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5th International Conference on Electronic engineering and Informatics, EEI 2023

作者： Li, Chenwei Pan, Mengyuan Yang, Bo Zhang, Hengwei Henan Key Laboratory of Information Security State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China Guizhou University School of Chemistry and Chemical Engineering Guiyang China

ISBN: (纸本)9798350327076

Convolutional neural networks are widely used in computer vision and image processing. However, when the original input is added with manually imperceptible perturbations, these deep network models mostly tend to output incorrect predictions. The vulnerability of these models poses great threat to intelligent applications, and these manually imperceptible perturbations are called adversarial examples. Current baseline methods have achieved considerable white-box attack success rate, but black-box rate remains to be improved. To boost the adversarial generalization, ensemble models method is introduced to the process of generating adversarial examples. This paper proposes multiple ensemble strategies with baseline attack methods based on existing ensemble strategy used by former methods. Experiment on ImageNet dataset empirically verifies the optimal ensemble strategy on boosting adversarial generalization. © 2023 IEEE.

关键词： Image classification

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Highly efficient and stable electrocatalyst for hydrogen evolution by molybdenum doped Ni-Co phosphide nanoneedles at high current density

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Nano Research 2024年第3期17卷 1066-1074页

作者： Chengyu Huang Zhonghong Xia Jing Wang Jing Zhang Chenfei Zhao Xingli Zou Shichun Mu Jiujun Zhang Xionggang Lu Hong Jin Fan Shengjuan Huo Yufeng Zhao College of Sciences&Institute for Sustainable Energy Shanghai UniversityShanghai 200444China State Key Laboratory of Metastable Materials Science and Technology Hebei Key Laboratory of Heavy Metal Deep-Remediation in Water and Resource ReuseYanshan UniversityQinhuangdao 066004China State Key Laboratory of Advanced Special Steel and Shanghai Key Laboratory of Advanced Ferrometallurgy School of Materials Science and EngineeringShanghai UniversityShanghai 200444China State Key Laboratory of Advanced Technology for Materials Synthesis and Processing Wuhan University of TechnologyWuhan 430070China School of Physical and Mathematical Sciences Nanyang Technological UniversitySingapore639798

There is an increasingly urgent need to develop cost-effective electrocatalysts with high catalytic activity and stability as alternatives to the traditional Pt/C in catalysts in water *** this study,microspheres composed of Mo-doped NiCoP nanoneedles supported on nickel foam were prepared to address this *** results show that the nanoneedles provide sufficient active sites for efficient electron transfer;the small-sized effect and the micro-scale roughness enhance the entry of reactants and the release of hydrogen bubbles;the Mo doping effectively improves the electrocatalytic performance of NiCoP in alkaline *** catalyst exhibits low hydrogen evolution overpotentials of 38.5 and 217.5 mV at a current density of 10 mA·cm^(-2) and high current density of 500 mA·cm^(-2),respectively,and only 1.978 V is required to achieve a current density of 1000 mA·cm^(-2) for overall water *** functional theory(DFT)calculations show that the improved hydrogen evolution performance can be explained as a result of the Mo doping,which serves to reduce the interaction between NiCoP and intermediates,optimize the Gibbs free energy of hydrogen adsorption(△G_(*H)),and accelerate the desorption rate of **** study provides a promising solution to the ongoing challenge of designing efficient electrocatalysts for high-current-density hydrogen production.

关键词： transition metal phosphides Mo-doped NiCoP hydrogen evolution reaction gradient hydrothermal water splitting

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An Improved Authenticated key Agreement Protocol for IoT and Cloud Server 5th

An Improved Authenticated Key Agreement Protocol for IoT an...

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5th International Conference on Security and Privacy in New computing Environments, SPNCE 2022

作者： Ma, Yongliu Ma, Yuqian Cheng, Qingfeng State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China Strategic Support Force Information Engineering University Zhengzhou450001 China

ISBN: (纸本)9783031306228

The internet of things (IoT) is a complex network system with applications in all walks of life. However, there are various risks in the process of information transmission between IoT devices and servers. Recently, research on the security of authenticated key agreement (AKA) protocols in the IoT environment has gradually increased. Iqbal et al. proposed an AKA protocol between IoT and cloud servers and proved that it was secure under the eCK model. This paper shows that the Iqbal et al.’s protocol has two security flaws, which are resisting ephemeral key leakage attack and key compromise impersonation attack, and proposes a new AKA protocol in the IoT environment. Through the security proof and formal analysis, it is proved that the new protocol is secure under the eCK model. Comparing the protocol proposed in this paper with other similar protocols, it is found that the protocol in this paper achieves a balance between security performance and communication consumption. © 2023, ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering.

