text:
# AWS API client for Elastic Load Balancing

**Warning: This is a generated library, some operations may not work.**

*About the service:*
A load balancer distributes incoming traffic across targets, such as your
EC2 instances. This enables you to increase the availability of your
application. The load balancer also monitors the health of its registered
targets and ensures that it routes traffic only to healthy targets. You
configure your load balancer to accept incoming traffic by specifying one or
more listeners, which are configured with a protocol and port number for
connections from clients to the load balancer. You configure a target group
with a protocol and port number for connections from the load balancer to
the targets, and with health check settings to be used when checking the
health status of the targets.

## Links

- [Other AWS libraries](https://github.com/agilord/aws_client/tree/master/generated).
- [Issue tracker](https://github.com/agilord/aws_client/issues).
- [AWS API definitions](https://github.com/aws/aws-sdk-js/tree/master/apis).

## Contributors

- [Jonathan Böcker](https://github.com/Schwusch)
- [Istvan Soós](https://github.com/isoos)


text:
# swissknife
A simple gem with sneaky commands!
***
If you're tired of having to type out long commands excessively, then meet swissknife!
The whole purpose of the project is to provide a slew of shortened and simple commands to help developers.

## Adding a command
To create a new command, start by opening up the `swissknife.rb` file in `lib`, and create a new definition in the `Util`
class, like this:
``` ruby
# lib/swissknife.rb

# put any helper functions in the SwissKnife module, but not in the Util class

# class Util
def hello
  puts 'Hello World!'
end
```
Next, create a new file without an extension with the name of your command. Having it match the name of the definition
you created earlier is recommended. For my example, it would be `hello`. Finally, all that's needed is to add a shebang,
require for the module, and method initialization, as follows:
``` ruby
# bin/hello

#!/usr/bin/env ruby

require 'swissknife'

SwissKnife::Util.new.hello
```

### [Available commands](https://github.com/T145/swissknife/tree/master/bin)
| Command | Description |
|:-------:|:-----------:|
| archive | Creates a simple zip archive painlessly! |
| camo | (Alias for `hide`) |
| c | Clears the console screen (puts the cursor on the bottom for easy console browsing) |
| clean | Removes some of the junk we all encounter, such as the `.DS_Store` |
| computer | (Alias for `pc`) |
| cpy | Simply cross-platform copy command! |
| delete | (Alias for `r`) |
| erase | (Alias for `r`) |
| ga | Basically just like `git add`, but with the added advantage of having the `-A` option when no files are specified |
| gb | Creates a new Git branch or switches to an exsisting one |
| gc | Either clones a repo or commits some content; having `.git` inside the input determines whether or not to clone |
| gopath | Sets the `GOPATH` variable for Go users |
| gp | The equivalent of `git push` |
| hide | Adds a period to the front of a file's name |
| install | A sleek command that can install Homebrew and/or RVM (installs the latest Ruby version alongside it) |
| move | Moves a file or directory from one place to another! |
| pack | Bundles a Ruby project's dependencies to `vendor/bundle` |
| pc | Simple command for toggling the computer's power |
| r | Deletes a file or directory |
| remove | (Alias for `r`) |
| unarchive | Unzips a zip archive |
| wd | Shows the working directory; pretty straight-forward |
| x | Exits a console |

## Contributing
1. Fork it
2. Add or remove some stuff
3. Commit your changes (`git commit -am "My awesome change!"`)
4. Create a new pull request!

license: MIT

branch_name: refs/heads/master

extension: md

gha_language: C#

gha_license_id: MIT

license_type: permissive


text:
---
title: "Contribution overview"
slug: "contributor-overview"
excerpt: "The flow for making changes to Pants."
hidden: false
createdAt: "2020-05-16T22:54:21.867Z"
---
We welcome contributions of all types: from fixing typos to bug fixes to new features. For further questions about any of the below, please refer to the [community overview](doc:the-pants-community).

> 👍 Help wanted: identifying bad error messages
>
> We strive—but sometimes fail—to make every error message easy to understand and to give insight into what went wrong and how to fix it.
>
> If you ever encounter a confusing or mediocre error message, we would love your help to identify the error message. Please open a [GitHub issue](https://github.com/pantsbuild/pants/issues) with the original Pants command, the error message, and what you found confusing or think could be improved.
>
> (If you'd be interested in then changing the code, we'd be happy to point you in the right direction!)

Documentation Fixes
-------------------

To suggest edits to Pants documentation, fork the [Pants repository](https://github.com/pantsbuild/pants), make changes to files in the `docs/` directory, and submit a PR against the `main` branch. Address feedback from maintainers and, once approved, your changes will be incorporated into the official documentation.

Pants's tech stack
------------------

Most of Pants is written in Python 3. The majority of contributions touch this Python codebase.

We rely on several Python features that you will want to acquaint yourself with:

- [Type hints and MyPy](https://mypy.readthedocs.io/en/stable/)
- [Dataclasses](https://realpython.com/python-data-classes/)
- [`async`/`await` coroutines](https://www.python.org/dev/peps/pep-0492)
  - We do _not_ use `asyncio`. The scheduler is implemented in Rust. We only use `async` coroutines.
- [Decorators](https://realpython.com/primer-on-python-decorators/)
- [Comprehensions](https://www.geeksforgeeks.org/comprehensions-in-python/)

Pants's engine is written in Rust. See [Developing Rust](doc:contributions-rust) for a guide on making changes to the internals of Pants's engine.

First, share your plan
----------------------

Before investing your time into a code change, it helps to share your interest.

To share your plan, please either open a [GitHub issue](https://github.com/pantsbuild/pants/issues) or message us on [Slack](doc:getting-help#slack) (you can start with the #general channel). Briefly describe the change you'd like to make, including a motivation for the change.

If we do not respond within 24 business hours, please gently ping us by commenting "ping" on your GitHub issue or messaging on Slack asking if someone could please take a look.

> 📘 Tip: Can you split out any "prework"?
>
> If your change is big, such as adding a new feature, it can help to split it up into multiple pull requests. This makes it easier for us to review and to get passing CI.
>
> This is a reason we encourage you to share your plan with us - we can help you to scope out if it would make sense to split into multiple PRs.

Design docs
-----------

Changes that substantially impact the user experience, APIs, design or implementation, may benefit from a design doc that serves as a basis for discussion.

We store our design docs in [this Google Drive folder](https://drive.google.com/drive/folders/1LtA1EVPvalmfQ5AIDOqGRR3LV86_qCRZ). If you want to write a design doc, [let us know](https://www.pantsbuild.org/docs/getting-help) and if necessary we can give you write access to that folder.

We don't currently have any guidelines on the structure or format of design docs, so write those as you see fit.

Developing your change
----------------------

To begin, [set up Pants on your local machine](doc:contributor-setup).

To run a test, run:

```bash
$ pants test src/python/pants/util/frozendict_test.py
```

Periodically, you will want to run MyPy and the autoformatters and linters:

```bash
# Format un-committed changes
$ pants --changed-since=HEAD fmt

# Run the pre-commit checks, including `check` and `lint`
$ build-support/githooks/pre-commit
```

See our [Style guide](doc:style-guide) for some Python conventions we follow.

> 📘 You can share works in progress!
>
> You do not need to fully finish your change before asking for feedback. We'd be eager to help you while iterating.
>
> If doing this, please open your pull request as a "Draft" and prefix your PR title with "WIP". Then, comment on the PR asking for feedback and/or post a link to the PR in [Slack](doc:the-pants-community).

Opening a pull request
----------------------

When opening a pull request, start by providing a concise and descriptive title. It's okay if you aren't sure what to put - we can help you to reword it.

Good titles:

- Fix typo in `strutil.py`
- Add Thrift code generator for Python
- Fix crash when running `test` with Python 3.9

Bad titles:

- Fix bug
- Fix #8313
- Add support for Thrift code generation by first adding the file `codegen.py`, then hooking it up, and finally adding tests

Then, include a description. You can use the default template if you'd like, or use a normal description instead. Link to any corresponding GitHub issues.

Finally—if you have the permissions—add exactly one of the following labels to your PR. Otherwise, a maintainer will do this for you:

- `category:new feature` for new features
- `category:user api change` for changes that affect how end-users interact with Pants
- `category:plugin api change` for changes that affect how plugin authors interact with Pants internals
- `category:performance` for changes focused on improving performance
- `category:bugfix` for bugfixes
- `category:documentation` for documentation changes, including logging and help messages
- `category:internal` for miscellaneous, internal-facing changes

Pick the first of these that applies to your change. I.e., if you have modified the user API in a change that also improves performance, use `category:user api change`.

These labels are used to generate the changelist for each release.

> 📘 Tip: Review your own PR
>
> It is often helpful to other reviewers if you proactively review your own code. Specifically, add comments to parts where you want extra attention.
>
> For example:
>
> - "Do you know of a better way to do this? This felt clunky to write."
> - "This was really tricky to figure out because there are so many edge cases. I'd appreciate extra attention here, please."
> - "Note that I did not use a dataclass here because I do not want any of the methods like `__eq__` to be generated."

> 📘 FYI: we squash merge
>
> This means that the final commit message will come from your PR description, rather than your commit messages.
>
> Good commit messages are still very helpful for people reviewing your code; but, your PR description is what will show up in the changelog.

### CI

We use GitHub Actions for CI. Look at the "Checks" tab of your PR.

> 📘 Flaky tests?
>
> We unfortunately have some flaky tests. If CI fails and you believe it is not related to your change, please comment about the failure so that a maintainer may investigate and restart CI for you.
>
> Alternatively, you can push an empty commit with `git commit --allow-empty` to force CI to restart. Although we encourage you to still point out the flake to us.

### Review feedback

One or more reviewers will leave feedback. If you are confused by any of the feedback, please do not be afraid to ask for clarification!

If we do not respond within 24 business hours, please gently ping us by commenting "ping" on your pull request or messaging on Slack asking if someone could please take a look.

Once one or more reviewers have approved—and CI goes green—a reviewer will merge your change.

> 📘 When will your change be released?
>
> Your change will be included in the next weekly dev release, which usually happens every Friday or Monday. If you fixed a bug, your change may also be cherry-picked into a release candidate from the prior release series.
>
> See [Release strategy](doc:release-strategy).

repository: pantsbuild/pants

branch_name: refs/heads/main

gha_language: Python

repo_name: pantsbuild/pants


text:
---
name: Design Doc Draft
about: Provides a minimal design doc structure to get started
title: ''
labels: 'design doc draft'
assignees: ''

---

**Objective**

<!-- In a few sentences, describe the key objectives of the design. -->

**Background**

<!-- Things to know before reading the main design. -->

**Overview**

<!-- Summerize the highlight of the design in one paragraph. -->

**Details**

<!-- Expand the discussion and list all the details of the design. -->

**Caveats**

<!-- Describe why more obvious approaches don't work? -->

**Testing Plan**

<!-- How to ensure it doesn't break in the future? -->

**Work Estimates**

<!-- How much work is expected to implement the design? -->

**Plan B**

<!-- What to do if the feature breaks? -->

**No We Don't Do X**

<!-- What is strictly not in the scope of the design. Knowing what not to do is equally important as knowing what to do. -->

gha_language: Dart


text:
# Charts
## Available charts
The next charts are used for CI:
- `cluster-issuers` - cert-manager crd. Get certificate from letsencrypt for ingress controller

- `aws-fsx-csi-driver` - The Amazon FSx for Lustre Container Storage Interface (CSI) Driver implements CSI specification for container orchestrators (CO) to manage lifecycle of Amazon FSx for Lustre filesystems.

repository: RustamGimadiev/swiss-army-kube

repo_name: RustamGimadiev/swiss-army-kube


text:
# zsh-comp-gsettings
It is a zsh completion script for gsettings.
It takes advantage of compsys functions of zsh.

