CONSOLEHELPER-GTK
Section: Maintenance Commands (8)Updated: 2009-10-05
Index Return to Main Contents
NAME
consolehelper-gtk - A helper program for consolehelperSYNOPSIS
consolehelper-gtkDESCRIPTION
consolehelper-gtk is a program that accomplishes the same job as consolehelper, except graphically. It is executed by consolehelper if an X display is detected. This program is not intended to be run by users directly.SEE ALSO
consolehelper(8)
Index
This document was created by man2html, using the manual pages.
Time: 05:34:25 GMT, December 24, 2015


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Adair County Kentucky Court Records Directory
Adair County Kentucky free online court records, court records, court directory and information on finding court records and cases in Adair County Kentucky .
http://www.courtreference.com/Adair-County-Kentucky-Courts.htm
Adair County, Kentucky | Learn | FamilySearch.org
Beginning Dates for Adair County, Kentucky Government Records . Adair County Courthouse 424 Public .
http://familysearch.org/learn/wiki/en/Adair_County,_Kentucky
Court Docket - Kentucky Court of Justice
Kentucky Court of Justice (Banner Imagery) - click to go to homepage. . NONE County CIRCUIT Court Docket for 04/05/2013.  This site is a detailed .
http://adair.countycriminal.com/court-records-search/KY
Adair County Public Records | Search Kentucky Government ...
Adair County, KY Public Records. Circuit & District Court 201 Campbellsville St, Ste 101. Columbia, KY 42728 courts.ky.gov/courts/clerks/Pages... Hours: .
http://www.brbpub.com/kentucky/adair/
Adair County, Kentucky - Wikipedia, the free encyclopedia
Adair County Courthouse in Columbia, Kentucky . It is represented in District 51 in the Kentucky House of Representatives by the Republican, John "Bam" .
http://en.wikipedia.org/wiki/Adair_County,_Kentucky
Kentucky: Adair County - County Clerk
Jun 6, 2007 . Go to Kentucky.gov home page. KY Agencies · KY Services . Adair County ( banner goes here) . Adair County Court Clerk. County Clerk .
http://www.adaircounty.ky.gov/cogov/departments/coclerk.htm
Adair County, KY
Apr 3, 2008 . Please Note, I do not live in Kentucky any longer and can not do research for anyone. . Adair County Court Clerk Sheila Blair has announced that her office will . Early Supreme Court Cases - the first cases, from 1785-1792 .
http://www.rootsweb.ancestry.com/~kyadair/
News for Columbia, KY and Adair County on ColumbiaMagazine.com
Bobby Keltner, age 77 years of the Milltown Community of Adair County, KY . Court Docket Search the docket for Adair County (and other KY counties) for .
http://www.columbiamagazine.com/
Kentucky Court Records Online
Go to Kentucky.gov home page, Jump to Main Content . Kentucky Court of Justice (Banner Imagery) - click to go to homepage.
http://apps.courts.ky.gov/courtrecords/
Adair County, KY Mugshots - Mugshots.com -Search Inmate Arrest ...
Adair County, KY Mugshots - Find Latest Inmate Mugshots online, Search Latest Arrested Inmate Records, Newest Booking Records. .  EVERY .
http://mugshots.com/US-Counties/Kentucky/Adair-County-KY/
Adair County, Kentucky Public Records Directory - Free Public ...
Directory of court locations in Adair County, Kentucky. Includes local links to searching court records, dockets, legal research, self help, and more. Contact Info .
http://publicrecords.onlinesearches.com/KY_Adair.htm
Adair County District Court KY
Adair County District Court information, including phone, map, address, etc.
http://www.town-court.com/KY/adair-county/adair-county-district-court
Kentucky Courts. Find State, County and City Courts in KY.
Comprehensive listing of all courts in Kentucky. Find addresses .
http://www.50states.com/kentucky/state_courts.htm
3 Step Divorce: Kentucky Divorce Courts
Kentucky divorce courts and locations for filing for a divorce with the court. . In the majority of Kentucky divorce cases, the filing spouse files in the county where he or she lives. The Kentucky court locations . Adair County Judicial Center, .
http://www.3stepdivorce.com/courts/kentucky.shtml
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| Channel | Name | cooking ribs on agrill | small greenies smart chip biscuits | Programming |
|---|---|---|---|---|
| 53.1 | adair county kentucky court dockets DT | paula deen ultimate coffee cake | south american noni juice | Main adair county kentucky court dockets programming / MyNetworkTV |
| 53.2 | WOLF DT | trace adkins sunday morning coming down | fort mason festival pavilion | Simulcast of low carb alcoholic beverages |
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Adair County, Kentucky Public Records
Public records in Adair include death records, marriage records, court . County of Adair Resources & Statistics . Adair County, Kentucky from Wikipedia .
http://www.recordsbase.com/resources/public-records/kentucky/adair
[magellan's life contrasted to columbus] References
[to to increase wireless headphone range] External links
- s t o p organization
- air bag light hyundai
- ron paul's skeletons in the closet
- recipe salted roasted peanuts in shell
- discounted cheap domestic airfare
- day larine movie actress
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|


#include "XMP.hpp"
Include dependency graph for XMP.incl_cpp:
This is an overall client source file of XMP toolkit glue, the only XMP-specific one that clients should build in projects. This ensures that all of the client-side glue code for the XMP toolkit gets compiled.
You cannot compile this file directly, because the template's string type must be declared and only the client can do that. Instead, include this in some other source file. For example, to use std::string
you only need these two lines:
#include <string> #include "XMP.incl_cpp"


Configuration¶
Theme options¶
The following options can be defined in your project’s conf.py
file, using
the :confval:`html_theme_options <sphinx:html_theme_options>` configuration option.
For example:
html_theme_options = {
'canonical_url': '',
'analytics_id': 'UA-XXXXXXX-1', # Provided by Google in your dashboard
'logo_only': False,
'display_version': True,
'prev_next_buttons_location': 'bottom',
'style_external_links': False,
'vcs_pageview_mode': '',
'style_nav_header_background': 'white',
# Toc options
'collapse_navigation': True,
'sticky_navigation': True,
'navigation_depth': 4,
'includehidden': True,
'titles_only': False
}
Table of contents options¶
The following options change how sphinx:toctree
directives generate
documentation navigation.
Note
Setting :confval:`collapse_navigation` to False
and using a high value
for :confval:`navigation_depth` on projects with many files and a deep file
structure can cause long compilation times and can result in HTML files that
are significantly larger in file size.
Miscellaneous options¶
File-wide metadata¶
The following options can be used as file-wide metadata:
Other configuration¶
Adding a logo¶
Using the Sphinx standard option :py:confval:`html_logo <sphinx:html_logo>`, you can set an image file to be used as a logo at the top of the sidebar. The theme option :py:confval:`logo_only` also allows for only the logo to be shown at the top of the sidebar.
Adding custom CSS or Javascript¶
Adding custom CSS or Javascript can help you alter the look and feel of this theme without forking the theme for local use.
In order to add custom CSS or Javascript without disrupting the existing theme files, you can add files to be included in your documentation output.
How the table of contents displays¶
Currently the left menu will build based upon any toctree
directives defined
in your source files. It outputs 4 levels of depth by default, to allow for
quick navigation through topics. If no TOC trees are defined, Sphinx’s default
behavior is to use the page headings instead.
It’s important to note that if you don’t follow the same styling for your reST headings across your documents, the TOC tree will build incorrectly, and the resulting menu might not show the correct depth when it renders.
Also note that by default the table of contents is set with
includehidden=True
. This allows you to set a hidden TOC in your index file
with the :hidden: property that will allow you
to build a TOC without it rendering in your index.
By default, the navigation will “stick” to the screen as you scroll. However if your TOC is not tall enough, it will revert to static positioning. To disable the sticky navigation altogether, change the :confval:`sticky_navigation` theme option.


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|
Max API
8.2.0
|
Here are some techniques for implementing additional features found in most signal objects.
To implement unit generators such as filters and ramp generators, you need to save internal state between calls to your object's perform routine. Here is a very simple low-pass filter (it just averages successive samples) that saves the value of the last sample in a vector to be averaged with the first sample of the next vector. First we add a field to our data structure to hold the value:
Then, in our dsp method, we pass a pointer to the object as one of the DSP chain arguments. The dsp method also initializes the value of the internal state, to avoid any noise when the audio starts.
Here is the perform routine, which obtains the internal state before entering the processing loop, then stores the most recent value after the loop is finished.
The third argument to the dsp method is an array of numbers that enumerate the number of objects connected to each of your objects inputs and outputs. More advanced dsp methods can use this information for optimization purposes. For example, if you find that your object has no inputs or outputs, you could avoid calling 'dsp_add64' altogether. The MSP signal operator objects (such as +~ and *~) use this to implement a basic polymorphism: they look at the connections count to determine whether the perform routine should use scalar or signal inputs. For example, if the right input has no connected signals, the user can add a scalar value sent to the right inlet.
To implement this behavior, you have a few different options. The first option is to write two different perform methods, one which handles the two-signal case, and one which handles the scalar case. The dsp method looks at the count
array and passes a different function to dsp_add64.
The second option is to store the value of the count array for a particular signal in your object's struct. Then the perform method can make the decision whether to use the signal value or a scalar value that has been stored by the object. In this case, many objects use a single sample value from the signal as a substitute for the scalar. Using the first sample (i.e., the value at index 0) is a technique that works for any vector size, since vector sizes could be as small as a single sample. Here is an example of this technique for an object that has two inputs and one output. The connection count for the right input signal is stored in a struct member named m_count:
Here is a perform routine that uses the connection count information as passed in the format shown above:
To access a named buffer~ object for either reading or writing sample values, refer to the Buffers reference.