关键词： Internet of things

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Haica: A High Performance computing & Artificial Intelligence Fused computing Architecture 1

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22nd International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2022

作者： Chen, Zhengbo Zheng, Fang Guo, Feng Yu, Qi Chen, Zuoning Information Engineering University Zhengzhou China State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi China Chinese Academy of Engineering Beijing China

ISBN: (数字)9783031226779

ISBN: (纸本)9783031226762

In recent years, HPC and AI fusion, which refers to applying AI technology to traditional HPC applications, has become a new trend. HPC and AI fusion requires supports for multiple precisions used in both domains. While, supporting multiple precisions in a single computing unit is not easy. Prior research typically targets on modifying a high-precision FMA to support low precisions. However, such efforts suffer from disadvantages of increased latency and limited compute throughput for low-precision operations, high area and power overhead, and limited support of new data formats appeared in AI domain. To address this issue, we propose Haica, a double-precision FMA and low-precision systolic array fused architecture, to achieve HPC and AI fusion. Our work has two innovations. First, we propose a low-cost multiple-low-precision FMA by taking advantage of the commonality among FP16, BF16, and TF32. Second, inspired by the idea of splicing high precision with low precision, we replace the multiply and merge modules in a double-precision FMA with a modified systolic array that is composed of the proposed low-precision FMAs. We implement the logic design of Haica using RTL and evaluate its overhead. Results show that compared to the naive combination of a double-precision FMA and single-half-mixed-precision systolic array, Haica provides extra support for BF16 and TF32 with only 7.87% area and 33.26% power overhead. This demonstrates that Haica achieves HPC and AI fusion in a cost-effective manner. © 2023, Springer Nature Switzerland AG.

关键词： Artificial intelligence

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Multiuser Computation Offloading for Long-Term Sequential Tasks in Mobile Edge computing Environments

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Tsinghua Science and Technology 2023年第1期28卷 93-104页

作者： Huanhuan Xu Jingya Zhou Wenqi Wei Baolei Cheng School of Computer Science and Technology Soochow UniversitySuzhou 215006China State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi 214125China Provincial Key Laboratory for Computer Information Processing Technology Soochow UniversitySuzhou 215006China School of Computer Science Georgia Institute of TechnologyAtlantaGA 30332USA

Mobile edge computing has shown its potential in serving emerging latency-sensitive mobile applications in ultra-dense 5G networks via offloading computation workloads from the remote cloud data center to the nearby network ***,current computation offloading studies in the heterogeneous edge environment face multifaceted challenges:Dependencies among computational tasks,resource competition among multiple users,and diverse long-term *** applications typically consist of several functionalities,and one huge category of the applications can be viewed as a series of sequential *** this study,we first proposed a novel multiuser computation offloading framework for long-term sequential ***,we presented a comprehensive analysis of the task offloading process in the framework and formally defined the multiuser sequential task offloading ***,we decoupled the long-term offloading problem into multiple single time slot offloading problems and proposed a novel adaptive method to solve *** further showed the substantial performance advantage of our proposed method on the basis of extensive experiments.

关键词： mobile edge computing sequential tasks computation offloading dependency

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Color Image Steganalysis Algorithm Based on Quaternion Convolutional Neural Network 30th

Color Image Steganalysis Algorithm Based on Quaternion Convo...

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30th International Conference on Computational and Experimental engineering and Sciences, ICCES 2024

作者： Xu, Meng Wang, Jinwei State Key Laboratory of Mathematical Engineering and Advanced Computing Henan450001 China Department of Computer and Software Nanjing University of Information Science and Technology Nanjing210044 China

ISBN: (纸本)9783031687747

Most of the images on the Internet are color images, and steganalysis of color images is a very critical issue in the field of steganalysis. The current proposed color image steganalysis features mainly rely on manual design, and the steganalysis features do not fully consider the internal connections between the three channels of color images. Most steganalyzers based on deep learning are also designed for grayscale images. In recent years, the continuous introduction of advanced steganography techniques for color images has also brought more severe challenges to the steganography analysis of color images. Quaternions are a tool that can effectively represent color images, and utilizing quaternion transformations can fully exploit the correlation between color image channels. Combining quaternion theory with convolutional neural networks, this paper proposed a color image steganalysis algorithm based on quaternion convolutional neural networks. Firstly, the image is represented by quaternion, and then input into the quaternion convolutional neural network. The convolution kernel in the network is composed of quaternions, and then perform convolution and pooling. The experimental results have demonstrated the significant effectiveness of the proposed network. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Convolutional neural networks

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Grover algorithm circuit optimization and noise analysis on integer factorization 7

Grover algorithm circuit optimization and noise analysis on ...

