检索结果-内蒙古大学图书馆

Knowledge-routed visual question reasoning: Challenges for deep representation embedding

学校读者我要写书评

暂无评论

arXiv 2020年

作者： Cao, Qingxing Li, Bailin Liang, Xiaodan Wang, Keze Lin, Liang School of Intelligent Systems Engineering Sun Yat-sen University China DarkMatter AI Research China Department of statistics University of California Los AngelesCA90095 United States School of Computer Science and Engineering Sun Yat-sen University Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education Engineering Research Center for Advanced Computing Engineering Software of Ministry of Education China

Though beneficial for encouraging the Visual Question Answering (VQA) models to discover the underlying knowledge by exploiting the input-output correlation beyond image and text contexts, the existing knowledge VQA datasets are mostly annotated in a crowdsource way, e.g., collecting questions and external reasons from different users via the internet. In addition to the challenge of knowledge reasoning, how to deal with the annotator bias also remains unsolved, which often leads to superficial over-fitted correlations between questions and answers. To address this issue, we propose a novel dataset named Knowledge-Routed Visual Question Reasoning for VQA model evaluation. Considering that a desirable VQA model should correctly perceive the image context, understand the question, and incorporate its learned knowledge, our proposed dataset aims to cutoff the shortcut learning exploited by the current deep embedding models and push the research boundary of the knowledge-based visual question reasoning. Specifically, we generate the question-answer pair based on both the Visual Genome scene graph and an external knowledge base with controlled programs to disentangle the knowledge from other biases. The programs can select one or two triplets from the scene graph or knowledge base to push multi-step reasoning, avoid answer ambiguity, and balanced the answer distribution. In contrast to the existing VQA datasets, we further imply the following two major constraints on the programs to incorporate knowledge reasoning: i) multiple knowledge triplets can be related to the question, but only one knowledge relates to the image object. This can enforce the VQA model to correctly perceive the image instead of guessing the knowledge based on the given question solely;ii) all questions are based on different knowledge, but the candidate answers are the same for both the training and test sets. We make the testing knowledge unused during training to evaluate whether a model can unde

