantly outperformed all other model types and categories due to its specific pretraining on mental data as well as rigorous end-to-end fine tuning gave it an edge for detecting nuanced linguistic mental distress indicators from the complex contextual textual corpus. This insights from the results acknowledges the ML and NLP technologies high potential for developing complex AI systems for its intervention in the domain of mental health analysis. This thesis lays the foundation and directs the future work demonstrating the need for collaborative approach of different domain experts as well as to explore next generational large language models to develop robust and clinically approved mental health AI systems.
This thesis analyzes the online attention towards scientists and their research topics. The studies compare the attention dynamics towards the winners of important scientific prizes with scientists who did not receive a prize. Web signals such as Wikipedia page views, Wikipedia edits, and Google Trends were used as a proxy for online attention. One study focused on the time between the creation of the article about a scientist and their research topics. It was discovered that articles about research topics were created closer to the articles of prize winners than to scientists who did not receive a prize. One possible explanation could be that the research topics are more closely related to the scientist who got an award. This supports that scientists who received the prize introduced the topics to the public. Another study considered the public attention trends towards the related research topics before and after a page of a scientist was created. It was observed that after a page about a scientist was created, research topics of prize winners received more attention than the topics of scientists who did not receive a prize. Furthermore, it was demonstrated that Nobel Prize winners get a lower amount of attention before receiving the prize than the potential nominees from the list of Citation Laureates of Thompson Reuters. Also, their popularity is going down faster after receiving it. It was also shown that it is difficult to predict the prize winners based on the attention dynamics towards them.
Tractography on HARDI data
(2011)
Diffusion weighted imaging is an important modality in clinical imaging and the only possibility to gain insight into the human brain noninvasively and in-vivo. The applications of this imaging technique are diversified. It is used to study the brain, its structure, development and the functionality of the different areas. Further, important fields of application are neurosurgical planning, examinations of pathologies, investigation of Alzheimer-, strokes, and multiple sclerosis. This thesis gives a brief introduction to MRI and diffusion MRI. Based on this, the mostly used data representation in diffusion MRI in clinical imaging, the diffusion tensor, is introduced. As the diffusion tensor suffers from severe limitations new techniques subsumed under the term HARDI (high angular resolution diffusion imaging) are introduced and discussed in detail. Further, an extensive introduction to tractography, approaches that aim at reconstructing neuronal fibers, is given. Based on the knowledge fromthe theoretical part established tractography algorithms are redesigned to handle HARDI data and, thus, improve the reconstruction of neuronal fibers. Among these algorithms, a novel approach is presented that successfully reconstructs fibers on phantom data as well as on human brain data. Further, a novel global classification approach is presented to cluster voxels according to their diffusion properties.
In this thesis, the performance of the IceCube projects photon propagation
code (clsim) is optimized. The process of GPU code analysis and perfor-
mance optimization is described in detail. When run on the same hard-
ware, the new version achieves a speedup of about 3x over the original
implementation. Comparing the unmodified code on hardware currently
used by IceCube (NVIDIA GTX 1080) against the optimized version run on
a recent GPU (NVIDIA A100) a speedup of about 9.23x is observed. All
changes made to the code are shown and their performance impact as well
as the implications for simulation accuracy are discussed individually.
The approach taken for optimization is then generalized into a recipe.
Programmers can use it as a guide, when approaching large and complex
GPU programs. In addition, the per warp job-queue, a design pattern used
for load balancing among threads in a CUDA thread block, is discussed in
detail.
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.
The Internet of Things (IoT) is a fast-growing, technological concept, which aims to integrate various physical and virtual objects into a global network to enable interaction and communication between those objects (Atzori, Iera and Morabito, 2010). The application possibilities are manifold and may transform society and economy similarly to the usage of the internet (Chase, 2013). Furthermore, the Internet of Things occupies a central role for the realisation of visionary future concepts, for example, Smart City or Smart Healthcare. In addition, the utilisation of this technology promises opportunities for the enhancement of various sustainability aspects, and thus for the transformation to a smarter, more efficient and more conscious dealing with natural resources (Maksimovic, 2017). The action principle of sustainability increasingly gains attention in the societal and academical discourse. This is reasoned by the partly harmful consumption and production patterns of the last century (Mcwilliams et al., 2016). Relating to sustainability, the advancing application of IoT technology also poses risks. Following the precautionary principle, these risks should be considered early (Harremoës et al., 2001). Risks of IoT for sustainability include the massive amounts of energy and raw materials which are required for the manufacturing and operation of IoT objects and furthermore, the disposal of those objects (Birkel et al., 2019). The exact relations in the context of IoT and sustainability are insufficiently explored to this point and do not constitute a central element within the discussion of this technology (Behrendt, 2019). Therefore, this thesis aims to develop a comprehensive overview of the relations between IoT and sustainability.
