Contributions published at Department of Informatics (Burkhard Stiller)

This study examines how different types of users experience and perceive gamification on Stack Overflow. In addition, the game balance and users’ opinions on the level of challenging tasks for members of different skill sets are explored. The study data were obtained using a mixed method approach that combined quantitative and qualitative methods. Quantitative data were collected through a survey, and qualitative data were obtained by interviewing 10 Stack Overflow users. The results suggest that guidelines that are applied in Stack Overflow reduce the need for competence and autonomy, and as a result, discourage expert and novice users from contributing to Stack Overflow. Furthermore, the Stack Overflows’ reward system awards trendy questions over complicated and niche questions. The results indicate that novice users may feel that they cannot contribute to the platform. In addition, they struggle with finding adequately challenging tasks to solve, resulting in them being discouraged from contributing.

Polkadot’s validator selection process employs an iterative algorithm, which is dependent on the size of the staking system. As Polkadot’s staking network is growing, I propose a machine learning alternative approach to the current implementation, that is more independent of scale. The algorithm, the sequential Phragmén, aims to reduce a graph of nominator-validator edges to a subset of validators, the active set, and distribute the stake backing them, as evenly as possible. The goal of this thesis is to produce superior results, consequently improving the overall security or to provide solutions of equal quality in faster time. In order to achieve the goal, a pipeline is setup, that gathers data and transforms it such that it is suitable for machine learning models. Predictions are made, which are adjusted to fit the requirements set by Polkadot. The adjusted results are scored and ultimately compared to the solutions discovered by sequential Phragmén. An analysis of the training data reveals, that the active set remains highly static, with only 10 validators on average changing from era to era. This lack of diversity raised concerns regarding potential attack vectors for adversaries. Furthermore, it was observed that many nominators are acting inefficiently. Many of them do not execute their right to nominate up to 16 validators, which would maximize their chance of having a validator included in the active set. Additionally, many of them include validators, which are not eligible targets. This occurs since nominators frequently ignore their duty to actively tend to their validator preferences. They set them once and do not update them. Eligible validators become inactive (intentionally or unintentionally) and consequently remain as part of the nominators preferences. The prediction task was split up into three models: The first model predicts the next active set, the second model predicts the sum of stake each validator receives and the third predicts the individual stake distribution. The results show, that the first two models are trained well and produce satisfactory results. However, the learning curves of the third model reveal a bias, which make the predictions suboptimal. The source of the bias is likely the substantial changes in target values introduced by a slight shift of active set. We conclude that it is unlikely to outperform the sequential Phragmén using a supervised approach under the described conditions. Therefore, we recommend exploring an unsupervised approach for further research. Furthermore, we recommend the development of a tool for nominators, that could increase the convenience and the security of the overall staking system as a consequence.

This thesis aims to increase the capabilities of the Polkadot Data Preprocessing Pipeline of the Blockchain Observatory (BCO), a project of the Blockchain and Distributed Ledger Technologies Group (BDLT) at University of Zurich (UZH). This pipeline currently collects data of the Polkadot Relay chain. With the recent launch of Parachains, the Polkadot ecosystem expanded considerably. The aim of this thesis is to expand the pipeline to two Parachains, namely Moonbeam, a Ethereum Virtual Machine (EVM) compatible Parachain, and Interlay, a bridge to Bitcoin (BTC). Furthermore, Cross-Consensus Message Transfer (XCM) between the chains should also be handled. The new Pipelines consist of an archive node, a producer module and a preprocessing module per chain. The node provides raw data, the producer stores the raw data, while making sure the data is valid and checking and correcting the database integrity. The preprocessor finally preprocesses the raw data received, making use of the node for storage queries and web3 interactions in the case of Moonbeam. The data collected focuses on historical balance and transfer data, staking and reward data and data concerning the specific Parachains, such as ERC-20 Tokens on Moonbeam and vaults on Interlay. A part of the thesis was dedicated to the optimization of the pipeline to increase the speed of data collection by restructuring the preprocessor to only use batched queries per block processed. The memory footprint was reduced by removing redundant data. Finally, some queries and visualization are showcased to highlight interesting aspects of the data and to demonstrate the capabilities of the preprocessor, as well as providing examples.

