text:
# ComponentsManager
[![](https://jitpack.io/v/ValeryPonomarenko/ComponentsManager.svg)](https://jitpack.io/#ValeryPonomarenko/ComponentsManager)
![VERSION](https://api.bintray.com/packages/valeryponomarenko/ComponentsManager/Core/images/download.svg)
[![Codacy Badge](https://api.codacy.com/project/badge/Grade/10bc687809e44c2a8c8f49c3cbd7cfbe)](https://www.codacy.com/app/ValeryPonomarenko/ComponentsManager?utm_source=github.com&utm_medium=referral&utm_content=ValeryPonomarenko/ComponentsManager&utm_campaign=Badge_Grade)
[![Android Arsenal](https://img.shields.io/badge/Android%20Arsenal-Components%20Manager-brightgreen.svg?style=flat)](https://android-arsenal.com/details/1/7402)

README for the **1.x.x** of the library lives [here](https://github.com/ValeryPonomarenko/ComponentsManager/blob/master_v1/README.md).

## Benefits
- No need to save the components somewhere
- No need to remove the component when the Activity/Fragment is going to be destroyed
- The components will be saved while the rotation changes

## Getting started
This library is available on jitpack and ~~jcenter~~.

To download it from the jitpack, add these lines in your root build.gradle at the end of repositories:

```gradle
allprojects {
    repositories {
        ...
        maven { url 'https://jitpack.io' }
    }
}
```
If you are using **AndroidX**

```gradle
implementation "com.github.valeryponomarenko.componentsmanager:androidx:LATEST_VERSION"
```
If you are using **AppCompat**

```gradle
implementation "com.github.valeryponomarenko.componentsmanager:appcompat:LATEST_VERSION"
```

## Idea
The idea of the library is to save dagger components and return them when they are needed.
Every component is saved in the static store and removed when the owner is going to be destroyed.

## What's new
### 2.1.0
`*InjectionManager`s have two new methods to find a component. The methods return `null` if a component was not found and no exceptions are thrown.

```kotlin
// finds a component by type
XInjectionManager
    .findComponentOrNull<SomeComponent>()
    ?.someMethod()

// finds a component by predicate
XInjectionManager
    .findComponentOrNull { /* predicate */ }
    ?.someMethod()
```

The `ComponentNotFoundException` class that is inside `me.vponomarenko.injectionmanager.exeptions` package is deprecated, because the `exeptions` was misspelled, so use `ComponentNotFoundException` that is inside `me.vponomarenko.injectionmanager.exceptions` package. The new `ComponentNotFoundException` class is inherited from the old one.

### 2.0.1
If you use the `*InjectionManager.findComponent()` method and the component was not found, the ComponentNotFoundException will be more informative, beucase the type of the component will be printed.
```
//before
Caused by: me.vponomarenko.injectionmanager.exeptions.ComponentNotFoundException:
    Component for the Function1<java.lang.Object, java.lang.Boolean> was not found
...

//after
Caused by: me.vponomarenko.injectionmanager.exeptions.ComponentNotFoundException:
    Component of the FragmentChildB type was not found
...
```
But if you use the `*InjectionManager.findComponent(predicate)` method, the exception's massage will be the same as it was in 2.0.0.

### 2.0.0
The main difference between the **2.0.0** version and the **1.1.0** version that the **IHasComponent** interface is a generic one. Therefore, you must specify the class of the component.
```kotlin
//before
class MyFragment : Fragment(), IHasComponent {
    override fun onCreate(savedInstanceState: Bundle?) {
        super.onCreate(savedInstanceState)
        XInjectionManager
            .bindComponent<MyComponent>(this)
            .inject(this)
    }

    override fun getComponent(): Any = DaggerMyComponent.create()
}

//after
class MyFragment : Fragment(), IHasComponent<MyComponent> {
    override fun onCreate(savedInstanceState: Bundle?) {
        super.onCreate(savedInstanceState)
        XInjectionManager
            .bindComponent(this)
            .inject(this)
    }

    override fun getComponent(): MyComponent = DaggerMyComponent.create()
}
```

### 1.1.0
Add static methods into the (X/Compat)InjectionManager, so there will no need to get the instance and then call the needed method.
Examples:
```kotlin
//before
fun foo() {
    XInjectionManager.instance.init(this)
    XInjectionManager.instance.bindComponent<AppComponent>(this)
}

//after
fun foo() {
    XInjectionManager.init(this)
    XInjectionManager.bindComponent<AppComponent>(this)
}
```

## How to use
The following example will be for the **AndroidX**. If you want to use this library for the **AppCompat** packages, just change **XInjectionManager** to **CompatInjectionManager**.

First thing first, add the lifecycle callbacks listeners. At this step the library registers the lifecycle listener for the future activities and the fragments so the components that are bound to the activity or fragment will be destroyed right after the destruction of the owner.

```kotlin
class App : Application() {
    override fun onCreate() {
        super.onCreate()
        XInjectionManager.init(this)
    }
}
```

For example, the `FirstFragment` (also it works for the activities too) has a component, so you must implement the `IHasComponent` interface and call the `bindComponent` method of the `XInjectionManager` class. When the component is bound, it is available for other classes, but make sure, that these classes will not live longer than the owner of the component.

```kotlin
class FirstFragment : Fragment(), IHasComponent<FirstFeatureComponent> {
    //code...
    override fun onCreate(savedInstanceState: Bundle?) {
        super.onCreate(savedInstanceState)
        XInjectionManager.bindComponent(this).inject(this)
    }

    override fun getComponent(): FirstFeatureComponent =
        DaggerFirstFeatureComponent.builder()
            .build()
}
```

If the fragment doesn’t have its own component and uses the `AppComponent` to inject the dependencies, just call the findComponent method and specify the class of the component and that is all.

```kotlin
class SecondFragment : Fragment() {
    //code...
    override fun onCreate(savedInstanceState: Bundle?) {
        super.onCreate(savedInstanceState)
        XInjectionManager.findComponent<AppComponent>().inject(this)
    }
}
```

Also, this method might be used for getting dagger dependencies to build some components.

```kotlin
class AnotherFragment : Fragment(), IHasComponent<AnotherFeatureComponent> {
    //code...
    override fun getComponent(): AnotherFeatureComponent =
        DaggerAnotherFeatureComponent.builder()
            .appDependency(XInjectionManager.findComponent())
            .build()
}
```

That's all. There is no need to write code that will save, search or remove components anymore.

For more information, please, read the [wiki pages](https://github.com/ValeryPonomarenko/ComponentsManager/wiki).

## Links
[Lifecycle aware Dagger components - ProAndroidDev](https://proandroiddev.com/lifecycle-aware-dagger-components-8c74d01fa15)

If you have any questions, feel free to ask me on [LinkedIn](https://www.linkedin.com/in/ponomarenkovalery/).

repository: ValeryPonomarenko/ComponentsManager

gha_language: Kotlin

repo_name: ValeryPonomarenko/ComponentsManager


text:
## ![AAAAXY](logo.png)

AAAAXY is a nonlinear puzzle platformer taking place in non-Euclidean
geometry.

Although your general goal is reaching the surprising end of the game,
you are encouraged to set your own goals while playing. Exploration will
be rewarded, and secrets await you\!

So jump and run around, and enjoy losing your sense of orientation in
this World of Wicked Weirdness. Find out what Van Vlijmen will make you
do.

### Download now\!

[Download for Linux and Windows on
x86\_64](https://github.com/divVerent/aaaaxy/releases)

### Further Resources

[User Guide](userguide.md) | [Technical
Support](https://github.com/divVerent/aaaaxy/issues) | [Discussion
Forum](https://github.com/divVerent/aaaaxy/discussions) |
[Chat](https://matrix.to/#/#aaaaxy:matrix.org) | [Pull
Requests](https://github.com/divVerent/aaaaxy/pulls) |
[Speedrunning](speedrunning.md)

### Screenshots

[![shot1](screenshots/shot1.jpg)](https://raw.githubusercontent.com/divVerent/aaaaxy/main/docs/screenshots/shot1.png)
[![shot2](screenshots/shot2.jpg)](https://raw.githubusercontent.com/divVerent/aaaaxy/main/docs/screenshots/shot2.png)
[![shot3](screenshots/shot3.jpg)](https://raw.githubusercontent.com/divVerent/aaaaxy/main/docs/screenshots/shot3.png)
[![shot4](screenshots/shot4.jpg)](https://raw.githubusercontent.com/divVerent/aaaaxy/main/docs/screenshots/shot4.png)
[![shot5](screenshots/shot5.jpg)](https://raw.githubusercontent.com/divVerent/aaaaxy/main/docs/screenshots/shot5.png)
[![shot6](screenshots/shot6.jpg)](https://raw.githubusercontent.com/divVerent/aaaaxy/main/docs/screenshots/shot6.png)
[![shot7](screenshots/shot7.jpg)](https://raw.githubusercontent.com/divVerent/aaaaxy/main/docs/screenshots/shot7.png)
[![shot8](screenshots/shot8.jpg)](https://raw.githubusercontent.com/divVerent/aaaaxy/main/docs/screenshots/shot8.png)

### More Information

Deeper technical information and source code is to be found on the
[GitHub repository](https://github.com/divVerent/aaaaxy).

repository: isgasho/aaaaxy

branch_name: refs/heads/main

extension: md

license_type: permissive

repo_name: isgasho/aaaaxy


text:
---
title: CDS syntax check
cNumber: CHECK_67
rfc: false
index: 67
---

### CDS syntax check

Reports errors in CDS(DDLS)

Changing data types in DDIC can cause syntax errors in DDLS

Make sure to use object collector which includes DDLS

### Configuration
![Configuration](/img/default_conf.png)

license: MIT

repository: larshp/abapOpenChecks

gha_language: ABAP

gha_license_id: MIT

repo_name: larshp/abapOpenChecks


text:
---
layout: post
title:  "F# - Function overload"
subtitle: "Little win of Friday"
date:   2019-01-04 11:54:00
categories: [fsharp]
tags: [fsharp, aws, lambda]
---

I recently updated one of our Lambda functions from .NET Core version from 2.0 to 2.1 and suddenly got this error message in the following code snippet:

{% highlight fsharp %}
let tryParseInt =
    Int32.TryParse >> function
    | true, v -> Some v
    | false, _ -> None
{% endhighlight %}

"A unique overload for method 'TryParse' could not be determined based on type information prior to this program point. A type annotation may be needed. Candidates: Int32.TryParse(s: ReadOnlySpan<char>, result: byref<int>) : bool, Int32.TryParse(s: string, result: byref<int>) : bool"

Mhhh, interesting...

After a short research I found out that I can directly specify which overload I want to use of the given function.

This did the trick
{% highlight fsharp %}
let tryParseInt =
    (Int32.TryParse : string -> bool * int) >> function
    | true, v -> Some v
    | false, _ -> None
{% endhighlight %}

branch_name: refs/heads/master

extension: markdown


text:
---
layout: post
title: slashes in source code are interpreted once by javac and then again by the
  regex engine
date: 2006-05-13 13:14:00.000000000 +08:00
categories:
- 技术
tags:
- Java
- trick
status: publish
type: post
published: true
meta:
  _edit_last: '1'
  dsq_thread_id: '1532652931'
author:
  login: admin
  email<EMAIL_ADDRESS>  display_name: KONG
  first_name: ''
  last_name: ''
---
今天用string.replaceAll("\\\\n","\\n");本义是想将字符串里的字符串\\n转换成字符\\n,哪知道却没效果,心想可能是转义字符有问题,于是试着改成replaceAll("\\\\",test")想看看是啥状况,哪知却出现了Unexpected internal error near index 1的错误.google了一下,居然在sun的[_Bug Database_][0]里面找到了,不过已经被定性为 Closed, not a bug.**Evaluation** 里写道:This is not a bug. As mentioned in the spec, **slashes in source code are interpreted once by javac and then again by the regex engine**.

也就是说,对于replaceAll("\\\\",test"),\\\\先被javac解释成\\,然后交给正则引擎,正则引擎看到\\后认为这是一个转义字符,于是去找后面的字符,却发现没有了,然后报错.string.replaceAll("\\\\n","\\n") doesn't work的原因也很好解释了,\\\\n到了正则引擎处就成了\\n(换行符)了,而不是\\n

知道原因就好办了,改成string.replaceAll("\\\\\\\\n","\\n"); //it works!

