Trending December 2023 # Android: All You Need To Know About Root, Custom Recovery And Roms # Suggested January 2024 # Top 14 Popular

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When you buy a new Android device, you don’t just get what it comes preloaded with. You also get a number of things you really haven’t thought of before. Android is customizable to a large extent, and that’s what the third-party developers have leveraged – to cook up some of the great goodies for the devices. Root, Custom Recovery, and Custom ROMs are three of the customizations you should definitely make use of when you get an Android device. These are the things that make your experience with Android much smoother and faster. First off, you should know exactly what these things are and what they do, then you can move forward and get them on your device, right? Read on to learn more.

Rooting an Android Device

Root refers to the administrative access to the system files on your Android device. In simple worlds, it means once you are rooted, you can access (and modify) those system files that are usually restricted by the OS. You might wonder why you would ever need access to system files. Well, there are various reasons for that, some of them being:

You might want to change the appearance of your device

You might want to remove some apps that have come preloaded on your device

You might want to do some tricks with your phones

Custom Recovery

Each Android device that you buy comes with a recovery, and it’s called stock recovery. A custom recovery refers to the recovery that has been developed by third-party developers and not the device manufacturer. There are some limitations attached with the stock recovery, and therefore to get full access to your system, you need a custom recovery.

There are a number of custom recoveries available for Android devices with ClockworkMod and TWRP being two of the popular ones.

A custom recovery lets you:

Install custom ROMs that are available in ZIP format

Backup and Restore your system image

Format your device

etc…

You can flash a custom recovery only after unlocking the device. By means of “lock”, we are referring to the internal mechanism that prevents anyone from modifying the internal system of the phone. Once unlocked, you will be able to root the phone and flash a custom recovery. After you have a recovery up and running on your device, switching between various ROMs would be a breeze for you.

Custom ROMs

An Android ROM (aka system image) is basically a file that contains the executable instructions to run the Android OS. A stock ROM is the one developed by the device manufacturer and comes shipped in the phone. The good thing about Android is that it is open-source, and everyone can access the code. When someone takes the code, adds in their own stuff and distributes it, that is known as a custom ROM. There are a number of custom ROMs available for Android devices. Here are some of the popular ones:

1. CyanogenMod

CyanogenMod is one of the best custom ROMs available out there for Android devices. With its unique features and appearance, it has largely been accepted by a wide population of Android users. It’s a great ROM and is available for a lot of Android devices.

2. MIUI ROM

MIUI claims it redefines Android, and that’s true for the most part as the ROM does offer a number of features and enhancements that are lacking with the stock ROMs. It customized almost every part of the Android and made the user’s experience much smoother and faster.

3. AOKP

Android Open Kang Project, often abbreviated as AOKP, is a third-party custom ROM that lets you enjoy more on your device than what you get with your stock ROM. It offers features like ribbon, navigation ring, and vibration patterns that I think are more distinctive than the ones we find in other ROMs.

4. Paranoid Android

Ever wanted a ROM that provides a clutter-free experience on your device? Paranoid Android is the one you should be using. The ROM, like any other ROMs available out there, has a number of features and unique customizations that enhance your user-experience as well as lets you have something new and cool on your device. It is worth giving a try to this ROM.

Conclusion

An Android device can’t do everything out-of-the box but it does have some capabilities that can be unlocked by using various customization options mentioned above. Feel free to give a shot to all of these customizations and let us know how it worked for you!

Mahesh Makvana

Mahesh Makvana is a freelance tech writer who’s written thousands of posts about various tech topics on various sites. He specializes in writing about Windows, Mac, iOS, and Android tech posts. He’s been into the field for last eight years and hasn’t spent a single day without tinkering around his devices.

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All You Need To Know About Recommendation Systems

This article will support data scientists in furthering their studies on recommendation systems so that they can develop applications for professional use. We introduce the content-based filtering, for the recommendation system, using this filtering, we learn here how to use this system and how to predict items, we use an amazon dataset.

