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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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Code Clone Detection Based on Code Semantic Enhancement Model

Code Clone Detection Based on Code Semantic Enhancement Mode...

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2023 International Conference on Intelligent Communication and Computer engineering, ICICCE 2023

作者： Chunyan, Zhang Ziang, Shen Chen, Liang State Key of Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China Beijing University of Civil Engineering and Architecture Department of Electrical and Information Engineering Beijing China

ISBN: (纸本)9798350395617

Code clones are code fragments that are similar in composition or semantics of code statements, which can significantly improve software development efficiency and reduce costs by improving the quality of reusable code. However, existing code clone detection methods ignore the source code structure information and sequence information seriously. In this paper, we launched our research on the harder-to-break Type IV code clone detection, and propose a new method, namely Aug_Clone, to complete code clone detection task. Firstly, we utilize our published code semantic enhancement model to obtain the code semantic vector. Secondly, we perform code clone detection by calculating the similarity between these semantic vectors. At last, we evaluate our method on a large public data set, and experimental results show improvement over the state-of-the-art benchmarks, which demonstrates the effectiveness and efficiency of our method. © 2023 IEEE.

关键词： Semantics

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A Cross-Platform Instant Messaging User Association Method Based on Supervised Learning 4th

A Cross-Platform Instant Messaging User Association Method B...

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4th International Conference on Big Data and Security, ICBDS 2022

作者： Zhou, Pei Luo, Xiangyang Du, Shaoyong Shi, Wenqi Guo, Jiashan School of Cyber Science and Engineering Zhengzhou University Zhengzhou450000 China State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou450000 China

ISBN: (纸本)9789819932993

To solve the multi-platform user association problem of complex trajectory matching process and high time cost in cross-platform association positioning of instant messaging users, and at the same time make full use of the information in user trajectories, this paper proposes a supervised learning-based cross-platform instant messaging user association positioning method. The algorithm firstly places probes in the area where the target may appear to obtain user information;then gets user trajectories through the obtained user distance information and time information;selects user features through the classification algorithm of supervised learning, and designs a cross-platform instant messaging user association localization method based on supervised learning, so as to increase the association efficiency and accuracy of cross-platform instant messaging user association. The method conducts specific experiments for the most commonly used instant messaging tools in China, WeChat and Stranger users, and the results show that the method can achieve efficient and reliable association for these two types of instant messaging users. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Trajectories

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Solve the False-Positive Problem of the Mimic System Based on the Best Mimic Component Set

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China Communications 2022年第5期19卷 253-266页

作者： Yuwen Shao Zheng Zhang Xiaomei Wang Chuanxing Pan Jiangxing Wu State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou 450001China China National Digital Switching System Engineering and Technological R&D Center Zhengzhou 450002China

At present,there is a problem of false positives caused by the too vast mimic scope in mimic transformation *** studies have focused on the“compensation”method to deal with this problem,which is expensive and cannot fundamentally solve *** paper provides new insights into coping with the ***,this study summarizes the false-positive problem in the mimic transformation,analyzes its possible harm and the root ***,three properties about the mimic scope are *** on the three properties and security quantification technology,the best mimic component set theory is put forward to solve the false-positive *** are two algorithms,the supplemental method and the subtraction *** best mimic component set obtained by these two algorithms can fundamentally solve the mimic system’s false-positive problem but reduce the cost of mimic *** make up for the lack of previous researches.

关键词： mimic scope the best mimic component set false-positive rate mimic transformation

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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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Towards optimized tensor code generation for deep learning on sunway many-core processor

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Frontiers of Computer Science 2024年第2期18卷 1-15页

作者： Mingzhen LI Changxi LIU Jianjin LIAO Xuegui ZHENG Hailong YANG Rujun SUN Jun XU Lin GAN Guangwen YANG Zhongzhi LUAN Depei QIAN State Key Laboratory of Software Development Environment Beijing 100191China School of Computer Science and Engineering Beihang UniversityBeijing 100191China National University of Singapore Singapore 119077Singapore State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi 214000China Science and Technology on Special System Simulation Laboratory Beijing Simulation Center Beijing 100854China Department of Computer Science and Technology Tsinghua UniversityBeijing 100084China

