---
title: "Easiest Way to Make Quick Parmesan Garlic Bread Tasty"
slug: parmesan-garlic-bread
description: "Parmesan Garlic Bread Recipe - This is a recipe I came up with myself. It is great with most Italian dishes. It can be doubled as needed. Recipe of Quick Parmesan Garlic Bread Tasty"
date: 2021-05-12T13:13:00Z
image: https://cdn.statically.io/img/images.media-allrecipes.com/userphotos/336276.jpg
thumbnail: https://cdn.statically.io/img/images.media-allrecipes.com/userphotos/336276.jpg
cover: https://cdn.statically.io/img/images.media-allrecipes.com/userphotos/336276.jpg
author: Lili
ratingvalue: 4.2
reviewcount: 913
recipeingredient:
- "½ cup butter, melted"
- "1 teaspoon garlic salt"
- "¼ teaspoon dried rosemary"
- "⅛ teaspoon dried basil"
- "⅛ teaspoon dried thyme"
- "⅛ teaspoon garlic powder"
- "1 tablespoon grated Parmesan cheese"
- "1 (1 pound) loaf French bread, halved lengthwise"
recipeinstructions:
- "Preheat oven to 300 degrees F (150 degrees C)."
- "In a small bowl, mix butter, garlic salt, rosemary, basil, thyme, garlic powder and Parmesan cheese."
- "Spread each half of the French bread with equal portions of the butter mixture. Sprinkle with additional Parmesan cheese, if desired."
- "Place bread halves, crusts down, on a medium baking sheet. Bake in the preheated oven 10 to 12 minutes, or until the edges are very lightly browned."
categories:
- "Appetizers and Snacks"
- "Garlic Bread Recipes"
- "Recipes"
tags:
- "appetizers and snacks"
- "garlic bread recipes"
katakunci: parmesan garlic bread, appetizers and snacks, garlic bread recipes, .
nutrition: 267 calories
recipecuisine: Western
preptime: "P0DT0H15M"
cooktime: "P0DT0H12M"
totaltime: "P0DT0H27M"
recipeyield: "8 servings"
recipecategory:
- Appetizers and Snacks
- Garlic Bread Recipes
quotes: Who needs love when you can eat all the chocolate in the world.
---
Parmesan Garlic Bread - Hey everyone, hope youre having an amazing day today. Its me again Lili , welcome to our recipe website. Today, I will show you a way to make a special dish, Parmesan Garlic Bread. One of my favorites food recipes. For mine, Im gonna make it a little bit unique. This will be really delicious.
Parmesan Garlic Bread is This is a recipe I came up with myself. It is great with most Italian dishes. It can be doubled as needed.
Parmesan Garlic Bread is one of the most popular of recent trending meals in the world. It is easy, its fast, it tastes delicious. its appreciated by millions every day. its simple, it is fast, it tastes yummy. Parmesan Garlic Bread is something that I ve loved my whole life. They are nice and they look wonderful. They re nice and they look fantastic. Parmesan Garlic Bread is something that I have loved my whole life.
Recipe of Ultimate Parmesan Garlic Bread Tasty
To get started with this particular recipe, we must first prepare a few ingredients. You can have Parmesan Garlic Bread using 8 ingredients and 4 steps. Here is how you can achieve that.

-
In a small bowl, mix butter, garlic salt, rosemary, basil, thyme, garlic powder and Parmesan cheese.
-
Spread each half of the French bread with equal portions of the butter mixture. Sprinkle with additional Parmesan cheese, if desired.
-
Place bread halves, crusts down, on a medium baking sheet. Bake in the preheated oven 10 to 12 minutes, or until the edges are very lightly browned.
So that is going to wrap this up with this exceptional food Parmesan Garlic Bread recipe. Thank you very much for reading. I am confident that you will make this at home. There is gonna be another interesting food at home recipes coming up. Dont forget to bookmark this page in your browser, and share it to your family, colleague and friends. Thanks again for reading. Go on get cooking!
Reviews of Parmesan Garlic Bread:
-
caliboo - Made some changes that brought this up to a five star rating! One, don't melt the butter, soften it. Then mix the other ingredients {I omitted the rosemary and left out the garlic salt in favor of additional garlic powder} in the with softened butter and spread on the bread. When you have only five minutes of baking time left, add a liberal amount of mozzarella cheese to the top. After five minutes it will be just melted and nice and gooey when you take it out of the oven! Perfect!!!
-
Stephanie Louie - The flavor of the butter is absolutely delicious on the French bread. I will, however, omit the garlic salt next time as it is too salty. This recipe makes more than is needed for one loaf of bread (probably just the right amount for one and one-half to two loaves).
-
Sarah Jo - I cut this recipe in half, used garlic powder instead of garlic salt and I used sandwich bread that needed to be used up. (Ghetto? Sure. But it works just as any other bread would.) Family thought this was very tasty. I did not have any, I just looked at it with longing.
-
RWFINS - My family loved this didn't have a french loaf just some "wonder" slice bread slightly toasted the bread and then put spread on both sides and cook about 5 minutes longer then the recipe called for let sit a couple of minutes it turned out great. Oh yeah the most important thing use unsalted butter that way when you use the garlic salt it will not turn out too salty.
-
JJBURKE81 - This is the BEST garlic bread I've ever tasted!!!! The only changes that i made were to just soften the butter instead of melting it....it spread much easier and i also added more parmesan on top before i baked it. I will definately used this recipe again and again!
-
skram - I thought that this recipe was great. I didn't have any garlic salt so I just used more garlic powder and it turned out perfect!
-
KATMARNIN - I thought this was good except it was too salty. Next time I would cut the garlic salt in half and add more garlic powder.
-
Valerie Brunmeier - Great garlic bread. I recommend using more garlic powder (3/4 tsp.) and less garlic salt (1/2 tsp.). Otherwise good as stated. This way it will be more garlicky than it is salty which is how we like it. It was gobbled up at a recent bbq. Thanks!
-
PAMMMY - I made this recipe last weekend for a dinner party. It was AWESOME! The one reviewer who said it didn't have the right combination of spices must have done something wrong because this is the best and easiest garlic bread i've ever made! Also the recipe calls for 1/2 cup of butter which is one stick not two as one reviewer said. The rosemary gives it it's unique flavor. I followed the recipe exactly and added just a scant bit of freshly grated parmesan cheese on top. This is my new 'bread'! Thanks for sharing.
-
MILDRED5 - When making garlic bread for my grandchildren I use hot dog buns. I finish spliting them and the kids love them. They are easier for little hands to hold.


| Hydrogeological Toolbox |
| What is Hytool? | Describes Hytool main features |
| Starting Example | A starting example showing how to load data, and interpret them with the Theis (1935) solution. |
| Syntax convention | Describes the syntax convention used within Hytool |
| Hytool |
© 1998-2017 Philippe Renard and Co. Terms of Use Acknowledgments


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*FMF Campus Team*
duVergne, Brooke, Adriana, Francesca, Miranda and Kari
P.S. If you have any questions, please contact us at<EMAIL_ADDRESS>You have received this e-mail because of your interest in women’s issues. To unsubscribe, please click here. [ http://salsa.democracyinaction.org/o/1400/p/salsa/supporter/unsubscribe/public/index.sjs?unsubscribe_page_KEY=494 ]


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{% extends "base.html" %} {% block nav_home %}active{% endblock %} {% block styles %} {{ super() }} {% endblock %} {% block javascript %} {{ super() }} {% endblock %} {% block page_id %}generator{% endblock %} {% block content %}
FPO means For Placement Only and is commonly used for providing a placeholder image where actual content will reside in a publication, website, or similar.
The goal of FPOImg.com is simple—-provide an easy way to generate placeholder images for your designs whether it be for web development, print, or a school paper
Using FPOImg.com is simple, append the size of an image you would like to generate after fpoimg.com/
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--- layout: post title: Blog aims to record everyday life date: 2006-10-17 00:00:00 category: digital-media ---
Blog aims to record everyday life: "The National Trust is encouraging people to record a diary of their day on a website, as part of what is being called "Britain's biggest blog". The blogs will then be stored by the British Library and at other locations."


|
||||||||||
| PREV CLASS NEXT CLASS | FRAMES NO FRAMES | |||||||||
| SUMMARY: NESTED | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |
public interface SlingScript
The SlingScript
defines the API for objects which encapsulate
a script resolved by the SlingScriptResolver
. To have a script
evaluated prepare a SlingBindings
instance of variables used as
global variables to the script and call the eval(SlingBindings)
method.
Objects implementing this interface are returned by the
SlingScriptResolver.findScript(org.apache.sling.api.resource.ResourceResolver, String)
method.
| Method Summary | |
|---|---|
java.lang.Object |
call(SlingBindings props,
java.lang.String method,
java.lang.Object... args)
Evaluates this script using the bound variables as global variables to the script and then calls the given method with the arguments. |
java.lang.Object |
eval(SlingBindings props)
Evaluates this script using the bound variables as global variables to the script. |
Resource |
getScriptResource()
Returns the Resource providing the script source code. |
| Method Detail |
|---|
Resource getScriptResource()
java.lang.Object eval(SlingBindings props)
props
- The SlingBindings
providing the bound variables for
evaluating the script. Any bound variables must conform to the
requirements of the SlingBindings
predefined variables
set.
ScriptEvaluationException
- If an error occurrs executing the
script or preparing the script execution. The cause of the
evaluation execption is available as the exception cause.java.lang.Object call(SlingBindings props, java.lang.String method, java.lang.Object... args)
props
- The SlingBindings
providing the bound variables for
evaluating the script. Any bound variables must conform to the
requirements of the SlingBindings
predefined variables
set.method
- The name of the method to call.args
- The arguments for the method call.
ScriptEvaluationException
- If an error occurrs executing the
script or preparing the script execution. The cause of the
evaluation execption is available as the exception cause.
|
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| PREV CLASS NEXT CLASS | FRAMES NO FRAMES | |||||||||
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--- layout: post title: Making it to the ‘What If’ Era date: '2012-03-05T10:12:15-08:00' tags: tumblr_url: http://alexhwilliams.tumblr.com/post/18797183195/making-it-to-the-what-if-era ---
http://bit.ly/A0VKuE


Searches a string for a specified value and returns a new string where all the occurrences of the specified value are replaced. Case sensitive.
| Box Name | Type | Description |
|---|---|---|
| Variable | String | Variable name containing the original string. |
| Replace | String | Value that will be replaced by the new value. |
| New String | String | The value to replace the search value with. Can be empty to completely remove it. |
| Value to search for | Value to replace it with | String | Result |
|---|---|---|---|
| “l” | ”/” | “Hello” | “He//o” |
| ” “ | ”-“ | “Hello World” | “Hello-World” |
| “black” | “white” | “Mr.Black has a black car and a black cat.” | “Mr.Black has a white car and a white cat.” |


Copyright (c) 1998 Great American Trading Company
This game is played on the following board.
Each player also starts with 10 stones of his color, plus 10 neutral stones.
|
| An
example
In this position, the player that moves next wins by dropping one of his stones at cell b2. Red by making a direct 3 in-a-row, Black by creating an open 2 that cannot be stopped by the next red move. |
Since there are 20 neutral stones and only 19 cells, the board is sooner or later (if it doesn't end before that) totally filled.
Each player must keep a balance between neutral and colored stones. Running out of either is bad. No neutral stones implies that colored stones should be dropped on potential bad cells (the opponent can play neutral stones exactly on those places). No colored stones left to drop almost always means disaster.


