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A couple of days ago, we told you about Virtual Home–a brand new jailbreak tweak that allowed users to simulate a press of the Home button using Touch ID enabled hardware. Virtual Home was one of the first jailbreak tweaks that modified the behavior of the iPhone 5s’ Touch ID sensor, and because of that, it proved to be very popular among jailbreakers.

Adding to the popularity of Touch ID tweaks is a recent update to Ryan Petrich’s Activator—an absolute staple of a jailbreak tool that all users should have installed from day one. Petrich’s latest 1.8.3 update brings support for a single press of the Touch ID sensor.

But Activator goes far beyond the scope of Virtual Home, because you can, in theory at least, assign any Activator action to a Touch ID gesture. Have a look inside to see how it works.

To assign a Touch ID gesture to an Activator action, you’ll need to make sure you have the latest version of Activator installed on your device. Activator version 1.8.3 is the release that brings support for Touch ID.

Once installed, head into Activator’s settings by means of the Activator app icon on the Home screen, or via the stock Settings App. Select the Anywhere panel, and then select the Single Press panel under the Fingerprint Sensor section. You can then assign any action to the Touch ID single press gesture.

For starters, it’s probably best if you assign the Home Button gesture—a gesture that simulates the single press of the Home button—in order to get the hang of how things work. It’s also a good litmus test for comparing Activator’s implementation to Virtual Home’s.  Once you  get the hang of how Touch ID gestures work in Activator, feel free to assign any other action to the gesture.

A few points of note:

Although there is no support yet for a double tap, you can use two single taps in succession to simulate a double press of the Home button. It takes a little practice, and isn’t always accurate, but it worked fairly well in my tests.

If you want to use the Home button like normal for getting back to the Home screen, invoking the app switcher, etc., you need to keep in mind that doing so will likely invoke the action assigned to the Touch ID sensor as well. For example, if I have Control Center assigned to the Touch ID sensor via Activator, and I press in on the Home button to get back to the Home screen, it will go back to the Home screen and invoke Control Center at roughly the same time. This causes some obvious usability issues and graphical glitches at times. It’s an interesting phenomenon, and one that may not be that easy to work around. I’m curious to see how Petrich plans on handling this issue going forward.

With the aforementioned in mind, I would suggest using Activator’s built-in Touch ID support primarily to simulate the Home button. If you’ve experienced issues with Virtual Home, this is a good alternative for you to try.

Not to be overlooked are Activator’s other 1.8.3 features and fixes. Here is the full change log:

Fix arm64-specific crashes

Fix long reboot times on iOS 7

Update localizations

Add fingerprint event on iPhone 5s

Better detect device capabilities on iOS 7

It’s an exciting time to be a jailbroken iOS 7 user. It’s even more exciting to think about the potential of Touch ID in the hands of jailbreakers. Activator is and always has been at the forefront of innovation in the jailbreak community, and this latest release continues that trend.

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How To Print To Pdf On Iphone With 3D Touch

You can save nearly anything as a PDF from iPhone, all it takes is using a little known 3D Touch trick available only in Sharing action menus. Essentially this trick allows you to perform the iOS equivalent of Print to PDF like you would see on desktops like a Mac or Windows PC, except it’s on the mobile iOS world and available to iPhone users with 3D Touch devices.

You can perform the Print to PDF trick in iOS from just about any app, as long as it has the Sharing button and could theoretically print from it. This includes Safari, Pages, Notes, and other apps you’d expect to have this feature in. For demonstration purposes here, we’ll walk through this with Safari where we will use the print to PDF trick on a web page.

How to Print to PDF on iPhone with 3D Touch

This trick works the same to save just about anything as a PDF by using the print function within iOS, here’s how it works:

Open Safari (or another app you want to print to PDF from) and go to what you want to save as a PDF file

Tap the Sharing action button, it looks like a square with an arrow flying out of it

Now tap on “Print”

Next, perform a 3D Touch firm press on the first page preview to access the secret print to PDF screen option, this will open into a new preview window

Again tap on the Sharing action button at this new Print to PDF screen

Choose to save or share the document as a PDF – you can print to PDF and send it through messages, email, AirDrop, copy it to your clipboard, save the printed PDF to iCloud Drive, add it to DropBox, import it into iBooks, or any of the other options available in the sharing and saving actions

Your freshly printed PDF file will be available with whatever means you shared or saved the PDF. I typically choose to print the PDF and save it into iCloud Drive, but if you plan on sending it to another person through Messages or email to get a signature on the document or something similar, or send with AirDrop from the iPhone or iPad to a Mac, you can easily do that as well.

