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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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Comparison of spin-correlation and polarization variables of spin density matrix for top quark pairs at the LHC and New Physics implications

arXiv

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arXiv 2022年

作者： Cakir, Altan Kolay, Orcun Department of Physics Engineering Istanbul Technical University Maslak Istanbul Turkey Istanbul Technical University Artificial Intelligence Data Science Research and Application Center Maslak Istanbul Turkey

Precise determination of top-quark pairs is an essential tool for understanding the overall consistency of the standard model (SM) expectations, understanding limited New Physics (NP) models, through spin-spin correlation and polarization parameters, and has a critical impact on the analyses strategies at upcoming LHC programs. In this work, we review and discuss various state-of-the-art Monte Carlo (MC) methodologies as MadGraph5_aMC@NLO, Sherpa, Powheg-Box and Pythia8, which are Matrix Element (ME)/Parton Shower (PS) matching generators including a complete set of spin correlation and polarization in top quark pair production with dileptonic final states. This is the first such study that not only compares the effects of different MC event generator approaches on spin density matrix elements and polarization parameters, but also investigates the effects of leading order (LO) and next-to-leading order (NLO) accuracy in QCD, and electroweak (EW) corrections via Sherpa. Moreover, as a continuation of the work, the prospects for possible NP scenarios through top-quark spin-spin correlation and polarization measurements for Supersymmetry (R parity conserved and violated models) and Dark Matter (top quarks associated mediator) models during upcoming LHC runs are briefly outlined. We find that all SM MC predictions for the defined set of variables are generally consistent with the experimental data and theoretical predictions within the uncertainty variations. Besides, for the distributions of the cos , laboratory-frame observables (cos lab and |∆φll|) and the observables generated by parity (P) and charge-parity (CP) conserving interactions, we conclude some clues that the considered signals and beyond may well be separated from experimental data and located above the SM predictions. © 2022, CC BY.

关键词： Forecasting

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Touchstone benchmark: are we on the right way for evaluating ai algorithms for medical segmentation? 24

Touchstone benchmark: are we on the right way for evaluating...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Pedro R. A. S. Bassi Wenxuan Li Yucheng Tang Fabian Isensee Zifu Wang Jieneng Chen Yu-Cheng Chou Saikat Roy Yannick Kirchhoff Maximilian Rokuss Ziyan Huang Jin Ye Junjun He Tassilo Wald Constantin Ulrich Michael Baumgartner Klaus H. Maier-Hein Paul Jaeger Yiwen Ye Yutong Xie Jianpeng Zhang Ziyang Chen Yong Xia Zhaohu Xing Lei Zhu Yousef Sadegheih Afshin Bozorgpour Pratibha Kumari Reza Azad Dorit Merhof Pengcheng Shi Ting Ma Yuxin Du Fan Bai Tiejun Huang Bo Zhao Haonan Wang Xiaomeng Li Hanxue Gu Haoyu Dong Jichen Yang Maciej A. Mazurowski Saumya Gupta Linshan Wu Jiaxin Zhuang Hao Chen Holger Roth Daguang Xu Matthew B. Blaschko Sergio Decherchi Andrea Cavalli Alan L. Yuille Zongwei Zhou Department of Computer Science Johns Hopkins University and Department of Pharmacy and Biotechnology University of Bologna and Center for Biomolecular Nanotechnologies Istituto Italiano di Tecnologia Department of Computer Science Johns Hopkins University NVIDIA Division of Medical Image Computing German Cancer Research Center (DKFZ) and Helmholtz Imaging German Cancer Research Center (DKFZ) ESAT-PSI KU Leuven Division of Medical Image Computing German Cancer Research Center (DKFZ) and Faculty of Mathematics and Computer Science Heidelberg University Division of Medical Image Computing German Cancer Research Center (DKFZ) and Faculty of Mathematics and Computer Science Heidelberg University and HIDSS4Health - Helmholtz Information and Data Science School for Health Shanghai Jiao Tong University Shanghai Artificial Intelligence Laboratory Division of Medical Image Computing German Cancer Research Center (DKFZ) Division of Medical Image Computing German Cancer Research Center (DKFZ) and Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Helmholtz Imaging German Cancer Research Center (DKFZ) and Interactive Machine Learning Group (IML) DKFZ School of Computer Science and Engineering Northwestern Polytechnical University Australian Institute for Machine Learning The University of Adelaide College of Computer Science and Technology Zhejiang University Hong Kong University of Science and Technology (Guangzhou) Hong Kong University of Science and Technology (Guangzhou) and Hong Kong University of Science and Technology Faculty of Informatics and Data Science University of Regensburg Faculty of Electrical Engineering and Information Technology RWTH Aachen University Faculty of Informatics and Data Science University of Regensburg and Fraunhofer Institute for Digital Medicine MEVIS Electronic & Information Engineering School Harbin Institute of Technology (Shenzhen) Shanghai Jiao Tong University and Beijing Academy of Artificial Intelligence (BAAI) S

