Autism Spectrum Disorder (ASD) is a neuro-developmental disorder reported to affect around 58 million people across the world. Due to their struggles with social communication in real life, they prefer to use online mediums to form social connections and are better able to maintain meaningful relationships through their communications over these online mediums. However, empirical studies have revealed that autistic users are not as efficient in using these online mediums when they have to search for information. While studies in the past have resulted in a growing list of guidelines to improve the web search experience of autistic users, whether existing online ISTs adhere to these guidelines has not been studied extensively. Thus while we know what the autistic users need to efficiently look for information on the web, we do not know if and how the ISTs are catering to their accessibility needs.

To advance knowledge in the accessibility of popular online ISTs when catering to the information needs of autistic users, we conducted an empirical exploration focusing on the accessibility of the textual
information in the IST responses. We specifically examined the responses on three aspects of text accessibility for autistic users - (1) text structure, (2) text readability, and (3) text concreteness using
“accessibility indicators” i.e. quantifiable features created by us based on widely accepted web content accessibility guidelines for autistic users. Due to the lack of standard query datasets for autistic users, we had to generate synthetic queries representative of autistic users’ information needs to collect responses from four popular ISTs Google, Bing, Gemini, and ChatGPT. We examined the differences in the way ISTs respond to queries characteristic to autistic users in contrast to queries typically asked by the general public, as well as the differences in IST responses across different kinds of queries asked by autistic users. We also investigated if an autistic user were to reformulate their query and explicitly state that they are autistic in their query would nudge the IST to respond differently to their query.

Juxtaposing the IST responses generated for control and ASD group queries revealed that the ISTs not only responded differently to the queries asked by the two user groups, but the responses generated for the control group were surprisingly more accessible for autistic users than the IST responses generated for their queries. For queries typically asked by autistic users, none of the ISTs produced responses
suitable for autistic users. However, each IST had its strengths in the context of the accessibility which can employed in the other ISTs to improve the general accessibility of the responses generated by all ISTs. We also found that our query and prompt reformulation strategies affected the accessibility of each IST differently. In general, the reformulations did nudge the ISTs to generate responses that are more suitable for autistic users. This presents an optimistic solution to the issue of ISTs not catering to the accessibility needs of autistic users when they search for information on the web. Our results highlight areas where popular ISTs can be improved to make their responses more accessible to autistic users. Our systematic empirical investigation pipeline can also be extended to investigate IST responses on other factors that influence the accessibility of web content for autistic users.

Indoor localization technology has become increasingly crucial as the demand for precise and reliable positioning systems grows across various applications. Traditional methods, such as vision-based techniques, radio signal-based technologies (including UWB, WiFi, RFID, and Bluetooth), and visible light-based technologies, offer unique advantages and limitations. Among these, visible light positioning (VLP) stands out for its potential to provide high accuracy by leveraging the characteristics of light signals.

This thesis explores the integration of VLP with a balloon-enabled drone—a novel UAV setup featuring a buoyant balloon that extends flight duration. A balloon-enabled drone introduces both opportunities and challenges for VLP methods due to its size. Its large surface area can block light paths, which may impact signal reception and positioning accuracy. On the other hand, it also allows for the use of multiple receivers across the surface, potentially improving positioning reliability.

Traditional VLP systems typically utilize multiple transmitters and a single receiver; however, our approach takes advantage of the large surface area of a balloon-enabled drone by using only a single transmitter with multiple receivers strategically positioned on the balloon. This setup leverages the balloon’s curved surface to capture a diverse range of light intensities and angles, thereby improving positioning accuracy. We developed a 2D+H RSS-based VLP model specifically designed for balloon-enabled drones. This model takes into account factors like light transmission and optical channel loss. Our VLP system includes multiple receivers placed on the balloon’s surface and a single transmitter. We analyzed the optimal number and placement of these receivers to enhance positioning accuracy.

