Builds on the basics we have been exploring by working on more advanced editing, including work flow ideas, techniques for repairing gaps, improving FPS, automatic captioning, scrolling credits, films within a film, and having one person use multiple avatars while filming.
Donghyun Nam - 어도비 플래시로 게임만들기
Making Games with Adobe Flash
Seminar Info (https://wiki.ubuntu.com/KoreanTeam/activities/14)
Watch Spaeker's Slide! http://mwiki.ubuntu.or.kr/index.php/%ED%8C%8C%EC%9D%BC:FLASH%EB%B0%9C%ED%91%9C.pptx
Place / Date - 서울 토즈 대학로점 / 2014Y 8M 30D 토요일
~ About Speaker ~
Donghyun Nam
Video Capture : 장태희 (jang0913@gmail.com)
본 영상 녹화는 발표 진행 전 발표자와의 동의 하에 진행되었습니다.
What is Ubuntu Linux?
Ubuntu is an ancient African word meaning 'humanity to others'.
It also means 'I am what I am because of who we all are'.
The Ubuntu operating system brings the spirit of Ubuntu to the world of computers.
http://www.ubuntu.com
Why don't you stop by Ubuntu Korea Community?
Forum (http://www.ubuntu-kr.org/)
Facebook (https://www.facebook.com/groups/ubuntu.ko/)
Wiki (http://mwiki.ubuntu.or.kr/index.php/%EB%8C%80%EB%AC%B8)
The goal is to introduce some digital video concepts with a simple vocabulary, lots of visual elements and practical examples when possible.
We're going to introduce the main mechanics behind a generic video codec but most of these concepts are useful and used in modern codecs such as VP9, AV1 and HEVC. Be sure to understand that we're going to simplify things a LOT. Sometimes we'll use a real example (mostly H.264) to demonstrate a technique.
Builds on the basics we have been exploring by working on more advanced editing, including work flow ideas, techniques for repairing gaps, improving FPS, automatic captioning, scrolling credits, films within a film, and having one person use multiple avatars while filming.
Donghyun Nam - 어도비 플래시로 게임만들기
Making Games with Adobe Flash
Seminar Info (https://wiki.ubuntu.com/KoreanTeam/activities/14)
Watch Spaeker's Slide! http://mwiki.ubuntu.or.kr/index.php/%ED%8C%8C%EC%9D%BC:FLASH%EB%B0%9C%ED%91%9C.pptx
Place / Date - 서울 토즈 대학로점 / 2014Y 8M 30D 토요일
~ About Speaker ~
Donghyun Nam
Video Capture : 장태희 (jang0913@gmail.com)
본 영상 녹화는 발표 진행 전 발표자와의 동의 하에 진행되었습니다.
What is Ubuntu Linux?
Ubuntu is an ancient African word meaning 'humanity to others'.
It also means 'I am what I am because of who we all are'.
The Ubuntu operating system brings the spirit of Ubuntu to the world of computers.
http://www.ubuntu.com
Why don't you stop by Ubuntu Korea Community?
Forum (http://www.ubuntu-kr.org/)
Facebook (https://www.facebook.com/groups/ubuntu.ko/)
Wiki (http://mwiki.ubuntu.or.kr/index.php/%EB%8C%80%EB%AC%B8)
The goal is to introduce some digital video concepts with a simple vocabulary, lots of visual elements and practical examples when possible.
We're going to introduce the main mechanics behind a generic video codec but most of these concepts are useful and used in modern codecs such as VP9, AV1 and HEVC. Be sure to understand that we're going to simplify things a LOT. Sometimes we'll use a real example (mostly H.264) to demonstrate a technique.
C5 Instances and the Evolution of Amazon EC2 Virtualization - CMP332 - re:Inv...Amazon Web Services
Over the last 11 years, the Amazon EC2 virtualization platform has quietly evolved to take advantage of unique hardware and silicon, an accelerated network and storage architecture, and with the launch of C5 instances, a bespoke hypervisor to deliver the maximum amount of resources and performance to instances. Come to this deep dive to get a behind-the-scenes look at how our virtualization stack has evolved, including a peak at how our latest generation platform works under the covers.
Original presentation by Tobias Schneider (Twitter: @tobeytailor), on Gordon, his JavaScript implementation of a Flash player.
