Contributions in Merlin

Estimating biodiversity of a local environment can help us decrease the rate of its degradation. The Beelivingsensor project aims to utilize honey bees as living sensors that could help us estimate biodiversity. However, in order to analyze the honey bees, and therefore, extract the valuable information, we need to process a lot of data. This need is fulfilled by the Beelivingsensor platform, as it accepts data from multiple sources, and processes them to provide us with useful information. Our thesis aims to improve the maintainability, robustness, and extensibility of this platform. Our hope is that we will help developers focus on improving the platform, rather than making sure it is operational. Moreover, we want to make sure that in case no experienced developers are available, and our platform encounters errors that could endanger its functionality, the platform will stay operational, and fixing any problems will be as easy as possible. As a result of our research, we were able to identify five common problems affecting cloud platforms according to the literature. We have then used this knowledge to improve the maintainability of our platform in eight key areas. Additionally, by conducting two experiments, we were able to objectively prove that outside of our own positive experience, these improvements are indeed beneficial. During the experiments, we have also noted a few unexpected observations, which could be further explored in the future research. In the end, we have helped the platform to stay operational for longer periods of time, and we have decreased the amount of attention it needs from its administrators. The research can also help other Kubernetes platform maintainers to reconsider the tools they should prioritize, and they might be able to avoid some pitfalls that we have discovered during our experiments.

Quality control in the pharmaceutical industry serves the purpose that only high-quality and safe drugs reach the market. This highly complex process needs to be monitored and planned carefully. Therefore, Meocon GmbH, a management consulting boutique for the pharmaceutical industry created the Q_alizer, an intelligent tool that analyses and visualizes the sample flow processes in quality control organizations and helps to uncover weaknesses. Since Q_alizer is in its early stages, no requirements engineering had taken place so far to check if all of the users’ needs are fulfilled. Therefore, this thesis explores Q_alizer users’ needs and Meocon’s vision of the further development of the tool. A new prototype with new functionalities for KPI insights, planning, distributing and forecasting and a new design has been developed in Figma focusing on the outcomings of the requirements engineering. In evaluation, users were very impressed by the new features and new design but also made suggestions for further improvement.

Privacy is becoming increasingly important, especially when handling data for dataanalytics tasks. Differential Privacy is often provided as a solution to guarantee privacy and is used by many large companies today. The parameter epsilon (") is at the core of Differential Privacy and controls the trade-off between utility and privacy. The goal of this study is two-fold, firstly, to designed and come up with representations of epsilon for the layman, secondly, to investigate what range of epsilon values are preferred for different scenarios. To achieve this goal, we ran an online survey with 29 participants and found that people are motivated by personal financial incentives more than financial gain of their community and care more about their own privacy than the people around them.

In recent years, research has made tremendous progress in the classification of facial attributes through convolutional neural networks (CNNs). Such neural networks specialize in extracting various facial attributes from images. In this thesis, we investigate how these attributes correlate with identity and whether such correlations can be used to improve the classification accuracy of extractions from neural networks. We introduce a method for calculating correction terms based on statistical metrics of each attribute of ran identity. These correction terms are then applied to the extractions of neural networks, resulting in reweighted values which possess a lower average error rate than the original extractions. We offer approaches for both unbalanced and balanced CNNs. For the balanced networks, we adapt our correction terms to the domain distribution in order to consider the imbalance of classes. We show that this approach achieves similar results for manually annotated identity labels and identity labels that are inferred from the clustering of features that are extracted with neural networks. Our results show that it is indeed possible to lower the average attribute classification error rates for both neural networks based on the correlation of these attributes with identity.

Remote Electronic Voting (REV) becomes an increasing topic of interest, since citizens are used to complete many other tasks online. But REV systems need to provide a very high level of security, hindering deployment of adequate solutions. The Communication Systems Group at the University of Zurich implements the REV system Provotum. This thesis explores deployment of a USB dongle in the Remote Electronic Voting (REV) system Provotum to improve security. This work proposes a design centered around a set of 6 - mostly mutually exclusive - functions running on a USB dongle that provide additional protection against theft and selling of private keys, as well as against manipulation by malware on the voter's computer. A USB dongle is selected and all functions are implemented in a firmware running on that dongle, as well as additional system components to constitute a Proof-of-Concept that demonstrates end-to-end integration of the functionality. All finite-field cryptography of the Provotum system (except RSA signatures) is changed to Elliptic Curve Cryptography (ECC) to achieve better firmware runtime performance. The implementation overcomes several challenges that are documented in this work and achieves to oer a strong baseline codebase that facilitates future development of further dongle-enabled Provotum functionality. Additionally, since all proposed dongle functions are implemented in firmware, varying levels of a tradeoff between security and usability can be controlled in the system.

