Many studies have shown that collaborative tasks in immersive virtual reality (VR) and augmented reality (AR) environments can enhance collaborative outcomes in learning, training, and game-based contexts. However, the literature offers limited information on how these environments differentially shape social presence and collaborative engagement, particularly among unfamiliar users. This study addresses this gap by examining differences in social presence and collaborative engagement between VR and AR environments for unfamiliar pairs. A between-subjects experiment used an escape room game that featured three collaborative tasks, identically implemented in both VR and AR. The key difference was that the VR experience was stationary, while the AR experience required physical movement between rooms. The study involved 52 participants, divided into VR and AR groups, where two unfamiliar participants were paired into teams to complete the tasks. The results indicate significant differences in collaborative dynamics and user perception between the two environments. Specifically, VR pairs reported a stronger sense of immersion and flow state, whereas AR pairs demonstrated greater contextual awareness and behavioral coordination. Cybersickness measures also differed between conditions; given the locomotion mismatch, this pattern should be interpreted cautiously and not attributed to the environment alone. This finding improves understanding of the impact of immersive environments on collaborative processes and offers insights for designing collaborative XR applications (e.g., training and game-based teamwork), particularly for unfamiliar users.

Recent advancements in immersive virtual reality (IVR) have highlighted its expanding potential in educational settings. However, the influence of learners’ interpersonal relationships on their preferences for gamification elements in collaborative environments remains underexplored. This study introduces an avatar-based multiplayer IVR game designed to promote collaborative learning through improved social interaction. The game features a maze with three challenging puzzles, which require teamwork and problem-solving skills, encouraging communication and information sharing among participants. In addition, the study investigates the impact of different gamification elements on groups consisting of either peers or strangers, involving a total of 44 participants. The findings revealed significant improvements in communication and collaboration skills, along with increased motivation and engagement, across both group types. Moreover, participants exhibited distinct preferences for specific gamification elements, largely influenced by the nature of their social relationships and individual tendencies. This study offers valuable insights into the design of IVR-based collaborative educational environments with an emphasis on social interaction and game mechanics. It also identifies key factors shaping individual preferences for gamification elements, particularly in relation to social dynamics and personal preferences.

This paper investigates secure communication in multi-UAV networks, where each UAV employs rate-splitting multiple access (RSMA) to simultaneously deliver downlink data services to multiple ground terminals (GTs) under eavesdropping threats. To enhance network security, we propose a two-stage collaborative RSMA transmission scheme. Based on this scheme, we study the optimization of multi-UAV cooperative trajectory, time-step sharing and jamming power (MUCTSJ) to maximize the network’s secrecy rate. Additionally, to ensure fairness in throughput allocation among GTs, we incorporate two typical UAV service principles—Channel Quality First (CQF) and Fair Service First (FSF)—into the optimization objectives. Given the non-convex and NP-hard nature of this optimization problem, we reformulate it as a Markov Decision Process (MDP) and introduce a multi-agent reinforcement learning (MARL) framework based on the Centralized Training and Decentralized Execution (CTDE) paradigm. To address the dynamic topological changes induced by UAV mobility and time-varying channel states, as well as the gradient interference among multiple learning tasks, we design a Multi-Task Attention Deep Recurrent Network (MTA-DRNN). This architecture effectively captures the distinct observed attributes of each UAV while enhancing the coordination between diverse actions, thereby improving the adaptability of the agent and the stability of training. Simulation results demonstrate the superiority of the proposed solution enhances the security of multi-UAV networks over other baseline schemes. Furthermore, deployment on corresponding hardware platforms confirms the solution’s effectiveness and robustness in practical applications.

Recent Monte Carlo denoising methods have achieved impressive progress in generating visually compelling images, which accelerate path-tracing based rendering. However, most of them rely on strong supervision, resulting in a high computational burden for creating paired data and potential overfitting issues. Instead, we present an unsupervised approach based on contrastive disentanglement representation. Specifically, we introduce disentanglement representation combined with cycle-consistency and adversarial loss to factorize noise and content features from a noisy input. Besides, we enforce contrastive learning on abundant samples generated in the backbone to regularize the extracted content distribution, reducing noise features. Moreover, we introduce a multi-scale modulation to refine the representation of auxiliary features, providing better denoising guidance. Experimental results demonstrate that our approach performs favor-ably against existing state-of-the-art unsupervised methods and generates comparable results against supervised models while requiring less inference time.