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Introduction to Caffe TensorFlow

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How does Caffe TensorFlow work?

It is an open-source GitHub repository which consumes prototxt file as an input parameter and converts it to a python file. Thus, with this, the Caffe model can be easily deployed in the TensorFlow environment. The pre-trained baseline models can be easily validated by using a validator file written in Python. For the older Caffe Models, upgrade_net_proto_text and upgrade_net_proto_binary files have to be used for first upgrading them to the latest version supported by Caffe and then following the subsequent steps mentioned inline to deploy it to the TensorFlow environment. It has one constraint that is the user needs to have a Python 2.7 environment to access it. Also, Caffe and TensorFlow models cannot be invoked concurrently. So, a two-stage process is followed. First, the parameters are extracted and converted using the converter file, which is then fed into the TensorFlow in the last stage. Also, the user’s border values and padding have to be taken care of as it is handled differently in both Caffe and TensorFlow.

The below steps describe how the user can use the above repository on his/her local machine.

To install Caffe-TensorFlow, use git clone command with the repository path to map it to your local folder.

It uses TensorFlow GPU environment by default which consumes more memory. To avoid getting into this, uninstall the default environment and install TensorFlow CPU.

Convert the Caffe model into TensorFlow by using python executable command with the chúng tôi file. It will take verbose parameters like Caffe model path, the prototxt file path, the output path where weights and other parameters related to the model are stored, the converted code path and a standalone output path which has a pb file generated if the executed command is successful. This file stores the model weights and the corresponding architecture.

Following steps can be followed by the user:

The model weights can be combined into a single file using a combine python file available as a gist on GitHub. The associated weights in it can be loaded into the user’s TensorFlow computational graph.

The ordering of complex layers used in TensorFlow and Caffe models are different. E.g. the concatenation of the LSTM gates is ordered differently for both TensorFlow and Caffe. Thus, the user needs to have a deeper look at the source code for both the frameworks, which is open-source.

A potential rudimentary first up approach which can be used easily by the user is as follows:

The Caffe Model weights can be exported into a NumPy n-dimensional matrix.

A simple model example can be run for the preliminary N layers of the Caffe Model. The corresponding output can be stored in a flat-file.

The user can load the above weights into his/her TensorFlow computational graph.

Step 2 can be repeated for the TensorFlow computational graph.

The corresponding output can be compared with the output stored in the flat file.

If the output does not match, then the user can check whether the above steps were executed correctly or not.

N’s value can be incremented after every iteration, and the above steps are repeated for its updated value.

Benefits of Caffe TensorFlow

The Caffe Models are stored into a repository called Caffe Model Zoo. This is accessed by the researchers, academicians, scientists, students etc. all over the world. The corresponding models associated with it can be easily converted into TensorFlow. This makes it computationally faster, cheaper, less memory-intensive etc. Also, it increases the user’s flexibility and usage as the user does not have to implement the same Caffe Model into TensorFlow from scratch. It has also been used to train ImageNet models with a fairly good amount of accuracy. It can be in image classification, speech processing, Natural Language Processing, detecting facial landmarks etc. where Convolutional Networks, LSTM, Bi-LSTM models etc. are used.

Conclusion

The Caffe-TensorFlow Model finds its usage across all industry domains as model deployment is required for both popular deep learning frameworks. However, the user needs to be wary of its limitations and overcome the same while developing the model in Caffe and deploying it in TensorFlow.

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Linux System Call In Detail

Introduction

The system call is a mechanism in Linux that allows user-space applications to connect with the kernel, which is also a component of what makes up the operating system’s core. A user-space application needs to send a system call to the kernel that is used to have a privileged functioning performed on its behalf, including writing or reading information to a file or beginning a new process. In this article, we will be discussing the Linux System Call in detail along with its various types.

How Linux System Calls Work?

The system calls are carried out in the kernel as performs that consumer-space applications can access through the C library’s standard library functions. Among them are open(), read(), write(), close(), fork(), exec(), and numerous other functions.

In most cases, a user-space application puts together a set of disputes, such as the URL of a file to be opened, and moves them to the suitable typical library perform to make a system call. The library operates and then organizes all of the arguments in order that they can be enacted to the kernel, generally by replicating them into the system called table, a special buffering in memory.

