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Research on regional spatial logistics information integration method based on big data

International Journal of Information Technology and Management 2021年第3期20卷 234-249页

作者： Chen, Xiang-Dong Kalra, Gregory School of Mathematics and Information Science Nanjing Normal University of Special Education Jiangsu Nanjing China Ma'anshan Teacher's College Department of Software Engineering Anhui Ma'anshan China Department of Mathematics and Computer Science Clark University MA01610 United States

Aiming at the shortcomings of current regional spatial logistics, such as low efficiency of logistics resource utilisation, high cost of logistics transportation and slow speed of goods transportation, a regional spatial logistics information integration method based on large data is proposed. Firstly, the integration principle and process of regional spatial logistics information are described. Then, the logistics transportation route optimisation model is assumed. Finally, the integration of regional spatial logistics information is realised by using the logistics transportation route optimisation model with time windows. The experimental results show that the proposed regional spatial logistics information integration method can improve the utilisation efficiency of logistics resources, and the data consistency can reach 96.9%. When the number of goods is 10,000 the transportation cost of the proposed method is the lowest of 12,300 yuan, so the transportation time of the method is the shortest. © 2021 Inderscience Enterprises Ltd.. All rights reserved.

关键词： Transportation routes

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TEVA: Training-Efficient and Verifiable Aggregation for Federated Learning for Consumer Electronics in Industry 5.0

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IEEE Transactions on Consumer Electronics 2024年

作者： Xia, Yuanjun Liu, Yining Chen, Jingxue Liang, Yangfan Khan, Fazlullah Alturki, Ryan Wang, Xiaopei Guilin University of Electronic Technology Guangxi Key Laboratory of Trusted Software School of Computer and Information Security Guilin541004 China Wenzhou University of Technology School of Data Science and Artificial Intelligence Wenzhou325027 China University of Technology School of Mathematical and Physical Sciences SydneyNSW2007 Australia Jiaxing University Provincial Key Laboratory of Multimodal Perceiving and Intelligent Systems The Key Laboratory of Medical Electronics and Digital Health of Zhejiang Province The Engineering Research Center of Intelligent Human Health Situation Awareness of Zhejiang Province Jiaxing314001 China University of Nottingham Ningbo China School of Computer Science Faculty of Science and Engineering Zhejiang Ningbo315104 China Umm AI-Qura University Makkah Department of Software Engineering College of Computing Saudi Arabia Riverside Department of Computer Science and Engineering University of California CA92521 United States

Federated learning (FL) has been widely used for privacy-preserving model updates in Industry 5.0, facilitated by 6G networks. Despite FL's privacy-preserving advantages, it remains vulnerable to attacks where adversaries can infer private data from local models or manipulate the central server (CS) to deliver falsified global models. Current privacy-preserving approaches, primarily based on the FedAvg algorithm, fail to optimize training efficiency for non-independent and identically distributed (non-IID) data. This article proposes training-efficient and verifiable aggregation (TEVA) for FL to resolve these issues. This scheme combines threshold Paillier homomorphic encryption (TPHE), verifiable aggregation, and an optimized double momentum update mechanism (OdMum). TEVA not only leverages TPHE to protect the privacy of local models but also ensures the integrity of the global model through a verifiable aggregation mechanism. Additionally, TEVA integrates the OdMum algorithm to effectively address the challenges posed by non-IID data, promoting rapid model convergence and significantly enhancing overall training efficiency. Security analysis indicates that TEVA meets the requirements for privacy protection. Extensive experimental results demonstrate that TEVA can accelerate model convergence while incurring lower computational and communication overheads. © 1975-2011 IEEE.

关键词： Differential privacy

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Model for Recognition of Sign Language and Hand Gesture by Hard Hearing People

Model for Recognition of Sign Language and Hand Gesture by H...

