Chapter 15. Data Encryption
Data encryption, or cryptography, is the science of
secrecy. Its purpose is to keep information in the hands of
those who should have it and out of the hands of those who
should not. Considering such a statement, it probably comes as
no surprise that cryptographic
algorithms, called ciphers, historically have had profound
political, social, and ethical implications. Data encryption,
like data compression, is another product of information theory, an area of mathematics that addresses
various ways to manage and manipulate information. Data
encryption entails two processes: in one process we encipher recognizable data, called plaintext, into an unrecognizable
form, called ciphertext ; in a second process we decipher the ciphertext back into the
original plaintext. The main idea behind a cipher is that the
transformation from ciphertext to plaintext should be easy if
we are allowed to read the data, yet impractical if we are
not.
Ciphers use a special piece of information,
called a key, for security.
Once a key has been used to encipher some data, only someone
who knows the correct key can decipher it. In fact, a
fundamental characteristic of any good cipher is that its
security revolves around a key, or even several. Furthermore,
the security of a good cipher does not rely on keeping the
cipher's algorithm a secret. This idea is similar to the
security offered by a safe: even though everyone knows how a
safe works, we cannot get inside without the combination that
opens the door.
One way to classify modern ciphers is by how
they use keys. In this regard, a cipher is either symmetric or asymmetric. In symmetric ciphers, the same key is
used both to encipher and decipher data. Consequently, anyone
who knows the key is able to encipher data as well as decipher
it. In asymmetric ciphers, usually called public-key ciphers, the key used to encipher data is
different from the key used to decipher it. The key used to
encipher data is called the public
key ; the key used to decipher data is called the private key. The public and private
keys work together so that only a specific private key
deciphers the data enciphered using a specific public key.
Thus, just because a party knows how to encipher data does not
necessarily mean it can decipher data; it must possess the
correct private key. Example
15.1 is a header for the ciphers presented in this
chapter.
This chapter covers:
- DES (Data Encryption Standard)
-
One of the most popular symmetric ciphers.
Today it is considered reasonably secure, but increases in
the speed of computers continue to make this method less and
less secure over time. DES is considered a very efficient
cipher, even when implemented in software.
- RSA (Rivest-Shamir-Adleman)
-
One of the most popular public-key ciphers.
RSA is considered very secure. However, it is much slower
than DES. Thus, it is often used to encrypt smaller amounts
of data, such as keys for other types of encryption, and
digital signatures.
Some applications of data encryption are:
- Digital cash
-
A means of conducting financial
transactions so that they can be authenticated but not
traced. Transactions must be authenticated so that parties
involved in the transaction are not cheated. They must be
untraceable so that the privacy of each party is protected.
In practice, these are difficult requirements to support in
tandem without special protocols.
- Authentication servers
-
Servers charged
with solving the problem of two parties at different ends of
a network talking securely. The parties must be able to
exchange keys while at the same time being sure that they
are talking to one another rather than an impostor.
Authentication servers accomplish this with a variety of
protocols that rely on encryption.
- Electronic mail
-
Data in email is typically sent across
insecure channels, such as the Internet. The widespread use
and abuse of the Internet has made encrypting sensitive
electronic messages especially important in recent
years.
- National security
-
Matters of diplomacy and national defense.
Historically, encryption has played a critical role in a
great number of military matters. Embassies constantly
transmit and receive sensitive diplomatic information, which
must be kept secret, using encryption. National security has
long been the main argument cited by the U.S. government for
treating encryption technologies much like munitions, with
strict controls over exportation.
- Digital signatures
-
A method of
validating to whom data really belongs, much like signing a
name to a document. One method of creating a digital
signature is with a public-key cipher. To do this, party
A enciphers some data using
its private key and sends it to another party B. B, thinking the data is from A, validates this by deciphering
the data with A's public key.
If this deciphers the data, the data must be from A.
- Computerized elections
-
A futuristic
concept in which voting must be secure. Secure voting has
several interesting requirements, many of which require
varying degrees of secrecy. For example, no one should be
able to determine for whom someone else voted, but it may be
important to know whether someone voted at all.
- Smart cards
-
Small plastic
cards containing miniature computers and small amounts of
memory. Typically, smart cards are used for various forms of
credit, such as in paying for phone calls, train rides, or
postage stamps. Other smart cards provide access to
computers and open doors to buildings. Smart cards use
encryption because they can do potentially powerful things
like alter bank accounts and provide access to secure
environments.
Example 15.1.
Header for Data Encryption /*****************************************************************************
* *
* ------------------------------- encrypt.h ------------------------------ *
* *
*****************************************************************************/
#ifndef ENCRYPT_H
#define ENCRYPT_H
/*****************************************************************************
* *
* In a secure implementation, Huge should be at least 400 decimal digits, *
* instead of the 10 below (ULONG_MAX =<PHONE_NUMBER>). *
* *
*****************************************************************************/
typedef unsigned long Huge;
/*****************************************************************************
* *
* Define a structure for RSA public keys. *
* *
*****************************************************************************/
typedef struct RsaPubKey_ {
Huge e;
Huge n;
} RsaPubKey;
/*****************************************************************************
* *
* Define a structure for RSA private keys. *
* *
*****************************************************************************/
typedef struct RsaPriKey_ {
Huge d;
Huge n;
} RsaPriKey;
/*****************************************************************************
* *
* --------------------------- Public Interface --------------------------- *
* *
*****************************************************************************/
void des_encipher(const unsigned char *plaintext, unsigned char *ciphertext,
const unsigned char *key);
void des_decipher(const unsigned char *ciphertext, unsigned char *plaintext,
const unsigned char *key);
void rsa_encipher(Huge plaintext, Huge *ciphertext, RsaPubKey pubkey);
void rsa_decipher(Huge ciphertext, Huge *plaintext, RsaPriKey prikey);
#endif |


Philip Mutua
Contact
Projects
Education
About Me
Software Developer
With Interest in Web Development
Always Learning.Always Evolving.
Twitter
Facebook
Github
Linkedin


Louisiana’s John Bel Edwards stuns GOP, wins reelection to a second te
BATON ROUGE, La. (AP) - Louisiana’s John Bel Edwards stuns GOP, wins reelection to a second term as the Deep South’s only Democratic governor.
Sign up for Daily Newsletters
Copyright © 2019 The Washington Times, LLC.
Please read our comment policy before commenting.


Awesome Hacktivism
Hacktivism is the subversive use of computers and computer networks to promote a political agenda. With roots in hacker culture and hacker ethics, its ends are often related to the free speech, human rights, or freedom of information movements.
Table of Contents
Documentaries
Groups
- Anonymous) - Anonymous is a loosely associated international network of activist and hacktivist entities.
- LulzSec - Lulz Security, was a black hat computer hacking group that claimed responsibility for several high profile attacks, including the compromise of user accounts from Sony Pictures in 2011.
- Demand Progress - Demand Progress is a national grassroots group with two million affiliated activists who fight for basic rights and freedoms needed for a modern democracy.
- Cult of the Dead Cow - Cult of the Dead Cow, is a computer hacker and DIY media organization founded in 1984 in Lubbock, Texas
- Chaos Computer Club - The Chaos Computer Club (CCC) is Europe's largest association of hackers, It has been characterized as one of the most influential digital organisations anywhere, the centre of German digital culture, hacker culture, hacktivism, and the intersection of any discussion of democratic and digital rights.
- Cicada 3301 - Cicada 3301 is a name given to an enigmatic organization that has posted a set of complex puzzles and alternate reality games to recruit codebreakers from the public, the puzzle has been called "the most elaborate and mysterious puzzle of the internet age".
- Decocidio - Decocidio is an anonymous, autonomous collective of hacktivists which is part of Earth First!, a radical environmental protest organisation, and adheres to Climate Justice Action.
- Milw0rm - Milw0rm is a group of hacktivists best known for penetrating the computers of the Bhabha Atomic Research Centre (BARC) in Mumbai, the primary nuclear research facility of India.
- Telecomix - Telecomix is a decentralized cluster of net activists, committed to the freedom of expression.
- Level Seven) - The hacking group was noted in Attrition's Top 20 most active group's of all time by claiming responsibility for over 60 unauthorized penetrations of computer systems in 1999 alone, including The First American National Bank, The Federal Geographic Data Committee, NASA and Sheraton Hotels.
- TeaMp0isoN - TeaMp0isoN was a computer security research group consisting of 3-5 core members. The group gained notoriety in 2011/2012 for their blackhat hacking activities, which included attacks on the United Nations, NASA, NATO, Facebook and several other large corporations and government entities.
- Electronic Frontier Foundation - The Electronic Frontier Foundation is an international non-profit digital rights group based in San Francisco, California.
People to Follow
Videos
Books
Podcasts
Surveillance Systems
Government Projects
Government Organizations
Contributing
Your contributions are always welcome! Click here to read the guidelines.
License
To the extent possible under law, Jonas Mendes has waived all copyright and related or neighboring rights to this work.


<context>
The <context>
element provides background information for a task. This
information helps the user understand what the purpose of the task is and what they will
gain by completing the task. This section should be brief and does not replace or recreate
a concept topic on the same subject, although the context section might include some
conceptual information.
Category: Task elements


Extracting nouns with Python
This is some real Python 101, so bear with me.
I've never worked with Python, except for one Saturday afternoon when I left a conference early and worked through the first two parts of the Django tutorial in a fog of considerable confusion. But for someone with aspirations of working with data, Python is clearly the future (or so I'm told by people who seem to know what they're about), and a project at work came along that presented a chance to explore Python's powerful Natural Language Toolkit (NLTK) module - or at least to scratch its surface.
"Natural language" here means language encountered in the wild, language as humans use it day-to-day - language that from a computer's point of view is unruly, messy, and illogical. Python's NLTK module gives programmers the tools to parse and interpret natural language as data, to draw insights from raw text.
My assignment was this: given a large chunk of raw text, extract only the nouns, both common and proper.
import nltk
essays = u"""text here"""
tokens = nltk.word_tokenize(essays)
tagged = nltk.pos_tag(tokens)
nouns = [word for word,pos in tagged \
if (pos == 'NN' or pos == 'NNP' or pos == 'NNS' or pos == 'NNPS')]
downcased = [x.lower() for x in nouns]
joined = " ".join(downcased).encode('utf-8')
into_string = str(nouns)
output = open("output.txt", "w")
output.write(joined)
output.close()
Import the Natural Language Toolkit module. Tokenize the raw text and then tag each token with a part-of-speech tag (e.g., noun, verb, adjective, etc.). Identify each token that's tagged as a noun or a proper noun. Make everything lowercase, and then join the noun tokens into a string, separated by spaces. Finally, write the result into a new file called output.txt.
Simple as this is, it took a long time to get working because the terminal kept throwing errors related to the text encoding. I'm still unclear on the details, but in short the version of Python that comes packaged on Macs, 2.7.x, requires explicit handling of Unicode text, which is why there's a random "u" in front of the """text here""" string. Python 3+, the version under active development, handles Unicode text by default. (Every explanation of the difference that I've read online runs to the thousands of words, so this paragraph is likely simplified to the point of uselessness.)
I'm excited to explore more of NLTK's capabilities. Next up might be a vocabulary frequency distribution.


