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#assign n0_docs="true"> <#assign n1_query_find="true"> <#assign findList = "true">
Returns a list of beans for the query.
```java List<|Customer> customers = new QCustomer() .email.istartsWith("rob") .findList(); ```Note that we hold all the beans in memory when executing this query. As such it is good when we expect to fetch hundreds of beans but we should consider using a findEach query when we look to query and read a very large number of results.


Web Design and development for your products or services. Deployment to free or paid hosting. Technical updates and maintenance.
Website optimization to increase search engine ranking of your business. Itegration with social media networks.
Promotion of your products or services via social media. Registration and maintenance of internet business accounts (Yelp!, Google Business).
Hi! I'm Serhii
I am a marketing agency of one. I help small business with promoting their products and services via internet. I will increase your business exposure, attract new customers and raise your website ranking in search engines. Let's get in touch and chat about growing your business!


Note: This Java Plug-in guide describes features released prior to the Java SE 6 update 10 release. See Java Rich Internet Applications Development and Deployment document for information on the next generation Java Plug-in released in Java SE 6 update 10.
Java™ Plug-in enables applets written to the Java SE Platform 6 specification to be run in Mozilla and Internet Explorer web browsers. Java Plug-in comes with the 6 JRE/JDK.
Java Plug-in Developer Guide The Java Plug-in Developer Guide provides documentation in the following areas:


fork me at https://github.rtp.raleigh.ibm.com/et-dev-advocates/bluemix-car-rally.git
Data on car charging locations comes from Open Charge Map.
The mapping capability comes from the Google Static Maps API.
The sample customer name data was generated from generatedata.com.
The sample city/state/geo data was generated from the US National Weather Service.
package: {{pkg.name}}, version: {{pkg.version}}


array_values
(PHP 4, PHP 5, PHP 7)
array_values — 返回数组中所有的值
说明
array array_values ( array $input )
array_values() 返回 input 数组中所有的值并给其建立数字索引。
参数
input
数组。
返回值
返回含所有值的索引数组。
范例
Example #1 array_values() 例子
"XL", "color" => "gold");
print_r(array_values($array));
?>
以上例程会输出：
Array
(
[0] => XL
[1] => gold
)
参见
• array_keys() - 返回数组中部分的或所有的键名
User Contributed Notes 25 notes
===============================================================================
Remember, array_values() will ignore your beautiful numeric indexes, it will
renumber them according tho the 'foreach' ordering:
11,
1 => 22,
2 => 33,
);
$a[0] = 44;
print_r( array_values( $a ));
==>
Array(
[0] => 11
[1] => 22
[2] => 33
[3] => 44
)
?>
===============================================================================
This is another way to get value from a multidimensional array, but for
versions of php >= 5.3.x
1 ? $val : array_pop($val);
}
$arr = array(
'foo' => 'foo',
'bar' => array(
'baz' => 'baz',
'candy' => 'candy',
'vegetable' => array(
'carrot' => 'carrot',
)
),
'vegetable' => array(
'carrot' => 'carrot2',
),
'fruits' => 'fruits',
);
var_dump(array_value_recursive('carrot', $arr)); // array(2) { [0]=> string(6)
"carrot" [1]=> string(7) "carrot2" }
var_dump(array_value_recursive('apple', $arr)); // null
var_dump(array_value_recursive('baz', $arr)); // string(3) "baz"
var_dump(array_value_recursive('candy', $arr)); // string(5) "candy"
var_dump(array_value_recursive('pear', $arr)); // null
?>
===============================================================================
Most of the array_flatten functions don't allow preservation of keys. Mine
allows preserve, don't preserve, and preserve only strings (default).
// recursively reduces deep arrays to single-dimensional arrays
// $preserve_keys: (0=>never, 1=>strings, 2=>always)
function array_flatten($array, $preserve_keys = 1, &$newArray = Array()) {
foreach ($array as $key => $child) {
if (is_array($child)) {
$newArray =& array_flatten($child, $preserve_keys, $newArray);
} elseif ($preserve_keys + is_string($key) > 1) {
$newArray[$key] = $child;
} else {
$newArray[] = $child;
}
}
return $newArray;
}
// Tests
$array = Array(
'A' => Array(
1 => 'foo',
2 => Array(
'a' => 'bar'
)
),
'B' => 'baz'
);
echo 'var_dump($array);'."\n";
var_dump($array);
echo 'var_dump(array_flatten($array, 0));'."\n";
var_dump(array_flatten($array, 0));
echo 'var_dump(array_flatten($array, 1));'."\n";
var_dump(array_flatten($array, 1));
echo 'var_dump(array_flatten($array, 2));'."\n";
var_dump(array_flatten($array, 2));
?>
===============================================================================
code till dawn! -mark meves!
===============================================================================
If you are looking for a way to count the total number of times a specific
value appears in array, use this function:
$value)
{
if ($value == $match)
{
$count++;
}
}
return $count;
}
?>
This should really be a native function of PHP.
===============================================================================
I needed a function that recursively went into each level of the array to order
(only the indexed) arrays... and NOT flatten the whole thing.
Solution:
$val)
if(array_values($val) === $val)
$arr[$key] = array_values_recursive($val);
return $arr;
}
?>
===============================================================================
"XL", "color" => "gold","x" => " ","y" => "gold","z"
=> "");
print_r(array_values($array));
?>
wil result:
Array ( [0] => XL [1] => gold [2] => [3] => gold [4] => )
===============================================================================
'apple',
'fruit2' => 'orange',
'fruit3' => ' ',
'fruit4' => ' ',
'fruit5' => 'apple');
for ($i = 0; $i < count($array); $i++) {
$key=key($array);
$val=$array[$key];
if ($val<> ' ') {
echo $key ." = ". $val ."