## How to Use
It can be automatically loaded when you login or run zsh.

1. Copy the script named _gsettings into any one of your fpath directories.
2. Restart zsh

You can see where are your fpath directories by running `echo $fpath`.If every fpath direcotory is system one and you want to install the script into your personal directory, you can do that. Copy the script into your personal directory, and add the direcotry into the fpath in your ~/.zshrc. That has to be done before calling compinit. For your information, perhaps that part of your .zshrc will look like the following:

```
typeset -U fpath
fpath=($fpath $your_directory)
autoload -Uz compinit
compinit
```

## Completion to be More Complete
You can help me improve the gsettings completion. If you find any problems or you have other preferences for completion style, feel free to file your issues on the [issue page](https://github.com/jmatsuzawa/zsh-comp-gsettings/issues). Any patch is welcome.

## Tested Version
* gsettings: 2.36
* zsh: 5.0.2

## About gsettings
[Spec of gsettings command](https://developer.gnome.org/gio/2.36/gsettings-tool.html)

repository: jmatsuzawa/zsh-comp-gsettings

repo_name: jmatsuzawa/zsh-comp-gsettings


text:
---
---

### Sharing Data in AngularJS

Suppose you are building a bookstore.  A group of users select some books, put them in the shopping cart, then later realize they haven't logged into the system.  You as the chief developer must make sure the users see the same books in the shopping cart after logging into the system.

In AngularJS, there are two popular ways to ensure information is shared:  `$rootScope` and `service`.  (Actually, `$rootScope` is a service, but let's not worry about it for now).

### Using $rootScope

The first concept that one picks up in AngularJS is the concept of a `$scope`.  A `$scope` is how an Angular controller binds with the view.  However, some tricks must be used to share `$scope` amongst different controllers.

In Angular, a child scope can have access to a parent scope.  Since `$rootScope`is the parent of all scopes, a quick a dirty way is to use `$rootScope` as follows:

{% highlight bash %}
var app = angular.module("BookApp", []).run(function($rootScope) {
    $rootScope.books = ["1984", "A Tale of Two Cities", "A Brave New World"];
});

app.controller("BookCtrl", function( $scope, $rootScope) {
  $scope.books = $rootScope.books;

});

app.controller("FirstBookCtrl", function($scope, $rootScope) {
  $scope.firstBook = $rootScope.books[0];
});
{% endhighlight %}

And on your view, put the following:

{% highlight bash %}

{{book}}

    Book: {{firstBook}}

{% endhighlight %}

Check out the code [here](http://codepen.io/nelsonlho/pen/NNjNwo)

Basically, both the `BookCtrl` and `FirstBookCtrl` have access to the `$rootScope`, which contains all of the books chosen, however `BookCtrl` shows all of these books while `FirstBookCtrl` only shows the first.

You may wonder why we would need another way of sharing between controllers.  However, be reminded that in a complicated application, many controllers will have access to the same `$rootScope`.  Giving other controllers to change what every controller may be dependent on may simply not be a good practice.

The same idea may go to other child/parent scope relationships, while children may have access to their parents' scopes.  What if you want to take away the ability for these controllers to alter the information that many other parts of your application may need to share?

### Using A Factory Service

Another way is to use a factory to create a singleton object.  You will need to alter your Angular code as follows:

{% highlight text %}
var app = angular.module("BookApp", []);

app.factory("BookService", function() {
  var books = ["1984", "A Tale of Two Cities", "A Brave New World"];

  return {
    all: function() {
      return books;
    },
    first: function() {
      return books[0];
    }
  };
});

app.controller("BookCtrl", function($scope, BookService) {
  $scope.books = BookService.all();
});

app.controller("FirstBookCtrl", function($scope, BookService) {
  $scope.firstBook = BookService.first();
});
{% endhighlight %}

View the code [here](http://codepen.io/nelsonlho/pen/pydqrJ).

Voila!  You will still see the same results in your view.

---

Copyright © [Nelson Ho](http://www.github.com/nelsonlho).

extension: markdown


text:
Note
----
These are strictly backend tests running against an actual database. Ensure that the api
is running with

    NODE_ENV == 'development'

So it will write to the dev database, and *not* the production database.

Simple examples would be CRUD operations, making sure the database works.

repository: prestonp/edibls-api

repo_name: prestonp/edibls-api


text:
# 🧑face-verification-with-siamese-networks

### 📝Description
---
In this project, we are going to implement an End-to-End Deep Learning/Machine Learning project. What this web application does is that, first it gets two gray scale faces(that were extracted by `Haar Cascade` face detection algorithm) from user. As soon as user clicks on the `Match` button, model recieves both faces, applies some image preprocessing actions on them and then passes these two images into the model itself. Take note that this specific siamese network recieves two images as inputs and then applies `Contrastive Loss` which we will discuss later on. In the next step, the model returns a number which is the `distance` between two faces. If the distance is greater or equal to a specific threshold (in this case the threshold is equal to `0.4`) then returns `Not Matched`; which means these two images **does not** belong to the same person. Otherwise it returns `Matched` which means these two faces belong to the same person.

### ⚡Face Recognition vs Face Detection vs Face Validation
---
Before we start off and dig into this project, first we have to comprehend some terminologies and their differences in the face field.

First of all it has to be mentioned that face detection and face recognition are completely two different terminologies. With face detection, as its name suggests, we can detect and localize available face(s) in an image. Face detection algorithm tells you that where is the face exactly in the image. But on the other hand, a face recognition algorithm is a different algorithm. face recognizer gets the ROI of the image where the face is exactly located in that specific region and performs some actions on the ROI and then identifies the person that this face belongs to. We have various methods to detect and extract face(s) in the image, some of them are `Haar Cascades` , OpenCV's deep learning based face detector , `HOG + Linear SVM` and etc.

The next terminology is `Face Verification` which is completely different from previous terminologies. In face verification we select a face and then we compare it to one or more other faces. By doing this, we recieve two possible outputs from model, `same` or `different`. But in face recognition the output is the name or ground truth of the recognized face.

### 📐Siamese Networks
---
Siamese Networks are a type of neural network architectures that consists of two or more convolutional neural networks(CNNs) that work in parallel. We call these CNNs subnetworks. All of the subnetworks are identical. In addition to this, these subnetworkshave the same parameters, architecture and weights. Any parameter updates are **mirrored** across both subnetworks; meaning that if the weights of one of the subnetworks is updated then the weights of all of the other networks will be updated as well.

As mentioned, each subnetwork consists of a CNN network. Each CNN network recieves a gray scale face image with a shape of `(62, 47, 1)` with a batch size of 4. Then the input process into these `Conv2D => BN => ReLU => Pooling` sequence and then it is converterd into a `48-d` vector by a `GlobalMaxPooling` layer. After this process, these networks return the vectore in order to model be able to calculate the `euclidean distance` between vectors and do the further processings.

For the loss function, `Contrastive Loss` is used. The reason is that in many ways this loss function outperforms and has more accurate results than `Binary Cross-Entropy`. The contrasive loss function is calculated by the formula down below:

### 📉Project Results
---
The model has been created and put into a web application and you can see the performance and the output of the model down below:

![Screen Record No 2](https://user-images.githubusercontent.com/56585524/134305841-9062aa5a-2090-4800-84e9-20506faa9057.gif)
---
![Screen Record No 1](https://user-images.githubusercontent.com/56585524/134305957-83af21c6-a32c-489c-8824-c450a66de128.gif)

In addition to this, you can observe the loss function of the model for both training and test set:

### 🖥 Installation
---
The Code is written in Python 3.7.5. If you don't have Python installed you can find it [here](https://www.python.org/downloads/). If you are using a lower version of Python you can upgrade using the pip package, ensure you have the latest version of pip. To install the required packages and libraries, run this command in the project directory after cloning the repository:
```
git clone git@github.com:Kasra1377/lbp-face-recognition.git
```
or
```
git clone https://github.com/Kasra1377/lbp-face-recognition.git
```
After you cloned this repository, you have to download and install the Anaconda. You can find the download link from this [link](https://www.anaconda.com/products/individual). After the installation, open the Anaconda Prompt and type the code down below:
```
conda create -n verification python=3.7
```
This command creates a virtual env with a name of `verification`. Note that you can choose your own arbitrary name for virtual enviroment. After virtual enviroment is created,  switch to the directory of the project folder via Anaconda Prompt and then type:

```
conda activate verification
```
This command activates your own virtual enviroment that you have just created. In order to install all of the libraries that used in this project, you have to type:

```
conda install file requirements.txt
```

By doing this and downloading all of the required packages, you are ready to run this project on your local computer.

To run the web app on your computer, first open `app.py` python file by your own IDE and switch from current virtual env of your IDE into your newly created virtual enviroment. After that open your Git Bash and type the following commands respectively:

```
export FLASK_APP=app.py
```

```
export FLASK_ENV=development
```

```
FLASK_DEBUG=1 flask run
```

Now the web app is opened in your browser locally. You can use the available cropped faces in `samples` folder; in order to upload and match the faces.

### 🛠Technologies Used
---
**IDE**:  ![Visual Studio Code](https://img.shields.io/badge/Visual%20Studio%20Code-0078d7.svg?style=for-the-badge&logo=visual-studio-code&logoColor=white)

**Language(s):**  ![Python](https://img.shields.io/badge/python-3670A0?style=for-the-badge&logo=python&logoColor=ffdd54)

**Libraries:**  ![OpenCV](https://img.shields.io/badge/opencv-%23white.svg?style=for-the-badge&logo=opencv&logoColor=white) ![Keras](https://img.shields.io/badge/Keras-%23D00000.svg?style=for-the-badge&logo=Keras&logoColor=white) ![scikit-learn](https://img.shields.io/badge/scikit--learn-%23F7931E.svg?style=for-the-badge&logo=scikit-learn&logoColor=white) ![TensorFlow](https://img.shields.io/badge/TensorFlow-%23FF6F00.svg?style=for-the-badge&logo=TensorFlow&logoColor=white)  ![NumPy](https://img.shields.io/badge/numpy-%23013243.svg?style=for-the-badge&logo=numpy&logoColor=white)

**Front-End:**  ![HTML5](https://img.shields.io/badge/html5-%23E34F26.svg?style=for-the-badge&logo=html5&logoColor=white) ![CSS3](https://img.shields.io/badge/css3-%231572B6.svg?style=for-the-badge&logo=css3&logoColor=white)  ![JavaScript](https://img.shields.io/badge/javascript-%23323330.svg?style=for-the-badge&logo=javascript&logoColor=%23F7DF1E)

**Backend:**  ![Flask](https://img.shields.io/badge/flask-%23000.svg?style=for-the-badge&logo=flask&logoColor=white)

**Others:** ![Jupyter Notebook](https://img.shields.io/badge/jupyter-%23FA0F00.svg?style=for-the-badge&logo=jupyter&logoColor=white)

### ❌Bugs & Issues
---
If you ever encountered any bugs or any technical issues in this projects you can report it by `issues` section of this repository or you can contact me by my email address.

### 👥Contributers
---
Kasra1377

### 🔻References
---
[Building image pairs for siamese networks with Python](https://www.pyimagesearch.com/2020/11/23/building-image-pairs-for-siamese-networks-with-python/)

[Contrastive Loss for Siamese Networks with Keras and TensorFlow](https://www.pyimagesearch.com/2021/01/18/contrastive-loss-for-siamese-networks-with-keras-and-tensorflow/)


text:
# TimeWarperUE

This project is a small game prototype made in the Unreal engine, where you have a small time window to destroy all red targets by shooting them. After the time runs out the level resets and you respawn. Another character also spawns and executes exactly the actions that you did last round.

This prototype was developed by me over the course of three days in 2017 (I never used Unreal before).

[Click here for a longer gameplay video.](https://youtu.be/GpQBGy5b9eU)

repository: johannesCmayer/TimeWarperUE

repo_name: johannesCmayer/TimeWarperUE


text:
# giveaway:start

Start a giveaway in the channel. After sending this method, the 'HypeBot' user will publicly announce the winner of the giveaway, who will be randomly selected.

