Future 6G smartphones are proposed to embed transparent on-screen antenna arrays that use communication signals for passive finger tracking. Our research proposes two novel localisation methods that exploit the finger's electromagnetic backscattering response. Using model-generated time-series data, we simulate the spatiotemporal backscattering of a finger hovering above a transparent planar array at sub-terahertz frequencies. We compare a classical matched filter and subspace methods against our proposed approaches: a CNN-adapted matched filter (MF-CNN) and a multi-tone CNN position regressor (MT-CNN), alongside a near-field subspace baseline. The learned methods achieve sub-millimeter accuracy and remain robust\newline across variations in signal-to-noise ratio, array size, dielectric properties, and hover height, with MT-CNN offering the best trade-off between accuracy and latency. ... Read more

Reminders are often used to support people's prospective memory (PM). Research in Human-Robot Interaction (HRI) has primarily focused on when and how a robot reminder could better help humans, while the content of the reminder itself has received less systematic attention. This research investigates whether the content of a robot-delivered reminder affects users' subjective experience. Specifically, it compares episode-grounded reminders, which reference the situation in which the PM intention was originally formed, with reminders that omit this context, measuring effects on confidence in remembering, trust in the robot, and perceived usefulness. A between-subjects experiment (N = 21) was conducted on a browser-based 2D platform that uses a cooking scenario to impose concurrent cognitive demand with four PM tasks. Participants received either episode-grounded (EE1) or plain (EE0) reminders. Results showed that EE1 reminders were associated with significantly higher confidence compared to EE0 reminders (p = .034 after correction for multiple comparisons); trust and perceived usefulness did not differ between conditions. These findings contribute a theoretically grounded design procedure and a reusable experimental protocol for episode-grounded reminder content, and suggest that grounding reminders in episode context may shape users' confidence in remembering. ... Read more

As machine learning expands into diverse domains, TinyML has emerged as a crucial paradigm for deploying models on highly resource-constrained microcontrollers, which typically feature less than 256~KB of RAM. However, executing complex mathematical operations on these devices remains a significant challenge, necessitating novel model designs and hardware-aware optimization.
The Mamba architecture, built around State-Space Model, is a promising candidate due to its compact parameterization and strong performance on long-context tasks. Nevertheless, Mamba was originally designed for highly parallelized GPUs, making its adaptation for TinyML non-trivial. This paper evaluates Mamba deployment strategies on microcontrollers using TensorFlow Lite Micro.
We propose architecture modifications and optimization techniques tailored specifically to microcontroller constraints. Our deployment of a quantized Mamba model achieves a 60.4~KB peak RAM footprint on a Keyword Spotting task, a 74\% memory reduction compared to state-of-the-art work (MambaLite-Micro). Furthermore, we analyze the trade-offs of quantization, demonstrating that while it substantially reduces memory, it can introduce latency overhead on hardware lacking acceleration of INT8 operations.
To mitigate code size and loop-unrolling overheads, we introduce a model-splitting technique that enables the execution of larger models. Our findings demonstrate that while Mamba is a viable architecture for TinyML, further research is required to fully optimize State Space Model implementations for edge hardware. ... Read more

The growing deployment of AI-assisted diagnostics on resource-constrained microcontrollers raises an underexplored question: do the memory and latency limitations of embedded hardware reshape the fairness–accuracy–privacy trade-offs that practitioners must navigate in healthcare applications? We present a controlled, multi-objective empirical study evaluating Gaussian noise injection, post-training INT8 quantization, and classification threshold calibration. Fairness and privacy interventions are evaluated on the Pima Indians Diabetes dataset (768 samples, age-stratified protected group) using a lightweight MLP and a logistic regression baseline; quantization efficiency is additionally validated on a larger hospital readmission dataset (∼100,000 samples, ∼154,800-parameter model) to characterise scale-dependent compression behaviour. The key findings are fourfold: (i) INT8 quantization efficiency is scale-dependent: no benefit and up to 67% fairness degradation at sub-300 parameters, versus 3.87× compression and 3.6× speedup at ∼155k parameters; (ii) low-magnitude noise (σ=0.05) is a safe privacy proxy with negligible accuracy cost; (iii) higher noise levels create a non-monotonic privacy–fairness tension, destabilising group-level fairness without predictably improving it; (iv) post-hoc threshold calibration to τ =0.7 reduces equalized odds gap by 18.4% relative at only 1.2 pp accuracy cost, out-performing all training-time interventions with zero embedded overhead. These findings show that embedded constraints do not introduce new fairness–accuracy trade-offs but shift design priorities toward post-deployment calibration. ... Read more

