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International Joint Conference on computer science and Software engineering (JCSSE)

作者： Tao-Ku Chang Ching-Nung Yangw Fu-Hao Yeh Department of Computer Science and Information Engineering National Dong Hwa University Hualien Taiwan National Dong Hwa University Shoufeng TW Program of Information Technology Fooyin University Kaohsiung Taiwan

ISBN: (纸本)9781509020348

Recent studies indicate that customer awareness of and interest in mobile payments have been increasing. With customers worrying about their personal data being hacked or intercepted, major barriers to adoption are security and privacy risks. We are presenting in this paper the design of a secure scheme for mobile payments which guarantees mutual non-repudiation between the customer, merchant, and banker. With the proposed scheme, a customer can make a payment with the same payword chains of a single account from multiple devices.

关键词： Mobile communication Security computer science Mobile handsets Software engineering Electronic mail Business

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A repeating fast radio burst source localised to a nearby spiral galaxy

arXiv

引用

arXiv 2020年

作者： Marcote, B. Nimmo, K. Hessels, J.W.T. Tendulkar, S.P. Bassa, C.G. Paragi, Z. Keimpema, A. Bhardwaj, M. Karuppusamy, R. Kaspi, V.M. Law, C.J. Michilli, D. Aggarwal, K. Andersen, B. Archibald, A.M. Bandura, K. Bower, G.C. Boyle, P.J. Brar, C. Burke-Spolaor, S. Butler, B.J. Cassanelli, T. Chawla, P. Demorest, P. Dobbs, M. Fonseca, E. Giri, U. Good, D.C. Gourdji, K. Josephy, A. Kirichenko, A.Yu. Kirsten, F. Landecker, T.L. Lang, D. Lazio, T.J.W. Li, D.Z. Lin, H.-H. Linford, J.D. Masui, K. Mena-Parra, J. Naidu, A. Ng, C. Patel, C. Pen, U.-L. Pleunis, Z. Rafiei-Ravandi, M. Rahman, M. Renard, A. Scholz, P. Siegel, S.R. Smith, K.M. Stairs, I.H. Vanderlinde, K. Zwaniga, A.V. Oude Hoogeveensedijk 4 PD Dwingeloo7991 Netherlands ASTRON Netherlands Institute for Radio Astronomy Oude Hoogeveensedijk 4 PD Dwingeloo7991 Netherlands Anton Pannekoek Institute for Astronomy University of Amsterdam Science Park 904 Amsterdam1098 XH Netherlands Department of Physics McGill University 3600 University Street MontréalQCH3A 2T8 Canada McGill Space Institute McGill University 3550 University Street MontréalQCH3A 2A7 Canada Max-Planck-Institut für Radioastronomie Auf dem Hügel 69 BonnD-53121 Germany Department of Astronomy and Owens Valley Radio Observatory California Institute of Technology PasadenaCA91125 United States Department of Physics and Astronomy West Virginia University P.O. Box 6315 MorgantownWV26506 United States Center for Gravitational Waves and Cosmology West Virginia University Chestnut Ridge Research Building MorgantownWV26505 United States School of Mathematics Physics and Statistics University of Newcastle Newcatle upon TyneNE1 7RU United Kingdom Lane Department of Computer Science and Electrical Engineering MorgantownWV26506 United States Academia Sinica Institute of Astronomy and Astrophysics 645 N. A’ohoku Place HiloHI96720 United States National Radio Astronomy Observatory SocorroNM87801 United States Dunlap Institute for Astronomy & Astrophysics University of Toronto 50 St. George Street TorontoONM5S 3H4 Canada Department of Astronomy & Astrophysics University of Toronto 50 St. George Street TorontoONM5S 3H4 Canada Perimeter Institute for Theoretical Physics WaterlooONN2L 2Y5 Canada Department of Physics and Astronomy University of Waterloo WaterlooONN2L 3G1 Canada Department of Physics and Astronomy University of British Columbia 6224 Agricultural Road VancouverBCV6T 1Z1 Canada Instituto de Astronomía Universidad Nacional Autónoma de México Apdo. Postal 877 EnsenadaBaja California22800 Mexico Ioffe Institute 26 Politekhnicheskaya st. St. Petersburg194021 Russia Department o

