Collecting user data is ubiquitous in the modern web, because it enables different kinds of services like personalised recommendations. Specific data shared by the users can be analysed further to infer additional information - e.g., gender, age, appearance, or preferences. Such inferences are challenging to predict for users when deciding on whether or not to share data. In this paper, we explore privacy avatars as a way to visualise collected and inferred data to users by a picture. For this, we conducted semi-structured interviews (N=20) where participants were shown privacy avatars based on products they bought in a fictitious online-shopping scenario and additional privacy avatars with a varied level of detail and information. Our results show that participants were shocked by avatars that accurately depicted parts of their visual appearance and perceived inaccurate avatars as privacy-preserving. However, participants preferred information they considered sensitive (e.g., gender) to be accurate creating a tension between privacy and accuracy of shared or inferred information. We conclude with a discussion on how such privacy avatars could be generated and used in practice to offer intuitive and informative privacy interfaces.
Driving is a cognitively demanding activity increasingly shaped by interactions with in-vehicle systems. However, current voice assistants lack contextual understanding and often struggle to interpret ambiguous user intent that requires disambiguation. This research explores how large language model (LLM)-based in-car assistants can interpret and act on user intent in a safe, context-sensitive, and human-like manner. It is guided by two main research questions: (1) how users express intent through multimodal cues such as speech, gaze, and touch to provide contextual grounding for the LLM, and (2) how the assistant should adapt its timing, output modality, and level of proactivity to the driving situation and social context.
Mid-air haptic technology enables touchless interaction through focused ultrasound and holds promise for inclusive design in mobile and ubiquitous multimedia systems. However, accessibility aspects remain largely unexplored. This poster introduces an accessibility-centered framework for mid-air haptic interaction, building on prior reviews of user experience and technical design. We systematically analyzed 42 studies published between 2014 and 2024 and conducted twelve expert interviews, including accessibility specialists and a visually impaired participant. The analysis highlights three main challenges: low stimulus clarity, limited multimodal feedback, and insufficient personalization. Our proposed framework addresses these gaps through four design dimensions-perceptual optimization, multimodal integration, spatial configuration, and individual calibration. By integrating literature and expert insights, this work advances a structured foundation for accessible mid-air haptic systems and identifies future research directions toward inclusive, touchless interaction in mobile, public, and mixed-reality contexts.
Traditional methods for teaching electromagnetism often fall short, constrained by complex math and static 2D diagrams that fail to convey the dynamic, 3D nature of Electromagnetic fields. Even modern interactive tools frequently offer only scripted experiences, limiting exploration. This work presents a novel VR application designed to overcome these challenges by providing an interactive, real-time simulation of fundamental electromagnetic phenomena. Our core innovation is a compute shader GPU engine optimized for commodity hardware. It integrates Finite-Difference Time-Domain for field evolution with Particle-in-Cell for dynamic charges. This architecture allows for the stereoscopic visualization of complex 3D vector fields and provides low-latency user interaction. Students can directly manipulate charges and magnetic dipoles, observing the changes in the electromagnetic field. This seamless cause-and-effect loop is engineered to rapidly build physical intuition. Future work will focus on expanding the simulation features, as well as conducting empirical studies to validate the application's impact on learning.
As living and working spaces become scarce and costly, interiors transitioning between living, working, and sleeping configurations while enabling customized setups are in demand. Traditional furniture consumes space and is cumbersome to rearrange. Shape-changing furniture could solve this, yet existing options lack resolution, stability, or adaptability. We present AirClick, which facilitates on-demand room transformation using modular interactive inflatables. Our fabrication process supports personally fabricated and retrofitted retail inflatables. The touch-actuated modules connect to a floor-based air connector grid, facilitating interaction while integrating with traditional furniture. In a lab study (N=20) across four scenarios (office, meeting room, apartment room, multipurpose hall), participants rapidly transformed rooms and perceived AirClick as significantly more usable with higher intention to use in the everyday scenarios than in the hall, indicating suitability for routine activities with low to medium requirements for robustness. User feedback highlights AirClick's usefulness and scalability in diverse settings, hence showing AirClick's space-saving and customizable design can enhance the functionality and adaptability of living and working spaces.
