Trending February 2024 # Yourport: A Friendly And Feature # Suggested March 2024 # Top 11 Popular

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“YourPort” is a unique startpage that combines all these fortes into a singular package.

Introduction Usage and Features

When you first visit YourPort you will view search engine features at the top of the site’s page. To search for anything you can type in your query, select the sort of results you want – web, images, maps, or video – and then execute the search.

By default the search engine is Google; this can always be changed to one of the alternatives offered: Yahoo, AltaVista, Bing, AOL, or Ask.

In the top left of these shortcuts you will notice buttons that let you create custom tabs that include your own shortcuts. New tabs can be created and existing ones can be renamed or deleted.

For fresh tabs you will find empty shortcut slots as shown in the image below.

Alternatively you can select from a list of popular sites already available in YourPort’s options. To access these options place the mouse pointer in the top right of an empty slot. A ‘Popular Sites’ button will appear that will contain options to add the shortcuts for popular websites; these websites will be sorted in the menu categorically.

While adding site shortcuts you do not need to adhere to any positioning rules. You can start filling the shortcuts in whichever order you are comfortable with. You can fill all the empty shortcut slots or only some of them.

The “Share this tab” option lets you quickly share your customized tabs with your contacts. A direct URL can be obtained for the tab so you can land on your favorite ports tab rather than the default tab.

Conclusion

With its simple interface, user-friendliness, and richness of features YourPort serves as an excellent startpage. To quickly access your most frequented websites, all you have to do is start using YourPort. Once you begin using the site it will quite easily replace whichever startpage you are currently using.

Visit YourPort

Hammad

Hammad is a Business student and computer geek who cover latest technology news and reviews at AppsDaily. Apart from that, I like to review web services and softwares which can be helpful for the readers.

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A Friendly Introduction To Real

Overview

Real-time object detection is taking the computer vision industry by storm

Here’s a step-by-step introduction to SlimYOLOv3, the latest real-time object detection framework

We look at the various aspects of the SlimYOLOv3 architecture, including how it works underneath to detect objects

Introduction

Humans can pick out objects in our line of vision in a matter of milliseconds. In fact – just look around you right now. You’ll have taken in the surroundings, quickly detected the objects present, and are now looking at this article. How long did that take?

That is real-time object detection. How cool would be it if we could get machines to do that? Now we can! Thanks primarily to the recent surge of breakthroughs in deep learning and computer vision, we can lean on object detection algorithms to not only detect objects in an image – but to do that with the speed and accuracy of humans.

Do you want to learn real-time object detection but aren’t sure where to start? Do you want to build a computer vision model that detects objects like the above video? Then this article is for you!

Do you want to learn real-time object detection but aren’t sure where to start? Do you want to build a computer vision model that detects objects like the above video? Then this article is for you!

We will first look at the various nuances of object detection (including the potential challenges you might face). Then, I will introduce the SlimYOLOv3 framework and deep dive into how it works underneath to detect objects in real-time. Time to get excited!

If you’re new to the wonderful world of computer vision, we have designed the perfect course for you! Make sure you check it out here:

Table of Contents

What is Object Detection?

Applications of Object Detection

Why Real-Time Object Detection?

Challenges during Real-Time Object Detection

Introduction to SlimYOLOv3

Understanding the Architecture of SlimYOLOv3

What is Object Detection?

Before we dive into how to detect objects in real-time, let’s cover our basics first. This is especially important if you’re relatively new to the world of computer vision.

Object detection is a technique we use to identify the location of objects in an image. If there is a single object in the image and we want to detect that object, it is known as image localization. What if there are multiple objects in an image? Well, that’s what object detection is!

Let me explain this using an example:

The image on the left has a single object (a dog) and hence detecting this object will be an image localization problem. The image on the right has two objects (a cat and a dog). Detecting both these objects would come under object detection.

If you wish to get an in-depth introduction to object detection, feel free to refer to my comprehensive guide:

Now, you might be wondering – why is object detection required? ANd more to the point, why do we need to perform real-time object detection? We’ll answer these questions in the next section.

