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Knowledge and Data Engineering

Dynamic Network Identification From Non-stationary Vector Autoregressive Time Series


Learning the dynamics of complex systems features a large
number of applications in data science. Graph-based modeling
and inference underpins the most prominent family of
approaches to learn complex dynamics due to their ability to
capture the intrinsic sparsity of direct interactions in such systems.
They also provide the user with interpretable graphs
that unveil behavioral patterns and changes. To cope with
the time-varying nature of interactions, this paper develops
an estimation criterion and a solver to learn the parameters

Paper Details

Authors:
Daniel Romero, Bakht Zaman, Baltasar Beferull-Lozano
Submitted On:
11 December 2018 - 4:54am
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Dynamic network identification poster

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[1] Daniel Romero, Bakht Zaman, Baltasar Beferull-Lozano, "Dynamic Network Identification From Non-stationary Vector Autoregressive Time Series", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3841. Accessed: Dec. 13, 2018.
@article{3841-18,
url = {http://sigport.org/3841},
author = {Daniel Romero; Bakht Zaman; Baltasar Beferull-Lozano },
publisher = {IEEE SigPort},
title = {Dynamic Network Identification From Non-stationary Vector Autoregressive Time Series},
year = {2018} }
TY - EJOUR
T1 - Dynamic Network Identification From Non-stationary Vector Autoregressive Time Series
AU - Daniel Romero; Bakht Zaman; Baltasar Beferull-Lozano
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3841
ER -
Daniel Romero, Bakht Zaman, Baltasar Beferull-Lozano. (2018). Dynamic Network Identification From Non-stationary Vector Autoregressive Time Series. IEEE SigPort. http://sigport.org/3841
Daniel Romero, Bakht Zaman, Baltasar Beferull-Lozano, 2018. Dynamic Network Identification From Non-stationary Vector Autoregressive Time Series. Available at: http://sigport.org/3841.
Daniel Romero, Bakht Zaman, Baltasar Beferull-Lozano. (2018). "Dynamic Network Identification From Non-stationary Vector Autoregressive Time Series." Web.
1. Daniel Romero, Bakht Zaman, Baltasar Beferull-Lozano. Dynamic Network Identification From Non-stationary Vector Autoregressive Time Series [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3841

Diversity in Fashion Recommendation Using Semantic Parsing


Developing recommendation system for fashion images is challenging due to the inherent ambiguity associated with what criterion a user is looking at. Suggesting multiple images where each output image is similar to the query image on the basis of a different feature or part is one way to mitigate the problem. Existing works for fashion recommendation have used Siamese or Triplet network to learn features between a similar pair and a similar dissimilar triplet respectively.

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Authors:
Sukhad Anand, Chetan Arora, Atul Rai
Submitted On:
8 October 2018 - 4:26am
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Fashion recommendation based on contextual similarity

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[1] Sukhad Anand, Chetan Arora, Atul Rai, "Diversity in Fashion Recommendation Using Semantic Parsing", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3622. Accessed: Dec. 13, 2018.
@article{3622-18,
url = {http://sigport.org/3622},
author = {Sukhad Anand; Chetan Arora; Atul Rai },
publisher = {IEEE SigPort},
title = {Diversity in Fashion Recommendation Using Semantic Parsing},
year = {2018} }
TY - EJOUR
T1 - Diversity in Fashion Recommendation Using Semantic Parsing
AU - Sukhad Anand; Chetan Arora; Atul Rai
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3622
ER -
Sukhad Anand, Chetan Arora, Atul Rai. (2018). Diversity in Fashion Recommendation Using Semantic Parsing. IEEE SigPort. http://sigport.org/3622
Sukhad Anand, Chetan Arora, Atul Rai, 2018. Diversity in Fashion Recommendation Using Semantic Parsing. Available at: http://sigport.org/3622.
Sukhad Anand, Chetan Arora, Atul Rai. (2018). "Diversity in Fashion Recommendation Using Semantic Parsing." Web.
1. Sukhad Anand, Chetan Arora, Atul Rai. Diversity in Fashion Recommendation Using Semantic Parsing [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3622

Randomized Sampling-based Fly Local Sensitive Hashing


Fly Local Sensitive Hashing (FLSH) is a biomimetic data-independent hashing method inspired by the mechanism of odor processing system in drosophila. In this paper,we propose a novel Randomized Sampling-based Fly Local Sensitive Hashing (rs-FLSH) to model the randomness occurred during the establishment of synapses between neurons.Significant performance improvement can be achieved by applying a novel randomized sampling scheme in rs-FLSH,in which the sample rate is modeled by a Gaussian random variable rather than a fixed value in FLSH.