 That's why I'm riding in Reach the Beach for the ALA.  That's why I'm riding in Reach the Beach for the ALA. Donate to help me get there at http://action.lung.org/site/TR/Bike/ALAMP_Mountain_Pacific?px=4758632&pg=personal&fr_id=9543
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Please note: Loading class histogram may be very expensive, depending on the size and
layout of the heap. Alternatively, use the jcmd utility: jcmd <process id/main class> GC.class_histogram
Loading...
Last loaded: {{lastLoaded | date: 'yyyy-MM-dd hh:mm:ss'}}


A software engineer with experience in consultancy companies, Apple and startup. My experience with javascript and nodejs in the backend came through courses and small projects.


- acceptTrade - Static variable in class cmput301t4.gameswap.R.id
-
- acceptTradeButtonClicked(View) - Method in class cmput301t4.gameswap.Activities.DecideTradeActivity
-
This is called when the user attempts to accept the
trade
- action_settings - Static variable in class cmput301t4.gameswap.R.id
-
- action_settings - Static variable in class cmput301t4.gameswap.R.string
-
- activity_add_friend - Static variable in class cmput301t4.gameswap.R.layout
-
- activity_add_item - Static variable in class cmput301t4.gameswap.R.layout
-
- activity_cancel_create_trade - Static variable in class cmput301t4.gameswap.R.layout
-
- activity_counter_trade - Static variable in class cmput301t4.gameswap.R.layout
-
- activity_create_profile - Static variable in class cmput301t4.gameswap.R.layout
-
- activity_decide_trade - Static variable in class cmput301t4.gameswap.R.layout
-
- activity_edit_item - Static variable in class cmput301t4.gameswap.R.layout
-
- activity_edit_profile - Static variable in class cmput301t4.gameswap.R.layout
-
- activity_friend_inventory - Static variable in class cmput301t4.gameswap.R.layout
-
- activity_friend_profile - Static variable in class cmput301t4.gameswap.R.layout
-
- activity_friends_ntrading - Static variable in class cmput301t4.gameswap.R.layout
-
- activity_horizontal_margin - Static variable in class cmput301t4.gameswap.R.dimen
-
- activity_main - Static variable in class cmput301t4.gameswap.R.layout
-
- activity_mine_inventory - Static variable in class cmput301t4.gameswap.R.layout
-
- activity_my_inventory - Static variable in class cmput301t4.gameswap.R.layout
-
- activity_my_profile - Static variable in class cmput301t4.gameswap.R.layout
-
- activity_offer_trade - Static variable in class cmput301t4.gameswap.R.layout
-
- activity_search_friend - Static variable in class cmput301t4.gameswap.R.layout
-
- activity_search_people - Static variable in class cmput301t4.gameswap.R.layout
-
- activity_select_from_friend_inventory - Static variable in class cmput301t4.gameswap.R.layout
-
- activity_select_task - Static variable in class cmput301t4.gameswap.R.layout
-
- activity_trades - Static variable in class cmput301t4.gameswap.R.layout
-
- activity_vertical_margin - Static variable in class cmput301t4.gameswap.R.dimen
-
- activity_view_item - Static variable in class cmput301t4.gameswap.R.layout
-
- add(Item) - Method in class cmput301t4.gameswap.Models.Inventory
-
Adds an Item to the current users Inventory
- add(Trade) - Method in class cmput301t4.gameswap.Models.TradeList
-
Adds a trade to trade list
- add - Static variable in class cmput301t4.gameswap.R.string
-
- addFriend(String) - Static method in class cmput301t4.gameswap.Managers.FriendManager
-
Adds a friend
- addFriend(String) - Method in class cmput301t4.gameswap.Models.FriendList
-
Stores a User as a friend
- addFriendSide(Item) - Static method in class cmput301t4.gameswap.Managers.CreateTradeManager
-
Adds an item to friend inventory to trade with
- addImageCapture - Static variable in class cmput301t4.gameswap.R.id
-
- addImageGallery - Static variable in class cmput301t4.gameswap.R.id
-
- addImageOption(View) - Method in class cmput301t4.gameswap.Activities.AddItemActivity
-
opens camera to take photo
- addImageOption(View) - Method in class cmput301t4.gameswap.Activities.EditItemActivity
-
- addItem(String, String, boolean, Integer, Integer, String) - Static method in class cmput301t4.gameswap.Managers.InventoryManager
-
This adds the items to the inventory
- AddItemActivity - Class in cmput301t4.gameswap.Activities
-
Class to add an item to user's inventory
- AddItemActivity() - Constructor for class cmput301t4.gameswap.Activities.AddItemActivity
-
- addItemAfterTrade(Inventory, User) - Method in class cmput301t4.gameswap.Models.Inventory
-
adds exchanged item to inventory
- addItemButton() - Method in class cmput301t4.gameswap.Activities.AddItemActivity
-
- addItemsToCache(ArrayList<Item>) - Method in class cmput301t4.gameswap.Models.Cache
-
Method to add an ArrayList of Items to the Cache
- addItemTest() - Method in class cmput301t4.gameswap.UI_TestCases.BasicInventoryTest
-
- addItemToCache(Item) - Method in class cmput301t4.gameswap.Models.Cache
-
Method to add an Item to the Cache
- addNewItem(View) - Method in class cmput301t4.gameswap.Activities.myInventoryActivity
-
Called when user clicks on add item button
- addOwnerSide(Item) - Static method in class cmput301t4.gameswap.Managers.CreateTradeManager
-
Adds an item to owner inventory to trade for
- addTraderButton - Static variable in class cmput301t4.gameswap.R.id
-
- addTradesToCache(ArrayList<Trade>) - Method in class cmput301t4.gameswap.Models.Cache
-
Method to add an ArrayList of Trades to the Cache
- addTradeToCache(Trade) - Method in class cmput301t4.gameswap.Models.Cache
-
Method to add a Trade to the Cache
- app_name - Static variable in class cmput301t4.gameswap.R.string
-
- APPLICATION_ID - Static variable in class cmput301t4.gameswap.BuildConfig
-
- ApplicationTest - Class in cmput301t4.gameswap
-
- ApplicationTest() - Constructor for class cmput301t4.gameswap.ApplicationTest
-
- AppTheme - Static variable in class cmput301t4.gameswap.R.style
-
Customize your theme here.


Prospective members are invited to contract the club's Secretary<EMAIL_ADDRESS>regarding club shares for sale. If an existing club member is interested in selling their membership, the Club will prioritize putting a prospective new member into contact with the prospective seller if desired. Otherwise, a limited number of club-owned memberships are available at a cost of $6,000. Total membership is limited to 15 per aircraft.
Whether a club membership is purchased or there is a member-to-member sale, the Club must approve the prospective new member. The club will handle the sale proceeds, aircraft checkout and keys, will facilitate access privileges to the aircraft operating area.


{{defaultCard.fullName}}
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According to research, sales increase by 2.5% for every 2000 cards you hand out


