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1st EAI International Conference on Computer science and Engineering, COMPSE 2016

作者： Gui, Yan Liu, Yang Li, Feng Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation Changsha University of Science and Technology Changsha Hunan410114 China

ISBN: (纸本)9781631901362

The texture synthesis and design starts with an initial elements arrangement and expands it outward by using local and global growth, to obtain the new larger distribution of texture elements. There are two types of synthesized distributions, and their diversity consists in changing the layout of texture elements and decreasing or increasing the number of texture elements according to user's creation. Furthermore, we apply a set of deformation operations to locally change the shapes of texture elements when placing the extracted texture elements into the synthesized distribution, in order to guarantee structure consistency in final synthesized textures. Experimental results show that our method creates a large variety of textures from a given texture sample.

关键词： Computer graphics

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Enabling quality control for entity resolution: A human and machine cooperation framework

arXiv

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arXiv 2017年

作者： Chen, Zhaoqiang Chen, Qun Fan, Fengfeng Wang, Yanyan Wang, Zhuo Nafa, Youcef Li, Zhanhuai Liu, Hailong Pan, Wei School of Computer Science Northwestern Polytechnical University Key Laboratory of Big Data Storage and Management Northwestern Polytechnical University Ministry of Industry and Information Technology 1 Dongxiang Road Xi'an Shaanxi China

Even though many machine algorithms have been proposed for entity resolution, it remains very challenging to find a solution with quality guarantees. In this paper, we propose a novel HUman and Machine cOoperation (HUMO) framework for entity resolution (ER), which divides an ER workload between the machine and the human. HUMO enables a mechanism for quality control that can flexibly enforce both precision and recall levels. We introduce the optimization problem of HUMO, minimizing human cost given a quality requirement, and then present three optimization approaches: a conservative baseline one purely based on the monotonicity assumption of precision, a more aggressive one based on sampling and a hybrid one that can take advantage of the strengths of both previous approaches. Finally, we demonstrate by extensive experiments on real and synthetic datasets that HUMO can achieve high-quality results with reasonable return on investment (ROI) in terms of human cost, and it performs considerably better than the state-of-the-art alternatives in quality control. Copyright © 2017, The Authors. All rights reserved.

关键词： Quality assurance

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Evaluation of algorithms for multi-modality whole heart segmentation: An open-access grand challenge

arXiv

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arXiv 2019年

作者： Zhuang, Xiahai Li, Lei Payer, Christian Štern, Darko Urschler, Martin Heinrich, Mattias P. Oster, Julien Wang, Chunliang Smedby, Örjan Bian, Cheng Yang, Xin Heng, Pheng-Ann Mortazi, Aliasghar Bagci, Ulas Yang, Guanyu Sun, Chenchen Galisot, Gaetan Ramel, Jean-Yves Brouard, Thierry Tong, Qianqian Si, Weixin Liao, Xiangyun Zeng, Guodong Shi, Zenglin Zheng, Guoyan Wang, Chengjia MacGillivray, Tom Newby, David Rhode, Kawal Ourselin, Sebastien Mohiaddin, Raad Keegan, Jennifer Firmin, David Yang, Guang School of Data Science Fudan University Shanghai200433 China Fudan-Xinzailing Joint Research Center for Big Data Fudan University Shanghai200433 China School of Biomedical Engineering Shanghai Jiao Tong University Shanghai200240 China Institute of Computer Graphics and Vision Graz University of Technology Graz8010 Austria Ludwig Boltzmann Institute for Clinical Forensic Imaging Graz8010 Austria Institute of Medical Informatics University of Lubeck Lubeck23562 Germany Inserm Université de Lorraine U1254 IADI Nancy France School for Technology and Health KTH Royal Institute of Technology StockholmSE-10044 Sweden School of Biomed. Eng. Health Science Centre Shenzhen University Shenzhen518060 China Dept. of Comp. Sci. and Eng. Chinese University of Hong Kong Hong Kong China University of Central Florida Orlando32816 United States School of Computer Science and Engineering Southeast University Nanjing210096 China Université de Tours 64 avenue Jean Portalis Tours37200 France School of Computer Science Wuhan University Wuhan430072 China Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Technology SIAT Shenzhen China Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Institute for Surgical Technology & Biomechanics University of Bern Bern3014 Switzerland BHF Centre for Cardiovascular Science University of Edinburgh Edinburgh United Kingdom Edinburgh Imaging Facility QMRI University of Edinburgh Edinburgh United Kingdom Department of Imaging Sciences & Biomedical Engineering Kings College London London United Kingdom Cardiovascular Research Centre Royal Brompton Hospital LondonSW3 6NP United Kingdom National Heart and Lung Institute Imperial College London London LondonSW7 2AZ United Kingdom