";
}
next($array);
}
/*
fruit1 = apple
fruit2 = orange
fruit5 = apple
*/
?>
===============================================================================
Just a warning that re-indexing an array by array_values() may cause you to
reach the memory limit unexpectly.
For example, if your PHP momory_limits is 8MB,
and says there's a BIG array $bigArray which allocate 5MB of memory.
Doing this will cause PHP exceeds the momory limits:
It's because array_values() does not re-index $bigArray directly,
it just re-index it into another array, and assign to itself later.
===============================================================================
extract all values from a multi dimesnsional array or a nexted json object
function array_keys_multi($array,&$vals)
{
foreach ($array as $key => $value) {
if (is_array($value)) {
array_keys_multi($value,$vals);
}else{
$vals[] = $value;
}
}
return $vals;
}
===============================================================================
Please note that 'wellandpower at hotmail.com's recursive merge doesn't work.
Here's the fixed version:
===============================================================================
/**
* Return the new array from the offset index
*/
function array_values_from($array, $offset_index = 0) {
if (!is_array($array))
return null;
$index = (int)$offset_index;
foreach($array as $i => $value)
$array_return[$index++] = $value;
return $array_return;
}
===============================================================================
Non-recursive simplest array_flatten.
===============================================================================
A modification of wellandpower at hotmail.com's function to perform
array_values recursively. This version will only re-index numeric keys, leaving
associative array indexes alone.
$value) {
if (is_numeric($key)) {
$temp[] = is_array($value) ? array_values_recursive($value) :
$value;
} else {
$temp[$key] = is_array($value) ? array_values_recursive($value) :
$value;
}
}
return $temp;
}
?>
===============================================================================
This function is an improved and faster version of the one posted by
<27-Apr-2004 09:47>
===============================================================================
The function here flatterns an entire array and was not the behaviour I
expected from a function of this name.
I expected the function to flattern every sub array so that all the values were
aligned and it would return an array with the same dimensions as the imput
array, but as per array_values() adjusting the keys rater than removing them.
In order to do this, you will want this function:
function array_values_recursive($array) {
$temp = array();
foreach ($array as $value) {
if(is_array($value)) { $temp[] = array_values_recursive($value); }
else { $temp[] = $value; }
}
return $temp;
}
Hopefully this will assist.
===============================================================================
A comment on array_merge mentioned that array_splice is faster than array_merge
for inserting values. This may be the case, but if your goal is instead to
reindex a numeric array, array_values() is the function of choice. Performing
the following functions in a 100,000-iteration loop gave me the following
times: ($b is a 3-element array)
array_splice($b, count($b)) => 0.410652
$b = array_splice($b, 0) => 0.272513
array_splice($b, 3) => 0.26529
$b = array_merge($b) => 0.233582
$b = array_values($b) => 0.151298
===============================================================================
same array_flatten function, compressed and preserving keys.
function array_flatten($a,$f=array()){
if(!$a||!is_array($a))return '';
foreach($a as $k=>$v){
if(is_array($v))$f=array_flatten($v,$f);
else $f[$k]=$v;
}
return $f;
}
===============================================================================
Remember, that the following way of fetching data from a mySql-Table will do
exactly the thing as carl described before: An array, which data may be
accessed both by numerical and DB-ID-based Indexes:
Hope I haven't misunderstood anything here.. :)
===============================================================================
Indeed you can, and that's what's so great about it. I have, for instance, a
function that returns the results of a database query as an array. I want to
keep the order that the entries were returned in, but at the same time I want
to be able to access them _either_ by the position _or_ by some other index
(such as some sort of ID in the database, gotten from elsewhere). In this case,
I can make the function return an array from id to [array of values], and by a
simple call to array_values() this is transformed into an array indexed from 0
to count()-1. Useful.
===============================================================================
Note that in a multidimensional array, each element may be identified by a
_sequence_ of keys, i.e. the keys that lead towards that element. Thus
"preserving keys" may have different interpretations. Ivan's function for
example creates a two-dimensional array preserving the last two keys. Other
functions below create a one-dimensional array preserving the last key. For
completeness, I will add a function that merges the key sequence by a given
separator and a function that preserves the last n keys, where n is arbitrary.
0) {
list($prefix, $array) = array_pop($stack);
foreach ($array as $key => $value) {
$new_key = $prefix . strval($key);
if (is_array($value))
array_push($stack, array($new_key . $sep, $value));
else
$result[$new_key] = $value
}
}
return $result;
}
/*
* Flattening a multi-dimensional array into an
* n-dimensional one. The last n keys of each element are
* preserved. If this results in ambiguities, results are
* undefined.
*
* a[x_1][x_2]...[x_m] becomes a[x_{m-n+1}]...[x_m]
*/
function array_flatten_n($array, $n) {
$result = array();
$stack = array();
array_push($stack, array(array(), $array));
while (count($stack) > 0) {
list($prefix, $array) = array_pop($stack);
foreach ($array as $key => $value) {
if (is_array($value)) {
$new_prefix = array_values($prefix);
array_push($new_prefix, $key);
if (count($new_prefix) >= n)
array_shift($new_prefix);
array_push($stack, array($new_prefix, $value));
} else {
$array = $result;
foreach ($prefix as $pkey) {
if (!is_array($array[$pkey]))
$array[$pkey] = array();
$array = $array[$pkey];
}
$array[$key] = $value;
}
}
}
return $result;
}
?>
===============================================================================
Good function, if you want to acces associative array element by position:
'apple', 'juice'=>'orange', 'color'=>'lime');
$array = array_values($array);
echo $array[2];
?>
===============================================================================
@Yassin Ezbakhe
When we have to flatten multidimensional array of strings or numbers this
method could be much faster.
