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arXiv 2024年

作者： Wang, Chaoqian Su, Qi Department of Computational and Data Sciences George Mason University FairfaxVA22030 United States Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai China

Understanding the emergence of cooperation in spatially structured populations has advanced significantly in the context of pairwise games, but the fundamental theory of group-based public goods games (PGGs) remains less explored. Here, we provide theoretical conditions under which cooperation thrive in spatial PGGs on any population structure, which are accurate under weak selection. We find that PGGs can support cooperation across all kinds of model details and on almost all network structures in contrast to pairwise games. For example, a class of networks that would otherwise fail to produce cooperation, such as star graphs, are particularly conducive to cooperation in spatial PGGs. This fundamental advantage of spatial PGGs derives from reciprocity through second-order interactions, allowing local structures such as the clustering coefficient to play positive roles. We also verify the robustness of spatial PGGs on empirical networks where pairwise games cannot support cooperation, which implies that PGGs could be a universal interaction mode in real-world systems. © 2024, CC BY.

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Delay-Induced Watermarking for Detection of Replay Attacks in Linear systems

Delay-Induced Watermarking for Detection of Replay Attacks i...

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American Control Conference (ACC)

作者： Christoforos Somarakis Raman Goyal Erfaun Noorani Shantanu Rane Data Science and Applied Mathematics Group Merck & Co West Point PA USA Palo Alto Research Center - An SRI Company Palo Alto CA USA Department of Electrical and Computer Engineering the Institute for System Research (ISR) at the University of Maryland College Park College Park MD USA A. James Clark School of Engineering

ISBN: (数字)9798350382655

ISBN: (纸本)9798350382662

A state-feedback watermarking signal design for the detection of replay attacks in linear systems is proposed. The control input is augmented with a random time-delayed term of the system state estimate, in order to secure the system against attacks of replay type. We outline the basic analysis of the closed-loop response of the state-feedback watermarking in a LQG controlled system. Our theoretical results are applied on a temperature process control example. While the proposed secure control scheme requires very involved analysis, it, nevertheless, holds promise of being superior to conventional, feed-forward, watermarking schemes, in both its ability to detect attacks as well as the secured system performance.

关键词： Linear systems system performance Process control Watermarking Control systems Temperature control Signal design

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Physics-informed Neural Network for system identification of rotors ⁎

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IFAC-PapersOnLine 2024年第15期58卷 307-312页

作者： Xue Liu Wei Cheng Ji Xing Xuefeng Chen Zhibin Zhao Rongyong Zhang Qian Huang Jinqi Lu Hongpeng Zhou Wei Xing Zheng Wei Pan The National Key Laboratory of Aerospace Power System and Plasma Technology and School of Mechanical Engineering Xi’an Jiaotong University Xi’an China China Nuclear Power Engineering Co. Ltd. Beijing China Shanghai Apollo Machinery Co. Ltd. Shanghai China Department of Computer Science University of Manchester Manchester United Kingdom School of Computer Data and Mathematical Sciences Western Sydney University Sydney Australia

The condition of the rotor system remains difficult to assess due to system nonlinearity and nosiy measurements. To deal with the problem, we proposed a hierarchical physics-informed neural network (HPINN) to discover the ordinary differential equation (ODE) of a healthy/faulty rotor system from noise measurements and then assess the machine condition based on the discovered ODE. Specifically, the ODE of a healthy rotor system is first stably identified from noise measurement through HPINN guided by rotor dynamics. Based on the identified healthy ODE, the extra fault terms in the ODE of the faulty rotor system are then sparsely regressed from the predefined library embedded in HPINN. Moreover, with the mathematical terms of discovered fault, the potential fault and the health indicator (HI) are diagnosed and constructed to assess the condition of the rotor system, respectively. Finally, the effectiveness of the proposed method is verified by the data set collected on the circulating water test bench, showing the potential for practical applications.

