OrgMode with Nikola
Original post on Nikola
Thanks to Fabien Dubosson. Blogging in Org Mode With Nikola
Steps for installation
Add Makefile
The difference between this and the tutorial is the fact that I've used python3 -m venv instead of pyenv
ENVIRONMENT := _env/
default: help
$(ENVIRONMENT):
python3 -m venv "$@"
clean:
nikola clean
rm -rf $(ENVIRONMENT)
setup:
@echo "make $(ENVIRONMENT);"
@echo "source $(ENVIRONMENT)bin/activate;"
@echo "pip install --upgrade pip 'Nikola[extras]';"
help:
@echo "To setup Nikola environment, please use 'eval \$$(make setup)'."
Install with the bash script
type
$ eval $(make setup)
Create a repo github
I added unlicense (as the license)
Create nikola dir
Create nikola directory and then move the files into the upper repository
- initialize nikola website
$ nikola init website mv website
- move its files into the upper directory (cleaner)
$ mv website/* .
- remove old empty folder
$ rmdir website
Install orgmode into nikola
$ nikola plugin -i orgmode
Build webpage
- option 1
- nicola build
- //The port can be choosen freely
- nicola server -p 35729
- option 2 (build and serve with autoupdate)
- nicola auto -p 35729
Save all files into github
In here I recomment to use manual push
- check new files
$ git status
- add git files
$ git add <file or folder>
- commit
$ git commit -am "message"
- push
git push
Modify Python File for org Mode
when this was mentionned
# Add the orgmode compiler to your COMPILERS dict.
COMPILERS["orgmode"] = ('.org',)
# Add org files to your POSTS, PAGES
POSTS = POSTS + (("posts/*.org", "posts", "post.tmpl"),)
PAGES = PAGES + (("stories/*.org", "stories", "story.tmpl"),)
I thought that it was about things in the emacs file. Finally it was about things in the conf.py file. So search the posts and search the compilers and add them into the conf.py file. Don't forget to commit.
Error about system language
Error happened when this command :
nikola new_post 999_nikola_install.org
Error picture
solution: Article set locale (askubuntu.com)
- $ locale
to check all the locales configured on ubuntu
- $ sudo locale-gen "en_US.UTF-8"
- $ sudo dpkg-reconfigure locales
- From the list I search for en_US.UTF-8, which happens to be number 149
- I type 149 and press enter
- The command prompt asks me to choose between
- None
- C.UTF-8
- en_US.UTF-8
- I choose C.UTF8
- I rerun the command
- nikola new_post 999_nikola_install.org
- it works!
Adding a picture to orgmode
from
To add a picture simply type:
[[file:<link>]]
Generating post
- type following command
$ nikola new_post -e -f orgmode
- quit editor
- cd into the post folder
- use emacs to add orgmode stuff to the file
- rebuild with
$ nikola build


The number of members you can add to a project depends on your organization's tier. The following table shows the number of collaborators each tier can have per project:
| Tier | Collaborators per Project |
|---|---|
| Free | Unsupported |
| Quant Researcher | Unsupported |
| Team | 10 |
| Trading Firm | Unlimited |
| Institution | Unlimited |


Learning English
Beginner
Author: Umidjon Sobirov
Language: Uzbek
Pre-Intermediate
Author: Umidjon Sobirov
Language: Uzbek
Intermediate
Author: Umidjon Sobirov
Language: Uzbek
Upper-Intermediate
Author: Umidjon Sobirov
Language: Uzbek
Advanced
Author: Umidjon Sobirov
Language: Uzbek
English Extra
Author: Umidjon Sobirov
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IELTS
Author: Umidjon Sobirov
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In this post i’ll describe how to bypass NAC ( Network Access Control ). During an internal pentest, I had a problem connecting to the client network ( all network devices 802.1x (including VoIP devices), after a few hours trying to find out why, I found out that the client had implemented NAC, but what is Network access protocol (NAC) ?
Network Access Control (NAC) is a solution used on corporate networks to prevent, or even hinder, unauthorized hosts from accessing internally available services and systems. This access restriction can occur only at the network layer, preventing an IP address from being obtained through the DHCP service, or completely segmenting the wrong hosts from the valid ones at the link layer level. Below are descriptive steps to circumvent NAC, remembering that this does not work in all cases.
1º Connect a valid host to hub
2º Attach the attacker’s host to the hub
3º Intercept the ARP frames to identify the valid host MAC address
4º Connect hub / switch to the network point
5º Wait for authentication of valid host
6º Clone the valid host MAC address into the attacker’s host
7º Enter the victim’s IP address statically in the attacker’s host
This will allow the attacker to communicate with corporate networks without requiring authentication. The following illustration shows the diagram representing such a bypass of the NAC:
After setup, wireshark was used to get IP and MAC address from ARP packets. As ilustrate bellow
The next step was to clone the victim’s MAC address, IP address, configure network settings, and then connect the attacker’s machine to the hub. The following illustration shows when the required NAC bypass settings are made:
As a proof of concept concept, a scan was performed on the 10.75.X.X/X subnet to find available hosts with the nmap tool. The image below reveals the fact:
Thats all folks.. If you would like more posts related to internal pentest, leave your comment at


|
ActiveTcl User Guide
|
||
bell - Ring a display's bell
bell ?-displayof window? ?-nice?
This command rings the bell on the display for window and returns an empty string. If the -displayof option is omitted, the display of the application's main window is used by default. The command uses the current bell-related settings for the display, which may be modified with programs such as xset.
If -nice is not specified, this command also resets the screen saver for the screen. Some screen savers will ignore this, but others will reset so that the screen becomes visible again.
Copyright © 1994 The Regents of the University of California. Copyright © 1994-1996 Sun Microsystems, Inc. Copyright © 2000 Ajuba Solutions. Copyright © 1995-1997 Roger E. Critchlow Jr.


---
title: "Simple Way to Make Ultimate Chicken and Stuffing Casserole Delicious"
slug: chicken-and-stuffing-casserole
description: "Chicken and Stuffing Casserole Recipe - This casserole got me and my family our '10 minutes of fame'. It was entered in the "News Channel 7 Kitchen Recipes" and I had 6 news crew members here at my home eating it! Not only is it easy to make, it is truly delicious, too! Serve with a fresh green salad if desired. Recipe of Perfect Chicken and Stuffing Casserole Delicious"
date: 2021-05-25T07:09:52Z
image: https://cdn.statically.io/img/images.media-allrecipes.com/userphotos/656838.jpg
thumbnail: https://cdn.statically.io/img/images.media-allrecipes.com/userphotos/656838.jpg
cover: https://cdn.statically.io/img/images.media-allrecipes.com/userphotos/656838.jpg
author: Ani
ratingvalue: 4.6
reviewcount: 518
recipeingredient:
- "1 (3 pound) whole chicken"
- "8 ounces dry bread stuffing mix"
- "1 (10.75 ounce) can condensed cream of chicken soup"
- "1 (10.75 ounce) can condensed cream of celery soup"
- "1 ¼ cups chicken broth (from boiling the chicken)"
- "1 cup evaporated milk"
recipeinstructions:
- "Preheat oven to 350 degrees F (175 degrees C)."
- "Layer the deboned chicken meat in a lightly greased 9x13 inch baking dish. Sprinkle stuffing mix over the chicken layer."
- "In a large bowl mix together the soups, broth and evaporated milk. Stir well. Pour this mixture over the stuffing mix, making sure it gets to the bottom of the baking dish (poke around with a fork to let it saturate into the bottom layer). Bake in the preheated oven for 35 to 45 minutes, until it starts to bubble in the center."
categories:
- "Main Dish Recipes"
- "Casserole Recipes"
- "Recipes"
tags:
- "main dish recipes"
- "casserole recipes"
katakunci: chicken and stuffing casserole, main dish recipes, casserole recipes, .
nutrition: 579.5 calories
recipecuisine: Western
preptime: ""
cooktime: ""
totaltime: ""
recipeyield: "8 servings"
recipecategory:
- Main Dish Recipes
- Casserole Recipes
quotes: Food shared is happiness multiplied.
---
Chicken and Stuffing Casserole - Hello everybody, I hope you are having an amazing day today. Its me again Ani , welcome to my recipe site. Today, I will show you a way to prepare a distinctive dish, Chicken and Stuffing Casserole. One of my favorites recipes. This time, Im gonna make it a little bit tasty. This will be really delicious.
Chicken and Stuffing Casserole is This casserole got me and my family our '10 minutes of fame'. It was entered in the "News Channel 7 Kitchen Recipes" and I had 6 news crew members here at my home eating it! Not only is it easy to make, it is truly delicious, too! Serve with a fresh green salad if desired.
Chicken and Stuffing Casserole is one of the most favored of current trending meals in the world. its easy, its quick, it tastes yummy. It is enjoyed by millions every day. its easy, its quick, it tastes delicious. Chicken and Stuffing Casserole is something that I ve loved my entire life. They re nice and they look wonderful. They are fine and they look fantastic. Chicken and Stuffing Casserole is something which I have loved my whole life.
Recipe of Perfect Chicken and Stuffing Casserole Tasty
To begin with this recipe, we must first prepare a few components. You can have Chicken and Stuffing Casserole using 6 ingredients and 3 steps. Here is how you can achieve it.

