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Identification of Important FPGA Modules Based on Complex Network

computers, Materials & Continua 2024年第1期78卷 1027-1047页

作者： Senjie Zhang Jinbo Wang Shan Zhou Jingpei Wang Zhenyong Zhang Ruixue Wang School of Aeronautics and Astronautics University of Chinese Academy of SciencesBeijing101408China Technology and Engineering Center for Space Utilization Chinese Academy of SciencesBeijing100094China College of Control Science and Engineering Zhejiang UniversityHangzhou310063China College of Computer Science and Technology Guizhou UniversityGuiyang550025China

The globalization of hardware designs and supply chains,as well as the integration of third-party intellectual property(IP)cores,has led to an increased focus from malicious attackers on computing ***,existing defense or detection approaches often require additional circuitry to perform security verification,and are thus constrained by time and resource *** the scale of actual engineering tasks and tight project schedules,it is usually difficult to implement designs for all modules in field programmable gate array(FPGA)*** studies have pointed out that the failure of key modules tends to cause greater damage to the ***,under limited conditions,priority protection designs need to be made on key modules to improve protection *** have conducted research on FPGA designs including single FPGA systems and multi-FPGA systems,to identify key modules in FPGA *** the single FPGA designs,considering the topological structure,network characteristics,and directionality of FPGA designs,we propose a node importance evaluationmethod based on the technique for order preference by similarity to an ideal solution(TOPSIS)***,for the multi-FPGA designs,considering the influence of nodes in intra-layer and inter-layers,they are constructed into the interdependent network,and we propose a method based on connection strength to identify the important ***,we conduct empirical research using actual FPGA designs as *** results indicate that compared to other traditional indexes,node importance indexes proposed for different designs can better characterize the importance of nodes.

关键词： Hardware security FPGA circuits node importance interdependent network

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Parameter Efficient Point Cloud Prompt Tuning for Unified Point Cloud Understanding

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年 1-16页

作者： Fei, Ben Liu, Liwen Yang, Weidong Li, Zhijun Chen, Wen-Ming Ma, Lipeng School of Computer Science Fudan University Shanghai China School of Mechanical Engineering Tongji University Shanghai China Academy for Engineering and Technology Fudan University Shanghai China

The prevailing paradigm in 3D vision involves fully fine-tuning all the backbone parameters of pre-trained models. However, this approach poses challenges due to the large number of parameters requiring tuning, resulting in unexpected storage demands. To address these issues and alleviate the computational cost and storage burden associated with full fine-tuning, we propose Point Cloud Prompt Tuning (PCPT) as an effective method for large Transformer models in point cloud processing. PCPT offers a powerful and efficient solution to mitigate the costs associated with full fine-tuning. Drawing inspiration from recent advancements in efficient tuning of large-scale language models and 2D vision models, PCPT leverages less than 0.05 % of trainable parameters, while keeping the pre-trained parameters of the Transformer backbone unchanged. To evaluate the effectiveness of PCPT, extensive experiments were conducted on four discriminative datasets (ModelNet40, few-shot ModelNet40, ScanObjectNN, ShapeNetPart) and four generation datasets (PCN, MVP, ShapeNet55, and ShapeNet34/Unseen21). The results demonstrate that the task-specific prompts utilized in PCPT enable the Transformer model to adapt effectively to the target domains, yielding results comparable to those obtained through other full fine-tuning methods. This highlights the versatility of PCPT across various domains and tasks. Our code is available at https://***/Fayeben/PCPT. IEEE

关键词： Three dimensional displays

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Medical Knowledge Graph:Data Sources,Construction,Reasoning,and Applications

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Big Data Mining and Analytics 2023年第2期6卷 201-217页

作者： Xuehong Wu Junwen Duan Yi Pan Min Li Computer Science and Engineering Central South UniversityChangsha 410083China School of Computer Science Hunan First Normal UniversityChangsha 410006China School of Computer Science and Engineering Central South UniversityChangsha 410083China Computer Science and Control Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518000China

