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ICASSP 2020

ICASSP is the world’s largest and most comprehensive technical conference focused on signal processing and its applications. The ICASSP 2020 conference will feature world-class presentations by internationally renowned speakers, cutting-edge session topics and provide a fantastic opportunity to network with like-minded professionals from around the world. Visit website.

Combining cGAN and MIL for Hotspot Segmentation in Bone Scintigraphy

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Authors:
Hang Xu, Shijie Geng, Yu Qiao, Kuan Xu, Yueyang Gu
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21 May 2020 - 11:00pm
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Paper 2748 ICASSP 2020.pdf

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[1] Hang Xu, Shijie Geng, Yu Qiao, Kuan Xu, Yueyang Gu, "Combining cGAN and MIL for Hotspot Segmentation in Bone Scintigraphy", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5429. Accessed: Aug. 12, 2020.
@article{5429-20,
url = {http://sigport.org/5429},
author = {Hang Xu; Shijie Geng; Yu Qiao; Kuan Xu; Yueyang Gu },
publisher = {IEEE SigPort},
title = {Combining cGAN and MIL for Hotspot Segmentation in Bone Scintigraphy},
year = {2020} }
TY - EJOUR
T1 - Combining cGAN and MIL for Hotspot Segmentation in Bone Scintigraphy
AU - Hang Xu; Shijie Geng; Yu Qiao; Kuan Xu; Yueyang Gu
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5429
ER -
Hang Xu, Shijie Geng, Yu Qiao, Kuan Xu, Yueyang Gu. (2020). Combining cGAN and MIL for Hotspot Segmentation in Bone Scintigraphy. IEEE SigPort. http://sigport.org/5429
Hang Xu, Shijie Geng, Yu Qiao, Kuan Xu, Yueyang Gu, 2020. Combining cGAN and MIL for Hotspot Segmentation in Bone Scintigraphy. Available at: http://sigport.org/5429.
Hang Xu, Shijie Geng, Yu Qiao, Kuan Xu, Yueyang Gu. (2020). "Combining cGAN and MIL for Hotspot Segmentation in Bone Scintigraphy." Web.
1. Hang Xu, Shijie Geng, Yu Qiao, Kuan Xu, Yueyang Gu. Combining cGAN and MIL for Hotspot Segmentation in Bone Scintigraphy [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5429

Q-GADMM: Quantized Group ADMM for Communication Efficient Decentralized Machine Learning


In this paper, we propose a communication-efficient decentralized machine learning (ML) algorithm, coined quantized group ADMM (Q-GADMM). Every worker in Q-GADMM communicates only with two neighbors, and updates its model via the group alternating direct method of multiplier (GADMM), thereby ensuring fast convergence while reducing the number of communication rounds. Furthermore, each worker quantizes its model updates before transmissions, thereby decreasing the communication payload sizes.

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Authors:
Anis Elgabli, Jihong Park, Amrit Bedi, Mehdi Bennis, Vaneet Aggarwal
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21 May 2020 - 3:34pm
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icassp2020_final.pdf

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[1] Anis Elgabli, Jihong Park, Amrit Bedi, Mehdi Bennis, Vaneet Aggarwal, "Q-GADMM: Quantized Group ADMM for Communication Efficient Decentralized Machine Learning", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5428. Accessed: Aug. 12, 2020.
@article{5428-20,
url = {http://sigport.org/5428},
author = {Anis Elgabli; Jihong Park; Amrit Bedi; Mehdi Bennis; Vaneet Aggarwal },
publisher = {IEEE SigPort},
title = {Q-GADMM: Quantized Group ADMM for Communication Efficient Decentralized Machine Learning},
year = {2020} }
TY - EJOUR
T1 - Q-GADMM: Quantized Group ADMM for Communication Efficient Decentralized Machine Learning
AU - Anis Elgabli; Jihong Park; Amrit Bedi; Mehdi Bennis; Vaneet Aggarwal
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5428
ER -
Anis Elgabli, Jihong Park, Amrit Bedi, Mehdi Bennis, Vaneet Aggarwal. (2020). Q-GADMM: Quantized Group ADMM for Communication Efficient Decentralized Machine Learning. IEEE SigPort. http://sigport.org/5428
Anis Elgabli, Jihong Park, Amrit Bedi, Mehdi Bennis, Vaneet Aggarwal, 2020. Q-GADMM: Quantized Group ADMM for Communication Efficient Decentralized Machine Learning. Available at: http://sigport.org/5428.
Anis Elgabli, Jihong Park, Amrit Bedi, Mehdi Bennis, Vaneet Aggarwal. (2020). "Q-GADMM: Quantized Group ADMM for Communication Efficient Decentralized Machine Learning." Web.
1. Anis Elgabli, Jihong Park, Amrit Bedi, Mehdi Bennis, Vaneet Aggarwal. Q-GADMM: Quantized Group ADMM for Communication Efficient Decentralized Machine Learning [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5428