A marker (super-particle) in PIC simulations represents a group of physical particles, which has their own distribution function given by
|
| (22) |
where the subscript p denotes quantities on a marker, wp is the weight of the marker deﬁned above, Sv(v − vp) and S(r − rp) are the shape functions in the velocity space and real space, respectively. Here Sv and S have physical diemsion of 1∕length3 and 1∕velocity 3 so that the physical dimension of fp given in expression (22) is consistent with the physical dimension of a distribution function.
In standard PIC simulation, the shape of markers in the velocity space is always a Dirac delta function:
|
| (23) |
where δ3(v − vp) is the three-dimensional Dirac delta function. In this case, all the physical particles represnted by a marker have the same veloicty vp. [In terms of general velocity coordinates (vα,vβ,vγ), the three-dimensional Dirac delta function is deﬁned via the 1D Dirac delta function as follows:
|
| (24) |
where 𝒥 is the the Jacobian of the general velocity coordinate system (vα,vβ,vγ). Note that 𝒥v is dimensionless and the 1D Dirac delta function δ(vα − vαp) has a physical dimension of 1∕vα.]
The shape function in the real space, S(r − rp), can also be chosen as the Dirac delta function. In practice, ﬁnite-size shape in spatial space is often adopted, i.e., the physical particles represented by a marker have diﬀerent spatial locations. Along with the cell-averaging (discussed later), ﬁnite-size shape function has the eﬀect of making the resulting self-consistent ﬁeld more smooth, reducing (ideally completely removing) the artiﬁcial collision eﬀects (the interaction among close particles) associated with using very few markers to approximate a plasma that has many physical particles in a Debye sphere[7].
A 3D shape function can be constructed by combining three 1D shape functions and the Jacobian of the coordinate system. For example, in (α,β,γ) coordinate system, a 3D shape function is written as
|
| (25) |
where 𝒥r = 𝒥r(α,β,γ) is the Jacobian of (α,β,γ) coordinate system, Δαp, Δβp, and Δγp are the scale-lengths of the support of the 1D shape function S1D along α, β, and γ directions, respectively. The 1D shape function S1D should satisfy the following normalization:
|
| (26) |
where ξ is one of the spatial coordinates. The reason to include the Jacobian in the deﬁnition of the 3D shape function is that this makes fp be consistent with the deﬁnition of a distribution function, i.e., satisfy the following normalization:
|
| (27) |
i.e.,
|
| (28) |
While not strictly necessary, symmetric shapes are usually chosen, i.e. S1D has the property: S1D(ξ −ξp) = S1D(ξp −ξ). The support of S1D should be compact (i.e. it is zero outside a small region) so that physical particles represnted by the marker are localized to a small portion of the space. Compact support of S1D can make the deposition and gathering operations (deﬁned later) computationally eﬃcient. Modern PIC codes usually use the so-called b-splines as the spatial shape functions. The b-spline functions are a series of consecutively higher order functions obtained from each other by integration:
|
| (29) |
with the 0th b-spline being the ﬂat-top function deﬁned as
|
| (30) |
Then, by using Eqs. (29) and (30), it is ready to derive the expression of b1(ξ), which is given by
|
| (31) |
The graphics of the ﬁrst two b-splines, b0(ξ) and b1(ξ), are shown in Fig. 1.
The 1D shape function based on the b-spline function is then given by
|
| (32) |
Most PIC codes use b0, i.e, the ﬂat-top function, as the shape function and choose the support of the shape function Δp equal to the grid spacing. (k-spectra of particle shape functions, to be continued)
The shape functions are essentially identical to the basis functions used in ﬁnite element methods.
The spatial shape of markers determines how physical particles represented by a marker are distributed to spatial cells (called deposition) and also how the force on a marker is related to the nearby electromagnetic ﬁeld. Note that the phase-space volume occupied (or sampled) by a marker is diﬀerent from the concept of the spatial-shape of a marker.
The physical particle distribution is the sum of all the particle elements given in expression (22), i.e.,
|
| (33) |


|
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public interface AdapterFactory
The AdapterFactory
interface defines the API for helpers which
may be provided to enhance the adaptability of adaptable objects.
Implementations of this interface are registered as OSGi services and are
used by the AdapterManager
to adapt objects on demand. The
AdapterFactory
services are not really intended to be used by
clients directly.
| Field Summary | |
|---|---|
static String |
ADAPTABLE_CLASSES
The service registration property listing the fully qualified names of classes which can be adapted by this adapter factory (value is "adaptables"). |
static String |
ADAPTER_CLASSES
The service registration property listing the fully qualified names of classes to which this factory can adapt adaptables (value is "adapters"). |
static String |
SERVICE_NAME
The service name to use when registering implementations of this interface as services (value is "org.apache.sling.osgi.commons.AdapterFactory"). |
| Method Summary | ||
|---|---|---|
|
getAdapter(Object adaptable,
Class<AdapterType> type)
Adapt the given object to the adaptable type. |
| Field Detail |
|---|
static final String SERVICE_NAME
static final String ADAPTABLE_CLASSES
getAdapter(Object, Class)
method must be an instance of any of
these classes for this factory to be able to adapt the object.
static final String ADAPTER_CLASSES
| Method Detail |
|---|
<AdapterType> AdapterType getAdapter(Object adaptable, Class<AdapterType> type)
ADAPTABLE_CLASSES
services registration property. The type
parameter is on of the classes listed in the ADAPTER_CLASSES
service registration properties.
This method may return null
if the adaptable object may not
be adapted to the adapter (target) type for any reason. In this case, the
implementation should log a message to the log facility noting the cause
for not being able to adapt.
Note that the adaptable
object is not required to implement
the Adaptable
interface, though most of the time this method
is called by means of calling the Adaptable.adaptTo(Class)
method.
AdapterType
- The generic type of the adapter (target) type.adaptable
- The object to adapt to the adapter type.type
- The type to which the object is to be adapted.
null
if this factory instance
cannot adapt the object.
|
||||||||||
| PREV CLASS NEXT CLASS | FRAMES NO FRAMES | |||||||||
| SUMMARY: NESTED | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |


§334-85 Further hearings. A judicial determination under this part regarding the detention of a patient at the same psychiatric facility may not be demanded as of right more often than once in a period of six months. [L 1967, c 259, pt of §1; HRS §334-85]


« Back to FileHandler
No XSRF
You submitted a form that did not provide an XSRF token.
{% import yaml %}
{{ yaml.dump(handler.args) }}


The SanitizeHelper module provides a set of methods for scrubbing text of undesired HTML elements. These helper methods extend Action View making them callable within your template files.
Sanitizes HTML input, stripping all tags and attributes that aren't whitelisted.
It also strips href/src attributes with unsafe protocols like
javascript:
, while also protecting against attempts to use
Unicode, ASCII, and hex character references to work around these protocol
filters.
The default sanitizer is Rails::Html::WhiteListSanitizer. See Rails HTML Sanitizers for more information.
Custom sanitization rules can also be provided.
Please note that sanitizing user-provided text does not guarantee that the
resulting markup is valid or even well-formed. For example, the output may
still contain unescaped characters like <
,
>
, or &
.
Options
-
:tags
- An array of allowed tags. -
:attributes
- An array of allowed attributes. -
:scrubber
- A Rails::Html scrubber or Loofah::Scrubber object that defines custom sanitization rules. A custom scrubber takes precedence over custom tags and attributes.
Examples
Normal use:
<%= sanitize @comment.body %>
Providing custom whitelisted tags and attributes:
<%= sanitize @comment.body, tags: %w(strong em a), attributes: %w(href) %>
Providing a custom Rails::Html scrubber:
class CommentScrubber < Rails::Html::PermitScrubber
def allowed_node?(node)
!%w(form script comment blockquote).include?(node.name)
end
def skip_node?(node)
node.text?
end
def scrub_attribute?(name)
name == 'style'
end
end
<%= sanitize @comment.body, scrubber: CommentScrubber.new %>
See Rails HTML Sanitizer for documentation about Rails::Html scrubbers.
Providing a custom Loofah::Scrubber:
scrubber = Loofah::Scrubber.new do |node|
node.remove if node.name == 'script'
end
<%= sanitize @comment.body, scrubber: scrubber %>
See Loofah’s documentation for more information about defining custom Loofah::Scrubber objects.
To set the default allowed tags or attributes across your application:
# In config/application.rb
config.action_view.sanitized_allowed_tags = ['strong', 'em', 'a']
config.action_view.sanitized_allowed_attributes = ['href', 'title']
Source: show
# File<EMAIL_ADDRESS>line 83 def sanitize(html, options = {}) self.class.white_list_sanitizer.sanitize(html, options).try(:html_safe) end
Sanitizes a block of CSS code. Used by sanitize
when it comes
across a style attribute.
Source: show
# File<EMAIL_ADDRESS>line 88 def sanitize_css(style) self.class.white_list_sanitizer.sanitize_css(style) end
Strips all link tags from html
leaving just the link text.
strip_links('Ruby on Rails')
# => Ruby on Rails
strip_links('Please e-mail me at <a href="mailto:me<EMAIL_ADDRESS># => Please e-mail me at<EMAIL_ADDRESS>strip_links('Blog: Visit.')
# => Blog: Visit.
Source: show
# File<EMAIL_ADDRESS>line 116 def strip_links(html) self.class.link_sanitizer.sanitize(html) end
Strips all HTML tags from html
, including comments.
strip_tags("Strip these tags!")
# => Strip these tags!
strip_tags("Bold no more! See more here...")
# => Bold no more! See more here...
strip_tags("
Welcome to my website!
")
# => Welcome to my website!
Source: show


Use all the models to predict corresponding concepts and objects in the image.
Please note! The uploaded image is used to predict corresponding concepts using each model expicitly. It is possible, expected and very likely to see vague enteries in some models, given how relevant the image is to that model. This is done to showcase all the models in one go.
This model identifies objects/concepts that are are not usually fit for public forums and may require moderation.
It looks for things such as gore, drungs, expicit or suggestive nudity.
Also, to showcase the use of this model more effectively, the image is blurred if it is found to contain any above mentioned things. This shows that manual moderation may very well be replaced with an automated system.
This model can predict the dish, cuisine, and even the food items down to ingridients level. This may be used in apps like Zomato, Swiggy, etc and any app that that involves food items (health & wellness apps, etc.).
The current dataset doesn't include Indian cuisines.
This model can predict the clothings, accessories, etc. This may be used in e-commerce sites/apps like Myntra, Flipkart, and fashion curation sites as well.
The model can, at the moment, predict close to 100 such concepts.
This model, as opposed to just face detection, analyzes and tries detecting the probable age, the gender, the ethnic/racial origin based on his/her appearance.
Given the nature of this model, it may be used by organizations relying on such information or collecting such data.


{% extends "skeleton/_body_md.html" %} {%hyde title: "Templates" navname: "Templates" created: 2011-04-18 modified: 2011-04-18 %} {% block content_md %} Templates are special files which have access to the different context variables and are rendered when the instance is created and upgraded. Templates also allow user to define extra variables which can be then accessed in all the template files. This for example allow you to define some application specific settings which can than be accessed in all the templates. 1. [Context Variables](#context_variables) 2. [Template Tags](#template_tags) 1. [Accessing a variable value](#accessing_variable_value) 2. [For loop](#for_loop) 3. [If statement](#if_statement)


Bakery Manager Admin Dashboard
Generate ReportProduced
Today
Sold
Today
Product
stock quantity
Count of
empty ingredients
Manage your
users
- Name: Surname: Is active:
- Deactivate Activate
Sold history
Sold Product per Month
Summary
Yesterday sales
PLN
Current Month total
PLN
Current month total + Backlog (stock products)
PLN
Sales this month Year Before
PLN


Web audio API example: takes audio input from microphone visulizes the waveform and spectrogram in real-time.


