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Practical approach for detecting k-nonseparability of multipartite quantum states

Science china(Physics,Mechanics & Astronomy) 2025年第1期68卷 28-44页

作者： Jie Guo Jinchuan Hou Xiaofei Qi Kan He College of Mathematics Taiyuan University of TechnologyTaiyuan 030006China School of Mathematics and Statistics Shanxi UniversityTaiyuan 030006China Key Laboratory of Complex Systems and Data Science of Ministry of Education Shanxi UniversityTaiyuan 030006China

The instantaneous detection of entanglement in quantum states poses a significant challenge in the fields of quantum computation and quantum information,and there are no practical methods or tools to complete this task satisfactorily so *** propose a sufficient and necessary criterion of k-nonseparability for any n-partite finite or infinite dimensional systems,2≤k≤*** criterion serves as the foundation for a practical scheme designed to detect entanglement and k-nonseparability across multipar-tite finite-dimensional *** exemplify the application of our scheme,we have developed a software tool that facilitates swift and precise identification of entanglement,k-nonseparability,and genuine entanglement within n-qubit systems,specifically tailored for systems with 2≤n≤4.

关键词： multipartite systems entanglement states entanglement witnesses k-nonseparability

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On construction of strong orthogonal arrays and column-orthogonal strong orthogonal arrays of strength two plus

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Science china Mathematics 2025年第5期68卷 1219-1242页

作者： Bochuan Jiang Yaping Wang Fasheng Sun School of Mathematics and Statistics Beijing Jiaotong UniversityBeijing100044China Key Laboratory of Advanced Theory and Application in Statistics and Data Science Ministry of EducationSchool of StatisticsEast China Normal UniversityShanghai200062China Key Laboratory for Applied Statistics of Ministry of Education and School of Mathematics and Statistics Northeast Normal UniversityChangchun130024China

Computer experiments require space-filling designs with good low-dimensional projection *** orthogonal arrays are a type of space-filling design that provides better stratifications in low dimensions than ordinary orthogonal *** this paper,we address the problem of constructing strong orthogonal arrays and column-orthogonal strong orthogonal arrays of strength two *** methods typically rely on regular designs or specific nonregular designs as base orthogonal arrays,limiting the sizes of the final ***,we propose two general methods that are easy to implement and applicable to a wide range of base orthogonal *** methods produce space-filling designs that can accommodate a large number of factors,provide significant flexibility in terms of run sizes,and possess appealing low-dimensional projection ***,these designs are ideal for computer experiments.

关键词： computer experiment difference scheme strong orthogonal array column-orthogonality space-filling property

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A numerical magnitude aware multi-channel hierarchical encoding network for math word problem solving

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Neural Computing and Applications 2025年第3期37卷 1651-1672页

作者： Xu, Junjie Chen, Yuzhong Xiao, Lingsheng Liao, Hongmiao Zhong, Jiayuan Dong, Chen College of Computer and Data Science Fuzhou University Fujian Province Fuzhou350108 China Engineering Research Center of Big Data Intelligence Ministry of Education Fuzhou China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fujian Province Fuzhou350108 China

Math word problem (MWP) represents a critical research area within reading comprehension, where accurate comprehension of math problem text is crucial for generating math expressions. However, current approaches still grapple with unresolved challenges in grasping the sensitivity of math problem text and delineating distinct roles across various clause types, and enhancing numerical representation. To address these challenges, this paper proposes a Numerical Magnitude Aware Multi-Channel Hierarchical Encoding Network (NMA-MHEA) for math expression generation. Firstly, NMA-MHEA implements a multi-channel hierarchical context encoding module to learn context representations at three different channels: intra-clause channel, inter-clause channel, and context-question interaction channel. NMA-MHEA constructs hierarchical constituent-dependency graphs for different levels of sentences and employs a Hierarchical Graph Attention Neural Network (HGAT) to learn syntactic and semantic information within these graphs at the intra-clause and inter-clause channels. NMA-MHEA then refines context clauses using question information at the context-question interaction channel. Secondly, NMA-MHEA designs a number encoding module to enhance the relative magnitude information among numerical values and type information of numerical values. Experimental results on two public benchmark datasets demonstrate that NMA-MHEA outperforms other state-of-the-art models. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Semantics

