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Methods for solving equations with errors based on the HHL algorithm

The Journal of china Universities of Posts and Telecommunications 2022年第4期29卷 9-20页

作者： LüLihui Wang Hong Ma Zhi Duan Qianheng Fei Yangyang Meng Xiangdong State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou 450001China Henan Key Laboratory of Network Cryptography Technology Zhengzhou 450001China

To solve polynomial systems,Harrow,Hassidim,and Lloyd(HHL)proposed a quantum algorithm called HHL *** on the HHL algorithm,Chen et *** an algorithm,the solving the Boolean solutions of polynomial systems(PoSSoB)***,Ding et *** the Boolean Macaulay matrix and analyzed the lower bound on the condition *** by Ding et al.’s research,several related algorithms are proposed in this ***,the improved PoSSoB algorithm using the Boolean Macaulay matrix is proved to have lower ***,for solving equations with errors,a quantum algorithm for the max-polynomial system solving(Max-PoSSo)problem is proposed based on the improved PoSSoB ***,the Max-PoSSo algorithm is extended to the learning with errors(LWE)problem and its special case,the learning parity with noise(LPN)problem,providing a quantitative criterion,the condition number,for the security of these basic problems.

关键词： Harrow Hassidim and Lloyd polynomial system solving max-polynomial system solving learning parity with noise learning with errors

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A Network Deception Defense Mechanism Based on Virtual Topology Generation

A Network Deception Defense Mechanism Based on Virtual Topol...

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2023 International Conference on Intelligent Systems, Communications, and Computer Networks, ISCCN 2023

作者： Wang, Binghui Lu, Bin State Key Laboratory of Mathematical Engineering and Advanced Computing Information Engineering University Zhengzhou China

ISBN: (纸本)9781510666238

To address the problem that traditional network defense techniques are difficult to cope with scanning attacks launched by internal attackers, this paper proposes a Virtual Network Topology Deception Defense mechanism(VNTDD)based on the idea of deception defense. In order to solve the problem of lack of randomness and security of virtual network topology, we divide the virtual network topology into three categories: forwarding nodes, real nodes and virtual nodes, and analyze their deployment locations and numbers respectively to generate a random virtual network topology, and enables the deployment of virtual network topology on the underlying real network through traffic control mechanisms. Finally, through experimental analysis, the VNTDD deception defense mechanism proposed in this paper can effectively prolong the scanning process of the intranet by internal attackers. © 2023 SPIE. All rights reserved.

关键词： Topology

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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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The Causal Reasoning Ability of Open Large Language Model: A Comprehensive and Exemplary Functional Testing 23

The Causal Reasoning Ability of Open Large Language Model: A...

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23rd IEEE International Conference on Software Quality, Reliability, and Security, QRS 2023

作者： Li, Shun-Hang Zhou, Gang Li, Zhi-Bo Lu, Ji-Cang Huang, Ning-Bo State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China Information Engineering University Zhengzhou China

ISBN: (纸本)9798350319583

As the intelligent software, the development and application of large language models are extremely hot topics recently, bringing tremendous changes to general AI and software industry. Nonetheless, large language models, especially open source ones, incontrollably suffer from some potential software quality issues such as instability, inaccuracy, and insecurity, making software testing necessary. In this paper, we propose the first solution for functional testing of open large language models to check full-scene availability and conclude empirical principles for better steering large language models, particularly considering their black box and intelligence properties. Specifically, we focus on the model's causal reasoning ability, which is the core of artificial intelligence but almost ignored by most previous work. First, for comprehensive evaluation, we deconstruct the causal reasoning capability into five dimensions and summary the forms of causal reasoning task as causality identification and causality matching. Then, rich datasets are introduced and further modified to generate test cases along with different ability dimensions and task forms to improve the testing integrity. Moreover, we explore the ability boundary of open large language models in two usage modes: prompting and lightweight fine-tuning. Our work conducts comprehensive functional testing on the causal reasoning ability of open large language models, establishes benchmarks, and derives empirical insights for practical usage. The proposed testing solution can be transferred to other similar evaluation tasks as a general framework for large language models or their derivations. © 2023 IEEE.

关键词： Black-box testing

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Network traffic classification based on multi-head attention and deep metric learning 8

Network traffic classification based on multi-head attention...

