Designing an actor model game architecture with PonyNick Pruehs
Introduction to Pony, actor model, reference capabilities and making concurrent DirectX games with Pony.
Presented at MVP Fusion #3.
http://mvpfusion.azurewebsites.net/
Forth chapter of the lecture Unreal Engine Basics taught at SAE Institute Hamburg.
- Getting familiar with behavior trees in general
- Learning how to set up and use behavior trees in Unreal Engine
- Learning about the very basics of the Unreal Engine navigation system
Designing an actor model game architecture with PonyNick Pruehs
Introduction to Pony, actor model, reference capabilities and making concurrent DirectX games with Pony.
Presented at MVP Fusion #3.
http://mvpfusion.azurewebsites.net/
Forth chapter of the lecture Unreal Engine Basics taught at SAE Institute Hamburg.
- Getting familiar with behavior trees in general
- Learning how to set up and use behavior trees in Unreal Engine
- Learning about the very basics of the Unreal Engine navigation system
Machine learning for IoT - unpacking the blackboxIvo Andreev
Have you ever considered Machine Learning as a black box? It sounds as a kind of magic happening. Although being one among many solutions available, Azure ML has proved to be a great balance between flexibility, usability and affordable price. But how does Azure ML compare with the other ML providers? How to choose the appropriate algorithm? Do you understand the key performance indicators and how to improve the quality of your models? The session is about understanding the black box and using it for IoT workload and not only.
Sparking Science up with Research RecommendationsMaya Hristakeva
I gave this presentation at the first Spark Summit EU in Amsterdam, 29th October, 2015.
Finding relevant and related publications is an important task of researchers’ activities. At Mendeley, we have tens of millions of research articles that we try to recommend to millions of researchers, requiring a large scale solution to this problem. Spark’s implementations of recommender systems have recently attracted much attention. In this presentation, we demonstrate how Spark can be used to generate scientific article recommendations for researchers. We share Mendeley’s experiences of moving from Apache Mahout's machine learning libraries to Spark, the challenges that we faced and the solutions that we put in place.
https://github.com/mayahhf/spark-cf-recommender
https://spark-summit.org/eu-2015/events/sparking-science-up-with-research-recommendations/
Machine learning for IoT - unpacking the blackboxIvo Andreev
Have you ever considered Machine Learning as a black box? It sounds as a kind of magic happening. Although being one among many solutions available, Azure ML has proved to be a great balance between flexibility, usability and affordable price. But how does Azure ML compare with the other ML providers? How to choose the appropriate algorithm? Do you understand the key performance indicators and how to improve the quality of your models? The session is about understanding the black box and using it for IoT workload and not only.
Sparking Science up with Research RecommendationsMaya Hristakeva
I gave this presentation at the first Spark Summit EU in Amsterdam, 29th October, 2015.
Finding relevant and related publications is an important task of researchers’ activities. At Mendeley, we have tens of millions of research articles that we try to recommend to millions of researchers, requiring a large scale solution to this problem. Spark’s implementations of recommender systems have recently attracted much attention. In this presentation, we demonstrate how Spark can be used to generate scientific article recommendations for researchers. We share Mendeley’s experiences of moving from Apache Mahout's machine learning libraries to Spark, the challenges that we faced and the solutions that we put in place.
https://github.com/mayahhf/spark-cf-recommender
https://spark-summit.org/eu-2015/events/sparking-science-up-with-research-recommendations/
Title: Sista: Improving Cog’s JIT performance
Speaker: Clément Béra
Thu, August 21, 9:45am – 10:30am
Video Part1
https://www.youtube.com/watch?v=X4E_FoLysJg
Video Part2
https://www.youtube.com/watch?v=gZOk3qojoVE
Description
Abstract: Although recent improvements of the Cog VM performance made it one of the fastest available Smalltalk virtual machine, the overhead compared to optimized C code remains important. Efficient industrial object oriented virtual machine, such as Javascript V8's engine for Google Chrome and Oracle Java Hotspot can reach on many benchs the performance of optimized C code thanks to adaptive optimizations performed their JIT compilers. The VM becomes then cleverer, and after executing numerous times the same portion of codes, it stops the code execution, looks at what it is doing and recompiles critical portion of codes in code faster to run based on the current environment and previous executions.
Bio: Clément Béra and Eliot Miranda has been working together on Cog's JIT performance for the last year. Clément Béra is a young engineer and has been working in the Pharo team for the past two years. Eliot Miranda is a Smalltalk VM expert who, among others, has implemented Cog's JIT and the Spur Memory Manager for Cog.