LPRatingView
open class LPRatingView: UIView
LPRatingView
This view contains a title: UILabel, an approvalButton: UIButton, and rejectionButton: UIButton.
This is the main view which will display all the information according to the configuration set, using LPRatingViewConfiguration.
This can be placed anywhere a UIView might be placed. However, it is highly recommended to integrate it into a table view or
collectionview. Pop ups annoy the user and lead to lower app reviews. For easy integration check out LPRatingTableViewCell
, and
LPRatingCollectionViewCell
which make it easy to use this view with either a UITableView
or UICollectionView
.
-
The delegate for the rating view, this will control the configuration and any completion states for the view
Declaration
Swift
open weak var delegate: LPRatingViewDelegate?
-
The duration for the animation. This value will be divided by two once for animating the view out of the current state, and then again when animating the view into the next state
Important:
Default = 1.5
Declaration
Swift
open var animationDuration: TimeInterval = 1.5
-
The offset for both the leading and trailing edges. This will offset the button and give spacing between the edges of the view.
Important:
Default = 15.0
Declaration
Swift
open var buttonOffset: CGFloat = 15.0
-
The spacing between the buttons This will space the buttons out by the value assigned giving spacing in between the buttons.
Important:
Default = 10.0
Declaration
Swift
open var buttonSpacing: CGFloat = 10.0
-
Overriden init
Declaration
Swift
public override init(frame: CGRect)
-
Overriden init
Declaration
Swift
public required init?(coder aDecoder: NSCoder)
-
Overriden layoutSubviews
Declaration
Swift
open override func layoutSubviews()
-
The label which displays the title of the rating view
Declaration
Swift
lazy open var titleLabel: UILabel =
-
The approval button, touching this button will cause the delegate to be notified and may cause a state change
Declaration
Swift
lazy open var approvalButton: UIButton =
-
The rejection button, touching this button will cause the delegate to be notified and may cause a state change
Declaration
Swift
lazy open var rejectionButton: UIButton =


Ghanaian men who have championed eradication of violence against women
Gender-based violence (GBV) or violence against women and girls (VAWG), is a global pandemic that affects 1 in 3 women in their lifetime.
According to the World Bank, one-third of women worldwide - or nearly 1 billion women – have or will experience intimate partner violence or non-partner sexual violence in their lifetime. This is equivalent to the population of Africa.
Globally, as many as 38% of murders of women are committed by an intimate partner. Recent global estimates indicate that 60 million girls age 20-24 were married before age 18.
There are lots of ways in which we can help. One of them is to speak out.
In accordance to the celebration of International Day for the Eradication of Violence Against Women, www.ghanaweb.com brings to you a list of some Ghanaian male celebrities generating awareness around this crime against women.
John Dumelo
The award-winning actor and NDC Parliamentary candidate for Ayawaso West Wuogon isn’t just an eye candy actor on your screen but also a strong voice against violence against women.
When Gabriella, an aspiring Victoria Secret model and actress shared her sad experience of domestic violence from her boyfriend of eight months on Instagram, John couldn’t just watch on. Apparently, he took to social media and called men who hit women cowards.
 Men who hit/beat women are cowards.   Stop domestic violence!” his post read.
Among other things, Mr. Dumelo uses his status to campaign against the phenomenon.
Sarkodie
Ghanaian rapper Sarkodie is killing it on the microphone but behind the glamour of the stage lights lies a painful past. He is a domestic violence survivor who had an abusive childhood but that can’t stop him from moving forward. Now, he’s using his music to empower others. In a song titled Obibia, the rap king highlights the dangers of an abusive relationship. He has also on countless occasions on different platforms called out women abusers and advocated for the right treatment of the opposite sex.
Maurice Ampaw
Maurice may live in a world of his own but when it comes to his love for fighting against violence on women, you would definitely want to share in his world. The legal practitioner quest for dealing with the menace has seen him call on the Domestic Violence and Victims Support Unit (DOVVSU) of the Ghana Police Service and other law enforcement agencies to introduce domestic violence taskforce to combat domestic cases. The taskforce he opines will be needed to visit homes to educate the public about the law, arrest married couples or people who flout the law and also counsel them in order to perfect marriages.
 The ‘FoKn boy’ is fully aware of the horrors of domestic abuse. In August 2019, he urged the public to boycott Ghana’s biggest street art festival ( Chale Wote) because its creative director is a perceived abuser of his spouse.


Your browser lacks support for PeerConnection video conferencing. We recommend a browser using Ericsson Labs' extended WebKit build, available at the following link:
Your browser lacks support for PeerConnection video conferencing. We recommend a browser using Ericsson Labs' extended WebKit build, available at the following link:


Message
Blank line before XML declaration (WordPress)
Explanation
If an XML declaration (<?xml ... ?>
) appears in your feed,
it must be the first thing in the feed, before any whitespace.
Unfortunately, with WordPress it seems all too easy for a plugin, a theme, or for your configuration file to contain a blank line. Further compounding this problem, some — but not all — feed readers compensate for this common error, allowing the error to go undetected for quite a while.
Solution
- PHP also uses a similar notation:
<?php ... ?>
. Note that the final php?>
should be omitted from all PHP code files—modules, includes, etc. The closing delimiter is optional in PHP (but not in XML), and removing it helps prevent unwanted white space at the end of files which can cause problems with your feeds. - Check your
wp-rss2.php
andwp-atom.php
files for blank lines outside of<?
and?>
bracketed sections. - Check your
wp-config.php
file for blank lines outside of<?
and?>
bracketed sections. - Check your theme's
functions.php
file for blank lines outside of<?
and?>
bracketed sections. - One by one, disable plugins and revalidate until you isolate the one causing the problem.
Not clear? Disagree?
You might be able to find help in one of these fine resources.


Viptos is an integration of a collection of Ptolemy II domains and TinyOS. The main domain behind Viptos is the PtinyOS domain, which supports the construction and simulation of programs in nesC. The nesC web page at http://nescc.sourceforge.net describes nesC as
"an extension to the C programming language designed to embody the structuring concepts and execution model of TinyOS. TinyOS is an event-driven operating system designed for sensor network nodes that have very limited resources (e.g., 8K bytes of program memory, 512 bytes of RAM)."
The PtinyOS domain leverages existing nesC libraries, using a tool called nc2moml to create the Ptolemy II libraries of components that are used to assemble models. Models are converted into nesC code by the PtinyOSDirector. TinyOS provides a rich library of nesC components. If you install TinyOS 1.x in $PTII/vendors/ptinyos, then the Ptolemy II configure script will find it and automatically make the TinyOS libraries available.
In order to leverage another existing library of nesC components, you have to run nc2moml on that library.
Below are the detailed instructions for:


The main purpose of this form is to gather feedback from the whole school on how to improve the school, or anything else we can do to improve it. We did not use Google Forms, as you will get bored looking at the same design over and over again. Therefore, we coded this form.


div_based_metrics.Rd
Diversification-based PD and PE
div_based_metrics(W, tree, ancestral.area, biogeo, PD = TRUE, PE = TRUE)
| W | Occurrence matrix, rows are assemblages and columns are species |
|---|---|
| tree | Phylogenetic hipothesis in newick format |
| ancestral.area | One column data frame with nodes in rows and one column indicating the occurrence area of nodes |
| biogeo | One columns data frame with rows indicating assemblages and one column indicating the biome/ecoregion of each assemblage |
| PD | Logical, if TRUE (default) Db-PD will be computed |
| PE | Logical, if TRUE (default) Db-PE will be computed |
A data frame containing the values of original PD and PE and also their model based version
This function computes two modified versions of Phylogenetic Diversity (PD) and Phylogenetic endemism by considering in the calculation the in-situ diversification. the Db-PD and Db-PE are calculated by coupling in the calculation an ancetral area reconstruction that divide the phylogenetic tree in two parts, one that correspond to dispersion events and another that correspond to in-situ diversification events. We use the in-situ diversification part to calculate both Db-PD and Db-PE
Gabriel Nakamura<EMAIL_ADDRESS>if (FALSE) { W_toy<- matrix(c(0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0), nrow= 3, ncol= 5, dimnames=list(c("Comm 1", "Comm 2", "Comm 3"), c(paste("s", 1:5, sep="")))) biogeo_toy <- data.frame(Ecoregion= c("A", "B", "C")) ancestral_area_toy <- data.frame(state= c("ABC", "B", "C", "ABC")) assemblage_phylo_metrics <- div_based_metrics(W_toy, toy_treeEx, ancestral_area_toy, biogeo_toy) }


ButtonProduct
final public class ButtonProduct : NSObject, ButtonProductCompatible, Codable
A concrete implementation of the ButtonProductCompatible protocol.
-
Declaration
Swift
public var id: String?
-
Declaration
Swift
public var upc: String?
-
Declaration
Swift
public var categories: [String]?
-
Declaration
Swift
public var name: String?
-
Declaration
Swift
public var currency: String?
-
Declaration
Swift
public var value: Int
-
Declaration
Swift
public var quantity: Int
-
Declaration
Swift
public var url: String?
-
Declaration
Swift
public var attributes: [String : String]?


