检索结果-内蒙古大学图书馆

11th International Conference on Computer Engineering and Networks, CENet2021

作者： Jiao, Ge College of Computer Science and Technology Hengyang Normal University HengyangHunan421002 China Hunan Provincial Key Laboratory of Intelligent Information Processing and Application HengyangHunan421002 China

ISBN: (纸本)9789811665530

Adaptive steganography emplaces the message into the hard-to-detect noise area or the complex texture area of the image, so the steganography analysis method based on artificial design features needs to design a very complex feature extraction algorithm to detect the steganography image. In view of the advantages of deep learning steganography in automatic extraction of image features and high detection accuracy, an image steganography analysis algorithm is designed by using deep convolutional neural network, and an image steganography analysis system based on deep learning is developed. The system realizes the functions of neural network structure analysis, image steganography and verification, steganography analysis and so on. The system simplifies the operation process of algorithm comparison, reduces the complexity of algorithm performance evaluation, and verifies the feasibility of the proposed algorithm. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Feature extraction

来源：评论

学校读者我要写书评

暂无评论

FPGA optimal implementation of PRINCE against power analysis attacks 5

FPGA optimal implementation of PRINCE against power analysis...

引用

5th International Conference on information Science and Cloud Computing, ISCC 2017

作者： Zou, Yi Li, Lang College of Computer Science and Technology Hengyang Normal University Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Hengyang421002 China

The algorithm PRINCE proposed in ASIACRYPT 2012 is a lightweight cipher, currently suitable for the implementation on RFID Smart Card of the IoT. It's studied to achieve the optimal implementation of PRINCE encryption algorithm, and to add a fixed random mask to enhance the anti power attacking ability of PRINCE. In this paper, we constructed the same operations into a module called PrinceRound, and used counter to control the PrinceRound module, while the same round operation runs repeatedly. As a result, it can save registers and effectively reduce the amount of computation. Furthermore, we used a special method by adding the random mask to S-box. The experimental results show that the optimized PRINCE occupies area 9.8% fewer than original PRINCE on FPGA and the encryption with a fixed random mask can run correctly. Last but not least, our research is the first one about the implementation of PRINCE algorithm on FPGA, and can serve as a reference for further application of IOT encryption. © Copyright owned by the author(s).

关键词： Field programmable gate arrays (FPGA)

来源：评论

学校读者我要写书评

暂无评论

Variational Graph Autoencoders Method Based on Attentional Mechanisms for Overlapping Community Detection 12th

Variational Graph Autoencoders Method Based on Attentional M...

引用

12th International Conference on Computer Engineering and Networks, CENet 2022

作者： Wen, Kunhui Lin, Mugang Zhu, Xuanying Zhao, Huihuang College of Computer Science and Technology Hengyang Normal University Hunan Hengyang421008 China Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Hunan Hengyang421008 China

ISBN: (纸本)9789811969003

Detecting overlapping communities of an attribute network is an important and difficult issue in network science. Traditional methods for overlapping community detection generally considered only its topological structure and neglected node attribute so that the detected communities could be inaccurate. Graph neural networks (GNN) have recently gained attention because of their learning representation ability for node attributes and the topology of a graph and demonstrating superior performance in natural language processing, data mining and pattern recognition, etc. However, GNN methods are still less for overlapping community detection. In this paper, we present an overlapping community detection method based on GNN for attribute networks. It applies a variational graph autoencoder based on attentional mechanisms to learn the representation of nodes in the graph and enhances the representation learning capability of GNN through semi-supervised learning. Various real-world networks of publicly available datasets are used to verify the effectiveness of the proposed approach. The experimental results show that the proposed method outperforms many other methods. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Graph neural networks

来源：评论

学校读者我要写书评

暂无评论

A New Scrambling Method Based on Semi-frequency Domain and Chaotic System

A New Scrambling Method Based on Semi-frequency Domain and C...

引用

2005 International Conference on Neural Networks and Brain(ICNN&B’05)

作者： Tao Li Shaowu Zhou Zhaofu Zeng Qingli Ou School of Information and Electrical Engineering Hunan University of Science and Technology Key Laboratory of Education Ministry for Image Processing and Intelligent Control Huazhong University of Science & Technology

ISBN: (纸本)0780394224

A new digital image scrambling method based on DCT and chaos maps is presented in this *** the chaotic system in semi-frequency domain,all the pixels of the original image are rearranged and *** procedure of the algorithm is roughly divided into three steps:first,an original image is decomposed for its first variable by 1-D DCT, then modulated by chaos system,a primary scrambling result is achieved,at last,after by inverse 1-D DCT for the scrambling result,repeating the same procedure for the second variable,a final scrambling result is obtained for the original *** confirm the robustness of the novel method,some robustness testing experiments are carried out on the scrambling *** experimental results shows that the method succeeds in enduring several kinds of common image attacks,such as cropping,noise and rotation.

