Trending November 2023 # How To Recolour Vector Images With Adobe Firefly. # Suggested December 2023 # Top 20 Popular

You are reading the article How To Recolour Vector Images With Adobe Firefly. updated in November 2023 on the website We hope that the information we have shared is helpful to you. If you find the content interesting and meaningful, please share it with your friends and continue to follow and support us for the latest updates. Suggested December 2023 How To Recolour Vector Images With Adobe Firefly.

If you are playing around with vector images and need to recolour something but don’t have any dedicated vector editing software. This article will show you how to quickly and easily use Adobe Firefly to change the colour scheme of vector images using AI. The process is super easy and has some pretty interesting methods and options for changing vector colours.

Vector images are digital graphics that are created using mathematical equations to define geometric shapes such as lines, curves, and points. Unlike traditional raster images, which are made up of pixels, vector images can be scaled up or down infinitely without losing resolution or quality which is why they are such popular image formats for logos and other content that needs clean sharp lines and edges. They also work flawlessly with printers and print mediums.

The only problem with Vector files is that they require speciality software to create and edit. While there are plenty of tools around they can be expensive and have a rather steep learning curve to master the basics. This is where Adobe Firefly aims to simplify things. Using Adobe Firefly – Recolour Vectors, you can quickly and easily change the colours and colour theme of vector image files.

How do you Recolour Vector Images with Adobe Firefly? Adobe Firefly Vector Recolour.

This will take you to the main page where you can upload your SVG file. Sadly this is the only file format that works with Adobe Firefly Recolour but there is a good reason for that which we explained earlier. Recolouring different file formats would have far less desirable and quality results. It’s just the nature of the beast.

Now that you have uploaded your file you’ll be able to start recolouring. So enter the colour scheme you want to achieve for your image. Sometimes really descriptive requests work well, while others keeping things simple works better. You’ll need to experiment a little.

On the next page, you’ll be given quite a few more colour options that you can play with and some extra options under the Harmony heading. The options under the Harmoney heading change the way colours are replaced giving slightly different effects. You’ll need to experiment with these because they do vastly different things and don’t seem to have a specific general rule. At least with the example SVG vector file I’m using.

When you find an image that you like the colour scheme of, hover your mouse over the image to reveal some extra colour options. This will show you a shuffle icon and a list of colours which will update the image using those colours in different ways. This option has a surprising amount of variability so it’s worth experimenting with a little.

You're reading How To Recolour Vector Images With Adobe Firefly.

Make Your Images Clearer And Crisper – Denoise Images With Autoencoders

This article was published as a part of the Data Science Blogathon


“Tools can be the same for everyone, but how a wielder use it makes the difference”

We often think that we should be aware of all the services or tools that are out there in the market to fulfill a required task. Well, I agree with this mentality so much so you can keep exploring and keep experimenting with different combinations of tools to produce useful inferences. But this doesn’t mean that I don’t favor the mentality of thinking creatively with what you already have. Many of the times, we all have a certain range of knowledge regarding any domains and promoters that can make the work easy and simple and at the same time no 2 people can know exactly the same amount and type of knowledge. This would mean that we all are unaware of various possibilities that collectively other people might know.

Today, I will try to focus on the idea of thinking creatively with what you already know.

This article talks about a very popular technique or architecture in deep learning to handle various unsupervised learning problems: AutoEncoders. 

AutoEncoders is a name given to a specific type of neural network architecture that comprises 2 networks connected to each other by a bottleneck layer (latent dimension layer). These 2 networks are opposite in terms of their functionality and what they provide with their execution. The first network is called an Encoder, which takes the input examples and generates numerical values to represent that data in smaller dimensions (latent dimension). The encoded data is targeted to be responsible for understanding the raw input in a lower number of values (saving the nature and features of the raw input as much as possible). This output that you get as a smaller dimension encoded representation of input data is what we call a bottleneck layer(latent dimension layer).

The second network in the architecture is connected just after the bottleneck layer, making the network work in continuation as those latent dimensions are now the input to the second network, also known as Decoder.

 Here is how a basic Autoencoder network looks like:

Autoencoder Network

Let us look at the code to teach a neural network, how to generate images from the fashion MNIST dataset. Check more information about the dataset on the attached link and for your brief understanding, just know that this dataset contains greyscale images of size (28,28) which have 10 classes based on fashion clothing objects.

