检索结果-内蒙古大学图书馆

SSRN 2022年

作者： Wang, Jingyi Feng, Jianwen Xu, Chen Wu, Xiaoqun Lü, Jinhu College of Mathematics and Statistics Shenzhen University Shenzhen518060 China School of Mathematics and Statistics Hubei Key Laboratory of Computational Science Wuhan University Wuhan China State Key Laboratory of Software Development Environment School of Automation Science and Electrical Engineering Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing China

This letter investigates almost sure exponential stabilization of continuous-time Markov jump linear systems (MJLSs) under communication data-rate constraints by introducing sampling and quantization into the feedback control. Different from previous works, the sampling times and the jump times are independent of each other in this letter. The quantization is recursively adjusted on the sampling time, and its updating strategy does not depend on the switching in a sampling interval. In other words, the explicit value of the switching signal in a sampling interval is not necessary. The numerically testable condition is developed to ensure almost sure exponential stabilization of MJLSs under the proposed communication and control protocols. We also drop the assumption of stabilizability of all individual modes required in previous works about the switched systems. Moreover, we extend the result to the case of continuous-time semi-Markov jump linear systems (semi-MJLSs) via the semi-Markov kernel approach. Finally, some numerical examples are presented to illustrate the effectiveness on stabilization of the proposed communication and control protocols. © 2022, The Authors. All rights reserved.

关键词： Markov processes

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A survey of coverage-guided greybox fuzzing with deep neural models

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Information and Software Technology 2025年 186卷

作者： Qiu, Junyang Jiang, Yupeng Miao, Yuantian Luo, Wei Pan, Lei Zheng, Xi State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi 214123 China School of Computing Macquarie University Sydney 2109 NSW Australia School of Information and Physical Sciences University of Newcastle Newcastle 2308 NSW Australia School of Information Technology Deakin University Melbourne 3125 VIC Australia

Coverage-guided greybox fuzzing (CGF) has emerged as a powerful technique for software vulnerability detection, yet traditional techniques often struggle with the increasing complexity of modern software systems and the vastness of input spaces. Deep neural networks (DNNs) have begun to fundamentally transform CGF by addressing these limitations through automated feature extraction, adaptive input generation, and intelligent path prioritization. However, despite these advancements, critical gaps persist in understanding the state-of-the-art landscape. Existing studies often lack rigorous benchmarks to evaluate scalability and generalizability, fail to address the interpretability of neural-guided decisions, and overlook the integration of emerging paradigms such as large language models (LLMs) and neurosymbolic reasoning. This survey systematically bridges these gaps by providing a comprehensive taxonomy of DNN-driven CGF techniques, analyzing their strengths and limitations across key fuzzing stages—seed generation, selection, and mutation. We find that although DNNs have significantly improved fuzzing efficiency, challenges such as semantically invalid seeds, high computational overhead, and limited cross-domain adaptability remain unresolved. Most importantly, we identify two transformative directions with the potential to redefine CGF: (1) LLM-powered fuzzing, which combines generative AI with domain-specific fine-tuning to produce context-aware inputs;and (2) neurosymbolic integration, which merges the precision of symbolic execution with the scalability of neural networks to tackle path explosion. By synthesizing these insights, this survey not only clarifies the state-of-the-art but also outlines a roadmap for developing robust, explainable, and widely applicable intelligent fuzzers. The future of CGF lies in hybrid models that integrate data-driven learning with formal methods, paving the way for autonomous vulnerability discovery in an era of increasingly c

关键词： Code coverage Deep learning Greybox fuzzing Large language models Software vulnerability

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Multi-Variant Execution Research of Software Diversity

Multi-Variant Execution Research of Software Diversity

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2019 International Conference on Artificial Intelligence Technologies and Applications, ICAITA 2019