Preparation time
less than 30 mins
Cooking time
10 to 30 mins
Serves
Serves 6 - 8
This recipe takes the classic poached eggs with asparagus, and adds big parsley flavours and a scattering of bacon.
For the soup, fry the butter, shallots and thyme in a medium saucepan for two minutes.
Add the vegetable stock and bring to the boil.
Add the asparagus stalks, reserving the tips, and cook for a minute.
Add the parsley and watercress and simmer for two minutes, until the asparagus is just tender.
Remove from the heat and blend with a hand blender, or allow to cool for 3-4 minutes and place into a food blender. Blend the soup until smooth.
Return the soup to the hob over a medium heat and season, to taste, with salt and freshly ground black pepper.
Meanwhile, bring a pan of salted water to the boil, add the asparagus tips and simmer for 2-3 minutes until just tender.
Drain the asparagus tips and mix with a little olive oil, salt and black pepper.
For the garnish, preheat a deep fat fryer to 160C/325F, or alternatively heat vegetable oil in a deep sided, heavy based pan until a breadcrumb sizzles and turns golden-brown when dropped into it. (CAUTION: hot oil can be dangerous. Do not leave unattended.)
 Lift out with a slotted spoon and set aside on a plate.
Increase the temperature of the fat fryer to 190C/375F.
Return the pork rind to the fat fryer and fry for another 2-3 minutes until crisp. Lift out with a slotted spoon and drain onto kitchen paper. Sprinkle with salt.
In aa frying pan, fry the bacon until brown and crisp, then set aside onto kitchen paper to drain.
Bring a small pan of water to a gentle simmer then add the vinegar.
Stir the simmering water vigorously to create a vortex, then carefully crack two of the eggs into the water. Poach for 2-3 minutes, or until the eggs are cooked to your liking, then carefully remove from the pan using a slotted spoon and set aside to drain on kitchen paper.
Repeat the poaching process with the remaining two eggs.
Alternatively, if using later, drop the poached eggs straight into iced water and chill in the fridge until ready to use. Bring a pan of water to the boil, then add the poached eggs and cook for a minute just to heat through. Drain onto kitchen paper.
To serve, lay the asparagus tips into the centre of the soup plate then nestle the poached egg in the centre of the pile. Ladle in the soup around the edge then scatter over the pork scratchings and bacon. Finish by scattering the parsley and watercress cress over the top.


-
AI is broken
-
Slash dataset: A toy dataset that stymies most non-convolutional models.
TLDR
I’m releasing a dataset “Slash dataset” that is easy for convolutional neural nets to learn but difficult for most other models.
It will live here soph.info/data/slash_data.npz and will be licensed under a Creative Commons Attribution 4.0 International License.
Code for generating the dataset and replicating the analysis is at the bottom of this post. Cheers!
Background
The convolution is an extremely important mathematical operation. It’s frequently used in digital signal processing, including compression techniques for images and audio, and it’s the namesake and heart of “Convolutional Neural Network”.
But what actually is a convolution? There are a bunch of mesmerizing gifs like the one below, and there are some good explanations out there, but I think there’s room to improve.
I’m in the middle of a project to explain exactly what convolution does and, specifically, why it’s so useful for machine learning. As part of that effort, I’ve put together a toy dataset designed to be as simple as possible while also being difficult for all models except for convolutional ones.
Data
The dataset includes 1,000 images, each of which include a slash. It is divided into two classes according to whether the slash is downward facing (like the backslash character “”) or upward facing (like the forward slash ). Below is a random selection along with class labels.
Each image is 30x30 grayscale pixels. I’ve added a little bit of noise to the images to make things interesting.
Results
1 4 5 6 2 3 0 7 8 Model Random Forest SVM - Linear SVM - RBF kernel SVM - Polynomial kernel Logistic Regression K Nearest Neighbors KNN, k=1 Vanilla CNN Custom CNN Trainable params 9183<PHONE_NUMBER>00 675000 901<PHONE_NUMBER>00 1041 80 Train score 1 0.994667 0.838667 0.84 0.98 0.842667 1 1 1 Test score 0.472 0.5 0.532 0.532 0.54 0.604 0.908 1 1 Hopefully by now, you’re looking at this dataset and thinking “gee, that doesn’t look too difficult”. This would be a boring task for any human and it might be difficult at first to see why it is so challenging for most models.
I’m going to save a detailed explanation of exactly why that is for later. For now, I want to share my results: I tested out several off-the-shelf models as well as two convolutional models.
Of the off-the-shelf models, I didn’t waste my time tuning most of them, as I feel quite confident that even when tuned they would do poorly. (I’d love to be proved wrong on that 😉.) The one that I did tune was K-Nearest Neighbors, so you’ll see a result for one with the default parameters as well as one with the best performing parameters ($k=1$).
All of the models perform at or close enough to chance (50%) to dismiss them. KNN does pretty well when we tune it (90% test accuracy) but this performance looks less impressive when we note that KNN is memorizing the entire dataset and then comparing new examples to memorized ones. That strategy is not one that we would expect to hold up in the real world where data is much bigger than a few hundred B&W thumbnail images. Because of this, I’ve listed the trainable parameters for each model.
On the other hand, the ConvNets both get perfect scores on both train and test sets. The Vanilla CNN uses the same cookie-cutter structure that I would apply as a first attempt on just about any image data (two rounds of Convolution →Batch Normalization→Pooling). This Vanilla CNN is not tuned to the particular dataset. The Custom CNN is one I built and hand-tuned to demonstrate how ConvNets can really punch above their weight in terms of parameters. This architecture was pretty fragile (tweaking the numbers slightly is likely to break it) when testing but it’s mostly there to make a point.
Methods
All code used to generate this dataset is included below. Be on the lookout for more Convolution content shortly!
-
An opinionated guide to imbalanced classes
-
Building Generative Adversarial Networks In Tensorflow and Keras
-
How to make a gif in Colab
I wrote a little demo of how to make a gif in Colab. You can check it out below or you can open it and run it yourself by clicking on the “Open in Colab” button at the top.


DNP 3.0 Analyzer
DNP 3.0 Analyzer
Welcome! Enter the amazing world of DNP 3.0 protocol
×
Datagram Analysis
Datagram History


