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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.
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.
As new sensors are added to VR headsets, more data can be collected. This introduces a new potential threat to user privacy. We focused on the feasibility of extracting personal information from eye-tracking. To achieve this, we designed a preliminary user study focusing on the pupil response to audio stimuli. We used a variation of machine learning models to test the collected data to determine the feasibility of obtaining information such as the age or gender of the participant. Several of the experiments show promise for obtaining this information. We were able to extract with reasonable certainty whether caffeine was consumed and the gender of the participant. This demonstrates the unknown threat that embedded sensors pose to users. A further studies are planned to verify the results.
Die biologische Ammoniumoxidation ist ein zentraler Bestandteil des globalen Stickstoffkreislaufs. Angesichts der extremen Massen Stickstoff anthropogenen Ursprungs in der Umwelt, liegt die Entfernung reaktiven Stickstoffs im Interesse der Umwelt und der öffentlichen Gesundheit. In der folgenden Arbeit werden Bedingungen zur anaeroben Ammoniumoxidation mit Nitrat in einem Anammox-Reaktor untersucht. Dabei wurden 2 Laborreaktoren für eine Zeit von insgesamt 116 Tagen betrieben und beobachtet, die ausschließlich als Elektronendonatoren und Akzeptoren Ammonium und Nitrat enthielten. Zusätzlich wurden Batchkulturen mit Zellen eines Reaktors angezüchtet und auf ihre Gaszusammensetzung abhängig unterschiedlicher Eigenschaften untersucht. Hierbei wurde eine Reihe unterschiedlicher analytischer Quantifizierungsmethoden genutzt und es konnte gezeigt werden, dass ein Abbau unter den Bedingungen stattfindet.
Die aktuelle Forschung zu dieser Reaktion ist spärlich und verleiht der Bachelorarbeit dadurch Relevanz.
This thesis investigates the efficacy of four machine learning algorithms, namely linear regression, decision tree, random forest and neural network in the task of lead scoring. Specifically, the study evaluates the performance of these algorithms using datasets without sampling and with random under-sampling and over-sampling using SMOTE. The performance of each algorithm is measure using various performance metrics, including accuracy, AUC-ROC, specificity, sensitivity, precision, recall, F1 score, and G-mean. The results indicate that models trained on the dataset without sampling achieved higher accuracy than those trained on the dataset with either random under-sampling or random over-sampling using SMOTE. However, the neural network demonstrated remarkable results on each dataset compared to the other algorithms. These findings provide valuable insights into the effectiveness of machine learning algorithms for lead scoring tasks, particularly when using different sampling techniques. The findings of this study can aid lead management practices in selecting the most suitable algorithm and sampling technique for their needs. Furthermore, the study contributes to the literature by providing a comprehensive evaluation of the performance of machine learning algorithms for lead scoring tasks. This thesis has practical implications for businesses looking to improve their lead management practices, and future research could extend the analysis to other machine learning algorithms or more extensive datasets.
Crowd-Powered Medical Diagnosis : The Potential of Crowdsourcing for Patients with Rare Diseases
(2023)
With the recent rise in medical crowdsourcing platforms,
patients with chronic illnesses increasingly broadcast their
medical records to obtain an explanation for their complex
health conditions. By providing access to a vast pool of
diverse medical knowledge, crowdsourcing platforms have
the potential to change the way patients receive a medical
diagnosis. We developed a conceptual model that details
a set of variables. To further the understanding of
crowdsourcing as an emerging phenomenon in health care,
we provide a contextualization of the various factors that
drive participants to exert effort. For this purpose, we used
CrowdMed.com as a platform from which we gathered and
examined a unique dataset that involves tasks of diagnosing
rare medical conditions. By promoting crowdsourcing
as a robust and non-discriminatory alternative to seeking
help from traditional physicians, we contribute to the acceptance
and adoption of crowdsourcing services in health
economics.
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.
The digital transformation of higher education demands effective and efficient methods for learning support and assessment of learning processes. This paper relates learning support and assessment to each other in the context of learning management systems. It refers to previous studies carried out in multiple introductory economic courses of the University of Applied Sciences Mittweida which examine possible connections between the use of digital tests and learning success, investigate student’s acceptance and self-perceived learning success with respect to the webbased portion of a blended course and a purely online based course. Based on a survey (n = 71) and a quantitative analysis (n = 214) with logging and exam assessment data, the previous work shows that students approached the web-based course portion with rather reserved attitudes. Still, they perceived the individual course elements, namely videos, podcasts, interactive worksheets, online tests, and a comprehensive PDF file to be beneficial to their learning experience. Especially we could indicate a positive correlation between the points students achieved in the online tests and the exam results.
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.
As the cryptocurrency ecosystem rapidly grows, interoperability has become increasingly crucial, enabling assets and data to interact seamlessly across multiple chains. This work describes the concept and implementation of a trustless connection between the Bitcoin Lightning Network and EVM-compatible blockchains, allowing the transfer of assets between the two ecosystems. Establishing such a connection can significantly contribute to the growth of both ecosystems as they can benefit from each other’s advantages and emerge new pos- sibilities.
To investigate the effects of climate change on interactions within ecosystems, a microcosm experiment was conducted. The effects of temperature increase and predator diversity on Collembola communities and their decomposition rate were investigated. The predators used were mites and Chilopods, whose predation effects on several response variables were analysed. This data included Collembola abundance, biomass and body mass as well as basal respiration and microbial biomass carbon. These response variables were tested against the predictors in several models. Temperature showed high significance in interaction with mite abundance in almost all models. Furthermore, the results of the basal respiration and microbial biomass carbon support the suggestion of a trophic cascade within the animal interaction.
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.
Reputation is indispensable for online business since it supports customers in their buying decisions and allows sellers to justify premium prices. While IS research has investigated reputation systems mainly as review systems on online platforms for business-to-consumer (B2C) transactions, no proper solutions have been developed for business-to-business (B2B) transactions yet. We use blockchain technology to propose a new class of reputation systems that apply ratings as voluntary bonus payments: Before a transaction is performed, customers commit to pay a bonus that is granted if a service provider has performed a service properly. As opposed to rival reputation systems that build on cumulated ratings or reviews, our system enables monetized reputation mechanisms that are inextricably linked with online transactions. We expect this system class to provide more trustworthy ratings, which might reduce agency costs and serve quality providers to establish a reputation towards new customers.
Over recent years, Maximal Extractable Value (MEV) has gained significant importance within the decentralized finance (DeFi) ecosystem. Remarkably, within just two years of its emergence, MEV has seen an extraction of approximately 600 million USD - a phenomenon that has sparked concerns regarding potential threats to blockchain stability.
With growing interest in the Ethereum network and the growing DeFi sector, research surrounding MEV has substantially increased. This work aims to offer a comprehensive understanding of MEV. Additionally, this research quantifies the largest types of MEV (Arbitrage, Sandwich and Liquidations) from March 2022 to March 2023. The data are then compared to other sources, revealing a general upward trend, with a particularly noticeable increase in Sandwich Attacks.
The cryptocurrency ecosystem has seen significant growth with Ethereum and Bitcoin as foundational pillars. Ethereum introduced smart contracts revolutionizing decentralized applications (dApps) across various domains. Scalability challenges led to alternative ecosystems like Binance Smart Chain and Polygon, maintaining compatibility through the Ethereum Virtual Machine (EVM). Bitcoin also faces scalability issues, leading to the Lightning Network's development—an off-chain solution with payment channels for scalable instant transactions. Interoperability is increasingly crucial as the cryptocurrency ecosystem continues to grow, enabling seamless interactions between assets and data across multiple blockchain platforms. EVM-compatible blockchains and the Lightning Network offer unique advantages in their respective use cases. This paper utilizes atomic swaps to create a secure, fast, and user-friendly trustless bridge between the Lightning Network and EVM-compatible blockchains, fostering the growth of both ecosystems and unlocking novel opportunities.
