检索结果-内蒙古大学图书馆

40th IEEE International Conference on data Engineering, ICDE 2024

作者： Hou, Wenzhe Zhao, Xiang Tang, Bo National University of Defense Technology Laboratory for Big Data and Decision China Southern University of Science and Technology Department of Computer Science and Engineering China

ISBN: (纸本)9798350317152

Graphs are valuable data structures used to represent complex relationships between entities in a wide range of applications, such as social networks and chemical reactions. Subgraph counting problem is a well-known hard problem, as its core subroutine, the subgraph matching, is NP-complete. In this work, we propose an efficient and unified deep learning-based solution framework LearnSC, which solves the subgraph counting problem approximately. This framework offers two key advantages: (i) it is a generic solution that is orthogonal to the existing techniques of learning-based solutions;and (ii) it is equipped with a suite of optimizations to significantly improve the accuracy of the estimated results. Our experimental results on 7 datasets demonstrate that our proposal is highly accurate, robust, and scalable, making it an excellent solution for subgraph counting problem among all statistics-based and learning-based competitors. © 2024 IEEE.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Achieving dynamic privacy measurement and protection based on reinforcement learning for mobile edge crowdsensing of IoT

引用

Digital Communications and Networks 2024年第2期10卷 380-388页

作者： Renwan Bi Mingfeng Zhao Zuobin Ying Youliang Tian Jinbo Xiong Fujian Provincial Key Laboratory of Network Security and Cryptology College of Computer and Cyber SecurityFujian Normal UniversityFuzhou350117China Guangxi Key Laboratory of Trusted Software Guilin University of Electronic TechnologyGuilin541004China Faculty of Data Science City University of Macao999078MacaoChina State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyang550025China

With the maturity and development of 5G field,Mobile Edge CrowdSensing(MECS),as an intelligent data collection paradigm,provides a broad prospect for various applications in ***,sensing users as data uploaders lack a balance between data benefits and privacy threats,leading to conservative data uploads and low revenue or excessive uploads and privacy *** solve this problem,a Dynamic Privacy Measurement and Protection(DPMP)framework is proposed based on differential privacy and reinforcement ***,a DPM model is designed to quantify the amount of data privacy,and a calculation method for personalized privacy threshold of different users is also ***,a Dynamic Private sensing data Selection(DPS)algorithm is proposed to help sensing users maximize data benefits within their privacy ***,theoretical analysis and ample experiment results show that DPMP framework is effective and efficient to achieve a balance between data benefits and sensing user privacy protection,in particular,the proposed DPMP framework has 63%and 23%higher training efficiency and data benefits,respectively,compared to the Monte Carlo algorithm.

关键词： Mobile edge crowdsensing Dynamic privacy measurement Personalized privacy threshold Privacy protection Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

Machine Learning Based Predictive Analysis of Consumer Behavior in Webcasting Bandwagons 24

Machine Learning Based Predictive Analysis of Consumer Behav...

引用

2024 Guangdong-Hong Kong-Macao Greater Bay Area International Conference on Digital Economy and Artificial Intelligence, DEAI 2024

作者： Zhu, Lixing Yang, Xue Jiang, Xue Chen, Shufen Chen, Jie Xiao, Yunqing Guangdong University of Science and Technology Dongguan China Alibaba Cloud Big Data Application College Zhuhai College of Science and Technology Zhuhai China

ISBN: (纸本)9798400717147

This paper addresses the problem of predicting consumer behavior in live webcast banding and explores the application of machine learning algorithms in this field and their effectiveness. The study mainly focuses on the use of two machine learning models, Decision Tree and Random Forest, to analyze and predict consumer purchase behavior in live streaming bandwagon campaigns. By simulating a dataset containing 15,000 records, this study conducted detailed training and testing of the models to evaluate their performance on key metrics such as accuracy, recall, precision and F1 score. The experimental results show that the Random Forest model outperforms the Decision Tree model on all the evaluated metrics, especially on accuracy and F1 score. This finding highlights the advantages of integrated learning methods when dealing with complex datasets. In addition, the feature selection process revealed that live streaming viewing duration, number of interactions, and anchor ratings are key factors influencing consumers' purchasing decisions. The findings of this study provide valuable insights for the optimization of marketing strategies for webcast bandwagons. By understanding the key factors influencing consumer purchasing behavior, e-commerce platforms and companies can more effectively position their target markets and optimize their advertising and product promotion strategies. © 2024 ACM.

