 Ali Yassine, Paint-it-Gray: Modelling, Partitioning, and Analysis of User Transaction Networks in the Bitcoin Blockchain, University of Zurich, Faculty of Business, Economics and Informatics, 2023. (Master's Thesis)
 
The rise of Blockchain technology and cryptocurrencies has enabled the creation of decentralized alternative payment systems, without the need for third party financial institutions. However, this decentralization and pseudo-anonymity have also facilitated the emergence of darknet markets (DNMs) offering illicit products. This study introduces the"grayscale diffusion framework", with the aim of modeling and understanding the propagation of dark assets in the Bitcoin network. Formulated and implemented with a combination of on-chain and off-chain data, this approach utilizes address clustering, haircut tainting, and community partitioning to offer a unique analysis perspective on dark asset proliferation across the Bitcoin blockchain. The framework uncovers interesting patterns in the assortative nature of Bitcoin transactions based on the darkness level of assets, pointing to the existence of non-random clusters and communities that facilitate dark asset diffusion. Our research not only addresses key questions related to the effective modeling and tracking of tainted asset flow, but also provides valuable insights. Keywords: Bitcoin, Address Clustering, Darknet Marketplaces, Gray-scale Diffusion, User Transaction Networks. |
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Nevio Liberato, Creation and Comparative Visualization of Rankings Derived From Pairwise Comparisons, University of Zurich, Faculty of Business, Economics and Informatics, 2023. (Bachelor's Thesis)
Rankings serve to structure large datasets. They are an integral part of decision-making in various fields. To address the complex task of interpreting and comparing ranking algorithms and the results they produce, we created a Visual Analytics (VA) tool called RankViz. This prototype allows users to visualize and explore the output of various ranking algorithms and includes multiple metrics to assess the quality and differences of the rankings. The pairwise comparison data we used to construct the rankings was collected in a previous study by Barth et al. This thesis reviews different ranking algorithms, details the functionality of RankViz, demonstrates its utility with usage scenarios, and discusses potential future work in this field. |
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Viachaslau Berasneu, Design and Implementation of a System for Reproducible Machine and Deep Learning Models, University of Zurich, Faculty of Business, Economics and Informatics, 2023. (Bachelor's Thesis)
In recent years, small and midsize enterprises (SMEs) have become increasingly reliant on technology, but lag in terms of investment into cybersecurity. This renders them vulnerable to malware attacks, which are increasingly targeting companies rather than individuals, with great economic impact. This project proposes and implements a prototype tool, which allows for machine learning models to be trained, stored, and tested within the SecBox sandbox environment. Both classification and anomaly detection models are implemented through Scikit-learn, in order to provide predictions about known malware types (binary and multiclass classification), as well as detecting the presence of unseen malware in real-time during the SecBox execution. The models are trained using the system call and resource usage file execution logs available from the SecBox, which are transformed into suitable formats using frequency-based and sequence-based data preprocessing. Model reproducibility is ensured by generating configuration files with references to the random seeds, the datasets used in training, as well as other model parameters, which can be used to re-train the same model. To evaluate and compare model performance, each model type is tested in a realistic scenario of the execution of Monti ransomware within the SecBox, creating a confusion matrix as well as calculating the accuracy, precision, recall and F1-score metrics based on the model predictions. The system call classifier models are shown to have the best performance when classifying Monti malware samples, and the project is concluded by specifying several relevant research areas to be investigated further. |
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Florian Andreas Herzog, Fully Fledged SDN in a LoRa Mesh, University of Zurich, Faculty of Business, Economics and Informatics, 2023. (Bachelor's Thesis)