Medical knowledge graphs(MKGs)are the basis for intelligent health care,and they have been in use in a variety of intelligent medical ***,understanding the research and application development of MKGs will be crucial for future relevant research in the biomedical *** this end,we offer an in-depth review of MKG in this *** research begins with the examination of four types of medical information sources,knowledge graph creation methodologies,and six major themes for MKG ***,three popular models of reasoning from the viewpoint of knowledge reasoning are discussed.A reasoning implementation path(RIP)is proposed as a means of expressing the reasoning procedures for *** addition,we explore intelligent medical applications based on RIP and MKG and classify them into nine major ***,we summarize the current state of MKG research based on more than 130 publications and future challenges and opportunities.

关键词： medical knowledge graph knowledge graph construction knowledge reasoning intelligent medical applications intelligent healthcare

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Machine learning design of 400 MPa grade biodegradable Zn-Mn based alloys with appropriate corrosion rates

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International Journal of Minerals,Metallurgy and Materials 2024年第12期31卷 2727-2736页

作者： Wangzhang Chen Wei Gou Yageng Li Xiangmin Li Meng Li Jianxin Hou Xiaotong Zhang Zhangzhi Shi Luning Wang Beijing Advanced Innovation Center for Materials Genome Engineering State Key Laboratory for Advanced Metals and MaterialsSchool of Materials Science and EngineeringUniversity of Science and Technology BeijingBeijing 100083China Institute of Materials Intelligent Technology Liaoning Academy of MaterialsShenyang 110004China School of Computer and Communication Engineering Shunde Innovation SchoolUniversity of Science and Technology BeijingShunde 528399China

The commonly used trial-and-error method of biodegradable Zn alloys is costly and *** this study,based on the self-built database of biodegradable Zn alloys,two machine learning models are established by the first time to predict the ultimate tensile strength(UTS)and immersion corrosion rate(CR)of biodegradable Zn alloys.A real-time visualization interface has been established to design Zn-Mn based alloys;a representative alloy is *** tensile mechanical properties and immersion corrosion rate tests,its UTS reaches 420 MPa,and the prediction error is only 0.95%.CR is 73μm/a and the prediction error is 5.5%,which elevates 50 MPa grade of UTS and owns appropriate corrosion ***,influences of the selected features on UTS and CR are discussed in *** combined application of UTS and CR model provides a new strategy for synergistically regulating comprehens-ive properties of biodegradable Zn alloys.

关键词： Zn alloys machine learning alloy design performance prediction

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Exploring the possibilities of integration of cyber-psychology for human behaviour in a smart city

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International Journal of Information and Communication technology 2024年第2期25卷 150-167页

作者： Wen, Liping Ting, Zhou Zheng, Huang Alfred Daniel, J. Antonidoss, A. Department of Psychology University of Chinese Academy of Sciences Beijing100101 China Institute of Psychology Chinese Academy of Sciences Beijing100101 China Department of Medical Psychology School of Health Humanities Peking University Beijing100191 China Department of Computer Science & Engineering SNS College of Technology India Department of Computer Science and Engineering Hindustan Institute of Technology and Science India

The smart city idea differs between cities and nations. In all meanings and characteristics of a smart city, public involvement is the only thing that remains common. Therefore, it is a very significant field to study human behaviour and development in smart cities. This paper presents a framework for identifying qualities necessary for people to be classified as intelligent persons and to integrate these human behavioural characteristics in cyber technology. Human behaviour in a smart city has been analysed using the machine learning algorithm and big data analytics. The integrated machine learning and big data analytics framework (iML-BD) classifies the cyber behaviour of intelligent persons in a smart city by observing the cyber activities performed by the individuals. Furthermore, this paper handles the risk factors for cyber-acquired and cyber-dependant crime violence and abuse that vulnerable internet and public access devices using blockchain technology. Blockchain is a method of storing data that takes too long to alter, modify, or manipulate. A blockchain is an electronic accounting system that is reproduced and spread through the Bitcoin protocol’s entire communication network. The case study performed on iML-BD has resulted in the highest performance in terms of prediction accuracy of 94.98%. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Smart city

