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2021 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2021

作者： Cui, Jianan Xie, Yutong Gong, Kuang Kim, Kyungsang Yang, Jaewon Larson, Peder Hope, Thomas Behr, Spencer Seo, Youngho Liu, Huafeng Li, Quanzheng Zhejiang University State Key Laboratory of Modern Optical Instrumentation College of Optical Science and Engineering Hangzhou China Massachusetts General Hospital Harvard Medical School Center for Advanced Medical Computing and Analysis BostonMA United States Peking University Beijing China Massachusetts General Hospital Harvard Medical School Gordon Center for Medical Imaging BostonMA United States Department of Radiology and Biomedical Imaging CA94107 United States

ISBN: (纸本)9781665421133

The structure of the deep neural network is constantly changing, and its performance is constantly breaking through. Recently, a new network, Nouveau variational auto-encoder (NVAE), has been proposed and gained great attention. In addition to the ability to generate high-quality images, the more important nature of NVAE is that it can generate a distribution that makes it possible to measure the uncertainty. In this work, we proposed to use NVAE for PET image denoising and estimate the uncertainty from both training data and model at the same time. 2.5D training based on 28 patients was performed and quantification based on 7 real patient data showed that NVAE has a good performance for PET denoising, which outperforms the Unet. The variance of 1000 sample output was calculated to show the uncertainty map. © 2021 IEEE.

关键词： Deep neural networks

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Optimizing the Memory Hierarchy by Compositing Automatic Transformations on Computations and Data

Optimizing the Memory Hierarchy by Compositing Automatic Tra...

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IEEE/ACM International Symposium on Microarchitecture (MICRO)

作者： Jie Zhao Peng Di State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China Huawei Technologies Co. Ltd Beijing China

ISBN: (数字)9781728173832

ISBN: (纸本)9781728173849

Optimizing compilers exploit the memory hierarchy using loop tiling and fusion, but these two transformations usually interfere with each other due to the oversight of transformations on data in memories. We present a novel composition of loop tiling and fusion in this paper. Unlike existing tiling-after-fusion algorithms that only transform computation spaces, our approach first applies rectangular/parallelogram tiling to live-out computation spaces for fitting the memory hierarchy, followed by the computation of the memory footprints required by each tile. The upwards exposed data extracted from the memory footprints are used to determine the tile shapes of intermediate computation spaces, allowing the construction of arbitrary tile shapes. Finally, our technique implements a post-tiling fusion strategy for maximizing data locality without losing tilability or parallelism of live-out computation spaces, thereby enabling storage reduction and reuse, and optimizing the memory hierarchy. We demonstrate that our approach can achieve superior performance on both CPU and GPU architectures over the state of the art by experimenting on 11 benchmarks extracted from numerous domains including neural networks, image processing, sparse matrix computation and linear algebra. Also, the results of the ResNet-50 model on an AI accelerator show that our approach can obtain 16% performance improvement.

关键词： Shape Tiles Memory management Transforms Benchmark testing Sparse matrices Optimization

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A novel extreme multistability system coupled by HR neuron and unidirectional cyclic HNN and its application in medical image encryption

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Chaos, Solitons & Fractals 2025年 199卷

作者： Zhi Huang Zhen Li Qiao Wang Weijie Tan Xianming Wu College of Big Data and Information Engineering Guizhou University Guiyang 550025 China State Key Laboratory of Public Big Data Guizhou University Guiyang 550025 China Guizhou Key Laboratory of Artificial Intelligence and Brain-inspired Computing College of Mathematics and Big Data Guizhou Education University Guiyang 550018 China State Key Laboratory of Public Big Data Key Laboratory of Advanced Manufacturing Technology of Ministry of Education Guizhou University Guiyang 550025 China School of Mechanical and Electrical Engineering Guizhou Normal University Guiyang 550025 China

