Cell-free DNA (cfDNA) fragmentomics using liquid biopsy data has emerged as a promising minimally invasive approach for cancer detection. While multiple fragmentomic features, including the Fragment Short Long Ratio (FSLR), Motif Diversity Score (MDS), and Copy Number Alterations (CNA), have individually demonstrated predictive value, their complementarity and optimal integration remain insufficiently explored.

In this study, we investigate the relationships between these fragmentomic features in a genome-wide setting and evaluate their complementarity through multi-view intermediate integration for a binary classification task. Variance decomposition with correlation analysis showed that FSLR, MDS, and CNA capture partially non-redundant aspects of tumour-derived cfDNA signals, with only 7.2% overlap among outliers.

This biological complementarity did not translate into substantially improved predictive performance. The strongest downstream model was the PCA-based concatenation baseline using all three views, achieving an AUC of 0.969, with only marginal gains over CNA alone. In contrast, MOFA+ did not improve classification performance, reflecting a mismatch between its variance-maximization objective and cancer–healthy discrimination in cfDNA data. Similarly, Contrastive Multi-View Kernel Learning (CMK) failed to yield separable representations under an unsupervised objective, with meaningful class structure emerging only when supervision was introduced, yet still not surpassing the concatenation baseline.

Across all methods, CNA was consistently the most discriminative single feature, while FSLR provided an additional independent signal. MDS performed worst in all settings and contributed little to predictive performance. This limited contribution may reflect the chosen 5 Mb resolution rather than an inherent lack of biological signal, suggesting that feature-specific resolution optimization should be considered prior to integration. ... Read more

While Automatic Speech Recognition (ASR) systems have achieved near-human performance for typical adult speech, they remain highly unreliable for children with atypical language development, such as Developmental Language Disorder (DLD). A major bottleneck in improving ASR for this demographic is the severe scarcity of annotated speech data due to strict privacy and ethical constraints. To address this, this study investigates the use of Text-to-Speech (TTS) synthetic data augmentation to improve ASR performance for Dutch children with DLD. We propose a linguistically motivated pipeline that applies rule-based morphological and phonological transformations to typical Dutch child transcripts, simulating realistic DLD error patterns. These transformed transcripts are then synthesized using a zero-shot multi-speaker TTS model (XTTS v2) to generate a controlled dataset of DLD-like child speech. By augmenting ASR training data under various experimental conditions, we evaluate the impact of synthetic phoneme error probability and speaker variability on downstream recognition accuracy.

Our results demonstrate that applying a moderate probability of linguistically motivated DLD transformations significantly outperforms standard, error-free childlike synthetic speech. Despite this relative linguistic success, the synthetic fine-tuning did not surpass the absolute performance of the unadapted zero-shot baseline. Furthermore, experiments indicate that scaling synthetic speaker variability introduces disruptive generation artifacts rather than improving model robustness. These findings demonstrate the validity of targeted linguistic simulation for atypical speech while exposing the severe acoustic limitations of current zero-shot TTS models in clinical, data-scarce domains. ... Read more

Unit testing is an important step in the software development workflow to detect bugs and ensure system correctness. Recently, Large Language Models (LLMs) have been explored to automate unit test generation and have demonstrated promising results. However, the generated tests are not always reliable, as they may contain syntax errors, hallucinations, test smells, or failing assertions. We conjecture that providing developers with feedback on such issues will increase the adoption of LLMs in realworld workflows. To address this, we propose PyTestGuard, a PyCharm plugin that allows developers to generate and refine unit tests directly within the Integrated Development Environment (IDE). Beyond test generation, PyTestGuard helps users evaluate test quality by detecting test smells and reporting issues such as missing arguments or references to non-existing objects. We conducted a user study with nine participants to assess PyTestGuard’s usefulness as a testing assistant and to identify areas for improvement. Participants reported that the tool’s feedback on test quality, along with its summarised error messages and coverage information, supported them while writing unit tests. However, they also faced challenges and suggested improvements before completely trusting LLM-based test generation in their development workflow. Based on these findings, we highlight several design recommendations for future tools that aim to integrate LLMs into software testing workflows. ... Read more

