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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Jing-Hong Wang Chang-Xin Li Jia-Teng Yang College of Computer and Cyber Security Hebei Normal University Shijiazhuang China Hebei Key Laboratory of Network and Information Security Hebei Normal University Shijiazhuang China Hebei Provincial Engineering Research Center for Supply Chain Big Data Analytics & Security Hebei Normal University Shijiazhuang China Hebei Polytechnic Institute Shijiazhuang China

Graph Attention Network (GAT) is a popular method for graph embedding as they effectively leverage attention mechanisms to aggregate first-order neighborhood node information. However, they have limitations in their ability to consider high-order neighborhood nodes and structural information. Directly incorporating high-order neighborhood node information into traditional GAT may result in over-smoothing. To address this challenge, in this paper, we propose a joint attention graph embedding model based on similar networks and structural correlation. secondly, we introduce the concept of structural correlation to the original graph attentional method, and consider not only node content feature but also joint node topological structure feature when calculating the attention score. Finally, the graph attention mechanism module performs feature extraction to generate embedding representations of the nodes. The proposed algorithm achieved an accuracy of 85.7%, 74.30%, and 84.10% on the Cora, Citeseer, and Pubmed datasets respectively in node classification tasks. This represents an improvement of 2.70%, 3.94%, and 2.60% respectively compared to the traditional graph attention model. It can be seen that the proposed method can obtain a better representation of node embedding.

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Deep Learning for Automated Quantification of Irradiation Defects in TEM data: Relating Pixel-level Errors to Defect Properties

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Microscopy and microanalysis : the official journal of Microscopy Society of America, Microbeam Analysis Society, Microscopical Society of Canada 2023年第1期29卷 1559-1560页

作者： Sainju, Rajat Roberts, Graham Chen, Wei-Ying Hutchinson, Brian Yang, Qian Ding, Caiwen Edwards, Danny J. Li, Meimei Zhu, Yuanyuan Department of Materials Science and Engineering University of Connecticut Storrs CT United States Department of Computer Science and Engineering University of Connecticut Storrs CT United States Nuclear Science and Engineering Division Argonne National Laboratory Lemont IL United States Computer Science Department Western Washington University Bellingham WA United States National Security Directorate AI and Data Analytics Division Pacific Northwest National Laboratory Richland WA United States Energy and Environment Directorate Nuclear Sciences Division Pacific Northwest National Laboratory Richland WA United States

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Federated Learning-Based Intelligent Indoor Smoke and Fire Detection System for Smart Buildings

Federated Learning-Based Intelligent Indoor Smoke and Fire D...

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2024 International Conference on Telecommunications and Intelligent Systems, ICTIS 2024

作者： Aymene Berkani, Mohamed Rafik Chouchane, Ammar Himeur, Yassine Abdennebi, Anes Sagiroglu, Seref Amira, Abbes Medea Algeria University Biskra University Center of Barika Laboratory of LI3C Amdoukal Road Barika05001 Algeria University of Dubai College of Engineering and Information Technology Dubai United Arab Emirates École de Technologie Supérieure Software and Information Technology Engineering Montréal Canada Analytics Security R&D Center Gazi University Artificial Intelligence and Big Data Ankara Turkey University of Sharjah Department of Computer Science Sharjah United Arab Emirates

ISBN: (纸本)9798331527396

Ensuring safety in smart buildings is crucial due to the increasing prevalence of smoke and fire hazards in modern environments. This paper introduces a novel privacy-preserving FL approach based on a CNN1D for smoke and fire detection in smart buildings. Our system integrates data from wearable environmental sensors to train a lightweight, edge-deployable DL-CNN1D model, ensuring data privacy while enabling collaborative learning across distributed clients using Federated Averaging (FedAvg) aggregation. Experiments conducted on a comprehensive air measurement dataset for smoke and fire detection demonstrate exceptional performance, with the global model achieving 99.97% accuracy, 99.96% precision, in smoke and fire recognition. Our model demonstrates a low communication cost of 0.4 MB, underscoring its efficiency for real-time applications. Our FL-based approach represents a significant step towards balancing the need for robust safety systems with growing privacy concerns in smart building environments. © 2024 IEEE.

