cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7986931
title: Epiphytic bryophyte and lichen transplant dataset, Oregon, USA

description: This dataset consist of epiphytic bryophyte and lichen transplants, which were transplanted along elevation gradient in Oregon, USA in 2010. The data is part of the publication: "Epiphytic bryophyte and lichen transplant niches in changed environments along an elevational gradient in Pacific Northwest conifer forests". The data were collected with a financial support of Fulbright Scholar programme 2009/2010 and processed with a financial support of postdoctoral grant “Epiphyte metapopulation dynamics in boreo-nemoral forest landscape” (Nr. 1.1.1.2/VIAA/3/19/469).
DOI: 10.5281/zenodo.7986930
title: Epiphytic bryophyte and lichen transplant dataset, Oregon, USA

description: This dataset consist of epiphytic bryophyte and lichen transplants, which were transplanted along elevation gradient in Oregon, USA in 2010. The data is part of the publication: "Epiphytic bryophyte and lichen transplant niches in changed environments along an elevational gradient in Pacific Northwest conifer forests". The data were collected with a financial support of Fulbright Scholar programme 2009/2010 and processed with a financial support of postdoctoral grant “Epiphyte metapopulation dynamics in boreo-nemoral forest landscape” (Nr. 1.1.1.2/VIAA/3/19/469).
DOI: 10.5281/zenodo.7986957
title: amazona_auropalliata_decontaminated_gx.fasta.gz

description: .
DOI: 10.5281/zenodo.7986956
title: amazona_auropalliata_decontaminated_gx.fasta.gz

description: .
DOI: 10.6084/m9.figshare.22712365
title: Maximum and Minimum temperature at a coarser resolution (PISCOt v1.2)

description: Maximum and Minimum air temperature from PISCOt v1.2. at coarser resolution: 0.05° and 0.10° spatial resolution. Normal (average 1981-2010) and daily values from 1981 to 2020.
DOI: 10.6084/m9.figshare.22712365.v2
title: Maximum and Minimum temperature at a coarser resolution (PISCOt v1.2)

description: Maximum and Minimum air temperature from PISCOt v1.2. at coarser resolution: 0.05° and 0.10° spatial resolution. Daily values from 1981 to 2020.
DOI: 10.5281/zenodo.7982540
title: hybrid-graph-benchmark

description: This is a benchmark dataset for evaluating <strong>hybrid-graph</strong> (hypergraph and hierarchical graph) learning algorithms. It contains: 21 real-world higer-order graphs from the domains of biology, social media, and wikipedia For accessing additional functionalities, please access through Project Page Built-in functionalities for preprocessing hybrid-graphs A framework to easily train and evaluate Graph Neural Networks
DOI: 10.5281/zenodo.7982539
title: hybrid-graph-benchmark

description: This is a benchmark dataset for evaluating <strong>hybrid-graph</strong> (hypergraph and hierarchical graph) learning algorithms. It contains: 21 real-world higer-order graphs from the domains of biology, social media, and wikipedia For accessing additional functionalities, please access through Project Page Built-in functionalities for preprocessing hybrid-graphs A framework to easily train and evaluate Graph Neural Networks
DOI: 10.6084/m9.figshare.23261801
title: Distinctive and homogenized patterns of topsoil nitrogen and phosphorus across urban forests in eastern China

description: The datasets include data on topsoil (0-20cm) total N concentration, total P concentration across a north-south transect of urban and natural forests in eastern China. They support the findings of the manuscript entitled "Distinctive and homogenized patterns of topsoil nitrogen and phosphorus across urban forests in eastern China".
DOI: 10.6084/m9.figshare.23261801.v1
title: Distinctive and homogenized patterns of topsoil nitrogen and phosphorus across urban forests in eastern China

description: The datasets include data on topsoil (0-20cm) total N concentration, total P concentration across a north-south transect of urban and natural forests in eastern China. They support the findings of the manuscript entitled "Distinctive and homogenized patterns of topsoil nitrogen and phosphorus across urban forests in eastern China".
DOI: 10.25422/azu.data.21265959.v1
title: Raw Data for "Effects of β-Caryophyllene and Oxygen Availability on Cholesterol and Fatty Acids in Breast Cancer Cells"