ESM is an important step towards improving mental health care and its efficiency. Most research in this field has focused on the client as its end user. However, mental health practitioners can also use the data gathered using ESM to gain insights into their clients. To discover what methods of visualization practitioners find most insightful and intuitive to identify mental health conditions, and why, a user evaluation containing mock-ups based on existing literature has been performed. In total, 8 people participated in the study, 6 of which were psychology students, 1 was a psychology researcher and 1 was a mental health practitioner. Based on the user evaluation, it was concluded that the use of spider plots comparing the average and variability of the ESM data of a client to that of a cohort with a certain mental health condition is an intuitive visualization method to identify mental health conditions in clients. ... Read more

Experience Sampling Method (ESM) has emerged as a technique for capturing real-time mental health data in natural environments, offering advantages over traditional retrospective assessments by reducing recall bias and providing contextual understanding of emotional patterns. Despite its benefits, ESM remains underutilized due to limited tools for transforming complex datasets into interpretable insights for clinicians and patients. This study developed and evaluated a network graph visualization to represent behavior-emotion relationships from ESM data. Six mental health practitioners evaluated the system through structured surveys assessing usability, clinical relevance, and interpretive capability. Participants rated visualization intuitiveness at 3.8/7 and visual design at 3.2/5. Comparative evaluations were mixed, with participants rating the approach as better (n=2), equivalent (n=2), worse (n=1), or much worse (n=1) than traditional methods. Despite usability challenges related to visual complexity and dynamic node movement, participants successfully extracted clinically relevant behavior-emotion patterns. Color coding was the most effective design element, while interactive filtering functionality was crucial for pattern recognition. Network visualization shows potential for making ESM data more accessible to mental health practitioners, though design refinements addressing visual complexity and temporal dynamics integration are needed to improve clinical utility. ... Read more

Background: The Experience Sampling Method (ESM) enables the collection of momentary self-reports on thoughts, emotions, and behaviour in daily life. However, there is limited practical guidance on how to visualize this data to support practitioners.
Objective: This study aimed to explore how ESM data can be visualized to effectively communicate treatment effectiveness to mental health practitioners.
Methodology: A design-based approach was used to guide the development of two ESM visualizations. Visualization 1 consisting of a single temporal line graph and Visualization 2 consisting of a dashboard-style layout with four graphs. Practitioners, psychology students and medicine students completed a questionnaire evaluating the clarity, interpretability, and perceived clinical utility of the visualizations. Thematic analysis was conducted on the responses.
Results: Analysis of the responses from ten participants revealed four themes: (1) preference for single temporal line graphs due to their clarity and intuitive presentation of change over time, (2) usability challenges in complex dashboards, particularly with dual axes and ambiguous terminology, (3) value of contextual variables, such as interactions and homework completion, in understanding therapy progress and (4) a strong desire for customization to fit client-specific needs.
Conclusion: Clear visualizations of ESM data may support practitioners in assessing therapy effectiveness, especially when they enable reasoning about both therapeutic change and contextual influences. However, individual differences in interpretation underscore the need for flexibility and customizability.
... Read more

The Experience Sampling Method (ESM) is increasingly recognized for its ability to capture fine-grained, real-time insights into individuals’ emotional and behavioral states in their everyday environments. While the utility of ESM in clinical contexts has been well-documented, its integration into practical tools for mental health professionals remains underexplored. This study investigates how meaningful insights can be derived and presented from ESM data to assist clinicians in the identification of mental health symptoms and intervention planning. A series of prototype visualizations, in the form of line charts, pie and bar charts and network diagrams, were developed and then evaluated for usefulness, user-friendliness and intuitiveness by a total of 8 participants. Results suggest that no single visualization is sufficient on its own, but that a combination of temporal, contextual, and relational visualizations provides a more complete view of a patient’s emotional patterns. While bar charts were preferred for clarity and comparison, pie charts were appreciated for quickly identifying dominant factors by a few participants. Network diagrams, though initially less intuitive, were still valued when paired with descriptive captions. These findings support the potential of multi-format visual tools to assist clinicians in extracting meaningful insights from ESM data. ... Read more

Formative assessment has been shown to improve student engagement and learning outcomes across several subject domains in K-12 education. However, its effectiveness within the subject domain of digital tooling remains understudied. This research investigated the effect of computer assisted formative assessment on learning outcomes and behavioral student engagement, with the latter as a potential mediating variable, within the subject domain of digital tooling.

