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Proceedings of the 3rd International Conference on advanced Information Science and System

作者： Jinjin Liu Zhifeng Chen Xiaonan Li Tongxin Wei School of Computer Science Zhongyuan University of Technology China and State Key Laboratory of Mathematical Engineering and Advanced Computing China State Key Laboratory of Mathematical Engineering and Advanced Computing China School of Computer Science Zhongyuan University of Technology China School of Cyberspace Security Zhengzhou University China

ISBN: (纸本)9781450385862

In view of the bandwidth consumption caused by data stream transmission in video analysis system and the demand for accurate online real-time analysis of massive data, this paper proposes a deep learning model framework for face recognition employed in the embedded system. Through data collaboration, the cloud could build a more complex data set with a small amount of uploaded data gathered by the end devices. And the framework collaboration makes sure that the fully-trained cloud model directly download or distillate knowledge to the end devices. Experiments show that the deep model not only realizes the real-time response and the accurate response of the cloud system, but also greatly reduces the bandwidth consumption caused by sample data transmission in the model training process.

关键词： Deep learning

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Controllable Majorana vortex states in iron-based superconducting nanowires

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National Science Review 2022年第9期9卷 195-203页

作者： Chuang Li Xun-Jiang Luo Li Chen Dong E.Liu Fu-Chun Zhang Xin Liu School of Physics and Institute for Quantum Science and Engineering Huazhong University of Science and Technology Wuhan National High Magnetic Field Center and Hubei Key Laboratory of Gravitation and Quantum Physics State Key Laboratory of Low Dimensional Quantum Physics Department of Physics Tsinghua University Kavli Institute for Theoretical Sciences and CAS Center for Excellence in Topological Quantum Computing University of Chinese Academy of Sciences Collaborative Innovation Center of Advanced Microstructures Nanjing University

To reve al the non-Abelian braiding statistics of Major ana zero modes(MZMs),it is crucial to design a Majorana platform,in which MZMs can be easily manipulated in a broad topological nontrivial parameter *** is also an essential step to confirm their *** this study,we propose an iron-based superconducting nanowire system with Majorana vortex states to satisfy desirable *** system has a radius-induced topological phase transition,giving a lower bound for the nanowire *** the topological phase,the iron-based superconducting nanowires have only one pair of MZMs over a wide range of radii,chemical potential and external magnetic *** wave function of MZMs has a sizable distribution at the side edge of the *** property enables the control of the interaction of MZMs in neighboring vortex nanowires and paves the way for Majorana fusion and braiding.

关键词： iron-based superconductor topological phase transition superconducting vortex Majorana zero modes nanowire

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The Executors Scheduling Algorithm for the Web Server Based on the Attack Surface

The Executors Scheduling Algorithm for the Web Server Based ...

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2020 IEEE International Conference on Advances in Electrical engineering and Computer Applications, AEECA 2020

作者： Nie, Guanglai Zhang, Zheng Zhao, Yufeng Meac State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (纸本)9781728165202

In the existing scheduling algorithms of mimicry structure, the random algorithm cannot solve the problem of large vulnerability window in the process of random scheduling. Based on known vulnerabilities, the algorithm with diversity and complexity as scheduling indicators can not only fail to meet the characteristic requirements of mimic's endogenous security for defense, but also cannot analyze the unknown vulnerabilities and measure the continuous differences in time of mimic Executive Entity. In this paper, from the Angle of attack surface is put forward based on mimicry attack the mimic Executive Entity scheduling algorithm, its resources to measure analysis method and mimic security has intrinsic consistency, avoids the random algorithm to vulnerability and modeling using known vulnerabilities targeted, on time at the same time can ensure the diversity of the Executive body, to mimic the attack surface web server scheduling system in continuous time is less, and able to form a continuous differences. Experiments show that the minimum symbiotic resource scheduling algorithm based on time continuity is more secure than the random scheduling algorithm. © 2020 IEEE.

关键词： Scheduling algorithms

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Implementation of Multi-compartment Neuron Model with Synaptic Plasticity Using FPGA 21

Implementation of Multi-compartment Neuron Model with Synapt...