To achieve this aim, this thesis utilises the methodology of Grounded Theory in combination with a comprehensive literature review. The analysed literature primarily consists of research contributions in the field of Information Technology (IT). Based on this literature, aspects, solution approaches, effects and challenges in the context of IoT and sustainability were elaborated. The analysis revealed two central perspectives in this context. IoT for Sustainability (IoT4Sus) describes the utilisation and usage of IoT-generated information to enhance sustainability aspects. In contrast, Sustainability for IoT (Sus4IoT) fo-cuses on sustainability aspects of the applied technology and highlights methods to reduce negative impacts, which are associated with the manufacturing and operation of IoT. Elaborated aspects and relations were illustrated in the comprehensive CCIS Framework. This framework represents a tool for the capturing of relevant aspects and relations in this context and thus supports the awareness of the link between IoT and sustainability. Furthermore, the framework suggests an action principle to optimise the performance of IoT systems regarding sustainability.
The central contribution of this thesis is represented by the providence of the CCIS Framework and the contained information regarding the aspects and relations of IoT and sustainability.
Software systems are often developed as a set of variants to meet diverse requirements. Two common approaches to this are "clone-and-owning" and software product lines. Both approaches have advantages and disadvantages. In previous work we and collaborators proposed an idea which combines both approaches to manage variants, similarities, and cloning by using a virtual platform and cloning-related operators.
In this thesis, we present an approach for aggregating essential metadata to enable a propagate operator, which implements a form of change propagation. For this we have developed a system to annotate code similarities which were extracted throughout the history of a software repository. The annotations express similarity maintenance tasks, which can then either be executed automatically by propagate or have to be performed manually by the user. In this work we outline the automated metadata extraction process and the system for annotating similarities; we explain how the implemented system can be integrated into the workflow of an existing version control system (Git); and, finally, we present a case study using the 101haskell corpus of variants.
The publication of open source software aims to support the reuse, the distribution and the general utilization of software. This can only be enabled by the correct usage of open source software licenses. Therefore associations provide a multitude of open source software licenses with different features, of which a developer can choose, to regulate the interaction with his software. Those licenses are the core theme of this thesis.
After an extensive literature research, two general research questions are elaborated in detail. First, a license usage analysis of licenses in the open source sector is applied, to identify current trends and statistics. This includes questions concerning the distribution of licenses, the consistency in their usage, their association over a period of time and their publication.
Afterwards the recommendation of licenses for specific projects is investigated. Therefore, a recommendation logic is presented, which includes several influences on a suitable license choice, to generate an at most applicable recommendation. Besides the exact features of a license of which a user can choose, different methods of ranking the recommendation results are proposed. This is based on the examination of the current situation of open source licensing and license suggestion. Finally, the logic is evaluated on the exemplary use-case of the 101companies project.
The Internet of Things (IoT) recently developed from the far-away vision of ubiquitous computing into very tangible endeavors in politics and economy, implemented in expensive preparedness programs. Experts predict considerable changes in business models that need to be addressed by organizations in order to respond to competition. Although there is a need to develop strategies for upcoming transformations, organizational change literature did not turn to the specific change related to the new technology yet. This work aims at investigating IoT-related organizational change by identifying and classifying different change types. It therefore combines the methodological approach of grounded theory with a discussion and classification of identified change informed by a structured literature review of organizational change literature. This includes a meta-analysis of case studies using a qualitative, exploratory coding approach to identify categories of organizational change related to the introduction of IoT. Furthermore a comparison of the identified categories to former technology-related change is provided using the example of Electronic Business (e-business), Enterprise Resource Planning (ERP) systems, and Customer Relationship Management (CRM) systems. As a main result, this work develops a comprehensive model of IoT-related business change. The model presents two main themes of change indicating that personal smart things will transform businesses by means of using more personal devices, suggesting and scheduling actions of their users, and trying to avoid hazards. At the same time, the availability of information in organizations will further increase to a state where information is available ubiquitously. This will ultimately enable accessing real time information about objects and persons anytime and from any place. As a secondary result, this work gives an overview on concepts of technology-related organizational change in academic literature.
