Titlebook: Collaborative Computing: Networking, Applications and Worksharing; 19th EAI Internation Honghao Gao,Xinheng Wang,Nikolaos Voros Conference

LAST

https://doi.org/10.1007/978-3-031-57009-4ry physical objects that can be independently addressed can be interconnected. In the face of the IoT produces a large of time series data, which is very necessary to detect anomaly data. Transformer has proven to be a powerful tool in several areas, but still has some limitations, such as the predi

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Flatter

Heike Pantelmann,Sabine Blackmore of main memory (DRAM). Fortunately, a promising solution has emerged in the form of hybrid memory systems (HMS) which combine DRAM and persistent memory (PMEM) to enable data-centric graph computing. However, directly transitioning existing DRAM-based models to HMS can lead to inefficiency issues,

ASSET

Heike Pantelmann,Sabine Blackmorere events is to understand historical trends and extract the information most likely to affect the future, i.e., the TKG reasoning task is both influenced by the trends of time-evolving graphs and directly driven by the facts relevant to a specific query. Existing methods mostly build models separat

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https://doi.org/10.1007/978-3-658-40467-3 naturally represented as graphs, Graph Neural Networks (GNNs) have proven highly effective for learning graph representations of source code. Pooling, as an essential operation for GNN-based models, is limited in its ability to leverage the rich hierarchical information presented in tree-like graph

Cholecystokinin

清真寺

Myocyte

Task Offloading in UAV-to-Cell MEC Networks: Cell Clustering and Path PlanningD position of the UAV is determined according to the quality of user service, and the double deep Q-network (DDQN) algorithm is used to determine the trajectory of the UAV. Simulation experiments demonstrate the effectiveness and efficiency of our proposed strategy by comparing it with the baselines

臭了生气

LAMB: Label-Induced Mixed-Level Blending for Multimodal Multi-label Emotion Detectionerent labels to attend to the most relevant blended tokens adaptively using a transformer-based decoder, which facilitates the exploration of label-to-modality dependency. Unlike common low-order strategies in multi-label learning, correlations among multiple labels can be learned by self-attention

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