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80b42aca3e9b7ed16c04e671d713d608a4c3f623 | 1,880 | md | Markdown | _posts/2019-05-30-MCHAOS.md | spoj-solution/spoj-solution.github.io | 9b31cccb4ba76c1c78262683ceff15cb89934160 | [
"MIT"
] | null | null | null | _posts/2019-05-30-MCHAOS.md | spoj-solution/spoj-solution.github.io | 9b31cccb4ba76c1c78262683ceff15cb89934160 | [
"MIT"
] | 1 | 2022-02-26T04:57:17.000Z | 2022-02-26T04:57:17.000Z | _posts/2019-05-30-MCHAOS.md | spoj-solution/spoj-solution.github.io | 9b31cccb4ba76c1c78262683ceff15cb89934160 | [
"MIT"
] | 2 | 2019-07-10T14:10:59.000Z | 2020-05-10T05:23:53.000Z | ---
layout: post
title: MCHAOS - Chaos Strings
categories: ['bitmasks']
code: MCHAOS
src: MCHAOS.cpp
---
### **Statement**
[English](/problems/MCHAOS/en/) | [Vietnamese](/problems/MCHAOS/vn/)
---|---
Little Lovro likes to play games with words. During the last few weeks he
realized that some words don't like each other.
The words A and B don't like each other if the word A is lexicographically
before the word B, but the word B' is lexicographically before the word A',
where X' stands for the word X reversed (if X="kamen" then X'="nemak"). For
example, the words "lova" and "novac" like each other, but the words "aron"
and "sunce" don't like each other.
Given some set of the words, we define the degree of chaos of the set as the
number of pairs of different words that don't like each other.
Write a program that, given a set of words, finds the chaos degree for the
set.
### Input
The first line of input contains an integer N, 2 ≤ N ≤ 100 000.
Each of the following N lines contains one word – a sequence of at most 10
lowercase letters of the English alphabet ('a'-'z'). There will be no two
identical words in the set.
### Output
The first and only line of output should contain a single integer – the chaos
degree of the given set of words.
Note: use 64-bit signed integer type (int64 in Pascal, long long in C/C++).
### Sample
input
2
lopta
kugla
output
0
input
4
lova
novac
aron
sunce
output
3
input
14
branimir
vladimir
tom
kruz
bred
pit
zemlja
nije
ravna
ploca
ko
je
zapalio
zito
output
48
#### **Solution**
| 18.613861 | 77 | 0.599468 | eng_Latn | 0.997982 |
80b92397a4002dc89c958ce6826f076af8d9a611 | 1,067 | markdown | Markdown | _posts/2018-08-29-commercial_real_estate_cli.markdown | JohnMarkLudwick/JohnMarkLudwick.github.io | 1b502d7c8ecc288cf30a819c3b01662dc94992fa | [
"MIT"
] | null | null | null | _posts/2018-08-29-commercial_real_estate_cli.markdown | JohnMarkLudwick/JohnMarkLudwick.github.io | 1b502d7c8ecc288cf30a819c3b01662dc94992fa | [
"MIT"
] | null | null | null | _posts/2018-08-29-commercial_real_estate_cli.markdown | JohnMarkLudwick/JohnMarkLudwick.github.io | 1b502d7c8ecc288cf30a819c3b01662dc94992fa | [
"MIT"
] | null | null | null | ---
layout: post
title: "Commercial Real Estate CLI"
date: 2018-08-29 14:09:05 +0000
permalink: commercial_real_estate_cli
---
My CLI project scrapes a major commercial real estate search website and provides users with a list of property locations. After the list of properties is returned, users can select a property (level one) and view details (i.e. location, cost, size) about the selected location (level two.)
The site I scraped is called [Loopnet](http://www.loopnet.com/). Loopnet is a Zillow-like commercial real estate search platform that returns thousands of properties for a user based on geographic and property specific queries. A command line interface that returns all of Loopnet’s property information would be beneficial to real estate companies interested in merging an internal database with the data provided by services like Loopnet. While the functionality of my CLI doesn’t come close to achieving the full scope of value described above, the experience of developing a small CLI from scratch was challenging and gratifying.
| 88.916667 | 633 | 0.794752 | eng_Latn | 0.999389 |
80b97850502d0d8800d73922a08843ee8185710b | 198 | md | Markdown | exampleSite/content/projects/hugo.md | jakeryang/jake-hugo-theme | a4fc8a89791b2037f04b7ca2801dfd6df20f8e39 | [
"MIT"
] | null | null | null | exampleSite/content/projects/hugo.md | jakeryang/jake-hugo-theme | a4fc8a89791b2037f04b7ca2801dfd6df20f8e39 | [
"MIT"
] | null | null | null | exampleSite/content/projects/hugo.md | jakeryang/jake-hugo-theme | a4fc8a89791b2037f04b7ca2801dfd6df20f8e39 | [
"MIT"
] | null | null | null | ---
title: "Hugo"
description: "A static site generator written in Go!"
image_url: "/images/hugo-logo-wide.png"
redirect_url: "https://gohugo.io/"
stack: ["Go"]
date: 2020-08-15T12:09:01-05:00
---
| 19.8 | 53 | 0.686869 | eng_Latn | 0.233133 |
80b9e3f6a79b0bc147dc322577ab20d72068b36e | 86 | md | Markdown | share/doc/homebrew/Homebrew-2.md | infynyxx/homebrew | eb34409da5a58fead35a8a0439b23cb1e2a3b55b | [
"BSD-2-Clause"
] | 383 | 2015-01-15T21:47:56.000Z | 2022-03-19T22:53:14.000Z | share/doc/homebrew/Homebrew-2.md | infynyxx/homebrew | eb34409da5a58fead35a8a0439b23cb1e2a3b55b | [
"BSD-2-Clause"
] | 430 | 2015-01-07T06:43:26.000Z | 2022-03-30T20:43:30.000Z | share/doc/homebrew/Homebrew-2.md | infynyxx/homebrew | eb34409da5a58fead35a8a0439b23cb1e2a3b55b | [
"BSD-2-Clause"
] | 148 | 2015-01-15T11:40:43.000Z | 2022-02-27T17:05:52.000Z | # Homebrew 2
Idea for Homebrew 2:
* https://github.com/Homebrew/homebrew/issues/11041
| 21.5 | 51 | 0.767442 | kor_Hang | 0.164859 |
80bc8b9031e71cba45a0a62b5444df1aa30bfc45 | 270 | md | Markdown | CloudServices/MonitorDebugCloudService.md | TamirAl/70-532_StudyKit | 0f3582069bfd0ade77056d0dd1169287f7b3ea63 | [
"Apache-2.0"
] | null | null | null | CloudServices/MonitorDebugCloudService.md | TamirAl/70-532_StudyKit | 0f3582069bfd0ade77056d0dd1169287f7b3ea63 | [
"Apache-2.0"
] | null | null | null | CloudServices/MonitorDebugCloudService.md | TamirAl/70-532_StudyKit | 0f3582069bfd0ade77056d0dd1169287f7b3ea63 | [
"Apache-2.0"
] | 1 | 2019-05-16T07:41:26.000Z | 2019-05-16T07:41:26.000Z | ## Monitor and debug a cloud service
1. Configure diagnostics using the SDK or configuration file, profile resource consumption, enable remote debugging, establish a connection using Remote Desktop CmdLets in Windows PowerShell, debug using IntelliTrace or the emulator | 90 | 232 | 0.833333 | eng_Latn | 0.950368 |
80bd14249d7496ae91533da76e1fb2f05b4b8855 | 221 | md | Markdown | _posts/2018-06-11-hamzabegovic-python-11-12.md | Ademir161/univie-tnt-2018-summer.github.io | 72a02ab6a01a7aa845ec4af7cf4619dfb0b39755 | [
"MIT"
] | null | null | null | _posts/2018-06-11-hamzabegovic-python-11-12.md | Ademir161/univie-tnt-2018-summer.github.io | 72a02ab6a01a7aa845ec4af7cf4619dfb0b39755 | [
"MIT"
] | null | null | null | _posts/2018-06-11-hamzabegovic-python-11-12.md | Ademir161/univie-tnt-2018-summer.github.io | 72a02ab6a01a7aa845ec4af7cf4619dfb0b39755 | [
"MIT"
] | null | null | null | ---
layout: post
title: Python Unit 11-12 Completion!
image: /img/hello_world.jpeg
---
Here is the confirmation of the completion of the units 11 and 12 of Python:
| 22.1 | 76 | 0.751131 | eng_Latn | 0.900224 |
80bd9a0b2e429d8b633fb64aee908148ffe6e5d1 | 5,162 | md | Markdown | src/Static/markdown/enDoc.md | winterland1989/jsmServer | 80416729eb79e15cc2938c19908d8583b30415fc | [
"MIT"
] | null | null | null | src/Static/markdown/enDoc.md | winterland1989/jsmServer | 80416729eb79e15cc2938c19908d8583b30415fc | [
"MIT"
] | null | null | null | src/Static/markdown/enDoc.md | winterland1989/jsmServer | 80416729eb79e15cc2938c19908d8583b30415fc | [
"MIT"
] | null | null | null | Javascript Snippet Manager
==========================
Jsm is a very lightweight frontend code manager, it's particularily suitable to install and publish short code snippet. Beside Javascript, jsm support coffeescirpt, livescript and jsx.
Email: [[email protected]](mailto:[email protected]).
jsm i | install
---------------
Jsm-client run on node.js，use npm to install to global：
npm install -g https://github.com/winterland1989/jsm-client.git
Now create your entry file，require the snippet you need，for example if you need `lodash`：
require('./jsm/base/lodash3')
Assuming your entry called `index.js`：
jsm install index.js
You will find `lodash3` is installed to the right place and ready to be packed：
├── index.js
├── jsm
│ ├── base
│ │ ├── lodash3.js
+ Version and namespace management
Version and namespace are directly written into your code, never add file extensions:
require('./jsm/base/lodash3')
require('./jsm/base/zepto1')
require('./jsm/winter/mss1')
Jsm will looking for `lodash3` and `zepto1` from user `base`，`mss1` from user `winter`, and place them accordingly.
+ What if there're more than one entry?
Just
jsm install A.js B.js C.coffee ...
+ What about the files not directly require by entry?
All files that under `jsm` will be install, assuming `index.coffee` require `./widget/Dialog.coffee`, and `./widget/Dialog.coffee` require `./jsm/base/lodash3`, then after `jsm install index.js`, the tree should be like this:
├── index.coffee
├── jsm
├── widget
│ ├── Dialog.coffee
│ │ ├── jsm
│ │ │ ├── lodash3.js
Most of the time we don't need multiple `jsm` folders all over the place, so change the require line in `./widget/Dialog.coffee` into:
require('../jsm/base/lodash3')
Note that we use `../jsm` to as relative path.
+ Can we change the name `jsm`
No, it's a keyword hard coded to recognize files under `jsm` management, all snippets will be install to a `jsm` folder.
jsm p | publish
---------------
Run following command to publish a snippet to jsm:
jsm publish ./.../.../userName/snippetNameVersion.js
`jsm` will try to parse `userName` and `Version` for you, just follow the prompt.
Pleas make sure your snippet meet following requirements:
+ only require code already on jsm
+ require snippet path correctly
For example, myModule1.js is the snippet you want to share:
├── index.coffee
├── jsm
│ ├── base
│ │ ├── jquery2.js
│ ├── myName
│ │ ├── myModule1.js
│ │ ├── myModule2.js
And you need to require `jquery2.js` and `myModule2`
var $ = require('../base/jquery2')
var myModule2 = require('../base/myModule2')
+ Add version number (use `0` to mark beta, see below), if any API lead a breaking change, increase version like `foo2 -> foo3`
+ Provide some document with snippet.
jsm u | update
--------------
Same as install, provide single or multiple entry files, `jsm` will update all snippet they required to latest revision, and warning about DEPRECATED snippets.
jsm d | deprecate
-----------------
Provide a snippet file path, `jsm` will try to parse author , title and version, and mark the snippet DEPRECATED.
jsm s | server
--------------
Provide an entry file and a port number(default = 8080), `jsm` will start a test server, serving an empty HTML page with bundled entry file. The server also use port 35729 to do live-reload.
jsm w | webpack
---------------
Provide your entry files, `jsm` will generate a minimium [webpack](http://webpack.github.io/) config file `webpack.config.js` with javascrit, coffeescirpt and livescript support.
jsm i pageA.js pageB.js
jsm w pageA.js pageB.js
webpack
Will bundle `pageA.bundle.js`, `pageB.bundle.js`.
Conventions
===========
+ snippet's name need to be /^[a-zA-Z]+\d+$/
```
camelCase1 //singleton, function...
CamelCase2 // Class，Factory...
```
+ The default version is `0` even you don't add a version number to file , version `0` marked a testing snippet with possible API changes, so think before you require a version `0` snippet, ready to modify your code while the author are working on it.
+ `jsm` won't distinguish es3/5/6 files，you need to use `jsx` extension to use bable transcompiler.
+ After modified other's code, it's highly recommended to move them to your own namespace, if you do need lock version, add following to the top scope of your file:
```js
//-jsm-update: false
```
coffee/live:
```coffee
#-jsm-update: false
```
After that, `jsm update` will ignore the marked file.
+ It's highly recommended to add `jsm` folder to your VCS, with help of `-jsm-update`, you can easily achieve portable build.
+ `jsm` will parse filename to get keyword for searching:
imageLoader.js
Will get `image`, `Loader` and `imageLoader`(searching is case-insensitive), if you want to add more, add a comment to the top level of the code:
```js
//-jsm-keywords: async process ...
```
or in coffee/live:
```coffee
#-jsm-keywords: async process ...
```
`jsm publish` will display all keywords it found.
| 29.666667 | 251 | 0.680744 | eng_Latn | 0.982789 |
80be1d41c692c895a50e36e3e432f63daba5f68a | 114 | md | Markdown | docs/commands/p.md | arbaregni/nushell | 1297499d7abeba1986ed174067bc467abd72ec9e | [
"MIT"
] | 18,814 | 2019-06-19T16:38:16.000Z | 2022-03-31T23:27:56.000Z | docs/commands/p.md | arbaregni/nushell | 1297499d7abeba1986ed174067bc467abd72ec9e | [
"MIT"
] | 3,743 | 2019-06-19T19:02:07.000Z | 2022-03-31T22:18:29.000Z | docs/commands/p.md | arbaregni/nushell | 1297499d7abeba1986ed174067bc467abd72ec9e | [
"MIT"
] | 1,102 | 2019-07-14T03:23:06.000Z | 2022-03-30T05:24:48.000Z | # p
Go to previous shell.
## Usage
```shell
> p {flags}
```
## Flags
* -h, --help: Display this help message
| 9.5 | 39 | 0.578947 | eng_Latn | 0.932873 |
80be87b0145c854de57781ab3f4ee512414377d3 | 172 | md | Markdown | TeamCode/src/main/java/org/firstinspires/ftc/teamcode/readme.md | cybots5975/cybots2018 | 082db31bdf9987c05a15d5c1fc8cf6c1bac33412 | [
"MIT"
] | 2 | 2018-11-02T19:03:00.000Z | 2021-12-10T03:51:41.000Z | TeamCode/src/main/java/org/firstinspires/ftc/teamcode/readme.md | cybots5975/cybots2018 | 082db31bdf9987c05a15d5c1fc8cf6c1bac33412 | [
"MIT"
] | null | null | null | TeamCode/src/main/java/org/firstinspires/ftc/teamcode/readme.md | cybots5975/cybots2018 | 082db31bdf9987c05a15d5c1fc8cf6c1bac33412 | [
"MIT"
] | null | null | null | ## TeamCode Module
Welcome!
This module, TeamCode, is the place where we have all our robot code. This includes OpModes
for teleop and autonomous as well as the backend.
| 24.571429 | 91 | 0.77907 | eng_Latn | 0.999761 |
80bf502e4b121ba2a2974f9d3f017976bc15034a | 1,074 | md | Markdown | dhlgitee_springbooruningjar_readme.md | lua-study/0 | 161010a7530d62864e917d1d3253e1fa0a8413f9 | [
"Apache-2.0"
] | 3 | 2021-06-08T07:57:41.000Z | 2022-02-03T18:50:19.000Z | dhlgitee_springbooruningjar_readme.md | lua-study/0 | 161010a7530d62864e917d1d3253e1fa0a8413f9 | [
"Apache-2.0"
] | null | null | null | dhlgitee_springbooruningjar_readme.md | lua-study/0 | 161010a7530d62864e917d1d3253e1fa0a8413f9 | [
"Apache-2.0"
] | 5 | 2021-03-11T07:42:05.000Z | 2021-09-08T05:43:56.000Z | # springbooruningjar
#### 介绍
一个springboot可运行的jar
#### 软件架构
基于springboot，用的阿里的镜像地址
#### 安装教程
1. 本地需要安装jdk，我使用的是8，还需要安装maven，通过maven构建
2. 安装好jdk之后在本地运行命令 java -version 出现结果表明安装成功
3. 导出本工程，在本地使用maven命令构建本工程，mvn package
4. 导出jar之后，运行命令 java -jar smptools-0.0.1.jar
5. 浏览器访问： http://localhost:8081/index http://localhost:8081/hello
(http://localhost:8081/index 直接返回的接口，http://localhost:8081/hello通过html返回)
#### 使用说明
1. 暂无
#### 参与贡献
1. Fork 本仓库
2. 新建 Feat_xxx 分支
3. 提交代码
4. 新建 Pull Request
#### 码云特技
1. 使用 Readme\_XXX.md 来支持不同的语言，例如 Readme\_en.md, Readme\_zh.md
2. 码云官方博客 [blog.gitee.com](https://blog.gitee.com)
3. 你可以 [https://gitee.com/explore](https://gitee.com/explore) 这个地址来了解码云上的优秀开源项目
4. [GVP](https://gitee.com/gvp) 全称是码云最有价值开源项目，是码云综合评定出的优秀开源项目
5. 码云官方提供的使用手册 [https://gitee.com/help](https://gitee.com/help)
6. 码云封面人物是一档用来展示码云会员风采的栏目 [https://gitee.com/gitee-stars/](https://gitee.com/gitee-stars/)
# 良心友情链接
[腾讯QQ群快速检索](http://u.720life.cn/s/8cf73f7c)
[软件免费开发论坛](http://u.720life.cn/s/bbb01dc0) | 23.866667 | 92 | 0.6946 | yue_Hant | 0.748402 |
80bfe83d2161bdbae05444bf0f3f0b6a44f5da1f | 1,225 | md | Markdown | README.md | justkhant/Mangamop | 6544ea93ab11f247c445355ef127405a51a85304 | [
"MIT"
] | 1 | 2020-08-25T22:56:09.000Z | 2020-08-25T22:56:09.000Z | README.md | justkhant/Mangamop | 6544ea93ab11f247c445355ef127405a51a85304 | [
"MIT"
] | null | null | null | README.md | justkhant/Mangamop | 6544ea93ab11f247c445355ef127405a51a85304 | [
"MIT"
] | null | null | null | # Mangamop
Automated Manga Cleaning Web Application using a Deep Learning Pix2Pix Model.
## Training the Model
The deep learning model, named MANGAClean, uses the PyTorch implementation of the pix2pix GAN network, forked from pytorch-CycleGAN-and-pix2pix. The goal of training this model is to convert raw dirty scans of manga pages into high-quality, denoised images, and to effectively automate the cleaning process in manga scanlation procedures.
Check out https://github.com/justkhant/pytorch-MANGAClean for more information about training the deep learning model, as well as more background about manga cleaning procedures.
## Plan
After training the MANGAClean model, the plan is the create a website where users can upload images of low quality, raw manga scans, and receive clean, denoised versions of the manga pages as output. Below is the high level architecture diagram of the project:
<img src='images/Mangamop_Architecture_Diagram.png' width ="800px"/>
* Write the web app in Python with Flask as the framework.
* Deploy the web app on the Heroku Cloud Platform.
## Web App (In Progress)
This section will be updated with snapshots of the website, as well as the link to it after it is deployed.
| 61.25 | 338 | 0.794286 | eng_Latn | 0.994787 |
80c14f70ddde10312a6b585ad8cb8a55006d5b91 | 5,093 | md | Markdown | _posts/2020-04-21-themoviedb_project_dataengineer.md | CrashLaker/Crashlaker.github.io | ff59ce753939119095b29f7c8b3aef8f73f4a654 | [
"MIT"
] | null | null | null | _posts/2020-04-21-themoviedb_project_dataengineer.md | CrashLaker/Crashlaker.github.io | ff59ce753939119095b29f7c8b3aef8f73f4a654 | [
"MIT"
] | null | null | null | _posts/2020-04-21-themoviedb_project_dataengineer.md | CrashLaker/Crashlaker.github.io | ff59ce753939119095b29f7c8b3aef8f73f4a654 | [
"MIT"
] | null | null | null | ---
layout: post
title: "TheMovieDB Project DataEngineer"
comments: true
date: "2020-04-21 04:52:17.979000+00:00"
categories: [projects]
tags: [dataviz, data-engineer, superset, spark, hadoop, airflow, pyspark]
---
### Project Diagram
### Database Schema
**Stack used**
* 1VM
* Cloudera Hortonworks Ambari
* HDFS
* Hive
* Superset
* JupyterLab
* Experiments
* Airflow
* Python
* PySpark
### Cloudera Hortonworks Ambari
**Start Thrift**
`./usr/hdp/3.0.1.0-187/spark2/sbin/start-thriftserver.sh`
**Reset Ambari Admin Password**
`ambari-admin-password-reset`
### Airflow
https://airflow.apache.org/installation.html
https://airflow.apache.org/start.html
```bash
yum -y install python36 python36-pip python36-devel
export AIRFLOW_HOME=~/airflow
# install from pypi using pip
pip install apache-airflow
# initialize the database
airflow initdb
# start the web server, default port is 8080
airflow webserver -p 8080
# start the scheduler
airflow scheduler
# visit localhost:8080 in the browser and enable the example dag in the home page
```
or docker
```bash
docker rm -f airflow1
docker run -dit --name airflow1 -p 8124:8080 \
--network cda \
-v /root/airflow-dags:/root/airflow/dags \
-v /root/airflow-scripts:/scripts \
-v /root/airflow-scratch:/scratch \
airflow1
airflow scheduler
```
## Spark
**Install**
```bash
yum -y install wget
#export SPARK_URL=http://ftp.unicamp.br/pub/apache/spark/spark-2.4.3/spark-2.4.3-bin-hadoop2.7.tgz
export SPARK_URL=http://ftp.unicamp.br/pub/apache/spark/spark-2.4.4/spark-2.4.4-bin-hadoop2.7.tgz
export SPARK_HOME=/opt/spark
wget ${SPARK_URL} -O /tmp/spark.tgz
tar xzvf /tmp/spark.tgz -C /opt/ && rm -rf /tmp/spark.tgz && mv /opt/spark* /opt/spark
pip3 install pyspark findspark
yum -y install java-1.8.0-openjdk
export JAVA_HOME="/usr/lib/jvm/java-1.8.0-openjdk-1.8.0.222.b10-1.el7_7.x86_64/jre/"
cat <<EOF > /etc/profile.d/spark.sh
export SPARK_HOME=/opt/spark
export JAVA_HOME="/usr/lib/jvm/java-1.8.0-openjdk-1.8.0.222.b10-1.el7_7.x86_64/jre/"
EOF
```
## Hive
https://medium.com/@o20021106/how-to-connect-to-remote-hive-cluster-with-pyspark-fabc04c42283
1. Copy core-site.xml, hdfs-site.xml, hive-site.xml, hbase-site.xml, from your cluster running hive, and paste it to your spark’s /conf directory
2. add any jar files to spark’s /jar directory.
3. run pyspark
4. Create a spark session and make sure to enable hive support.
```bash
wget /assets/files/0-35ynhUx_0_hive-site-1.xml -O /opt/spark/conf/hive-site.xml
wget /assets/files/0-35ynhUx_1_core-site.xml -O /opt/spark/conf/core-site.xml
wget /assets/files/0-35ynhUx_2_hdfs-site.xml -O /opt/spark/conf/hdfs-site.xml
wget http://coreto1:8081/download/codimd/71f4d5e87b4c4adfa32a0de1b11f5787.jar/hive-jdbc-standalone.jar
-O /opt/spark/jars/hive-jdbc-standalone.jar
```
## PySpark
```python
from pyspark import SparkConf
from pyspark.sql import SparkSession
from pyspark.sql import Row
spark = SparkSession \
.builder \
.appName("Python Spark SQL Hive integration example") \
.enableHiveSupport() \
.getOrCreate()
spark.sql("show databases").show()
```
## SuperSet
**Centos**
```bash
yum -y install mariadb-devel
pip3 install mysqlclient
yum install -y cyrus-sasl-plain cyrus-sasl-devel cyrus-sasl-gssapi
pip3 install pyhive[hive]
```
**Ubuntu**
`apt install -y libsasl2-dev`
```bash
Install superset 0.34.1
export LC_ALL=en_US.utf-8
export LANG=en_US.utf-8
yum -y install python36 python36-pip python36-devel gcc gcc-c++
```
**Connect to Hive**
`hive://hive@localhost:10016/movies`
## Results
## Known Caveats
### Can't sync spark to hive
https://community.cloudera.com/t5/Support-Questions/Spark2-shell-is-not-displaying-all-Hive-databases-only/td-p/193774
On Spark 2 and HDP 3.x . Edit file "/usr/hdp/3.1.4.0-315/spark2/conf/hive-site.xml"
Remove property below:
<property>
<name>metastore.catalog.default</name>
<value>spark</value>
</property>
### Hive managed table doesn't work in with non transactional in hive 3
https://community.cloudera.com/t5/Support-Questions/Hive-managed-table-doesn-t-work-in-with-non-transactional-in/td-p/237514
https://xgkfq28377.i.lithium.com/t5/image/serverpage/image-id/14232i8E84A1CF23E989AB/image-dimensions/1000?v=1.0&px=-1
### SQL Lab query indefinitely in PENDING #4733
https://github.com/apache/incubator-superset/issues/4733
`Point to spark2 hiveserver port 10016`
| 23.910798 | 145 | 0.732378 | yue_Hant | 0.206344 |
80c180f5b0c988064cc55b2fec1bd45bf0eba1f3 | 3,240 | md | Markdown | README.md | werner/bones | a2b6410646aa167007f0693fe5a322970960c886 | [
"MIT"
] | 1 | 2018-11-04T12:17:07.000Z | 2018-11-04T12:17:07.000Z | README.md | werner/bones | a2b6410646aa167007f0693fe5a322970960c886 | [
"MIT"
] | null | null | null | README.md | werner/bones | a2b6410646aa167007f0693fe5a322970960c886 | [
"MIT"
] | null | null | null | # bones [](https://travis-ci.org/werner/bones)
A SQL Query Builder for Crystal.
## Installation
Add this to your application's `shard.yml`:
```yaml
dependencies:
bones:
github: werner/bones
```
## Usage
```crystal
require "bones"
class Person < Bones::TableDef
table_name person
column id : Int32
column age : Int32
column name : String
column gender : Char
has_many :worker
has_many :position
has_many :vehicle
has_many :department
end
class Worker < Bones::TableDef
table_name worker
column id : Int32
column person_id : Int32
column name : String
belongs_to :person
end
class Position < Bones::TableDef
table_name position
column id : Int32
column person_id : Int32
column name : String
belongs_to :person
end
class Vehicle < Bones::TableDef
table_name vehicle
column id : Int32
column person_id : Int32
column name : String
belongs_to :person
end
class Department < Bones::TableDef
table_name department
column id : Int32
column person_id : Int32
column name : String
belongs_to :person
end
person = Person.new
worker = Worker.new
position = Position.new
vehicle = Vehicle.new
department = Department.new
person_id = person.id
worker_id = worker.id
position_id = position.id
worker_person_id = worker.person_id
position_person_id = position.person_id
vehicle_person_id = vehicle.person_id
department_person_id = department.person_id
person_age = person.age
person_name = person.name
worker_name = worker.name
department_name = department.name
person_gender = person.gender
sql = Bones::SQL::SQL.new
sql.select(person_id, person_name, worker_name, sql.sum(person_age))
.from(person)
.inner_join(to_table: worker, on: person_id.dup.eq(worker_person_id))
.inner_join(to_table: position, on: person_id.dup.eq(position_person_id))
.left_join(to_table: vehicle, on: person_id.dup.eq(vehicle_person_id))
.right_join(to_table: department, on: person_id.dup.eq(department_person_id))
.where(worker_name.eq("Jhon").and(person_gender.eq('M')).or(person_age.gt(20)).and(person_id.is_not(nil)))
.order_by(person_id.asc)
.group_by(person_id, person_name, worker_name)
.having(sql.sum(person_age).lt(100).and(sql.count(person_id).gt(1)))
.limit(100)
.offset(2)
.to_sql_string
# SELECT person.id, person.name, worker.name, SUM(person.age) FROM person INNER JOIN worker ON person.id = worker.person_id
# INNER JOIN position ON person.id = position.person_id
# LEFT JOIN vehicle ON person.id = vehicle.person_id
# RIGHT JOIN department ON person.id = department.person_id
# WHERE worker.name = 'Jhon' AND person.gender = 'M' OR person.age > 20
# AND person.id IS NOT NULL
# GROUP BY person.id, person.name, worker.name
# HAVING SUM(person.age) < 100 AND COUNT(person.id) > 1
# ORDER BY person.id ASC
# LIMIT 100 OFFSET 2
```
## Contributing
1. Fork it ( https://github.com/werner/bones/fork )
2. Create your feature branch (git checkout -b my-new-feature)
3. Commit your changes (git commit -am 'Add some feature')
4. Push to the branch (git push origin my-new-feature)
5. Create a new Pull Request
## Contributors
- [werner](https://github.com/werner) werner - creator, maintainer
| 25.116279 | 124 | 0.74537 | eng_Latn | 0.460308 |
80c3afe79529cdeb2b4712702561923383be6bd8 | 1,551 | md | Markdown | 2017-04-27-Arch-Linux-on-Macbook/steps.md | stefanfoulis/install-logs | 7fda0800e5a16a66680ea10ae448b28c80b370b8 | [
"BSD-3-Clause"
] | null | null | null | 2017-04-27-Arch-Linux-on-Macbook/steps.md | stefanfoulis/install-logs | 7fda0800e5a16a66680ea10ae448b28c80b370b8 | [
"BSD-3-Clause"
] | null | null | null | 2017-04-27-Arch-Linux-on-Macbook/steps.md | stefanfoulis/install-logs | 7fda0800e5a16a66680ea10ae448b28c80b370b8 | [
"BSD-3-Clause"
] | null | null | null | Followed https://medium.com/@philpl/arch-linux-running-on-my-macbook-2ea525ebefe3 up to "The rest of it".
Kept a 200GB Partition gap I plan to use for a docker native filesystem later.
Instead of putting kernel module names into ``/etc/modules`` as suggested in the howto, I create a file called ``/etc/modueles-load.d/macbook.conf`` and put them in there, which is more like it is suggested in "Automatic module handling" in https://wiki.archlinux.org/index.php/kernel_modules .
```
coretemp
applesmc
```
Sync the pacman package library:
```
pacman -Syyu
```
Install dhcpd, so that we have networking after the reboot.
```
pacman -S dhcpcd
systemctl enable dhcpcd
```
Set a password: ``passwd``
## Install a bootloader
```
pacman -S dosfstools
bootctl --path=/boot install
```
Save this into ``/boot/loader/entries/arch.conf``
```
title Arch Linux
linux /vmlinuz-linux
initrd /initramfs-linux.img
options root=/dev/sda2 rw elevator=deadline quiet splash resume=/dev/sda3 nmi_watchdog=0
```
make this the default boot entry
```
echo "default arch" > /boot/loader/loader.conf
```
Now we can boot into the system.
```
exit
reboot
```
Remove the usb drive!
# Installation
Following *Setting up Arch Linux* in https://medium.com/@philpl/arch-linux-running-on-my-macbook-2ea525ebefe3 to setup GNOME now.
```
groupadd users # "users" group already existed for me
useradd -m -g users -G wheel -s /bin/bash stefanfoulis
pacman -S sudo
echo "%wheel ALL=(ALL) ALL" > /etc/sudoers.s/wheel.conf
passwd stefanfoulis
passwd -l root
````
```
| 20.959459 | 294 | 0.732431 | eng_Latn | 0.782903 |
80c53bf8346dc81751b4196ba202cb17c58432eb | 707 | md | Markdown | README.md | Mangara/PlanetWarsBot | 16984d56140e4093bd6a98d3b723f33a2a12b6be | [
"MIT"
] | null | null | null | README.md | Mangara/PlanetWarsBot | 16984d56140e4093bd6a98d3b723f33a2a12b6be | [
"MIT"
] | null | null | null | README.md | Mangara/PlanetWarsBot | 16984d56140e4093bd6a98d3b723f33a2a12b6be | [
"MIT"
] | null | null | null | # Planet Wars AI
My submission for the 2010 Google AI Challenge, hosted by the University of Waterloo. It ended at #21 out of 4619 participants (top 0.5%).
## Getting Started
To build the project, open it with a recent version of [NetBeans](https://netbeans.org/).
Run the bot with
java -jar tools/PlayGame-1.2.jar <map> 1000 1000 log.txt "java -jar dist/PlanetWarsBot.jar" "java -jar <other bot>" | java -jar tools/ShowGame-1.2.jar
See [Play BullyBot.bat](Play BullyBot.bat) and the others for examples.
## Authors
* **Sander Verdonschot** - [Mangara](https://github.com/Mangara)
## License
This project is licensed under the MIT License - see the [LICENSE.txt](LICENSE.txt) file for details
| 32.136364 | 154 | 0.729844 | eng_Latn | 0.881771 |
80c55ff3481c809857cf6ca25eec9f0bb9046403 | 8,553 | markdown | Markdown | _posts/2019-07-10-AndroidShimmer-2019.markdown | fangxiong/fangxiong.github.io | a7e8464876287119cd87a8e687d25121f7646dbf | [
"Apache-2.0"
] | null | null | null | _posts/2019-07-10-AndroidShimmer-2019.markdown | fangxiong/fangxiong.github.io | a7e8464876287119cd87a8e687d25121f7646dbf | [
"Apache-2.0"
] | 1 | 2021-03-29T21:54:22.000Z | 2021-03-29T21:54:22.000Z | _posts/2019-07-10-AndroidShimmer-2019.markdown | fangxiong/fangxiong.github.io | a7e8464876287119cd87a8e687d25121f7646dbf | [
"Apache-2.0"
] | 1 | 2019-12-31T10:05:25.000Z | 2019-12-31T10:05:25.000Z | ---
layout: post
title: "Android 实现流光字体显示"
subtitle: " \"Android 实现流光字体显示。\""
date: 2019-07-10 19:00:00
author: "ML"
header-img: "/img/post-bg-2019.jpg"
music-id: 471385043
tags:
- 编程语言
- Android
---
* 目录
{:toc}
# Android 实现流光字体显示
<!--  -->
## 实现方式一
> 借助Facebook开源的项目[Shimmer](https://facebook.github.io/shimmer-android/)，非常方便强大的一款开源项目。
## 实现方式二
> 开源项目[Shimmer-android](https://github.com/RomainPiel/Shimmer-android),方便使用，可去参考。
## 实现方式三
> 自定义view实现，参考方式二实现方式，代码如下，重写onSizeChanged设置一些基本参数，在onDraw方法中绘制图层，通过改变Shader实现，最后通过postInvalidateDelayed方法进行重绘图层。
```java
import android.content.Context;
import android.graphics.Canvas;
import android.graphics.LinearGradient;
import android.graphics.Matrix;
import android.graphics.Paint;
import android.graphics.Shader;
import android.util.AttributeSet;
import android.widget.TextView;
import java.util.logging.Logger;
public class Test extends TextView {
private Logger logger= Logger.getLogger("Test");
private LinearGradient mLinearGradient;
private Matrix mGradientMatrix;
private Paint mPaint;
private int mViewWidth = 0;
private int mTranslate = 0;
private boolean mAnimating = true;
public Test(Context context, AttributeSet attrs) {
super(context, attrs);
}
@Override
protected void onSizeChanged(int w, int h, int oldw, int oldh) {
super.onSizeChanged(w, h, oldw, oldh);
logger.info("onSizeChanged");
if (mViewWidth == 0) {
mViewWidth = getMeasuredWidth();
if (mViewWidth > 0) {
mPaint = getPaint();
mLinearGradient = new LinearGradient(-mViewWidth, 0, 0, 0,
new int[]{0x33ffffff, 0xffffffff, 0x33ffffff},
new float[]{0.0f, 0.5f, 1.0f}, Shader.TileMode.CLAMP);
mPaint.setShader(mLinearGradient);
mGradientMatrix = new Matrix();
}
}
}
@Override
protected void onDraw(Canvas canvas) {
super.onDraw(canvas);
logger.info("onDraw");
if (mAnimating && mGradientMatrix != null) {
mTranslate += mViewWidth / 10;
if (mTranslate > 2 * mViewWidth) {
mTranslate = -mViewWidth;
}
mGradientMatrix.setTranslate(mTranslate, 0);
mLinearGradient.setLocalMatrix(mGradientMatrix);
postInvalidateDelayed(30);
}
}
}
```
> 2019-07-12 修改实现方式，将流光效果封装到对象中，使用只需要调用对象设置paint即可。
* 接口
```kotlin
import android.graphics.Color
import android.graphics.LinearGradient
import android.graphics.Matrix
import android.graphics.Shader
import com.xjlmh.classic.view.sticker.Sticker
interface ShimmerStyle{
fun setAnimating(boolean: Boolean=true)
fun setLinearGradient(linearGradient: LinearGradient?=null)
fun setGradientMatrix(matrix: Matrix?=null)
fun setDefaultTranslate(default: Int=0)
fun setLinearGradientColors(colors:IntArray?)
fun setLinearGradientPositions(position:FloatArray?)
fun initShimmerDefault(){
println("initShimmerDefault")
setAnimating()
setLinearGradient()
setGradientMatrix()
setDefaultTranslate()
}
}
```
* 实现类
```kotlin
import android.graphics.*
import com.xjlmh.classic.instrument.log.DebugLog
class Shimmer() : ShimmerStyle {
private var mLinearGradient: LinearGradient? = null
private var mGradientMatrix: Matrix? = null
var mPaint = Paint()
private var mViewWidth = 0
private var isAnimating = true
private var mTranslate = 0
var mWidthDivideBy = 5
private val DEFAULT_COLORS = intArrayOf(0xff00ffff.toInt(), 0xffffffff.toInt(), 0xff00ffff.toInt())
private val DEFAULT_POSITIONS = floatArrayOf(0.0f, 0.5f, 1.0f)
private val DEFAULT_SHADER_TILEMODE=Shader.TileMode.CLAMP
private var colors = DEFAULT_COLORS//intArrayOf(0xff00ffff.toInt(), 0xffffffff.toInt(), 0xff00ffff.toInt())
private var positions = DEFAULT_POSITIONS
private var content: String = ""
private var tileMode:Shader.TileMode= DEFAULT_SHADER_TILEMODE
/**
* 构建基础数据
*/
constructor(content: String) : this() {
this.content = content
// if (content.length>7)
// setLinearGradientColors(intArrayOf(0xFF4DDA11.toInt(), 0xffffffff.toInt(), 0xFF4DDA11.toInt()))
DebugLog.i("Shimmer", "shimmer create")
println("shimmer create")
val rect = Rect()
mPaint.textSize = 60f
mPaint.getTextBounds(content, 0, 1, rect)
mViewWidth = mPaint.measureText(content).toInt()
}
/**
* 初始化，所有配置设置好以后调用，调用以后设置关联， LinearGradient首次实例化之后不可修改，因此采用每次修改都需实例化
*/
fun init(){
initShimmerDefault().apply {
DebugLog.i("Shimmer", "initShimmerDefault")
}
mPaint.shader = mLinearGradient
}
override fun setShaderTileMode(tileMode: Shader.TileMode?) {
tileMode?.let {
this.tileMode=it
return
}
this.tileMode= DEFAULT_SHADER_TILEMODE
}
override fun setLinearGradientColors(colors: IntArray?) {
DebugLog.i("Shimmer", "setLinearGradientColors before:${this.colors.toList()} ")
colors?.let {
this.colors=it
DebugLog.i("Shimmer", "setLinearGradientColors after: ${this.colors.toList()}")
return
}
this.colors=DEFAULT_COLORS
}
override fun setLinearGradientPositions(position: FloatArray?) {
DebugLog.i("Shimmer", "setLinearGradientPositions before:${this.positions.toList()} ")
position?.let {
this.positions=it
DebugLog.i("Shimmer", "setLinearGradientPositions after: ${this.positions.toList()}")
return
}
this.positions=DEFAULT_POSITIONS
}
override fun setAnimating(b: Boolean) {
DebugLog.i("Shimmer", "setAnimating:$b")
isAnimating = b
}
override fun setLinearGradient(linearGradient: LinearGradient?) {
DebugLog.i("Shimmer", "setLinearGradient:$linearGradient")
mLinearGradient = linearGradient ?: LinearGradient((-mViewWidth).toFloat(), 0f, 0f, 0f,
colors,
positions,tileMode)
}
override fun setGradientMatrix(matrix: Matrix?) {
DebugLog.i("Shimmer", "setGradientMatrix:$matrix")
mGradientMatrix = matrix ?: Matrix()
}
override fun setDefaultTranslate(i: Int) {
DebugLog.i("Shimmer", "setDefaultTranslate:$i")
mTranslate = i
}
/**
* 绘制函数,调用画出阴影
*/
fun run() {
DebugLog.i("Shimmer", "run")
if (isAnimating && mGradientMatrix != null) {
mTranslate += mViewWidth / mWidthDivideBy
if (mTranslate > 2 * mViewWidth) {
mTranslate = 0
}
DebugLog.i("Shimmer", "mTranslate :$mTranslate")
mGradientMatrix!!.setTranslate(mTranslate.toFloat(), 0f)
this.mLinearGradient?.setLocalMatrix(mGradientMatrix)
}
log()
}
/**
* 关闭流光效果
*/
fun stop() {
isAnimating = false
}
fun log(){
DebugLog.i("Shimmer","content:$content mViewWidth:$mViewWidth isAnimating:$isAnimating :mTranslate:$mTranslate mWidthDivideBy:$mWidthDivideBy colors:${colors.toList()} positions:${positions.toList()}")
}
fun getContent()=content
}
```
* 使用
```java
private val shimmerList = ArrayList<Shimmer>()
----------------------------------------------
shimmerList.add(new Shimmer("你好呀！")) ;
shimmerList.add(new Shimmer("欢迎你哈啊阿达！")) ;
shimmerList.get(0).setMWidthDivideBy(10);
shimmerList.get(1).setMWidthDivideBy(10);
shimmerList.get(1).setLinearGradientColors(new int[]{0xFF4DDA11, 0xffffffff, 0xFF4DDA11});
shimmerList.get(0).init();
shimmerList.get(1).init();
----------------------------------------------
canvas.drawText(shimmerList.get(0).getContent(), 200, 600, shimmerList.get(0).getMPaint());
```
### 扩展
* Shader知识点
+ [Paint#setShader](https://www.jianshu.com/p/6ab058329ca8)
+ [自定义控件之Shader(着色器/渲染器)](https://blog.csdn.net/asd7364645/article/details/52761204)
+ [Android中的LinearGradient](https://www.jianshu.com/p/a9d09cb7577f)
+ [Android笔记：invalidate()和postInvalidate() 的区别及使用](https://blog.csdn.net/Mars2639/article/details/6650876)
+ [Android自定义View之invalidate方法和postInvalidate方法](https://www.jianshu.com/p/f2b51180b705)
| 30.437722 | 210 | 0.644452 | yue_Hant | 0.470249 |
80c8338cca737402241e327197906c86dd23fd24 | 14,353 | md | Markdown | docs/bem.js.md | alexander-shipilov/bem-classnames | 85977db655c392981b375d9f5d46371e00b6af18 | [
"MIT"
] | null | null | null | docs/bem.js.md | alexander-shipilov/bem-classnames | 85977db655c392981b375d9f5d46371e00b6af18 | [
"MIT"
] | 2 | 2021-05-07T16:32:18.000Z | 2021-06-16T02:02:30.000Z | docs/bem.js.md | alexander-shipilov/bem-classnames | 85977db655c392981b375d9f5d46371e00b6af18 | [
"MIT"
] | null | null | null | ## Classes
<dl>
<dt><a href="#ReactHelper">ReactHelper</a></dt>
<dd><p>React BEM Helper</p>
</dd>
</dl>
## Functions
<dl>
<dt><a href="#bem">bem([props])</a> ⇒ <code><a href="#BEM">BEM</a></code></dt>
<dd><p>Creates an instance of <a href="#BEM">BEM</a>.</p>
</dd>
<dt><a href="#config">config([configProps])</a> ⇒ <code><a href="#BEMConfig">Readonly.<BEMConfig></a></code></dt>
<dd><p>Sets / returns configuration</p>
</dd>
<dt><a href="#block">block(blockName, [mods], ...mixins)</a> ⇒ <code>string</code></dt>
<dd><p>Creates block classes</p>
</dd>
<dt><a href="#element">element(blockName, elementName, [mods], ...mixins)</a> ⇒ <code>string</code></dt>
<dd><p>Creates element classes</p>
</dd>
<dt><a href="#modifiers">modifiers(className, [mods])</a> ⇒ <code>string</code></dt>
<dd><p>Creates modifiers classes</p>
</dd>
</dl>
## Typedefs
<dl>
<dt><a href="#BEMDelimiter">BEMDelimiter</a> : <code>string</code></dt>
<dd><p>Selector delimiter: non-empty string that consists of symbols: '_', '-', e.g. '--'</p>
</dd>
<dt><a href="#BEMFormatter">BEMFormatter</a> ⇒ <code>string</code></dt>
<dd><p>Function to format a part of BEM selector.</p>
</dd>
<dt><a href="#BEMModifiers">BEMModifiers</a> : <code>string</code> | <code>Array.<string></code> | <code>Set.<string></code> | <code>Object.<string, *></code> | <code>Map.<string, *></code></dt>
<dd></dd>
<dt><a href="#BEMConfig">BEMConfig</a> : <code>Object</code></dt>
<dd></dd>
<dt><a href="#BEM">BEM</a> : <code>Object</code></dt>
<dd></dd>
<dt><a href="#ComponentPropsWithClassName">ComponentPropsWithClassName</a> : <code>Object</code></dt>
<dd><p>Component props with <code>className</code> property</p>
</dd>
<dt><a href="#ComponentClassWithClassName">ComponentClassWithClassName</a> : <code>function</code></dt>
<dd><p>Component class with <code>className</code> property</p>
</dd>
<dt><a href="#ComponentWithClassName">ComponentWithClassName</a> : <code>Object</code></dt>
<dd><p>Component with <code>className</code> property</p>
</dd>
</dl>
<a name="ReactHelper"></a>
## ReactHelper
React BEM Helper
**Kind**: global class
* [ReactHelper](#ReactHelper)
* [new ReactHelper([configProps])](#new_ReactHelper_new)
* [.config()](#ReactHelper+config) ⇒ [<code>BEMConfig</code>](#BEMConfig)
* [.block(component, [mods], ...mixins)](#ReactHelper+block) ⇒ <code>string</code>
* [.blockModifiers(component, [mods])](#ReactHelper+blockModifiers) ⇒ <code>string</code>
* [.element(component, elementName, [mods], ...mixins)](#ReactHelper+element) ⇒ <code>string</code>
* [.elementModifiers(component, elementName, [mods])](#ReactHelper+elementModifiers) ⇒ <code>string</code>
<a name="new_ReactHelper_new"></a>
### new ReactHelper([configProps])
| Param | Type | Description |
| --- | --- | --- |
| [configProps] | [<code>BEMConfig</code>](#BEMConfig) | Configuration |
<a name="ReactHelper+config"></a>
### reactHelper.config() ⇒ [<code>BEMConfig</code>](#BEMConfig)
Returns configuration
**Kind**: instance method of [<code>ReactHelper</code>](#ReactHelper)
<a name="ReactHelper+block"></a>
### reactHelper.block(component, [mods], ...mixins) ⇒ <code>string</code>
Creates block classes for Component
**Kind**: instance method of [<code>ReactHelper</code>](#ReactHelper)
| Param | Type | Description |
| --- | --- | --- |
| component | [<code>ComponentWithClassName</code>](#ComponentWithClassName) \| [<code>ComponentClassWithClassName</code>](#ComponentClassWithClassName) | Component |
| [mods] | [<code>BEMModifiers</code>](#BEMModifiers) | Modifiers |
| ...mixins | <code>ClassValue</code> | Mixins |
**Example**
```js
import React, { Component } from 'react';
import { ReactHelper } from '@helioscompanies/bem-classnames';
const bem = new ReactHelper();
function FooBar(props) {
return (
<div className={ bem.block(FooBar, null, props.classNames) } />
);
}
FooBar.className = 'FooBar';
render(<FooBar className="test" />, document.getElementById('test'));
// <div class="FooBar test" />
```
<a name="ReactHelper+blockModifiers"></a>
### reactHelper.blockModifiers(component, [mods]) ⇒ <code>string</code>
Creates modifiers classes for Component block
**Kind**: instance method of [<code>ReactHelper</code>](#ReactHelper)
| Param | Type | Description |
| --- | --- | --- |
| component | [<code>ComponentWithClassName</code>](#ComponentWithClassName) \| [<code>ComponentClassWithClassName</code>](#ComponentClassWithClassName) | Component |
| [mods] | [<code>BEMModifiers</code>](#BEMModifiers) | Modifiers |
<a name="ReactHelper+element"></a>
### reactHelper.element(component, elementName, [mods], ...mixins) ⇒ <code>string</code>
Creates classes for Component element
**Kind**: instance method of [<code>ReactHelper</code>](#ReactHelper)
| Param | Type | Description |
| --- | --- | --- |
| component | [<code>ComponentWithClassName</code>](#ComponentWithClassName) \| [<code>ComponentClassWithClassName</code>](#ComponentClassWithClassName) | Component |
| elementName | <code>string</code> | Element name |
| [mods] | [<code>BEMModifiers</code>](#BEMModifiers) | Modifiers |
| ...mixins | <code>ClassValue</code> | Mixins |
**Example**
```js
import React, { Component } from 'react';
import { ReactHelper } from '@helioscompanies/bem-classnames';
const bem = new ReactHelper();
class FooBar extends Component {
render() {
return (
<div className={ bem.block(this) }>
<div className={ bem.element(this, 'inner') } />
</div>
);
}
}
FooBar.className = 'FooBar';
render(<FooBar className="test" />, document.getElementById('test'));
// <div class="FooBar test">
// <div class="FooBar__inner" />
// </div>
```
<a name="ReactHelper+elementModifiers"></a>
### reactHelper.elementModifiers(component, elementName, [mods]) ⇒ <code>string</code>
Creates modifiers classes for Component element
**Kind**: instance method of [<code>ReactHelper</code>](#ReactHelper)
| Param | Type | Description |
| --- | --- | --- |
| component | [<code>ComponentWithClassName</code>](#ComponentWithClassName) \| [<code>ComponentClassWithClassName</code>](#ComponentClassWithClassName) | Component |
| elementName | <code>string</code> | Element name |
| [mods] | [<code>BEMModifiers</code>](#BEMModifiers) | Modifiers |
<a name="bem"></a>
## bem([props]) ⇒ [<code>BEM</code>](#BEM)
Creates an instance of [BEM](#BEM).