Knowledge of whole heart anatomy is a prerequisite for many clinical applications. Whole heart segmentation (WHS), which delineates substructures of the heart, can be very valuable for modeling and analysis of the anatomy and functions of the heart. However, automating this segmentation can be arduous due to the large variation of the heart shape, and different image qualities of the clinical data. To achieve this goal, a set of training data is generally needed for constructing priors or for training. In addition, it is difficult to perform comparisons between different methods, largely due to differences in the datasets and evaluation metrics used. This manuscript presents the methodologies and evaluation results for the WHS algorithms selected from the submissions to the Multi-Modality Whole Heart Segmentation (MM-WHS) challenge, in conjunction with MICCAI 2017. The challenge provides 120 three-dimensional cardiac images covering the whole heart, including 60 CT and 60 MRI volumes, all acquired in clinical environments with manual delineation. Ten algorithms for CT data and eleven algorithms for MRI data, submitted from twelve groups, have been evaluated. The results show that many of the deep learning (DL) based methods achieved high accuracy, even though the number of training datasets were limited. A number of them also reported poor results in the blinded evaluation, probably due to overfitting in their training. The conventional algorithms, mainly based on multi-atlas segmentation, demonstrated robust and stable performance, even though the accuracy is not as good as the best DL method in CT segmentation. The challenge, including provision of the annotated training data and the blinded evaluation for submitted algorithms on the test data, continues as an ongoing benchmarking resource via its homepage (***/zhuangxiahai/0/mmwhs/). Copyright © 2019, The Authors. All rights reserved.

关键词： Heart

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Image representation based on multi-features

Image representation based on multi-features

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International Conference on Intelligent System and Knowledge Engineering, ISKE

作者： Xianzhong Long Lei Chen Qun Li College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China Jiangsu Key Laboratory of Big Data Security & Intelligent Processing Nanjing Jiangsu China School of Computer Science & Technology Nanjing University of Posts and Telecommunications Nanjing China

As one kind of popular application in computer vision, image clustering has attracted many attentions. Some machine learning algorithms have been widely employed, such as K-Means, Non-negative Matrix Factorization (NMF), Graph regularized Non-negative Matrix Factorization (GNMF) and Locally Consistent Concept Factorization (LCCF). These methods possess respective strength and weakness. The common problem existing in these clustering algorithms is that they only use one kind of feature. However, different kinds of features complement each other and can be used to improve performance results. In this paper, in order to make use of the complementarity between different features, we propose an image representation method based on multi-features. Clustering results on several benchmark image data sets show that the proposed scheme outperforms some classical methods.

关键词： Clustering algorithms Image representation Feature extraction Optimization Symmetric matrices Machine learning algorithms Signal processing algorithms

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A parse tree-based NoSQL injection attacks detection mechanism

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Journal of Information Hiding and Multimedia Signal Processing 2017年第4期8卷 916-928页

作者： Ma, Hong Wu, Tsu-Yang Chen, Min Yang, Rong-Hua Pan, Jeng-Shyang Fujian Provincial Key Laboratory of Big Data Mining and Applications National Demonstration Center for Experimental Electronic Information and Electrical Technology Education College of Information Science and Engineering Fujian University of Technology No3 Xueyuan Road University Town Fuzhou350118 China

