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作者： Bing Liu Hui Shu Fei Kang State Key Laboratory of Mathematical Engineering and Advanced Computing

With the wide application of network attached storage(NAS), the security problem is becoming more and more serious, together with the increasingly fierce attacks against NAS devices of different manufacturers. In order to better capture various types of attacks against NAS devices and detect security threats in time, this paper proposes a solution named NAS honeypot,mining the potential security threats through modeling and analyzing the NAS threats. Then a NAS honeypot based on device virtualization technology and depth monitoring and attack induction was designed on the basis of the construction NAS attack chain. Experimental results show that the NAS honeypot can effectively capture, record and analyze network attacks against multi-types NAS devices with a strong guiding effect in mastering popular attack method of NAS devices and alleviating the security threats of NAS.

关键词： NAS Threat modeling Attack chain Honeypot

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Ultra-Fast Mini License Plate Recognition System Based-on Vision Processing Unit 2

Ultra-Fast Mini License Plate Recognition System Based-on Vi...

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2nd International Conference on Big-Data Service and Intelligent Computation, BDSIC 2020

作者： Wang, Junhui Ren, Shuangyin He, Jiezhong Ji, Xiaolan Huang, Diqing College of Computer National University of Defense Technology State Key Laboratory of Mathematical Engineering and Advanced Computing China College of Computer National University of Defense Technology

ISBN: (纸本)9781450388399

As more embedded environments need license plate recognition systems, how to recognize car plates with high speed/accuracy and low energy has become an important and challenging problem. In this paper, we propose a ultra-Fast miNi (FaNi) license plate recognition (LPR) system. The FaNi system are divided into one training sub-system and one inference sub-system. The former are used to get some offline features;then, the latter is deployed online to recognize license numbers with nearly real-time speed. The inference system is comprised of the vision processing unit (VPU) and the display unit. These two parts are both implemented with hardware logic. Experiments show that the FaNi system can obtain high accuracy and high speed with low resource cost. © 2020 ACM.

关键词： Field programmable gate arrays (FPGA)

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Software Supply Chain Analysis Based on Function Similarity

Software Supply Chain Analysis Based on Function Similarity

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作者： Wenjie Sun Zheng Shan Fudong liu Xingwei Li Meng Qiao Chunyan Zhang State Key Laboratory of Mathematical Engineering and Advanced Computing

The supply of open source and open source components is growing at an alarming rate,while vulnerabilities in open source components are *** supply chain analysis aims to discover third-party components and open source code used in a software,and analyze the software's dependence on *** this paper,we propose a software component analysis method and a known vulnerabilities detecting *** scanning the open source components of the binary file and conducting vulnerability analysis,the known vulnerabilities are *** paper mainly solves the problem of detecting known vulnerabilities in the supply chain of binary *** conducted a case analysis and achieved good results.

关键词： OpenSSL Software Supply Chain Analysis Based on Function Similarity

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Research on the Protection Mechanism of Cisco IOS Exploit

Research on the Protection Mechanism of Cisco IOS Exploit

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作者： Jinzhu Wang Ruijie Cai Shengli Liu State Key Laboratory of Mathematical Engineering and Advanced Computing

Cisco IOS is an operating system that runs on Cisco routing and switching *** security is important to the entire *** vulnerability attacks have become one of the main sources of threat to Cisco IOS security,it is necessary to analyze the defense mechanism of IOS against vulnerability *** purpose of this paper is to find out the deficiencies of IOS vulnerability defense mechanism in time and put forward improvement methods and preventive ***,this paper introduces the protection mechanism of Cisco IOS vulnerability,including CheckHeaps,DEP,*** principle of these mechanisms and the idea of vulnerability protection are *** then,after analyzing the shortcomings of these protection mechanisms,the basic idea of bypassing these protection mechanisms is ***,in view of the shortcomings of Cisco IOS vulnerability defense mechanism and the proposed bypass methods,some advices about how to better protect Cisco IOS from vulnerability exploitation are given.

关键词： Research on the Protection Mechanism of Cisco IOS Exploit Cisco IOS

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Optimizing execution for pipelined-based distributed deep learning in a heterogeneously networked GPU cluster

Optimizing execution for pipelined-based distributed deep le...

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作者： Zhang, Jinghui Zhan, Jun Li, Jiange Jin, Jiahui Qian, Lei School of Computer Science and Engineering Southeast University Nanjing China State Key Lab of Mathematical Engineering and Advanced Computing Wuxi China

Exorbitant resources (computing and memory) are required to train a deep neural network (DNN). Often researchers deploy an approach that uses distributed parallel training to acquire larger models faster on GPUs. This approach has its detriments, though;on one hand, a GPU's expanded capacity to compute also produces bigger bottlenecks in inter-GPU's communications during model training, and multi-GPU systems lead to complex connectivity. Workload schedulers then end up having to consider hardware topology and requirements for workload communication, in hopes of allocating GPU resources to optimize execution time and improve usage in a heterogeneous environment. On the other hand, the high memory requirements to train a DNN model make running the training processes on GPUs onerous. To contend with this, we introduce two execution optimization methods based on pipeline-hybrid parallelism (using both data and model parallelism) in a GPU cluster with heterogeneous networking. First, we propose a model partition algorithm that accelerates pipeline-hybrid parallelism training between heterogeneously network-connected GPUs. Second, we introduce a cost-balanced recomputing algorithm to reduce memory usage in the pipeline mode. Experiments show that our solution (Pipe-Torch) averages a speedup of 1.4× compared with data parallelism, and reduces the memory footprint while maintaining pipelined load-balanced training. © 2020 John Wiley & Sons Ltd