[0]: http://spaces.msn.com/bugdatabase/


text:
---
layout: default
---

# 1994-12-13 - Re: Authentication vs encryption: CPs on the web

## Header Data

From: "Amanda Walker"<EMAIL_ADDRESS>To<EMAIL_ADDRESS>Message Hash: 6880954b6045db5d863d0765de499bdc15c41c66c0012199c1bada834e6c96f4

Message ID<EMAIL_ADDRESS>Reply To: _N/A_

UTC Datetime: 1994-12-13 21:46:08 UTC

Raw Date: Tue, 13 Dec 94 13:46:08 PST

## Raw message

```
{% raw  %}From: "Amanda Walker"<EMAIL_ADDRESS>Date: Tue, 13 Dec 94 13:46:08 PST
To<EMAIL_ADDRESS>Subject: Re: Authentication vs encryption: CPs on the web
Message-ID<EMAIL_ADDRESS>MIME-Version: 1.0
Content-Type: text/plain

> I notice that the people who come to this topic from an
> institutional point of view tend to be more interested in
> the authentication aspects.
> This seems to fit better into the control-oriented mindset.
> With authentication you can track what people are doing
> better; non-repudiable signatures could actually work in some ways
> against the signer.  I think that may be one reason Phil Zimmermann
> is famous for not signing his messages. :-)  But encryption can
> actually work against institutional interests (compared to individual
> ones) by making it harder to keep track of people's activities.

Very much agreed.  This is why, in my description of the rough criteria I sent
out, I included mention of self-signed certificates (which only show that you
do in fact have the private key corresponding to a given public key), bare
keys, and so on.

There clearly needs to be facilities for encrypted anonymous use.  To expand
on my example of wanting sales literature to be signed so I know it's genuine,
I correspondingly *don't* want to include a certificate with identifying
information in my own query--I get enough junk mail already, and I don't want
vendors to be able to capture market research at the browsing level (I haven't
looked at WIRED's server for precisely this reason).

Amanda Walker
InterCon Systems Corporation

{% endraw %}
```

## Thread

+ Return to [December 1994](/archive/1994/12)

+ Return to "["Amanda Walker" <amanda@intercon.com>](/authors/amanda_walker_amanda_at_intercon_com_)"

+ 1994-12-13 (Tue, 13 Dec 94 13:46:08 PST) - Re: Authentication vs encryption: CPs on the web - _"Amanda Walker"<EMAIL_ADDRESS>


text:
# AutomatedLoanPredictor
This repository contains file to automate the loan eligibility process based on customer details provided while filling online application form

##Description

The task is to predict whether the loan will be approved or not based on the details provided by customers. Here is the problem statement for this project:
Based on the details provided by customers, we have to create a model that can decide where or not their loan should be approved.

Here are a couple of factors that are helpful with respect to this project:

- Amount of loan: The total amount of loan applied by the customer.
- Income of applicanhttps://github.com/eetinosa/AutomatedLoanPredict/blob/main/data.csvhttps://github.com/eetinosa/AutomatedLoanPredict/blob/main/data.csvt: The income of the applicant (customer) can also be a deciding factor. A higher income will lead to higher probability of loan approval.
- Education of applicant: Educational qualification of the applicant can also be a vital factor to predict the loan status of a customer.

## Data exploration and pre-processing phase.
To explore the dataset and pre-process it. Find the dataset [here](https://github.com/eetinosa/AutomatedLoanPredict/blob/main/data.csv)The steps taken are as follows:

- Univariate Analysis
- Bivariate Analysis
- Missing Value Treatment
- Outlier Treatment
- Feature Engineering

Explore the variables individually which is called the univariate analysis. Exploring the effect of one variable on the other, or exploring two variables at a time is the bivariate analysis. Look for any missing values or outliers that might be present in the dataset and deal with them. And also create new features using the existing features (Encoding) which is referred to as feature engineering.

##Model building
It is a classification problem, Any of the classification models like the logistic regression, decision tree, random forest,can be used etc. Here, I used a random forest as the predictive model for this project.

Random forest classifier creates a set of decision trees from randomly selected subset of training set. It then aggregates the votes from different decision trees to decide the final class of the test object.

Tuning Parametersused for this model

- n_estimators : Number of trees in forest. Default is 10.
- max_depth : represents the depth of each tree in the forest. Here I used 5
- random_state : this is used as a seed to ensure consistency.
- min_samples_split: minimum number of working set size at node required to split. Default is 2.

![The Loan PRedictor Webpage](https://github.com/eetinosa/AutomatedLoanPredict/blob/main/Loan%20Predictor.PNG)
##How to run the model
On a terminal, run

```bash
git clone https://github.com/eetinosa/AutomatedLoanPredict
cd AutomatedLoanPredict/
code .
source loan/bin/activate
pip install -r requirements.txt
streamlit run app.py
```

repository: eetinosa/AutomatedLoanPredict

repo_name: eetinosa/AutomatedLoanPredict


text:
# HTTP Shortcuts for Android

![Version](https://img.shields.io/badge/version-1.28.0-green.svg)

A simple Android app that allows you to create shortcuts that can be placed on your home screen. Each shortcut, when clicked, triggers an HTTP request.

Get it on the [Play Store](https://play.google.com/store/apps/details?id=ch.rmy.android.http_shortcuts) or [download the latest APK](https://github.com/Waboodoo/HTTP-Shortcuts/releases) directly.

Help me translate this app! Feel free to open a pull request to add new languages or to fix translation mistakes. Or contact me and I can grant you access to the translation tool.

Become a [Beta Tester](https://play.google.com/apps/testing/ch.rmy.android.http_shortcuts)!

## Features
- HTTP and HTTPS
- GET, POST, PUT, DELETE, PATCH, HEAD, OPTIONS and TRACE
- Basic Authentication, Digest Authentication & Bearer Authentication
- Custom request headers
- Custom request body (text only)
- Option to accept all certificates (if you know what you're doing)
- Built-in icons, support for Ipack icons and custom icons
- Display response as a toast, window or dialog, or run silently
- Grouping into categories through tabs
- Value injection through variables (e.g., text, numbers, passwords, selections, colors, dates, …)
- Run arbitrary JavaScript before & after execution, including special actions (e.g., vibrate, show toast, parse and process response…),
- Import & Export in JSON or cURL format
- 3rd party integration (e.g. Tasker)
- Themes (with Dark Mode support)

## Screenshots

![Image](/Screenshots/1.png)
![Image](/Screenshots/2.png)
![Image](/Screenshots/3.png)
![Image](/Screenshots/4.png)
![Image](/Screenshots/5.png)
![Image](/Screenshots/6.png)
![Image](/Screenshots/7.png)
![Image](/Screenshots/8.png)

## FAQ

Check out the [FAQ](https://http-shortcuts.rmy.ch/#faq) page, or go to the official [forum](https://http-shortcuts.rmy.ch/forum).

## Hints for Experts

### Inspect Network Requests
This app uses [Stetho](https://github.com/facebook/stetho), which means you can debug the internal state and the network requests that are sent:
1. Connect your phone to a PC with a USB cable
2. Open the app on the phone
3. Open Chrome on the PC
4. Go to chrome://inspect
5. Click the *inspect* button below "HTTP Shortcuts (powered by Stetho)"

### Bulk Creation / Modifying of Shortcuts
If you want to add or edit a lot of shortcuts at once it can be very cumbersome to do so through the app's UI. An easy solution is to go to the apps settings and use the export feature. It provides you with a JSON file that you can easily modify with any tool you want. Once you are done you can import it again and voila: you have all your changes in the app.

### Customize look-and-feel of shortcuts
As opposed to Android's home screen widgets, home screen shortcuts are very limitted in how they can be customized, i.e. only icon and label can be specified. If you wish to change the look-and-feel of your shortcuts beyond that, I recommend you have a look at some third-party apps that allow you to create custom widgets. I recommend [Elixir 2 - Widgets](https://play.google.com/store/apps/details?id=com.bartat.android.elixir.widget&hl=en), which I personally use to combine multiple shortcuts into a nice looking widget.

### Voice Support
The app has very basic voice support, though only experimental at this point. Try "Ok Google, search \[Name of Shortcut\] in HTTP Shortcuts".

## Acknowledgments

### Features
- Adding basic voice command support: [Adrián López](https://github.com/adrianlzt)
- Improving quality of generated icons: [idmadj](https://github.com/idmadj)

### Translations
- Brazilian Portuguese: [Eduardo Folly](https://github.com/edufolly)
- Chinese: [Kevin Mao](https://github.com/yuanrunmao), [Chu Jinkai](https://github.com/cjkzwe)
- French: [Flavien](https://github.com/Flavien06)
- German: [Roland Meyer](https://github.com/Waboodoo)
- Italian: [Flavio Barisi](https://github.com/flavio20002), [Roberto Bellingeri](https://github.com/bellingeri)
- Norwegian (Bokmål): [Imre Kristoffer Eilertsen](https://github.com/DandelionSprout)
- Persian: [Amir Motahari](https://github.com/a-motahari)
- Spanish: [iprockbyte](https://github.com/iprockbyte)
- Turkish: [Bilal Bayrak](https://github.com/bayrakbilal)

repository: arashilmg/HTTP-Shortcuts

repo_name: arashilmg/HTTP-Shortcuts


text:
{
  "title": "Excited that #ggspatial works with #gganimate out of the box! This is a first try at zooming around multiple core locations from our latest Halifax lakes paper! https://t.co/XnTLa7dfm5 https://t.co/zuLsBLb428 #rstats #dataviz #gis https://t.co/tD8jtiQ8g9",
  "date": "2018-08-05"
}

# rstats

> **NumFOCUS** (@NumFOCUS; 78/15): Excited that #ggspatial works with #gganimate out of the box! This is a first try at zooming around multiple core locations from our latest Halifax lakes paper! https://t.co/XnTLa7dfm5 https://t.co/zuLsBLb428 #rstats #dataviz #gis https://t.co/tD8jtiQ8g9  [↪](https://twitter.com/dataandme/status/1026195093101965320)

<!-- -->

> **Tom B** (@teebuckl; 62/22): ICYMI, 👷 Great guide from @_ColinFay's workshop:
📦 "Building a package that lasts"
https://t.co/s79uOBRhFa #rstats https://t.co/Txp8jOIP2m  [↪](https://twitter.com/dataandme/status/1026090966187487234)

<!-- -->

> **tidyverse tweets** (@tidyversetweets; 59/12): R CHECK passed, README written, unit tests passed, blog finalized, newsletter prepared, Waterworld video generated...
>
3D mapping 🕶rayshader update release: tomorrow 👍#rstats #rayshader https://t.co/Pkk9y5Zy6o  [↪](https://twitter.com/dataandme/status/1026177519161102336)

<!-- -->

> **CRAN Package Updates** (@CRANberriesFeed; 52/15): Estimating treatment effects and ICCs from (G)LMMs on the observed scale using Bayes, Part 1: https://t.co/4nnWaMC3gR #rstats #DataScience  [↪](https://twitter.com/dataandme/status/1026123251896991744)

<!-- -->

> **tidyverse tweets** (@tidyversetweets; 29/7): Putting Hawaii and Alaska into their place. #rstats #dataviz #gganimate https://t.co/JOMWlVv3A9  [↪](https://twitter.com/dataandme/status/1026190740370731009)

<!-- -->

> **CRAN Package Updates** (@CRANberriesFeed; 25/12): New blog! "Estimating treatment effects and ICCs from (G)LMMs on the observed scale using Bayes, Part 1: lognormal models" https://t.co/D9Io8yXK6q #rstats https://t.co/by8mnt7tpK  [↪](https://twitter.com/dataandme/status/1026144246246662145)

<!-- -->

> **CRAN Package Updates** (@CRANberriesFeed; 25/8): In case you missed it, we are excited to announce that Sherri Rose (@sherrirose) will be taking over for Jeff Leek (@jtleek)  to co-edit @biostatistics  with @drizopoulos starting in January 2019! #rstats #biostatistics  [↪](https://twitter.com/dataandme/status/1026156134921641984)

