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Introduction to Prolog list

The prolog list is a function for collecting several values to operate on large-size chúng tôi list is a data structure for grouping the entity to handle the application’s chúng tôi list is a collection to store multiple data items using chúng tôi list is a data structure to create groups of the different values using square chúng tôi prolog list is functional to store various data items, and it differs by using comma chúng tôi prolog list is used to insert, delete, update, and append operations of the large chúng tôi list is a function to handle and operate different entities in similar or different categories.

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Syntax

The prolog list uses square brackets [prolog list] to store data.

The data list differs by a comma after a single value.

The list syntax is shown below.

[Value1, Value2, Value3, …, Value ]

Description

The prolog list contains values of a similar category.

The square bracket contains values and differentiates these values using commas.

The prolog empty list syntax is shown below.

[ ]

Description

The prolog list is either empty or non-empty.

If the list is empty, then the bracket does not contain any value.

The prolog list works with the head.

The list works with a tail.

Description

The vertical bar separates the head and tail of the list.

The first value is called the head of the list.

The other values except the first are called tail values.

The list works with variables using the head.

The list works with variables using a tail.

Tail = [value1, value2, value]

Description

The prolog creates variables and contains lists with values.

If this variable list is required in the main list, then include it in the head or tail part.

Some prolog list syntaxes are given below.

How does the list work in Prolog?

Create a file with the “pl” extension.

Create a list with list multiple entities.

Plist([Australia, india, japan, UK, USA]).

Open console or interpreter.

Set the directory path of the “pl” file.

Use the given prolog file.

[main].

Use the list in the console.

Plist([Australia, india, japan, UK, USA]).

Directly use the list in the prolog console.

[1, 2, 3, 4, 5] = [Australia, India, Japan, UK, USA].

Method1: use with multiple variables.

[1, 2, 3, 4, 5] = [Australia, India, Japan, UK, USA].

The “1” number is assigned for Australia.

Australia = 1

The “2” number is assigned for India.

India = 2

The “3” number is assigned for Japan.

Japan = 3

The “4” number is Assigned for the Uk.

UK = 4

The “5” number is assigned for the USA.

USA = 5

Method2: use with a single variable.

A = [Australia, India, Japan, UK, USA]. Examples

Here are the following examples mentioned below

Example 1

The basic list with a single variable example and output are below.

Prolog console

Output

Prolog console

Output

Prolog console

Output

Description:

The list contains a value in number, lowercase, and uppercase format.

The list assigns a single variable and displays the same as a program.

Example 2

The basic prolog list with multiple variables examples and output is below.

Prolog console

[1, 2, 3, 4, 5] = [Australia, India, Japan, UK, USA].

Output

Prolog console

[A, B, C, D, E] = [Australia, India, Japan, UK, USA].

Output

Prolog console

[A, B, C, D, E] = [australia, india, japan, uk, usa].

Output

Description:

The list contains a value in number, lowercase, and uppercase format.

The list assigns multiple variables and displays values with variables.

Example 3

The list with tail example and the output shown below.

Prolog console

Output

Description:

The prolog list specifies head and tail using a vertical bar.

The other values except the first value are called a tail of the list.

Example 4

The list with a head example with a single variable.

Prolog console

Output

Description:

The list specifies head and tail using a vertical bar.

The first value is called the head of the list.

Example5: the list with the required entity example and the output shows below.

Prolog console

Output

Prolog console

Output

Prolog console

Output

Description:

The list displays the required value using variable and underscores symbols.

You specify the position of the value using a comma and underscore symbol.

Example 6

The basic list example and the output shows below.

pl file

cntry([australia, india, japan, england, america]).

Prolog console

Output

Description:

The file contains a list with the required value.

You assign a list name with the required variable name.

The output displays a list with the variable name.

Prolog console

Output

Description:

The list specifies head and tail using a vertical bar.

The first value is called the head of the list.

The other value is called the tail of the list.

Conclusion

The list maintains a collection of the values in the applications.

The prolog list operates on large size data of the database.

The list creates applications that are user-friendly, fast, and lightweight.

 The list operates efficiently.

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What’s Microsoft 365 Copilot, And How Does It Works? Here’s The Answer.

Microsoft 365 Copilot is a new chatbot for the suite of Office apps and other Microsoft products and services. It uses the same technology already available on Bing Chat, but this new experience is tailored for work.

