React has a Virtual DOM object, for each DOM object which acts as a representation for that original object. While updating DOM virtual DOM acts as the middle layer and only updates real DOM when there are any changes to virtual DOM. In that case react will update only the updated components and not the entire DOM.
AWS offers several methods to cost-effectively deliver live video content on the cloud. This answer provides an AWS solution that combines AWS Elemental Cloud, a service that enables customers to rapidly deploy multiscreen offerings for live and on-demand content, with other AWS services to build a highly resilent and scalable architecture that delivers your live content worldwide.
Namespacing is a technique employed to avoid collisions with other objects or variables in the global namespace. Its Important for helping organized blocks of functionality in the application into easily manageable groups which can be uniquely identified.
What is the difference between supervised and unsupervised machine learning?
Supervised learning requires training labeled data. For example, in order to do classification (a supervised learning task), you’ll need to first label the data and need to train the model to classify data into your labeled groups. Unsupervised learning, in contrast, does not require labeling data explicitly.
Data mining is the extraction of implicit, formerly unknown, and potentially useful information from data. It is applied in a wide range of domains and its practices have become fundamental for several applications.
This article is about the tools used in real Data Mining for finding and describing structural patterns in data using Python. In recent years, Python has been used for the development of data-centric.
DATA IMPORTING AND VISUALIZATION
The very first step of a data analysis consists of obtaining the data and loading the data into the user’s work environment. User can easily download data using the following Python capability:
In the above snippet user has used the library urllib2 to access a file on the website and saved it to the disk using the methods of the File object provided by the standard library. The file contains the iris dataset, which is a multivariate dataset that consists of 50 samples from each of three species of Iris flowers. Each sample has four features that is the length and the width of sepal and petal, in centimetres.
The dataset is stored in the CSV format. It is appropriate to parse the CSV file and to store the informa tion that it contains using a more suitable data structure. The dataset has 5 rows, the first 4 rows contain the values of the features while the last row signifies the class of the samples. The CSV can be easily parsed using the function genfromtxt of the numpy library:
from numpy import genfromtxt, zeros
# read the first 4 columns
data = genfromtxt(‘iris.csv’,delimiter=’,’,usecols=(0,1,2,3))
In the above example user has created a matrix with the features and a vector that contains the classes. The user can also confirm the size of the dataset looking at the shape of the data structures loaded:
print set(target) # build a collection of unique elements
set([‘setosa’, ‘versicolor’, ‘virginica’])
An important task when working with a new data is to understand what information the data contains and how it is structured. Visualization helps the user to explore the information graphically in such a way to gain understanding and insight into the data.
Classification is a data mining function that allocates samples in a dataset to target classes. The models that implement this function are called classifiers. There are two basic steps for using a classifiers: training and classification. The library sklearn contains the implementation of many models for classification.
t = zeros(len(target))
t[target == ‘setosa’] = 1
t[target == ‘versicolor’] = 2
t[target == ‘virginica’] = 3
The classification can be done with the predict method and it is easy to test it with one of the sample:
In this case the predicted class is equal to the correct one (setosa), but it is important to assess the classifier on a wider range of samples and to test it with data not used in the training process.
We do not have labels attached to the data that tell us the class of the samples. The user has to analyse the data in order to group them on the basis of a similar criteria where groups are sets of similar samples. This kind of analysis is called unsupervised data analysis. One of the most famous clustering tools is the k-means algorithm, which can be run as follows:
The snippet above runs the algorithm and groups the data in 3 clusters (as specified by the parameter k). Now the user can use the model to assign each sample to one of the clusters:
c = kmeans.predict(data)
And the user can evaluate the results of clustering, comparing it with the labels that they already have using the completeness and the homogeneity of the score:
from sklearn.metrics import completeness_score, homogeneity_score
The wholeness of the score approaches 1 when most of the data points that are members of a given class are elements of the same cluster while the homogeneity score approaches 1 when all the clusters contain almost only data points that are member of a single class.
