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MC-Nonlocal-PINNs:Handling Nonlocal Operators in PINNs Via Monte Carlo Sampling

Numerical Mathematics(Theory,Methods and Applications) 2023年第3期16卷 769-791页

作者： Xiaodong Feng Yue Qian Wanfang Shen Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems ScienceChinese Academy of SciencesBeijingChina Shandong Key Laboratory of Blockchain Finance Shandong University of Finance and EconomicsJinan 250014China

We propose Monte Carlo Nonlocal physics-informed neural networks(MC-Nonlocal-PINNs),which are a generalization of MC-fPINNs in *** et al.(*** ***.400(2022),115523)for solving general nonlocal models such as integral equations and nonlocal *** to MC-fPINNs,our MC-Nonlocal-PINNs handle nonlocal operators in a Monte Carlo way,resulting in a very stable approach for high dimensional *** present a variety of test problems,including high dimensional Volterra type integral equations,hypersingular integral equations and nonlocal PDEs,to demonstrate the effectiveness of our approach.

关键词： Nonlocal models PINNs Monte Carlo sampling deep neural networks

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An Integrated Approach for Fault Detection in Industrial Processes Using Approximate Entropy and Slow Feature Analysis 43

An Integrated Approach for Fault Detection in Industrial Pro...

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43rd Chinese Control Conference, CCC 2024

作者： Yang, Chuangyan Li, Peng Wu, Jiande Lang, Xun Ai, Mingxi Yunnan University School of Information Science and Engineering Yunnan Kunming650500 China Yunnan University Yunnan Key Laboratory of Intelligent Systems and Computing China

ISBN: (纸本)9789887581581

Multivariate statistical process monitoring has been widely used in industry process. However, traditional algorithms often ignore the complexity dynamic features of actual industry process. This study proposes a novel algorithm based on Approximate Entropy and Slow Feature Analysis (AE-SFA), which has completed the full-condition monitoring of a dynamic complexity system and quantitative divided static components and dynamic components. The algorithm achieves the optimal detection rate under T2 and SPE control limits. To enrich the experiment, we used the Tennessee Eastman process dataset to demonstrate the superiority of our algorithm. From the comprehensive score of the optimal detection rate, the accuracy of the proposed method is improved by 3.24% and 10.50% of T2 and SPE statistics, respectively. © 2024 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Statistical process control

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Deep Self-supervised Subspace Clustering with Triple Loss 30th

Deep Self-supervised Subspace Clustering with Triple Loss

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30th International Conference on MultiMedia Modeling, MMM 2024

作者： Bu, Xiaotong Dong, Jiwen Zhang, Mengjiao Feng, Guang Gao, Xizhan Niu, Sijie School of Information Science and Engineering University of Jinan Jinan250022 China Shandong Provincial Key Laboratory of Network Based Intelligent Computing Jinan China

ISBN: (纸本)9783031533075

Deep subspace clustering (DSC) methods are widely used in various fields such as motion segmentation, image segmentation, and text mining. It uses the deep neural network to map high-dimensional features into low-dimensional latent subspace to achieve effective division of data. Nevertheless, DSC simply tends to learn representations based on auto-encoder, which can’t fully exploit the intrinsic structure of the data. In this paper, we design a novel approach called Deep self-supervised subspace clustering with triple data (DSSCT), which aims to uncover supervised information inherent in the data. Specifically, DSSCT leverages data augmentation and triple contrastive loss to obtain more effective low-dimensional representations that capture the similarity and difference among different samples. In addition, we introduce a dual self-expression matrix fusion strategy to further enhance the discriminant of the self-expression matrix used in DSSCT. To evaluate the performance of our proposed method, we conduct extensive experiments on several widely used public datasets and achieved excellent performance when compared with other state-of-art methods. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Matrix algebra

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Enhanced Fake News Detection through the Fusion of Deep Learning and Repeat Vector Representations 2

Enhanced Fake News Detection through the Fusion of Deep Lear...