This research conducted a quasi-experiment with 122 second-grade students from a Havo/Vwo high school during a course on Google Spreadsheets. The experimental group had access to a button that let the computer instantly check their homework and provide feedback, while the control group did not. At the end of the course, students in the experimental group completed a questionnaire to share their experiences.

This research found no significant effects of the intervention on behavioral student engagement and learning outcomes, nor was a mediated relationship established. However, students did report several cognitive and metacognitive benefits. Namely: enhanced motivation, enhanced self-regulated learning, enhanced autonomy, feedback that helped them move forward and an improved understanding of the material. On the other hand, they reported some downsides. Namely: increased time consumption, a lack of added value and a lack of feedback quality.

Since this research did not find effects of computer assisted formative assessment similar to those observed in other subject areas, it raises the question of whether the subject domain of digital tooling interacts differently with this type of intervention. Further research with a greater sample size and statistical power is needed to explore this possibility. Future research could investigate which characteristics make this subject domain distinct, in which contexts within digital tooling this type of intervention is most effective and which benefits it should aim to provide.
... Read more

The curse of dimensionality poses a fundamental challenge in autonomous negotiations: as the number of issues and their interdependencies increase, exhaustive evaluation of the outcome space quickly becomes infeasible. This thesis addresses this problem by introducing a surrogate-based method that approximates uncertain hypercubic constraint-based utility functions with quadratic polynomials. An autonomous negotiation agent can then search for high-utility outcomes in this surrogate model. The research objective was to investigate how efficiently an autonomous negotiation agent can identify high-utility bids with this approach, and how this approach compares to linear approximations and established benchmark agents.

The main contributions of this thesis are threefold. First, it introduces a probabilistic complexity measure for these hypercubic functions, capturing how parameters such as dimensionality, constraint width, the number of constraints, and the number of issues interact to shape the function's complexity. Second, it develops a novel agent that leverages a regression model with quadratic basis functions to construct a surrogate model of a hypercubic constraint-based utility function. Third, it evaluates the agent through extensive experiments, demonstrating how performance scales with complexity. Following the steps outlined in this thesis, the performance of surrogate models can be directly compared.

The results demonstrate that the surrogate-based method is a promising approach, as the agent constructed in this thesis outperforms the agents from the 2014 Automated Negotiating Agent Competition which used similar scenarios as those considered in this thesis. These agents all have in common that they directly search the utility function as opposed to a surrogate model of it. Furthermore, the results indicate that simple basis functions, such as quadratic ones, enable the agent to reach the global maximum of its utility function in low-complexity hypercubic cases, with performance scaling reasonably well up to medium complexity. Beyond this point, however, performance deteriorates rapidly, clearly signaling the need for more expressive surrogate models. ... Read more

Continual learning (CL) enables intelligent systems to continually acquire, adapt, and apply knowledge, representing a dynamic paradigm in AI. For embodied agents—interacting with their environment physically and cognitively—CL enhances adaptability and reduces training costs significantly. In this literature review, we contribute by focusing on the application of CL in such agents, showcasing the approaches, means of evaluation and practical uses of this cognitive framework in real-world scenarios. We conclude that while CL holds promise for embodied agents, there exists a notable gap between the theoretical evaluation of CL and the complex real-world scenarios these agents operate in. ... Read more

Virtual agents have demonstrated remarkable progress in both competitive and cooperative en- vironments. Embodied agents, which enhance AI interactions with the physical world, show great promise for a variety of use cases in both virtual and non-virtual settings. This literature review ex- amines the intersection of embodied virtual agents with cognitive and social frameworks derived from human behavior, focusing on open-ended learning and intrinsic motivation. These paradigms, inspired by human learning and adaptability, offer a path to- wards addressing the limitations of current artificial systems. The literature survey provides a thorough analysis of the research landscape, discussing the definitions, applications, and benefits of embodied agents in virtual settings. Furthermore, it evaluates the methods and benchmarks used to assess the ca- pabilities of these agents, while offering possible solutions for developing the next generation of embodied agents. ... Read more

