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International Conference on Informatics, Electronics & Vision (ICIEV)

作者： Rollyn Labuguen Dean Karlo Bardeloza Salvador Blanco Negrete Jumpei Matsumoto Kenichi Inoue Tomohiro Shibata Graduate School of Life Science and Systems Engineering Kyushu Institute of Technology Kitakyushu Japan Department of Electronics Computer and Communications Engineering Ateneo de Manila University Quezon Philippines System Emotional Science University of Toyama Toyama Japan Systems Neuroscience Section Kyoto University

The analysis and understanding of primate behavior play a fundamental role in fields such as neuroscience, medicine, psychology, genetics, and more. This paper demonstrates an automatic detection of primate features by using an open-source deep learning toolset, DeepLabCut. We trained the deep neural network to locate 17 features and extract the monkey pose by relating the set of features detected. The model is trained with 5,967 manually annotated monkey images, which achieved train and test set errors of 3.61 and 19.72 unit pixels respectively. We also plotted the feature trajectories across multiple frames to show that the trained model can be used for analyzing behavior.

关键词： Feature extraction Training Predictive models Wrist Task analysis Nose Pose estimation

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Solving Lur'e equations through zeroing neural networks

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Information sciences 2025年 718卷

作者： Yu He Xiaojuan Wang Yafei Tie Huanqi Yang Theodore E. Simos Spyridon D. Mourtas Vasilios N. Katsikis School of Computer Science and Artificial Intelligence Huanghuai University Zhumadian 463000 China Henan International Joint Laboratory of Behavior Optimization Control for Smart Robots Henan 463000 China Henan Key Laboratory of Smart Lighting Zhumadian 46300 China Hangzhou Dianzi University School of Mechanical Engineering Er Hao Da Jie 1158 Xiasha Hangzhou 310018 China Center for Applied Mathematics and Bioinformatics Gulf University for Science and Technology West Mishref 32093 Kuwait Section of Mathematics Dept. of Civil Engineering Democritus Univ. of Thrace Xanthi 67100 Greece Department of Economics Division of Mathematics-Informatics and Statistics-Econometrics National and Kapodistrian University of Athens Sofokleous 1 Street 10559 Athens Greece Laboratory “Hybrid Methods of Modelling and Optimization in Complex Systems” Siberian Federal University Prosp. Svobodny 79 660041 Krasnoyarsk Russia

Solving Lur'e equations plays a critical role in addressing linear-quadratic optimal control (LQOC) problems, especially in cases where the control cost matrices are singular. This paper introduces, for the first time, two novel zeroing neural network (ZNN) models—ZNNLE and ZNNLE-LQOC—specifically designed to solve the Lur'e equation system and the LQOC problem, respectively. The proposed models extend the applicability of the ZNN methodology to these challenging scenarios by offering robust and efficient solutions to time-varying matrix equations. Theoretical analyses confirm the validity of both models, while numerical simulations and practical applications demonstrate their effectiveness. Moreover, a comparative study with an enhanced alternating-direction implicit (ADI) method highlights the superior performance of the ZNNLE-LQOC model in solving LQOC problems.

关键词： Dynamic system Linear-quadratic optimal control Lur'e equations Singular optimal control Zeroing neural network

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Automatic Extraction of Medication Mentions from Tweets-Overview of the BioCreative VII Shared Task 3 Competition

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Database : the journal of biological databases and curation 2023年第2023期2023卷 baac108页