In recommendation systems, we have two techniques, In this bog we major focus on content-based filtering.

Collaborative Filtering.

Content-based Filtering.

Today in real-world recommendation systems are an integral part of our lives. In amazon Roughly 35% of revenue is made by a Recommendation system, hence we can say the Recommendation system contributes to the major chunk of revenue in amazon. Working on recommendation algorithms is one of my favourite things to do. When I come across a recommendation engine on a website, I immediately want to dissect it and, how it works. It’s one of the many perks of a data scientist!

Collaborative Filtering

In this filtering, we use user and item reviews and then using this review we find a common user who has the same interest-as other users.

Content-based Filtering

Content-based filtering we recommend to what the user likes, based on their interest.

Here we will focus on a content-based Recommendation System and we understand using real-life data,amazon-apparel dataset.

Source: Wikipedia

Table of Contents

Table of Contents:-

1. What is the Recommtations system?

2. Overview of the data.

3. Data preprocessing.

5. Text Preprocessing.

6. Apply the different techniques to convert text to vector.

7. Measuring the effectiveness of the solution.

What is the Recommendation System?

Let’s take one real-life example. all of the done shopping on Amazon. So when you search for one product and then amazon shows a similar item. In nutshell, we can say this similar product is nothing but it is a recommendation system for you, so it’s all about a recommendation. But, how a recommendation system works? We will learn about it in this blog. To understand better we take an amazon woman apparel dataset.

Overview of Data

Here we have an item title, brand name, the colour of the item, price of the item, etc. Using an amazon API we take data from amazon, we have a total of 183k datapoint(product) and 19 features available here.

The Feature List data.columns # prints column-names or feature-names.

Index([‘sku’, ‘asin’, ‘product_type_name’, ‘formatted_price’, ‘author’,

‘color’, ‘brand’, ‘publisher’, ‘availability’, ‘reviews’,

‘large_image_url’, ‘availability_type’, ‘small_image_url’,

‘editorial_review’, ‘title’, ‘model’, ‘medium_image_url’,

‘manufacturer’, ‘editorial_reivew’],

dtype=’object’)

Of these 19 features, we will be using only 6 features in this blog

1. asin ( identification number)

2. brand ( brand to which the product belongs)

3. color ( Color information of apparel)

4. product_type_name (type of the apparel, ex: SHIRT/T-SHIRT )

5. medium_image_url ( URL of the image )

6. title (title of the product.)

7. formatted_price (the price of the product)

data = data[['asin', 'brand', 'color', 'medium_image_url', 'product_type_name', 'title', 'formatted_price']] print ('Number of data points : ', data.shape[0], 'Number of features:', data.shape[1]) data.head() # prints the top rows in the table.

Source: Author’s GitHub Profile

Data Preprocessing

For the data preprocessing we remove all the datapoint where feature value is not present.

After the remove datapoint where colour and price value is null and after this we have 28k datapoint available.

Remove some text from the title

Eg of duplicates data points:

Titles 1:

16. woman’s place is in the house and the senate shirts for Womens XXL White

17. woman’s place is in the house and the senate shirts for Womens M Grey

Title 2:

25. tokidoki The Queen of Diamonds Women’s Shirt X-Large

26. tokidoki The Queen of Diamonds Women’s Shirt Small

27. tokidoki The Queen of Diamonds Women’s Shirt Large

Here we have some title that looks like this where the meaning of the title is the same, except the few words. from the eg titles1 where we can show

both titles is the same they talk about the same shirts, the only difference is the size of shirt.

so here we remove this type of data title.

Remove the same Image.

There is some image is available where the product is the same but different only that is product colour. So, we remove that product where the product is the same but the colour is different.

Source: Author’s GitHub Profile

Text Preprocessing

Here we have the product title and to convert this title into vector first we have to do text processing.