Importing necessary libraries/modules:

import tensorflow as tf import numpy as np import matplotlib.pyplot as plt import pandas as pd from tensorflow.keras.datasets import fashion_mnist from tensorflow.keras import layers, losses from tensorflow.keras.models import Model 1. Retrieve the data

Once, we retrieve the data from tensorflow.keras.datasets, we need to rescale the values of pixel range from (0-255) to (0,1), and for such we will divide all the pixel values with 255.0 to normalize them. NOTE: As autoencoders are capable of unsupervised learning (without Labels) and this is what we wish to achieve through this article, we will ignore labels from the training and testing dataset for fashionMNIST.

(x_train, _), (x_test, _) = fashion_mnist.load_data() x_train = x_train.astype('float32')/255. x_test = x_test.astype('float32')/255. print(x_train.shape) print(x_test.shape)

As you can see, after running this code, we have 60,000 thousand training images and 10,000 testing images.

2. Adding the Noise

The raw dataset doesn’t contain any noise in the images but for our task, we can only learn to denoise the images that have noise already in them. So for our case, let’s add some noise in our data.

Here we are working with greyscale images, that have only 1 channel but that channel is not mentioned as an additional dimension in the dataset, so for the purpose of adding noise, we need to add a dimension of value ‘1’, which corresponds to the greyscale channel for each image in the training and testing set.

x_train = x_train[..., tf.newaxis] x_test = x_test[..., tf.newaxis] print(x_train.shape) ## Adding Noise, with Noise intensity handled by noise_factor noise_factor = 0.2 x_train_noisy = x_train + noise_factor*tf.random.normal(shape=x_train.shape) x_test_noisy = x_test + noise_factor * tf.random.normal(shape=x_test.shape) x_train_noisy = tf.clip_by_value(x_train_noisy, clip_value_min = 0., clip_value_max = 1.) x_test_noisy = tf.clip_by_value(x_test_noisy, clip_value_min = 0., clip_value_max = 1.)

Let’s visualize these noisy images:

n = 10 plt.figure(figsize=(20,2)) for i in range(n): plt.subplot(1, n, i+1) plt.imshow(tf.squeeze(x_train_noisy[i])) plt.title('Original + Noise') plt.gray() 3. Create a Convolutional Autoencoder network

we create this convolutional autoencoder network to learn the meaningful representation of these images their significantly important features. Through this understanding, we will be able to generate a denoised version of this dataset from the latent dimensional values of these images.

class Conv_AutoEncoder(Model): def __init__(self): super(Conv_AutoEncoder, self).__init__() self.encoder = tf.keras.Sequential([ layers.Input(shape=(28,28,1)), layers.Conv2D(16, (3,3), activation='relu', padding='same', strides=2), layers.Conv2D(8, (3,3), activation='relu', padding='same', strides=2) ]) self.decoder = tf.keras.Sequential([ layers.Conv2DTranspose(8, kernel_size=3, strides = 2, activation='relu', padding='same'), layers.Conv2DTranspose(16, kernel_size=3, strides=2, activation='relu', padding='same',), layers.Conv2D(1, kernel_size=(3,3), activation='sigmoid', padding='same') ]) def call(self,x): encoded = self.encoder(x) decoded = self.decoder(encoded) return decoded conv_autoencoder = Conv_AutoEncoder()

We have created this network using TensorFlow’s Subclassing API. We inherited the Model class in our Conv_AutoEncoder class and defined the network as individual methods to this class (self.encoder and self.decoder).

Encoder: We have chosen an extremely basic model architecture for the demonstration purpose, which consists of an Input Layer, 2 Convolutional 2D layers.

Decoder: For upsampling the images to their original size through transpose convolutions, we are using 2 Convolutional2D Transpose layers and 1 mainstream convolutional 2D layer to get the channel dimension as 1 (same as for grayscale images).

Small to-do task for you: use conv_autoencoder.encoder.summary() and conv_autoencoder.decoder.summary() to observe a more detailed architecture of these networks.

4. Training the Model and Testing it

As discussed above already, we don’t have labels for these images, but what we do have is original images and noisy images, we can train our model to understand the representation of original images from latent space created by noisy images. This will give us a trained decoder network to remove the noise from the noisy image’s latent representations and get clearer images.