The Dirty Little Secrets They Didn’t Teach You In Pentesting Class Chris Gates
Derbycon 2011
This talk is about methodologies and tools that we use or have coded that make our lives and pentest schedule a little easier, and why we do things the way we do. Of course, there will be a healthy dose of Metasploit in the mix.
Embracing a Taxonomy of Types to Simplify Machine Learning with Leah McGuireDatabricks
Salesforce has created a machine learning framework on top of Spark ML that builds personalized models for businesses across a range of applications. Hear how expanding type information about features has allowed them to deal with custom datasets with good results.
By building a platform that automatically does feature engineering on rich types (e.g. Currency and Percentages rather than Doubles; Phone Numbers and Email Addresses rather than Strings), they have automated much of the work that consumes most data scientists’ time. Learn how you can do the same by building a single model outline based on the application, and then having the framework customize it for each customer.
The Key to Machine Learning is Prepping the Right Data with Jean Georges Perrin Databricks
Machine learning has its challenges, and understanding the algorithms is not always easy. In this session, you’ll discover methods to make these challenges less daunting.
Intended for software engineers who need to understand the requirements and constraints of data scientists, and data scientists who need to implement or help implement production systems, the session will begin with a quick introduction to data quality and a level-set on common vocabulary. You’ll then explore the formats that are required by Spark ML to run its algorithms, and see how to automate the build through user-defined functions and other techniques. Automation will make reproducibility easy, minimize errors and increase the efficiency of data scientists.
Key takeaways will include:
– How to build the required tool set in Java
– Understanding the formats required by Spark ML (a new vocabulary)
– Learning fundamentals about data quality and how to make sure the data is usable
Cutting Edge Computer Vision for EveryoneIvo Andreev
Microsoft offers a wide range of tools and advanced solutions to support you in managing computer vision related tasks.
From purely coding approaches with ML.NET, through zero-code ComputerVision.ai to advanced and flexible AI service in Azure ML, there is a solution for every need and each type of person.
From running on premises, through managed infrastructure to completely cloud services the speed of getting to the desired results and the return of investment are guaranteed.
Join this session to get insights about the options, deployment, pricing, pros and cons compared and select the most appropriate tech for your business case.
Similar to What is jubatus? How it works for you? (20)
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.
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The Internet of Things (IoT) is a revolutionary concept that connects everyday objects and devices to the internet, enabling them to communicate, collect, and exchange data. Imagine a world where your refrigerator notifies you when you’re running low on groceries, or streetlights adjust their brightness based on traffic patterns – that’s the power of IoT. In essence, IoT transforms ordinary objects into smart, interconnected devices, creating a network of endless possibilities.
Here is a blog on the role of electrical and electronics engineers in IOT. Let's dig in!!!!
For more such content visit: https://nttftrg.com/
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.
Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.
HEAP SORT ILLUSTRATED WITH HEAPIFY, BUILD HEAP FOR DYNAMIC ARRAYS.
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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.
Hierarchical Digital Twin of a Naval Power SystemKerry Sado
A hierarchical digital twin of a Naval DC power system has been developed and experimentally verified. Similar to other state-of-the-art digital twins, this technology creates a digital replica of the physical system executed in real-time or faster, which can modify hardware controls. However, its advantage stems from distributing computational efforts by utilizing a hierarchical structure composed of lower-level digital twin blocks and a higher-level system digital twin. Each digital twin block is associated with a physical subsystem of the hardware and communicates with a singular system digital twin, which creates a system-level response. By extracting information from each level of the hierarchy, power system controls of the hardware were reconfigured autonomously. This hierarchical digital twin development offers several advantages over other digital twins, particularly in the field of naval power systems. The hierarchical structure allows for greater computational efficiency and scalability while the ability to autonomously reconfigure hardware controls offers increased flexibility and responsiveness. The hierarchical decomposition and models utilized were well aligned with the physical twin, as indicated by the maximum deviations between the developed digital twin hierarchy and the hardware.
Saudi Arabia stands as a titan in the global energy landscape, renowned for its abundant oil and gas resources. It's the largest exporter of petroleum and holds some of the world's most significant reserves. Let's delve into the top 10 oil and gas projects shaping Saudi Arabia's energy future in 2024.
6. Architecture
• It looks as if one server running
– You can use single local Jubatus server for develop
– Multiple Jubatus server cluster for production
Client
Jubatus RPC
The same RPC!