Remove the stop word # we use the list of stop words that are downloaded from nltk lib. import nltk nltk.download('stopwords') stop_words = set(stopwords.words('english')) print ('list of stop words:', stop_words)

{“couldn’t”, ‘such’, ‘where’, ‘too’, ‘are’, ‘ve’, ‘your’, ‘him’, ‘this’, “wouldn’t”, “didn’t”, ‘has’, ‘than’, ‘ll’, ‘very’, ‘who’, ‘having’, ‘for’, “should’ve”, ‘mightn’, ‘of’, ‘until’, ‘we’, ‘haven’, “you’d”, ‘while’, “shouldn’t”, ‘doing’, “mightn’t”, ‘just’, ‘through’, ‘own’, ‘o’, ‘what’, ‘any’, ‘will’, “weren’t”, ‘have’, “hadn’t”, ‘my’, ‘weren’, ‘most’, “aren’t”, ‘it’, ‘had’, ‘further’, ‘more’, ‘those’, ‘on’, ‘against’, “doesn’t”, ‘himself’, ‘their’, ‘few’, ‘being’, ‘you’, ‘below’, ‘in’, ‘here’, ‘be’, “mustn’t”, “wasn’t”, ‘nor’, ‘then’, ‘how’, “that’ll”, ‘a’, ‘hasn’, ‘mustn’, “needn’t”, ‘shouldn’, ‘by’, ‘doesn’, ‘hadn’, ‘y’, ‘herself’, “she’s”, ‘shan’, ‘do’, ‘d’, ‘an’, ‘ourselves’, ‘the’, ‘that’, ‘after’, ‘there’, “you’re”, ‘them’, ‘was’, ‘itself’, ‘hers’, ‘yours’, ‘needn’, ‘down’, ‘its’, “you’ll”, ‘didn’, “won’t”, ‘both’, ‘these’, ‘up’, ‘again’, ‘his’, ‘did’, ‘our’, ‘when’, ‘only’, ‘s’, ‘over’, ‘because’, ‘wasn’, ‘should’, ‘so’, ‘re’, ‘couldn’, ‘under’, ‘ain’, ‘at’, “it’s”, ‘as’, ‘he’, ‘all’, ‘does’, “don’t”, ‘won’, ‘whom’, ‘to’, ‘i’, “haven’t”, ‘ma’, ‘were’, “hasn’t”, ‘m’, ‘above’, ‘each’, ‘she’, “isn’t”, ‘between’, ‘they’, ‘am’, ‘no’, ‘myself’, ‘yourself’, ‘during’, ‘t’, ‘out’, ‘off’, ‘wouldn’, “you’ve”, ‘or’, ‘with’, ‘ours’, ‘before’, ‘same’, ‘which’, ‘into’, ‘now’, “shan’t”, ‘if’, ‘themselves’, ‘isn’, ‘about’, ‘yourselves’, ‘theirs’, ‘and’, ‘don’, ‘not’, ‘from’, ‘can’, ‘me’, ‘but’, ‘is’, ‘once’, ‘why’, ‘some’, ‘her’, ‘aren’, ‘been’, ‘other’}

Apply Stemming from nltk.stem.porter import * stemmer = PorterStemmer() print(stemmer.stem('arguing')) print(stemmer.stem('fishing'))

Output.

argu fish Apply the Different Techniques to Convert Text to Vector TF-IDF Base Word to Vector

Here we use a TF-IDF to convert a text to a vector and after this, we got a vector for each title.

Source: Towards Data Science

Now we have a vector and for this find, similarity we use a Euclidean distance, which product dist is very small to the query product we can defined-as a similar product.

