10.78. Release 0.66
Type System
In this release we have replaced the existing simple fixed type system with a fully extensible type system and have added several new types. We have also expanded the function system to support custom arithmetic, comparison and cast operators. For example, the new date/time types include an operator for adding an INTERVAL to a TIMESTAMP.
Existing functions have been updated to operate on and return the newly added types. For example, the ANSI color functions now operate on a COLOR type, and the date/time functions operate on standard SQL date/time types (described below).
Finally, plugins can now provide custom types and operators in addition to connectors and functions. This feature is highly experimental, so expect the interfaces to change over the next few releases. Also, since in SQL there is only one namespace for types, you should be careful to make names for custom types unique as we will add other common SQL types to Presto in the near future.
Date/Time Types
Presto now supports all standard SQL date/time types: DATE, TIME, TIMESTAMP and INTERVAL. All of the date/time functions and language constructs now operate on these types instead of BIGINT and perform temporal calculations correctly. This was previously broken due to, for example, not being able to detect whether an argument was a DATE or a TIMESTAMP. This change comes at the cost of breaking existing queries that perform arithmetic operations directly on the BIGINT value returned from the date/time functions.
As part of this work, we have also added the date_trunc() function which is convenient for grouping data by a time span. For example, you can perform an aggregation by hour:
SELECT date_trunc('hour', timestamp_column), count(*)
FROM ...
GROUP BY 1
Time Zones
This release has full support for time zone rules, which are needed to perform date/time calculations correctly. Typically, the session time zone is used for temporal calculations. This is the time zone of the client computer that submits the query, if available. Otherwise, it is the time zone of the server running the Presto coordinator.
Queries that operate with time zones that follow daylight saving can produce unexpected results. For example, if we run the following query to add 24 hours using in the America/Los Angeles time zone:
SELECT date_add('hour', 24, TIMESTAMP '2014-03-08 09:00:00');
=>
2014-03-09 10:00:00.000
The timestamp appears to only advance 23 hours. This is because on March 9th clocks in America/Los Angeles are turned forward 1 hour, so March 9th only has 23 hours. To advance the day part of the timestamp, use the day unit instead:
SELECT date_add('day', 1, TIMESTAMP '2014-03-08 09:00:00');
=>
2014-03-09 09:00:00.000
This works because the date_add() function treats the timestamp as list of fields, adds the value to the specified field and then rolls any overflow into the next higher field.
Time zones are also necessary for parsing and printing timestamps. Queries that use this functionality can also produce unexpected results. For example, on the same machine:
SELECT TIMESTAMP '2014-03-09 02:30:00';
The above query causes an error because there was no 2:30 AM on March 9th in America/Los_Angeles due to a daylight saving time transition.
In addition to normal TIMESTAMP values, Presto also supports the TIMESTAMP WITH TIME ZONE type, where every value has an explicit time zone. For example, the following query creates a TIMESTAMP WITH TIME ZONE:
SELECT TIMESTAMP '2014-03-14 09:30:00 Europe/Berlin';
=>
2014-03-14 09:30:00.000 Europe/Berlin
You can also change the time zone of an existing timestamp using the AT TIME ZONE clause:
SELECT TIMESTAMP '2014-03-14 09:30:00 Europe/Berlin'
AT TIME ZONE 'America/Los_Angeles';
=>
2014-03-14 01:30:00.000 America/Los_Angeles
Both timestamps represent the same instant in time; they differ only in the time zone used to print them.
The time zone of the session can be set on a per-query basis using the X-Presto-Time-Zone HTTP header, or via the PrestoConnection.setTimeZoneId(String) method in the JDBC driver.
Localization
In addition to time zones, the language of the user is important when parsing and printing date/time types. This release adds localization support to the Presto engine and functions that require it: date_format() and date_parse(). For example, if we set the language to Spanish:
SELECT date_format(TIMESTAMP '2001-01-09 09:04', '%M');
=>
enero
If we set the language to Japanese:
SELECT date_format(TIMESTAMP '2001-01-09 09:04', '%M');
=>
1月
The language of the session can be set on a per-query basis using the X-Presto-Language HTTP header, or via the PrestoConnection.setLocale(Locale) method in the JDBC driver.
Optimizations
- We have upgraded the Hive connector to Hive 0.12 which includes performance improvements for RCFile.