Applications of Object Detection

Object Detection is being widely used in the industry right now. Anyone harboring ambitions of working in computer vision should know these applications by heart.

The use cases of object detection range from personal security to automated vehicle systems. Let’s discuss some of these current and ubiquitous applications.

Self-Driving Cars

This is one of the most interesting and recent applications of Object detection. Honestly, it’s one I am truly fascinated by.

Self-driving cars (also known as autonomous cars) are vehicles that are capable of moving by themselves with little or no human guidance. Now, in order for a car to decide its next step, i.e. either to move forward or to apply breaks, or to turn, it must know the location of all the objects around it. Using Object Detection techniques, the car can detect objects like other cars, pedestrians, traffic signals, etc.

Face Detection and Face Recognition

Face detection and recognition are perhaps the most widely used applications of computer vision. Every time you upload a picture on Facebook, Instagram or Google Photos, it automatically detects the people in the images. This is the power of computer vision at work.

Action Recognition

Object Counting

Now, here’s the thing – most of the applications require real-time analysis. The dynamic nature of our industry leans heavily towards instant results and that’s where real-time object detection comes into the picture.

Why Real-Time Object Detection?

Let’s take the example of self-driving cars. Consider that we have trained an object detection model which takes a few seconds (say 2 seconds per image) to detect objects in an image and we finally deployed this model in a self-driving car.

Do you think this model will be good? Will the car be able to detect objects in front of it and take action accordingly?

Certainly not! The inference time here is too much. The car will take a lot of time to make decisions which might lead to serious situations like accidents as well. Hence, in such scenarios, we need a model that will give us real-time results. The model should be able to detect objects and make inferences within microseconds.

Some of the commonly used algorithms for object detection include RCNN, Fast RCNN, Faster RCNN, and YOLO.

The aim of this article is not to deep dive into these techniques but to understand the SlimYOLOv3 architecture for real-time object detection. If you wish to learn more about these techniques, check out the below tutorials:

These techniques work really well when we do not need real-time detection. Unfortunately, they tend to stumble and fall when faced with the prospect of real-time analysis. Let’s look at some of the challenges you might encounter when trying to build your own real-time object detection model.

Challenges of Performing Real-Time Object Detection

Real-time object detection models should be able to sense the environment, parse the scene and finally react accordingly. The model should be able to identify what all types of objects are present in the scene. Once the type of objects have been identified, the model should locate the position of these objects by defining a bounding box around each object.

So, there are two functions here. First, classifying the objects in the image (image classification), and then locating the objects with a bounding box (object detection).

We can potentially face multiple challenges when we are working on a real-time problem:

How do we deal with variations? The variations might be of difference in the shape of objects, brightness level, etc.

Deploying object detection models. This generally takes A LOT of memory and computation power, especially on machines we use on a daily basis

Finally, we must also keep a balance between detection performance and real-time requirements. Generally, if the real-time requirements are met, we see a drop in performance and vice versa. So, balancing both these aspects is also a challenge

So how can we overcome these challenges? Well – this is where the crux of the article begins- the SlimYOLOv3 framework! SlimYOLOv3 aims to deal with these limitations and perform real-time object detection with incredible precision.

Let’s first understand what SlimYOLOv3 is and then we will look at the architecture details to have a better understanding of the framework.

Introduction to SlimYOLOv3

Can you guess how a deep learning pipeline works? Here’s a quick summary of a typical process:

First, we design the model structure

Fine-tune the hyperparameters of that model

Train the model and

Finally, evaluate it

There are multiple components or connections in the model. Some of these connections, after a few iterations, become redundant and hence we can remove these connections from the model. Removing these connections is referred to as pruning.

Pruning will not significantly impact the performance of the model and the computation power will reduce significantly. Hence, in SlimYOLOv3, pruning is performed on convolutional layers. We will learn more about how this pruning is done in the next section of this article.

After pruning, we fine-tune the model to compensate for the degradation in the model’s performance.

