arXiv subjects

Antonio Krüger

Publications and source records attributed to Antonio Krüger.

At least 19 recordsLinked to original sources

arXiv2026

Neural Signatures of Programming Expertise: Classifying Programmer Skill Levels Using EEG Data

Accurately assessing a programmer's skill level is critical for hiring, team composition, and performance evaluation in the software industry. Conventional methods, such as coding tests or interviews, often fail to capture the full spectrum of cognitive abilities underlying programming expertise. This study explores using electroencephalography (EEG) and machine learning to investigate neural correlates of programming skill. We analyzed an existing EEG dataset recorded during code comprehension from 37 programmers with 1 to 30 years of experience (8.1 +/- 6.3 years) to examine relationships between neural activity and expertise. Additionally, we conducted classification experiments using Random Forest classifiers with diverse features for binary (experts vs. novices) and multi-class (experts, intermediates, novices) setups. We identified EEG features and brain regions associated with programming expertise. Specifically, EEG entropy showed the strongest correlation with skill level. Furthermore, experts' brains were characterized by highly localized centro-frontal activation, whereas frontal activation in other groups was part of a more distributed network. Regarding classification, our setup achieved an average accuracy of 91.83% (binary) and 78.15% (multi-class) in stratified 10-fold cross-validation, while leave-one-subject-out validation achieved 85.00% and 58.80%, respectively. Individual frequency bands outperformed full-spectrum analyses, and both program comprehension and resting-state data yielded strong results. These findings demonstrate that EEG features effectively capture neural correlates across different skill levels and highlight the potential of neural data to complement traditional methods of skill assessment.

cs.HC↗

arXiv2026

International AI Safety Report 2026

The International AI Safety Report 2026 synthesises the current scientific evidence on the capabilities, emerging risks, and safety of general-purpose AI systems. The report series was mandated by the nations attending the AI Safety Summit in Bletchley, UK. 29 nations, the UN, the OECD, and the EU each nominated a representative to the report's Expert Advisory Panel. Over 100 AI experts contributed, representing diverse perspectives and disciplines. Led by the Report's Chair, these independent experts collectively had full discretion over the report's content.

cs.CY↗

arXiv2025

International AI Safety Report 2025: Second Key Update: Technical Safeguards and Risk Management

This second update to the 2025 International AI Safety Report assesses new developments in general-purpose AI risk management over the past year. It examines how researchers, public institutions, and AI developers are approaching risk management for general-purpose AI. In recent months, for example, three leading AI developers applied enhanced safeguards to their new models, as their internal pre-deployment testing could not rule out the possibility that these models could be misused to help create biological weapons. Beyond specific precautionary measures, there have been a range of other advances in techniques for making AI models and systems more reliable and resistant to misuse. These include new approaches in adversarial training, data curation, and monitoring systems. In parallel, institutional frameworks that operationalise and formalise these technical capabilities are starting to emerge: the number of companies publishing Frontier AI Safety Frameworks more than doubled in 2025, and governments and international organisations have established a small number of governance frameworks for general-purpose AI, focusing largely on transparency and risk assessment.

cs.CY↗

arXiv2025

International AI Safety Report 2025: First Key Update: Capabilities and Risk Implications

Since the publication of the first International AI Safety Report, AI capabilities have continued to improve across key domains. New training techniques that teach AI systems to reason step-by-step and inference-time enhancements have primarily driven these advances, rather than simply training larger models. As a result, general-purpose AI systems can solve more complex problems in a range of domains, from scientific research to software development. Their performance on benchmarks that measure performance in coding, mathematics, and answering expert-level science questions has continued to improve, though reliability challenges persist, with systems excelling on some tasks while failing completely on others. These capability improvements also have implications for multiple risks, including risks from biological weapons and cyber attacks. Finally, they pose new challenges for monitoring and controllability. This update examines how AI capabilities have improved since the first Report, then focuses on key risk areas where substantial new evidence warrants updated assessments.

cs.CY↗

arXiv2025

Implicit Search Intent Recognition using EEG and Eye Tracking: Novel Dataset and Cross-User Prediction

For machines to effectively assist humans in challenging visual search tasks, they must differentiate whether a human is simply glancing into a scene (navigational intent) or searching for a target object (informational intent). Previous research proposed combining electroencephalography (EEG) and eye-tracking measurements to recognize such search intents implicitly, i.e., without explicit user input. However, the applicability of these approaches to real-world scenarios suffers from two key limitations. First, previous work used fixed search times in the informational intent condition -- a stark contrast to visual search, which naturally terminates when the target is found. Second, methods incorporating EEG measurements addressed prediction scenarios that require ground truth training data from the target user, which is impractical in many use cases. We address these limitations by making the first publicly available EEG and eye-tracking dataset for navigational vs. informational intent recognition, where the user determines search times. We present the first method for cross-user prediction of search intents from EEG and eye-tracking recordings and reach 84.5% accuracy in leave-one-user-out evaluations -- comparable to within-user prediction accuracy (85.5%) but offering much greater flexibility

cs.HC↗

arXiv2025

Distinguishing Target and Non-Target Fixations with EEG and Eye Tracking in Realistic Visual Scenes