Business Process Querying (BPQ) is a discipline in the field of Business Process Man- agement which helps experts to understand existing process models and accelerates the development of new ones. Its queries can fetch and merge these models, answer questions regarding the underlying process, and conduct compliance checking in return. Many languages have been deployed in this discipline but two language types are dominant: Logic-based languages use temporal logic to verify models as finite state machines whereas graph-based languages use pattern matching to retrieve subgraphs of model graphs directly. This thesis aims to map the features of both language types to features of the other to identify strengths and weaknesses. Exemplarily, the features of Computational Tree Logic (CTL) and The Diagramed Modeling Language (DMQL) are mapped to one another. CTL explores the valid state space and thus is better for behavioral querying. Lacking certain structural features and counting mechanisms it is not appropriate to query structural properties. In contrast, DMQL issues structural queries and its patterns can reconstruct any CTL formula. However, they do not always achieve exactly the same semantic: Patterns treat conditional flow as sequential flow by ignoring its conditions. As a result, retrieved mappings are invalid process execution sequences, i.e. false positives, in certain scenarios. DMQL can be used for behavioral querying if these are absent or acceptable. In conclusion, both language types have strengths and are specialized for different BPQ use cases but in certain scenarios graph-based languages can be applied to both. Integrating the evaluation of conditions would remove the need for logic-based languages in BPQ completely.
The internet is becoming more and more important in daily life. Fundamental changes can be observed in the private sector as well as in the public sector. In the course of this, active involvement of citizens in planning political procedures is more and more supported electronically. The expectations culminate in the assumption that information and communication technology (ICT) can enhance civic participation and reduce disenchantment with politics. Out of these expectations, a lot of eparticipation projects were initiated in Germany. Initiatives were established, e.g. the "Initiative eParticipation", which gave many incentives of electronic participation for policy and administration in order to strengthen decision-making processes with internet supported participation practices. This thesis consists of two major parts. In the first part, definitions of the essential terms are presented. The position of e-participation within the dimension of ebusiness is pointed out. In order to explain e-participation, basics of the classical offline participation are delivered. It will be shown that a change is in progress, not only because of the deployment of ICT. Subsequently, a framework to characterize eparticipation is presented. The European Union is encouraging the implementation of e-participation. So, the city of Koblenz should be no exception. But what is the current situation in Koblenz? To provide an answer to this question, the status quo was examined with the help of a survey among the citizens of Koblenz, which was developed, conducted and evaluated. This is the second major part of this thesis.
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.
“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 growing numbers of breeding rooks (Corvus frugilegus) in the city of Landau (Rhineland- Palatinate, Germany) increase the potential for conflict between rooks and humans, which is mainly associated with noise and faeces. Therefore, the aim of this work is a better understanding of the breeding tree selection of the rook in order to develop options for action and management in the future.
Part I of this thesis provides general background information on the rook and includes mapping of the rookeries in the Anterior Palatinate and South Palatinate including Landau in the year 2020. That mapping revealed that the number of rural colonies has decreased, while the number of urban colonies has increased in the study area in the last few years. In line with current literature, tree species and tree size were important criteria for breeding tree selection. However, the mapping showed that additional factors must be important as well.
Therefore, as rooks seem to often breed along traffic axes, Part II of this thesis examines how temperature, artificial light and noise, which are all linked to traffic axes, affect the breeding tree selection of the rook in the city of Landau. The following three hypotheses are developed: (1) manually selected breeding trees (Bm) have a warmer microclimate than manually selected non-breeding trees (Nm) or randomly selected non-breeding trees (Nr), (2) Bm are exposed to a higher light level than Nm or Nr and (3) Bm are exposed to a higher noise level than Nm or Nr. To test these hypotheses, 15 Bm, 13 Nm and 16 Nr are investigated.