This bachelor-thesis tries to incorporate Software Defined Networking (SDN) mechanisms into a Long Range (LoRa) Internet of Things (IoT) mesh. Typically, devices used in LoRa-based wireless sensor networks (WSN) are limited in range. Therefore, in a scenario where a significant amount of nodes would be out of range of the LoRa Wide Area Network (LoRaWAN), deploying a mesh topology is a simple yet effective way to connect far away nodes using multi-hop communication. SDN, on the other hand, aims to improve network performance by analyzing the network and applying smart optimization algorithms. The hardware used in this thesis, are nodes being a Raspberry Pi, a popular choice inIoT, and the E32-868T20D LoRa Shield, a budget-friendly option to adapt LoRa technology. The software is implemented in Java, a programming language that promotes human-readability in code and benefits from decades of experience in practical software development. While LoRa networks with mesh topologies have already been subject of previous research using various devices and programming languages, the goal of this thesis is to test the effectiveness of SDN-based mechanisms in improving a LoRa mesh network and finally providing a user-friendly API as a service to other applications as a transmitter of data. Disclaimer: Neither the product, nor the analysis of SDN-based mechanisms have reached a state of success. |
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Gregory Frommelt, Linux on Tensilica Xtensa, University of Zurich, Faculty of Business, Economics and Informatics, 2023. (Bachelor's Thesis)
The Internet-of-Things (IoT) is becoming increasingly integral to modern living, offering a wide range of applications across diverse devices employing a multitude of different operating systems (OS). While Linux is the most prevalent OS in the IoT landscape, its resource requirements often prevent its use on less powerful, cost-efficient devices like those in the ESP32 family of microcontrollers (MCUs). The goal of this thesis is therefore to explore the feasibility of porting Linux to ESP32 devices, motivated by both economic and IoT standardization incentives. A cost analysis reveals an approximately 80% reduction in expenses when using the ESP32-WROVER-IE module compared to the Raspberry Pi Zero W. A tool chain was constructed to compile a Linux kernel image, which was successfully ported to an ESP32-S3-DevKitC-1 board. Initial evaluations indicate that the ported system offers basic functionality suitable for IoT tasks, although certain limitations currently restrict its practical utility. |
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Muyao Dong, Design and Implementation of a Business-driven Threat Quantification Framework, University of Zurich, Faculty of Business, Economics and Informatics, 2023. (Master's Thesis)
Nowadays, companies and organizations invest in cybersecurity more and more as they are operating with digital information systems. Cyber risk management presents a well-defined path toward the management of critical assets, threats, and countermeasures. Within cyber risk management, threat modeling is a structured process to identify potential threats, and in this process, it is significant to evaluate each threat and estimate its potential impacts.
Although threat modeling methodologies have been developed in depth, most of them focus on threat identification in di↵erent contexts, while how to quantify their impact for further inspection is less discussed. This thesis works on designing a framework to fill in this gap. The main outcome of this thesis is a framework that guides users to evaluate and quantify cyber threats in business contexts. The framework integrates applicable business impacts, calculates and visualizes the impacts of cyber threats, providing users with an intuitive picture of cyber threats analysis in the view of business. The prototype is well developed and properly evaluated, and the usability of the prototype is of satisfaction. |
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Jordi Küffer, ARTIS - Art Tracking with IoT and Blockchain, University of Zurich, Faculty of Business, Economics and Informatics, 2023. (Bachelor's Thesis)
This thesis delves into the convergence of the Internet of Things (IoT) and Blockchain technologies, focusing on the innovative application of these technologies within artwork transportation. The main goal is to introduce a system that capitalizes on IoT and blockchain to enhance the tracking and management of artwork during transportation processes.
In pursuit of this goal, the study adopts a dual-pronged methodology. A comprehensive literature review provides a foundational understanding of the underlying principles. Subsequently, an applied research approach is employed, culminating in designing, implementing, and evaluating a prototype tailored to the intricacies of artwork transportation.
The outcome of this thesis is ARTIS, a real-world prototype that effectively supports the targeted artwork tracking use case. However, it is acknowledged that further strides are needed to refine the prototype, particularly in safeguarding sensitive data and optimizing sensor accuracy.
