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E-commerce Scheme Based on Proxy t-out-of-n Oblivious Signature

International Journal of Network Security 2024年第5期26卷 851-860页

作者： Chen, Jingyu Gong, Linming Ma, Xiangxiang Wang, Daoshun The Shaanxi Key Laboratory of Clothing Intelligence School of Computer Science Xi’an Polytechinic University Xi’an710048 China State and Local Joint Engineering Research Center for Advanced Networking and Intelligent Information Services School of Computer Science Xi’an Polytechnic University Shaanxi Xi’an710048 China Department of Computer Science and Technology Tsinghua University Beijing100084 China No. 58 Shan-gu Road Lintong Xi’an710600 China

With the development of the internet and the demand for online shopping, intelligent online shopping systems have received widespread attention in recent years. However, existing online shopping systems’ security and privacy issues have not been well addressed. This article proposes a shopping scheme based on proxy t-out-of-n oblivious signatures, which uses t-out-of-n oblivious signature technology to ensure that users select multiple messages from a predetermined set of messages, ensuring the legality and privacy of the selection while using proxy signature technology to provide the attribute of signature unforgeability. We have analyzed the correctness of the proposed scheme and demonstrated its security in terms of integrity, unforgeability, confidentiality of selected messages, selection restriction, non-repetitive, verifiability, distinguishability, non-repudiation, and prevention of abuse. Our scheme has lower computational and communication costs than other oblivious signature schemes. In addition, the privacy-preserving multi-choice shopping scheme we propose enables users to shop more securely and conveniently. © (2024), (International Journal of Network Security). All rights reserved.

关键词： Differential privacy

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Design and implementation of a dedicated 5G communication terminal for distributed power sources 3

Design and implementation of a dedicated 5G communication te...

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3rd International Conference on Electronics technology and Artificial Intelligence, ETAI 2024

作者： Gao, Zunhai Lu, Hongxia Zhou, Kang Yu, Yue School of Information and Artificial Intelligence Nanchang Institute of Science and Technology Nanchang China Economics and Management School Hubei Business College Wuhan China School of Mathematics and Computer Science Wuhan Polytechnic University Wuhan China

ISBN: (纸本)9781510683143

With the increasing integration of renewable energy into the power grid, the demand for efficient communication systems for managing and controlling distributed power sources has become urgent. Here, we introduce the design and implementation of a 5G communication terminal tailored specifically for the unique requirements of distributed power sources. The proposed terminal aims to improve communication reliability, latency, and throughput, thereby optimizing the overall performance of the distribution network, and designs a set of self-feedback fault elimination. The system architecture, key features, and experimental results were discussed in detail, demonstrating the feasibility and effectiveness of the proposed solution. © 2024 SPIE.

关键词： Power distribution networks

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Improved Quaternion Discriminant Analysis for Feature Extraction and Classification of Hyperspectral Image

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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2025年 18卷 12925-12945页

作者： Wang, Xinpeng Ling, Bingo Wing-Kuen Yu, Long Dai, Qingyun Liu, Shaopeng Zhao, Jiandong Kuang, Weichao Guangdong Polytechnic Normal University Faculty of Computer Science Guangzhou China Guangdong University of Technology School of Information Engineering Guangzhou China Sun Yat-sen University School of Geography and Planning Guangzhou510275 China Dongguan University of Technology Department of Electronic Engineering and Intellectualization Dongguan China

Real domain discriminant analysis (RDA) can not solve the crosstalk of strong correlation between adjacent spectral bands, and also causes spectral information loss of hyperspectral image (HSI). As a result, the effect of feature extraction is not ideal. Besides, in the case of small samples, using quaternion discriminant analysis (QDA) based on Cos-sin Decomposition, the matrix obtained by the first orthogonal trigonometric decomposition is not a diagonal matrix, that makes the projection axis not optimal. To solve these problems, firstly, a quaternion discriminant analysis method based on stable quaternion Cossin Decomposition (QDASG) is proposed to solve the small sample problem. Secondly, this paper theoretically analyzes that when the number of projection axes in QDASG is smaller than that in traditional DA methods, the discriminant formula can achieve optimal results, and verifies this conclusion through experiments. Then, according to the number of training samples, the implementation process of QDA is discussed in two cases. Finally, spatial filtering is used to extract spatial information, and the distance quotient formula between classes is proposed as the standard for selecting the the optimal number of stacking layers for the filter. Experimental results show that this method is effective compared with deep learning method and and other traditional machine learning methods, especially in the case of small samples. © 2008-2012 IEEE.