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2nd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Mahmud, Tanjim Hasan, Imran Aziz, Mohammad Tarek Rahman, Taohidur Hossain, Mohammad Shahadat Andersson, Karl Rangamati Science and Technology University Department of Computer Science and Engineering Rangamati4500 Bangladesh University of Chittagong Department of Computer Science and Engineering Chittagong4331 Bangladesh Pervasive and Mobile Computing Laboratory Luleå University of Technology Luleå97187 Sweden

ISBN: (纸本)9798350327533

The rapid dissemination of information across social networks, online news portals, and digital platforms has given rise to a pervasive issue - fake news. This misinformation, deliberately propagated by groups with ulterior motives, has the potential to divert societies in alarming directions. In a digital landscape inundated with millions of daily stories, distinguishing fact from fabrication has become a formidable challenge, casting a dark cloud of uncertainty over the information age. In response to this critical issue, this paper presents an innovative approach for enhanced fake news detection through the fusion of deep learning and Repeat Vector representations. In an era where individuals of all ages increasingly rely on online news sources, the dissemination of misinformation has become an alarming norm. It is a challenge to discern deceptive narratives that aim to sow discord and create undue *** confront this challenge head-on, we propose a deep neural network model that combines Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) architectures using the Repeat Vector method. This hybrid model excels in deciphering complex patterns within 1D data, effectively tackling the menace of fake news. Our rigorous evaluation demonstrates that our proposed model outperforms contemporary methods, achieving an impressive validation accuracy of up to 98.94% on our *** a world increasingly plagued by misinformation, our approach represents a significant stride toward safeguarding the integrity of information in the digital age. © 2024 IEEE.

关键词： Autoencoder Technique Convolutional Neural Network Deep Learning Long Short-Term Memory

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Set pair three-way overlapping community discovery algorithm for weighted social internet of things

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Digital Communications and Networks 2023年第1期9卷 3-13页

作者： Chunying Zhang Jing Ren Lu Liu Shouyue Liu Xiaoqi Li Liya Wang College of Science North China University of Science and TechnologyTangshanHebeiChina Hebei Engineering Research Center for the Intelligentization of Iron Ore Optimization and Ironmaking Raw Materials Preparation Processes North China University of Science and TechnologyTangshanHebeiChina Hebei Key Laboratory of Data Science and Application North China University of Science and TechnologyTangshanHebeiChina The Key Laboratory of Engineering Computing in Tangshan City North China University of Science and TechnologyTangshanHebeiChina Tangshan Intelligent Industry and Image Processing Technology Innovation Center North China University of Science and TechnologyTangshanHebeiChina

There are many problems in Social Internet of Things(IoTs),such as complex topology information,different degree of association between nodes and overlapping *** idea of set pair information grain computing and clustering is introduced to solve the above problems so as to accurately describe the similarity between nodes and fully explore the multi-community structure.A Set Pair Three-Way Overlapping Community Discovery Algorithm for Weighted Social Internet of Things(WSIoT-SPTOCD)is *** the local network structure,which fully considers the topological information between nodes,the set pair connection degree is used to analyze the identity,difference and reverse of neighbor *** similarity degree of different neighbor nodes is defined from network edge weight and node degree,and the similarity measurement method of set pair between nodes based on the local information structure is *** to the number of nodes'neighbors and the connection degree of adjacent edges,the clustering intensity of nodes is defined,and an improved algorithm for initial value selection of k-means is *** nodes are allocated according to the set pair similarity between nodes and different ***-way community structures composed of a positive domain,boundary domain and negative domain are generated ***,the overlapping node set is generated according to the calculation results of community node ***,experiments are carried out on artificial networks and real *** results show that WSIoT-SPTOCD performs well in terms of standardized mutual information,overlapping community modularity and F1.

关键词： Social internet of things Set pair analysis K-means Local information structure Overlapping community

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Matching Gains with Pays: Effective and Fair Learning in Multi-Agent Public Goods Dilemmas 27

Matching Gains with Pays: Effective and Fair Learning in Mul...