The significance of this work lies in its innovative amalgamation of IoT and blockchain technologies, presenting a novel avenue for addressing challenges in the artwork transportation domain. By demonstrating the feasibility of such a system, this thesis lays the groundwork for future endeavors to advance this concept into a production-ready solution. |
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Özgür Acar Güler, Explaining CNN-Based Active Tuberculosis Detection in Chest X-Rays through Saliency Mapping Techniques, University of Zurich, Faculty of Business, Economics and Informatics, 2023. (Master's Thesis)
Tuberculosis (TB) is an infectious disease caused by the bacterium Mycobacterium tuberculosis, which is one of the leading causes of death worldwide. Various Deep Convolutional Neural Network models have gained popularity to help during the TB screening process by detecting
patients with active Tuberculosis from their Chest X-Rays. To help with further advancing the research, a new publicly available dataset, TBX11K, has been used to increase the number of samples during training for existing replaceable state-of-the-art models. In the first step, the model’s performance was evaluated to see if an improvement through the addition of more TB-related data was observable. It was shown that state-of-the-art replicable binary classifier models could further be improved through the inclusion of more data. Further, there is a lack of focus on generating and evaluating explanations for such models. The preferred methods currently are saliency mapping techniques such as Grad-CAM, to generate visual explanations based on the model’s
decision-making process, by overlaying heatmaps over the Chest X-Rays. The selected TBX11K dataset includes ground truth bounding box labels, which makes it possible to evaluate if the visualisations were correct. There are various evaluation metrics to evaluate the faithfulness and localisation performance of the saliency mapping techniques according to ground truth labels. Two of them have been identified to be useful, namely RemOve and Debias, and Proportional Energy. RemOve and Debias was used to observe if there is one universal saliency mapping technique that performs well for all models for the task of active Tuberculosis detection. Further, based on these two metrics, a new metric was proposed, ROAD-Normalised PropEng Average, to measure the overall best-performing model and Saliency Mapping Technique combination. From the evaluation with RemOve and Debias, it was concluded that there does not seem to be a universal saliency mapping technique that performs well on all model architectures for the de-
tection of active Tuberculosis. Thus, it is recommended to always consider the underlying model before choosing the optimal saliency mapping technique. Further, through the use of the ROAD-Normalised PropEng Average, it was concluded that one model in combination with a saliency mapping technique offered the best trade-off between faithfulness and correctness of the visualisations. This was the multi-label DenseNet-121 model with Eigen-CAM. To obtain accurate clas-
sifications of active Tuberculosis with explainable and correct visualisations, it is recommended to use this model and visualisation technique combination. |
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Remo Hertig, Deep Radial Basis-Function Networks for Open-Set Classification, University of Zurich, Faculty of Business, Economics and Informatics, 2023. (Master's Thesis)
A problem with modern deep learning recognition systems is that they often respond to stimuli of an unknown class overly confident, but wrong. Open-set recognition highlights this behavior and provides evaluation methods to estimate the generalization capability of models beyond the classic train/test set split. In this thesis, we incorporate a Radial Basis Function (RBF) layer into deep convolutional networks to model the deep feature distribution. We evaluate such networks on standard open-set evaluation protocols and compare their performance with standard Softmax classification models. Additionally, we utilize negative training samples and compare with the Entropic Open-Set Loss Softmax extension. We show that standard deep RBF network with Gaussian activation functions does not outperform Softmax based methods in open-set recognition. We extend the RBF network in two ways, which both show increased open-set recognition performance over the baseline RBF network. Based on these results we conjecture that solely using an RBF layer for the classification sub-system of a deep neural network might not be sufficient to solve the open-set recognition problem. |
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Shaoyan Li, Unsupervised Shape representations for 3D reconstruction, University of Zurich, Faculty of Business, Economics and Informatics, 2023. (Master's Thesis)
Non-uniform rational B-Spline surfaces (NURBS surface), a kind of parametric surface, are widely used in 3D modeling. This work explores NURBS surface reconstruction via the NURBS-Diff module. The NURBS-Diff module enables NURBS surfaces differentiable using the PyTorch framework. With supervised parameters, the module reconstructs the NURBS-based point cloud efficiently. This work introduces several pipelines by utilizing the NURBS-Diff module in unsupervised cases. The unsupervised pipelines make use of supersampling methods to obtain unstructured input and propose various metrics for point cloud and surface evaluation. The baseline unsupervised method is adapted from the original supervised pipeline. An extension of the NURBS-Diff module
is also presented. The unsupervised pipelines are evaluated against the baseline. The pipelines serve as a stepping stone to further investigation into NURBS surface reconstruction based on unstructured input. |
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Turki Alahmadi, MFExplain: An Interactive Tool for Explaining Movie Recommendations Generated with Matrix Factorization, University of Zurich, Faculty of Business, Economics and Informatics, 2023. (Master's Thesis)
