检索结果-内蒙古大学图书馆

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

来源：评论

学校读者我要写书评

暂无评论

Unsupervised Community Discovery Algorithm via Reconstructed Graph Neural NEtwork

Unsupervised Community Discovery Algorithm via Reconstructed...

引用

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.

关键词：

来源：评论

学校读者我要写书评

暂无评论

FedDSV: Shapley Value-based Contribution Estimation in Federated Learning with Dynamic Participation

引用

IEEE Transactions on Mobile Computing 2025年

作者： Lei, Kaijia Ren, Xuebin Yang, Shusen Wang, Xiaocheng Zhao, Fangyuan Xi'an Jiaotong University School of Computer Science and Technology Shaanxi Xi'an 710049 China Xi'an Jiaotong University National Engineering Laboratory for Big Data Analytics Shaanxi Xi'an 710049 China Xi'an Jiaotong University National Engineering Laboratory for Big Data Analytics (NEL-BDA) Shaanxi Xi'an 710049 China Xi'an Jiaotong University Ministry of Education Key Lab for Intelligent Networks and Network Security (MOE KLINNS Lab) Shaanxi Xi'an 710049 China

Federated Learning (FL) succeeds in collaborative and privacy-preserving ML model training among multiple distributed data owners. To maintain a healthy FL ecosystem, it is crucial to estimate the contributions of all participants fairly. Due to provable fairness, Shapley value (SV) is widely used for contribution estimation in FL. However, current studies focus on static scenarios with fixed participants and neglect the dynamic settings with the random joining or leaving of participants in practice. This paper fills the gap by proposing FedDSV, a novel contribution estimation framework for FL with dynamic participation. FedDSV supports flexible weighting mechanisms and is compatible with the SV fairness properties in dynamic scenarios. To reduce the computational complexity, we propose a Monte Carlo variant sampling method (SMC), which can adapt well to dynamic scenarios and approximate the true SVs. To evaluate the effectiveness and efficiency of our proposed approaches, extensive experiments under different settings (e.g., frequency switching, low-quality detection, etc.) are conducted on both i.i.d and non-i.i.d. distributions. Experimental results demonstrate that FedDSV can reflect the real utility contribution of data sources for dynamic FL, and SMC can approximate the exact dynamic SVs with larger similarities in a much shorter time than the state-of-the-art methods. © 2002-2012 IEEE.

关键词： Contribution estimation dynamic participation federated learning shapley value

来源：评论

学校读者我要写书评

暂无评论

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

arXiv

引用

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

来源：评论

学校读者我要写书评

暂无评论

Fault Diagnosis for Rolling Bearing Based on RF-LSTM

Fault Diagnosis for Rolling Bearing Based on RF-LSTM

引用

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

来源：评论

学校读者我要写书评

暂无评论

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

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

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

arXiv

引用

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

来源：评论

学校读者我要写书评

暂无评论

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

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

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

arXiv

引用

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

来源：评论

学校读者我要写书评

暂无评论

Modelling COVID-19 transmission dynamics in Laos under non-pharmaceutical interventions, vaccination, and replacement of SARS-CoV-2 variants

引用

BMC global and public health 2024年第1期2卷 38页

作者： Xu-Sheng Zhang Hong Luo Andre Charlett Daniela DeAngelis Wei Liu Peter Vickerman Mark Woolhouse Linxiong Wu Statistics Modelling and Economics Data Analytics & Surveillance UK Health Security Agency London UK. xu-sheng.zhang@UKHSA.gov.uk. Education College Yunnan University Kunming Yunnan People's Republic of China. Statistics Modelling and Economics Data Analytics & Surveillance UK Health Security Agency London UK. Medical Research Council Biostatistics Unit University Forvie Site Robinson Way Cambridge UK. School of Public Health Kunming Medical University Kunming Yunnan People's Republic of China. Population Health Sciences University of Bristol Bristol UK. Usher Institute University of Edinburgh Edinburgh UK. School of Public Health Kunming Medical University Kunming Yunnan People's Republic of China. wlx0871@***. Yunnan Provincial Key Laboratory of Public Health and Biosafety Kunming Yunnan People's Republic of China. wlx0871@***.

BACKGROUND:Understanding how the COVID-19 pandemic evolved under control measures is crucial to tackle the SARS-CoV-2 virus spread. Laos, a country bordering China but with late occurrence and low burden of COVID-19 compared to its neighbouring countries, was used for a case study. METHODS:A transmission model with disease reporting was proposed to investigate the impact of control measures on the SARS-CoV-2 virus spread in Laos from April 2021 to May 2022. It was assumed that the transmission rate changed with people's behaviours, control measures and emerging variants; susceptibility decreased with vaccination and infection. Bayesian inference was used for model calibration to data of confirmed cases, deaths, and recoveries, and the deviance information criterion was used to select the best model variant. RESULTS:Our model including Non-pharmaceutical interventions (NPIs), behaviour change, vaccination, and changing variants well explained the three waves in Laos. The Alpha variant was estimated to have a basic reproduction number of 1.55 (95% CrI: 1.47-1.64) and was replaced by the Delta variant from September 2021 which was 1.88 (95% CrI: 1.77-2.01) times more transmissible; the Delta variant was replaced by Omicron variant from March 2022 which was 3.33 (95% CrI: 2.84-3.74) times more transmissible. The Delta variant was the most severe with a case fatality rate of 1.05% (95% CrI: 0.96-1.15%) while the Alpha variant and Omicron variant were much milder. The ascertainment rate was low and variable: first decreasing from 13.2 to 1.8% by 23 May 2021, and then increasing to 23.4% by 15 March 2022. Counterfactual simulations indicated that vaccination played strong roles in reducing infections even under the emergence of immune escape variants while behaviour change delayed but might not flatten the peak of outbreaks. CONCLUSIONS:The three waves of Laos' epidemics were due to the invasion of more transmissible and immune escape variants that affected the herd immuni

关键词： Bayesian estimation COVID-19 Control measures Google mobility Modelling Non-pharmaceutical interventions (NPIs) Oxford Government COVID-19 policy index SARS-CoV-2 Vaccination Variant

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：