Distinguishing target from non-target fixations during visual search is a fundamental building block to understand users' intended actions and to build effective assistance systems. While prior research indicated the feasibility of classifying target vs. non-target fixations based on eye tracking and electroencephalography (EEG) data, these studies were conducted with explicitly instructed search trajectories, abstract visual stimuli, and disregarded any scene context. This is in stark contrast with the fact that human visual search is largely driven by scene characteristics and raises questions regarding generalizability to more realistic scenarios. To close this gap, we, for the first time, investigate the classification of target vs. non-target fixations during free visual search in realistic scenes. In particular, we conducted a 36-participants user study using a large variety of 140 realistic visual search scenes in two highly relevant application scenarios: searching for icons on desktop backgrounds and finding tools in a cluttered workshop. Our approach based on gaze and EEG features outperforms the previous state-of-the-art approach based on a combination of fixation duration and saccade-related potentials. We perform extensive evaluations to assess the generalizability of our approach across scene types. Our approach significantly advances the ability to distinguish between target and non-target fixations in realistic scenarios, achieving 83.6% accuracy in cross-user evaluations. This substantially outperforms previous methods based on saccade-related potentials, which reached only 56.9% accuracy.

cs.CV↗

arXiv2025

Grasp Prediction based on Local Finger Motion Dynamics

The ability to predict the object the user intends to grasp offers essential contextual information and may help to leverage the effects of point-to-point latency in interactive environments. This paper explores the feasibility and accuracy of real-time recognition of uninstrumented objects based on hand kinematics during reach-to-grasp actions. In a data collection study, we recorded the hand motions of 16 participants while reaching out to grasp and then moving real and synthetic objects. Our results demonstrate that even a simple LSTM network can predict the time point at which the user grasps an object with a precision better than 21 ms and the current distance to this object with a precision better than 1 cm. The target's size can be determined in advance with an accuracy better than 97%. Our results have implications for designing adaptive and fine-grained interactive user interfaces in ubiquitous and mixed-reality environments.

cs.HC↗

arXiv2025

Your Interface, Your Control: Adapting Takeover Requests for Seamless Handover in Semi-Autonomous Vehicles

With the automotive industry transitioning towards conditionally automated driving, takeover warning systems are crucial for ensuring safe collaborative driving between users and semi-automated vehicles. However, previous work has focused on static warning systems that do not accommodate different driver states. Therefore, we propose an adaptive takeover warning system that is personalised to drivers, enhancing their experience and safety. We conducted two user studies investigating semi-autonomous driving scenarios in rural and urban environments while participants performed non-driving-related tasks such as text entry and visual search. We investigated the effects of varying time budgets and head-up versus head-down displays for takeover requests on drivers' situational awareness and mental state. Through our statistical and clustering analyses, we propose strategies for designing adaptable takeover systems, e.g., using longer time budgets and head-up displays for non-hazardous takeover events in high-complexity environments while using shorter time budgets and head-down displays for hazardous events in low-complexity environments.

cs.HC↗

arXiv2025

International AI Safety Report

The first International AI Safety Report comprehensively synthesizes the current evidence on the capabilities, risks, and safety of advanced AI systems. The report was mandated by the nations attending the AI Safety Summit in Bletchley, UK. Thirty nations, the UN, the OECD, and the EU each nominated a representative to the report's Expert Advisory Panel. A total of 100 AI experts contributed, representing diverse perspectives and disciplines. Led by the report's Chair, these independent experts collectively had full discretion over the report's content.

cs.CY↗

arXiv2024

AdaptoML-UX: An Adaptive User-centered GUI-based AutoML Toolkit for Non-AI Experts and HCI Researchers

