text:
# Swagger harvester-api

## Overview
Harvester App API for Mobile app to manage harvest quotas, reports and track crop harvest progeress.
This Node app demonstrates use of 12 factor app methodology and Swagger for API design and documentation. The fetures of this example include a web template with REST API, PostgresDB access methods using SQL, promise and ORM methods. This app also demonstartes use of pipelines, github for deployment, and Express and Authentication with Social logins.

For quick startup use this Heroku button below.

[![Deploy](https://www.herokucdn.com/deploy/button.svg)](https://heroku.com/deploy?template=https://github.com/iandrosov/harvester-api)

This server was generated by the [swagger-codegen](https://github.com/swagger-api/swagger-codegen) project.  By using the [OpenAPI-Spec](https://github.com/OAI/OpenAPI-Specification) from a remote server, you can easily generate a server stub.  This is an example of building a node.js server.

This example uses the [expressjs](http://expressjs.com/) framework.

### Running the server
To run the server, run:

```
npm start
```

To view the Swagger UI interface:

```
open http://localhost:8080/docs
```

# Heroku Deployment

This process uses automatic git deploy to Heroku to push builds on dev, stage or prod.
Typical setup can be PROD to master branch, STAGE linked to stage branch and DEV linked to dev branch.

However, this can lead to deployed targets being out of sync especially when using Heroku Pipelines. We found that simple and more stable way to use auto deploy with pipelines is to link one of your envionments to github for deploy. Then once app is tested use pipeline promotion to push the builds upstrem to stage and prod.

In this example we link DEV with master branch.
Then following git flow to push builds to Heroku. Creating pul requests for features is good method to test with review apps. New pull request can crreate a new review app for validation. After review app is confirmed this pull request can be merged to master and this will push the buld to dev. Then we can promote this buuild via pipeline to stage for QA and then prod environments.

This project leverages the mega-awesome [swagger-tools](https://github.com/apigee-127/swagger-tools) middleware which does most all the work.


text:
materialui-component-autocomplete [![Release](https://img.shields.io/badge/version-0.1.0-orange.svg)](https://github.com/Pushpamk/materialui-component-sublime-autocomplete/releases/tag/0.1.0)
=============

### MaterialUi Component Sublime Plugin

Auto-complete plugin for MaterialUi Component props .

### MaterialUi

MaterialUi is a React components for faster and easier web development. Build your own design system, or start with Material Design.

* Homepage: [https://material-ui.com/](https://material-ui.com/)

### Installation

You can clone this repository to the Packages directory of your Sublime installation.

```bash
git clone https://github.com/Pushpamk/materialui-component-sublime-autocomplete.git
```

OR

Download and paste in Installed Packages from [release](https://github.com/Pushpamk/materialui-component-sublime-autocomplete/releases/tag/0.1.0)

### Usage

**Props name autocompletion**: When inside double quotes of a class attribute (`class="[HERE]"`), suggestions will appear for all `uk-` classes.

![guide-1](test/tutorial.gif)
![guide-2](test/tutorial.png)

**Component attributes autocompletion**: When inside a tag, but outside of a quoted attribute string (`<div [HERE] >`), suggestions will appear for all props.

**Note**: In order for the suggestions to appear, your document needs to have the syntax set to `JS`. You can set the syntax from the menu *View > Syntax* or via the Command Palette.

To make a new version of the plugin available via package control, a new version tag needs to be created.

## Copyright and license

Licensed under the [MIT License](LICENSE)

license: MIT

branch_name: refs/heads/master

extension: md

license_type: permissive


text:
# [![](https://pushme-pullyou.github.io/tootoo-2021/lib/assets/icons/mark-github.svg )](https://github.com/theo-armour/sdg-2021/ "Source code on GitHub" ) [Sustainable Development Goals 2021 Read Me]( https://theo-armour.github.io/sdg-2021/ "Home page" )

## The Goals

1. [No poverty]( https://theo-armour.github.io/sdg-2021/#sdg-overview/01-no-poverty.md )
2. [Zero hunger]( https://theo-armour.github.io/sdg-2021/#sdg-overview/02-zero-hunger.md)
3. [Good health and well being]( https://theo-armour.github.io/sdg-2021/#sdg-overview/03-good-health-and-well-being.md )
4. [Quality education]( https://theo-armour.github.io/sdg-2021/#sdg-overview/04-quality-education.md )
5. [Gender equality]( https://theo-armour.github.io/sdg-2021/#sdg-overview/05-gender-equality.md )
6. [Clean water and sanitation]( https://theo-armour.github.io/sdg-2021/#sdg-overview/06-clean-water-and-sanitation.md )
7. [Affordable and clean energy]( https://theo-armour.github.io/sdg-2021/#sdg-overview/07-affordable-and-clean-energy.md )
8. [Decent work and economic growth]( https://theo-armour.github.io/sdg-2021/#sdg-overview/08-decent-work-and-economic-growth.md )
9. [Industry, innovation and infrastructure]( https://theo-armour.github.io/sdg-2021/#sdg-overview/08-decent-work-and-economic-growth.md)]
10. [Reduced inequalities]( https://theo-armour.github.io/sdg-2021/#sdg-overview/10-reduced-inequalities.md )
11. [Sustainable cities and communities]( https://theo-armour.github.io/sdg-2021/#sdg-overview/11-sustainable-cities-and-communities.md )
12. [Responsible consumption and production]( https://theo-armour.github.io/sdg-2021/#sdg-overview/12-responsible-consumption-and-production.md )
13. [Climate action]( https://theo-armour.github.io/sdg-2021/#sdg-overview/13-climate-action.md )
14. [Life below water]( https://theo-armour.github.io/sdg-2021/#sdg-overview/14-life-below-water.md )
15. [Life on land]( https://theo-armour.github.io/sdg-2021/#sdg-overview/15-life-on-land.md )
16. [Peace, justice and strong institutions]( https://theo-armour.github.io/sdg-2021/#sdg-overview/16-peace-justice-and-strong-institutions.md )
17. [Partnerships for the goals]( https://theo-armour.github.io/sdg-2021/#sdg-overview/17-partnerships-for-the-goals.md )

## Links of Interest

* https://ta.medium.com/awesome-list-of-united-nations-sustainable-development-goals-33ff192f0e85

### UN

* https://sdgs.un.org/goals ~

* https://unstats.un.org/SDGAPI/swagger/
* https://sustainabledevelopment.un.org/globalsdreport
* https://unstats.un.org/sdgs/
* https://unstats.un.org/sdgs/report/2020/

the Brundtland Report ~ 1987

* http://www.un-documents.net/wced-ocf.htm
* http://www.un-documents.net/our-common-future.pdf
* https://en.wikipedia.org/wiki/Our_Common_Future

Agenda 21

* https://en.wikipedia.org/wiki/Agenda_21

Rio Declaration

* https://en.wikipedia.org/wiki/Rio_Declaration_on_Environment_and_Development
* https://en.wikipedia.org/wiki/United_Nations_Conference_on_Sustainable_Development ~ 2012

More
* https://sdg.data.gov/
* https://en.wikipedia.org/wiki/Commission_on_Sustainable_Development
* https://en.wikipedia.org/wiki/Three_generations_of_human_rights
* https://www.wbgu.de/fileadmin/user_upload/wbgu/publikationen/hauptgutachten/hg2019/pdf/Charter_EN_WBGU.pdf

### Trends

* https://trends.google.com/trends/explore?date=all&geo=US&q=%2Fm%2F0r8knlf

### California

* https://sdg.lamayor.org/about-la-sdgs
* https://ajed.assembly.ca.gov/content/sustainable-development-goals
* http://bay.stanford.edu/sdg

## Green Party

* Green Party USA: https://www.gp.org/platform
	* Written before SDGs??

## Union des Associations Internationales

* https://uia.org/
* https://uia.org/s/sdg/en/ ~ Wow! looks like a great resource
* http://encyclopedia.uia.org/en ~ The Encyclopedia of World Problems and Human Potential

## More

* https://www.iisd.org/
* https://www.iisd.org/about-iisd/sustainable-development?q=topic/sustainable-development
* https://www.globalgoals.org/
* https://sdg-pathfinder.org/
* https://www.sdgactioncampaign.org/
* http://www.sustainablefriends.com/what-is-environmental-sustainability-why-is-it-important/

## To Do / Wish List

## Issues

## Change Log

### 2021-06-27

SDG 2021-06-27

### 2021-02-20

* First commit Readme

***

 ❦


text:
mapping with the dot
--------------------
:nnoremap <leader>g :grep -R something .<cr>
If you've read :help grep this should be pretty easy to understand. We've looked at lots of mappings before, and there's nothing new here.

something something

something somthing something
something somthing something
something somthing something
something somthing something

Obviously we're not done yet, so let's refine this mapping until it meets our simplified goal.

The Search Term
First we need to search for the word under the cursor, not the string something. Run the following command:
adding a search term
---------------------
:nnoremap <leader>g :grep -R <cword> .<cr>
Now try it out. <cword> is a special bit of text you can use in Vim's command-line mode, and Vim will replace it with "the word under the cursor" before running the command.

You can use <cWORD> to get a WORD instead of a word. Run this command:

using <cWORD>
----------------
:nnoremap <leader>g :grep -R <cWORD> .<cr>
Now try the mapping when your cursor is over something like foo-bar. Vim will grep for foo-bar instead of just part of the word.

operator-pending mappings
==========================

The original vi did not have separate mappings for
Normal/Visual/Operator-pending mode and for Insert/Command-line mode.
Therefore the ":map" and":map!" commands enter and display mappings for
several modes.  In Vim you can use the ":nmap", ":vmap", ":omap", "cmap" and
":imap" commands to enter mappings for each mode separately.

text for testing onoremap
----------------------------
return person.get_pets(type="cat", fluffy_only=True)
return person.get_pets(type="cat", fluffy_only=True)
return person.get_pets(type="cat", fluffy_only=True)
return person.get_pets()

map1
------------
onoremap p i(
type dp

using :copen
--------------

repository: gkeary/vim-gregg

extension: markdown

repo_name: gkeary/vim-gregg


text:
# FEIP0001

## Introdction
FEIP defines the ID and LABELs of a user from Freecash ecology.

## Protocol
|Name|Type|Length|Content|
|:--:|:--:|:----:|:------|
|Protocol name|bytes|4|0x46,0x45,0x49,0x50 (The HEX of "FEIP")|
|Protocol number|bytes|4|0x30,0x30,0x30,0x31 (The HEX of "0001")|
|operation|bytes|4||
|Nickname|bytes|1-16||
|Labels|bytes|0-200|Begins with '0x23',can be multiple|

## Operation
|Name|Code|Description|
|:--:|:----:|:------|
|Create/Update|0000|Create or Update the ID for a user|
|Destroy|0001|Report the loss of private key|

## OP_RETURN Example
Create an ID:
```
FEIP00010000Deisler-JJ#Developer
FEIP00010000Otto#Tech manager#Developer@Freecash First Foundation
```

Report the loss of private key:
```
FEIP00010001
```

## Recommended presentation format in Dapps
```
Deisler-JJ@Ly6j
```
`Ly6j` is the last four characters of the input address of FEIP0001 transaction.
## Transaction Protocol
Send at least 0.01 FCH to account `FCEfmLBfZ7C7YUzQxdp5bJ68ogN3dKRnSw`.

The hash160 of `FCEfmLBfZ7C7YUzQxdp5bJ68ogN3dKRnSw` is `4645495030303031303030303030303030303030`(HEX).

The FCH in `FCEfmLBfZ7C7YUzQxdp5bJ68ogN3dKRnSw` will be destroyed,because no one knows the private key.