Visible Light Positioning (VLP) uses LEDs for accurate indoor localization. However, structured illumination drift caused by LED aging, optical contamination, thermal effects, blockages, and device failures can reduce the long-term accuracy of RSS-based VLP systems. This thesis investigates how this drift can be modeled and compensated for using lightweight algorithms suitable for microcontrollers.

The proposed method combines scaling-based compensation for gradual degradation with anomaly detection for sudden degradation events such as broken LEDs. This method is tested through a long-term deployment simulation using the DenseVLC dataset and is also implemented on a Raspberry Pi Pico to assess embedded feasibility. The results show that VLP systems suffer increasing errors over time, while degradation-aware compensation improves long-term robustness. However, embedded deployment introduces accuracy trade-offs due to quantization and memory constraints.

These results show that modeling and compensating for degradation mechanisms is important for reliable long-term VLP deployment, and that compensation methods need to account for both gradual and sudden changes in received signal strength. ... Read more

With radio communication bandwidth becoming increasingly scarce and expensive, researchers have turned toward the light medium, namely the field of Visible Light Communication (VLC). Although the field of Visible Light Communication (VLC) was pioneered in the late 1800s, it faced criticism from scientists of that era, with radio communications being preferred instead. VLC has since regained attention by complementing existing radio communication methods. This research paper focuses on exploring different short-range multiple-input multiple-output (MIMO) screen-to-camera VLC schemes operating solely on the red optical channel. The transmitting screen is a 4×6 LED grid on a prototype board, while the receiver is an off-the-shelf smartphone back camera. The chosen modulation technique is on-off keying (OOK) with Manchester encoding (ME), while demodulation is performed using three different strategies, the first two using Otsu thresholding and the last using a sliding window approach. Our experiments show that, while the modulation scheme achieves a transmission rate of 6 symbols per LED per frame (up to 144 symbols per frame) and a bit error rate (BER) of less than 10⁻¹, the limited resolution and frame rate make it difficult to reliably include important data frame header fields such as the sequence number. ... Read more

In multi-member human-agent teams the communication and shared mental models within the team are essential for good teamwork and team performance. In some ways the mediating processes are even more important than in human-only team because the artificial agents of today lack many of the innate social behaviours that humans naturally possess. Research into human-agent teams have allowed designers of such teams to anticipate for complex interactions such as trust violation and repair scenarios. In this study a human-agent-agent team undertakes a search- and rescue mission with the human in a leading role, one of the agents free-roaming and the other agent under the human's direct control. Approximately one-third of the way through the mission, the autonomous agents initiated actions independently of human approval, thereby undermining operator trust. As a trust repair strategy the agent employs a promise to do better and a novel authority change by lowering its level of automation and presenting the option of restricting cooperation with the other agent.
We conducted the experiment with thirty participants divided into a two groups with differing trust repair strategies (promise only, promise with the authority change) and measured trust perception at three different time steps.
Results show no significant difference between the two trust repair strategies when directly comparing to trust. A positive correlation between the authority change trust repair strategy and task load on trust recovery was found. Through thematic analysis we did find that the shared mental model and communication richness to be dissonant to what participants expected which is in line with literature on the complexity of triadic teams. ... Read more

