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Image/Video Processing

Using Multimodal Data for Automated Fidelity Evaluation in Pivotal Response Treatment Videos


Research has shown that caregivers implementing
pivotal response treatment (PRT) with their child with autism
spectrum disorder (ASD) helps the child develop social and
communication skills. Evaluation of caregiver fidelity to PRT in
training programs and research studies relies on the evaluation
of video probes depicting the caregiver interacting with his
or her child. These video probes are reviewed by behavior
analysts and are dependent on manual processing to extract
data metrics. Using multimodal data processing techniques and

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Authors:
Corey Heath, Hemanth Venkateswara, Troy McDaniel, Sethuraman Panchanathan
Submitted On:
11 November 2019 - 8:34pm
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GlobalSIP_Presentation.pptx

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[1] Corey Heath, Hemanth Venkateswara, Troy McDaniel, Sethuraman Panchanathan, "Using Multimodal Data for Automated Fidelity Evaluation in Pivotal Response Treatment Videos", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4946. Accessed: Nov. 12, 2019.
@article{4946-19,
url = {http://sigport.org/4946},
author = {Corey Heath; Hemanth Venkateswara; Troy McDaniel; Sethuraman Panchanathan },
publisher = {IEEE SigPort},
title = {Using Multimodal Data for Automated Fidelity Evaluation in Pivotal Response Treatment Videos},
year = {2019} }
TY - EJOUR
T1 - Using Multimodal Data for Automated Fidelity Evaluation in Pivotal Response Treatment Videos
AU - Corey Heath; Hemanth Venkateswara; Troy McDaniel; Sethuraman Panchanathan
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4946
ER -
Corey Heath, Hemanth Venkateswara, Troy McDaniel, Sethuraman Panchanathan. (2019). Using Multimodal Data for Automated Fidelity Evaluation in Pivotal Response Treatment Videos. IEEE SigPort. http://sigport.org/4946
Corey Heath, Hemanth Venkateswara, Troy McDaniel, Sethuraman Panchanathan, 2019. Using Multimodal Data for Automated Fidelity Evaluation in Pivotal Response Treatment Videos. Available at: http://sigport.org/4946.
Corey Heath, Hemanth Venkateswara, Troy McDaniel, Sethuraman Panchanathan. (2019). "Using Multimodal Data for Automated Fidelity Evaluation in Pivotal Response Treatment Videos." Web.
1. Corey Heath, Hemanth Venkateswara, Troy McDaniel, Sethuraman Panchanathan. Using Multimodal Data for Automated Fidelity Evaluation in Pivotal Response Treatment Videos [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4946

FHDR: HDR Image Reconstruction from a Single LDR Image using Feedback Network


High dynamic range (HDR) image generation from a single exposure low dynamic range (LDR) image has been made possible due to the recent advances in Deep Learning. Various feed-forward Convolutional Neural Networks (CNNs) have been proposed for learning LDR to HDR representations. To better utilize the power of CNNs, we exploit the idea of feedback, where the initial low level features are guided by the high level features using a hidden state of a Recurrent Neural Network.

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Authors:
Zeeshan Khan, Mukul Khanna, Shanmuganathan Raman
Submitted On:
10 November 2019 - 4:07am
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FHDR Presentation.pdf

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[1] Zeeshan Khan, Mukul Khanna, Shanmuganathan Raman, "FHDR: HDR Image Reconstruction from a Single LDR Image using Feedback Network", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4943. Accessed: Nov. 12, 2019.
@article{4943-19,
url = {http://sigport.org/4943},
author = {Zeeshan Khan; Mukul Khanna; Shanmuganathan Raman },
publisher = {IEEE SigPort},
title = {FHDR: HDR Image Reconstruction from a Single LDR Image using Feedback Network},
year = {2019} }
TY - EJOUR
T1 - FHDR: HDR Image Reconstruction from a Single LDR Image using Feedback Network
AU - Zeeshan Khan; Mukul Khanna; Shanmuganathan Raman
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4943
ER -
Zeeshan Khan, Mukul Khanna, Shanmuganathan Raman. (2019). FHDR: HDR Image Reconstruction from a Single LDR Image using Feedback Network. IEEE SigPort. http://sigport.org/4943
Zeeshan Khan, Mukul Khanna, Shanmuganathan Raman, 2019. FHDR: HDR Image Reconstruction from a Single LDR Image using Feedback Network. Available at: http://sigport.org/4943.
Zeeshan Khan, Mukul Khanna, Shanmuganathan Raman. (2019). "FHDR: HDR Image Reconstruction from a Single LDR Image using Feedback Network." Web.
1. Zeeshan Khan, Mukul Khanna, Shanmuganathan Raman. FHDR: HDR Image Reconstruction from a Single LDR Image using Feedback Network [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4943

