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ICASSP 2018

ICASSP is the world’s largest and most comprehensive technical conference focused on signal processing and its applications. The 2019 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.

Bandlimited Spatiotemporal Field Sampling with Location and Time Unaware Mobile Sensors


Sampling of smooth spatiotemporally varying fields is a well studied topic in the literature. Classical approach assumes that the field is observed at known sampling locations and known timestamps ensuring field reconstruction. In a first, in this work the sampling and reconstruction of a spatiotemporal bandlimited field is addressed, where the samples are obtained by a location-unaware, time-unaware mobile sensor. The spatial and temporal order of samples is assumed to be known. It is assumed that the field samples are affected by measurement-noise.

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Authors:
Animesh Kumar
Submitted On:
20 April 2018 - 6:42pm
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Bandlimited Spatiotemporal Field Sampling with Location and Time Unaware Sensors Poster

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[1] Animesh Kumar, "Bandlimited Spatiotemporal Field Sampling with Location and Time Unaware Mobile Sensors", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3116. Accessed: Sep. 28, 2020.
@article{3116-18,
url = {http://sigport.org/3116},
author = {Animesh Kumar },
publisher = {IEEE SigPort},
title = {Bandlimited Spatiotemporal Field Sampling with Location and Time Unaware Mobile Sensors},
year = {2018} }
TY - EJOUR
T1 - Bandlimited Spatiotemporal Field Sampling with Location and Time Unaware Mobile Sensors
AU - Animesh Kumar
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3116
ER -
Animesh Kumar. (2018). Bandlimited Spatiotemporal Field Sampling with Location and Time Unaware Mobile Sensors. IEEE SigPort. http://sigport.org/3116
Animesh Kumar, 2018. Bandlimited Spatiotemporal Field Sampling with Location and Time Unaware Mobile Sensors. Available at: http://sigport.org/3116.
Animesh Kumar. (2018). "Bandlimited Spatiotemporal Field Sampling with Location and Time Unaware Mobile Sensors." Web.
1. Animesh Kumar. Bandlimited Spatiotemporal Field Sampling with Location and Time Unaware Mobile Sensors [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3116

MODE DOMAIN SPATIAL ACTIVE NOISE CONTROL USING SPARSE SIGNAL REPRESENTATION


Active noise control (ANC) over a sizeable space requires a large number of reference and error microphones to satisfy the spatial Nyquist sampling criterion, which limits the feasibility of practical realization of such systems. This paper proposes a mode-domain feedforward ANC method to attenuate the noise field over a large space while reducing the number of microphones required.

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Authors:
Yuki Mitsufuji, Thushara Abhayapala
Submitted On:
20 April 2018 - 5:10pm
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ICASSP2018_poster.pdf

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[1] Yuki Mitsufuji, Thushara Abhayapala, "MODE DOMAIN SPATIAL ACTIVE NOISE CONTROL USING SPARSE SIGNAL REPRESENTATION", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3115. Accessed: Sep. 28, 2020.
@article{3115-18,
url = {http://sigport.org/3115},
author = {Yuki Mitsufuji; Thushara Abhayapala },
publisher = {IEEE SigPort},
title = {MODE DOMAIN SPATIAL ACTIVE NOISE CONTROL USING SPARSE SIGNAL REPRESENTATION},
year = {2018} }
TY - EJOUR
T1 - MODE DOMAIN SPATIAL ACTIVE NOISE CONTROL USING SPARSE SIGNAL REPRESENTATION
AU - Yuki Mitsufuji; Thushara Abhayapala
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3115
ER -
Yuki Mitsufuji, Thushara Abhayapala. (2018). MODE DOMAIN SPATIAL ACTIVE NOISE CONTROL USING SPARSE SIGNAL REPRESENTATION. IEEE SigPort. http://sigport.org/3115
Yuki Mitsufuji, Thushara Abhayapala, 2018. MODE DOMAIN SPATIAL ACTIVE NOISE CONTROL USING SPARSE SIGNAL REPRESENTATION. Available at: http://sigport.org/3115.
Yuki Mitsufuji, Thushara Abhayapala. (2018). "MODE DOMAIN SPATIAL ACTIVE NOISE CONTROL USING SPARSE SIGNAL REPRESENTATION." Web.
1. Yuki Mitsufuji, Thushara Abhayapala. MODE DOMAIN SPATIAL ACTIVE NOISE CONTROL USING SPARSE SIGNAL REPRESENTATION [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3115

