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Author Correction: The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data

Scientific data 2021年第1期8卷 72页

作者： Gilberto Pastorello Carlo Trotta Eleonora Canfora Housen Chu Danielle Christianson You-Wei Cheah Cristina Poindexter Jiquan Chen Abdelrahman Elbashandy Marty Humphrey Peter Isaac Diego Polidori Markus Reichstein Alessio Ribeca Catharine van Ingen Nicolas Vuichard Leiming Zhang Brian Amiro Christof Ammann M Altaf Arain Jonas Ardö Timothy Arkebauer Stefan K Arndt Nicola Arriga Marc Aubinet Mika Aurela Dennis Baldocchi Alan Barr Eric Beamesderfer Luca Belelli Marchesini Onil Bergeron Jason Beringer Christian Bernhofer Daniel Berveiller Dave Billesbach Thomas Andrew Black Peter D Blanken Gil Bohrer Julia Boike Paul V Bolstad Damien Bonal Jean-Marc Bonnefond David R Bowling Rosvel Bracho Jason Brodeur Christian Brümmer Nina Buchmann Benoit Burban Sean P Burns Pauline Buysse Peter Cale Mauro Cavagna Pierre Cellier Shiping Chen Isaac Chini Torben R Christensen James Cleverly Alessio Collalti Claudia Consalvo Bruce D Cook David Cook Carole Coursolle Edoardo Cremonese Peter S Curtis Ettore D'Andrea Humberto da Rocha Xiaoqin Dai Kenneth J Davis Bruno De Cinti Agnes de Grandcourt Anne De Ligne Raimundo C De Oliveira Nicolas Delpierre Ankur R Desai Carlos Marcelo Di Bella Paul di Tommasi Han Dolman Francisco Domingo Gang Dong Sabina Dore Pierpaolo Duce Eric Dufrêne Allison Dunn Jiří Dušek Derek Eamus Uwe Eichelmann Hatim Abdalla M ElKhidir Werner Eugster Cacilia M Ewenz Brent Ewers Daniela Famulari Silvano Fares Iris Feigenwinter Andrew Feitz Rasmus Fensholt Gianluca Filippa Marc Fischer John Frank Marta Galvagno Mana Gharun Damiano Gianelle Bert Gielen Beniamino Gioli Anatoly Gitelson Ignacio Goded Mathias Goeckede Allen H Goldstein Christopher M Gough Michael L Goulden Alexander Graf Anne Griebel Carsten Gruening Thomas Grünwald Albin Hammerle Shijie Han Xingguo Han Birger Ulf Hansen Chad Hanson Juha Hatakka Yongtao He Markus Hehn Bernard Heinesch Nina Hinko-Najera Lukas Hörtnagl Lindsay Hutley Andreas Ibrom Hiroki Ikawa Marcin Jackowicz-Korczynski Dalibor Janouš Wilma Jans Rachhpal Jassal Shicheng Jiang Tomomichi Kato Myr Computational Research Division Lawrence Berkeley National Laboratory Berkeley CA 94720 USA. gzpastorello@lbl.gov. DIBAF University of Tuscia Viterbo 01100 Italy. Euro-Mediterranean Centre on Climate Change Foundation (CMCC) Lecce 73100 Italy. Climate & Ecosystem Sciences Division Lawrence Berkeley National Laboratory Berkeley CA 94720 USA. Computational Research Division Lawrence Berkeley National Laboratory Berkeley CA 94720 USA. Department of Civil Engineering California State University Sacramento CA 95819 USA. Department of Geography Environment and Spatial Sciences Michigan State University East Lansing MI 48823 USA. Department of Computer Science University of Virginia Charlottesville VA 22904 USA. TERN Ecosystem Processes Menzies Creek VIC3159 Australia. Max Planck Institute for Biogeochemistry Jena 07701 Germany. Laboratoire des Sciences du Climat et de l'Environnement (LSCE) CEA CNRS UVSQ UPSACLAY Gif sur Yvette 91190 France. Key Laboratory of Ecosystem Network Observation and Modeling Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing 100101 China. Department of Soil Science University of Manitoba Winnipeg MB R3T2N2 Canada. Department of Agroecology and Environment Agroscope Research Institute Zürich 8046 Switzerland. School of Geography and Earth Sciences McMaster University L8S4K1 Hamilton ON Canada. Department of Physical Geography and Ecosystem Science Lund University Lund 22362 Sweden. Department of Agronomy and Horticulture University of Nebraska-Lincoln Lincoln NE 68583 USA. School of Ecosystem and Forest Sciences The University of Melbourne Richmond VIC3121 Australia. Department of Biology Research Group PLECO University of Antwerp Antwerp 2610 Belgium. Joint Research Centre European Commission Ispra 21027 Italy. TERRA Teaching and Research Center University of Liege Gembloux B-5030 Belgium. Finnish Meteorological Institute Helsinki 00560 Finland. ES

