text:
---
layout: default
---

# 1996-02-16 - Re: Online Zakat Payment: Religious tithe.

## Header Data

From<EMAIL_ADDRESS>(tallpaul)

To<EMAIL_ADDRESS>Message Hash: 434948c1bc96fa19de617b328b4de00cb0646814a9c128ae9127f4063b536ee2

Message ID<EMAIL_ADDRESS>Reply To: _N/A_

UTC Datetime: 1996-02-16 06:47:17 UTC

Raw Date: Fri, 16 Feb 1996 14:47:17 +0800

## Raw message

```
{% raw  %}From<EMAIL_ADDRESS>(tallpaul)
Date: Fri, 16 Feb 1996 14:47:17 +0800
To<EMAIL_ADDRESS>Subject: Re: Online Zakat Payment: Religious tithe.
Message-ID<EMAIL_ADDRESS>MIME-Version: 1.0
Content-Type: text/plain

Why oh why am I getting the idea that "cypherpunks" would better be called
"cryptoauthoritarians." From murder-as-political-liberation, to the
universalization of the libertarians "feelings" onto everyone else in the
world, to the mass nuclear bombings of civilians to the mass nuclear
elimination of religions.

My, my. For a group of people so uspet at taxes you certainly have faith in
the ability of private individuals to generate the capital for things like
the Manhattan Project and high-cost nuke delivery systems!

On Feb 15, 1996 20:03:56, 'Sean Gabb<EMAIL_ADDRESS>wrote:

>True, there are risks in generalising from the actions of one individual
>to the group as a whole.  Nevertheless, where Islam is concerned, there
>are so many individuals behaving badly that we are justified in thinking
>the whole religion a force for bad.
>
>Doubtless, I shall be told that "true Islam" is "true tolerance", "true
>freedom", and even "true apple pie".  I may be told that the horrors of
>actually existing Islam - remember this phrase, or something like it? -
>are all somehow the fault of the West.  But the fact is that most Moslems

>venerate old men in beards, who think that anyone who disagrees
>with them about God should be put to death, that a woman with a
>clitoris is a kind of devil, and that Western classical music is evil.
>
>By my standards, these people are disgusting.  My only dispute with Tim
>is in our manner of expressing our disgust.
>
>Sean Gabb
>Editor
>Free Life.
>

I was wondering if S. Gabb, as a self-declared expert on Islam, if he might
explain the difference between the Sunni and Shi'ite sects, and the
respective size of each?

I am also curious if he could point us to a single written example where he
or anyone else has ever been told the various things that he "doubtless"
will be told?

In short, is this important history posted on cypherpunks, more libertarian
political demagogery,  or another J. Bell-like reference to the
universalization of the libertarian's feelings onto the rest of the world?

--tallpaul
  "Gentle Jesus
  "Meek and mild
  "Bless me
  "While I nuke this child.
  "But reassure'em
  "As I wack'em
  "I love their rights;
  "I'll never tax'em."

{% endraw %}
```

## Thread

+ Return to [February 1996](/archive/1996/02)

+ Return to "[Sean Gabb <cea01sig@gold.ac.uk>](/authors/sean_gabb_cea01sig_at_gold_ac_uk_)"
+ Return to<EMAIL_ADDRESS>(tallpaul)](/authors/tallpaul_at_pipeline_com_tallpaul_)"

+ 1996-02-16 (Fri, 16 Feb 1996 14:47:17 +0800) - Re: Online Zakat Payment: Religious tithe. -<EMAIL_ADDRESS>(tallpaul)_
  + 1996-02-16 (Fri, 16 Feb 1996 20:41:47 +0800) - [Re: Online Zakat Payment: Religious tithe.](/archive/1996/02/760d1e19d3a3bd7cfd3cd762c3e28ad0cc2e4a2aecb7bf9a804e3aee0804d21c) - _Sean Gabb<EMAIL_ADDRESS>


text:
---
hrs_structure:
  division: '2'
  volume: '8'
  title: '23'
  chapter: 414D
  section: 414D-283
type: hrs_section
tags:
  - Business
menu:
  hrs:
    identifier: HRS_0414D-0283
    parent: HRS0414D
    name: 414D-283 Grounds for revocation of certificate of authority
weight: 25810
title: Grounds for revocation of certificate of authority
full_title: 414D-283 Grounds for revocation of certificate of authority
---
**§414D-283 Grounds for revocation of certificate of authority.** The department director may commence a proceeding under section 414D-284 to revoke the certificate of authority of a foreign corporation authorized to transact business in this State if:

(1) The corporation fails to:

(A) Pay any fees prescribed by law;

(B) File its annual report for a period of two years;

(C) Appoint and maintain an agent for service of process as required; or

(D) File a statement of a change in the name or business address of the agent as required by chapter 425R; or

(2) A misrepresentation has been made of any material matter in any application, report, affidavit, or other record or document submitted by the corporation. [L 2001, c 105, pt of §1; am L 2002, c 130, §67; am L 2003, c 124, §39; am L 2009, c 55, §27]

license: MIT

repository: SamMade/BlueAndKrabby

branch_name: refs/heads/master

extension: md

license_type: permissive

repo_name: SamMade/BlueAndKrabby


text:
# Core components API

Temporary file for core components API. List of props, public methods and data that are available to use via mixin insertion.
In case of injectable components (like modal) or the ones triggered by Vuex actions you should write them down also. Feel free to write some description/new api proposal for each docummented component.

## AddToCart
This component represents a single button that when pressed adds a product to cart. Right now this component is used on Product page only.
### Props
**product** - An instance of a product
### Data
No data
### Methods
#### addToCart (product)
Dispatches 'cart/addItem' action and passes a product instance as a parameter.
**Parameters:**
*product* - An instance of a product

## Breadcrumbs
This component represents a hierarchy of the current page in relation to the application's structure. It is used in Product, Category, My Account and Compare pages.
### Props
**routes** - An array of route objects, each representing name and path to a parent page.
**activeRoute** - A name of the current page
### Data
No data
### Methods
No methods

## ColorButton
This components represents a button that is used for visualizing different options, specifically product filters. It is used on category's Sidebar and Product pages.
### Props
**label** - label that is shown on the component button.
**id** - identifier that unifies an option among others in array.
**code** - a name of an option that the component is being used to represent (currently 'color').
**context** - a name of an entity that the component belongs to (currently one of 'category' or 'product').
### Data
No data
### Methods
No methods

## Loader
This component is used for visualizing loading process, when something is happening in the background. It is currently used when account is being registered, password is being reset and user is logging in.
### Props
No props
### Data
**message** - A message that is shown while loading process is on.
**isVisible** - Computed property which is equal to UI-store's *loader* property. This prop defines whether to show or hide the spinner.
### Methods
#### show (message = null)
Sets message property and calls UI-store mutation 'ui/setLoader', which causes a spinner to show up.
**Parameters:**
*message* - A text that is shown when loading process is on.
#### hide
Calls UI-store mutation 'ui/setLoader', which hides a spinner.
**Parameters:**
No parameters
### Mounted
Two listeners are defined:
*'notification-progress-start'* - calls *show* method.
*'notification-progress-stop'* - calls *hide* method.

## Logo
This component is intended to serve an image of an application's logo and to navigate the user to a Home page when the logo is pressed.
### Props
No props
### Data
No data
### Methods
No methods

## Newsletter popup (should be using modal)

### Props

### Data

### Methods

## Notification

### Props

### Data

### Methods

## OfflineBadge

### Props

### Data

### Methods

## Overlay

### Props

### Data

### Methods

## PriceButton

### Props

### Data

### Methods

## ProductAttribute

### Props

### Data

### Methods

## ProductLinks

### Props

### Data

### Methods

## ProductListing

### Props

### Data

### Methods

## ProductSlider

### Props

### Data

### Methods

## ProductTile

### Props

### Data

### Methods

## SizeButton

### Props

### Data

### Methods

## Tooltip

### Props

### Data

### Methods

## ValidationError

### Props

### Data

### Methods

repository: ionutindreica/vue-storefront

path: /doc/components/[temporary] core components API.md

filename: [temporary] core components API.md

repo_name: ionutindreica/vue-storefront


text:
# bigcommerce/mock-injector
[![Build Status](https://scrutinizer-ci.com/g/bigcommerce/mock-injector/badges/build.png?b=master&s=6b0bd1c0db716ba85e2c12a64d640ab1a4d85369)](https://scrutinizer-ci.com/g/bigcommerce/mock-injector/build-status/master)
[![Scrutinizer Code Quality](https://scrutinizer-ci.com/g/bigcommerce/mock-injector/badges/quality-score.png?b=master&s=c0811fd76bcb31f9e73a0beb8114e5de34d9f0ca)](https://scrutinizer-ci.com/g/bigcommerce/mock-injector/?branch=master)
[![Code Coverage](https://scrutinizer-ci.com/g/bigcommerce/mock-injector/badges/coverage.png?b=master&s=a592e4140397a936727ce28d598ca19078aa0a64)](https://scrutinizer-ci.com/g/bigcommerce/mock-injector/?branch=master)

Auto-mocking test Dependency Injector component

Will automatically mock all injected dependencies of an object using the BigCommerce Injector. This allows objects to be
refactored to have dependencies removed or new ones added without needing to rebuild all the mock definitions or modify
many tests, making decoupled DI driven development much easier.

Mocks can be configured with expectations by calling getMock(CLASS_NAME) on the TestInjector within your tests. This
will return an ObjectProphecy object (a mock generated by Prophecy - the mocking library included in PHPUnit).

A single mock instance is created per Fully Qualified Class Name, so if the object you're testing takes two of the same
typed parameter, the same mock will be passed to both (so ensure your expectations are set appropriately).

Construction Usage:
````
// All constructor dependencies resolved through mocking
$testObject = $testInjector->create(My\Test\Object::class);

// Parameter injection - will provide parameters either by type, name or position - otherwise resolve via mocking
$testObject = $testInjector->create(My\Test\Object::class, [
    "enabled" => true,              // Named
    7 => "fish",                    // Positional
    Logger::class => new Logger(),  // Type
]);
````

Invocation usage
````
// Call the method 'createUser' on an existing object
$result = $testInjector->invoke($instanceOfThing, "createUser");

// Parameter injection - will provide parameters either by type, name or position - otherwise resolve via mocking
$result = $testInjector->invoke($instanceOfThing, "createUser", [
    "enabled" => true,              // Named
    7 => "fish",                    // Positional
    Logger::class => new Logger(),  // Type
]);
````

For integration with your PHPUnit tests, see: [AutoMockingTest.php](src/AutoMockingTest.php)

(The MIT License)
Copyright (C) 2015-2017 BigCommerce Inc.
All rights reserved.

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.


text:
# Chess AI
## A Chess AI that uses reinforcement learning to play either against itself, or versus a human.

