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arXiv 2020年

作者： Chatterjee, Soumick Saad, Fatima Sarasaen, Chompunuch Ghosh, Suhita Krug, Valerie Khatun, Rupali Mishra, Rahul Desai, Nirja Radeva, Petia Rose, Georg Stober, Sebastian Speck, Oliver Nürnberger, Andreas Data and Knowledge Engineering Group Otto von Guericke University Magdeburg Germany Faculty of Computer Science Otto von Guericke University Magdeburg Germany Genomics Research Centre Human Technopole Italy Institute for Medical Engineering Otto von Guericke University Magdeburg Germany Research Campus STIMULATE Otto von Guericke University Magdeburg Germany Biomedical Magnetic Resonance Otto von Guericke University Magdeburg Germany Artificial Intelligence Lab Otto von Guericke University Magdeburg Germany Department of Mathematics and Computer Science University of Barcelona Barcelona Spain Translational Radiobiology Department of Radiation Oncology Universitätsklinikum Erlangen Erlangen Germany Apollo Hospitals Bilaspur India HCG Cancer Centre Vadodara India Computer Vision Centre Cerdanyola Barcelona Spain Centre for Behavioural Brain Sciences Magdeburg Germany German Centre for Neurodegenerative Diseases Magdeburg Germany

The outbreak of COVID-19 has shocked the entire world with its fairly rapid spread and has challenged different sectors. One of the most effective ways to limit its spread is the early and accurate diagnosing infected patients. Medical imaging, such as X-ray and Computed Tomography (CT), combined with the potential of Artificial Intelligence (AI), plays an essential role in supporting medical personnel in the diagnosis process. Thus, in this article five different deep learning models (ResNet18, ResNet34, InceptionV3, InceptionResNetV2 and DenseNet161) and their ensemble, using majority voting have been used to classify COVID-19, pneumoniæ and healthy subjects using chest X-ray images. Multilabel classification was performed to predict multiple pathologies for each patient, if present. Firstly, the interpretability of each of the networks was thoroughly studied using local interpretability methods - occlusion, saliency, input X gradient, guided backpropagation, integrated gradients, and DeepLIFT, and using a global technique - neuron activation profiles. The mean Micro-F1 score of the models for COVID-19 classifications ranges from 0.66 to 0.875, and is 0.89 for the ensemble of the network models. The qualitative results showed that the ResNets were the most interpretable models. This research demonstrates the importance of using interpretability methods to compare different models before making a decision regarding the best performing model. Copyright © 2020, The Authors. All rights reserved.

关键词： COVID-19

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Publisher Correction: Increasing the presence of BIPOC researchers in computational science

Nature computational science

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Nature computational science 2024年第10期4卷 798页

作者： Christine Yifeng Chen Alan Christoffels Roger Dube Kamuela Enos Juan E Gilbert Sanmi Koyejo Jason Leigh Carlo Liquido Amy McKee Kari Noe Tai-Quan Peng Karaitiana Taiuru Cosmochemical and Isotopic Signatures Group Nuclear and Chemical Sciences Division Lawrence Livermore National Laboratory Livermore CA USA. chen127@llnl.gov. South African MRC (SAMRC) Bioinformatics Unit South African National Bioinformatics Institute University of the Western Cape Cape Town South Africa. alan@sanbi.ac.za. Africa Centers for Disease Control and Prevention African Union Addis Ababa Ethiopia. alan@sanbi.ac.za. College of Science Rochester Institute of Technology Rochester NY USA. rrdube@***. Office of Indigenous Knowledge and Innovation University of Hawai'i Honolulu HI USA. kenos@hawaii.edu. Computer & Information Science & Engineering Department University of Florida Gainesville FL USA. juan@ufl.edu. Department of Computer Science Stanford University Stanford CA USA. sanmi@cs.stanford.edu. Department of Information and Computer Sciences University of Hawai'i at Mānoa Honolulu HI USA. leighj@hawaii.edu. Pi'ikū Co. Kailua HI USA. carlo.h.liquido@***. Department of Educational Foundations University of Hawai'i at Mānoa Honolulu HI USA. amymckee@hawaii.edu. 'ĀinaQuest Honolulu HI USA. amymckee@hawaii.edu. Office of Indigenous Knowledge and Innovation University of Hawai'i Honolulu HI USA. karinoe@hawaii.edu. Department of Communication Michigan State University East Lansing MI USA. pengtaiq@msu.edu. Taiuru & Associates Limited Auckland New Zealand. karaitiana@taiuru.maori.nz. Ngāi Tahu Ngāti Kahungunu Ngāti Toa Auckland New Zealand. karaitiana@taiuru.maori.nz.