<!-- -->

> **Dr. GP Pulipaka** (@gp_pulipaka; 24/21): Credit Card Fraud Detection using Autoencoders in #Keras — #TensorFlow for Hackers (Part VII). #BigData #Analytics #DeepLearning #MachineLearning #AI #FinTech #Python #RStats #JavaScript #ReactJS #VueJS #GoLang #CloudComputing #Serverless #DataScientist
https://t.co/LU6GZrZn8a https://t.co/n9utd4DYsk  [↪](https://twitter.com/dataandme/status/1026143372254347264)

<!-- -->

> **Oli Darley** (@CatchingPebbles; 22/19): Introduction to AI with Dinosaurs. #BigData #Analytics #DeepLearning #MachineLearning #DataScience #AI #IoT #IIoT #Python #RStats #TensorFlow #Java #JavaScript #ReactJS #VueJS #GoLang #CloudComputing #Serverless #DataScientist #GeneticAlgorithms
https://t.co/VbmG3By72m https://t.co/NW21TrU829  [↪](https://twitter.com/dataandme/status/1026142301725319169)

<!-- -->

> **tidyverse tweets** (@tidyversetweets; 19/1): Lo que aprendí ayer...
>
Yo te 🖤, str_wrap( )
>
#rstats https://t.co/YKlViACvf5  [↪](https://twitter.com/dataandme/status/1026167430295552003)

<!-- -->

> **Shrestha Diwash** (@diwastha; 18/17): Arm's Transformative Device-to-Data Platform for Connected IoT. #BigData #Analytics #MachineLearning #DataScience #AI #IoT #IIoT #EdgeComputing #Python #RStats #TensorFlow #Java #JavaScript #ReactJS #VueJS #GoLang #CloudComputing #Serverless #DataScientist
https://t.co/YtwneNnrCh https://t.co/OruNTGaiRj  [↪](https://twitter.com/dataandme/status/1026142804161044481)

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> **Analytics France** (@AnalyticsFrance; 11/6): Note: I'm doing this analysis in #rstats with 'tidytext' and am using the NRC Sentiment Lexicon to identify words associated with anger. https://t.co/cEsXnRs55B  [↪](https://twitter.com/dataandme/status/1026122139001339904)

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> **CRAN Package Updates** (@CRANberriesFeed; 10/4): How Do We Draw a Line? https://t.co/5av6N72zK3 #rstats #DataScience  [↪](https://twitter.com/dataandme/status/1026155930889711616)

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> **tidyverse tweets** (@tidyversetweets; 9/1): Spent a lot of time developing in #rstats over the past few weeks - devtools and roxygen2 have been blowing my mind... merlin 🧙♂ 📦 heading to CRAN this week #software #datascience  [↪](https://twitter.com/dataandme/status/1026176264770646017)

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> **Dr. GP Pulipaka** (@gp_pulipaka; 8/18): On My Way to LAX in Few Hours. The Future of #DataScience via @missdkingsbury. #BigData #Analytics #DeepLearning #MachineLearning #AI #Python #RStats #TensorFlow #Java #JavaScript #ReactJS #VueJS #GoLang #CloudComputing #Serverless #DataScientist #Books
https://t.co/KO3KPYDV2b https://t.co/oFq0zaZ4D8  [↪](https://twitter.com/dataandme/status/1026159664881459200)

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> **Joshua Rosenberg** (@jrosenberg6432; 7/3): This was not the plot I wanted to have, but I like it
#rstats
@accidental__aRt https://t.co/P8qfBm69hN  [↪](https://twitter.com/dataandme/status/1026109066823835654)

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> **Will Lowe** (@conjugateprior; 6/9): Boarding in an Hour! #BigData Appliances for #InMemoryComputing via @missdkingsbury. #Analytics #MachineLearning #DataScience #AI #IoT #IIoT #Python #RStats #TensorFlow #JavaScript #ReactJS #VueJS #GoLang #CloudComputing #Serverless #DataScientist #Books
https://t.co/IbkkSX0USd https://t.co/2tp1YMUzsQ  [↪](https://twitter.com/dataandme/status/1026205389027569664)

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> **Mirjam Eiswirth** (@MirjamIce; 5/17): Perform Advanced #GeoSpatial Analysis with Spatial Join—Now Available in #Tableau. #BigData #Analytics #GIS #DataScience #Python #RStats #TensorFlow #Java #JavaScript #ReactJS #VueJS #GoLang #CloudComputing #Serverless #DataScientist
https://t.co/yLweYGMjjl https://t.co/1ow8jKyYfT  [↪](https://twitter.com/dataandme/status/1026144867380953088)

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> **Mara Averick** (@dataandme; 5/11): React Framer X and ES6. #BigData #Analytics #MachineLearning #DataScience #AI #Python #RStats #TensorFlow #Java #JavaScript #ReactJS #VueJS #GoLang #CloudComputing #Serverless #DataScientist #ES6
https://t.co/kyQVdL2Bul https://t.co/6JL0xFGLMY  [↪](https://twitter.com/dataandme/status/1026143828439457792)

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> **Krissy Hopkins** (@kghopkin; 5/5): Day91: I am learning about multiple linear regression  and Logistic regression via "ISLR" book.
#100DaysOfCode
#rstats  [↪](https://twitter.com/dataandme/status/1026161689509732352)

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> **Sharon Machlis** (@sharon000; 5/3): ⌨ keypress ⇨ code profile: "rstudio:addins pt 5 - Profile your code… in the background, with no dependencies"  👨💻 @jozefhajnala  https://t.co/1pJxebrVix #rstats #rstudio https://t.co/oJtU84WrGw  [↪](https://twitter.com/dataandme/status/1026193650773057536)

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> **Data Scientists** (@DataScientistsF; 5/2): Lancaster university library with #rstats #tableau and some #dataviz classics https://t.co/JluOUKq67g  [↪](https://twitter.com/dataandme/status/1026126297444102144)

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> **Joshua Rosenberg** (@jrosenberg6432; 5/2): Fast unconditional but incidental-parameter-bias-corrected fixed effect logit models: https://t.co/bfZSnUCTrb and an #Rstats package to run them with: https://t.co/MYWX8vtAWN HT @jenniferdoleac  [↪](https://twitter.com/dataandme/status/1026100221523226624)

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> **Stanisław M. Stanuch** (@dziennikarz; 4/2): Morning walk along Union canal, afternoon #rstats , enjoying #Edfringe tonight - there are countless reasons to love Edinburgh :) https://t.co/iUIFGbaY6f  [↪](https://twitter.com/dataandme/status/1026135981706158080)

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> **Analytics France** (@AnalyticsFr; 4/1): I used #rstats data.table exclusively for a project this year with data close to the limits of memory. It's a wonderful tool- expressive and fast. https://t.co/g2Vyondeof  [↪](https://twitter.com/dataandme/status/1026130944745324544)

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> **Christopher Peters** (@statwonk; 4/1): 🍋😂🤣🔊 #rstats beepr::beep_on_error custom .wav file https://t.co/Z94jfqNTVY  [↪](https://twitter.com/dataandme/status/1026099051849306112)

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> **John Coene** (@jdatap; 3/3): If I had the knowledge and skills I'd develop some sort of codepen/jsfiddle for #rstats; is this feasible? It'd bring so much to the community!  [↪](https://twitter.com/dataandme/status/1026200331229847552)

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> **PyData** (@PyData; 3/1): New blog post benchmarking row binding a list of data frames in #rstats. Compares dplyr, purrr, and data.table: https://t.co/Q6buCcLgif  [↪](https://twitter.com/dataandme/status/1026186470787305487)

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> **Dr. GP Pulipaka** (@gp_pulipaka; 2/3): We derive confidence from the t-distribution this week.
#DataScience #MachineLearning #rstats #Statistics
https://t.co/BmsBkmyw43 https://t.co/alRhg5BPCb  [↪](https://twitter.com/dataandme/status/1026211566490935296)

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> **NumFOCUS** (@NumFOCUS; 2/2): @AnserGIS Not sure I get your rbind() point. Reproducible example please ? #rstats #gis  [↪](https://twitter.com/dataandme/status/1026176618862202881)

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> **Antonio S. Chinchón** (@aschinchon; 2/2): Announcing:
>
The NumFOCUS Project Forum for Core Developers and Critical Users
Sept 24-25 in NYC
>
Check out the schedule & learn more: https://t.co/Sp3zu3JzcG
>
Register: https://t.co/jFH6g4XUHW
>
#opensource #Sustainability #pydata #data #science #python #rstats #julialang #ML #AI https://t.co/fZQlcnHKFP  [↪](https://twitter.com/dataandme/status/1026163518528266242)

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> **CRAN Package Updates** (@CRANberriesFeed; 2/2): After some great feedback from @CivicAngela, looking for more feedback from the #rstats community thats interested in #transit and #gtfs on the tidytransit package: https://t.co/DHkyA3YH60  [↪](https://twitter.com/dataandme/status/1026149484118462464)

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> **John Yagecic** (@J_Yagecic; 2/2): @benmarwick @CDWren @archaeo_ecology If I may, my Beginner's Guide to R #rstats
https://t.co/quQUboKSvU  [↪](https://twitter.com/dataandme/status/1026110058688638976)

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> **Naresh Devineni** (@realDevineni; 2/1): #rstats and #ESA2018 are like peas in a pod these days... if you need to get a jump start on learning R, need a resource for your lab, or a resource for teaching, check out our book at the #OUP stand.  Author @beckerhopper is around too!  [↪](https://twitter.com/dataandme/status/1026197729805066240)

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> **ai korg** (@ai_korg; 1/3): Comprehensive Collection of One-page Survival Guides for doing #DataScience with R: https://t.co/Ibnh1uzKy0 #abdsc #Rstats #BigData #MachineLearning #PredictiveAnalytics #Coding #DataScientists https://t.co/fpNpNKsrke  [↪](https://twitter.com/dataandme/status/1026143368785608706)

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> **Antonio S. Chinchón** (@aschinchon; 1/2): Support the #opensource #datascience tools you use every day — become a NumFOCUS member!
>
https://t.co/1JT8FzHcnl
>
#python #pydata #julialang #rstats #bayesian #bigdata #machinelearning #dataviz #openscience #opendata https://t.co/lWSNAMrEsd  [↪](https://twitter.com/dataandme/status/1026188924174118912)

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> **Kristoffer Magnusson** (@krstoffr; 1/1): ggplot: adjust X ticks positions in boxplots #tidyverse #rstats https://t.co/OhUJiVB5Xq  [↪](https://twitter.com/dataandme/status/1026129651020775424)

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> **R-bloggers** (@Rbloggers; 1/1): How Do We Draw a Line? | Fronkonstin https://t.co/jcsA1bgU2Z #rstats #dataviz #BigData #opendata (tutorial w/Code)  [↪](https://twitter.com/dataandme/status/1026212436079202305)

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> **Michael Crowther** (@Crowther_MJ; 1/1): Visualizing Inequalities in Life Expectancy
#ggplot2 #Rstats https://t.co/v25aLOaBmJ  [↪](https://twitter.com/dataandme/status/1026191244354048000)

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> **GettingStartedWithR** (@GSwithR; 1/1): Keep up with new developments from @NumFOCUS sponsored projects — join our monthly email newsletter: https://t.co/TTwkhvlTkd
>
#jupyter #matplotlib #pandas #python #pydata #julialang #rstats #bigdata #machinelearning #dataviz #openscience #opendata #opensource #oss #datascience https://t.co/F4cjZkdqMH  [↪](https://twitter.com/dataandme/status/1026192702629666816)

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> **tidyverse tweets** (@tidyversetweets; 1/1): My Sunday check on the health of the Indian Rupee vis-a-vis the US dollar, and its strength relative to the Euro, the British pound and the Chinese Yuan. Data from @bankofengland . Charts in #rstats and #ggplot2 https://t.co/19ckg0ERCL  [↪](https://twitter.com/dataandme/status/1026174779705962496)

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> **mikefc** (@coolbutuseless; 1/1): From QUANTARCH's FB group, In Memoriam, George Cowgill, HOW I GOT TO WHERE I AM NOW: One Thing after Another, a (Mostly) Linear Narrative. Ancient Mesoamer, 19:2, 2008, 165–73. His journey to #QUANTARCH & the legacy he hoped to build https://t.co/EKoFIodfrN #rstats #archaeology https://t.co/fRiTFvCQFG  [↪](https://twitter.com/dataandme/status/1026162337525456897)

