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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

引用

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-Objective Squirrel Search Algorithm for EEG Feature Selection

SSRN

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SSRN 2022年

作者： Wang, Chao Li, Songjie Shi, Miao Zhao, Jie Acharya, U. Rajendra Xie, Neng-Gang Cheong, Kang Hao College of Civil Engineering and Architecture Anhui Polytechnic University Anhui Wuhu241000 China Anhui Province Key Laboratory of Multidisciplinary Management and Control of Complex Systems Anhui Ma’anshan 243002 China Department of Management science and Engineering Anhui University of Technology Anhui Maanshan243002 China 8 Somapah Road Singapore487372 Singapore Department of Mechanical Engineering Anhui University of Technology Maanshan243002 China Department of Electronics and Computer Engineering Ngee Ann Polytechnic S599489 Singapore Department of Biomedical Engineering School of Science and Technology SUSS University Singapore Department of Biomedical Informatics and Medical Engineering Asia University Taichung Taiwan

Feature selection plays a critical role in the application of Brain computer Interface (BCI) systems. Many methods have been used to solve the feature selection problem, but they model it as a single-objective problem, considering only classification accuracy or number of features. To close this critical gap, we improve the squirrel search algorithm by combining it with the grid method, and propose a Multi-Objective Squirrel Search Algorithm (MOSSA) to solve the feature selection problem in BCI. We conduct experiments on three publicly available motion imagery datasets, and the experimental results reveal the best classification results of the method on dataset 1. The average classification accuracy of dataset 2 is 96.71%, with the number of selected features reduced to 18 on average. The highest classification accuracy of dataset 3 is 83.57% on the training set and 82.86% on the test set. In addition, we compare MOSSA with other algorithms and the results show the superiority of our proposed method in solving the feature selection problem. Finally, we combine MOSSA with an online application of BCI, where subjects visualise controlling the robot to perform the corresponding actions by the left and right hand movements. The average recognition rate of the three subjects is approximately 70%. In summary, the MOSSA is an effective method for solving the feature selection problem and is useful for the development of online © 2022, The Authors. All rights reserved.

关键词： Brain computer interface

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HydraMini: An FPGA-based Affordable Research and Education Platform for Autonomous Driving

HydraMini: An FPGA-based Affordable Research and Education P...

引用

International Conference on Connected and Autonomous Driving

作者： Tianze Wu Yifan Wang Weisong Shi Joshua Lu SKL of Computer Architecture Institute of Computing Technology CAS Beijing China Department of Computer Science Wayne State University Michigan USA Xilinx Shanghai China

ISBN: (数字)9781728160597

ISBN: (纸本)9781728160603

Autonomous driving has been a hot topic recently, so many industrial and academic groups are putting much engineering and research efforts into this topic. However, it is difficult for most researchers or students to afford a car as a research platform to conduct experiments for autonomous driving. Further, we believe that only when more people have the chance to make contributions will this area be more prosperous. Therefore, in this paper, we present HydraMini, an affordable experimental research and education platform supporting the experiments from hardware systems to vision algorithms, and its high flexibility makes it easily extended and modified. It is equipped with the Xilinx PYNQ-Z2 board as the computing platform, which deploys the Deep Learning Processing Unit (DPU) in FPGA to accelerate the deep learning inference. It also provides useful tools like a simulator for model training and testing in a virtual environment to facilitate the use of HydraMini. Our platform will help researchers and students build and test their own solutions for autonomous driving algorithms and systems easily and efficiently.

关键词： Platform Hardware system Automobile driving Virtual environments Field programmable gate arrays Cars simulator Students Chance Education Model trains motivation

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A Cost Model for Data Discovery in Large-Scale IoT Networks of Smart Cities

A Cost Model for Data Discovery in Large-Scale IoT Networks ...