The new technology aims to change how people work in the office by using Artificial Intelligence to complete a wide range of tasks that otherwise could take a long time, helping users be more productive and use their time more efficiently.

What’s Microsoft 365 Copilot?

Copilot is a new AI digital assistant built with ChatGPT version 4 and Microsoft Graphs for Microsoft 365 (Office) applications, including Word, Excel, PowerPoint, Outlook, OneNote, Microsoft Teams, and other products and services. Yes, you will need a Microsoft 365 subscription to access the feature.

The chatbot will appear as a sidebar on the application (similar to the Bing Chat integration available on Microsoft Edge) that you can use to ask complex questions and command the app using natural language to perform different tasks.

The things you can do with Copilot will depend on the app you are using. For example, in Microsoft Word, you can ask Copilot to create content for a document on a specific topic or based on data available in another document. On PowerPoint, you can ask to create a presentation or style the presentation in a certain way.

In Excel, using natural language, you can prompt the chatbot to create or analyze the data in the table. In Outlook, the AI can summarize emails, help you compose email replies, and it can help you make sense of the information on a thread of emails. Finally, on Teams, the AI can summarize meetings, prepare people with updates on specific projects, determine the time to schedule a meeting, and more.

Another feature that sets Copilot apart is that it can’t only analyze information in the current files but also in other documents you have stored in the cloud. For instance, in Word, you can query the chatbot to create a proposal based on the customer notes you have in OneNote and another Word document.

Copilot will also be available in other products as well, such as Business Chat, Viva Engage, and others. 

How does Microsoft 365 Copilot works?

Copilot doesn’t just connect to ChatGPT to Microsoft 365. Instead, the technology uses the “Copilot system,” which combines the Microsoft 365 apps with the Microsoft Graph data and the technology of ChatGPT version 4.

If you are wondering how Copilot works, this will clear things up. If you ask a question in Word, the chatbot will send the data to the Microsoft Graph to analyze and make sense of the query, and then the data is sent to the ChatGPT language model. 

Once ChatGPT has the answer, it sends the data back to the Microsoft Graph for further grounding, security, and compliance checks before showing the answer to the user inside the app.

Microsoft also emphasizes that the new Copilot is not perfect and it’ll make mistakes. However, the company is touting the mistakes as something “usefully wrong” that will still give you a head start on the topic.

How It Works: The Pocket

We may earn revenue from the products available on this page and participate in affiliate programs. Learn more ›

HD: Deconstructed

As cameras continue to shrink in size and weight, an often overlooked side effect is their lack of image stability. Naturally, the heavier the camera, the less your shaky hands move its lens. Via optical image stabilization, the Panasonic HDC-SD5 keeps footage rock solid while maintaining a pocket-size form factor.

How to Create Color-Rich, Steady-Handed Video

Light enters the front of the camcorder and passes through a series of aspherical lenses and so-called low-dispersion lenses, which are made of ultra-pure glass. Together, these bend the light to magnify the images but don’t spread out colors the way a prism or cheap lens would. (That can lead to green or red smears in the footage.) A motor-driven zoom lens slides back and forth to change the view from slightly wider than the naked eye’s to an 8.5-fold magnification. With a large aperture (f/1.8), the camera captures enough light to record clear action in a candlelit room.

Light then passes to an optical image stabilizer that smoothes out jitters from shaky handheld shooting. Gyrosensors below the lens barrel measure the camcorder’s minute movements up and down and left and right. A processor analyzes the data and sends signals to the stabilizer, where a lens floats in a magnetic field. Adjusting the electromagnets nudges the lens in the opposite direction of the camera jitter, as frequently as 4,000 times per second, to compensate for the movement and deliver a steady beam of light to the image sensors.

Before the image can be recorded, the light beam has to be divided into the red, green and blue components that your TV will later use to reassemble the video. Most camcorders split the light with alternating color filters over the individual pixels on a single imaging chip. But those filters absorb much of the light and dull the colors. Panasonic instead uses a series of prisms to separate the beam into three color streams. Each stream strikes a separate image sensor that measures its intensity on 560,000 pixels to produce more-vivid video than single-sensor cameras can.

A Look Inside

The main circuit board holds the image processor, which controls the autofocus and iris, cleans up the raw data from the sensors, and compresses the video up to 60-fold to fit on an SD memory card. A separate processor controls the image-stabilizer lenses.