The ability to print to PDF is very popular and widely used, so it’s a bit of a mystery as to why iOS has this feature hidden behind a secret 3D Touch gesture within the Print function, rather than available as an obvious menu item within the Print menus like Print to PDF is on a Mac. As far as I can tell, there is absolutely nothing obvious to suggest this feature exists at all and it’s basically hidden, which is a little weird given how useful it is to save things like web pages or documents as PDF files. But now that you know it exists, you can print to PDF to your hearts delight, right from your iPhone. Perhaps a future version of iOS will make this great trick a bit more obvious, we’ll see.


How To Use Logstash Aws With Examples?

Introduction to Logstash AWS

Logstash AWS is a data processing pipeline that collects data from several sources, transforms it on the fly, and sends it to our preferred destination. Elasticsearch, an open-source analytics and search engine, is frequently used as a data pipeline. AWS offers Amazon OpenSearch Service (successor to Amazon Elasticsearch Service), a fully managed service that provides Elasticsearch with an easy connection with Logstash to make it easy for clients.

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What is Logstash AWS?

Create an Amazon OpenSearch Service domain and start loading data from our Logstash server. The Amazon Web Services Free Tier allows us to try Logstash and Amazon OpenSearch Service for free.

To make it easier to import data into Elasticsearch, Amazon OpenSearch Service has built-in connections with Amazon Kinesis Data Firehose, Amazon CloudWatch Logs, and AWS IoT.

We can also create our data pipeline using open-source tools like Apache Kafka and Fluentd.

All common Logstash input plugins, including the Amazon S3 input plugin, are supported. In addition, depending on our Logstash version, login method, and whether our domain uses Elasticsearch or OpenSearch, OpenSearch Service now supports the following Logstash output plugins.

Logstash output amazon es, signs, and exports Logstash events to OpenSearch Service using IAM credentials.

Logstash output OpenSearch, which only supports basic authentication at the moment. When building or upgrading to an OpenSearch version, select Enable compatibility mode in the console to allow OpenSearch domains.

We can also use the OpenSearch cluster settings API to enable or disable the compatibility of logstash.

Depending on our authentication strategy, we can use either the normal Elasticsearch plugin or the logstash output amazon es plugin for an Elasticsearch OSS domain.

Step-By-Step Guide logstash AWS

Configuration is comparable to any other OpenSearch cluster if our OpenSearch Service domain supports fine-grained access control and HTTP basic authentication.

The input for this sample configuration file comes from Filebeat’s open-source version.

Below is the configuration of the available search service domain as follows.

Code –


The configuration differs depending on the Beats app and the use situation.

All requests to OpenSearch Service must be signed using IAM credentials if our domain has an IAM-based domain access policy or fine-grained access control with an IAM master user.

In this scenario, the logstash output amazon es plugin is the simplest way to sign requests from Logstash OSS.

After configuring the available search service domain, we are installing the plugin name as logstash-output-amazon_es.

After exporting the plugin, we are exporting the IAM credential. And finally, we are changing the configuration files to use the plugin.

How to Use Logstash AWS?

Logstash provides several outputs that allow us to route data wherever we want, opening up a downstream application.

Over 200 plugins are available in Logstash’s pluggable structure. To work in pipeline harmony, mix, match, and orchestrate various inputs, filters, and outputs.

While Elasticsearch is our preferred output for searching and analyzing data, it is far from the only option.

We provide a great API for plugin development and a plugin generator to assist us in getting started and sharing our work.

The below step shows how to use logstash in AWS as follows.

The first step is to forward logs to OpenSearch Service using our security ports as 443.

The second step is to update the configurations for Logstash, filebeat, and OpenSearch Services.

The third step is to set up filebeat on the Amazon Elastic Compute Cloud instance we want to use as a source. Then, finally, check that our YAML config file is correctly installed and set up.

The fourth step is to set up Logstash on a separate Amazon EC2 instance to send logs.

Install logstash AWS

The below step shows install logstash on AWS as follows.

Create a new Ec2 instance –

In this step, we are creating a new instance of EC2 to install the logstash. In the below example, we have created the t2.micro instance.

Create a yum repository for installing the logstash –

In this step, we create the yum repository for installing the logstash in the Amazon EC2 instance.

[logstash-7.x] Type = rpm-md

Install the logstash on the newly created EC2 instance –

In this step, we install the logstash on a newly created instance using the yum command.

Start the logstash and check the status –

In this step, we are starting the logstash and checking the status of the logstash as follows.

# service logstash start

# service logstash status

Setup a Logstash Server for AWS

The below step shows the setup logstash for AWS as follows.