Optimizing Bayesian HMM Based x-vector Clustering for theSecond DIHARD Speech Diarization Challenge

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Authors:
Mireia Diez, Lukas Burget, Federico Landini, Shuai Wang, Honza Cernocky
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21 May 2020 - 9:13am
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[1] Mireia Diez, Lukas Burget, Federico Landini, Shuai Wang, Honza Cernocky, "Optimizing Bayesian HMM Based x-vector Clustering for theSecond DIHARD Speech Diarization Challenge", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5427. Accessed: Aug. 12, 2020.
@article{5427-20,
url = {http://sigport.org/5427},
author = {Mireia Diez; Lukas Burget; Federico Landini; Shuai Wang; Honza Cernocky },
publisher = {IEEE SigPort},
title = {Optimizing Bayesian HMM Based x-vector Clustering for theSecond DIHARD Speech Diarization Challenge},
year = {2020} }
TY - EJOUR
T1 - Optimizing Bayesian HMM Based x-vector Clustering for theSecond DIHARD Speech Diarization Challenge
AU - Mireia Diez; Lukas Burget; Federico Landini; Shuai Wang; Honza Cernocky
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5427
ER -
Mireia Diez, Lukas Burget, Federico Landini, Shuai Wang, Honza Cernocky. (2020). Optimizing Bayesian HMM Based x-vector Clustering for theSecond DIHARD Speech Diarization Challenge. IEEE SigPort. http://sigport.org/5427
Mireia Diez, Lukas Burget, Federico Landini, Shuai Wang, Honza Cernocky, 2020. Optimizing Bayesian HMM Based x-vector Clustering for theSecond DIHARD Speech Diarization Challenge. Available at: http://sigport.org/5427.
Mireia Diez, Lukas Burget, Federico Landini, Shuai Wang, Honza Cernocky. (2020). "Optimizing Bayesian HMM Based x-vector Clustering for theSecond DIHARD Speech Diarization Challenge." Web.
1. Mireia Diez, Lukas Burget, Federico Landini, Shuai Wang, Honza Cernocky. Optimizing Bayesian HMM Based x-vector Clustering for theSecond DIHARD Speech Diarization Challenge [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5427

Detecting Mismatch between Text Script and Voice-over Using Utterance Verification Based on Phoneme Recognition Ranking


The purpose of this study is to detect the mismatch between text script and voice-over. For this, we present a novel utterance verification (UV) method, which calculates the degree of correspondence between a voice-over and the phoneme sequence of a script. We found that the phoneme recognition probabilities of exaggerated voice-overs decrease compared to ordinary utterances, but their rankings do not demonstrate any significant change.

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Authors:
Yoonjae Jeong, Hoon-Young Cho
Submitted On:
21 May 2020 - 7:57am
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[1] Yoonjae Jeong, Hoon-Young Cho, "Detecting Mismatch between Text Script and Voice-over Using Utterance Verification Based on Phoneme Recognition Ranking", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5426. Accessed: Aug. 12, 2020.
@article{5426-20,
url = {http://sigport.org/5426},
author = {Yoonjae Jeong; Hoon-Young Cho },
publisher = {IEEE SigPort},
title = {Detecting Mismatch between Text Script and Voice-over Using Utterance Verification Based on Phoneme Recognition Ranking},
year = {2020} }
TY - EJOUR
T1 - Detecting Mismatch between Text Script and Voice-over Using Utterance Verification Based on Phoneme Recognition Ranking
AU - Yoonjae Jeong; Hoon-Young Cho
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5426
ER -
Yoonjae Jeong, Hoon-Young Cho. (2020). Detecting Mismatch between Text Script and Voice-over Using Utterance Verification Based on Phoneme Recognition Ranking. IEEE SigPort. http://sigport.org/5426
Yoonjae Jeong, Hoon-Young Cho, 2020. Detecting Mismatch between Text Script and Voice-over Using Utterance Verification Based on Phoneme Recognition Ranking. Available at: http://sigport.org/5426.
Yoonjae Jeong, Hoon-Young Cho. (2020). "Detecting Mismatch between Text Script and Voice-over Using Utterance Verification Based on Phoneme Recognition Ranking." Web.
1. Yoonjae Jeong, Hoon-Young Cho. Detecting Mismatch between Text Script and Voice-over Using Utterance Verification Based on Phoneme Recognition Ranking [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5426