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Chinese text-line detection from web videos with fully convolutional networks

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Big Data Analytics 2018年第1期3卷 1-11页

作者： Yang, Chun Pei, Wei-Yi Wu, Long-Huang Yin, Xu-Cheng Department of Computer Science and Technology University of Science and Technology Beijing Beijing China Beijing Key Laboratory of Materials Science Knowledge Engineering University of Science and Technology Beijing Beijing China

In recent years, video becomes the dominant resource of information on the Web, where the text within video usually carries significant semantic information. Video text extraction and recognition plays an essential role in web multimedia understanding and retrieval for big visual data analytics and applications. To deal with challenging backgrounds and embedding noises, most conventional approaches usually tend to design sophisticated pre-processing and post-progressing steps before and after text detection. In this paper, we present a simple yet powerful pipeline that directly and uniformly detects Chinese text lines for embedded captions from web videos. In this Chinese text-line detection system, a fully convolutional network with local context is adopted to localize via an end-to-end learning way. The produced caption predictions are with the word level that could be directly fed into the character classifier. Text-line construction is then performed by heuristic strategies. A variety of experiments are conducted on several real-world web video datasets and demonstrated the effectiveness and efficiency of our proposed method. The proposed system can directly detect the English word and Chinese characters in the caption text-lines without word or character segmentation with the high performance on real-world web video datasets.

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Publisher Correction: Global estimates on the number of people blind or visually impaired by cataract: a meta-analysis from 2000 to 2020

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Eye (London, England) 2024年第11期38卷 2229-2231页

