Effective EEG classification plays a crucial role in brain-computer interface (BCI) research, which helps to accurately identify various neural patterns. Traditional EEG decoding methods have primarily focused on individual brain analysis, neglecting the importance of interactions among multiple brains. With the advancement of hyperscanning techniques, researchers have paid increasing attention to inter-brain interactions in collaborative tasks and their impact on neural pattern recognition. We propose a mamba-inspired dual-brain interaction network (MDI-Net), which is an EEG classification framework specifically designed for dyadic interaction scenarios. MDI-Net consists of three modules: temporal-spatial feature extraction (TSFE) module, mamba-inspired feature interaction (MIFI) module, and feature fusion enhancement (FFE) module. EEG signals are firstly sent to the TSFE module to extract spatio-temporal specific features of individual brains. Then the MIFI module is employed to capture long-range dependencies in EEG and obtain high-level features of inter-brain interactions. Furthermore, the features from paired brains are fused by the FFE module to enhance the representational capability. Comprehensive experiments and evaluations were conducted on two public datasets, and the results demonstrate that our proposed MDI-Net model outperforms both traditional and state-of-the-art methods.

Modeling continuous-time dynamics constitutes a foundational challenge, and uncovering inter-component correlations within complex systems holds promise for enhancing the efficacy of dynamic modeling. The prevailing approach of integrating graph neural networks with ordinary differential equations has demonstrated promising performance. However, they disregard the crucial signed information potential on graphs, impeding their capacity to accurately capture real-world phenomena and leading to subpar outcomes. In response, we introduce a novel approach: a signed graph neural ordinary differential equation, adeptly addressing the limitations of miscapturing signed information. Our proposed solution boasts both flexibility and efficiency. To substantiate its effectiveness, we seamlessly integrate our devised strategies into three preeminent graph-based dynamic modeling frameworks: graph neural ordinary differential equations, graph neural controlled differential equations, and graph recurrent neural networks. Rigorous assessments encompass three intricate dynamic scenarios from physics and biology, as well as scrutiny across four authentic real-world traffic datasets. Remarkably outperforming the trio of baselines, empirical results underscore the substantial performance enhancements facilitated by our proposed approach. Our code can be found at https://github.com/beautyonce/SGODE.

Anomaly detection in multivariate time series has emerged as a critical challenge in the time series research community with significant application potentials in various scenarios, ranging from fault diagnosis to system state estimation in Industrial Control Systems (ICSs). Meanwhile, the demand for high availability and extensibility of ICSs necessitates their deployment in the JointCloud environment. Therefore, the performance of the multivariate time series anomaly detection model is expected to be enhanced in the JointCloud environment when encountering dynamic network conditions among multiple clouds. Impressed by the effectiveness of diffusion models in anomaly detection, we have chosen diffusion models for empowering our anomaly detection model. Specifically, we propose Progressively Distilled Diffusion Anomaly Detection model (ProDiffAD) in the JointCloud environment to seek for the balance between effectiveness and efficiency. Moreover, our proposed model is capable of being adaptive with the dynamic network conditions in the JointCloud environment by modeling the intercloud network conditions. To validate the effectiveness and efficiency of our model, comprehensive experiments are conducted on two real and five synthetic datasets. The experimental results demonstrate that our proposed model achieves more accurate and faster multivariate time series anomaly detection in the JointCloud environment under dynamic network conditions compared to state-of-the-art models.

In the Internet of Vehicles (IoV), the classic TCP/IP still plays an important role for data transmission, traffic control and address assignment. However, with increasing requirements on content retrieve efficiency in IoV, the drawbacks of traditional TCP/IP stacks, such as weak scalability in large networks, low efficiency in dense environment and unreliable addressing in high mobility circumstance, have incurred significant performance degradations in vehicular environments. Fortunately, the emerging Named Data Network (NDN) technology provides a good choice to address above issues in vehicular environment by proving content caching capability with introduced content store module, and boosts the research activity of Vehicular Named Data Network (VNDN) in the last few years. In this paper, to improve the service performance, e.g., reducing the delay of data acquisition, a data caching scheme is proposed by taking the spatial-temporal characteristics of data into account. At first, we divided the data in a VNDN into emergency safety message, traffic efficiency message and service message, according to the application requirements. Then, we analyze the spatial-temporal characteristics of these three message categories and design the caching strategy according to these characteristics. Experimental results from NDNSim platform show that our designed scheme has an approximately 50% performance enhancement compared with Leave Copy Everywhere (LCE), Pro(0.7), and Pro(0.2) data caching protocols in terms of average hit rate, average hop count and average cache replacement times, which verifies the reliability and effectiveness of our proposed data caching scheme.

Detecting driver’s statuses is favorable for reducing the incidence of traffic accidents and ensuring driving security. This paper aims to develop an efficient system for driving stress detection under real driving circumstances. Multiple physiological signals, i.e., electrocardiogram (ECG), galvanic skin response (GSR), and respiration (RESP) were collected and multi-modal features were extracted from time, spectral, and wavelet domains. The proposed approaches are motivated by three points: 1) Obvious individual difference affects the transferability of trained models to a new drive. Then, through dissimilarity evaluation and hierarchical clustering, we searched for subgroups of drives that presented relatively consistent feature distributions. Performing cross-drive modeling within each subgroup enables us to identify driver statuses more precisely with less computation cost; 2) fusing the high-dimensional physiological data from multiple views is beneficial to achieve a reliable assessment but brings new challenges for existing techniques. We adopted Multi-view Intact Space Learning (MISL) to integrate rich information from multiple perspectives by constructing a latent intact representation of the data; 3) most of the existing systems are offline. The current study made both offline and online analysis to validate the effectiveness of this research. Experimental results reveal that the proposed approaches can achieve competitive performance to state-of-the-art methods and can be developed into intelligent in-vehicle systems to detect driver’s unfavorable statuses, better adjust their negative affection, and avoid traffic accidents.