Nowadays, many IT giants such as Facebook, Google, and Amazon adopt non-relational database (NoSQL, Not only SQL) technologies to manage their systems. Although these kind of database technologies have made outstanding contributions to the development of the IT industry, it also exposed some security risks such as SQL injection attacks. Up to now, there are many solutions to counter SQL injection attacks in SQL databases. However, there exist few approaches to counter injection attacks in NoSQL databases. So, how to design an effective NoSQL injection attacks detecting mechanism becomes a subject worthy of in-depth study. In this paper, we based on semantic structure analysis of execution statements to propose a detection approach using parse tree. Based on this approach, we focus on MongoDB to propose a dynamic NoSQL injection attacks detection mechanism in the web environment called DND. It does not require access to or modifying source codes, rewriting source codes with extra libraries, or complex assisted devices. Finally, the experimental results are shown that DND has high accuracy rates, low false positive rates, and low response time. © 2017, Ubiquitous International. All rights reserved.

关键词： Semantics

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Improved kernel and algorithm for claw and diamond free edge deletion based on refined observations

arXiv

引用

arXiv 2017年

作者： Li, Wenjun Peng, Huan Yang, Yongjie School Of Computer And Communication Engineering Hunan Provincial Key Laboratory Of Intelligent Processing Of Big Data On Transportation Changsha University Of Science And Technology Changsha China Economic Theory Saarland University Saarbrücken Germany

In the {CLAW, DIAMOND}-FREE EDGE DELETION problem (CDFED), we are given a graph G and an integer k > 0, and the question is whether there are at most k edges whose deletion results in a graph without claws and diamonds as induced subgraphs. Based on some refined observations, we propose a kernel of O(k3) vertices and O(k4) edges, significantly improving the previous kernel of O(k12) vertices and O(k24) edges. In addition, we derive an O∗(3.792k)-time algorithm for CDFED. Copyright © 2017, The Authors. All rights reserved.

关键词： Diamonds

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Search for CP violation in Ξb−→pK−K− decays

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Physical Review D 2021年第5期104卷 052010-052010页

