by Nexlogica Team | Dec 27, 2022 | Uncategorized
Git is a version control tool that helps a developer to track what all changes that he/she has done in his code. Git’s user interface is fairly similar to these other VCSs, but Git stores and thinks about information in a very different way. Git thinks of its data more like a series of snapshots of a miniature filesystem. With Git, every time you commit, or save the state of your project, Git basically takes a picture of what all your files look like at that moment and stores a reference to that snapshot. GIT allows you to analyze all code changes with great accuracy. If necessary, you can also use a very important function that allows you to restore the selected version of the file. This is especially useful when developer made a mistake that caused the software to stop working properly.
Most operations in Git need only local files and resources to operate — generally no information is needed from another computer on your network. If you’re used to a CVCS where most operations have that network latency overhead, this aspect of Git will make you think that the gods of speed have blessed Git with unworldly powers. Because you have the entire history of the project right there on your local disk, most operations seem almost instantaneous.
Everything in Git is checksummed before it is stored and is then referred to by that checksum. This means it’s impossible to change the contents of any file or directory without Git knowing about it. This functionality is built into Git at the lowest levels and is integral to its philosophy. You can’t lose information in transit or get file corruption without Git being able to detect it.
When you do actions in Git, nearly all of them only add data to the Git database. It is hard to get the system to do anything that is not undoable or to make it erase data in any way. As with any VCS, you can lose or mess up changes you haven’t committed yet, but after you commit a snapshot into Git, it is very difficult to lose, especially if you regularly push your database to another repository. Thanks to the fact that previous versions of the code are saved, programmers do not have to worry about “breaking something” – they can experiment with the code and test different solutions.
The GIT software also has some very useful advantage – allow you to work in teams, what is very often in the IT industry. Thanks to GIT, every team member has access to exactly the same, up-to-date version, and the risk of errors is decreased to an absolute minimum.
You can read more about GIT here.
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by Nexlogica Team | Dec 23, 2022 | Artificial Intelligence - Machine Learning, Uncategorized
Artificial neural networks and related deep learning are conquering other areas of the industry.
It underpins most deep learning models. As a result, deep learning may sometimes be referred to as deep neural learning or deep neural networking. The use of networks built of artificial neurons allows to create software that imitates the work of the human brain, which translates into an increase in the efficiency of business processes and companies.
The Neural Network is constructed from 3 type of layers:
- Input layer — initial data for the neural network.
- Hidden layers — intermediate layer between input and output layer and place where all the computation is done.
- Output layer — produce the result for given inputs.
The input layer is used to retrieve data and pass it on to the first hidden layer.
In hidden layers, calculations are performed, as well as the learning process itself.
The output layer calculates the output values obtained from the entire network, and then sends the obtained results to the outside.
Each node has a weight and a threshold – when the threshold value exceeds the allowable value, it activates and sends data to the next layer. Neural networks need training data from which they learn to function properly. As they receive more data, they can improve their performance.
Neural networks come in several different forms, including recurrent neural networks, convolutional neural networks, artificial neural networks and feedforward neural networks, and each has benefits for specific use cases. However, they all function in somewhat similar ways — by feeding data in and letting the model figure out for itself whether it has made the right interpretation or decision about a given data element.
Neural networks involve a trial-and-error process, so they need massive amounts of data on which to train. It’s no coincidence neural networks became popular only after most enterprises embraced big data analytics and accumulated large stores of data. Because the model’s first few iterations involve somewhat educated guesses on the contents of an image or parts of speech, the data used during the training stage must be labeled so the model can see if its guess was accurate. This means, though many enterprises that use big data have large amounts of data, unstructured data is less helpful. Unstructured data can only be analyzed by a deep learning model once it has been trained and reaches an acceptable level of accuracy, but deep learning models can’t train on unstructured data.
Deep learning will be developed, and deep neural networks will find application in completely new areas. It is already predicted that they can be used in driving autonomous cars or in the entertainment sector to analyze the behavior of users of a streaming service, or add sound to silent movies.
You can read more about Artificial Neural Network here.
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by Nexlogica Team | Dec 22, 2022 | Artificial Intelligence - Machine Learning
Deep learning is a type of machine learning and artificial intelligence (AI) that imitates the way humans gain certain types of knowledge. Deep learning is an important element of data science, which includes statistics and predictive modeling. It is extremely beneficial to data scientists who are tasked with collecting, analyzing and interpreting large amounts of data; deep learning makes this process faster and easier. At its simplest, deep learning can be thought of as a way to automate predictive analytics. While traditional machine learning algorithms are linear, deep learning algorithms are stacked in a hierarchy of increasing complexity and abstraction.
Computer programs that use deep learning go through much the same process as the toddler learning to identify things around him. Each algorithm in the hierarchy applies a nonlinear transformation to its input and uses what it learns to create a statistical model as output. Iterations continue until the output has reached an acceptable level of accuracy. The number of processing layers through which data must pass is what inspired the label deep.
Unlike the toddler, who will take weeks or even months to understand the concept of eg. bed, a computer program that uses deep learning algorithms can be shown a training set and sort through millions of images, accurately identifying which images have beds in them within a few minutes.
To achieve an acceptable level of accuracy, deep learning programs require access to immense amounts of training data and processing power, neither of which were easily available to programmers until the era of big data and cloud computing. Because deep learning programming can create complex statistical models directly from its own iterative output, it is able to create accurate predictive models from large quantities of unlabeled, unstructured data. This is important as the internet of things (IoT) continues to become more pervasive because most of the data humans and machines create is unstructured and is not labeled.
