As artificial intelligence transitions into physically embodied collaborative roles, systems must be designed to support rather than thwart human autonomy. In a between-subjects experiment (N = 20 dyads), we investigated how the conversational delivery style (Assertive vs. Supportive) of an LLM-driven Pepper robot affected perceived user autonomy and creative output during a live campus-improvement brainstorming task. While both conditions yielded a similar quantity of unique ideas (Assertive M = 5.90, Supportive M = 5.70, t(18) = 0.40, p = 0.697), the Supportive delivery style marginally increased autonomy frustration (M = 2.43) compared to the Assertive style (M = 2.03; t(38) = 2.02, p = 0.050, d = 0.64). Qualitative analysis of the interaction logs revealed that the Supportive robot's conversational pacing frequently triggered interruptions during natural cognitive silences, actively seizing the conversational floor and disrupting the dyads' workflow. These findings demonstrate that in live human-robot interaction, the structural implementation of interaction timing and turn-taking is more critical to preserving perceived user autonomy than empathetic semantic framing. ... Read more

This study investigates whether proactive robot interventions undermine autonomy compared to reactive interventions during collaborative ideation tasks. A Pepper robot was implemented as a conversational agent capable of generating spoken solution suggestions using a locally hosted large language model, speech recognition, and text-to-speech functionality. The study used a between-subjects design with two conditions: a proactive condition, in which the robot autonomously intervenes based on conversational cues such as silence or signs of struggle, and a reactive condition, in which it only responds when explicitly addressed. Participants completed a collaborative task focused on improving campus life, after which perceived autonomy was measured using an adapted autonomy subscale of the Basic Psychological Need Satisfaction and Frustration Scale. Behavioral data, including robot intervention frequency, timing, and number of ideas generated, were also collected. Results showed no statistically significant differences between conditions, suggesting that proactive interventions do not substantially reduce participants’ sense of autonomy. Groups in the proactive condition generated an average of 7.70 ideas compared to 5.80 in the reactive condition, but this difference was not significant  (p = .067). These findings suggest that unsolicited robot participation may not inherently undermine perceived autonomy in short brainstorming tasks, while highlighting the importance of intervention design. ... Read more

Social robots deployed in collaborative groups reshape interaction between human participants, yet how different verbal strategies produce distinct shaping effects remains untested. This study compared assertive and supportive robot strategies across 20 dyads in a creative ideation task with a Pepper robot. Transcripts were coded using Mercer’s talk taxonomy and post-session interviews were analysed thematically. The supportive strategy produced a significantly higher frequency of exploratory discourse than the assertive strategy. The interaction strategy had no significant effect on participation balance, disputational talk, cumulative talk, group cohesion, or ingroup identification. Four unintended cross-condition shaping effects emerged, including attention redistribution, partner solidarity, speech formalisation,
and emotional suppression. Condition-specific effects were also observed, including creative suppression and expert deference. These results show that a robot’s verbal register shapes the quality of human collaboration, and that robot presence alone restructures interaction beyond any programmed design intent. ... Read more

Social robots can shape group interaction, but robot facilitation is often studied at the level of the robot as a whole rather than at the level of the specific utterances through which the robot intervenes. This paper investigates how three spoken elicitation strategies delivered by a social robot -generative, elaborative, and perspective-shifting prompts- shape participant engagement and contribution substantiveness in a two-person creative ideation task about improving the TU Delft campus experience. The study used a within-subjects design in which each of the 20 groups received one prompt from each strategy during divergence and one during convergence, while the task, robot, scheduling rule, and technical setup were kept constant. Engagement was measured using self-reports, response delay, speaking time, vocal activation, and connection cues; contribution substantiveness was measured using manually coded idea count, elaboration units, and consecutive same-subject turns. The descriptive results suggest that convergence produced higher self-reported engagement than divergence, generative prompts were most associated with idea breadth, and elaborative prompts were most associated with developed and sustained discussion. Perspective-shifting prompts appeared more useful once participants already had ideas to evaluate. The findings do not establish a universally superior strategy, but they show that robot facilitation should be designed as phase-sensitive prompt behaviour rather than treated as a general effect of robot presence. ... Read more

Long distance Haptic Bilateral Teleoperation (HBT) is used in applications such as surgery, training, remote operation, and disaster relief. One of the main challenges in these systems is communication delay. When force feedback relies directly on sensors in the remote environment, increasing delay quickly makes the system unstable and hard to control. To overcome this, previous approaches generated force feedback using a local simulation of the remote environment, while providing visual feedback through live video [20]. This reduces timing constraints, but only works when the simulation accurately represents reality. When the robot encounters objects that are not modeled and not visible to the operator, unpredictable forces occur and the system no longer behaves correctly.
This thesis investigates how such unpredictable forces can be handled in delayed HBT. We introduce a method that keeps predictive force feedback through simulation, while correcting the simulation using sensor data from the robot. A small 3D printed attachment was developed to detect unexpected contact events. These measurements are sent back to the operator side and used to update the virtual environment, allowing force feedback to be generated even for unknown objects. The approach was evaluated in a user study with 13 participants under varying delays and visibility conditions. The results show that when visibility is limited, reaction based feedback can be used instead of prediction based feedback. The findings indicate that combining simulation based prediction with remote sensing offers a practical solution for dealing with unpredictable forces in long distance HBT. ... Read more