The results show that Bm were exposed to more noise than both types of non-breeding trees (μBm, noise = 36.52481 dB, μNm, noise = 31.27229 dB, μNr, noise = 29.17417 dB) where the difference between Bm and Nr was significant. In addition, there was a tendency for Bm to be exposed to less light (μBm, light = 0.356 lx) than Nm (μNm, light = 0.4107692 lx) and significantly less light than Nr (μNr, light = 1.995 lx), while temperature did not differ between the groups (μBm, temp = 16.90549 °C, μNm, temp = 16.93118 °C, μNr, temp = 17.28639 °C).
This study shows for the first time that rooks prefer trees which are exposed to low light levels and high noise levels, i.e. more intense traffic noise, for breeding. It can only be speculated that the cause of this is lower enemy pressure at such sites. The fact that temperature does not seem to have any influence on breeding tree selection may be due to only small temperature differences at nest height, which might be compensated by breeding behaviour. Consequently, in the long term one management approach could be to divert traffic from inner-city areas, especially schools and hospitals, to bypasses. If tree genera suitable for rooks, such as plane trees, are planted along the bypasses, those sites could provide suitable alternative habitats to inner-city breeding locations, which become less attractive for breeding due to noise reduction. In the short term in addition to locally implemented repellent measures the most effective approach is to strengthen rook acceptance among the population. However, further research is needed to verify the results of this thesis and to gain further insights into rook breeding site selection in order to develop effective management measures.
Artificial neural networks is a popular field of research in artificial intelli-
gence. The increasing size and complexity of huge models entail certain
problems. The lack of transparency of the inner workings of a neural net-
work makes it difficult to choose efficient architectures for different tasks.
It proves to be challenging to solve these problems, and with a lack of in-
sightful representations of neural networks, this state of affairs becomes
entrenched. With these difficulties in mind a novel 3D visualization tech-
nique is introduced. Attributes for trained neural networks are estimated
by utilizing established methods from the area of neural network optimiza-
tion. Batch normalization is used with fine-tuning and feature extraction to
estimate the importance of different parts of the neural network. A combi-
nation of the importance values with various methods like edge bundling,
ray tracing, 3D impostor and a special transparency technique results in a
3D model representing a neural network. The validity of the extracted im-
portance estimations is demonstrated and the potential of the developed
visualization is explored.
The development of a pan-European public E-Procurement system is an important target of the European Union to enhance the efficiency, transparency and competitiveness of public procurement procedures conducted within the European single market. A great obstacle for cross-border electronic procurement is the heterogeneity of national procurement systems in terms of technical, organizational and legal differences. To overcome this obstacle the European Commission funds several initiatives that contribute to the aim of achieving interoperability for pan-European public procurement. Pan European Public Procurement OnLine (PEPPOL) is one of these initiatives that aims at piloting an interoperable pan-European E-Procurement solution to support businesses and public purchasing entities from different member states to conduct their procurement processes electronically.rnrnAs interoperability and inter-connection of distributed heterogeneous information systems are the major requirements in the European procurement domain, and the VCD sub-domain in particular, service-oriented architecture (SOA) seems to provide a promising approach to realize such an architecture, as it promotes loose coupling and interoperability. This master thesis therefore discusses the SOA approach and how its concepts, methodologies and technologies can be used for the development of interoperable IT systems for electronic public procurement. This discussion is enhanced through a practical application of the discussed SOA methodologies by conceptualizing and prototyping of a sub-system derived from the overall system domain of the Virtual Company Dossier. For that purpose, important aspects of interoperability and related standards and technologies will be examined and put into the context of public electronic procurement. Furthermore, the paradigm behind SOA will be discussed, including the derivation of a top-down development methodology for service-oriented systems.
Topic models are a popular tool to extract concepts of large text corpora. These text corpora tend to contain hidden meta groups. The size relation of these groups is frequently imbalanced. Their presence is often ignored when applying a topic model. Therefore, this thesis explores the influence of such imbalanced corpora on topic models.