A pruned model results in fewer trainable parameters and lower computation requirements in comparison to the original YOLOv3 and hence it is more convenient for real-time object detection.

Let’s now discuss the architecture of SlimYOLOv3 to get a better and clearer understanding of how this framework works underneath.

Understanding the Architecture of SlimYOLOv3

The below image illustrates how SlimYOLOv3 works:

SlimYOLOv3 is the modified version of YOLOv3. The convolutional layers of YOLOv3 are pruned to achieve a slim and faster version. But wait – why are we using YOLOv3 in the first place? Why not other object detection algorithms like RCNN, Faster RCNN?

Why YOLOv3?

There are basically two types (or two categories) of deep object detection models:

Detectors belonging to the RCNN family fall under two-stage detectors. The process contains two stages. First, we extract region proposals and then classify each proposal and predict the bounding box. These detectors generally lead to good detection accuracy but the inference time of these detectors with region proposals requires huge computation and run-time memory

Detectors belonging to the YOLO series fall under single stage detectors. It is a single-stage process. These models utilize the predefined anchors that cover spatial position, scales, and aspect ratios across an image. Hence, we do not need an extra branch for extracting region proposals. Since all computations are in a single network, they are more likely to run faster than the two-stage detectors. YOLOv3 is also a single stage detector and currently the state-of-the-art for object detection

Sparsity training

The next step is the sparsity training of this YOLOv3 model:

Here, we prune the YOLOv3 model using the following steps:

First, we evaluate the importance of each component of the YOLOv3 model. I will discuss the details of how to decide the importance of these components shortly

Once the importance is evaluated, we remove the less important components

The removed components can either be an individual neural connection or the network structures. To define the importance of each component, we rank each neuron of the network based on their contribution. There are multiple ways to do it:

We can take the L1/L2 regularized means of neuron weights

The mean activation of each neuron

Number of times the output of a neuron wasn’t zero

In SlimYOLOv3, the importance is calculated based on the L1 regularized means of neuron weights which are considered as the scaling factor. The absolute value of these scaling factors is the importance of a channel. To accelerate the convergence and improve the generalization of the YOLOv3 model, the batch normalization layer is used after every convolutional layer.

SlimYOLOv3

We then define a global threshold, let’s say ŷ, and discard any channel that has a scaling factor less than this threshold. In this way, we prune the YOLOv3 architecture and get the SlimYOLOv3 architecture:

While evaluating the scaling factor, the maxpool layers and the upsample layers of the YOLOv3 architecture have not been considered since they have nothing to do with the channel number of the layer number.

Fine-tuning

We now have the SlimYOLOv3 model, so what’s next?

We fine-tune it so as to compensate for the degradation in performance and finally evaluate the fine-tuned model to determine whether the pruned model is suitable for deployment.

In SlimYOLOv3, a penalty factor of α = 0.0001 is used to perform channel pruning.

End Notes

We’ve covered a lot of ground in this article. We saw the different object detection algorithms like RCNN, Fast RCNN, Faster RCNN, as well as the current state-of-the-art for object detection YOLO. Then, we looked at the SlimYOLOv3 architecture which is the pruned version of YOLO and can be used for real-time object detection.

I’m excited to get my hands on the code for SlimYOLOv3! I will try to implement SlimYOLOv3 and will share my learning with you guys.

Related

Phonerescue – A Friendly And Speedy Android Data Recovery Tool

This is a sponsored article and was made possible by iMobie. The actual contents and opinions are the sole views of the author who maintains editorial independence, even when a post is sponsored.

Data on your phone can be a fragile thing. One second you’re tapping through your family albums, the next second you’re uploading them to Facebook without realising, then POOF, before you know it, you’ve swiped them out of existence.

PhoneRescue claims to offer a solution to that, letting you recover not only photos, but also contacts, call logs, messages, videos, music and app documents. The mobile data recovery app has already earned itself a solid reputation for the iPhone, and now it’s arrived on Android. I got the chance to put it through its paces.