10. Architecture
• Multilanguage client library
– gem, pip, cpan, maven Ready!
– It essentially uses a messagepack-rpc.
• So you can use OCaml, Haskell, JavaScript, Go with your own
risk.
Client
Jubatus RPC
12. Classifier
• Task: Classification of Datum
import sys
def fib(a):
if a == 1 or a == 0:
return 1
else:
return fib(a-1) + fib(a-2)
if __name__ == “__main__”:
print(fib(int(sys.argv[1])))
def fib(a)
if a == 1 or a == 0
1
else
return fib(a-1) + fib(a-2)
end
end
if __FILE__ == $0
puts fib(ARGV[0].to_i)
end
Sample Task: Classify what programming language used
It’s It’s
13. Classifier
• Set configuration in the Jubatus server
ClassifierFreature
Extractor
"converter": {
"string_types": {
"bigram": {
"method": "ngram",
"char_num": "2"
}
},
"string_rules": [
{
"key": "*",
"type": "bigram",
"sample_weight": "tf",
"global_weight": "idf“
}
]
}
Feature Extractor
14. Classifier
• Configuration JSON
– It does “feature vector design”
– very important step for machine learning
"converter": {
"string_types": {
"bigram": {
"method": "ngram",
"char_num": "2"
}
},
"string_rules": [
{
"key": "*",
"type": "bigram",
"sample_weight": "tf",
"global_weight": "idf“
}
]
}
setteings for extract feature from string
define function named “bigram”
original embedded function “ngram”
pass “2” to “ngram” to create “bigram”
for all data
apply “bigram”
feature weights based on tf/idf
see wikipedia/tf-idf
16. Feature Extractor
• What bigram extractor does?
bigram
extractor
import sys
def fib(a):
if a == 1 or a == 0:
return 1
else:
return fib(a-1) + fib(a-2)
if __name__ == “__main__”:
print(fib(int(sys.argv[1])))
key value
im 1
mp 1
po 1
... ...
): 1
... ...
de 1
ef 1
... ...
Feature Vector
17. Classifier
• Training model with feature vectors
key value
im 1
mp 1
po 1
... ...
): 1
... ...
de 1
ef 1
... ...
Classifier
key value
pu 1
ut 1
... ...
{| ...
|m 1
m| 1
{| 1
en 1
nd 1
key value
@a 1
$_ 1
... ...
my ...
su 1
ub 1
us 1
se 1
... ...
18. Classifier
• Set configuration in the Jubatus server
Classifier
"method" : "AROW",
"parameter" : {
"regularization_weight" : 1.0
}
Feature Extractor
bigram
extractor Classifier Algorithms
• Perceptron
• Passive Aggressive
• Confidence Weight
• Adaptive Regularization of Weights
• Normal Herd
19. Classifier
• Use model to classification task
– Jubatus will find clue for classification
AROW
key value
si 1
il 1
... ...
{| 1
... ...
It’s
20. Classifier
• Use model to classification task
– Jubatus will find clue for classification
AROW
key value
re 1
): 1
... ...
s[ 1
... ...
It’s
21. Via RPC
• call feature extraction and classification from
client via RPC
AROWbigram
extractor
lang = client.classify([sourcecode])
import sys
def fib(a):
if a == 1 or a == 0:
return 1
else:
return fib(a-1) + fib(a-2)
if __name__ == “__main__”:
print(fib(int(sys.argv[1])))
key value
im 1
mp 1
po 1
... ...
): 1
... ...
de 1
ef 1
... ...
It may be
22. What classifier can do?
• You can
– estimate the topic of tweets
– trash spam mail automatically
– monitor server failure from syslog
– estimate sentiment of user from blog post
– detect malicious attack
– find what feature is the best clue to classification
23. What classifier cannot do
• You cannot
– train model from data without supervised answer
– create a class without knowledge of the class
– get fine model without correct feature designing
24. How to use?
• see examples in
http://github.com/jubatus/jubatus-example
– gender
– shogun
– malware classification
– language detection
25. Recommender
• Task: what datum is similar to the datum?
Name
Star
Wars
Harry
Potter
Star Trek Titanic Frozen
John 4 3 2 2
Bob 5 3
Erika 1 3 4 5
Jack 2 5
Ann 4 5
Emily 1 4 2 5 4
Which movie should we recommend Ann?
