This talk introduces a main aspect in any machine learning application, i.e., the necessity of validating a machine learning method after it has been learned from training data.
Visual Impression Localization of Autonomous Robots_#CASE2015Soma Boubou
This paper proposes a novel localization approach based on visual impressions. We define a visual impression as the representation of a HSV color distribution of a place. The representation uses clustering feature (CF) tree to manage the color distribution and we propose to weight each CF entry to indicate its importance. The method compares the navigating tree, which is created by the robot from its observations, with the available reference trees of the environment. In addition, we propose a new similarity measure to compare two CF trees which represent the visual impressions of the corresponding two places. The method is tested on two data sets collected in different environments. The results of the experiments show the effectiveness of the proposed method.
This talk introduces a main aspect in any machine learning application, i.e., the necessity of validating a machine learning method after it has been learned from training data.
Visual Impression Localization of Autonomous Robots_#CASE2015Soma Boubou
This paper proposes a novel localization approach based on visual impressions. We define a visual impression as the representation of a HSV color distribution of a place. The representation uses clustering feature (CF) tree to manage the color distribution and we propose to weight each CF entry to indicate its importance. The method compares the navigating tree, which is created by the robot from its observations, with the available reference trees of the environment. In addition, we propose a new similarity measure to compare two CF trees which represent the visual impressions of the corresponding two places. The method is tested on two data sets collected in different environments. The results of the experiments show the effectiveness of the proposed method.
Covering important topics of Classical Machine Learning in 16 hours, in preparation for the following 10 weeks of Deep Learning courses at Taiwan AI academy from 2018/02-2018/05. Topics include regression (linear, polynomial, gaussian and sigmoid basis functions), dimension reduction (PCA, LDA, ISOMAP), clustering (K-means, GMM, Mean-Shift, DBSCAN, Spectral Clustering), classification (Naive Bayes, Logistic Regression, SVM, kNN, Decision Tree, Classifier Ensembles, Bagging, Boosting, Adaboost) and Semi-Supervised learning techniques. Emphasis on sampling, probability, curse of dimensionality, decision theory and classifier generalizability.
Ee184405 statistika dan stokastik statistik deskriptif 2 numerikyusufbf
Statistika adalah suatu bidang ilmu yang mempelajari cara-cara mengumpulkan data untuk selanjutnya dapat dideskripsikan dan diolah, kemudian melakukan induksi/inferensi dalam rangka membuat kesimpulan, agar dapat ditentukan keputusan yang akan diambil berdasarkan data yang dimiliki.
DATA =============> PROSES STATISTIK ===========> INFORMASI
Statistik Deskriptif adalah suatu cara menggambarkan persoalan yang berdasarkan data yang dimiliki yakni dengan cara menata data tersebut sedemikian rupa agar karakteristik data dapat dipahami dengan mudah sehingga berguna untuk keperluan selanjutnya.
A pre conference workshop on Machine Learning was organized as a part of #doppa17, DevOps++ Global Summit 2017. The workshop was conducted by Dr. Vivek Vijay and Dr. Sandeep Yadav. All the copyrights are reserved with the author.
Covering important topics of Classical Machine Learning in 16 hours, in preparation for the following 10 weeks of Deep Learning courses at Taiwan AI academy from 2018/02-2018/05. Topics include regression (linear, polynomial, gaussian and sigmoid basis functions), dimension reduction (PCA, LDA, ISOMAP), clustering (K-means, GMM, Mean-Shift, DBSCAN, Spectral Clustering), classification (Naive Bayes, Logistic Regression, SVM, kNN, Decision Tree, Classifier Ensembles, Bagging, Boosting, Adaboost) and Semi-Supervised learning techniques. Emphasis on sampling, probability, curse of dimensionality, decision theory and classifier generalizability.
Ee184405 statistika dan stokastik statistik deskriptif 2 numerikyusufbf
Statistika adalah suatu bidang ilmu yang mempelajari cara-cara mengumpulkan data untuk selanjutnya dapat dideskripsikan dan diolah, kemudian melakukan induksi/inferensi dalam rangka membuat kesimpulan, agar dapat ditentukan keputusan yang akan diambil berdasarkan data yang dimiliki.
