Trending February 2024 # Microsoft Indic Language Input Tool Allows You To Type In Different Indian Languages # Suggested March 2024 # Top 4 Popular

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English is the most widely used language. Whether it Is to write an email or a blog post, the most preferred and widely used language is English. If you want to type in a language other than English, say Hindi, it becomes a challenging task. This is because to type in the Hindi language, you should learn Hindi typing. But now, the time has changed. If you do not know Hindi typing, you can still type in Hindi. There are many free tools available for this purpose, like Google Inputs. In this article, we will talk about the Microsoft Indic Language Input Tool that allows you to type in different Indian languages.

What is Microsoft Indic Language Input Tool?

Microsoft Indic Language Input Tool allows you to type in different Indian languages. If you install it on your computer, you need not learn to type in another language, say Hindi. You can use your keyboard to type in your native language. When you press the spacebar, it will convert the typed word to your preferred language.

Microsoft Indic Language Input Tool lets you type in different Indian languages

You should have .NET version 2.0 or higher installed on your system to install Indic Language Input Tool. If you do not have it, you will see the following error message:

Microsoft Input Indic Language Tool chúng tôi Framework 2.0 or higher. Please install .NET Framework 2.0 and restart the setup.

The .NET Framework setup will launch automatically. Follow the on-screen wizard to install the required .NET Framework. If the setup does not launch by itself, you can install it via Windows Features.

How to use Microsoft ​Indic Language Input Tool

Using this tool is easy. After installing it, the tool will be available in the notifications area of the Taskbar. Follow the steps written below:

Select the preferred language.

Start typing in your preferred language.

To type with this tool, it is not necessary that you know how to type in that particular language. You can type it by using your Qwerty keyboard. When you press the spacebar, it will change the typed word in the preferred language (see the above screenshot). It also shows word suggestions while typing so that you can select the desired word. If you do not select the word from the suggestions, it replaces the word on the top of the list automatically by pressing the spacebar.

Related: How to add Hinglish keyboard to Windows PC

As compared to Google inputs, you can use it online as well as offline.

You can type in supported Indian languages on different apps on Windows 11/10.

It is not necessary to learn typing in other languages. You can type by using your keyboard and when you press the spacebar, it converts the typed word into the selected language. I also tested it in Gujarati. While writing, I typed in Hindi and when I pressed the spacebar, it converted my text into Gujarati. I don’t know Gujarati, hence, I converted the typed text on Google Translate and it showed me exactly what I typed. I did not test it in other languages because I do not know other Indian languages.

It does not work on some apps, like Notepad, Microsoft Word, Excel, etc. On the other hand, I was able to type in Hindi in some apps, including Notepad++, Microsoft PowerPoint, etc.

How do you use Microsoft Indic Language Input Tool?

You can use the Microsoft Indic Language Input Tool to type in different Indian languages without having the skills to type in that language. To use this tool, install the SDK version of the desired language and then start typing. When you press the spacebar, it will convert the typed word into the desired language.

Read next: Best 5 free Hindi typing software for Windows PC.

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3 Important Nlp Libraries For Indian Languages You Should Try Out Today!

Overview

Ever wondered how to use NLP models in Indian languages?

This article is all about breaking boundaries and exploring 3 amazing libraries for Indian Languages

We will implement plenty of NLP tasks in Python using these 3 libraries and work with Indian languages

Introduction

Language is a wonderful tool of communication – its powered the human race for centuries and continues to be at the heart of our culture. The sheer amount of languages in the world dwarf our ability to master them all.

In fact, a person born and brought up in part of the country might struggle to communicate with a fellow person from a different state (yes, I’m talking about India!). It’s a challenge a lot of us face in today’s borderless world.

This is a research area that Natural Language Processing (NLP) techniques have not yet managed to master. The majority of breakthroughs and state-of-the-art frameworks we see are developed in the English language. I have long wondered if we could use that and build NLP applications in vernacular languages.

Human beings by nature are diverse and multilingual, so it makes sense, right?

Since the Indian subcontinent itself has a multitude of languages, dialects and writing styles spoken by more than a billion people, we need tools to work with them. And that’s the topic of this article.

We will learn how to work with these languages using existing NLP tools, compare them relatively in terms of various parameters, and learn some challenges/limitations that this area faces.

Here’s what we’ll cover in this article:

What are the Languages of the Indian Subcontinent?

Text Processing for Indian Languages using Python

iNLTK

Indic NLP Library

StanfordNLP

Trends in Multilingual NLP Research

What are the Languages of the Indian Subcontinent?

The Indian Subcontinent is a combination of many nations, here’s what Wikipedia says:

The Indian subcontinent is a term mainly used for the geographic region surrounded by the Indian Ocean: Bangladesh, Bhutan, India, Maldives, Nepal, Pakistan and Sri Lanka.

