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computer Software 2024年第2期41卷 60-79页

作者： Maeda, Hiroyuki Tanaka, Kazuaki Fukuoka Industrial Technology Center Japan Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology Japan

In this paper, we propose hardware acceleration to improve a performance of scripting programming languages for embedded developments. Scripting programming languages enable more efficient software developments and scripting programming languages are used in several system developments such as IoT developments. However, scripting programming languages are an interpreted language and the program is executed by a virtual machine, so it takes a long time to execute. Therefore, we implemented hardware acceleration for scripting programming language "mruby/c" targeting small embedded devices. The hardware acceleration supports bottleneck processing of a virtual machine of mruby/c, execution speeds are improved. This paper describes the configuration of the hardware acceleration for mruby/c and the performance evaluation. © 2024 Japan Society for Software science and technology. All rights reserved.

关键词： Virtual machine

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DuFNet:Dual Flow Network of Real-Time Semantic Segmentation for Unmanned Driving Application of Internet of Things

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computer Modeling in engineering & sciences 2023年第7期136卷 223-239页

作者： Tao Duan Yue Liu Jingze Li Zhichao Lian d Qianmu Li School of Computer Science and Engineering Nanjing University of Science and TechnologyNanjing210094China School of Cyberspace Security Nanjing University of Science and TechnologyWuxi320200China

The application of unmanned driving in the Internet of Things is one of the concrete manifestations of the application of artificial intelligence *** semantic segmentation can help the unmanned driving system by achieving road accessibility *** segmentation is also a challenging technology for image understanding and scene *** focused on the challenging task of real-time semantic segmentation in this *** this paper,we proposed a novel fast architecture for real-time semantic segmentation named *** from the existing work of Bilateral Segmentation Network(BiSeNet),DuFNet proposes a novel Semantic Information Flow(SIF)structure for context information and a novel Fringe Information Flow(FIF)structure for spatial *** also proposed two kinds of SIF with cascaded and paralleled structures,*** SIF encodes the input stage by stage in the ResNet18 backbone and provides context information for the feature *** from previous stages usually contain rich low-level details but high-level semantics for later *** convolutions embed in Parallel SIF aggregate the corresponding features among different stages and generate a powerful global context representation with less computational *** FIF consists of a pooling layer and an upsampling operator followed by projection convolution *** concise component provides more spatial details for the *** with BiSeNet,our work achieved faster speed and comparable performance with 72.34%mIoU accuracy and 78 FPS on Cityscapes Dataset based on the ResNet18 backbone.

关键词： Real-time semantic segmentation convolutional neural network feature fusion unmanned driving fringe information flow

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DERNNet:Dual Encoding Recurrent Neural Network Based Secure Optimal Routing in WSN

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computer Systems science & engineering 2023年第5期45卷 1375-1392页

作者： A.Venkatesh S.Asha School of Computer Science and Engineering Vellore Institute of TechnologyChennaiIndia Centre for Cyber Physical Systems&School of Computer Science and Engineering Vellore Institute of TechnologyChennaiIndia

A Wireless Sensor Network(WSN)is constructed with numerous sensors over geographical *** basic challenge experienced while designing WSN is in increasing the network lifetime and use of low *** sensor nodes are resource constrained in nature,novel techniques are essential to improve lifetime of nodes in *** energy is considered as an important resource for sensor node which are battery powered *** WSN,energy is consumed mainly while data is being transferred among nodes in the *** research works are carried out focusing on preserving energy of nodes in the network and made network to live ***,this network is threatened by attacks like vampire attack where the network is loaded by fake ***,Dual Encoding Recurrent Neural network(DERNNet)is proposed for classifying the vampire nodes s node in the ***,the Grey Wolf Optimization(GWO)algorithm helps for transferring the data by determining best solutions to optimally select the aggregation points;thereby maximizing battery/lifetime of the network *** proposed method is evaluated with three standard approaches namely Knowledge and Intrusion Detection based Secure Atom Search Routing(KIDSASR),Risk-aware Reputation-based Trust(RaRTrust)model and Activation Function-based Trusted Neighbor Selection(AF-TNS)in terms of various *** existing methods may lead to wastage of energy due to vampire attack,which further reduce the lifetime and increase average energy consumed in the ***,the proposed DERNNet method achieves 31.4%of routing overhead,23%of end-to-end delay,78.6%of energy efficiency,94.8%of throughput,28.2%of average latency,92.4%of packet delivery ratio,85.2%of network lifetime,and 94.3%of classification accuracy.

