Refine
Document Type
- Conference Proceeding (16)
- Master's Thesis (12)
- Bachelor Thesis (7)
- Final Report (4)
Year of publication
- 2023 (39) (remove)
Language
- English (39) (remove)
Keywords
- Blockchain (9)
- Maschinelles Lernen (6)
- Vektorquantisierung (3)
- Bitcoin (2)
- Graphentheorie (2)
- Neuronales Netz (2)
- Smart contract (2)
- Algorithmus (1)
- Ammoniumverbindungen (1)
- Anthropocene Disease (1)
- Anämie (1)
- Bibliometric analysis (1)
- Biometrie (1)
- Bodenorganismus (1)
- Bridge (1)
- Business Reputation System (1)
- COVID-19 (1)
- Cryptocurrency (1)
- DAO (1)
- DNA Barcoding (1)
- DeSci (1)
- Dezentralisation (1)
- Direct Laser Interference Patterning (1)
- E-Learning (1)
- Education (1)
- Epidemiologie (1)
- Fernunterricht (1)
- Fledermäuse (1)
- Gesichtserkennung (1)
- Globalisierung (1)
- Identitätsverwaltung (1)
- Immunologische Diagnostik (1)
- Integriertes Lernen (1)
- Intelligent methods (1)
- Interkulturelle Kompetenz (1)
- Kind (1)
- Klimaänderung (1)
- Kommunikationsstrategie (1)
- Kugelspalt (1)
- Künstliche Intelligenz (1)
- Laser beam welding (1)
- Lebensraum (1)
- Lernerfolg (1)
- Luftschall (1)
- Malaria (1)
- Mathematisches Modell (1)
- Maximal Extractable Value (1)
- Migration (1)
- Non-Fungible Token (1)
- Objekterkennung (1)
- Oxidation (1)
- Pandemie (1)
- Paper-based Coffee Cups (1)
- Polygon scanner processing (1)
- Prozessüberwachung (1)
- Risiko (1)
- SARS-Cov- 2 (1)
- Sandwich Attacks (1)
- Satellitenfunk (1)
- Satellitentechnik (1)
- Siliziumbearbeitung (1)
- Smart Contract Programming (1)
- Spaltströmung (1)
- Stakeholder (1)
- Stickstoffverbindungen (1)
- Supply Chain Management (1)
- Surface texturing (1)
- Thulium (1)
- Tiefschweißen (1)
- Traceability (1)
- Transparenz (1)
- Trust (1)
- Tutte-Polynom (1)
- Ultrafast (1)
- Ultrakurzpulslaser (1)
- User Generated Content (1)
- Virtuelle Währung (1)
- Web of Things (1)
- Wildtiere (1)
- Zeitreihe (1)
- atomic swaps (1)
- collective trauma (1)
- cross cultural work environment (1)
- decentralized science (1)
- high repetition rate (1)
- high throughput (1)
- high troughput (1)
- ightning network (1)
- industrial lasers (1)
- launch strategies (1)
- learning motivation (1)
- mev-inspect (1)
- micro drilling (1)
- mindful leadership (1)
- mint mechanism (1)
- negatively-valenced emotions (1)
- optical coherence tomography (1)
- optimization (1)
- pandemic (1)
- polygon scanner (1)
- pricing strategy (1)
- scholar publishing (1)
- scientific paper token (1)
- sensor technology (1)
- silicon processing, high power (1)
- temporal energy deposition (1)
- trauma studies (1)
- ultra-fast (1)
- ultrafast laser (1)
- workplace mental health (1)
- Ökosystem (1)
Institute
We report on our recent progress in creating a new type of compact laser that uses thulium-based fiber CPA technology to emit a central wavelength of 2 μm. This laser can produce pulse energies of >100 μJ and an average power of >15 W. It is designed to be long-lasting and is built for industrial use, making it a great fit for integration into laser machines used for materials processing. These laser parameters are ideal for working with semiconductors like silicon, allowing for tasks such as micro-welding, cutting of filaments, dicing, bonding and more.
A Systematic Literature Review on Blockchain Oracles: State of Research, Challenges, and Trends
(2023)
To enable data exchange between the Blockchain protocol (on-chain) and the real world (off-chain), e.g., non-Blockchain-based applications and systems, a software called Oracle is used [3]. Blockchain oracle is an important component in the use of off-chain data for on-chain smart contracts. However, there is limited scientific literature available on this important blockchain topic. Therefore, in this paper, a novel systematic literature review based on intelligent methods, e.g., information linking, topic clustering and focus identification through frequency calculations, is proposed. Thus, the current state of scientific research interest, content and challenges, and future research directions for blockchain oracles are identified. This paper shows that there is little unbiased literature that does not call oracles a problem. From the results of this new literature review framework, relevant areas of data handling and verification with blockchain oracles are identified for future research.
Derived from the Ancient Greek word τραῦμα (engl. wound,
damage), the word trauma refers to either physical or emotional wounds. Nowadays, it is mostly used in the context of psychological wounds, inflicted by an identity-shattering event – an event that causes the traumatised to not be able to reconcile their lived reality with the expectation of a human universal experience anymore. The last decade, the last two years in particular, and the last two weeks ad absurdum, have scarred the global landscape of human existence beyond recognition. From Putin’s unexpected reimposition of mutually assured destruction doctrines via the global SARS-Cov-2 pandemic to the lingering threat of climate doom, people all over the globe have been faced with persistent threats to their most basic perceptions of ontological safety. This article seeks to examine the impact of the SARS-Cov-2 pandemic and to which degree it is justified to speak of a pandemic trauma. In addition, it engages with the liminality of pandemic trauma as a shared, collective and an isolated, individual experience, and potential mitigation strategies for building community resilience.