-
Layer the deboned chicken meat in a lightly greased 9x13 inch baking dish. Sprinkle stuffing mix over the chicken layer.
-
In a large bowl mix together the soups, broth and evaporated milk. Stir well. Pour this mixture over the stuffing mix, making sure it gets to the bottom of the baking dish (poke around with a fork to let it saturate into the bottom layer). Bake in the preheated oven for 35 to 45 minutes, until it starts to bubble in the center.
So thats going to wrap this up for this exceptional food Chicken and Stuffing Casserole recipe. Thank you very much for reading. Im confident that you will make this at home. Theres gonna be another interesting food in home recipes coming up. Remember to save this page on your browser, and share it to your family, colleague and friends. Thanks again for reading. Go on get cooking!
Reviews of Chicken and Stuffing Casserole:
-
SHARON8799 - Like a lot of people, I change almost every recipe to suit my family's taste and this was no exception. My only changes were to par-cook the chicken with some spices, could only find a 12-oz can of milk so I put the whole thing in, and stirred the stuffing into the wet mix before spooning it over the chicken. My husband loved it (said, "Three thumbs up") but he'll eat anything that doesn't get up and walk away. My super-finicky 6-yr-old: TWO servings! That NEVER happens. Yep, this is a keeper! The only thing I'd do differently next time is to make one of the cans of soup a low-sodium type. We like salty stuff, but this was a bit much, even for us. Otherwise, a perfect dish!! (Did you read that part about my youngest eating TWO servings? TWO!!)
-
MELODY JUNE - This is one of the easiest receipes I've found. I bake boneless, skinless chicken breasts in the morning while I'm getting ready for work (30 - 40 minutes max). When I come home I spend 10 minutes putting the entire casserole together and just pop it in the oven. This has been one of the more requested dinner menu's now! My son asks me to make a double receipe as he likes it so much he wants it for lunch the next day! A true favorite!
-
bracystake - Very tasty recipe and simple. I enjoy the texture of fresh vegetables in my stuffing so I added diced carrots celery and onion to the mix. I also used unseasoned stuffing. To enhance the flavor I salted the chicken added salt and pepper to the liquid ingredients as well as a good tsp each of rubbed sage thyme and basil. Turned out great!
-
PattyJoy - I used this recipe as a springboard for my own version.  Sometimes I make my own stuffing from left-over home-made whole wheat bread cubes, tossed with melted butter and poultry seasoning, (which I make myself from a recipe on this site), and sometimes I just use the "off the shelf" stuffing mix. I usually add some fresh vegetable or other, (last night it was broccoli), and add it to the water and butter that's melting in the pan to be added to the stuffing mix, just to give the recipe a nutritional, color, and texture boost. If I think it needs more moisture, I make my own home-made "cream soup" and pour it over the top and shake it through. If I have any grated cheese handy, (just about any kind works!), I sprinkle that on the top, too, before baking. This has become one of my kids' and husband's favorite recipes. Left-overs are great!
-
KRISTY916 - Very good and very easy. I adapted this recipe slightly by 1) using a pre-cooked rotisserie chicken (and once, a pre-cooked rotisserie turkey breast)and using canned broth in place of the boiled chicken water. I simply pulled apart the chicken and kept some aside for sandwiches and such, and chopped the rest of up for layering into the casserole; 2) in addition to the crunch of celery and veggies, I scattered craisens (dried cranberries) onto the stuffing mixture. They absorbed the liquid of the casserole and became soft and tender, but held their intregrity, for a bit of sharp cranberry contrast which for me, completes the "Thanksgiving in a hot dish" flavor combo. 3) Evap Milk was not required. I've used milk and also FF half and half. The finished texture of this casserole is not "stiff." It's rather soupy, like a very thick Chicken Pot pie mixture, so don't expect anything you can "slice."
-
Claire - This is very good. I've made this a few times and it always gets rave reviews. I've experimented with adding different soups and have never had a bad outcome (cream of mushroom is a good one to sub for the cream of celery). I usually also just use chicken breast meat instead of a whole chicken. Great recipe! Thanks:)
-
GODDESS77 - I love this recipe! It makes great comfort food! However, I like to use Campbell's "chicken with herbs" soup instead of the cream of chicken and cream of celery.
-
bug177 - Love this recipe! I make it even simpler by stirring dry stuffing mix with a little water and the 2 cans creamed soup.Then stir in cubed raw chicken breast, transfer to a casserole and bake. I serve with canned cranberry sauce and it tastes like thanksgiving with only ten minutes to prep!
-
Linda - Like a previous reviewer I used the breast tenderloins rather than the whole chicken. Otherwise I changed nothing about the recipe. The 4 children (ages 5-12) were raving about it and had 2nd helpings - a rarity for the youngest. Without a doubt this is a KEEPER!
-
Roxy - It tasted like canned soup.


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The interval of steps between the contour lines
can be done either in multiples of even distribution
with a logarithmic contour interval or in a user defined contour interval. In the first case
five contour lines are calculated and automatically plotted. The steps between
the contour lines are arranged in a logarithmic scale between 1 and the
maximum counting density. in the first option
the size of the counting calotte is fitted to the number of data, in the
second option it is defined manually.
In the menu 'Colour\Contour lines' you can also choose different grades
of gray for the plot of contour lines.
In the menu ‚Lines‚ the line type for each
contour line can be changed.
In the menu ‚Contouring methods‚ further methods
for the visualisation of counting densities are
offered.
The menu ‚smoothing factor is used only for
the Scalar product-method .
It is also possible to draw closed polylines
which follow the edges between the colours. These polylines can be saved
in a *.dxf file. This file can be treated with
Autocad, CorelDraw, or any other graphic programm which reads dxf-files.
The procedure is: Click 'Method of contouring/Distance method/In steps'
and wait until the diagram is finished. Then click 'Graphic/Save/DXF'. On
the right side a panel appears, in which the saved coordinates and the radius
are shown. Change the numbers, if necessary. Click on 'Create plot file'
and type the name of the plot file. Wait until the polylines appear. Now you
can import the Dxf file to a graphic program.
The Menu ‚Diagram‚ ist the same as in the other programs.


intuit
Businessmodel of Intuit
Customer Segments
Intuit provides services to three main segments: general consumers, accounting professionals and small and medium-sized businesses. The Company provides personal finance and consumer tax products to general users, comprising both sole traders and general professionals, wishing to gain greater control of their finances and access easier methods of managing tax requirements.
Its more advanced tools and products are used by accountancy firms as well as to enterprises. The Company’s business customers operate within various industries, including the energy, construction, education, technology, travel and financial services sectors, with customer including big names such as Google, Procter and Gamble, TomTom, and Kayak.
Intuit serves customers across the world, but its main market is in the US where it is the market leader.
Value Propositions
The key values of Intuit are the flexibility, accessibility and reliability of its products, its support and community networks and its industry reputation.
Intuit is one of the most established names in the tax and accounting software market, and is a market leader in its native US. The Company has a reputation for providing reliable, easy-to-use products offering substantial functionality.
Intuit products can be accessed via desktop and mobile websites, as well as mobile and tablet apps, enabling users to keep track of their finances while on the move. It also allows users to access its software products via the cloud or through direct downloads.
Additionally, Intuit maintains a strong support and community networks forits users through its website.
Channels
The majority of Intuit’s products are available via its desktop and mobile websites at www.intuit.com. The Company has also developed a range of apps corresponding to its various different products – including for Mint, Intuit Tax Online Accountant, Online Payroll and QuickBooks online – that are available through the Apple App Store and the Google Play marketplace.
Once users have subscribed to Intuit services, software can be downloaded directly - on both Mac and Windows computers – or accessed via a cloud-based software-as-a-service platform, depending on the product.
For more complicated or tailored subscriptions customers can contact the Intuit sales team for pricing.
Customer Relationships
The majority of Intuit’s consumer, business and accountancy products are available on a self-service basis, with subscriptions, purchases and downloads being managed via the Intuit website with no interference from the Company itself.
Some larger, more advances subscriptions, however, require a greater degree of personal care, with sales representatives negotiating prices directly with customers.
Intuit provides ongoing tax, payroll and quickbooks support to its customers in the form of tutorials, FAQs and training resources. The Company’s support staff can also be contacted directed via the Intuit website.
Intuit also hosts community forums for its small business customers and tax customers, providing answers to specific queries and enabling users to interact with one another.
The Company also updates its customers through its small business, tax and accounting blogs, as well as via its various social networking accounts.
Key Activities
Intuit develops and markets business and financial management software solutions for individuals, small and medium-sized businesses, financial institutions, and accounting professionals, including tax preparation, payroll, accounting and personal finance products.
Its activities are divided into three principal segments: Small Business, Consumer Tax and Professional Tax. Additionally, the Company operates an application marketplace which facilitates the sale of third-party applications, and provides small business lending services in collaboration with several financial and tech partners.
The Company also manages a network of other partners, including developers, resellers, distributors and affiliates
Key Partners
Intuit partners with a variety of companies in order to provide reliable and efficient products and extend its marketing and sales network.
The Company’s partners are divided into five separate programs: the QuickBooks Online Distribution Partner Program, comprising tech companies and accounting services firms; the Intuit Reseller Program, comprising authorised resellers and independent software vendors; the QuickBooks Affiliate Program; the Intuit Developer Network, comprising software developers, cloud services companies and tech firms; and the Turbo Tax Affiliate Program.
In addition, the Company partners with a number of sourcing companies, through which it procures the goods and services required to operate fully. Intuit’s partners include e-commerce company Shopify, payments platform developer Stripe, ride-sharing company Uber and tech firm Fundbox.
Intuit also collaborates with a number of authorised hosting partners, including Xcentric, CloudRunner, Right Networks and Skyline Cloud Services.
Key Resources
Intuit’s key resources are its software products, its IT infrastructure, its network of partnerships and its personnel.
The Company has a number of patent applications filed with the US Patent and Trademark Office, including applications titled ‘Personal savings plan’, ‘Method and system for intrusion and extrusion detection’, and Method and system for providing a payroll preparation platform with user contribution-based plugins’.
Cost Structure
Intuit’s cost relate principally to the development of its products, cost of sales, support services, retention of personnel and maintenance of IT infrastructure.
The Company reportedly employs a workforce of more than 8,000, including large sales and support teams that incur costs through salaries and benefits.
The Company also operates a network of servers and data centres which accrues operating and rental costs, as do Intuit’s offices across the US, Australia, Canada, India, Brazil, Singapore, France and the UK.
Revenue Streams
Intuit primarily generates revenue through the sale of various tax preparation, payroll and accounting software products. This accounted for the vast majority of Intuit’s 2015 fiscal revenue of $4.2 billion.
The majority of Intuit’s products are available on a monthly subscription basis, with the exception of certain one-off tax services. Intuit’s suite of products is divided into those for individuals, comprising Mint, QuickBooks Self-Employed and TurboTax; those for small businesses, including Intuit Payroll Services, QuickBooks and QuickBooks Payments; and those for accountants, notably Intuit Tax Online, Lacerte, and ProSeries.
Subscription pricing ranges from as little as $5 per month for the most basic QuickBooks Individual package and up to $1,890 per year for the Company’s ProSeries tax product for accountants. For larger accountancies and businesses subscription pricing can be negotiated with members of the Intuit sales team. Intuit additionally sells a range of personal finance products through its Quicken brand.
Intuit also generates a smaller amount of revenue through its app marketplace, where it sells a range of third-party applications, as well as through its Intuit Quickbooks Financing products, that include invoice financing, short-term loans, and credit card services.


