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arXiv 2021年

作者： Nabipour, Saeideh Javidan, Javad Khorrami, Majid Azimzadeh, Jila University of Mohaghegh Ardabili Department of Electronic and Computer Engineer Ardabil Iran The National Institute of Standards and Technology BoulderCO80305 United States Rice University HoustonTX77005 United States The Electrical Engineering Department University of Colorado BoulderCO80309 United States The National Research Institute for Metals Tsukuba Japan

Watermarking is a technique for hiding of data in a medium coverage so that its presence is not detectable by a human eye and is recoverable only by the authorized recipient. Two of the most important features of watermarked image are transparency and robustness which are largely related to the security of watermarking algorithms. In this paper, an image watermarking scheme based on BCH error correction code in Discrete Cosine Transformation (DCT) domain is considered. Before embedding process, the watermark is encoded through BCH coding. Then it is embedded into the Discrete Cosine Transformation (DCT) coefficients of cover image. In order to decrease embedding complexity and speed up the process of finding the best position to insert a watermark signal, lookup table method is utilized. The key features of proposed method include the reduction of time required in the process embedding of information, security and ability to correct the error caused by variety of attacks and destructions as well. Watermarked image robustness has been investigated against different kinds attacks and the simulation results indicate that the proposed algorithm outperforms the existing methods in terms of imperceptibility, robustness and security. © 2021, CC BY.

关键词： Table lookup

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Learning-Analytics based Intelligent Simulator for Personalised Learning

Learning-Analytics based Intelligent Simulator for Personali...

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2020 International Conference on Advancement in Data Science, E-Learning and Information Systems, ICADEIS 2020

作者： Sharef, Nurfadhlina Mohd Azmi Murad, Masrah Azrifah Mansor, Evi Indriasari Nasharuddin, Nurul Amelina Omar, Muhd Khaizer Samian, Normalia Arshad, Noreen Izza Ismail, Waidah Shahbodin, Faaizah Universiti Putra Malaysia Faculty of Computer Science and Information Technology Selangor43400 Malaysia Positive Computing Research Center Institute of Autonomous Systems Universiti Teknologi Petronas Perak32610 Malaysia Universiti Sains Islam Malaysia Faculty of Science and Technology Bandar Baru Nilai Sembilan71800 Malaysia Centre for Academic Excellence and Scholarship Universiti Teknikal Malaysia Melaka Melaka76100 Malaysia

ISBN: (纸本)9781728182728

Personalised learning enables instructions to be tailored specific to students learning needs, while making sure learning outcomes are attained. Instructors require information that could facilitate them in adapting their pedagogy design so the learning delivery could be optimized. However, existing solutions are limited to descriptive analytic and intervention facilitation is confined to students at risk prediction based on their course engagement frequency. Tools to predict final grade is available but very scarce. Besides, realtime monitoring of reaction to learning events are not available. Therefore, this paper proposes a solution that integrates Internet of Things, learning analytic and chatbot to fill the said gaps. The paper also presents the experience of pilot developments towards the current version of solution. © 2020 IEEE.

关键词： Artificial Intelligence in Education Chatbot for Learning Learning Analytic Learning Experience Personalised Learning

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An Image Stabilization Technique for Long-durational Outdoor Footages Obtained by Visual IoT Systems

An Image Stabilization Technique for Long-durational Outdoor...

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International Symposium on Wireless Personal Multimedia Communications (WPMC)

作者： Yuki Murakami Ken T. Murata Kazutaka Kikuta Michiharu Niimi Tomohiro Kawanabe Takamichi Mizuhara Toshiki Aoki Kazunori Yamamoto Tsutomu Nagatsuma Kazuki Kobayashi Keiichiro Fukuzawa Praphan Pavarangkoon ICT Testbed Research and Development Promotion Center National Institute of Information and Communications Technology (NICT) Tokyo Japan Resilient ICT Reserach Center National Institute of Information and Communications Technology (NICT) Sendai Japan Faculty of Computer Science and Systems Engineering Kyushu Institute of Technology (Kyutech) Fukuoka Japan Clealink Technology Kyoto Japan Faculty of Engineering Shinshu University Nagano Japan Academic Center for Computing and Media Studies (ACCMS) Kyoto University Kyoto Japan Faculty of Information Technology King Mongkut’s Institute of Technology Ladkrabang Bangkok Thailand