Setup

There are quite a few things you need to do before your phone is ready for the data recovery, but at least the app is honest about them by giving you a list of “Quick Tips” to consider before doing your data recovery. It doesn’t say any of them are mandatory, but seeing as it’s worded like a bunch of instructions rather than tips, it’s probably best you do what it says.

During setup the app tells you that you need to have a rooted device to access the “Deep Scan” functionality and even offers to do your rooting for you. While a nice idea in principle, this option sadly didn’t work for me. As there’s a separate Android app for each manufacturer (Samsung, HTC, Sony etc.), maybe it got confused because I have an unlocked HTC One M8 running LineageOS, rather than the default HTC UI.

My bootloader was already unlocked, and it was just a case of me flashing SuperSU to get root privileges, so getting root access wasn’t much of a problem for me. With that said, if you’re phone’s unrooted and you want a deep scan, be prepared to find your own rooting solutions if the built-in one here doesn’t work.

You’ll also need to enable USB debugging and grant root access to PhoneRescue, adding a couple more hoops to the process before you’re ready.

Using PhoneRescue

Once it’s made its findings, you can choose to recover files to your PC or directly to your phone, which is welcome. It does the job quickly, too, so you won’t be twiddling your thumbs for too long while waiting for it to complete its search.

The developers’ website has plenty of tips for using PhoneRescue, including guides on how to recover photos and how to recover messages, respectively.

Is PhoneRescue Reliable?

It’s the most crucial question. After all that rooting and effort to get it working, does PhoneRescue do the job? The answer is yes, to an extent. I’ve factory-reset my phone a couple of times in the past, and PhoneRescue didn’t manage to find anything from before those resets. (There’s a good chance they’ve been completely overwritten by now, so it may well have been an impossible task.)

PhoneRescue did, however, do a good job of finding files I’d deleted from my device since the reset – photos, messages, and a video, to be exact. It’s also very quick and easy on the eyes, making it less intimidating than certain other recovery software.

Conclusion

PhoneRescue (the HTC version, at least) is a very good tool but doesn’t yet match the deep-scanning feature set of the iPhone version which includes more tools and tweaks than this one. With time, however, it can catch up. Less experienced users or those just wanting a quick-and-easy tool for recovering their phone data will find everything they need here, and the fact that it does its job so quickly makes it a winner.

Download PhoneRescue (Android version)

Robert Zak

Content Manager at Make Tech Easier. Enjoys Android, Windows, and tinkering with retro console emulation to breaking point.

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A List Of Major Apps That Support Apple’s Shareplay Feature

Check out this list of iPhone, iPad, Apple TV and Mac apps updated with support for SharePlay, a feature that facilitates shared app and media experiences on FaceTime.

These major apps support Apple’s SharePlay feature

SharePlay is supported in Apple’s major apps including Music, TV and Fitness+.

The company has already highlighted several major third-party apps that now support SharePlay, including NBA, TikTok, Twitch, Paramount+ and Showtime. And now, we’ve put together the definitive list of all the popular apps for iPhone, iPad, Mac and Apple TV from both Apple and third-party developers. These apps either already feature support for SharePlay or its developers are working on implementing SharePlay in a future update.

Audio streaming Video streaming Health and fitness Games Other

SharePlay facilitates media sharing via FaceTime, but it can also power shared app experiences in creative ways. This includes Apple, which has leveraged SharePlay to implement synchronized group workouts and meditations on its Fitness+ service.

Creative uses for SharePlay

On TikTok, for instance, the For You feed becomes For Us when using SharePlay. The name change not only reflects the user has switched from a solitary experience to a group SharedPlay experience but also actually fills the feed with video recommendations that apply to everyone on the call. In a way, it’s a synced page between all of the devices participating in a SharePlay session that also mixes everyone’s interests.