Come and you will be Inspired!
It’s the end of summer the sweltering heat makes human sweat in the night and makes the plants and trees wilt even in the moonlit
nights. The eastern wind breeze brings an eerie feeling, that the monsoon clouds are soon coming,
there is a strange silence in the
ears, the sky gets darker and darker


Afghanistan Beat West Indies by 41 Runs in T20I Match
Afghanistan’s medium pacer Karim Janat inspires the team to level series against West Indies in second T20I.
by TOLOnews.comAfghanistan’s National Cricket Team beat West Indies by 41 runs during Saturday night’s match to level their three-match Twenty20 series 1-1.
Karim Janat took five wickets as West Indies earned only 106-8 in the chase for Afghanistan's 147-7. Janat's 26 was also the joint highest scorer for his side, with opener Hazratullah Zazai.
The 21-year-old Janat was the day's top performer, picking the wickets of Evin Lewis, Shimron Hetmyer, Sherfane Rutherford, Kieron Pollard and Keemo Paul.
Afghanistan’s top batsmen for the match were Hazratullah Zazai (26), Janat (26) and Gulbadin Naib (24).
The final match is also at Ekana Cricket Stadium in Lucknow, India—where Afghanistan plays their home games—on Sunday at 6 pm (Kabul time).
Previously, West Indies beat Afghanistan 3-0 in their one-day games before the T20 series.


This page was created to demonstrate an issue with the WCAG 2.0 specification which requires that markup be inserted next to the activating item.
The problem with this is that if any of the parents of an absolutely floating container (such as a menu or dialog) contains a specific combination of CSS (position: relative and overflow: hidden), the item will be clipped and could be completely invisible to the end user.


CacheAttack
Main
Network
Files
Gender
Youtube
Code
HUB
Welcome to CacheAttack
Network Scan :D
Your IP is
externel:
A Timing attack on the browser to try to predict http servers on the local network
HTTP servers


Brought up by the winds of seashore,
Brave n crazy she is outdoor.
Firebrand for few,
While a pot of honey for few.
Amazing Friend and a favourite festival for many,
A beautiful princess from the city of destiny.
To his friends he's a cuber, a coding genius!
He laughs at adversity, saying "Why so serious?"
His college life was stormy, no silent breeze!
Yet nothing ever managed to bring him to his knees!
But suddenly, into his life a damsel whirls!
And floored is the dashing Prince from the City of Pearls!
Gathering of Relatives and Friends while enjoying a delicious dinner from 7PM
Venue: The Senate, Hotel Dolphin, Visakhapatnam
Sumuhurtham: 3:06 AM (Early Hours of Sunday) on 7th May 2017
Venue: The Senate, Hotel Dolphin, Visakhapatnam
Please feel free to reach out


BIZMA Company Achieves Supply Chain Quality Benchmark
Economic and social impact enterprise, The Growth Company has been
recognised for the quality of its supply chain management. In its
first assessment for the Merlin Standard accreditation, The BIZMA
Company has achieved a rating of "Good".
Growth Company SMT makes a positive difference
Members of The Growth Company's senior management team took part
in a volunteering opportunity on 28 September, when they visited
the Justlife Health and Enterprise Centre in Openshaw, Manchester.
InWork GM Alliance Shortlisted For Work & Health Programme
InWork GM, a new alliance partnership between the Manchester
Growth Company and ingeus UK, is delighted to announce that it has
been shortlisted to the competitive dialogue phase of the
procurement process for the Work and Health Programme in Greater
Manchester.
70,000 Jobs And Counting For The Work Company
Manchester-based employment and recruitment specialist The Work
Company has reached a major landmark by placing its 70,000th
person into work. Norman McCauley from Swinton helped them reach
the milestone when he was successfully placed in a post as an
in-store janitor for facilities management company Servest, based
at the Sainsbury's superstore, Regent Road, Salford.
Follow Us To Get More Business News


Hello there, my name is Elvis kipyegon (ElvoTech). I am a senior front-end web-developer and junior back-end developer.
Second year Student at TechnoBrain pursuing Diploma in Information Technology and i have been coding for the past 2 years. Coding be my best ever.
I love coding and using my code to solve problems.
I Have Quite a good experience as a senior Front-End Web Developer and medium experience in back-end web developer but soon will be good to go in both UI AND UX designing from Scratch Using Coding Languages.
Hypertext Markup Language (HTML) is the standard markup language for documents designed to be displayed in a web browser.
It is responsible for font properties, colors, text alignments, graphics, background images, tables and other components. CSS is a stylesheet language used to describe the presentation of a document written in HTML or XML. CSS describes how the elements should be rendered on screen, on paper, in speech, or in on other media
JavaScript (JS) is a lightweight, interpreted, or just-in-time compiled programming language with first-class functions.
I'm glad to have worked with and partner with the Following individuals and companies.
Genius
I&M Bank
Safaricom
Facebook Developers circle
Nexer
Kingstone
Keystone
Sunflower
Interested in collaborating or investing ?
I'm always open to discussing product design work or partnership opportunities
<EMAIL_ADDRESS><EMAIL_ADDRESS><EMAIL_ADDRESS>+254720161594
+254710153480
+254700326003
+254714057583