Recommender systems have become integral in guiding users through the overwhelming abundance of online content. As these systems assume an ever-increasing role in shaping user decisions and preferences, there is a growing demand for clarity in their decision-making processes to instill trust. Recommendation algorithms with a high degree of accuracy such as matrix factorization are highly regarded and widely adopted. Nonetheless, these algorithms tend to exhibit high complexity in their logic and architecture, rendering them challenging to explain to end-users. This issue has been recognized and many tools have presented possible solutions. Many of the implemented approaches, however, have demonstrated shortcomings due to disregarding some user-centered properties or overly concentrating on unraveling the underlying algorithmic intricacy. This work presents MFExplain, an innovative tool for explaining movie recommendations generated with matrix factorization. The tool aims to explain recommendations by relying on the provision of intuitive justifications. Leveraging interactivity and cutting-edge dimensionality reduction techniques enables the tool to also encourage exploration, allow user feedback, and foster many desirable recommender system properties that enrich the user experience. |
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Dogan Parlak, An Open-Source Implementation of FIFA’s Enhanced Football Intelligence, University of Zurich, Faculty of Business, Economics and Informatics, 2023. (Master's Thesis)
This thesis addresses the implementation of concepts outlined in FIFA's Enhanced Football Intelligence (EFI) document through an open-source library, filling the gap with accessible implementations for these concepts. The EFI document provides descriptions for various metrics related to football performance analysis used in the FIFA World Cup 2022. Existing packages in football analytics do not fully incorporate the latest methodologies used in the FIFA World Cup 2022, essential for the creation of a source that aligns with FIFA's definitions. The implemented concepts cover possession control, phases of play, ball recovery time, line breaks, receptions behind midfield and defensive lines, defensive line height and team length, team shape, final third entries, pressure on the ball, forced turnovers, and expected goals (xG). Utilizing the explanations of these concepts, the thesis formulates a main approach and involves refinements. The level of stability varies, with methods that incorporate fewer heuristics tending to be more stable, while those that rely on a greater number of heuristics tend to be less stable. However, during implementation, limitations were encountered, including the lack of technical details and absence of FIFA's resources regarding the technology they have employed. Specifically, the lack of heuristics mentioned in the definitions of the concepts was a notable gap. Challenges were also observed, such as specific matches that are labeled as outliers due to their performance in distinct concepts. Despite these limitations and challenges, the implementation overall offers stable and accurate performance, aligned with FIFA's outcomes.
In future work, these limitations can be addressed through a comprehensive approach. Firstly, revisiting the concepts with additional information regarding their descriptions will enhance the understanding of the underlying factors. Secondly, the expansion of datasets will not only provide a broader foundation for analysis but also improve the heuristics employed, leading to enhanced accuracy and stability of the outcomes. Additionally, the application of advanced technologies, similar to those employed by FIFA, can significantly contribute to improving the reliability and effectiveness of the results. By considering these avenues, future research can overcome the identified limitations. This thesis contributes to advancing football performance analysis by addressing these challenges and provides a valuable resource for researchers, analysts, and football enthusiasts seeking to reproduce FIFA's match reports and gain insights into football performance.
Keywords: Enhanced Football Intelligence, FIFA, open-source implementation, football performance analysis, sports analytics. |
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Alexander Wyss, DaedalusData: Exploring and Labeling of a Large High-Dimensional Unlabeled Image Dataset; Analysis of Particle Contamination in Global Operations Consumables, University of Zurich, Faculty of Business, Economics and Informatics, 2023. (Master's Thesis)
With the surge in the volume and dimensionality of large image datasets across fields such as medicine, manufacturing, and quality monitoring, there is an increased emphasis on efficiently curating these datasets.
This design study explores the challenges associated with labeling and exploring large, high-dimensional, and unlabeled image datasets.
Traditional tools prioritize either data visualization using techniques like dimensionality reduction or labeling automation using AI learning mechanisms.
This binary focus often comes at the cost of extensive labeling functionalities or comprehensive overviews, since user interaction is reduced.
This research bridges this gap by introducing DaedalusData, an interactive visual analytics approach that combines meaningful visual exploration with efficient labeling and intuitive feedback loops.
DaedalusData presents an interactive platform that enables pattern and anomaly exploration, efficient image labeling by integrating metadata, and preliminary steps toward labeling automation.
The tool was developed alongside domain experts and built for a dataset containing particle contamination in consumables at Roche Diagnostics.
As a design study this thesis, solved a real-world problem, through close collaboration with domain experts.