ISBN: (纸本)9798331314385

How can we test ai performance? This question seems trivial, but it isn't. Standard benchmarks often have problems such as in-distribution and small-size test sets, oversimplified metrics, unfair comparisons, and short-term outcome pressure. As a consequence, good performance on standard benchmarks does not guarantee success in real-world scenarios. To address these problems, we present Touchstone, a large-scale collaborative segmentation benchmark of 9 types of abdominal organs. This benchmark is based on 5,195 training CT scans from 76 hospitals around the world and 5,903 testing CT scans from 11 additional hospitals. This diverse test set enhances the statistical significance of benchmark results and rigorously evaluates ai algorithms across out-of-distribution scenarios. We invited 14 inventors of 19 ai algorithms to train their algorithms, while our team, as a third party, independently evaluated these algorithms. In addition, we also evaluated pre-existing ai frameworks—which, differing from algorithms, are more flexible and can support different algorithms—including MONai from NVIDIA, nnU-Net from DKFZ, and numerous other open-source frameworks. We are committed to expanding this benchmark to encourage more innovation of ai algorithms for the medical domain.

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LOPO: An Out-of-order Layer Pulling Orchestration Strategy for Fast Microservice Startup

LOPO: An Out-of-order Layer Pulling Orchestration Strategy f...

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IEEE Annual Joint Conference: INFOCOM, IEEE computer and Communications Societies

作者： Lin Gu Junhao Huang Shaoxing Huang Deze Zeng Bo Li Hai Jin National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China School of Computer Science China University of Geosciences Wuhan China Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong

Container based microservices have been widely applied to promote the cloud elasticity. The mainstream Docker containers are structured in layers, which are organized in stack with bottom-up dependency. To start a microservice, the required layers are pulled from a remote registry and stored on its host server, following the layer dependency order. This incurs long microservice startup time and hinders the performance efficiency. In this paper, we discover that, for the first time, the layer pulling order can be adjusted to accelerate the microservice startup. Specifically, we address the problem on microservice layer pulling orchestration for startup time minimization and prove it as NP-hard. We propose a Longest-chain based Out-of-order layer Pulling Orchestration (LOPO) strategy with low computational complexity and guaranteed approximation ratio. Through extensive real-world trace driven experiments, we verify the efficiency of our LOPO and demonstrate that it reduces the microservice startup time by 22.71% on average in comparison with state-of-the-art solutions.

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Design of Intelligent Detection and Alarm System for Accident Anti-collision Based on ARM Technology 5th

Design of Intelligent Detection and Alarm System for Acciden...

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5th International Conference on Smart Computing and Communication, SmartCom 2020

作者： Tan, Minfu Hong, Yujie Quan, Guiyan Lin, Yinhua Xi, Erhui Zhang, Shiwen Huang, Weihong Big Data Development and Research Center Guangzhou College of Technology and Business Guangzhou528138 China College of Computer Science and Electronic Engineering Hunan University Changsha410000 China Department of Computer Science and Engineering Guangzhou College of Technology and Business Guangzhou528138 China School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan411201 China

ISBN: (纸本)9783030747169

With the increase of social transportation, traffic accidents are not uncommon, and various disputes often arise in accident responsibility. Law enforcement agencies can only understand the situation at the scene of the accident through the two parties who caused the accident, and the two sides of the accident often have their own opinions. This article designs an anti-collision intelligent detection and alarm system. When cars collide, the collision video recorded during driving can be used as direct evidence, saving a lot of time for both sides of the accident and law enforcement agencies, and improving the efficiency of law enforcement. This article first introduces the research significance of the ARM-based accident collision detection and alarm system, and then deeply analyzes the key technologies used in the system, including the main control A10, embedded system Melis, bus I2C and acceleration sensor BMA250. Then analyze the entire hardware module of the system. After that, it analyzes in detail the initialization and control of the acceleration sensor BMA250 in the software, and the alarm processing process when an accident occurs. Finally, we summarized the design and research of the entire system, analyzed the system's shortcomings, and looked forward to the work that needs to be improved in the future. © 2021, Springer Nature Switzerland AG.