Knowledge workers profit immensely from modern technologies and use them for many aspects of their work. But these technologies also come with drawbacks, as people are now more than ever expected to be available at all times via their work devices. This can lead to blurring boundaries between ones work and private life, which in turn can have negative effects on the ability to psychologically detach from work and enjoy non-work time. In this study we introduce the concept of artefact-based reflection, a method through which knowledge workers can reflect on so called work-artefacts, like tabs, files or e-mails, that they worked with throughout their day and "clean up their workplace" at the end of their workday. Based on existing research and this new concept we developed Downtime, an application which aims at helping knowledge workers in facilitating psychological detachment through artefact-based reflection. We then conducted a user study over two weeks to evaluate the effects of our application. Our findings suggest that knowledge workers can increase their detachment from work and create mental boundaries between their work and non-work lives through reflecting upon the work-artefacts they used that day, especially when they were not actively seeking detachment from their work beforehand. Further research is necessary to evaluate the long-term effects of artefact-based reflection on a broader range of knowledge workers.

This study examined the portrayal of artificial intelligence (AI) in video games and explored the potential correlation between video game exposure and individuals’ level of technological acceptance of AI virtual assistants. A Qualitative Content Analysis (QCA) of several popular video games is conducted to analyze their depiction of AI characters, their roles, and interactions within game narratives. Additionally, this study used a previously validated survey instrument based on the Technological Acceptance Model (TAM) to assess how certain video game playing habits and trust of AI virtual assistants are correlated. We found that portrayal of AI characters in the games analyzed show that AI is often portrayed as humanized and more advanced than its real-world counterpart, and that it is often hostile to humans. The analysis of the survey results found that there is a moderate positive correlation between playing video games featuring AI and willingness to use AI virtual assistant technologies. The findings of this study will contribute to the growing field of AI portrayals in popular media and provide insights into the influence of video games on individuals’ perceptions and acceptance of AI technology.

Since artificial intelligence (AI) has already penetrated large parts of our daily lives and is likely to become even more prominent in the future, it is not surprising that AIs are also increasingly used in movies, books, series, video games, and so on. This work focuses specifically on media for children because, on the one hand, they can be influenced by the media, and on the other hand, there is little known about the depiction of AIs in this media. Still, at the same time, there is a chance to give children a differentiated and critical image of AIs at an early age. For this purpose, a total of 13 media were analyzed using a framework created and mappings were assembled that also included currently used AIs from the industry in order to compare them with each other. Through this analysis, the following categories of AI characters in children's media could be identified: "Side-Kick", "Big Bad Evil", "Virtual Assistant" and "Alternative Human".

Ultra-Wideband (UWB) technology has gained significant popularity in indoor localization applications. These applications often generate vast amounts of personal information, increasing the need to ensure compliance with privacy-preserving principles to safeguard user data. In this thesis, the privacy-preserving characteristics of several UWB localization architectures were analyzed. Firstly, UWB localization architectures were examined based on their privacy-preserving characteristics. Subsequently, two versions of a time difference of arrival (tdoa) localization system were implemented, including privacy best practices provided by the IEEE 802.15.4 standard during the implementation process. Additionally, the privacy-preserving characteristics of the implemented UWB localization systems were evaluated with the help of a privacy criteria catalog based on COPri V.2 ontology. This thesis found that the localization system employing a passively listening tag fulfills seven out of eight privacy criteria. In contrast, the system where the tag actively sends out UWB signals only fulfilled three out of eight criteria in its minimal version. However, the privacy-preserving characteristics of the active system could be greatly improved by using tools such as dynamic addressing, encrypting packages containing personal information, using a message integrity code (MIC), and using a scrambled time sequence (STS). Finally, the limitations of the current systems' implementations are addressed which provides directions for future research.

Federated learning has become increasingly more popular due to limitations of the traditional machine learning methods regarding the data privacy. In addition due to technological evolution, the data volume in general has increased by a lot. Mobile devices are capable of storing more and more data. While traditional machine learning methods struggle to deal with these concerns, federated learning emerged from these problems. Two main approaches have been mainly used namely Centralized and Decentralized Federated Learning. The former one has gotten much more attention in comparison with its counterpart and thus possesses many aggregation rules which are resistant to attacks. The goal of this thesis is to propose a new aggregation rule which is resistant to attacks against the machine learning model for the decentralized setting to fill a gap where the research has no reached yet. This is done by extending an existing framework fedstellar, for federated learning. The case studies as part of the evaluation evaluate the algorithm on performance and resource consumption related metrics. They indicate that the performance of the algorithm depends on the situation. They also show the limitation of the algorithm and possibilities of expanding the algorithm to other applications.

