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Ontology Alignment Evaluation Initiative Benchmark test library


The goal of the benchmark test library is to offer a set of tests which are wide in feature coverage, progressive and stable. It serves the purpose of evaluating the strength and weakness of matchers (by being progressive and wide coverage) and measuring the progress of matchers (by being stable and reusable over the years).

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Dataset Details

Citation Author(s):
Submitted by:
Zhengxiang Yang
Last updated:
Mon, 06/04/2018 - 04:34
DOI:
10.21227/H2HM2Z
Links:
 
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[1] , "Ontology Alignment Evaluation Initiative Benchmark test library", IEEE Dataport, 2018. [Online]. Available: http://dx.doi.org/10.21227/H2HM2Z. Accessed: Jun. 18, 2018.
@data{h2hm2z-18,
doi = {10.21227/H2HM2Z},
url = {http://dx.doi.org/10.21227/H2HM2Z},
author = { },
publisher = {IEEE Dataport},
title = {Ontology Alignment Evaluation Initiative Benchmark test library},
year = {2018} }
TY - DATA
T1 - Ontology Alignment Evaluation Initiative Benchmark test library
AU -
PY - 2018
PB - IEEE Dataport
UR - 10.21227/H2HM2Z
ER -
. (2018). Ontology Alignment Evaluation Initiative Benchmark test library. IEEE Dataport. http://dx.doi.org/10.21227/H2HM2Z
, 2018. Ontology Alignment Evaluation Initiative Benchmark test library. Available at: http://dx.doi.org/10.21227/H2HM2Z.
. (2018). "Ontology Alignment Evaluation Initiative Benchmark test library." Web.
1. . Ontology Alignment Evaluation Initiative Benchmark test library [Internet]. IEEE Dataport; 2018. Available from : http://dx.doi.org/10.21227/H2HM2Z
. "Ontology Alignment Evaluation Initiative Benchmark test library." doi: 10.21227/H2HM2Z

test 20180517a


test

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Dataset Details

Citation Author(s):
Submitted by:
Kun Lee
Last updated:
Thu, 05/17/2018 - 03:07
DOI:
10.21227/H28D5V
 
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[1] , "test 20180517a", IEEE Dataport, 2018. [Online]. Available: http://dx.doi.org/10.21227/H28D5V. Accessed: Jun. 18, 2018.
@data{h28d5v-18,
doi = {10.21227/H28D5V},
url = {http://dx.doi.org/10.21227/H28D5V},
author = { },
publisher = {IEEE Dataport},
title = {test 20180517a},
year = {2018} }
TY - DATA
T1 - test 20180517a
AU -
PY - 2018
PB - IEEE Dataport
UR - 10.21227/H28D5V
ER -
. (2018). test 20180517a. IEEE Dataport. http://dx.doi.org/10.21227/H28D5V
, 2018. test 20180517a. Available at: http://dx.doi.org/10.21227/H28D5V.
. (2018). "test 20180517a." Web.
1. . test 20180517a [Internet]. IEEE Dataport; 2018. Available from : http://dx.doi.org/10.21227/H28D5V
. "test 20180517a." doi: 10.21227/H28D5V

IS-RNN


This is the code and data for IS-RNN validation and research

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Dataset Details

Citation Author(s):
Submitted by:
Fei Wang
Last updated:
Fri, 05/11/2018 - 05:40
DOI:
10.21227/H2V953
Data Format:
 
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[1] , "IS-RNN", IEEE Dataport, 2018. [Online]. Available: http://dx.doi.org/10.21227/H2V953. Accessed: Jun. 18, 2018.
@data{h2v953-18,
doi = {10.21227/H2V953},
url = {http://dx.doi.org/10.21227/H2V953},
author = { },
publisher = {IEEE Dataport},
title = {IS-RNN},
year = {2018} }
TY - DATA
T1 - IS-RNN
AU -
PY - 2018
PB - IEEE Dataport
UR - 10.21227/H2V953
ER -
. (2018). IS-RNN. IEEE Dataport. http://dx.doi.org/10.21227/H2V953
, 2018. IS-RNN. Available at: http://dx.doi.org/10.21227/H2V953.
. (2018). "IS-RNN." Web.
1. . IS-RNN [Internet]. IEEE Dataport; 2018. Available from : http://dx.doi.org/10.21227/H2V953
. "IS-RNN." doi: 10.21227/H2V953

QRNG Machine Learning


Dataset used in paper "Machine Learning Cryptanalysis of a Quantum Random Number Generator" submitted to IEEE TIFS.

 

Dataset Files

No Data files have been uploaded.

