OpenLLM-France/wikimedia huggingface dataset:
# The implementation of E-Government/E-Government by country/Thailand/Unified state web portal of electronic government

The E-Government Portal offers one-stop e-Services center which can be accessed through SMS and Web Portal, etc. The system also serves as an efficient tool for the government to communicate with various government agencies, the business sector and the general public.
Citizens could have easy access to significant information and services, they will enjoy having greater convenience, more opportunity for career advancement, enhance self development and promote public participation in government’s activities.

## E-services

The government-based e-services are developed in the Unified sign-in form, totaling 38 systems. Apparently, some of the most remarkable systems have been designed to empower significant progress, for instance, database management, rehabilitation for victims of unrest in southern border provinces, sub-district data administration, provincial welfare database, Internet-based inquiry services, medical rehabilitation services initiated by Sirindhorn National Medical Rehabilitation Institute (SNMRI), and donation allocation and management for needy people.
E-government-service are divided into 24 categories:
* The welfare of the government;
* Business investments;
* Consumer Protection;
* Tender and form;
* Law and safety;
* Environment, resources and agriculture;
* State property and auctions;
* Food and Drug Administration;
* Culture and Sports;
* Immigration and visas;
* Education and welfare;
* Employment and well-being;
* Families, communities and residences;
* Passport and travel;
* Public service;
* Apply for a certificate of authenticity;
* Elections;
* Business investments;
* Taxes and finances;
* Transport and Transportation;
* Social security and protection of employees;
* Public utility;
* Services for people with disabilities;
* Other services.
G-News – is a newly developed application providing information on government services that can be obtained directly by the general public. The application can provide individual message alerts on specific transactions, such as notifications of tax payment or public utility payments. People are also allowed to search for their required information or specific locations where they want to visit.
# The implementation of E-Government/E-Government by country/Ukraine/A national electronic healthcare system

The web portal of electronic health system E-Health is a system that helps patients to receive, and doctors - to provide quality medical services. And also, it allows you to control how effectively public funds allocated for health care are spent and prevent abuse.

## E-services

The web portal of the National Electronic Healthcare System provides its services to the following categories of users:
* Head of medical institution. To register in the system user need:
** electronic digital signature (EDS);
** e-mail address;
** documents of the institution;
** passport;
** tax number;
* Doctor of the first link:
** EDS of the employee of the medical institution;
** e-mail address;
** patient documents;
* Authorized person:
** EDS of the employee of the medical institution;
** e-mail address;
** patient documents;
* a patient:
** mobile phone;
** tax number;
** proof of identity.
In the future, the Е-Health system will enable everyone to quickly get their medical information, and doctors - correctly diagnose taking into account the whole picture of the patient's health. There will no longer be a need for paper medical records and printed references that are lost and forgotten. Doctors will write out electronic prescriptions that can not be lost or faked. The system will contain the entire medical history of the patient, and it will be available to both the patient and his doctors.
# The implementation of E-Government/E-Government by country/Thailand/The Stock Exchange

The web portal of Stock Exchange of Thailand (SET) displays all the information in the following sections:
* About the SET;
* Rules/Regulations;
* Market data;
* Companies/Securities Information;
* Related persons;
* News.

## E-services

The SET provides e-services:
* Information services:
** Service Detail;
** Subscription;
** Data Vendors;
** SET Market Data Usage Policy;
* Listing services:
** Listing on SET;
** Bond Listing;
* Institutional services:
** Trading Channel for Institutional Investors;
** Direct Market Access (DMA);
** Program Trading;
** Co-location service. The Stock Exchange of Thailand provides co-location services for the installation of servers at the SET Data Center (Primary Site). Services support trading orders and pricing data for the equities market (SET) and the derivatives market (TFEX). Co-location allows for fast, reliable data transfer within the SET Data center itself, and this immediate proximity provides rapid order placement and data transfer for participating clients.
# Stata

Stata
* Basics
** Introduction
** Settings
** Documentation
** Probability Function
** Random Number Generation
** Data Management
** Graphics
** Programming
** Mata
** Natural Language Processing
** Publication Quality Output
** Descriptive Statistics
* Methods
** Maximum Likelihood
** Bayesian Methods
** Bootstrap
** Non Parametric
** Matching
** Quantile Regression
* Topics
** Linear Models
** Binomial Outcome Models
** Multinomial Models
** Count Data Models
** Tobit and Selection Models
** Duration Analysis
** Multilevel Modeling
** Time Series
** Spatial Econometrics
** Factor Analysis
* Problems
* Stata is a statistical package for data management, data analysis, and graphics. It is commercially distributed by StataCorp LP.
* Share your knowledge on Stata on this wikibook.
# Stata/Descriptive Statistics

