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Redundant Data Detection and Deletion to Meet Privacy Protection Requirements in Blockchain-Based Edge Computing Environment

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China Communications 2024年第3期21卷 149-159页

作者： Zhang Lejun Peng Minghui Su Shen Wang Weizheng Jin Zilong Su Yansen Chen Huiling Guo Ran Sergey Gataullin Cyberspace Institute Advanced Technology Guangzhou UniversityGuangzhou 510006China College of Information Engineering Yangzhou UniversityYangzhou 225127China School Math&Computer Science Quanzhou Normal UniversityQuanzhou 362000China Computer Science Department City University of Hong KongHong Kong 999077China School of Computer and Software Nanjing University of Information Science and TechnologyNanjing 21004China Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and TechnologyAnhui UniversityHefei 230601China Department of Computer Science and Artificial Intelligence Wenzhou UniversityWenzhou 325035China Guangzhou University Library Guangzhou UniversityGuangzhou 510006China Central Economic and Mathematics Institute Russian Academy of Sciences MIREA-Russian Technological University Moscow RegionRussia

With the rapid development of information technology,IoT devices play a huge role in physiological health data *** exponential growth of medical data requires us to reasonably allocate storage space for cloud servers and edge *** storage capacity of edge nodes close to users is *** should store hotspot data in edge nodes as much as possible,so as to ensure response timeliness and access hit rate;However,the current scheme cannot guarantee that every sub-message in a complete data stored by the edge node meets the requirements of hot data;How to complete the detection and deletion of redundant data in edge nodes under the premise of protecting user privacy and data dynamic integrity has become a challenging *** paper proposes a redundant data detection method that meets the privacy protection *** scanning the cipher text,it is determined whether each sub-message of the data in the edge node meets the requirements of the hot *** has the same effect as zero-knowledge proof,and it will not reveal the privacy of *** addition,for redundant sub-data that does not meet the requirements of hot data,our paper proposes a redundant data deletion scheme that meets the dynamic integrity of the *** use Content Extraction Signature(CES)to generate the remaining hot data signature after the redundant data is *** feasibility of the scheme is proved through safety analysis and efficiency analysis.

关键词： blockchain data integrity edge computing privacy protection redundant data

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Multi-Agent Graph Reinforcement Learning Based On-Demand Wireless Energy Transfer in Multi-UAV-Aided IoT Network

Multi-Agent Graph Reinforcement Learning Based On-Demand Wir...

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International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt)

作者： Ze Yu Zhao Yueling Che Sheng Luo Kaishun Wu Victor C. M. Leung College of Computer Science and Software Engineering Shenzhen University China

This paper proposes a new on-demand wireless energy transfer (WET) scheme of multiple unmanned aerial vehicles (UAVs). Unlike the existing studies that simply pursuing the total or the minimum harvested energy maximization at the Internet of Things (IoT) devices, where the IoT devices' own energy requirements are barely considered, we propose a new metric called the hungry-level of energy (HoE), which reflects the time-varying energy demand of each IoT device based on the energy gap between its required energy and the harvested energy from the UAVs. With the purpose to minimize the overall HoE of the IoT devices whose energy requirements are not satisfied, we optimally determine all the UAVs' trajectories and WET decisions over time, under the practical mobility and energy constraints of the UAVs. Although the proposed problem is of high complexity to solve, by excavating the UAVs' self-attentions for their collaborative WET, we propose the multi-agent graph reinforcement learning (MAGRL) based approach. Through the offline training of the MAGRL model, where the global training at the central controller guides the local training at each UAV agent, each UAV then distributively determines its trajectory and WET based on the well-trained local neural networks. Simulation results show that the proposed MAGRL-based approach outperforms various benchmarks for meeting the IoT devices' energy requirements.

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Federated Machine Learning Based Fetal Health Prediction Empowered with Bio-Signal Cardiotocography

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computers, Materials & Continua 2024年第3期78卷 3303-3321页

作者： Muhammad Umar Nasir Omar Kassem Khalil Karamath Ateeq Bassam SaleemAllah Almogadwy Muhammad Adnan Khan Muhammad Hasnain Azam Khan Muhammad Adnan Department of Computer Sciences Bahria University Lahore CampusLahore54000Pakistan Faculty of Information Technology Liwa CollegeAbu Dhabi00000United Arab Emirates Department of Computing Skyline University CollegeSharjah1797United Arab Emirates Department of Computer Science Taibah UniversityMedina42353Saudi Arabia Riphah School of Computing and Innovation Faculty of ComputingRiphah International UniversityLahore CampusLahore54000Pakistan Department of Engineering and Development Dubizzle LabsLahore54000Pakistan Department of Software Faculty of Artificial Intelligence and SoftwareGachon UniversitySeongnam-si13120Korea