The user can also visualize the result of the clustering and compare the assignments with the real labels visually:
subplot(211) # top figure with the real classes
subplot(212) # bottom figure with classes assigned automatically
Despite the rapid adoption of cloud computing and its positive impact on business, there are still some myths and risks, concerns and misinformation around deploying and running applications in the cloud.
In this article we will be going to discuss the myths and the risks and provide the correct information and make you take an informed decision for your business or organization.
MYTH: EVERYTHING WORKS BETTER IN THE CLOUD
Cloud computing has its place in every business and organization, from multi-national corporations and financial institutions to online businesses and start-ups. The common profits of the cloud are faster time to market and streamlined processes and flexible infrastructure costs which are difficult to ignore. But just because the cloud has its place in every business, does not mean it is right for every task.
The public cloud might be best for some actions, private cloud for others, and dedicated hosting for legacy applications. Getting the right overall solution often requires a combination public, private, and dedicated infrastructure.
RISK: SECURITY RISKS AT THE VENDOR
When a cloud service vendor provides a critical service for your business and stores critical data – such as customer payment data and the mailing lists then you are placing the life of your business in the vendor’s hands.
Many small businesses know almost nothing about the people and technology behind the cloud services they use.
When the person is depending on a cloud service for their business-critical task, then they put the trust of their business into the hands of other people and the quality of their work.
The reputation is no longer depends on the integrity of only their business – now it also depends on the integrity of the vendor’s business. And that’s a cloud computing risk.
MYTH: DATA IS NOT AS SECURE IN THE CLOUD
A security breach could bring down your site and may cause the users to lose their valuable revenue. Therefore, it is not surprising that security concerns are one of the largest hurdles for many businesses considering the cloud. However, security is also one of the easiest cloud myths to debunk.
In reality, security risks in the cloud are the same as those faced by traditional IT solutions, but with one main difference: When operating in the cloud, security no longer rests on your shoulders alone. Instead, security is a shared responsibility with the cloud hosting provider.
RISK: RISKS RELATED TO LACK OF CONTROL
When the user host and maintain a service on a local network, then the user has complete control over the features he/she choose to use. However, when the user uses a cloud service provider, the vendor will have the control. The user has no guarantee that the features using today will be provided for the same price tomorrow or not. The vendor can double its price, and if the clients are depending on that service, then the user might be forced to pay. Also, who controls access to the user’s data in a cloud service? What happens if the user is not able to make payment?
If the user gets behind on their bill, then the user may be surprised to find their data is held hostage by the vendor. The user cannot access the service and export the data until he/she pay up.
MYTH: IT IS ALWAYS CHEAPER TO RUN IN THE CLOUD
It is not always cheaper to run in the cloud, but it can often be more cost effective. If you need all your servers running 24x7x365, it is likely you can get the same compute power for less cost using a dedicated server.
Cloud works best for variable demands and workloads, where you have higher demand at certain times and lower demand at others. The cloud allows to switch servers off during periods of lower demand to improve cost efficiency by more closely matching the cost pattern to the user’s revenue/demand pattern.
RISK: AVAILABILITY RISKS
No service can guarantee 100% uptime. When you rely on a cloud service for a business-critical task, then you are putting the feasibility of the business in the hands of two services: the cloud vendor and ISP.
If the user’s internet access goes down, then it will also take your vendor’s cloud service with it.
MYTH: CLOUD TECHNOLOGY IS STILL IN ITS INFANCY
A latest ISACA study revealed that cloud computing is fast approaching, within the next few years, you can expect to see constant innovation at an ever-increasing pace. Constant refinement will help to ensure that cloud computing meets the needs of every size and type of business. Those who harness the cloud now will be the first to reap its long-term rewards.