**Kind**: global function
**Throws**:
- <code>TypeError</code> specified delimiters should be a string described by [BEMDelimiter](#BEMDelimiter)
- <code>TypeError</code> specified formatters should be a function
- <code>TypeError</code> resulting `modifierDelimiter` should not contain resulting `elementDelimiter`
| Param | Type |
| --- | --- |
| [props] | [<code>Partial.<BEMConfig></code>](#BEMConfig) |
**Example**
```js
import { kebabCase } from 'lodash';
import { bem } from 'bem-classnames';
const bem1 = bem();
const bem2 = bem({ formatter: kebabCase, blockFormatter: (name) => `my-${name}` });
bem1.element('AlertBlock', 'messageWrapper', { foo: 'bar', ted: true }, 'mixin1', { 'mixin2': true });
// 'AlertBlock__messageWrapper_foo_bar AlertBlock__messageWrapper_ted mixin1 mixin2'
bem2.element('AlertBlock', 'messageWrapper', { foo: 'bar', ted: true }, 'mixin1', { 'mixin2': true });
// 'my-alert-block__message-wrapper_foo_bar my-alert-block__message-wrapper_ted mixin1 mixin2'
```
<a name="config"></a>
## config([configProps]) ⇒ [<code>Readonly.<BEMConfig></code>](#BEMConfig)
Sets / returns configuration
**Kind**: global function
**Throws**:
- <code>TypeError</code> specified delimiters should be a string described by [BEMDelimiter](#BEMDelimiter)
- <code>TypeError</code> specified formatters should be a function
- <code>TypeError</code> resulting `modifierDelimiter` should not contain resulting `elementDelimiter`
| Param | Type | Description |
| --- | --- | --- |
| [configProps] | [<code>Partial.<BEMConfig></code>](#BEMConfig) | Configuration |
**Example**
```js
import { kebabCase } from 'lodash';
import { config, element } from 'bem-classnames';
config({
formatter: kebabCase,
blockFormatter: (name) => `prefix-${name}`
});
element('AlertBlock', 'messageWrapper', { foo: 'bar', ted: true }, 'mixin1', { 'mixin2': true });
// 'prefix-alert-block__message-wrapper_foo_bar prefix-alert-block__message-wrapper_ted mixin1 mixin2'
```
<a name="block"></a>
## block(blockName, [mods], ...mixins) ⇒ <code>string</code>
Creates block classes
**Kind**: global function
**Throws**:
- <code>TypeError</code> `blockName` should not contain delimiters
- <code>TypeError</code> `mods` should be one of the type listed in [BEMModifiers](#BEMModifiers)
- <code>TypeError</code> `mods` should not contain delimiters
| Param | Type | Description |
| --- | --- | --- |
| blockName | <code>string</code> | Block name |
| [mods] | [<code>BEMModifiers</code>](#BEMModifiers) | Modifiers |
| ...mixins | <code>ClassValue</code> | Mixins |
**Example**
```js
block('block');
// 'block'
block('block', 'foo');
// 'block_foo'
block('block', ['foo', 'bar']);
// 'block_foo block_bar'
block('block', { foo: 'bar', ted: true })
// 'block_foo_bar block_ted'
block('block', { foo: 'bar', ted: true }, 'mixin1', { 'mixin2': true })
// 'block_foo_bar block_ted mixin1 mixin2'
```
<a name="element"></a>
## element(blockName, elementName, [mods], ...mixins) ⇒ <code>string</code>
Creates element classes
**Kind**: global function
**Throws**:
- <code>TypeError</code> `blockName` should not contain delimiters
- <code>TypeError</code> `elementName` should not contain delimiters
- <code>TypeError</code> `mods` should be one of the type listed in [BEMModifiers](#BEMModifiers)
- <code>TypeError</code> `mods` should not contain delimiters
| Param | Type | Description |
| --- | --- | --- |
| blockName | <code>string</code> | Block name |
| elementName | <code>string</code> | Element name |
| [mods] | [<code>BEMModifiers</code>](#BEMModifiers) | Modifiers |
| ...mixins | <code>ClassValue</code> | Mixins |
**Example**
```js
element('block', 'element');
// 'block__element'
element('block', 'element', 'foo');
// 'block_element_foo'
element('block', 'element', ['foo', 'bar']);
// 'block__element_foo block__element_bar'
element('block', 'element', { foo: 'bar', ted: true })
// 'block__element_foo_bar block__element_ted'
element('block', 'element', { foo: 'bar', ted: true }, 'mixin1', { 'mixin2': true })
// 'block__element_foo_bar block__element_ted mixin1 mixin2'
```
<a name="modifiers"></a>
## modifiers(className, [mods]) ⇒ <code>string</code>
Creates modifiers classes
**Kind**: global function
**Throws**:
- <code>TypeError</code> `className` should not contain `modifierDelimiter`
- <code>TypeError</code> `mods` should be one of the type listed in [BEMModifiers](#BEMModifiers)
- <code>TypeError</code> `mods` should not contain delimiters
| Param | Type |
| --- | --- |
| className | <code>string</code> |
| [mods] | [<code>BEMModifiers</code>](#BEMModifiers) |
**Example**
```js
modifies('block');
// ''
modifies('block', { foo: false, bar: 0, baz: '', ted: undefined, qux: null });
// ''
modifies('block', 'foo');
// 'block_foo'
modifies('block', ['foo', 'bar']);
// 'block_foo block_bar'
modifies('block', { foo: 'bar', ted: true })
// 'block_foo_bar block_ted'
modifies(element('block', 'element'), { foo: 'bar', ted: true })
// 'block__element_foo_bar block__element_ted'
```
<a name="BEMDelimiter"></a>
## BEMDelimiter : <code>string</code>
Selector delimiter: non-empty string that consists of symbols: '_', '-', e.g. '--'
**Kind**: global typedef
<a name="BEMFormatter"></a>
## BEMFormatter ⇒ <code>string</code>
Function to format a part of BEM selector.
**Kind**: global typedef
| Param | Type | Description |
| --- | --- | --- |
| part | <code>string</code> | Part of BEM selector |
<a name="BEMModifiers"></a>
## BEMModifiers : <code>string</code> \| <code>Array.<string></code> \| <code>Set.<string></code> \| <code>Object.<string, \*></code> \| <code>Map.<string, \*></code>
**Kind**: global typedef
<a name="BEMConfig"></a>
## BEMConfig : <code>Object</code>
**Kind**: global typedef
**Properties**
| Name | Type | Default | Description |
| --- | --- | --- | --- |
| [elementDelimiter] | [<code>BEMDelimiter</code>](#BEMDelimiter) | <code>__</code> | Element delimiter |
| [modifierDelimiter] | [<code>BEMDelimiter</code>](#BEMDelimiter) | <code>_</code> | Modifier delimiter. Should not contain `elementDelimiter` |
| [formatter] | [<code>BEMFormatter</code>](#BEMFormatter) | | Formatter |
| [blockFormatter] | [<code>BEMFormatter</code>](#BEMFormatter) | | Block formatter |
| [elementFormatter] | [<code>BEMFormatter</code>](#BEMFormatter) | | Element formatter |
| [modifierFormatter] | [<code>BEMFormatter</code>](#BEMFormatter) | | Modifier name formatter |
| [modifierValueFormatter] | [<code>BEMFormatter</code>](#BEMFormatter) | | Modifier value formatter |
<a name="BEM"></a>
## BEM : <code>Object</code>
**Kind**: global typedef
**Properties**
| Name | Type | Description |
| --- | --- | --- |
| config | <code>function</code> | [config](#config) |
| block | <code>function</code> | [block](#block) |
| element | <code>function</code> | [element](#element) |
| modifiers | <code>function</code> | [modifiers](#modifiers) |
<a name="ComponentPropsWithClassName"></a>
## ComponentPropsWithClassName : <code>Object</code>
Component props with `className` property
**Kind**: global typedef
**Properties**
| Name | Type |
| --- | --- |
| className? | <code>string</code> |
<a name="ComponentClassWithClassName"></a>
## ComponentClassWithClassName : <code>function</code>
Component class with `className` property
**Kind**: global typedef
**Properties**
| Name | Type |
| --- | --- |
| className? | <code>string</code> |
<a name="ComponentWithClassName"></a>
## ComponentWithClassName : <code>Object</code>
Component with `className` property
**Kind**: global typedef
**Properties**
| Name | Type |
| --- | --- |
| className? | <code>string</code> |
| constructor | [<code>ComponentClassWithClassName</code>](#ComponentClassWithClassName) |
| props | [<code>ComponentPropsWithClassName</code>](#ComponentPropsWithClassName) |
| 32.546485 | 218 | 0.663973 | yue_Hant | 0.195092 |
80c835425d92924e5f157517e16efbc8a8111156 | 6,244 | md | Markdown | tutorials/action_system/working_with_custom_actions.md | pettermahlen/intellij-sdk-docs | 1fd57016f2bf34afb3277d7094da091e0d84876b | [
"Apache-2.0"
] | 1 | 2019-10-01T11:25:19.000Z | 2019-10-01T11:25:19.000Z | tutorials/action_system/working_with_custom_actions.md | pettermahlen/intellij-sdk-docs | 1fd57016f2bf34afb3277d7094da091e0d84876b | [
"Apache-2.0"
] | null | null | null | tutorials/action_system/working_with_custom_actions.md | pettermahlen/intellij-sdk-docs | 1fd57016f2bf34afb3277d7094da091e0d84876b | [
"Apache-2.0"
] | null | null | null | ---
title: 1. Registering an Action
---
An action is technically a class, derived from the
[AnAction](upsource:///platform/editor-ui-api/src/com/intellij/openapi/actionSystem/AnAction.java)
class.
To update the state of the action, the method `AnAction.update()` is periodically called by IDEA.
The object of type
[AnActionEvent](upsource:///platform/editor-ui-api/src/com/intellij/openapi/actionSystem/AnActionEvent.java)
passed to this method carries the information about the current context for the action,
and in particular, the specific presentation which needs to be updated.
### 1.1. Creating actions
To create a new action we need to extend
[AnAction](upsource:///platform/editor-ui-api/src/com/intellij/openapi/actionSystem/AnAction.java)
class:
```java
public class SimpleAction extends AnAction {
}
```
### 1.2. Overriding actionPerformed()
The only method of an inheritor of
[AnAction](upsource:///platform/editor-ui-api/src/com/intellij/openapi/actionSystem/AnAction.java)
which is required to be overridden is `public void actionPerformed(AnActionEvent anActionEvent);`, and it should contain the code to be executed when the action has been invoked.
In this case the action does nothing.
```java
public class SimpleAction extends AnAction {
@Override
public void actionPerformed(AnActionEvent anActionEvent) {
}
}
```
### 1.3. Registering actions
To register a newly created action, `<action>` attribute should be added to the `<actions>` section of the plugin configuration file
[plugin.xml](https://github.com/JetBrains/intellij-sdk-docs/blob/master/code_samples/register_actions/resources/META-INF/plugin.xml).
IntelliJ IDEA has an embedded inspection that spots unregistered actions.
To register the action and set up its attributes press ***Alt + Enter*** while the caret is placed on the action's declaration.
Fill the **New Action** form to set up action's parameters such as: action's name and description, a UI component the action is bound to,
visual position of the menu item the action is bound to, and a shortcut for invoking the action.
In our case the action will be available in the **Tools Menu**, it will be placed on top, and will have no shortcuts.
After filling the **New Action** form and applying the changes `<actions>` section of our
[plugin.xml](https://github.com/JetBrains/intellij-sdk-docs/blob/master/code_samples/register_actions/resources/META-INF/plugin.xml)
file will look like this:
```xml
<actions>
<!-- Add your actions here -->
<action id="org.jetbrains.tutorials.actions.SimpleAction" class="org.jetbrains.tutorials.actions.SimpleAction"
text="Simple Action" description="IntelliJ Action System Demo">
<add-to-group group-id="ToolsMenu" anchor="first"/>
</action>
</actions>
```
### 1.4. Setting attributes manually
You can configure additional attributes of the action by editing its registration in the plugin.xml file.
Please refer to the [Action System documentation](/basics/action_system.html#registering-actions) for the full list
of supported attributes.
After performing the steps described above we need to compile and run the plugin to the the newly created action available as a Tools Menu item:
### 1.5. Performing an action
In order to make the action do something we need to implement `public void actionPerformed(AnActionEvent anActionEvent);` method.
In the following example action invokes a dialog that shows information about a selected Project View Item and has no icon and any pre-selected default option:
```java
@Override
public void actionPerformed(AnActionEvent anActionEvent) {
Project project = anActionEvent.getProject();
Navigatable navigatable = anActionEvent.getData(CommonDataKeys.NAVIGATABLE);
if (project != null && navigatable != null) {
Messages.showMessageDialog(project, navigatable.toString(), "Selected Element", Messages.getInformationIcon());
}
}
```
### 1.6. Setting up action's visibility and availability
To manipulate with action's visibility and availability we need to override `public void update(@NotNull AnActionEvent e);`
Default implementation of this method does nothing.
Override this method to provide the ability to dynamically change action's state and(or) presentation depending on the context.
```java
public class SimpleAction extends AnAction {
@Override
public void actionPerformed(AnActionEvent anActionEvent) {
//...
}
@Override
public void update(AnActionEvent anActionEvent) {
}
}
```
The following code sample illustrates how to make the action visible and available only when the following conditions are met:
there's a project available and there's an item you can navigate to selected in the project view pane:
```java
public class SimpleAction extends AnAction {
@Override
public void actionPerformed(AnActionEvent anActionEvent) {
//...
}
@Override
public void update(AnActionEvent anActionEvent) {
Project project = anActionEvent.getProject();
Navigatable navigatable = anActionEvent.getData(CommonDataKeys.NAVIGATABLE);
anActionEvent.getPresentation().setEnabledAndVisible(project != null && navigatable != null);
}
}
```
Parameter `anActionEvent` carries information on the invocation place and data available.
**Note** This method can be called frequently: for instance, if an action is added to a toolbar, it will be updated twice a second.
This means that this method is supposed to work really fast, no real work should be done at this phase.
For example, checking selection in a tree or a list, is considered valid but working with the file system is not.
If you cannot understand the state of the action fast you should do it in the
[AnActionEvent](upsource:///platform/editor-ui-api/src/com/intellij/openapi/actionSystem/AnActionEvent.java)
method and notify the user that the action cannot be executed if it's the case.
After compiling and running the plugin project and invoking the action, the dialog will pop up:
| 41.350993 | 178 | 0.767937 | eng_Latn | 0.970321 |
80c8942706521962cc129b525b9e334d10958502 | 4,483 | md | Markdown | build/windows.md | admariner/everything-curl | db9f4527658b5bf266b20ad57d57913b66ea311b | [
"CC-BY-4.0"
] | null | null | null | build/windows.md | admariner/everything-curl | db9f4527658b5bf266b20ad57d57913b66ea311b | [
"CC-BY-4.0"
] | null | null | null | build/windows.md | admariner/everything-curl | db9f4527658b5bf266b20ad57d57913b66ea311b | [
"CC-BY-4.0"
] | null | null | null | # On Windows
You can build curl on Windows in several different ways. We recommend using
the MSVC compiler from Microsoft or the free and open source mingw
compiler. The build process is, however, not limited to these.
If you use mingw, you might want to use the [autotools](autotools.md) build
system.
## winbuild
This is how to build curl and libcurl using the command line.
Build with MSVC using the `nmake` utility like this:
cd winbuild
Decide what options to enable/disable in your build. The `README.md` file in
that directly details them all, but an example command line could look like
this (split into several lines for readability):
nmake WITH_SSL=dll WITH_NGHTTP2=dll ENABLE_IPV6=yes \
WITH_ZLIB=dll MACHINE=x64
## Visual C++ project files
curl tarballs ship with pre-generated project files that you can load and
build curl with.
Project files are provided for several different Visual C++ versions.
To build with VC++, you will of course have to first install VC++ which is
part of Visual Studio.
Once you have VC++ installed you should launch the application and open one of
the solution or workspace files. The VC directory names are based on the
version of Visual C++ that you will be using. Each version of Visual Studio
has a default version of Visual C++. We offer these versions:
- VC10 (Visual Studio 2010 Version 10.0)
- VC11 (Visual Studio 2012 Version 11.0)
- VC12 (Visual Studio 2013 Version 12.0)
- VC14 (Visual Studio 2015 Version 14.0)
- VC14.10 (Visual Studio 2017 Version 15.0)
- VC14.30 (Visual Studio 2022 Version 17.0)
Separate solutions are provided for both libcurl and the curl command line
tool as well as a solution that includes both projects. libcurl.sln, curl.sln
and curl-all.sln, respectively. We recommend using curl-all.sln to build both
projects.
For example, if you are using Visual Studio 2022 then you should be able to
use `VC14.30\curl-all.sln` to build curl and libcurl.
## Running DLL based configurations
If you are a developer and plan to run the curl tool from Visual Studio (eg
you are debugging) with any third-party libraries (such as OpenSSL, wolfSSL or
libSSH2) then you will need to add the search path of these DLLs to the
configuration's PATH environment. To do that:
1. Open the 'curl-all.sln' or 'curl.sln' solutions
2. Right-click on the 'curl' project and select Properties
3. Navigate to 'Configuration Properties > Debugging > Environment'
4. Add `PATH='Path to DLL';C:\Windows\system32;C:\Windows;C:\Windows\System32\Wbem`
... where 'Path to DLL` is the configuration specific path. For example the
following configurations in Visual Studio 2010 might be:
DLL Debug - DLL OpenSSL (Win32):
PATH=..\..\..\..\..\openssl\build\Win32\VC10\DLL Debug;C:\Windows\system32;
C:\Windows;C:\Windows\System32\Wbem
DLL Debug - DLL OpenSSL (x64):
PATH=..\..\..\..\..\openssl\build\Win64\VC10\DLL Debug;C:\Windows\system32;
C:\Windows;C:\Windows\System32\Wbem
DLL Debug - DLL wolfSSL (Win32):
PATH=..\..\..\..\..\wolfssl\build\Win32\VC10\DLL Debug;C:\Windows\system32;
C:\Windows;C:\Windows\System32\Wbem
DLL Debug - DLL wolfSSL (x64):
PATH=..\..\..\..\..\wolfssl\build\Win64\VC10\DLL Debug;C:\Windows\system32;
C:\Windows;C:\Windows\System32\Wbem
If you are using a configuration that uses multiple third-party library DLLs
(such as `DLL Debug - DLL OpenSSL - DLL LibSSH2`) then `Path to DLL` will need
to contain the path to both of these.
## Notes
The following keywords have been used in the directory hierarchy:
- `<platform>` - The platform (For example: Windows)
- `<ide>` - The IDE (For example: VC10)
- `<architecture>` - The platform architecture (For example: Win32, Win64)
- `<configuration>` - The target configuration (For example: DLL Debug, LIB
Release - LIB OpenSSL)
If you are using the source code from the git repository, rather than a
release archive or nightly build, you will need to generate the project
files. Please run "generate -help" for usage details.
Should you wish to help out with some of the items on the TODO list, or find
bugs in the project files that need correcting, and would like to submit
updated files back then please note that, whilst the solution files can be
edited directly, the templates for the project files (which are stored in the
git repository) will need to be modified rather than the generated project
files that Visual Studio uses.
| 39.324561 | 84 | 0.74236 | eng_Latn | 0.989429 |
80c911ae1f0e3da3f102d9107dcc0c405f2f74c6 | 520 | md | Markdown | README.md | felizang/IIC-pytorch3 | c16928fd497089b3776c7dc3a2ac89b863314a62 | [
"MIT"
] | 1 | 2020-09-18T23:37:58.000Z | 2020-09-18T23:37:58.000Z | README.md | felizang/IIC-pytorch3 | c16928fd497089b3776c7dc3a2ac89b863314a62 | [
"MIT"
] | null | null | null | README.md | felizang/IIC-pytorch3 | c16928fd497089b3776c7dc3a2ac89b863314a62 | [
"MIT"
] | null | null | null | # IIC-pytorch3
This repository contains the [IIC paper](https://arxiv.org/abs/1807.06653) code, which is converted for running on Python 3 and torch 1.6.
The original code is from https://github.com/xu-ji/IIC
Currently, ./iic/cluster_sobel_twohead.py has been converted and can be run.
IIC is an unsupervised clustering algorithm which was introduced in ICCV2019.
The paper title is ["Invariant Information Clustering for Unsupervised Image Classification and Segmentation"](https://arxiv.org/abs/1807.06653)
| 52 | 144 | 0.782692 | eng_Latn | 0.985624 |
80c93e9c785dc46b56201457a65c2def9c1ced2e | 1,480 | md | Markdown | README.md | riccardobucco/wikipedia-downloader | e78ff79cc3d89489e370cd8b733e29835cfcfa2a | [
"MIT"
] | null | null | null | README.md | riccardobucco/wikipedia-downloader | e78ff79cc3d89489e370cd8b733e29835cfcfa2a | [
"MIT"
] | 3 | 2019-01-05T16:07:42.000Z | 2019-02-05T10:36:23.000Z | README.md | riccardobucco/wikipedia-downloader | e78ff79cc3d89489e370cd8b733e29835cfcfa2a | [
"MIT"
] | 2 | 2019-07-31T05:36:44.000Z | 2021-01-29T13:23:18.000Z | # Wikipedia downloader
*wikipedia_downloader* is a Python module that makes it easy to download Wikipedia data dumps.
## Installation
To install *wikipedia_downloader*, simply run:
```
pip install wikipedia_downloader
```
## Documentation
### Functions
- wikipedia_downloader.**download_sql_dump**(*language*, *file*, *dump="latest"*, *target_dir="."*)
Downloads and decompresses a Wikipedia SQL dump.
Arguments:
- *language*: Wikipedia name (language code).
- *file*: File name.
- *dump*: Dump version.
- *target_dir*: Target directory.
#### Example
```Python
import wikipedia_downloader as wpd
wpd.download_sql_dump("en", "pagelinks", dump="20190101", target_dir="./dumps")
```
- wikipedia_downloader.**get_dataframe**(*language*, *file*, *dump="latest"*, *select=None*, *where=None*)
Builds a pandas.DataFrame from a Wikipedia SQL dump.
Arguments:
- *language*: Wikipedia name (language code).
- *file*: File name.
- *dump*: Dump version.
- *select*: Columns to be kept.
- *where*: Functions used to filter records.
Returns: *pandas.DataFrame*
#### Example
```python
import wikipedia_downloader as wpd
select = ["page_id", "page_namespace", "page_title"]
where = {"page_namespace": lambda x: x == 0 or x == 14}
df = wpd.get_dataframe("en", "page", dump="20190101", select=select, where=where)
```
## License
This project is licensed under the MIT License - see the [LICENSE.md](LICENSE.md) file for details. | 31.489362 | 106 | 0.691216 | eng_Latn | 0.807328 |
80c963763c212dfb05ff6a2493cbcc36f74ed328 | 192 | md | Markdown | Khush Sachdeva.md | sanxy/hacktoberfest-1 | 913582b310688d496602e8b1bc9166cb64866e38 | [
"MIT"
] | null | null | null | Khush Sachdeva.md | sanxy/hacktoberfest-1 | 913582b310688d496602e8b1bc9166cb64866e38 | [
"MIT"
] | null | null | null | Khush Sachdeva.md | sanxy/hacktoberfest-1 | 913582b310688d496602e8b1bc9166cb64866e38 | [
"MIT"
] | 1 | 2020-09-30T18:53:05.000Z | 2020-09-30T18:53:05.000Z | ### YOUR FULL NAME
- Photo: https://raw.githubusercontent.com/khush3211/HacktoberFest/main/KhushSachdeva.jpg.jpg
- Location: Panipat, Haryana, India
- Github: https://github.com/khush3211
***
| 32 | 93 | 0.760417 | yue_Hant | 0.845636 |
80c9d25cdff872bf2537243dcbfa4000d52b4c2b | 310 | md | Markdown | README.md | SWJTU-TEC/homepage | 71e5557ef6c2d4ceb6261108c5430041cf2d4bea | [
"CC0-1.0"
] | null | null | null | README.md | SWJTU-TEC/homepage | 71e5557ef6c2d4ceb6261108c5430041cf2d4bea | [
"CC0-1.0"
] | 2 | 2020-07-07T07:53:41.000Z | 2020-07-12T15:18:45.000Z | README.md | SWJTU-TEC/homepage | 71e5557ef6c2d4ceb6261108c5430041cf2d4bea | [
"CC0-1.0"
] | null | null | null | # SWJTU
TEC 新教学
# 介绍
TEC是一个教学交流的平台，我们关注新时代教学中的思想、精神、文化。
平台的名字——“TEC新教学”最早有郭永春老师提出。“T”指唐山，也代表思想。“E”指峨眉，代表精神，也指向我们关注的领域是教育。“C”指成都，也代表文化。
TEC也是唐山工学院英文——“Tangshan Engineering College”的简称，以此缅怀历史，面向未来。
# 关注领域
* 多维学术、全人教育
* 跨学科教育
* 新工科
* 新文科
* 超学科
* 线上教学
* 混合式教学
* 设计思维
* MOOCS
# 关于我们
西南交通大学热爱教学的老师和他们的朋友圈。
| 14.090909 | 80 | 0.716129 | yue_Hant | 0.995872 |
80ca24402b46d4c1b5937ce57ea364d15fb215b7 | 2,441 | md | Markdown | README.md | pakitovic/executor-s3 | 7651556373cf2a838af9e0889f911838e5c765f8 | [
"MIT"
] | null | null | null | README.md | pakitovic/executor-s3 | 7651556373cf2a838af9e0889f911838e5c765f8 | [
"MIT"
] | null | null | null | README.md | pakitovic/executor-s3 | 7651556373cf2a838af9e0889f911838e5c765f8 | [
"MIT"
] | 5 | 2020-07-09T09:44:17.000Z | 2021-02-14T20:53:24.000Z | <p align="center">
<a href="http://runnerty.io">
<img height="257" src="https://runnerty.io/assets/header/logo-stroked.png">
</a>
<p align="center">Smart Processes Management</p>
</p>
[![NPM version][npm-image]][npm-url] [![Downloads][downloads-image]][npm-url] [![Dependency Status][david-badge]][david-badge-url]
<a href="#badge">
<img alt="code style: prettier" src="https://img.shields.io/badge/code_style-prettier-ff69b4.svg">
</a>
# S3 executor for [Runnerty]:
### Installation:
Through NPM
```bash
npm i @runnerty/executor-s3
```
You can also add modules to your project with [runnerty-cli]
```bash
npx runnerty-cli add @runnerty/executor-s3
```
This command installs the module in your project, adds example configuration in your `config.json` and creates an example plan of use.
If you have installed [runnerty-cli] globally you can include the module with this command:
```bash
rty add @runnerty/executor-s3
```
### Configuration sample:
Add in [config.json]:
```json
{
"id": "s3_default",
"type": "@runnerty-executor-s3",
"apiVersion": "2006-03-01",
"accessKeyId": "ABC123",
"secretAccessKey": "ABC123",
"region": "eu-west-1",
"bucket": "my.s3bucket.com"
}
```
### Plan samples:
Add in [plan.json]:
- Upload
```json
{
"id": "s3_default",
"method": "upload",
"local_file": "/tmp/test.txt",
"remote_file": "dir_one/dir_two/test_up.txt"
}
```
- Download
```json
{
"id": "s3_default",
"method": "download",
"remote_file": "test.txt",
"local_file": "/tmp/test_down.txt"
}
```
- Delete
```json
{
"id": "s3_default",
"method": "delete",
"remote_path": "folder_test/foo.txt"
}
```
- Delete array of files
```json
{
"id": "s3_default",
"method": "delete",
"remote_path": ["folder_test/sample.txt", "folder_test/sample.zip"]
}
```
- Delete glob pattern
```json
{
"id": "s3_default",
"method": "delete",
"remote_path": "folder_test/*.txt"
}
```
[runnerty]: http://www.runnerty.io
[downloads-image]: https://img.shields.io/npm/dm/@runnerty/executor-s3.svg
[npm-url]: https://www.npmjs.com/package/@runnerty/executor-s3
[npm-image]: https://img.shields.io/npm/v/@runnerty/executor-s3.svg
[david-badge]: https://david-dm.org/runnerty/executor-s3.svg
[david-badge-url]: https://david-dm.org/runnerty/executor-s3
[config.json]: http://docs.runnerty.io/config/
[plan.json]: http://docs.runnerty.io/plan/
[runnerty-cli]: https://www.npmjs.com/package/runnerty-cli
| 20.686441 | 134 | 0.669398 | yue_Hant | 0.199167 |
80cb670a6d5c27c2e982e00db6c1dfe67baa4967 | 2,386 | md | Markdown | README.md | elberthcorniell/tic-tac-toe | 97a5ce91e7e3a2175fe1b14c348c0b8851c2846f | [
"MIT"
] | 1 | 2021-01-03T17:11:39.000Z | 2021-01-03T17:11:39.000Z | README.md | elberthcorniell/tic-tac-toe | 97a5ce91e7e3a2175fe1b14c348c0b8851c2846f | [
"MIT"
] | null | null | null | README.md | elberthcorniell/tic-tac-toe | 97a5ce91e7e3a2175fe1b14c348c0b8851c2846f | [
"MIT"
] | null | null | null |
# Tic-tac-toe
The classic tic-tac-toe game to be played in your terminal.
## About the Project
This project implements the tic-tac-toe game using object-oriented Ruby.
## Built With
- Ruby
- Visual Studio, Git & GitHub
## About Tic-tac-toe
Tic-tac-toe is a game for two players, "X" and "O", who take turns marking the spaces in a 3×3 grid.
The player who succeeds in placing three of their marks in a horizontal, vertical, or diagonal row is the winner.
## Getting Started
### Prerequisites
- Ruby
- A terminal
### Setup
- Clone or download this repository
- If necessary, unzip the downloaded directory
### Usage
- Go into the "tic-tac-toe" directory
- Execute the "main.rb" file
```
./bin/main.rb
```
- Follow the "How to play" instructions
## How to Play
1. The first player (Player 1) will choose their symbol "X" or "O". The remaining symbol will be assigned to the other player (Player 2);
2. Enter Player 1's name;
3. Enter Player 2's name;
4. Starting with Player 1, turn after turn, each player will type a number (1 to 9) corresponding to their desired movement;
5. The players continue making movements until one of them get three of their marks in a horizontal, vertical, or diagonal row;
6. If no player gets three of their marks in a row after all the board's spots are marked, the game ends in a draw.
### Testing methods
For testing please escute the next steps:
- Execute the following code on the terminal to get the Gems needed:
```
bundle install
```
- Run the test with the following command:
```
rspec
```
## Authors
👨💻 **Elbert Corniell**
- GitHub: [@elberthcorniell](https://github.com/elberthcorniell)
- Twitter: [@elberthcorniell](https://twitter.com/elberthcorniell)
- LinkedIn: [Elbert Corniell](https://www.linkedin.com/in/elbert-corniell-989183159/)
👨💻 **Breno Xavier**
- GitHub: [@brenoxav](https://github.com/brenoxav)
- Twitter: [@brenoxav](https://twitter.com/brenoxav)
- LinkedIn: [Breno Xavier](https://www.linkedin.com/in/brenoxav)
## 🤝 Contributing
Contributions, issues, and feature requests are welcome!
Feel free to check the [issues page](https://github.com/brenoxav/tic-tac-toe/issues).
## Show your support
Give a ⭐️ if you like this project!
## 📝 License
This project is [MIT](./LICENSE) licensed. | 22.509434 | 137 | 0.722967 | eng_Latn | 0.982601 |
80cc1b2d2c9e3f76f252bed584d46ef4b98f4922 | 261 | md | Markdown | _posts/2015-04-29-hello-weechat.md | Maffblaster/maffblaster.github.io | 64ea9a6be87434f3c5127c1c28e49a457357c799 | [
"MIT"
] | 1 | 2015-12-17T23:03:10.000Z | 2015-12-17T23:03:10.000Z | _posts/2015-04-29-hello-weechat.md | Maffblaster/maffblaster.github.io | 64ea9a6be87434f3c5127c1c28e49a457357c799 | [
"MIT"
] | null | null | null | _posts/2015-04-29-hello-weechat.md | Maffblaster/maffblaster.github.io | 64ea9a6be87434f3c5127c1c28e49a457357c799 | [
"MIT"
] | null | null | null | ---
layout: post
title: WeeChat is great!
description: In this entry I exclaim I moved away from Irssi towards WeeChat
date: 2015-04-29 16:46:50 -07:00
tags: "WeeChat, Irssi"
---
Goodbye Irssi.
Hello [WeeChat](https://en.wikipedia.org/wiki/WeeChat)! | 23.727273 | 77 | 0.704981 | eng_Latn | 0.490883 |
80cc3ba1fe1810b352c8e6bc296a3041baf5a236 | 824 | md | Markdown | README.md | Timothykagondu/Delani-studio | bc46ef6fbf967d4bd1647cc5f0664d0d3b5ec442 | [
"MIT"
] | null | null | null | README.md | Timothykagondu/Delani-studio | bc46ef6fbf967d4bd1647cc5f0664d0d3b5ec442 | [
"MIT"
] | null | null | null | README.md | Timothykagondu/Delani-studio | bc46ef6fbf967d4bd1647cc5f0664d0d3b5ec442 | [
"MIT"
] | null | null | null | # Delani-studio
Timothy Kagondu.
Description
This project is a web application for Delani studio that shows details about the services it offers, has information about the studio, a portfolio section and a form that enables users to send their messages and concern to Delani studios.
Technologies Used
HTML5
CSS
Bootstrap
jQuery
markdown
BDD
Behaviour Input Output
Enter your name:
Enter your Email Address:
Enter your message or comment I would love to work with you:
Press Submit Pop up will appear, we have received your message. Thank you for reaching out to us.
The site is not responsive enough for some devices.(Mostly Android)
Contact Information:
If you have any question or contributions, please email me at [email protected]
License:
MIT License:
Copyright (c) 2022 Timothy kagondu
| 17.913043 | 238 | 0.787621 | eng_Latn | 0.997087 |
80ceae7457aaf1688f6331496f078caf094edc15 | 643 | md | Markdown | README.md | edwinKR/CoinOn | d5bfba4e373cb060378dededa8ad00ef8efbe50f | [
"MIT"
] | null | null | null | README.md | edwinKR/CoinOn | d5bfba4e373cb060378dededa8ad00ef8efbe50f | [
"MIT"
] | null | null | null | README.md | edwinKR/CoinOn | d5bfba4e373cb060378dededa8ad00ef8efbe50f | [
"MIT"
] | null | null | null | # "CoinOn" Project
CoinOn is a scatter plot graph viewing the top cryptocurrencies in the market. This was a project where I developed an algorithm implementing data visualization in a four-day Hackachton for my bootcamp. The objective was to learn a new stack and I chose D3.js with React and used the CoinMarketCap API for the data.
Hover or click on one of the dots and you will be provided with the crypto's:
* name
* symbol
* price per USD
* rank
* availability of supply (%)
Just to add a little bit of fun to it, you can change the size of the bubbles by moving the button at the very bottom. Do it slowly to see the gradual change.
| 49.461538 | 315 | 0.772939 | eng_Latn | 0.999838 |
80cf151cb1f7fc85922a968f5c7daa14c6873888 | 335 | md | Markdown | articles/includes/task-guide-banner.md | MicrosoftDocs/dynamics-365-project-operations-pr.ro-ro | 79664de6167f582156d39eee0e1a3c03e1f9f208 | [
"CC-BY-4.0",
"MIT"
] | 1 | 2021-04-20T21:13:46.000Z | 2021-04-20T21:13:46.000Z | articles/includes/task-guide-banner.md | MicrosoftDocs/dynamics-365-project-operations-pr.ro-ro | 79664de6167f582156d39eee0e1a3c03e1f9f208 | [
"CC-BY-4.0",
"MIT"
] | null | null | null | articles/includes/task-guide-banner.md | MicrosoftDocs/dynamics-365-project-operations-pr.ro-ro | 79664de6167f582156d39eee0e1a3c03e1f9f208 | [
"CC-BY-4.0",
"MIT"
] | null | null | null | > [!IMPORTANT]
> Dynamics 365 for Finance and Operations a evoluat în aplicații create special pentru a vă ajuta să gestionați funcții specifice de afaceri. Pentru mai multe informații despre aceste modificări, consultați [Ghidul de licențiere Dynamics 365](https://mbs.microsoft.com/Files/public/365/Dynamics365LicensingGuide.pdf).
| 83.75 | 317 | 0.814925 | ron_Latn | 0.999881 |
80cf4d826c118b5b3e50b99f8ce85544aadf5844 | 25 | md | Markdown | test/check-references/readme-is-ignored/tests/README.md | vegaprotocol/approbation | 9bb46abd898fb7abd0dd54249acb8244e276903a | [
"Unlicense"
] | 1 | 2022-03-07T15:45:45.000Z | 2022-03-07T15:45:45.000Z | test/check-references/readme-is-ignored/tests/README.md | vegaprotocol/approbation | 9bb46abd898fb7abd0dd54249acb8244e276903a | [
"Unlicense"
] | 18 | 2022-02-14T14:50:48.000Z | 2022-03-18T15:49:41.000Z | test/check-references/readme-is-ignored/tests/README.md | vegaprotocol/approbation | 9bb46abd898fb7abd0dd54249acb8244e276903a | [
"Unlicense"
] | null | null | null | This file is also ignored | 25 | 25 | 0.84 | eng_Latn | 1.00001 |
80d0053a7f53ed0c4c6025639ac0636fd4f31fc6 | 241 | md | Markdown | README.md | AmongUsModLoader/ModLoaderApi | 2b2a8838c747d8b37046cd93c4e52444568231a5 | [
"MIT"
] | 1 | 2020-11-05T08:33:20.000Z | 2020-11-05T08:33:20.000Z | README.md | AmongUsModLoader/ModLoaderApi | 2b2a8838c747d8b37046cd93c4e52444568231a5 | [
"MIT"
] | null | null | null | README.md | AmongUsModLoader/ModLoaderApi | 2b2a8838c747d8b37046cd93c4e52444568231a5 | [
"MIT"
] | 1 | 2020-12-02T05:14:46.000Z | 2020-12-02T05:14:46.000Z | # ModLoaderApi
WIP Among Us API wrapper and mod loader
Example [Here](https://github.com/0SoggyMustache0/AmongUsExampleMod)
## Credits
* [Ashley Wright](https://github.com/MsRandom)
* [George Kazanjian](https://github.com/0SoggyMustache0)
| 26.777778 | 68 | 0.771784 | yue_Hant | 0.723689 |
80d0ee55209ea8c27ce121de0a1fdddffea12d25 | 1,681 | md | Markdown | server-2013/lync-server-2013-new-voice-mail-feature.md | MicrosoftDocs/OfficeDocs-SkypeforBusiness-Test-pr.de-de | 41875522f81c5db6dcade5cfdece0ecb9bb5a10b | [
"CC-BY-4.0",
"MIT"
] | 1 | 2020-05-19T19:28:03.000Z | 2020-05-19T19:28:03.000Z | server-2013/lync-server-2013-new-voice-mail-feature.md | MicrosoftDocs/OfficeDocs-SkypeforBusiness-Test-pr.de-de | 41875522f81c5db6dcade5cfdece0ecb9bb5a10b | [
"CC-BY-4.0",
"MIT"
] | 36 | 2018-04-26T15:58:54.000Z | 2018-08-23T00:35:22.000Z | server-2013/lync-server-2013-new-voice-mail-feature.md | MicrosoftDocs/OfficeDocs-SkypeforBusiness-Test-pr.de-de | 41875522f81c5db6dcade5cfdece0ecb9bb5a10b | [
"CC-BY-4.0",
"MIT"
] | 13 | 2018-05-29T23:18:03.000Z | 2021-11-15T11:23:43.000Z | ---
title: 'Lync Server 2013: Neue Voicemailfunktion'
TOCTitle: Neue Voicemailfunktion
ms:assetid: 84d13238-67ef-42cc-801a-2d8147ba3b7f
ms:mtpsurl: https://technet.microsoft.com/de-de/library/JJ688117(v=OCS.15)
ms:contentKeyID: 49890822
ms.date: 05/19/2016
mtps_version: v=OCS.15
ms.translationtype: HT
---
# Neue Voicemailfunktion in Lync Server 2013
_**Letztes Änderungsdatum des Themas:** 2012-10-05_
Mit Lync Server 2013 wird Voice Mail Escape, eine verbesserte Funktion zum Verwalten von Voicemail, eingeführt. Mit dieser neuen Funktion kann ermittelt werden, wenn ein Anruf an Voicemail weitergeleitet wurde, und es wird verhindert, dass der Anruf sofort an die Mobiltelefon-Voicemail des Besitzers weitergeleitet wird, ohne dass der Benutzer die Möglichkeit hat, den Anruf entgegenzunehmen. Dieses Szenario tritt ein, wenn der Benutzer das gleichzeitige Klingeln auf dem Mobiltelefon aktiviert und das Mobiltelefon abgeschaltet ist, das Mobiltelefon entladen ist oder keinen Empfang hat. Voicemail Escape erkennt, dass der Anruf sofort von der Mobiltelefon-Voicemail des Benutzers beantwortet wurde und unterbricht den Anruf mit der Mobiltelefon-Voicemail. Der Anruf klingelt auf anderen Endpunkten vom Benutzer weiter, womit der Benutzer die Möglichkeit hat, den Anruf entgegenzunehmen. Sollte der Benutzer den Anruf nicht entgegennehmen, wird der Anruf zur Voicemail des Unternehmens umgeleitet.
## Siehe auch
#### Aufgaben
[Konfigurieren des Wechsels der Voicemail in Lync Server 2013](lync-server-2013-configuring-voice-mail-escape.md)
#### Konzepte
[Neue Enterprise-VoIP-Funktionen in Lync Server 2013](lync-server-2013-new-enterprise-voice-features.md)
| 56.033333 | 1,000 | 0.812612 | deu_Latn | 0.993577 |
80d1b44c949e68a47749c4602113b510b49f90c8 | 929 | md | Markdown | README.md | picandocodigo/howlong | 00004373a36f29ec74b4564fd8ea0d509f6c2c4d | [
"MIT"
] | 1 | 2022-03-28T06:09:54.000Z | 2022-03-28T06:09:54.000Z | README.md | picandocodigo/howlong | 00004373a36f29ec74b4564fd8ea0d509f6c2c4d | [
"MIT"
] | null | null | null | README.md | picandocodigo/howlong | 00004373a36f29ec74b4564fd8ea0d509f6c2c4d | [
"MIT"
] | null | null | null | # How long
[](https://travis-ci.org/picandocodigo/howlong) <a href="https://codeclimate.com/github/picandocodigo/howlong/maintainability"><img src="https://api.codeclimate.com/v1/badges/72c9febd52a3b6293ae7/maintainability" /></a> <a href="https://codeclimate.com/github/picandocodigo/howlong/test_coverage"><img src="https://api.codeclimate.com/v1/badges/72c9febd52a3b6293ae7/test_coverage" /></a>
Ruby script to search for a running process and tell you how long it's been running:
```bash
$ howlong emacs
Process /usr/bin/emacs24 has been active for 2 days, 2 hours, 12 minutes and 37 seconds
$ howlong iceweasel
Process iceweasel has been active for 13 hours, 58 minutes and 21 seconds
```
With IRB:
```bash
$ rake console
2.1.2 :001 > Howlong.run "emacs"
Process emacs has been active for 33 minutes and 25 seconds
=> nil
2.1.2 :002 >
```
| 38.708333 | 465 | 0.756728 | eng_Latn | 0.672723 |
80d1ec44039f5fe8b615ed88eb2188a50733ff3c | 949 | md | Markdown | terraform/gcp/initial_setup_01.md | OmnidApps/Documentation | e83dd5004f22d1646ff0fb5c10412452cc689fcb | [
"MIT"
] | null | null | null | terraform/gcp/initial_setup_01.md | OmnidApps/Documentation | e83dd5004f22d1646ff0fb5c10412452cc689fcb | [
"MIT"
] | null | null | null | terraform/gcp/initial_setup_01.md | OmnidApps/Documentation | e83dd5004f22d1646ff0fb5c10412452cc689fcb | [
"MIT"
] | null | null | null | # Google Cloud Terraform Initial Setup
## Requirements
- Project Admin on a Google Cloud project
- `gcloud` cli installed (for troubleshooting)
- `terraform` >= 0.13.2
## Scope
This tutorial will prepare us for provisioning resources using Google Compute Cloud (GCP) Compute Engine. As such, we will be granting permissions to a service account to work with these resources.
## Preparing Environment
In order to be able to interact with our GCP project using `terraform`, we will need a service account.
For the purpose of this tutorial, we will create a service account with a lot of permissions to make getting started easy.
In a production environment, however, we will want to make sure we make very specific service account roles based upon which resources they will be editing.
### Roles
- Compute Admin
- Allows access to `compute.*`, which includes all compute engine resources such as storage, zones, instances, networking, etc.
| 39.541667 | 197 | 0.776607 | eng_Latn | 0.994743 |
80d3fa691e2429651eb57ce815052fd127ef10a2 | 1,988 | md | Markdown | _posts/springboot/2018-11-03-springboot-1.md | moregorenine/moregorenine.github.io | 56ac501699d2a48f785dfc27e06c7e4e8cd1d8b7 | [
"MIT"
] | null | null | null | _posts/springboot/2018-11-03-springboot-1.md | moregorenine/moregorenine.github.io | 56ac501699d2a48f785dfc27e06c7e4e8cd1d8b7 | [
"MIT"
] | null | null | null | _posts/springboot/2018-11-03-springboot-1.md | moregorenine/moregorenine.github.io | 56ac501699d2a48f785dfc27e06c7e4e8cd1d8b7 | [
"MIT"
] | 1 | 2021-10-11T11:10:29.000Z | 2021-10-11T11:10:29.000Z | ---
title: "Spring Boot Quick Start #1"
excerpt: "Spring Boot에 대해 알아보자 설정에서 부터"
categories:
- springboot
tags:
- springboot
last_modified_at: 2018-11-03T21:00:00+09:00
toc: true
toc_sticky: true
---
## Intro
Spring Boot에 대해 알아보자 설정에서 부터
[Spring Boot Quick Start 강좌 - Java Brains](https://javabrains.io/courses/spring_bootquickstart/ "Spring Boot Quick Start 강좌 Link")
[연습예제 github](https://github.com/moregorenine/study/tree/master/spring-boot-quick-start "연습예제 github Link")
## Spring Boot의 시작은?
처음 spring boot를 접했을 땐 tomcat 설정도 필요없이 혼자 알아서 작동되는 걸 보고 신기했었다. spring에서 처음 설정해주던 .xml도 보이지 않았으니깐 마법의 시작은 아래 한줄이였다.
> SpringApplication.run(App.class, args);
이 한줄이 하는 역활이 무엇일까?
- 기본 configuration 설정
- spring application 시작
- classpath 스캔
- 톰캣서버 시작
## Embedded 톰캣 특징
- 편리하다
- Servlet Container의 설정 파일이 application의 설정 파일로 대체
- 독립적인 applicaiton
- microservices 아키텍쳐에 유용함.
## 스프링 부트 어떻게 시작할까요?
스프링 부트 프로젝트 생성을 어떻게 시작할지 아래 3가지 방법에 대해서 알아본다.
* STS IDE를 통한 방법
* 예제 [링크](https://javabrains.io/courses/spring_bootquickstart/lessons/Using-the-STS-IDE/)
* 공식 스프링 사이트에서 프로젝트 생성 다운로드 [https://start.spring.io](https://start.spring.io)
* 예제 [링크](https://javabrains.io/courses/spring_bootquickstart/lessons/Using-Spring-Initializr/)
* 스프링 부트 CLI 명령어를 통한 방법
* 예제 [링크](https://javabrains.io/courses/spring_bootquickstart/lessons/Using-Spring-Boot-CLI/)
## application.properties
스프링 설정파일 정보만 해도 어마어마하게 방대하다. 이걸 일일이 외우는 사람은 없겠죠? 자신의 버젼에 맞는 스프링 API 문서에서 찾을 수 있습니다.
current의 항상 최신 버젼의 스프링 API를 예로 들면
current version의 [Spring Boot Reference Guide](https://docs.spring.io/spring-boot/docs/current/reference/html/index.html)
문서양만 해도 어마무시한데요 무려 10번째 항목 Common application properties 에서 찾을 수 있으니 참고세요..
* X. Appendices
* A. Common application properties
이런 방대한 양의 설정들을 `application.properties` or `application.yml` 에서 간단하게 설정할 수 있도록 도와주니 고마운 일이죠.
## Related Posts
Spring Boot Quick Start #2 [link](https://moregorenine.github.io/springboot/springboot-2/ "Spring Boot Quick Start #2")
| 35.5 | 132 | 0.747485 | kor_Hang | 0.999961 |
80d5e958dd452f1c5eac579a707588543ed239e7 | 84 | md | Markdown | datasafe-storage/datasafe-storage-impl-db/README.md | YunLemon/datasafe | 0d6064f96ad55e48a90c5702405f28ca7a7655b7 | [
"Apache-2.0"
] | null | null | null | datasafe-storage/datasafe-storage-impl-db/README.md | YunLemon/datasafe | 0d6064f96ad55e48a90c5702405f28ca7a7655b7 | [
"Apache-2.0"
] | 26 | 2019-03-26T15:33:14.000Z | 2019-04-09T07:34:53.000Z | datasafe-storage/datasafe-storage-impl-db/README.md | adorsys/datasafe2 | 6339d7f4e9339b3c7a452570c839a91ed4cd1a78 | [
"Apache-2.0"
] | 1 | 2019-04-01T13:11:13.000Z | 2019-04-01T13:11:13.000Z | # Database storage adapter
This adapter is used for store user profiles in database | 28 | 56 | 0.821429 | eng_Latn | 0.990033 |
80d63cf9b56ce7dd729db6a6ab3b26c4924fc02b | 560 | md | Markdown | readme.md | silvermistshadow/movie-tickets | 4b401e168f6da4ec306bb3561d6433cd53e11dc4 | [
"MIT"
] | 1 | 2019-08-13T03:56:11.000Z | 2019-08-13T03:56:11.000Z | readme.md | silvermistshadow/movie-tickets | 4b401e168f6da4ec306bb3561d6433cd53e11dc4 | [
"MIT"
] | null | null | null | readme.md | silvermistshadow/movie-tickets | 4b401e168f6da4ec306bb3561d6433cd53e11dc4 | [
"MIT"
] | null | null | null | # _Movie Tickets_
#### _Displays price of a movie ticket, 7-24-2019_
#### By _**BM**_
## Description
_Takes input from the user and displays the price of a movie ticket based on some values_
## Setup/Installation Requirements
* _Load site_
* _Enter values_
* _Get an example price_
## Known Bugs
## Support and contact details
_{Let people know what to do if they run into any issues or have questions, ideas or concerns. Encourage them to contact you or make a contribution to the code.}_
## Technologies Used
_Javascript_
### License
*MIT*
| 16.470588 | 163 | 0.732143 | eng_Latn | 0.995933 |
80d68c59b8d3abf5a6935585753a5909b3e6b990 | 1,781 | md | Markdown | TODO.md | mtiosejo/hrext04-my-cruddy-app | c49ec58377506989b3d4751ff626c8822036be88 | [
"MIT"
] | null | null | null | TODO.md | mtiosejo/hrext04-my-cruddy-app | c49ec58377506989b3d4751ff626c8822036be88 | [
"MIT"
] | null | null | null | TODO.md | mtiosejo/hrext04-my-cruddy-app | c49ec58377506989b3d4751ff626c8822036be88 | [
"MIT"
] | null | null | null | # MVP
- [√] store items
- [√] create new item
- [√] select item
- [√] edit/update item
- [√] delete item
# Detail Todo
- [ ] Front End
- [√] display items
- [√] enter new item
- [√] select item for edit
- [√] select item for delete
- [√] a form!
- [ ] UI/UX
- [ ] Confirmation of delete/update
- [ ] Sortable list
- [ ] Navigation/Pagination
- [ ] Mouse over Preview
- [ ] Searching/Filtering
- [ ] Animations/Transitions
- [ ] Library Considerations
- [ ] underscore
- [√] jquery
- [ ] moment.js
- [ ] c3.js (charts.js)
## Next Steps
- [ ] factor out common functionality
- [ ] testing
## Feature list
* denotes on my to do for the project
3 days = 30 hours
My plan is to have an interactive note taking/to do list app inspired by Google Keep which I use daily. I like to implement the features I've kept from the list we created as a class below and I believe it'd take me roughly 30 hours to complete all the necessary features. My end goal is to practice the skills we've learned here at HREXT and showcase my skills as well as deliver a clean product that delivers on its functionality.