26. Recommender
• Do recommendation based on Nearest Neighbor
Movie Rating(high-dimensional)
Science Fiction
Star Trek lover
John
Jack
Love Romance
Fantasy
Erika
Ann
StarWars lover
Bob
Emily
Near
Far
27. Recommender
• Ann and Emily is near
– we should recommend Flozen for Ann
Name
Star
Wars
Harry
Potter
Star Trek Titanic Frozen
Ann 4 5 ★
Emily 1 4 2 5 4
I bet Ann would like it!
28. Recommender with Feature Extractor
• Recommender server consist of Feature Extractor
and Recommender engine.
– Jubatus calculates distance between feature vectors
RecommenderFeature
Extractor
Recommender Engine can use
• Minhash
• Locality Sensitive Hashing
• Euclid Locality Sensitive Hashing
for defining distance.
29. Recommender with Feature Extractor
• Jubatus maps data in feature space
– There are distances between data
• How are they near or far?
key value
pu 1
ut 1
... ...
{| ...
|m 1
m| 1
{| 1
Feature
Extractor
key value
im 1
mp 1
... ...
... ...
“{ 1
fo 1
... ...
key value
Ma 1
ap 1
... ...
in 1
nt 1
te 1
er 1
Recommender
Ruby
Python
Java
30. What Recommender can do?
• You can
– create recommendation engine in e-commerce
– calculate similarity of tweets
– find similar directional NBA player
– visualize distance between “Star Wars” and “Star Trek”
31. What Recommender cannot do?
• You cannot
– Label data(use classifier!)
– get decision tree
– get a-priori based recommendation
34. Anomaly Detection
• Distance based detection is not good
– We cannot decide appropriate threshold of distance
Distance is equal!
35. Anomaly Detection with Feature Extractor
• Anomaly detection server consist of Feature
Extractor and anomaly detection engine.
– Jubatus finds outlier from feature vectors
Anomaly
Detection
Feature
Extractor
Anomaly Detection Engine can use
• Minhash
• Locality Sensitive Hashing
• Euclid Locality Sensitive Hashing
for defining distance.
36. Anomaly Detection
• jubaanomaly can do it!
– It base on local outlier factor algorithm
key value
pu 1
ut 1
... ...
{| ...
|m 1
m| 1
{| 1
Feature
Extractor
key value
im 1
mp 1
... ...
... ...
“{ 1
fo 1
... ...
key value
Ma 1
ap 1
... ...
in 1
nt 1
te 1
er 1
Anomaly
Detection
Outlier!
37. What Anomaly Detection can do?
• You (might) can
– find outlier
– grasp the trend and overview of current data stream
– detect or predict server's failure
– protect Web services from zero-day attacks
38. What Anomaly Detection cannot do?
• You cannot
– know the cluster distribution of data
– find any kinds of outliers with 100% accuracy
– easily understand how each outlier occurs
– know why a datum is assigned high outlier score
39. Conclusion
• Jubatus have embedded feature extractor with
algorithms.
• User should configure both feature extractor and
algorithm properly
• Client use configured machine learning via
Jubatus-RPC
• Classifier and Recommender and Anomaly may
be useful for your task.
Hello, I’ll speak about Jubatus.
You may heard about jubatus, but I’m afraid you don’t know jubatus well.
In this speak, I wish you’d realize what jubatus can do, or how to use it for your task.
Jubatus has 3 feature.
Jubatus is a distributed online machine-learning framework.
Distributed means resilient to machine failure.
And Jubatus can increase its performance for your task by coordinate multi-machine cluster.
Online means fixed time computation.
Jubatus developer carefully designed Jubatus API so that users can balance between performance and computation time.
Machine-Learning is key factor of Big Data age.
You’ll need more than “word count”
This is a overview of Jubatus process.
This red rectangle is one Jubatus process.
Inside process, there is two component exists.
Feature Extractor and Machine-Learning-Model.
You can connect your program with jubatus via Jubatus RPC.
So you can do machine learning with client-server model.
You can combine this process in cluster each other.
Jubatus in cluster communicate and make more fast and reliable machine learning.
Whole model is shared and resilient to machine failure.
If there are many Jubatus servers running and continue to mixing
User can communicate with cluster via jubatus proxy as if it is single jubatus server.
The communication protocol between Jubatus server and client is completely the same with that of Jubatus proxy and client.
It is useful for developers because they can run jubatus in local machine for developing environment, and deploy the client code for production clusters.
A big benefit of distributed system, Jubatus can scale performance out.
In your production environment, if there is too heavy RPC request for the throughput of clusters
You can append machine to cluster, cluster will increase its performance.