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> **Data Scientists** (@DataScientistsF; 1/1): Does the Dog Get Adopted?? -- P2 - Sorry it has got a bit long. Let me know your thoughts on the final model #rstats #TidyTuesday https://t.co/W4mIR6fHNY https://t.co/H7cDK2Yps1  [↪](https://twitter.com/dataandme/status/1026129352885526531)

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> **Malcolm Barrett 🦁 +👨** (@malco_barrett; 1/1): @CDWren @archaeo_ecology FYI you can also type example("whateverfunction") to *run* all the example code in an #rstats function's help documentation.  [↪](https://twitter.com/dataandme/status/1026112152720105476)

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> **PossibilityUnlimited** (@Earriffic; 1/1): @benmarwick @CDWren @archaeo_ecology I also maintain a list of my favorite R learning resources at @Computerworld here:  #rstats
https://t.co/DpweiYoq5e  [↪](https://twitter.com/dataandme/status/1026110632742068230)

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> **Dewey Dunnington** (@paleolimbot; 1/0): Great cocktails tonight at #rstats recommended bar. One of mine came in a bag! Thanks @_inundata & @srsupp for the suggestion :) https://t.co/ekpJUyMhmE  [↪](https://twitter.com/dataandme/status/1026217911868944385)

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> **Tyler Hampton** (@tylerbhampton; 0/4): How To Get #DataAnalytics Professionals Who Understand #Business
https://t.co/pMWsfOsLf8
#careerarc #HR #datascience #analytics #datadriven #BI #BusinessIntelligence #skillsdevelopment #leadership #python #rstats #scala #java #hadoop #spark #MachineLearning #ML https://t.co/Fj1iMDCSCr  [↪](https://twitter.com/dataandme/status/1026200681802358786)

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> **CRAN Package Updates** (@CRANberriesFeed; 0/2): No post abaixo, exploramos a contribuição dos nove grupos do IPCA para a inflação cheia, de modo a gerar uma previsão combinada para a mesma. #weLoveR #rstats #rlang #r #datascience https://t.co/pygIFzUDpn  [↪](https://twitter.com/dataandme/status/1026156839807770624)

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> **CRAN Package Updates** (@CRANberriesFeed; 0/2): hi #rstats, I kind of want to help out on packages if anyone needs, I (sort of) know how to use R's C API, Rcpp and C++11/14, and bad C. Are there any problems I can help with? I'm not great but maybe we can learn together!  [↪](https://twitter.com/dataandme/status/1026149752235122688)

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> **statstools.com** (@statstools; 0/2): Your #Manchester #python #rstats #data community is growing every day! Join our slack channel https://t.co/jCeWqxF8Rb so you can meet your fellow members, join the conversation, ask questions, post links to blogs, podcasts, talks, discuss issues, and tell us what you want!  [↪](https://twitter.com/dataandme/status/1026112165688819717)

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> **Mara Averick** (@dataandme; 0/1): thanks to CRAN team and tests, #rstats helper package for #d3js updated with better DESCRIPTION and bug fixes  [↪](https://twitter.com/dataandme/status/1026216533167669251)

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> **Tyler Morgan-Wall** (@tylermorganwall; 0/1): #rstats  [↪](https://twitter.com/dataandme/status/1026216180908879872)

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> **R-bloggers** (@Rbloggers; 0/1): A quick #rstats function to input a dataframe and build out a @Rasa_HQ NLU training data file format for user here: https://t.co/jS0iHBD7ie … #nlu #chatbot   https://t.co/aWiMVzvgeU  [↪](https://twitter.com/dataandme/status/1026214302972366849)

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> **Claus Wilke** (@ClausWilke; 0/1): How to prevent marrangeGrob opening a graphics device #tidyverse #rstats https://t.co/WPesngRmke  [↪](https://twitter.com/dataandme/status/1026213993608830978)

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> **Biostatistics** (@biostatistics; 0/1): How can I state legends in ggplot? #tidyverse #rstats https://t.co/ehkm8ZXNPe  [↪](https://twitter.com/dataandme/status/1026155066930155520)

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> **Dr. GP Pulipaka** (@gp_pulipaka; 0/1): Averaging multiple values in a single column to create a new variable in a tidy dataframe in R #tidyverse #rstats https://t.co/1M7fHmTuxw  [↪](https://twitter.com/dataandme/status/1026182770534621190)

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> **Dr. GP Pulipaka** (@gp_pulipaka; 0/1): Renaming multiple columns and gathering with dplyr in R #tidyverse #rstats https://t.co/28i18Aeb7x  [↪](https://twitter.com/dataandme/status/1026210260955086849)

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> **Sebastián Garrido** (@segasi; 0/1): How to annotate line plot with arrow and maximum value in R #tidyverse #rstats https://t.co/r3fT5WHDc2  [↪](https://twitter.com/dataandme/status/1026198918441435136)

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> **Dr. GP Pulipaka** (@gp_pulipaka; 0/1): create new variables based on other variables, with a loop over variables names in R #tidyverse #rstats https://t.co/x06e1wSroa  [↪](https://twitter.com/dataandme/status/1026197804627177477)

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> **Jay Ulfelder** (@dtchimp; 0/1): Error Switching geom_tile plot to Stat_Density_2D Plot #tidyverse #rstats https://t.co/QCh61Jlu4Q  [↪](https://twitter.com/dataandme/status/1026175146455904256)

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> **Ingo Rohlfing** (@ingorohlfing; 0/1): Can I get `stat_summary` to calculate summary values before transforms applied rather than after?
>
1. Yes - with a bit of effort
2. Not by default in the future as the github issue got closed pretty fast  :(
>
https://t.co/h2pCpbIRhi
>
https://t.co/s4UuB5hDp9
>
#rstats #ggplot2 https://t.co/ijqvM1RCup  [↪](https://twitter.com/dataandme/status/1026210905355214848)

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> **Dr. GP Pulipaka** (@gp_pulipaka; 0/1): Thanks @naupakaz for a great #rstats #ggplot #dataviz workshop at #ESA2018! If you missed it, check out the materials here, https://t.co/spBkFvNANk  [↪](https://twitter.com/dataandme/status/1026206894514417665)

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> **Clark Fitzgerald** (@clarkfitzg; 0/1): Time to Accept It: publishing in the Journal of Statistical Software #rstats #datascience https://t.co/dAL2hFeXeF  [↪](https://twitter.com/dataandme/status/1026203157154521088)

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> **Chris Hammill** (@CFHammill; 0/1): R functions for Bayesian Model Statistics and Summaries #rstats #stan #brms - #PrescientInfo https://t.co/gQipx4jn5B  [↪](https://twitter.com/dataandme/status/1026198203656560643)

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> **James David Smith** (@TheRealJimShady; 0/1): New false positive! Current false positive rate = 0.033 alpha = .05 #rstats  [↪](https://twitter.com/dataandme/status/1026196164041826304)

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> **Christian Bartens** (@cbartens; 0/1): @AnserGIS Oh right, yes I get what you mean. I've tended to use rbindlist() when this becomes an issue for me. #rstats #gis  [↪](https://twitter.com/dataandme/status/1026192483154313216)

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> **JayEnAar** (@GorwayGlobal; 0/1): Strange - whenever I use `df_list %>% purrr::map(~ggplot(.` etc within JupyterLab, it returns the names of the list items smack dab in the middle of the output plots. Not really a critical issue but weird nonetheless - any advice on how to suppress this behaviour? #rstats https://t.co/oHksVGNKFz  [↪](https://twitter.com/dataandme/status/1026182513247617024)

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> **Anthroinformatics** (@Dr_EOC; 0/1): #rdata #rstats: blighty internal function and datasets - https://t.co/Gf9qwGfPWs  [↪](https://twitter.com/dataandme/status/1026181894872936450)

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> **The Part Time Analyst** (@parttimeanalyst; 0/1): New CRAN package tidytransit with initial version 0.2.3 https://t.co/0QdYpl5sce #rstats  [↪](https://twitter.com/dataandme/status/1026181647627104257)

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> **Sharon Machlis** (@sharon000; 0/1): CRAN updates: fitur tsibble https://t.co/y5W2NU2tPr #rstats  [↪](https://twitter.com/dataandme/status/1026181689532284928)

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> **Sharon Machlis** (@sharon000; 0/1): New CRAN package BMSC with initial version 0.1 https://t.co/0QdYpl5sce #rstats  [↪](https://twitter.com/dataandme/status/1026181578597298178)

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> **Natalie Cooper** (@nhcooper123; 0/1): New CRAN package survsup with initial version 0.0.1 https://t.co/0QdYpl5sce #rstats  [↪](https://twitter.com/dataandme/status/1026181627364474880)

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> **Gemini Analytics** (@gemini_finance; 0/1): New CRAN package PELVIS with initial version 1.0.4 https://t.co/0QdYpl5sce #rstats  [↪](https://twitter.com/dataandme/status/1026181614156558337)

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> **Vítor Wilher** (@vitorwilherbr; 0/1): CRAN updates: MKmisc reticulate tidyRSS https://t.co/y5W2NTKSXT #rstats  [↪](https://twitter.com/dataandme/status/1026136312707993601)

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> **Sean Wu** (@slwu89; 0/1): CRAN removals: RVsharing https://t.co/t6KpMVXnWp #rstats  [↪](https://twitter.com/dataandme/status/1026136261906636800)

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> **PyData Manchester** (@PyDataMCR; 0/1): New CRAN package behaviorchange with initial version 0.0.1 https://t.co/0QdYpl5sce #rstats  [↪](https://twitter.com/dataandme/status/1026181567213961216)

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> **timelyportfolio** (@timelyportfolio; 0/1): New CRAN package localIV with initial version 0.1.0 https://t.co/0QdYpl5sce #rstats  [↪](https://twitter.com/dataandme/status/1026181600776802306)

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> **Chris Buddenhagen** (@CEBuddenhagen; 0/1): CRAN updates: cgraph https://t.co/y5W2NTKSXT #rstats  [↪](https://twitter.com/dataandme/status/1026121178820370432)

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> **Brock Tibert** (@BrockTibert; 0/1): New CRAN package nbc4va with initial version 1.1 https://t.co/0QdYpl5sce #rstats  [↪](https://twitter.com/dataandme/status/1026151367411412992)

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> **tidyverse tweets** (@tidyversetweets; 0/1): If you want to be a data champion, #rstats makes for an excellent weapon...  [↪](https://twitter.com/dataandme/status/1026173550447734786)

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> **tidyverse tweets** (@tidyversetweets; 0/1): Najbardziej angażujący tweet o #rstats w ciągu ostatnich 12h. https://t.co/cefFCaeh9a  [↪](https://twitter.com/dataandme/status/1026170061743112192)

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> **tidyverse tweets** (@tidyversetweets; 0/1): @hadleywickham @stoltzmaniac Interesting discussion. As you see it @hadleywickham, one should avoid relying on the #TidyVerse for building the functions in a package, but make sure that the package functions are compatible with TidyVerse use? So, #Rstats base for building functions, TidyVerse for using them?  [↪](https://twitter.com/dataandme/status/1026164952128778241)

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> **R Weekly Live** (@rweekly_live; 0/1): Posted by aschinchon, now on R-bloggers: How Do We Draw a Line? #rstats https://t.co/4u5RECAuPH  [↪](https://twitter.com/dataandme/status/1026160198967341057)

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> **Robert Sills** (@robertnsills; 0/1): Posted by Kristoffer Magnusson, now on R-bloggers: Estimating treatment effects and ICCs from (G)LMMs on the observed scale using Bayes, Part 1: lognormal models #rstats https://t.co/lEZpt2gZS7  [↪](https://twitter.com/dataandme/status/1026124995221696514)

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> **cor(Bot)** (@CorrelationsBot; 0/1): Estimating treatment effects and ICCs from (G)LMMs on the observed scale using Bayes, Part 1: lognormal models - https://t.co/uZdfBi5mZo #rstats  [↪](https://twitter.com/dataandme/status/1026123498308202497)

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> **James David Smith** (@TheRealJimShady; 0/1): How Do We Draw a Line? - https://t.co/2wNBCHbcS7 #rstats  [↪](https://twitter.com/dataandme/status/1026160002392842240)