These nations represent great diversity in languages, cultures, cuisines etc.

Even within India itself, there are a multitude of languages that are spoken and used in day to day life which itself showcases the basic need to be able to build NLP based applications in vernacular languages.

These are some of the languages of the Indian Subcontinent that are supported by libraries we’ll see in this article (each library lists only unique languages it supports as there are many overlapping languages like hindi):

iNLTK- Hindi, Punjabi, Sanskrit, Gujarati, Kannada, Malyalam, Nepali, Odia, Marathi, Bengali, Tamil, Urdu

Indic NLP Library- Assamese, Sindhi, Sinhala, Sanskrit, Konkani, Kannada, Telugu,

StanfordNLP- Many of the above languages

Text Processing for Indian Languages using Python

There are a handful of Python libraries we can use to perform text processing and build NLP applications for Indian languages. I’ve put them together in this diagram:

All of these libraries are prominent projects that researchers and developers are actively utilizing and improving for working with multiple languages. Each library has its own strengths and that’s why we will explore them one by one.

1. iNLTK (Natural Language Toolkit for Indic Languages)

As the name suggests, the iNLTK library is the Indian language equivalent of the popular NLTK Python package. This library is built with the goal of providing features that an NLP application developer will need.

iNLTK provides most of the features that modern NLP tasks require, like generating a vector embedding for input text, tokenization, sentence similarity etc. in a very intuitive and easy API interface.

Let’s explore the features of this library.

Installing iNLTK

iNLTK has a dependency on PyTorch 1.3.1, hence you have to install that first:

You can then install iNLTK using pip:

pip install inltk Language support

iNLTK currently supports 12 languages of the Indian Subcontinent:

That’s quite a diverse collection of languages!

Setting the language

iNLTK has language models trained for different languages and in order to use one, we have to download its files first. We will be working with Hindi text, so let’s set “Hindi” as our language:

from inltk.inltk import setup setup

(

'hi'

)

This will download all the necessary files to make inferences for Hindi.

Tokenization

The first step we do to solve any NLP task is to break down the text into its smallest units or tokens. iNLTK supports tokenization of all the 12 languages I showed earlier:

View the code on Gist.

Let’s look at the output of the above code:

The input text in Hindi is nicely split into words and even the punctuations are captured. This was a basic task – let’s now see some interesting applications of iNLTK!

Generate similar sentences from a given text input

Since iNLTK is internally based on a Language Model for each of the languages it supports, we can do interesting stuff like generate similar sentences given a piece of text!

View the code on Gist.

The first parameter is the input sentence. Next, we pass the number of similar sentences we want (here it’s 5) and then we pass the language code which is ‘hi’ for Hindi.

Here’s the model’s output:

This feature of iNLTK is very useful for text data augmentation as we can just multiply the sentences in our training data by populating it with sentences that have a similar meaning.

Identify the language of a text

Knowing what language a particular text is written in can be very useful when building vernacular applications or working with multilingual data. iNLTK provides this very useful functionality as well:

Above is an example of a sentence written in Malayalam that iNLTK correctly identifies.

Extract embedding vectors

When we are training machine learning or deep learning-based models for NLP tasks, we usually represent the text data by an embedding like TF-IDF, Word2vec, GloVe, etc. These embedding vectors capture the semantic information of the text input and are easier to work with for the models (as they expect numerical input).

iNLTK under the hood utilizes the ULMFiT method of training language models and hence it can generate vector embeddings for a given input text. Here’s an example:

View the code on Gist.

We get two embedding vectors, one for each word in the input sentence:

Notice that each word is denoted by an embedding of 400 dimensions.

Text completion

Text completion is one of the most exciting aspects of language modeling. We can use it in multiple situations. Since iNLTK internally uses language models, you can easily use it to auto-complete the input text.

In this example, I have taken a Bengali sentence that says “The weather is nice today”:

View the code on Gist.

Here, the fourth parameter is to adjust the “randomness” of the model to make different generations (you can play with this value). The model gives a prompt output:

'আবহাওয়া চমৎকারভাবে, সরলভাবে এক-একটি সৃষ্টির দিনক্ষণ'

This roughly translates to ‘The weather is excellent, simply a day of creation’ (according to Google Translate). It’s an interestingly smooth output, isn’t it?

We can often use text generation abilities of a language model to augment the text dataset, and since we usually have small datasets for vernacular languages, this feature of iNLTK comes in handy.

Finding similarity between two sentences

iNLTK provides an API to find semantic similarities between two pieces of text. This is a really useful feature! We can use the similarity score for feature engineering and even building sentiment analysis systems. Here’s how it works:

View the code on Gist.