Currently, the Internet of Things (IoT) is connected to the virtual world through the Web of Things (WoT), allowing efficient utilization of real-world objects with Internet technologies. The WoT facilitates abstract interaction between applications and connected IoT devices, allowing owners to switch between devices while using multiple ones. To achieve this, virtual assets in WoT devices can be tokenized through smart contracts and transferred using hashed proof as transactions within blockchain networks that support virtual currencies. The goal of Web of Things is to establish connectivity, interoperability, and integration among IoT devices using web standards and protocols, reducing reliance on device manufacturers. This enables easy integration of Web 3.0 cryptocurrency for device management. This study proposes a solution for WoT applications involving different cryptocurrency definitions. Finally, simulation results are presented to demonstrate the tokenization-based ownership transfer in the Web of Things.
Recently a deep neural network architecture designed to work on graph- structured data have been capturing notice as well as getting implemented in various domains and application. However, learning representation (feature embedding) from graphical data picking pace in research and constructing graph(s) from dataset remains a challenge. The ability to map the data to lower dimensions further makes the task easier while providing comfort in applying many operations. Graph neural network (GNN) is one of the novel neural network models that is catching attention as it is outperforming in various applications like recommender systems, social networks, chemical synthesis, and many more. This thesis discusses a unique approach for a fundamental task on graphs; node classification. The feature embedding for a node is aggregated by applying a Recurrent neural network (RNN), then a GNN model is trained to classify a node with the help of aggregated features and Q learning supports in optimizing the shape of neural networks. This thesis starts with the working principles of the Feedforward neural network, recurrent units like simple RNN, Long short-term memory (LSTM), and Gated recurrent unit (GRU), followed by concepts of Reinforcement learning (RL) and the Q learning algorithm. An overview of the fundamentals of graphs, followed by the GNN architecture and workflow, is discussed subsequently. Some basic GNN models are discussed in brief later before it approaches the technical implementation details, the output of the model, and a comparison with a few other models such as GraphSage and Graph attention network (GAN).
In the field of satellites it is common practice to combine multiple ground stations into one network, to increase communication times with satellites. This work focuses on TIM, which is an international academic colaborative project. Important criteria for this project are elaborated and used to evaluate existing ground station networks. It concludes that there is no appropriate solution availiable for this specific use case and establish a proposed solution. The proposed ground station network software will be elaborated and evaluated.
Assessment of COI and 16S for insect species identification ti determine the diet of city bats
(2023)
Despite the numerous benefits of urbanization to human living conditions, urbanization has also negatively affected humans, their environment, and other organisms that share urban habitats with humans. Undoubtedly adverse while some wild animals avoid living in urban areas, others are more tolerant or prefer life in urban habitats. There are more than 1,400 species of bats in the world.
Therefore, they have the potential to contribute significantly to the mammalian biodiversity in urban areas. Insectivorous bats species play a key role in agriculture by improving yields and reducing chemical pesticide costs. Using metabarcoding, it is possible to determine the prey consumed by these noctule mammals based on the DNA fragments in their fecal pellets. This study
aimed to evaluate COI and 16S metabarcodes for insect species identification to determine the diet of metropolitan bats. For this purpose, COI and 16S metabarcodes were extracted, amplified, and sequenced from 65 bat feces collected in the Berlin metropolitan areas. Following a taxonomic annotation, I found that 73% of all identified insects could only be detected using the COI method, while 15% could be recovered using the 16S approach. Just 12% of all detected insects were identified simultaneously by both markers. According to this result, COI is more suitable for the taxonomic identification of insects from bat feces. However, given the bias of COI primers, it is recommended to use both markers for a more precise estimation of species diversity. Additionally,based on the insect species identified, I noticed that urban bats fed mainly on Diptera, Coleoptera,and Lepidoptera. The bat species Nyctalus noctula was most abundant in the samples. His diet analysis revealed that 91% of the samples contained the insect species Chironomus plumosus. 14 pest insect species were also found in his diet.
This desk research will initiate an exploration of present and potential blockchain applications in the higher education sector of Europe. The aim of this research is to create a theoretical base for a further postgraduate research and analysis, so to create an effective model/framework to augment the integration of blockchain technology into existing organizational processes, initially in higher educational institutions, but which may be adaptable and generalizable to other specific uses. Due to the novelty of the topic, academic resources related to the research area are limited. Most studies seem to focus on blockchain-based applications in industries such as finance, healthcare, and supply chain management, and there is little evidence of the impact of blockchain technology on education. This paper discusses present and suggests some potential blockchain-based applications in education in Europe and beyond. This research provides a groundwork for education and academia stakeholders, policymakers and researchers to exploit the potential of blockchain in different functions of an education system.
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.
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.
In the swiftly changing world of academic publishing, the Sea of Wisdom platform seizes the opportunity to innovate. By combining the technologies of blockchain, decentralized finance (DeFi), and Non-Fungible Tokens (NFTs) with traditional scholarly communication, we present a groundbreaking, decentralized solution. Our design, although adaptable, primarily uses Ethereum's Virtual Machine, tapping into its robust scientific community.
In laser drilling, one challenge is to achieve a high drilling quality in high aspect ratio drilling. Ultra-short pulsed lasers use different concepts like thin disks, fibers and rods. The slab technology is implemented because of their flexibility and characteristics. They bring together both advantages and deliver high pulse energies at high repetition rates. Materials with a thickness > 1.5 mm demand specialized optics handling the high power and pulse energies with adapted processing strategies, integrated in a machine setup. In this contribution, we focus on all the necessary components and strategies for drilling high precision holes with aspect ratios up to 1:40.
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
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.
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.
For monitoring laser beam welding processes and detecting or actively avoiding process defects, acoustic based measurements can be used in addition to optical measurement methods such as pyrometry. To reliably detect process events, it is essential to position the respective sensors in such a way that specific signal characteristics are reproducible and significant. However, there are only few investigations regarding the positioning for airborne sound sensors, especially for the detection of process emissions in the ultrasonic range. Therefore, in this research, the influence of the process distance as well as the angle and orientation of the microphone to a laser beam deep penetration welding process is investigated with respect to the detectability of process emissions in different frequency bands. It is shown that for a wide ultrasonic range a flat sensor angle with respect to the sample surface leads to an increased signal strength of the acoustic emissions compared to steep angles.
This study explores the opportunities and risks associated with user-generated content (UGC) in the communication strategies of marketing departments from a business perspective. With the rise of social media and online platforms, UGC has become a powerful tool for brands to engage with their audience, build trust, and enhance brand awareness. However, implementing UGC also comes with inherent risks, including the loss of control over brand messaging, potential negative user-generated content, and legal implications.
To investigate these dynamics, an empirical mixed-methods approach was employed, including expert interviews and a comprehensive literature review. The findings indicate that UGC offers significant opportunities for marketing departments, such as increased customer loyalty, enhanced authenticity, brand awareness, as well as a diverse set of possible content. However, the study also reveals the potential risks associated with UGC, highlighting the importance of managing these risks effectively.
The Tutte polynomial is an important tool in graph theory. This paper provides an introduction to the two-variable polynomial using the spanning subgraph and rank-generating polynomials. The equivalency of definitions is shown in detail, as well as evaluations and derivatives. The properties and examples of the polynomial, i.e. the universality, coefficient relations, closed forms and recurrence relations are mentioned. Moreover, the thesis contains the connection between the dichromate and other significant polynomials.
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.