## Note
If there are more than one inputs in the FEIP0001 transaction,the address of First input will be bound to the Nickname.

repository: freefch/FEIP

gha_license_id: MIT

repo_name: freefch/FEIP


text:
# zombie-lord

Zombie lord is a controller for a set of headless browser instances running somewhere (in the cloud, on your device, wherever).

The zombie lord can accept incoming requests (over websocket, or http) and uses its minions to run tasks.

## Notes for install

Note that when we install Litewait/Zombie Lord into a user's directory, all that needs to be done is gclone litewait (or cp an existing repo) and call npm i (which will rebuild each node_modules for each module).

But the first time we install on a new instance / system, we need to install chrome (dl_chrome.sh) and do (deb.deps) for chrome first (or a diff deps for other systems, I think aws install can also be used for centos type systems). And also do font.deps (to make sure we have emojis and chinese fonts etc).

Perhaps these font deps are not important for things like litewait / app minifications / text only, but I think they still matter.

Also we can always turn on sending screenshot frames, so font's do matter. And having them there probably prevents weird render issues anyway that might create instability for chrome.


text:
#Vimeo

*embed Vimeo video's onto your website*

This app allows Vimeo video's to be embedded directly onto your site, so people can see videos you've made, or maybe just a few of your favorites. This app supports autoplay and multiple videos on one page

![Imgur](http://i.imgur.com/mMuEjqo.png)

repository: cloudflare-apps/vimeo

repo_name: cloudflare-apps/vimeo


text:
# Stasher

Stasher is an in-memory reimplementation of the Google App Engine datastore. It is written in C++ and is designed as a replacement datastore stub when running unit tests. The project aims are:

 - To massively outperform the SQLite stub by not serializing to protobufs
 - To provide a Python API for monkey-patching the Python GAE testbed
 - To use an architecture that will allow for persistent storage in future

# Building

Stasher requires CMake. To build do the following:

- mkdir build && cd build
- cmake ..
- make
- ./tests/stasher_tests

I'm trying to avoid dependencies where possible :)

repository: Kazade/stasher

repo_name: Kazade/stasher


text:
# README Generator
  [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)

  Table of Contents |
  ----------------- |
  [Description](#description) |
  [Installation](#installation) |
  [Usage](#usage) |
  [License](#license) |
  [Contributions](#contributions) |
  [Questions](#questions) |

  ## Description
  This is a command line README Generator. Open your terminal, install the required packages, then run "node index" to start the program. Answer the prompts and the program will create a README file for your next project, presented in a professional and easy to read format.

  ## Installation
  Download source code, open terminal, enter ``` npm i ```, enter ``` node index ```

  ## Usage
  Used for creating an auto-formatted README file. Video for demonstration:

  ![Demo](assets/demo-video.gif)
  Link to video: https://drive.google.com/file/d/1GtF-Bk2VFGkeuvK9WBJyo8gRscAANdg0/view

  ## License
  This application is licensed under MIT open-source licensing.

  ## Contributions
  Contributors: Emily Tassoni

  If you wish to contribute, please contact the email below. You may fork this repository to use for testing purposes. Thank you!

  ## Questions
   Please contact [tassoni13<EMAIL_ADDRESS>with questions.
   Visit my [GitHub](https://github.com/tassoni13) to see past and current projects.

   * This README file was generated with this README Generator

branch_name: refs/heads/main


text:
# Custom Bedrock with WP Project Scaffolding MU-Plugin

This is a customized fork of the popular [Bedrock](https://roots.io/bedrock/) boilerplate for WordPress projects. [Read the original Bedrock readme...](https://github.com/roots/bedrock)

## Customizations

* bundles a must-use plugin called **WP Project Scaffolding** to handle a lot of basic project functionality
  * it contains the following base classes (these are always loaded)
    * *Config* - the usual WordPress configuration, but more flexible: constants are automatically defined from environment vars, from the `composer.json` file's `->extra->constants` and from default values, in that priority (this class is instantiated from `wp-config.php`)
    * *PluginAutoloader* - allows loading must-use plugins just like normal plugins (copied from the original Bedrock project)
    * *Sunrise* - multisite domain mapping (this class is instantiated from `sunrise.php`)
    * *ThemeFallback* - handles the default theme directory (copied from the original Bedrock project)
  * it contains the following module classes (optional, can be enabled via `composer.json` file's `->extra->settings`; those settings are passed to the respective class which handles them)
    * *AutoUpdater* - handles WordPress Auto Updates, allows to easily adjust which parts of WordPress are auto-updated
    * *GithubUpdater* - if the Github Updater is used, this allows to specify access tokens to private repositories and more
* `/config/` directory is gone, configuration is instead handled by the custom *Config* class (the three environments from the original Bedrock are still supported though)
* WordPress is now installed into `/web/core/` instead of `/web/wp`
* some dependencies have been adjusted

repository: felixarntz/bedrock

gha_language: PHP

repo_name: felixarntz/bedrock


text:
### [CVE-2020-13563](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-13563)
![](https://img.shields.io/static/v1?label=Product&message=phpGACL&color=blue)
![](https://img.shields.io/static/v1?label=Version&message=n%2Fa&color=blue)
![](https://img.shields.io/static/v1?label=Vulnerability&message=CWE-80%3A%20Improper%20Neutralization%20of%20Script-Related%20HTML%20Tags%20in%20a%20Web%20Page%20(Basic%20XSS)&color=brighgreen)

### Description

A cross-site scripting vulnerability exists in the template functionality of phpGACL 3.3.7. A specially crafted HTTP request can lead to arbitrary JavaScript execution. An attacker can provide a crafted URL to trigger this vulnerability in the phpGACL template group_id parameter.

### POC

#### Reference
- https://talosintelligence.com/vulnerability_reports/TALOS-2020-1177

#### Github
No PoCs found on GitHub currently.

repository: trickest/cve

gha_language: HTML

repo_name: trickest/cve


text:
# How to save a servable model of Paddle Serving?

([简体中文](./SAVE_CN.md)|English)

## Save from training or prediction script
Currently, paddle serving provides a save_model interface for users to access, the interface is similar with `save_inference_model` of Paddle.
``` python
import paddle_serving_client.io as serving_io
serving_io.save_model("imdb_model", "imdb_client_conf",
                      {"words": data}, {"prediction": prediction},
                      fluid.default_main_program())
```
`imdb_model` is the server side model with serving configurations. `imdb_client_conf` is the client rpc configurations.

Serving has a dictionary for `Feed` and `Fetch` variables for client to assign. In the example, `{"words": data}` is the feed dict that specify the input of saved inference model. `{"prediction": prediction}` is the fetch dic that specify the output of saved inference model. An alias name can be defined for feed and fetch variables. An example of how to use alias name
 is as follows:
 ``` python
 from paddle_serving_client import Client
import sys

client = Client()
client.load_client_config(sys.argv[1])
client.connect(["<IP_ADDRESS>:9393"])

for line in sys.stdin:
    group = line.strip().split()
    words = [int(x) for x in group[1:int(group[0]) + 1]]
    label = [int(group[-1])]
    feed = {"words": words, "label": label}
    fetch = ["acc", "cost", "prediction"]
    fetch_map = client.predict(feed=feed, fetch=fetch)
    print("{} {}".format(fetch_map["prediction"][1], label[0]))
 ```

## Export from saved model files
If you have saved model files using Paddle's `save_inference_model` API, you can use Paddle Serving's` inference_model_to_serving` API to convert it into a model file that can be used for Paddle Serving.
```python
import paddle_serving_client.io as serving_io
serving_io.inference_model_to_serving(dirname, serving_server="serving_server", serving_client="serving_client", model_filename=None, params_filename=None )
```
Or you can use a build-in python module called `paddle_serving_client.convert` to convert it.
```python
python -m paddle_serving_client.convert --dirname ./your_inference_model_dir
```
Arguments are the same as `inference_model_to_serving` API.
| Argument | Type | Default | Description |
|--------------|------|-----------|--------------------------------|
| `dirname` | str | - | Path of saved model files. Program file and parameter files are saved in this directory. |
| `serving_server` | str | `"serving_server"` | The path of model files and configuration files for server. |
| `serving_client` | str | `"serving_client"` | The path of configuration files for client. |
| `model_filename` | str | None | The name of file to load the inference program. If it is None, the default filename `__model__` will be used. |
| `paras_filename` | str | None | The name of file to load all parameters. It is only used for the case that all parameters were saved in a single binary file. If parameters were saved in separate files, set it as None. |

repository: MRXLT/Serving

branch_name: refs/heads/develop

repo_name: MRXLT/Serving


text:
# Production-ish Server

None of this has anything to do with React Router, but since we're
talking about web servers, we might as well take it one step closer to
the real-world. We'll also need it for server rendering in the next
section.

Webpack dev server is not a production server. Let's make a production
server and a little environment-aware script to boot up the right server
depending on the environment.

Let's install a couple modules:

```
npm install express if-env compression --save
```

First, we'll use the handy `if-env` in `package.json`.   If yes, it runs `npm run start:prod`, if not, it runs
`npm run start:dev`.

Now we're ready to create a production server with Express and add a new file at root dir. Here's a
first attempt:

```js
// server.js
var express = require('express')
var path = require('path')
var compression = require('compression')

var app = express()

// serve our static stuff like index.css
app.use(express.static(__dirname))

// send all requests to index.html so browserHistory in React Router works
app.get('*', function (req, res) {
  res.sendFile(path.join(__dirname, 'index.html'))
})

var PORT = process.env.PORT || 8080
app.listen(PORT, function() {
  console.log('Production Express server running at localhost:' + PORT)
})
```

Now run:

```sh
NODE_ENV=production npm start
# For Windows users:
# SET "NODE_ENV=production" && npm start
```

Congratulations! You now have a production server for this app. After
clicking around, try navigating to [http://localhost:8080/package.json](http://localhost:8080/package.json).
Whoops.  Let's fix that. We're going to shuffle around a couple files and
update some paths scattered across the app.

1. make a `public` directory.
2. Move `index.html` and `index.css` into it.

Now let's update `server.js` to point to the right directory for static
assets:

```js
// server.js
// ...
// add path.join here
app.use(express.static(path.join(__dirname, 'public')))

// ...
app.get('*', function (req, res) {
  // and drop 'public' in the middle of here
  res.sendFile(path.join(__dirname, 'public', 'index.html'))
})
```

We also need to tell webpack to build to this new directory:

```js
// webpack.config.js
// ...
output: {
  path: 'public',
  // ...
}
```

And finally (!) add it to the `--content-base` argument to `npm run start:dev` script:

```json
"start:dev": "webpack-dev-server --inline --content-base public --history-api-fallback",
```

If we had the time in this tutorial, we could use the `WebpackDevServer`
API in a JavaScript file instead of the CLI in an npm script and then
turn this path into config shared across all of these files. But, we're
already on a tangent, so that will have to wait for another time.

Okay, now that we aren't serving up the root of our project as public
files, let's add some code minification to Webpack and gzipping to
express.

```js
// webpack.config.js

// make sure to import this
var webpack = require('webpack')

module.exports = {
  // ...

  // add this handful of plugins that optimize the build
  // when we're in production
  plugins: process.env.NODE_ENV === 'production' ? [
    new webpack.optimize.DedupePlugin(),
    new webpack.optimize.OccurrenceOrderPlugin(),
    new webpack.optimize.UglifyJsPlugin()
  ] : [],

  // ...
}
```

And compression in express:

```js
// server.js
// ...
var compression = require('compression')

var app = express()
// must be first!
app.use(compression())
```

Now go start your server in production mode:

```
NODE_ENV=production npm start
```

You'll see some UglifyJS logging and then in the browser, you can see
the assets are being served with gzip compression.