ISBN: (纸本)9781665427616

Disaster mitigation is a significant issue where modern wireless network systems are expected to play a crucial role. It is believed that Visual IoT is one of the key techniques because of its monitoring abilities of urban and rural areas. The outdoor Visual IoT systems generally transfer a large number of footage every day. To detect information from large-size footage datasets, automatic time subtractions between frames are effective. However, to extract even tiny difference between frames, camera shake causes a serious damage. In this study we first survey long-durational footage transmitted from outdoor cameras installed in a city to examine that the stabilization techniques based on feature keypoints are effective to camera shake. Based on this survey we define a matching index to judge if the stabilization is of use or not for every couple of frames. The index is implemented with help of a camera calibration library in OpenCV using AKAZE feature. We then propose a method to stabilize footage continuously obtained by outdoor Visual IoT systems. Using the matching index, we examine one-day footage to find that stabilization is occasionally not applicable in case when the matching index is relatively larger or smaller than 1 (when matching is complete) or the number of matching pairs using AKAZE keypoints are too few. According to the results we set a threshold value of the matching index. We finally perform this technique to footage recorded on a couple of strongly windy days. The efficiency is numerically and visually confirmed on each footage successfully.

关键词： Visualization Cloud computing Wireless networks Urban areas Cameras Feature extraction Libraries

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Patch based semi-supervised linear regression for face recognition

Patch based semi-supervised linear regression for face recog...

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作者： Ding, Yuhua Liu, Fan Rui, Ting Tang, Zhenmin School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China College of Computer and Information Hohai University Nanjing210098 China Nantong Ocean and Coastal Engineering Research Institute Hohai University Nanjing210098 China Engineering Institute of Engineer Crops PLA University of Science and Technology Nanjing210007 China

To deal with single sample face recognition, this paper presents a patch based semi-supervised linear regression (PSLR) algorithm, which draws facial variation information from unlabeled samples. Each facial image is divided into overlapped patches, and a regression model with mapping matrix will be constructed on each patch. Then, we adjust these matrices by mapping unlabeled patches to . The solutions of all the mapping matrices are integrated into an overall objective function, which uses ∂2,1-norm minimization constraints to improve discrimination ability of mapping matrices and reduce the impact of noise. After mapping matrices are computed, we adopt majority-voting strategy to classify the probe samples. To further learn the discrimination information between probe samples and obtain more robust mapping matrices, we also propose a multistage PSLR (MPSLR) algorithm, which iteratively updates the training dataset by adding those reliably labeled probe samples into it. The effectiveness of our approaches is evaluated using three public facial databases. Experimental results prove that our approaches are robust to illumination, expression and occlusion. © 2019 KSII.

关键词： Face recognition

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Cognitive Inspired Sound-Based Automobile Problem Detection: A Step Toward Xai

SSRN

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SSRN 2024年

作者： Hamad, Abdulsattar Abdullah Nasim, Muhammad Fawad Jaffar, Arfan Khalaf, Osamah Ibrahim Ouahada, Khmaies Hamam, Habib Akram, Sheeraz Siddique, Abubakar Faculty of Computer Science and Information Technology The Superior University Lahore54000 Pakistan Lahore55150 Pakistan University of Samarra College of Education Department of physics Iraq Lahore 54000 Lahore55150 Pakistan Department of Solar Al-Nahrain Research Center for Renewable Energy Al-Nahrain University Baghdad Jadriya Iraq School of Electrical Engineering Dept. of Electrical and Electronic Eng. Science University of Johannesburg Johannesburg2006 South Africa Faculty of Engineering Uni de Moncton NBE1A3E9 Canada Hodmas University College Somalia Bridges for Academic Excellence Taleh Area Mogadishu Centre-Ville Tunis Tunisia School of Electrical Engineering University of Johannesburg South Africa Riyadh12571 Saudi Arabia Wellington Institute of Technology Te Pukenga Wellington6022 New Zealand