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> **Ahmed Hasan** (@ahmedrhasan; 0/1): Estimating treatment effects and ICCs from (G)LMMs on the observed scale using Bayes, Part 1: lognormal models - https://t.co/wUMvWHcY6Y #rstats  [↪](https://twitter.com/dataandme/status/1026124610851557377)

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> **#rstats data** (@rstatsdata; 0/1): How Do We Draw a Line? - https://t.co/K02G9AGcEm #rstats  [↪](https://twitter.com/dataandme/status/1026159681646075906)

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> **CRAN Package Updates** (@CRANberriesFeed; 0/1): Estimating treatment effects and ICCs from (G)LMMs on the observed scale using Bayes, Part 1: lognormal models #rstats https://t.co/9lVY9nJ01x  [↪](https://twitter.com/dataandme/status/1026123947392282624)

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> **CRAN Package Updates** (@CRANberriesFeed; 0/1): How Do We Draw a Line? #rstats https://t.co/uapN97VNTt  [↪](https://twitter.com/dataandme/status/1026159179764064256)

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> **CRAN Package Updates** (@CRANberriesFeed; 0/1): How Do We Draw a Line? - https://t.co/gAHsESRrmx #rstats  [↪](https://twitter.com/dataandme/status/1026158783108665349)

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> **CRAN Package Updates** (@CRANberriesFeed; 0/1): Estimating treatment effects and ICCs from (G)LMMs on the observed scale using Bayes, Part 1: lognormal models - https://t.co/xVcSdfc7A3 #rstats  [↪](https://twitter.com/dataandme/status/1026126015158992896)

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> **Leon Eyrich Jessen** (@jessenleon; 0/1): #memobootr #rstats #moderation
Check out the updates to the MeMoBootR package - double two-way moderation! https://t.co/pkr7KRkRKc  [↪](https://twitter.com/dataandme/status/1026135809668407297)

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> **Thomas Hütter** (@DerFredo; 0/1): @MaartenvSmeden We'll just have to do an #rstats version some day 😜 (although it looks like there's some supplemental code somewhere I'll have to track down!)  [↪](https://twitter.com/dataandme/status/1026134529893777408)

<!-- -->

> **Thomas Hütter** (@DerFredo; 0/1): R functions for Bayesian Model Statistics and Summaries #rstats #stan #brms - #PrescientInfo https://t.co/oSdL0c0M5i  [↪](https://twitter.com/dataandme/status/1026132778147106816)

<!-- -->

> **Data Scientists** (@DataScienceFr; 0/1): More high flows in the #DelawareRiver  this time originating in the upper basin.  See the @DRBC1961 near-real-time #dashboard at https://t.co/SYyx6GISTA made with #rstats and @USGS and @NOAA data https://t.co/ElOZGx4EJg  [↪](https://twitter.com/dataandme/status/1026132747033690112)

<!-- -->

> **Data Scientists** (@DataScienceFr; 0/1): It gets quite complicated because you can add varying slopes, other  random effects & other approaches seem to be becoming more widely used. I'm esp. curious about use of ERGMs/ergm package in #rstats. Anyone with experience with either of these approaches who can weigh in? [2/2]  [↪](https://twitter.com/dataandme/status/1026132609385066496)

<!-- -->

> **Analytics France** (@AnalyticsFr; 0/1): Trying out social network selection models. I learned about the P2 model in @kenfrank20's class: It is basically a regression (or logistic regression if predicting tie instead of weight) with individual-specific (random) effects. You can fit it using lme4/lmer() in #rstats. [1/2]  [↪](https://twitter.com/dataandme/status/1026132040259973120)

<!-- -->

> **Analytics France** (@AnalyticsFrance; 0/1): 🤔 Nothing to say about the deep statistics history of experimental design, @yudapearl?
>
Also how is supervised machine learning different from fitting very general non-specific statistical models?
>
The excerpt is from @yudapearl's "The Book of Why" #python #rstats https://t.co/4By5ak7BBg  [↪](https://twitter.com/dataandme/status/1026123724255309824)

<!-- -->

> **Bruno Rodrigues** (@brodriguesco; 0/1): @JoachimStatist1 Very nice welcome! Start following the people that tweet with the #rstats hashtag and soon you'll become addicted 😁  [↪](https://twitter.com/dataandme/status/1026093800668758017)

<!-- -->

repository: batpigandme/twitter-blogdown

repo_name: batpigandme/twitter-blogdown


text:
connect-history-api-fallback

Middleware to proxy requests through a specified index page, useful for Single Page Applications that utilise the HTML5 History API.

Table of Contents

- [Introduction](#introduction)
- [Usage](#usage)
- [Options](#options)
	- [index](#index)
	- [rewrites](#rewrites)
	- [verbose](#verbose)
	- [htmlAcceptHeaders](#htmlacceptheaders)
	- [disableDotRule](#disabledotrule)

## Introduction

Single Page Applications (SPA) typically only utilise one index file that is
accessible by web browsers: usually `index.html`. Navigation in the application
is then commonly handled using JavaScript with the help of the
[HTML5 History API](http://www.w3.org/html/wg/drafts/html/master/single-page.html#the-history-interface).
This results in issues when the user hits the refresh button or is directly
accessing a page other than the landing page, e.g. `/help` or `/help/online`
as the web server bypasses the index file to locate the file at this location.
As your application is a SPA, the web server will fail trying to retrieve the file and return a *404 - Not Found*
message to the user.

This tiny middleware addresses some of the issues. Specifically, it will change
the requested location to the index you specify (default being `/index.html`)
whenever there is a request which fulfills the following criteria:

 1. The request is a `GET` or `HEAD` request
 2. which accepts `text/html`,
 3. is not a direct file request, i.e. the requested path does not contain a
    `.` (DOT) character and
 4. does not match a pattern provided in options.rewrites (see options below)

## Usage

The middleware is available through NPM and can easily be added.

```
npm install --save connect-history-api-fallback
```

Import the library

```javascript
var history = require('connect-history-api-fallback');
```

Now you only need to add the middleware to your application like so

```javascript
var connect = require('connect');

var app = connect()
  .use(history())
  .listen(3000);
```

Of course you can also use this piece of middleware with express:

```javascript
var express = require('express');

var app = express();
app.use(history());
```

## Options
You can optionally pass options to the library when obtaining the middleware

```javascript
var middleware = history({});
```

### index
Override the index (default `/index.html`). This is the request path that will be used when the middleware identifies that the request path needs to be rewritten.

This is not the path to a file on disk. Instead it is the HTTP request path. Downstream connect/express middleware is responsible to turn this rewritten HTTP request path into actual responses, e.g. by reading a file from disk.

```javascript
history({
  index: '/default.html'
});
```

### rewrites
Override the index when the request url matches a regex pattern. You can either rewrite to a static string or use a function to transform the incoming request.

The following will rewrite a request that matches the `/\/soccer/` pattern to `/soccer.html`.
```javascript
history({
  rewrites: [
    { from: /\/soccer/, to: '/soccer.html'}
  ]
});
```

Alternatively functions can be used to have more control over the rewrite process. For instance, the following listing shows how requests to `/libs/jquery/jquery.1.12.0.min.js` and the like can be routed to `./bower_components/libs/jquery/jquery.1.12.0.min.js`. You can also make use of this if you have an API version in the URL path.
```javascript
history({
  rewrites: [
    {
      from: /^\/libs\/.*$/,
      to: function(context) {
        return '/bower_components' + context.parsedUrl.pathname;
      }
    }
  ]
});
```

The function will always be called with a context object that has the following properties:

 - **parsedUrl**: Information about the URL as provided by the [URL module's](https://nodejs.org/api/url.html#url_url_parse_urlstr_parsequerystring_slashesdenotehost) `url.parse`.
 - **match**: An Array of matched results as provided by `String.match(...)`.
 - **request**: The HTTP request object.

### verbose
This middleware does not log any information by default. If you wish to activate logging, then you can do so via the `verbose` option or by specifying a logger function.

```javascript
history({
  verbose: true
});
```

Alternatively use your own logger

```javascript
history({
  logger: console.log.bind(console)
});
```

### htmlAcceptHeaders
Override the default `Accepts:` headers that are queried when matching HTML content requests (Default: `['text/html', '*/*']`).

```javascript
history({
  htmlAcceptHeaders: ['text/html', 'application/xhtml+xml']
})
```

### disableDotRule
Disables the dot rule mentioned above:

> […] is not a direct file request, i.e. the requested path does not contain a `.` (DOT) character […]

```javascript
history({
  disableDotRule: true
})
```

repository: bripkens/connect-history-api-fallback

gha_language: JavaScript

repo_name: bripkens/connect-history-api-fallback


text:
hactool currently recognizes the following keys (## represents a hexadecimal number between 00 and 1F):

```
secure_boot_key                 : Secure boot key for use in key derivation. NOTE: CONSOLE UNIQUE
tsec_key                        : TSEC key for use in key derivation. NOTE: CONSOLE UNIQUE.
keyblob_mac_key_source          : Seed for keyblob MAC key derivation.
keyblob_mac_key_##              : The ##th Keys used to validate keyblobs. NOTE: CONSOLE UNIQUE.
keyblob_key_source_##           : The ##th Seeds for keyblob keys.
keyblob_key_##                  : The ##th Actual keys used to decrypt keyblobs. NOTE: CONSOLE UNIQUE.
encrypted_keyblob_##            : The ##th Actual encrypted keyblobs (EKS). NOTE: CONSOLE UNIQUE.
keyblob_##                      : The ##th Actual decrypted keyblobs (EKS).
master_key_source               : Seed for master key derivation.
master_key_##                   : The ##th Firmware Master Key. Obtainable with TrustZone code execution.
package1_key_##                 : The ##th Package1 key. Obtainable with Package1ldr code execution.
package2_key_source             : Found in TrustZone .rodata.
package2_key_##                 : The ##th Package2 key. Derivable from master_key_## and package2_key_source.
aes_kek_generation_source       : Found in TrustZone .rodata.
aes_key_generation_source       : Found in TrustZone .rodata.
titlekek_source                 : Found in TrustZone .rodata.
titlekek_##                     : The ##th Titlekek. Derivable from master_key_## and titlekek_source.
key_area_key_application_source : Found in FS .rodata.
key_area_key_ocean_source       : Found in FS .rodata.
key_area_key_system_source      : Found in FS .rodata.
key_area_key_application_##     : Derivable from master_key_##, aes generation sources, and key_area_key_application_source.
key_area_key_ocean_##           : Derivable from master_key_##, aes generation sources, and key_area_key_ocean_source.
key_area_key_system_##          : Derivable from master_key_##, aes generation sources, and key_area_key_system_source.
sd_card_kek_source              : Seed for SD card kek.
sd_card_nca_key_source          : Seed for SD card encryption NCA key.
sd_card_save_key_source         : Seed for SD card encryption save key.
header_kek_source               : Found in FS .rodata.
header_key_source               : Found in FS .rodata.
header_key                      : Derivable from master_key_##, aes generation sources, and header sources.
```

license: ISC

repository: SocraticBliss/hactool

repo_name: SocraticBliss/hactool


text:
---
title: You can now play on your Cozy
tags:
  - Cozy
---

Cozy started as a personal private cloud and app platform, and is now evolving into a universal tool that can help you organize your life and make you more productive by automating routine tasks. But it can also host any [client side app](https://blog.cozycloud.cc/post/2016/02/18/Client-Side-applications) you want, including games such as 2048 or esviji.

If you already have your Cozy, you just have to copy/paste this url into the “Install an app from it repository” field in your cozy’s store page: [https://github.com/jsilvestre/cozy-esviji/](https://github.com/jsilvestre/cozy-esviji/).

Thanks a lot [Joseph](https://github.com/jsilvestre) for this!

