In ultrasound elastography, the noise present in the measured displacement fields has been a critical factor affecting the quality of the strain or elastic distribution reconstruction. Existing partial differential eq...
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Federated Learning (FL) is a distributed privacy-protecting machine learning paradigm that enables collaborative training among multiple parties without the need to share raw data. This mode of training renders FL par...
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We propose an unsupervised insider threat detection system that learns normal user behaviors through audit data using neural networks equipped with multi-head self-attention mechanisms. The attention mechanisms learn ...
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Amidst the rapid advancements in artificial intelligence technology, it is imperative to apply these technological developments to the realm of education to enhance information-based teaching methodologies. This artic...
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The complexity of apps running on cloud platforms is evident in their nature. Every application has distinct needs for processing power and memory at various times. In order to effectively cater to tenants' varied...
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In the education sector, an increasing amount of research is beginning to explore the application of blockchain technology to credit banks. This paper proposes a consortium blockchain consensus mechanism tailored for ...
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Fatigue driving is one of the main causes of traffic accidents. Effective fatigue driving detection technology can reduce traffic accidents caused by fatigue driving. Traditional fatigue driving detection methods usua...
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software-defined networking (SDN) is transforming network management, yet it grapples with performance bottlenecks in large-scale deployments. Multi-controller solutions have been proposed to address this issue. Howev...
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With the rapid development of Internet of Things(IoT)technology,IoT systems have been widely applied in health-care,transportation,home,and other ***,with the continuous expansion of the scale and increasing complexit...
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With the rapid development of Internet of Things(IoT)technology,IoT systems have been widely applied in health-care,transportation,home,and other ***,with the continuous expansion of the scale and increasing complexity of IoT systems,the stability and security issues of IoT systems have become increasingly ***,it is crucial to detect anomalies in the collected IoT time series from various ***,deep learning models have been leveraged for IoT anomaly ***,owing to the challenges associated with data labeling,most IoT anomaly detection methods resort to unsupervised learning ***,the absence of accurate abnormal information in unsupervised learning methods limits their *** address these problems,we propose AS-GCN-MTM,an adaptive structural Graph Convolutional Networks(GCN)-based framework using a mean-teacher mechanism(AS-GCN-MTM)for anomaly *** performs better than unsupervised methods using only a small amount of labeled *** Teachers is an effective semi-supervised learning method that utilizes unlabeled data for training to improve the generalization ability and performance of the ***,the dependencies between data are often unknown in time series *** solve this problem,we designed a graph structure adaptive learning layer based on neural networks,which can automatically learn the graph structure from time series *** not only better captures the relationships between nodes but also enhances the model’s performance by augmenting key *** have demonstrated that our method improves the baseline model with the highest F1 value by 10.4%,36.1%,and 5.6%,respectively,on three real datasets with a 10%data labeling rate.
Federated Learning (FL) offers significant advancements in user/data privacy, learning quality, model efficiency, scalability, and network communication latency. However, it faces notable security challenges, particul...
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