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26th Annual Computational Neuroscience Meeting (CNS*2017) of the Organization for Computational Neuroscience Antwerp, Belgium, July 15-20, 2017

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BMC NEUROscience 2017年第SUPPL 1期18卷 59-59页

作者： [Anonymous] Indiana University Purdue University Indianapolis Indianapolis IN 46032 USA Stark Neurosciences Research Institute Indiana University School of Medicine Indianapolis IN 46032 USA Department of Mathematics East Carolina University Greenville NC 27858 USA Jülich Supercomputing Centre Forschungszentrum Jülich 52425 Jülich Germany Future Systems Swiss National Supercomputing Centre 8092 Zurich Switzerland User Engagement and Support Swiss National Supercomputing Centre 6900 Lugano Switzerland Institut de Neurosciences des Systèmes Aix Marseille Univ 13005 Marseille France Simulation Lab Neuroscience Forschungszentrum Jülich Jülich Germany Department of Experimental Psychology Ghent University 9000 Ghent Belgium Donders Center for Cognitive Neuroimaging Radboud University 6525HR Nijmegen The Netherlands Department of Electrical Computer and Energy Engineering University of Colorado Boulder CO 80309 USA Department of Neurosurgery Johns Hopkins School of Medicine Baltimore MD 21287 USA Department of Neurology Johns Hopkins School of Medicine Baltimore MD 21287 USA Department of Otolaryngology Johns Hopkins School of Medicine Baltimore MD 21287 USA INSERM U968 Paris France Sorbonne Universités UPMC University Paris 06 UMR_S 968 Institut de la Vision Paris France CNRS UMR_7210 Paris France Department of Computer Architecture and Technology University of Granada (CITIC) Granada Spain Sorbonne Universités UPMC Univ Paris 06 INSERM CNRS Institut de la Vision Paris France Department of Adaptive Machine Systems Osaka University Osaka Japan Department of Computer Science University of Cergy-Pontoise Cergy-Pontoise France Department of Physics and Astronomy College of Charleston Charleston SC 29424 USA School of Physics Faculty of Science University of Sydney Sydney NSW 2006 Australia Center of Excellence for Integrative Brain Function Australian Research Council Sydney Australia Max Planck Institute for Human Cognitive and Brain Sciences Saxony Lei

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A quality assurance support system for learning-object development

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2012 International Workshops on Web-Based Learning, ICWL 2012 - KMEL, SciLearn, and CCSTED

作者： Kalayanapan, Nopachat Wuwongse, Vilas Dittawit, Kornschnok Computer Science and Information Management Asian Institute of Technology Thailand

ISBN: (纸本)9783662434536

Use of e-Learning has been expanding rapidly in recent years with the development of ICT and e-Learning tools. The trend shows that it will continue to grow as technology is becoming cheaper and more accessible. Despite the trend, e-Learning suffers from low quality learning objects (LOs) due to the lack of supporting tools for proper electronic LO development. This paper proposes a quality assurance support system for LO development that aims to raise quality awareness throughout the development process. The system's underlying data model makes use of RDF to model and implement a workflow for LO development following the ISO/IEC 19796-1 standard. The system also integrates Quality Function Deployment (QFD) and achievement standards from Achievement Standard Network (ASN) to support the proposed workflow. Preliminary evaluation of the system shows that the system can satisfactorily assure the quality of resulting LOs. © 2014 Springer-Verlag.

关键词： Quality function deployment

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A comparative study of key phrase extraction for cross-domain document collections

Lecture Notes in Computer Science (including subseries Lectu...