Detection of Malicious VBScript Using Static and Dynamic Analysis with Recurrent Deep Learning

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Authors:
Jack W. Stokes, Rakshit Agrawal, Geoff McDonald
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21 May 2020 - 1:25am
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[1] Jack W. Stokes, Rakshit Agrawal, Geoff McDonald, "Detection of Malicious VBScript Using Static and Dynamic Analysis with Recurrent Deep Learning", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5425. Accessed: Aug. 12, 2020.
@article{5425-20,
url = {http://sigport.org/5425},
author = {Jack W. Stokes; Rakshit Agrawal; Geoff McDonald },
publisher = {IEEE SigPort},
title = {Detection of Malicious VBScript Using Static and Dynamic Analysis with Recurrent Deep Learning},
year = {2020} }
TY - EJOUR
T1 - Detection of Malicious VBScript Using Static and Dynamic Analysis with Recurrent Deep Learning
AU - Jack W. Stokes; Rakshit Agrawal; Geoff McDonald
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5425
ER -
Jack W. Stokes, Rakshit Agrawal, Geoff McDonald. (2020). Detection of Malicious VBScript Using Static and Dynamic Analysis with Recurrent Deep Learning. IEEE SigPort. http://sigport.org/5425
Jack W. Stokes, Rakshit Agrawal, Geoff McDonald, 2020. Detection of Malicious VBScript Using Static and Dynamic Analysis with Recurrent Deep Learning. Available at: http://sigport.org/5425.
Jack W. Stokes, Rakshit Agrawal, Geoff McDonald. (2020). "Detection of Malicious VBScript Using Static and Dynamic Analysis with Recurrent Deep Learning." Web.
1. Jack W. Stokes, Rakshit Agrawal, Geoff McDonald. Detection of Malicious VBScript Using Static and Dynamic Analysis with Recurrent Deep Learning [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5425

Privacy-Preserving Phishing Web Page Classification Via Fully Homomorphic Encryption

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Authors:
Edward J. Chou, Arun Gururajan, Kim Laine, Nitin Kumar Goel, Anna Bertiger, Jack W. Stokes
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21 May 2020 - 1:29am
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[1] Edward J. Chou, Arun Gururajan, Kim Laine, Nitin Kumar Goel, Anna Bertiger, Jack W. Stokes, "Privacy-Preserving Phishing Web Page Classification Via Fully Homomorphic Encryption", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5424. Accessed: Aug. 12, 2020.
@article{5424-20,
url = {http://sigport.org/5424},
author = {Edward J. Chou; Arun Gururajan; Kim Laine; Nitin Kumar Goel; Anna Bertiger; Jack W. Stokes },
publisher = {IEEE SigPort},
title = {Privacy-Preserving Phishing Web Page Classification Via Fully Homomorphic Encryption},
year = {2020} }
TY - EJOUR
T1 - Privacy-Preserving Phishing Web Page Classification Via Fully Homomorphic Encryption
AU - Edward J. Chou; Arun Gururajan; Kim Laine; Nitin Kumar Goel; Anna Bertiger; Jack W. Stokes
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5424
ER -
Edward J. Chou, Arun Gururajan, Kim Laine, Nitin Kumar Goel, Anna Bertiger, Jack W. Stokes. (2020). Privacy-Preserving Phishing Web Page Classification Via Fully Homomorphic Encryption. IEEE SigPort. http://sigport.org/5424
Edward J. Chou, Arun Gururajan, Kim Laine, Nitin Kumar Goel, Anna Bertiger, Jack W. Stokes, 2020. Privacy-Preserving Phishing Web Page Classification Via Fully Homomorphic Encryption. Available at: http://sigport.org/5424.
Edward J. Chou, Arun Gururajan, Kim Laine, Nitin Kumar Goel, Anna Bertiger, Jack W. Stokes. (2020). "Privacy-Preserving Phishing Web Page Classification Via Fully Homomorphic Encryption." Web.
1. Edward J. Chou, Arun Gururajan, Kim Laine, Nitin Kumar Goel, Anna Bertiger, Jack W. Stokes. Privacy-Preserving Phishing Web Page Classification Via Fully Homomorphic Encryption [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5424

'TEXCEPTION: A Character/Word-Level Deep Learning Model for Phishing URL Detection

