Accurate classification of sleep stages based on bio-signals is fundamental not only for automatic sleep stage annotation, but also for clinical health management and continuous sleep monitoring. Traditionally, this task relies on experienced clinicians to manually annotate data, a process that is both time-consuming and labor-intensive. In recent years, deep learning methods have shown promise in automating this task. However, three major challenges remain: (1) deep learning models typically require large-scale labeled datasets, making them less effective in real-world settings where annotated data is limited; (2) significant inter-individual variability in bio-signals often results in inconsistent model performance when applied to new subjects, limiting generalization; and (3) existing approaches often overlook the high-order relationships among bio-signals, failing to simultaneously capture signal heterogeneity and spatial-temporal dependencies. To address these issues, we propose MetaSTH-Sleep, a few-shot sleep stage classification framework based on spatial-temporal hypergraph enhanced meta-learning. Our approach enables rapid adaptation to new subjects using only a few labeled samples, while the hypergraph structure effectively models complex spatial interconnections and temporal dynamics simultaneously in EEG signals. Experimental results demonstrate that MetaSTH-Sleep achieves substantial performance improvements across diverse subjects, offering valuable insights to support clinicians in sleep stage annotation.

Next Point-of-Interest (POI) recommendation aims to predict users’ next locations by leveraging historical check-in sequences. Although existing methods have shown promising results, they often struggle to capture complex high-order relationships and effectively adapt to diverse user behaviors, particularly when addressing the cold-start issue. To address these challenges, we propose Hypergraph-enhanced Meta-learning Adaptive Network (HyperMAN), a novel framework that integrates heterogeneous hypergraph modeling with a difficulty-aware meta-learning mechanism for next POI recommendation. Specifically, three types of heterogeneous hyperedges are designed to capture high-order relationships: user visit behaviors at specific times (Temporal behavioral hyperedge), spatial correlations among POIs (spatial functional hyperedge), and user long-term preferences (user preference hyperedge). Furthermore, a diversity-aware meta-learning mechanism is introduced to dynamically adjust learning strategies, considering users behavioral diversity. Extensive experiments on real-world datasets demonstrate that HyperMAN achieves superior performance, effectively addressing cold start challenges and significantly enhancing recommendation accuracy.

With the advancement of human society, ensuring the security of images in the Internet of Everything (IoE) has become a critical concern. In this paper, the two-dimensional coupled Logistic, Sine, and Hénon Map systems (2D-CLSHS) are theoretically derived. Comparative experiments demonstrate that the Lyapunov Exponent (LE) reaches 91.1764, Specific Entropy (SE) is 2.061, and the 0-1 test value is 0.998, significantly surpassing other chaotic models proposed in recent years. Additionally, this paper combines Discrete Cosine Transform (DCT) domain steganography with multi-scale pyramid representation, utilizing the 2D-CLSHS system to encrypt secret messages. Furthermore, the integration of reinforcement learning and chaotic encryption is achieved through Bayesian optimization-based reinforcement learning algorithms. Comparative experiments indicate that the Number of Pixel Change Rate (NPCR) is 99.6094% and the Unified Average Changing Intensity (UACI) is 33.4635%. Despite the high bit-per-pixel rate (3BPP), the proposed method maintains a Peak Signal-to-Noise Ratio (PSNR) of 48.5225 and a Structural Similarity Index (SSIM) of 0.9987, outperforming other schemes in recent years. This provides a novel approach to image security in the IoE environment.

Accurate time synchronization is crucial for multisensor fusion, which is widely used in mobile robotics, autonomous driving, and virtual reality. Despite many advancements, precise multi-sensor synchronization is still challenging due to the sensors’ internal characteristics, data filtering, disjointed clock reference, and transmission delay caused by operation system scheduling. This paper proposes a novel hardware-based synchronization solution to achieve synchronization in microsecond-level precision. By introducing a Sensor Adaptor board that provides a unified clock reference, the proposed hardware architecture enables high-precision synchronization across multiple sensors. Furthermore, we develop a method for Visual-Inertial time synchronization that actively controls the exposure duration using an ambient light sensor. By managing the IMU clock signal and exposure trigger, we align the camera’s sampling moment with the authentic IMU sampling time and significantly reduce the time discrepancy in the Visual-Inertial system. Experiments are conducted to evaluate the efficiency of the proposed method and system, including comparisons with previous work. The results indicate that our method can achieve precise time synchronization and be successfully implemented in multi-sensor systems.

Factorization models have been successfully applied to the recommendation problems and have significant impact to both academia and industries in the field of Collaborative Filtering ( CF ). However, the intermediate data generated in factorization models’ decision making process (or training process , footprint ) have been overlooked even though they may provide rich information to further improve recommendations. In this article, we introduce the concept of Convergence Pattern, which records how ratings are learned step-by-step in factorization models in the field of CF. We show that the concept of Convergence Patternexists in both the model perspective (e.g., classical Matrix Factorization ( MF ) and deep-learning factorization) and the training (learning) perspective (e.g., stochastic gradient descent ( SGD ), alternating least squares ( ALS ), and Markov Chain Monte Carlo ( MCMC )). By utilizing the Convergence Pattern, we propose a prediction model to estimate the prediction reliability of missing ratings and then improve the quality of recommendations. Two applications have been investigated: (1) how to evaluate the reliability of predicted missing ratings and thus recommend those ratings with high reliability. (2) How to explore the estimated reliability to adjust the predicted ratings to further improve the predication accuracy. Extensive experiments have been conducted on several benchmark datasets on three recommendation tasks: decision-aware recommendation, rating predicted, and Top- N recommendation. The experiment results have verified the effectiveness of the proposed methods in various aspects.