The battery holds lithium-ion cells that can power about an hour of shooting.

The LCD receives live video from the processor in real time.

Jacks provide video and audio outputs and a computer connection.

An SD memory card records up to five hours of high-def video on a 32-gigabyte card.

More How It Works:

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How Does The Round Operation Work In Pyspark?

Introduction to PySpark Round

Round is a function in PySpark that is used to round a column in a PySpark data frame. It rounds the value to scale decimal place using the rounding mode. PySpark Round has various Round function that is used for the operation. The round-up, Round down are some of the functions that are used in PySpark for rounding up the value.

The round function is essential in PySpark as it rounds up the value to the nearest value based on the decimal function. The return type of the Round function is the floating-point number. The round function offers various options for rounding data, and we decide the parameters based on the rounding requirement.

Syntax:

The syntax for the function is:

from pyspark.sql.functions import round, col b.select("*",round("ID",2)).show()

b: The Data Frame used for the round function.

select():  You can use the select operation. This syntax allows you to select all the elements from the Data Frame.

round(): The Round Function to be used

It takes on two-parameter:

The Column name and the digit allowed the possible round-up number.

Screenshot:

How does the ROUND operation work in PySpark?

The round operation works on the data frame column, taking the column values as the parameter and iterating over the column values to round up the items. It accepts one parameter from which we can decide the position to which the rounding off needs to be done. If you don’t provide any parameters, the function will round to the nearest value and return a data frame from it.

Let’s check the creation and usage with some coding examples.

Examples

Let’s see a few examples. Let’s start by creating simple data.

data1 = [{'Name':'Jhon','ID':21.528,'Add':'USA'},{'Name':'Joe','ID':3.69,'Add':'USA'},{'Name':'Tina','ID':2.48,'Add':'IND'},{'Name':'Jhon','ID':22.22, 'Add':'USA'},{'Name':'Joe','ID':5.33,'Add':'INA'}]

A sample data is created with Name, ID, and ADD as the field.

a = sc.parallelize(data1)

RDD is created using sc. parallelize.

b = spark.createDataFrame(a) b.show()

Created Data Frame using Spark.createDataFrame.

Output:

Let us round the value of the ID and use the round function on it.

b.select("*",round("ID")).show()

This selects the ID column of the data frame and works over each and every element rounding up the value out of it. The data frame generates a new column, which you can further use for analysis.

The ceil function is a PySpark function that is a Round-up function that takes the column value and rounds up the column value with a new column in the PySpark data frame.

from pyspark.sql.functions import ceil, col b.select("*",ceil("ID")).show()

Output:

This is an example of a Round-Up Function.

The floor function is a round-down function that takes the column value and rounds down the column value with a new column in the data frame.

from pyspark.sql.functions import floor, col b.select("*",floor("ID")).show()

This is an example of the Round Down Function.

Output:

The round function Rounds the column value to the nearest integer with a new column in the PySpark data frame.

b.select("*",round("ID")).show()

Output:

The round-off function takes up the parameter and rounds it up to the nearest decimal place with a new column in the data frame.

b.select("*",round("ID",2)).show()

Output:

Note:

ROUND is a ROUNDING function in PySpark.

It rounds up the data to a given value in the Data frame.

You can use it to round up or down the values in a Data Frame.

PySpark ROUND function results can create new columns in the Data frame.

It uses the function ceil and floor for rounding up the value.

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How Unwind Works In Mongodb?

Definition of Mongodb unwind

MongoDB unwind operator is used to deconstructing the array field from input to output documents, it will be used for each element from the document. The difference between input and output document in unwind operator is very simple, the output document value of a field of array is replaced by a single item of the input array of documents. MongoDB unwind operator is basically used for transfer complex documents into simple documents, it will improve the documents readability and understanding. Using unwind operator in MongoDB we can also perform operations like grouping and sorting on the data.

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Syntax:

Below is the syntax of unwind operator in MongoDB.

2)

}

1) Unwind operator –

This operator is used to deconstruct the documents in MongoDB. Every output and input documents depend on each other to deconstruct the value. We have passing the input parameter with unwind operator to display the result. Unwind operator is used with prefix as $ while using in MongoDB.