description: The Excel and CSV files contain raw data for the data presented in the main text figures.<br><br> <em>For inquiries regarding the contents of this dataset, please contact the Corresponding Author listed in the README.txt file. Administrative inquiries (e.g., removal requests, trouble downloading, etc.) can be directed to data-management@arizona.edu</em>
DOI: 10.25422/azu.data.21265959
title: Raw Data for "Effects of β-Caryophyllene and Oxygen Availability on Cholesterol and Fatty Acids in Breast Cancer Cells"

description: The Excel and CSV files contain raw data for the data presented in the main text figures.<br><br> <em>For inquiries regarding the contents of this dataset, please contact the Corresponding Author listed in the README.txt file. Administrative inquiries (e.g., removal requests, trouble downloading, etc.) can be directed to data-management@arizona.edu</em>
DOI: 10.5281/zenodo.7987028
title: Ranking snapshot for the query "THE ROLE OF HUMAN INTELLIGENCE IN ARTIFICIAL INTELLIGENCE" performed on Google Scholar

description: This deposit provides a snapshot of results obtained through a Google Scholar search query on the topic of "THE ROLE OF HUMAN INTELLIGENCE IN ARTIFICIAL INTELLIGENCE". The search was performed by Alessandro Lotta, from Unipd, on May 30, 2023 at 6:34:32 PM. The captured data includes 2 pages of search results, which have been saved in the output-data.jsonld file. In addition to the citation data, the deposit includes PNG format screenshots of the search results, allowing visual reference to the captured information. The metadata for the Research Object Crate is also included in JSON format, providing essential details about the contents. The citation snapshot presented here is generated using the Unipd Ranking Citation Tool, a tool developed by Gianmaria Silvello and Alessandro Lotta (University of Padua). This tool, accessible at https://rankingcitation.dei.unipd.it
description: The citation snapshot presented here is generated using the Unipd Ranking Citation Tool, a tool developed by Gianmaria Silvello and Alessandro Lotta (University of Padua). This tool, accessible at https://rankingcitation.dei.unipd.it
DOI: 10.5281/zenodo.7987027
title: Ranking snapshot for the query "THE ROLE OF HUMAN INTELLIGENCE IN ARTIFICIAL INTELLIGENCE" performed on Google Scholar

description: This deposit provides a snapshot of results obtained through a Google Scholar search query on the topic of "THE ROLE OF HUMAN INTELLIGENCE IN ARTIFICIAL INTELLIGENCE". The search was performed by Alessandro Lotta, from Unipd, on May 30, 2023 at 6:34:32 PM. The captured data includes 2 pages of search results, which have been saved in the output-data.jsonld file. In addition to the citation data, the deposit includes PNG format screenshots of the search results, allowing visual reference to the captured information. The metadata for the Research Object Crate is also included in JSON format, providing essential details about the contents. The citation snapshot presented here is generated using the Unipd Ranking Citation Tool, a tool developed by Gianmaria Silvello and Alessandro Lotta (University of Padua). This tool, accessible at https://rankingcitation.dei.unipd.it
description: The citation snapshot presented here is generated using the Unipd Ranking Citation Tool, a tool developed by Gianmaria Silvello and Alessandro Lotta (University of Padua). This tool, accessible at https://rankingcitation.dei.unipd.it
DOI: 10.5281/zenodo.7987033
title: amazona_ochrocephala_decontaminated_gx.fasta.gz

description: .
DOI: 10.5281/zenodo.7987032
title: amazona_ochrocephala_decontaminated_gx.fasta.gz

description: .
DOI: 10.5281/zenodo.7987056
title: amazona_ventralis_decontaminated_gx.fasta.gz

description: .
DOI: 10.5281/zenodo.7987055
title: amazona_ventralis_decontaminated_gx.fasta.gz

description: .
DOI: 10.17037/data.00003472
title: Clinical codelist - Smoking - Aurum

description: Read, SNOME and EMIS codes for smoking.
DOI: 10.5281/zenodo.7986829
title: Model dataset for the journal publication entitled "New features and enhancements in Community Land Model (CLM5) snow albedo modeling: description, sensitivity, and evaluation"

description: This is the global 1-deg CLM5-SNICAR model simulation dataset for the journal publication entitled "New features and enhancements in Community Land Model (CLM5) snow albedo modeling: description, sensitivity, and evaluation".
DOI: 10.5281/zenodo.7987060
title: amazona_farinosa_decontaminated_gx.fasta.gz

