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CEUR Workshop Proceedings 2020年 2690卷

作者： Tagarelli, Andrea Zumpano, Ester Latifić, Aida Kamialić Calì, Andrea 87036 Italy Faculty of Electrical Engineering and Computer Science University of Maribor Slovenia Dept. of Computer Science and Information Systems Birkbeck University of London United Kingdom

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Designing a virtual reality system for clinical education and examination

Computers and Education: X Reality

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computers and Education: X Reality 2024年 5卷

作者： Akinwale, Olawale Babatunde Abiona, Olatunde Oluwatope, Ayodeji Oludola Otuyemi, Olayinka Donald Ijarotimi, Omotade Adebimpe Olubusola Komolafe, Abiola Aregbesola, Stephen Babatunde Kolawole, Babatope Ayodeji Adetutu, Olufemi Mayowa Agunbiade, Ojo Melvin Ayinde, Adeboye Titus Idowu, Lanre Okunola, Oluseye Ademola Adediwura, Alaba Adeyemi Department of Electronic and Electrical Engineering Faculty of Technology Obafemi Awolowo University Ile-Ife 220005 Nigeria Computer Information Systems Department College of Arts and Sciences Indiana University Northwest 3400 Broadway Gary 46408 IN United States Department of Computer Science and Engineering Obafemi Awolowo University Ile-Ife 220005 Nigeria Department of Child Dental Health Faculty of Dentistry College of Health Sciences Obafemi Awolowo University Ile-Ife 220005 Nigeria Department of Obstetrics Gynaecology and Perinatology Faculty of Clinical Sciences College of Health Sciences Obafemi Awolowo University Ile-Ife 220005 Nigeria Department of Nursing Science Faculty of Basic Medical Science College of Health Sciences Obafemi Awolowo University Ile-Ife 220005 Nigeria Department of Oral and Maxillofacial Surgery Faculty of Dentistry College of Health Sciences Obafemi Awolowo University Ile-Ife 220005 Nigeria Department of Medicine Faculty of Clinical Sciences College of Health Sciences Obafemi Awolowo University Ile-Ife 220005 Nigeria Department of Dept. of Demography and Social Statistics Faculty of Social Sciences Obafemi Awolowo University Ile-Ife 220005 Nigeria Department of Sociology and Anthropology Faculty of Social Sciences Obafemi Awolowo University Ile-Ife 220005 Nigeria Department of Psychology Faculty of Social Sciences Obafemi Awolowo University Ile-Ife 220005 Nigeria Department of Educational Technology and Library Studies Obafemi Awolowo University Ile-Ife 220005 Nigeria Department of Educational Foundation and Counselling Obafemi Awolowo University Ile-Ife 220005 Nigeria

The COVID-19 pandemic clearly showed how much medical and clinical students’ education relied on physical instruction and examination. The work presented here was born by the fact that medical and clinical education had to be suspended during the social distancing phase of the pandemic. This work presents the lessons learned from creating a virtual reality system (VTRACS) for educating and assessing clinical students on a limited budget. Our work showed that clinical education could be done in a virtual space with a minimal virtual reality setup and a low-cost virtual reality headset. © 2024 The Authors

关键词： Clinical education Haptic feedback Low-cost Medical education Virtual reality

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Data-Intensive Routing in Delay-Tolerant Networks

Data-Intensive Routing in Delay-Tolerant Networks

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IEEE Annual Joint Conference: INFOCOM, IEEE computer and Communications Societies

作者： Kazuya Sakai Min-Te Sun Wei-Shinn Ku Dept. of Electrical Eng. and Comput. Sci. Tokyo Metropolitan University 6-6 Asahigaoka Hino Tokyo Japan Department of Computer Science and Information Engineering National Central University Taoyuan Taiwan Department of Computer Science and Software Engineering Auburn University Auburn Alabama

Mobile users and wireless devices are now the sources of a large volume of data. In such data-intensive mobile and wireless computing systems, delay-tolerant network (DTN) routing plays a critical role in data routing, dissemination, and collection. In this paper, we first introduce a new routing problem in DTNs - data-intensive routing - where data transmitted from one node to another is very large with respect to the size of data which can be transmitted in a single contact and available buffer size at relay nodes. In the proposed opportunistic path model, the contact frequency, contact duration, and buffer constraint are all integrated into a single routing metric. Then, we design the data-intensive routing (DIR) protocol where the path with the highest bottleneck link capacity is defined as the path weight. In addition, we propose the advanced DIR (A-DIR) protocol which focuses on the probability that the last message block will be delivered to its destination within the time constraint. Both the DIR and A-DIR protocols forward messages to better relays or to their destinations based on a greedy strategy with the proposed path metric. Simulations using real mobility traces demonstrate that the proposed DIR and A-DIR protocols achieve their design goals.