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21st IEEE International Conference on Communication Technology, ICCT 2021

作者： DIng, Yiwen Fu, Wenlong Cao, Lihong Communication University of China State Key Laboratory of Media Convergence and Communication Beijing China Neuroscience and Intelligent Media Institute Communication University of China Beijing China State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi China

ISBN: (纸本)9781665432061

Computational neuroscience is interdisciplinary and plays an essential role in facilitating the development of cognitive neuroscience and Artificial General Intelligence (AGI). To explore the computational efficiency of Spiking Neural Network (SNN) on a computational hardware platform, we deploy SNN on the Xilinx Zynq7 Zedboard Field Programmable Gate Array (FPGA). Specifically, we use multi-compartmental Hodgkin-Huxley (HH) neuron model to construct neural circuits via C++ code and adopt a biologically plausible calcium concentration-based plasticity model as the learning rule of SNN. Our experimental results show that the FPGA platform can support parallel computation of the multi-compartment HH model with synaptic plasticity. Compared to other methods, FPGA-based computing has a significant speed advantage, suggesting that FPGA can be a good choice for implementing brain-inspired computation. © 2021 IEEE.

关键词： Field programmable gate arrays (FPGA)

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MDFA: A Malware Detection Framework Based on Attention Mechanism with Bi-LSTM

MDFA: A Malware Detection Framework Based on Attention Mecha...

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International Conference on Applied Machine Learning (ICAML)

作者： Xuyan Qi Huanwei Wang Yonghe Tang Wei Liu Jiangtao Xie State Key Laboratory of Mathematic Engineering and Advanced Computing Zhengzhou China

As the existing malware intelligent detection methods have shortcomings and low accuracy of manual feature extraction and feature processing, a Malware Detection Framework with Attention mechanism based on Bi-directional Long Short-Term Memory (MDFA) was proposed. To training this method, We have collected 15, 893 malware and 10, 959 benign program samples and constructed a dataset. Then, the sample data was labeled and sanitized by determining the results based on VirusTotal analysis. Finally, the Bi-directional Long Short-Term Memory model injected into the attention mechanism was built and trained to complete the malware detection. Experimental results show that the accuracy of MDFA was improved by at least 2. 3%, and the recall rate was improved significantly compared with the existing methods, verifying the effectiveness of MDFA.

关键词： Training Computer viruses Bidirectional control Manuals Machine learning Feature extraction Transformers

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Cluster-Based Routing Algorithm for WSN Based on Subtractive Clustering 16

Cluster-Based Routing Algorithm for WSN Based on Subtractive...

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16th IEEE International Wireless Communications and Mobile computing Conference, IWCMC 2020

作者： Chen, Ling Liu, Wenwen Gong, Daofu Chen, Yan State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou450002 China

ISBN: (纸本)9781728131290

The Cluster-based Routing Algorithm for Wireless Sensor Network based on Subtractive Clustering (SCC algorithm) generates nodes of cluster head in dense area of the node by relying on subtractive clustering. The application of this algorithm effectively solves the attribution problem of non-cluster head node in conventional algorithms, and consumption is evenly distributed throughout the network. Through simulation experiments, it can be found that the application of the SCC algorithm can map the distribution of nodes of cluster head reasonably and delay the death time of the first node to prolong the lifetime of network and balance the energy consumption of nodes. © 2020 IEEE.

关键词： Routing algorithms

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Research on the Protection Mechanism of Cisco IOS Exploit 4

Research on the Protection Mechanism of Cisco IOS Exploit

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4th International Conference on Data Mining, Communications and Information Technology, DMCIT 2020

作者： Wang, Jinzhu Cai, Ruijie Liu, Shengli State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou450001 China

Cisco IOS is an operating system that runs on Cisco routing and switching equipment. Its security is important to the entire network. Since vulnerability attacks have become one of the main sources of threat to Cisco IOS security, it is necessary to analyze the defense mechanism of IOS against vulnerability attacks. The purpose of this paper is to find out the deficiencies of IOS vulnerability defense mechanism in time and put forward improvement methods and preventive measures. Firstly, this paper introduces the protection mechanism of Cisco IOS vulnerability, including CheckHeaps, DEP, ASLR. The principle of these mechanisms and the idea of vulnerability protection are analyzed. And then, after analyzing the shortcomings of these protection mechanisms, the basic idea of bypassing these protection mechanisms is proposed. Finally, in view of the shortcomings of Cisco IOS vulnerability defense mechanism and the proposed bypass methods, some advices about how to better protect Cisco IOS from vulnerability exploitation are given. © Published under licence by IOP Publishing Ltd.