Active inference is a theory of the human brain characterising behaviour that minimises surprise. The free energy principle accounts for the adaptive behaviours of organisms through action, perception, and learning aimed at optimising reward or surprise. This study systematically reviews relevant literature to address their methodologies, relevance to mimicking social human behaviour, challenges, and limitations to guide future research by succinctly reporting previous findings and research gaps. Active inference models are extended with deep active inference, free-energy models, multimodal deep belief networks, predictive coding, and probabilistic programming. These models employ goal-directed, epistemic, reward-seeking, and decision-making behaviours and simulate cumulative culture. However, some of these models do not translate well to complex real-life applications due to their simplicity, computational demands, or the assumptions upon which they are based. Challenges with real-life applications include difficulty scaling to high-dimensional data and model simplicity. Furthermore, some experiments did not have enough data to validate or train their models. ... Read more

This research paper aims to present how Theory of Mind (ToM) - the ability that allows humans to attribute mental states to others - can be used in the context of physically and virtually embodied computational agents. The focus is on using ToM for perspective-taking in environments with multiple computational agents interacting together. A Systematic Literature Review (SLR) was conducted providing a structured search process for collecting the literature. The findings from this review were obtained from 38 selected papers found in the literature since 2018. The review presented how computational ToM was inspired by the human ToM. Further, it summarised the current applications for perspective-taking for multi-agent settings, and it collected insights on high-level implementations of ToM agents. The findings highlight the complexity of ToM agents and the importance of ToM for agent interactions. ... Read more

In the future, autonomous social robots are expected to seamlessly integrate into our society. To be perceived as interactive partners rather than mere tools, these robots must be embodied and capable of navigating complex, dynamic environments. This study explores the critical role of embodiment and examines the application of human-inspired socio-cognitive mechanisms in embodied agents. We conducted a systematic scoping review of 36 publications sourced from Scopus, IEEE Xplore, Web of Science, and PubMed. Our findings reveal a diverse array of human-inspired implementations in embodied agents, including curiosity-driven learning, categorical thinking, and predictive learning. Notably, human-like eye-gaze plays a crucial role in enhancing the anthropomorphism of these agents. By drawing inspiration from human interactions, we can transition from pre-programmed robots to fully autonomous agents that exhibit emergent behaviours and adapt to ever-changing conditions. Embodiment allows agents to communicate their intentions and desires through various modes, facilitating richer interactions. Additionally, multiple studies underscore the importance of using embodied agents to study human behaviours. Furthermore, the choice of embodiment type must depend on the task at hand; for some tasks, virtual embodiment is more effective, while for others, physical embodiment is preferable. This review provides a comprehensive starting point for future researchers interested in developing human-inspired embodied agents. ... Read more

Due to climate change, man-made conflicts, and rising inflation, a growing number of people around the world are struggling to have consistent access to safe and nutritious food. This phenomenon is known as food insecurity (FI). Therefore, we take in this thesis the first steps towards developing a monitoring process for assessing FI using Human-Centered AI (HCAI). We developed this process for, and in collaboration with, the Integrated Food Security Phase Classification (IPC). The IPC is an organization that helps countries classify levels of food insecurity in their regions to inform humanitarian decision-making. During our research process, we found that any form of HCAI for the IPC would need to be informed by input from their domain experts, and we concluded that we could not start implementing HCAI until we found a way to formalize their input in a way that was robust and suited their technical capabilities. To this end, we ran an experiment with 18 IPC experts in Malawi to see whether they could quantify their assumptions by setting thresholds for food security drivers. The results are encouraging but show that there is still much to be done to bridge the gap between domain knowledge and technical expertise. We also show in this thesis that there is a lack of real-life case studies on HCAI development and share therefore our lessons learned from our real-world HCAI case study on FI monitoring. In this way, we hope to promote the development of a practice-informed methodology for HCAI. ... Read more