Inconvenience of this method is, that its speed depends on size of strings/
numbers, which array contains - bigger strings, lower efficiency.
Conclusion: Use this method for small amount of data in arrays (less than 500B
per element in my case) which have many dimensions, in other case, use Yassin
Ezbakhe method.
===============================================================================
$value) {
if (is_array($value)) {
$result = array_merge($result, array_flatten($value));
}
else {
$result[$key] = $value;
}
}
return $result;
}
?>
===============================================================================
In case you want to replace all keys in multiarrays by integers starting at 0,
the following function might help.


{% extends "base.html" %} {% block content %}
Code for America Brigades are local volunteer groups that bring together community members to help make government work better. Brigades use technology to build new tools to help with local civic issues. Code for America supports Brigade chapters with resources, tools, and access to the wider civic technology movement.


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Show Path
Hello... DADDY!!
Ready to have a peek at the roller coaster ride of your life??
↓
Fasten your seat belt!! GET.. SET.. GO!!
↓
So.. That's Mr.Handsome!
Sema comedy pa.. ! :D
Trust me guys! I am learning..!
THAT DEMANDS..
AN EXPLANATION!!
Dad.. u r kidding right??
Mr.Handsome strikes again!
Canada - Here I come!
Hangouts!!
Nope.. There is no way out..!
Made for each other!
Fashion.. i guess..
As-salamu alaykum!
Meha.. COMING SOON!
The cutest girl of the era! :)
That's what u call a happy family!
Our endless trips!
Me.. in the safest hands!
Our dream house!
Love you dad! You are the best!
HAPPY FATHER'S DAY!


AdaBoost
1. Boosting
Problems in machine learning often consist of a large number of features. This can cause difficulties in training and generalization properties. Boosting is a type of algorithm that focuses on the most relevant features iteratively, selecting only those features that improve the model.
Consider a binary classification model with labels \(\{-1,1\}\). In adaboost or adaptive boosting, we fit a series of weak-learners iteratively. A weak-learner is an algorithm that performs only slightly better than chance. An example can be a decision tree with small depth. At each step in adaboosting, a new weak learner is fitted to the data but using different weights so that the algorithm focus on the datapoints it finds harder to classify.
After the mth-step the classifier will have the form
\[C_{m}(x)=\alpha_1h_1(x)+\ldots+\alpha_{m}h_{m}(x)\]In each step we minimize the exponential loss function by choosing $\alpha$. At the mth step this loss function is
\[\frac{1}{N}\sum_{i=1}^N e^{-y_i C_m(x_i)}=\frac{1}{N}\sum_{i=1}^N e^{-y_i C_{m-1}(x_i)-y_ih_m(x_i)\alpha_m}=\sum_i \omega_i e^{-y_ih_m(x_i)\alpha_m}\]where $\omega_i=e^{-y_iC_{m-1}(x_i)}/N$ is a weight, and we fit the mth weak learner $h_m(x)$ on the data weighted by $\omega_i$. Differentiating with respect to $\alpha_m$ and setting to zero we obtain
\[\begin{equation*}\begin{split}&\sum_i\omega_i y_ih_m(x_i)e^{-y_ih_m(x_i)\alpha_m}=0\iff \\ &\sum_{y_i=h_m(x_i)}\omega_ie^{-\alpha_m}-\sum_{y_i\neq h_m(x_i)}\omega_ie^{\alpha_m}=0\iff \frac{\sum_{y_i=h_m(x_i)}\omega_i}{\sum_{y_i\neq h_m(x_i)}\omega_i}=e^{2\alpha_m}\end{split}\end{equation*}\]Normalizing the weights such that $\sum_i\omega_i=1$, we calculate the parameter $\alpha_m$ as
\[\alpha_m=\frac{1}{2}\ln\Big(\frac{1-\sum_{y_i\neq h_m(x_i)}\omega_i}{\sum_{y_i\neq h_m(x_i)}\omega_i}\Big)=\frac{1}{2}\ln\Big(\frac{1-\epsilon_m}{\epsilon_m}\Big)\]where $\epsilon_m$ is the weighted error
\[\epsilon_m=\sum_{y_i\neq h_m(x_i)}\omega_i\]For $m=1$, the first step, the weights are $\omega_i=1/N$.
In summary the algorithm consists:
w=1/N # weight initialization
learners=[] # list of weak learners
for i in range(T):
Weak_Learner.fit(x_train,y_train,weights=w)
error=Weak_Learner.score(x_train,y_train,weights=w)
alpha=0.5 log(1-error/error)
learners.append(alpha*Weak_Learner)
w=Weight.recalculate(w)
#predictor function
def C(x):
prediction=0
for wl in learners:
prediction+=wl.predict(x)
return sign(prediction)
2. Decision Boundary
We fit an Adaboost classifier to a dataset consisting of two sets of points, red and blue, normally distributed. Below is the Adaboost prediction after six steps.
And below we present the prediction of its six estimators in the order of training, from left to right
At each step we superimpose the prediction from the previous estimatores:
One can see that at each step the alogrithm tries to “fix” the misclassified points.
