Contributions in Merlin

This thesis examines the impact of Blockchain on the United Nations’ Sustainable Development Goals (SDGs). It includes two parts: Firstly, a comprehensive literature review is conducted to investigate published evidence of Blockchain as an enabler/inhibitor of the 169 targets stated in the SDGs. The study observed a positive impact of Blockchain on achieving targets in financial inclusion, supply chain management, fundraising, energy usage, environmental surveillance, health insurance and govern-ance. Increased energy consumption and money laundering due to cryptocurrencies are assessed as inhibitors. In the second part of the work, four fundraising platforms and four supply chain service providers are interviewed to examine practical evidence of Blockchain as an enabler/inhibitor for achieving the targets. A contribution to the targets’ achievement can be determined for all startups. However, it is observed that the Blockchain is only one piece of the puzzle, whereby the design of the business idea and the combination of additional technologies such as the Internet of Things (IoT) as a whole must be considered.

The objective of this bachelor thesis is to describe the development of an electronic B2B marketplace in Europe and to define its requirements on the part of the various users. First, an analysis of an existing electronic marketplace is presented with focus on the success factors of this very marketplace. In addition to the results of interviews with potential users of the marketplace a description of functions offer substantial added value to all parties in a procurement relationship. The development of an electronic marketplace in Europe also increases the digital visibility of European production sites, which in turn benefits individual companies as well as the European workplace as a whole. The influence of directories on the digital visibility of companies described by Hauck et al. (2020) confirms this positive effect. This bachelor thesis is relevant for readers who want to understand how an electronic marketplace works. It also serves as a helpful guide for people who want to develop an electronic marketplace for Europe in defining its requirements and success factors.

The aim of this study is to devise deep learning (DL) approaches trained on paired and unpaired data that are able to generate realistic synthetic computed tomography (CT) images for magnetic resonance-guided radiotherapy (MRgRT) in the area of the abdomen and to assess its clinical applicability. The imaging data of 76 patients with a tumour in the abdomen who were treated with MRgRT at USZ was collected retrospectively and divided in training (59) and test sets (17). To improve the current state-of-the-art of DL technologies by studying different architectures and ensembles of configurations, the following experiments were conducted: (a) evaluating the influence of the different GAN architectures trained on paired (Pix2pix) and unpaired data (CycleGAN and CUT, which firstly applied for the purpose of sCT generation); (b) investigating the footprint of different preprocessing methods (Nyul, novel N-peaks); (c) improving spatial consistency of results by adjusting the network configuration (2D, pseudo3D); (d) testing different GAN training objectives (LSGAN, WGAN-GP); (e) estimating the influence of the loss function on the generated results (per-pixel L1 loss, VGG19 perceptual loss). The quality of sCT generation was assessed using both, image similarity and dosimetric accuracy metrics. The dosimetric accuracy of the best performing models was estimated by comparing the dose distribution of MRgRT treatment plans calculated from synthetic CT and original CT images using dose-volume histogram (DVH) parameters to allow assessment of the clinical applicability of the DL methods. Our results suggest that DL models trained with unpaired data achieve similar performance as models requiring perfectly aligned image pairs, and even perform better in the bone and air pocket areas. The mean absolute errors (mean ± SD) calculated within the body contour are 71.0±20, 73.4±21 and 84.5 ±19 HU when using the best performing configuration of pix2pix, CycleGAN and CUT, respectively. The proposed DL-based synthetic CT generation methods may be considered clinically applicable for treatment planning in the abdominal area with the mean DVH indicator discrepancies with the original plan of less than 1% for all models, and less than 0.5% for all tumour DVH indicators for the best performing model, CycleGAN. The study confirmed that generation of synthetic CT using a DL approach from low field magnetic resonance images in the abdomen is feasible and allows a reliable calculation of irradiation plans in MRgRT.

The growing usage of IoT devices brings in itself multiple different new use cases. From healthcare, location tracking to process automations and crowdsensing, IoT devices are being used more than ever. In parallel there has been a growing cybersecurity concern, as IoT devices are becoming a desirable target for cyber attackers. IoT devices, depending on their purpose can have access to large amounts of data which makes them an attractive target for cyber criminals. To further this issue, these devices are poorly secured and inherently, as they are resource constrained, can not support conventional cybersecurity software. IoT devices have been the targets of different kinds of malware, from botnets and backdoors to rootkits, ransomwares and others. A feasible way to sever these cyber security concerns and prevent these targeted malware attacks from happening, is with the help of Intrusion Detection Systems (IDSs). Nevertheless, traditional IDSs are powerless when it comes to detecting new unknown malware attacks, other wise known as zero day attacks. For this reason, new research is relying heavily on Machine Learning (ML) and Deep Learning (DL) decision engine based IDSs. A key component that determines the efficacy of these IDSs is a quality dataset, containing the behavior of a device under normal behavior and also the behavior when it has been compromised by novel malware, with which the ML or DL based IDS can be trained. A ML or DL based IDS with a quality dataset is then statistically better suited to detect novel malware. In spite of the importance of these datasets, quality datasets, especially ones modelling the internal behavior of IoT devices in a normal state and when under attack by zero day attacks such as botnets, backdoors and others, are scarce. In wake of this limitation, this thesis aims to create a quality dataset that accurately represents the internal behavior of an IoT device, both when it is functioning normally and when it is under attack. In order to accomplish this, the system calls of the IoT device, which in this specific case is an ElectroSense sensor, are monitored under normal behavior, gathered, cleaned and stored in a centralized directory. Then, the device is infected with current malware affecting IoT devices, such as the bashlite botnet, thetick backdoor, bdvl rootkit and a ransomware proof of concept and the monitoring process is repeated for each malware. The infections are sequential, meaning that the device is not infected with more than one malware at a time. Finally the generated dataset contains normal and anomalous behavior classified by malware. It is then evaluated through analyzing the sequences and frequencies of the system calls statistically.