## Permissions

To start a giveaway you must use an OAuth token with the `chat:giveaway_start` scope.

## Examples

### Request

```json
{ "type": "method", "method": "giveaway:start", "arguments": [], "id": 11 }
```

### Response

```json
{ "type": "reply", "error": null, "id": 11, "data": "Starting a giveaway" }
```

repository: pedroslopez/developer-docs

repo_name: pedroslopez/developer-docs


text:
---
title: GraphQL newbie tutorial - introduction
date: '2018-10-31T10:23:04.284Z'
author: Robert
---

I’m Robert - Head of Growth at grapqhleditor.com. I’m responsible for every non-tech part of the project -  talking with users, building feature roadmap, building traffic and promoting it in tech community. To grow my tech skills I’ve also decided to learn GraphQL. Since I’m a newbie, this tutorial is also for newbies.

## Where GraphQL came from?

GraphQL was introduced in 2015 by Facebook as an alternative to REST. Back then It wasn’t just an experiment - it was already in production inside facebook mobile apps since 2012.  Just after public release, It became extremely popular, and around that time many companies were working on similar solutions. This technology started to gain attention since it can be used with many other languages and there’re many ready to go libraries to most popular styles. Right now according to Stackshare there’re nearly 2k companies using it. Early adopters included Coursera, Github, ProductHunt, Yelp or Twitter.

## What is GraphQL?

To introduce GraphQL we I’ll use an example of data and compare functionality with REST. This example will show you why new technology is more efficient, simpler to use and what was motivation to create it.

Let’s assume that we are creating a backend for application with two versions (web and mobile). The core of the system will contain information about football players, teams they played and some other details. Mobile version of the application should show less information.

| Web                       | Mobile     |
| -------------| -------------|
| Name| Name|
| Bio| Team (Years)|
| Team (Description, Years) | Team (Years)|
| Team (Description, Years) ||

If we would create API based on REST, therefore, we would need to write two requests:

    GET /player/{id}
    {
      "id": „101”
      "name": „David Beckham”
      "bio": „…”
      „age”: „43”
    }

    GET /player/{id}/teams
    {
      „Teams”: [{
        „id": „201”,
        „name”: „Real Madrid”,
        „description": „…”,
        „goals”: „…”,
        „matches”: „…”,
        „years”: „2003-2007”
      },
      ...
      ]
    }

By looking at the response, you can see that we get more data than we need in both cases. It is essential in mobile apps because of mobile data and the speed of loading.  Of course, we could overcome that by writing different endpoints which will get us only what we want. Another way is to implement API in a way that we could choose what we want I.e. GET /player/{id}/teams?only=name. But that’s hard the solution to maintain in agile software development where requirements are changing every week.

## Opposite to that, we can introduce GraphQL.

Backend with GraphQL is more flexible because it has only one endpoint. Customers write a query and specify what do you want exactly and gets data in JSON. In our football example, we can create queries for each platform (web and mobile).

**Web application:**

    query {
      player(id: "101") {
        name
        bio
        teams {
          title
          description
        }
      }
    }

**Resposne:**

    {
      "data": {
        "player": {
          "name": "David Beckham",
          "bio": "...",
          "teams": [{
            "title": "Real Madrid",
            "description": "..."
          }, ...]
        }
      }
    }

**Mobile app**

    query {
      player(id: "101") {
        name
        teams {
          title
        }
      }
    }

**Resposne:**

    {
      "data": {
        "player": {
          "name": "David Beckham",
          "teams": [{
            "title": "Real Madrid"
          }, ...]
        }
      }
    }

As you can see with each query, we are getting only what we want. That’s the power of GraphQL - flexibility. You don’t need to create endpoints for different screens. Various clients can use a once defined schema in a way that they need to.

## Fewer requests.

In traditional REST approach usually, there are many GET requests to show data on one screen. It’s not super efficient when it comes to mobile apps - it slows them down and needs more internet data transfer. GraphQL allows a user to create only one endpoint. Thanks to technology introduced by Facebook engineers communication between client and a server is better optimised.

## Query language

The most important feature of GraphQL is that it’s language agnostic. It’s not created to work with specific technology. It’s a query language with its own rules which for most popular programming languages. There’re many supportive libraries on the internet for: C#/.NET, PHP. Javascript, Java, Python, Ruby and many more.

## Summary

GraphQL is a query language and runtime that we can use to build and expose APIs as a strongly-typed schema instead of hundreds of REST endpoints. Your clients see the schema. They write a query for what they want. They send it over and get back exactly the data they asked for and nothing more. GrapqhQL advantages:

- fewer data and requests for mobile apps,
- simplicity and visible API structure
- can be used with many technologies
- is used by Twitter, Facebook, Github and many more

repository: graphql-editor/graphql-blog

gha_language: JavaScript

repo_name: graphql-editor/graphql-blog


text:
---
id: quick-example
title: Quick Example
---

Following is an example showing how to:

- use `KafkaConsumer#records` in order to stream records from Kafka,
- use `produce` to produce newly created records to Kafka,
- use `commitBatchWithin` to commit consumed offsets in batches.

```scala mdoc
import cats.effect.{IO, IOApp}
import fs2.kafka._
import scala.concurrent.duration._

object Main extends IOApp.Simple {
  val run: IO[Unit] = {
    def processRecord(record: ConsumerRecord[String, String]): IO[(String, String)] =
      IO.pure(record.key -> record.value)

    val consumerSettings =
      ConsumerSettings[IO, String, String]
        .withAutoOffsetReset(AutoOffsetReset.Earliest)
        .withBootstrapServers("localhost:9092")
        .withGroupId("group")

    val producerSettings =
      ProducerSettings[IO, String, String]
        .withBootstrapServers("localhost:9092")

    val stream =
      KafkaConsumer.stream(consumerSettings)
        .subscribeTo("topic")
        .records
        .mapAsync(25) { committable =>
          processRecord(committable.record)
            .map { case (key, value) =>
              val record = ProducerRecord("topic", key, value)
              committable.offset -> ProducerRecords.one(record)
            }
        }.through { offsetsAndProducerRecords =>
          KafkaProducer.stream(producerSettings).flatMap { producer =>
            offsetsAndProducerRecords.evalMap {
              case (offset, producerRecord) =>
                producer.produce(producerRecord)
                  .map(_.as(offset))
            }.parEvalMap(Int.MaxValue)(identity)
          }
        }.through(commitBatchWithin(500, 15.seconds))

    stream.compile.drain
  }
}
```

gha_language: Scala


text:
---
layout: kanji
v4: 59
v6: 63
kanji: 元
keyword: beginning
elements: beginning, two, human legs
strokes: 4
image: E58583
on-yomi: ゲン、ガン
kun-yomi: もと
permalink: /rtk/元/
prev: 児
next: 頁
---

1) [mukade] 10-7-2007(236): In the beginning were just two who walked on human legs.

2) [Tsuki11] 29-10-2007(71): In the beginning God created woman, and God asked her how she liked his work. Woman replied, "Well, you know how I have two eyes, two arms, two legs... it seems redundant to have three breasts. It would look more symmetrical with two." God nodded, and the middle breast fell off... and it was good.  But what do I do with this useless boob?" And God created man. That's why the women today only have two boobs to match their two human legs.

3) [smiro] 18-11-2007(46): Every journey begins with your two legs.

4) [Tatiana] 10-10-2006(14): Grandpa Woody always began stories from his youth with "In the beginning, when I had two legs...".

5) [domokun1134] 24-11-2009(8): In the beginning we start out with nothing but our own two legs.


text:
---
title: How Microsoft Saved Apple and Accidentally Revolutionized Personal Computing
layout: post
date: '2023-08-13 15:55:35'
author: Jiten Thakkar
comments: 'true'
tags:
- history
- microsoft
- apple
- steve jobs
- bill gates
---

{% include image.html file="steve_jobs_speech.png" description="**Steve Jobs giving graduation speech at Standford, 2005**" %}

In the tech world, it's common to hear stories of fierce competition and bitter rivalries. One of the most famous of these is the rivalry between Apple and Microsoft. However, there is a lesser-known story about how Microsoft actually helped save Apple from financial ruin.

In 1997, Apple was struggling and had no clear direction. The company had just acquired NeXT computers, which laid the foundation for their future operating system, MacOS. Steve Jobs, who had previously been fired from Apple, returned as the interim CEO and set out to clean up the company. He knew that Apple needed partners and capital injection to survive and avoid laying off talented people.

{% include image.html file="billgatesMacworld1997.jpeg" description="**Bill Gates at MacWorld, 1997**" %}

At the same time, Microsoft was the undisputed leader in the PC market with over 90% market share. Apple and Microsoft were in patent infringement negotiations[1] that were leading to frustration on both sides. Jobs went to Bill Gates and negotiated a deal where Microsoft would invest $150 million in Apple, guarantee support for Microsoft Office on MacOS for the next 5 years, and drop a patent infringement lawsuit. It led to the famous Apple event where Bill Gates appeared as the towering image above Steve Jobs on the stage, much to everyone's dismay because Microsoft stood for everything that they hated about consumer software.

This deal was a pivotal moment in tech history that saved Apple from financial ruin. Without it, Apple may not have survived as a company. From Microsoft's point of view, it was a small price to pay for avoiding a potentially expensive lawsuit. It allowed Apple to survive and ultimately revolutionize personal computing with the introduction of the iPhone and rest is history.

So, the next time you're using an Apple product, take a moment to thank Microsoft for their role in saving the company and changing the course of history.

This interesting nugget of history is from book [Becoming Steve Jobs](https://www.goodreads.com/book/show/22318382-becoming-steve-jobs). Good read.

[1] [https://en.wikipedia.org/wiki/Apple_Computer,_Inc._v._Microsoft_Corp](https://en.wikipedia.org/wiki/Apple_Computer,_Inc._v._Microsoft_Corp)

gha_language: HTML


text:
# XDMDemo

Demo showing how to send data from a webpage to a Hootsuite Campaigns iFrame.

## Develop

```sh
docker-compose up
```

Get the ngrok url

```sh
curl -Ls $(docker port xdmdemo_ngrok_1 4040)/api/tunnels/command_line | python -c 'import sys, json; print json.load(sys.stdin)["public_url"]'
```

Add the content from the campaigns.js file into the campaign.

repository: hssolutionarchitects/xdmdemo

repo_name: hssolutionarchitects/xdmdemo


text:
# google-natural-language-sampler

Code examples for Google Natural Language API written in Python.

## Description

Example codes has following features:

* Sentiment Analysis
* Named Entity Recognition
* Syntax Analysis
* Entity Sentiment Analysis
* Text Classification
* Text Annotation

## Requirement

* Python 3.x
* Credentials

## Setup

To install necessary library, simply use pip:

```bash
pip install google-cloud-language
```

or,

```bash
pip install -r requirements.txt
```

Next, set up to authenticate with the Cloud Natural Language API using your project's service account credentials. Then, set the GOOGLE_APPLICATION_CREDENTIALS environment variable to point to your downloaded service account credentials:

```bash
export GOOGLE_APPLICATION_CREDENTIALS=/path/to/your/credentials-key.json
```

## Quick Start: Running the Example

### Sentiment Analysis

```bash
$ python examples/sentiment_analysis.py "President Obama looks very happy."
    Sentiment score: 0.4000000059604645
Sentiment magnitude: 0.4000000059604645
```

For more information, see [Analyzing sentiment](https://cloud.google.com/natural-language/docs/analyzing-sentiment).

### Named Entity Recognition

```bash
$ python examples/named_entities.py "President Obama is speaking at the White House."
====================
         name: Obama
         type: PERSON
     salience: 0.9082207679748535
wikipedia_url: -
====================
         name: White House
         type: LOCATION
     salience: 0.09177924692630768
wikipedia_url: https://en.wikipedia.org/wiki/White_House
```

For more information, see [Analyzing entities](https://cloud.google.com/natural-language/docs/analyzing-entities).