关键词： rotor system fault diagnosis health indicator physics-informed neural network

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Automated identification and quantification of metastatic brain tumors and perilesional edema based on a deep learning neural network

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Journal of Neuro-Oncology 2024年第1期166卷 167-174页

作者： Chou, Chi-Jen Yang, Huai-Che Chang, Po-Yao Chen, Ching-Jen Wu, Hsiu-Mei Lin, Chun-Fu Lai, I-Chun Peng, Syu-Jyun Division of Neurosurgery Department of Surgery Kaohsiung Veterans General Hospital Kaohsiung Taiwan School of Medicine College of Medicine National Yang Ming Chiao Tung University Hsinchu Taiwan Department of Neurosurgery Neurological Institute Taipei Veterans General Hospital Taipei Taiwan Department of Electrical Engineering National Central University Taoyuan Taiwan Department of Neurological Surgery University of Virginia Health System Charlottesville 22903 VA United States Department of Radiology Taipei Veterans General Hospital Taipei Taiwan Department of Heavy Particles & Radiation Oncology Taipei Veterans General Hospital Taipei Taiwan Professional Master Program in Artificial Intelligence in Medicine College of Medicine Taipei Medical University No.250 Wuxing St. Xinyi Dist. Taipei City 110 Taiwan Clinical Big Data Research Center Taipei Medical University Hospital Taipei Medical University Taipei Taiwan

Purpose: This paper presents a deep learning model for use in the automated segmentation of metastatic brain tumors and associated perilesional edema. Methods: The model was trained using Gamma Knife surgical data (90 MRI sets from 46 patients), including the initial treatment plan and follow-up images (T1-weighted contrast-enhanced (T1cWI) and T2-weighted images (T2WI)) manually annotated by neurosurgeons to indicate the target tumor and edema regions. A mask region-based convolutional neural network was used to extract brain parenchyma, after which the DeepMedic 3D convolutional neural network was in the segmentation of tumors and edemas. Results: Five-fold cross-validation demonstrated the efficacy of the brain parenchyma extraction model, achieving a Dice similarity coefficient of 96.4%. The segmentation models used for metastatic tumors and brain edema achieved Dice similarity coefficients of 71.6% and 85.1%, respectively. This study also presents an intuitive graphical user interface to facilitate the use of these models in clinical analysis. Conclusion: This paper introduces a deep learning model for the automated segmentation and quantification of brain metastatic tumors and perilesional edema trained using only T1cWI and T2WI. This technique could facilitate further research on metastatic tumors and perilesional edema as well as other intracranial lesions. © 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

关键词： Deep learning neural network Identification Metastatic brain tumors Perilesional edema Quantification

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PrintListener: Uncovering the Vulnerability of Fingerprint Authentication via the Finger Friction Sound

arXiv

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arXiv 2024年

作者： Zhou, Man Su, Shuao Wang, Qian Li, Qi Zhou, Yuting Ma, Xiaojing Li, Zhengxiong School of Cyber Science and Engineering Huazhong University of Science and Technology China School of Cyber Science and Engineering Wuhan University China Institute for Network Sciences and Cyberspace Tsinghua University China Department of Computer Science and Engineering University of Colorado Denver United States Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology China

Fingerprint authentication has been extensively employed in contemporary identity verification systems owing to its rapidity and cost-effectiveness. Due to its widespread use, fingerprint leakage may cause sensitive information theft, enormous economic and personnel losses, and even a potential compromise of national security. As a fingerprint that can coincidentally match a specific proportion of the overall fingerprint population, MasterPrint rings the alarm bells for the security of fingerprint authentication. In this paper, we propose a new side-channel attack on the minutiae-based Automatic Fingerprint Identification system (AFIS), called PrintListener, which leverages users’ fingertip swiping actions on the screen to extract fingerprint pattern features (the first-level features) and synthesizes a stronger targeted PatternMasterPrint with potential second-level features. The attack scenario of PrintListener is extensive and covert. It only needs to record users’ fingertip friction sound and can be launched by leveraging a large number of social media platforms. Extensive experimental results in real-world scenarios show that Printlistener can significantly improve the attack potency of MasterPrint. © 2024, CC0.