作者： Liu, Zhenwu Zhang, Zheng Zhang, Jiexin Liu, Hao State Key Laboratory of Mathematical Engineering and Advanced Computing PLA Strategic Support Force Information Engineering University ZhengZhou HeNan450001 China

As more and more software products are threatened by malicious reverse analysis, along with software products are pirated, tampered with and so on, it is of great significance to study software security protection technology in depth. As a software security protection technology, software diversification introduces uncertainty into the target program and provides probabilistic protection for the target program. Multi-Variant Execution (MVE) are fine-grained implementation of software diversification that produces functionally identical variants at the system call level. This article first introduces the related concepts of multi-variant execution. Secondly, it expounds the key technologies of multi-variant execution implementation-variant generation, variant monitor, input/output and synchronization, monitor-variant communication. Security of monitors and different variant communication technologies are analyzed and compared, their advantages and defects are pointed out respectively. Several implementation methods of multi-variant execution design are summarized. Finally, the reasons for the false negatives and false positives of attack events are analyzed, and the prospects and challenges of using multi-variant execution techniques to implement security systems are summarized. © 2019 IOP Publishing Ltd. All rights reserved.

关键词： Artificial intelligence

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Uncovering the Isothermal Transformation Dynamics Behavior and Mechanism of Reversed Austenite in Ph13-8mo Steel

SSRN

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SSRN 2023年

作者： Cui, Puchang Wang, Yuqi Jia, Changhao Wang, Hongli Bai, Qingqing Zhang, Zhihong Zheng, Huaibei Lai, Zhonghong Liu, Yong Zhu, Jingchuan School of Materials Science and Engineering Harbin Institute of Technology Harbin150001 China Chengdu Advanced Metal Materials Industrial Technology Institute Co. Ltd. Chengdu610300 China National Key Laboratory for Precision Hot Processing of Metals Harbin Institute of Technology Harbin150001 China Center for Analysis Measurement and Computing Harbin Institute of Technology Harbin150001 China State Key Laboratory of Metal Material for Marine Equipment and Application Anshan114009 China

The isothermal transformation kinetics and microstructural evolution of the reversed austenite have a significant influence on the properties of PH13-8Mo steel. However, the microscopic phase transformation mechanism during isothermal processes for PH13-8Mo steel needs further examination. Herein, we systematically investigate the isothermal transformation behavior and mechanism of reversed austenite in PH13-8Mo steel. Phase transformation kinetic measurements unravel that the complete elimination of residual austenite can be achieved. Only hierarchical martensitic microstructure without austenite was detected after the solution-cooling treatment at 927 °C with a cooling rate of 10 °C/s, determining the critical martensite start and end temperature to be 176.6 °C, 11.6 °C. The isothermal kinetics of reverted austenite satisfies the Johnson-Mehl-Avrami relationship. The fraction of reversed austenite increases from 0.14% at 510 °C to 8.73% at 593 °C. The lath martensite boundaries with large kernel average misorientation values promote elemental diffusion at the interface. Further, the underlying mechanisms of diffusion-controlled dynamics are elucidated during the isothermal aging process. The interfacial model indicates the growing width of the reversed austenite up to 100 nm and the compositional gradient formed by the elemental diffusion during aging for 0-5 h at 593 °C. The interdiffusion coefficient calculations interpret the diffusion direction of alloying element Ni from martensite to reversed austenite;Cr and Mo may form alloying carbides at the austenite-martensite interface;Al possesses the fastest diffusivity in martensite and austenite phases. The present work unveils the phase transformation kinetics of PH13-8Mo steel, achieving quantitative regulation for maraging stainless steel. © 2023, The Authors. All rights reserved.

