The field of Tactile Internet (TI) is transporting physical interactions over vast distances as if they were near. This teleoperation method increases the quality of tele-engineering, telesurgery and many other fields. However, it requires the strict end to end latency constraints of a few ms, which is still out of reach. This thesis looks at the first step of this pipeline, the Human Machine Interface (HMI). We redesign a SenseGlove MK1, A VR haptic feedback glove, to reduce the latency to under 0.5 ms. We then use prediction to have an effective latency of less than -0.8 ms, creating the first Negative Latency Glove. This gives the rest of the TI system a larger latency budget than before.

Generative Adversarial Networks (GANs) brought rapid developments in generating synthetic images by mimicking structures in the training data. With the list of application of GANs growing drastically, it has lately become an exciting technology to explore for designers to communicate their ideas and arts through technology and create engaging experiences for humans. Nevertheless, translating human experiences to artificial intelligence and creating visually pleasant imagery is a challenging task due to complex semantics of human perception. To address this issue, we introduce an iterative training approach in which the generated images are curated by humans and the most pleasing ones are fed back into the network to retrain. Additionally, we do a factorial analysis to investigate how the aesthetic quality and the diversity are affected by the size of training data and training length. In experiments, we validate that this method can significantly improve the aesthetic quality of generated images regardless of the dataset size and training length, however the use of smaller datasets comes with a cost of reduction in the image diversity and novelty in the output images. The aesthetic bias towards certain contexts can also deteriorate the diversity and affect the model evaluations. On the other hand, no significant relationship has been found regarding the training length, however this could possibly be due to instabilities that happen during the model convergence progress.

In the software engineering field the code review process has been widely adopted as a quality control, in the current day code reviews have also multiple additional advantages. In this research we explored how the code review process is used at Greenchoice, a Dutch energy supplier. Subsequently, we created a code review checklist which was used by the developers in order to improve the process. In the first step of the exploration we used a questionnaire to retrieve Greenchoice's motivation for the code review process. In the results we show that ensuring quality is the main motivation followed by knowledge sharing and functionality check. Secondly, we held 13 semi-structured interviews, performed a data analysis on the metadata of 8458 pull requests and performed a data analysis of 5400 code review comments to explore Greenchoice's code review process further. In the results of the interviews we show that the reviewers tend to focus on understanding the change and finding obvious mistakes. They also mentioned code reviews could be performed stricter improving the maintainability and evolvability aspects. However, it can be hard to know where to check on for these aspects. In the results of the metadata analysis we show that the code reviews are performed quick, frequent and small changes are made, similar to the modern code reviews processes described in the literature. In the results of the code review comments analysis we show that most comments are related to explaining or understanding the change. Next, we created together with a scrum team a code review checklist which was was used by three scrum teams for two periods of two weeks. In order to measure the effectiveness of the code review checklist we used a pre questionnaire to gauge the current situation and the expectations of the usage. A post questionnaire was used to gauge the new situation and whether the expectations were met. After comparing the results we show in the results that the checklist is less appropriate to guide the reviewer step by step in a code review. Furthermore, we show that the items in the checklists should be applicable to the different changes in the pull requests. The strength of the checklist lies in addressing the items which are often forgotten in the development process and the code reviews.

Web3Recommend is a decentralized Social Recommender System implementation that enables Web3 Platforms on Android to generate recommendations that balance trust and relevance. Generating recommendations in decentralized networks is a non-trivial problem because these networks lack a global perspective due to the absence of a central authority. Further, decentralized networks are prone to Sybil Attacks in which a single malicious user can generate multiple fake or "Sybil" identities. Web3Recommend relies on a novel graph-based content recommendation design inspired by GraphJet, a recommendation system used in Twitter enhanced with MeritRank, a decentralized reputation scheme that provides Sybil-resistance to the system. By adding MeritRank's decay parameters to the vanilla Social Recommender Systems’ personalized SALSA graph algorithm, we can provide theoretical guarantees against Sybil Attacks in the generated recommendations. Similar to GraphJet, we focus on generating real-time recommendations by only acting on recent interactions in the social network, allowing us to cater temporally contextual recommendations while keeping a tight bound on the memory usage in resource-constrained devices, allowing for a seamless user experience. As a proof-of-concept, we integrate our system with MusicDAO, an open-source Web3 music-sharing platform, to generate personalized, real-time recommendations. Thus, we provide the first Sybil-resistant Social Recommender System, allowing real-time recommendations beyond classic user-based collaborative filtering. The system is also rigorously tested with extensive unit and integration tests. Further, our experiments demonstrate the trust-relevance balance of recommendations against multiple adversarial strategies in a test network generated using data from real music platforms.

