Overview
Features
- Detect: Monitors whether target elements scroll in/out of the visible view of a scroll container (not just the window). Allows configuring a logical size of the container to affect where the target must actually be located before triggering events.
- Query: Provides location information identifying where the target element is located relative to the container (top, left, bottom, right).
- Change: Enables animated scrolling of the target into the container with any of the built-in jQuery easing options and flexible positioning settings.
Download
Usage
A scrolling element is either the window or an element with the overflow
style set to
auto
or scroll
$('#myelement').scrollable()
This will add myelement to the list of elements being monitored for their scroll position. If the element scrolls into
the visible viewport defined by its container, then an in
event is triggered. When it scrolls out, an
out
event is triggered. Each event is triggered only one time when an element moves in and out of view.
Options
direction
- Determines if monitoring/positioning will occur for either both
, vertical
only, or
horizontal
only. Defaults to both
.
$('#myelement').scrollable({ direction: 'vertical' });
offset
- Adjust the logical size of the container such that the detection compares to a different
sized box than the physical one displayed in the browser. Accepts pixels and percentages. Accepts left
,
top
, right
, and bottom
to set each side individually or vertical
/y
and
horizonal
/x
which will split the value evenly between the top/bottom and left/right, respectively.
Any missing values will default to zero. Positive value shrink the box while negative expand it.
// Adjust the bounds of the top/left side of the detection zone
$('#myelement').scrollable({ offset: { left: '40%', top: '40%' } });
// Adjust the bounds of the left/right only (20% for each) and leave top/bottom alone
$('#myelement').scrollable({ offset: { x: '40%' } });
See the example to see how it works.
Methods
position
- returns an object hash describing the state of the element relative to the scrolling container's
visible viewport:
-
container
[Object] - The normalized (after applyingoffset
) left/right/top/bottom points of the container element. -
element
[Object] - The left/right/top/bottom points of the target element. -
inside
[Boolean] - Is the element inside the defined visible viewport. -
outside
[Boolean] - Is the element outside the defined visible viewport. -
left
[Boolean] - Is the element left of the defined visible viewport. -
right
[Boolean] - Is the element right of the defined visible viewport. -
top
[Boolean] - Is the element above the defined visible viewport. -
bottom
[Boolean] - Is the element below the defined visible viewport.
goto
- Scrolls the element into the visible viewport. Uses jQuery.animate to smooth the transistion.
Takes an optional config object with the following options:
-
onlyOutside
- Should the element be scrolled if its already in the visible area. -
my
- Determines the final positioning of the target element relative to the container. -
at
- Determines where the element should be positioned relative to the container.
See the documentation on jQuery UI Position for the format ofmy
andat
. -
duration
- Same options as jQuery.animate() -
easing
- Accepts all the easing options available in jQuery UI -
complete
- callback function to execute once animation completes
See the example to experiment with the positioning options.
Events
in
- triggered when the element scrolls into the defined viewport.
$('#myelement').scrollable({
in: function ( e, ui ) {
...
}
});
or
$('#myelement').on('scrollin', function ( e, ui ) {
...
});
e
is the original scroll event.
The ui
object contains the following keys:
-
container
[jQuery] - The closest scrolling ancestor container of the target element -
element
[jQuery] - The target element -
position
[Object] - The same object hash that is returned by theposition
method.
out
- triggered when the element scrolls out of the defined viewport. Same parameters and usage as in
.
Demos
in
event fires which handles updating the navigation bar. Click each section on the
navigation menu on the left will animate the section into view using the goto
method.


An enthusiastic and motivated graduate with a master's degree focused in Computer Science and Engineering from National Cheng Kung University. Currently studying for a master's degree in Software Engineering at California State University Fullerton. Solid experience in object-oriented programming; developing, testing and debugging code. Experienced in JavaScript, HTML, CSS, and can use responsive technologies such as Bootstrap. Can use online version control tools such as GitHub. Quickly learn and master new technologies; enjoy working in both team and individually.
GPA: 3.63 / 4.00
GPA: 4.16 / 4.30
GPA: 3.84 / 4.00
For outdoor activities, I like to take a walk which helps me relax. Traveling is also one of my favorite interests, since it broadens my horizons. I also enjoy hiking and jogging. I think doing these outdoor activites can not only make me healthy, but also peace my mind.
As for indoor activities, I watch action, sci-fi, hero and many genres of movies and television series.Also, I love to cook. Listening to music, such as electronic, pop, and rock music, is another activity that I like to do when I am free and I like to explore the information about latest technology advancements.


Welcome User
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{# Licensed to the Apache Software Foundation (ASF) under one or more contributor license agreements. See the NOTICE file distributed with this work for additional information regarding copyright ownership. The ASF licenses this file to you 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. #} {% extends 'appbuilder/baselayout.html' %} {% from 'airflow/_messages.html' import show_message %} {% block page_title -%} {% if title is defined -%} {{ title }} - {{ appbuilder.app_name }} {% else -%} {{ appbuilder.app_name }} {% endif%} {% endblock %} {% block head_meta %} {{ super() }} {% if scheduler_job is defined and (scheduler_job and scheduler_job.is_alive()) %} {% endif %} {% endblock %} {% block messages %} {% include 'appbuilder/flash.html' %} {% if scheduler_job is defined and (not scheduler_job or not scheduler_job.is_alive()) %} {% call show_message(category='warning', dismissible=false) %}
The scheduler does not appear to be running. {% if scheduler_job %} Last heartbeat was received . {% endif %}
The DAGs list may not update, and new tasks will not be scheduled.
{% endcall %} {% endif %} {% if triggerer_job is defined and (not triggerer_job or not triggerer_job.is_alive()) %} {% call show_message(category='warning', dismissible=false) %}The triggerer does not appear to be running. {% if triggerer_job %} Last heartbeat was received . {% endif %}
Triggers will not run, and any deferred operator will remain deferred until it times out and fails.
{% endcall %} {% endif %} {% endblock %} {% block footer %} {% if not current_user.is_anonymous %} {% set version_label = 'Version' %} {% endif %} {% endblock %} {% block head_css %} {{ super() }} {% if not appbuilder.app_theme %} {# airflowDefaultTheme.css file contains the styles from local bootstrap-theme.css #} {% endif %} {% endblock %} {% block tail_js %} {{ super() }} {% if analytics_tool is defined and analytics_tool %} {% include "analytics/" + analytics_tool + ".html" %} {% endif %} {% endblock %}


Extends an MBO control object with options for multipoint proposal.
setMBOControlMultiPoint(control, method = NULL, cl.lie = NULL,
moimbo.objective = NULL, moimbo.dist = NULL, moimbo.selection = NULL,
moimbo.maxit = NULL, moimbo.sbx.eta = NULL, moimbo.sbx.p = NULL,
moimbo.pm.eta = NULL, moimbo.pm.p = NULL)
Arguments
| control |
[MBOControl ]
Control object for mbo. |
| method |
[character(1) ]
Method used for proposal of multiple infill points, for parallel batch evaluation.
Possible values are:
“cb”: Proposes points by optimizing the confidence bound “cb” criterion,
propose.points times. Each lambda value for “cb” is drawn randomly from an
exp(1)-distribution, so do not define infill.opt.cb.lambda .
The optimizer for each proposal is configured in the same way as for the single point case,
i. e., by specifying infill.opt and related stuff.
“moimbo”: Proposes points by multi-objective infill criteria via evolutionary multi-objective optimization.
The EA is a (mu+1) type of algorithm and runs for moimbo.maxit generations.
The population size is set to propose.points .
The selection criterion is moimbo.selection .
If this method is selected the infill criterion in setMBOInfillCrit is ignored.
“cl”: Proposes points by constant liar strategy.
Only meaningful if infill.crit == "cb"
In the first step the kriging model is fitted based on the real data and the best point is calculated
according to the regular EI-criterion.
Then, the function value of the best point is simply guessed by the worst seen function evaluation.
This lie is used to update the model in order to propose the subsequent point.
The procedure is applied until the number of best points achieves propose.points .
Default is cb . |
| cl.lie |
[function ]
Function used by constant liar method for lying. Default is min . |
| moimbo.objective |
[character(1) ]
Objectives which are optimized in multi-objective approach.
Possible values are: “mean.dist”, “ei.dist”, “mean.se”, “mean.se.dist”.
Default is “ei.dist”. |
| moimbo.dist |
[character(1) ]
Distance function used in multi-objective EA.
Possible values are: “nearest.neighbor”, “nearest.better”.
Default is “nearest.better”. |
| moimbo.selection |
[character(1) ]
Method used for selecting 1 element for removal from the population
in each iteration of the multi-objective EA.
Possible values are:
“hypervolume”: Non-dominated sorting + hypervolume contribution.
“crowdingdist”: Non-dominated sorting + crowding distance based ranking.
“first”: Non-dominated sorting + first objective of moimbo.objective as criterion.
“last”: Non-dominated sorting + last objective of moimbo.objective as criterion.
Default is hypervolume . |
| moimbo.maxit |
[character(1) ]
Number of generations for multi-objective EA.
Default is 100. |
| moimbo.sbx.eta |
[numeric(1) ]
Distance parameter of crossover distribution, see sbx_operator .
Default is 15. |
| moimbo.sbx.p |
[numeric(1) ]
Probability of 1-point crossover, see sbx_operator .
Default is 1. |
| moimbo.pm.eta |
[numeric(1) ]
Distance parameter of mutation distribution, see pm_operator .
Default is 15. |
| moimbo.pm.p |
[numeric(1) ]
Probability of 1-point mutation, see pm_operator .
Default is 1. |
Value
[MBOControl
].
See also
Other MBOControl: makeMBOControl
,
setMBOControlInfill
,
setMBOControlMultiObj
,
setMBOControlTermination


The Xholon
framework for building loosely-organized systems
captures object interactions at runtime,
and creates UML 2.0 sequence diagrams using UMLGraph.
phpCallGraph
can output its analysis in UMLGraph format, allowing the
automatic production of UML sequence diagrams from PHP code.