### difficulty scale 1-10. 1 meaning it's already finished 10 meaning what?!?!
*(5) add due date
*(2) time stamps (moment.js)
*(2) ability to select and delete multiple items (checkbox?)
*(5) add status/priority and allow for sorting based on status/priority
*(5) upload files/images
*(2) drag and drop to arrange
*(3) reminders? (push operation?)
*(1<) font changes/choices? (google fonts)
*(1<) Panic Button
*(2) Highlight based on status/priority/due date
*(3) add more items button, that shows another input section allowing for multiple items to be added at once
| 32.381818 | 434 | 0.659742 | eng_Latn | 0.982677 |
80d760b7503755bb949f18c94907dc8870aac8fa | 649 | md | Markdown | _posts/2020-04-08-self-emplo.md | pipiscrew/pipiscrew.github.io | 9d81bd323c800a1bff2b6d26c3ec3eb96fb41004 | [
"MIT"
] | null | null | null | _posts/2020-04-08-self-emplo.md | pipiscrew/pipiscrew.github.io | 9d81bd323c800a1bff2b6d26c3ec3eb96fb41004 | [
"MIT"
] | null | null | null | _posts/2020-04-08-self-emplo.md | pipiscrew/pipiscrew.github.io | 9d81bd323c800a1bff2b6d26c3ec3eb96fb41004 | [
"MIT"
] | null | null | null | ---
title: Self employed? This app makes managing invoices a breeze.
author: PipisCrew
date: 2020-04-08
categories: [news]
toc: true
---
TL;DR: As of April 8, you can get a subscription to the Fiskl Mobile Invoicing app for up to 91% off. Freelancing has quickly become a popular career alternative, with many folks turning to the work-from-home lifestyle to bring in some extra dough during these difficult times.
https://mashable.com/shopping/april-8-fiskl-plus-mobile-invoicing-app-sale/
origin - https://www.pipiscrew.com/2020/04/self-employed-this-app-makes-managing-invoices-a-breeze/ self-employed-this-app-makes-managing-invoices-a-breeze | 49.923077 | 277 | 0.784284 | eng_Latn | 0.934339 |
80d7b3e5e3b161242b2a3a5d0431e0b4bd895370 | 583 | md | Markdown | _posts/2015-09-24-Anna-Elyse-Dee-Dee.md | hyperdressyou/hyperdressyou.github.io | 9849173bcaac57aa413f9b885975103e554300f1 | [
"MIT"
] | null | null | null | _posts/2015-09-24-Anna-Elyse-Dee-Dee.md | hyperdressyou/hyperdressyou.github.io | 9849173bcaac57aa413f9b885975103e554300f1 | [
"MIT"
] | null | null | null | _posts/2015-09-24-Anna-Elyse-Dee-Dee.md | hyperdressyou/hyperdressyou.github.io | 9849173bcaac57aa413f9b885975103e554300f1 | [
"MIT"
] | null | null | null | ---
layout: post
date: 2015-09-24
title: "Anna Elyse Dee Dee"
category: Anna Elyse
tags: [Anna Elyse]
---
### Anna Elyse Dee Dee
Just **$289.99**
###
<table><tr><td>BRANDS</td><td>Anna Elyse</td></tr></table>
<a href="https://www.readybrides.com/en/anna-elyse/86808-anna-elyse-dee-dee.html"><img src="//img.readybrides.com/225934/anna-elyse-dee-dee.jpg" alt="Anna Elyse Dee Dee" style="width:100%;" /></a>
<!-- break -->
Buy it: [https://www.readybrides.com/en/anna-elyse/86808-anna-elyse-dee-dee.html](https://www.readybrides.com/en/anna-elyse/86808-anna-elyse-dee-dee.html)
| 36.4375 | 196 | 0.682676 | yue_Hant | 0.456413 |
80d93778bc1da91bd05cf1e123da76cce1895a68 | 101 | md | Markdown | _blocks/feature-43.md | jamesETF/EthicalFrenchie_Final | 7b803665d53a4c3520ca8711886ede8a85691c46 | [
"MIT"
] | 1 | 2021-05-24T15:38:24.000Z | 2021-05-24T15:38:24.000Z | site/_blocks/feature-43.md | vladignatyev/base64tool | e90402c2358c3943ed6a509237ab67e065fd443c | [
"MIT"
] | 5 | 2021-03-10T20:46:10.000Z | 2022-02-26T08:25:27.000Z | _blocks/feature-43.md | jamesETF/EthicalFrenchie_Final | 7b803665d53a4c3520ca8711886ede8a85691c46 | [
"MIT"
] | null | null | null | ---
title: Call To Action
icon: ios-rocket.svg
---
Chose from three call to action section layouts.
| 14.428571 | 48 | 0.722772 | eng_Latn | 0.995534 |
80d976ada18f95ef2e15cc716f0a050de71f96bb | 19,842 | md | Markdown | docs/UserGuide.md | Silvernitro/tp | eb84a77482364548a0d4d50f31b039a5e9853a3b | [
"MIT"
] | null | null | null | docs/UserGuide.md | Silvernitro/tp | eb84a77482364548a0d4d50f31b039a5e9853a3b | [
"MIT"
] | null | null | null | docs/UserGuide.md | Silvernitro/tp | eb84a77482364548a0d4d50f31b039a5e9853a3b | [
"MIT"
] | null | null | null | ---
layout: page
title: User Guide
---
* Table of Contents
{:toc}
--------------------------------------------------------------------------------------------------------------------
## GradPad
We are a team of **Computer Science Undergraduates** working together to solve **module management** and **graduation planning** problems
faced by fellow Computer Science students. Below is the detailed User Guide for effective use of GradPad which can help you
ease and improve your **module planning process**.
GradPad is a one-stop solution to **module management** for Computer Science Undergraduates.
Planning for modules has always been a tedious process but it does not have to be.
The current approach to planning and tracking graduation requirements is to open up tabs after tabs of NUS resources such as
[NUSMods](https://nusmods.com/) and [websites](https://www.comp.nus.edu.sg/programmes/ug/cs/curr/) of School of Computing,
which can be messy at times.
So, our team has come up with the idea of an **easy-to-use**, **all-in-one application** that
can ease the process of **module management** for Computer Science Undergraduates.
GradPad **consolidates** the modules you have taken and **displays** the remaining required modules to
ease your module planning process. A **module searching platform** is also included in GradPad, providing easy
**navigation** of modules.
This user guide introduces what you can do with GradPad and the steps needed to do them. In addition,
it includes a [quick start guide](#quick-start) that walks you through the installation and set-up process.
With this guide, you'll be ready to use GradPad in no time.
--------------------------------------------------------------------------------------------------------------------
## NUSMods integration
To streamline your module searching process with added convenience, GradPad integrates data from [NUSMods](https://nusmods.com/). With this,
you no longer have to visit NUSMods separately to look up module information. Instead, GradPad offers you all that data
in the same app you use to plan your modules itself.
We have requested and was granted permission from NUSMods to utilise their platform to retrieve data such as NUS course catalogues
and **module information**. These data are retrieved on the go in **realtime**, directly from NUSMods.
--------------------------------------------------------------------------------------------------------------------
## Quick start
Follow the steps below to learn how to get started with GradPad!
1. Ensure you have Java `11` or above installed in your Computer.
1. Download the latest `GradPad.jar` from [here](https://github.com/AY2021S1-CS2103T-T09-1/tp/releases).
1. Copy the file to the folder you want to use as the _home folder_ for your GradPad.
1. Ensure you have **internet connectivity** to fully utilise GradPad with the latest data from [NUSMods](https://nusmods.com/).
1. Double-click the file to start the app. The Main Page similar to the below should appear in a few seconds. Note how the app contains some sample data.<br>
1. Type the command in the command box and press Enter to execute it. e.g. typing **`help`** and pressing Enter will open the help window.<br>
Some example commands you can try:
* **`list`** : Lists all added modules.
* **`add`** `CS2103T` : Adds a module named `CS2103T` to the GradPad.
* **`delete`** `CS2103T` : Deletes the module named `CS2103T` in the current list.
* **`search`** `CS2103T` : Search for module named `CS2103T` from NUSMods database(if internet is available) or from local saved file.
* **`exit`** : Exits the app.
<div markdown="block" class="alert alert-info">
**:information_source: Note:**<br>
Detailed explanation of each command can be found [here](#commands).
Refer to the [Feature Summary List](#feature-summary-list) below for a summary of all commands.
</div>
--------------------------------------------------------------------------------------------------------------------
## Feature Summary List
A consolidated view of GradPad feature list is listed below in a table form for easy reference.
Features | Description
----------------------------------------|------------
Add a Module | Allows you to add any module of choice to the `Completed Modules` list for tracking purposes
View Added Modules | Allows you to check all the modules you've added to `Completed Modules`
Find Modules from Completed Modules | Allows you to look for modules in `Completed Modules` using keywords
Delete a Module | Allows you to delete unwanted modules in `Completed Modules`
Edit a Module | Allows you to edit the details of a module you've already added to `Completed Modules`
Check Modular Credits | Allows you to check your current modular credit tally
View all Required Modules | Allows you to display all required modules in the Computer Science curriculum onto the `Result Display`
View all General Education Modules | Allows you to display all available General Education Modules offered in NUS, onto the `Result Display`
View all Science Modules | Allows you to display all viable Science modules in the Computer Science curriculum onto the `Result Display`
Search for a Module | Allows you to search for any module available in NUS and display the module details in the `Result Display`
Find Tags from Completed Modules | Allows you to check all tags that are used in `Completed Modules`
View Help | Allows you to view a list of all possible commands
Exit the Program | Allows you to terminate the GradPad program
Refer to [Commands](#commands) below for details on all commands.
## Commands
Detailed explanations of each commands and their respective usage are listed under this section.
<div markdown="block" class="alert alert-info">
**:information_source: Notes about the Command Format and Feature Descriptions:**<br>
* When a command requires parameters, they can be entered in any order.<br>
For example, if the command specifies `edit MODULE_CODE c/NEW_MODULE_CODE t/core`, `edit MODULE_CODE t/core c/NEW_MODULE_CODE
` is also acceptable.
* Highlighted words (e.g. `this`) refer to actual commands or sections on the GradPad user interface.
</div>
### Adding a module: `add`
Scenario:<br>
You have just completed the CS2101 module, and you want to add the module into GradPad to track your academic progress.
You found the module to be enjoyable so you would like to add tags to remind yourself of how fun the module was.
The `add` command allows you to add a module you have completed into the Completed Modules list.
You can add a module by specifying a valid module code. You can also include multiple tags using the prefix `t/` but they are optional.
Conveniently, you don't have to specify the module title or modular credits of the module you are adding - GradPad
automatically retrieves them for you using data from NUSMods.
<div markdown="block" class="alert alert-info">
**:information_source: Note:**<br>
* You cannot add a module that has been previously added as GradPad does not allow duplicate modules.
* You can use multiple words in a tag. However, there should only be a single space between any 2 words.
</div>
To add the module:
1. Type `add cs2101 t/superbmodule t/superfun` into the command box, and press **Enter** to execute it.<br>
2. The result display box will display the message "The following module has been successfully added", along with the details of the module. You will then be able to see the CS2101 module at the bottom of the Completed Modules list.<br>
### Viewing all added modules: `list`
Scenario:<br>
You have filtered the Completed Modules list to show modules whose module codes contain 'cs1', and you wish to restore the list to view all the modules you have added.
The `list` command allows you to view all the modules you have added into the Completed Modules list in GradPad using this command. This is especially useful when you have previously used a command that filters the list (i.e. `find`) and you wish to restore the list to its original state.
To view all modules:
1. Type `list` into the command box, and press **Enter** to execute it.
2. The result display box will display the message "All your modules have been listed!", and you will be able to view all your previously added modules in the Completed Modules list.
### Viewing all added tags: `tags`
Scenario:<br>
You wish to filter your `Completed Modules` list to display modules tagged as "foundation". However, you've added
tons of tags to tons of modules and so you can't remember if this tag actually exists.
The `tags` command allows you to view all the tags currently in your `Completed Modules` list in
GradPad. This is useful when you want to see what tags you can filter your `Completed Modules` by (see the
`find` command below).
To view all tags:
1. Type `tags` into the command box, and press **Enter** to execute it.
2. The result display box will display a list of all existing tags.
### Editing module details: `edit`
Scenario:<br>
When you were a freshman, you added a module with tags "superbmodule" and "superfun". But now that you're graduating,
you actually think that the module was the best you'd taken in NUS.
The `edit` command allows you to edit the details of a module that you have added into GradPad.
You can edit a module by specifying the module code of the module in the Completed Modules list, followed by the
fields you wish to edit with their respective prefixes, `c/` for new module code, `t/` for new tag. You may edit multiple fields in a
single `edit` command.
Module title and modular credits for respective module code will be automatically updated with data from NUSMods.
<div markdown="block" class="alert alert-info">
**:information_source: Note:**<br>
* When you edit tags, instead of changing the descriptions of the existing tags, editing tags replaces
them with new ones.
* Instead of deleting that module and adding it again with new tags, you can simply edit the module and replace the tags.
</div>
To edit the tag of an existing module:
1. Type `edit cs2101 t/bestmodule` into the command box, and press **Enter** to execute it.<br>
2. The result display box will display the message "The following module has been successfully edited", along with the newly edited details of the module. You will also be able to see that the tags of the module displayed in the Completed Modules list has been replaced.
### Finding a specific module or a group of modules: `find`
Scenario:<br>
Let's say you have been adding a huge number of modules into GradPad, and you have lost track of the modules you
have added. You want to check and see all CS-coded modules with the "core" tag.
The `find` command allows you to filter the Completed Modules list to display the modules that you want to see.
You can do this by specifying parts of the module code of the module(s) you wish to display.
Additionally, you can also specify the tags of the module(s) you wish to display. Likewise, you can also simply
specify parts of a tag instead of the entire tag.
Instead of scrolling through the long Completed Modules list and checking the modules one by one, you can easily filter the list to display that module by `find`-ing said module.
To find the module:
1. Type `find ma bestmodule` into the command box, and press **Enter** to execute it.<br>
2. The result display box will display the message "2 modules found!" and you will be able to see only the modules
whose module codes contain "ma" and modules with "bestmodule" as a tag in the Completed Modules list.
### Deleting a Module: `delete`
Scenario:<br>
You have added several modules into GradPad but you realised that you accidentally added an additional module 'CS2107' that you have not completed yet. If you no longer want that module in your Completed Modules list, you can simply delete it.
The `delete` command allows you to remove a module you have added into GradPad.
You can delete a module by specifying the **module code** of the module. (Case insensitive)
To delete the module:
1. Type `delete cs2107` into the command box, and press **Enter** to execute it.<br>
2. When you are prompted for a confirmation, type `yes` into the command box, and press **Enter**.<br>
3. The result display will display the message "The following module has been successfully deleted" along with the details of the module you deleted.
### Checking total modular credits: `checkmc`
**Note:** A **Modular Credit** (MC) is a unit of the effort, stated in terms of time, expected of a typical student in managing his/her workload, with
an average of 20 MCs per semester.
Scenario:<br>
You want to make sure you have hit the minimum criteria of 70MCs required for applying internship modules or the 160MCs graduation requirements.
The `checkmc` command allows you to check the total amount of modular credits you have accumulated so far using this command.
This gives you a rough gauge of the progress of your graduation planning.
To check total modular credits:
1. Type `checkmc` into the command box, and press **Enter** to execute it.
2. The result display box will display a message telling you how many modular credits you have accumulated so far.
### Checking required modules: `required`
Scenario: <br>
You have added several modules into your GradPad so far but you are unsure if they are part of the Computer Science curriculum. You can simply check by using the `required` command.
The `required` command allows you to check the modules of the Computer Science curriculum you have yet to take, based on your `Completed Modules` list.
To check required modules:
1. Type `required` into the command box, press **Enter** to execute it.
2. The result display box will then display all the modules you have yet to take for each particular field, as shown below.
### Checking General Education Modules: `gem`
Scenario: <br>
You wish to take some GE modules to clear your GE requirement, but you are unsure what modules are available. You can simply check by using the `gem` command.
The `gem` command allows you to check all the available General Education (GE) modules that are available in NUS. To ease your planning, the `gem` command indicates which modules are available in Semester 1 and/or 2.
To check GE modules:
1. Type `gem` into the command box, press **Enter** to execute it.
2. The result display box will then display all the available GE modules that you can take, categorised into Semester 1 and 2, as shown below.
### Checking Science modules: `science`
Scenario: <br>
You wish to take a Science module to clear your Science requirement, but you forgot what modules are available. You can simply check by using the `science` command.
The `science` command allows you to check all the available Science modules of the Computer Science curriculum.
To check Science modules:
1. Type `science` into the command box, press **Enter** to execute it.
2. The result display box will then display all the available Science modules that you can take, as shown below.
### Searching for a module: `search`
Scenario: <br>
After viewing the required modules you have yet to take, you wish to find out more about those modules (Eg. What are they about?
Do they have any pre-requisites?). You can simply look up those details by using the `search` command.
The `search` command allows you to search for any module available in NUS.
You can search for a module by specifying the module code of the module. The essential module details will then be retrieved
from NUSMods database directly and displayed in the result display box.
<div markdown="block" class="alert alert-info">
**:information_source: Note:**<br>
If your internet is down, you can only search for modules that are of the Computer Science curriculum.
</div>
1. Type `search MA1521` into the command box, press **Enter** to execute it.
2. The result display box will then display all the relevant information about the module "MA1521", as shown below.
### Showing the help display: `help`
Scenario: <br>
You are in the midst of planning your modules, but you suddenly forgot what commands are available to you. Simply refresh your memory by using the `help` command to see all available commands and their respective functions.
The `help` command displays all of GradPad's available commands and how to use them, to you. This command is useful for first-timers or users who generally do not know how to use GradPad.
To show the help display:
1. Type `help` into the command box, and press **Enter** to execute it.<br>
1. The result display box will display all of GradPad's commands along with examples on how to use them.<br>
### Exiting GradPad: `exit`
Besides clicking the "close" button (red button) at the top of the GradPad window, you can exit GradPad using the `exit` command.
To exit:
1. Type `exit` into the command box, and press **Enter** to execute it.<br>
2. The result display box will display the message "Take care!" and after a 1.5 second delay, the application will terminate.<br>
--------------------------------------------------------------------------------------------------------------------
## FAQ
**Q**: How do I transfer my data to another Computer?<br>
**A**: Install the app in the other computer and overwrite the empty data file it creates with the file that contains the data of your previous GradPad home folder.
--------------------------------------------------------------------------------------------------------------------
## Command summary
This section is the table of all the commands available and their respective usage example in GradPad.
<div markdown="block" class="alert alert-info">
**:information_source: Notes about the Command summary:**<br>
* Words in curly braces (e.g. `{module code}`) refers to **compulsory** input that is required for the command to function.
* Words in square brackets (e.g. `[c/module code]`) refers to **optional** input that can be entered.
</div>
Action | Format | Example
--------|-------|----------
**Add** | `add {module code} [t/tags]` | `add CS2100 t/core`
**Edit** | `edit {module code} [c/module code] [t/tags]` | `edit cs2103 c/CS2103T t/core`
**Delete** | `delete {module code}` | `delete CS2103T`
**Completed Modules** | `list`
**Find** | `find {module code or tag}` | `find CS2103T foundation fun`
**Check MCs** | `checkmc`
**Required Modules** | `required`
**General Education Modules** | `gem`
**Science Modules** | `science`
**Search module details** | `search {module code}` | `search CS2103T`
**Check existing Tags** | `tags`
**Help** | `help`
**Exit** | `exit`
| 47.697115 | 289 | 0.717468 | eng_Latn | 0.995371 |
80d9ee88060b936a3e5e2ec4dbaf77318c6c7cda | 15,099 | md | Markdown | fabric-sdk-go/25863-28594/27416.md | hyperledger-gerrit-archive/fabric-gerrit | 188c6e69ccb2e4c4d609ae749a467fa7e289b262 | [
"Apache-2.0"
] | 2 | 2021-01-08T04:06:04.000Z | 2021-02-09T08:28:54.000Z | fabric-sdk-go/25863-28594/27416.md | cendhu/fabric-gerrit | 188c6e69ccb2e4c4d609ae749a467fa7e289b262 | [
"Apache-2.0"
] | null | null | null | fabric-sdk-go/25863-28594/27416.md | cendhu/fabric-gerrit | 188c6e69ccb2e4c4d609ae749a467fa7e289b262 | [
"Apache-2.0"
] | 4 | 2019-12-07T05:54:26.000Z | 2020-06-04T02:29:43.000Z | <strong>Project</strong>: fabric-sdk-go<br><strong>Branch</strong>: master<br><strong>ID</strong>: 27416<br><strong>Subject</strong>: [FABG-769] query chaincode's collection configuration<br><strong>Status</strong>: MERGED<br><strong>Owner</strong>: 乔伦 徐 - [email protected]<br><strong>Assignee</strong>:<br><strong>Created</strong>: 11/7/2018, 2:24:22 AM<br><strong>LastUpdated</strong>: 11/21/2018, 9:13:05 AM<br><strong>CommitMessage</strong>:<br><pre>[FABG-769] query chaincode's collection configuration
Change-Id: Ie6e82ba1980c930652e9ad5ad736ef2c6b92da81
Signed-off-by: 乔伦 徐 <[email protected]>
</pre><h1>Comments</h1><strong>Reviewer</strong>: 乔伦 徐 - [email protected]<br><strong>Reviewed</strong>: 11/7/2018, 2:24:22 AM<br><strong>Message</strong>: <pre>Uploaded patch set 1.</pre><strong>Reviewer</strong>: Hyperledger Jobbuilder - [email protected]<br><strong>Reviewed</strong>: 11/7/2018, 2:24:32 AM<br><strong>Message</strong>: <pre>Patch Set 1:
Build Started https://jenkins.hyperledger.org/job/fabric-sdk-go-tests-verify-s390x/4245/ (1/2)</pre><strong>Reviewer</strong>: 乔伦 徐 - [email protected]<br><strong>Reviewed</strong>: 11/7/2018, 2:27:48 AM<br><strong>Message</strong>: <pre>Topic set to FABG-769</pre><strong>Reviewer</strong>: Hyperledger Jobbuilder - [email protected]<br><strong>Reviewed</strong>: 11/7/2018, 2:30:01 AM<br><strong>Message</strong>: <pre>Patch Set 1:
Build Started https://jenkins.hyperledger.org/job/fabric-sdk-go-tests-verify-x86_64/4109/ (2/2)</pre><strong>Reviewer</strong>: Hyperledger Jobbuilder - [email protected]<br><strong>Reviewed</strong>: 11/7/2018, 3:00:23 AM<br><strong>Message</strong>: <pre>Patch Set 1: Verified-1
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Logs: https://logs.hyperledger.org/production/vex-yul-hyp-jenkins-3/fabric-sdk-go-tests-verify-x86_64/4109</pre><strong>Reviewer</strong>: 乔伦 徐 - [email protected]<br><strong>Reviewed</strong>: 11/7/2018, 3:06:10 AM<br><strong>Message</strong>: <pre>Patch Set 1:
reverify</pre><strong>Reviewer</strong>: Hyperledger Jobbuilder - [email protected]<br><strong>Reviewed</strong>: 11/7/2018, 3:06:20 AM<br><strong>Message</strong>: <pre>Patch Set 1: -Verified
Build Started https://jenkins.hyperledger.org/job/fabric-sdk-go-tests-verify-s390x/4246/ (1/2)</pre><strong>Reviewer</strong>: Hyperledger Jobbuilder - [email protected]<br><strong>Reviewed</strong>: 11/7/2018, 3:11:30 AM<br><strong>Message</strong>: <pre>Patch Set 1:
Build Started https://jenkins.hyperledger.org/job/fabric-sdk-go-tests-verify-x86_64/4110/ (2/2)</pre><strong>Reviewer</strong>: Hyperledger Jobbuilder - [email protected]<br><strong>Reviewed</strong>: 11/7/2018, 3:38:10 AM<br><strong>Message</strong>: <pre>Patch Set 1: Verified-1
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https://jenkins.hyperledger.org/job/fabric-sdk-go-tests-verify-x86_64/4110/ : SUCCESS
Logs: https://logs.hyperledger.org/production/vex-yul-hyp-jenkins-3/fabric-sdk-go-tests-verify-x86_64/4110</pre><strong>Reviewer</strong>: 乔伦 徐 - [email protected]<br><strong>Reviewed</strong>: 11/7/2018, 9:10:48 AM<br><strong>Message</strong>: <pre>Patch Set 1:
reverify</pre><strong>Reviewer</strong>: Hyperledger Jobbuilder - [email protected]<br><strong>Reviewed</strong>: 11/7/2018, 9:10:56 AM<br><strong>Message</strong>: <pre>Patch Set 1: -Verified
Build Started https://jenkins.hyperledger.org/job/fabric-sdk-go-tests-verify-s390x/4247/ (1/2)</pre><strong>Reviewer</strong>: Hyperledger Jobbuilder - [email protected]<br><strong>Reviewed</strong>: 11/7/2018, 9:16:23 AM<br><strong>Message</strong>: <pre>Patch Set 1:
Build Started https://jenkins.hyperledger.org/job/fabric-sdk-go-tests-verify-x86_64/4111/ (2/2)</pre><strong>Reviewer</strong>: Hyperledger Jobbuilder - [email protected]<br><strong>Reviewed</strong>: 11/7/2018, 9:40:05 AM<br><strong>Message</strong>: <pre>Patch Set 1: Verified-1
Build Failed
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https://jenkins.hyperledger.org/job/fabric-sdk-go-tests-verify-x86_64/4111/ : SUCCESS
Logs: https://logs.hyperledger.org/production/vex-yul-hyp-jenkins-3/fabric-sdk-go-tests-verify-x86_64/4111</pre><strong>Reviewer</strong>: 乔伦 徐 - [email protected]<br><strong>Reviewed</strong>: 11/7/2018, 10:21:55 AM<br><strong>Message</strong>: <pre>Patch Set 1:
reverify</pre><strong>Reviewer</strong>: Hyperledger Jobbuilder - [email protected]<br><strong>Reviewed</strong>: 11/7/2018, 10:22:02 AM<br><strong>Message</strong>: <pre>Patch Set 1: -Verified
Build Started https://jenkins.hyperledger.org/job/fabric-sdk-go-tests-verify-s390x/4250/ (1/2)</pre><strong>Reviewer</strong>: Hyperledger Jobbuilder - [email protected]<br><strong>Reviewed</strong>: 11/7/2018, 10:27:21 AM<br><strong>Message</strong>: <pre>Patch Set 1:
Build Started https://jenkins.hyperledger.org/job/fabric-sdk-go-tests-verify-x86_64/4114/ (2/2)</pre><strong>Reviewer</strong>: Hyperledger Jobbuilder - [email protected]<br><strong>Reviewed</strong>: 11/7/2018, 10:58:56 AM<br><strong>Message</strong>: <pre>Patch Set 1: Verified+1
Build Successful
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https://jenkins.hyperledger.org/job/fabric-sdk-go-tests-verify-s390x/4250/ : SUCCESS
Logs: https://logs.hyperledger.org/production/vex-yul-hyp-jenkins-3/fabric-sdk-go-tests-verify-s390x/4250</pre><strong>Reviewer</strong>: Troy Ronda - [email protected]<br><strong>Reviewed</strong>: 11/20/2018, 1:47:59 PM<br><strong>Message</strong>: <pre>Patch Set 1: Code-Review+1
(2 comments)</pre><strong>Reviewer</strong>: 乔伦 徐 - [email protected]<br><strong>Reviewed</strong>: 11/20/2018, 10:20:01 PM<br><strong>Message</strong>: <pre>Uploaded patch set 2.</pre><strong>Reviewer</strong>: Hyperledger Jobbuilder - [email protected]<br><strong>Reviewed</strong>: 11/20/2018, 10:20:10 PM<br><strong>Message</strong>: <pre>Patch Set 2:
Build Started https://jenkins.hyperledger.org/job/fabric-sdk-go-tests-verify-s390x/4275/ (1/2)</pre><strong>Reviewer</strong>: Hyperledger Jobbuilder - [email protected]<br><strong>Reviewed</strong>: 11/20/2018, 10:24:19 PM<br><strong>Message</strong>: <pre>Patch Set 2:
Build Started https://jenkins.hyperledger.org/job/fabric-sdk-go-tests-verify-x86_64/4135/ (2/2)</pre><strong>Reviewer</strong>: Hyperledger Jobbuilder - [email protected]<br><strong>Reviewed</strong>: 11/20/2018, 10:30:09 PM<br><strong>Message</strong>: <pre>Patch Set 2: Verified-1
Build Failed
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https://jenkins.hyperledger.org/job/fabric-sdk-go-tests-verify-x86_64/4135/ : FAILURE
Logs: https://logs.hyperledger.org/production/vex-yul-hyp-jenkins-3/fabric-sdk-go-tests-verify-x86_64/4135</pre><strong>Reviewer</strong>: 乔伦 徐 - [email protected]<br><strong>Reviewed</strong>: 11/20/2018, 11:03:42 PM<br><strong>Message</strong>: <pre>Uploaded patch set 3.</pre><strong>Reviewer</strong>: Hyperledger Jobbuilder - [email protected]<br><strong>Reviewed</strong>: 11/20/2018, 11:03:59 PM<br><strong>Message</strong>: <pre>Patch Set 3:
Build Started https://jenkins.hyperledger.org/job/fabric-sdk-go-tests-verify-s390x/4276/ (1/2)</pre><strong>Reviewer</strong>: Hyperledger Jobbuilder - [email protected]<br><strong>Reviewed</strong>: 11/20/2018, 11:07:07 PM<br><strong>Message</strong>: <pre>Patch Set 3:
Build Started https://jenkins.hyperledger.org/job/fabric-sdk-go-tests-verify-x86_64/4136/ (2/2)</pre><strong>Reviewer</strong>: Hyperledger Jobbuilder - [email protected]<br><strong>Reviewed</strong>: 11/20/2018, 11:33:50 PM<br><strong>Message</strong>: <pre>Patch Set 3: Verified-1
Build Failed
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https://jenkins.hyperledger.org/job/fabric-sdk-go-tests-verify-x86_64/4136/ : SUCCESS
Logs: https://logs.hyperledger.org/production/vex-yul-hyp-jenkins-3/fabric-sdk-go-tests-verify-x86_64/4136</pre><strong>Reviewer</strong>: 乔伦 徐 - [email protected]<br><strong>Reviewed</strong>: 11/21/2018, 1:13:11 AM<br><strong>Message</strong>: <pre>Patch Set 3:
reverify</pre><strong>Reviewer</strong>: Hyperledger Jobbuilder - [email protected]<br><strong>Reviewed</strong>: 11/21/2018, 1:13:20 AM<br><strong>Message</strong>: <pre>Patch Set 3: -Verified
Build Started https://jenkins.hyperledger.org/job/fabric-sdk-go-tests-verify-s390x/4277/ (1/2)</pre><strong>Reviewer</strong>: Hyperledger Jobbuilder - [email protected]<br><strong>Reviewed</strong>: 11/21/2018, 1:14:45 AM<br><strong>Message</strong>: <pre>Patch Set 3:
Build Started https://jenkins.hyperledger.org/job/fabric-sdk-go-tests-verify-x86_64/4137/ (2/2)</pre><strong>Reviewer</strong>: Hyperledger Jobbuilder - [email protected]<br><strong>Reviewed</strong>: 11/21/2018, 1:42:45 AM<br><strong>Message</strong>: <pre>Patch Set 3: Verified+1
Build Successful
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Logs: https://logs.hyperledger.org/production/vex-yul-hyp-jenkins-3/fabric-sdk-go-tests-verify-s390x/4277
https://jenkins.hyperledger.org/job/fabric-sdk-go-tests-verify-x86_64/4137/ : SUCCESS
Logs: https://logs.hyperledger.org/production/vex-yul-hyp-jenkins-3/fabric-sdk-go-tests-verify-x86_64/4137</pre><strong>Reviewer</strong>: Bob Stasyszyn - [email protected]<br><strong>Reviewed</strong>: 11/21/2018, 8:24:10 AM<br><strong>Message</strong>: <pre>Patch Set 3: Code-Review+2</pre><strong>Reviewer</strong>: Troy Ronda - [email protected]<br><strong>Reviewed</strong>: 11/21/2018, 8:29:20 AM<br><strong>Message</strong>: <pre>Patch Set 3: Code-Review+1</pre><strong>Reviewer</strong>: Troy Ronda - [email protected]<br><strong>Reviewed</strong>: 11/21/2018, 8:29:40 AM<br><strong>Message</strong>: <pre>Change has been successfully merged by Troy Ronda</pre><strong>Reviewer</strong>: Hyperledger Jobbuilder - [email protected]<br><strong>Reviewed</strong>: 11/21/2018, 9:13:05 AM<br><strong>Message</strong>: <pre>Patch Set 3:
Build Failed
https://jenkins.hyperledger.org/job/fabric-sdk-go-tests-merge-s390x/889/ : FAILURE
Logs: https://logs.hyperledger.org/production/vex-yul-hyp-jenkins-3/fabric-sdk-go-tests-merge-s390x/889
https://jenkins.hyperledger.org/job/fabric-sdk-go-tests-merge-x86_64/940/ : SUCCESS
Logs: https://logs.hyperledger.org/production/vex-yul-hyp-jenkins-3/fabric-sdk-go-tests-merge-x86_64/940</pre><h1>PatchSets</h1><h3>PatchSet Number: 1</h3><blockquote><strong>Type</strong>: REWORK<br><strong>Author</strong>: 乔伦 徐 - [email protected]<br><strong>Uploader</strong>: 乔伦 徐 - [email protected]<br><strong>Created</strong>: 11/7/2018, 2:24:22 AM<br><strong>UnmergedRevision</strong>: [22de7a29087a24b16327a52d14bdb84a9a64695a](https://github.com/hyperledger-gerrit-archive/fabric-sdk-go/commit/22de7a29087a24b16327a52d14bdb84a9a64695a)<br><br><strong>Approver</strong>: Hyperledger Jobbuilder - [email protected]<br><strong>Approved</strong>: 11/7/2018, 10:58:56 AM<br><strong>Type</strong>: Verified<br><strong>Value</strong>: 1<br><br><strong>Approver</strong>: Troy Ronda - [email protected]<br><strong>Approved</strong>: 11/20/2018, 1:47:59 PM<br><strong>Type</strong>: Code-Review<br><strong>Value</strong>: 1<br><br><h2>Comments</h2><strong>Commenter</strong>: Troy Ronda - [email protected]<br><strong>CommentLine</strong>: [pkg/client/resmgmt/resmgmt_test.go#L472](https://github.com/hyperledger-gerrit-archive/fabric-sdk-go/blob/22de7a29087a24b16327a52d14bdb84a9a64695a/pkg/client/resmgmt/resmgmt_test.go#L472)<br><strong>Comment</strong>: <pre>suggest testing this return value's contents.</pre><strong>Commenter</strong>: Troy Ronda - [email protected]<br><strong>CommentLine</strong>: [test/integration/pkg/client/resmgmt/resmgmt_queries_pvt_test.go#L52](https://github.com/hyperledger-gerrit-archive/fabric-sdk-go/blob/22de7a29087a24b16327a52d14bdb84a9a64695a/test/integration/pkg/client/resmgmt/resmgmt_queries_pvt_test.go#L52)<br><strong>Comment</strong>: <pre>suggest testing additional fields in the response.</pre></blockquote><h3>PatchSet Number: 2</h3><blockquote><strong>Type</strong>: REWORK<br><strong>Author</strong>: 乔伦 徐 - [email protected]<br><strong>Uploader</strong>: 乔伦 徐 - [email protected]<br><strong>Created</strong>: 11/20/2018, 10:20:01 PM<br><strong>UnmergedRevision</strong>: [9d23d2208b040be3aecf1e7c56ce14660ae30a98](https://github.com/hyperledger-gerrit-archive/fabric-sdk-go/commit/9d23d2208b040be3aecf1e7c56ce14660ae30a98)<br><br><strong>Approver</strong>: Hyperledger Jobbuilder - [email protected]<br><strong>Approved</strong>: 11/20/2018, 10:30:09 PM<br><strong>Type</strong>: Verified<br><strong>Value</strong>: -1<br><br></blockquote><h3>PatchSet Number: 3</h3><blockquote><strong>Type</strong>: REWORK<br><strong>Author</strong>: 乔伦 徐 - [email protected]<br><strong>Uploader</strong>: 乔伦 徐 - [email protected]<br><strong>Created</strong>: 11/20/2018, 11:03:42 PM<br><strong>GitHubMergedRevision</strong>: [946ab57cfea50c5bb3282f76b47e14409e977327](https://github.com/hyperledger-gerrit-archive/fabric-sdk-go/commit/946ab57cfea50c5bb3282f76b47e14409e977327)<br><br><strong>Approver</strong>: Hyperledger Jobbuilder - [email protected]<br><strong>Approved</strong>: 11/21/2018, 1:42:45 AM<br><strong>Type</strong>: Verified<br><strong>Value</strong>: 1<br><br><strong>Approver</strong>: Troy Ronda - [email protected]<br><strong>Approved</strong>: 11/21/2018, 8:29:20 AM<br><strong>Type</strong>: Code-Review<br><strong>Value</strong>: 1<br><br><strong>MergedBy</strong>: Troy Ronda<br><strong>Merged</strong>: 11/21/2018, 8:29:40 AM<br><br><strong>Approver</strong>: Bob Stasyszyn - [email protected]<br><strong>Approved</strong>: 11/21/2018, 8:24:10 AM<br><strong>Type</strong>: Code-Review<br><strong>Value</strong>: 1<br><br></blockquote> | 122.756098 | 3,569 | 0.763627 | kor_Hang | 0.304643 |
80db073bd7b61a39267b10dd0c5a65bb13ae0d6a | 1,374 | md | Markdown | README.md | msharov/pinentry-xlib | 334b7467f19006323aaeb4c4520fc316596dea9a | [
"MIT"
] | 6 | 2017-12-24T20:10:53.000Z | 2022-01-17T19:01:49.000Z | README.md | msharov/pinentry-xlib | 334b7467f19006323aaeb4c4520fc316596dea9a | [
"MIT"
] | 1 | 2017-11-05T01:19:33.000Z | 2017-11-15T00:47:38.000Z | README.md | msharov/pinentry-xlib | 334b7467f19006323aaeb4c4520fc316596dea9a | [
"MIT"
] | null | null | null |
# pinentry-xlib
Pinentry is the program called by background gpg and gpg-agent processes
to prompt the user for passwords. The stock gpg package includes several
[pinentry programs](https://github.com/gpg/pinentry), including one
for the terminal using curses, and X versions using gtk, gtk2, qt, or
qt4 toolkits. These UI toolkits are quite large, and the gtk2 pinentry
ends up linked to 60 (!) shared libraries with a combined size of
60M. Because pinentry does little more than display a dialog box and
input the password, this seems rather excessive. So this project is a
toolkit-less version of pinentry using only the basic X API, loading
only 6 shared libraries and having a 3M footprint.
There is also a similar program, ssh-askpass, used by ssh and ssh-agent
to ask for passwords. It can also be replaced by pinentry-xlib.
To compile you need X11 libs and gcc or another C11-supporting compiler:
```sh
./configure --prefix=/usr
make
make install
```
pinentry and ssh-askpass use symlinks to allow keeping multiple versions
installed simultaneously, so to enable pinentry-xlib you will need to:
```sh
cd /usr/bin
rm pinentry ssh-askpass
ln -s pinentry-xlib pinentry
ln -s pinentry-xlib ssh-askpass
```
For usage instructions consult pinentry info page installed with gpg.
Report bugs on [project bugtracker](https://github.com/msharov/pinentry-xlib/issues).
| 36.157895 | 85 | 0.786026 | eng_Latn | 0.998399 |
80dc2902eb4df0b78a462b4fa93db13c0a7117f1 | 2,586 | md | Markdown | README.md | athei/flio | 62c0dc2e6025e163519c5045ab93e7b4c9dbc877 | [
"MIT"
] | null | null | null | README.md | athei/flio | 62c0dc2e6025e163519c5045ab93e7b4c9dbc877 | [
"MIT"
] | null | null | null | README.md | athei/flio | 62c0dc2e6025e163519c5045ab93e7b4c9dbc877 | [
"MIT"
] | null | null | null | # flio
flio is an effort to write a secure and modular fileserver using the SMB2 (and higher) protocol. Besides all kind of popritary implementations (Apple, Microsoft, ...) there is only Samba as mature open source implementation. Samba is a fine piece of software but really hard to use when you only want a file server for your embedded device:
* It is huge (> 20MB) and not modular. You pay for all enterprise stuff when alle you want is a simple file server.
* It is GPLv3 licencsed which may hinder adoption.
* It is is written in C which is less than optimal for a software that must handle adversarial input
# Goals
We align our goals so that we improve on these points by the following means.
* flio is written in safe Rust which offers much stronger security properties than C
* flio is MIT licensed for easy adoption
* Every optional component should be easily removeable (through cargo features) to reduce the minimum size
# Project Structure
The project shall consist of the following crates.
## smb2-packet
This create parses the wire representation of the protocol to semantically strong data structures. It also does the reverse: Serializing data structures to the wire representation. No other crate should ever touch wire data. This way we contain most of the critical handling of adversarial data in this crate.
## smb2-server
The server state machine that implements the smb2 protocol from a server perspective and depends on the smb2-packet crate. It implements everything but I/O. Therefore it does not impose any execution model (asynchronous vs. synchronous) on the user or does any I/O at all. These tasks are all delegated to the users of this crate (probably through traits). This allows us to easily test this crate by suppyling mock traits.
## flio
The actual server implementation that handles the I/O and execution model (probably using tokio wtith async/await).
# no_std
All crates but the actual server flio should be able to work in a no_std environment to allow for the development of alternative servers on smb2-server that work in more constraint environments.
# Current State
The only crate that exists today is smb2-packet which is approximatly half done to parse
all the required requests. After that the serializing part will be implemented which is arguably
eassier. The automated testing is done with packet traces from various implementations talking to each other.
The interface is still a little awkward because without a user it is not clear what is needed here.
This will improve once the smb2-server and flio crates are started.
| 73.885714 | 423 | 0.800851 | eng_Latn | 0.99985 |
80dd91f809d3cb32678ab1eb8b0c8fe13a730dd5 | 96 | md | Markdown | _posts/0000-01-02-IanJPeek.md | IanJPeek/github-slideshow | a9e5bc0d42cb93300ec1f8672e96c2083a17e41d | [
"MIT"
] | null | null | null | _posts/0000-01-02-IanJPeek.md | IanJPeek/github-slideshow | a9e5bc0d42cb93300ec1f8672e96c2083a17e41d | [
"MIT"
] | 5 | 2019-09-26T18:11:49.000Z | 2021-09-27T23:14:42.000Z | _posts/0000-01-02-IanJPeek.md | IanJPeek/github-slideshow | a9e5bc0d42cb93300ec1f8672e96c2083a17e41d | [
"MIT"
] | null | null | null | layout: slide
title: "Welcome to our second slide!"
---
Your text
"Witty comment inserted here"
| 16 | 37 | 0.739583 | eng_Latn | 0.993738 |
80df60db1c2a3a16a1b224faae8c10304b4e86e3 | 635 | md | Markdown | README.md | sjsafranek/GeoSkeletonDB | e2485f88060d707895b0f3b8486bcf65c8b6e095 | [
"MIT"
] | 1 | 2020-05-13T20:35:16.000Z | 2020-05-13T20:35:16.000Z | README.md | sjsafranek/GeoSkeletonDB | e2485f88060d707895b0f3b8486bcf65c8b6e095 | [
"MIT"
] | null | null | null | README.md | sjsafranek/GeoSkeletonDB | e2485f88060d707895b0f3b8486bcf65c8b6e095 | [
"MIT"
] | null | null | null | [](https://godoc.org/github.com/sjsafranek/GeoSkeletonDB)
[](https://goreportcard.com/report/github.com/sjsafranek/geoskeletondb)
[](http://sjsafranek.github.io/geoskeletondb/)
# GeoSkeletonDB
Simple wrapper for BoltDB to store GeoJSON data
## Documentation
https://godoc.org/github.com/sjsafranek/GeoSkeletonDB
## ToDo
- Test with travis-ci.org
- https://img.shields.io/travis/USER/REPO.svg | 48.846154 | 153 | 0.782677 | yue_Hant | 0.538372 |
80e09ced649205f5abb191511a2af95087763b7e | 6,781 | md | Markdown | README.md | omarsou/wav2vec_xlsr_cv_exp | 4e7d779f6ae9b0bbc4762ac856a3866ce6912127 | [
"Apache-2.0"
] | 3 | 2021-03-26T08:16:48.000Z | 2021-11-24T16:14:55.000Z | README.md | omarsou/wav2vec_xlsr_cv_exp | 4e7d779f6ae9b0bbc4762ac856a3866ce6912127 | [
"Apache-2.0"
] | null | null | null | README.md | omarsou/wav2vec_xlsr_cv_exp | 4e7d779f6ae9b0bbc4762ac856a3866ce6912127 | [
"Apache-2.0"
] | null | null | null | # wav2vec_xlsr_cv_exp
Experiments on out of training languages (from common voice https://commonvoice.mozilla.org/) using Wav2Vec
# Repo Description
**notebook_template.ipynb** is a notebook that you can use to experiment with wav2vec2 models. \
The folder **utils** is a python package where I put all the necessary functions ro run the notebook_template. \
The folder **exp** contains notebooks of our experiments (the notebooks are not the same as the template one,
they are not well organized but they follow the same procedure/approach as the notebook_template.) \
**trainer.py** is a module made to run a training.
# Introduction
Low resources languages have always been an issue for automatic speech recognition. Indeed, most speech recognition systems were trained using the available transcriptions, and when they were not sufficient, the model would be trained on a language with huge resources (typically english, french, etc...), and then finetuned on the low resource language. Although this approach was very intuitive and feasible, it still made use of annotated data. The problem is that it is very exhausting to annotate speech samples, especially for low resource languages. On the other hand, we gather every day countless of new unlabelled data, that can't be used for training with the precedent approach.
Fortunately, unsupervised pretraining on large quantity of audio has proven to be very successful in learning speech features. Pretrained models achieve very good results on several speech tasks on low resource languages (i.e languages with few amount of labelled data 10mn - 10h), and have outperformed fully supervised approaches. In this project, we will examine if the features learned in one language (Wav2Vec2-LV60) or in multiple languages (XLSR-53) are good enough on other out-of-training low resource languages.
# Wav2Vec 2.0 Model
The model is composed of a multi-layer convolutional feature encoder
*f* : *X* → *Z* which takes as input raw audio *X* and outputs the
latent speech representations *z*<sub>1</sub>, ...., *z*<sub>*T*</sub>.
This latter is fed to a context network *g* : *Z* → *C* that follows the
Transformer architecture and uses convolutional layers to learn relative
positional embedding, which output representations
*c*<sub>1</sub>, ....., *c*<sub>*T*</sub> capturing information from the
entire sequence.
The output of the feature encoder (and not of the context transformer)
is discretized to *q*<sub>*t*</sub> in parallel using a quantization
module *Z* → *Q* that relies on product quantization. These
*q*<sub>1</sub>, ....., *q*<sub>*T*</sub> represent the targets in the
pretraining task.
The model architecture is summarized below (Source : [2]) : \
\
The pretraining of this model consists of masking a certain proportion of time steps in the latent feature encoder space, and the objective is to identify the correct quantized latent audio representation in a set of distractors of each masked time step.
For the experimentation we consider two versions of the wav2vec 2.0 architecure [2].
The first one is the XLSR-53 [3], which was pretrained on 56k hours of speech samples, which represent fifty three different languages from the multilingual librispeech, the common voice dataset and the babel dataset. \
The second is the wav2vec LARGE LV-60 which was pretrained on sixty thousand hours of only english speech samples from LibriVox. \
# Dataset Preparation
**Cross-Lingual** : For the cross-lingual experimentation, we consider
out-of-training language datasets from the Common Voice Dataset [4] . We
have identified languages that were not used for the pretraining of the
XLSR-53 model and for which we have sufficient annotated data to work
with (Ukrainian (30h) and Czech (36h)).
**Monolingual** : To examine the impact of multilingual training, we
took an english audio dataset that none of these models have been
pretrained on, thus we consider the TIMIT dataset.\
For both experimentation, we retrieve the non-aligned phoneme
transcription of each audio sample by running the open-source tool
phonemizer[1] on their corresponding text scripts. We follow partially
the defined splits in the dataset sources : the test set was kept
intact, we merge the training and validation set into one set, and we
split this latter into two set (80% training / 20% validation). For the
cross-lingual experimentation, we used different subsets of the training
set (10mn, 1h and the maximum number of hours ≈ 8h/9h).
# Results
## Monolingual : TIMIT English Dataset
<div class="center">
| Model | PER on dev. set | PER on test set |
|:-----------:|:---------------:|:---------------:|
| XLSR-53 | 4.7% | 7.3% |
| Large LV-60 | 3.9% | 6.1% |
</div>
It seems like on an english task, the wav2vec 2.0 Large LV60 is more suited. This actually shows a very
important result : cross-lingual pretraining does not improve the performances over a robust automatic
speech recognition system trained on a very large dataset in english.
## Cross-Lingual : Ukrainian / Czech
<div class="tabular">
| Language | 10min | 1h | 10h |
|:-------------------:|:-----:|:----:|:----:|
| Czech (XLSR) | 19.0 | 15.5 | 11.4 |
| Czech (wav2vec 2.0) | 24.7 | 17.2 | 11.7 |
| Czech (scratch) | \- | \- | 35.8 |
| Ukrainian (XLSR) | 27.2 | 21.4 | 16.7 |
| Ukrainian (scratch) | \- | \- | 48.2 |
</div>
Using pretrained model (English Only LV60 or Multilingual XLSR) was more
efficient than training from scratch the model. We can see that using
only 10 minutes of training data, we achieve a 20% gain in PER compared
to the training from scratch with 10 hours of data. Furthermore, XLSR
performs better on the Czech language in comparison with the Wav2Vec 2.0
LV model, this shows that Cross-Lingual pretraining still preferred for
low resource languages.
# Models
Models can be found here : [Google Drive](https://drive.google.com/drive/folders/1D7wWF74esD93pe6C5UX52RLkQJzNPn2U?usp=sharing)
# References
[1] https://pypi.org/project/phonemizer (built from source espeak-ng to
access ukrainian support)
[2] A. Baevski, H. Zhou, A. Mohamed, and M. Auli. wav2vec2.0: A framework for self-supervised learning of speech rep-resentations, 2020.
[3] A. Conneau, A. Baevski, R. Collobert, A. Mohamed, andM. Auli. Unsupervised cross-lingual representation learningfor speech recognition, 2020.