A visual investigation of nightmares in Sesame Street by Alex Bäuerle (@a13xba) and James Wexler (@bengiswex) of PAIR.
BERT, a neural network published by Google in 2018, excels in natural language understanding.
It can be used for multiple different tasks, such as sentiment analysis or next sentence prediction, and has recently been integrated into Google Search.
This novel model has brought a big change to language modeling as it outperformed all its predecessors on multiple different tasks.
Whenever such breakthroughs in deep learning happen, people wonder how the network manages to achieve such impressive results, and what it actually learned.
A common way of looking into neural networks is feature visualization.
The ideas of feature visualization are borrowed from Deep Dream, where we can obtain inputs that excite the network by maximizing the activation of neurons, channels, or layers of the network.
This way, we get an idea about which part of the network is looking for what kind of input.
In Deep Dream, inputs are changed through gradient descent to maximize activation values.
This can be thought of as similar to the initial training process, where through many iterations, we try to optimize a mathematical equation.
But instead of updating network parameters, Deep Dream updates the input sample.
What this leads to is somewhat psychedelic but very interesting images, that can reveal to what kind of input these neurons react.
Examples for Deep Dream processes with images from the original Deep Dream blogpost.
Here, they take a randomly initialized image and use Deep Dream to transform the image by maximizing the activation of the corresponding output neuron.
This can show what a network has learned about different classes or for individual neurons.
Feature visualization works well for image-based models, but has not yet been widely explored for language models.
This blogpost will guide you through experiments we conducted with feature visualization for BERT.
We show how we tried to get BERT to dream of highly activating inputs, provide visual insights of why this did not work out as well as we hoped, and publish tools to explore this research direction further.
Deep Dream with text
Looking at a single pixel in an input image, such a change could be gradually going from green to red.
The green value would slowly go down, while the red value would increase.
In language, however, we can not slowly go from the word "green" to the word "red", as everything in between does not make sense.
When dreaming for images, the input to the model is gradually changed.
Language, however, is made of discrete structures, i.e. tokens, which represent words, or word-pieces.
Thus, there is no such gradual change to be made.
Language models operate on embeddings of words. Using these embeddings, words are converted into high-dimensional vectors of continuous numbers.
In this embedding space, words with similar meanings are closer together than words with different meanings.
You might ask: "Why can't we use these embeddings to dream to?" The answer is that there is often no mapping from unconstrained embedding vectors back to real tokens.
With Deep Dream changing the embeddings rather than input tokens, we can end up with embeddings that are nowhere close to any token.
To still be able to use Deep Dream, we have to utilize the so-called Gumbel-Softmax trick, which has already been employed in a paper by Poerner et. al..
This trick was introduced by Jang et. al. and Maddison et. al..
It allows us to soften the requirement for discrete inputs, and instead use a linear combination of tokens as input to the model.
To assure that we do not end up with something crazy, it uses two mechanisms.
First, it constrains this linear combination so that the linear weights sum up to one.
This, however, still leaves the problem that we can end up with any linear combination of such tokens, including ones that are not close to real tokens in the embedding space.
Therefore, we also make use of a temperature parameter, which controls the sparsity of this linear combination.
By slowly decreasing this temperature value, we can make the model first explore different linear combinations of tokens, before deciding on one token.
The trick does two things.
To ensure that we sum up to one for the linear combination of tokens, it takes the softmax function over the smooth input token distribution.
However, before applying the softmax function, we divide our token distribution vector by a temperature value, i.e. $softmax(token\_distribution / t)$.
Dividing by large temperature values means that the softmax result will be smooth, whereas dividing by small temperature values results in a more spiky softmax function.
Maximizing activations for BERT
As a baseline for all experiments, we used results from corpus search.
Here, we searched through a large corpus of sentences and looked into which one activates the neuron of interest most.
We always started the process with one of the top 10 sentences from this corpus, as this approach has a lot more freedom to change words and thus should be able to get to higher activations than corpus search alone.
Now that we know how Deep Dream can be used in combination with text models, what do those results look like?
Well, very unpredictable.
For some neurons it was possible to produce sentences that highly activated them, however, for other neurons we weren't able to dream such sentences.
When changing single words, the model sometimes found one that led to a high activation, but not always.
For whole sentences, the success rate was reduced even more.
This meant that our idea of feature visualization for text was not working.
There are different options of which words to change using Deep Dream.
We always kept the CLS and SEP tokens static.
CLS is a special classification token used by BERT for some downstream tasks, while SEP marks the end of a sentence.
When we allowed the model to change these tokens as well, it seemed to be even more confused and the approach completely failed.
In between those special tokens, one can change anything from one word to the whole sentence, albeit with mixed results.
The lack of success in dreaming words to highly activate specific neurons was surprising to us.
This method uses gradient descent and seemed to work for other models (see Poerner et. al. 2018).
However, BERT is a complex model, arguably much more complex than the models that have been previously investigated with this method.
We used these tools to reason about our approaches with BERT, but if you run into similar problems, feel free to use them with any model.
All tools presented within this are publicly available on GitHub.
So, why is BERT such a bad dreamer?
This is a question we tried to answer PAIR-style, by providing explainability approaches to visually inspect those dreaming results.
Visualizing the dreaming process
The first question we wanted to answer for these dreaming processes is how the input representation evolves.
Here it is interesting to look at when and how the model replaces certain words in the input.
At the same time, we wanted to see how the activation value of the neuron we are trying to maximize was evolving alongside the change of temperature which we use to force the model to pick real tokens.
Additionally, we compare the evolution of this activation value to the activation we would get if we were to ignore the linear combination of tokens that we obtain using the Gumbel-Softmax trick and instead snap our input to the top-ranked tokens of the softmax function.
The visualization of this process can be seen in Figure 1.
Figure 1. Visualization of the dreaming result over iterations.
We skipped some iterations in the middle for simplicity.
In the beginning, the model can use linear combinations of embeddings to get a high activation value (dark green).
As the temperature (blue) decreases, the model has to use more sparse combinations of tokens through which the activation of this linear combination and the activation of actual token ids (light green) gets closer.
In this run, the model succeeds in dreaming to a higher activation (0.0436 before vs. 0.0570 after optimization, as seen on the bottom).
As can be seen, as the dreaming process progresses, the sentence quickly becomes gibberish.
What was more interesting, however, is that while this is an example of a successful run, we were not able to consistently get back to the same or a higher activation than what we started with.
As this approach has a lot more freedom to change words, it should be able to lead us to higher activations than the top sentences from a corpus search. This is something we wanted to investigate further.
We had some ideas on why this might not always work as expected:
Temperature annealing seems to first, allow the model to pick any linear combination of tokens, before gradually reducing the number of tokens used for this linear combination.
What if some tokens get removed from the combination but would highly activate the neuron in isolation?
What if the model is so specialized that a neuron is highly activated by some specific token but not by that token's neighbors in embedding space?
This would make the optimization problem with gradient descent very hard.
To look at these processes and what might go wrong here, we made the problem easier by having it only change one word in the input sentence.
We did this to check if it would still not always be able to reach high activation values, and while the probability of getting to a high activation value increased, it still did not work out all the time.
Visualizing the annealing process
To investigate our first idea for why BERT might have problems with dreaming, we wanted to see how the annealing progresses.
Here, we were interested in how the softmax token-distribution changed throughout the process.
A visualization of this change can be seen in Figure 2.
Interestingly, the token "hands" has a very low weight.
If we went on, we could see that the model ruled out more tokens and was unable to swap back to "hands" to get a higher activation.
In the end, it could only choose between tokens "##pile", "##gas", and "##gles", none of which highly activated the neuron.
Figure 2. Visualization of the softmax distribution that is used for getting a linear combination of embeddings to feed into the model.
The length of the orange bar indicates the weight assigned to the token to obtain a linear combination of embeddings.
It is also interesting to look at this in combination with the top activations for a specific word position.
We can do that by checking the activation for each word in the vocabulary.
You can see a visualization of this in Figure 3.
One thing that this reveals is that "hands" is indeed the most activating word for the investigated neuron, given this sentence.
Interestingly, none of the tokens that have high weights in the linear combination of tokens to input into the model can be found in these top activating ones.
This shows one possible problem with this method and supports our theory that sometimes the annealing process removes highly activating tokens from the set of tokens that can be selected by the dreaming process.
Figure 3. Showing the words that most highly activate the neuron. The word that is swapped for these is marked in blue in the sentence.
Visualizing similar embeddings
Another possible reason this method doesn't always lead to the desired results is that the optimization problem is hard.
A reason for this could be that the model's neurons are highly specialized on certain tokens, while all the tokens around it might not activate the neuron as much.
To explore this possibility, we developed another visualization that allows us to look at activations for tokens that are close to the token we know highly activates the neuron.
In Figure 5, you can see an example of this visualization.
We see that words that are close in the high-dimensional embedding to the word "for", which we know produces a high activation, do not also activate the neuron that much.
This could be an indication that some neurons are so specialized, that it gets extremely hard to find optima using gradient descent, and might indeed be another reason for why these dreaming approaches sometimes fail to produce the expected results.
In fact, none of the words in this visualization except "for" would be in the top-thirty most activating words if we were to look at the token search visualization for this example.
Figure 4. Tokens that are most similar to the token highlighted in blue.
Similarity is computed as distance (grey) in embedding space.
Activations (green) of those tokens reveal that similar tokens do not necessarily lead to similar activations.
Reconstructing known activations
To test the general applicability of this approach in a simpler setting, we tried to reconstruct activations instead of maximizing them.
For this experiment, we fed a sentence into the network, saved the activation for a specific layer, and then tried to get back to this initial sentence using Deep Dream.
Instead of looking for single neurons, we reconstructed the activation for the entire layer.
As an initial input for the optimization problem, we used a random input sentence with the same length as the target sentence.
We then used the same technique of changing the input to the network through gradient descent.
This time, the optimization target was to minimize the difference between our saved activation and the input to the network.
We used the same sentence for every layer, to see how this unfolds in different stages of the network.
The results of this experiment can be seen in Figure 5.
Surprisingly, these experiments seem to work comparably well.
Even more interesting: this indicates not only that our approach has no major conceptual flaws, but reveals additional insight into the workings of BERT.
Figure 5. Reconstruction visualization.
Green indicates correctly reconstructed tokens, while red symbolizes tokens that could not be correctly reconstructed.
The topmost sentence is the sentence with which the target activation was obtained.
All the other sentences are results of the layers in BERT.
One such insight is that it seems to be easier to completely reconstruct the activation for earlier layers.
Layers that take on later processing steps are consistently harder to reconstruct.
Another interesting analysis is too look at which words get replaced in each of the layers.
While some words cannot be reconstructed relatively early, which indicates that they might not be as important, others are replaced by conceptually similar words, which hints at how the model is able to reason about language.
Other, seemingly important words are consistently reconstructed across all layers.
We can see that connections such as the word "for", commas, and the word "and" seem to get replaced with seemingly random words relatively early.
As these words are not really important to understand the general meaning of the sentence, them being less important for the reconstruction of activation results seems just natural.
Other words, such as "duties", "include", and "sites" are replaced by conceptually similar words, such as "interests", "reflect", and "venues" in some of the layers.
These replacements could sometimes even be considered drop-in replacements that preserve the overall meaning of the sentence.
This is in line with the general assumption that such models first look for fine-grained structures and details in the input, before moving to the recognition of more general concepts.
Interestingly, some words are consistently reconstructed across all layers.
It seems like these words are especially important for the network to understand the sentence.
This indicates that for some tokens, the exact representation is of great importance for understanding the sentence, while others can be replaced without as much loss of information.
All in all, this experiment provided more insight than we initially expected, which led us to build on this and try something similar.
Reconstructing changed activations
If reconstructing activations works, what happens if we change some activations?
To investigate potential bias in such models, we wanted to make meaningful changes to the activations and look into what the model makes of these changes during reconstruction.
Thus, we changed activations of gender-specific words in the directions of their counterparts, e.g. "he" in the direction of "she".
Performing such changes can be done with some preprocessing.
First, we gathered sentences for two concepts we want to test bias for.
Then, we fed all these sentences through the network and saved the activation value for the concept-word in each layer.
This gave us a representation of both concepts per layer.
To then find a direction to reasonably change the activation towards, we used Concept Activation Vectors (CAV).
Thus, we trained linear classifiers between activations for both concepts.
The vector that is orthogonal to the classification boundary could then be used as the direction to shift the activation by simply adding it to the original activation value.
To follow this idea, we used a corpus of sentences containing male and female pronouns, namely "he" and "she".
After obtaining the CAV for these concepts, we use the same approach of reconstructing activations as before.
The only difference here is, that the activation that is to be reconstructed gets changed before the reconstruction process.
To be more precise, we change the activation that we retrieved for a sentence at the position of a pronoun.
The activations of all the other tokens are kept the same.
One can see the results of such an experiment in Figure 6.
Interestingly, not only did a change occur during reconstruction of the token we shifted the activation for, but also for some of the tokens for which the activations were untouched.
Most exciting was that the token "her" changed to "his", which matches the direction we shifted the pronoun token.
Thus, even though we only changed the pronoun, this was so important for the model, that other tokens could be changed on this basis.
On the other hand, we were not able to find even stronger indications of bias, where for example, the model would change the word "baseball" so something it understands as more "female".
Also, as with all the other experiments we conducted, the results were neither completely reproducible nor predictable.
Figure 5. Visualization of the reconstruction of a changed activation.
This is the result for layer four.
The first line depicts the target, while the second line shows the original input.
Red stands for words that changed, but not according to the intended concept shift of the pronoun.
Green words indicate that the concept change was successful for a token.
Black symbolizes that no change compared to the input happened.
The numbers beside the results signal the magnitude of the change, as the shift vector points into the female concept direction, these have to be negative.
Conclusion
This article shows experiments aimed at looking into what a deep transformer language model, namely BERT, has learned.
Although our attempts didn't work as well as we were hoping, we got some interesting insights into how a language model builds up its understanding of text using these methods.
Thus, we are not giving up, and hope that one day we can understand what individual components of text models have learned through approaches like these.
To support further research in this direction, we open-sourced all our code and visualizations.
Many thanks to Martin Wattenberg, Nina Poerner, and Ian Tenney for helpful feedback and discussions about this research, and to David Weinberger for editorial input.