关键词： Friction

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Estimating the value of a statistical life in China: A contingent valuation study in six representative cities

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Chinese Journal of Population,Resources and Environment 2023年第4期21卷 269-278页

作者： Chaoji Cao Xinke Song Wenjia Cai Yichao Li Jianhui Cong Xueying Yu Mengzhao Gao Can Wang Ministry of Education Key Laboratory for Earth System Modeling Department of Earth System ScienceTsinghua UniversityBeijing100084China Center for Healthy Cities Institute for China Sustainable UrbanizationTsinghua UniversityBeijing100084China Tsinghua Urban Institute Tsinghua UniversityBeijing100084China State Key Joint Laboratory of Environment Simulation and Pollution Control School of EnvironmentTsinghua UniversityBeijing100084China Center for Statistical Science Tsinghua UniversityBeijing100084China Department of Industrial Engineering Tsinghua UniversityBeijing100084China School of Economics and Management Shanxi UniversityTaiyuan030000China School of Economics and Management Beihang UniversityBeijing100191China Beijing Advanced Innovation Center for Big Data-based Precision Medicine Beihang UniversityBeijing100191China Key Laboratory of Big Data-Based Precision Medicine(Beihang University) Ministry of Industry and Information TechnologyBeijing100191China

The value of a statistical life(VSL)is a crucial tool for monetizing health *** explore the VSL in China,this study examines people’s willingness to pay(WTP)to reduce death risk from air pollution in six representative cities in China based on face-to-face contingent valuation interviews(n=3936)from March 7,2019 to September 30,*** results reveal that the WTP varied from CNY 455 to 763 in 2019(USD 66-111),corresponding to a VSL range of CNY 3.79-6.36 million(USD 549395-921940).The VSL in China in 2019 is estimated to be CNY 4.76 million(USD 689659).The statistics indicate that monthly expenditure levels,environmental concerns,risk attitudes,and assumed market acceptance,which have seldom been dis‐cussed in previous studies,significantly impact WTP and *** findings will serve as a reference for ana‐lyzing mortality risk reduction benefits in future research and for policymaking.

关键词： Air pollution Contingent valuation method Willingness to pay Value of a statistical life China

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Real-Time Epileptic Seizure Detection Based on Deep Learning*

Real-Time Epileptic Seizure Detection Based on Deep Learning...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Tianshu Zhou Yulang Feng Jianda Wang Yu Tian Jianhua Feng Jingsong Li Research Center for Healthcare Data Science Zhejiang Laboratory China Engineering Research Center of EMR and Intelligent Expert System Ministry of Education Zhejiang University China Department of Pediatrics The Second Affiliated Hospital of Zhejiang University School of Medicine China

Epilepsy is one of the most common neurological diseases, and video EEG is the most commonly used examination method for epilepsy diagnosis. However, since the video EEG examination lasts for hours, the escort has a heavy burden, and the large amount of video EEG data needs to be visually checked by the doctor. The real-time detection of epileptic seizures can reduce the stress of the escort and provide a mark for the doctor to check the EEG efficiently. In this paper, we propose a deep neural network with specified signal representation for real-time seizure detection and add a smoothing filter on the model output to enhance performance. First, we compare the performance of real-time epileptic seizure detection model under different signal representations. Then we use the best signal representation for further analysis in real-time scenario. In the experiment, the EEG data of 9 patients in the CHB-MIT public data set was used, and a patient-specific neural network was trained for each individual. The recall was 97%, the false alarm was 0.219 times per hour, and the latency time was 3.4s for real-time seizure event detection. The results show that this method can realize the real-time detection of epileptic seizures, which is of great significance to the subsequent system design combined with actual scenes.

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Rainfall-Induced Landslide Prediction using Graph-Based Convolutional Network Models

Rainfall-Induced Landslide Prediction using Graph-Based Conv...