The flourish of deep learning frameworks and hardware platforms has been demanding an efficient compiler that can shield the diversity in both software and hardware in order to provide application *** the existing deep learning compilers,TVM is well known for its efficiency in code generation and optimization across diverse hardware *** the meanwhile,the Sunway many-core processor renders itself as a competitive candidate for its attractive computational power in both scientific computing and deep learning *** paper combines the trends in these two ***,we propose swTVM that extends the original TVM to support ahead-of-time compilation for architecture requiring cross-compilation such as *** addition,we leverage the architecture features during the compilation such as core group for massive parallelism,DMA for high bandwidth memory transfer and local device memory for data locality,in order to generate efficient codes for deep learning workloads on *** experiment results show that the codes generated by swTVM achieve 1.79x improvement of inference latency on average compared to the state-of-the-art deep learning framework on Sunway,across eight representative *** work is the first attempt from the compiler perspective to bridge the gap of deep learning and Sunway processor particularly with productivity and efficiency in *** believe this work will encourage more people to embrace the power of deep learning and Sunwaymany-coreprocessor.

关键词： sunway processor deep learning compiler code generation performance optimization

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HM-QCNN: Hybrid Multi-branches Quantum-Classical Neural Network for Image Classification 9th

HM-QCNN: Hybrid Multi-branches Quantum-Classical Neural Netw...

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19th International Conference on advanced Data Mining and Applications, ADMA 2023

作者： Liu, Haowen Gao, Yufei Shi, Lei Wei, Lin Shan, Zheng Zhao, Bo School of Cyber Science and Engineering Songshan Laboratory Zhengzhou University Zhengzhou China State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (纸本)9783031466632

Quantum machine learning has been developing in recent years, demonstrating great potential in various research domains and promising applications for pattern recognition. However, due to the constraints of quantum hardware, the input qubits are restricted caused by small circuit size, and the fuzziness in all dimensions caused by the features that are difficult to be effectively mined. Besides, previous studies focus on binary classification, but multi-classification received little attention. To address the difficulty in multi-classification, this paper proposed a hybrid multi-branches quantum-classical neural network (HM-QCNN) that utilizes a multi-branch strategy to construct the convolutional part. The part consists of three branches to extract the features of different scales and morphologies. Two quantum convolutional layers apply quantum CRZ gates and rotational gates to design a random quantum circuit (RQC) with 4 qubits and full qubits measurements. The experiments on three public datasets (MNIST, Fashion MNIST, and MedMNIST) demonstrate that HM-QCNN outperforms other prevalent methods with accuracy, precision, and convergence speed. Compared with the classical CNN and the hybrid neural network without multi-branches, HM-QCNN reached 97.40% and improved the accuracy of classification by 6.45% and 1.36% on the MNIST dataset, respectively. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Qubits

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Path test data generation using adaptive simulated annealing particle swarm optimization

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Soft computing 2024年第17-18期28卷 9587-9607页

作者： Jiao, Chongyang Zhou, Qinglei State Key Laboratory of Mathematical Engineering and Advanced Computing PLA Strategic Support Force Information Engineering University Zhengzhou450001 China Henan Information Engineering School Zhengzhou Vocational College of Industrial Safety Zhengzhou450000 China School of Computer and Artificial Intelligence Zhengzhou University Zhengzhou450001 China

Software testing is an effective means of ensuring software quality. The cost of software testing is the main component of the total cost of software development. The generation of test data is very important in testing, because the efficiency of testing depends on the test data used. A significant part of software testing automation is the test data generation. How to automatically generate test datasets is still an open problem. Search-based software testing (SBST) is a process of generating test data that employs meta-heuristic methods to resolve tough NP-hard problems. The test data generation problem belongs to NP-hard problems. This paper focuses on the automatic generation of test data for path coverage based on control flow criteria in structural testing. Some meta-heuristic algorithms have been used to search for software test data. To advance the efficiency and effectiveness of path test data generation, an adaptive simulated annealing particle swarm optimization (ASAPSO) algorithm is proposed. The probabilistic jumping property of simulated annealing (SA) algorithm is introduced into the particle swarm optimization (PSO) algorithm to make the particle accept bad solution at a certain probability during the searching process, and therefore boost the capacity of the proposed algorithm to bounce from the local optimum. The fitness function is constructed by the branch function superposition to properly guide the search process. In addition, to improve the convergence speed of the algorithm, an adaptive adjustment scheme based on inertial weight and an adjustment scheme based on learning factor are proposed as well. The experimental results show that the proposed method can effectually avoid premature convergence and effectively upgrades the efficiency of generating test data automatically. It is also competitive in solving other complicated optimization problems. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Natur

关键词： Software testing

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