关键词： Martensite

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GNN-Geo: A Graph Neural Network-based Fine-grained IP geolocation Framework

arXiv

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arXiv 2021年

作者： Ding, Shichang Luo, Xiangyang Wang, Jinwei Fu, Xiaoming State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou276800 China School of Computer and Software Nanjing University of Information Science & Technology Nanjing210044 China Institute of Computer Science University of Göttingen Göttingen37077 Germany

Rule-based fine-grained IP geolocation methods are hard to generalize in computer networks which do not follow hypothetical rules. Recently, deep learning methods, like multi-layer perceptron (MLP), are tried to increase generalization capabilities. However, MLP is not so suitable for graph-structured data like networks. MLP treats IP addresses as isolated instances and ignores the connection information, which limits geolocation accuracy. In this work, we research how to increase the generalization capability with an emerging graph deep learning method - Graph Neural Network (GNN). First, IP geolocation is re-formulated as an attributed graph node regression problem. Then, we propose a GNN-based IP geolocation framework named GNN-Geo. GNN-Geo consists of a preprocessor, an encoder, messaging passing (MP) layers and a decoder. The preprocessor and encoder transform measurement data into the initial node embeddings. MP layers refine the initial node embeddings by modeling the connection information. The decoder maps the refined embeddings to nodes' locations and relieves the convergence problem by considering prior knowledge. The experiments in 8 real-world IPv4/IPv6 networks in North America, Europe and Asia show the proposed GNN-Geo clearly outperforms the state-of-art rule-based and learning-based baselines. This work verifies the great potential of GNN for fine-grained IP geolocation. Copyright © 2021, The Authors. All rights reserved.

关键词： Deep learning

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Discovery of High-Temperature Superconducting Ternary Hydrides via Deep Learning

arXiv

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arXiv 2025年

作者： Wang, Xiaoyang Zhang, Chengqian Wang, Zhenyu Liu, Hanyu Lv, Jian Wang, Han Weinan, E. Ma, Yanming National Key Laboratory of Computational Physics Institute of Applied Physics and Computational Mathematics Fenghao East Road 2 Beijing100094 China Academy for Advanced Interdisciplinary Studies Peking University Beijing100871 China Key Laboratory of Material Simulation Methods & Software of Ministry of Education State Key Laboratory of Superhard Materials College of Physics Jilin University Changchun130012 China International Center of Future Science Jilin University Changchun130012 China HEDPS CAPT College of Engineering Peking University Beijing100871 China AI for Science Institute Beijing100080 China Center for Machine Learning Research Peking University Beijing100871 China School of Mathematical Sciences Peking University Beijing100871 China

The discovery of novel high-temperature superconductor materials holds transformative potential for a wide array of technological applications. However, the combinatorially vast chemical and configurational search space poses a significant bottleneck for both experimental and theoretical investigations. In this study, we employ the design of high-temperature ternary superhydride superconductors as a representative case to demonstrate how this challenge can be well addressed through a deep-learning-driven theoretical framework. This framework integrates high-throughput crystal structure exploration, physics-informed screening, and accurate prediction of superconducting critical temperatures. Our approach enabled the exploration of approximately 36 million ternary hydride structures across a chemical space of 29 elements, leading to the identification of 144 potential high-Tc superconductors with predicted Tc > 200 K and superior thermodynamic stability at 200 GPa. Among these, 129 compounds spanning 27 novel structural prototypes are reported for the first time, representing a significant expansion of the known structural landscape for hydride superconductors. This work not only greatly expands the known repertoire of high-Tc hydride superconductors but also establishes a scalable and efficient methodology for navigating the complex landscape of multinary hydrides. © 2025, CC BY.

