PROBLEM Recent years have witnessed the rapid progress of self-supervised language models (LMs)[1],especially large language models (LLMs)[2].LLMs not only achieved state-of-the-art performance on many natural languag...
PROBLEM Recent years have witnessed the rapid progress of self-supervised language models (LMs)[1],especially large language models (LLMs)[2].LLMs not only achieved state-of-the-art performance on many natural language processing tasks,but also captured widespread attention from the public due to their great potential in a variety of real-world applications (***,search engines,writing assistants,etc.)through providing general-purpose intelligent services.A few of the LLMs are becoming foundation models,an analogy to infrastructure,that empower hundreds of downstream applications.
Infrared target detection is now applied in many fields, such as medical imaging, military detection, autonomous driving, and environmental monitoring with drones. Due to the small size of these targets, complex envir...
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In multi-institutional patient data sharing scenarios, maintaining fine-grained access control while safeguarding privacy and adapting to real-world environments is crucial. Traditional attribute-based encryption (ABE...
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Flood forecasting methods based on deep learning rely on a large number of observational data, and are facing serious challenges in areas with scarce data. Aiming at the problems of flood inundated range prediction in...
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Originally presented in previous work to capture the set of fundamental elements of the UML state machine specification, Common Declarative Language (CDL) provides a model that can aid in the validation and verificati...
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This study provides an innovative architectural model for e-Health systems that aims to improve cyber resilience while maintaining high availability under fluctuating traffic loads. We examined typical cybersecurity i...
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With the rapid development of intelligent systems, Multi-Agent Systems (MAS) have shown unique advantages in solving complex decision-making problems. Particularly in the field of Multi-Agent Reinforcement Learning (M...
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Hyperspectral images (HSIs) provide rich spectral information, but acquiring high-resolution data is costly and challenging, making spectral super-resolution essential. Inspired by the near-linear efficiency of state ...
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Time series anomaly detection is crucial in various industrial applications to identify unusual behaviors within the time series *** to the challenges associated with annotating anomaly events,time series reconstructi...
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Time series anomaly detection is crucial in various industrial applications to identify unusual behaviors within the time series *** to the challenges associated with annotating anomaly events,time series reconstruction has become a prevalent approach for unsupervised anomaly ***,effectively learning representations and achieving accurate detection results remain challenging due to the intricate temporal patterns and dependencies in real-world time *** this paper,we propose a cross-dimension attentive feature fusion network for time series anomaly detection,referred to as ***,a series and feature mixing block is introduced to learn representations in 1D ***,a fast Fourier transform is employed to convert the time series into 2D space,providing the capability for 2D feature ***,a cross-dimension attentive feature fusion mechanism is designed that adaptively integrates features across different dimensions for anomaly *** results on real-world time series datasets demonstrate that CAFFN performs better than other competing methods in time series anomaly detection.
Human Action Recognition (HAR) has widespread applications in areas such as human-computer interaction, elderly care, and home healthcare. However, current sensor-based HAR faces challenges of low fine-grained recogni...
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