The automobile has become an essential part of modern human life, and one of the critical factors that determine a vehicle’s value is its reliability. Engine breakdowns are not only hazardous but also financially burdensome. Early detection of engine failure can help save costs and improve the average lifespan of a car. Traditionally, manual inspection of engine sounds by expert mechanics has been used as an effective approach for the early detection of engine problems. There have been recent attempts to develop sound detection-based methods for the automatic diagnosis of engine problems. However, the majority of these techniques are neither robust nor explainable. These systems act like black boxes, making their decision-making process difficult to *** Learning Classifier System, also known as LCS, is a machine learning technique that operates on a set of rules. It has shown promise in producing robust, explainable, and generalizable solutions for many problem domains. The objective of this work is to develop a novel LCS-based system for the automatic detection of engine problems. The developed system’s decision-making process is interpretable, which is a step toward eXplainable artificial intelligence. Using time domain, frequency domain, and time-frequency domain features, the innovative system’s generalizability is assessed. While the developed system’s robustness is evaluated using noisy sound data collected under various normal and abnormal conditions, The experimental results indicate that the novel approach outperformed conventional state-ofthe-art techniques by 2.6% − 6.0% and achieved the highest performance accuracy of 98.6%. © 2024, The Authors. All rights reserved.

关键词： Frequency domain analysis

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Overview of the recent experimental research on the J-TEXT tokamak

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Nuclear Fusion 2024年第11期64卷 112005-112005页