Image Alpha Matting via Residual Convolutional Grid Network


Alpha matting is an important topic in areas of computer vision. It has various applications, such as virtual reality, digital image and video editing, and image synthesis. Conventional approaches for alpha matting do not perform well when they encounter complicated background or when foreground and background color distributions overlap. It is also difficult to extract alpha matte accurately when the foreground objects are semi-transparent or hairy.

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Authors:
Yang Zhou, Lei Chen, Jiying Zhao
Submitted On:
8 November 2019 - 1:26pm
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Image Alpha Matting via Residual Convolutional Grid Network_Poster.pdf

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[1] Yang Zhou, Lei Chen, Jiying Zhao, "Image Alpha Matting via Residual Convolutional Grid Network", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4933. Accessed: Nov. 12, 2019.
@article{4933-19,
url = {http://sigport.org/4933},
author = {Yang Zhou; Lei Chen; Jiying Zhao },
publisher = {IEEE SigPort},
title = {Image Alpha Matting via Residual Convolutional Grid Network},
year = {2019} }
TY - EJOUR
T1 - Image Alpha Matting via Residual Convolutional Grid Network
AU - Yang Zhou; Lei Chen; Jiying Zhao
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4933
ER -
Yang Zhou, Lei Chen, Jiying Zhao. (2019). Image Alpha Matting via Residual Convolutional Grid Network. IEEE SigPort. http://sigport.org/4933
Yang Zhou, Lei Chen, Jiying Zhao, 2019. Image Alpha Matting via Residual Convolutional Grid Network. Available at: http://sigport.org/4933.
Yang Zhou, Lei Chen, Jiying Zhao. (2019). "Image Alpha Matting via Residual Convolutional Grid Network." Web.
1. Yang Zhou, Lei Chen, Jiying Zhao. Image Alpha Matting via Residual Convolutional Grid Network [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4933

Compressive Super-Pixel LiDAR for High-Framerate 3D Depth Imaging


We propose a new sampling and reconstruction framework for full frame depth imaging using synchronised, programmable laser diode and photon detector arrays. By adopting a measurement scheme that probes the environment with sparse, pseudo-random patterns, our method enables eyesafe LiDAR operation, while guaranteeing fast reconstruction of

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Authors:
Brian Stewart, Joao F.C. Mota, Andrew M. Wallace
Submitted On:
8 November 2019 - 6:40am
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SuperPixel LiDAR GlobalSIP19 print.pdf

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[1] Brian Stewart, Joao F.C. Mota, Andrew M. Wallace, "Compressive Super-Pixel LiDAR for High-Framerate 3D Depth Imaging", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4930. Accessed: Nov. 12, 2019.
@article{4930-19,
url = {http://sigport.org/4930},
author = {Brian Stewart; Joao F.C. Mota; Andrew M. Wallace },
publisher = {IEEE SigPort},
title = {Compressive Super-Pixel LiDAR for High-Framerate 3D Depth Imaging},
year = {2019} }
TY - EJOUR
T1 - Compressive Super-Pixel LiDAR for High-Framerate 3D Depth Imaging
AU - Brian Stewart; Joao F.C. Mota; Andrew M. Wallace
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4930
ER -
Brian Stewart, Joao F.C. Mota, Andrew M. Wallace. (2019). Compressive Super-Pixel LiDAR for High-Framerate 3D Depth Imaging. IEEE SigPort. http://sigport.org/4930
Brian Stewart, Joao F.C. Mota, Andrew M. Wallace, 2019. Compressive Super-Pixel LiDAR for High-Framerate 3D Depth Imaging. Available at: http://sigport.org/4930.
Brian Stewart, Joao F.C. Mota, Andrew M. Wallace. (2019). "Compressive Super-Pixel LiDAR for High-Framerate 3D Depth Imaging." Web.
1. Brian Stewart, Joao F.C. Mota, Andrew M. Wallace. Compressive Super-Pixel LiDAR for High-Framerate 3D Depth Imaging [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4930