description: .
DOI: 10.5281/zenodo.7987059
title: amazona_farinosa_decontaminated_gx.fasta.gz

description: .
DOI: 10.17632/v2vfnnwrgf.1
title: Data for article Quantifying carbon storage and sequestration by native and non-native forests under contrasting climate types

description: This data was used to write the article "Quantifying carbon storage and sequestration by native and non-native forests under contrasting climate types", published in Global Change Biology
DOI: 10.17632/44zs8pmh6s.1
title: 160-member pyramid structure Sap2000 File

description: This is the Sap2000 file of the 160-member pyramid structure. There are two versions of the structure named aluminum and steel.
DOI: 10.17632/c9vzct5f4x.1
title: 2016-member double-layer space truss Sap2000 files

description: Sap2000 files (steel and aluminum) of the 2016-member double-layer space truss.
DOI: 10.17632/4zjjwhr576.1
title:  Demographic Characteristics of GMS-D, by Sex, Race/Ethnicity, and Sexual Orientation

description:  Demographic Characteristics of GMS-D, by Sex, Race/Ethnicity, and Sexual Orientation
DOI: 10.17632/88rpmmnmkm.3
title: HEVC-SVS: Low-level HEVC features and CNN features for OVP and SUMME datasets 

description: Proposed HEVC feature sets along with CNN features from GoogleNet, AlexNet, Inception-ResNet-V2, and VGG16 for OVP and SUMME datasets.
The new modified datasets are names "HEVC-SVS-OVP" and "HEVC-SVS-SUMME".

The datasets contain the original ground truth data they came with, and these stayed unmodified.

The following are the HEVC features with their descriptions:

Feature ID	Feature variable
Averaged per frame
	1	Number of CU parts
	2	MVD bits per CU
	3	CU bits excluding MVD bits
	4	Percentage of intra CU parts
	5	Percentage of skipped CU parts
	6	Number of CUs with depth 0 (i.e 64x64)
	7	Number of parts with depth 1 (i.e 32x32)
	8	Number of CUs with depth 2 (i.e 16x16)
	9	Number of parts with depth 3 (i.e 8x8)
10-18	Standard deviation of feature IDs 1-9 per frame
19	Max CU depth per frame
20	For CUs with depth > 0, log_2⁡〖(|sum of MVD|)〗?
21	For CUs with depth = 0, log_2⁡〖(|sum of MVD|)〗?
Averaged per frame
	22	Row-wise SAD of the CU prediction error
	23	Column-wise SAD of the CU prediction error
	24	Ratio of gradients (i.e feature 22 divided by feature 23) per CU
	25	Total distortion per CU as computed by the HEVC encoder
26-29	Standard deviation of feature IDs 22-25 per frame
30	Per frame: Summation of variance of the x and y components of all MVs
31-47	Histogram of x-component of all MVs per frame (using 16 pins)
48-64	Histogram of y-component of all MVs per frame (using 16 pins)

Upon using any of these datasets, please do cite our publication where we proposed the HEVC 64 feature set for the first time:

O. Issa and T. Shanableh, "CNN and HEVC Video Coding Features for Static Video Summarization," in IEEE Access, 2022, doi: 10.1109/ACCESS.2022.3188638.
Online: https://ieeexplore.ieee.org/document/9815254

Along with the original authors for each of the datasets:

OVP:

@article{Avila,
title = "VSUMM: A mechanism designed to produce static video summaries and a novel evaluation method",
journal = "Pattern Recognition Letters",
volume = "32",
number = "1",
pages = "56 - 68",
year = "2011",
note = "<ce:title>Image Processing, Computer Vision and Pattern Recognition in Latin America</ce:title>",
issn = "0167-8655",
doi = "10.1016/j.patrec.2010.08.004",
author = "Sandra Eliza Fontes de Avila and Ana Paula Brand„o Lopes and Antonio da Luz Jr. and Arnaldo de Albuquerque Ara˙jo",
}

SUMME:

@inproceedings{GygliECCV14,
   author ={Gygli, Michael and Grabner, Helmut and Riemenschneider, Hayko and Van Gool, Luc},
   title = {Creating Summaries from User Videos},
   booktitle = {ECCV},
   year = {2014}
}
DOI: 10.17632/88rpmmnmkm.4
title: HEVC-SVS: Low-level HEVC features and CNN features for TVSum, SumMe, OVP and VSUMM datasets

description: **HEVC-SVS Datasets**

Proposed HEVC feature sets along with CNN features from GoogleNet, AlexNet, Inception-ResNet-V2, and VGG16 for TVSum, SumMe, OVP and VSUMM datasets. The new modified datasets names are "HEVC-SVS-TVSum", "HEVC-SVS-SumMe", "HEVC-SVS-OVP" and "HEVC-SVS-VSUMM", respectively.

The datasets contain the original ground truth data they came with, and these stayed unmodified.

Upon using any of these datasets, please do cite our publication where we proposed the HEVC feature set for the first time:

If you are using (HEVC-SVS-OVP) and/or (HEVC-SVS-VSUMM) datasets:

@article{issa_cnn_2022,
	title = {{CNN} and {HEVC} {Video} {Coding} {Features} for {Static} {Video} {Summarization}},
	volume = {10},
	copyright = {Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC-BY-NC-ND)},
	issn = {2169-3536},
	url = {https://ieeexplore.ieee.org/document/9815254/},
	doi = {10.1109/ACCESS.2022.3188638},
	urldate = {2022-09-29},
	journal = {IEEE Access},
	author = {Issa, Obada and Shanableh, Tamer},
	year = {2022},
	pages = {72080--72091},
}

If you are using (HEVC-SVS-TVSum) and/or (HEVC-SVS-SumMe) datasets:

{ PENDING }

Make sure to also cite the original authors for each of the datasets:

TVSum:

@INPROCEEDINGS{7299154,
  author = {Yale Song and Vallmitjana, Jordi and Stent, Amanda and Jaimes, Alejandro},
  booktitle = {2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, 
  title = {TVSum: Summarizing web videos using titles}, 
  year = {2015},
  volume = {},
  number = {},
  pages = {5179-5187},
  doi = {10.1109/CVPR.2015.7299154}
}

SumMe:

@inproceedings{GygliECCV14,
   author ={Gygli, Michael and Grabner, Helmut and Riemenschneider, Hayko and Van Gool, Luc},
   title = {Creating Summaries from User Videos},
   booktitle = {ECCV},
   year = {2014}
}

OVP and VSUMM:

@article{Avila,
    title = "VSUMM: A mechanism designed to produce static video summaries and a novel evaluation method",
    journal = "Pattern Recognition Letters",
    volume = "32",
    number = "1",
    pages = "56 - 68",
    year = "2011",
    note = "<ce:title>Image Processing, Computer Vision and Pattern Recognition in Latin America</ce:title>",
    issn = "0167-8655",
    doi = "10.1016/j.patrec.2010.08.004",
    author = "Sandra Eliza Fontes de Avila and Ana Paula Brand„o Lopes and Antonio da Luz Jr. and Arnaldo de Albuquerque Ara˙jo",
}

Acknowledgement:
The work in this repository is supported by the American University of Sharjah under research grant number FRG22-E-E44. This work represents the opinions of the author(s) and does not mean to represent the position or opinions of the American University of Sharjah.
DOI: 10.17632/88rpmmnmkm.5
title: HEVC-SVS: Low-level HEVC features and CNN features for TVSum, SumMe, OVP and VSUMM datasets

description: **HEVC-SVS Datasets**

Proposed HEVC feature sets along with CNN features from GoogleNet, AlexNet, Inception-ResNet-V2, and VGG16 for TVSum, SumMe, OVP and VSUMM datasets. The new modified datasets names are "HEVC-SVS-TVSum", "HEVC-SVS-SumMe", "HEVC-SVS-OVP" and "HEVC-SVS-VSUMM", respectively.

The datasets contain the original ground truth data they came with, and these stayed unmodified.