关键词： Image security image scrambling chaotic sequences DCT(the discrete cosine transform)

来源：评论

学校读者我要写书评

暂无评论

Attribute Selection Method Based on Artificial Bee Colony Algorithm and Neighborhood Discrimination Matrix Optimization 16th

Attribute Selection Method Based on Artificial Bee Colony Al...

引用

16th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2021

作者： Ji, Yuxuan Ye, Jun Yang, Zhenyu Ao, Jiaxin Wang, Lei Nanchang Institute of Technology Jiangxi Nanchang330000 China Key Laboratory of Water Information Cooperative Perception and Intelligent Processing of Jiangxi Province Jiangxi Nanchang330000 China

ISBN: (纸本)9789811912559

At present, the existing attribute reduction algorithm combining artificial bee colony and neighborhood rough set basically uses the attribute dependence and the number of attribute subsets as parameters to construct the fitness function, while ignoring the role of heuristic information. As a result, the number of bee colony iterations increases, and the convergence speed is slow. Aiming at this kind of problem, an improved method is proposed. First, a discernibility matrix under the neighborhood rough set is defined;secondly, an attribute importance measurement method of the discernibility matrix under the neighborhood decision system is proposed;The attribute importance of the domain discrimination matrix constructs a new fitness function for the heuristic factor;finally, an attribute selection algorithm for artificial bee colony algorithm and neighborhood discrimination matrix importance optimization is designed. Compared with the original algorithm, the new method reduces the number of generations, accelerates the convergence speed, and retains the minimum attribute reduction collected during each iteration, and multiple minimum attribute reductions can be obtained. The experimental results on the UCI data set prove the feasibility and effectiveness of the algorithm. © 2022, Springer Nature Singapore Pte Ltd.

关键词： Matrix algebra

来源：评论

学校读者我要写书评

暂无评论

Research on Two-Dimensional Code Packaging Advertising and Anti-counterfeiting Based on Blockchain 1

引用

2nd EAI International Conference on Advanced Hybrid information processing, ADHIP 2018

作者： Wu, Yuezhong Chen, Rongrong Tan, Yanxi Shao, Zongmiao College of Artificial Intelligence Hunan University of Technology Zhuzhou412007 China Intelligent Information Perception and Processing Technology Hunan Province Key Laboratory Zhuzhou412007 China College of Business Hunan University of Technology Zhuzhou412007 China Department of Mechanical and Electrical Information Engineering Zhuzhou Industrial Secondary Specialized School Zhuzhou412008 China

ISBN: (数字)9783030190866

ISBN: (纸本)9783030190859

With the rapid development of Internet technology and the national economy, the brand awareness of consumers and businesses is getting stronger and stronger. However, how to identify application solutions for counterfeit and shoddy goods is a serious challenge for commodity brand. This paper designs and implements a two-dimensional code packaging advertising and anti-counterfeiting model based blockchain. It is applied to process the commodity information in the packaging field, based on the decentralization, openness, autonomy, anonymity and non-tamper ability of blockchain, combined with two-dimensional code technology. The model is based on network transmission interaction, has the advantages of high unforgeability, low cost, easy implementation, fast access, etc. It has a good technical reference value for implementing packaging advertising, anti-counterfeiting and blockchain application. © 2019, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Blockchain

来源：评论

学校读者我要写书评

暂无评论

Prediction of GDP Carbon Emission Based on Grey Model and Neural Network 4th

Prediction of GDP Carbon Emission Based on Grey Model and Ne...

引用

4th EAI International Conference on Robotic Sensor Networks, 2020

作者： Zhang, Feng Zhao, Huihuang Ramasamy, Manimaran College of Computer Science and Technology Hengyang Normal University Hengyang China Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Hengyang China

ISBN: (纸本)9783030704506

In order to improve the prediction accuracy of Gross Domestic Product (GDP) carbon emission, combining the advantages of grey prediction and neural network, a discrete grey neural network prediction model named DGMBP is established. This model not only has the advantages of less data sample and larger development coefficient range of GM 1,1 model but also combines the advantages of neural network to deal with nonlinear data sample. Taking the GDP carbon emission data of China, India, the United States and the European Union as an example, the prediction results obtained are stable, and the accuracy of the prediction data is over 95%, which is much higher than that obtained by the linear regression equation and the logical regression equation. Finally, the carbon emission of GDP in 2020–2030 is predicted by using this model. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

Double time slot RFID anti-collision algorithm based on gray code 5

Double time slot RFID anti-collision algorithm based on gray...