The study posits that merging interactivity, human expertise, and automated processes offer a promising direction for managing large image datasets, with DaedalusData serving as a foundational step. |
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Marco Zimmermann, Performanceauswirkung der Beimischung von Emerging Markets in ein Aktienportfolio, University of Zurich, Faculty of Business, Economics and Informatics, 2023. (Master's Thesis)
Diese Arbeit untersuchte, ob sich über die letzten 20 Jahre eine Beimischung der Emerging Markets in ein Aktienportfolio fortgeschrittener Volkswirtschaften gelohnt hat. Ausserdem sollten relevante Treiber der Emerging Markets Rendite identifiziert werden und deren Einfluss gemessen werden. Aus der Untersuchung resultierte, dass sich eine Beimischung über 5,10 und 15 Jahre nicht gelohnt hat, über 20 Jahre schon. Es resultierte ein signifikant positiver Einfluss der Rohstoffentwicklung und ein signifikant negativer Einfluss der Dollar Stärke. Die GDP Wachstums-
differenz zwischen Emerging Markets und Developed Markets hatte keinen signifikanten Einfluss. Das GDP-Wachstum der Emerging Markets lieferte in einem drei Variablen Modell einen signifikant positiven Einfluss. |
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Alexander John Völker, Persönlichkeit und Investitionsentscheidungen bei Wohneigentum, University of Zurich, Faculty of Business, Economics and Informatics, 2023. (Master's Thesis)
Mit dieser quantitativen empirischen Studie mit 207 Probanden wird gezeigt, dass die Big-Five-
Persönlichkeitsdimensionen einen Einfluss auf ausgewählte Kauf- und Finanzierungspräferenzen bei Wohneigentum in Deutschland ausüben, wohingegen die
Persönlichkeitsdimension ‹Eigenschaftsangst› diese Präferenzen nicht beeinflusst. Die Auswertung erfolgte mittels ‹Ordinary Least Squares›-Regressionsanalyse. Des Weiteren besteht kein Zusammenhang zwischen den Big Five und der Preisbereitschaft bei nachhaltigen Gebäudeattributen. Ausserdem wurde festgestellt, dass sich die Kauf- und
Finanzierungspräferenzen im Mittelwert länderüber-
greifend zwischen Israel und Deutschland signifikant voneinander unterscheiden. Diese Ergebnisse unter-
streichen einerseits die Bedeutung von Persönlich-
keitsdimensionen in Entscheidungsprozessen und andererseits die Notwendigkeit, auch kulturelle Faktoren in zukünftige Forschungen einzubeziehen, um
Abweichungen besser zu verstehen. |
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Laura Hofmann, A Single-Case Study on CEO Activism and its Evaluation by News Media Around the World, University of Zurich, Faculty of Business, Economics and Informatics, 2023. (Master's Thesis)
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Yoram Bielefeldt, Persistente CSR-Vermeidung: Fallstudie zur FIFA im Vorlauf der Fussball Weltmeisterschaft 2022 in Katar von 2009 bis 2022, University of Zurich, Faculty of Business, Economics and Informatics, 2023. (Bachelor's Thesis)
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David Moser, The Dynamics of Code Review: Understanding the Impact of Change Size Through Eye Tracking Analysis, University of Zurich, Faculty of Business, Economics and Informatics, 2023. (Bachelor's Thesis)
This research utilized eye tracking technology to gain a better comprehension of the code review process. We collected data from 14 participants, ranging from inexperienced Java users to experienced Java developers and code reviewers with more than a decade of experience. By analyzing the eye tracking data, we were able to identify differences in attention patterns based on the size of the code changes and the focus on various code elements. Notably, smaller code changes received more detailed attention to specific code elements than larger ones. Our results provide useful information that can be used to improve code review processes and developer training. |
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Wouter van Dijk, Pattern and Signal Detection using Machine Learning for Algorithmic Trading, University of Zurich, Faculty of Business, Economics and Informatics, 2023. (Master's Thesis)
Predicting equity returns is a complex task in finance. This paper examines the volume profile
as a predictive tool using machine learning techniques. We process and summarize the volume
profile into features, using an XGBoost classifier to forecast stock return direction. Our approach is
validated across two equity sets, demonstrating its capability to identify high-return periods. Based
on the probability estimates, trading strategies are created and shown to be able to outperform the
benchmark on a risk-adjusted and total return basis. Overall, the results indicate the predictive
potential of the volume profile leveraged by the model. |
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Gino Steffen, Impact of financial development on economic indicators in advanced countries in Asia, University of Zurich, Faculty of Business, Economics and Informatics, 2023. (Bachelor's Thesis)
The relationship between economic indicators and financial development has long been a topic of
particular interest for researchers and policymakers alike. Despite extensive research in the past, the
true nature of the relationship remained unknown. The goal of this thesis is to analyze this research
question for multiple advanced Asian countries with various economic and financial indicators. The
analysis is conducted by means of ordinary least squares regression. The results reveal that many
indicators have a rather negative impact but not consistently across all countries, making it difficult
to draw a general conclusion about the observed relationship. |
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