[4] R. Ardila, M. Branson, K. Davis, M. Henretty, M. Kohler,J. Meyer, R. Morais, L. Saunders, F. M. Tyers, and G. Weber.Common voice: A massively-multilingual speech corpus. InProceedings of the 12th Conference on Language Resourcesand Evaluation (LREC 2020), pages 4211–4215, 2020 | 62.211009 | 690 | 0.741189 | eng_Latn | 0.996972 |
80e15201b27382533199bc2368e158000dd5967f | 842 | md | Markdown | _posts/2021-04-09-bash-introduction-4-exercise-try-bash-.md | avicoder/mslearn | d864219a93bfa551c113003450f9284002299508 | [
"MIT"
] | null | null | null | _posts/2021-04-09-bash-introduction-4-exercise-try-bash-.md | avicoder/mslearn | d864219a93bfa551c113003450f9284002299508 | [
"MIT"
] | null | null | null | _posts/2021-04-09-bash-introduction-4-exercise-try-bash-.md | avicoder/mslearn | d864219a93bfa551c113003450f9284002299508 | [
"MIT"
] | 1 | 2022-03-09T17:33:15.000Z | 2022-03-09T17:33:15.000Z | ---
layout: post
title: Introduction to Bash - Exercise - Try Bash
description: nil
summary: nil
tags: nil
---
<a target="_blank" href="https://docs.microsoft.com/en-us/learn/modules/bash-introduction/4-exercise-try-bash/"><i class="fas fa-external-link-alt"></i> </a>
<img align="right" src="https://docs.microsoft.com/en-us/learn/achievements/student-evangelism/bash-introduction-badge.svg">
#### 1. What directory would you switch to if you entered the Bash command cd .?
<i class='far fa-square'></i> My special "home" directory
<i class='far fa-square'></i> The parent directory
<i class='far fa-square'></i> The first alphabetical subdirectory
<i class='fas fa-check-square' style='color: Dodgerblue;'></i> I wouldn't switch directories
<br />
<br />
<br />
| 33.68 | 158 | 0.691211 | eng_Latn | 0.446424 |
80e159be2fbe6af7b820542e7d89deb3d595b2aa | 2,073 | md | Markdown | docs/interfaces/_api_types_.trackattributes.md | qingqibing/vue-onvif-rx | b41842ea88a0852a9fc52a80786007ed3298f7cf | [
"MIT"
] | 1 | 2021-11-19T01:33:45.000Z | 2021-11-19T01:33:45.000Z | docs/interfaces/_api_types_.trackattributes.md | meepobrother/onvif-rx | bd0f312bdb4af3dc5e4a057f6dd9c5a2653d69bd | [
"MIT"
] | 3 | 2021-05-11T12:50:18.000Z | 2022-01-22T11:55:55.000Z | docs/interfaces/_api_types_.trackattributes.md | meepobrother/onvif-rx | bd0f312bdb4af3dc5e4a057f6dd9c5a2653d69bd | [
"MIT"
] | null | null | null | [onvif-rx](../README.md) > ["api/types"](../modules/_api_types_.md) > [TrackAttributes](../interfaces/_api_types_.trackattributes.md)
# Interface: TrackAttributes
The basic information about the track. Note that a track may represent a single contiguous time span or consist of multiple slices.
## Hierarchy
**TrackAttributes**
## Index
### Properties
* [AudioAttributes](_api_types_.trackattributes.md#audioattributes)
* [Extension](_api_types_.trackattributes.md#extension)
* [MetadataAttributes](_api_types_.trackattributes.md#metadataattributes)
* [TrackInformation](_api_types_.trackattributes.md#trackinformation)
* [VideoAttributes](_api_types_.trackattributes.md#videoattributes)
---
## Properties
<a id="audioattributes"></a>
### `<Optional>` AudioAttributes
**● AudioAttributes**: *[AudioAttributes](_api_types_.trackattributes.md#audioattributes)*
*Defined in [api/types.ts:3761](https://github.com/patrickmichalina/onvif-rx/blob/f117e44/src/api/types.ts#L3761)*
___
<a id="extension"></a>
### `<Optional>` Extension
**● Extension**: *[TrackAttributesExtension](_api_types_.trackattributesextension.md)*
*Defined in [api/types.ts:3763](https://github.com/patrickmichalina/onvif-rx/blob/f117e44/src/api/types.ts#L3763)*
___
<a id="metadataattributes"></a>
### `<Optional>` MetadataAttributes
**● MetadataAttributes**: *[MetadataAttributes](_api_types_.trackattributes.md#metadataattributes)*
*Defined in [api/types.ts:3762](https://github.com/patrickmichalina/onvif-rx/blob/f117e44/src/api/types.ts#L3762)*
___
<a id="trackinformation"></a>
### TrackInformation
**● TrackInformation**: *[TrackInformation](_api_types_.trackinformation.md)*
*Defined in [api/types.ts:3759](https://github.com/patrickmichalina/onvif-rx/blob/f117e44/src/api/types.ts#L3759)*
___
<a id="videoattributes"></a>
### `<Optional>` VideoAttributes
**● VideoAttributes**: *[VideoAttributes](_api_types_.trackattributes.md#videoattributes)*
*Defined in [api/types.ts:3760](https://github.com/patrickmichalina/onvif-rx/blob/f117e44/src/api/types.ts#L3760)*
___
| 29.197183 | 133 | 0.76218 | kor_Hang | 0.155324 |
80e1ada555f61b4a5282ef17575da1c0790862bc | 151 | md | Markdown | SUMMARY.md | Jiavan/coding-is-interesting | 853a0d11320bc0c06145f5b76fac29346b8292de | [
"MIT"
] | null | null | null | SUMMARY.md | Jiavan/coding-is-interesting | 853a0d11320bc0c06145f5b76fac29346b8292de | [
"MIT"
] | null | null | null | SUMMARY.md | Jiavan/coding-is-interesting | 853a0d11320bc0c06145f5b76fac29346b8292de | [
"MIT"
] | null | null | null | # Summary
## 前言
* [Introduction](README.md)
## 程序员的基本修养
* [自由软件与开源协议.md](fsf-and-license.md)
## StackPath
* [什么是 CDN](stackpath/what-is-cdn.md)
| 10.066667 | 37 | 0.649007 | yue_Hant | 0.770155 |
80e1f9e624a156a4989559879b6439f58b60ca3e | 5,802 | md | Markdown | source/docs/documentation/Foundry/voltmx_foundry_user_guide/Content/AppReconfig.md | kumari-h/volt-mx-docs | 7b361299d49abedd1162cbb1640bad0cd04d3140 | [
"Apache-2.0"
] | null | null | null | source/docs/documentation/Foundry/voltmx_foundry_user_guide/Content/AppReconfig.md | kumari-h/volt-mx-docs | 7b361299d49abedd1162cbb1640bad0cd04d3140 | [
"Apache-2.0"
] | null | null | null | source/docs/documentation/Foundry/voltmx_foundry_user_guide/Content/AppReconfig.md | kumari-h/volt-mx-docs | 7b361299d49abedd1162cbb1640bad0cd04d3140 | [
"Apache-2.0"
] | null | null | null | ---
layout: "documentation"
category: "voltmx_foundry_user_guide"
---
User Guide: [Publish](Publish.html) > [Reconfiguration at Publish](ReconfigPublish.html) > App Reconfiguration
App Reconfiguration
===================
During app publishing, Volt MX Foundry allows you to reconfigure the default values of app services specific to an environment and publish the app to another environment.
App Key and App Secret are used by a Mobile app to identify and communicate with a published instance of a Volt MX Foundry app. You can configure specific values or let the system auto-generate those.
> **_Important:_** For more details about How to Publish an App, refer to [Publish](Publish.html).
> **_Important:_** **Full** publish method is selected by default, you can switch to **Incremental** publish to reduce publish duration. Refer to [Publish > Delta Publish](Publish.html).
Use Cases
---------
You can use service reconfiguration based on the following scenarios:
A developer can use an app configuration in the current workspace and publish the reconfigured app to another environment. A developer can reconfigure only a few entities (such as base URL, User ID, and password).
* For example, currently an endpoint URL of an integration service of the app is configured as `http://sample.test.com`, which is a test environment. But after an app is published in a production environment, a developer wants the integration service to communicate to a production endpoint - for example, `https://sample.com`. In such cases, a developer reconfigures the endpoint URL from http://sample.test.com to https://sample.com, and publishes the app to a production environment.
To display the app reconfiguration page while publishing an app, follow these steps:
1. After you complete configuring services in an app, click the **Publish** tab.
2. In the **[Publish](Publish.html)** page, select an environment. The **CONFIGURE & PUBLISH** and **PUBLISH** buttons are active only after you select an environment.
3. Click **CONFIGURE & PUBLISH** to display the app reconfiguration page.
The **App Configuration** page appears with the following properties:
| App Details | Properties |
| --- | --- |
| Primary App Key | Primary App Key/Secret for Web ChannelSecondary App Key/Secret for Native Channel> **_Note:_** For more information on separate app key/secret, refer to [Separate Appkey/Secret for Native and Web Channels](SeparateAppKey-Secret.html) Note that App Key needs to be unique. Additionally App Key and App Secret should only contain alpha numeric characters or '-' and must contain at least 5 and a max of 60 characters. |
| Primary Secret |
| Secondary App Key |
| Secondary Secret |
1. Configure the following for Incremental publish. Full publish method is selected by default.
| Publish Method | Details |
| --- | --- |
| Incremental Publish | If you select this option, the current publish will only deploy the services that have changed since the last publish to this environment. This will reduce the deployment time. |
| Full Publish | If you select this option, the current publish will deploy all services associated to this App to the environment. |
| Exclusions in Publish | Details |
| Engagement Services Configurations | If you select this check box, the current publish will skip swagger files. Enabling this setting skips swagger generation during app publish and improves publish time. |
| Generated OpenAPI (Swagger) API Docs | If you select this check box, the current publish will skip swagger files. Enabling this setting skips swagger generation during app publish and improves publish time. |
2. To change any value, click the **Edit** button.
The system displays the original value in the text box, and the **save** and **cancel** buttons next to the text box. The text field is active.
3. Change the value in the text field.
4. Click **save**.
5. If you have configured sync services in your app and also uploaded a binary file for Web client, do the following:
1. Under **Synchronization**, choose **Yes** to keep the previous sync configuration in the application history. By default, the synchronization is set to **No**.
> **_Note:_** You are publishing an update to your sync configuration. If you keep the previous sync config in the application history, any devices that already have a local copy of the data can attempt to sync only the schema changes and avoid a full sync. If you do not keep the previous sync config in application history, all devices will be required to fully rebuild their local copy of the data using a full sync. In either case, the client app must be rebuilt with the new sync configuration and updated to all devices.
2. Under the **Web Client**, Select the required version from the drop-down list. When you select a version and publish the app, the system overrides the existing version of the `.war` file with the selected new version in the Server. If there is no published version, the system adds the new Web client in the server.
* **Skip**: If you select Skip, Web Client Assets will not be included in this Publish. If you select a version of Web Client, the last published Web Client will be deleted and the said Version of Web Client will be published to Server.
* **Remove**: If you select Remove, the Web Client is removed if present in the Server.
7. Click **SAVE & PUBLISH** to start the publishing. The process of publishing the app begins.
| 73.443038 | 530 | 0.743192 | eng_Latn | 0.997763 |
80e31b6f2ffb4635343c79a5aae9768fed85e069 | 198 | md | Markdown | README.md | stephenluong/DMIT-1531---LAMP-STACK | 1f2e528b489d38b66072e2cbb7c7f32267deed80 | [
"MIT"
] | null | null | null | README.md | stephenluong/DMIT-1531---LAMP-STACK | 1f2e528b489d38b66072e2cbb7c7f32267deed80 | [
"MIT"
] | null | null | null | README.md | stephenluong/DMIT-1531---LAMP-STACK | 1f2e528b489d38b66072e2cbb7c7f32267deed80 | [
"MIT"
] | null | null | null |
# DMIT 1531 - LAMP STACK
Based on the exercises from the class, highly condensed, highly effective.
Go here for LAMP Scripts
https://gist.github.com/stephenluong/7846bb2d14264b8b16bd9e47665a8039
| 33 | 74 | 0.808081 | eng_Latn | 0.719206 |
80e39516b0862419970a9fce1b434a076ae46f07 | 3,398 | md | Markdown | docs/metrikker.md | navikt/poa-arbeidssokerregistrering | 12cba4e69213258f7d7e1f8db5cd5f759d919ff1 | [
"MIT"
] | null | null | null | docs/metrikker.md | navikt/poa-arbeidssokerregistrering | 12cba4e69213258f7d7e1f8db5cd5f759d919ff1 | [
"MIT"
] | null | null | null | docs/metrikker.md | navikt/poa-arbeidssokerregistrering | 12cba4e69213258f7d7e1f8db5cd5f759d919ff1 | [
"MIT"
] | null | null | null | # Metrikker
Vi har ulike metrikker for hvordan løsningen brukes av arbeidssøker og rent tekniske metrikker for hvordan løsningen har det i produksjon.
- [Dashboard i Amplitude](https://analytics.amplitude.com/nav/dashboard/jbs02pj) for bruk av løsningen
- [Dashboard i Grafana](https://grafana.nais.io/d/000000283/nais-app-dashbord?orgId=1&refresh=1m&var-interval=$__auto_interval_interval&var-datasource=prod-gcp&var-team=tbd&var-app=poa-arbeidssokerregistrering&var-namespace=paw&var-docker_image=c6938a0fe9fbc0cd6076ebb2ffa13e1069e6f4ce&var-ingress_url=All) teknisk
# Metrikker i Amplitude
Dersom du vil lage egne rapporter utover de som ligger på dashboardet er det satt målinger omkring følgende hendelser.
## Stoppsituasjoner
Situasjoner som gjør at arbeidssøkeren ikke får registrert seg.
Kan hentes ut ved å sjekke `arbeidssokerregistrering.stoppsituasjoner` og gruppere på `situasjon`.
På situasjonen `Arbeidssøkeren må reaktivere seg` kan du også gruppere på `brukergruppe` for å se hvilke grupper som møtte stoppsituasjonen
### Situasjoner
`Arbeidssøkeren må reaktivere seg`
Arbeidssøkeren har falt ut av NAV-systemet og det har gått kortere tid enn 28 dager siden det skjedde.
Her er det mulig å gruppere på `brukergruppe` for å se hvilken status arbeidssøkeren hadde før hen falt ut.
`Arbeidssøkeren får ikke reaktivert seg`
Arbeidssøkeren har satt igang reaktivering, men får en feilmelding tilbake.
Her er det mulig å grupper på `aarsak` for å se hvilken feil baksystemet gir
`Arbeidssøkeren mangler arbeidstillatelse eller er utvandret`
Denne kan skyldes at NAV ikke har oppdaterte opplysninger om arbeidssøkeren i vår systemer.
I de tilfellene må en veileder gjøre en manuell jobb for å oppdatere opplysningene.
`Arbeidssøkeren er allerede registrert`
Denne kommer opp for brukere som ikke får registrert seg på grunn av mismatch i baksystemene til NAV.
Det kan være at de er i gruppen IARBS eller annet.
`Arbeidssøkeren får en feilmelding`
Dette er en samlemelding over feilsituajoner hvor vi ikke kan gi en god forklaring på hva som har skjedd elelr hvordan den kan løses.
Typisk skjer dette ved uforutsette tekniske feil i baksystemene og hvor bruker bare får beskjed om å prøve igjen senere.
`Arbeidssøkeren får ikke registrert seg pga nedetid`
Denne vises dersom systemet er togglet av pga for eksempel planlagt vedlikehold av Arena.
## Brukeradferd
Målinger omkring brukeradferd.
Kan hentes ut ved å sjekke `arbeidssokerregistrering.aktiviteter` og `arbeidssokerregistrering.besvarelser`
- Hvor lang tid bruker man på å fylle ut skjemaet?
- Hvor mange treffer feilsituasjoner?
- Hvor mange får registrert seg vellykket?
- Hva svarer man på du ulike spørsmålene?
- Hvilken flyt havner brukeren på?
- Hva gjør de etter registrering?
### Aktiviteter
Kan hentes ut ved å sjekke `arbeidssokerregistrering.aktiviteter` og vil kunne grupperes på `aktivitet`.
Vil i noen tilfeller inneholde informasjon om `registreringstype`m `tidBruktForAaFullforeSkjema` eller `innsatsgruppe`
### Besvarelser
Kan hentes ut ved å sjekke `arbeidssokerregistrering.besvarelser` og grupperes på `sporsmalId`
## Eksperimenter
Egne målinger satt opp omkring eksperimenter vi kjører.
Kan hentes ut ved å sjekke `arbeidssokerregistrering.eksperimenter`.
Kan grupperes på `eksperiment` og vil i noen tilfeller også ha med info om `situasjon` og `brukergruppe`
| 44.12987 | 316 | 0.809594 | nob_Latn | 0.998416 |
80e42c37cda0cd1fc7040ff7531fd288d0faec93 | 263 | md | Markdown | README.md | lodegaard/Chromecast-remote | c2b2dd1a4e79b5176251cab188a2eb1e60be78e5 | [
"MIT"
] | null | null | null | README.md | lodegaard/Chromecast-remote | c2b2dd1a4e79b5176251cab188a2eb1e60be78e5 | [
"MIT"
] | null | null | null | README.md | lodegaard/Chromecast-remote | c2b2dd1a4e79b5176251cab188a2eb1e60be78e5 | [
"MIT"
] | null | null | null | [](https://github.com/lodegaard/Chromecast-remote/actions/workflows/cmake.yml)
# Chromecast-remote
Use a presenter remote as a controller for chromecast trough node-red
| 65.75 | 172 | 0.809886 | eng_Latn | 0.220841 |
80e48f4ed321507f660b13c49966234e01e741ee | 151 | md | Markdown | app/src/main/java/Chapter05/P106_FillingSettingArray/README.md | K3cs1/Java-Coding-Problems | 477632a9cebb2ca0954be9ac153db87a99b90e66 | [
"MIT"
] | 427 | 2019-10-08T11:17:47.000Z | 2022-03-30T10:26:57.000Z | app/src/main/java/Chapter05/P106_FillingSettingArray/README.md | K3cs1/Java-Coding-Problems | 477632a9cebb2ca0954be9ac153db87a99b90e66 | [
"MIT"
] | 7 | 2019-12-07T22:32:09.000Z | 2021-06-04T22:03:30.000Z | app/src/main/java/Chapter05/P106_FillingSettingArray/README.md | K3cs1/Java-Coding-Problems | 477632a9cebb2ca0954be9ac153db87a99b90e66 | [
"MIT"
] | 284 | 2019-09-27T13:31:55.000Z | 2022-03-31T14:18:51.000Z | # Filling and setting an array
Write several examples for fill up an array and set all elements based on a generator function to compute each element.
| 50.333333 | 119 | 0.807947 | eng_Latn | 0.999944 |
80e5e4d9ee2d1c245a775e01bb864986cc5cf520 | 27,812 | md | Markdown | aws/r/aws_kinesisanalyticsv2_application.md | chrisjaimon2012/tfwriter | 1ea629ed386bbe6a8f21617a430dae19ba536a98 | [
"MIT"
] | 78 | 2021-01-15T14:10:30.000Z | 2022-02-14T09:17:40.000Z | aws/r/aws_kinesisanalyticsv2_application.md | chrisjaimon2012/tfwriter | 1ea629ed386bbe6a8f21617a430dae19ba536a98 | [
"MIT"
] | 5 | 2021-04-09T15:21:28.000Z | 2022-01-28T19:02:05.000Z | aws/r/aws_kinesisanalyticsv2_application.md | chrisjaimon2012/tfwriter | 1ea629ed386bbe6a8f21617a430dae19ba536a98 | [
"MIT"
] | 30 | 2021-01-17T13:16:57.000Z | 2022-03-21T12:52:08.000Z | # aws_kinesisanalyticsv2_application
[back](../aws.md)
### Index
- [Example Usage](#example-usage)
- [Variables](#variables)
- [Resource](#resource)
- [Outputs](#outputs)
### Terraform
```terraform
terraform {
required_providers {
aws = ">= 3.35.0"
}
}
```
[top](#index)
### Example Usage
```terraform
module "aws_kinesisanalyticsv2_application" {
source = "./modules/aws/r/aws_kinesisanalyticsv2_application"
# description - (optional) is a type of string
description = null
# name - (required) is a type of string
name = null
# runtime_environment - (required) is a type of string
runtime_environment = null
# service_execution_role - (required) is a type of string
service_execution_role = null
# tags - (optional) is a type of map of string
tags = {}
application_configuration = [{
application_code_configuration = [{
code_content = [{
s3_content_location = [{
bucket_arn = null
file_key = null
object_version = null
}]
text_content = null
}]
code_content_type = null
}]
application_snapshot_configuration = [{
snapshots_enabled = null
}]
environment_properties = [{
property_group = [{
property_group_id = null
property_map = {}
}]
}]
flink_application_configuration = [{
checkpoint_configuration = [{
checkpoint_interval = null
checkpointing_enabled = null
configuration_type = null
min_pause_between_checkpoints = null
}]
monitoring_configuration = [{
configuration_type = null
log_level = null
metrics_level = null
}]
parallelism_configuration = [{
auto_scaling_enabled = null
configuration_type = null
parallelism = null
parallelism_per_kpu = null
}]
}]
sql_application_configuration = [{
input = [{
in_app_stream_names = []
input_id = null
input_parallelism = [{
count = null
}]
input_processing_configuration = [{
input_lambda_processor = [{
resource_arn = null
}]
}]
input_schema = [{
record_column = [{
mapping = null
name = null
sql_type = null
}]
record_encoding = null
record_format = [{
mapping_parameters = [{
csv_mapping_parameters = [{
record_column_delimiter = null
record_row_delimiter = null
}]
json_mapping_parameters = [{
record_row_path = null
}]
}]
record_format_type = null
}]
}]
input_starting_position_configuration = [{
input_starting_position = null
}]
kinesis_firehose_input = [{
resource_arn = null
}]
kinesis_streams_input = [{
resource_arn = null
}]
name_prefix = null
}]
output = [{
destination_schema = [{
record_format_type = null
}]
kinesis_firehose_output = [{
resource_arn = null
}]
kinesis_streams_output = [{
resource_arn = null
}]
lambda_output = [{
resource_arn = null
}]
name = null
output_id = null
}]
reference_data_source = [{
reference_id = null
reference_schema = [{
record_column = [{
mapping = null
name = null
sql_type = null
}]
record_encoding = null
record_format = [{
mapping_parameters = [{
csv_mapping_parameters = [{
record_column_delimiter = null
record_row_delimiter = null
}]
json_mapping_parameters = [{
record_row_path = null
}]
}]
record_format_type = null
}]
}]
s3_reference_data_source = [{
bucket_arn = null
file_key = null
}]
table_name = null
}]
}]
vpc_configuration = [{
security_group_ids = []
subnet_ids = []
vpc_configuration_id = null
vpc_id = null
}]
}]
cloudwatch_logging_options = [{
cloudwatch_logging_option_id = null
log_stream_arn = null
}]
}
```
[top](#index)
### Variables
```terraform
variable "description" {
description = "(optional)"
type = string
default = null
}
variable "name" {
description = "(required)"
type = string
}
variable "runtime_environment" {
description = "(required)"
type = string
}
variable "service_execution_role" {
description = "(required)"
type = string
}
variable "tags" {
description = "(optional)"
type = map(string)
default = null
}
variable "application_configuration" {
description = "nested block: NestingList, min items: 0, max items: 1"
type = set(object(
{
application_code_configuration = list(object(
{
code_content = list(object(
{
s3_content_location = list(object(
{
bucket_arn = string
file_key = string
object_version = string
}
))
text_content = string
}
))
code_content_type = string
}
))
application_snapshot_configuration = list(object(
{
snapshots_enabled = bool
}
))
environment_properties = list(object(
{
property_group = set(object(
{
property_group_id = string
property_map = map(string)
}
))
}
))
flink_application_configuration = list(object(
{
checkpoint_configuration = list(object(
{
checkpoint_interval = number
checkpointing_enabled = bool
configuration_type = string
min_pause_between_checkpoints = number
}
))
monitoring_configuration = list(object(
{
configuration_type = string
log_level = string
metrics_level = string
}
))
parallelism_configuration = list(object(
{
auto_scaling_enabled = bool
configuration_type = string
parallelism = number
parallelism_per_kpu = number
}
))
}
))
sql_application_configuration = list(object(
{
input = list(object(
{
in_app_stream_names = list(string)
input_id = string
input_parallelism = list(object(
{
count = number
}
))
input_processing_configuration = list(object(
{
input_lambda_processor = list(object(
{
resource_arn = string
}
))
}
))
input_schema = list(object(
{
record_column = list(object(
{
mapping = string
name = string
sql_type = string
}
))
record_encoding = string
record_format = list(object(
{
mapping_parameters = list(object(
{
csv_mapping_parameters = list(object(
{
record_column_delimiter = string
record_row_delimiter = string
}
))
json_mapping_parameters = list(object(
{
record_row_path = string
}
))
}
))
record_format_type = string
}
))
}
))
input_starting_position_configuration = list(object(
{
input_starting_position = string
}
))
kinesis_firehose_input = list(object(
{
resource_arn = string
}
))
kinesis_streams_input = list(object(
{
resource_arn = string
}
))
name_prefix = string
}
))
output = set(object(
{
destination_schema = list(object(
{
record_format_type = string
}
))
kinesis_firehose_output = list(object(
{
resource_arn = string
}
))
kinesis_streams_output = list(object(
{
resource_arn = string
}
))
lambda_output = list(object(
{
resource_arn = string
}
))
name = string
output_id = string
}
))
reference_data_source = list(object(
{
reference_id = string
reference_schema = list(object(
{
record_column = list(object(
{
mapping = string
name = string
sql_type = string
}
))
record_encoding = string
record_format = list(object(
{
mapping_parameters = list(object(
{
csv_mapping_parameters = list(object(
{
record_column_delimiter = string
record_row_delimiter = string
}
))
json_mapping_parameters = list(object(
{
record_row_path = string
}
))
}
))
record_format_type = string
}
))
}
))
s3_reference_data_source = list(object(
{
bucket_arn = string
file_key = string
}
))
table_name = string
}
))
}
))
vpc_configuration = list(object(
{
security_group_ids = set(string)
subnet_ids = set(string)
vpc_configuration_id = string
vpc_id = string
}
))
}
))
default = []
}
variable "cloudwatch_logging_options" {
description = "nested block: NestingList, min items: 0, max items: 1"
type = set(object(
{
cloudwatch_logging_option_id = string
log_stream_arn = string
}
))
default = []
}
```
[top](#index)
### Resource
```terraform
resource "aws_kinesisanalyticsv2_application" "this" {
# description - (optional) is a type of string
description = var.description
# name - (required) is a type of string
name = var.name
# runtime_environment - (required) is a type of string
runtime_environment = var.runtime_environment
# service_execution_role - (required) is a type of string
service_execution_role = var.service_execution_role
# tags - (optional) is a type of map of string
tags = var.tags
dynamic "application_configuration" {
for_each = var.application_configuration
content {
dynamic "application_code_configuration" {
for_each = application_configuration.value.application_code_configuration
content {
# code_content_type - (required) is a type of string
code_content_type = application_code_configuration.value["code_content_type"]
dynamic "code_content" {
for_each = application_code_configuration.value.code_content
content {
# text_content - (optional) is a type of string
text_content = code_content.value["text_content"]
dynamic "s3_content_location" {
for_each = code_content.value.s3_content_location
content {
# bucket_arn - (required) is a type of string
bucket_arn = s3_content_location.value["bucket_arn"]
# file_key - (required) is a type of string
file_key = s3_content_location.value["file_key"]
# object_version - (optional) is a type of string
object_version = s3_content_location.value["object_version"]
}
}
}
}
}
}
dynamic "application_snapshot_configuration" {
for_each = application_configuration.value.application_snapshot_configuration
content {
# snapshots_enabled - (required) is a type of bool
snapshots_enabled = application_snapshot_configuration.value["snapshots_enabled"]
}
}
dynamic "environment_properties" {
for_each = application_configuration.value.environment_properties
content {
dynamic "property_group" {
for_each = environment_properties.value.property_group
content {
# property_group_id - (required) is a type of string
property_group_id = property_group.value["property_group_id"]
# property_map - (required) is a type of map of string
property_map = property_group.value["property_map"]
}
}
}
}
dynamic "flink_application_configuration" {
for_each = application_configuration.value.flink_application_configuration
content {
dynamic "checkpoint_configuration" {
for_each = flink_application_configuration.value.checkpoint_configuration
content {
# checkpoint_interval - (optional) is a type of number
checkpoint_interval = checkpoint_configuration.value["checkpoint_interval"]
# checkpointing_enabled - (optional) is a type of bool
checkpointing_enabled = checkpoint_configuration.value["checkpointing_enabled"]
# configuration_type - (required) is a type of string
configuration_type = checkpoint_configuration.value["configuration_type"]
# min_pause_between_checkpoints - (optional) is a type of number
min_pause_between_checkpoints = checkpoint_configuration.value["min_pause_between_checkpoints"]
}
}
dynamic "monitoring_configuration" {
for_each = flink_application_configuration.value.monitoring_configuration
content {
# configuration_type - (required) is a type of string
configuration_type = monitoring_configuration.value["configuration_type"]
# log_level - (optional) is a type of string
log_level = monitoring_configuration.value["log_level"]
# metrics_level - (optional) is a type of string
metrics_level = monitoring_configuration.value["metrics_level"]
}
}
dynamic "parallelism_configuration" {
for_each = flink_application_configuration.value.parallelism_configuration
content {
# auto_scaling_enabled - (optional) is a type of bool
auto_scaling_enabled = parallelism_configuration.value["auto_scaling_enabled"]
# configuration_type - (required) is a type of string
configuration_type = parallelism_configuration.value["configuration_type"]
# parallelism - (optional) is a type of number
parallelism = parallelism_configuration.value["parallelism"]
# parallelism_per_kpu - (optional) is a type of number
parallelism_per_kpu = parallelism_configuration.value["parallelism_per_kpu"]
}
}
}
}
dynamic "sql_application_configuration" {
for_each = application_configuration.value.sql_application_configuration
content {
dynamic "input" {
for_each = sql_application_configuration.value.input
content {
# name_prefix - (required) is a type of string
name_prefix = input.value["name_prefix"]
dynamic "input_parallelism" {
for_each = input.value.input_parallelism
content {
# count - (optional) is a type of number
count = input_parallelism.value["count"]
}
}
dynamic "input_processing_configuration" {
for_each = input.value.input_processing_configuration
content {
dynamic "input_lambda_processor" {
for_each = input_processing_configuration.value.input_lambda_processor
content {
# resource_arn - (required) is a type of string
resource_arn = input_lambda_processor.value["resource_arn"]
}
}
}
}
dynamic "input_schema" {
for_each = input.value.input_schema
content {
# record_encoding - (optional) is a type of string
record_encoding = input_schema.value["record_encoding"]
dynamic "record_column" {
for_each = input_schema.value.record_column
content {
# mapping - (optional) is a type of string
mapping = record_column.value["mapping"]
# name - (required) is a type of string
name = record_column.value["name"]
# sql_type - (required) is a type of string
sql_type = record_column.value["sql_type"]
}
}
dynamic "record_format" {
for_each = input_schema.value.record_format
content {
# record_format_type - (required) is a type of string
record_format_type = record_format.value["record_format_type"]
dynamic "mapping_parameters" {
for_each = record_format.value.mapping_parameters
content {
dynamic "csv_mapping_parameters" {
for_each = mapping_parameters.value.csv_mapping_parameters
content {
# record_column_delimiter - (required) is a type of string
record_column_delimiter = csv_mapping_parameters.value["record_column_delimiter"]
# record_row_delimiter - (required) is a type of string
record_row_delimiter = csv_mapping_parameters.value["record_row_delimiter"]
}
}
dynamic "json_mapping_parameters" {
for_each = mapping_parameters.value.json_mapping_parameters
content {
# record_row_path - (required) is a type of string
record_row_path = json_mapping_parameters.value["record_row_path"]
}
}
}
}
}
}
}
}
dynamic "kinesis_firehose_input" {
for_each = input.value.kinesis_firehose_input
content {
# resource_arn - (required) is a type of string
resource_arn = kinesis_firehose_input.value["resource_arn"]
}
}
dynamic "kinesis_streams_input" {
for_each = input.value.kinesis_streams_input
content {
# resource_arn - (required) is a type of string
resource_arn = kinesis_streams_input.value["resource_arn"]
}
}
}
}
dynamic "output" {
for_each = sql_application_configuration.value.output
content {
# name - (required) is a type of string
name = output.value["name"]
dynamic "destination_schema" {
for_each = output.value.destination_schema
content {
# record_format_type - (required) is a type of string
record_format_type = destination_schema.value["record_format_type"]
}
}
dynamic "kinesis_firehose_output" {
for_each = output.value.kinesis_firehose_output
content {
# resource_arn - (required) is a type of string
resource_arn = kinesis_firehose_output.value["resource_arn"]
}
}
dynamic "kinesis_streams_output" {
for_each = output.value.kinesis_streams_output
content {
# resource_arn - (required) is a type of string
resource_arn = kinesis_streams_output.value["resource_arn"]
}
}
dynamic "lambda_output" {
for_each = output.value.lambda_output
content {
# resource_arn - (required) is a type of string
resource_arn = lambda_output.value["resource_arn"]
}
}
}
}
dynamic "reference_data_source" {
for_each = sql_application_configuration.value.reference_data_source
content {
# table_name - (required) is a type of string
table_name = reference_data_source.value["table_name"]
dynamic "reference_schema" {
for_each = reference_data_source.value.reference_schema
content {
# record_encoding - (optional) is a type of string
record_encoding = reference_schema.value["record_encoding"]
dynamic "record_column" {
for_each = reference_schema.value.record_column
content {
# mapping - (optional) is a type of string
mapping = record_column.value["mapping"]
# name - (required) is a type of string
name = record_column.value["name"]
# sql_type - (required) is a type of string
sql_type = record_column.value["sql_type"]
}
}
dynamic "record_format" {
for_each = reference_schema.value.record_format
content {
# record_format_type - (required) is a type of string
record_format_type = record_format.value["record_format_type"]
dynamic "mapping_parameters" {
for_each = record_format.value.mapping_parameters
content {
dynamic "csv_mapping_parameters" {
for_each = mapping_parameters.value.csv_mapping_parameters
content {
# record_column_delimiter - (required) is a type of string
record_column_delimiter = csv_mapping_parameters.value["record_column_delimiter"]
# record_row_delimiter - (required) is a type of string
record_row_delimiter = csv_mapping_parameters.value["record_row_delimiter"]
}
}
dynamic "json_mapping_parameters" {
for_each = mapping_parameters.value.json_mapping_parameters
content {
# record_row_path - (required) is a type of string
record_row_path = json_mapping_parameters.value["record_row_path"]
}
}
}
}
}
}
}
}
dynamic "s3_reference_data_source" {
for_each = reference_data_source.value.s3_reference_data_source
content {
# bucket_arn - (required) is a type of string
bucket_arn = s3_reference_data_source.value["bucket_arn"]
# file_key - (required) is a type of string
file_key = s3_reference_data_source.value["file_key"]
}
}
}
}
}
}
dynamic "vpc_configuration" {
for_each = application_configuration.value.vpc_configuration
content {
# security_group_ids - (required) is a type of set of string
security_group_ids = vpc_configuration.value["security_group_ids"]
# subnet_ids - (required) is a type of set of string
subnet_ids = vpc_configuration.value["subnet_ids"]
}
}
}
}
dynamic "cloudwatch_logging_options" {
for_each = var.cloudwatch_logging_options
content {
# log_stream_arn - (required) is a type of string
log_stream_arn = cloudwatch_logging_options.value["log_stream_arn"]
}
}
}
```
[top](#index)
### Outputs
```terraform
output "arn" {
description = "returns a string"
value = aws_kinesisanalyticsv2_application.this.arn
}
output "create_timestamp" {
description = "returns a string"
value = aws_kinesisanalyticsv2_application.this.create_timestamp
}
output "id" {
description = "returns a string"
value = aws_kinesisanalyticsv2_application.this.id
}
output "last_update_timestamp" {
description = "returns a string"
value = aws_kinesisanalyticsv2_application.this.last_update_timestamp
}
output "status" {
description = "returns a string"
value = aws_kinesisanalyticsv2_application.this.status
}
output "version_id" {
description = "returns a number"
value = aws_kinesisanalyticsv2_application.this.version_id
}
output "this" {
value = aws_kinesisanalyticsv2_application.this
}
```
[top](#index) | 32.414918 | 111 | 0.507047 | eng_Latn | 0.791969 |
80e760322f263f3f9af5a4045b57f5ce619cac26 | 620 | md | Markdown | EvidenciaLabs/evidencias/lab01/Lab1evidencia.md | valnicky/AZ-204-DevelopingSolutionsforMicrosoftAzure | 69f22e1b28c67e247fa1bad80a325c929bce31d2 | [
"MIT"
] | null | null | null | EvidenciaLabs/evidencias/lab01/Lab1evidencia.md | valnicky/AZ-204-DevelopingSolutionsforMicrosoftAzure | 69f22e1b28c67e247fa1bad80a325c929bce31d2 | [
"MIT"
] | null | null | null | EvidenciaLabs/evidencias/lab01/Lab1evidencia.md | valnicky/AZ-204-DevelopingSolutionsforMicrosoftAzure | 69f22e1b28c67e247fa1bad80a325c929bce31d2 | [
"MIT"
] | null | null | null | 
| 13.478261 | 25 | 0.667742 | yue_Hant | 0.050732 |
80e7ed7d927ae3253d95c5ef4db939542a69f4d9 | 826 | md | Markdown | Demos/CognitoWebIdentityFederation/STAGE5 - Cleanup.md | FabioORamos/pingnoran | 7ea7e65e6f4f22aa9c67755a7032e20213fb2c18 | [
"MIT"
] | null | null | null | Demos/CognitoWebIdentityFederation/STAGE5 - Cleanup.md | FabioORamos/pingnoran | 7ea7e65e6f4f22aa9c67755a7032e20213fb2c18 | [
"MIT"
] | null | null | null | Demos/CognitoWebIdentityFederation/STAGE5 - Cleanup.md | FabioORamos/pingnoran | 7ea7e65e6f4f22aa9c67755a7032e20213fb2c18 | [
"MIT"
] | null | null | null | # Advanced Demo - Web Identity Federation
# Delete the Google API Project & Credentials
https://console.developers.google.com/cloud-resource-manager
Select `PetIDF` and click `DELETE`
Type in the ID of the project, which might have a slightly different name (shown above the text box) click `Shut Down`
# Delete the Cognito ID Pool
Move to the cognito console
Click `Manage Identity Pools`
Click on `PetIDFIDPool`
Click `Edit Identity Pool`
Locate and expand `Delete identity pool`
Click `Delete Identity Pool`
Click `Delete Pool`
# Delete the IAM Roles
Move to the IAM Console
Select `Roles`
Select both `Cognito_PetIDF*` roles
Click `Delete Role`
Click `Yes Delete`
# Delete the CloudFormation Stack
Move to the cloud formation console
Select `WEBIDF`, click `Delete` then `Delete Stack` | 30.592593 | 120 | 0.749395 | eng_Latn | 0.462797 |
80e83326268edcb61a2d1323ccf303e839312b17 | 652 | md | Markdown | _posts/2022-04-23-Operator.md | dctsoft2022/dctsoft2022.github.io | 9a1584f79745f77d837c473bd3e58b8e262ac013 | [
"MIT"
] | null | null | null | _posts/2022-04-23-Operator.md | dctsoft2022/dctsoft2022.github.io | 9a1584f79745f77d837c473bd3e58b8e262ac013 | [
"MIT"
] | null | null | null | _posts/2022-04-23-Operator.md | dctsoft2022/dctsoft2022.github.io | 9a1584f79745f77d837c473bd3e58b8e262ac013 | [
"MIT"
] | null | null | null | ---
layout: single
title: "Operator"
---
## Operator (Java)
아래 코드를 실행시켰을때 출려물을 맞춰보세요.
```java
class OperatorTest
{
public static void main(String[] args)
{
int num1 = 10, num2 = 10, num3 =10;
int result1 = 0, result2 = 0;
result1 = ++num1;
result2 = num2++;
System.out.println("num1 = " + num1 + " num2 = " + num2);
System.out.println("result1 = " + result1 + " result2 = " + result2);
System.out.println("num3++ " + num3++);
System.out.println("num3 " + num3);
}
}
```
num을 실행시키는 것과 result를 실행시키는 것의 차이를 이해해야하고,
num++ 와 ++num의 차이를 이해해야한다.
> 정답
> num1 = 11 num2 = 11
> result1 = 11 result2 = 10
> num3++ 10
> num3 11
| 18.111111 | 71 | 0.596626 | kor_Hang | 0.905561 |
80ea2905fb2ece85788c0541b14eeaea7cd3b0ee | 499,300 | md | Markdown | content/publication/moral-judgment-as-categorization/index.md | cillianmiltown/website_march_19 | 29ad840014207a92daa750e253ca5eb4005251d7 | [
"MIT"
] | null | null | null | content/publication/moral-judgment-as-categorization/index.md | cillianmiltown/website_march_19 | 29ad840014207a92daa750e253ca5eb4005251d7 | [
"MIT"
] | null | null | null | content/publication/moral-judgment-as-categorization/index.md | cillianmiltown/website_march_19 | 29ad840014207a92daa750e253ca5eb4005251d7 | [
"MIT"
] | null | null | null | +++
title = "Moral Judgment as Categorization (MJAC)"
date = 2021-07-15T00:00:00
# Authors. Comma separated list, e.g. `["Bob Smith", "David Jones"]`.
authors = ["Cillian McHugh", "Marek McGann", "Eric R. Igou", "Elaine L. Kinsella"]
# Publication type.
# Legend:
# 0 = Uncategorized
# 1 = Conference proceedings
# 2 = Journal
# 3 = Work in progress
# 4 = Technical report
# 5 = Book
# 6 = Book chapter
publication_types = ["2"]
shorttitle = "MJAC"
url_source = "https://journals.sagepub.com/doi/full/10.1177/1745691621990636"
#url = "https://journals.sagepub.com/doi/full/10.1177/1745691621990636"
doi = "10.1177%2F1745691621990636"
abstract = "Observed variability and complexity of judgments of 'right' and 'wrong' cannot be readily accounted for within extant approaches to understanding moral judgment. In response to this challenge, we present a novel perspective on categorization in moral judgment. Moral judgment as categorization (MJAC) incorporates principles of category formation research while addressing key challenges of existing approaches to moral judgment. People develop skills in making context-relevant categorizations. They learn that various objects (events, behaviors, people, etc.) can be categorized as morally 'right' or 'wrong'. Repetition and rehearsal results in reliable, habitualized categorizations. According to this skill formation account of moral categorization, the learning and the habitualization of the forming of moral categories, occurs within goal-directed activity that is sensitive to various contextual influences. By allowing for the complexity of moral judgments, MJAC offers greater explanatory power than existing approaches while also providing opportunities for a diverse range of new research questions."
publication = "Perspectives on Psychological Science"
url_pdf = "https://journals.sagepub.com/doi/pdf/10.1177/1745691621990636"
