DASECount: Domain-Agnostic Sample-Efficient Wireless Indoor Crowd Counting via Few-shot Learning

no code implementations • 18 Nov 2022 • Huawei Hou, Suzhi Bi, Lili Zheng, Xiaohui Lin, Yuan Wu, Zhi Quan

In this paper, we propose a Domain-Agnostic and Sample-Efficient wireless indoor crowd Counting (DASECount) framework that suffices to attain robust cross-domain detection accuracy given very limited data samples in new domains.

Crowd Counting Few-Shot Learning +1

Molecular distance matrix prediction based on graph convolutional networks

no code implementations • 1 Jul 2021 • Xiaohui Lin, Yongquan Jiang, Yan Yang

In order to indicate the effect of DMGCN model, the model is compared with the model DeeperGCN-DAGNN and the method of calculating molecular conformation in RDKit.

Detection of Insider Attacks in Distributed Projected Subgradient Algorithms

no code implementations • 18 Jan 2021 • Sissi Xiaoxiao Wu, Gangqiang Li, Shengli Zhang, Xiaohui Lin

Simulation results demonstrate that the proposed AI-based methods are beneficial to improve performance of detecting and localizing malicious agents over score-based methods, and the peer-to-peer neural network model is indeed robust to target issues.

Federated Learning