In brain-like research, investigating the impact of heterogeneous neurons on specific neural network structures is of significant importance. In this paper, a heterogeneous memristive synapse-coupled system (HMSCS) is proposed, which is coupled by Hindmarsh–Rose (HR) and unidirectional cyclic Hopfield neural network (HNN), and is established by presenting a new locally active memristor with multi-stable behavior to simulate the synaptic connection between HR neuron and HNN. The dynamic analysis results show that the locally active region of the memristor can significantly affect the chaos range of HMSCS, and the initial value of the memristor can drive a shift in the phase of the attractor, resulting in extreme multistability. In addition, the numerical analyses are validated by circuit design and simulation. Furthermore, a novel medical image encryption scheme is proposed based on the HMSCS chaotic system, in which an improved Hilbert curve permutation method is given, where the traversal direction and starting point of the Hilbert curve are dynamically selected in each round, thereby effectively enhance permutation performance. Additionally, a region-constrained initial value mapping method is proposed to prevent multistability systems from entering periodic orbits due to improper initial value selection, which is a common problem in existing multistability chaotic systems applied to image encryption. Some common security analyses results show that our proposed encryption scheme has excellent security performance for medical image application scenarios.

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A new static vulnerabilities analysis algorithm for PHP codes 3

A new static vulnerabilities analysis algorithm for PHP code...

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3rd Annual International Conference on Network and Information Systems for Computers, ICNISC 2017

作者： Yan, Xue-Xiong Ma, Heng-Tai Institute of Software Chinese Academy of Sciences Beijing China State Key Laboratory of Mathematical Engineering and Advanced Computing Beijing China

ISBN: (纸本)9781538616185

as for detecting taint-style vulnerabilities in PHP codes, this paper introduces function calling control vulnerability, which is a new kind of taint-style vulnerabilities in PHP codes without sensitive function, and enriches classical update rules for taint analysis with a new transfer function definition, which is used to deal with statements with a single function call. The new static vulnerabilities analysis algorithm is implemented in a tool named POSE, and the experiment results of the POSE show that the new algorithm is valid for detecting more type of taint-style vulnerabilities in PHP codes. © 2019 IEEE.

关键词： Codes (symbols)

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SmartGift: Learning to Generate Practical Inputs for Testing Smart Contracts

SmartGift: Learning to Generate Practical Inputs for Testing...

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International Conference on Software Maintenance (ICSM)

作者： Teng Zhou Kui Liu Li Li Zhe Liu Jacques Klein Tegawendé F. Bissyandé Nanjing University of Aeronautics and Astronautics Nanjing China Key Laboratory of Safety-Critical Software Nanjing University of Aeronautics and Astronautics Nanjing China State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi China Monash University Melbourne Australia Interdisciplinary Centre for Security Reliability and Trust (SnT) University of Luxembourg Luxembourg

With the boom of Initial Coin Offerings (ICO) in the financial markets, smart contracts have gained rapid popularity among consumers. Smart contract vulnerabilities however made them a prime target to malicious attacks that are leading to huge losses. The research community is thus applying various software engineering technologies to smart contracts to address them. In general, to detect vulnerabilities in smart contracts, mutation and fuzz based testing approaches have been widely studied and indeed achieved promising performance on benchmark datasets. Generating test inputs with mutation approaches essentially relies on the available test cases in a smart contract program. In our preliminary study, however, we observed that 56.4% of 218 identified open-source smart contract project repositories do not provide any test case for validation. Fuzzing test inputs leads to random values and lacks practical usefulness. Our work addresses this problem: we propose an approach, Smartgift, which generates practical inputs for testing smart contracts by learning from the transaction records of real-world smart contracts. Leveraging a collected set of over 60 thousand transaction records, Smartgift is able to generate relevant test inputs for ~77% smart contract functions, largely outperforming the traditional fuzzing approach (successful for only 60% functions). We further demonstrate the practicality of the test inputs by using them to replace the test inputs of the ContractFuzzer state of the art smart contract vulnerability detector: with inputs by Smartgift, ContractFuzzer can now detect 131 of the 154 vulnerabilities in its benchmark.

