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Biological Psychiatry Global Open Science 2025年第5期5卷

作者： Angela Koloi Nabila P.R. Siregar Rick Quax Antonis I. Sakellarios Femke Lamers Arja Rydin Kevin Dobretz Costas Papaloukas Dimitrios I. Fotiadis Jos A. Bosch Unit of Medical Technology and Intelligent Information Systems Department of Materials Science and Engineering University of Ioannina Ioannina Greece Department of Biological Applications and Technology University of Ioannina Ioannina Greece Department of Clinical Psychology University of Amsterdam Amsterdam the Netherlands Department of Medical Psychology Amsterdam University Medical Center Amsterdam the Netherlands Computational Science Laboratory Institute of Informatics University of Amsterdam Amsterdam the Netherlands Biomedical Engineering of the Department of Mechanical Engineering and Aeronautics University of Patras Patras Greece Department of Psychiatry Amsterdam University Medical Center VU Amsterdam Amsterdam the Netherlands Amsterdam Public Health Mental Health Program Amsterdam the Netherlands Cardiology Geneva University Hospitals Geneva Switzerland Department of Medicine University of Geneva Geneva Switzerland Biomedical Research Institute Foundation for Research and Technology - Hellas Ioannina Greece

Background Major depressive disorder (MDD) and cardiovascular diseases (CVDs) often co-occur whereby comorbidity results in poorer clinical outcomes, presumably due to shared immunometabolic pathways. Identifying shared biomarkers for MDD-CVD comorbidity may provide targets for prevention or treatment. Methods Using data from the NESDA (Netherlands Study of Depression and Anxiety) ( n = 2256, 66.3% female, mean age 41.86 ± 13.08 years at baseline), validated with the UK Biobank (UKB) data ( n = 35,668, 56.14% female, mean age 63.95 ± 7.74 years), this study aimed to identify 1) biomarkers, closely associated with current MDD, and 2) longitudinal pathways linking MDD and atherosclerotic CVD. Plasma metabolites (nuclear magnetic resonance) and inflammatory markers were used as exposures within a machine learning framework. Influential biomarkers were integrated into a temporal network analysis linking MDD to subsequent CVDs, exploring longitudinal pathways through causal discovery, validated by sensitivity analysis and centrality assessment. External validation included mediation and regression analysis adjusting for covariates. Results Network analysis identified stable direct paths from MDD to CVDs via tumor necrosis factor α (TNF-α), tyrosine, and fatty acids and indirect paths via acetate, high-density lipoprotein (HDL) diameter, interleukin 6, AGP, high-sensitivity C-reactive protein, and low-density lipoprotein triglycerides. Among these, acetate, tyrosine, AGP (α 1 -acid glycoprotein), and HDL diameter potentially mediated the MDD-CVD connection, given that these were identified as key nodes within the network. UKB validation confirmed HDL diameter (β = 0.004) and AGP (β = 0.003) as significant depression-CVD mediators (both p < .001), after adjusting for age, sex, deprivation index, alcohol consumption, smoking status, physical activity, and body mass index. Conclusions These analyses identified biomarkers shared in MDD and CVDs and may drive comorbid patholog

关键词： Biomarkers CVD Machine learning MDD Mediators

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Learn-Morph-Infer: a new way of solving the inverse problem for brain tumor modeling

arXiv

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arXiv 2021年

作者： Ezhov, Ivan Scibilia, Kevin Franitza, Katharina Steinbauer, Felix Shit, Suprosanna Zimmer, Lucas Lipkova, Jana Kofler, Florian Paetzold, Johannes Canalini, Luca Waldmannstetter, Diana Menten, Martin J. Metz, Marie Wiestler, Benedikt Menze, Bjoern Department of Informatics TUM Munich Germany TranslaTUM - Central Institute for Translational Cancer Research TUM Munich Germany Department of Mechanical Engineering TUM Munich Germany Department of Quantitative Biomedicine UZH Zurich Switzerland Department of Pathology Brigham and Women’s Hospital Harvard Medical School BostonMA United States Broad Institute of Harvard MIT CambridgeMA United States Data Science Program Dana-Farber Cancer Institute BostonMA United States Fraunhofer MEVIS Bremen Germany Neuroradiology Department of Klinikum Rechts der Isar TUM Munich Germany