- GROUP BY and JOIN operators are now compiled to byte code and are significantly faster.
- Reduced memory usage of GROUP BY and SELECT DISTINCT, which previously required several megabytes of memory per operator, even when the number of groups was small.
- The planner now optimizes function call arguments. This should improve the performance of queries that contain complex expressions.
- Fixed a performance regression in the HTTP client. The recent HTTP client upgrade was using inadvertently GZIP compression and has a bug in the buffer management resulting in high CPU usage.
SPI changes
In this release we have made a number of backward incompatible changes to the SPI:
- Added Type and related interfaces
- ConnectorType in metadata has been replaced with Type
- Renamed TableHandle to ConnectorTableHandle
- Renamed ColumnHandle to ConnectorColumnHandle
- Renamed Partition to ConnectorPartition
- Renamed PartitionResult to ConnectorPartitionResult
- Renamed Split to ConnectorSplit
- Renamed SplitSource to ConnectorSplitSource
- Added a ConnectorSession parameter to most ConnectorMetadata methods
- Removed most canHandle methods
General Bug Fixes
- Fixed CLI hang after using USE CATALOG or USE SCHEMA
- Implicit coercions in aggregations now work as expected
- Nulls in expressions work as expected
- Fixed memory leak in compiler
- Fixed accounting bug in task memory usage
- Fixed resource leak caused by abandoned queries
- Fail queries immediately on unrecoverable data transport errors
Hive Bug Fixes
- Fixed parsing of timestamps in the Hive RCFile Text SerDe (ColumnarSerDe) by adding configuration to set the time zone originally used when writing data
Cassandra Bug Fixes
- Auto-reconnect if Cassandra session dies
- Format collection types as JSON


Corrie's Andy Whyment is new favourite to win after funny first I'm A Celeb trial
Coronation Street star Andy Whyment is now the public's favourite to win I'm A Celeb this year after his funny entrance
by Brogan-Leigh HurstSoap star Andrew Whyment is the new favourite to win I'm A Celebrity following his hilarious first Bushtucker trial entrance.
The Coronation Street sensation, 38, entered the Jungle tonight alongside Cliff Parisi - who played Minty in EastEnders.
But the pair were thrown right in at the deep end with a Bushtucker trial before they could meet campmates.
They arrived in Snake Rock which had been transformed into a Jungle jail.
It was extremely basic with ground sheets on the floor.
"Me and Cliff walked in and it was very basic. I've probably stayed in better hostels," Andy joked.
And in response, Cliff said: "I'm on the floor.
"I've only just got into the Jungle and I mean there's creepy crawlies and stuff and I've just had to deal with that and now I'm going to have to deal with them all night," he explained, referring to his new sleeping arrangements.
They swept the groundsheets in an attempt to make the camp more habitable.
Andy and Cliff were delighted that they had managed to find eight stars.
"I think the camp will eat well so hopefully they'll like us for that," hopeful actor Andy said.
Fans loved his first appearance and were going crazy on Twitter over it.
One tweeted: "Andy Whyment is hilarious! @imacelebrity #ImACeleb ".
Another penned: "Andy Whyment during that Bushtucker Trial is the funniest thing I've seen in an age. #ImACeleb ".
And a third wrote: "Andy whyment to win I'm @imacelebrity ".
Do you have a story to sell? Get in touch with us at<EMAIL_ADDRESS>or call us direct 0207 29 33033


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Last night my wife and I took 4-AcO-DiPT, bought from a research chemical supplier, for the first time. This report is about her extremely negative reaction to the substance:
t+0:00 - We dosed at around 6:30pm, Zippie (female) took 20-25mg, I (Dirk) took 25-30mg. We had eaten a light meal at 5:00pm.
t+0:20 - First signs noticed, quite similar to the initial onset of LSD, body-wise, but with little visuals.
t+0:45 - Kicking in pretty strong for me, although Zippie was already saying that she would need a higher dose the next time, and appeared slightly agitated by the fact that it was hitting me so much harder than her.
t+1:00-1:30 - Plateaued somewhere during this time, roughly 8:00pm. Zippie and I went upstairs to give it the 'ol' sex test' (that was one of the main reasons she decided to try it with me, as she's not big on psychoactives otherwise) sometime around now; the substance was predominantly a body-feel, and the sex was great. Took an extra long time to orgasm, however.
t+2:30 - Initial signs of stomach pains began with Zippie; I was fine.
t+3:00 - Around 9:30 Zippie's stomach pain was getting bad enough that she headed off to bed; she was tired and thought that the drug's effect had reduced itself enough that she could sleep. I watched TV and enjoyed feeling like I'd taken a couple grams of shrooms.
t+3:30-5:30 - Zippie came back downstairs after only 10 or so minutes, and spent the next couple hours in the bathroom, having minor vomiting and diarrhea. By midnight we decided that she needed to go to the hospital.