Summary. The purpose of this document is to test LaTeX math in DocOnce with various output formats. Most LaTeX math constructions are renedered correctly by MathJax in plain HTML, but some combinations of constructions may fail. Unfortunately, only a subset of what works in html MathJax also works in sphinx MathJax. The same is true for markdown MathJax expresions (e.g., Jupyter notebooks). Tests and examples are provided to illustrate what may go wrong.
The recommendation for writing math that translates to MathJax in html, sphinx, and markdown is to stick to the environments \[ ... \]
, equation
, equation*
, align
, align*
, alignat
, and alignat*
only. Test the math with sphinx output; if it works in that format, it should work elsewhere too.
The current version of the document is translated from DocOnce source to the format pandoc.
We can get an inline equation $u(t)=e^{-at}$
rendered as \(u(t)=e^{-at}\).
An equation with number,
!bt
\begin{equation} u(t)=e^{-at} \label{eq1a}\end{equation}
!et
looks like
\[ \begin{equation} u(t)=e^{-at} \label{eq1a}\end{equation} \] Maybe this multi-line version is what we actually prefer to write:
!bt
\begin{equation}
u(t)=e^{-at}
\label{eq1b}
\end{equation}
!et
The result is the same:
\[
\begin{equation}
u(t)=e^{-at} \label{eq1b}
\end{equation}
\] We can refer to this equation through its label eq1b
: \eqref{eq1b}.
MathJax has historically had some problems with rendering many LaTeX math environments, but the align*
and align
environments have always worked.
!bt
\begin{align*}
u(t)&=e^{-at}\\
v(t) - 1 &= \frac{du}{dt}
\end{align*}
!et
Result:
\[ \begin{align*} u(t)&=e^{-at}\\ v(t) - 1 &= \frac{du}{dt} \end{align*} \]
Here, we use align
with user-prescribed labels:
!bt
\begin{align}
u(t)&=e^{-at}
\label{eq2b}\\
v(t) - 1 &= \frac{du}{dt}
\label{eq3b}
\end{align}
!et
Result:
\[ \begin{align} u(t)&=e^{-at} \label{eq2b}\\ v(t) - 1 &= \frac{du}{dt} \label{eq3b} \end{align} \] We can refer to the last equations as the system \eqref{eq2b}-\eqref{eq3b}.
In LaTeX, equations within an align
environment is automatically given numbers. To ensure that an html document with MathJax gets the same equation numbers as its latex/pdflatex companion, DocOnce generates labels in equations where there is no label prescribed. For example,
!bt
\begin{align}
u(t)&=e^{-at}
\\
v(t) - 1 &= \frac{du}{dt}
\end{align}
!et
is edited to something like
!bt
\begin{align}
u(t)&=e^{-at}
\label{_auto5}\\
v(t) - 1 &= \frac{du}{dt}
\label{_auto6}
\end{align}
!et
and the output gets the two equation numbered.
\[ \begin{align} u(t)&=e^{-at}\\ v(t) - 1 &= \frac{du}{dt} \end{align} \]
The align
environment can be used with two &
alignment characters, e.g.,
!bt
\begin{align}
\frac{\partial u}{\partial t} &= \nabla^2 u, & x\in (0,L),
\ t\in (0,T]\\
u(0,t) &= u_0(x), & x\in [0,L]
\end{align}
!et
The result in pandoc becomes
\[ \begin{align} \frac{\partial u}{\partial t} &= \nabla^2 u, & x\in (0,L), \ t\in (0,T]\\ u(0,t) &= u_0(x), & x\in [0,L] \end{align} \]
A better solution is usually to use an alignat
environment:
!bt
\begin{alignat}{2}
\frac{\partial u}{\partial t} &= \nabla^2 u, & x\in (0,L),
\ t\in (0,T]\\
u(0,t) &= u_0(x), & x\in [0,L]
\end{alignat}
!et
with the rendered result
\[ \begin{alignat}{2} \frac{\partial u}{\partial t} &= \nabla^2 u, & x\in (0,L), \ t\in (0,T]\\ u(0,t) &= u_0(x), & x\in [0,L] \end{alignat} \]
If DocOnce had not rewritten the above equations, they would be rendered in pandoc as
\[ \begin{alignat}{2} \frac{\partial u}{\partial t} &= \nabla^2 u, & x\in (0,L), \ t\in (0,T]\\ u(0,t) &= u_0(x), & x\in [0,L] \end{alignat} \]
Let us try the old eqnarray*
environment.
!bt
\begin{eqnarray*}
u(t)&=& e^{-at}\\
v(t) - 1 &=& \frac{du}{dt}
\end{eqnarray*}
!et
which results in
\[ \begin{eqnarray*} u(t)&=& e^{-at}\\ v(t) - 1 &=& \frac{du}{dt} \end{eqnarray*} \]
Here we use eqnarray
with labels:
!bt
\begin{eqnarray}
u(t)&=& e^{-at}
\label{eq2c}\\
v(t) - 1 &=& \frac{du}{dt}
\label{eq3c}
\end{eqnarray}
!et
which results in
\[ \begin{eqnarray} u(t)&=& e^{-at} \label{eq2c}\\ v(t) - 1 &=& \frac{du}{dt} \label{eq3c} \end{eqnarray} \] Can we refer to the last equations as the system \eqref{eq2c}-\eqref{eq3c} in the pandoc format?
multiline
environment with label and numberThe LaTeX code
!bt
\begin{multline}
\int_a^b f(x)dx = \sum_{j=0}^{n} \frac{1}{2} h(f(a+jh) +
f(a+(j+1)h)) \\
=\frac{h}{2}f(a) + \frac{h}{2}f(b) + \sum_{j=1}^n f(a+jh)
\label{multiline:eq1}
\end{multline}
!et
gets rendered as
\[ \begin{multline} \int_a^b f(x)dx = \sum_{j=0}^{n} \frac{1}{2} h(f(a+jh) + f(a+(j+1)h)) \\ =\frac{h}{2}f(a) + \frac{h}{2}f(b) + \sum_{j=1}^n f(a+jh) \label{multiline:eq1} \end{multline} \] and we can hopefully refer to the Trapezoidal rule as the formula \eqref{multiline:eq1}.
Although align
can be used to split too long equations, a more obvious command is split
:
!bt
\begin{equation}
\begin{split}
\int_a^b f(x)dx = \sum_{j=0}^{n} \frac{1}{2} h(f(a+jh) +
f(a+(j+1)h)) \\
=\frac{h}{2}f(a) + \frac{h}{2}f(b) + \sum_{j=1}^n f(a+jh)
\end{split}
\end{equation}
!et
The result becomes
\[ \begin{equation} \begin{split} \int_a^b f(x)dx = \sum_{j=0}^{n} \frac{1}{2} h(f(a+jh) + f(a+(j+1)h)) \\ =\frac{h}{2}f(a) + \frac{h}{2}f(b) + \sum_{j=1}^n f(a+jh) \end{split} \end{equation} \]
First we use the plain old pmb package for bold math. The formula
!bt
\[ \frac{\partial\u}{\partial t} +
\u\cdot\nabla\u = \nu\nabla^2\u -
\frac{1}{\varrho}\nabla p,\]
!et
and the inline expression $\nabla\pmb{u} (\pmb{x})\cdot\pmb{n}$
(with suitable newcommands using pmb) get rendered as
\[ \frac{\partial\pmb{u}}{\partial t} + \pmb{u}\cdot\nabla\pmb{u} = \nu\nabla^2\pmb{u} - \frac{1}{\varrho}\nabla p, \] and \(\nabla\pmb{u} (\pmb{x})\cdot\pmb{n}\).
Somewhat nicer fonts may appear with the more modern \bm
command:
!bt
\[ \frac{\partial\ubm}{\partial t} +
\ubm\cdot\nabla\ubm = \nu\nabla^2\ubm -
\frac{1}{\varrho}\nabla p,\]
!et
(backslash ubm
is a newcommand for bold math \(u\)), for which we get
\[ \frac{\partial\boldsymbol{u}}{\partial t} + \boldsymbol{u}\cdot\nabla\boldsymbol{u} = \nu\nabla^2\boldsymbol{u} - \frac{1}{\varrho}\nabla p. \] Moreover,
$\nabla\boldsymbol{u}(\boldsymbol{x})\cdot\boldsymbol{n}$
becomes \(\nabla\boldsymbol{u}(\boldsymbol{x})\cdot\boldsymbol{n}\).
Warning. Note: for the pandoc format, \bm
was substituted by DocOnce to \boldsymbol
.
Finally, we collect some problematic formulas in MathJax. They all work fine in LaTeX. Most of them look fine in html too, but some fail in sphinx, ipynb, or markdown.
The LaTeX code
!bt
\[ {\color{blue}\frac{\partial\u}{\partial t}} +
\nabla\cdot\nabla\u = \nu\nabla^2\u -
\frac{1}{\varrho}\nabla p,\]
!et
results in
\[ {\color{blue}\frac{\partial\pmb{u}}{\partial t}} + \nabla\cdot\nabla\pmb{u} = \nu\nabla^2\pmb{u} - \frac{1}{\varrho}\nabla p, \]
Sometimes one must be extra careful with the LaTeX syntax to get sphinx MathJax to render a formula correctly. Consider the combination of a bar over a bold math symbol:
!bt
\[ \bar\f = f_c^{-1}\f,\]
!et
which for pandoc output results in
\[ \bar\boldsymbol{f} = f_c^{-1}\boldsymbol{f}. \]
With sphinx, this formula is not rendered. However, using curly braces for the bar,
!bt
\[ \bar{\f} = f_c^{-1}\f,\]
!et
makes the output correct also for sphinx:
\[ \bar{\boldsymbol{f}} = f_c^{-1}\boldsymbol{f}, \]
Here is an align
environment with a label and the pmatrix
environment for matrices and vectors in LaTeX.
!bt
\begin{align}
\begin{pmatrix}
G_2 + G_3 & -G_3 & -G_2 & 0 \\
-G_3 & G_3 + G_4 & 0 & -G_4 \\
-G_2 & 0 & G_1 + G_2 & 0 \\
0 & -G_4 & 0 & G_4
\end{pmatrix}
&=
\begin{pmatrix}
v_1 \\
v_2 \\
v_3 \\
v_4
\end{pmatrix}
+ \cdots
\label{mymatrixeq}\\
\begin{pmatrix}
C_5 + C_6 & -C_6 & 0 & 0 \\
-C_6 & C_6 & 0 & 0 \\
0 & 0 & 0 & 0 \\
0 & 0 & 0 & 0
\end{pmatrix}
\frac{d}{dt} &=
\begin{pmatrix}
v_1 \\
v_2 \\
v_3 \\
v_4
\end{pmatrix} =
\begin{pmatrix}
0 \\
0 \\
0 \\
-i_0
\end{pmatrix}
\end{align}
!et
which becomes
\[ \begin{align} \begin{pmatrix} G_2 + G_3 & -G_3 & -G_2 & 0 \\ -G_3 & G_3 + G_4 & 0 & -G_4 \\ -G_2 & 0 & G_1 + G_2 & 0 \\ 0 & -G_4 & 0 & G_4 \end{pmatrix} &= \begin{pmatrix} v_1 \\ v_2 \\ v_3 \\ v_4 \end{pmatrix} + \cdots \label{mymatrixeq}\\ \begin{pmatrix} C_5 + C_6 & -C_6 & 0 & 0 \\ -C_6 & C_6 & 0 & 0 \\ 0 & 0 & 0 & 0 \\ 0 & 0 & 0 & 0 \end{pmatrix} \frac{d}{dt} &= \begin{pmatrix} v_1 \\ v_2 \\ v_3 \\ v_4 \end{pmatrix} = \begin{pmatrix} 0 \\ 0 \\ 0 \\ -i_0 \end{pmatrix} \end{align} \]
The same matrices without labels in an align*
environment:
!bt
\begin{align*}
\begin{pmatrix}
G_2 + G_3 & -G_3 & -G_2 & 0 \\
-G_3 & G_3 + G_4 & 0 & -G_4 \\
-G_2 & 0 & G_1 + G_2 & 0 \\
0 & -G_4 & 0 & G_4
\end{pmatrix}
&=
\begin{pmatrix}
v_1 \\
v_2 \\
v_3 \\
v_4
\end{pmatrix}
+ \cdots \\
\begin{pmatrix}
C_5 + C_6 & -C_6 & 0 & 0 \\
-C_6 & C_6 & 0 & 0 \\
0 & 0 & 0 & 0 \\
0 & 0 & 0 & 0
\end{pmatrix}
\frac{d}{dt} &=
\begin{pmatrix}
v_1 \\
v_2 \\
v_3 \\
v_4
\end{pmatrix} =
\begin{pmatrix}
0 \\
0 \\
0 \\
-i_0
\end{pmatrix}
\end{align*}
!et
The rendered result becomes
\[ \begin{align*} \begin{pmatrix} G_2 + G_3 & -G_3 & -G_2 & 0 \\ -G_3 & G_3 + G_4 & 0 & -G_4 \\ -G_2 & 0 & G_1 + G_2 & 0 \\ 0 & -G_4 & 0 & G_4 \end{pmatrix} &= \begin{pmatrix} v_1 \\ v_2 \\ v_3 \\ v_4 \end{pmatrix} + \cdots \\ \begin{pmatrix} C_5 + C_6 & -C_6 & 0 & 0 \\ -C_6 & C_6 & 0 & 0 \\ 0 & 0 & 0 & 0 \\ 0 & 0 & 0 & 0 \end{pmatrix} \frac{d}{dt} &= \begin{pmatrix} v_1 \\ v_2 \\ v_3 \\ v_4 \end{pmatrix} = \begin{pmatrix} 0 \\ 0 \\ 0 \\ -i_0 \end{pmatrix} \end{align*} \]