Doubly Linked-list implementation.
qlist container is a doubly Linked-List implementation. qlist provides uniformly named methods to add, get, pop and remove an element at the beginning and end of the list. These operations allow qlist to be used as a stack, queue, or double-ended queue.
[Conceptional Data Structure Diagram] last~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~+ | +-----------+ doubly +-----------+ doubly +-|---------+ first~~~|~> 0 <~|~~~~~~~~~~|~> 1 <~|~~~~~~~~~~|~> N | +-----|-----+ linked +-----|-----+ linked +-----|-----+ | | | +-----v---------------+ | +-----v-----+ | DATA A | | | DATA N | +---------------------+ | +-----------+ +---------------------v------------------+ | DATA B | +----------------------------------------+
// create a list. qlist_t *list = qlist(); // insert elements list->addlast(list, "e1", sizeof("e1")); list->addlast(list, "e2", sizeof("e2")); list->addlast(list, "e3", sizeof("e3")); // get char *e1 = (char*)list->getfirst(list, NULL, true)); // malloced char *e3 = (char*)list->getat(list, -1, NULL, false)); // no malloc (...omit...) free(e1); // pop (get and remove) char *e2 = (char*)list->popat(list, 1, NULL)); // get malloced copy (...omit...) free(e2); // debug output list->debug(list, stdout, true); // travesal qdlobj_t obj; memset((void*)&obj, 0, sizeof(obj)); // must be cleared before call list->lock(list); while (list->getnext(list, &obj, false) == true) { printf("DATA=%s, SIZE=%zu\n", (char*)obj.data, obj.size); } list->unlock(list); // free object list->free(list);
Functions | |
| qlist_t * | qlist (void) |
| Create new qlist_t linked-list container. | |
| static size_t | setsize (qlist_t *list, size_t max) |
| qlist->setsize(): Limit maximum number of elements allowed in this list. | |
| static bool | addfirst (qlist_t *list, const void *data, size_t size) |
| qlist->addfirst(): Inserts a element at the beginning of this list. | |
| static bool | addlast (qlist_t *list, const void *data, size_t size) |
| qlist->addlast(): Appends a element to the end of this list. | |
| static bool | addat (qlist_t *list, int index, const void *data, size_t size) |
| qlist->addat(): Inserts a element at the specified position in this list. | |
| static void * | getfirst (qlist_t *list, size_t *size, bool newmem) |
| qlist->getfirst(): Returns the first element in this list. | |
| static void * | getlast (qlist_t *list, size_t *size, bool newmem) |
| qlist->getlast(): Returns the last element in this list. | |
| static void * | getat (qlist_t *list, int index, size_t *size, bool newmem) |
| qlist->getat(): Returns the element at the specified position in this list. | |
| static bool | getnext (qlist_t *list, qdlobj_t *obj, bool newmem) |
| qlist->getnext(): Get next element in this list. | |
| static void * | popfirst (qlist_t *list, size_t *size) |
| qlist->popfirst(): Returns and remove the first element in this list. | |
| static void * | poplast (qlist_t *list, size_t *size) |
| qlist->getlast(): Returns and remove the last element in this list. | |
| static void * | popat (qlist_t *list, int index, size_t *size) |
| qlist->popat(): Returns and remove the element at the specified position in this list. | |
| static bool | removefirst (qlist_t *list) |
| qlist->removefirst(): Removes the first element in this list. | |
| static bool | removelast (qlist_t *list) |
| qlist->removelast(): Removes the last element in this list. | |
| static bool | removeat (qlist_t *list, int index) |
| qlist->removeat(): Removes the element at the specified position in this list. | |
| static size_t | size (qlist_t *list) |
| qlist->size(): Returns the number of elements in this list. | |
| static size_t | datasize (qlist_t *list) |
| qlist->size(): Returns the sum of total element size. | |
| static void | reverse (qlist_t *list) |
| qlist->reverse(): Reverse the order of elements. | |
| static void | clear (qlist_t *list) |
| qlist->clear(): Removes all of the elements from this list. | |
| static void * | toarray (qlist_t *list, size_t *size) |
| qlist->toarray(): Returns the serialized chunk containing all the elements in this list. | |
| static char * | tostring (qlist_t *list) |
| qlist->tostring(): Returns a string representation of this list, containing string representation of each element. | |
| static bool | debug (qlist_t *list, FILE *out) |
| qlist->debug(): Prints out stored elements for debugging purpose. | |
| static void | lock (qlist_t *list) |
| qlist->lock(): Enters critical section. | |
| static void | unlock (qlist_t *list) |
| qlist->unlock(): Leaves critical section. | |
| static void | free_ (qlist_t *list) |
| qlist->free(): Free qlist_t. |
| qlist_t* qlist | ( | void | ) |
Create new qlist_t linked-list container.
| errno | will be set in error condition. -ENOMEM : Memory allocation failure. |
qlist_t *list = qlist();
| static size_t setsize | ( | qlist_t * | list, | |
| size_t | max | |||
| ) | [static] |
qlist->setsize(): Limit maximum number of elements allowed in this list.
| list | qlist_t container pointer. | |
| max | maximum number of elements. 0 means no limit. |
| static bool addfirst | ( | qlist_t * | list, | |
| const void * | data, | |||
| size_t | size | |||
| ) | [static] |
qlist->addfirst(): Inserts a element at the beginning of this list.
| list | qlist_t container pointer. | |
| data | a pointer which points data memory. | |
| size | size of the data. |
| errno | will be set in error condition.
|
// create a sample object. struct my_obj obj; // create a list and add the sample object. qlist_t *list = qlist(); list->addfirst(list, &obj, sizeof(struct my_obj));
| static bool addlast | ( | qlist_t * | list, | |
| const void * | data, | |||
| size_t | size | |||
| ) | [static] |
qlist->addlast(): Appends a element to the end of this list.
| list | qlist_t container pointer. | |
| data | a pointer which points data memory. | |
| size | size of the data. |
| errno | will be set in error condition.
|
| static bool addat | ( | qlist_t * | list, | |
| int | index, | |||
| const void * | data, | |||
| size_t | size | |||
| ) | [static] |
qlist->addat(): Inserts a element at the specified position in this list.
| list | qlist_t container pointer. | |
| index | index at which the specified element is to be inserted. | |
| data | a pointer which points data memory. | |
| size | size of the data. |
| errno | will be set in error condition.
|
first last new
Linked-list [ A ]<=>[ B ]<=>[ C ]?==?[ ]
(positive index) 0 1 2 3
(negative index) -3 -2 -1
qlist_t *list = qlist(); list->addat(list, 0, &obj, sizeof(obj)); // same as addfirst(). list->addat(list, -1, &obj, sizeof(obj)); // same as addlast().
| static void* getfirst | ( | qlist_t * | list, | |
| size_t * | size, | |||
| bool | newmem | |||
| ) | [static] |
qlist->getfirst(): Returns the first element in this list.
| list | qlist_t container pointer. | |
| size | if size is not NULL, element size will be stored. | |
| newmem | whether or not to allocate memory for the element. |
| errno | will be set in error condition.
|
size_t size; void *data = list->getfirst(list, &size, true); if (data != NULL) { (...omit...) free(data); }
| static void* getlast | ( | qlist_t * | list, | |
| size_t * | size, | |||
| bool | newmem | |||
| ) | [static] |
qlist->getlast(): Returns the last element in this list.
| list | qlist_t container pointer. | |
| size | if size is not NULL, element size will be stored. | |
| newmem | whether or not to allocate memory for the element. |
| errno | will be set in error condition. ENOENT : List is empty. ENOMEM : Memory allocation failure. |
| static void* getat | ( | qlist_t * | list, | |
| int | index, | |||
| size_t * | size, | |||
| bool | newmem | |||
| ) | [static] |
qlist->getat(): Returns the element at the specified position in this list.
| list | qlist_t container pointer. | |
| index | index at which the specified element is to be inserted | |
| size | if size is not NULL, element size will be stored. | |
| newmem | whether or not to allocate memory for the element. |
| errno | ||
| errno | will be set in error condition. -ERANGE : Index out of range. -ENOMEM : Memory allocation failure. |
first last Linked-list [ A ]<=>[ B ]<=>[ C ] (positive index) 0 1 2 (negative index) -3 -2 -1
| static bool getnext | ( | qlist_t * | list, | |
| qdlobj_t * | obj, | |||
| bool | newmem | |||
| ) | [static] |
qlist->getnext(): Get next element in this list.
| list | qlist_t container pointer. | |
| obj | found data will be stored in this structure | |
| newmem | whether or not to allocate memory for the element. |
| errno | will be set in error condition. -ENOENT : No next element. -ENOMEM : Memory allocation failure. |
qlist_t *list = qlist(); (...add data into list...) qdlobj_t obj; memset((void*)&obj, 0, sizeof(obj)); // must be cleared before call list->lock(list); // can be omitted in single thread model. while (list->getnext(list, &obj, false) == true) { printf("DATA=%s, SIZE=%zu\n", (char*)obj.data, obj.size); } list->unlock(list); // release lock.
| static void* popfirst | ( | qlist_t * | list, | |
| size_t * | size | |||
| ) | [static] |
qlist->popfirst(): Returns and remove the first element in this list.
| list | qlist_t container pointer. | |
| size | if size is not NULL, element size will be stored. |
| errno | will be set in error condition. -ENOENT : List is empty. -ENOMEM : Memory allocation failure. |
| static void* poplast | ( | qlist_t * | list, | |
| size_t * | size | |||
| ) | [static] |
qlist->getlast(): Returns and remove the last element in this list.
| list | qlist_t container pointer. | |
| size | if size is not NULL, element size will be stored. |
| errno | will be set in error condition. -ENOENT : List is empty. -ENOMEM : Memory allocation failure. |
| static void* popat | ( | qlist_t * | list, | |
| int | index, | |||
| size_t * | size | |||
| ) | [static] |
qlist->popat(): Returns and remove the element at the specified position in this list.
| list | qlist_t container pointer. | |
| index | index at which the specified element is to be inserted | |
| size | if size is not NULL, element size will be stored. |
| errno | will be set in error condition. -ERANGE : Index out of range. -ENOMEM : Memory allocation failure. |
first last Linked-list [ A ]<=>[ B ]<=>[ C ] (positive index) 0 1 2 (negative index) -3 -2 -1
| static bool removefirst | ( | qlist_t * | list | ) | [static] |
qlist->removefirst(): Removes the first element in this list.
| list | qlist_t container pointer. |
| errno | will be set in error condition. -ENOENT : List is empty. |
| static bool removelast | ( | qlist_t * | list | ) | [static] |
qlist->removelast(): Removes the last element in this list.
| list | qlist_t container pointer. |
| errno | will be set in error condition. -ENOENT : List is empty. |
| static bool removeat | ( | qlist_t * | list, | |
| int | index | |||
| ) | [static] |
qlist->removeat(): Removes the element at the specified position in this list.
| list | qlist_t container pointer. | |
| index | index at which the specified element is to be removed. |
| errno | will be set in error condition. -ERANGE : Index out of range. |
| static size_t size | ( | qlist_t * | list | ) | [static] |
qlist->size(): Returns the number of elements in this list.
| list | qlist_t container pointer. |
| static size_t datasize | ( | qlist_t * | list | ) | [static] |
qlist->size(): Returns the sum of total element size.
| list | qlist_t container pointer. |
| static void reverse | ( | qlist_t * | list | ) | [static] |
qlist->reverse(): Reverse the order of elements.
| list | qlist_t container pointer. |
| static void clear | ( | qlist_t * | list | ) | [static] |
qlist->clear(): Removes all of the elements from this list.
| list | qlist_t container pointer. |
| static void* toarray | ( | qlist_t * | list, | |
| size_t * | size | |||
| ) | [static] |
qlist->toarray(): Returns the serialized chunk containing all the elements in this list.
| list | qlist_t container pointer. | |
| size | if size is not NULL, chunk size will be stored. |
| errno | will be set in error condition. -ENOENT : List is empty. -ENOMEM : Memory allocation failure. |
| static char* tostring | ( | qlist_t * | list | ) | [static] |
qlist->tostring(): Returns a string representation of this list, containing string representation of each element.
| list | qlist_t container pointer. |
| errno | will be set in error condition. -ENOENT : List is empty. -ENOMEM : Memory allocation failure. |
| static bool debug | ( | qlist_t * | list, | |
| FILE * | out | |||
| ) | [static] |
qlist->debug(): Prints out stored elements for debugging purpose.
| list | qlist_t container pointer. | |
| out | output stream FILE descriptor such like stdout, stderr. |
| errno | will be set in error condition. -EIO : Invalid output stream. |
| static void lock | ( | qlist_t * | list | ) | [static] |
qlist->lock(): Enters critical section.
| list | qlist_t container pointer. |
| static void unlock | ( | qlist_t * | list | ) | [static] |
qlist->unlock(): Leaves critical section.
| list | qlist_t container pointer. |
| static void free_ | ( | qlist_t * | list | ) | [static] |
qlist->free(): Free qlist_t.
| list | qlist_t container pointer. |


The animals have all gotten out of their cages, and you have to get through the enclosures to get to the zoo-keeper’s house to help him get the animals back!
Along the way you will learn facinating facts about the areas of the zoo as you arrive.
Pick your character below to start playing!


We are an all around design and branding agency specializing in creative design with use of the latest digital technology. With our really creative and positive team we take on any assignment, from logo design to concept campains or web development. Find out what we can do for you !


module-info.java
on packages which are not exported from named modules.
Such configuration may occur when some modules in the project are already migrated to Java modules but others are still non-modular. By analogy to the JDK, such non-modular code should not get access to the code in named modules which is not explicitly exported.