关键词： Routing Routing protocols Relays Measurement Wireless communication Buffer storage

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Market-based model in CR-IoT: A Q-probabilistic multi-agent learning approach

arXiv

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arXiv 2019年

作者： Wang, Dan Zhang, Wei Song, Bin Du, Xiaojiang Guizani, Mohsen State Key Laboratory of Integrated Services Networks Xidian University 710071 China Science and Technology on Information Systems Engineering Laboratory National University of Defense Technology China Dept. of Computer and Information Sciences Temple University PhiladelphiaPA19122 United States Dept. of Electrical and Computer Engineering University of Idaho MoscowID83844 United States

The ever-increasingly urban population and its material demands have brought an unprecedented burden to cities. Smart cities leverage emerging technologies like CR-IoT to provide better QoE and QoS for all citizens. However, resource allocation is an important problem in CR-IoT. Currently, this problem is handled by auction theory or game theory. To make CR-IoT nodes smarter and more autonomously allocate resources, we propose a multiagent reinforcement learning (MARL) algorithm to learn the optimal resource allocation strategies in the oligopolistic market model. Firstly, we model a multi-agent scenario with two primary users (PUs) as sellers and multiple secondary users (SUs) as buyers, which is a Bertrand market model. Then, we propose the Q-probabilistic multiagent learning (QPML) and apply it for allocating resource in a market. In the situation of multiagent interaction, the PUs and SUs learn strategies to maximize their revenue. Experimental results prove our multiagent learning approach performs well and convergence also well. Copyright © 2019, The Authors. All rights reserved.

关键词： Resource allocation

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Improving Defect Inspection Quality of Deep-Learning Network in Dense Beans by Using Hough Circle Transform for Coffee Industry

Improving Defect Inspection Quality of Deep-Learning Network...

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IEEE International Conference on systems, Man and Cybernetics

作者： Cheng-Ju Kuo Ding-Chau Wang Tzu-Ting Chen Yung-Chien Chou Mao-Yuan Pai Gwo-Jiun Horng Min-Hsiung Hung Yu-Chuan Lin Tz-Heng Hsu Chao-Chun Chen Department of Computer Science and Information Engineering Institute of Manufacturing Information and Systems National Cheng Kung University (NCKU) Taiwan Dept. of Comp. Sci. & Info. Engr. Southern Taiwan University of Science and Technology (STUST) Taiwan General Research Service Center National Pingtung University of Science and Technology Taiwan Department of Computer Science and Information Engineering Chinese Culture University Taiwan

In this paper, we propose a novel Hough circle-assisting deep-network inspection scheme (HCADIS), aiming at identifying defects in dense coffee beans. The proposed HCADIS plays a critical role in a camera-based defect removal system to collect defective bean positions for picking all defects off. The idea of the HCADIS is to mix intermediate data from a deep network and a feature engineering method call Hough circle transform for utilizing advantages of both methods in inspecting beans. The Hough circle transform is adopted because it performs quite stable and bean shapes are highly close to circles in nature. A set of core mechanisms are designed for collaboration between the deep network and the Hough circle transform for precisely and accurately inspecting defective beans. Finally, we implement a prototype of the HCADIS and conduct experiments for testing the proposed scheme. The test results reveal that the HCADIS indeed successfully inspect defects among dense beans with superior performance in various metrics. This work provides industrial participants useful experiences for creating deep-learning solutions to bean products in coffee industries.

