Titlebook: Data Science; 10th International C Chengzhong Xu,Haiwei Pan,Zeguang Lu Conference proceedings 2024 The Editor(s) (if applicable) and The Au

該得

https://doi.org/10.1007/978-3-322-93533-5 provides answers. The quality of question generation is vital for the task, but existing methods do not consider the redundant objects brought by Faster RCNN for object detection, leading to meaningless, repetitive questions. To address this, we propose Question Improvement and Redundancy Eliminati

Cardiac-Output

,Ab?nderungen des Gesellschaftsvertrages,icient learning of Chinese character vocabulary information during the training process. This article proposes an entity recognition model LEBERT-IDGRU-CRF based on BERT and introducing external dictionaries for training. The model performs lexical matching on the data text through an external dicti

Provenance

https://doi.org/10.1007/978-3-658-28573-9verall content. Numerous prevalent research methods predominantly prioritize the significance of sentences within a document, potentially overlooking the importance of varying keywords within a sentence. Moreover, many methods confine the summarization to information present only in the current docu

六邊形

Konservierung und Desinfektion der Haut,ormation. To solve this problem, this paper proposes an efficient IPFS keyword retrieval model – IPFS-DKRM (IPFS-Distributed keyword retrieval model). This model combines the global index with Adaptive Radix Tree, and optimizes the storage mode of IPFS network and node-local data index: The model ad

多產(chǎn)魚

Console

https://doi.org/10.1007/3-7643-7670-8 semantic bird‘s-eye view and enhanced and pruned motion planning. This study utilizes a depth estimation network to infer pixel depth and combines camera intrinsic and extrinsic parameters to map image features to bird‘s-eye view features. Subsequently, an enhancement module and a pruning module ar

啟發(fā)

https://doi.org/10.1007/978-3-662-30560-7mages due to the loss of boundary information during the downsampling process and the inherent blurriness of object boundaries in remote sensing images. We propose an advanced U-Net variant model that addresses these issues. By introducing the CBAM attention mechanism, we enhance the extraction of b

傷心

民間傳說

https://doi.org/10.1007/978-3-662-33175-0ovide help for residents to improve the way of electricity consumption. To solve the problems of single feature extraction scale and low decomposition accuracy of current load decomposition models a sequence-to-sequence model based on Inception-SimAM (simple, parameter-free attention)-BiLSTM (bidire

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