text:
# Angular_idle #

[![Codacy Badge](https://api.codacy.com/project/badge/Grade/94f190553d604a3895b999cb82e2622b)](https://www.codacy.com/app/tushar/Angular_idle?utm_source=github.com&utm_medium=referral&utm_content=nimjetushar/Angular_idle&utm_campaign=Badge_Grade)
<br/>
[![GitHub license](https://img.shields.io/github/license/nimjetushar/Angular_idle.svg)](https://github.com/nimjetushar/Angular_idle/blob/master/LICENSE)

This README would normally document whatever steps are necessary to get your application up and running.

### What is this repository for? ###

* Quick summary - This library is use to determine the whether application is idle or not and to perform
required operation if idle for some-time
* Version 1.0

### How do I get set up? ###

* Summary of set up
For basic configuration setup just look at the example.

* Configuration
In angular config can perform following configuration setting. Just have to inject idleProvider to get working.

1: Setting idle timer:
This is used to set the idle timer for which the application will get idle complete event
idleProvider.idle( *specify time in ms* ).
The input should be in integer.
eg- idleProvider.idle( 60000 ) this specify idle time of 1 min
Default time is 30 min

2: Auto resume:
Based on this the idle timer will resume
idleProvider.autoResume( *boolean* ).
By default auto resume is true

3: Select interrupt:
Interrupt are the event for which you want to reset the idle timer
idleProvider.interrupt( *string* ).
eg- interrupt: 'mousemove keydown DOMMouseScroll mousewheel mousedown touchstart touchmove scroll'

4: Disable Title change:
This will change the title of the application when application is idle.
idleProvider.titleDisabled( *boolean* )
If this is set to true document's title change will be disabled.
By default it is set to false

5: Document title on idle:
This is used to change the document title based on your choice of text
idleProvider.title( *string* ).

* Dependencies
Require Angular.js version 1.X

* Methods

1: watch:
Watch is used to initialize the idle timer

2: unwatch:
This is used to cancel the currently running timer

3: Destroy:
This is used to destroy the event whic is used to reset the idle timer.
If you call destroy none of the functionality will work as the event itself is destroyed.

4: event "idleComplete"
Have to apply event listener i.e. $rootScope.$on('idleComplete', function () {}) to perform your operations.
This is broadcast when application is idle for specified time.

Note:
If auto resume is disabled then to reinitialize the idle timer you have to call "start" explicitly


text:
# Universal Weakly Supervised Segmentation by Pixel-to-Segment Contrastive Learning
By [Tsung-Wei Ke](https://www1.icsi.berkeley.edu/~twke/), [Jyh-Jing Hwang](https://jyhjinghwang.github.io/), and [Stella X. Yu](http://www1.icsi.berkeley.edu/~stellayu/)

Weakly supervised segmentation requires assigning a label to every pixel based on training instances with partial annotations such as image-level tags, object bounding boxes, labeled points and scribbles. This task is challenging, as coarse annotations (tags, boxes) lack precise pixel localization whereas sparse annotations (points, scribbles) lack broad region coverage. Existing methods tackle these two types of weak supervision differently: Class activation maps are used to localize coarse labels and iteratively refine the segmentation model, whereas conditional random fields are used to propagate sparse labels to the entire image.

We formulate weakly supervised segmentation as a semi-supervised metric learning problem, where pixels of the same (different) semantics need to be mapped to the same (distinctive) features. We propose 4 types of contrastive relationships between pixels and segments in the feature space, capturing low-level image similarity, semantic annotation, co-occurrence, and feature affinity They act as priors; the pixel-wise feature can be learned from training images with any partial annotations in a data-driven fashion. In particular, unlabeled pixels in training images participate not only in data-driven grouping within each image, but also in discriminative feature learning within and across images. We deliver a universal weakly supervised segmenter with significant gains on Pascal VOC and DensePose.

## Code Base
This release of code is based on [SegSort](https://github.com/jyhjinghwang/SegSort) in ICCV 2019.

### Prerequisites

1. Linux
2. Python3 (>=3.5)
3. Cuda >= 9.2 and Cudnn >= 7.6

### Required Python Packages

1. pytorch >= 1.6
2. numpy
3. scipy
4. tqdm
5. easydict == 1.9
6. PyYAML
7. PIL
8. opencv
9. pydensecrf

## Data Preparation

### Pascal VOC 2012

1. Augmented Pascal VOC training set by [SBD](http://home.bharathh.info/pubs/codes/SBD/download.html). [Download link](https://github.com/jyhjinghwang/SegSort/blob/master/dataset/voc12/sbd_clsimg.zip) provided by [SegSort](https://github.com/jyhjinghwang/SegSort). Please unzip it and put it besides the VOC2012/ folder as sbd/dataset/clsimg/.
2. Ground-truth semantic segmentation masks are reformatted as grayscale images. [Download link](https://www.dropbox.com/sh/fd2m7s87gk7jeyh/AAC6tN6syhFKmEaDYCwUIgnXa?dl=0) provided by [SegSort](https://github.com/jyhjinghwang/SegSort).
3. The over-segmentation masks are generated by combining contour detectors with gPb-owt-ucm. [HED-owt-ucm masks](https://www.dropbox.com/sh/fd2m7s87gk7jeyh/AAC6tN6syhFKmEaDYCwUIgnXa?dl=0) provided by [SegSort](https://github.com/jyhjinghwang/SegSort).
4. Scribble and point annotations by [ScribbleSup](https://jifengdai.org/downloads/scribble_sup/). We follow ["Regularized Losses for Weakly-supervised CNN Segmentation"](https://github.com/meng-tang/rloss/blob/master/data/pascal_scribble/convertscribbles.m) to process scribble annotations. You can download the processed ground-truth scribble([100%](https://drive.google.com/file/d/1R25yksUYCo59ljtuXxC9l2cd6iZFsjpW/view?usp=sharing), [80%](https://drive.google.com/file/d/1A8mAFya8o22bKO-3yQuBPJR_Ki-4NFCO/view?usp=sharing), [50%](https://drive.google.com/file/d/1d38S_K-y3oLUtNAa2IcjuJsmjzyEus40/view?usp=sharing) and [30%](https://drive.google.com/file/d/1cV19sv0JHxEi95dUr-_fF3oXUbMztwf3/view?usp=sharing)) and [point](https://drive.google.com/file/d/13CsLen7i8aHCXpqnjkHcjrpX5wAcVJLl/view?usp=sharing) annotations, and put them under VOC2012/scribble folder. For scribbles, we set dilation size to 3; For points, we set dilation size to 6.
5. Cam annotations by [SEAM](https://github.com/YudeWang/SEAM). You can download the processed CAMs for [image tag](https://drive.google.com/file/d/1bIHgMfzfiewabO57SJXA6IorknpVQUk-/view?usp=sharing) and [bounding box](https://drive.google.com/file/d/1zhGAqNc3OguUfmipOyYnaXERbAwv3T_S/view?usp=sharing) annotations, and put them under VOC2012/cam. For image tags, we set `alpha` to 6 and probability threshold to 0.2. Pixels with probability less than 0.2 are considered as unlabeled regions. For bounding boxes, we normalize CAM to the range of 0 and 1 within each box, and set the probability threshold to 0.5. The pixel labels outside the boxes as background class.
6. Dataset layout:
```
   $DATAROOT/
       |-------- sbd/
       |          |-------- dataset/
       |                       |-------- clsimg/
       |
       |-------- VOC2012/
                  |-------- JPEGImages/
                  |-------- segcls/
                  |-------- hed/
                  |-------- scribble/
                  |            |-------- dilate_3/
                  |            |-------- dilate_6_0.0/
                  |
                  |-------- cam/
                               |-------- seam_a6_th0.2/
                               |-------- seambox_a6_th0.5/
```

### DensePose

1. Images from [MSCOCO](https://cocodataset.org). Download 2014 Train and Val Images ([here](https://cocodataset.org/#download)).
2. Ground-truth semantic segmentation by [DensePose](http://densepose.org/). You can download the processed ground-truth segmentation [here](https://drive.google.com/file/d/1vNGgamOjw7J0p8AAASy9igVpUCDjvytb/view?usp=sharing), and put them under segcls/ folder.
3. Ground-truth point annotation [here](https://drive.google.com/file/d/17zH3OFKXEr9y0QWNYP-9ry22SkhmhEmt/view?usp=sharing). Put them under segcls/ folder.
4. The over-segmentation masks by [PMI](http://web.mit.edu/phillipi/pmi-boundaries/) and gPb-owt-ucm. Download [PMI-owt-ucm masks](https://drive.google.com/file/d/1KZEEyZJBfTQYS63DkdasY4-DnFiFCBHN/view?usp=sharing) and put them under seginst/ folder.
5. Dataset layout:
```
   $DATAROOT/
       |-------- images/
       |          |-------- train2014/
       |          |-------- val2014/
       |
       |-------- segcls/
       |          |-------- densepose/gray/
       |          |-------- densepose_points/gray/
       |
       |-------- seginst/
                  |-------- pmi_0.1_256/
```