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Authors:
Yu QIAO
Submitted On:
4 October 2018 - 9:49pm
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ICIP Poster.pdf

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[1] Yu QIAO, "Randomized Sampling-based Fly Local Sensitive Hashing", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3475. Accessed: Dec. 13, 2018.
@article{3475-18,
url = {http://sigport.org/3475},
author = {Yu QIAO },
publisher = {IEEE SigPort},
title = {Randomized Sampling-based Fly Local Sensitive Hashing},
year = {2018} }
TY - EJOUR
T1 - Randomized Sampling-based Fly Local Sensitive Hashing
AU - Yu QIAO
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3475
ER -
Yu QIAO. (2018). Randomized Sampling-based Fly Local Sensitive Hashing. IEEE SigPort. http://sigport.org/3475
Yu QIAO, 2018. Randomized Sampling-based Fly Local Sensitive Hashing. Available at: http://sigport.org/3475.
Yu QIAO. (2018). "Randomized Sampling-based Fly Local Sensitive Hashing." Web.
1. Yu QIAO. Randomized Sampling-based Fly Local Sensitive Hashing [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3475

Alternating autoencoders for matrix completion


We consider autoencoders (AEs) for matrix completion (MC) with application to collaborative filtering (CF) for recommedation systems. It is observed that for a given sparse user-item rating matrix, denoted asM, an AE performs matrix factorization so that the recovered matrix is represented as a product of user and item feature matrices.

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Authors:
Kiwon Lee, Yong H. Lee, Changho Suh
Submitted On:
4 June 2018 - 2:48pm
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Poster_Lee.pdf

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[1] Kiwon Lee, Yong H. Lee, Changho Suh, "Alternating autoencoders for matrix completion", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3223. Accessed: Dec. 13, 2018.
@article{3223-18,
url = {http://sigport.org/3223},
author = {Kiwon Lee; Yong H. Lee; Changho Suh },
publisher = {IEEE SigPort},
title = {Alternating autoencoders for matrix completion},
year = {2018} }
TY - EJOUR
T1 - Alternating autoencoders for matrix completion
AU - Kiwon Lee; Yong H. Lee; Changho Suh
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3223
ER -
Kiwon Lee, Yong H. Lee, Changho Suh. (2018). Alternating autoencoders for matrix completion. IEEE SigPort. http://sigport.org/3223
Kiwon Lee, Yong H. Lee, Changho Suh, 2018. Alternating autoencoders for matrix completion. Available at: http://sigport.org/3223.
Kiwon Lee, Yong H. Lee, Changho Suh. (2018). "Alternating autoencoders for matrix completion." Web.
1. Kiwon Lee, Yong H. Lee, Changho Suh. Alternating autoencoders for matrix completion [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3223

Profit Maximizing Logistic Regression Modeling for Credit Scoring


Multiple classification techniques have been employed for different business applications. In the particular case of credit scoring, a classifier which maximizes the total profit is preferable. The recently proposed expected maximum profit (EMP) measure for credit scoring allows to select the most profitable classifier. Taking the idea of the EMP one step further, it is desirable to integrate the measure into model construction, and thus obtain a profit maximizing model.

Paper Details

Authors:
Arnout Devos, Jakob Dhondt, Eugen Stripling, Bart Baesens, Seppe vanden Broucke, Gaurav Sukhatme
Submitted On:
30 May 2018 - 8:30pm
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PosterA1_Arnout_Devos_DSW2018.pdf