Cardiotocography measures the fetal heart rate in the fetus during pregnancy to ensure physical health because cardiotocography gives data about fetal heart rate and uterine shrinkages which is very beneficial to detect whether the fetus is normal or suspect or *** cardiotocography measures infer wrongly and give wrong predictions because of human *** traditional way of reading the cardiotocography measures is the time taken and belongs to numerous human errors as *** condition is very important to measure at numerous stages and give proper medications to the fetus for its *** the current period Machine learning(ML)is a well-known classification strategy used in the biomedical field on various issues because ML is very fast and gives appropriate results that are better than traditional *** techniques play a pivotal role in detecting fetal disease in its early *** research article uses Federated machine learning(FML)and ML techniques to classify the condition of the *** study proposed a model for the detection of bio-signal cardiotocography that uses FML and ML techniques to train and test the ***,the proposed model of FML used numerous data preprocessing techniques to overcome data deficiency and achieves 99.06%and 0.94%of prediction accuracy and misprediction rate,respectively,and parallel the proposed model applying K-nearest neighbor(KNN)and achieves 82.93%and 17.07%of prediction accuracy and misprediction accuracy,***,by comparing both models FML outperformed the KNN technique and achieved the best and most appropriate prediction results as compared with previous studies the proposed study achieves the best and most accurate results.

关键词： Cardiotocography ML FML fetal disease bio-signal

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Tour Spot Recommendation System via Content-Based Filtering 16

Tour Spot Recommendation System via Content-Based Filtering

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16th International Conference on Open Source Systems and Technologies, ICOSST 2022

作者： Muneer, Mishal Rasheed, Uzair Khalid, Sadia Ahmad, Muhammad University of Lahore Department of Computer Science and It Lahore Pakistan College of Electrical and Mechanical Engineering Nust Department of Computer and Software Engineering Islamabad Pakistan University of Engineering and Technology Department of Computer Engineering Lahore Pakistan

ISBN: (纸本)9781665464772

Online recommendation systems have gained prominence. The online recommendation systems provide a better and faster way to choose Tour Spots for traveling and transactions. Recommender systems are powerful emerging technologies that help consumers to find the best places for touring according to their interests. The recommendation system recommends the most appropriate destinations for the end users. Internet travel portals also compete with each other regularly by taking into consideration several characteristics. Recommendation systems are one of the best methods to raise income and attract consumers. Established systems retrieved irrelevant information that resulted in low customer satisfaction and disappointment of customers. In this research, the Tour Spot recommendation system through content-based filtering is proposed and designed. It recommends the best picnic spot according to the budget and interest of the user. The proposed system improves the tourist experience and recommends the best place according to the things they would like;most significantly it is budget friendly as it suggests different places according to the interests and needs of the user. Data collection, including user information, consolidated user engagement reports, and tour spot data, is the primary challenge of implementing a travel recommendation system. User information originates mainly from information entered by the user during the registration process. When the system has the data of user likes and dislikes, then the system compares the user preferences to the dataset that the author makes for the tour recommendation systems in the dataset first identifies all the features that must be considered at the time of selection of travel destination. This Article also considers user likes and dislikes and the previous history of users to recommend a similar tour spot. © 2022 IEEE.

关键词： Recommender systems

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Research on Topological Deformation of 3D Human Image Based on Laplace Optimization 4

Research on Topological Deformation of 3D Human Image Based ...

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4th International Conference on High Performance Big Data and Intelligent Systems, HDIS 2022

作者： Zhou, Dongdong Chen, Xiaobing Zhang, Chuangchuang Guo, Shuxin Ashim, Khadka Lin, Jianchu Huaiyin Insititute of Technology Faculty of Computer and Software Engineering Huai'an China Gandaki College of Engineering and Science Pokhara University Pokhara Nepal

ISBN: (数字)9781665491440

ISBN: (纸本)9781665491440

Image deformation technology is an important technology in computer graphics image processing, which can satisfy people's requirements for visual and sensory effects, and its research results have been widely used in film and television animation, game development, virtual reality and other fields. Mesh-based deformation technology has been a research emphasis in image deformation technology, through the virtual model for the mesh division, the use of mesh nodes control, to achieve the deformation of the model. However, the more complex the model is, the greater number of mesh control nodes, which greatly increases the complexity of mesh deformation. In this paper, the deformation of 3D virtual character model is studied based on the method of image topological deformation. In order to solve the problem of large number of network nodes in 3D model, the Laplace deformation method is further combined to reduce the number of control nodes in the network. The experimental results show that the deformation can be effectively completed using a small number of control nodes by using the laplace coordinate system for adjacent topological mesh node control. © 2022 IEEE.