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Sustainable Communication Networks and Application (ICSCNA), International Conference on

作者： Manjula R Jagadeesan S Venkadeshan Ramalingam Vetriselvi T Johnpeter T Department of Computing Technologies College of Engineering and Technology SRM Institute of Science and Technology Chennai India Department of Software and Systems Engineering School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore Tamilnadu India Information Technology Department University of Technology and Applied Sciences Sultanate of Oman Department of IOT School of Computer Science and Engineering Vellore Institute of Technology Vellore Tamilnadu India Department of Computer Science and Engineering K.Ramakrishnan College of Engineering Trichy India

Hard hearing and deaf people are communicating with other people using the sign language. This is the only way of communication of such people to convey Since this is the only means by which these individuals can communicate, it is imperative that others be able to understand their language. Sign language recognition by computer begins with the acquisition of sign gestures and ends with recognition and conversion to text or voice. Static and dynamic are the two ways of sign gestures used. This study aims at focusing on the dynamic gesture recognition and the steps for recognizing sign language are discussed. The collected data are preprocessed and feature are extracted for classification. Finally the results are all investigated and developed for single user.

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mRS-DBFT: An improved consensus mechanism suitable for end-edge-cloud hybrid trust models

mRS-DBFT: An improved consensus mechanism suitable for end-e...

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Frontiers Technology of Information and computer (ICFTIC), IEEE International Conference on

作者： Xue Zhai Shanchen Pang Sibo Qiao Shihang Yu Qingdao institute of software college computer science and technology China University of Petroleum (East China) Qingdao China School of Mechanical Engineering Tiangong University Tianjin China

As a three-terminal collaborative architecture of terminal, edge and cloud, end-edge-cloud is jointly participated by multiple institutions or organizations, and has the characteristics of a hybrid trust architecture of “partially trustworthy, globally untrustworthy”. Traditional consensus algorithm designs are mostly used in relatively unified trust model environments and do not provide a mechanism for cross-organizational cooperation. Therefore, they cannot effectively solve the trust and consensus issues between organizations. We propose an improved DPoS-PBFT consensus mechanism based on reputation scoring (mRS-DBFT). The core idea of the mRS-DBFT is to add a reputation scoring mechanism based on the DPOS-PBFT consensus algorithm. The reputation score is calculated based on factors such as the node’s historical behavior, contribution, and credibility. Nodes with higher reputation score have greater influence and decision-making power in the consensus process. This mechanism can solve the problem of mutual distrust among nodes of different organizations in the end-edge-cloud architecture, and each node participates in the consensus process based on its reputation score, thus forming a credible and consistent system, achieving cross-organizational consensus, and ensuring node trust and data consistency in an open end-edge-cloud environment. The experimental results demonstrate that the mRS-DBFT consensus algorithm helps select nodes with higher stability to participate in the consensus by evaluating the reputation of computing devices, it can reduce fluctuations in delay and improve the stability of the consensus process.

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Computation Offloading From Edge to Equipment for Smart Manufacturing

Computation Offloading From Edge to Equipment for Smart Manu...

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IEEE International Conference on Utility and Cloud Computing (UCC)

作者： Hung H. Nguyen Yi Zhou Kushagra Kushagra Xiao Qin Department of Computer Science and Software Engineering Auburn University Auburn AL USA TSYS School of Computer Science Columbus State University Columbus GA USA

In smart manufacturing, data management systems are built with a multi-layer architecture, in which the most significant layers are the edge and the cloud. The edge layer renders support to data analysis that genuinely demands low latency. Cloud platforms store vast amounts of data while performing extensive computations such as machine learning and big data analysis. This type of data management system has a limitation rooted in the fact that all data needs to be transferred from the equipment layer to the edge layer in order to perform all data analyses. Even worse, data transferring adds delays to computation processes in smart manufacturing. We investigate an offloading strategy to shift a selection of computation tasks towards the equipment layer. Our computation offloading mechanism opts for smart manufacturing tasks that are not only light weight but also have no need to save data at the edge/cloud end. In our empirical study, we demonstrate that the edge layer can judiciously offload computing tasks to the equipment layer, which curtails computing latency and slashes the amount of transferred data during smart manufacturing process. Our experimental results confirm that our offloading strategy offers the capability for data analysis computing in real-time at the equipment level- an array of smart devices is slated to speed up the data analysis process in semiconductor manufacturing.