I have given two sentences as input above. The first one roughly translates to “I like food” while the second one means “I appreciate food that tastes good” in Hindi. The model gives out a cosine similarity of 0.67 which means that the sentences are pretty close, and that’s correct.

Apart from cosine similarity, you can pass your own comparison function to the cmp parameter if you want to use a custom distance metric.

Additionally, there are many interesting features that the library provides and I urge you to check out iNLTK’s documentation page for more information.

2. Indic NLP Library

Here is what the official documentation says about Indic NLP’s objective:

“The Indic NLP Library is built to support most of the common text processing and NLP capabilities for Indian languages.

Indian languages share a commonality in terms of script, phonology, language syntax, etc. and this library is an attempt to provide a general solution to very commonly required toolsets for Indian language text.”

This library provides the following set of functionalities:

Text Normalization

Script Information

Tokenization

Word Segmentation

Script Conversion

Romanization

Indicization

Transliteration

Translation

We’ll explore all of them one by one in this article. But first, let’s have a look at the different languages this library supports out of the box and which functionality is available for what language:

As you can see, the Indic NLP Library supports a few more languages than iNLTK, including Konkani, Sindhi, Telugu, etc. Let’s explore the library further!

Installing the Indic NLP Library

You can install the library using pip:

pip install indic-nlp-library

Next, you have to download the resources folder that contains the models for different languages. You can do that by cloning the indic_nlp_resources repository from GitHub:

# download the resource

Apart from its API, this library also provides certain scripts that are useful for NLP. You can clone the GitHub folder itself to get them:

# download the repo

Now that all the files are downloaded, you can set the path so that Python knows where to find these on your computer:

View the code on Gist.

The above steps might take some time due to the size of the resources. Once you are done with these steps, you are ready to start!

Splitting input text into sentences

Indic NLP Library supports many basic text processing tasks like normalization, tokenization at the word level, etc. But sentence level tokenization is what I find interesting because this is something that different Indian languages follow different rules for.

Here is an example of how to use this sentence splitter:

View the code on Gist.

Here is the output:

तो क्या विश्व कप 2023 में मैच का बॉस टॉस है? यानी मैच में हार-जीत में टॉस की भूमिका अहम है? आप ऐसा सोच सकते हैं। विश्वकप के अपने-अपने पहले मैच में बुरी तरह हारने वाली एशिया की दो टीमों पाकिस्तान और श्रीलंका के कप्तान ने हालांकि अपने हार के पीछे टॉस की दलील तो नहीं दी, लेकिन यह जरूर कहा था कि वह एक अहम टॉस हार गए थे।

Now, what if I tell you that you can do the same for all 15 Indian languages that Indic NLP Library supports? Fascinating, isn’t it?

Transliteration among various Indian Language Scripts

Transliteration is when you convert a word written in one language such that it is written using the alphabet of the second language. Note that this is very different from “Translation” wherein you also convert the word itself to the second language so that it’s “meaning” is maintained.

Here is an example to illustrate the difference:

Here is how you can perform transliteration using the Indic NLP Library:

View the code on Gist.

In the above example, we have a sentence written in Hindi and we want to transliterate it to Telugu. This is the output of the model:

ఆజ మౌసమ అచ్ఛా హై౤ సూరజ ఉజ్జ్వల హై ఔర బారిశ కే కోఈ సంకేత నహీం హైం౤ ఇసలిఏ హమ ఆజ ఖేల సకతే హైం!

This is a near-perfect transliteration!

Converting Indian Languages to Roman Script

This is a feature that will be very helpful when working with social media data of non-native English speakers as they have a tendency to mix and interchange language every now and then in their posts.

English follows Roman Script for the alphabet, hence we can “Transliterate” any Indian language text to English using this library:

View the code on Gist.

Here is what the model gives as output:

aaja mausama achchaa hai. isalie hama aaja khela sakate hai !

Very cool, isn’t it? This is something most of us can relate to as a lot of times we type our local language using English alphabets (I’m looking at all you texting people!).

Understanding the phonetics of a character

Phonetics of a character describe the speech property of that character (like how will it sound, how much tongue should be rolled to pronounce it, etc.)

Here is an example of a phonetic property that defines how the character “k” is spoken:

The Indian Sub-Continent languages have strong phonetics for their alphabet and that’s why in the Indic NLP Library, each character has a phonetic vector associated with it that defines its properties.

How is this useful? Well, you can basically take the character of a new language and just learn almost everything about it – from whether it is a vowel or consonant to how is the tongue rolled to pronounce that word?

Here is an example where we take the simple Hindi character ‘आ’ :

View the code on Gist.

Here is the output:

How similar do two characters sound?