### ImageNet Pre-Trained Models
We use the same ImageNet pretrained ResNet101 as [EMANet](https://github.com/XiaLiPKU/EMANet). You can download the pretrained models [here](https://drive.google.com/file/d/1I5WIu8jHQF-BksWiMrttiuNp1JnyRslh/view?usp=sharing) and put it under a new directory SPML/snapshots/imagenet/trained/. **Note: we do not use MSCOCO pretrained ResNet.**

### Pascal VOC 2012 Trained Models.
We provide the download links for our SPML models trained using image-level tag/bounding box/scribble/point annotations on PASCAL VOC, and summarize the performance as follows. **Note: we report the performance with denseCRF post-processing.**

| Annotations   |      val      |     test      |
| ------------- | ------------- | ------------- |
|  [Image Tags](https://drive.google.com/file/d/1lLgpINx2vm1s2Gkq_S4TAg9tmUOvI0XL/view?usp=sharing)   |     69.5      |      71.6        |
| [Bounding Box](https://drive.google.com/file/d/1uZBPKJRHXPBkNdALXfBoXLSwUmub8G0s/view?usp=sharing)  |     73.5      |      74.7        |
| [Scribbles](https://drive.google.com/file/d/1uU7l9BWC2BaQQk50vp3tF-u72jM9iQJW/view?usp=sharing)  |     76.1      |      76.4        |
| [Points](https://drive.google.com/file/d/1kgYItTXZS2p6vL4xHU1JEpspSbt0Lau6/view?usp=sharing)  |     73.2     |      74.0        |

### DensePose Trained Models.

We provide the download link for our SPML models trained using point annotations on DensePose [here](https://drive.google.com/file/d/1NVBf0rQJjsLryNow7ne_wtKUHCR0nuFF/view?usp=sharing). We achieve 44.15% of mIoU on minival2014 set.

## Bashscripts to Get Started

### Pascal VOC 2012

* SPML with image-level tag.
```
source bashscripts/voc12/train_spml_tag.sh
```

* SPML with bounding box.
```
source bashscripts/voc12/train_spml_box.sh
```

* SPML with scribbles.
```
source bashscripts/voc12/train_spml_scribble.sh
```

* SPML with points.
```
source bashscripts/voc12/train_spml_point.sh
```

### DensePose

* SPML with points.
```
source bashscripts/densepose/train_spml_point.sh
```

## Citation

If you find this code useful for your research, please consider citing our paper Universal Weakly Supervised Segmentation by Pixel-to-Segment Contrastive Learning.
```
@inproceedings{ke2021spml,
  title={Universal Weakly Supervised Segmentation by Pixel-to-Segment Contrastive Learning},
  author={Ke, Tsung-Wei and Hwang, Jyh-Jing and Yu, Stella X},
  booktitle={International Conference on Learning Representations},
  pages={},
  year={2021}
}
```

## License

SPML is released under the MIT License (refer to the LICENSE file for details).

license: MIT

branch_name: refs/heads/master

extension: md

license_type: permissive


text:
# PandaFPS
An FPS controller made in Panda3D

# System Requirements
A Windows PC (XP+)
Any Linux distribution that support the Panda3D SDK
Panda3D installed properly (the PIP version)
Python3 with PIP

# About
This is a simple FPS controller made by a one-person development studio. Raycast bullets/advanced object collision will be added soon!

# How to install
You need the latest version of Panda3D from the official website. The PIP version was used in development so it is reccomended that you use that. As Panda3D is bundled with a special version of Python with PATH requirements satisfied, you only need Python for downloading with PIP. To insert your models into the game, add your .BAM files into the models folder of your Panda3D installation. You may add your music into the music folder (see the music path in the code).
# More
This movement controller has some problems with framerate drops (due to models with an in-efficient amount of meshes. If you find some way to improve this movement controller please create a push request and I will review and put your code into the main branch.

# Buy me a coffee!
Being a developer is hard work and requires a lot of time. Supporting me on Patreon really means a lot and will also allow me to release more updates/bugfixes to this project.

repository: InstytutXR/PandaFPSController

branch_name: refs/heads/main

repo_name: InstytutXR/PandaFPSController


text:
If you are building a website, you may naturally still want to minify/bundle your assets, but sadly virtually all JavaScript and CSS minifiers out there are written in Node.Js and so it's a major PITA to use them.

I was so fed up with this that I put together this thing that packages [terser](https://github.com/terser/terser) and [clean-css](https://github.com/clean-css/clean-css) in a single stand-alone executable that you can just copy to wherever you need it and you don't have to worry about crappy Node.js and it's weekly breaking changes and countless other nuisances.

# usage
    ./minify terser|cleancss <option>

    # Invoke terser
    ./minify terser --help

    # Invoke clean-css
    ./minify cleancss --help

repository: megakraken/minify

repo_name: megakraken/minify


text:
# STIB stub

This small Flask server stubs the [STIB-MIVB
API](https://opendata.stib-mivb.be/).

## Endpoints

### Supported:

  - `/token`: stubs the OAuth2 Client Credentials token endpoint, returns a
    Bearer token. Not secure! (see below)
  - `/OperationMonitoring/1.0/VehiclePositionByLine/<ids>` *(incomplete)*:
    returns a JSON file containing *only* `pointId` values for the *first* id
    requested in the comma-separated list. The other keys are not returned. The
    `pointId` values change over time at a regular interval.

### Not supported:

  - `/OperationMonitoring/1.0/PassingTimeByPoint/<ids>`
  - `/Files/2.0/Gtfs`
  - `/Files/2.0/Shapefiles`

## OAuth2 compliance

### Authentication
This stub completely disregards authentication:

  - The token endpoint ignores the provided client credentials and always
    outputs the same token.
  - The other API endpoints ignore the presence and validity of the attached
    tokens.

### Security
This stub server is not protected by TLS. This can be a problem with compliant
OAuth2 client implementations, as the standard states that client credentials
and access tokens must not be transmitted in the clear.

With [oauthlib](https://github.com/oauthlib/oauthlib), setting the environment
variable `OAUTHLIB_INSECURE_TRANSPORT` to `1` relaxes this security
requirement.

repository: cebamps/stib-stub

repo_name: cebamps/stib-stub


text:
cfml.dictionary
========
The CFML dictionary project.  Aims to have the different vendor and version, versions.

```xml
<dependency>
    <groupId>com.github.cfparser</groupId>
    <artifactId>cfml.dictionary</artifactId>
    <version>2.5.0</version>
</dependency>
```

repository: rcastagno/cfparser

repo_name: rcastagno/cfparser


text:
# Reaper

A simple module that turns DeprecationWarning into errors when your library
version or requirements change

It is notoriously difficult to keep track of deprecated function and clean dead
code regularly release after release. In particular, when marking a function as
deprecated, it is common to forgot mentioning the timeline of deprecation.

This forces you to actually pass a simple specification of when the deprecation
should happen, and dead code be removed.

## Use case

Deprecate component of your own library.

Let's say the current version of reaper have bad API, I intend to remove for
0.5, but want to keep around for the time being. I can use the following:

```python

from reaper import deprecate

@deprecate("reaper", ">=0.5.0")
def throw_warnings(predicate, category, version, message):
    """
    This is too hard to use !
    """
    pass

```

This will show a `DeprecationWarning` to user calling the function in any
version from 0.1.x to 0.4.x, and raise a `DeprecationException` to any user
using 0.5.0-dev, or above.

## Future Plans

- Allow to raise early on Continuous Integration system to catch using
  deprecated API early in development process, and still allowing you to use
  code locally.

- Ability to do the same base on time/date

- versions which is not a decorator/ context manager

- ability to mark version of dependency supported, and warn/raise when the
  minimal dependency list change:

```python

if deprecate(`tornado`, `<=4.3.1`):
    #do stuff
else:
    # do other stuff
```

The first branch (or actually the predicate) should warn/raise as soon as
something like `tornado>=4.4` appears in requirements.txt, that some dead code
has been found.

repository: pombredanne/reaper

repo_name: pombredanne/reaper


text:
# laravel-multidomain (tests' notes)
We provide some information about laravel-multidomain's tests.