作者： Ding, Yonghua Wang, Nengchao Chen, Zhongyong Xia, Donghui Yang, Zhoujun Chen, Zhipeng Zheng, Wei Yan, Wei Li, Da Zhou, Song Xu, Xin Zhang, Xiaoyi Mao, Feiyue Fang, Jiangang Ren, Zhengkang Chen, Xixuan Zhang, Junli Zhang, Xiaobo He, Ying Zhang, Qi Li, You Bai, Wei Mou, Lingke Li, Feng Long, Ting Ke, Rui Gao, Li Shi, Peng Shen, Chengshuo Liu, Jiaxing Guo, Weixin Wang, Lu Li, Hanhui Jiang, Zhonghe Zhang, Xiaoqing Li, Jianchao Rao, Bo Cheng, Zhifeng Zhu, Ping Liu, Minghai Xu, Tao Ma, Shaoxiang Yang, Yong Li, Chuan Wang, Zhijiang Zhang, Ming Yu, Kexun Hu, Xiwei Liang, Y. Yu, Q. Gentle, K.W. Pan, Yuan Gentle, Kenneth William Yu, Qingquan Liang, Yunfeng Jiang, Wei Yi, Lin Zhang, Ya Hu, Qiming Li, Jianchao Huang, Mingxiang Li, Haolong Huang, Zhuo Huang, Jie Wang, Weijun He, Mei Zhao, Qing Xu, Jiayu Zhou, Gangyi Ba, Weigang Yang, Cheng Yao, Junjie Wang, Chuliang He, Bo Hu, Yongzhen Deng, Mingyang Fu, Jingwei Liang, Meiling Guan, Yehong Wu, Hongyan Zhuang, Ge Ding, Weixing Lan, Tao Zeng, Shiyong Hussain, Malik Sadam Lin, Zhifang Liu, Hai Sun, Youwen Zeng, Long Liu, Shaocheng Liao, Liang Zhong, Wulyu Shi, Zhongbing Jiang, Min Dong, Yunbo Xu, Min Wang, Aike Zhang, Xiaolong Liu, Linzi Tong, Ruihai Cheng, Jun Nie, Lin Li, Hui Xu, Jianqiang Yang, Zengchen Wen, Jie Liang, Anshu Zhang, Yipo Wu, Yifan Chen, Chengyuan Zhao, Kaijun Shi, Yuejiang Huang, He Austin, Max Lee, Sanggon Luhmann, Neville C. Domier, Calvin W. Zhu, Yilun Brower, David L. Chen, Jie Ida, K. Takemura, Y. Suzuki, Y. Nagasaki, K. Drews, P. Knieps, A. Han, Xiang Krämer-Flecken, A. Henkel, M. He, Jiyang Zhou, Yinan Jia, Ruo Peng, Yue Wang, Pengyu Li, Jiawei Yang, Jie Lu, Conghui Lu, Yiwei Wang, Rumeng Zhang, Hongqi Zeng, Yingqin Zhang, Guodong Ma, Fangyuan Bala, Abba Alhaji Li, Dingchen Hua, Jiankun Ai, Xinkun Tao, Qinlin Zhong, Yu Li, Yangbo He, Xiangrui Wan, Sui Huang, Shanni Ma, Yiming Qu, Zitong Yu, Shimin Huang, Zhangsheng Lei, Chi School of Electric and Electronic Engineering Huazhong University of Science and Technology Wuhan430074 China Southwestern Institute of Physics Chengdu China Institute of Plasma Physics Chinese Academy of Sciences Hefei China Guizhou University of Commerce Guiyang China Wuhan Institute of Technology Wuhan China ITER Organization Route de Vinon-sur-Verdon CS 90 046 Saint-Paul-lez-DuranceCedex 13067 France Forschungszentrum Jülich GmbH IEK-Plasmaphysik Jülich Germany Max-Planck-Institut für Plasmaphysik Garching Germany Institute of Fusion Studies University of Texas at Austin AustinTX United States University of Texas at Austin AustinTX78712 United States School of Physics Huazhong University of Science and Technology Wuhan430074 China Department of Physics Wuhan University of Technology Wuhan430070 China Princeton Plasma Physics Laboratory PO Box 451 PrincetonNJ08543 United States Hubei Key Laboratory of Optical Information and Pattern Recognition Wuhan Institute of Technology Wuhan430205 China College of Computer Science South-Central University for Nationalities Wuhan430074 China Shenzhen University Shenzhen518060 China University of Science and Technology of China Hefei230026 China School of Electrical Engineering & Automation Jiangsu Normal University Xuzhou221116 China Southwest Jiaotong University Chengdu China College of Nuclear Science and Engineer East China University of Technology PO Box Nanchang330013 China ENN Science and Technology Development Co. Ltd Hebei Key Laboratory of Compact Fusion Langfang065001 China National Fusion Research Institute Daejeon305-333 Korea Republic of University of California DavisCA95616 United States University of California Los Angeles Los AngelesCA90095 United States National Institute for Fusion Science Toki509-5292 Japan Graduate School of Advanced Science and Engineering Hiroshima University Hiroshima739-8527 Japan Institute of Advanced Energy Kyoto University Gokasho Ky

The J-TEXT capability is enhanced compared to two years ago with several upgrades of its diagnostics and the increase of electron cyclotron resonance heating (ECRH) power to 1 MW. With the application of electron cyclotron wave (ECW), the ECW assisted plasma startup is achieved;the tearing mode is suppressed;the toroidal injection of 300 kW ECW drives around 24 kA current;fast electrons are generated with toroidal injected ECW and the runaway current conversion efficiency increases with ECRH power. The mode coupling between 2/1 and 3/1 modes are extensively studied. The coupled 2/1 and 3/1 modes usually lead to major disruption. Their coupling can be either suppressed or avoided by external resonant magnetic perturbation fields and hence avoids the major disruption. It is also found that the 2/1 threshold of external field is significantly reduced by a pre-excited 3/1 mode, which can be either a locked island or an external kink mode. The disruption control is studied by developing prediction methods capable of cross tokamak application and by new mitigation methods, such as the biased electrode or electromagnetic pellet injector. The high-density operation and related disruptions are studied from various aspects. Approaching the density limit, the collapse of the edge shear layer is observed and such collapse can be prevented by applying edge biasing, leading to an increased density limit. The density limit is also observed to increase, if the plasma is operated in the poloidal divertor configuration or the plasma purity is increased by increasing the pre-filled gas pressure or ECRH power during the start-up phase. © 2024 The Author(s).