Notary Nodes candidates proposals. Contribute to KomodoPlatform/NotaryNodes development by creating an account on GitHub.
Deleted User11-Apr-19 07:22 PM
some are autoreelected and still... run(edited)
^^
Decker11-Apr-19 07:22 PM
I'm preparing this right now, also will be a sort by region ... but first should finish fill list.
4
phm8711-Apr-19 07:23 PM
Thank you for your work
Audo11-Apr-19 07:24 PM
the http://komodoelection.com site is hosted through Github (uses Jekyll). Anyone can take a look and issue pull requests that help improve the site.
https://komodoelection.com/candidates/ <- this page should list candidates by region, but there's some problem
I had a goal in mind running for 3, but NA is my 1st choice and I didn't want the chance of "just" SH
Deleted User11-Apr-19 07:48 PM
lol siu
I think even withtout saying it just typing will have the effect :p
siu - Chainmakers11-Apr-19 07:49 PM
but it seems like some candates submitted proposal quite recently
last time i counted on april 2 it was 12
Deleted User11-Apr-19 07:49 PM
yeah it s so cool
a lot of newcomers
ComputerGenie 👴👴👴11-Apr-19 07:49 PM
spread the love (provided they can do the job)
Deleted User11-Apr-19 07:50 PM
testnet is here ^^ people learn
1
in any case it s positive
(and alright and joker fix and break stuff, they should have a team name)
siu - Chainmakers11-Apr-19 07:51 PM
"Dont change horses at mid race, stay with Chainmakers, the original service providers!"
Deleted User11-Apr-19 07:51 PM
blackjocker and Mortimalright
SHossain11-Apr-19 07:53 PM
Candidates who has been running LABS nodes are doing better on this testnet. They already know the basics and more.
1
Deleted User11-Apr-19 07:53 PM
can t argue with what s obvious
SHossain11-Apr-19 07:53 PM
For next year, LABS will be a great place for newbies to start with
for sure NN needs dedicated, capable and reliable ops
zatJUM11-Apr-19 07:54 PM
It depends... I'm running in testnet with a crappy vps (my kmdlabs node is much better!)
Deleted User11-Apr-19 07:55 PM
labs means dedication and stuff above nn tasks
not sure above is the good word : more advanced
from labs comes innovation
1
1
ComputerGenie 👴👴👴11-Apr-19 07:56 PM
they should have a team name isn't good enough?
1
SHossain11-Apr-19 07:57 PM
i mentioned the beginning of this testnet. if you want to show your performance and that you are capable use a better node.
also, kolo may reward the participants based on the performance.
another thing is, it is indeed a good test for your future server that you finally want to use if you get elected. you will find out easily if there is any issue or not.
trust me, you don't want to change server quite often in the mainnet
zatJUM11-Apr-19 07:58 PM
I believe you, but I don't want to spend 300$/month for a server if I'm not elected
Deleted User11-Apr-19 07:59 PM
it s like poker
siu - Chainmakers11-Apr-19 07:59 PM
Its a chance you need to take
Deleted User11-Apr-19 07:59 PM
btw this testnet seems quite fun with attacks etc
PHBA206111-Apr-19 07:59 PM
well, but spending 300$ for a month before the election may give you more chance to get elected
zatJUM11-Apr-19 08:00 PM
I'm 15th of the competition (I entered 19th), with a 15$ vps I was using for other services
SHossain11-Apr-19 08:00 PM
if you spend $300 and show good performance you have more chance to get votes
just think it that way
if your performance is poor, what voters may think.(edited)
zatJUM11-Apr-19 08:01 PM
I think it's not poor by entering 19th and being 15th with a crappy vps
Deleted User11-Apr-19 08:01 PM
maybe they can think he did good on labs and as he s not cresus he had to get the better of it with a crappy vps
but I just love labs too much
zatJUM11-Apr-19 08:01 PM
exactly!
Deleted User11-Apr-19 08:02 PM
yet he decided to run in SH
destroy him
1
ComputerGenie 👴👴👴11-Apr-19 08:02 PM
well, but spending 300$ for a month before the election may give you more chance to get elected
I doubt it, my test server is $40, the other $260 is just pissing away $ if you do that
SHossain11-Apr-19 08:03 PM
don't need to have $300 server. i just replied with the amount he posted
you can use less for a good server
i also use VPS for LABS and getting good performance out of that
Deleted User11-Apr-19 08:03 PM
btw alien server have no price, they await us on moon
4
SHossain11-Apr-19 08:05 PM
of course you don't have to follow what i said. those are just my 2c
and from my experience
Deleted User11-Apr-19 08:06 PM
(from the top player I just can say gj ^^)
1
this was a really cool year, and couldn t pass alien tech
ComputerGenie 👴👴👴11-Apr-19 08:07 PM
and from my experience
yes, ymmv on a $40 server but it does me well when I do my job
1
SHossain11-Apr-19 08:07 PM
@Deleted User i hope this year new ops can take over me
Deleted User11-Apr-19 08:08 PM
competition awaits
will be fun to see what all this becomes in a year
ComputerGenie 👴👴👴11-Apr-19 08:08 PM
can take over me We can if you leave Paris
Deleted User11-Apr-19 08:08 PM
barely anyone autosplit, nodes are managed, stats and reports
this is very cool
SHossain11-Apr-19 08:09 PM
i still use autosplit
Deleted User11-Apr-19 08:09 PM
yrah
SHossain11-Apr-19 08:09 PM
manual is a nightmare for me(edited)
Deleted User11-Apr-19 08:09 PM
but alien tech
I think one processor is the price of my car xD
but my car don t notarize
2
i believe processor is not the only factor btw
we saw nvme early adopters are on the ending line easier than conventionnal ssd
some still have SSD classic but most of top of em have specific tweaks, I believe
testnet is a bit different as you have to restart quite often. it s not a longrun but a sysadmin rush to fix and get back to work
NutellaLicka11-Apr-19 08:17 PM
It proves people are capable of doing the job
cipi11-Apr-19 08:18 PM
the most important thing is network connectivity... problem is, that you don't know how good the hoster is beforehand, because most of them don't make their peering partners and network infrastructure public, so you will have to try it out...
i found a very good method to do it, but it takes 1 month till you have the result... and that is: run a tor relay and look what the "advertised bandwith" is after 1 month
NutellaLicka11-Apr-19 08:19 PM
On that note. Having a node in Australia when everyone else is in the northern hemisphere hurts.
cipi11-Apr-19 08:21 PM
yes, that is a problem... exactly the problem i have with my AR-node that is located in HongKong... it hardly made it to 7th place last season... my other node (Dallas, USA) is second and the hardware is very similar, even the hoster is the same


Design Name E:/git/clay/Circuit/R6_1/allegro_c6r3/CLAYc6r3.brd
Date Sun Mar 06 07:33:32 2016
Note: This report identifies the unused B/B vias associated with a via stack structure. A stack structure can consist of coincidently placed micro, B/B vias or a combination. As an example of what the report detects, consider the stack Micro1-2, BB2-7, Micro7-8; if a trace connects to the stack on Layers 3 and 6, Micro1-2 and Micro7-8 would be identified as "unused". Click on the hyperlink to navigate to their location.
Unused Blind/Buried Vias
| Net Name |
Padstack Name |
Start/Stop Layer |
Location (x y) |


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Definition at line 2 of file AStructChangeTracker.m.
◆ AStructChangeTracker()
| function mxberry::core::struct::AStructChangeTracker::AStructChangeTracker |
( |
| ) |
|
◆ applyAllLaterPatches()
| virtual function mxberry::core::struct::AStructChangeTracker::applyAllLaterPatches |
( |
in |
self, |
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in |
SInput, |
|
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in |
startRev |
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) |
| |
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virtual |
◆ applyPatches()
| virtual function mxberry::core::struct::AStructChangeTracker::applyPatches |
( |
in |
self, |
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in |
SInput, |
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in |
startRev, |
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in |
endRev, |
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in |
isInclusiveVec |
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) |
| |
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virtual |
◆ getLastRevision()
| virtual function mxberry::core::struct::AStructChangeTracker::getLastRevision |
( |
in |
self | ) |
|
|
virtual |
The documentation for this class was generated from the following file:


Literature Survey
Text classification is one of the major segments in a data classification system which is the procedure of organizing the type of text file automatically based on the given dataset. Several text classification concepts are available in the research world based on different supervised and unsupervised machine learning concepts.
Khudran Alzhrani, Ethan M. Ruddy, Terrance E. Boulty and C. Edward Chow "Automated Big Text Security Classification"[1]
The threat of leakage of sensitive information from inside attacks can be minimized by using DLP techniques. DLP techniques are composed with several phases as the discovering phase, detecting phase, protecting phase and monitoring phase [1].
Figure I illustrate the general components of a general DLP solution. This system is focused on developing the existing solutions to bring out a general statistical structure including text classification mechanisms [1].
Figure I: A prototypical Data Leak Prevention (DLP) framework
Fang Miao, Pu Zhang, Libiao Jin, Hongda Wu "Chinese News Text Classification Based on Machine learning algorithm"
Most of the developed classification algorithms are based on machine learning principles. A machine learning text classification system can be broken down into 4 components as text pretreatment, text representation, classifier training and classification. This classification system is a Chinese news text classifier which is based on K-nearest neighbor, Naive Bayes and Support vector machine [2].
This research paper is based on the above mentioned components and this proposed system is developed to train the classifier module depending on the existing datasets which will classify the unclassified news. The shown Figure II illustrates the four components of a text classification system and the basic relationship between them [2].
Figure II: Text Classification Model
The results of the training set and the test set derived from the news corpus of the Fudan University after classifying from this system is shown below in Table I.
Table I: Result of Classification
Based on the above collected results the precision value, recall and F-value is calculated. The obtained results are indicated on the below Table II.
Table II: Performance Evaluation
After analyzing the data in Table I and Table II , it is clear that the precision value, recall and the F-value are the highest in the SVM algorithm. However, the SVM algorithm is time consuming. Therefore it is only suitable for small datasets. Other two algorithms produced similar results [2].
Ge Peng, Sipei Liu and Jin Wang "Sensitivity-Proof Content Advertising Based on Two-Stage Text Classification" [3]
Social media is one of the most prominent information sharing platforms in the modern world. There are some advantages as well as some disadvantages of using social media. Sharing of the sensitive information in an unauthorized manner is one of the major disadvantages of social media. Sex-related information, violate news are some of them. This research paper is based on content classification based on sensitivity level. The proposed system is developed under two stages [3].
The solution heavily focuses on two classifiers, general advertisement classifier and sensitivity detector. General advertisement classifier will classify the content based on the text and assign the advertisements to the respective Ad class. The sensitivity detector will then classify the pre-classified advertisements again based on the sensitive information to produce results. The hierarchical architecture developed within the system enables the functioning of the system efficiently which is shown in the Figure III below [3].
Figure III: Architecture of two-stage Classification
The policy implemented for the classification process is shown below in Table III.
Table III: Decision Policy Table
Figure IV: Text Classification Methodology
Another method to classify files is based on the image content of the file. Image classification is referred to as a process that is able to classify an image according to its visual content. An image classification algorithm has to be designed to analyze the content of the image. David Kaeli, in his book which is heterogeneous Computing with OpenCL 2.0 states how important the BoW model is related to document classification and natural language processing. Furthermore he highlights the fact that the BoW model is used to classify images. For this purpose, features of the image should be identified and should be fed for the machine learning algorithm. A feature generation algorithm can be used to convert an image to a set of features which is also called as the signature of the image.
A high level algorithm for image classification includes
Figure V: High level algorithm for Image Classification
Sergios Theodoridis and Konstantinos Koutroumbas in their book, Pattern Recognition, define diffusely about features, feature hectors and classifiers. By showing two images with distinct regions inside it, authors try to convince the fact that the two regions of the image are visually different. This process is aided by already available image databases with a number of patterns which leads to machine learning.
Figure VI: Image Comparison
Moreover, Robert C. Weih, Jr. and Norman D. Riggan, Jr. proposed OBJECT-BASED CLASSIFICATION VS. PIXEL-BASED CLASSIFICATION: COMPARATIVE IMPORTANCE OF MULTI-RESOLUTION IMAGERY and verbosely defined about the 3 main techniques of image classification in remote sensing [6].
Figure VII: Unsupervised Classification
Figure VIII: Supervised Classification
Figure IX: Object based Classification
In real world situations there are a number of complex policies, in these access decisions depend on the rule. According to the different application rules are coming, for example, from organizational regulations, practices and government laws. To develop the access controls, the system needs to ensure that the availability of the resources, confidentiality and integrity of the data.
There are three main types of access control policies.
Mandatory access control (MAC), in this policy users has no authority to override the policies and it is controlled centrally by the security policy administrator. This is used when the use of resources is determined by the characteristics of the resource and the subject. Here the resource owner does not have a say in controlling access. The security policy administrator defines the utilization of resources and their access policy, which cannot be overridden by the end users, and the policy, will decide who has authority to access the particular programs and files. MAC is mostly used in a system where confidentiality is the priority.[3]
Discretionary access control (DAC), this policy Contrast with Mandatory Access Control (MAC) which is determined by the system administrator while DAC policies are determined by the end user with permission. So there each resource will have a resource owner. In DAC, a user has complete authority over all resources it owns and also determines the permissions for other users who have those resources and programs.[4]
Role-based access control (RBAC), this policy is rather simple in comparison to others. In RBAC roles are assigned by the system administrator statically. In which access is controlled depending on the roles that the users have in a system. (RBAC) is mostly used to control the access to computer or network resources depending on the roles of individual users within an organization. In this context we discuss the different access control policies and models that have been proposed by the researchers, whilst finding the current status of access control systems and their low level implementation in terms of security mechanisms. This review gives the idea of different access control policies to develop the access control systems and gives the overview comparison of the different security policies and their mechanisms.[4]
Throughout this research paper Role Based Access Controls are put into use. After a successful user login, the role of the user will be identified and since the permissions are mapped to the roles beforehand, the logged in user will only be able to perform the specified activities on the files.[4]
Figure X: Role Based Access Controls
The system protection is based on the permission that describes a given access right to a particular object or set of objects. In the RBAC model we are dealt with unauthorized access to the computer system resources and data [29]. Since we have considered only the access rights that users are required to execute a particular transaction on a particular object from the defined set of objects.[4]
Figure XI: User role permission mapping
A permission p is a pair < trans, object>, where trans represents the transaction that executes on the set of objects that is object .Consider P indicate the universal set of permissions, Trans indicate the universal set of transactions, and Obj indicates the set of objects. We can define the association between permission/transaction and permission/object with the following functions. Trans (p) : P → Tr, It gives the associated transaction to the specified permission p. ObP (p) : P → 2Obj ,It gives the associated set of objects to the specified permission p. A role is created by collecting permissions according to the functional and logical requirements to this role should represent. Each role has a name associated with this and it uniquely identifies this role in the system. A role r is a pair of < rn, pset>, where rn indicates the role name and pset indicates the set of role permissions. The mapping between roles and permissions can be defined with the following function: PR(r): R → 2P, It gives the associated set of permissions to the specified role r. Here R indicates the universal set of roles. While allocating permissions to roles it is needed to ensure the principle of least privilege that is each role should have only required rights for its functional requirements.[4]
It is needed to take necessary measures to achieve the integrity and security of data. The classified data should only be accessed by the authorized individuals while preventing the access by unauthorized individuals. A paper presented by Rizwana Shaikh,Dr. M. Sasikumar mentioned Data can be classified based on several factors as outlined by the above figure. It mentions that Access Control, Content and Storage can be used as characteristics to classify data. Likewise, we can use the characteristics which we use to classify text and image as a base to preserve the classification levels that will be implemented in the proposed data classification scheme [3]. Another paper presented by Bokefode Jayant. D, Ubale Swapnaja A, Apte Sulabha S, Modani Dattatry G, it is declared access control based models can be used for achieving security[4]. Mainly access control is based on three models; Discretionary Access Control(DAC), Role Based Access Control(RBAC) and Mandatory Access Control(MAC). Out of those three Role Based Access Control best suits the proposed data classification scheme. In RBAC, there are defined sets of roles which predefined permissions on objects. The users are assigned to the roles, the user will only be able to perform the tasks defined for that particular role [4].
Access Control will be effective only if the users are authenticated before that. There are several authentication mechanisms currently ruling the authentication system.
Authentication can be made possible in different ways. A basic authentication technique is a page containing a login form. After filling out the login form, the input data will be validated against a server and will return a session cookie. The idea behind cookies is that they help to maintain continuity and states on the web. When the user visits the same website again, the browser sends the cookie to the web server which uses this cookie to identify the user. The purpose of a cookie is to provide information for the subsequent server-browser communications. For instance, a merchant website could use a cookie that contains the user’s name and credit card number, and this information can be used to make future payment transactions easier.
Figure XII: Cookie Authentication
If the credentials are correct, the system continues to step two, which returns a cookie back to the system. The client then sends this cookie to the service. Based on this cookie, it decides whether the user is granted access to the data. In some cases, the service contacts the authentication server to verify whether the cookie is still valid.
While the user stays logged in, the cookie would be sent along with every subsequent request. The server can then compare the session id stored on the cookie against the session information stored in the memory to verify the user’s identity and it sends the response with the corresponding state!
A token-based authentication mechanism has a different approach. A user enters the name and password into the client (client means the browser or mobile devices etc.). The client then sends these credentials (i.e. username and password) to the Authorization Server. Then the Authorization Server authenticates the client credentials (i.e. username and password) and then it generates and returns an access token. This Access Token contains enough information to identify a user and also contains the token expiry time the client application then includes the Access Token in the Authorization header of the HTTP request to access the restricted resources from the Resource Server until the token is expired.
Figure XIII: Token Based Authentication
This concept alone takes care of many of the problems with having to store information on the server. The biggest difference here is that the user’s state is not stored on the server, as the state is stored inside the token on the client side instead.
Data protection is important, critical, and vital for any organization. Loss of data effect the privacy and security of the organization. In the new era data has grown up massively in terms of volume. Data has value and if it is fall in to the wrong hand it can have drastic consequences. It has been predicted the volume of data would reach 44 trillion gigabytes by the year 2021. And protecting data necessitates robust DLP. A report from Gartner predicts DLP to grow at 10% [1]. There have theft for sensitive data earlier, but they have become more frequent nowadays.
Data breaches with the loss of sensitive enterprises can affect reputation of the enterprises, cause financial loss and it may have to face lawsuits sometimes. So it is vital to know the importance of data protection, as a research team have to find smart and efficient way to prevent data loss and detect data modification in an organization caused by intentional or unintentional attack to the data [2]. So, it’s very clear that enterprises are in need to implement a robust system along with DLP technologies.
This writing is mainly focusing on integrity of the stored data and an innovative solution that allows to detect intentional/unintentional modification of data while increasing the effectiveness of the system. The author is proposing several solutions based on the literature survey and finally proposing a novel scheme relying on fuzzy hashing for the detection of unauthorized data modification by the internal users of the system.
Hashing standards to detect the file modification
Hashing is a cryptographic sequence of letters and numbers generated when a file is analyzed by a hashing algorithm. When it comes to ensuring the integrity of file or file content the best answer is to use hashes. It is a crucial Data Structure which is designed to implement a special function named the Hash function which is used to map a given value with a key for faster access of elements. The efficiency of mapping depends on the efficiency of the hash function used.
For cryptographic hash function, the following properties are required: Preimage resistance: it is computationally infeasible to find any input which hashes to any pre-specified output. Second preimage resistance: it is computationally infeasible to find any second input which has the same output as any specified input. Collision resistance: it is computationally infeasible to find a collision, i.e. two distinct inputs that hash to the same result [3]. Therefore, it is evident that hashing can be utilized to exactly figure out whether a file is altered or not. Research world proved that there are several hashing algorithms out in the world to achieve integrity of our data. There are 2 most widely used algorithms so far that are MD5 and SHA-256.
Message Digest5
MD stands for Message Digest and describes a mathematical function that can take place on a variable length string. The number 5 simply depicts that MD5 was the successor to MD4. MD5 is essentially a checksum that is used to validate the authenticity of a file or a string and this is one of its most common uses. It makes large amounts of information to be compressed into a confidential format before signing the private key by digital signatures (that is, any length byte string is transformed into a certain length of big integer) [4].
Md5 hashes goes through several steps to compare 2 files and verify the integrity of files.
Without loss of generality of the file content, it supposes that the original data padding is always added until its length in bits is congruent.
Figure XIV: MD5 Working principle of an iterated hash function
Figure XV: One step of the compression function
Parameters of MD5
Below equation shows a single MD5 operation.
Secure Hash Algorithm 256
SHA-256 is an emerging technology that is considered a stronger option for conducting a checksum for file integrity. SHA-256 uses 256-bits compared to 128 used for an MD5, which exponentially increases the different letter/number combinations that can be generated and decreases the plausibility of a collision or repeatability, which theoretically exists with MD5 [5].
Figure XVI: SHA-256 mechanism
SHA 256 is also going through several rounds of steps to obtain the message digest [7].
Figure XVII: SHA-256 mechanism
Parameters of SHA 256
Below equation shows a single SHA operation.
Table IV: Comparison between MD5 and SHA
Figure XVIII: Performance chart of hashing algorithms
Research Gap
No matter how advanced the current technology is, there is still a slow development and inventions in the cause of securing data at rest. The monopoly that the high standard organizations maintain to keep their social reputation is a major reason for this. They demotivate small scale organizations in securing their personal information which will enable them to collect data through internal attacks. For example top rated stock broking firms use employees of upcoming stock broking companies to collect information about pending bids by paying them.
Throughout history various organizations and governments introduced different solutions to address the problem of Data Loss. The Enigma machine can be considered as one such solution which was able creates a turning point in the field of Cyber Security. But in the recent past, traditional countermeasures such as manually controlled inbound storage spaces or file based storage systems have proven ineffective against internal attacks causing serious problems. This has created a huge loophole in the field of information security making it really difficult for the officials to control. Due to the negligence of the authorities, a huge gap is now created making it difficult for the security enthusiasts to recover soon.
On the other hand with the development of technology and the rapid increment of the use of the internet, security of sensitive information is at a higher risk. Usually these problems are addressed through enforcing security measures to the whole system. But modern advanced technologies are continuously causing problems to these measures proving them unreliable, expensive and resource consuming. Therefore classification of data can be considered as another approach designed to protect information other than enforcing security measures on the system as a whole. Although the approach is presented and several systems are developed, there is a requirement for a single integrated hybrid system which can classify documents.
Organizations store heaps of information. Controlling, managing and manipulating these information is a huge challenge to any organization in the presence of modern technology. Also these information should be classified based on the sensitivity level in order to fulfill the security requirements of the organization. Although the prevailing solutions are capable of classifying information based on few specific purposes, there is no one integrated machine learning module to classify organizational documents which are specific to various industry domains.
Moreover due to the fact that modern technologies are rapidly used in most of the organizations worldwide, there is a huge gap in the industrial world for technicians who are capable of handling latest technology based products. One main example to prove the above point is that there should be a specially recruited team to configure the Banking Software everyday which is just running the same piece of code in all machines. But there is a possibility to reduce the IT support team if the configuration is automated using an understandable user interface which does not need any technical knowledge about that.
