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Pattern recognition and classification (MLR-PATT)

Serious Games and ML for Detecting MCI


Our work has focused on detecting Mild Cognitive Impairment (MCI) by developing Serious Games (SG) on mobile devices, distinct from games marketed as 'brain training' which claim to maintain mental acuity. One game, WarCAT, captures players' moves during the game to infer processes of strategy recognition, learning, and memory. The purpose of our game is to use the generated game-play data combined with machine learning (ML) to help detect MCI. MCI is difficult to detect for several reasons.

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Authors:
Mahmood Aljumaili, Robert D McLeod, Marcia Friesen
Submitted On:
12 November 2019 - 12:45am
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GlobalSIP 2019 - Serious Games and ML for Detecting MCI.pdf

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[1] Mahmood Aljumaili, Robert D McLeod, Marcia Friesen, "Serious Games and ML for Detecting MCI", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4949. Accessed: Dec. 12, 2019.
@article{4949-19,
url = {http://sigport.org/4949},
author = {Mahmood Aljumaili; Robert D McLeod; Marcia Friesen },
publisher = {IEEE SigPort},
title = {Serious Games and ML for Detecting MCI},
year = {2019} }
TY - EJOUR
T1 - Serious Games and ML for Detecting MCI
AU - Mahmood Aljumaili; Robert D McLeod; Marcia Friesen
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4949
ER -
Mahmood Aljumaili, Robert D McLeod, Marcia Friesen. (2019). Serious Games and ML for Detecting MCI. IEEE SigPort. http://sigport.org/4949
Mahmood Aljumaili, Robert D McLeod, Marcia Friesen, 2019. Serious Games and ML for Detecting MCI. Available at: http://sigport.org/4949.
Mahmood Aljumaili, Robert D McLeod, Marcia Friesen. (2019). "Serious Games and ML for Detecting MCI." Web.
1. Mahmood Aljumaili, Robert D McLeod, Marcia Friesen. Serious Games and ML for Detecting MCI [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4949

Combining TD-IDF with symptom features to differentiate between lymphoma and tuberculosis case reports

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Authors:
Moanda Diana Pholo, Yskandar Hamam, AbdelBaset Khalaf, Chunling Du
Submitted On:
9 November 2019 - 3:41pm
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globalsip-2019-Slide-Diana-v2.pdf

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[1] Moanda Diana Pholo, Yskandar Hamam, AbdelBaset Khalaf, Chunling Du, "Combining TD-IDF with symptom features to differentiate between lymphoma and tuberculosis case reports", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4941. Accessed: Dec. 12, 2019.
@article{4941-19,
url = {http://sigport.org/4941},
author = {Moanda Diana Pholo; Yskandar Hamam; AbdelBaset Khalaf; Chunling Du },
publisher = {IEEE SigPort},
title = {Combining TD-IDF with symptom features to differentiate between lymphoma and tuberculosis case reports},
year = {2019} }
TY - EJOUR
T1 - Combining TD-IDF with symptom features to differentiate between lymphoma and tuberculosis case reports
AU - Moanda Diana Pholo; Yskandar Hamam; AbdelBaset Khalaf; Chunling Du
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4941
ER -
Moanda Diana Pholo, Yskandar Hamam, AbdelBaset Khalaf, Chunling Du. (2019). Combining TD-IDF with symptom features to differentiate between lymphoma and tuberculosis case reports. IEEE SigPort. http://sigport.org/4941
Moanda Diana Pholo, Yskandar Hamam, AbdelBaset Khalaf, Chunling Du, 2019. Combining TD-IDF with symptom features to differentiate between lymphoma and tuberculosis case reports. Available at: http://sigport.org/4941.
Moanda Diana Pholo, Yskandar Hamam, AbdelBaset Khalaf, Chunling Du. (2019). "Combining TD-IDF with symptom features to differentiate between lymphoma and tuberculosis case reports." Web.
1. Moanda Diana Pholo, Yskandar Hamam, AbdelBaset Khalaf, Chunling Du. Combining TD-IDF with symptom features to differentiate between lymphoma and tuberculosis case reports [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4941

SURFACE EMG-BASED HAND GESTURE RECOGNITION VIA DILATED CONVOLUTIONAL NEURAL NETWORKS


The recent evolution of Artificial Intelligence (AI) and deep learning models coupled with advancements of assistive robotic systems have shown great potential in significantly improving myoelectric control of prosthetic devices. In this regard, the paper proposes a novel deep-learning-based architecture for processing surface Electromyography (sEMG) signals to classify and recognize upper-limb hand gestures via incorporation of dilated causal convolutions.

