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Human-centered artificial intelligence in education: Seeing the invisible through the visible

computers and Education: Artificial Intelligence 2021年 2卷

作者： Yang, Stephen J.H. Ogata, Hiroaki Matsui, Tatsunori Chen, Nian-Shing Department of Computer Science and Information Engineering National Central University No. 300 Zhongda Rd. Zhongli District Taoyuan City32001 Taiwan Academic Center for Computing and Media Studies Kyoto University Yoshidahonmachi Sakyo Ward Kyoto606-8501 Japan School of Human Sciences Waseda University 1-104 Totsukamachi Shinjuku-ku Tokyo169-8050 Japan Department of Applied Foreign Languages National Yunlin University of Science and Technology 123 University Road Section 3 Douliou CityYunlin64002 Taiwan

The inevitable rise and development of artificial intelligence (AI) was not a sudden occurrence. The greater the effect that AI has on humans, the more pressing the need is for us to understand it. This paper addresses research on the use of AI to evaluate new design methods and tools that can be leveraged to advance AI research, education, policy, and practice to improve the human condition. AI has the potential to educate, train, and improve the performance of humans, making them better at their tasks and activities. The use of AI can enhance human welfare in numerous respects, such as through improving the productivity of food, health, water, education, and energy services. However, the misuse of AI due to algorithm bias and a lack of governance could inhibit human rights and result in employment, gender, and racial inequality. We envision that AI can evolve into human-centered AI (HAI), which refers to approaching AI from a human perspective by considering human conditions and contexts. Most current discussions on AI technology focus on how AI can enable human performance. However, we explore AI can also inhibit the human condition and advocate for an in-depth dialog between technology- and humanity-based researchers to improve understanding of HAI from various perspectives. © 2021 The Author(s)

关键词： Artificial intelligence

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Accurate Virus Identification with Interpretable Raman Signatures by Machine Learning

arXiv

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arXiv 2022年

作者： Ye, Jiarong Yeh, Yin-Ting Xue, Yuan Wang, Ziyang Zhang, Na Liu, He Zhang, Kunyan Ricker, Ryeanne Yu, Zhuohang Roder, Allison Lopez, Nestor Perea Organtini, Lindsey Greene, Wallace Hafenstein, Susan Lu, Huaguang Ghedin, Elodie Terrones, Mauricio Huang, Shengxi Huang, Sharon Xiaolei College of Information Sciences and Technology The Pennsylvania State University University ParkPA United States Department of Physics The Pennsylvania State University University ParkPA United States Department of Electrical and Computer Engineering Johns Hopkins University BaltimoreMD United States Department of Electrical and Computer Engineering The Pennsylvania State University University ParkPA United States Department of Biomedical Engineering George Washington University WashingtonDC United States Systems Genomics Section Laboratory of Parasitic Diseases National Institute of Allergy and Infectious Diseases National Institutes of Health BethesdaMD United States Department of Biochemistry and Molecular Biology The Pennsylvania State University University ParkPA United States Department of Pathology and Laboratory Medicine Division of Clinical Pathology The Pennsylvania State University College of Medicine HersheyPA United States Department of Veterinary and Biomedical Sciences The Pennsylvania State University University ParkPA United States 413G Eric. J. Barron Innovation Hub 123 S. Burrowes Street State CollegePA16801 United States

Rapid identification of newly emerging or circulating viruses is an important first step toward managing the public health response to potential outbreaks. A portable virus capture device coupled with label-free Raman Spectroscopy holds the promise of fast detection by rapidly obtaining the Raman signature of a virus followed by a machine learning approach applied to recognize the virus based on its Raman spectrum, which is used as a fingerprint. We present such a machine learning approach for analyzing Raman spectra of human and avian viruses. A Convolutional Neural Network (CNN) classifier specifically designed for spectral data achieves very high accuracy for a variety of virus type or subtype identification tasks. In particular, it achieves 99% accuracy for classifying influenza virus type A vs. type B, 96% accuracy for classifying four subtypes of influenza A, 95% accuracy for differentiating enveloped and nonenveloped viruses, and 99% accuracy for differentiating avian coronavirus (infectious bronchitis virus, IBV) from other avian viruses. Furthermore, interpretation of neural net responses in the trained CNN model using a full-gradient algorithm highlights Raman spectral ranges that are most important to virus identification. By correlating ML-selected salient Raman ranges with the signature ranges of known biomolecules and chemical functional groups-for example, amide, amino acid, carboxylic acid-we verify that our ML model effectively recognizes the Raman signatures of proteins, lipids and other vital functional groups present in different viruses and uses a weighted combination of these signatures to identify viruses. © 2022, CC BY.