关键词： Feature extraction Transforms Inspection Industries Object detection Shape Data structures

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Roadmap on Deep Learning for Microscopy

arXiv

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arXiv 2023年

作者： Volpe, Giovanni Wählby, Carolina Tian, Lei Hecht, Michael Yakimovich, Artur Monakhova, Kristina Waller, Laura Sbalzarini, Ivo F. Metzler, Christopher A. Xie, Mingyang Zhang, Kevin Lenton, Isaac C.D. Rubinsztein-Dunlop, Halina Brunner, Daniel Bai, Bijie Ozcan, Aydogan Midtvedt, Daniel Wang, Hao Sladoje, Nataša Lindblad, Joakim Smith, Jason T. Ochoa, Marien Barroso, Margarida Intes, Xavier Qiu, Tong Yu, Li-Yu You, Sixian Liu, Yongtao Ziatdinov, Maxim A. Kalinin, Sergei V. Sheridan, Arlo Manor, Uri Nehme, Elias Goldenberg, Ofri Shechtman, Yoav Moberg, Henrik K. Langhammer, Christoph Špačková, Barbora Helgadottir, Saga Midtvedt, Benjamin Argun, Aykut Thalheim, Tobias Cichos, Frank Bo, Stefano Hubatsch, Lars Pineda, Jesus Manzo, Carlo Bachimanchi, Harshith Selander, Erik Homs-Corbera, Antoni Fränzl, Martin de Haan, Kevin Rivenson, Yair Korczak, Zofia Adiels, Caroline Beck Mijalkov, Mite Veréb, Dániel Chang, Yu-Wei Pereira, Joana B. Matuszewski, Damian Kylberg, Gustaf Sintorn, Ida-Maria Caicedo, Juan C. Cimini, Beth A. Bell, Muyinatu Lediju A. Saraiva, Bruno M. Jacquemet, Guillaume Henriques, Ricardo Ouyang, Wei Le, Trang Gómez-De-Mariscal, Estibaliz Sage, Daniel Muñoz-Barrutia, Arrate Lindqvist, Ebba Josefson Bergman, Johanna Department of Physics University of Gothenburg Gothenburg41296 Sweden Dept. IT and SciLifeLab Uppsala University Sweden Department of Electrical and Computer Engineering Boston University BostonMA02215 United States Department of Biomedical Engineering Boston University BostonMA02215 United States Görlitz Germany Department of Renal Medicine Division of Medicine University College London Royal Free Hospital Campus London United Kingdom Artificial Intelligence for Life Sciences CIC Dorset United Kingdom Roche Pharma International Informatics Roche Diagnostics GmbH Mannheim Germany Department of Electrical Engineering and Computer Sciences UC Berkeley United States Technische Universität Dresden Faculty of Computer Science Dresden Germany Max Planck Institute of Molecular Cell Biology and Genetics Center for Systems Biology Dresden Dresden Germany University of Maryland College Park United States Klosterneuburg3400 Austria School of Mathematics and Physics The University of Queensland Brisbane4072 Australia Australian Research Council Centre of Excellence for Engineered Quantum Systems The University of Queensland Brisbane4072 Australia Département d’Optique P. M. Duffieux Institut FEMTO-ST Université Franche-Comté CNRS UMR 6174 Besançon France Department of Electrical and Computer Engineering University of California Los Angeles Los AngelesCA United States Department of Information Technology Uppsala University Sweden Department of Biomedical Engineering Rensselaer Polytechnic Institute TroyNY12180 United States Elephas 1 Erdman Pl. Madison53705 United States University of Wisconsin–Madison Department of Medical Physics MadisonWI53705 United States Department of Molecular and Cellular Physiology Albany Medical College AlbanyNY12208 United States Research Laboratory of Electronics Electrical Engineering and Computer Science Massachusetts Institute of Technology BostonMA United States Center for Nanophase Materials Sciences Oak R

Through digital imaging, microscopy has evolved from primarily being a means for visual observation of life at the micro- and nano-scale, to a quantitative tool with ever-increasing resolution and throughput. Artificial intelligence, deep neural networks, and machine learning are all niche terms describing computational methods that have gained a pivotal role in microscopy-based research over the past decade. This Roadmap is written collectively by prominent researchers and encompasses selected aspects of how machine learning is applied to microscopy image data, with the aim of gaining scientific knowledge by improved image quality, automated detection, segmentation, classification and tracking of objects, and efficient merging of information from multiple imaging modalities. We aim to give the reader an overview of the key developments and an understanding of possibilities and limitations of machine learning for microscopy. It will be of interest to a wide cross-disciplinary audience in the physical sciences and life sciences. Copyright © 2023, The Authors. All rights reserved.

关键词： Image enhancement

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

arXiv

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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-dept. 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

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Tethered Balloon Technology for Green Communication in Smart Cities and Healthy Environment 1

Tethered Balloon Technology for Green Communication in Smart...