## Organization
We provide 3 test suites:

 1. `CommandsTestCase` where we include a test for each of the commands
 provided by the `DomainConsoleServiceProvider` provider.

 2. `HttpTestCase` where we include some tests for simulating HTTP requests
 changing the domain name. We provide two distinct settings (other than the default one)
 for two additional domains, namely `site1.test` and `site2.test`.

 3. `ArtisanTestCase` where we include some tests for simulating the `--domain`
 option applied to Artisan commands.

## Requirements and setup
We make use of the fantastic [Orchestra/Testbench](https://github.com/orchestral/testbench)
package together with its browser-kit extension.

However, in order to setup the Laravel application with the machinery provided
by our package, we provide a standard `.env.example` file and some configuration files
to be used by the simulated Orchestra's Laravel application.

Moreover we created an adapted copy of the Laravel's `artisan` script in order to
  use the `--domain` option in the Artisan commands launched by shell.

  Note that, as pointed out in the package documentation, the `--domain` option does
  not work from within a Laravel application, so we needed to simulate command launches
  directly from shell.

We perform above operations in the `setUp` sections of the suites.

We provide tests under the `mysql` connection (set in `phpunit.xml`).
You need to create two databases, namely (`site1` and `site2`) in your DB server
in order to run tests correctly.

## Running tests
As usual, we run tests from the package folder launching:

```bash
../../../vendor/bin/phpunit
```

However, as we need to simulate more than one domain via `$_SERVER['SERVER_NAME']` values
and as in the `phpunit.xml` file we can't put a conditional environmentt variable,
 in order to fullyperform all the tests, we require to launch also the following
 commands:

```bash
SERVER_NAME=site1.test ../../../vendor/bin/phpunit
```

```bash
SERVER_NAME=site2.test ../../../vendor/bin/phpunit
```

repository: gecche/laravel-multidomain

gha_language: PHP

gha_license_id: MIT

repo_name: gecche/laravel-multidomain


text:
brackets-occurrences-marker
===========================

Brackets [http://brackets.io/] extension that marks occurrences of a selected text or the word under the cursor. The occurrences of the word are marked with a small delay so that you can continue uninterrupted editing.

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

Install using Extension Manager in the File menu.

If you wish to manually install the extension you can use two options:

1. Use Download ZIP to download zipped extension from github and then open Extension Manager in Brackets and drag the file there

2. Use Download ZIP to download zipped extension from github and then open Extension Folder from Brackets (Help -> Show Extension Folder). Unzip the file into the user subfolder.

Usage
-----

The extension has its own preferences dialog. Go to (Edit -> Occurrences Marker Preferences). You can enable/disable the extension, exclude certain file types, choose marking mode and setup how to mark the occurrences.

Selected text is marked at once. Marking the word under cursor can be delayed to allow normal editing.

Change Log
----------

2016-07-05
- Exclude preferences are now an array. You can clean old thevirtualeuoccur.preferences.exclude_* from your brackers.json
- Background color setting can contain CSS styles
- Initial background setting is: background-color: #555; opacity: 0.7; border-radius: 6px;

2016-01-11
- Allow time interval to be set from 0sec to 10sec.
- 0sec interval wil cause instant marking.
- Default value is set to 0.2sec.

2016-01-07
- Added configurable time interval.
- Fixed unmarking.

repository: yaddran/brackets-occurrences-marker

gha_language: JavaScript

repo_name: yaddran/brackets-occurrences-marker


text:
# declaration-block-no-shorthand-property-overrides

Disallow shorthand properties that override related longhand properties.

<!-- prettier-ignore -->
```css
a { background-repeat: repeat; background: green; }
/**                            ↑
 * This overrides the longhand property before it */
```

In almost every case, this is just an authorial oversight. For more about this behavior, see [MDN's documentation of shorthand properties](https://developer.mozilla.org/en-US/docs/Web/CSS/Shorthand_properties).

The [`message` secondary option](../../../docs/user-guide/configure.md#message) can accept the arguments of this rule.

## Options

### `true`

The following patterns are considered problems:

<!-- prettier-ignore -->
```css
a {
  padding-left: 10px;
  padding: 20px;
}
```

<!-- prettier-ignore -->
```css
a {
  transition-property: opacity;
  transition: opacity 1s linear;
}
```

<!-- prettier-ignore -->
```css
a {
  -webkit-transition-property: opacity;
  -webkit-transition: opacity 1s linear;
}
```

<!-- prettier-ignore -->
```css
a {
  border-top-width: 1px;
  top: 0;
  bottom: 3px;
  border: 2px solid blue;
}
```

The following patterns are _not_ considered problems:

<!-- prettier-ignore -->
```css
a { padding: 10px; padding-left: 20px; }
```

<!-- prettier-ignore -->
```css
a { transition-property: opacity; } a { transition: opacity 1s linear; }
```

<!-- prettier-ignore -->
```css
a { transition-property: opacity; -webkit-transition: opacity 1s linear; }
```

repository: stylelint/stylelint

repo_name: stylelint/stylelint


text:
# Labour Relations Board Website Rebuild

![img](https://img.shields.io/badge/Lifecycle-Experimental-339999)

Build and deployment configurations to stand-up a Drupal instance, backed by a MariaDB database, in OpenShift.

The project uses the [openshift-developer-tools](https://github.com/BCDevOps/openshift-developer-tools) for processing the templates and provisioning the builds/deployment configurations.

repository: garywong-bc/jag-lrb-web

repo_name: garywong-bc/jag-lrb-web


text:
# Changelog

## 1.3.0 (planed July 2020)
* Support [Angular materials](https://material.angular.io/components/categories) elements
* All tasks [angular](https://github.com/jdi-testing/jdi-light/issues?q=is%3Aissue+label%3AAngular+)

## 1.2.7 (release 05.05.2020)
* Add log option 'log.info.details' for different levels of the element details in logs #1917
* Use methods in JDIAction (Step) in logs #1920
* Fix issue #1916

## 1.2.6 (release 03.05.2020)
* Add Menu2D

## 1.2.5 (release 01.05.2020)
* Add is().expand() / is().collapsed() functionality for all dropdowns
* Fix Smart Search identification for UI_AND_ELEMENTS option
* Add click() method for all Sections
* Add doubleClick(), rightClick(), dragAndDrop() methods for all elements (fot ICoreElement)

## 1.2.4 (release 31.04.2020)
* Fix small issues with dropdowns isEmpty and isNotEmpty for invisible elements

## 1.2.3 (release 22.04.2020)
* Add StartIndex setting #1888
* Fix Download Driver issue. Always set X32 version for Windows #1889

repository: AlMcRey/jdi-light

repo_name: AlMcRey/jdi-light


text:
# Java based Service Orchestration Engine sample (SOE)

Java based Voice Proxy for integrating Voice Gateway/Voice Agent with Watson, Watson Conversation Service and a clients backend APIs.
Banking Example

## Purpose

The purpose of this project is to show how a developer can integrate the Watson Conversation service with the Voice Gateway/Voice Agent with Watson in a more realistic way. A way that is more common in a clients environment either for a proof of concept or a production implementation. This demo focuses on a banking example, where the caller can ask about account balances and perform basic credit card actions on a sample database of user profiles.

## Background

By default the IBM Voice Gateway (VGW)/Voice Agent with Watson (VAW) can communicate with the Watson Conversation Service (WCS) by using the REST services provided by the WCS. The service uses a single REST API for all conversation interactions. VGW/VAW exploits this API by creating the appropriate JSON payload when transcribing the spoken words from the caller and invoking the API. The challenge with VGW/VAW communicating directly with WCS is that there is no way to personalize the conversation interactions. WCS only maintains static information based on any one particular question being asked. If there is a desire to have dynamic responses, there needs to be a way for the runtime to make API calls to other services to lookup the additional information and provide it as part the response to either WCS or to VGW/VAW. This is where the service orchestration engine (SOE) comes in.

Since WCS only has one API for the conversation, it is very easy to impersonate (proxy) that API. Also since the JSON format used in WCS is well documented, it is easy to ensure that the proxy can make updates and communicate with WCS on behalf of any invoker. VGW being the most interesting.