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8th International Conference on Electronic Technology and Information Science, ICETIS 2023

作者： Lv, Zhuo Lu, Bin Li, Xue Qi, Zan State Key Laboratory of Mathematical Engineering and Advanced Computing PLA Information Engineering University Zhengzhou China

ISBN: (纸本)9781510666535

Network traffic classification plays an important role in network resource management and security. The application of encryption techniques and the rapid increase in the size of network traffic have placed higher demands on traffic classification. In this paper, we design multi-headed attention (MHA) and deep metric learning (DML) in our model for network traffic classification. In addition, MHA-DML also extracts more subtle and highly differentiated features through the improved triplet measurement loss. Experimental results demonstrate that the model achieves the best classification on all three publicly available web traffic datasets. The MHA-DML guarantees detection accuracy even when facing a classification task with many categories. © 2023 SPIE.

关键词： Classification (of information)

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Resolving Anonymous Nodes Based on "Watermelon Patterns" Towards Constructing Router-Level Topology in Mesh Network 29th

Resolving Anonymous Nodes Based on "Watermelon Patterns" Tow...

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29th International Conference on Computational and Experimental engineering and Sciences, ICCES 2023

作者： Li, Ruixiang Feng, Xinzhu Liu, Chong Yuan, Fuxiang Ding, Shichang State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China Key Laboratory of Cyberspace Situation Awareness of Henan Province Zhengzhou China Sichuan University Chengdu China

ISBN: (纸本)9783031425141

Router-level topology, portraying the connection relationship between network devices, is important for discovering key devices in network. In actual networks, there are some anonymous routers, which do not respond traceroute due to network congestion, firewall security settings, router response rate limitations, and other factors. As these anonymous routers are difficult to eliminate, it makes the constructed router-level topology huge in size. Currently, the time cost of finding key nodes is high. Resolving anonymous nodes for reducing the topology size is necessary to improve the efficiency in finding key node. Although anonymous route resolving methods have been available, they are difficult to handle two-hop anonymous route structures in mesh topologies. To this end, based on the four structures summarized in Graph Based Induction (GBI) method, we propose a method to resolve anonymous nodes based on "watermelon patterns". Combining with Kapar, an alias IP merging method, to build router-level topologies in mesh networks. We have conducted experiments in 8 cities, such as New York, Tokyo, Seoul and so on. The experimental results show that, our method can handle the local topology containing two-hop anonymous nodes. And using our method to further reduce the size of the router-level topology built using GBI and Kapar, which is conducive to improving the efficiency of discovering key nodes in the topology. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Topology

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Towards Efficient and Privacy-Preserving Anomaly Detection of Blockchain-Based Cryptocurrency Transactions 25th

Towards Efficient and Privacy-Preserving Anomaly Detection ...

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25th International Conference on Information and Communications Security, ICICS 2023

作者： Song, Yuhan Zhu, Yuefei Wei, Fushan State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China Henan Key Laboratory of Network Cryptography Technology Zhengzhou China

ISBN: (纸本)9789819973552

In recent years, a growing number of breaches targeting cryptocurrency exchanges have damaged the credibility of the entire cryptocurrency ecosystem. To prevent further harm, it’s crucial to detect the anomalous behaviors hidden within cryptocurrency transactions and offer predictive suggestions. However, details of transaction records must be carefully analyzed for effective detection, and this information could be exploited by adversaries to launch attacks such as de-anonymization and model interference. As a result, it is essential to prioritize privacy preservation when designing an anomaly detection system for cryptocurrency transactions. In this paper, we propose a privacy-preserving anomaly detection (PPad) scheme for cryptocurrency transactions based on a decision tree model, which achieves privacy preservation by using additively homomorphic encryption and matrix perturbation techniques. We also design and implement PPad’s underlying protocol in a cloud outsourcing environment. The correctness and privacy properties of PPad have been proven through detailed analysis. Experimental results show that our scheme can offer privacy assurance with desirable detection effectiveness and efficiency, making it suitable for real-world applications. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Decision trees

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Stance Detection for Social Text: Inference-Enhanced Multi-Task Learning with Machine-Annotated Supervision

Stance Detection for Social Text: Inference-Enhanced Multi-T...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Pi, Qiankun Lu, Jicang Sun, Yepeng Fan, Qinlong Zhou, Xukun Shang, Shouxin State Key Laboratory of Mathematical Engineering and Advanced Computing Henan Zhengzhou China School of Information Renmin University of China Beijing China