In this project we will focus on the topic of inspirational people. We want to motivate everyone to develop their creative potential, to talk about successful practical cases and to provide information that will help start their projects and implement ideas.
Why?
Because, first, we consider creativity as a driving force for the development of modern society. We are convinced that this is not an abstract concept, but a characteristic inherent in every person from birth. We just need to develop.
Secondly, because it is interesting to our readers. Leader stories have always attracted the greatest interest in the audience. We decided to talk about it even more.
Third, because it's a challenge.


| 002:006 Khan | : |
Verily, those who disbelieve, it is the same to them whether you (O Muhammad Peace be upon him ) warn them or do not warn them, they will not believe. |
| 002:006 Maulana | : |
Those who disbelieve , it being alike to them whether thou warn them or warn them not , they will not believe. |
| 002:006 Pickthal | : |
As for the Disbelievers, Whether thou warn them or thou warn them not it is all one for them; they believe not. |
| 002:006 Rashad | : |
As for those who disbelieve, it is the same for them; whether you warn them, or not warn them, they cannot believe. |
| 002:006 Sarwar | : |
Those who deny your message will not believe whether you warn them or not. |
| 002:006 Shakir | : |
Surely those who disbelieve, it being alike to them whether you warn them, or do not warn them, will not believe. |
| 002:006 Sherali | : |
Those who have disbelieved - it being alike to them whether thou warn them or warn them not - they will not believe. |
| 002:006 Yusufali | : |
As to those who reject Faith, it is the same to them whether thou warn them or do not warn them; they will not believe. |
Concepts/Themes Covered: أركان الإسلام > التوحيد > الكافرون > صفات الكفار Pillars of Islam> Islamic> The Disbelievers> The Characteristics of the Disbelievers الإيمان > الإيمان بالله > حقيقة الإيمان Faith> Belief In Allah> The Reality of Faith الإيمان > الإيمان بالله > الهداية إلى الإيمان Faith> Belief In Allah> Guiding to Faith العمل > العمل الصالح > العمل المفضي إلى النجاح Action(Work)> The Good Deeds>Achtion which Leads to Success
This tool uses eight English parallel translations for the Holy Quran imported from Mr. Clay Smith. You can also visit Qurany Concepts for browsing the Quran themes or topics. If you want to search the Holy Quran for keywords, please visit Qurany Keywords.


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| /**
* Copyright 2017 Google Inc.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
import * as firestore from '@firebase/firestore-types';
/**
* Error Codes describing the different ways Firestore can fail.  */
OK: 'ok' as Code,
/** The operation was cancelled (typically by the caller). */
CANCELLED: 'cancelled' as Code,
/** Unknown error or an error from a different error domain. */
UNKNOWN: 'unknown' as Code,
/**
* Client specified an invalid argument. Note that this differs from
* FAILED_PRECONDITION. INVALID_ARGUMENT indicates arguments that are
* problematic regardless of the state of the system (e.g., a malformed file
* name).
*/
INVALID_ARGUMENT: 'invalid-argument' as Code,
/**
* Deadline expired before operation could complete. For operations that
* change the state of the system, this error may be returned even if the
* operation has completed successfully. For example, a successful response
* from a server could have been delayed long enough for the deadline to
* expire.
*/
DEADLINE_EXCEEDED: 'deadline-exceeded' as Code,
/** Some requested entity (e.g., file or directory) was not found. */
NOT_FOUND: 'not-found' as Code,
/**
* Some entity that we attempted to create (e.g., file or directory) already
* exists.
*/
ALREADY_EXISTS: 'already-exists' as Code,
/**
* The caller does not have permission to execute the specified operation.
* PERMISSION_DENIED must not be used for rejections caused by exhausting
* some resource (use RESOURCE_EXHAUSTED instead for those errors).
* PERMISSION_DENIED must not be used if the caller can not be identified
* (use UNAUTHENTICATED instead for those errors).
*/
PERMISSION_DENIED: 'permission-denied' as Code,
/**
* The request does not have valid authentication credentials for the
* operation.
*/
UNAUTHENTICATED: 'unauthenticated' as Code,
/**
* Some resource has been exhausted, perhaps a per-user quota, or perhaps the
* entire file system is out of space.
*/
RESOURCE_EXHAUSTED: 'resource-exhausted' as Code,
/**
* Operation was rejected because the system is not in a state required for
* the operation's execution. For example, directory to be deleted may be
* non-empty, an rmdir operation is applied to a non-directory, etc.
*
* A litmus test that may help a service implementor in deciding
* between FAILED_PRECONDITION, ABORTED, and UNAVAILABLE:
* (a) Use UNAVAILABLE if the client can retry just the failing call.
* (b) Use ABORTED if the client should retry at a higher-level
* (e.g., restarting a read-modify-write sequence).
* (c) Use FAILED_PRECONDITION if the client should not retry until
* the system state has been explicitly fixed. E.g., if an "rmdir"
* fails because the directory is non-empty, FAILED_PRECONDITION
* should be returned since the client should not retry unless
* they have first fixed up the directory by deleting files from it.
* (d) Use FAILED_PRECONDITION if the client performs conditional
* REST Get/Update/Delete on a resource and the resource on the
* server does not match the condition. E.g., conflicting
* read-modify-write on the same resource.
*/
FAILED_PRECONDITION: 'failed-precondition' as Code,
/**
* The operation was aborted, typically due to a concurrency issue like
* sequencer check failures, transaction aborts, etc.
*
* See litmus test above for deciding between FAILED_PRECONDITION, ABORTED,
* and UNAVAILABLE.
*/
ABORTED: 'aborted' as Code,
/**
* Operation was attempted past the valid range. E.g., seeking or reading
* past end of file.
*
* Unlike INVALID_ARGUMENT, this error indicates a problem that may be fixed
* if the system state changes. For example, a 32-bit file system will
* generate INVALID_ARGUMENT if asked to read at an offset that is not in the
* range [0,2^32-1], but it will generate OUT_OF_RANGE if asked to read from
* an offset past the current file size.
*
* There is a fair bit of overlap between FAILED_PRECONDITION and
* OUT_OF_RANGE. We recommend using OUT_OF_RANGE (the more specific error)
* when it applies so that callers who are iterating through a space can
* easily look for an OUT_OF_RANGE error to detect when they are done.
*/
OUT_OF_RANGE: 'out-of-range' as Code,
/** Operation is not implemented or not supported/enabled in this service. */
UNIMPLEMENTED: 'unimplemented' as Code,
/**
* Internal errors. Means some invariants expected by underlying System has
* been broken. If you see one of these errors, Something is very broken.
*/
INTERNAL: 'internal' as Code,
/**
* The service is currently unavailable. This is a most likely a transient
* condition and may be corrected by retrying with a backoff.
*
* See litmus test above for deciding between FAILED_PRECONDITION, ABORTED,
* and UNAVAILABLE.
*/
UNAVAILABLE: 'unavailable' as Code,
/** Unrecoverable data loss or corruption. */
DATA_LOSS: 'data-loss' as Code
};
/**
* An error class used for Firestore-generated errors. Ideally we should be
* using FirebaseError, but integrating with it is overly arduous at the moment,
* so we define our own compatible error class (with a `name` of 'FirebaseError'
* and compatible `code` and `message` fields.)
*/
export class FirestoreError extends Error implements firestore.FirestoreError {
name = 'FirebaseError';
stack?: string;
constructor(readonly code: Code, readonly message: string) {
super(message);
// HACK: We write a toString property directly because Error is not a real
// class and so inheritance does not work correctly. We could alternatively
// do the same "back-door inheritance" trick that FirebaseError does.
this.toString = () => `${this.name}: [code=${this.code}]: ${this.message}`;
}
}
|


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Advancement of technology at a rapid pace has yielded new forms of waging wars. With connectivity being constantly upgraded and computers integrating into our evryday life at an increasing speed, our dependency on technology and connectivity has grown drastically. And in this vulnerability, cyber attacks seek shelter. Motivated to gain information, deny service, sway public mentality in a desired direction supporting a political propaganda or even access your personal accounts. When it's targeted at an individual or a small group, its a common case of Cybercrime. But when the world nations and multinational companies get involved, it calls for The Committee for Control and Regulation of Cyberspace.
As we stand today witnessing rising policies of anonymity on the Internet, we are always under the eyes of someone. Is absolute anonymity a fairy-tale? Clearly isn't. Welcome to the Dark Web. Absolutely no ears, no eyes, seriously. That's when it turns shady. As much a liberating tool it is, also is open doors to crime. Can we stop it? No we can't. Can we minimize it? Yes we can. This year, at the simulation for Committee for Legalization of Banned Technologies discuss "Establishing frameworks for the Regulation of Dark Web".
Humanity as we know it is changing rapidly. But is this change all for the better? With development at rates unheard of before and innovation in all spheres known to us, we seem to have forgotten what makes this progress possible - Earth. All this advancement comes at the cost of the decline of the environment and the world we live in. Nobody is a stranger to terms like climate change and global warming which makes our next committee, the Committee on Renewable Energy and Sustainable Development (CRESD) of paramount importance in the modern world. Discussing the feasibility and implementation of non-conventional sources of energy, this committee promises to be challenging and will keep you on your toes throughout the conference.
For the past decade or so, there has been one common objective for nearly every country in the world - Oil monopoly. So when speculations emerged about oil reserves in the Arctic, it came to no surprise that a multitude of nations began one of the most heated territorial disputes in today’s world. Who owns the Arctic? Presenting the final committee of the conference, the Continuous Crisis Committee (CCC), where a multifarious group of nations come together to resolve once and for all, this intense, exhilarating emerging crisis. Demanding the perfect balance between diplomacy and action, make sure to keep your thinking hats on, to face every dilemma thrown your way.