Many languages have multiple characters that have a similar sound or are spoken similarly but used in different settings in words. Can you think of any off the top of your head?

In English, it would be the characters “k” and “c”. While growing up, I’d often wonder why it was written as “school” but pronounced as “skool”? That’s exactly what I’m talking about here.

Similarly, in Hindi, we have characters ‘क’ and ‘ख’ that are confused a lot due to their sound being very similar.

Let’s find how phonetically similar these characters are using the Indic NLP Library:

View the code on Gist.

I have also used a third character ‘भ’ for comparison purposes. Let’s see what output the model gives:

As expected, there is a higher similarity between ‘क’ and ‘ख’ than ‘क’ and ‘भ’.

Splitting words into Syllables

Source

We can use the Indic NLP Library to split words of Indian Languages into their syllables. This is really useful because languages have unique rules that govern what makes a syllable.

For example, when we consider the case of Indian Languages in general and Hindi, in particular, you’d notice that the concept of matras is very important when considering syllables.  Here’s an example in Hindi:

This type of syllabification is known as Orthographic Syllabification. Let’s see how we can do this in Python:

View the code on Gist.

We have given the Hindi word ‘जगदीशचंद्र’ as input and here’s the output:

ज ग दी श च ंद्र

Notice how the various syllables have been properly identified! If you want to learn more about Orthographic Syllabification, you can read the paper – Orthographic Syllable as a basic unit for SMT between Related Languages.

Now that we have learned a fair bit of NLP tasks that we can perform with Indian Languages, let’s go to the next step with StanfordNLP.

3. StanfordNLP

StanfordNLP is an NLP library right from Stanford’s Research Group on Natural Language Processing.

The most striking feature of this library is that it supports around 53 human languages for text processing!

Out of these languages, StanfordNLP supports Hindi and Urdu that belong to the Indian Sub-Continent.

StanfordNLP is good for generating features of Computational Linguistics like Named Entity Recognition (NER), Part of Speech (POS) tags, Dependency Parsing, etc. Let’s see a glimpse of this library!

Installing StanfordNLP

1. Install the StanfordNLP library:

pip install stanfordnlp

2. We need to download a language’s specific model to work with it. Launch a Python shell and import StanfordNLP:

import stanfordnlp

3. Then download the language model for Hindi (“hi”):

stanfordnlp.download('hi')

This can take a while depending on your internet connection. These language models are pretty huge (the English one is 1.96GB).

Note: You need Python 3.6.8/3.7.2 or later to use StanfordNLP.

Extracting Part of Speech (POS) Tags for Hindi

StanfordNLP comes with built-in processors to perform five basic NLP tasks:

Tokenization

Multi-Word Token Expansion

Lemmatization

Parts of Speech Tagging

Dependency Parsing

Let’s start by creating a text pipeline:

nlp = stanfordnlp.Pipeline(processors = "pos")

Now, we will first take a piece of Hindi text and run the StanfordNLP pipeline on it:

hindi_doc = nlp("""केंद्र की मोदी सरकार ने शुक्रवार को अपना अंतरिम बजट पेश किया. कार्यवाहक वित्त मंत्री पीयूष गोयल ने अपने बजट में किसान, मजदूर, करदाता, महिला वर्ग समेत हर किसी के लिए बंपर ऐलान किए. हालांकि, बजट के बाद भी टैक्स को लेकर काफी कन्फ्यूजन बना रहा. केंद्र सरकार के इस अंतरिम बजट क्या खास रहा और किसको क्या मिला, आसान भाषा में यहां समझें""")

Once you have done this, StanfordNLP will return an object containing the POS tags of the input text. You can use the below code to extract the POS tags:

View the code on Gist.

Once we call the extract_pos(hindi_doc) function, we will able to see the correct POS tags for each word in the input sequence along with their explanations:

An interesting fact about StanfordNLP is that its POS tagger performs accurately for a majority of words. It is even able to pick the tense of a word (past, present or future) and whether the word is in base or plural form.

If you want to read more about StanfordNLP and how you can use it for other tasks, feel free to this article.

End notes

You’d have already noticed in this article that there are useful libraries to perform NLP on Indian languages, but even then these libraries have a long way to go in terms of functionality when compared with the likes of spaCy, NLTK and other NLP libraries that majorly support European languages.

Good news is that the research in multilingual NLP has only risen over the last couple of years and in no time you should be able to see a plethora of options to choose from.

Related

Majestic Updates Backlink Tool – You Might Need To See This

Majestic has totally rewritten their crawler and have created an improved backlink reporting tool. It contains many useful features powerful tools that makes link researching easier, allowing you to focus on the actual link building.

For me, Majestic was more of a site auditing tool than a link building tool. So when I was asked if I was interested in trying a demonstration version of their new tool, I was a little skeptical.