作者： Davy Weissenbacher Karen O'Connor Siddharth Rawal Yu Zhang Richard Tzong-Han Tsai Timothy Miller Dongfang Xu Carol Anderson Bo Liu Qing Han Jinfeng Zhang Igor Kulev Berkay Köprü Raul Rodriguez-Esteban Elif Ozkirimli Ammer Ayach Roland Roller Stephen Piccolo Peijin Han V G Vinod Vydiswaran Ramya Tekumalla Juan M Banda Parsa Bagherzadeh Sabine Bergler João F Silva Tiago Almeida Paloma Martinez Renzo Rivera-Zavala Chen-Kai Wang Hong-Jie Dai Luis Alberto Robles Hernandez Graciela Gonzalez-Hernandez Department of Computational Biomedicine Cedars-Sinai Medical Center Los Angeles CA USA. DBEI The Perelman School of Medicine University of Pennsylvania Philadelphia PA USA. Department of Computer Science and Information Engineering National Central University No. 300 Zhongda Rd Zhongli District Taoyuan 320 Taiwan. IoX Center National Taiwan University Da'an District Section 4 Roosevelt Rd No. 1 Barry Lam Hall Taipei 106 Taiwan. Research Center for Humanities and Social Sciences Academia Sinica No. 128 Section 2 Academia Rd Nangang District Taipei 115 Taiwan. Computational Health Informatics Program Boston Children's Hospital Boston MA USA. Department of Pediatrics Harvard Medical School Boston MA USA. NVIDIA Santa Clara CA USA. Department of Statistics Florida State University Tallahassee FL USA. Data and Analytics Chapter F. Hoffmann-La Roche Ltd Switzerland. Pharmaceutical Research and Early Development Roche Innovation Center Basel Switzerland. Speech and Language Technology Lab DFKI Berlin Germany. Department of Biology Brigham Young University Provo UT USA. Department of Computational Medicine and Bioinformatics Medical School University of Michigan Ann Arbor MI USA. Department of Learning Health Sciences Medical School University of Michigan Ann Arbor MI USA. School of Information University of Michigan Ann Arbor MI USA. Department of Computer Science Georgia State University Atlanta GA USA. CLaC Labs Concordia University Montreal Canada. DETI Institute of Electronics and Informatics Engineering of Aveiro University of Aveiro Portugal. Department of Computation University of A Coruña Spain. Computer Science and Engineering Department Universidad Carlos III de Madrid Madrid Spain. Big Data Laboratory Chunghwa Telecom Laboratories Taoyuan Taiwan. Department of Computer Science National Yang Ming Chiao Tung University Hsinchu Taiwan. Department of Electrical Engineering College of Electrical Engineering and Computer Sci

This study presents the outcomes of the shared task competition BioCreative VII (Task 3) focusing on the extraction of medication names from a Twitter user's publicly available tweets (the user's 'timeline'). In general, detecting health-related tweets is notoriously challenging for natural language processing tools. The main challenge, aside from the informality of the language used, is that people tweet about any and all topics, and most of their tweets are not related to health. Thus, finding those tweets in a user's timeline that mention specific health-related concepts such as medications requires addressing extreme imbalance. Task 3 called for detecting tweets in a user's timeline that mentions a medication name and, for each detected mention, extracting its span. The organizers made available a corpus consisting of 182 049 tweets publicly posted by 212 Twitter users with all medication mentions manually annotated. The corpus exhibits the natural distribution of positive tweets, with only 442 tweets (0.2%) mentioning a medication. This task was an opportunity for participants to evaluate methods that are robust to class imbalance beyond the simple lexical match. A total of 65 teams registered, and 16 teams submitted a system run. This study summarizes the corpus created by the organizers and the approaches taken by the participating teams for this challenge. The corpus is freely available at https://***/tasks/biocreative-vii/track-3/. The methods and the results of the competing systems are analyzed with a focus on the approaches taken for learning from class-imbalanced data.

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Corrigendum to ‘An Unmanned Aerial System (UAS) for concurrent measurements of solar-induced chlorophyll fluorescence and hyperspectral reflectance toward improving crop monitoring’ Agricultural and Forest Meteorology, Volume 294, 15 November 2020, 108145

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Agricultural and Forest Meteorology 2021年 300卷

作者： Christine Y. Chang Ruiqing Zhou Oz Kira Samhita Marri Joseph Skovira Lianhong Gu Ying Sun School of Integrative Plant Science Soil and Crop Sciences Section Cornell University Ithaca NY USA College of Biosystems Engineering and Food Science Zhejiang University Hangzhou Zhejiang China Department of Electrical and Computer Engineering Cornell University Ithaca NY USA Environmental Sciences Division and Climate Change Institute Oak Ridge National Laboratory Oak Ridge TN USA

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Corrigendum to "Novel dynamic fuzzy Decision-Making framework for COVID-19 vaccine dose recipients" [J. Adv. Res. 37 (2022) 147-168]

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Journal of advanced research 2023年 45卷 193页