关键词： Wireless sensor network vampire nodes lifetime optimal routing energy encryption decryption trust value optimization

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On Designs of Decentralized Reputation Management for Permissioned Blockchain Networks

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computer Modeling in engineering & sciences 2024年第5期139卷 1755-1773页

作者： Jinyu Chen Long Shi Qisheng Huang Taotao Wang Daojing He School of Electronic and Optical Engineering Nanjing University of Science and TechnologyNanjing210094China School of Mechanical Engineering and Automation Harbin Institute of TechnologyShenzhen518055China College of Electronics and Information Engineering Shenzhen UniversityShenzhen518060China School of Computer Science and Technology Harbin Institute of TechnologyShenzhen518055China

In permissioned blockchain networks,the Proof of Authority(PoA)consensus,which uses the election of authorized nodes to validate transactions and blocks,has beenwidely advocated thanks to its high transaction throughput and fault ***,PoA suffers from the drawback of centralization dominated by a limited number of authorized nodes and the lack of anonymity due to the round-robin block proposal *** a result,traditional PoA is vulnerable to a single point of failure that compromises the security of the blockchain *** address these issues,we propose a novel decentralized reputation management mechanism for permissioned blockchain networks to enhance security,promote liveness,and mitigate centralization while retaining the same throughput as traditional *** paper aims to design an off-chain reputation evaluation and an on-chain reputation-aided ***,we evaluate the nodes’reputation in the context of the blockchain networks and make the reputation globally verifiable through smart ***,building upon traditional PoA,we propose a reputation-aided PoA(rPoA)consensus to enhance securitywithout sacrificing *** particular,rPoA can incentivize nodes to autonomously form committees based on reputation authority,which prevents block generation from being tracked through the randomness of reputation ***,we develop a reputation-aided fork-choice rule for rPoA to promote the network’s ***,experimental results show that the proposed rPoA achieves higher security performance while retaining transaction throughput compared to traditional PoA.

关键词： Blockchain reputation management PoA throughput security decentralization

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Machine Learning-Based Chronic Kidney Cancer Prediction Application: A Predictive Analytics Approach

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Biomedical Materials and Devices 2024年第2期2卷 1028-1048页

作者： Uddin, Khandaker Mohammad Mohi Nahid, Md. Nuzmul Hossain Ullah, Md. Mehedi Hasan Mazumder, Badhan Khan, Md. Saikat Islam Dey, Samrat Kumar Department of Computer Science and Engineering Dhaka International University Faculty of Computer Science and Engineering Patuakhali Science and Technology University School of Science and Technology Bangladesh Open University

Chronic Kidney Cancer (CKC) is a disease that hindrances the blood-filtering mechanism of the kidney and is increasing at an alarming rate in the recent few years. As CKC does not show any earlier symptoms, the earlier prediction will be very effective to elevate its effect. In this paper, a machine learning-based method for diagnosing CKC at an early stage is proposed. SVM, Decision Tree, Random Forest, KNN, Native Bayes, Logistic Regression, LGBM, CatBoost, and AdaBoost are employed for classification purposes, and among these algorithms, Random Forest, AdaBoost, and LGBM give 99% accuracy. Data pre-processing and feature selection help improve the accuracy of the proposed model. The outcome of the comparative analysis of our proposed work with eight existing approaches proves the robustness and supremacy of the work. Furthermore, the best model is also used to develop a user-friendly web application. © The Author(s), under exclusive licence to Springer science+Business Media, LLC 2023.