# Featured image
# Place your image in the `static/img/` folder and reference its filename below, e.g. `image = "example.jpg"`.
#[header]
#image = "20181101_080705.jpg"
#caption = ""
+++
<style>
/* toc style */
.toc {
position: fixed;
top: 55%;
left: 3%;
width: 20%;
transform: translateY(-50%);
/*background-color: #f6f6f6;*/
/*border: solid 1px #c9c9c9;*/
border-radius: 5px;
}
.nav ul li a:hover {
text-decoration: underline;
font-weight: bold;
color: #B10DC9;
}
.toc-nav ul {
/* overflow: scroll; */
/* width: 80%; */
white-space: nowrap;
line-height: 1rem;
}
/* ignore h1 header */
.toc-nav ul ul ul {
margin-left: 2rem;
}
.toc-nav .nav-link {
text-overflow: ellipsis;
/* overflow: hidden; */
color: #333;
}
/* Media Queries */
@media (max-width: 1080px) {
main {
max-width: 100%;
}
.toc {
display: none;
}
}
</style>
<aside>
<nav id="TableOfContents">
<ul>
<li><a href="#moral-judgment-as-categorization-mjac">Moral Judgment as Categorization (MJAC)</a>
<ul></ul></li>
<li><a href="#moral-judgment-and-categorization-in-the-literature">Moral Judgment and Categorization in the Literature</a>
<ul></ul></li>
<li><a href="#introducing-mjac">Introducing MJAC</a><ul>
<li><a href="#type-token-interpretation">Type-Token Interpretation</a>
<li><a href="#context-sensitivity">Context Sensitivity</a></li>
<li><a href="#habitualization">Habitualization</a></li>
</ul></li>
<li><a href="#applying-mjac">Applying MJAC</a><ul>
<li><a href="#moral-dumbfounding">Moral Dumbfounding</a></li>
<li><a href="#categorizing-people-versus-categorizing-actions">Categorizing people versus categorizing actions</a></li>
<li><a href="#moral-categorization-involving-known-others">Moral Categorization Involving Known Others</a></li>
<li><a href="#moral-categorization-involving-unknown-others">Moral Categorization Involving Unknown Others</a></li>
</ul></li>
<li><a href="#examining-the-explanatory-power-of-mjac">Examining the Explanatory Power of MJAC</a><ul>
<li><a href="#beyond-unidimensional-conceptions-of-morality">Beyond Unidimensional Conceptions of Morality</a></li>
<li><a href="#moral-phenomena-with-domain-general-categorization-explanations">Moral Phenomena with Domain General (Categorization) Explanations</a></li>
<li><a href="#summarizing-the-differences-between-mjac-and-existing-approaches">Summarizing the Differences Between MJAC and Existing Approaches</a></li>
</ul></li>
<li><a href="#challenges-limitations-and-responses">Challenges, Limitations, and Responses</a><ul>
<li><a href="#effects-not-directly-predicted-by-mjac">Effects not Directly Predicted by MJAC</a></li>
</ul></li>
<li><a href="#novelties-and-conclusion">Novelties and Conclusion</a><ul>
</ul></li>
<li><a href="#conclusion">Conclusion</a>
<ul></ul></li>
</ul>
</nav>
</aside>
<div id="moral-judgment-as-categorization-mjac" class="section level1">
<iframe width="560" height="315" src="https://www.youtube.com/embed/VZahP87TCqE" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
<br>
(presentation to IMP seminar series 31st May 2021)
<h1>Moral Judgment as Categorization (MJAC)</h1>
<blockquote>
<p>It’s terribly simple. The good-guys are stalwart and true. The bad-guys are easily distinguished by their pointy horns or black hats and we always defeat them and save the day. Nobody ever dies…and everybody lives happily ever after. <span class="citation">(Whedon <a href="#ref-whedon_lie_1997" role="doc-biblioref">1997</a>, 41:55)</span></p>
</blockquote>
<p>In the above quotation from the TV show Buffy the Vampire Slayer, Buffy is being comforted by her mentor Giles. He paints a picture of morality where good and evil are easily distinguishable. He is, of course, lying. Even in this fantastical world of monsters and demons, there is no consistent, clear way to tell right from wrong.</p>
<p>Morality is a morass of complexity, and this complexity poses a considerable challenge to our understanding of how people make moral judgments. Drawing on moral philosophy, psychologists have adopted labels such as “deontology”, “utilitarianism”, as well as more niche concepts such as “virtue”, and “care” to help make sense of people’s varied judgments. Many of the questions about variability or apparent inconsistency in judgments have been understood in terms of people shifting in their implicit (or explicit) moral theories from deontological to utilitarian principles or vice-versa. However, attempts to reduce morality to pointy horns or black hats are ultimately doomed to fail, and despite important insights, no theory of moral judgment can yet fully account for the complexity of moral judgment.</p>
<p>In response to these challenges, we present “Moral Judgment as Categorization” (MJAC), which has three premises:</p>
<ul>
<li>The making of a moral judgment is a process of categorizing something as <em>MORALLY RIGHT</em>, or <em>MORALLY WRONG</em>, (or indeed <em>NOT MORALLY RELEVANT</em>).</li>
<li>The process of categorization involved in the making of a moral judgment is a domain general one (not unique or specific to the moral domain).</li>
<li>Moral categorization occurs as part of on-going goal-directed behavior and, as such, is highly dynamic and sensitive to a range of contextual influences.</li>
</ul>
<p>We argue that contemporary dynamic approaches to concept and category formation <span class="citation">(e.g., Barsalou <a href="#ref-barsalou_situated_2003" role="doc-biblioref">2003</a>, <a href="#ref-barsalou_cognitively_2017" role="doc-biblioref">2017</a>; Barsalou and Wiemer-Hastings <a href="#ref-barsalou_situating_2005" role="doc-biblioref">2005</a>; see also Sloman, Lombrozo, and Malt <a href="#ref-sloman_domain_2008" role="doc-biblioref">2008</a>; Barrett, Wilson-Mendenhall, and Barsalou <a href="#ref-barrett_psychological_2014" role="doc-biblioref">2014</a>)</span> provide the best framework for making sense of the complexity of moral judgment. MJAC encompasses the same phenomena addressed by extant theories of moral judgment (and phenomena not directly addressed by these theories) but does so in a more parsimonious way. By assuming dynamism and context-sensitivity from the outset, MJAC is not subject to ad hoc additions or refinements to cope with variability in moral judgments or to account for less frequently discussed phenomena of moral judgment.</p>
<p>In what follows, we first discuss previous attempts to align moral psychology with the literature on categorization. Second, we present our model, MJAC, in more detail. Third, we assess the strength of evidence for MJAC by contrasting its assumptions, explanations, and predictions with existing moral judgment theories. Fourth, we address specific limitations of MJAC. Finally, we conclude by offering a brief summary of the key advantages and novel research avenues offered by MJAC.</p>
</div>
<div id="moral-judgment-and-categorization-in-the-literature" class="section level1">
<h1>Moral Judgment and Categorization in the Literature</h1>
<p>We propose that research on categorization provides the best understanding of the complexities of moral judgment. Similar arguments have previously been proposed by <span class="citation">Stich (<a href="#ref-stich_moral_1993" role="doc-biblioref">1993</a>)</span>, <span class="citation">Harman, Mason, and Sinnott-Armstrong (<a href="#ref-harman_moral_2010" role="doc-biblioref">2010</a>)</span>, and <span class="citation">Prinz (<a href="#ref-prinz_passionate_2005" role="doc-biblioref">2005</a>)</span>; however, these approaches were limited in their ability to account for the full dynamism and context-sensitivity of categorization or moral judgments.</p>
<p><span class="citation">Stich (<a href="#ref-stich_moral_1993" role="doc-biblioref">1993</a>)</span> highlights common examples of moral ambiguity to illustrate that the concepts of right and wrong similarly cannot be defined by a set of necessary and sufficient conditions, thus rejecting the classical view of concepts <span class="citation">(Stich <a href="#ref-stich_moral_1993" role="doc-biblioref">1993</a>)</span>. However, it is not clear which alternative approach could be adopted in its place. In acknowledging limitations in existing approaches to categorization, Stich argued that developments in both categorization research and morality research should be considered in parallel.</p>
<p><span class="citation">Harman, Mason, and Sinnott-Armstrong (<a href="#ref-harman_moral_2010" role="doc-biblioref">2010</a>)</span> also reject the classical view of concepts, proposing that moral judgments can be understood in terms of exemplar models of categorization: “stored representations of instances” of a concept <span class="citation">(Harman, Mason, and Sinnott-Armstrong <a href="#ref-harman_moral_2010" role="doc-biblioref">2010</a>, 234)</span>. However, categorizations can be highly variable, sensitive to contextual influences (including sensory-motor, proprioceptive, introspective, and situational influences), and occur as part of goal-directed activity – posing a challenge to exemplar models that assume that categorization is modular, stable, and implicitly taxonomic in organization <span class="citation">(for review see Barsalou <a href="#ref-barsalou_situated_2003" role="doc-biblioref">2003</a>)</span>.</p>
<p><span class="citation">Prinz (<a href="#ref-prinz_passionate_2005" role="doc-biblioref">2005</a>)</span> describes the development of dynamic concepts and categories, and extends this to the moral domain, representing an important development in the literature. This approach, however, gives more weight to emotions as the source of moral judgments [e.g., “Emotions, I will suggest, are perceptions of our bodily states. To recognize the moral value of an event is, thus, to perceive the perturbation that it causes”, <span class="citation">Prinz (<a href="#ref-prinz_passionate_2005" role="doc-biblioref">2005</a>)</span>, p. 99) while the role of the cognitive processes remains unclear. This is particularly challenging in view of recent work advocating a more measured view of the role of emotion in the making of moral judgment <span class="citation">(Huebner, Dwyer, and Hauser <a href="#ref-huebner_role_2009" role="doc-biblioref">2009</a>; Landy and Goodwin <a href="#ref-landy_does_2015" role="doc-biblioref">2015</a>; May <a href="#ref-may_does_2014" role="doc-biblioref">2014</a>)</span>.</p>
<p>Finally, research on categorization processes has also had an indirect influence on theorizing about moral judgment. In presenting and defending the theory of dyadic morality <span class="citation">(TDM, Schein and Gray <a href="#ref-schein_theory_2018" role="doc-biblioref">2018</a>)</span>, the moral domain is described with reference to the broader literature on concepts and categories <span class="citation">(e.g., Gray, Waytz, and Young <a href="#ref-gray_moral_2012" role="doc-biblioref">2012</a>, 206; Gray, Young, and Waytz <a href="#ref-gray_mind_2012" role="doc-biblioref">2012</a>, 42; Schein and Gray <a href="#ref-schein_theory_2018" role="doc-biblioref">2018</a>, 42)</span>. However, TDM does not explicitly align with any specific categorization approach. Furthermore, TDM adopts an essentialist position (see below) that is inconsistent with modern understandings of categorization <span class="citation">(Barsalou <a href="#ref-barsalou_situated_2003" role="doc-biblioref">2003</a>; Harman, Mason, and Sinnott-Armstrong <a href="#ref-harman_moral_2010" role="doc-biblioref">2010</a>; McCloskey and Glucksberg <a href="#ref-mccloskey_natural_1978" role="doc-biblioref">1978</a>; Mervis and Rosch <a href="#ref-mervis_categorization_1981" role="doc-biblioref">1981</a>; Oden <a href="#ref-oden_fuzziness_1977" role="doc-biblioref">1977</a>; Rosch <a href="#ref-rosch_cognitive_1975" role="doc-biblioref">1975</a>; Rosch and Mervis <a href="#ref-rosch_family_1975" role="doc-biblioref">1975</a>; Stich <a href="#ref-stich_moral_1993" role="doc-biblioref">1993</a>)</span>, and this is problematic for explaining key phenomena in the moral domain.</p>
<p>Essentialism in categorization has been described a bias to perceive categories as natural kinds, or as having an underlying causal structure or “essence” <span class="citation">(Gelman <a href="#ref-gelman_essential_2003" role="doc-biblioref">2003</a>, 7)</span>. For the purposes of the current discussion we highlight two types of essentialism. First, we note essentialism in the general population as part of every-day (moral) categorization <span class="citation">(see Heiphetz <a href="#ref-heiphetz_development_2020" role="doc-biblioref">2020</a> for moe detailed discussion)</span>. Second, essentialism on the part of moral psychologists, whereby authors attempt to identify or define a moral “essence”, that distinguishes a moral domain as distinct from other domains, complete with moral specific psychological processes. We view this as a manifestation of the essentialism error in psychology <span class="citation">(see Mesquita, Barrett, and Smith <a href="#ref-mesquita_mind_2010" role="doc-biblioref">2010</a>)</span>. Indeed, the limitations of attempting to identify a moral “essence” (or necessary and sufficient conditions for something to be moral) have been well documented <span class="citation">(Bucciarelli, Khemlani, and Johnson-Laird <a href="#ref-bucciarelli_psychology_2008" role="doc-biblioref">2008</a>; Knobe <a href="#ref-knobe_there_2018" role="doc-biblioref">2018</a>; Sinnott-Armstrong <a href="#ref-sinnott-armstrong_does_2012" role="doc-biblioref">2012</a>; Sinnott-Armstrong and Wheatley <a href="#ref-sinnott-armstrong_are_2014" role="doc-biblioref">2014</a>; Stich <a href="#ref-stich_moral_1993" role="doc-biblioref">1993</a>, <a href="#ref-stich_moral_2018" role="doc-biblioref">2018</a>)</span>. MJAC does not attempt to define a moral “essence”. Instead, we assume that moral categorization involves domain general processes.</p>
</div>
<div id="introducing-mjac" class="section level1">
<h1>Introducing MJAC</h1>
<p>The premise of MJAC is that moral judgment is the result of domain general skill<a href="#fn1" class="footnote-ref" id="fnref1"><sup>1</sup></a> acquisition in making relevant categorizations in particular instances <span class="citation">(Barsalou <a href="#ref-barsalou_situated_2003" role="doc-biblioref">2003</a>, <a href="#ref-barsalou_cognitively_2017" role="doc-biblioref">2017</a>)</span>. Consider the formation of the ad-hoc goal-derived category <em>THINGS TO PACK INTO A SUITCASE</em> <span class="citation">Barsalou (<a href="#ref-barsalou_deriving_1991" role="doc-biblioref">1991</a>)</span>. Items that fall into this category (toothbrush, spare clothes, etc.) are not generally categorized as such on a day-to-day basis. The category emerges as required; that is, when a person needs to pack things into a suitcase. A person who travels frequently will be able to form the category <em>THINGS TO PACK INTO A SUITCASE</em> more readily due to repetition and the emerging skill. <span class="citation">Barsalou (<a href="#ref-barsalou_situated_2003" role="doc-biblioref">2003</a>)</span> argues that categorization more generally occurs through the same process. We propose that this basic process also holds for moral categories, that is, when people encounter a behavior in certain circumstances, they may learn that it is morally wrong, and this behavior becomes associated with the category <em>MORALLY WRONG</em>. Each subsequent time this behavior is encountered in a context where its moral value is relevant or it is identified as a member of the category <em>MORALLY WRONG</em> (either explicitly or implicitly), the person’s skill in deploying this category is strengthened. This same process holds for <em>MORALLY RIGHT</em>. With the increasing frequency of such categorizations, they become increasingly habitual and automatic <span class="citation">(see Barsalou <a href="#ref-barsalou_situated_2003" role="doc-biblioref">2003</a>)</span>.</p>
<p>A key strength of the approach adopted here is the ability to account for the dynamism and context-dependency (including sensory-motor, proprioceptive, and situational influences) that poses a challenge to other approaches <span class="citation">(Barsalou <a href="#ref-barsalou_situated_2003" role="doc-biblioref">2003</a>)</span>. Our interactions with (or exposure to) category members occur as part of goal-directed activity (e.g., general social interactions, gossip, political discussion, setting a ‘good’ example/attempting to appear likable or virtuous, engaging with fiction, jury duty, etc.). This allows for the encountering of an infinite number of categories (e.g., ad-hoc goal-derived categories) and category members. Here we are primarily concerned with the super-ordinate categories of <em>MORALLY RIGHT</em> and <em>MORALLY WRONG</em>. These categories display considerable variability. For example, consider the range of emotions associated with the behaviors categorized as <em>MORALLY WRONG</em>, including anything from murder to stealing office supplies. People may be shocked or angered by murder but might barely show mild contempt in response to stealing paper-clips. Similar variability is also observed for the same member of the category depending on the context. Consider (a) lying about the actions of a third party, to either cover for them or provide them with an opportunity to come forward themselves; (b) cold-blooded and calculated murder versus killing in a passionate rage, (c) a “massacre of innocent civilians” versus “collateral damage”.</p>
<p>Despite the variability and context-dependency, people’s categorizations show sufficient consistency to give the impression of relative stability. Bananas and apples are reliably categorized as fruit, just as murder and harm are widely categorized as wrong. Barsalou <span class="citation">(<a href="#ref-barsalou_situated_2003" role="doc-biblioref">2003</a>, <a href="#ref-barsalou_cognitively_2017" role="doc-biblioref">2017</a>)</span> provides a framework for the emergence of this relative stability, while still accounting for the observed dynamism and context sensitivity.</p>
<div id="type-token-interpretation" class="section level2">
<h2>Type-Token Interpretation</h2>
<p>Barsalou <span class="citation">(<a href="#ref-barsalou_situated_2003" role="doc-biblioref">2003</a>; <a href="#ref-barsalou_perceptual_1999" role="doc-biblioref">1999</a>)</span> proposes that the learning and maintaining of categorizations occurs through the process of type-token interpretation, defined as the binding of specific tokens (category members) to general types (category). For the category <em>THINGS TO PACK INTO A SUITCASE</em> <span class="citation">(Barsalou <a href="#ref-barsalou_situated_2003" role="doc-biblioref">2003</a>; Barsalou, Solomon, and Wu <a href="#ref-barsalou_perceptual_1999" role="doc-biblioref">1999</a>)</span>, this entails identifying a given item (token) as something that you pack or do not pack into a suitcase (type). Crucially, this process can be implicit, simply involving treating an item as a member or not a member of a particular category within an appropriate context for action, in this case, packing it or not packing it. Skill in forming the categories emerges from repetition and rehearsal of the type-token interpretation; people become skilled at deploying categories that they encounter frequently.</p>
</div>
<div id="context-sensitivity" class="section level2">
<h2>Context Sensitivity</h2>
<p>Type-token interpretation occurs every time a given token is encountered such that every categorization of a given token (object/item/event) is subject to contextual influences of the current situation. This results in dynamic and complex categories, without necessary and sufficient conditions, and without even stable best exemplars or prototypes. The properties of an object relevant to that particular context become salient, and the categorization process is accented by the details of the particular circumstances in which the actions are being taken. Stable or recurring properties (both object and contextual) can be learned, and their identification or recognition become a part of the subsequent engagement in the relevant goal-directed activity and the enactment of different relevant type-token interpretations of objects. This is dependent on the experience and learning history of the individual and not inherent in the categories themselves, however, which is what gives rise to the complex, dynamic aspects of concepts central to Barsalou’s approach.</p>
<p>Consider a study by <span class="citation">Barsalou (<a href="#ref-barsalou_contextindependent_1982" role="doc-biblioref">1982</a>)</span>. Participants were presented with a series of sentences involving particular items. For example: “The basketball was used when the boat sank”; or “The basketball was well worn from much use” <span class="citation">(Barsalou <a href="#ref-barsalou_contextindependent_1982" role="doc-biblioref">1982</a>, see also <a href="#ref-barsalou_situated_2003" role="doc-biblioref">2003</a>)</span>. Following each sentence, participants were asked to verify whether particular properties were true for the item; for example whether or not “floats” is true for “basketball” following reading either of the above sentences. The fact that basketballs float is relevant to the first sentence, and thus this property is inferred from reading this sentence. In the second sentence, this property (while still true for basketball) is irrelevant and does not become salient by reading the sentence. Thus, while what is true for basketball does not change depending in the situation, the properties that are inferred in a given instance do. This is evident in that participants were faster at verifying “floats” as true for basketball following reading the first sentence than the second <span class="citation">(Barsalou <a href="#ref-barsalou_contextindependent_1982" role="doc-biblioref">1982</a>, <a href="#ref-barsalou_situated_2003" role="doc-biblioref">2003</a>)</span>. Other studies have yielded similar results, demonstrating that different sentences cause different properties to become salient depending on these properties’ relevance to the given sentence <span class="citation">(Greenspan <a href="#ref-greenspan_semantic_1986" role="doc-biblioref">1986</a>; Tabossi <a href="#ref-tabossi_effects_1988" role="doc-biblioref">1988</a>; Yeh and Barsalou <a href="#ref-yeh_situated_2006" role="doc-biblioref">2006</a>)</span>. The contextually relevant inferences made when we encounter category members are not limited to object properties but can also include situational and introspective inferences <span class="citation">(e.g., Barsalou and Wiemer-Hastings <a href="#ref-barsalou_situating_2005" role="doc-biblioref">2005</a>)</span>.</p>
</div>
<div id="habitualization" class="section level2">
<h2>Habitualization</h2>
<p>Highly skilled categorizations become habitualized (automatic/intuitive) to the point where these categorizations provide an illusion of “stable categories”. Typically, these “stable categories” mirror real-world categories or classes and social norms that are frequently and reliably encountered in day-to-day life. This reflects the use of these categories in (a) interacting effectively with the world and (b) communicating with others. Natural kinds and social norms would constitute prototypical classes of such frequently encountered and reliably implemented categories <span class="citation">(e.g., Keil, Rozenblit, and Mills <a href="#ref-keil_what_2004" role="doc-biblioref">2004</a>)</span>. In some cases, categories that can be referenced to natural kinds may take on the causal rules that distinguish natural kinds. For example, fruit is distinct from vegetables in that the agreed scientific classification of fruit (in our culture) is as containing the seeds. This causal rule is not necessarily operationalized in everyday interactions with fruit and vegetables; however, in certain situations, it may be referenced to aid in the classification of ambiguous items.</p>
<p>More abstract categories are more difficult to define because there may not be a set of causal rules governing membership to draw on. There is a large body of literature documenting the search for causal rules or identifying characteristics of particular emotion categories, for instance, but no approach has fully answered this question <span class="citation">(Griffiths <a href="#ref-griffiths_what_1997" role="doc-biblioref">1997</a>; see also Barrett, Wilson-Mendenhall, and Barsalou <a href="#ref-barrett_psychological_2014" role="doc-biblioref">2014</a>; Mesquita, Barrett, and Smith <a href="#ref-mesquita_mind_2010" role="doc-biblioref">2010</a>)</span>.</p>
<p><span class="citation">Barsalou and Wiemer-Hastings (<a href="#ref-barsalou_situating_2005" role="doc-biblioref">2005</a>)</span> directly address this question of abstract concepts, demonstrating that the content of increasingly abstract concepts contains increasingly situational and introspective focus. Consider the possible inferences associated with the categorization of <em>SOFA</em> versus <em>FREEDOM</em>. Various properties of <em>SOFA</em> will remain relatively stable across contexts. However, to make sense, any conceptualization of <em>FREEDOM</em> needs to be embedded in a specific situational (e.g., freedom from oppression) or introspective (e.g., feeling free) context. Inferences regarding <em>FREEDOM</em> are necessarily more context-dependent. This results in greater situational or introspective inferences being made for abstract categories, while concrete categories allow for more object-level inferences.</p>
<p>The abstract nature of moral categories means they are similarly rich in situational and introspective inferences. That is, whether a particular behavior is viewed as right or wrong varies depending on the situation and may be categorized as right or wrong in different ways, specific to the context and the goal-directed activity in which the person is engaged. The link of introspection and the abstract nature of moral categories is supported by recent approaches that stress the tight coupling of moral judgments and emotions <span class="citation">(e.g., Cameron, Payne, and Doris <a href="#ref-cameron_morality_2013" role="doc-biblioref">2013</a>; Huebner, Dwyer, and Hauser <a href="#ref-huebner_role_2009" role="doc-biblioref">2009</a>; Royzman et al. <a href="#ref-royzman_cad_2014" role="doc-biblioref">2014</a>; Rozin et al. <a href="#ref-rozin_cad_1999" role="doc-biblioref">1999</a>; Valdesolo and DeSteno <a href="#ref-valdesolo_manipulations_2006" role="doc-biblioref">2006</a>)</span>.</p>
<p>As with the mapping of habitualized categorizations on to real-world natural kinds, moral categories may appear to follow principles or rules, reflecting social norms of society or a specific social group. A behavior that is encountered frequently and consistently identified as <em>MORALLY RIGHT</em>, may emerge as a “good example”, or a <em>Token</em><a href="#fn2" class="footnote-ref" id="fnref2"><sup>2</sup></a> for <em>MORALLY RIGHT</em>. Over time, people develop a range of <em>Tokens</em> for the categories <em>MORALLY RIGHT</em> (and for <em>MORALLY WRONG</em>). Furthermore, similar behaviors may become categorized together, for example, continued identification of “hitting people” as <em>WRONG</em>, and “kicking people” as <em>WRONG</em> may lead a person to form a superordinate category <em>CAUSING HARM TO PEOPLE</em>, which is consistently identified as WRONG. This may then be taken a step further, and “don’t harm people” and “don’t harm animals” may merge to form <em>INFLICTING HARM</em>, which is consistently identified as <em>WRONG</em>.</p>
<p>The emergence of habitualized, highly generalized, morally grounded <em>Tokens</em> may form the basis of what we call values. Furthermore, as more and more <em>Tokens</em> are developed and become increasingly generalized, these generalized <em>Tokens</em> become arranged hierarchically in terms of severity. This essentially becomes our “moral code”. There is not necessarily an underlying set of rules (or moral principles) governing this moral code, it is based on a large collection of <em>Tokens</em>, and a process of categorization that is sensitive to context and on-going actions. Some of the generalized <em>Tokens</em> (values) may appear to exhibit sufficient powers of “governance” to constitute rules. However, these are not true rules; as with the mapping of stable categorizations onto natural kinds, it may be possible to construct plausible (and often true) causes for the associations that define many categories, however, the process of categorization remains grounded in type-token interpretation <span class="citation">(rather than the rules that can be inferred from referencing observable categories, Barsalou <a href="#ref-barsalou_situated_2003" role="doc-biblioref">2003</a>; Barsalou and Wiemer-Hastings <a href="#ref-barsalou_situating_2005" role="doc-biblioref">2005</a>)</span>. MJAC provides a framework for the emergence of what appears to be relative stability in categorization while simultaneously accounting for the observed variability and context-dependency that pose a challenge to existing theories of moral judgment.</p>
</div>
</div>
<div id="applying-mjac" class="section level1">
<h1>Applying MJAC</h1>
<div id="moral-dumbfounding" class="section level2">
<h2>Moral Dumbfounding</h2>
<p>The processes underlying moral judgment, according to MJAC, predict the phenomenon of moral dumbfounding. Moral dumbfounding occurs when people defend a moral judgment even though they cannot provide a reason to support it <span class="citation">(Haidt <a href="#ref-haidt_emotional_2001" role="doc-biblioref">2001</a>; Haidt, Björklund, and Murphy <a href="#ref-haidt_moral_2000" role="doc-biblioref">2000</a>; McHugh et al. <a href="#ref-mchugh_searching_2017a" role="doc-biblioref">2017</a>)</span>. Typically, moral dumbfounding occurs for harmless taboo behaviors (consensual incest, cannibalism involving a body that is already dead). Consider the learning of taboo behaviors as wrong through type-token interpretation and typical interaction with such behavior. The taboo nature of these topics means that they are consistently identified as morally wrong, without much discussion <span class="citation">(the Scottish public petitions committee notably dismissed a call to legalize incest with no discussion at all; see Sim <a href="#ref-sim_msps_2016" role="doc-biblioref">2016</a>)</span>. This leads to a high degree of stability in categorizing them as WRONG. However, while other behaviors may be discussed or disputed, generating a deeper knowledge surrounding the rationale for identifying as right or wrong, the taboo nature of these behaviors prevents them from being discussed. This means that a typical encounter with such behavior involves little more than identifying it as wrong, possibly with an expression of disgust, and changing the subject <span class="citation">(Sim <a href="#ref-sim_msps_2016" role="doc-biblioref">2016</a>)</span>. Identifying causal rules that govern the behavior’s membership of the category <em>MORALLY WRONG</em> is likely problematic, in that a person would have limited experience at attempting to do so. In this view, type-token interpretation of taboo behaviors logically leads to moral dumbfounding.</p>
<p>Phenomena similar to moral dumbfounding have been observed in the non-moral domain. While these have not been explicitly identified as “dumbfounding” we suggest that dumbfounding also occurs for categories other than <em>MORALLY WRONG</em>. For example, Boyd and Keil <span class="citation">(Boyd <a href="#ref-boyd_what_1989" role="doc-biblioref">1989</a>, <a href="#ref-boyd_realism_1991" role="doc-biblioref">1991</a>; Keil <a href="#ref-keil_concepts_1989" role="doc-biblioref">1989</a>; see also Griffiths <a href="#ref-griffiths_what_1997" role="doc-biblioref">1997</a>)</span> found that participants struggled to explain their reasons for categorizing an imagined creature as A CAT or NOT A CAT. Descriptions of participants’ responding in such situations bear a striking similarity, whether the target categorization is in the moral domain or not. In discussing their work on the illusion of explanatory depth, <span class="citation">Keil, Rozenblit, and Mills (<a href="#ref-keil_what_2004" role="doc-biblioref">2004</a>)</span> describe the sensation of being “surprised by our inability to explain something” <span class="citation">(<a href="#ref-keil_what_2004" role="doc-biblioref">2004</a>, 277)</span>. Similarly, in discussing moral dumbfounding, Haidt describes how people “express surprise at their inability to find supporting reasons” <span class="citation">(Haidt <a href="#ref-haidt_emotional_2001" role="doc-biblioref">2001</a>, 817)</span>. The illusion of explanatory depth and moral dumbfounding are likely phenomena with common underpinnings.</p>
</div>
<div id="categorizing-people-versus-categorizing-actions" class="section level2">
<h2>Categorizing people versus categorizing actions</h2>
<p>In line with <span class="citation">Barsalou and Wiemer-Hastings (<a href="#ref-barsalou_situating_2005" role="doc-biblioref">2005</a>)</span>, we have been describing the cognitive processes in relation to the development of the abstract categories <em>MORALLY WRONG</em> and <em>MORALLY RIGHT</em>. In reality, people do not deal with these abstractions, rather moral categorization is situated in specific contexts, occurring as part of goal-directed behavior. In some situations, we may identify specific actions as morally questionable or morally praiseworthy, while in others, we may identify specific actors as morally questionable or morally praiseworthy. While, the action or actor may belong to the super-ordinate category <em>MORALLY WRONG</em>, or <em>MORALLY RIGHT</em> (or <em>NOT MORALLY RELEVANT</em>), it is likely that in everyday interactions people are more concerned with the subordinate categories in question, for example, <em>BAD</em>/<em>GOOD PERSON</em> or <em>BAD</em>/<em>GOOD ACTION</em>.</p>
<p>Previous authors have argued that when people make moral judgments, the primary evaluation is of the character of the person committing the act <span class="citation">(e.g., Uhlmann, Pizarro, and Diermeier <a href="#ref-uhlmann_personcentered_2015" role="doc-biblioref">2015</a>; Landy and Uhlmann <a href="#ref-landy_morality_2018" role="doc-biblioref">2018</a>; see also Siegel, Crockett, and Dolan <a href="#ref-siegel_inferences_2017" role="doc-biblioref">2017</a>; Siegel et al. <a href="#ref-siegel_beliefs_2018" role="doc-biblioref">2018</a>)</span>. MJAC does not adopt this position, rather we recognize that there are many potential contextual factors that influence whether the target of any given moral categorization is the actor or on the action (or both). The variability relating to the target of moral categorization can influence which super-ordinate category is eventually implicated, that is, whether the final judgment is <em>MORALLY WRONG</em>, or <em>MORALLY RIGHT</em> (or <em>NOT MORALLY RELEVANT</em>); for example, if a corrupt politician helps a neighbor with shopping, even though this action may be categorized as good, the actor is likely to still be categorized as a bad.</p>
</div>
<div id="moral-categorization-involving-known-others" class="section level2">
<h2>Moral Categorization Involving Known Others</h2>
<p>MJAC assumes that moral categorization is dynamic and context-dependent. We propose that consideration of the goal-directed nature of moral categorizations provides a key insight into some of the contexts that may affect the target of a given categorization. Consider the following two scenarios:</p>
<ol style="list-style-type: lower-alpha">
<li>you find out that a colleague has been fired for stealing from your employer - they have been bringing home office equipment for their own personal use, and they have been exaggerating their expense claims;</li>
<li>a close friend of yours reveals to you that they have been stealing from their employer - they have been bringing home office equipment for their own personal use, and they have been exaggerating their expense claims.</li>
</ol>
<p>It seems intuitive that people should judge (b) differently from (a), and we predict that people will be more lenient in their judgments of (b) than of (a). Despite the historical paucity of research investigating the influence of the relationship between the person making a judgment and the apparent perpetrator <span class="citation">(relative to the literature investigating people’s judgments of strangers, see Hester and Gray <a href="#ref-hester_moral_2020" role="doc-biblioref">2020</a>, see also <span class="citation">Feltz and May <a href="#ref-feltz_means_2017" role="doc-biblioref">2017</a></span>)</span>, recent findings support this prediction <span class="citation">(Forbes <a href="#ref-forbes_when_2018" role="doc-biblioref">2018</a>; Heiphetz and Craig <a href="#ref-heiphetz_dehumanization_2020" role="doc-biblioref">2020</a>; Hofmann et al. <a href="#ref-hofmann_morality_2014" role="doc-biblioref">2014</a>; Lee and Holyoak <a href="#ref-lee_he_2020" role="doc-biblioref">2020</a>; McManus, Kleiman-Weiner, and Young <a href="#ref-mcmanus_what_2020" role="doc-biblioref">2020</a>; Weidman et al. <a href="#ref-weidman_punish_2020" role="doc-biblioref">2020</a>)</span>. Several studies have demonstrated that people appear to be more lenient in their judgments of people they are close to versus strangers <span class="citation">(Forbes <a href="#ref-forbes_when_2018" role="doc-biblioref">2018</a>; Hofmann et al. <a href="#ref-hofmann_morality_2014" role="doc-biblioref">2014</a>; Lee and Holyoak <a href="#ref-lee_he_2020" role="doc-biblioref">2020</a>; Weidman et al. <a href="#ref-weidman_punish_2020" role="doc-biblioref">2020</a>)</span>. Further evidence that close others are judged differently to strangers has been found by <span class="citation">Heiphetz and Craig (<a href="#ref-heiphetz_dehumanization_2020" role="doc-biblioref">2020</a>)</span>. They showed that a tendency to dehumanize racists (and sexists) is associated with a greater tendency to view strangers’ ambiguous actions as racially biased (or sexist), but not the ambiguous actions of friends <span class="citation">(Heiphetz and Craig <a href="#ref-heiphetz_dehumanization_2020" role="doc-biblioref">2020</a>)</span>. The importance of accounting for possible relationships in moral judgment research is not limited to the relationship between the <em>observer</em> and the relevant actors. Recent work has shown that people are judged more favorably for helping strangers than helping kin, while a failure to help kin is judged more harshly, suggesting a stronger obligation towards kin than towards strangers <span class="citation">(McManus, Kleiman-Weiner, and Young <a href="#ref-mcmanus_what_2020" role="doc-biblioref">2020</a>)</span>.</p>
<p>A further prediction is that for (b), the target of categorization will be the <em>action</em> rather than the <em>actor</em>. People are motivated to see close others positively <span class="citation">(Forbes <a href="#ref-forbes_when_2018" role="doc-biblioref">2018</a>; Murray, Holmes, and Griffin <a href="#ref-murray_benefits_1996" role="doc-biblioref">1996</a><a href="#ref-murray_benefits_1996" role="doc-biblioref">a</a>, <a href="#ref-murray_selffulfilling_1996" role="doc-biblioref">1996</a><a href="#ref-murray_selffulfilling_1996" role="doc-biblioref">b</a>)</span>. If faced with a situation where a close other committed a moral transgression, people would be motivated to avoid making a negative judgment of the person <span class="citation">(Ditto, Pizarro, and Tannenbaum <a href="#ref-ditto_motivated_2009" role="doc-biblioref">2009</a>; Murray, Holmes, and Griffin <a href="#ref-murray_benefits_1996" role="doc-biblioref">1996</a><a href="#ref-murray_benefits_1996" role="doc-biblioref">a</a>, <a href="#ref-murray_selffulfilling_1996" role="doc-biblioref">1996</a><a href="#ref-murray_selffulfilling_1996" role="doc-biblioref">b</a>; Proulx and Inzlicht <a href="#ref-proulx_five_2012" role="doc-biblioref">2012</a>)</span>. One way to avoid this is to make the target of the categorization the action rather than the actor.<a href="#fn3" class="footnote-ref" id="fnref3"><sup>3</sup></a></p>
<p>In making the <em>action</em> the target of the categorization rather than the <em>actor</em>, people can reduce the degree to which they view their close others negatively. However, this strategy is implemented <em>in addition</em> to making judgments that are more lenient. Making more lenient judgments about specific transgressions based on the <em>actor</em> introduces context-specific inconsistency in regard to the categorization of that transgression. MJAC predicts that this inconsistency may threaten the long term stability of the categorization. Specifically, we predict that leniency towards close others for a specific behavior should eventually lead to more general leniency towards that behavior. This development of more general leniency should occur independently of deliberate attempts to present as consistent (although it could be accelerated by attempts to be consistent). For instance, an increased tolerance of “locker room talk” by people who would otherwise disapprove of sexism.</p>
</div>
<div id="moral-categorization-involving-unknown-others" class="section level2">
<h2>Moral Categorization Involving Unknown Others</h2>
<p>Drawing on the goal-directed nature of moral categorization, we further predict any prospective relationships between the observer and the actor. Success in social interactions involves successfully predicting the actions of others <span class="citation">(Waytz and Young <a href="#ref-waytz_morality_2018" role="doc-biblioref">2018</a>)</span>. As such, a key goal of moral categorization is to distinguish “good” from “bad” people <span class="citation">(Uhlmann, Pizarro, and Diermeier <a href="#ref-uhlmann_personcentered_2015" role="doc-biblioref">2015</a>)</span>, by attempting to identify a person’s moral “essence” <span class="citation">(e.g., Dunlea and Heiphetz <a href="#ref-dunlea_children_2020" role="doc-biblioref">2020</a>; Heiphetz and Dunlea <a href="#ref-heiphetz_children_2019" role="doc-biblioref">2019</a>)</span>, or “character” <span class="citation">(Klein and O’Brien <a href="#ref-klein_tipping_2016" role="doc-biblioref">2016</a>; Siegel, Crockett, and Dolan <a href="#ref-siegel_inferences_2017" role="doc-biblioref">2017</a>; Siegel et al. <a href="#ref-siegel_beliefs_2018" role="doc-biblioref">2018</a>)</span>. This enables people to establish relationships or pursue continued interactions with “good” people, and to limit their interactions with “bad” people (or at least treat interactions with “bad” people with caution).</p>
<p>Thus, evaluations of strangers’ actions should show a bias for categorizing the <em>actor</em> rather than the <em>action</em>. Furthermore, this bias should be more pronounced in situations when people anticipate that there may be follow-up interactions with the stranger. Research on reciprocity and repeated interactions with strangers or partners <span class="citation">(e.g., Fehr and Gächter <a href="#ref-fehr_cooperation_2000" role="doc-biblioref">2000</a>, <a href="#ref-fehr_fairness_2003" role="doc-biblioref">2003</a>)</span> provides an ideal framework that could be adapted to test this prediction. In conditions where participants are partnered, their initial evaluations should be more focused on their partner’s character than in conditions where participants interact with a new “stranger” for each trial.</p>
<p>Drawing on the well-established tendency for negative information to be weighted more heavily than positive information <span class="citation">(e.g., Kahneman and Tversky <a href="#ref-kahneman_prospect_1979" role="doc-biblioref">1979</a>; Rozin and Royzman <a href="#ref-rozin_negativity_2001" role="doc-biblioref">2001</a>; Smith <a href="#ref-smith_theory_1759" role="doc-biblioref">1759</a>)</span>, we predict that people will be more sensitive to negative actions than positive actions. Indeed, this has been shown to be the case. <span class="citation">Klein and O’Brien (<a href="#ref-klein_tipping_2016" role="doc-biblioref">2016</a>)</span> presented participants with vignettes describing changes in patterns of behavior. Participants were asked to indicate how many consecutive instances of the new behavior would need to occur to convince them that the actor’s “moral character had transformed” <span class="citation">(Klein and O’Brien <a href="#ref-klein_tipping_2016" role="doc-biblioref">2016</a>, 152)</span>. Participants perceived negative transformations much quicker than positive transformations, which was true for commencing negative behaviors and ceasing positive behaviors <span class="citation">(Klein and O’Brien <a href="#ref-klein_tipping_2016" role="doc-biblioref">2016</a>)</span>. A general heightened sensitivity to negative information means that people appear to be quicker to categorize an actor as “bad” (vs. “good”).</p>
<p>This identification of “bad” actors appears to be present from an early age, such that even pre-verbal infants show a preference for good actors over bad actors <span class="citation">(Hamlin, Wynn, and Bloom <a href="#ref-hamlin_social_2007" role="doc-biblioref">2007</a>, <a href="#ref-hamlin_threemontholds_2010" role="doc-biblioref">2010</a>; Hamlin and Wynn <a href="#ref-hamlin_young_2011" role="doc-biblioref">2011</a>; cf. Margoni and Surian <a href="#ref-margoni_infants_2018" role="doc-biblioref">2018</a>; Schlingloff, Csibra, and Tatone <a href="#ref-schlingloff_15monthold_2020" role="doc-biblioref">2020</a>; Steckler, Woo, and Hamlin <a href="#ref-steckler_limits_2017" role="doc-biblioref">2017</a>)</span>. We do not claim that infants in these studies have acquired fully developed categories of <em>MORALLY WRONG</em> and <em>MORALLY RIGHT</em>, and that they assign different actors to these categories. Rather, type-token interpretation predicts that category members should be treated as similar, independently of whether or not a person can describe the category, or even the relationship between the category members.<a href="#fn4" class="footnote-ref" id="fnref4"><sup>4</sup></a> Previous research has demonstrated that we implicitly treat similar items as similar even though we may not be able to articulate what makes them similar <span class="citation">(e.g., recognising “good decks” from “bad decks” in the Iowa Gambling Task: Bechara and Damasio <a href="#ref-bechara_somatic_2005" role="doc-biblioref">2005</a>; Damasio <a href="#ref-damasio_descartes_1994" role="doc-biblioref">1994</a>; or implicit identification of abstract patterns, Proulx and Heine <a href="#ref-proulx_connections_2009" role="doc-biblioref">2009</a>; Whitson and Galinsky <a href="#ref-whitson_lacking_2008" role="doc-biblioref">2008</a>)</span>.</p>
<p>These findings should not be interpreted as categorizations of “bad” actors are more stable than categorizations of “good” actors. Indeed, the opposite is the case <span class="citation">(Siegel et al. <a href="#ref-siegel_beliefs_2018" role="doc-biblioref">2018</a>)</span>, where beliefs about “bad” agents are more volatile than beliefs about “good” agents. MJAC explains this volatility in the categorization of “bad” agents relative to “good” as emerging due to the relative consistency with which categorizations are made. As noted by Siegel et al., “bad people often behave morally, but good people rarely behave immorally” <span class="citation">(<a href="#ref-siegel_beliefs_2018" role="doc-biblioref">2018</a>, 750)</span>. The contexts in which actors are categorized as “good” are more consistent than the contexts in which they are categorized as “bad”. This consistency makes the categorization “good” actor a more stable categorization than “bad” actor. This apparent stability categorizing “good” actors relative to “bad” actors can also be seen in research on moral essentialism, people show a greater tendency to attribute essence based on moral goodness than moral badness <span class="citation">(Heiphetz <a href="#ref-heiphetz_development_2020" role="doc-biblioref">2020</a>; Newman, Bloom, and Knobe <a href="#ref-newman_value_2014" role="doc-biblioref">2014</a>)</span>.</p>
<p>The findings discussed above reflect the goal-directed nature of moral categorization. Specifically, people are motivated to understand and predict others’ actions to guide future interactions <span class="citation">(Uhlmann, Pizarro, and Diermeier <a href="#ref-uhlmann_personcentered_2015" role="doc-biblioref">2015</a>; Waytz and Young <a href="#ref-waytz_morality_2018" role="doc-biblioref">2018</a>)</span>. If we understand that some behaviours are associated with positive experiences and some with negative outcomes, then it is not surprising that we show a preference for people who behave in a more positive way, even from a very young age <span class="citation">(Hamlin and Wynn <a href="#ref-hamlin_young_2011" role="doc-biblioref">2011</a>)</span>.</p>
<p>Interestingly, distinguishing between categorizing an <em>action</em> or categorizing an <em>actor</em> has implications for behavior, specifically when the <em>actor</em> in question is the self. In a series of studies by <span class="citation">Bryan, Adams, and Monin (<a href="#ref-bryan_when_2013" role="doc-biblioref">2013</a>)</span>, participants took part in tasks in which cheating for financial gain (at the expense of the experimenter) was possible. When task instructions discouraging cheating used the term “cheater”, participants’ rates of cheating was significantly lower than when the term used was “cheating”. Committing an action that might fall into the category <em>MORALLY WRONG</em> is less aversive than being categorized as a <em>BAD PERSON</em>.</p>
</div>
</div>
<div id="examining-the-explanatory-power-of-mjac" class="section level1">
<h1>Examining the Explanatory Power of MJAC</h1>
<p>To evaluate the strength of evidence for MJAC we turn to examine its explanatory power compared to several existing theories of moral judgment. We argue that MJAC ultimately provides greater explanatory power than those models while also keeping the account of the processes involved in moral judgment parsimonious with the currently dominant account of general concept and category formation.</p>
<p>We group the range of extant approaches roughly into two. On the one hand, it may be that variations in moral judgments are a product of variations in the basic functioning of the cognitive system. The expansive range of dual-processing theories has a long history of addressing cognitive variation in such terms. Still, in the case of morality, there are a number of such theories highlighting slightly different forms of a dual-processing distinction. Here we compare MJAC with three theories, each with a slightly different take on the dual-processing view: Greene’s dual-process model <span class="citation">(Greene <a href="#ref-greene_secret_2008" role="doc-biblioref">2008</a>, <a href="#ref-greene_why_2016" role="doc-biblioref">2016</a>; Greene et al. <a href="#ref-greene_fmri_2001" role="doc-biblioref">2001</a>, <a href="#ref-greene_neural_2004" role="doc-biblioref">2004</a>)</span>; more recent “softer” interpretations of Greene’s approach <span class="citation">(Byrd and Conway <a href="#ref-byrd_not_2019" role="doc-biblioref">2019</a>; Conway et al. <a href="#ref-conway_sacrificial_2018" role="doc-biblioref">2018</a>; Conway and Gawronski <a href="#ref-conway_deontological_2013" role="doc-biblioref">2013</a>; Goldstein-Greenwood et al. <a href="#ref-goldstein-greenwood_how_2020" role="doc-biblioref">2020</a>)</span>; and the model-based/model-free interpretation proposed by both <span class="citation">Cushman (<a href="#ref-cushman_action_2013" role="doc-biblioref">2013</a>)</span> and <span class="citation">Crockett (<a href="#ref-crockett_models_2013" role="doc-biblioref">2013</a>)</span>.</p>
<p>On the other hand, it may be the case that moral judgment arises due to morality-specific processing, where some conditions are met to trigger such morality-focused cognition. MJAC, which follows a dynamical theory of categorization that undermines any form of reliable essence to moral categories runs counter to this claim. We use the Theory of Dyadic Morality <span class="citation">(TDM, Gray, Waytz, and Young <a href="#ref-gray_moral_2012" role="doc-biblioref">2012</a>; Gray, Young, and Waytz <a href="#ref-gray_mind_2012" role="doc-biblioref">2012</a>; Schein and Gray <a href="#ref-schein_theory_2018" role="doc-biblioref">2018</a>)</span> as a foil to explore this issue.</p>
<p>A key difference between MJAC and all of the alternative approaches that we identify is that it does not align particular aspects of morality or moral judgment with a single underlying process or processing distinction. On the face of it, this might be seen as complicating matters rather than making sense of them; however, we argue that it has two significant benefits. Firstly, it acknowledges the already clear evidence of complexity and variety when it comes to the making of moral judgments. Secondly, it makes available a rich set of theoretical resources parsimonious with research in a more general domain of cognitive psychology.</p>
<p>In what follows, we show that the evidence runs against any straightforward mapping between single moral judgment dimensions and underlying cognitive processing. This helps distinguish our account from those already extant in the literature while also providing reasons for seeing moral judgment as being underpinned by more context-sensitive or generic forms of processing.</p>
<p>We then show how what appears to be a wealth of unreliable and inconsistent findings in the domain of moral judgment can be seen as suggesting parallels between behavior in moral judgment tasks and well-studied phenomena in categorization research. While only suggestive at present, MJAC, at the very least, predicts that such similarities should exist and offers a framework within which systematic relationships between these various phenomena can be sought.</p>
<div id="beyond-unidimensional-conceptions-of-morality" class="section level2">
<h2>Beyond Unidimensional Conceptions of Morality</h2>
<div id="dual-process-theories-of-moral-judgment" class="section level3">
<h3>Dual-Process Theories of Moral Judgment</h3>
<p>The three dual-processing theories of moral judgment that we address here each use some form of one-to-one mapping between a key dimension of moral judgment and the underlying differences in information processing expressed in that dual-processing account. Identification of the moral dimension is usually made through categorization of responses to challenges such as the trolley problem (the moral judgment literature is, unfortunately, replete with vehicular homicides).</p>
<p>For instance, Greene’s theory describes the distinction between deontological and consequentialist outcomes to moral judgments as a qualitative difference in processing, where deontological judgments are grounded in implicit, emotional, automatic processing, and consequentialist judgments involve deliberate, controlled processing <span class="citation">(Greene <a href="#ref-greene_why_2016" role="doc-biblioref">2016</a>)</span>. Byrd and Conway’s <span class="citation">(<a href="#ref-byrd_not_2019" role="doc-biblioref">2019</a>)</span> softer approach is less dichotomous, such that deontological judgments are viewed as involving relatively more affective processing. For both Crockett’s <span class="citation">(<a href="#ref-crockett_models_2013" role="doc-biblioref">2013</a>)</span> and Cushman’s <span class="citation">(<a href="#ref-cushman_action_2013" role="doc-biblioref">2013</a>)</span> model-free vs. model-based accounts the logic is similar, though the emphasis is reversed. While for <span class="citation">Greene (<a href="#ref-greene_why_2016" role="doc-biblioref">2016</a>)</span>, and <span class="citation">Byrd and Conway (<a href="#ref-byrd_not_2019" role="doc-biblioref">2019</a>)</span>, the form of processing drives the form of moral judgments, for both Cushman and Crockett, the framing of the moral task drives the kind of processing that is likely to result. Crockett and Cushman both avoid the simple deontological/consequentialist divide but focus instead on evaluating either moral actions or moral outcomes, which give rise to model-free or model-based judgments respectively. As with Greene and Byrd and Conway, however, they hold a stable one-to-one mapping between this dimension of the content of the moral judgment and the underlying processing.</p>
<p>The clarity of these mappings is appealing, but we argue here that the complexity and inconsistency of the findings in the existing literature on these relationships are disconfirming for these accounts <span class="citation">(e.g., De Neys and Białek <a href="#ref-deneys_dual_2017" role="doc-biblioref">2017</a>; Gamez-Djokic and Molden <a href="#ref-gamez-djokic_affective_2016" role="doc-biblioref">2016</a>; Gubbins and Byrne <a href="#ref-gubbins_dual_2014" role="doc-biblioref">2014</a>; Körner and Volk <a href="#ref-korner_concrete_2014" role="doc-biblioref">2014</a>; McPhetres et al. <a href="#ref-mcphetres_reflecting_2018" role="doc-biblioref">2018</a>; Pizarro and Bloom <a href="#ref-pizarro_intelligence_2003" role="doc-biblioref">2003</a>; Reynolds and Conway <a href="#ref-reynolds_not_2018" role="doc-biblioref">2018</a>)</span>. We note that research on categorization also predicts reliably distinguishable patterns of response along the lines of many dual-processes accounts, distinguished by individual learning histories and experience in performing given categorizations in different circumstances. For clarity and consistency, we will refer to this distinction as one between habitual versus deliberative responses, positioned at either end of a continuum <span class="citation">(Kruglanski and Gigerenzer <a href="#ref-kruglanski_intuitive_2011" role="doc-biblioref">2011</a>)</span>.</p>
<p>We follow the categorization research in identifying as a key dimension the extent to which specific categorizations (instances of type-token interpretations) are well-rehearsed and thus, become fluent, stable, and habitual within frequently enacted goal-directed activities <span class="citation">(Barsalou <a href="#ref-barsalou_perceptions_1999" role="doc-biblioref">1999</a>, <a href="#ref-barsalou_situated_2003" role="doc-biblioref">2003</a>, <a href="#ref-barsalou_cognitively_2017" role="doc-biblioref">2017</a>)</span>. Less experience with a particular type-token interpretation will result in less consistent deployment of the category and demand more deliberative consideration of the situation and appropriate action.</p>
<p>Therefore, this key dimension in underlying processing is not predicted by MJAC to map straightforwardly onto any aspect of task content or framing in moral judgment, such as habitual judgments being deontological while deliberative ones are consequentialist. While well-worn deontic exhortations (“It’s wrong to hurt people”, “Thou shalt not kill”, “You shouldn’t hit your sister”) will no doubt develop a strong habitual foundation, within the MJAC framework, consequentialist judgments that are well-practiced will also be supported by habitual responses <span class="citation">(associated with quick intuitive or affective reactions to moral judgments as studied by deneys_dual_2017; Gubbins and Byrne <a href="#ref-gubbins_dual_2014" role="doc-biblioref">2014</a>; Reynolds and Conway <a href="#ref-reynolds_not_2018" role="doc-biblioref">2018</a>)</span>. Consequentialist reasoning, likely requiring explicit moral argument to arise, may be somewhat less commonly practiced, but also some deontological situations have novel characteristics that therefore also require deliberation [as illustrated by the likes of <span class="citation">Gamez-Djokic and Molden (<a href="#ref-gamez-djokic_affective_2016" role="doc-biblioref">2016</a>)</span>; <span class="citation">Körner and Volk (<a href="#ref-korner_concrete_2014" role="doc-biblioref">2014</a>)</span>; <span class="citation">McPhetres et al. (<a href="#ref-mcphetres_reflecting_2018" role="doc-biblioref">2018</a>)</span>; <span class="citation">Pizarro and Bloom (<a href="#ref-pizarro_intelligence_2003" role="doc-biblioref">2003</a>)</span>).</p>
<p>This variation in the relationship between deontological and consequentialist judgments and the ways (habitual vs. deliberative) they get made undermines both Greene’s and Byrd and Conway’s accounts. Neither <span class="citation">Cushman (<a href="#ref-cushman_action_2013" role="doc-biblioref">2013</a>)</span> nor <span class="citation">Crockett (<a href="#ref-crockett_models_2013" role="doc-biblioref">2013</a>)</span> connect the moral perspective with a specific form of processing. Still, they do map the distinction between action- and outcome-focused judgments onto the distinction between model-free and model-based processing. While this can accommodate such variability in deontological or utilitarian perspectives depending on circumstances, it runs afoul of what is termed the “doctrine of double effect” <span class="citation">(Doris <a href="#ref-doris_moral_2010" role="doc-biblioref">2010</a>; Mikhail <a href="#ref-mikhail_rawls_2000" role="doc-biblioref">2000</a>)</span>. The doctrine of double effect concerns the difference between causing harm as a means to an end being seen as different to causing harm as a side-effect of achieving the same ends, even when the actions taken are the same <span class="citation">(e.g., Mikhail <a href="#ref-mikhail_rawls_2000" role="doc-biblioref">2000</a>; see also Klein et al. <a href="#ref-klein_many_2017" role="doc-biblioref">2017</a>)</span>. It is unclear what about such cases could trigger a difference in processing that would explain differential judgments for model theories. These theories are also challenged by versions of the trolley problem presented in virtual reality environments <span class="citation">(Francis et al. <a href="#ref-francis_virtual_2016" role="doc-biblioref">2016</a>)</span>, where a usual pattern of responding (preference for inaction over pushing someone onto the track to stop the tram) was reversed. This runs directly counter to the predictions of the action-outcome mapping to form of processing made by these model theories. However, the shift to a more deliberative, calculating mode of thinking is perhaps less surprising for MJAC, given the novelty of the mode of presentation.</p>