With more estimators, the decision boundary becomes more complex
3. Python Implementation
We build a class node that stores the weak learners. The attribute “next” points to the next weak-learner in the series.
class node:
def __init__(self,alpha=None,tree=None):
self.tree=tree
self.alpha=alpha
self.next=None
def insert(self,alpha,tree):
if self.next is None:
self.next=node(alpha,tree)
else:
return self.next.insert(alpha,tree)
The class Adaboost contains fit and predict methods.
class AdaBoost:
def __init__(self,T):
self.T=T
self.head=node(alpha=1,tree=DecisionTreeClassifier(max_depth=1))
def step(self,ypred,y,sample_weight):
error=1-accuracy_score(y,ypred,sample_weight=sample_weight)
if error==1.0:
return 'stop'
else:
alpha=0.5*np.log((1-error)/error)
sample_weight=sample_weight*np.exp(-y*ypred*alpha)
sample_weight=sample_weight/sample_weight.sum()
return alpha, sample_weight
def fit(self,x,y):
sample_weight=np.ones(x.shape[0])
self.head.tree.fit(x,y)
ypred=self.head.tree.predict(x)
alpha, sample_weight=self.step(ypred,y,sample_weight)
self.head.alpha=alpha
for i in range(1,self.T):
tree=DecisionTreeClassifier(max_depth=1)
tree.fit(x,y,sample_weight=sample_weight)
ypred=tree.predict(x)
alpha, sample_weight=self.step(ypred,y,sample_weight)
self.head.insert(alpha,tree)
def read(self,node,x):
ypred=node.tree.predict(x)
ypred=node.alpha*ypred
if node.next is None:
return ypred
else:
return ypred+self.read(node.next,x)
def predict(self,x):
ypred=self.read(self.head,x)
ypred=np.sign(ypred)
return ypred


Please Enjoy This Extremely Bizarre Vance Joy Parody From Matt And Alex
by Joseph EarpMatt and Alex, one of the most beloved radio duos in recent memory, are back in our lives for a very limited time, reuniting in order to co-host the ‘Best Video’ award at the ARIAs. So, in true Matt and Alex style, they’ve celebrated the occasion by making the world’s weirdest ‘Riptide’ tribute.
Yep, while most legacy acts would spend their time back in the spotlight together doing pat interviews and making sweet, nostalgic videos, Matt and Alex have instead decided to throw their entire energies behind a minute-long clip set to the Vance Joy megahit and filled with imagery plucked straight from your most melodic night terror.
Related
So there’s shots of the pair performing budget dentistry upon one another, picking bathing costumes out of their butts, wielding giant foam hands, and generally making surrealistic nuisances of themselves.
Why ‘Riptide’? Uh, well, I mean it kinda fits in with the fact that they’ll be presenting the award Best Video, presented by YouTube Music — and the Vance Joy clip has become iconic in its own way. But also, it doesn’t really fit with anything, and it’s the kind of genial lunacy that has defined the career of radio’s two loosest units.
Watch the entire bizarre clip over on YouTube Music here.


Welcome tinyweb test website
Basic test
signle html
signle image
html & image & js & css
Special test
304 response
404 response
Dynamic test
Fcgi Get method test, handle by dynamic_get.php
Fcgi Post method test, handle by dynamic_post.php
Upload file test, handle by dynamic_post.php


home
about
product
our store
contact us
caption
content
caption
content
caption
content
caption
content


Who we are ?
We are a bunch of overly optimistic, hardworking students working towards creating I.I.T. Kanpur's first Autonomous Under Water Vehicle. The current team consists of 20 students spanning various branches of engineering like Mechanical, Electrical, Computer Science, Physics, Material Science and Chemical at Indian Institute of Technology Kanpur.
Our mission is to participate in AUV competitions like NIOT SAVe, Robosub. The competition is a platform for students to display their skills in underwater robotics and build a connection with industries working along similar verticals.
Our vision is to lower the entry barier for research in underwater robotics. For this we have designed Varun such that any of its mechanical, electrical or software component can be individually improved and tested. Thus enabling researchers to focus on there field of research and using other pre-baked components.


Kenya census: Why counting people can be controversial
by Reality Check teamKenya's population census figures have been released, causing controversy among some groups who feel the results are not fair or accurate.
It found that the total population of the country is now 47.6 million, nine million more than in 2009.
But some regions have experienced a decrease in population.
These outcomes can be hugely controversial because the size of the local population has important implications for the level of government funding they receive.
Why does population size matter?
Kenya's population is made up of many different ethnic groups, closely aligned to competing political parties.
The government has yet to release all the data on the ethnic composition of the country, but the changes in population in certain regions from this latest census have already caused arguments.
The outcome of such surveys can embolden or weaken claims made by groups for political representation or resources.
In one area of the north-east territories bordering Ethiopia and Somalia, the census indicates a decrease in the population, prompting local political leaders looking to retain funding for their provinces to question the veracity of the survey.
The figures also reveal a high gender imbalance in the area, with significantly more males than females, leading some to speculate it might be due to a higher incidence of female mortality or an influx of single men who have moved there to work.
"This data, which was released this week, is false and not fit for purpose," says Aden Duale, a politician who represents a constituency with a large Somali population.
He argues that as Somali men normally have more than one wife, you would expect the numbers of women to be higher.
The Kenyan statistics bureau has defended the accuracy of the figures.
What is a census and how does it work?
A census is when a government collects information about its people.