DATA =============> PROSES STATISTIK ===========> INFORMASI
Statistik Deskriptif adalah suatu cara menggambarkan persoalan yang berdasarkan data yang dimiliki yakni dengan cara menata data tersebut sedemikian rupa agar karakteristik data dapat dipahami dengan mudah sehingga berguna untuk keperluan selanjutnya.
A pre conference workshop on Machine Learning was organized as a part of #doppa17, DevOps++ Global Summit 2017. The workshop was conducted by Dr. Vivek Vijay and Dr. Sandeep Yadav. All the copyrights are reserved with the author.
Safalta Digital marketing institute in Noida, provide complete applications that encompass a huge range of virtual advertising and marketing additives, which includes search engine optimization, virtual communication advertising, pay-per-click on marketing, content material advertising, internet analytics, and greater. These university courses are designed for students who possess a comprehensive understanding of virtual marketing strategies and attributes.Safalta Digital Marketing Institute in Noida is a first choice for young individuals or students who are looking to start their careers in the field of digital advertising. The institute gives specialized courses designed and certification.
for beginners, providing thorough training in areas such as SEO, digital communication marketing, and PPC training in Noida. After finishing the program, students receive the certifications recognised by top different universitie, setting a strong foundation for a successful career in digital marketing.
Synthetic Fiber Construction in lab .pptxPavel ( NSTU)
Synthetic fiber production is a fascinating and complex field that blends chemistry, engineering, and environmental science. By understanding these aspects, students can gain a comprehensive view of synthetic fiber production, its impact on society and the environment, and the potential for future innovations. Synthetic fibers play a crucial role in modern society, impacting various aspects of daily life, industry, and the environment. ynthetic fibers are integral to modern life, offering a range of benefits from cost-effectiveness and versatility to innovative applications and performance characteristics. While they pose environmental challenges, ongoing research and development aim to create more sustainable and eco-friendly alternatives. Understanding the importance of synthetic fibers helps in appreciating their role in the economy, industry, and daily life, while also emphasizing the need for sustainable practices and innovation.
Macroeconomics- Movie Location
This will be used as part of your Personal Professional Portfolio once graded.
Objective:
Prepare a presentation or a paper using research, basic comparative analysis, data organization and application of economic information. You will make an informed assessment of an economic climate outside of the United States to accomplish an entertainment industry objective.
Acetabularia Information For Class 9 .docxvaibhavrinwa19
Acetabularia acetabulum is a single-celled green alga that in its vegetative state is morphologically differentiated into a basal rhizoid and an axially elongated stalk, which bears whorls of branching hairs. The single diploid nucleus resides in the rhizoid.
2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
Biological screening of herbal drugs: Introduction and Need for
Phyto-Pharmacological Screening, New Strategies for evaluating
Natural Products, In vitro evaluation techniques for Antioxidants, Antimicrobial and Anticancer drugs. In vivo evaluation techniques
for Anti-inflammatory, Antiulcer, Anticancer, Wound healing, Antidiabetic, Hepatoprotective, Cardio protective, Diuretics and
Antifertility, Toxicity studies as per OECD guidelines
Embracing GenAI - A Strategic ImperativePeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
Introduction to AI for Nonprofits with Tapp NetworkTechSoup
Dive into the world of AI! Experts Jon Hill and Tareq Monaur will guide you through AI's role in enhancing nonprofit websites and basic marketing strategies, making it easy to understand and apply.