The phenomenon of one element moving and progressively overlaying another is common in nature, such as waves swashing and backwashing, or eyelids moving over eyeballs while blinking. Folding Texture, which was proposed by Thorben, can simulate this texture “folding” visual effect in real-time without changing geometry.
However, to date, no tool has been developed to assist in the design and synthesis of folding textures. Applications of the technique so far are achieved through manual creation of the folding texture, which is a tedious process.
This thesis explores the problem of folding-texture design and synthesis. A novel approach is proposed for animating still images based on the folding texture technique. The approach uses a semi-automatic, user-assisted method that combines texture editing, motion profile specification, and folding texture synthesis into one seamless process, reducing the need for extensive manual work. It enables novice users to utilize the technique with a fair level of prior knowledge of folding texture. ... Read more

Software testing, a critical phase in the software development lifecycle, is often hindered by the time-intensive and costly manual creation of test cases. While automating test case generation could mitigate these challenges, its adoption in the industry has been limited due to difficulties in comprehending the generated test cases. To address this, our study presents an approach for clustering test cases and evaluates its impact on the comprehensibility of test suites through empirical research. Our approach clusters test cases based on their covered objectives, grouping together those with similar attributes to enhance developer understanding. The core of our empirical research evaluates developer agreement with our clustering method and contrasts the comprehensibility of clustered versus non-clustered test suites. Findings suggest a broad agreement among developers in favor of our clustering approach, with clustered test suites facilitating faster software maintenance tasks. Notably, the effectiveness of task completion remained comparable between both suite types. In summary, our research introduces and validates an innovative test case clustering strategy, striving to enhance the comprehensibility of automatically generated test suites. ... Read more

Recently, a few children-centered recommendation systems have been created and evaluated. How- ever, these systems required user interaction to cre- ate ground truth to evaluate the result. This research aims to compare some of the traditional recommen- dation models and explore which trait could impact the recommendation process most for different age group users. The result shows the children friendly model does not achieve higher accuracy than the traditional recommendation model. But the book length model and emotion analysis model shows the potential of a good RS that can help children choose books, and cover image recommendation models are only working with younger age ... Read more

Reading is an essential skill for any child to learn, and finding enjoyment in it can greatly contribute to developing proper reading comprehension. Finding the books they like could prove to be difficult. Utilizing collaborative filtering recommender systems to recommend books to children is a tricky task, the lack of user feedback makes it difficult to accurately recommend books they would enjoy. Using content based recommender systems might be preferable, but what book features could a recommender system like this base recommendations on? This research explores the idea of utilizing the textual complexity of books and their descriptions as such a possible feature. By evaluating how accurate readability formulas can predict the age a book is intended for, how the variability and length of sentences vary per age and analysing the difficulty of words used, this paper finds that the descriptions of books intended for younger audiences might not be aimed at them, but instead at their parents. These findings imply that basing recommendations of the textual complexity of book descriptions might not be the most useful feature to base recommendations of. ... Read more

De Kindertelefoon is a children's helpline aimed at providing (pre-)adolescents with a person to talk to for a variety of subjects such as bullying, sex, and abuse. These people who talk for De Kindertelefoon need proper training and guidance. Among the tools that De Kindertelefoon provides is the 5-phase model, a conversational model. Researchers set up a simulation of a virtual child and De Kindertelefoon to help teach the 5-phase model. However, simulation alone was not enough, and we wanted to see if a feedback system could improve the results, with immediate feedback being the focus.
Immediate feedback is when the user fills in an answer and the system immediately provides information to strengthen knowledge.
When applied to an academic environment, both immediate feedback and student engagement have been proven to be important for completing a task.

Using a literature review, we found that immediate feedback can be directly linked to educational techniques such as self-improvement, self-efficacy, and the Self-Determination Theory. Through a focus group, we also found that constructive immediate feedback is an important pillar of De Kindertelefoon.

The design was achieved by looking at the existing and limited 5-phase model as a graph and trying to find an optimal path through that graph.

After conducting a within-subjects study experiment with 34 participants, the results were inconclusive, with neither condition appearing to be more useful for the group of participants, nor either condition being better at teaching them the 5-phase model.
As the results were inconclusive, the data was explored more by looking at it as a between-subject study, which showed that the explanation sheet might perform better for knowledge.

The research shows the possible strength of feedback in a practical manner. From the results, the immediate feedback is neither more nor less resonant than a classical approach to teaching the 5-phase model. ... Read more