Dataset Details

Citation Author(s):
Submitted by:
Nhan Truong
Last updated:
Tue, 05/08/2018 - 03:51
DOI:
10.21227/H2108P
Data Format:
 
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[1] , "QRNG Machine Learning", IEEE Dataport, 2018. [Online]. Available: http://dx.doi.org/10.21227/H2108P. Accessed: Jun. 18, 2018.
@data{h2108p-18,
doi = {10.21227/H2108P},
url = {http://dx.doi.org/10.21227/H2108P},
author = { },
publisher = {IEEE Dataport},
title = {QRNG Machine Learning},
year = {2018} }
TY - DATA
T1 - QRNG Machine Learning
AU -
PY - 2018
PB - IEEE Dataport
UR - 10.21227/H2108P
ER -
. (2018). QRNG Machine Learning. IEEE Dataport. http://dx.doi.org/10.21227/H2108P
, 2018. QRNG Machine Learning. Available at: http://dx.doi.org/10.21227/H2108P.
. (2018). "QRNG Machine Learning." Web.
1. . QRNG Machine Learning [Internet]. IEEE Dataport; 2018. Available from : http://dx.doi.org/10.21227/H2108P
. "QRNG Machine Learning." doi: 10.21227/H2108P

Tourists Reviews Dataset


This dataset contains user online reviews of two tourist places namely London' Parks and Art Museums.

London Parks Reviews

In this dataset, the top five most visited parks are selected, such as St. James' Park, Hyde Park, Regent's Park, Kensington Park and Greenwich Park. For each park, 600 reviews from Jan to Sep 2017 are present in CSV format.

London Art Museums

Dataset Files

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Dataset Details

Citation Author(s):
Submitted by:
Muhammad Usman
Last updated:
Mon, 05/07/2018 - 12:07
DOI:
10.21227/H29H4T
Data Format:
 
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[1] , "Tourists Reviews Dataset", IEEE Dataport, 2018. [Online]. Available: http://dx.doi.org/10.21227/H29H4T. Accessed: Jun. 18, 2018.
@data{h29h4t-18,
doi = {10.21227/H29H4T},
url = {http://dx.doi.org/10.21227/H29H4T},
author = { },
publisher = {IEEE Dataport},
title = {Tourists Reviews Dataset},
year = {2018} }
TY - DATA
T1 - Tourists Reviews Dataset
AU -
PY - 2018
PB - IEEE Dataport
UR - 10.21227/H29H4T
ER -
. (2018). Tourists Reviews Dataset. IEEE Dataport. http://dx.doi.org/10.21227/H29H4T
, 2018. Tourists Reviews Dataset. Available at: http://dx.doi.org/10.21227/H29H4T.
. (2018). "Tourists Reviews Dataset." Web.
1. . Tourists Reviews Dataset [Internet]. IEEE Dataport; 2018. Available from : http://dx.doi.org/10.21227/H29H4T
. "Tourists Reviews Dataset." doi: 10.21227/H29H4T

Eyes in the Skies: A Data-driven Fusion Approach to Identifying Drug Crops from Remote Sensing Images Dataset


Automatic classification of sensitive content in remote sensing images, such as drug crop sites, is a promising task as it can aid law-enforcement institutions fighting illegal drug dealers worldwide, while, at the same time, it can help monitoring legalized crops in countries that regulate them. However, existing art on detecting drug crops from remote sensing images is limited in some key factors not taking full advantage of the available hyperspectral info for analysis.

Dataset Files

No Data files have been uploaded.

Dataset Details

Citation Author(s):
Submitted by:
Anselmo Ferreira
Last updated:
Sun, 04/29/2018 - 01:50
DOI:
10.21227/H2WD42
Data Format:
 
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[1] , "Eyes in the Skies: A Data-driven Fusion Approach to Identifying Drug Crops from Remote Sensing Images Dataset", IEEE Dataport, 2018. [Online]. Available: http://dx.doi.org/10.21227/H2WD42. Accessed: Jun. 18, 2018.
@data{h2wd42-18,
doi = {10.21227/H2WD42},
url = {http://dx.doi.org/10.21227/H2WD42},
author = { },
publisher = {IEEE Dataport},
title = {Eyes in the Skies: A Data-driven Fusion Approach to Identifying Drug Crops from Remote Sensing Images Dataset},
year = {2018} }
TY - DATA
T1 - Eyes in the Skies: A Data-driven Fusion Approach to Identifying Drug Crops from Remote Sensing Images Dataset
AU -
PY - 2018
PB - IEEE Dataport
UR - 10.21227/H2WD42
ER -
. (2018). Eyes in the Skies: A Data-driven Fusion Approach to Identifying Drug Crops from Remote Sensing Images Dataset. IEEE Dataport. http://dx.doi.org/10.21227/H2WD42
, 2018. Eyes in the Skies: A Data-driven Fusion Approach to Identifying Drug Crops from Remote Sensing Images Dataset. Available at: http://dx.doi.org/10.21227/H2WD42.
. (2018). "Eyes in the Skies: A Data-driven Fusion Approach to Identifying Drug Crops from Remote Sensing Images Dataset." Web.
1. . Eyes in the Skies: A Data-driven Fusion Approach to Identifying Drug Crops from Remote Sensing Images Dataset [Internet]. IEEE Dataport; 2018. Available from : http://dx.doi.org/10.21227/H2WD42
. "Eyes in the Skies: A Data-driven Fusion Approach to Identifying Drug Crops from Remote Sensing Images Dataset." doi: 10.21227/H2WD42

Machine Learning: A Science Mapping Analysis


Machine learning is becoming increasingly important for companies and the scientific community. It allows us to generate solutions for several problems faced by society. In this study, we perform a science mapping analysis on the machine learning research, in order to provide an overview of the scientific work during the last decade in this area and to show trends that could be the basis for future developments in the field of computer science. This study was carried out using the CiteSpace and SciMAT tools based on results from Scopus and Clarivate Web of Science.