关键词： Pipelines

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Strain-tunable magnetic compensation temperature of epitaxial Tb3Fe5O12 thin films

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Physical Review B 2023年第18期108卷 184403-184403页

作者： Yufei Li Xihui Yang Hua Bai Mingzhi Wang Dashuai Cheng Cheng Song Zhe Yuan Yi Liu Zhong Shi Shanghai Key Laboratory of Special Artificial Microstructure Materials and Technology and Pohl Institute of Solid State Physics and School of Physics Science and Engineering Tongji University Shanghai 200092 China Center for Advanced Quantum Studies and Department of Physics Beijing Normal University Beijing 100875 China Key Laboratory of Advanced Materials School of Materials Science and Engineering Beijing Innovation Center for Future Chips Tsinghua University Beijing 100084 China Institute for Nanoelectronic Devices and Quantum Computing Fudan University Shanghai 200433 China

High-quality rare-earth iron garnet (ReIG) Tb3Fe5O12 (TbIG) thin films are epitaxially grown on a series of (111)-oriented garnet substrates with various lattice constants. The coherent growth induces a substrate-dependent in-plane tensile or compressive strain in the TbIG film. Measurements of the anomalous Hall-like effect in TbIG/Pt heterostructures show that the compensation temperature of TbIG films monotonically changes with the film strain. The strain results in a variation of the distances between magnetic atoms in the TbIG crystal and therefore the corresponding exchange interactions. The latter is explicitly calculated as a function of the lattice strain based on density-functional theory, reproducing the observed experimental results. This work provides a versatile way to optimize ReIG-based spin-orbit torque devices.

关键词： Anomalous Hall effect Epitaxial strain Exchange interaction Ferrimagnetism Magnetoelastic effect

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Personalized Federated Learning with Collaborative Aggregation Networks for Multi-Site Brain Disorder Diagnosis

Personalized Federated Learning with Collaborative Aggregati...

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Industrial Automation, Robotics and Control engineering (IARCE), International Conference on

作者： Qian Si Yang Li School of Cyber Science and Technology Beihang University Beijing China Department of Automation Science and Electrical Engineering Beijing Advanced Innovation Center for Big Data and Brain Computing State Key Laboratory of Virtual Reality Technology and Systems Advanced Institute of Information Technology Peking University Beihang University Beijing China

ISBN: (数字)9798350380323

ISBN: (纸本)9798350380330

In multi-site brain disease diagnosis studies, traditional centralized training methods necessitate sharing medical data, posing significant privacy risks. Federated learning (FL) offers a privacy-preserving solution by enabling global model training through aggregating locally trained models from multiple data centers without sharing raw data. However, current FL approaches rely on a server-based network topology, where central server failure disrupts training. Additionally, data heterogeneity across sites often slows convergence and reduces accuracy. To overcome these issues, we introduce a decentralized personalized federated learning collaborative aggregation network (pFedCAN). This framework has two core components: (1) separating local models into shared and personalized layers, and (2) forming a collaborative aggregation network via similarity detection in the shared layers. Specifically, each center trains its local model, then separates it into shared and personalized layers. The shared layer is exchanged with other centers, while the personalized layer remains local. Data centers analyze similarities in received shared layers to build a collaborative network, where shared layers from similar centers are aggregated to refine the model. This approach flexibly adapts to varying levels of data heterogeneity, enhancing model training efficiency. Validation on public datasets, ABIDE I and ADHD, shows that the proposed method outperforms current leading techniques.

关键词： Training Adaptation models Data centers Federated learning Service robots Collaboration Brain modeling Data models Servers Medical diagnostic imaging

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Transverse-momentum-dependent wave functions of the pion from lattice QCD

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Physical Review D 2024年第9期109卷 L091503-L091503页