Create IAM policy –

Create a logstash configuration file –


Start the logstash and check the logs –

# service logstash start


Logstash AWS is a data processing pipeline that collects data from several sources, transforms it on the fly, and sends it to our preferred destination. Depending on our authentication strategy, we can use either the normal Elasticsearch plugin or the logstash-output-amazon_es plugin for an Elasticsearch OSS domain.

Recommended Articles

This is a guide to Logstash AWS. Here we discuss how to use logstash AWS, its installation and setup process, and a detailed explanation. You may also have a look at the following articles to learn more –

How To Use Tensorflow Gather With Example?

Introduction to TensorFlow gather

Tensorflow gather is used to slice the input tensor object based on the specified indexes. The Keras is the library available in deep learning, which is a subtopic of machine learning and consists of many other sub-libraries such as TensorFlow and Theano. These are used in Artificial intelligence and robotics as this technology uses algorithms developed based on the patterns in which the human brain works and is capable of self-learning. Tensorflow Keras is one of the most popular and highly progressing fields in technology right now, as it possesses the potential to change the future of technology.

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TensorFlow gather overviews

The output or value returned by the gather function consists of a tensor with the datatype equivalent to the one whose value is passed in arguments.

TensorFlow gather Args

The arguments of parameters used in the gather function are listed in detail one by one –

Arguments – It is nothing but the source tensor or matrix with the rank equivalent to or greater than the value of axis +1.

Index 1, index 2, …. – The data type of this tensor is either int 64 or int 32, and the value of this parameter should be in the range of 0 to arguments. Shape [axis].

Dimensions of the batch – This is an integer value representing the count or number of batch dimensions present. This value should be equivalent to the rank of indices or less than that.

Axis – This value should be equivalent to or greater than batch dimensions and have the data type 64 or 32. The indices’ value helps specify the axis as it should be higher than or equal to batch dimensions. In addition, this value helps us specify the index from where the value should be gathered.

Operation name – Hels in the specification of the operation’s name to be performed.

Validation for the specified indices – This parameter is deprecated and has no usage as, by default, the validation of indexes always happens and is done by the CPU internally.

Note that inside the CPU, if the bound index is found, an error occurs, and in the case of GPU, if we find out the bound index, then the output will contain its corresponding value.

How to use TensorFlow gather?

We will need to follow certain steps to use the TensorFlow gather function. Some of them are as listed below –

The required libraries should be imported at the top of the code file.

The input data and the required objects should be assigned the initial value.

You can optionally print your input data if you want to observe before and after the difference.

Make use of the gather function to calculate and get the resultant.

After gathering, you can again print the tensor data to observe the difference made by the gather function.

Run the code and observe the results.

We need to note certain things when we are using the gather function. First, the gather function starts slicing the tensor from arguments that are params axis, taking along the consideration of indexes. The value of the index can be any integer that can have the tensor of any given dimension. Most often, it is one-dimensional.

The use of the function tensorObj. getitem happens in case of scalar quantities, sclices of python, and tf. newaxis. To handle the indices of the tensor, the tensorObj.gather extends its functionality. When used in its most simple format, it resembles scalar indexing.

The most commonly used case is when we have to pass only a single axis index tensor. As this data does not contain the sequential pattern, it cannot be recognized as a slice of python. The arguments or parameters can have any shape. The selection of the slices is made based on any axis that depends on the argument of the axis whose default value is 0.

TensorFlow gather Examples

Sample Educba Example 1:

print (‘resultant: ‘, resultant)


After executing and running the above code, we get the following output –

Screenshot of the output –

print (‘resultant: ‘, resultant)

After executing and running the above code, we get the following output –

Screenshot of the output –


The use of tensorflow gather is used to prepare the slices of the input tensor along the axis depended on the indices that are passed as the argument. Some of the functions that are related with tensorObj.gather include tensorObj.scatter, tensorObj.getItem, tensorObj.gather_nd, tensorObj.boolean_mask, tensorObj.slice and tensorObj.strided_slice.

Recommended Articles

This is a guide to TensorFlow gather. Here we discuss the Introduction, overviews, args, How to use TensorFlow gather, and Examples with code implementation. You may also have a look at the following articles to learn more –

How To Use Pytorch Amd With Examples?

Introduction to PyTorch AMD

Web development, programming languages, Software testing & others

In this article, we will try to dive into the topic of PyTorch AMD and will try to understand What is PyTorch AMD, how to use PyTorch AMD, image classification models in AMD, its associated examples, and finally give our concluding statement on it.

What is PyTorch AMD?

PyTorch AMD is the container of the framework, allowing us to run the container of AMD’s machine learning framework. For doing so, it is necessary that the docker environment of your system should support the AMD GPU.