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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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Robust second-order correlation of twin parametric beams generated by amplified spontaneous parametric down-conversion

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Chinese Optics Letters 2020年第12期18卷 63-67页

作者： Kunpeng Jia Xiaohan Wang Xinjie LV Ping Xu Zhenlin Wang ZhiWei Wuang Gang Zhao Yan-Xiao Gong Zhenda Xie Shining Zhu National Laboratory of Solid State Microstructures School of PhysicsSchool of Electronic Science and EngineeringCollege of Engineering and Applied Sciencesand Collaborative Innovation Center of Advanced MicrostructuresNanjing UniversityNanjing 210093China Institute for Quantum Information and State Key Laboratory of High Performance Computing.College of Computing National University of Defense TechnologyChangsha 410073China Mesoscopic Optics and Quantum Electronics Laboratory University of California Los AngelesCciliforniaCA 90095USA

We report an observation of the second-order correlation between twin beams generated by amplified spontaneous parametric down-conversion operating above threshold with kilowatt-level peak power, from a periodically poled Li Ta O3 crystal via a single-pass scheme. Photocurrent correlation was measured because of the bright photon streams, with raw visibility of 37.9% or 97.3% after electronic filtering. As expected in our theory, this correlation is robust and insensitive to parametric gain and detection loss, enabling important applications in optical communications, precision measurement, and nonlocal imaging.

关键词： amplified spontaneous parametric down-con version robust second-order correlation high-gain twin beams

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Double JPEG Compression Detection Based on Markov Model 18th

Double JPEG Compression Detection Based on Markov Model

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18th International Workshop on Digital Forensics and Watermarking, IWDW 2019

作者： Wang, Jinwei Huang, Wei Luo, Xiangyang Shi, Yung-Qing Nanjing University of Information Science and Technology Nanjing210044 China Department of Computer and Software Nanjing University of Information Science and Technology NanjingJiangsu210044 China Shanxi Key Laboratory of Network and System Security Xidian University Xi’an710071 China State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou450001 China State Key Laboratory of Information Security Institute Information Engineering Chinese Academy of Sciences Beijing100093 China Department of Electrical Computer Engineering New Jersey Institute of Technology NewarkNJ07102 United States

ISBN: (纸本)9783030435745

In this paper, a feature based on the Markov model in quaternion discrete cosine transform (QDCT) domain is proposed for double JPEG compression detection. Firstly, a given JPEG image is extracted from blocked images to obtain amplitude and three angles (ψ, φ, and θ). Secondly, when extracting the Markov features, we process the transition probability matrix with the corresponding refinement. Our proposed refinement method not only reduces redundant features, but also makes the acquired features more efficient for detection. Finally, a support vector machine (SVM) is employed for NA-DJPEG compression detection. It is well known that detecting NA-DJPEG compressed images with (Formula Presented) is a challenging task, and when the images with small size (i.e., 64 × 64), the detection will be more difficult. The experimental result indicates that our method can still achieve a high classification accuracy in this case. © 2020, Springer Nature Switzerland AG.

关键词： Support vector machines

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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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Influence of Small-Size Contaminations on Thin Film Structural Properties

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Chinese Physics Letters 2019年第3期36卷 82-85页

作者： F.V.Grigoriev V.B.Sulimov Jinlong Zhang Xinbin Cheng Zhanshan Wang A.V.Tikhonravov Research Computing Center M.V.Lomonosov Moscow State University MOE Key Laboratory of Advanced Micro-Structured Materials Institute of Precision Optical Engineering School of Physics Science and EngineeringTongji University IFSA Collaborative Innovation Center Shanghai Jiao Tong University

An approach for studying the influence of nano-particles on the structural properties of deposited thin films is proposed. It is based on the molecular dynamic modeling of the deposition process in the presence of contaminating nano-particles. The nano-particle is assumed to be immobile and its interaction with film atoms is described by a spherically symmetric potential. The approach is applied to the investigation of properties of silicon dioxide films. Visualization tools are used to investigate the porosity associated with nano-particles. The structure of the film near the nano-particle is studied using the radial distribution function. It is found that fluctuations of film density near the nano-particles are essentially different in the cases of low-energy and high-energy deposition processes.

关键词： Influence Small-Size Properties

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