## Goal

The goal of this sample is for the user to get an understanding of how to create an SOE that communicates with both VGW/VAW and WCS. You can leverage this code or you can see the high level flow and create your own if Java isn't your thing. Some might prefer Node.JS, Python or even Node-Red.

## Sample Watson Conversation Workspace

We have provided a sample Conversation Workspace as a part of the tutorial. The JSON is in the conversation_skill folder. The sample workspace is a typical call center like scenario for a banking service.

We have 3 Entities define as part of the tutorial.

1. Loans (Auto loan, Mortgage and Student Loan)
2. Accounts (Checking, Savings and Money Market)
3. Credit Card

We have defined four intents. When you open the workspace you can see what they are. But at a high level the following intents are available:

1. Information: General information on the loans or accounts
2. Balance: Check the balance of an account or a loan
3. Payment: Make a payment on a loan from one of the accounts
4. Activation: Activate a credit card

## How to start the sample

### Pre-requisites

* For running this SOE sample you must first have either an IBM Voice Gateway or Voice Agent with Watson deployment set up with all the credentials for Text, Speech and Speech to Text.

* In your Watson Assistant workspace create an agent or use an existing agent and create a new skill with the json configuration in the conversation_skill folder

* If you're using Voice Agent fill in the credentials used in Watson Assistant with the values for the skill created in the previous step except the conversation url which will point to our SOE. If you're using Voice Gateway you only need to specify the conversation url which will point to our SOE

* You're ready to start running the sample!

### Starting SOE

**For running this sample you need java and maven installed on your machine**

#### Quick deployment

Using the maven Open liberty plugin you can quickly start the application as is
1. Clone the project to your machine and in a teminal window change directory to this sample

2. In the src/main/wlp directory modify the server.env to include the credentials needed for connecting to Watson Assistant

```
WORKSPACE_ID=Newly created workspace id
CONVERSATION_VERSION=2016-07-11
CONVERSATION_USERNAME=Username for connecting to watson assistant
CONVERSATION_PASSWORD=Password for connecting to watson assistant
CONVERSATION_URL=Url for connecting to the Watson Assistant API
```

3. On the terminal window run `mvn clean package liberty:run` and this will start the SOE.  You can change these in the pom.xml of the project in the properties section `testServerHttpPort` and `testServerHttpsPort`.

4. Your SOE sample should be running now!

**Important:** In Voice Gateway configuration set the WATSON_CONVERSATION_URL to the URL of the sample that just started. In Voice Agent setup your agent to use a Service Ochestration Engine and set the URL to the URL of the sample that just started. The URL is: `{protocol}://{host}:{port}/banking/rest/bankWebhook` change the placeholders to fit your sample but remember to end the URL with **/banking/rest/bankWebhook**

#### Build this project from source and play around with it

1. Clone the project to your machine and in a teminal window change directory to this sample.

2. You can import this project as a maven project in eclipse for modifying the sample

3. Create a Liberty server with the server.xml included in the src/main/wlp directory and in the server.env of that server include and fill the credentials necessary for connecting to Watson Assistant

```
WORKSPACE_ID=Newly created workspace id
CONVERSATION_VERSION=2016-07-11
CONVERSATION_USERNAME=Username for connecting to watson assistant
CONVERSATION_PASSWORD=Password for connecting to watson assistant
CONVERSATION_URL=Url for connecting to the Watson Assistant API
```

4. Package the app running in a terminal window `mvn clean package`

5. Move the packaged war app to the dropins of the server and start the server

6. Your SOE sample should be running now!

**Important:** In you Voice Gateway/Voice Agent configuration change the conversation url to the URL of the sample that just started which is: `{protocol}://{host}:{port}/banking/rest/bankWebhook` change the placeholders to fit your sample but remember to end the URL with **/banking/rest/bankWebhook**

### How it works

* All the data for the customer are stored in the src/main/webapp/WEB-INF/callerProfile.csv. The SOE acesses this data for getting the info and validating the customer information

* If the SOE recognizes an intent that applies to it, it will take a turn and get the information from the csv and include it for the conversation with Watson Assistant. If not, it will just forward requests to Watson Assistant without interfering.

### Deploying to IBM Cloud

You can also deploy this sample application to IBM Cloud as an easy way to connect to it from Voice Agent, as well as a Voice Gateway deployment.

**Requirements:**

* Sign up for [IBM Cloud](https://cloud.ibm.com/registration)
* Install the [IBM Cloud Standalone CLI](https://cloud.ibm.com/docs/cli/reference/ibmcloud/download_cli.html#install_use)

**Steps:**

1. Login to your IBM Cloud account in the CLI and set up the target for the cloud foundry running `ibmcloud target --cf`
2. Once you have that, follow all the steps to set up as described above if you haven't done so yet and package the app and server running `mvn clean package` in the directory of the sample
3. Once the server is packaged you will see it in the target directory as *banking.zip*
4. Run `ibmcloud cf push banking-SOE -p target/banking.zip` to push the app to the cloud. Sometimes you would need to manually adjust the memory of the app in the cloud
5. Once the app is running you can set up your Agent or Gateway by filling the url as before but with the cloud foundry URL of the newly pushed app which would tipically be something like `https://banking-soe.mybluemix.net/banking/rest/bankWebhook` and remember to always include the end path `banking/rest/bankWebhook`
6. Test out calling the Gateway or Agent and following on of the perdetermined conversations.

# Sample Conversation Scripts

In an effort to help with using the demo it would be beneficial to know what questions to ask WCS. Below are the sample questions. Remember, there is an API that keeps track of the balances, so if you withdraw too much money you will get errors.

### Conversation Flow 1:

**Watson:** Hi I am Watson. How can I help you today?

**Caller:** "I would like to pay my mortgage"

**Watson:** I need some personal information. Please say your first name.

**Caller:** "My name is Brian"

**Watson:** I have your first name as Brian, is that correct?

**Caller:** "Yes it is"

**Watson:** Please clearly say our password.

**Caller:** "baseball"

**Watson:** Okay you want to pay your mortgage. What account do you want to use?

**Caller:** "I want to use my checking account"

**Watson:** We will use your checking account. How much would you like to pay?

**Caller:** "I want to pay two hundred dollars"

**Watson:** Okay we will move $200 from your checking account. Is there anything else I can help with?

### Conversation Flow 2:

**Watson:** Hi I am Watson. How can I help you today?

**Caller:** "I want to add a credit card"

**Watson:** I need some personal information. Please say your first name.

**Caller:** "Olivia"

**Watson:** I have your first name as Olivia, is that correct?

**Caller:** "Yes"

**Watson:** Please clearly say our password

**Caller:** "Gymnastics"

**Watson:** What type of card would you like to add?

**Caller:** "Mastercard"

**Watson:** Please enter your card number

**Caller:** (Input card number with keypad)

**Watson:** Okay I have added your card. Is there anything else I can help you with?


text:
# Installing Packer For Image Creation
### July 3, 2019

Now that we have [installed chefdk](/articles/installing-chefdk-on-mac)
and are connected to our [aws via saml](/articles/connecting-to-aws-using-saml)
let's install [Packer](https://www.packer.io/) to help us
create (and later automate) the creation of a preconfigured AWS server.

First we need to install [Golang](https://golang.org/).  For this,
I used Brew as [installing from source](https://golang.org/doc/install/source)
see way too complicated.

```
brew install go
```

For packer however, once [Golang](https://golang.org/) is installed, it seems rather straight forward
to [install from source](https://www.packer.io/intro/getting-started/install.html#compiling-from-source).

```
mkdir -p ~/src && \
  cd ~/src && \
  git clone https://github.com/hashicorp/packer.git && \
  cd packer && \
  make dev
```

The installed things into `~/src/packer`. Let's make the packer
tools available from anywhere on your machine by adding it
to your path.

```bash
vi ~/.bash_profile
```

And in there add a line that looks like the following

```bash
...
PATH="$HOME/src/packer/bin:$PATH"
...
export PATH
```

If everything worked as expected then open up a new terminal, and
check the version of your installed `packer`

```bash
$ packer -v
```


text:
# AirBnb - Rent API

This API created by Data Scientists uses an XGBoost Regressor model to take existing pricing data and return predicted optimal prices for property rental on Airbnb.

## Endpoint

The deployed Heroku app can be found [here](https://air-bnb-price.herokuapp.com/).

## Data

The final XGBRegressor model was trained on data from [opendatasoft](https://public.opendatasoft.com/explore/dataset/airbnb-listings/export/?disjunctive.host_verifications&disjunctive.amenities&disjunctive.features).