It is clear that although there are several attempts carried out in this purpose, a solution which can address the above mentioned problems is still in need. Therefore the main focus of this research is to develop a solution which will cover all the required aspects and to make DocSpace the best among the list.
Research Problem
With the expansion of modern technology, the lifestyle of people is mostly managed and relied on their computers and mobile phones to an increasing extent. On that premise, File classification can be introduced as a technique to the modern storage systems which can be introduced as a hierarchical arrangement of classification levels in an efficient approach to address the problem of how to secure information. But the major problem in the industrial world is that there is no solution implemented to classify documents using the principle idea of using file properties such as date and time the file was created, size of the file, owner and access patterns and classifies files.
However to address this problem, technological approaches should be analyzed covering the main objective. Based on the developed classification structures it was clear that machine learning technologies are used to achieve the utmost goal of classifying files in an effective manner. But it was required to analyze on how to come up with a solution which can introduce a unified methodology to classify and secure data with the aid of security levels which can be considered as a valuable solution to the industrial space over many security related issues. However, the requirement of analyzing data before classifying in order to get a clear understanding about the data that is about to be classified should be looked after to obtain the maximum output. Although several organizations have implemented different standards and policies, lack of understanding of the general public on this matter is still acting as a barrier making it difficult to step up as a community. Moreover, it is required to find out how to implement appropriate measures to prohibit accessing files in lower security levels to higher security levels without proper authorization. This will enable the small scale organizations to keep their sensitive information private and also to come up with innovative solutions which will make them unique and successful. Although governments are still not keen on paying some attention to address this issue as a nation, the impact made by intruders and attackers on governing bodies is still at a very high level.
Organizations are experiencing serious problems due to the Higher Cost and Higher Resource Consumption Rate when Implementing Data Prevention Techniques. As discussed in the previous section this is one of the main reasons why organizations are neglecting the security of their data. Further Most of existing systems require Expert Knowledge to achieve Maximum Performance. Therefore it is needed to wait for a technical support officer to make the required changes in order to proceed with the work in any kind of minor uncleariblity. Based on the above details it is clear that a solution with lower resource consumption and lower implementation cost is required. Further the system should be able to address the problem of complex operational knowledge. There is no evidence to prove the existence of a Single Integrated Solution that can classify industrial specific files based on the level of sensitivity of the information.
The most important requirement in developing security solutions is the control of the access aspect of the system. The particular solution won’t be even to clothe term security if the proper access controls are not there. At the present time, the security solutions which are using access control mechanisms couples with authentication mechanisms which are obsolete and which are highly prone to attacks. Therefore, there is a clear line between the use of the proper access control mechanisms along with industry standard authentication protocol to secure the resources which need to be protected and the securing of users which are using the security solutions.
There is also a major problem in the process of updating from the current security level of the file after each modification to improve accuracy. This can be considered as a problem based on securing the classified files which are stored. Comparatively there is a lack of focus on securing the classified files in both traditional classification methods and latest classification methods. Therefore it is required to find out how to keep the files continuously safe. This proposed model is based on the question how to develop a single integrated based hybrid data classification system using high level Microservice architecture to prevent intentional or unintentional misbehaviors.
Research Objectives
Almost all the activities of the world have some sort of digital intervention to it. Let it be simply reserving a book from a library to voting. All these services or products take the advantage of digital devices and systems to speed up the process or to have an effective and efficient outcome. Digital devices and systems use data as a main input. Therefore, it is crucial that the data which is fed into these systems are kept and handled in a secure and organized manner. Data classification is meant to set labels and categories for data based on the sensitivity of data.
In the recent past, traditional countermeasures have proven inefficient when dealing with inside attacks. Classification of data is one of the most prominent and modern security measures followed by organizations in order to obtain DLP from insider attacks. With the rapid enhancement of the technology and the continuous nature of the information collection process an efficient system to classify data is required. After analyzing the aspects of the research problems, a set of specified objectives were constructed to design a new product covering all the identified gaps.
Main Objective
The main objective of this research is to develop machine learning based single integrated hybrid data classification solution to perform text and image classification together, to re-classify the updated classified files and to prevent intentional or unintentional miss behavior using the classification implemented. Further the specification of the solution to the industrial world which the prevailing solutions are not capable of was also focused throughout the process. In addition to the main objective several specific objectives were designed to increase the productivity and efficiency of the product while maintaining the commercial quality. The total product which includes 4 major components is designed completely based on this objective scope.
Specific Objectives
The main focus of this section is on the secondary objectives that can be either specific or different to each component related to the product. Although they are described under the secondary objectives section, it is really important to keep in mind that these conditions should also function accordingly in order for the complete system to run. Also to ensure that the assumptions made during the project development period.
Methodology
The solution proposed under the research topic is broken down into 4 major components in order to fulfill all the objectives required to cover the prevailing research problem.
Files need to be classified into security levels since not every file contains the information that should be ready by normal people. Therefore, it is a must to classify files. Before classifying the files, the data needs to be analyzed, in order to get a clear understanding about what sort of data is inside and what can be done using that data. Once files are analyzed they are categorized as Image Files or Text Files. Then, the files will be classified using machine learning technologies based on their levels of significance with regard to the predefined parameters such as sensitivity depending on specifications such as Health Insurance Portability and Accountability Act, Payment Card Industry Data Security Standard and General Data Protection Regulation. Classifying data itself is not enough. There should be steps implemented to secure the classified data. Appropriate measures will be taken to prohibit accessing files in lower security level to another higher security level without appropriate authorization. Finally, the security level of the file is constantly updated after each modification based on files to improve the accuracy.
The above introduction on the complete mechanism of the solution explains how the 04 major components collaboratively function together. The main focus of this document is on the text classification component which is the initial sector of DocSpace. All the other components are developed around this component while covering all the discussed objectives
Text classification is entirely based on machine learning algorithms. Depending on the prevailing datasets, the classifier is trained accordingly. The trained model is then used to classify the unclassified data and the model is proposed to be implemented under three processes as follows [6].
A. Text representation
B. Classifier training
C. Classification
Figure XXIII: Text Classification model of DocSpace
Training model and Classification
This component is assigned with the task to label the text of unknown categories. Therefore the most important part of classification is the selection of the algorithm. Currently the most popular algorithms are based on machine learning techniques. Hence when an unclassified file is received, based on the trained dataset, a security level is assigned. With regard to a data set, a classification algorithm is most appropriate as these data require data metrics to evaluate the results such as precision (P), recall (R), and F-measure (F).[6]
P - Represents how many of the positive samples classified are actually positive samples
R - Indicates how many positive examples in the sample are correctly predicted
This system is developed so that the precision can be denoted in the proportion of the document number that the classifier correctly judges them to be the category and the document number in this category. And recall is the proportion of the document number that the classifier correctly judges them to be the category and the document number in the sample. In general, the precision and recall rate are contradictory, so the results can be evaluated by the F-measure, which is the weighted average of precision and recall rate of the model, and its calculation formula is as follow:
Where P is precision and R is recall rate.
This solution is implemented using all 3 major classification models namely K-nearest neighbor algorithm, Naïve Bayesian algorithm and Support Vector Machine algorithm (SVM). But after analyzing the results the Naive Bayesian algorithm is used as the main algorithm used in the classifier. The main reason for that is although the precision value, recall and the F-value are the highest in SVM algorithm; it is time consuming.[6]
The Naive Bayesian algorithm is a very simple classification algorithm based on Bayes theorem. Bayes theorem is estimating the probability of occurrence of something by calculating the frequency of occurrence of that particular object in the past. Its results indicate that the probability distribution for the random variable and can also be interpreted as the possibility of different levels of trust. [6] The Bayesian formula is:
Image Classification
There are three main techniques of Image classification in remote sensing namely Unsupervised Image Classification, Supervised Image Classification and Object-based Image Classification. The two most common approaches are said to be Unsupervised and Supervised techniques but lately object based classification has been used more for work related to high resolution data.[7]
Figure XXV: Unsupervised classification
Figure XXVI: Supervised classification
Figure XXVII: Object based classification
In this document we have used object based classification. This approach allows for consideration of shape, size, and context as well as spectral content. Relationships between objects can play a crucial role in their identification and classification. Object-oriented methods are often more effective than pixel-based methods when classifying high-resolution imagery, as spatial resolution increases, the more variability there may be in the spectral content of individual pixels all belonging to the same class.
The image processing mechanism used in this component is Optical character recognition or optical character reader (OCR). OCR is the utilization of technology to recognize printed or written by hand message characters inside digital images of physical documentation, for example, a scanned paper report. The essential procedure of OCR includes looking at the content of a record and making an interpretation of the characters into code that can be utilized for information handling. OCR is now and then additionally alluded to as text recognition. [9] During the implementation process Pytesseract library is used to implement OCR. Python Imaging Library (PIL) is used for image processing tasks. Python Imaging Library is a free library for Python programming. During the OCR process the image is converted into a text.
Then this text is passed into a word cleaner function. The created file is then forwarded into the classification algorithm for classification. Spell checker used to minimize the errors caused by the OCR implementation
Prevention Techniques
Based on the research problem identified the research is mainly focused towards implementing access control and authentication in Web Applications. As the first phase of the research the best Access Control Mechanism Was identified. The Role Based Access Control Mechanism was identified by analyzing the advantages and disadvantages. In addition, the literature reviews pointed out that Role Based Access Control was able to deliver better results.
The access control analysis was done considering the complexity of management, ease of use and the usage of resources.
The Web application which was used was built using micro services architecture and was developed using python programming language to perform adaptive file classification. The classified files are to be stored in a database. Therefore, since the database itself is major security component after analyzing several databases and the ways of connecting the database to the application Mongo ATLAS cloud database was used. A Single API was used to contact the database from each and every Micro Service rather than having one API for each and every Micro service. This adds an extra layer of security for the application.
Role Based Access Control was used as the access control mechanism. It provides fine-grained control and offers a simple, manageable approach to access management that is less prone to error than assigning permissions to users individually. The File Classification scheme has predefined set of users which will be using the system. The actions which they can perform on the resource are always the same and confined to their job role. Role Based Access Control satisfies all those requirements. Not just that, Role Based Access Control comes with the ease of administration as well. The administrator doesn’t have to implement policies to each and every user. Instead the administrator has to do few tasks. Map the necessary permissions to the roles and assign users to the roles according to their job roles. That way the access control of the users will be secured as well as the management of the control of access will be much easier.
According to the research problem, access control wasn’t enough for a comprehensive security solution. The access control should be aligned with a proper authentication mechanism. There are several authentication mechanisms available.
A token-based authentication was selected instead of session-based authentication. By the analysis it was seen that it provides a high level of security and since the scaling problems are also solved with the use of a token-based authentication mechanism rather than with the use of session-based authentication.
File Modification Detection
Even though SHA256 hashing works fine, the authors needed to improve the system to eliminate user mistakes / faults. In the context of user mistakes includes dissent deletion of lines or paragraphs, unintended alterations of the document or any other accidental faults.in order to detect user mistakes system has to compare the document with the alterations and the document prior. There are various ways to measure similarity levels between two files.
Following are the options authors considered
Calculating Similarities Between Vectors
This method is done via representing the document in vector form. But for this scenario it wasn't a good solution since calculating similarities between vectors is performed regardless of positions relative to one another (Semantic
Similarity).
Output of this process is many numbers of hashes. Now the algorithm takes short signatures from each hash and concatenates them together.
How algorithms detect similarities is, if a user has done a small change then it only produces different hashes for one or very few segments. Other segments remain with the same hash values. So, it doesn't output a totally different hash. Only corresponding values are changed. Thus, the change of score totally depends on the changes the user does to the original document.
After testing all the possibilities authors found fuzzy hashing is the best option for the task of eliminating user mistakes mentioned above. so a fuzzy Hashing mechanism is introduced to the system to achieve that goal. For the implementation SSdeep project is used.
Figure XXIX: System methodology
A new file will create in the user environment and user supposed to enter data and upload it to the system. As soon as the file is uploaded to the system it will automatically label the file with a File ID and the system generates fuzzy
hash value according to the file uploaded and stores it in the database. Whenever a user is in need of alteration, the user should download the file from the system and then the user can do modifications as desired. User will upload the
file again to the system with or without any alterations. System has to identify whether the uploaded file is modified or not. In order to do so the system will generate new fuzzy hash values to compare hash values in line with file ID
from the database. If fuzzy hash values are not the same, then it will be sent out for reclassification. Before sending it to reclassification the user will get a notification indicating how much of changes has happened to the document so
that the user has more confidence over the document being altered.
Prompting Similarity levels are as follows
Audience
The factors that are explained in the previous sections prove that one of the major reasons for not using classification as a security implementation to secure data is the lack of focus on developing a solution that is fixed to a wider range of users. As an example the second solution explained under literature survey is based on Chinese news classification which is useful only to a minor social sector. Therefore this research is developed with the objective of expanding the audience to motivate the organizations to use these types of solutions.
Therefore DocSpace is designed based on a complex mechanism which is advanced enough to be used in any industry domain where data classification is required. It can vary from industries which require lower level security implementations such as Education, Agriculture and Transportation to industries which require higher level security implementations such as Military, Banking and Finance. However the dataset which is to be used should be changed and tweaked based on the industry that the product will be used. For the development purpose we have trained the model based on the Banking and Finance industry which is one area where a solution similar to this is essential.
That is the probability of event A occurring on the premise that event B has already occurred. In general, we usually use another form:
In the Naive Bayesian classification algorithm, if the unclassified feature set is represented by A, the classification result set is represented by B, then:
Thus, we can find out the category that makes P(category|feature) the biggest, this category is the classification result of the text.
The Naive Bayesian method algorithm takes less time, and it is easy to implement. However, the Naive Bayesian classification model needs to meet the requirement that the conditional independence assumption between features. But even so, it can still get better classification results in this matter.[6]
Image Classification
There are three main techniques of Image classification in remote sensing namely Unsupervised Image Classification, Supervised Image Classification and Object-based Image Classification. The two most common approaches are said to be Unsupervised and Supervised techniques but lately object based classification has been used more for work related to high resolution data.[7]