TRAINING:, x_train, epochs=10, validation_data = (x_test_noisy, x_test))

Training loss and validation loss, both seem to be almost exact which means there is no overfitting and also there is no significant decrease in both the losses for the last 4 epochs, which says that it has reached the almost optimal representation between the images.


conv_encoded_images = conv_autoencoder.encoder(x_test_noisy).numpy() conv_decoded_images = conv_autoencoder.decoder(conv_encoded_images).numpy()

Visualizing the Effect of Denoising:

n = 10 plt.figure(figsize=(20, 4)) for i in range(n): # display original ax = plt.subplot(2, n, i + 1) plt.imshow(x_test_noisy[i]) plt.title("original") plt.gray() ax.get_xaxis().set_visible(False) ax.get_yaxis().set_visible(False) # display reconstruction ax = plt.subplot(2, n, i + 1 + n) plt.imshow(conv_decoded_images[i]) plt.title("reconstructed") plt.gray() ax.get_xaxis().set_visible(False) ax.get_yaxis().set_visible(False)

Well, that was it for this article, I hope you understood the basic functionality of autoencoders and how they can be used for reducing the noise in images.

Gargeya Sharma

For getting more info check out my Github Homepage

LinkedIn       GitHub

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How To Make 3D Vector Globe In Illustrator

Knowing how to create a 3D vector globe in Illustrator can be very rewarding. Globes sometimes play a big part in logos, branding, and other artwork for companies and individuals alike. With the tools and features available, anyone can use Illustrator to achieve their goals. There are so many ways to create the same artwork, so no one method is exclusive.

How to make 3D Vector Globe in Illustrator

There are many ways to create 3D vector globes but as long as you and your client are satisfied with the result it is ok. The way that this article will show you is super easy, and anyone can follow. There will be two 3D vector globes demonstrated here, they are easy to follow and recreate.

Decide on the purpose

Go to Illustrator

Vector globe with horizontal lines

Vector globe with horizontal and vertical lines

1] Decide on the purpose

The purpose of the globe will decide on the look and color that is used. The purpose will decide the color and the size of the canvas. The globe may be a small part of a bigger project, for example, the globe may be used to fill in the letter O in a word logo. The globe may also be the base for a logo so it will need to be larger.  Make sketches of the whole project and where the globe will fit. Decide on the type of globe that you want, is it the globe with horizontal lines or the globe with horizontal and vertical lines? Decide if the globe will be for screen only, print only, or a mix of both, this will help to decide the resolution that you will choose when creating the new document. For display on screens, only a resolution of 72 ppi is ok.  For print, you would need to have a resolution of 300 ppi. The use of the globe will also decide the color mode whether CMYK or RGB. CMYK is best for print and has fewer color options, it is not as bright as RGB. RGB is best for display on screens. Screens usually have a wide range of colors that can be displayed so RGB color mode is best.

2] Go to Illustrator

Open Illustrator and create a new file for the globe. Since the globe is a circle, the canvas can be made to be a square. However. If you decide to design more artwork that will include the globe, it will decide on the orientation and the size of the canvas.

While in Illustrator go to File then New and a New document dialogue window will open. In the new document dialogue box, you will choose the options that you want. For this project, the globe is the only thing that will be created, the Width is 1200 px, the Height 1200 px, the Color mode is CMYK, and the resolution is 300 ppi. You can use any values that you want basing on your needs. Remember that this will be a vector image so stretching or shrinking it will not affect the quality. There is just one thing that you need to do after it is finished and that will be discussed later in the article. When you have finished choosing the options, press Ok to confirm or Cancel to close the window. you will see the canvas appear based on the options chosen. As mentioned before two globes will be designed to show you how they are done. The first will be the globe with only horizontal likes then the other will be the globe with horizontal and vertical lines.

3] Vector globe with Horizontal lines

This globe will use horizontal lines so draw the rectangle wider horizontally and narrower vertically. Make the rectangle any color, gradient, or pattern you want. Here is the one that will be used for this globe. The colors can be changed when the globe is finished so you can leave them black if you so choose. Note also that you do not have to make the rectangles large, they can be normal size. They will be made to fit whatever size your globe is.