Hi, I'm David. A full-stack Web developer experienced in Django & nodejs
A platform for web dev students to showcase their projects, view other students projects and rate them.
An application that helps people find movie shops near them.
A football application that shows football results, league standings and game fixtures.
On top of HTML5 and CSS3, I am skilled in front end frameworks and libraries like nodejs, angular, jquery, bootstrap and css-grid to make immersive beautiful responsive websites
Highly skilled in python frameworks like flask and Django with sql databases like postgreSQL and sqlite to create web applications and REST apis


RMI-IIOP Programmer's Guide
|
RMI-IIOP Documentation |
This document discusses how to write Java Remote Method Invocation (RMI) programs that can access remote objects by using the Internet Inter-ORB Protocol (IIOP). By making your RMI programs conform to a small set of restrictions, your CORBA clients in any language can access RMI-IIOP servers. RMI-IIOP gives you RMI ease-of-use coupled with CORBA/IIOP language interoperability.
RMI lacks interoperability with other languages because it does not use CORBA-IIOP as the communication protocol.
The Java(TM) 2 Platform, Standard Edition (J2SE) CORBA/IIOP implementation is known as Java IDL. Along with the idlj compiler, Java IDL can be used to define, implement, and access CORBA objects from the Java programming language.
The Java IDL web page gives you a good, Java-centric view of CORBA/IIOP programming. To get a quick introduction to writing Java IDL programs, see the Getting Started: Hello World web page.
Here are the primary rmic flags that support the CORBA/IIOP functionality:
-iiop -- Generates IIOP stubs/ties.
-iiop -poa -- Generates IIOP stubs/ties that work with a Portable Object Adapter (POA).
-idl -- Generates IDL.
The following options are used in conjunction with the -idl option.
-noValueMethods -- Stops generation of IDL for methods and constructors within IDL valuetypes.
-always -- Forces regeneration even when existing stubs/ties/idl are newer than the input class. Only valid when -iiop and/or -idl flags are present.
-idlModule <fromJavaPackage<.class>> <toIDLModule> -- Specifies IDLEntity package mapping. For example: -idlModule foo.bar my::real::idlmod
-idlFile <fromJavaPackage<.class>> <toIDLFile> -- Specifies IDLEntity file mapping. For example: -idlFile test.pkg.X TEST16.idl
For more detailed information on the rmic compiler, read the rmic documentation.
Stub classes are also generated for abstract interfaces. An abstract interface is an interface that does not extend java.rmi.Remote, but whose methods all throw either java.rmi.RemoteException or a superclass of java.rmi.RemoteException. Interfaces that do not extend java.rmi.Remote and have no methods are also abstract interfaces.
QUESTION: I have a RMI-IIOP application which was running on J2SE 1.3. When I generate new stubs and skeletons using rmic -iiop -poa
, it does not work. Why ? ANSWER: There are 2 different ways to compile and run RMI-IIOP programs, which are listed below. We do not recommend that you combine these.
- The default RMI-IIOP behavior. An example of an application that uses the default RMI-IIOP behavior can be found at Tutorial: Getting Started Using RMI-IIOP.
- POA-based RMI-IIOP. An example of an application that uses the non-standard POA-based RMI-IIOP behavior can be found at Getting Started Using RMI-IIOP: Example Using POA-based server-side model.
New to this release of J2SE is the -iiop -poa option. Using the -iiop flag with the -poa option changes the inheritance from org.omg.CORBA_2_3.portable.ObjectImpl to org.omg.PortableServer.Servant. This type of mapping is nonstandard and is not specified by the Java Language to OMG IDL Language Mapping Specification.
The PortableServer module for the Portable Object Adapter (POA) defines the native Servant type. In the Java programming language, the Servant type is mapped to the Java org.omg.PortableServer.Servant class. It serves as the base class for all POA servant implementations and provides a number of methods that may be invoked by the application programmer, as well as methods which are invoked by the POA itself and may be overridden by the user to control aspects of servant behavior.
Note: After the OMG IDL is generated using rmic -idl, use the generated IDL with an IDL-to-C++ or other language compiler, but not with the IDL-to-Java language compiler. "Round tripping" is not recommended and should not be necessary. The IDL generation facility is intended to be used with other languages such as C++. Java clients or servers can use the original RMI-IIOP types.
IDL provides a purely declarative, programming language independent means for specifying the API for an object. The IDL is used as a specification for methods and data that can be written in and invoked from any language that provides CORBA bindings. This includes Java and C++ among others. See the Java Language to IDL Mapping (OMG) document for a complete description.
Note: The generated IDL can only be compiled using an IDL compiler that supports the CORBA 2.3 extensions to IDL.
See the RMIC tool page (Solaris Version/Windows version) for a complete rmic description.
import javax.naming.*; ... Context ic = new InitialContext();
import java.rmi.*; ... Naming.rebind("MyObject", myObj);use:
import javax.naming.*; ... ic.rebind("MyObject", myObj);
import java.util.*; import javax.naming.*; ... Hashtable env = new Hashtable(); env.put("java.naming.applet", this); Context ic = new InitialContext(env);
On the server side, use the PortableRemoteObject.toStub() call to obtain a stub, then use writeObject() to serialize this stub to an ObjectOutputStream. The code to do this looks something like:
On the client side, use readObject() to deserialize a remote reference to the object from an ObjectInputStream, with code like:org.omg.CORBA.ORB myORB = org.omg.CORBA.ORB.init(new String[0], null); Wombat myWombat = new WombatImpl(); javax.rmi.CORBA.Stub myStub = (javax.rmi.CORBA.Stub)PortableRemoteObject.toStub(myWombat); myStub.connect(myORB); // myWombat is now connected to myORB. To connect other objects to the // same ORB, use PortableRemoteObject.connect(nextWombat, myWombat); FileOutputStream myFile = new FileOutputStream("t.tmp"); ObjectOutputStream myStream = new ObjectOutputStream(myFile); myStream.writeObject(myStub);
FileInputStream myFile = new FileInputStream("t.tmp"); ObjectInputStream myStream = new ObjectInputStream(myFile); Wombat myWombat = (Wombat)myStream.readObject(); org.omg.CORBA.ORB myORB = org.omg.CORBA.ORB.init(new String[0], null); ((javax.rmi.CORBA.Stub)myWombat).connect(myORB); // myWombat is now connected to myORB. To connect other objects to the // same ORB, use PortableRemoteObject.connect(nextWombat, myWombat);
_<implementionName>_Tie.class _<interfaceName>_Stub.class
orbd -ORBInitialPort port#You must specify the port number when starting ORBD.
java -Djava.naming.factory.initial=com.sun.jndi.cosnaming.CNCtxFactory -Djava.naming.provider.url=iiop://<hostname>:1050 <appl_class>This example uses the name service port number of 1050. If you specified a different port in step 7, you need to use the same port number in the provider URL here. The <hostname> in the provider URL is the host name that was used to start the CosNaming server in step 7.
java.naming.factory.initial=com.sun.jndi.cosnaming.CNCtxFactory java.naming.provider.url=iiop://<hostname>:1050This example uses the name service port number of 1050. If you specified a different port in step 7, you need to use the same port number in the provider URL here. The <hostname> in the provider URL is the host name that was used to start the CosNaming server in step 7.
For tutorials that describe creating a new RMI-IIOP application, link to Getting Started Using RMI-IIOP: Example Using POA-based server-side model or Tutorial: Getting Started Using RMI-IIOP.
The PortableRemoteObject.exportObject() call only creates a tie object and caches it for future usage. The created tie does not have a delegate or an ORB associated. This is known as explicit invocation.
The PortableRemoteObject.exportObject() happens automatically when the servant instance is created. This happens when a PortableRemoteObject constructor is called as a base class. This is known as implicit invocation.
Later, when PortableRemoteObject.toStub() is called by the application, it creates the corresponding Stub object and associates it with the cached Tie object. But since the Tie is not connected and does not have a delegate, the newly created stub also does not have a delegate or ORB.
The delegate is set for the stub only when the application calls Stub.connect(orb). Thus, any operations on the stub made before the ORB connection is made will fail.
The Java Language to IDL Mapping Specification says the following about the Stub.connect() method:
The connect method makes the stub ready for remote communication using the specified ORB object orb. Connection normally happens implicitly when the stub is received or sent as an argument on a remote method call, but it is sometimes useful to do this by making an explicit call (e.g., following deserialization). If the stub is already connected to orb (i.e., has a delegate set for orb), then connect takes no action. If the stub is connected to some other ORB, then a RemoteException is thrown. Otherwise, a delegate is created for this stub and the ORB object orb.
For servants that are not POA-activated, Stub.connect(orb) is necessary as a required setup.
NOTE: Although it is true that ORBs written in different languages should be able to talk to each other, we haven't tested the interoperability of the Java ORB with other vendor's ORBs.
UnicastRemoteObject should be used as the superclass for the object implementation in RMI programming. PortableRemoteObject should be used in RMI-IIOP programming. If PortableRemoteObject is used, you can switch the transport protocol to either JRMP or IIOP during runtime.
ulimit -n 90