<p>According to MJAC, the making of moral judgments is dynamical, and context-dependent, and occurs as part of goal-directed activity; thus we should expect to see this observed variability that poses a challenge to any stable mapping between content and form of processing or judgment outcome. MJAC also assumes that relative stability in moral categorizations emerges as a result of continued and consistent type-token interpretation, such that particular categorizations become habitualized (and hence intuitive). Thus, we should expect a variety of contextual factors affecting people’s moral judgments, not limited to any single key dimension. Constraints on space mitigate against exploring each of these in detail. Still, the sheer range of such factors that have been reported offers compelling evidence that whatever underlies variation in moral judgment is a complex of issues and is not unidimensional in any given situation [the reader is referred to the wealth of literature examining such factors as emotional influences, <span class="citation">Cameron, Payne, and Doris (<a href="#ref-cameron_morality_2013" role="doc-biblioref">2013</a>)</span>; intentionality, evitability, benefit recipient, <span class="citation">Christensen et al. (<a href="#ref-christensen_moral_2014" role="doc-biblioref">2014</a>)</span>; <span class="citation">Christensen and Gomila (<a href="#ref-christensen_moral_2012" role="doc-biblioref">2012</a>)</span>; action-outcome distinction <span class="citation">Crockett (<a href="#ref-crockett_models_2013" role="doc-biblioref">2013</a>)</span>; cushman_action_2013; trustworthiness and social evaluation <span class="citation">Everett, Pizarro, and Crockett (<a href="#ref-everett_inference_2016" role="doc-biblioref">2016</a>)</span>; <span class="citation">Everett et al. (<a href="#ref-everett_costs_2018" role="doc-biblioref">2018</a>)</span>; personal-impersonal distinction, <span class="citation">Greene et al. (<a href="#ref-greene_fmri_2001" role="doc-biblioref">2001</a>)</span>; doctrine of double effect, <span class="citation">Mikhail (<a href="#ref-mikhail_rawls_2000" role="doc-biblioref">2000</a>)</span>; level of physical contact, <span class="citation">Valdesolo and DeSteno (<a href="#ref-valdesolo_manipulations_2006" role="doc-biblioref">2006</a>)</span>; order effects, <span class="citation">Wiegmann, Okan, and Nagel (<a href="#ref-wiegmann_order_2012" role="doc-biblioref">2012</a>)</span>).</p>
</div>
<div id="theory-of-dyadic-morality" class="section level3">
<h3>Theory of Dyadic Morality</h3>
<p>The theory of dyadic morality <span class="citation">(TDM, Gray, Young, and Waytz <a href="#ref-gray_mind_2012" role="doc-biblioref">2012</a>)</span> that has recently been presented by Gray and colleagues would also seem to be grounded in generic categorization processes <span class="citation">(Gray, Waytz, and Young <a href="#ref-gray_moral_2012" role="doc-biblioref">2012</a>, 206; gray_mind_2012, p. 102; Schein and Gray <a href="#ref-schein_theory_2018" role="doc-biblioref">2018</a>, 42)</span>. As such, the approach is not heavily focused on a single processing dimension explaining moral judgment (or the variation therein). While TDM has not been identified with a specific theory of categorization, Gray et al. (<span class="citation">(<a href="#ref-gray_moral_2012" role="doc-biblioref">2012</a>, 206)</span> make reference to “prototypes or exemplar sets”, and it is here that the divergence with MJAC becomes clear. <span class="citation">Barsalou (<a href="#ref-barsalou_situated_2003" role="doc-biblioref">2003</a>)</span> summarized a range of findings indicating that neither prototype nor exemplar approaches can adequately explain the dynamic and variable nature of performance in categorization tasks.</p>
<p>More problematically, though TDM has been linked to exemplar and prototype theories, its proponents highlight moral situations as those involving a set of necessary and sufficient conditions - those which involve “an intentional agent causing damage to a vulnerable patient” <span class="citation">(Schein and Gray <a href="#ref-schein_theory_2018" role="doc-biblioref">2018</a>, 33)</span>, or "an intentional moral agent and a suffering moral patient <span class="citation">(Gray, Young, and Waytz <a href="#ref-gray_mind_2012" role="doc-biblioref">2012</a>, 101)</span>. Such appeals to essentialism are at odds with decades of research demonstrating dynamism and context-dependency in categorization <span class="citation">(Barsalou <a href="#ref-barsalou_contextindependent_1982" role="doc-biblioref">1982</a>, <a href="#ref-barsalou_instability_1987" role="doc-biblioref">1987</a>, <a href="#ref-barsalou_situated_2003" role="doc-biblioref">2003</a>, <a href="#ref-barsalou_cognitively_2017" role="doc-biblioref">2017</a>; Harman, Mason, and Sinnott-Armstrong <a href="#ref-harman_moral_2010" role="doc-biblioref">2010</a>; McCloskey and Glucksberg <a href="#ref-mccloskey_natural_1978" role="doc-biblioref">1978</a>; Mervis and Rosch <a href="#ref-mervis_categorization_1981" role="doc-biblioref">1981</a>; Oden <a href="#ref-oden_fuzziness_1977" role="doc-biblioref">1977</a>; Rosch <a href="#ref-rosch_cognitive_1975" role="doc-biblioref">1975</a>; Rosch and Mervis <a href="#ref-rosch_family_1975" role="doc-biblioref">1975</a>; Stich <a href="#ref-stich_moral_1993" role="doc-biblioref">1993</a>)</span>, and returns us to a unidimensional approach to moral judgment, this time identifying the moral character of a situation as the extent to which it involves harm. While intuitively appealing, this does not bear empirical scrutiny.</p>
<p>Proponents of TDM argue that even in ostensibly harmless moral transgressions, people perceive harm <span class="citation">(Gray, Schein, and Ward <a href="#ref-gray_myth_2014" role="doc-biblioref">2014</a>)</span>. This perception of harm guides participants’ judgments in moral dumbfounding scenarios <span class="citation">(Schein and Gray <a href="#ref-schein_theory_2018" role="doc-biblioref">2018</a>; Schein <a href="#ref-schein_importance_2020" role="doc-biblioref">2020</a>)</span>. Dumbfounding is displayed when people maintain a moral judgment in the absence of a means of justifying their judgment, usually evoked by vignettes of supposedly “harmless wrongs” such as consensual incest or cannibalism of an already-dead body <span class="citation">(Haidt, Björklund, and Murphy <a href="#ref-haidt_moral_2000" role="doc-biblioref">2000</a>; McHugh et al. <a href="#ref-mchugh_searching_2017a" role="doc-biblioref">2017</a>)</span>. <span class="citation">Schein and Gray (<a href="#ref-schein_theory_2018" role="doc-biblioref">2018</a>)</span> point to a series of studies by <span class="citation">Royzman, Kim, and Leeman (<a href="#ref-royzman_curious_2015" role="doc-biblioref">2015</a>)</span>, to support their appeal to perceived harm in the “moral dumbfounding” paradigm. royzman_curious_2015 investigating the case of consensual incest, included additional questions that appear to demonstrate that people’s judgments were (at least in part) grounded in perceptions of harm.</p>
<p>However, more recent dumbfounding work fails to support the TDM perspective on this matter <span class="citation">(McHugh et al. <a href="#ref-mchugh_reasons_2020" role="doc-biblioref">2020</a>)</span>. In addressing specific methodological limitations of the <span class="citation">Royzman, Kim, and Leeman (<a href="#ref-royzman_curious_2015" role="doc-biblioref">2015</a>)</span> study, <span class="citation">McHugh et al. (<a href="#ref-mchugh_reasons_2020" role="doc-biblioref">2020</a>)</span> found that people do not cite harm as a reason for their judgment. Participants were asked to judge a vignette describing consensual incest, asked to provide reasons for their judgment, and then provided with the questions examining perceptions of harm developed by <span class="citation">Royzman, Kim, and Leeman (<a href="#ref-royzman_curious_2015" role="doc-biblioref">2015</a>)</span>. The responses to the harm-based questions provided one measure of participants’ perceptions of harm, that is, did participants endorse a harm-based reason for their judgment when it was presented to them? Another measure of perceptions of harm was taken by coding the reasons provided for whether or not participants mentioned harm as justifying their judgment. Figure 1 presents a matrix plotting rows of participants’ judgments (wrong vs. not wrong) against columns of their endorsing of harm (left matrix), or whether or not they mentioned harm (right matrix) across three studies (<em>N</em> = 723).<a href="#fn5" class="footnote-ref" id="fnref5"><sup>5</sup></a> According to TDM, all participants should be located in either the top left (harm/wrong) or the bottom right (no harm/not wrong) quadrants, the responding of participants in either of the other two quadrants cannot be explained by TDM.</p>
<p><img 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" 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0IQvOAf7bE1HM7sTsyUW5Bw/T8dZBU6vzFH+pW81GKn4U0NvXupr9kdjx99w6f2JoIZG59dPVlSQpD24AnHc9YcGn1/8rE3Kdn2mo321nlv/u+PonhTX/1H/3b0fmpk712pUw0Mcf5vq62+u0VjlxTnPHLCwYRM5Wc/DGU/X3bVhxbdMYYzj+dMNbx4gVv+YndiK03T2qpni+TntTGsWxjUwHuXT41l5AVuf6XF0bFWHX0we6rZIkh94Jj1pTGjS2j80+v92Qs45o5v7U9/NnX8pZgd1clPvPeQ5Td37blz/aG2/JU9t0yeatHa55tdk0TNuzkI4Glae+apVVP9xbygHX+3Hf1OG8C74ZJ2Gur888uKOh91vhXU+cTiqfOtIYAHAK93XmPOBWw05owVv86Z7LbgRf5k7zRNz4kPrpzsECFl135iq+VncScWpPzsW6Wje9J5jRV+3B177afhMdbtXsW20Ajh8ifP2m82mHdD8WPv+n72D6v1B+85NxmS4Kc8cMzhjqiBdy5hvw0v9OoPpss0Qyk+uX7qib1w+ZNVlq258xtzvNAffKp0uMPhNy9k1zsS73xLcf53OXp/Mhu7kLK1z9ofaMzQnHhoqfkEQKsWBtXz921sBwYpKfhd9TR7pEa+vDVuqoMw/q7vra6imVxCVN8rW9jvK1r7nKM2jaH8has2bWQU73jKogPDxefO9YfakqntT0XneglJv6s/HHO8+wXBYQBP05rSXy2f6ozkhV76TqeNy2G6AN4dl7TTUOdbFBZ1Pup8y92hzicWT51vDevAAwAQBD/lpx9VVDHOHn5qvcjx26dbyNcSKVl9533F/uT07yQIgiB4wZt3rvWx/+OgkKkMxIKUHTvSBfbfGxw6XbZi59DKhvrec8u/CuKXxNv/SIKgR7759DvVuYPEX3LjPZeETPPVycALf3rdZFuRMNZ8t7/X0fKwvMjLb9rm73CH8vAwub0vbqz+31dtk0vZ8oJ23ntrmqOvQxCEJP/2Hxc6SKdLK7//35GJc9+YlG26/eaMafZIBpT85Hp2rKupe+/XlQ5Xbp7mEiJ9ZFNjbI3NldUOlgEX5N7z4XffM/Z9+chaofkPXXzuXH+orfAikhLOtaNpTX1t2xzWl14gJPkP/mVX1mRnHzX4xYP3f9Q30zrL/Ze0h6HOnw7qfNT5dj4adb7nIYAHACAIQhqekpnFyEwKETt+s766vMYwkwadcOWVlyY6X9/ykzJSHTUn+Xz2+TYhSEpNcnR7NH/vXJjamiZvk6RPVHSQo7ut7sQ+tmXDj9l2Uf40x5MgCEKcd8G6yVy+tKG2osZB04b0WVW0SjrNDnl2vzg9eOJY4+TueUE7rt8WOG3bgRd32ZWr7S1mSxDGimOlbFtOsv6Ki8Knb40Il196MXvqjK0nTvY5ar9OdwlJlqTGTn5hqv9fP7/znbqJaYtgg2vPnRsOtTV+ZEzkua9ubG9qMzj/mwuWrPDRP9/BjnKlej++/5f/HpxZCOr+S9rTUOdPA3U+6nx7UOd7HAJ4AOAWFyQ0mgNa23/ms6evu/6F2pn0LfGjVqyInkF1y/MP8HPy7aTA13e6Zo1L6Ntbus415sxunjaZ2irOKs81TEjR8oLcafq2GOK0LHbcHaVsaRm231rmJ2ZnypzZp23GhuqGydNHCnMLVzqzL15EXn6cvZNCD9XVDkz2AvCX5K+YvslCEIQgKz+H7UIxNtQ0OLimpr2EhLmXXMRe4rS+6b2b87IuuOPZD4+2js2ke8LF5871h9oGs+uRnmhr8eqgcd74bvj1i7cmTubHNnX9876H/6eYQQDq/kvaeajzz4M6f+ZQ59uHOp+bphnWAACweBlUPS319Q2NTc0tLS0trS1NDXU1ta3DWmqmoyn5sYmxM2pKkqS3PV2lVQrlZAcUKff3ddRYMeu4IUg/3vCpAwecuNfQI4NTd2FqeHCYIuw1GXmBwdar4zpP19PFfhQvaunS6cYLMgTpS9MFRJPNlpGpt6uX/cbC1Mxk5+6tktTMRD7BDKOkh7t7tQRhr+00/SUkWXP/7us/ufYf7UbmNNETbfte/uW+lx8UBiQuL1y3fv36devWrc1PC5XM47lz/aG2gfSduh4p5bCSQucEQRBkQMlTf7pxz6VvdJgIgiBM7W/d89g1a/+82clB3e6/pL0S6vwpqPNR59uHOt/TEMADAJgzDld99f4/PvzPt4dPVrUrdTNuuNlC+gU4PRfSS9HjavXkoSClUocNgomBAdXkHZzq//SezZ/O+OMo5fAIRRB2mi+kj8xn9seTVo2p2NPKCwxxcr4o6RMUKCUJna0Lgh5Vjk1+Y1IWEOBkzMILCJochUjQlFKhpAh7vXBOXEK8yCte3vN38uq7361WmxWSNihbTu5pObnnnecIkieNyC7aeuGl19xwbUl6gI3D69pz54ZDbYtIKhWQzCxlWq1Wu+JPdiEggy/83fPXfH3NP7tNBEHQxuZXd/3m6pPPF8md+WX3X9LeA3W+TajzUec7CXW+B3h9vQoAMF/0XXufviI7Kefiu5559+uy1hHbLTlSEpF3yQVpM5mwRUokM8nQ4o1ozYSGbcyJHX4dWjMxMdf7Ka3X6x3uYw6HkzYZjey+SZlc5uS+SLmvvbfSep2efZt5aqFp9mj+4bRO5+ArO3cJ+WTe/PbJij3P3bI22mYZaErTW/H1W0/fsSMrNvOSxz9vtJwy6eJz54ZDbfPtUweH1oxrFmNjzjYy7LJnf39ZODtVte6vd/32uHPTZN1/SXsF1Pn2oc5Hne8k1PkegAAeAIAgCEJb9dIVq3c8+mnd2HlNOJInDkrIXlNy2Q13PvjUi2999n1F51D3qQ/uyBXa3dOCRIolbA8MrXd/u9ygd1teGlIknmoX0RPj485+F41Ga+9HIhF7PdATE842J2itRjvV1hEKBK5o8fskbfv564fbOqu+fv3JOy9fmxIksrFXmlI3fPHEFas2PvDNgMtP5dS5c8ehtoHW69mhviLOx00uxYu69vndF4ZMhvD6qhd+9vsyp46tF13S7oI63yHU+ajznYU6f/5hCD0AAEGMH3r06vu+7J58dEzyfJM2XXfLDy7dVrw2O97PquGmm+/yeRwpm3o+fl4bxMZbzQc7itb8oebwL5Z408Ni0jcoUDQ5WI8aGXIwbvM8OuWIvcYIzy/Aj0d0UwRBEPT4iNLg3Mxfekw5OtWYk/vJXdcMEQRnbrklc8stjxLUeE/V8UMHDx48eOjgwePVAxqa/UhKWfr89TelnPzy9qRzJ8jF584Nh9oWo0YzGV2Qcqe7fBYJXuwNf3rq/cN3fj1CEQRBa8uf/dkfLz/40LJpLlDvu6RdDXX+NFDno86fOdT588Wb/sAAADyD6njnNy/XTd4PeEHrHtpTU7P3lYdv3r7CRktuUSJ9/f14k0PW1CqHt1ppRCQ7zY8aHRn1tgyxwug4NlUS1V1V7SD3sRljY02j0V5jLiI6kp3YaGisbXIuX7Wusa6VXW/HNzLKzx3NEJ4satkF19z1m79+uO9sz2D7kX/+5uql/uy9nxre+9vnD0x1fbj43Ln+UNtAj6vZ65H0C3DLUeQyfuKtL/56w+RhoSdOPn3Xn2un6+z05kvaFVDnTwt1Pur8WUCdP08QwAPAokf3f/UZuw4qL+yKv3781NaYaZ6m0zN4WLwgiOMSos7dMai+HofLtgiSM9iUvKaOhqaZjIybD/yULHY6K204c7TUmVnB9HDZqWZ7OXJ5oenpoZP3U1NzWbnSmcvDWH/qjGryjYLUrFS3j4njyWLXXPfYBycO7N7AtudMPd9+Xck2Pl187lx/qG2g+nv6J1dBEscmRLlmEeyFRJB6x58fLZSzwdjhJ+9+ZZqAgyuX9CyhzncC6nzU+TOHOn+eIIAHgEXPWH+2bnI+FS9g6w8vDp/uee6EesIlqYo5RJCQnHDuLk+purtHHXx9Xkz+ymh2kdaj3x3VOPMB42VvPfbwQw899NBDDz382D8q3DhilfRfVbh0ssVCDX/5/tfKaX+H6vn3x4fsp8gV5q7JnxyHSGsOfvrl4PSXh7Hq83/XTbai+NGrVsXO/o6sL3t11513MH72q4+naQr55Nz1f1eFsa257vYutjHn4nPnhkNtzdTT2TO5XHXMkgSx87+5eAizdv3lwfzJLFf06PeP73qzzeFV4ulL2r1Q5zsBdT7qfLtQ53uat9atAADzhlKNscuokMERYdMOoFQdO1jmtnw7XooMSUmeXBHG1NnW6ai5IMzbVjKZ+prq/fjP73dNOyqP6vng8Xuf+t3u3bt37979h/91it3ZM8FP3rotbaqJ8cWf3mqYZvyjvvK1l/c7yFNEBhRvWy1hezi/ffWd6fZIjH3/yjs17GDK8JLteXMZuDtS/vErLzP+9tzbh6frDRKGhk0t72OeNtjV5871h9oKPdLRcW7YJylckpa0GDtjnCDO/cVff5EzeY1Siq8fffgzR6NbPX5JuxXqfCegzkedbwfqfM9DAA8Aix7PP4AdXEYNdnY7HkJGK/Y99uB7DscTLkjC3ILl55K9mlrP1qgdvVe64cZrJm+qlGLPw3e+0eSw8UsPffnoE1+d6+EhhVkXXZji1luyYNm11y8XmY0ovuc1RwXU1//tF8+fcZiFmRd12Q9L2GnGmuO773+73VF7d/z47gfeZN/BT7zmhg3SGXwBS/z4pPjJI0brju75bsRha4gaOF3GfjgvKi7GrPnl4nPn+kNtyVhXWXuugShIW5UXsBinQzpFuvL//nJ35uS5oEba25WOajEPX9LuhTrfGajzUefbgjrfGyCAB4BFj78kPZmdtDX29ZsfdthtqelaP79v2xUvVpmN9qIpalE07MjAlavPPVendTWVdQ7v8OI1u35ePNm2ofr+u+vSn33UbKeRTI+d+sN1t7JtH9J/2z0/znbzI3V+xq0PXB7OtuAVX9930Z2ftOttvdXY9fldlz74/dg07Qsy/Kpf/GjJ5KJA1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" width="672" /></p>
<p>Even in taking the most generous measure of perceptions of harm (left), the responding of 17% of participants (9% plus 8%) cannot be explained by TDM. Taking the stricter <span class="citation">(and arguably more accurate, see McHugh et al. <a href="#ref-mchugh_reasons_2020" role="doc-biblioref">2020</a>)</span> measure of perceptions of harm further reduces the explanatory power of TDM – only 45% of participants responded in line with the predictions of TDM. In addition to evidence for harmless wrongs, the same set of studies had questions explicitly related to the wrongness of behaviors linked with harm and potential harm. While participants were not explicitly asked about their perceptions of harm for boxing or contact team sports, they were presented with a question “How would you rate the behavior of two people who engage in an activity that could potentially result in harmful consequences for either of them?”. Only 50% of participants across two studies (<em>N</em> = 613) rated this as wrong, providing clear evidence for the idea of “wrongless harms” that is rejected by TDM <span class="citation">(Schein and Gray <a href="#ref-schein_theory_2018" role="doc-biblioref">2018</a>)</span>.</p>
<p>So far, there is nothing uniquely “moral” in moral judgment. The people researchers have studied do not appear to apply any given mode of processing or content in a sufficiently consistent manner to provide stable account of moral judgment. We argue, therefore, that a more successful approach is to explore what the capacity to identify morally right and morally wrong actors, actions and outcomes has in common with people’s capacity to identify categories more generally.</p>
</div>
</div>
<div id="moral-phenomena-with-domain-general-categorization-explanations" class="section level2">
<h2>Moral Phenomena with Domain General (Categorization) Explanations</h2>
<p>MJAC assumes that moral categorization is a dynamical, context-dependent process, and as such, we predict the same phenomena as have been found within the categorization literature at large. In this section, we briefly outline some evidence for this predicted similarity, though we note that at present, these patterns are more suggestive than conclusive. However, we argue that these patterns should be seen not as noise obscuring an underlying stable moral category but a signal of the complexity of the processes that give rise to that category. We believe that the phenomenon of moral judgment is no more undermined or challenged by this complexity than the cognitive psychology of concepts, and category formation is more generally. These include such phenomena as order effects, language effects, the impact of emotions, and typicality of instance.</p>
<div id="order-effects" class="section level3">
<h3>Order effects</h3>
<p>In morality research, responses to different moral dilemmas have been found to vary depending on the order of presentation <span class="citation">(Petrinovich and O’Neill <a href="#ref-petrinovich_influence_1996" role="doc-biblioref">1996</a>; Wiegmann, Okan, and Nagel <a href="#ref-wiegmann_order_2012" role="doc-biblioref">2012</a>)</span>. MJAC can explain these in the same way as order effects in non-moral categorization are explained. That is, they occur as a result of priming. The scenario that is presented first causes some features of the second scenario to become more salient. The salience of these features leads to a different judgment than if the initial scenario was not presented. In the case of categorization, the effect of this type of priming is primarily studied concerning reaction times. For example, a study by Barsalou <span class="citation">(<a href="#ref-barsalou_contextindependent_1982" role="doc-biblioref">1982</a>, <a href="#ref-barsalou_situated_2003" role="doc-biblioref">2003</a>)</span> showed that reading sentences that made particular features of a given object salient influenced the speed at which participants verified related properties of the given object <span class="citation">(see also Tabossi <a href="#ref-tabossi_effects_1988" role="doc-biblioref">1988</a>)</span>. We predict similar reaction time variability should be observed when participants are primed with relevant properties for making moral categorizations.</p>
<p>There is also evidence that priming people with particular concepts can influence their subsequent categorizations. In a study by <span class="citation">Higgins, Bargh, and Lombardi (<a href="#ref-higgins_nature_1985" role="doc-biblioref">1985</a>)</span>, participants completed a task in which they were required to create sentences from a selection of words. Some of the words presented were selected to prime a particular concept, e.g., “bold”, “courageous”, and “brave” primed “Adventurous”; “careless”, “foolhardy”, and “rash” primed “Reckless” <span class="citation">(Higgins, Bargh, and Lombardi <a href="#ref-higgins_nature_1985" role="doc-biblioref">1985</a>, 63)</span>. Participants were later presented with a description of ambiguous behavior. It was found that the categorizations of these behaviors were influenced by the concept that was primed. A similar study demonstrated the same effect <span class="citation">(Srull and Wyer <a href="#ref-srull_role_1979" role="doc-biblioref">1979</a>)</span>. We predict that this same effect should occur for moral categorizations, for example, participants responses to descriptions of behavior that could be viewed as either “moral” or “self-righteous”, or a behavior that could be viewed as either “immoral” or “crafty” should be subject to the same effect as described by <span class="citation">Higgins, Bargh, and Lombardi (<a href="#ref-higgins_nature_1985" role="doc-biblioref">1985</a>)</span>.</p>
</div>
<div id="language-effects" class="section level3">
<h3>Language effects</h3>
<p>Though the influence of language on the categories available to a given person has a long and controversial history in psychology, recent research has made it increasingly clear that a given language forms a significant constraint on categorization tasks due to the resources of vocabulary and grammatical structure that it provides <span class="citation">(Cubelli et al. <a href="#ref-cubelli_effect_2011" role="doc-biblioref">2011</a>; Davidoff <a href="#ref-davidoff_language_2001" role="doc-biblioref">2001</a>)</span>. Second language acquisition also impacts how categorizations are formed, as a person learns to deploy new linguistic resources in the service of their goal-directed activities <span class="citation">(Athanasopoulos <a href="#ref-athanasopoulos_bilinguals_2007" role="doc-biblioref">2007</a>)</span></p>
<p>People’s moral judgments have been shown to vary depending on whether they read a moral scenario in their first language or in a second language <span class="citation">(the “foreign language effect”, e.g., Cipolletti, McFarlane, and Weissglass <a href="#ref-cipolletti_moral_2016" role="doc-biblioref">2016</a>; Costa et al. <a href="#ref-costa_your_2014" role="doc-biblioref">2014</a>; Driver <a href="#ref-driver_switching_2020" role="doc-biblioref">2020</a>; Geipel, Hadjichristidis, and Surian <a href="#ref-geipel_how_2015" role="doc-biblioref">2015</a>; Hayakawa et al. <a href="#ref-hayakawa_thinking_2017" role="doc-biblioref">2017</a>)</span>. Specifically, people appear to be more willing to endorse action in the <em>Footbridge</em>/<em>Push</em> version of the trolley dilemma when this dilemma is presented a language other than their native language. According to MJAC, deontological judgments become intuitive as a result of consistency across contexts. The changing of the language presents a novel context, which means the inferences associated with the regular context (e.g., emotional inferences) of encountering or this scenario are not as salient. Evidence for this interpretation comes from research investigating people’s reactions to non-moral taboo words in their first language vs. a second language. <span class="citation">Harris, Ayçiçeĝi, and Gleason (<a href="#ref-harris_taboo_2003" role="doc-biblioref">2003</a>)</span> measured skin conductance of English speakers and Turkish speakers when rating different types of words in their first language and in their second language. It was found that (non-moral) taboo words led to greater arousal when presented in participants’ first language than when presented in a second language <span class="citation">(see also, Colbeck and Bowers <a href="#ref-colbeck_blinded_2012" role="doc-biblioref">2012</a>)</span>, suggesting that the emotional inferences associated with the footbridge dilemma are less salient when it is presented in a foreign language.</p>
</div>
<div id="emotion-effects" class="section level3">
<h3>Emotion effects</h3>
<p>Emotion is perhaps the most widely discussed contextual influence on moral judgments <span class="citation">(e.g., Cameron, Payne, and Doris <a href="#ref-cameron_morality_2013" role="doc-biblioref">2013</a>; Giner-Sorolla <a href="#ref-giner-sorolla_functional_2018" role="doc-biblioref">2018</a>; Huebner, Dwyer, and Hauser <a href="#ref-huebner_role_2009" role="doc-biblioref">2009</a>; Landy and Goodwin <a href="#ref-landy_does_2015" role="doc-biblioref">2015</a>; May <a href="#ref-may_does_2014" role="doc-biblioref">2014</a>; Prinz <a href="#ref-prinz_passionate_2005" role="doc-biblioref">2005</a>; Royzman et al. <a href="#ref-royzman_cad_2014" role="doc-biblioref">2014</a>; Rozin et al. <a href="#ref-rozin_cad_1999" role="doc-biblioref">1999</a>; Russell and Giner-Sorolla <a href="#ref-russell_moral_2011" role="doc-biblioref">2011</a>; Valdesolo and DeSteno <a href="#ref-valdesolo_manipulations_2006" role="doc-biblioref">2006</a>)</span>. Above, we have outlined how specific emotions may become associated with particular types of judgment; that is, the emergence of relative stability in making specific categorizations is linked with consistency in relevant contextual features, where the relevant contextual features include emotions. In other words, the emotions that may be experienced when a moral categorization is learned (or reinforced/consolidated) are likely to also be present during later categorizations. A corollary of this is that the experience of the specific emotion may provide a contextual cue, reminding people of previous experiences, making a particular categorization more salient <span class="citation">(e.g., Barsalou <a href="#ref-barsalou_situated_2003" role="doc-biblioref">2003</a>; Barsalou and Wiemer-Hastings <a href="#ref-barsalou_situating_2005" role="doc-biblioref">2005</a>; Damasio <a href="#ref-damasio_descartes_1994" role="doc-biblioref">1994</a>; Damasio and Damasio <a href="#ref-damasio_cortical_1994" role="doc-biblioref">1994</a>; Rosenfield <a href="#ref-rosenfield_invention_1988" role="doc-biblioref">1988</a>)</span>.</p>
<p>In line with the prediction that manipulations designed to suppress the salience of these contextual factors <span class="citation">(Smith and Vela <a href="#ref-smith_environmental_2001" role="doc-biblioref">2001</a>)</span>, we predict the same type of manipulations should have similar effects on the influences of emotions on moral categorizations. The foreign language <span class="citation">(Colbeck and Bowers <a href="#ref-colbeck_blinded_2012" role="doc-biblioref">2012</a>; Costa et al. <a href="#ref-costa_your_2014" role="doc-biblioref">2014</a>; Driver <a href="#ref-driver_switching_2020" role="doc-biblioref">2020</a>; Geipel, Hadjichristidis, and Surian <a href="#ref-geipel_how_2015" role="doc-biblioref">2015</a>; Harris, Ayçiçeĝi, and Gleason <a href="#ref-harris_taboo_2003" role="doc-biblioref">2003</a>; Hayakawa et al. <a href="#ref-hayakawa_thinking_2017" role="doc-biblioref">2017</a>)</span> effect described above provides some evidence for this, whereby the salience of the emotional content is reduced by being presented in the second language. Similar effects should be observed using mindset manipulations <span class="citation">(Igou <a href="#ref-igou_when_2011" role="doc-biblioref">2011</a>; Igou and Bless <a href="#ref-igou_undesirable_2007" role="doc-biblioref">2007</a>)</span>.</p>
<p>The specific contextual influences discussed above provide just a sample of the broader contextual factors known to influence the making of moral judgment. MJAC assumes that moral judgments are dynamical and context-dependent, and as such, it is the approach that is best positioned to understand the diverse contextual influences on moral judgment. It is beyond the scope of the current paper to describe and account for all the known contextual influences on moral judgment <span class="citation">(e.g., an incomplete list would include: Bostyn, Sevenhant, and Roets <a href="#ref-bostyn_mice_2018" role="doc-biblioref">2018</a>; Christensen et al. <a href="#ref-christensen_moral_2014" role="doc-biblioref">2014</a>; Christensen and Gomila <a href="#ref-christensen_moral_2012" role="doc-biblioref">2012</a>; Costa et al. <a href="#ref-costa_your_2014" role="doc-biblioref">2014</a>; Cushman et al. <a href="#ref-cushman_simulating_2012" role="doc-biblioref">2012</a>; Everett, Pizarro, and Crockett <a href="#ref-everett_inference_2016" role="doc-biblioref">2016</a>; Everett et al. <a href="#ref-everett_costs_2018" role="doc-biblioref">2018</a>; Forbes <a href="#ref-forbes_when_2018" role="doc-biblioref">2018</a>; Francis et al. <a href="#ref-francis_virtual_2016" role="doc-biblioref">2016</a>, <a href="#ref-francis_simulating_2017" role="doc-biblioref">2017</a>; Lee and Holyoak <a href="#ref-lee_he_2020" role="doc-biblioref">2020</a>; Petrinovich and O’Neill <a href="#ref-petrinovich_influence_1996" role="doc-biblioref">1996</a>; Rozin et al. <a href="#ref-rozin_cad_1999" role="doc-biblioref">1999</a>; Schein <a href="#ref-schein_importance_2020" role="doc-biblioref">2020</a>; Timmons and Byrne <a href="#ref-timmons_moral_2019" role="doc-biblioref">2019</a>; Uhlmann, Pizarro, and Diermeier <a href="#ref-uhlmann_personcentered_2015" role="doc-biblioref">2015</a>; Valdesolo and DeSteno <a href="#ref-valdesolo_manipulations_2006" role="doc-biblioref">2006</a>; Vasquez et al. <a href="#ref-vasquez_cultural_2001" role="doc-biblioref">2001</a>; Vasudev and Hummel <a href="#ref-vasudev_moral_1987" role="doc-biblioref">1987</a>)</span>. However, MJAC predicts understanding these diverse context effects depends on (a) accounting the learning history (e.g., in the cases of emotional influences and the foreign language effect) and, (b) viewing moral categorization as occurring as part of goal-directed activity (e.g., categorization of actor versus action discussed above). Incorporating both of these considerations into a program of research inevitably leads to attempts to make the study of moral judgment reflective of real-world moral decision making <span class="citation">(Bauman et al. <a href="#ref-bauman_revisiting_2014" role="doc-biblioref">2014</a>; Bostyn, Sevenhant, and Roets <a href="#ref-bostyn_mice_2018" role="doc-biblioref">2018</a>; Gilligan <a href="#ref-gilligan_different_1977" role="doc-biblioref">1977</a>, <a href="#ref-gilligan_different_1993" role="doc-biblioref">1993</a>; Hester and Gray <a href="#ref-hester_moral_2020" role="doc-biblioref">2020</a>; Hofmann et al. <a href="#ref-hofmann_morality_2014" role="doc-biblioref">2014</a>; Schein <a href="#ref-schein_importance_2020" role="doc-biblioref">2020</a>; Watkins <a href="#ref-watkins_morality_2020" role="doc-biblioref">2020</a>)</span>.</p>
</div>
<div id="typicality" class="section level3">
<h3>Typicality</h3>
<p>Finally, one of the most salient phenomena within the field of categorization concerns the fact that there are “better” and “worse” examples of any given category <span class="citation">(McCloskey and Glucksberg <a href="#ref-mccloskey_natural_1978" role="doc-biblioref">1978</a>; Oden <a href="#ref-oden_fuzziness_1977" role="doc-biblioref">1977</a>)</span>, for example, a chair is viewed as a more typical member of the category FURNITURE than bookends <span class="citation">(McCloskey and Glucksberg <a href="#ref-mccloskey_natural_1978" role="doc-biblioref">1978</a>)</span>. Such judgments are made even for those categories with supposedly logical or sharp boundaries such as geometric figures <span class="citation">(Bourne <a href="#ref-bourne_typicality_1982" role="doc-biblioref">1982</a>; Feldman <a href="#ref-feldman_bias_2000" role="doc-biblioref">2000</a>)</span>.</p>
<p>MJAC predicts that this same phenomena of typicality should be observed for moral categorizations, e.g., cold-blooded murder versus violence in pursuit of a cause. We further predict that relative typicality should be related to the relative consistency with which category members are identified as members of the given category (and should be independent of perceived severity). This facet of moral judgment has already seen some discussion in the existing moral judgment theoretical literature. Cushman <span class="citation">(<a href="#ref-cushman_action_2013" role="doc-biblioref">2013</a>, 282)</span> makes a passing reference – that pushing someone “with your hands” is more typically harmful than pushing someone “with your buttocks”. However, typicality sees more substantial discussion in the context of TDM <span class="citation">(Gray and Keeney <a href="#ref-gray_impure_2015" role="doc-biblioref">2015</a>; Schein and Gray <a href="#ref-schein_theory_2018" role="doc-biblioref">2018</a>)</span>.</p>
<p>Typicality ratings in moral judgments, as described by TDM, are related to the degree to which a given scenario matches the defined prototype of morality, as an “intentional agent causing damage to a vulnerable patient” <span class="citation">(Schein and Gray <a href="#ref-schein_theory_2018" role="doc-biblioref">2018</a>, 32)</span>. An act that more clearly involves harm is rated as more typically wrong than an action where the perceived harm is less. Similarly, if there are evident intentional agent and vulnerable patient, an action is rated as more typically wrong than if the actors are more similar in their intentionality and vulnerability <span class="citation">(Gray and Keeney <a href="#ref-gray_impure_2015" role="doc-biblioref">2015</a>; Schein and Gray <a href="#ref-schein_theory_2018" role="doc-biblioref">2018</a>)</span>.</p>
<p>This account of typicality is based on assumptions related to content (agent-patient, harm) and does not inform our understanding of the cognitive processes underlying moral judgments. As such, it cannot clearly distinguish between typicality and severity. Indeed the strong overlap between severity of an act and it’s typicality as an example of moral wrongness is acknowledged: “By definition, more severe acts are more immoral; that is, they are better examples of the category”immorality" <span class="citation">(Gray and Keeney <a href="#ref-gray_impure_2015" role="doc-biblioref">2015</a>, 860)</span>.</p>
<p>With MJAC, we propose that typicality is related to both frequency and consistency of exposure; that is, behaviors that are frequently encountered and consistently identified as members of a given moral category should emerge as typical category members. Given the consistency with which harm related transgressions are identified as wrong, the emergence of the prototypical template described by Gray and colleagues is not surprising <span class="citation">(Gray and Keeney <a href="#ref-gray_impure_2015" role="doc-biblioref">2015</a>; Schein and Gray <a href="#ref-schein_theory_2018" role="doc-biblioref">2018</a>)</span>. However, we attribute these typicality ratings to the learning history rather than to perceptions of harm and of agents and patients.</p>
<p>Given the possible confounding influence of severity on typicality ratings, unpacking this difference in interpretation will prove challenging; however, we believe it will be a worthwhile endeavor. We hypothesize typicality ratings are related to the learning history and not linked to specific content. This predicts differences in typicality ratings when controlling for severity (either by focusing on harmless dilemmas or by keeping the severity of harm constant). This also predicts differences in typicality ratings within populations, through individual differences in moral values <span class="citation">(e.g., Graham et al. <a href="#ref-graham_moral_2012" role="doc-biblioref">2012</a>; Haidt and Joseph <a href="#ref-haidt_moral_2008" role="doc-biblioref">2008</a>)</span>, and between populations through cultural variation <span class="citation">(e.g., Haidt, Koller, and Dias <a href="#ref-haidt_affect_1993" role="doc-biblioref">1993</a>)</span>. Furthermore, this view of typicality of moral categorizations predicts that perceptions of typicality will be context sensitive, that is intra-personal variability should be observed depending on current context, and crucially depending on current goal-directed activity. A professor grading papers would rate straight plagiarism as more typically wrong than plagiarism by omitting references. Whereas when not grading papers, the same professor may be more concerned with the ethics of her colleagues’ precarious contracts and entirely indifferent to the shortcuts students may take in their assignments. Or a sports fan may claim to view cheating as wrong, where different cheating behaviors vary in their typicality (e.g., overt fouling, cynical fouling, feigning injury so that the referee penalizes the other team) however, the same fan may turn a blind eye to these behaviors when committed by members of the team she supports.</p>
<p>It is worth noting that this sensitivity to the context of moral judgment implies that the importance of understanding moral judgments in more real-life contexts rather than through the study of abstract decontextualized dilemmas has been well documented <span class="citation">(e.g., Bauman et al. <a href="#ref-bauman_revisiting_2014" role="doc-biblioref">2014</a>; Bostyn, Sevenhant, and Roets <a href="#ref-bostyn_mice_2018" role="doc-biblioref">2018</a>; Gilligan <a href="#ref-gilligan_different_1977" role="doc-biblioref">1977</a>, <a href="#ref-gilligan_different_1993" role="doc-biblioref">1993</a>; Hester and Gray <a href="#ref-hester_moral_2020" role="doc-biblioref">2020</a>; Hofmann et al. <a href="#ref-hofmann_morality_2014" role="doc-biblioref">2014</a>; Schein <a href="#ref-schein_importance_2020" role="doc-biblioref">2020</a>; Watkins <a href="#ref-watkins_morality_2020" role="doc-biblioref">2020</a>)</span>. By focusing specifically on context-sensitive categorizations occurring as part of goal-directed activity, MJAC offers a framework for attempting to make the study of moral judgments more reflective of the making of moral judgments in everyday life. Furthermore, in recognizing the broader array of contextual influences on moral categorizations, rather than focusing on specific contextual influences on specific types of judgments, MJAC is uniquely positioned to incorporate known context effects into a coherent parsimonious framework. This would provide opportunities for the combined influences of these contextual factors to be studied relative to each other, with the potential to identify clear boundary conditions to understand how and when specific contextual factors influence moral categorizations more than others.</p>
</div>
</div>
<div id="summarizing-the-differences-between-mjac-and-existing-approaches" class="section level2">
<h2>Summarizing the Differences Between MJAC and Existing Approaches</h2>
<p>Above, we have outlined how MJAC differs from existing theories in terms of assumptions and explanation. These theories make assumptions based on content, and this results in essentialist theorizing, either implicit or explicit attempts to define an “essence” of morality. In contrast, MJAC rejects essentialism, instead assuming moral categorizations are dynamical, context-dependent, and occurring as part of goal-directed activity. Each of the theories discussed is explicitly or implicitly <span class="citation">(e.g., Schein and Gray <a href="#ref-schein_theory_2018" role="doc-biblioref">2018</a>, 41)</span> based on dual-process assumptions, with related dichotomous assumptions regarding the cognitive mechanisms (where these mechanisms are specified). MJAC does not assume distinct, separable processes, adopting type-token interpretation, occurring as part of goal-directed activity <span class="citation">(Barsalou <a href="#ref-barsalou_situated_2003" role="doc-biblioref">2003</a>, <a href="#ref-barsalou_cognitively_2017" role="doc-biblioref">2017</a>)</span>, as the mechanism that underlies moral categorization. These differences in assumptions underlie the differences in the explanation discussed above. These differences are summarized in Table 1.</p>
<p>Table 1: Specific points of divergence between MJAC and existing theories</p>
<table style="width:100%;">
<colgroup>
<col width="16%" />
<col width="16%" />
<col width="16%" />
<col width="16%" />
<col width="16%" />
<col width="16%" />
</colgroup>
<thead>
<tr class="header">
<th></th>
<th>Greene’s Dual-process theory</th>
<th>“Soft” dual-process theory</th>
<th>Model-based accounts</th>
<th>TDM</th>
<th>MJAC</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td><strong>Assumptions</strong>:</td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
</tr>
<tr class="even">
<td><em>Content</em></td>
<td>Deontology-utilitarianism / personal-impersonal</td>
<td>Deontology-utilitarianism</td>
<td>Action-outcome</td>
<td>Harm-based, dyadic</td>
<td>Dynamical Context-dependent Goal-directed</td>
</tr>
<tr class="odd">
<td><em>Moral “Essence”</em></td>
<td>(Implicit)</td>
<td>(Not discussed)</td>
<td>(Implicit)</td>
<td>Explicit</td>
<td>Rejected</td>
</tr>
<tr class="even">
<td><em>Processes</em></td>
<td>Dual-processes</td>
<td>Dual-processes</td>
<td>Dual-processes</td>
<td>(implicitly dual-process)</td>
<td>Continuum</td>
</tr>
<tr class="odd">
<td><em>Mechanisms</em></td>
<td>Intuition (emotion) / cognition</td>
<td>Emotion / cognition</td>
<td>Model-based / model-free</td>
<td>Categorization (unspecified)</td>
<td>Type-token interpretation</td>
</tr>
<tr class="even">
<td><strong>Phenomena Explained</strong>:</td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
</tr>
<tr class="odd">
<td><em>Dumbfounding (harmless wrongs)</em></td>
<td>(Not discussed)</td>
<td>(Not discussed)</td>
<td>Explained</td>
<td>Denied</td>
<td>Explained: learning history</td>
</tr>
<tr class="even">
<td><em>Wrongless harms</em></td>
<td>(Not discussed)</td>
<td>(Not discussed)</td>
<td>(Not discussed)</td>
<td>Denied</td>
<td>Explained: learning history</td>
</tr>
<tr class="odd">
<td><em>Typicality</em></td>
<td>(Not discussed)</td>
<td>(Not discussed)</td>
<td>(Not discussed)</td>
<td>Matching of “prototype”</td>
<td>Context-dependent</td>
</tr>
<tr class="even">
<td><em>Contextual influences</em></td>
<td>Specific: Personal-impersonal</td>
<td>Specific: Emotion / cognition</td>
<td></td>
<td>Specific: Action-outcome</td>
<td>Specific: Harm-based</td>
</tr>
</tbody>
</table>
</div>
</div>
<div id="challenges-limitations-and-responses" class="section level1">
<h1>Challenges, Limitations, and Responses</h1>
<p>MJAC assumes that both relative stability and various contextual influences can be understood in terms of the learning history of the person. Given this assumption, a key challenge associated with MJAC is that it is impossible to gain access to the complete learning history of any person. That said, this limitation is not specific to MJAC; learning influences on moral judgment have been widely discussed <span class="citation">(e.g., Campbell <a href="#ref-campbell_learning_2017" role="doc-biblioref">2017</a>; Crockett <a href="#ref-crockett_models_2013" role="doc-biblioref">2013</a>; Cushman <a href="#ref-cushman_action_2013" role="doc-biblioref">2013</a>; Haidt <a href="#ref-haidt_emotional_2003" role="doc-biblioref">2003</a>; Kohlberg <a href="#ref-kohlberg_stages_1969" role="doc-biblioref">1969</a>, <a href="#ref-kohlberg_kohlberg_1985" role="doc-biblioref">1985</a>; Narvaez <a href="#ref-narvaez_neokohlbergian_2005" role="doc-biblioref">2005</a>; Pizarro and Bloom <a href="#ref-pizarro_intelligence_2003" role="doc-biblioref">2003</a>; Railton <a href="#ref-railton_moral_2017" role="doc-biblioref">2017</a>)</span>. MJAC proposes making the role of the learning history an explicit consideration in attempting to understand the making of moral judgments. This will be a challenging yet, in our view, worthwhile endeavor, integrating a diverse range of methods and requiring greater interdisciplinary collaboration between the various domains of moral psychology.</p>
<div id="effects-not-directly-predicted-by-mjac" class="section level2">
<h2>Effects not Directly Predicted by MJAC</h2>
<p>Despite predicting a broad range of contextual variability, there remain some influences on moral judgment that are not directly predicted by MJAC. Three such phenomena are the doctrine of double effect, moral luck, and moral conviction. While not directly predicted, these phenomena further illustrate the variability and complexity that theories of moral judgment must account for.</p>
<p>Firstly, the doctrine of double effect is the name given to the finding that people view causing harm as a means to achieving a goal as worse than causing harm as a side-effect of achieving a goal <span class="citation">(Doris <a href="#ref-doris_moral_2010" role="doc-biblioref">2010</a>; Mikhail <a href="#ref-mikhail_rawls_2000" role="doc-biblioref">2000</a>)</span>. Above, we have presented the doctrine of double effect as a limitation of model-based approaches <span class="citation">(Crockett <a href="#ref-crockett_models_2013" role="doc-biblioref">2013</a>; Cushman <a href="#ref-cushman_action_2013" role="doc-biblioref">2013</a>)</span>; the action-outcome distinction does not adequately explain why people should make a distinction between harm as a means and harm as a side effect (henceforth, means-side effect). Similarly, this means-side effect distinction is not directly predicted by MJAC. Interestingly, it has been found that people apply this distinction even though they cannot reliably articulate it <span class="citation">(Cushman, Young, and Hauser <a href="#ref-cushman_role_2006" role="doc-biblioref">2006</a>; Hauser et al. <a href="#ref-hauser_dissociation_2007" role="doc-biblioref">2007</a>)</span>. This suggests a similarity with moral dumbfounding, and the possibility of a common explanation. In the case of moral dumbfounding, MJAC posits that people implicitly learn (through continued and consistent type-token interpretation) that something is wrong and that learning the categorization occurs independently of learning the reasons for the categorization. Distinguishing side effects from means is much more subtle than distinguishing different types of actions, however, there is no reason for such a distinction not to emerge through the same process of type-token interpretation if others are making the same distinction in their moral judgments <span class="citation">(Cushman, Young, and Hauser <a href="#ref-cushman_role_2006" role="doc-biblioref">2006</a>; Hauser et al. <a href="#ref-hauser_dissociation_2007" role="doc-biblioref">2007</a>; Mikhail <a href="#ref-mikhail_rawls_2000" role="doc-biblioref">2000</a>)</span>. In this way, while it is not an obvious a priori prediction of MJAC, the doctrine of double effect is not inconsistent with its assumptions.</p>
<p>The second known effect that is not directly predicted by MJAC is the phenomenon of moral luck. Moral luck demonstrates that different outcomes can lead to different evaluations of the same behavior <span class="citation">(Nagel <a href="#ref-nagel_moral_1979" role="doc-biblioref">1979</a>, <a href="#ref-nagel_moral_2013" role="doc-biblioref">2013</a>; Williams <a href="#ref-williams1981moral" role="doc-biblioref">1982</a>; Wolf <a href="#ref-wolf_moral_2001" role="doc-biblioref">2001</a>; Young, Nichols, and Saxe <a href="#ref-young_investigating_2010" role="doc-biblioref">2010</a>)</span>. Consider the following two scenarios <span class="citation">(adapted from Wolf <a href="#ref-wolf_moral_2001" role="doc-biblioref">2001</a>; see also Royzman and Kumar <a href="#ref-royzman_consequential_2004" role="doc-biblioref">2004</a>; Williams <a href="#ref-williams1981moral" role="doc-biblioref">1982</a>)</span>:</p>
<p><em>Jo</em></p>
<blockquote>
<p>A truck driver (Jo), needs to make an emergency stop. Jo has neglected to check the brakes of the truck recently. When attempting to stop the truck, Jo loses control and the truck crashes into the ditch.</p>
</blockquote>
<p><em>Pat</em></p>
<blockquote>
<p>A truck driver (Pat), needs to make an emergency stop. Pat has neglected to check the brakes of the truck recently. When attempting to stop the truck, Pat loses control and the truck runs over a child.</p>
</blockquote>
<p>The actions of Jo and Pat are the same, however, previous research has shown that in situations like this, people are likely to view Pat as more morally blameworthy than Jo <span class="citation">(Walster <a href="#ref-walster_assignment_1966" role="doc-biblioref">1966</a>; Wells and Gavanski <a href="#ref-wells_mental_1989" role="doc-biblioref">1989</a>; Young, Nichols, and Saxe <a href="#ref-young_investigating_2010" role="doc-biblioref">2010</a>)</span>. People are more harsh in their moral judgments of the same actions when the actions result in negative outcomes. Williams <span class="citation">(<a href="#ref-williams1981moral" role="doc-biblioref">1982</a>; see Wolf <a href="#ref-wolf_moral_2001" role="doc-biblioref">2001</a>)</span> is attributed with coining the phrase “moral luck” to describe this asymmetry of judgments of actions based on outcomes.</p>
<p>As with the trolley problem, and the emergence of typicality, MJAC explains the phenomenon of moral luck with reference to the consistency of previous categorizations. Causing harm to another person is relatively consistently categorized as <em>MORALLY WRONG</em> <span class="citation">(Cushman et al. <a href="#ref-cushman_simulating_2012" role="doc-biblioref">2012</a>; Schein and Gray <a href="#ref-schein_theory_2018" role="doc-biblioref">2018</a>; though not with perfect consistency, e.g., Alicke <a href="#ref-alicke_selfinjuries_2012" role="doc-biblioref">2012</a>; McHugh et al. <a href="#ref-mchugh_reasons_2020" role="doc-biblioref">2020</a>)</span>. This relative consistency means that encountering an event in which the actions of an agent cause harm is highly likely to be categorized as <em>MORALLY WRONG</em>. The actions described in classic moral luck scenarios are typically ambiguous or minimally problematic. That is, they are not categorized as wrong with the same consistency. This mismatch in the consistency with which the actions vs the outcomes are categorized as wrong that leads to what we observe as moral luck. In effect, the harmful outcome may be viewed as a contextual influence that leads to harsher judgments of actions.</p>
<p>A third phenomenon that is not directly addressed by MJAC is moral conviction <span class="citation">(e.g., Skitka <a href="#ref-skitka_psychology_2010" role="doc-biblioref">2010</a>)</span>, or zeal in moral positions <span class="citation">(e.g., McGregor <a href="#ref-mcgregor_zeal_2006" role="doc-biblioref">2006</a>)</span>. While MJAC does not make specific claims about moral conviction, previous research has linked this to identity and identification with particular groups <span class="citation">(e.g., Greene <a href="#ref-greene_moral_2013" role="doc-biblioref">2013</a>; see also Proulx and Inzlicht <a href="#ref-proulx_five_2012" role="doc-biblioref">2012</a>; Heine, Proulx, and Vohs <a href="#ref-heine_meaning_2006" role="doc-biblioref">2006</a>)</span>, and more recently attitude strength has been linked with connectivity <span class="citation">(e.g., Dalege et al. <a href="#ref-dalege_network_2019" role="doc-biblioref">2019</a>)</span> We suggest that the meaning maintenance model provides an ideal framework for understanding zeal in moral categorization. According to the meaning maintenance model <span class="citation">(Heine, Proulx, and Vohs <a href="#ref-heine_meaning_2006" role="doc-biblioref">2006</a>)</span>, there are four primary domains of meaning: certainty, self-esteem, social relations, and mortality. Where non-moral category knowledge constitutes meaning in the domain of certainty <span class="citation">(Heine, Proulx, and Vohs <a href="#ref-heine_meaning_2006" role="doc-biblioref">2006</a>)</span>, moral knowledge additionally holds meaning in the social domain <span class="citation">(Greene <a href="#ref-greene_moral_2013" role="doc-biblioref">2013</a>; see also Proulx and Inzlicht <a href="#ref-proulx_five_2012" role="doc-biblioref">2012</a>; Heine, Proulx, and Vohs <a href="#ref-heine_meaning_2006" role="doc-biblioref">2006</a>)</span>. We hypothesize that it is this spanning of both the certainty and the social domains of meaning that leads to moral zeal.</p>
<p>When we apply this insight to the broader framework of MJAC, it appears that some contexts (namely social/group contexts) matter more in the development of <em>robust</em> moral categories. We hypothesize that robustness in moral categorization is related to the consistency of categorization across multiple (social) contexts. Consider the categorization of sexist jokes as <em>MORALLY WRONG</em>. Some groups would endorse this categorization, and there are groups who would disagree. The degree to which a person will be motivated to defend this categorization will be related to the social groups they are members of, and the consistency across these groups. Someone who agrees with this categorization but spends a lot of time tolerating “locker room talk” will be less zealous than someone who socializes with people who openly identify as feminists.</p>
</div>
</div>
<div id="novelties-and-conclusion" class="section level1">
<h1>Novelties and Conclusion</h1>
<p>MJAC builds on the assumption that moral categorization is dynamical, context-dependent, and occurs as part of goal-directed activity. Given these assumptions, we propose that the best way to understand variability in moral categorization is by accounting for both the learning history and the current goals. Drawing on this, we have identified two general core predictions of MJAC:</p>
<ul>
<li>Stability in moral categorization emerges through continued and consistent type-token interpretation</li>
<li>Robustness in moral categorization emerges through consistency across multiple contexts</li>
</ul>
<p>In addition to these general core predictions, throughout the preceding discussion, we have identified a number of more specific predictions, these are summarized in Table 2. While some predictions are consistent with existing approaches, other predictions are novel and informed by MJAC.</p>
<p>Table 2: Specific predictions of MJAC</p>
<table>
<colgroup>
<col width="33%" />
<col width="33%" />
<col width="33%" />
</colgroup>
<thead>
<tr class="header">
<th>Phenomenon</th>
<th>Explanation / General Prediction</th>
<th>Specific predictions</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td>Typicality</td>
<td>Continued and consistent type-token interpretation</td>
<td>Depends on current goals, personal judgments of typicality can vary depending on context/culture</td>
</tr>
<tr class="even">
<td>Dumbfounding</td>
<td>- Categorization is learned independently of reasons</td>
<td>- Order effects / Prior commitments <br> - Inconsistencies readily ignored where possible <br> - Competing goals (consistency vs principled)</td>
</tr>
<tr class="odd">
<td></td>
<td>- Can occur for any category</td>
<td>- Individual differences in categorizations that lead to dumbfounding</td>
</tr>
<tr class="even">
<td>Order effects</td>
<td>- Priming</td>
<td>- Equivalent reaction time effects <span class="citation">(e.g., Barsalou <a href="#ref-barsalou_contextindependent_1982" role="doc-biblioref">1982</a>, <a href="#ref-barsalou_situated_2003" role="doc-biblioref">2003</a>)</span> <br> - Equivalent flexibility in moral categorization <span class="citation">(Higgins, Bargh, and Lombardi <a href="#ref-higgins_nature_1985" role="doc-biblioref">1985</a>)</span></td>
</tr>
<tr class="odd">
<td>Foreign-language effect</td>
<td>- Foreign-language creates a novel context, reducing influence of contextual influences</td>
<td>- Should be reduced by fluency (but not proficiency) where fluency reflects immersive experience with the language, allowing for the these contextual influences to be re-established</td>
</tr>
<tr class="even">
<td>Emotional influences</td>
<td>- Mood dependent memory</td>
<td>- Mindset manipulations <br> - Drawing attention to possible influence of emotion</td>
</tr>
<tr class="odd">
<td>Actor / character</td>
<td>- We are motivated to view close others positively:</td>
<td>- Categorize the action when close other transgresses <br> - Categorize the actor when close other is virtuous</td>
</tr>
<tr class="even">
<td></td>
<td>- We are motivated to understand others so we can predict their behavior</td>
<td>- Bias in favor of categorizing actors rather than actions when evaluating the actions of strangers <br> - Especially if there is a possibility for future interactions</td>
</tr>
<tr class="odd">
<td>Robustness / Zeal</td>
<td>- Consistency across social contexts leads to more robustness</td>
<td>- People with a more diverse range of social groups should have more tolerance towards alternative views <br> - For any individual, categorizations that are consistent across multiple social groups should be more robust than categorizations that vary between groups</td>
</tr>
</tbody>
</table>
<p>We have identified specific phenomena that MJAC can explain better than existing approaches. Furthermore, we have identified particular goals that predict specific patterns of variability in the making of moral judgments (e.g., appearing consistent, viewing close others positively, predicting others’ behavior in anticipation of future interactions). We do not present an exhaustive list, however, we illustrate the value of accounting for goal-directed activity in attempting to understand moral categorization.</p>
<p>In addition to the explanatory and predictive power outlined above, a further strength of MJAC is parsimony. If the processes of categorization and making moral judgments have identical underlying cognitive mechanisms, it will be possible to draw on our knowledge about the nature of category formation to further our understanding of moral judgments.</p>
</div>
<div id="conclusion" class="section level1">
<h1>Conclusion</h1>
<p>It is <em>not</em> terribly simple, the good guys are <em>not</em> always stalwart and true, and the bad guys are <em>not</em> easily distinguished by their pointy horns or black hats. Knowing right from wrong is not a simple process of applying an abstract principle to a particular situation. Decades of research in moral psychology have shown that our moral judgments can vary from one situation to the next, while a growing body of evidence indicates that people cannot always provide reasons for their moral judgments. Understanding the making of moral judgments requires accounting for the full complexity and variability of our moral judgments. MJAC provides a framework for studying moral judgment that incorporates this dynamism and context-dependency into its core assumptions. We have argued that this sensitivity to the dynamical and context-dependent nature of moral judgments provides MJAC with superior explanations for known moral phenomena while simultaneously providing MJAC with the power to explain a greater and more diverse range of phenomena than existing approaches.</p>
</div>
<div id="references" class="section level1 unnumbered">
<h1 class="unnumbered">References</h1>
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</div>
<div class="footnotes">
<hr />
<ol>
<li id="fn1"><p>We use the term “skill” to refer to the type of automaticity that is developed through practice/rehearsal. This does not imply any objectivist moral truth that can be accessed by moral “experts”, we refer only to the cognitive processes involved.<a href="#fnref1" class="footnote-back">↩︎</a></p></li>
<li id="fn2"><p>Where <em>Token</em> denotes a token that may be viewed as ‘prototypical’ for a given type.<a href="#fnref2" class="footnote-back">↩︎</a></p></li>
<li id="fn3"><p>Relatedly, for favourable judgements, we predict the opposite effect. That is, if for morally praiseworthy actions are being performed by a close other, the target of the categorisation is more likely to be the actor than the action, helping to maintain a positive view of the close other <span class="citation">(Forbes <a href="#ref-forbes_when_2018" role="doc-biblioref">2018</a>; Murray, Holmes, and Griffin <a href="#ref-murray_benefits_1996" role="doc-biblioref">1996</a><a href="#ref-murray_benefits_1996" role="doc-biblioref">a</a>, <a href="#ref-murray_selffulfilling_1996" role="doc-biblioref">1996</a><a href="#ref-murray_selffulfilling_1996" role="doc-biblioref">b</a>)</span>.<a href="#fnref3" class="footnote-back">↩︎</a></p></li>
<li id="fn4"><p>Recall the discussion regarding the ad-hoc category <em>THINGS TO PACK INTO A SUITCASE</em> <span class="citation">(Barsalou <a href="#ref-barsalou_deriving_1991" role="doc-biblioref">1991</a>, <a href="#ref-barsalou_situated_2003" role="doc-biblioref">2003</a>)</span>.<a href="#fnref4" class="footnote-back">↩︎</a></p></li>
<li id="fn5"><p>These figures are not reported in <span class="citation">McHugh et al. (<a href="#ref-mchugh_reasons_2020" role="doc-biblioref">2020</a>)</span>, however see <span class="citation">McHugh et al. (<a href="#ref-mchugh_reasons_2018a" role="doc-biblioref">2018</a>)</span> for full data sets.<a href="#fnref5" class="footnote-back">↩︎</a></p></li>
</ol>
</div>
</div>
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80ebe48d821c402e741ffa8c0d7d879d19a38498 | 1,446 | md | Markdown | CatchingUp-With-HaskellForCats.md | HaskellForCats/HaskellForCats | 2d7a15c0cdaa262c157bbf37af6e72067bc279bc | [
"MIT"
] | 2 | 2019-08-19T17:59:00.000Z | 2019-11-06T16:52:35.000Z | CatchingUp-With-HaskellForCats.md | HaskellForCats/HaskellForCats | 2d7a15c0cdaa262c157bbf37af6e72067bc279bc | [
"MIT"
] | null | null | null | CatchingUp-With-HaskellForCats.md | HaskellForCats/HaskellForCats | 2d7a15c0cdaa262c157bbf37af6e72067bc279bc | [
"MIT"
] | null | null | null | Catching Up with Us:
We are currently on video 10 in Erik Meyer's functional programming series.