作者： O S Albahri A A Zaidan A S Albahri H A Alsattar Rawia Mohammed Uwe Aickelin Gang Kou F M Jumaah Mahmood M Salih A H Alamoodi B B Zaidan Mamoun Alazab Alhamzah Alnoor Jameel R Al-Obaidie Computer Techniques Engineering Department Mazaya University College Thi-Qar Nassiriya Iraq. Faculty of Engineering & IT British University in Dubai United Arab Emirates. Electronic address: aws.alaa@fskik.upsi.edu.my. Iraqi Commission for Computers and Informatics (ICCI) Baghdad Iraq. Faculty of Engineering & IT British University in Dubai United Arab Emirates. School of Computing and Information Systems University of Melbourne 700 Swanston Street Victoria 3010 Australia. School of Business Administration Faculty of Business Administration Southwestern University of Finance and Economics Chengdu 611130 China. Department of Advanced Applications and Embedded Systems Intel Corporation Plot 6 Bayan Lepas Technoplex 11900 Georgetown Pulau Pinang Malaysia. Department of Computer Science Computer Science and Mathematics College Tikrit University Tikrit Iraq. Department of Computing Faculty of Arts Computing and Creative Industry Universiti Pendidikan Sultan Idris (UPSI) Perak Malaysia. Future Technology Research Center National Yunlin University of Science and Technology 123 University Road Section 3 Douliou Yunlin 64002 Taiwan ROC. College of Engineering IT and Environment Charles Darwin University NT Australia. School of Management Universiti Sains Malaysia Pulau Pinang Malaysia. Department of Biology Faculty of Science and Mathematics Universiti Pendidikan Sultan Idris Tanjong Malim Perak 35900 Malaysia.

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Reading race: AI recognizes patient's racial identity in medical images

arXiv

引用

arXiv 2021年

作者： Banerjee, I. Bhimireddy, A.R. Burns, J.L. Celi, L.A. Chen, L. Correa, R. Dullerud, N. Ghassemi, M. Gichoya, J.W. Huang, S. Kuo, P. Lungren, M.P. Price, B.J. Purkayastha, S. Pyrros, A.A. Oakden-Rayner, L. Okechukwu, C. Seyyed-Kalantari, L. Trivedi, H. Wang, R. Zaiman, Z. Zhang, H. Department of Biomedical Informatics Emory University 100 Woodruff Circle AtlantaGA30322 United States School of Informatics and Computing Indiana University Purdue University 535 W Michigan St. IT 475E IndianapolisIN46202 United States Institute for Medical Engineering and Science Massachusetts Institute of Technology 45 Carleton Street E25- 330 CambridgeMA02142 United States Department of Medicine Beth Israel Deaconess Medical Center 330 Brookline Avenue BostonMA02215 United States Department of Computer Science National Tsing Hua University No. 101 Section 2 Kuang-Fu Road Hsinchu30013 Taiwan Department of Computer Science Emory University 201 Dowman Drive AtlantaGA30322 United States Department of Computer Science University of Toronto 214 College St TorontoONM5T 3A1 Canada Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology 77 Massachusetts Avenue CambridgeMA02139 United States Department of Radiology Emory University 1364 Clifton Rd AtlantaGA30322 United States Stanford University School of Medicine 291 Campus Drive Palo AltoCA94305 United States Florida State University College of Medicine 1115 W Call St TallahasseeFL32304 United States Australian Institute for Machine Learning University of Adelaide AIML Building Lot Fourteen Cnr North Terrace and Frome Road AdelaideSA5000 Australia Department of Computer Science Georgia Institute of Technology 266 Ferst Drive AtlantaGA30332 United States Lunenfeld-Tanenbaum Research Institute Sinai Health 600 University Ave TorontoONM5G 1X5 Canada Dupage Medical Group 40 S Clay St HinsdaleIL60521 United States

Background: In medical imaging, prior studies have demonstrated disparate AI performance by race, yet there is no known correlation for race on medical imaging that would be obvious to the human expert interpreting the images. We conduct a comprehensive evaluation of AI's ability to recognize patient's racial identity from medical images. Methods: Using private and public datasets we evaluate: A) performance quantification of deep learning models to detect race from medical images, including the ability of these models to generalize to external environments and across multiple imaging modalities, B) assessment of possible confounding anatomic and phenotype population features, such as disease distribution and body habitus as predictors of race, and C) investigation into the underlying mechanism by which AI models can recognize race. Findings: Standard deep learning models can be trained to predict race from medical images with high performance across multiple imaging modalities. Our findings hold under external validation conditions, as well as when models are optimized to perform clinically motivated tasks. We demonstrate this detection is not due to trivial proxies or imaging-related surrogate covariates for race, such as underlying disease distribution. Finally, we show that performance persists over all anatomical regions and frequency spectrum of the images suggesting that mitigation efforts will be challenging and demand further study. Interpretation: We emphasize that model ability to predict self-reported race is itself not the issue of importance. However, our findings that AI can trivially predict self-reported race - even from corrupted, cropped, and noised medical images - in a setting where clinical experts cannot, creates an enormous risk for all model deployments in medical imaging: if an AI model secretly used its knowledge of self-reported race to misclassify all Black patients, radiologists would not be able to tell using the same data the model ha