关键词： Raman spectroscopy

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Intelligent Backpropagated Neural Networks for Numerical Computations for MHD Squeezing Fluid Suspended by Nanoparticles between Two Parallel Plates

Research Square

引用

Research Square 2021年

作者： Shoaib, Muhammad Zahoor Raja, Muhammad Asif Farhat, Imrana Shah, Zahir Kumam, Poom Department of Mathematics COMSATS University Islamabad Attock Campus Pakistan Future Technology Research Center National Yunlin University of Science and Technology 123 University Road Section .3 Douliou Yunlin64002 Taiwan Department of Electrical and Computer Engineering COMSATS University Islamabad Attock Campus Pakistan Department of Mathematics University of Lakki Marwat Lakki Marwat Khyber Pakhtunkhwa 28420 Pakistan 126 Pracha Uthit Rd. Bang Mod Thung Khru Bangkok10140 Thailand 126 Pracha Uthit Rd. Bang Mod Thung Khru Bangkok10140 Thailand

In the present research, artificial intelligence based backpropagated neural networks with Levenberg-Marquardt algorithm (BNN-LMA) are utilized to interpret the numerical computation for squeezing 2D magneto-hydrodynamic (MHD) nanofluid flow between two parallel plates. The non-linear system of ODEs represents the magneto-hydrodynamic, squeezing nanofluidic flow model (MHD-SNFM). A reference dataset for BNN-LMA is formulated by utilizing Adam numerical solver for different scenarios of MHD-SNFM by variation of squeezing number, Hartmann Number and heat source parameter. The validation, testing and training processes of BNN-LMA are exploited to analyze the approximate solution of MHD-SNFM for different scenarios and correctness of proposed BNN-LMA is verified by comparison of reference outcomes. The performance of BNN-LMA to solve the MHD-SNFM is validated through regression analysis, histogram studies and mean square error (MSE). © 2021, CC BY.

关键词： Regression analysis

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Integrating Cellphone-based Hardware Wallet with Visional Certificate Verification System

Integrating Cellphone-based Hardware Wallet with Visional Ce...

引用

GLOBECOM 2020 - 2020 IEEE Global Communications Conference

作者： Lo-Yao Yeh Wan-Hsin Hsu Jiun-Long Huang Chih-Ya Shen Chi Wu-Lee National Chi Nan University Nantou Taiwan Institute of Computer Science and Engineering National Chiao Tung University Hsinchu Taiwan National Chiao Tung University Hsinchu Taiwan National Tsing Hua University Hsinchu Taiwan Blockchain Technology Section Institute for Information Industry Taipei Taiwan

ISBN: (数字)9781728182988

ISBN: (纸本)9781728182995

With the rapid growth of blockchain, more and more people possess cryptocurrencies or use decentralized applications (Dapp). A wallet in blockchain not only stores your assets but also represents your online identity. Nowadays, document notary using blockchain is getting mature to prevent the problem of the counterfeit certificate. In this paper, we integrate a cellphone-based wallet with a novel visional certificate verification system. By the trusted execution environment (TEE) protection, a user can isolate his/her private key without the leakage attack. Two-factor authentication is required to generate a signature. Different from existing platforms, our system adopts IPFS, a P2P network for storing and sharing data, for storing the certificate image, which offers the function of visional verification for better persuasiveness. Furthermore, the unique time-limited verification can restrict the accessing period of the verifier for better privacy protection. As a result, our verification system provides several promising features to enhance security and privacy strength.