Stata
* Basics
** Introduction
** Settings
** Documentation
** Probability Function
** Random Number Generation
** Data Management
** Graphics
** Programming
** Mata
** Natural Language Processing
** Publication Quality Output
** Descriptive Statistics
* Methods
** Maximum Likelihood
** Bayesian Methods
** Bootstrap
** Non Parametric
** Matching
** Quantile Regression
* Topics
** Linear Models
** Binomial Outcome Models
** Multinomial Models
** Count Data Models
** Tobit and Selection Models
** Duration Analysis
** Multilevel Modeling
** Time Series
** Spatial Econometrics
** Factor Analysis
* Problems
In this section we show how to do summary statistics using Stata. This section include three subsection. The first one deals with commands to describe a whole dataset, the second one, with commands to describe a single variable and the third one with command to describe a set of variables.

## Describe a dataset

* 'des' (describe) : gives the size of the file, the number of observation, the number of variable, the list, labels, types for each variable.
* 'des, s' (describe short) : gives only the size of the file, the number of observations, the number of variables.
* 'des' return the number of changes since last save, the number of variables 'r(k)', the number of observations 'r(N)'.
* codebook
* inspect

## Univariate statistics
### Continuous variables

* su
* su, d
* robmean : robust mean

### Discrete variables

* ta

## Multivariate statistics
### Continuous variables

* corr returns the matrix of linear correlation between a set of variables.
** corr, cov returns the covariance matrix.
Here is an example. We first simulate a y and x such that there is a positive correlation between them. We plot the two variables and look at the correlation.
* wincorr returns the winsorized correlation : tails have replaced by a limit value. This is useful if some extreme values have a big influence on the correlation coefficient.
* spearman and spearman2 gives the Spearman's rank correlation between two variables. This statistics is less sensitive to outliers than Pearson's linear correlation. This is often useful as a robustness check.

### Discrete variables

* ta

### Continuous and discrete variables

* catgraph : plotting means of a continuous variable by categories
* table
# Stata/Graphics

Stata
* Basics
** Introduction
** Settings
** Documentation
** Probability Function
** Random Number Generation
** Data Management
** Graphics
** Programming
** Mata
** Natural Language Processing
** Publication Quality Output
** Descriptive Statistics
* Methods
** Maximum Likelihood
** Bayesian Methods
** Bootstrap
** Non Parametric
** Matching
** Quantile Regression
* Topics
** Linear Models
** Binomial Outcome Models
** Multinomial Models
** Count Data Models
** Tobit and Selection Models
** Duration Analysis
** Multilevel Modeling
** Time Series
** Spatial Econometrics
** Factor Analysis
* Problems

## References

* UCLA Stata Graph Gallery
* A visual guide to Stata Graphics
** The 1st chapter

## Standard Error Bar Chart

Standard Error Bar Chart are very useful to displain confidence interval.

## Setting for Graphics

* the list of symbols :
query graphics gives all the settings.
* The scheme defines the general appearance of the graph.
* You can download more scheme on the Visual Guide to Stata Graphics website :

## Exporting/Saving graphics

You can export a graph to png, jpg, ps or pdf format by using once the graph is done the graph export command.
See h gr export to see the list of all available graph devices.
You can also store a graph in the Stata graph format using the graph save command. This format can only be read by Stata.

## Maps

* tmap
* spmap (http://www.stata.com/support/faqs/graphics/spmap.html)
# Stata/Binomial Outcome Models

Stata
* Basics
** Introduction
** Settings
** Documentation
** Probability Function
** Random Number Generation
** Data Management
** Graphics
** Programming
** Mata
** Natural Language Processing
** Publication Quality Output
** Descriptive Statistics
* Methods
** Maximum Likelihood
** Bayesian Methods
** Bootstrap
** Non Parametric
** Matching
** Quantile Regression
* Topics
** Linear Models
** Binomial Outcome Models
** Multinomial Models
** Count Data Models
** Tobit and Selection Models
** Duration Analysis
** Multilevel Modeling
** Time Series
** Spatial Econometrics
** Factor Analysis
* Problems

## References

* Regression Models for Categorical Dependent Variables Long and Freese. Example datasets and programs are available on this webpage

## Binomial outcome models

* The logit model can be estimated using logit or glm and the probit model with probit or glm.

