Artificial intelligence (AI) is becoming more widely used in a number of industries, including in the field of education. Applications of artificial intelligence (AI) are becoming crucial for schools and universities, whether for automated evaluation, smart educational systems, individualized learning, or staff support. ChatGPT, anAI-based chatbot, offers coherent and helpful replies based on analyzing large volumes of data. Integrating ChatGPT, a sophisticated Natural Language Processing (NLP) tool developed by OpenAI, into higher education has sparked significant interest and debate. Since the technology is already adapted by many students and teachers, this study delves into analyzing the sentiments expressed on university websites regarding ChatGPT integration into education by creating a comprehensive sentiment analysis framework using Hierarchical Residual RSigELU Attention Network (HR-RAN). The proposed framework addresses several challenges in sentiment analysis, such as capturing fine-grained sentiment nuances, including contextual information, and handling complex language expressions in university review data. The methodology involves several steps, including data collection from various educational websites, blogs, and news platforms. The data is preprocessed to handle emoticons, URLs, and tags and then, detect and remove sarcastic text using the eXtreme Learning Hyperband Network (XLHN). Sentences are then grouped based on similarity and topics are modeled using the Non-negative Term-Document Matrix Factorization (NTDMF) approach. Features, such as lexico-semantic, lexico structural, and numerical features are extracted. Dependency parsing and coreference resolution are performed to analyze grammatical structures and understand semantic relationships. Word embedding uses the Word2Vec model to capture semantic relationships between words. The preprocessed text and extracted features are inputted into the HR-RAN classifier to categorize sentiments as positive, negative, or neutral. The sentiment analysis results indicate that 74.8% of the sentiments towards ChatGPT in higher education are neutral, 21.5% are positive, and only 3.7% are negative. This suggests a predominant neutrality among users, with a significant portion expressing positive views and a very small percentage holding negative opinions. Additionally, the analysis reveals regional variations, with Canada showing the highest number of sentiments, predominantly neutral, followed by Germany, the UK, and the USA. The sentiment analysis results are evaluated based on various metrics, such as accuracy, precision, recall, F-measure, and specificity. Results indicate that the proposed framework outperforms conventional sentiment analysis models. The HR-RAN technique achieved a precision of 98.98%, recall of 99.23%, F-measure of 99.10%, accuracy of 98.88%, and specificity of 98.31%. Additionally, word clouds are generated to visually represent the most common terms within positive, neutral, and negative sentiments, providing a clear and immediate understanding of the key themes in the data. These findings can inform educators, administrators, and developers about the benefits and challenges of integrating ChatGPT into educational settings, guiding improvements in educational practices and AI tool development.

In international business relationships, such as international railway operations, large amounts of data can be exchanged among the parties involved. For the exchange of such data, a limited risk of being cheated by another party, e.g., by being provided with fake data, as well as reasonable cost and a foreseeable benefit, is expected. As the exchanged data can be used to make critical business decisions, there is a high incentive for one party to manipulate the data in its favor. To prevent this type of manipulation, mechanisms exist to ensure the integrity and authenticity of the data. In combination with a fair exchange protocol, it can be ensured that the integrity and authenticity of this data is maintained even when it is exchanged with another party. At the same time, such a protocol ensures that the exchange of data only takes place in conjunction with the agreed compensation, such as a payment, and that the payment is only made if the integrity and authenticity of the data is ensured as previously agreed. However, in order to be able to guarantee fairness, a fair exchange protocol must involve a trusted third party. To avoid fraud by a single centralized party acting as a trusted third party, current research proposes decentralizing the trusted third party, e.g., by using a distributed ledger based fair exchange protocol. However, for assessing the fairness of such an exchange, state-of-the-art approaches neglect costs arising for the parties conducting the fair exchange. This can result in a violation of the outlined expectation of reasonable cost, especially when distributed ledgers are involved, which are typically associated with non-negligible costs. Furthermore, the performance of typical distributed ledger-based fair exchange protocols is limited, posing an obstacle to widespread adoption. To overcome the challenges, in this thesis, we introduce the foundation for a data exchange platform allowing for a fully decentralized fair data exchange with reasonable cost and performance. As a theoretical foundation, we introduce the concept of cost fairness, which considers cost for the fairness assessment by requesting that a party following the fair exchange protocol never suffers any unilateral disadvantages. We prove that cost fairness cannot be achieved using typical public distributed ledgers but requires customized distributed ledger instances, which usually lack complete decentralization. However, we show that the highest unilateral cost are caused by a grieving attack. To allow fair data exchanges to be conducted with reasonable cost and performance, we introduce FairSCE, a distributed ledger-based fair exchange protocol using distributed ledger state channels and incorporating a mechanism to protect against grieving attacks, reducing the possible unilateral cost that have to be covered to a minimum. Based on our evaluation of FairSCE, the worst-case cost for data exchange, even in the presence of malicious parties, is known, which allows an estimate of the possible benefit and, thus, the preliminary estimate of economic utility. Furthermore, to allow for an unambiguous assessment of the correct data being transferred while still allowing for sensitive parts of the data to be masked, we introduce an approach for the hashing of hierarchically structured data, which can be used to ensure integrity and authenticity of the data being transferred.