### Syntax Analysis

```bash
$ python examples/syntax_analysis.py "President Obama is speaking at the White House."
NOUN: President
NOUN: Obama
VERB: is
VERB: speaking
ADP: at
DET: the
NOUN: White
NOUN: House
PUNCT: .
```

For more information, see [Analyzing syntax](https://cloud.google.com/natural-language/docs/analyzing-syntax).

### Entity Sentiment Analysis

```bash
$ python examples/entity_sentiment.py "President Obama is speaking at the White House."
====================
         name: Obama
         type: PERSON
     salience: 0.9082207679748535
wikipedia_url: -
    magnitude: 0.10000000149011612
        score: 0.0
====================
         name: White House
         type: LOCATION
     salience: 0.09177924692630768
wikipedia_url: https://en.wikipedia.org/wiki/White_House
    magnitude: 0.0
        score: 0.0
```

For more information, see [Analyzing entity sentiment](https://cloud.google.com/natural-language/docs/analyzing-entity-sentiment).

### Text Classification

```bash
$ python examples/classify_text.py "On Saturday, Sevilla FC announced the signing of Spanish defender Aleix Vidal from defending LaLiga champions FC Barcelona. Via their official website, Barcelona said they were to receive €8.5 million transfer as well as two million in variables."
====================
name            : /Sports/Team Sports/Soccer
confidence      : 0.9900000095367432
====================
name            : /News
confidence      : 0.550000011920929

```

For more information, see [Classifying text](https://cloud.google.com/natural-language/docs/classifying-text).
In [the content category page](https://cloud.google.com/natural-language/docs/categories), You can see all categories returned by classify_text method.

### Text Annotation

```bash
$ python examples/annotate_text.py "President Obama looks very happy."
    Sentiment score: 0.4000000059604645
Sentiment magnitude: 0.4000000059604645
====================
         name: Obama
         type: PERSON
     salience: 1.0
wikipedia_url: https://en.wikipedia.org/wiki/Barack_Obama
```

For more information, see [AnnotateTextRequest](https://cloud.google.com/natural-language/docs/reference/rpc/google.cloud.language.v1#google.cloud.language.v1.AnnotateTextRequest).

## Licence

[MIT](https://github.com/Hironsan/google-vision-sampler/blob/master/LICENSE)

## Author

[Hironsan](https://github.com/Hironsan)

## References

* [Natural Language](https://googlecloudplatform.github.io/google-cloud-python/latest/language/usage.html)
* [Natural Language API Client Libraries](https://cloud.google.com/natural-language/docs/reference/libraries)
* [Method: documents.classifyText](https://cloud.google.com/natural-language/docs/reference/rest/v1/documents/classifyText)


text:
# CInner

Pronounced **sinner**, CInner is a basic CI system which can be used to run CI tests using GitHub's APIs. More details available in the code. If you have a question, create an issue to ask us. :)


text:
# reCAPTCHA.AspNetCore
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT) [![nuget](https://img.shields.io/nuget/v/reCAPTCHA.AspNetCore.svg)](https://www.nuget.org/packages/reCAPTCHA.AspNetCore/)

Google reCAPTCHA v2/v3 for .NET Standard 2.0, ASP.NET Core 2, and .NET Framework.

# Install

From the a command prompt
```bash
dotnet add package reCAPTCHA.AspNetCore
```

```bash
Install-Package reCAPTCHA.AspNetCore
```

You can also search for package via your nuget ui / website:

https://www.nuget.org/packages/reCAPTCHA.AspNetCore/

# Requirements
You must first have a **secret key** and a **site key** in order to use the reCAPTCHA service. This package supports v2 and v3 api keys. You can read more about reCAPTCHA v2, and v3 as well as sign up for free here: https://www.google.com/recaptcha/intro/

# Configure

Choose how you want to configure the storage of your RecaptchaSettings. This contains your site key, and site secret so it's recommended to use secrets.json with Azure Key Vault (or similar setup). However you can also just add the section to your appconfig.json file.

# Versions

- [v2](https://developers.google.com/recaptcha/docs/display)
- [v2-invis](https://developers.google.com/recaptcha/docs/invisible)
- [v3](https://developers.google.com/recaptcha/docs/v3)

#### appconfig.json

Add the follow entry to the file make sure to paste in your secret key and site key followed by setting the correct version to v2 or v3 depending on your key type:
```json
"RecaptchaSettings": {
    "Enabled": true,
    "SecretKey": "paste secret key here",
    "SiteKey": "paste site key here",
    "Version": "v2"
  }
```

#### secrets.json
Right click on your project file and goto Manage Secrets.

This will open secrets.json. Add the follow entry to the file make sure to paste in your secret key and site key followed by setting the correct version to v2 or v3 depending on your key type:
```json
"RecaptchaSettings": {
    "SecretKey": "paste secret key here",
    "SiteKey": "paste site key here",
    "Version": "v2"
  }
```

Note: This will also require you to have a setup such as Azure Key Vault (or similar setup) when running in production.

# Examples

Open Startup.cs and add the following code as shown below to your ConfigureServices method:

```csharp
services.Configure<RecaptchaSettings>(Configuration.GetSection("RecaptchaSettings"));
services.AddTransient<IRecaptchaService, RecaptchaService>();
```

# Usage

In order to prevent having to copy and paste your site key all over your view files (a nightmare to update later). You can inject your settings from the Startup method by adding the following code to top of your view file:

```csharp
@inject IOptions<RecaptchaSettings> RecaptchaSettings
```

You can then freely include the Recaptcha script inside of forms you wish to vaidate later in your controller (supports multiple forms).
```csharp
@using (Html.BeginForm("SomeMethod", "SomeController")) {
  @Html.Recaptcha(RecaptchaSettings.Value)
}
```

If you wish to trigger a JavaScript function on callback you can pass a method name to the Html helper.
```csharp
@using (Html.BeginForm("SomeMethod", "SomeController")) {
  @Html.Recaptcha(RecaptchaSettings.Value, successCallback:"methodName")
}
```
```html
<script>
  function methodName() {
    alert('Hello world!');
  }
</script>
```

You may find that you need to add a reference to Microsoft.Extensions.Options before you can use IOptions.

```csharp
@using Microsoft.Extensions.Options
@using reCAPTCHA.AspNetCore
```

You can see a tested example of usage in the [Contact.cshtml](https://github.com/TimothyMeadows/reCAPTCHA.AspNetCore/blob/master/reCAPTCHA.AspNetCore.Example/Views/Home/Contact.cshtml) view. However you will need to configure it with your key information before running yourself. You should also take note of the allowed domains security policy in the Google Recaptcha docs.

# Validation

You can validate the recaptcha attempts using the ValidateRecaptchaAttribute on your HttpPost method:

```csharp
[HttpPost]
[ValidateRecaptcha]
public async Task<IActionResult> SomeMethod(SomeModel model)
{
  return View(model);
}
```

You can see a tested example of usage in the [HomeController.cs](https://github.com/TimothyMeadows/reCAPTCHA.AspNetCore/blob/master/reCAPTCHA.AspNetCore.Example/Controllers/HomeController.cs) controller. However you will need to configure it with your key information before running yourself. You should also take note of the allowed domains security policy in the Google Recaptcha docs.

# Recaptcha.net

Users who suffer from censorship concerns can now bypass the libraries default of www.google.com to www.recaptcha.net using the following static variable in the RecaptchaService class. You can best set this static during the ConfigureService method.

```csharp
RecapthaService.UseRecapchaNet = true; // Default is false
```


text:
### Floppy Directory

Put floppy images in this directory. Valid image formats are flat sector images
of the type created by WinImage, dd or other such utilities, typically with \*.img
or \*.ima extensions. Compressed \*.imz images are not supported.

MartyPC will adjust the floppy drive size within the capabilities of the currently
emulated machine, based on the size of the image loaded.  There is no need to configure
this setting.

However, if emulating a IBM PC or XT, note that certain floppy sizes will not work
as the machine/BIOS does not know how to handle a disk of that size.

Floppy images must be one of these exact file sizes:

   |bytes|type| |
   |----|----|-------|
   |163,840   | 160KB floppy, single-sided | Rare, only used by the earliest PCs.|
   |184,320   | 180KB floppy, single-sided | Rare, only used by the earliest PCs.|
   |327,680   | 320KB floppy, double-sided | Somewhat rare. |
   |368,640   | 360KB floppy, double-sided | Extremely common. |
   |737,280   | 720KB floppy, double-sided | Extremely common. |
   |1,228,800 | 1.2MB floppy, double-sided | Extremely common. |

   The exception is anything smaller than 163,840 bytes will be loaded as that size,
   padded with 0s.
   This is a convenience feature for development of boot sector software or loading
   of boot sector demos and games.

repository: dbalsom/martypc

gha_language: Rust

gha_license_id: NOASSERTION

repo_name: dbalsom/martypc


text:
Meal Planner

Meal Planner is an application that will allow you to select the meals for the week and based off the plan print out a shopping list. With this app you can plan your meals ahead of time in order to make healthy choices. There will no longer be the headache of thinking what to make. No more endless trips to the grocery store. It will also cut down on food waste because you will be buying ingredients based on a plan.

Users can create a profile, create their own recipes, then select from those recipes or other recipes to plan for the week ahead and get an organized shopping list.

ASP.NET core 3.0 EntityFramework


text:
A simple ROS tracker wrapper for the catkinized AprilTag library [https://github.com/icoderaven/apriltags_catkin]

Publishes all detected AprilTags in the subscribed image stream in custom messages defined in msg/ folder.

Also includes a Boost Python Wrapper for the same to use within python seamlessly.

Tag parameters need to be specified in a yaml format. An example is included in config/params.yaml

Two executables exist:
apriltag_detector_node : To only publish the tag messages.
apriltag_tracker_node : To publish the PnP output of the tag in the camera frame.

Enabling the debug parameter publishes a debug image with an overlay of the tags.


text:
# Intro

 The aim of this project is to recover the data stored on DNA sequences from millions of noisy reads. In this regard, we first encoded a sample zip file, then induce errors and erasures to the encoded DNA sequences, then cluster the reads using Locality-Sensitive Hashing method, then by performing Multiple Sequence Alignment and Majority Voting on each cluster we generate a number of candidates and by putting them into a decoder we receover the original file. A step-by-step approach for an example is provided below.

==============

# Setup and Installation
Assuming the experiment is being performed in a docker container, the following libraries and packages need to be installed.

        apt-get update
        apt-get install gcc
        apt-get install make
        apt-get install git
        apt-get install libboost-all-dev
		apt-get install python3.6
		apt-get install python-numpy
		apt-get install python-biopython
		apt-get install python-sklearn

The encoder/decoder I used is a Reed-Solomon encoder/decoder written by [Reinhard Heckel](https://github.com/reinhardh). Download [dna_rs_coding](https://github.com/reinhardh/dna_rs_coding) as a zip file and extract it, then put the LSH_clustering.ipynb jupyter notebook in the main directory where you extracted that zip file.

compile the encoder/decoder by running the following commands:

	cd simulate
 	make texttodna

For multiple sequence alignment, I used MUSCLE command line. Please download the proper version of the software from http://www.drive5.com/muscle/downloads.htm and put it in the main directory. LSH_clustering.ipynb is written for linux, so if you are using another operating system, after downloading the proper version of MUSCLE software and putting it in the main directory, you need to change "muscle_exe" in the "multiple_alignment_muscle" function (cell #12 in LSH_clustering.ipynb) to the name of the file you dowloaded.

==============

# Run and reproduce the results
Now, all you need to do is to open and LSH_clustering.ipynb or simply run the following code in the command line.