关键词： Federated learning

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Unsupervised Community Discovery Algorithm via Reconstructed Graph Neural NEtwork

Unsupervised Community Discovery Algorithm via Reconstructed...

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Jinghong Wang Hui Wang Jiateng Yang Xizhao Wang College of Computer and Cyber Security Hebei Normal University Shijiazhuang China Hebei Key Laboratory of Network and Information Security Hebei Normal University Shijiazhuang China Hebei Provincial Engineering Research Center for Supply Chain Big Data Analytics & Data Security (Hebei Normal University) Shijiazhuang China College of Artificial Intelligence and Big Data Hebei Polytechnic Institute Shijiazhuang China College of Computer Science and Software Engineering ShenZhen University Shenzhen China

Community discovery can help discover potential community structures in the network, which is a fundamental and important issue in network science. Graph neural network-based algorithms are receiving increasing attention. However, there is relatively little work on community discovery directly using graph neural networks in reconstruction design. This paper presents an Unsupervised Community Discovery Algorithm based on reconstructed graph neural network (UCDA) to resolve the above issues. To be specific, this paper proposes a framework that learns the representation based on network structure and attribute information. By designing input network features, encoders, decoders, this algorithm uses an unsupervised loss function to train neural networks to extract communities in an integrated way. These designs integrate higher-order modularity information with network features and use variational graph auto-encoders to train data end-to-end under the graph neural network framework. Based on a series of experiments with a wide range of datasets and advanced algorithms, UCDA has achieved superior performance and shows strong competitiveness. Finally, the last section reports the results analysis, t-SNE visualization and hyperparameter analysis, which depicts stable performance and powerful network modularity ability.

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SoK: Training Machine Learning Models over Multiple Sources with Privacy Preservation

arXiv

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arXiv 2020年

作者： Song, Lushan Lin, Guopeng Wang, Jiaxuan Wu, Haoqi Ruan, Wenqiang Han, Weili Laboratory for Data Analytics and Security Fudan University China

Nowadays, gathering high-quality training data from multiple data sources with privacy preservation is a crucial challenge to training high-performance machine learning models. The potential solutions could break the barriers among isolated data corpus, and consequently enlarge the range of data available for processing. To this end, both academic researchers and industrial vendors are recently strongly motivated to propose two main-stream folders of solutions mainly based on software constructions: 1) Secure Multi-party Learning (MPL for short);and 2) Federated Learning (FL for short). The above two technical folders have their advantages and limitations when we evaluate them according to the following five criteria: security, efficiency, data distribution, the accuracy of trained models, and application scenarios. Motivated to demonstrate the research progress and discuss the insights on the future directions, we thoroughly investigate these protocols and frameworks of both MPL and FL. At first, we define the problem of Training machine learning Models over Multiple data sources with Privacy Preservation (TMMPP for short). Then, we compare the recent studies of TMMPP from the aspects of the technical routes, the number of parties supported, data partitioning, threat model, and machine learning models supported, to show their advantages and limitations. Next, we investigate and evaluate five popular FL platforms. Finally, we discuss the potential directions to resolve the problem of TMMPP in the future. Copyright © 2020, The Authors. All rights reserved.

关键词： Machine learning

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Estimating Smart Grid Stability with Hybrid RNN+LSTM Deep Learning Approach

Estimating Smart Grid Stability with Hybrid RNN+LSTM Deep Le...