关键词： Alarm systems

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Reversible data Hiding With Secret Encrypted Image Sharing and Adaptive Coding

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IEEE Internet of Things Journal 2025年

作者： Fang, Guangtian Wang, Feng Zhao, Chenbin Qin, Chuan Chang, Ching-Chun Chang, Chin-Chen Fujian University of Technology College of Computer Science and Mathematics Fuzhou350118 China Fujian University of Technology College of Computer Science and Mathematics Key Laboratory of Big Data Mining and Applications Fuzhou350118 China Monash University Faculty of IT Department of Software Systems and Cybersecurity VIC3800 Australia Ministry of Education School of Cyber Science and Engineering Key Laboratory of Aerospace Information Security and Trusted Computing Wuhan430072 China University of Shanghai for Science and Technology School of Optical-Electrical and Computer Engineering Shanghai200093 China Feng Chia University Information and Communication Security Research Center Taichuang 40724 Taiwan Feng Chia University Department of Information Engineering and Computer Science Taichung40724 Taiwan

To ensure the security of image information and facilitate efficient management in the cloud, the utilization of reversible data hiding in encrypted images (RDHEI) has emerged as pivotal. However, most existing RDHEI schemes suffer from lower security and limited embedding capacity. To tackle these challenges, we propose a reversible data hiding with secret encrypted image sharing and adaptive coding scheme. Specifically, in the encryption phase, we introduce an improved secret sharing (SS) encryption method based on the chinese remainder theorem for polynomials (CRTP). This method not only improves the security of encrypted images but also vacates a larger room for embedding. In the embedding phase, we introduce an adaptive coding embedding approach using XOR preservation (XORP) and huffman coding, which provides high embedding capacity. Experimental results and security analysis demonstrate that our proposed encryption method achieves optimal values in security indicators for encrypted images, such as information entropy, histograms, NPCR and UACI. The proposed embedding method is superior to some state-of-the-art schemes in terms of embedding capacity. Furthermore, in datasets BOSSBase and BOWS2, the average embedding rates of the proposed embedding approach can reach 2.1745 bpp and 2.0656 bpp, respectively. © 2025 IEEE.

关键词： adaptive coding chinese remainder theorem for polynomials encrypted images Reversible data hiding secret sharing

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IoT-Enhanced local attention dual networks for pathological image restoration in healthcare

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Measurement: Sensors 2024年 33卷

作者： Hassan, Abdelwahab Said Thakare, Anuradha Bhende, Manisha Prasad, K.D.V. Singh, Pavitar Parkash Byeon, Haewon Department of Mathematics Faculty of Science Suez Canal University Egypt Department of Computer Engineering Pimpri Chinchwad College of Engineering Pune411044 India Dr. D. Y. Patil Vidyapeeth Pune Dr. D. Y. Patil School of Science & Technology Tathawade Pune India Hyderabad Pune India Department of Management Lovely Professional University India Department of AI and Software Inje University Gimhae50834 Korea Republic of Inje University Medical Big Data Research Center Gimhae50834 Korea Republic of

High-resolution pathological images play a pivotal role in accurate disease diagnosis and are important in precision medicine. However, obtaining real-time high-resolution images faces challenges due to hardware limitations and scanning time constraints. Conventional image super-resolution restoration algorithms struggle to provide satisfactory results for pathological images, resulting in blurred and unrealistic restorations. In response to this challenge, this research proposes a pioneering approach by integrating Internet of Things (IoT) technology with Local Attention Dual Network (IOT-LAT) for super-resolution restoration of pathological images. The enhanced IOT-LATincorporates IoT-based data acquisition and processing, IoT-Enhanced non-local attention mechanisms, Gaussian constraint, and parameter-sharing strategies in up-sampling and down-sampling branches. This integration enables real-time super-resolution restoration of pathological images with improved accuracy. The reconstructed images exhibit a structural similarity of 0.914 and a peak signal-to-noise ratio of 30.84 dB. These results validate the effectiveness of the suggested approach in precisely reconstructing high-frequency details and improving modelling efficiency via a lightweight non-local attention mechanism enhanced by the Internet of Things. This work discusses IoT and non-local attention dual networks to improve pathological image super-resolution restoration. This allows for faster and more accurate disease diagnosis and treatment in precision medicine. © 2024 The Authors

关键词： Internet of things

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ANTIBIOLOG: An Advanced Tool for Combat Antibiotic Resistance With Enhanced Multinomial Logistic Regression

ANTIBIOLOG: An Advanced Tool for Combat Antibiotic Resistanc...