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Progressive Growth for Point Cloud Completion by Surface-Projection Optimization

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年第5期9卷 1-15页

作者： Fei, Ben Zhang, Rui Yang, Weidong Li, Zhijun Chen, Wen-Ming School of Computer Science Fudan University Shanghai China School of Mechanical Engineering Tongji University Shanghai China Academy for Engineering and Technology Fudan University Shanghai China

Point cloud completion concentrates on completing geometric and topological shapes from incomplete 3D shapes. Nevertheless, the unordered nature of point clouds will hamper the generation of high-quality point clouds without predicting structured and topological information of the complete shapes and introducing noisy points. To effectively address the challenges posed by missing topology and noisy points, we introduce SPOFormer, a novel topology-aware model that utilizes surface-projection optimization in a progressive growth manner. SPOFormer consists of three distinct steps for completing the missing topology: (1) Missing Keypoints Prediction. A topology-aware transformer auto-encoder is integrated for missing keypoint prediction. (2) Skeleton Generation. The skeleton generation module produces a new type of representation named skeletons with the aid of keypoints predicted by topology-aware transformer auto-encoder and the partial input. (3) Progressively Growth. We design a progressive growth module to predict final output under Multi-scale Supervision and Surface-projection Optimization. Surface-projection Optimization is firstly devised for point cloud completion, aiming to enforce the generated points to align with the underlying object surface. Experimentally, SPOFormer model achieves an impressive Chamfer Distance-$\ell _{1}$ (CD) score of 8.11 on PCN dataset. Furthermore, it attains average CD-$\ell _{2}$ scores of 1.13, 1.14, and 1.70 on ShapeNet-55, ShapeNet-34, and ShapeNet-Unseen21 datasets, respectively. Additionally, the model achieves a Maximum Mean Discrepancy (MMD) of 0.523 on the real-world KITTI dataset. These outstanding qualitative and quantitative performances surpass previous approaches by a significant margin, firmly establishing new state-of-the-art performance across various benchmark datasets. Our code is available at https://***/kiddoray/SPOFormer IEEE

关键词： Topology

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Timelog: Genetic AI Enabled Timetable Generation 2

Timelog: Genetic AI Enabled Timetable Generation

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2nd IEEE International Conference for Women in Innovation, technology and Entrepreneurship, ICWITE 2024

作者： Singh, Divanshu Khandelwal, Avish Kumar, Aniket Bawaskar, Harsh Ugale, Prajakta Mit Academy of Engineering Information Technology Pune Alandi India Mit Academy of Engineering Computer Science Pune Alandi India Mit Academy of Engineering School of Computer Engineering Pune Alandi India

ISBN: (纸本)9798350383287

Efficient timetable creation is paramount for organizational productivity, yet often a labor-intensive task. In this paper, we present 'Timelog', a groundbreaking innovation that harnesses the power of Genetic Algorithms to generate bespoke, conflict-free timetables tailored to user-defined requirements. Timelog empowers users with the ability to customize key parameters such as density, duration, and compatibility, ensuring schedules that perfectly align with their unique *** evaluate the effectiveness of Timelog, we introduce a set of performance metrics, including schedule efficiency and resource utilization, providing a comprehensive assessment of its capabilities. Remarkably, Timelog consistently achieves an astounding accuracy rate of 99% when used in educational institutions compliant with UGC (University Grants Commission) norms, making it an ideal solution for *** seamlessly integrating genetic algorithms, Timelog represents a transformative leap in the scheduling domain, offering organizations a streamlined, efficient, and highly customizable scheduling solution. This innovation promises to revolutionize the scheduling process, significantly enhancing organizational efficiency and productivity. © 2024 IEEE.