Source: Tutorial Example

All You Need To Know About Autoencoders In 2023

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

Autoencoders are unsupervised learning techniques based on neural network frameworks, trained to copy inputs to outputs. Neural networks are designed to create bottlenecks in the network. Internally, the hidden layer h describes the code used to represent the input. An autoencoder network consists of three parts. First, the encoder compresses the image and generates code using the encoder function h = f(x). Then comes a bottleneck where we have a compressed knowledge representation of the original input, followed by a decoder that forms the reconstruction r = g(h). The autoencoder scheme is shown in Figure 1. Data is compressed and restructured as it moves through the architecture. This compression and reconstruction process is complicated when the input features are independent. However, ere is some correlation within the input data, the existing dependencies can be learned and used when the input is forced through the network bottleneck.

Figure 1 – Diagram showing the schematic of a typical Autoencoder

In the following subsection, we will take a detailed look into the network architecture and the corresponding hyperparameters of an Autoencoder.

The Architecture of an AutoEncoders

You must already have a faded idea of what an autoencoder would look like. In this section, we will add more depth to your understanding. We would be particularly interested in the hyperparameters you need to take care of while designing an autoencoder.

As mentioned earlier, an autoencoder consists of three parts: encoder, code, and decoder. Both the encoder and decoder are simple feedforward neural networks. The code is a single layer of ANN with selected dimensions. For input and output layers, the number of nodes is determined by the input data X. Therefore, the input and output layers have the same number of nodes, and both correspond to high-dimensional representations. The middle hidden layer with the fewest nodes corresponds to the low-dimensional representation. The goal of the training process is to minimize the squared reconstruction error between the network’s inputs and outputs. For learning algorithms, the most commonly used strategy is backpropagation. The initial weights of the network are important for the encoder to find a good solution. Backpropagation works more effectively when the initial weights are closer to the optimal solution. Many algorithms have been developed to find good initial weights.

Before training the autoencoder, we need to set four hyperparameters.

The number of nodes in the middle layer, i.e., the code layer. A smaller size of the code layer would result in more compression.

The number of nodes per layer is the third hyperparameter we need to tune. Typically the encoder and decoder are symmetric in terms of the layer structure, and the number of nodes in each subsequent layer of the encoder keeps decreasing till the code layer is reached and then keeps increasing similarly in the decoder architecture.

The choice of the loss function is the fourth hyperparameter. The most frequently used loss functions include the mean squared error or binary cross entropy.

The most important tradeoff in autoencoders is the bias-variance tradeoff. At the same time, the autoencoder architecture should reconstruct the input well (reducing the reconstruction error) while generalizing the low representation to something meaningful. Therefore, to achieve this property, let’s look at the various architectures developed to address this trade-off.

Autoencoders types to tackle the tradeoff 1. Sparse Autoencoders

These networks offer an alternative method of introducing bottlenecks without requiring node count reduction. It handles the trade-off by forcing sparsity on hidden activations. They can be added over or in place of bottlenecks. There are two ways to apply sparse regularization. The first is by using L1 regularization, and the second is by implementing KL divergence. I won’t go into the mathematical details of the regularization technique, but a brief overview is sufficient for this blog.

Figure 2 – Schematic representation of a Sparse Autoencoder

2. Denoising Autoencoders

Autoencoders have been considered neural networks with identical inputs and outputs. The main goal is reproducing the input as accurately as possible while avoiding information bottlenecks. However, another way to design an autoencoder is to slightly perturb the input data but keep the pure data as the target output. With this approach, the model cannot simply create a mapping from input data to output data because they are no longer similar. So using this regularization option introduces some noise into the input while the autoencoder is expected to reconstruct a clean version of the input.

Figure 3 – Schematic representation of a Denoising Autoencoder

While in the previous case, the emphasis was on making the encoder more resilient to some input perturbations, in these types of architectures, the emphasis is on making the feature extraction less sensitive to small perturbations. It is written. This is achieved by having the encoder ignore changes in the input that are not significant for reconstruction by the decoder. The main idea behind this regularization technique is that potential representations that are not important for reconstruction are reduced by the regularization factor. In contrast, important variations remain because they have a large impact on the reconstruction error is.