The influence is tested by training LDA on samples with varying size relations. The samples are generated from data sets containing a large group differences i.e language difference and small group differences i.e. political orientation. The predictive performance on those imbalanced corpora is judged using perplexity.
The experiments show that the presence of groups in training corpora can influence the prediction performance of LDA. The impact varies due to various factors, including language-specific perplexity scores. The group-related prediction performance changes for groups when varying the relative group sizes. The actual change varies between data sets.
LDA is able to distinguish between different latent groups in document corpora if differences between groups are large enough, e.g. for groups with different languages. The proportion of group-specific topics is under-proportional to the share of the group in the corpus and relatively smaller for minorities.
While Virtual Reality has been around for decades it gained new life in recent years. The release of the first consumer hardware devices allows fully immersive and affordable VR for the user at home. This availability lead to a new focus of research on technical problems as well as psychological effects. The concepts of presence, describing the feeling of being in the virtual place, body ownership and their impact are central topics in research for a long time and still not fully understood.
To enable further research in the area of Mixed Reality, we want to introduce a framework that integrates the users body and surroundings inside a visual coherent virtual environment. As one of two main aspects we want to merge real and virtual objects to a shared environment in a way such that they are no longer visually distinguishable. To achieve this the main focus is not supposed to be on a high graphical fidelity but on a simplified representation of reality. The essential question is, what level of visual realism is necessary to create a believable mixed reality environment that induces a sense of presence in the user? The second aspect considers the integration of virtual persons. Can characters be recorded and replayed in a way such that they are perceived as believable entities of the world and therefore act as a part of the users environment?
The purpose of this thesis was the development of a framework called Mixed Reality Embodiment Platform. This inital system implements fundamental functionalities to be used as a basis for future extensions to the framework. We also provide a first application that enables user studies to evaluate the framework and contribute to aforementioned research questions.
Our work finds the fine grained edits in context of neighbouring tokens in Wikipedia articles. We cluster those edits according to similar neighbouring context. We encode neighbouring context into vector space using word vectors. We evaluate clusters returned by our algorithm on extrinsic and intrinsic metric and compare it with previous work. We analyse the relation between extrinsic and intrinsic measurements of fine grained edit tokens.
Constituent parsing attempts to extract syntactic structure from a sentence. These parsing systems are helpful in many NLP applications such as grammar checking, question answering, and information extraction. This thesis work is about implementing a constituent parser for German language using neural networks. Over the past, recurrent neural networks have been used in building a parser and also many NLP applications. In this, self-attention neural network modules are used intensively to understand sentences effectively. With multilayered self-attention networks, constituent parsing achieves 93.68% F1 score. This is improved even further by using both character and word embeddings as a representation of the input. An F1 score of 94.10% was the best achieved by constituent parser using only the dataset provided. With the help of external datasets such as German Wikipedia, pre-trained ELMo models are used along with self-attention networks achieving 95.87% F1 score.
Der Zweck dieser Arbeit ist es, sich auf die kritischen Forschungsherausforderungen und -themen zu konzentrieren, die UI/UX-Designprinzipien umgeben, mit einem Schwerpunkt auf kulturübergreifenden Konzepten aus der Perspektive von E-Learning-Plattformen. Zu diesem Zweck betrachten wir zunächst die kulturellen Dimensionen auf der Grundlage des Hofstede-Rahmens mit dem Ziel, wichtige kulturelle Werte zu identifizieren. Als zweites Ziel der Forschung erleichtert eine Reihe von Kriterien, die so genannte Usability-Heuristik von Nielsen, die Erkennung von Usability Problemen bei der Gestaltung von Benutzeroberflächen (UI). Die Usability-Heuristiken umfassen zehn Variablen, die die Interaktion zwischen dem Benutzer und einem Produkt oder System beeinflussen. Wenn wir uns näher mit
diesen Themen befassen, werden wir in der Lage sein, eine Matrix mit Beziehungen zwischen der heuristischen Bewertung von Nielsen und dem kulturellen Rahmen von Geert Hofstede aufzudecken. Abschließend erörtern wir das mögliche Potenzial kultureller Werte zur Beeinflussung von Benutzeroberflächen für E-Learning-Plattformen. In der Tat gibt es einige Funktionen in E-Learning-Plattformen, die aufgrund der Kultur weniger diskutiert werden, obwohl sie sehr praktisch in die Plattformen integriert werden können.
