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EPPDL: An efficient privacy-preserving distributed ledger for digital asset transfer in Web3.0

FUTURE GENERATION computer systems-THE international JOURNAL OF ESCIENCE 2025年 167卷

作者： Ma, Lichuan Zhou, Lu Huang, Hang Qu, Youyang Liu, Xuefeng Xidian Univ State Key Lab Integrated Serv Networks 2 South Taibai Rd Xian 710071 Shaanxi Peoples R China Shaanxi Key Lab Blockchain & Secure Comp 2 South Taibai Rd Xian 710071 Shaanxi Peoples R China CSIRO Data61 Res Way Clayton Vic 3168 Australia

Nowadays, anew generation of decentralized internet framework, coined as Web3.0, is emerging. However, due to the insufficient computing power on the user side and the know-your-customer regulatory requirements, it is unrealistic to fully achieve decentralization in Web3.0 currently. The service provider-intermediated architecture seems more practical by including federated service providers. At the same time, in order to fully stimulate users to create and share contents in the era of Web3.0, the importance of digital assets, e.g., digit tokens and cryptocurrencies, is increasing. Asa result, whether digital asset transfer can be securely and efficiently accommodated determines the further development of Web3.0. Blockchain is believed to be one effective solution to guarantee the security of digital asset transfer. However, existing works either target fully decentralized scenarios or fail to settle digital asset transfers with high efficiency. Thus in this paper, a formal yet novel service provider-intermediated architecture is firstly proposed to closely align with the practical requirements of Web3.0. Then, an efficient privacy-preserving distributed ledger construction protocol, coined as EPPDL, is proposed to safeguard digital asset transfer among users registered at different service providers. Concrete security analysis proves that the proposed EPPDL is secure against different types of adversaries, while comprehensive experiments verify its efficiency and effectiveness.

关键词： Web3.0 Privacy preserving distributed ledger Blockchain

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Satellite Image Classification using Convolutional Neural Network with Huber Loss Function for Land Cover Mapping in Forest Ecosystem

Satellite Image Classification using Convolutional Neural Ne...

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2024 international Conference on distributed systems, computer networks and Cybersecurity, ICDSCNC 2024

作者： Al-Fatlawy, Ramy Riad Raviprakash, P. Vybhavi, Gummagatta Yajaman Jagadish, Sripelli Sundaram, S Meenakshi College of Technical Engineering The Islamic University Department of Computers Techniques Engineering Najaf Iraq Hyderabad India Gokaraju Rangaraju Institute of Engineering and Technology Bachupally Department of Cse Hyderabad India School of Computer Science & Artificial Intelligence Sr University Warangal India Nitte Meenakshi Institute of Technology Department of Cse Bengaluru India

ISBN: (纸本)9798350375442

Satellite image classification for land cover involves determining high-resolution imagery to recognize and classify various types of land covers. This process assists in monitoring forest health, managing resources, and assessing biodiversity effectively. However, accurately classifying images for land cover is difficult because of diverse and complex nature of forest environments and varying image qualities. In this research, the Convolutional Neural Network with Huber Loss Function (CNN-HLF) is proposed to classify the satellite images accurately for landcover mapping in forest ecosystem. The CNN-HLF effectively manage the variability and complexity of forest environments which improves the performance of land cover. Initially, the Eurosat dataset is used to determine the CNN-HLF performance. Resizing and image sharpening is applied to adjust the size and enhance the clarity of features. DenseNet201 is employed for extracting features effectively from pre-processed images. The proposed CNN-HLF achieves a high accuracy of 99.87% using Eurosat dataset compared to existing methods like GoogleNet with pre-processing and Lightweight Parallel CNN (LPCNN). © 2024 IEEE.

关键词： Convolutional neural networks

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A Beetle Antennae Search-Based Sparrow Search Algorithm for Network Intrusion Detection

A Beetle Antennae Search-Based Sparrow Search Algorithm for ...