Upon using any of these datasets, please do cite our publication where we proposed the HEVC feature set for the first time:

If you are using (HEVC-SVS-OVP) and/or (HEVC-SVS-VSUMM) datasets:

@article{issa_cnn_2022,
	title = {{CNN} and {HEVC} {Video} {Coding} {Features} for {Static} {Video} {Summarization}},
	volume = {10},
	copyright = {Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC-BY-NC-ND)},
	issn = {2169-3536},
	url = {https://ieeexplore.ieee.org/document/9815254/},
	doi = {10.1109/ACCESS.2022.3188638},
	urldate = {2022-09-29},
	journal = {IEEE Access},
	author = {Issa, Obada and Shanableh, Tamer},
	year = {2022},
	pages = {72080--72091},
}

If you are using (HEVC-SVS-TVSum) and/or (HEVC-SVS-SumMe) datasets:

{ PENDING }

Make sure to also cite the original authors for each of the datasets:

TVSum:

@INPROCEEDINGS{7299154,
  author = {Yale Song and Vallmitjana, Jordi and Stent, Amanda and Jaimes, Alejandro},
  booktitle = {2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, 
  title = {TVSum: Summarizing web videos using titles}, 
  year = {2015},
  volume = {},
  number = {},
  pages = {5179-5187},
  doi = {10.1109/CVPR.2015.7299154}
}

SumMe:

@inproceedings{GygliECCV14,
   author ={Gygli, Michael and Grabner, Helmut and Riemenschneider, Hayko and Van Gool, Luc},
   title = {Creating Summaries from User Videos},
   booktitle = {ECCV},
   year = {2014}
}

OVP and VSUMM:

@article{Avila,
    title = "VSUMM: A mechanism designed to produce static video summaries and a novel evaluation method",
    journal = "Pattern Recognition Letters",
    volume = "32",
    number = "1",
    pages = "56 - 68",
    year = "2011",
    note = "<ce:title>Image Processing, Computer Vision and Pattern Recognition in Latin America</ce:title>",
    issn = "0167-8655",
    doi = "10.1016/j.patrec.2010.08.004",
    author = "Sandra Eliza Fontes de Avila and Ana Paula Brand„o Lopes and Antonio da Luz Jr. and Arnaldo de Albuquerque Ara˙jo",
}

Acknowledgement:
The work in this research project is supported by the American University of Sharjah under research grant number FRG22-E-E44. This research work represents the opinions of the author(s) and does not mean to represent the position or opinions of the American University of Sharjah.
DOI: 10.17632/88rpmmnmkm.6
title: HEVC-SVS: Low-level HEVC features and CNN features for TVSum, SumMe, OVP and VSUMM datasets

description: **HEVC-SVS Datasets**

Proposed HEVC feature sets along with CNN features from GoogleNet, AlexNet, Inception-ResNet-V2, and VGG16 for TVSum, SumMe, OVP and VSUMM datasets. The new modified datasets names are "HEVC-SVS-TVSum", "HEVC-SVS-SumMe", "HEVC-SVS-OVP" and "HEVC-SVS-VSUMM", respectively.

The datasets contain the original ground truth data they came with, and these stayed unmodified.

Upon using any of these datasets, please do cite our publication where we proposed the HEVC feature set for the first time:

If you are using (HEVC-SVS-OVP) and/or (HEVC-SVS-VSUMM) datasets:

@article{issa_cnn_2022,
	title = {{CNN} and {HEVC} {Video} {Coding} {Features} for {Static} {Video} {Summarization}},
	volume = {10},
	copyright = {Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC-BY-NC-ND)},
	issn = {2169-3536},
	url = {https://ieeexplore.ieee.org/document/9815254/},
	doi = {10.1109/ACCESS.2022.3188638},
	urldate = {2022-09-29},
	journal = {IEEE Access},
	author = {Issa, Obada and Shanableh, Tamer},
	year = {2022},
	pages = {72080--72091},
}

If you are using (HEVC-SVS-TVSum) and/or (HEVC-SVS-SumMe) datasets:

{ PENDING }

Make sure to also cite the original authors for each of the datasets:

TVSum:

@INPROCEEDINGS{7299154,
  author = {Yale Song and Vallmitjana, Jordi and Stent, Amanda and Jaimes, Alejandro},
  booktitle = {2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, 
  title = {TVSum: Summarizing web videos using titles}, 
  year = {2015},
  volume = {},
  number = {},
  pages = {5179-5187},
  doi = {10.1109/CVPR.2015.7299154}
}

SumMe:

@inproceedings{GygliECCV14,
   author ={Gygli, Michael and Grabner, Helmut and Riemenschneider, Hayko and Van Gool, Luc},
   title = {Creating Summaries from User Videos},
   booktitle = {ECCV},
   year = {2014}
}

OVP and VSUMM:

@article{Avila,
    title = "VSUMM: A mechanism designed to produce static video summaries and a novel evaluation method",
    journal = "Pattern Recognition Letters",
    volume = "32",
    number = "1",
    pages = "56 - 68",
    year = "2011",
    note = "<ce:title>Image Processing, Computer Vision and Pattern Recognition in Latin America</ce:title>",
    issn = "0167-8655",
    doi = "10.1016/j.patrec.2010.08.004",
    author = "Sandra Eliza Fontes de Avila and Ana Paula Brand„o Lopes and Antonio da Luz Jr. and Arnaldo de Albuquerque Ara˙jo",
}

Acknowledgement:
The work in this research project is supported by the American University of Sharjah under research grant number FRG22-E-E44. This research work represents the opinions of the author(s) and does not mean to represent the position or opinions of the American University of Sharjah.
DOI: 10.17632/88rpmmnmkm.7
title: HEVC-SVS: Low-level HEVC features and CNN features for TVSum, SumMe, OVP and VSUMM datasets

description: **HEVC-SVS Datasets**

Proposed HEVC feature sets along with CNN features from GoogleNet, AlexNet, Inception-ResNet-V2, and VGG16 for TVSum, SumMe, OVP and VSUMM datasets. The new modified datasets names are "HEVC-SVS-TVSum", "HEVC-SVS-SumMe", "HEVC-SVS-OVP" and "HEVC-SVS-VSUMM", respectively.

The datasets contain the original ground truth data they came with, and these stayed unmodified.

Upon using any of these datasets, please do cite our publication where we proposed the HEVC feature set for the first time:

If you are using (HEVC-SVS-OVP) and/or (HEVC-SVS-VSUMM) datasets:

@article{issa_cnn_2022,
	title = {{CNN} and {HEVC} {Video} {Coding} {Features} for {Static} {Video} {Summarization}},
	volume = {10},
	copyright = {Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC-BY-NC-ND)},
	issn = {2169-3536},
	url = {https://ieeexplore.ieee.org/document/9815254/},
	doi = {10.1109/ACCESS.2022.3188638},
	urldate = {2022-09-29},
	journal = {IEEE Access},
	author = {Issa, Obada and Shanableh, Tamer},
	year = {2022},
	pages = {72080--72091},
}

If you are using (HEVC-SVS-TVSum) and/or (HEVC-SVS-SumMe) datasets:

{ PENDING }

Make sure to also cite the original authors for each of the datasets:

TVSum:

@INPROCEEDINGS{7299154,
  author = {Yale Song and Vallmitjana, Jordi and Stent, Amanda and Jaimes, Alejandro},
  booktitle = {2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, 
  title = {TVSum: Summarizing web videos using titles}, 
  year = {2015},
  volume = {},
  number = {},
  pages = {5179-5187},
  doi = {10.1109/CVPR.2015.7299154}
}

SumMe:

@inproceedings{GygliECCV14,
   author ={Gygli, Michael and Grabner, Helmut and Riemenschneider, Hayko and Van Gool, Luc},
   title = {Creating Summaries from User Videos},
   booktitle = {ECCV},
   year = {2014}
}

OVP and VSUMM:

@article{Avila,
    title = "VSUMM: A mechanism designed to produce static video summaries and a novel evaluation method",
    journal = "Pattern Recognition Letters",
    volume = "32",
    number = "1",
    pages = "56 - 68",
    year = "2011",
    note = "<ce:title>Image Processing, Computer Vision and Pattern Recognition in Latin America</ce:title>",
    issn = "0167-8655",
    doi = "10.1016/j.patrec.2010.08.004",
    author = "Sandra Eliza Fontes de Avila and Ana Paula Brand„o Lopes and Antonio da Luz Jr. and Arnaldo de Albuquerque Ara˙jo",
}

Acknowledgement:
The work in this research project is supported by the American University of Sharjah under research grant number FRG22-E-E44. This research work represents the opinions of the author(s) and does not mean to represent the position or opinions of the American University of Sharjah.