Gezondheidszorg en toekomst, kennis delen, 18 juni 2015Robert Muijsers
Bijeenkomst Kennis delen in de gezondheidszorg,
Schouwburg ‘Orpheus’ Apeldoorn, 18 juni 2015
Door: Maverick advocaten, Blokvoort advocaten, Infense advocaten, Velink en De Die Advocaten, Drijber advocaten en Partners
Gezondheidszorg en toekomst, kennis delen, 18 juni 2015Robert Muijsers
Bijeenkomst Kennis delen in de gezondheidszorg,
Schouwburg ‘Orpheus’ Apeldoorn, 18 juni 2015
Door: Maverick advocaten, Blokvoort advocaten, Infense advocaten, Velink en De Die Advocaten, Drijber advocaten en Partners
(BDT207) Real-Time Analytics In Service Of Self-Healing EcosystemsAmazon Web Services
Netflix strives to provide an amazing experience to each member. To accomplish this, Netflix needs to maintain very high availability across our systems. However, at a certain scale, humans can no longer scale their ability to monitor the status of all systems, making it critical for Netflix to build tools and platforms that can automatically monitor their production environments and make intelligent real-time operational decisions to remedy the problems they identify. In this session, we discuss how Netflix uses data mining and machine learning techniques to automate decisions in real-time with the goal of supporting operational availability, reliability, and consistency. We review how we got to the current states, the lessons we learned, and the future of real-time analytics at Netflix. While Netflix's scale is larger than most other companies, we believe the approaches and technologies we discuss are highly relevant to other production environments, and audience members should come away with actionable ideas that are implementable in, and benefit, most other environments.
Contour-Constrained Superpixels for Image and Video ProcessingNAVER Engineering
발표자: 이세호(고려대 박사과정)
발표일: 2017.8.
개요:
슈퍼픽셀 알고리즘은 입력 영상을 다수의 의미 있는 영역으로 과분할 하는 기법이다. 입력 영상을 픽셀 단위로 표현할 때와 비교하여, 슈퍼픽셀 단위의 표현은 입력 영상의 단위의 수를 크게 줄이는 장점이 있다. 각 슈퍼픽셀은 객체의 윤곽선을 넘어서는 영역을 포함하지 않는 동시에, 단일 객체만을 담아야 한다. 본 발표에서는 객체의 윤곽선 정보를 고려한 윤곽선 제약 슈퍼픽셀 기법(contour-constrained superpixel algorithm)을 제안한다.
Writing Machine Learning code is now possible with .NET native library ML.NET that has recently reached 1.0 milestole. Let's look what we can do with this lib, which scenarios can be handled.
LEXT OLS4100 3D Measuring Laser MicroscopeOlympus IMS
LEXT OLS4100 3D Measuring Laser Microscope details: http://bit.ly/16J4CMF
The LEXT OLS4100 is a Laser Scanning Microscope to perform non-contact 3D observations and measurements of surface features at 10 nanometer resolutions. It also features a fast image acquisition and a high-resolution image over a wider area.
Contact us: http://bit.ly/1rDmq94
Sign up for our Newsletter: http://bit.ly/1j5FOTy
Improving Hardware Efficiency for DNN ApplicationsChester Chen
Speaker: Dr. Hai (Helen) Li is the Clare Boothe Luce Associate Professor of Electrical and Computer Engineering and Co-director of the Duke Center for Evolutionary Intelligence at Duke University
In this talk, I will introduce a few recent research spotlights by the Duke Center for Evolutionary Intelligence. The talk will start with the structured sparsity learning (SSL) method which attempts to learn a compact structure from a bigger DNN to reduce computation cost. It generates a regularized structure with high execution efficiency. Our experiments on CPU, GPU, and FPGA platforms show on average 3~5 times speedup of convolutional layer computation of AlexNet. Then, the implementation and acceleration of DNN applications on mobile computing systems will be introduced. MoDNN is a local distributed system which partitions DNN models onto several mobile devices to accelerate computations. ApesNet is an efficient pixel-wise segmentation network, which understands road scenes in real-time, and has achieved promising accuracy. Our prospects on the adoption of emerging technology will also be given at the end of this talk, offering the audiences an alternative thinking about the future evolution and revolution of modern computing systems.