关键词： Tokamak devices

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The medical algorithmic audit (vol 4, pg e384, 2022)

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LANCET DIGITAL HEALTH 2022年第6期4卷 E405-E405页

作者： Liu, X. Glocker, B. McCradden, M. M. Ghassemi, M. Denniston, A. K. Oakden-Rayner, L. Academic Unit of Ophthalmology Institute of Inflammation and Ageing College of Medical and Dental Sciences University of Birmingham UK Department of Ophthalmology University Hospitals Birmingham NHS Foundation Trust Birmingham UK Moorfields Eye Hospital NHS Foundation Trust London UK Health Data Research UK London UK Birmingham Health Partners Centre for Regulatory Science and Innovation University of Birmingham Birmingham UK Biomedical Image Analysis Group Department of Computing Imperial College London London UK The Hospital for Sick Children Toronto ON Canada Dalla Lana School of Public Health Toronto ON Canada Institute for Medical Engineering and Science and Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge MA USA National Institute of Health Research Biomedical Research Centre for Ophthalmology Moorfields Hospital London NHS Foundation Trust London UK University College London Institute of Ophthalmology London UK Australian Institute for Machine Learning University of Adelaide Adelaide SA Australia. lauren.oakden-rayner@adelaide.edu.au

Artificial intelligence systems for health care, like any other medical device, have the potential to fail. However, specific qualities of artificial intelligence systems, such as the tendency to learn spurious correlates in training data, poor generalisability to new deployment settings, and a paucity of reliable explainability mechanisms, mean they can yield unpredictable errors that might be entirely missed without proactive investigation. We propose a medical algorithmic audit framework that guides the auditor through a process of considering potential algorithmic errors in the context of a clinical task, mapping the components that might contribute to the occurrence of errors, and anticipating their potential consequences. We suggest several approaches for testing algorithmic errors, including exploratory error analysis, subgroup testing, and adversarial testing, and provide examples from our own work and previous studies. The medical algorithmic audit is a tool that can be used to better understand the weaknesses of an artificial intelligence system and put in place mechanisms to mitigate their impact. We propose that safety monitoring and medical algorithmic auditing should be a joint responsibility between users and developers, and encourage the use of feedback mechanisms between these groups to promote learning and maintain safe deployment of artificial intelligence *** © 2022 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license. Published by Elsevier Ltd.. All rights reserved.

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Developing an Intelligent Resume Screening Tool With AI-Driven Analysis and Recommendation Features

Applied AI Letters

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Applied AI Letters 2025年第2期6卷

作者： K. L. Abhishek M. Niranjanamurthy Shonit Aric Syed Immamul Ansarullah Anurag Sinha G. Tejani Mohd Asif Shah Department of Artificial Intelligence & Machine Learning BMS Institute of Technology Affiliated to Visvesvaraya Technological University Bangalore Karnataka India Software Engineer Fidelity Investments Bangalore India Department of Management Studies University of Kashmir North Campus Srinagar Jammu and Kashmir India Tech School Computer Science Department ICFAI University Ranchi Jharkhand India Department of Computer Science RC University Palamue Bishrampur Jharkhand India Jadara University Research Center Jadara University Amman Jordan Dean of Faculty Department of Economics Kardan University Kabul Afghanistan Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Rajpura Punjab India Chitkara Centre for Research and Development Chitkara University Baddi Himachal Pradesh India Division of Research and Development Lovely Professional University Phagwara Punjab India

