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The number of Internet of Things (IoT) devices is increasing rapidly. The Trustless Incentivized Remote Node Network, in short IN3 (Incubed), enables trustworthy and fast access to a blockchain for a large number of low-performance IoT devices. Although currently IN3 only supports the verification of Ethereum data, it is not limited to one blockchain due to modularity. This thesis describes the fundamentals, the concept and the implementation of the Bitcoin verification in IN3.
In this thesis two novel methods for removing undesired background illumination are de-veloped. These include a wavelet analysis based approach and an enhancement of a deep learning method. These methods have been compared with conventional methods, using real confocal microscopy images and synthetic generated microscopy images. These synthetic images were created utilizing a generator introduced in this thesis.
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.
This work emphasises the synergy between anthropologi-cal research on human skeletal remains and suitable doc-umentation strategies. Highlighting the significance of data recording and the use of digital databases in various aspects of anthropological work on bones, including scien-tific standards, skeletal collections, analysis of research re-sults, ethical considerations, and curation, it provides a comprehensive examination of these topics to demonstrate the value of investing time and resources in this field, countering the existing lack of funding that has led to sig-nificant deficiencies. Additionally, the paper outlines the requirements and challenges associated with standard data protocoling and suggests that digital data manage-ment frameworks and technologies such as ontologies and semantic web technologies for anthropological information should be a central focus in developing solutions.
In this paper, we conduct experiments to optimize the learning rates for the Generalized Learning Vector Quantization (GLVQ) model. Our approach leverages insights from cog- nitive science rooted in the profound intricacies of human thinking. Recognizing that human-like thinking has propelled humankind to its current state, we explore the applica- bility of cognitive science principles in enhancing machine learning. Prior research has demonstrated promising results when applying learning rate methods inspired by cognitive science to Learning Vector Quantization (LVQ) models. In this study, we extend this approach to GLVQ models. Specifically, we examine five distinct cognitive science-inspired GLVQ variants: Conditional Probability (CP), Dual Factor Heuristic (DFH), Middle Symmetry (MS), Loose Symmetry (LS), and Loose Symme- try with Rarity (LSR). Our experiments involve a comprehensive analysis of the performance of these cogni- tive science-derived learning rate techniques across various datasets, aiming to identify optimal settings and variants of cognitive science GLVQ model training. Through this research, we seek to unlock new avenues for enhancing the learning process in machine learning models by drawing inspiration from the rich complexities of human cognition. Keywords: machine learning, GLVQ, cognitive science, cognitive bias, learning rate op- timization, optimizers, human-like learning, Conditional Probability (CP), Dual Factor Heuristic (DFH), Middle Symmetry (MS), Loose Symmetry (LS), Loose Symmetry with Rarity (LSR).
Adversarial robustness of a nearest prototype classifier assures safe deployment in sensitive use fields. Much research has been conducted on artificial neural networks regarding their robustness against adversarial attacks, whereas nearest prototype classifiers have not chalked similar successes. This thesis presents the learning dynamics and numerical stability regarding the Crammer-normalization and the Hein-normalization for adversarial robustness of nearest prototype classifiers. Results of conducted experiments are penned down and analyzed to ascertain the bounds given by Saralajew et al. and Hein et al. for adversarial robustness of nearest prototype classifiers.
With globalization and the increasing diversity of the workforce, organizations are faced with the challenge of effectively managing multicultural teams. Understanding how employee engagement and job satisfaction are influenced by multicultural factors is crucial for organizations to create inclusive work environments that foster productivity and wellbeing. This literature review aims to explore the relationship between employee engagement, job satisfaction, and multi-cultural workplaces. It examines relevant studies and provides insights into the key factors, challenges, and strategies for enhancing employee engagement and job satisfaction in multicultural workplaces. The findings will shed light upon the author's research area on the factors influencing employee engagement and job satisfaction in multicultural work environments and contribute to a deeper understanding of cross-cultural dynamics in the workplace.
Traditional user management on the Internet has historically required individuals to give up control over their identities. In contrast, decentralized solutions promise to empower users and foster decentralized interactions. Over the last few years, the development of decentralized accounts and tokens has significantly increased, aiming at broader user adoption and shared social economies.
This thesis delves into smart contract standards and social infrastructure for Ethereum-based blockchains to enable identity-based data exchange between abstracted blockchain accounts. In this regard, the standardization landscapes of account and social token developments were analyzed in-depth to form guidelines that allow users to retain complete control over their data and grant access selectively.
Based on the evaluations, a pioneering Solidity standard is presented, natively integrating consensual restrictive on-chain assets for abstracted blockchain accounts. Further, the architecture of a decentralized messaging service has been defined to outline how new token and account concepts can be intertwined with efficient and minimal data-sharing principles to ensure security and privacy, while merging traditional server environments with global ledgers.
Laser engraving requires a precise ablation per pulse through all layers of a depth map. To transform this process towards areas of a square meter and more within an acceptable time, needs high-power ultra-short pulsed lasers for the precision and a high scan speed for the beam distribution. Scan speeds in the range of several 100 m/s can be achieved with a polygon scanner. In this work, a polygon scanner has been utilized within a roll-engraving machine to treat an 800 x 220 mm² (L x Dia) roll with 0.55 m² in a laser engraving process. The machine setup, the processing strategy and the data handling has been investigated and result in an efficient large area process. Pre-tests were performed with a multi-MHz-frequency nanosecond-pulsed laser, to investigate the processing strategy. A method to overcome the duty cycle of the polygon scanner was found in the synchronization of two polygons, enabling the use on a single laser source in a time-sharing concept. The throughput and the utilization of the laser source can be increased by the factor of two
In this work, Direct Laser Interference Patterning (DLIP) is used in conjunction with the polygon scanner technique to fabricate textured polystyrene and nickel surfaces through ultra-fast beam deflection. For polystyrene, the impact of scanning speed and repetition rate on the structure formation is studied, obtaining periodic features with a spatial period of 21 μm and reaching structure heights up to 23 μm. By applying scanning speeds of up to 350 m/s, a structuring throughput of 1.1 m²/min has been reached. Additionally, the optical configuration was used to texture nickel electrode foils with line-like patterns with a spatial period of 25 μm and a maximum structure depth of 15 μm. Subsequently, the structured nickel electrodes were assessed in terms of their performance for the Hydrogen Evolution Reaction (HER). The findings revealed a significant improvement in HER efficiency, with a 22% increase compared to the untreated reference electrode.