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Authors:
Farid Tajaddodianfar, Jack W. Stokes, Arun Gururajan
Submitted On:
21 May 2020 - 1:35am
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[1] Farid Tajaddodianfar, Jack W. Stokes, Arun Gururajan, "'TEXCEPTION: A Character/Word-Level Deep Learning Model for Phishing URL Detection", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5423. Accessed: Aug. 12, 2020.
@article{5423-20,
url = {http://sigport.org/5423},
author = {Farid Tajaddodianfar; Jack W. Stokes; Arun Gururajan },
publisher = {IEEE SigPort},
title = {'TEXCEPTION: A Character/Word-Level Deep Learning Model for Phishing URL Detection},
year = {2020} }
TY - EJOUR
T1 - 'TEXCEPTION: A Character/Word-Level Deep Learning Model for Phishing URL Detection
AU - Farid Tajaddodianfar; Jack W. Stokes; Arun Gururajan
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5423
ER -
Farid Tajaddodianfar, Jack W. Stokes, Arun Gururajan. (2020). 'TEXCEPTION: A Character/Word-Level Deep Learning Model for Phishing URL Detection. IEEE SigPort. http://sigport.org/5423
Farid Tajaddodianfar, Jack W. Stokes, Arun Gururajan, 2020. 'TEXCEPTION: A Character/Word-Level Deep Learning Model for Phishing URL Detection. Available at: http://sigport.org/5423.
Farid Tajaddodianfar, Jack W. Stokes, Arun Gururajan. (2020). "'TEXCEPTION: A Character/Word-Level Deep Learning Model for Phishing URL Detection." Web.
1. Farid Tajaddodianfar, Jack W. Stokes, Arun Gururajan. 'TEXCEPTION: A Character/Word-Level Deep Learning Model for Phishing URL Detection [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5423

A Generalized Framework for Domain Adaptation of PLDA in Speaker Recognition


This paper proposes a generalized framework for domain adaptation of Probabilistic Linear Discriminant Analysis (PLDA) in speaker recognition. It not only includes several existing supervised and unsupervised domain adaptation methods but also makes possible more flexible usage of available data in different domains. In particular, we introduce here the two new techniques described below. (1) Correlation-alignment-based interpolation and (2) covariance regularization.

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Authors:
Qiongqiong Wang, Koji Okabe, Kong Aik Lee, Takafumi Koshinaka
Submitted On:
20 May 2020 - 8:49pm
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[1] Qiongqiong Wang, Koji Okabe, Kong Aik Lee, Takafumi Koshinaka, "A Generalized Framework for Domain Adaptation of PLDA in Speaker Recognition", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5422. Accessed: Aug. 12, 2020.
@article{5422-20,
url = {http://sigport.org/5422},
author = {Qiongqiong Wang; Koji Okabe; Kong Aik Lee; Takafumi Koshinaka },
publisher = {IEEE SigPort},
title = {A Generalized Framework for Domain Adaptation of PLDA in Speaker Recognition},
year = {2020} }
TY - EJOUR
T1 - A Generalized Framework for Domain Adaptation of PLDA in Speaker Recognition
AU - Qiongqiong Wang; Koji Okabe; Kong Aik Lee; Takafumi Koshinaka
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5422
ER -
Qiongqiong Wang, Koji Okabe, Kong Aik Lee, Takafumi Koshinaka. (2020). A Generalized Framework for Domain Adaptation of PLDA in Speaker Recognition. IEEE SigPort. http://sigport.org/5422
Qiongqiong Wang, Koji Okabe, Kong Aik Lee, Takafumi Koshinaka, 2020. A Generalized Framework for Domain Adaptation of PLDA in Speaker Recognition. Available at: http://sigport.org/5422.
Qiongqiong Wang, Koji Okabe, Kong Aik Lee, Takafumi Koshinaka. (2020). "A Generalized Framework for Domain Adaptation of PLDA in Speaker Recognition." Web.
1. Qiongqiong Wang, Koji Okabe, Kong Aik Lee, Takafumi Koshinaka. A Generalized Framework for Domain Adaptation of PLDA in Speaker Recognition [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5422

Fast and High-Quality Singing Voice Synthesis System based on Convolutional Neural Networks


The present paper describes singing voice synthesis based on convolutional neural networks (CNNs). Singing voice synthesis systems based on deep neural networks (DNNs) are currently being proposed and are improving the naturalness of synthesized singing voices. As singing voices represent a rich form of expression, a powerful technique to model them accurately is required. In the proposed technique, long-term dependencies of singing voices are modeled by CNNs.