关键词： Virtual reality

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On Designing Multi-UAV aided Wireless Powered Dynamic Communication via Hierarchical Deep Reinforcement Learning

arXiv

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arXiv 2023年

作者： Zhao, Ze Yu Che, Yue Ling Luo, Sheng Luo, Gege Wu, Kaishun Leung, Victor C.M. College of Computer Science and Software Engineering Shenzhen University China

This paper proposes a novel design on the wireless powered communication network (WPCN) in dynamic environments under the assistance of multiple unmanned aerial vehicles (UAVs). Unlike the existing studies, where the low-power wireless nodes (WNs) often conform to the coherent harvest-then-transmit protocol, under our newly proposed double-threshold based WN type updating rule, each WN can dynamically and repeatedly update its WN type as an E-node for non-linear energy harvesting over time slots or an I-node for transmitting data over sub-slots. To maximize the total transmission data size of all the WNs over T slots, each of the UAVs individually determines its trajectory and binary wireless energy transmission (WET) decisions over times slots and its binary wireless data collection (WDC) decisions over sub-slots, under the constraints of each UAV’s limited on-board energy and each WN’s node type updating rule. However, due to the UAVs’ tightly-coupled trajectories with their WET and WDC decisions, as well as each WN’s time-varying battery energy, this problem is difficult to solve optimally. We then propose a new multi-agent based hierarchical deep reinforcement learning (MAHDRL) framework with two tiers to solve the problem efficiently, where the soft actor critic (SAC) policy is designed in tier-1 to determine each UAV’s continuous trajectory and binary WET decision over time slots, and the deep-Q learning (DQN) policy is designed in tier-2 to determine each UAV’s binary WDC decisions over sub-slots under the given UAV trajectory from tier-1. Both of the SAC policy and the DQN policy are executed distributively at each UAV. Finally, extensive simulation results are provided to validate the outweighed performance of the proposed MAHDRL approach over various state-of-the-art benchmarks. Copyright © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

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Mind-Body TaoRelax: Relieving Stress Through Immersive Virtual Reality Relaxation Training in a Taoist Atmosphere

Mind-Body TaoRelax: Relieving Stress Through Immersive Virtu...

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Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW), IEEE Conference on

作者： Hao Yan Ding Ding Zhuying Li College of Software Engineering Southeast University China School of Computer Science and Engineering Southeast University China

ISBN: (数字)9798350374490

ISBN: (纸本)9798350374506

Virtual reality technology is providing a new method for psychologi-cal relaxation training, but most systems focus primarily on medi-tation as a singular relaxation method. We propose a novel virtual reality relaxation system called TaoRelax. Building upon traditional Taoist culture, the system integrates both meditation and progressive muscle relaxation training. It combines electromyography (EMG) as a means of feedback and assessment during training, providing users with a dual experience of relaxation for both body and mind. Our preliminary results indicate that after training in our system, participants experienced a significant reduction in psychological stress and a notable improvement in meditation abilities.

关键词： Training Three-dimensional displays Conferences Psychology Virtual reality Human factors User interfaces

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MSE-CapsPPISP: Spatial Hierarchical Protein-Protein Interaction Sites Prediction Using Squeeze-and-Excitation Capsule Networks

MSE-CapsPPISP: Spatial Hierarchical Protein-Protein Interact...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Lv, Weipeng Jiang, Changkun Li, Jianqiang Shenzhen University College of Computer Science and Software Engineering National Engineering Laboratory for Big Data System Computing Technology Shenzhen China