关键词： Decision trees

来源：评论

学校读者我要写书评

暂无评论

Green technology innovation guiding policies under the synergy of government and financial institutions

引用

Sustainable Operations and Computers 2024年第1期5卷 269-277页

作者： Ma, Yanhong Zhao, Hui School of Economics and Management Beijing Information Science and Technology University Beijing100192 China Beijing Key Lab of Green Development Decision Based on Big Data Beijing100192 China

Guiding enterprises towards green technology innovation (GTI) is a pivotal strategy for mitigating pollution and energy wastage, thereby facilitating the transition towards a green and low-carbon economy. In order to solve the single irrationality of the existing guidance policy and improve its implementation effect, parameters of environmental regulation and green credit policy are hereby introduced to construct a tripartite evolutionary game model of the government, financial institutions, and enterprises. The evolutionary stability of the government and financial institutions in guiding enterprises' GTI is analyzed. Simulation analysis method is used to visualize the evolution path of the game system under different policy options. The study results show that increasing interest rate subsidies can promote the financial institutions and the government to formulate GTI guidance policies. Besides, there is a significant synergistic effect between interest rate subsidies and green credits, which can efficiently foster GTI within enterprises and decrease their innovation costs. Building on this foundation, further increasing the carbon tax rate can lead to a reduction in government subsidy expenditures, improve the evolution rate of the ideal strategy, and expedite the realization of a green, low-carbon transformation. © 2024

关键词： Green economy

来源：评论

学校读者我要写书评

暂无评论

Graph Attention Network-based Multimodal Fusion for Fake Food Reviews Detection 24

Graph Attention Network-based Multimodal Fusion for Fake Foo...

引用

9th International Conference on Cyber Security and Information Engineering, ICCSIE 2024

作者： Yang, Jiajie Mao, Dianhui Zhang, Denghui Beijing Key Laboratory of Big Data Technology for Food Safety Computer Science Department Beijing Technology and Business University China

ISBN: (纸本)9798400718137

In the field of computational intelligence and data analytics, the detection of fake food reviews has emerged as a pressing challenge, exacerbated by the widespread use of social media. These fraudulent reviews, particularly prevalent in the food and beverage industry, distort consumer decision-making and disrupt market fairness. To address this, we propose GMF-R, an innovative multimodal fusion framework based on Graph Attention Network (GAT), designed to effectively detect fake food reviews. The framework features end-to-end trainability and introduces advanced multimodal feature processing mechanisms, including fine-grained cross-modal interactions, a shared feature space, and multimodal graph construction. By utilizing GAT, GMF-R can adaptively assign attention weights to key nodes and accurately capture complex inter-modal relationships. Experimental results demonstrate that GMF-R achieves a 97.4% accuracy rate and a 98.1% F1 score on the Yelp reviews website dataset, significantly outperforming previous state-of-the-art models. © 2024 Copyright held by the owner/author(s).

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Evaluating RISC-V Vector Instruction Set Architecture Extension with Computer Vision Workloads

引用

Journal of Computer science & technology 2023年第4期38卷 807-820页

作者：李若时彭平邵志远金海郑然 National Engineering Research Center for Big Data Technology and System Huazhong University of Science and Technology Wuhan 430074China Services Computing Technology and System Laboratory Huazhong University of Science and TechnologyWuhan 430074 China Cluster and Grid Computing Lab Huazhong University of Science and TechnologyWuhan 430074China

Computer vision(CV)algorithms have been extensively used for a myriad of applications *** the multimedia data are generally well-formatted and regular,it is beneficial to leverage the massive parallel processing power of the underlying platform to improve the performances of CV *** Instruction Multiple data(SIMD)instructions,capable of conducting the same operation on multiple data items in a single instruction,are extensively employed to improve the efficiency of CV *** this paper,we evaluate the power and effectiveness of RISC-V vector extension(RV-V)on typical CV algorithms,such as Gray Scale,Mean Filter,and Edge *** our examinations,we show that compared with the baseline OpenCV implementation using scalar instructions,the equivalent implementations using the RV-V(version 0.8)can reduce the instruction count of the same CV algorithm up to 24x,when processing the same input ***,the actual performances improvement measured by the cycle counts is highly related with the specific implementation of the underlying RV-V *** our evaluation,by using the vector co-processor(with eight execution lanes)of Xuantie C906,vector-version CV algorithms averagely exhibit up to 2.98x performances speedups compared with their scalar counterparts.

关键词： RISC-V vector extension single instruction multiple data(SIMD) computer vision OpenCV

来源：评论

学校读者我要写书评

暂无评论

The Evaluation Benchmark for SP ARQL Federated Query

The Evaluation Benchmark for SP ARQL Federated Query

引用

2023 Asia-Pacific Conference on Image Processing, Electronics and Computers, IPEC 2023

作者： Xu, Jiao Tian, Pingfang Gu, Jinguang Big Data Science and Engineering Research Institute Wuhan University of Science and Technology Wuhan China

ISBN: (纸本)9781665457033

In SP ARQL federated query system, cardinality estimation is one of the most critical steps that affect query performance. In recent years, a large number of federated query cardinality estimation methods have been produced, which is particularly important for evaluating the performance of different methods. However, the existing research shows that there is no unified test benchmark when evaluating cardinality estimation methods, and the commonly used precision index Q-error has also been proved to be unable to generate the optimal query plan. In order to solve the above problems, this paper proposes a test benchmark (FQCEB) for SP ARQL federated query cardinality estimation. FQCEB consists of three components: benchmark data set, query generator and evaluation index, including 32 templates created manually, 1600 unique queries and more than 200K query subplans. The experimental results show that when the performance of radix estimation method is evaluated by using the test benchmark in this paper, it shows the applicability of the benchmark to various methods, and the P-error evaluation index in the benchmark shows the correlation with the query running time, and the correlation coefficient is as high as 85%, which proves that P-error is more suitable for evaluating the actual performance of radix estimation method than Q-error. © 2023 IEEE.