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HTOTP: Honey Time-Based One-Time Passwords

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IEEE Transactions on Information Forensics and Security 2025年 20卷 4438-4453页

作者： Ding, Zixuan Wang, Ding Nankai University College of Cryptology and Cyber Science Key Laboratory of Data and Intelligent System Security Ministry of Education Tianjin300350 China Chinese Academy of Sciences Key Laboratory of Cyberspace Security Defense Institute of Information Engineering Beijing100085 China

One-Time Passwords (OTPs) play a crucial role in Two-Factor Authentication (2FA) and Multi-Factor Authentication (MFA) by adding an additional layer of security. OTPs effectively reduce the risk of static passwords being intercepted and reused. Nevertheless, both academic schemes and industrial solutions face security threats stemming from server/device compromises and OTP factor forgery. Chain-based asymmetric OTP schemes are a promising approach to addressing the problem of server compromise but still face threats from device compromise and pre-generated chain leakage. We emphasize that since devices directly store the OTP seed, OTP authentication is essentially equivalent to verifying device possession. This means that in existing OTP schemes, OTP forgery and device compromise remain prevalent and difficult to overcome. In this work, we propose a brand new scheme to address OTP factor forgery and server/device compromises. For the first time, our scheme constructs a tightly coupled architecture between the password factor and the OTP factor. The OTP seed is derived from a password and a device-stored salt, preventing OTP seed extraction and OTP forgery even in the event of a device compromise. Through the integration of "honeywords" with the tightly coupled OTP architecture, the server stores decoy OTP seeds generated by decoy passwords, providing resistance against server compromises and partial password guessing from devices. We conduct a comprehensive evaluation of our OTP schemes. The computational overhead is correlated with the number of honeywords, and with the recommended set size of 20, the total verification overhead is approximately 0.24~ms . Additionally, we propose formal security properties and application metrics, and rigorously prove our scheme’s resistance against server/device compromise attacks and guessing attacks. Our scheme is the first to achieve comprehensive OTP security with low overhead. © 2005-2012 IEEE.

关键词： Authentication

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T³Planner: Multi-Phase Planning Across Structure-Constrained Optical, IP, and Routing Topologies

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IEEE Journal on Selected Areas in Communications 2025年第5期43卷 1823-1839页

作者： Hao, Yijun Yang, Shusen Li, Fang Zhang, Yifan Zhao, Cong Ren, Xuebin Zhao, Peng Xu, Chenren Wang, Shibo Xi’an Jiaotong University National Engineering Laboratory for Big Data Analytics Xi’an710049 China Xi’an Jiaotong University National Engineering Laboratory for Big Data Analytics Ministry of Education Key Laboratory for Intelligent Networks and Network Security Xi’an710049 China Peking University School of Computer Science Beijing100871 China The Chinese University of Hong Kong Department of Computer Science and Engineering Hong Kong National Engineering Laboratory for Big Data Analytics Xi'an Jiaotong University Xi'an710049 China

Network topology planning is an essential multi-phase process to build and jointly optimize the multi-layer network topologies in wide-area networks (WANs). Most existing practices target single-phase/layer planning, and are incapable of satisfying all rigorous topological structure constraints (e.g., dual-homing rings) defined by network standards and operators, especially in large-scale networks. These significantly limit their usability and performance in production networks. We consider a general topology planning problem with typical structure constraints over three essential phases (greenfield, reconfiguration, and site expansion) and topological layers (optical, IP, and routing topologies). We present, T3Planner, a novel practical solver to this problem in production. Specifically, we develop a structure-driven encoder based on graph neural network (GNN) for concise structure encoding, and design a new learning framework with optical-centric layer compression/reconstruction and rule-aided reinforcement learning (RL) for fast convergence and high performance. Extensive experiments on nine real topologies demonstrate that T3Planner scales to large optical networks with hundreds of sites, saves 46.6% cost, and supports 3.12x more demand when compared to related existing approaches. © 1983-2012 IEEE.