The minimum requirements of the single node server are that it should have X86-64 CPU or CPUs along with GPU(s) of AMD instinct MI100 and GPU of Radeon instinct MI50 (S). The operating system used for this should be Centos 8.3 or higher OR Ubuntu 18.04 or higher version. The driver for ROCm should be compatible with the 4.2 version, and the Docker Engine Singularity container is used at runtime.

By default, the considerations and suppositions made by PyTorch AMD container of frameworks are that the server should contain x-86-64 single or multiple CPUs and should have a minimum of one listing AMD GPU. Furthermore, to run the docker container, the server should have the listed ROCm driver with a specified or higher version installed on it and the required operating system. Finally, the server should contain the docker engine in it to run the container.

If you want to go for installing the Docker engine, kindly visit this link. In order to install singularity in its latest version, if the use of singularity is already planned, then visit this link. For installation of procedures of ROCm as well as validity checks, kindly go through this link.

How to Use PyTorch AMD?

AMD is an open-source platform and has high performance and flexibility. It comes along with various libraries, compilers, and languages that can be used by developers and communities working in Machine Learning, Artificial Intelligence, and HPC technology to make their task of coding easy, fast, and implementing complex logic and functionalities in it. The biggest task is that PyTorch AMD provides you with containers.

Docker pull “name of the container”

For example, for AMDih container of PyTorch, the command would be –

docker pull AMDih / PyTorch : rocm4.2_ubuntu18.04_py3.6_PyTorch_1.9.0 Image classification models in AMD

Broadly, image classification can be done by using either of the two technologies of PyTorch or tensorflow in AMD. Some of the products include AMD Radeon instinct MI50, AMD Instinct MI100, while for tensorflow same products can be used.

There are various models that can be used in AMD for image classifications. Some of them are as listed below –

Efficent b0 and b7

Resnet 101 and 50

Inceptive v3

For running a particular image container in PyTorch AMD, you will have to give a check on the operating system on which you have installed and the software and its version that you have installed in the process of running the containers of AMD. Some of the steps that need to be followed for running the containers are as given below –

A search of the tab named Tags and then hit into the container image release that is located inside it, which you are about to run.

You will have to paste the command you have copied by opening a command prompt in your system. At this step, the beginning of pulling the image of the container happens. Before you go for the next step, make sure that the pulling of the docker completes.

Now, the container image that is pulled should be run. For running the container, you will have to choose the mode of interactive or non-interactive as per necessity and scenario.

While running the command, the “-it” parameter stands for the interactive mode running.

The option “–rm” specifies that the container should be deleted after finishing.

The parameter “-v” is used for specifying the directory for mounting.

The absolute path to the file or directory of the host system, which we will need to access in the container, is specified by using the parameter local_dir.

The target directory’s absolute path is specified by using the container_dir parameter when you are present in the container.

xx is the version of the container.

When you want to run particular command inside the image, the command parameter should be specified.

Examples of PyTorch AMD def sampleEducbaExample(args, educbaModel, sampleEducbaExample_loader): educbaModel.eval() corectionPrecision = 0 cumulativeCount = 0 with torch.no_grad(): for data, target in sampleEducbaExample_loader: achievedOutput = educbaModel(data) predictedValue = achievedOutput.argmax(dim=1) corectionPrecision += predictedValue.eq(target.view_as(predictedValue)).sum() cumulativeCount += args.sampleEducbaExample_batch_size cumulativeCorrections = corectionPrecision.copy().get().float_precision().long().item() print('Test set: Accuracy: {}/{} ({:.0f}%)'.format( cumulativeCorrections, cumulativeCount, 100. * cumulativeCorrections / cumulativeCount))

The output of the execution of the above program gives the following result on the console panel –

Here, we have carried out the predictions in a secure manner from end to end. Both server and client are completely unaware. The server has no idea about the output of classification and input of data, and the client is not aware of the server’s model weights.


We can use PyTorch AMD to improve the user’s data protection and secure machine learning. The docker container of PyTorch 3.6 internally provides AMD support.

Recommended Articles

This is a guide to PyTorch AMD. Here we discuss What is PyTorch AMD, how to use PyTorch AMD, image classification models in AMD. You may also have a look at the following articles to learn more –

Htc Touch Diamond2 And Touch Pro2

HTC have announced two new devices at MWC today, the HTC Touch Diamond2 and the HTC Touch Pro2, each updates of the original devices. The Touch Diamond2 has a 3.2-inch VGA touchscreen display, while the Touch Pro2 has a 3.6-inh WVGA touchscreen and a slide-out QWERTY keyboard. Both run Windows Mobile, currently 6.1 but we’re expecting to hear that each will ship with 6.5 when Microsoft officially announce it – as expected – later on today.