As remote work is becoming more prevalent, the informal, spontaneous conversations regularly encountered in co-located work become less frequent because knowledge workers lack essential cues about their colleagues, such as their state of attention or current location. The lack of such social interactions can lead to feelings of isolation at work. While existing approaches focus mainly on improving team awareness to ameliorate coordination and collaboration problems caused by remote work, fewer tools focus on fostering informal, spontaneous communication to reduce the feeling of isolation. To address this gap, our approach focuses on people, their moods and current status, and opportunities for spontaneous interactions to create more social awareness. To this end, we developed AmbientTeams, an unobtrusive and informal tool that aims to reduce the perceived distance between distant colleagues. AmbientTeams seeks to achieve this goal through a mood-based micro-blogging approach that allows knowledge workers to share moods and status updates with their team and provides various ways to respond to what has been shared. In a preliminary evaluation, we tested our research prototype on a group of five knowledge workers who used the tool for one week. The results show that AmbientTeams facilitated getting to know each other by sharing moods and bringing more natural communication, which is otherwise often lost in a remote setting. In general, the encouraging results show that our novel approach of allowing knowledge workers to quickly and easily share moods with their team can benefit them by enabling and encouraging a more informal, lighthearted way of communicating.

The purpose of this thesis is the design and development of an app-prototype which supports intersession activities of patients undergoing psychotherapy. Intersession processes can include thoughts and feelings that patients experience between therapy sessions. To accomplish this, important requirements of the app are selected in an iterative process involving existing literature and feedback of experts in psychology. Suitable technologies and architectures for the implementation are selected in accordance with the gathered requirements. As a result, a fully functioning mobile app-prototype is developed and eventually evaluated. The participants provide initial feedback and generally show a high intention to use the app. The findings of the evaluation suggest functionalities, design principles, and potential challenges of selected aspects of the app. Initial insights and foundations for future development result.

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Ontologies are becoming a key component of numerous applications and research fields. But knowledge captured within ontologies is not static. Some ontology updates potentially have a wide ranging impact; others only affect very localised parts of the ontology and their applications. Investigating the impact of the evolution gives us insight into the editing behaviour but also signals ontology engineers and users how the ontology evolution is affecting other applications. However, such research is in its infancy. Hence, we need to investigate the evolution itself and its impact on the simplest of applications: the materialisation. In this work, we define impact measures that capture the effect of changes on the materialisation. In the future, the impact measures introduced in this work can be used to investigate how aware the ontology editors are about consequences of changes. By introducing five different measures, which focus either on the change in the materialisation with respect to the size or on the number of changes applied, we are able to quantify the consequences of ontology changes. To see these measures in action, we investigate the evolution and its impact on materialisation for nine open biomedical ontologies, most of which adhere to the EL++ description logic. Our results show that these ontologies evolve at varying paces but no statistically significant difference between the ontologies with respect to their evolution could be identified. We identify three types of ontologies based on the types of complex changes which are applied to them throughout their evolution. The impact on the materialisation is the same for the investigated ontologies, bringing us to the conclusion that the effect of changes on the materialisation can be generalised to other similar ontologies. Further, we found that the materialised concept inclusion axioms experience most of the impact induced by changes to the class inheritance of the ontology and other changes only marginally touch the materialisation.

Die vorliegende Arbeit analysiert, welche Kryptowährungen und Stablecoins während der COVID-19 Pandemie am besten performt haben und untersucht deren gemeinsamen Eigenschaften. Die resultierenden krisenresistenten Charakteristiken werden mit Aussagen bestehender Studien sowie Interviews mit Fachpersonen verglichen. Schliesslich erweisen sich ein sinnvoller Nutzen, eine starke Tokenomics, die dahinterstehenden Personen und ihre Reputation, Handelbarkeit, Ausgabe-Möglichkeit, Fungibilität, Netzwerk- sicherheit, ein begrenztes und vorhersehbares Angebot sowie der hauptsächliche Handel gegen Tether als krisenresistente Eigenschaften für Kryptowährungen. Eine off-chain Besicherung, der US-Dollar und Gold als Peg, die allgemeine Existenz eines Collaterals, eine komplette Besicherung und das Vertrauen sind dabei resistenzfördernde Charakteristiken für Stablecoins.