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Dataset Details

Citation Author(s):
Submitted by:
Juan Rincon-Patino
Last updated:
Sat, 04/21/2018 - 22:28
DOI:
10.21227/H2337Z
Data Format:
 
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[1] , "Machine Learning: A Science Mapping Analysis", IEEE Dataport, 2018. [Online]. Available: http://dx.doi.org/10.21227/H2337Z. Accessed: Jun. 18, 2018.
@data{h2337z-18,
doi = {10.21227/H2337Z},
url = {http://dx.doi.org/10.21227/H2337Z},
author = { },
publisher = {IEEE Dataport},
title = {Machine Learning: A Science Mapping Analysis},
year = {2018} }
TY - DATA
T1 - Machine Learning: A Science Mapping Analysis
AU -
PY - 2018
PB - IEEE Dataport
UR - 10.21227/H2337Z
ER -
. (2018). Machine Learning: A Science Mapping Analysis. IEEE Dataport. http://dx.doi.org/10.21227/H2337Z
, 2018. Machine Learning: A Science Mapping Analysis. Available at: http://dx.doi.org/10.21227/H2337Z.
. (2018). "Machine Learning: A Science Mapping Analysis." Web.
1. . Machine Learning: A Science Mapping Analysis [Internet]. IEEE Dataport; 2018. Available from : http://dx.doi.org/10.21227/H2337Z
. "Machine Learning: A Science Mapping Analysis." doi: 10.21227/H2337Z

Phase Characterization of the Nonuniform Reflect arrays


A microstrip reflectarray antenna is a hybrid design of a reflector antenna and a planar phased array antenna where a feed element illuminates reflecting surface, which can be either flat, slightly curved or a non-uniform plane in order to convert a spherical wave produced by its feed into a plane wave. On the reflecting side of the surface, there might be a series of the printed patch, dipole, loop elements or just a dielectric layer without any power division network.

Dataset Files

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Dataset Details

Citation Author(s):
Submitted by:
peyman mahouti
Last updated:
Fri, 03/30/2018 - 13:22
DOI:
10.21227/H2BW8F
Data Format:
 
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[1] , "Phase Characterization of the Nonuniform Reflect arrays", IEEE Dataport, 2018. [Online]. Available: http://dx.doi.org/10.21227/H2BW8F. Accessed: Jun. 18, 2018.
@data{h2bw8f-18,
doi = {10.21227/H2BW8F},
url = {http://dx.doi.org/10.21227/H2BW8F},
author = { },
publisher = {IEEE Dataport},
title = {Phase Characterization of the Nonuniform Reflect arrays},
year = {2018} }
TY - DATA
T1 - Phase Characterization of the Nonuniform Reflect arrays
AU -
PY - 2018
PB - IEEE Dataport
UR - 10.21227/H2BW8F
ER -
. (2018). Phase Characterization of the Nonuniform Reflect arrays. IEEE Dataport. http://dx.doi.org/10.21227/H2BW8F
, 2018. Phase Characterization of the Nonuniform Reflect arrays. Available at: http://dx.doi.org/10.21227/H2BW8F.
. (2018). "Phase Characterization of the Nonuniform Reflect arrays." Web.
1. . Phase Characterization of the Nonuniform Reflect arrays [Internet]. IEEE Dataport; 2018. Available from : http://dx.doi.org/10.21227/H2BW8F
. "Phase Characterization of the Nonuniform Reflect arrays." doi: 10.21227/H2BW8F

DataSet for paper


The dataset provides data for the article " LSTM-based Argument Recommendation for Non-API Methods"

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Dataset Details

Citation Author(s):
Submitted by:
Guangjie Li
Last updated:
Thu, 03/29/2018 - 04:49
DOI:
10.21227/H24D47
Data Format:
 
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[1] , "DataSet for paper", IEEE Dataport, 2018. [Online]. Available: http://dx.doi.org/10.21227/H24D47. Accessed: Jun. 18, 2018.
@data{h24d47-18,
doi = {10.21227/H24D47},
url = {http://dx.doi.org/10.21227/H24D47},
author = { },
publisher = {IEEE Dataport},
title = {DataSet for paper},
year = {2018} }
TY - DATA
T1 - DataSet for paper
AU -
PY - 2018
PB - IEEE Dataport
UR - 10.21227/H24D47
ER -
. (2018). DataSet for paper. IEEE Dataport. http://dx.doi.org/10.21227/H24D47
, 2018. DataSet for paper. Available at: http://dx.doi.org/10.21227/H24D47.
. (2018). "DataSet for paper." Web.
1. . DataSet for paper [Internet]. IEEE Dataport; 2018. Available from : http://dx.doi.org/10.21227/H24D47
. "DataSet for paper." doi: 10.21227/H24D47