引用

5th International Conference on information Science and Cloud Computing, ISCC 2017

作者： Deng, Hongwei Li, Lang Wang, Hui School of Computer Science and Technology Hengyang Normal University School of Information Science and Engineering Central South University Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Hunan 421002 China

To solve the radio frequency identification problem, an anti-collision algorithm based on the Gray code (BSGC) was developed. The proposed algorithm reduces the effort required to search for tag prefixes in accordance with Gray code regulations and enhances the identification speed through efficient division and branching of the collision tags. The results of simulation and subsequent analysis of the proposed algorithm confirm that it effectively reduces the number of collisions and transmission delays, enhances the throughput rate, and increases the tag identification efficiency. © Copyright owned by the author(s).

关键词： Radio frequency identification (RFID)

来源：评论

学校读者我要写书评

暂无评论

A Personalized Collaborative Filtering Recommendation System of Network Document Resource Based on Knowledge Graph 3rd

A Personalized Collaborative Filtering Recommendation System...

引用

3rd International Conference on Cyberspace Data and Intelligence, Cyber DI 2019, and the International Conference on Cyber-Living, Cyber-Syndrome, and Cyber-Health, CyberLife 2019

作者： Wu, Yuezhong Chen, Rongrong Li, Changyun Chen, Shuhong College of Artificial Intelligence Hunan University of Technology Zhuzhou China Intelligent Information Perception and Processing Technology Hunan Province Key Laboratory Zhuzhou China College of Business Hunan University of Technology Zhuzhou China School of Computer Science and Cyber Engineering Guangzhou University Guangzhou China School of Computer and Communication Hunan Institute of Engineering Xiangtan China

ISBN: (纸本)9789811519215

The Internet has become one of the important channels for users to obtain information and knowledge. It is crucial that how to acquire personalized requirement of users accurately and effectively from huge amount of network document resource. This paper proposes a personalized collaborative filtering recommendation system for network document resource exploration using knowledge graph which can solve the problem of information overload and resource trek effectively. Extensive system test has been carried out in the field of big data application in packaging industry. The experimental results show that the proposed system recommends network document resource more accurately, and further improves recommendation quality using knowledge graph. Therefore, it can meet people’s personalized resource need more effectively. © 2019, Springer Nature Singapore Pte Ltd.

关键词： Recommender systems

来源：评论

学校读者我要写书评

暂无评论

Lightweight Image Super-Resolution via Weighted Multi-Scale Residual Network

引用

IEEE/CAA Journal of Automatica Sinica 2021年第7期8卷 1271-1280页

作者： Long Sun Zhenbing Liu Xiyan Sun Licheng Liu Rushi Lan Xiaonan Luo Guangxi Key Laboratory of Image and Graphic intelligent processing Guilin University of Electronic TechnologyGuilin 541004China National Local Joint Engineering Research Center of Satellite Navigation and Location Service Guilin University of Electronic TechnologyGuilin 541004China the College of Electrical and Information Engineering Hunan UniversityChangsha 410082China

The tradeoff between efficiency and model size of the convolutional neural network(CNN)is an essential issue for applications of CNN-based algorithms to diverse real-world *** deep learning-based methods have achieved significant improvements in image super-resolution(SR),current CNNbased techniques mainly contain massive parameters and a high computational complexity,limiting their practical *** this paper,we present a fast and lightweight framework,named weighted multi-scale residual network(WMRN),for a better tradeoff between SR performance and computational *** the modified residual structure,depthwise separable convolutions(DS Convs)are employed to improve convolutional operations’***,several weighted multi-scale residual blocks(WMRBs)are stacked to enhance the multi-scale representation *** the reconstruction subnetwork,a group of Conv layers are introduced to filter feature maps to reconstruct the final high-quality *** experiments were conducted to evaluate the proposed model,and the comparative results with several state-of-the-art algorithms demonstrate the effectiveness of WMRN.

关键词： Convolutional neural network(CNN) lightweight framework multi-scale super-resolution

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：