You can also [read the documentation](https://dev.cozy.io/clientsideapp.html) to create your own applications for Cozy.

gha_language: CSS


text:
## Goals
* one user story:
  * user click buy button in product detail page,
  * show the price of the order: including deduction,shipping fee,user can adjust deduction and pay method
  * user submit the order: deduction, decrease stock in shelf and warehouse
  * user pay the order: generating brokerage

## Deploy:
* web server: Vert.X, SpringBoot
* msg queue: Kafka or RabbitMQ
* microservices
  * order
  * shelf: price related
  * stock:
  * user: brokerage/coupon deduction,brokerage generation

## Detail
### webserver
* convert http request to a command/query message, send to message queue.
* subsribe response message from message queue，convert to http response and send back

### Rest API
POST /order
general data from header such as token,

### Fundamental layer:
  * ServiceFramework: convert message to service method invocation
  * ServiceFramework: other aspect support: security:current user & permission (Spring security?),log,transaction?
  * Infrustructure:

//TODO a architect diagram
Fundamental -> Service -> Domain

### Event Storming
<img width="1536" alt="WeChat50900ac91619bd26a41162508596ab51" src="https://user-images.githubusercontent.com/7393184/130709982-c333ba3f-a068-45c6-af70-ada427810801.png">

### Security Service
* Authentication & Authroization,fire UserLoginEvent

### OrderService
* fire OrderCreatedEvent

### ShelfService
* onOrderCreatedEvent: fill shelf price to order,and fire OrderPricedEvent

### UserService
* onOrderCreatedEvent: fill balance and brokerage that can be deducted to ther order,and fire OrderBalanceDeductedEvent


text:
# Update March 2021

This repository has been archived and is no longer maintained. Russbot had a good life on Slack, and may live again on Discord in the future.

---

# russbot

A Slack bot for KSU CIS Slack Channel. It is maintained by students and faculty at Kansas State University.

---

## Examples

Some of the plugins of russbot include:
* `!xkcd` - Posts a random xkcd comic
* `!rss` - Reads and posts from RSS feeds
* `!lmgtfy` - Let me Google that for you
* `!wolfram` - Wolfram Alpha Search
* `!beocat` - Beocat Break In, a text Based RPG Game

---

## Installation

#### Notes
* You will need to add a bot to your slack channel and get an API key prior to starting the setup.
* russbot uses gradle to build and run the bot.

#### Instructions

1. To get started copy the config file `russbot.cfg.example` to `russbot.cfg`
2. Inside the config file it should have a spot for several settings
    1. token - the Slack token
    2. wolfram - the Wolfram API token
    3. channels - the Channels russbot is allowed to access (plugins can choose from a subset of these)
    4. test - boolean (true, false) to put the system into test mode
3. Create a **data** folder in the root of the project. `mkdir data`
3. Once you enter those go ahead and build with gradle as normal. `./gradlew build`
4. After you successfully build it you can try running it. `./gradlew run`

#### Testing

Use `./gradlew run -q -PrunArgs="['-t']"` to run in test mode and interact with russbot via command-line. You can also set the test property to "true" to enable test mode.

While in test mode:
`#<channel>` will switch channel
`@<user>` will switch user

---

## License

The MIT License (MIT)

repository: russfeld/russbot

gha_language: Java

repo_name: russfeld/russbot


text:
---
title: "Internal deeplearnjs - math"
layout: post
date: 2017-10-06 20:47:29 +0900
image: 'images/'
description:
tag: ["math", "deeplearnjs", "DeepLearning", "JavaScript", "TypeScript"]
blog: true
author: "lewuathe"
---

[deeplearnjs](https://github.com/PAIR-code/deeplearnjs) is a new deep learning framework running on browser accelerated by WebGL hardware acceleration as I introduced before in this [post](/try-deeplearn-js.html). I was interested in the library from the beginning and submitted [some patches](https://github.com/PAIR-code/deeplearnjs/commits?author=Lewuathe) including new optimizer implementations. I learned some internal codebase of the library during this process. So I want to explain some points interesting to me in this post. This time is regarding the component about mathematical calculation in deeplearnjs.

## Mathematical calculation

`NDArrayMath` is a component which is responsible for doing a tensor calculation. This class provides kernel interface like `exp`, `add` and convolution etc.  `NDArrayMath` is an abstract component. Actual calculation will be delegated to implementations on CPU and GPU, `NDArrayMathCPU` and `NDArrayMathGPU` respectively. These implementations are delegated from template method of `NDArrayMath` abstract class.

```typescript
  /**
   * Adds two NDArrays element-wise, A + B. Supports broadcasting.
   * For a stricter version without broadcasting use math.addStrict().
   *
   * @param a The first NDArray to add element-wise.
   * @param b The second NDArray to add element-wise.
   */
  add(a: NDArray, b: NDArray): NDArray {
    util.assertAndGetBroadcastedShape(a.shape, b.shape);
    return this.executeOp('add', () => this.addInternal(a, b));
  }
  protected abstract addInternal(a: NDArray, b: NDArray): NDArray;
```

`NDArrayMathCPU` and `NDArrayMathGPU` should implement `addInternal` method to provide this kernel function in their platform, CPU and GPU. CPU implementation is very simple.

```typescript
  protected addInternal<T extends NDArray>(a: T, b: T): T {
    return this.scaledArrayAddInternal<T>(Scalar.ONE, a, Scalar.ONE, b);
  }
```

It's delegated to `scaledArrayAddInternal` further.

```
  protected scaledArrayAddInternal<T extends NDArray>(
      c1: Scalar, a: T, c2: Scalar, b: T): T {
    const newShape = util.assertAndGetBroadcastedShape(a.shape, b.shape);
    const newValues = new Float32Array(util.sizeFromShape(newShape));

    const aValues = a.getValues();
    const bValues = b.getValues();
    const c1Val = c1.get();
    const c2Val = c2.get();
    for (let i = 0; i < newValues.length; ++i) {
      newValues[i] = c1Val * aValues[i % a.size] + c2Val * bValues[i % b.size];
    }
    return NDArray.make(newShape, {values: newValues}) as T;
  }
```

In contrast, understanding GPU implementation may require a little WebGL familiarity. This is the implementation in `NDArrayMathGPU`.

```typescript
  protected addInternal<T extends NDArray>(a: T, b: T): T {
    const program = new BinaryOpProgram(binaryop_gpu.ADD, a.shape, b.shape);
    return this.compileAndRun<NDArray, T>(program, [a, b]);
  }
```

It writes a shader program in a plain string. `BinaryOpProgram` just keeps the shader source program string and some metadata. `NDArrayMathGPU` compiles the program and send it to GPU through WebGL API. If you are familiar with the shader pipeline of WebGL, it is not so difficult to understand the process.

`NDArray` is a data treated as a tensor entity in deeplearnjs. If you touch Python numpy, the interface may look similar to numpy array. We learned how to run kernel program in WebGL GPU for now but how deeplearnjs send a data in `NDArray` to GPU?

The answer is texture.

## Texture Manager

A data in `NDArray` is copied to GPU frame buffer as a texture. Texture manager is responsible for managing the data in GPU frame buffer.

```typescript
function createAndConfigureTexture(
    gl: WebGLRenderingContext, width: number, height: number,
    numChannels: number): WebGLTexture {
  webgl_util.validateTextureSize(gl, width, height);
  const texture = webgl_util.createTexture(gl);

  const tex2d = gl.TEXTURE_2D;
  const internalFormat = getTextureInternalFormat(gl, numChannels);
  const format = getTextureFormat(gl, numChannels);
  webgl_util.callAndCheck(gl, () => gl.bindTexture(tex2d, texture));
  webgl_util.callAndCheck(
      gl, () => gl.texParameteri(tex2d, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE));
  webgl_util.callAndCheck(
      gl, () => gl.texParameteri(tex2d, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE));
  webgl_util.callAndCheck(
      gl, () => gl.texParameteri(tex2d, gl.TEXTURE_MIN_FILTER, gl.NEAREST));
  webgl_util.callAndCheck(
      gl, () => gl.texParameteri(tex2d, gl.TEXTURE_MAG_FILTER, gl.NEAREST));
  webgl_util.callAndCheck(
      gl,
      () => gl.texImage2D(
          tex2d, 0, internalFormat, width, height, 0, format, gl.FLOAT, null));
  webgl_util.callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, null));
  return texture;
}
```

`createAndConfigureTexture` create texture frame buffer and bind it to the context. A frame buffer in a GPU memory space is allocated. Who send the data into that space?

## NDArray

`runProgram` method in `gpgpu_math.ts` gets inputs and outputs of the program.

```typescript
const outTex = output.getTexture();
```

`getTexture()` actually copies the data into frame buffer allocated in advance. This is a method in `NDArray`.

```typescript
  getTexture(preferredShapeRC?: [number, number]): WebGLTexture {
    if (this.data.texture == null) {
      this.uploadToGPU(preferredShapeRC);
    }
    return this.data.texture;
  }
```

Finally it is delegated to `uploadDataToTexture` method in `gpgpu_util.ts`.

```typescript
function uploadDataToTexture(
    gl: WebGLRenderingContext, texture: WebGLTexture, width: number,
    height: number, data: Float32Array, numChannels: number) {
  const textureFormat = getTextureFormat(gl, numChannels);

  webgl_util.validateTextureSize(gl, width, height);
  webgl_util.callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, texture));
  webgl_util.callAndCheck(
      gl,
      () => gl.texSubImage2D(
          gl.TEXTURE_2D, 0, 0, 0, width, height, textureFormat, gl.FLOAT,
          data));
  webgl_util.callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, null));
}
```

`gl.bindTexture` send a command to GPU to select the frame buffer allocated in advance for this kernel program.  Then it copies data with `texSubImage2D` command as 2D texture image internally. (But it is 2D tensor actually). So the kernel program can find the data in frame buffer after this method is called.

## Problem

Copying the data into GPU memory can be an overhead. It is desirable to copy data as much as possible at the same time. The data should not be changed in a batch training in a deeplearning framework. Bulk copying still more important. deeplearnjs now copies the data every time each kernel program runs. There is some room to be improved regarding copying data into GPU memory.

gha_language: HTML


text:
---
title: 'How to Convert a PowerPoint Presentation into Microlearning Content?'
og_title: 'How to Convert a PowerPoint Presentation into Microlearning Content?'
description: Lengthy PowerPoint presentations don’t keep learners engaged. Learn what it takes to convert your training slide decks into beautiful and intuitive microlearning feeds.
og_description: Lengthy PowerPoint presentations don’t keep learners engaged. Learn what it takes to convert your training slide decks into beautiful and intuitive microlearning feeds.
categories: ["ACM"]
id: 108
author:
  name: SmartWinnr
  email<EMAIL_ADDRESS>weight: -108
slug: "/how-to-convert-a-powerpoint-presentation-into-microlearning-content"
featured_image: "/images/108.how-to-convert-a-powerpoint-presentation-into-microlearning-content.jpg"
isFeatured: false
date: '2020-09-04T10:00:32+08:00'
tags: ["Training", "Micro-learning", "Knowledge Retention"]
relatedBlogs : ["Microlearning- An effective way to train millennials", "Spaced Repetition and Microlearning: Two peas in a pod", "Ways to Challenge the Forgetting Curve", "3 insights from Cognitive Science that will drive your employee knowledge", "Training the Millennials 5 Strategies that Work", "‘Work from Home Warriors’: Sales Training Game for Remote Sales Teams", "How to Transform your 2-day Face-to-face Training into a Microsoft Teams Virtual Training?", "How to Deliver Training to your Remote Sales Staff?"]
---

### **PowerPoint Presentation: A thing of the past**

Don’t get this wrong. Microsoft power-point is undoubtedly the most powerful tool to present information in many creative ways. It’s been around since 1987 and been producing 30 million presentations every day. But when it comes to training, it is perhaps not the most optimal tool.

Sure, it is effective for instructor-led training sessions. But to make that training stick, you need something more than just a ppt.

Training has evolved so much from the 1980s to today. Now, with workplaces filled with the millennial workforce, you need a supplement that caters to:

* Shorter attention spans

* Just-in-time learning needs

* Better retention rate

* And other aspects of quality learning

Wondering what you could use as a supplement?

**Microlearning** is the key.

### **What is Microlearning?**

Microlearning is the process of breaking down lengthy training content into smaller nuggets. Converting those nuggets into interesting formats like infographics, videos, podcasts, etc, and delivering them to the learner regularly through mobile app or desktop. This method of learning helps the learners to:

* Digest the information quickly and easily

* Improves their knowledge retention

* Enables them to learn anytime, anywhere

So, in order to supplement your training and meet the training needs of Gen Z learners-- you can convert your PowerPoint training content into microlearning feeds. Here are some helpful ways to convert.

