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Information-theoretic learning (MLR-INFO)

Deep Learning for Classroom Activity Detection from Audio


Increasingly, post-secondary instructors are incorporating innovative teaching practices into their classrooms to improve student learning outcomes. In order to assess the effect of these techniques, it is helpful to quantify the types of activity being conducted in the classroom. Unfortunately, self-reporting is unreliable and manual annotation is tedious and scales poorly.

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Authors:
Robin Cosbey, Allison Wusterbarth, Brian Hutchinson
Submitted On:
10 May 2019 - 4:33pm
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[POSTER] Deep Learning for Classroom Activity Detection from Audio

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[1] Robin Cosbey, Allison Wusterbarth, Brian Hutchinson, "Deep Learning for Classroom Activity Detection from Audio", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4404. Accessed: Aug. 17, 2019.
@article{4404-19,
url = {http://sigport.org/4404},
author = {Robin Cosbey; Allison Wusterbarth; Brian Hutchinson },
publisher = {IEEE SigPort},
title = {Deep Learning for Classroom Activity Detection from Audio},
year = {2019} }
TY - EJOUR
T1 - Deep Learning for Classroom Activity Detection from Audio
AU - Robin Cosbey; Allison Wusterbarth; Brian Hutchinson
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4404
ER -
Robin Cosbey, Allison Wusterbarth, Brian Hutchinson. (2019). Deep Learning for Classroom Activity Detection from Audio. IEEE SigPort. http://sigport.org/4404
Robin Cosbey, Allison Wusterbarth, Brian Hutchinson, 2019. Deep Learning for Classroom Activity Detection from Audio. Available at: http://sigport.org/4404.
Robin Cosbey, Allison Wusterbarth, Brian Hutchinson. (2019). "Deep Learning for Classroom Activity Detection from Audio." Web.
1. Robin Cosbey, Allison Wusterbarth, Brian Hutchinson. Deep Learning for Classroom Activity Detection from Audio [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4404

Feature Selection for Multi-labeled Variables via Dependency Maximization


Feature selection and reducing the dimensionality of data is an essential step in data analysis. In this work, we propose a new criterion for feature selection that is formulated as conditional information between features given the labeled variable. Instead of using the standard mutual information measure based on Kullback-Leibler divergence, we use our proposed criterion to filter out redundant features for the purpose of multiclass classification.

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Authors:
Salimeh Yasaei Sekeh, Alfred O. Hero
Submitted On:
9 May 2019 - 9:36am
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ICASSP2019-Salimeh-V2.pdf

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[1] Salimeh Yasaei Sekeh, Alfred O. Hero, "Feature Selection for Multi-labeled Variables via Dependency Maximization", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4202. Accessed: Aug. 17, 2019.
@article{4202-19,
url = {http://sigport.org/4202},
author = {Salimeh Yasaei Sekeh; Alfred O. Hero },
publisher = {IEEE SigPort},
title = {Feature Selection for Multi-labeled Variables via Dependency Maximization},
year = {2019} }
TY - EJOUR
T1 - Feature Selection for Multi-labeled Variables via Dependency Maximization
AU - Salimeh Yasaei Sekeh; Alfred O. Hero
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4202
ER -
Salimeh Yasaei Sekeh, Alfred O. Hero. (2019). Feature Selection for Multi-labeled Variables via Dependency Maximization. IEEE SigPort. http://sigport.org/4202
Salimeh Yasaei Sekeh, Alfred O. Hero, 2019. Feature Selection for Multi-labeled Variables via Dependency Maximization. Available at: http://sigport.org/4202.
Salimeh Yasaei Sekeh, Alfred O. Hero. (2019). "Feature Selection for Multi-labeled Variables via Dependency Maximization." Web.
1. Salimeh Yasaei Sekeh, Alfred O. Hero. Feature Selection for Multi-labeled Variables via Dependency Maximization [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4202

ICASSP 2019 Poster for Paper #3198: PRIVACY-AWARE FEATURE EXTRACTION FOR GENDER DISCRIMINATION VERSUS SPEAKER IDENTIFICATION