Gaze estimation systems powered by deep neural networks are commonly used in sensitive applications such as driver assist or human-computer interaction. While backdoor attacks have been widely studied for classification tasks, vulnerability of regression networks like gaze estimators to these kind of attacks still remain underexplored. This research investigates the effectiveness of full-image backdoor attacks on appearance-based gaze estimation models. Specifically, the study explores dirty-label attacks with two types of global backdoor triggers: a spatial-domain sinusoidal pattern and a randomized frequency-domain perturbation. Experimental results on the MPIIFaceGaze dataset demonstrate that both triggers can reliably induce malicious outputs while preserving high accuracy on clean data, with the frequency-domain trigger offering superior stealth. These findings highlight a significant vulnerability in deep regression models, emphasizing the need for defensive mechanisms in real-world gaze estimation systems. ... Read more

The global adoption of 5G technology is rapidly accelerating and 5G traffic is growing exponentially. This increase in demand compels network operators to evaluate whether their current and upcoming 5G infrastructure can effectively accommodate the growing data traffic. A key component within the 5G network is the User Plane Function (UPF), which connects the end-devices to the data networks. Therefore, it is vital for both equipment manufacturers and service providers to analyze the performance of existing UPF implementations. This paper presents an initial approach to assess the Open5GS UPF performance by conducting stress testing in virtualized environment. We focus on finding the optimal UPF configuration by measuring the resource consumption and latency of the UPF under varying intensity of generated traffic, and providing a simple queueing model for the UPF. Numerical results show that a CPU load of 70-80% balances between latency and throughput while ensuring that 99% of the packets are forwarded within 150 ms. ... Read more

Academic research in 5G networking faces a lack of accessible, realistic packet-level datasets, limiting innovation and reproducibility. This paper evaluates two state-of-the-art machine learning approaches, PAC-GAN and TabularARGN, for generating synthetic 5G TCP/IP packet headers. Using a real 5G packet-capture dataset, we adapt both models to include inter-packet timing and rigorously assess them on protocol validity, marginal distribution alignment, and joint distribution fidelity. Results show that PAC-GAN produces highly valid and statistically faithful synthetic packets, effectively modeling complex header dependencies and temporal patterns. While TabularARGN ensures strict protocol compliance, it struggles to capture higher-order correlations and traffic diversity. Our findings establish convolutional generative models like PAC-GAN as practical tools for producing realistic, protocol-compliant synthetic 5G traffic, broadening access to datasets for benchmarking and security testing. ... Read more

With the exponential growth of mobile traffic in 5G networks, accurate forecasting is essential for efficient resource management. This research provides a comparative analysis of time series forecasting models for predicting near-future network traffic. Using a public dataset from a 5G base station in Barcelona, this study evaluates the performance of a traditional statistical model, against deep learning models: a Recurrent Neural Network (RNN), a Long Short-Term Memory (LSTM) and a Google timesFM model. The results demonstrate that while the SARIMAX model struggles to capture near-future traffic demand, the deep learning approaches yield significantly higher predictive accuracy. Specifically, a simple LSTM architecture shows great results, outperforming even a more complex one. However, the timesFM model, in particular, shows the most robust generalization capabilities. Additionally, the models trained on data from one base station do not generalize well to others, highlighting significant differences in traffic characteristics even between geographically close locations. This suggests that while locally trained LSTMs are a powerful tool, future work should focus on developing more adaptive and transferable models, such as those using federated learning or graph neural networks.^p ... Read more

Modern mobile networks must adapt to rapidly changing traffic patterns and increasing user demands. A key challenge is understanding where user traffic terminates and how these destinations vary over time. This thesis addresses this challenge by introducing an open-source, modular analysis framework that analyzes passive Internet traffic traces, enriches them with geolocation and organizational metadata, and infers latency stability and routing dynamics, in order to characterize the infrastructures that terminate user traffic and assess their performance and reliability over time. The results show a long-term shift towards content-centric traffic, highlight geographic and temporal variations in performance, and demonstrate that content networks typically offer greater stability than enterprise or research destinations. These findings support adaptive traffic management strategies in 5G and future 6G networks. ... Read more