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[1] Arnout Devos, Jakob Dhondt, Eugen Stripling, Bart Baesens, Seppe vanden Broucke, Gaurav Sukhatme, "Profit Maximizing Logistic Regression Modeling for Credit Scoring", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3221. Accessed: Dec. 13, 2018.
@article{3221-18,
url = {http://sigport.org/3221},
author = {Arnout Devos; Jakob Dhondt; Eugen Stripling; Bart Baesens; Seppe vanden Broucke; Gaurav Sukhatme },
publisher = {IEEE SigPort},
title = {Profit Maximizing Logistic Regression Modeling for Credit Scoring},
year = {2018} }
TY - EJOUR
T1 - Profit Maximizing Logistic Regression Modeling for Credit Scoring
AU - Arnout Devos; Jakob Dhondt; Eugen Stripling; Bart Baesens; Seppe vanden Broucke; Gaurav Sukhatme
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3221
ER -
Arnout Devos, Jakob Dhondt, Eugen Stripling, Bart Baesens, Seppe vanden Broucke, Gaurav Sukhatme. (2018). Profit Maximizing Logistic Regression Modeling for Credit Scoring. IEEE SigPort. http://sigport.org/3221
Arnout Devos, Jakob Dhondt, Eugen Stripling, Bart Baesens, Seppe vanden Broucke, Gaurav Sukhatme, 2018. Profit Maximizing Logistic Regression Modeling for Credit Scoring. Available at: http://sigport.org/3221.
Arnout Devos, Jakob Dhondt, Eugen Stripling, Bart Baesens, Seppe vanden Broucke, Gaurav Sukhatme. (2018). "Profit Maximizing Logistic Regression Modeling for Credit Scoring." Web.
1. Arnout Devos, Jakob Dhondt, Eugen Stripling, Bart Baesens, Seppe vanden Broucke, Gaurav Sukhatme. Profit Maximizing Logistic Regression Modeling for Credit Scoring [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3221

PSEUDO-SUPERVISED APPROACH FOR TEXT CLUSTERING BASED ON CONSENSUS ANALYSIS


In recent years, neural networks (NN) have achieved remarkable
performance improvement in text classification due to
their powerful ability to encode discriminative features by
incorporating label information into model training. Inspired
by the success of NN in text classification, we propose a
pseudo-supervised neural network approach for text clustering.
The neural network is trained in a supervised fashion
with pseudo-labels, which are provided by the cluster labels
of pre-clustering on unsupervised document representations.

Paper Details

Authors:
Peixin Chen, Wu Guo, Lirong Dai, Zhenhua Ling
Submitted On:
13 April 2018 - 3:58am
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ICASSP-Chen Peixin_v2.pdf

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[1] Peixin Chen, Wu Guo, Lirong Dai, Zhenhua Ling, "PSEUDO-SUPERVISED APPROACH FOR TEXT CLUSTERING BASED ON CONSENSUS ANALYSIS", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2640. Accessed: Dec. 13, 2018.
@article{2640-18,
url = {http://sigport.org/2640},
author = {Peixin Chen; Wu Guo; Lirong Dai; Zhenhua Ling },
publisher = {IEEE SigPort},
title = {PSEUDO-SUPERVISED APPROACH FOR TEXT CLUSTERING BASED ON CONSENSUS ANALYSIS},
year = {2018} }
TY - EJOUR
T1 - PSEUDO-SUPERVISED APPROACH FOR TEXT CLUSTERING BASED ON CONSENSUS ANALYSIS
AU - Peixin Chen; Wu Guo; Lirong Dai; Zhenhua Ling
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2640
ER -
Peixin Chen, Wu Guo, Lirong Dai, Zhenhua Ling. (2018). PSEUDO-SUPERVISED APPROACH FOR TEXT CLUSTERING BASED ON CONSENSUS ANALYSIS. IEEE SigPort. http://sigport.org/2640
Peixin Chen, Wu Guo, Lirong Dai, Zhenhua Ling, 2018. PSEUDO-SUPERVISED APPROACH FOR TEXT CLUSTERING BASED ON CONSENSUS ANALYSIS. Available at: http://sigport.org/2640.
Peixin Chen, Wu Guo, Lirong Dai, Zhenhua Ling. (2018). "PSEUDO-SUPERVISED APPROACH FOR TEXT CLUSTERING BASED ON CONSENSUS ANALYSIS." Web.
1. Peixin Chen, Wu Guo, Lirong Dai, Zhenhua Ling. PSEUDO-SUPERVISED APPROACH FOR TEXT CLUSTERING BASED ON CONSENSUS ANALYSIS [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2640

