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2020 International Conference on Electronics, Communications and Information Technology, CECIT 2020

作者： Liu, X.N. Hu, C.C. Yin, M.J. Jin, L. School of Information Engineering Zhengzhou University Zhengzhou China State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China School of Cyberspace and Security Information Engineering University Zhengzhou China

With the continuous development of quantum communication in recent years, quantum key distribution (QKD) has also become a key technology in the field of quantum communication. Due to the limitation of physical resources and experimental conditions, the effectiveness of different communication protocols cannot be accurately analyzed. To solve this problem, a protocol simulation method is proposed, BB84 and B92 protocols are simulated and analyzed on a classic computer by this method. On the Eclipse IDE Developers platform, the simulation program of the quantum communication protocol was written using Java language, the simulation process under the ideal environment and the eavesdropping environment was analyzed with emphasis. The simulation results show that the key distribution efficiency of BB84 is about 50% without eavesdroppers, which is much higher than 25% of B92, and the key distribution efficiency under eavesdropping conditions is about 25% and 18%. The experimental results are consistent with theoretical expectations, verifying the correctness of the protocol simulation, and also proving the feasibility of using computers for quantum communication protocol simulation. © Published under licence by IOP Publishing Ltd.

关键词： Computer software

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An Adaptive Proximal Inexact Gradient Framework and Its Application to Per-Antenna Constrained Joint Beamforming and Compression Design

arXiv

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arXiv 2025年

作者： Fan, Xilai Jiang, Bo Liu, Ya-Feng State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China Ministry of Education Key Laboratory of NSLSCS School of Mathematical Sciences Nanjing Normal University Nanjing210023 China

In this paper, we propose an adaptive proximal inexact gradient (APIG) framework for solving a class of nonsmooth composite optimization problems involving function and gradient errors. Unlike existing inexact proximal gradient methods, the proposed framework introduces a new line search condition that jointly adapts to function and gradient errors, enabling adaptive stepsize selection while maintaining theoretical guarantees. Specifically, we prove that the proposed framework achieves an ϵ-stationary point within O(ϵ−2) iterations for nonconvex objectives and an ϵ-optimal solution within O(ϵ−1) iterations for convex cases, matching the best-known complexity in this context. We then custom-apply the APIG framework to an important signal processing problem: the joint beamforming and compression problem (JBCP) with per-antenna power constraints (PAPCs) in cooperative cellular networks. This customized application requires careful exploitation of the problem’s special structure such as the tightness of the semidefinite relaxation (SDR) and the differentiability of the dual. Numerical experiments demonstrate the superior performance of our custom-application over state-of-the-art benchmarks for the JBCP. © 2025, CC BY.

关键词： Beamforming

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Dual-axial self-attention network for text classification

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Science China(Information Sciences) 2021年第12期64卷 80-90页

作者： Xiaochuan ZHANG Xipeng QIU Jianmin PANG Fudong LIU Xingwei LI State Key Laboratory of Mathematical Engineering and Advanced Computing Shanghai Key Laboratory of Intelligent Information Processing Fudan University School of Computer Science Fudan University

Text classification is an important task in natural language processing and numerous studies aim to improve the accuracy and efficiency of text classification models. In this study, we propose an effective and efficient text classification model which is based on self-attention solely. The recently proposed multidimensional self-attention significantly improved the performance of self-attention. However, existing models suffer from two major limitations:(1) the previous multi-dimensional self-attention models are quite timeconsuming;(2) the dependencies of elements along the feature axis are not taken into account. To overcome these problems, in this paper, a much more computational efficient multi-dimensional self-attention model is proposed, and two parallel self-attention modules, called dual-axial self-attention, are applied to capture rich dependencies along the feature axis as well as the text axis. A text classification model is then *** experimental results on eight representative datasets show that the proposed text classification model can obtain state-of-the-art results and the proposed self-attention outperforms conventional self-attention models.

关键词： text classification dual-axial self-attention feature-axial dependency

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A Software Diversity-Based Lab in Operating System for Cyber Security Students 3

A Software Diversity-Based Lab in Operating System for Cyber...

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3rd IEEE International Conference on Computer Science and Educational Informatization, CSEI 2021

作者： Wang, Junchao Pang, Jianmin Shan, Zheng Wei, Jin Yao, Jinyang Liu, Fudong State Key Laboratory of Mathematical Engineering and Advanced Computing Zheng Zhou China School of Computer Science Data Arena Institute Fudan University Zheng Zhou China

ISBN: (纸本)9781728184739

The course of operating system's labs usually fall behind the state of art technology. In this paper, we propose a Software Diversity-Assisted Defense (SDAD) lab based on software diversity, mainly targeting for students majoring in cyber security and computer science. This lab is consisted of multiple modules and covers most of the important concepts and principles in operating systems. Thus, the knowledge learned from the theoretical course will be deepened with the lab. For students majoring in cyber security, they can learn this new software diversity-based defense technology and understand how an exploit works from the attacker's side. The experiment is also quite stretchable, which can fit all level students. © 2021 IEEE.