This paper investigates the latency and resilience of user-plane anchor reconfiguration in a fully virtualized 5G core environment using Open5GS and UERANSIM. The experiment spans five VirtualBox virtual machines, each hosting a key component of the 5G core or radio stack: 5G-core gNB, UPF1, UPF2, and a single UE. All nodes communicate over a shared internal network, ensuring controland user-plane traffic remains isolated from external variability. The UE is initially anchored to UPF 1 via DNN “internet.” After the initial tunnel is established and validated, a re-anchoring procedure is triggered by calling the SMF’s REST API. Although the endpoint is intended to perform a PFCP Session Modification, Open5GS tears down the session and creates a new one on UPF 2 instead. By analyzing timestamped UE logs—capturing tunnel setup, session release, and re-establishment—we measure the latency of user-plane reattachment. Our results reveal high variability in recovery times, ranging from sub-second to over 50 seconds. These inconsistencies are attributed to limitations in Open5GS’s session handling, the lack of true migration support, and hardware limitations of the used machine. Despite these challenges, the study offers insights into the practical behavior of PFCP-driven anchor reconfiguration and the operational gaps that remain in open-source 5G core implementations. ... Read more

Event cameras are bio-inspired sensors with high dynamic range, high temporal resolution, and low power consumption. These features enable precise motion detection even in challenging lighting conditions and fast-changing scenes, rendering them well-suited for optical flow estimation. However, event camera output is sparse and unstructured, making it challenging to process. Transformer architectures have shown to be effective in capturing long-term temporal dependencies and processing sparse input, hence they might be better suited to processing this output by leveraging the fine time granularity inherent to event camera data.
We introduce E-GMFlow, an approach for event-based optical flow inspired by the recent success in terms of accuracy of transformer-based models for frame-based optical flow. We explore the effect of temporal details on the accuracy of this transformer architecture by changing the number of temporal bins in which events are discretized. We observe that the increase in the number of temporal bins generally causes higher accuracy and comment on the limitations of this study. ... Read more

Computer vision tasks have shown to benefit greatly from both developments in deep learning networks, and the emergence of event cameras. Deep networks can require a large amount of training data, which is not readily available for event cameras, specifically for optical flow estimation. The need for simulating this data in a realistic, physics-driven manner is therefore crucial. This paper compares the state of the art event camera simulators on different criteria, including event timestamp modeling, performance under low illumination, bandwidth simulation, computation speed and various types of noise simulation. We also summarize the shortcomings of some commonly used optical flow event datasets. For generating high-quality, realistic events, The V2E and DVS-Voltmeter simulators have shown to produce the most accurate data. ... Read more

Optical flow estimation with event cameras encompasses two primary algorithm classes: model-based and learning-based methods. Model-based approaches, do not require any training data while learning-based approaches utilize datasets of events to train neural networks. To effectively apply these algorithms, it's essential to understand their respective strengths and weaknesses.
This study compares model-based and learning-based optical flow estimation methods using event cameras, aiming to provide guidance for real-world applications. We evaluated these methods on the MVSEC and DSEC datasets, focusing on their accuracy and runtime. Our findings indicate that model-based methods excel on the MVSEC dataset, characterized by small motions, while learning-based approaches perform better on the more dynamic DSEC dataset. To investigate potential overfitting of learning-based methods to DSEC, we retrained the IDNet and TMA models on the BlinkFlow dataset. The retrained models demonstrated competitive accuracy, surpassing model-based methods which indicates that learning-based models perform better on datasets like DSEC even when not able to overfit. Finally, our analysis on runtime showed that model-based methods achieve real-time performance on CPUs and learning-based methods require a GPU to run in real-time. ... Read more

Heart rate data and other data collected by consumer-grade wearable devices can give away quite useful information about the user. It can for example be used by machine learning algorithms such as Deep Neural Networks (DNN) to learn patterns about cardiovascular disease and fitness, or be used for identification. Heart rate patterns can also change quickly within the span of several months, which could make older heart rate data less useful when training a DNN. This paper shows that the DNN did indeed perform significantly worse when trying to identify people on older data compared to recent data. The accuracy calculated from the test set was 63.64% when trained on the most recently available training data, in comparison to 33.88% when trained on the least recent data which was more than 200 days older. When changing the recentness of training data only for a single user, there was also always an improvement in the accuracy of the model to identify that particular person. The accuracy to identify all users however did not necessarily increase, and sometimes even decreased. Using more data for training still outperforms using a smaller amount of samples of more recent data by slight margins, showing the trade-off between the recentness of data and the amount of data used for training. However, if fast training times are required, taking the most recent data windows can still lead to a similar performance as when training on all available data. ... Read more