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Authors:
Elahe Rahimian, Soheil Zabihi, Seyed Farokh Atashzar, Amir Asif, Arash Mohammadi$
Submitted On:
7 November 2019 - 10:35am
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Hand Gesture Recognition via Dilated Causal Convolutions

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[1] Elahe Rahimian, Soheil Zabihi, Seyed Farokh Atashzar, Amir Asif, Arash Mohammadi$, "SURFACE EMG-BASED HAND GESTURE RECOGNITION VIA DILATED CONVOLUTIONAL NEURAL NETWORKS", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4924. Accessed: Dec. 12, 2019.
@article{4924-19,
url = {http://sigport.org/4924},
author = {Elahe Rahimian; Soheil Zabihi; Seyed Farokh Atashzar; Amir Asif; Arash Mohammadi$ },
publisher = {IEEE SigPort},
title = {SURFACE EMG-BASED HAND GESTURE RECOGNITION VIA DILATED CONVOLUTIONAL NEURAL NETWORKS},
year = {2019} }
TY - EJOUR
T1 - SURFACE EMG-BASED HAND GESTURE RECOGNITION VIA DILATED CONVOLUTIONAL NEURAL NETWORKS
AU - Elahe Rahimian; Soheil Zabihi; Seyed Farokh Atashzar; Amir Asif; Arash Mohammadi$
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4924
ER -
Elahe Rahimian, Soheil Zabihi, Seyed Farokh Atashzar, Amir Asif, Arash Mohammadi$. (2019). SURFACE EMG-BASED HAND GESTURE RECOGNITION VIA DILATED CONVOLUTIONAL NEURAL NETWORKS. IEEE SigPort. http://sigport.org/4924
Elahe Rahimian, Soheil Zabihi, Seyed Farokh Atashzar, Amir Asif, Arash Mohammadi$, 2019. SURFACE EMG-BASED HAND GESTURE RECOGNITION VIA DILATED CONVOLUTIONAL NEURAL NETWORKS. Available at: http://sigport.org/4924.
Elahe Rahimian, Soheil Zabihi, Seyed Farokh Atashzar, Amir Asif, Arash Mohammadi$. (2019). "SURFACE EMG-BASED HAND GESTURE RECOGNITION VIA DILATED CONVOLUTIONAL NEURAL NETWORKS." Web.
1. Elahe Rahimian, Soheil Zabihi, Seyed Farokh Atashzar, Amir Asif, Arash Mohammadi$. SURFACE EMG-BASED HAND GESTURE RECOGNITION VIA DILATED CONVOLUTIONAL NEURAL NETWORKS [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4924

VayuAnukulani: Adaptive memory networks for air pollution forecasting


Air pollution is the leading environmental health hazard globally due to various sources which include factory emissions, car exhaust and cooking stoves. As a precautionary measure, air pollution forecast serves as the basis for taking effective pollution control measures, and accurate air pollution forecasting has become an important task. In this paper, we forecast fine-grained ambient air quality information for 5 prominent locations in Delhi based on the historical and realtime ambient air quality and meteorological data reported by Central Pollution Control board.

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Authors:
Divyam Madaan, Radhika Dua, Prerana Mukherjee, Brejesh Lall
Submitted On:
13 November 2019 - 9:53am
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VayuAnukulani_globalSip.pdf

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[1] Divyam Madaan, Radhika Dua, Prerana Mukherjee, Brejesh Lall, "VayuAnukulani: Adaptive memory networks for air pollution forecasting", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4917. Accessed: Dec. 12, 2019.
@article{4917-19,
url = {http://sigport.org/4917},
author = {Divyam Madaan; Radhika Dua; Prerana Mukherjee; Brejesh Lall },
publisher = {IEEE SigPort},
title = {VayuAnukulani: Adaptive memory networks for air pollution forecasting},
year = {2019} }
TY - EJOUR
T1 - VayuAnukulani: Adaptive memory networks for air pollution forecasting
AU - Divyam Madaan; Radhika Dua; Prerana Mukherjee; Brejesh Lall
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4917
ER -
Divyam Madaan, Radhika Dua, Prerana Mukherjee, Brejesh Lall. (2019). VayuAnukulani: Adaptive memory networks for air pollution forecasting. IEEE SigPort. http://sigport.org/4917
Divyam Madaan, Radhika Dua, Prerana Mukherjee, Brejesh Lall, 2019. VayuAnukulani: Adaptive memory networks for air pollution forecasting. Available at: http://sigport.org/4917.
Divyam Madaan, Radhika Dua, Prerana Mukherjee, Brejesh Lall. (2019). "VayuAnukulani: Adaptive memory networks for air pollution forecasting." Web.
1. Divyam Madaan, Radhika Dua, Prerana Mukherjee, Brejesh Lall. VayuAnukulani: Adaptive memory networks for air pollution forecasting [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4917

Stochastic Tucker-Decomposed Recurrent Neural Networks for Forecasting


The growing edge computing paradigm, notably the vision of the internet-of-things (IoT), calls for a new epitome of lightweight algorithms. Currently, the most successful models that learn from temporal data, which is prevalent in IoT applications, stem from the field of deep learning. However, these models evince extended training times and heavy resource requirements, prohibiting training in constrained environments. To address these concerns, we employ deep stochastic neural networks from the reservoir computing paradigm.