Originally presented on Day 1 of JSConf 2010; more info here in case you missed it: http://palagpat-coding.blogspot.com/2010/04/in-case-you-missed-it-jsconfus-2010-day.html
Streaming Apps and Poison Pills: handle the unexpected with Kafka Streams (Lo...confluent
Apache Kafka's Streams API lets us process messages from different topics with very low latency. Messages may have different formats, schemas and may even be serialised in different ways. What happens when an undesirable message comes in the flow? When an error occurs, real-time applications can't always wait for manual recovery and need to handle such failures. Kafka Streams lets you use a few techniques like sentinel value or dead letter queues-in this talk we'll see how. This talk will give an overview of different patterns and tools available in the Streams DSL API to deal with corrupted messages. Based on a real-life use case, it also includes valuable experiences from building and running Kafka Streams projects in production. The talk includes live coding and demonstrations.
Kanban for Software Development and Kaizen CultureAcquate
Talk #1 - Kanban for Developers
There is much hype about Kanban since it was perfected and introduced to the world by Toyota. Since then, not only manufacturers but all businesses looked into this simple but extremely powerful approach.
As software developers are yearning for better project management and visibility on all aspects of their work, Kanban naturally blipped on their radar.
In this session, we will look at the origins of Kanban, how it applies to software development along with misunderstandings and myths. We will also compare manufacturing practices with software development techniques and see how we can benefit from their experiences. During the session we will have some interactive exercises to help us better understand Kanban and becoming more efficient and productive by limiting the amount of work we do.
Talk #2 - Kaizen: Continuous Process Improvement
Enterprises can utilize Process Improvement to improve their workflow, allowing them to be more efficient, eliminate bottlenecks and problem areas, and as a result, achieve reduced costs, on-time delivery and increased profits. Currently, enterprises are facing stiffer competition to win customer acceptance through quality, and the need to develop more customer-oriented products and services faster than ever before.
Einstein said "We can't solve problems by using the same kind of thinking we used when we created them.", and that is what lies at the heart of Kaizen. Improvement comes with a different look at the problems and doing this continuously every day and everywhere. It is more of a company culture than a project.
But where do we start improving? British comic writer Douglas Adams said "See first, think later, then test. But always see first. Otherwise you will only see what you were expecting.". And that is exactly where we start. By looking deep into our workflow and process.
In this session, we will look at Kaizen philosophy, why change is important and very hard to do. we'll also analyze waste and why it is bad for our business and see how quality-first approach makes us better at what we do. I will also provide some test cases and finish off by looking into implementing a Kaizen culture at the office by involving everybody.
Web Presentations, deck.js and Extensionsremiemonet
See it in HTML at:
http://twitwi.github.io/Presentation-2013-SoftShake-DeckJs/
Introduction to web presentations, deck.js and some of its extensions.
Smiley by inky2010 http://openclipart.org/detail/77641/smiley-face-by-inky2010
C5 Instances and the Evolution of Amazon EC2 Virtualization - CMP332 - re:Inv...Amazon Web Services
Over the last 11 years, the Amazon EC2 virtualization platform has quietly evolved to take advantage of unique hardware and silicon, an accelerated network and storage architecture, and with the launch of C5 instances, a bespoke hypervisor to deliver the maximum amount of resources and performance to instances. Come to this deep dive to get a behind-the-scenes look at how our virtualization stack has evolved, including a peak at how our latest generation platform works under the covers.
Original presentation by Tobias Schneider (Twitter: @tobeytailor), on Gordon, his JavaScript implementation of a Flash player.
Originally presented on Day 1 of JSConf 2010; more info here in case you missed it: http://palagpat-coding.blogspot.com/2010/04/in-case-you-missed-it-jsconfus-2010-day.html
Streaming Apps and Poison Pills: handle the unexpected with Kafka Streams (Lo...confluent
Apache Kafka's Streams API lets us process messages from different topics with very low latency. Messages may have different formats, schemas and may even be serialised in different ways. What happens when an undesirable message comes in the flow? When an error occurs, real-time applications can't always wait for manual recovery and need to handle such failures. Kafka Streams lets you use a few techniques like sentinel value or dead letter queues-in this talk we'll see how. This talk will give an overview of different patterns and tools available in the Streams DSL API to deal with corrupted messages. Based on a real-life use case, it also includes valuable experiences from building and running Kafka Streams projects in production. The talk includes live coding and demonstrations.