Current treatment planning of patients diagnosed with a brain tumor, such as glioma, could significantly benefit by accessing the spatial distribution of tumor cell concentration. Existing diagnostic modalities, e.g. magnetic resonance imaging (MRI), contrast sufficiently well areas of high cell density. In gliomas, however, they do not portray areas of low cell concentration, which can often serve as a source for the secondary appearance of the tumor after treatment. To estimate tumor cell densities beyond the visible boundaries of the lesion, numerical simulations of tumor growth could complement imaging information by providing estimates of full spatial distributions of tumor cells. Over recent years a corpus of literature on medical image-based tumor modeling was published. It includes different mathematical formalisms describing the forward tumor growth model. Alongside, various parametric inference schemes were developed to perform an efficient tumor model personalization, i.e. solving the inverse problem. However, the unifying drawback of all existing approaches is the time complexity of the model personalization which prohibits a potential integration of the modeling into clinical settings. In this work, we introduce a deep learning based methodology for inferring the patient-specific spatial distribution of brain tumors from T1Gd and FLAIR MRI medical scans. Coined as Learn-Morph-Infer, the method achieves real-time performance in the order of minutes on widely available hardware and the compute time is stable across tumor models of different complexity, such as reaction-diffusion and reaction-advection-diffusion models. We believe the proposed inverse solution approach not only bridges the way for clinical translation of brain tumor personalization but can also be adopted to other scientific and engineering domains. Copyright © 2021, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

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A Robust Ensemble Algorithm for Ischemic Stroke Lesion Segmentation: Generalizability and Clinical Utility Beyond the ISLES Challenge

arXiv

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arXiv 2024年

作者： de la Rosa, Ezequiel Reyes, Mauricio Liew, Sook-Lei Hutton, Alexandre Wiest, Roland Kaesmacher, Johannes Hanning, Uta Hakim, Arsany Zubal, Richard Valenzuela, Waldo Robben, David Sima, Diana M. Anania, Vincenzo Brys, Arne Meakin, James A. Mickan, Anne Broocks, Gabriel Heitkamp, Christian Gao, Shengbo Liang, Kongming Zhang, Ziji Siddiquee, Md Mahfuzur Rahman Myronenko, Andriy Ashtari, Pooya Van Huffel, Sabine Jeong, Hyun-Su Yoon, Chi-Ho Kim, Chulhong Huo, Jiayu Ourselin, Sebastien Sparks, Rachel Clèrigues, Albert Oliver, Arnau Lladó, Xavier Chalcroft, Liam Pappas, Ioannis Bertels, Jeroen Heylen, Ewout Moreau, Juliette Hatami, Nima Frindel, Carole Qayyum, Abdul Mazher, Moona Puig, Domenec Lin, Shao-Chieh Juan, Chun-Jung Hu, Tianxi Boone, Lyndon Goubran, Maged Liu, Yi-Jui Wegener, Susanne Kofler, Florian Ezhov, Ivan Shit, Suprosanna Hernandez Petzsche, Moritz R. Menze, Bjoern Kirschke, Jan S. Wiestler, Benedikt Department of Quantitative Biomedicine University of Zurich Zurich Switzerland Department of Informatics Technical University Munich Germany Icometrix Leuven Belgium ARTORG Center for Biomedical Research University of Bern Bern Switzerland Department of Radiation Oncology University Hospital Bern University of Bern Switzerland University of Bern Bern Switzerland Chan Division of Occupational Science and Occupational Therapy University of Southern California Los AngelesCA United States Stevens Neuroimaging and Informatics Institute Department of Neurology Keck School of Medicine University of Southern California United States University Institute of Diagnostic and Interventional Neuroradiology Inselspital Bern Switzerland University Institute of Diagnostic and Interventional Neuroradiology University Hospital Bern Inselspital University of Bern Bern Switzerland Department of Diagnostic and Interventional neuroradiology University Medical Center Hamburg-Eppendorf Hamburg Germany Department of Medical Imaging Radboud University Medical Center Institute for Health Sciences Nijmegen Netherlands Deepwise AI Lab Beijing China Beijing University of Posts and Telecommunications Bejing China School of Computing and Augmented Intelligence Arizona State University TempeAZ United States NVIDIA Santa ClaraCA United States STADIUS Center for Dynamical Systems Signal Processing and Data Analytics KU Leuven Leuven Belgium Pohang Korea Republic of School of Biomedical Engineering & Imaging Sciences King’s College London United Kingdom Institute of Computer Vision and Robotics University of Girona Spain Wellcome Centre for Human Neuroimaging University College London London United Kingdom Laboratory of Neuro Imaging Stevens Institute for Neuroimaging and Informatics Keck School of Medicine University of Southern California Los Angeles United States KU Leuven Leuven Belgium CREATIS Université Lyon1 CNRS UMR5220 INSERM U1206 INSA-Lyon Villeurbanne696