20+ Photos That Show Family Life Is No Joke
Family isn’t all sunshine and rainbows, but you can have the time of your life trolling your significant other or your kids once in a while. Whether you’re making only your half of the bed, or you’re even bringing your car inside the house, you’ll definitely be nailing family life with a little humor.
We at Bright Side gathered some photos that perfectly sum up the endless entertainment of trolling your loved ones and we hope you’ll have a good laugh.
1. “Husband said he was going to make a bed frame. Turns out it was for the cat.”
2. “You left the toilet seat up.”
3. What happens when the wife spends too much time in Target:
4. “I broke the toilet seat. This is how I told my wife...”
5. “My wife forgot to leave the car seat for me to take our son to the babysitter’s house. This is the picture I sent when she asked how I was going to get him there.”
6.
7. “Pettiness level 100,000,00...... My husband was angry this morning so he decided he was only making his half of the bed.”
8. “I took a picture of the moment my 2 kids realized all the donuts at the party had been eaten while they were off playing on the swings.”
9. “I think my husband’s going to realize I borrowed one of his extension cords.”
10. The joys of having a little brother
11. Family life can be exhausting.
12. My daughter’s solution when her piggy bank deposit slot wasn’t wide enough to put rolled money into.
13. “My dad is a huge Golden State fan, my husband is not. This is what my father woke up to this morning after a Raptors win!”
14. When your 4-year-old daughter decides to give herself a haircut and the only words you can tell her are “It will grow back”:
15. “My husband, Ian, insisted that our new puppy Nala get her own stocking. I thought it was sweet until I realized he had ulterior motives...”
16. “My daughter said the bagel wouldn’t fit in the toaster so I told her to cut it in half...”
17.
18. The wall was freshly painted.
19. “Apparently my wife trapped a bee in her cup holder.”
20. “My mom gave my daughter 2 $1 bills. She took them upstairs and “made more money.”
21. The struggle is real when your wife thinks you’re on a diet, but you are craving junk food.
22. “My daughter... trying to hide when she is supposed to be in bed.”
23. “I found out that our Volkswagen fits in the den. We’ll see what the wife thinks when she gets home.”
Which situation made you laugh the most? Do you have funny stories or photos of your family life to share with us? We’d love to find them in the comment section!
Preview photo credit ninnuh / reddit