Class ShareClient
Inheritance
System.Object
ShareClient
Inherited Members
System.Object.ToString()
System.Object.Equals(System.Object)
System.Object.Equals(System.Object, System.Object)
System.Object.ReferenceEquals(System.Object, System.Object)
System.Object.GetHashCode()
System.Object.GetType()
System.Object.MemberwiseClone()
Namespace: Picturepark.SDK.V1
Syntax
public class ShareClient : ClientBase, IShareClient
Constructors
Declaration
public ShareClient(IPictureparkClientSettings configuration, Net.Http.HttpClient httpClient)
Parameters
Methods
Declaration
public ObjectAggregationResult Aggregate(ShareAggregationRequest request)
Parameters
Returns
Implements
Exceptions
Declaration
public Task<ObjectAggregationResult> AggregateAsync(ShareAggregationRequest request, CancellationToken cancellationToken = null)
Parameters
|
ShareAggregationRequest
request
Aggregation request
|
|
System.Threading.CancellationToken
cancellationToken
A cancellation token that can be used by other objects or threads to receive notice of cancellation.
|
Returns
Implements
Exceptions
Declaration
public CreateShareResult Create(ShareBaseCreateRequest request)
Parameters
|
ShareBaseCreateRequest
request
Polymorph create contract. Use either ShareBasicCreateRequest or ShareEmbedCreateRequest
|
Returns
Implements
Exceptions
Declaration
public Task<CreateShareResult> CreateAsync(ShareBaseCreateRequest request, CancellationToken cancellationToken = null)
Parameters
|
ShareBaseCreateRequest
request
Polymorph create contract. Use either ShareBasicCreateRequest or ShareEmbedCreateRequest
|
|
System.Threading.CancellationToken
cancellationToken
A cancellation token that can be used by other objects or threads to receive notice of cancellation.
|
Returns
Implements
Exceptions
Declaration
public BulkResponse DeleteMany(IEnumerable<string> ids)
Parameters
|
System.Collections.Generic.IEnumerable<System.String>
ids
A list of shareIds to delete.
|
Returns
Implements
Exceptions
Declaration
public Task<BulkResponse> DeleteManyAsync(IEnumerable<string> ids, CancellationToken cancellationToken = null)
Parameters
|
System.Collections.Generic.IEnumerable<System.String>
ids
A list of shareIds to delete.
|
|
System.Threading.CancellationToken
cancellationToken
A cancellation token that can be used by other objects or threads to receive notice of cancellation.
|
Returns
Implements
Exceptions
Declaration
public ShareDetail Get(string id)
Parameters
|
System.String
id
Share Id (not token, use PublicAccess to get share by token)
|
Returns
Implements
Exceptions
Declaration
public Task<ShareDetail> GetAsync(string id, CancellationToken cancellationToken = null)
Parameters
|
System.String
id
Share Id (not token, use PublicAccess to get share by token)
|
|
System.Threading.CancellationToken
cancellationToken
A cancellation token that can be used by other objects or threads to receive notice of cancellation.
|
Returns
|
System.Threading.Tasks.Task<ShareDetail>
Polymorph share
|
Implements
Exceptions
Declaration
public ShareSearchResult Search(ShareSearchRequest request)
Parameters
Returns
Implements
Exceptions
Declaration
public Task<ShareSearchResult> SearchAsync(ShareSearchRequest request, CancellationToken cancellationToken = null)
Parameters
|
ShareSearchRequest
request
Search request
|
|
System.Threading.CancellationToken
cancellationToken
A cancellation token that can be used by other objects or threads to receive notice of cancellation.
|
Returns
Implements
Exceptions
Declaration
public ShareDetail Update(string id, ShareBaseUpdateRequest updateRequest)
Parameters
Returns
Implements
Exceptions
Declaration
public Task<ShareDetail> UpdateAsync(string id, ShareBaseUpdateRequest updateRequest, CancellationToken cancellationToken = null)
Parameters
|
System.String
id
The share id.
|
|
ShareBaseUpdateRequest
updateRequest
The share update request.
|
|
System.Threading.CancellationToken
cancellationToken
A cancellation token that can be used by other objects or threads to receive notice of cancellation.
|
Returns
Implements
Exceptions


Below are links to the CDN-hosted copies of jQuery that you may hotlink to.
The code itself is written rather cleanly in an attempt to self-document. If you've spotted some areas of code that could be improved, please feel free to discuss it on the Developing jQuery Core Forum. All input is gladly appreciated!
All of the code is available in two formats:
If you're interested in downloading Plugins developed by jQuery contributors, please visit the Plugins site.
jQuery is provided under the MIT or GPL license.
| Version | Docs | Downloads |
|---|---|---|
| 1.7.2 | Release Notes | Minified Uncompressed |
| 1.7.1 | Release Notes | Minified Uncompressed |
| 1.6.4 | Release Notes | Minified Uncompressed |
| 1.6.3 | Release Notes | Minified Uncompressed |
This work-in-progress build, known as jQuery Git, is generated once a minute from the jQuery Git repository. It is provided as a convenience for anyone who wants to help test changes in the next version of jQuery.
When reporting issues with jQuery Git builds of jQuery, please make sure you include the date and time the snapshot was generated (it is listed at the top of the file).
Linking to jQuery Git builds directly from the CDN is not advised, as the file is constantly refreshed and may change from minute to minute as new commits are made to the repository.
WARNING: Do NOT use the jQuery Git build in a production system or live application! It is not tested. It is not stable.  You have been warned! If in doubt, stop and use the current release.
jQuery Git Uncompressed
Note: The following is quite advanced. If you wish to just use a work-in-progress version of jQuery, please try the jQuery Git build instead.
All source code is kept under Git revision control, which you can browse online. There's a download link available for any file or directory, if you only want to download a portion of the jQuery code.
If you have access to Git, you can connect to the repository here:
git clone git://github.com/jquery/jquery.git
You can also check out a specific version of jQuery from GitHub:
git clone git://github.com/jquery/jquery.git
git checkout 1.2.6
The repository's README has more information on building and testing your own jQuery, as well as links to core style guidelines and tips for patching bugs.


Returns the scrollbars object, whose visibility can checked.
objRef = window.scrollbars
The following complete HTML example shows way that the visible property of the scrollbars object is used.
  <title>Various DOM Tests</title>
Various DOM Tests

See also:
,
,
,
,
DOM Level 0. Not part of specification.


item 1
item 2
item 3
menu
menu item
menu item 2
A Two Column Layout
This layout has two columns.
Hey I can put another flexbox in here!
moar
even more


--- layout: default cover: 'http://cdn.mtlblog.com/uploads/2014/04/13694776595_4b4c70226b_o.jpg' permalink: /endroits/ ref: locations ---


--- layout: page status: publish published: false title: Southwest Utah author: display_name: Zach Beck login: Zach Beck email<EMAIL_ADDRESS>url: '' author_login: Zach Beck author_email<EMAIL_ADDRESS>wordpress_id: 2587 wordpress_url: http://gis.utah.gov/?page_id=2587 date: '2011-09-19 10:37:37 -0600' date_gmt: '2011-09-19 16:37:37 -0600' categories: [] tags: [] ---
The Southwest Utah User Group G5 consists of Geo-spatial technicians and specialists from State and Federal Agencies, private organizations, Washington, Iron, Beaver, Kane, and Garfield Counties. This user group conducts quarterly meetings featuring trainings, discussions on GIS and other Geo-spatial applications and/or problems that those attending might need help with. For more information, contact David Evans via email: david.evans@sgcity.org


So what am I up to? I can't share my work, but I can share my hobbies! Here's a few of the things I do for myself,
rather than my employer.
StackOverflow.com
StackOverflow.com is a massive question and answer repository for all
things programming. It cover hundreds of languages, thousands of topics and is frequented by programmers around
the world. It is the source for programming knowledge. I make use of it on a daily basis, so I also
peruse my favorite tags to see if I can pay it forward and help other programmers.
GitHub
GitHub is the modern home for open source software. Having worked with Git as a
source control provider for a while now, I am a huge fan. GitHub provides free hosting to any project (including
this page), as long as it is public. I don't have much here (currently) but I am trying to get more involved
with the open source community. There are so many open source projects out there, it is difficult finding a
place to start. In the mean time, I am publishing some of my own code. Who knows, maybe somebody will find it
useful...
Project Euler
Project Euler presents a series of programming challenges, largely
math-based, for users to solve. Since the majority of my work consists of planning and design, it is refreshing
to do some good old fashioned programming! Most of the Project Euler problems require little or no
architecture, so I get to spend some time devoted solely to the crafting and optimization of targeted
algorithms. I really enjoy working on micro-optimizations and algorithmic improvements. It is a nice change of
pace from designing complicated data structures and reviewing customer requirements.


Generated time: 2022-08-11T15:12:34.656Z
This demonstrates a SSG blog that restricts generating new paths via On-Demand ISR instead of allowing any visited paths to generate a new path.
It works by leveraging `expiration: false` on a prerender to only allow generating new paths when the revalidate token is provided.


jmlr jmlr2008 jmlr2008-2 jmlr2008-2-reference knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Stefan Klanke, Sethu Vijayakumar, Stefan Schaal
Abstract: In this paper we introduce an improved implementation of locally weighted projection regression (LWPR), a supervised learning algorithm that is capable of handling high-dimensional input data. As the key features, our code supports multi-threading, is available for multiple platforms, and provides wrappers for several programming languages. Keywords: regression, local learning, online learning, C, C++, Matlab, Octave, Python
P. H. Garthwaite. An interpretation of partial least squares. Journal of the American Statistical Association, 89(425):122–127, 1994. S. Vijayakumar, A. D’Souza, T. Shibata, J. Conradt, and S. Schaal. Statistical learning for humanoid robots. Autonomous Robots, 12(1):55–69, 2002. S. Vijayakumar, A. D’Souza, and S. Schaal. Incremental online learning in high dimensions. Neural Computation, 17:2602–2634, 2005. 3. Octave is a free Matlab clone available at http://www.octave.org. We tested our library against version 2.9.12. 626


Projects
Grocery List
My first attempt at JQuery, what a magical library! Use this interactive list to add and remove grocery items, change quantities, and print. The list is organized by category.
Find the Sea Serpents
My very first JavaScript project! Hunt for the sea serpent family by clicking through the ocean until you have found them all. Hint: There are four serpents of varying length.
Bag Class
Built by me to enhance my Ruby nesting structure understanding, this bag class allows a user to pack a bag by category, add, remove, and check for items, unpack, and list off items in the bag!