Many modern autonomous systems (such as autonomous cars or robots1) rely on “reward functions”, which encode the robot’s desired behavior; in other words, reward functions effectively tell a robot, in any given situation, which action is good and which is bad.
Today, in practice, reward functions are designed by hand via an iterative process of trial-and-error. Such an approach is not only time-consuming but, more concerningly, very prone to failures. For example, consider a simple autonomous driving task:
It may seem reasonable to choose a reward function that incentivizes the robot to maintain a high speed on the road; however, when an autonomous car is provided with said reward function, it instead does the following: (Credit: Mat Kelcey)
The approach of hand-designing reward functions scales poorly with the complexity of the system: as robots and their tasks become more complex, it becomes increasingly more difficult for a system designer to design a reward function that encodes good behavior in every possible setting the robot may encounter2. Thus, if we want to deploy robots in the infinitely-complex real world, we’re going to need a better approach.
One such approach is to learn the reward function directly from humans. This can be done in a variety of ways: by having the human demonstrate the task to the robot, by querying the human directly to learn their preferences, by having humans physically provide feedback to the robot while it is operating, etc. In reward learning, and more generally in problems that involve learning from humans, we typically pick one such mode of feedback and use data of that form when learning. In this work however, we investigate, in the specific context of reward learning, how we can _combine multiple modes of human feedback to learn from humans more effectively. _But first, we discuss some current state-of-the-art approaches to reward learning.
Inverse Reinforcement Learning. Inverse Reinforcement Learning (IRL)3 is one popular approach for learning reward functions. IRL leverages the idea that while a human may not be able to specify a reward function for the task, they can instead show the robot how to perform the task. In IRL, a human provides demonstrations of the robot’s desired behavior on the specified task; the algorithm then infers a reward function from these demonstrations. For example, in autonomous driving, the human may provide demonstrations of them driving a car themselves; or in robotics, the expert human may provide demonstrations by teleoperating the robot using a controller.
IRL can work remarkably well when the demonstrations are of high-quality. However, this is rarely the case in robotics. Consider, for example, the simple task below where the robot needs to smoothly move its arm to the black pad while avoiding the obstacle (as in the marked blue line):
We tasked several users with providing demonstrations on this task using a simple joystick controller. One such user’s demonstration is shown below:
The above user had extensive prior experience with controlling robots, but despite this, we can see that (1) the user doesn’t manage to fully get the robot’s arm to the goal and that (2) his demonstration is extremely choppy. Other users remarked about the system:
- “I had a hard time controlling the robot.”
- “[It] would be nice if the controller for the [robot] was easier to use”
It is difficult to control complex robots with many joints using intuitive joystick controllers since the robot has many more degrees-of-freedom (i.e., joints) than the joystick does. While more complex controllers may resolve this issue, people still struggle to provide high-quality demonstrations with such controllers since they are unintuitive to use. So, if people cannot provide effective good demonstrations, how else can we learn the reward function?
Preference-Based Learning. In such situations where users cannot provide informative demonstrations, we can instead learn about the true reward function by using structured queries to elicit that information directly from the user. One such method for doing this, known as preference-based learning, queries the user for her preference between two examples of the robot attempting the task. Each of the user’s responses encode some information about the true reward function. Therefore, by repeatedly querying the user we can infer the true reward function. For example, in the robotics task discussed above, the user may be presented with the following two trajectories in a preference query:
While neither trajectory perfectly accomplishes the task, the trajectory on top gets much closer to the goal and so we would expect the human to pick that one. This process of generating a pair of trajectories and querying the human for her preference between them is repeated many times until the algorithm can accurately infer a reward function for the robot.
It is much easier for a human to accurately respond to a preference query, i.e., choose between two trajectories, than to provide a full-length demonstration. Thus, we expect the human input in preference-based learning to be of very high quality; as a result, we expect the learned reward function to accurately reflect the human’s desired behavior for the robot.
However, preference-based learning methods face several challenges of their own. Most notably, these methods tend to be very data-inefficient. Each preference query gives us little information about the true reward function, especially when compared to a full-length demonstration. Since the space of all possible reward functions is very large and complex, it often takes many, many preference queries – and therefore, a lot of human time – to find the true reward function with preference-based learning.
Our key insight is that preference queries are comparatively more accurate but less informative than demonstrations. In our work, we leverage this insight to derive a new framework for reward learning, DemPref. DemPref, as the name suggests, utilizes both demonstrations and preferences to learn an accurate reward function efficiently by amplifying each of their respective benefits and dampening their respective drawbacks.
DemPref
In DemPref, we learn the reward function in two stages. In the first stage, the human provides demonstrations, which are used to construct a probabilistic distribution over reward functions. This can be done in several ways, but we choose to do so using Bayesian IRL4, where we learn a distribution by performing a Bayesian update over all possible reward functions ( here denotes a given reward function, and denotes the th human demonstration):
This distribution can be thought of as a prior over reward functions, which upweights those reward functions more likely to be the true reward function while downweighting those less likely. Since the demonstrations are imperfect, this prior over reward functions will be imperfect as well; however, this prior still contains valuable information about the true reward function and allows us to effectively shrink the space of possible reward functions.
Hence, in the second stage of DemPref, we can use a preference-based learning algorithm with this prior to find the true reward function much more quickly than if were starting with no prior, since we now only have to search over a much smaller space of possible reward functions. Many different preference-based learning algorithms can be used here, and we choose to use an active preference-based learning algorithm based on the maximum volume removal method5; this algorithm attempts to generate at each step, the query that is most informative about the true reward function and therefore generally requires fewer queries than other methods.
By specifically avoiding using preference queries at the initial stages and demonstrations at the latter stages, our method diminishes the impact of their relatively uninformative and inaccurate nature respectively. In other words, we leverage (1) the information-rich nature of demonstrations to speed up the process of learning the reward function and (2) the accurate nature of preference queries to ensure that the reward function we learn is accurate.
Our simulated experimental results show that using DemPref significantly reduces the number of queries required to learn the true reward function, when compared to the same preference-based learning algorithm that doesn’t use any demonstrations.
Evaluation of DemPref in simulation. Here, is the number of demonstrations used in DemPref; corresponds to standard preference-based learning. measures how close the learned reward function is to the true reward function; implies that the learned reward function is exactly the true reward function.
Dealing with non-convexity
One challenge with the active preference-based learning algorithm we use is that it generates the queries at each step by approximately solving a non-convex optimization problem: these problems are especially difficult to solve because they have many possible solutions which “look optimal” to optimization algorithms, but which are actually sub-optimal. Hence, for complex problems, the algorithm can end up generating a query in which neither option yields much information about the human’s true reward function. See, for example, the uninformative query below, which the algorithm generated for the robotics task from above:
Since neither trajectory reflects the desired behavior of the robot, it is very difficult for a human to choose between the two trajectories in a manner that is consistent with their internal reward function. Hence, the human’s answers to such queries can mislead the algorithm and significantly slow down the process of learning the true reward function. This is a common problem with such preference-based learning methods and was first identified by Bıyık and Sadigh (2018)6.
In our work, we derive a new preference-based learning algorithm to be used in conjunction with DemPref, that can alleviate this issue. In the standard DemPref framework, the demonstration is only useful in learning a prior to generate preference queries with. However, we instead retain the demonstration during the second phase, and use them to ground the preference queries: at each step, we let the human choose between the two generated trajectories or the demonstration she initially provided. If the human chooses one of the generated trajectories, that trajectory will take the place of the demonstration in the next set of preference queries. This ensures that there is always at least one trajectory in the query that is reasonably reflective of the human’s true reward function:
We can think of this preference-based learning method as iteratively finding better trajectories, starting from the initial demonstration; hence, we refer to this preference-based learning method as Iterated Correction (IC).
Rankings instead of preferences
In IC, when the human has to respond to a preference query, she now has to pick between three trajectories, instead of two. However, by only asking the human to pick her most preferred of the three trajectories, we are discarding valuable information since the human’s preference between the remaining two trajectories also contains information about the true reward function. Hence, in our algorithm, we instead allow the human to rank all three trajectories, instead of simply specifying her preference between them. \
Our simulation experiments show that DemPref with IC significantly reduces the number of queries required to learn the true reward function, over standard DemPref:
The following diagram summarizes our full algorithm:
User study. Thus far we have shown that DemPref is indeed more sample efficient than prior preference based learning algorithms, but have yet to demonstrate that it allows for more accurate learning over prior IRL algorithms. A common application for IRL is enabling humans to teach real, physical robots to accomplish specific tasks7. Hence, to investigate whether DemPref was more effective than IRL at learning reward functions from real humans for this application, we ran a user study with 15 participants. Each of the users were tasked at training the robot on the same robotics task discussed above using both DemPref and IRL. The learned reward function was then used to train the robot on the same task, but in a slightly different domain. (Below, the training environment is shown on the left, while the testing environment is shown on the right. Desired trajectories are shown in blue in both diagrams.)
Below, we show an example (filmed during a user study) of the behavior of the IRL robot (top) and the DemPref robot (bottom):
Here, the IRL robot tries so hard to avoid the obstacle that it doesn’t even bother with trying to reach the goal; this behavior was induced by the low-quality demonstrations received from the user. In contrast, the DemPref robot did not suffer from these issues, since the feedback received from preference queries were of a high-quality.
This was not an isolated incident; many users encountered similar issues with the IRL robot. When the users were asked to rate the behavior of the robots trained with DemPref and the robot trained with IRL on the new task, the users overwhelmingly rated the DemPref robot as being better at the task than the IRL robot. Additionally, when asked about their preferences between the two methods for training the robot, the users also overwhelmingly preferred the DemPref robot over the IRL robot.
User study preferences among the IRL robot (grey) and DemPref robot (orange). The asterisk denotes statistically significant differences.
Future work
It is interesting to note that despite the challenges that users faced with controlling the robot, they did not rate the DemPref system as being “easier” to use than the IRL system.
This was largely due to the time-taken to generate each query in the preference-based learning stage. Recall that the preference-based learning algorithm generated queries by solving a non-convex (hard!) optimization problem. This optimization is fairly time-consuming; for example, in our task, it took roughly ~45 seconds to generate each query. Since each query is generated according to the human’s previous answers, the queries cannot be precomputed or parallelized either. Several users specifically referred to the time it took to generate each query as negatively impacting their experience with the DemPref system: “I wish it was faster to generate the preference [queries]”, “In overall human time the [IRL] system… actually took less time”, “The [DemPref system] will be even better if time cost is less.”
Additionally, one user expressed difficulty in evaluating the preference queries themselves, commenting that “It was tricky to understand/infer what the preferences were [asking]. Would be nice to communicate that somehow to the user (e.g. which [trajectory] avoids collision better)!” These are valid critiques of the framework, and indeed, we are currently investigating approaches to (a) generate preferences more quickly via distributed optimization and (b) generate more interpretable preferences as directions for future work.
Conclusion
In this work, we showed that we can learn reward functions more efficiently and accurately by leveraging multiple modes of human feedback in such a way that amplifies their respective strengths while dampening their respective weaknesses. While our work was grounded in the context of reward learning, we are optimistic about the potential that this fundamental idea – of leveraging data from multiple modes of human feedback in a structured manner – holds more broadly for the field of learning from humans. We believe that, applied correctly, this idea can improve the process of learning from humans greatly, in a large number of domains.
This blog post is based on the following paper that will appear at RSS 2019:
Learning Reward Functions by Integrating Human Demonstrations and Preferences. Malayandi Palan*, Nicholas C. Landolfi*, Gleb Shevchuk, Dorsa Sadigh. RSS, 2019. PDF
* Denotes equal contribution.
This project was partially supported by grants from Toyota Research Institute and Future of Life Institute. We are grateful for the helpful feedback provided by Michelle Lee and Andrey Kurenkov.
-
We henceforth use the term robots to refer generically to an autonomous system. ↩
-
See here for an extensive list of well-known examples where hand-designed reward functions fail (sometimes catastrophically). ↩
-
Often referred to as Learning from Demonstrations (LfD), and closely related to Imitation Learning. ↩
-
Ramachandran, Deepak, and Eyal Amir. “Bayesian Inverse Reinforcement Learning.” IJCAI, 2007. ↩
-
Sadigh, Dorsa, Anca D. Dragan, Shankar Sastry, and Sanjit A. Seshia. “Active preference-based learning of reward functions.” RSS, 2017. ↩
-
Bıyık, Erdem, and Dorsa Sadigh. “Batch Active Preference-Based Learning of Reward Functions.” CORL, 2018. ↩
-
Usually, it would not be practical to run a preference-based learning algorithm on a real, high degree-of-freedom robot such as a manipulator, as the algorithm would be too slow; however, using DemPref, we are able to learn reward functions on real robots due to the efficiency gains from additionally using demonstrations. ↩