Diffusion-weighted MRI (DWI) is essential for stroke diagnosis, treatment decisions, and prognosis. However, image and disease variability hinder the development of generalizable AI algorithms with clinical value. We address this gap by presenting a novel ensemble algorithm derived from the 2022 Ischemic Stroke Lesion Segmentation (ISLES) challenge. ISLES’22 provided 400 patient scans with ischemic stroke from various medical centers, facilitating the development of a wide range of cutting-edge segmentation algorithms by the research community. By assessing them against a hidden test set, we identified strengths, weaknesses, and potential biases. Through collaboration with leading teams, we combined top-performing algorithms into an ensemble model that overcomes the limitations of individual solutions. Our ensemble model combines the individual algorithms’ strengths and achieved superior ischemic lesion detection and segmentation accuracy (median Dice score: 0.82, median lesion-wise F1 score: 0.86) on our internal test set compared to individual algorithms. This accuracy generalized well across diverse image and disease variables. Furthermore, the model excelled in extracting clinical biomarkers like lesion types and affected vascular territories. Notably, in a Turing-like test, neuroradiologists consistently preferred the algorithm’s segmentations over manual expert efforts, highlighting increased comprehensiveness and precision. Validation using a real-world external dataset (N=1686) confirmed the model’s generalizability (median Dice score: 0.82, median lesion-wise F1 score: 0.86). The algorithm’s outputs also demonstrated strong correlations with clinical scores (admission NIHSS and 90-day mRS) on par with or exceeding expert-derived results, underlining its clinical relevance. This study offers two key findings. First, we present an ensemble algorithm that detects and segments ischemic stroke lesions on DWI across diverse scenarios on par with expert (neuro)rad

关键词： Diagnosis

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Designing the Application of Security Text Messages into Audio Files Using Data Encryption Standard (DES) Algorithms Using the End of File (EOF) Method 1

Designing the Application of Security Text Messages into Aud...

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1st Workshop on Environmental Science, Society, and Technology, WESTECH 2018