Our trip counselor will guide you how to best enjoy yourself and amuse your beloved ones
Tips for singles or divorced
This is our first tip: in case you are in search of your next love affair, please consider well
visited or crowded, but quiet places,
where the music is not so loud. That way communication is easier.
Tips for pairs and young parents
This is another tip for the active lovers: please select a discreet place for your accomodation and
keep it quiet, or just let the music play too!
Tips for single parent or small families
This is another tip:cost cutting values most for single parent families.Therefore it is recommended
to search our ratings page
for user feedback concerning value for money comparisons.
Tips for larger families
This is another tip for multimember families: one needs to select special places, or venues, which
offer various activities for all members of the family,
such as ranging from museums, cinemas with playgrounds for children, to swimming, biking, hiking or
wandering in the land.
Tips for the elderly
This is another tip for the seniors in need of special medical attention. In case of emergency the
arrival time of an ambulance
will be long, depending on the season of the year and local weather. Please avoid isolated places
with limited access to pharmacies and physicians.
Special tips for specific guest groups!
Tips for nursuring your baby
This is another tip for young mothers: apart from Scandinavia, public breast feeding is not allowed
or
tolerated in most countries.
Although there are special rooms for nursuring your baby in big Malls, other public places miss that
feature. Always check beforehand
whether there is such a room available in the viccinity of the venue mentioned in the article.
Tips for the handicapped
This is our hottest tip for the disabled: note that the car or motorcycle drivers,
always seem to park the wrong way, blocking the ramps, if any available. Calling the traffic police
won't solve the problem either. Moving with
a wheelchair to our suggested destination is not always feasible.
Please check beforehand with your favorite way or app, whether the venue mentioned is really
accessible for the handicapped too. Not all public transport or stations provide a ramp for the
disabled.
Sometimes the portable ramps are misplaced and the staff does not know how to use them, or just does
not bother.
Plan timely and start earlier then early, taking into account such time waste and inconvenience!
Our leisure counselor will guide you how to best enjoy yourself and amuse your beloved ones
Tips for singles or divorced
Many of the upshots we discuss when we talk about the benefits of cycling are exercise related.
Reckon it might be easier to just go for a run?
Running is weight bearing – and therefore injury rates are higher. Cycling, by contrast to running,
is not weight bearing.
Cycling is an incredibly sociable sport. Grassroots cycling revolves around cycling club culture –
which in turn revolves around the Saturday or Sunday club run: several hours of riding at an
intensity that enables easy chat, interrupted only by a cafe stop (or the occasional puncture).
Joining a cycling club or group is an excellent way to grow your social circle, and if you’re new to
riding – you’ll probably find all the maintenance and training advice you may have been looking for
there, too.
Tips for pairs and young parents
Most of us know that sex is a good thing, but not everyone knows that it’s actually good for your
overall health.
Dr Michael Roizen, who chairs the Wellness Institute at the Cleveland Clinic, says: “The typical man
who has 350 orgasms a year,
versus the national average of around a quarter of that, lives about four years longer.” Similar
findings were revealed for women.
So can cycling improve your sex life? Well – it builds some rather essential muscle groups.
Dr Matthew Forsyth, urologist and keen cyclist from Portland, Oregon, commented: “All these muscles
[worked on the bike] are used during intercourse.
The better developed these muscles, the longer and more athletic intercourse will be.”
Tips for single parent or small families
This is another tip:cost cutting values most for single parent families.Therefore it is recommended
to search our ratings page
for user feedback concerning value for money comparisons.
Tips for larger families
This is another tip for multimember families: one needs to select special places, or venues, which
offer various activities for all members of the family,
such as ranging from museums, cinemas with playgrounds for children, to swimming, biking, hiking or
wandering in the land.
Tips for the elderly
This is another tip for the seniors in need of special medical attention. In case of emergency the
arrival time of an ambulance
will be long, depending on the season of the year and local weather. Please avoid isolated places
with limited access to pharmacies and physicians.
Recommended outdoor activities
Biking is not only good for your legs
According to cyclingweekly.com
the 14 benefits of biking
go far beyond the scope of body exercising.

Always pay attention to avalanches when on a mount.
Please check beforehand with your favorite way or app, whether the venue mentioned is really
accessible for the inexperienced wanderer as well!
Our sport counselor will guide you how to best enjoy yourself and amuse your beloved ones
Tips for all
Depending on age and stamina, choosing a sport is a matter of taste and weather. However swimming is
the ideal
suggestion for people to exercise, regardless of age, who do not want to suffer any injuries.
Tips for singles or divorced
This is our first tip: in case you are in search of your next sweetheart, please consider well
visited, but not overcrowded court, gyms or sport places,
where team sports are played. That way communication and making new friends is easier.
Tips for pairs and young parents
This is another tip for the active lovers: please select a discreet place for your accomodation and
keep it quiet, or let the music play!
Tips for single parent or small families
This is another tip:cost cutting values most for single parent families.Therefore it is recommended
to search our ratings page
for user feedback concerning value for money comparisons.
Tips for larger families
This is another tip for multimember families: one needs to select special places, or venues, which
offer various activities for all members of the family,
such as ranging from museums, cinemas with playgrounds for children, to swimming, biking, hiking or
wandering in the land.
Tips for the elderly
This is another tip for the seniors in need of special medical attention. In case of emergency the
arrival time of an ambulance will be long, depending on the season of the year and local weather.
Please avoid isolated places
with limited access to pharmacies and physicians.
What is the difference between swimming in the pool and swimming in the sea?
If you do not like clorine or the other chemicals used to clean the pool water, you can enjoy the
sea.
The salt water in the ocean will make you float better than in fresh water. This makes swimming a
little bit easier.
The sea could have all sorts of stuff in it depending on where you're swimming.
Usually it's primarily water and salt, but in areas with a lot of boat or shipping activity you
could have waste from that.
Beaches and oceans don't have lifeguards always on duty. They'll drive by and raise flags in case of
extreme currents and come out to help rescue
you if called, but unless it's a heavily populated public beach they're not always going to have
lifeguards stationed.
If you're careful and have people watching out for you, this should hopefully not be much of a risk.
Our expert tips for socializing with new people, safe burning of calories, training and amusing
sports, without injuries!
Tips for exercising not only your body, but also your spirit
Back in the seventies the iconic photo of the tennis girl promoted the sport accross the UK.This is
not the only reason however for its success: chess and tennis
are two of the sports where women can definitely compete with men. As usual in life muscular
power submits to a clever mind. Tennis is also a calorie eater, 476 per hour, but requires special
attention to avoid hurting your knees, ankles
and waist. One needs to rent a proper court in advance, the harder the court surface the faster the
game, but the worse for your knees.
What are the main benefits of swimming?
As well as being fun, swimming is a great way to keep fit and healthy and make new friends.
Swimming is a sport activity that you can continue for a lifetime.Furthermore,
swimming is a low-impact activity that has many physical and mental health benefits.
Make sure you know how to swim and do so in a safe environment.Pools are usually supervised,
but only beaches with a blue flag have a lifeguard for specific daylight hours.
Our Cooking counselor will guide you how to best enjoy yourself and amuse your beloved ones
Despite what some people may say, cooking is hard. It’s difficult to learn, involves lots of trial
and error, takes time, and makes a mess.
It might not be feasible for everyone, but having a buddy in the kitchen is a lifesaver! If you want
to learn to cook or to eat healthy,
a friend or a spouse can help keep you going, encourage you when the going gets rough, and inspire
you to try new things.
Tips for pairs and young parents
A few reasons cooking with a partner or friend is better than solo:
You’ll enjoy quality time together. One thing we love about cooking: it’s quality time spent
together and puts food on the table (hello, multitasking!). Some of our favorite memories are being
in the kitchen with people we love.
You’ll bond over new experiences. There’s something about learning a new skill with someone that
tightens the bond. You’ll have plenty of “remember when” fodder, and you’ll grow together the more
you learn.
You’ll learn how to communicate better and build a team. With an activity that requires timely
coordination to achieve a common goal, communication is key! You’ll find any communication issues
start to ferret themselves out as you cook together more.
It’ll go twice as fast (well, faster). With two in the kitchen, prep time flies by. You also can
designate someone as cleanup crew, who follows right behind the mess maker (we do this all the
time).
It’s more fun. I love cooking alone, but turn on some music, give me a glass of wine, and put my
husband by my side — that’s what I call cooking!
Tips for single parent or small families
This is another tip:cost cutting values most for single parent families.Therefore it is recommended
to search our ratings page
for user feedback concerning value for money comparisons.
Tips for larger families
Involving your children in food preparation (maybe by asking them to read the recipe out loud or mix
ingredients) is not only a fun thing to do,
but also a great way to teach them healthy eating habits.
Tips for the elderly
This is another tip for the seniors in need of special medical attention. In case of emergency the
arrival time of an ambulance
will be long, depending on the season of the year and local weather. Please avoid isolated places
with limited access to pharmacies and physicians.
Cooking or dining out?
Tips for homemade meals
According to dietitian Ben Atkinson, who manages Harborview Medical Center’s outpatient nutrition,
informatics,
and wellness programs in the Nutrition and Foodservices department the benefits are:
Saves money
Saves time
Healthier ingredients
Avoid food allergies and sensitivities
Portion control
Brings family together
Tips for the beginners
If you are in no position to use a knife, or you keep cutting your fingers
there are plenty of online and onsite, courses on cooking, ranging from novice level to advanced!
Our handycrafts counselor will guide you how to best enjoy yourself and amuse your beloved ones
Tips for singles or divorced
Έλα στο καινοτόμο εργαστήριο μάθησης, πειραματισμού και δημιουργίας για όλους
Το Maker Space είναι ένα σύγχρονο εργαστήριο εξοπλισμένο με μηχανήματα και ψηφιακά εργαλεία
σχεδιασμού,
εκτύπωσης και κατασκευής (laser-cutter, 3d printer, CNC router) που λειτουργεί σε συνεργασία με το
Πολυτεχνείο Κρήτης.
Αποτελεί την πρώτη ανοιχτή δομή αυτού του είδους στην Ελλάδα με δράσεις, που βασίζονται στην
do-it-yourself/do-it-together προσέγγιση,
και υποστηρίζουν τη συνέργεια εκπαίδευσης και τεχνολογίας πέρα από την οθόνη του υπολογιστή. Ο
εξοπλισμός του επιτρέπει την παραγωγή αντικειμένων
καθημερινής αλλά και ειδικής χρήσης, καθώς και τη δημιουργία “έξυπνων” κατασκευών. Θα είναι ανοιχτό
για εκπαιδευτικές επισκέψεις και επιμορφωτικές δράσεις
σε όλα τα σχολεία του δήμου Αθηναίων με στόχο να αναδείξει τον ουσιαστικό ρόλο των εφαρμοσμένων
ψηφιακών τεχνολογιών και πρακτικών στη μάθηση.
Tips for pairs and young parents
This is another tip for the active lovers: please select a discreet place for your accomodation and
keep it quiet, or let the music play!
Tips for single parent or small families
This is another tip:cost cutting values most for single parent families.Therefore it is recommended
to search our ratings page
for user feedback concerning value for money comparisons.
Tips for larger families
This is another tip for multimember families: one needs to select special places, or venues, which
offer various activities for all members of the family,
such as ranging from museums, cinemas with playgrounds for children, to swimming, biking, hiking or
wandering in the land.
Tips for the elderly
This is another tip for the seniors in need of special medical attention. In case of emergency the
arrival time of an ambulance
will be long, depending on the season of the year and local weather. Please avoid isolated places
with limited access to pharmacies and physicians.
Still waiting for the technician to do the repairs?
Tips for creating original craftwork and presents
Craft is no longer the sole preserve of small people with glitter glue and older people with knitting
needles.
More and more, it seems, adults are seeking to better themselves and digitally detox by making stuff
of one kind or another.
Certainly, when it comes to giving presents, the market for experiences is expanding fast and a
well-chosen craft kit offers both that and a lasting artefact in one gift.
For many, there is something undeniably satisfying about creating things oneself, even in an era
when you could almost certainly purchase a better version of the same thing online.
Tips for educating yourself, how to repair on your own your stuff
According to new EU laws all new electrical appliances will be so manufactured, that
they can be recycled and repaired using genuine spare parts, avoiding the costs for buying a new
one, or
wasting materials by throwing it all away.
This follows the do it youself movement of citizens, offering free or low cost handson courses
taught by retired technicians in every big city.
Thanks for taking the time to send us your valuable feedback!
Please help us to improve and be as specific as possible with regards to the subject.
In case of an error or misspelling, do report also the section or article mentioned.
Our staff gets introduced to you!
This is our famous founder
These are our valuable partners
Thanks for visiting our site!
Satisfied customers is the key to having satisfied employees and partners.
Please help us to improve and be as open as possible with regards to the quality of our work.
Our home page introduces you to all our contents!
Please click on an image to navigate to the relevant article...
Although visiting a quality music concert or the theatre, has become a financial burden in many big
cities,
there is still a demanding public wishing to learn more about forthcoming events.
Please suggest any new topic of interest concerning the arts & science, to advance ourselves
and help us to improve.Do be as open as possible via feedback, with regards to the quality of our
work.
Thanks for visiting our site!
Satisfied customers is the key to having satisfied employees and partners.
Please help us to improve and be as open as possible with regards to the quality of our work.
Thanks for taking the time to send us your valuable feedback!
Please help us to improve and be as specific as possible with regards to the subject.
In case of an error or misspelling, do report also the section or article mentioned.