This thesis introduces and evaluates a novel platform that uses Open-source intelligence (OSINT) to identify a primary subject and an associated event using publicly accessible data. As a starting point, the platform utilizes LoRa (Long Range) datasets. This novel tool will make use of web scraping techniques, the power of OpenAI's large language model GPT-3.5, and a custom matching score algorithm. The objective is to collect a comprehensive image of the primary subject and infer potential participants of the specific location and time covered by the LoRa dataset. Evaluating our approach demonstrates its effectiveness in identifying 14 out of 16 actual participants, showcasing its ability to create a relevant dataset of potential participants. Looking at the accuracy, the model manages to achieve a precision score of 0.75, while the recall score of 0.46 indicates some true positives were not captured. The results reflect the difficulty in identifying participants in a private event with a limited public presence. Despite the challenging scenario, this tool represents an innovative approach to merging OSINT techniques with LoRa data. Future work will focus on enhancing the tool's robustness, expanding its coverage to additional social media platforms, improving adaptability across diverse scenarios, and exploring advanced language models.

Visualization guidelines are one of the core elements of the visualization field. Guidelines pose an essential role for practitioners, researchers and students alike and therefore they should not just be accepted as they are proposed, but discussed, evolved and verified as thoroughly as possible. Currently, only a small amount of the proposed visualization guidelines go through this in-depth process, with the majority of them being the more prominent guidelines, such as the Data-Ink-Ratio or the Chart-Junk debate. While there are tools showcasing categorizations of visualization guidelines and forums for discussing them, there is no tool that acts as an accessible and easily navigable central source of knowledge for visualization guidelines. In the scope of this thesis, a tool is developed that provides guidance and context on visualization guidelines by providing additional information such as taxonomies and related scientific resources. To showcase the functionality of the tool, additional data required to provide guidance on visualization guidelines will be collected. The collected data is added to an existing collection of visualization guideline related data. The complete data will then be displayed on the VisKnowsBest tool to showcase a tool with the potential of acting as a central repository for visualization guidelines.

The number of people suffering from chronic diseases worldwide is rapidly increasing, leading to rising costs and decreased quality of life. Lifestyle changes are an effective measure in the treatment of chronic diseases such as obesity. However, treatment adherence is consistently low. This thesis aims to analyse how the initial phases of the Digital Companion influence patient empowerment as well as self-perceived and actual adherence to the treatment plan. We analyse 18 patient interviews conducted during the field study of the Digital Companion. The results indicate that the structural empowerment provided by the Health Care Professional (HCP) together with the app have a positive effect on patients’ psychological empowerment. Patients report high levels of self-perceived empowerment and predicted adherence to the treatment plan which they planned with their HCP. The evaluation of patient-generated data recorded in the app shows a very high average actual adherence of 80%.

Chatbots have experienced a significant surge in popularity in recent months, which can largely be attributed to the utilization of Large Language Models (LLMs). Among these platforms, ChatGPT has shown the fastest growth, amassing one million users within a only five days. This can be attributed to the inherent contextual understanding and impressive capabilities exhibited by LLMs, which continue to be explored. Recognizing the potential of chatbots, particularly their adaptability to custom interfaces, our aim is to develop a tailored assistant to help adults in corporate identity E-learning. Our pedagogical conversational agent serves as a supportive guide throughout the learning process. Given the current boom in chatbot usage, coupled with the dearth of prior research on chatbots in the field of corporate identity and limited exploration in the realm of adult learning, our study seeks to address the following questions: How do users interact with the assistant and what types of messages are exchanged? The findings of this thesis will shed light on the dynamics of user-agent interaction, the frequency of exchanged messages, and the intended functions of users. As our final product relies on an LLM, which serves as the backbone of the chatbot, we encountered various challenges, such as incorporating external functions and managing the LLM's knowledge limitations. To ensure optimal performance, this thesis includes a comprehensive prompt creation manual, which we utilized to refine our assistant and deliver the most effective learning experience for trainees.

Background: Gamification and storytelling have been implemented in slow breathing training apps, a proven effective way to reduce stress and improve mental wellbeing, with the goal of keeping users interested and engaged. However, it is widely acknowledged that users’ cultural background and socioeconomic status (SES) affect their perceptions of apps and therefore intentions to use it on a regular basis. Objective: Based on previous research on user engagement in digital health interventions, cross-cultural human-computer interaction (HCI) and technology acceptance theories, this thesis investigates the intention to use three distinct versions of a slow breathing training app (Stressless©: control group, Breeze©: with gamification, Tragic Kingdom©: with gamification and storytelling) between German and American users. Methods: Adult US and German nationals were recruited from online crowdsourcing platform Prolific. They were randomly assigned to one of the three condition groups and watched a 1-minute introduction video about the corresponding breathing apps in their preferred language. Afterwards, they reported intention to use in a survey, alongside with other attributes related to technology acceptance that are not evaluated in this thesis. As the dependent variable, aggregated intention to use were summed up based on four survey questions. Independent variables were participants’ nationality and app versions coded as categorical variables. Results: A total of 325 participants completed the study (153 German participants and 144 from US Americans). The results show that while national culture does not play a significant role in intention to use, the effect of app version is strong. Although not statistically significant, SES differences in intention to use is observed with no moderating effect of national culture. Conclusion: Even though most hypothesis cannot be rejected; this thesis still provides meaningful guidelines for future studies on cross-cultural and other demographic comparisons on digital health gamification design.