### Setup

```bash
# Install yarn
curl --compressed -o- -L https://yarnpkg.com/install.sh | bash

# Install typescript, angular cli
yarn global add typescript @angular/cli

# In root directory install node_modules
yarn

# Serve the angular app
ng serve
```

### Additional Notes

- MIT Licensed :heart:
- Learn more about the project at https://chess.chriscates.ca


text:
[![idea](http://i.imgur.com/BGMt0Ne.png)](#)

# `$ idea`

 [![Support me on Patreon][badge_patreon]][patreon] [![Buy me a book][badge_amazon]][amazon] [![PayPal][badge_paypal_donate]][paypal-donations] [![Ask me anything](https://img.shields.io/badge/ask%20me-anything-1abc9c.svg)](https://github.com/IonicaBizau/ama) [![Version](https://img.shields.io/npm/v/idea.svg)](https://www.npmjs.com/package/idea) [![Downloads](https://img.shields.io/npm/dt/idea.svg)](https://www.npmjs.com/package/idea) [![Get help on Codementor](https://cdn.codementor.io/badges/get_help_github.svg)](https://www.codementor.io/johnnyb?utm_source=github&utm_medium=button&utm_term=johnnyb&utm_campaign=github)

> A lightweight CLI tool and module for keeping ideas in a safe place quick and easy.

## :cloud: Installation

You can install the package globally and use it as command line tool:

```sh
# Using npm
npm install --global idea

# Using yarn
yarn global add idea
```

Then, run `idea --help` and see what the CLI tool can do.

```
$ idea --help
idea help
usage: idea [command] <idea|filter|id>

A lightweight CLI tool and module for keeping your ideas in a safe place quick and easy.

[command]
  create <idea>           Creates and saves a new idea. Example: `idea create "Implement something very cool"`
  list                    Lists all ideas. Example: `idea list`
  filter <filter>         Lists filtered ideas. Example: `idea filter '{"state": "SOLVED"}'`
  solve <id>              Solves an idea. Example `idea solve 1`
  help                    Prints this help.

Documentation can be found at https://github.com/IonicaBizau/idea
```

## :clipboard: Example

Here is an example how to use this package as library. To install it locally, as library, you can use `npm install idea` (or `yarn add idea`):

```js
// Dependencies
var Idea = require("idea");

// Initialize the idea object
var idea = new Idea("./ideas.json", err => {
    if (err) throw err;
    idea.create("Implement something cool.", err => {
        if (err) throw err;
    });
});
```

## :question: Get Help

There are few ways to get help:

 1. Please [post questions on Stack Overflow](https://stackoverflow.com/questions/ask). You can open issues with questions, as long you add a link to your Stack Overflow question.
 2. For bug reports and feature requests, open issues. :bug:
 3. For direct and quick help, you can [use Codementor](https://www.codementor.io/johnnyb). :rocket:

## :memo: Documentation

For full API reference, see the [DOCUMENTATION.md][docs] file.

## :yum: How to contribute
Have an idea? Found a bug? See [how to contribute][contributing].

## :sparkling_heart: Support my projects
I open-source almost everything I can, and I try to reply to everyone needing help using these projects. Obviously,
this takes time. You can integrate and use these projects in your applications *for free*! You can even change the source code and redistribute (even resell it).

However, if you get some profit from this or just want to encourage me to continue creating stuff, there are few ways you can do it:

 - Starring and sharing the projects you like :rocket:
 - [![Buy me a book][badge_amazon]][amazon]—I love books! I will remember you after years if you buy me one. :grin: :book:
 - [![PayPal][badge_paypal]][paypal-donations]—You can make one-time donations via PayPal. I'll probably buy a ~~coffee~~ tea. :tea:
 - [![Support me on Patreon][badge_patreon]][patreon]—Set up a recurring monthly donation and you will get interesting news about what I'm doing (things that I don't share with everyone).
 - **Bitcoin**—You can send me bitcoins at this address (or scanning the code below): `1P9BRsmazNQcuyTxEqveUsnf5CERdq35V6`

    ![](https://i.imgur.com/z6OQI95.png)

Thanks! :heart:

## :scroll: License

[MIT][license] © [Ionică Bizău][website]

[license]: /LICENSE
[website]: https://ionicabizau.net
[contributing]: /CONTRIBUTING.md
[docs]: /DOCUMENTATION.md
[badge_patreon]: https://ionicabizau.github.io/badges/patreon.svg
[badge_amazon]: https://ionicabizau.github.io/badges/amazon.svg
[badge_paypal]: https://ionicabizau.github.io/badges/paypal.svg
[badge_paypal_donate]: https://ionicabizau.github.io/badges/paypal_donate.svg
[patreon]: https://www.patreon.com/ionicabizau
[amazon]: http://amzn.eu/hRo9sIZ
[paypal-donations]: https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&hosted_button_id=RVXDDLKKLQRJW

repository: melodyjerry/idea

repo_name: melodyjerry/idea


text:
# BookItUp
Book Sharing Android Application

Produced for the Mobile Application Developement course at Politechnic of Turin.

Team members:

 Giorgio Crepaldi https://github.com/Gogo693

 Catia Blengino https://github.com/I-Tac

 Gianpaolo Bontempo https://github.com/mrfreak94

Provided functionalities:

 Login using firebase

 Create and edit profile

 Add a book to the platform

 ISBN book scan and recognition using google api

 Browse books sorted by different criteria using Algolia

 Display books on map

 Chat implemented in Kotlin to contact other users to organize the book exchange

 Reserve and return a book

 Manage rented, owned and lent books

 Notifications using firebase chat

Feel free to fork the project and improve it!

This project was developed for educational purposes, I do not assure a completely bug-free code.

repository: modusV/BookItUp

repo_name: modusV/BookItUp


text:
# ppmd
Converts PowerPoint OOXML (PPTX) files to Markdown-based presentations

Operation:

* call `python3 main.py [path/to/file.pptx]`

Options:

* **`--slidesep`** - determines how individual slides will be separated. Five dashes (`-----`) by default.
* **`--headingsfirst`/`--noheadings`** - sets whether to assume the first text on each slide is a heading. Y by default.
* **`--includenotes`/`--excludenotes`** - sets whether to include slide notes in the output. Includes by default
* **`--notesprefix`** - sets the string used to prefix lines in the notes output - three > (`>>>`) by default

repository: NathanCastle/ppmd

repo_name: NathanCastle/ppmd


text:
# Talking Blogs
Django-based blogging application where users can write/listen/read textual and visual blogs

## Features

1. Personal blogs: User can maintain one's personal blog for personal interests
2. Group blogs: Users with access can collaborate and, evenetually, publish their blog
3. Blog Categories: Users can allocate a caterogry to their blog.
4. Paid Blogs: Optional, false by default, althoyugh a user can set a price tag for someone to access their blog
5. Talking Blogs: Text-To-Speech (TTS) is implemented so user can listen on a go without having to read the text
6. Blog Comments: Users can comment on other users blogs with text and images
7. Blog Ratings: Users can react to others' blogs with 'thumbs up', 'love','insightful','
8. Blog Followers: Users can follow/get followers on their personal/group blogs. Followers of a blog will see its content in their news feed.
9. Blog Layout: 'Advanced' tweaks can be implemented by a user to vary the appearance of one's blog.
10. Blog Stats: Detailed, visual analytics of user's blog performance based on listening hours, ratings, comments & followers
11. Blog Earnings: Users may earn by monetizing their blog by placing ads on their blogs.
12. Blog Ads: Users may advertise their blog/post by initiating an ad capmaign.
13. Blog Wish List: If a user doesn't have the funds to purchase a blog, one may add it to the wish list
14. Blog Reading List: Users may add an owned/free blog to their reading list which contains blogs that a user will read at a later time.
15. Blog Share: Extension that allows for sharing the blog to social medias
16. Blog Reports: In a scenario whereby the content of the blog is inappropriate (based on the regulations), the blog may be reported by the users!
17. Author/Blog Blocks: Blocking a publicator will ensure that the user won't encounter the blocked instance.
18. Author/Blog Mutes: Blocking a publicator will ensure that the user won't see the publications of a blocked instance.
19. Customized Recommendation: User will get recommendations in one's feed based on one's followers, reading engagement, reactions & mutes/blocks.
20. Notifications: User will be notified when one of the following instances occurr: blog reaction/comment/follow/
21. Email Notifications: Above instances are reported by email by default.
22. Messaging: Messaging channels in which users can share blogs to each other and exchange with textual/visual messages

## Functions

1. Registration, Log-in via email & password/third party, forget password --> send a verification code --> set new password
2. Adjusting the style of layout of the blog page
3. Modifying user profile page
4. Writing blogs
5. Commenting on blogs
6. Reacting to blogs
7. Using the reading list

## Third Party Plugins

repository: blogfosters/blog

branch_name: refs/heads/main

repo_name: blogfosters/blog


text:
## :trophy: Evaluation on [GTSDB](http://benchmark.ini.rub.de/?section=gtsdb&subsection=dataset)

Evaluation on the public GTSDB traffic sign detection benchmark follows the following steps: (1) Convert ground truth to an appropriate format, (2) run detector on benchmark images, (3) call evaluation software. The repository contains convenience scripts for these tasks. In the following description we store all evaluation data in a folder ``/tmp/gtsdb-evaluation``. This is not a specific requirement and you may change this location to something different.

### :raising_hand: Convert ground truth

Download [GTSDB](http://benchmark.ini.rub.de/?section=gtsdb&subsection=news) dataset. This dataset contains images in ``PPM`` format as well as the ground truth annotation in ``gt.txt``. Use the following command to convert the dataset ground truth:
```
convert-gtsdb --gtsdb-label-map=resources/labels/gtsdb-label-to-name.yaml  <path_to_gtsdb>/FullIJCNN2013/gt.txt /tmp/gtsdb-evaluation/groundtruth
```
The file [gtsdb-label-to-name.ymal](https://github.com/moabitcoin/Signfeld/blob/master/resources/labels/gtsdb-label-to-name.yaml) contains the mapping from GTSDB class ID to class name as it is used in the synthetic signs dataset.

### Run detector on benchmark

Use the script [detect-synthetic-signs](https://github.com/moabitcoin/Signfeld/blob/master/bin/detect-synthetic-signs) to generate detections on GTSDB images. Following previous example commands the call would look as follows.
```
detect-synthetic-signs --images=<path_to_gtsdb>/*.ppm \
                       --label-map=resources/labels/synthetic-signs-169.yaml \
                       --target-label-map=resources/labels/gtsdb-label-to-name.yaml \
                       --config=experiments/synthetic-signfeld-dataset/logs/config.yaml \
                       --weights=experiments/synthetic-signfeld-dataset/logs/model_final.pth \
                       --output_dir=/tmp/gtsdb-evaluation/detections-signfeld
```
In addition to the detectors configuration (``--config``), weights (``--weights``) and label map (``--label-map``), we also supply the label map of the GTSDB dataset (``--target-label-map``). By supplying a target label map, the script suppresses all detections which are of a class which is not part of the GTSDB dataset. Otherwise, they would be counted as false alarms during evaluation.