2) Path –

Type of this parameter in MongoDB unwind operator is a string. This is the path field of an array, path is used to specify the path of documents. This is a mandatory parameter while using unwind operator in MongoDB.

3) IncludeArrayIndex –

This is an optional parameter while using unwind operator in MongoDB. Type of this parameter in MongoDB unwind operator is a string. This states that the new name of a field is used to hold the index array for the element. This parameter name does not start with the $ sign.

4) PreserveNullEmptyArrays –

Type of this parameter in MongoDB unwind operator is Boolean. PreserveNullEmptyArrays is an optional parameter while using unwind operator in MongoDB. If the path of this parameter contains the true value unwind operator will show the output, if the path of this parameter contains the false value then unwind operator will not show the output.

How unwind work in Mongodb?

MongoDB unwind operator will deconstructs the documents for every document. Unwind operator is basically works on array elements. We can also use embedded documents with unwind operators.

I suppose our array contains the ABC student mark as {50, 55, 60, 70, 75}. Unwind operator will return the output as below.

{Name: “ABC”, mark: 75}

The above output shows that the array will be deconstructs into multiple documents. Our array contains the single documents value, but we can see the output will show the multiple documents in it.

We can pass includeArrayIndex and preserveNullEmptyArrays parameter while using unwind operator in MongoDB. Both parameters are optional while using unwind operator.

Unwind operator will duplicates each array element into different documents. This is used in an array that contains the data like a month, day of the week, and year.

Unwind operator is also working with the non-array path field. Before MongoDB version 3.2 if we have used a non-array path field it will show an error. After version 3.4 every non-array path field will not show any error it will return single elements of an array.

In the below example, we have used the non-array path field as a name. After using the non-array path field the array element will retrieve the single document.

Code:

db.MongoDB_Update.find ()

Figure – Unwind operator is work with non-array path field in MongoDB.

If we have missed any value in the path, unwind operator will not generate any output if we have entered an incorrect value.

Code:

Figure – unwind operator will not generate any output if we have entered any incorrect value.

In the above example, we have used id field in unwind operator, but id field is not present in MongoDB_Update collection. So unwind operator will return the empty result in output.

Example

The below example shows unwind operator in MongoDB.

1) Unwind operator with array field –

In the below example, we have used the array field name as lap_storage. After using the array field we can see that result will display each document with a different field.

Lap_storage contains the 6 array elements and MongoDB_Update collection contains the 2 documents, so we can see that unwind operator displays output as 12 documents.

db.MongoDB_Update.find ()

Figure – Example of unwind operator with array field.

2) Unwind operator with includeArrayIndex parameter –

In the below example, we have used includeArrayIndex parameter with unwind operator. We have used array field name as lap_storage and includeArrayIndex field as MongoDBIndex.

MongoIndex is a user-defined field that was used to capture the array index from lap_storage field.

Code:

db.MongoDB_Update.aggregate ([{$unwind: {path: “$lap_storage”, includeArrayIndex: “MongoDBIndex”}}])

Figure – Example of Unwind operator with includeArrayIndex parameter.

3) Unwind operator with preserveNullEmptyArrays parameter with true value –

In the below example, we have used preserveNullEmptyArrays parameter with unwind operator. We have used array field name as lap_storage and preserveNullEmptyArrays parameter value as true.

db.MongoDB_Update.find ()

Figure – Example of Unwind operator with preserveNullEmptyArrays parameter with true value

4) Unwind operator with preserveNullEmptyArrays parameter with false value –

In the below example, we have used preserveNullEmptyArrays parameter with unwind operator. We have used array field name as lap_storage and preserveNullEmptyArrays parameter value as false.

Code:

db.MongoDB_Update.find ()

Figure – Example of Unwind operator with preserveNullEmptyArrays parameter with false value.

5) Unwind operator using embedded documents –

The below example shows that unwind operator using embedded documents. We have embedded document field name as lap_spec.

Also, we have used preserveNullEmptyArrays parameter with unwind operator. We have set the value of preserveNullEmptyArrays parameter as true.

Code:

db.MongoDB_Update.find ()

Figure – Example of unwind operator using embedded documents in MongoDB.

Conclusion

Unwind operator is very useful and important in MongoDB to deconstruct the array field. We have using preserveNullEmptyArrays and includeArrayIndex optional parameter while using unwind operator. We can also use embedded documents with unwind operators. We can transfer complex documents into simple documents by using unwind operator in MongoDB.