Each handset also has the TouchFLO 3D GUI, 3G, WiFi b/g and Bluetooth 2.0 with A2DP stereo wireless. The Touch Diamond2 has a 5-megapixel camera with autofocus, while the Touch Pro2 has a 3.2-megapixel camera with autofocus.

The HTC Touch Diamond2 will be available in European and Asian markets in early Q2 2009 with broader global availability coming later in the year. The Touch Pro2 will be available across major global markets beginning in early summer. No word on prices as yet.

Press Release:

new HTC Touch Diamond2 and HTC Touch Pro2 SIGNAL a new wave in communication

New phones simplify information access with HTC Push Internet and unify personal communication with single-view contact integration

BARCELONA ó Feb 16, 2009 ó HTC Corporation, a global designer of mobile phones, today unveiled two new flagship devices, the HTC Touch Diamond2??and HTC Touch Pro2?. Integrating innovative simplicity with unique style and an intuitive interface, the devices balance function, form and cutting-edge technology to personalize the communication and mobile Internet experience.

ìThe HTC Touch Pro2 and HTC Touch Diamond2 introduce a mobile communication experience that simplifies how we communicate with people in our lives whether through voice, text or email,î said Peter Chou, president and CEO, HTC Corp. ìHTC is delivering the latest, cutting-edge sophistication in a broad portfolio of mobile phones that improve how people live, work and communicate.î

HTC TouchFLO 3D Integrated with Windows Mobile

The HTC Touch Diamond2 and HTC Touch Pro2 utilize HTCís latest TouchFLO 3D interface. TouchFLO 3D has been more deeply integrated into a customized version of Windows Mobile 6.1 to deliver more consistency throughout Windows Mobile applications and menus. Focused on making navigation easier and more intuitive, TouchFLO 3D brings important information to the top-level user interface, including quick access to people, messaging, email, photos, music and weather. As part of this improved Windows Mobile integration the touch focus areas have been enlarged to be more finger-touch friendly.


With the HTC Touch Diamond2 and HTC Touch Pro2, HTC is introducing a new people-centric communication approach, providing a single contact view that displays the individual conversation history of contacts regardless of whether voice, text or email were used. This can be viewed from the contact card or the in-call screen during a phone conversation, ensuring the latest communication contact-by-contact is always at hand.


Continuing its commitment to making the mobile Internet easier and more enjoyable, the HTC Touch Diamond2 and HTC Touch Pro2 introduce HTCís Push Internet technology. HTC Push Internet alleviates slow downloading and rendering of Web pages on a mobile phone. Users can preselect their favorite Websites to get immediate access to them when needed.

HTC Touch Diamond2

The HTC Touch Diamond2 is the next step in the evolution of the successful HTC Touch Diamond. Crafted to fit perfectly into the hand, the Touch Diamond2 evolves the compact design and iconic style of the original HTC Touch Diamond. It incorporates a larger 3.2-inch high-resolution wide-screen VGA display for a greater viewing area in a design just 13.7mm thick. The phone also includes a new touch sensitive zoom bar for even faster zooming of Web pages, emails, text messages, photos or documents.

With fifty-percent better battery life, a five mega-pixel auto focus camera, expandable memory, gravity sensor and an ambient light sensor, the Touch Diamond2 brings the most sophisticated capabilities to a broad consumer audience looking for the professional benefits of a smartphone without sacrificing size, looks or functionality.


Designed for business professionals, the HTC Touch Pro2 is architected with distinct style and strength while delivering the most powerful productivity experience available on a mobile phone. Leveraging HTCís TouchFLO 3D, people-centric communication and Push Internet technology, the Touch Pro2 features a high-resolution 3.6-inch widescreen VGA display for an expanded viewing area and large finger-friendly QWERTY keyboard. With improved battery life, expandable memory, a touch-sensitive zoom bar as well as gravity, proximity and ambient light sensors, the Touch Pro2 is optimized for touch as well as heavy email use.

Introducing HTC Straight Talk? for HTC Touch Pro2

The new HTC Touch Pro2 leverages voice in a new way to create one of the most sophisticated communication experiences found on a mobile phone. † HTCís new Straight Talk technology delivers an integrated email, voice and speakerphone experience. Users can transition seamlessly from email to single or multi-party conference calls and turn any location into a conference room.


The HTC Touch Diamond2 will be available to customers across major European and Asian markets in early Q2 2009 with broader global availability coming later in the year. The Touch Pro2 will be available across major global markets beginning in early summer.

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