The GeoFlow II experiment aims to replicate Earth’s core dynamics using a rotating spherical container with controlled temperature differences and simulated gravity. During the GeoFlow II campaign, a massive dataset of images was collected, necessitating an automated system for image processing and fluid flow visualization in the northern hemisphere of the spherical container. From here, we aim to detect the special structures appearing on the post processed images. Recognizing YOLOv5’s proficiency in object detection, we apply Yolov5 model for this task.
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.
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.
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.
Decentralization is one of the key attributes associated with blockchain technology. Among the different developments in recent years, decentralized autonomous organizations (DAOs) have been of growing interest. DAOs are currently a key part of another emerging use case, namely decentralized science (DeSci). Given the novelty of the field, an integrative definition of DeSci has not been established, but some inherent concepts and ideas can be traced back to the Open Science movement. Although the DeSci movement has the potential to benefit the public, for example through funding underrepresented research areas or more inclusive and transparent research in general, some negative aspects of decentralization should not be neglected. Due to the novelty of blockchain and emerging use cases, research can and should precede mass adoption, to which this paper aims to contribute.
In this work, we identify similarities between Adversarial Examples and Counterfactual Explanations, extend already stated differences from previous works to other fields of AI such as dimensionality, transferability etc. and try to observe these similarities and differences in different classifier with tabular and image data. We note that this topic is an open discussion and the work here isn’t definite and canbe further extended or modified in the future, if new discoveries found.
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.
This thesis comprehensively explores factors contributing to malaria-induced anemia and severe malarial anemia (SMA). The study utilizes a comprehensive dataset to investigate immunological interactions, genetic variations, and temporal dynamics. Findings highlight the complex interplay between immune markers, genetic traits, and cohort-specific influences. Notably, age, HIV status, and genetic variations emerge as crucial factors influencing anemia risk. The incorporation of Poisson regression models sheds light on the genetic underpinnings of SMA, emphasizing the need for personalized interventions. Overall, this research provides valuable insights into the multifaceted nature of malaria-induced complications, paving the way for further molecular investigations and targeted interventions.
In this thesis, we implement, correct, and modify the compartmental model described in “Transmission Dynamics of Large Coronavirus Disease Outbreak in Homeless Shelter, Chicago, Illinois, USA, 2020”. Our objective is to engage in reading and understanding scientific literature, reproduce the results, and modify or generalize an existing mathematical model. We provide an overview of epidemiological models, focusing on simple compartmental SEIR models. We correct inaccuracies and misprints in the original implementation and use the limited-memory Broyden–Fletcher–Goldfarb–Shanno algorithm to fit the model’s parameters. Furthermore, we modify the model by introducing an additional compartment. The resulting model has a more intuitive interpretation and relies on fewer assumptions. We also perform the fitting process for this alternative model. Finally, we demonstrate the advantages of our modified implementations and discuss other possible approaches.
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.
In Machine Learning, Learning Vector Quantization(LVQ) is well known as supervised learning method. LVQ has been studied to generate optimal reference vectors because of its simple and fast learning algorithm [12]. In many tasks of classification, different variants of LVQ are considered while training a model. In this thesis, the two variants of LVQ, Generalized Matrix Learning Vector Quantization(GMLVQ) and Generalized Tangent Learning Vector Quantization(GTLVQ) have been discussed. And later, transfer learning technique for different variants of LVQ has been implemented, visualized and we have compared the results using different datasets.
This master thesis covers the topics of Studying customers’ behavior on the example of skin care brand Nivea. There are presented theoretical basis for the following research about marketing, customers’ behavior and conducting marketing research properly. Then, there is the analysis of German market. Since Nivea is the brand of Beiersdorf company, there is a description of Beiersdorf’s activity and operation work. The main idea of the paper work is to analyze customers’ behavior of Nivea. Therefore, the work contains huge research about the brand along with its’ micro- and macroenvironment. There also were conducted an in-depth interview and a survey to understand customers’
current needs. With all the results the author of the work proposed some ideas for Nivea brand.
Pollinating insects are of vital importance for the ecosystem and their drastic decline imposes severe consequences for the environment and humankind. The comprehension of their interaction networks is the first step in order to preserve these highly complex systems. For that purpose, the following study describes a protocol for the investigation of honey bee pollen samples from different agro-environmental areas by DNA extraction, PCR amplification and nanopore sequencing of the barcode regions rbcL and ITS. It was shown, that the most abundant species were classified consistently by both DNA barcodes, while species richness was enhanced by single-barcode detection of less abundant species. The analysis of the the different landscape variables exhibited a decline of species richness, Shannon diversity index, and species evenness with increasing organic crop area. However, sampling was only carried out in August and further investigations are suggested to display a more complete picture of honey bee foraging throughout the seasons.
Where does the cocoa, which we consume on a regular basis, come from? Supply chains are not always transparent, much less easily comprehensible. The cocoa industry faces ongoing challenges. Whether it be the chocolate manufacturers’ promise to maintain a sustainable and ethical supply chain, the minimal impact on the environment or the maximum adherence to human rights in their production process. This paper revises important steps which lead to the compliance with UN standards and questions the role of consumers in the construct of ethical chocolate products.
This scientific work deals with the current opportunities of business development. Purpose of the work is study and analysis of the organization's development strategy and its development. The subject of the study is the mechanism of formation of an organization's development strategy, understanding of business development and its core methodologies and branches. This thesis is based on the operations of the real engineering company and main part of the research could be applied in reality. Main goal of the thesis is to find recommendations on the implementation of strategic changes organization's development strategy.
Noise in the oceans is a constantly increasing factor. The growing industrialisation due to shipping, offshore wind parks, seismic studies and other anthropogenic noise is putting the eco system under immense stress. The focus of this thesis is on the assessment of continuous underwater noise from ships. Based on existing strategies in air as well as underwater and a comparison of both an alternative strategy for the assessment of con-tinuous noise from ships is given. The concept developed is based on published, scien-tifically observed responses of animals to ship passes with an indication of an effect range. A model is created to describe the strategy using publicly available data for cargo ships as an example. The results are summarized in maps depicting the affected area for an MRU of the OSPAR II region and the MPA “Borkum Riffgrund”. The strategy is discussed and evaluated on the basis of these results. From this, further improvements and the need for additional information in publicly available data on vessel traffic are derived.
Applications and Potential Impacts of Blockchain Technology in Logistics and Supply Chain Areas
(2022)
The motive of the present thesis is to analyze the applications and potential impacts of blockchain technology in the logistics and supply chain areas. For this purpose, the literature from different sources has been used to analyze and get an overview of the current status and role of blockchain technology within the logistics and supply chain areas. Different use cases, as well as pilot projects from organizations all over the world and also from Germany, have been included. Suggestions for further applications and implementations of blockchain technology along with their potential impacts have been made. Additionally, the cost of implementing blockchain-based solutions and applications has been estimated along with providing recommendations and suggestions for important and key points to be considered before preparing and deciding to implement blockchain-based solutions in any organization.
Influenza A viruses are responsible for the outbreak of epidemics as well as pandemics worldwide. The surface protein neuraminidase of this virus is responsible, among other things, for the release of virions from the cell and is thus of interest in pharmacological research. The aim of this work is to gain knowledge about evolutionary changes in sequences of influenza A neuraminidase through different methods. First, EVcouplings is used with the goal of identifying evolutionary couplings within the protein sequences, but this analysis was unsuccessful. This is probably due to the great sequence length of neuraminidase. Second, the natural vector method will be used for sequence embedding purposes, in hopes to visualize sequential progression of the virus protein over time. Last, interpretable machine learning methods will be applied to examine if the data is classifiable by the different years and to gain information if the extracted information conform to the results from the EVcouplings analysis. Additionally to using the class label year, other labels such as groups or subtypes are used in classification with varying results. For balanced classes the machine learning models performed adequately, but this was not the case for imbalanced data. Groups and subtypes can be classified with a high accuracy, which was not the case for the years, continents or hosts. To identify the minimal number of features necessary for linear separation of neuraminidase group 1 subtypes, a logistic regression was performed at last, resulting in the identification of 15 combinations of nine amino acid frequencies. Since the sequence embedding as well as the machine learning methods did not show neuraminidase evolution over time, further research is necessary, for example with focus on one subtype with balanced data.