The trends of industry 4.0 and the further enhancements toward an ever changing factory lead to more mobility and flexibility on the factory floor. With that higher need of mobility and flexibility the requirements on wireless communication rise. A key requirement in that setting is the demand for wireless Ultra-Reliability and Low Latency Communication (URLLC). Example use cases therefore are cooperative Automated Guided Vehicles (AGVs) and mobile robotics in general. Working along that setting this thesis provides insights regarding the whole network stack. Thereby, the focus is always on industrial applications. Starting on the physical layer, extensive measurements from 2 GHz to 6 GHz on the factory floor are performed. The raw data is published and analyzed. Based on that data an improved Saleh-Valenzuela (SV) model is provided. As ad-hoc networks are highly depended onnode mobility, the mobility of AGVs is modeled. Additionally, Nodal Encounter Patterns (NEPs) are recorded and analyzed. A method to record NEP is illustrated. The performance by means of latency and reliability are key parameters from an application perspective. Thus, measurements of those two parameters in factory environments are performed using Wireless Local Area Network (WLAN) (IEEE 802.11n), private Long Term Evolution (pLTE) and 5G. This showed auto-correlated latency values. Hence, a method to construct confidence intervals based on auto-correlated data containing rare events is developed. Subsequently, four performance improvements for wireless networks on the factory floor are proposed. Of those optimization three cover ad-hoc networks, two deal with safety relevant communication, one orchestrates the usage of two orthogonal networks and lastly one optimizes the usage of information within cellular networks. Finally, this thesis is concluded by an outlook toward open research questions. This includes open questions remaining in the context of industry 4.0 and further the ones around 6G. Along the research topics of 6G the two most relevant topics concern the ideas of a network of networks and overcoming best-effort IP.

Currently, there are a variety of digital tools in the humanities, such as annotation, visualization, or analysis software, which support researchers in their work and offer them new opportunities to address different research questions. However, the use of these tools falls far short of expectations. In this thesis, twelve improvement measures are developed within the framework of a design science theory to counteract the lack of usage acceptance. By implementing the developed design science theory, software developers can increase the acceptance of their digital tools in the humanities context.

Social media provides a powerful way for people to share opinions and sentiments about a specific topic, allowing others to benefit from these thoughts and feelings. This procedure generates a huge amount of unstructured data, such as texts, images, and references that are constantly increasing through daily comments to related discussions. However, the vast amount of unstructured data presents risks to the information-extraction process, and so decision making becomes highly challenging. This is because data overload may cause the loss of useful data due to its inappropriate presentation and its accumulation. To this extent, this thesis contributed to the field of analyzing and detecting feelings in images and texts. And that by extracting the feelings and opinions hidden in a huge collection of image data and texts on social networks After that, these feelings are classified into positive, negative, or neutral, according to the features of the classified data. The process of extracting these feelings greatly helps in decision-making processes on various topics as will be explained in the first chapter of the thesis. A system has been built that can classify the feelings inherent in the images and texts on social media sites, such as people’s opinions about products and companies, personal posts, and general messages. This thesis begins by introducing a new method of reducing the dimension of text data based on data-mining approaches and then examines the sentiment based on neural and deep neural network classification algorithms. Subsequently, in contrast to sentiment analysis research in text datasets, we examine sentiment expression and polarity classification within and across image datasets by building deep neural networks based on the attention mechanism.