The Chamber (1996) A film review by Steve Rhodes Copyright 1996 Steve Rhodes
RATING (0 TO ****): *
Racism, anti-Semitism, the Klan, capital punishment, vast conspiracy, bankable stars and a famous author. What more could the viewing public want? Believability would be nice. Absent that, perhaps a mystery that goes somewhere. What they get instead is balderdash.
In a film that has only one even mildly interesting performance and where the other characters are created out of cardboard, it helps if you approach THE CHAMBER with as low an expectation as possible to avoid disappointment. Having read "The Chamber" and all of the other John Grisham books, I entered the theater not expecting a lot, but it was considerable worse than I feared.
In the hands of director James Foley and screenwriters William Goldman and Chris Reese, they manage to take the least of enjoyable author Grisham's novels and excise its only strength which is the mystery. Although they do leave an outline of the mystery, what is left is like a maze where someone has erased most of the dividing lines. The film plays like it was a four hour movie where all of the key sections have been purged to get it down to size. Only the dull parts and the sensation portions have been left intact. The characters don't relate to each other. Rather than chemistry between them, what we get is a string of serial monologues masquerading as a script.
There are many fine pictures that discuss the subjects in THE CHAMBER in a responsible and enlightening manner. Foley's vision here is limited to the exploitive. He takes controversial subjects and taunts the audience with racial and religious epithets solely for gratuitous shock value.
In easily the worse performance of his career, Chris O'Donnell is badly miscast as lawyer Adam Hall. Adam is the grandson of famous racist and KKK bomber Sam Cayhall (Gene Hackman), and Adam comes back to town to save his granddaddy from execution. Ever reliable Hackman does the only real acting in the show, but even he is not as good as he usually is. Directors and writers do make a difference.
As the story opens, Cayhall is going to die in 28 days unless Adam can get him off with an appeal or a pardon. Since Cayhall admits to killing two innocent boys, Adam has his work cut out for him.
The South in THE CHAMBER is even more stereotyped than most Grisham novels. White society is dichotomized into rich people who live in white mansion with big columns and the rest who are rednecks living in shacks. They are almost all racist except for the hero lawyer. The KKK, of course, is omnipresent. Caricature is effective to a point, but THE CHAMBER is way over the line. Cliche follows cliche in an unending drive for manipulating the audience.
When Adam arrives at the ante-bellum mansion of Cayhall's daughter Lee Bowen (Faye Dunaway), she tells him, "Pretty good for poor white trash. How well I'll be accepted after everybody finds out I'm Hitler's daughter is another thing." She later tells him that, "We come from a long line of hate."
"Save me.
Soon Adam finds out Gov. McCallister (David Marshall Grant) was somehow a partner in Cayhall's criminal activities. Moreover, the governor hints that if Cayhall will shut up, he will pardon him. Imagine that, a government leader hinting of pardons if his partner in crime will just keep quiet. Couldn't happen in America.
The governor's attractive female aide Nora Stark (Lela Rochon) tells Adam, "You're in Mississippi now - land of the secrets. There are bodies buried everywhere." The governor also suggest she sleep with Adam so she can find out what he knows.
Cayhall keeps wanting Adam to stay away from the conspiracy admonishing him, "If you spend half as much time trying to be a lawyer instead of trying to be Dick Tracy, I might not be dead in five days." I will not discuss the ending other than to say it is as manipulative as the rest of the film.
Ian Baker's cinematography is dull and lifeless. For a major motion picture, it is strikingly ugly. The music by Carter Burwell sounds like it was lifted from an old B movie.
I left the theater with many questions. How could they make such a incredible mistake as to cast Chris O'Donnell as the lawyer? Why was the script such an insult to the viewer's intelligence? And the biggest question of all, why did I stay, hoping in vain that the film would somehow get better?
THE CHAMBER runs forever at 1:51 thanks to the sloppy editing of Mark Warner. It is rated R for some violence and some bad language, and the film would probably be acceptable for teenagers. I hated the show, and I recommend people of all ages avoid it. Only out of respect for Gene Hackman do I award it a single *. My immediate reaction when I left the theater was not even to rate it that high.
**** = One of the top few films of this or any year. A must see film. *** = Excellent show. Look for it. ** = Average movie. Kind of enjoyable. * = Poor show.  Totally unbearable.
REVIEW WRITTEN ON: October 18, 1996
Opinions expressed are mine and not meant to reflect my employer's.
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


It''s really easy!
Just click on any row''s edit icon (the one with the little pencil) or just click the "name" column and edit its values.
- The "name" column refers to what you''ll see on the context menu.
- The "Search URL" refers to what the search engine needs to run your query.
- The %s symbol will be replaced with the selected text on your browser.
You can decide the order of your search engines by dragging them up and down... do it over the "#" column, where the up and down arrows appear. Your mouse pointer will change when you''re over the right spot, and you''ll figure it out.

- Then just copy the URL that was generated within the site, that might look like this, for example, on IMDB: https://www.imdb.com/find?s=all;q=TESTSEARCH.
- Now just replace TESTSEARCH with %s and paste it here, on the "Search URL" column.
- Then indicate a name to that Search URL and you''re done!
Make sure you check first if the site you''d like to add is on the "Other search engines" table. I''m updating that table every now and the. If you have any suggestions for that list, they are more than welcome.
You can sort the URLs order by hovering over the first column.


Selected Works at FTChinese.com
London | Apr 28 2016
@silvashih
About me
- Covering business in Taiwan over 5 ys.
- Switched to digital world 3 yrs ago.
- Teach digital media/ddj to graduate level students in Taiwan.
- Data editor at FTC based in Beijing. Making data-driven stories and reporting on Taiwan.
About FTC
- 50% on the site are translated from the FT.com.
- 50% are editorials, Op-Eds and columns run and produced by FTC editors.
I expected my role at FTC is...
...but it's more close to
Works so far
1. Make a Chinese version of FT's work
2. Produce weekly/ biweekly data stories.
3. Produce monthly mulitmedia pieces.
1. Migrate FT's works to the FTC site.
w/ interactive team: "What is at stake at the Paris climate change conference?"
w/ graphics team: "China debt load reaches record high"
2. FTC data stories
weekly • biweekly
"Changes of words-used in China's Two Sessions official documents"
Can "red alert" saves Beijing's air?
（PM 2.5 index in 12 hours since red-alert had issued)
Can 'red alert' save Beijing's air?
（PM 2.5 index comparison in the past four alerts.)
China budget deficit on soil remeditation.
Why Taiwan's ruling KMT fails?
Why do we choose the design/framework above?
Blame the CMS!
A single, static html page is friendly to the site.
Consider the soical network enviroment.
What I've learned so far
GFW is evil. !important
DDJ in China is emerging. A lot of space to be explored.


{% extends 'base.html'%} {% import "bootstrap/wtf.html" as wtf %} {% block content %}
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Blurring and Sharpening
First of all in order to well understand the topic we have to learn about convolution operation which is the base of the concept
Convolution is a mathematical operation performed on two functions producing a third function which is typically a modified version of the original function ie.
output_image(modified_image) = original_image(first_function) x kernel_function(second_function)
Here the output_image can be the sharpened/blurred image of the original_image and kernel_function is a second function with which we do convolution operation on our original image
Now kernel is responsible for the modification of original image.Different kernels are designed for different operations(blurring, sharpening, edge detection)
Here I is original image and k is kernel and we performing convolution bw I and K.
Basically kernel is a matrix. We perform the convolution operation by sliding this kernel/filter over the input/original image. At every location, we do element-wise matrix multiplication and sum the result. This sum goes into the feature map(output of operation or modified image). The red area where the convolution operation takes place with blue kernel is called the receptive field. Due to the size of the filter the receptive field is also 3x3.And green area is the ouput of the repective convolution operation. Likewise we perform the convolution operation on all over the input image with kernel and then we get the output as shown in figure.
So sharpening and blurring will get performed by choosing appropriate kernel
Like wise we choose kernel and apply convolution.
Now there are various inbuilt opencv functions that can do such convolution operation with predefined kernels and give us blurred images or sharpened images, whatever we want. Various functions are as follows:
-
Blurring
- MedianBlur() - This will do convolution with such kernel that will extract the median form the inout image. ie here the central element of the original image is replaced by the median of all pixels in the kernel area.
- GaussianBlur() - This uses the gaussian kernel to produce the blurred image
- bilateralFilter() - This will blur while keeping the edges sharp. It also takes a Gaussian filter/kernel in space, but one more gaussian filter which is a function of pixel difference. The pixel difference function makes sure that only those pixels with similar intensity to central pixel is considered for blurring. So it preserves the edges since pixels at edges will have large intensity variation.
-
Sharpening
- Here we use our defined kernel and then apply convolution operation by using filter function of opencv on our input image
Implementation in Jupyter Notebook
So you have completed this topic. Congratulations
Hope you learnt something new
Happy Learning!! 📚


Design Your Canvas
Welcome!  Click on 'Create New' button above to define how big your canvas should be. Afterwards, just select your favorite color and start coloring these pixels! ;)
You can erase a cell with a righ-click of your mouse! Have fun!
Improvements to come.
I'm sorry. This input value is not valid. Please try again.
Only values between 1 and 100 will be accepted.
Your canvas
Pick a Color
This Project
This project was developed for the "Google Developer Challenge Scholarship".
Stuff used
To develop this project I used HTML / Bootstrap, FontAwesome, CSS, JavaScript and jQuery functionalities. All these amazing languages and tools are working together to give life to this page! :D Isn't this pretty?
Motivation
I'm really inspired and have many improvements in mind for this 'Art Maker'. Future implementations include: possibility to save your art in different kinds of formats; eraser and undo/foward options; possibility to send your art to someone per email or social media; etc.
About Me
My name is Isabella Navarro, I'm from Brazil, but I live in Germany. I studied Biology, but I'm also a technology lover. Working hard to be the best Bioinformatician ever! :)


Before Change
else:
keyed_request, result = self._maybe_cache_get(step)
if result is None:
result = step(self._node_builder)
self._maybe_cache_put(keyed_request, result)
promise.success(result)
return submitted
def _await_one(self, in_flight):
After Change
Submit pending while there"s capacity, and more than `n` items in pending_submission.
to_submit = min(len(pending_submission) - n, self._pool_size - len(in_flight))
submitted = 0
completed = []
for _ in range(to_submit):
step, runnable = pending_submission.popitem(last=False)
if self._is_async_node(step.node):
// Run in a future.
if step in in_flight:
raise InFlightException("{} is already in_flight!".format(step))
future = self._pool.submit(functools.partial(self._execute_step, step, runnable))
in_flight[step] = future
self._pending.add(future)
future.add_done_callback(self._processed_node_callback)
submitted += 1
else:
// Run inline.
completed.append(self._execute_step(step, runnable))
return submitted, completed
def _await_one(self, in_flight):
Await one completed step, and remove it from in_flight.