The extensive literature in the data visualization field indicates that the process of creating efficient data visualizations requires the data designer to have a large set of skills from different fields (such as computer science, user experience, and business expertise). However, there is a lack of guidance about the visualization process itself. This thesis aims to investigate the different processes for creating data visualizations and develop an integrated framework to guide the process of creating data visualizations that enable the user to create more useful and usable data visualizations. Firstly, existing frameworks in the literature will be identified, analyzed and compared. During this analysis, eight views of the visualization process are developed. These views represent the set of activities which should be done in the visualization process. Then, a preliminary integrated framework is developed based on an analysis of these findings. This new integrated framework is tested in the field of Social Collaboration Analytics on an example from the UniConnect platform. Lastly, the integrated framework is refined and improved based on the results of testing with the help of diagrams, visualizations and textual description. The results show that the visualization process is not a waterfall type. It is the iterative methodology with the certain phases of work, demonstrating how to address the eight views with different levels of stakeholder involvement. The findings are the basis for a visualization process which can be used in future work to develop the fully functional methodology.
Tracking is an integral part of many modern applications, especially in areas like autonomous systems and Augmented Reality. For performing tracking there are a wide array of approaches. One that has become a subject of research just recently is the utilization of Neural Networks. In the scope of this master thesis an application will be developed which uses such a Neural Network for the tracking process. This also requires the creation of training data as well as the creation and training of a Neural Network. Subsequently the usage of Neural Networks for tracking will be analyzed and evaluated. This includes several aspects. The quality of the tracking for different degrees of freedom will be checked as well as the the impact of the Neural Network on the applications performance. Additionally the amount of required training data is investigated, the influence of the network architecture and the importance of providing depth data as part of the networks input. This should provide an insight into how relevant this approach could be for its adoption in future products.
Mapping ORM to TGraph
(2017)
Object Role Modeling (ORM) is a semantic modeling language used to describe objects and their relations amongst each other. Both objects and relations may be subject to rules or ORM constraints.
TGraphs are ordered, attributed, typed and directed graphs. The type of a TGraph and its components, the edges and vertices, is defined using the schema language graph UML (grUML), a profiled version of UML class diagrams. The goal of this thesis is to map ORM schemas to grUML schemas in order to be able to represent ORM schema instances as TGraphs.
Up to this point, the preferred representation for ORM schema instances is in form of relational tables. Though mappings from ORM schemas to relational schemas exist, those publicly available do not support most of the constraints ORM has to offer.
Constraints can be added to grUML schemas using the TGraph query language GReQL, which can efficiently check whether a TGraph validates the constraint or not. The graph library JGraLab provides efficient implementations of TGraphs and their query language GReQL and supports the generation of grUML schemas.
The first goal of this work is to perform a complete mapping from ORM schemas to grUML schemas, using GReQL to sepcify constraints. The second goal is to represent ORM instances in form of TGraphs.
This work gives an overview of ORM, TGraphs, grUML and GReQL and the theoretical mapping from ORM schemas to grUML schemas. It also describes the implementation of this mapping, deals with the representation of ORM schema instances as TGraphs and the question how grUML constraints can be validated.
The Internet of Things (IoT) is a network of addressable, physical objects that contain embedded sensing, communication and actuating technologies to sense and interact with their environment (Geschickter 2015). Like every novel paradigm, the IoT sparks interest throughout all domains both in theory and practice, resulting in the development of systems pushing technology to its limits. These limits become apparent when having to manage an increasing number of Things across various contexts. A plethora of IoT architecture proposals have been developed and prototype products, such as IoT platforms, been introduced. However, each of these architectures and products apply their very own interpretations of an IoT architecture and its individual components so that IoT is currently more an Intranet of Things than an Internet of Things (Zorzi et al. 2010). Thus, this thesis aims to develop a common understanding of the elements forming an IoT architecture and provide high-level specifications in the form of a Holistic IoT Architecture Framework.