All-told, we spent 5 hours at the hospital in the ER. The doctors knew almost nothing, if anything at all, about 4-AcO-DiPT; I basically had to educate them about it. They put Zippie on an IV and eventually gave her some anti-nausea medicine, which killed both the nausea and stomach pain, and was the reason we finally were allowed to go home.
During the time there, they had to call in a Toxicologist, who actually knew something about the substance and other similar 'drug' topics (he really knew his stuff...but he was also pierced up and reminded me of myself, so you can make your own guesses as to why he actually knew as much as he did). Even he had no clue why she was having this reaction, and couldn't even attribute it to an interaction with the only 2 things in her system - Zippie was on the birth-control patch, and had also been taking 1/2 daily dose of a weight-loss suppliment (non-ephedrine based). According to him, the 4-AcO chain on the DiPT makes it a non-polarized molecule, which is why it is absorbable through the stomach lining, and as such it isn't a MAOI and so the reaction wasn't an MAOI hypertensive/metabolic crisis.
All in all, I'm submitting this report as a warning to anyone who has read the other information available on the net regarding 4-AcO-DiPT; just because no one else has reported a truly adverse reaction to a substance doesn't mean that it can't happen, and just because there is no obvious pharmacological reason for a bad interaction to occur doesn't mean you're not taking a risk.
 Just don't look at it as a harmless miracle drug or anything - my wife found out the hard way that nothing in life is free.
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.


In particular, there is a mathematical formula that places an upper bound on the probability of two randomly chosen (distinct) strings hashing to the same fingerprint. By choosing a large enough fingerprint, the probability of a collision can be made vanishingly small. Hence, the fingerprint of a text T can be used as a fixed-size unique identifier for T. @author Allan Heydon


--- layout: post title: Active deflation of an airbag thumb: airbag pub: title: A finite-element/boundary-element method for three-dimensional, large-displacement fluid–structure-interaction author: T.M. van Opstal, E.H. van Brummelen, G.J. van Zwieten journal: Computer Methods in Applied Mechanics and Engineering, volume 284 url: http://dx.doi.org/10.1016/j.cma.2014.09.037 ---
Coupled solution of (interior and exterior) Stokes flow and Kirchhoff-Love thin walled shell interaction, simulating the deflation of a fully extended airbag. Numerical techniques used are the boundary element method and isogeometric analysis.


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java.lang.Object dxf2svg.util.Database
This class is a simple database that could incorporate numerous tables, each with a number of records. Records are made up of a primary key (String value) and the record data itself which is stored in the form of String arrays (String[]).
Because the underlying structure is a Hashtable
, adding a
record that duplicates another will result in the first record being over
written.
A typical application is the FigureSheetDatabase
which stores
all of a publication's figures as individual tables; each of these tables
stores the sheet number as a key and a Vector of spotcall titles etc as the value.