Fast radio bursts (FRBs) are brief, bright, extragalactic radio flashes1, 2. Their physical origin remains unknown, but dozens of possible models have been postulated3. Some FRB sources exhibit repeat bursts4–7. Though over a hundred FRB sources have been discovered to date8, only four have been localised and associated with a host galaxy9–12, with just one of the four known to repeat9. The properties of the host galaxies, and the local environments of FRBs, provide important clues about their physical origins. However, the first known repeating FRB has been localised to a low-metallicity, irregular dwarf galaxy, and the apparently non-repeating sources to higher-metallicity, massive elliptical or star-forming galaxies, suggesting that perhaps the repeating and apparently non-repeating sources could have distinct physical origins. Here we report the precise localisation of a second repeating FRB source6, FRB 180916.J0158+65, to a star-forming region in a nearby (redshift z = 0.0337 ±0.0002) massive spiral galaxy, whose properties and proximity distinguish it from all known hosts. The lack of both a comparably luminous persistent radio counterpart and a high Faraday rotation measure6 further distinguish the local environment of FRB 180916.J0158+65 from that of the one previously localised repeating FRB source, FRB 121102. This demonstrates that repeating FRBs have a wide range of luminosities, and originate from diverse host galaxies and local environments. Copyright © 2020, The Authors. All rights reserved.

关键词： Galaxies

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Author Correction: The power of genetic diversity in genome-wide association studies of lipids

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Nature 2023年第7965期618卷 E19-E20页

作者： Sarah E Graham Shoa L Clarke Kuan-Han H Wu Stavroula Kanoni Greg J M Zajac Shweta Ramdas Ida Surakka Ioanna Ntalla Sailaja Vedantam Thomas W Winkler Adam E Locke Eirini Marouli Mi Yeong Hwang Sohee Han Akira Narita Ananyo Choudhury Amy R Bentley Kenneth Ekoru Anurag Verma Bhavi Trivedi Hilary C Martin Karen A Hunt Qin Hui Derek Klarin Xiang Zhu Gudmar Thorleifsson Anna Helgadottir Daniel F Gudbjartsson Hilma Holm Isleifur Olafsson Masato Akiyama Saori Sakaue Chikashi Terao Masahiro Kanai Wei Zhou Ben M Brumpton Humaira Rasheed Sanni E Ruotsalainen Aki S Havulinna Yogasudha Veturi QiPing Feng Elisabeth A Rosenthal Todd Lingren Jennifer Allen Pacheco Sarah A Pendergrass Jeffrey Haessler Franco Giulianini Yuki Bradford Jason E Miller Archie Campbell Kuang Lin Iona Y Millwood George Hindy Asif Rasheed Jessica D Faul Wei Zhao David R Weir Constance Turman Hongyan Huang Mariaelisa Graff Anubha Mahajan Michael R Brown Weihua Zhang Ketian Yu Ellen M Schmidt Anita Pandit Stefan Gustafsson Xianyong Yin Jian'an Luan Jing-Hua Zhao Fumihiko Matsuda Hye-Mi Jang Kyungheon Yoon Carolina Medina-Gomez Achilleas