---

[Next: Navigating](../12-navigating/)

repository: yisainan/react

gha_language: JavaScript

repo_name: yisainan/react


text:
# AWS ECR Login

This script and container aid in loging into AWS EC2 Container Registries (ECR) and updating the docker.tar.gz files on a host (for Docker 1.6+)

## Script Usage
```
./ecr-login.sh [options]
    -r|--region        If not provided, the region will be looked up via AWS metadata (optional on EC2-only).
    -g|--registries    The AWS account IDs to use for the login. Space separated. (Ex: "123456789101,<PHONE_NUMBER>1")
    -f|--file-location Where the docker.tar.gz should be saved.
    -i|--interval      How often to loop and refresh credentials (optional - default is 21600 - 6 hours).
```

The script can be run from within a Docker container or stand-alone on either AWS infrastructure or traditional servers. When run in a container, you must mount the host path where you wish the `docker.tar.gz` file to be written.

### Docker Container
```
$ docker pull adobeplatform/aws-ecr-login
$ docker run --name aws-ecr-login -v /home/core:/home/core:rw -e "REGION=us-east-1" -e "REGISTRIES=12345678901" -e "FILE_LOCATION=/home/core/docker.tar.gz" -e "INTERVAL=21600"
```

## Usage on Marathon
To run this on Marathon 1.7/1.8 or newer, here's a sample JSON:

(Following example is derived from running Marathon on CoreOS)

```
{
  "volumes": null,
  "id": "/ecr-login",
  "cmd": null,
  "args": null,
  "user": null,
  "env": {
    "REGISTRIES": "<ACCOUNT NUMBER (e.g.<PHONE_NUMBER>89 from ECR repo)>",
    "FILE_LOCATION": "/home/core/docker.tar.gz",
    "AWS_ACCESS_KEY_ID": "<FILL THIS IN>",
    "REGION": "<FILL THIS IN>",
    "AWS_SECRET_ACCESS_KEY": "<FILL THIS IN>",
    "AWS_DEFAULT_REGION": "us-east-1",
    "INTERVAL": "21600"
  },
  "instances": 4,
  "cpus": 0.01,
  "mem": 64,
  "disk": 0,
  "gpus": 0,
  "executor": null,
  "constraints": [
    [
      "hostname",
      "UNIQUE"
    ],
    [
      "hostname",
      "GROUP_BY"
    ]
  ],
  "fetch": null,
  "storeUrls": null,
  "backoffSeconds": 1,
  "backoffFactor": 1.15,
  "maxLaunchDelaySeconds": 3600,
  "container": {
    "docker": {
      "image": "wistiaanders/aws-ecr-login:latest",
      "forcePullImage": true,
      "privileged": false,
      "network": "HOST"
    },
    "type": "DOCKER",
    "volumes": [
      {
        "containerPath": "/home/core",
        "hostPath": "/home/core",
        "mode": "RW"
      }
    ]
  },
  "healthChecks": [
    {
      "protocol": "COMMAND",
      "command": {
        "value": "if [[ $(expr $(date +%s) - $(date +%s -r /home/core/docker.tar.gz)) -gt 21600 ]]; then exit 1; else exit 0; fi"
      },
      "gracePeriodSeconds": 60,
      "intervalSeconds": 60,
      "timeoutSeconds": 20,
      "maxConsecutiveFailures": 2,
      "portType": "PORT_INDEX"
    }
  ],
  "readinessChecks": null,
  "dependencies": null,
  "upgradeStrategy": {
    "minimumHealthCapacity": 1,
    "maximumOverCapacity": 1
  },
  "labels": {},
  "acceptedResourceRoles": [
    "slave_public",
    "*"
  ],
  "ipAddress": null,
  "residency": null,
  "secrets": null,
  "taskKillGracePeriodSeconds": null,
  "portDefinitions": [
    {
      "protocol": "tcp",
      "port": 10000
    }
  ],
  "requirePorts": false
}
```

Service deploys on Marathon that require ECR credentials will need to reference docker.tar.gz (or in this case `file:///home/core/docker.tar.gz`) in the URI field - so Marathon can pull the credentials before pulling off your desired Docker image off of ECR.

### Usage on AWS Infrastructure
Running on AWS means that the region can be determined automatically via the AWS metadata service and does not need to be provided via the command line.

### Usage on Traditional Servers
Running on traditional servers means that the region must be provided. Failure to provide the region will result in the script hanging as it attempts to contact the (unavailable) AWS metadata service.

```
$ ./ecr-login.sh --region us-east-1 --registries<PHONE_NUMBER>1 --file-location /home/core/docker.tar.gz --interval 21600
```

repository: chen-anders/marathon-ecr-login

repo_name: chen-anders/marathon-ecr-login


text:
###changes made in the project

add features and components folders into client/app section

move admin and dashboard folders into features folder

change routes in admin.route.spec and admin.route to reflect where our files have changed path as we have moved folders around, same with dashboard.route.spec and with dashboard.route, then restart gulp serve-dev to allow wiredep to refind links, wiredep will do this automatically.

remove modules from app/app.modules

copy and paste app.module into features folder and change name to features.module.js (check project to see how its done) change name in admin and features to app.features.admin and app.features.dashboard. this needs to be changed in all files except html file. re-serve gulp.

add in module 'app.features' into app.module. (main one)

features.module should be .module('app.features', [etc...])

go into dashboard.html and we can start taking out bootstrap items and simply leave scaffolding on the page and add in components to be dropped onto the page. We're going to ceate a people grid and refactor out the people widget (row 29 - 54)

create new folder in components. called peoplegrid.

copy features module into components folder and add in module app.components, with app.components.peopleGrid as the first dependency injection.

then copy components module into people grid folder.

into peoplegrid add html file and directive file.

write out directive file and give it a named function. (see how it's done in project, look at file); templateUrl needs to have a path from app folder. the static path already points at client, so that's where it starts.

repository: Bbouley/hottowel-angular-workshop

repo_name: Bbouley/hottowel-angular-workshop


text:
---
title: F.A.Q.
sitemap:
  changefreq: monthly
  priority: 0.5
---

## Where can I download karaoke songs?

Below is a non-exhaustive, probably U.S.-centric list of sources for licensed MP3+G or MP4 files (additions welcome):

- [SunFly Karaoke](https://www.sunflykaraoke.com)
- [PartyTyme Karaoke](https://www.partytyme.net)
- [Karaoke Version](https://www.karaoke-version.com)
- [All Star Karaoke](https://www.allstardl.com)
- [SBI Karaoke](https://downloads.sbikaraoke.com)
- [PCDJ](https://www.pcdj.com/hd-mp4-karaoke-download-packs/)

Karaoke tracks require original recording and production. Please support the creators!

## Is a microphone required?

No, but... is it still karaoke without a mic? :)

Karaoke Forever makes no assumptions about audio input so that it can work with any microphone setup, including none at all. The <a href='{{< ref "docs/index.md#player" >}}'>player's output (music) can be mixed with microphones in software or an outboard mixer (see below).

## What's the recommended audio setup?

There are generally 2 ways to mix Karaoke Forever's player output (the music) with microphones. In either case, at least 2 mics are recommended.

  - Software mixing: The system running the player uses a USB or Thunderbolt audio interface that has mic(s) connected.
  - Hardware mixer: An external/outboard mixer has mic(s) connected, as well as the audio from the system running the player.

license: ISC

repository: rpelletierrefocus/karaoke-forever

gha_license_id: ISC

repo_name: rpelletierrefocus/karaoke-forever


text:
# TiDB Design Documents

Many changes, including bug fixes and documentation improvements can be implemented and reviewed via the normal GitHub pull request workflow.

Some changes though are "substantial", and we ask that these be put through a bit of a design process and produce a consensus among the TiDB community.

The process described in this page is intended to provide a consistent and controlled path for new features to enter the TiDB projects, so that all stakeholders can be confident about the direction the projects is evolving in.

## Who should initiate the design document?

Everyone is encouraged to initiate a design document, but before doing it, please make sure you have an intention of getting the work done to implement it.

## Before creating a design document

A hastily-proposed design document can hurt its chances of acceptance. Low-quality proposals, proposals for previously-rejected features, or those that don't fit into the near-term roadmap, may be quickly rejected, which can be demotivating for the unprepared contributor. Laying some groundwork ahead of the design document can make the process smoother.

Although there is no single way to prepare for submitting a design document, it is generally a good idea to pursue feedback from other project developers beforehand, to ascertain that the design document may be desirable; having a consistent impact on the project requires concerted effort toward consensus-building.

The most common preparations for writing and submitting a design document for now is [creating an issue for discussion](https://github.com/pingcap/tidb/issues/new/choose), which is going to be converted into a tracking issue of the design implementation.

## What is the process?

1. Create a pull request with a design document based on the [template](./TEMPLATE.md) under this directory as `YYYY-MM-DD-my-feature.md`.
2. Discussion takes place, and the text is revised in response.
3. The design document is accepted or rejected when at least two committers reach consensus and no objection from the committer.
4. If accepted, [create a tracking issue](https://github.com/pingcap/tidb/issues/new/choose) for the design document or convert one from a previous discuss issue. The tracking issue basically tracks subtasks and progress. And refer the tracking issue in the design document replacing placeholder in the template.
5. Merge the pull request of design.

Please update the tracking issue according to the progress of succeeding implementation pull requests.

An example that almost fits into this model is the proposal "Support global index for partition table", without following the latest template.

- Its tracking issue: https://github.com/pingcap/tidb/issues/18032
- Its pull request: https://github.com/pingcap/tidb/pull/18982
- Its design document: https://github.com/pingcap/tidb/blob/master/docs/design/2020-08-04-global-index.md

repository: pingcap/tidb

gha_language: Go

repo_name: pingcap/tidb


text:
# blockchainsudoku

A Sudoku grid represented in the Blockchain.
Simply send Bitcoin to the relevant wallet to change the value of the grid. The cell value is the first character of the received balance in the particular wallet. For example, if the balance is 400 Satoshi, the value displayed is 4. 2356 would display a 2 etc.

Files:
index.php - provides a basic rendering of the Sudoku grid from data.csv, and list of the wallet addresses from wallets.csv.
getBalance.php - gets the current received Satoshi from the wallets. Recommended to be scheduled as a cron job. This script saves the current received balances to balance.csv, then copies it over data.csv. This prevents unnecesary requests through the API.
wallets.csv - defines the wallets and their positions on the grid.
balance.csv - stores the received balances of the wallets.
data.csv - a copy of balance.csv

Remember to give the getBalance.csv 755 permissions, and also set the path of where you have stored the CSV's.

repository: blockchainsudoku/blockchainsudoku

repo_name: blockchainsudoku/blockchainsudoku


text:
# Phellinus quercinus (J.D. Zhao) J.D. Zhao, 1992 SPECIES

#### Status
SYNONYM

#### According to
The Catalogue of Life, 3rd January 2011

#### Published in
Biblthca Mycol. 145: 350 (1992)

#### Original name
Fuscoporia quercina J.D. Zhao, 1986

### Remarks
null

repository: mdoering/backbone

path: /life/Fungi/Basidiomycota/Agaricomycetes/Hymenochaetales/Hymenochaetaceae/Phellinus/Phellinus neoquercinus/ Syn. Phellinus quercinus/README.md

repo_name: mdoering/backbone


text:
# Change Log

All notable changes to this project will be documented in this file.
See [Conventional Commits](https://conventionalcommits.org) for commit guidelines.