关键词： High temperature superconductors

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6G Enabled advanced Transportation Systems

arXiv

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arXiv 2023年

作者： Liu, Ruiqi Hua, Meng Guan, Ke Wang, Xiping Zhang, Leyi Mao, Tianqi Zhang, Di Wu, Qingqing Jamalipour, Abbas The State Key Laboratory of Mobile Network and Mobile Multimedia Technology Shenzhen518055 China The Wireless and Computing Research Institute ZTE Corporation Beijing100029 China The Department of Electrical Engineering City University of Hong Kong 999077 Hong Kong The State Key Laboratory of Advanced Rail Autonomous Operation The School of Electronic and Information Engineering Beijing Jiaotong University Beijing100044 China The Advanced Research Institute of Multidisciplinary Science Beijing Institute of Technology Beijing100081 China The School of Electrical and Information Engineering Zhengzhou University Zhengzhou450001 China The School of Electrical Engineering Korea University Seoul02841 Korea Republic of Department of Electronic Engineering Shanghai Jiao Tong University Shanghai200240 China School of Electrical and Information Engineering University of Sydney CamperdownNSW2006 Australia

With the emergence of communication services with stringent requirements such as autonomous driving or on-flight Internet, the sixth-generation (6G) wireless network is envisaged to become an enabling technology for future transportation systems. In this paper, two ways of interactions between 6G networks and transportation are extensively investigated. On one hand, the new usage scenarios and capabilities of 6G over existing cellular networks are firstly highlighted. Then, its potential in seamless and ubiquitous connectivity across the heterogeneous space-air-ground transportation systems is demonstrated, where railways, airplanes, high-altitude platforms and satellites are investigated. On the other hand, we reveal that the introduction of 6G guarantees a more intelligent, efficient and secure transportation system. Specifically, technical analysis on how 6G can empower future transportation is provided, based on the latest research and standardization progresses in localization, integrated sensing and communications, and security. The technical challenges and insights for a road ahead are also summarized for possible inspirations on 6G enabled advanced transportation. © 2023, CC BY-NC-ND.

关键词： Wireless networks

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Semantic segmentation of track image based on deep neural network

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The Journal of China Universities of Posts and Telecommunications 2020年第5期27卷 23-33页

作者： Wang Zhaoying Zhou Junhua Liao Zhonghua Zhai Xiang Zhang Lianping School of Electronic Engineering Beijing University of Posts and TelecommunicationsBeijing 100876China Beijing Complex Product Advanced Manufacturing Kngineering Research Center Beijing Simulation CenterBeijing 100854China State Key Laboratory of Intelligent Manufacturing System Technology Beijing Institute of Electronic System EngineeringBeijing 100854China Alibaba Cloud Computing Beijing 100020China

In this paper,deep learning technology was utilited to solve the railway track recognition in intrusion detection *** railway track recognition can be viewed as semantic segmentation task which extends image processing to pixel level *** encoder-decoder architecture DeepLabv3+model was applied in this work due to its good performance in semantic segmentation *** images of the railway track collected from the video surveillance of the train cab were used as experiment dataset in this work,the following improvements were made to the *** first aspect deals with over-fitting problem due to the limited amount of training *** augmentation and transfer learning are applied consequently to rich the diversity of data and enhance model robustness during the training ***,different gradient descent methods are compared to obtain the optimal optimizer for training model *** third problem relates to data sample imbalance,cross entropy(CE)loss is replaced by focal loss(FL)to address the issue of serious imbalance between positive and negative *** of the improved DeepLabv3+model with above solutions is demonstrated by experiment results with different system parameters.

关键词： railway track recognition convolutional neural networks semantic segmentation DeepLabv3+

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Efficient stego key recovery based on distribution differences of extracting message bits

Efficient stego key recovery based on distribution differenc...

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作者： Liu, Jiufen Gan, Junjun Wang, Junchao Xu, Che Luo, Xiangyang Zhengzhou Science and Technology Institute Zhengzhou450001 China State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou450001 China