ICASSP2018_Dynamic Matrix Recovery from Partially Observed and Erroneous Measurements

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20 April 2018 - 4:30pm
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ICASSP_MW.pdf

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[1] , "ICASSP2018_Dynamic Matrix Recovery from Partially Observed and Erroneous Measurements", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3113. Accessed: Sep. 28, 2020.
@article{3113-18,
url = {http://sigport.org/3113},
author = { },
publisher = {IEEE SigPort},
title = {ICASSP2018_Dynamic Matrix Recovery from Partially Observed and Erroneous Measurements},
year = {2018} }
TY - EJOUR
T1 - ICASSP2018_Dynamic Matrix Recovery from Partially Observed and Erroneous Measurements
AU -
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3113
ER -
. (2018). ICASSP2018_Dynamic Matrix Recovery from Partially Observed and Erroneous Measurements. IEEE SigPort. http://sigport.org/3113
, 2018. ICASSP2018_Dynamic Matrix Recovery from Partially Observed and Erroneous Measurements. Available at: http://sigport.org/3113.
. (2018). "ICASSP2018_Dynamic Matrix Recovery from Partially Observed and Erroneous Measurements." Web.
1. . ICASSP2018_Dynamic Matrix Recovery from Partially Observed and Erroneous Measurements [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3113

Autoencoder-based image compression: can the learning be quantization independent?

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Authors:
Aline Roumy, Christine Guillemot
Submitted On:
20 April 2018 - 3:18pm
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presentation_icassp_2018.pdf

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[1] Aline Roumy, Christine Guillemot, "Autoencoder-based image compression: can the learning be quantization independent?", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3112. Accessed: Sep. 28, 2020.
@article{3112-18,
url = {http://sigport.org/3112},
author = {Aline Roumy; Christine Guillemot },
publisher = {IEEE SigPort},
title = {Autoencoder-based image compression: can the learning be quantization independent?},
year = {2018} }
TY - EJOUR
T1 - Autoencoder-based image compression: can the learning be quantization independent?
AU - Aline Roumy; Christine Guillemot
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3112
ER -
Aline Roumy, Christine Guillemot. (2018). Autoencoder-based image compression: can the learning be quantization independent?. IEEE SigPort. http://sigport.org/3112
Aline Roumy, Christine Guillemot, 2018. Autoencoder-based image compression: can the learning be quantization independent?. Available at: http://sigport.org/3112.
Aline Roumy, Christine Guillemot. (2018). "Autoencoder-based image compression: can the learning be quantization independent?." Web.
1. Aline Roumy, Christine Guillemot. Autoencoder-based image compression: can the learning be quantization independent? [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3112

Adversarial Advantage Actor-Critic Model for Task-Completion Dialogue Policy Learning


This paper presents a new method --- adversarial advantage actor-critic (Adversarial A2C), which significantly improves the efficiency of dialogue policy learning in task-completion dialogue systems. Inspired by generative adversarial networks (GAN), we train a discriminator to differentiate responses/actions generated by dialogue agents from responses/actions by experts.

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Authors:
Baolin Peng, Xiujun Li, Jianfeng Gao, Jingjing Liu, Yun-Nung Chen, Kam-Fai Wong
Submitted On:
20 April 2018 - 12:23pm
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poster_icassp2018_v2.pptx