As robots and virtual agents are increasingly envisioned as long-term companions rather than simply tools, it becomes essential to ensure that human–robot relationships are grounded in appropriate forms of trust. This study investigates how cognitive and affective dimensions of trust develop differently over time in social human–robot interaction. We conducted a 2 (social attitude: social, baseline) × 3 (time: t1, t2, t3) mixed-design user study using a novel, card-based conversational task designed to encourage trust formation. Results show that while cognitive trust remained stable over time, affective trust increased gradually across repeated interactions. Moreover, social cues enhance both cognitive and affective trust. These findings provide empirical support for the theoretical distinction between cognitive and affective trust, offering new evidence that affective trust develops more slowly, consistent with interpersonal trust theories. ... Read more

Pose estimation models offer promising opportunities for automated feedback in cricket training, but their practical impact is limited by the lack of personalized and understandable explanations. This study investigates how explanation formats can be tailored to users’ expertise levels, focusing on beginner, intermediate, and expert levels, to improve the effectiveness of AI-generated feedback. Based on a literature review of explanation needs and generation methods, we propose a taxonomy linking expertise levels to suitable explanation modes: visual, comparative, and statistical. We implement a set of explanation prototypes aligned with this taxonomy and evaluate them through a user study involving 17 participants across the three expertise levels. Results show that participants rated explanations tailored to their skill level as more useful, trustworthy, and easier to interpret. Statistical validation using Kruskal-Wallis and Dunn’s tests confirmed significant differences in perception between user groups, especially between beginners and experts. These findings demonstrate the value of expertise-based explanation design in cricket analytics and offer design guidelines for future explainable pose estimation systems in sports
... Read more

Explainable Artificial Intelligence (XAI) has the potential to enhance user understanding and trust in AI systems, especially in domains where interpretability is crucial, such as cricket training. This study investigates the impact of different explanation formats on user experience within a cricket-specific context. Two prototypes were developed, each including four explanation formats: textual, visual, rule-based, and mixed. The second prototype introduced interactive features to examine their influence on user experience and explanation effectiveness. A small-scale user study evaluated the explanations based on satisfaction and trust. Results show that rule-based explanations were significantly less preferred in terms of satisfaction than the other explanation formats. Furthermore, the addition of interactive features led to a significant increase in user trust, though they did not enhance satisfaction levels. These findings highlight the importance of selecting appropriate explanation formats and the potential of interactive features to enhance trust in AI-generated explanations in a cricket-specific context. ... Read more

Computer Science education, particularly at the beginner level, often presents challenges due to vague and unhelpful error messages. This problem is particularly significant for students with low self-efficacy, leading to hindered learning experiences. Large Language Models (LLMs) offer a promising solution by generating more comprehensible and supportive error messages. This study aims to assess whether the rewriting of error messages using LLMs can improve self-efficacy among high school students, focusing on self-efficacy and study success as indicators of improved learning experiences. Through in-class experiments with 32 participants, the findings revealed that LLM rewritten error messages, although consistent with existing research, did not produce statistically significant effects. Therefore, more research is needed to evaluate their impact on learning outcomes and explore the most effective types of prompt. This research contributes to understanding the role of LLMs in educational settings, providing empirical insights into their effectiveness in real-world scenarios. ... Read more

Hand gestures play a crucial role in communication, especially in social interactions. This research investigates the viability of using coding schemes to describe hand gestures and how accurately they can be classified in crowded environments by using fine-tuned visual transformers such as VideoMAE. The dataset used during training is based on the Conflab dataset and contains top-view video recordings of social interactions in a crowded social setting. The videos are manually annotated for gesture phases (preparation, hold, stroke, recovery) and gesture units. The two classifiers obtain high accuracies after fine tuning, with an overall accuracy of 95% for the gesture phase classification and 93% for classifying whether a clip is a gesture unit or not. These findings suggest that the proposed approach is effective in crowded environments and can be adapted for real-time applications. ... Read more