This Bachelor thesis investigates the learning rules of the Hebbian, Oja and BCM neuron models for their convergence to, and the stability of, the fixed points. Existing research is presented in a structured manner using consistent notation. Hebbian learning is neither convergent nor stable. Oja learning converges to a stable fixed point, which is the eigenvector corresponding to the largest eigenvalue of the covariance matrix of the input data. BCM learning converges to a fixed point which is stable, when assuming a discrete distribution of orthogonal inputs that occur with equal probability. Hebbian learning can therefore not be used in further applications, where convergence to a stable fixed point is required. Furthermore, this Bachelor thesis came to the conclusion that determining the fixed points of the BCM learning rule explicitly involves extensive calculation and other methods for verifying the stability of possible fixed points should be considered.
Digital data is rising day by day and so is the need for intelligent, automated data processing in daily life. In addition to this, in machine learning, a secure and accurate way to classify data is important. This holds utmost importance in certain fields, e.g. in medical data analysis. Moreover, in order to avoid severe consequences, the accuracy and reliability of the classification are equally important. So if the classification is not reliable, instead of accepting the wrongly classified data point, it is better to reject such a data point. This can be done with the help of some strategies by using them on top of a trained model or including them directly in the objective function of the desired training model. We discuss such strategies and analyze the results on data sets in this thesis.
In the past few years Generative models have become an interesting topic in the field of Machine Learning (ML). Variational Autoencoder (VAE) is one of the popular frameworks of generative models based on the work of D.P Kingma and M. Welling [6] [7]. As an alternative to VAE the authors in [12] proposed and implemented Information Theoretic Learning (ITL) based Autoencoder. VAE and ITL Autoencoder are a combination of the neural networks and probabilistic graphical models (PGM) [7]. In modern statistics it is difficult to compute the approximation ofthe probability densities. In this paper we make use of Variational Inference (VI) technique from machine learning that approximate the distributions through optimization. The closeness between the distributions are measured by the information theoretic divergence measures such as Kullbach-Liebler, Euclidean and Cauchy Schwarz divergences. In this thesis, we study theoretical and experimental results of two different frameworks of generative models which generate images of MNIST handwritten characters [8] and Yale face database B [3]. The results obtained show that the proposed VAE and ITL Autoencoder are capable of generating the underlying structure of the example datasets
Studying and understanding the metabolism of plants is essential to better adapt them to future climate conditions. Computational models of plant metabolism can guide this process by providing a platform for fast and resource-saving in silico analyses. The reconstruction of these models can follow kinetic or stoichiometric approaches with Flux Balance Analysis being one of the most common one for stoichiometric models. Advances in metabolic modelling over the years include the increasing number of compartments, the automation of the reconstruction process, the modelling of plant-environment interactions and genetic variants or temporally and spatially resolved models. In addition, there is a growing focus on introducing synthetic pathways in plants to increase their agricultural potential regarding yield, growth and nutritional value. One example is the β-hydroxyaspartate cycle (BHAC) to bypass photorespiration. After the implementation in a stoichiometric C3 plant model, in silico flux analyses can help to understand the resulting metabolic changes. When comparing with in vivo experiments with BHAC plants, the metabolic model can reproduce most results with exceptions regarding growth and oxaloacetate. To evaluate whether the BHAC is suitable to establish a synthetic C4 cycle, the pathway is implemented in a two-cell type model that is capable of running a C4 cycle. The results show that the BHAC is only beneficial under light limitation in the bundle sheath cell. An additional engineering target for improved performance of plants is malate synthase. This work serves as the basis for further analyses combining the different factors boosting the advantages of the BHAC and for in vivo experiments in C3 and C4 plants.
Embeddings for Product Data
(2022)
The E-commerce industry has grown exponentially in the last decade, with giants like Amazon, eBay, Aliexpress, and Walmart selling billions of products. Machine learning techniques can be used within the e-commerce domain to improve the overall customer journey on a platform and increase sales. Product data, in specific, can be used for various applications, such as product similarity, clustering, recommendation, and price estimation. For data from these products to be used for such applications, we have to perform feature engineering. The idea is to transform these products into feature vectors before training a machine learning model on them. In this thesis, we propose an approach to create representations for heterogeneous product data from Unite’s platform in the form of structured tabular records. These tables consist of attributes having different information ranging from product-ids to long descriptions. Our model combines popular deep learning approaches used in natural language processing to create numerical representations, which contain mostly non-zeros elements in an array or matrix called as dense representation for all products. To evaluate the quality of these feature vectors, we validate how well the similarities between products are captured by these dense representations. The evaluations are further divided into two categories. The first category directly compares the similarities between individual products. On the other hand, the second category uses these dense vectors in any of the above- mentioned applications as inputs. It then evaluates the quality of these dense representation vectors based on the accuracy or performance of the defined application. As result, we explain the impact of different steps within our model on the quality of these learned representations.
Differentiation is ubiquitous in the field of mathematics and especially in the field of Machine learning for calculations in gradient-based models. Calculating gradients might be complex and require handling multiple variables. Supervised Learning Vector Quantization models, which are used for classification tasks, also use the Stochastic Gradient Descent method for optimizing their cost functions. There are various methods to calculate these gradients or derivatives, namely Manual Differentiation, Numeric Differentiation, Symbolic Differentiation, and Automatic Differentiation. In this thesis, we evaluate each of the methods mentioned earlier for calculating derivatives and also compare the use of these methods for the variants of Generalized Learning Vector Quantization algorithms.
Sequences are an important data structure in molecular biology, but unfortunately it is difficult for most machine learning algorithms to handle them, as they rely on vectorial data. Recent approaches include methods that rely on proximity data, such as median and relational Learning Vector Quantization. However, many of them are limited in the size of the data they are able to handle. A standard method to generate vectorial features for sequence data does not exist yet. Consequently, a way to make sequence data accessible to preferably interpretable machine learning algorithms needs to be found. This thesis will therefore investigate a new approach called the Sensor Response Principle, which is being adapted to protein sequences. Accordingly, sequence similarity is measured via pairwise sequence alignments with different sequence alignment algorithms and various substitution matrices. The measurements are then used as input for learning with the Generalized Learning Vector Quantization algorithm. A special focus lies on sequence length variability as it is suspected to affect the sequence alignment score and therefore the discriminative quality of the generated feature vectors. Specific datasets were generated from the Pfam protein family database to address this question. Further, the impact of the number of references and choice of substitution matrices is examined.
In this thesis, we focus on using machine learning to automate manual or rule-based processes for the deduplication task of the data integration process in an enterprise customer experience program. We study the underlying theoretical foundations of the most widely used machine learning algorithms, including logistic regression, random forests, extreme gradient boosting trees, support vector machines, and generalized matrix learning vector quantization. We then apply those algorithms to a real, private data set and use standard evaluation metrics for classification, such as confusion matrix, precision, and recall, area under the precision-recall curve, and area under the Receiver Operating Characteristic curve to compare their performances and results.
The aim of this bachelor thesis is to find out how the use of artificial intelligence, specifically the one used in combat situations, can increase the playing time or even the replay value of games in the action role-playing genre. Thereby, it focuses mainly on combat situations between a player and an artificial intelligence.