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International Smart Grid Workshop and Certificate Program (ISGWCP)

作者： Saadin Oyucu Şeref Sağıroğlu Ahmet Aksöz Emre Biçer Department of Computer Engineering Faculty of Engineering Adıyaman Türkiye Gazi University Artificial Intelligence and Big Data Analytics Security R&D Center Ankara Türkiye Mobilers Team Sivas Cumhuriyet University Sivas Türkiye Faculty of Engineering and Natural Sciences Battery Research Laboratory Sivas University of Science and Technology Sivas Türkiye

ISBN: (数字)9798350361612

ISBN: (纸本)9798350361629

Smart grids are faced with a range of challenges, such as the development of communication infrastructure, cybersecurity threats, data privacy, and the protection of user information, due to their complex structure. Another key challenge faced by smart grids is the stability issues arising from variable energy sources and consumption patterns. In these complex grid systems where energy demand and supply need to be balanced instantly, stability predictions play a significant role in foreseeing potential disruptions and optimizing energy flow. Therefore, within the scope of this study, a hybrid structure utilizing Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) networks is employed for stability classification to predict grid stability. This hybrid model combines the ability of RNN to recognize relationships between consecutive data points with LSTM’s capability to preserve long-term dependencies. The results obtained indicate that the model exhibited stable performance with accuracy rates of 98.06% and 98.02% at 50 and 100 epochs, respectively. The findings of this study contribute valuable insights to research on the management and stability of smart grids, enabling energy systems to be operated more reliably and efficiently.

关键词： Deep learning data privacy Recurrent neural networks Power system stability Stability analysis data models Smart grids

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Fault Diagnosis for Rolling Bearing Based on RF-LSTM

Fault Diagnosis for Rolling Bearing Based on RF-LSTM

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Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS), CAA Symposium on

作者： Qiuting Li Xiuqing Wang Yunpeng Yang Ruiyi Wang Feng Lv College of Computer and Cyber Security Hebei Normal University Shijiazhuang China Hebei Provincial Key Laboratory of Network & Information Security Shijiazhuang China Hebei Provincial Engineering Research Center for Supply Chain Big Data Analytics & Data Security Shijiazhuang China

ISBN: (纸本)9781665401166

In the era of big data, it is of great significance to study data-driven fault diagnostic methods for equipment in complex industrial environments. In this paper, a data-driven fault diagnostic method for rolling bearing based on RF-LSTM is proposed, which combines random forest (RF) algorithm with long and short term memory (LSTM) networks. RF is used to select sensor data’s features, which have great influence on rolling bearing fault diagnosis, and LSTM is used for prediction rolling bearing’s working states. The proposed method is used for fault diagnosis of the Case Western Reserve University (CWRU) bearing dataset and the experimental results validated the effectiveness of the proposed RF-LSTM model. Experimental results also show that the performance of the RFLSTM model is better than that of RF, LSTM, RF-RF, PCA-RF, PCA-LSTM models.

关键词： Fault diagnosis Radio frequency Fault detection Time series analysis Rolling bearings Forestry Predictive models

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SS-GNN: A Simple-Structured Graph Neural Network for Affinity Prediction

arXiv

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arXiv 2022年

作者： Zhang, Shuke Jin, Yanzhao Liu, Tianmeng Wang, Qi Zhang, Zhaohui Zhao, Shuliang Shan, Bo Software College Hebei Normal University Shijiazhuang050024 China Shijiazhuang Xianyu Digital Biotechnology Co. Ltd Shijiazhuang050024 China College of Computer and Cyber Security Hebei Normal University Shijiazhuang050024 China Hebei Provincial Key Laboratory of Network and Information Security Shijiazhuang050024 China Hebei Provincial Engineering Research Center for Supply Chain Big Data Analytics & Data Security Shijiazhuang050024 China