## Contributing
Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

Please make sure to update tests as appropriate.

## Authors and Acknowledgment
API Engineering: Henry Mead, Drew Bordelon

Data Modeling: Timothy Carroll

## License
[MIT](https://opensource.org/licenses/MIT

repository: drewamorbordelon/AirBnB-Price

repo_name: drewamorbordelon/AirBnB-Price


text:
---
title: "Countries in Africa"
excerpt: "A Cartography Design exercise"
header:
  image: /images/carto_africa.png
  teaser: /images/carto_africa.png
sidebar:
  - title: "Scope"
    image: /images/logo.png
    image_alt: "logo"
    text: "Exercise"
  - title: "Data sources"
    text: Unknown
---

A Cartography Design exercise mentored by the great [Mamata Akella](https://github.com/makella). The school-like result using just CartoCSS is simply amazing.

{% raw %}
<iframe width="100%" height="520" frameborder="0" src="https://team.carto.com/u/abel/builder/b9867174-dba1-402f-8d9f-54da08f17f52/embed" allowfullscreen webkitallowfullscreen mozallowfullscreen oallowfullscreen msallowfullscreen></iframe>
{% endraw %}

[Link to the map](https://team.carto.com/u/abel/builder/b9867174-dba1-402f-8d9f-54da08f17f52/embed)


text:
---
title: Hiding Content for Accessibility
subtitle: Implementing .sr-only The Right Way™
date: 2023-04-15
published: true
---
Sometimes a website that seems clear and easy to use for sighted users can cause
some confusion for those who rely on a screen reader for navigation.

In addition to [semantic elements](https://developer.mozilla.org/en-US/docs/Glossary/Semantics#semantics_in_html),
we need to [label regions](https://www.w3.org/WAI/tutorials/page-structure/labels/)
to make a page easier to navigate.

When content is added for screen readers that would seem redundant to sighted
users, or otherwise disrupt the design, it should be visually hidden.  These
styles are recommended by WebAIM for hiding content off-screen:

```css
.sr-only {
  position: absolute;
  left: -10000px;
  top: auto;
  width: 1px;
  height: 1px;
  overflow: hidden;
}
```

In [CSS in Action](https://webaim.org/techniques/css/invisiblecontent/)
WebAIM covers this and several other techniques for visually hiding content.

repository: VAggrippino/blog

gha_language: SCSS

repo_name: VAggrippino/blog


text:
# Quantitative-Microplankton-Tracker

By Harshith Bachimanchi, Benjamin Midtvedt, Daniel Midtvedt, Erik Selander, and Giovanni Volpe.

The quantitiative microplankton tracker is a deep-learning based framework to predict dry masses and three-dimensional positions of microplankton from experimental holographic images.

This repository contains source code and data for the article, [Microplankton life histories revealed by holographic microscopy and deep learning](https://elifesciences.org/articles/79760)

## Description
The quantitative microplankton tracker uses two neural network in sequence to predict the dry masses and 3D positions of the microplanktons in holographic images. The first neural network, **Regression U-Net (RU-Net)**, segments single plankton holograms from a large field-of-view holographic image and predicts the dry masses and vertical positions. The second neural network, **Weighted Average Convolutional Neural network (WAC-Net)**, is then applied to the cropped image sequences of planktons to refine the plankton dry masses and vertical positions predicted by **RU-Net**.

## Usage
### Training-tutorials
We provide tutorials to train the neural networks, **RU-Net**, and, **WAC-Net**, from scratch in [training-tutorials](https://github.com/softmatterlab/Quantitative-Microplankton-Tracker/tree/main/training-tutorials). PDF versions of **training-tutorials** are included for quick viewing.

1. [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/softmatterlab/Quantitative-Microplankton-Tracker/blob/main/training-tutorials/1-training_RU-Net.ipynb) [1-training_RU-Net.ipynb](https://github.com/softmatterlab/Quantitative-Microplankton-Tracker/blob/main/training-tutorials/1-training_RU-Net.ipynb) demonstrates the training process of **RU-Net**.

2. [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/softmatterlab/Quantitative-Microplankton-Tracker/blob/main/training-tutorials/2-training_WAC-Net_drymass.ipynb) [2-training_WAC-Net_drymass.ipynb](https://github.com/softmatterlab/Quantitative-Microplankton-Tracker/blob/main/training-tutorials/2-training_WAC-Net_drymass.ipynb) demonstrates the training process of **WAC-Net** to predict plankton dry masses.

3. [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/softmatterlab/Quantitative-Microplankton-Tracker/blob/main/training-tutorials/3-training_WAC-Net_vertical_positions.ipynb) [3-training_WAC-Net_vertical_positions.ipynb](https://github.com/softmatterlab/Quantitative-Microplankton-Tracker/blob/main/training-tutorials/3-training_WAC-Net_vertical_positions.ipynb) demonstrates the training process of **WAC-Net** to predict plankton vertical positions.

### Usage examples
We also provide additional examples in [examples](https://github.com/softmatterlab/Quantitative-Microplankton-Tracker/tree/main/examples) on how to apply pre-trained neural networks on experimental holographic images. PDF versions of **examples** are included for quick viewing.

1. [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/softmatterlab/Quantitative-Microplankton-Tracker/blob/main/examples/1-example_RU-Net.ipynb) [1-example_RU-Net.ipynb](https://github.com/softmatterlab/Quantitative-Microplankton-Tracker/blob/main/examples/1-example_RU-Net.ipynb) demonstrates how to use **RU-Net** on experimental holographic images. The notebook also generates **figure 1** and **figure 2** of the paper.

2. [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/softmatterlab/Quantitative-Microplankton-Tracker/blob/main/examples/2-example_WAC-Net.ipynb) [2-example_WAC-Net.ipynb](https://github.com/softmatterlab/Quantitative-Microplankton-Tracker/blob/main/examples/2-example_WAC-Net.ipynb) demonstrates how to use **WAC-Net** on cropped image sequences of planktons to obtain a refined dry mass value. The notebook also generates **figure 3** and **figure 4** in the paper.

3. [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/softmatterlab/Quantitative-Microplankton-Tracker/blob/main/examples/3-example_Analysis.ipynb) [3-example_Analysis.ipynb](https://github.com/softmatterlab/Quantitative-Microplankton-Tracker/blob/main/examples/3-example_Analysis.ipynb) demonstrates the complete analysis pipeline, i.e., using **RU-Net** and **WAC-Net** in sequence.

## Citation
If you use this code for your research, please cite our paper:

https://elifesciences.org/articles/79760

```
Harshith Bachimanchi, Benjamin Midtvedt, Daniel Midtvedt, Erik Selander, Giovanni Volpe (2022).
"Microplankton life histories revealed by holographic microscopy and deep learning."
eLife 11:e79760.
https://doi.org/10.7554/eLife.79760
```

See also [DeepTrack2.1](https://github.com/softmatterlab/DeepTrack-2.0/tree/master):

```
Benjamin Midtvedt, Saga Helgadottir, Aykut Argun, Jesús Pineda, Daniel Midtvedt, Giovanni Volpe.
"Quantitative Digital Microscopy with Deep Learning."
Applied Physics Reviews 8 (2021), 011310.
https://doi.org/10.1063/5.0034891
```

## Funding
This work was partly supported by the H2020 European Research Council (ERC) Starting Grant ComplexSwimmers (Grant No. 677511), the Horizon Europe ERC Consolidator Grant MAPEI (Grant No. 101001267), the Knut and Alice Wallenberg Foundation (Grant No. 2019.0079), and the Swedish Research Council (VR, Grant No. 2019-05238).


text:
# Configuring ColorHelper

## `color_rules`

The `color_rules` option configures how ColorHelper interacts with a given file. In order for ColorHelper to inject
color previews, translate colors, and various other tasks, it needs context, and rules provide that context.

```js
    "color_rules": [
        {
            "name": "HTML/CSS",
            "base_scopes": [
                "source.css",
                "text.html"
            ],
            "color_class": "css-level-4",
            "scanning": [
                // https://packagecontrol.io/packages/CSS3
                "meta.declaration-list.css -support.type.property-name.css -comment -string",
                // CSS, CSS in HTML etc. (based on: Sublime Default)
                "meta.property-value.css -comment -string",
                // CSS3, CSS3 in HTML etc. (based on: https://packagecontrol.io/packages/CSS3)
                "meta.value.css -comment -string",
                // HTML attributes (based on: Sublime Default)
                "meta.tag.inline.any.html string.quoted -constant.character.entity.html",
                "meta.tag.any.html meta.attribute-with-value.style.html"
            ]
        }
    ]
```

A rule will define what kind of colors ColorHelper should look for, and where in the file valid colors are found.

