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Time series anomaly detection is instrumental in maintaining system availability in various domains. Current work in this research line mainly focuses on learning data normality deeply and comprehensively by devising advanced neural network structures and new reconstruction/prediction learning objectives. However, their one-class learning process can be misled by latent anomalies in training data (i.e., anomaly contamination) under the unsupervised paradigm. Their learning process also lacks knowledge about the anomalies. Consequently, they often learn a biased, inaccurate normality boundary. To tackle these problems, this paper proposes calibrated one-class classification for anomaly detection, realizing contamination-tolerant, anomaly-informed learning of data normality via uncertainty modeling-based calibration and native anomaly-based calibration. Specifically, our approach adaptively penalizes uncertain predictions to restrain irregular samples in anomaly contamination during optimization, while simultaneously encouraging confident predictions on regular samples to ensure effective normality learning. This largely alleviates the negative impact of anomaly contamination. Our approach also creates native anomaly examples via perturbation to simulate time series abnormal behaviors. Through discriminating these dummy anomalies, our one-class learning is further calibrated to form a more precise normality boundary. Extensive experiments on ten real-world datasets show that our model achieves substantial improvement over sixteen state-of-the-art contenders.

Graph neural networks (GNN) have been proven to be mature enough for handling graph-structured data on node-level graph representation learning tasks. However, the graph pooling technique for learning expressive graph-level representation is critical yet still challenging. Existing pooling methods either struggle to capture the local substructure or fail to effectively utilize high-order dependency, thus diminishing the expression capability. In this paper we propose HAP, a hierarchical graph-level representation learning framework, which is adaptively sensitive to graph structures, i.e., HAP clusters local substructures incorporating with high-order dependencies. HAP utilizes a novel cross-level attention mechanism MOA to naturally focus more on close neighborhood while effectively capture higher-order dependency that may contain crucial information. It also learns a global graph content GCont that extracts the graph pattern properties to make the pre- and post-coarsening graph content maintain stable, thus providing global guidance in graph coarsening. This novel innovation also facilitates generalization across graphs with the same form of features. Extensive experiments on ten datasets show that HAP significantly outperforms twelve popular graph pooling methods on graph classification task with an maximum accuracy improvement of 20.18%, and exceeds the performance of state-of-the-art graph matching and graph similarity learning algorithms by over 3.42% and 16%.

Edge Intelligence (EI) offers an attractive approach for local AI processing at the network edge for privacy protection and reduced transmission, but deploying resource-intensive neural networks on edge devices remains a challenge. The neural architecture search (NAS) technique, known for its automation and minimal manual intervention, serves as a pivotal tool for EI. However, existing methods typically concentrate on optimizing resource consumption for specific hardware, leading to hardware-specific neural architectures with limited generalizability. In response, we propose OnceNAS, a novel method that designs and optimizes on-device inference neural networks for resource-constrained edge devices. OnceNAS simultaneously optimizes for parameter count and inference latency in addition to inference accuracy, producing lightweight neural networks while maintaining their inference performance. Meanwhile, we introduce an efficient evaluation strategy that can simultaneously assess multiple metrics. Experimental results demonstrate the effectiveness of OnceNAS, achieving high-performing architectures with substantial size reduction (10.49x) and speedup (5.45x). As a result, OnceNAS offers practical value by generating efficient on-device inference neural architectures for resource-constrained edge devices, facilitating real-world applications like autonomous driving and smart healthcare. Furthermore, we contribute DARTS-Bench, an open-source dataset providing candidate architectures with hardware-related information and a user-friendly API, facilitating future research in lightweight NAS. • Carefully Selected Optimization Objectives: We choose hardware-agnostic objectives to control the model complexity. • Efficient Neural Architecture Search Strategy: We introduce a novel NAS method for resource-constrained edge devices. • Accurate Architecture Performance Evaluation: We propose an efficient neural architecture performance evaluation method. • Dataset Contribution: We contribute DARTS-Bench, including corresponding runtime information and API functions. [ABSTRACT FROM AUTHOR]