This generally includes demographic data such as household population, religion and ethnicity. Censuses are usually carried out every 10 years.
It's a huge undertaking that involves mapping every home in the country and then visiting them to gather answers to a series of questions.
Some countries are ditching pen and paper for a digital process, in which people fill out a form online.
In Kenya the census is carried out by officials who go door-to-door, recording information on electronic tablets, which send the information to a central database.
Collecting information can sometimes prove difficult. Communities with little trust in their government can be unwilling to reveal personal details.
How do governments use the information?
The outcome of the census can have a real-world impact on its citizens.
In Kenya, not only is the distribution of government funds directly linked to regional population size, it's also the case that constituency boundaries are drawn up using census data.
The more divided a country, the more contentious a census tends to be.
Ethiopia has postponed its census twice since 2017 because of protests and ethnic violence.
When it does finally happen, the outcome will be significant for groups vying for more regional powers - the larger the ethnic group, the stronger it will feel its claim for self-determination under Ethiopia's ethnically-based federal system.
The Sidama ethnic group in southern Ethiopia, for example, is campaigning to have its own state.
Nigeria has a long history of controversial censuses and was due to hold another one in 2016, but a lack of funds and logistical issues mean it's yet to happen.
And in Lebanon, the quota of representatives in parliament from different religious communities is linked to population data in the census.
However, issues of demography there are so controversial there that there hasn't been a full census in Lebanon since 1932.


|
Class Summary |
| AESEngine |
an implementation of the AES (Rijndael), from FIPS-197. |
| AESFastEngine |
an implementation of the AES (Rijndael), from FIPS-197. |
| AESLightEngine |
an implementation of the AES (Rijndael), from FIPS-197. |
| AESWrapEngine |
an implementation of the AES Key Wrapper from the NIST Key Wrap
Specification. |
| BlowfishEngine |
A class that provides Blowfish key encryption operations,
such as encoding data and generating keys. |
| CamelliaEngine |
Camellia - based on RFC 3713. |
| CamelliaLightEngine |
Camellia - based on RFC 3713, smaller implementation, about half the size of CamelliaEngine. |
| CamelliaWrapEngine |
An implementation of the Camellia key wrapper based on RFC 3657/RFC 3394. |
| CAST5Engine |
A class that provides CAST key encryption operations,
such as encoding data and generating keys. |
| CAST6Engine |
A class that provides CAST6 key encryption operations,
such as encoding data and generating keys. |
| DESedeEngine |
a class that provides a basic DESede (or Triple DES) engine. |
| DESedeWrapEngine |
Wrap keys according to
draft-ietf-smime-key-wrap-01.txt. |
| DESEngine |
a class that provides a basic DES engine. |
| ElGamalEngine |
this does your basic ElGamal algorithm. |
| GOST28147Engine |
implementation of GOST 28147-89 |
| Grain128Engine |
Implementation of Martin Hell's, Thomas Johansson's and Willi Meier's stream
cipher, Grain-128. |
| Grainv1Engine |
Implementation of Martin Hell's, Thomas Johansson's and Willi Meier's stream
cipher, Grain v1. |
| HC128Engine |
HC-128 is a software-efficient stream cipher created by Hongjun Wu. |
| HC256Engine |
HC-256 is a software-efficient stream cipher created by Hongjun Wu. |
| IDEAEngine |
A class that provides a basic International Data Encryption Algorithm (IDEA) engine. |
| IESEngine |
support class for constructing intergrated encryption ciphers
for doing basic message exchanges on top of key agreement ciphers |
| ISAACEngine |
Implementation of Bob Jenkin's ISAAC (Indirection Shift Accumulate Add and Count). |
| NaccacheSternEngine |
NaccacheStern Engine. |
| NoekeonEngine |
A Noekeon engine, using direct-key mode. |
| NullEngine |
The no-op engine that just copies bytes through, irrespective of whether encrypting and decrypting. |
| RC2Engine |
an implementation of RC2 as described in RFC 2268
"A Description of the RC2(r) Encryption Algorithm" R. |
| RC2WrapEngine |
Wrap keys according to RFC 3217 - RC2 mechanism |
| RC4Engine |
|
| RC532Engine |
The specification for RC5 came from the RC5 Encryption Algorithm
publication in RSA CryptoBytes, Spring of 1995. |
| RC564Engine |
The specification for RC5 came from the RC5 Encryption Algorithm
publication in RSA CryptoBytes, Spring of 1995. |
| RC6Engine |
An RC6 engine. |
| RFC3211WrapEngine |
an implementation of the RFC 3211 Key Wrap
Specification. |
| RFC3394WrapEngine |
an implementation of the AES Key Wrapper from the NIST Key Wrap
Specification as described in RFC 3394. |
| RijndaelEngine |
an implementation of Rijndael, based on the documentation and reference implementation
by Paulo Barreto, Vincent Rijmen, for v2.0 August '99. |
| RSABlindedEngine |
this does your basic RSA algorithm with blinding |
| RSABlindingEngine |
This does your basic RSA Chaum's blinding and unblinding as outlined in
"Handbook of Applied Cryptography", page 475. |
| RSAEngine |
this does your basic RSA algorithm. |
| Salsa20Engine |
Implementation of Daniel J. |
| SEEDEngine |
Implementation of the SEED algorithm as described in RFC 4009 |
| SEEDWrapEngine |
An implementation of the SEED key wrapper based on RFC 4010/RFC 3394. |
| SerpentEngine |
Serpent is a 128-bit 32-round block cipher with variable key lengths,
including 128, 192 and 256 bit keys conjectured to be at least as
secure as three-key triple-DES. |
| SkipjackEngine |
a class that provides a basic SKIPJACK engine. |
| TEAEngine |
An TEA engine. |
| TwofishEngine |
A class that provides Twofish encryption operations. |
| VMPCEngine |
|
| VMPCKSA3Engine |
|
| XTEAEngine |
An XTEA engine. |


Kevin Durant uses fake Twitter account to defend himself from haters
The Golden State Warriors forward is one of the NBA's most active tweeters. But on Sunday, unflattering details from his Twitter past emerged:
KD has secret accounts that he uses to defend himself and forgot to switch to them when he was replying to this guy I'm actually speechless pic.twitter.com/9245gnpa3c
— 🐗 1-1 / ✭ 1-1 (@harrisonmc15) September 18, 2017


Click on the red button labeled 'Register'. You will be taken to the BeADonor.ca website for Organ and Tissue Donor Registration.