The library function calls a trap when its parameters are in the system call table, and this moves the command from user space to the kernel. The kernel then executes the action requested using the system call table arguments and converts the outcome to the user-space utilization via the framework call’s exchange value.

System calls are essential components of the operating system Linux because they enable user-space programs to perform privileged activities in a controlled and safe manner. It also provides an established interface for gaining access to kernel performance, which aids utilization along with system compatibility and seamless integration.

Types of System Calls in Linux

System calls in Linux are classified into five types based on the operation they perform. These are the categories −

Process management system calls − These system calls are used to manage processes, such as starting new ones, stopping existing ones, and waiting for them to finish. Fork(), exec(), wait(), and exit() are all examples of process management system calls ().

fork() − This system call duplicates the calling process to create a new process. The child process is the new process that runs the same program as the parent process.

wait() − A parent process uses this system call to wait for its child process to terminate. The parent process is halted until the child process completes.

exit() − The present-day process is terminated and a status code is returned to the originating process.

File management system calls − These calls to the system are used to open, read, write, and close documents, as well as to create, rename, and delete them. Some file management system calls () are open(), read(), write(), close(), mkdir(), and rmdir().

open() − broadens a file and goes back to a file descriptor (an integer identifying the open file.

read() − The above system call examines data from an open file into a memory buffer.

write() − It is an internal call that is used to write information stored in a buffer of memory to an open file.

close() − It is a system call for closing a file determined by its file descriptive term.

Device management system calls − These system calls are used to manage I/O devices, such as reading from and writing to them, setting device attributes, and controlling device operations. System calls for device management include read(), write(), ioctl(), and select ().

write() − It is a system call that is used to write data from a memory buffer to an output device.

ioctl() − This system call controls the behavior of a device by setting or retrieving device attributes.

select() − This system call is used to wait for I/O operations to complete on multiple devices.

Network management system calls − These system calls are used to manage network resources such as connecting and disconnecting from networks, sending and receiving data over networks, and resolving network addresses. Socket(), connect(), send(), and recv() are examples of network management system calls ().

socket() − It establishes a socket, and this is a network of things endpoint which can be used to communicate.

connect() − It is an internal call used to establish a connection to an external network endpoint.

send() − It is an application call used to transfer data across a computer’s connection.

recv() − A network connection is used to send data to this system call.

System information system calls − Such system calls are utilized to query and change system parameters such as time, configuration, and performance statistics. System information system calls () include getpid(), getuid(), gethostname(), and sysinfo().

getpid() − The program ID of the process that is currently running is returned by this system call.

getuid() − The system call in question is used to get the user ID of the current process.

gethostname() − This call to the system is used to get the hostname of the system.

sysinfo() − The above system call returns information about the system, such as the amount of memory free and the overall amount of processes.

Conclusion

System calls are a vital component in Linux and other systems of operation that allow consumer-space programs to make use of kernel functionality via a standardized interface. System calls are classified into five categories based on the type of operation they perform: process management, file management, device management, network management, and system information. A single system call group contains a set of operations that allow consumer-space applications to execute particular kinds of operations on the operating system that is underneath. System calls contribute to application and operating system compatibility and interoperability by offering a defined interface for accessing the kernel that is used functionality.

Techniques And Their Implementation In Tensorflow

Introduction to Keras Regularization

Keras regularization allows us to apply the penalties in the parameters of layer activities at the optimization time. Those penalties were summed into the function of loss, and it will optimize the network. It applies on a per-layer basis. The exact API depends on the layer, but multiple layers contain a unified API. The layer will expose arguments of 3 keywords.

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Key Takeaways

Suppose we need to configure the regularization using multiple arguments, then implement the subclass into the keras regularization.

We can also implement the class method and get the config to support the serialization. We can also use regularization parameters.

What is Keras Regularization?

The keras regularization prevents the over-fitting penalizing model from containing large weights. There are two popular parameters available, i.e., L1 and L2. L1 is nothing but the Lasso, and L2 is called Ridge. Both of these parameters are defined at the time of learning the linear regression. When working with tensorflow, we can implement the regularization using an optimizer. We are adding regularization to our code by adding a parameter name as kernel_regularizer. While adding L2 regularization, we need to pass the keras regularizers.l2 () function.