Veris Product
|
Case Set
|
|
|
Execution Environment
|
|
|
CC Report
|
|
| Case Set |
Schedule Type |
Effective Pattern |
Creator |
Control |
| UPC Offer Case Set |
Interval |
Everyday 06:45 |
hanll |
|
| UPC Product Case Set |
Timing |
Every 23:05 From 06-12 To 06-24 |
hanll |
|
| UPC Service Case Set |
Timing |
Every 06:00 From 06-10 To 06-23 |
tianhj |
|


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Oh noes! The page you're looking for could not be found. ¯\_(ツ)_/¯


Problem
Blacks in global media plagued by:
Lack of representation,
Stereotypes & tropes,
#OscarsSoWhite
Independent Black Diaspora Filmmakers limited by
Film distribution channels,
Gatekeepers of Hollywood
Solution: Noirfest
Highly curated global Black content Paired with the African Diaspora: New York, DC, Los Angeles, Chicago


This project serves as proxy to a Convolutional Neural Network that can detect and interpret ASL from images and live video. The model used in this project was built from scratch using the Keras Functional API (running Tensorflow 2.1.0 back-end). CNN was trained on 87000 jpg images of hands, 3000 images per letter of the American Sign Language alphabet, as well as 3000 images for 'delete', 'space', and 3000 images of blank rooms categorized as 'nothing'. Image data can be found and downloaded [here](https://www.kaggle.com/grassknoted/asl-alphabet). The goal of this project was to gain a deeper understanding of Convolutional Neural Network architecture, as well as to explore real time classification using computer vision. Due to the initial time constraints on deployment, currently this page serves purely as a demonstration of the model's performance on the provided validation data. You can explore how the model performs classifying a live video feed by cloning down the repository for this project, and following the instructions in the Readme.md filename
Select a validation image from below to see the model's classification


by Kristof Hracza
Click on / hover over the project cards, to reveal the story behind them.
(ON PHONE: Click on the card, then if you have read it,
click on the side of the screen to reset it.)
Date:
21 June 2020
This time I wanted to do something, a bit more interactive and useful, with the help of Javascript. I am using Trello board, for most of my projects, and for the website as well. Just to keep track of everything. So because I wanted to write something harder than a website, but use JS as the main language, I have started to make this todo app. It is not the best, specially not at the time when I am writing this (21 June 2020), but the fact that I like this, will help me to produce a better looking app. You can use the app, at the "Apps" section of this site.
Date:
12 June 2020
Since I have re-designed the whole website back in May 2020, I wanted to do something that was again related to python. It is not hard, to figure out that I am really into hacking, and that I love making tools, that break already working stuff. In this case I wanted to build a Hash Cracker tool.  Hash every word in the password file, then compare it to the hash file. If there is any match, then the hash was successfully "broken". I know, it is not the most advanced stuff, that I could come up with, but it seemed like a great idea, and it only took about 4-5 hours.
Creation Date:
2 February 2020
Retirement Date:
2 June 2020
As of June 2 2020, I have completely re-designed my website. Before it had an "arcade" look. I was happy with it, but I felt like, giving the website a more "professional" look, would make it more appealing to someone, who would visit the website for the 1st time. So the old version was colorful, it mostly contained, blue, pink, cyan, and purple colour. The new one is more minimalist, and it just looks better in my opinion. You can still check the old version out, by clicking on the picture below.
Date:
31 May 2020
Since the Instagram attack, I wanted to use selenium, as I found it interesting. So, I've got an idea, I was sure that I wanted to make a GUI app. Then I realised that I could do something that checks the price of a product. I chose amazon, as the base of my project, because that seemed easy. I have finished the project in 1 night (roughly 5-6 hours). The 2 most important module that I have used are: Tkinter, and Selenium. I have published the app both in PY and EXE format.
Date:
24 May 2020
Back in 2019 I have made a Space Invaders "game", but it was a catastrophe. So now, that I have more experience in programming, I have remade it. I have used Python, and mostly the turtle module. The program itself was pretty easy to implement, as I was able to use some mechanics from my Flappy Bird game, such as gravity. With all the code, and the design, it took about 2-3 days to finish it. The game is available both in PY and EXE format.
Date:
16 May 2020
After making Slow Loris, I felt like its the path I want to work more on. So I made an Instagram dictionary attack, which uses TOR. I implemented this in Python, using the selenium module for generating the webpage and handling the attack, plus the requests. I also used stem, to change IP whenever it was needed. This project was done, roughly in two days.
Date:
2 May 2020
Slow Loris is a DoS method, which anyone can use even with a low bandwidth. It is making numerous connections to a webserver, then sending data every 10 / 15 seconds, just when the webserver would close the connection, so that it would not close. I made 2 versions of this script. One you can use with your own IP, but that is not, recommended. The second script, is using TOR Proxies, so your IP won't be visible. Both of the scripts are written using Python.
Date:
4 April 2020
I made this game both in Python and javascript, but I will only talk about the Javascript version, as I feel like the Python one, was a bit of a fail. I will probably remake it in the future, as I got much better since. So, as I mentioned the Python version was a big fail, and since I shifted to JS, I wanted to give it another try. In a few days, I learnt the basics of JS so I got going and it was much easier to write, than the Python implementation. After a few days of programming I finished the game, so that it worked both on PCs and Phones.
Date:
25 February 2020
The inspiration to make this app came from one of my friends. He asked me if I use password managers. I said, no. But than something clicked. People pay for these services, so instead of paying for a password manager, why not make my own?! I had to learn Tkinter, for this project, but it went overall pretty well. In about a week, I made a usable Password Manager, that can store passwords, and encrypt (only the login password) in Sha1.
Date:
2 February 2020
Obviously my biggest project is, this website.
I started making my website so that I will be able to write in HTML, CSS and JS.
I also wanted a place where I would be able to, document my projects throughout my programming,
"carrer". I believe that this website was needed for me, to be really proud of what I do.
Also this is not just a one timer project. It will last years, as I will always update it.
Date:
16 September 2019
Being a self described nerd, I was interested in programming, and I was desperate to make my own game This was my very first game, written in Python, by using the Turtle Module. Roughly it took 2 weeks to finish it. After that I knew all the basic syntaxes of Turtle and I was ready for a new challenge.


