We present a visual encoding method for semantic mapping in indoor environments, designed to minimize redundancy in image data and support federated learning across a fleet of service robots. Our pipeline combines 2D LiDAR-based segmentation with RGB image filtering based on geometric orientation, distance, visibility, and uniqueness. The result is a compact set of representative visual samples suitable for downstream semantic tasks such as object recognition or language grounding. We evaluate our method in a Gazebo simulation using a TurtleBot platform and compare it against a naive odometry-based sampling strategy. Our approach achieves up to 57.5% reduction in collected images while preserving scene coverage. Additionally, we demonstrate how multiple robots can collaboratively improve the visual map in a federated setup, reducing collection time and enabling model generalization across diverse environments. The proposed method offers an efficient and scalable solution for semantic mapping under bandwidth and computation constraints.
While artificial intelligence (AI) is improving the performance of O-RAN, it will also expose the network to adversarial machine learning (AML) attacks. For this reason, in this paper, we are the first to investigate AML in the context of deep reinforcement learning (DRL)-based O-RAN xApps. What separates AML in O-RAN from traditional settings is the need to design and analyze adversarial attacks based on RAN-specific Key Performance Measures (KPMs) such as transmitted bit rate, downlink buffer occupancy, transmitted packets, etc. As such, we propose the AdvO-RAN framework, which includes (i) a new adversarial perturbation generator using preference-based reinforcement learning (PbRL) to learn the perturbation that most violate the user service level agreements (SLA) and (ii) a robust training module for enhancing DRL agent resilience to the attacks in (i). We experimentally evaluate AdvO-RAN on the Colosseum network emulator. Experimental results show that AdvO-RAN can enhance xApp performance by reducing SLA violations from 44% to 27% on average and reducing by 46% the latency under the most challenging attack scenario for Ultra-Reliable Low-Latency Communications (URLLC) traffic. AdvO-RAN can improve up to 75% of throughput in the victim Enhanced Mobile Broadband (eMBB) slice users during a constant bit-rate traffic scenario.
Personal characteristics fundamentally shape virtual reality (VR) experiences, yet their integration into predictive models remains underexplored. This paper studies how to incorporate personal attributes (age, gender, prior VR experience) into Bayesian networks for cybersickness prediction via: (i) direct inclusion as root nodes, (ii) a two-stage model that learns a susceptibility score from personal attributes, and (iii) a stratified model. Using 26,040 samples from VR maze-navigation experiments, direct inclusion attains 82.53% accuracy (+14.02 percentage points over a 68.51% no-personal baseline). The two-stage approach reaches 77.32% while supporting cold-start prediction for unseen users, and stratified models achieve 73.62%. Using participant-level cross-validation to avoid subject leakage, we find that personalization consistently improves cybersickness prediction. These results argue that personal attributes should be treated as first-class signals in cybersickness models, with clear design trade-offs between maximal accuracy and deployability for unseen users, informing personalized VR systems and adaptive content delivery.
Cognitive attacks in mixed reality (MR), e.g., latency perturbations that induce frame-time jitter, can divert visual attention and degrade task performance. We study 2D gaze prediction under such disturbances and propose a time-aware sequence model that handles irregular sampling by supplying elapsed times triangle t between observations and conditions on sparse event/object context available at prediction time via learned token embeddings. Using time-based windows, we evaluate within-user and cross-user temporal generalization on MR recordings spanning multiple attack intensities. Results indicate accurate, time-robust gaze regression under latency perturbations, supporting adaptive MR interfaces in adversarial settings.
Internet browsing exposes private user information to websites and intermediate network providers, such as geographically linkable IP addresses and browsing history. Privacy proxies aim to address this internet privacy problem at scale. They are affordable for a large user base and do not compromise performance or usability-with a trend towards enabling them by default. Due to network encryption and closed-source code, their privacy promises cannot be verified using passive observations. We are the first to analyze the client-side implementations of three prominent privacy proxies available to over a billion users: Apple's iCloud Private Relay, Google's IP Protection, and Microsoft's Edge Secure Network VPN. We develop a deep understanding of these systems by reverse-engineering closed-source components and creating a client to bring Apple's, Google's, and Microsoft's solutions to Firefox on Linux. While privacy proxies offer privacy-enhancing features, we identify multiple issues deeply anchored in their architecture that weaken user privacy and security promises across all solutions. Our responsible disclosure and design recommendations aim to further strengthen this novel technology.