关键词： Knowledge based systems

FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Lekadir, Karim Feragen, Aasa Fofanah, Abdul Joseph Frangi, Alejandro F. Buyx, Alena Emelie, Anais Lara, Andrea Porras, Antonio R. Chan, An-Wen Navarro, Arcadi Glocker, Ben Botwe, Benard O. Khanal, Bishesh Beger, Brigit Wu, Carol C. Cintas, Celia Langlotz, Curtis P. Rueckert, Daniel Mzurikwao, Deogratias Fotiadis, Dimitrios I. Zhussupov, Doszhan Ferrante, Enzo Meijering, Erik Weicken, Eva González, Fabio A. Asselbergs, Folkert W. Prior, Fred Krestin, Gabriel P. Collins, Gary S. Tegenaw, Geletaw S. Kaissis, Georgios Misuraca, Gianluca Tsakou, Gianna Dwivedi, Girish Kondylakis, Haridimos Jayakody, Harsha Woodruf, Henry C. Mayer, Horst Joachim Aerts, Hugo JWL Walsh, Ian Chouvarda, Ioanna Buvat, Irène Tributsch, Isabell Rekik, Islem Duncan, James Kalpathy-Cramer, Jayashree Zahir, Jihad Park, Jinah Mongan, John Gichoya, Judy W. Schnabel, Julia A. Kushibar, Kaisar Riklund, Katrine Mori, Kensaku Marias, Kostas Amugongo, Lameck M. Fromont, Lauren A. Maier-Hein, Lena Alberich, Leonor Cerdá Rittner, Leticia Phiri, Lighton Marrakchi-Kacem, Linda Donoso-Bach, Lluís Martí-Bonmatí, Luis Cardoso, M. Jorge Bobowicz, Maciej Shabani, Mahsa Tsiknakis, Manolis Zuluaga, Maria A. Bielikova, Maria Fritzsche, Marie-Christine Camacho, Marina Linguraru, Marius George Wenzel, Markus De Bruijne, Marleen Tolsgaard, Martin G. Ghassemi, Marzyeh Ashrafuzzaman, Md Goisauf, Melanie Yaqub, Mohammad Abadía, Mónica Cano Mahmoud, Mukhtar M.E. Elattar, Mustafa Rieke, Nicola Papanikolaou, Nikolaos Lazrak, Noussair Díaz, Oliver Salvado, Olivier Pujol, Oriol Sall, Ousmane Guevara, Pamela Gordebeke, Peter Lambin, Philippe Brown, Pieta Abolmaesumi, Purang Dou, Qi Lu, Qinghua Osuala, Richard Nakasi, Rose Zhou, S. Kevin Napel, Sandy Colantonio, Sara Albarqouni, Shadi Joshi, Smriti Carter, Stacy Klein, Stefan Petersen, Steffen E. Aussó, Susanna Awate, Suyash Raviv, Tammy Riklin Cook, Tessa Mutsvangwa, Tinashe E.M. Rogers, Wendy A. Niessen, Wiro J. Puig-Bosch, Xènia Zeng, Yi Mohammed, Yunusa G. Aquino, Yves Saint James Salahuddin, Zohaib Starmans, Martijn P.A. Department de Matemàtiques i Informàtica Universitat de Barcelona Barcelona Spain Barcelona Spain DTU Compute Technical University of Denmark Kgs Lyngby Denmark Department of Mathematics and Computer Science Faculty of Science and Technology Milton Margai Technical University Freetown Sierra Leone Center for Computational Imaging & Simulation Technologies in Biomedicine Schools of Computing and Medicine University of Leeds Leeds United Kingdom Cardiovascular Science and Electronic Engineering Departments KU Leuven Leuven Belgium Institute of History and Ethics in Medicine Technical University of Munich Munich Germany Faculty of Engineering of Systems Informatics and Sciences of Computing Galileo University Guatemala City Guatemala Department of Biostatistics and Informatics Colorado School of Public Health University of Colorado Anschutz Medical Campus AuroraCO United States Department of Medicine Women’s College Research Institute University of Toronto Toronto Canada Universitat Pompeu Fabra BarcelonaBeta Brain Research Center Barcelona Spain Department of Computing Imperial College London London United Kingdom School of Biomedical & Allied Health Sciences University of Ghana Accra Ghana Department of Midwifery & Radiography School of Health & Psychological Sciences City University of London United Kingdom Kathmandu Nepal European Heart Network Brussels Belgium Department of Thoracic Imaging University of Texas MD Anderson Cancer Center Houston United States IBM Research Africa Nairobi Kenya Departments of Radiology Medicine and Biomedical Data Science Stanford University School of Medicine Stanford United States Institute for AI and Informatics in Medicine Klinikum rechts der Isar Technical University Munich Munich Germany Department of Computing Imperial College London London United Kingdom Muhimbili University of Health and Allied Sciences Dar es Salaam Tanzania United Republic of Ioannina Greece Almaty AI Lab Almaty Kazakhstan

Background: Despite major advances in artificial intelligence (AI) research for healthcare, the deployment and adoption of AI technologies remain limited in clinical practice. In recent years, concerns have been raised about the clinical, technical, ethical and legal risks associated with healthcare AI. To increase adoption in the real world, it is essential that AI technologies are trusted and accepted by patients, clinicians, health organisations and authorities. This paper describes the FUTURE-AI framework as the first international consensus guideline for trustworthy AI in healthcare. Methods: The FUTURE-AI consortium was founded in 2021 and now comprises 117 interdisciplinary experts from 50 countries representing all continents, including AI scientists, clinical researchers, biomedical ethicists, and social scientists. Over a two-year period, the consortium established guiding principles and best practices for trustworthy and deployable AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. Findings: The FUTURE-AI framework was established based on six guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 30 best practices were defined, addressing technical, clinical, socio-ethical and legal dimensions of trustworthy AI. The recommendations cover the entire lifecycle of healthcare AI, from design, development and validation to regulation, deployment, and monitoring. Interpretation: FUTURE-AI is a structured, risk-informed framework which provides guidance for constructing healthcare AI tools that will be trusted, deployed and adopted in real-world clinical practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of healthcare AI. Copyright © 2023, The Authors. All rights