Virtual reality is a growing field of interest as it provides a particular intuitive way of user-interaction. However, there are still open technical issues regarding latency — the delay between interaction and display reaction — and the trade-off between visual quality and frame-rate of real-time graphics, especially when taking visual effects like specular and semi-transparent surfaces and volumes into account. One solution, a distributed rendering setup, is presented in this thesis, in which the image synthesis is divided into an accurate but costly physically based rendering thread with a low refresh rate and a fast reprojection thread to remain a responsive interactivity with a high frame-rate. Two novel reprojection techniques are proposed that cover reflections and refractions produced by surface ray-tracing as well as volumetric light transport generated by volume ray-marching. The introduced setup can enhance the VR experience within several domains. In this thesis, three innovative training applications have been realized to investigate the added value of virtual reality to the three learning stages of observation, interaction and collaboration. For each stage an interdisciplinary curriculum, currently taught with traditional media, was transferred to a VR setting in order to investigate how virtual reality is capable of providing a natural, flexible and efficient learning environment

Eine didaktisch-methodische Ausarbeitung einer Unterrichtsreihe zur Online-Plattform InstaHub mit dem Themenschwerpunkt Datenschutz. Die Unterrichtsreihe ist für die Klassenstufe 5-6 (und höher) eines Gymnasiums geeignet. Weiterhin werden Anregungen für Datenschutz im Unterricht gegeben.

The term “Software Chrestomaty” is defined as a collection of software systems meant to be useful in learning about or gaining insight into software languages, software technologies, software concepts, programming, and software engineering. 101companies software chrestomathy is a community project with the attributes of a Research 2.0 infrastructure for various stakeholders in software languages and technology communities. The core of 101companies combines a semantic wiki and confederated open source repositories. We designed and developed an integrated ontology-based knowledge base about software languages and technologies. The knowledge is created by the community of contributors and supported with a running example and structured documentation. The complete ecosystem is exposed by using Linked Data principles and equipped with the additional metadata about individual artifacts. Within the context of software chrestomathy we explored a new type of software architecture – linguistic architecture that is targeted on the language and technology relationships within a software product and based on the megamodels. Our approach to documentation of the software systems is highly structured and makes use of the concepts of the newly developed megamodeling language MegaL. We “connect” an emerging ontology with the megamodeling artifacts to raise the cognitive value of the linguistic architecture.

Skalierbarkeit und garantierte Ausliererung sind essentielle Eigenschaften eines jeden Routingalgorithmus. Beides bietet bei drahtlosen Ad-hoc Netzwerken die Kombination aus Greedy- und Face- Routing, sofern ein planarer Graph zur Verfügung steht. Doch gerade die fehlerfreie Planarisierung bereitet bei realistischen Netzwerken Schwierigkeiten. Daher soll mit dieser Arbeit die Frage beantwortet werden, zu welcher Fehlerrate es führt, wenn der Graph lediglich mit lokalen Methoden teilplanarisiert wird. Dazu wurde eine Simulationsumgebung geschaffen, um unter Anwendung des Log-Normal-Shadowing-Modells zufällige Konnektivitätsgraphen zu generieren. Diese wurden anschließend durch zwei unterschiedliche, lokale Strategien teilplanarisiert. Es wurden neun verschiedene Settings definiert, die sich aus drei unterschiedlichen Graphendichten und drei unterschiedlichen Werten für den Sigmaparameter des Log-Normal-Shadowing-Modells ergeben. Für jedes Setting wurde in 2000 Simulationsdurchläufen das Verhalten von Greedy-, Face- und kombiniertem Greedy-Face-Routing untersucht und ausgewertet. Zum Abschluss wurden die Ergebnisse dieser Simulation bewertet und diskutiert.

Using semantic data from general-purpose programming languages does not provide the unified experience one would want for such an application. Static error checking is lacking, especially with regards to static typing of the data. Based on the previous work of λ-DL, which integrates semantic queries and concepts as types into a typed λ-calculus, this work takes its ideas a step further to meld them into a real-world programming language. This thesis explores how λ-DL's features can be extended and integrated into an existing language, researches an appropriate extension mechanism and produces Semantics4J, a JastAdd-based Java language semantic data extension for type-safe OWL programming, together with examples of its usage.