Current resume screening relies on manual review, causing delays and errors in evaluating large volumes of resumes. Lack of automation and data extraction leads to inefficiencies and potential biases. Recruiters face challenges in identifying qualified candidates due to oversight and time constraints. Inconsistent evaluation criteria hinder decision-making. These issues result in prolonged hiring processes, missed opportunities, and potential bias in candidate selection. The goal of this project is to develop an AI-powered Resume Analysis and Recommendation Tool, catering to the trend of recruiters spending less than 2 min on each CV. The tool will rapidly analyze all resume components while providing personalized predictions and recommendations to applicants for improving their CVs. It will present user-friendly data for recruiters, facilitating export to CSV for integration into their recruitment processes. Additionally, the tool will offer insights and analytics on popular roles and skills within the job market. Its user section will enable applicants to continually test and track their resumes, encouraging repeat usage and driving traffic. Colleges can benefit from gaining insights into students' resumes before placements. Overall, this AI-powered tool aims to enhance the resume evaluation process, benefiting both job seekers and employers. The primary aim of this project is to develop a Resume Analyzer using Python, incorporating advanced libraries such as Pyresparser, NLTK (Natural Language Toolkit), and MySQL. This automated system offers an efficient solution for parsing, analyzing, and extracting essential information from resumes. The user-friendly interface, developed using Streamlit, allows for seamless resume uploading, insightful data visualization, and analytics. The Resume Analyzer significantly streamlines the resume screening process, providing recruiters with valuable insights and enhancing their decision-making capabilities.

关键词： natural language processing parsing resume analysis tokenization

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Assessing Trustworthy AI in Times of COVID-19: Deep Learning for Predicting a Multiregional Score Conveying the Degree of Lung Compromise in COVID-19 Patients

IEEE Transactions on Technology and Society

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IEEE Transactions on technology and Society 2022年第4期3卷 272-289页

作者： Allahabadi, Himanshi Amann, Julia Balot, Isabelle Beretta, Andrea Binkley, Charles Bozenhard, Jonas Bruneault, Frederick Brusseau, James Candemir, Sema Cappellini, Luca Alessandro Chakraborty, Subrata Cherciu, Nicoleta Cociancig, Christina Coffee, Megan Ek, Irene Espinosa-Leal, Leonardo Farina, Davide Fieux-Castagnet, Genevieve Frauenfelder, Thomas Gallucci, Alessio Giuliani, Guya Golda, Adam Van Halem, Irmhild Hildt, Elisabeth Holm, Sune Kararigas, Georgios Krier, Sebastien A. Kuhne, Ulrich Lizzi, Francesca Madai, Vince I. Markus, Aniek F. Masis, Serg Mathez, Emilie Wiinblad Mureddu, Francesco Neri, Emanuele Osika, Walter Ozols, Matiss Panigutti, Cecilia Parent, Brendan Pratesi, Francesca Moreno-Sanchez, Pedro A. Sartor, Giovanni Savardi, Mattia Signoroni, Alberto Sormunen, Hanna-Maria Spezzatti, Andy Srivastava, Adarsh Stephansen, Annette F. Theng, Lau Bee Tithi, Jesmin Jahan Tuominen, Jarno Umbrello, Steven Vaccher, Filippo Vetter, Dennis Westerlund, Magnus Wurth, Renee Zicari, Roberto V. Ey Netherlands Enterprise Intelligence Department Amsterdam1083 HP Netherlands Eth Zurich Health Ethics and Policy Lab Department of Health Sciences and Technology Zürich8092 Switzerland Center for Diplomatic and Strategic Studies Postgraduate Studies in Diplomacy and International Relations Paris75015 France Pisa56124 Italy Hackensack Meridian Health Bioethics Center EdisonNJ08820 United States University of Oxford Faculty of Philosophy OxfordOX2 6GG United Kingdom Collège André- Laurendeau Philosophie Department MontrealQCH8N 2J4 Canada Université du Québec À Montréal École des Médias MontrealQCH2L 2C4 Canada Pace University Philosophy Department New YorkNY10038 United States The Ohio State University Wexner Medical Center Department of Radiology ColumbusOH43210 United States Humanitas Research Hospital Department of Radiology Milan20089 Italy Humanitas University Department of Biomedical Sciences Milan20089 Italy University of New England Faculty of Science Agriculture Business and Law ArmidaleNSW2351 Australia University of Technology Sydney Faculty of Engineering and Information Technology SydneyNSW2007 Australia Scuola Superiore Sant'Anna European Centre of Excellence on the Regulation of Robotics and Ai Pisa56127 Italy University of Bremen Group of Computer Architecture Bremen28359 Germany New York University Grossman School of Medicine Division of Infectious Diseases and Immunology Department of Medicine New YorkNY10016 United States Digital Institute Ai Research Section Stockholm16731 Sweden Arcada University of Applied Sciences Department of Business Management and Analytics Helsinki00550 Finland University of Brescia Radiological Sciences and Public Health Department of Medical and Surgical Specialties Brescia25121 Italy Sncf Reseau Sa Ethique Groupe La Plaine93418 France Institute of Diagnostic and Interventional Radiology University Hospital Zurich Zürich8091 Switzerland Eindhoven University of Tech