关键词： Clinical research

Validating polyp and instrument segmentation methods in colonoscopy through Medico 2020 and MedAI 2021 Challenges

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Jha, Debesh Sharma, Vanshali Banik, Debapriya Bhattacharya, Debayan Roy, Kaushiki Hicks, Steven A. Tomar, Nikhil Kumar Thambawita, Vajira Krenzer, Adrian Ji, Ge-Peng Poudel, Sahadev Batchkala, George Alam, Saruar Ahmed, Awadelrahman M.A. Trinh, Quoc-Huy Khan, Zeshan Nguyen, Tien-Phat Shrestha, Shruti Nathan, Sabari Gwak, Jeonghwan Jha, Ritika K. Zhang, Zheyuan Schlaefer, Alexander Bhattacharjee, Debotosh Bhuyan, M.K. Das, Pradip K. Fan, Deng-Ping Parasa, Sravanthi Ali, Sharib Riegler, Michael A. Halvorsen, Pål de Lange, Thomas Bagci, Ulas Machine & Hybrid Intelligence Lab Department of Radiology Northwestern University Chicago United States SimulaMet Oslo Norway Oslo Metropolitan University Oslo Norway Jadavpur University Kolkata India Indian Institute of Technology Guwahati India Institute of Medical Technology and Intelligent Systems Technische Universität Hamburg Germany University of Oslo Norway Julius-Maximilian University of Würzburg Germany Faculty of Information Technology University of Science VNU-HCM Viet Nam Department of IT Convergence Engineering Gachon University Seongnam13120 Korea Republic of School of Computing University of Leeds LeedsLS2 9JT United Kingdom College of Engineering Australian National University Canberra Australia Department of Engineering Science University of Oxford Oxford United Kingdom National University of Computer and Emerging Sciences Karachi Campus Pakistan Swedish Medical Center Seattle United States Kathmandu Nepal Couger Inc Tokyo Japan Department of Software Korea National University of Transportation Chungju Korea Republic of University of Bergen Bergen Norway Department of Medicine and Emergencies Mölndal Sahlgrenska University Hospital Region Västra Götaland Sweden Department of Molecular and Clinical Medicin Sahlgrenska Academy University of Gothenburg Sweden ETH Zurich Zurich Switzerland

Automatic analysis of colonoscopy images has been an active field of research motivated by the importance of early detection of precancerous polyps. However, detecting polyps during the live examination can be challenging due to various factors such as variation of skills and experience among the endoscopists, lack of attentiveness, and fatigue leading to a high polyp miss-rate. Therefore, there is a need for an automated system that can flag missed polyps during the examination and improve patient care. Deep learning has emerged as a promising solution to this challenge as it can assist endoscopists in detecting and classifying overlooked polyps and abnormalities in real time, improving the accuracy of diagnosis and enhancing treatment. In addition to the algorithm's accuracy, transparency and interpretability are crucial to explaining the whys and hows of the algorithm's prediction. Further, conclusions based on incorrect decisions may be fatal, especially in medicine. Despite these pitfalls, most algorithms are developed in private data, closed source, or proprietary software, and methods lack reproducibility. Therefore, to promote the development of efficient and transparent methods, we have organized the "Medico automatic polyp segmentation (Medico 2020)" and "MedAI: Transparency in Medical Image Segmentation (MedAI 2021)" competitions. The Medico 2020 challenge received submissions from 17 teams, while the MedAI 2021 challenge also gathered submissions from another 17 distinct teams in the following year. We present a comprehensive summary and analyze each contribution, highlight the strength of the best-performing methods, and discuss the possibility of clinical translations of such methods into the clinic. Our analysis revealed that the participants improved dice coefficient metrics from 0.8607 in 2020 to 0.8993 in 2021 despite adding diverse and challenging frames (containing irregular, smaller, sessile, or flat polyps), which are frequently missed during a