To begin with, this bachelor thesis examines the action role-playing genre in order to find a suitable definition for it. Accordingly, action role-playing games involve titles that send the player on a hero’s journey-like adventure in which they must prove their skills in combat against virtual opponents. The greatest challenge of these real-time battles comes from the required quick reflexes, skill queries and hand-eye coordination.
Next, six means of increasing the replayability of a game are explored: Experience and Nostalgia, Variety and Randomness, Goals and Completion, Difficulty, Learning, and Social Aspect. The paper then proceeds to give an explanation for the term Artificial Intelligence and examines the various methods used to create intelligent behavior as well as the general advancement of the research field. Special attention is given to the implementation methods of Finite State Machines and Behavior Trees, as they are the most widely used methods for creating behavioral patterns of virtual characters.
Finally, a study conducted as part of the bachelor thesis is described, which compares a mathematically balanced artificial intelligence with a behaviorally balanced one in terms of game performance regarding the willingness of test subjects to purchase and play through the game as well as its replay value. The thesis concludes with the findings that while the behavioral approach is more promising than the mathematical approach, a combination of the two methods ultimately leads to the best outcome. Furthermore, the study shows that the use of artificial intelligence to individualize gaming experiences is promising for the future of the gaming industry.
Simulating complex physical systems involves solving nonlinear partial differential equations (PDEs), which can be very expensive. Generative Adversarial Networks (GAN) has recently been used to generate solutions to PDEs-governed complex systems without having to numerically solve them.
However, concerns are raised that the standard GAN system cannot capture some important physical and statistical properties of a complex PDE-governed system, along side with other concerns for difficult and unstable training, the noisy appearance of generated samples and lack of robust assessment methods of the sample quality apart from visual examination. In this thesis, a standard GAN system is trained on a data set of Heat transfer images. We show that the generated data set can capture the true distribution of training data with respect to both visual and statistical properties, specifically the vertical statistical profile. Furthermore, we construct a GAN model which can be conditioned using variance-induced class label. We show that the variance threshold t = 0. 01 constructs a good conditional class label, such that the generated images achieve 96% accuracy
rate in complying with the given conditions.
This thesis aims to research the platform YouTube and whether “being a YouTuber” qualifies as a profession or not and what leads to this. The author combines existing scientific data and information provided by YouTubers doing this as a job and uses the compilation method. The author merges that material and uses it to create a bachelor thesis that covers both the theoretical and practical approach. The aim was to find out if there is a success recipe that can be followed that leads to views and clicks which are essential for the profession as a YouTuber. To do this, the author created two channels to see how the factors mentioned in this thesis are applied and if the approach leads to success. The findings of this thesis showed, that although the profession of a YouTuber can be classified as a job, it needs to be viewed differently from commonly known and in society accepted careers. Becoming a YouTuber and making money from this business, therefore, cannot be guaranteed.
Classification label security determines the extent to which predicted labels from classification results can be trusted. The uncertainty surrounding classification labels is resolved by the security to which the classification is made. Therefore, classification label security is very significant for decision-making whenever we are encountered with a classification task. This thesis investigates the determination of the classification label security by utilizing fuzzy probabilistic assignments of Fuzzy c-means. The investigation is accompanied by implementation, experimentation, visualization and documentation of the results.
Due to the intractability of the Discrete Logarithm Problem (DLP), it has been widely used in the field of cryptography and the security of several cryptosystems is based on the hardness of computation of DLP. In this paper, we start with the topics on Number Theory and Abstract Algebra as it will enable one to study the nature of discrete logarithms in a comprehensive way, and then, we concentrate on the application and computation of discrete logarithms. Application of discrete logarithms such as Diffie Hellman key exchange, ElGamal signature scheme, and several attacks over the DLP such as Baby-step Giant-step method, Silver Pohlig Hellman algorithm, etc have been analyzed. We also focus on the elliptic curve along with the discrete logarithm over the elliptic curve. Attacks for the elliptic curve discrete logarithm problem, ECDLP have been discussed. Moreover, the extension of several discrete logarithms-based protocols over the elliptic curve such as the elliptic curve digital signature algorithm, ECDSA have been discussed also.
With the growing market of cryptocurrencies, blockchain is becoming central to various research areas relevant from a mathematical and cryptographic point of view. Moreover, it is capable of transforming the traditional methods involving centralized network operations into decentralized peer-to-peer functionalities. At the same time, it provides an alternative to digital payments in a robust and tamperproof manner by adding the element of cryptography, consequently making it traversable for an individual who is a part of the blockchain network. Furthermore, for a blockchain to be optimal and efficient, it must handle the blockchain trilemma of security, decentralization, and scalability constraints in an effective manner. Algorand, a blockchain cryptocurrency protocol intended to solve blockchain’s trilemma, has been studied and discussed. It is a permissionless (public) blockchain protocol and uses pure proof of stake as its consensus mechanism.
As economies are getting more and more interconnected, the importance of the global logistics sector grew accordingly. However, both structural challenges and current events lead to recent supply chain disruptions, exposing the vulnerabilities of the sector. Simultaneously, blockchain has emerged as a key innovative technology with use cases going far beyond the exchange of virtual currencies. This paper aims to analyze how the technology is transforming global logistics and its challenges. Therefore, six use cases, are presented to give an overview of the technological possibilities of blockchain and smart contracts. The analysis combines theoretical approaches from scientific journals and combines them with findings from real-world implementations. The paper finds that the technology can change supply chain design fundamentally, with processes and decisions being automated and power within supply chain structures changing. However, implementations also face technological, environmental, and organizational challenges that need to be solved for wide-spread adoption.
More than 10 years after the invention of Bitcoin, the underlying blockchain technology is having an increasing effect on today’s society. Although one of the most popular application areas of blockchain is still the field of cryptocurrencies, the technological concepts are crossing into further application domains such as international supply chains. Fast-changing markets, high costs of time and risk management as well as biased relationships between the actors pose big challenges to an appropriate supply chain management. Based on a case study about sensor tracking, this paper explores the potential impact of blockchain on small and medium enterprises within an international supply chain. We will show that blockchain technologies offers a high potential to reduce inequalities of power relations between involved actors within supply chains. To achieve this, the requirements for the use of blockchain in supply chain management will be analyzed by means of a conducted case study and an expert survey of the companies concerned.
Current research in identity management is focusing on decentralized trust establishment for distributed identities. One of these decentralized trust models is Self-Sovereign Identities (SSI). With SSI each entity should be able to independently present and manage provable information about itself as well as request and review evidence from other entities. Using a distributed blockchain, information for verifying the authenticity of this evidence can be obtained from any other entity. This concept can be used not only for people, but also for authentication and authorization during the life cycle of devices in the Internet of Things (IoT). This paper presents an SSI-based concept for authentication and authorization of IoT devices among each other, intended to contribute to the change in trust on the internet. The SSI methodology employing a blockchain offers the possibility to establish mutual trust and proof of ownership without relying on any third party. The paper describes the concept, offers a reference implementation, and gives a discussion of the approach.
As part of the research project Trusted Blockchains for the Open, Smart Energy Grid of the Future (tbiEnergy), one of the objectives is to investigate how a holistic blockchain approach for the realization of a local energy market could be accomplished and how corresponding hardware security mechanisms can be integrated. This paper provides an overview of the implemented prototype and describes the system and its processes.
Digital Power of Attorney catalyzed by Software Requirements for Blockchain-based Applications
(2022)
Blockchain Technology (BT) with so-called web3 is at an inflection point between new sub-theme hypes and world-wide industrialization over last three years thanks to large companies like MicroStrategy [1], Facebook [2] and several Venture-Capital formations [3] who are already fighting over market share and community growth. Our work represents insights from Literature-based Software Requirement (SR) elicitation for a specific Blockchain-based Application, which is creation, managing and control of digital Power of Attorney (POA). The context of POA is not only a financial driven use-case it is by far a heavy weight universal legal transaction. We use a morphological box and reduced PRIMS-P to synthesis a generic specification for further Blockchain-based Application development. Formulated SRs in POA context are reflected on our core actors which are Grantor and authorized, trusted, external Entities. Proposed characteristics for relationship and effects are visualized in a reference model originally used in digital platform ecosystems [4]. This design and modelling approach facilitated closing discussion of BT and its future eCommerce perspective.
