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Sparse Reconstructive Evidential Clustering for Multi-View Data

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iEEE/CAA Journal of Automatica Sinica 2024年第2期11卷 459-473页

作者： Chaoyu Gong Yang You School of computing National University of SingaporeSingapore

Although many multi-view clustering(MVC) algorithms with acceptable performances have been presented, to the best of our knowledge, nearly all of them need to be fed with the correct number of clusters. in addition, these existing algorithms create only the hard and fuzzy partitions for multi-view objects,which are often located in highly-overlapping areas of multi-view feature space. The adoption of hard and fuzzy partition ignores the ambiguity and uncertainty in the assignment of objects, likely leading to performance degradation. To address these issues, we propose a novel sparse reconstructive multi-view evidential clustering algorithm(SRMVEC). Based on a sparse reconstructive procedure, SRMVEC learns a shared affinity matrix across views, and maps multi-view objects to a 2-dimensional humanreadable chart by calculating 2 newly defined mathematical metrics for each object. From this chart, users can detect the number of clusters and select several objects existing in the dataset as cluster centers. Then, SRMVEC derives a credal partition under the framework of evidence theory, improving the fault tolerance of clustering. Ablation studies show the benefits of adopting the sparse reconstructive procedure and evidence theory. Besides,SRMVEC delivers effectiveness on benchmark datasets by outperforming some state-of-the-art methods.

关键词： Evidence theory multi-view clustering(MVC) optimization sparse reconstruction

Resource management at the network edge for federated learning

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Digital Communications and Networks 2024年第3期10卷 765-782页

作者： Silvana Trindade Luiz F.Bittencourt Nelson L.S.da Fonseca institute of computing State University of CampinasCampinasBrazil

Federated learning has been explored as a promising solution for training machine learning models at the network edge,without sharing private user *** limited resources at the edge,new solutions must be developed to leverage the software and hardware resources as the existing solutions did not focus on resource management for network edge,specially for federated *** this paper,we describe the recent work on resource manage-ment at the edge and explore the challenges and future directions to allow the execution of federated learning at the *** such as the discovery of resources,deployment,load balancing,migration,and energy effi-ciency are discussed in the paper.

关键词： Resource management Edge computing Federated learning Machine learning

Simulation study on the security of consensus algorithms in DAG-based distributed ledger

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Frontiers of Computer Science 2024年第3期18卷 171-182页

作者： Shuzhe Li Hongwei XU Qiong Li Qi HAN Faculty of computing Harbin Institute of TechnologyHarbin 150001China

Due to the advantages of high volume of transactions and low resource consumption,Directed Acyclic Graph(DAG)-based Distributed Ledger Technology(DLT)has been considered a possible next-generation alternative to ***,the security of the DAG-based system has yet to be comprehensively *** at verifying and evaluating the security of DAG-based DLT,we develop a Multi-Agent based iOTA Simulation platform called *** MAiOTASim,we model honest and malicious nodes and simulate the configurable network environment,including network topology and *** double-spending attack is a particular security issue related to *** perform the security verification of the consensus algorithms under multiple double-spending attack *** simulations show that the consensus algorithms can resist the parasite chain attack and partially resist the splitting attack,but they are ineffective under the large weight *** take the cumulative weight difference of transactions as the evaluation criterion and analyze the effect of different consensus algorithms with parameters under each attack ***,MAiOTASim enables users to perform largescale simulations with multiple nodes and tens of thousands of transactions more efficiently than state-of-the-art ones.

关键词： distributed ledger iOTA Multi-Agent system simulation

Single image super-resolution:a comprehensive review and recent insight

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Frontiers of Computer Science 2024年第1期18卷 139-156页

作者： Hanadi AL-MEKHLAFi Shiguang LiU College of intelligence and computing Tianjin UniversityTianjin 300350China

Super-resolution(SR)is a long-standing problem in image processing and computer vision and has attracted great attention from researchers over the *** main concept of SR is to reconstruct images from low-resolution(LR)to high-resolution(HR).it is an ongoing process in image technology,through up-sampling,de-blurring,and *** neural network(CNN)has been widely used to enhance the resolution of images in recent *** alternative methods use deep learning to improve the progress of image super-resolution based on ***,we review the recent findings of single image super-resolution using deep learning with an emphasis on distillation knowledge used to enhance image super-resolution.,it is also to highlight the potential applications of image super-resolution in security monitoring,medical diagnosis,microscopy image processing,satellite remote sensing,communication transmission,the digital multimedia industry and video ***,we present the challenges and assess future trends in super-resolution based on deep learning.