SPS Malaysia Chapter Member-Driven Initiative: Professional Knowledge Transfer Workshop 2017: Arduino Simple Programming


Winter 2017 Seasonal School Workshop, Malaysia. The Arduino Simple Programming at school was held at five different schools near Parit Raja, Batu Pahat, Johor from October-November 2017. The schools are SK Jelutong, SMK Sri gading, SK Pintas Puding, SK Bukit Kuari and SK Seri Sabak Uni. The objective of Professional Knowledge Transfer Workshop is to give an exposure to the students of primary and secondary schools on engineering and to inspire them to be engineers. This program was organized by the Institute of Electrical and Electronics Engineers (IEEE) UTHM Student Branch with cooperation of IEEE Signal Processing Society (SPS) Malaysia Chapter. The program is fully funded by IEEE Signal Processing Society (SPS) under the IEEE SPS Member-Driven Initiative Program with the amount of USD 1,400. The fund had been used to buy 30 Arduino Uno kits, purchasing stationery, and refreshments for 30 students from each school, 10 postgraduate students from UTHM IEEE student branch and 13 Academic staff from FKEE, UTHM.

Paper Details

Authors:
Dr Mohd Norzali Hj Mohd
Submitted On:
13 December 2017 - 11:05am
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Fasa3_latestEdited module.pdf

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[1] Dr Mohd Norzali Hj Mohd, "SPS Malaysia Chapter Member-Driven Initiative: Professional Knowledge Transfer Workshop 2017: Arduino Simple Programming", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2372. Accessed: Dec. 13, 2018.
@article{2372-17,
url = {http://sigport.org/2372},
author = {Dr Mohd Norzali Hj Mohd },
publisher = {IEEE SigPort},
title = {SPS Malaysia Chapter Member-Driven Initiative: Professional Knowledge Transfer Workshop 2017: Arduino Simple Programming},
year = {2017} }
TY - EJOUR
T1 - SPS Malaysia Chapter Member-Driven Initiative: Professional Knowledge Transfer Workshop 2017: Arduino Simple Programming
AU - Dr Mohd Norzali Hj Mohd
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2372
ER -
Dr Mohd Norzali Hj Mohd. (2017). SPS Malaysia Chapter Member-Driven Initiative: Professional Knowledge Transfer Workshop 2017: Arduino Simple Programming. IEEE SigPort. http://sigport.org/2372
Dr Mohd Norzali Hj Mohd, 2017. SPS Malaysia Chapter Member-Driven Initiative: Professional Knowledge Transfer Workshop 2017: Arduino Simple Programming. Available at: http://sigport.org/2372.
Dr Mohd Norzali Hj Mohd. (2017). "SPS Malaysia Chapter Member-Driven Initiative: Professional Knowledge Transfer Workshop 2017: Arduino Simple Programming." Web.
1. Dr Mohd Norzali Hj Mohd. SPS Malaysia Chapter Member-Driven Initiative: Professional Knowledge Transfer Workshop 2017: Arduino Simple Programming [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2372

Automatic Question-answering Using a Deep Similarity Neural Network


Automatic question-answering is a classical problem in natural language processing, which aims at designing systems that can automatically answer a question, in the same way as human does. In this work, we propose a deep learning based model for automatic question-answering. First the questions and answers are embedded using neural probabilistic modeling. Then a deep similarity neural network is trained to find the similarity score of a pair of answer and question. Then for each question, the best answer is found as the one with the highest similarity score.

Paper Details

Authors:
Shervin Minaee, Zhu Liu
Submitted On:
13 November 2017 - 9:10am
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GlobalSIP2017Poster-QAEngine.pdf

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[1] Shervin Minaee, Zhu Liu, "Automatic Question-answering Using a Deep Similarity Neural Network", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2335. Accessed: Dec. 13, 2018.
@article{2335-17,
url = {http://sigport.org/2335},
author = {Shervin Minaee; Zhu Liu },
publisher = {IEEE SigPort},
title = {Automatic Question-answering Using a Deep Similarity Neural Network},
year = {2017} }
TY - EJOUR
T1 - Automatic Question-answering Using a Deep Similarity Neural Network
AU - Shervin Minaee; Zhu Liu
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2335
ER -
Shervin Minaee, Zhu Liu. (2017). Automatic Question-answering Using a Deep Similarity Neural Network. IEEE SigPort. http://sigport.org/2335
Shervin Minaee, Zhu Liu, 2017. Automatic Question-answering Using a Deep Similarity Neural Network. Available at: http://sigport.org/2335.
Shervin Minaee, Zhu Liu. (2017). "Automatic Question-answering Using a Deep Similarity Neural Network." Web.
1. Shervin Minaee, Zhu Liu. Automatic Question-answering Using a Deep Similarity Neural Network [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2335