As autonomous systems are increasingly integrated as a team member for collaborative tasks, trust in human-agent teams (HAT) becomes crucial to foster success. In many real world scenarios, trust violations are expected, thus demanding the use of trust repair strategies to restore damaged trust. Previous research has shown that expressing regret and providing explanations are effective strategies to rebuild human-agent trust. However, the role of various team dynamics, such as interdependence relationships, remains unexplored. The aim of this paper is to examine the influence of interdependence levels on the effectiveness of trust repair strategies. To investigate this, an experiment was conducted in a collaboration environment with an urban search and rescue mission. Two interdependence conditions were introduced to analyse their effect on trust and collaboration fluency. No significant evidence was found to support a relationship between interdependence and trust repair or collaboration fluency. However, as this study only considers two interdependence conditions, there is much more room for future work to explore further. This study can bring meaningful insights to design and facilitate agents that are more trustworthy in human-agent collaboration settings.

Nowadays, Human Autonomy Teams (HATs) are incorporated in many fields, where humans and autonomous agents work collaboratively to combine their capabilities with the ultimate goal of performing tasks more efficiently. In such environments, it is imperative to sustain a high level of trust between the agents as collaboration is not possible without mutual trust. Naturally, this implies that recovering trust following trust violation is also a crucial aspect of HATs. Moreover, besides team performance, the fluency of collaboration is another important factor to consider when evaluating the success of the teams. This paper aims to investigate the effect of opportunistic (soft) interdependence between the agents on trust violation, trust repair, and on collaboration fluency when compared against a baseline (complete independence) condition. In this paper, interdependence relationships refer to how the agents complement/combine each other's competence. The experimental results were obtained through a user study, using questionnaires and logged objective metrics. Our research found that teams with opportunistic interdependence relationships were significantly affected by trust violations compared to the baseline condition. Furthermore, although not as significant as the effect of trust violation, they also experienced a significant trust recovery during the tasks. Finally, the results of analyzing both subjective and objective fluency metrics did not give any significant result that indicates the difference in the level of collaboration fluency between the two conditions.

Artificial Intelligence (AI) is increasingly helping people with all kinds of tasks, due to its promising capabilities. In some tasks, an AI system by itself will take over tasks, but in other tasks, an AI system making decisions on its own would be undesired due to ethical and legal reasons. In those cases, AI can still be of help by forming human-AI teams, in which humans get advice from the AI system helping them with making their final decisions. Human-AI teams are for instance used in the medical and legal fields. One problem arises, in which instances should one trust an AI system and in which not? Trusting the AI system when it is correct and trusting yourself when you are correct, results in a high appropriate reliance. If users appropriately rely on AI systems, it is possible to achieve complementary team performance, which is better than any single teammate. However, as known from previous literature, people struggle with assessing their performance and knowing how well they perform compared to peers. When one overestimates their performance this can be because of a dual burden, due to the lack of skill they also lack the skill to accurately estimate their performance. This phenomenon is called the Dunning-Kruger Effect (DKE). This raises questions about whether the inability to estimate their own capabilities would also reflect on their assessment of the AI system its performance. In this thesis we look at how the DKE affects (appropriate) reliance on AI systems and if so, how such effects due to the DKE can be mitigated. The effects of the DKE and possible mitigation are being tested through an empirical study (N = 249). The attempt at mitigation is done by including a tutorial intervention, which has been proved in previous research to be useful in decreasing the DKE. The tutorial intervention is aimed at revealing the weaknesses of the participant and making them aware of their miscalibrated self-estimation. Furthermore, in this thesis, the effects of revealing how the AI system makes its decisions through explainable AI (XAI) are explored. The XAI consisted of highlights from logic unit-based explanations, it should allow participants to gain more understanding of the AI advice. This thesis shows how this will affect user self-assessment and reliance on the AI system. We found that participants who overestimate themselves tend to rely less on the AI system, compared to participants that had an accurate or underestimation of their performance. After the tutorial participants have a better calibration of their self-assessment. While the designed tutorial intervention can help participants calibrate their self-assessment, it fails to promote (appropriate) reliance. Furthermore, the logic units-based explanations did not improve accurate self-assessing or increase user (appropriate) reliance on AI systems. This thesis shows the importance of considering cognitive biases when dealing with human-AI teams and invites more research on how to handle and mitigate the DKE in human-AI decision making.