### :game_die: Evaluate model

First, download and install the evaluation software:
```
wget https://github.com/rafaelpadilla/Object-Detection-Metrics/archive/v0.2.zip -O Object-Detection-Metrics-v0.2.zip
unzip Object-Detection-Metrics-v0.2.zip
```
Now call the software to evaluate detections against ground truth. The script prints per-class AP and mean average precision according to Pascal VOC metrics.
```
python Object-Detection-Metrics-0.2/pascalvoc.py -gt /tmp/gtsdb-evaluation/groundtruth -det /tmp/gtsdb-evaluation/detections-retinanet-02-11-2019-200K -gtformat xyrb -detformat xyrb -np
```

### :tv: Visualise results.

The repository contains a [script](https://github.com/moabitcoin/Signfeld/blob/master/bin/visualize-synthetic-sign-detections) to visualise detection results. For the data generated in this section, call it as follows:
```
visualize-synthetic-sign-detections --images=<path to GTSB folder>/*.ppm \
                                    --template-dir=synthetic-signs/templates \
                                    --detections=/tmp/gtsdb-evaluation/detections-retinanet-02-11-2019-200K \
                                    --destination=/tmp/gtsdb-evaluation/images-retinanet-02-11-2019-200K \
                                    --min-confidence=0.5
```

repository: memari-majid/signfeld

repo_name: memari-majid/signfeld


text:
# Falco Community Call

[More Information](https://github.com/falcosecurity/community)
[Zoom Link](https://zoom.us/my/cncffalcoproject)
[Recording](https://youtube.com/playlist?list=PLgVVUpW8NIJAaZtszf1_nxB2y6mQrlY4s)

## 2023-02-15

### MC

- Leonardo Grasso ([leogr](https://github.com/leogr))

### Who joined

- Luca Guerra ([LucaGuerra](https://github.com/LucaGuerra))
- Leonardo Grasso ([leogr](https://github.com/leogr))
- Vicente JJ. Miras ([vjjmiras](https://github.com/vjjmiras))
- Aldo Lacuku ([alacuku](https://github.com/alacuku))
- Samuele Cappellin ([@cappellinsamuele](https://github.com/cappellinsamuele))
- Jason Dellaluce ([@jasondellaluce](https://github.com/jasondellaluce))
- Jacqueline Salinas ((https://github.com/jsalinas29)
- Andrea Terzolo ([Andreagit97](https://github.com/Andreagit97))
- Adao Oliveira Junior ([junior](https://github.com/junior))
- Aizhamal Nurmamat kyzy ([aijamalnk](https://github.com/aijamalnk))
- Federico Di Pierro ([@fededp](https://github.com/FedeDP))
- Sumit Bindal ([@sumitb](https://github.com/sumitb))
- Lorenzo Susini ([@loresuso](https://github.com/loresuso))
- Fred Araujo ([@araujof](https://github.com/araujof))
- Marut Pandya ([@pandyamarut](https://github.com/pandyamarut))

### Weekly highlights

### Agenda

- Certificate issues in falco container[alacuku]
    - https://github.com/falcosecurity/falco/pull/2412
    - https://github.com/falcosecurity/libs/pull/871
- Proposing a 0.34.1 patch release for the issues above and... [LucaGuerra]
    - engine version in Falco was not incremented
    - stability fixes in libs
    - we agree to release an hotfix
    - aldo volountered as release manager!!!
- [Jacque] Falco community survey is live - we need your feedback!
    - https://survey.typeform.com/to/JseepIQh
    - closes March 18th!
    - get your swag Falco water bottle + tshirt
    - kicking off contributor of the month
- [jacque] [Falco monthly newsletter](https://docs.google.com/document/d/1lW3ywb2SFvHP0ipkJuZ1LgKoc5gIO1IB9Z3CEr0b6ss/edit?usp=sharing) - feedback and contributions welcomed!
    - will publish each month on the last day of the month
- [Aiz] Falco site improvements plan [GitHub board](https://github.com/orgs/falcosecurity/projects/3/views/1?query=is%3Aopen+sort%3Aupdated-desc)
    - Umbrella issue: https://github.com/falcosecurity/falco-website/issues/826

## 2023-02-08

### MC

- Jacqueline Salinas [jsalinas32](https://github.com/jsalinas32)

### Who joined

- Thomas Labarussias [Issif](https://github.com/Issif)
- Federico Di Pierro ([@fededp](https://github.com/FedeDP))
- Vicente JJ. Miras ([vjjmiras](https://github.com/vjjmiras))
- Stanley Chan ([happy-dude](https://github.com/happy-dude))
- Angelo Puglisi [@deepskyblue86](https://github.com/deepskyblue86)
- Samuele Cappellin ([@cappellinsamuele](https://github.com/cappellinsamuele))
- Jason Dellaluce ([@jasondellaluce](https://github.com/jasondellaluce))
- Aldo Lacuku ([alacuku](https://github.com/alacuku))
- Mauro Moltrasio ([Molter73](https://github.com/Molter73))
- Roberto Scolaro ([therealbobo](https://github.com/therealbobo))
- Aizhamal Nurmamat kyzy ([aijamalnk](https://github.com/aijamalnk))
- Teryl Taylor ([terylt](https://github.com/terylt))
- Andrea Terzolo ([Andreagit97](https://github.com/Andreagit97))
- Luca Guerra ([LucaGuerra](https://github.com/LucaGuerra))

### Weekly highlights

- Falco 0.34 is officially out
    - blog post: https://falco.org/blog/falco-0-34-0/

### Agenda

- Release update on 0.34.0 (yay!)
- Big thank you to the community for helping with this release (including testing, blogging, and other activities)
- SSL (CA) issues are still there https://github.com/falcosecurity/falco/issues/2395#issuecomment-1422635105

repository: falcosecurity/community

repo_name: falcosecurity/community


text:
# ld34
ludum dare entry

# Asset Creds

[pot god](http://opengameart.org/content/pot-god-statue)
[tileset2](http://opengameart.org/content/98-pixel-art-rpg-icons)
[tileset1](http://opengameart.org/content/tileset-1bit-color-extention)
[tileset3](http://opengameart.org/content/recolor-all-the-items)
[potted palm](http://opengameart.org/content/potted-palm)
[scythe and pot](http://opengameart.org/content/scythe-and-pot)
[vegetation](http://opengameart.org/content/paramecijs-vegetation-base-texture-pack)

# Developer notes

## installing

`npm install`
`npm start`

## developing

`npm install --dev` for development

static site in ./client
vendor js is just copied from node_modules to client/js

all prices are in millibitcoin (just for fun)

### TODO

* build a shop function
  * shop.newItem({ texture, name, cost });
  * shop.rejectItem(id)

day clock

http://jsfiddle.net/popopome/uy5m2n1d/1/


text:
# Carlia filicum (Desm.) Höhn., 1918 SPECIES

#### Status
SYNONYM

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

#### Published in
Annls mycol. 16(1/2): 62 (1918)

#### Original name
Sphaeria filicum Desm., 1840

### Remarks
null

repository: mdoering/backbone

path: /life/Fungi/Ascomycota/Dothideomycetes/Capnodiales/Mycosphaerellaceae/Mycosphaerella/Mycosphaerella filicum/ Syn. Carlia filicum/README.md

repo_name: mdoering/backbone


text:
+++
title = "Freestanding Mode"
weight = 100
sort_by = "weight"
insert_anchor_links = "right"
+++

As of version 7.20, the WWT WebGL engine supports a “freestanding mode” that
allows it to run in a purely self-contained fashion — it will not make any
requests to the `worldwidetelescope.org` servers for supporting data. This
disables a number of the engine’s capabilities, but may be of interest for WWT
adopters who are unwilling or unable to depend on external services.

## Limitations of Freestanding Mode

Activating the freestanding mode causes the engine to be limited in the
following ways:

- **No default sky map.** WWT’s built-in DSS sky map will not be
  available. The sky view will only be able to show an empty, black sky unless
  you import your own all-sky survey(s).
- **No built-in image sets.** Likewise, WWT’s built-in database of all-sky maps
  and “study” images will not be available. If you wish to show any surveys or
  studies, you must load them in yourself.
- **No 3D “solar system” mode.** The engine won't prevent you from activating
  this mode, but it will be virtually useless. Many aspects of this mode rely on
  WWT APIs, including:
  - COSMOS galaxies database
  - Hipparcos star database
  - Minor planets database
  - Planet and moon textures
- **Most tours will not work.** If a tour relies on assets provided by the core
  WWT APIs, they will not have been loaded and will be missing. If you construct
  a tour that does *not* rely on these assets, it will work as expected.
- **No automatic proxying.** When fetching web resources, if the WWT engine runs
  into a problem that seems to be due to HTTP/HTTPS mismatches or CORS
  restrictions, it will reroute the relevant requests through a proxy service on
  `worldwidetelescope.org` that works around the issue(s). In freestanding mode,
  this behavior is disabled.
- **No constellation artwork.** These images are pulled from a WWT web API.
- **No ISS (International Space Station) coordinate frame or 3D model.** WWT’s
  support for showing the ISS depends on web APIs to provide its current orbital
  parameters and a 3D model, so this support must be disabled in freestanding
  mode.
- **Tour URLs in WTML folders must be specified.** In the standard mode,
  references to tours can be resolved to a URL that fetches their data based on
  a UUID registered with the WWT webservice. This is not possible in
  freestanding mode.
- **No automatic ID-based thumbnail images for tours.** As above. These will use
  a generic star thumbnail image instead.
- **No automatic name-based thumbnail images for Places in WTML folders.** These
  will use a generic star thumbnail image instead.
- **No automatic Bing-based DEM data for Mercator projection imagesets.** Some
  Earth data have elevation data automatically attached. This feature will be
  deactivated.

## Activating Freestanding Mode

If you are using WWT in [the Vue/Pinia component model](../getting-started/vue-component-model.md),
activate freestanding mode by specifying the [Vue prop] `wwt-freestanding-asset-baseurl`:

```xml
<template>
  <div>
    <WorldWideTelescope
      id="wwt"
      wwt-freestanding-asset-baseurl="https://myassets.org/wwtengine"
    ></WorldWideTelescope>
    <div id="my-ui-controls">...</div>
  </div>
</template>
```

[Vue prop]: https://vuejs.org/guide/components/props.html

This value is a base URL that will be used to look up simple, static data assets
used by the engine. The default value used in the production version of WWT is
`https://web.wwtassets.org/engine/assets`, and you can use that value here if
you don’t mind depending on `wwtassets.org`.

If you're using the [bundled TypeScript
model](../getting-started/bundled-typescript-model.md), you can activate the
mode with an analogous parameter,
{{helpersapi(p="interfaces/InitControlSettings.html#freestandingAssetBaseurl",t="InitControlSettings#freestandingAssetBaseurl")}},
when calling {{helpersapi(p="classes/WWTInstance.html#constructor",t="the WWTInstance constructor")}}
provided in the {{helpersapi(p="index.html",t="@wwtelescope/engine-helpers")}} package.

Finally, at the lowest levels of abstraction, such as what you would have in the
[hosted JavaScript model](../getting-started/hosted-javascript-model.md), you
can call the {{engineapi(p="classes/WWTControlBuilder.html#freestandingMode",
t="freestandingMode() method")}} on the
{{engineapi(p="classes/WWTControlBuilder.html", t="WWTControlBuilder class")}}
exported in the {{engineapi(p="index.html",t="@wwtelescope/engine")}} package.
Once again, this API takes the same asset baseurl parameter as seen above.