A generalized Lambert function as described below:
The exponential function can be generalized by the expression
\[ f(x) = \left[ 1 + (1-n) x \right]^\frac{1}{1-n} \]The derivative of this function is a power of itself
\[ \frac{d f(x)}{dx} = f(x)^n \]and matches the exponential function exactly in the limit \( n \to 1 \). This prompts one to consider using it to define a Lambert-type function:
\[ W(x) \left[ 1 + (1-n) W(x) \right]^\frac{1}{1-n} = x \]The principle branch of this function is evaluated by numerical root finding.
This example requires the higher-level mathematics of Math.
Complete code for this example:


An n-dimensional sparese array. This class abstracts away the concept of an n-dimensional array, it should be easy to create an array of type T on N dimensions. It differes from an array in that, when instantiated, it consumes no memory. When you begin to fill cells of the array with data, only then does memory begin to fill up. Accessing a cell without data returns "defaultValue", which is set in the constructor.
More...
#include <sparse_array.hpp>
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| | sparse_array_n (const Allocator &a=Allocator()) |
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| | sparse_array_n (const Allocator &a, const T &dv, unsigned int d0,...) |
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| | sparse_array_n (const T &dv, unsigned int d0,...) |
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| sparse_array_n (const T &dv, unsigned int *d) |
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| | ~sparse_array_n () |
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| unsigned int | linear_index (unsigned int d0,...) const |
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| unsigned int | linear_index (unsigned int d0, va_list vl) const |
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| unsigned int | linear_index (unsigned int *idx) const |
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| T & | operator() (unsigned int d0,...) |
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| const T & | operator() (unsigned int d0,...) const |
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| T & | operator() (unsigned int *d) |
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| const T & | operator() (unsigned int *d) const |
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const bool | has_value (unsigned int d0,...) const |
| | Returns whether an index has a value For a multidimensional index, this returns whether or not that index has a value. The value at that index will always be readable, use this to know whether or not it's a true value, or an automatically generated value.
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const bool | has_value (unsigned int *d) const |
| | Returns whether an index has a value For a multidimensional index, this returns whether or not that index has a value. The value at that index will always be readable, use this to know whether or not it's a true value, or an automatically generated value.
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bool | dump_to_stream (std::ofstream &stream) const |
| | Dumps this array to a file stream at its current index.
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bool | read_from_stream (std::ifstream &stream) |
| | Reads data from a filestream.
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template<class T, int N, class Allocator = std::allocator<T>>
class sisl::sparse_array_n< T, N, Allocator >
An n-dimensional sparese array. This class abstracts away the concept of an n-dimensional array, it should be easy to create an array of type T on N dimensions. It differes from an array in that, when instantiated, it consumes no memory. When you begin to fill cells of the array with data, only then does memory begin to fill up. Accessing a cell without data returns "defaultValue", which is set in the constructor.
◆ sparse_array_n() [1/3]
template<class T , int N, class Allocator = std::allocator<T>>
Default constructor. Default constructor, allocates nothing.
◆ sparse_array_n() [2/3]
template<class T , int N, class Allocator = std::allocator<T>>
N-dim constructor. Allocates an array with N dimensions with maximum dimensions given by the parameters.
◆ sparse_array_n() [3/3]
template<class T , int N, class Allocator = std::allocator<T>>
N-dim constructor. Allocates an array with N dimensions with maximum dimensions given by the parameters.
◆ ~sparse_array_n()
template<class T , int N, class Allocator = std::allocator<T>>
N-dim constructor. Allocates an array with N dimensions with maximum dimensions given by the parameters.
◆ linear_index() [1/3]
template<class T , int N, class Allocator = std::allocator<T>>
/brief Returns a unique index for this index. This returns a unique index for a point index, this should generally be an index between 0 and max_dim_1*max_dim_2*...*max_dim_N - 1.
◆ linear_index() [2/3]
template<class T , int N, class Allocator = std::allocator<T>>
| unsigned int sisl::sparse_array_n< T, N, Allocator >::linear_index |
( |
unsigned int |
d0, |
|
|
va_list |
vl |
|
) |
| const |
|
inline |
/brief Returns a unique index for this index. This returns a unique index for a point index, this should generally be an index between 0 and max_dim_1*max_dim_2*...*max_dim_N - 1.
◆ linear_index() [3/3]
template<class T , int N, class Allocator = std::allocator<T>>
/brief Returns a unique index for this index. This returns a unique index for a point index, this should generally be an index between 0 and max_dim_1*max_dim_2*...*max_dim_N - 1.
◆ operator()() [1/4]
template<class T , int N, class Allocator = std::allocator<T>>
/brief Accessor functions. Access a value in the array, returns a reference so it is possible to write to indices via this overload.
◆ operator()() [2/4]
template<class T , int N, class Allocator = std::allocator<T>>
/brief Accessor functions. Access a value in the array, returns a reference so it is possible to write to indices via this overload.
◆ operator()() [3/4]
template<class T , int N, class Allocator = std::allocator<T>>
/brief Accessor functions. Access a value in the array, returns a reference so it is possible to write to indices via this overload.
◆ operator()() [4/4]
template<class T , int N, class Allocator = std::allocator<T>>
/brief Accessor functions. Access a value in the array, returns a reference so it is possible to write to indices via this overload.
The documentation for this class was generated from the following file:


CNN Anchor Scorns Devin Nunes’ ‘Horsesh*t’ Attacks Against The Network
CNN chief correspondent John King hit back at Rep. Devin Nunes (R-CA) on Monday after the congressman accused CNN of engaging in “criminal activity” by reporting on his alleged contacts in Ukraine.
Nunes attacked CNN on Sunday after the network published indicted Giuliani associated Lev Parnas’ allegation that Nunes had met with former Ukrainian Prosecutor General Viktor Shokin in Vienna in 2018 to dig up dirt on Joe Biden.
Though he had told Breitbart on Friday that the story was “demonstrably false,” Nunes declined to say on Sunday how CNN had gotten it wrong and instead claimed that the network had potentially broken the law by talking to Parnas. He also vowed to sue CNN after Thanksgiving.
“Forgive me, but horseshit,” King said to the panel of “Inside Politics” after playing a clip of the Republican’s comments on Fox News’ “Sunday Morning Futures.”
“This is easy. This is easy,” he continued. “He was on a plane with staff that went somewhere during this time frame. There are either passports stamped, or if they did it on a classified basis, there’s a crew on the plane.”
King pointed out that if the report were truly as bogus as Nunes claims it to be, then the lawmaker could simply provide travel documents to prove he did not go to Vienna during his four-day trip to Europe with several aides last year.
“If it’s demonstrably false, demonstrate it,” the CNN anchor said.
Watch King below:


Stephen King Finally Gets His Own POP! Vinyl Toys from Funko
by John SquiresCountless Stephen King characters have been turned into POP! vinyl toys by Funko over the years, and now King himself has finally been immortalized with his own Funko toys!
Two different Stephen King POP! toys are on the way:
- Black shirt & grey pants – holding book
- Barnes & Noble Exclusive – blood-covered, holding axe & book
Both of these are listed as “Coming Soon.”


