Zhijun Guo¹, Yun Sun^2,*, Ying Wang¹, Chaoqi Fu³, Jilong Zhong^4,*

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2375-2398, 2024, DOI:10.32604/cmc.2024.048112 - 15 May 2024

Abstract Due to the time-varying topology and possible disturbances in a conflict environment, it is still challenging to maintain the mission performance of flying Ad hoc networks (FANET), which limits the application of Unmanned Aerial Vehicle (UAV) swarms in harsh environments. This paper proposes an intelligent framework to quickly recover the cooperative coverage mission by aggregating the historical spatio-temporal network with the attention mechanism. The mission resilience metric is introduced in conjunction with connectivity and coverage status information to simplify the optimization model. A spatio-temporal node pooling method is proposed to ensure all node location features… More >

Chuiju You¹, Guanjun Lin^1,*, Jinming Qiu¹, Ning Cao¹, Yundong Sun², Russell Higgs³

Computer Systems Science and Engineering, Vol.46, No.2, pp. 1277-1292, 2023, DOI:10.32604/csse.2023.026150 - 09 February 2023

Abstract A sensor graph network is a sensor network model organized according to graph network structure. Structural unit and signal propagation of core nodes are the basic characteristics of sensor graph networks. In sensor networks, network structure recognition is the basis for accurate identification and effective prediction and control of node states. Aiming at the problems of difficult global structure identification and poor interpretability in complex sensor graph networks, based on the characteristics of sensor networks, a method is proposed to firstly unitize the graph network structure and then expand the unit based on the signal More >

Jie Chen¹, Jiabao Xu¹, Xuefeng Xi^1,*, Zhiming Cui¹, Victor S. Sheng²

CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.1, pp. 747-761, 2023, DOI:10.32604/cmes.2022.020771 - 24 August 2022

Abstract Interpreting deep neural networks is of great importance to understand and verify deep models for natural language processing (NLP) tasks. However, most existing approaches only focus on improving the performance of models but ignore their interpretability. In this work, we propose a Randomly Wired Graph Neural Network (RWGNN) by using graph to model the structure of Neural Network, which could solve two major problems (word-boundary ambiguity and polysemy) of Chinese NER. Besides, we develop a pipeline to explain the RWGNN by using Saliency Map and Adversarial Attacks. Experimental results demonstrate that our approach can identify More >

Baiyan Zhang¹, Hefei Ling^1,*, Ping Li¹, Qian Wang¹, Yuxuan Shi¹, Lei Wu¹, Runsheng Wang¹, Jialie Shen²

CMC-Computers, Materials & Continua, Vol.68, No.2, pp. 1505-1517, 2021, DOI:10.32604/cmc.2021.016851 - 13 April 2021

Abstract The majority of existing graph-network-based few-shot models focus on a node-similarity update mode. The lack of adequate information intensifies the risk of overtraining. In this paper, we propose a novel Multi-head Attention Graph Network to excavate discriminative relation and fulfill effective information propagation. For edge update, the node-level attention is used to evaluate the similarities between the two nodes and the distribution-level attention extracts more in-deep global relation. The cooperation between those two parts provides a discriminative and comprehensive expression for edge feature. For node update, we embrace the label-level attention to soften the noise More >