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Authors:
Zachariah Carmichael, Dhireesha Kudithipudi
Submitted On:
29 October 2019 - 2:12pm
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Presentation Slides

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[1] Zachariah Carmichael, Dhireesha Kudithipudi, "Stochastic Tucker-Decomposed Recurrent Neural Networks for Forecasting", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4899. Accessed: Dec. 12, 2019.
@article{4899-19,
url = {http://sigport.org/4899},
author = {Zachariah Carmichael; Dhireesha Kudithipudi },
publisher = {IEEE SigPort},
title = {Stochastic Tucker-Decomposed Recurrent Neural Networks for Forecasting},
year = {2019} }
TY - EJOUR
T1 - Stochastic Tucker-Decomposed Recurrent Neural Networks for Forecasting
AU - Zachariah Carmichael; Dhireesha Kudithipudi
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4899
ER -
Zachariah Carmichael, Dhireesha Kudithipudi. (2019). Stochastic Tucker-Decomposed Recurrent Neural Networks for Forecasting. IEEE SigPort. http://sigport.org/4899
Zachariah Carmichael, Dhireesha Kudithipudi, 2019. Stochastic Tucker-Decomposed Recurrent Neural Networks for Forecasting. Available at: http://sigport.org/4899.
Zachariah Carmichael, Dhireesha Kudithipudi. (2019). "Stochastic Tucker-Decomposed Recurrent Neural Networks for Forecasting." Web.
1. Zachariah Carmichael, Dhireesha Kudithipudi. Stochastic Tucker-Decomposed Recurrent Neural Networks for Forecasting [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4899

EEG SIGNAL DIMENSIONALITY REDUCTION AND CLASSIFICATION USING TENSOR DECOMPOSITION AND DEEP CONVOLUTIONAL NEURAL NETWORKS

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17 October 2019 - 3:25pm
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MLSP-poster.pdf

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[1] , "EEG SIGNAL DIMENSIONALITY REDUCTION AND CLASSIFICATION USING TENSOR DECOMPOSITION AND DEEP CONVOLUTIONAL NEURAL NETWORKS", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4878. Accessed: Dec. 12, 2019.
@article{4878-19,
url = {http://sigport.org/4878},
author = { },
publisher = {IEEE SigPort},
title = {EEG SIGNAL DIMENSIONALITY REDUCTION AND CLASSIFICATION USING TENSOR DECOMPOSITION AND DEEP CONVOLUTIONAL NEURAL NETWORKS},
year = {2019} }
TY - EJOUR
T1 - EEG SIGNAL DIMENSIONALITY REDUCTION AND CLASSIFICATION USING TENSOR DECOMPOSITION AND DEEP CONVOLUTIONAL NEURAL NETWORKS
AU -
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4878
ER -
. (2019). EEG SIGNAL DIMENSIONALITY REDUCTION AND CLASSIFICATION USING TENSOR DECOMPOSITION AND DEEP CONVOLUTIONAL NEURAL NETWORKS. IEEE SigPort. http://sigport.org/4878
, 2019. EEG SIGNAL DIMENSIONALITY REDUCTION AND CLASSIFICATION USING TENSOR DECOMPOSITION AND DEEP CONVOLUTIONAL NEURAL NETWORKS. Available at: http://sigport.org/4878.
. (2019). "EEG SIGNAL DIMENSIONALITY REDUCTION AND CLASSIFICATION USING TENSOR DECOMPOSITION AND DEEP CONVOLUTIONAL NEURAL NETWORKS." Web.
1. . EEG SIGNAL DIMENSIONALITY REDUCTION AND CLASSIFICATION USING TENSOR DECOMPOSITION AND DEEP CONVOLUTIONAL NEURAL NETWORKS [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4878

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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Mouath_Aouayeb_ieee_mlsp2019.pdf

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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: Dec. 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

Robust importance-weighted cross-validation under sample selection bias


Cross-validation under sample selection bias can, in principle, be done by importance-weighting the empirical risk. However, the importance-weighted risk estimator produces sub-optimal hyperparameter estimates in problem settings where large weights arise with high probability. We study its sampling variance as a function of the training data distribution and introduce a control variate to increase its robustness to problematically large weights.

