PaperEdits is a proofreading and editing service that aims to help students improve their writing. It offers services like editing documents to fix grammar, punctuation, and organization, as well as typing handwritten documents and providing document outlines. The business wants to boost students' grades and career prospects through high-quality writing assistance. It plans to target high school and college students in Chicago on a budget of $731.49, with projections of editing 700 documents in the first year for $11,200 in revenue. Marketing will involve flyers, social media, and word-of-mouth. The founder is qualified through AP English courses and plans to expand the business by adding services and hiring employees.
The document outlines strategies for rapid online responses to crises, emphasizing the need for preparedness and the effective use of social media and technology in engagement. Following the 2010 Haiti earthquake, it highlights the limitations of waiting until an emergency occurs and the importance of having established contacts and processes in place. Key considerations include staff readiness, emergency response planning, and ensuring technological capabilities can handle increased demands.
This document discusses using BPEL and Java EE to create composite applications. It begins by explaining why services and composite applications are important in SOA. It then provides an overview of BPEL, including how it allows the orchestration of services defined by WSDL. The document demonstrates how to build a sample loan processing composite application in BPEL that integrates existing Java EE services. It summarizes that SOA enables flexible applications, BPEL is used to orchestrate services, and Java EE and JBI provide the runtime environment.
This 3-step guide shows how to prevent headphones from tangling by wrapping them in a figure eight, securing them with a binder clip, and providing links to purchase headphones from popular retailers. Wrapping the headphones tightly in a figure eight shape and then clipping them together with a binder clip keeps them neatly organized and untangled so they are ready for use.
This document provides a comprehensive exam response for an MSN Nursing Education degree. It discusses several key concepts:
1) It summarizes Florence Nightingale's early contributions to nursing theory and the role of theory in nursing education.
2) It analyzes Virginia Henderson's Principles and Practice of Nursing theory and how it aligns with the philosophy of "see one, do one, teach one".
3) It examines the National League for Nursing's eight core competencies for nursing education and provides examples of how to demonstrate competencies related to facilitating learning and learner development.
惆惘 悋 悋惘悋悧 惡 惆惘悽惠 惠惶 惆惘 惆悋惆 擧悋 拆惘惆悋悽惠 愆惆 惡 悋 悴惆惆 惆惘 悋 惘悋惡愀 擯惘惆悛惘 愆惆 悋愕惠 .
In this presentation, the decision tree has been introduced and some new concepts have been gathered in this topic.
my email address for question is :
kh.asaditavakkol@gmail.com
Life of Points (Machine learning with Orange flavor) khalooei
油
Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it learn for themselves.
1) EIRDH-P combines public key cryptography and steganography by encrypting images and embedding secret messages within the encrypted images.
2) It involves an Image Provider who encrypts a cover image using public key encryption. A Data Hider then embeds a secret message within the encrypted image.
3) The receiver decrypts the stego-image to extract the secret message and recover the original cover image using their private key.
The document discusses using genetic algorithms and memetic algorithms to optimize wireless sensor network design parameters for energy efficiency while meeting application requirements. It proposes encoding sensor network characteristics and applying genetic operators to minimize energy use and maximize sensing uniformity over time. A memetic algorithm hybridizes this genetic algorithm with local searches that change sensor operating modes based on battery thresholds to further improve energy conservation. Evaluation shows the memetic algorithm enhances network lifetime compared to the genetic algorithm alone.
惆惘 悋 悋惘悋悧 惡 惆惘悽惠 惠惶 惆惘 惆悋惆 擧悋 拆惘惆悋悽惠 愆惆 惡 悋 悴惆惆 惆惘 悋 惘悋惡愀 擯惘惆悛惘 愆惆 悋愕惠 .
In this presentation, the decision tree has been introduced and some new concepts have been gathered in this topic.
my email address for question is :
kh.asaditavakkol@gmail.com
Life of Points (Machine learning with Orange flavor) khalooei
油
Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it learn for themselves.
1) EIRDH-P combines public key cryptography and steganography by encrypting images and embedding secret messages within the encrypted images.
2) It involves an Image Provider who encrypts a cover image using public key encryption. A Data Hider then embeds a secret message within the encrypted image.
3) The receiver decrypts the stego-image to extract the secret message and recover the original cover image using their private key.