关键词： Network topology

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A data representation method using distance correlation

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Frontiers of Computer Science 2025年第1期19卷 1-14页

作者： Xinyan LIANG Yuhua QIAN Qian GUO keyin ZHENG Institute of Big Data Science and Industry Shanxi UniversityTaiyuan 030006China Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education Shanxi UniversityTaiyuan 030006China School of Computer Science and Technology Taiyuan University of Science and TechnologyTaiyuan 030024China Shanxi Key Laboratory of Big Data Analysis and Parallel Computing Taiyuan University of Science and TechnologyTaiyuan 030024China

Association in-between features has been demonstrated to improve the representation ability of data. However, the original association data reconstruction method may face two issues: the dimension of reconstructed data is undoubtedly higher than that of original data, and adopted association measure method does not well balance effectiveness and efficiency. To address above two issues, this paper proposes a novel association-based representation improvement method, named as AssoRep. AssoRep first obtains the association between features via distance correlation method that has some advantages than Pearson’s correlation coefficient. Then an improved matrix is formed via stacking the association value of any two features. Next, an improved feature representation is obtained by aggregating the original feature with the enhancement matrix. Finally, the improved feature representation is mapped to a low-dimensional space via principal component analysis. The effectiveness of AssoRep is validated on 120 datasets and the fruits further prefect our previous work on the association data reconstruction.

关键词： association representation distance correlation classification

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Recognition of tea sprouts based on improved deeplabV3 + model

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Multimedia Tools and Applications 2025年 1-18页

作者： Liu, Ruixin Wang, Dengzhun Chen, Zhonghui Li, Zhilin Xiao, Hui Yan, Chunyu Yan, Jianwei Lu, Ping Xie, Benliang College of Big Data and Information Engineering Guizhou University Guiyang550025 China Power Semiconductor Device Reliability Engineering Center of the Ministry of Education Guiyang550025 China College of Mechanical Engineering Guizhou University Guiyang550025 China State Key Laboratory Breeding Base of Green Pesticide and Agricultural Bioengineering Key Laboratory of Green Pesticide and Agricultural Bioengineering Ministry of Education Guizhou University Guiyang550025 China

The recognition of tea sprouts is the premise of realize the intelligence of the premium tea picking. DeepLabV3 +, as the latest semantic segmentation algorithm of DeepLab family, can well recognize the tea sprouts in images. However, the model still has the problem of large number of parameters, and there is still room for improvements in segmentation accuracy and speed. To solve the multi-label recognition and segmentation of background, single bud, one leaf and two leaves in the tea sprout images, an improved deep convolutional neural network algorithm is proposed. This method realizes the recognition of different postures of high-quality tea sprouts on the premise of efficiency and accuracy. The DMResNet18 model is used as the feature extraction network to lightweight the network model and increase the segmentation speed. The feature joint attention network is added to optimize the spatial weights and channel weights of the model, which improves the accuracy of segmentation. Additionally, the low-level feature fusion method is applied to obtain rich initial global information of features and improves the overall performance of the model. The results show that the segmentation accuracy of improved DeepLabV3 + model reaches 92.58%, which is improved by 4.26% compared with the original DeepLabV3 + model. The model size and the speed of detection are improved by 80.67% and 52.24%, respectively. The proposed method offers a solution for the intelligent recognition of tea sprouts. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Semantic Segmentation

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Enhancing Adversarial Example Transferability via Regularized Constrained Feature Layer

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Computers, Materials & Continua 2025年第4期83卷 157-175页

作者： Xiaoyin Yi Long Chen Jiacheng Huang Ning Yu Qian Huang School of Computer Science and Technology Chongqing University of Posts and TelecommunicationsChongqing400065China Chongqing Key Laboratory of Public Big Data Security Technology Chongqing401420China School of Cyber Security and Information Law Chongqing University of Posts and TelecommunicationsChongqing400065China Key Laboratory of Cyberspace Big Data Intelligent Security Ministry of EducationChongqing400065China Artificial Intelligence and Big Data College Chongqing Polytechnic University of Electronic TechnologyChongqing401331China