You should have something like this. Don’t worry if it looks slightly different, your rectangles may be narrower or have less space. But you can adjust as needed or leave it as is.

When you drag them into the Symbols pallet a window will appear asking you to name the symbols. You can give them a name or just press ok. You will see the rectangles appear in the symbols pallet. Note that those symbols will only be available in the document they were created in. If you open a new document. You will not see them there. The rectangles will remain on the screen so you can just drag them off the canvas to make space for you to work. Don’t delete them as they can be used for the next globe.

The 3D Revolve options window will appear. look to the bottom of the window and press Preview so you can see changes on the half circle in real-time. When you press the preview button you will see the circle complete with a 3D look.

This is what it looks like after exiting the 3D options window.

This is the globe, it has been colored to show the different sections.

The globe is two in one, which is what helps with the 3D effect. They can be kept together as it is shown above or they can be separated and one can be deleted or used for something else.

4] Vector globe with horizontal and vertical lines

This second globe is just a demonstration that this principle can be used to make a whole lot of other designs. You can experiment with making the lines go in any direction and the outcome would look different.

You would follow all the steps above for the globe with the horizontal lines, what would be different is the fact the rectangle strips would be placed in the form of a grid. You can achieve this grid by following the steps above and making the rectangle strips horizontally, do the first one then copy and paste the second one then use Ctrl + D to duplicate as many as you need.

This is what the grid will look like when the process is finished.

With the grid completed, you would follow all the steps above for the previous globe to complete this new globe.

The new globe will come out looking almost like the first one. The only difference is that the new globe has vertical and horizontal lines. The option for various designs is only limited by your imagination.

You can pull apart the globe and you will see the two separate parts.

This is the globe with both parts together and some gradient added. The piece at the back should be given a different and darker gradient than the front so that the 3D effect can be created. They will still look 3D when they are apart as shown above.

Both globe designs are great for logos, branding, and other artwork for personal or professional use. Look closely at them and see if you have seen these designs or similar logos or branding for real companies.

Vector graphics are very handy because they do not distort when they are stretched or shrunk. However when you create art in Illustrator there is an important step to take to keep the art from changing when you stretch or shrink it. You need to select the artwork and go to Object then Path then Outline stroke.

Read: How to turn Hand Drawings into Vector with Illustrator

How do you make a 3D globe in Illustrator?

To create a 3D globe in Illustrator you need to first draw a sphere. You start by drawing a circle and cut that circle into a half circle. Then add the 3D revolve effect and you have a perfect sphere. You even have some control over the surface texture and the light source. You can then use the Symbols pallet on the right to add lines or map on the sphere.

How do you make a globe shape in Illustrator?

In order make a globe shape in Illustrator, you need to make the help of the Eclipse tool first to make the framework. However, as it makes a 2D globe, you need to use the 3D Revolve tool to make it a 3D shape. Once the 3D shape is made, you can customize it with various color, and other options as per your requirements.

Adobe Slate Review: Make Beautiful Stories To Share With Others

Whether you are creating a Christmas newsletter to send to family or a weekly update on the progress of a project to your boss, you can make your presentation look fantastic with Adobe Slate for iPad.

Adobe’s new creativity app allows users to build their stories from specially designed templates, which are uploaded to a website so they can be shared with anyone. We’ve got an app review of Adobe Slate for you today.


The app is built around the idea that anyone can present an attractive story, newsletter, or announcement for others using pictures, text and website links. The final presentation is uploaded to a special web page on chúng tôi Users can then share the story to others via the unique link. You can keep the page private, or allow anyone to see your masterpiece.


The pre-made stories serve as an inspiration to help you create something. Whether you are showing off your family vacation photos in a scrapbook setting, inviting family to your college graduation, or telling your customers about an upcoming sale, you can get ideas for your story by exploring others’.

App Use

Users must log in using their Adobe ID to use this app. If you don’t have one, an account is free and you do not need a subscription to Creative Cloud to use it. Once logged in, you can either explore the story feed or start creating your own project.

When creating a new project, you will start with a Title and subtitle if necessary. For the title portion, you can add a background image. Then, you will add new sections below.

When adding a text section, you can adjust the font size between normal, Heading 1 and Heading 2. You can also make a numbered or bulleted list, and put the text in italicized quotes.