作者： Min-Huan Chu Jin-Chen He Jun Hua Jian Liang Xiangdong Ji Andreas Schäfer Hai-Tao Shu Yushan Su Ji-Hao Wang Wei Wang Yi-Bo Yang Jun Zeng Jian-Hui Zhang Qi-An Zhang INPAC Shanghai Key Laboratory for Particle Physics and Cosmology Key Laboratory for Particle Astrophysics and Cosmology (MOE) School of Physics and Astronomy Shanghai Jiao Tong University Shanghai 200240 China Yang Yuanqing Scientific Computering Center Tsung-Dao Lee Institute Shanghai Jiao Tong University Shanghai 200240 China Department of Physics University of Maryland College Park Maryland 20742 USA Key Laboratory of Atomic and Subatomic Structure and Quantum Control (MOE) Guangdong Basic Research Center of Excellence for Structure and Fundamental Interactions of Matter Institute of Quantum Matter South China Normal University Guangzhou 510006 China Guangdong-Hong Kong Joint Laboratory of Quantum Matter Guangdong Provincial Key Laboratory of Nuclear Science Southern Nuclear Science Computing Center South China Normal University Guangzhou 510006 China Institut für Theoretische Physik Universität Regensburg D-93040 Regensburg Germany CAS Key Laboratory of Theoretical Physics Institute of Theoretical Physics Chinese Academy of Sciences Beijing 100190 China School of Fundamental Physics and Mathematical Sciences Hangzhou Institute for Advanced Study UCAS Hangzhou 310024 China Southern Center for Nuclear-Science Theory (SCNT) Institute of Modern Physics Chinese Academy of Sciences Huizhou 516000 Guangdong Province China International Centre for Theoretical Physics Asia-Pacific Beijing/Hangzhou China School of Physical Sciences University of Chinese Academy of Sciences Beijing 100049 China School of Science and Engineering The Chinese University of Hong Kong Shenzhen 518172 China Center of Advanced Quantum Studies Department of Physics Beijing Normal University Beijing 100875 China School of Physics Beihang University Beijing 102206 China

We present a lattice QCD calculation of the transverse-momentum-dependent wave function (TMDWF) of the pion using large-momentum effective theory. Numerical simulations are based on one ensemble with 2+1+1 flavors of highly improved staggered quarks with lattice spacing a=0.121 fm from the MILC collaboration, and one with 2+1 flavor clover fermions and tree-level Symanzik gauge action generated by the CLS collaboration with a=0.098 fm. As a key ingredient, the soft function is first obtained by incorporating the one-loop perturbative contributions and a proper normalization. Based on this and the equal-time quasi-TMDWF simulated on the lattice, we extract the TMDWF. The results for both lattice ensembles are compatible and a comparison with a phenomenological parametrization is made. Our studies provide a first attempt of ab initio calculation of TMDWFs which will eventually lead to crucial theory inputs for making predictions for exclusive processes under QCD factorization.

关键词： Lattice QCD Transverse momentum dependent distribution Pions

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Feature Rotation Invariance Learning for Point Cloud Analysis

Feature Rotation Invariance Learning for Point Cloud Analysi...

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Smart World Congress (SWC), IEEE

作者： Lu Shi Qi Cao Guoqing Zhang Jin Yi Yansen Huang Yigang Cen State Key Laboratory of Advanced Rail Autonomous Operation the School of Computer Science and Technology and Visual Intellgence +X International Cooperation Joint Laboratory of MOE Beijing Jiaotong University Beijing China the School of Computing Science University of Glasgow Singapore With the Key Laboratory of Big Data & Artificial Intelligence in Transportation Ministry of Education the State Key Laboratory of Advanced Rail Autonomous Operation and the School of Computer and Information Technology Beijing Jiaotong University Beijing China the College of Civil Engineering Guizhou University Guizhou Lianjian Civil Engineering Quality Testing Monitoring Center Co. LTD Guizhou China

ISBN: (数字)9798331520861

ISBN: (纸本)9798331520878

While deep learning has significantly advanced point cloud analysis, extracting effective features from their disordered structure remains challenging. Existing approaches often rely on complex network architectures or manual feature engineering to achieve rotation invariance, which increases inference latency. This paper offers a novel perspective on this challenge, showing that rotation-sensitive models can achieve performance comparable to rotation-invariant models when trained with rotation-augmented data. Given the significant differences in the feature distributions of rotation-sensitive and rotation-invariant models, we propose Feature Rotation Invariance Learning (FRIL), a framework that leverages Distance Metric Learning and rotation-based constraints to help rotation-sensitive models extract rotation-invariant features. This approach eliminates the need for additional modules, enabling robust performance against rotations. Extensive experiments demonstrate that FRIL consistently outperforms existing rotation-invariant methods across various visual tasks.

关键词： Point cloud compression Representation learning Analytical models Solid modeling Visualization Three-dimensional displays Semantics Cloning Feature extraction Data models

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Taking Myopic Best Response Against The Hedge Algorithm

Taking Myopic Best Response Against The Hedge Algorithm

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Chinese Control Conference (CCC)

作者： Xinxiang Guo Yifen Mu School of Mathematical Sciences University of Chinese Academy of Sciences Beijing China The State Key Laboratory of Scientific and Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing China The Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing China

With the rapid development of artificial intelligence (AI), the game between the human and machine/AI becomes more and more common. The related theoretical analysis becomes significant and necessary, which however is still rare. This problem involves evolution analysis and optimal control of dynamic game systems driven by learning algorithms. In this paper, we consider a finitely repeated two-player zero-sum game. We study the dynamic evolution process of the game system, in which one player employs the Hedge algorithm and the other player takes the myopic best response. By the updating formula of the Hedge algorithm, we define a quantity called the state and construct the state Transition Triangle Graph (STTG). Then, we prove that the game system is periodic after o(T) stages.

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