It is suitable for Cloud Computing era.
And jubatus cluster is resilient for cluster failure.
Whenever servers break down, the proxy server conceal the machine failure so the service will continue.
So you can append or remove cluster machine dynamically.
And Jubatus client library is implemented in many language.
you can get jubatus client library via gem, pip, cpan, maven.
If you want to use it in other language, you can use messagepack-rpc client with your own risk.
It will work! (I tried Javascript
And Jubatus has many kind of machine-learning module.
You can use these machine learning rapidly.
Among 6 machine learning modules, Classsifier and Recommender and Anomaly Detection will be great help of you.
I’ll introduce these 3 machine learning modules.
classifier can classify data.
A sample task, you may want to detect programming language of source code.
In this case, you can classify language from sequence of text.
First of all, you have to set configuration in the jubatus server.
The configuration is written in JSON.
In this case, you choose embedded ngram function, and passing number 2 to ngram. You can get bigram function.
And set rule. In this rule, all data inserted will be handled with bigram.
Regulating the weights of words with tf/idf scheme.
Now, the Feature Extractor becomes “bigram extractor”
with this bigram extractor, all datum to be splited into two character words.
“import” will become “im”, “mp”, “po”, “or”, “rt” with bigram scheme.
This form of datum representation if Feature Vector.
bigram extractor extracts bigram from datum and get Feature Vector.
You extracting feature vectors from many language source code.
Jubatus Classifier learns from feature vectors and create model.
Next, the classifier algorithm should be configured.
You can select Classifier Algorithm from Perceptron or Passive Aggressive or the others.
the trained model can classify datum from feature vector.
In this case, Jubatus classifier finds a Ruby characteristic feature like "{|"
and highly score for ruby, then Jubatus estimate this source code is Ruby.
Another datum, Jubatus find Python characteristic feature like “):”
Jubatus scores high for this feature and it estimate this source code should be python.
You can do these procedure via Jubatus RPC.
On RPC, giving datum for classification, and Jubatus returns the classification result.
All you have to do is write precise JSON configuration and client source code.
You can
estimate the topic of a tweet
trash spam mail automatically
monitor server failure from syslog
estimate sentiment from blog post
detect attacking via network
calculate what feature is the best clue to classification
You cannot
train model from data without supervised answer
create a class without knowledge of the class
get fine model without correct feature designing
Other information for using classifier is available at jubatus official example repository.
These 4 sample may be useful for study.
Next Jubatus algorithm is recommender.
With this “movie and review rating matrix” which movie should we recommend Ann?
Jubatus can answer.
An imaginary field of highly dimensional rating space.
Star Wars lover and Star Trek lover is relatively close.
Both of them movie is a kind of Science Fiction.
Ann and Emily is relatively close.
These distance is useful for recommendation.
Because Preferences of the human is tend to be similar.
In this case, Ann would like Frozen
Jubatus recommender server consists of Feature Extractor and recommender engine.
Feature extractor is completely the same with classifier’s one.
Jubatus calculates distance between feature vectors.
From former example, jubatus recommender extracts feature vector from source code, and recommender engine maps each vectors in feature space.
You can
create recommendation engine
calculate similarity of tweets
find similar directional NBA player
visualize distance between “Star Wars” and “Star Trek
notice that you can use recommender more than recommender.
Recommender is based on unsupervised algorithm.
So that
You cannot Labeling data(use classifier!)
get decision tree
And it is nearest-neighbor based recommendation so that
get a-priori based recommendation
Another algorithm is Anomaly Detection
It calculates “How this datum is far from others?”
Jubatus can detect the outlier from mass of data.
In easy way, you may use recommender’s distance score for finding outlier
Distance is not homogeneous, it can not be used to discover outliers.
anomaly detection server consists of Feature Extractor and anomaly detection engine.
Feature extractor is completely the same with classifier and recommender’s one.
Jubatus finds outlier from feature vectors
The same wit recommender, Jubatus detect anomaly from Feature Vector
You should access this procedure via RPC too.
You (might) can
find outlier
detect or prediction of server’s failure
protect service against zero-day attack
know the trend of the entire data stream
You cannot
get mostly common datum
get cluster map of data
give a diagnosis the outlier reason automatically
Jubatus have embedded feature extractor with algorithms.
User should configure both feature extractor and algorithm properly
Client use configured machine learning via Jubatus-RPC
Classifier and Recommender and Anomaly may be useful for your task.