Electrocardiogram(ECG) signal analysis is crucial in healthcare service computing. Ensuring accurate assessment of ECG signal quality is vital to prevent wastage of transmission bandwidth and ineffective analysis caused by noise. This enables the efficient utilization of service resources. However, existing ECG signal quality assessment(SQA) methods primarily focus on single-modal learning, overlooking the interrelation of ECG in a multimodal feature space and failing to effectively exploit available information for pattern mining. In this paper, we model the SQA for ECG as an anomaly detection problem and propose a multimodal unsupervised SQA method. It jointly explores the boundaries between high-quality ECG and noise in both the time and frequency domains by introducing time-frequency pseudo anomalies. Specifically, we first simulate real ECG noise from the time-domain using a combination of a series of noises and convert it to the frequency-domain to form time-frequency pseudo-anomalies. Next, we map the time-frequency pseudo anomalies onto hyperspheres and jointly refine the hyperspheres learned only from high-quality ECG samples in both feature spaces. Finally, the noise score is defined as the distance from the joint time-frequency features to the center of the hypersphere. Multiple experiments on various real-world ECG datasets validate the superior performance of our proposed method.

Cyber-Physical System needs high security to ensure the safe operation. Anomaly detection is one of the mainstream security technologies, the core of which is data analysis and learning. Unsupervised Deep-Learning-based Anomaly Detection Methods can be used in the scenarios that collects large amounts of unlabeled data and are more in line with the actual needs of CPS. However, the correlation among data did not attract enough attention to exploring their implicit relationship, and the adaptive training was deficient. Therefore, we propose an Adaptive-Correlation-aware Unsupervised Deep Learning (ACUDL) for anomaly detection in CPS. It constructs a directed graph structure to represent the implicit correlation among data and adaptively updates with dynamic graph; then, designs a dual-autoencoder to extract the original non-correlation, correlation, and reconstruction features, and builds an estimation network using the Gaussian mixture model (GMM) to estimate the anomaly energy. Experimental results on several CPS data scenarios show that ACUDL can be well adapted to many application scenarios with different data characteristics and achieves better overall results than some up-to-date DL-ADMs.

Capturing the complex interaction between the node attribute and the network structure is important for attributed network embedding and anomaly detection. However, there are few methods to explicitly model the correlation between these two views of the node attribute and the network structure. In this article, we propose an attributed network anomaly detection (CaCo) method based on the canonical correlation analysis, which assumes that there should be a strong correlation between the attribute and structure features of normal nodes, and a weak correlation one between those abnormal nodes, in the attributed networks. Consequently, a joint learning mechanism is designed in CaCo to explicitly measure the correlation between two views in the latent space. Specifically, the backbone of a weight-sharing graph convolutional network is employed to encode the node feature from two views of attribute and structure in the latent space, respectively. Then, a Kullback–Leibler divergence regularization is used to align the distributions of the two views. Finally, the parameters of CaCo are optimized by maximizing the correlation between attribute and structure features of normal nodes in the training phase, and anomalies can be detected by measuring the correlation between two views in the testing phase. Extensive experiments on six real-world datasets demonstrate the effectiveness of the proposed method compared to the state-of-the-art techniques.

Semi-supervised learning (SSL) has proven to be a powerful algorithm in different domains by leveraging unlabeled data to mitigate the reliance on the tremendous annotated data. However, few efforts consider the underlying temporal relation structure of unlabeled time series data in the semi-supervised learning paradigm. In this work, we propose a simple and effective method of Semi-supervised Time series classification architecture (termed as SemiTime) by gaining from the structure of unlabeled data in a self-supervised manner. Specifically, for the labeled time series, SemiTime conducts the supervised classification directly under the supervision of the annotated class label. For the unlabeled time series, the segments of past-future pair are sampled from time series, where two segments of pair from the same time series candidate are in positive temporal relation, while two segments from the different candidates are in negative temporal relation. Then, the temporal relation between those segments is predicted by SemiTime in a self-supervised manner. Finally, by jointly classifying labeled data and predicting the temporal relation of unlabeled data, the useful representation of unlabeled time series can be captured by SemiTime. Extensive experiments on multiple real-world datasets show that SemiTime consistently out-performs the state-of-the-arts, which demonstrates the effectiveness of the proposed method. Code and data are publicly available at https://haoyfan.github.io.