## Engine Assets

Currently, the engine assets are not indexed elsewhere. To serve up your own assets,
download the following files from the default baseurl given above:

- `callisto.png`
- `circle.png`
- `ConstellationNamePositions_EN.txt`
- `constellations.txt`
- `earth.png`
- `europa.png`
- `figures.txt`
- `ganymede.png`
- `glyphs1.png`
- `glyphs1.xml`
- `io.png`
- `jupiter.png`
- `mars.png`
- `mercury.png`
- `moon.png`
- `moons.txt`
- `moonshadow.png`
- `neptune.png`
- `pins.png`
- `pluto.png`
- `saturn.png`
- `StarProfileAlpha.png`
- `sun.png`
- `sunCorona.png`
- `thumb_folder.jpg`
- `thumb_folderup.jpg`
- `thumb_star.jpg`
- `uranus.png`
- `venus.png`

The following assets are only used in 3D mode and so are unlikely to be needed if
you are using WWT in freestanding mode:

- `galimg/gal_$N.jpg` for *N* ranging from `0000` to `0255`, inclusive
- `milkywaybar.jpg`
- `saturnringsshadow.png`
- `saturnringsstrip.png`

repository: WorldWideTelescope/wwt-webgl-engine

gha_language: C#

gha_license_id: MIT

repo_name: WorldWideTelescope/wwt-webgl-engine


text:
---
layout: page
permalink: /project2/
---

Projects in this page are organized by **technology**.
- [Supervised Learning](#supervised-learning)
  - [Classification](#classification)
  - [Regression](#regression)
- [Unsupervised Learning](#unsupervised-learning)
  - [Clustering](#clustering)
  - [Recommendation](#recommendation)
- [Computer Vision](#computer-vision)
  - [Convolutional Neural Network](#convolutional-neural-network)
  - [Fully Convolutional Network](#fully-convolutional-network)
  - [MobileNet Single Shot Detection](#mobilenet-single-shot-detection)
  - [Object Detection API](#object-detection-api)
- [Hidden Markov Models](#hidden-markov-models)
- [Reinforcement Learning](#reinforcement-learning)
- [Recurrent Neural Network](#recurrent-neural-network)
- [Database](#database)
  - [SQL](#sql)
  - [NoSQL](#nosql)
- [Behavior Modeling](#behavior-modeling)
- [Visualization](#visualization)
- [Tools I use](#tools-i-use)

----
## Supervised Learning
### Classification
**Finding Donors for Charity** - utilized several supervised algorithms to accurately model individuals' income using data collected from the 1994 U.S. Census. We then chose the best candidate algorithm from preliminary results and further optimize this algorithm to best model the data.

**Predicting Stock Out** - used a variety of different supervised learning approaches (SVMs, KNNs, Decision Trees, Neural Networks) to predict stock out of products for a company, and ultimately found that a Nearest Neighbors model got the best accuracy of 99%. I used Numpy and Sklearn to help preprocess the data and create the models.

**Titanic Survival Exploration** - The project revolved around taking in a dataset of all the passengers in the Titanic disaster, which includes data such as age, gender, ticket price and etc. to predict whether or not they survived.

**Vehicle Detection I** - performed a Histogram of Oriented Gradients (HOG) feature extraction on a labeled training set of images and train a classifier Linear SVM classifier to detect vehicles on the road in a video from a front-facing camera on a car.

### Regression
**Analyzing Boston Housing** - predicted house price in suburbs of Boston, Massachusetts by leveraging the data of a house, such as the average number of rooms among homes in the neighborhood, the percentage of homeowners in the neighborhood considered "lower class" (working poor) and the ratio of students to teachers in primary and secondary schools in the neighborhood.

**Lightning strike Induced Outages** - This research analyzed lightning strikes and weather conditions, and investigated how lighting-induced outages of airport infrastructure and facilities impacted airports' performance from an economic perspective.

----
## Unsupervised Learning
### Clustering
**Customer Segmentation** - this project was to best describe the variation in different types of customers that a wholesale distributor interacted with by analyzing customers' annual spending amounts on diverse product categories.
### Recommendation
**Select Genre of Next Movie** - to pick the genre of our studio’s next movie using the dataset of IMDB movie ratings. I came up with two metrics to evaluate the popularity and reputation of a movie in a cerain genre which guide us to make recommendations.

----
## Computer Vision
**Detecting Lane Lines I** - detected lane lines on a highway by creating thresholded binary image as well as calculated curvature and vehicle's position in a video by fitting 2nd order polynomial functions.
### Convolutional Neural Network
**Developing A Dog Identification App** - aimed at developing an algorithm that could accept any user-supplied image as input. If a dog is detected in the image, it would provide an estimate of the dog's breed. If a human is detected, it would provide an estimate of the dog breed that is most resembling.

**Traffic Sign Recognition** - used LeNet architecture to train a CNN to recognize traffic signs.
### Fully Convolutional Network
**Detecting Lane Lines II** - used Fully Convolutional Network to identify free space on the road in a video clip.
### MobileNet Single Shot Detection
**Vehicle Detection II** - used MobileNets separable depthwise convolutions and Single Shot Detection (SSD) to build a pipeline to detect cars, people, bikes in a image.
### Object Detection API
**Traffic Light Detection and Classification** - used Google Tensorflow Object Detection API to train a classifier to detect and classify traffic light in videos.

----
## Hidden Markov Models
**Sign Language Recognition System** - employed hidden Markov models (HMM's) to analyze a series of measurements taken from videos of American Sign Language (ASL) collected for research.

----
## Reinforcement Learning
**Train a Smartcab to Drive** - applied reinforcement learning techniques for a self-driving agent in a simplified world to aid it in effectively reaching its destinations in the allotted time.

----
## Recurrent Neural Network
**Predicting Stock Price** - used a Recurrent Neural Network regressor to forecast stock prices 7 years in advance.

**Create A Text Generator** - used a Recurrent Neural Network (RNN) architecture to create an English language sequence generator capable of building semi-coherent English sentences from scratch by building them up character-by-character.

----
## Database
### SQL
**Wrangle Open Street Map Data** - I selected San Diego, CA, United States in [Open Street Map](https://www.openstreetmap.org) and used data munging techniques, such as assessing the quality of the data for validity, accuracy, completeness, consistency and uniformity, to clean the raw data. I used SQL as the data schema to complete the project.
### NoSQL

----
## Behavior Modeling
**A Study on Airlines’ Schedule Block Time Adjustment Behavior** - built discrete choice models to identify factors that affect airlines’ schedule block time (the yearly time differences of scheduled departure times and arrival times) and quantify their impacts.

----
## Visualization
**Visualizing Impact of Component Shortage on Finished Goods** - created a dashboard using [Bokeh](https://bokeh.pydata.org/en/latest/) and [Heroku](https://dashboard.heroku.com/) to visualize monetary loss of finished goods due to shortage of certain component/components. Please note that it may take some time to load the website cause I used a free-tier Heroku account to deploy the app.

----
## Tools I use
* Language: Python, R, scala, C++
* Database: MySQL, MongoDB
* Data Analysis: pandas, numpy, sklearn
* Big Data: Hadoop, Spark
* Deep Learning: tenforflow, Keras
* Data Visualization: D3.js, Shiny, Tableau
* Spatial Analytics: ArcGIS, QGIS


text:
## About

This script will:

 1. download the Ubuntu 12.04 alternate server, 32bit iso
 2. ... do some magic to turn it into a vagrant box file
 3. output package.box

## Usage

    ./build.sh

This should do everything you need.

### Keith's notes

Changed to work on an Ubuntu host, to create a 32bit image, and localized for the US.


text:
# Youtube Detox
🧘♀ Get control over YouTube and be distraction free

## Contributing

Feel free to contribute to this project and treat it like your own. 😊

## Useful Links

- Website: [https://youtubedetox.skynox.tech](https://youtubedetox.skynox.tech)

## License

MIT License

## Author

[Sarthak Sharma](https://twitter.com/sarthology)

## Acknowledgments

Icons used from [flaticon](https://flaticon.com).

repository: YouuTeee/youtube-detox

repo_name: YouuTeee/youtube-detox


text:
# Proposal: Update Angular 6 Version

Since the last release of Angular, [version 6](https://blog.angular.io/version-6-of-angular-now-available-cc56b0efa7a4), the aerobase-angular is not working anymore due the changes in<EMAIL_ADDRESS>
---

##  Table of Contents

* [Proposed By](#prb)
* [History](#his)
* [Reason to add](#rta)
* [Implementation Details](#imd)

---

##  Proposed By

* Name: Mauricio Gemelli Vigolo
* Date: 2018-05-08
* Github user: [aerobase](https://github.com/aerobase)

###  History

|    Date    |                        User                         | Details                          |
| :--------: | :-------------------------------------------------: | -------------------------------- |
| 2018-05-08 | [aerobase](https://github.com/aerobase) | Initial version of this document |

##  Reason to add

With the release of Angular 6 the aerobase-angular is not compiling anymore due changes with rxjs@6. Also trying to
update to Angular 5 was [causing compilation errors](<(https://github.com/angular/angular/issues/19607)>) for those using Angular 4, so the dev dependencies of aerobase-angular
were frozen in version 4.4.4.
The problem in 4.4.4 was that dev dependencies were outdated as [david-dm](https://david-dm.org/aerobase/aerobase-angular?type=dev) report which is also not good for the library.

##  Implementation Details

The release 6.0.0 of aerobase-angular will change the versioning mechanism, as described bellow:

* aerobase-angular 6.x.x will be compatible with angular 6
* aerobase-angular 5.x.x will be compatible with angular 5
* aerobase-angular 4.x.x will be compatible with angular 4

This way the library will support the newest angular versions not forgetting legacy applications.

repository: aerobase/aerobase-angular

repo_name: aerobase/aerobase-angular


text:
# Repeatable Fields

[Plugin's Homepage with Demo](http://www.rhyzz.com/repeatable-fields.html)

## Description

Repeatable Fields is a jQuery plugin which let's you create a set of fields that can be made to repeat. You can do the following to a field that is repeatable:

* Add new instance
* Remove existing instance
* Reposition an instance

The functionality that is provided by this plugin can also be referred to as:

* Dynamic Fields Plugin
* Dynamic Rows plugin
* Add Remove Rows Plugin

## Requirements

This plugin requires [jQuery](http://jquery.com/) and [jQuery UI Sortable](https://jqueryui.com/sortable/).

### Example

#### HTML

					Add

					Move Row
					Move Up
					<input type="text" class="move-steps" value="1" />
					Move Down

    			An Input Field

    				<input type="text" name="an-input-field[{{row-count-placeholder}}]" />

    			Remove

#### JavaScript

    jQuery(function() {
    	jQuery('.repeat').each(function() {
    		jQuery(this).repeatable_fields();
    	});
    });

## Options

```
wrapper: '.wrapper',
container: '.container',
row: '.row',
add: '.add',
remove: '.remove',
move: '.move',
move_up: '.move-up',
move_down: '.move-down',
move_steps: '.move-steps',
template: '.template',
is_sortable: true,
before_add: null,
after_add: self.after_add,
before_remove: null,
after_remove: self.after_remove,
sortable_options: null,
row_count_placeholder: '{{row-count-placeholder}}',
```

wrapper

Specifies an element that acts as a wrapper.

container

Specifies an element within the wrapper which acts as a container.

row

Specifies an element within the container that acts as a row holder. The row is what is repeated.

add

Specifies an element within the wrapper which let's you add more more

remove

Specifies an element within the row which let's you remove the current row

move

Specifies an element within the row which let's you reposition the current row.

move_up

Specifies an element clicking on which moves the row up

move_down

Specifies an element clicking on which moves the row down

move_steps

Specifies a field using which steps can be provided for the move_up and move_down functionality. An element will move up or down this number of steps. Default value is 1 step. -1 moves the item all the way up or down.

template

Specifies an element within the container which acts as a row template.

is_sortable

Specifies whether rows can be sorted

before_add

Specifies a function to run before a row is added

after_add

Specifies a function to run after a row is added

before_remove

Specifies a function to run before a row is removed

after_remove

Specifies a function to run after a row is removed

sortable_options

Specifies an object that can contain Options, Methods and Events which are passed to jQuery UI Sortable

row_count_placeholder

Specifies the row count placeholder to be used

repository: jaanusnurmoja/repeatable-fields

repo_name: jaanusnurmoja/repeatable-fields


text:
laser gun
==========

A javascript Raygun.io plugin alternative to:
- [https://github.com/MindscapeHQ/raygun4node](raygun4node)
- [https://github.com/MindscapeHQ/raygun4js](raygun4js).