Amid the emergence of numerous new digital platforms for refugees in Switzerland developed by NGOs and the public administration, the question of refugee needs is becoming increasingly important. This research paper analyzes the significance of digital platforms in supporting refugees in Switzerland. In doing so, it applies and extends the Public Value concept by Faulkner and Kaufmann. The study is based on 15 interviews with primarily Syrian refugees, which were evaluated using qualitative content analysis. The results offer insights from the refugees' perspective and identify critical aspects in dealing with digital platforms. Building on this, an expanded Public Value concept has been developed. This new concept includes the newly added dimensions of "Information Value", "Community Engagement", and "Role of the Platform". The extension of the model offers a new approach to evaluating and optimizing the use of digital platforms targeted at refugees.

The growth of privately held digital platforms has had a significant impact on socioeconomic development and technical innovation. While most of these platforms are primarily focused on generating business value, there is a subset that operates as public service platforms, providing citizens with access to public services. However, it is essential to determine whether these privately held public service platforms contribute to or detract from public value. This research aims to address two primary research questions within the Swiss context. Firstly, it investigates how Swiss-based privately-owned digital platforms contribute to and destroy public value creation. Secondly, it explores citizens' expectations regarding public value creation through Swiss-based privately-owned digital platforms. A multiple case study approach was employed, analyzing three platforms: Coople, Homegate, and Ricardo. By employing the Public Value Scorecard framework, the analysis focused on assessing various dimensions of public value. The research involved conducting interviews and conducting a comprehensive analysis of grey literature, including Google Play reviews and newspaper articles sourced from the Swissdox database. The findings suggest that users highly value the efficiency aspect and the resulting transparency offered by these platforms. However, concerns arise regarding the quality of service, the level of personalization, as well as the platforms' responsibility and accountability.

Incorporating Pedagogical Conversational Agents (PCAs) into learning environments has shown promising benefits for learning. However, most of the research has been performed in the field of academia. Building on a previous paper, this study focuses on the application of PCAs for organisational workplace learning. Having a strong Corporate Identity (CI) has become a competitive advantage for companies. In sectors, like public administrations that have a distinct service side, training staff on CI can be beneficial for the organisation. This thesis is part of a bigger research project that designed and implemented a CI Training for two public administrations in Southern Germany. Using Action Design Research, this thesis focused on designing a learning platform and learning videos that aimed to train staff on external communication, based on existing theories about multimedia learning and practice-inspired research. The main role of this thesis in the project was to design the usability of the learning platform, the experience of the learners, as well as the learning videos. So far, no uniform guidelines for the design of learning platforms that include a Pedagogical Agent (PA) could be found. The aim of this thesis was therefore to investigate the interaction of the agent, the platform, and the learner, thus contributing towards answering the question how a successful workplace training environment, that is supported by a PCA, can be designed. The results showed that incorporating a PCA into the learning environment increased the overall usability of the platform, especially the perceived control that the learners have over the interactions with the platform. The agent initially motivated the participants, however, this motivation declined as the agent did not perform to their expectations. No effect of the PCA-supported learning treatments on self-efficacy or self-determination could be found, in comparison to traditional e-learning without a PCA. Based on the results and on the qualitative feedback from the participants, design principles for PCA-supported learning environments were formulated. The data and results of this thesis can be used for further iterations of the overarching research project. Simultaneously, it can serve as a source of information for the design of other PCA-supported learning environments.

The Ukrainian refugee crisis beginning in 2022 revealed challenges for both refugees and organisations in the Swiss asylum process. While refugees are faced with time-consuming and cumbersome administrative tasks and repetitive forms, the many involved authorities and organisations are overwhelmed with slow and paper-based processes, redundant work, and wrong data. In this thesis, I suggest a digital ID stored in a smartphone-based “wallet” applying the concept and technology of Self-Sovereign Identity (SSI) to mitigate these issues. Following a design science research approach, I iteratively developed a prototype and evaluated it with a total of 14 refugees and five experts from different organisations. The results show that a digital ID would ease the lives of refugees and possibly empower them. Concerning organisations, the potential for improved interoperability is limited, but experts agree that efficiency in processes would likely be increased. Ultimately, this thesis derives learnings from the results on how to design an SSI-based ID for refugees and discusses its potential.