作者： R. Aaij C. Abellán Beteta T. Ackernley B. Adeva M. Adinolfi H. Afsharnia C. A. Aidala S. Aiola Z. Ajaltouni S. Akar J. Albrecht F. Alessio M. Alexander A. Alfonso Albero Z. Aliouche G. Alkhazov P. Alvarez Cartelle S. Amato Y. Amhis L. An L. Anderlini A. Andreianov M. Andreotti F. Archilli A. Artamonov M. Artuso K. Arzymatov E. Aslanides M. Atzeni B. Audurier S. Bachmann M. Bachmayer J. J. Back P. Baladron Rodriguez V. Balagura W. Baldini J. Baptista Leite R. J. Barlow S. Barsuk W. Barter M. Bartolini F. Baryshnikov J. M. Basels G. Bassi B. Batsukh A. Battig A. Bay M. Becker F. Bedeschi I. Bediaga A. Beiter V. Belavin S. Belin V. Bellee K. Belous I. Belov I. Belyaev G. Bencivenni E. Ben-Haim A. Berezhnoy R. Bernet D. Berninghoff H. C. Bernstein C. Bertella A. Bertolin C. Betancourt F. Betti Ia. Bezshyiko S. Bhasin J. Bhom L. Bian M. S. Bieker S. Bifani P. Billoir M. Birch F. C. R. Bishop A. Bitadze A. Bizzeti M. Bjørn M. P. Blago T. Blake F. Blanc S. Blusk D. Bobulska J. A. Boelhauve O. Boente Garcia T. Boettcher A. Boldyrev A. Bondar N. Bondar S. Borghi M. Borisyak M. Borsato J. T. Borsuk S. A. Bouchiba T. J. V. Bowcock A. Boyer C. Bozzi M. J. Bradley S. Braun A. Brea Rodriguez M. Brodski J. Brodzicka A. Brossa Gonzalo D. Brundu A. Buonaura C. Burr A. Bursche A. Butkevich J. S. Butter J. Buytaert W. Byczynski S. Cadeddu H. Cai R. Calabrese L. Calefice L. Calero Diaz S. Cali R. Calladine M. Calvi M. Calvo Gomez P. Camargo Magalhaes P. Campana A. F. Campoverde Quezada S. Capelli L. Capriotti A. Carbone G. Carboni R. Cardinale A. Cardini I. Carli P. Carniti L. Carus K. Carvalho Akiba A. Casais Vidal G. Casse M. Cattaneo G. Cavallero S. Celani J. Cerasoli A. J. Chadwick M. G. Chapman M. Charles Ph. Charpentier G. Chatzikonstantinidis C. A. Chavez Barajas M. Chefdeville C. Chen S. Chen A. Chernov V. Chobanova S. Cholak M. Chrzaszcz A. Chubykin V. Chulikov P. Ciambrone M. F. Cicala X. Cid Vidal G. Ciezarek P. E. L. Clarke M. Clemencic H. V. Cliff J. Closier J. L. Cobbledick V. Coco J. A. B. Coelho J. Cogan E. Cogneras Nikhef National Institute for Subatomic Physics Amsterdam Netherlands Physik-Institut Universität Zürich Zürich Switzerland Oliver Lodge Laboratory University of Liverpool Liverpool United Kingdom Instituto Galego de Física de Altas Enerxías (IGFAE) Universidade de Santiago de Compostela Santiago de Compostela Spain H.H. Wills Physics Laboratory University of Bristol Bristol United Kingdom Université Clermont Auvergne CNRS/IN2P3 LPC Clermont-Ferrand France University of Michigan Ann Arbor United States associated to Syracuse University Syracuse New York USA INFN Sezione di Milano Milano Italy University of Cincinnati Cincinnati Ohio USA Fakultät Physik Technische Universität Dortmund Dortmund Germany European Organization for Nuclear Research (CERN) Geneva Switzerland School of Physics and Astronomy University of Glasgow Glasgow United Kingdom ICCUB Universitat de Barcelona Barcelona Spain Department of Physics and Astronomy University of Manchester Manchester United Kingdom Petersburg Nuclear Physics Institute NRC Kurchatov Institute (PNPI NRC KI) Gatchina Russia Cavendish Laboratory University of Cambridge Cambridge United Kingdom Universidade Federal do Rio de Janeiro (UFRJ) Rio de Janeiro Brazil Université Paris-Saclay CNRS/IN2P3 IJCLab Orsay France INFN Sezione di Firenze Firenze Italy INFN Sezione di Ferrara Ferrara Italy Physikalisches Institut Ruprecht-Karls-Universität Heidelberg Heidelberg Germany Institute for High Energy Physics NRC Kurchatov Institute (IHEP NRC KI) Protvino Russia Protvino Russia Syracuse University Syracuse New York USA Yandex School of Data Analysis Moscow Russia Aix Marseille Univ CNRS/IN2P3 CPPM Marseille France Laboratoire Leprince-Ringuet CNRS/IN2P3 Ecole Polytechnique Institut Polytechnique de Paris Palaiseau France Institute of Physics Ecole Polytechnique Fédérale de Lausanne (EPFL) Lausanne Switzerland Department of Physics University of Warwick Coventry United Kingdom Centro Brasileiro de P

A search for CP violation in charmless three-body Ξb−→pK−K− decays is performed using pp collision data recorded with the LHCb detector, corresponding to integrated luminosities of 1 fb−1 at a center-of-mass energy s=7 TeV, 2 fb−1 at s=8 TeV and 2 fb−1 at s=13 TeV. A good description of the phase-space distribution is obtained with an amplitude model containing contributions from Σ(1385), Λ(1405), Λ(1520), Λ(1670), Σ(1775) and Σ(1915) resonances. The model allows for CP -violation effects, which are found to be consistent with zero. The branching fractions of Ξb−→Σ(1385)K−, Ξb−→Λ(1405)K−, Ξb−→Λ(1520)K−, Ξb−→Λ(1670)K−, Ξb−→Σ(1775)K− and Ξb−→Σ(1915)K− decays are also reported. In addition, an upper limit is placed on the product of ratios of Ωb− and Ξb− fragmentation fractions and the Ωb−→pK−K− and Ξb−→pK−K− branching fractions.