In the field of Blockchain Technology applications and research, non-fungible tokens (NFTs) have gained significant attention in recent years. Whilst current research is focused on NFT use cases or the purchase of NFTs from an investor’s perspective, the NFT launch (i.e. primary market) from a creator’s perspective remains uncovered. However, the launch strategy is considered to be an important factor for the success of a product. Therefore, our research paper aims to explore launch strategies of NFTs. Thereby, we discuss the marketing mix instruments price (i.e. pricing strategy), place (i.e. mint mechanism), and promotion. Through an empirical approach of conducting eight expert interviews, we examine parameters that are used to define an NFT launch strategy and assess their preference of different stakeholders.
Machine learning models for timeseries have always been a special topic of interest due to their unique data structure. Recently, the introduction of attention improved the capabilities of recurrent neural networks and transformers with respect to their learning tasks such as machine translation. However, these models are usually subsymbolic architectures, making their inner working hard to interpret without comprehensive tools. In contrast, interpretable models such learning vector quantization are more transparent in the ability to interpret their decision process. This thesis tries to merge attention as a machine learning function with learning vector quantization to better handle timeseries data. A design on such a model is proposed and tested with a dataset used in connection with the attention based transformers. Although the proposed model did not yield the expected results, this work outlines improvements for further research on this approach.
Analysis of Continuous Learning Strategies at the Example of Replay-Based Text Classification
(2023)
Continuous learning is a research field that has significantly boosted in recent years due to highly complex machine and deep learning models. Whereas static models need to be retrained entirely from scratch when new data get available, continuous models progressively adapt to new data saving computational resources. In this context, this work analyzes parameters impacting replay-based continuous learning approaches at the example of a data-incremental text classification task using an MLP and LSTM. Generally, it was found that replay improves the results compared to naive approaches but achieves not the performance of a static model. Mainly, the performances increased with more replayed examples, and the number of training iterations has a significant influence as it can partly control the stability-plasticity-trade-off. In contrast, the impact of balancing the buffer and the strategy to select examples to store in the replay buffer were found to have a minor impact on the results in the present case.
Analysis of the Forensic Preparation of Biometric Facial Features for Digital User Authentication
(2023)
Biometrics has become a popular method of securing access to data as it eliminates the need for users to remember a password. Although exploiting the vulnerabilities of biometric systems increased with their usage, these could also be helpful during criminal casework.
This thesis aims to evaluate approaches to bypass electronic devices with forged faces to access data for law enforcement. Here, obtaining the necessary data in a timely manner is critical. However, unlocking the devices with a password can take several years with a brute force attack. Consequently, biometrics could be a quicker alternative for unlocking.
Various approaches were examined to bypass current face recognition technologies. The first approaches included printing the user's face on regular paper and aimed to unlock devices performing face recognition in the visible spectrum. Further approaches consisted of printing the user's infrared image and creating three-dimensional masks to bypass devices performing face recognition in the near-infrared. Additionally, the underlying software responsible for face recognition was reverse-engineered to get information about its operation mode.
The experiments demonstrate that forged faces can partly bypass face recognition and obtain secured data. Devices performing face recognition in the visible spectrum can be unlocked with a printed image of the user's face. Regarding devices with advanced near-infrared face recognition, only one could be bypassed with a three-dimensional face mask. In addition, its underlying software provided evidence about the demands of face recognition. Other devices under attack remained locked, and their software provided no clues.
Safety, quality, and sustainability concerns have arisen from global supply chains. Stakeholders incur risk regarding these factors, given their significance and complexity. Thus, each business's supply chain risk management must prioritize product characteristics. Accordingly, an effective traceability solution that can monitor and regulate product and supply chain aspects is crucial, especially in a given scenario. This re-search paper elucidates the potential of smart contracts in blockchain to enhancing the efficacy of business transactions and ensuring comprehensive traceability within the supply chain of paper-based coffee cups The improved levels of transaction transparency and security in traditional supply chains have been achieved through the digitization of supply chain ecosystem interactions and transactions. This approach makes verifying sources, manufacturing procedures, and quality standards easier in complex supply chains. Accordingly, the integration helps stakeholders monitor and track the whole ecosystem, promoting transparency, predictability, and dependability.
Laser welding of hidden T-joints, connecting the web-sheet through the face-sheet of the joint can provide advantages like increased lightweight potential in manufacturing sandwich structures with thin-walled cores. However, maintaining the correct positioning of the beam relative to the joint is challenging. A method to reduce the effort of positioning is using optical coherence tomography (OCT), that interferometrically measures the reflection distance inside of the keyhole during laser deep penetration welding. In this study new approaches for targeted data processing of the OCT-signal to automatically detect misalignments are presented. It is shown that considering multiple components from the inference pattern and the respective signal intensities improve the detection accuracy of misalignments.
Aspects of Mindful Leadership Upon the Psychological Health of Employees in an Intercultural Context
(2023)
Across the globe, organizations are in the midst of rapid transformation. Immigration, digitalization and the push for sustainability are just to name a few. Organizational structures are being pushed for more agility, co-opetition, integration, tenable and resilient workplaces. Social structures of companies are being reformed and the weight of cooperation and integration lays upon the leaders and employees. But from this weight of integration what psychological effects does it play upon the migrant and domestic employees to be engaged at work? What role does the leadership style impact the mental health and engagement in the cross-cultural workplace? Previous work has shown the importance of workplace integration, however, the impact of the mental health of domestic employees needs more attention from the scholars in this new context. The object of the research is to define the connection of mindful leadership and the psychological health of employees within a cross-cultural workplace and to develop strategies to improve workplace engagement.