I’ll get to the point: Majestic’s new version of their backlink checker tool blew me away. It’s ability to simplify the research part of link building, to take the chore out of it, is impressive.

More than a List of Links

One problem with link tools is that they give you a list of backlinks, but they are missing information about the context of the link.

Majestic’s new backlink tool quickly shows:

Where on the page the link is located (footer, content, navigation)

Whether there is surrounding text

How many outbound links are on the page and how many are internal or external links

The improvements are not limited to the above. There are more features that allow you to fine tune your backlink search in so many different ways.

Majestic Crawler Rewritten

Majestic has rewritten and completely updated their crawler. The crawler now divides every web page it finds into up to 40 segments. Majestic can not only instantly show a visual representation of links on a web page, but you can also use that information to find link pages with specific link densities.

For example you can filter to find backlinks with the lowest link density and that are dofollow. This can reveal article type opportunities.

By searching for higher link densities you will be able to find “links” pages, pages that link out to useful web pages.

Majestic Context Search

Majestic’s new Context search can find links with a specific context, such as a specific word close to the link.

You can also use the Context feature to find specific kinds of link pages, like Resources pages.

Link Density Charts

Majestic is dividing each web page into 40 parts.

Majestic then displays a Link Density Chart that visualizes the link density of the web page that a link is on. It also shows you (with a green line) where on the page the backlink is.

The Link Density Chart allows you to see at a glance whether a site represents a good link opportunity.

Here’s how Majestic explains the Link Density Charts:

“The width of a profile shows the Link Density in each segment, so a wider line means that the section of text is more saturated with links.”

Fine Tune Your Link Search

Wading through a list of poor quality links in order to find a handful of link targets is a boring chore that wastes time and money.

A set of features that elevates Majestic above other link discovery tools is the ability to segment links by different qualities.

Segmenting links is one of my link discovery shortcuts. Segmenting links allows you to filter out the low quality links and quickly discover the highest quality and most relevant link prospects.

Segment by Link Density

Segmenting by link density can help you avoid finding backlinks that are hosting too many links. By searching for low density link pages you can discover article pages. These represent link opportunities related to suggesting an article.

If you search for high density links you can quickly identify link resources types of pages. These are the kinds of pages that are useful for suggest-a-link campaigns.

Search for Backlinks By Topic

Majestic leverages it’s in-depth topical trust flow information to enable you to search by the topic of the backlink. This is a powerful way to search for a specific kind of topical backlink and avoid wading through long lists of irrelevant links.

Segment by Source URL

This is a powerful way to indicate a quality about the URL. For example, you can search for all .edu backlinks.

You can input a search parameter like .edu to find all the dot edu backlinks:

Or you can search just for backlinks that have the word “links” in the URL:

As you can see in the above screenshot, I input the word “links” into the Source URL tab (it is superimposed over a result in the above image) and it returned backlinks with the word “links” in the URL.

Is Majestic Link Researching Tool the Best?

I received no free pass and no free access to the tool. I was only granted access to a demonstration version of the tool where I could try out the new features with one URL. My review is unbiased.

I have used Majestic in the past. Their Trust Flow metric has always been useful for diagnosing backlink problems. That’s useful site audit feature though, not so useful for link building.

The new version of Majestic significantly improves it’s usefulness for researching backlinks. It is without a doubt a better backlink research tool than before. But is it the best?

Whether something is best depends on how you approach  link building. My way of building links uses the process of segmenting the backlinks into a group of sites I want links from.

Some tools can only provide a list of links with only a minimal way to drill down to the best links according to the features that you are looking for. Majestic appears to solve that problem.

Researching backlinks should not be time consuming. Majestic’s ability to segment links makes the research part of link building so much easier. That in turn will contribute to keeping you focused on the link building rather than the link research.

This new version of Majestic is an exciting new tool that is useful for link building or as part of a site audit toolkit. I am impressed!

Read more about Majestic’s new Link Context backlink checker here.

Top 10 Programming Languages To Study At College In 2023

These top 10 programming languages provide all-around services and can be used for several purposes

Programming languages are the ones that help to develop software. Languages like Javascript, Python, C++ and more are used by programmers (developers) to give instructions to a machine so that the computer can perform the assigned tasks. The world is now more focused on technology. Over the years, the software development industry has exponentially grown at a much higher rate than ever imagined before, paving the way for an even lucrative career option for several aspiring software developers and programmers. There are several programming languages to learn, but only the top programming languages provide all-around services and can be used for several purposes. This article lists the top 10 programming languages to study at college in 2023.