Dynamic object roles and corresponding contexts can model complex applications with higher-level abstraction. These abstracted applications can be used in wider areas such as financial institutions, health care, and supply chain network. Role management which consists of the creation of role objects, and binding role object between core objects still suffers from non-intrusive logging-monitoring, auditing, and resilient data source for role-based applications. Moreover, immutable smart contracts cause problems concerning bug fixing and maintenance without dynamic binding to new smart contract objects. An object that is created from a smart contract (contract class) can be transparently attached to a role object utilizing the Role Object Pattern (ROP). However, ROP itself does not contain a context definition and context-specific role assignment grouping the definition of smart contract relationships in abstracted data types. In this study, we would like to implement an extended version of the role object pattern called Context-based Role Object Pattern (ContextROP) with an onchain smart contract language called Solidity to solve fundamental problems. To evaluate the proposal, we will implement a use case with the design pattern proceeding with qualitative and quantitative analysis.
The topic of soulbound, non-transferable tokens is getting lots of interest within the blockchain space lately as decentralized societies become more tangible with Web3 social media applications and DAOs. In this article, I want to outline how such tokens function, their problems for adoption and standardization, and how they differ from verifiable credentials in the SSI field. As such soulbound assets will likely rely on extended recovery and asset management schemes to become viable identities that safely gain reputation and trust, features like social recovery and contract-based accounting are incorporated. By combining those new technologies and the theoretical crypto-native identity construct, the paper will give an impression of the future user-centric data economy.
Humans started using the principles of insurance thousands of years ago when they lived in tribes in smaller villages. If one of the tribe members were injured, the others would take care of him and his family. The basic principle of insurance is several people covering each other against a particular risk. Today, most people in regions like Europe have access to insurance, while many people worldwide still have no access at all. The cost and accessibility may be improved with a blockchain-based parametric approach. The insurance process in a parametric approach is exclusively based on data, and decisions are made objectively. Blockchain is a necessary and integral part of the approach to create transparency and connect the customer’s and investor’s risk capital. The paper offers an overview of the opportunities and challenges of blockchain-based parametric insurance, a catalog of criteria for such insurance, a description of all components and their interaction for implementation on Ethereum, and a reference implementation of a train delay insurance in Germany.
Probabilistic Micropayments
(2022)
Probabilistic micropayments are important cryptography research topics in electronic commerce. The Probabilistic micropayments have the potential to be researched in order to obtain efficient algorithms with low transaction costs and high speeding computer power. To delve into the topic, it is vital to scrutinize the cryptographic preliminaries such as hash functions and digital signatures. This thesis investigates the important probabilistic methods based on a centralized or decentralized network. Firstly, centralized networks such as lottery-based tickets, Payword, coin-flipping, and MR2 are described, and an approach based on blind signatures is also discussed. Then, decentralized network methods such as MICROPAY3, a transferable scheme on the blockchain network, along with an efficient model for cryptocurrencies, are explained. Then we compare the different probabilistic micropayment methods by improving their drawback with a new technique. To set the results from the theoretical analysis of different methods into some context, we analyze the attacks that reduce the security and, therefore, the system’s efficiency. Particularly, we discuss various methods for detecting double-spending and eclipse attacks occurrence
This scientific work reveals the potential for the development of the renewable energy market, due to many reasons. The reasons are the unstable political situation in the world, rising energy prices, environmental degradation and the growing demand of Ger man residents for government measures to reduce the negative impact on the environment. This work is related to business planning and development using strategies based on the above reasons. The purpose of the study is to develop methods for successfully regulating the market for renewable resources to solve the problem of environmental pollution through the promotion of environmentally friendly products. The work explores the driving forces and problems hindering the development of the market for renewable resources. The problems raised concerned all interested parties, from consumers and producers to the state body for regulating and stimulating the industry . An analysis was also made of the methods of environmentally oriented companies and the tools they use to strengthen their positions in the market. Based on the data obtained from the conducted research, a concept and business strategy for a new environmentally oriented generation” was created. The business consulting company “Sun’s idea of the new company is to involve all parties using marketing tools, creating a healthy competitive environment among commercial companies and benefiting not only the companies themselves but also the end user of the products and the German government.
Fermat proposed fermat’s little theorem in 1640, but a proof was not officially published until 1736. In this thesis paper, we mainly focus on different proofs of fermat’s little theorem like combinatorial proof by counting necklaces, multinomial proofs, proof by modular arithmetic, dynamical systems proof, group theory proof etc. We also concentrate on the generalizations of fermat’s little theorem given by Euler and Laplace. Euler was the first scientist to prove the fermat’s little theorem. We will also go through three different proofs given by Euler for fermat’s little theorem. This theorem has many applications in the field of mathematics and cryptography. We focus on applications of fermat’s little theorem in cryptography like primality testing and publickey cryptography. Primality test is used to determine if the given number n is a prime number or composite number. In this paper, we also concentrate on fermat primality test and Miller-Rabin primality test, which is an extension of fermat primality test. We also discuss the most widely used public-key cryptosystem i.e, the RSA Algorithm, named after its developers R. Rivest, A. Shamir, and L. Adleman. The algorithm was invented in 1978 and depends heavily on fermat’s little theorem.
A classical topic in the theory of random graphs is the probability of at least one isolated vertex in a given random graph. An isolated node has a huge impact on social networks which can be given by a random graph. We present a distribution on the number of isolated vertex using the probability generating function. We discuss the relationship between isolated edges and extended cut polynomials, extended matching polynomials using the principle of inclusion exclusion. We introduce an algorithm based on colored graphs for general graphs. We apply this to the components of a graph as well. Finally, we implement the idea on a special class of graphs like cycle, bipartite graph, path, and others. We discuss recursive procedure based on the analogous coloring rules for ladder and fan graphs.
Introducing natural adversarial observations to a Deep Reinforcement Learning agent for Atari Games
(2021)
Deep Learning methods are known to be vulnerable to adversarial attacks. Since Deep Reinforcement Learning agents are based on these methods, they are prone to tiny input data changes. Three methods for adversarial example generation will be introduced and applied to agents trained to play Atari games. The attacks target either single inputs or can be applied universally to all possible inputs of the agents. They were able to successfully shift the predictions towards a single action or to lower the agent’s confidence in certain actions, respectively. All proposed methods had a severe impact on the agent’s performance while producing invisible adversarial perturbations. Since natural-looking adversarial observations should be completely hidden from a human evaluator, the negative impact on the performance of the agents should additionally be undetectable. Several variants of the proposed methods were tested to fulfil all posed criteria. Overall, seven generated observations for two of three Atari games are classified as natural-looking adversarial observations.
We investigate the folding and thermodynamic stability of a tertiary contact of baker's yeast ribosomal ribonucleic acid (rRNA), which is supposed to be essential for the maturation process of ribosomes in eukaryotes at lower temperatures1. Ribosomes are cellular machines essential for all living organisms. RNA is at the center of these machines and responsible for translation of genetic information into proteins2,3. Only recently, the rRNA tertiary contact of interest was discovered in Zurich by the research group of Vikram Govind Panse. Gerhardy et al.1 showed in vitro that within the 60s-preribosome under defined metal ion concentrations the tertiary contact become visible between a GAAA-tetraloop and a kissing loop motif. Our aim is now to understand this RNA structure, especially the formation of the rRNA tertiary contact, in terms of thermodynamics and kinetics at various experimental conditions, such as temperature and metal ion concentration of K(I), Na(I) and Mg(II). Therein, we use optical spectroscopy like UV/VIS spectroscopy and ensemble Förster or Fluorescence Resonance Energy Transfer (FRET) folding studies. Our findings will help to further characterize this newly discovered ribosomal RNA contact and to elucidate its function within the ribosomal maturation process.