Our Mission
HairCare 3 is here to find your perfect hairstylist by giving stylists a platform to promote themselves on. You can add and read reviews, ratings, and view photos to find the hairstylist for you.
HC3 Features
Lorem ipsum, dolor sit amet consectetur adipisicing elit. Blanditiis, asperiores dolores. Commodi repellat esse provident. Reiciendis consequatur reprehenderit voluptatem nam illo sequi animi, recusandae libero non, soluta suscipit minus cupiditate! Lorem, ipsum dolor sit amet consectetur adipisicing elit. Magni iure aliquid eius deleniti cupiditate molestias quas fuga possimus nemo porro? Placeat nam numquam molestiae labore dignissimos, sunt consequuntur cupiditate commodi.
User Features
- Search nearby stylists by address
- Upload review and rating
- Update/delete reviews
Stylist Features
- Create a detailed profile
- View other stylists to research competitors
- Update/delete Profile


ctrl + hjkl to Arrow Keys (Vim)
ctrl + h = left_arrow
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Links and Lists
Click on the menu on the lower left
Click on terminal
Enter your clone code
create a index file by typing in gedit index.html
Paste all the information from your template file
Add text in the different areas
Save your work
Start up terminal and type in python -m SimpleHTTPServer
Go into your browser and type in localhost:8000
http://www.w3schools.com/
https://www.codecademy.com
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http://www.htmlgoodies.com/


Popovers provide a way to display more information via hover. Use this to provide secondary information like recent changes, or related entities
Popovers are on a 250ms delay so as not to distract power users but can still provide commentary for new users or someone taking a new action


Purpose of the this hands-on training is to dockerize an Python Flask application with Dockerfile and Docker compose.
At the end of the this hands-on training, students will be able to;
build a Docker images.
configure Docker Compose to run Python Flask app.
Part 1 - Launch a Docker Machine Instance and Connect with SSH
Part 2 - Configuring Multi Containers (Python Flask App and MySQL) with Docker Compose
Launch a Compose enabled Docker machine on Amazon Linux 2 AMI with security group allowing SSH connections using the Cloudformation Template for Docker Machine Installation.
Connect to your instance with SSH.
ssh -i .ssh/call-training.pem<EMAIL_ADDRESS>mkdir to-do-api
cd to-do-api
to-do-api.py
with following coding.# Import Flask modules
from flask import Flask, jsonify, abort, request, make_response
from flaskext.mysql import MySQL
# Create an object named app
app = Flask(__name__)
# Configure mysql database
app.config['MYSQL_DATABASE_HOST'] = 'database'
app.config['MYSQL_DATABASE_USER'] = 'clarusway'
app.config['MYSQL_DATABASE_PASSWORD'] = 'Clarusway_1'
app.config['MYSQL_DATABASE_DB'] = 'todo_db'
app.config['MYSQL_DATABASE_PORT'] = 3306
mysql = MySQL()
mysql.init_app(app)
connection = mysql.connect()
connection.autocommit(True)
cursor = connection.cursor()
# Write a function named `init_todo_db` which initializes the todo db
# Create P table within sqlite db and populate with sample data
# Execute the code below only once.
def init_todo_db():
drop_table = 'DROP TABLE IF EXISTS todo_db.todos;'
todos_table = """
CREATE TABLE todo_db.todos(
task_id INT NOT NULL AUTO_INCREMENT,
title VARCHAR(100) NOT NULL,
description VARCHAR(200),
is_done BOOLEAN NOT NULL DEFAULT 0,
PRIMARY KEY (task_id)
) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4 COLLATE=utf8mb4_unicode_ci;
"""
data = """
INSERT INTO todo_db.todos (title, description, is_done)
VALUES
("Project 2", "Work on project 2 with teammates", 1 ),
("Cloudformation Documentation", "Study and learn how to read cloudformation docs", 0),
("Work on CC Phonebook", "Solve python coding challenge about phonebook app", 0);
"""
cursor.execute(drop_table)
cursor.execute(todos_table)
cursor.execute(data)
# Write a function named `get_all_tasks` which gets all tasks from the todos table in the db,
# and return result as list of dictionary
# `[{'task_id': 1, 'title':'XXXX', 'description': 'XXXXXX', 'is_done': 'Yes' or 'No'} ]`.
def get_all_tasks():
query = """
SELECT * FROM todos;
"""
cursor.execute(query)
result = cursor.fetchall()
tasks =[{'task_id':row[0], 'title':row[1], 'description':row[2], 'is_done': bool(row[3])} for row in result]
return tasks
# Write a function named `find_task` which finds task using task_id from the todos table in the db,
# and return result as list of dictionary
# `{'task_id': 1, 'title':'XXXX', 'description': 'XXXXXX', 'is_done': 'Yes' or 'No'}`.
def find_task(id):
query = f"""
SELECT * FROM todos WHERE task_id={id};
"""
cursor.execute(query)
row = cursor.fetchone()
task = None
if row is not None:
task = {'task_id':row[0], 'title':row[1], 'description':row[2], 'is_done': bool(row[3])}
return task
# Write a function named `insert_task` which inserts task into the todos table in the db,
# and return the newly added task as dictionary
# `{'task_id': 1, 'title':'XXXX', 'description': 'XXXXXX', 'is_done': 'Yes' or 'No'}`.
def insert_task(title, description):
insert = f"""
INSERT INTO todos (title, description)
VALUES ('{title}', '{description}');
"""
cursor.execute(insert)
query = f"""
SELECT * FROM todos WHERE task_id={cursor.lastrowid};
"""
cursor.execute(query)
row = cursor.fetchone()
return {'task_id':row[0], 'title':row[1], 'description':row[2], 'is_done': bool(row[3])}
# Write a function named `change_task` which updates task into the todos table in the db,
# and return updated added task as dictionary
# `{'task_id': 1, 'title':'XXXX', 'description': 'XXXXXX', 'is_done': 'Yes' or 'No'}`.
def change_task(task):
update = f"""
UPDATE todos
SET title='{task['title']}', description = '{task['description']}', is_done = {task['is_done']}
WHERE task_id= {task['task_id']};
"""
cursor.execute(update)
query = f"""
SELECT * FROM todos WHERE task_id={task['task_id']};
"""
cursor.execute(query)
row = cursor.fetchone()
return {'task_id':row[0], 'title':row[1], 'description':row[2], 'is_done': bool(row[3])}
# Write a function named `remove_task` which removes task from the todos table in the db,
# and returns True if successfully deleted or False.
def remove_task(task):
delete = f"""
DELETE FROM todos
WHERE task_id= {task['task_id']};
"""
cursor.execute(delete)
query = f"""
SELECT * FROM todos WHERE task_id={task['task_id']};
"""
cursor.execute(query)
row = cursor.fetchone()
return True if row is None else False
# Write a function named `home` which returns 'Welcome to the Callahan's To-Do API Service',
# and assign to the static route of ('/')
@app.route('/')
def home():
return "Welcome to Callahan's To-Do API Service"
# Write a function named `get_tasks` which returns all tasks in JSON format for `GET`,
# and assign to the static route of ('/todos')
@app.route('/todos', methods=['GET'])
def get_tasks():
return jsonify({'tasks':get_all_tasks()})
# Write a function named `get_tasks` which returns the task with given task_id in JSON format for `GET`,
# and assign to the static route of ('/todos/<int:task_id>')
@app.route('/todos/<int:task_id>', methods = ['GET'])
def get_task(task_id):
task = find_task(task_id)
if task == None:
abort(404)
return jsonify({'task found': task})
# Write a function named `add_task` which adds new task using `POST` methods,
# and assign to the static route of ('/todos')
@app.route('/todos', methods=['POST'])
def add_task():
if not request.json or not 'title' in request.json:
abort(400)
return jsonify({'newly added task':insert_task(request.json['title'], request.json.get('description', ''))}), 201
# Write a function named `update_task` which updates an existing task using `PUT` method,
# and assign to the static route of ('/todos/<int:task_id>')
@app.route('/todos/<int:task_id>', methods=['PUT'])
def update_task(task_id):
task = find_task(task_id)
if task == None:
abort(404)
if not request.json:
abort(400)
task['title'] = request.json.get('title', task['title'])
task['description'] = request.json.get('description', task['description'])
task['is_done'] = int(request.json.get('is_done', int(task['is_done'])))
return jsonify({'updated task': change_task(task)})
# Write a function named `delete_task` which updates an existing task using `DELETE` method,
# and assign to the static route of ('/todos/<int:task_id>')
@app.route('/todos/<int:task_id>', methods=['DELETE'])
def delete_task(task_id):
task = find_task(task_id)
if task == None:
abort(404)
return jsonify({'result':remove_task(task)})
# Write a function named `not_found` for handling 404 errors which returns 'Not found' in JSON format.
@app.errorhandler(404)
def not_found(error):
return make_response(jsonify({'error': 'Not found'}), 404)
# Write a function named `bad_request` for handling 400 errors which returns 'Bad Request' in JSON format.
@app.errorhandler(400)
def bad_request(error):
return make_response(jsonify({'error': 'Bad request'}), 400)
# Add a statement to run the Flask application which can be reached from any host on port 80.
if __name__== '__main__':
init_todo_db()
# app.run(debug=True)
app.run(host='<IP_ADDRESS>', port=80)
requirements.txt
in your project folder, add the followings as package list.flask
flask-mysql
FROM python:alpine
COPY . /app
WORKDIR /app
RUN pip install -r requirements.txt
EXPOSE 80
CMD python ./to-do-api.py
docker-compose.yml
in your project folder with following setup.version: "3.7"
services:
database:
image: mysql:5.7
environment:
MYSQL_ROOT_PASSWORD: R1234r
MYSQL_DATABASE: todo_db
MYSQL_USER: clarusway
MYSQL_PASSWORD: Clarusway_1
networks:
- clarusnet
myapp:
build: .
restart: always
depends_on:
- database
ports:
- "80:80"
networks:
- clarusnet
networks:
clarusnet:
driver: bridge
docker-compose up -d
docker container ls
to-do-api_myapp
as image name.docker image ls
to-do-api_clarusnet
.docker network ls
Check if the To-Do App is running by entering http://<ec2-host-name>
in a browser.
Test the application.
To Do List
API using /todos
path and HTTP GET
method with curl
command.curl http://<ec2-host-name>/todos
id=3
using /todos/3
path and HTTP GET
method with curl
command.curl http://<ec2-host-name>/todos/3
To Do List
using /todos
path and HTTP POST
method with curl
command.curl -H "Content-Type: application/json" -X POST -d '{"title":"Get some REST", "description":"REST in Peace"}' http://<ec2-host-name>/todos
id=1
using /todos/1
path and HTTP DELETE
method with curl
command.curl -H "Content-Type: application/json" -X DELETE http://<ec2-host-name>/todos/1
To Do List
API using /todos
path and HTTP GET
method with curl
command.curl http://<ec2-host-name>/todos
Stop and remove containers, networks, images.
docker-compose down
docker image ls
docker container ls
docker network ls