Researching hand gestures in real-world social interactions requires very careful analysis. While gesture coding schemes were created with that purpose in mind, they are not widely utilised in research. Moreover, studies on gesture classification rarely focus on the physical nature of movements involved in gesturing, despite the fact that being able to quantify the motion could reveal useful patterns and correlations. To address those points, this research proposes the following approach: using machine learning models to automatically classify physical features of hand gestures, according to a coding scheme. Two such classifiers were created, for the left and right hand respectively. Overall, the results are quite promising - despite a small and imbalanced training set and complex features both models achieved an accuracy of roughly 60%. Moreover, the results indicate that by avoiding some of the simplifications that this research makes, and by using more balanced training data, the accuracy could be significantly increased. This is concrete evidence that machine learning models can indeed be used to classify the physical aspects of hand gestures, as defined
by a coding scheme, in social interactions in the wild.
... Read more

With the increasing influence of Machine Learning (ML) on our lives, the need for education on this topic is growing. A key component of education is assessment and improving this aspect could lead to better student learning performance. This study aimed to investigate the influence of different assessment methods on students' learning performance in k-means clustering. Two different assessment methods were used: a closed-book problem-based assignment and an open-book short answer exam. Participants were notified of their assessment method, after which they were instructed to watch a video lecture and take the assessment. Results show a significantly improved learning gain when using the open-book assessment, where learning gain was defined as the difference in score between the pre- and post-test. Between these two methods the open-book assessment is therefore favourable. However, future research is needed to develop a validated concept inventory for k-means clustering and identify other possible assessment techniques. ... Read more

Machine learning education often involves complex topics that can be challenging to teach engagingly, leading to difficulties in maintaining student focus and achieving optimal learning outcomes. This study aims to bridge the gap between machine learning-specific teaching techniques and those centred on student engagement by conducting a comprehensive analysis of related works and an empirical experiment. The related works section reveals differences between traditional and engagement-focused teaching methods. To address the knowledge gap regarding the impact of engagement-focused methods on learning outcomes, a controlled experiment was conducted, comparing a conventional 16-minute video lecture followed by practice questions against the same content divided into four shorter video segments, each followed by a subset of the practice questions. The results demonstrate that the experimental group achieved significantly higher average quiz scores and reported consistently higher satisfaction ratings, suggesting that even a simple engagement-boosting technique can substantially improve learning outcomes and student satisfaction in machine learning education. This study highlights the importance of prioritising student engagement as the field of machine learning continues to evolve. ... Read more

The rapid advance of AI and ML asks for better and earlier education on these topics. However, research on teaching AI and ML topics is relatively underdeveloped. Especially applying the teaching method gamification has not yet been thoroughly tested. This research aims to explore the effectiveness of gamifying ML education in high schools. An experiment was designed based on previous research. This experiment revolves around a computer game where students learn to train a car to drive itself. Despite small sample size, results show increased motivation and low anxiety. ... Read more

This study investigates the impact of scaffolded assignments on student learning, confidence, and the development of an empirical mindset in a Machine Learning (ML) course at TU Delft. Unlike traditional Computer Science subjects, ML requires an experimental approach, challenging students used to design-first methodologies. Through surveys of 25 students from the CSE2510 course, the study found that scaffolded assignments significantly enhance student confidence and perceived learning benefits, despite no positive correlation between the number of assignments completed and course grades. Qualitative feedback highlighted the value of scaffolded assignments in understanding the ML design process by providing structured guidance and enabling practice of specific sub-tasks. These findings suggest scaffolded learning is crucial in developing an experimental mindset and boosting student confidence in ML education. This study also proposes a second methodology for future research during an edition of the course to further explore this topic. ... Read more

The increasing demand for machine learning expertise calls for effective teaching methods for university-level courses. This research compares static versus interactive teaching methods in the context of machine learning, with the latter focusing on the student engaging more with the material. Specifically, this study investigates the impact of interactive visualisations on students' understanding of receiver operating characteristics (ROC) curve analysis, a critical concept in evaluating machine learning algorithms. Traditional static teaching methods often fall short of conveying complex ideas like ROC curves, which are pivotal in various fields, including medicine and psychology. This research also compares the efficacy of interactive versus static visualisations in enhancing student motivation. Twenty first-year computer science students from Delft University of Technology participated in the experiment and were randomly assigned to control (static visualisation) and experimental (interactive visualisation) groups. The results of the experiment were determined by analyzing the pre- and post-test scores, along with surveys measuring motivation. These indicate significant improvements in understanding for both groups, with a greater gain observed in the experimental group. This suggests that interactive visualisations may offer a superior approach to teaching complex machine learning concepts, but the experiment conducted in the study does not show a statistically significant difference between the static and interactive visualisations. The research also compares the student's motivation after completing an instructional material focused on the ROC, but the interactive visualisations did not provide better results. The study underscores the potential of interactive teaching tools to enhance educational outcomes in machine learning and highlights the need for further research into interactive methods for the teaching of machine learning. ... Read more