Eriks video series: https://channel9.msdn.com/Series/C9-Lectures-Erik-Meijer-Functional-Programming-Fundamentals/Lecture-Series-Erik-Meijer-Functional-Programming-Fundamentals-Chapter-1
For catching up with us the following might be more helpful.
"Learn You a Haskell for a Greater Good"
http://learnyouahaskell.com/chapters
Most important for playing along at home:
Downloading and installing the G.H.C. (Glasgow Haskell Compiler) which is part of: http://www.haskell.org/platform/
For OSX-Maverick heres some special instructions, http://haskell-workshop.github.io/tutorials/osx/2013-10-23-mavericks-ghc.html
NOTE! the Haskell-platform, Most of what we need. The book and video both use the deprecated Hugs compiler; it works okay; but the Glasgow Haskell Compiler GHC-7.6.x, is the current standard.
If you end up with GHC-7.4.x it will also work but the latest haskell-web-frameworks require GHC-7.6.x
When it's installed and you open a command prompt (terminal) and type ghci you should get the prelude prompt,
Prelude>
And you are ready to go.
Any text editor will do, but some do a better job of highlighting and such.
Popular choices are Emacs, and Vim.
Sublime also work Notepad++.
Not matter the editor
It's a good idea to set tabs to spaces otherwise GHC will complain.
| 38.052632 | 193 | 0.759336 | eng_Latn | 0.989177 |
80ec34dd06d2ebd0d7906afe466ebf6fb24e0a50 | 3,238 | markdown | Markdown | _posts/2018-03-12-some-common-api-data-types-to-put-to-use.markdown | mgboydcom/api-evangelist | f99c16b197adaa5f635c34782a89728ab7e9a53d | [
"CC-BY-3.0"
] | null | null | null | _posts/2018-03-12-some-common-api-data-types-to-put-to-use.markdown | mgboydcom/api-evangelist | f99c16b197adaa5f635c34782a89728ab7e9a53d | [
"CC-BY-3.0"
] | 2 | 2021-02-05T22:55:24.000Z | 2021-02-11T03:16:55.000Z | _posts/2018-03-12-some-common-api-data-types-to-put-to-use.markdown | mgboydcom/api-evangelist | f99c16b197adaa5f635c34782a89728ab7e9a53d | [
"CC-BY-3.0"
] | 1 | 2021-02-08T01:31:38.000Z | 2021-02-08T01:31:38.000Z | ---
published: true
layout: post
title: Some Common API Data Types To Put To Use
date: 2018-03-12T13:00:00.000Z
tags:
- API Evangelist
- Design
- Standards
- Definitions
image: >-
https://s3.amazonaws.com/kinlane-productions/algo-rotoscope/stories/carryload_dali_three.jpg
---
<p><img src="{{ page.image }}" width="45%" align="right" style="padding: 15px;" /></p>I'm continuing my exploration of the API design guidance published by leading API providers. This time, I am taking a look at the API design guide publish by Adidas, [specifically the portion of it addressing what some of the common API types should be](https://adidas-group.gitbooks.io/api-guidelines/content/application/common-data-types.html). Providing some API design essentials, that all of us API providers should be baking into our APIs by default.
Here is the short list of [default standards Adidas](https://adidas-group.gitbooks.io/api-guidelines/) is supporting across their API operations:
- **Date and Time Format** - Date and Time MUST always conform to the [ISO 8601 format](https://en.wikipedia.org/wiki/ISO_8601) e.g.: 2017-06-21T14:07:17Z (date time) or 2017-06-21 (date), it MUST use the UTC (without time offsets).
- **Duration Format** - Duration format MUST conform to the [ISO 8601 format](https://en.wikipedia.org/wiki/ISO_8601) standard e.g.: P3Y6M4DT12H30M5S (three years, six months, four days, twelve hours, thirty minutes, and five seconds).
- **Time Interval Format** - Time Interval format MUST conform to the [ISO 8601 format](https://en.wikipedia.org/wiki/ISO_8601) standard e.g.: 2007-03-01T13:00:00Z/2008-05-11T15:30:00Z.
- **Standard Time Stamps** - Where applicable, a resource representation SHOULD contain the standard timestamps: createdAt, updatedAt, and finishedAt, using the [ISO 8601 format](https://en.wikipedia.org/wiki/ISO_8601).
- **Language Code Format** - Language codes MUST conform to [the ISO 639](https://en.wikipedia.org/wiki/List_of_ISO_639-1_codes) e.g.: en for English.
- **Country Code Format** - Country codes MUST conform to [the ISO 3166-1](https://en.wikipedia.org/wiki/ISO_3166-1_alpha-2) alpha-2 e.g.: DE for Germany.
- **Currency Format** - Currency codes MUST conform to [the ISO 4217](https://en.wikipedia.org/wiki/ISO_4217) e.g.: EUR for Euro.
These are the common data formats we are all using across our APIs. We should be working to support standard formats wherever possible, embracing the wider web, and investing in the longevity and usability of our API designs. Making our APIs sure that they speak to as wide of audience as they possibly can, and operate in a consistent way, no matter which API you are integrating with.
I want to also give a shout out to the [API Stylebook](http://apistylebook.com/), where I'm easily finding these API design guides that I'm using for these stories, and including in my API design guidance in my consulting work. Also, I'd like to encourage other API providers to reuse the patterns present in these guides, as well as publishing their own API design governance guidance whenever possible. It is critical that we all share these stories, so that we can learn from each other, and emulate the healthiest patterns already in use across the space.
| 111.655172 | 559 | 0.765596 | eng_Latn | 0.957588 |
80ec5341c2b493d38b5593dc79a39e81d92a2c84 | 248 | md | Markdown | README.md | NanoCommons/workshops | 79524aee969d43842eab3fa006485943ca0a3d61 | [
"CC0-1.0"
] | null | null | null | README.md | NanoCommons/workshops | 79524aee969d43842eab3fa006485943ca0a3d61 | [
"CC0-1.0"
] | 1 | 2021-12-03T13:50:25.000Z | 2021-12-03T14:54:56.000Z | README.md | NanoCommons/workshops | 79524aee969d43842eab3fa006485943ca0a3d61 | [
"CC0-1.0"
] | null | null | null | # workshops
Repository for workshop material
* [Annotating Your Experimental Data workshop](2020-04-28/README.md) (April 2020)
* [Nanosafety and the semantic web: from natural language to computational processing](2021-12-08/README.md) (Dec 2021)
| 41.333333 | 119 | 0.78629 | eng_Latn | 0.833385 |
80ecaa0ab7dde1734dc767d19de1ef1aa4b55e6b | 63 | md | Markdown | tests/library/README.md | plantain-00/js-project-initializer | c96267ec74b555d81ab9dae9902d25490e939a50 | [
"MIT"
] | 1 | 2018-12-01T04:45:59.000Z | 2018-12-01T04:45:59.000Z | tests/library/README.md | plantain-00/js-project-initializer | c96267ec74b555d81ab9dae9902d25490e939a50 | [
"MIT"
] | 1 | 2020-07-17T22:40:58.000Z | 2020-07-17T22:40:58.000Z | tests/library/README.md | plantain-00/js-project-initializer | c96267ec74b555d81ab9dae9902d25490e939a50 | [
"MIT"
] | null | null | null | # rpc-on-ws
A lightweight RPC library on websocket connection.
| 21 | 50 | 0.793651 | eng_Latn | 0.876921 |
80ee5793154ea30e20a1f42d0af526eeddd7874d | 1,539 | md | Markdown | README.md | geological-survey-of-queensland/geoadmin-features-ont | 3f05ae9e3a1a377f9fd7a3deb86e37cd5dc6bbc1 | [
"CC-BY-4.0"
] | null | null | null | README.md | geological-survey-of-queensland/geoadmin-features-ont | 3f05ae9e3a1a377f9fd7a3deb86e37cd5dc6bbc1 | [
"CC-BY-4.0"
] | null | null | null | README.md | geological-survey-of-queensland/geoadmin-features-ont | 3f05ae9e3a1a377f9fd7a3deb86e37cd5dc6bbc1 | [
"CC-BY-4.0"
] | null | null | null | <img src="images/gsq.jpg" style="width:25%" />
# GSQ Geological Administrative Features Ontology
This ontology defines a series of subclasses of the [GeoSPARQL Ontology's](https://www.opengeospatial.org/standards/geosparql) `Feature` class that are used by the [Geological Survey of Queensland](https://linked.data.gov.au/org/gsq) to admininster geologically-based resource extraction in the Australian state of Queensland. The classes include `Block`, `Permit` etc., some of which are defined in Queensland or Australian legislation.
## License
The content of this API is licensed for use under the [Creative Commons 4.0 License](https://creativecommons.org/licenses/by/4.0/). See the [license deed](LICENSE) all details.
## Citation
If you wish to cite this ontology, please do so like this:
Geological Survey of Queensland (2019) "Geological Administrative Features Ontology". An OWL ontology defining classes of geospatial Feature relevant to the mineral adminstration duties of the Geological Survey of Queensland. https://linked.data.gov.au/def/geoadminfeatures
## Contacts
*owner*:
**Geological Survey of Queensland**
*Within the Queensland Department of Natural Resources, Mines & Energy*
1 William St, Brisbane, Queensland, Australia
<https://www.business.qld.gov.au/industries/mining-energy-water/resources/geoscience-information/gsq>
<[email protected]>
*author*:
**Nicholas Car**
SURROUND Australia Pty Ltd
<[email protected]>
<http://orcid.org/0000-0002-8742-7730>
| 51.3 | 437 | 0.776478 | eng_Latn | 0.867756 |
80eebec0e2777aeb8528d48fef2178c1a06ef736 | 4,409 | md | Markdown | docs/index.md | mattgiguere/pyutil | 3b44323d9e0b991998f1959b0e903a9a0164420f | [
"MIT"
] | 3 | 2016-10-07T12:54:00.000Z | 2021-03-15T16:26:08.000Z | docs/index.md | mattgiguere/pyutil | 3b44323d9e0b991998f1959b0e903a9a0164420f | [
"MIT"
] | null | null | null | docs/index.md | mattgiguere/pyutil | 3b44323d9e0b991998f1959b0e903a9a0164420f | [
"MIT"
] | 1 | 2016-01-18T19:27:11.000Z | 2016-01-18T19:27:11.000Z | # Welcome to pyutil
###A Python Library of Matt's Useful Utility Files
Project Directory Layout
========================
- **docs**: This documentation
- **pyutil**: The pyutil code
- **site**: The mkdocs build of this site
- **tests**: Unit testing code
Code Documentation
==================
##blazeFit.py
A recursive routine that fits the blaze function of an echelle spectrum. This is useful for cross correlation or equivalent width determination. The algorithm will continue until either the rms reaches `maxrms` or `numcalls` has been reached, whichever comes first.
**Parameters**:
- *wav*: the wavelength
- *spec*: the relative flux
- *maxrms*: the maximum root mean square of the residuals
- *numcalls*: the maximum number of recursive calls to make.
**Returns**:
The returned `fit` array contains the `numpy.polyfit` 7th order polynomial coefficients of the best fit to the continuum.
**Example**:
```python
from pyutil.blazeFit import blazeFit
fit = blazeFit(wav, spec, maxrms, numcalls=150)
```
##connectChironDB.py
A routine for establishing a connection to the CHIRON MySQL database.
**Parameters**:
- *legacy*: [Optional] Boolean argument specifying what will be returned. The default is False, which returns an SQLAlchemy+PyMySQL engine. If True, a PyMySQL connection is returned instead. The SQLAlchemy+PyMySQL engine returned as the default was added because the MySQL connections are deprecated in pandas.
**Returns**:
Either a SQLAlchemy+PyMySQL engine or a PyMySQL connection, depending on the input.
**Examples**:
- For a pymysql connection:
```python
from pyutil import connectChironDB as ccdb
conn = ccdb.connectChironDB(legacy=True)
```
- For an SQLAlchemy `mysql+pymysql` engine:
The MySQL connection flavor has been deprecated in `pandas` and will be removed in a future version. However, MySQL will still be supported through SQLAlchemy. To prepare for this (and get rid of the annoying warning messages), the default object returned from `connectChironDB` is now an SQLAlchemy engine:
```python
from pyutil import connectChironDB as ccdb
engine = ccdb.connectChironDB()
```
##getChironSpec.py
A routine for retrieving and continuum normalizing spectra from the CHIRON Spectrometer. Continuum normalization in this case divides each echelle order by a polynomial fit to the respective order in the master flat field.
**Parameters**:
- *obnm*: The observation name to restore. CHIRON observation names are in the format chiyymmdd.####, where yymmdd are the year, month, and day, and #### is the chronological sequence number that identifies the observation.
- *normalized*: [Optional] Boolean argument specifying if the returned spectrum should be normalized or not. The default is True. If True, the blaze function is determined by fitting the nightly quartz exposures with a polynomial model.
**Returns**:
A CHIRON spectrum that is optionally normalized.
**Example**:
```python
from pyutil.getChironSpec import getChironSpec
normspec = getChironSpec('chi150223.1125', normalized=True)
```
##pearsonBootstrapPvalue.py
A routine for determining the p-value to the Pearson linear correlation coefficient through a Bootstrap analysis.
##wgtdMeans.py
A routine for binning heteroscedastic unevenly sampled time series data. This is similar to velplot in IDL. Specified time bins are used to bin the data. For each bin a weighted mean value is calculated.
**Parameters**:
- *df*: An input pandas DataFrame with at least 3 columns: the observation times, the measurements, and the associated uncertainties.
- *time*: [Optional] The column name of the observation time column in the DataFrame. The default value is "JD".
- *rv*: [Optional] The column name of the observation column. The default value is "mnvel".
- *unc*: [Optional] The default column name for the uncertainty column. If not specified, "errvel" will be used.
- *timebin*: [Optional] The bin size, in the same units as the time column of the input DataFrame. The default value is 0.5.
- *phase*: [Optional] The fractional amount the bin center time will be shifted by. For example, if the input time column is days, and timebin=2, setting a phase=0.5 will shift the times of the returned bins by one day.
**Returns**:
A pandas DataFrame with the binned weighted mean values.
**Example**:
```python
dfout = wgtdMean(dfin, time="JD", rn="mnvel", unc="errvel", timebin=0.5, phase=0)
```
| 40.081818 | 310 | 0.759809 | eng_Latn | 0.993831 |
80eedc96ae3c73ce4270e3824545fc9ffa84d5da | 746 | md | Markdown | README.md | yoggy/simple_slack_incoming_webhook | b560cb7bf4e5c632843e8b396fac256accbfdbef | [
"MIT"
] | null | null | null | README.md | yoggy/simple_slack_incoming_webhook | b560cb7bf4e5c632843e8b396fac256accbfdbef | [
"MIT"
] | null | null | null | README.md | yoggy/simple_slack_incoming_webhook | b560cb7bf4e5c632843e8b396fac256accbfdbef | [
"MIT"
] | null | null | null | # simple_slack_incoming_webhook
```
$ go build
$ ./simple_slack_incoming_webhook
usage: ./simple_slack_incoming_webhook slack_incoming_webhook_url
example:
$ somecommand | ./simple_slack_incoming_webhook https://hooks.slack.com/services/xxxxxxxxxxxx
$ date | ./simple_slack_incoming_webhook https://hooks.slack.com/services/xxxxxxxxxxxx
2022/01/25 18:43:21 [+] url=https://hooks.slack.com/services/xxxxxxxxxxxx
2022/01/25 18:43:21 [+] {"text":"2022年 1月 25日 火曜日 18:43:21 JST\n"}
2022/01/25 18:43:22 [*] 200 OK
$ crontab -e
0 10 * * * date | ./simple_slack_incoming_webhook https://hooks.slack.com/services/xxxxxxxxxxxx 2>/dev/null
```
## Copyright and license
Copyright (c) 2022 yoggy
Released under the [MIT license](LICENSE.txt)
| 27.62963 | 108 | 0.75067 | yue_Hant | 0.627829 |
80efef56e4e5ef1ebfd6cd54372c8621970fd87e | 1,065 | md | Markdown | _posts/2021-08-25-101-things-i-learned-in-urban-design-school.md | baneteasnow/new | 8e96ad361213a407d86a446337a7d74ecfaf1efe | [
"MIT"
] | 1 | 2022-01-04T21:29:53.000Z | 2022-01-04T21:29:53.000Z | _posts/2021-08-25-101-things-i-learned-in-urban-design-school.md | baneteasnow/new | 8e96ad361213a407d86a446337a7d74ecfaf1efe | [
"MIT"
] | null | null | null | _posts/2021-08-25-101-things-i-learned-in-urban-design-school.md | baneteasnow/new | 8e96ad361213a407d86a446337a7d74ecfaf1efe | [
"MIT"
] | null | null | null | ---
layout: post
category: book
title: "101 Things I Learned in Urban Design School | Matthew Frederick, Vikas Mehta (Kitap)"
kitap: "101 Things I Learned in Urban Design School"
yazar: "Matthew Frederick, Vikas Mehta"
yil: "2020"
sayfa: "216"
goodreads: "https://www.goodreads.com/book/show/39730422-101-things-i-learned-in-urban-design-school"
last_modified_at: 2021-08-25
published: true
description: "101 Things I Learned in Urban Design School, şehir planlama ile ilgili temel kavramları ele alıyor."
posted: 2021-08-25
tag: "şehir planlama"
image: "/assets/new/101-things-i-learned-in-urban-design-school.jpg"
---
101 Things I Learned in Urban Design School, şehir planlama ile ilgili temel kavramları ele alıyor. Başlıklardan bazıları uygulamaya yönelik tasarım ilkelerini içerirken bazıları da problem çözmeye yönelik olarak yaratıcı ve pratik fikirler veriyor.
Kitabın öncelikle şehir planlamaya odaklanıyor. Her gün içinde yürüdüğümüz sokaklar ve parkların tasarımının, binaların konumunun ne olması gerektiği noktasında da sıradan okuyucuya aktarıyor.
| 50.714286 | 249 | 0.802817 | tur_Latn | 0.999521 |
80f0a10f8c2d9912f29f4ffb18fc07d3b512a772 | 975 | md | Markdown | content/sections/3-About.pl.md | karpinski1994/lbrobotyziemne.pl | 4d9ff1af1a8775e888dc6e9589cff91dcad45a03 | [
"MIT"
] | null | null | null | content/sections/3-About.pl.md | karpinski1994/lbrobotyziemne.pl | 4d9ff1af1a8775e888dc6e9589cff91dcad45a03 | [
"MIT"
] | null | null | null | content/sections/3-About.pl.md | karpinski1994/lbrobotyziemne.pl | 4d9ff1af1a8775e888dc6e9589cff91dcad45a03 | [
"MIT"
] | null | null | null | ---
anchor: "O firmie"
header: "Firma Usługowa Bartosz Ludynia"
subheader: ""
content: "FU Bartosz Ludynia to przedsiębiorstwo zorientowane wokół prac ziemnych. Wykopy są moją pasją od dzieciństwa. Pasję do prac związanych z budownictwem wraz z bezcenną wiedzą oraz doświadczeniem odziedziczyłem po ojcu, prowadzącym firmę w tej branży z sukcesami od wielu lat. Do grona moich stałych klientów można zaliczyć osoby indywidualne oraz duże podmioty jak kamieniarstwa nagrobkowe czy nawet kopalnie. Moje usługi cechuje fachowość, rzetelność a także prostota oraz elastyczność w zakresie komunikacji. Na swoje prace daję gwarancję zadowolenia oraz spokoju na lata."
subcontent: "Zapraszam do kontaktu Bartosz Ludynia."
teamMember: [
{
imageFileName: "team/lb-logo.jpg",
header: "",
subheader: "Przedsiębiorstwo świadczące usługi z zakresu transportu oraz robót ziemnych.",
social: {
twitter: "#",
facebook: "#",
linkedin: "#"
}
},
]
---
| 48.75 | 583 | 0.754872 | pol_Latn | 0.999978 |
80f1c4182c571465cbb3bdd62ff01913a57e493e | 1,548 | md | Markdown | articles/active-directory/active-directory-reporting-sign-ins-from-possibly-infected-devices.md | OpenLocalizationTestOrg/azure-docs-pr15_hr-HR | 94470f6d3849fb1d48d443d49ffe0217ddba2f80 | [
"CC-BY-3.0",
"CC-BY-4.0",
"MIT"
] | null | null | null | articles/active-directory/active-directory-reporting-sign-ins-from-possibly-infected-devices.md | OpenLocalizationTestOrg/azure-docs-pr15_hr-HR | 94470f6d3849fb1d48d443d49ffe0217ddba2f80 | [
"CC-BY-3.0",
"CC-BY-4.0",
"MIT"
] | null | null | null | articles/active-directory/active-directory-reporting-sign-ins-from-possibly-infected-devices.md | OpenLocalizationTestOrg/azure-docs-pr15_hr-HR | 94470f6d3849fb1d48d443d49ffe0217ddba2f80 | [
"CC-BY-3.0",
"CC-BY-4.0",
"MIT"
] | null | null | null | <properties
pageTitle="Prijavite se ins vjerojatno zaražene uređaja"
description="Izvješće koje sadrži znak u pokušaja izvršen s uređaja na kojem može imati neke od zlonamjernog softvera (zlonamjernog softvera)."
services="active-directory"
documentationCenter=""
authors="SSalahAhmed"
manager="gchander"
editor=""/>
<tags
ms.service="active-directory"
ms.workload="identity"
ms.tgt_pltfrm="na"
ms.devlang="na"
ms.topic="article"
ms.date="03/04/2016"
ms.author="saah;kenhoff"/>
# <a name="sign-ins-from-possibly-infected-devices"></a>Prijavite se ins vjerojatno zaražene uređaja
Izvješće nije pokušava prepoznati vaših korisnika uređaje koje ste postaju špijunskim i sada su dio na botnet. Ne možemo povezivanje IP adrese korisničku prijavu programskog dodatka prema IP adresama koje ćemo znati da je kontaktu s poslužiteljima botnet.
Preporuka: Ovom izvješću zastavicama IP adresa nije uređaje. Preporučujemo da se obratite se korisniku i pregled svih korisnika uređaje da biste bili sigurni. Moguće je i je li špijunskim korisničke osobnim uređajem ili nekome tko nije korisnika koji je koristio istu IP adresu kao korisnik, ima zaraženih uređaja.
Dodatne informacije o softverom adresu zlonamjernog softvera potražite u članku [Centar za zaštitu od zlonamjernog softvera](http://go.microsoft.com/fwlink/?linkid=335773).
| 55.285714 | 314 | 0.784884 | hrv_Latn | 0.882835 |
80f356e6559de3121195ad3ee3d6ea9151596f07 | 1,115 | md | Markdown | vendor/github.com/hashicorp/vault/website/source/docs/commands/operator/rotate.html.md | al4/vault-unsealer | 6d263fa4afedf3c6a4f8dcabb7065e0b01810ee4 | [
"Apache-2.0"
] | null | null | null | vendor/github.com/hashicorp/vault/website/source/docs/commands/operator/rotate.html.md | al4/vault-unsealer | 6d263fa4afedf3c6a4f8dcabb7065e0b01810ee4 | [
"Apache-2.0"
] | null | null | null | vendor/github.com/hashicorp/vault/website/source/docs/commands/operator/rotate.html.md | al4/vault-unsealer | 6d263fa4afedf3c6a4f8dcabb7065e0b01810ee4 | [
"Apache-2.0"
] | null | null | null | ---
layout: "docs"
page_title: "operator rotate - Command"
sidebar_current: "docs-commands-operator-rotate"
description: |-
The "operator rotate" rotates the underlying encryption key which is used to
secure data written to the storage backend. This installs a new key in the key
ring. This new key is used to encrypted new data, while older keys in the ring
are used to decrypt older data.
---
# operator rotate
The `operator rotate` rotates the underlying encryption key which is used to
secure data written to the storage backend. This installs a new key in the key
ring. This new key is used to encrypted new data, while older keys in the ring
are used to decrypt older data.
This is an online operation and does not cause downtime. This command is run
per-cluster (not per-server), since Vault servers in HA mode share the same
storage backend.
## Examples
Rotate Vault's encryption key:
```text
$ vault operator rotate
Key Term 3
Install Time 01 May 17 10:30 UTC
```
## Usage
There are no flags beyond the [standard set of flags](/docs/commands/index.html)
included on all commands.
| 30.135135 | 80 | 0.756951 | eng_Latn | 0.998327 |
80f3fe9918ef31f87c7a5ebb0e3523175bd353d9 | 333 | md | Markdown | README.md | scaled/markdown-mode | cc2277440e182bbd688577b51ae927fe1969f35d | [
"BSD-2-Clause"
] | null | null | null | README.md | scaled/markdown-mode | cc2277440e182bbd688577b51ae927fe1969f35d | [
"BSD-2-Clause"
] | null | null | null | README.md | scaled/markdown-mode | cc2277440e182bbd688577b51ae927fe1969f35d | [
"BSD-2-Clause"
] | null | null | null | # Scaled Markdown Mode
A [Scaled] major editing mode for editing Markdown files.
Documentation TBD.
## Distribution
Scaled Markdown Mode is released under the New BSD License. The most recent version of the code is
available at http://github.com/scaled/markdown-mode
[Scaled]: https://github.com/scaled/scaled "Scalable Editor"
| 25.615385 | 98 | 0.780781 | eng_Latn | 0.908276 |
80f40057f60ea3e73627d3e8173fb78806f41e17 | 10,631 | md | Markdown | _posts/2021-04-05-deploying-rails-vps-capistrano-systemd.md | J-Swift/talkingquickly.github.io | c301ec309399abba4e19816c950b01883b1a4586 | [
"MIT"
] | 2 | 2015-02-23T10:29:44.000Z | 2019-09-03T13:10:28.000Z | _posts/2021-04-05-deploying-rails-vps-capistrano-systemd.md | J-Swift/talkingquickly.github.io | c301ec309399abba4e19816c950b01883b1a4586 | [
"MIT"
] | null | null | null | _posts/2021-04-05-deploying-rails-vps-capistrano-systemd.md | J-Swift/talkingquickly.github.io | c301ec309399abba4e19816c950b01883b1a4586 | [
"MIT"
] | 1 | 2021-04-06T13:36:35.000Z | 2021-04-06T13:36:35.000Z | ---
layout : post
title: Setting up Ubuntu 20.04 for Rails app Deployment
date: 2021-04-04 15:40:00
categories: devops
biofooter: false
bookfooter: true
docker_book_footer: false
permalink: '/setting-up-ubuntu-20-04-focal-fossa-vps-for-rails-app-deployment'
---
Deploying Rails to a VPS with Capistrano remains one of the simplest and most reliable methods for getting a Rails app up-and running. With the likes of Hetzner Cloud, Digital Ocean and Linode providing inexpensive, reliable virtual machines, Rails app serving substantial amounts of traffic can be hosted with minimal cost and complexity.
We'll first use Chef to provision a VPS including securing and hardening the server, installing the correct Ruby version(s) and setting up Postgres and Redis. We'll then use Capistrano to deploy our Rails app, including appropriate systemd units to ensure our services are started automatically on boot .
<!--more-->
This tutorial is in two parts:
- **[Setting up a VPS for Rails app Deployment](/setting-up-ubuntu-20-04-focal-fossa-vps-for-rails-app-deployment)**
- [Deploying Rails to Ubuntu 20.04 with Capistrano](/deploying-rails-to-a-vps-with-capistrano-v3-and-systemd)
Note that this post is intended to be a tutorial rather than a reference, so the focus will be on the steps that need to be completed rather than exploring the why.
## The Stack
- Ubuntu 20.04 Focal Fossa (Current [LTS](https://ubuntu.com/about/release-cycle))
- PostgreSQL 13 (Other versions selectable)
- Ruby 3.0 (Other versions selectable)
- Redis, Memcached (optional)
## Chef
Rather than executing lots of commands by hand, we'll use Chef to automate the setup of the server. This means that when we need to provision another, identical server, we need just one command rather than having to try and remember all the shell commands. Chef is similar to tools such as Puppet or Ansible with the advantage for our use case that it's both written in and leverages for configuration, Ruby.
## Installing Chef
On OSX we can Install Chef by executing the following in a terminal:
```
curl https://packages.chef.io/files/stable/chef-workstation/21.2.303/mac_os_x/11.0/chef-workstation-21.2.303-1.x86_64.dmg --output /tmp/chef-workstation.dmg
hdiutil attach /tmp/chef-workstation.dmg
```
Then visiting the newly mounted "Chef Workstation" volume in finder, double clicking on the `.pkg` and following the installation prompts.
On Ubuntu / Debian based systems, Chef Workstation can be installed with:
```
curl https://packages.chef.io/files/stable/chef-workstation/21.2.303/debian/10/chef-workstation_21.2.303-1_amd64.deb --output /tmp/chef-workstation.deb
sudo dpkg -i /tmp/chef-workstation.deb
```
Alternatively the latest version can be downloaded from `https://downloads.chef.io/products/workstation`, this tutorial has been tested with version `21.2.303` but should be broadly compatible with `21.x` releases.
Once this step is complete, executing `chef` in a local terminal should give help output from Chef, rather than a command not found error.
## Installing Knife
Knife is the CLI tool that we will use to interact with Chef, we can install it by executing:
```
chef gem install knife-zero
```
Note this command should not be executed from within a folder with a `Gemfile` as this may lead to hard to debug `bundler not found` errors.
## Creating a VPS
We should now head to our favourite VPS provider. My current preference is Hetzner Cloud with Digital Ocean and Linode in close second.
For a non-trivial Rails application we probably don't want to go below 1GB of RAM, likewise since we're going to be running both our application and database servers on a single machine, we probably want at least 2 cores. 3 Cores and 4GB RAM is generally a comfortable starting point which is equivalent to Hetzners CPX21.
We can now choose Ubuntu 20.04 as the system image, boot it up and make a note of the IP address.
## Setup key based SSH
If we configured key based authentication as part of the VPS creation process, this step can be skipped.
If however when we execute `ssh USERNAME@SERVER_IP` (where `USERNAME` is the username our VPS provider gave us to use and `SERVER_IP` is our servers IP address) we are required to enter a password then we need to setup key based auth.
Key based authentication allows us to authenticate using our local public / private key pair rather than a password. This is important because as part of the server hardening process, we will later disable password based authentication completely.
We can copy our public key to the server with:
```
ssh-copy-id USERNAME@SERVER_IP
```
This will prompt us for our password one more time and then add our public key to the list of allowed keys in the remote servers `~/.ssh/authorized_keys`.
We should then be able to execute `ssh USERNAME@SERVER_IP` to login to our remote server without being required to enter a password. Note if we're still being required to enter the passphrase for our local SSH key, we can avoid this by executing `ssh-add` to temporarily store credentials in the local ssh agent.
## Getting the sample code
Next we need to clone the sample Chef [repository](https://github.com/TalkingQuickly/rails-server-template):
```
git clone [email protected]:TalkingQuickly/rails-server-template.git
```
And then enter the sample code folder with `cd rails-server-template`
## Preparing the node
We can now prepare the server (node in Chef terminology) for provisioning with the following command:
```
knife zero bootstrap SERVER_IP --connection-user SSH_USER --node-name NODE_NAME
```
replacing `SERVER_IP` with our servers IP address, `SSH_USER` with the same username we used when setting up key based login above and `NODE_NAME` with a friendly name for the node, e.g. `rails_app_server`.
This will connect to the remote server, install Chef and generate the local file `nodes/NODE_NAME.json`. This is the file where all details about the node and what should be installed on it will be stored.
If we've never connected to the node via SSH before, we may be asked to confirm the servers fingerprint by entering `Y` and pressing enter.
## Configuring the Node
The above step created a JSON file which stores information about the node in `nodes/NODE_NAME.json` but we should not edit that file directly.
Instead we can use the following command to edit the node definition:
```
knife node edit NODE_NAME
```
Replacing `NODE_NAME` with the name used above. This will open a JSON file in the editor defined in `knife.rb`, by default this will be `vim`, but we can change it to any editor we want by updating the `knife[:editor]` variable in `knife.rb`, e.g. `code --wait` for VSCode or `subl -n -w` for sublime text.
We can then update our configuration
```json
{
"name": "NODE_NAME",
"chef_environment": "_default",
"normal": {
"postgresql": {
"version" : "POSTGRES_VERSION",
"password": {
"postgres": "SOME_RANDOM_PASSSWORD"
}
},
"rbenv": {
"rubies": [
"RUBY_VERSION"
],
"global": "RUBY_VERSION",
"gems": {
"RUBY_VERSION": [
{
"name": "bundler"
}
]
}
},
"knife_zero": {
"host": "SERVER_IP"
},
"tags": [
]
},
"policy_name": null,
"policy_group": null,
"run_list": [
"role[server]",
"role[nginx-server]",
"role[postgres-server]",
"role[rails-app]",
"role[redis-server]",
"role[memcached-server]"
]
}
```
Replacing:
- `NODE_NAME` with the node name we've been using
- `SERVER_IP` with the server ip address
- `SOME_RANDOM_PASSWORD` with the password we want to be set for the postgres master user
- `RUBY_VERSION` in three places with the Ruby version we need for our Rails application
- `POSTGRES_VERSION` with the Postgres version we want, if in doubt 13 is the current stable release and should work for most setups
We can then save and close the node definition.
## Setting up users
Our final step is to create a non-root user which will later be used by Capistrano to deploy our application.
To do this we first generate a password for the user by executing the following locally:
```
openssl passwd -1 "SOME_RANDOM_PASSWORD"
```
And making a note of the output.
We then create a Chef data bag called `users` with an entry `deploy` using the following command:
```
knife data_bag create users deploy
```
This will create the file `data_bags/users/deploy.json` which we should then open and replace the contents with:
```json
{
"id": "deploy",
"password": "ENCRYPTED_PASSWORD",
"ssh_keys": [
"PUBLIC_KEY"
],
"groups": [
"sysadmin"
],
"shell": "/bin/bash"
}
```
Replacing `ENCRYPTED_PASSWORD` with the output of the `openssl` command above and `PUBLIC_KEY` with your public key, generally the contents of your local `~/.ssh/id_rsa.pub`.
## Applying configuration to the node
We're now ready to apply our configuration to the node with the following command:
```
knife zero converge "name:NODE_NAME" --ssh-user `SSH_USER`.
```
Replacing `NODE_NAME` with the name we used above and `SSH_USER` with the user we setup key based authentication for (often `root`).
The first time we run this, it will take a while as it has to install all of the servers components including compiling our Ruby version.
If we make changes to our configuration, for example by editing the node definition and overriding more values from roles, we simply run the above command again to have the changes applied.
## What we've set up
We now have a hardened server, ready to deploy a Rails application for. We can create an unlimited number of identical servers simply by following the above process.
The [book](https://leanpub.com/deploying_rails_applications) provides more detail on exactly what's going on behind the scenes and how to customise it but at a high level we have:
- Installed a firewall which limits access to ports 22 (SSH), 80 (HTTP) and 443 (HTTPS)
- Disabled password based SSH login and installed fail2ban to block suspicious logins
- Enabled automatic OS and core package updates
- Installed Nginx, Postgres, Redis and Memcached
## Next
Assuming everything has gone well, we can now continue to [deploying our Rails application with Capistrano](deploying-rails-to-a-vps-with-capistrano-and-systemd)
If you've run into any issues, please feel free to ping me on Twitter where I'm [@talkingquickly](https://www.twitter.com/talkingquickly) or open an issue on the [sample code repository](https://github.com/TalkingQuickly/rails-server-template) and I'll do my best to help. | 42.354582 | 408 | 0.751576 | eng_Latn | 0.995986 |
80f4f9bb1cf2ed1b63569c2072463989df13bf38 | 6,641 | md | Markdown | content/post/videopembelajaran/index.md | ferilee/website-idt | 495ea5ba69c3b047f05cd3b45bdc7ee16efc554b | [
"MIT"
] | null | null | null | content/post/videopembelajaran/index.md | ferilee/website-idt | 495ea5ba69c3b047f05cd3b45bdc7ee16efc554b | [
"MIT"
] | null | null | null | content/post/videopembelajaran/index.md | ferilee/website-idt | 495ea5ba69c3b047f05cd3b45bdc7ee16efc554b | [
"MIT"
] | null | null | null | ---
title: "Workshop Merancang Video Pembelajaran"
date: 2021-09-11T23:42:15+07:00
draft: false
author: "Ferilee"
authorLink: "https://t.me/ferilee"
slug: workshop
tags: ["video", "video pembelajaran", "free web apps"]
categories:
- Workshop
- Video Pembelajaran
---
| MATERI | JAM PELATIHAN |
| :------: | :-----------: |
| Pengenalan Canva | 2 |
| Dasar - dasar Canva | 3 |
| Bermain dengan Teks | 4 |
| Elemen Canva | 4 |
| Video dan Animasi | 6 |
| Bekerja Kolaboratif | 5 |
| Tugas Proyek | 8 |
| Total | 32 |
## Pengantar
Konten visual sangat menunjang pemahaman siswa terhadap materi pembelajaran. Kualitas visual pada sebuah konten mempengaruhi belajar siswa. Oleh karena itu sebagai pengajar, kita dituntut untuk membuat desain media pembelajaran yang menarik dan mampu meningkatkan minat belajar siswa.
`Mengapa harus konten visual ?` Karena otak manusia memproses informasi lebih cepat melalui konten visual daripada teks. Konten visual dapat lebih cepat dipahami sedangkan teks membutuhkan waktu lebih lama untuk dibaca dan dipahami.
Anda tentu tidak asing lagi dengan aplikasi pengolah grafis seperti `Adobe photoshop, CorelDraw, Adobe Illustrator, Gimp, Inkscape`, dan lainnya. Namun sayangnya aplikasi-aplikasi berbasis desktop tersebut tidak cocok digunakan untuk pengguna awam yang tidak memahami konsep-konsep desain. Aplikasi-aplikasi ini menjadi senjata andalan para desainer profesional dalam menciptakan desain yang bagus dan keren.
Banyak pengembang aplikasi berlomba-lomba menciptakan alat bantu desain yang dibutuhkan oleh guru dan siswa untuk digunakan sebagai media yang mendukung proses pembelajaran baik di kelas maupun ***online class*** menggunakan LMS. Tidak hanya mudah dioperasikan, tetapi juga mampu dijadikan alat bantu desain yang kolaboratif dan mendukung pembelajaran.
Pada kesempatan kali ini, kita akan belajar membuat konten visual untuk pembelajaran dengan menggunakan Canva.
## Pengenalan Canva
Canva adalah alat bantu desain dan publikasi online dengan misi memberdayakan semua orang di seluruh dunia agar dapat membuat desain apa pun dan mempublikasikannya di mana pun.
Canva diluncurkan pada tahun 2013 dengan lebih dari 60 juta pengguna aktif bulanan yang tersebar di 190 negara dan mendukung 100 bahasa. Hingga saat ini, lebih dari 7 miliar desain telah dibuat oleh pengguna Canva.
Jenis konten visual yang bisa dibuat dengan Canva adalah
* logo,
* banner,
* poster,
* konten media sosial,
* invoice,
* infografis,
* newsletter,
* desain kemasan produk,
* iklan,
* presentasi,
* desain media pembelajaran,
* dan lain-lain.
## Dasar-dasar Canva
> **Langkah pertama**, sebelum memulai membuat desain di Canva, Anda harus membuat akun terlebih dahulu. Anda bisa membuatnya melalui `akun google, akun facebook, akun apple, atau surel (email)`.
>**Langkah kedua**, bergabung dengan tim melalui undangan yang dikirimkan via surel. Administrator telah membuat tim untuk bekerja bersama di Canva. Klik tombol `Gabung ke kelas` untuk mulai bergabung dengan anggota tim.
Setelah berhasil bergabung, Canva akan memberikan ucapan selamat bergabung seperti ini
>**Langkah ketiga**, untuk menciptakan konten visual Anda bisa memulai dengan tiga cara yaitu: ` mendesain dengan template, mendesain dari awal, mengunggah atau mengimpor PDF desain Anda.`
 
### Mendesain dengan template
Canva menyediakan ribuan template gratis siap pakai, mulai kategori pendidikan hingga bisnis. Anda hanya perlu mencari template yang sesuai dengan konten yang akan Anda buat.
Jika Anda sudah menemukan desain yang sesuai dengan keinginan, terapkan dengan menekan tombol `Sesuaikan template ini`.
Berikutnya Anda akan diarahkan menuju area kerja Canva seperti pada gambar di bawah ini
### Mendesain dari awal
Jika Anda menginginkan membuat desain baru dari awal, tentukan dimensi (ukuran desain yang diinginkan), kemudian klik tombol `buat desain baru`
### Mengunggah atau mengimpor PDF desain Anda
Fitur ini masih dalam tahap beta, sehingga masih belum optimal hasilnya. Disarankan untuk tidak menggunakan fitur ini sampai versi stabilnya. Jika Anda ingin mencobanya, silakan siapkan file PDFnya kemudian impor ke Canva.
### Bagian - bagian Canva
Bagian-bagian Canva bisa dilihat pada gambar berikut ini:
* tools (template, elemen, unggahan, foto, teks, gaya, audio, latar belakang, logo, lainnya). Canva menyediakan tools untuk Anda gunakan dalam membuat konten visual.
* Stage and timeline, pada bagian ini (stage) Anda akan melihat tampilan hasil dan timeline yang berfungsi untuk mengatur waktu desain tersebut muncul.
Seperti halnya pada presentasi, Anda bisa menambahkan efek transisi dan animasi halaman atau elemen untuk mempercantik tampilan.
## Bermain dengan Teks
Canva menyediakan beragam jenis font, mulai font reguler sampai kombinasi (*typography*). Anda boleh memilih jenis font yang disukai dan menggantinya dengan teks yang diinginkan.
## Elemen Canva
Selain teks, Canva juga menyediakan banyak elemen-elemen grafis seperti garis dan bentuk, audio, video, foto, bagan, bingkai, dan lain-lain.
## Video dan Animasi
Canva memungkinkan untuk digunakan sebagai alat bantu desain yang berisikan konten video dan animasi. Canva menyediakan koleksi video dan animasi yang bisa Anda gunakan dalam karya Anda.
Apabila Anda tidak menemukan aset video pada koleksi Canva, Anda bisa mengunggah video pembelajaran melalui bilah unggahan.
## Bekerja Kolaboratif
Siapapun bisa bekerja secara kolaboratif di Canva. Setiap orang bisa mengubah desain untuk menghasilkan karya yang sempurna.
## Tugas Proyek
Silakan buat desain media pembelajaran sesuai mapel yang diampu dan kemudian diekspor dalam bentuk video dengan format mp4. Hasilnya diunggah ke media sosial (facebook, instagram, youtube, dan lain-lain).
{{< canva >}}
| 55.341667 | 408 | 0.793254 | ind_Latn | 0.98574 |
80f62cc0eaccac1f189d125ea621ec01b87bb495 | 4,697 | md | Markdown | natives/camera-api.md | efraim9/relisoft_core | 395c50410de27eaaf62f081365d4cc1efe33d56f | [
"MIT"
] | 24 | 2020-01-12T12:53:29.000Z | 2022-03-15T12:59:58.000Z | natives/camera-api.md | efraim9/relisoft_core | 395c50410de27eaaf62f081365d4cc1efe33d56f | [
"MIT"
] | 3 | 2020-02-22T18:49:23.000Z | 2020-10-06T07:01:27.000Z | natives/camera-api.md | efraim9/relisoft_core | 395c50410de27eaaf62f081365d4cc1efe33d56f | [
"MIT"
] | 22 | 2020-01-10T15:42:47.000Z | 2022-03-08T04:39:43.000Z | ---
description: Create cool camera effects!
---
# Camera API
Simple example of pinkcage motel animation from air camera point to door
{% code title="your\_clientside.lua" %}
```lua
function playCam()
local camPos = vector3(331.09,-268.47,79.74)
local doorPosCam = vector3(316.5,-205.47,55.65)
local doorPosPoint = vector3(311.95,-203.68,55.4)
local pointTo = vector3(320.92,-207.27,57.28)
rcore:cinematicLook(true)
rcore:disableControls(true)
local camera = rcore:createCamera('main',camPos,vector3(0,0,0))
camera.render()
camera.pointTo(pointTo)
Citizen.Wait(2000)
camera.changePosition(doorPosCam,doorPosPoint)
camera.destroy()
rcore:cinematicLook(false)
rcore:disableControls(false)
rcore:stopRendering()
end
RegisterCommand('testhotel',function(source,args)
Citizen.CreateThread(function()
ESX.Scaleform.ShowFreemodeMessage('~g~pinkcage motel', '~y~Prave sis zakoupil pokoj cislo 3!', 5)
end)
playCam()
end)
```
{% endcode %}
## Cinematic cool and disable controls
When you turning with a camera and creating cool position animation you don\`t want allow player movement or shooting, a player is on a same spot on the ground but a camera is somewhere else because that rcore offers simple toggle command for disable controls and start cinematic look which will create borders at top and bottom in black colour for a movie experience
{% code title="your\_clientside.lua" %}
```lua
rcore = exports.rcore
rcore:cinematicLook(true)--Turn on disable controls & cinematic look
rcore:disableControls(true)
--Here do camera animation
Citizen.Wait(5000)--We will wait 5 sec
rcore:cinematicLook(false)
rcore:disableControls(false) --Turn off disable controls & cinematic look
```
{% endcode %}
### Creating camera
Creating camera is so easy... After we create our API of course. Camera has a name which need to be unique and position.