关键词： Performance evaluation Cloud computing Data analysis Optimized production technology Machine learning Semiconductor device manufacture Real-time systems

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Quantum Forgery Attacks on COPA, AES-COPA and Marble Authenticated Encryption Algorithms

arXiv

引用

arXiv 2023年

作者： Xu, Yinsong Liu, Wenjie Yu, Wenbin School of Computer and Software Nanjing University of Information Science and Technology Nanjing210044 China Engineering Research Center of Digital Forensics Ministry of Education Nanjing210044 China

Since the classic forgery attacks on COPA, AES-COPA and Marble authenticated encryption algorithms need to query about 2n/2 times and their success probability are not high. To solve this problem, the corresponding quantum forgery attacks on COPA, AES-COPA and Marble authenticated encryption algorithms are presented. In the quantum forgery attacks on COPA and AES-COPA, we use Simon’s algorithm to find the period of the tag generation function in COPA and AES-COPA by querying in superposition, and then generate a forged tag for a new message. While in the quantum forgery attack on Marble, Simon’s algorithm is used to recover the secret parameter L, and the forged tag can be computed with L. Compared with classic forgery attacks on COPA, AES-COPA and Marble, our attack can reduce the number of queries from O(2n/2) to O(n) and improve success probability close to 100%. © 2023, CC BY.

关键词： Marble

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Precise Facial Landmark Detection by Dynamic Semantic Aggregation Transformer

arXiv

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arXiv 2024年

作者： Wan, Jun Liu, He Wu, Yujia Lai, Zhihui Min, Wenwen Liu, Jun School of Information Engineering Zhongnan University of Economics and Law Wuhan430073 China College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China School of Information Science and Technology Sanda University Shanghai201209 China School of Information Science and Engineering Yunnan University Yunnan Kunming650091 China Information Systems Technology and Design Pillar Singapore University of Technology and Design Singapore487372 Singapore

At present, deep neural network methods have played a dominant role in face alignment field. However, they generally use predefined network structures to predict landmarks, which tends to learn general features and leads to mediocre performance, e.g., they perform well on neutral samples but struggle with faces exhibiting large poses or occlusions. Moreover, they cannot effectively deal with semantic gaps and ambiguities among features at different scales, which may hinder them from learning efficient features. To address the above issues, in this paper, we propose a Dynamic Semantic-Aggregation Transformer (DSAT) for more discriminative and representative feature (i.e., specialized feature) learning. Specifically, a Dynamic Semantic-Aware (DSA) model is first proposed to partition samples into subsets and activate the specific pathways for them by estimating the semantic correlations of feature channels, making it possible to learn specialized features from each subset. Then, a novel Dynamic Semantic Specialization (DSS) model is designed to mine the homogeneous information from features at different scales for eliminating the semantic gap and ambiguities and enhancing the representation ability. Finally, by integrating the DSA model and DSS model into our proposed DSAT in both dynamic architecture and dynamic parameter manners, more specialized features can be learned for achieving more precise face alignment. It is interesting to show that harder samples can be handled by activating more feature channels. Extensive experiments on popular face alignment datasets demonstrate that our proposed DSAT outperforms state-of-the-art models in the literature. Our code is available at https://***/GERMINO-LiuHe/DSAT. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep neural networks

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Digital Human Techniques for Education Reform 24

Digital Human Techniques for Education Reform

引用

7th International Conference on Educational Technology Management, ICETM 2024

作者： Guo, Peirong Zhang, Qi Tian, Chunwei Xue, Wanli Feng, Xiaocheng School of Software Northwestern Polytechnical University Shaanxi Xi’an China School of Economics and Management Harbin Institute of Technology at Weihai Shandong Weihai China Shenzhen Research Institute of Northwestern Polytechnical University Northwestern Polytechnical University Guangdong Shenzhen China School of Computer Science and Engineering Tianjin University of Technology Tianjin China Research Center for Social Computing and Information Retrieval Harbin Institute of Technology Heilongjiang Harbin China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen Key Laboratory of Media Security Shenzhen University Guangdong Shenzhen China