[![Build Status](https://travis-ci.org/psirenny/lasergun.png?branch=master)](https://travis-ci.org/psirenny/lasergun)

![lasergun](/index.png?raw=true)

Motivation
----------

The raygun4node plugin does not work when browserified.
I found it difficult to handle errors in isomorphic web apps.
Creating a wrapper around raygun4node and raygun4js is hard because each plugin has a different api.

I also wanted the plugin to have a chainable fluent interface.
Passing custom data, tags, etc. in the other plugins was cumbersome.

Installation
------------

    $ npm install lasergun --save

Usage
-----

    var raygun = require('lasergun');

    raygun()
      .version('1.0.0')
      .apiKey(...)
      .data({foo: 'bar'})
      .tags(['baz', 'quz'])
      .message('testing')
      .send(err);

repository: psirenny/lasergun

repo_name: psirenny/lasergun


text:
## FiSpace

PHP Library

------------

CREATOR BY FLEXICE

Based on 2019-12-13 23:18:04 Friday

------------

`
composer require flexice/fispace
`

```php
// Require Auto Loader
require_once "vendor/autoload.php";

// Uses

use flexice\space\FiSpace;
use flexice\space\FiUse;

// Create Example Class FiSpace And Add In His Arguments
$FiSpace = new FiSpace(new FiUse());
```

[YouTrack ♥](https://fisystems.myjetbrains.com/youtrack/projects/26130ed0-e9e4-414c-9826-4bf79e27e724 "YouTrack")

[YouTrack Issues ♥](https://fisystems.myjetbrains.com/youtrack/issues "YouTrack Issues")

[Packagist ♥](https://packagist.org/packages/flexice/fispace "Packagist")


text:
---
id: 0001
path: "/hello-world"
date: "2018-07-02T19:59:33.962Z"
title: "🎉 Hello World 🎉"
---
This is how it's supposed to start right, with a "Hello World"? At least that's the lesson I learned whilst reading the book that inspired this project '97 things every programmer should know'.

The New Coder is my effort to create a resource for those who have already found themselves in the tech industry but are still wanting to learn from their peers and maybe share their experiences so that we may all continue to improve and connect.

I think the web is saturated with 'Learn to code' material and there are some wonderful resources for those taking their first steps into coding or levelling up with new tech but I've struggled to find as much for actually being a coder *. The daily grind, how to navigate the office politics, learning to speak up during retrospectives, debugging live code when the app is on fire. The things they don't teach you because then you'd run a mile.

I'm also interested in all the little things you pick up along the way that make such a huge difference to your daily life as a coder. For example learning Resharper was a must early on as I work in C#, for others, learning intellisense or shortkeys make no sense (you see what I did there). That's a boring statement, not a story. You won't remember it in 5 minutes.

But...if I told you that the reason I know this to be true is because my mentor at work would keep a tally of the times I used my mouse to navigate to a base type or member implementation instead of `ctrl + F12` or any other shortcut I failed to use. Then, at the end of my probabtion, and after he had totted up the total average time I'd wasted each day, showed me that I had lost a potential afternoon of productivity or beer time!

Well that's a little more interesting, not a lot, but a little. You see I'm not much of a writer but I love to read stories. That's why I need your help. Remember at the start I said I wanted this to be a resource, well that's true, but I can't create it all by myself, that would be one snoozefest let me tell you.

I personally know of more than a few new coders whose stories I would love to hear and if they know a few that could share their stories and they knew a few, we could have the beginnings of a joke about recursion and more importantly the beginnings of a great collection of real stories from real coders making their first waves in tech.

If you don't wish to contrubute by sharing your story I'd be grateful if you can help spread the word by sharing on social media where you can.

Yours in coding,

## _The New Coder_
❤

* _For every time I refer to coding or coder please note that I wish this to be transferable with programmer or software engineer, DevOps person, or developer... whichever vernacular you prefer._

repository: haletothewood/TheNewCoder

gha_language: CSS

repo_name: haletothewood/TheNewCoder


text:
---
id: 885
title: 'It could be me. (Draw #07)'
date: 2010-04-19 11:06:13 +00:00
author: mark
layout: post
tags:
  - it could be me
  - lottery
---
I'm writing this, so you know i haven't hit the jackpot.

Numbers drawn: **3, 10, 29, 31, 36, 44** + bonus ball **45**

My Numbers: **6, 9, 16, 23, 27, 32**

No. of matching numbers: **0**

Price of ticket: **£1**

Amount won: **£0**

Financial outcome: **-£1**

And thus…

**Lottery balance: £-7  //  Not A Lottery balance: £7**

gha_license_id: NOASSERTION


text:
# Shims

[![Build Status](https://travis-ci.org/djspiewak/shims.svg?branch=master)](https://travis-ci.org/djspiewak/shims)

Once upon a time, [scalaz](https://github.com/scalaz/scalaz) was the standard library for functional and generally higher-order abstractions in the Scala community.  Library and framework authors could confidently write and publish functions in terms of `scalaz.Monad`, `scalaz.State` and so on.

Unfortunately, for various reasons, this is no longer the case.  [Cats](https://github.com/non/cats) is now on the scene, and many users may want to use it rather than scalaz in their downstream projects.  As a library author, we do not want to force a specific choice upon our users.  We would ideally like to supply our users with a single dependency that could work with scalaz *or* cats, without introducing classpath polution or code duplication.

Shims is a tool for achieving this goal.  It is *explicitly* targeted at upstream library authors, not downstream, user-facing projects!  If you have already committed to specifically scalaz or cats, or (more importantly) you have no reason to support both, **you do not need shims!**  However, if you are a library or framework author who does not want to force your downstream users into one universe or another, shims will solve all your problems.

The way this works is you divide your framework into (at minimum) three submodules: for example, *emm-core*, *emm-scalaz* and *emm-cats*.  You put all of your code into the *core* submodule, which has a dependency on *shims-core*.  You do *not* depend on scalaz or cats in your *core!*  When you need typeclasses (e.g. `Monad` or `Functor`), you use the types provided by shims (literally, `shims.Monad`).  Then, your *scalaz* and *cats* submodules are defined to depend on your *core* as well as *shims-scalaz* or *shims-cats*, respectively.  You can optionally define an `object scalaz extends shims.Implicits` (or analogously for cats), which insulates your end users from ever needing to know about shims.  This also gives you the ability to add custom shims in a uniform and user-transparent fashion.

From a user standpoint, they must add a single additional import which would not be required if you wrote specifically against cats *or* scalaz.  For example, for the emm project, the following *pair* of imports are required:

```scala
import emm._
import emm.compat.scalaz._
```

If emm were written specifically against scalaz, the second import would be unnecessary.  Users will also need to add a second SBT dependency on the *-scalaz* or *-cats* submodule of your project (e.g. *emm-scalaz*).  Transitive dependencies will take care of the rest!

Note that it is highly recommended that you use the following convention in your own projects if at all possible.  Or at least, it is recommended that you shy away from something like this:

```scala
// do NOT do this!
import emm._
import emm.scalaz._             // no no no no!
```

The problem with this revision (droping the `compat`) is hierarchical imports.  Importing `emm._` brings `scalaz` into scope, which masks the `_root_.scalaz` package, which is where all of the real scalaz stuff lives!  Tucking your compatibility objects off into their own package, which won't be independently imported by users, avoids this problem.

## SBT Setup

```sbt
libraryDependencies += "com.codecommit" %% "shims-core" % ShimsVersion
```

In your *-scalaz* and *-cats* subprojects, add the following dependencies:

```sbt
libraryDependencies += "com.codecommit" %% "shims-scalaz-71" % ShimsVersion        // for scalaz 7.1

// or!

libraryDependencies += "com.codecommit" %% "shims-scalaz-72" % ShimsVersion        // for scalaz 7.2

// or!

libraryDependencies += "com.codecommit" %% "shims-cats" % ShimsVersion        // for cats 0.4.1
```

The current stable version of shims is **0.3**:

```sbt
val ShimsVersion = "0.3"
```

## Features

At present, the only non-typeclass feature which shims provides is a default right-projected syntax for `scala.util.Either`.  It provides this primarily in lieu of providing an `EitherLike`, which may change in the future.  The syntax also provides scalaz-style symbolic aliases for `Either`, so you can use it without tearing your hair out.  The syntax can be brought in by importing `shims.syntax.either._`

### Typeclasses

*TODO*

### Type Shapes

*TODO*

### Providing Instances

*TODO*

## What Shims is NOT

Shims is *not* a replacement for scalaz or cats!  It is not a competitor.  It does not fill the same needs.  If you think you need shims and you're not an upstream library author, chances are you actually need scalaz or cats.  Shims is a compatibility layer, nothing more.  If you're writing a "downstream" project (i.e. you deploy to a server, instead of to bintray/sonatype), you should absolutely not see "shims" in your SBT files.

## Contributing

This is a lazily-evaluated library.  Currently, it contains only just enough to make [emm](https://github.com/djspiewak/emm) and [http4s](http://http4s.org) operational.  If you need more than that, PRs are very much welcome.  Please note that this is a compatibility layer *specifically* for typeclasses!  It is not a replacement for scalaz *or* cats.  For example, we will not implement an `Xor` (or `\/`) delegate.  `State`, `Kleisli` and anything ending in `T` are similarly out of scope.  The whole point is just to write code which works with either cats or scalaz typeclasses, where they are equivalent.  I reserve the right to be pointlessly opinionated about what is and isn't out of scope.  Also, when we implement something, I reserve the right to be pointlessly opinionated about the names and types thereof.  So basically, welcome to my bikeshed.  :-)

repository: edmundnoble/shims

repo_name: edmundnoble/shims


text:
# Typescript Migration Linter
Bash script to help with your javascript to typescript migration

It will check the changed files for your branch against a base branch (usually master) and if it finds any files still end in .js or .jsx it will exit with code 1.  If used in a CI/CD pipeline it will protect your team from touching or adding new javascript files without converting them to typescript.

## Usage
`./main.sh <BASE_BRANCH> <APP_DIRECTORY> <BLACKLIST_DIRECTORY>`

* BASE_BRANCH - this is the base branch that you plan on merging into.  For many teams this is going to be master
* APP_DIRECTORY - this is the relative path to where your application JS code is located.  Additional directories can be separated with a comma delimiter.
* BLACKLIST_DIRECTORY - (optional) this is the relative path to directories that you wish to ignore for the JS rule enforcement.  Additional directories can be separated with a comma delimiter.