The Data Privacy Vocabulary (DPV) provides terms (classes and properties) to describe and represent information related to processing of personal data based on established requirements such as for the EU General Data Protection Regulation (GDPR). The DPV is structured as a top-down hierarchical vocabulary with the core or base concepts of personal data categories, purposes of processing and types of processing, data controller(s) associated, recipients of personal data, legal bases or justifications used, technical and organisational measures and restrictions (e.g. storage locations and storage durations), applicable rights, and the risks involved.
The namespace for DPV terms is http://www.w3.org/ns/dpv#
The suggested prefix for the DPV namespace is dpv
The DPV and its documentation is available on GitHub.
Contributing to the DPV and its extensions The DPVCG welcomes participation regarding the DPV, including expansion or refinement of its terms, addressing open issues, and welcomes suggestions on their resolution or mitigation.
While we welcome participation via any and all mediums - e.g., via Github pull requests or issues, emails, papers, or reports - the formal resolution of contributions takes place only through the DPVCG meeting calls and mailing lists. We therefore suggest joining the group to participate in these discussions for formal approval.
The Data Privacy Vocabulary provides terms (classes and properties) to describe and represent information about personal data handling. In particular, the vocabulary provides extensible taxonomies of terms to describe the following components:
Entities such as Recipients, Data Controllers, Data Subjects
Rights
Risks
These terms are intended to repersent personal data handling as machine-readable information by specifying personal data categories undergoing processing, its purpose(s), the data controller(s) involved, recipient(s) of this data, the legal bases or justifications used (e.g. consent or legitimate interest), involving technical and organisational measures and restrictions (e.g. storage location and storage duration), the applicable rights, and possibility of risks.
As the Legal Bases are dependant on legal jurisdictions, we provide the legal bases defined by GDPR as a separate 'extension' of the DPV called DPV-GDPR. The DPV is intended to be a 'general vocabulary', with extensions used to extend any apply it in jurisdiction, domain, and use-case specific requirements.
Examples of applications where the concepts provided by the DPV can be used are:
represent policies for personal data handling
represent information about consent e.g. provenance of consent
log/document personal data handling actions e.g. by a data controller
support automated checking of legal compliances of data handling ex ante (prior to processing), or ex post (i.e. check compliance after processing)
Namespaces
The namespace for DPV vocabulary is http://www.w3.org/ns/dpv#. The table below indicates the full list of namespaces and prefixes used in this document.
Prefix
Namespace
dct
http://purl.org/dc/terms/
dpv
http://www.w3.org/ns/dpv#
dpv-gdpr
http://www.w3.org/ns/dpv-gdpr#
dpv-nace
http://www.w3.org/ns/dpv-nace#
odrl
http://www.w3.org/ns/odrl/2/
owl
http://www.w3.org/2002/07/owl#
rdf
http://www.w3.org/1999/02/22-rdf-syntax-ns#
rdfs
http://www.w3.org/2000/01/rdf-schema#
skos
http://www.w3.org/2004/02/skos/core#
spl
http://www.specialprivacy.eu/langs/usage-policy#
svd
http://www.specialprivacy.eu/vocabs/data#
svdu
http://www.specialprivacy.eu/vocabs/duration#
svl
http://www.specialprivacy.eu/vocabs/locations#
svpu
http://www.specialprivacy.eu/vocabs/purposes#
svpr
http://www.specialprivacy.eu/vocabs/processing#
svr
http://www.specialprivacy.eu/vocabs/recipients
xsd
http://www.w3.org/2001/XMLSchema#
The DPV, as it is provided, does not recommend any specific way to use its concepts. Adopters are free to utilise their preferred models (e.g. RDFS-style, OWL2-style, or simply as a list of terms), though we strongly recommend being aware of the implications of using a specific model regarding interpretation, reasoning, and interoperability.
Base Vocabulary
Concepts in the Base vocabulary are available as an individual module here.
The 'Base' or 'Core' vocabulary describes the top-level classes required for defining a 'policy' for personal data handling. Classes and properties for each top-level class are further elaborated using sub-vocabularies, for example a taxonomy of personal data categories. While all concepts within the vocabulary share a single namespace, the modular approach makes it possible to use only the specific taxonomies or sub-vocabularies, for example to refer only to purposes. The DPV provides the following as top-level concepts and generic properties to associate them with other concepts:
Class
Property
Description
[=PersonalDataCategory=]
[=hasPersonalDataCategory=]
Personal data categories
[=Purpose=]
[=hasPurpose=]
Purpose of Processing
[=Processing=]
[=hasProcessing=]
Category or type of processing of personal data
[=DataController=]
[=hasDataController=]
Data Controller responsible for processing
[=DataSubject=]
[=hasDataSubject=]
Data Subject or Individual whose data is being processing
[=Recipient=]
[=hasRecipient=]
Recipient of personal data
[=TechnicalOrganisationalMeasure=]
[=hasTechnicalOrganisationalMeasure=]
Technical and/or Organisational measures associated with processing
[=LegalBasis=]
[=hasLegalBasis=]
Legal bases or justifications for processing
[=Right=] and [=DataSubjectRight=]
[=hasRight=]
Rights applicable or provided
[=Risk=]
[=hasRisk=]
Risks applicable or probable regarding processing
Along with these, the DPV defines the concept of [=PersonalDataHandling=] for representing a 'policy' associating the top-level concepts with one another. For example, using [=PersonalDataHandling=] it is possible to indicate the application of a specific purpose and processing to categories of personal data relating to data subjects (or individual), along with the data controller responsible, the recipients of data, legal basis used, the technical and organisational measures involved, rights provided, and the possibility of risks.
The DPV does not mandate the use of [=PersonalDataHandling=]. Adopters can define their own interpretation of what concepts personal data handling involves, or define a separate concept similar to [=PersonalDataHandling=]. For example, one may specify that a [=PersonalDataHandling=] is only associated with [=Purpose=], [=Processing=], [=PersonalDataCategory=], and [=Recipient=] where the legal basis and technical and organisational measures are either assumed or defined externally. In continuation of this, the DPV also does not provide any constraints on the inclusion or exclusion of concepts used to define an instance of [=PersonalDataHandling=]. Possibilities for specifying such constraints include use of OWL2 semantics and SHACL to specify mandatory concepts. For example, requiring every instance of [=PersonalDataHandling=] must have at least one Personal Data Category, Controller, Purpose, and Legal Basis.
A high-level Class to describe 'data handling'. This can consist of personal data being processed for a purpose, involving entities, using technical and organisational, applicable risks, rights, and legal basis.
Axel Polleres,
Bud Bruegger,
Harshvardhan J. Pandit,
Javier Ferenandez,
Mark Lizar
has right
Term:
hasRight
Description:
Indicates applicability of a Right.
Status:
accepted
Created:
Contributor(s):
Harshvardhan J. Pandit
has risk
Term:
hasRisk
Description:
Indicates applicability of a Risk.
Status:
accepted
Created:
Contributor(s):
Harshvardhan J. Pandit
has technical and organisational measure
Term:
hasTechnicalOrganisationalMeasure
Description:
Indicates use of a Technical or Organisational measure.
Status:
accepted
Created:
Contributor(s):
Axel Polleres,
Bud Bruegger,
Harshvardhan J. Pandit,
Javier Ferenandez,
Mark Lizar
Personal Data Categories
Concepts related to Personal Data Categories are available as an individual module here.
DPV provides broad top-level personal data categories adapted from the taxonomy contributed by R. Jason Cronk [[EnterPrivacy]]. The top-level concepts in this taxonomy refer to the nature of information (financial, social, tracking) and to its inherent source (internal, external). Each top-level concept is represented in the DPV vocabulary as a Class, and is further elaborated by subclasses for referring to specific categories of information - such as preferences or demographics.
While this taxonomy is by no means exhaustive, the aim is to provide a sufficient coverage of abstract categories of personal data which can be extended using the subclass mechanism to represent concepts used in the real-world.
Regulations such as the GDPR define personal data at an abstract level as information (directly or indirectly) associated with an individual or a category of individuals. DPV defines classes representing categories that are inclusive of information associated with them (e.g. name consisting of first name, last name), with their instances representing specific elements of persona data (e.g. “John Doe” as name).
The categories defined in the personal data categories taxonomy can be used directly or further extended by subclassing the respective classes to depict specialised concepts, such as “likes regarding movies” or combined with classes to indicate specific contexts. The class [=DerivedPersonalData=] provides one such context to indicate information has been derived from existing information, e.g. inference of opinions from social media. Additional classes can be defined to specify contexts such as use of machine learning, accuracy, and source. Similarly, the class [=SpecialCategoryPersonalData=] represents categories that are ‘special’ or ‘sensitive’ and require additional conditions, e.g. as per GDPR’s Article 9.
The following is an overview of the concepts provided for personal data within the DPV:
Concept
Description
[=PersonalDataCategory=]
Category of Personal Data
[=SpecialCategoryPersonalData=]
Indicate personal data is sensitive or belongs to a special category
Information about web browsing referrer or referral, which can be based on location, targeted referrals, direct, organic search, social media or actions, campaigns.
Elmar Kiesling; Harshvardhan J. Pandit,
Fajar Ekaputra
Life History
Term:
LifeHistory
Description:
Information about personal history regarding events or activities - including their occurences that might be directly related or have had an influence (e.g. World War, 9/11)
Elmar Kiesling; Harshvardhan J. Pandit,
Fajar Ekaputra
PIN Code
Term:
PINCode
Description:
Information about Personal identification number (PIN), which is usually used in the process of authenticating the individual as a user accessing a system.
Elmar Kiesling; Harshvardhan J. Pandit,
Fajar Ekaputra
Secret Text
Term:
SecretText
Description:
Information about secret text used in the process of authenticating the individual as a user accessing a system, e.g., when recovering a lost password.
Elmar Kiesling; Harshvardhan J. Pandit,
Fajar Ekaputra
Purposes
Concepts related to Purposes are available as an individual module here.
DPV at the moment defines a hierarchically organized taxonomy of generic categories of purposes (for processing of personal data). Regulations, such as GDPR, require the purpose to be declared in a specific and understandable manner. We therefore suggest to declare the purpose being used as an instance of one or several dpv:Purpose categories and to always declare the specific purpose with a human readable description (e.g. by using rdfs:label and rdfs:comment).
DPV provides a way to indicate purposes are restricted or fall within a specific business sector using the class [=Sector=] and the property [=hasSector=]. Hierarchies for defining business sectors include NACE maintained by EU [[NACE]], NAICS maintained by USA [[NAICS]], ISIC maintained by UN [[ISIC]], and GICS maintained by commercial organisations MSCI and S&P [[GICS]]. Multiple classifications can be used through mappings between sector codes such as the NACE to NAICS alignment provided by EU [[NACE-NAICS]].
We provide an interpretation of the NACE revision 2 codes which uses rdfs:subClassOf to specify the hierarchy, available here. The NACE codes have the namespace dpv-nace and are represented as dpv-nace:NACE-CODE.
Purposes can be further restricted to specific contexts using the [=Context=] and the property [=hasContext=]. In this case, 'context' refers to a generic context which can be expanded as applicable within a use-case or domain.
For using purposes, we suggest selecting the most appropriate or applicable purpose over more abstract ones by selecting or extending the relevant classes in the purpose taxonomy. For example, the purpose [=ServiceOptimization=] is further sub-classed to indicate optimisation for consumer as [=OptimisationForConsumer=] and for controller as [=OptimisationForController=].
The following is an overview of the concepts within the purpose taxonomy:
Class
Property
Description
[=Purpose=]
[=hasPurpose=]
Indicate purpose
[=Sector=]
[=hasSector=]
Indicate sector of organisation or restrict purpose to sector
carry out advertising i.e. process or artefact used to call attention to a product, service, etc. through announcements, notices, or other forms of communication.
Indiciates a purpose is restricted to the specified context(s)
Status:
accepted
Created:
has sector
Term:
hasSector
Description:
Indicates the purpose is associated with activities in the indicated (Economic) Sector(s)
Status:
accepted
Created:
Processing Categories
Concepts related to Processing are available as an individual module here.
DPV provides a hierarchy of classes to specify the operations associated with the processing of personal data. Declaring the processing or processing categories associated with personal data is required by regulations such as the GDPR. Processing operations (e.g. collect, share, and use) can have specific constraints or obligations which makes it necessary to accurately represent them. While the term ‘use’ is liberally used to refer to a broad range of processing categories in privacy notices, we suggest to select and use appropriate terms to accurately reflect the nature of processing where applicable.
There are a variety of terms used for describing processing operations depending on specific interpretations within the technological, legal, or sociological domain. We consolidate these terms and define the follow 'top-level' concepts to create a hierarchical taxonomy for categories of processing: [=Disclose=], [=Copy=], [=Obtain=], [=Remove=], [=Store=], [=Transfer=], [=Transform=], and [=Use=]. Each of these are then further expanded using subclasses within the taxonomy.
Although the DPV taxonomy of processing categories includes terms mentioned in the definition of processing in GDPR (Article 4-2), their interpretation is based on common understanding (i.e. dictionary definition) and legal interpretation. Where the interpretation of a term differs significantly within a jurisdiction, it is advisable to declare it in a separate vocabulary as an extension to the DPV, similar to DPV-GDPR. An example of where terms differ between common understanding and jurisdiction-dependent definitions is the term 'sell' mentioned within the California Consumer Protection Act (2018) 1798.140(t), which includes "selling, renting, releasing, disclosing, disseminating, making available, transferring, or otherwise communicating".