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International Conference on Mobile Data Management

作者： Torbjørn Kirkevik Soltvedt Amir Sinaeepourfard Dirk Ahlers Norwegian University of Science and Technology (NTNU) Trondheim Norway Department of Computer Science Norwegian University of Science and Technology (NTNU) Trondheim Norway Department of Architecture and Planning Norwegian University of Science and Technology (NTNU) Trondheim Norway

ISBN: (数字)9781728146638

ISBN: (纸本)9781728146645

A smart city with huge numbers of physical (e.g., sensors and actuators) and non-physical (e.g., external databases) data sources will continuously produce high amounts of massive city-data. Distributed data storage across the city may store the produced city-data. City managers through different update mechanisms may send the produced city-data from distributed data storage to centralized data storage (e.g., Cloud data storage). Hence, the data discovery issues are in a vital position in the smart city concepts because the produced city-data may exist in different data storage platforms from distributed to centralized data. In this paper, we will first present our proposed Distributed-to-Centralized Information and Communications technology (D2C-ICT) architecture for the Zero Emission Neighborhoods (ZEN) center. This proposed D2C-ICT architecture can provide multiple facilities from the joined benefits of distributed and centralized technologies in smart cities. Second, we will show how the Multi-Attribute Utility Theory (MAUT) cost model can be beneficial to find the appropriate data for building city services across the different storage platforms on the city scale as well as can be applied in the ZEN center and its pilots.

关键词： Conferences

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Real-Time Measurement-Driven Reinforcement Learning Control Approach for Uncertain Nonlinear Systems

arXiv

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arXiv 2023年

作者： Abouheaf, Mohamed Boase, Derek Gueaieb, Wail Spinello, Davide Al-Sharhan, Salah College of Technology Architecture and Applied Engineering Bowling Green State University Bowling GreenOH43403-0001 United States School of Electrical Engineering and Computer Science University of Ottawa 800 King Edward Avenue OttawaONK1N 6N5 Canada Department of Mechanical Engineering 161 Louis Pasteur OttawaONK1N 6N5 Canada Machine Intelligence Research Labs AuburnWA98071-2259 United States

The paper introduces an interactive machine learning mechanism to process the measurements of an uncertain, nonlinear dynamic process and hence advise an actuation strategy in real-time. For concept demonstration, a trajectory-following optimization problem of a Kinova robotic arm is solved using an integral reinforcement learning approach with guaranteed stability for slowly varying dynamics. The solution is implemented using a model-free value iteration process to solve the integral temporal difference equations of the problem. The performance of the proposed technique is benchmarked against that of another model-free high-order approach and is validated for dynamic payload and disturbances. Unlike its benchmark, the proposed adaptive strategy is capable of handling extreme process variations. This is experimentally demonstrated by introducing static and time-varying payloads close to the rated maximum payload capacity of the manipulator arm. The comparison algorithm exhibited up to a seven-fold percent overshoot compared to the proposed integral reinforcement learning solution. The robustness of the algorithm is further validated by disturbing the real-time adapted strategy gains with a white noise of a standard deviation as high as 5%. © 2023, CC BY-NC-ND.

关键词： Reinforcement learning

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Blending effects of boron carbide and lanthanum(III) oxide reinforcements on the structural, physical, mechanical, and radiation shielding properties of 316L-stainless steel composites for nuclear applications

引用

Annals of Nuclear Energy 2025年 221卷

作者： ALMisned, Ghada Yayla, Nihal Albayrak, M.Gökhan Güler, Ömer Baykal, Duygu Sen Alkarrani, Hessa Tekin, H.O. Department of Physics College of Science Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh 11671 Saudi Arabia Munzur University Department of Strategic Raw Materials and Advanced Technology Applications Tunceli Turkey Fırat University Metallurgical and Materials Engineering Department Engineering Faculty Elazığ Turkey Rare Earth Elements Application and Research Center Munzur University Tunceli 62000 Turkey Istanbul Nisantasi University Faculty of Engineering and Architecture Mechatronics Engineering Istanbul 34398 Turkey Research Institute for Medical and Health Sciences University of Sharjah Sharjah Sharjah 27272 United Arab Emirates Medical Diagnostic Imaging Department College of Health Sciences University of Sharjah Sharjah 27272 United Arab Emirates Istinye University Faculty of Engineering and Natural Sciences Computer Engineering Department Istanbul 34396 Turkey