Fraction Field of Integral Domains#
AUTHORS:
William Stein (with input from David Joyner, David Kohel, and Joe Wetherell)
Burcin Erocal
Julian Rüth (2017-06-27): embedding into the field of fractions and its section
EXAMPLES:
Quotienting is a constructor for an element of the fraction field:
sage: R.<x> = QQ[]
sage: (x^2-1)/(x+1)
x - 1
sage: parent((x^2-1)/(x+1))
Fraction Field of Univariate Polynomial Ring in x over Rational Field
The GCD is not taken (since it doesn’t converge sometimes) in the inexact case:
sage: Z.<z> = CC[]
sage: I = CC.gen()
sage: (1+I+z)/(z+0.1*I)
(z + 1.00000000000000 + I)/(z + 0.100000000000000*I)
sage: (1+I*z)/(z+1.1)
(I*z + 1.00000000000000)/(z + 1.10000000000000)
- sage.rings.fraction_field.FractionField(R, names=None)#
Create the fraction field of the integral domain
R
.INPUT:
R
– an integral domainnames
– ignored
EXAMPLES:
We create some example fraction fields:
sage: FractionField(IntegerRing()) Rational Field sage: FractionField(PolynomialRing(RationalField(),'x')) Fraction Field of Univariate Polynomial Ring in x over Rational Field sage: FractionField(PolynomialRing(IntegerRing(),'x')) Fraction Field of Univariate Polynomial Ring in x over Integer Ring sage: FractionField(PolynomialRing(RationalField(),2,'x')) Fraction Field of Multivariate Polynomial Ring in x0, x1 over Rational Field
Dividing elements often implicitly creates elements of the fraction field:
sage: x = PolynomialRing(RationalField(), 'x').gen() sage: f = x/(x+1) sage: g = x**3/(x+1) sage: f/g 1/x^2 sage: g/f x^2
The input must be an integral domain:
sage: Frac(Integers(4)) Traceback (most recent call last): ... TypeError: R must be an integral domain.
- class sage.rings.fraction_field.FractionFieldEmbedding#
Bases:
DefaultConvertMap_unique
The embedding of an integral domain into its field of fractions.
EXAMPLES:
sage: R.<x> = QQ[] sage: f = R.fraction_field().coerce_map_from(R); f Coercion map: From: Univariate Polynomial Ring in x over Rational Field To: Fraction Field of Univariate Polynomial Ring in x over Rational Field
- is_injective()#
Return whether this map is injective.
EXAMPLES:
The map from an integral domain to its fraction field is always injective:
sage: R.<x> = QQ[] sage: R.fraction_field().coerce_map_from(R).is_injective() True
- is_surjective()#
Return whether this map is surjective.
EXAMPLES:
sage: R.<x> = QQ[] sage: R.fraction_field().coerce_map_from(R).is_surjective() False
- section()#
Return a section of this map.
EXAMPLES:
sage: R.<x> = QQ[] sage: R.fraction_field().coerce_map_from(R).section() Section map: From: Fraction Field of Univariate Polynomial Ring in x over Rational Field To: Univariate Polynomial Ring in x over Rational Field
- class sage.rings.fraction_field.FractionFieldEmbeddingSection#
Bases:
Section
The section of the embedding of an integral domain into its field of fractions.
EXAMPLES:
sage: R.<x> = QQ[] sage: f = R.fraction_field().coerce_map_from(R).section(); f Section map: From: Fraction Field of Univariate Polynomial Ring in x over Rational Field To: Univariate Polynomial Ring in x over Rational Field
- class sage.rings.fraction_field.FractionField_1poly_field(R, element_class=<class 'sage.rings.fraction_field_element.FractionFieldElement_1poly_field'>)#
Bases:
FractionField_generic
The fraction field of a univariate polynomial ring over a field.
Many of the functions here are included for coherence with number fields.
- class_number()#
Here for compatibility with number fields and function fields.
EXAMPLES:
sage: R.<t> = GF(5)[]; K = R.fraction_field() # optional - sage.rings.finite_rings sage: K.class_number() # optional - sage.rings.finite_rings 1
- function_field()#
Return the isomorphic function field.
EXAMPLES:
sage: R.<t> = GF(5)[] # optional - sage.rings.finite_rings sage: K = R.fraction_field() # optional - sage.rings.finite_rings sage: K.function_field() # optional - sage.rings.finite_rings Rational function field in t over Finite Field of size 5
See also
sage.rings.function_field.RationalFunctionField.field()
- maximal_order()#
Return the maximal order in this fraction field.
EXAMPLES:
sage: K = FractionField(GF(5)['t']) # optional - sage.rings.finite_rings sage: K.maximal_order() # optional - sage.rings.finite_rings Univariate Polynomial Ring in t over Finite Field of size 5
- ring_of_integers()#
Return the ring of integers in this fraction field.
EXAMPLES:
sage: K = FractionField(GF(5)['t']) # optional - sage.rings.finite_rings sage: K.ring_of_integers() # optional - sage.rings.finite_rings Univariate Polynomial Ring in t over Finite Field of size 5
- class sage.rings.fraction_field.FractionField_generic(R, element_class=<class 'sage.rings.fraction_field_element.FractionFieldElement'>, category=Category of quotient fields)#
Bases:
Field
The fraction field of an integral domain.
- base_ring()#
Return the base ring of
self
.This is the base ring of the ring which this fraction field is the fraction field of.
EXAMPLES:
sage: R = Frac(ZZ['t']) sage: R.base_ring() Integer Ring
- characteristic()#
Return the characteristic of this fraction field.
EXAMPLES:
sage: R = Frac(ZZ['t']) sage: R.base_ring() Integer Ring sage: R = Frac(ZZ['t']); R.characteristic() 0 sage: R = Frac(GF(5)['w']); R.characteristic() # optional - sage.rings.finite_rings 5
- construction()#
EXAMPLES:
sage: Frac(ZZ['x']).construction() (FractionField, Univariate Polynomial Ring in x over Integer Ring) sage: K = Frac(GF(3)['t']) # optional - sage.rings.finite_rings sage: f, R = K.construction() # optional - sage.rings.finite_rings sage: f(R) # optional - sage.rings.finite_rings Fraction Field of Univariate Polynomial Ring in t over Finite Field of size 3 sage: f(R) == K # optional - sage.rings.finite_rings True
- gen(i=0)#
Return the
i
-th generator ofself
.EXAMPLES:
sage: R = Frac(PolynomialRing(QQ,'z',10)); R Fraction Field of Multivariate Polynomial Ring in z0, z1, z2, z3, z4, z5, z6, z7, z8, z9 over Rational Field sage: R.0 z0 sage: R.gen(3) z3 sage: R.3 z3
- is_exact()#
Return if
self
is exact which is if the underlying ring is exact.EXAMPLES:
sage: Frac(ZZ['x']).is_exact() True sage: Frac(CDF['x']).is_exact() False
- is_field(proof=True)#
Return
True
, since the fraction field is a field.EXAMPLES:
sage: Frac(ZZ).is_field() True
- is_finite()#
Tells whether this fraction field is finite.
Note
A fraction field is finite if and only if the associated integral domain is finite.
EXAMPLES:
sage: Frac(QQ['a','b','c']).is_finite() False
- ngens()#
This is the same as for the parent object.
EXAMPLES:
sage: R = Frac(PolynomialRing(QQ,'z',10)); R Fraction Field of Multivariate Polynomial Ring in z0, z1, z2, z3, z4, z5, z6, z7, z8, z9 over Rational Field sage: R.ngens() 10
- random_element(*args, **kwds)#
Return a random element in this fraction field.
The arguments are passed to the random generator of the underlying ring.
EXAMPLES:
sage: F = ZZ['x'].fraction_field() sage: F.random_element() # random (2*x - 8)/(-x^2 + x)
sage: f = F.random_element(degree=5) sage: f.numerator().degree() == f.denominator().degree() True sage: f.denominator().degree() <= 5 True sage: while f.numerator().degree() != 5: ....: f = F.random_element(degree=5)
- ring()#
Return the ring that this is the fraction field of.
EXAMPLES:
sage: R = Frac(QQ['x,y']) sage: R Fraction Field of Multivariate Polynomial Ring in x, y over Rational Field sage: R.ring() Multivariate Polynomial Ring in x, y over Rational Field
- some_elements()#
Return some elements in this field.
EXAMPLES:
sage: R.<x> = QQ[] sage: R.fraction_field().some_elements() [0, 1, x, 2*x, x/(x^2 + 2*x + 1), 1/x^2, ... (2*x^2 + 2)/(x^2 + 2*x + 1), (2*x^2 + 2)/x^3, (2*x^2 + 2)/(x^2 - 1), 2]
- sage.rings.fraction_field.is_FractionField(x)#
Test whether or not
x
inherits fromFractionField_generic
.EXAMPLES:
sage: from sage.rings.fraction_field import is_FractionField sage: is_FractionField(Frac(ZZ['x'])) True sage: is_FractionField(QQ) False


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Hairdresser / Trainer
Nail Technician
Nail Technician
Hairdresser


@* * Copyright 2021 HM Revenue & Customs * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. *@ @import helper.CSRF @import testOnly.models.TestHeadersModel @import views.html.templates.main_template @(form: Form[TestHeadersModel], headers: Map[String, String])(implicit request: Request[_], messages: Messages, appConfig: config.FrontendAppConfig) @main_template(title = "Test Headers") { @helper.form(action = testOnly.controllers.routes.TestHeadersController.submitTestHeaders) { @CSRF.formField