关键词： super-resolution deep learning single-image interpolation-based learning-based reconstruction-based

Reinforcement Learning-Empowered Graph Convolutional Network Framework for Data integrity Attack Detection in Cyber-Physical Systems

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CSEE Journal of Power and Energy Systems 2024年第2期10卷 797-806页

作者： Edeh Vincent Mehdi Korki Mehdi Seyedmahmoudian Alex Stojcevski Saad Mekhilef School of Science Computingand Engineering TechnologiesSwinburne University of TechnologyMelbourneAustralia

The massive integration of communication and information technology with the large-scale power grid has enhanced the efficiency, safety, and economical operation of cyber-physical systems. However, the open and diversified communication environment of the smart grid is exposed to cyber-attacks. Data integrity attacks that can bypass conventional security techniques have been considered critical threats to the operation of the grid. Current detection techniques cannot learn the dynamic and heterogeneous characteristics of the smart grid and are unable to deal with non-euclidean data types. To address the issue, we propose a novel Deep-Q-Network scheme empowered with a graph convolutional network (GCN) framework to detect data integrity attacks in cyber-physical systems. The simulation results show that the proposed framework is scalable and achieves higher detection accuracy, unlike other benchmark techniques.

关键词： Deep reinforcement learning graph convolutional network heterogeneous smart grid network

Transformer-Aided Deep Double Dueling Spatial-Temporal Q-Network for Spatial Crowdsourcing Analysis

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Computer Modeling in Engineering & Sciences 2024年第4期139卷 893-909页

作者： Yu Li Mingxiao Li Dongyang Ou Junjie Guo Fangyuan Pan Department of computing Hangzhou Dianzi UniversityHangzhou310018China

With the rapid development ofmobile internet,spatial crowdsourcing has becomemore andmore *** crowdsourcing consists of many different types of applications,such as spatial crowd-sensing *** terms of spatial crowd-sensing,it collects and analyzes traffic sensing data from clients like vehicles and traffic lights to construct intelligent traffic prediction *** collecting sensing data,spatial crowdsourcing also includes spatial delivery services like DiDi and *** task assignment and worker selection dominate the service quality for spatial crowdsourcing *** research conducted task assignments via traditional matching approaches or using simple network ***,advanced mining methods are lacking to explore the relationship between workers,task publishers,and the spatio-temporal attributes in ***,in this paper,we propose a Deep Double Dueling Spatial-temporal Q Network(D3SQN)to adaptively learn the spatialtemporal relationship between task,task publishers,and workers in a dynamic environment to achieve optimal ***,D3SQNis revised through reinforcement learning by adding a spatial-temporal transformer that can estimate the expected state values and action advantages so as to improve the accuracy of task *** experiments are conducted over real data collected fromDiDi and ELM,and the simulation results verify the effectiveness of our proposed models.

关键词： Historical behavior analysis spatial crowdsourcing deep double dueling Q-networks

A Deep Learning Approach for Landmines Detection Based on Airborne Magnetometry imaging and Edge computing

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Computer Modeling in Engineering & Sciences 2024年第5期139卷 2117-2137页

作者： Ahmed Barnawi Krishan Kumar Neeraj Kumar Bander Alzahrani Amal Almansour Faculty of computing and information Technology King Abdulaziz UniversityJeddahSaudi Arabia Department of Computer Science and Engineering Thapar Institute of Engineering and TechnologyDeemed to be UniversityPatialaIndia

