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2023 IEEE International Conference on Unmanned systems, ICUS 2023

作者： Li, Zhe Liu, Jin Dong, Yibo Ren, Jiaqi Li, Weili National University of Defense Technology Science and Technology on Information Systems Engineering Laboratory Changsha China

ISBN: (纸本)9798350316308

In order to solve the problem that it is difficult to effectively express and quantify various uncertain factors in infrastructure network attacker-defender game modelling, an infrastructure network attacker-defender game model based on intuitionistic fuzzy sets is established by combining game theory with intuitionistic fuzzy sets. After converting the exact payoff matrix into an intuitionistic fuzzy matrix according to certain rules, the nonlinear programming solver is used to solve the model to obtain the equilibrium strategy of both sides. Finally, the experimental results are analysed to show the effectiveness and feasibility of the model. © 2023 IEEE.

关键词： Nonlinear programming

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DADN: Data Augmentation-Driven Dual Network Framework for Node Classification 2

DADN: Data Augmentation-Driven Dual Network Framework for No...

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2nd International Conference on Automation, Robotics and Computer engineering, ICARCE 2023

作者： Liu, Dengfeng Pan, Zhiqiang Wang, Siyuan Li, Peihong Liu, Shixian Mao, Yanying National University of Defense Technology Science and Technology on Information Systems Engineering Laboratory Changsha China

ISBN: (纸本)9798350308341

Node classification is an important task in graph neural networks (GNNs), aiming to predict the category of nodes in the graph according to their neighbours and their own characteristics. Current methods mainly focus on one level of data augmentation, ignoring huge potential in other underlying levels. Furthermore, existing models cannot fully exploit the information contained in the existing data, which undermines the performance of node classification. In order to solve the above problems, we propose a novel Data Augmentation-driven Dual Network (DADN) framework. Specifically, we first design a composite data augmentation module that comprehensively considers the local neighborhood level, the node level, and the edge level to augment the original features. Then, we copy the original features and the augmented features into the dual network framework with dropout for model training to capture the innermost features. Finally, we experimentally evaluate our method on three real-world public datasets, and the results show that our method outperforms the baseline models in the node classification task, except for individual metrics. © 2023 IEEE.

关键词： Classification (of information)

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Collaborative Learning for Cross-Organizational Data Sharing Using Hyperledger Fabric 8

Collaborative Learning for Cross-Organizational Data Sharing...

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8th International Conference on Data science in Cyberspace, DSC 2023

作者： Zheng, Yaling Cheng, Ziwen Liu, Yi Wang, Bowen Zhu, Cheng National University of Defense Technology Science and Technology on Information Systems Engineering Laboratory Changsha China

ISBN: (纸本)9798350331035

The rise of edge networking presents both opportunities and challenges. The ever-expanding array of IoT devices generates vast amounts of data, enabling collaboration and smart applications. However, managing decentralized data, resolving heterogeneity, and ensuring privacy poses challenges. The combination of blockchain and federated learning offers a solution. But trust management, inefficiencies, and ambiguous assessment of data contribution have become new obstacles. In this paper, we propose a data sharing model that supports cross-organizational collaborative learning based on Hyperledger Fabric, in order to enhance the high-quality data sharing of collaborative learning and improve the effect of learning. The experimental results show that the proposed model can realize decentralized and efficient data sharing and improve the accuracy and stability of collaborative learning under acceptable communication load. © 2023 IEEE.

关键词： blockchain collaborative learning data sharing edge network Hyperledger Fabric

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Improving the spaceborne GNSS-R altimetric precision based on the novel multilayer feedforward neural network weighted joint prediction model

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Defence technology（防务技术） 2024年第2期32卷 271-284页

作者： Yiwen Zhang Wei Zheng Zongqiang Liu Qian Xuesen Laboratory of Space Technology China Academy of Space TechnologyBeijing 100094China China Academy of Aerospace Science and Innovation Beijing 100176China State Key Laboratory of Space-Ground Integrated Information Technology Beijing Institute of Satellite Information EngineeringBeijing 100194China

Global navigation satellite system-reflection(GNSS-R)sea surface altimetry based on satellite constellation platforms has become a new research direction and inevitable trend,which can meet the altimetric precision at the global scale required for underwater *** present,there are still research gaps for GNSS-R altimetry under this mode,and its altimetric capability cannot be specifically ***,GNSS-R satellite constellations that meet the global altimetry needs to be ***,the matching precision prediction model needs to be established to quantitatively predict the GNSS-R constellation altimetric ***,the GNSS-R constellations altimetric precision under different configuration parameters is calculated,and the mechanism of the influence of orbital altitude,orbital inclination,number of satellites and simulation period on the precision is analyzed,and a new multilayer feedforward neural network weighted joint prediction model is ***,the fit of the prediction model is verified and the performance capability of the model is tested by calculating the R2 value of the model as 0.9972 and the root mean square error(RMSE)as 0.0022,which indicates that the prediction capability of the model is ***,using the novel multilayer feedforward neural network weighted joint prediction model,and considering the research results and realistic costs,it is proposed that when the constellation is set to an orbital altitude of 500 km,orbital inclination of 75and the number of satellites is 6,the altimetry precision can reach 0.0732 m within one year simulation period,which can meet the requirements of underwater navigation precision,and thus can provide a reference basis for subsequent research on spaceborne GNSS-R sea surface altimetry.