The extraction of embedded messages, i.e., extraction attacks are the ultimate purpose of steganalysis, with great practical significance to obtain covert communication content and covert communication forensics. For steganography using a stego key, the extraction attacks are equivalent to the stego key recovery. This paper mainly studies the methods of efficient stego key recovery for LSB steganography in JPEG domain. According to the distribution differences of extracting message bits generated by the correct and incorrect keys, stego key recovery is transformed into the hypothesis test of the message bits distribution extracted by the correct and incorrect keys. Firstly, by fitting the message bits distribution extracted by the correct key, a stego key recovery method based on nonparametric hypothesis test is proposed. Secondly, by utilizing the distribution differences between message bits extracted by the correct and incorrect keys, a stego key recovery method based on parametric hypothesis test is proposed. And then, formulas are given for calculating the sample size and threshold in the proposed stego key recovery model, on the basis of type I error and type II error. Experimental results show that the proposed method can successfully recover the stego key of OutGuess 0.13b, OutGuess0.2, JPEG domain random LSB matching steganography and random F3 steganography, the performance is superior to that of existing methods. © 2018, Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Extraction

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Two-dimensional ferromagnetic superlattices

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National Science Review 2020年第4期7卷 745-754页

作者： Shanshan Liu Ke Yang Wenqing Liu Enze Zhang Zihan Li Xiaoqian Zhang Zhiming Liao Wen Zhang Jiabao Sun Yunkun Yang Han Gao Ce Huang Linfeng Ai Ping Kwan Johnny Wong ANDrew Thye Shen Wee Alpha T.N'Diaye Simon A.Morton Xufeng Kou Jin Zou Yongbing Xu Hua Wu Faxian Xiu State Key Laboratory of Surface Physics and Department of Physics Fudan University Institute for Nanoelectronic Devices and Quantum Computing Fudan University Laboratory for Computational Physical Sciences (MOE) Fudan University Department of Electronic Engineering Royal Holloway University of London School of Electronic Science and Engineering Nanjing University Materials Engineering The University of Queensland Department of Physics National University of Singapore Centre for Advanced 2D Materials and Graphene Research Centre National University of Singapore Lawrence Berkeley National Laboratory School of Information Science and Technology Shanghai Tech University Centre for Microscopy and Microanalysis The University of Queensland Collaborative Innovation Center of Advanced Microstructures

Mechanically exfoliated two-dimensional ferromagnetic materials(2 D FMs) possess long-range ferromagnetic order and topologically nontrivial skyrmions in few layers. However, because of the dimensionality effect, such few-layer systems usually exhibit much lower Curie temperature(TC) compared to their bulk counterparts. It is therefore of great interest to explore effective approaches to enhance their TC, particularly in wafer-scale for practical applications. Here, we report an interfacial proximity-induced high-TC2 D FM FeGe Te(FGT) via A-type antiferromagnetic material Cr Sb(CS) which strongly couples to FGT. A superlattice structure of(FGT/CS)n, where n stands for the period of FGT/CS heterostructure, has been successfully produced with sharp interfaces by molecular-beam epitaxy on 2-inch wafers. By performing elemental specific X-ray magnetic circular dichroism(XMCD) measurements, we have unequivocally discovered that TCof 4-layer FeGeTecan be significantly enhanced from 140 K to230 K because of the interfacial ferromagnetic coupling. Meanwhile, an inverse proximity effect occurs in the FGT/CS interface, driving the interfacial antiferromagnetic Cr Sb into a ferrimagnetic state as evidenced by double-switching behavior in hysteresis loops and the XMCD spectra. Density functional theory calculations show that the Fe-Te/Cr-Sb interface is strongly FM coupled and doping of the spin-polarized electrons by the interfacial Cr layer gives rise to the TCenhancement of the FeGeTefilms, in accordance with our XMCD measurements. Strikingly, by introducing rich Fe in a 4-layer FGT/CS superlattice, TCcan be further enhanced to near room temperature. Our results provide a feasible approach for enhancing the magnetic order of few-layer 2 D FMs in wafer-scale and render opportunities for realizing realistic ultra-thin spintronic devices.

关键词： 2D ferromagnetic material room temperature 2-inch Fe3GeTe2 film wafers proximity effect (Fe3GeTe2/CrSb)nsuperlattice

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