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IEEE International WIE Conference on Electrical and computer engineering (WIECON-ECE)

作者： Sweta Padma Routray Amlan Deep Nayak Debasish Swapnesh Kumar Nayak Swayamprabha Sahoo Tripti Swarnkar Center for Biotechnology Siksha ‘O’ Anusandhan Deemed to be University Bhubaneswar India Department of Computer Application Institute of Technical Education and Research Siksha ‘O’ Anusandhan Deemed to be University Bhubaneswar India Department of Computer Science & Engineering Institute of Technical Education and Research Siksha ‘O’ Anusandhan Deemed to be University Bhubaneswar India

Pseudomonas aeruginosa, a multidrug-resistant organism with clinical significance, complicates the treatment of critically ill patients. Since traditional susceptibility testing takes at least 24 hours, timely identification and treatment are crucial in the face of rising Antimicrobial Resistance (AMR). In this study, we present the development of ANTIBIOLOG, a cutting-edge Machine Learning (ML) model. This model predicts P. aeruginosa antibiotic susceptibility based on patient demographics and antibiotic susceptibility testing results, using a dataset of 49 patient samples from IMS & SUM Hospital. Multinomial logistic regression was selected for dealing with multiple classes and providing probabilistic interpretations. The performance of the model is evaluated using various metrics such as accuracy, precision, recall, specificity, F1 score, and MCC. Through a user-friendly web interface, it simplifies resistance prediction and guides empirical antibiotic treatment. Unlike generic tools, it focuses on P. aeruginosa and provides accurate therapy selection. ANTIBIOLOG predicted antibiotic susceptibility with a promising 77.77% accuracy. This ML-based tool enables healthcare professionals to combat multidrug-resistant P. aeruginosa infections and address AMR issues.

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A Simple Dashboard for OpenStack Horizon 2021

A Simple Dashboard for OpenStack Horizon

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2021 4th International Conference on Computing and Big data

作者： Hongbin Wang Shaoxu Li Dan Deng Leixiao Li Jing Gao Jie Li College of Data Science and Application Inner Mongolia University of Technology China and Inner Mongolia Autonomous Region Software Service Engineering Technology Research Center based on Big Data China College of Computer and Information Engineering Inner Mongolia Agricultural University China

ISBN: (纸本)9781450387194

OpenStack is an open source cloud computing management project, which allows flexible resources applying. The traditional OpenStack dashboard is inflexible, thus a simple dashboard is designed. The simple dashboard integrates keystone, horizon, grace, nova, neutron, swift and other components, communicates with the traditional OpenStack dashboard and runs in the form of docker container. We implemented simple dashboard on a small cloud platform. Compared with the traditional dashboard, the interface of simple dashboard is simpler, more flexible and easy to use.

关键词： Cloud computing Horizon OpenStack Simple dashboard

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An External Stability Audit Framework to Test the Validity of Personality Prediction in ai Hiring

arXiv

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arXiv 2022年

作者： Rhea, Alene K. Markey, Kelsey D’Arinzo, Lauren Schellmann, Hilke Sloane, Mona Squires, Paul Khan, Falaah Arif Stoyanovich, Julia Center for Data Science New York University Center for Responsible AI Tandon School of Engineering The MITRE Corporation BedfordMA United States Arthur L. Carter Journalism Institute New York University United States Center for Responsible AI Tandon School of Engineering New York University United States Department of Psychology Arts & Science New York University United States Center for Responsible AI Tandon School of Engineering Computer Science & Engineering Tandon School of Engineering Center for Data Science New York University United States

Automated hiring systems are among the fastest-developing of all high-stakes ai systems. Among these are algorithmic personality tests that use insights from psychometric testing, and promise to surface personality traits indicative of future success based on job seekers’ resumes or social media profiles. We interrogate the validity of such systems using stability of the outputs they produce, noting that reliability is a necessary, but not a sufficient, condition for validity. Crucially, rather than challenging or affirming the assumptions made in psychometric testing — that personality is a meaningful and measurable construct, and that personality traits are indicative of future success on the job — we frame our audit methodology around testing the underlying assumptions made by the vendors of the algorithmic personality tests themselves. Our main contribution is the development of a socio-technical framework for auditing the stability of algorithmic systems. This contribution is supplemented with an open-source software library that implements the technical components of the audit, and can be used to conduct similar stability audits of algorithmic systems. We instantiate our framework with the audit of two real-world personality prediction systems, namely Humantic ai and Crystal. The application of our audit framework demonstrates that both these systems show substantial instability with respect to key facets of measurement, and hence cannot be considered valid testing instruments. © 2022, CC BY-SA.

关键词： Employment

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