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[1] Baolin Peng, Xiujun Li, Jianfeng Gao, Jingjing Liu, Yun-Nung Chen, Kam-Fai Wong, "Adversarial Advantage Actor-Critic Model for Task-Completion Dialogue Policy Learning", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3110. Accessed: Sep. 28, 2020.
@article{3110-18,
url = {http://sigport.org/3110},
author = {Baolin Peng; Xiujun Li; Jianfeng Gao; Jingjing Liu; Yun-Nung Chen; Kam-Fai Wong },
publisher = {IEEE SigPort},
title = {Adversarial Advantage Actor-Critic Model for Task-Completion Dialogue Policy Learning},
year = {2018} }
TY - EJOUR
T1 - Adversarial Advantage Actor-Critic Model for Task-Completion Dialogue Policy Learning
AU - Baolin Peng; Xiujun Li; Jianfeng Gao; Jingjing Liu; Yun-Nung Chen; Kam-Fai Wong
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3110
ER -
Baolin Peng, Xiujun Li, Jianfeng Gao, Jingjing Liu, Yun-Nung Chen, Kam-Fai Wong. (2018). Adversarial Advantage Actor-Critic Model for Task-Completion Dialogue Policy Learning. IEEE SigPort. http://sigport.org/3110
Baolin Peng, Xiujun Li, Jianfeng Gao, Jingjing Liu, Yun-Nung Chen, Kam-Fai Wong, 2018. Adversarial Advantage Actor-Critic Model for Task-Completion Dialogue Policy Learning. Available at: http://sigport.org/3110.
Baolin Peng, Xiujun Li, Jianfeng Gao, Jingjing Liu, Yun-Nung Chen, Kam-Fai Wong. (2018). "Adversarial Advantage Actor-Critic Model for Task-Completion Dialogue Policy Learning." Web.
1. Baolin Peng, Xiujun Li, Jianfeng Gao, Jingjing Liu, Yun-Nung Chen, Kam-Fai Wong. Adversarial Advantage Actor-Critic Model for Task-Completion Dialogue Policy Learning [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3110

Acoustic Reflector Localization and Classification


The process of understanding acoustic properties of environments is important for several applications, such as spatial audio, augmented reality and source separation. In this paper, multichannel room impulse responses are recorded and transformed into their direction of arrival (DOA)-time domain, by employing a superdirective beamformer. This domain can be represented as a 2D image. Hence, a novel image processing method is proposed to analyze the DOA-time domain, and estimate the reflection times of arrival and DOAs. The main acoustically reflective objects are then localized.

Paper Details

Authors:
Luca Remaggi, Hansung Kim, Philip J. B. Jackson, Filippo M. Fazi, Adrian Hilton
Submitted On:
20 April 2018 - 12:07pm
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Remaggietal_ICASSP2018.pdf

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[1] Luca Remaggi, Hansung Kim, Philip J. B. Jackson, Filippo M. Fazi, Adrian Hilton, "Acoustic Reflector Localization and Classification", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3109. Accessed: Sep. 28, 2020.
@article{3109-18,
url = {http://sigport.org/3109},
author = {Luca Remaggi; Hansung Kim; Philip J. B. Jackson; Filippo M. Fazi; Adrian Hilton },
publisher = {IEEE SigPort},
title = {Acoustic Reflector Localization and Classification},
year = {2018} }
TY - EJOUR
T1 - Acoustic Reflector Localization and Classification
AU - Luca Remaggi; Hansung Kim; Philip J. B. Jackson; Filippo M. Fazi; Adrian Hilton
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3109
ER -
Luca Remaggi, Hansung Kim, Philip J. B. Jackson, Filippo M. Fazi, Adrian Hilton. (2018). Acoustic Reflector Localization and Classification. IEEE SigPort. http://sigport.org/3109
Luca Remaggi, Hansung Kim, Philip J. B. Jackson, Filippo M. Fazi, Adrian Hilton, 2018. Acoustic Reflector Localization and Classification. Available at: http://sigport.org/3109.
Luca Remaggi, Hansung Kim, Philip J. B. Jackson, Filippo M. Fazi, Adrian Hilton. (2018). "Acoustic Reflector Localization and Classification." Web.
1. Luca Remaggi, Hansung Kim, Philip J. B. Jackson, Filippo M. Fazi, Adrian Hilton. Acoustic Reflector Localization and Classification [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3109

OBJECT-ORIENTED ANOMALY DETECTION IN SURVEILLANCE VIDEOS


Detecting and localizing anomalies in surveillance videos is an ongoing challenge. Most existing methods are patch or trajectory-based, which lack semantic understanding of scenes and may split targets into pieces. To handle this prob-lem, this paper proposes a novel and effective algorithm by incorporating deep object detection and tracking with full utilization of spatial and temporal information.