作者： Pardede, A.M.H. Pramana, Anggi Zarlis, Muhammad Iskandar, Akbar Manurung, Rosida Tiurma Sriadhi, S. Kurniawan, Citra Hasibuan, Abdurrozzaq Siregar, Nurintan Asyiah Sari, Ayu Esteka Mulyaningsih, Indrya Iskandar Alam, Teguh Hidayat Ibrahim Listyorini, Tri Winarno, Edy Tulus STMIK Kaputama Binjai Sumatera Utara Indonesia Department of Computer Science Universitas Sumatera Utara Medan Indonesia Department of Informatics STMIK AKBA Makassar Indonesia Graduate Program in Scientific Psychology Universitas Kristen Maranatha Indonesia Department of Electrical Engineering Universitas Negeri Medan Indonesia Department of Electrical Engineering Sekolah Tinggi Teknik Malang Indonesia Faculty of Engineering Universitas Islam Sumatera Utara Medan Indonesia Department of Management STIE Labuhanbatu Sumatera Utara Indonesia Department of Management STIE Sakti Alam Kerinci Jambi Indonesia Department of Indonesian Language Teaching Institut Agama Islam Negeri Syekh Nurjati Cirebon Indonesia Department of Informatics Engineering Universitas Muhammadiyah Sorong Sorong Indonesia Department of English Education Universitas Muhammadiyah Sorong Sorong Indonesia Department of Informatics Universitas Muria Kudus Indonesia Faculty of Information Technology Universitas Stikubank Semarang Indonesia Department of Mathematics Universitas Sumatera Utara Medan Indonesia

There are several ways to deal with security issues of confidential data sent via the internet, including using cryptographic techniques and steganography. Steganography is the science and art of concealing information / messages in a media in such a way that its existence is not detected by other parties who are not entitled to the information. Instead, cryptography disguises the meaning of a message, but does not hide that there is a message because the file looks suspicious. The Steganography technique used here was End Of File (EOF). The EOF technique described how to add data or files at the end of the image file. For this technique, the size of data or files that will be hidden could be larger than the size of the image file. The hidden data will be inserted at the end of the file so that it will not affect the image. This steganography application is also equipped with a cryptographic function Data Encryption Standard (DES) at the time of insertion of data that functions as a generator code and encrypts data, so that the security of a data in the file is more protected and protected from those who are not entitled to know the data. © Published under licence by IOP Publishing Ltd.

关键词： Steganography

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STREAMLINE: AN AUTOMATED MACHINE LEARNING PIPELINE FOR BIOMEDICINE APPLIED TO EXAMINE THE UTILITY OF PHOTOGRAPHY-BASED PHENOTYPES FOR OSA PREDICTION ACROSS INTERNATIONAL SLEEP CENTERS

arXiv

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arXiv 2023年

作者： Urbanowicz, Ryan J. Bandhey, Harsh Keenan, Brendan T. Maislin, Greg Hwang, Sy Mowery, Danielle L. Lynch, Shannon M. Mazzotti, Diego R. Han, Fang Li, Qing Yun Penzel, Thomas Tufik, Sergio Bittencourt, Lia Gislason, Thorarinn de Chazal, Philip Singh, Bhajan McArdle, Nigel Chen, Ning-Hung Pack, Allan Schwab, Richard J. Cistulli, Peter A. Magalang, Ulysses J. Department of Computational Biomedicine Cedars Sinai Medical Center Los AngelesCA United States Division of Sleep Medicine Department of Medicine University of Pennsylvania PhiladelphiaPA United States Institute for Biomedical Informatics University of Pennsylvania PhiladelphiaPA United States Department of Biostatistics Epidemiology and Informatics University of Pennsylvania PhiladelphiaPA United States Cancer Prevention and Control Program Fox Chase Cancer Center PhiladelphiaPA United States Division of Medical Informatics Department of Internal Medicine University of Kansas LawrenceKS United States Division of Pulmonary Critical Care and Sleep Medicine Department of Internal Medicine University of Kansas LawrenceKS United States Division of Sleep Medicine Peking University People’s Hospital Beijing China Department of Respiratory and Critical Care Medicine Ruijin Hospital Shanghai Jiao Tong University School of Medicine Shanghai China Interdisciplinary Center of Sleep Medicine Charite University Hospital Berlin Germany Department of Psychobiology Federal University of Sao Paulo Sao Paulo Brazil Department of Sleep Medicine Landspitali University Hospital Reykjavik Iceland University of Iceland Faculty of Medicine Reykjavik Iceland Charles Perkins Center Faculty of Engineering University of Sydney Sydney Australia Department of Pulmonary Physiology and Sleep Medicine Sir Charles Gairdner Hospital Nedlands Australia University of Western Australia Crawley Australia Department of Pulmonary and Critical Care Medicine Sleep Center Linkou Chang Gung Memorial Hospital Taoyuan Taiwan Charles Perkins Centre Faculty of Medicine and Health University of Sydney Royal North Shore Hospital Sydney Australia Division of Pulmonary Critical Care and Sleep Medicine Department of Medicine Ohio State University Wexner Medical Center ColumbusOH United States 700 N. San Vicente Blvd. Pacific Design Center Suite G541E Los AngelesCA90069 United States