The script will segment the changed files by their git diff filter: added, modified, and renamed.  Please see example output below. Both directory parameters accept regex.

### Example
`./main.sh master app1/,app2 app1/scripts/`

`./main.sh production app/`

`./main.sh production app/ app/.*__tests__/`

### Example output
```
Started typescript file check
  Added:
      app/api/booger.js
  Modified:
      app/api/collectionService.js
  Renamed:
      app/api/NotesService.js

error: JS(X)? files detected, be sure to update the files above before proceeding
Exiting with code: 1
```


text:
### Short Video-Analytics

#### Introduction
   ###### 1. This small project is conducted to analyze the candidate's answer during video interview.
   ###### 2. Project used existing pre-trained models
            * For video/image emotion analysis : FER library is used which has pre-trained model based on Harcascade and MTCNN
            * For speech to Text analysis :Speech_recognition library is used
            * For text sentiment analysis :VADER from ntlk library is used.VADER(Valence Aware Dictionary and sentiment Reasoner)
   ###### 3. The Analysis is presented in the form of Heat-Maps showing neagtive-positive sentiments from words and negative and positive emotions of face in video.

#### Emotions-Sentiment Heat Maps

###### The sample of result/outcome of the code.

![](https://github.com/RammySekham/Short-Video-Analytics/blob/main/Images/HeatMaps.PNG)

#### How to run this code
       1. Select directory ($ cd <directory>)
       2. Clone the repo  ($ git clone <repo-url>)
       3. Install dependencies
          pip install -r requirements.txt
                OR individual modules using
          pip install <module_name>
       4. Place mp4 video file of small size 20-40sec introductory video in your working directory
       5. Change file_name in settings.py file
       6. Run the code "Video_Analytics.py"


text:
# Collective Knowledge repository for unified AI components and workflows

[![compatibility](https://github.com/ctuning/ck-guide-images/blob/master/ck-compatible.svg)](https://github.com/ctuning/ck)
[![License](https://img.shields.io/badge/License-BSD%203--Clause-blue.svg)](https://opensource.org/licenses/BSD-3-Clause)

# Introduction
This is a [Collective Knowledge](http://cKnowledge.org) repository to unify and share
AI workflows (code, data sets and models) as portable, customizable and reusable components
with a common Python API and JSON meta-information.
Such workflows help to continuously optimize and co-design
the whole SW/HW stack for AI/ML workloads across diverse platforms, frameworks, libraries,
models and data sets (see [ACM ReQuEST tournaments](http://cKnowledge.org/request),
[MLPerf benchmark](http://mlperf.org) and the [public CK repository with crowdsourced expeirments](http://cKnowledge.org/repo))
while helping researchers and developers to adapt to technological chaos:

[![logo](http://cknowledge.org/images/ai-cloud-resize.png)](http://cKnowledge.org/ai)

You can browse reusable AI artifacts from this repository [online](http://cKnowledge.org/ai-artifacts).

CK supports the following AI frameworks:
[TensorFlow](https://github.com/ctuning/ck-tensorflow),
[MXNet](https://github.com/ctuning/ck-mxnet),
[PyTorch](https://github.com/ctuning/ck-pytorch)
[Caffe](https://github.com/dividiti/ck-caffe),
[Caffe2](https://github.com/ctuning/ck-caffe2),
and [CNTK](https://github.com/ctuning/ck-cntk).

# Coordination of development
* [CK consortium](http://cKnowledge.org/partners.html)
* [ReQuEST consortium](http://cKnowledge.org/request.html)
* [cTuning Foundation](http://cTuning.org)
* [dividiti](http://dividiti.com)

# Installing and customizing CK

First you need to install Collective Knowledge framework (CK) as described
[here](https://github.com/ctuning/ck#Installation).

If you have never used CK, we also suggest you to check this
[CK getting started guide](https://github.com/ctuning/ck/wiki/First-Steps).

You may also want to check how to [customize your CK installation](https://github.com/ctuning/ck/wiki/Customization).
For example, you can force CK to install all packages (code, data sets and models) to your scratch file system
instead of the default ${HOME}/CK-TOOLS by specifying the path using environment variable "CK_TOOLS"!
You can also specify where to install all CK repositories instead of the default ${HOME}/CK
using environment variable "CK_REPOS".

*Note that [CK](https://github.com/ctuning/ck/wiki)
is a continuously evolving community project similar to Wikipedia,
so if you don't like something or something is not working,
please do not hesitate to send your feedback
to the [public mailing list](https://groups.google.com/forum/#!forum/collective-knowledge),
open tickets in related [CK GitHub repositories](http://cKnowledge.org/shared-repos.html),
or even contribute patches, updates, new workflows and research components!*

# Installing and using this repository

You can pull this CK repo with all dependencies as follows:

```
$ ck pull repo:ck-ai

$ ck ls ai-artifact

$ ck dashboard ai-artifact
```

Add new artifact description:
```
$ ck add ai-artifact:{AI alias}
```

Follow interactive instructions to add an artifact description.

Feel free to ask further questions via [CK public mailing list](http://groups.google.com/group/collective-knowledge).

# Related Publications
See [CK publications](https://github.com/ctuning/ck/wiki/Publications).

## Next steps

We are gradually adding references about [all existing AI artifacts](http://cknowledge.org/repo/web.php?template=cknowledge&sort_by_uoa=yes&search_by_tags=tensorflow|caffe|caffe2|cntk|mxnet&aview=yes&ignore_without_alias=yes&archive_all=yes&force_limit=200&repo_list=ck-caffe,ck-tensorflow,ck-caffe2,ck-cntk,ck-pytorch,ck-mxnet,ck-mvnc)
in the CK format including their installation, usage and optimization.
The [CK consortium](http://cKnowledge.org/partners.html) and [MLPerf](http://mlperf.org) are also working on a unification of all AI APIs and meta descriptions!

repository: ctuning/ck-ai

repo_name: ctuning/ck-ai


text:
---
type: page
title: Code of Conduct
---

We value the participation of each member of the community and want everyone to have an enjoyable and fulfilling experience. Accordingly, all members are expected to show respect and courtesy to other members and the community at large.

To make clear what is expected, all members, organizers and volunteers associated with the Philly CocoaHeads community are required to conform to the following Code of Conduct.

## Code of Conduct

The Philly CocoaHeads community is dedicated to providing a harassment-free experience for everyone, regardless of gender, sexual orientation, disability, physical appearance, body size, race, or religion. We do not tolerate harassment of participants in any form.

All communication should be appropriate for a professional audience including people of many different backgrounds. Sexual language and imagery is not appropriate.

Be kind to others. Do not insult or put down other members. Behave professionally. Remember that harassment and sexist, racist, or exclusionary jokes are not appropriate for the Philly CocoaHeads community.

These expectations are to be considered during your participation at all group related events and gatherings. It also extends to your behavior in the community at large.

Members violating these rules may be asked to leave the group or disciplined at the sole discretion of the organizers.

Thank you for helping make this a welcoming, friendly group for all.

## What is Harassment?

Participants asked to stop any harassing behavior are expected to comply immediately.

Be careful in the words that you choose.  Excessive swearing and offensive jokes are not appropriate for the Philly CocoaHeads community.

## Contact Information

If you are being harassed, notice that someone else is being harassed, or have any other concerns, please contact an organizer immediately.

Email<EMAIL_ADDRESS>
## License

This Code of Conduct is based on the [PyCon Code of Conduct](https://us.pycon.org/2013/about/code-of-conduct/) which was forked from the example policy from the [Geek Feminism wiki, created by the Ada Initiative](http://geekfeminism.wikia.com/wiki/Conference_anti-harassment/Policy) and other volunteers under a Creative Commons Zero license.

Code of Conduct for the Philly CocoaHeads group is licensed under a [Creative Commons Attribution 3.0 Unported License.](http://creativecommons.org/licenses/by/3.0/)

repository: phillycocoa/website

gha_language: HTML

repo_name: phillycocoa/website


text:
# SuperSaaS Python SDK

Online bookings/appointments/calendars in Python using the SuperSaaS scheduling platform - https://supersaas.com

## Prerequisites

1. [Register for a (free) SuperSaaS account](https://www.supersaas.com/accounts/new), and
2. get your account name and password.

##### Dependencies

Python 2.7 or 3.*

No external libraries. The supporting `urllib`/`urllib2` and `json`/`simplejson` standard libs are loaded conditionally.

## Installation

The SDK is available from PyPi and can be installed using pip, e.g.

    $ pip install supersaas-api-client

## Configuration

Set the SuperSaaS `Client` authorization credentials (e.g. with keys/values from system environment) by calling the `configure` method:

    from SuperSaaS.SDK import Client
    Client.instance().configure(
        account_name=ENV['your-env-supersaas-account-name'],
        password = ENV['your-env-supersaas-account-password'],
        user_name = ENV['your-env-supersaas-user-name']
    )

## API Methods

Details of the data structures, parameters, and values can be found on the developer documentation site:

https://www.supersaas.com/info/dev

#### Create User

Create a user with user attributes params:

    Client.instance().users.create({'full_name': 'Example Name', 'email'<EMAIL_ADDRESS>'slot_id': 12345}, true, true) #=> <SuperSaaS.SDK.Models.User.User>

#### Update User

Update a user by `user_id` with user attributes params:

    Client.instance().users.update(12345, {'full_name': 'New Name'}) #=> {}

## Additional Information

+ [SuperSaaS Registration](https://www.supersaas.com/accounts/new)
+ [Product Documentation](https://www.supersaas.com/info/support)
+ [Developer Documentation](https://www.supersaas.com/info/dev)
+ [Ruby API Client](https://github.com/SuperSaaS/supersaas-ruby-api)
+ [PHP API Client](https://github.com/SuperSaaS/supersaas-php-api)
+ [NodeJS API Client](https://github.com/SuperSaaS/supersaas-nodejs-api)
+ [C# API Client](https://github.com/SuperSaaS/supersaas-csharp-api)
+ [Objective-C API Client](https://github.com/SuperSaaS/supersaas-objc-api)
+ [Go API Client](https://github.com/SuperSaaS/supersaas-go-api)

Contact: [support<EMAIL_ADDRESS>
## Releases

The gem follows semantic versioning, i.e. MAJOR.MINOR.PATCH

**MAJOR**releases add or refactor API features and are likely to contain incompatible breaking changes

**MINOR**releases add new backwards-compatible functionality

**PATCH**releases apply backwards-compatible bugfixes or documentation updates

## License

The SuperSaaS Python API Client is available under the MIT license. See the LICENSE file for more info.

repository: TertiumQuid/supersaas-python-sdk

repo_name: TertiumQuid/supersaas-python-sdk


text:
# CS1632 Deliverable 4
Spring Semester 2015

DUE 24 MAR 2016

## Deliverable 4

For this assignment, you will either:
1. Develop a combinatorial test and explain it
2. Write your own JUnit-based property-based test to check Arrays.sort(int[] arr) method in Java

There are no partners for this deliverable.

For the combinatorial testing, you will need to develop a combinatorial test with at least five parameters (variables).  You may generate the combinatorial table using the NIST ACTS tool or another combinatorial generator.  To download the NIST ACTS tool, you may follow the instructions here: http://csrc.nist.gov/groups/SNS/acts/documents/comparison-report.html.  I recommend you do this as soon as possible to ensure everything works on your system and it can be downloaded.  Since this requires at least one manual step on the part of NIST researchers, do it as soon as possible!