Four composite samples were fabricated, maintaining 10 % B4C and varying La2O3 content from 0 % to 20 %. X-ray Diffraction (XRD), Scanning Electron Microscopy (SEM), and Energy Dispersive X-ray Spectroscopy (EDX) anal... 详细信息

Four composite samples were fabricated, maintaining 10 % B₄C and varying La₂O₃ content from 0 % to 20 %. X-ray Diffraction (XRD), Scanning Electron Microscopy (SEM), and Energy Dispersive X-ray Spectroscopy (EDX) analyses confirmed the successful incorporation of reinforcements, preserving the austenitic structure of 316L-SS. Elastic modulus calculations revealed a gradual reduction from 225.0 GPa to 215.0 GPa with increasing La₂O₃ due to clustering and reduced homogeneity. Gamma-ray shielding properties demonstrated significant improvements, particularly at low and intermediate photon energies, with Sample 4 with 20 % La₂O₃ addition, achieving a 13.6 % higher linear attenuation coefficients (LAC) at 0.015 MeV. Neutron shielding highlighted Sample 4 as one of the most effective materials, outperforming conventional alloys. This study demonstrates the potential of 316L Stainless-Steel (316L-SS) + B₄C + La₂O₃ composites as multifunctional materials for radiation-intensive applications, offering superior attenuation and structural stability. It can be concluded that optimization of reinforcement distribution would play an important role in balancing mechanical and shielding performance in next-generation nuclear materials. © 2025 Elsevier Ltd

关键词： 316L stainless-steel Boron carbide Lanthanum(III) oxide Nuclear radiation shielding

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Pricing Strategy for Household Energy-Saving Option (Heso): A Novel Option-Based Intervention for Promoting Household Energy Efficiency

SSRN

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SSRN 2022年

作者： Xu, Qian Li, Shengping Shen, Liyin Chang, Ruidong Wang, Qian-Cheng Liu, Xuan Department of the Built Environment College of Design and Engineering National University of Singapore 4 Architecture Drive Singapore117566 Singapore Department of Infrastructure Engineering Faulty of Engineering and Information Technology The University of Melbourne VIC3010 Australia School of Spatial Planning and Design Zhejiang University City College Hangzhou China School of Management Science and Real Estate Chongqing University Chongqing China School of Architecture and Built Environment Faculty of Engineering Computer and Mathematical Sciences The University of Adelaide SA5005 Australia Department of Land Economy University of Cambridge CambridgeCB3 9EP United Kingdom Department of the Built Environment Eindhoven University of Technology Eindhoven5600MB Netherlands

Household energy-saving interventions are essential in reducing energy consumption in residential buildings. One of the effective interventions is Household Energy-Saving Option (HESO). However, the pricing strategy of this innovative intervention has not been explored. This study aims to establish a feasible valuation strategy for HESO to facilitate the application of market-based energy-saving interventions among households. This study first provides a pricing strategy of HESO from a market zero-profit perspective. It continues by developing a price cap and floor for HESO's market price. The theoretical pricing model was validated by a field experiment consisting of two-tiered HESO trading among 67 households in Singapore. The results revealed decisive implications for both environmental policymakers and HESO suppliers. Policymakers are suggested to launch HESO in countries or regions with stable and relatively high electricity prices and low discount rates. HESO suppliers may choose to adjust rewards and duration accordingly when the market price deviates from the zero-profit equilibrium price of HESO. The valuation strategy of HESO also helps quantitative estimation of households’ perceived value of HESO. © 2022, The Authors. All rights reserved.