## [0.1.64](https://github.com/thi-ng/umbrella/compare/@thi.ng/sparse@0.1.63<EMAIL_ADDRESS>(2021-01-22)

**Note:** Version bump only for package @thi.ng/sparse

## [0.1.63](https://github.com/thi-ng/umbrella/compare/@thi.ng/sparse@0.1.62<EMAIL_ADDRESS>(2021-01-21)

**Note:** Version bump only for package @thi.ng/sparse

## [0.1.62](https://github.com/thi-ng/umbrella/compare/@thi.ng/sparse@0.1.61<EMAIL_ADDRESS>(2021-01-13)

**Note:** Version bump only for package @thi.ng/sparse

## [0.1.61](https://github.com/thi-ng/umbrella/compare/@thi.ng/sparse@0.1.60<EMAIL_ADDRESS>(2021-01-10)

**Note:** Version bump only for package @thi.ng/sparse

## [0.1.60](https://github.com/thi-ng/umbrella/compare/@thi.ng/sparse@0.1.59<EMAIL_ADDRESS>(2021-01-02)

**Note:** Version bump only for package @thi.ng/sparse

## [0.1.59](https://github.com/thi-ng/umbrella/compare/@thi.ng/sparse@0.1.58<EMAIL_ADDRESS>(2020-12-22)

**Note:** Version bump only for package @thi.ng/sparse

# 0.1.0 (2019-02-17)

### Features

* **sparse:** add CSC, update all other matrix impls, remove adjacency ([cd773c9](https://github.com/thi-ng/umbrella/commit/cd773c9))
* **sparse:** re-import & update [@thi](https://github.com/thi).ng/sparse (MBP2010) ([a2d1cc9](https://github.com/thi-ng/umbrella/commit/a2d1cc9))


text:
# ImageLetterIcon
Material letter icon with circle background. Also supports for image for user contact.

[![Android Arsenal](https://img.shields.io/badge/Android%20Arsenal-ImageLetterIcon-green.svg?style=true)](https://android-arsenal.com/details/1/2762)

 [ ![Download](https://api.bintray.com/packages/akashandroid90/maven/image-letter-icon/images/download.svg) ](https://bintray.com/akashandroid90/maven/image-letter-icon/_latestVersion)

![Square Initials](images/one.png)
![Square Initials](images/two.png)
![Alternate Initial Square Image](images/three.png)
![Alternate Initial Circular Image](images/four.png)
Gradle
-------------------------

```

dependencies {
    compile 'com.github.akashandroid90:imageletter:1.8@aar'
}

```

ChangeLog
-----

bug fixes

vector drawable support

Usage
-----

Declare in XML (see xml attributes below for customization):

```xml
<com.github.akashandroid90.imageletter.MaterialLetterIcon
    android:layout_width="@dimen/letter_icon_size"
    android:layout_height="@dimen/letter_icon_size" />
```

Or static initializer (see xml attributes below for customization):

```java
MaterialLetterIcon icon = new MaterialLetterIcon.Builder(context) //
            .shapeColor(getResources().getColor(R.color.circle_color))
            .setShapeType(MaterialLetterIcon.SHAPE_CIRCLE)
            .letter("S")
            .letterColor(getResources().getColor(R.color.letter_color))
            .letterSize(26)
            .lettersNumber(1)
            .letterTypeface(yourTypeface)
            .initials(false)
            .initialsNumber(2)
            .create();
```

Configure using xml attributes or setters in code:

```java
app:shape_color="@color/black"      // shape color
app:shape_type="circle"             // shape type
app:letter=""                       // letter or string to get first letter from
app:letter_color="@color/white"     // letter color
app:letter_size="26"                // letter size SP
app:letters_number="1"              // number of letters to get from string
app:initials="false"                // turn on initials mode
app:initials_number="2"             // number of initials to be showed
app:corner_radius="1dp"             // radius of image to make at corner
app:is_oval="true"                  // to make a circular image
app:border_width="1dp"              // border width around the image
app:border_color="@color/black"     //border color around image
```

Donations
---------

This project needs you! If you would like to support this project's further development, the creator of this project or the continuous maintenance of this project, feel free to donate. Your donation is highly appreciated (and I love food, coffee and beer). Thank you!

**PayPal**

* **[Donate $5]**: Thank's for creating this project, here's a coffee (or some beer) for you!
* **[Donate $10]**: Wow, I am stunned. Let me take you to the movies!
* **[Donate $15]**: I really appreciate your work, let's grab some lunch!
* **[Donate $25]**: That's some awesome stuff you did right there, dinner is on me!
* **[Donate $50]**: I really really want to support this project, great job!
 This project saved me hours (if not days) of struggle and hard work, simply awesome!

Of course, you can also choose what you want to donate, all donations are awesome!

Developed By
--------------------
Akash Jain

[Donate $5]: 		https://www.paypal.me/akashandroid/5
[Donate $10]:  		https://www.paypal.me/akashandroid/10
[Donate $15]:  		https://www.paypal.me/akashandroid/15
[Donate $25]:  		https://www.paypal.me/akashandroid/25
[Donate $50]: 		https://www.paypal.me/akashandroid/50
[Donate $100]: 		https://www.paypal.me/akashandroid/100
[Donate $2799]: 	https://www.paypal.me/akashandroid/2799

repository: Subratsss/ImageLetterIcon

repo_name: Subratsss/ImageLetterIcon


text:
### [CVE-2018-16840](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2018-16840)
![](https://img.shields.io/static/v1?label=Product&message=curl&color=blue)
![](https://img.shields.io/static/v1?label=Version&message=n%2Fa&color=blue)
![](https://img.shields.io/static/v1?label=Vulnerability&message=CWE-416&color=brighgreen)

### Description

A heap use-after-free flaw was found in curl versions from 7.59.0 through 7.61.1 in the code related to closing an easy handle. When closing and cleaning up an 'easy' handle in the `Curl_close()` function, the library code first frees a struct (without nulling the pointer) and might then subsequently erroneously write to a struct field within that already freed struct.

### POC

#### Reference
No PoCs from references.

#### Github
- https://github.com/KorayAgaya/TrivyWeb
- https://github.com/Mohzeela/external-secret
- https://github.com/siddharthraopotukuchi/trivy
- https://github.com/simiyo/trivy
- https://github.com/t31m0/Vulnerability-Scanner-for-Containers
- https://github.com/umahari/security


text:
# Basic AMA App

Epicodus-JavaScript, Week 2 Independent Project 08.11.2017

## Description

This app allows the user to enter a question, and other users to enter answers to said question. The app displays questions as links, and can be answered on their individual pages.

## Prerequisites

You will need the following things properly installed on your computer.

* [Git](https://git-scm.com/)
* [Node.js](https://nodejs.org/) (with NPM)
* [Ember CLI](https://ember-cli.com/)
* [PhantomJS](http://phantomjs.org/)

You will also need a [Firebase](https://console.firebase.google.com/) account if you wish to use your own database. Follow prompts to add the app to your database. Edit the 'firebase' section of environment.js with your own information.

## Configuration/dependencies

* ember-cli
* emberfire
* ember-bootstrap

The app will use ember-cli and its myriad of pre-loaded assets.

## Setup/Installation

* `git clone <repository-url>` this repository
* `cd ama`
* `npm install`

## Running / Development

* `ember serve`
* Visit your app at [http://localhost:4200](http://localhost:4200).

### Code Generators

Make use of the many generators for code, try `ember help generate` for more details

### Running Tests

* `ember test`
* `ember test --server`

### Building

* `ember build` (development)
* `ember build --environment production` (production)

## Specifications

| Behavior | Input | Output |
|----------|-------|--------|
| User visits home page, chooses to ask question | Click ask question button | Home page displays form to enter question|
| User enters question | How do I install nodejs? | Question displays on home page as link with author's name|
| User views a question | Click any question link | Question page displays with the content, edit and delete buttons, and an answer button |
| User chooses to answer question | Click answer button | A form to enter answer displays on the question page |
| User enters answer | Go to nodejs site and follow directions | The answer displays below the question along with previous answers |

## Further Reading / Useful Links

* [ember.js](http://emberjs.com/)
* [ember-cli](https://ember-cli.com/)
* Development Browser Extensions
  * [ember inspector for chrome](https://chrome.google.com/webstore/detail/ember-inspector/bmdblncegkenkacieihfhpjfppoconhi)
  * [ember inspector for firefox](https://addons.mozilla.org/en-US/firefox/addon/ember-inspector/)

### License

MIT License

Copyright © 2017 Nathan Stewart

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.

 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: GStewartN/AMA

repo_name: GStewartN/AMA


text:
# GitHub Trending (2023-03-11)

![](https://img.shields.io/badge/Python-New%204-green?style=flat-square&logo=appveyor)
- [microsoft/visual-chatgpt](https://github.com/microsoft/visual-chatgpt): VisualChatGPT

![](https://img.shields.io/badge/TypeScript-New%201-green?style=flat-square&logo=appveyor)
- [yetone/openai-translator](https://github.com/yetone/openai-translator): 基于 ChatGPT API 的划词翻译浏览器插件和跨平台桌面端应用 - Browser extension and cross-platform desktop application for translation based on ChatGPT API.

![](https://img.shields.io/badge/none-New%20376-green?style=flat-square&logo=appveyor)
- [sindresorhus/awesome](https://github.com/sindresorhus/awesome): 😎 Awesome lists about all kinds of interesting topics

![](https://img.shields.io/badge/TypeScript-New%20172-green?style=flat-square&logo=appveyor)
- [yuvalsuede/ai-component-generator](https://github.com/yuvalsuede/ai-component-generator):

![](https://img.shields.io/badge/C%23-New%2034-green?style=flat-square&logo=appveyor)
- [jasontaylordev/CleanArchitecture](https://github.com/jasontaylordev/CleanArchitecture): Clean Architecture Solution Template for .NET 7

![](https://img.shields.io/badge/TypeScript-New%20220-green?style=flat-square&logo=appveyor)
- [futantan/OpenGpt](https://github.com/futantan/OpenGpt): Create your own ChatGPT App in seconds.

![](https://img.shields.io/badge/none-New%20131-green?style=flat-square&logo=appveyor)
- [dair-ai/ML-Papers-Explained](https://github.com/dair-ai/ML-Papers-Explained): Explanation to key concepts in ML

![](https://img.shields.io/badge/none-New%20111-green?style=flat-square&logo=appveyor)
- [Elfocrash/.NET-Backend-Developer-Roadmap](https://github.com/Elfocrash/.NET-Backend-Developer-Roadmap): Roadmap for a .NET Backend Developer working with Microservices

![](https://img.shields.io/badge/none-New%20162-green?style=flat-square&logo=appveyor)
- [merveenoyan/my_notes](https://github.com/merveenoyan/my_notes): My small cheatsheets for data science, ML, computer science and more.

![](https://img.shields.io/badge/Python-New%2038-green?style=flat-square&logo=appveyor)
- [Azure-Samples/azure-search-openai-demo](https://github.com/Azure-Samples/azure-search-openai-demo): Demonstration of how to leverage Azure OpenAI and Cognitive Search to enable Information Search and Discovery over organizational content

![](https://img.shields.io/badge/none-New%20734-green?style=flat-square&logo=appveyor)
- [davinci1010/pinduoduo_backdoor](https://github.com/davinci1010/pinduoduo_backdoor): 拼多多apk内嵌提权代码，及动态下发dex分析

![](https://img.shields.io/badge/JavaScript-New%2013-green?style=flat-square&logo=appveyor)
- [therealgliz/blooket-hacks](https://github.com/therealgliz/blooket-hacks): Multiple game hacks to use so the game become easier to play!