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Authors:
Xiaodan Li, Weihai Li, Bin Liu, Qiankun Liu, Nenghai Yu
Submitted On:
20 April 2018 - 10:55am
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ICASSP2018-2394.pdf

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[1] Xiaodan Li, Weihai Li, Bin Liu, Qiankun Liu, Nenghai Yu, "OBJECT-ORIENTED ANOMALY DETECTION IN SURVEILLANCE VIDEOS", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3108. Accessed: Sep. 28, 2020.
@article{3108-18,
url = {http://sigport.org/3108},
author = {Xiaodan Li; Weihai Li; Bin Liu; Qiankun Liu; Nenghai Yu },
publisher = {IEEE SigPort},
title = {OBJECT-ORIENTED ANOMALY DETECTION IN SURVEILLANCE VIDEOS},
year = {2018} }
TY - EJOUR
T1 - OBJECT-ORIENTED ANOMALY DETECTION IN SURVEILLANCE VIDEOS
AU - Xiaodan Li; Weihai Li; Bin Liu; Qiankun Liu; Nenghai Yu
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3108
ER -
Xiaodan Li, Weihai Li, Bin Liu, Qiankun Liu, Nenghai Yu. (2018). OBJECT-ORIENTED ANOMALY DETECTION IN SURVEILLANCE VIDEOS. IEEE SigPort. http://sigport.org/3108
Xiaodan Li, Weihai Li, Bin Liu, Qiankun Liu, Nenghai Yu, 2018. OBJECT-ORIENTED ANOMALY DETECTION IN SURVEILLANCE VIDEOS. Available at: http://sigport.org/3108.
Xiaodan Li, Weihai Li, Bin Liu, Qiankun Liu, Nenghai Yu. (2018). "OBJECT-ORIENTED ANOMALY DETECTION IN SURVEILLANCE VIDEOS." Web.
1. Xiaodan Li, Weihai Li, Bin Liu, Qiankun Liu, Nenghai Yu. OBJECT-ORIENTED ANOMALY DETECTION IN SURVEILLANCE VIDEOS [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3108

RCDFNN: Robust Change Detection based on Convolutional Fusion Neural Network


Video change detection, which plays an important role in computer vision, is far from being well resolved due to the complexity of diverse scenes in real world. Most of the current methods are designed based on hand-crafted features and perform well in some certain scenes but may fail on others. This paper puts up forward a deep learning based method to automatically fuse multiple basic detections into an optimal

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Submitted On:
20 April 2018 - 10:43am
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ICASSP2018_poster.pdf

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[1] , " RCDFNN: Robust Change Detection based on Convolutional Fusion Neural Network", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3107. Accessed: Sep. 28, 2020.
@article{3107-18,
url = {http://sigport.org/3107},
author = { },
publisher = {IEEE SigPort},
title = { RCDFNN: Robust Change Detection based on Convolutional Fusion Neural Network},
year = {2018} }
TY - EJOUR
T1 - RCDFNN: Robust Change Detection based on Convolutional Fusion Neural Network
AU -
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3107
ER -
. (2018). RCDFNN: Robust Change Detection based on Convolutional Fusion Neural Network. IEEE SigPort. http://sigport.org/3107
, 2018. RCDFNN: Robust Change Detection based on Convolutional Fusion Neural Network. Available at: http://sigport.org/3107.
. (2018). " RCDFNN: Robust Change Detection based on Convolutional Fusion Neural Network." Web.
1. . RCDFNN: Robust Change Detection based on Convolutional Fusion Neural Network [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3107

END-TO-END SOUND SOURCE ENHANCEMENT USING DEEP NEURAL NETWORK IN THE MODIFIED DISCRETE COSINE TRANSFORM DOMAIN

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20 April 2018 - 10:06am
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ICASSP_2018_koizumi_r03.pdf