Kanban for Software Development and Kaizen CultureAcquate
Talk #1 - Kanban for Developers
There is much hype about Kanban since it was perfected and introduced to the world by Toyota. Since then, not only manufacturers but all businesses looked into this simple but extremely powerful approach.
As software developers are yearning for better project management and visibility on all aspects of their work, Kanban naturally blipped on their radar.
In this session, we will look at the origins of Kanban, how it applies to software development along with misunderstandings and myths. We will also compare manufacturing practices with software development techniques and see how we can benefit from their experiences. During the session we will have some interactive exercises to help us better understand Kanban and becoming more efficient and productive by limiting the amount of work we do.
Talk #2 - Kaizen: Continuous Process Improvement
Enterprises can utilize Process Improvement to improve their workflow, allowing them to be more efficient, eliminate bottlenecks and problem areas, and as a result, achieve reduced costs, on-time delivery and increased profits. Currently, enterprises are facing stiffer competition to win customer acceptance through quality, and the need to develop more customer-oriented products and services faster than ever before.
Einstein said "We can't solve problems by using the same kind of thinking we used when we created them.", and that is what lies at the heart of Kaizen. Improvement comes with a different look at the problems and doing this continuously every day and everywhere. It is more of a company culture than a project.
But where do we start improving? British comic writer Douglas Adams said "See first, think later, then test. But always see first. Otherwise you will only see what you were expecting.". And that is exactly where we start. By looking deep into our workflow and process.
In this session, we will look at Kaizen philosophy, why change is important and very hard to do. we'll also analyze waste and why it is bad for our business and see how quality-first approach makes us better at what we do. I will also provide some test cases and finish off by looking into implementing a Kaizen culture at the office by involving everybody.
Web Presentations, deck.js and Extensionsremiemonet
See it in HTML at:
http://twitwi.github.io/Presentation-2013-SoftShake-DeckJs/
Introduction to web presentations, deck.js and some of its extensions.
Smiley by inky2010 http://openclipart.org/detail/77641/smiley-face-by-inky2010
Birds of a Feather 2017: 邀請分享 Light Up The Korean DarkWeb - Dasom KimHITCON GIRLS
2017年12月10日 - Birds of a Feather ( 簡稱BoF ),語意上是指鳥類會與相同類型的鳥群一起飛翔,之後衍伸為讓志同道合的人們聚集在一起或舉辦非正式聚會。
https://hitcon-girls.blogspot.tw/2017/12/Birds-of-a-Feather.html
Birds of a Feather 2017: 邀請分享 Glance into the Enterprise InfoSec Field - HowardHITCON GIRLS
2017年12月10日 - Birds of a Feather ( 簡稱BoF ),語意上是指鳥類會與相同類型的鳥群一起飛翔,之後衍伸為讓志同道合的人們聚集在一起或舉辦非正式聚會。
https://hitcon-girls.blogspot.tw/2017/12/Birds-of-a-Feather.html
Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
We have compiled the most important slides from each speaker's presentation. This year’s compilation, available for free, captures the key insights and contributions shared during the DfMAy 2024 conference.
Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...Dr.Costas Sachpazis
Terzaghi's soil bearing capacity theory, developed by Karl Terzaghi, is a fundamental principle in geotechnical engineering used to determine the bearing capacity of shallow foundations. This theory provides a method to calculate the ultimate bearing capacity of soil, which is the maximum load per unit area that the soil can support without undergoing shear failure. The Calculation HTML Code included.
NUMERICAL SIMULATIONS OF HEAT AND MASS TRANSFER IN CONDENSING HEAT EXCHANGERS...ssuser7dcef0
Power plants release a large amount of water vapor into the
atmosphere through the stack. The flue gas can be a potential
source for obtaining much needed cooling water for a power
plant. If a power plant could recover and reuse a portion of this
moisture, it could reduce its total cooling water intake
requirement. One of the most practical way to recover water
from flue gas is to use a condensing heat exchanger. The power
plant could also recover latent heat due to condensation as well
as sensible heat due to lowering the flue gas exit temperature.
Additionally, harmful acids released from the stack can be
reduced in a condensing heat exchanger by acid condensation. reduced in a condensing heat exchanger by acid condensation.