作者： Konrad Pesudovs Hugh R. Taylor Tabassom Sedighi Rupert R. A. Bourne Shahina Pardhan Van Charles Lansingh John H. Kempen Ian Tapply Arthur G. Fernandes Maria Vittoria Cicinelli Alessandro Arrigo Nicolas Leveziel Serge Resnikoff Seth Flaxman Mukkharram M. Bikbov Tasanee Braithwaite Alain Bron Ching-Yu Cheng Monte A. Del Monte Joshua R. Ehrlich Leon B. Ellwein David Friedman João M. Furtado Gus Gazzard Ronnie George M. Elizabeth Hartnett Jost B. Jonas Rim Kahloun Moncef Khairallah Rohit C. Khanna Janet Leasher Julie-Anne Little Vinay Nangia Michal Nowak Tunde Peto Pradeep Ramulu Fotis Topouzis Mitiadis Tsilimbaris Ya Xing Wang Ningli Wang Paul Svitil Briant Theo Vos Katrin Burkart Kaleb Coberly Xiaochen Dai Stephen S. Lim Tomislav Mestrovic Ali H. Mokdad Christopher J. L. Murray Jaimie D. Steinmetz Awoke Misganaw Yohannes Habtegiorgis Abate Mohammad Abdollahi Mozhan Abdollahi Ayele Mamo Abebe Olumide Abiodun Richard Gyan Aboagye Woldu Aberhe Abrha Hasan Abualruz Hiwa Abubaker Ali Eman Abu-Gharbieh Salahdein Aburuz Tadele Girum Girum Adal Mesafint Molla Adane Isaac Yeboah Addo Qorinah Estiningtyas Sakilah Adnani Muhammad Sohail Afzal Muhammad Umair Shahin Aghamiri Bright Opoku Ahinkorah Aqeel Ahmad Sajjad Ahmad Ali Ahmadi Siamak Sabour Ayman Ahmed Haroon Ahmed Ahmad Samir Alfaar Abid Ali Syed Shujait Shujait Ali Awais Altaf Hubert Amu Emmanuel Manu Sofia Androudi Rodrigo Anguita Abhishek Anil Rimple Jeet Kaur Muhammad Aaqib Shamim Saeid Anvari Anayochukwu Edward Anyasodor Francis Appiah Jalal Arabloo Haleh Ayatollahi Mosab Arafat Damelash Areda Reza Arefnezhad Brhane Berhe Aregawi Akeza Awealom Asgedom Tahira Ashraf Seyyed Shamsadin Athari Bantalem Tilaye Tilaye Atinafu Birhan Tsegaw Taye Maha Moh’d Wahbi Atout Alok Atreya Ahmed Y. Azzam Hassan Babamohamadi Sara Bagherieh Yogesh Bahurupi Atif Amin Baig Shumaila Nargus Biswajit Banik Mainak Bardhan Saurav Basu Kavita Batra Nebiyou Simegnew Bayileyegn Fatemeh Bazvand Addisu Shunu Beyene Awoke Masrie Asrat Derese Devidas S. Bhagat Akshaya Srikanth Bhagavathula Pankaj Bh Medicine & Health University of New South Wales Sydney NSW Australia School of Optometry and Vision Science University of New South Wales Sydney NSW Australia School of Population and Global Health University of Melbourne Carlton VIC Australia Vision and Eye Research Institute Anglia Ruskin University Cambridge UK HelpMeSee Instituto Mexicano de Oftalmologia Santiago de Querétaro Mexico University of Miami Gables FL USA University of Utah Salt Lake City UT USA Chief Medical Office HelpMeSee New York NY USA Mexican Institute of Ophthalmology Queretaro Mexico Department of Ophthalmology Massachusetts Eye and Ear/Shepens Eye Research Institute/Harvard Medical School Boston MA USA Eye Unit MyungSung Christian Medical Center (MCM) Comprehensive Specialized Hospital and MyungSun Medical College Addis Ababa Ethiopia Department of Ophthalmology Addis Ababa University Addis Ababa Ethiopia Sight for Souls Bellevue WA USA Department of Ophthalmology Harvard University Boston MA USA Eye Unit MyungSung Medical College Addis Ababa Ethiopia Addenbrooke’s Hospital Cambridge UK Department of Ophthalmology Cambridge University Hospitals Cambridge UK Federal University of Sao Paolo Sao Paolo SP Brazil University of Calgary Calgary AB Canada Department of Ophthalmology and Visual Sciences Federal University of São Paulo Sao Paulo Brazil School of Medicine Vita-Salute San Raffaele University Milan Italy Department of Ophthalmology IRCCS San Raffaele Scientific Institute Milan Italy Department of Ophthalmology San Raffaele Scientific Institute Milano Italy IRCCS San Raffaele Scientific Institute Vita-Salute University Milan Italy Scientific Institute San Raffaele Hospital Vita-Salute University Milan Italy University of Poitiers Poitiers France CHU de Poitiers Poitiers France Ophthalmology Department CHU de Poitiers (Poitiers University Hospital) Poitiers France Unité 1084 National Institute of Health and Medical Research (INSERM) Poitiers France

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Toward evaluating the level of crowd wisdom using interval estimates

Toward evaluating the level of crowd wisdom using interval e...

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作者： Nguyen, Van Du Truong, Hai Bang Merayo, Mercedes G. Nguyen, Ngoc Thanh Division of Knowledge and System Engineering for ICT Ton Duc Thang University Ho Chi Minh City Viet Nam Faculty of Information Technology Ton Duc Thang University Ho Chi Minh City Viet Nam Viet Nam Department Sistemas Informáticos y Computación Universidad Complutense de Madrid Spain Faculty of Computer Science and Management Wroclaw University of Science and Technology Wyb. Wyspianskiego 27 Wroclaw50-370 Poland Faculty of Information Technology Nguyen Tat Thanh University Ho Chi Minh city Viet Nam