The increasing integration of machine learning across various domains has underscored the necessity for accessible systems that non-experts can utilize effectively. To address this need, the field of automated machine learning (AutoML) has developed tools to simplify the construction and optimization of ML pipelines. However, existing AutoML solutions often lack efficiency in creating online pipelines and ease of use for Human-Computer Interaction (HCI) applications. Therefore, in this paper, we introduce AdaptoML-UX, an adaptive framework that incorporates automated feature engineering, machine learning, and incremental learning to assist non-AI experts in developing robust, user-centered ML models. Our toolkit demonstrates the capability to adapt efficiently to diverse problem domains and datasets, particularly in HCI, thereby reducing the necessity for manual experimentation and conserving time and resources. Furthermore, it supports model personalization through incremental learning, customizing models to individual user behaviors. HCI researchers can employ AdaptoML-UX (\url{https://github.com/MichaelSargious/AdaptoML_UX}) without requiring specialized expertise, as it automates the selection of algorithms, feature engineering, and hyperparameter tuning based on the unique characteristics of the data.

cs.HC↗

arXiv2024

Toward a Surgeon-in-the-Loop Ophthalmic Robotic Apprentice using Reinforcement and Imitation Learning

Robot-assisted surgical systems have demonstrated significant potential in enhancing surgical precision and minimizing human errors. However, existing systems cannot accommodate individual surgeons' unique preferences and requirements. Additionally, they primarily focus on general surgeries (e.g., laparoscopy) and are unsuitable for highly precise microsurgeries, such as ophthalmic procedures. Thus, we propose an image-guided approach for surgeon-centered autonomous agents that can adapt to the individual surgeon's skill level and preferred surgical techniques during ophthalmic cataract surgery. Our approach trains reinforcement and imitation learning agents simultaneously using curriculum learning approaches guided by image data to perform all tasks of the incision phase of cataract surgery. By integrating the surgeon's actions and preferences into the training process, our approach enables the robot to implicitly learn and adapt to the individual surgeon's unique techniques through surgeon-in-the-loop demonstrations. This results in a more intuitive and personalized surgical experience for the surgeon while ensuring consistent performance for the autonomous robotic apprentice. We define and evaluate the effectiveness of our approach in a simulated environment using our proposed metrics and highlight the trade-off between a generic agent and a surgeon-centered adapted agent. Finally, our approach has the potential to extend to other ophthalmic and microsurgical procedures, opening the door to a new generation of surgeon-in-the-loop autonomous surgical robots. We provide an open-source simulation framework for future development and reproducibility at https://github.com/amrgomaaelhady/CataractAdaptSurgRobot.

cs.RO↗

arXiv2024

SynthoGestures: A Novel Framework for Synthetic Dynamic Hand Gesture Generation for Driving Scenarios

Creating a diverse and comprehensive dataset of hand gestures for dynamic human-machine interfaces in the automotive domain can be challenging and time-consuming. To overcome this challenge, we propose using synthetic gesture datasets generated by virtual 3D models. Our framework utilizes Unreal Engine to synthesize realistic hand gestures, offering customization options and reducing the risk of overfitting. Multiple variants, including gesture speed, performance, and hand shape, are generated to improve generalizability. In addition, we simulate different camera locations and types, such as RGB, infrared, and depth cameras, without incurring additional time and cost to obtain these cameras. Experimental results demonstrate that our proposed framework, SynthoGestures (https://github.com/amrgomaaelhady/SynthoGestures), improves gesture recognition accuracy and can replace or augment real-hand datasets. By saving time and effort in the creation of the data set, our tool accelerates the development of gesture recognition systems for automotive applications.

cs.CV↗

arXiv2024

Looking for a better fit? An Incremental Learning Multimodal Object Referencing Framework adapting to Individual Drivers

The rapid advancement of the automotive industry towards automated and semi-automated vehicles has rendered traditional methods of vehicle interaction, such as touch-based and voice command systems, inadequate for a widening range of non-driving related tasks, such as referencing objects outside of the vehicle. Consequently, research has shifted toward gestural input (e.g., hand, gaze, and head pose gestures) as a more suitable mode of interaction during driving. However, due to the dynamic nature of driving and individual variation, there are significant differences in drivers' gestural input performance. While, in theory, this inherent variability could be moderated by substantial data-driven machine learning models, prevalent methodologies lean towards constrained, single-instance trained models for object referencing. These models show a limited capacity to continuously adapt to the divergent behaviors of individual drivers and the variety of driving scenarios. To address this, we propose \textit{IcRegress}, a novel regression-based incremental learning approach that adapts to changing behavior and the unique characteristics of drivers engaged in the dual task of driving and referencing objects. We suggest a more personalized and adaptable solution for multimodal gestural interfaces, employing continuous lifelong learning to enhance driver experience, safety, and convenience. Our approach was evaluated using an outside-the-vehicle object referencing use case, highlighting the superiority of the incremental learning models adapted over a single trained model across various driver traits such as handedness, driving experience, and numerous driving conditions. Finally, to facilitate reproducibility, ease deployment, and promote further research, we offer our approach as an open-source framework at \url{https://github.com/amrgomaaelhady/IcRegress}.