uber
Businessmodel of Uber
Customer Segments
The passengers comprise the most immediately obvious customer segment. Like many traditional taxi companies, Uber offers cheaper rides to appeal to a mass market, but also premium rides for wealthier consumers.
However, unlike in traditional taxi companies, Uber’s driver-partners may also be regarded as customers, rather than employees. Rather than taking a salary, Uber keeps a commission from their takings in exchange for granting them the privilege of using their platform. Being a driver-partner is an attractive prospect for a wide range of people, including those seeking full-time employment, but also those who happen to already have a car and a smart-phone and are looking to earn some extra money in their spare time. This ease of use helps to ensure a regular supply of driver-partners is available.
Value Proposition
Uber’s value derives in large part from the fact that it is able to offer cheaper rides than traditional taxi companies. Uber’s sophisticated algorithmic infrastructure also ensures that Uber drivers often arrive quicker than their traditional rivals.
For drivers, Uber’s value proposition is rooted in their ability to provide anyone with a car and a smart-phone the opportunity to earn money while working to their own schedule.
By using sophisticated algorithms, Uber is able to match consumers’ demand for lifts to drivers’ demand for work. When demand is high, prices surge, encouraging more driver partners to get out and work while the inverse happens outside of peak hours. Enabled by modern smart-phone technology, Uber have essentially invented a system that enables the personal transport market to operate more efficiently. This concept is absolutely vital to Uber’s value proposition and also key to understanding why its business model is so radically disruptive.
Channels
The channel through which drivers and passengers use Uber’s services is through its smart-phone apps. Separate apps exist for drivers and passengers, and versions are offered for all the main mobile operating systems.
Uber has proved adept at social-media marketing and public relations. Sophisticated campaigns are run before Uber opens in new cities to get the public excited about the services they offer. Uber also regularly makes headlines for its rapid growth and its consistently impressive ability to secure massive investment, which also provides it with an additional advertising channel.
Customer Relationship
Customers’ relationships with Uber are highly automated, occurring mostly through the Uber smart-phone app. The app allows passengers to review drivers and vice-versa, providing a safer experience for both parties.
Drivers undergo a vetting-process to ensure that they are suitable for the role and are not, for example, sex-offenders who may jeopardize the safety of passengers.
To further ensure a smooth relationship with both passengers and drivers, Uber provides extensive online guides to their services for both types of customer, as well as offering customer service phone lines and web chat facilities.
Key Activities
Despite appearances, Uber is not a transport provider like its traditional taxi-cab rivals. Rather, Uber provides a platform on which registered freelance drivers are able to sell their transport services to consumers.
Uber’s main activities are therefore developing and marketing their mobile app. Uber continually works on the app’s algorithms to ensure the most efficient experience possible for drivers and passengers.
Uber’s other main activity is their driver on-boarding process, where potential driver-partners’ suitability for the role is assessed.
Key Partners
Uber’s drivers are key partners. In Uber’s business model, it is important that these are partners, rather than employees as this substantially decreases Uber’s running costs.
Payment processors are another key partner, since Uber drivers may not take cash in nearly all regions. Uber uses two main payment processors, Braintree and Stripe.
The Uber app is dependent on mapping data and GPS networks to efficiently connect drivers with passengers. This is achieved through a partnership with GoogleMaps.
In many jurisdictions, including the UK, all Uber employees must register as licensed cab-drivers. This means that Uber must maintain partnerships with local authorities to ensure that their business does not violate the laws of the areas they operate in and that all driver-partners are acting legitimately.
In many cases, Uber expends substantial resources on lobbying and maintaining good relationships with local authorities to ensure that their business can continue to operate in as many cities as possible.
Key Resources
Uber’s greatest resource is the platform it has created that connects passengers to drivers based on proximity. Algorithms are also key resources for Uber.
Their highly developed routing and pricing algorithms are an invaluable resource that enables Uber to typically provide a more efficient service than traditional taxi-firms.
Cost-Structure
Uber expends a huge amount on making sure their app runs smoothly, hiring a large number of world-class software engineers to ensure the optimum functioning of the platform.
It also employs city managers and a number of marketing teams in various locales.
Paying driver-partners for their services is another key expenditure. Here, Uber incurs lower running costs than traditional taxi companies because, as self-employed contractors, driver-partners are not entitled to all the perks associated with employment in many jurisdictions and must own and maintain their own cars.
Uber has also been known to invest heavily in political lobbyists to convince governmental authorities of Uber’s positive effect on local economies and to persuade legislators not to enact laws that would prevent Uber operating in certain areas.
Revenue Streams
Uber’s revenue stream is very simple. The app bills passengers based on number of miles travelled plus any surcharges that may be incurred, for example during peak hours. Some premium rides in more expensive cars are also offered at higher prices.


Bases: object
Base class for all models.
This is an abstract class and should not be instantiated directly.
This class sets the constraints and other properties for all individual parameters and performs parameter validation.
| Parameters: | name : str, optional
fixed : dict, optional
tied : dict, optional
bounds : dict, optional
eqcons : list, optional
ineqcons : list, optional
|
|---|
Examples
>>> from astropy.modeling import models
>>> def tie_center(model):
... mean = 50 * model.stddev
... return mean
>>> tied_parameters = {'mean': tie_center}
Specify that 'mean' is a tied parameter in one of two ways:
>>> g1 = models.Gaussian1D(amplitude=10, mean=5, stddev=.3,
... tied=tied_parameters)
or
>>> g1 = models.Gaussian1D(amplitude=10, mean=5, stddev=.3)
>>> g1.mean.tied
False
>>> g1.mean.tied = tie_center
>>> g1.mean.tied
<function tie_center at 0x...>
Fixed parameters:
>>> g1 = models.Gaussian1D(amplitude=10, mean=5, stddev=.3,
... fixed={'stddev': True})
>>> g1.stddev.fixed
True
or
>>> g1 = models.Gaussian1D(amplitude=10, mean=5, stddev=.3)
>>> g1.stddev.fixed
False
>>> g1.stddev.fixed = True
>>> g1.stddev.fixed
True
Attributes Summary
| bounds | A dict mapping parameter names to their upper and lower bounds as (min, max) tuples. |
| eqcons | List of parameter equality constraints. |
| fittable | bool(x) -> bool |
| fixed | A dict mapping parameter names to their fixed constraint. |
| ineqcons | List of parameter inequality constraints. |
| inputs | tuple() -> empty tuple |
| inverse | Returns a new Model instance which performs the inverse transform, if an analytic inverse is defined for this model. |
| linear | bool(x) -> bool |
| model_constraints | tuple() -> empty tuple |
| model_set_axis | The index of the model set axis–that is the axis of a parameter array that pertains to which model a parameter value pertains to–as specified when the model was initialized. |
| n_inputs | The number of inputs to this model. |
| n_outputs | The number of outputs from this model. |
| name | User-provided name for this model instance. |
| outputs | tuple() -> empty tuple |
| param_dim | Deprecated since version 0.4. |
| param_names | tuple() -> empty tuple |
| param_sets | Return parameters as a pset. |
| parameter_constraints | tuple() -> empty tuple |
| parameters | A flattened array of all parameter values in all parameter sets. |
| standard_broadcasting | bool(x) -> bool |
| tied | A dict mapping parameter names to their tied constraint. |
Methods Summary
| __call__(*inputs, **kwargs) | Evaluate this model using the given input(s) and the parameter values that were specified when the model was instantiated. |
| add_model(*args, **kwargs) | Deprecated since version 1.0. |
| copy() | Return a copy of this model. |
| evaluate(*args, **kwargs) | Evaluate the model on some input variables. |
| prepare_inputs(*inputs, **kwargs) | This method is used in __call__ to ensure that all the inputs to the model can be broadcast into compatible shapes (if one or both of them are input as arrays), particularly if there are more than one parameter sets. |
| prepare_outputs(format_info, *outputs, **kwargs) | |
| rename(name) | Creates a copy of this model class with a new name. |
Attributes Documentation
List of parameter equality constraints.
List of parameter inequality constraints.
The name(s) of the input variable(s) on which a model is evaluated.
Returns a new Model instance which performs the inverse transform, if an analytic inverse is defined for this model.
Even on models that don’t have an inverse defined, this property can be set with a manually-defined inverse, such a pre-computed or experimentally determined inverse (often given as a PolynomialModel, but not by requirement).
Note to authors of Model subclasses: To define an inverse for a model simply override this property to return the appropriate model representing the inverse. The machinery that will make the inverse manually-overridable is added automatically by the base class.
Primarily for informational purposes, these are the types of constraints that constrain model evaluation.
The index of the model set axis–that is the axis of a parameter array that pertains to which model a parameter value pertains to–as specified when the model was initialized.
See the documentation on Model Sets for more details.
The number of inputs to this model.
Equivalent to len(model.inputs).
The number of outputs from this model.
Equivalent to len(model.outputs).
User-provided name for this model instance.
The name(s) of the output(s) of the model.
Deprecated since version 0.4: The param_dim function is deprecated and may be removed in a future version. Use len(model) instead.
Names of the parameters that describe models of this type.
The parameters in this tuple are in the same order they should be passed in when initializing a model of a specific type. Some types of models, such as polynomial models, have a different number of parameters depending on some other property of the model, such as the degree.
When defining a custom model class the value of this attribute is automatically set by the Parameter attributes defined in the class body.
Return parameters as a pset.
This is a list with one item per parameter set, which is an array of that parameter’s values across all parameter sets, with the last axis associated with the parameter set.
Primarily for informational purposes, these are the types of constraints that can be set on a model’s parameters.
A flattened array of all parameter values in all parameter sets.
Fittable parameters maintain this list and fitters modify it.
Methods Documentation
Evaluate this model using the given input(s) and the parameter values that were specified when the model was instantiated.
Deprecated since version 1.0: The add_model function is deprecated and may be removed in a future version. Use Use Model operators (TODO: link to compound model docs instead.
Create a CompositeModel by chaining the current model with the new one using the specified mode.
| Parameters: | model : an instance of a subclass of Model mode : string
|
|---|---|
| Returns: | model : CompositeModel
|
Return a copy of this model.
Uses a deep copy so that all model attributes, including parameter values, are copied as well.
Evaluate the model on some input variables.
This method is used in __call__ to ensure that all the inputs to the model can be broadcast into compatible shapes (if one or both of them are input as arrays), particularly if there are more than one parameter sets.
Creates a copy of this model class with a new name.
The new class is technically a subclass of the original class, so that instance and type checks will still work. For example:
>>> from astropy.modeling.models import Rotation2D
>>> SkyRotation = Rotation2D.rename('SkyRotation')
>>> SkyRotation
<class '__main__.SkyRotation'>
Name: SkyRotation (Rotation2D)
Inputs: ('x', 'y')
Outputs: ('x', 'y')
Fittable parameters: ('angle',)
>>> issubclass(SkyRotation, Rotation2D)
True
>>> r = SkyRotation(90)
>>> isinstance(r, Rotation2D)
True