This function takes one parameter, which contains the strength of regularization. We pass L1 regularizers by replacing the l2 function with the l1 function. Suppose we need to use L2 and l1 regularization this is called the elastic net. The weight regularization provides an approach to reducing the overfitting of neural network models for deep learning. Activity regularization encourages the neural network to learn the sparse features of internal representations for the raw observations. It is common to seek the representation of spark known for autoencoders called sparse encoders.

How to Add Keras Regularization?

It will generally reduce the model overfitting and help the model generalize. The regularization is a penalized model for overfitting, as we know it has two parameters. Below we are using the l1 parameter for adding keras regularization.

Below steps shows how we can add keras regularization as follows:

Code:

python -m pip install tensorflow python –m pip install keras

Output:

2. After installing the module of keras and tensorflow now we are checking the installation by importing both modules as follows.

Code:

import tensorflow as tf from keras.layers import Dense

Output:

3. After checking the installation now in this step we are importing the required model which was used in it. Basically, we are importing the dense, sequential, l1, and activation modules. We are importing the dense module from the layers library, a sequential module from the library, an l1 module from the regularizers library, and an activation module from the layers library.

Code:

from sklearn.datasets import make_circles ….. from keras.layers import Activation

Output:

4. After importing the dataset now in this step we are preparing the dataset for it. We are preparing the dataset by using x and y values. Also, we are defining the value of X_train, y_train, X_test, and y_test as follows.

Code:

X, y = make_circles() train = 25 X_train, X_test = X[] y_train, y_test = y[]

Output:

5. After creating the dataset in this step we are creating the neural network model and adding the regularizer into the input layer as follows. We are adding a sequential model and defining the dense layer as follows.

Code:

mod = Sequential() mod.add() mod.add(Activation('relu')) mod.add(Dense(2, activation = 'relu')) mod.summary()

Output:

Keras Regularization Layer

The weight regularization layer of keras is applying penalties to the parameters of layers. The weight regularization layer will expose three keyword arguments as follows:

Kernel Regularizer

Bias Regularizer

Activity Regularizer

The below example shows keras weight regularization layer as follows. This layer is dividing the input batch size.

Code:

from tensorflow.keras import layers from tensorflow.keras import regularizers we_lay = layers.Dense( units = 44, kernel_regularizer = regularizers.L1L2(), … activity_regularizer = regularizers.L2 (1e-5) ) ten = chúng tôi (shape = (7, 7)) * 3.0 out = we_lay(ten) print(tf.math.reduce_sum (we_lay.losses))

Output:

The L1 and L2 regularizers are available as part of a module of regularizers. The below example shows the L1 class regularizers module.

Code:

from tensorflow.keras import layers from tensorflow.keras import regularizers we_lay = layers.Dense ( units = 44, kernel_regularizer = regularizers.L1L2(), … activity_regularizer = regularizers.L2(1e-5) ) ten = tf.keras.regularizers.L1(l1=0.01 * 3.0) print (tf.math.reduce_sum (we_lay.losses))

Output:

The below example shows the L1 class regularizers module as follows. We are importing the layers and regularizers model.

Code:

from tensorflow.keras import layers from tensorflow.keras import regularizers we_lay = layers.Dense( units = 44, kernel_regularizer = regularizers.L1L2(), … activity_regularizer = regularizers.L2 (1e-5) ) ten = tf.keras.regularizers.L2 (l2 = 0.01 * 3.0) print(tf.math.reduce_sum(we_lay.losses))

Output:

Examples of Keras Regularization Example #1

In the below example we are using L2 arguments.

Code:

from sklearn.datasets import make_circles ….. from keras.layers import Activation X, y = make_circles() train = 25 X_train, X_test = X [] y_train, y_test = y [] mod = Sequential() mod.add() mod.add(Activation ('relu')) mod.add(Dense(2, activation = 'relu')) mod.summary()

Output:

Example #2

In the below example, we are using L1 arguments.