关键词： AdaBoost Chronic kidney cancer Machine learning Random forest and LGBM Web application

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Enhanced Deep Reinforcement Learning Strategy for Energy Management in Plug-in Hybrid Electric Vehicles with Entropy Regularization and Prioritized Experience Replay

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Energy engineering 2024年第12期121卷 3953-3979页

作者： Li Wang Xiaoyong Wang School of Computer Science and Engineering Anhui University of Science&TechnologyHuainan232001China School of Information Engineering Huainan Union UniversityHuainan232001China

Plug-in Hybrid Electric Vehicles(PHEVs)represent an innovative breed of transportation,harnessing diverse power sources for enhanced *** management strategies(EMSs)that coordinate and control different energy sources is a critical component of PHEV control technology,directly impacting overall vehicle *** study proposes an improved deep reinforcement learning(DRL)-based EMSthat optimizes realtime energy allocation and coordinates the operation of multiple power *** DRL algorithms struggle to effectively explore all possible state-action combinations within high-dimensional state and action *** often fail to strike an optimal balance between exploration and exploitation,and their assumption of a static environment limits their ability to adapt to changing ***,these algorithms suffer from low sample ***,these factors contribute to convergence difficulties,low learning efficiency,and *** address these challenges,the Deep Deterministic Policy Gradient(DDPG)algorithm is enhanced using entropy regularization and a summation tree-based Prioritized Experience Replay(PER)method,aiming to improve exploration performance and learning efficiency from experience ***,the correspondingMarkovDecision Process(MDP)is ***,an EMSbased on the improvedDRLmodel is *** simulation experiments are conducted against rule-based,optimization-based,andDRL-based *** proposed strategy exhibitsminimal deviation fromthe optimal solution obtained by the dynamic programming(DP)strategy that requires global *** the typical driving scenarios based onWorld Light Vehicle Test Cycle(WLTC)and New European Driving Cycle(NEDC),the proposed method achieved a fuel consumption of 2698.65 g and an Equivalent Fuel Consumption(EFC)of 2696.77 *** to the DP strategy baseline,the proposed method improved the fuel efficiency variances(FEV)by 18.13%

关键词： Plug-in hybrid electric vehicles deep reinforcement learning energy management strategy deep deterministic policy gradient entropy regularization prioritized experience replay

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A new technology perspective of the Metaverse:Its essence,framework and challenges

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Digital Communications and Networks 2024年第6期10卷 1653-1665页

作者： Feifei Shi Huansheng Ning Xiaohong Zhang Rongyang Li Qiaohui Tian Shiming Zhang Yuanyuan Zheng Yudong Guo Mahmoud Daneshmand School of Computer and Communication Engineering University of Science and Technology BeijingBeijing100083China Jinzhong University Shanxi030619China Department of Business Intelligence and Analytics and the Department of Computer Science Stevens Institute of TechnologyHobokenNJ07030USA

The Metaverse depicts a parallel digitalized world where virtuality and reality are *** has economic and social systems like those in the real world and provides intelligent services and *** this paper,we introduce the Metaverse from a new technology perspective,including its essence,corresponding technical framework,and potential technical ***,we analyze the essence of the Metaverse from its etymology and point out breakthroughs promising to be made in time,space,and contents of the Metaverse by citing Maslow's Hierarchy of ***,we conclude four pillars of the Metaverse,named ubiquitous connections,space convergence,virtuality and reality interaction,and human-centered communication,and establish a corresponding technical ***,we envision open issues and challenges of the Metaverse in the technical *** work proposes a new technology perspective of the Metaverse and will provide further guidance for its technology development in the future.

关键词： Metaverse Technical framework Ubiquitous connections Space convergence Virtuality and reality interaction Human-centered communication

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GazeREC-Net: Advancing Gaze Restoration in Low-Light Conditions

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2024年第12期51卷 2034-2042页

作者： Ku, Jiayin Wang, Li School of Computer Science and software Engineering University of Science and Technology Liaoning Anshan114051 China College of Computer Science and Tech nology Liaoning Anshan114051 China