Objective: While machine learning (ML) includes a valuable array of tools for analyzing biomedical data with multivariate and complex underlying associations, significant time and expertise is required to assemble effective, rigorous, comparable, reproducible, and unbiased pipelines. Automated ML (AutoML) tools seek to facilitate ML application by automating a subset of analysis pipeline elements. In this study we develop and validate a Simple, Transparent, End-to-end Automated Machine Learning Pipeline (STREAMLINE) and apply it to investigate the added utility of photography-based phenotypes for predicting obstructive sleep apnea (OSA);a common and underdiagnosed condition associated with a variety of health, economic, and safety consequences. Methods: STREAMLINE is designed to tackle biomedical binary classification tasks while (1) avoiding common mistakes, (2) accommodating complex associations and common data challenges, and (3) allowing scalability, reproducibility, and model interpretation. It automates the majority of established, generalizable, and reliably automatable aspects of an ML analysis pipeline while incorporating cutting edge algorithms and providing opportunities for human-in-the-loop customization. We present a broadly refactored and extended release of STREAMLINE, validating and benchmarking performance across simulated and real-world datasets. Then we applied STREAMLINE to evaluate the utility of demographics (DEM), self-reported comorbidities (DX), symptoms (SYM), and photography-based craniofacial (CF) and intraoral (IO) anatomy measures in predicting ‘any OSA’ or ‘moderate/severe OSA’ using 3,111 participants from Sleep Apnea Global Interdisciplinary Consortium (SAGIC). Results: Benchmarking analyses validated the efficacy of STREAMLINE across data simulations with increasingly complex patterns of association including epistatic interactions and genetic heterogeneity. OSA analyses identified a significant increase in ROC-AUC when adding CF t

关键词： Machine learning

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Correction: Characterizing country-specific human and ecosystem health impact and damage cost of agricultural pesticides: the case for Thailand

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The International Journal of Life Cycle Assessment 2022年第12期27卷 1377-1377页

作者： Mankong, Phatchari Fantke, Peter Phenrat, Tanapon Mungkalasiri, Jitti Gheewala, Shabbir H. Prapaspongsa, Trakarn Graduate Program in Environmental and Water Resources Engineering Department of Civil and Environmental Engineering Faculty of Engineering Mahidol University Nakhon Pathom Thailand Quantitative Sustainability Assessment Department of Environmental and Resource Engineering Technical University of Denmark Kongens Lyngby Denmark Department of Civil Engineering Faculty of Engineering Naresuan University Phitsanulok Thailand Technology and Informatics Institute for Sustainability (TIIS) National Metal and Materials Technology Center (MTEC) National Science and Technology Development Agency (NSTDA) Pathum Thani Thailand The Joint Graduate School of Energy and Environment (JGSEE) King Mongkut’s University of Technology Thonburi Bangkok Thailand Centre of Excellence On Energy Technology and Environment Ministry of Higher Education Science Research and Innovation Bangkok Thailand

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Computer Modelling of the Information Properties of Hyper Chaotic Lorenz System and Its Application in Secure Communication System

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Journal of Physics: Conference Series 2021年第1期1764卷