Several algorithms have been proposed for the testing of series-parallel graphs in linear time. We give our alternate algorithms for testing series-parallel graphs, their tree decompositions, and the independence number when the input is undirected biconnected series-parallel graphs, which run (approximately) linearly in polynomial time.
VQ-VAE is a successful generative model which can perform lossy compression. It combines deep learning with vector quantization to achieve a discrete compressed representation of the data. We explore using different vector quantization techniques with VQ-VAE, mainly neural gas and fuzzy c-means. Moreover, VQ-VAE consists of a non-differentiable discrete mapping which we will explore and propose changes to the original VQ-VAE loss to fit the alternative vector quantization techniques.
Cryptocurrencies are characterized by high volatility, both in the short and long term. Experienced traders exploit this to make profits from price fluctuations by swing trading. However, this requires closely observing and analyzing the prices and trading positions at the right time. Only a few specialists, who spend time focusing on this, or optimized trading bots are able to actually make continuously profits. The autradix protocol is a selfoptimizing and self-learning parametric trading algorithm that analyzes price actions in real-time and adaptively optimizes the algorithm’s parameters to realize the user’s investment objective. Embedded in an adaptive genetic algorithm, possible parameterizations are simulated and the optimal for the investigated trading pairs are calculated. The generic trading protocol API enables coupling with various crypto exchanges and decentralized protocols. A smart contract based decentralized, trustless, and tokenized fund, controlled by a DAO, enables users to invest, operate trading agents, and to participate in the profits generated according to their share.
With the advancement in cryptography and emerging internet technology, electronic voting is gaining popularity since it ensures ballot secrecy, voter security, and integrity. Many commercial startups and e-Voting systems have been proposed, but due to lack of trust, privacy, transparency, and hacking issues, many solutions have been suspended. Blockchain, along with cryptographic primitives, has emerged as a promising solution due to its transparent, immutable, and decentralized nature. In this paper, we summarized the properties that existing solutions should satisfy and explained some cryptographic primitives like ZKP, Ring signatures along with their security limitations. We gave a comprehensive review of some blockchain-based e-Voting systems and discussed their strengths and weaknesses based on the given properties with table of comparison.
Abstract: Blockchain Technology has become an innovative, mature tool for digital transformation, disrupting more and more application areas in their business processes, values, or even economic models. This paper leverages more than 30 academic publications on prototypes and their Blockchain-based use cases to transact certificates in the context of public education. The conceptual design and guiding ideas are reflected in the practical application development for the Federal Ministry of Education and Research ECHT! project within the showcase region WIR! in Mittweida and are used for the research design. During this approach we applied agile methods and the current certificate process to propose a comprehensive disclosure of a new software prototype including a three-layered architecture with multi-stakeholder components. The artefact instantiation contributes to the practical knowledge base within Information System Research and specifically in digital certificate processes starting from creation, searching, and proofing up to revoking by consideration of an existing IT landscape as well as organizational hierarchy.
Over the last two decades, the rapid advances in digitization methods put us on the fourth industrial era’s cusp. It is an era of connectivity and interactivity between various industrial processes that need a new, trusted environment to exchange and share information and data without relying on third parties. Blockchain technologies can provide such a trusted environment. This paper focuses on utilizing the blockchain with its characteristics to build machine-to-machine (M2M) communication and digital twin solutions. We propose a conceptual design for a system that uses smart contracts to construct digital twins for machines and products and executes manufacturing processes inside the blockchain. Our solution also employs the decentralized identifiers standard (DIDs) to provide self-sovereign digital identities for machines and products. To validate the approach and demonstrate its applicability, the paper presents an actual implementation of the proposed design to a simulated case study done with the help of Fischertechnik factory model.
Bitcoin's energy consumption and social costs in relation to its capacity as a settlement layer
(2021)
Bitcoin runs on energy. The decentralized network’s amount of energy consumption has resulted in multifaceted discussions about its efficiency and environmental impact. To put Bitcoin’s energy consumption into perspective, we propose to relate (a) the energy consumption in TWh and (b) resulting social costs in the form of carbon emissions to the Dollar value settled on the Bitcoin network. Both metrics allow to relate and quantify the capacity of Bitcoin as a settlement layer to the network’s energy consumption and resulting carbon missions, or social costs. We find that in early 2021 Bitcoin (a) settles between $2,333 and $7,555 for each Dollar spent on energy and (b) that, on average, a Dollar settled on the Bitcoin blockchain causes in social costs between 0.007% and 0.01%, depending on the estimated energy consumption converted into the costs of carbon emissions. These results help to assess the efficiency, cost and sustainability of Bitcoin and may allow a comparison of Bitcoin with existing settlement base layers such as Fedwire or gold
Blockchain and other distributed ledger technologies are evolving into enabling infrastructures for innovative ICT-solutions. Numerous features, such as decentralization, programmability, and immutability of data, have led to a multitude of use cases that range from cryptocurrencies, tracking and tracing to automated business protocols or decentralized autonomous systems. For organizations that seek blockchain adoption, the overwhelming spectrum of potential application areas requires guidance reducing complexity and support the development of blockchain-based concepts. This paper introduces a classification approach to provide design and implementation guidance that goes beyond current textbook classifications. As an outcome, a typology for management and business architects is developed, before the paper concludes with an instantiation of existing use cases and a discussion of their classes.
Mapping identities, digital assets, and people’s profiles on the internet is getting much traction in the blockchain cosmos lately. The new technology is currently forming architectures that will further pave new ways to reach fundamental mechanisms to interact in a decentralized, user-centered manner. These schemes are often declared as the next generation of the web. Within the article will be shown, how the internet has evolved in managing identities, what problems arose, and how new data architectures help build applications on top of privacy rights. Both technological and ethical perspectives are viewed to answer which guidelines should be considered to fulfill the upcoming branch of decentralized services and what we can learn from historical schemes regarding their privacy, accounting, and user data.
Global challenges like climate change, food security, and infectious diseases such as the COVID-19 pandemic are nearly impossible to tackle when established experts and upstart innovators work in silos. If research organizations, governments, universities, NGOs, and the private sector could collaborate on these challenges more easily, lasting solutions would certainly come more quickly. Aligned with the United Nations’ Sustainable Development Goals, SAIRA connects key players in different arenas: scientists and engineers at research and technology organizations (RTOs) looking to collaborate on sustainable development projects, companies seeking R&D support to tackle their most challenging problems, and startups with innovative ideas and a desire to scale. The platform is a blockchain-secured open innovation platform, anchored on Max Plank Digital Library's blockchain network bloxberg, that assures the authenticity and integrity of all user-generated content and collaboration processes.
While blockchain technology is still in an early stage of its development, it is already of surging economic importance.
In the literature, blockchain is referred to as either being a disruptive, institutional, foundational, or general purpose technology. There is still no consensus about the economic theory that should apply for analyzing its economic effects. This article draws on use cases from the coffee supply chain to explore, which theories could potentially apply to an emerging blockchain economy.