A Novel Blurring based Method for Video Compression

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4 November 2019 - 11:35pm
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Video Compression Poster

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[1] , "A Novel Blurring based Method for Video Compression ", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4912. Accessed: Nov. 12, 2019.
@article{4912-19,
url = {http://sigport.org/4912},
author = { },
publisher = {IEEE SigPort},
title = {A Novel Blurring based Method for Video Compression },
year = {2019} }
TY - EJOUR
T1 - A Novel Blurring based Method for Video Compression
AU -
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4912
ER -
. (2019). A Novel Blurring based Method for Video Compression . IEEE SigPort. http://sigport.org/4912
, 2019. A Novel Blurring based Method for Video Compression . Available at: http://sigport.org/4912.
. (2019). "A Novel Blurring based Method for Video Compression ." Web.
1. . A Novel Blurring based Method for Video Compression [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4912

INCREMENTAL LABEL PROPAGATION ON FACIAL IMAGES


The increasing computational complexity of label propagation-based facial image annotation when applied on multimedia data whose cardinality increases over the time (e.g., when analyzing video or movie content on-line), can be reduced by using an incremental approach. In this paper, a method for incremental label propagation on facial images is described. The similarity matrix is incrementally constructed by employing the kd-tree nearest neighbor algorithm.

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Authors:
Efstratios Kakaletsis, Anastasios Tefas, Nikos Nikolaidis, Ioannis Pitas
Submitted On:
25 October 2019 - 7:04am
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Poster_MLSP_2019_Incremental_Label_Propagation.pdf

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[1] Efstratios Kakaletsis, Anastasios Tefas, Nikos Nikolaidis, Ioannis Pitas, "INCREMENTAL LABEL PROPAGATION ON FACIAL IMAGES", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4893. Accessed: Nov. 12, 2019.
@article{4893-19,
url = {http://sigport.org/4893},
author = {Efstratios Kakaletsis; Anastasios Tefas; Nikos Nikolaidis; Ioannis Pitas },
publisher = {IEEE SigPort},
title = {INCREMENTAL LABEL PROPAGATION ON FACIAL IMAGES},
year = {2019} }
TY - EJOUR
T1 - INCREMENTAL LABEL PROPAGATION ON FACIAL IMAGES
AU - Efstratios Kakaletsis; Anastasios Tefas; Nikos Nikolaidis; Ioannis Pitas
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4893
ER -
Efstratios Kakaletsis, Anastasios Tefas, Nikos Nikolaidis, Ioannis Pitas. (2019). INCREMENTAL LABEL PROPAGATION ON FACIAL IMAGES. IEEE SigPort. http://sigport.org/4893
Efstratios Kakaletsis, Anastasios Tefas, Nikos Nikolaidis, Ioannis Pitas, 2019. INCREMENTAL LABEL PROPAGATION ON FACIAL IMAGES. Available at: http://sigport.org/4893.
Efstratios Kakaletsis, Anastasios Tefas, Nikos Nikolaidis, Ioannis Pitas. (2019). "INCREMENTAL LABEL PROPAGATION ON FACIAL IMAGES." Web.
1. Efstratios Kakaletsis, Anastasios Tefas, Nikos Nikolaidis, Ioannis Pitas. INCREMENTAL LABEL PROPAGATION ON FACIAL IMAGES [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4893

High Resolution Water Segmentation for Autonomous Unmanned Surface Vehicles: A Novel Dataset and Evaluation


Even though Unmanned Surface Vehicles (USVs) are increasingly used to perform various laborious and expensive off-shore tasks, they still require an extensive dedicated crew supporting and ensuring the safety of their operations. The recent developments in computer vision and robotics further fueled the interest on developing \textit{autonomous} USVs that will overcome the aforementioned limitations, unleashing their full potential. One of the most vital and fundamental tasks in order to automate and ensure the safety of USV operations is to perform water segmentation.