Gaze estimation technology is essential for applications such as human-computer interaction, augmented reality, and virtual reality. However, its accuracy is significantly compromised in low-light conditions due to degraded image quality. To address this, we developed GazeREC-Net, an innovative gaze restoration method. We simulated low-light conditions on the MPIIFaceGaze and ColumbiaGaze datasets, creating a specialized degraded dataset for training and testing our model. GazeREC-Net combines Fourier transform techniques with advanced image restoration algorithms, significantly enhancing image quality and optimizing gaze information recovery and extraction in low-light environments. Our evaluations using Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) metrics demonstrated a PSNR improvement of 7.64% and an SSIM improvement of 5.75% compared to HINet. Additionally, GazeREC-Net outperformed CFTNet and other existing gaze estimation models, including Gaze-TR, Dilated-Net, CA-Net, L2CS-Net, and MTGLS, in reducing gaze estimation error. These findings validate the effectiveness of GazeREC-Net in low-light conditions and offer new research directions for applying gaze estimation technologies in complex lighting environments. © (2024), (International Association of Engineers). All rights reserved.

关键词： Augmented reality

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Research on Network Traffic Classification Based on Graph Neural Network

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2024年第12期51卷 2043-2050页

作者： Han, Yue Dai, Hong University of Science and Technology Liaoning Liaoning Anshan114051 China School of Computer Science and Software Engineering University of Science and Technology Liaoning Liaoning Anshan China

Network traffic classification is a critical concern in network security and management, essential for accurately differentiating among various network applications, optimizing service quality, and improving user experience. The exponential increase in worldwide Internet users and network traffic is continuously augmenting the diversity and complexity of network applications, rendering the Internet environment increasingly intricate and dynamic. Conventional machine learning techniques possess restricted processing abilities for network traffic attributes and struggle to address the progressively intricate traffic classification tasks in contemporary networks. In recent years, the swift advancement of deep learning technologies, particularly Graph Neural Networks (GNN), has yielded significant improvements in network traffic classification. GNN can capture the structured information among network nodes and extract the latent features of network traffic. Nonetheless, current network traffic classification models continue to exhibit deficiencies in the thoroughness of feature extraction. To tackle the problem, this research proposes a method for constructing traffic graphs utilizing numerical similarity and byte distance proximity by exploring the latent correlations among bytes, and it constructs a model, SDA-GNN, based on Graph Isomorphic Networks (GIN) for the categorization of network traffic. In particular, the Dynamic Time Warping (DTW) distance is employed to evaluate the disparity in byte distributions, a channel attention mechanism is utilized to extract additional features, and a Long Short-Term Memory Network (LSTM) enhances the stability of the training process by extracting sequence characteristics. Experimental findings on two actual datasets indicate that the SDA-GNN model surpasses other baseline techniques across multiple assessment parameters in the network traffic classification task, achieving classification accuracy enhancements of 2.19% and 1.49%

关键词： Long short-term memory

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From Generative AI to Generative Internet of Things: Fundamentals, Framework, and Outlooks

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IEEE Internet of Things Magazine 2024年第3期7卷 30-37页

作者： Wen, Jinbo Nie, Jiangtian Kang, Jiawen Niyato, Dusit Du, Hongyang Zhang, Yang Guizani, Mohsen College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics China School of Computer Science and Engineering Nanyang Technological University China School of Automation Guangdong University of Technology China United Arab Emirates

Generative Artificial Intelligence (GAI) possesses the capabilities of generating realistic data and facilitating advanced decision-making. By integrating GAI into modern Internet of Things (IoT), Generative Internet of Things (GIoT) is emerging and holds immense potential to revolutionize various aspects of society, enabling more efficient and intelligent IoT applications, such as smart surveillance and voice assistants. In this article, we present the concept of GIoT and conduct an exploration of its potential prospects. Specifically, we first overview four GAI techniques and investigate promising GIoT applications. Then, we elaborate on the main challenges in enabling GIoT and propose a general GAI-based secure incentive mechanism framework to address them, in which we adopt Generative Diffusion Models (GDMs) for incentive mechanism designs and apply blockchain technologies for secure GIoT management. Moreover, we conduct a case study on modern Internet of Vehicle traffic monitoring, which utilizes GDMs to generate effective contracts for incentivizing users to contribute sensing data with high quality. Numerical results demonstrate the superiority of the proposed scheme. Finally, we suggest several open directions worth investigating for the future popularity of GIoT. © 2018 IEEE.

关键词： Internet of things

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