The system’s performance was tested through both static and dynamic experiments. In static tests, our configuration achieved an average positioning error of 4 cm. During dynamic tests, which involved movement and tilt, the mean error increased to 10-12 cm, largely due to difficulties in estimating height and managing tilt angles. Overall, our system shows an improvement over existing positioning methods like Crazyflie, while also maintaining low energy consumption and computational complexity. This work highlights the potential of our VLP model to improve the positioning accuracy of balloon-enabled drones for various applications.

This thesis involves the development and testing of an educational tool named ”Playlist,” aimed at improving the learning experience of Computer Science & Engineering bachelor students at the TU Delft. The Playlist tool was developed as a feature for the existing tool called Skill Circuits, supporting students in creating personalized sequences of learning activities for their study sessions. This research contributes to the complexities of modern digital learning environments and the challenges students face in navigating vast amounts of educational content.

This report holds the full description of the integrated thesis. The first part including the chapters of ”Fostering a personal sequence of learning activities in Skill Circuits”.
The study uses a design-based research approach, which involves iterative development and research cycles and including students in the evaluation phases. By drawing on educational theories and addressing practical needs identified at TU Delft, the Playlist tool was designed to seamlessly integrate into the existing learning environment. The tool’s functionalities were prioritized based on user feedback provided by Computer Science students and gathered through surveys and focus group sessions.

Key findings indicate that playlists can improve student engagement, motivation, and overall learning experience by supporting students in finding learning activities aligned with their learning preferences and goals. The research highlights the importance of providing students with tools that improve navigating the learning environment and support them in self-directed learning. The Playlist feature not only aids students in organizing their learning activities but also promotes a holistic approach to education by incorporating supporting activities such as reflection and taking breaks.

The integrated thesis hopes to demonstrate the potential and importance of designing tools to personalize learning and support students in achieving their learning goals. It also explains why personalization tools in higher education should be implemented in an iterative manner.

A Room Impulse Response (RIR) is a mathematical model for sound propagation in a room. Estimating RIR parameters such as the reverberation time (T60) allows Automatic Speech Recognition (ASR) systems to adapt to reverberation in input signals by changing their behavior based on these estimates. Currently, machine learning techniques provide the most accurate T60 estimations. We propose a novel methodology by using intrusive Speech Intelligibility Measures (SIMs) beyond their traditional application. In this study we utilize SIIB, SIIB^Gauss, STOI and ESTOI as SIMs. For each SIM we find a best fit curve with respect to the reverberation time (T60) using a statistical approach. The statistical analysis is applied on simulated RIRs obtained by using the Image Source Method. The estimator for SIIB^Gauss achieves the lowest Mean Squared Error of 0.353 on simulated data. Although this does not outperform state-of-the-art models, we offer recommendations for possible improvements. Preliminary experiments suggest that enhancing noise robustness is crucial and that the estimators could be generalized to real-world scenarios. However, further research is necessary to confirm this.

Within the field of computer science (CS), women are under-represented in the workforce and education settings. As Massive Open Online Courses (MOOCs) grow in popularity, understanding the gender differences in reasons for enrolment and engagement remains crucial to improving learner outcomes. This study investigates why men and women enrol in introductory CS MOOCs and how they interact with these courses. This is done with data from four MOOCs offered by TU Delft between 2015 and 2022.

Using survey data for the learners' reasons for enrolment and clickstream data for their behavioural engagement, we applied k-means clustering to identify engagement patterns. Our analysis reveals that the three most important reasons for men and women are career-related, interest-related, and degree-related, in that order. Women are more likely to enrol for career-related reasons than men, while men are more driven by interest in the topic than women. Women also tend to show lower engagement levels compared to men, who are more likely to complete the courses. We found no significant association between reasons for enrolment and engagement for men and women.

These findings highlight the need for gender-sensitive course design strategies to enhance engagement and completion rates. Providing mentorship opportunities, fostering peer interaction platforms, and highlighting role models in the field could also help create a more inclusive learning environment. Future research should explore specific learner challenges and incorporate a more comprehensive engagement model.