Figure 4 – Schematic representation of a Contractive Autoencoder

Applications of Autoencoders

If you’ve read this far, you should have the theoretical background you need to know about autoencoders. You must be wondering where the application of these structures lies in machine learning. This section sheds light on the applications of these structures.

Dimensionality reduction was one of the first applications of representation learning. Reducing dimensions can help improve the model performance in several cases.

Another task that bene more than dimensionality reduction is information retrieval.

Other applications of autoencoders include anomaly detection, image processing, data denoising, drug discovery, popularity prediction, and machine translation.

Conclusion

That’s why I talked about autoencoders in today’s blog. Autoencoders are architectures originally designed to help with dimensionality reduction. However, its applications have multiplied many times over time. First, we briefly introduced the structure of an autoencoder and how data compression is achieved at the code layer. We then discussed different types of autoencoders and how each one helps to deal with bias-variance tradeoffs. Finally, we have finished discussing the scenarios in which autoencoders are applied in today’s world. So the key takeaways from this article are:

The general architectural approach towards autoencoders

The bias-variance tradeoff faced by the autoencoders

How applying different regularization techniques can enable us to handle the tradeoff. This would enable you to think of more such algorithms and develop newer architectures.

The areas where this type of architecture finds applicability.

I believe I could leave you with a deep theoretical understanding of the architecture and use cases of Autoencoders from this discussion in the blog. If this article excites you, I urge you to go ahead and develop one such architecture for yourself. It’s a good project to have with you.

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All You Need To Know About Defi Cryptocurrencies: Hypaswap

With the introduction of DeFi, the entire banking system was transformed into a much more adaptable framework where anyone can lend and borrow money without encountering needless complications. It’s safe to conclude that this system is far more capable and risk-aware than the conventional banking system, even though it isn’t fully risk-free given how recent the technology is. A decentralised non-custodial liquidity protocol called

HypaSwap (HYPA) – The New Token In Town

HypaSwap is a hotspot of the decentralised economy as it is a decentralised liquidity protocol. The protocol’s main focus is on lending and borrowing. HypaSwap has implemented several safeguards addressing collateralisation, the state of the liquidity pool, and external penetrations to ensure fair practices and hassle-free transactions.

With lending and borrowing as its main areas of focus, HypaSwap has developed a solid system that allows users to conduct these transactions without running the danger of losing money to fraud or bad loans. The lenders are reimbursed with the interest rate, and the borrowers are compelled to overpay for the loan. HypaSwap also established a thorough framework for members of the community to actively engage in extra-banking activities like staking and collateral exchanging. To maximise their investment returns and receive incentives for their active participation, members are urged to stake additional tokens.

Features Of The HypaSwap Ecosystem Borrowing

Borrowers withdraw funds from the liquidity pool in exchange for collateral when they borrow money. The collateral is released after the amount and interest have been paid. Contrary to centralised banking, the collateral must be significantly more valuable than the amount being borrowed. The needed sum for HypaSwap is equal to 150 per cent of the borrowed sum. As a result, for every 100 ETH borrowed, the borrower must put up 150 ETH as collateral. The collateral is liquidated and disbursed among the lenders if the borrower defaults on the loan. If the value of the collateral drops below 150 per cent of the loaned amount due to price volatility, the collateral is liquidated and dispersed among the lenders.

Lending

The HypaSwap protocol allows users to lend their assets to build a liquidity pool made up of various cryptocurrencies, including ETH and BNB. Every transaction results in the creation of a derivative with a 1:1 valuation that can be saved, exchanged, or redeemed. This implies that the value of the derivative token is still free even while the underlying asset is still locked in the liquidity pool. The owner of the derivative token, also called a fToken, receives ownership of the lent sum once the token is sold.

Source

Earning Through Lending

Interest rates and HypaSwap Incentives are the two ways that lenders profit in the HypaSwap ecosystem. Since funding the liquidity pool is the first stage of DeFi, lenders are never exposed to risk because loan repayment is guaranteed and profits are promised. Every time a borrower pays off their loan with interest, the money is divided up among all the lenders whose money was used, and they each receive a portion of the interest rate. If the borrower doesn’t make payments, the lenders will be reimbursed by selling the collateral. Lenders are additionally encouraged by HypaSwap Incentives to continue lending and lock in for extended periods because lending is what keeps the protocol operational.