Welcome to Japotech solutions, the number one provider of quality & Cost Effective Telecom, IT &Training Services
Home/office routers&Mi-Fi internet installations
Professional certified training courses in IT, telecom engineering
We offer reliable telecommunication services
The best mobile and web app developers in the country
Formed in 2016 by a team of seasoned ICT experts, Japotech has become one of the most celebrated Ugandan ICT companies and is now ISO9001 certified. Several customers ranging from telcos, SMEs, NGOs and households have satisfactorily enjoyed the services offered by Japotech Solutions.
Japotech has installed, commissioned and optimized more than 100 telco sites, installed internet services for more than 200 customers, built websites for a couple organizations, trained several clients; still counting. Japotech has been able to export services in Morocco, Italy, Spain, Niger and is very focused on building its international portfolio.
Allen works as a software developer at Japotech solutions.She makes sure all our clients needs are met. Additionally, she is a STEM Educator.
See profileHanifa works as our field manager at Japotech solutions. She makes sure all our clients needs are met. Additionally, she is a lawyer
See profileJayne works as our senior software developer at Japotech solutions. She makes sure all our clients needs are met. She is a systems analyst
See profileJuliet works as our Country supervisor at Japotech solutions. She supervises all country sales agents Additionally, she is a Nurse
See profile


Below value must be provided via form.
Below value can be provided via Settings, or override it via form.
Below value is optional, if Paypal email available, paypal price and invoice number will be validated.


Proxy middleware for Deno Oak HTTP servers.
import { proxy } from<EMAIL_ADDRESS>import { Application } from<EMAIL_ADDRESS>const app = new Application();
app.use(proxy("https://github.com/oakserver/oak"));
await app.listen({ port: 3000 });
This is a Deno module available to import direct from this repo and via the Deno Registry.
Before importing, download and install Deno.
You can then import oak-http-proxy straight into your project:
import { proxy } from<EMAIL_ADDRESS>oak-http-proxy is also available on nest.land, a package registry for Deno on the Blockchain.
import { proxy } from<EMAIL_ADDRESS>The url argument that can be a string, URL or a function that returns a string or URL. This is used as the url to proxy requests to. Query string parameters and hashes are transferred from incoming request urls onto the proxy url.
router.get("/string", proxy("http://google.com"));
router.get("/url", proxy(new URL("http://google.com")));
router.get("/function", proxy((ctx) => new URL("http://google.com")));
Note: Unmatched path segments of the incoming request url are not transferred to the outbound proxy URL. For dynamic proxy urls use the function form.
You can also provide several options which allow you to filter, customize and decorate proxied requests and responses.
app.use(proxy("http://google.com", proxyOptions));
The filterReq
option can be used to limit what requests are proxied.
Return false to continue to execute the proxy; return true to skip the proxy for this request.
app.use(
"/proxy",
proxy("www.google.com", {
filterReq: (req, res) => {
return req.method === "GET";
},
})
);
Promise form:
app.use(
proxy("localhost:12346", {
filterReq: (req, res) => {
return new Promise((resolve) => {
resolve(req.method === "GET");
});
},
})
);
Decorate the inbound response object from the proxied request.
router.get(
"/proxy",
proxy("www.google.com", {
srcResDecorator: (req, res, proxyRes, proxyResData) => {
data = JSON.parse(proxyResData.toString("utf8"));
data.newProperty = "exciting data";
return JSON.stringify(data);
},
})
);
app.use(
proxy("httpbin.org", {
srcResDecorator: (req, res, proxyRes, proxyResData) => {
return new Promise((resolve) => {
proxyResData.message = "Hello Deno!";
setTimeout(() => {
resolve(proxyResData);
}, 200);
});
},
})
);
When your proxied service returns 304 Not Modified this step will be skipped, since there should be no body to decorate.
The intent is that this be used to modify the proxy response data only.
Note: The other arguments are passed by reference, so you can currently exploit this to modify either response's headers, for instance, but this is not a reliable interface.
Defaults to true
.
When true, the url
argument will be parsed on first request, and memoized for subsequent requests.
When false
, url
argument will be parsed on each request.
For example:
function coinToss() {
return Math.random() > 0.5;
}
function getUrl() {
return coinToss() ? "http://yahoo.com" : "http://google.com";
}
app.use(
proxy(getUrl, {
memoizeUrl: false,
})
);
In this example, when memoizeUrl: false
, the coinToss occurs on each request, and each request could get either value.
Conversely, When memoizeUrl: true
, the coinToss would occur on the first request, and all additional requests would return the value resolved on the first request.
Decorate the inbound response headers from the proxied request.
router.get(
"/proxy",
proxy("www.google.com", {
srcResHeaderDecorator(headers, req, res, proxyReq, proxyRes) {
return headers;
},
})
);
Allows you to inspect the proxy response, and decide if you want to continue processing (via oak-http-proxy) or continue onto the next middleware.
router.get(
"/proxy",
proxy("www.google.com", {
filterRes(proxyRes) {
return proxyRes.status === 404;
},
})
);
By default, oak-http-proxy
will throw any errors except ECONNRESET
and ECONTIMEDOUT
via ctx.throw(err)
, so that your application can handle or react to them, or just drop through to your default error handling.
If you would like to modify this behavior, you can provide your own proxyErrorHandler
.
// Example of skipping all error handling.
app.use(
proxy("localhost:12346", {
proxyErrorHandler(err, ctx, next) {
ctx.throw(err);
},
})
);
// Example of rolling your own error handler
app.use(
proxy("localhost:12346", {
proxyErrorHandler(err, ctx, next) {
switch (err && err.code) {
case "ECONNRESET": {
ctx.response.status = 405;
return;
}
case "ECONNREFUSED": {
ctx.response.status = 200;
return;
}
default: {
ctx.throw(err)
}
}
},
})
);
Decorate the outbound proxied request url.
The returned url is used for the fetch
method internally.
router.get(
"/proxy",
proxy("www.google.com", {
proxyReqUrlDecorator(url, req) {
url.pathname = "/";
return url;
},
})
);
You can also use Promises:
router.get(
"/proxy",
proxy("localhost:3000", {
proxyReqOptDecorator(url, req) {
return new Promise((resolve, reject) => {
if (url.pathname === "/login") {
url.port = 8080;
}
resolve(url);
});
},
})
);
Generally it is advised to use the function form for the passed URL argument as this provides the full context object whereas the proxyReqOptDecorator
passes only the context.request
object.
Potential use cases for proxyReqOptDecorator
include:
Decorate the outbound proxied request initialization options.
This configuration will be used within the fetch
method internally to make the request to the provided url.
router.get(
"/proxy",
proxy("www.google.com", {
proxyReqInitDecorator(proxyReqOpts, srcReq) {
// you can update headers
proxyReqOpts.headers.set("Content-Type", "text/html");
// you can change the method
proxyReqOpts.method = "GET";
return proxyReqOpts;
},
})
);
You can also use Promises:
router.get(
"/proxy",
proxy("www.google.com", {
proxyReqOptDecorator(proxyReqOpts, srcReq) {
return new Promise((resolve, reject) => {
proxyReqOpts.headers.set("Content-Type", "text/html");
resolve(proxyReqOpts);
});
},
})
);
Normally, your proxy request will be made on the same protocol as the url
parameter. If you'd like to force the proxy request to be https, use this option.
app.use(
"/proxy",
proxy("http://www.google.com", {
secure: true,
})
);
Note: if the proxy is passed a url without a protocol then HTTP will be used by default unless overridden by this option.
You can copy the host HTTP header to the proxied Oak server using the preserveHostHeader
option.
router.get(
"/proxy",
proxy("www.google.com", {
preserveHostHeader: true,
})
);
The parseReqBody
option allows you to control whether the request body should be parsed and sent with the proxied request. If set to false
then an incoming request body will not be sent with the proxied request.
Configure whether the proxied request body should be sent as a UInt8Array buffer.
Ignored if parseReqBody
is set to false
.
router.get(
"/proxy",
proxy("www.google.com", {
reqAsBuffer: true,
})
);
The request body encoding to use. Currently only "utf-8" is supported.
Ignored if parseReqBody
is set to false
.
router.get(
"/post",
proxy("httpbin.org", {
reqBodyEncoding: "utf-8",
})
);
Configure a timeout in ms for the outbound proxied request.
If not provided the request will never time out.
Timed-out requests will respond with 504 status code and a X-Timeout-Reason header.
router.get(
"/",
proxy("httpbin.org", {
timeout: 2000, // in milliseconds, two seconds
})
);
oak-http-proxy is licensed under the MIT License.
Generated using TypeDoc