In today’s world, much of Internet’s traffic is from mobile devices. This makes mobile users just as much of a target as computer users. Mobile scams and malware are on the rise, and scammers are budding to get smarter at tricking the user into giving up the user’s data. Explore the different types of mobile scams below and the best ways to identify them and keep your data safe.
SOCIAL MEDIA SCAM
If you are on Twitter, Facebook, and other social media sites you know how annoying it is to be followed by a confused name with zero followers, then receive a tweet with little more than a link. Facebook is full of fake profiles, some of which have acquitted intentions, but most are there to provide fake Likes on demand – or to spam genuine users with phishing links. However, never click on a link sent by an unfamiliar person. Even if it seems harmless enough at the time, you could end up either giving your valuable personal details to a criminal or in worse scenario have malware inserted onto your mobile.
Same old-fashioned methods are applied over SMS. User’s will get a message from unknown numbers influencing the user to reply to a number or to click on a link and open it in the user’s phone browser. This might likely insert malware onto the user’s phone or alerts the scammers to the fact that the number is active and worth targeting again.
These scams can present themselves in many forms. For instance, misleading offers such as “free” ringtones, lotteries offer, and much more. There are few ways scammers can get the user with ringtone scams. Sometimes those “free” ringtones are an actual subscription service, and if the user’s respond he/she may unknowingly have signed up for the service. Other times, they come embedded with malware.
SMS phishing or Smishing will use some scary tactic to get a quick reply without much time to think about the action. When in the form of a notice from a bank or financial institution, the phisher requests immediate action or the user’s account will be closed. If the user feels that his/her accounts are in danger, check for the company’s customer service phone number online and call them to verify the text.
Scanners will read the user’s cell phone identity, including the number and its unique serial number, then they program another phone with the same details and make calls at the user’s expense.
VOTING WITH PHONE
People will receive text messages or recorded messages offering them the chance to cast their vote through phone, simply by pressing a key for each of the candidates.
This is one trick targeting the voters of one political persuasion or another, to stop the voters from actually casting their vote for real.
SINGLE RING SCAMS
Scammers will call once and end up the call, with the objective of tempting the user to call back to that number. These numbers can be high toll lines, and by calling them the user might end up paying a premium rate for the phone call.
2016 was good for DevOps: We saw more enterprise adoption of containers and more organizations throwing their hats into the container ring. That doesn’t mean that the tools surrounding DevOps are mature. According to experts, 2016 set the stage for security enhancements, containerization, and consolidation.
The next phase of DevOps will focus more on security. In 2017, building security practices as code will be part of application development, rather than applying it post-facto. This will lead to DevOps going beyond Dev, QA, and Ops. So, explore this article and the top five DevOps predictions for 2017.
TOP 5 DEVOPS
Considering technology on front, we will see increase in popularity of containerization solutions Docker because of its ability to provide constant environment from development to production. Next year it will be more popular with non-production environments, and as it develops it will see similar popularity for production environments. One of the key reasons for this popularity is its portability across multi-cloud platforms.
EFFORTS TO ADDRESS ENTERPRISE CONCERNS WILL INCREASE
With an increased experience of implementing next gen platforms and automatically generating containers, there will be a greater focus on enterprise concerns, such as access controls, audit trails, and network technologies that can implement “virtual firewalls” at the level of the orchestration tier.
DevOps will extend into pay-as-you-go
We will see more cloud implementations of DevOps to meet the needs of an on-demand model. Technology solutions which orchestrate across cloud providers will only accelerate that adoption by abolishing the risk of cloud provider locking. Customers can easily switch over to a low-cost provider and can profit from the elastic nature of the cloud pricing model.
MORE AUTOMATED CODE
In 2016, organizations began introducing tools to decrease the tedium of finding a line of error code in applications, and 2017 will see more automation for developers. The automation will revolve around code testing, gathering and formatting data, reporting, and notifications.
“Coding through automation and machine learning will be more dominant than previous years, but now it is possible due to new hardware and techniques” such as GPUs and parallel computing.