### Scobit

* Scobit (Skewed logistic regression) was developped by Jonathan Nagler 1994 (American Journal of Political Science). The idea is two estimate a skewness parameter of the underlying distribution.
# Stata/Documentation

Stata
* Basics
** Introduction
** Settings
** Documentation
** Probability Function
** Random Number Generation
** Data Management
** Graphics
** Programming
** Mata
** Natural Language Processing
** Publication Quality Output
** Descriptive Statistics
* Methods
** Maximum Likelihood
** Bayesian Methods
** Bootstrap
** Non Parametric
** Matching
** Quantile Regression
* Topics
** Linear Models
** Binomial Outcome Models
** Multinomial Models
** Count Data Models
** Tobit and Selection Models
** Duration Analysis
** Multilevel Modeling
** Time Series
** Spatial Econometrics
** Factor Analysis
* Problems
* Stata's website includes a huge quantity of resources including
** a bookstore
*** Mitchell's introduction to Stata Graphics
** The Stata Journal
* The UCLA Stata computing page includes many tutorials.
** UCLA Stata Graph Gallery
* Statistical Software Component
* Antoine Bozio's introduction to Stata (in French)
* Edwin Leuven's Introduction to Stata

## Video

* 'Installing Commands from the Command Line in Stata'

## Blog

* Gabriel Rossman
# Stata/Bayesian Methods

Stata
* Basics
** Introduction
** Settings
** Documentation
** Probability Function
** Random Number Generation
** Data Management
** Graphics
** Programming
** Mata
** Natural Language Processing
** Publication Quality Output
** Descriptive Statistics
* Methods
** Maximum Likelihood
** Bayesian Methods
** Bootstrap
** Non Parametric
** Matching
** Quantile Regression
* Topics
** Linear Models
** Binomial Outcome Models
** Multinomial Models
** Count Data Models
** Tobit and Selection Models
** Duration Analysis
** Multilevel Modeling
** Time Series
** Spatial Econometrics
** Factor Analysis
* Problems

# References

* Using winbugs from Stata
* Bayesian analysis using WinBugs and Stata
# Stata/Bootstrap

Stata
* Basics
** Introduction
** Settings
** Documentation
** Probability Function
** Random Number Generation
** Data Management
** Graphics
** Programming
** Mata
** Natural Language Processing
** Publication Quality Output
** Descriptive Statistics
* Methods
** Maximum Likelihood
** Bayesian Methods
** Bootstrap
** Non Parametric
** Matching
** Quantile Regression
* Topics
** Linear Models
** Binomial Outcome Models
** Multinomial Models
** Count Data Models
** Tobit and Selection Models
** Duration Analysis
** Multilevel Modeling
** Time Series
** Spatial Econometrics
** Factor Analysis
* Problems

## Bootstrap/Resampling Methods

* bs
* bstrap
* bstat
* bsample
# Stata/Linear Models

Stata
* Basics
** Introduction
** Settings
** Documentation
** Probability Function
** Random Number Generation
** Data Management
** Graphics
** Programming
** Mata
** Natural Language Processing
** Publication Quality Output
** Descriptive Statistics
* Methods
** Maximum Likelihood
** Bayesian Methods
** Bootstrap
** Non Parametric
** Matching
** Quantile Regression
* Topics
** Linear Models
** Binomial Outcome Models
** Multinomial Models
** Count Data Models
** Tobit and Selection Models
** Duration Analysis
** Multilevel Modeling
** Time Series
** Spatial Econometrics
** Factor Analysis
* Problems

## References

* UCLA Computing STATA Regression page

## Simple Linear Model

We generate a simple fake data set :
leanout is a prefix which simplifies the output. This command does not display useless ancillary statistics and focus and confidence intervals rather than null hypothesis testing.

## Performing multiple regression on the same subsample

Sometimes you want to perform multiple regressions on the same subsample. This is not obvious since when one of the variable of the model is missing the observation is dropped. One way to be sure that you use the same subsample is to use the 'e(sample)' command which returns the list of all used observations. In the example below qui store the result of 'e(sample)' in variables 'samp1' and 'samp2' and we perform the model conditioning on 'samp1==1 & samp2 == 1'. Thus we are sure that both estimation are done using the same observations.