Although existing work demonstrates that the usage of Scratch strengthens programming skills, little is known about the effect of children encouraging or criticizing each other when programming on the development of their programming skills. To address this gap, we conducted a data analysis to reveal the correlation (or lack thereof) between the sentiment of comments on a Scratch project and the creator's inclination to continue producing projects.

In particular, using a known dataset of Scratch projects, which we enriched with new projects, we examined several independent variables that capture sentiment on comments from different perspectives, e.g. the absolute or relative number of comments with a particular sentiment score that a user receives, or the ratio of comments that fall in a certain category. We also examined several dependent variables that capture the creator's inclination to continue producing projects, e.g. the number of projects they create or the total time that they are active on the platform.

The results of our experiments reveal that the absolute and relative number of comments with a specific sentiment score given to a user have a low correlation to the time that a user is active on the platform. Additionally, the ratio of the number of positive and negative comments over the total number of comments given to a user has a low correlation to the time that a user is active on the platform. Finally, the number of comments that a user receives has a low correlation to the number of projects a user creates.

Scratch is a block-based programming language. It is designed to be simple and syntax error avoidant. This makes Scratch an accessible platform for cultivating coding skills. Many young learners are taught about different programming skills using various project types as examples. For instance, games are used as an engagement tool, and various games can motivate new learners to make their own. This influx of new published projects are manually classified into different types by using tags in the project descriptions. However, this manual classification only happens when the user adds the appropriate tags. This calls for an extension of that feature, which could help classify all projects published on the Scratch website. This has the goal of improving the browsing process, especially for new projects. To address this gap, in this initial iteration we leverage the fact that there are likely similarities, or even various project type defining features that would help improve the accuracy of classification through machine learning. Filling this gap also opens the possibility of automatic classification, depending on the accuracy of the results. Within this study, various machine learning models were tested with quantifiable project features as input data. The accuracy scores were compared to draw conclusions on how well various features extracted from Scratch projects performed for classification.

Scratch is a popular, visual programming language aimed at children, and is used by teachers and after school code clubs to teach their students about programming. Measuring whether they understand the underlying concepts, however, is a difficult task. In this research, we tried clustering Scratch projects by complexity to help students improve their programming skills. We did this by selecting an existing data set to extract features that indicate code complexity. Before, researchers attempted clustering on one metric that globalises the project’s complexity. Different researchers set out to measure the growth of the students by clustering the projects the students created.
With that in mind, we adopt a partition-based clustering algorithm to cluster the projects, as this method indicates outliers. We examine the quality of these clusters using the silhouette coefficient. We set up five experiments with different input vectors to make out the impact each input has on the clusters. We did not find a clear indication of the projects being clustered by the selected features. This could mean that Scratch projects are not suitable to measure a high-level understanding of programming concepts. Including the project name in the input vector had a negligible effect on the outcome of the experiments.

Atrial Fibrillation or AF is the most common heart rhythm anomaly affecting millions of people. This work explores the possibilities of reinterpreting speech processing techniques for use in atrial fibrillation detection. An existing method of modelling single heartbeat, single lead ECG signals by means of an ARMA model's amplitude response as a time domain signal is implemented. The parameters of the models are then used for AF detection by means of detecting P wave absence. For this detection, the distribution of the P wave associated parameters is compared to a GMM model of normal sinus rhythm beats obtained from a large number of recordings from different sources.

Indoor localization is a field in a development process. Different solutions have been introduced in recent years. Some of the solutions use beacons, WI-FI access points, different smartphone sensors, or acoustic sensing to make localizations. This paper is presenting an application that uses acoustic sensing data to perform localization with different deep models. The research aims to explore different models and evaluate their performance metrics in the classification of three different acoustic data sets and their overhead on the system. Two different architecture designs are implemented - a client-server one as the models are stored on the server and one only front-end oriented as in this case compressed models are used. The results show that the client-server approach outperforms the front-end only design as the former's models reach classification results of 98\%, 90\% and 90\% tested on three different data sets, despite taking longer to fetch a prediction result from the server compared to the compressed models stored on a smartphone device.