{% code title="your\_clientside.lua" %}
```lua
--Name need to be unique - mandatory field
--Position is vector3(x,y,z) - mandatory field
--Rotation is vector3(x,y,z) - if not fill it will use vector3(0,0,0)
--Fox is integer and its default value is 60 you can change it or leave it empty
--This function returning table object with functions to control camera
createCamera(name, pos, rot, fov)
--Minimum params
local pos = vector3(2580.6,150.0,26.0)
local camera = rcore:createCamera('main',pos)
--Long params
local pos = vector3(2580.6,150.0,26.0)
local rot = vector3(0,10,0)
local fov = 120
local camera = rcore:createCamera('main',pos,rot,fov)
```
{% endcode %}
### Camera object
Object that will return function createCamera is table that allows you call function on it that mean that all data and function are in camera object you dont need to use other function everything is there.
{% code title="camera object params" %}
```lua
cam -- id of camera object
position -- position of camera
rotation -- rotation of camera
fov -- fov of camera
name -- name of camera
lastPoinTo -- if you called pointTo function there will be last location or nil
pointTo(pos) -- function that will point your camera to specific point with vector3(x,y,z)
render() --Set cam active and render its
changeCam(newCam, duration)--Change camera with animation (first parameter is CAM ID not object)
destroy() --Destroy camera
changePosition(newPos,newPoint,newRot,duration)--Change camera with animation to specific position and point
```
{% endcode %}
#### pointTo\(position\)
Point your camera to specific position, allowed parameter is vector3\(\) as position
#### render\(\)
Set your camera active and render it to player
#### changeCam\(newCamId, duration\)
Change camera to another with animation between position, you need to create new camera for this and first parameter is **camera ID** not whole object
#### changePosition\(newPos, newPoint, newRot, duration\)
Change camera with animation to a new position for this will rcore create automatically one new camera and make animation change of position to a new point and then your current camera will receive new data so you can continue using it. Last two parameters is option for newRot is default vector3\(0,0,0\) and for duration its 4000 its in milliseconds
#### destroy\(\)
Remove camera and destroy it but its still not stop rendering
### Stop rendering and remove everything
if you want stop rendering your cams you have to use specific function stopRendering which will render player game camera again and stop rendering yours camera - dont forget that stopRendering will not destroy your camera
{% code title="your\_clientside.lua" %}
```lua
rcore = exports.rcore
rcore:stopRendering()
```
{% endcode %}
| 35.315789 | 368 | 0.748989 | eng_Latn | 0.979635 |
80f65b9d333b849aabbcf664d350767966e90639 | 38 | md | Markdown | README.md | xichuangzhu/xichuanzhu-one | f110275c3912618cf4d98a9c9b3a5d151e7d4599 | [
"MIT"
] | null | null | null | README.md | xichuangzhu/xichuanzhu-one | f110275c3912618cf4d98a9c9b3a5d151e7d4599 | [
"MIT"
] | null | null | null | README.md | xichuangzhu/xichuanzhu-one | f110275c3912618cf4d98a9c9b3a5d151e7d4599 | [
"MIT"
] | null | null | null | 一句
======
http://one.xichuangzhu.com
| 7.6 | 26 | 0.605263 | yue_Hant | 0.949543 |
80f6b9a8a02d3b87e16e8683c5359a42924fb1f8 | 41 | md | Markdown | fluid/flows/C/400/README.md | idancali/carmel | 1245a2da43d55192c8861afd8854262601679cd9 | [
"MIT"
] | null | null | null | fluid/flows/C/400/README.md | idancali/carmel | 1245a2da43d55192c8861afd8854262601679cd9 | [
"MIT"
] | null | null | null | fluid/flows/C/400/README.md | idancali/carmel | 1245a2da43d55192c8861afd8854262601679cd9 | [
"MIT"
] | null | null | null | ## C400: Chris sets the master password.
| 20.5 | 40 | 0.731707 | eng_Latn | 0.956766 |
80f7997aadea6f70ef4e6e3ba6e784b26b2ceaf3 | 194 | md | Markdown | README.md | ladder1984/backbonejs_todos_with_Django | 892c3f73f2eebb193ca3df2e0c0aa7ac9cb49820 | [
"MIT"
] | null | null | null | README.md | ladder1984/backbonejs_todos_with_Django | 892c3f73f2eebb193ca3df2e0c0aa7ac9cb49820 | [
"MIT"
] | null | null | null | README.md | ladder1984/backbonejs_todos_with_Django | 892c3f73f2eebb193ca3df2e0c0aa7ac9cb49820 | [
"MIT"
] | null | null | null | #backbonejs_todos_with_Django
#说明
backbonejs.org官网示例Todos的Django适配，参考自https://github.com/the5fire/the5fire-servertodos
#当前版本
Django:1.7.7
Backbone.js:1.1.2
Underscore.js:1.6.0
jQuery:1.8.3
| 13.857143 | 84 | 0.798969 | pol_Latn | 0.090861 |
80f7a59b1139acb775bd4a7652fc81dfa1857bcd | 4,700 | md | Markdown | _posts/2021-01-27-VulnHub-y0usef1靶场渗透测试.md | yanmie-art/yanmie-art.github.io- | f2c8f121ec7e49ff3d865a3b0b3efc44e0cf4a85 | [
"MIT"
] | 5 | 2019-12-25T15:57:36.000Z | 2021-07-21T08:35:24.000Z | _posts/2021-01-27-VulnHub-y0usef1靶场渗透测试.md | yanmie-art/yanmie-art.github.io- | f2c8f121ec7e49ff3d865a3b0b3efc44e0cf4a85 | [
"MIT"
] | null | null | null | _posts/2021-01-27-VulnHub-y0usef1靶场渗透测试.md | yanmie-art/yanmie-art.github.io- | f2c8f121ec7e49ff3d865a3b0b3efc44e0cf4a85 | [
"MIT"
] | 2 | 2020-11-20T11:06:48.000Z | 2022-01-26T10:06:58.000Z | ---
layout: post # 使用的布局（不需要改）
title: VulnHub-y0usef1靶场渗透测试 # 标题
subtitle: #副标题
date: 2021-01-27 # 时间
author: yanmie # 作者
header-img: img/.jpg ##标签这篇文章标题背景图片
catalog: true # 是否归档
tags:
- Vulnhub
---
### About Release
* **Name**: y0usef: 1
* **Date release**: 10 Dec 2020
* downpload：[https://www.vulnhub.com/entry/y0usef-1,624/](https://www.vulnhub.com/entry)
* Get two flag
* Difficulty : easy
## 一、环境搭建
使用 `virtual Box` 搭建靶机。
win10 攻击机，使用 kali 作为辅助攻击机。
但是 我 kali 是在 vmware 上安装的，所以需要配置一下网络。
virtual box：
选用仅主机模式，在注意一下下边的 `界面名称`。
配置 VM ware
我在保留原有设置的基础上，在添加一块网卡 VMnet3，将其桥街到 virtualBOX 选用的那块网卡上，这样就可以在一个网络上了。
配置完成，开始操作。
## 二、信息收集
在这里先普及一下 nmap 主机发现的基础知识。
* -PN nmap进行其他扫描之前都会对目标进行一个ping扫描。如果目标对ping 扫描无反应将结束整个扫描过程。这种方法可以跳过那些没有响应的主机，从而节省大量时间，但如果目标在线只是采用某种手段屏蔽了ping 扫描，从而躲过我们的其他扫描操作，我们可以指定无论目标是否响应ping 扫描，都要将整个扫描过程完整的参数呈现出来；如 nmap -PN 192.168.56.1/24。
* -P\* 选项(用于选择 ping的类型)可以被结合使用。 `PS/PA/PU/PY[portlist]: TCP SYN/ACK, UDP or SCTP discovery to given ports`
* -PR 使用ARP协议发现主机。如： `nmap -PR 192.168.56.1/24`
* -sP 使用 ping 协议发现主机。如：`nmap -sP 192.168.56.1/24`
* -sS 半开放扫描，使用 TCP 协议发现主机. 如：`nmap -sS 192.168.56.1/24`
* -sT 全开放扫描，使用 TCP 协议发现主机。如：`nmap -sT 192.168.56.1/24`
* -sU 使用 UDP 协议发现主机。如：`nmap -sU 192.168.56.1/24`
我们使用命令 `nmap -sP -T5 192.168.56.1/24` 来进行 ping 主机发现。
#### 1\. 目标主机发现
先发现目标主机
然后进行端口扫描。
依旧使用 nmap 神器，再来看看看 nmap 端口扫描的基础知识：
nmap 端口扫描分成六个状态:
* open(开放的)
* closed(关闭的)
* filtered(被过滤的)
* unfiltered(未被过滤的)
* open\|filtered\(开放或者被过滤的\)
* closed\|filtered\(关闭或者被过滤的\)
#### 2\. 端口扫描
常见端口扫描选项：
* -sS TCP SYN 扫描，快速，不建立 TCP 连接，所以不易被发现。如 `nmap -sS 192.168.56.101`
* -sT TCP connect 扫描，较慢，三次握手建立 TCP 连接，容易被发现。如：`nmap -sT 192.168.56.101`
* -sA TCP ACK 扫描，用于发现防火墙规则，确定它们是有状态的还是无状态的，哪些端口是被过滤的。 如： `nmap -sA 192.168.56.101`
* -sU UDP 扫描，较快。如： `namp -sU 192.168.56.101`
* -p (只扫描指定的端口) 。 默认情况下，Nmap用指定的协议对端口1到1024以及nmap-services 文件中列出的更高的端口在扫描。如： `nmap -p 1-65535 192.168.56.101`
* -p- 从端口1扫描到65535。
* -T 设置延时，一般使用 `-T4`
在这里我们使用命令 `nmap -sY -T4 -p- 192.168.56.101`
显示所有端口都被过滤。。。
使用 TCP 进行扫描
发现 80 端口和 22 端口开放。在渗透测试过程中，单单经考单一的方法是不够的，需要多种方法结合起来才能展现最佳效果。
#### 3. 服务探测
这里我们直接先开始探测 靶机 80 端口。
这里也可以使用 `nmap` 进行服务版本探测
可以看到SSH 版本以及 apache 版本。
访问网站 `http://192.168.56.101/`
php 站，（此插件为 Wappalyzer）
访问发现一句话，此网站正在建设中，正在运行中。
#### 4. 目录扫描
发现有个目录比较特殊，访问一下，
确实很特殊，应为这个是代码限制的访问的。
ok,这应该是个管理目录，既然是代码限制，所以尝试 XFF 伪造 `127.0.0.1` ,
成功伪造。（此插件为 `X-Forwarded-For Header`）
## 三、操作
`We'll never share your username with anyone else.`
右键源代码也啥也没发现，
手动进行弱密码测试直接解开，
`admin/admin` ,省去了 burp 爆破的功夫。
进入后台，发现一处可以上传文件的操作
直接上传 php 马被限制，那就进行绕过。
修改`Content-Type` 即可绕过。
并且返回了路径。
蚁剑上手。
想去 root 目录看看但是没权限。
在 `/home/` 目录下发现 `user.txt`
打开查看是：
```txt
c3NoIDogCnVzZXIgOiB5b3VzZWYgCnBhc3MgOiB5b3VzZWYxMjM=
```
burp base64解码得到 ssh 账号密码。
```txt
ssh :
user : yousef
pass : yousef123
```
正好靶机开放 22 端口，我们进行连接。
#### 提权
最简单的 前面加 `sudo`
还可以 `sudo su ` 输入当前用户`yousef` 密码即可成为root.
又得到一串 base64 字符串
```txt
You've got the root Congratulations any feedback content me twitter @y0usef_11
```
结束。
## 四、总结
主机发现 ---> 端口扫描 ---> 服务探测 ---> 目录扫描 ---> 伪造IP ---> 提权 | 25.824176 | 194 | 0.736809 | yue_Hant | 0.52885 |
80f88b9b0c06118214e15add89aaa11800168018 | 4,923 | md | Markdown | src/pages/languages/420.420.md | FractalHQ/HelloWorlds | c6f90363a5f34ae6040858502fd5714ec3184512 | [
"MIT"
] | null | null | null | src/pages/languages/420.420.md | FractalHQ/HelloWorlds | c6f90363a5f34ae6040858502fd5714ec3184512 | [
"MIT"
] | null | null | null | src/pages/languages/420.420.md | FractalHQ/HelloWorlds | c6f90363a5f34ae6040858502fd5714ec3184512 | [
"MIT"
] | null | null | null | 420
blaze it blaze it blaze it blaze it blaze it blaze it blaze it blaze it blaze it blaze it blaze it blaze it blaze it blaze it blaze it blaze it blaze it blaze it blaze it blaze it
blaze it blaze it blaze it blaze it blaze it blaze it blaze it blaze it blaze it blaze it blaze it blaze it blaze it blaze it blaze it blaze it blaze it blaze it blaze it blaze it
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blaze it blaze it blaze it blaze it blaze it blaze it blaze it blaze it blaze it blaze it blaze it blaze it blaze it blaze it blaze it blaze it
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blaze it blaze it blaze it blaze it blaze it blaze it blaze it blaze it blaze it blaze it blaze it blaze it blaze it
blaze it blaze it blaze it blaze it blaze it blaze it blaze it blaze it blaze it blaze it blaze it blaze it blaze it blaze it blaze it blaze it
blaze it blaze it blaze it blaze it blaze it blaze it blaze it blaze it blaze it blaze it
blaze it blaze it blaze it blaze it blaze it blaze it blaze it blaze it blaze it blaze it blaze it blaze it blaze it blaze it blaze it
blaze it blaze it blaze it blaze it blaze it blaze it blaze it blaze it blaze it blaze it blaze it blaze it blaze it
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blaze it blaze it blaze it blaze it blaze it blaze it blaze it blaze it blaze it blaze it blaze it blaze it blaze it blaze it blaze it blaze it blaze it blaze it
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blaze it blaze it
blaze it blaze it blaze it blaze it blaze it blaze it blaze it blaze it blaze it
blaze it blaze it blaze it blaze it blaze it blaze it blaze it blaze it blaze it blaze it blaze it
blaze it blaze it blaze it blaze it blaze it blaze it
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blaze it blaze it blaze it blaze it blaze it blaze it blaze it blaze it blaze it blaze it blaze it
blaze it blaze it blaze it blaze it blaze it blaze it blaze it
blaze it blaze it blaze it blaze it blaze it blaze it blaze it blaze it blaze it blaze it blaze it blaze it
blaze it blaze it blaze it blaze it blaze it blaze it
blaze it blaze it blaze it blaze it blaze it blaze it blaze it blaze it blaze it blaze it blaze it blaze it
blaze it blaze it blaze it
blaze it blaze it blaze it blaze it blaze it blaze it blaze it blaze it blaze it blaze it blaze it blaze it
blaze it blaze it blaze it blaze it blaze it blaze it blaze it
blaze it blaze it blaze it blaze it blaze it blaze it blaze it blaze it blaze it blaze it blaze it blaze it
blaze it blaze it blaze it blaze it blaze it blaze it
blaze it blaze it blaze it blaze it blaze it blaze it blaze it blaze it blaze it blaze it
blaze it blaze it blaze it blaze it blaze it blaze it blaze it blaze it blaze it blaze it blaze it blaze it blaze it blaze it blaze it blaze it blaze it blaze it blaze it blaze it blaze it blaze it blaze it blaze it blaze it blaze it blaze it blaze it blaze it blaze it blaze it blaze it blaze it blaze it blaze it blaze it blaze it blaze it blaze it
blaze it blaze it blaze it
blaze it blaze it blaze it blaze it blaze it blaze it blaze it blaze it
blaze it blaze it blaze it blaze it blaze it blaze it blaze it blaze it blaze it blaze it blaze it
blaze it blaze it blaze it blaze it blaze it blaze it
| 80.704918 | 350 | 0.776965 | bos_Latn | 0.721796 |
80f95e3c2bd3d67914d9cdf00b11b4bcf1c9e1d2 | 692 | md | Markdown | README.md | jmartin4849/ECE444-F2020-Lab3 | fca960f3a42c44397825b823d7c05ed7971a3d41 | [
"MIT"
] | null | null | null | README.md | jmartin4849/ECE444-F2020-Lab3 | fca960f3a42c44397825b823d7c05ed7971a3d41 | [
"MIT"
] | null | null | null | README.md | jmartin4849/ECE444-F2020-Lab3 | fca960f3a42c44397825b823d7c05ed7971a3d41 | [
"MIT"
] | null | null | null | # ECE444-F2020-Lab3
Johnathon Martin
this repo is a clone of
https://github.com/miguelgrinberg/flasky
Activity 1
Activity 2
Activity 3: Differences between SQL and noSQL
The major difference is that SQL is relational and noSQL is not. This means that SQL has a more defined structure
and must use a pre defined schema that is accessed via queries whereas noSQL is more dynamic with no strict underlying structure. SQL is table based
where as uses document, key-value, graph or wide-column storages.
| 34.6 | 149 | 0.786127 | eng_Latn | 0.986446 |
222164714b7d4835739e41e44180aebf4f5e453d | 221 | md | Markdown | _posts/2019-10-19-update.md | dret/HTML5-overview | 2916dd7028f2793ddc0db2f5684d76bbbf33a33f | [
"Unlicense"
] | 128 | 2015-01-30T02:30:33.000Z | 2020-10-31T07:53:20.000Z | _posts/2019-10-19-update.md | dret/HTML5-overview | 2916dd7028f2793ddc0db2f5684d76bbbf33a33f | [
"Unlicense"
] | 33 | 2015-02-18T01:47:51.000Z | 2020-02-25T02:23:45.000Z | _posts/2019-10-19-update.md | dret/HTML5-overview | 2916dd7028f2793ddc0db2f5684d76bbbf33a33f | [
"Unlicense"
] | 12 | 2015-04-28T19:26:54.000Z | 2020-06-02T04:59:47.000Z | ---
layout: post
title: "changed status of \"High Resolution Time Level 2\" from CR to PR"
date: 2019-10-19
tags: [ hr-time-2 ]
---
changed status of "[High Resolution Time Level 2](/spec/hr-time-2)" from CR to PR
| 22.1 | 81 | 0.665158 | eng_Latn | 0.807232 |
222240a531ba052062e308d3463a27b3f38fa635 | 1,001 | md | Markdown | content/blog/2021-03-07.md | ytkg/prospi_antenna | ab3a621b3ea2e0350a353f337a5e851369efc059 | [
"RSA-MD"
] | null | null | null | content/blog/2021-03-07.md | ytkg/prospi_antenna | ab3a621b3ea2e0350a353f337a5e851369efc059 | [
"RSA-MD"
] | null | null | null | content/blog/2021-03-07.md | ytkg/prospi_antenna | ab3a621b3ea2e0350a353f337a5e851369efc059 | [
"RSA-MD"
] | null | null | null | ---
title: "[2021-03-07] お待たせしました。3600菅野&岡本が巨人純正に帰ってきます【プロスピA】# 533 他"
date: 2021-03-07
---
###【V.I.P】
#####お待たせしました。3600菅野&岡本が巨人純正に帰ってきます【プロスピA】# 533
<iframe width="560" height="315" src="https://www.youtube.com/embed/q5H9wDFVzrQ" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
###しらたき白瀧
#####【プロスピA】2021グランドオープン重要なポジション追加予想！どこが来そう？佐藤輝明選手がヤバい…佐藤選手を獲得できるイベントがもうすぐで登場！【プロ野球スピリッツA】
<iframe width="560" height="315" src="https://www.youtube.com/embed/FrgazT53JGI" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
###カンタチャンネル
#####【プロスピA】イベント・ガチャ更新は何が来る？明日～やるべき事！グランドオープン前の我慢の時期！【プロ野球スピリッツA】
<iframe width="560" height="315" src="https://www.youtube.com/embed/TMr9bbL-wsw" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
| 58.882353 | 219 | 0.763237 | eng_Latn | 0.077519 |
2223048108f9a1b7a0d23957838c4b5a9a430b1e | 406 | md | Markdown | README.md | airdb-wiki/hugo-book | 49790413985a619d399e2eda542478c335492e45 | [
"MIT"
] | null | null | null | README.md | airdb-wiki/hugo-book | 49790413985a619d399e2eda542478c335492e45 | [
"MIT"
] | null | null | null | README.md | airdb-wiki/hugo-book | 49790413985a619d399e2eda542478c335492e45 | [
"MIT"
] | null | null | null | # Hugo Wiki Theme
[](https://gohugo.io)
[](LICENSE)
本主题基于 [Hugo-book](https://github.com/alex-shpak/hugo-book) v9 版本改进.
Demo: [Hugo Wiki](https://airdb-wiki.github.io/hugo-book)
| 36.909091 | 99 | 0.721675 | yue_Hant | 0.376092 |
222522f0d41f5d4b12a3024818ba1ad54309bf96 | 63 | md | Markdown | README.md | rjrivero/cubietools | e489457605dcbc5e0893b96af1da9ab31b80b966 | [
"MIT"
] | null | null | null | README.md | rjrivero/cubietools | e489457605dcbc5e0893b96af1da9ab31b80b966 | [
"MIT"
] | null | null | null | README.md | rjrivero/cubietools | e489457605dcbc5e0893b96af1da9ab31b80b966 | [
"MIT"
] | null | null | null | # cubietools
Helper scripts and tools to manage my home server
| 21 | 49 | 0.809524 | eng_Latn | 0.997944 |
2225c419a0ada9d22eeefae436fa80aba1c95cc1 | 584 | md | Markdown | README.md | mrdrivingduck/uc-os-ii-code-notes | 403b112f5ca24ef6f7cd8c8a296d9ac7bab264d1 | [
"MIT"
] | 4 | 2020-05-08T09:07:20.000Z | 2022-01-03T19:46:25.000Z | README.md | mrdrivingduck/uc-os-ii-code-notes | 403b112f5ca24ef6f7cd8c8a296d9ac7bab264d1 | [
"MIT"
] | null | null | null | README.md | mrdrivingduck/uc-os-ii-code-notes | 403b112f5ca24ef6f7cd8c8a296d9ac7bab264d1 | [
"MIT"
] | 2 | 2019-11-27T06:17:19.000Z | 2020-03-09T06:50:28.000Z | # uc-os-ii-code-notes
🐤 Notes of reading μC/OS-II code.
Created by : Mr Dk.
2019 / 11 / 04 @Nanjing, P.R.China
---
I have been curious for a long time about the differences between embedded operating systems and general OS like Linux.
Since I have understood how Linux works on a high level (very very high and abstract 😅), I'm turning to embedded RTOS now.
The book I choose is _嵌入式实时操作系统 μC/OS-II 教程 (第二版)_ by _程文娟、吴永忠、苗刚中_
It will be easier for me to understand a Chinese book.
Move on! 😈
---
## License
Copyright © 2019, Jingtang Zhang. ([MIT License](LICENSE))
---
| 20.137931 | 122 | 0.714041 | eng_Latn | 0.993885 |
2225cf01e533cf467f5d6a3c7faaeda47f84ad24 | 1,229 | md | Markdown | README.md | Samiul-Islam-Niloy/Treasure-Guardian | fbc5ff7fc725ddc23d2ef808ebc24ec36c430dc9 | [
"MIT"
] | null | null | null | README.md | Samiul-Islam-Niloy/Treasure-Guardian | fbc5ff7fc725ddc23d2ef808ebc24ec36c430dc9 | [
"MIT"
] | null | null | null | README.md | Samiul-Islam-Niloy/Treasure-Guardian | fbc5ff7fc725ddc23d2ef808ebc24ec36c430dc9 | [
"MIT"
] | null | null | null | # Treasure Guardian
Treasure Guardian is a single player 2D action-adventure game developed in C/C++ with iGraphics library.
Term Project, Software Development I [CSE 1200]
Department of Computer Science and Engineering<br>
Ahsanullah University of Science and Technology, Bangladesh
## Screenshots:
## Project Information:
**Project Title:** Treasure Guardian<br>
**Project Type:** 2D Game
**Language:** C/C++<br>
**Tools:** iGraphics Library<br>
**IDE:** Visual Studio 2010<br>
**Platform:** Windows
## Co-authors:
* Robin Rezwan
* Al Noman Limon
## License:
This project is licensed under the terms of the MIT license. Therefore, it's free for use or modification. See the [LICENSE](LICENSE) for more info.
| 32.342105 | 148 | 0.781123 | eng_Latn | 0.221938 |
22266e96616c88934d7ae5e335956bcdd1445690 | 567 | md | Markdown | docs/gr_funzioni/geometria/point_n.md | gbvitrano/HfcQGIS_md | 8a9114f95edb9e9ecf834ed1df5230653f9b637f | [
"CC0-1.0"
] | null | null | null | docs/gr_funzioni/geometria/point_n.md | gbvitrano/HfcQGIS_md | 8a9114f95edb9e9ecf834ed1df5230653f9b637f | [
"CC0-1.0"
] | null | null | null | docs/gr_funzioni/geometria/point_n.md | gbvitrano/HfcQGIS_md | 8a9114f95edb9e9ecf834ed1df5230653f9b637f | [
"CC0-1.0"
] | 1 | 2021-11-17T09:14:12.000Z | 2021-11-17T09:14:12.000Z | # point_n
Restituisce un nodo specifico da una geometria.
## Sintassi
point_n(geometry, index)
## Argomenti
* _geometry_ oggetto geometria
* _index_ indice del nodo da restituire, dove 1 è il primo nodo
## Esempi
* `geom_to_wkt(point_n(geom_from_wkt('POLYGON((0 0, 4 0, 4 2, 0 2, 0 0))'),2)) → 'Point (4 0)'`
## nota bene
--
## osservazioni
--
---
## Esempi correlati
[esempio nro 4](/esempi/add_col_z.md) - Come aggiungere la quota Z alla tabella attributi
| 15.75 | 95 | 0.680776 | ita_Latn | 0.966259 |
2226cc617d48a7ebbb30456d03f4cd4919c1e913 | 1,490 | md | Markdown | CONTRIBUTING.md | LaisRast/sage | 5fb2a6ea44400e469caee82748cf863ca0c5f724 | [
"BSL-1.0"
] | null | null | null | CONTRIBUTING.md | LaisRast/sage | 5fb2a6ea44400e469caee82748cf863ca0c5f724 | [
"BSL-1.0"
] | null | null | null | CONTRIBUTING.md | LaisRast/sage | 5fb2a6ea44400e469caee82748cf863ca0c5f724 | [
"BSL-1.0"
] | null | null | null | # Contributing to SageMath #
SageMath is an open-source project and has a very welcoming developer
community. We invite newcomers to help improve the code or contribute
in other ways.
Our mission is:
Creating a viable free open source alternative to
Magma, Maple, Mathematica and Matlab.
SageMath can give students and other contributors a wonderful
experience in developing in a large-scale, supportive, and rigorous
environment.
All code is peer-reviewed, all functions must be documented and
unit-tested on a variety of platforms and systems. Python is used as a
base language, Git is used as the source code revision system, and
Trac is used to track all support/development issues. All of these
tools are very capable and used in many other small and large-scale
projects. From a software engineering standpoint, SageMath is a
wonderful project to be engaged in.
The mailing lists are extremely supportive and responsive. There are
several hundred people that have contributed code directly to SageMath
over the years, including many leaders in mathematics, as well as
software professionals, graduate students, and undergraduate students.
Contributions of all sorts are heartily welcomed, but attention is
paid to making sure that the system does not become bloated and
disorganized. The mission statement allows for a very wide variety of
contributions.
See [FAQ: Contributing to Sage](https://doc.sagemath.org/html/en/faq/faq-contribute.html)
for more information.
| 41.388889 | 89 | 0.808054 | eng_Latn | 0.999697 |
2226d88ede6e78316307f6500accca5411dcd055 | 2,423 | md | Markdown | README.md | patriciahall2/vacation-suggester | 6bda3d36127f488a34d63b0e5b2b53ec697c7480 | [
"Unlicense",
"MIT"
] | null | null | null | README.md | patriciahall2/vacation-suggester | 6bda3d36127f488a34d63b0e5b2b53ec697c7480 | [
"Unlicense",
"MIT"
] | null | null | null | README.md | patriciahall2/vacation-suggester | 6bda3d36127f488a34d63b0e5b2b53ec697c7480 | [
"Unlicense",
"MIT"
] | null | null | null | # Vacation-Suggester by Patricia Hall
https://github.com/patriciahall2/vacation-suggester
#### SYNOPSIS
Vacation Suggester is a webpage designed to suggest a vacation destination based on your preferences. The website asks you 5 questions and after receiving your answers will suggest a vacation destination for you.
#### Program Setup Instructions
This project was written using the Unix operating system, Atom text editor, in HTML language, using CSS for styling, Bootstrap, JavaScript,
and jQuery. The project was tested and found to be successfully displayed on the Google Chrome web browser. The Bootstrap boilerplate can
be downloaded at: http://getbootstrap.com/ I used the uncompressed jQuery version 1.12.0 for this project which can be downloaded here:
http://code.jquery.com/jquery-1.12.0.js. To use this website:
1. Open a terminal window on your computer.
2. Type this command in the terminal: git clone https://github.com/patriciahall2/vacation-suggester
3. A clone of the Vacation Suggester directory should appear in your current working directory.
4. Open vacation-suggester.html in your Google Chrome browser.
5. Answer the questions on the webpage. After completing the questionnaire click the button at the bottom of the page and scroll down. A vacation suggestion will appear at the bottom of the page.
#### The MIT License (MIT)
Copyright (c) 2016 Patricia Hall
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Vacation Suggester"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
| 105.347826 | 460 | 0.802724 | eng_Latn | 0.92602 |
22277af5a40eb27a6cb2ff959a03fdd959b45f98 | 9,668 | md | Markdown | _posts/tempest-crossing/teller-of-worlds/2020-09-16-evar-part-1.md | Atemosta/feeling-responsive | 41dda3076737a936a4b0dcee29939fb2a26594b9 | [
"MIT"
] | null | null | null | _posts/tempest-crossing/teller-of-worlds/2020-09-16-evar-part-1.md | Atemosta/feeling-responsive | 41dda3076737a936a4b0dcee29939fb2a26594b9 | [
"MIT"
] | 1 | 2022-02-16T22:07:19.000Z | 2022-02-16T22:07:19.000Z | _posts/tempest-crossing/teller-of-worlds/2020-09-16-evar-part-1.md | Atemosta/feeling-responsive | 41dda3076737a936a4b0dcee29939fb2a26594b9 | [
"MIT"
] | 2 | 2021-04-18T00:11:09.000Z | 2021-04-18T00:18:35.000Z | ---
layout: page
subheadline: "Creative's Workshop 2020"
title: "EvAr Part 1"
teaser: "The magic inside you."
#header:
# image_fullwidth: "waiting_raw.jpg"
# caption: Waiting for the new VRMMO release of Sword Art Online
# caption_url: "https://en.wikipedia.org/wiki/Sword_Art_Online"
#image:
# thumb: thumbnail_waiting.jpg
# homepage: waiting_raw.jpg
categories:
- tempest-crossing/teller-of-worlds
tags:
- teller-of-worlds
author: Ousikai
show_meta: true
comments: true
---
[quote="lainastanford, post:110, topic:27179"]
I am amused by the terrible pun.
[/quote]
I learn from only the best :smirk:
[quote="lainastanford, post:110, topic:27179"]
YEAH! Oh, the power of the dorky magnet!
[/quote]
I'm glad your cameo was satisfactory to your liking!
I will now take a quick detour and continue the next part following the short story of [Arrival](https://pro2.akimbo.com/t/oscar-k-sandoval-rivera-dailies-tempest-crossing-begins/27179/91?u=mtfallsvr) as it's important to the ending of the Refrigerator story arc...
## Day 35: EvAr Part 1
After a lovely housewarming with the family of Araceli and Teyo, the three of us sat outside the small abode to take in the afterglow of such a delicious and filling meal. After settling down to enjoy the afternoon air, I decided to ask more about their lives.
"So what do you two do when you're not at home?"
Araceli took the lead of the conversation: "Well, when we aren't doing chores, homework, or walking to/from school several hours away on foot, we help out with our dad in the fields. Fortune must have smiled upon my ancestors, because we own just enough land to grow crops for the entire year to make sure our family never starves,and also have a little bit left over to sell in town to make a bit of extra cash!"
"In fact, you came at just the right time!" Teyo exclaimed. "Right now is just about harvesting season for our corn yield, and we'd love to take you along for the experience."
"Teyo!" Araceli scolded. "You don't ask guests to help with farm labor, that's just rude!"
"Not to worry, Araceli! I was actually looking for a way to repay all your hospitality. If getting my hands dirty even pays back just a bit of your family's kindness, I'd call that more than a fair exchange," I gleamed.
"Well..." Araceli began to trail off, but Teyo took her silence as acceptance of his suggestion, and a devious smile quickly flashed across his face. I decided to not make note of it as it didn't seem like a big deal at the time. After all, how hard could harvesting corn be?
----
*You know, maybe generosity and acts of kindness are not the keys to the kingdom*, I thought to myself as I tumbled into the ground for what seemed like the umpeeth time that hour. Araceli was looking on with a worried expression while Teyo could not contain himself in laughter, and neither could the bumble husk apparently as it buzzed about amusingly.
According to Teyo, these bumble husks were a natural phenomena whenever farmers tried to harvest their corn, as mostly harmless but extremely irritating pests that grew in maturity as their corny home began to ripen. In a last ditch effort to protect their corny counterparts, the husks found themselves a new life as giant bumbling insect-like authorities. Their only means of defense was quite literally bumbling into any would-be harvesters, and anyone not well-versed in the way of the bumble husks would find themselves quickly infuriated at the whole ordeal.
What was not before me was a bumble husk, however, but a rival mocking my very existence. Fuming in the head, Teyo took my very apparent frustration as a sign that he should finally step in.
"Want to learn the secret to taking care of these baddies?" Teyo asked confidently.
"SURE. WHATEVER. IT'S NOT LIKE YOU COULD HAVE TOLD ME 30 MINUTES AGO WHEN I ASKED IF I NEEDED TO KNOW ANYTHING," I snapped coldly.
At Teyo's signal, he told everyone watching to be quiet and focus. He said to "wait for the wind," and it would be obvious after a quick demonstration. Just as Teyo promised, as soon as the first waves of wind hit the corn fields, he sprung to life. Using the wind's momentum, he carried himself towards the nearest bumble husk, preparing for the attack. The bumble husk sensed his intent, and moved accordingly to what it expected Teyo's imminent attack pattern to be. Unfortunately for the bumble husk, Teyo was just a bit faster than what it was expecting, and found itself on the receiving side of a nasty wallop to the face. Teyo repeated this tactic three more times before the bumble husk could stand his attacks no more, and disintegrated into an infinite number of blue and white shards.
Disintegrate? I asked the two if this how things "die" in this world, and Araceli confirmed that my hypothesis was correct. According to Araceli, every "thing" in this world had a numerical value to represent its durability, and once its durability reached zero, the "thing" would disintegrate into nothingness. A chill ran down my spine wondering what happens if a person's durability reached zero, and Teyo offered to take me down to the nearby graveyard so I could find out myself. Afraid yet intrigued at the offer, I told Teyo I would have to take a rain-check as I still had one more battle immediately ahead of me: the last tango between the bumble husk and I.
Using Teyo's simple advice, I began to calm myself. I focused on only myself in that corn field, and quickly blocked out all the sights, sounds, and feelings of the worlds. Once I found myself isolated in this empty void, I opened my senses just a bit to pick up traces of the bumble husk. Once it was nothing but me and the bumble husk, I once again opened my my senses just a little bit more to await for traces of the wind. A calm but soothing wind soon found itself at my backside, and then something strange happened.
My left arm began to instinctively tingle, and just as if I had done this technique thousands of times prior to this moment, I let the wind ride by my side. Slowly I caressed the wind from my shoulders all the way to my palm, and once it reached my palm it began to aggregate. At my go, I pointed all the compressed air dead-on into the direction of the bumble husk, and released.
In that moment, the entire field went silent. Teyo, Araceli, and even the bumble husk itself watched in awe as I conjured a wind gale seemingly out of nowhere and had it instantly collide with the unexpecting bumble husk. The bumble husk soared across the field quickly before instantly turning into the well-known blue and white particles, and then, another moment of silence met our trio.
"That... was... so.... awesome!!! How did you do that Met??? Can you teach me to do that too???" Teyo excitedly declared as he ran to come and meet me. Araceli, however, was not equally as impressed. Worried, in fact, was the only expression crossing her face.
"Did you... just use an EvAr?"
"EvAr? What the heck is that?", I asked inquisitively.
"EvAr is short for Evolving Art. Is it a gift granted to us by the gods where we are able to give life to the inanimate magic inside of us. EvAr... is something only given to those deemed worthy, and they are the pinnacle of the society that stands ontop our current world. Which is to say, the rich and ruling class and not people like me and Teyo."
Araceli then paused and coughed nervously before asking the next question. "Why... do you have an EvAr, Met?"
Sensing the rising hostility emanating from Araceli, I decided to reflect on what I knew so far before giving her an unsatisfactory answer. Could my EvAr stem from some facet of my unknown past? And why a Wind EvAr?
Just as I was fixated on my Wind EvAr, a menu screen popped up in front of me.
*Menu screen?*
I focused my vision on the menu screen, and noted it's simplicity:
*Wind Evolving Art: Level 1.*
*Sub Arts: None*
*Experience to Next Level: 9.*
Weird, why is a game menu appearing in the real world? Could Araceli and Teyo also see this menu? I decided to then try and see if either of the two also had a Wind EvAr, and was taken aback by what I saw. Or rather, what I didn't see. The same menu as mine popped up alonside each person, but the Level and accompanying information was all blurred out violently like it wasn't meant to be seen.
"Do you two... know what the level of your Wind EvAr you are?"
"Level? What the heck are you talking about? You're sure still doing alright, Met? You just used EvAr, and you got knocked upside the head one too many times thanks to a certain someone..." Araceli began glaring at Teyo, but continuing keeping one eye on me.
"Come on, Met, you gotta show me how you did that! Please Araceli, it's not everyday you meet someone who can use EvAr AND they actually treat us like normal people", Teyo pleaded.
Just as Araceli looked like she was about to shut down Teyo's insane request, I decided to step in and save face while I still could. "Listen Araceli, I know you're probably suspicious of me and quite frankly, I am too. But if you claim that only the *rich and gifted* can learn EvAr, what's the harm in trying to teach Teyo. After all, if it were up to me, I would say that EvAr is for ***EvAryone***!"
Araceli sighed, both at the ridiculous request and my shoe-horned in pun, but against her maternal instincts, decided to just give it a go and see what would happen. Teyo celebrated and quickly ran to give his sister a big ol' hug, then quickly got down to business to see what he could learn from me.
---
And on that day, the world received its first ever peasant EvAr user.
@dragon @homeroom11 | 91.207547 | 797 | 0.769963 | eng_Latn | 0.999803 |
2227ffaf8cd48461703658135716d3e1fffe3cec | 117 | md | Markdown | Examples/iOSExample/iOSExample/readme.md | buscarini/SwiftStroke | fa4a41758363fb4245cd044ffb1ee774678d9c1a | [
"MIT"
] | null | null | null | Examples/iOSExample/iOSExample/readme.md | buscarini/SwiftStroke | fa4a41758363fb4245cd044ffb1ee774678d9c1a | [
"MIT"
] | null | null | null | Examples/iOSExample/iOSExample/readme.md | buscarini/SwiftStroke | fa4a41758363fb4245cd044ffb1ee774678d9c1a | [
"MIT"
] | null | null | null | # Readme
## Cocoapods
Use this as a reference of a pod swift project:
[https://github.com/kylef/JSONWebToken.swift] | 19.5 | 47 | 0.752137 | eng_Latn | 0.729149 |
2228220eaf1c8f193073bb1483ac51117c644a51 | 1,593 | md | Markdown | README.md | garfeng/clothes | a85f653b2f5199ec81102f4c467195e6c3d0e4d7 | [
"MIT"
] | 3 | 2018-01-06T11:21:16.000Z | 2019-09-22T01:50:04.000Z | README.md | garfeng/clothes | a85f653b2f5199ec81102f4c467195e6c3d0e4d7 | [
"MIT"
] | null | null | null | README.md | garfeng/clothes | a85f653b2f5199ec81102f4c467195e6c3d0e4d7 | [
"MIT"
] | 3 | 2018-01-06T11:24:27.000Z | 2019-09-22T01:54:30.000Z | # 衣服生成器 for RPGmaker
衣服 = 版型 + 花色
其中，版型管理阴影和轮廓，为灰度图；花色为简单的色块。
将花色在版型上遮罩一层，即可生成一件新的衣服。
版型制作复杂，一个可以多用。
花色制作简单，制作时不需考虑阴影和高光。
以下为**相同版型**搭配**不同花纹**的示例：
 +  = 
 +  = 
调用了 RPGmaker MV 自带的 rpg_core.js 和 rpg_manager.js，以及pixi.js
## 在线版
[链接](http://garfeng.github.io/clothes)
## 离线版
### github仓库下载
1. 下载本仓库后，双击运行根目录下的webserver.exe，保持窗口不要关闭。
2. 打开谷歌浏览器，访问 http://localhost:8920 。
### 百度网盘下载
[链接](https://pan.baidu.com/s/1sk9LQUT#path=/myprogram/ClothesGenerator)
有两个文件，其中，
40M+的那个是自带浏览器的完整版本，如果你的电脑里没有安装谷歌浏览器等一些比较新的浏览器，请下载该版本。
2.4M那个是自带webserver的精简版，如果你安装了谷歌浏览器，请下载该版本。
## 添加您自己的素材
### 版型
1. 制作3x4格式的行走图版型，版型需要是黑白的，需要把袖子切下来单独放置。
2. 两张图放进images/model目录下，文件名分别为：
```
总体行走图：xxx_1.png
袖子行走图：xxx_2.png
```
3. 编写data/data.json
```
{
"model":["xxx","xxx","添加你的xxx"],
"pattern":[……]
}
```
### 花色
1. 制作宽度为一个行走单位宽度两倍，高度和它相同的两个花色。
比如xp默认行走图32x48，花色图为64x48，其中，前32像素为正面，后32为背面。
制作两个花色，分别给总体行走图和袖子图上色。
2. 放进images/pattern目录下，文件名为：
```
总体行走图的花色：xxx_1.png
袖子行走图的花色：xxx_2.png
```
3. 编写data/data.json
```
{
"model":[……],
"pattern":["a","b","添加你的xxx"]
}
```
### 刷新
浏览器刷新页面，即可看到新添加的图。 | 18.102273 | 257 | 0.724419 | yue_Hant | 0.374975 |
22283d27b0d8606781618606de668de275317018 | 1,089 | md | Markdown | _posts/2022-03-14-JekyllLayout.md | StOney44/stoney44.github.io | ec70e34213fbe8a431af49c6cbd87083fe23a14b | [
"MIT"
] | null | null | null | _posts/2022-03-14-JekyllLayout.md | StOney44/stoney44.github.io | ec70e34213fbe8a431af49c6cbd87083fe23a14b | [
"MIT"
] | null | null | null | _posts/2022-03-14-JekyllLayout.md | StOney44/stoney44.github.io | ec70e34213fbe8a431af49c6cbd87083fe23a14b | [
"MIT"
] | null | null | null | ---
title: "Layout"
excerpt: "(작성중) Layout의 종류"
categories:
- Blog
tags:
- [Blog, Jekyll]
toc: true # table of contents
toc_sticky: true
author_profile: false # 좌측 프로필 접기(포스트에 들어가면 꺼짐)
sidebar:
nav: "docs"
date: 2022-03-14
last_modified_at: 2022-03-14
---
# Layout
Layout outer, middle, inner, file.md 적용
# `default`
- 전체 HTML tag를 만드는 가장 바깥의 Layout
# `archive`
- dafault를 Layout으로 갖는 Layout
# `sidebar`
- 메뉴 리스트와 프로필이 나타나는 Layout
- 문서마다 `sidebar.nav` 변수를 수동으로 입력해야함
# `home`
- 최상위 경로의 `/index.html`에서 사용하는 Layout
# `Single`
- post의 Layout, 갖는 속성값은 아래와 같다
```
- Header Image: 사이트 전체화면을 관통하는 image와 header를 넣을 수 있음
- Breadcrumb
- sidebar
- SEO: title, description, published_date 등 포스트 고유의 <meta> tag가 추가
- title
- content: post의 내용이 되는 {{ content }} 삽입
- category: 현재 post가 속해있는 category 보여줌
- date: 포스팅 날짜
- share: 포스트를 소셜에서 공유할 수 있게 Link를 제공
- pagination: 목차로 돌아갈 필요 없이 previous/next 버튼 제공
- comment: 댓글 기능
- related post
```
[<cite>Reference</cite> - ExtraBrain 블로그](https://seungwubaek.github.io/blog/mmistake_layout/)
| 19.8 | 94 | 0.665748 | kor_Hang | 0.999975 |
22283e6868478fd7ecc2369bbb392fe9e56f1d1c | 2,230 | md | Markdown | examples/emoji/README.md | meseguera/m3o-js | e2f61fd876a3477e8f587aedc3e2a81fa2e28d30 | [
"Apache-2.0"
] | 9 | 2021-09-09T12:58:46.000Z | 2022-03-14T08:09:11.000Z | examples/emoji/README.md | meseguera/m3o-js | e2f61fd876a3477e8f587aedc3e2a81fa2e28d30 | [
"Apache-2.0"
] | 2 | 2021-11-11T17:32:22.000Z | 2021-11-25T14:47:34.000Z | examples/emoji/README.md | meseguera/m3o-js | e2f61fd876a3477e8f587aedc3e2a81fa2e28d30 | [
"Apache-2.0"
] | 3 | 2021-11-25T14:29:06.000Z | 2022-03-07T18:08:09.000Z | # Emoji
An [m3o.com](https://m3o.com) API. For example usage see [m3o.com/Emoji/api](https://m3o.com/Emoji/api).
Endpoints:
## Find
Find an emoji by its alias e.g :beer:
[https://m3o.com/emoji/api#Find](https://m3o.com/emoji/api#Find)
```js
const { EmojiService } = require('m3o/emoji');
const emojiService = new EmojiService(process.env.M3O_API_TOKEN)
// Find an emoji by its alias e.g :beer:
async function findEmoji() {
const rsp = await emojiService.find({
"alias": ":beer:"
})
console.log(rsp)
}
findEmoji()
```
## Flag
Get the flag for a country. Requires country code e.g GB for great britain
[https://m3o.com/emoji/api#Flag](https://m3o.com/emoji/api#Flag)
```js
const { EmojiService } = require('m3o/emoji');
const emojiService = new EmojiService(process.env.M3O_API_TOKEN)
// Get the flag for a country. Requires country code e.g GB for great britain
async function getFlagByCountryCode() {
const rsp = await emojiService.flag({
"alias": "GB"
})
console.log(rsp)
}
getFlagByCountryCode()
```
## Print
Print text and renders the emojis with aliases e.g
let's grab a :beer: becomes let's grab a 🍺
[https://m3o.com/emoji/api#Print](https://m3o.com/emoji/api#Print)
```js
const { EmojiService } = require('m3o/emoji');
const emojiService = new EmojiService(process.env.M3O_API_TOKEN)
// Print text and renders the emojis with aliases e.g
// let's grab a :beer: becomes let's grab a 🍺
async function printTextIncludingEmoji() {
const rsp = await emojiService.print({
"text": "let's grab a :beer:"
})
console.log(rsp)
}
printTextIncludingEmoji()
```
## Send
Send an emoji to anyone via SMS. Messages are sent in the form '<message> Sent from <from>'
[https://m3o.com/emoji/api#Send](https://m3o.com/emoji/api#Send)
```js
const { EmojiService } = require('m3o/emoji');
const emojiService = new EmojiService(process.env.M3O_API_TOKEN)
// Send an emoji to anyone via SMS. Messages are sent in the form '<message> Sent from <from>'
async function sendAtextContainingAnEmojiToAnyoneViaSms() {
const rsp = await emojiService.send({
"from": "Alice",
"message": "let's grab a :beer:",
"to": "+44782669123"
})
console.log(rsp)
}
sendAtextContainingAnEmojiToAnyoneViaSms()
```
| 21.650485 | 104 | 0.70583 | yue_Hant | 0.384017 |
2229adf4ea6c44a5bf842c68b139a6fc4633b662 | 1,246 | md | Markdown | Week2-FinanceConcepts.md | nomadic-me/CodingCheatSheets | a4109cce611dcfc81668db3a18c2c50d75a66ba4 | [
"MIT"
] | null | null | null | Week2-FinanceConcepts.md | nomadic-me/CodingCheatSheets | a4109cce611dcfc81668db3a18c2c50d75a66ba4 | [
"MIT"
] | null | null | null | Week2-FinanceConcepts.md | nomadic-me/CodingCheatSheets | a4109cce611dcfc81668db3a18c2c50d75a66ba4 | [
"MIT"
] | 1 | 2022-03-10T03:11:03.000Z | 2022-03-10T03:11:03.000Z | # Additional Finance Concepts
## Corresponding Jupyter Notebook:
[Jupyter Notebook](PythonJupyterNotebooks/Week2-Day3-Quick1.ipynb)
### From Investopedia: Understanding the Time Value of Money
#### https://www.investopedia.com/articles/03/082703.asp
### From Harvard Business Review: A Refresher on Net Present Value
#### https://hbr.org/2014/11/a-refresher-on-net-present-value
### From Investopedia: Discounted Cash Flow (DCF)
#### https://www.investopedia.com/terms/d/dcf.asp
.png)
| 62.3 | 279 | 0.789727 | eng_Latn | 0.507554 |
2229caf4a5ee2fdac4b69106484bac87700dd76e | 407 | md | Markdown | README.md | OpenSoftwareSolutions/bonita-restclient | 6829eeddaedcced252cf78001ca370fe93a13967 | [
"Apache-2.0"
] | null | null | null | README.md | OpenSoftwareSolutions/bonita-restclient | 6829eeddaedcced252cf78001ca370fe93a13967 | [
"Apache-2.0"
] | null | null | null | README.md | OpenSoftwareSolutions/bonita-restclient | 6829eeddaedcced252cf78001ca370fe93a13967 | [
"Apache-2.0"
] | null | null | null | # bonita-restclient
This Bonita Rest Client shows an example on how to read processed files from a repository that a mobile device has produced.
# License
Apache License V2.0
# Used with bonita-connector-digitalforms
see the tutorial on how to [Integrate BonitaSoft with DigitalForms](https://github.com/OpenSoftwareSolutions/bonita-connector-digitalforms/wiki/Integration-DigitalForms-with-BonitaSoft)
| 45.222222 | 186 | 0.823096 | eng_Latn | 0.93376 |
222a3d1881b70d91992e17bd4807ee7663d2bf4f | 210 | md | Markdown | packages/explorer-2.0/README.md | fakecoinbase/livepeerslashlivepeerjs | 7fc9fddf8c4c582b61884128efc021b1f0358cde | [
"MIT"
] | null | null | null | packages/explorer-2.0/README.md | fakecoinbase/livepeerslashlivepeerjs | 7fc9fddf8c4c582b61884128efc021b1f0358cde | [
"MIT"
] | null | null | null | packages/explorer-2.0/README.md | fakecoinbase/livepeerslashlivepeerjs | 7fc9fddf8c4c582b61884128efc021b1f0358cde | [
"MIT"
] | null | null | null | # Livepeer Explorer
## Getting started
1. Rename .env.example to .env
2. Install it and run:
```bash
yarn
yarn dev
```
## Developing on Rinkeby
Rename .env.example to .env.rinkeby and run `yarn dev:rinkeby` | 15 | 62 | 0.714286 | eng_Latn | 0.964294 |
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