Dear customer,
pick up seats
SCREEN
Movie:
Gingerclown
Time:
November 3, 21:00
Seat:
Tickets:
0
Total:
$
0
BUY


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The requested page could not be found.


bigfile
OS/161 Reference Manual
Name
bigfile - create a large file in small chunks
Synopsis
/testbin/bigfile filename size
Description
bigfile creates a file of the specified size in fairly small chunks.
Requirements
bigfile uses the following system calls:
bigfile should run on emufs once assignment 2 is complete, and should
run on SFS once assignment 4 is complete. Sufficiently small files
should work on SFS even before assignment 4.


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Home
Output
The output window tracks all the events occurring during the execution of the plugin, as a sort of plugin trace log.
Each event is tracked reporting the followin info:
- Timestamp: date and time of the event
- Level: severity level of the event message (INFO, DEBUG, WARN, ERROR, FATAL)
- Message: the description of the event
- Exception: details of the occurred exception, if any.


Omid Rejali comes from a very dynamic background. As a first generation child of family of immigrants he spent 10 years of his life growing up in Tehran. Living in a third world country has given Omid the ability to appreciate the legal rights our wonderful but not perfect justice system has to offer.
Omid has a passion for the court room and believes that every individual's rights should be fully vindicated no matter what the circumstances are. His struggles while growing up have made him a very strong person who is able to overcome anything that stands in his way. He is a staunch believer in government transparency and making sure each and every one of his clients' rights are fully vindicated.
While in law school Omid has won many awards, including The Jefferson Medal, and California Award for Excellence in international business transactions and legal synthesis. He has also been placed on the deans list for his excellent academic performance. Additionally, he received a certificate of achievement from the Pro Bono Honors Program for giving over 200 hours of legal assistance to those in need in the community.
Prior to opening his firm Omid externed for the Honorable Judge Jack Staton at the Department of Justice, Office of Immigration Review in El Centro where he obtained valuable hands on experience and learned how the immigration court operates. He also gained exposure to the courtroom while working at the El Cajon public defenders office.
Omid is fluent in speaking Farsi and is licensed to practice in all California State Courts and in the United States District Court for the Southern District of California.


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--- layout: img_banner_style permalink: /history bannerImage: assets/img/general/historyRusty.png bannerCaption: Team History ---
{{ histSeg.content | markdownify }}


Prokill wrote:Currently when i want to invite friends into a game i am playing.
I drag their name into the block at the bottom left
Now it seems i can invite 1 and then it bugs out the name just hovers there. i have confirmed with a few players and all have the same issue.
Thx


For sending logs (usage) from your application you will need to have a LoggingAPI instance up and you can use following setup:


fast_ml fast_ml-2013 fast_ml-2013-32 knowledge-graph by maker-knowledge-mining
Source: html
Introduction: Perhaps the most common format of data for machine learning is text files. Often data is too large to fit in memory; this is sometimes referred to as big data. But do you need to load the whole data into memory? Maybe you could at least pre-process it line by line. We show how to do this with Python. Prepare to read and possibly write some code. The most common format for text files is probably CSV. For sparse data, libsvm format is popular. Both can be processed using csv module in Python. import csv i_f = open( input_file, 'r' ) reader = csv.reader( i_f ) For libsvm you just set the delimiter to space: reader = csv.reader( i_f, delimiter = ' ' ) Then you go over the file contents. Each line is a list of strings: for line in reader: # do something with the line, for example: label = float( line[0] ) # .... writer.writerow( line ) If you need to do a second pass, you just rewind the input file: i_f.seek( 0 ) for line in re
sentIndex sentText sentNum sentScore
1 import csv i_f = open( input_file, 'r' ) reader = csv. [sent-10, score-0.437]
2 reader( i_f ) For libsvm you just set the delimiter to space: reader = csv. [sent-11, score-0.438]
3 Each line is a list of strings: for line in reader: # do something with the line, for example: label = float( line[0] ) # . [sent-13, score-0.69]
4 writerow( line ) If you need to do a second pass, you just rewind the input file: i_f. [sent-18, score-0.362]
5 It’s purpose is to randomly split the file into two, such that some lines from the original file go to the first ouput file and the rest go to the second. [sent-23, score-1.037]
6 csv We’d also like to specify the probability of writing to the first file, so that for example 90% go to the train set and the rest to the test set: python split. [sent-29, score-0.473]
7 We need to import a few modules and read the file names: import csv import sys import random input_file = sys. [sent-35, score-0.902]
8 9 Random seed It might be useful to be able to split the file again in the future in exactly the same way. [sent-41, score-0.974]
9 To get the same split every time, we’ll seed a random number generator. [sent-45, score-0.773]
10 You give it a seed - an arbitrary string - and it will behave exactly the same every time. [sent-46, score-0.637]
11 So we’d like to be able to specify a seed on the command line as the final argument: python split. [sent-48, score-1.006]
12 argv[5] except IndexError: seed = None if seed: random. [sent-54, score-0.571]
13 seed( seed ) Readers and writers Let’s open the files and create a CSV reader and two writers. [sent-55, score-1.121]
14 If you’re on Windows, it’s important to open the files for writing in binary mode ( 'wb' ), otherwise you might get some extra new lines. [sent-56, score-0.524]
15 write( line ) The headers Some files have headers in the first line. [sent-62, score-0.856]
16 If that’s the case with your data, you can read the first line and write it to both output files: headers = reader. [sent-63, score-0.727]
17 We read a line and then (pseudo)randomly decide whether to write it to one file or the other. [sent-68, score-0.783]
18 We compare this number to P and on this basis decide which file to write to. [sent-72, score-0.452]
19 writerow( line ) Note that it’s an inexact method of splitting: if you have a thousand lines, you’ll get roughly 900 in the first file and roughly 100 in the second with P = 0. [sent-76, score-0.661]
20 An exact split If you wanted to split the file exactly 900/100, here’s a way to do it. [sent-78, score-0.587]
wordName wordTfidf (topN-words)
[('seed', 0.49), ('line', 0.289), ('reader', 0.285), ('open', 0.229), ('headers', 0.225), ('file', 0.191), ('split', 0.155), ('csv', 0.152), ('import', 0.152), ('indexerror', 0.122), ('specify', 0.122), ('files', 0.117), ('memory', 0.115), ('write', 0.11), ('read', 0.103), ('delimiter', 0.102), ('integers', 0.102), ('shuffle', 0.102), ('xrange', 0.102), ('indexes', 0.091), ('module', 0.09), ('decide', 0.09), ('float', 0.09), ('exactly', 0.086), ('except', 0.081), ('pass', 0.081), ('writing', 0.075), ('range', 0.075), ('second', 0.073), ('format', 0.07), ('random', 0.067), ('go', 0.067), ('really', 0.065), ('every', 0.061), ('compare', 0.061), ('randomly', 0.061), ('something', 0.058), ('rest', 0.057), ('lines', 0.057), ('list', 0.054), ('roughly', 0.054), ('final', 0.054), ('might', 0.052), ('otherwise', 0.051), ('command', 0.051), ('libsvm', 0.051), ('common', 0.051), ('processed', 0.051), ('readers', 0.051), ('formats', 0.051)]
simIndex simValue blogId blogTitle
same-blog 1 1.0 32 fast ml-2013-07-05-Processing large files, line by line
Introduction: Perhaps the most common format of data for machine learning is text files. Often data is too large to fit in memory; this is sometimes referred to as big data. But do you need to load the whole data into memory? Maybe you could at least pre-process it line by line. We show how to do this with Python. Prepare to read and possibly write some code. The most common format for text files is probably CSV. For sparse data, libsvm format is popular. Both can be processed using csv module in Python. import csv i_f = open( input_file, 'r' ) reader = csv.reader( i_f ) For libsvm you just set the delimiter to space: reader = csv.reader( i_f, delimiter = ' ' ) Then you go over the file contents. Each line is a list of strings: for line in reader: # do something with the line, for example: label = float( line[0] ) # .... writer.writerow( line ) If you need to do a second pass, you just rewind the input file: i_f.seek( 0 ) for line in re
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Introduction: Recently we proposed to pre-process large files line by line. Now it’s time to introduce phraug *, a set of Python scripts based on this idea. The scripts mostly deal with format conversion (CSV, libsvm, VW) and with few other tasks common in machine learning. With phraug you currently can convert from one format to another: csv to libsvm csv to Vowpal Wabbit libsvm to csv libsvm to Vowpal Wabbit tsv to csv And perform some other file operations: count lines in a file sample lines from a file split a file into two randomly split a file into a number of similiarly sized chunks save a continuous subset of lines from a file (for example, first 100) delete specified columns from a csv file normalize (shift and scale) columns in a csv file Basically, there’s always at least one input file and usually one or more output files. An input file always stays unchanged. If you’re familiar with Unix, you may notice that some of these tasks are easily ach
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Introduction: Last time we talked about the small data branch of Best Buy contest . Now it’s time to tackle the big boy . It is positioned as “cloud computing sized problem”, because there is 7GB of unpacked data, vs. younger brother’s 20MB. This is reflected in “cloud computing” and “cluster” and “Oracle” talk in the forum , and also in small number of participating teams: so far, only six contestants managed to beat the benchmark. But don’t be scared. Most of data mass is in XML product information. Training and test sets together are 378MB. Good news. The really interesting thing is that we can take the script we’ve used for small data and apply it to this contest, obtaining 0.355 in a few minutes (benchmark is 0.304). Not impressed? With simple extension you can up the score to 0.55. Read below for details. This is the very same script , with one difference. In this challenge, benchmark recommendations differ from line to line, so we can’t just hard-code five item IDs like before
4 0.133012 25 fast ml-2013-04-10-Gender discrimination
Introduction: There’s a contest at Kaggle held by Qatar University. They want to be able to discriminate men from women based on handwriting. For a thousand bucks, well, why not? Congratulations! You have spotted the ceiling cat. As Sashi noticed on the forums , it’s not difficult to improve on the benchmarks a little bit. In particular, he mentioned feature selection, normalizing the data and using a regularized linear model. Here’s our version of the story. Let’s start with normalizing. There’s a nice function for that in R, scale() . The dataset is small, 1128 examples, so we can go ahead and use R. Turns out that in its raw form, the data won’t scale. That’s because apparently there are some columns with zeros only. That makes it difficult to divide. Fortunately, we know just the right tool for the task. We learned about it on Kaggle forums too. It’s a function in caret package called nearZeroVar() . It will give you indexes of all the columns which have near zero var
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Introduction: We’re back to Kaggle competitions. This time we will attempt to predict advertised salaries from job ads and of course beat the benchmark. The benchmark is, as usual, a random forest result. For starters, we’ll use a linear model without much preprocessing. Will it be enough? Congratulations! You have spotted the ceiling cat. A linear model better than a random forest - how so? Well, to train a random forest on data this big, the benchmark code extracts only 100 most common words as features, and we will use all. This approach is similiar to the one we applied in Merck challenge . More data beats a cleverer algorithm, especially when a cleverer algorithm is unable to handle all of data (on your machine, anyway). The competition is about predicting salaries from job adverts. Of course the figures usually appear in the text, so they were removed. An error metric is mean absolute error (MAE) - how refreshing to see so intuitive one. The data for Job salary prediction con
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simIndex simValue blogId blogTitle
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Introduction: Perhaps the most common format of data for machine learning is text files. Often data is too large to fit in memory; this is sometimes referred to as big data. But do you need to load the whole data into memory? Maybe you could at least pre-process it line by line. We show how to do this with Python. Prepare to read and possibly write some code. The most common format for text files is probably CSV. For sparse data, libsvm format is popular. Both can be processed using csv module in Python. import csv i_f = open( input_file, 'r' ) reader = csv.reader( i_f ) For libsvm you just set the delimiter to space: reader = csv.reader( i_f, delimiter = ' ' ) Then you go over the file contents. Each line is a list of strings: for line in reader: # do something with the line, for example: label = float( line[0] ) # .... writer.writerow( line ) If you need to do a second pass, you just rewind the input file: i_f.seek( 0 ) for line in re
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Introduction: Recently we proposed to pre-process large files line by line. Now it’s time to introduce phraug *, a set of Python scripts based on this idea. The scripts mostly deal with format conversion (CSV, libsvm, VW) and with few other tasks common in machine learning. With phraug you currently can convert from one format to another: csv to libsvm csv to Vowpal Wabbit libsvm to csv libsvm to Vowpal Wabbit tsv to csv And perform some other file operations: count lines in a file sample lines from a file split a file into two randomly split a file into a number of similiarly sized chunks save a continuous subset of lines from a file (for example, first 100) delete specified columns from a csv file normalize (shift and scale) columns in a csv file Basically, there’s always at least one input file and usually one or more output files. An input file always stays unchanged. If you’re familiar with Unix, you may notice that some of these tasks are easily ach
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Introduction: Last time we talked about the small data branch of Best Buy contest . Now it’s time to tackle the big boy . It is positioned as “cloud computing sized problem”, because there is 7GB of unpacked data, vs. younger brother’s 20MB. This is reflected in “cloud computing” and “cluster” and “Oracle” talk in the forum , and also in small number of participating teams: so far, only six contestants managed to beat the benchmark. But don’t be scared. Most of data mass is in XML product information. Training and test sets together are 378MB. Good news. The really interesting thing is that we can take the script we’ve used for small data and apply it to this contest, obtaining 0.355 in a few minutes (benchmark is 0.304). Not impressed? With simple extension you can up the score to 0.55. Read below for details. This is the very same script , with one difference. In this challenge, benchmark recommendations differ from line to line, so we can’t just hard-code five item IDs like before
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Introduction: We’re back to Kaggle competitions. This time we will attempt to predict advertised salaries from job ads and of course beat the benchmark. The benchmark is, as usual, a random forest result. For starters, we’ll use a linear model without much preprocessing. Will it be enough? Congratulations! You have spotted the ceiling cat. A linear model better than a random forest - how so? Well, to train a random forest on data this big, the benchmark code extracts only 100 most common words as features, and we will use all. This approach is similiar to the one we applied in Merck challenge . More data beats a cleverer algorithm, especially when a cleverer algorithm is unable to handle all of data (on your machine, anyway). The competition is about predicting salaries from job adverts. Of course the figures usually appear in the text, so they were removed. An error metric is mean absolute error (MAE) - how refreshing to see so intuitive one. The data for Job salary prediction con
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Introduction: The promise What’s attractive in machine learning? That a machine is learning, instead of a human. But an operator still has a lot of work to do. First, he has to learn how to teach a machine, in general. Then, when it comes to a concrete task, there are two main areas where a human needs to do the work (and remember, laziness is a virtue, at least for a programmer, so we’d like to minimize amount of work done by a human): data preparation model tuning This story is about model tuning. Typically, to achieve satisfactory results, first we need to convert raw data into format accepted by the model we would like to use, and then tune a few hyperparameters of the model. For example, some hyperparams to tune for a random forest may be a number of trees to grow and a number of candidate features at each split ( mtry in R randomForest). For a neural network, there are quite a lot of hyperparams: number of layers, number of neurons in each layer (specifically, in each hid
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topicId topicWeight
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Introduction: Recently we proposed to pre-process large files line by line. Now it’s time to introduce phraug *, a set of Python scripts based on this idea. The scripts mostly deal with format conversion (CSV, libsvm, VW) and with few other tasks common in machine learning. With phraug you currently can convert from one format to another: csv to libsvm csv to Vowpal Wabbit libsvm to csv libsvm to Vowpal Wabbit tsv to csv And perform some other file operations: count lines in a file sample lines from a file split a file into two randomly split a file into a number of similiarly sized chunks save a continuous subset of lines from a file (for example, first 100) delete specified columns from a csv file normalize (shift and scale) columns in a csv file Basically, there’s always at least one input file and usually one or more output files. An input file always stays unchanged. If you’re familiar with Unix, you may notice that some of these tasks are easily ach
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Introduction: There’s a contest at Kaggle held by Qatar University. They want to be able to discriminate men from women based on handwriting. For a thousand bucks, well, why not? Congratulations! You have spotted the ceiling cat. As Sashi noticed on the forums , it’s not difficult to improve on the benchmarks a little bit. In particular, he mentioned feature selection, normalizing the data and using a regularized linear model. Here’s our version of the story. Let’s start with normalizing. There’s a nice function for that in R, scale() . The dataset is small, 1128 examples, so we can go ahead and use R. Turns out that in its raw form, the data won’t scale. That’s because apparently there are some columns with zeros only. That makes it difficult to divide. Fortunately, we know just the right tool for the task. We learned about it on Kaggle forums too. It’s a function in caret package called nearZeroVar() . It will give you indexes of all the columns which have near zero var
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Introduction: We’re back to Kaggle competitions. This time we will attempt to predict advertised salaries from job ads and of course beat the benchmark. The benchmark is, as usual, a random forest result. For starters, we’ll use a linear model without much preprocessing. Will it be enough? Congratulations! You have spotted the ceiling cat. A linear model better than a random forest - how so? Well, to train a random forest on data this big, the benchmark code extracts only 100 most common words as features, and we will use all. This approach is similiar to the one we applied in Merck challenge . More data beats a cleverer algorithm, especially when a cleverer algorithm is unable to handle all of data (on your machine, anyway). The competition is about predicting salaries from job adverts. Of course the figures usually appear in the text, so they were removed. An error metric is mean absolute error (MAE) - how refreshing to see so intuitive one. The data for Job salary prediction con
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Introduction: The promise What’s attractive in machine learning? That a machine is learning, instead of a human. But an operator still has a lot of work to do. First, he has to learn how to teach a machine, in general. Then, when it comes to a concrete task, there are two main areas where a human needs to do the work (and remember, laziness is a virtue, at least for a programmer, so we’d like to minimize amount of work done by a human): data preparation model tuning This story is about model tuning. Typically, to achieve satisfactory results, first we need to convert raw data into format accepted by the model we would like to use, and then tune a few hyperparameters of the model. For example, some hyperparams to tune for a random forest may be a number of trees to grow and a number of candidate features at each split ( mtry in R randomForest). For a neural network, there are quite a lot of hyperparams: number of layers, number of neurons in each layer (specifically, in each hid
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9 0.35793561 17 fast ml-2013-01-14-Feature selection in practice
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The Mummy A review by Joe Chamberlain
Starring Brendan Fraser; Rachel Weisz; John Hannah; Arnold Voslo & Kevin J. O'Connor Brendan Fraser stars in this remake of the 1930's Boris Karloff classic. This new mummy is not only a tribute to the original but it is very blatantly paying homage to Raiders Of The Lost Ark as well. Brendan Fraser is no Indiana Jones, but he comes pretty darn close. I'll admit right off the bat that I wasn't expecting a whole lot out of The Mummy. I figured on a few nifty special effects and not a whole lot else. I was in for quite the surprise. The Mummy is a whole lot of fun, and will probably rank as one of my favorite movies of the year. Fraser plays a former soldier turned fortune hunter who, in no small way, resembles a certain character that Harrison Ford made famous. Although, Fraser's character doesn't have a bullwhip. Rick O'Connell (Fraser) is one of the few people who has ever seen the Lost City of the Dead in Egypt. It is reported to contain an immense treasure and volumes of great knowledge. He is hired by a librarian (Rachel Weisz) and her brother (John Hannah) to take them to the city so that they can try to find its hidden treasures. Once there they run into a bit more than they bargained for. They inadvertently bring back to life a mummy which has been sealed in the ancient city along with a very bad curse. Basically, once this Mummy gets reawakened he isn't a real nice guy. And once he gets up to full strength he will be able to control the world. By the way, the way he gains strength is to replace the long rotted parts of his body with new flesh from living victims. The Mummy has as his command the power to create plagues of biblical proportion, such as locusts, making the water turn to blood and fireballs falling from the sky. All and all, not the sort of fellow that you want to be enemies with.
The plot of the movie is a little on the popcorn side of things. It is your typical light summer fare. This is not a difficult movie to follow, yet on the other hand there are enough spills, thrills and one liners to keep the audience thoroughly entertained throughout the movie. I was not really expecting a whole lot in either the dialogue or acting portions of this movie. On both counts I was happily wrong. This isn't Oscar-caliber dialogue mind you, but it is witty and often pretty funny. I found most of it was designed to give the movie that Indiana Jones feel.  The acting was very impressive. Brendan Fraser has never really been in a movie that ever really impressed the heck out of me. This time he did. Fraser comes off as a young Harrison Ford in this movie. He might not have quite as much talent as Ford, but he certainly has the charisma and screen presence to make up for it. Quite honestly, he is no slouch in the talent department either. Rachel Weisz does a fine job as the take charge librarian who eventually falls for Fraser's character. The actor who I enjoyed the most was John Hannah as Weisz's somewhat roguish brother. His character was pure comic relief, and Hannah delivered all the way. Arnold Voslo was also good playing the mummy. Although his scenes were somewhat limited because of the nature of his character.
The reason Voslo's scenes were limited was the fact that for a good portion of the movie, the Mummy was purely a computer generated character. More or less a walking skeleton with a little rotting flesh on him. Although there was more and more meat on him as the movie progressed and the body count mounted. The CGI Mummy was probably the most impressive CGI character that I have ever seen in a movie. I would say it will be a long time before the effects were surpassed if it weren't for the fact that Star Wars: Episode I - The Phantom Menace wasn't being released a week and a half after this film. The Mummy itself wasn't the only impressive computer generated horror in this film. Special effects wizards also used computers to generate a very impressive killer windstorm as well as the Mummy's undead henchmen. One of the neatest touches was the CGI flesh eating beetles -- I'll say no more.
If, when you think of The Mummy, you picture a slow moving creature wrapped in gauze chasing people at speeds approaching 1/4 of a mile per hour, you are in for a shock. This new Mummy is slick, scary and a whole lot of fun. The Mummy might not go down as a "classic" like the original, but it will provide you with one of the most enjoyable movie experiences that you are likely to see in a summer film.
9/10
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| Class | Description |
|---|---|
| AbstractFeatureMojo |
Base class for all mojos.
|
| AbstractIncludingFeatureMojo | |
| AbstractIncludingFeatureMojo.FeatureScanner | |
| AbstractRepositoryMojo | |
| Aggregate | |
| AggregateFeaturesMojo |
Aggregate multiple features into a single one.
|
| AnalyseFeaturesMojo |
Analyse the feature.
|
| ApisJarMojo |
Generates the APIs JARs for the selected feature files.
|
| Archive |
Several features can be selected to be put into a single archive
|
| AttachFeatureArchivesMojo |
Create a feature model archive and attach it.
|
| AttachFeaturesMojo |
Attach the feature as a project artifact.
|
| CollectArtifactsMojo | |
| DependencyLifecycleParticipant |
Maven lifecycle participant which adds the artifacts of the model to the dependencies.
|
| EmbedFeaturesMojo |
Embed the features in the resources
|
| ExtractExtensionMojo | |
| FeatureLauncherMojo |
Launches the given Feature File
|
| FeaturesDiffMojo |
Compares different versions of the same Feature Model.
|
| FeatureSelectionConfig | |
| HelpMojo |
Display help information on slingfeature-maven-plugin.
Call mvn slingfeature:help -Ddetail=true -Dgoal=<goal-name> to display parameter details. |
| IncludeArtifactMojo |
This goal creates a Feature Model file that includes the Module Artifact as
bundle (or extension) so that the Artifact can be added through a FM into a
FM project.
|
| InfoMojo |
Extract information from a feature This mojo does not require a project, it
can be run by just pointing it at a feature file.
|
| Repository | |
| RepositoryMojo |
Create a Maven repository structure from the referenced artifacts in the features.
|
| Scan | |
| UpdateVersionsMojo |
Update the bundles/artifact versions
|
| UpdateVersionsMojo.ArtifactUpdate | |
| UpdateVersionsMojo.SimpleFeatureJSONReader | |
| UpdateVersionsMojo.SimpleFeatureJSONWriter | |
| UpdateVersionsMojo.UpdateConfig | |
| UpdateVersionsMojo.UpdateResult |
Copyright © 2007–2020 The Apache Software Foundation. All rights reserved.