Recently, the use of the wisdom of crowds (WoC) for finding solutions to a wide range of real-life problems has dramatically expanded. Prior studies have revealed that diversity, independence, decentralization, and aggregation are the determinants of collective wisdom. However, these findings are often based on the so-called point estimates - single values are used as the representations of individual predictions on the task of estimating unknown quantities or predicting outcomes of future events. In some situations, interval values, which are often called interval estimates, can be used for such representations. Accordingly, one can provide an individual prediction in the form of an interval value including a lower and an upper bounds. Taking into account this kind of representation, in this paper, we present a case study in which collectives of randomly selected predictions can outperform those of most accurate predictions. Then, we evaluate the WoC level by taking into account diversity and cardinality. The computational experiments have indicated that diversity is positively related to collective wisdom. Finally, we discuss some related theoretical and practical implications for further research. © 2019 - IOS Press and the authors. All rights reserved.

关键词： Forecasting

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Automatic knowledge Graph Construction: A Report on the 2019 ICDM/ICBK Contest

Automatic Knowledge Graph Construction: A Report on the 2019...

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IEEE International Conference on Data Mining (ICDM)

作者： Xindong Wu Jia Wu Xiaoyi Fu Jiachen Li Peng Zhou Xu Jiang Mininglamp Academy of Sciences Mininglamp Technology Beijing China Key Laboratory of Knowledge Engineering with Big Data (Hefei Univ. of Technology) Ministry of Education Hefei China Department of Computing Macquarie University Sydney Australia School of Computer Science and Technology Anhui University Hefei China

Automatic knowledge graph construction seeks to build a knowledge graph from unstructured text in a specific domain or cross multiple domains, without human intervention. IEEE ICDM 2019 and ICBK 2019 invited teams from both degree-granting institutions and industrial labs to compete in the 2019 knowledge Graph Contest by automatically constructing knowledge graphs in at least two different domains. This article reports the outcomes of the Contest. The participants were expected to build a model to extract knowledge represented as triplets from text data and develop a web application to visualize the triplets. Awards were given to five teams. Their models and key techniques used to construct knowledge graphs are summarized.

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Advances in Machine Learning for the Behavioral sciences

arXiv

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arXiv 2019年

作者： Kliegr, Tomáš Bahník, Štěpán Fürnkranz, Johannes Department of Information and Knowledge Engineering Faculty of Informatics and Statistics University of Economics Prague Czech Republic Department of Management Faculty of Business Administration University of Economics Prague Czech Republic Computational Data Analytics Group Department of Computer Science Johannes Kepler Universität Linz Altenbergerstraße 66B Linz4040 Austria

The areas of machine learning and knowledge discovery in databases have considerably matured in recent years. In this article, we briefly review recent developments as well as classical algorithms that stood the test of time. Our goal is to provide a general introduction into different tasks such as learning from tabular data, behavioral data, or textual data, with a particular focus on actual and potential applications in behavioral sciences. The supplemental appendix to the article also provides practical guidance for using the methods by pointing the reader to proven software implementations. The focus is on R, but we also cover some libraries in other programming languages as well as systems with easy-to-use graphical interfaces. Copyright © 2019, The Authors. All rights reserved.

关键词： Data mining

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License and template access control for geospatial linked data 3

License and template access control for geospatial linked da...

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3rd International Workshop on Geospatial Linked Data and the 2nd Workshop on Querying the Web of Data, GeoLD-QuWeDa 2018

作者： Meehan, Alan Fatema, Kaniz Brennan, Rob Clinton, Éamonn McNerney, Lorraine O'Sullivan, Declan ADAPT Centre Knowledge and Data Engineering Group School of Computer Science and Statistics Trinity College Dublin Ireland Department of Electronics Computing and Mathematics University of Derby Kedleston Road Derby United Kingdom Ordnance Survey Ireland Phoenix Park Dublin Ireland

As Linked Data technologies mature geospatial data institutions are seeking to exploit these advantages and selectively serve their commercially valuable data to customers on the web. An access control solution is required to ensure customers are not accessing unauthorized data. In this paper we present a fine grained and flexible access control method for geospatial Linked Data. Our declarative approach provides a vocabulary to model data licenses and templates. A license models what a customer is allowed to access at a fine granularity and a template models how data is allowed to be accessed. Each template contains variables and the values that these variables are allowed to contain are specified. This provides great flexibility and allows the creation of templates for a range of different data access use cases. We propose an implementation architecture for our approach which proposes a license and template-based access control web service that sits on top of a triple store, SPARQL processor and a RESTful API for access. We also present a case study where a prototype implementation of our access control approach was applied to Ordnance Survey Ireland's controlled access geospatial Linked Data. © CEUR-WS. All rights reserved.