Media analysis reflects the most important facts about public opinion and the actors involved in the most current topics. It is beneficial for the government and the public administration to identify the information gaps in the public media to intervene and assist in specific areas concerning the reception and acclimatization process of Ukrainian refugees in Switzerland. This study aims to explore the potential of combining the three Natural Language Processing (NLP) techniques, Named Entity Recognition (NER), Sentiment Analysis (SA), and Topic Modeling (TM), with Machine Learning (ML) models to facilitate decision-making by the authorities. The data used for this study consist of German- and English-speaking articles retrieved from Swiss news media related to the Russian-Ukrainian war. The data includes articles between February 2022 and January 2023. The research approach is split into three main parts, data exploration, the modeling phase, and the evaluation of the models. During data exploration, the data was preprocessed and filtered in relation to Ukrainian refugees. In the modeling phase, the used ML models were introduced, fine-tuned, and applied to the data, leading to the final results. Using the three abovementioned NLP techniques, the most common topics, the article's sentiment over time, and the most common entities in the data could be identified. SA reveals a change of 5% from positive to negative articles regarding the total sum. TM presents the most common topics related to the Ukrainian refugee crisis in Switzerland. NER uncovers the most affected actors, locations, and organizations impacted by the crisis. In the evaluation phase, the model's performances were analyzed, which resulted in a remarkable accuracy score of 96.5% for the NER model, an average accuracy score of 62.5% for the SA model, and a coherence score of 0.65 for BERTopic, the TM model. To conclude, the research shows the potential of using ML-based NLP techniques on news media data to extract beneficial facts from a huge amount of data regarding the case of the Russian-Ukrainian war.

This thesis aims to address a key challenge in time-series data analysis - efficiently identifying correlations between various data streams. As the prominence of sensor networks rises, the analysis of time-series data has become increasingly crucial. The insights derived from these data streams hold significant intelligence, but extracting them efficiently remains a complex task. This thesis brings into focus the dimensionality-reduction filter-and-refine techniques, designed to expedite the process of identifying correlations. Despite their utility, these techniques lack a comprehensive comparative analysis over streaming systems, and this thesis seeks to fill this gap. The core objective is to implement these techniques within a streaming platform, enabling benchmarking under realistic conditions. The thesis is divided into several chapters that provide a comprehensive overview of the problem, delve into the dimensionality reduction algorithms, and discuss their implementation within a streaming system. Particular emphasis is placed on the Filter-and-Refine Algorithm on the Kafka Streaming Platform. Two distinct design approaches, one based on Kafka Streams and another simpler Producer-Consumer design, are implemented, compared, and evaluated. The thesis culminates with an exhaustive series of experiments assessing the performance of the implemented algorithms. The ultimate goal is to provide a framework that not only implements the techniques but also evaluates their performance, aiming to contribute to the field of time-series data analysis.

With an aging population across the world, dementia and neurological diseases, such as Alzheimer's disease (AD), are on the rise. The disproportionate rise in AD cases in developing countries gives rise to a low-cost, robust way to diagnose early-onset AD. The EARDREAM project takes up the fight against AD in these countries using low-density electroencephalography (EEG) device, with the goal of developing a digital biomarker of early-onset AD. To make the collected health data accessible and enable large-scale analysis, there is a need for accessible, scaleable, secure solutions for EEG data analysis. This thesis presents the design and implementation of the Wondernap platform, a novel, cloud-based system dedicated to enhancing the current process of interacting with the data generated using the EARDREAM EEG device. The evolution of the platform through two iterative prototypes is described, highlighting the transition from an initial prototype to a scaleable, secure, cloud-based solution. The initial prototype laid the groundwork for a scaleable, modular, and transferable architecture capable of accounting for the unique requirements of the EARDREAM project, employing state-of-the-art technologies for EEG data analysis architectures. Deploying a Flask backend and Apache HBase for EEG data storage, with MongoDB for patient data, the first iteration validated its usability through a user evaluation, scoring 84/100 on the System Usability Scale. Building upon user feedback and stakeholder input, the second prototype accentuated the applicability of cloud computing to the current architecture, demonstrating its scalability and portability. Using infrastructure-as-code and incorporating Apache Phoenix, this prototype showcased enhancements in fault tolerance, security, and performance. In summary, the developed platform offers a fault-tolerant, scaleable, secure cloud architecture supporting a user-friendly frontend allowing its users to gain insights into the data generated using the EARDREAM portable EEG device.