`jupyter notebook LSH_clustering.ipynb`


text:
# Vowel-Recognition
Speaker independent recognition of the eleven steady state vowels of British English using a specified training set of lpc derived log area ratios.

This project describes the application of a variety of feed-forward networks
to the task of recognition of vowel sounds from multiple speakers.
The vowel data was collected by
Deterding [Deterding89], who recorded examples of the eleven steady state
vowels of English spoken by __fifteen speakers__ for a speaker normalisation
study.

(An ascii approximation to) the International Phonetic Association (I.P.A.)
symbol and the word in which the eleven vowel sounds were recorded.
The word was uttered once by each of the fifteen speakers.  Four
male and four female speakers were used to train the networks, and the other
four male and three female speakers were used for testing the performance.

The speech signals were low pass filtered at 4.7kHz and then digitised to 12
bits with a 10kHz sampling rate.  Twelfth order linear predictive analysis was
carried out on six 512 sample Hamming windowed segments from the steady part
of the vowel.  The reflection coefficients were used to calculate 10 log area
parameters, giving a 10 dimensional input space.Each speaker thus yielded six frames of speech from eleven vowels.  This gave
528 frames from the eight speakers used to train the networks and 462 frames
from the seven speakers used to test the networks.

-----

### Trial 1 -with 100 epochs and optimizer as SGD(Stochastic gradient descent)

![github logo](https://github.com/anishsingh20/Vowel-Recognition/blob/master/Pass1-100epochs.png)


text:
# rules_verilog

Utilities for working with Verilog within [Bazel](https://bazel.build/).

## gtkwave_wrapper

Wrapper for [GTKWave](http://gtkwave.sourceforge.net/) to view traces generated by Bazel rules.

## verilog_module

Bazel representation of a Verilog module's source files, dependencies, and top module.

```Starlark
load("@rules_verilog//verilog:defs.bzl", "verilog_module")

verilog_module(
    name = "counter",
    top = "counter",
    srcs = ["counter.sv"],
)

verilog_module(
    name = "counter_top",
    top = "counter_top",
    srcs = [
        "counter_top.sv",
    ],
    deps = [":counter"],
)
```

## WORKSPACE

To incorporate `rules_verilog` into your project at the following to your `WORKSPACE` file.

```Starlark
load("@bazel_tools//tools/build_defs/repo:http.bzl", "http_archive")

http_archive(
    name = "rules_verilog",
    # See release page for latest version url and sha.
)
```


text:
# riscv-parser

This is the basis of a parser for RISCV-V that does not use regex or third-party parsing libraries. It could be handy in making your own linter, prettifier, or assembler for RISC-V. I made this for a research project since I couldn't find a small, existing parser or even a grammar for RISC-V assembly.

Note that this is only a parser (and a minimalist one at that). It doesn't check the semantics or produce binary code. The parser supports *most* of the GNU assembler's syntax.

For more details, read my blog post about the parser: [Parsing RISC-V](https://web.eecs.utk.edu/~azh/blog/parsingriscvhtml).

Try it out:
```
clang++ *.cpp -std=c++11
./a.out examples/a7.S
```

repository: iCodeIN/riscv-parser

repo_name: iCodeIN/riscv-parser


text:
# TimcardsCore Project

This project contains the domain classes that define the application at its
core, plus business logic for manipulating objects created from these classes.

The domain classes also serve as data transfer objects:  they are independent of
their Entity Framework counterparts, so they can hold information more in line
with the application itself.

This project contains:

* A custom EventArgs class, used in the bulk data operation to raise an event
while the import process is executing.  This is handled by the UI layer to
show the progress of the import to the user.
* Custom Exception classes, to make exception handling logic clear.
* An extension method for providing user-friendly descriptions of enum values.
* An extension method for making it easier to add attributes to an XML node.
* Interfaces for allowing the core to access objects from the outside layers.
* Business logic classes that separate logic from the data they manipulate.
* Model (or domain) classes.

repository: danhickok/timecards

repo_name: danhickok/timecards


text:
# CouponsTracker
App developed as part of coursework for Udacity Android Nanodegree. CouponsTracker is the Capstone Project.

Coupons Tracker helps you keep track of all currently active offers provided by various merchants. It is an offline solution so you do not need an internet connection to view coupons. There are daily notifications that inform the user about coupons expiring each day. Coupons can also be backed up to Google drive due to which the data can be imported at any time on another device.

## Features
* Locally save offer / discount coupons from various merchants or retailers.
* View upcoming and past coupons.
* View all the coupons used.
* Share, edit or delete any coupon.
* Set daily notification reminders about coupons expiring each day.
* Add widget to quickly browse coupons that can be availed.
* Backup data to Google drive and import the same on another device.

## More Information
Details of this app can be found [here](https://github.com/darsh2/CouponsTracker/blob/master/Capstone_Stage1.pdf) and [here](https://github.com/darsh2/CouponsTracker/blob/master/Updated_Capstone_Stage1.pdf).

## How to use
* Clone this project and import in Android Studio.
* Run the installRelease gradle task to use the release apk.
* Install the generated app-release.apk file in a real Android device or emulator.

## License
    Copyright 2017 Darshan Dorai.

    Licensed under the Apache License, Version 2.0 (the "License");
    you may not use this file except in compliance with the License.
    You may obtain a copy of the License at

       http://www.apache.org/licenses/LICENSE-2.0

    Unless required by applicable law or agreed to in writing, software
    distributed under the License is distributed on an "AS IS" BASIS,
    WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
    See the License for the specific language governing permissions and
    limitations under the License.

repository: masheransari/CouponsTracker

repo_name: masheransari/CouponsTracker


text:
PyroCMS Module Generator
=======================

### Version 2.0

This generator will allow you to fill in a form and generate a module. It is built with [PhalconPHP](http://phalconphp.com/en/) because phalcon is crazy fast and easy to make small apps with.

# YOU MUST HAVE PHALCONPHP INSTALLED TO USE THIS APP

[Install Phalcon First](http://phalconphp.com/en/download).

### Usage

Just throw it in your localhost and point your browser to it. There is no database. It just writes and renames files for you.

If you you **used a custom url** (and didn't just clone as `pyro-module-generator`), then open the [config/config.php and change the baseUri](https://github.com/james2doyle/pyro-module-generator/blob/master/config/config.php#L7).

##### Writeable Folders

* cache/volt/
* public/generated/

#### Genrated Modules

The generated module is in the `public/generated/` folder.

![screenshot](https://raw.githubusercontent.com/james2doyle/pyro-module-generator/master/screenshot.jpeg)

### Todo

* More than just text and textarea forms
* Clean up
* Use DIRECTORY_SEPARATOR more

### License

(The MIT License)

Copyright (c) 2014 James Doyle(@james2doyle) <EMAIL_ADDRESS>
Permission is hereby granted, free of charge, to any person obtaining
a copy of this software and associated documentation files (the
'Software'), to deal in the Software without restriction, including
without limitation the rights to use, copy, modify, merge, publish,
distribute, sublicense, and/or sell copies of the Software, and to
permit persons to whom the Software is furnished to do so, subject to
the following conditions:

The above copyright notice and this permission notice shall be
included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED 'AS IS', WITHOUT WARRANTY OF ANY KIND,
EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY
CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE
SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

repository: adzzie/pyro-module-generator

repo_name: adzzie/pyro-module-generator


text:
# steamcmd-rcon

Barebone steamcmd in Ubuntu with rcon healthcheck

[![Docker Build Status](https://img.shields.io/docker/build/sfoxdev/steamcmd-rcon.svg?style=flat-square)]()
[![Docker Build Status](https://img.shields.io/docker/automated/sfoxdev/steamcmd-rcon.svg?style=flat-square)]()
[![Docker Build Status](https://img.shields.io/docker/pulls/sfoxdev/steamcmd-rcon.svg?style=flat-square)]()
[![Docker Build Status](https://img.shields.io/docker/stars/sfoxdev/steamcmd-rcon.svg?style=flat-square)]()

## Usage

### Envs

See [sfoxdev/steamcmd](https://hub.docker.com/r/sfoxdev/steamcmd/) plus

- `RCON_HOST=xyz` (default `localhost`): host of the rcon server, in case it is bound to a specific address
- `RCON_PORT=xyz` (default `27015`): port of the rcon server
- `RCON_PASS=xyz` (default `""`): password of the rcon server
- `RCON_HEALTH_COMMAND` (**mandatory**): command that is transmitted to the server which must responded with a non-zero-length answer for the container to be considered healthy
- `RCON_HEALTH_REGEXP` (default `""`): regexp that, if set, must match the rcon response for the container to be considered healthy

## Credits

- Uses SourceRcon.py from [frostschutz/SourceLib](https://github.com/frostschutz/SourceLib) under MIT license

repository: sfoxdev/docker-steamcmd-rcon

repo_name: sfoxdev/docker-steamcmd-rcon


text:
# Assistant VM

Assistant VM is slightly modified student VM. This document outlines the changes.

## VM modifications

- Change conky header from "Embedded (2021-2022)" to "Embedded Asssistant (2021-2022).
- Add https://schooner.utu.fi/assistant.html shortcut to Desktop.
- Download, extract and install https://schooner.utu.fi/utu-schooner-laa-1.0.1.tar.gz (as root)

**NOTE:** Version 1.0.2 MPLAB X startup doesn't work (does not connect to X Window). Due to time constraints, this will be ignored and fixed for Spring 2022.

## Resources

- [Schooner local agent GitHub repository](https://github.com/Jasata/utu-schooner-laa)
- [Schooner GitHub repository](https://github.com/Jasata/utu-schooner)

repository: Jasata/utu-schooner

path: /doc/Assistant VM.md

filename: Assistant VM.md

repo_name: Jasata/utu-schooner


text:
# GDA Extraction
This repository contains the source code to train and test Biomedical Relation Extraction (BioRE) models on the TBGA dataset. TBGA is a large-scale, semi-automatically annotated dataset for Gene-Disease Association (GDA) extraction. In addition, the repository contains scripts to compute dataset statistics and to convert other BioRE datasets in the required format.
 TBGA dataset is available at: https://doi.org/10.5281/zenodo.5911097.
  TBGA paper can be found at: https://rdcu.be/cKkY2.

### Usage

Clone this repository

```bash
git clone https://github.com/GDAMining/gda-extraction.git
```

Then install all the requirements:

```bash
pip install -r requirements.txt
```

**Note**: Please choose appropriate PyTorch version based on your machine (related to your CUDA version).
 For details, refer to https://pytorch.org/.

Then install the OpenNRE package with
```bash
cd ./OpenNRE
python setup.py install
```

If users also want to modify the code, run this instead:
```bash
cd ./OpenNRE
python setup.py install
python setup.py develop
```

## Datasets

Users can go into the `benchmark` folder and download TBGA using the script `download_TBGA_dataset.sh`.

If interested in running models on BioRel and DTI, users can download and store these datasets as follows.

BioRel:

Download in `/benchmark/biorel/` the `train.json`, `dev.json`, `test.json`, and `relation2id.json` files from https://bit.ly/biorel_dataset.

Then, run the `convert_biorel2opennre.sh` file in `/convert2opennre`.

DTI:

Download in `/benchmark/dti/` the `train.json`, `valid.json`, and `test.json` files from https://cloud.tsinghua.edu.cn/d/c9651d22d3f94fb7a4f8/.

Then, run the `convert_dti2opennre.sh` file in `/convert2opennre`.