Building a Better Friend Selector
tip:
add ?n=X to the querystring to test with various list sizes, this demo supports up to 50,000 friends (beware!)
i apologize for the long loading time, it's just your browser downloading my massive sample data file. it should be fine once that's cached


Home Page
The People Route, implements multiple concepts.
- Subscribing to slice of State
- Dispatching an action ngOnInit
- Using @ngrx/effects to dispatch a service call to electron IPC
- Listening for a response on IPC and dispatching another action to load serialized data from the Electron Main process.


Adventure SCI-FI Terror
New Movies
15 October 2015 (Brazil)
Drama
7.0
Adventure, SCI-FI
Adventure, SCI-FI, Terror
Action, Drama
Adventure, Terror
Adventure, Action
SCI-FI
9.0
Suspense, Drama
10
7.5


Founder & Visionary
Leader
Product developer & engineer
Front-end developer
Designer
Back-end developer
Engineer
Space, zero gravity and sex! Hell yeah!


|
|
IFXNeighborResController Class ReferenceInheritance diagram for IFXNeighborResController:
List of all members.
|
Public Types |
| enum | EdgeType { KeeperEdge,
CollapseEdge,
EdgeEdge
} |
Public Member Functions |
| | IFXNeighborResController () |
| virtual | ~IFXNeighborResController () |
| IFXRESULT IFXAPI | InitMesh (IFXMeshGroup *pMeshGroup, IFXCLODManager *pCLODManager, IFXNeighborMesh *pNeighborMesh, IFXUpdatesGroup *pUpdatesGroup, IFXVertexMapGroup *pVertexMapGroup) |
| IFXRESULT IFXAPI | Initialize (IFXNeighborMesh *pNeighborMesh, IFXUpdatesGroup *pUpdatesGroup) |
| IFXDistalEdgeMerge *IFXAPI | GetDistalEdgeMerges (U32 resolution) |
| void IFXAPI | SetDistalEdgeMerges (U32 resolution, IFXDistalEdgeMerge *pList) |
| IFXDistalEdgeMerge *IFXAPI | AllocateDistalEdgeRecord () |
| void IFXAPI | SetResolution (U32 resolution) |
| void IFXAPI | IncreaseResolution () |
| void IFXAPI | DecreaseResolution () |
Private Member Functions |
| IFXRESULT | BuildEdgeMap () |
| void | DeleteEdgeMap () |
| void | ResetCLODFlags () |
| void | DetermineCollapsedEdges () |
| void | AnalyzeMergingEdges (U32 meshIndex, U32 resolution) |
| BOOL | CheckForDistalMerge (U32 other, U32 child, U32 parent) |
| void | AddDistalMergeRecord (U32 resolution, U32 other, U32 child, U32 parent) |
| void | UpdateEdgesInMap (U32 meshIndex) |
| void | FindNonDeletedFaceForEdge (U32 v1, U32 v2) |
| void | ApplyEdgeMerge (IFXDistalEdgeMerge *pMerge) |
| void | DecrementFaceCount (U32 meshIndex) |
| BOOL | IsDeletedFace (U32 meshIndex, U32 faceIndex) |
| void | MarkDuplicatesFromCorner (U32 meshIndex, U32 faceIndex, U32 cornerIndex) |
| void | MarkDuplicates (U32 meshIndex, U32 faceIndex) |
| BOOL | IsNotDuplicate (U32 meshIndex, U32 faceIndex) |
| EdgeType | ClassifyEdge (U32 meshIndex, U32 faceIndex, U32 cornerIndex) |
| BOOL | FindCollapseEdge (U32 meshIndex, U32 *collapseFaceIndex, U32 *collapseCornerIndex) |
| BOOL | FindEdgeEdge (U32 meshIndex, U32 *collapseFaceIndex, U32 *collapseCornerIndex) |
| void | MarkCollapseEdges (U32 meshIndex, U32 faceIndex, U32 cornerIndex) |
| void | MarkCollapseEdgesInMesh (U32 meshIndex) |
| void | AltRemoveFaces (U32 meshIndex) |
| void | RemoveFaces (U32 meshIndex) |
| void | RemoveFace (U32 meshIndex, U32 faceIndex, U32 xCornerIndex) |
| void | AddFaces (U32 meshIndex) |
| void | AddFace (U32 meshIndex, U32 faceIndex, U32 xCornerIndex) |
Private Attributes |
| EdgeMap * | m_pEdgeMap |
| IFXDistalEdgeMerge ** | m_distalMerges |
| IFXMeshGroup * | m_pMeshGroup |
| IFXCLODManager * | m_pCLODManager |
| IFXNeighborMesh * | m_pNeighborMesh |
| IFXUpdatesGroup * | m_pUpdatesGroup |
| IFXCoincidentVertexMap * | m_pVertexMap |
| U32 | m_resolution |
| U32 | m_finalMaxResolution |
| U32 | m_numMeshes |
| ResolutionState * | m_pMeshStates |
Classes |
| class | EdgeEntry |
| class | EdgeMap |
| struct | ResolutionState |
Member Enumeration Documentation
|
|
- Enumerator:
-
| KeeperEdge |
|
| CollapseEdge |
|
| EdgeEdge |
|
|
Constructor & Destructor Documentation
| IFXNeighborResController::IFXNeighborResController |
( |
|
) |
|
|
| IFXNeighborResController::~IFXNeighborResController |
( |
|
) |
[virtual] |
|
Member Function Documentation
| void IFXNeighborResController::AddDistalMergeRecord |
( |
U32 |
resolution, |
|
|
U32 |
other, |
|
|
U32 |
child, |
|
|
U32 |
parent |
|
) |
[private] |
|
| void IFXNeighborResController::AddFace |
( |
U32 |
meshIndex, |
|
|
U32 |
faceIndex, |
|
|
U32 |
xCornerIndex |
|
) |
[private] |
|
| void IFXNeighborResController::AddFaces |
( |
U32 |
meshIndex |
) |
[private] |
|
| IFXINLINE IFXDistalEdgeMerge * IFXNeighborResController::AllocateDistalEdgeRecord |
( |
|
) |
[virtual] |
|
| void IFXNeighborResController::AltRemoveFaces |
( |
U32 |
meshIndex |
) |
[private] |
|
| void IFXNeighborResController::AnalyzeMergingEdges |
( |
U32 |
meshIndex, |
|
|
U32 |
resolution |
|
) |
[private] |
|
| IFXRESULT IFXNeighborResController::BuildEdgeMap |
( |
|
) |
[private] |
|
| BOOL IFXNeighborResController::CheckForDistalMerge |
( |
U32 |
other, |
|
|
U32 |
child, |
|
|
U32 |
parent |
|
) |
[private] |
|
| void IFXNeighborResController::DecreaseResolution |
( |
|
) |
[virtual] |
|
| void IFXNeighborResController::DecrementFaceCount |
( |
U32 |
meshIndex |
) |
[private] |
|
| void IFXNeighborResController::DeleteEdgeMap |
( |
|
) |
[private] |
|
| void IFXNeighborResController::DetermineCollapsedEdges |
( |
|
) |
[private] |
|
| BOOL IFXNeighborResController::FindCollapseEdge |
( |
U32 |
meshIndex, |
|
|
U32 * |
collapseFaceIndex, |
|
|
U32 * |
collapseCornerIndex |
|
) |
[private] |
|
| BOOL IFXNeighborResController::FindEdgeEdge |
( |
U32 |
meshIndex, |
|
|
U32 * |
collapseFaceIndex, |
|
|
U32 * |
collapseCornerIndex |
|
) |
[private] |
|
| void IFXNeighborResController::FindNonDeletedFaceForEdge |
( |
U32 |
v1, |
|
|
U32 |
v2 |
|
) |
[private] |
|
| void IFXNeighborResController::IncreaseResolution |
( |
|
) |
[virtual] |
|
|
|
Methods for progressive download of clod neighbor mesh.
Initilize resolution controller with a clodolution neighbor mesh that is ready for clod operations. This is used in conjuction with progressive download of clod neighbor mesh. Requires setting distal edge merge records as appropriate.
- Note:
- Can return IFX_E_OUT_OF_MEMORY
Implements IFXNeighborResControllerInterface. |
| BOOL IFXNeighborResController::IsDeletedFace |
( |
U32 |
meshIndex, |
|
|
U32 |
faceIndex |
|
) |
[private] |
|
| BOOL IFXNeighborResController::IsNotDuplicate |
( |
U32 |
meshIndex, |
|
|
U32 |
faceIndex |
|
) |
[private] |
|
| void IFXNeighborResController::MarkCollapseEdges |
( |
U32 |
meshIndex, |
|
|
U32 |
faceIndex, |
|
|
U32 |
cornerIndex |
|
) |
[private] |
|
| void IFXNeighborResController::MarkCollapseEdgesInMesh |
( |
U32 |
meshIndex |
) |
[private] |
|
| void IFXNeighborResController::MarkDuplicates |
( |
U32 |
meshIndex, |
|
|
U32 |
faceIndex |
|
) |
[private] |
|
| void IFXNeighborResController::MarkDuplicatesFromCorner |
( |
U32 |
meshIndex, |
|
|
U32 |
faceIndex, |
|
|
U32 |
cornerIndex |
|
) |
[private] |
|
| void IFXNeighborResController::RemoveFace |
( |
U32 |
meshIndex, |
|
|
U32 |
faceIndex, |
|
|
U32 |
xCornerIndex |
|
) |
[private] |
|
| void IFXNeighborResController::RemoveFaces |
( |
U32 |
meshIndex |
) |
[private] |
|
| void IFXNeighborResController::ResetCLODFlags |
( |
|
) |
[private] |
|
| void IFXNeighborResController::SetDistalEdgeMerges |
( |
U32 |
resolution, |
|
|
IFXDistalEdgeMerge * |
pList |
|
) |
[virtual] |
|
|
|
Set the list of distal edge merges for a resolution.
pList can be NULL.
NOTE: - The m_otherVertex value is not used.
- The distal edge records are allocated by the calling
- procedure, but this destructor will free them.
Implements IFXNeighborResControllerInterface. |
| void IFXNeighborResController::SetResolution |
( |
U32 |
resolution |
) |
[virtual] |
|
| void IFXNeighborResController::UpdateEdgesInMap |
( |
U32 |
meshIndex |
) |
[private] |
|
Member Data Documentation
|
|
Maximum resolution after all updates have been downloaded.
|
© 1999 - 2006 Intel Corporation. All rights reserved.
* Other names and brands may be claimed as the property of others.
|
|