关键词： Baryons CP violation

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Measurement of the prompt-production cross-section ratio σ(χc2)/σ(χc1) in pPb collisions at sNN=8.16 TeV

引用

Physical Review C 2021年第6期103卷 064905-064905页

作者： R. Aaij C. Abellán Beteta T. Ackernley B. Adeva M. Adinolfi H. Afsharnia C. A. Aidala S. Aiola Z. Ajaltouni S. Akar J. Albrecht F. Alessio M. Alexander A. Alfonso Albero Z. Aliouche G. Alkhazov P. Alvarez Cartelle S. Amato Y. Amhis L. An L. Anderlini A. Andreianov M. Andreotti F. Archilli A. Artamonov M. Artuso K. Arzymatov E. Aslanides M. Atzeni B. Audurier S. Bachmann M. Bachmayer J. J. Back S. Baker P. Baladron Rodriguez V. Balagura W. Baldini J. Baptista Leite R. J. Barlow S. Barsuk W. Barter M. Bartolini F. Baryshnikov J. M. Basels G. Bassi B. Batsukh A. Battig A. Bay M. Becker F. Bedeschi I. Bediaga A. Beiter V. Belavin S. Belin V. Bellee K. Belous I. Belov I. Belyaev G. Bencivenni E. Ben-Haim A. Berezhnoy R. Bernet D. Berninghoff H. C. Bernstein C. Bertella A. Bertolin C. Betancourt F. Betti Ia. Bezshyiko S. Bhasin J. Bhom L. Bian M. S. Bieker S. Bifani P. Billoir M. Birch F. C. R. Bishop A. Bitadze A. Bizzeti M. Bjørn M. P. Blago T. Blake F. Blanc S. Blusk D. Bobulska J. A. Boelhauve O. Boente Garcia T. Boettcher A. Boldyrev A. Bondar N. Bondar S. Borghi M. Borisyak M. Borsato J. T. Borsuk S. A. Bouchiba T. J. V. Bowcock A. Boyer C. Bozzi M. J. Bradley S. Braun A. Brea Rodriguez M. Brodski J. Brodzicka A. Brossa Gonzalo D. Brundu A. Buonaura C. Burr A. Bursche A. Butkevich J. S. Butter J. Buytaert W. Byczynski S. Cadeddu H. Cai R. Calabrese L. Calefice L. Calero Diaz S. Cali R. Calladine M. Calvi M. Calvo Gomez P. Camargo Magalhaes A. Camboni P. Campana A. F. Campoverde Quezada S. Capelli L. Capriotti A. Carbone G. Carboni R. Cardinale A. Cardini I. Carli P. Carniti L. Carus K. Carvalho Akiba A. Casais Vidal G. Casse M. Cattaneo G. Cavallero S. Celani J. Cerasoli A. J. Chadwick M. G. Chapman M. Charles Ph. Charpentier G. Chatzikonstantinidis C. A. Chavez Barajas M. Chefdeville C. Chen S. Chen A. Chernov V. Chobanova S. Cholak M. Chrzaszcz A. Chubykin V. Chulikov P. Ciambrone M. F. Cicala X. Cid Vidal G. Ciezarek P. E. L. Clarke M. Clemencic H. V. Cliff J. Closier J. L. Cobbledick V. Coco J. A. B. Coelho J Nikhef National Institute for Subatomic Physics Amsterdam Netherlands Physik-Institut Universität Zürich Zürich Switzerland Oliver Lodge Laboratory University of Liverpool Liverpool United Kingdom Instituto Galego de Física de Altas Enerxías (IGFAE) Universidade de Santiago de Compostela Santiago de Compostela Spain H.H. Wills Physics Laboratory University of Bristol Bristol United Kingdom Université Clermont Auvergne CNRS/IN2P3 LPC Clermont-Ferrand France University of Michigan Ann Arbor Michigan USA associated to68 INFN Sezione di Milano Milano Italy University of Cincinnati Cincinnati Ohio USA Fakultät Physik Technische Universität Dortmund Dortmund Germany European Organization for Nuclear Research (CERN) Geneva Switzerland School of Physics and Astronomy University of Glasgow Glasgow United Kingdom ICCUB Universitat de Barcelona Barcelona Spain Department of Physics and Astronomy University of Manchester Manchester United Kingdom Petersburg Nuclear Physics Institute NRC Kurchatov Institute (PNPI NRC KI) Gatchina Russia Cavendish Laboratory University of Cambridge Cambridge United Kingdom Universidade Federal do Rio de Janeiro (UFRJ) Rio de Janeiro Brazil Université Paris-Saclay CNRS/IN2P3 IJCLab Orsay France INFN Sezione di Firenze Firenze Italy INFN Sezione di Ferrara Ferrara Italy Physikalisches Institut Ruprecht-Karls-Universität Heidelberg Heidelberg Germany Institute for High Energy Physics NRC Kurchatov Institute (IHEP NRC KI) Protvino Russia Protvino Russia Syracuse University Syracuse New York USA Yandex School of Data Analysis Moscow Russia Aix Marseille Univ CNRS/IN2P3 CPPM Marseille France Laboratoire Leprince-Ringuet CNRS/IN2P3 Ecole Polytechnique Institut Polytechnique de Paris Palaiseau France Institute of Physics Ecole Polytechnique Fédérale de Lausanne (EPFL) Lausanne Switzerland Department of Physics University of Warwick Coventry United Kingdom Imperial College London London United Kingdom Centro Brasileiro