![](https://img.shields.io/badge/Python-New%2082-green?style=flat-square&logo=appveyor)
- [geohot/tinygrad](https://github.com/geohot/tinygrad): You like pytorch? You like micrograd? You love tinygrad! ❤

![](https://img.shields.io/badge/TypeScript-New%20153-green?style=flat-square&logo=appveyor)
- [drizzle-team/drizzle-orm](https://github.com/drizzle-team/drizzle-orm): TypeScript ORM for SQL

![](https://img.shields.io/badge/JavaScript-New%2086-green?style=flat-square&logo=appveyor)
- [iptv-org/iptv](https://github.com/iptv-org/iptv): Collection of publicly available IPTV channels from all over the world

![](https://img.shields.io/badge/none-New%2026-green?style=flat-square&logo=appveyor)
- [autonomousvision/stylegan-t](https://github.com/autonomousvision/stylegan-t): [ARXIV'23] StyleGAN-T: Unlocking the Power of GANs for Fast Large-Scale Text-to-Image Synthesis

![](https://img.shields.io/badge/C-New%20541-green?style=flat-square&logo=appveyor)
- [ggerganov/whisper.cpp](https://github.com/ggerganov/whisper.cpp): Port of OpenAI's Whisper model in C/C++

![](https://img.shields.io/badge/C%23-New%2055-green?style=flat-square&logo=appveyor)
- [RadekVyM/MarvelousMAUI](https://github.com/RadekVyM/MarvelousMAUI): .NET MAUI clone of the Wonderous app - a visual showcase of eight wonders of the world.

![](https://img.shields.io/badge/Python-New%2090-green?style=flat-square&logo=appveyor)
- [sczhou/CodeFormer](https://github.com/sczhou/CodeFormer): [NeurIPS 2022] Towards Robust Blind Face Restoration with Codebook Lookup Transformer

![](https://img.shields.io/badge/none-New%20147-green?style=flat-square&logo=appveyor)
- [mli/paper-reading](https://github.com/mli/paper-reading): 深度学习经典、新论文逐段精读

![](https://img.shields.io/badge/JavaScript-New%20134-green?style=flat-square&logo=appveyor)
- [Postcatlab/postcat](https://github.com/Postcatlab/postcat): Postcat 是一个可扩展的 API 工具平台。集合基础的 API 管理和测试功能，并且可以通过插件简化你的 API 开发工作，让你可以更快更好地创建 API。An extensible API tool.

![](https://img.shields.io/badge/JavaScript-New%208-green?style=flat-square&logo=appveyor)
- [EddieHubCommunity/LinkFree](https://github.com/EddieHubCommunity/LinkFree): Connect to your audience with a single link. Showcase the content you create and your projects in one place. Make it easier for people to find, follow and subscribe.

![](https://img.shields.io/badge/C%23-New%2034-green?style=flat-square&logo=appveyor)
- [dotnet/maui](https://github.com/dotnet/maui): .NET MAUI is the .NET Multi-platform App UI, a framework for building native device applications spanning mobile, tablet, and desktop.

![](https://img.shields.io/badge/Python-New%2013-green?style=flat-square&logo=appveyor)
- [ansible/ansible](https://github.com/ansible/ansible): Ansible is a radically simple IT automation platform that makes your applications and systems easier to deploy and maintain. Automate everything from code deployment to network configuration to cloud management, in a language that approaches plain English, using SSH, with no agents to install on remote systems. https://docs.ansible.com.

![](https://img.shields.io/badge/Vue-New%2021-green?style=flat-square&logo=appveyor)
- [honghuangdc/soybean-admin](https://github.com/honghuangdc/soybean-admin): A fresh and elegant admin template, based on Vue3,Vite3,TypeScript,NaiveUI and UnoCSS [一个基于Vue3、Vite3、TypeScript、NaiveUI 和 UnoCSS的清新优雅的中后台模版]

repository: Hsins/Daily-GitHub-Trending

repo_name: Hsins/Daily-GitHub-Trending


text:
# bflutter

BeeSight Soft Flutter core using BLoC Design Pattern (Business Logic Component)

## Newbie

- [[Newbie] Chapter 1. Flutter overview](https://nhancv.com/newbie-chapter-1-flutter-overview/)

- [[Newbie] Chapter 2. Flutter getting started](https://nhancv.com/newbie-chapter-2-flutter-getting-started/)

- [[Newbie] Chapter 3. Flutter initial setup](https://nhancv.com/newbie-chapter-3-flutter-initial-setup/)

- [[Newbie] Chapter 4. Widget’s state](https://nhancv.com/newbie-chapter-4-widgets-state/)

- [[Newbie] Chapter 5. Basic tickets](https://nhancv.com/newbie-chapter-5-basic-tickets/)

## Flutter package

https://pub.dartlang.org/packages/bflutter

repository: beesightsoft/bflutter

gha_language: Dart

repo_name: beesightsoft/bflutter


text:
# 🌶🌶 - Bluetooth Low energy: Broadcast current values in a Bluetooth advertisement

This shows how to create a Bluetooth advertisement publisher with custom values.

Putting values in an advertisement saves having to connect to device and read the value.
This sample puts a 1 byte count followed by a 32bit integer into a Manufacturer Data Section.

To collect data from this sample create a simple Bluetooth Watcher and save data coming from
device with local name "MyValues".

You will be able see advertisements from device with ManufacterData value changing every second.
Suitable Phone applications: "LightBlue" or "nRF Connect"

See [main Bluetooth sample's readme](../README.md) for more information.

repository: AdrianSoundy/Samples

repo_name: AdrianSoundy/Samples


text:
##Coursework 1 - Coursework Specification

	Module Name:	 Multimedia I

	Module Code:	 TECH1015

	Title of Assessment:	 Coursework 1

	This coursework item is:	 Summative / Formative

	The learning outcomes that are assesses by this coursework are

 Develop a standalone web-site incorporating animated and dynamic content

Use a range of sources in order to help solve technical problems related to basic web authoring and evaluate the results of your efforts in an objective manner

Develop a basic understanding of the languages of the web, HTML, CSS and JavaScript

	This coursework constitutes 40% of the overall module mark.

	This coursework is:	 Individual / Group

	Date set:	 30th September

	Date and time due:	 12pm (midday) on Friday 13th December

	Your marked coursework and feedback will be available to you on:	Monday 20th January

	If for any reason this is not forthcoming by the due date your module leader will let you know why and when it can be expected. The Associate Professor Student Experience (<a href='mailto:CEMstudentexperience<EMAIL_ADDRESS>should be informed of any issues relating to the return of marked coursework and feedback.

	Note that you should normally receive feedback on your coursework by no later than 20 University working days after the formal hand-in date, provided that you have met the submission deadline.

	When completed you are required to submit your coursework to

The report should be submitted through the Turnitin link in the Assessments section of the TECH1015 Blackboard page

 The website should be in your public_html folder

			If you need any support or advice on completing this coursework please visit the Student Matters tab on the Faculty of Technology Blackboard page.

	Late submission of coursework policy

	Late submissions will be processed in accordance with current University regulations which state: “the time period during which a student may submit a piece of work late without authorisation and have the work capped at 40% [50% at PG level] if passed is 14 calendar days. Work submitted unauthorised more than 14 calendar days after the original submission date will receive a mark of 0%.  These regulations apply to a student’s first attempt at coursework. Work submitted late without authorisation which constitutes reassessment of a previously failed piece of coursework will always receive a mark of 0%.”

	Academic Offences and Bad Academic Practices

	These include plagiarism, cheating, collusion, copying work and reuse of your own work, poor referencing or the passing off of somebody else's ideas as your own. If you are in any doubt about what constitutes an academic offence or bad academic practice you must check with your tutor. Further information and details of how DSU can support you, if needed, is available at:
http://www.dmu.ac.uk/dmu-students/the-student-gateway/academic-support-office/academic-offences.aspx and
http://www.dmu.ac.uk/dmu-students/the-student-gateway/academic-support-office/bad-academic-practice.aspx

***

###Overview
Coursework 1 is an individual piece of web work that will see you demonstrate knowledge and use of the core principles of media presentation through the web. To this end, you will create a two page website showcasing a film. This will include imagery, audio and video content, as well as a simple style. Alongside the practical work, you will also submit documentation that provides an overview of the production process and evaluates your finished work.

**All written work must be submitted through the Turnitin link on Blackboard**

The submission deadline for this work is midday on Friday 13th December. You are encouraged to seek feedback from your lab tutor throughout the first semester on your ongoing work. To this end, the lab session in the week before submission is entirely given over to completion of this work. You may have the opportunity in earlier lab sessions to complete work towards this assignment should you finish the prescribed task for that session early, though don’t rely on this.

Coursework 1 is worth 40% of your module mark.

###The Task

You are to produce a two page website for an upcoming film. Which film this is remains up to you. It can be new, old, made up. From the viewers’ point of view, several things should be included:

* A trailer for the film
	- so you’ll need to include video
* At least one audio interview
	- so you’ll need to include audio
* Images of key cast
	- so you’ll need some images in there
* A synopsis of the film
	- so you’ve got some text content

Your website will be across two pages. The first page will include the synopsis and trailer. The second page will have cast pictures and audio interviews.

You don’t need to include the real trailer, or real interviews with the task, but they do need to be actual video and audio files. You’re being marked on how you include these, not the quality of the media itself.

There is a prescribed layout for each of the two pages. You should use CSS to attempt to achieve these layouts. The fonts, colours, and other embellishments are up to you.

![Required Website Layout](https://www.dropbox.com/s/141m48lm8t7ungv/TECH1015%20CW1%20Layout.png?raw=1)

While you can add to this for better visual clarity, such as by boxing up each row of cast information and adding more rows, you should maintain as much of this layout as possible. The menu will only have two options in it, one for each page. The footer should have something suitably generic, such as a copyright notice for example.

You should pay close attention to the marking criteria below for the specific points you will be marked on.

If you’re at all unsure about what you are meant to do, ask your lab tutor for guidance.

###The Documentation

You are also to produce documentation alongside this. This has two main aims. Firstly, you should provide an overview of your production process including the reason for making the choices you have. For example, how did you lay out the cast rows and why did you choose that method. We want to see two things here, that you understand what you’ve done, and that you’ve put some thought into possible approaches. Screenshots and code snippets are useful here for helping to show what you’re talking about.

The second part of your documentation is the evaluation. This should include feedback from peers. You’ll see from the marking criteria that you’ll lose marks if you don’t include this. Ideally, you’ll have a range of people try out your website and then provide structured feedback on it. You might want to create a questionnaire that has a few questions soliciting a numerical answer (quality of design between 1 and 5, clarity of content etc) and then a couple of opportunities for the user to provide some more text-based feedback. You should then include this data in your report, and very importantly, comment on it.

You should then go on to form your own critical opinion of your work, building in reflection on your peer feedback. You need to be able to stand back from your work and be objective about it. This is crucial. What works about your site, what doesn’t. What did you find difficult, or what did you fail to achieve. Be completely honest. You wont lose any marks for pointing out something that doesn’t work. Indeed, you’ll gain marks for having a good critical approach.

Again, ask your lab tutor if you are at all unsure about what you should be writing.

###Submission

Your work should be submitted in two places, before midday on Friday 13th December.

####Turnitin
Turnitin is a facility that allows for the submission of documentation and includes a similarity checker to ensure your work is original. Your report should be submitted through the Turnitin link available on the module Blackboard shell. You'll find it in the Assessments section.

####public_html
In order to be able to view your website through a web server, you should transfer your website to your `public_html` folder, uncompressed, with its complete folder structure intact. On doing so, you should view your website through the server to ensure that all links and media still work.
**Do not make any changes after the deadline**

###Marking Criteria

There are two components here, the website and the documentation, so each is marked separately.

####Website
The website makes up 70% of your mark. You have to demonstrate a number of principles. You will receive a mark for each depending on the degree to which you successfully achieve this. They are:

**Include Images**
Full marks for at least three images included using the `<img>` element, with suitable alt text, and originals that are close to the final display size of the image.

**Include Audio**
Full marks for the inclusion of audio using the `<audio>` element with at least two different file formats properly used.

**Include Video**
Full marks for the inclusion of video using the `<video>` element, with at least two different file formats properly used and a poster image in place.

**Separate Style Sheet**
Full marks for using a single .css file that uses properly formed CSS and linking it to both pages of the website.