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2024 international Conference on distributed systems, computer networks and Cybersecurity, ICDSCNC 2024

作者： Baig, Ayub Reddy, R. Archana Muhsin Hassan, Muntather Subramanya, S.G. Manu, Hajari Department of Infornation Technology Hyderabad India Sr University Department of Computer Science and Engineering Warangal India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute of Technology Department of Cse Bengaluru India Gokaraju Rangaraju Institute of Engineering and Technology Department of Ai&ml Hyderabad India

ISBN: (纸本)9798350375442

Nowadays, the network Intrusion Detection System (IDS) is a crucial cybersecurity technique that predicts unauthorized access, and exploits malfunction of computer networks. Traditional IDS struggle with classifying multiple, varied attack patterns which often lead to poor performance in detecting critical threats. In order to improve detection accuracy, identify numerous attacks, developing an efficient system becomes crucial which handles imbalanced data. Thus, this paper presents an Adaptive network IDS that employs a hybrid approach by combining the Improved Beetle Antenna Search (BAS) Algorithm and the Sparrow Search Algorithm (SSA) to enhance detection accuracy and adaptability in identifying cyber threats on NSL-KDD and CSE-CIC-IDS2018 datasets. Additionally, a Bi-directional Long Short-Term Memory (Bi-LSTM) network is utilized to reduce the False Positive Rate (FPR) and increase the entire efficiency of the system by effectively overcoming the challenges such as imbalanced data and low detection rate. Experimental evaluations on benchmark intrusion datasets such as NSL-KDD and CSE-CIC-IDS2018 using proposed IBAS-SSA + Bi-LSTM demonstrate higher detection results with accuracy of 98.12% and 98.64% respectively, when compared to other traditional IDS methods. © 2024 IEEE.

关键词： Network intrusion

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Hierarchical Federated Learning for Internet of Vehicles networks: A Multi-Tiered Approach to Efficient and Adaptive distributed Learning 6

Hierarchical Federated Learning for Internet of Vehicles Net...

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6th international Conference on Sustainable Technologies for Industry 5.0, STI 2024

作者： Protim, Joy Sharmin, Zeseya Sultana, Babe Green University of Bangladesh Department of Computer Science and Engineering Bangladesh School of Information Technology Deakin University Melbourne Australia

ISBN: (纸本)9798331531973

The Internet of Vehicles IoV) has emerged as a critical paradigm in intelligent transportation systems, generating vast amounts of data that can be leveraged for various applications. However, the distributed nature of vehicular networks, privacy concerns, and the non-IID (non-independent and identically distributed) characteristics of the data pose significant challenges to traditional machine learning approaches. This paper proposes a novel Hierarchical Federated Learning (HFL) architecture for IoV networks, introducing an intermediate tier of regional cloud servers between vehicles and the central server. Our approach addresses key challenges in communication efficiency, data heterogeneity, and model performance. Experimental results demonstrate that HFL achieves up to 30% reduction in communication overhead and a 15% improvement in model accuracy compared to conventional federated learning methods in IoV scenarios. © 2024 IEEE.

关键词： Self-supervised learning

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A Cost-Efficient Failure-Tolerant Scheme for distributed DNN Training 41

A Cost-Efficient Failure-Tolerant Scheme for Distributed DNN...

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41st IEEE international Conference on computer Design (ICCD)

作者： Chen, Menglei Hua, Yu Bai, Rong Huang, Jianming Huazhong Univ Sci & Technol WNLO Wuhan Hubei Peoples R China

ISBN: (纸本)9798350342918

distributed deep neural network (DNN) training is important to support artificial intelligence (AI) applications, such as image classification, natural language processing, and autonomous driving. Unfortunately, the distributed property makes the DNN training vulnerable to system failures. Check-pointing is generally used to support failure tolerance, which however suffers from high runtime overheads. In order to enable high-performance and low-latency checkpointing, we propose a lightweight checkpointing system for distributed DNN training, called LightCheck. To reduce the checkpointing overheads, we leverage fine-grained asynchronous checkpointing by pipelining checkpointing in a layer-wise way. To further decrease the checkpointing latency, we leverage the software-hardware codesign methodology by coalescing new hardware devices into our checkpointing system via a persistent memory (PM) manager. Experimental results on six representative real-world DNN models demonstrate that LightCheck offers more than 10x higher checkpointing frequency with lower runtime overheads than stateof-the-art checkpointing schemes. We have released the opensource codes for public use in https://***/LighT-chenml/ ***.