This paper introduces a deep neural network based feature extraction scheme that aims to improve the trade-off between utility and privacy in speaker classification tasks. In the proposed scenario we develop a feature representation that helps to maximize the performance of a gender classifier while minimizing additional speaker

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Authors:
Rainer Martin
Submitted On:
8 May 2019 - 2:50am
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ICASSP2019_poster_Nelus.pdf

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[1] Rainer Martin, "ICASSP 2019 Poster for Paper #3198: PRIVACY-AWARE FEATURE EXTRACTION FOR GENDER DISCRIMINATION VERSUS SPEAKER IDENTIFICATION", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4027. Accessed: Aug. 17, 2019.
@article{4027-19,
url = {http://sigport.org/4027},
author = {Rainer Martin },
publisher = {IEEE SigPort},
title = {ICASSP 2019 Poster for Paper #3198: PRIVACY-AWARE FEATURE EXTRACTION FOR GENDER DISCRIMINATION VERSUS SPEAKER IDENTIFICATION},
year = {2019} }
TY - EJOUR
T1 - ICASSP 2019 Poster for Paper #3198: PRIVACY-AWARE FEATURE EXTRACTION FOR GENDER DISCRIMINATION VERSUS SPEAKER IDENTIFICATION
AU - Rainer Martin
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4027
ER -
Rainer Martin. (2019). ICASSP 2019 Poster for Paper #3198: PRIVACY-AWARE FEATURE EXTRACTION FOR GENDER DISCRIMINATION VERSUS SPEAKER IDENTIFICATION. IEEE SigPort. http://sigport.org/4027
Rainer Martin, 2019. ICASSP 2019 Poster for Paper #3198: PRIVACY-AWARE FEATURE EXTRACTION FOR GENDER DISCRIMINATION VERSUS SPEAKER IDENTIFICATION. Available at: http://sigport.org/4027.
Rainer Martin. (2019). "ICASSP 2019 Poster for Paper #3198: PRIVACY-AWARE FEATURE EXTRACTION FOR GENDER DISCRIMINATION VERSUS SPEAKER IDENTIFICATION." Web.
1. Rainer Martin. ICASSP 2019 Poster for Paper #3198: PRIVACY-AWARE FEATURE EXTRACTION FOR GENDER DISCRIMINATION VERSUS SPEAKER IDENTIFICATION [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4027

ICASSP 2019 Poster for Paper #3198: PRIVACY-AWARE FEATURE EXTRACTION FOR GENDER DISCRIMINATION VERSUS SPEAKER IDENTIFICATION

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8 May 2019 - 2:50am
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ICASSP2019_poster_Nelus.pdf

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[1] , "ICASSP 2019 Poster for Paper #3198: PRIVACY-AWARE FEATURE EXTRACTION FOR GENDER DISCRIMINATION VERSUS SPEAKER IDENTIFICATION", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4025. Accessed: Aug. 17, 2019.
@article{4025-19,
url = {http://sigport.org/4025},
author = { },
publisher = {IEEE SigPort},
title = {ICASSP 2019 Poster for Paper #3198: PRIVACY-AWARE FEATURE EXTRACTION FOR GENDER DISCRIMINATION VERSUS SPEAKER IDENTIFICATION},
year = {2019} }
TY - EJOUR
T1 - ICASSP 2019 Poster for Paper #3198: PRIVACY-AWARE FEATURE EXTRACTION FOR GENDER DISCRIMINATION VERSUS SPEAKER IDENTIFICATION
AU -
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4025
ER -
. (2019). ICASSP 2019 Poster for Paper #3198: PRIVACY-AWARE FEATURE EXTRACTION FOR GENDER DISCRIMINATION VERSUS SPEAKER IDENTIFICATION. IEEE SigPort. http://sigport.org/4025
, 2019. ICASSP 2019 Poster for Paper #3198: PRIVACY-AWARE FEATURE EXTRACTION FOR GENDER DISCRIMINATION VERSUS SPEAKER IDENTIFICATION. Available at: http://sigport.org/4025.
. (2019). "ICASSP 2019 Poster for Paper #3198: PRIVACY-AWARE FEATURE EXTRACTION FOR GENDER DISCRIMINATION VERSUS SPEAKER IDENTIFICATION." Web.
1. . ICASSP 2019 Poster for Paper #3198: PRIVACY-AWARE FEATURE EXTRACTION FOR GENDER DISCRIMINATION VERSUS SPEAKER IDENTIFICATION [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4025