作者： Volodymyr Rusyn Mujiarto Mustafa Mamat Firmansyah Azharul W. S. Mada Sanjaya Aceng Sambas Estiyan Dwipriyoko Akhmad Sutoni Yuriy Fedkovych Chernivtsi National University Kotsybynsky str. 2 Chernivtsi 58012 Ukraine. Department of Mechanical Engineering Universitas Muhammadiyah Tasikmalaya Indonesia Faculty of Informatics and Computing Universiti Sultan Zainal Abidin Kuala Terengganu Malaysia Department of Mechanical Engineering Sekolah Tinggi Teknologi Cileungsi Indonesia Department of Physics Universitas Islam Negeri Sunan Gunung Djati Bandung Indonesia Informatics Study Program Universitas Langlangbuana Bandung Indonesia Department of Industrial Engineering Universitas Suryakancana Cianjur Indonesia

This paper presents computer modeling, analysis and research of the hyper-chaotic Lorenz system based on programming interface that has been developed in LabView software environment. This study allows for generating and research of the main information properties of hyper-chaotic Lorenz system, focusing on time distribution of the four chaotic coordinates, phase portraits and Lyapunov exponents. The programming interface demonstrates the algorithm of masking and decrypt of the information carrier.

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Corrigendum to "Novel model of multiple sclerosis induced by EBV-like virus generate a unique B cell population" [Journal of Neuroimmunology volume 394 (2024) 578408]

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Journal of neuroimmunology 2025年 404卷 578625页

作者： J L Deffenbaugh K-J Jung S P Murphy Y Liu C N Rau C Petersen-Cherubini P L Collins D Chung A E Lovett-Racke Department of Microbial Infection & Immunity The Ohio State University Wexner Medical Center USA. Department of Computer Science and Engineering The Ohio State University USA. Department of Microbial Infection & Immunity The Ohio State University Wexner Medical Center USA Neuroscience Graduate Program The Ohio State University USA. Department of Biomedical Informatics The Ohio State University Wexner Medical Center USA Pelotonia Institute for Immuno-Oncology The Ohio State University Wexner Medical Center USA. Department of Neuroscience The Ohio State University Wexner Medical Center USA. Electronic address: amylovettracke@***.

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Coconut waste to green nanomaterial: Large scale synthesis of N-doped graphene nano sheets

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Nano-Structures and Nano-Objects 2023年 36卷

作者： Siburian, Rikson Tang, Liang Wei Alias, Yatimah Tok, Alfred Iing Yoong Goei, Ronn Simanjuntak, Crystina Tarigan, Kerista Paiman, Suriati Goh, Boon Tong Anshori, Isa Kurniawan, Cepi Postgraduate Program Department of Chemistry Faculty of Mathematics and Natural Sciences Universitas Sumatera Utara Medan 20155 Indonesia Department of Chemistry Faculty of Mathematics and Natural Sciences Universitas Sumatera Utara Medan 20155 Indonesia Carbon and Frankincense Research Center Universitas Sumatera Utara Medan 20155 Indonesia Department of Chemistry Faculty of Science Universiti Malaya Kuala Lumpur 50603 Malaysia University Malaya Centre for Ionic Liquids (UMCiL) Universiti Malaya Kuala Lumpur 50603 Malaysia School of Materials Science and Engineering Nanyang Technological University 50 Nanyang Avenue Singapore 639798 Singapore Department of Physics Faculty of Mathematics and Natural Sciences Universitas Sumatera Utara Medan 20155 Indonesia Physics Department Faculty of Science Universiti Putra Malaysia Selangor (Serdang) Seri Kembangan 43400 Malaysia Low Dimensional Materials Research Centre Department of Physics Faculty of Science University of Malaya Kuala Lumpur 50603 Malaysia Biomedical Engineering Department School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung 40132 Indonesia Research Center of Nanoscience and Nanotechnology Bandung Institute of Technology Bandung 40132 Indonesia Chemistry Department Faculty of Mathematics and Natural Sciences Universitas Negeri Semarang Semarang 50229 Indonesia Functional Material Research Center Universitas Negeri Semarang Semarang 50229 Indonesia