关键词： computer systems Checkpointing systems Failure tolerance Deep neural networks

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26th international Conference on distributed computer and Communication networks: Control, Computation, Communications, DCCN 2023

26th International Conference on Distributed Computer and Co...

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26th international Conference on distributed computer and Communication networks: Control, Computation, Communications, DCCN 2023

ISBN: (纸本)9783031504815

The proceedings contain 41 papers. The special focus in this conference is on distributed computer and Communication networks: Control, Computation, Communications. The topics include: Efficient Transmission of Holographic Images: A Novel Approach Toward 6G Telepresence Services;the Simulation of Finite-Source Retrial Queues with Two-Way Communication to the Orbit, Incorporating a Backup Server;on Real-Time Model Inversion Attacks Detection;distributed System for Scientific and Engineering Computations with Problem Containerization and Prioritization;Overview of Research Works on Applications of UHF RFID on Vehicles for Data Transmission;on the Identification of a Finite Automaton by Its Input and Output Sequences in Case of Distortions;analysis of Tethered Unmanned High-Altitude Platform Reliability;information Spreading in Non-homogeneous Evolving networks with Node and Edge Deletion;comparative Analysis of a Resource Loss System with the Finite Buffer and Different Service Disciplines;batch Service Polling System: Mathematical Analysis and Simulation Modeling;analysis of the Queueing System Describing a Mobile Network Subscriber’s Processing Under Varying Modulation Schemes and Correlated Batch Arrivals;analysis of Queuing systems under N Policy with Different Server Activation Strategies;on Asymptotic Insensitivity of Reliability Function of a 2-out-of-n Model Under Quick Recovery of Its Components;on the Variance Reduction Methods for Estimating the Reliability of the Multi-phase Gaussian Degradation System;multiphase Queuing System of Blocking Queues and a Single Common Orbit Retrial Queue with Limited Buffer;analysis of Procedures for Joint Servicing of Multiservice Traffic in Access Nodes;recovery of Real-Time Clusters with the Division of Computing Resources into the Execution of Functional Queries and the Restoration of Data Generated Since the Last Backup;numerical Study of Queuing-Inventory systems with Catastrophes Under Base Stock Policy;a Machine-Lear

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A Service-Oriented Middleware Enabling Decentralised Deployment in Mobile Multihop networks

A Service-Oriented Middleware Enabling Decentralised Deploym...

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20th international Conference on Service-Oriented Computing (ICSOC)

作者： Hogie, Luc CNRS Paris France Univ Cote Azur Lab I3S Nice France Inria Sophia Antipolis Biot France

ISBN: (纸本)9783031265068;9783031265075

The number of computing devices, mostly smartphones is tremendous. The potential for distributed computing on them is no less huge. But developing applications for such networks is challenging especially as most middleware solutions for distributed computing are tailored to managed grids and clusters, so they lacks the elasticity needed to deal with the difficult conditions brought by multi-hops, mobility, heterogeneity, untrustability, etc. To solve this, several middleware were released, but none of them feature workable deployment solutions. This paper presents the deployment service of the Idawi middleware, which implements a fully decentralized and automatised deployment strategy into a Open Source middleware tailored to enabling distributed computing in difficult networking conditions like in the IoT/fog/edge.

关键词： Decentralised systems Overlay networks Middleware Deployment IoT Java

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TinyML for EEG Decoding on Microcontrollers 56

TinyML for EEG Decoding on Microcontrollers

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56th IEEE international symposium on Circuits and systems (ISCAS)

作者： Tragoudaras, Antonios Antoniadis, Charalampos Massoud, Yehia King Abdullah Univ Sci & Technol KAUST Innovat Technol Labs ITL Thuwal Saudi Arabia

