Linkable and traceable anonymous authentication with fine-grained access control
To prevent misuse of privacy, numerous anonymous authentication schemes with linkability and/or traceability have been proposed to ensure different types of accountabilities. Previous schemes cannot simultaneously achieve public linking and ...
Nonconvex and discriminative transfer subspace learning for unsupervised domain adaptation
Unsupervised transfer subspace learning is one of the challenging and important topics in domain adaptation, which aims to classify unlabeled target data by using source domain information. The traditional transfer subspace learning methods often ...
Integrating element correlation with prompt-based spatial relation extraction
Spatial relations in text refer to how a geographical entity is located in space in relation to a reference entity. Extracting spatial relations from text is a fundamental task in natural language understanding. Previous studies have only focused ...
A comprehensive survey on graph neural network accelerators
Deep learning has gained superior accuracy on Euclidean structure data in neural networks. As a result, non-Euclidean structure data, such as graph data, has more sophisticated structural information, which can be applied in neural networks as ...
Labeling-based centrality approaches for identifying critical edges on temporal graphs
Edge closeness and betweenness centralities are widely used path-based metrics for characterizing the importance of edges in networks. In general graphs, edge closeness centrality indicates the importance of edges by the shortest distances from ...
FPSMix: data augmentation strategy for point cloud classification
Data augmentation is a widely used regularization strategy in deep neural networks to mitigate overfitting and enhance generalization. In the context of point cloud data, mixing two samples to generate new training examples has proven to be ...
Exploring & exploiting high-order graph structure for sparse knowledge graph completion
Sparse Knowledge Graph (KG) scenarios pose a challenge for previous Knowledge Graph Completion (KGC) methods, that is, the completion performance decreases rapidly with the increase of graph sparsity. This problem is also exacerbated because of ...
SSA: semantic structure aware inference on CNN networks for weakly pixel-wise dense predictions without cost
The pixel-wise dense prediction tasks based on weakly supervisions currently use Class Attention Maps (CAMs) to generate pseudo masks as ground-truth. However, existing methods often incorporate trainable modules to expand the immature class ...
Foundation models for topic modeling: a case study
In summary, GPT-4 outperforms the other two LLMs throughout the entire process, primarily due to the limitations of the models’ scale. Meanwhile, Qwen-72B performs considerably better than Qwen-14B and achieves comparable results, albeit slightly ...