Convolutional Factor Analysis Inspired Compressvie Sensing


We solve the compressive sensing problem via convolutional factor analysis, where the convolutional dictionaries are learned in situ from the compressed measurements. An alternating direction method of multipliers (ADMM) paradigm for compressive sensing inversion based on convolutional factor analysis is developed. The proposed algorithm provides reconstructed images as well as features, which can be directly used for recognition (e:g:, classification) tasks.

Paper Details

Authors:
Yunchen Pu
Submitted On:
17 September 2017 - 11:10am
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Slides_ICIP_2017.pdf

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[1] Yunchen Pu, "Convolutional Factor Analysis Inspired Compressvie Sensing", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2216. Accessed: Dec. 13, 2018.
@article{2216-17,
url = {http://sigport.org/2216},
author = {Yunchen Pu },
publisher = {IEEE SigPort},
title = {Convolutional Factor Analysis Inspired Compressvie Sensing},
year = {2017} }
TY - EJOUR
T1 - Convolutional Factor Analysis Inspired Compressvie Sensing
AU - Yunchen Pu
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2216
ER -
Yunchen Pu. (2017). Convolutional Factor Analysis Inspired Compressvie Sensing. IEEE SigPort. http://sigport.org/2216
Yunchen Pu, 2017. Convolutional Factor Analysis Inspired Compressvie Sensing. Available at: http://sigport.org/2216.
Yunchen Pu. (2017). "Convolutional Factor Analysis Inspired Compressvie Sensing." Web.
1. Yunchen Pu. Convolutional Factor Analysis Inspired Compressvie Sensing [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2216

AMOS: An Automated Model Order Selection Algorithm for Spectral Graph Clustering


One of the longstanding problems in spectral graph clustering (SGC) is the so-called model order selection problem: automated selection of the correct number of clusters. This is equivalent to the problem of finding the number of connected components or communities in an undirected graph. In this paper, we propose AMOS, an automated model order selection algorithm for SGC.

Paper Details

Authors:
Pin-Yu Chen, Thibaut Gensollen, Alfred Hero
Submitted On:
5 March 2017 - 11:06pm
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ICASSP_AMOS_2017.pdf

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[1] Pin-Yu Chen, Thibaut Gensollen, Alfred Hero, "AMOS: An Automated Model Order Selection Algorithm for Spectral Graph Clustering", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1643. Accessed: Dec. 13, 2018.
@article{1643-17,
url = {http://sigport.org/1643},
author = {Pin-Yu Chen; Thibaut Gensollen; Alfred Hero },
publisher = {IEEE SigPort},
title = {AMOS: An Automated Model Order Selection Algorithm for Spectral Graph Clustering},
year = {2017} }
TY - EJOUR
T1 - AMOS: An Automated Model Order Selection Algorithm for Spectral Graph Clustering
AU - Pin-Yu Chen; Thibaut Gensollen; Alfred Hero
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1643
ER -
Pin-Yu Chen, Thibaut Gensollen, Alfred Hero. (2017). AMOS: An Automated Model Order Selection Algorithm for Spectral Graph Clustering. IEEE SigPort. http://sigport.org/1643
Pin-Yu Chen, Thibaut Gensollen, Alfred Hero, 2017. AMOS: An Automated Model Order Selection Algorithm for Spectral Graph Clustering. Available at: http://sigport.org/1643.
Pin-Yu Chen, Thibaut Gensollen, Alfred Hero. (2017). "AMOS: An Automated Model Order Selection Algorithm for Spectral Graph Clustering." Web.
1. Pin-Yu Chen, Thibaut Gensollen, Alfred Hero. AMOS: An Automated Model Order Selection Algorithm for Spectral Graph Clustering [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1643

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