关键词： Privacy Conferences Authentication Blockchain Hardware Global communication Cryptography

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Corrigendum to “Review of artificial neural networks-contribution methods integrated with structural equation modeling and multi-criteria decision analysis for selection customization” [Eng. Appl. Artif. Intell. 124 (2023) 106643]

引用

engineering Applications of Artificial Intelligence 2024年 128卷

作者： A.A. Zaidan Alhamzah Alnoor O.S. Albahri R.T. Mohammed A.H. Alamoodi A.S. Albahri B.B. Zaidan Salem Garfan Hamsa Hameed Mohammed S. Al-Samarraay Ali Najm Jasim R.Q. Malik SP Jain School of Global Management Sydney Australia Management Technical College Southern Technical University Basrah Iraq Department of Computer Science and Information Technology La Trobe University Melbourne VIC Australia Computer Techniques Engineering Department Mazaya University College Thi-Qar Nassiriya Iraq Department of Computing Science Faculty of Science Komar University of Science and Technology (KUST) Sulaymaniyah Iraq Department of Computing and META Technology (FKMT) Universiti Pendidikan Sultan Idris Tanjung Malim 35900 Malaysia MEU Research Unit Middle East University Amman Jordan Iraqi Commission for Computers and Informatics (ICCI) Baghdad Iraq Technical College Imam Ja'afar Al-Sadiq University Baghdad Iraq Future Technology Research Center National Yunlin University of Science and Technology 123 University Road Section 3 Douliou Yunlin 64002 Taiwan Faculty of Human Development Sultan Idris University of Education (UPSI) Tanjung Malim Malaysia Foundation of Alshuhda Baghdad Iraq Medical Instrumentation Techniques Engineering Department Al-Mustaqbal University College Babylon Iraq

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Simulation of Crushed Ore Particles Motion in the Pulp Under the Influence of High-energy Ultrasound Dynamic Effects

Simulation of Crushed Ore Particles Motion in the Pulp Under...

引用

IEEE International Conference on Electronics and Nanotechnology (ELNANO)

作者： Vladimir Morkun Natalia Morkun Andrey Pikilniak Nikolay Podgorodetskiy Irina Haponenko Arkadii Davidcovich Computer Science and Technology Kryvyi Rih National University Kryvyi Rih Ukraine Kryvyi Rih National University Kryvyi Rih Ukraine Donbas National Academy of Civil Engineering and Architecture Donetsk Ukraine R&D section Kryvyi Rih National University Kryvyi Rih Ukraine MCRSS Boston USA

ISBN: (数字)9781728197135

ISBN: (纸本)9781728197142

The software described in the article provides a method for determining the particle size distribution of the solid pulp phase, first implemented in world practice. The program for numerical analysis and representation of the simulation results of changes in the particle size distribution of the pulp solid phase under the controlling influence of high-energy ultrasound radiation pressure is developed. The particle distribution function according to the useful component disclosure degree is built.

关键词： ultrasound crushed ore pulp radiation pressure Particle size distribution Dental Pulp Pulp RADIATION PRESSURE Ultrasonic Waves PARTICLE MOTION simulating HIGH ENERGY crushing

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Outlier detection based on cluster outlier factor and mutual density

引用

International Journal of Intelligent Information and Database Systems 2019年第1-2期12卷 91-108页

作者： Zhang, Zhongping Zhu, Mengfan Qiu, Jingyang Liu, Cong Zhang, Debin Qi, Jie School of Information Science and Engineering Yanshan University Qinghuangdao Hebei066004 China Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province 438 West Section of Hebei Street Haigang District Qinhuangdao City Hebei Province066004 China Hebei Education Examinations Authority 231 Hongqi Street Shijiazhuang City Hebei Province050000 China First Middle School of Qian An Country 347 Wutong Road Qianan Jilin131400 China

Outlier detection is an important task in data mining with numerous applications. Recent years, the study on outlier detection is very active, many algorithms were proposed including based on clustering. However, most outlier detection algorithms based on clustering often need parameters and it is very difficult to select a suitable parameter for different data set. In order to solve this problem, an outlier detection algorithm called outlier detection based on cluster outlier factor and mutual density is proposed in this paper. The mutual density and γ density is used to construct decision graph. The data points with γ density anomalously large in decision graph are treated as cluster centres. This algorithm detect the boundary of outlier cluster using cluster outlier factor called cluster outlier factor (COF), it can automatic find the parameter. This method can achieve good performance in clustering and outlier detection which be shown in the experiments. Copyright © 2019 Inderscience Enterprises Ltd.