#### **1. Infographics**

Do you know that-- “a good infographic is worth a thousand word”? That’s the power infographics carry. Visual representation of information helps in capturing your audiences’ attention.

Go through your training presentation and convert any of the following into beautiful and intuitive infographics:

* bullet points,

* statistical data,

* comparisons,

* timelines,

* hierarchies

If you don’t want to get into the effort of creating an infographic, you can also present a slide itself as an infographic provided the content is short and crisp. Remember that ‘micro’ is key here. The intention should be to get the learners to focus on small pieces of information at a time.

Some helpful tools for creating infographics:

Canva

Venngage

Piktochart

easel.ly

Visme

Infogram

#### **2. Videos**

Videos are everywhere nowadays!  YouTube viewers consume more than a billion hours of videos per day!  And more than half of YouTube’s views come from mobile devices.

How did this number of views get so high? Quite simply. These videos are easy to access and they maintain viewers’ attention. So, why not use them in your learning as well to effectively engage your learners?

Here is how to convert the training content PPT into videos:

1. Pick a concept in the ppt which you want to convert into a video.

2. Club all the slides that explain that concept in a separate ppt.

3. Then add PowerPoint features like animations, transitions, and audio to the slides in order to add movement to the presentation.

4. You can also choose to add narration to each slide. This helps to engage the learners more effectively since they aren’t reduced to only reading the slides.

Another way to create these videos is by using video creation tools that are available in the market. They help in generating some awesome videos/ animated videos within no time.

**Best Tools for Creating Videos:**

Moovly

Animoto

Toonly

Animaker

Pro-Tip: Here are certain guidelines to follow in order to create highly engaging videos:

Follow a well-defined structure while presenting the information in the video. Make sure that the content flows seamlessly

Add screenshots, images, and, animations wherever you can

According to research from various social media platforms-- viewers’ engagement with the videos drops sharply after 2-3 minutes. So, make sure that your microlearning videos are no more than 2 minutes. Preferably 60 seconds.

#### **3. Podcasts**

In this fast-paced world, your learners do not have the time to keep up with the latest updates and trends of their industry. So you need to create content which they can consume on the go.

This is where podcasts come into the picture. Podcasts or audio files provide an efficient way for the learners to listen and learn while

* They are commuting to home on the train

* At the gym

* While running errands

* While working from home

It doesn’t take much to develop these podcasts.

Every month, invite an expert to talk on a topic for 5-10 minutes. Topics can be varied like

The market landscape

Industry trends

Compliance and policies

Current sales trends

Record the talk using the podcast record tools mentioned below and send the audio file to the learners on their mobile devices.

Some best tools to record podcasts:

Skype

Zoom

Adobe Audition

Audacity

Garage Band

Pro-Tip: Occasionally, you can do podcast series featuring industry experts. Or get some famous personalities to deliver a motivational talk. This way you will be able to excite and engage your audience effectively with your podcasts.

#### **4. Storyboards**

We all have enjoyed comic books as children and some of us still do. What makes comic books special?

* The animations

* And the presentation of the story in the form of a storyboard

You can spark the same nostalgic emotion and engagement amongst your learners by introducing storyboards in their learning.

Here are some topics which are suitable for converting into storyboards:

Sales Conversations

Workflows

Standard operating procedures

Ideal customer conversations

Best tools for Creating Storyboards:

StoryboardThat

StoryBinder

Adobe Photoshop

Storyboarder

Plot

Frameforge Storyboard Studio

Automate the process of sending microlearning feeds to your learners by using SmartWinnr’s SmartFeeds. It helps you to regularly send bite-sized insights that matter and allows your learners to like and react to a SmartFeed.

### **Bring everything together: The Heart-Head-Hands Technique**

Now let’s look at an interesting technique called “Heart-Head-Hands”. This technique provides a framework to organize and send your microlearning modules. This helps in maximizing the interest of the learners and their knowledge retention.

Here is how the technique works:

* **Heart**: This is the emotional hook that you need to establish at the beginning (before sending the actual training content). Try to get the learner to care about what you have to say by striking an emotional chord with them. Send microlearning feeds on:

  * What topics you are going to cover in the next one week or 15 days

  * What knowledge are you are trying to reinforce

  * What outcome do you expect out of it

  * What is in it for them

    **What to use**: Infographics and videos

* **Head**: This is where you provide the learners with the actual training content. Send one microlearning feed every day or once in 3 days. Make sure not to overwhelm your learners by limiting to only one concept per microlearning.

    **What to use**: Infographics, videos, and podcasts

* **Hands**: Finally, wrap things up by reinforcing practical knowledge. Send feeds on:

  * Real-life scenarios

  * Realistic customer conversation examples

  * Appropriate actions that should be taken in various work scenarios

  * How to apply a concept they have learned in a realistic scenario etc

    This provides hands-on knowledge to your learners.

    **What to use**: Storyboard

#### **Want to know more about microlearning?**

Spaced Repetition and Microlearning: Two peas in a pod

Use Micro-Learning to Turbo-Charge your Sales Team

How does Microlearning Support Virtual Training

#### **Looking for a platform to automate the process of delivering microlearning feeds for your learners?**

Explore SmartWinnr’s SmartFeeds. SmartFeeds help you create microlearning feeds and deliver them to your learners through the SmartWinnr mobile app regularly. Your learners can then like and react to these feeds. This helps in increasing the engagement of the learners.

Interested to see it in action? Book a demo today!

repository: smartwinnr/site-smartwinnr

repo_name: smartwinnr/site-smartwinnr


text:
# payments

To add new plan you've to do the following:

* Create plan in Stripe
  * For test env, this can be done by adding it [here](https://github.com/koding/koding/blob/master/go/src/socialapi/workers/payment/paymentplan/paymentplan.go#L42)
  * You'll have to create it manually for production
* Add migrations [here](https://github.com/koding/koding/tree/master/go/src/socialapi/db/sql/payment_definition), [here](https://github.com/koding/koding/tree/master/go/src/socialapi/db/sql/migrations) and finally [here](https://github.com/koding/koding/blob/master/config/generateRunFile.coffee#L191).

repository: f/koding

gha_language: CoffeeScript

repo_name: f/koding


text:
# Template for project

## Built with
* Docker (infrastructure)
* ReactJS + Foundation CSS (client side)
* Django (server side)
* Postgres (database)
* Nginx (reverse proxy and cache for static assets)

## Getting Started
These instructions will get you a copy of the project up and running on your local machine for development and testing purposes.

### Prerequisites

* Install Docker for Mac if using Mac (https://store.docker.com/editions/community/docker-ce-desktop-mac)
* Add your SSH key to your Bitbucket settings
* Install NPM (https://docs.npmjs.com/getting-started/installing-node)

Add these lines to .zshrc to setup our custom Docker command
```
alias dcdev='docker-compose -f docker-compose.yml -f docker-compose.dev.yml'
```

Then, run this command to load your changes
```
source ~/.zshrc
```

### Setup project

### Set up environmental variables
```
cp .env.sample .env
cp db.env.sample db.env
```

#### Install node packages
```
cd ~/Desktop/projectname/frontend
yarn install
```

NOTE: should not have to manually do yarn install. Also, there may be issues with installing new node packages while devloping, making you have to restart the Docker container, will need to investigate further...

http://dchua.com/2016/02/07/getting-npm-packages-to-be-installed-with-docker-compose/

#### Generate Django secret key

#### Start the project
Spin up containers using the alias we set up earlier

* Check to make sure Docker is running, when it is, you will be able to use docker-compose commands
* This may take a few minutes if this is your first time running the command since Docker will have to build the images that we will use to run our Docker containers

```
cd ~/Desktop/projectname
dcdev up
```

#### View in browser
* go to localhost:3000 to view our user interface
* go to localhost/api/ for server side and to see API response data (e.g. localhost/api/stories?bySpeaker=true&byTitle=true for all the stories)

You are now ready to start developing and testing!

#### If you want to stop the containers, run this command
```
dcdev down
```

### Developing

Any changes to the Dockerfiles will require you to rebuild the images
```
dcdev build
```

## Production

### Build Static files
```
cd ~/Desktop/projectname/frontend
npm run build
```

repository: ucsd-field-lab/namuti-webapp-template

repo_name: ucsd-field-lab/namuti-webapp-template


text:
# carp.deployments [![Maven Central](https://maven-badges.herokuapp.com/maven-central/dk.cachet.carp.deployments/carp.deployments.core/badge.svg)](https://mvnrepository.com/artifact/dk.cachet.carp.deployments) [![Sonatype Nexus (Snapshots)](https://img.shields.io/nexus/s/dk.cachet.carp.deployments/carp.deployments.core?server=https%3A%2F%2Foss.sonatype.org)](https://oss.sonatype.org/content/repositories/snapshots/dk/cachet/carp/deployments/)

Maps the information specified in a study protocol to runtime configurations used by the 'clients' subystem to run the protocol on concrete devices (e.g., a smartphone) and allow researchers to monitor their state.
To start collecting data, participants need to be invited, devices need to be registered, and consent needs to be given to collect the requested data.

## Deployment sequence

The following diagram depicts a typical sequence of calls to [application services in the deployments subsystem](#application-services) to create a study deployment, invite participants, and deploy it successfully on a client device.
The events between `DeploymentService` and `ParticipationService` are opaque to the user and are merely an implementation detail.
The application and domain services in `carp.studies` and `carp.client` abstract away this entire sequence.
Matching code for these calls can be found in the main README as part of the [carp.deployments](../README.md#example-deployments) and [carp.client](../README.md#example-client) examples.

![Study deployment sequence diagram](https://i.imgur.com/5vhCqwu.png)

## Study and device deployment state

Most of the [the `DeploymentService` endpoints](#application-services) return the current status of a study deployment after the requested operation has been executed.
Depending on the current state of the deployment, different operations are available.
This is represented by [`StudyDeploymentStatus`](../carp.deployments.core/src/commonMain/kotlin/dk/cachet/carp/deployments/application/StudyDeploymentStatus.kt), which reflects the underlying state machine:

![Study deployment state machine](https://i.imgur.com/GTk5OHe.png)

The overall deployment state depends on the aggregate of individual device deployment states.
Each device within the study deployment has a corresponding [`DeviceDeploymentStatus`](../carp.deployments.core/src/commonMain/kotlin/dk/cachet/carp/deployments/application/DeviceDeploymentStatus.kt):

![Device deployment state machine](https://i.imgur.com/acxD0Vw.png)

## Application services

The _'Require'_ and _'Grant'_ column lists claim-based authorization recommendations for implementing infrastructures.
Respectively, the required claims and claims to grant upon a successful request.

### [`DeploymentService`](../carp.deployments.core/src/commonMain/kotlin/dk/cachet/carp/deployments/application/DeploymentService.kt)

Allows deploying study protocols to participants and retrieving primary device deployments for participating primary devices as defined in the protocol.

| Endpoint | Description | Require | Grant |
| --- | --- | --- | --- |
| `createStudyDeployment` | Instantiate a study deployment for a given protocol with invited participants. | | manage deployment: `studyDeploymentId`, in deployment: `studyDeploymentId` |
| `removeStudyDeployments` | Remove study deployments and all related data to it. | manage deployment: (all) `studyDeploymentId`| |
| `getStudyDeploymentStatus` | Get the status for a study deployment. | in deployment: `studyDeploymentId` | |
| `getStudyDeploymentStatusList` | Get the status for a set of deployments. | in deployment: (all) `studyDeploymentIds` | |
| `registerDevice` | Register a device for a study deployment. | in deployment: `studyDeploymentId` | |
| `unregisterDevice` | Unregister a device for a study deployment. | in deployment: `studyDeploymentId` | |
| `getDeviceDeploymentFor` | Get the deployment configuration for a primary device in a study deployment. | in deployment: `studyDeploymentId` | |
| `deviceDeployed` | Indicate to stakeholders in a study deployment that a primary device was deployed successfully, i.e., that the study deployment was loaded on the device and that the necessary runtime is available to run it. | in deployment: `studyDeploymentId` | |
| `stop` | Stop a study deployment. No further changes to this deployment will be allowed and no more data will be collected. | in deployment: `studyDeploymentId` | |

### [`ParticipationService`](../carp.deployments.core/src/commonMain/kotlin/dk/cachet/carp/deployments/application/ParticipationService.kt)

Allows retrieving participations for study deployments,
and managing data related to participants which is input by users.

| Endpoint | Description | Require | Grant |
| --- | --- | --- | --- |
| `getActiveParticipationInvitations` | Get all participations of active study deployments a specified account has been invited to. | authenticated: `accountId` | |
| `getParticipantData` | Get currently set data for all expected participant data. | in deployment: `studyDeploymentId` | |
| `getParticipantDataList` |  Get currently set data for all expected participant data for a set of study deployments. | in deployment: `studyDeploymentId` | |
| `setParticipantData` | Set participant data for a specified study deployment. | in deployment: `studyDeploymentId` | |

branch_name: refs/heads/develop


text:
---
layout: page
title: "404 Error"
date: 2012-10-26 16:25
comments: false
sharing: false
footer: false
---

Sorry, the page you're looking for cannot be found. I migrated my blog from tumblr so some links might not work anymore.