**Well Formed and Appropriate Markup**
Full marks for a site that has properly written HTML and makes full use of HTML semantic elements such as `nav`.

**Interlinking**
Full marks for both successfully linking the two pages together, and providing links to external pages i.e. other website(s).

**Layout Implementation**
Full marks for using CSS to successfully achieve the given layout.

For each of these seven criteria you will receive a mark out of 10, giving you a total out of 70.

###Documentation
The documentation is worth 30% of the coursework grade. You will be marked against three criteria, again with a mark out of 10 for each one:

**Justification of Choices**
Your documentation should provide an overview of the production process, including justification for your approach to problems and the solutions you used.

**Consideration of Third-Party Feedback**
You should seek opinion from others on the quality of your work and reflect on this. Have a handful of people use your site and complete a short questionnaire for example. Include the results, but then go on to discuss them.

**Quality of Self-Evaluation**
A good evaluation will demonstrate an ability to stand back from your work and critically evaluate it. What worked, what didn’t, and what could be improved.

repository: thomcorah/dmu-multimedia

gha_language: CSS

repo_name: thomcorah/dmu-multimedia


text:
# 01 ⋯ Introduction to Machine Learning

[Introductory Topics](http://www.fields.utoronto.ca/talks/Introductory-Topics)

[Roger Grosse](http://danroy.org/)
* Assistant Professor of Computer Science, University of Toronto
* Founding Member of the Vector Institute

---

### Resources

**Publications**

- [Publications on Google Scholar](https://scholar.google.com/citations?user=xgQd1qgAAAAJ&hl=en)
- [Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations](http://people.ee.duke.edu/~lcarin/DeepBelief.pdf) - 2009

**Courses**

- CSC 421/2516 ⋯ W 2019 ⋯ Neural Networks and Deep Learning
    * [Course Overview / Assignments / Lectures / Tutorials](http://www.cs.toronto.edu/~rgrosse/courses/csc421_2019/)
    * [Lecture Slides 01: Introduction](http://www.cs.toronto.edu/~rgrosse/courses/csc421_2019/slides/lec01.pdf)
- CSC 2541 ⋯ Topics of Machine Learning
    * F 2018 ⋯ [Deep Reinforcement Learning](https://csc2541-f18.github.io/)
    * F 2017 ⋯ [Scalable and Flexible Models of Uncertainty](https://csc2541-f17.github.io/)

- CSC 411/2515 ⋯ F 2018 ⋯ Machine Learning and Data Mining
    * [Course Syllabus](https://www.cs.toronto.edu/~rgrosse/courses/csc411_f18/syllabus.pdf)
* [Course Overview / Assignments / Lectures / Tutorials](https://www.cs.toronto.edu/~rgrosse/courses/csc411_f18/)
    * [Lecture Slides 01](http://www.cs.toronto.edu/~rgrosse/courses/csc411_f18/slides/lec01-slides.pdf): Introduction to Machine Learning
- CSC 321 ⋯ Intro to Neural Networks
    * W 2018 ⋯ [Intro to Neural Networks and Machine Learning](http://www.cs.toronto.edu/~rgrosse/courses/csc321_2018/)
    * F 2018 ⋯ [Intro to Neural Networks](http://www.cs.toronto.edu/~rgrosse/courses/csc321_2018/)
    * W 2017 ⋯ [Intro to Neural Networks](http://www.cs.toronto.edu/~rgrosse/courses/csc321_2017/)
    * W 2015 ⋯ [Intro to Neural Networks](http://www.cs.toronto.edu/~rgrosse/csc321/)

## Abstract

This lecture will introduce basic machine learning and deep learning concepts which will be used throughout the course. I’ll briefly review linear regression and classification methods, and then introduce neural networks and the back propagation algorithm.

## Core Topics

* Topics Overview
* ML Concepts
   * LR (Linear Regression) / CLAS (Classification)
   * GD (Gradient Descent)
   * MLP (Multi-Layer Perceptrons)
   * BP (Backpropagation)
* What is ML
* Why ML
* Machine Learning Workflow
* Implementing ML Systems
* Suggested Further Readings

## Lecture Notes

#### X. [Topic]()

0. TOPIC
* Sub topic
    * Nested sub topic
    * 𝐷𝑛 = boolean of 𝑛

| No. | Classification | Value |
|-----|----------------|-------|
|  00 | Class w/ Float | 0.00  |

#### A. [Topics Overview]()

ML Concepts

1. LR (Linear Regression) / CLAS (Classification)
2. GD (Gradient Descent)
3. MLP (Multi-Layer Perceptrons)
4. BP (Backpropagation)

#### B. [What is ML]()

1. Behaviours difficult to program by hand currently:

* recognizing people and objects for their dimensions in time
* details in human speech

2. A learning ALGM (algorthim) approach:

Program an ALGM where its data or experience is learnt where auto adjusted behaviours are made based on the instructions placed

3. Use a learning ALGM, when:

| Trigger                                                        | Product Type.          |
|----------------------------------------------------------------|------------------------|
| it's hard to code a solution by hand                           | Vision / Speech        |
| a system needs instant adapation to environment changes.       | Spam Detection         |
| you want the system to perform *better* than human programmers |                        |
| privacy / fairness is needed without / with less biast         | Ranking Search Results |

4. ML Types

| Type                   | Objective                                       | Product Type |
|------------------------|-------------------------------------------------|-------|
| Supervised Learning    | label things, seeking binary behaviour   | Object Recognition / Natural Language Processing |
| Unsupervised Learning  | no labels, seeking interesting patterns from data, to create a likeness from its source | Generative Modeling |
| Reinforcement Learning | an agent interacting, learning from its policy (mapped environment) from periodic rewards | DeepMind & Atari games |

#### C. [ML Concept Workflows]()

1. Linear Models

* Overview
* Problem Setup
* Vectorization
* Optimization Issue
* Partial Derivatives
* Linear Regression
* 1A) Gradient Descent
   * Feature Mapping
   * Generalization

2. Linear Classification

* Binary Linear Classification
* Logistic Regression

3. Multiclass Classification

4. MLP (MultiLayer Perceptrons)

* Neural Network
*

4. BP (Backpropagation)

## Lecture Materials

|  Lecture    | [Lecture Title]() ⌇ Fields ﹊  28/03/2019 |
| --------- | :--------- |
| [![Placeholder ALT IMG TEXT](http://img.youtube.com/vi/aLFJ5ERxt2c/0.jpg)](https://www.youtube.com/watch?v=aLFJ5ERxt2c&ap=%3D18%2526fmt)        | ℙ  |
| Downloads  |  [📼](placeholder) 4.13 GB ⌇ 📄 [PDF](placeholder) 3.87 MB      |

#### Material Topic Summary

ℙ

#### External Lecture Materials

|  Lecture    | [Lecture Title]() ⌇ Fields ﹊  28/03/2019 |
| --------- | :--------- |
| [![Placeholder ALT IMG TEXT](http://img.youtube.com/vi/aLFJ5ERxt2c/0.jpg)](https://www.youtube.com/watch?v=aLFJ5ERxt2c&ap=%3D18%2526fmt)        | ℙ  |
| Downloads  |  [📼](placeholder) 4.13 GB ⌇ 📄 [PDF](placeholder) 3.87 MB      |

## Resources

1. [Deep Learning](https://www.deeplearningbook.org/)

2. University of Toronto: Computer Science Department - [Machine Learning](http://www.learning.cs.toronto.edu/courses.html)

3. [Introduction to Machine Learning](https://medium.com/@charliechenyuzhang/introduction-to-machine-learning-ml-e6b43d59b76f), Charlie Chenyu Zhang - Sep 17, 2018
4. Princeton University, COS 324 ⋯ F 2018 - [Introduction to Machine Learning](https://www.cs.princeton.edu/courses/archive/fall18/cos324/)
5. [Understanding Machine Learning:
From Theory to Algorithms](http://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning/), Shai Shalev-Shwartz and Shai Ben-David - 2014

6. Machine Learning and Medicine: [ML Resources](https://sgfin.github.io/learning-resources/), Sam Finlayson with Zak Kohane
7. [Summaries of papers on machine learning](https://github.com/fregu856/papers), Fredrik Gustafsson
8. [Optical Illusion](http://cs229.stanford.edu/proj2006/Bolouki-OpticalIllusion.pdf), Sara Bolouki, Roger Grosse, Honglak Lee, Andrew Ng - 2006
9. [Introducing K-FAC](https://towardsdatascience.com/introducing-k-fac-and-its-application-for-large-scale-deep-learning-4e3f9b443414): A Second-Order Optimization Method for Large-Scale Deep Learning, Kazuki Osawa - Dec 14, 2018
10. [Metacademy](https://metacademy.org/)


text:
## 1.4.0
* Remove width restriction of title in legend
* Added new option to drawDefaultLabelsForDataPointChart function to control behavior of collided labels

## 1.3.0
* Updated packages

## 1.2.0
* Added 'disableNiceOnlyForScale' to 'CreateAxisOptions' interface
and added verification with this property to createAxis func

## 1.1.0
* Removed `lodash`
* Updated dependencies

## 1.0.1
* Update ChartUtils to use SVG utils version 1.0.0
* Add CHANGELOG

repository: BlueGranite/pbiHexbinScatterplot

repo_name: BlueGranite/pbiHexbinScatterplot


text:
---
layout: post
title:  "Costs of cubesat"
image: ''
date:   2016-03-12 00:06:31
tags:
- mongodb
description: ''
categories:
- Learn GH
---

# COST
 CubeSat forms a cost-effective independent means of getting a payload into orbit.
 After delays from low-cost launchers such as Interorbital Systems,launch prices have been about $100,000 per unit,
 but newer operators are offering lower pricing.Some CubeSats have complicated components or instruments,
 such as LightSail-1, that pushes their construction cost into the millions,but a basic 1U CubeSat can cost
 about $50,000 to construct so CubeSats are a viable option for some schools and universities;
 as well as small businesses to develop CubeSats for commercial purposes.


text:
## CS294-112 Assignment Solutions
Solutions to the fall 2018 edition of the assignments for [CS294-112: Deep Reinforcement Learning](http://rail.eecs.berkeley.edu/deeprlcourse/)

Based on the video lectures from the [Fall 2017](https://www.youtube.com/playlist?list=PLkFD6_40KJIznC9CDbVTjAF2oyt8_VAe3) edition of the course.

## Installation
[Starting code](https://github.com/berkeleydeeprlcourse/homework)

To be able to run the code for each homework, please start by following the instructions from each homework's PDF
(linked with each assignment below) about how to install the homework. As for setting up a Python environment, the
provided `cs294.yml` file is a `conda` requirements file that you can use to create a conda environment using the
following command:
```bash
conda env create -f cs294.yml
```
This will create a conda environment named `cs294`.

### [Assignment 1: Imitation Learning](http://rail.eecs.berkeley.edu/deeprlcourse-fa18/static/homeworks/hw1.pdf):
- Behavioral Cloning (_Done_)
- DAgger

### [Assignment 2: Policy Gradients](http://rail.eecs.berkeley.edu/deeprlcourse-fa18/static/homeworks/hw2.pdf):
- Computation Graph (_Done_)
- Policy Gradient (_Done_)
- Neural Network Baselines (_Done_)

### [Assignment 3: Q-Learning and Actor-Critic](http://rail.eecs.berkeley.edu/deeprlcourse-fa18/static/homeworks/hw3.pdf):
- Q-Learning
- Actor-Critic

### [Assignment 4: Model-Based Reinforcement Learning](http://rail.eecs.berkeley.edu/deeprlcourse-fa18/static/homeworks/hw4.pdf):
- Problem 1
- Problem 2
- Problem 3a
- Problem 3b


text:
---
description: >-
  Curious about whether Athens is right for you or how to get started? Learn how
  to download Athens for your desktop, explore the basics, and get help from the
  Athens community.
---

# Getting Started

### What is Athens?

Athens is an open-source knowledge graph for research and notetaking. It was built as an [outliner](https://en.wikipedia.org/wiki/Outliner) on a [graph database](https://en.wikipedia.org/wiki/Graph_database).