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[1] , "END-TO-END SOUND SOURCE ENHANCEMENT USING DEEP NEURAL NETWORK IN THE MODIFIED DISCRETE COSINE TRANSFORM DOMAIN", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3106. Accessed: Sep. 28, 2020.
@article{3106-18,
url = {http://sigport.org/3106},
author = { },
publisher = {IEEE SigPort},
title = {END-TO-END SOUND SOURCE ENHANCEMENT USING DEEP NEURAL NETWORK IN THE MODIFIED DISCRETE COSINE TRANSFORM DOMAIN},
year = {2018} }
TY - EJOUR
T1 - END-TO-END SOUND SOURCE ENHANCEMENT USING DEEP NEURAL NETWORK IN THE MODIFIED DISCRETE COSINE TRANSFORM DOMAIN
AU -
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3106
ER -
. (2018). END-TO-END SOUND SOURCE ENHANCEMENT USING DEEP NEURAL NETWORK IN THE MODIFIED DISCRETE COSINE TRANSFORM DOMAIN. IEEE SigPort. http://sigport.org/3106
, 2018. END-TO-END SOUND SOURCE ENHANCEMENT USING DEEP NEURAL NETWORK IN THE MODIFIED DISCRETE COSINE TRANSFORM DOMAIN. Available at: http://sigport.org/3106.
. (2018). "END-TO-END SOUND SOURCE ENHANCEMENT USING DEEP NEURAL NETWORK IN THE MODIFIED DISCRETE COSINE TRANSFORM DOMAIN." Web.
1. . END-TO-END SOUND SOURCE ENHANCEMENT USING DEEP NEURAL NETWORK IN THE MODIFIED DISCRETE COSINE TRANSFORM DOMAIN [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3106

ON SPEECH ENHANCEMENT USING MICROPHONE ARRAYS IN THE PRESENCE OF CO-DIRECTIONAL INTERFERENCE

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Authors:
Xin Leng, Jingdong Chen, Jacob Benesty, Israel Cohen
Submitted On:
20 April 2018 - 9:23am
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ICASSP_2018.pdf

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[1] Xin Leng, Jingdong Chen, Jacob Benesty, Israel Cohen, "ON SPEECH ENHANCEMENT USING MICROPHONE ARRAYS IN THE PRESENCE OF CO-DIRECTIONAL INTERFERENCE", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3104. Accessed: Sep. 28, 2020.
@article{3104-18,
url = {http://sigport.org/3104},
author = {Xin Leng; Jingdong Chen; Jacob Benesty; Israel Cohen },
publisher = {IEEE SigPort},
title = {ON SPEECH ENHANCEMENT USING MICROPHONE ARRAYS IN THE PRESENCE OF CO-DIRECTIONAL INTERFERENCE},
year = {2018} }
TY - EJOUR
T1 - ON SPEECH ENHANCEMENT USING MICROPHONE ARRAYS IN THE PRESENCE OF CO-DIRECTIONAL INTERFERENCE
AU - Xin Leng; Jingdong Chen; Jacob Benesty; Israel Cohen
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3104
ER -
Xin Leng, Jingdong Chen, Jacob Benesty, Israel Cohen. (2018). ON SPEECH ENHANCEMENT USING MICROPHONE ARRAYS IN THE PRESENCE OF CO-DIRECTIONAL INTERFERENCE. IEEE SigPort. http://sigport.org/3104
Xin Leng, Jingdong Chen, Jacob Benesty, Israel Cohen, 2018. ON SPEECH ENHANCEMENT USING MICROPHONE ARRAYS IN THE PRESENCE OF CO-DIRECTIONAL INTERFERENCE. Available at: http://sigport.org/3104.
Xin Leng, Jingdong Chen, Jacob Benesty, Israel Cohen. (2018). "ON SPEECH ENHANCEMENT USING MICROPHONE ARRAYS IN THE PRESENCE OF CO-DIRECTIONAL INTERFERENCE." Web.
1. Xin Leng, Jingdong Chen, Jacob Benesty, Israel Cohen. ON SPEECH ENHANCEMENT USING MICROPHONE ARRAYS IN THE PRESENCE OF CO-DIRECTIONAL INTERFERENCE [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3104

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