137 Dundas Street, London, ON N6H 1E9
<EMAIL_ADDRESS>(226)504-8888


Apache Lucene - Scoring
Introduction
Lucene scoring is the heart of why we all love Lucene. It is blazingly fast and it hides almost all of the complexity from the user. In a nutshell, it works. At least, that is, until it doesn't work, or doesn't work as one would expect it to work. Then we are left digging into Lucene internals or asking for help on<EMAIL_ADDRESS>to figure out why a document with five of our query terms scores lower than a different document with only one of the query terms.
While this document won't answer your specific scoring issues, it will, hopefully, point you to the places that can help you figure out the what and why of Lucene scoring.
Lucene scoring uses a combination of the Vector Space Model (VSM) of Information Retrieval and the Boolean model to determine how relevant a given Document is to a User's query. In general, the idea behind the VSM is the more times a query term appears in a document relative to the number of times the term appears in all the documents in the collection, the more relevant that document is to the query. It uses the Boolean model to first narrow down the documents that need to be scored based on the use of boolean logic in the Query specification. Lucene also adds some capabilities and refinements onto this model to support boolean and fuzzy searching, but it essentially remains a VSM based system at the heart. For some valuable references on VSM and IR in general refer to the Lucene Wiki IR references.
The rest of this document will cover Scoring basics and how to change your Similarity. Next it will cover ways you can customize the Lucene internals in Changing your Scoring -- Expert Level which gives details on implementing your own Query class and related functionality. Finally, we will finish up with some reference material in the Appendix.
Scoring
Scoring is very much dependent on the way documents are indexed, so it is important to understand indexing (see Apache Lucene - Getting Started Guide and the Lucene file formats before continuing on with this section.) It is also assumed that readers know how to use the Searcher.explain(Query query, int doc) functionality, which can go a long way in informing why a score is returned.
Fields and Documents
In Lucene, the objects we are scoring are Documents. A Document is a collection of Fields. Each Field has semantics about how it is created and stored (i.e. tokenized, untokenized, raw data, compressed, etc.) It is important to note that Lucene scoring works on Fields and then combines the results to return Documents. This is important because two Documents with the exact same content, but one having the content in two Fields and the other in one Field will return different scores for the same query due to length normalization (assumming the DefaultSimilarity on the Fields).
Score Boosting
Lucene allows influencing search results by "boosting" in more than one level:
- Document level boosting - while indexing - by calling document.setBoost() before a document is added to the index.
- Document's Field level boosting - while indexing - by calling field.setBoost() before adding a field to the document (and before adding the document to the index).
- Query level boosting - during search, by setting a boost on a query clause, calling Query.setBoost().
Indexing time boosts are preprocessed for storage efficiency and written to the directory (when writing the document) in a single byte (!) as follows: For each field of a document, all boosts of that field (i.e. all boosts under the same field name in that doc) are multiplied. The result is multiplied by the boost of the document, and also multiplied by a "field length norm" value that represents the length of that field in that doc (so shorter fields are automatically boosted up). The result is decoded as a single byte (with some precision loss of course) and stored in the directory. The similarity object in effect at indexing computes the length-norm of the field.
This composition of 1-byte representation of norms (that is, indexing time multiplication of field boosts & doc boost & field-length-norm) is nicely described in Fieldable.setBoost().
Encoding and decoding of the resulted float norm in a single byte are done by the static methods of the class Similarity: encodeNorm() and decodeNorm(). Due to loss of precision, it is not guaranteed that decode(encode(x)) = x, e.g. decode(encode(0.89)) = 0.75. At scoring (search) time, this norm is brought into the score of document as norm(t, d), as shown by the formula in Similarity.
Understanding the Scoring Formula
This scoring formula is described in the Similarity class. Please take the time to study this formula, as it contains much of the information about how the basics of Lucene scoring work, especially the TermQuery.
The Big Picture
OK, so the tf-idf formula and the Similarity is great for understanding the basics of Lucene scoring, but what really drives Lucene scoring are the use and interactions between the Query classes, as created by each application in response to a user's information need.
In this regard, Lucene offers a wide variety of Query implementations, most of which are in the org.apache.lucene.search package. These implementations can be combined in a wide variety of ways to provide complex querying capabilities along with information about where matches took place in the document collection. The Query section below highlights some of the more important Query classes. For information on the other ones, see the package summary. For details on implementing your own Query class, see Changing your Scoring -- Expert Level below.