The document discusses using genetic algorithms and memetic algorithms to optimize wireless sensor network design parameters for energy efficiency while meeting application requirements. It proposes encoding sensor network characteristics and applying genetic operators to minimize energy use and maximize sensing uniformity over time. A memetic algorithm hybridizes this genetic algorithm with local searches that change sensor operating modes based on battery thresholds to further improve energy conservation. Evaluation shows the memetic algorithm enhances network lifetime compared to the genetic algorithm alone.
This document contains assembly code macros and variables for a game involving planes, ships, and helicopters on different levels. It defines macros for drawing graphics, handling input, random number generation, and dynamic movement of objects. Variables track positions, speeds, and statuses of objects and the level parameters. The code implements game logic and object behavior through calls to the macros.
2. 悋悋惠
1. Large-scale experimental evaluation of GPU
strategies for evolutionary machine learning
Mar鱈a A. Franco, Jaume Bacardit
2016 Elsevier B.V. All rights reserved
2. Improving the scalability of rule-based
evolutionary learning
Jaume Bacardit, Edmund K. Burke, Natalio Krasnogor
2009
2
9. BioHEL
Procedure BioHEL general workflow
Input : TrainingSet
RuleSet =
stop = false
Do
BestRule = null
For repetition=1 to NumRepetitionsRuleLearning
CandidateRule = RunGA(TrainingSet)
If CandidateRule is better than BestRule
BestRule = CandidateRule
EndIf
EndFor
Matched = Examples from TrainingSet matched by BestRule
If class of BestRule is the majority class in Matched
Remove Matched from TrainingSet
Add BestRule to RuleSet
Else
stop = true
EndIf
While stop is false
Output : RuleSet
9
25. Experimental design
Intel(R) Core(TM) i7 CPU
8 cores
3.07 GHz
12 GB of RAM
Tesla C2070
6 GB of internal memory
448 CUDA cores
14 multiprocessors x 32 CUDA Cores/MP
25
26. Experimental design
the serial experiments are run in Intel Xeon E5472 processors at 3.0 GHz
26
27. Experimental design
Pentium 4
3.6 GHz
hyper-threading
2 GB of RAM
Tesla C1060
4 GB of global memory
30 multiprocessors
27
28. Synthetic datasets Result
28
Run-time of the two GPU strategies on
synthetic problems
#Windows=1 and population size=500
Log scale is used for both axis
disc = discrete attributes
real = continuous attributes
29. Synthetic datasets Result
29
Run-time per instance of the two GPU strategies on synthetic problems
#Attributes=300, #Windows=1 and population size=500
Log scale in x axis
disc = discrete attributes
real = continuous attributes
30. Synthetic datasets Result
30
Run-time per attribute of the two GPU strategies on synthetic problems
#Instances=1 M, #Windows=1 and population size=500
Log scale in x axis
disc = discrete attributes
real = continuous attributes
31. Synthetic datasets Result
31
Run-time per individual of the two GPU strategies on synthetic problems
#Instances=1 M, #Attributes = 300 and #Windows=1
disc = discrete attributes
real = continuous attributes
32. Synthetic datasets Result
32
Run-time per individual of the two GPU strategies on synthetic problems
#Instances=1 M, #Attributes = 300 and #Windows=1
disc = discrete attributes
real = continuous attributes
33. Synthetic datasets Result
33
Execution time in seconds of the evaluation process of the serial version and
both CUDA fitness functions with windowing disabled
34. Experiments on real-world datasets
34
Speedup against the serial algorithm without using
windowing of the different parallelisation
approaches ran on different architectures
35. Experiments on real-world datasets
35
Independent evaluation process, coarse-grained
strategy on the C1060 GPU Card
Problems:
black = continuous
red = mixed
blue = discrete
(For interpretation of the references to colour in this
figure legend, the reader is referred to the web
version of this article)
36. Experiments on real-world datasets
36
Independent evaluation process, coarse-grained
strategy on the C2070 GPU Card
Problems:
black = continuous
red = mixed
blue = discrete
(For interpretation of the references to colour in this
figure legend, the reader is referred to the web
version of this article)
37. Experiments on real-world datasets
37
Independent evaluation process, fine-grained
strategy on the C2070 GPU Card
Problems:
black = continuous
red = mixed
blue = discrete
(For interpretation of the references to colour in this
figure legend, the reader is referred to the web
version of this article)