关键词： Discriminant analysis

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Fc-gcn: A formal concept-enhanced graph convolution network model

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Soft Computing 2025年第6期29卷 2715-2725页

作者： Wu, Kai Zhang, Chao Hao, Fei Li, Jinhai Wan, Qing Park, Kyuwon Qin, Xueyang Loia, Vincenzo School of Computer Science Shaanxi Normal University Xi’an710119 China Intelligent Policing Key Laboratory of Sichuan Province Sichuan Police College Luzhou646000 China Faculty of Science Kunming University of Science and Technology Kunming650500 China School of Science Xi’an Polytechnic University Xi’an710048 China AI·SW Education Institute Soonchunhyang University Asan31538 Korea Republic of School of Computer Science and Technology Dalian University of Technology Dalian116024 China Dipartimento di Scienze Aziendali - Management & Innovation Systems University of Salerno Fisciano84098 Italy

Graph convolution network (GCN) is a promising deep learning method, mainly used to process graph data, such as social network, knowledge graph, etc. The basic idea of GCN is to use local information to update the node feature vectors, so as to obtain global information and realize graph-level tasks. When dealing with large-scale graphs, the computational and storage overhead of GCN are expensive, which may lead to a decline in generalization performance. Fortunately, formal concept analysis (FCA) as a data mining method based on lattice theory, is mainly used to extract the conceptual relationship between data. By classifying and clustering the data, a complete concept hierarchy can be established to help people better understand the internal relationship between data and provide strong support for subsequent data analysis. To this end, this paper proposes a formal concept-enhanced graph convolution network (FC-GCN), which uses the FCA methodology to preprocess large-scale graphs, extract graph data information and deploy the graph-related services. Technically, the maximal cliques and concept stability are used for feature update to implement downstream tasks such as node classification, link prediction, and community detection thereby overcoming the problems of graph convolution methods on large-scale graphs. The FC-GCN model is trained with Cora dataset, and the performance of model accuracy with different parameters is analyzed using node classification. Compared with baselines on real-world datasets, FC-GCN has better performance on vertice classification, accuracy improvements range from 4.2 to 6.1%. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Formal concept analysis

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Explaining Machine Learning Based Speed Anomaly Detection System Using eXplainable Artificial Intelligence 1

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13th International Conference on Information Systems and Advanced Technologies, ICISAT 2023

作者： Idris, Hussaini Aliyu Ahmad, Aminu Diginsa, Muhammad Umar Aliyu, Buhari Muhammad, Adamu Yahaya, Abdulgaffar Abubakar Suleiman, Sabiu Usman Alexandria Egypt School of Computer Science and Engineering Central South University Hunan Changsha China Department of Computer Engineering Technology Bilyaminu Usman Polytechnic Hadejia Hadejia Nigeria Department of Computer Science Jigawa State Polytechnic Dutse Jigawa Nigeria Department of Electrical Electronics Engineering Jigawa State Polytechnic Dutse Jigawa Nigeria Department of Computer Science Mewar University Gangarar Chittorgarh Rajasthan India