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Authors:
Kazuhiro Nakamura, Shinji Takaki, Kei Hashimoto, Keiichiro Oura, Yoshihiko Nankaku, Keiichi Tokuda
Submitted On:
20 May 2020 - 8:26pm
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[1] Kazuhiro Nakamura, Shinji Takaki, Kei Hashimoto, Keiichiro Oura, Yoshihiko Nankaku, Keiichi Tokuda, "Fast and High-Quality Singing Voice Synthesis System based on Convolutional Neural Networks", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5421. Accessed: Aug. 12, 2020.
@article{5421-20,
url = {http://sigport.org/5421},
author = {Kazuhiro Nakamura; Shinji Takaki; Kei Hashimoto; Keiichiro Oura; Yoshihiko Nankaku; Keiichi Tokuda },
publisher = {IEEE SigPort},
title = {Fast and High-Quality Singing Voice Synthesis System based on Convolutional Neural Networks},
year = {2020} }
TY - EJOUR
T1 - Fast and High-Quality Singing Voice Synthesis System based on Convolutional Neural Networks
AU - Kazuhiro Nakamura; Shinji Takaki; Kei Hashimoto; Keiichiro Oura; Yoshihiko Nankaku; Keiichi Tokuda
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5421
ER -
Kazuhiro Nakamura, Shinji Takaki, Kei Hashimoto, Keiichiro Oura, Yoshihiko Nankaku, Keiichi Tokuda. (2020). Fast and High-Quality Singing Voice Synthesis System based on Convolutional Neural Networks. IEEE SigPort. http://sigport.org/5421
Kazuhiro Nakamura, Shinji Takaki, Kei Hashimoto, Keiichiro Oura, Yoshihiko Nankaku, Keiichi Tokuda, 2020. Fast and High-Quality Singing Voice Synthesis System based on Convolutional Neural Networks. Available at: http://sigport.org/5421.
Kazuhiro Nakamura, Shinji Takaki, Kei Hashimoto, Keiichiro Oura, Yoshihiko Nankaku, Keiichi Tokuda. (2020). "Fast and High-Quality Singing Voice Synthesis System based on Convolutional Neural Networks." Web.
1. Kazuhiro Nakamura, Shinji Takaki, Kei Hashimoto, Keiichiro Oura, Yoshihiko Nankaku, Keiichi Tokuda. Fast and High-Quality Singing Voice Synthesis System based on Convolutional Neural Networks [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5421

Sparse Directed Graph Learning for Head Movement Prediction in 360 Video Streaming


High-definition 360 videos encoded in fine quality are typically too large in size to stream in its entirety over bandwidth (BW)-constrained networks. One popular remedy is to interactively extract and send a spatial sub-region corresponding to a viewer's current field-of-view (FoV) in a head-mounted display (HMD) for more BW-efficient streaming. Due to the non-negligible round-trip-time (RTT) delay between server and client, accurate head movement prediction that foretells a viewer's future FoVs is essential.

Paper Details

Authors:
Gene Cheung, Patrick Le Callet, Jack Z. G. Tan
Submitted On:
20 May 2020 - 7:49pm
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[1] Gene Cheung, Patrick Le Callet, Jack Z. G. Tan, "Sparse Directed Graph Learning for Head Movement Prediction in 360 Video Streaming", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5420. Accessed: Aug. 12, 2020.
@article{5420-20,
url = {http://sigport.org/5420},
author = {Gene Cheung; Patrick Le Callet; Jack Z. G. Tan },
publisher = {IEEE SigPort},
title = {Sparse Directed Graph Learning for Head Movement Prediction in 360 Video Streaming},
year = {2020} }
TY - EJOUR
T1 - Sparse Directed Graph Learning for Head Movement Prediction in 360 Video Streaming
AU - Gene Cheung; Patrick Le Callet; Jack Z. G. Tan
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5420
ER -
Gene Cheung, Patrick Le Callet, Jack Z. G. Tan. (2020). Sparse Directed Graph Learning for Head Movement Prediction in 360 Video Streaming. IEEE SigPort. http://sigport.org/5420
Gene Cheung, Patrick Le Callet, Jack Z. G. Tan, 2020. Sparse Directed Graph Learning for Head Movement Prediction in 360 Video Streaming. Available at: http://sigport.org/5420.
Gene Cheung, Patrick Le Callet, Jack Z. G. Tan. (2020). "Sparse Directed Graph Learning for Head Movement Prediction in 360 Video Streaming." Web.
1. Gene Cheung, Patrick Le Callet, Jack Z. G. Tan. Sparse Directed Graph Learning for Head Movement Prediction in 360 Video Streaming [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5420

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