关键词： GNSS-R satellite constellations Sea surface altimetric precision Underwater navigation Multilayer feedforward neural network

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Unveiling the Impact of Network Structure in Enhancing information Dissemination Efficiency for Military Command and Control : A SIR Modeling Study 9

Unveiling the Impact of Network Structure in Enhancing Infor...

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9th International Conference on Big Data and information Analytics, BigDIA 2023

作者： Liu, Shixian Jiang, Miao Chen, Honghui Li, Peihong Mao, Yanying Wang, Siyuan Liu, Dengfeng National University of Defense Technology Science and Technology on Information Systems Engineering Laboratory Changsha China

ISBN: (纸本)9798350330076

At the intersection of network science, communication models, and military command and control, this study explores the critical impact of network structure on the efficiency of information dissemination. We discuss how these findings can be applied to real-world military situations. Our research employs the SIR model and network simulation to replicate the information propagation process within the network. We discovered that various aspects of the network structure, such as node relationships, topology, and information propagation paths, exert a significant influence on the speed and scope of information dissemination. Furthermore, we explore the practical applications of these theories and findings in military command and control strategies. By considering network structure, we can design follow-up experiments tailored to specific network classifications within complex and realistic environments, aiming to optimize communication methods. In summary, this study introduces an innovative perspective in the military domain, with far-reaching implications for future research and the practical implementation of military command decision-making. © 2023 IEEE.

关键词： Network topology

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A Survey on Security of UAV Authentication Based on Blockchain under New C2 paradigm 9

A Survey on Security of UAV Authentication Based on Blockcha...

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9th International Conference on Big Data and information Analytics, BigDIA 2023

作者： Zheng, Yaling Liu, Yi Cheng, Ziwen Pan, Yongqi Zhu, Cheng National University of Defense Technology Science and Technology on Information Systems Engineering Laboratory Changsha China

ISBN: (纸本)9798350330076

Advancements in information technology and artificial intelligence have given rise to a new military command and control (C2) paradigm where unmanned aerial vehicles (UAVs) play a critical role. However, due to open wireless communication channels and evolving security challenges for UAVs in this paradigm, traditional centralized authentication methods are no longer sufficient. In this context, this paper conducts a systematic literature review (SLR) approach to explore the application of blockchain technology in UAV authentication. It categorizes and reviews relevant solutions published in recent years, and analyzes the advantages of blockchain in addressing centralized dependencies, cross-domain heterogeneity and other issues. The review finds dynamic management and efficiency improvement are key points needing attention. This paper provides valuable references on integrating UAV identity authentication with blockchain technology under new C2 paradigms. © 2023 IEEE.

关键词： Authentication

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Federated Learning in Heterogeneous Cloud Computing Environment 9

Federated Learning in Heterogeneous Cloud Computing Environm...

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9th International Conference on Big Data and information Analytics, BigDIA 2023

作者： Li, Yaoyu Jin, Shuai Zhu, Jianghan National University of Defense Technology Science and Technology on Information Systems Engineering Laboratory Changsha China

ISBN: (纸本)9798350330076

This study examines key characteristics of cloud computing technology within the domain of federated learning, with the primary objective of exploring the principles concerning distributed computing acceleration ratio and storage input/output efficiency across various network segments. Combining the heterogeneous characteristics of cloud segments, we present a novel approach for distributed model training strategies by using the federated learning technique. This approach can keep the accuracy of the model, enhance training efficiency, and protect user privacy. The experiment demonstrates that the proposed strategy outperforms the baseline method, which does not account for variations in different segments. © 2023 IEEE.

关键词： Cloud computing

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An Explainable Recommendation Method based on Diffusion Model 9

An Explainable Recommendation Method based on Diffusion Mode...