cs.HC↗

arXiv2023

Using Abstract Tangible Proxy Objects for Interaction in Optical See-through Augmented Reality

Interaction with virtual objects displayed in Optical See-through Augmented Reality is still mostly done with controllers or hand gestures. A much more intuitive way of interacting with virtual content is to use physical proxy objects to interact with the virtual objects. Here, the virtual model is superimposed on a physical object, which can then be touched and moved to interact with the virtual object. Since it is not possible to use an exact replica as a tangible proxy object for every use case, we conducted a study to determine the extent to which the shape of the physical object can deviate from the shape of the virtual object without massively impacting performance and usability, as well as the sense of presence. Our study, in which we investigated different levels of abstraction for a sofa model, shows that the physical proxy object can be abstracted to a certain degree. At the same time, our results indicate that the physical object must have at least a similar shape as the virtual object in order to serve as a suitable proxy.

cs.HC↗

arXiv2023

Designing for Passengers' Information Needs on Fellow Travelers: A Comparison of Day and Night Rides in Shared Automated Vehicles

Shared automated mobility-on-demand promises efficient, sustainable, and flexible transportation. Nevertheless, security concerns, resilience, and their mutual influence - especially at night - will likely be the most critical barriers to public adoption since passengers have to share rides with strangers without a human driver on board. As related work points out that information about fellow travelers might mitigate passengers' concerns, we designed two user interface variants to investigate the role of this information in an exploratory within-subjects user study (N = 24). Participants experienced four automated day and night rides with varying personal information about co-passengers in a simulated environment. The results of the mixed-method study indicate that having information about other passengers (e.g., photo, gender, and name) positively affects user experience at night. In contrast, it is less necessary during the day. Considering participants' simultaneously raised privacy demands poses a substantial challenge for resilient system design.

cs.HC↗

arXiv2023

Teach Me How to Learn: A Perspective Review towards User-centered Neuro-symbolic Learning for Robotic Surgical Systems

Recent advances in machine learning models allowed robots to identify objects on a perceptual nonsymbolic level (e.g., through sensor fusion and natural language understanding). However, these primarily black-box learning models still lack interpretation and transferability and require high data and computational demand. An alternative solution is to teach a robot on both perceptual nonsymbolic and conceptual symbolic levels through hybrid neurosymbolic learning approaches with expert feedback (i.e., human-in-the-loop learning). This work proposes a concept for this user-centered hybrid learning paradigm that focuses on robotic surgical situations. While most recent research focused on hybrid learning for non-robotic and some generic robotic domains, little work focuses on surgical robotics. We survey this related research while focusing on human-in-the-loop surgical robotic systems. This evaluation highlights the most prominent solutions for autonomous surgical robots and the challenges surgeons face when interacting with these systems. Finally, we envision possible ways to address these challenges using online apprenticeship learning based on implicit and explicit feedback from expert surgeons.

cs.RO↗

arXiv2021

A Long-Term Investigation on the Effects of (Personalized) Gamification on Course Participation in a Gym

Gamification is frequently used to motivate people getting more physically active. However, most systems follow a one-size-fits-all gamification approach, although past research has shown that interpersonal differences exist in the perception of gamification elements. Also, most studies investigating the effects of gamification are rather short, although it has been shown that gamification can suffer from novelty effects. In this paper, we address both these issues by investigating whether gamification elements, integrated into a fitness course booking system, have an effect on how frequently users participate in fitness courses in a gym (N=52) over a duration of 275 days (548 days including baseline). Also, the gamification elements that we implemented are tailored to specific Hexad user types, which allows us to investigate whether using suitable gamification elements leads to an increased course participation. Our results show that gamification increased the participation in fitness courses significantly and that users who received a suitable set of gamification elements - according to their Hexad user type - increased their participation significantly more than others.

cs.HC↗

arXiv2021

Design Guidelines to Increase the Persuasiveness of Achievement Goals for Physical Activity

Achievement goals are frequently used to support behavior change. However, they are often not specifically designed for this purpose nor account for the degree to which a user is already intending to perform the target behavior. In this paper, we investigate the perceived persuasiveness of different goal types as defined by the 3x2 Achievement Goal Model, what people like and dislike about them and the role that behavior change intentions play when aiming at increasing step counts. We created visualizations for each goal type based on a qualitative pre-study (N=18) and ensured their comprehensibility (N=18). In an online experiment (N=118), we show that there are differences in the perception of these goal types and that behavior change intentions should be considered to maximize their persuasiveness as goals evolve. Next, we derive design guidelines on when to use which type of achievement goal and what to consider when using them

cs.HC↗