CONDITIONAL DISTRIBUTION LEARNING WITH NEURAL NETWORKS AND ITS APPLICATION TO UNIVERSAL IMAGE DENOISING


A simple and scalable denoising algorithm is proposed that can be applied to a wide range of source and noise models. At the core of the proposed CUDE algorithm is symbol-by-symbol universal denoising used by the celebrated DUDE algorithm, whereby the optimal estimate of the source from an unknown distribution is computed by inverting the empirical distribution of the noisy observation sequence by a deep neural network, which naturally and implicitly aggregates multiple contexts of similar characteristics and estimates the conditional distribution more accurately.

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Authors:
Jongha Jon Ryu, Young-Han Kim
Submitted On:
8 October 2018 - 3:38am
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icip2018-poster-cude.pdf

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[1] Jongha Jon Ryu, Young-Han Kim, "CONDITIONAL DISTRIBUTION LEARNING WITH NEURAL NETWORKS AND ITS APPLICATION TO UNIVERSAL IMAGE DENOISING", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3616. Accessed: Aug. 17, 2019.
@article{3616-18,
url = {http://sigport.org/3616},
author = {Jongha Jon Ryu; Young-Han Kim },
publisher = {IEEE SigPort},
title = {CONDITIONAL DISTRIBUTION LEARNING WITH NEURAL NETWORKS AND ITS APPLICATION TO UNIVERSAL IMAGE DENOISING},
year = {2018} }
TY - EJOUR
T1 - CONDITIONAL DISTRIBUTION LEARNING WITH NEURAL NETWORKS AND ITS APPLICATION TO UNIVERSAL IMAGE DENOISING
AU - Jongha Jon Ryu; Young-Han Kim
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3616
ER -
Jongha Jon Ryu, Young-Han Kim. (2018). CONDITIONAL DISTRIBUTION LEARNING WITH NEURAL NETWORKS AND ITS APPLICATION TO UNIVERSAL IMAGE DENOISING. IEEE SigPort. http://sigport.org/3616
Jongha Jon Ryu, Young-Han Kim, 2018. CONDITIONAL DISTRIBUTION LEARNING WITH NEURAL NETWORKS AND ITS APPLICATION TO UNIVERSAL IMAGE DENOISING. Available at: http://sigport.org/3616.
Jongha Jon Ryu, Young-Han Kim. (2018). "CONDITIONAL DISTRIBUTION LEARNING WITH NEURAL NETWORKS AND ITS APPLICATION TO UNIVERSAL IMAGE DENOISING." Web.
1. Jongha Jon Ryu, Young-Han Kim. CONDITIONAL DISTRIBUTION LEARNING WITH NEURAL NETWORKS AND ITS APPLICATION TO UNIVERSAL IMAGE DENOISING [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3616

ON THE GEOMETRY OF MIXTURES OF PRESCRIBED DISTRIBUTIONS

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23 April 2018 - 12:06am
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GeometryMixtures-Poster-ICASSP2018.pdf

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[1] , "ON THE GEOMETRY OF MIXTURES OF PRESCRIBED DISTRIBUTIONS", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3143. Accessed: Aug. 17, 2019.
@article{3143-18,
url = {http://sigport.org/3143},
author = { },
publisher = {IEEE SigPort},
title = {ON THE GEOMETRY OF MIXTURES OF PRESCRIBED DISTRIBUTIONS},
year = {2018} }
TY - EJOUR
T1 - ON THE GEOMETRY OF MIXTURES OF PRESCRIBED DISTRIBUTIONS
AU -
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3143
ER -
. (2018). ON THE GEOMETRY OF MIXTURES OF PRESCRIBED DISTRIBUTIONS. IEEE SigPort. http://sigport.org/3143
, 2018. ON THE GEOMETRY OF MIXTURES OF PRESCRIBED DISTRIBUTIONS. Available at: http://sigport.org/3143.
. (2018). "ON THE GEOMETRY OF MIXTURES OF PRESCRIBED DISTRIBUTIONS." Web.
1. . ON THE GEOMETRY OF MIXTURES OF PRESCRIBED DISTRIBUTIONS [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3143