Advancements in the precision and accuracy of consumer-grade wearables, such as a Fitbit, have enabled the identification and therefore authentication of individuals based on their emitted heart frequencies using these wrist-worn devices. With this type of authentication, a password is essentially sent out every second. This makes it a perfect form of authentication in fields where constant authentication is crucial. However, not much is known about how different types of cardiac diagnosis (e.g. fit or obese) influence the accuracy of this type of authentication. In this paper, it will be shown how different types of cardiac diagnosis affect the accuracy of Deep Neural Networks to identify individuals by heart rate. This study is done with data obtained from 14 subjects, having different types of cardiac diagnosis. A deep neural network consisting of multiple convolutional layers is being used to conduct the experiments. It has been shown that subjects with a paroxysmal atrial fibrillation diagnosis improve the accuracy the most, compared to the reference (normal healthy) subjects. On the other hand, (very) fit subjects decrease the accuracy the most. Heart failure and obese subjects have a similar accuracy compared to reference subjects. ... Read more

The aim of this paper is to complete the gap in the knowledge and experiment using as little as only the heart rate of some subjects to manage to successfully authorise them in some supposed system. The focus will be on the Gaussian Mixture model and the One Class Support Vector Machine, both outlier detectors, because most of the past research was focused on supervised models. Using these two, this paper will experiment with recognising intruders with models trained to distinguish one authorised person and multiple authorised persons. In the first case, multiple data processing methods and hyperparameters will be tested together and compared. In the second case, the goal will be to use and modify the best-found parameters of the first case to train models that are able to detect multiple persons as authorised. This time, there are two methods that this paper is going to look into and compare their performance: training one single model to detect all the subjects and training multiple models, one per subject. The most notable results are with one authorised person, with a score of 0.936, with two authorised, 0.88, and with 12 authorised, 0.713, when using the area under the curve metric. ... Read more

Outlier detection is an essential part of modern systems. It is used to detect anomalies in behaviour or performance of systems or subjects, such as fall detection in smartwatches or voltage irregularity detection in batteries. This provides early indications of something of potential problems.

A part of outlier detection that is not often analysed is the performance of algorithms in environments with data from only one subject, versus environments with data from multiple subjects. This paper aims to answer the questions regarding the performance of Gaussian Mixture Models (GMM) and DBSCAN in these different environments. This paper focuses on time series data collected from consumer-grade wearables like smartwatches. In this paper, the outliers are defined manually, as the used data set did not contain predefined outliers. This research considers both outliers defined within the subject data, and the use of other subjects as outliers.

Results from this paper indicate that the amount of subjects in the environment is not the sole factor in the performance of these algorithms. Rather, it is a combination of the amount of subjects in the environment and the type of outlier to be detected. Results show that a GMM has difficulty distinguishing subjects that are similar when using another subject as outlier data. On average, DBSCAN outperforms a GMM in almost all cases, and DBSCAN is a lot more consistent in its performance than a GMM. ... Read more

In recent years, with the rapid expansion of IoT (Internet of Things) devices, more and more research and commercial projects have focused on various application areas of IoT. Signify, as a leading player in the smart home industry, has been deeply involved in this field for many years, particularly focusing on smart lighting for smart homes and providing consumers with a whole-house smart lighting solution (Hue System). The Hue System consists of several IoT devices, such as the Hue Bridge, Hue Bulbs, and Hue Accessories. This research paper specifically targets the IoT field and aims to reduce the firmware update cycle of the Hue Bridge.

The Hue Bridge, which serves as a central device in the Philips Hue internet-connected lighting system, connects users with other Hue devices. However, the Hue Bridge faces the challenge of inefficient firmware updates, which require validation engineers to wait for 6-8 weeks to ensure firmware reliability. To address this issue, this paper proposes a virtual system solution that improves the virtualization procedure of the Hue Bridge devices and utilizes Kubernetes for large-scale deployment to accelerate the generation of diagnostic data. Furthermore, a Use Case Model is established based on users' daily data, and a model based on Frequent Pattern Mining is applied to simulate users' daily behaviors in the Kubernetes Deployment.

To validate our virtual system, we designed validation experiments from multiple perspectives, including validation of the use case model and automated feedback. Our validation results demonstrate that this system enables more efficient and convenient acquisition of automated feedback (issues/bugs), while significantly enhancing the generation of diagnostic data in the firmware update cycle. Moreover, it offers advantages such as high availability, convenience, and cost-effectiveness in deployment. This research provides valuable references and insights for firmware update-related studies in the IoT domain ... Read more

Badnets are a type of backdoor attack that aims at manipulating the behavior of Convolutional Neural Networks. The training is modified such that when certain triggers appear in the inputs the CNN is going to behave accordingly. In this paper, we apply this type of backdoor attack to a regression task on gaze estimation. We examine different triggers to discover which of them lead to better performance and thus infer which trigger aspects one can take the most advantage from. It turns out that placing frames around and drawing multiple lines across the images are the most effective for the training of Badnets. ... Read more