Pitsillides Jouke Jan Hottenga Gonneke Willemsen Andrew R Wood Yingji Ji Zishan Gao Simon Haworth Ruth E Mitchell Jin Fang Chai Mette Aadahl Jie Yao Ani Manichaikul Helen R Warren Julia Ramirez Jette Bork-Jensen Line L Kårhus Anuj Goel Maria Sabater-Lleal Raymond Noordam Carlo Sidore Edoardo Fiorillo Aaron F McDaid Pedro Marques-Vidal Matthias Wielscher Stella Trompet Naveed Sattar Line T Møllehave Betina H Thuesen Matthias Munz Lingyao Zeng Jianfeng Huang Bin Yang Alaitz Poveda Azra Kurbasic Claudia Lamina Lukas Forer Markus Scholz Tessel E Galesloot Jonathan P Bradfield E Warwick Daw Joseph M Zmuda Jonathan S Mitchell Christian Fuchsberger Henry Christensen Jennifer A Brody Mary F Feitosa Mary K Wojczynski Michael Preuss Massimo Mangino Paraskevi Christofidou Niek Verweij Jan W Benjamins Jorgen Engmann Rachel L Kember Roderick C Slieker Ken Sin Lo Nuno R Zilhao Phuong Le Marcus E Kleber Graciela E Delgado Shaofeng Huo Dai Department of Internal Medicine Division of Cardiology University of Michigan Ann Arbor MI USA. VA Palo Alto Health Care System Palo Alto CA USA. Department of Medicine Division of Cardiovascular Medicine Stanford University School of Medicine Stanford CA USA. Department of Computational Medicine and Bioinformatics University of Michigan Ann Arbor MI USA. William Harvey Research Institute Barts and the London School of Medicine and Dentistry Queen Mary University of London London UK. Department of Biostatistics and Center for Statistics Genetics University of Michigan Ann Arbor MI USA. Department of Genetics Perelman School of Medicine University of Pennsylvania Philadelphia PA USA. Clinical Pharmacology William Harvey Research Institute Barts and The London School of Medicine and Dentistry Queen Mary University of London London UK. Endocrinology Boston Children's Hospital Boston MA USA. Medical and Population Genetics Broad Institute Cambridge MA USA. Department of Genetic Epidemiology University of Regensburg Regensburg Germany. McDonnell Genome Institute and Department of Medicine Washington University St Louis MO USA. Department of Precision Medicine Division of Genome Science National Institute of Health Cheongju-si South Korea. Tohoku Medical Megabank Organization Tohoku University Sendai Japan. Sydney Brenner Institute for Molecular Bioscience Faculty of Health Sciences University of the Witwatersrand Johannesburg South Africa. Center for Research on Genomics and Global Health National Human Genome Research Institute National Institutes of Health Bethesda MD USA. Blizard Institute Barts and the London School of Medicine and Dentistry Queen Mary University of London London UK. Wellcome Sanger Institute Hinxton UK. Department of Epidemiology Emory University Rollins School of Public Health Atlanta GA USA. Atlanta VA Health Care System Decatur GA USA. Malcolm Randall VA Medical Center Gainesville FL USA. Division of Vascular Sur