关键词： Students

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Stepwise DOA Estimation for Coherent and Uncorrelated Mixed Signals on Coprime Array

Stepwise DOA Estimation for Coherent and Uncorrelated Mixed ...

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IEEE advanced Information Management,Communicates,Electronic and Automation Control Conference (IMCEC)

作者： Heng Jiang Lichun Li Hailong Zhang Hongyi Pan Xue Li College of Information System Engineering Information Engineering University Zhengzhou China State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (纸本)9781665479691

The coprime array constructs virtual array to obtain higher degrees of freedom (DOF), but the premise of doing so is that the signals are independent of each other. Once there are coherent signals, the signal model of virtual array is destroyed and the method fails. To solve these problems, we propose the stepwise estimation of the Direction of Arrival (DOA) for a mixture of coherent and uncorrelated signals. Firstly, the symmetry of the two coprime subarrays is used to eliminate the uncorrelated signals, and combined with the properties of prime numbers, the coherent source DOA is jointly estimated. The obtained coherent source DOA was combined with the optimization algorithm to remove the coherent signal in the mixed signal. Finally, the virtual domain signal processing method was used to estimate the independent source DOA. This method can effectively reduce the influence of coherent signals on the accuracy of DOA estimation without loss of degrees of freedom. In this paper, the feasibility of the proposed method is proved by theoretical derivation, and the effectiveness of the method is also verified by experimental simulations.

关键词： Direction-of-arrival estimation Automation Simulation Estimation Signal processing algorithms Interference Signal processing

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Multi-objective Firefly Algorithm for Test Data Generation with Surrogate Model 7th

Multi-objective Firefly Algorithm for Test Data Generation w...

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7th International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2021

作者： Zhang, Wenning Zhou, Qinglei Jiao, Chongyang Xu, Ting State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou450000 China Zhongyuan University of Technology Zhengzhou450000 China Zhengzhou University Zhengzhou450000 China

ISBN: (纸本)9789811659393

To solve the emerging complex optimization problems, multi objective optimization algorithms are needed. By introducing the surrogate model for approximate fitness calculation, the multi objective firefly algorithm with surrogate model (MOFA-SM) is proposed in this paper. Firstly, the population was initialized according to the chaotic mapping. Secondly, the external archive was constructed based on the preference sorting, with the lightweight clustering pruning strategy. In the process of evolution, the elite solutions selected from archive were used to guide the movement to search optimal solutions. Simulation results show that the proposed algorithm can achieve better performance in terms of convergence iteration and stability. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Multiobjective optimization

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Metaheuristic Optimization Methods and Techniques for Multiple Sclerosis Detection 4th

Metaheuristic Optimization Methods and Techniques for Multip...

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4th International Conference on IoT and Big Data Technologies for Health Care, IotCare 2023

作者： Wang, Jiaji Wu, Meng Chen, Shuwen Zhu, Huisheng School of Computing and Mathematical Sciences University of Leicester LeicesterLE1 7RH United Kingdom School of Physics and Information Engineering Jiangsu Second Normal University Jiangsu Nanjing210016 China State Key Laboratory of Millimeter Waves Southeast University Nanjing210096 China

ISBN: (纸本)9783031900839

This paper explores the application of metaheuristic optimization methods in the context of Multiple Sclerosis (MS) detection, addressing the challenges of early and accurate diagnosis. It introduces Genetic Algorithms, Simulated Annealing, and Particle Swarm Optimization as effective tools for feature selection and classification in MS detection. Additionally, the paper discusses emerging metaheuristic optimization methods and their potential contributions. Through critical evaluation, it emphasizes the need for method selection based on task-specific requirements. In conclusion, this research highlights the promise of metaheuristic optimization in revolutionizing MS detection, urging further development and validation on larger datasets. Future directions include hybrid algorithms and advanced machine learning integration for improved diagnostic precision. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering 2025.