关键词： Network security

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An evaluation algorithm of the importance of network node based on community influence 1

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5th International Conference on Data Mining and Big Data, DMBD 2020

作者： He, Gongzhen Luo, Junyong Yin, Meijuan State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou450001 China

ISBN: (数字)9789811572050

ISBN: (纸本)9789811572043

Identifying nodes in social networks that have great influence on information dissemination is of great significance for monitoring and guiding information dissemination. There are few methods to study the influence of communities on social networks among the existing node importance evaluation algorithms, and it is difficult to find nodes that promote information dissemination among communities. In view of this reason, this paper proposes a node importance evaluation algorithm based on community influence (abbreviated as IEBoCI algorithm), which evaluates the importance of the nodes based on the influence degree of the nodes on the communities and the ability to disseminate information the communities to which the nodes are connected. This algorithm firstly calculates the activation probability of nodes to other nodes, which is used to divide communities and evaluate influence. Secondly, the network is divided into communities based on LPA algorithm. Finally, the importance of the node is calculated by combining the influence of the community itself and the influence of the node on the community. Experiments are carried out on real social network data and compared with other community-based methods to verify the effectiveness of the algorithm. © Springer Nature Singapore Pte Ltd. 2020.

关键词： Complex networks

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ThyroidNet:A Deep Learning Network for Localization and Classification of Thyroid Nodules

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Computer Modeling in engineering & Sciences 2024年第4期139卷 361-382页

作者： Lu Chen Huaqiang Chen Zhikai Pan Sheng Xu Guangsheng Lai Shuwen Chen Shuihua Wang Xiaodong Gu Yudong Zhang Ultrasonic Department Zhongda Hospital Affiliated to Southeast UniversityNanjing210009China School of Physics and Information Engineering Jiangsu Second Normal UniversityNanjing211200China School of Computing and Mathematical Sciences University of LeicesterLeicesterLE17RHUK Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia State Key Laboratory of MillimeterWaves Southeast UniversityNanjing210096China Jiangsu Province Engineering Research Center of Basic Education Big Data Application Jiangsu Second Normal UniversityNanjing211200China School of Software Engineering Quanzhou Normal UniversityQuanzhou362000China Department of Biological Sciences Xi’an Jiaotong-Liverpool UniversitySuzhou215123China

Aim:This study aims to establish an artificial intelligence model,ThyroidNet,to diagnose thyroid nodules using deep learning techniques ***:A novel method,ThyroidNet,is introduced and evaluated based on deep learning for the localization and classification of thyroid ***,we propose the multitask TransUnet,which combines the TransUnet encoder and decoder with multitask ***,we propose the DualLoss function,tailored to the thyroid nodule localization and classification *** balances the learning of the localization and classification tasks to help improve the model’s generalization ***,we introduce strategies for augmenting the ***,we submit a novel deep learning model,ThyroidNet,to accurately detect thyroid ***:ThyroidNet was evaluated on private datasets and was comparable to other existing methods,including U-Net and *** results show that ThyroidNet outperformed these methods in localizing and classifying thyroid *** achieved improved accuracy of 3.9%and 1.5%,***:ThyroidNet significantly improves the clinical diagnosis of thyroid nodules and supports medical image analysis *** research directions include optimization of the model structure,expansion of the dataset size,reduction of computational complexity and memory requirements,and exploration of additional applications of ThyroidNet in medical image analysis.

关键词： ThyroidNet deep learning TransUnet multitask learning medical image analysis

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Network representation learning based extended matrix factorization for recommendation 6th

Network representation learning based extended matrix factor...

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6th International Conference on Artificial Intelligence and Security,ICAIS 2020

作者： Xu, Jinmao Gong, Daofu Liu, Fenlin Tan, Lei State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou450001 China

ISBN: (纸本)9789811581007

The growing heterogeneous data in the Internet effectively improves the performance of recommender systems (RS). The main problem faced by the traditional matrix factorization (MF) is how to fuse more heterogeneous information data in MF to improve the performance of RS. In view of this, this paper proposes an Extended Matrix Factorization (EMF) based on network representation learning. EMF integrates multiple types of data in Heterogeneous Information Network (HIN) to improve the accuracy of prediction rating. The nodes in HIN are first mapped to a low-dimensional representation vector by network representation learning (NRL). Then the representation vector is used as the input of the EMF, the parameters are optimized by the gradient descent, and finally the prediction model is obtained. The experiments on two real data sets show the effectiveness of the EMF. Compared with the baseline algorithms, the EMF model can obtain more accurate prediction rating. © Springer Nature Singapore Pte Ltd 2020.

关键词： Recommender systems

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