关键词： Errors

来源：评论

学校读者我要写书评

暂无评论

On the Convergence of Prey Predator Algorithm

引用

International Journal of Applied and Computational Mathematics 2024年第2期10卷 1-23页

作者： Tilahun, Surafel Luleseged Department of Mathematics HPC and Big Data Analytics Center of Excellence Addis Ababa Science and Technology University Addis Ababa 16417 Ethiopia

Unlike their success in solving complex and difficult problems, there is a lack of proper mathematical analysis and discussion of metaheuristic algorithms. Even though some researches are done on these aspects, the gap is still wide and different researchers outlined that more needs to be done to understand and analyse the search behaviour mathematically in order to move towards explainable AI. Convergence analysis is one of the issues researchers are trying to address. In this paper, a discussion on the convergence of prey predator algorithm will be presented. It is a swarm based algorithm which shows promising results. Furthermore, some initial researches show that it is a generalized algorithm where some well known algorithms fall as its special case. Hence, its analysis can be customized and used for other algorithms as well. Based on mathematical arguments, this paper discusses the convergence analysis of the algorithm to a near optimal solution with appropriate implementation, i.e. appropriate parameter setting. Prey predator algorithm is shown to be an ϵ-convergent algorithm that means there is a non infinity iteration number to get at least epsilon less quality from an optimal solution. © The Author(s), under exclusive licence to Springer Nature India Private Limited 2024.

关键词： Convergence Metaheuristic Prey predator algorithm Swarm intelligence

来源：评论

学校读者我要写书评

暂无评论

Fake Face Detection Based on Fusion of Spatial Texture and High-Frequency Noise

引用

Chinese Journal of Electronics 2025年第1期34卷 212-221页

作者： Dengyong Zhang Feifan Qi Jiahao Chen Jiaxin Chen Rongrong Gong Yuehong Tian Lebing Zhang Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation School of Computer and Communication Engineering Changsha University of Science and Technology School of Computer and Communication Engineering Changsha University of Science and Technology Changsha Social Work College Changkuangao Beijing Technology Co. Ltd. School of Computer and Artificial Intelligence Huaihua University

The rapid development of the Internet has led to the widespread dissemination of manipulated facial images, significantly impacting people's daily lives. With the continuous advancement of Deepfake technology, the generated counterfeit facial images have become increasingly challenging to distinguish. There is an urgent need for a more robust and convincing detection method. Current detection methods mainly operate in the spatial domain and transform the spatial domain into other domains for analysis. With the emergence of transformers, some researchers have also combined traditional convolutional networks with transformers for detection. This paper explores the artifacts left by Deepfakes in various domains and, based on this exploration, proposes a detection method that utilizes the steganalysis rich model to extract high-frequency noise to complement spatial features. We have designed two main modules to fully leverage the interaction between these two aspects based on traditional convolutional neural networks. The first is the multi-scale mixed feature attention module, which introduces artifacts from high-frequency noise into spatial textures, thereby enhancing the model's learning of spatial texture features. The second is the multi-scale channel attention module, which reduces the impact of background noise by weighting the features. Our proposed method was experimentally evaluated on mainstream datasets, and a significant amount of experimental results demonstrate the effectiveness of our approach in detecting Deepfake forged faces, outperforming the majority of existing methods.

关键词： Deepfakes Adaptation models Attention mechanisms Noise Transforms Transformers Feature extraction Internet Convolutional neural networks Faces

来源：评论

学校读者我要写书评

暂无评论

Research on one-stop traditional Chinese medicine service system based on character recognition

Research on one-stop traditional Chinese medicine service sy...

引用

Information technology,big data and Artificial Intelligence (ICIBA), International Conference on IEEE International Conference on

作者： Beile Wang Qingqiu Yang Huafeng Kong Department of Information Engineering Wuhan Business University Wuhan China Major in Data Science and Big Data Technology

ISBN: (数字)9798350363609

ISBN: (纸本)9798350363616

In recent years, China has introduced numerous policies aimed at fostering the growth of the traditional Chinese medicine (TCM) industry. Concurrently, the enrichment of TCM healthcare resources has s purred an increase in demand for TCM services. To address the needs of TCM practitioners and to offer the public more high-quality, precise, and intelligent TCM services, we have designed a comprehensive WeChat applet for TCM services that incorporates text recognition technology. This applet leverages text recognition to analyze the ingredients listed in prescriptions and employs a cold extraction method to prepare Chinese patent medicines, thereby reducing the cost of TCM treatments for consumers. Developed using the WeChat developer tool, the applet boasts a user-friendly interface and a suite of integrated functionalities, presenting a valuable service model for the TCM sector.

关键词： Industries Patents Costs Text recognition Social networking (online) Medical services big data Message services Character recognition Information technology

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：