Once a Query has been created and submitted to the IndexSearcher, the scoring process begins. (See the Appendix Algorithm section for more notes on the process.) After some infrastructure setup, control finally passes to the Weight implementation and its Scorer instance. In the case of any type of BooleanQuery, scoring is handled by the BooleanWeight2 (link goes to ViewVC BooleanQuery java code which contains the BooleanWeight2 inner class), unless the static BooleanQuery#setUseScorer14(boolean) method is set to true, in which case the BooleanWeight (link goes to ViewVC BooleanQuery java code, which contains the BooleanWeight inner class) from the 1.4 version of Lucene is used by default. See CHANGES.txt under release 1.9 RC1 for more information on choosing which Scorer to use.
Assuming the use of the BooleanWeight2, a BooleanScorer2 is created by bringing together all of the Scorers from the sub-clauses of the BooleanQuery. When the BooleanScorer2 is asked to score it delegates its work to an internal Scorer based on the type of clauses in the Query. This internal Scorer essentially loops over the sub scorers and sums the scores provided by each scorer while factoring in the coord() score.
Query Classes
For information on the Query Classes, refer to the search package javadocs
Changing Similarity
One of the ways of changing the scoring characteristics of Lucene is to change the similarity factors. For information on how to do this, see the search package javadocs
Changing your Scoring -- Expert Level
At a much deeper level, one can affect scoring by implementing their own Query classes (and related scoring classes.) To learn more about how to do this, refer to the search package javadocs
Appendix
Class Diagrams
Sequence Diagrams
FILL IN HERE. Volunteers?
Algorithm
This section is mostly notes on stepping through the Scoring process and serves as fertilizer for the earlier sections.
In the typical search application, a Query is passed to the Searcher , beginning the scoring process.
Once inside the Searcher, a Hits object is constructed, which handles the scoring and caching of the search results. The Hits constructor stores references to three or four important objects:
- The Weight object of the Query. The Weight object is an internal representation of the Query that allows the Query to be reused by the Searcher.
- The Searcher that initiated the call.
- A Filter for limiting the result set. Note, the Filter may be null.
- A Sort object for specifying how to sort the results if the standard score based sort method is not desired.
Now that the Hits object has been initialized, it begins the process of identifying documents that match the query by calling getMoreDocs method. Assuming we are not sorting (since sorting doesn't effect the raw Lucene score), we call on the "expert" search method of the Searcher, passing in our Weight object, Filter and the number of results we want. This method returns a TopDocs object, which is an internal collection of search results. The Searcher creates a TopDocCollector and passes it along with the Weight, Filter to another expert search method (for more on the HitCollector mechanism, see Searcher .) The TopDocCollector uses a PriorityQueue to collect the top results for the search.
If a Filter is being used, some initial setup is done to determine which docs to include. Otherwise, we ask the Weight for a Scorer for the IndexReader of the current searcher and we proceed by calling the score method on the Scorer .
At last, we are actually going to score some documents. The score method takes in the HitCollector (most likely the TopDocCollector) and does its business. Of course, here is where things get involved. The Scorer that is returned by the Weight object depends on what type of Query was submitted. In most real world applications with multiple query terms, the Scorer is going to be a BooleanScorer2 (see the section on customizing your scoring for info on changing this.)
Assuming a BooleanScorer2 scorer, we first initialize the Coordinator, which is used to apply the coord() factor. We then get a internal Scorer based on the required, optional and prohibited parts of the query. Using this internal Scorer, the BooleanScorer2 then proceeds into a while loop based on the Scorer#next() method. The next() method advances to the next document matching the query. This is an abstract method in the Scorer class and is thus overriden by all derived implementations. If you have a simple OR query your internal Scorer is most likely a DisjunctionSumScorer, which essentially combines the scorers from the sub scorers of the OR'd terms.


id
level
field
country
language
university
link
{{ onescholarship.id }}
{{ onescholarship.level }}
{{ onescholarship.field }}
{{ onescholarship.country }}
{{ onescholarship.language }}
{{ onescholarship.university }}
{{ onescholarship.link }}


{% extends "base.html" %} {% load staticfiles %} {% block title %}Training{% endblock %} {% block heading %}
{{ error_message }}
Oops, something went wrong. No labels were found.


Everything that gets registered in Lua is registered in a given Lua table, in a namespace (Lua table at given name), or in the global scope (called module). All registrations must be surrounded by its scope. To define a module, the luabind::module class is used. It is used like this:
module(L)
[
// declarations
];
This will register all declared functions or classes in the global namespace in Lua.
You can also register into a custom Lua table wrapped in a Object:
object mod = newtable(L);
module(L, mod)
[
// declarations
];
You can then directly use all object manipulation functions on mod.
If you just want to have a namespace for your module (like the standard libraries) you can give a name to the constructor, like this:
module(L, "my_library")
[
// declarations
];
Here all declarations will be put in the my_library table.
If you want nested namespace’s you can use the luabind::namespace_ class. It works exactly as luabind::module except that it doesn’t take a lua_State* in it’s constructor. An example of its usage could look like this:
module(L, "my_library")
[
// declarations
namespace_("detail")
[
// library-private declarations
]
];
As you might have figured out, the following declarations are equivalent:
module(L)
[
namespace_("my_library")
[
// declarations
]
];
module(L, "my_library")
[
// declarations
];
Each declaration must be separated by a comma, like this:
module(L)
[
def("f", &f),
def("g", &g),
class_<A>("A")
.def(constructor<int, int>),
def("h", &h)
];
More about the actual declarations in the Binding functions to Lua and Binding classes to Lua sections.