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A deep neural network for time-domain signal reconstruction 40

A deep neural network for time-domain signal reconstruction

引用

40th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015

作者： Wang, Yuxuan Wang, Deliang Department of Computer Science and Engineering Ohio State University United States Center for Cognitive and Brain Sciences Ohio State University United States

ISBN: (纸本)9781467369978

Supervised speech separation has achieved considerable success recently. Typically, a deep neural network (DNN) is used to estimate an ideal time-frequency mask, and clean speech is produced by feeding the mask-weighted output to a resynthesizer in a subsequent step. So far, the success of DNN-based separation lies mainly in improving human speech intelligibility. In this work, we propose a new deep network that directly reconstructs the time-domain clean signal through an inverse fast Fourier transform layer. The joint training of speech resynthesis and mask estimation yields improved objective quality while maintaining the objective intelligibility performance. The proposed system significantly outperforms a recent non-negative matrix factorization based separation system in both objective speech intelligibility and quality. © 2015 IEEE.

关键词： Deep neural network speech separation time-domain signal time-frequency masking

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Mapping the Human Body at Cellular Resolution -- The NIH Common Fund Human BioMolecular Atlas program

arXiv

引用

arXiv 2019年

作者： Snyder, Michael P. Lin, Shin Posgai, Amanda Atkinson, Mark Regev, Aviv Rood, Jennifer Rozenblatt-Rosen, Orit Gaffney, Leslie Hupalowska, Anna Satija, Rahul Gehlenborg, Nils Shendure, Jay Laskin, Julia Harbury, Pehr Nystrom, Nicholas A. Bar-Joseph, Ziv Zhang, Kun Börner, Katy Lin, Yiing Conroy, Richard Procaccini, Dena Roy, Ananda L. Pillai, Ajay Brown, Marishka Galis, Zorina S. Cai, Long Shendure, Jay Trapnell, Cole Lin, Shin Jackson, Dana Snyder, Michael P. Nolan, Garry Greenleaf, William James Lin, Yiing Plevritis, Sylvia Ahadi, Sara Lee, Hayan Nevins, Stephanie Esplin, Ed Horning, Aaron Bahmani, Amir Zhang, Kun Sun, Xin Jain, Sanjay Hagood, James Kharchenko, Peter Atkinson, Mark Bodenmiller, Bernd Brusko, Todd Clare-Salzler, Michael Nick, Harry Otto, Kevin Posgai, Amanda Wasserfall, Clive Maffioletti, Sergio Caprioli, Richard M. Spraggins, Jeffrey M. Gutierrez, Danielle Patterson, Nathan Heath Neumann, Elizabeth K. Harris, Raymond deCaestecker, Mark de Plas, Raf Van Lau, Ken Cai, Long Yuan, Guo-Cheng Zhu, Qian Dries, Ruben Yin, Peng Saka, Sinem K. Kishi, Jocelyn Y. Wang, Yu Goldaracena, Isabel Laskin, Julia Ye, DongHye Burnum-Johnson, Kristin E. Piehowski, Paul D. Ansong, Charles Zhu, Ying Harbury, Pehr Desai, Tushar Mulye, Jay Chou, Peter Nagendran, Monica Bar-Joseph, Ziv Teichmann, Sarah A. Paten, Benedict Murphy, Robert F. Ma, Jian Kiselev, Vladimir Yu Kingsford, Carl Ricarte, Allyson Ruffalo, Matthew Gehlenborg, Nils Kharchenko, Peter Vella, Margaret McCallum, Chuck Börner, Katy Cross, Leonard E. Friedman, Samuel H. Heiland, Randy Herr, Bruce Macklin, Paul Quardokus, Ellen M. Record, Lisel Sluka, James P. Weber, Griffin M. Nystrom, Nicholas A. Silverstein, Jonathan C. Blood, Philip D. Ropelewski, Alexander J. Shirey, William E. Mabee, Paula Lenhardt, W. Christopher Robasky, Kimberly Michailidis, Stavros Satija, Rahul Marioni, John Regev, Aviv Butler, Andrew Stuart, Tim Fisher, Eyal Ghazanfar, Shila Rood, Jennifer Gaffney, Leslie Ersalan, Gokcen Biancalani, Tommaso Conroy, Richard Procaccini, Dena Roy, Anan School of Medicine Stanford University 300 Pasteur Drive United States Department of Genetics Stanford School of Medicine StanfordCA94305 United States Department of Medicine University of Washington SeattleWA98195 United States Department of Pathology University of Florida Diabetes Institute GainesvilleFL32610 United States Klarman Cell Observatory Broad Institute of MIT and Harvard CambridgeMA02142 United States Howard Hughes Medical Institute Koch Institute of Integrative Cancer Research Department of Biology Massachusetts Institute of Technology CambridgeMA02140 United States New York Genome Center New York CityNY10013 United States New York University New YorkNY10012 United States Department of Biomedical Informatics Harvard Medical School BostonMA02115 United States Department of Genome Sciences University of Washington SeattleWA98105 United States Department of Chemistry Purdue University West LafayetteIN47907 United States Department of Biochemistry Stanford University School of Medicine StanfordCA94305 United States Pittsburgh Supercomputing Center Carnegie Mellon University PittsburghPA15213 United States Department of Computational Biology School of Computer Science Carnegie Mellon University PittsburghPA15213 United States Department of Bioengineering University of California San Diego La JollaCA92093 United States Department of Intelligent Systems Engineering School of Informatics Computing and Engineering Indiana University BloomingtonIN47408 United States Department of Surgery Washington University School of Medicine St LouisMO63110 United States Division of Program Coordination Planning and Strategic Initiatives National Institutes of Health BethesdaMD20892 United States National Human Genome Research Institute National Institutes of Health BethesdaMD20892 United States National Heart Lung and Blood Institute National Institutes of Health BethesdaMD20892 United States Caltech-UW TMC Departmen