ISBN: (纸本)9781665468190

The discovery of protein-protein interaction sites (PPIs) is vital for exploring the principle of PPI and understanding the nature of life activities. Developing computational approaches to predict PPIs can effectively compensate for the shortcomings of biological experiments, which are mostly time-consuming and vulnerable to noise. In recent years, deep learning has been used to develop PPIs prediction models. Most of them consider the contextual information of the target amino acid residues and use a local protein sequence to represent the targets. However, the traditional deep-learning techniques, e.g., deep neural networks (DNNs) and convolutional neural networks (CNNs), disregard the important spatial hierarchies contained in the features of protein sequences, leading to their failure to effectively distinguish the interaction sites from different residue regions. In this work, we design MSE-CapsPPISP, a new deep-learning model to address the PPIs prediction with spatial hierarchies. The key idea of MSE-CapsPPISP is to take into account the hierarchical relationships between the features of protein sequences. We characterize the hierarchical relationships by designing a tailored Capsule Network (CapsNet) model, which is a novel type of neural network with vector neurons. Moreover, to make the network representation more robust, MSE-CapsPPISP uses multi-scale CNNs to extract multi-scale features of protein sequences and Squeeze-and-Excitation blocks to recalibrate the features. The validation results show that our MSE-CapsPPISP outperforms the baseline CNNs-based architecture DeepPPISP and other competing schemes in the PPIs prediction task. © 2022 IEEE.

关键词： Forecasting

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Slicing Input Features to Accelerate Deep Learning: A Case Study with Graph Neural Networks

arXiv

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arXiv 2024年

作者： Xu, Zhengjia Lyu, Dingyang Zhang, Jinghui College of Software Engineering Southeast University China School of Computer Science and Engineering Southeast University China

As graphs grow larger, full-batch GNN training becomes hard for single GPU memory. Therefore, to enhance the scalability of GNN training, some studies have proposed sampling-based mini-batch training and distributed graph learning. However, these methods still have drawbacks, such as performance degradation and heavy communication. This paper introduces SliceGCN, a feature-sliced distributed large-scale graph learning method. SliceGCN slices the node features, with each computing device, i.e., GPU, handling partial features. After each GPU processes its share, partial representations are obtained and concatenated to form complete representations, enabling a single GPU's memory to handle the entire graph structure. This aims to avoid the accuracy loss typically associated with mini-batch training (due to incomplete graph structures) and to reduce inter-GPU communication during message passing (the forward propagation process of GNNs). To study and mitigate potential accuracy reductions due to slicing features, this paper proposes feature fusion and slice encoding. Experiments were conducted on six node classification datasets, yielding some interesting analytical results. These results indicate that while SliceGCN does not enhance efficiency on smaller datasets, it does improve efficiency on larger datasets. Additionally, we found that SliceGCN and its variants have better convergence, feature fusion and slice encoding can make training more stable, reduce accuracy fluctuations, and this study also discovered that the design of SliceGCN has a potentially parameter-efficient nature. Copyright © 2024, The Authors. All rights reserved.

关键词： Encoding (symbols)

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Hybrid Deep Learning Algorithm For MRI Brain Alzheimer's Disease Prediction

Hybrid Deep Learning Algorithm For MRI Brain Alzheimer's Dis...

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2023 International Conference on Energy, Materials and Communication engineering, ICEMCE 2023

作者： Karthikeyan, D. Nivasini, R. Raja, Kiruba Thangam Madhivadhani, D. Sivakumar, B. Krishna Priya, A.T.R. Srm Institute of Science and Technology Department of Electrical and Electronics Engineering Kattankulathur Chennai India Iitm Research Park Guvi Geek Networks Tamilnadu Chennai India Vit University School of Computer Science Engineering and Information Systems Department of Software and Systems Engineering Vellore India Kings Engineering College Department of Electronics and Communication Engineering Sriperumbudur Chennai India Rohini College of Engineering and Technology Department of Computer Science and Engineering Kanyakumari India

ISBN: (纸本)9798350393378

Magnetic resonance imaging (MRI) has been used to study the structural makeup of the brain and analyse several neurological disorders and diseased areas. For the adoption of preventative measures, early recognition of Alzheimer's disease (AD) patients is essential. Here, a thorough inspection of the tissue arrangements obtained by MRI images of Outcome and Assessment Information (OASIS) dataset enables an exact characterization of certain brain diseases. There have been a number of division techniques for diagnosing AD that range in complexity. Compassion has been tested by deep learning techniques used to segment the structure of the brain and classify AD because they have the potential to uncover important information from vast amounts of data. In this paper the deep learning technique of Hybrid Dragonfly based GWO convolutional Neural Network (CNN) is achieved promising result for the diagnosis of AD. At image preprocessing wiener filter is used for removing the additive noises and the Gray Level Co-Occurrence Matrix (GLCM) extraction is implemented for texture analyzing. As a result, hybrid deep learning methods of CNN has the accuracy as 90% and the result of the image prediction are presented in this paper. © 2023 IEEE.

关键词： Magnetic resonance imaging

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