Indoor localization is an actively researched field due to there not being a universal solution found yet. Applications of such systems include but are not limited to indoor wayfinding and automated tour guides. In previous years multiple solutions were proposed. This work looks into the performance of an indoor location sensing system in the presence of background music and tries to improve the accuracy in such a scenario. To achieve that a denoising autoencoder is proposed as a preprocessing step aiming to remove the noise from the fingerprints used for localization. In the end, it is shown that the use of such a technique introduces a tradeoff between an accuracy drop in quiet environments but an accuracy increase in environments with music.

Indoor localization is an important field of research for advancing robotics and providing more accurate estimations of indoor locations for users. There are many indoor localization algorithm implementations, but many of them underperform under certain environmental changes or restrictions. This research will present a way of combining already existing indoor localization techniques to more accurately deduce the user’s location within a building. An experiment was conducted within campus building Pulse, where multiple fingerprints of locations where gathered, and then used to train and test the combined classification models. By fusing active acoustic location sensing and WiFi localization using weighted averaging, ensemble stacking, and 2-step localization, the combination of classifiers was able to outperform individual classifiers by up to 5% of localization accuracy. Additionally, 2-step localization and weighted averaging methods did not add any performance overhead.

The ability to accurately determine the location within indoor settings is crucial for various applications such as indoor navigation, interactive floor plans, and room-specific services. While GPS technology has revolutionized outdoor positioning, it falls short in providing precise location information within buildings due to signal blockage. To address this limitation, specialized indoor positioning systems utilizing acoustic sensing have been explored, leveraging deep learning models. This paper presents a comparative study of passive and active acoustic sensing systems for room recognition. Passive sensing involves capturing existing background noise in a room and using it as an identifier, while active sensing emits acoustic signals and analyzes the resulting echoes. Previous research has primarily focused on active sensing, achieving high classification accuracy but facing challenges related to device orientation and the presence of multiple individuals. Moreover, the emission of high-frequency chirps used in active sensing may cause discomfort to pets. The results indicate that passive sensing achieves an accuracy of 73.7%, slightly outperforming active sensing at 63.5% in baseline conditions. However, in the presence of constant background noise, passive sensing accuracy drops to 21.7%, while active sensing exhibits better resilience with an accuracy of 59.7%. Furthermore, when the device orientation is altered by 90 degrees, active sensing results in a lower in accuracy (45.5%), while passive sensing maintains better performance at 71%. The impact of multiple individuals in the room had a relatively minor effect on passive sensing systems, achieving an accuracy of 72.2%. Active sensing was shown to be not as resilient, reaching an accuracy of 44.2%.

Cardiovascular diseases are one of the primary causes of mortality worldwide. Paroxysmal atrial fibrillation is a specific type that is difficult to detect and diagnose in a short time frame. To overcome this, we investigated if long-term wearable data can be used for the detection of heart diseases. The BigIdeasLab_STEP dataset and long-term Fitbit data from the ME-TIME study were used to examine this.

Our analysis showed a correlation between the window/stride size and accuracy when performing activity classification with the BigIdeasLAB_STEP dataset. Moreover, variability was found between subjects due to differences in the physical structure of their hearts. Normalization proved to be a crucial step to minimize the subject variability and significantly improved performance. Grouping subjects and performing classification inside a group helped to improve performance and decrease inter-subject variability. Integrating handcrafted features in deep learning networks also improved classification performance.

Analysis of the long-term Fitbit data showed that there is a difference between individuals based on their health condition. Classification of individual peaks was possible and worked best when utilizing a time series-specific support vector machine and grouping peaks together. Grouping peaks per week from a person and calculating a percentage of heart disease-predicted peaks also worked relatively well to distinguish between heart disease and reference subjects. Like with the previous dataset, normalization proved to be a crucial step to minimize subject variability.

The findings indicate that heart rate time series can be utilized for classification tasks like predicting activity or the detection of heart diseases. However, normalization and grouping techniques need to be chosen carefully to minimize the issue of subject variability.