Along with information about the processing 'operation', regulations (e.g. GDPR) also require additional information such as scale of processing, extent of automation and human involvement, source of data, consequences, and algorithmic logic. DPV declares such concepts as top-level classes which can be used in combination with the processing (or other concepts such as purposes) to indicate their application.
Terms such as evaluation or scoring are defined within the processing categories because they relate to the specific operations or activities taking place over personal data. This is not to be confused as indicating a purpose, since they still need to be applied or defined towards a specific 'purpose' for the processing. For example, consider an use-case for scoring an individual for rankings in an online competition - here the 'scoring' is indicative of the processing operations while 'rankings' is the purpose.
The following is an overview of the concepts provided within the DPV processing taxonomy:
Class
Property
Description
[=Processing=]
[=hasProcessing=]
Specifies the processing operations over personal data
[=DataSource=]
[=hasDataSource=]
Indicates source of personal data used in processing
[=SystematicMonitoring=]
Specifies processing involves systematic monitoring (of data subjects)
[=EvaluationScoring=]
Specifies processing involves evaluating or scoring (of data subjects)
[=MatchingCombining=]
Specifies processing involves matching or combining of data
[=AutomatedDecisionMaking=]
Specifies processing produces automated decisions (regarding data subjects)
[=LargeScaleProcessing=]
Specifies processing takes place at 'large scales'
[=InnovativeUseOfNewTechnologies=]
Specifies processing involves use of innovative and new technologies
[=hasAlgorithmicLogic=]
specifies the algorithmic logic for processing
[=hasConsequences=]
Specifies consequences arising from processing
[=hasHumanInvolvement=]
Specifies the extent of human involvement regarding processing
to irreversibly alter personal data in such a way that a unique data subject can no longer be identified directly or indirectly or in combination with other data
Indicates the logic used in procdessing such as for automated decision making
Status:
accepted
Created:
Contributor(s):
Georg P. Krog,
Harshvardhan J. Pandit,
Paul Ryan
has consequences
Term:
hasConsequences
Description:
Indicates consequences of processing such as for those for Data Subjects in relation to automated decision making
Status:
accepted
Created:
Contributor(s):
Georg P. Krog,
Harshvardhan J. Pandit,
Paul Ryan
has data source
Term:
hasDataSource
Description:
Indicates the source or origin of data being processed
Status:
accepted
Created:
Contributor(s):
Georg P. Krog,
Harshvardhan J. Pandit,
Paul Ryan
has human involvement
Term:
hasHumanInvolvement
Description:
Indicates Involvement of humans in processing such as within automated decision making process
Status:
accepted
Created:
Contributor(s):
Georg P. Krog,
Harshvardhan J. Pandit,
Paul Ryan
Technical and Organisational Measures
Concepts related to Technical and Organisational Measures are available as an individual module here.
Technical and Organisational measures consist of activities, processes, or procedures used in connection with ensuring data protection, carrying out processing in a secure manner, and complying with legal obligations. Such measures are required by regulations depending on the context of processing involving personal data. For example, GDPR (Article 32) states implementing appropriate measures by taking into account the state of the art, the costs of implementation and the nature, scope, context and purposes of processing, as well as risks, rights and freedoms. Specific examples of measures in the article include:
the pseudonymisation and encryption of personal data
the ability to ensure the ongoing confidentiality, integrity, availability and resilience of processing systems and services
the ability to restore the availability and access to personal data in a timely manner in the event of a physical or technical incident
a process for regularly testing, assessing and evaluating the effectiveness of technical and organisational measures for ensuring the security of the processing
To represent these requirements, the DPV defines a hierarchical taxonomy of technical and organisational measures through the top-level concept of [=TechnicalOrganisationalMeasure=], which is further distinguished as [=TechnicalMeasure=] and [=OrganisationalMeasure=]. A technical measure is an implementation detail or technology used to achieve a specific goal or object, such as authentical protocol used to validate identity. In contrast, an organisational measure is a process or procedure used by the organisation, for example authorisation procedure to decide who should be granted access within an organisation.
Measures can be associated using the generic property [=measureImplementedBy=]. The value or object of this property can be an IRI (or URL) representing a specific measure or standard used to implement it, or a String representing relevant information.
In the future, we plan to provide a collection of terms and URIs for specifying standards (e.g. ISO) and best practices (e.g. certifications, seals). Whether this should be provided within the DPV itself or as a separate extension similar to DPV-GDPR is to be decided. We welcome participation and contributions for this work.
DPV provides specific measures for storage of personal data in the form of [=StorageRestriction=], with specialised variants for duration as [=StorageDuration=], location as [=StorageLocation=], deletion as [=StorageDeletion=], and restoration as [=StorageRestoration=].
The generic properties [=hasStorage=], [=hasLocation=], and [=hasDuration=] enable representing information about storage, location, and duration respectively. These can be used to specify restrictions or conditions, such as for storage of personal data, its processing, or information about recipients.
For indicating the mitigation of [=Risk=], DPV provides [=RiskMitigationMeasure=] as a top-level concept within the Technical and Organisational measures taxonomy. The property [=mitigatesRisk=] is used to indicate the relationship between risk and its mitigation.
The following provides an overview of the important and top-level concepts within the Technical Organisational measures taxonomy:
Class
Property
Description
[=TechnicalOrganisationalMeasure=]
[=hasTechnicalOrganisationalMeasure=]
Specifies the technical and organisational measures utilised or applicale
Axel Polleres,
Harshvardhan J. Pandit,
Mark Lizar,
Rob Brennan
Anonymization
Term:
Anonymization
Description:
Alterting personal data irreversibly such that a data subject can no longer be identified directly or indirectly, either by the data controller alone or in collaboration with any other party
Axel Polleres,
Harshvardhan J. Pandit,
Mark Lizar,
Rob Brennan
Pseudo-Anonymization
Term:
PseudoAnonymization
Description:
PseudoAnonmyization or 'pseudonymisation’ means the processing of personal data in such a manner that the personal data can no longer be attributed to a specific data subject without the use of additional information, provided that such additional information is kept separately and is subject to technical and organisational measures to ensure that the personal data are not attributed to an identified or identifiable natural person;
Axel Polleres,
Harshvardhan J. Pandit,
Mark Lizar,
Rob Brennan
Risk Management Procedure
Term:
RiskManagementProcedure
Description:
Risk management refers to a coordinated set of activities and methods that is used to direct an organization and to control the many risks that can affect its ability to achieve objectives. The term risk management also refers to the programme that is used to manage risk. This programme includes risk management principles, a risk management framework, and a risk management process.
Axel Polleres,
Harshvardhan J. Pandit,
Mark Lizar,
Rob Brennan
measure implemented by
Term:
measureImplementedBy
Description:
a generic Property to describe how the measure is implemented
Status:
accepted
Created:
Contributor(s):
Axel Polleres
mitigates risk
Term:
mitigatesRisk
Description:
Indicates mitigation of risk(s)
Status:
accepted
Created:
Contributor(s):
Harshvardhan J. Pandit
Entities
Concepts related to Entities are available as an individual module here.
Entities refer to individuals, organisations, institutions, authorities, agencies, or any similar 'actor'. Defining and representing them is important given their rights and responsibilities under legal obligations. To represent such entities, DPV defines the [=LegalEntity=] class as a generic concept which if further extended to represent the different categories of entities.
The DPV core vocabulary consists concepts of [=DataSubject=], [=DataController=], and [=Recipient=] which are subclasses of [=LegalEntity=]. Consequently, they are not described in this section to avoid duplicity.
To describe the entities that act as recipients regarding personal data and its processing, the concepts of [=DataProcessor=], [=DataSubProcessor=], and [=ThirdParty=] are defined. Defining recipients is important in the context of data protection and privacy as it allows tracking the entities personal data is shared/transfered with. The concepts of [=Child=] and [=VulnerableDataSubject=] represent specific categories of data subjects based on relevance in legal requirements. The concept [=Authority=] represents an entity with legal authority, and is extended to represent [=DataProtectionAuthority=] for a specific authority concerned with data protection and privacy. To represent an 'agent' of an organisation, the concept [=Representative=] is provided. Similarly, [=DataProtectionOfficer=] refers to a specific entity associated with monitoring data protection and privacy within (or on behalf of) an organisation.
The concept 'child' can be legally distinct from 'minor', although they are also used as synonyms in several cases. DPV uses 'child' as the commonly used term to signify an individual below a certain legally defined age. This is influenced from the use of the term 'child' within the GDPR and by CJEU in its judgements. It is important to note that the relevant age for determining a child (or a minor child) varies by jurisdiction.
To represent information about entities, DPV provides the following properties: [=hasName=] to indicate name, [=hasAddress=] to indicate address, [=hasContact=] to indicate contact or communication channels, [=hasIdentifier=] to indicate an identifier associated with the entity, and [=hasRepresentative=] to indicate an 'agent' or representative of the entity.
An entity within or authorised by an organisation to monitor internal compliance, inform and advise on your data protection obligations and act as a contact point for data subjects and the supervisory authority.
Beatriz Esteves,
Georg Krog,
Harshvardhan J. Pandit,
Paul Ryan
Third Party
Term:
ThirdParty
Description:
A ‘third party’ means a natural or legal person, public authority, agency or body other than the data subject, controller, processor and persons who, under the direct authority of the controller or processor, are authorised to process personal data;
Specifies address of a legal entity such as street address or pin code
Created:
Contributor(s):
Beatriz Esteves,
Georg P Krog,
Harshvardhan J.Pandit,
Paul Ryan
has contact
Term:
hasContact
Description:
Specifies contact details of a legal entity such as phone or email
Created:
Contributor(s):
Beatriz Esteves,
Georg P Krog,
Harshvardhan J.Pandit,
Paul Ryan
has identifier
Term:
hasIdentifier
Description:
Specifies an identifier for the entity such as registeration number or official ID
Created:
Contributor(s):
Beatriz Esteves,
Georg P Krog,
Harshvardhan J.Pandit,
Paul Ryan
has name
Term:
hasName
Description:
Specifies name of a legal entity
Created:
Contributor(s):
Beatriz Esteves,
Georg P Krog,
Harshvardhan J.Pandit,
Paul Ryan
has representative
Term:
hasRepresentative
Description:
Specifies representative of the legal entity
Created:
Contributor(s):
Beatriz Esteves,
Georg P Krog,
Harshvardhan J.Pandit,
Paul Ryan
Consent
The concepts related to Consent are available as an individual module here.
Consent is one of the legal bases or legal justifications for the processing of personal data, whose legal validity is assocaited with requirements and obligations based on jurisdictional laws. DPV provides concepts to describe consent and its attributes to represent a 'record' of consent from a compliance perspective.
[=Consent=] represents the concept of 'consent', with the DPV classes and properties used to associate the relevant information it is about. If using [=PersonalDataHandling=], the use of consent can be indicated through the [=hasLegalBasis=] property, and the relevant information associated using properties, such as [=hasPurpose=] and [=Purpose=]. Conversely, one can also create alternate forms of 'consent as a policy' where the purpose and other information is directly assocaited with [=Consent=] using the generic properties.
To specify additional information, DPV specifies provenance properties of [=hasProvisionTime=] and [=hasProvisionMethod=] for how consent was given, and [=hasWithdrawalTime=] and [=hasWithdrawalMethod=] to indicate how consent was withdrawn. The expiry of consent can be specified using the property [=hasExpiry=], with sub-properties provided to define expiry as a temporal entity using [=hasExpiryTime=] or as a condition/event using [=hasExpiryCondition=].
Requirements for 'informed' consent require provision of information before the consent is obtained so as to inform the individual. This information is typically provided through a 'notice', which can be specified using the property [=hasConsentNotice=]. Similarly, 'explicit' consent is a specific form of consent where the action of giving consent carries additional obligations of being explicitly performed by the individual. To represent this, the boolean property [=isExplicit=] ir provided.
The specific conditions under which consent can be considered valid, or informed, or explicit as defined under jurisdictional law. The terms provided within the DPV are generic concepts for representing these 'types' of consent. For using specific jurisdictional requirements and use of consent as a legal basis, it is advisable to declare such jurisdictional concepts in a separate extension to the DPV. For specific use of consent as a legal basis defined within GDPR, we provide DPV-GDPR.
To specify consent provided by delegation, such as in the case of a parent or guardian providing consent for a child, the properties [=hasProvisionByJustification=] and [=hasWithdrawalByJustification=] can be used to capture the nature of such ‘delegation’, with the fields [=hasProvisionBy=] and [=hasWithdrawalBy=] representing the legal entity who provided the consent. By default, the consent can be assumed to be provided by the associated Data Subject.