Transfer-based Adversarial Attacks(TAAs)can deceive a victim model even without prior *** is achieved by leveraging the property of adversarial *** is,when generated from a surrogate model,they retain their features if applied to other models due to their good ***,adversarial examples often exhibit overfitting,as they are tailored to exploit the particular architecture and feature representation of source ***,when attempting black-box transfer attacks on different target models,their effectiveness is *** solve this problem,this study proposes an approach based on a Regularized Constrained Feature Layer(RCFL).The proposed method first uses regularization constraints to attenuate the initial examples of low-frequency *** are then added to a pre-specified layer of the source model using the back-propagation technique,in order to modify the original adversarial ***,a regularized loss function is used to enhance the black-box transferability between different target *** proposed method is finally tested on the ImageNet,CIFAR-100,and Stanford Car datasets with various target models,The obtained results demonstrate that it achieves a significantly higher transfer-based adversarial attack success rate compared with baseline techniques.

关键词： Adversarial examples black-box transferability regularized constrained transfer-based adversarial attacks

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A level set model with shape prior constraint for intervertebral disc MRI image segmentation

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Multimedia Tools and Applications 2025年第9期84卷 6755-6783页

作者： Tian, Zhuangzhou Wang, Shengsheng College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China

Accurate intervertebral disc image segmentation is necessary for further treatment. However, existing methods are difficult to segment due to the intensity inhomogeneity of intervertebral disc MRI images and the similarity in intensity between the intervertebral disc and the surrounding regions. This paper proposes a novel level set model by introducing a shape prior constraint for automatic and accurate segmentation. We utilize a detection model and intensity change to obtain rectangle prior information, which is used to define a shape constraint term. Then, combining data and regularizing terms construct an energy function and employ the steepest descent method minimization to get segmentation results. Finally, we apply the proposed new method to segment intervertebral disc images and get promising results. Extensive experiments and comparisons show that our model is more efficient and accurate. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Image segmentation

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Dual AAV Cluster-Assisted Maritime Physical-Layer Secure Communications via Collaborative Beamforming

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IEEE Internet of Things Journal 2025年第9期12卷 12589-12607页

作者： Huang, Jiawei Wang, Aimin Sun, Geng Li, Jiahui Wang, Jiacheng Du, Hongyang Niyato, Dusit Jilin University College of Computer Science and Technology Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Changchun130012 China Nanyang Technological University College of Computing and Data Science Jurong West 639798 Singapore The University of Hong Kong Department of Electrical and Electronic Engineering Hong Kong

Autonomous aerial vehicles (AAVs) can be utilized as relay platforms to assist maritime wireless communications. However, complex channels and multipath effects at sea can adversely affect the quality of AAV transmitted signals. Collaborative beamforming (CB) can enhance the signal strength and range to assist the AAV relay for remote maritime communications. However, due to the open nature of AAV channels, security issue requires special consideration. This article proposes a dual AAV cluster-assisted system via CB to achieve physical-layer security in maritime wireless communications. Specifically, one AAV cluster forms a maritime AAV-enabled virtual antenna array (MUVAA) relay to forward data signals to the remote legitimate vessel, and the other AAV cluster forms an MUVAA jammer to send jamming signals to the remote eavesdropper. In this system, we formulate a secure and energy-efficient maritime communication multiobjective optimization problem (SEMCMOP) to maximize the signal-to-interference-plus-noise ratio (SINR) of the legitimate vessel, minimize the SINR of the eavesdropping vessel and minimize the total flight energy consumption of AAVs. Since the SEMCMOP is an NP-hard and large-scale optimization problem, we propose an improved swarm intelligence optimization algorithm with chaotic solution initialization and hybrid solution update strategies to solve the problem. Simulation results indicate that the proposed algorithm outperforms other comparison algorithms, and it can achieve more efficient signal transmission by using the CB-based method. © 2014 IEEE.

关键词： Multiobjective optimization

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