This article reports the first measurement of prompt χc1 and χc2 charmonium production in nuclear collisions at Large Hadron Collider energies. The cross-section ratio σ(χc2)/σ(χc1) is measured in pPb collisions at sNN=8.16 TeV, collected with the LHCb experiment. The χc1,2 states are reconstructed via their decay to a J/ψ meson, subsequently decaying into a pair of oppositely charged muons, and a photon, which is reconstructed in the calorimeter or via its conversion in the detector material. The cross-section ratio is consistent with unity in the two considered rapidity regions. Comparison with a corresponding cross-section ratio previously measured by the LHCb Collaboration in pp collisions suggests that χc1 and χc2 states are similarly affected by nuclear effects occurring in pPb collisions.

关键词： Particle & resonance production Relativistic heavy-ion collisions

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Survey of key technologies on millimeter-wave CMOS integrated circuits

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IOP Conference Series: Materials science and Engineering 2018年第1期351卷

作者： Fei Yu Lei Gao Lixiang Li Shuo Cai Wei Wang Chunhua Wang Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation & School of Computer and Communication Engineering Changsha University of Science and Technology Changsha 410114 China College of Information Science and Engineering Hunan University Changsha 410082 China

In order to provide guidance for the development of high performance millimeter-wave complementary metal oxide semiconductor (MMW-CMOS) integrated circuits (IC), this paper provides a survey of key technologies on MMW-CMOS IC. Technical background of MMW wireless communications is described. Then the recent development of the critical technologies of the MMW-CMOS IC are introduced in detail and compared. A summarization is given, and the development prospects on MMW-CMOS IC are also discussed.

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EasyHPC: An online programming platform for learning high performance computing

EasyHPC: An online programming platform for learning high pe...

引用

IEEE International Conference on Teaching, Assessment and Learning for Engineering (TALE)

作者： Zhepeng Zou Yuxiao Zhang Jiang Li Xiaojun Hei Yunfei Du Di Wu Guangdong Province Key Laboratory of Big Data Analysis and Processing Guangzhou China School of Data and Computer Science Sun Yat-sen University Guangzhou China National Supercomputer Center in Guangzhou Guangzhou China School of Electronic Information and Communications Huazhong University of Science and Technology Wuhan China

Lacking of appropriate learning facilities, the traditional approach to learn high-performance computing (HPC) is commonly theory-oriented without sufficient hands-on programming experiences. To improve the hands-on experiences of HPC learners, we design and implement a flexible and adaptive online HPC learning platform in this paper, called EasyHPC. This platform contains various online course modules such as quiz bank, interactive community, and virtual laboratory. In our system, various HPC theoretical and experimental learning activities can be conducted online, such as assigning HPC parallel programming tasks, and creating HPC questions and collecting students' submissions. It is convenient for students to study HPC-related courses in our platform, submit course assignments, exchange ideas, and complete HPC programming tasks. Our preliminary learning trials have shown that our system can effectively improve the hands-on experience of our students by providing an integrated HPC learning and programming environment. Our students are able to achieve various HPC capstone projects in our platform to develop their system capability.

关键词： Programming profession Servers High performance computing Education Supercomputers

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