`color_rules` is a list of rule sets, where each rule is a dictionary of options. Only one rule set can apply to a
given view at a time.

!!! tip
    New rules and rule overrides should be added to [`user_color_rules`](#user_color_rules) instead of modifying
    `color_rules` directly.

### `name`

Optional name. If a user creates a rule in `user_color_rules`, and it it shares the same `name` as a rule under
`color_rules`, a shallow merge of the two rules will be made which will allow the user rule to override the values
of top level keys.

```js
    "name": "HTML/CSS",
```

### `syntax_files`

Target a view using a syntax file from the given list. Defaults to an empty list.

When specifying syntax files, you would use full path relative to `Packages`. The extension should be omitted.

```js
    "syntax_files": [
        "PackageDev/Package/Sublime Text Color Scheme/Sublime Text Color Scheme",
        "PackageDev/Package/Sublime Text Theme/Sublime Text Theme"
    ],
```

### `syntax_filter`

Specify whether [`syntax_files`](#syntax_files) is an `allowlist` or `blocklist`.  Default's to `allowlist`.

```js
    "syntax_filter": "allowlist",
```

### `base_scopes`

Target a view whose base scope matches something from the list of scopes. Defaults to an empty list.

```js
    "base_scopes": [
        "source.css",
        "text.html"
    ],
```

### `extensions`

Target a view with an extension from the provided list. Defaults to an empty list.

```js
    "extensions": [".tmTheme"],
```

### `color_class`

A string defining the name of a color class to use for the scopes within the current view. Color class name should be
defined in [`color_classes`](#color_classes).

```js
    "color_class": "css-level-4",
```

If needed, you can define multiple color classes with a list of dictionaries. Each dictionary in the list should
contain a `class` and `scopes`:

- `scopes`: A string that defines a base scope that the color class applies to.

- `class`:  The name of the color class profile to use (defined in [`color_classes`](#color_classes)).

```js
    "color_class": [
        {"class": "css-level-4", "scopes": "-comment -string"},
        // etc.
    ]
```

### `scanning`

Scanning will only find colors within certain scopes in a file. This is to help avoid generating previews in areas of
a document that are undesirable. Often used to avoid generating previews in comments etc.

This option is a list containing scopes that should be scanned for colors.

```js
    "scanning": true,
```

### `color_trigger`

It is slow to iterate an entire buffer directly with the color class to match colors, so do a quick search for tokens
that should trigger a color check. That way we only test in places where we think we might have a valid color. For
instance, the color class can translate colors in the form `rgb(1 1 1 / 1)`, so we can specify `rgb(` as a color
trigger. If we find `rgb(`, we will test that spot's scope and attempt to read in a color at that location. Defaults to:

```js
"(?i)(?:\b(?<![-#&])(?:color|hsla?|lch|lab|hwb|rgba?)\(|\b(?<![-#&])[\w]{3,}(?!\()\b|(?<![&])#)"
```

```js
"color_trigger": "(?i)(?:\\b(?<![-#&])[\\w]{3,}(?!\\()\\b|(?!<&)\\#)",
```

### `allow_scanning`

This is an easy way to disable just scanning within a certain rule set. Defaults to `true`.

```js
"allow_scanning": true,
```

### `enable`

This can be used to disable a color rule set entirely. Defaults to `true`.

```js
"enable": true,
```

## `user_color_rules`

Follows the same format as `color_rules`, but is made so users can append to existing color rules. If a `name` is
specified, and it matches a rule `name` in the default `color_rules`, the top level key values in the user rule will
override the values in the default rules.

```js
    // User rules. These will be appended to the normal `color_rules` unless they
    // share the same name. In that case, a shallow merge will be performed allowing
    // the values of top level keys to be overridden and new keys to be added.
    "user_color_rules": [],
```

## `generic`

This defines a generic fallback rule for files that don't match anything in `color_rules`. This allows color tools to
work anywhere by providing them with sane defaults. By default, scanning is not enabled in files with generic rules,
but you can enable it if you wish.

Generally, `generic` accepts all rules that can be applied to `color_rules` except rules that filter out specific
views such as: `sytnax_files`, `syntax_filter`, `base_scopes`, and `extensions`. Additionally, `color_class` only
accepts a string defining a single color class. It will not accept multiple color classes.

```js
    "generic": {
        "allow_scanning": false,
        "scanning": ["-comment -string"],
        "color_class": "css-level-4"
    },
```

## `color_classes`

ColorHelper uses the `Color` class from the [`coloraide`][coloraide] dependency to manage, manipulate, and translate
colors. By default, these color classes accept inputs that match valid CSS. They also output colors in the form of valid
CSS.

It may be desirable to filter out certain color spaces, or even alter a color space to accept different input formats
and generate different output formats. This can all be done by subclassing the `Color` class.

`color_classes` allows you to configure the `Color` class, or point to a custom `Color` class and configure it.

`color_classes` is a dictionary of color profiles that link to a specific `Color` class. You can tweak options
specifically related to the `Color` class. The **key** is the name of the color profile  which can be referenced by
[`color_rules`](#color_rules). The **value** is a dictionary of options.

### `output`

This can be used to specify the output options available when converting a color or inserting a color from the color
picker or other tools. Specify the color `space` from the `Color` class to use, and the options to supply to the `Color`
class' `to_string` method. Defaults to:

```js
[
    {"space": "srgb", "format": {"hex": True}},
    {"space": "srgb", "format": {"comma": True, "precision": 3}},
    {"space": "hsl", "format": {"comma": True, "precision": 3}},
    {"space": "hwb", "format": {"comma": False, "precision": 3}},
    {"space": "lch", "format": {"comma": False, "precision": 3}},
    {"space": "lab", "format": {"comma": False, "precision": 3}},
    {"space": "xyz", "format": {}}
]
```

To learn more about available options, see [`coloraide`'s documentation][coloraide-strings].

### `class`

This allows a user to specify a custom color class derived from `coloraide.Color`. This could be used to reference
a custom color class that can recognize different formats when scanning for colors. A custom color class will often also
provide different string outputs and string output options.

The value should be the full import path for the `Color` class.

ColorHelper provides a few custom color classes in `ColorHelper.custom`. You can check out those to see how to create
your own.

```js
"class": "ColorHelper.custom.tmtheme.ColorSRGBX11",
```

### `filters`

A list that restricts color recognition to only the specified color spaces. Default is an empty list which allows all
color spaces.

Allowed color spaces are `srgb`, `hsl`, `hwb`, `hsv`, `lch`, `lab`, `xyz`, `display-p3`, `rec2020`, `a98-rgb`, and
`prophoto-rgb`.

```js
"filters": ["srgb", "hsl"],
```

### `edit_mode`

Optional parameter that controls the "edit" tool that is used in the info panel when you click the large color preview.
The default value is `default`, but can also be be set to `st-colormod` to use an edit mode that mimics Sublimes
`color-mod` implementation.

This was mainly added so users could specify `st-colormod` when using a compatible color profile for color schemes and
themes.

```js
"edit_mode": "st-colormod",
```

## `user_color_classes`

Follows the same format as `color_classes`, but is made so users can append to existing color classes. If a `name` is
specified, and it matches a entry's `name` in the default `color_classes`, the top level key values in the user color
class will override the values in the default color class.

```js
    // User color classes. These will be added to the normal `color_classes` unless they
    // share the same name with an existing entry. In that case, a shallow merge will be performed allowing
    // the values of top level keys to be overridden and new keys to be added.
    "user_color_classes": {}
```

--8<-- "refs.md"

repository: adaminfinitum/ColorHelper

repo_name: adaminfinitum/ColorHelper


text:
<meta http-equiv="Content-Type" content="text/html; charset=UTF-8">

Description:

This is a PHP class for generating images with simple mathematical questions (Math CAPTCHAs) to protect the forms of your website from spambots.

How to Install:

	You can install the class very easily by using composer. You just have to run:

composer require magkopian/php-math-captcha:1.0.*

	Or add it as a dependency in you `composer.json` file:

{
	"require": {
		"magkopian/php-math-captcha": "1.0.*"
	}
}

	And then run:

composer update

	Also, don't forget to include composer `autoload.php` file to your code.