关键词： Access control

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Intensive summer course in robotics – Robotcraft

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Applied Computing and Informatics 2018年第1-2期16卷 155-179页

作者： Ferreira, N.M. Fonseca Araujo, André Couceiro, M.S. Portugal, David Department of Electrical Engineering (DEE) Engineering Institute of Coimbra (ISEC) Polytechnic of Coimbra (IPC) Rua Pedro Nunes – Quinta da Nora Coimbra Portugal GECAD - Knowledge Research Group on Intelligent Engineering and Computing for Advanced Innovation Development of the Engineering Institute of Porto (ISEP) Polytechnic Institute of Porto (IPP) Porto Portugal INESC TEC – Institute for Systems and Computer Engineering Technology and Science Porto Portugal Ingeniarius Lda Coimbra Portugal

This paper describes a two-month summer intensive course designed to introduce participants with a hands-on technical craft on robotics and to acquire experience in the low-level details of embedded systems. Attendants started this course with a brief introduction to robotics;learned to draw, design and create a personalized 3D structure for their mobile robotic platform and developed skills in embedded systems. They were familiarize with the practices used in robotics, learning to connect all sensors and actuator, developing a typical application on differential kinematic using Arduino, exploring ROS features under Raspberry Pi environment and Arduino – Raspberry Pi communication. Different paradigms and some real applications and programming were addressed on the topic of Artificial Intelligence. Throughout the course, participants were introduced to programming languages (including Python and C++), advanced programming concepts such as ROS, basic API development, system concepts such as I2C and UART serial interfaces, PWM motor control and sensor fusion to improve robotic navigation and localization. This paper describes not just the concept, layout and methodology used on RobotCraft 2017 but also presents the participants knowledge background and their overall opinions, leading to focus on lessons learned and suggestions for future editions. © 2018, N.M. Fonseca Ferreira, André Araujo, M.S. Couceiro and David Portugal.

关键词： International trends in educational robotics Project-based learning ROS

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The Computational Complexity of Angry Birds

arXiv

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arXiv 2018年

作者： Stephenson, Matthew Renz, Jochen Ge, Xiaoyu Department of Data Science and Knowledge Engineering Maastricht University Maastricht Netherlands Research School of Computer Science Australian National University Canberra Australia

The physics-based simulation game Angry Birds has been heavily researched by the AI community over the past five years, and has been the subject of a popular AI competition that is currently held annually as part of a leading AI conference. Developing intelligent agents that can play this game effectively has been an incredibly complex and challenging problem for traditional AI techniques to solve, even though the game is simple enough that any human player could learn and master it within a short time. In this paper we analyse how hard the problem really is, presenting several proofs for the computational complexity of Angry Birds. By using a combination of several gadgets within this game’s environment, we are able to demonstrate that the decision problem of solving general levels for different versions of Angry Birds is either NP-hard, PSPACE-hard, PSPACE-complete or EXPTIME-hard. Proof of NP-hardness is by reduction from 3-SAT, whilst proof of PSPACE-hardness is by reduction from True Quantified Boolean Formula (TQBF). Proof of EXPTIME-hardness is by reduction from G2, a known EXPTIME-complete problem similar to that used for many previous games such as Chess, Go and Checkers. To the best of our knowledge, this is the first time that a single-player game has been proven EXPTIME-hard. This is achieved by using stochastic game engine dynamics to effectively model the real world, or in our case the physics simulator, as the opponent against which we are playing. These proofs can also be extended to other physics-based games with similar mechanics. Copyright © 2018, The Authors. All rights reserved.

关键词： Computational complexity

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