3. Components of the recognition
system
Analysis
-Face Tracking
Intelligence
-Support Vector Machine
Classifier
Shape
Parameters
Training
with stereo
Data Classifier
Testing
with mono
New
Data
Output
7. p - degrees of freedom
Stereo Tracking
Data Monocular Tracking
And Classification
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8. Support Vector Machines (SVM)
- Best discriminating hypersurface
between two class of objects
- Map the data to high dimension
using a map function
- The hypersurface in the feature
space corresponds to a hyperplane
in the mapped space
Training
Data Classifier
Testing
(Classifier)
New Data Output
10. LUKAS TOMASI KANADE OPTICAL
FLOW TRACKER EXTENDED TO 3D
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TIME t+1
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11. One to Many Application of
Support Vector Machines (SVM)
- One hypersurface per class is calculated
- A new data is tested for each hypersurface
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- A different probability is assigned to ith class
13. -Training (Stereo) with 2 people, totally 240 frames
- Testing with 3 people
- 5 expressions: neutral, open mouth, close mouth,
smile, raise eyebrow
- velocity term is added to the shape vector:
3
n
n
n
new
n
- Two other classifiers were tested:
1 - Clustering 2 – N-Nearest Neighbor
16. Decision of the system
Input
Neutral Open
mouth
Close
mouth
Smile Raise
eyebrow
Neutral (44) 32 6 3 0 3
Open mouth (80) 0 76 4 0 0
Close Mouth (50) 0 1 49 0 0
Smile (87) 2 0 0 81 4
Raise Eyebrow (21) 3 0 0 0 18
Performance of the system for different
expressions
Table 1
17. Comparison Between Different Methods
SVM with
kernel
erbf
SVM with
kernel rbf
Clustering N-Nearest
with N=9
N-Nearest
with N=5
Same
person
176/182 170/182 161/182 173/182 173/182
Total 256/282 253/282 242/283 255/282 253/282
Table 2
18. -Training (Stereo) with 1 person, totally 130 frames
- Testing with 3 people
- 5 expressions: neutral, open mouth, close mouth,
smile, raise eyebrow
Comparison Between Different Methods with only one person training set
SVM with
kernel erbf
SVM with
kernel rbf
Clustering N-Nearest
with N=9
N-Nearest
with N=5
Same person 98/110 99/110 109/110 109/110 110/110
Total 216/282 207/282 233/282 231/282 229/282
Table 3
19. -Training (Stereo) with 2 people, totally 240 frames
- Testing with 3 people
- 3 emotional expressions: neutral, happy, surprise
- Transition between expressions are separated
Comparison Between Different Methods with three emotional expressions
SVM with
kernel erbf
SVM
with
kernel
rbf
Cluster
ing
N-
Nearest
with N=9
N-Nearest
with N=5
N-Nearest
with N=3
N-Nearest with
N=1
Same
person
164/165 165/165 152/165 163/165 164/165 164/165 164/165
Total 222/228 223/228 213/228 225/228 224/228 223/228 223/228
Table 4
20. Performance Comparison Between Previous Expression Recognition Work
Recognition
Rate
Pose
Change
Number of
Expressions
Test/Train
Subject
Number of
Data
Comments
Chen et.al,
ICME 2000
%89 Direct
camera
view
7 Different
subject
470
images
Problem with
different people
Wang et.al,
AFGR 1998
%96 Direct
camera
view
3 Different
subject
29 image
sequence
Sequence
classification
(easier)
Lien et.al,
AFGR 1998
%85-%93 ~10
degrees
rotation
4 Different
subject
~130
images
Only upper part
of the face is
classified
Hiroshi et.al,
ICPR 1996
%70 ~45-60
degrees
rotation
5 Same
subject
900
images
Permits for
rotations, but
rates are not as
good
Chang et.al,
IJCNN 1999
%92 Direct
camera
view
3 Different
subject
38 images Small test and
training set
Matsuno et.al,
ICCV 1995
%80 Direct
camera
view
4 Different
subject
45 images Small test and
training set
Hong et.al,
AFGR 1998
%65-%85 Direct
camera
view
7 Same and
different
subject
~250
images
%85 with known
person % 65 with
unknown person
Hong et.al,
AFGR 1998
%81-%97 Direct
camera
view
3 Same and
different
subject
~250
images
%97 with known
person % 81 with
unknown person
Sakaguchi
et.al, ICPR
1996
%84 Direct
camera
view
6 Same
subject
- The test and
training set not
mentioned
Our Work %91 ~70-80
degrees
rotation
5 Different
subject
282
images
Table 2
Our Work %98 ~70-80
degrees
3 Different
subject
228
images
Table 4 -
Emotional
22. Future Work
Conclusions
- Breakthrough facial expression recognition rates .
- 3-D is the right way to go…
- Test with more subjects and expressions.
- further application to face recognition (?)