How to Use:

To generate a captcha you simply:

session_start();

$mathCaptcha = new MathCaptcha\MathCaptcha();

$mathCaptcha->generate();
$mathCaptcha->output();

The `MathCaptcha` class makes use of session variables so you have to call the `session_start()` function before instantiating a `MathCaptcha` object.

You can optionally supply an identifier for the captcha, to the constructor of the `MathCaptcha` class, if you want to use multiple captchas in your website.

To verify the user's answer you simply:

session_start();

$mathCaptcha = new MathCaptcha\MathCaptcha();

if ( $mathCaptcha->check($captcha_answer) === true ) {
	// Correct answer
}
else {
	// Incorrect answer
}

If you use more than one captchas in your website you need also to supply the identifier of the captcha, to the constructor of the `MathCaptcha` class.

Check out the `test_form.php` and `math_captcha.php` files for a working example.

Requirements:

PHP 5, GD 2.0.1 or later (2.0.28 or later is recommended)


text:
* General workflow for using user support scripts

  1. Copy and paste the desired script to your working directory.
     Preferably under `xpdUser/userScripts`.

  2. After enter `xpdui` and get to the ipython session, run the
     script with interactive mode (-i option):

     ```
     %run -i userScripts/<script_name.py>
     ```

  3. Open the script and follow the `Example` section in the scan
     plan doc string and execute it.

* All the script in this repo are tested in the simulation mode
  before updating, howeve, please help us report issue when actual
  instruments.


text:
Initially a straight-up port of the old Perl [ISS-RT tools](https://github.com/kraigu/ISS-RT) to Python.

Requires python3.

Requires the [python-rt module](https://github.com/CZ-NIC/python-rt). Built and tested against 1.0.9.

Some functionality (RTIR) requires RT 4.4 with an updated version of RTIR, due to the way incidents queues work.

Requires a .rtrcp file in your home directory as such:

```
[rt]
username=yourusername
hostname=yourrtinstance
password=guessme
```

TODO
----

* [X] Automated testing
* [ ] Pretty print function for individual tickets?

License
-------

BSD-new. Original author Mike Patterson<EMAIL_ADDRESS>for the University of Waterloo, 2016-2018.

repository: kraigu/ISS-RTpy

gha_language: Python

repo_name: kraigu/ISS-RTpy


text:
## Envreplace

A small, statically linkable cli utility written in GoLang that replaces strings in files with the value of env vars.

The primary usage is for dockerizing existing applications. It's useful when you have an application that is looking somewhere in the filesystem for its configuration, and you'd prefer to pass that configuration as an environment variable.

It's roughly similar in functionality to [my node.js module with the same name](https://www.npmjs.com/package/envreplace), but since it can be statically linked and precompiled with no dependencies, it's much better suited for use in a docker container where you might not want a whole node.js install.

### A simple example

If you have a config file `/usr/src/conf/myconfig.properties`
```
my.property.foobar=#FOOBAR#
```
That you want to end up in `/usr/local/tomcat/conf` with its value `#FOOBAR#` taken from the environment variable `FOOBAR`

Then you'd call `envreplace` like this:
```bash
FOOBAR="Some value"
envreplace /usr/src/conf/myconfig.properties /usr/local/tomcat/conf/myconfig.properties
```
And you'd end up with the `/usr/local/tomcat/conf/myconfig.properties`
```
my.property.foobar=Some value
```

### Multiple files at once

If you have two files in the `/usr/src/conf/` folder that you want replaced, you can do multiple at once by creating an envreplace configuration json file.

The `files` entry in the JSON object a map of source files to destination files to be processed by `envreplace`.
You could create a file `/usr/src/conf/envreplace.json`
```json
{
  "files": {
    "./myconfig.properties": "/usr/local/tomcat/conf/myconfig.properties",
    "./somethingelse.file": "/usr/local/tomcat/conf/whatever.name.file"
  }
}
```

Then you'd run:
```bash
FOOBAR="Some value"
envreplace /usr/src/conf/envreplace.json
```
And both the files declared would have their `#FOOBAR#` replaced with `Some value`

## Configuration file options
The .json file that contains the config has two keys:
* `variableRegex` a string version of a RegEx.  It must contain a single capture group that will be used to pull out the environment variable name.
* `files` a json object.  This is used as a key/value map of file src to file destination. The value can be a string, or an array of strings.  If it is an array of strings, the src file is written to the multiple destinations specified in the array.
```
files: {
  "/path/to/source.file": "/path/to/destination.file",
  "/path/to/some/other/source.file": "/another/destination.file",
  "/path/to/a/file/that/should/go/two/places.file": ["/somewhere/place1.file", "/somewhereElse/place2.file"]
}
```

File paths listed in the files map are relative to the location of the configuration file.

## CLI options:

### Usage:
  * envreplace <srcFile> <destFile>
      Calls envreplace on <srcFile> and put the result into <destFile>

  * envreplace <configfile>
      Call envreplace with configuration stored in json formated file <configfile>

### Flags:
  * -help
      Prints help text
  * -regex string
      Regular expression that parses variable name into capture group 1. Remember to quote and escape to pass it through your shell. If no value is provided the default is '#([A-Z0-9_]+)#'
  * -silent
      Skips printing count of files and substitutions made
  * -verbose
      Prints info about each substitution made

## Fails on missing env vars
This tool will exit with a non-zero exit code and prints a helpful message if any of the files specified in the files option have match the variableRegex but do not have a matching env var.  This prevents you from accidentally using config that did not get substituted properly.

## Building
There's a very simple included makefile

* `make` build the executable
* `make test` build the executable and run the tests
* `make install` build the executable, run the tests, and copy the file to /usr/local/bin

## License (MIT)

Copyright (c) 2017 Ian Taylor

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

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

 IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.

repository: itaylor/envreplace-go

repo_name: itaylor/envreplace-go


text:
# EveCloud Miner
![Discord](https://img.shields.io/discord/801148733902553158?style=for-the-badge) ![Downloads](https://img.shields.io/github/downloads/evecloud/evecloud-miner/total?style=for-the-badge) ![Version](https://img.shields.io/github/package-json/v/evecloud/evecloud-miner?style=for-the-badge)
### Requirements

- NodeJS 14
- CPU (x64/ARMv8)
- Discord Account (linked to the [dashboard](https://dash.evecloud.xyz/profile))

### Download

- [Binary releases](https://github.com/EveCloud/EveCloud-Miner/releases)
- [Download from source](https://github.com/EveCloud/EveCloud-Miner/tree/main/build)

### Usage

Run ```evecloud-miner-(linux/macos/win.exe)``` and then follow the instructions.

### Report Bugs

To report bugs and issues please use the GitHub issue reporting tool: https://github.com/EveCloud/EveCloud-Miner/issues. Any bugs and issues reports are very much appreciated since it helps us to improve EveCloud Miner. Thank you.

### License
```
Copyright (c) 2021 Hiekki4<EMAIL_ADDRESS>and Contributors

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

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

 IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
```

repository: jjjjjjjjjjjjas/EveCloud-Miner

repo_name: jjjjjjjjjjjjas/EveCloud-Miner


text:
---
layout: header
---

I received an M.Sc. in Communication Systems from the [Swiss Federal Institute of Technology in Lausanne (EPFL)](https://www.epfl.ch/en/) and a B.Sc. in Telematics Engineering at [Comillas Pontifical University](https://www.comillas.edu/en/), in Madrid.

### Experience

#### 2019 · Machine Learning Engineer Intern at Schindler
During my time at Schindler, I designed and implemented a pipeline to classify patent documents into categories that facilitate the matching of patents to specific elevator systems and components.
I had the opportunity to work with a patent attorney and a product engineer during the entire process, allowing me to make sensible decisions regarding the design of the pipeline and evaluate the final results. I submitted this work as my M.Sc. thesis.

#### 2018 · Data Scientist Intern at Dathena
I worked as an intern at [Dathena](https://www.dathena.io/), a data security company based in Singapore. At the time of my internship the company still had not exceeded 50 staff, making it possible to learn about the tasks carried out at each department.
During my time at the company, I deployed new features to the data protection pipeline and worked on an OCR pipeline for noisy documents with the data science team.

### University Labs

#### 2018 · HCI in Learning and Instruction Lab (EPFL)
I analyzed writing strategies and roles taken by students during online collaborative writing tasks.
As part of this project, I defined a set of features to analyze paragraph quality and consistency and calculated the semantic similarity between authors at different stages of the writing process.