RATE-OPTIMAL META LEARNING OF CLASSIFICATION ERROR


Meta learning of optimal classifier error rates allows an experimenter to empirically estimate the intrinsic ability of any estimator to discriminate between two populations, circumventing the difficult problem of estimating the optimal Bayes classifier. To this end we propose a weighted nearest neighbor (WNN) graph estimator for a tight bound on the Bayes classification error; the Henze-Penrose (HP) divergence. Similar to recently proposed HP estimators [berisha2016], the proposed estimator is non-parametric and does not require density estimation.

Paper Details

Authors:
Morteza Noshad, Alfred Hero
Submitted On:
16 April 2018 - 12:28am
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icassp-poster-v3.pdf

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[1] Morteza Noshad, Alfred Hero, "RATE-OPTIMAL META LEARNING OF CLASSIFICATION ERROR", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2907. Accessed: Aug. 17, 2019.
@article{2907-18,
url = {http://sigport.org/2907},
author = {Morteza Noshad; Alfred Hero },
publisher = {IEEE SigPort},
title = {RATE-OPTIMAL META LEARNING OF CLASSIFICATION ERROR},
year = {2018} }
TY - EJOUR
T1 - RATE-OPTIMAL META LEARNING OF CLASSIFICATION ERROR
AU - Morteza Noshad; Alfred Hero
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2907
ER -
Morteza Noshad, Alfred Hero. (2018). RATE-OPTIMAL META LEARNING OF CLASSIFICATION ERROR. IEEE SigPort. http://sigport.org/2907
Morteza Noshad, Alfred Hero, 2018. RATE-OPTIMAL META LEARNING OF CLASSIFICATION ERROR. Available at: http://sigport.org/2907.
Morteza Noshad, Alfred Hero. (2018). "RATE-OPTIMAL META LEARNING OF CLASSIFICATION ERROR." Web.
1. Morteza Noshad, Alfred Hero. RATE-OPTIMAL META LEARNING OF CLASSIFICATION ERROR [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2907

Rate-optimal Meta Learning of Classification Error


Meta learning of optimal classifier error rates allows an experimenter to empirically estimate the intrinsic ability of any estimator to discriminate between two populations, circumventing the difficult problem of estimating the optimal Bayes classifier. To this end we propose a weighted nearest neighbor (WNN) graph estimator for a tight bound on the Bayes classification error; the Henze-Penrose (HP) divergence. Similar to recently proposed HP estimators [berisha2016], the proposed estimator is non-parametric and does not require density estimation.

Paper Details

Authors:
Morteza Noshad, Alfred Hero
Submitted On:
16 April 2018 - 12:28am
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icassp-poster-v3.pdf

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[1] Morteza Noshad, Alfred Hero, "Rate-optimal Meta Learning of Classification Error", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2906. Accessed: Aug. 17, 2019.
@article{2906-18,
url = {http://sigport.org/2906},
author = {Morteza Noshad; Alfred Hero },
publisher = {IEEE SigPort},
title = {Rate-optimal Meta Learning of Classification Error},
year = {2018} }
TY - EJOUR
T1 - Rate-optimal Meta Learning of Classification Error
AU - Morteza Noshad; Alfred Hero
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2906
ER -
Morteza Noshad, Alfred Hero. (2018). Rate-optimal Meta Learning of Classification Error. IEEE SigPort. http://sigport.org/2906
Morteza Noshad, Alfred Hero, 2018. Rate-optimal Meta Learning of Classification Error. Available at: http://sigport.org/2906.
Morteza Noshad, Alfred Hero. (2018). "Rate-optimal Meta Learning of Classification Error." Web.
1. Morteza Noshad, Alfred Hero. Rate-optimal Meta Learning of Classification Error [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2906

A multi-layer image representation using Regularized Residual Quantization: application to compression and denoising


A learning-based framework for representation of domain-specific images is proposed where joint compression and denoising can be done using a VQ-based multi-layer network. While it learns to compress the images from a training set, the compression performance is very well generalized on images from a test set. Moreover, when fed with noisy versions of the test set, since it has priors from clean images, the network also efficiently denoises the test images during the reconstruction.