You can add pictures directly from your iPad, or access them from Creative Cloud, Lightroom, or Dropbox. You can also search for images from copyright-free content provided by Adobe.

Photos can be set as inline, which displays them similar to a blog roll, inline with the rest of the story, fill screen, which fits the image to the screen, but still within the story’s feed, window, which sets the image in the background so that the text or photos above and below it scroll over the top of it, or full width, which displays the picture in its full size.

You can also include a grid of photos, for which it appears you can add an unlimited number of images. I stopped at 24 in my test.

When adding a link, you will create a button. Name the button and then add the URL address. As far as I can tell, there is no way to include a link in the body of the text.

There are 11 different themes, which slightly alters the look of the story with different fonts, background colors, and layout styles.

When finished, tap the Share icon to upload the story to the unique web page. Here you can set the link to private so that only people you give it to can see it, or leave it public so that Adobe can share it on their Explore feed for others to see. You can also select to share it on Facebook and Twitter, and through email or text. If you have a website, you can create an embed code for it, as well.

The Good

It is incredibly easy to put together a story with this app. I built a quick newsletter for a friend who owns a comic book shop in a matter of minutes. Everything is easy to use and works seamlessly.

You can also duplicate a story. So, if you send out weekly emails with some of the same content (like business addresses, etc.) you can make a copy and edit the duplicate with new information, preserving the original template design.

Any edits you make will also be saved as long as you hit the Share icon again so the changes are uploaded.

The Bad

I didn’t find anything wrong with this app. It worked perfectly and was easy to use. I was never confused or stuck trying to figure out how to add or remove anything.


Adobe Slate is free to download and use. If you are a Creative Cloud subscriber, you can access that content from within the app. There are no in-app purchases. Everything available is provided to all Adobe ID account holders, whether you pay for a subscription or not.


This is a great program for people who send weekly letters or updates to friends, family, or customers. I highly recommend it for anyone wishing to reach out to others with an attractive format, but don’t have the time (or skills) to build it themselves. Download it in the App Store today.

Related Apps

Adobe Voice is very similar, but acts more as a slideshow or mini movie, using your voice to tell the story.

Support Vector Machine – Simplified

The first time I heard the name “Support Vector Machine”, I felt, if the name itself sounds so complicated the formulation of the concept will be beyond my understanding. Luckily, I saw a few university lecture videos and realized how easy and effective this tool was. In this article, we will talk about how support vector machine works. This article is suitable for readers who do not know much about this algorithm and have a curiosity to learn a new technique. In following articles we will explore the technique in detail and analyze cases where such techniques are stronger than other techniques.

What is a classification analysis?

Let’s consider an example to understand these concepts. We have a population composed of 50%-50% Males and Females. Using a sample of this population, you want to create some set of rules which will guide us the gender class for rest of the population. Using this algorithm, we intend to build a robot which can identify whether a person is a Male or a Female. This is a sample problem of classification analysis. Using some set of rules, we will try to classify the population into two possible segments. For simplicity, let’s assume that the two differentiating factors identified are : Height of the individual and Hair Length. Following is a scatter plot of the sample.

The blue circles in the plot represent females and green squares represents male. A few expected insights from the graph are :

1. Males in our population have a higher average height.

2. Females in our population have longer scalp hairs.

If we were to see an individual with height 180 cms and hair length 4 cms, our best guess will be to classify this individual as a male. This is how we do a classification analysis.

What is a Support Vector and what is SVM?

Support Vectors are simply the co-ordinates of individual observation. For instance, (45,150) is a support vector which corresponds to a female. Support Vector Machine is a frontier which best segregates the Male from the Females. In this case, the two classes are well separated from each other, hence it is easier to find a SVM.

How to find the Support Vector Machine for case in hand?

There are many possible frontier which can classify the problem in hand. Following are the three possible frontiers.

How do we decide which is the best frontier for this particular problem statement?

The easiest way to interpret the objective function in a SVM is to find the minimum distance of the frontier from closest support vector (this can belong to any class). For instance, orange frontier is closest to blue circles. And the closest blue circle is 2 units away from the frontier. Once we have these distances for all the frontiers, we simply choose the frontier with the maximum distance (from the closest support vector). Out of the three shown frontiers, we see the black frontier is farthest from nearest support vector (i.e. 15 units).