Condensation of vapors in flue gas is a complicated
phenomenon since heat and mass transfer of water vapor and
various acids simultaneously occur in the presence of noncondensable
gases such as nitrogen and oxygen. Design of a
condenser depends on the knowledge and understanding of the
heat and mass transfer processes. A computer program for
numerical simulations of water (H2O) and sulfuric acid (H2SO4)
condensation in a flue gas condensing heat exchanger was
developed using MATLAB. Governing equations based on
mass and energy balances for the system were derived to
predict variables such as flue gas exit temperature, cooling
water outlet temperature, mole fraction and condensation rates
of water and sulfuric acid vapors. The equations were solved
using an iterative solution technique with calculations of heat
and mass transfer coefficients and physical properties.
Final project report on grocery store management system..pdfKamal Acharya
In today’s fast-changing business environment, it’s extremely important to be able to respond to client needs in the most effective and timely manner. If your customers wish to see your business online and have instant access to your products or services.
Online Grocery Store is an e-commerce website, which retails various grocery products. This project allows viewing various products available enables registered users to purchase desired products instantly using Paytm, UPI payment processor (Instant Pay) and also can place order by using Cash on Delivery (Pay Later) option. This project provides an easy access to Administrators and Managers to view orders placed using Pay Later and Instant Pay options.
In order to develop an e-commerce website, a number of Technologies must be studied and understood. These include multi-tiered architecture, server and client-side scripting techniques, implementation technologies, programming language (such as PHP, HTML, CSS, JavaScript) and MySQL relational databases. This is a project with the objective to develop a basic website where a consumer is provided with a shopping cart website and also to know about the technologies used to develop such a website.
This document will discuss each of the underlying technologies to create and implement an e- commerce website.
Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
Understanding Inductive Bias in Machine LearningSUTEJAS
This presentation explores the concept of inductive bias in machine learning. It explains how algorithms come with built-in assumptions and preferences that guide the learning process. You'll learn about the different types of inductive bias and how they can impact the performance and generalizability of machine learning models.
The presentation also covers the positive and negative aspects of inductive bias, along with strategies for mitigating potential drawbacks. We'll explore examples of how bias manifests in algorithms like neural networks and decision trees.
By understanding inductive bias, you can gain valuable insights into how machine learning models work and make informed decisions when building and deploying them.
6th International Conference on Machine Learning & Applications (CMLA 2024)ClaraZara1
6th International Conference on Machine Learning & Applications (CMLA 2024) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of on Machine Learning & Applications.
Forklift Classes Overview by Intella PartsIntella Parts
Discover the different forklift classes and their specific applications. Learn how to choose the right forklift for your needs to ensure safety, efficiency, and compliance in your operations.
For more technical information, visit our website https://intellaparts.com
5. 5
LET’S TALK ABOUT DEEPFAKES!
1. In December 2017, a user
named “DeepFakes”
announce the tool on
Reddit Community of
developers.
2. DeepFakes is a tool that
utilizes deep learning to
recognize and swap faces
in pictures and videos.
5
11. HOW DEEPFAKES WORK?
>Steps for: DeepFaceLab
2 31
Extract image
from source
video.
Extract faces
from source
and target
image.
4
Manually
remove
error image.
5
Model
Training
6
Extract image
from target
video.
Debug & Convert
image to mp4
11
12. HOW DEEPFAKES WORK?
>Extract image from source&target video.
FFmpeg
FFmpeg is the leading
multimedia framework,
able to decode, encode,
transcode, mux, demux,
stream, filter and play
pretty much anything
that humans and
machines have created.
ffmpeg -i clipname -vf fps=framerate -qscale:v 2 "imagename%04d.jpg"Command_
12
13. HOW DEEPFAKES WORK?
>Extract faces from source and target image
(Ref: Facial landmarks with dlib, OpenCV, and Python)
Divided a face into the following areas:
1. eyes(left/right)
2. eyebrows(left/right)