Code:

from sklearn.datasets import make_circles ….. from keras.layers import Activation X, y = make_circles() train = 35 X_train, X_test = X[] y_train, y_test = y[] mod = Sequential() mod.add() mod.add(Activation('relu')) mod.add(Dense(2, activation = 'relu')) mod.summary()

Output:

FAQ

Given below are the FAQs mentioned:

Q1. What is the use of keras regularization?

Answer: It is the technique for preventing the model from large weights. The regularization category is applied to the per-layer basis.

Q2. How many types of weight regularization are in keras?

Answer: Basically there are multiple types of weight regularization like vector norms, L1 and L2. It will require the hyper parameter which is configured.

Q3. Which modules do we need to import at the time of using keras regularization?

Answer: We need to import the keras and tensorflow module at the time of using it. Also, we need to import is a dense layer.

Conclusion

There are two popular keras regularization parameters available i.e. L1 and L2. In that L1 is nothing but the Lasso and L2 is called Ridge. It allows us to apply the penalties to the parameters of layer activities at the time of optimization.

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Different Versions Of Imagemagick In Detail

Introduction to Imagemagick version

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Versions of Imagemagick

Imagemagick was created in 1987 by John Cristy, where initially, it was used to convert 24-bit images to 8-bit images with fewer colors than its parent. It became a hit and was freely released to the public in 1990 August. However, after the initial release, there was reporting of bugs that the developers would fix occasionally, and hence there were many changes from the initial release. This made John Cristy release version 4.2.9 by the mid-1990s.

Imagemagick version 5 was developed when the user interface was made more friendly to beginners. More scripts and algorithms were included in the user interface functionalities. Version 5 made users to transfer scripts and algorithms from other languages and use them in Imagemagick. Though Imagemagick was developed in C, the enhancements and modules were developed in C++, and it is called Magick++. Several functionalities such as module loader, file identification, and test suites were added to Imagemagick using C++.

Imagemagick had changed its look and form in version 5. Going forward from version 5, a bug was found in the command line where if the users had many images to manage, it looks bulky and confusing. It became important to fix the command line as most users work with the command-line interface than the application’s user interface. The scripts used were mostly BASH and Perl that made necessary changes to the command line, which made the impossible possible by creating canvas in the command line interface. Initially, batch scripts were used that made the work easy in Windows, but it was difficult to use in Linux and other operating systems. So, windows batch scripts were modified to PHP scripts, and Bash scripts were introduced for other operating systems.

Version 6 also made it possible to use any scripts on the command line interface comfortable for the users and make it work on the functionalities. This works only for a single image at a time, and the user must create the API if he/she is developing in their own scripting language. We can also generate scripts by inputting images into the application. We can generate a text file, and the application produces images of the same on the web page. This helps to download the images directly from the application. It should be noted that images have different formats and hence browser support is necessary to get the image in the desired format. Imagemagick changes the font to Arial or Times New Roman font without any warning if the required font is not present.

Different versions of Imagemagick 6 saw changes in command line scripts mainly in the form of geometry, blurs, sharpening images, color changes, edging of images, and noise removal in the images. Furthermore, in addition to the C++ wrapper in Imagemagick, a .NET wrapper was provided in this version that helps users to make enhancements in their application either with C++ or .NET.

We have only versions from 6 available on the website, which was released in 2024. Previous releases are archived, and version 6 are legacy releases that can either be kept by the user or updated to the newer version. We can download these versions from the index of the Imagemagick webpage and use it further for any document creation. The versions available are 6.5, 6.6, 6.7, 6.8, 6.9, and the subversions of the same are available for the users to download and use for raster image editing.

In addition to RGBA images, CMYK and CMYKA images are also supported in newer versions of Imagemagick. For example, colorspaces and pixel channel support is provided in Imagemagick version 7 with any arbitrary images provided by the user, or the application takes an arbitrary version by itself. Hence, the support is provided to arbitrary Colorspaces where pixel channels are stored as floats, and hence the band values are rounded off, ignoring the error.

