Automatic sleep staging plays a vital role in assessing sleep quality and diagnosing sleep disorders. Most existing methods rely heavily on long and continuous EEG recordings, which poses significant challenges for data acquisition in resource-constrained systems, such as wearable or home-based monitoring systems. In this paper, we propose the task of resource-efficient sleep staging, which aims to reduce the amount of signal collected per sleep epoch while maintaining reliable classification performance. To solve this task, we adopt the masking and prompt learning strategy and propose a novel framework called Mask-Aware Sleep Staging (MASS). Specifically, we design a multi-level masking strategy to promote effective feature modeling under partial and irregular observations. To mitigate the loss of contextual information introduced by masking, we further propose a hierarchical prompt learning mechanism that aggregates unmasked data into a global prompt, serving as a semantic anchor for guiding both patch-level and epoch-level feature modeling. MASS is evalutaed on four datasets, demonstrating state-of-the-art performance, especially when the amount of data is very limited. This result highlights its potential for efficient and scalable deployment in real-world low-resource sleep monitoring environments.

The proliferation of hate speech on social media presents an escalating threat to both public discourse and individual mental well-being. Traditional strategies that prioritize detection and removal often neglect to engage directly with hate speakers or address the underlying causes of their hostility. This article proposesHateMediator, a dialogue-based intervention framework that fine-tunes large language models (LLMs) to generate persuasive, context-aware counter-hate speech. The framework emphasizes two core aspects: the generation of effective counter-hate responses and their evaluation through multiturn dialogues. Our fine-tuning approach integrates tutorial-based learning with critical token guidance, enabling LLMs to recognize and reproduce strategic rhetorical patterns observed in expert interventions. To support training and evaluation, we introduce theMedHatedataset, grounded in social science theory, comprising complete dialogue records from 85 real-world hate incidents (including 255 dialogues), expert-crafted counter-responses, and feedback from the original hate speakers. Experimental results show thatHateMediatorconsistently outperforms baseline LLMs across multiple evaluation dimensions. This study advances both the technical frontier of hate speech intervention and the ethical deployment of LLMs in addressing complex social issues.

Sleep staging is a crucial method for the evaluation of sleep quality and the diagnosis of sleep disorders. In recent years, rapid progress has been made in sleep research through the application of fabric computing and neural networks. Flexible fabric sensors introduced by fabric computing minimize the discomfort of data collection devices on individuals, while neural network-based algorithms can automatically perform sleep staging based on the collected signals. However, there are two key challenges hinder the integration of automatic sleep staging networks with fabric computing: (1) signals in fabric-based environments exhibit strong heterogeneity due to the wide range of individuals, and (2) interactions between individuals and the fabric space introduce behavioral dynamics to the system. In this paper, we propose a masked autoencoder-based sleep staging neural networks (MAESleepNet), designed to integrate automatic sleep staging algorithm with fabric space. Specifically, MAESleepNet addresses the challenge of signal heterogeneity by learning fine-grained representations from local signals. Furthermore, MAESleepNet tackle the challenge of behavioral dynamics through stochastic masking and reconstruction pre-training. Experiments were conducted on three public datasets: (1) Sleep-EDF-20, (2) Sleep-EDF-78 and (3) SHHS. MAESleepNet achieves overall accuracies of 88.9%, 85.5%, and 87.3%, respectively, outperforming other state-of-the-art models. Furthermore, feature visualization and reconstruction visualization experiments were also conducted. The results demonstrates that MAESleepNet is an effective solution to the aforementioned challenges, paving the way for seamless integration into the fabric space.

A multi-tenant metaverse enables multiple users in a common virtual world to interact with each other online. Information sharing will occur when interactions between a user and the environment are multicast to other users by an interactive metaverse (IM) service. However, ineffective information-sharing strategies intensify competitions among users for limited resources in networks, and fail to interpret optimization intent prompts conveyed in high-level natural languages, ultimately diminishing user immersion. In this paper, we explore reliable information sharing in a multi-tenant metaverse with time-varying resource capacities and costs, where IM services are unreliable and alter the volumes of data processed by them, while the service provider dynamically adjusts global intent to minimize multicast delays and costs. To this end, we first formulate the information sharing problem as a Markov decision process and show its NP-hardness. Then, we propose a learning-based system GTP, which combines the proximal policy optimization reinforcement learning with feature extraction networks, including graph attention network and gated recurrent unit, and a Transformer encoder for multi-feature comparison to process a sequence of incoming multicast requests without the knowledge of future arrival information. The GTP operates through three modules: a deployer that allocates primary and backup IM services across the network to minimize a weighted goal of server computation costs and communication distances between users and services, an intent extractor that dynamically infers provider intent conveyed in natural language, and a router that constructs on-demand multicast routing trees adhering to users, the provider, and network constraints. We finally conduct theoretical and empirical analysis on the proposed algorithms for the system. Experimental results show that the proposed algorithms are promising, and superior to their comparison baseline algorithms.

Human digital twin bridges humans with digital avatars in the fabric metaverse, assisting users and healthcare professionals with real-time visualization, analysis, and prediction of personal data sensed by fabric sensors. The human digital twin-assisted healthcare monitoring (HHM) service refreshment refers to sending personal health data to corresponding services hosted on nearby edge servers and receiving the results to update local digital avatars continuously. However, the malicious nature and resource limitations of edge servers may lead to user privacy leaks and refreshment timeout, thereby impacting diagnostics. In this paper, we investigate a novel privacy-enhanced HHM service refreshment maximization problem in the fabric metaverse by considering privacy data encryption, model compression, and personalized user requirements. To this end, we first formulate the above issue as an Integer Linear Programming (ILP) problem, and prove its NP-hardness. Then, a resource scheduler named Wiper is designed, consisting of a shallow-deep distiller and an agile refresher library. To enable efficient inference while preserving user privacy, the former replaces violation modules in existing models with approximations and conducts shallow distillation on model layers to meet operation type and depth limits of homomorphic encryption, and then deep distillation on model parameters to decrease end-to-end refreshment delay. Finally, to satisfy user requirements on accuracy and delay during encrypted refreshments while maximizing the throughput of HHM services in offline and online situations with different problem scales, a series of HHM service refreshment algorithms are merged into the latter, including exact, performance-guaranteed approximation, and residual diffusion reinforcement learning algorithms. Theoretical analyses and experiments demonstrate that our algorithms are promising compared with baseline algorithms.