关键词： Medical imaging

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Correction to: Global surface features contribute to human haptic roughness estimations

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Experimental brain research 2022年第4期240卷 1293页

作者： Huazhi Li Jiajia Yang Yinghua Yu Wu Wang Yulong Liu Mengni Zhou Qingqing Li Jingjing Yang Shiping Shao Satoshi Takahashi Yoshimichi Ejima Jinglong Wu Graduate School of Interdisciplinary Science and Engineering in Health Systems Okayama University 3-1-1 Tsushima-Naka Kita-ku Okayama 700-8530 Japan. Graduate School of Interdisciplinary Science and Engineering in Health Systems Okayama University 3-1-1 Tsushima-Naka Kita-ku Okayama 700-8530 Japan. yang@okayama-u.ac.jp. Section On Functional Imaging Methods National Institute of Mental Health Bethesda MD USA. yang@okayama-u.ac.jp. Section On Functional Imaging Methods National Institute of Mental Health Bethesda MD USA. School of Psychological and Cognitive Sciences Peking University Beijing China. Department of Teacher Education Wenzhou University Wenzhou China. School of Computer Science and Technology Changchun University of Science and Technology Changchun China. School of Social Welfare Yonsei University Seoul Korea. School of Mechatronical Engineering Beijing Institute of Technology Beijing China.

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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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Multi-layer attention mechanism for speech keyword recognition

arXiv

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arXiv 2019年

作者： Luo, Ruisen Sun, Tianran Wang, Chen Du, Miao Tang, Zuodong Zhou, Kai Gong, Xiaofeng Yang, Xiaomei College of Electrical Engineering Sichuan University 24 South Section 1 One Ring Road Chengdu610065 Department of Computer Science Rutgers University-New Brunswick Piscataway New Jersey08854 United States

Abstract As an important part of speech recognition technology, automatic speech keyword recognition has been intensively studied in recent years. Such technology becomes especially pivotal under situations with limited infrastructures and computational resources, such as voice command recognition in vehicles and robot interaction. At present, the mainstream methods in automatic speech keyword recognition are based on long short-term memory (LSTM) networks with attention mechanism. However, due to inevitable information losses for the LSTM layer caused during feature extraction, the calculated attention weights are biased. In this paper, a novel approach, namely Multi-layer Attention Mechanism, is proposed to handle the inaccurate attention weights problem. The key idea is that, in addition to the conventional attention mechanism, information of layers prior to feature extraction and LSTM are introduced into attention weights calculations. Therefore, the attention weights are more accurate because the overall model can have more precise and focused areas. We conduct a comprehensive comparison and analysis on the keyword spotting performances on convolution neural network, bi-directional LSTM cyclic neural network, and cyclic neural network with the proposed attention mechanism on Google Speech Command datasets V2 datasets. Experimental results indicate favorable results for the proposed method and demonstrate the validity of the proposed method. The proposed multi-layer attention methods can be useful for other researches related to object spotting. Copyright © 2019, The Authors. All rights reserved.

关键词： Long short-term memory

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Privacy preservation for ehealth big data in cloud accessed using resource-constrained devices: Survey

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International Journal of Network Security 2019年第2期21卷 312-325页

作者： Kibiwott, Kittur Philemon Fengli, Zhang Victor, K. Kimeli Anyembe, Omala A. Opoku-Mensah, Eugene School of Information and Software Engineering University of Electronic Science and Technology of China No.4 Section 2 Jianshe Rd North Chengdu610054 China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China University of Eldoret P. O. Box 1125-30100 Eldoret Kenya

Mobile technology is proving to offer unprecedented advantage to health professionals by providing a more efficient transmission and access to health services. However, mobile devices are resource-constrained. This is setback whenever storage and computation are required on ehealth big data. To mitigate this drawback, mobile computing is integrated with scalable cloud computing. While this is an advantage on mobile user's side by enlarging the limited resources of the device, it also gives rise to security and privacy concerns. In order to overcome these challenges associated with security and privacy, the data owner (hospital) encrypts data using Attribute Based Encryption (ABE) primitive due to the fine-grained access control advantage it offers then sends ciphertext to the cloud. To realize fast data access, the resource-constrained device securely outsources heavy computations to resource abundant cloud server on its behalf with the guarantee that the server cannot learn anything about plaintext. In this paper, a survey of ABE with outsourced decryption of the existing works that is applicable to resource-constrained device for accessing eHealth big data is provided. © 2019 Femto Technique Co., Ltd.

关键词： Big data

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