Spotify is also working on implementing support for the SharePlay features. With it, Spotify fans will be able to organize listening parties via FaceTime as long as they’re subscribers. Read: How to use SharePlay to share music and videos on FaceTime calls

With SharePlay in Night Sky, you can stargaze remotely with your friends on iPhone and iPad in real-time. With the quizz-making app Kahoot!, you can take quizzes with others over FaceTime. Another example is Piano with Friends, which has great use for SharePlay — you can remotely draw on a single canvas with friends on a FaceTime call.

And the Crouton app lets you cook with friends via SharePlay in step-by-step mode.

How can developers add support for SharePlay?

SharePlay comes with a typical Apple touch, as per the Apple Newsroom announcement:

SharePlay sessions offer shared playback controls, so anyone on the FaceTime call can play, pause or jump ahead while enjoying synced media. With dynamic volume controls, audio from the streaming content will automatically lower when a FaceTime participant is speaking, making it easy to continue the conversation with friends despite a loud scene or climactic chorus.

And:

When users prefer to have uninterrupted sound, they can simply tap on the Messages button in the FaceTime controls to jump to a shared thread and keep the conversation going. Each participant in the SharePlay session streams directly from the relevant app on their own device, delivering high-fidelity audio and video. Apple TV supports SharePlay so users can watch shared shows or movies on the big screen while using their personal device to continue connecting with friends over FaceTime.

Apple provides developers with the tools and resources to implement SharePlay in their apps, should they wish to do so, through the Apple Developer website.

SharePlay is currently available on the following devices:

iPhone and iPod touch with iOS 15.1 and later

iPad with iPadOS 15.1 and later

Apple TV with tvOS 15.1 and later.

SharePlay for Mac is launching later in 2023 via an update to macOS Monterey.

If you have an Apple TV, you can watch shared media on the big screen while using FaceTime on your iPhone or iPad. With screen sharing support, you and your friends can browse the web together, look at photos or check out something in a favorite app.

Parameters Of Camera Tracking Feature

Introduction to Camera Tracking in After Effects

World-famous company Adobe Systems offers you the most popular video editing software, which is After Effect. We have different types of techniques and features in this software for making our work easy. Camera Tracking is one of its important features and used for giving tracking effects to any object with the motion of background objects. We can use Camera tracking for any type of video footage or animated object. Here in this article, we will understand Camera Tracking in a very simple manner and analyze important parameters of it to easily handle it for our graphics designing purpose.

How to Use Camera Tracking in After Effect?

Before starting our learning, let us have a quick look at the user interface of Adobe After Effect software for our better understanding of this software throughout this article.

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Step 1: The user screen of this software is divided into many sections such as Menu bar for making the different adjustment in this software, Toolbar which provides the different type of tool for designing work, Project panel, which shows a number of the composition of any project, Effect Control shows you parameters of any effect or preset which is used in current composition, at the button of this screen we have Layer section which shows you parameters of layers of any project, next is Timeline section which is used for handling the parameters of animation.

Step 2: For Learning Camera Tracking in After Effect, we have to take a video. You can take your own video or can download it from the internet. For placing that video in After Effect software, go to that folder of your personal computer where you have saved your video. Pick this video from there with the help of the mouse button and drop it in the Project Panel section of After Effect software.

Step 4: Now adjust the time of animation of this video according to your requirement from the Timeline section.

Step 6: Or you can choose a camera tracking option from the Animation menu of the Menu Bar. A drop-down list will be open to choosing the ‘Track Camera’ option from here.

Step 8: Once the camera initialized, this type of screen will be opened.

Step 10: The text area will be generated here with white color. Select this area with the help of the mouse cursor. A Character parameter box will be open on the right side of the working screen.

Step 12: Now, type text according to you which you want to animate with the help of a camera tracker.

Step 13: Now adjust your text where you want to place it with the help of the “move and selection’ tool.

Step 17: Now adjust the value of Orientation for adjusting the text like this. You can set the parameters of text property according to you.

Step 20: Now, type your desired name here. I will type ‘shadow’ as the name of this duplicate layer.

Step 24: Choose black as the shadow color of this layer and press the Ok button of this color box.

Step 25: Go to the Opacity property of this shadow layer and decrease the value of opacity up to 60% or you can set this value according to you.