### Demo Athens

Not sure Athens is right for you? [Demo Athens in your browser.](https://athensresearch.github.io/athens)

Click to watch video demoing the value of Athens:

[![7f9876cb28bd455a9de52673efefa2c8-00001](https://user-images.githubusercontent.com/8952138/115828768-00a9a480-a3c3-11eb-9b44-ae5488434ce2.gif)](https://www.loom.com/share/7f9876cb28bd455a9de52673efefa2c8)

### The Basics

If you're new to Athens, read our [basics guide](../basics.md). In just a few minutes, you will learn how to use the outliner to take notes and organize your knowledge, and create bidirectional links. From there, navigate your growing knowledge graph and manipulate or format blocks of content.

### Guide Book

The rest of our guide book contains information about using Athens, such as [key features](../feature-list/) and a [PDF version](https://github.com/ddauber/athens-cheat-sheet/blob/main/athens_cheatsheet.pdf) of an Athens cheatsheet.

### Get Help

Have a question about how Athens works? Our community is active on [Discord](https://discord.gg/as9h8yHNfD) and [GitHub](https://github.com/athensresearch/athens/discussions).

### Get Involved

Contribute code

Become a moderator on Discord

File bugs

Test new features

Request new features

Help troubleshoot

Help translate

Help document

Create templates and how-tos

Mentor

repository: joelhans/handbook

repo_name: joelhans/handbook


text:
# Node Email Extractor

[![npm](https://nodei.co/npm/node-email-extractor.png?downloads=true&downloadRank=true&stars=true)](https://www.npmjs.com/package/alexa-rank-nodejs)

![npm version](https://img.shields.io/npm/v/node-email-extractor)
![lisence](https://img.shields.io/npm/l/node-email-extractor)
[![issues](https://img.shields.io/github/issues/binsarjr/node-email-extractor)](https://github.com/binsarjr/node-email-extractor/issues)
![downloads month](https://img.shields.io/npm/dm/node-email-extractor)
![downloads](https://img.shields.io/npm/dt/node-email-extractor)

Extract emails from text and also from a site page

# Includes
- [request-promise-native](https://www.npmjs.com/package/request-promise-native)

# Requirements
- [NodeJS](https://nodejs.org/en/download/)
- [npm](https://www.npmjs.com/)

# Instalation
Instalation is done using `npm install` command
```
$ npm install node-email-extractor
```

# Feutures
- Extract email from plaintext
- Extract emails from website content
- this module already supports [typescript](https://www.typescriptlang.org/)

# Usage
### javascript
```js
const email = require('node-email-extractor').default;

(async () => {
    var data = await email.url('https://www.****.com/contact-us/')
    console.log(data);
})()

var data = email.text(`Contact Details
Phone: +267 72301363 / 73316322

Email<EMAIL_ADDRESS>
Registered with: `)

console.log(data)
```

### typescript
```js
import EmailExtractor from "node-email-extractor";

(async () => {
    var data = await EmailExtractor.url('https://www.****.com/contact-us/')
    console.log(data);
})()

var data = EmailExtractor.text(`Contact Details
Phone: +267 72301363 / 73316322

Email<EMAIL_ADDRESS>
Registered with: `)

console.log(data)

```

### results
```js
{ domains: [ 'gmail.com' ], emails: [<EMAIL_ADDRESS>] }
{ domains: [ 'gmail.com' ], emails: [<EMAIL_ADDRESS>] }
```
Yes, it's really all you need to get started, Thank You ❤

repository: pentest-tools/node-email-extractor

repo_name: pentest-tools/node-email-extractor


text:
# Detecting Pneumonia using Deep Learning

---

## Description
Pneumonia is a severe lung infection that inflames and fills the air sacs in the lungs with fluid or puss. It's a common illness that affects many people each year in the US and around the world. To diagonose this disease, Radiologists looks for the presence and the severity of infiltrate on patient's chest X-Ray image. However, studies have shown that even experienced radiologists often have a hard time correctly identify whether something is an infiltrate on the X-ray. This causes delays in diagnosis, which increases the disease severity and associated mortality rate.

Can AI help fill in the gap for humans, spotting Pneumonia the way that human's eye aren't able to do?

## Objective
Build a deep learning model that detects Pneumonia on patient's chest X-Ray images.

## Methodology
### 1. Data Collection
Data is obtained from [Kaggle](https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia), which are chest X-Ray images of pediatric patients under age of 5 from a Medical Center in Guangzhou, China. It contains a total of 5,856 images in different sizes.

### 2. Data Exploring
As the validation dataset arranged by Kaggle is too small, I've re-split the entire dataset into this ratio: 70% train, 19% validation, and 11% test. The training dataset is distributed as 79% of X-rays has pneumonia and 21% doesn't.

### 3. Image Preprocessing
To ensure efficient computation, **pixels were normalized** to values from 0 to 1. The **images were also augmented** to prevent model from overfitting. Lastly, since a chest X-ray shows the parts of the chest in different shades of black and white based on the amount of radiation the tissue can absorb, it makes sense to decrease the complexity of the model by **converting the images to grayscale** (one color channel).

### 4. Data Modeling
#### Model Architecture
The basic structure of a CNN model consists of CNN layer, followed by a pooling layer and an optional dropout, then fully connected layers with activations and output layer. I've tried different combinations of activations, dropout, and batch normalization, and below is the high-level architecture of my final and best performing convolutional neural network (CNN) model.

![CNN Model](cnn.png)

Since this is a binary classification problem, we used ReLU activation to output non-negative values and fed into a sigmoid softmax function that predicts an output probability, which can be converted to class values based on our threshold.

#### Optimize for AUC score
For the case of Pneumonia, we will aim to have high recall as any delayed diagnosis means that someone may get really sick and potentially lose their life. We do that by first having our model optimize for AUC score, focusing on having a good measure of separability for our binary classification. Then, we can crank the threshold up and down (from 0.5) if the business feels more comfortable with a higher or lower FP or FN.

## Results

### Fit
Through each epoch during the learning process, validation loss and training loss approach each other, which means our model doesn’t seem to have much overfitting or underfitting. Moreover, training and validation AUC score also converges to a point where they’re almost equal in the end.

### Metrics
- The model has an AUC score of 97.98%, indicating that our CNN model can separate 97.98% of the images in the test set.
- The model achieves 98.72% for recall, with 8.49% of False Positives and 0.80% of False Negatives, which is impressive and exactly what we’re aiming for.

## Workflow
Follow the jupyter notebook in the order below:
- 00 Download dataset from [Kaggle](https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia)
- 01 EDA.ipynb
- 02 CNN Model.ipynb

repository: WorqHat/Detect-Pneumonia-using-ML

gha_language: Jupyter Notebook

repo_name: WorqHat/Detect-Pneumonia-using-ML


text:
# RestReview (Client + Rest API)
**Rest Review**: A platform that enables users to search and review restaurants.

* The REST API is available at **https://jmarques.pythonanywhere.com/**.
* The Angular Interface is available at **https://rest-review.herokuapp.com**.

## Implemented Features

### API's Project
* **Django Models**: We used django models to create and model entities into a database. Regarding entity relations, we implemented N:M, N:1 and 1:1 relations;
* **Django Authentication**: The mechanisms provided by Django Authentication were widely used to allow different users to log in into your web application;
* **Django Authorization**: We decided to restrict some areas of our service to specific groups of users. This being said, we use Django Authorization mechanisms to make sure that only the allowed users could perform certain operations. For instance, a regular user can see restaurants and even submit reviews, but cannot create a new restaurant or edit the information of an existent restaurant. Such operation can only be done by the restaurant's owner.

### Angular Project
* **Components**: We used components for every view and all the logic associated with it;
* **Data Binding**: Mostly for showing persistent data, we used data binding;
* **Directives**: Directives were used for providing additional behavior for DOM elements;
* **Service**: Every data fetched from the server was called from a service;
* **Dependency Injection**: Injection of the service into the components;
* **Routing**: All the navigation in the platform is made with routing;
* **Bootstrap**: Bootstrap is used as a style tool for the interface;
* **Observables**: For dealing with asynchronous programming, we used observables;

## How to Run
1. Delete the database, if it's already created;
2. Build the database;
3. Create a superuser;
4. Run both project;
5. Once the project is running, access to `/reload_database` in the rest api to populate te database with some default data.

## Accesses

### Client

| Username  | Password |
| ------------- | ------------- |
|<EMAIL_ADDRESS>| client1  |
|<EMAIL_ADDRESS>| client2  |
|<EMAIL_ADDRESS>| client3  |

### Owner

| Username  | Password |
| ------------- | ------------- |
|<EMAIL_ADDRESS>| owner1  |
|<EMAIL_ADDRESS>| owner2  |
|<EMAIL_ADDRESS>| owner3  |

## Features

**/reload_database** : Clean and put the database default data

**/register**: Page with registration options as owner or client

**/register-client**: Register a client account

**/register-owner**: Register an owner account

**/login** : Sign in to platform

**/**: Provides the home page with the top 2 restaurants and search feature

**/list-restaurants**: Listing of all restaurants with the ability to search for specific restaurants, restaurants in a city, or order the results by some characteristics

**/owner/{name}**: Access the public profile of some given owner

**/restaurant/{number}**: See all the information of a given restaurant (info, reviews, location, owner, etc)

#### All the following features require a previous login:

**/new-restaurant**: Create a new restaurant (only owners can do this)

**/client**: Access client own profile

**/owner**: Access client own owner

**/my-restaurants**: Access all the restaurants a loged owner has (only owners can do this)

**/edit-restaurant/{number}**: Edit information of a given restaurant (only the owner of that restaurant can do this)

**my-favorites**: Access the user favorites' restaurants (only a client can do this and each client can only access his own favorites list)

### Features not in URL

* A client can add some restaurant to his favorites in `list-restaurants` and `home`.
* From the detail page of restaurant it's possible to access the reviews and the owner profile.
* The location of each restaurant in the detail page is done using google maps api.
* In each edit feature (restaurant, client and owner) it's also possible to eliminate the object (restaurant, object, owner). This action will also remove all related entries in other tables of the database.

## Authors
* **Vasco Ramos:** [vascoalramos](https://github.com/vascoalramos)
* **João Marques:** [joao-p-marques](https://github.com/joao-p-marques)

## License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.

repository: vascoalramos/rest-review

gha_language: Python

repo_name: vascoalramos/rest-review


text:
# cmake

> Cross-platform build system generator.
> It generates Makefiles, Visual Studio projects or others, depending on the target system.
> Homepage: <https://cmake.org/cmake/help/v3.2/manual/cmake.1.html>.

- Generate a Makefile and use it to compile a project in the same directory as the source:

`cmake && make`

- Generate a Makefile and use it to compile a project in a separate "build" directory (out-of-source build):

`cmake -H. -B{{build}} && make -C {{build}}`

- Run cmake in interactive mode (it will ask for each variable, instead of using defaults):

`cmake -i`

repository: chrillebile/tldr

repo_name: chrillebile/tldr


text:
# Tests Sampling

Sometimes it's useful to run profilers against randomly chosen tests. Unfortunately, test frameworks don't support such functionality. That's why we've included small patches for RSpec and Minitest in TestProf.