Smart ultrafast laser processing with rotating beam – Laser micro drilling, cutting and turning
(2021)
Current micro drilling, cutting and turning processes are mainly based on EDM, milling, stamping, honing or grinding. All these technologies are using a tool with a predefined geometry that is transferred to the working piece. In contrast the laser is a highly flexible tool, which can adapt its size very fast by changing only a software setting. Thanks to the efforts in laser development during the last years, stable ultrafast lasers with sufficient average power and high repetition rates became industrially available. For using as many pulses as possible, a cost-efficient production demands for innovative processes and machining setups with fast axes movement and special optics for beam manipulation. GFH has developed a helical drilling optics, which rotates the beam up to 30.000 rpm in a very precise circle and allows furthermore to adjust the diameter and the incidence angle. This enables the laser to be used for high precision drilling and cutting and also for micro turning processes.
The shape-memory Nitinol as a nickel-titanium alloy is widely used in actuator and medical applications. However, the connection of a flange to the rod is a critical point. Therefore, laser rod end melting enables material accumulations to generate a preform at the end of a rod, followed by die forming, so that the flange can be generated. This process has been successfully applied on 1.4301 steel. This study is aimed to investigate laser rod end melting of shape-memory Nitinol regarding the resultant surface quality of the preforms. The results showed that spherical preforms could be generated without visible surface discoloration due to oxidation. By using different scan rates, different solidification conditions occurred which led to significantly different surface structures. These findings show that laser rod end melting can principally be applied on Nitinol to generate preforms for flanges whereby the surface quality depends on the solidification conditions.
Beam shaping and splitting with diffractive optics for high performance laser scanning systems
(2021)
Diffractive optical elements (DOEs) enable novel high performance and process-tailored scanning strategies for galvanometer-based scan heads. Here we present several such concepts integrating DOEs with laser scanners and the respective application use cases. Beam shaping DOEs providing a homogeneous fluence over a custom defined profile, such as a rectangular Top-Hat, enable increased process quality in Laser-Induced Forward Transfer (LIFT) compared to the Gaussian beam of the laser source. We show that aberrations which occur over the necessary large wafer-sized image field can be eliminated through the use of a synchronous XY-stage motion. Another application that benefits from the use of DOEs is laser drilling. Drilling in display and electronics manufacturing demands high throughput that can only be achieved through the use of beam splitting DOEs for parallel processing. To this end, the joint MULTISCAN project is developing a variable multi-beam tool capable of scanning and switching each individual beamlet for increased control.
Increasing speed in laser processing is driven by the development of high-power lasers into ranges of more than 1 kW. Additionally, a proper distribution of these laser power is required to achieve high quality processing results. In the case of high pulse repletion rates, a proper distribution of the pulses can be obtained from ultrafast beam deflection in the range of several 100 m/s. A two-dimensional polygon mirror scanner has been used to distribute a nanosecond pulsed laser with up to 1 kW average power at a wavelength of 1064 nm for multi pass laser engraving. The pulse duration of this laser can be varied between 30 ns and 240 ns and the pulse repetition rate is set between 1 and 4 MHz. The depth information is included in greyscale bitmaps, which were used to modulate the laser during the scanning accordingly to the lateral position and the depth. The process allows high processing rates and thus high throughput.
Pulsed laser processing of vacuum component surfaces is a promising method for electron cloud mitigation in particle accelerators. By generating a hierarchically structured surface, the escape probability of secondary electrons is reduced. The choice of laser treatment parameters – such as laser power, scanning speed and line distance – has an influence on the resulting surface morphology as well as on its performance. The impact of processing parameters on the surface properties of copper is investigated by Secondary Electron Yield (SEY) measurements, Scanning Electron Microscopy (SEM), ablation depth measurements in an optical microscope and particle release analysis. Independent of the laser wavelength (532nm and 1064nm), it was found that the surface morphology changes when varying the processing parameters. The ablation depth increases and the SEY reduces with increasing laser fluence. The final application requires the capability to treat tens of meters of vacuum pipes. The limiting factors of this type of surface treatment for the applicability in particle accelerators are discussed.
We demonstrate a thulium-based fiber amplifier delivering pulses tunable between <120fs and 2ps duration at up to 228 μJ of pulse energy at a center wavelength of 1940 nm and 500-kHz repetition rate. Due to the excellent long-term stability, this system proves the ability of this technology to be integrated into ultra-fast material processing machines.
The wind energy sector is undergoing digitalization processes that span multi-tier supply chains of turbine components and wind farm maintenance, amongst others. In an industrial use case that includes Siemens Gamesa Renewable Energy, Vestas and APQP4Wind, the processes of producing, fastening, and servicing bolts in turbines are mapped to a digital model. The model follows the lifetime of turbine bolts from the manufacturing phase, to fastening in turbines and maintenance, until their replacement and recycling. The development of the digital model is iteratively addressed in a design science research approach, as the authors actively contribute to the project. Distributed ledgers (DLs) support the notary documentation of the bolts and turbines, from their registration phase to the assembly-, technical service verification- and recycling phases. The immutable and decentralized nature of DLs secures the data against tampering and prevents any changes taken unilaterally by engaging the service stakeholders and component providers in a blockchain consortium.
There are multiple ways to gain information about an individual and its health status, but an increasingly popular field in medicine has become the analysis of human breath, which carries a lot of information about metabolic processes within the individuals body. The information in exhaled breath consists of volatile (organic) compounds (VOCs). These VOCs are products of metabolic processes within the individuals body, thus might be an indicator for diseases disturbing those processes. The compounds are to be detected by mass-spectrometric (MS) or ion-mobility spectrometric (IMS) techniques, making the analysis of these compounds not only bounded to exhaled breath. The resulting data is spectral data, capturing concentrations of the VOCs indirectly through intensities. However, a number of about 3000 VOCs [1] could already be determined in human exhaled breath. The number of research paper about VOC-analysis and detection had risen nearly constantly over the last decade 1. Furthermore, the technique to identify VOCs could also be used to capture biomarker from alien species within the individuals body. Extracting VOCs from an individual can be done by non- or minimal invasive techniques. However, the manual identification of VOCs and biomarkers related to a certain disease or infection is not feasible due to the complexity of the sample and often unknown metabolic products, thus automized techniques are needed. [1–4] To establish breath analysis as a diagnosis tool, machine learning methodes could be used. Machine learning has become a popular and common technique when dealing with medical data, due to the rapid analysis. Taking this advantage, breath analysis using machine learning could become the model of choice for diagnosis, keeping in mind that conventional methodes are laboratory based and thus when trying detect bacterial infection need sometimes several days to identify the organism. [5]
The objective of this Bachelor Project is the creation of a tool that should support forensic investigators during IT forensic interventions. It uses Kismet as the base program and adds functionalities to it via the plugin interface. The installation of the plugin shall be explained, how the plugin works, and a recommendation on how to use it. To understand the underlying basics, an introduction about WLAN and Bluetooth is given. The tests that were performed with the new plugin are described as well as their results. It is therefore briefly discussed why the tool is applicable for locating Wi-Fi devices, especially access points, but not Bluetooth devices. Using all this a few ideas on how to improve the tool and what can be researched in this area are provided.
In this work, a protocol for portable nanopore sequencing of DNA from pollen collected from honey bees, bumble bees, and wild bees was developed. DNA metabarcoding is applied to identify genera within the mixed DNA samples. The DNA extraction and ITS and ITS2 PCR parameters tested for this purpose were applied to the collected pollen sample and the amplicons were then decoded using the Flongle sequencer adapter from Oxford Nanopore Technologies. It is shown that the main pollinator resources at the different sites can be identified in percentage proportions. The protocol generated in this study can be used for further ecological questions.
We propose a method for edge detection in images with multiplicative noise based on Ant Colony System (ACS). To adapt the Ant Colony System algorithm to multiplicative noise, global pheromone matrix is computed by the Coefficient of Variation. We carried out a performance comparison of the edge detection Ant Colony System algorithm among several techniques, the best results were found in the gradient and the coefficient of variation.