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Authors:
Jussi Taipalmaa, Nikolaos Passalis, Honglei Zhang, Moncef Gabbouj, Jenni Raitoharju
Submitted On:
24 October 2019 - 2:10pm
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[1] Jussi Taipalmaa, Nikolaos Passalis, Honglei Zhang, Moncef Gabbouj, Jenni Raitoharju, "High Resolution Water Segmentation for Autonomous Unmanned Surface Vehicles: A Novel Dataset and Evaluation", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4889. Accessed: Nov. 12, 2019.
@article{4889-19,
url = {http://sigport.org/4889},
author = {Jussi Taipalmaa; Nikolaos Passalis; Honglei Zhang; Moncef Gabbouj; Jenni Raitoharju },
publisher = {IEEE SigPort},
title = {High Resolution Water Segmentation for Autonomous Unmanned Surface Vehicles: A Novel Dataset and Evaluation},
year = {2019} }
TY - EJOUR
T1 - High Resolution Water Segmentation for Autonomous Unmanned Surface Vehicles: A Novel Dataset and Evaluation
AU - Jussi Taipalmaa; Nikolaos Passalis; Honglei Zhang; Moncef Gabbouj; Jenni Raitoharju
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4889
ER -
Jussi Taipalmaa, Nikolaos Passalis, Honglei Zhang, Moncef Gabbouj, Jenni Raitoharju. (2019). High Resolution Water Segmentation for Autonomous Unmanned Surface Vehicles: A Novel Dataset and Evaluation. IEEE SigPort. http://sigport.org/4889
Jussi Taipalmaa, Nikolaos Passalis, Honglei Zhang, Moncef Gabbouj, Jenni Raitoharju, 2019. High Resolution Water Segmentation for Autonomous Unmanned Surface Vehicles: A Novel Dataset and Evaluation. Available at: http://sigport.org/4889.
Jussi Taipalmaa, Nikolaos Passalis, Honglei Zhang, Moncef Gabbouj, Jenni Raitoharju. (2019). "High Resolution Water Segmentation for Autonomous Unmanned Surface Vehicles: A Novel Dataset and Evaluation." Web.
1. Jussi Taipalmaa, Nikolaos Passalis, Honglei Zhang, Moncef Gabbouj, Jenni Raitoharju. High Resolution Water Segmentation for Autonomous Unmanned Surface Vehicles: A Novel Dataset and Evaluation [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4889

Online Learning for Indoor Asset Detection


Building floor plans with locations of safety, security, and energy assets such as IoT sensors, fire alarms, etc. are vital for climate control, emergency response, safety, and maintenance of building infrastructure. Existing approaches to building survey are tedious, error prone, and involve an operator with a clipboard and pen, enumerating and localizing assets in each room. We propose an interactive method for a human operator to use an app on a smartphone, which can detect, classify, and localize assets of interest, to expedite such a task.

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Authors:
Adith Balamurugan, Avideh Zakhor
Submitted On:
14 October 2019 - 8:33pm
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OneShotMLSP.pdf

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[1] Adith Balamurugan, Avideh Zakhor, "Online Learning for Indoor Asset Detection", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4872. Accessed: Nov. 12, 2019.
@article{4872-19,
url = {http://sigport.org/4872},
author = {Adith Balamurugan; Avideh Zakhor },
publisher = {IEEE SigPort},
title = {Online Learning for Indoor Asset Detection},
year = {2019} }
TY - EJOUR
T1 - Online Learning for Indoor Asset Detection
AU - Adith Balamurugan; Avideh Zakhor
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4872
ER -
Adith Balamurugan, Avideh Zakhor. (2019). Online Learning for Indoor Asset Detection. IEEE SigPort. http://sigport.org/4872
Adith Balamurugan, Avideh Zakhor, 2019. Online Learning for Indoor Asset Detection. Available at: http://sigport.org/4872.
Adith Balamurugan, Avideh Zakhor. (2019). "Online Learning for Indoor Asset Detection." Web.
1. Adith Balamurugan, Avideh Zakhor. Online Learning for Indoor Asset Detection [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4872