J. P. Vermeer(3)
Product(9)
Brand Development
Identity
IP and Patent Development
Requirement and Solution Development
Focused Agile Implementations
System Architecture
Sales Strategy & Presentation
Developing Product Life-cycles
Client Relationship Facilitation
Program(4)
Front-End Applications(11)
Angular.JS
Protractor.JS
JQuery
AJAX
JavaScript
Three.JS
HTML5 Canvas
LESS
Bootstrap
LESS
Concrete5
Large-Volume Data Systems(6)
Hadoop
Graph Database Topologies
SQL
Data Optimization
Trend Analysis
Application and Platform Integration
Hardware(6)
Process Automation
Physical Security Systems and Interfaces
Optical Character Recognition
Data Optimization
Trend Analysis
Application and Platform Integration
Expert Systems(6)
Requirements and Needs Assessment
SQL and Data Design
Business Process Applications
Software Development Lifecycle
Project Management
Test Design
QA, Testing
Production and Release
Principles(10)
Loyalty to the Business
Team-Oriented Success
Readable Code
Reusable Code
Leadership
Leadership creates Leaders
Product, Brand and Identity
Marketer-friendly, Market Savvy
Production & Release
Shipping Great Products


If your router supports NAT-PMP, UPnP, or you have Apple AirPort, Transmission can do this automatically; just tick the checkbox in Preferences -> Network.
eg. If your upload connection is 256 Kilobits/sec, then you should cap it at 21 KB/sec ((256 / 8) * 0.66 = 21).
Remember, your download speed is proportional to how fast you upload. If there are many transfers running, then each transfer will only receive a small proportion of your upload bandwidth, reducing their respective download speeds. To avoid spreading your upload too thinly, a good rule of thumb is to have at least 128 KBit/sec of upload bandwidth for every torrent you wish to run simultaneously.
eg. If your upload bandwidth is 256 KBit/sec, then you should only have two (256/128 = 2) downloading transfers in the queue.


But isn’t that better than the actual goal?
Unclear on that whole ‘garbage in’ concept.
by SharkyAt the company where this pilot fish is working as a contractor, forms are scanned, reviewed, edited, corrected and uploaded to a database. None of this is relevant to what fish is working on, but he’s interested when he learns that the link between the database entries and the scanned images isn’t working.
So even after the contract ends, he manages to find out what was going on: The manager of data entry misunderstood the turnaround time requirement. They had to upload documents within 48 hours of data entry, but the manager got the notion that it was 48 hours after receipt of the documents. No matter what, he told the data entry clerks, they had to upload those documents within 48 hours of receiving them.
Apparently, marvels fish, they had a “shove it on up there” button they could press to bypass the review and editing steps.
Upload your true tale of IT life to Sharky and I’ll review and edit it myself. Send it to me at<EMAIL_ADDRESS>You can also subscribe to the Daily Shark Newsletter.