ISBN: (数字)9783031605949

ISBN: (纸本)9783031605932

The goal of combating road accidents through the application of advanced communication and network technologies in transportation system called intelligent transportation system (ITS) has recorded a remarkable success. Nevertheless, the complex nature of the ITS makes cyberattacks a major concern even with cryptographic techniques employed for authentication of users, as they cannot prevent attacks from authenticated users (insider attackers). To solve this problem, several data-centric approaches for anomaly detection using machine learning (ML) are proposed by the research community. However, those approaches are limited in interpretability and explainability due to the complexity of some ML algorithms and black-box nature of neural networks which makes threat intelligence and analysis difficult. In this paper, we proposed an eXplainable artificial intelligence (XAI)-based speed anomaly detection system using trusted vehicles method with more than 99% accuracy, precision, recall and F1-Score where Shapley Additive exPlanation (SHAP) is further employed to explain the predictions made by the models. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Machine learning

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Multi-feedback Pairwise Ranking via Adversarial Training for Recommender

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Chinese Journal of Electronics 2020年第4期29卷 615-622页

作者： WANG Jianfang FU Zhiyuan NIU Mingxin ZHANG Pengbo ZHANG Qiuling College of Computer Science and Technology Henan Polytechnic University

Personalized recommendation systems predict potential demand by analyzing user ***, user feedback information is inferred from implicit feedback or explicit ***,feedback can be contaminated by user's mis-operations or malicious operations, and may thus lead to incorrect *** propose a novel Multi-feedback pairwise ranking method via Adversarial training(AT-MPR) for recommender to enhance the robustness and overall performance in the event of rating *** MPR method extends Bayesian personalized ranking(BPR) to cover three types of feedback: positive, negative, and *** obtains user preferences in a probabilistic way through multiple feedbacks at different *** reduce the impact of feedback noise, we train an MPR objective function using minimax adversarial *** on two datasets show that the AT-MPR model achieves satisfactory performance and outperforms the state-of-the-art implicit feedback collaborative ranking models in two evaluation metrics.

关键词： Adversarial training Pairwise ranking Collaborative filtering Recommender system

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Edge Detection of COVID-19 CT Image Based on GF_SSR, Improved Multiscale Morphology, and Adaptive Threshold

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computer Modeling in Engineering & sciences 2022年第7期132卷 81-94页

作者： Shouming Hou Chaolan Jia Kai Li Liya Fan Jincheng Guo Mackenzie Brown School of Computer Science and Technology Henan Polytechnic UniversityJiaozuo454000China City College Xi’an Jiaotong UniversityXi’an710018China Department of Thoracic Surgery Jiaozuo Second People’s HospitalJiaozuo454000China School of Engineering Edith Cowan UniversityJoondalup6027Australia

Edge detection is an effective method for image segmentation and feature ***,extracting weak edges with the inhomogeneous gray of Corona Virus Disease 2019(COVID-19)CT images is extremely *** morphology has been widely used in the edge detection of medical images due to its excellent boundary detection *** this paper,we propose a weak edge detection method based on Gaussian filtering and singlescale Retinex(GF_SSR),and improved multiscale morphology and adaptive threshold binarization(IMSM_ATB).As all the CT images have noise,we propose to remove image noise by Gaussian *** edge of CT images is enhanced using the SSR *** addition,based on the extracted edge of CT images using improved Multiscale morphology,a particle swarm optimization(PSO)algorithm is introduced to binarize the image by automatically getting the optimal *** evaluate our method,we use images from three datasets,namely COVID-19,Kaggle-COVID-19,and COVID-Chestxray,*** average values of results are worthy of reference,with the Shannon information entropy of 1.8539,the Precision of 0.9992,the Recall of 0.8224,the F-Score of 1.9158,running time of ***,three types of lesion images in the COVID-19 dataset are selected to evaluate the visual effects of the proposed *** with the other four algorithms,the proposed algorithm effectively detects the weak edge of the lesion and provides help for image segmentation and feature extraction.

关键词： COVID-19 SSR multiscale morphology PSO adaptive threshold edge detection

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Parameter optimization strategy of random forest algorithm for land use classification 4

Parameter optimization strategy of random forest algorithm f...