As music video streaming occupies a significant market share in how people consume music, gaining an understanding of user behavioural patterns becomes increasingly crucial. This understanding can enable better music video streaming experiences by tailoring them towards more personalized and user-centric designs. Though prior works have emphasized user behaviour during solely listening to music, understanding user actions/clicks while consuming music videos remains largely unexplored. Given the unique experience offered by the combination of audio and visual elements, there is a need for focused research in this area. Therefore this study attempts to bridge this research gap by collecting and analysing a large dataset of streaming sessions from a music video streaming company - XITE. In total, we analyzed 1.8 million sessions from approximately 270,000 unique users. The behaviour exhibited during those sessions is interpreted as a language and modelled using the Language Model - Doc2Vec. This facilitated the conversion of session action sequences into embeddings. Our findings suggest that music video streaming sessions exhibit cohesive user interaction patterns, which can be grouped into distinct clusters, thereby enabling the detection of distinct behavioural patterns across user sessions. Furthermore, previous studies have indicated that user interactions with multimedia streaming platforms can be influenced by the context in which content is consumed. Extending these findings, our analysis of behavioural clusters revealed that certain user behaviours while consuming music videos are associated with specific music video genres and temporal factors. For instance, we discovered that passive sessions predominantly commence around 10 am, while sessions requiring more active engagement typically start in the evening. The insights derived from this study are valuable for improving user-centric design in music video streaming platforms and providing businesses with data-driven recommendations for strategic planning.

Web surveys have increasingly been used to collect data from respondents over the years. They offer several advantages compared to other methods of obtaining data. Researchers benefit from a broad demographic representation to make generalized conclusions, and satisfaction surveys allow employees to explain shortcomings or improvements anonymously. Both examples demand comprehensive information, thereby requiring a lengthy survey. However, dropout increases with the length of a survey, which is a big problem on web surveys as it decreases the statistical significance of the results. Proposed solutions, such as reducing the number of questions or rewarding respondents with an incentive, may not always be feasible due to the preciseness of information required or limited financial capabilities. Achievement primes have been shown to reduce dropout on short surveys targeting extrinsically motivated respondents without additional costs or the need to reduce survey length. As repeated exposure to primes reinforces the stimuli, long surveys may also benefit from achievement primes. In this study, respondents are exposed to a questionnaire of more than 15 minutes on health whilst working behind a computer containing either no prime, passive achievement primes, or active achievement primes. Besides extrinsically motivated respondents, recruited via the crowdworking platform Prolific, intrinsically motivated respondents are also targeted in this study, recruited via snowball sampling. Through a 2 times 3 factorial design, we discovered no statistical difference in dropout, perceived workload, and user engagement across the three questionnaire variants when evaluating intrinsically (N=88) and extrinsically motivated respondents (N=140) individually. By comparing intrinsically with extrinsically motivated respondents, we discovered extrinsically motivated respondents were more engaged and dropped out less.

A significant cardiovascular health risk is insufficient physical activity. The World Health Organization recommends 150 minutes of strenuous physical activity every week. Inadequate physical activity increases the risk of chronic diseases and other health conditions like cholesterol and obesity. This thesis researches the role of data monitoring as a persuasion strategy in monitoring a user’s progress in their journey to becoming more physically active and how it can be leveraged to decrease the risk of cardiovascular diseases. Specifically, the focus of the thesis is to determine the effectiveness of expert-generated tailored messages to motivate a user in their physical activity behaviour. We designed the content of the messages by adapting an existing ontology for tailoring motivational messages in the context of physical activity. Messages were then generated by experts through a scenario-based feedback generation process, where the scenarios were tailored to a user’s mood, self-efficacy and progress. The design of these tailored messages was tested against generic messages to determine which type of message was more motivating to the user. An experiment was conducted by recruiting crowd workers who were asked to rate the motivational levels of the two message types with respect to a given scenario. The results of the experiment supported the initial hypothesis that messages tailored to mood, self-efficacy and progress are more motivating than generic messages. Additionally, we have shown a systematic and reproducible way to obtain motivating messages. We have also provided a dataset of motivational messages that can be used during various stages of a user’s physical activity intervention, along with a set of scenarios representing different levels of a user’s state (mood, self-efficacy and progress).