Step 26: Now go to the Preview tab for playing the camera tracking, which is on the right side of the working screen, or you can press the Space bar of the keyboard as the short cut of playing animation.

Step 27: Your animation will look like this.

In this way, you can use Camera Tracking in After Effect software and handle the parameters of Camera Tracking for getting the best result from this feature of After Effect software.

Conclusion

Now, after going through this article, you can better understand ‘what is Camera Tracking’ and how you can use it in After Effect software. You also have a look at the basic parameters of the Camera Tracking feature of After Effect so that you can easily handle this feature for making a highly professional project in the video editing field.

Recommended Articles

This is a guide to Camera Tracking in After Effect. Here we discuss an introduction and track a camera in after-effects in a step-by-step manner. You can also go through our other related articles to learn more –

Asus G73Jw: Speedy, Smooth, Feature

Gamers looking for a powerful yet portable machine can stop looking. The Asus G73Jw is here. It’s only a mild update to Asus’s most recent previous lean, mean, gaming machine, the G73Jh. But with a slightly faster processor (a 1.73GHz Core i7 740QM vs. a 1.6GHz 720QM), a Blu-ray combo drive, and improved battery life (2.5 hours vs. 1.75 hours), the G73Jw is definitely an upgrade. Unfortunately, Asus didn’t redesign the notebook’s exterior features at all, and the keyboard remains a serious weakness.

The G73Jw looks exactly like the earlier G73Jh. It weighs a hefty 8.8 pounds (though is less of a burden to carry if you stow it in the Asus Republic of Gamers laptop backpack, which came bundled with our model) and measures 16.6 inches wide by 12.8 inches long by 2.3 inches thick. The G73Jw’s power brick is considerably lighter than power bricks of some competing high-power desktop replacement laptops (the HP Elitebook 8740w comes to mind), but it still adds 2 pounds to your bag if you decide to carry this monster around.

Asus houses the G73Jw in a dark gray chassis that tapers off at an angle on all sides. A rubbery, fingerprint-resistant dark gray material covers the lid and makes it easy to grip. I didn’t test it , but the rubbery material seems likely to be much more prone to scratching from normally nonthreatening metal objects (such as paperclips and keys) than a typical hard-plastic lid is.

The G73Jw has much the same array of ports as other notebooks in its league. Its shining feature is a USB 3.0 port, but it also has three USB 2.0 ports, an HDMI output, a VGA output, an ethernet port, microphone and headphone jacks, and an eight-in-one card reader. It lacks an ExpressCard slot and eSATA ports, but most people aren’t looking for these in a laptop today. The G73Jw also carries a Blu-ray combo drive, which is perfect for your HD movie needs.

The G73Jw’s keyboard is one of the worst I’ve seen–a huge disappointment in a gaming-oriented desktop replacement. Most 17-inch notebooks have great (or at least well-laid-out) keyboards, but the G73Jw’s keyboard is cramped, slippery, and generally frustrating to type on.

The interior of the notebook is as smooth and minimalist as the exterior–and in this case, that’s not a good thing. A wide (at least 1.5 inches on either side) dark gray bezel surrounds the keyboard, which is crammed together at the center of the notebook. The keys are small and hard, and they offer virtually no tactile feedback for both typists and gamers, who depend on it. The tiny size of the keys on the Chiclet-style keyboard wouldn’t be such a problem if they weren’t so smooth and so closely packed together. The close proximity combined with the smooth surfaces guarantees that your fingers will repeatedly slip off one key and onto another.

Asus may have missed the keyboard memo, but it certainly got the trackpad right: It’s enormous and comes with two separate, easy-to-use buttons below it. The buttons offer plenty of feedback, and the trackpad supports multitouch gestures.

Video playback was very good. High-def streaming video played perfectly, and Blu-ray discs looked fantastic. Audio was quite satisfactory, considering that laptops aren’t known for their speakers. The G73Jw’s speakers are situated just above the keyboard and deliver full, loud sound.

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