Design Science Research (DSR) is used in this thesis to develop the architecture framework based on the pertinent literature. The development of the Holistic IoT Architecture Framework includes the identification of two new IoT Architecture Perspectives that became apparent during the analysis of the IoT architecture proposals identified in the extant literature. While applying these novel perspectives, the need for a new component for the architecture framework, which was merely implicitly mentioned in the literature, became obvious as well. The components of various IoT architecture proposals as well as the novel component, the Thing Management System, were combined, consolidated and related to each other to develop the Holistic IoT Architecture Framework. Subsequently, it was shown that the specifications of the architecture framework are suitable to guide the implementation of a prototype.
This contribution provides a common understanding of the basic building blocks, actors and relations of an IoT architecture.
The Material Point Method (MPM) has proven to be a very capable simulation method in computer graphics that is able to model materials that were previously very challenging to animate [1, 2]. Apart from simulating singular materials, the simulation of multiple materials that interact with each other introduces new challenges. This is the focus of this thesis. It will be shown that the self-collision capabilities of the MPM can naturally handle multiple materials interacting in the same scene on a collision basis, even if the materials use distinct constitutive models. This is then extended by porous interaction of materials as in[3], which also integrates easily with MPM.It will furthermore be shown that regular single-grid MPM can be viewed as a subset of this multi-grid approach, meaning that its behavior can also be achieved if multiple grids are used. The porous interaction is generalized to arbitrary materials and freely changeable material interaction terms, yielding a flexible, user-controllable framework that is independent of specific constitutive models. The framework is implemented on the GPU in a straightforward and simple way and takes advantage of the rasterization pipeline to resolve write-conflicts, resulting in a portable implementation with wide hardware support, unlike other approaches such as [4].
Digital transformation is a prevailing trend in the world, especially in dynamic Asia. Vietnam has recorded remarkable changes in the economy as domestic enterprises have made new strides in the digital transformation process. MB Bank, one of the prestigious financial groups in Vietnam, also takes advantage of digital transformation to have the opportunity to break through to become a large-scale technology enterprise with many factors such as improving customer experience, increasing customer base and increasing customer satisfaction. enhance competitiveness, build trust and loyalty for customers. However, in the process of converting MB, there are also many challenges that require banks to have appropriate policies to handle. It can be said that MB Bank is a typical case study of digital transformation in the banking sector in Vietnam.
Challenges of Implementing Innovation Strategies at Large Organizations: A case of Lotte Group
(2023)
For many decades, one of the most important focuses of research has been on determining whether or not there is a correlation between the size of an organization and its level of innovation. Unlike small companies, large companies often have well-established structure that are hard to change and change managements seems to be much more difficult especially related to innovation. Nevertheless, there are many examples to prove the opposites. Some large organization like Apple, Amazon... always show great innovation efforts and keep changing in a much positive way. Therefore, the aim of this thesis is to discuss of how large organization can be able to implement innovation when having much drawbacks compare to SMEs. Through the use of a qualitative research approach, researcher was able to explore essential information on the innovation strategies that large companies are using in order to innovate and how they could overcome existing challenges by studying the working process of Lotte Group – one of the biggest companies in Korea.
FinTech is deemed to be an underexplored phenomenon even in academic and real environments. Among (1) “Sustainable FinTech” – the application of information technology as innovation in established financial services providers’ business operation; and (2) “Disruptive FinTech” – the provision of financial products and services by non-incumbents which in most cases are information technology entrepreneurs, the former receives more attention. In order to contribute to Disruptive FinTech category, the thesis strive to examine Entrepreneurial Strategy framework applied for technology players taking part in Vietnam financial market.
The purpose of this master thesis is to enable the Robot Lisa to process complex commands and extract the necessary information in order to perform a complex task as a sequence of smaller tasks. This is intended to be achieved by the improvement of the understanding that Lisa has of her environment by adding semantics to the maps that she builds. The complex command itself will be expected to be already parsed. Therefore the way the input is processed to become a parsed command is out of the scope of this work. Maps that Lisa builds will be improved by the addition of semantic annotations that can include any kind of information that might be useful for the performance of generic tasks. This can include (but not necessarily limited to) hierarchical classifications of locations, objects and surfaces. The processing of the command in addition to some information of the environment shall trigger the performance of a sequence of actions. These actions are expected to be included in Lisa- currently implemented tasks and will rely on the currently existing modules that perform them.