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Authors:
Sohrab Ferdowsi, Slava Voloshynovskiy, Dimche Kostadinov
Submitted On:
15 September 2017 - 10:56am
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Multi-layer image representation

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[1] Sohrab Ferdowsi, Slava Voloshynovskiy, Dimche Kostadinov, "A multi-layer image representation using Regularized Residual Quantization: application to compression and denoising", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2138. Accessed: Aug. 17, 2019.
@article{2138-17,
url = {http://sigport.org/2138},
author = {Sohrab Ferdowsi; Slava Voloshynovskiy; Dimche Kostadinov },
publisher = {IEEE SigPort},
title = {A multi-layer image representation using Regularized Residual Quantization: application to compression and denoising},
year = {2017} }
TY - EJOUR
T1 - A multi-layer image representation using Regularized Residual Quantization: application to compression and denoising
AU - Sohrab Ferdowsi; Slava Voloshynovskiy; Dimche Kostadinov
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2138
ER -
Sohrab Ferdowsi, Slava Voloshynovskiy, Dimche Kostadinov. (2017). A multi-layer image representation using Regularized Residual Quantization: application to compression and denoising. IEEE SigPort. http://sigport.org/2138
Sohrab Ferdowsi, Slava Voloshynovskiy, Dimche Kostadinov, 2017. A multi-layer image representation using Regularized Residual Quantization: application to compression and denoising. Available at: http://sigport.org/2138.
Sohrab Ferdowsi, Slava Voloshynovskiy, Dimche Kostadinov. (2017). "A multi-layer image representation using Regularized Residual Quantization: application to compression and denoising." Web.
1. Sohrab Ferdowsi, Slava Voloshynovskiy, Dimche Kostadinov. A multi-layer image representation using Regularized Residual Quantization: application to compression and denoising [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2138

AN EFFICIENT INTRA CODING ALGORITHM BASED ON STATISTICAL LEARNING FOR SCREEN CONTENT CODING


Screen content has different characteristics compared with natural content captured by cameras. To achieve more efficient compression, some new coding tools have been developed in the High Efficiency Video Coding (HEVC) Screen Content Coding (SCC) Extension, which also increase the computational complexity of encoder. In this paper, complexity analysis are first conducted to explore the distribution of complexities.

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Authors:
Liquan Shen, Ping An
Submitted On:
15 September 2017 - 5:05am
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ICIP2017 poster of paper #1561

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[1] Liquan Shen, Ping An, "AN EFFICIENT INTRA CODING ALGORITHM BASED ON STATISTICAL LEARNING FOR SCREEN CONTENT CODING", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2114. Accessed: Aug. 17, 2019.
@article{2114-17,
url = {http://sigport.org/2114},
author = {Liquan Shen; Ping An },
publisher = {IEEE SigPort},
title = {AN EFFICIENT INTRA CODING ALGORITHM BASED ON STATISTICAL LEARNING FOR SCREEN CONTENT CODING},
year = {2017} }
TY - EJOUR
T1 - AN EFFICIENT INTRA CODING ALGORITHM BASED ON STATISTICAL LEARNING FOR SCREEN CONTENT CODING
AU - Liquan Shen; Ping An
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2114
ER -
Liquan Shen, Ping An. (2017). AN EFFICIENT INTRA CODING ALGORITHM BASED ON STATISTICAL LEARNING FOR SCREEN CONTENT CODING. IEEE SigPort. http://sigport.org/2114
Liquan Shen, Ping An, 2017. AN EFFICIENT INTRA CODING ALGORITHM BASED ON STATISTICAL LEARNING FOR SCREEN CONTENT CODING. Available at: http://sigport.org/2114.
Liquan Shen, Ping An. (2017). "AN EFFICIENT INTRA CODING ALGORITHM BASED ON STATISTICAL LEARNING FOR SCREEN CONTENT CODING." Web.
1. Liquan Shen, Ping An. AN EFFICIENT INTRA CODING ALGORITHM BASED ON STATISTICAL LEARNING FOR SCREEN CONTENT CODING [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2114

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