What if we do not find a clean frontier which segregates the classes?

Our job was relatively easier finding the SVM in this business case. What if the distribution looked something like as follows :

In such cases, we do not see a straight line frontier directly in current plane which can serve as the SVM. In such cases, we need to map these vector to a higher dimension plane so that they get segregated from each other. Such cases will be covered once we start with the formulation of SVM. For now, you can visualize that such transformation will result into following type of SVM.

Each of the green square in original distribution is mapped on a transformed scale. And transformed scale has clearly segregated classes. Many algorithms have been proposed to make these transformations and some of which will be discussed in following articles.

End Notes

Support Vector Machines are very powerful classification algorithm. When used in conjunction with random forest and other machine learning tools, they give a very different dimension to ensemble models. Hence, they become very crucial for cases where very high predictive power is required. Such algorithms are slightly harder to visualize because of the complexity in formulation. You will find these algorithm very useful to solve some of the Kaggle problem statement.

Did you find the article useful? Have you used any other machine learning tool recently? How do you think SVM is different when compared to CART/CHAID models? Do you plan to use SVM in any of your business problems? If yes, share with us how you plan to go about it.

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How To Blur Images On A Mac

Posting a picture online comes with risks. Your photo may contain sensitive info or a depiction of someone you don’t want others to see. The good news is that you can easily blur these images before posting them online. There are dozens of available Mac apps to accomplish this. Here, we take you through how you can easily and quickly blur images on your Mac using Skitch or the built-in Photos app.

Blur Images With Skitch

Part of the popular Evernote family of products, Skitch is a fantastic product that everyone should have on their computer.

Launch Skitch if you already have it downloaded or grab it from the Mac App Store.

Look on the left side of the app in the vertical toolbar for “Pixelate,” the second-to-last option. You can also identify it by its icon, which is blurry or pixelated.

Use your mouse to drag the cursor around the area you wish to blur out. This works as a square or rectangle, and you can go back over each blurred area a few times just to be 100 percent sure nothing is visible.

Both ways work the same, so the image doesn’t appear differently if you use one method over the other.

With Skitch, it is really easy to blur images on the Mac. However, if downloading a separate app isn’t for you, there is another method that works with the Photos app, which is pre-installed on Macs.

Blur Images Using the Photos App

Editing a photo using the Photos app won’t “blur” an image in the same way the Skitch method will. Instead, using the edit features available through the Photos app, you can “Retouch” an image and remove any sensitive or unwanted info.

Open the preinstalled Photos app on your Mac. The app should be in your deck or available through Mission Control by pressing F4 on your keyboard.

When the edit screen pops up, look for the “Retouch” option about halfway down on the right side of the app.

With the Retouch tool engaged, drag the mouse icon across any part of an image that you wish to hide. You can increase or decrease the size of the pointer to help cover more or less of an image. Whereas Skitch actually blurs using its feature, the Retouch option is more of a smudge.

If you really need to remove something from an image, Skitch is likely the more preferred option. However, Photos is also free and immediately available on your Mac computer, making it an easy choice to get the job done.

Blur Images Using SnagIt

If Skitch isn’t your app of choice and the Photos app isn’t something you want to use, apps like SnagIt can do a similar job. Apart from just blurring images, SnagIt is jam-packed with a bunch of features, like taking screenshots, recording your screen, and editing photos and videos.

Press and hold your left mouse button and drag your cursor across the part of the image you want to blur.

Frequently Asked Questions Does blurring images affect image size?

In some cases, blurring images can increase or decrease the image size depending on the software you use to edit your image. It can also happen if your exported image is saved in a different file format than its original one.

How to blur parts of images online?

Here are some websites that are free of charge that allow you to blur images or parts of images. To use some of these options, however, you may have to create an account.

What are some additional Mac screen capture software that can blur images?

Here are a few more screen capture software options for Mac that can also edit and blur images.




All screenshots by Ojash Yadav & David Joz

Ojash Yadav

Ojash has been writing about tech back since Symbian-based Nokia was the closest thing to a smartphone. He spends most of his time writing, researching, or ranting about Bitcoin. Ojash also contributes to other popular sites like MakeUseOf, SlashGear, and MacBookJournal.

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