## Dataset Statistics

Users can compute dataset statistics to understand the differences between datasets. For instance, if a user wants to compute statistics for TBGA, they can run

```bash
python data_stats.py --benchmark_fpath ./benchmark/TBGA/
```

## Pretrain
Pretrained embeddings can be downloaded by running scripts in the ``pretrain`` folder. For instance, if a user wants to download BioWordVec embeddings, they can run

```bash
cd ./pretrain
bash download_biowordvec.sh
```

Once downloaded, pretrained embeddings need to be tailored to the considered dataset. For instance, if a user wants to experiment with TBGA, they have to run

```bash
python prepare_embeddings.py --embs_fpath ./pretrain/biowordvec/ --benchmark_fpath ./benchmark/TBGA/
```

## Training

Users can train RE models on the provided datasets using ``train_model.py``, where ``model`` can be CNN, PCNN, BiGRU, BiGRU-ATT, or BERE. For instance, a user can run the following script to train and test the CNN (AVE) bag-level model on the TBGA dataset:
```bash
python train_cnn.py \
    --metric auc \
    --dataset TBGA \
    --bag_strategy ave \
    --hidden_size 250 \
    --optim sgd \
    --lr 0.2 \
    --batch_size 64 \
    --max_epoch 20
```

BioWordVec pretrained embeddings (obtained from ``download_biowordvec.sh``) are used to train RE models on TBGA and BioRel. Biomedical Word2Vec pretrained embeddings (obtained from ``download_biow2v.sh``) are required to train RE models on DTI. Results are reported in terms of Area Under the Precision-Recall Curve (AUPRC) and (micro) F1 score.

## Cite

If you use or extend our work, please cite the following:

```
@article{marchesin-silvello-2022,
  title = "TBGA: a large-scale Gene-Disease Association dataset for Biomedical Relation Extraction",
  author = "S. Marchesin and G. Silvello",
  journal = "BMC Bioinformatics",
  year = "2022",
  url = "https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-022-04646-6",
  doi = "10.1186/s12859-022-04646-6",
  volume = "23",
  number = "1",
  pages = "111"
}
```

```
@dataset{marchesin-silvello-2022-gda,
  title = "TBGA: A Large-Scale Gene-Disease Association Dataset for Biomedical Relation Extraction",
  author = "S. Marchesin and G. Silvello",
  publisher = "Zenodo",
  year = "2022",
  version = "1.0",
  url = "https://doi.org/10.5281/zenodo.5911097",
  doi = "10.5281/zenodo.5911097"
}
```

```
@inproceedings{han-etal-2019-opennre,
  title = "{O}pen{NRE}: An Open and Extensible Toolkit for Neural Relation Extraction",
  author = "X. Han and T. Gao and Y. Yao and D. Ye and Z. Liu and M. Sun",
  booktitle = "Proceedings of EMNLP-IJCNLP: System Demonstrations",
  year = "2019",
  url = "https://www.aclweb.org/anthology/D19-3029",
  doi = "10.18653/v1/D19-3029",
  pages = "169--174"
}
```

If you use the BERE RE model or the DTI dataset, please cite the following:

```
@article{hong-etal-2020-bere,
  title = "A novel machine learning framework for automated biomedical relation extraction from large-scale literature repositories",
  author = "L. Hong and J. Lin and S. Li and F. Wan and H. Yang and T. Jiang and D. Zhao and J. Zeng",
  journal = "Nature Machine Intelligence",
  year = "2020",
  url = "https://www.nature.com/articles/s42256-020-0189-y",
  doi = "10.1038/s42256-020-0189-y",
  volume = "2",
  pages = "347--355"
}
```

If you use the BioRel dataset, please cite the following:

```
@article{xing-etal-2020-biorel,
  title     = "BioRel: towards large-scale biomedical relation extraction",
  author    = "R. Xing and J. Luo and T. Song",
  journal   = "BMC Bioinformatics",
  year      = "2020",
  url = "https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-020-03889-5",
  doi = "10.1186/s12859-020-03889-5",
  volume    = "21-S",
  number    = "16",
  pages     = "543"
}
```

repository: GDAMining/gda-extraction

repo_name: GDAMining/gda-extraction


text:
---
layout: default
---

# 1997-11-05 - Re: Why porn must be stopped at all costs, by Jodi Hoffman (fwd)

## Header Data

From: Brandon Crosby<EMAIL_ADDRESS>To<EMAIL_ADDRESS>Message Hash: 95895e920a2194bb51c81712fead8854efd1626cf5c6c2c654f2057cf20640f3

Message ID<EMAIL_ADDRESS>Reply To: _N/A_

UTC Datetime: 1997-11-05 16:12:44 UTC

Raw Date: Thu, 6 Nov 1997 00:12:44 +0800

## Raw message

```
{% raw  %}From: Brandon Crosby<EMAIL_ADDRESS>Date: Thu, 6 Nov 1997 00:12:44 +0800
To<EMAIL_ADDRESS>Subject: Re: Why porn must be stopped at all costs, by Jodi Hoffman (fwd)
Message-ID<EMAIL_ADDRESS>MIME-Version: 1.0
Content-Type: text

>
> > Someone on this list, I forget who, has made numerous attempts at
> > convincing us that pornography 'does no harm' to children.  It is
> > exactly at this point that I must draw a line. Studies have shown that
> > an event which lasts even so much as three-tenths of a second, within
> > five to ten minutes has produced a structural change in the brain.
> > Exposure to porn causes actual brain damage, especially in a child.
>
> let me guess . . . scientology? it sounds to me as if you just despise
> porn so much that you're seeing red, and hence . . . not seeing the
> facts straight (or believing whatever you [want to] hear).
>
Take a young child to the mall. He or she sits on santa's lap.

Does the brain change at this point? The simple fact that the brain has
been altered in some way only proves that the child is concious.

-Brandon Crosby

{% endraw %}
```

## Thread

+ Return to [November 1997](/archive/1997/11)

+ Return to "[Brandon Crosby <bcrosby@mncs.k12.mn.us>](/authors/brandon_crosby_bcrosby_at_mncs_k12_mn_us_)"

+ 1997-11-05 (Thu, 6 Nov 1997 00:12:44 +0800) - Re: Why porn must be stopped at all costs, by Jodi Hoffman (fwd) - _Brandon Crosby<EMAIL_ADDRESS>


text:
---
layout: default
---

# 1996-11-12 - Taxation Thought Experiment

## Header Data

From: "Joseph M. Reagle Jr."<EMAIL_ADDRESS>To<EMAIL_ADDRESS>Message Hash: c0d5c5561e9db98a60c7e87073cb97dda5aa034df49977f99e654c174fa59cf3

Message ID<EMAIL_ADDRESS>Reply To: _N/A_

UTC Datetime: 1996-11-12 20:25:29 UTC

Raw Date: Tue, 12 Nov 1996 12:25:29 -0800 (PST)

## Raw message

```
{% raw  %}From: "Joseph M. Reagle Jr."<EMAIL_ADDRESS>Date: Tue, 12 Nov 1996 12:25:29 -0800 (PST)
To<EMAIL_ADDRESS>Subject: Taxation Thought Experiment
Message-ID<EMAIL_ADDRESS>MIME-Version: 1.0
Content-Type: text/plain

Another thought experiment, comments welcomed:

o TAXES THOUGHT EXPERIMENT

 1) I generate $100 of productivity for my company
 2) Company is taxed %30, $70 left
 3) Company pay shareholders and costs, $30 is left
 4) Company pays me
 5) I pay 40% in taxes, so $18 left
 6) With $18 I can buy a $16.82 object (%07 sales tax).

Results:
 1) I see $16.82 realization from $100 productivity increase.
 * Govt. gets $49.26 of my productivity, or nearly 3 times the amount I get.
_______________________
Regards,          Our greatest glory is not in never failing, but in rising
                  up every time we fail.  -Ralph Waldo Emerson
Joseph Reagle     http://rpcp.mit.edu/~reagle/home.html
<EMAIL_ADDRESS>  E0 D5 B2 05 B6 12 DA 65  BE 4D E3 C1 6A 66 25 4E

{% endraw %}
```

## Thread

+ Return to [November 1996](/archive/1996/11)

+ Return to<EMAIL_ADDRESS>(Dr.Dimitri Vulis KOTM)](/authors/dlv_at_bwalk_dm_com_dr_dimitri_vulis_kotm_)"
+ Return to<EMAIL_ADDRESS>(Igor Chudov @ home)](/authors/ichudov_at_algebra_com_igor_chudov__at__home_)"
+ Return to "["Joseph M. Reagle Jr." <reagle@rpcp.mit.edu>](/authors/joseph_m_reagle_jr__reagle_at_rpcp_mit_edu_)"
+ Return to "["Michael Froomkin - U.Miami School of Law" <froomkin@law.miami.edu>](/authors/michael_froomkin__u_miami_school_of_law_froomkin_at_law_miami_edu_)"
+ Return to "[Open Net Postmaster <postmaster@opennet.net.au>](/authors/open_net_postmaster_postmaster_at_opennet_net_au_)"
+ Return to "["Timothy C. May" <tcmay@got.net>](/authors/timothy_c_may_tcmay_at_got_net_)"

+ 1996-11-12 (Tue, 12 Nov 1996 12:25:29 -0800 (PST)) - Taxation Thought Experiment - _"Joseph M. Reagle Jr."<EMAIL_ADDRESS>  + 1996-11-12 (Tue, 12 Nov 1996 15:17:58 -0800 (PST)) - [Re: Taxation Thought Experiment](/archive/1996/11/46a5eaf063a055a172f08bd332e0af0cc53612514f0650feb063337f87e8ef03) - _"Timothy C. May"<EMAIL_ADDRESS>  + 1996-11-13 (Tue, 12 Nov 1996 17:26:08 -0800 (PST)) - [Returned mail: User Unknown](/archive/1996/11/014f93da069ab44e327aae45049713e63131bd253415d185f1172e4aae04477f) - _Open Net Postmaster<EMAIL_ADDRESS>  + 1996-11-15 (Fri, 15 Nov 1996 08:20:10 -0800 (PST)) - [Re: Taxation Thought Experiment](/archive/1996/11/52e0d98267157ede40a406a316055c606e297ced33a9589c4fc86fb4b250a1de) - _"Michael Froomkin - U.Miami School of Law"<EMAIL_ADDRESS>    + 1996-11-16 (Sat, 16 Nov 1996 12:10:50 -0800 (PST)) - [Re: Taxation Thought Experiment](/archive/1996/11/7d42c8e68242c3068ad005e6f6d77846327077dcff0628347fbbf88e30e7dda1) -<EMAIL_ADDRESS>(Dr.Dimitri Vulis KOTM)_
      + 1996-11-16 (Sat, 16 Nov 1996 15:50:33 -0800 (PST)) - [Re: Taxation Thought Experiment](/archive/1996/11/0d9cf0b9bb28f4cc98b23ae47519ab8d84e747b0b250a39a6e381ddc341a8755) -<EMAIL_ADDRESS>(Igor Chudov @ home)_
        + 1996-11-17 (Sun, 17 Nov 1996 15:02:37 -0800 (PST)) - [Re: Taxation Thought Experiment](/archive/1996/11/30cae6d2f044d5651f61379e619fae15beb2a03844841a5762a0ee13db61968f) -<EMAIL_ADDRESS>(Dr.Dimitri Vulis KOTM)_
          + 1996-11-18 (Sun, 17 Nov 1996 18:41:00 -0800 (PST)) - [Re: Taxation Thought Experiment](/archive/1996/11/2022ab2c85a76f8bdff993339bd9f32b8db2cadbf8b753c10997689810e79b37) -<EMAIL_ADDRESS>(Igor Chudov @ home)_
            + 1996-11-19 (Mon, 18 Nov 1996 17:01:13 -0800 (PST)) - [Re: Taxation Thought Experiment](/archive/1996/11/2379e90df02d25dcb890aeaee359ff3ff299fa4f02b2d9c7bdd7b0e2f5a8e421) -<EMAIL_ADDRESS>(Dr.Dimitri Vulis KOTM)_
          + 1996-11-18 (Mon, 18 Nov 1996 00:58:38 -0800 (PST)) - [Re: Taxation Thought Experiment](/archive/1996/11/01abc7573dc3ec8c67072060837a83946a3a3cd0463e9f2070576592deccb143) -<EMAIL_ADDRESS>(Dr.Dimitri Vulis KOTM)_


text:
## [1.1.16](https://github.com/UziTech/sync-settings-git-location/compare/v1.1.15...v1.1.16) (2022-04-16)