Landmines continue to pose an ongoing threat in various regions around the world,with countless buried landmines affecting numerous human *** detonation of these landmines results in thousands of casualties reported worldwide ***,there is a pressing need to employ diverse landmine detection techniques for their *** effective approach for landmine detection is UAV(Unmanned Aerial Vehicle)based AirborneMagnetometry,which identifies magnetic anomalies in the local terrestrial magnetic *** can generate a contour plot or heat map that visually represents the magnetic field *** the effectiveness of this approach,landmine removal remains a challenging and resource-intensive task,fraughtwith *** computing,on the other hand,can play a crucial role in critical drone monitoring applications like landmine *** processing data locally on a nearby edge server,edge computing can reduce communication latency and bandwidth requirements,allowing real-time analysis of magnetic field *** enables faster decision-making and more efficient landmine detection,potentially saving lives and minimizing the risks involved in the ***,edge computing can provide enhanced security and privacy by keeping sensitive data close to the source,reducing the chances of data exposure during *** paper introduces the MAGnetometry imaging based Classification System(MAGiCS),a fully automated UAV-based system designed for landmine and buried object detection and *** have developed an efficient deep learning-based strategy for automatic image classification using magnetometry dataset *** simulating the proposal in various network scenarios,we have successfully detected landmine signatures present in themagnetometry *** trained models exhibit significant performance improvements,achieving a maximum mean average precision value of 97.8%.

关键词： CNN deep learning landmine detection magnetometer mean average precision UAV

Structural failure analysis with CMS-based ground motion selection using innovative cost function and weight factors

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Earthquake Engineering and Engineering Vibration 2024年第4期23卷 899-918页

作者： Samadian, Delbaz Muhit, imrose B. Dawood, Nashwan School of computing Engineering and Digital Technologies Teesside University Middlesbrough UK

The selection and scaling of ground motion records is considered a primary and essential task in performing structural analysis and design. Conventional methods involve using ground motion models and a conditional spectrum to select ground motion records based on the target spectrum. This research demonstrates the influence of adopting different weighted factors for various period ranges during matching selected ground motions with the target hazard spectrum. The event data from the Next Generation Attenuation West 2 (NGA-West 2) database is used as the basis for ground motion selection, and hazard de-aggregation is conducted to estimate the event parameters of interest, which are then used to construct the target intensity measure (iM). The target iMs are then used to select ground motion records with different weighted vector-valued objective functions. The weights are altered to account for the relative importance of iM in accordance with the structural analysis application of steel moment resisting frame (SMRF) buildings. instead of an ordinary objective function for the matching spectrum, a novel model is introduced and compared with the conventional cost function. The results indicate that when applying the new cost function for ground motion selection, it places higher demands on structures compared to the conventional cost function. Moreover, submitting more weights to the first-mode period of structures increases engineering demand parameters. Findings demonstrate that weight factors allocated to different period ranges can successfully account for period elongation and higher mode effects.

关键词： weighted objective function ground motion selection steel moment resisting frame hazard analysis

Enhancing ioT Security:Quantum-Level Resilience against Threats

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Computers, Materials & Continua 2024年第1期78卷 329-356页

作者： Hosam Alhakami College of computing Umm Al-Qura UniversityMakkahSaudi Arabia

The rapid growth of the internet of Things(ioT)operations has necessitated the incorporation of quantum computing technologies tomeet its expanding *** integration ismotivated by the need to solve the specific issues provided by the expansion of ioT and the potential benefits that quantum computing can offer in this *** combination of ioT and quantum computing creates new privacy and security *** study examines the critical need to prevent potential security concerns from quantum computing in ioT *** investigate the incorporation of quantum computing approaches within ioT security frameworks,with a focus on developing effective security *** research,which uses quantum algorithms and cryptographic protocols,provides a unique solution to protecting sensitive information and assuring the integrity of ioT *** rigorously analyze critical quantum computing security properties,building a hierarchical framework for systematic *** offer concrete solutions flexible to diverse aswell as ambiguous opinions through using a unified computational model with analytical hierarchy process(AHP)multi-criteria decision-making(MCDM)as the technique for ordering preferences by similarity to ideal solutions(TOPSiS)in a fuzzy *** study adds practical benefit by supporting practitioners in recognizing,choosing,and prioritizing essential security factors from the standpoint of quantum *** approach is a critical step towards improving quantum-level security in ioT systems,strengthening their resilience against future threats,and preserving the ioT ecosystem’s long-term prosperity.

关键词： Quantum security quantum computing internet of Things fuzzy decision-making