We have both 64-bit and 32-bit versions for each release of Imagemagick. 7.0.10 version was released in January, and the most recent 7.1.0 was released in August. Whenever any bugs are found, new version updates are released by the Imagemagick team, making users work with the most recent updates always. Major updates come with the release change in numbers, and this change will be published on the website. If the user prefers to go with the older version, they can download the same from the website and use it without making any updates to the software. Changes in scripts and images can be made either via the command line or user interface so that image modifications and color addition can be done through commands without seeing the images.

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Top 10 Alternative Of Greenshot In Detail

Introduction to Greenshot

Web development, programming languages, Software testing & others

List of Greenshot Alternative

Given below is the list of Greenshot Alternative:

1. SimpleScreenRecorder

SimpleScreenRecorder is a software solution from the Qt-based screencast to help you record the entire computer screen or part of that video audio. The software is designed specifically for those wishing to record a demo, play games, and other tasks. Solution with all the tools and functionality that helps you to capture and modify your recording is easy and easy to use. Furthermore, the programme synchronizes the video-audio-captured property, which reduces the video frame rates if the computer is too slow and multi-threaded.

2. Lightscreen

Lightscreen is a Microsoft Windows-designed lightweight screen shooting software solution. The software is used for the automation of screenshot saving and cataloging. It works as a hidden background process, invoking a hotkey and saving screenshot files to your disc as the user wants. The solution, which allows you to easily capture the screen and share it without limitation, is quite simple and easy to use. An interesting fact is that you can capture the area of the screen and capture what you need, resize and adjust it for maximum flexibility.

3. HotShots

HotShots was a tool that allows users to take and edit a screenshot. The software operates both on Linux and on Windows. It also enables users to edit their screenshots, for instance, to highlight an area. Even users can scroll through the software to capture or shoot an entire webpage. Users can point to any part where, on the one hand, likewise, any part of the screenshot may be removed. In addition, users can automate tasks such as showing a fast work menu, copying or annotating the image to the clipboard. During screenshots, users can hide the interface and add or remove the mouse cursor. Furthermore, hotShots enable users to add date and time in the filename, and when all tasks have been completed, they can also enable volumes. Finally, on your screenshots, users can draw forms, lines and other characters.

4. ScreenRec 5. Recordit 6. FastStone Capture

FastStone Capting is an all-in-one universally available Windows operating systems screenshot shot and screen recording platform used to take a snap of the selected area of the application that has been opened in Windows and record what is going on the screen. It is one of the best applications with two primary functions for screenshotting and user recording.

7. ShareX

ShareX is a free and open-source screenshot capture and screen capture application which is incorporated into various productivity and tools. Features and functions include capturing the complete screenshot of the display in contrast to the traditional Windows printing system. These frames are available in complete image capture, window recording, monitor capture, rectangle image capture. In addition, all captured screenshots are available in multiple frames.

8. PicPick 9. Screenpresso

Screenpresso is an ultimate screen collection tool based on the two areas of the screenshot and takes either the whole window or some given region with snapshots. Besides capturing screenshots, it also offers capturing of video screens. This tool is intended for those interested in organizing training sessions and presenting detailed work to the audience.

10. Skitch Conclusion – Greenshot Alternative

In this article, we have seen various Greenshot alternatives for capturing screenshots. You can choose any of them based on your requirements.

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Entity Framework One To Many

Introduction to Entity Framework One to Many

Entity Framework One to Many relationships occurs when one table’s primary key develops into another table’s foreign key. The One to Many relationships in each row of the data in one table is linked to more records in the second table. The One to Many relationships is not the property of records, but it is the relationship of itself.

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Overview of Entity Framework One to Many

Entity Framework One to Many relationships occurs while the primary key of one table becomes the foreign key of another table. When the foreign key is defined in the table represents many ends of the relationship. The One to Many relationships in a relational database occurs when the row of one table-1 is linked with many rows in table-2, but one of the rows in table-2 is the relationship to only one row in table-1.

To better understand one-to-many relationships, create two entities, PeopleAddress, and People; both entities contain records like peopleID, name, and peopleAddressID, address, city, state, and country, respectively. One person’s data has many address records; in our example, PeopleID is the primary key for the Person table, and that primary key takes part in the Primary and Foreign Key of the PeopleAddress table.