You will come up with your own system (software) to test.  This can be already existing software, or software that you make up.  Build the system using NIST ACTS (or a similar tool), which will generate a covering array.  You should make tests which covers all pairs (2-way interactions).  Based on this covering array, generate a test plan (in the same format as Deliverable 1) which indicates how to test all of pairs (as discussed in class).

You may not use "fonts and font effects" as your software.  You should come up with your own idea.

For the JUnit-based property-based tests, generate a minimum of 100 different random arrays of different sizes, and test different properties (many examples were discussed in the lecture on property-based testing) of sorting them.  You may use Java's built-in Arrays.sort() method.  You should test at least three different properties of each sorted array.  You should use traditional JUnit test procedures (e.g., use assertions, don't use System.out.println during normal execution, etc.)  Since we are testing a built-in Java method, I don't expect any failures, but who knows, you may be the one to find a bug in Java's own libraries!

You may do this either all in one JUnit test method, or with one method per property.

For either of these projects, I expect an approximately one-page (3 or 4 paragraphs) description of why you chose this project, how you went about doing it, any issues you faced, and what you learned.  If you are doing the combinatorial testing option, use this space to explain what you are testing.  If it is existing software, please provide a link; if it is software that you have thought of yourself, please explain it in enough detail that I can understand what you are testing.

Both of these options also will require a screenshot.  Please put this on its own page.  For the combinatorial testing project, I expect a screenshot of NIST ACTS or whatever other tool you are using, specifically showing the all-pairs covering array.  For the property-based testing project, I expect a screenshot of the executed JUnit test(s).

## Format
Every assignment should have a title page with:
* The name of the student
* The title "CS 1632 - DELIVERABLE 4"
* The name of the project you worked on - "COMBINATORIAL TESTING" or "PROPERTY-BASED TESTING"

## Grading (Combinatorial Testing)
* Summary - 10%
* Screenshot of NIST ACTS (or similar) tool with your system - 20%
* Test Plan - 70%

## Grading (Property-Based Testing)
* Summary - 10%
* Screenshot of completed test - 20%
* Test Code - 70%

Please feel free to email me at<EMAIL_ADDRESS>or come to office hours to discuss any problems you have.


text:
# IlmoitusTaulu
RSS Feed -> Telegram integration

Fetches posts from RSS feed and posts them to a Telegram channel. Uses Azure Cosmos DB to store timestamp value of latest post.
Should be used as a triggered scheduled Webjob or triggered by Task Scheduler.

# How to deploy
1. Fill in needed settings in appsettings_example.json and rename it to appsettings.json
2. Run or schedule application

repository: juilijoel/IlmoitusTaulu

repo_name: juilijoel/IlmoitusTaulu


text:
---
title: "More Resources"
disableToc: true
---

Discover more AWS resources for building and running your application on AWS:

* AWS IoT Lens - https://d1.awsstatic.com/whitepapers/architecture/AWS-IoT-Lens.pdf
* MQTT topics Whitepaper - https://d1.awsstatic.com/whitepapers/Designing_MQTT_Topics_for_AWS_IoT_Core.pdf
* Connected home site - https://aws.amazon.com/iot/solutions/connected-home/
* Industrial - https://aws.amazon.com/quickstart/architecture/aws-industrial-time-series-data-connector/
* Cognito user pools with AWS IoT - https://aws.amazon.com/pt/blogs/iot/configuring-cognito-user-pools-to-communicate-with-aws-iot-core/
* Device Defender - https://aws.amazon.com/pt/blogs/iot/detect-anomalies-connected-devices/
* Greengrass Dive Deep - https://www.youtube.com/watch?v=1N7Y_gxZ9Wg

repository: reisjr/aws-iot-labguide

repo_name: reisjr/aws-iot-labguide


text:
---
title: dvdupgrd.exe | DVDUpgrd
excerpt: What is dvdupgrd.exe?
---

# dvdupgrd.exe

* File Path: `C:\windows\SysWOW64\dvdupgrd.exe`
* Description: DVDUpgrd

## Hashes

Type | Hash
-- | --
MD5 | `05E6467411B53FA9A9BDA3ED902100D3`
SHA1 | `3DD411A7B6B12992C55D99671F2867EEFE8014F7`
SHA256 | `95CCE95AB9100F71CA6BE4C393432A06CD08EB6BA34B43A514F663C8BF7B898A`
SHA384 | `93E3A7318BEBCBC00C0D4BC088E50A1B7E3DD86113D7CDFDBD9EC44A3B43DECCF2554B543CF7B58C20F747B7F95409E8`
SHA512 | `5F7E017E2792C5D8D8834F10318254F73F0D22A6EB144FC7B75403C57B83ACEAA55DF5D32590794D0F7183A9A9A582368212997AD38516FB06459D2BBA04943E`
SSDEEP | `384:WwHPRzPUmikmIBRbxL1E4Sx1/iLpEXgE8Nnu/xxyvGZWu3WIG:MmRmIjxBE31ErZNu/xx4G/`

## Signature

* Status: The file C:\windows\SysWOW64\dvdupgrd.exe is not digitally signed. You cannot run this script on the current system. For more information about running scripts and setting execution policy, see about_Execution_Policies at http://go.microsoft.com/fwlink/?LinkID=135170
* Serial: ``
* Thumbprint: ``
* Issuer:
* Subject:

## File Metadata

* Original Filename: DVDUpgrd.EXE.MUI
* Product Name: Microsoft Windows Operating System
* Company Name: Microsoft Corporation
* File Version: 6.3.9600.16384 (winblue_rtm.130821-1623)
* Product Version: 6.3.9600.16384
* Language: English (United States)
* Legal Copyright:  Microsoft Corporation. All rights reserved.

MIT License. Copyright (c) 2020-2021 Strontic.


text:
#gruntFront

###Introduction

A Grunt.js, Jade, LESS & SASS starter pack for front-end designers/developers

####1) Install Git

1.1) Mac (you need MacPorts already installed). Run the following command in **Terminal**

    sudo port install git-core +svn +doc +bash_completion +gitweb

1.2) To test your Git install run the following command in **Terminal**

    git --version

You should see something like this

    git version <IP_ADDRESS>

####2) Install node.js via Mac OSX Installer

2.1) Uninstall any previous Node.js versions you may already have

2.2) Download & Install the Node.js Mac OSX Installer - [http://nodejs.org/dist/v0.10.23/node-v0.10.23.pkg](http://nodejs.org/dist/v0.10.23/node-v0.10.23.pkg "")

2.3) To ensure the correct permissions are set by running the following in **Terminal**:

    sudo chown -R $USER /usr/local

2.4) Test your node.js install;

    node -v

you should see something like

    v0.10.24

2.5) node.js comes with an excellent package manager called **npm**. Next test it to see that its working OK:

    npm -v

you should see something like this;

    1.3.21

####3) Install Grunt.js

In **Terminal** run;

    npm install -g grunt-cli

####4) Grab the demo code from GitHub

In **Terminal** run;

    git clone https://github.com/ianscrivener/starter4Russ
    cd start4Russ

This will create a new directory called 'starter4Russ'.. something like this

    |source
        |img
        |jade
        |less
        |sass
        index.jade
    | wwwroot
        |css
        |img
        index.html
    Gruntfile.js
    package.json
    README.md

####5) Install the projects

In **Terminal** run;

    npm init

This will download & install all the Grunt.js packages requiredf or our project.

####6) Start grunt

In **Terminal** run;

    grunt

This *should* compile all the files (Jade, LESS, SASS), start a web browser and open the development site in your web browser.

To create minifed files use

    grunt production

repository: ianscrivener/gruntFront

repo_name: ianscrivener/gruntFront


text:
# Abstract

The Hotels50k project is a large-scale database and image recognition project aimed at
combating sex trafficking. This is done by trying to recognize images from hotel rooms that
contain victims of sex trafficking. Image recognition is an extraordinarily challenging task due to
poor image quality, different cameras, perspectives, and many other factors. In addition to
creating a dataset of over 1 million images from booking websites and crowd-sourced through a
mobile app, the original authors developed a baseline model for image recognition based on the
Hotels50k dataset. In this paper, I will present a novel method of feature extraction and
recognition designed to improve the image recognition accuracy of the Hotels50k project.

# Introduction

There is an extraordinary number of photos taken of victims of sex trafficking online today, and
many of such pictures are taken in hotel rooms. Thus, the ability to recognize hotel rooms from
an image is of great interest and importance when trying to combat sex trafficking. This
relationship is what sparked the idea for the original Hotels50k dataset and image recognition
task. However, image recognition of hotels is a unique challenge in comparison to more generic
image recognition tasks that are placed in one of the following categories (Grauman and Leibe
2011):

1. Basic-level categories (e.g., ‘building’)
2. Specialized categories (e.g., ‘church’)
3. Exact instances (e.g., ‘the Notre-Dame’).

The Hotels50k domain spans the latter two of these generic categories. To learn what a specific
hotel looks like requires the learning of shared features across chains and what features make
certain chains identifiable.

This paper has three main contributions. First, providing a novel method of extracting features
from an image using state-of-the-art semantic segmentation networks. Second, collecting
important data on the similarities and differences amongst semantic classes to facilitate future
work on this problem. Third, proposing a unique method for choosing the "best" result from the
multi-feature kNN model built for this project.

# Methodology

First, I set up a state-of-the-art Resnet50+Pyramid Pooling model here. Several other models
were provided, however, I chose only to experiment with the Resnet50+Pyramid Pooling (PPM)
model due to its high level of reported accuracy and my familiarity with the model. The PPM
model is trained on the ADE20k dataset with 150 semantic classes - an issue considering the
semantically labeled Hotels50k data uses only 27 semantic classes.

To overcome this, I conducted two groups of training and evaluation experiments. The first
group was the model trained on the 150 semantic classes from the ADE20K dataset, and
evaluated on the Hotels50k data.

The Hotels50k data was adapted to these 150 semantic classes by by-hand mapping of Hotels50k
semantic classes to ADE20K semantic classes. To approach the second group, training on the 27

semantic classes from Hotels50k, I adapted the model above utilizing transfer learning. Each
layer of the encoder (Resnet50) was frozen, with the output layers for the decoder (PPM) adapted
to 27 semantic classes. Due to higher evaluation accuracy on the models trained with 150
classes, and because the models trained on 27 classes did not achieve the same separation in
distributions between the closest vector in the same and different semantic classes as in the 150
classes. Only the model trained on 150 classes was used to produce features.

In the original Hotels50k paper, a representative feature was computed over an entire image,
whereas the aim in this project is to create an average feature for each semantic class in each
image, which we call "semantic pooling". "Semantic pooling” is based on the assumption that the
average “bed” feature is similar from hotel to hotel in the same chain. To extract the average
semantic class features from each image, the image was first fed through the Resnet50+Pyramid
Pooling (PPM) semantic segmentation model. The predictions for the image were saved, along
with a 2048-dimensional feature map produced by the ResNet50 encoder. Each semantic class
present in the prediction, whose surface area also totaled over 10% (an arbitrary number) of the
total surface area of the image, was recorded. The coordinates of the location within each image
of each semantic class that met the surface area requirement were interpolated from the
prediction to the 2048-dimensional feature map. The average of the 2048-dimensional vectors
from each semantic class was calculated, giving a representative 2048-dimensional feature for
each semantic class in the image. The average semantic features were serialized and recorded in
an index file.