RNAseq in Bioconductor
The Course
This course introduces RNAseq analysis in Bioconductor.
Course material and exercises are available to view as rendered HTML at https://rockefelleruniversity.github.io/RU_RNAseq/.
All material is available to download under GPL v2 license.
Setting up.
Install R.
R can be installed from the R-project website.
R 3.1.0 or higher is required for this course.
Install RStudio.
RStudio can be installed from the R-project website.
Install required packages.
Option 1 - (For your own personal computers)
Having downloaded R and RStudio, some additional packages are required (rmarkdown and ggplot2).
To install these,
- First launch RStudio
- Install the packages in the R console using devtools
install.packages("devtools",dependencies=TRUE) devtools::install_github("rockefelleruniversity/RU_RNAseq",subdir="RU_RNAseq",build_vignettes=TRUE)
Option 3 - (For anywhere)
Having downloaded R and RStudio, some additional packages are required (rmarkdown and ggplot2).
To install these,
- First launch RStudio
- Install the packages in the R console using install.packages
source("https://bioconductor.org/biocLite.R") biocLite("BSgenome.Mmusculus.UCSC.mm10") biocLite("BSgenome.Hsapiens.UCSC.hg19") biocLite("TxDb.Hsapiens.UCSC.hg19.knownGene") biocLite("TxDb.Mmusculus.UCSC.mm10.knownGene") biocLite("Org.Mm.eg.db") biocLite("Org.Hs.eg.db") biocLite("Biostrings") biocLite("GenomicRanges") biocLite("rtracklayer")
Download the material
The material can either be downloaded as a zip
wget https://github.com/rockefelleruniversity/RU_RNAseq/archive/master.zip ./
RNAseq part1
RNAseq part1
This section introduces the analysis of RNAseq data in Bioconductor
Session sections:
- Preprocessing RNAseq data in R
- Alignment of data
- Quantification of gene, exon and transcript expression levels
Link to single page, printable HTML - RNAseq part1
Link to R code included in presentation- - RNAseq part1
RNAseq part2
RNAseq part2
This section introduces the analysis of RNAseq data in Bioconductor
Session sections:
- Import of transcript quantifications into gene level summaries
- Differential gene expression analysis of RNAseq data in R
- Functional enrichment analysis
Link to single page, printable HTML - RNAseq part2
Link to R code included in presentation- - RNAseq part2
RNAseq part3
This section introduces the analysis of RNAseq data in Bioconductor
Session sections:
- Working with MSigDB gene set collection in R
- Gene set enrichment using goseq
- Gene set enrichment using fgsea
RNAseq part3
RNAseq part4
This section introduces the analysis of RNAseq data in Bioconductor
Session sections:
- Differential exon usage
- Plotting DEU for transcripts
- Differential gene expression analysis for multiple sample groups
Link to single page, printable HTML - RNAseq part4
Link to R code included in presentation- - RNAseq part4


MSFVenom can encode with a few options. Typical is:
-e x86/shikata_ga_nai
However, this is so well known that many AV block it
Attempt to try other MSFVenom encoding options
Look at the Veil framework
Other options are surely available


CrimPapers
Articles published on 6 February 2020
On 6 February 2020, 4 new articles were published in 4 journals.
Organized Crime and Criminal Careers by Edward Kleemans, Vere van Koppen
Match-Fixing Under the State Monopoly Sports Betting System: A Case Study of the 2011 k-League Scandal
Eyewitnesses in the Courtroom: A Jury-Level Experimental Examination of the Impact of the Henderson Instructions
Health Professionals’ Experiences of Providing Trauma-Informed Care in Acute Psychiatric Inpatient Settings: A Scoping Review by Carol O’dwyer, Laura Tarzia, Sabin Fernbacher, Kelsey Hegarty


public class EarthQuakeEntry extends java.lang.Object implements DataEntry, java.lang.Comparable<EarthQuakeEntry>
Overrides the equals
, hashCode
, toString
methods
| Constructor and Description |
|---|
EarthQuakeEntry(java.lang.String info,
java.lang.String locationPoints,
java.lang.String elevation,
java.lang.String age)
Sole Constructor for the POJO class to initialize all final fields (Immutable).
|
| Modifier and Type | Method and Description |
|---|---|
int |
compareTo(EarthQuakeEntry anotherQuake)
This method defines the Natural Ordering using the exact magnitude to sort in descending order
|
boolean |
equals(java.lang.Object o)
This method used to separate Objects using the text Location and exact magnitude
|
java.lang.String |
getAge()
Gets the String representation of the age of earthquake
|
double |
getDepth()
Gets the depth of the earthquake.
|
java.math.BigDecimal |
getExactDepth()
Gets the exact depth of the earthquake for exact double comparisons.
|
java.math.BigDecimal |
getExactMagnitude()
Gets the exact magnitude of the earthquake for exact double comparisons.
|
float |
getLatitude()
Gets the latitude of the earthquake.
|
java.lang.String |
getLocationTitle()
Gets the descriptive location of the earthquake.
|
float |
getLongitude()
Gets the longitude of the earthquake.
|
double |
getMagnitude()
Gets the magnitude of the earthquake.
|
java.lang.String |
getTitle()
Gets the descriptive location of the earthquake along with its magnitude.
|
int |
hashCode()
This method computes the HashCode so if put in hashed structure, using the text Location and the exact magnitude, matching the equals method
|
java.lang.String |
toString()
This method gets the String Representation of the POJO object for debugging and logging.
|
public EarthQuakeEntry(java.lang.String info, java.lang.String locationPoints, java.lang.String elevation, java.lang.String age)
info
- The string representation of the earthquake from the RSS feedlocationPoints
- The String representation of latitude, longitudeelevation
- The string representation of elevation that will be used to compute depthage
- The string representation of the age of the earthquakepublic java.lang.String getLocationTitle()
public java.lang.String getTitle()
public double getMagnitude()
public float getLongitude()
public java.lang.String getAge()
public java.math.BigDecimal getExactMagnitude()
public java.math.BigDecimal getExactDepth()
public double getDepth()
public float getLatitude()
public boolean equals(java.lang.Object o)
equals
in class java.lang.Object
o
- Object holding the other EarthQuakeEntry
to be compared for equality withpublic int hashCode()
hashCode
in class java.lang.Object
public int compareTo(EarthQuakeEntry anotherQuake)
compareTo
in interface java.lang.Comparable<EarthQuakeEntry>
anotherQuake
- An object that holds another EarthQuakeEntry
to be compared againstpublic java.lang.String toString()
for eg. Title : 18km E of Little Lake, CA , lat, lon : 35.926334, -117.71233 , Magnitude: 0.7, Depth: 8.96 , Age: Past Week
toString
in class java.lang.Object


Upload a melody file in the required template format for testing harmonisation in the selected style
file_upload
Upload Melody
Not sure how a template should look like? Have a look at
this page
and/or download
this example template file
Style 1
Mode 1
Style 2
Mode 2
Tonal Difference
0
1
2
3
4
5
6
7
8
9
10
11
Options
Use Chord Families
Voice Leading
NoVL
BBVL
Harmonise
Style 1
Style 2
Blend
Harmonise
send
play_arrow
play
stop
stop
file_download
download
home
Harmonise Again!


[dscrdeql] <CowboyCrypto- Founder> Not a bad start!
GatewayBOT22-Nov-18 02:52 AM
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GatewayBOT22-Nov-18 03:53 AM
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GatewayBOT22-Nov-18 04:38 AM
[dscrdeql] <CowboyCrypto- Founder> Pungo wallet is pretty cool
GatewayBOT22-Nov-18 04:52 AM
[dscrdeql] <AndronicusBass-Founder> Pungo wallet is nice. Its in development mode on android so dont use with funds just yet.
GatewayBOT22-Nov-18 06:05 AM
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GatewayBOT22-Nov-18 06:13 AM
[dscrdeql] <Jellysmash> You wouldn't bother putting a large amount in it though if you couldn't stake it or get masternode rewards with it.
GatewayBOT22-Nov-18 08:53 AM
[dscrdeql] <SwimedDragon> I'm going for masternode rewards
GatewayBOT22-Nov-18 09:11 AM
[dscrdeql] <SwimedDragon> I'm just gonna try and leave crypto alone for a few day's and see how it goes. Don't think I can do it but I can try
GatewayBOT22-Nov-18 09:48 AM
[dscrdeql] <Buddz1993> Do you know how long does it take to have our EQLI in Blockfolio what are the requirements?
[dscrdeql] <Jellysmash> No I don't know
[dscrdeql] <Jellysmash> I've never used blockfolio
[dscrdeql] <Jellysmash> @SwimedDragon it's hard to give up crypto, I've never been successful
[dscrdeql] <Buddz1993> What about delta ???
[dscrdeql] <Buddz1993> Coins like pirate and chainzilla are much younger and they have their information probidade there already
Did you know?
@DigitalPriceOrg is actively listing coins, about 2 a week.
We are actively looking for new and interesting coins to list.
If you think your coin might be interesting for us to list, please contact us to discuss the terms.

@DigitalPriceOrg is actively listing coins, about 2 a week.
We are actively looking for new and interesting coins to list.
If you think your coin might be interesting for us to list, please contact us to discuss the terms.


Berlin Umsonst is an app which list events happening in and around Berlin. Most of these events are free, but most comedy-events expect a small donation ca.5-10$. So if you have a good time make sure show them your appreciation. Events listed were gathered using web-srapping technologies, so NO GUARANTY here of safety and quality, If you decide to attend, you'll be doing at your own risk.
Try the AppThere are more than 500 upcoming events in more than 8 different categories listed, lot of stuff you should be looking forward to!
Enjoy the upcoming stars of stages. Many unknown comedians made their debut in these small pubs and alot are still at it. The comedy scene is fill with very and somewhat less talented artist just waiting for their moment to shine.
Always wanted to try a new sport but too lazy for it. Now your chance, try one of the free sport events without comiting yourself, if you dont like it, you dont have to go back.
The berlin art scene is somewhat unique. Difficult to say what is really art here, Painting, grafiti, street performance, Berlin is bursting out with energy, sometimes a little too much energy.