The increasing dangers of unfairness in machine learning (ML) are becoming a frequent subject of discussion, both, in academia and popular media. Recent literature focused on introducing and assessing algorithmic solutions to bias in ML. However, there is a disconnect between these solutions and practitioners' needs. By interviewing 30 ML practitioners of diverse backgrounds across 16 countries, and presenting them with a simulated use case, our study aims to investigate common fairness practices among professionals and how these are influenced by their backgrounds. The results reveal a superlative disparity among academia and industry practitioners. We also identify different practices in fairness and data exploration stages, influenced by the educational background as well as the level of experience of practitioners. Our study also finds how demographics have an impact on several aspects, such as willingness to accept and support legal actions taken against ML discrimination. In accordance with our findings, we suggest several actions that can be taken to improve fairness solutions, and we also highlight future directions for fairness research that can cause a positive impact on the way fairness is perceived by practitioners.

Research has shown how people anthropomorphize conversational agents (CA) and unconsciously bring their gender stereotypes into human-agent interaction. For this reason, there has been a long lasted dilemma on whether designers should design CAs that conform to or violate stereotypical expectations. Despite the urgency and importance of navigating through this dilemma, how to better design the gender identity of CA is still an open research question. In this thesis, we describe the problem space of CA identity design and argue that we can calibrate the gender effect by manipulating a metaphor we attach to the CAs. To this end, we approached these research questions from three angles: (1) developing a framework to address the ethical dilemma in CA identity design, (2) evaluating the effect of gender and metaphor in chatbot profiles, and (3) calibrating gender stereotyping through metaphor manipulation.

Due to COVID-19 the overall wellbeing worldwide decreased. Assessing and improving wellbeing became a more important subject. This article describes the design research that uses the My Wellness Check survey created by the Delft University for Technology and aims to create a dashboard for wellbeing. That includes a way of authenticating users to very sensitive data. Also, finding ways how to improve personal wellbeing by using a recommendation system based on different types of filtering and the additional elements that are needed for a recommendation system.

With the growing importance of teamwork in higher education, effective communication and goal congruence have become vital in improving the effectiveness of student teamwork. This study aims todesign and implement an innovative system that combines a goal-setting chatbot and an effort visualizer to facilitate effective collaboration in student teams. We found that unclear goals and lack ofengagement were the main challenges in teamwork. To address these challenges, we developed asystem that combines a chatbot and an effort visualizer designed to enhance team transparency andgoal alignment through technology. The chatbot guides users to set specific collaborative goals throughthe SMART framework. At the same time, the effort visualizer displays each member’s contribution,thus increasing accountability among team members and facilitating greater participation. The systemwe designed was validated in a creative writing task (N=84) in a collaborative scenario. The resultsshowed that either the chatbot or the effort visualizer alone positively impacted student engagementand collaboration. However, when using the chatbot and effort visualizer in combination, there neededto be more clear evidence to improve engagement and collaboration effects further. This finding suggests that we pay more attention to the characteristics of individual tools and combination strategieswhen designing collaboration enhancement tools. This study provides valuable insights into using chatbots and visualization techniques to improve team collaboration and suggests new directions for futureresearch in educational technology.

Longer lifespans and an ageing population put tremendous pressure on the care of the elderly. With the technology of robotics breakthroughs, it appears that the use of robotics in elderly care is ready to take off. Interestingly, more and more robots are now being created with a human-like appearance and demeanour, yet this could lead to issues like inappropriate mental models of robots in the user's mind. On the other hand, due to their minimal communication challenges, robot-like robots may function effectively in the human-robot interaction. Up to now, far too little attention has been paid to the increasing trend of the human-like robot. This thesis compared the performance of these two types of robots, which may help to explain why the trend is the way it is. Two research questions are created to accomplish this goal: Which type of robot, robot-like or human-like, produces the most fluent interaction in the elderly care scenario? Does the robot-like or human-like robot bring about the best memory performance in the music listening activity? This thesis project aims to contribute to the development of the social robot in elderly care. In this thesis, a music assistance robot was developed, it can converse with the user while listening to music and recalling memories associated with the song. The elder's health and well being are benefited by this pastime. To address the question of which type of robot performs better, two types of robots, a human-like robot and a robot-like robot, were created to compare in terms of fluency in human-robot interactions and performance of memory recall during music listening activities. Four key elements: appearance, behaviour, voice and dialogue were used in the construction of two robot identities. Based on the robot we created, a joint music listening experiment was carried out. For evaluation, a questionnaire and video recordings of the interaction were both employed. This was tested with 30 volunteers (The young adults were chosen for the experiment due to COVID restrictions and time limitation), and it was discovered that the human-like robot performed better overall.