A Spatiotemporal Deep Learning Solution For Automatic Micro-Expressions Recognition From Local Facial Regions

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14 October 2019 - 10:57am
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[1] , "A Spatiotemporal Deep Learning Solution For Automatic Micro-Expressions Recognition From Local Facial Regions", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4870. Accessed: Nov. 12, 2019.
@article{4870-19,
url = {http://sigport.org/4870},
author = { },
publisher = {IEEE SigPort},
title = {A Spatiotemporal Deep Learning Solution For Automatic Micro-Expressions Recognition From Local Facial Regions},
year = {2019} }
TY - EJOUR
T1 - A Spatiotemporal Deep Learning Solution For Automatic Micro-Expressions Recognition From Local Facial Regions
AU -
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4870
ER -
. (2019). A Spatiotemporal Deep Learning Solution For Automatic Micro-Expressions Recognition From Local Facial Regions. IEEE SigPort. http://sigport.org/4870
, 2019. A Spatiotemporal Deep Learning Solution For Automatic Micro-Expressions Recognition From Local Facial Regions. Available at: http://sigport.org/4870.
. (2019). "A Spatiotemporal Deep Learning Solution For Automatic Micro-Expressions Recognition From Local Facial Regions." Web.
1. . A Spatiotemporal Deep Learning Solution For Automatic Micro-Expressions Recognition From Local Facial Regions [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4870

Improving Neural Non-Maximum Suppression For Object Detection By Exploiting Interest-Point Detector


Non-maximum suppression (NMS) is a post-processing step in almost every visual object detector. Its goal is to drastically prune the number of overlapping detected candidate regions-of-interest (ROIs) and replace them with a single, more spatially accurate detection. The default algorithm (Greedy NMS) is fairly simple and suffers from drawbacks, due to its need for manual tuning. Recently, NMS has been improved using deep neural networks that learn how to solve a spatial overlap-based detections rescoring task in a supervised manner, where only ROI coordinates are exploited as input.

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Authors:
Charalampos Symeonidis, Ioannis Mademlis, Nikos Nikolaidis, Ioannis Pitas
Submitted On:
11 October 2019 - 10:38am
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mlsp_poster.pdf

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[1] Charalampos Symeonidis, Ioannis Mademlis, Nikos Nikolaidis, Ioannis Pitas, "Improving Neural Non-Maximum Suppression For Object Detection By Exploiting Interest-Point Detector", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4851. Accessed: Nov. 12, 2019.
@article{4851-19,
url = {http://sigport.org/4851},
author = {Charalampos Symeonidis; Ioannis Mademlis; Nikos Nikolaidis; Ioannis Pitas },
publisher = {IEEE SigPort},
title = {Improving Neural Non-Maximum Suppression For Object Detection By Exploiting Interest-Point Detector},
year = {2019} }
TY - EJOUR
T1 - Improving Neural Non-Maximum Suppression For Object Detection By Exploiting Interest-Point Detector
AU - Charalampos Symeonidis; Ioannis Mademlis; Nikos Nikolaidis; Ioannis Pitas
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4851
ER -
Charalampos Symeonidis, Ioannis Mademlis, Nikos Nikolaidis, Ioannis Pitas. (2019). Improving Neural Non-Maximum Suppression For Object Detection By Exploiting Interest-Point Detector. IEEE SigPort. http://sigport.org/4851
Charalampos Symeonidis, Ioannis Mademlis, Nikos Nikolaidis, Ioannis Pitas, 2019. Improving Neural Non-Maximum Suppression For Object Detection By Exploiting Interest-Point Detector. Available at: http://sigport.org/4851.
Charalampos Symeonidis, Ioannis Mademlis, Nikos Nikolaidis, Ioannis Pitas. (2019). "Improving Neural Non-Maximum Suppression For Object Detection By Exploiting Interest-Point Detector." Web.
1. Charalampos Symeonidis, Ioannis Mademlis, Nikos Nikolaidis, Ioannis Pitas. Improving Neural Non-Maximum Suppression For Object Detection By Exploiting Interest-Point Detector [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4851

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