Short-term time series forecasting is pivotal in various scientific and industrial fields. Recent advancements in deep learning-based technologies have significantly improved the efficiency and accuracy of short-term time series modeling. Despite advancements, current time short-term series forecasting methods typically emphasize modeling dependencies across time stamps but frequently overlook inter-variable dependencies, which is crucial for multivariate forecasting. We propose a multi patterns memory model discovering various dependency patterns for short-term multivariate time series forecasting to fill the gap. The proposed model is structured around two key components: the short-term memory block and the long-term memory block. These networks are distinctively characterized by their use of asymmetric convolution, each tailored to process the various spatial-temporal dependencies among data. Experimental results show that the proposed model demonstrates competitive performance over the other time series forecasting methods across five benchmark datasets, likely thanks to the asymmetric structure, which can effectively extract the underlying various spatial-temporal dependencies among data.

Pain is an experience of unpleasant sensations and emotions associated with actual or potential tissue damage. In the global context, billions of people are affected by pain disorders. There are particular challenges in the measurement and assessment of pain, and the commonly used pain measuring tools include traditional subjective scoring methods and biomarker-based measures. The main tools for biomarker-based analysis are electroencephalography (EEG), electrocardiography and functional magnetic resonance. The EEG-based quantitative pain measurements are of immense value in clinical pain management and can provide objective assessments of pain intensity. The assessment of pain is now primarily limited to the identification of the presence or absence of pain, with less research on multilevel pain. High power laser stimulation pain experimental paradigm and five pain level classification methods based on EEG data augmentation are presented. First, the EEG features are extracted using modified S-transform, and the time-frequency information of the features is retained. Based on the pain recognition effect, the 20-40[Formula: see text]Hz frequency band features are optimized. Afterwards the Wasserstein generative adversarial network with gradient penalty is used for feature data augmentation. It can be inferred from the good classification performance of features in the parietal region of the brain that the sensory function of the parietal lobe region is effectively activated during the occurrence of pain. By comparing the latest data augmentation methods and classification algorithms, the proposed method has significant advantages for the five-level pain dataset. This research provides new ways of thinking and research methods related to pain recognition, which is essential for the study of neural mechanisms and regulatory mechanisms of pain.

Multivariate time series prediction is a critical problem that is encountered in many fields, and recurrent neural network (RNN)‐based approaches have been widely used to address this problem. However, traditional RNN‐based approaches for predicting multivariate time series are still facing challenges, as time series are often related to each other and historical observations in real‐world applications. To address this limitation, this paper proposes a spatiotemporal self‐attention mechanism‐based LSTNet, which is a multivariate time series forecasting model. The proposed model leverages two self‐attention strategies, spatial and temporal self‐attention, to focus on the most relevant information among time series. The spatial self‐attention is used to discover the dependences between variables, while temporal attention is employed to capture the relationship among historical observations. Moreover, a standard deviation term is added to the objective function to track multivariate time series effectively. To evaluate the proposed method’s performance, extensive experiments are conducted on multiple benchmarked datasets. The experimental results show that the proposed method outperforms several baseline methods significantly. Therefore, the proposed spatiotemporal self‐attention‐based LSTNet is a promising approach for predicting multivariate time series.

Central neuropathic pain (CNP) after spinal cord injury (SCI) is related to the plasticity of cerebral cortex. The plasticity of cortex recorded by electroencephalogram (EEG) signal can be used as a biomarker of CNP. To analyze changes in the brain network mechanism under the combined effect of injury and pain or under the effect of pain, this paper mainly studies the changes of brain network functional connectivity in patients with neuropathic pain and without neuropathic pain after SCI. This paper has recorded the EEG with the CNP group after SCI, without the CNP group after SCI, and a healthy control group. Phase-locking value has been used to construct brain network topological connectivity maps. By comparing the brain networks of the two groups of SCI with the healthy group, it has been found that in the [Formula: see text] and [Formula: see text] frequency bands, the injury increases the functional connectivity between the frontal lobe and occipital lobes, temporal, and parietal of the patients. Furthermore, the comparison of brain networks between the group with CNP and the group without CNP after SCI has found that pain has a greater effect on the increased connectivity within the patients' frontal lobes. Motor imagery (MI) data of CNP patients have been used to extract one-dimensional local binary pattern (1D-LBP) and common spatial pattern (CSP) features, the left and right hand movements of the patients' MI have been classified. The proposed LBP-CSP feature method has achieved the highest accuracy of 98.6% and the average accuracy of 91.5%. The results of this study have great clinical significance for the neural rehabilitation and brain-computer interface of CNP patients.

Blockchain as a new decentralized chain structure can be used in edge computing to solve the security issue caused by edge nodes in model training. However, large amounts of data exchanges in the process of model training of edge computing reduce the performance of blockchain, and meanwhile, the block needed to be saved in the edge node challenges storage capacity of the edge node. Therefore, in the paper we propose a safe and efficient model training mechanism based on onchain and offchain collaboration. In the mechanism, edge nodes train models locally, store the model parameters in offchain and only return identifiers for model aggregation. By the method, the storage pressure of the edge node is reduced and the efficiency of executing consensus algorithms are increased. Moreover, in the mechanism we design a reputation evaluation model based on confidence factors to avoid the uploading of random and wrong data of edge nodes. Evaluation results show that our schemes can reduce the average delay and resources consumption, increase transaction throughput and maintain security compared with a state-of-the-art scheme.

Abstract The electromagnetic situation, which can promote the abilities of understanding and decision‐making for the battlefield, has attracted significant interest recently in information‐based warfare. This paper investigates the deep learning‐based electromagnetic situation analysis and judgment in a complicated battlefield environment. To comprehensively simulate the two‐sided battling process, a turn‐based confrontation strategy is proposed, and an electromagnetic situation analysis and judgment model are then designed based on the AlphaGo Zero algorithm to achieve efficient situation analysis and decision‐making. In addition, an electromagnetic situation‐based attack‐defense platform is developed to realize and evaluate this designed model. Simulation results demonstrate that this designed model achieves significant performance in electromagnetic situation analysis and judgment compared with the Monte Carlo Tree Search based baseline.

Abstract In this paper, we study the problem of authentication of spatio‐textual similarity join queries in untrusted cloud environments. To solve this problem, we first propose a pairwise authentication scheme, where object pairs are considered as basic processing units. In this scheme, an object hash tree is proposed for the data owner (DO) to build the authenticated data structure (ADS) over the data collection. Moreover, during join processing, a pair table is introduced for the service provider (SP) to construct the verification object (VO) for client's authentication. However, when clients authenticate the join results, they have large number of pairs to examine, which leads to excessive computation cost. Therefore, we then propose an optimized MR*‐tree‐based scheme. In this scheme, the DO spatially organizes the objects in an ADS called MR*‐tree, which combines the concepts of the Merkle hash tree and the R*‐tree. Based on the MR*‐tree, the SP employs the depth‐first search strategy to find the join results and constructs the VO according to the visited nodes of the MR*‐tree. Our extensive experiments on the real‐world dataset verify the effectiveness and efficiency of our proposed schemes on several performance metrics, including the data preprocessing time, running time, VO size, and authentication time. Copyright © 2016 John Wiley & Sons, Ltd.