ISBN: (纸本)9798350368741

Stance detection, a natural language processing technique, captures user attitudes on controversial social media topics. However, the semantic ambiguity of social texts makes accurate stance determination challenging. Existing annotated data is often domain-specific, resulting in poor model generalization for unseen targets and cross-domain scenarios. To tackle this challenge, we introduce inference tasks related to stance detection as auxiliary tasks and use a multitask learning approach to improve the model's understanding of textual semantics. Meanwhile, to alleviate the scarcity of annotated data, we use argumentation corpus with abundant resources as the source domain to train the basic stance detection model. We integrate this model with a large-scale framework to facilitate weakly supervised learning for machine annotation of unlabeled social texts, thereby boosting the model's adaptability across different targets and domains. Experimental results on four Twitter datasets demonstrate that our method can significantly improve the model's stance discriminative ability and generalization performance. © 2025 IEEE.

关键词： argumentation corpus machine annotation multitask learning stance detection

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LENSER: A Channel state Information Based Indoor Localization Scheme for Malicious Devices 20

LENSER: A Channel State Information Based Indoor Localizatio...

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20th IEEE International Conference on Mobile Ad Hoc and Smart Systems, MASS 2023

作者： Ma, Yongqiang Luo, Xiangyang Li, Ruixiang Du, Shaoyong Liu, Wenyan State Key Laboratory of Mathematical Engineering and Advanced Computing Key Laboratory of Cyberspace Situation Awareness of Henan Province Zhengzhou China

ISBN: (纸本)9798350324334

Rapid and accurate localization of illicit micro-recorders, cameras, and other wireless network devices is of great significance for ensuring public privacy and security. However, existing methods are susceptible to environmental noise, have low localization accuracy, high localization time overhead, and require extensive manual involvement. To address these issues, we propose LENSER, a channel state information based indoor localization scheme for illicit wireless network devices. First, we set detection points to capture signal amplitude distribution changes during the antenna array rotation process, thus determining whether there is a line-of-sight propagation relationship between the detection point and the malicious device. Next, we identify the room containing the malicious device based on the line-of-sight propagation relationship and calibrate the measured position and estimate the direction of the malicious device using existing methods within the room. Finally, we establish a linear space based on the direction of the malicious device and propose an iterative comparison method based on received signal strength indicator to localize malicious devices accessing public Wi-Fi networks within the room. We conducted 1200 experiments in four buildings with different layouts and building materials. The results show that LENSER's average localization time is 12.5 minutes, and the average localization accuracy is 26.4 centimeters, an improvement of 11.1% to 86.1% in average localization accuracy and a reduction of 11.1% to 58.2% in average localization time compared to existing methods, such as Spotfi, Lumos, SCamF, SNOOPDOG, and Region Division. © 2023 IEEE.

关键词： Channel state information

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An Opposition-Based Learning Adaptive Chaotic Particle Swarm Optimization Algorithm

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Journal of Bionic engineering 2024年第6期21卷 3076-3097页

作者： Chongyang Jiao Kunjie Yu Qinglei Zhou State Key Laboratory of Mathematical Engineering and Advanced Computing PLA Strategic Support Force Information Engineering UniversityZhengzhou450001China Henan Information Engineering School Zhengzhou Vocational College of Industrial SafetyZhengzhou450000China School of Electrical and Information Engineering Zhengzhou UniversityZhengzhou450001China School of Computer and Artificial Intelligence Zhengzhou UniversityZhengzhou450001China

To solve the shortcomings of Particle Swarm Optimization(PSO)algorithm,local optimization and slow convergence,an Opposition-based Learning Adaptive Chaotic PSO(LCPSO)algorithm was *** chaotic elite opposition-based learning process was applied to initialize the entire population,which enhanced the quality of the initial individuals and the population diversity,made the initial individuals distribute in the better quality areas,and accelerated the search efficiency of the *** inertia weights were adaptively customized during evolution in the light of the degree of premature convergence to balance the local and global search abilities of the algorithm,and the reverse search strategy was introduced to increase the chances of the algorithm escaping the local *** LCPSO algorithm is contrasted to other intelligent algorithms on 10 benchmark test functions with different characteristics,and the simulation experiments display that the proposed algorithm is superior to other intelligence algorithms in the global search ability,search accuracy and convergence *** addition,the robustness and effectiveness of the proposed algorithm are also verified by the simulation results of engineering design problems.

关键词： PSO Opposition-based learning Chaotic motion Inertia weight Intelligent algorithm

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