This systematic review examines harassment in virtual reality (VR), synthesizing findings from 85 studies published between 2017 and 2025. We propose a nuanced typology of harassment, encompassing spatial intrusion, sexual and verbal abuse, identity-based discrimination, group-targeted harassment, and systemic harms, and demonstrate how VR’s immersive and embodied features amplify risk and impact. Marginalized users, such as women, LGBTQ+ individuals, children, and people with disabilities, face disproportionate harm. We further analyze the psychological and behavioral consequences of harassment, as well as the effectiveness and limitations of current governance, design, and AI-driven interventions. Our review identifies persistent research gaps in theory, measurement, and inclusive protection, and advocates for ethical, participatory, and preventive approaches to platform safety. This work aims to guide researchers and designers in building more equitable and safe VR environments.

The rapid proliferation of emotion-aware web services has necessitated the analysis of multimodal user interactions. However, this introduces new vulnerabilities where adversaries exploit emotional signals to circumvent fraud detection systems. Despite its improved utility, the robustness of multimodal fraud detection against emotion-driven adversarial manipulation remains significantly underexplored. Existing paradigms often treat emotional cues as static features, overlooking the adversary's capability to strategically modulate multimodal signals (e.g., facial micro-expressions, vocal intonation, and textual styles) to mimic genuine behavior. Furthermore, prevalent evaluations are typically confined to unimodal perturbations and fail to account for context-consistent, cross-modal attacks, thereby compromising system reliability in real-world deployments. To bridge this gap, we propose Context-Emotion Adversarial Training (CEAT), a robust framework designed to fortify multimodal fraud detection against emotion-based attacks. CEAT leverages a Transformer-based architecture to synergistically model emotional features (e.g., visual dynamics and acoustic prosody) alongside semantic context derived from text, yielding a unified representation. Crucially, CEAT introduces a context-aware perturbation mechanism that injects noise into the emotional latent space during training. This process preserves semantic consistency while encouraging the learning of emotion-invariant and discriminative representations. Additionally, a contrastive learning objective is integrated to maximize the distributional divergence between genuine and adversarial samples within the latent manifold. Extensive experiments on multimodal benchmarks demonstrate that CEAT significantly outperforms state-of-the-art baselines, exhibiting superior robustness under simulated emotion-driven attack scenarios.

The diverse applications of internet of things (IoT) have significantly increased the demand for efficient and reliable wireless networks, making power consumption a critical concern. Reconfigurable intelligent surface (RIS) have been proposed as a solution to mitigate power consumption in wireless communication systems by dynamically adjusting the signal propagation direction between transmitters and receivers. Due to the operational status of IoT devices and the complex association relationships between RISs and devices, a dynamic and highly variable communication environment is typically resulted, which renders power consumption optimization more challenging, as compared to conventional methods that do not incorporate RISs. This paper addresses the optimization of power consumption and IoT device coverage rate in an indoor communication scenario to improve system performance. We design an Adaptive Hybrid Optimization Strategy based on the association between RISs and devices to maximize the device coverage rate. Additionally, we optimize the phase shifts of multiple RISs to minimize system power consumption using the relaxation transformative method while satisfying the coverage rate constraint. Extensive simulation results demonstrate that, in an indoor environment with several obstacles, the proposed algorithm achieves a higher device-centric coverage rate compared to a solution without RIS and exhibits lower power consumption compared to strategies that rely more on base stations.

With the rapid advancement of virtual reality (VR) technology, understanding developer discussions is essential for improving software quality and maintenance. This study is the first to systematically investigate how developer concerns across major VR platforms, namely SteamVR, Meta, and HTCVive, align with the ISO/IEC 25010 international software quality standard. We collected and analyzed 392,590 posts from 47,280 developers, using topic modeling and manual coding to map discussions to nine ISO/IEC 25010 quality characteristics. We further examined topic distributions, sentiment trends, and interaction patterns across platforms. Our findings show that developers are most challenged by interaction, compatibility, and functionality issues, emphasizing the need to enhance user experience, enable cross device integration, and maintain system stability. Discussion on performance has decreased, signaling a shift in priorities. Platform specific challenges also emerged, highlighting the need for tailored strategies for different VR ecosystems. Based on these insights, we suggest strategies that prioritize optimizing user interaction (e.g., intuitive controls, seamless navigation), strengthening cross platform compatibility (e.g., universal SDKs, shared asset pipelines), and implementing sustainable maintenance practices (e.g., clear codebases, regular updates) to foster a more robust VR software ecosystem.