关键词： Data mining

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Mechanical Sensors for Ultraheavy Dark Matter Searches via Long-range Forces

arXiv

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arXiv 2025年

作者： Qin, Juehang Amaral, Dorian W.P. Bhave, Sunil A. Cai, Erqian Carney, Daniel Lang, Rafael F. Li, Shengchao Marino, Alberto M. Marvinney, Claire Newton, Jared R. Taylor, Jacob M. Tunnell, Christopher Department of Physics and Astronomy Rice University HoustonTX77005 United States Quantum Science Center Oak Ridge National Laboratory Oak RidgeTN37831 United States OxideMEMS Lab Elmore Family School of Electrical and Computer Engineering Purdue University West LafayetteIN47907 United States Physics Division Lawrence Berkeley National Laboratory BerkeleyCA94720-8153 United States Department of Physics and Astronomy Purdue University West LafayetteIN47907 United States Quantum Information Science Section Computational Sciences and Engineering Division Oak Ridge National Laboratory One Bethel Valley Road Oak RidgeTN37831 United States Joint Quantum Institute National Institute of Standards and Technology College ParkMD20742 United States Joint Center for Quantum Information and Computer Science University of Maryland College ParkMD20742 United States Department of Computer Science Rice University HoustonTX77005 United States

Dark matter candidates with masses around the Planck-scale are theoretically well-motivated, and it has been suggested that it might be possible to search for dark matter solely via gravitational interactions in this mass range. In this work, we explore the pathway towards searching for dark matter candidates with masses around the Planck-scale using mechanical sensors while considering realistic experimental constraints, and develop analysis techniques needed to conduct such searches. These dark matter particles are expected to leave tracks as their signature in mechanical sensor arrays, and we show that we can effectively search for such tracks using statistical approaches to track-finding. We analyze a range of possible experimental setups and compute sensitivity projections for searches for ultraheavy dark matter coupling to the Standard Model via long-range forces. We find that while a search for Planck-scale dark matter purely via gravitational couplings would be exceedingly difficult, requiring > 70 dB of quantum noise reduction with a 1003 array of devices, there is a wide range of currently unexplored dark matter candidates which can be searched for with already existing or near-term experimental platforms. © 2025, CC BY.

关键词： Gravitational effects

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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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Realizing flexible bioelectronic medicines for accessing the peripheral nerves – technology considerations

引用

Bioelectronic Medicine 2018年第1期4卷 1页

作者： Giagka, Vasiliki Serdijn, Wouter A. Section Bioelectronics Department of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft Netherlands Technologies for Bioelectronics Group Department of System Integration and Interconnection Technologies Fraunhofer Institute for Reliability and Microintegration IZM Berlin Germany

Patients suffering from conditions such as paralysis, diabetes or rheumatoid arthritis could in the future be treated in a personalised manner using bioelectronic medicines (BEms) (Nat Rev Drug Discov 13:399–400, 2013, Proc Natl Acad Sci USA 113:8284–9, 2016, J Intern Med 282:37–45, 2017). To deliver this personalised therapy based on electricity, BEms need to target various sites in the human body and operate in a closed-loop manner. The specific conditions and anatomy of the targeted sites pose unique challenges in the development of BEms. With a focus on BEms based on flexible substrates for accessing small peripheral nerves, this paper discusses several system-level technology considerations related to the development of such devices. The focus is mainly on miniaturisation and long-term operation. We present an overview of common substrate and electrode materials, related processing methods, and discuss assembly, miniaturisation and long-term stability issues. © 2018, The Author(s).

关键词： Electrodes

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