Quantum machine learning is one of the promising application domains of quantum computing, which is expected to improve and accelerate the most resource-intensive machine learning calculations. This talk will explain how we implement quantum machine learning algorithms, what are the limits and challenges, and how these challenges can be addressed.
(BDT207) Real-Time Analytics In Service Of Self-Healing EcosystemsAmazon Web Services
Netflix strives to provide an amazing experience to each member. To accomplish this, Netflix needs to maintain very high availability across our systems. However, at a certain scale, humans can no longer scale their ability to monitor the status of all systems, making it critical for Netflix to build tools and platforms that can automatically monitor their production environments and make intelligent real-time operational decisions to remedy the problems they identify. In this session, we discuss how Netflix uses data mining and machine learning techniques to automate decisions in real-time with the goal of supporting operational availability, reliability, and consistency. We review how we got to the current states, the lessons we learned, and the future of real-time analytics at Netflix. While Netflix's scale is larger than most other companies, we believe the approaches and technologies we discuss are highly relevant to other production environments, and audience members should come away with actionable ideas that are implementable in, and benefit, most other environments.
Contour-Constrained Superpixels for Image and Video ProcessingNAVER Engineering
발표자: 이세호(고려대 박사과정)
발표일: 2017.8.
개요:
슈퍼픽셀 알고리즘은 입력 영상을 다수의 의미 있는 영역으로 과분할 하는 기법이다. 입력 영상을 픽셀 단위로 표현할 때와 비교하여, 슈퍼픽셀 단위의 표현은 입력 영상의 단위의 수를 크게 줄이는 장점이 있다. 각 슈퍼픽셀은 객체의 윤곽선을 넘어서는 영역을 포함하지 않는 동시에, 단일 객체만을 담아야 한다. 본 발표에서는 객체의 윤곽선 정보를 고려한 윤곽선 제약 슈퍼픽셀 기법(contour-constrained superpixel algorithm)을 제안한다.
Writing Machine Learning code is now possible with .NET native library ML.NET that has recently reached 1.0 milestole. Let's look what we can do with this lib, which scenarios can be handled.
LEXT OLS4100 3D Measuring Laser MicroscopeOlympus IMS
LEXT OLS4100 3D Measuring Laser Microscope details: http://bit.ly/16J4CMF
The LEXT OLS4100 is a Laser Scanning Microscope to perform non-contact 3D observations and measurements of surface features at 10 nanometer resolutions. It also features a fast image acquisition and a high-resolution image over a wider area.
Contact us: http://bit.ly/1rDmq94
Sign up for our Newsletter: http://bit.ly/1j5FOTy
Improving Hardware Efficiency for DNN ApplicationsChester Chen
Speaker: Dr. Hai (Helen) Li is the Clare Boothe Luce Associate Professor of Electrical and Computer Engineering and Co-director of the Duke Center for Evolutionary Intelligence at Duke University
In this talk, I will introduce a few recent research spotlights by the Duke Center for Evolutionary Intelligence. The talk will start with the structured sparsity learning (SSL) method which attempts to learn a compact structure from a bigger DNN to reduce computation cost. It generates a regularized structure with high execution efficiency. Our experiments on CPU, GPU, and FPGA platforms show on average 3~5 times speedup of convolutional layer computation of AlexNet. Then, the implementation and acceleration of DNN applications on mobile computing systems will be introduced. MoDNN is a local distributed system which partitions DNN models onto several mobile devices to accelerate computations. ApesNet is an efficient pixel-wise segmentation network, which understands road scenes in real-time, and has achieved promising accuracy. Our prospects on the adoption of emerging technology will also be given at the end of this talk, offering the audiences an alternative thinking about the future evolution and revolution of modern computing systems.
Quantum machine learning is one of the promising application domains of quantum computing, which is expected to improve and accelerate the most resource-intensive machine learning calculations. This talk will explain how we implement quantum machine learning algorithms, what are the limits and challenges, and how these challenges can be addressed.