With the rapid integration of the Internet of Things (IoT) and fifth-generation mobile communications (5G), the massive real-time computing demands generated on the terminal side have exceeded the processing capability of a single edge server (ES). How to efficiently offload computing tasks in parallel to multiple ESs for collaborative execution has emerged as a significant challenge in mobile edge computing (MEC). To address this, we propose a dynamic parallel offloading framework, CoESAC (Collaborative Edge with Soft Actor-Critic). On the one hand, we model the multi-objective offloading problem and the load allocation problem as a high-dimensional discrete decision-making task, demonstrating its intrinsic NP-hard complexity. On the other hand, based on a discrete Soft Actor-Critic (SAC) algorithm in deep reinforcement learning (DRL), the proposed method adopts a task sub-fragmentation and dynamic load adaptation mechanism to flexibly schedule the parallel computing capabilities of multiple ESs. Experimental results show that across diverse system conditions and execution scenarios, CoE-SAC reduces the average make-span by up to 50.16% compared with advanced baselines, and significantly lowers the failure rate by more than 30.73%. These improvements highlight the framework’s strong robustness and superior performance, offering new insights into multi-node collaborative computing under heterogeneous resources and high-concurrency workloads.

The rapid evolution of vehicular edge computing (VEC) poses critical challenges in distributed caching resource management, particularly in reducing content retrieval latency and improving cache utilization efficiency. We propose a community-aware caching framework tailored for VEC scenarios, comprising two main components: a Dynamic Thematic-Community Clustering (DTCC) algorithm based on Collapsed Gibbs sampling and a Potential Game-based Caching Optimization (GCO) strategy. The DTCC algorithm captures the temporal evolution of vehicular social networks, facilitating dynamic community partitioning and topic distribution extraction. Meanwhile, GCO formulates the caching decisions of vehicles and base stations as a non-cooperative game, whose community-aware utility function design guarantees both the existence and convergence of a Nash equilibrium. Extensive experiments on real-world datasets demonstrate that GCO consistently outperforms state-of-the-art baselines across diverse performance metrics, further validating its efficacy compared with existing caching solutions.

The immersive experience and 360-degree visual stimulation offered by virtual reality (VR) have contributed to its widespread adoption in games, education, and healthcare. The quality of experience (QoE), as a significant performance indicator, is used to measure user experience from subjective and objective perspectives and is required to satisfy high data rate, low delay, and high reliability in the wireless VR system. To achieve a higher data rate for VR users, a terahertz (THz) network is deployed. However, THz frequency experiences severe signal attenuation due to complex indoor obstacles, which can be alleviated by utilizing a reconfigurable intelligent surface (RIS) equipped with programmable metamaterial reflective elements. Taking inspiration from these considerations, this paper investigates a new framework for indoor multi-user multi-RIS-assisted THz wireless VR systems. Based on the scenario, an optimization problem is formulated to maximize the QoE by jointly optimizing the passive beamforming at RIS, the transmit power allocation among VR users, and the rendering capacity allocation among virtual objects. To achieve an optimal solution, we decompose the optimization problem into two stages: stage-1 aims to minimize BER and maximize data transmission rate, while stage-2 aims to maximize rendering capacity among virtual objects. Objective function conversion and alternative optimization (AO) methods are employed to address the two problems. Extensive simulations are conducted to validate the feasibility of the proposed system model and to showcase the superior performance of the proposed method in terms of QoE compared to other baseline methods.

Recently, technological development in the creation of programmable metamaterial has aided progress in the development of the Reconfigurable Intelligent Surface (RIS), which has been considered one of the fundamental technologies for future wireless communication systems. In this paper, we investigate the problem of maximizing the average achievable rate for multiple users indoor wireless communication environment assisted by multiple RISs. Unlike most existing works considering single-RIS single-user scenarios or single-RIS multi-user scenarios, the multi-RIS can reflect the signals from several transmission links in all destinations. The average achievable rate maximization problem for indoor communication systems is solved by optimizing the phase shifts of reflective elements. Thus, we propose a Multi-verse Optimizer approach to solve the problem. Our simulation results demonstrate that a communication system with multiple RISs provides considerable achievable rate gains relative to baseline schemes.