关键词： Computer aided diagnosis

Piquasso: A Photonic Quantum Computer Simulation software Platform

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Kolarovszki, Zoltán Rybotycki, Tomasz Rakyta, Péter Kaposi, Ágoston Poór, Boldizsár Jóczik, Szabolcs Nagy, Dániel T.R. Varga, Henrik El-Safty, Kareem H. Morse, Gregory Oszmaniec, Michal Kozsik, Tamás Zimborás, Zoltán Quantum Computing and Quantum Information Research Group HUN-REN Wigner Research Centre for Physics Konkoly-Thege Miklós út 29-33 BudapestH-1525 Hungary Department of Programming Languages and Compilers Eötvös Loránd University Pázmány Péter sétány 1/a BudapestH-1117 Hungary Center for Theoretical Physics Polish Academy of Sciences Al. Lotników 32/46 Warszawa02-668 Poland Department of Physics of Complex Systems Eötvös Loránd University Pázmány Péter sétány 1/a BudapestH-1117 Hungary Quantinuum 17 Beaumont Street OxfordOX1 2NA United Kingdom Robert Bosch Kft. Gyömrői út 104. BudapestH-1103 Hungary Department of Computer Engineering Technical University of Munich Arcisstraße 21 München80333 Germany Algorithmiq Ltd. Kanavakatu 3C Helsinki00160 Finland

We introduce the Piquasso quantum programming framework, a full-stack open-source software platform for the simulation and programming of photonic quantum computers. Piquasso can be programmed via a high-level Python programming interface enabling users to perform efficient quantum computing with discrete and continuous variables. Via optional high-performance C++ backends, Piquasso provides state-of-the-art performance in the simulation of photonic quantum computers. The Piquasso framework is supported by an intuitive web-based graphical user interface where the users can design quantum circuits, run computations, and visualize the results. Copyright © 2024, The Authors. All rights reserved.

关键词： Quantum computers

A Hybrid RSA algorithm in support of IoT greenhouse applications 2

学校读者我要写书评

暂无评论

A Hybrid RSA algorithm in support of IoT greenhouse applicat...

2nd IEEE International Conference on Industrial Internet Cloud, ICII 2019

作者： Aledhari, Mohammed Parizi, Reza M. Dehghantanha, Ali Choo, Kim-Kwang Raymond College of Computing and Software Engineering Kennesaw State University GA United States Cyber Science Lab School of Computer Science University of Guelph ON Canada Department of Information Systems and Cyber Security University of Texas at San Antonio TX United States

ISBN: (纸本)9781728129778

Internet of Things (IoT) is being utilized in a plethora of applications, many of which aim to improve system performance. IoT nodes suffer from several limitations, such as power supply, computational capability, and information security. The current state of IoT information and the potential for security breaches represent a significant untoward condition, especially regarding the organizational necessity for confidentiality and privacy. In this paper, we propose a strong, simple and energy conserving three-stage data encryption algorithm with a focus on securing IoT data in support of greenhouse applications. The stages include: (1) a novel implementation of the K-Map substitution functions;(2) the utilization of a chaotic equation to generate a sequence of random numbers, which are added to the result of the first stage;(3) the third stage incorporates the Rivest, Shamir, and Adelman (RSA) algorithm, performed on feeds from the output of the second stage, resulting in encrypted data, requiring private key decryption. The proposed algorithm eliminates the handshaking of the traditional RSA to exchange the keys (private and public) between IoT nodes and the cloud (server), then reduce the transmission time by 30%. The proposed cryptography algorithm is implemented and tested using two evaluation methods: a single micro-controller (standalone) and on a server (cloud). The algorithm is tested in both directions up/down link, and provides an acceptable and stable performance with 1.3 faster than the original RSA. © 2019 IEEE.