The semantically pooled features were computed for the entirety of the unconcluded test data set
(17k images), and the semantically pooled features for the training set are in the process of being
computed (~400k/1.1 million done).

In a change from regular kNN, the entire training dataset was not searched through. Instead, for
each testing image, the averaged semantic features were computed in an identical method as
described above for the training images. Then, instead of searching through the entire training
dataset for the closest semantic feature to each semantic feature in the test image, only the
training images with the same semantic features were compared. For example, if a bed and a wall
were found in the query image, the algorithm would conduct a kNN search through all the bed
features in the dataset and then all the wall features averaging the results, rather than the features
for every class. In line with the evaluation protocol created in the original Hotels50k paper, I
evaluated kNN accuracy on chains (Marriot, Days Inn, etc.) and instances (DC Marriot). The
average accuracy for each testing image over all of the semantic classes found in that image was
averaged and recorded. This was repeated for k=1,3,5. The higher values of k (k>10) could not
be computed as there were not enough features in each class.

# Results

In the first set of graphs shown on the right, there is a large separation between the closest of the
same class and the closest of a different class. In the second set on the right, some classes are
more unique to certain hotels. It was hoped that this would be seen naturally in the kNN,
however, in the final table, this is not the case.

# Conclusion

While the results were not as expected, there are several takeaways for continuing work on this
project and explanations for the results. There were several positive takeaways from this project.

First, there is now a method to extract semantic features from images. This method works
accurately and quickly, allowing for the large-scale extraction from datasets with millions of
photos. Second, as displayed in Figure 1, these features have clear separations supporting the
claim that distinct semantic classes are extracted from the images.

This is an important finding, as it gives a basis for the idea that we can search for the closest
feature in the same class to obtain the chain or instance that a query image originated.

However, the results of the kNN do not agree. There are some improvements or changes that
would yield better results in future experiments. First, there was not enough training data. There
was a large amount of unexpected server downtime leading to not all the training data features
being computed. This is an obvious problem, as now some chains and instances are represented
in the training data that are not represented or cannot be used to make an accurate inference.

Second, some classes were underrepresented or overrepresented in the training data. A solution
to this is recomputing features with a lower threshold for total surface area. In the experiments
run, the surface area threshold was set at 10%, meaning a semantic feature must take up 10% of
the picture to be computed and saved. While performing these experiments, it became clear that
even with this low margin, smaller, more distinct semantic classes such as lighting and art pieces
were underrepresented. The large semantic features like floors, walls, and ceilings were
overrepresented. In future experiments, I would reduce this surface area margin to obtain a more
representative distribution of the features.

Third, there needs to be a more robust scoring function to determine which k vectors are the
closest. The project needed a form of kNN that did not just minimize the distance between two
vectors but also considers how unique certain semantic classes are between hotel chains and

instances. This would most likely mean some form of weighting the distance between two
vectors, the unknown test feature and the known training image, by the likelihood that the
semantic class being computed are from the same class and chain(like the graphs to the left).
Instead of computing the Euclidean or cosine distance and taking the chain/instance with the
closest distance, we could look at how likely a semantic class is from a hotel chain and how
distinct that class is from that chain compared to other chains.

In addition to the flaws explored above, other paths would be prudent to explore in future
experiments. First, test different models for semantic segmentation. There were several models
given in the semantic segmentation suite, mentioned previously and they may be able to generate
more distinct features that would aid in searching.

Second, it would be worthwhile to explore the use of only the 27 classes from the Hotels50k
dataset. The 27 classes were not used because they did not achieve the same separation in
distributions between the closest vector in the same and different semantic classes as in the 150
classes. This could be due to the smaller number of semantic classes. There are several classes in
the Hotels50k dataset that could be further broken down into distinct micro classes rather than
more macro classes. Furthermore, more semantically labeled Hotels50k images would need to be
created. There were simply not enough to create a more accurate semantic segmentation module
that could be trained.

Finally, we could adapt what features are being computed. Instead of an average semantic class
feature for each image, a representative semantic class feature is created for each chain and
instance. Even within hotels, there can be variations in lighting and picture angle, which could

lead to different representations from one hotel room to another in the same hotel. Normalizing
the feature over the entire instance or chain would be more useful in catching the variations
within chains and instances.


text:
# Demo 4

Describe patterns to update

```
# Start the service stack in background
docker-compose up -d
```

- Configuration files are persisted on the local file system in `opennms-etc`.
- Customizations in etc-overlay and jetty-webinf-overlay for authentication
- Important! Always backup configs, RRD files and database.
- You can use the Docker container images to iniatilize a directory with a specific pristine configuration
- Use the configuration tester to validate a configuration directory against any specific version

```
# Test configuration with a specific version
docker run --rm -v $(pwd):opennms-etc-21.0.3-1:/opt/opennms/etc opennms/horizon-core-web:22.0.4-1 -t -a
```

```
# Get all files which are changed from a pristine and custom configuration within a version
ls -l
opennms-etc-21.0.3-1
opennms-etc-21.0.3-1-pristine
```
# Show all files which are changed between a pristine 21.0.3 and my custom config
diff -rq -EBbw opennms-etc-21.0.3-1 opennms-etc-21.0.3-1-pristine

Files opennms-etc-21.0.3-1/categories.xml and opennms-etc-21.0.3-1-pristine/categories.xml differ
Files opennms-etc-21.0.3-1/config.properties and opennms-etc-21.0.3-1-pristine/config.properties differ
Files opennms-etc-21.0.3-1/libraries.properties and opennms-etc-21.0.3-1-pristine/libraries.properties differ
Files opennms-etc-21.0.3-1/opennms-upgrade-status.properties and opennms-etc-21.0.3-1-pristine/opennms-upgrade-status.properties differ
Only in opennms-etc-21.0.3-1: org.opennms.features.datachoices.cfg
Only in opennms-etc-21.0.3-1: pluginManifestData.xml
Files opennms-etc-21.0.3-1/snmp-config.xml and opennms-etc-21.0.3-1-pristine/snmp-config.xml differ
Files opennms-etc-21.0.3-1/thresholds.xml and opennms-etc-21.0.3-1-pristine/thresholds.xml differ
Files opennms-etc-21.0.3-1/users.xml and opennms-etc-21.0.3-1-pristine/users.xml differ
Files opennms-etc-21.0.3-1/viewsdisplay.xml and opennms-etc-21.0.3-1-pristine/viewsdisplay.xml differ
```


text:
# RMI Deno Package

![](https://img.shields.io/badge/available%20on-deno.land/x-6B3E2E.svg?logo=deno&labelColor=555555&style=for-the-badge)
![](https://img.shields.io/github/v/release/swimauger/rmi.svg?color=C38452&style=for-the-badge)
![](https://img.shields.io/github/repo-size/swimauger/rmi?color=A1785C&label=Size&style=for-the-badge)
![](https://img.shields.io/github/license/swimauger/rmi?color=CCB494&style=for-the-badge)

## Remote Method Invocation in TypeScript for Deno Compiler

> Remote Method Invocation (RMI) is an architecture in which Server Side methods are exposed directly to the client and called on the server. Eliminating the repetitive process of programming the same API on both the client and  server.

## **Getting Started**
```javascript
  import { Registry, Side, Bundle } from "https://deno.land/x/rmi/mod.ts";
```

## **Creating and Registering a Remote Object and Route**
### *On the server:*
```typescript
  // Create a Remote Object for the "login" bundle
  class Authentication extends Bundle('login') {
    static init() {
      // Initialize database with key
    }

    @Side.Server
    static signup(username: string, password: string) {
      return someToken;
    }
  }

  Authentication.init();

  // Register Remote Object
  Registry.registerRemoteObject(Authentication);

  // Register a new route
  Registry.registerRoute({
    path: '/',
    method: 'get',
    response: function(req: ServerRequest) {
      req.respond({
        body: Deno.readFileSync('page.html')
      });
    }
  });

  // Start Server on port 8080
  Registry.createServer(8080);
```

### *On the client:*
```html
  <!-- Load the login bundle in a script -->
  <script src="/rmi/login"></script>

  <!-- Use the remote object API on the client -->
  <script>
    (async () => {
      const token = await Authentication.signup('johndoe', 'password123');
    })();
  </script>

  <!-- That's it!!! -->
```

## Scoping your Remote Object

### Bundle Scoping:
```typescript
  class Authentication extends Bundle('login')
```
- **Bundle Scoping allows the developer to Bundle multiple remote objects into a single script file**
- In the previous server example on the first line, you can see the Authentication remote object can be accessed in the login bundle.

### Side Scoping:
```typescript
  @Side.Server
  invokedOnServerCalledFromClient()

  @Side.Client
  invokedOnClientCalledFromServer()

  @Side.Serialized
  invokedOnClientCalledFromClient()

  // No Decorator
  invokedOnServerCalledFromServer()
```
- **Side Scoping is how you want methods to be invoked**
- Side.Server turns the function on the client to a request to the server
  - Used for logic you want to be called on the server from the client
- Side.Client turns the function on the client into an event listener for calls from the server
  - Used for logic you want to be called on the client from the server
- Side.Serialized keeps the exact same function on the client
  - Used for logic you want to use directly on the client
- No Decorator keeps the exact same function ONLY on the server
  - Used for logic you dont want to be accessible at all by the client

repository: swimauger/rmi

repo_name: swimauger/rmi


text:
# Page Layouts

## Introduction

**Page Layouts** provide configuration for the visual layout of various components within an app.

The components that can be configured are:

- [Master Panels](master_panels.md)
- [User Profile](user_profile.md)
- [Navigations](navigations.md)
- [Dashboards](dashboards.md)
- [Content Pages](content_pages.md)
- [Panel Views](panel_views.md)

Administration is provided in [Admin: Page Layouts](/admin/page_layouts)

## Assignment to App Types

Typically, each definition is assigned to an **App Type**. The current *app type* a user is using selects the appropriate definitions to apply.

A special case allows definitions to have a null *app type*, in which case the definition
is available to users irrespective of their current *app type* (i.e. in all *app types*). Typically definitions assigned to an *app type* appear ahead of those with a null *app type*. For definitions
where only a single definition can be used, one with an *app type* will override one
available to all *app types*.

## Contents

- [Detailed Options](detailed_options.md)

repository: consected/restructure

branch_name: refs/heads/develop

gha_language: PLpgSQL

repo_name: consected/restructure


text:
# Sections (Organisms)

Sections are analogy to [Organisms](https://bradfrost.com/blog/post/atomic-web-design/#organisms)
in atomic design methodology.

`Blocks` give us some building blocks to work with,
and we can now combine them together to form `sections`.
Sections are groups of `blocks` joined together to form a relatively complex,
distinct section of an interface.

`Sections` can consist of similar and/or different block types.
For example, a masthead `section` might consist of diverse components like a logo,
primary navigation, search form, and list of social media channels.
But a “product grid” `section` might consist of the same `block`
(possibly containing a product image, product title and price)
repeated over and over again.

Building up from blocks to sections encourages creating standalone, portable,
reusable components.

repository: YuriiF/react-blitz

gha_language: JavaScript

repo_name: YuriiF/react-blitz