In this paper, a breakthrough was achieved in large-scale production of N-doped Graphene Nano Sheets (N-GNS) using coconut fruits. The main objectives were to produce N-GNS on a large scale and assess its electrical conductivity. The process involved two key steps: first, producing GNS from coconut fruits through pyrolysis, and then generating N-GNS by doping with nitrogen using ammonia solution at room temperature. Various analytical techniques were used to characterize the produced N-GNS, including X-ray Diffraction (XRD), Scanning Electron Microscopy with Energy Dispersive Spectroscopy (SEM-EDS), Transmission Electron Microscopy (TEM), X-ray Photoelectron Spectroscopy (XPS), Raman spectroscopy, and electrochemical cyclic voltammetry and electrochemical impedance spectroscopy. The XRD data indicated that the C (002) peak of N-GNS shifted to a higher 2θ value (2θ = 24.72°) compared to GNS (2θ = 23.86°), suggesting an incorporation of nitrogen into carbon structure in N-GNS. This was further supported by XPS data, which identified N-pyridine (BE = 402.0 eV) and C-N (BE = 286.8 eV) in N-GNS. TEM images showed that N-GNS had a flat surface, and the distance between graphene layers slightly expanded (0.36 nm) compared to graphene layers (0.34 nm). SEM images and EDX data validated the morphology, resembling honeycomb lattices, with a significant content of N atoms. Raman data successfully identified the D-band and G-band in N-GNS, further validating its production. Most importantly, N-GNS exhibited electrical conductivity, making it a promising candidate for conductive and supporting materials in various applications. The significance of this research extends beyond N-GNS production, opening up new avenues for the sustainable synthesis of valuable nanomaterials from coconut fruit waste, contributing to both graphene technology and environmental conservation. © 2023 Elsevier B.V.

关键词： C-sp2 Electrical conductivity Graphene nano sheets N-graphene nano sheets N-pyridine XPS

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Turning social news curation into microtask crowdsourcing: a vision and research agenda

Turning social news curation into microtask crowdsourcing: a...

引用

IEEE International Conference on Systems, Man and Cybernetics

作者： Daniel Schneider António Correia Ramon Chaves Ana Paula Pimentel Marcio Antelio Edson Mello Lucas Marcos Antonio de Almeida Luiz Oliveira Jano Moreira de Souza Postgraduate Program in Informatics PPGI/UFRJ Rio de Janeiro Brazil Tércio Pacitti Institute of Computer Applications and Research NCE/UFRJ Rio de Janeiro Brazil INESC TEC and University of Trás-os-Montes e Alto Douro Vila Real Portugal Graduate School of Engineering CEFET-RJ Rio de Janeiro Brazil Polytechnic Institute IPRJ/UERJ Rio de Janeiro Brazil Systems Engineering and Computer Science Program (PESC/COPPE/UFRJ) Rio de Janeiro Brazil Federal Institute of Rio de Janeiro (IFRJ) Rio de Janeiro Brazil

ISBN: (数字)9781728185262

ISBN: (纸本)9781728185279

Over the past decade, online crowdsourcing has established itself as an emerging paradigm that industry and government have been using to harness the cognitive abilities of a multitude of users distributed around the world. In this context, microtask crowdsourcing has become the method of choice for addressing a wide range of diverse problems. Microtasks typically require a minimum of time and cognitive effort, but combined individual efforts have made it possible to accomplish great achievements. The goal of this paper is to contribute to the ongoing effort of understanding whether the same success that microtask crowdsourcing has achieved in other domains can be obtained in the field of social news curation. In particular, we ask whether it is possible to turn online news curation, typically a social and collaborative activity on the Web, into a model in which curatorial activities are mapped into microtasks to be performed by a crowd of online users.

关键词： Crowdsourcing Industries Conferences Government Collaboration Turning Cybernetics

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