关键词： Internet of things

Corrigendum to “An analysis of urban sprawl growth and prediction using remote sensing and machine learning techniques” [J. S. Am. Earth Sci. Volume 142 , 15 August 2024, 104988]

学校读者我要写书评

暂无评论

Journal of South American Earth Sciences 2025年 152卷

作者： Alanoud Al Mazroa Mashael Maashi Fadoua Kouki Kamal M. Othman Nahla Salih Mohamed Ahmed Elfaki Sabarunisha Begum S Department of Information Systems College of Computer and Information Sciences Princess Nourah bint Abdulrahman University (PNU) P.O. Box 84428 Riyadh 11671 Saudi Arabia Department of Software Engineering College of Computer and Information Sciences King Saud University Po box 103786 Riyadh 11543 Saudi Arabia Department of Financial and Banking Sciences Applied College at Muhail Aseer King Khalid University Saudi Arabia Department of Electrical Engineering College of Engineering and Islamic Architecture Umm Al-Qura University Makkah Saudi Arabia Department of Computer Science Applied College Imam Abdulrahman Bin Faisal University Saudi Arabia Department of Computer Science College of Computing and Information Technology Shaqra University Shaqra Saudi Arabia Department of Biotechnology P.S.R. Engineering College Sivakasi 626140 India

Thermal Analysis of Magnetized Bioconvective Flow of a Casson Nanofluid With Temperature Dependent Thermal Conductivity: A Numerical Study

学校读者我要写书评

暂无评论

International Journal of Energy Research 2025年第1期2025卷

作者： Shahzad Khattak Muhammad Naveed Khan Mohammad Yar Mohamed Hussien Taoufik Saidani Kaouther Ghachem Lioua Kolsi Department of Mathematics Faculty of Engineering and Computing National University of Modern Languages Islamabad Pakistan numl.edu.pk Department of Mathematics Quaid-i-Azam University Islamabad Pakistan qau.edu.pk Department of Mathematics Kabul Polytechnic University Kabul Afghanistan kpu.edu.af Department of Chemistry Faculty of Science King Khalid University Abha Saudi Arabia kku.edu.sa Center for Scientific Research and Entrepreneurship Northern Border University 73213 Arar Saudi Arabia nbu.edu.sa Department of Industrial and Systems Engineering College of Engineering Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh 11671 Saudi Arabia pnu.edu.sa Department of Mechanical Engineering College of Engineering University of Ha’il Ha’il City 81451 Saudi Arabia uoh.edu.sa

The current study has applications in various technical fields, including energy generation, fluid dynamics, geophysical fluid mechanics, and biomedical and industrial engineering. This study aims to investigate the explicit solution of a two-dimensional Casson nanofluid flow model incorporating an exponential heat exchanger and activation energy. The fluid motion is induced by an exponentially stretching surface under a convective boundary condition and variable thermal conductivity. Additionally, the influence of microorganisms and magnetohydrodynamics (MHDs) is examined. The governing system of coupled partial differential equations (PDEs) is transformed into a set of ordinary differential equations (ODEs) and numerically solved using the bvp4c solver in MATLAB. A comprehensive graphical and tabulated analysis is conducted for various governing parameters. The findings reveal that an increase in the Casson fluid and porosity parameters leads to a reduction in fluid velocity. Moreover, the microorganism density profile declines with increasing bioconvection Lewis number and Peclet number.

关键词： casson nanofluid model exponential stretching surface microorganism variable heat exchanger

A reinforcement-learning-based energy-efficient framework for multi-task video analytics pipeline

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Zhao, Yingying Dong, Mingzhi Wang, Yujiang Feng, Da Lv, Qin Dick, Robert P. Li, Dongsheng Lu, Tun Gu, Ning Shang, Li School of Computer Science Fudan University Shanghai China Shanghai Key Laboratory of Data Science Fudan University Shanghai China Shanghai Institute of Intelligent Electronics & Systems Shanghai China Department of Computing Imperial College London London United Kingdom Software Service Company Limited Beijing China University of Colorado Boulder BoulderCO United States Department of Electrical Engineering and Computer Science College of Engineering University of Michigan Ann ArborMI United States Microsoft Research Asia Shanghai China School of Computer Science Fudan University Shanghai China