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9th International Conference on Big Data and information Analytics, BigDIA 2023

作者： Guo, Yupu Cai, Fei Chen, Honghui Chen, Chonghao Zhang, Xin Zhang, Menxi National University of Defense Technology Science and Technology on Information Systems Engineering Laboratory Changsha China

ISBN: (纸本)9798350330076

To meet the requirements of precise recommendation in the era of information warfare, there has been a growing interest in explainable recommendations in the field of command and control in recent years. While Variational Autoencoders (VAE) have been effective in generating explanations, diffusion models have shown superior performance in fields like image processing and natural language processing. This paper proposes a Diffusion-based explainable recommendation model (DIEXRS) to enhance the effectiveness of explanations further. The DIEXRS model consists of two processes: diffusion (training) and reverse (inference). User and item embeddings are generated based on their IDs, along with comment embeddings from user comments. These embeddings are incorporated into the diffusion process, where the Transformer model is trained to fit the noise and improve the decoder's decoding capabilities. In the reverse process, a random Gaussian sample and the user's ID are used to obtain the item's ID and corresponding interpretation embedding, providing the recommended interpretation through the decoder. Experimental results on two public datasets demonstrate that the DIEXRS model achieves state-of-the-art performance in interpretable recommendations compared to other baselines. This highlights the potential of diffusion models in enhancing the explainability and effectiveness of recommendation systems © 2023 IEEE.

关键词： Embeddings

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LKMT:Linguistics Knowledge-Driven Multi-Task Neural Machine Translation for Urdu and English

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Computers, Materials & Continua 2024年第10期81卷 951-969页

作者： Muhammad Naeem Ul Hassan Zhengtao Yu Jian Wang Ying Li Shengxiang Gao Shuwan Yang Cunli Mao Faculty of Information Engineering and Automation Kunming University of Science and TechnologyKunming650500China Yunnan Key Laboratory of Artificial Intelligence Kunming University of Science and TechnologyKunming650500China

Thanks to the strong representation capability of pre-trained language models,supervised machine translation models have achieved outstanding ***,the performances of these models drop sharply when the scale of the parallel training corpus is *** the pre-trained language model has a strong ability for monolingual representation,it is the key challenge for machine translation to construct the in-depth relationship between the source and target language by injecting the lexical and syntactic information into pre-trained language *** alleviate the dependence on the parallel corpus,we propose a Linguistics Knowledge-Driven MultiTask(LKMT)approach to inject part-of-speech and syntactic knowledge into pre-trained models,thus enhancing the machine translation *** the one hand,we integrate part-of-speech and dependency labels into the embedding layer and exploit large-scale monolingual corpus to update all parameters of pre-trained language models,thus ensuring the updated language model contains potential lexical and syntactic *** the other hand,we leverage an extra self-attention layer to explicitly inject linguistic knowledge into the pre-trained language model-enhanced machine translation *** on the benchmark dataset show that our proposed LKMT approach improves the Urdu-English translation accuracy by 1.97 points and the English-Urdu translation accuracy by 2.42 points,highlighting the effectiveness of our LKMT *** ablation experiments confirm the positive impact of part-of-speech and dependency parsing on machine translation.

关键词： Urdu NMT(neural machine translation) Urdu natural language processing Urdu Linguistic features low resources language linguistic features pretrain model

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AE-TGN: Attribute-Empowered Temporal Graph Networks for Dynamic Graph Link Prediction

AE-TGN: Attribute-Empowered Temporal Graph Networks for Dyna...

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2023 IEEE International Conference on Dependable, Autonomic and Secure Computing, 2023 International Conference on Pervasive Intelligence and Computing, 2023 International Conference on Cloud and Big Data Computing, 2023 International Conference on Cyber science and technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2023

作者： Chen, Chonghao Luo, Aimin Huang, Yang National University of Defense Technology Science and Technology on Information Systems Engineering Laboratory Changsha China

ISBN: (纸本)9798350304602

Temporal/dynamic graph link prediction task requires the AI systems to predict the possible future edges based on the observation history of graph events. Existing works for the temporal graph learning mainly use the recurrent neural networks or self-attention mechanism to model the interaction information of graph, and ignore the node attribute features. In particular, they usually adopt a static attribute encoder where the attribute representation can not adjust according the graph structure evolution. To address this issue, we propose Attribute-Empowered Temporal Graph Networks (AE-TGN), which leverages a dynamic attribute leaning module to fine-tune the attribute embedding. In detail, we use the recurrent networks to encode the memory of nodes and the temporal self-attention mechanism to capture the spatial graph information. Next, a trainable multi-layer perceptron is employed to adjust the node attribute representation. Finally, the node attribute embeddings are concatenated with the memory vectors as the temporal embeddings of nodes, and fed into the link prediction decoder. Experimental results on two real-world datasets demonstrates the superiority of our proposal by beating competitive baselines. Specially, AE-TGN presents obvious improvements in the inductive setting, which validates that the attribute encoder can produce accurate node representation for the downstream task. © 2023 IEEE.

关键词： Graph neural networks

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