Kotlin

Kotlin is used extensively for Android apps, web applications, desktop applications, and server-side application development. Kotlin was built to be better than Java, and people who use this language are convinced. Most of the Google applications are based on Kotlin. Some companies using Kotlin as their programming language include Coursera, Pinterest, PostMates among many others. Kotlin developers earn an average of US$136,000 a year, with the potential to earn up to US$171,500.  

Python

Python probably has the largest support for data science and machine learning in general. While there are other languages like R and MATLAB which do offer competition, Python’s the strict ruler of the data science space. A majority of the frameworks and libraries used in machine learning are made in Python only, making it probably the best language to pick up if one wants to learn about machine learning (or data science in general).  

JavaScript

JavaScript is the most popular programming language for building interactive websites; “virtually everyone is using it,” Gorton says. When combined with chúng tôi programmers can use JavaScript to produce web content on the server before a page is sent to the browser, which can be used to build games and communication applications that run directly in the browser. A wide variety of add-ons extend the functionality of JavaScript as well.  

C++

C++ finds use in analytics, research as well as in-game development. The popular game development engine – the Unreal Engine – uses C++ as the scripting language for all of the functionality one can define while building a game. C++ also finds extensive use in software development. C++ probably has the largest learning community among all of the languages. Most students would start their algorithms courses building trees, linked lists, stacks, queues, and numerous other data structures in C++. Naturally, it is quite easy to pick up and learn as well as easy to master if one pays attention to details.  

Go

Go is a statically typed, compiled programming language designed at Google by Robert Griesemer, Rob Pike, and Ken Thompson. Go is syntactically similar to C, but with memory safety, garbage collection, structural typing, and CSP-style concurrency.  

PHP

Programmers mainly use PHP to write server-side scripts. But developers can also use this language to write command-line scripts, and programmers with high-level PHP coding skills can also use it to develop desktop applications. PHP is considered a relatively easy language to learn for beginning developers. PHP professionals have several dedicated online communities, making it easy to get support and answers to questions.  

Dart

The most famous use of Dart currently is in the framework of Flutter, a language used for mobile app development. Dart is simpler to learn than JavaScript and manages to simplify even difficult-to-understand cases well. With TypeScript and Dart both in the market, programmers are spoilt for choice when it comes to choosing a language they want to pick up.  

Java

Java is probably the language that most people were introduced to as part of an introductory computer programming course in college or school. Java is the language used for teaching object-oriented programming to the masses. Java is also highly respected in the field of analytics and research. The only problem with Java is that there are very few support packages and projects for the language at present. There’s very little community involvement – something that most mainstream languages have. Despite that, Java is a language that is very easy to pick up and learn – partly explaining the appeal for the language.  

Swift

In the realm of the iOS world, Swift is marketed as a go-to technology for app development. This programming language has knocked out Objective-C and sits comfortably in the top 20 in 2023. Swift requires fewer coding skills compared with other languages and it can be used with IBM Swift Sandbox and IBM Bluemix.  

R

Programming languages are the ones that help to develop software. Languages like Javascript, Python, C++ and more are used by programmers (developers) to give instructions to a machine so that the computer can perform the assigned tasks. The world is now more focused on technology. Over the years, the software development industry has exponentially grown at a much higher rate than ever imagined before, paving the way for an even lucrative career option for several aspiring software developers and programmers. There are several programming languages to learn, but only the top programming languages provide all-around services and can be used for several purposes. This article lists the top 10 programming languages to study at college in 2023.Kotlin is used extensively for Android apps, web applications, desktop applications, and server-side application development. Kotlin was built to be better than Java, and people who use this language are convinced. Most of the Google applications are based on Kotlin. Some companies using Kotlin as their programming language include Coursera, Pinterest, PostMates among many others. Kotlin developers earn an average of US$136,000 a year, with the potential to earn up to US$171,500.Python probably has the largest support for data science and machine learning in general. While there are other languages like R and MATLAB which do offer competition, Python’s the strict ruler of the data science space. A majority of the frameworks and libraries used in machine learning are made in Python only, making it probably the best language to pick up if one wants to learn about machine learning (or data science in general).JavaScript is the most popular programming language for building interactive websites; “virtually everyone is using it,” Gorton says. When combined with chúng tôi programmers can use JavaScript to produce web content on the server before a page is sent to the browser, which can be used to build games and communication applications that run directly in the browser. A wide variety of add-ons extend the functionality of JavaScript as well.C++ finds use in analytics, research as well as in-game development. The popular game development engine – the Unreal Engine – uses C++ as the scripting language for all of the functionality one can define while building a game. C++ also finds extensive use in software development. C++ probably has the largest learning community among all of the languages. Most students would start their algorithms courses building trees, linked lists, stacks, queues, and numerous other data structures in C++. Naturally, it is quite easy to pick up and learn as well as easy to master if one pays attention to chúng tôi is a statically typed, compiled programming language designed at Google by Robert Griesemer, Rob Pike, and Ken Thompson. Go is syntactically similar to C, but with memory safety, garbage collection, structural typing, and CSP-style concurrency.Programmers mainly use PHP to write server-side scripts. But developers can also use this language to write command-line scripts, and programmers with high-level PHP coding skills can also use it to develop desktop applications. PHP is considered a relatively easy language to learn for beginning developers. PHP professionals have several dedicated online communities, making it easy to get support and answers to chúng tôi most famous use of Dart currently is in the framework of Flutter, a language used for mobile app development. Dart is simpler to learn than JavaScript and manages to simplify even difficult-to-understand cases well. With TypeScript and Dart both in the market, programmers are spoilt for choice when it comes to choosing a language they want to pick chúng tôi is probably the language that most people were introduced to as part of an introductory computer programming course in college or school. Java is the language used for teaching object-oriented programming to the masses. Java is also highly respected in the field of analytics and research. The only problem with Java is that there are very few support packages and projects for the language at present. There’s very little community involvement – something that most mainstream languages have. Despite that, Java is a language that is very easy to pick up and learn – partly explaining the appeal for the chúng tôi the realm of the iOS world, Swift is marketed as a go-to technology for app development. This programming language has knocked out Objective-C and sits comfortably in the top 20 in 2023. Swift requires fewer coding skills compared with other languages and it can be used with IBM Swift Sandbox and IBM Bluemix.R is one of the computer programming languages used in the world of data science. Ruby is a well-known programming language, especially popular among start-ups. Such high flyers as Airbnb, Twitch, and GitHub were powered by Ruby. Its demand is bolstered on Ruby on Rails, which is a full-stack web application framework that fuels Ruby.