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7th International Conference on Intelligent computing and Signal Processing, ICSP 2022

作者： Song, Hui-Chao Liu, Xiao-Nan Jiang, Duo An, Jiale University of Information Engineering State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (纸本)9781665478571

The classical computer is restricted by Moore's law and other factors into the development bottleneck, quantum computer by virtue of its natural acceleration advantage into the field of vision. Quantum computing can achieve the acceleration effect compared with classical computing in many fields. In the field of integer decomposition, Shor algorithm can be solved in polynomial time, which breaks the security of RSA public key cryptosystem. However, the quantum bits (logn + 2, where n is the integer to be decomposed) required by Shor algorithm are difficult to be realized on today's quantum computers. Therefore, the hybrid quantum algorithm model of "classical + quantum (Grover algorithm)"is tried to realize the decomposition of large integers. In this paper, based on the previous hybrid quantum algorithm model, considering that the continuous accumulation of errors in the circuit of the quantum algorithm will lead to the failure of the algorithm as the depth increases, part of the circuit of the quantum algorithm oracle is optimized to reduce the consumption of the depth, quantum gate and quantum bit in the circuit of the quantum algorithm, so as to make it easier to run on the actual quantum computer. By running the optimized quantum circuit on the back end of the simulator of IBM Q quantum cloud platform, and trying to add thermal relaxation and depolarization noises to the circuit, the influence of different noises on the circuit under different iterations was explored. © 2022 IEEE.

关键词： Quantum computers

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A Combination Method of Resampling and Random Forest for Imbalanced Data Classification 4

A Combination Method of Resampling and Random Forest for Imb...

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4th International Conference on Advances in Computer Technology, Information Science and Communications, CTISC 2022

作者： Zheng, Liu Han, Qiu Junhu, Zhu Information & Engineering University State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (数字)9781665458726

ISBN: (纸本)9781665458726

In the research of imbalanced data classification, the resampling effect of the existing resampling and random forest combination technology is greatly affected by the characteristic dimension of the training set, resulting in the unsatisfactory classification effect of the model. To solve this problem, a combination method of resampling and Random Forest for imbalanced data classification is proposed. The core idea is to resample the original data set multiple times by using the feature subset of each subtree in the random forest. By reducing the number of features used in a single resampling, the influence of feature dimension on resampling effect is reduced;Different feature subsets are used for resampling in different subtrees to improve the diversity of base classifiers. Based on SMOTE, B-SMOTE and ADASYN resampling techniques, three different optimization algorithms PSRF, PBSRF and PADRF are realized respectively. Taking the geometric mean and recall rate as evaluation indexes, the experimental results on 10 groups of KEEL data show that compared with the algorithm before optimization, the geometric mean values of the three algorithms are increased by 0.85%, 2.93% and 0.79% respectively, and the recall rates are increased by 1.44%, 3.26% and 1.07% respectively. © 2022 IEEE.

关键词： Machine learning

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Research on Unpredetermined Behavior Recognition Techniques Based on Network Attacks 23

Research on Unpredetermined Behavior Recognition Techniques ...

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Proceedings of the 2023 5th International Conference on Internet of Things, Automation and Artificial Intelligence

作者： Yuxuan Xiao Jinlong Fei Junyi Wang State Key Laboratory of Mathematical Engineering and Advanced Computing China

ISBN: (纸本)9798400716485

With the increasing number of cyber-attack behaviors, it has caused great harm to the contemporary society. Currently, the cyber attack behavior recognition module is usually implemented by adopting a strategy based on rule-base matching, which often fails to identify the attack behaviors when faced with unpreset path attack behaviors of the actual attack process due to its reliance on predefined abnormal behavior patterns and attack labels. Therefore, this paper proposes an efficient and accurate unpreset behavior detection framework for cyber-attack behaviors, introduces machine learning techniques into the identification of unpreset attack behaviors, proposes an RF-Corr feature importance assessment method based on the Kendall correlation coefficient between features and the feature weight values, and designs an unpreset attack behavior identification method based on BiLSTM neural network, which improves the current effectiveness of identification and detection for unpresetted behavior of cyber attacks, and provides a solution for artificial intelligence detection of unpresetted behavior of cyber attacks.

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Rotation Model Enhancement for Adversarial Attack

Rotation Model Enhancement for Adversarial Attack

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2022 International Conference on Networking and Network Applications, NaNA 2022

作者： Zhang, Hengwei Li, Zheming Liu, Hao Yang, Bo Li, Chenwei Wang, Jindong State Key Laboratory of Mathematical Engineering and Advanced Computing Henan Key Laboratory of Information Security Zhengzhou China

ISBN: (数字)9781665461313

ISBN: (纸本)9781665461313

Current white-box attack to deep neural networks have achieved considerable success, but not for black-box attack. The main reason is poor transferability, as the adversarial examples are crafted with single deep neural networks model, and excessively depend on that model. To address that problem, we propose a rotation model enhancement algorithm to craft adversarial examples. We improve rotation method in model enhancement. This algorithm constructs a possibility model to randomly rotate original images, and generates multiple transformed images. Therefore, we craft adversarial examples with single model, and boost attack on multiple models, which demonstrate considerable transferability and success rate for black-box attack. The simulation indicates the algorithm boost black-box attack with a 89.2% success rate. © 2022 IEEE.

关键词： Deep neural networks

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