Nevertheless the aim of this work is not only to be able to use currently implemented tasks in a more complex sequence of actions but also make it easier to add new tasks to the complex commands that Lisa can perform.
Willingness to pay and willingness to accept on a two-sided platform - The use case of DoBeeDo
(2019)
It is widely known that especially for technology-based start-ups, entrepreneurs need to set up the boundaries of the business and define the product/service to offer in order to minimize the risk of failure. The goal of this thesis is to not only emphasize the importance of the business model development and evaluation but also show an example customer validation process for an emerging start-up named DoBeeDo, which is a mobile app operating on a two-sided market. During the process of customer validation a survey has been conducted to evaluate the interest of the target groups as well as the fit of their expectations using the Willingness to Pay and Willingness to Accept measures. The paper includes an analysis and evaluation of the gathered results and assesses whether the execution of the Customer Development Model can be continued.
In recent years head mounted displays (HMD) and their abilities to create virtual realities comparable with the real world moved more into the focus of press coverage and consumers. The reason for this lies in constant improvements in available computing power, miniaturisation of components as well as the constantly shrinking power consumption. These trends originate in the general technical progress driven by advancements made in smartphone sector. This gives more people than ever access to the required components to create these virtual realities. However at the same time there is only limited research which uses the current generation of HMDs especially when comparing the virtual and real world against each other. The approach of this thesis is to look into the process of navigating both real and virtual spaces while using modern hardware and software. One of the key areas are the spatial and peripheral perception without which it would be difficult to navigate a given space. The influence of prior real and virtual experiences on these will be another key aspect. The final area of focus is the influence on the emotional state and how it compares to the real world. To research these influences a experiment using the Oculus Rift DK2 HMD will be held in which subjects will be guided through a real space as well as a virtual model of it. Data will be gather in a quantitative manner by using surveys. Finally, the findings will be discussed based on a statistical evaluation. During these tests the different perception of distances and room size will the compared and how they change based on the current reality. Furthermore, the influence of prior spatial activities both in the real and the virtual world will looked into. Lastly, it will be checked how real these virtual worlds are and if they are sufficiently sophisticated to trigger the same emotional responses as the real world.
Commonsense reasoning can be seen as a process of identifying dependencies amongst events and actions. Understanding the circumstances surrounding these events requires background knowledge with sufficient breadth to cover a wide variety of domains. In the recent decades, there has been a lot of work in extracting commonsense knowledge, a number of these projects provide their collected data as semantic networks such as ConceptNet and CausalNet. In this thesis, we attempt to undertake the Choice Of Plausible Alternatives (COPA) challenge, a problem set with 1000 questions written in multiple-choice format with a premise and two alternative choices for each question. Our approach differs from previous work by using shortest paths between concepts in a causal graph with the edge weight as causality metric. We use CausalNet as primary network and implement a few design choices to explore the strengths and drawbacks of this approach, and propose an extension using ConceptNet by leveraging its commonsense knowledge base.
Thesis is devoted to the topic of challenges and solutions for human resources management (HRM) in international organizations. The aim is to investigate methodological approaches to assessment of HRM challenges and solutions, and to apply them on practice, to develop ways of improvement of HRM of a particular enterprise. The practical research question investigated is “Is the Ongoing Professional Development – Strategic HRM (OPD-SHRM) model a better solution for HRM system of PrJSC “Philip Morris Ukraine”?”
To achieve the aim of this work and to answer the research question, we have studied theoretical approaches to explaining and assessing HRM in section 1, analyzed HRM system of an international enterprise in section 2, and then synthesized theory and practice to find intersection points in section 3.
Research findings indicate that the main challenge of HRM is to balance between individual and organizational interests. Implementation of OPD-SHRM is one of the solutions. Switching focus from satisfaction towards success will bring both tangible and intangible benefits for individuals and organization. In case of PrJSC “Philip Morris Ukraine”, the maximum forecasted increase is 330% in net profit, 350% in labor productivity, and 26% in Employee Development and Engagement Index.