Important Indian Freedom Fighters

Introduction

Lala Lajpat Rai was one of the cornerstones in the history of the Indian freedom struggle. His contributions to the Indian freedom movement made him one of the notable cynosures in Indian history. He was later named the Punjab Kesari for his fierce participation in the Indian movement for freedom. He was one of the key members of the Indian National Congress (INC).

During the movement of Hindu supremacy, he was appointed as a leader. After seeing the condition of the poor children he decided to establish many schools for them.

Who Was Lala Lajpat Rai?

Lala Lajpat Rai was one of the famous authors in the period of independence He was also a well-known freedom fighter. He was born on 28th January 1865 in Dhudike which was located in the district of Punjab. He was considered to be one important member of the Lal- Bal-Pal triumvirates. Not only that, he made various necessary contributions to society among them was the establishment of the Punjab National Bank.

Lala Lajpat Rai

Punjab State Archives, CC BY-SA 4.0 , via Wikimedia Commons

Lala Lajpat Rai: Career and Early Life

The name of Lala Lajpat Rai’s father was Munshi Radha Krishan Agarwal and his mother’s name was Gulab Devi Agarwal. His father was a government school teacher in many Urdu and Persian schools. His family moved to Rewari in the year 1870. He started his education there only. Later on, his father was appointed as an Urdu teacher. He was influenced by his family toward the Hindu religion.

During the period of his youth, he decided to spread the importance of this religion through various works. Later on, he joined politics and became a member of a very old party known as the Indian National Congress (Moffat, 2023).

He passed Law at a Government College of Punjab in the year 1880. There he met many other freedom fighters like Lala Hans Raj and Pandit Guru Dutt. He was one of the chroniclers of the Arya Gazette in Punjab in the Lahore district. After finishing his educational life in the Lahore district, his family moved to Rohtak in 1884.

Lala Lajpat Rai: Contribution to the Indian Freedom Movement

Lala Lajpat Rai decided to devote himself to the freedom movement of India. During the year 1914, he left practising law and went to the United Kingdom and in 1917 he travelled to the United States. After joining the Indian National Congress, he was deported to Mandalay and he was mainly trapped in a conspiracy that was planned by the British Government for participating in political unrest that was organized in Punjab. Due to insufficient evidence and lack of proper proof he was declared guilty.

Contribution of Lala Lajpat Rai

In the year 1920, he was elected as a member of the INC in Calcutta. In 1921 he led the movement by creating social servants in the people’s society. Due to this, a non-profitable welfare association was moved to the Delhi headquarters which got proper finalization after the partition movement.

In today’s period also many branches exist in several places in the country. He was strictly against the rule of the poor people being untouchables.

He was put in jail from 1921 to 1923 after his release he was again elected as a member of the Legislative Assembly.