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1st International Conference of Intelligent Computing and engineering, ICOICE 2019

作者： Alsamhi, S.H. Mohsen Aljabali, Hasan Najim, Mohd Srivastava, Ashutosh Samar Ansari, Mohd Zhao, Liang Nguyen Van, Sau Gupta, S.K. Alammari, Amr A. Hatem Saber, Akram Hebah, Mohammed Y.A.M. Ahmed Abdullah Alasali, Marwan IBB University Yemen and Aligarh Muslim University Dept. of Electronics Engineering Ibb Yemen Aligarh Muslim University Department of Electrical Engineering Aligarh India University of Jaddah Department of Electrical and Electronics Engineering Jaddah Saudi Arabia Department of Electrical Engineering Varanasi India Software Research Institute Athlone Institute of Technology Athlone Ireland School of Computer Science Shenyang Aerospace University Shenyang China Biomedical of Information Technology Shenzhen Insitutes of Advanced Technology UCAS Shenzhen China School of Electronics and Communication Engineering Shri Mata Vaishno Devi University Katra India

ISBN: (纸本)9781728144870

The development and adopting of advanced communication technologies provide mobile users more convenience to connect any wireless network anytime and anywhere. Therefore, a large number of base stations (BS) are demanded keeping users connectivity, enhancing network capacity, and guarantee a sustained users Quality of Experiences (QoS). However, increasing the number of BS leads to an increase in the ecological ad radiation hazards. In order to green communication, many factors should be taken into consideration, i.e., saving energy, guarantee QoS, and reducing pollution hazards. Therefore, we propose tethered balloon technology that can replace a large number of BS and reduce ecological and radiation hazards due to its high altitude and feasible green and healthy broadband communication. The main contribution of this paper is to deploy tethered balloon technology at different altitude and measure the power density. Furthermore, we evaluate the measurement of power density from different height of tethered balloon comparison with traditional wireless communication technologies. The simulation results showed that tethered balloon technology can deliver green communication effectively and efficiently without any hazardous impacts. © 2019 IEEE.

关键词： Smart city

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Towards Reproducible Bioinformatics: The OpenBio-C Scientific Workflow Environment

Towards Reproducible Bioinformatics: The OpenBio-C Scientifi...

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IEEE Symposium on Bioinformatics and Bioengineering (BIBE)

作者： Alexandros Kanterakis Galateia Iatraki Konstantina Pityanou Lefteris Koumakis Nikos Kanakaris Nikos Karacapilidis George Potamias Foundation for Research and Technology-Hellas (FORTH) Institute of Computer Science Heraklion Greece Dept. of Electrical and Computer Engineering Hellenic Mediterranean University Heraklion Crete Greece Industrial Management and Information Systems Lab MEAD University of Patras Patras Greece

ISBN: (纸本)9781728146188

The highly competitive environment of post-genomics biomedical research is pushing towards the production of highly qualified results and their utilization in clinical practice. Due to this pressure, an important factor of scientific progress has been underestimated namely, the reproducibility of the results. To this end, it is critical to design and implement computational platforms that enable seamless and unhindered access to distributed bio-data, software and computational infrastructures. Furthermore, the need to support collaboration and form synergistic activities between the engaged researchers is also raised, as the mean to achieve consensus on respective scientific discoveries. The aforementioned needs and requirements underlie the motivations for the development of OpenBio-C workflow environment, and determine its fundamental objectives. The insufficiencies of current workflow editing and execution environments are explored, and the key-directions to advance over an environment that supports reproducibility are stated. The basic components and functionality of the OpenBio-C initial implementation (beta version) are detailed.

关键词： Bioinformatics Tools Collaboration Pipelines Genomics Market research Semantics

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The unified higher-order dependency pair framework

arXiv

引用

arXiv 2018年

作者： Fuhs, Carsten Kop, Cynthia Dept. of Computer Science and Information Systems Birkbeck University of London United Kingdom Dept. of Software Science Radboud University Nijmegen Netherlands

In recent years, two higher-order extensions of the powerful dependency pair approach for termination analysis of first-order term rewriting have been defined: the static and the dynamic approach. Both approaches offer distinct advantages and disadvantages. However, a grand unifying theory is thus far missing, and both approaches lack the modularity present in the dependency pair framework commonly used in first-order rewriting. Moreover, neither approach can be used to prove non-termination. In this paper, we aim to address this gap by defining a higher-order dependency pair framework, integrating both approaches into a shared formal setup. The framework has been implemented in the (fully automatic) higher-order termination tool WANDA. Copyright © 2018, The Authors. All rights reserved.

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