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4th International Conference on Geoscience and Remote Sensing Mapping, GRSM 2022

作者： Wei, Jieru Zhao, Bei Shang, Jiandong Han, Lin Li, Xiao Li, Xinzhao National Supercomputing Center in Zhengzhou Zhengzhou University Henan Zhengzhou450000 China The school of Computer and Artificial Intelligence Zhengzhou University Henan Zhengzhou450000 China The school of Geo-Science & Technology Zhengzhou University Henan Zhengzhou450000 China

ISBN: (数字)9781510662179

ISBN: (纸本)9781510662162

When using the random forest algorithm to classify remote sensing images of each target year in the study area, the number of decision trees and the maximum number of features for constructing the optimal model of decision trees have a great influence on the accuracy of the random forest classification results. Based on this, this paper proposes an adaptive parameter tuning strategy based on GridSearchCV to improve the random forest algorithm. The method can select the best parameters according to different sample data and study area conditions. By comparing with unoptimized random forest, decision tree, and support vector machine algorithms, the results suggest that: the optimized random forest algorithm has good classification accuracy, and the overall accuracy and Kappa coefficient of classification results are above 0.90. © 2023 SPIE.

关键词： Decision trees

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Exploring the Impact of ***'s Block-Based Coding Curriculum on Student Motivation in K-12 Education 12

Exploring the Impact of ***'s Block-Based Coding Curriculum ...

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12th International Conference on Information and Education technology, ICIET 2024

作者： Choi, Wan Chong Choi, Iek Chong Illinois Institute Of Technology Department Of Computer Science United States Macao Polytechnic University Faculty Of Applied Sciences China School Of Education City University Of Macau China

ISBN: (纸本)9798350371772

In an era where technological skills are essential, integrating coding curriculum within Macao's primary education is becoming increasingly prevalent. However, fostering programming skills among young learners is challenging, particularly when low motivation impedes learning. ***, a block-based programming website, has recently seen increasing use in universities, high schools, and primary schools. It incorporates gamification elements, allowing students to learn programming through a game-based challenge. This study employed a quasi-experimental approach to explore the impact of *** on enhancing students' motivation to learn coding. The participants comprised 20 grade three students who received three weeks of instruction, with six lessons each week, each lasting 40 minutes. The effectiveness of the *** intervention was assessed using the Instructional Materials Motivation Survey (IMMS), which measures the dimensions of the ARCS motivation model: Attention, Relevance, Confidence, and Satisfaction. The findings revealed a significant boost in learning motivation across all ARCS dimensions following the *** course. The study also demonstrated a strong correlation between these dimensions within the block-based coding curriculum provided by ***, suggesting the value of such platforms in motivating and improving programming education in primary schools. © 2024 IEEE.

关键词： Curricula

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Deep Learning-based Techniques for Precise Vehicle Detection and Distance Estimation in Autonomous Systems 11

Deep Learning-based Techniques for Precise Vehicle Detection...

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11th IEEE International Conference on Cloud Computing in Emerging Markets, CCEM 2023

作者： Srinivasa, C. Suresha, R. Manohar, N. Dharun, G.K. Sheela, T. Jipeng, Tian Department of Computer Science School of Computing Mysuru Amrita Vishwa Vidyapeetham India Mysuru India Computer Science College Zhongyuan University of Technology Henan Province Zhengzhou China

ISBN: (纸本)9798350360059

Vehicle detection, recognition, and distance estimation play a crucial role in computer vision, particularly in applications such as intelligent traffic systems. However, existing methods need help in achieving accurate vehicle detection, recognition, and distance estimation, often resulting in low detection accuracy and limited distance estimation capability. This paper proposes an integrated approach combining Faster R-CNN architecture with scale-based distance estimation to address these issues. Through experimental evaluations, the results validate the effectiveness of the proposed method in achieving 93.5%, 95.6%, and high detection accuracy, recognition accuracy, and accurate distance estimation. The proposed framework shows the potential to enhance the performance and robustness of vehicle detection and distance estimation systems, offering promising advancements in this field. ©2023 IEEE.

关键词： Intelligent systems

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