An entity can be related to other entities in the Entity Framework. The relationship between the entities includes two ends that describe the entity type and the diversity of the type. Those two ends of the relation are the Principle Role and the Dependent Role.

Configure Entity Framework One to Many

In Entity Framework Versions, we mostly do not need to configure the One to Many relationships because the relationship conventions complete all the conventions.

Let’s see the following examples:

Code First Conventions:

1. To add reference Navigation Property – Include the reference navigation property of the Author in the Book Entity Class.

Code:

public class AuthorMaster { public int Author_Id { get; set; } public string Author_Name { get; set; } } public class BookMaster { public int Book_Id { get; set; } public string Book_Title { get; set; } public AuthorMaster Author { get; set; } }

To include an Author Navigation property, it built the One to Many relationships between the two entities called AuthorMaster and BookMaster Table in the database by having the Foreign Key Author_Author_ID to Books Table.

2. To add the Collection Navigation Property – To accomplish the One-to-Many relationship by including the collection Navigation Property of the Book entity in the Author Entity Class.

Code:

public class AuthorMaster { public int Author_Id { get; set; } public string Author_Name { get; set; } } public class BookMaster { public int Book_Id { get; set; } public string Book_Title { get; set; } }

3. To add Navigation Properties in both entities – To include the navigation properties at two entities also helps in the One to Many relationships.

Code:

public class AuthorMaster { public int Author_Id { get; set; } public string Author_Name { get; set; } } public class BookMaster { public int Book_Id { get; set; } public string Book_Title { get; set; } public AuthorMaster Author { get; set; } }

4. Fully-Defined Relationship – This relationship at two ends will build the One-to-Many relationship; let’s see with one example, the entity BookMaster contains the foreign key property Author_ID with its reference property AuthorMaster and the AuthorMaster has the collection of Books.

Code:

public class AuthorMaster { public int Author_Id { get; set; } public string Author_Name { get; set; } } public class BookMaster { public int Book_Id { get; set; } public string Book_Title { get; set; } public int Author_Id { get; set; } public AuthorMaster Author { get; set; } }

The entire convention creates the same result in the database.

Entity Framework One to Many Convention

In Entity Framework, several conventions are followed in the domain classes; it automatically produces the result in One to Many relationships between two tables in the database.

Let’s see one example of the conventions that built the one-to-many relationship.

Convention – 1

To begin the One to Many relationships between the student and Grade tables or entities, there are several students which those students are associated with one Grade. Therefore, it defines that every student entity points to the Grade. It is achieved by adding the reference navigation property of the Grade type in the Student Class as coded below.

Code:

public class StudentMaster { public int Student_Id { get; set; } public string Student_Name { get; set; } public GradeMaster Student_Grade { get; set; } } public class GradeMaster { public int Grade_Id { get; set; } public string Grade_Name { get; set; } public string Grade_Section { get; set; } }

In this example, the StudentMaster class adds the reference navigation property of GradeMaster Class. Instead, there will be several students in a single grade, which results in a one-to-one relationship between the StudentMaster and grade master tables in the database.

Convention – 2

Code:

public class StudentMaster { public int Student_Id { get; set; } public string Student_Name { get; set; } } public class GradeMaster { public int Grade_Id { get; set; } public string Grade_Name { get; set; } public string Grade_Section { get; set; } } Entity Framework One to Many Fluent API

To configure the One to Many Relationship using the Fluent API, configuring the relationship using Fluent API in one place makes it controllable.

Look at the following example of two entity classes:

Code:

public class StudentMaster { public int Student_Id { get; set; } public string Student_Name { get; set; } public int S_CurrentGradeId { get; set; } public GradeMaster S_CurrentGrade { get; set; } } public class GradeMaster { public int Grade_Id { get; set; } public string Grade_Name { get; set; } public string Grade_Section { get; set; } }

To use Fluent API for configuring the relationship of two entities, we need to override the OnModelCreating method in the Context Class.

Code:

public class SchoolContext : DbContext { protected override void OnModelCreating(DbModelBuilder modelBuilder) { } } Conclusion

This article has explained Entity Framework One to Many Relationships. Furthermore, it contains the configuration and conventions of EF One to Many relations.

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