WebEngineCertificateError QML Type
A utility type for ignoring certificate errors or rejecting erroneous certificates. More...
| Import Statement: | import QtWebEngine 1.5 |
| Since: | QtWebEngine 1.1 |
Properties
- description : string
- error : enumeration
- overridable : bool
- url : url
Methods
- void defer()
- void ignoreCertificateError()
- void rejectCertificate()
Detailed Description
This QML type contains information about a certificate error that occurred. The error property holds the reason that the error occurred and the description property holds a short localized description of the error. The url property holds the URL that triggered the error.
The certificate can be rejected by calling rejectCertificate, which will stop loading the web engine request. By default, an invalid certificate will be automatically rejected.
The certificate error can be ignored by calling ignoreCertificateError, which will resume loading the request.
It is possible to defer the decision of rejecting a certificate by calling defer, which is useful when waiting for user input.
See also WebEngineView::certificateError.
Property Documentation
The type of the error.
| Constant | Description |
|---|---|
WebEngineCertificateError.SslPinnedKeyNotInCertificateChain | The certificate did not match the built-in public keys pinned for the host name. |
WebEngineCertificateError.CertificateCommonNameInvalid | The certificate's common name did not match the host name. |
WebEngineCertificateError.CertificateDateInvalid | The certificate is not valid at the current date and time. |
WebEngineCertificateError.CertificateAuthorityInvalid | The certificate is not signed by a trusted authority. |
WebEngineCertificateError.CertificateContainsErrors | The certificate contains errors. |
WebEngineCertificateError.CertificateNoRevocationMechanism | The certificate has no mechanism for determining if it has been revoked. |
WebEngineCertificateError.CertificateUnableToCheckRevocation | Revocation information for the certificate is not available. |
WebEngineCertificateError.CertificateRevoked | The certificate has been revoked. |
WebEngineCertificateError.CertificateInvalid | The certificate is invalid. |
WebEngineCertificateError.CertificateWeakSignatureAlgorithm | The certificate is signed using a weak signature algorithm. |
WebEngineCertificateError.CertificateNonUniqueName | The host name specified in the certificate is not unique. |
WebEngineCertificateError.CertificateWeakKey | The certificate contains a weak key. |
WebEngineCertificateError.CertificateNameConstraintViolation | The certificate claimed DNS names that are in violation of name constraints. |
WebEngineCertificateError.CertificateValidityTooLong | The certificate has a validity period that is too long. (Added in 5.7) |
WebEngineCertificateError.CertificateTransparencyRequired | Certificate Transparency was required for this connection, but the server did not provide CT information that complied with the policy. (Added in 5.8) |
A boolean that indicates whether the certificate error can be overridden and ignored.
[read-only] url : url |
The URL that triggered the error.
Method Documentation
This function should be called when there is a need to postpone the decision whether to ignore a certificate error, for example, while waiting for user input. When called, the function pauses the URL request until WebEngineCertificateError::ignoreCertificateError() or WebEngineCertificateError::rejectCertificate() is called.
The certificate error is ignored, and the web engine view continues to load the requested URL.
The certificate is rejected, and the web engine view stops loading the requested URL.


§92-13 Penalties. Any person who wilfully violates any provisions of this part shall be guilty of a misdemeanor, and upon conviction, may be summarily removed from the board unless otherwise provided by law. [L 1975, c 166, pt of §1]


Within Zend Framework applications, it’s often expedient to provide a mechanism for using class aliases instead of full class names to load adapters and plugins, or to allow using aliases for the purposes of slipstreaming alternate implementations into the framework.
In the first case, consider the adapter pattern. It’s often unwieldy to utilize a full class name (e.g., Zend\Cloud\DocumentService\Adapter\SimpleDb); using the short name of the adapter, SimpleDb, would be much simpler.
In the second case, consider the case of helpers. Let us assume we have a “url” helper; you may find that while the shipped helper does 90% of what you need, you’d like to extend it or provide an alternate implementation. At the same time, you don’t want to change your code to reflect the new helper. In this case, a short name allows you to alias an alternate class to utilize.
Classes implementing the ShortNameLocator interface provide a mechanism for resolving a short name to a fully qualified class name; how they do so is left to the implementers, and may combine strategies defined by other interfaces, such as PluginClassLocator.
Implementing a ShortNameLocator is trivial, and requires only three methods, as shown below.
1 2 3 4 5 6 7 8 | namespace Zend\Loader;
interface ShortNameLocator
{
public function isLoaded($name);
public function getClassName($name);
public function load($name);
}
|
This component defines no configuration options, as it is an interface.
Is the requested plugin loaded? isLoaded($name)
isLoaded() Implement this method to return a boolean indicating whether or not the class has been able to resolve the plugin name to a class.
Get the class name associated with a plugin name getClassName($name)
getClassName() Implement this method to return the class name associated with a plugin name.
Resolve a plugin to a class name load($name)
load() This method should resolve a plugin name to a class name.
Please see the Quick Start for the interface specification.
The source code of this file is hosted on GitHub. Everyone can update and fix errors in this document with few clicks - no downloads needed.


From existing provider optimizations to full-custom buildouts, we know what it takes to make your store stand out.
We care deeply about pixel-perfection in our designs. Maybe even too deeply. But probably just deeply enough.
You need something that doesn't exist yet. Why not have us build it for you?
DBS begins as an idea: what if we could make beautiful things that helped people?
Working with several different clients, we begin to develop long-term relationships and a reputation for great quality.
John reads a book by Rebecca Solnit and becomes fascinated with the notion that the deep blue at the horizon line is beautiful precisely because it is unattainable.
Expansion and growth, continued relationships with great ongoing clients, and the forging of meaningful relationships with new clients. The horizon is beautiful.


Both jquery and three.js are loaded from a cdn via a script tag inside the head
However they are required as usual inside our modules, i.e.
exposify makes the adjustements to make this all work
var $ = require('jquery')
Please open the dev console to see logged versions of both libraries and investigate the
main.jsin the sources tab in order to find out how it works (all jquery and three require calls were replaced with assignments from the window).