### Bug Fixes

* **deps:** update dependency fs-extra to ^10.1.0 ([#206](https://github.com/UziTech/sync-settings-git-location/issues/206)) ([1252987](https://github.com/UziTech/sync-settings-git-location/commit/12529872e493f102b5142017b269be36f111b1c1))

## [1.1.15](https://github.com/UziTech/sync-settings-git-location/compare/v1.1.14...v1.1.15) (2022-03-13)

### Bug Fixes

* **deps:** update dependency atom-modal-views to ^1.0.27 ([#202](https://github.com/UziTech/sync-settings-git-location/issues/202)) ([e1f11fc](https://github.com/UziTech/sync-settings-git-location/commit/e1f11fc5eb2f364deb5017595cb0e956b5edaa65))

## [1.1.14](https://github.com/UziTech/sync-settings-git-location/compare/v1.1.13...v1.1.14) (2021-11-02)

### Bug Fixes

* **deps:** update dependency atom-modal-views to ^1.0.21 ([#184](https://github.com/UziTech/sync-settings-git-location/issues/184)) ([2d2c419](https://github.com/UziTech/sync-settings-git-location/commit/2d2c419a64e043cf35a0f2426831c08febf6a02a))

## [1.1.13](https://github.com/UziTech/sync-settings-git-location/compare/v1.1.12...v1.1.13) (2021-06-12)

### Bug Fixes

* throw error on git auth prompts ([#156](https://github.com/UziTech/sync-settings-git-location/issues/156)) ([e8dfb97](https://github.com/UziTech/sync-settings-git-location/commit/e8dfb978431f721df0bf15302e5dac5bc35bc70d))

## [1.1.12](https://github.com/UziTech/sync-settings-git-location/compare/v1.1.11...v1.1.12) (2021-05-04)

### Bug Fixes

* **deps:** update dependency fs-extra to v10 ([cb4e0ea](https://github.com/UziTech/sync-settings-git-location/commit/cb4e0ea8bb911a09ae1cdd0ddd9711ba749620f1))

## [1.1.11](https://github.com/UziTech/sync-settings-git-location/compare/v1.1.10...v1.1.11) (2021-03-01)

### Bug Fixes

* **deps:** update dependency atom-modal-views to ^1.0.9 ([bbeb904](https://github.com/UziTech/sync-settings-git-location/commit/bbeb90441f338b2d7f8453fb3045f35921ec0c0f))

## [1.1.10](https://github.com/UziTech/sync-settings-git-location/compare/v1.1.9...v1.1.10) (2021-01-27)

### Bug Fixes

* **deps:** update dependency atom-modal-views to ^1.0.7 ([99757cb](https://github.com/UziTech/sync-settings-git-location/commit/99757cbe67f820dd8d55bf572a4e09f67300a0f3))

## [1.1.9](https://github.com/UziTech/sync-settings-git-location/compare/v1.1.8...v1.1.9) (2021-01-19)

### Bug Fixes

* **deps:** update dependency fs-extra to ^9.1.0 ([3805df5](https://github.com/UziTech/sync-settings-git-location/commit/3805df5b07cb1e1023b8db9d734452cc01233ff1))

## [1.1.8](https://github.com/UziTech/sync-settings-git-location/compare/v1.1.7...v1.1.8) (2020-12-11)

### Bug Fixes

* **deps:** update dependency atom-modal-views to ^1.0.6 ([7377c8b](https://github.com/UziTech/sync-settings-git-location/commit/7377c8b7846f7cdee96767d0edb0ad4ccb81d48b))

## [1.1.7](https://github.com/UziTech/sync-settings-git-location/compare/v1.1.6...v1.1.7) (2020-11-20)

### Bug Fixes

* **deps:** update dependency atom-modal-views to ^1.0.5 ([1e40646](https://github.com/UziTech/sync-settings-git-location/commit/1e40646509a313787efe9efce2e2b732470983ea))

## [1.1.6](https://github.com/UziTech/sync-settings-git-location/compare/v1.1.5...v1.1.6) (2020-11-16)

### Bug Fixes

* **deps:** update dependency atom-modal-views to ^1.0.4 ([eb09364](https://github.com/UziTech/sync-settings-git-location/commit/eb09364475d22d9f464ea64daf0580a85a44f898))

## [1.1.5](https://github.com/UziTech/sync-settings-git-location/compare/v1.1.4...v1.1.5) (2020-09-28)

### Bug Fixes

* **deps:** update dependency atom-modal-views to ^1.0.3 ([319625d](https://github.com/UziTech/sync-settings-git-location/commit/319625d335ffa0d501a00c57f949ad67bd1db9ba))

## [1.1.4](https://github.com/UziTech/sync-settings-git-location/compare/v1.1.3...v1.1.4) (2020-09-24)

### Bug Fixes

* **deps:** update dependency atom-modal-views to ^1.0.2 ([3e5a4dd](https://github.com/UziTech/sync-settings-git-location/commit/3e5a4dd2247ba3684e2144b753cc405e3e69895d))

## [1.1.3](https://github.com/UziTech/sync-settings-git-location/compare/v1.1.2...v1.1.3) (2020-08-28)

### Bug Fixes

* better error messages ([4de1110](https://github.com/UziTech/sync-settings-git-location/commit/4de11101701b856eaf7f7eb55f4483d1fe8a50e8))

## [1.1.2](https://github.com/UziTech/sync-settings-git-location/compare/v1.1.1...v1.1.2) (2020-08-19)

### Bug Fixes

* **deps:** update atom-modal-views to v1.0.1 ([a7ee23e](https://github.com/UziTech/sync-settings-git-location/commit/a7ee23e7f3eb943d612b45c7a97db24fe6cc0140))

## [1.1.1](https://github.com/UziTech/sync-settings-git-location/compare/v1.1.0...v1.1.1) (2020-08-18)

### Bug Fixes

* use atom-input-views for modal input ([e1763e8](https://github.com/UziTech/sync-settings-git-location/commit/e1763e8320364c80ac1351f772803f315f069a4c))

# [1.1.0](https://github.com/UziTech/sync-settings-git-location/compare/v1.0.8...v1.1.0) (2020-07-24)

### Bug Fixes

* check diff before commit ([d7825f5](https://github.com/UziTech/sync-settings-git-location/commit/d7825f5606b20ee6a78e5c1666c9a0e61129d82f))

### Features

* add commit message prompt setting ([2f00c00](https://github.com/UziTech/sync-settings-git-location/commit/2f00c00576e7d02538cefeff5c48d037bd78fdcd))
* add commit message setting ([e414333](https://github.com/UziTech/sync-settings-git-location/commit/e4143338cca43527405bb08bea3d1bed4a90d66b))

## [1.0.8](https://github.com/UziTech/sync-settings-git-location/compare/v1.0.7...v1.0.8) (2020-07-14)

### Bug Fixes

* **deps:** update dependency marked to ^1.1.1 ([a27903e](https://github.com/UziTech/sync-settings-git-location/commit/a27903ee6405f26517bae2ae5d67a06a5639072f))

## [1.0.7](https://github.com/UziTech/sync-settings-git-location/compare/v1.0.6...v1.0.7) (2020-06-04)

### Bug Fixes

* **deps:** update dependency fs-extra to ^9.0.1 ([758d8bd](https://github.com/UziTech/sync-settings-git-location/commit/758d8bdfb3e66226d5da4ef9fc9d6113c1abe606))

## [1.0.6](https://github.com/UziTech/sync-settings-git-location/compare/v1.0.5...v1.0.6) (2020-05-17)

### Bug Fixes

* **deps:** update dependency marked to ^1.1.0 ([9ff5c79](https://github.com/UziTech/sync-settings-git-location/commit/9ff5c79ff3be2d24678a2ac7ab9e7b4e9255e06f))

## [1.0.5](https://github.com/UziTech/sync-settings-git-location/compare/v1.0.4...v1.0.5) (2020-04-26)

### Bug Fixes

* **deps:** update dependency marked to v1 ([d3d1aff](https://github.com/UziTech/sync-settings-git-location/commit/d3d1aff0458289a34700ddd8fab6f0e5ffd10610))

## [1.0.4](https://github.com/UziTech/sync-settings-git-location/compare/v1.0.3...v1.0.4) (2020-04-08)

### Bug Fixes

* **deps:** update dependency fs-extra to v9 ([57773c4](https://github.com/UziTech/sync-settings-git-location/commit/57773c45607781ca1071374f54c0212adc0333d7))
* **deps:** update dependency marked to ^0.8.2 ([89e3cff](https://github.com/UziTech/sync-settings-git-location/commit/89e3cff84ef648502bc576a55bc7ef253cd23e47))

## [1.0.3](https://github.com/UziTech/sync-settings-git-location/compare/v1.0.2...v1.0.3) (2020-03-04)

### Bug Fixes

* get files as Buffer ([ec65abf](https://github.com/UziTech/sync-settings-git-location/commit/ec65abfa0150ae1e11cfe4136d3ff469d55987c5))

## [1.0.2](https://github.com/UziTech/sync-settings-git-location/compare/v1.0.1...v1.0.2) (2020-03-02)

### Bug Fixes

* release with semantic-release ([93d35e4](https://github.com/UziTech/sync-settings-git-location/commit/93d35e48dd50f5b412436897ae289c5c555c9861))

repository: UziTech/sync-settings-git-location

repo_name: UziTech/sync-settings-git-location


text:
## Factory Pattern

In factory pattern, we create objects without exposing the creation logic to the code that requires the object to be created.

- It provides an interface for constructing pre configured objects
- Code is cleaner
- It allows you to offer an easy to understand interface to your packages function

```javascript
// Factory.js

function deliveryFactory(address, item) {
  if (distance > 10 && distance < 50) {
    return new DeliveryByCar(address, item);
  }

  if (distance > 50) {
    return new DeliveryByTruck(address, item);
  }

  return new DeliveryByBike(address, item);
}

class DeliveryByBike {
  constructor(address, item) {
    this.address = address;
    this.item = item;
  }
}

class DeliveryByTruck {
  constructor(address, item) {
    this.address = address;
    this.item = item;
  }
}

class DeliveryByCar {
  constructor(address, item) {
    this.address = address;
    this.item = item;
  }
}

const newDelivery = deliveryFactory(
  "121 baily ave, Toronto, canada",
  "nitendo 360"
);
```

## Abstract Factory Pattern

In factory pattern, we take care of creating objects of same family whereas in abstract factory pattern we will provide a constructor for creating families of related objects, without specifying concrete classes or constructors.

```javascript
function abstractFactory(address, item, options) {
  if (options.isSameday) {
    return sameDayDeliveryFactory(address, item);
  }
  if (options.isExpress) {
    return expressDeliveryFactory(address, item);
  }

  return deliveryFactory(address, item);
}
```


text:
# Teensy 4.0 OVERDRIVE beta1

## Breakout Board
![](./teensy40_overdrive_beta1/teensy40_overdrive_beta1.png)

## Code
[teensy40_overdrive_beta1/teensy40_overdrive_beta1.ino](./teensy40_overdrive_beta1/teensy40_overdrive_beta1.ino)

## Wiring
![](./teensy40_overdrive_beta1/wire1.jpg)

![](./teensy40_overdrive_beta1/wire2.jpg)

![](./teensy40_overdrive_beta1/wire3.jpg)

![](./teensy40_overdrive_beta1/reverse.jpg)

## Tuning
Using an oscilloscope, enter the time (in microseconds) during which the voltage is HIGH.

Important:

* MIN is minimum pulse. You can forget which is left, right, forward, brake.
* Enter only the neutral pulse exactly. Only 2 microseconds of neutral error is allowed to support delicate control and high power rc car. The correct neutral shows 0.0 as Joystick value. You can also use DEBUG 1 to see the pulses using a serial monitor.

