For detecting anomalies which are unexpected behaviors in complex systems, deep learning-based anomaly detection algorithms for multivariate time series have gained a lot of attention recently. While many anomaly dete...
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ISBN:
(纸本)9783030922313;9783030922306
For detecting anomalies which are unexpected behaviors in complex systems, deep learning-based anomaly detection algorithms for multivariate time series have gained a lot of attention recently. While many anomaly detection algorithms have been widely proposed, there has been no work on how to perform these detection algorithms for multivariate data streams with a stream processing framework. To address this issue, we present a real-time deep learning-based anomaly detection approach for multivariate data streams with Apache Flink. We train a LSTM encoder-decoder model to reconstruct a multivariate input sequence and develop a detection algorithm that uses reconstruction error between the input sequence and the reconstructed sequence. We show that our anomaly detection algorithm can provide promising performance on a real-world dataset. Then, we develop a Flink program by implementing three operators which process and transform multivariate data streams in a specific order. The Flink program outputs anomaly detection results in real time, making system experts can easily receive notices of critical issues and resolve the issues by appropriate actions to maintain the health of the systems.
data selection has proven its merit for improving Neural Machine Translation (NMT), when applied to authentic data. But the benefit of using synthetic data in NMT training, produced by the popular back-translation tec...
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Voice conversion (VC) can be achieved by first extracting source content information and target speaker information, and then reconstructing waveform with these information. However, current approaches normally either...
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Machine learning is an important research area belonging to artificial intelligence technology. Machine learning has potent dataprocessing and prediction ability. This paper uses machine learning to build a data anal...
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Next point-of-interest (POI) recommendation aims to predict the next destination for users. In the past, most POI recommendation models were based on the user's historical check-in trajectory to achieve recommenda...
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ISBN:
(纸本)9783031301070;9783031301087
Next point-of-interest (POI) recommendation aims to predict the next destination for users. In the past, most POI recommendation models were based on the user's historical check-in trajectory to achieve recommendations. However, when these models are trained with sparse historical trajectory data, the learned user's sequence patterns are unstable, which is difficult to obtain good recommendations. In view of the above problem, we propose the next POI recommendation approach that combines neighbor information with location popularity to alleviate the sparsity of data. Specifically, we construct User-POI graph and POI-POI graph, and use graph neural networks (GNN) to capture neighbor information of effective users on these two graphs. In addition, considering that location popularity is influenced by different times and distances, we design a dynamic method to measure the impact of location popularity on the user's check-in preferences. In evaluating the experimental performance of two real-world datasets, our approach outperforms several classical next POI recommendation approaches.
The Eastern Mediterranean, Middle East, and North Africa (EMMENA) regions are rich in Cultural Heritage (CH) sites that have been subject to various threats, including conflicts, natural disasters, and urban developme...
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In this paper, we introduce GBC, an advanced framework for transforming low-resolution, monochrome video sequences into high-resolution, colorized, and geometrically accurate 3D models. GBC combines Bidirectional Opti...
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Adverse drug reactions (ADR) have become a common and serious problem faced by drug users worldwide, posing a significant threat to human life and health safety. How to achieve automatic evaluation of the quality of A...
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ISBN:
(纸本)9798350326970
Adverse drug reactions (ADR) have become a common and serious problem faced by drug users worldwide, posing a significant threat to human life and health safety. How to achieve automatic evaluation of the quality of ADR reports, and how to mine and evaluate the relevance between drugs and adverse drug reactions, has become an urgent problem that needs to be solved at present. In this study, a text classification technology based on deep learning were employed to establish an automated system to evaluate the information quality and relevance of ADR reports, using ADR reports from cooperative medical institutions and case studies in the literature as samples. The ERNIE+DCGNN model was used to train the ADR relevance evaluation model, and its effects were compared with other main-stream models. Comparative experimental results demonstrated that the ADR relevance evaluation model constructed in this paper had better experimental results.
Reversible data hiding in encrypted images (RDHEI) has become a hot topic, and a lot of methods are proposed to optimize this technology. However, these methods do not make full use of image spatial correlation, which...
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Leak detection is important to enable automatic and early identification of leakages in water distribution systems, which may prevent water wastage, reduce the environmental impact of leakages, and also avoid structur...
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