ISBN: (纸本)9798400717468

The rapid evolution of artificial intelligence, big data, and generative AI models has ushered in significant transformations across various sectors, including education. Digital Human Technique, an innovative technology grounded in advanced computer science and artificial intelligence, is reshaping educational paradigms by enabling virtual humans to simulate human behavior, express emotions, and interact with users. This paper explores the application of Digital Human Technique in education reform, focusing on creating immersive, intelligent classroom experiences that foster meaningful interactions between teachers and students. We define Digital Human Technique and delve into its key technical components such as character modeling and rendering, natural language processing, computer vision, and augmented reality technologies. Our methodology involves analyzing the role of educational digital humans created through these technologies, assessing their impact on educational processes, and examining various application scenarios in educational reform. Results indicate that Digital Human Technique significantly enhances the learning experience by enabling personalized teaching, increasing engagement, and fostering emotional connections. Educational digital humans serve as virtual teachers, interactive learning aids, and facilitators of emotional interaction, effectively addressing the challenges of traditional educational methods. They also promote a deeper understanding of complex concepts through simulated environments and interactive digital content. © 2024 Copyright held by the owner/author(s).

关键词： Digital elevation model

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Research on fan Operation evaluation and Error state judgment relying on improved neural network and intelligent computing 2

Research on fan Operation evaluation and Error state judgmen...

引用

2021 2nd International Conference on Applied Physics and Computing, ICAPC 2021

作者： Liu, Xueyi Dong, Junhao Tu, Guangyu School of Computer and Information Science School of Software Southwest University Chongqing400700 China College of Digital Technology and Engineering Ningbo University of Finance and Economics Zhejiang Ningbo315175 China School of Software Nanchang University Jiangxi Nanchang330000 China

Fan, as the most commonly used mechanical equipment, is widely used. In order to solve the problem of fan bearing fault diagnosis, this paper analyzes the main factors affecting fan spindle speed and power generation in operation. The input and output parameters of the performance prediction model are determined. The performance prediction model of wind turbine is established by using generalized regression neural network, and the smoothing factor of GRNN is optimized by comparing the prediction accuracy of the model. Based on this model, the sliding data window method is used to calculate the residual evaluation index of wind turbine speed and power in real time. When the evaluation index continuously exceeds the pre-set threshold, the abnormal state of wind turbine can be judged. In order to obtain wind turbine blades with better aerodynamic performance, a blade aerodynamic performance optimization method based on quantum heredity is proposed. The B é zier curve control point is used as the design variable to represent the continuous chord length and torsion angle distribution of the blade, the blade shape optimization model aiming at the maximum power is established, and the quantum genetic algorithm is used to optimize the chord length and torsion angle of the blade under different constraints. The optimization results of quantum genetic algorithm and classical genetic algorithm are compared and analyzed. Under the same parameters and boundary conditions, the proposed blade aerodynamic optimization method based on quantum genetic optimization is better than the classical genetic optimization method, and can obtain better blade aerodynamic shape and higher wind energy capture efficiency. This method makes up for the shortcomings of traditional fault diagnosis methods, improves the recognition rate of fault types and the accuracy of fault diagnosis, and the diagnosis effect is good. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： Neural networks

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An Effective Moving Cast Shadow Detection Framework for Surveillance Video Analysis

SSRN

引用

SSRN 2022年

作者： Dai, Jiangyan Zhang, Huihui Gao, Jin Yi, Yugen Wang, Bingshu School of Computer Engineering Weifang University Weifang China School of Computer Science and Information Engineering HeFei University of Technology Hefei China School of Software Jiangxi Normal University Nanchang China School of Software Taicang Campus Northwestern Polytechnical University Suzhou China

Moving object detection is crucial in intelligent surveillance system, such as object tracking, object classification, and so on. However, moving cast shadows seriously degrade the accuracy of moving object detection and are often detected as objects, resulting in object merging, object loss or shape distortion. To overcome the drawbacks, we propose an original moving shadow detection approach by incorporating the broad learning system. Firstly, according to the color constancy and texture consistency of shadows, the color, texture and gradient features are extracted by input frames and a background. Then, the broad learning system is introduced, which can map input samples into a more appropriate feature space. Meanwhile, the BLS offers an alternative way, which makes it classify moving cast shadows effectively and efficiently. Finally, a post-processing is designed for keeping complete moving objects. Extensive experiments verify the proposed method’s effectiveness and generalization ability on outdoor and indoor surveillance scenes. © 2022, The Authors. All rights reserved.

关键词： Textures

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