* MIN and MAX are 1000 and 2000 (us), unless facing obvious problems. However, neutral should be set really accurately. If neutral is perfect, the autonomous driving can reproduce the slowest possible driving with high power motor.

### Teensy
source: [teensy40_overdrive_beta1.ino](teensy40_overdrive_beta1/teensy40_overdrive_beta1.ino)
```
/* STEERING PULSE */
const int RECV_CH1_PULSE_LENGTH_MIN     = 1000; // maximum steering right value
const int NEUTRAL_STEERING_PULSE_LENGTH = 1509; // neutral steering value
const int RECV_CH1_PULSE_LENGTH_MAX     = 2000; // maximum steering left value

/* THROTTLE PULSE */
const int RECV_CH2_PULSE_LENGTH_MIN     = 1000; // maximum throttle forward value
const int NEUTRAL_THROTTLE_PULSE_LENGTH = 1520; // neutral throttle value
const int RECV_CH2_PULSE_LENGTH_MAX     = 2000; // maximum throttle brake value
```
If you use reversed throttle, then set REVERSE 1. (default 0)
```
#define REVERSE 1              // TS-50A ESC should be 1. This uses only for led controll.
```
If you want to use PCA9685 board, then set USE_PCA9685_EMULATOR 0 and use P1/P2 pin. (default 1)
```
#define USE_PCA9685_EMULATOR 0
```
If you use 3ch transmitter (Tamiya TT-02 XB's transmitter etc.), then set USE_3CH_TRANSMITTER 1. (default 0)
```
#define USE_3CH_TARNSMITTER 0  // 0: use 4 channel transmitter.(Futaba 7PX/4PM etc.) 1: use 3 channel transmitter.(Tamiya TTU-08 etc.)
```

### DonkeyCar
source: [myconfig.py](donkeycar311/myconfig.py.nano_120fps)
```
THROTTLE_FORWARD_PWM = 250       # 1000us
THROTTLE_STOPPED_PWM = 375       # 1520us?
THROTTLE_REVERSE_PWM = 500       # 2000us
```


text:
# Consensus Update Protocol

The purpose of the protocol is to handle updating the allocation and destination of channels running the consensus app to reach an outcome specified on initialization.

The protocol stores every new commitment it receives that represents a valid transition from the most recent commitment.
Commitments received in `COMMITMENTS_RECEIVED` are processed in order, and are ignored when they are an invalid transition.

When it is the participant's turn, and the protocol has been cleared to send,

- if the current proposal matches the desired outcome, the protocol votes in favour
- else, the protocol starts a new round proposing the desired outcome

In either case, the protocol moves to `CommitmentSent`.

The protocol returns failure precisely when it becomes the participant's turn when in the `CommitmentSent` state, and consensus has not been reached.
In this case, the protocol does not send a second commitment.
This prevents the possibility of an infinite loop.

The protocol returns success precisely when consensus is reached -- necessarily with the desired outcome, due to the above behaviour -- before the participant is required to send their second commitment.
Note that this could be the result of the protocol even if some of the subsequent commitments are invalid.

## State Machine

```mermaid
graph TD
linkStyle default interpolate basis
  St((start)) --> STSCO{Safe to send?}
  STSCO --> |Yes| CS
  STSCO --> |No| NSTS(NotSafeToSend)
  NSTS -->|CommitmentsReceived| CV2{Commitments valid?}
  CV2   -->|No| F((Failure))
  CV2 -->|Yes| STS{Safe to send?}
  NSTS -->|ClearedToSend| STS

  CS   -->|CommitmentsReceived| CV{Commitments valid?}
  STS -->|YES| RC
  STS -->|NO| NSTS
  CV   -->|No| F2((Failure))
  CV   -->|Yes| RC{Round complete?}

  RC   -->|No| CS(CommitmentsSent)
  RC   -->|Yes| S((Success))

  classDef logic fill:#efdd20;
  classDef Success fill:#58ef21;
  classDef Failure fill:#f45941;

  class St,STS,STSCO,FP,RC,CV,CV2 logic;
  class S Success;
  class F,F2 Failure;
```

repository: magmo/apps

gha_language: TypeScript

repo_name: magmo/apps


text:
# List

Examples have been built on the **B-L475E-IOT01A STM32L4 based board**

- [Install the arm-none-eabi-* toolchain](https://developer.arm.com/tools-and-software/open-source-software/developer-tools/gnu-toolchain/gnu-rm/downloads)
- [Install CMAKE](https://cmake.org/cmake/help/latest/index.html)
- [Fastbit Embedded Brain Academy Linker Script](https://www.youtube.com/watch?v=B7oKdUvRhQQ)
- [ARM CMSIS_5 Github](https://github.com/ARM-software/CMSIS_5)
- [STM32 Github](https://github.com/STMicroelectronics/STM32Cube_MCU_Overall_Offer)

## High Level Overview of Baremetal Cross Compilation Process

- Write / Get the linker script
  - Support for `.text`, `.data` and `.bss` sections
  - Support for C Standard Library
  - Support for C++ Standard Library
- Write the Startup File (CMSIS)
  - Initialize the ISR Vector Table
  - Set the Stack Pointer at the first address
  - Implement the Reset Handler
- Implement the Reset Handler
  - Copy the `.data` section from Flash to RAM (LMA to VMA)
  - Initialize the `.bss` section to 0
    - Data initialized to 0 is stored here
    - Data uninitialized also needs to be set to 0 and stored here
- Call the main service routine

## Important

### **dump** folder
  - contains various compilation dumps to learn the various flags that are used during compilation

## C Programming

- [x] Minimal_Linker
  - Basic understanding of Linker Scripts
  - Basic C Programming used
- [x] Minimal_Linker_CMake
  - Integrating the Basic example with CMake Cross compilation toolchain
- [x] Minimal_Blinky
  - Complete the Linker Script (base) with ALIGN keyword as well
  - Very minimal Blinky example (NOT Complete)
  - Does not contain complete startup, .bss implementation and copy functions
  - Can be flashed onto the microcontroller to see led blinking
- [x] Minimal_Blinky_Meson
  - Integrated the Meson build system
  - Forked from Minimal_Blinky example
- [x] Minimal_Startup
  - Using the Minimal Blinky to write startup files for your linker script
- [x] Minimal_CMSIS
  - Using the ARM CMSIS Base Toolchain
  - Device specific data added

## C++ Programming

- [x] Minimal_Cpp
  - Adding C++
  - Constructor
- [ ] Minimal_Cpp_Advanced
  - Templates
  - C++11/C++14 Features
  - Add other features and testing
- [ ] Minimal_Cpp_Stdlib
  - Using the C++ Standard libraries
  - `<algorithm>` and static allocation based functions


text:
# Lehen hitza euskaraz (Prefer Basque Language)

This add-on modifies HTTP [Accept-Language](https://www.w3.org/Protocols/rfc2616/rfc2616-sec14.html#sec14.4) headers sent by the web browser. It indicates that the user would prefer to view content in the [Basque language](https://en.wikipedia.org/wiki/Basque_language), if available.

Code released under the terms of the [MIT license](https://opensource.org/licenses/MIT).

More information at [LehenHitza.eus](https://www.lehenhitza.eus/). Thanks to contributions by [CodeSyntax](https://www.codesyntax.com/).

repository: callahad/lehen-hitza

repo_name: callahad/lehen-hitza


text:
## Contributing

For those who would like to help in growing this repository, here are some guidelines on how to get started.

### 1. Where do I go from here?

The greatest way to help out with  the project and learn a little about it yourself is bug testing.
If you've never really coded before, this may be a decent place to start.
If you believe you have a found bug or have a question, see if someone else has already created an issue in the tab above-
if not, go ahead and [create an issue](https://github.com/SPARC-Auburn/Lab-Assistant/issues/new).

### 2. Fork & create a branch

If the issue is something you think you can fix, create a fork of your own and a branch with a name
describing the problem. Then create a pull request to the main branch. This will allow you to work on the code
without changing the primary files

A good branch name would be (where issue #7 is the ticket you're working on):

```
Issue #7 - Slow Response times
```

The issue name for this would be "Slow Response Times"

### 3. Clone the files to your working environment and run

In order to start testing, download the repository. The best testing environment is a Linux distribution, and the Assistant can currently
be run by running the command

```
python Listen.py
```

In the terminal window while under the "PCKaren" directory.

### 4. Did you find a bug?

* **Ensure the bug was not already reported** by [searching all
  issues](https://github.com/SPARC-Auburn/Lab-Assistant/issues).

* If you're unable to find an open issue addressing the problem, [open a new
  one](https://github.com/SPARC-Auburn/Lab-Assistant/issues/new).  Be sure to
  include a **title and clear description**, as much relevant information as
  possible, and a **code sample** or an **executable test case** demonstrating
  the expected behavior that is not occurring.

* If possible, use the relevant bug report templates to create the issue.
  Simply copy the content of the appropriate template into a .py file, make the
  necessary changes to demonstrate the issue, and **paste the content into the
  issue description**.

### 5. Do you want to help development?

 * Currently, the best way to help with development is to look into the **projects tab**. This keeps track of the current roadmap
 for Lab-Assistant, and are the features that are intended to be rolled out.
 * Once you have your own fork to work on, take a look at the different versions under [Projects](https://github.com/SPARC-Auburn/Lab-Assistant/projects).
 * Each version should have a feature that is either planned, under development, or finished. Find a feature you'd like to work on and
 submit a pull request for us to take a look at!

### 5. Implement your fix or feature

If you have a fix or feature you'd like to add, feel free to change it at this point on your own fork!
This is your testing environment, so don't worry about breaking it- that helps you learn!
If your program becomes irreparable, you can always clone or download it again.

### 6. Make a Pull Request

At this point, you should ensure that your branch is up to date with the most current master branch of Lab-Assistant.
Doing this will ensure that the feature or fix you have written will work with the current version.
Then update your feature branch from your local copy of master, and push it!

Finally, go to GitHub and
[make a Pull Request](https://help.github.com/articles/creating-a-pull-request)!

The retainers of this repository will run your code through a few tests to ensure that it is working correctly and
will added it to the master branch as soon as it's been verified and is beneficial to what is being done.

We really appreciate all your help!

repository: todun/Lab-Assistant-IO

repo_name: todun/Lab-Assistant-IO


text:
# MCKP - Multiple-Choice Knapsack Problem

Project for the course of "[Problem Solving: Optimization Methods and Algorithms](https://www.unibo.it/en/teaching/course-unit-catalogue/course-unit/2018/384256)" AA.2018/2019

## About the project
The multiple-choice knapsack problem (MCKP) is a generalization of the ordinary knapsack problem, where the set of items is partitioned into classes. The binary choice of taking an item is replaced by the selection of exactly one item out of each class of items.

Solution implemented by using **dinamic programming**.

## Use Requirements
- g++ compile 8.2.0

## Build project [script: work in progress]
- **Dependences**:
`
g++ -std=c++11 Main.cpp ComputeSolution.h ComputeSolutionImp.cpp MultipleChoiceKnapsackProblem.cpp OneOrNoneForClass.cpp PrintMatrix.cpp UtilsVectors.h
`

- **Build**:
`
g++ -o main Main.cpp ComputeSolution.h ComputeSolutionImp.cpp MultipleChoiceKnapsackProblem.cpp OneOrNoneForClass.cpp PrintMatrix.cpp UtilsVectors.h
`

## Run project
It is possible to start the computation from the command line with the command:
`
  ./main
`

**Nota**: first it's necessary to build the project.

## Documentation [ITA]
A brief presentation of the system can be found in [doc folder](https://github.com/chiara-volonnino/mckp/tree/master/doc)

repository: dotrado/mckp

repo_name: dotrado/mckp