The Internet of Things (IoT) is a rapidly growing technology that connects millions of devices together. However, as more devices connect, the importance of ensuring security, privacy, and performance becomes paramount. Training performance is affected without proper protocols in place, and devices can get compromised. This research focuses on how to enhance IoT security, privacy and performance using federated learning and blockchain. We will first identify the current challenges concerning those three metrics for IoT. Then, we will introduce federated learning and blockchain and explore how to integrate them. Next, we will address a set of related work performed on the field through a series of surveys. Keeping those surveys in mind, we present and compare several novel solutions for various IoT applications that attempt to provide solutions to enhance IoT security. We complete this study by discussing the potential of those solutions, as well as their challenges and then we highlight possible directions for future research in this booming field.

The Internet of Things (IoT) consists out of billions of devices. This vast size magnifies the security and efficiency challenges the IoT faces. Blockchain (BC) features like decentralisation, immutability and smart contracts can negate these IoT challenges.
In this paper we discuss how BC based solutions can significantly increase the security and efficiency of data management in IoT networks.

To this end we provide reviews of existing related surveys that focus on the privacy and performance of BC solutions in IoT.
State-of-the-art research papers on this topic are reviewed and discussed.
We create summaries that categorize and compare all reviews in this paper. These comparisons include consensus algorithms, performance, security, privacy, pros and cons.
Finally, we discuss the reviewed papers and present potential future research directions that we found in this field based on the discussed research papers and surveys.

In this paper, we present a study to improve using neural networks for acoustic reflection localization. Our study focuses on the reimplementation of the proposed neural network model by Bologni et al. and investigates the effect of adding a third microphone to the microphone array. We reimplemented and trained the neural network using the same framework and hyperparameters as the original model, and then evaluated it using the same metrics. Our results show that the addition of a third microphone improves the amount of detected sources from 43% to 58%, it also improved the front-back ambiguity from 25% to 18%. Conclusively, have demonstrated the potential benefits of adding a third microphone to the neural network approach for acoustic reflection localization.

The dramatic increase in the number of Internet of Things (IoT) devices has created rapid growth for exploitation of security flaws and vulnerabilities. Particularly for critical infrastructure and real-time systems security threats can be highly damaging. Machine Learning (ML) algorithms have demonstrated the ability to combat the security threats and improve the efficiency of data management within IoT networks. This paper addresses how ML methods improve security and efficiency. A review of the current approaches is conducted and these approaches are categorized into detection systems as well as privacy and efficiency enhancements. The proposed future research directions are then presented to address the limitations of the state-of-the-art ML-based IoT security methods.

The Internet of Things (IoT) is producing significant amounts of data. Protecting this data from adversaries is therefore a prominent field of research. This paper conducts a review of the current state-of-the-art in the field of IoT integrated with Blockchain (BC) and Machine Learning (ML). The review focuses on the use of privacy and performance metrics to evaluate the effectiveness of these latest solutions. We first provide an overview of related works to have an understanding of what has been done. Then, we present five important privacy and performance metrics that have been used in the review. We then provide a detailed evaluation of state-of-the-art solutions. Finally, we identify and present open problems that need to be addressed in the form of future research directions. This paper provides valuable insights for researchers interested in improving privacy and performance in IoT applications, and opportunities for future research.

Bounding boxes are often used to communicate automatic object detection results to humans, aiding humans in a multitude of tasks. We investigate the relationship between bounding box localization errors and human task performance. We use observer performance studies on a visual multi-object counting task to measure both human trust and performance with different levels of bounding box accuracy. The results show that localization errors have no significant impact on human accuracy or trust in the system. Recall and precision errors impact both human performance and trust, suggesting that optimizing algorithms based on the F1 score is more beneficial in human-computer tasks. Lastly, the paper offers an improvement on bounding boxes in multi-object counting tasks with center dots, showing improved performance and better resilience to localization
inaccuracy.