## Instrumental Variables

Here is a data generating process for an instrumental variable setting. u is correlated with x which gives endogeneity. z is independant of u and correlated with x, which makes it eligible as a valid instrument for x.
It easy to see that the standard least square estimate is biased and the IV estimate is unbiased.
You can perform an overidentification test using overid or ivreg2

## Linear Panel Data

* xtset
* xtreg
* xtabond
* xtabond2
* ivreg2
* xtivreg2
* ivendog
* ivhettest
* overid : overidentification test
* xtoverid : overidentification test
* xttest2
* ivgmm0
* xtarsim
* xtdpd
* xtdpdsys

### Random effect estimator

We assume $y_{it}=1+x_{it}+z_{i}+f_{i}+u_{it}$. With f independant of x and z and u independant of x and z.

### Dynamic Linear Panel Data

Layard and Nickel unemployment dataset.
You can also generate fake data :
It is easy to see that standard random effect and fixed effect models are biased but instrumented random and fixed effect are unbiased :

## References

* ↑ Nathaniel Beck "leanout: A prefix to regress (and similar commands) to produce less output that is more useful" Stata Journal, forthcoming http://politics.as.nyu.edu/docs/IO/2576/sj_driver.pdf
# VHDL for FPGA Design/Printable version

The current, editable version of this book is available in Wikibooks, the open-content textbooks collection, at
https://en.wikibooks.org/wiki/VHDL_for_FPGA_Design

# Decoder
## Link to Video of ISE Navigation to RTL Schematic

Video of how to get to the Decoder RTL Schematic (circuit diagram) in the Xilinx ISE Project Navigator

## See Also

* Encoders-Decoders

# Multiplexer
## Video Links

* Video of Simulation
* Video of how to get to the Multiplexer RTL Schematic

# 4-Bit Adder

library ieee; use ieee.std_logic_1164.all; use ieee.std_logic_unsigned.all use ieee.std_logic_arith.all;
Entity Adder4 Is port ( a,b:in integer Range -8 to 7; sum:out integer Range -16 to 15 ); end Adder4;
Architecture Adder4arch of Adder4 Is Begin sum<=a+b;
end Adder4arch;

## Simulation Waveform
# 4-Bit ALU
## 4-Bit ALU VHDL Code

A combinatorial ALU with the following operations:
| Operation | Result | Flag | Description |
| 000 | Nibble1 + Nibble2 | Carry = Overflow | Addition |
| 001 | | Nibble1 - Nibble2 | | 1 if Nibble2 > Nibble1, 0 otherwise | Test / diff |
| 010 | Nibble1 AND Nibble2 | 0 | Bitwise AND |
| 011 | Nibble1 OR Nibble2 | 0 | Bitwise OR |
| 100 | Nibble1 XOR Nibble2 | 0 | Bitwise XOR |
| 101 | 15 - Nibble1 | 0 | Bitwise inverse of Nibble1 |
| 110 | 15 - Nibble2 | 0 | Bitwise inverse of Nibble2 |
| 111 | Nibble1 + Nibble2 + 1 | Carry = Overflow | Addition |
# VIP 922
## Contents

* Dish Network
# Vala Programming/Concepts

Vala Programming

## Concepts

Basic
Files. Namespaces.
Objects
Introduction To Object Oriented Programming (OOP). Classes.
Variables
Short description of the chapter.
Memory Management
Short description of the chapter.
Error Handling
Short description of the chapter.
Introduction to DBus
Short description of the chapter.
# Vala Programming

Vala is a new programming language that aims to bring modern programming language features to GNOME developers without imposing any additional runtime requirements or different Application Binary Intefaces (ABIs) compared to applications and libraries written in C.
This book aims to be a complete guide to learning how to program in Vala. While it is aimed primarily at new users of the language, it is also intended to be useful for experienced Vala programmers and as a comprehensive reference.

## Introduction

History
History. Language Features. Why Vala? Caveats
Programming Style
Coding conventions.
Getting Started
Getting Vala. First app (helloworld.vala). Explanation. Running the Code.

## Concepts

Basic
Files. Namespaces.
Objects
Introduction To Object Oriented Programming (OOP). Classes.
Variables
Short description of the chapter.
Memory Management
Short description of the chapter.
Error Handling
Short description of the chapter.
Introduction to DBus
Short description of the chapter.