Maybe these links might help you get your bearings:

* [Armory Online Bible College](https://github.com/gorrillamcd/Armory): The open source application that is used for [ibccmexico.com](http://ibccmexico.com)
* [The Homepage]({{ root_url }}): Techonamission.com homepage
* [Missionary-related Blog Posts]({{ root_url }}blog/categories/missions): Blog posts from the "Missions" category.
* [Technology-related Blog Posts]({{ root_url }}blog/categories/technology): Blog posts from the "Technology" category.

repository: gorrillamcd/techonamission-octopress

repo_name: gorrillamcd/techonamission-octopress


text:
![](/octobash.png)

# D-Lab Introduction to Bash + Git

This is the repository for D-Lab's Introduction to Bash + Git workshop. Laptop, Internet connection, and Zoom account required.

# Workshop Goals

An introduction to programming basics in Bash and GitHub that are often assumed, but that you might have never had good instruction on!
The first half of this workshop will introduce you to navigating your computer’s file system and basic Bash commands to remove the fear of working with the command line to give you the confidence to use it to increase your productivity.
The second half of this workshop will introduce you to Git, a powerful tool for keeping track of changes you make to the files in a project. You can use it to synchronize your work across computers, collaborate with others, and even deploy applications to the cloud. In this workshop, you will learn the basics to understand and use Git, including working with the popular "social coding" website, GitHub.

# Installation Instructions

In preparation for the upcoming workshop you will need to make sure you have Bash installed and have created a GitHub user account.

### 1) Install Bash

**OSX**

* Your system might have Git installed already. Test this by opening a terminal and typing 'git'.
* If not, install [xcode](https://apps.apple.com/us/app/xcode/id497799835?mt=12) from the app store. When this process completes, open a terminal and type 'git' to see if the process worked correctly.

**Windows**

* Windows users must install git from [here](https://git-scm.com/downloads).

**Linux**

* Use the package manager by typing: `$ apt-get install git`

### 2) Create a Github account

* All participants should create a free GitHub account [here](https://github.com/join?source=header-home).

### 3) Download the workshop materials

Download the workshop materials by visiting the [GitHub repository](https://github.com/dlab-berkeley/BashGit). To run these lessons on your laptop:

* Click the green "Clone or Download" button
* Click "Download Zip"
* Extract this file someplace familiar (we recommend Desktop)

> If you are a Git user, simply clone this repository by opening a terminal and typing: `git clone https://github.com/dlab-berkeley/BashGit.git`

We will take a few minutes at the start of the workshop to make sure everyone has Bash installed and the workshop materials downloaded. Please feel free to email [dlab-frontdesk<EMAIL_ADDRESS>or visit our [help desk](https://dlab.berkeley.edu/frontdesk) if you have any questions.

# Is Bash not working on your laptop?

Attend the workshop anyway, we can provide you with a cloud-based solution (DataHub or Binder) until you figure out the problems with your local installation.

If you have a Berkeley CalNet ID, you can run these lessons on UC Berkeley's DataHub by clicking [this link](https://datahub.berkeley.edu/hub/user-redirect/git-pull?repo=https%3A%2F%2Fgithub.com%2Fdlab-berkeley%2FBashGit&urlpath=tree%2FBashGit%2F&branch=master). By using this link, you can save your work and come back to it at any time. When you want to return to your saved work, just go straight to [DataHub](https://datahub.berkeley.edu), sign in, and you click on the introduction-to-bash-+-git folder.
If you don't have a Berkeley CalNet ID, you can still run these lessons in the cloud, by clicking this [Binder button](https://hub.mybinder.turing.ac.uk/user/dlab-berkeley-bashgit-in1h0k5a/tree). By using this button, you cannot save your work.

# Resources

[Software Carpentry](https://software-carpentry.org/lessons/)
* [The Unix Shell](http://swcarpentry.github.io/shell-novice/)
* [Version Control with Git](http://swcarpentry.github.io/git-novice/)

### D-Lab's Programming Fundamentals and Git Fundamentals workshops:

* [Programming Fundamentals](https://github.com/dlab-berkeley/programming-fundamentals)
* [Git Fundamentals](https://github.com/dlab-berkeley/git-fundamentals)

# About the UC Berkeley D-Lab
D-Lab works with Berkeley faculty, research staff, and students to advance data-intensive social science and humanities research. Our goal at D-Lab is to provide practical training, staff support, resources, and space to enable you to use R for your own research applications. Our services cater to all skill levels and no programming, statistical, or computer science backgrounds are necessary. We offer these services in the form of workshops such as R Fundamentals, one-to-one consulting, and working groups that cover a variety of research topics, digital tools, and programming languages.

Visit the [D-Lab homepage](http://dlab.berkeley.edu/) to learn more about us. View our [calendar](http://dlab.berkeley.edu/calendar-node-field-date) for upcoming events, and also learn about how to utilize our [consulting](http://dlab.berkeley.edu/consulting) and [data](http://dlab.berkeley.edu/data-resources) services.

# Contributors
* [Evan Muzzall](https://dlab.berkeley.edu/people/evan-muzzall)
* [Donald Max Ziff](https://dlab.berkeley.edu/people/donald-max-ziff)
* [Renata Barreto](https://dlab.berkeley.edu/people/renata-barreto)

repository: fingolightly/BashGit

repo_name: fingolightly/BashGit


text:
# The Meta-Memory in Alzheimer's Disease (MMAD) Package

The `mmad` package (*Meta-Memory in Alzheimer's Disease*) is designed to be used in preclinical AD cohorts. It allows to compute three different measure of awareness: the *Meta-Memory Ratio* (MMR), the *Awareness of Cognitive Decline Categorization* (ACDC) and the *Awareness for Cognitive Decline Index* (ACDI).

The MMR and the ACDC are based on the Participant-Performance Discrepancy, i.e. the difference between a participant's subjective appraisal of her/his cognition, and her/his objective performance.

The MMR is numeric continuous measure, centered around 0 which corresponds to a good match between subjective and objective performance. A positive score indicates a SCD, and a negative stands for a lack of awareness.

The ACDC, based on the same inputs, categorized the participant as:

  * *Normal*: Good objective and subjective performances.
  * *Amnestic*: Low objective and subjective performances.
  * *SCD*: Low subjective but normal objective performance.
  * *LowAwareness*: normal subjective but low subjective performance.

The ACDI is based on the Participant-Informant Discrepancy. As for the MMR, it's a numeric continuous measure, centered around 0 which corresponds to a good match between subjective and objective performance. A positive score indicates a SCD, and a negative stands for a lack of awareness.

All of the measures are designed to be used in multiple preclincal AD samples, either for one or multiples visits.

repository: GagGeo/MMAD

repo_name: GagGeo/MMAD


text:
# Field Simulation

This project uses an object-oriented approach with Java to simulate a field
consisting of rabbits, foxes, and hunters.
Rabbits and foxes move around randomly having a certain maximum ages, after some year they start
breading with a certain rate and finally they die. foxes also should
eat rabbits to stay alive. However, hunters don't move, breed or die. They just
hunt from places nearby randomly.

### Basic UML of The Project
![uml](Pics/uml.png)

### The Result After 200 iterations
![last iteration](Pics/final.png)
orange pixels represent rabbits, blue ones are foxes and hunters are black.
### Prerequisites
Inorder to get started with Java programming you can read:
* [Object first with Java](https://www.academia.edu/39217681/Objects_First_with_Java_5th_Edition)

more specifically you can read chapter 10 of the book to understand this project.

## License
This project is licensed under the Apache License 2.0 - see the [LICENSE](LICENSE) file for details


text:
# cat_n.sh

Write a bash program called `cat_n.sh` that mimics the behavior of `cat -n` where it will print the line number and line of an input file. If there are no arguments, it should print a "Usage" and exit *with an error code*. Your program will expect to receive an argument in `$1`. If the argument is not a file, it should notify the user and exit *with an error code*. It will iterate over the lines in the file and print the line number, a space, and the line of the file. Your output will differ from regular `cat -n` as I won't expect you to right-align the numbers.

# Expected Behavior

````
$ ./cat_n.sh
Usage: cat-n.sh FILE
$ ./cat_n.sh foo
foo is not a file
$ ./cat_n.sh files/sonnet-29.txt
1 Sonnet 29
2 William Shakespeare
3
4 When, in disgrace with fortune and men’s eyes,
5 I all alone beweep my outcast state,
6 And trouble deaf heaven with my bootless cries,
7 And look upon myself and curse my fate,
8 Wishing me like to one more rich in hope,
9 Featured like him, like him with friends possessed,
10 Desiring this man’s art and that man’s scope,
11 With what I most enjoy contented least;
12 Yet in these thoughts myself almost despising,
13 Haply I think on thee, and then my state,
14 (Like to the lark at break of day arising
15 From sullen earth) sings hymns at heaven’s gate;
16 For thy sweet love remembered such wealth brings
17 That then I scorn to change my state with kings.
````

# Testing

You have been provided a `Makefile` that will run a test suite. This is what it should look like when all tests are passing:

````
$ make test
pytest -v test.py
============================= test session starts ==============================
platform darwin -- Python 3.6.8, pytest-4.2.0, py-1.7.0, pluggy-0.8.1 -- /anaconda3/bin/python
cachedir: .pytest_cache
rootdir: /Users/kyclark/work/python/practical_python_for_data_science/ch02-unix-exercises/exercises/cat_n_a, inifile:
plugins: remotedata-0.3.1, openfiles-0.3.2, doctestplus-0.2.0, arraydiff-0.3
collected 3 items

test.py::test_usage PASSED                                               [ 33%]
test.py::test_bad_input PASSED                                           [ 66%]
test.py::test_run PASSED                                                 [100%]

=========================== 3 passed in 0.05 seconds ===========================
````

First the program is executed with no arguments to see if it produces a "usage"-type help messages. Then it is tested with bad input. Then it is tested that it produces the expected output when given good input.

This is the basis of "test-driven design" (TDD). We define a set of tests that describe what working software should do. When the tests pass, the software is done. When you are passing all the tests, you are done!


text:
# stegano/png

An image steganography implementation that outputs PNG steganograms. It accepts both JPEG and PNG images as input.

It satisfies the interface defined by [github.com/zanicar/stegano](https://github.com/zanicar/stegano)
A command line implementation is available [here](../cmd/stegano).

## Package Documentation

[https://pkg.go.dev/github.com/zanicar/stegano/png](https://pkg.go.dev/github.com/zanicar/stegano/png)

## How this package works

PNG images use lossless compression, thus data can be written to individual pixels without risk of being destroyed by the compression algorithm. For a JPEG implementation the data will have to be concealed within the compression algorithm's output matrices to prevent dataloss. This implementation also deliberately ignores the alpha channel for pixels, as alpha channels typically do not contain sufficient noise for concealment purposes.

The Conceal method conceals the given data in the pixels of the image decoded from reader and writes the result to writer as a new PNG image.

The Reveal method extracts data from the pixels of the image decoded from reader and writes the result to writer as a binary file.

Refer to the documentation given above for package usage.

## Examples

This tiny version of my profile image contains the link to this repository.

![steganogram](../examples/x.png)

More examples [here](../examples)

repository: zanicar/stegano

repo_name: zanicar/stegano