Transformative technologies are enabling the construction of three dimensional (3D) maps of tissues with unprecedented spatial and molecular resolution. Over the next seven years, the NIH Common Fund Human Biomolecular Atlas program (HuBMAP) intends to develop a widely accessible framework for comprehensively mapping the human body at single-cell resolution by supporting technology development, data acquisition, and detailed spatial mapping. HuBMAP will integrate its efforts with other funding agencies, programs, consortia, and the biomedical research community at large towards the shared vision of a comprehensive, accessible 3D molecular and cellular atlas of the human body, in health and various disease settings. Copyright © 2019, The Authors. All rights reserved.

关键词： Mapping

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Retraction: Effect of Types and Gap on Spark Plugs on Exhaust Emissions on the 110CC Double Wheel Vehicles and Its Dissemination Using Website (J. Phys.: Conf. Ser. 1114 012097)

引用

Journal of Physics: Conference Series 2018年第1期1114卷

作者： Tinus Ginting Hotler Manurung Astro Julida Harahap Erianto Ongko H Hartono Dahlan Abdullah Cut Ita Erliana Kundharu Saddhono S Sriadhi Supriyono Akbar Iskandar A A Saleh I Ketut Sudarsana Department of Mechanical Engineering Akademi Teknologi Industri Immanuel Indonesia Department of Informatics Akademi Teknologi Industri Immanuel Indonesia Department of Computer Science STMIK IBBI Medan Indonesia Department of Informatics Universitas Malikussaleh Aceh Indonesia Department of Industrial Engineering Universitas Malikussaleh Aceh Indonesia Graduate Program Universitas Sebelas Maret Surakarta Indonesia Department of Electrical Engineering Universitas Negeri Medan Indonesia Department of Geography Education Universitas Prof Dr Hazairin SH Bengkulu Indonesia Department of Informatics STMIK AKBA Makassar Indonesia Statistics Financial & Social Sciences Research Group Indonesia Department of Religious Education Institut Hindu Dharma Negeri Denpasar Denpasar Indonesia

This paper was retracted by IOP Publishing on 12 December 2018. This paper was published due to a technical error and was not intended to be included in this journal. Retraction published: 8 February 2019

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Deep neural networks for estimating speech model activations 40

Deep neural networks for estimating speech model activations

引用

40th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015

作者： Williamson, Donald S. Wang, Yuxuan Wang, Deliang Department of Computer Science and Engineering Ohio State University United States Center for Cognitive and Brain Sciences Ohio State University United States

ISBN: (纸本)9781467369978

This paper presents an approach for improving the perceptual quality of speech separated from background noise at low signal-to-noise ratios. Our approach uses two stages of deep neural networks, where the first stage estimates the ideal ratio mask that separates speech from noise, and the second stage maps the ratio-masked speech to the clean speech activation matrices that are used for nonnegative matrix factorization (NMF). Supervised NMF systems make assumptions about the relationship between the activation and basic matrices that do not always hold. Other two-stage approaches combining masking with NMF reconstruction do not account for mask estimation errors. We show that the proposed algorithm achieves higher objective speech quality and intelligibility compared to these related methods. © 2015 IEEE.

关键词： deep neural network nonnegative matrix factorization speech quality speech separation

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Effect of Types and Gap on Spark Plugs on Exhaust Emissions on the 110CC Double Wheel Vehicles and Its Dissemination Using Website