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27th European Conference on Artificial Intelligence, ECAI 2024

作者： Chen, Yitian Liu, Xuan Zhang, Shigeng Chen, Xinning Guo, Song School of Computer Science and Engineering Central South University China College of Computer Science and Electronic Engineering Hunan University China The Ministry of Education Key Laboratory of "Fusion Computing of Supercomputing and Artificial Intelligence" China Department of Computer Science and Engineering Hong Kong University of Science and Technology China

ISBN: (纸本)9781643685489

The training of multi-agent reinforcement learning (MARL) tasks with the public goods dilemma (PGD) is difficult because the selfish actions of individual agents for high personal rewards may reduce the collective utility of the whole group. Existing solutions to this problem, e.g., reward gifting or intrinsic rewards, although inducing cooperation among agents in small groups, cannot guarantee fairness among agents’ policies and fail to achieve optimal group utility in large-scale systems. In this paper, we propose F4PGD, an effective method to train large-scale MARL tasks with PGD in a decentralized manner, which is inspired by Adam’s equity theory that the match between a person’s payoff and his contribution is the key incentive for people to contribute to the common good. In F4PGD, a mechanism is designed to match an agent’s reward with its contribution, which suppresses agents from taking a free ride and meanwhile encourages well-learned agents to contribute to public goods. Experimental results show that F4PGD effectively learns optimal policies for the whole group and guarantees fairness among agents in several typical MARL tasks with PGD. © 2024 The Authors.

关键词： Adversarial machine learning

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Improved Particle Swarm optimization Task Scheduling Algorithm Based on Multiple Swarm Molecular Dynamics Theory 24

Improved Particle Swarm optimization Task Scheduling Algorit...

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5th International Conference on Computer Information and Big Data Applications, CIBDA 2024

作者： Liu, Zilai Yu, Liang Li, Qiqian College of Computer Science Guangdong University of Science and Technology Guangdong AIoT Application Innovation Joint Laboratory AIoT Edge Computing Engineering Technology Research Center Dongguan523000 China

ISBN: (纸本)9798400718106

In light of the objective to optimize task scheduling in Cyber-physical systems (CPS) by establishing an efficient mapping between system resources and user tasks, a novel particle swarm optimization task scheduling algorithm is proposed. This algorithm incorporates the principles of multiple swarm molecular dynamics theory, thereby enhancing its performance. By integrating molecular dynamics theory and KMTOA into the particle swarm optimization algorithm, the proposed approach aims to achieve improved task time span and load balancing capabilities. To achieve this, the algorithm initially divides the particle population into distinct sub-populations. Subsequently, different gravitational rules are applied to guide the evolution of particles in each sub-population. This division and rule implementation greatly enhance the diversity of the population in the early stages and enhance the local search ability in the later stages. Simulation and comparative experiments have been conducted to evaluate the effectiveness of the improved task scheduling algorithm. The results demonstrate that the algorithm exhibits superior task time span and load balancing capabilities, effectively enhancing the efficiency of resource mapping. © 2024 ACM.

关键词： Cyber Physical System

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Blockchain-Enabled Secure, Fair and Scalable Data Sharing in Zero-Trust Edge-End Environment

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IEEE Journal on Selected Areas in Communications 2025年第6期43卷 2056-2069页

作者： Xu, Xiaolong Meng, Ke Xiang, Haolong Cui, Guangming Xia, Xiaoyu Dou, Wanchun Nanjing210044 China Nanjing University of Information Science and Technology School of Software and the Jiangsu Province Engineering Research Center of Advanced Computing and Intelligent Services Nanjing210044 China RMIT University School of Computing Technologies MelbourneVIC3000 Australia Nanjing University State Key Laboratory for Novel Software Technology Nanjing210023 China