With the increasing development of artificial intelligence (AI), there is a more significant opportunity for humans and agents to collaborate in teamwork. In Human-Agent Teamwork (HAT) settings, collaboration requires communication, and the agent displaying emotion can impact how human teammates communicate and work together with the agent. This study investigated the impact of an explainable agent expressing emotion within explanations in a teamwork setting. We investigated how integrating an emotional component into an agent’s explanations influences trust in the agent, as well as humans’ perceptions of the agent’s anthropomorphism, animacy, likeability, and overall team performance when collaborating with the agent. With this goal, a pre-study was conducted using a focus-group meeting to investigate the relevant emotions to display in a simulated Search and Rescue (SAR) task and how these emotions can be incorporated into Explainable AI (XAI). Next, we conducted an in-between subject controlled experiment to study the effects of emotional components in explanations. The participants were divided into experimental and control groups, collaborating with agents that either displayed emotion or no emotion. The participants had to carry out a SAR task where they worked together with the agent to rescue victims. Our results confirmed that an agent displaying emotions increased perceived likeability, animacy, and anthropomorphism. Among these three, likeability and animacy are positively associated with trust. In contrast, an increase in anthropomorphism is associated with a decrease in trust. From the results, we could not conclude that team performance is directly affected by having emotion in the explanation. However, the results showed that emotion increases the messages sent from the human to the agent, and this increase in communication led to higher team performance. ... Read more

Human-agent teamwork (HAT) is becoming increasingly prevalent in fields such as search and rescue (SAR), where effective collaboration between humans and artificial agents is crucial. Previous studies have shown that trust plays a pivotal role in the success of HATs, influencing decision-making, communication, and potentially overall team performance.

This research investigates the impact of agent-provided explanations about the agent's trust in humans (artificial trust) and corresponding behavior changes on human trust in the agent and their satisfaction with explanations during a simulated SAR task. Two types of explanations were explored: Trust-Explained (TE) explanations, where the agent explains its trust level and trust-based decisions, and Trust-Unexplained (TU) explanations, which solely describe the agent’s behavior without reference to trust dynamics. Besides, this research also investigates the correlation between human trust and explanation satisfaction, and in the end, whether the differences in the provided explanations result in differences in team performance and artificial trust.

The study involved 40 participants divided into two groups: an experimental group (the trust-enhanced explanation group) receiving TE explanations and a control group (the non-trust explanation group) receiving TU explanations. Participants' trust in the agent, satisfaction with the explanations, and team performance and artificial trust were measured and analyzed. Contrary to initial expectations, no statistically significant differences in explanation satisfaction and human trust in the agent were found between the two groups. However, a strong positive correlation was observed between participants' satisfaction with the explanations and their trust in the agent, indicating that explanation quality plays a crucial role in human trust development. Furthermore, no significant differences in team performance were detected, suggesting that trust explanations may not directly influence task outcomes. In the analysis of artificial trust, the agent in the trust-enhanced explanation group exhibited more conservative adjustments in trust levels compared to the non-trust explanation group. This conservative approach may have influenced players in the trust-enhanced explanation group to adopt a more cautious or deliberate decision-making process, potentially prioritizing the comprehension of explanations over the optimization of task performance.

For future research, it may be worth delving deeper into the influence of trust explanations on user behavior, the more complex HAT task environments, the relationship between artificial trust and user behavior, the dynamic and adaptive explanations, and the causal relationship between explanation satisfaction and human trust in the agent to understand further how trust can be fostered in HAT.
... Read more