Temporal Superpixels Based on Proximity-Weighted Patch MatchingNAVER Engineering
발표자: 이세호(고려대 박사과정)
발표일: 2018.4.
슈퍼픽셀 알고리즘은 입력 영상을 다수의 의미 있는 영역으로 과분할하는 기법이다. 입력 영상을 픽셀 단위로 표현할 때와 비교하여, 슈퍼픽셀 단위의 표현은 입력 영상의 단위의 수를 크게 줄이는 장점이 있어, 여러 컴퓨터 비전 기법에 전처리로 이용된다. 또한 슈퍼픽셀 알고리즘을 동영상으로 확장한 동영상 슈퍼픽셀 (temporal superpixel) 알고리즘은 동영상 기반의 컴퓨터 비전 기법에 적용될 수 있다. 기존의 동영상 슈퍼픽셀 기법은 시간적 유사성을 유지하기 위하여 움직임 정보를 이용하는데, 움직임 정보의 추출에는 많은 계산 복잡도가 요구된다. 따라서 이를 보완하기 위해, 본 연구에서는 근접성 가중치 패치 정합 (proximity-weighted patch matching) 기반의 동영상 슈퍼픽셀 기법을 제안한다.
Non-Uniform Random Feature Selection and Kernel Density Scoring With SVM Base...Sathishkumar Samiappan
Traditional statistical classification approaches often
fail to yield adequate results with Hyperspectral imagery (HSI) because
of the high dimensional nature of the data, multimodal class
distribution and limited ground truth samples for training. Over
the last decade, Support VectorMachines (SVMs) andMulti-Classifier
Systems (MCS) have become popular tools for HSI analysis.
Random Feature Selection (RFS) forMCS is a popular approach to
produce higher classification accuracies. In this study, we present a
Non-Uniform Random Feature Selection (NU-RFS) within a MCS
framework using SVMas the base classifier.We propose a method
to fuse the output of individual classifiers using scores derived from
kernel density estimation. This study demonstrates the improvement
in classification accuracies by comparing the proposed approach
to conventional analysis algorithms and by assessing the
sensitivity of the proposed approach to the number of training samples.
These results are compared with that of uniform RFS and regular
SVM classifiers. We demonstrate the superiority of Non-Uniform
based RFS system with respect to overall accuracy, user accuracies,
producer accuracies and sensitivity to number of training
samples.
4. Activity Inference Process
Raw data
Discretization
• MDLP
• LGD
Classifier
construction
• Decision Tree
• Naïve Bayesian
• K-Nearest Neighbor
• SVM
4
5. Activity Inference Process
Raw data
Discretization
•MDLP
•LGD
Feature-Value
selection
•ONEFVAS
•GIFVAS
•CBFVAS
Classifier
construction
•Decision Tree
•Naïve Bayesian
•K-Nearest Neighbor
•SVM
5
6. Feature-Value Selection
• What is feature-value?
• A range of sensor reading
• e.g. Accelerometer magnitude high, GPS at home, light bright
• Why using feature-value?
• Sensor reading relation with activity: relevant or not
• e.g. Accelerometer magnitude value reading
Accelerometer: LowAccelerometer: Low Accelerometer: high 6
9. Iteration-based (GIFVAS)
• Looping on the threshold, selecting feature-values iteratively.