This article's main contributions are twofold: 1) to demonstrate how to apply the general European Union's High-Level Expert Group's (EU HLEG) guidelines for trustworthy AI in practice for the domain of healthcare and 2) to investigate the research question of what does 'trustworthy AI' mean at the time of the COVID-19 pandemic. To this end, we present the results of a post-hoc self-assessment to evaluate the trustworthiness of an AI system for predicting a multiregional score conveying the degree of lung compromise in COVID-19 patients, developed and verified by an interdisciplinary team with members from academia, public hospitals, and industry in time of pandemic. The AI system aims to help radiologists to estimate and communicate the severity of damage in a patient's lung from Chest X-rays. It has been experimentally deployed in the radiology department of the ASST Spedali Civili clinic in Brescia, Italy, since December 2020 during pandemic time. The methodology we have applied for our post-hoc assessment, called Z-Inspection®, uses sociotechnical scenarios to identify ethical, technical, and domain-specific issues in the use of the AI system in the context of the pandemic. © 2020 IEEE.

关键词： COVID-19

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Discovering Traffic Anomaly Propagation in Urban Space Using Traffic Change Peaks 6th

Discovering Traffic Anomaly Propagation in Urban Space Using...

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6th International Conference on Data Science, ICDS 2019

作者： Huang, Guang-Li Ji, Yimu Liu, Shangdong Zarei, Roozbeh School of Science RMIT University MelbourneVIC3000 Australia School of Computer Science Nanjing University of Posts and Telecommunications Nanjing210023 China Institute of High Performance Computing and Big Data Nanjing University of Posts and Telecommunications Nanjing210023 China Nanjing Center of HPC China Nanjing210003 China Jiangsu HPC and Intelligent Processing Engineer Research Center Nanjing210003 China School of Information Technology Deakin University GeelongVIC3125 Australia

ISBN: (纸本)9789811528095

Discovering traffic anomaly propagation enables a thorough understanding of traffic anomalies and dynamics. Existing methods, such as STOTree, are not accurate for two reasons. First, they discover the propagation pattern based on the detected anomalies. The imperfection of the detection method itself may introduce false anomalies and miss the real anomaly. Second, they develop a propagation tree of anomalies by searching continuous spatial and temporal neighborhoods rather than considering from a global perspective, and thus cannot find a complete propagation tree if a spatial or temporal gap exists. In this paper, we propose a novel discovering traffic anomaly propagation method using traffic change peaks, which can visualize the change of traffic anomalies (e.g., congestion and evacuation area) and thus accurately captures traffic anomaly propagation. Inspired by image processing techniques, the GPS trajectory dataset in each time period can be converted to one grid traffic image and be stored in the grid density matrix, in which the grid cell corresponds to the pixel and the density of grid cells corresponds to the Gray level (0–255) of pixels. An adaptive filter is developed to generate traffic change graphs from grid traffic images in consecutive periods, and clustering traffic change peaks along the road is to discover the propagation of traffic anomalies. The effectiveness of the proposed method has been demonstrated using a real-world GPS trajectory dataset. © Springer Nature Singapore Pte Ltd 2020.

关键词： Adaptive filters

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