Account id:
Client id:
Client secret:
Request type: Add video (pull-based) Add video (upload source files) Replace video (pull-based) Replace video (upload source files) Retranscode video
Video id: (Required for replace or retranscode request)
Video metadata:
Ingest request options:
File data:
Text tracks data:


You can use the height and width attributes to specify the size of the iframe:
To remove the default border of the iframe, use CSS:
With CSS, you can also change the size, style and color of the iframe's border:
When the target of a link matches the name of an iframe, the link will open in the iframe.


Police spokeswoman Alyson Edwards told reporters Friday the baby was recently born and police are "urgently" looking for the mother, who may be in a vulnerable physical and emotional state.
"We want this mother to come forward. We want to assist her in any way we can. That’s the priority."
Police say there is evidence of a recent birth, but believe the birth occurred at another location.
An early step in the search will be to contact health facilities, Edwards said.
"Perhaps someone knows of a woman who was pregnant recently and is not pregnant any longer but there is no evidence of a child in that person's life. They may have information that is critical to locating this mother."
Police were initially called to the scene, in the 400 block of 5th Avenue North, around 8:50 a.m., after a report of an unknown problem.
Forensic investigators could be seen blocking off a recycling dumpster with a large blue tarp. The dumpster was eventually hoisted onto a truck and removed from the scene.
Man helped lift baby out
A man who lives in a nearby apartment building said he was among those who discovered the baby Friday morning.
He said he came across a neighbour and a bottle-picker looking in the dumpster where the infant was found.
"The guy searching for bottles came across the infant, and I helped the other guy take the infant out, it was a baby girl, probably newborn," Jonathon Mercredi said in an interview.
"It was something I'll never forget actually."
Police have not shared the infant's gender publically.
Mercredi said the baby was wrapped in a reusable shopping bag that was also wrapped in plastic.
After the police were called, Mercredi and the man who helped him remove the baby returned it to the dumpster because they considered it a crime scene.
"It's going to be at the back of my mind, I don't know how I'm going to sleep tonight"
An autopsy is scheduled for Tuesday to learn about the cause of death and when it occurred, Edwards said.
Anyone with information about the mother is asked to contact the Saskatoon Police Service at<PHONE_NUMBER> or Crime Stoppers at 1-800-222-8477.
With files from CTV News Saskatoon's Laura Woodward, Jonathon Charlton and Saron Fanel


It is a long established fact that a reader will be distracted by the readable content of a page when looking at its layout. The point of using Lorem Ipsum is that it has a more-or-less normal distribution of letters
2020 All Rights Reserved. Design by Free html Templates


Puppet Class: solaris_tidy::protect_core_dumps
- Defined in:
- manifests/protect_core_dumps.pp
Overview
Solaris_tidy::Protect_core_dumps
Configure solaris to dump cores on a separate partition with restricted permissions
7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 |
# File 'manifests/protect_core_dumps.pp', line 7
class solaris_tidy::protect_core_dumps(
$pattern = "/var/cores/core_%n_%f_%u_%g_%t_%p",
) {
file { "/var/cores":
ensure => directory,
owner => "root",
group => "root",
mode => "0700"
}
# running coreadm with no arguments gives a table that looks like this on a
# stock solaris VM
#
# bash-3.2# coreadm
# global core file pattern:
# global core file content: default
# init core file pattern: core
# init core file content: default
# global core dumps: disabled
# per-process core dumps: enabled
# global setid core dumps: disabled
# per-process setid core dumps: disabled
# global core dump logging: disabled
#
# After running command it looks more like this (!!! == changed field):
# bash-3.2# coreadm
# global core file pattern: /var/cores/core_%n_%f_%u_%g_%t_%p !!!
# global core file content: default
# init core file pattern: core
# init core file content: default
# global core dumps: enabled !!!
# per-process core dumps: enabled
# global setid core dumps: disabled
# per-process setid core dumps: disabled
# global core dump logging: enabled !!!
#
# So we need to run if any of the above conditions incorrect
$coreadm = "coreadm | "
$grep_pattern = "${coreadm} grep 'global core file pattern: ${pattern}''"
$grep_dump = "${coreadm} grep 'global core dumps: enabled'"
$grep_log = "${coreadm} grep ' global core dump logging: enabled'"
exec { "coreadm file pattern":
command => "coreadm -g ${pattern}",
unless => $grep_pattern,
path => ['/usr/bin', '/bin'],
}
exec { "coreadm global dump":
command => "coreadm -e global",
unless => $grep_dump,
path => ['/usr/bin', '/bin'],
}
exec { "coreadm global log":
command => "coreadm -e log",
unless => $grep_log,
path => ['/usr/bin', '/bin'],
}
}
|


--- layout: post title: Small Touches Make a Big Difference in ProLiant Gen 8 Servers date: '2012-02-14T10:43:38-08:00' tags: tumblr_url: http://alexhwilliams.tumblr.com/post/17614514280/small-touches-make-a-big-difference-in-proliant-gen-8 ---
http://bit.ly/w9bsZ3


Hi, I'm Henry.
A Front-End Web Developer in Washington, DC
RECENT PROJECTS
SolSmart
Portfolio resource, and lead generation site for an organization that assists local governments with the process of bringing solar to their communities.
View Live SiteQuikPark
Online presence for parking company that includes 3rd party integration of parking management system.
View Live SiteShakespear Theatre
Online presence for theatre company that includes 3rd party integration for ticket sales as well as a membership and education resource component.
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