Lala Lajpat Rai: Influence and Legacy

Lala Lajpat Rai was a very heavy leader of the Nationalist movement of India. The movement was led by the INC and the Arya Samaj. He wrote many articles and published them with the help of the young generation. After reading his articles many other freedom fighters were inspired and decided to dedicate their own lives to the motherland. He was a founding member of many organizations during the late 19th’ and 20th centuries.

He was the founder of the Laxmi Insurance Company which still now bears his honour. His main wish was to establish a hospital in the name of his mother and after his mother’s death; he created the hospital which is situated in Pakistan. It is now the world’s largest hospital.

Lala Lajpat Rai: Literary Works

The literary works of Lala Lajpat Rai can be named as follows −

Young India: an interpretation

The Arya Samaj: an account

The problem of National Education in India in 1920

Unhappy India in 1928

England’s Debt to India in 1917

Conclusion

Lala Lajpat Rai was one of the most important pioneers in the freedom struggle. Many people come to know about his contributions to the Indian Freedom movement as well as in literature. He was from a middle-class family.

He received his education in Rewari. From childhood only he was a strong believer in Hinduism and gave respect to other religions also. He did not use to believe in the fact of untouchables which were strictly believed by most of the people of the Indian society. His contribution to the freedom struggle was very remarkable. He was also one of the authors who inspired many other freedom fighters through his writings.

FAQs

Q.1. What was the contribution of Lala Lajpat Rai to the struggles of the untouchables in India?

Ans. Lala Lajpat Rai mainly fought the movement with many lower-caste people. His contributions to the untouchables were one of his notable works as he believed all people are equal and everyone has equal status.

Q.2. What did the followers of Lala Lajpat Rai do in Surat?

Ans. Many of the followers of Lala Lajpat Rai tried to elect him as a member of the presidency of the party in Surat. They were mostly unable to make him eligible for that post due to his involvement in the freedom movements.

Q.3. What contributions did Lala Lajpat Rai make to women empowerment in India?

Ans. Lala Lajpat Rai fought for the equality of Indian women specifically for those who were restricted from being an equal part of society. He led many missions in rural areas to enable people to understand equality in society for women too.

Gui Testing In Different Ways

What is GUI Testing?

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Features of GUI

By understanding its different characteristics, GUI testing can be clarified further.

Thus, some critical GUI tests and other associated elements are described below:

This testing method is harder than the line interface test command.

Most test tools used for GUI testing focus mainly on regression testing.

It also confirms that the components, like the font and the pictures, comply with their design specifications.

GUI can face more challenges with automated testing, as the user interface often changes.

It is carried out from the viewpoint of the user and not the developer or tester.

It helps the team collect the data needed to decide whether an application can be deployed or not deployed.

GUI Testing in Different Ways

Manual Based Testing

Record and Replay

Model-Based Testing

1. Manual Based Testing

Testers manually verified all graphics for the company document with the prerequisites. For instance, the multiplication (33X5) can be checked by manual testing.

2. Record and Replay

Record and Replay is an automated Graphical User Interface tool with all the test records during testing.

3. Model-Based Testing Check-List of GUI Testing

Check GUI elements like length, width, size, font, etc.

Check for the correct error message display.

Size of font and font readability.

Pictures should be aligned correctly.

The positioning for various resolutions of all GUI elements.

Advantages

In addition, this testing offers other benefits such as:

Tests the interface from the point of view of customers.

Efficiently reduces the risk to the end of the development cycle.

Contributes to validating compliance with design specifications for the various icons and elements.

Improves product reliability and increases product quality.

More memory resources are needed, which can slow the system.

The testing method takes time and may involve additional GUI software.

Given the frequent change in the implementation interface, the team could need to refactor a test script to enhance its precision.

The testing method is difficult due to limited access or no access to the source code.

Examples

Test the height of the elements in size, location, and width.

Testing for the displayed error messages.

Test the various parts of the screen.

Test the font to determine whether or not it is readable.

We can also test the spelling.

Testing the screen in various sizes by zooming in and out, such as 600×800, 640 x 480, etc.

Texts and other components, such as buttons, icons, and so on, are in the correct location to test the alignment.

We can also test the font color.

Testing error messages colors and warning messages.

We can also test the clarity of the image

We can also test the Alignment of the image.

Hyperlink color testing.

How to Do GUI Testing?

Ensure a text box correctly aligns with the label “Source Folder.”

Ensure the text box is correctly aligned with the label “Package.”

Check that the “Browse” label is the button at the end of the TextBox named after the “Source Folder” label.

Make sure that the label text box “Name” is correctly aligned.

Verify that the ‘ Editors ‘ label consists of the public, default, private, and protected names of 4 radio buttons.

Verify that the “Super-Class” label under the “Modifier” label has to be correctly aligned.

Ensure that wherever needed, an error should be produced in RED color.

Display a correct confirmation message after updating any field.

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