引用

Journal of Physics: Conference Series 2018年第1期1114卷

作者： Ginting, Tinus Manurung, Hotler Astro Julida Harahap Ongko, Erianto Hartono, H Dahlan Abdullah Cut Ita Erliana Saddhono, Kundharu Sriadhi, S Supriyono Iskandar, Akbar Saleh, A A Sudarsana, I Ketut Department of Mechanical Engineering Akademi Teknologi Industri Immanuel Indonesia Department of Informatics Akademi Teknologi Industri Immanuel Indonesia Department of Computer Science STMIK IBBI Medan Indonesia Department of Informatics Universitas Malikussaleh Aceh Indonesia Department of Industrial Engineering Universitas Malikussaleh Aceh Indonesia Graduate Program Universitas Sebelas Maret Surakarta Indonesia Department of Electrical Engineering Universitas Negeri Medan Indonesia Department of Geography Education Universitas Prof Dr Hazairin SH Bengkulu Indonesia Department of Informatics STMIK AKBA Makassar Indonesia Statistics Financial & Social Sciences Research Group Indonesia Department of Religious Education Institut Hindu Dharma Negeri Denpasar Denpasar Indonesia

Based on the formulation of the problem to be discussed by the writer is to know: the influence of 0.4 mm spark gap variation, and standard spark plug type, to CO and HC, the influence of 0.5 mm spark plug gap variation, and variation of platinum spark plug on CO and HC, influence of spark gap variation 0,6mm, and variation of iridium spark plug type to CO and HC emission Method used to obtain data needed to support the writer that is research method with experiment, literature study method or literature study The result of this research concludes that there is spark gap influence 0.4 mm, with variations of standard plug, platinum and iridium spark plugs against CO and HC emissions, where the lowest CO value is 0.27% and HC 98 ppm, 0.5 mm spark plug with lowest CO 0.15% and HC 46 ppm, 0.6 mm spark plug gap the lowest CO value of 0.19% and HC 24 ppm. The results of this research will be disseminated using the website

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A novel dataset generating method for fine-grained vehicle classification with CNN

International Journal of Database Theory and Application

引用

International Journal of Database Theory and Application 2016年第6期9卷 45-52页

作者： Yu, Shaoyong Song, Zhijun Su, Songzhi Li, Wei Wu, Yun Zeng, Wenhua Department of Cognitive Science Xiamen University Xiamen361000 China School of Computer and Information Engineering Xiamen University of Technology Xiamen361024 China The 28th Research Institute of China Electronics Technology Group Corporation Nanjing210007 China

We focus on the issue of dataset generation for fine-grained vehicle classification with CNN. Traditionally, to build a large dataset, images must be first collected manually, and then be annotated with a lot of effort. All these work are time-consuming and cost-prohibitive. In this work we propose a novel method that can generate massive images automatically, and these generated images need no annotation. An AutoCAD 3D model of a car of specified make and model is imported into our system, and then images of different views of the car are generated, these images can describe all the details of a car. By taking these images as training dataset, we use a Convolutional Neural Network to train a model for fine-grained vehicle classification. Experimental results show that these images generated virtually by 3D model indeed work as effective as real images. © 2016 SERSC.

关键词： Neural networks

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Analysis of font legibility for Korean serif and sans serif types in printed and displayed settings 59

Analysis of font legibility for Korean serif and sans serif ...

引用

59th International Annual Meeting of the Human Factors and Ergonomics Society, HFES 2015

作者： Kim, Minjee Park, Sung-Hwan Ahn, Sung Hee Choi, Myung Bin Yun, Myung-Hwan Interdisciplinary Program in Cognitive Science Seoul National University Seoul Korea Republic of Department of Industrial Engineering Seoul National University Seoul Korea Republic of

ISBN: (纸本)9780945289470

Activities with smart devices have been more complicated and diversified, and font legibility influences these activities both directly and indirectly. This study investigated the difference of Korean font legibility between printed paper and VDT. Four Korean fonts, Dodum and Gulim - sans serif types, and Batang and Gungseo - serif types, were compared in experiments. The result showed that there was a significant difference in the font legibility for printed paper, but not for VDT. On printed paper, serif types were more legible than sans serif types. However, a significant difference between serif types and sans serif types were not found in VDT. Dodum, one of the sans serif font types achieved the highest score on preference in the survey questionnaire among the four Korean fonts. The paper suggests that serif types did not always provide the optimal legibility in VDT. Copyright 2015 Human Factors and Ergonomics Society.

关键词： Ergonomics

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