3. nose
4. chin
13
25. HOW DEEPFAKES WORK?
>Debug & Convert image to mp4
Debug Mode
Use predicted mask? 1
Erosion (-100 to +100): (default = 0) 1
Seamless Erosion (0 to 40): (default = 0) 20
Blur (-200 to +200): (default = 0) 40
Hist-match threshold (0 to 255): (default = 255) default
Face Scale (-50 to +50): (default = 0) default
Transfer Color from predicted face? (LCT/RCT/no): (default = no) RCT
Degrade Color Power of Final Image: (default = 0) default
25
26. HOW DEEPFAKES WORK?
>Debug & Convert image to mp4
Debug Mode
Use predicted mask? 1
Erosion (-100 to +100): (default = 0) 1
Seamless Erosion (0 to 40): (default = 0) 20
Blur (-200 to +200): (default = 0) 40
Hist-match threshold (0 to 255): (default = 255) default
Face Scale (-50 to +50): (default = 0) default
Transfer Color from predicted face? (LCT/RCT/no): (default = no) RCT
Degrade Color Power of Final Image: (default = 0) default
26
27. HOW DEEPFAKES WORK?
>Debug & Convert image to mp4
Debug Mode
Use predicted mask? 1
Erosion (-100 to +100): (default = 0) 1
Seamless Erosion (0 to 40): (default = 0) 20
Blur (-200 to +200): (default = 0) 40
Hist-match threshold (0 to 255): (default = 255) default
Face Scale (-50 to +50): (default = 0) default
Transfer Color from predicted face? (LCT/RCT/no): (default = no) RCT
Degrade Color Power of Final Image: (default = 0) default
27
28. HOW DEEPFAKES WORK?
>Debug & Convert image to mp4
Debug Mode
Use predicted mask? 1
Erosion (-100 to +100): (default = 0) 1
Seamless Erosion (0 to 40): (default = 0) 20
Blur (-200 to +200): (default = 0) 40
Hist-match threshold (0 to 255): (default = 255) default
Face Scale (-50 to +50): (default = 0) default
Transfer Color from predicted face? (LCT/RCT/no): (default = no) RCT
Degrade Color Power of Final Image: (default = 0) default
28
29. HOW DEEPFAKES WORK?
>Debug & Convert image to mp4
Debug Mode
Use predicted mask? 1
Erosion (-100 to +100): (default = 0) 1
Seamless Erosion (0 to 40): (default = 0) 20
Blur (-200 to +200): (default = 0) 40
Hist-match threshold (0 to 255): (default = 255) default
Face Scale (-50 to +50): (default = 0) default
Transfer Color from predicted face? (LCT/RCT/no): (default = no) RCT
Degrade Color Power of Final Image: (default = 0) default
29
30. HOW DEEPFAKES WORK?
>Debug & Convert image to mp4
Debug Mode
Use predicted mask? 1
Erosion (-100 to +100): (default = 0) 1
Seamless Erosion (0 to 40): (default = 0) 20
Blur (-200 to +200): (default = 0) 40
Hist-match threshold (0 to 255): (default = 255) default
Face Scale (-50 to +50): (default = 0) default
Transfer Color from predicted face? (LCT/RCT/no): (default = no) RCT
Degrade Color Power of Final Image: (default = 0) default
30
31. HOW DEEPFAKES WORK?
>Debug & Convert image to mp4
Debug Mode
Use predicted mask? 1
Erosion (-100 to +100): (default = 0) 1
Seamless Erosion (0 to 40): (default = 0) 20
Blur (-200 to +200): (default = 0) 40
Hist-match threshold (0 to 255): (default = 255) default
Face Scale (-50 to +50): (default = 0) default
Transfer Color from predicted face? (LCT/RCT/no): (default = no) RCT
Degrade Color Power of Final Image: (default = 0) default
31
40. CAN YOU TELL A FAKE VOICE FROM A REAL ONE?
40
(Ref: https://lyrebird.ai/)
41. • create a digitized version of your own voice.
• Uses deep learning frameworks on AWS to develop and
train its AI models
What is Lyrebird?
Record & Upload
voice samples
Train by DL Framework
on AWS EC2 P3
Digital Voice
Generated
Optput audio of
requested dialog
(Ref: https://lyrebird.ai/)41
42. LYREBIRD LIVE DEMO
This is my honored to talk to everyone here.
However, can you trust your eyes and ears to perceive reality?
Machine learning can artificially mimic natural sounds to create digital voice.
Just like me!
42
43. Why my voice sounds awful?
43
(Ref: https://lyrebird.ai/)