关键词： Costs

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SSMTL++: Revisiting self-supervised multi-task learning for video anomaly detection

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computer Vision and Image Understanding 2023年第1期229卷

作者： Barbalau, Antonio Ionescu, Radu Tudor Georgescu, Mariana-Iuliana Dueholm, Jacob Ramachandra, Bharathkumar Nasrollahi, Kamal Khan, Fahad Shahbaz Moeslund, Thomas B. Shah, Mubarak Department of Computer Science University of Bucharest 14 Academiei Street Bucharest010014 Romania SecurifAI 21D Mircea Voda Bucharest030662 Romania MBZ University of Artificial Intelligence Masdar City Abu Dhabi United Arab Emirates Department of Architecture Design and Media Technology Aalborg University Rendsburggade 14 Aalborg9000 Denmark Milestone Systems Banemarksvej 50C Brøndby2605 Denmark Geopipe Inc 460W 51st New York CityNY10019 United States Linköping University 581 83 Linköping Sweden University of Central Florida OrlandoFL32816 United States

A self-supervised multi-task learning (SSMTL) framework for video anomaly detection was recently introduced in literature. Due to its highly accurate results, the method attracted the attention of many researchers. In this work, we revisit the self-supervised multi-task learning framework, proposing several updates to the original method. First, we study various detection methods, e.g. based on detecting high-motion regions using optical flow or background subtraction, since we believe the currently used pre-trained YOLOv3 is suboptimal, e.g. objects in motion or objects from unknown classes are never detected. Second, we modernize the 3D convolutional backbone by introducing multi-head self-attention modules, inspired by the recent success of vision transformers. As such, we alternatively introduce both 2D and 3D convolutional vision transformer (CvT) blocks. Third, in our attempt to further improve the model, we study additional self-supervised learning tasks, such as predicting segmentation maps through knowledge distillation, solving jigsaw puzzles, estimating body pose through knowledge distillation, predicting masked regions (inpainting), and adversarial learning with pseudo-anomalies. We conduct experiments to assess the performance impact of the introduced changes. Upon finding more promising configurations of the framework, dubbed SSMTL++v1 and SSMTL++v2, we extend our preliminary experiments to more data sets, demonstrating that our performance gains are consistent across all data sets. In most cases, our results on Avenue, ShanghaiTech and UBnormal raise the state-of-the-art performance bar to a new level. © 2023 Elsevier Inc.

关键词： Anomaly detection

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Special Section Guest Editorial: Advances in Breast Imaging

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Journal of Medical Imaging 2019年第3期6卷 031401-031401页

作者： Krupinski, Elizabeth A. Astley, Sue Marti, Robert Tornai, Martin Zwiggelaar, Reyer Emory University Department of Radiology and Imaging Sciences AtlantaGA United States University of Manchester Division of Informatics Imaging and Data Science Manchester United Kingdom University of Girona Department of Architecture and Computer Technology Girona Spain Duke University Departments of Radiology and Biomedical Engineering DurhamNC United States Aberystwyth University Department of Computer Science Aberystwyth United Kingdom

This guest editorial introduces the special section on Advances in Breast Imaging.

关键词： Digital breast tomosynthesis Breast imaging Breast cancer Breast Cancer Digital mammography computer simulations Mammography Optical filters Radiology

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Identifying Users with Wearable Sensors based on Activity Patterns

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Procedia computer science 2020年 177卷 8-15页

作者： Muhammad Ehatisham-ul-Haq Maryam Naseer Malik Muhammad Awais Azam Usman Naeem Asra Khalid Mustansar Ali Ghazanfar Department of Computer Engineering University of Engineering and Technology Taxila Punjab 47050 Pakistan Whitecliffe Technologies Wellington New Zealand School of Electronic Engineering and Computer Science Queen Mary University of London United Kingdom School of Engineering & Computer Science Victoria University of Wellington New Zealand School of Architecture Computing and Engineering University of East London United Kingdom

We live in a world where ubiquitous systems surround us in the form of automated homes, smart appliances and wearable devices. These ubiquitous systems not only enhance productivity but can also provide assistance given a variety of different scenarios. However, these systems are vulnerable to the risk of unauthorized access, hence the ability to authenticate the end-user seamlessly and securely is important. This paper presents an approach for user identification given the physical activity patterns captured using on-body wearable sensors, such as accelerometer, gyroscope, and magnetometer. Three machine learning classifiers have been used to discover the activity patterns of users given the data captured from wearable sensors. The recognition results prove that the proposed scheme can effectively recognize a user’s identity based on his/her daily living physical activity patterns.

关键词： Activity recognition machine learning user identification wearable sensors

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