ISBN: (纸本)9781665451093

The accurate decoding of ElectroEncephaloGraphy (EEG) signals would bring us closer to understanding brain functionality, opening new pathways to fixing brain impairments and devising new Brain-computer Interface (BCI)-related applications. The impressive success of deep convolutional neural networks extracting information from raw data in computer vision and natural language processing has motivated their investigation into EEG signal decoding. Consequently, a number of deep convolutional neural network models with state-of-the-art performance have been proposed in the literature for EEG signal decoding. However, because all these works aimed to find the model architecture with the best decoding accuracy, the model's size was left unbounded in that exploration. Considering the model size in the design of deep convolutional neural networks for EEG decoding could make their implementation on low-power microcontrollers (that may be integrated into a wearable system) with limited memory feasible. Thus, in this paper, we search for the most accurate deep convolutional neural network on the BCI Competition IV 2a dataset that can also fit in a microcontroller with less than 256KB SRAM. Specifically, we use a Neural Architecture Search (NAS) algorithm that considers, apart from the model's accuracy, the model size, the latency, and the peak memory utilization when running the model's inference. We compare our models with the model with the best decoding accuracy in the literature on the BCI Competition IV 2a dataset (baseline). We show that the discovered models could achieve similar accuracy to the baseline model while shrinking the memory footprint during inference by a factor of approximate to x20, with a speedup in latency of up to x1.7 on average.

关键词： TinyAutoML EEG signals MCUs wearables Brain computer Interface

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An Exploratory Study of Deep Learning for Predicting Computational Tasks Behavior in HPC systems 35

An Exploratory Study of Deep Learning for Predicting Computa...

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35th IEEE international symposium on computer Architecture and High Performance Computing (SBAC-PAD)

作者： Porto, Alexandre H. L. Coelho, Micaella Ocana, Kary Osthoff, Carla Boito, Francieli Cardoso, Douglas O. Natl Lab Sci Comp Petropolis RJ Brazil Univ Bordeaux CNRS Bordeaux INP INRIALaBRI Talence France Polytech Inst Tomar Smart Cities Res Ctr Tomar Portugal

ISBN: (纸本)9798350381603

The scientific gateway BioinfoPortal for bioinformatics applications is hosted in the National Laboratory for Scientific Computing (LNCC) and is coupled to the Santos Dumont (SDumont) supercomputer environment. BioinfoPortal offers a catalog of bioinformatics software that benefits from the parallel and distributed architecture offered by LNCC. Task submissions consume SDumont nodes shared by other users of the supercomputer;thus, it is important they use the best configuration, which is defined as the best choice of the number of threads/nodes to be allocated for every task submission. This article presents an analysis using neural networks to estimate the computational time required to execute bioinformatics software in several scenarios using a pre-configured number of nodes and threads. Our goal is to demonstrate the performance behavior of software such as RAxML in Bioinfoportal, and which computational scenario can be chosen to efficiently execute software in SDumont. Results support that the neural networks are adequate to predict the variable elapsed time, Elapsed, to evaluate the relationships between input parameters, number of bootstraps (RAxML), number of threads, and number of nodes, and to identify the fastest configuration. The goal is to make BioinfoPortal a smart, efficient, and green gateway. In future studies, we propose to study more variables and predictors as well as other bioinformatics software in BioinfoPortal.

关键词： neural networks phylogenetic analysis extra trees performance prediction performance modeling

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SVG-based Network Topology Mapping Method and System Research for distributed Cloud Platforms 7

SVG-based Network Topology Mapping Method and System Researc...

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7th IEEE international Conference on Information systems and computer Aided Education, ICISCAE 2024

作者： Xingfu, Zhang Jinshuai, Zhang Shusong, Wang Beijing China-Power Information Technology Co. Ltd. Beijing China

ISBN: (纸本)9798350350760

—This study focuses on the visual representation of distributed cloud platform network topologies and introduces an innovative drawing method based on SVG technology. By thoroughly analyzing the unique advantages of SVG in network topology rendering, a data model and layout algorithm suited for complex distributed environments have been designed. The research results encompass a comprehensive topology drawing system, including core modules for data collection, topology computation, SVG rendering, and interaction. This system has demonstrated exceptional performance in real-world applications within large-scale cloud platforms, efficiently managing dynamically changing network structures and supporting multi-layered topology displays. The innovation of this paper lies in the seamless integration of SVG technology with distributed cloud architecture, offering a novel solution for the visualization management of complex networks. The research not only enhances the expressiveness and interactivity of network topologies but also provides a powerful tool for cloud platform operations and decision support, holding significant theoretical and practical value. ©2024 IEEE.

关键词： Cloud platforms

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