Deep learning examples
Because deep learning models process information in ways similar to the human brain, they can be applied to many tasks people do. Deep learning is currently used in most common image recognition tools, natural language processing (NLP) and speech recognition software. These tools are starting to appear in applications as diverse as self-driving cars and language translation services.
Use cases today for deep learning include all types of big data analytics applications, especially those focused on NLP, language translation, medical diagnosis, stock market trading signals, network security and image recognition.
Specific fields in which deep learning is currently being used include the following:
- Customer experience (CX). Deep learning models are already being used for chatbots. And, as it continues to mature, deep learning is expected to be implemented in various businesses to improve CX and increase customer satisfaction.
- Text generation. Machines are being taught the grammar and style of a piece of text and are then using this model to automatically create a completely new text matching the proper spelling, grammar and style of the original text.
- Aerospace and military. Deep learning is being used to detect objects from satellites that identify areas of interest, as well as safe or unsafe zones for troops.
Industrial automation. Deep learning is improving worker safety in environments like factories and warehouses by providing services that automatically detect when a worker or object is getting too close to a machine.
- Adding color. Color can be added to black-and-white photos and videos using deep learning models. In the past, this was an extremely time-consuming, manual process.
- Medical research. Cancer researchers have started implementing deep learning into their practice as a way to automatically detect cancer cells.
- Computer vision. Deep learning has greatly enhanced computer vision, providing computers with extreme accuracy for object detection and image classification, restoration and segmentation.
You can read more about Deep Learning here.
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by Nexlogica Team | Dec 21, 2022 | Security
Google Workspace, formerly known as Google Apps and later G Suite, is a collection of cloud computing, productivity and collaboration tools, software and products developed and marketed by Google. It consists of Gmail, Contacts, Calendar, Meet and Chat for communication; Currents for employee engagement; Drive for storage; and the Google Docs Editors suite for content creation. An Admin Panel is provided for managing users and services. Depending on edition Google Workspace may also include the digital interactive whiteboard Jamboard and an option to purchase such add-ons as the telephony service Voice. The education edition adds a learning platform Google Classroom and today has the name Workspace for Education.
The company developed mechanism to increase the security of mail data served by the Workspace (Gmail) service, calling it Client-Side Encryption (CSE). The solution gives Workspace customers the opportunity to implement their own mail encryption system, so data is protected before it reaches Google servers. Once the customer has enabled this encryption option, all attachments, emails, and embedded images are encrypted. However, CSE does not encrypt items such as email headers, subjects, timestamps, and recipient lists. Google explains that with CSE, content encryption is handled directly in the customer’s browser before any data is uploaded or stored in the Google cloud. This way Google’s servers cannot access the encryption keys.
CSE differs in one important respect from end-to-end encryption. For CSE, customers use encryption keys that are generated and stored in a cloud-based key management service. Therefore, administrators can control the keys and see who has access to them, and can always revoke a user’s access to the keys. With E2EE encryption, administrators have no control over customer keys and who can use them. They also cannot see what content users have encrypted. Those testing this mechanism should note that it is disabled by default and can be enabled at the domain or group level. Only then can the user click on the padlock icon to add CSE encryption to any message.
Google Workspace Client-side encryption is currently available for the following services:
- Google Drive for web browser, Drive for Desktop (non-Google file formats only), and Drive on Android and iOS (view-only for non-Google file formats).
- Google Meet for web browser only. CSE support for the Meet mobile app and meeting room hardware will be available in a later release.
- Google Calendar (beta) for web browser only.
You can read more about CSE here.
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by Nexlogica Team | Dec 20, 2022 | Uncategorized
The ear is one of the few parts of the body that remains relatively unchanged over our lifetime, making it a useful alternative to facial or fingerprint authentication technologies. This part of the body is unique to each person in the same way as a fingerprints. According to the researchers, even among identical twins, the shape of the ear is unique enough to still serve as a protection. An additional benefit is that, apart from the earlobe falling over time, the inside of the earlobe does not age as much over the years as our face.
The ear recognition software works similarly to face recognition. When a person gets a new phone, they have to register their fingerprint or face for the phone to recognize them. New devices often require users to place their fingers repeatedly over the sensor to get a full “picture” of their fingerprint. And face-recognition technology relies on users moving their faces in certain ways in front of their camera for the device to effectively capture their facial features. The ear recognition algorithm will work the same way.
While setting up a biometric device, the algorithm takes multiple samples of a person’s identity, such as facial images or fingerprints, and logs them into the device. When you go to unlock your device using a biometric, it takes a live sample to compare it to the logs on the device, such as a picture of your face or in this case, a picture of your ear.
Bourlai’s software uses an ear recognition algorithm to evaluate ear scans and determine if they are suitable for automated matching. He employed a variety of ear datasets with a wide range of ear poses to test the software.
The software that Professor Thirimachos Bourlai and his team are working on, has been tested on two large sets of ear images with accuracy of up to 97.25% of the time.
Ear recognition software could be used to enhance existing security systems, such as those used at airports around the world, and camera-based security systems, Bourlai said. His team also plans to enhance their proposed ear recognition algorithm to work well with thermal images as well to account for darker environments where it might be difficult to capture clear visible band images using conventional cameras.
You can read more about Ear Authentication Technology here.
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