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Mobile Networks and Wireless Communications (ICMNWC), IEEE International Conference on

作者： P. Rajasekar K. M. Devendraiah A Tamizhselvi Droupti Yadav A. Priyadharshini D. Suganthi Department of Data Science and Business System SRM Institute of Science and Technology India V. V. Sangha's Independent P U College Ballari India Department of Information Technology St. Joseph's College of Engineering Chennai India Department of Environmental Science Technology School of Basic Sciences CSJM University Kanpur India Department of Computer Science and Engineering (Cyber Security) Nandha Engineering College Erode India Saveetha College of Liberal Arts and Sciences SIMATS Chennai India

ISBN: (数字)9798331528348

ISBN: (纸本)9798331528355

Landslides are natural disasters that are devastating and unpredictable. Community notification and casualty reduction require early warning systems. Using data analytics and machine learning technology (MLT), IoT-enabled early warning models can monitor environmental variables and predict landslides. However, current models struggle to anticipate *** innovative strategy suggested by this work involves feature extraction, preprocessing, and model training. The first step to high-quality data is preparing the landslip grid points with Multicollinearity Analysis. Simulating salp chains improves feature selection during feature extraction with the Salp Swarm Algorithm (SSA). During training, a Graph-Based Convolutional Neural Network (G-CNN) is used for optimum predictions. The suggested strategy was more predictive than before, averaging 92.76% Due to its accuracy, the G-CNN outperforms graph-based methods and standard CNNs in handling complex environmental *** SSA-based feature extraction with G-CNN training improves landslip prediction accuracy, a promising early warning system development. This technique could improve natural catastrophe prediction and response by using IoT and ML advances.

关键词： Training Landslides Analytical models Accuracy Biological system modeling Disasters Predictive models Feature extraction Prediction algorithms Convolutional neural networks

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An Analysis of Load-Balancing Algorithms on Edge-Markovian Evolving Graphs

SSRN

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SSRN 2023年

作者： Shiraga, Takeharu Kijima, Shuji Department of Information and System Engineering Chuo University 1-13-27 Kasuga Bunkyo-ku Tokyo112-8551 Japan Faculty of Data Science Shiga University 1-1-1 Banba Hikone Shiga522-8522 Japan

The edge-Markovian is a simple and comprehensive model of time-varying networks (often called evolving graphs). Analysis of algorithms on the edge-Markovian graphs is, however, quite tough comparing with other models of evolving graphs with independent random graphs, and very few is known about the subject. This paper presents a new technique for analysis of algorithms on the edge-Markovian evolving graphs. Specifically, we give an analysis of random matching algorithms, which is a standard scheme for load-balancing on networks, and provide a nearly optimal load balance on edge-Markovian evolving graphs. To avoid the difficulty of an analysis caused by a complex correlation with the history of execution, we develop a simple proof technique based on history-independent bounds. © 2023, The Authors. All rights reserved.

关键词： Graphic methods

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Development of Modern Forecasting Models 16

Development of Modern Forecasting Models

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16th International Conference Management of Large-Scale system Development, MLSD 2023

作者： Ivanyuk, Vera Shuvalov, Konstantin Akhobadze, Gurami Malekova, Victoria Mikhailov, Alexey Levchenko, Kiril Financial University under the Government of the Russian Federation Department of Data Analysis and Machine Learning Moscow Russia Bauman Moscow State Technical University Department of Higher Mathematics Moscow Russia V.A.Trapeznikov Institute of Control Sciences of Ras Laboratory of Management of Large-Scale System Development Moscow Russia V. A.Trapeznikov Institute of Control Sciences of Ras Laboratory of Network Systems Control Moscow Russia Financial University under the Government of the Russian Federation Department of Financial Markets and Financial Engineering Moscow Russia Financial University under the Government of the Russian Federation Department of Mathematics Moscow Russia

ISBN: (纸本)9798350337907

关键词： Forecasting

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