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Authors:
Wouter M Kouw, Jesse H Krijthe, Marco Loog
Submitted On:
11 October 2019 - 4:48pm
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PDF of poster

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[1] Wouter M Kouw, Jesse H Krijthe, Marco Loog, "Robust importance-weighted cross-validation under sample selection bias", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4857. Accessed: Dec. 12, 2019.
@article{4857-19,
url = {http://sigport.org/4857},
author = {Wouter M Kouw; Jesse H Krijthe; Marco Loog },
publisher = {IEEE SigPort},
title = {Robust importance-weighted cross-validation under sample selection bias},
year = {2019} }
TY - EJOUR
T1 - Robust importance-weighted cross-validation under sample selection bias
AU - Wouter M Kouw; Jesse H Krijthe; Marco Loog
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4857
ER -
Wouter M Kouw, Jesse H Krijthe, Marco Loog. (2019). Robust importance-weighted cross-validation under sample selection bias. IEEE SigPort. http://sigport.org/4857
Wouter M Kouw, Jesse H Krijthe, Marco Loog, 2019. Robust importance-weighted cross-validation under sample selection bias. Available at: http://sigport.org/4857.
Wouter M Kouw, Jesse H Krijthe, Marco Loog. (2019). "Robust importance-weighted cross-validation under sample selection bias." Web.
1. Wouter M Kouw, Jesse H Krijthe, Marco Loog. Robust importance-weighted cross-validation under sample selection bias [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4857

A Benchmark Study of Backdoor Data Poisoning Defenses for Deep Neural Network Classifiers and A Novel Defense


While data poisoning attacks on classifiers were originally proposed to degrade a classifier's usability, there has been strong recent interest in backdoor data poisoning attacks, where the classifier learns to classify to a target class whenever a backdoor pattern ({\it e.g.}, a watermark or innocuous pattern) is added to an example from some class other than the target class.

Paper Details

Authors:
George Kesidis
Submitted On:
11 October 2019 - 1:35pm
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MLSP19 backdoor poster 1.1.pdf

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[1] George Kesidis, "A Benchmark Study of Backdoor Data Poisoning Defenses for Deep Neural Network Classifiers and A Novel Defense", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4855. Accessed: Dec. 12, 2019.
@article{4855-19,
url = {http://sigport.org/4855},
author = {George Kesidis },
publisher = {IEEE SigPort},
title = {A Benchmark Study of Backdoor Data Poisoning Defenses for Deep Neural Network Classifiers and A Novel Defense},
year = {2019} }
TY - EJOUR
T1 - A Benchmark Study of Backdoor Data Poisoning Defenses for Deep Neural Network Classifiers and A Novel Defense
AU - George Kesidis
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4855
ER -
George Kesidis. (2019). A Benchmark Study of Backdoor Data Poisoning Defenses for Deep Neural Network Classifiers and A Novel Defense. IEEE SigPort. http://sigport.org/4855
George Kesidis, 2019. A Benchmark Study of Backdoor Data Poisoning Defenses for Deep Neural Network Classifiers and A Novel Defense. Available at: http://sigport.org/4855.
George Kesidis. (2019). "A Benchmark Study of Backdoor Data Poisoning Defenses for Deep Neural Network Classifiers and A Novel Defense." Web.
1. George Kesidis. A Benchmark Study of Backdoor Data Poisoning Defenses for Deep Neural Network Classifiers and A Novel Defense [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4855

3D Facial Expression Recognition Based on Multi-View and Prior Knowledge Fusion

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Submitted On:
27 September 2019 - 9:02am
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MMSP2019.pdf

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[1] , "3D Facial Expression Recognition Based on Multi-View and Prior Knowledge Fusion", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4846. Accessed: Dec. 12, 2019.
@article{4846-19,
url = {http://sigport.org/4846},
author = { },
publisher = {IEEE SigPort},
title = {3D Facial Expression Recognition Based on Multi-View and Prior Knowledge Fusion},
year = {2019} }
TY - EJOUR
T1 - 3D Facial Expression Recognition Based on Multi-View and Prior Knowledge Fusion
AU -
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4846
ER -
. (2019). 3D Facial Expression Recognition Based on Multi-View and Prior Knowledge Fusion. IEEE SigPort. http://sigport.org/4846
, 2019. 3D Facial Expression Recognition Based on Multi-View and Prior Knowledge Fusion. Available at: http://sigport.org/4846.
. (2019). "3D Facial Expression Recognition Based on Multi-View and Prior Knowledge Fusion." Web.
1. . 3D Facial Expression Recognition Based on Multi-View and Prior Knowledge Fusion [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4846

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