关键词： Efficiency

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Federated Learning for Cybersecurity: A Promising Frontier 2

Federated Learning for Cybersecurity: A Promising Frontier

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2nd IEEE International Conference on IoT, Communication and Automation technology, ICICAT 2024

作者： Ananya, G. Bindu Madhavi, V. Murthy, Monisha Guruprasad, N. Global Academy of Technology Dept. of Computer Science and Engineering Karnataka Bengaluru India Buddha Institute of Technology Computer Science and Engineering Uttar Pradesh Gorakhpur India

ISBN: (纸本)9798350368109

In today's world, Artificial Intelligence and Machine Learning are transforming many industries, but they rely on huge amounts of data leading to privacy issues. A better alternative is Federated learning. Federated Learning involves training the model from multiple sources using the decentralization technique, meaning each device trains the model on its local data thereby reducing the strain on the single server. This is useful in cases where the data is too large to be sent and maintained on a central server or in handling privacy and security concerns. Cybersecurity is a prime domain where vulnerable attacks and breaches can be prevented using this technique. Other domains like finance, healthcare, IoT etc. have transformed the idea of data-driven decisions. This paper gives an insight into the concept of Federated learning and why it is a better choice. It includes the various algorithms that can be implemented in multiple applications depicted through a comparative analysis. The choice of algorithm depends upon its efficiency and the desired cybersecurity application. © 2024 IEEE.

关键词： Federated learning

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The governance technology for blockchain systems:a survey

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Frontiers of computer science 2024年第2期18卷 181-197页

作者： Guocheng ZHU Debiao HE Haoyang AN Min LUO Cong PENG School of Cyber Science and Engineering Wuhan UniversityWuhan 430072China Shandong Provincial Key Laboratory of Computer Networks Qilu University of Technology(Shandong Academy of Sciences)Jinan 250014China

After the Ethereum DAO attack in 2016,which resulted in significant economic losses,blockchain governance has become a prominent research ***,there is a lack of comprehensive and systematic literature review on blockchain *** deeply understand the process of blockchain governance and provide guidance for the future design of the blockchain governance model,we provide an in-depth review of blockchain *** this paper,first we introduce the consensus algorithms currently used in blockchain and relate them to governance ***,we present the main content of off-chain governance and investigate two well-known off-chain governance ***,we investigate four common on-chain governance voting techniques,then summarize the seven attributes that the on-chain governance voting process should meet,and finally analyze four well-known on-chain governance blockchain projects based on the previous *** hope this survey will provide an in-depth insight into the potential development direction of blockchain governance and device future research agenda.

关键词： blockchain governance off-chain governance on-chain governance voting

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Optimized Convolutional Neural Network Using Hierarchical Particle Swarm Optimization for Sensor Based Human Activity Recognition

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SN computer science 2024年第5期5卷 447页

作者： Ankalaki, Shilpa Thippeswamy, M.N. Department of Computer Science and Engineering Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal Karnataka 576104 India Department of Computer Science and Engineering (AI & amp

Hyperparameter optimization poses a significant challenge when developing deep neural networks. Building a convolutional neural network (CNN) for implementation can be an arduous and time-intensive task. This work proposed an approach to optimize the hyperparameters of one dimensional (1D-CNN) to improve the accuracy of human activity recognition (HAR). The framework includes a parametric depiction of 1D-CNNs along with an optimization process for hyperparameters aimed at maximizing the model's performance. This work designed the method called OPTConvNet for hyperparameter optimization of 1D-CNN using Hierarchical Particle Swarm Optimization (H-PSO). The H-PSO algorithm is designed to optimize the architectural, layer and training parameters of 1D-CNN. The H-PSO optimizes the architecture of the 1D-CNN at initial level. Layer and training hyperparameters will be optimized at the next level. The proposed approach employs an exponential-like inertia weight to fine-tune the balance between exploration and exploitation of particles to prevent premature convergence to a local optimum solution in the PSO algorithm. The H-PSO- CNN is evaluated on publicly available sensor- human activity recognition (S-HAR) datasets namely, UCI-HAR, Daphnet Gait, Opportunity and PAMPA2 datasets. © The Author(s) 2024.

关键词： 1D-convolutional neural network Hierarchical particle swarm optimization Human activity recognition Hyperparameters OPTConvNet Optimization

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