## Instructions

Require the corresponding patch:

```ruby
# For RSpec in your spec_helper.rb
require "test_prof/recipes/rspec/sample"

# For Minitest in your test_helper.rb
require "test_prof/recipes/minitest/sample"
```

And then just add `SAMPLE` env variable with the number examples you want to run:

```sh
SAMPLE=10 rspec
```

You can also run random set of example groups (or suites) using `SAMPLE_GROUPS` variable:

```sh
SAMPLE_GROUPS=10 rspec
```

Note that you can use tests sampling with RSpec filters:

```sh
SAMPLE=10 rspec --tag=api
SAMPLE_GROUPS=10 rspec -e api
```

That's it. Enjoy!

repository: test-prof/test-prof

gha_language: Ruby

repo_name: test-prof/test-prof


text:
[![Build Status](https://travis-ci.org/Cimpress-MCP/metafig.svg?branch=master)](https://travis-ci.org/Cimpress-MCP/metafig)
[![Code Climate](https://codeclimate.com/github/Cimpress-MCP/metafig/badges/gpa.svg)](https://codeclimate.com/github/Cimpress-MCP/metafig)
[![Test Coverage](https://codeclimate.com/github/Cimpress-MCP/metafig/badges/coverage.svg)](https://codeclimate.com/github/Cimpress-MCP/metafig/coverage)

[![dependencies Status](https://david-dm.org/Cimpress-MCP/metafig/status.svg)](https://david-dm.org/Cimpress-MCP/metafig)
[![devDependencies Status](https://david-dm.org/Cimpress-MCP/metafig/dev-status.svg)](https://david-dm.org/Cimpress-MCP/metafig?type=dev)

[![npm (scoped)](https://img.shields.io/npm/v/@cimpresscloud/metafig.svg)](https://www.npmjs.com/package/@cimpresscloud/metafig)
[![npm](https://img.shields.io/npm/dt/@cimpresscloud/metafig.svg)]()

## METAFIG

**Metafig** is a library for loading configuration values from external
services like AWS Parameter Store, Environment Variables, S3 objects, etc.
All of this is configurable from a single place, making it easy to synthesize
different values from different locations.

### Why?

 Some used S3 before lambda had encryption helpers for environment
variables. Some used environment variables. Some used the Parameter Store.

While we migrated most of them to the Parameter store, we wanted to:

1. Make it easier to mix-and-match
    * We had a few things too big for the Parameter Store
    * There are a few things that make more sense as environment variables.
2. Make it easier to migrate in the future if we change our mind.

### Installing

```bash
npm install --save @cimpresscloud/metafig
```

### Example

`./config.json`
```json
{
  "plugins": {
    "database": {
      "awsParam": {
        "path": "/production/myapp/database",
        "decryption": true
      }
    },
    "api": {
      "awsParam": {
        "path": "/production/myapp/api",
        "decryption": true
      },
      "environment": {
        "baseUrl": "API_BASE_URL"
      }
    }
  }
}
```

`./index.js`
```javascript
const metafig = require('metafig');
metafig(require('./config.json'), 'plugins')
  .then(config => {
    var db = new Db(config.plugins.database.connectionString);
    return db.query('SELECT * FROM MyTable');
  });
```

In this case, the `config` object looks something like:

```javascript
{
  plugins: {
    database: {
      connectionString: "sql://mydatabase.example.com:5678/db",
      username: "myApplication",
      password: "secret"
    },
    api: {
      token: "123456",
      baseUrl: "https://myapi.example.com"
    }
  }
}
```

As you can see, it pulled some items from environment variables, and some from
the AWS SSM Parameter Store.

### Providers

Metafig comes with 3 built-in configuration providers:

#### awsParam

The `awsParam` provider can pull down items from the AWS SSM Parameter Store.
It can take 2 parameters:

```javascript
"awsParam": {
  "path": "/app", // the path to the hierarchy in SSM to pull down
  "decryption": true // whether or not to decrypt SecureStrings
}
```

It will pull down every parameter listed under the specified path and put them
into the configuration object, nesting objects if there are deeper
configurations.

#### awsS3

The `awsS3` provider can pull down JSON configuration snippets as objects from
S3. It can accept any parameter for the [`getObject`](http://docs.aws.amazon.com/AWSJavaScriptSDK/latest/AWS/S3.html#getObject-property) API action:

```javascript
"awsS3": {
  "Bucket": "metafig-integration-test",
  "Key": "test.json"
}
```

It will pull down the specified object and run it through the JSON parser.

#### environment

The `environment` provider pulls values out of environment variables and maps
them to configuration items.

```javascript
"environment": {
  "myValue": "USERNAME"
}
```

Take the `USERNAME` environment variable and puts it into the `myValue` property
of your configuration.

#### literal

The `literal` provider just uses the literal value passed to it:

```javascript
"literal": {
  "username": "nmaclennan"
}
```

repository: Cimpress-MCP/metafig

gha_license_id: NOASSERTION

repo_name: Cimpress-MCP/metafig


text:
# youth-membership
Backend for youth membership profile

[![status](https://travis-ci.com/City-of-Helsinki/youth-membership.svg)](https://github.com/City-of-Helsinki/youth-membership)
[![pipeline status](https://gitlab.com/City-of-Helsinki/KuVa/github-mirrors/youth-membership/badges/develop/pipeline.svg)](https://gitlab.com/City-of-Helsinki/KuVa/github-mirrors/youth-membership/-/commits/develop)
[![codecov](https://codecov.io/gh/City-of-Helsinki/youth-membership/branch/develop/graph/badge.svg)](https://codecov.io/gh/City-of-Helsinki/youth-membership)

## Summary

Youth membership profile is implemented using Django and it provides a GraphQL API.

## Development with [Docker](https://docs.docker.com/)

Prerequisites:
* Docker engine: 18.06.0+
* Docker compose 1.22.0+

1. Create a `docker-compose.env.yaml` file in the project folder:
   * Use `docker-compose.env.yaml.example` as a base, it does not need any changes for getting the project running.
   * Set entrypoint/startup variables according to taste.
     * `DEBUG`, controls debug mode on/off
     * `TOKEN_AUTH_*`, settings for [tunnistamo](https://github.com/City-of-Helsinki/tunnistamo) authentication service
     * `OIDC_CLIENT_ID` tunnistamo client id for enabling OIDC admin login
     * `OIDC_CLIENT_SECRET` tunnistamo client secret for enabling OIDC admin login
     * `GDPR_API_QUERY_SCOPE` OAuth scope required for GDPR query operation
     * `GDPR_API_DELETE_SCOPE` OAuth scope required for GDPR delete operation
     * `ENABLE_GRAPHIQL`, enables GraphiQL interface for `/graphql/`
     * `APPLY_MIGRATIONS`, applies migrations on startup
     * `CREATE_ADMIN_USER`, creates an admin user with credentials `kuva-admin`:(password, see below)
<EMAIL_ADDRESS>     * `ADMIN_USER_PASSWORD`, the admin user's password. If this is not given, a random password is generated
     and written into stdout when an admin user is created automatically.
     * `HELSINKI_PROFILE_API_URL` URL for the Helsinki profile GraphQL API
     * `AUDIT_LOGGING_ENABLED`, enable audit logging for the backend
     * `AUDIT_LOG_USERNAME`, audit logs contain the username

2. Run `docker-compose up`
    * The project is now running at [localhost:8081](http://localhost:8081)

**Optional steps**

1. Run migrations:
    * Taken care by the example env
    * `docker exec youth-membership-backend python manage.py migrate`

2. Create superuser:
    * Taken care by the example env
    * `docker exec -it youth-membership-backend python manage.py add_admin_user`

## Development without Docker

Prerequisites:
* PostgreSQL 10
* Python 3.8

### Installing Python requirements

* Run `pip install -r requirements.txt`
* Run `pip install -r requirements-dev.txt` (development requirements)

### Database

To setup a database compatible with default database settings:

Create user and database

    sudo -u postgres createuser -P -R -S youth_membership  # use password `youth_membership`
    sudo -u postgres createdb -O youth_membership youth_membership

Allow user to create test database

    sudo -u postgres psql -c "ALTER USER youth_membership CREATEDB;"

### Daily running

* Create `.env` file: `touch .env`
* Set the `DEBUG` environment variable to `1`.
* Run `python manage.py migrate`
* Run `python manage.py add_admin_user`
* Run `python manage.py runserver 0:8000`

The project is now running at [localhost:8000](http://localhost:8000)

## Keeping Python requirements up to date

1. Install `pip-tools`:
    * `pip install pip-tools`

2. Add new packages to `requirements.in` or `requirements-dev.in`

3. Update `.txt` file for the changed requirements file:
    * `pip-compile requirements.in`
    * `pip-compile requirements-dev.in`

4. If you want to update dependencies to their newest versions, run:
    * `pip-compile --upgrade requirements.in`

5. To install Python requirements run:
    * `pip-sync requirements.txt`

## Code format

This project uses
[`black`](https://github.com/ambv/black),
[`flake8`](https://gitlab.com/pycqa/flake8) and
[`isort`](https://github.com/timothycrosley/isort)
for code formatting and quality checking. Project follows the basic
black config, without any modifications.

Basic `black` commands:

* To let `black` do its magic: `black .`
* To see which files `black` would change: `black --check .`

[`pre-commit`](https://pre-commit.com/) can be used to install and
run all the formatting tools as git hooks automatically before a
commit.

## Running tests

* Set the `DEBUG` environment variable to `1`.
* Run `pytest`.

## Management Commands

### common_utils/generate_notifications_from_templates

Example invocation
```bash
docker-compose exec django python /app/manage.py generate_notifications_from_templates --dev
```

The scripts uses the email templates under `templates/email` to generates notifications for supported languages. The script overwrites existing notifications. It has been configured to be executed when the docker container is ran.

The `--dev` flag instructs the script to save the generated templates under `generated` so they can be debugged or previewed in a browser.

## Issue tracking

* [Github issue list](https://github.com/City-of-Helsinki/youth-membership/issues)
* [Jira issues](https://helsinkisolutionoffice.atlassian.net/projects/YM/issues/?filter=allissues)

## API documentation

* [Generated GraphiQL documentation](https://jassari-api.test.kuva.hel.ninja/graphql/)

## Environments
Test: https://jassari-api.test.kuva.hel.ninja/graphql/

Production: https://jassari-api.prod.kuva.hel.ninja/graphql/

## CI/CD builds

Project is using [Gitlab](https://gitlab.com/City-of-Helsinki/KuVa/github-mirrors/youth-membership/pipelines)
for automated builds and deployment into the test environment.
The test environment is built automatically from the `develop` branch.

## Dependent services

For a complete service the following additional components are also required:
* [tunnistamo](https://github.com/City-of-Helsinki/tunnistamo) is used as the authentication service
* [open-city-profile](https://github.com/City-of-Helsinki/open-city-profile/) provides open city profile backend UI
* [open-city-profile-ui](https://github.com/City-of-Helsinki/open-city-profile-ui/) provides open city profile UI
* [youth-membership-ui](https://github.com/City-of-Helsinki/youth-membership-ui/) provides youth membership UI
* [youth-membership-admin-ui](https://github.com/City-of-Helsinki/youth-membership-admin-ui/) provides youth membership admin UI

repository: City-of-Helsinki/youth-membership

repo_name: City-of-Helsinki/youth-membership


text:
Based on https://github.com/opscode/chef-repo

How to Use:
===========
* Clone the repo locally
* Make sure Vagrant and Virtual Box are up and running
* Making sure librarian-chef is installed.
* Goto code directory and run librarian-chef install to download the cookbooks (by default cookbooks have been checked in, so this step can be skipped)
* Run vagrant up / reload to let vagrant / chef do there magic
* Login using ssh and setup the codebase and nginx config in /etc/nginx/sites-available/folder
* nxensite the new site.
* reload nginx

repository: shshankjain/chef-repo

repo_name: shshankjain/chef-repo