In edge computing, the Zero-Trust Security Model (ZTSM), as a key enabling technology for next-generation networks, plays a crucial role in providing authentication for addressing data sharing concerns, such as frequent data breaches, data misuse, and cyberattacks. However, due to the complexity and diversity of edge environments, ZTSM struggles to meet the security requirements of data sharing frameworks solely through enhanced authentication. Consequently, such frameworks with ZTSM still face challenges in ensuring data integrity, evaluating various node behaviors, and coping with the increasing complexity of node attributes. To address these issues, we propose a blockchain-enabled secure, fair and scalable data sharing framework in a zero-trust edge-end environment in this paper. Specifically, we first propose a Merkle forest-based data storage model for the classified storage of loosely coupled data, consequently enhancing the scalability of the model. Then, we design a node behavior-based reputation assessment mechanism to ensure fairness during data sharing. Moreover, a data sharing protocol supervised by smart contract is proposed, working with the aforementioned storage and assessment schemes, to ensure the security of data sharing. Finally, comprehensive security analysis validates the security, fairness and scalability of the proposed framework. Extensive experimental results show that, as transaction volume grows, the time cost of data traversal in the storage model becomes progressively more efficient. Additionally, when the size of the smart contract is increased tenfold, the maximum time cost of the data sharing protocol rises by only 4.98 times. © 1983-2012 IEEE.

关键词： Data integrity

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Machine learning empowered UAV-based beamforming design in ISAC systems

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science China(Information sciences) 2025年第5期68卷 285-286页

作者： Xiangyang DUAN Xiaoqi ZHANG Shuqiang XIA Zhongbin WANG Yihua MA Weijie YUAN State Key Laboratory of Mobile Network and Mobile Multimedia Technology Wireless and Computing Research InstituteZTE Corporation School of Electrical and Data Engineering University of Technology Sydney School of Automation and Intelligent Manufacturing Southern University of Science and Technology

Integrated sensing and communication (ISAC) has been proposed as an enabling technology for the realization of the next-generation wireless system,which focuses on performing wireless communication and sensing *** the various potential ISAC-based applications,unmanned aerial vehicle (UAV)-based ISAC plays a significant part in unlocking the potential of future next-generation wireless communication,facilitating low-latency data transmission in high-mobility *** by recent advancements,a variety of effective techniques have been investigated to optimize beamforming design in ISAC *** instance,the authors in [1] introduced an extended Kalman filtering (EKF)-based method tailored for millimeter wave (mmWave) ISAC ***,Ref.[2]proposed an extended interacting multiple model (IMM)-EKF framework designed for vehicular networks with intricate roadway *** these advancements,the aforementioned methods typically employ a separate scheme for channel prediction and beam alignment,which introduces additional signaling overhead in real-world ***,there is a demand for an end-to-end beamforming design approach specifically for UAV-based ISAC systems.

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Multi-Head Self-Attention Based Arabic News Recommendation System 6

Multi-Head Self-Attention Based Arabic News Recommendation S...

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6th International Conference on Advanced Aspects of Software engineering, ICAASE 2024

作者： Sabrina, Karboua Harrag, Fouzi Meziane, Farid Departement of Computer Science Mechatronics Laboratory Ferhat ABBAS University Setif 1 Setif Algeria Data Science Research Centre School of Computing and Engineering University of Derby United Kingdom

ISBN: (纸本)9798331522810

This paper introduces an Arabic News Recommendation System (ANRS) designed to enhance personalized news recommendations for Arabic-speaking users. By leveraging a multi-head self-Attention mechanism, the proposed model effectively captures complex dependencies within Arabic text, addressing challenges posed by the language's rich morphology and contextual nuances. The news encoder uses multi-head self-Attention to learn the meaning of Arabic news articles by analyzing interactions between words in titles, body text. Meanwhile, the user encoder models user preferences by examining previously read articles, also using multi-head self-Attention to capture connections between articles. Trained on a dataset of Arabic news articles and 15 million user interaction logs, ANRS achieves a high prediction accuracy of 98.6%, significantly outperforming traditional and deep learning-based Arabic recommendation models. © 2024 IEEE.

关键词： Recommender systems

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