关键词： Particle swarm optimization (PSO)

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Mechanistic investigation of highly bendable magnesium alloy sheet fabricated by short-process manufacturing

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Journal of Magnesium and Alloys 2024年第10期12卷 4289-4310页

作者： Shiwei Xu Peijie Xiao Xiaofan Wu John Holmes Frédéric Mompiou Zhenyu Xiao Chen Jin Tianjiao Li Kuaishe Wang Zhuoran Zeng College of Mechanical and Vehicle Engineering Hunan UniversityChangsha 410082China State Key Laboratory of Advanced Design and Manufacturing Technology for Vehicle Hunan UniversityChangsha 410082China Suzhou Research Institute of Hunan University Suzhou 215131China College of Engineering Computing and CyberneticAustralian National UniversityACT 2601Australia CEMES-CNRS Universitéde Toulouse29 rue J.MarvigToulouse 31055France Qinghai Salt Lake Teli Magnesium Co. Ltd.Xining 810000China International Joint Laboratory for Light Alloys(Ministry of Education) College of Materials Science and EngineeringChongqing UniversityChongqing 400044China School of Metallurgical Engineering Xi’an University of Architecture&TechnologyXi’an 710055China College of Materials Science and Engineering Hunan UniversityChangsha 410082China

In this study,a commercial magnesium alloy AZ31(Mg-3Al-1Zn-0.3Mn)sheet through a short manufacturing process was found to be ductile and highly formable in *** possessing a strong basal texture,the short-processed sheet without any annealing can be bent at a small radius,only 0.2 times its thickness in the 90°bending ***,it could withstand direct deformation by repeated *** in-situ microstructural characterization reveals that extension twin bands with strain localization appear in the bending *** subsequent flattening,these twin bands underwent detwinning,reducing local strain concentrations and enabling further bending *** outstanding bend formability originated from the significant-type dislocation loops slipping on the prismatic crystal planes within dynamic-recrystallized *** grains underwent a uniform refinement to several microns in the short manufacturing process and exhibited low residual *** active prismatic dislocation slip within refined grains was due to its much lower relative activation stress to basal slip(CRSSprism/CRSSbasal of only∼1.6)owing to the effective grain boundary ***,the prismatic dislocation activity was further enhanced when bypassing Al-Mn nano-particles during motion,leaving debris and loops that facilitated easy multiplication.

关键词： Magnesium Deformation Texture In-situ characterization Dislocation slip.

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Use Tabu Search Particle Swarm Optimization Algorithm to Detect COVID-19 4th

Use Tabu Search Particle Swarm Optimization Algorithm to Det...

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4th International Conference on IoT and Big Data Technologies for Health Care, IotCare 2023

作者： Chen, Shuwen Wang, Jiaji Zhu, Huisheng School of Physics and Information Engineering Jiangsu Second Normal University Jiangsu Nanjing210016 China State Key Laboratory of Millimeter Waves Southeast University Nanjing210096 China School of Computing and Mathematical Sciences University of Leicester LeicesterLE1 7RH United Kingdom

ISBN: (纸本)9783031900839

Efficient and accurate COVID-19 detection methods are paramount in the ongoing battle against the global pandemic. In this study, we present a novel approach that leverages the Tabu Search Particle Swarm Optimization (TS-PSO) algorithm for COVID-19 detection. Unlike conventional methods, our approach combines the strengths of both global and local search strategies, enhancing the accuracy of diagnosis. We conducted extensive experiments, benchmarking our TS-PSO-based approach against established methods. The results reveal that our TS-PSO method achieves competitive performance, with improvements in sensitivity, specificity, precision, accuracy, F1 score, MCC, and FMI. This innovative approach not only advances the field of COVID-19 detection but also demonstrates its potential as a valuable tool for healthcare professionals in the ongoing fight against the pandemic. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering 2025.

关键词： Tabu search

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A software upgrade security analysis method on network traffic classification using deep learning

A software upgrade security analysis method on network traff...

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2020 International Conference on Urban engineering and Management Science, ICUEMS 2020

作者： Zhang, Bing State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou450000 China

ISBN: (纸本)9781728188324

Nowadays, the vulnerability in the software upgrade process are extremely harmful to network security. However, the detection of upgrade vulnerability is facing serious difficulties and problems. Aiming at the current software upgrade vulnerability analysis problem, we propose a neural network model for traffic classification based on two levels of data packets and network flows. This method does not need to manually extract features and can learn the dependency relationship between data packets and networks well. It can make full use of comprehensive flow features for traffic classification. In order to evaluate the model, we built a dataset of software upgrade vulnerabilities and conducted relevant experiments on the CICIDS 2017 dataset. The experimental results show that compared with other RNN models, our model has the best performance, reaching a precision of 99.86% and an F1 score of 99.44%. At the same time, based on the pre-trained model, we use the software upgrade vulnerability dataset to fine-tuning the model, with the F1 score reaching 99.78%. © 2020 IEEE.

关键词： Neural network models

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