Efficient and effective drug-target binding affinity (DTBA) prediction is a challenging task due to the limited computational resources in practical applications and is a crucial basis for drug screening. Inspired by the good representation ability of graph neural networks (GNNs), we propose a simple-structured GNN model named SS-GNN to accurately predict DTBA. By constructing a single undirected graph based on a distance threshold to represent protein-ligand interactions, the scale of the graph data is greatly reduced. Moreover, ignoring covalent bonds in the protein further reduces the computational cost of the model. The GNN-MLP module takes the latent feature extraction of atoms and edges in the graph as two mutually independent processes. We also develop an edge-based atom-pair feature aggregation method to represent complex interactions and a graph pooling-based method to predict the binding affinity of the complex. We achieve state-of-the-art prediction performance using a simple model (with only 0.6M parameters) without introducing complicated geometric feature descriptions. SS-GNN achieves Pearson's Rp=0.853 on the PDBbind v2016 core set, outperforming state-of-the-art GNN-based methods by 5.2%. Moreover, the simplified model structure and concise data processing procedure improve the prediction efficiency of the model. For a typical protein-ligand complex, affinity prediction takes only 0.2 ms. All codes are freely accessible at https://***/xianyuco/SS-GNN. © 2022, CC BY-NC-SA.

关键词： Forecasting

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A high-frequency mobility big-data reveals how COVID-19 spread across professions, locations and age groups

arXiv

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arXiv 2022年

作者： Zhao, Chen Zhang, Jialu Hou, Xiaoyue Yeung, Chi Ho Zeng, An College of Computer and Cyber Security Hebei Normal University Shijiazhuang050024 China Hebei Key Laboratory of Network and Information Security Shijiazhuang050024 China Hebei Provincial Engineering Research Center for Supply Chain Big Data Analytics and Data Security Shijiazhuang050024 China Department of Science and Environmental Studies The Education University of Hong Kong Hong Kong School of Systems Science Beijing Normal University Beijing100875 China

As infected and vaccinated population increases, some countries decided not to impose non-pharmaceutical intervention measures anymore and to coexist with COVID-19. However, we do not have a comprehensive understanding of its consequence, especially for China where most population has not been infected and most Omicron transmissions are silent. This paper serves as the first study to reveal the complete silent transmission dynamics of COVID-19 overlaying a big data of more than 0.7 million real individual mobility tracks without any intervention measures throughout a week in a Chinese city, with an extent of completeness and realism not attained in existing studies. Together with the empirically inferred transmission rate of COVID-19, we find surprisingly that with only 70 citizens to be infected initially, 0.33 million becomes infected silently at last. We also reveal a characteristic daily periodic pattern of the transmission dynamics, with peaks in mornings and afternoons. In addition, retailing, catering and hotel staff are more likely to get infected than other professions. Unlike all other age groups and professions, elderly and retirees are more likely to get infected at home than outside home. Copyright © 2022, The Authors. All rights reserved.

关键词： COVID-19

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Automated Enforcement of the Principle of Least Privilege over data Source Access

Automated Enforcement of the Principle of Least Privilege ov...

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IEEE International Conference on Trust, security and Privacy in Computing and Communications (TrustCom)

作者： Haoqi Wu Zhengxuan Yu Dapeng Huang Haodong Zhang Weili Han Laboratory for Data Analytics and Security Fudan University Shanghai China

ISBN: (数字)9781665403924

ISBN: (纸本)9781665403931

The state-of-the-art database-backed web applications usually assign full privileges to connections between applications and data sources. This phenomenon, which would enable a malicious attacker to easily compromise the applications through arbitrarily manipulating the data sources without the restriction of privileges, seriously breaks the principle of least privilege (PLP), a fundamental law of system security. Motivated to counter this problem, we propose a framework PDA (PLP over data source Access) to automatically enforce this principle over data source access based on application-driven privilege separation. Our proposed PDA contributes from the following aspects: i) PDA achieves the privilege separation by intercepting database queries and enforcing privileged connections to database for each database query; ii) PDA can effectively defend against SQL-based vulnerabilities including buggy queries and SQL injection attacks. Lastly, we evaluate PDA on a widely used application platform, JForum, to demonstrate the effectiveness of PDA with a promising performance overhead of 8.13%.

关键词： data privacy Handheld computers databases Conferences SQL injection security

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