#### 2017 · Big Data Lab (Comillas Pontifical University)
As part of a collaboration grant, I deployed a pipeline using Kafka, Flume and Cassandra as streaming data sources for text analysis with Spark. In addition, I prepared a distributed programming environment with Docker and Spark and tested the capabilities of bluetooth sensors as an input to the processing pipeline.

#### 2016 · Transp-OR Laboratory (EPFL)
I completed my B.Sc. thesis as a research assistant in the [Transport and Mobility Lab](https://www.epfl.ch/labs/transp-or/). I worked on the statistical analysis of passenger satisfaction in railway networks, with the aim of identifying the factors with the greatest impact on passenger satisfaction.

### Relevant coursework

#### 2019 · M.Sc. Communication Systems (EPFL)
- *Machine Learning* – Analysis and implementation of fundamental techniques in ML using Python. The final project for this course consisted in the implementation of a **recommender system for movies based on user ratings**, for which we used an ensembled approach ([see on GitHub](https://github.com/lumosan/ml2017-projects/tree/master/project2/project_recommender_system)).
- *Deep Learning* – Mathematical basis and use of deep learning methods, as well as applied problems with PyTorch. For this course we trained a model based on **LSTM and CNN to predict finger movements from electroencephalograms** ([see on GitHub](https://github.com/lumosan/deeplearning2018/tree/master/Projects/Project1)).
- *Data visualization* – Created an **interactive data visualization** for insights on bills introduced in US Congress with D3.js and React.js ([GitHub repository](https://github.com/shaginhekvs/USSenate-dataViz), [deployment](https://luciams.com/USSenate-dataViz/)).
- *Distributed Information Systems* – Key concepts and algorithms in information retrieval and data mining using Python.
- *Automatic Speech Processing* – Formalisms, models and algorithms for the implementation of advanced speech processing applications.
- *Applied Data Analysis* – Development of data science pipelines with emphasis in practical skills using Python and several libraries for data science, including: Pandas, NumPy, SciPy, Matplotlib, Seaborn, Scikit Learn, Spacy, NLTK, BeautifulSoup and requests.

#### 2016 · B.Sc. Telematics Engineering (Comillas Pontifical University)
<!-- 2ND HIGHEST GPA IN MAJOR 7.95/10 -->
* *Architecture of Network Systems* – Python Flask application for language translation and visual recognition with IBM Cloud, using Travis CI (no longer hosted).
* *Computer Networks* – Cisco CCNA equivalent training using industry level network equipment.
* *Operations Research* – Optimal coverage in hexagonal grid with Linear Programming.

#### 2016 · Exchange Program in Communication Systems (EPFL)
* *Algorithms* – Analysis of computational complexity, modern algorithms and data structures.
* *Functional Programming* – Software project implementing a LISP interpreter using Scala.
* *Parallelism and Concurrency* – Functional parallelism using the actor model with Scala.


text:
# Fresh Tomatoes Movie Trailers

Fresh Tomatoes Movie Trailers is a website to share favorite movies.  It was done as part of the UDACITY's Full Stack Web Developer Nanodegree Program.

## Information

Fresh Tomatoes Movie Trailers website used open source projects to be created.

- Created on Python 2.7.13

## Installation

1. Clone or download the zip file
2. Unzip the movieproject-master to your local machine
3. On your location machine go to the movieproject-master folder just created
4. Open the movieproject-master folder
5. Double click on the fresh_tomatoes.html file

## Functionality
- Three movie posters will be shown on the Fresh Tomatoes Movie Trailers web page
- By clicking a movie poster a trailer for the movie is launched

## Attributions
- Starter code was from udacity/ud036_StarterCode.
- fresh_tomatoes.py was not altered

License
----

MIT


text:
**Quick Start**

This section briefly describes the minimum configuration required for the application to function properly.

## 1. Login/ Sing up

Go to the authentication page and complete the process successfully.

 GET
 https://cenit.io/users/sign_in?redirect_url=http://my.example.com/home

Once successfully authenticated, you will be redirected to the application's home page.

## 2. Install core collection
On the left side menu go to the option Integrations / Available integrations and install the core collection, as shown in the image below.

Once the installation of the core collection is complete, the status of the collection will be updated to "installed" as shown in the image.

## 3. Install integrations collections

After installing the core collection, proceed to install the collections of the marketplace in which you want to operate. The following example shows how to install the collection for Lazada marketplace.

After installing the collection for Lazada, the Integrations / Channels menu will shows Lazada's sales channels that can be linked, these channels must be connected and authorized so that the application can work with them.

Click on the + symbol of the desired channel to connect it, in this example the Lazada Malaysia channel was used. In the dialog box, check the Authorized option to automatically proceed to authorize the channel after pressing the Save button.

Then you will be redirected to the Lazada authentication page where you must authorize the application by entering your credentials.

Once the application is authorized in the corresponding sales channel, it will be indicated in the Integrations / Connected Integrations menu that the channel is ready for the synchronization of orders and products, as shown below.

## 4. Import product from channel X

To import products from a sale channel, click on the channel menu.

Select option import products.

A task will be executed in the background which will carry out the import process of the products when it is finished.

## 5. How to export product to channel Y

Select the product to link to the channel in Data / Products and click on the "Link" option as shown in the following image.

Select the channel to which you want to link the product, and click save.

A task will be executed in the background which will carry out the link process of the products when it is finished.

## 6. Import order from channel X

To import orders from a sale channel, click on channel menu.

Select option import orders.

A task will be executed in the background which will carry out the import process of the orders when it is finished.

## 7. Export order to channel Y

Select the order to be exported in Data / Orders menu and click on the order details option.

In the order details view select the Export order to another integration option.

Select the desired integration and click on save option.

A task will be executed in the background which will carry out the export process of the orders when it is finished.

repository: cenit-io/eCapi

gha_language: HTML

repo_name: cenit-io/eCapi


text:
---
layout: post
title: "Memory Based Online Learning of Deep Representations from Video Streams"
date: 2017-11-17 14:51:58
categories: arXiv_CV
tags: arXiv_CV Face Tracking
author: Federico Pernici, Federico Bartoli, Matteo Bruni, Alberto Del Bimbo
mathjax: true
---

* content
{:toc}

##### Abstract
We present a novel online unsupervised method for face identity learning from video streams. The method exploits deep face descriptors together with a memory based learning mechanism that takes advantage of the temporal coherence of visual data. Specifically, we introduce a discriminative feature matching solution based on Reverse Nearest Neighbour and a feature forgetting strategy that detect redundant features and discard them appropriately while time progresses. It is shown that the proposed learning procedure is asymptotically stable and can be effectively used in relevant applications like multiple face identification and tracking from unconstrained video streams. Experimental results show that the proposed method achieves comparable results in the task of multiple face tracking and better performance in face identification with offline approaches exploiting future information. Code will be publicly available.

##### Abstract (translated by Google)
我们提出了一种新的在线无监督方法从视频流中进行人脸识别学习。该方法利用深层描述符以及利用视觉数据的时间相关性的基于记忆的学习机制。具体而言，我们引入了一种基于反向最近邻的判别性特征匹配方案和一种特征遗忘策略，该方法可以在时间过程中检测冗余特征并进行适当的丢弃。结果表明，所提出的学习过程是渐近稳定的，可以有效地应用于相关应用，如无约束视频流的多人脸识别和跟踪。实验结果表明，所提出的方法在利用未来信息的离线方法的人脸识别中，在多人脸跟踪任务中取得了可比的结果。代码将公开可用。

##### URL
[https://arxiv.org/abs/1711.07368](https://arxiv.org/abs/1711.07368)

##### PDF
[https://arxiv.org/pdf/1711.07368](https://arxiv.org/pdf/1711.07368)


text:
# postman collection generation

[![PyPI version](https://badge.fury.io/py/postman-collection-generation.svg)](https://badge.fury.io/py/postman-collection-generation)   [![Downloads](https://pepy.tech/badge/postman-collection-generation)](https://pepy.tech/project/postman-collection-generation)  [![GitHub license](https://img.shields.io/github/license/Naereen/StrapDown.js.svg)](https://github.com/Naereen/StrapDown.js/blob/master/LICENSE)

## Installation

You can install the Real Python Feed Reader from [PyPI](https://pypi.org/project/postman_collection_generation/):

    pip install postman_collection_generation

## How to use

You can call the function :

    >>> from postman_collection_generation import generate_collection
    >>> generate_collection(flask_app,name='collection file name',url_domain='localhost',port=5000,output_path = '.')

repository: AliaHamwi/PostmanCollection

repo_name: AliaHamwi/PostmanCollection