• Evaluating accuracy for each iteration
• If accuracy reduction is big
• Then cancel the selection on this iteration, tag any feature-value
as special
• Any special feature-value will be remained until the last iteration
• Special feature-value
• Frequent but confusing
• Pure but infrequent
0.00%
50.00%
100.00%
1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0
Entropy Threshold
Accuracy
0.00%
50.00%
100.00%
1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0
Entropy Threshold
Feature-Value Pairs
9
10. Correlation-based
• Using Pearson correlation in the feature level
• Using entropy in the feature-value level
• For each feature-value pair
• Generate correlated feature-value
• Sort the correlated feature-value using entropy
• Pick only the best-N feature-value from it
• Discard other feature-value
80.00%
85.00%
90.00%
95.00%
100.00%
1 3 5 7 9 11 13 15 17
Best-N feature-value remained
Accuracy
Correlation
Original
350.00 KB
550.00 KB
750.00 KB
950.00 KB
1150.00 KB
1 3 5 7 9 11 13 15 17
Best-N feature-value remained
Model Size
Correlation
Original
10
11. Experiments
• Environments:
• Intel Quad Core 2.66GHz
• RAM 8 GB
• Java 7
• Weka 3.6.11 (all default parameter)
• Datasets:
• Collect from 11 participants
• At least 2 different activities, up to 6 activities
• Average 3 weeks, maximum 2 months
• Classifier Algorithm:
• Naïve Bayesian
• Decision Tree (J48)
• SVM (SMO)
• k-Nearest Neigbor (kNN) 11
12. Experiments (Model Size)
• Feature-value selection is not effective on Naïve Bayesian
• In general, feature-value selection works best on decision tree
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
80.00%
90.00%
100.00%
Original ONEFVAS GIFVAS CBFVAS
Model Size (LGD)
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
80.00%
90.00%
100.00%
Original ONEFVAS GIFVAS CBFVAS
Model Size (MDLP)
Naïve Bayes
Decision Tree
kNN
SVM
12
15. Conclusions
• Proposed feature-value selection for reducing model size
• ONEFVAS – Using entropy threshold
• GIFVAS – Using iteration on entropy threshold
• CBFVAS – Using correlation and entropy
• Proposed method is able to reduce model size while
maintaining accuracy performance
• Performance varies on discretization and classification algorithms
• Decision Tree gets the most benefit
15
16. Thank you
On Selecting Feature-Value Pairs on Smart Phones for Activity Inferences
Presented by: Gunarto Sindoro Njoo
GUNARTO.NCTU@GMAIL.COM
16
Editor's Notes
[Quick Explanation]
Motivation on activity inference
Helping on giving service by learning the context of the user
[1] Silent, Phone/Message Filtering
[2] Navigations, reading message out
[3] Tips, Review, Recommendations
[Quick Explanation]
Motivation on reducing the storage size for activity classification
Comparing between Computer, Smart phone, and Sensor Hub – Power Consumption
Computer : Hundreds Watts
Smart Phones: Several Watts
Sensor Hubs : mW
Activity inference process in general (Using Supervised learning)
Why we need discretization?
Because some of classification algorithms need interval so that it can work well,
e.g. decision tree, rule based.
In here we consider 2 supervised discretization methods,
using information theory (GINI for LGD and Information Gain for MDLP)
Feature-Value selection is inserted in the middle of discretization and classifier building
We introduce 3 methods to do feature-value selection.
Before going further, we need to explain what is feature value first.
Feature-Value selection’s goal is: reduce model size of classifier because some of the intervals have less meaning.
Why not feature selection ?
Because number of sensors in smart phone are limited and removing one will reduce the accuracy greatly.
We could also address that this approach could be a complement to feature-selection, and could do well in fewer features.
We could select the feature-value using threshold.
By doing so, we could reduce the number of feature-value well.
The problem is how we set the entropy threshold.
Doing iteration in searching for the best threshold.
Even though there are some “special feature-value” added back, the number of feature-value pairs still reduced a lot.
The problem here is that selection process is slow.
By using a big value for N, then the model size is bigger too.
Feature-value are grouped by their togetherness in the datasets:
If feature are correlated
If feature-value are together in the datasets.
Original is “Without feature-value selection”
ONEFVAS is using entropy threshold 0.
LGD and MDLP are discretization methods.
Naïve Bayesian uses matrix to represent the classification model, so the size is not reduced. Only the statistical attributes (mean, stdev, var) are changed.
So on the following slides, we remove Naïve Bayesian and focus on the rest.
On term of model size, ONEFVAS in Decision Tree is the best, but CBFVAS is more stable on most case.
SVM on MDLP couldn’t be run because of some limitation in weka, due to huge number of intervals generated by MDLP discretization.
Because of weka’s limitation on number of intervals, SVM can’t be run.
Based on those graphs, we can see that Decision Tree gets the most benefit (Low model size and Small accuracy reduction)