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How Encryption Works In Mongodb?

Definition of MongoDB Encryption

Mongodb encryption process involves to generate a master key of an entire database, after generating master key we are creating the unique keys for every database. Then we are encrypting our data with the database which was we have created, we can also encrypt our whole database by using master key. Any of the database involves the two forms either data at rest or data in motion, data at rest is the forms where data is not moving anywhere its static data forms. Data in motions will moves the data in network its static data forms.

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Syntax:

Below is the syntax of encryption in MongoDB.

1) Connect MongoDB instance by using encryption –

2) Connect MongoDB instance by using client certificate and certificate authority file –

3) Rotate KMIP master encryption key –

Parameter description syntax of MongoDB encryption are as follows.

1) Mongo – This parameter is used to login into MongoDB instance. In MongoDB we can login database instance using mongo command.

2) SSL – This is defined as login into the MongoDB database instance by using SSL authentication.

3) Host – The host and hostname is defined as IP or hostname used to login specified database instance in MongoDB. While login into any MongoDB database instance we need to use hostname.

4) sslCAFile – This is certificate authority file used to verify that certificate is present or not on database server. This file is used while login into the database server by using encryption.

5) sslPEMKeyFile – This file contains the certificate of mongo shell and this key is present on mongos or mongod instance.

6) enableEncryption – This parameter is define as use of encryption at the time of rotating master key.

7) kmipRotateMasterKey – This parameter is used to rotate master key of KMIP server. Using this parameter we can rotate master key in MongoDB.

8) kmipServerName – This is nothing but the KMIP server hostname which was used at the time of rotating master key.

9) kmipServerCAFile – This is certificate authority file of KMIP server. This file is used while rotating the master key.

10) kmipClientCertificateFile – This is client certificate file of KMIP server. This file is used while rotating the master key.

How encryption works in MongoDB?

MongoDB involves two types of data encryption forms.

2) Data in motion encryption

To encrypt the data using data at rest encryption enterprise MongoDB will provides the storage based and native symmetric key.

We can say that data at rest encryption is the data not moving over the network, we can say that it’s in static forms. Data at rest database encryption is also called as transparent data encryption its abbreviation is TDE. MongoDB uses the AES 256-bit standard encryption algorithm to encrypt the database. MongoDB uses the same encryption cipher key to encrypt as well as decrypt the data.

4) Fourth step is encrypt whole database by using the master key which was we have generated in first step.

In MongoDB, data is transacted between server application and database in two ways.

TLS and SSL are most secure protocols of encryption to send and receive data from two systems. This protocols is used in MongoDB encryption is some PEM file which was issued by the certificate authority. There are multiple settings available in MongoDB to configure the TLS and SSL protocol for client certificates.

We can also use sslCAFile to create certificate. We can store this file in MongoDB instance to use the encryption while login into the MongoDB instance. We can also rotate our encryption key. We can rotate our key by using KMIP master rotation.

Example

Below example shows encryption in MongoDB. Below steps shows how to use encryption in MongoDB.

1) First step is to create locally managed key file to manage the key management service. We can create by using OpenSSL. We have created the file name as mongodb_client.key.

Code:

# cat /encryption/mongodb_client.key

Figure – Example to create locally managed key file to manage the key management service.

2) After creating the key file, open the mongo shell command and login by using the keyfile, –shell, and –nodb option.

Code:

LOCAL_KEY

3) Third step involves load the documents of encryption using client-side encryption configuration.

Code:

}

Figure – Example to load the documents of encryption using client-side encryption configuration.

4) After setting configuration connect to the local host database.

Code:

csfleDatabaseConnection = Mongo(ClientSideFieldLevelEncryptionOptions)

Figure – Example to connect the local host database.

5) Fifth stage is show the database, connect to the database and show the collections from connected database.

Code:

show collections

Figure – show the database, connect to the database and show the collections.

Conclusion

Data at rest encryption and data in motion encryption has two forms of MongoDB data encryption. Data encryption is very important in MongoDB to secure data. Encryption involves generate master key for the database. We can rotate our master key using KMIP master rotation algorithm.

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This is a guide to MongoDB Encryption. Here we discuss the Definition, How encryption works in MongoDB? examples with code implementation respectively. You may also have a look at the following articles to learn more –

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