Poverty and hunger still prevails in many parts of the world. Sub-Saharan countries are particularly affected by severe food insecurity due to heavy reliance on agriculture and lack of sufficient farming equipment. Thus, finding a cost-effective method to introduce improved equipment to a high percentage of farmers in reasonable time is crucial. For this reason, we develop a modular agent-based model which simulates innovation diffusion among smallholder farmer households in Tanzania. It is based on proven innovation research as well as recent findings in an ongoing field study of the University of Zurich in Tanzania. Furthermore, we define different intervention strategies to accelerate the diffusion rate of an innovation among farmers. We outline how our model can be utilized to evaluate and compare such strategies in various ways. By applying algorithms from machine-learning on our diffusion simulation results, we show how key factors behind the performance of a strategy can be determined and demonstrate possibilities to predict the success rate of a strategy. Our findings present researchers an inexpensive alternative to assess intervention strategies effectiveness before launching them in the field.

This thesis examines the public value creation in digital ecosystems. It, therefore, aims to elaborate on success factors that lead to successful public value creation in digital ecosystems. For this purpose, interviews were conducted with representatives of digital ecosystems as part of a multiple case study, which was then evaluated using qualitative analysis. Through the analysis combined with the literature review, a number of success factors were determined that positively influence the process of public value creation. In a second step, a fuzzy-set Qualitative Comparative Analysis (fsQCA) was performed, which compiles configuration paths from these success factors. The results of this configuration analysis suggest different configurations paths that show in which configurations the success factors lead to successful public value creation for platform owners and complementors.

Since the inception of the Bitcoin blockchain in 2009 with the inclusion of the message "The Times 03/Jan/2009 Chancellor on brink of second bailout for banks" in its genesis block, blockchains have been used to store generic media. These include text, images, and documents. However such media is often not easily discoverable in the blockchains and is embedded deep within their binary data structures. The main goal of this thesis is to design and implement a tool that scans blockchains for their media content. The software tool developed for this work, the blockchain-parser, is capable of detecting text strings and files embedded in blockchains. The blockchains of the Bitcoin, Monero, and Ethereum cryptocurrencies were examined to find commonalities and differences between different blockchains in terms of their generic media storage usage. Prior related work has focused on the methods for storing media in Bitcoin. This thesis provides statistics and examples of the blockchain parser's detected media across Bitcoin, Monero, and Ethereum, which are presented and discussed herein. It concludes that Ethereum has been the most-used blockchain for media data storage of the three and might also be the best-suited blockchain for this task.

The goal of this thesis is to implement a working prototype for the detection of malicious DNS-over-HTTPS (DoH) traffic into the already existing System SecGrid, a platform for the extraction of internet traffic, its analysis, and the detection of cyber-attacks developed by the CSG-Group at the University of Zurich. The implementation contains a special feature extraction for DoH traffic based on TCP-flows and a two Layered Machine Learning pipeline for the detection of malicious DoH traffic. The evaluation proves that the prototype is extremely precise for single data-sets, but as soon as the models are trained and tested with different data the accuracy of the prototype deteriorates drastically. The conclusion is the diversification of the training data-sets into data-sets that are aligned with real-world browser settings and all available DoH resolvers and especially the quantitative and qualitative extension of the state-of-the-art data.

Tables are one of the most convenient ways to present complex, correlated, and structured information. While rule and heuristic based approaches have long dominated the table detection and structure recognition, their usefulness has been confined to a subset of tables that follow these rules. Significant research has been conducted to localize table structure, the majority of which focuses on using heuristics and rules with the assistance of optical character recognition (OCR) to manually select layout characteristics of the tables. With the rise of Deep Learning, new models have shown to be applicable across multiple unseen domains by incorporating transfer learning. This thesis presents an end-to-end object detection approach to detect tables and recognize their structures in a document and thus, help in table data extraction with the use of a deep learning model namely, Faster R-CNN. This work will also introduce a new metric based on Intersection over Union (IoU) for the task of table detection which does not penalize large bounding box predictions up to a defined extent and reduces the dependency of the F1 score on the chosen IoU threshold. A significant amount of experiments will be discussed on many popular publicly available datasets like ICDAR 2013, ICDAR 2019, ISRI-OCR, Marmot, TableBank, and PubTables-1M to carefully adapt and design the parameters of the Faster R-CNN model and demonstrate the robustness of the model across unseen datasets. The model present in this thesis outperforms other models including a transformer-based model to establish the state-of-the-art results on these datasets proving once again the superiority of the Faster R-CNN architecture.

This master’s thesis explores the opportunities real estate mezzanine financing o↵ers investors and borrowers through marketplace lending in Switzerland. To answer the research question, existing studies on marketplace lending were analysed, and a quantitative analysis was completed using data from the largest Swiss marketplace lending platform. The results indicate that using mezzanine financing through the alternative intermediary marketplace lending platforms creates new opportunities for investors and borrowers that are not available with traditional banks. On this basis, using this alternative option and optimising its results is advised.