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Lecture Notes in computer science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2014年 8839卷 393-398页

作者： Tantanasiriwong, Supaporn Haruechaiyasak, Choochart Guha, Sumanta Computer Science and Information Management School of Engineering and Technology Asian Institute of Technology Thailand Speech and Audio Technology Laboratory National Electronics and Computer Technology Center National Science and Technology Development Agency Thailand

ISBN: (纸本)9783319128221

An extraction tool, nowadays, has become useful for text mining researchers to find keywords and keyphrases from the documents. Performing keywords and keyphrases extraction for cross-domain information are more challenging since both domains of interest are different in word usage. In this paper, two popular keyphrases extraction tools, Maui and Carrot, are investigated, for extracting terms from cross-domain document databases. The characteristic of keywords or phrases matching among different domain collections is presented and used for determining the keyphrase extraction tool for patent documents and scientific publications. In our experiment, matching between a patent and its cited publication are the key point. For evaluation, the performance of cross-domain matching is measured by comparing the similarity measure among those extraction tool results. The experimental results show that Maui tool proves to be the appropriate keyphrases extraction tool with its best performance measured by Cosine similarity of 3.31% when compared with Carrot tool for cross-domain document collections matching. © Springer International Publishing Switzerland 2014.

关键词： Extraction

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An application profile for linked teacher profiles and teaching resources

Lecture Notes in Computer Science (including subseries Lectu...

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Lecture Notes in computer science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2014年 8839卷 138-148页

作者： Nonthakarn, Chariya Chawuthai, Rathachai Wuwongse, Vilas Computer Science and Information Management Asian Institute of Technology Pathumthani Thailand The Graduate University for Advanced Studies Japan and National Institute of Informatics Tokyo Japan Asian University Chonburi Thailand

ISBN: (纸本)9783319128221

This paper designs and develops an application profile for teaching resources management systems that emphasize how to link information of teacher profiles and teaching resources. The design and development are based on the Singapore Framework for a Dublin Core Application Profile, which is a framework for designing metadata schemas for maximum interoperability. The designed application profile integrates five entities – teacher profiles, course materials, academic works, events, and awards, all of which are considered to be important information about teachers, their academic contributions and achievements. By employing the Linked Open Data (LOD) principle to represent the application profile, teachers are able to share their teaching resources, and to cooperate with among themselves. The application profile has been implemented and evaluated against requirements collected from teachers. It has been found that the application profile is practically useful and the employment of application profile is an efficient means for the design of metadata schemas. © Springer International Publishing Switzerland 2014.

关键词： Metadata

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A distributed platform for archiving and viewing cultural artifacts in 3D

Lecture Notes in Computer Science (including subseries Lectu...

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Lecture Notes in computer science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2014年 8839卷 22-29页

作者： Chanhom, Weeraphan Anutariya, Chutiporn Guha, Sumanta Computer Science and Information Management Program AIT Asian Institute of Technology P.O. Box 4 Klong Luang Pathumthani12120 Thailand Asian University 89 Moo 12 Highway 331 Huay Yai Banglamung Chonburi20260 Thailand

ISBN: (纸本)9783319128221

In this paper we describe an architecture for the digital museum and propose the suitable museum data model for distributed digital contents, especially in 3D cultural objects. The development of a data model is viewing interoperability, standards, museum data model and feedback from two museums in Chiang Mai pilot sites under the Museum Thailand project. We analyzed the notable metadata enabling and supporting all of museum content management processes. In addition we also described a system that will integrate data from different museums. Finally, the museums can use this model to implement their system for providing services to cultural content experts and public entities. © Springer International Publishing Switzerland 2014.

关键词： Metadata

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Cross-Domain Citation Recommendation Based on Co-Citation Selection

Cross-Domain Citation Recommendation Based on Co-Citation Se...

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International Conference on Electrical Engineering/Electronics, computer, Telecommunications and information technology

作者： Supapom Tantanasiriwong Choochart Haruechaiyasak Information Management School of Engineering and Technology Asian Institute of Technology Speech and Audio Technology Laboratory National Electronics and Computer Technology Center National Science and Technology Development Agency

ISBN: (纸本)9781479929948

Recommending information across domains has recently gained much attention among research and academic communities. Traditionally, a cross-domain recommender system has emerged to assist users in finding relevant information from the target domain given the initial information from the source domain. However, in the area of citation recommendation, mapping terms across different domains could be problematic due to the term mismatch. In this paper, we propose a cross-domain citation recommendation framework to suggest relevant research publications given a patent as the source domain. Two main approaches are implemented and compared in this study. The first is a baseline approach which is based on simple keyword mapping technique. The second approach, Co-Citation Selection (CCS), is based on the collaborative filtering in which neighboring papers is selected and weighted into publication citation prediction. To compare between two approaches, we adopt the Cosine, Jaccard, and KL-Divergence as the similarity measurement. The evaluation results are reported in terms of mean precision, recall, F-measure, and reciprocal rank. The best improvement of 22.6% in mean reciprocal rank was achieved with the Jaccard similarity.