Deep-learning-based video processing has yielded transformative results in recent years. However, the video analytics pipeline is energy-intensive due to high data rates and reliance on complex inference algorithms, which limits its adoption in energy-constrained applications. Motivated by the observation of high and variable spatial redundancy and temporal dynamics in video data streams, we design and evaluate an adaptive-resolution optimization framework to minimize the energy use of multitask video analytics pipelines. Instead of heuristically tuning the input data resolution of individual tasks, our framework utilizes deep reinforcement learning to dynamically govern the input resolution and computation of the entire video analytics pipeline. By monitoring the impact of varying resolution on the quality of high-dimensional video analytics features, hence the accuracy of video analytics results, the proposed end-to-end optimization framework learns the best non-myopic policy for dynamically controlling the resolution of input video streams to globally optimize energy efficiency. Governed by reinforcement learning, optical flow is incorporated into the framework to minimize unnecessary spatio-temporal redundancy that leads to re-computation, while preserving accuracy. The proposed framework is applied to video instance segmentation which is one of the most challenging computer vision tasks, and achieves better energy efficiency than all baseline methods of similar accuracy on the YouTube-VIS dataset. Copyright © 2021, The Authors. All rights reserved.

关键词： Energy efficiency

3DTeethSeg’22: 3D Teeth Scan Segmentation and Labeling Challenge

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Ben-Hamadou, Achraf Smaoui, Oussama Rekik, Ahmed Pujades, Sergi Boyer, Edmond Lim, Hoyeon Kim, Minchang Lee, Minkyung Chung, Minyoung Shin, Yeong-Gil Leclercq, Mathieu Cevidanes, Lucia Prieto, Juan Carlos Zhuang, Shaojie Wei, Guangshun Cui, Zhiming Zhou, Yuanfeng Dascalu, Tudor Ibragimov, Bulat Yong, Tae-Hoon Ahn, Hong-Gi Kim, Wan Han, Jae-Hwan Choi, Byungsun van Nistelrooij, Niels Kempers, Steven Vinayahalingam, Shankeeth Strippoli, Julien Thollot, Aurélien Setbon, Hugo Trosset, Cyril Ladroit, Edouard Centre de Recherche en Numérique de Sfax Laboratory of Signals Systems Artificial Intelligence and Networks Technopôle de Sfax Sfax3021 Tunisia Udini 37 BD Aristide Briand Aix-en-Provence13100 France Inria Univ. Grenoble Alpes CNRS Grenoble INP LJK France Department of Computer Science and Engineering Seoul National University Korea Republic of School of Software Soongsil University Korea Republic of University of Michigan Ann Arbor United States University of North Carolina Chapel Hill United States Shandong University China ShanghaiTech University China Department of Computer Science University of Copenhagen Denmark Osstem Implant Co. Ltd. Seoul Korea Republic of Department of Oral and Maxillofacial Surgery Radboud University Nijmegen Medical Centre P.O. Box 9101 Nijmegen6500 HB Netherlands Department of Computing Science Radboud University Nijmegen Netherlands Department of Artificial Intelligence Radboud University Nijmegen Netherlands Berlin Institute of Health at Charité Universitätsmedizin Berlin Charitéplatz 1 Berlin10117 Germany

Teeth localization, segmentation, and labeling from intra-oral 3D scans are essential tasks in modern dentistry to enhance dental diagnostics, treatment planning, and population-based studies on oral health. However, developing automated algorithms for teeth analysis presents significant challenges due to variations in dental anatomy, imaging protocols, and limited availability of publicly accessible data. To address these challenges, the 3DTeethSeg’22 challenge was organized in conjunction with the International Conference on Medical Image computing and Computer Assisted Intervention (MICCAI) in 2022, with a call for algorithms tackling teeth localization, segmentation, and labeling from intraoral 3D scans. A dataset comprising a total of 1800 scans from 900 patients was prepared, and each tooth was individually annotated by a human-machine hybrid algorithm. A total of 6 algorithms were evaluated on this dataset. In this study, we present the evaluation results of the 3DTeethSeg’22 challenge. The 3DTeethSeg’22 challenge code can be accessed at: https://***/abenhamadou/3DTeethSeg22 challenge. © 2023, CC BY-SA.

关键词： Object detection