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Rewrite of Array.Copy fast path in C# by jkotas · Pull Request #27634 · dotnet/coreclr Contributes to #27106
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The history of the GC configs | .NET Blog Recently, Nick from Stack Overflow tweeted about his experience of using the .NET Core GC configs – he seemed quite happy with them (minus the fact they are not documented well which is something I’m talking to our doc folks about).
Blazor Component Lifecycle Diagram Blazor Component Lifecycle Diagram.pdf Get familiar with basic Blazor (Razor) Component Lifestyle Methods: SetParametersAsync OnInitialized, OnInitializedAsync OnParametersSet, OnParametersSetAsync…
07 Political and controversial matters The Communications Act 2003 prohibits political advertising. The term "political" is used in the Code in a wider sense than "party political". The prohibition includes, for example, campaigning for the purposes of influencing legislation or executive acti...
One solution (setup) to rule them all – Blazor Webassembly, Blazor Server, Blazor Electron Hello my dear friends and Blazorians :) This post will be a follow up on the previous post – Make it all dynamic in BLAZOR – Routing, Pages and Components But before we start, I want to give …
Credential stuffing explained: How to prevent, detect and mitigate The automated use of breached usernames and passwords to access accounts is low risk, high reward for cybercriminals. Here's how to make it harder for them to use credential stuffing.


--- layout: default title: Vizutil.js is a utility library for working with basic 2D geometry. ---
Utility methods for working with geopositioning (maps). Note: Most (all?) of these are from google utility libraries.
Utility methods for working with basic geometry.
Utility methods for working with hexagonal grids.
Basic math utilities.
Implementation of Park-Miller-Carta PRNG ported from:
http://lab.polygonal.de/2007/04/21/a-good-pseudo-random-number-generator-prng/
Methods


Before Change
unseen = [item for item in reader.values() if item.startswith("SEEN ")]
for key in binaries:
supportdir, version, name = binaries[key]
with BinaryUtil(supportdir, version, bases, 30, "/tmp").select_binary_stream(
key,
url_opener=reader) as stream:
self.assertEqual(stream(), "SEEN " + key.upper())
After Change
fake_base, fake_url = self._fake_base, self._fake_url
bases = [fake_base("apple"), fake_base("orange"), fake_base("banana"),]
binary_util = BinaryUtil(bases, 30, "/tmp")
binaries = {t[2]: t for t in (("bin/protobuf", "2.4.1", "protoc"),
("bin/ivy", "4.3.7", "ivy"),
("bin/bash", "4.4.3", "bash"))}


Jerusalem - القدس
The Eternal Capital of Palestine
Verified as the only capital of the state of Palestine
Jerusalem shall be ours
Jerusalem shall be free
Together we shall walk it's scented air
Our little hearts have turned into men
Soon we will pray under Al Aqsa's dome


Remove a GCM registration id and the list of vehicles associated with it.
The request method is DELETE.
For example:
DELETE /vehicleRecall/vehicles/registrationId/APA91bGKlMqU-9vISr8lDN589QDA1Bk0WSQ5IE7_r8NUMg4MaXKlnncaWgI1ST6m8E5agCA-uGxM8ksfAg-iOV8JZ67RLbtiB3s08fhFJj1k2Htt54jQ80a2W6GnX0NYQ2C440molF6YK_cdLgxBx0FOxYXgNeY_8kRk9nYGGU2xYrhsU5RBVDs Host: test.alpineconnectivity.com:8888
GET or DELETE requests pass data to a service is via the parameters of the request method.
DELETE /vehicleRecall/vehicles/registrationId/APA91bGKlMqU-9vISr8lDN589QDA1Bk0WSQ5IE7_r8NUMg4MaXKlnncaWgI1ST6m8E5agCA-uGxM8ksfAg-iOV8JZ67RLbtiB3s08fhFJj1k2Htt54jQ80a2W6GnX0NYQ2C440molF6YK_cdLgxBx0FOxYXgNeY_8kRk9nYGGU2xYrhsU5RBVDs Host: test.alpineconnectivity.com:8888
| registrationId (required) |
The "registrationId" field uniquely specifies which set of vehicles to delete. Note: At this time it is not necessary for a user to actually have an account to use this service. That's why we use the GCM registrationId to uniquely identify their vehicles instead of the alpine userId (email address). |
The following response codes are possible from this service.
(Client code must handle each of them.):
The content of the body of the HTTP Response will always be JSON.
Therefore, the following header will always be sent:
Content-Type: application/json
The body of the response should always contain JSON.
The type of JSON returned varies by the response code.
4 types of JSON are possible.
If the service executed successfully
JSON like the following will be returned in the body of the response:
HTTP/1.1 200 Success
If there was a major error the request that prevents us from even processing it,
the standard JSON describing the error will be returned in the body of the response:
HTTP/1.1 400 Bad Request { "errorCode": 1, "errorMessage": "INVALID JSON", "data": "registrationId not specified!" }
If a valid username/password was not passed via the Basic Authentication mechanism, this error code will be returned.
HTTP/1.1 401 Not Authorized WWW-Authenticate: Basic realm="alpineconnectivity.com" { "errorCode": 401, "errorMessage": "Not Authorized", "data": "Authorization header was not found." }
If an error occured on the server that prevents us from satisfying the request,
the standard JSON describing the error will be returned in the body of the response:
HTTP/1.1 500 Server Error { "errorCode": 3, "errorMessage": "SERVER ERROR", "data": "Could not connect to the database." }


transmission-cli
A lightweight, command-line BitTorrent client.
This tool has been deprecated, please see `transmission-remote`.
More information: home page
- Download a specific torrent:
transmission-cli url|magnet|path/to/file
- Download a torrent to a specific directory:
transmission-cli --download-dir path/to/download_directory url|magnet|path/to/file
- Create a torrent file from a specific file or directory:
transmission-cli --new path/to/source_file_or_directory
- Set the download speed limit to 50 KB/s:
transmission-cli --downlimit 50 url|magnet|path/to/file
- Set the upload speed limit to 50 KB/s:
transmission-cli --uplimit 50 url|magnet|path/to/file
- Use a specific port for connections:
transmission-cli --port port_number url|magnet|path/to/file
- Force encryption for peer connections:
transmission-cli --encryption-required url|magnet|path/to/file
- Use a Bluetack-formatted peer blocklist:
transmission-cli --blocklist blocklist_url|path/to/blocklist url|magnet|path/to/file


Before Change
filename = os.path.join("data", url.split("/")[-1])
// Download file if it doesn"t already exist
if not os.path.exists(filename):
os.makedirs("data")
print("Downloading...")
wget.download(url, filename)
print("Download complete!")
else:
print("File exists, skipping download.")
// Print 10 random lines
with open(filename, "r") as datafile:
lines = datafile.readlines()
// Last line in file is empty, we"ll deal with this later
lines = lines[:-1]
After Change
filename = "data/iris.data"
// Print 10 random lines
try:
datafile = open(filename, "r")
except IOError:
print("Cannot open data file: {}. Have you downloaded it yet?".format(filename))
exit()
lines = datafile.readlines()
// Last line in file is empty, we"ll deal with this later
lines = lines[:-1]
random.shuffle(lines)


Sorry for the migration delay. You can find our repo by clicking here.
Some parts of our site:TMSCA Test ViewerBalancePython


An element is a plain JavaScript object that represents a DOM node or component. Elements are pure and never mutated, and are cheap to create.
A component is a function or class. Components can have state and take props as input and return an element tree as output (although they can represent generic containers or wrappers and don’t necessarily have to emit DOM). Components can initiate side effects in lifecycle methods (e.g. AJAX requests, DOM mutations, interfacing with 3rd party libraries) and may be expensive to create.
const Component = () => "Hello"
const componentElement = <Component />
const domNodeElement = <div />