关键词： Cross-domain recommendation Citation recommendation information retrieval Collaborative filtering Similarity calculation Ranking measurement component

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Fast target redetection for CAMSHIFT using back-projection and histogram matching

Fast target redetection for CAMSHIFT using back-projection a...

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International Conference on computer Vision Theory and Applications (VISAPP)

作者： Abdul Basit Matthew N. Dailey Pudit Laksanacharoen Jednipat Moonrinta Department of Computer Science and Information Management Asian Institute of Technology Pathumthani Thailand Department of Computer Science and Information Technology University of Balochistan Quetta Pakistan Mechanical Engineering King Mongkut's University of Technology (North Bangkok) Bangsue Bangkok Thailand

ISBN: (纸本)9781479976867

Most visual tracking algorithms lose track of the target object (start tracking a different object or part of the background) or report an error when the object being tracked leaves the scene or becomes occluded in a cluttered environment. We propose a fast algorithm for mobile robots tracking humans or other objects in real-life scenarios to avoid these problems. The proposed method uses an adaptive histogram threshold matching algorithm to suspend the CAMSHIFT tracker when the target is insufficiently clear. While tracking is suspended, any method would need to continually scan the entire image in an attempt to redetect and reinitialize tracking of the specified object. However, searching the entire image for an arbitrary target object requires an extremely efficient algorithm to be feasible in real time. Our method, rather than a detailed search over the entire image, makes efficient use of the backprojection of the target object's appearance model to hypothesize and test just a few candidate locations for the target in each image. Once the target object is redetected and sufficiently clear in a new image, the method reinitializes tracking. In a series of experiments with four real-world videos, we find that the method is successful at suspending and reinitializing CAMSHIFT tracking when the target leaves and reenters the scene, with successful reinitialization and very low false positive rates.

关键词： Target tracking Histograms Robots Image color analysis Videos Adaptation models Search problems

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Low Cost Quality Aware Multi-tier Application Hosting on the Amazon Cloud

Low Cost Quality Aware Multi-tier Application Hosting on the...

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International Conference on Future Internet of Things and Cloud (FiCloud)

作者： Waheed Iqbal Matthew N. Dailey David Carrera Punjab University College of Information Technology University of the Punjab Lahore Pakistan Computer Science and Information Management Asian Institute of Technology Thailand Technical University of Catalonia Barcelona Supercomputing Center Spain

Public cloud providers offer a variety of services and resources enabling users to host their applications. In order to determine the set of resources that will best minimize hosting costs while simultaneously meeting performance requirements, cloud users must be aware of the cost-performance tradeoffs of the available resources. This is particularly true of multi-tier Web applications using dynamic scaling strategies to sustain specific response time requirements. In this paper, we provide a cost-performance analysis of multiple scale out strategies using the three most economically feasible Amazon EC2 compute resources (micro, small, and medium virtual machine instances) for a typical multi-tier Web application. We find that the special CPU allocation policy of the micro instance makes it especially suitable for satisfying Web application performance requirements at minimal cost on the Amazon cloud.

关键词： Time factors Virtual machining Resource management Databases Cloud computing Monitoring Throughput

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Real-time road lane detection with commodity hardware

Real-time road lane detection with commodity hardware

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International Electrical Engineering Congress (iEECON)

作者： Somchok Sakjiraphong Andre Pinho Matthew N. Dailey Mongkol Ekpanyapong Adriano Tavares Computer Science and Information Management Klong Luang Pathumthani Thailand Microelectronics and Embedded Systems Asian Institute of Technology Department of Industrial Electronics University of Minho Portugal

We present a real-time algorithm for lane boundary estimation based on matched filters and a RANSAC-based method for boundary marking identification. The algorithm is designed to run with minimal assumptions about the environment using a single monocular camera sensor and low-cost commodity compute hardware. With GPU acceleration and TBB, the algorithm runs in less than 25 ms on average on images of size 640 × 480 comparing with 0.12 seconds running on CPU baseline.

关键词： Graphics processing units Instruction sets Convolution Arrays Real-time systems Estimation Cameras

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