## Syntax

Projects and Files
Short description of the chapter.
Types
Short description of the chapter.
Variables
Variables. Scope.
Expressions
Short description of the chapter.
Statements
Short description of the chapter.
Classes
Short description of the chapter.

## Libraries

Vala Core Libraries
Short description of the chapter.
External Libraries
Short description of the chapter.

## Resources

* Vala Homepage
* Vala Binding Reference
# XQuery/Sequence Diagrams
## Background

Sequence Diagrams are tedious to draw, even with a diagramming tool. They are even worse to edit when the sequence changes. An alternative is to define an XML vocabulary to define the message sequencing and to use XQuery to render this description as XHTML. This textual approach also allows explanations to be revealed at each step and alternative renderings of the XML definition of the Sequence to be generated, such as a printed version.
This demonstrator uses a simplified meta-model, with only messages between actors and actions undertaken by actors.
(article under re-design - CW)

## Models
### 3-tier architecture

Here is a sample description of interaction in a 3-tier architecture: (badly needs re-writing)

## Rendering the diagram

The script 'displayDiagram' renders this model as an XHTML table:
Display

### Further example Diagrams

* A GoogleEarth application
Model Display

## Animating the Diagram

Rather than display the complete interaction, the diagram can be simply animated by displaying only the first n steps, together with explanatory text for the last step. The addition of some controls allows the user to step forward and backward in the sequence.
3 Tier GoogleEarth A function to compute the next step:
and call the function
A form to provide the controls and maintain the interaction state:
Limit the events displayed to the specified number of steps:
and display the explanation of the last step:

## Print the diagram

One of the advantages of having the whole diagram in XML is that the diagram can be displayed differently in print, so that each step can be shown with its description.
Fully expanded Descriptions
# XQuery/SPARQLing Country Calling Codes
## Motivation

Stimulated by Henry Story's blog entry, the following script works on the same problem. This script uses the functions defined in previous module to execute a SPARQL query on the dbpedia server, and to convert SPARQL Query results to tuples.

## First attempt

Run
In this script the resource uri is parsed to get the local name part of the resource URI in the fr:clean function.
The more sound alternative is to filter the multilingual rdfs:label property:
Run
but this query is naturally much slower.

## Discussion

This query returns a set of dbpedia resources which have a callingCode property. However, it includes resources which are not countries and it proves quite difficult to identify which resources are countries. It might be expected that either the skos:subject or rdfs:type predicates would identify countries, but this is not the case.
Of course, what entities are classified as countries is a debatable issue, as is currently illustrated by Kosova and by the documentation on ISO 3166. Perhaps countries are better identified by properties. There is a property countryCode which looks promising:
The SPARQL query becomes:
Run
However this shows that many countries have incomplete data in dbpedia, or that the coding of this property is inconsistent. This is not surprising because there are a number of types of country codes, which result in different definitions of country:
* ISO 3166-1 alpha-3
* ISO 3166-1 alpha-2
* ISO 3166-1 numeric
* IOC country codes
* License plate numbers
* Top-level domain codes

## Wikipedia scraping

In fact, International Calling codes are listed in a wikipedia entry Thus a more direct approach would be to generate the table by scraping wikipedia directly. However, now we err in the opposite direction, in that there are calling codes for telecom services as well as countries, and the format of numbers and names is inconsistent - some multiple numbers, some numbers with leading + , some countries with appended synonyms etc.
In this script, the path expression finds the anchor "Alphabetical_Listing" and then finds the following table.
Jan 2010 - the page layout had changed so that the previous path to this table :
to the current :
Wikipedia

## Export as RDF

An alternative is to export this table as RDF. Here the resource is the dbpedia resource and the property is defined in the dbpedia property namespace.
Similarly the structure of this table changed so this code needed to be updated.
RDF
# XQuery/Sending E-mail
## Motivation

You want to send an e-mail message from within an XQuery. This is frequently done when a report has finished running or when a key event such as a task update has been done.

## Method

eXist provides a simple interface to e-mail.

## Format of the send-email function

The email message is in the following format:

## Example of JSON API

Many e-mail services also provide a JSON API. For example, Mandrill has an e-mail service that allows you to send your first 12,000 emails per month for free. After you register for an account they will provide you with an API key. This API key can be used with the following to send an e-mail message:
Below is a refined version of the above Mandrill script, which does not use the XQJSON module to serialize the XML message to JSON.
This example was provided by Chris Misztur on Sept. 21, 2013.

## References

* eXist mail module