Tokenomics

Like any other conventional liquidity protocol, the HypaSwap protocol deals with a variety of tokens and currencies. HYPA, an ERC-20 token built on the Ethereum blockchain, operates as HypaSwap’s native coin. The HYPA token serves as the main medium of exchange for platform functions like interest rates, fines, staking rewards, etc. HYPA can be earned on the platform, or it can be purchased at the going rate on cryptocurrency exchanges. If you purchase HYPA tokens during stage 2 of the presale, you will receive 4 additional percent of tokens. If you purchase stage 1, you will receive a 6% bonus in HYPA tokens.

To find out more about this new cryptocurrency, see the links below:

HypaSwap (HYPA)

All You Need To Know About Google Maps New Feature For Covid

All You Need To Know About Google Maps New Feature for COVID-19

This Google Maps new feature is available on both Android and iOS and has been rolled out as quickly as all possible areas where the train and bus data was previously being tracked by Google. Some of the important new features included will be of great help to people who have to go out to work or buy out essentials.

Google Maps New Feature for COVID-19 New Feature 1: Provide timely and important alerts.

When a user will look for directions and services available under public transportation, he/she will get all the Covid-19 restrictions applied in the area of travel and also receive alerts from the local transit agencies. This update is currently rolled out in the United States, United Kingdom, Australia, France, Netherlands, Belgium and Spain. In the South Americas, this update is rolled out in Argentina, Brazil, Colombia and Mexico and in Asia this update has been released in India and Thailand.

New Feature 2: Provide Alerts and Guidelines for Medical and testing visits. New Feature 3: Avoid Crowds when using public transport.

As I mentioned earlier Social Distancing is an effective tools that human have against Covid-19 virus, the one place where we cannot avoid crowds is the public means of transportation. But with help from the latest Google Maps update, crowds can finally be avoided and proper Social distancing can be ensured. This data will help you to plan your trip as you can see at live data regarding at given places as well as predictions based on historical data.

New Feature 4: Other miscellaneous features.

The Google Maps new features also include other functions like Temperature, Accessibility, and Security on board, Women’s section, provisions for Wheelchair users and other facilities. Google is trying to help the travellers all over the world to travel safely and reduce the risk of Covid 19 Pandemic. Of course Google is seeking help from past riders and relying on the historical data it has collected depending if the location and internet services are turned on in a specific device.

Your Thoughts on All You Need To Know About Google Maps New Feature.

With the Google Maps new feature rolled out across the globe, users will have to just type the name of a train station or bus station on the Maps app and they will get complete details on arrival and departure times, crowd information and other restrictions in place. This will keep people at a distance from one, another, and reduce unnecessary travel.

Next Read:

Fix Google Maps Not Working on iPhone

Google Maps Alternatives

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About the author

Dheeraj Manghnani

Dheeraj Manghnani is a tech writer who writes about anything that has tech into it. He has written over a 1000 blogs on tech news, product comparisons, error solving and product reviews.

Samsung Galaxy S10: All You Need To Know

At a packed Galaxy Unpacked 2023 event on February 20th, Samsung unveiled the Galaxy S10, the device to mark the tenth anniversary in the Galaxy S series. Thanks to the plenty of rumors and leaks, we already knew the 2023 S10 has at least three variants – the standard S10, the Plus variant, and a smaller S10e model, but as you may have heard, there is also a 5G variant.

On this page, we have everything you need to know about the Galaxy S10e, Galaxy S10, Galaxy S10+, and Galaxy S10 5G, be it their specs, features, software updates, problems and their solutions, tips and tricks of getting the most out of them, the best accessories, deals, firmware download, and so on.