System Architect Picture "Category Valued Qualifier"
Parent Diagram:
Phenomenon Qualifier
(OMT Object Model)


a Software Developer
a IOS Software Developer
Taras Markevych
What I'm all about.
Hello, I'm developer.I'm like to trip, ride on bike, night walk, listen music, generade coffee to code strings. Last my work activity you can check on my github page.Life, get fun and do something great.
Progress bars?
Yes. I've been around.
IOS Software developer
IOS Software developer
Web-master, Frontend developer
Frelancer on top platforms like ODesk, Upwork.
I'm not lazy.
Electronic department.Bachelor's degree of Electronic systems and devices
Get in touch.


This tool finds central features and directional distributions. It can be used to answer questions such as:
If Use current map extent is checked, only the features in the input layer visible within the current map extent will be analyzed. If unchecked, all features in the input layer will be analyzed, even if they are outside the current map extent.
The point, line, or area layer that will be used to find central features and directional distributions.
The method of summarizing center and dispersion.
The field used to weight locations according to their relative importance.
The field used to group features for separate computations of central features or dispersion.
This is the name of the layer that will be created in My Content and added to the map. The default name is based on the tool name and the input layer name. If the layer name already exists, you will be prompted to provide another name.
Using the Save result in drop-down menu, you can specify the name of a folder in My Content where the result will be saved.


About this Presentation
I built this using reveal.js
Super simple Presentation framework using pure dom elements.
Using the Tools
⌘ (Command) + option + i or ctr + shift + i on windows
Or for those who dont like keyboard shortcuts
Right Click -> Inspect Element
Now what?
Elements
- Shows DOM structure
- Different than View Source
- Editable
- Can show shadow DOM
Network
- Shows all downloaded files
- Shows the status of files and time to download/blocking time
- Can be filtered by type of file
- Useful to see what files are taking long to download/are blocking other things from downloading
Sources
- Shows folder structure of the source files
- Files can be opened/inspected/edited/debugged
Timeline
- Can record activity and show all kinds of browser activity and various statistics
- Also shows frame/memory statistics
Profiles
- The CPU profiler shows execution time spent on javascript
- The Heap profiler shows memory usage by the DOM and javascript
- The CSS profiler shows time spent matching selectors to elements in the DOM
Resources
- Access to view/edit local/session storage and cookies
- Shows iframes on page
- Shows other varies font/img files as well that are cached
Audits
- Runs a test that shows warnings/recommendations to improve web page performance
- Can tell it to run on current state or reload the page then run the test (so it works on ajax apps)
Console
- Shows results of javascript console.log
- Can also run any valid javascript code
- Has access to the Command Line API
- Can be configured to show all XHR requests as well
- Can be brought up at any time with ESC key
Emulation
Chrome Dev tools have the ability to emulate both mobile devices and mobile networks.
This is a usefull tool when working with responsive design.
Opening Emulation
To turn on Emulation just click on the mobile device icon in the top left of the dev tools.
Choosing a Device
Just select a device from the dropdown list then refresh the page to reload user agent information.
Network Throttling
Throttling works the same way as selecting a device, just choose your level of throttling from the list.
Breakpoints
Viewing the breakpoints/media queries that are set up are easy as well.
Customizing the Look
While not functionally usefull customizing the look of the dev tools can make these tools much more enjoyable to use.
Enabling Customization
To enable customization go to
chrome://flags/#enable-devtools-experiments
and check Enable Developer Tools experiments.
Getting a Theme
Just go to the chrome web store and find a theme you like and the dev tools should be re themed, a chrome re-start might be required.
Quick link
Command Line API
For anyone who uses the javscript console the Command Line API is indespensable. It has a heap of tools that range in uses.
Do note that these are only available for use in the Chrome Dev Tools console and not in any actual script.
$ and $$
$ maps to
document.querySelector
and $$ maps to
document.querySelectorAll
Even without jQuery loaded on the page these work almost like jQuery selectors.
$_ and $0 - $4
$_ simply returns the value of the last evaluated expression
$n returns the nth selected DOM node from the elements list, from 0 - 4 are remembered.
Other Useful Methods
- $x(path) can be used to match a valid xpath expression.
- dir(object) can be used to display all of the methods and properties on a specified object, much like how jQuery objects are displayed in the console.
- getEventListeners(object) will return a list of all the registered event listeners on the specified object.


Fix It
Need something fixed?
Hand Made
Ask
Got a question in mind?
Housing
Looking for a new place?
Tutoring
Need tutoring services?
Rides
Need a ride to the store?


I am the owner of independent product design consultancy Scout Hawk Product Design Studio, where I have the good fortune to build things for entrepreneurs who are trying to make the world a better place for everyone.
I also advise a number of companies like Broadly, Samahope, and Archively on product strategy and design.
I've been a web designer since I was young. I used to build HTML websites as a 12 year old with crude paint programs and hand-coded HTML. Fast-forward 18 years and I have the good fortune to still be doing what I love — combining the technical with the creative.
Primarily, I think like a designer. While I spend an equal amount of time honing my design skills as I do my understanding of coding, user psychology, and business, I still take a creative, people-first approach to problem solving. I love to blend disciplines to create experiences that make people smile, and the capacity to create great experiences is why I love my job.
Check out my LinkedIn profile for more information.


Forms
Basic Form
Form Legend
Email address
Password
File input
Example block-level help text here.
Unchecked
Submit
Inline Form
Email address
Password
Remember me
Sign in
Horizontal form
Email
Password
Remember me
Sign in
Inputs
Option one is this and that—be sure to include why it's great
Option one is this and that—be sure to include why it's great
Option one is this and that—be sure to include why it's great
Option one is this and that—be sure to include why it's great
1
2
3
1
2
3
4
5
1 option
2 option
3 option
4 option
5 option
Static control
Email
<EMAIL_ADDRESS>Password
Disabled Input
Disabled fieldsets
Disabled input
Disabled select menu
Disabled select
Can't check this
Submit
Validation states
Input with success
Input with warning
Input with error
Control sizing
Height sizing
My Profile
My Friends
Messages
My Settings
Logout
My Profile
My Friends
Messages
My Settings
Logout
My Profile
My Friends
Messages
My Settings
Logout
My Profile
My Friends
Messages
My Settings
Logout
Form group sizing
Huge label
Large label
Default label
Small label
Column sizing
Help text
A block of help text that breaks onto a new line and may extend beyond one line.
Input groups
Basic example
@
.00
$
.00
Sizing
@
@
@
@
Button addons
Go!
Go!
Buttons with dropdowns
Action
Action
Another action
Something else here
Separated link
Action
Action
Another action
Something else here
Separated link
Segmented buttons
Action
Action
Another action
Something else here
Separated link
Action
Action
Another action
Something else here
Separated link


{{ _("We’ve emailed you instructions for setting your password, if an account exists with the email you entered. You should receive them shortly.") }}
{{ _("If you don’t receive an email, please make sure you’ve entered the address you registered with, and check your spam folder.") }}


I was just siting around the house, thinking about geting messed up. SO i decided to pay a visit to the local pharmacy. IT was alot harder than in thought it was going to be. The drug i was looking for was Corcidin Cough and Cold, but i had no luck. So i decide to go bak with what i had my first DXM trip with, Delsym. Once i had the medicine, i quickly downed 2.5 bottles of it, gagging the whole time. It was nasty, but i knew what was ahead.
I layed down for a while since it usually took about 1-2 hrs. to take affect. When i came to, everything was slowed down tremendously. Since there was nothing to do at home, i decided to go for a little walk. While i was walking down my driveway, it seemed as though i had been looking through a hole the size of a 50 cent piece. Then i just remember falling into a big hole. when i 'woke up' i was about 50 ft away from where i remember falling. Luckily someone that i knew found me and took me home. HE decided to have a little fun with me.
So we went next door and he decided to start trying to play basketball. he pelted me with the ball on repeated occasions. I knew what was going on but i didnt care cause my entire body was numb. So i took the punishment and decided to stay over there a little while longer. My neighbor who was home knew what was going on and decided to give me some food. i took a handful of chips and just started chewing. I chewed and chewed but i never swallowed my food. this was about an 2 hrs after i took the Delsym, so i decided to go home.
When i got home i was extremely cold, so i decided to go take a shower. I got in the shower, clothes and all, and proceeded to turn the hot water on full blast and barely turned on the cold. I layed down in the shower because i didnt want to fall. When i looked up at the ceiling i was seeing smoke coming out of the walls, and it made me freak out. But i couldn't move so i just stayed there.
Next thing i knew i was grabbing the faucet and holding on for dear life because the bathtub started to flip end over end. I could feel my body shift with the movement of the tub. AFter a efw minutes of flipping i layed back down. this was aobut 20 minutes after i got in the shower, and i suddenly felt my body sinking into the tub. I my hands went first, then my back and next my head, i remember looking up and seeing myslef from under the tub. It was the best feeling in the world. I dont know much about what happened after that because i always black out while on DXM. I remember waking up in the bathroom floor at like 3 in the morning. luckily my mom was asleep, so she didnt know what was going on. It was one of the best trips i had ever had, and one of the greatest feelings i had ever felt.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


Humans communicate with each other using natural languages like English, Spanish or Arabic and communicate with computers using programming languages like JavaScript, Python or Java.
JavaScript is arguably the most popular programming language with tons of libraries and frameworks that aim to ease the life of developers but still, you might find yourself in a situation when you really want to know how and why a piece of code works.