| Nested Class Summary | |
protected class |
Database.NullDatabaseObjectException
Gets thrown typically, if searches or entry creations are attempted with null objects. |
| Field Summary | |
protected java.util.Hashtable |
db
|
| Constructor Summary | |
Database()
Default constructor that initializes the database. |
| Method Summary | |
boolean |
containsKey(java.lang.String key)
This method returns true if the requested key was found in the database false otherwise. |
boolean |
createRecord(java.lang.String tableName,
java.lang.String key,
java.util.Vector values)
Creates a new record on the argument table. |
boolean |
delete(java.lang.String tableName)
Deletes an entire table (tableName). |
boolean |
delete(java.lang.String tableName,
java.lang.String key)
Deletes a record (named key) from the table 'tableName'. |
protected java.lang.Object |
get(java.lang.Object name)
Returns the object by the key of name. |
java.util.Vector |
getRecord(java.lang.String tableName,
java.lang.String key)
This method returns the values stored in a table called tableName by reference of the key argument. |
java.util.Hashtable |
getTable(java.lang.String tableName)
This method returns a Hashtable table with the name of the argument 'tableName'. |
java.lang.String[] |
getTableAndRecord(java.lang.String value)
This method returns an array of strings in which the first value is the name of the table and the second is the name of the key for any supplied value. |
java.util.Set |
keySet()
Returns a Set of the names of stored objects. |
| Methods inherited from class java.lang.Object |
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
| Field Detail |
protected java.util.Hashtable db
| Constructor Detail |
public Database()
| Method Detail |
public boolean createRecord(java.lang.String tableName, java.lang.String key, java.util.Vector values)
tableName
- of the table within the database.key
- Key String for the new record.values
- Vector of values to be stored in record.
Database.NullDatabaseObjectException
- if the table name or key are null.public java.util.Set keySet()
Set
of the names of stored objects.
public java.util.Vector getRecord(java.lang.String tableName, java.lang.String key)
Database.NullDatabaseObjectException
- if the table name or key are null.public java.util.Hashtable getTable(java.lang.String tableName)
public boolean delete(java.lang.String tableName, java.lang.String key)
public boolean delete(java.lang.String tableName)
protected java.lang.Object get(java.lang.Object name)
public java.lang.String[] getTableAndRecord(java.lang.String value)
Database.NullDatabaseObjectException
- if the value argument is a null String.public boolean containsKey(java.lang.String key)
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WITNESS LIST
Finance
March 3, 2011 10:30 AM - or upon adjournment
SB 1 - State Preservation Board
ON:
Bradley, Harry Superintendent (Texas State Cemetery)
Sneed, John Executive Director (State Preservation Board), Austin, TX
Registering, but not testifying:
On:
Herndon, Doaley State Employee (SPB), Austin, TX
SB 1 - Texas Commission on Environmental Quality
AGAINST:
Kitchens, Jerry Commander Lt. Dallas County Emissions (also providing written
testimony) (Dallas County Sheriff Emission Task Force), Mesquite, TX
McCain, Richard Constable (Travis County), Austin, TX
Nay, Terry L. Ellis County Constable, Pct. 2 (LIRAP & LIP Funding), Waxahachie, TX
Noel, Adele Air Quality, Gov't (Travis County), Austin, TX
Reed, Cyrus Conservation Director (also providing written testimony) (Sierra Club, Lone
Star Chapter), Austin, TX
Sumner, James C Chief Deputy Constable #4 Harris County (Harris County Constable 4),
Houston, TX
Vasquez, Michael Program Manager (Texas Clean Air Working Group; Texas Conference
of Urban Counties)
ON:
Adkisson, Tommy County Commissioner (also providing written testimony) (Bexar
County), San Antonio, TX
Gill, Bill Environmental Mgr (Near Nonattainment areas), Austin, TX
Hadden, Karen (Sustainable Energy & Economic Development (SEED) Coalition)
Lovell, Trevor Nuclear Program Coordinator (Public Citizen Texas), Austin
Morris, Michael Director of Transportation (also providing written testimony) (North
Central Texas Council of Governments)
Shaw, PhD, Bryan Chairman (TCEQ), Austin, TX
Sifuentez, Elizabeth Budget Director (TCEQ), Austin, TX
Torres, Raul Advocate (also providing written testimony) (Self)
Vickery, Mark Executive Director TCEQ (TCEQ), Austin, TX
Wittie, David (ADAPT of Texas)
Registering, but not testifying:
Against:
Delgado, Richard Police Officer (Harris County), Spring, TX
Hazel, Eddie Police Officer (Harris County), Spring, TX
Moore, Robert A. Travis County (Travis County / Constable Prct 3)
Saiovitz, Herwa (ADAPT of Texas), Austin, TX
Suits, Stacy Chief Deputy Constable, Travis Co. Pct. 3 (Travis County)
On:
Boyte, Melanie ADAPT (ADAPT)
1
WITNESS LIST
Finance
March 3, 2011 10:30 AM - or upon adjournment
Campos, Maelby Advocate (Self), El Paso, TX
Campos JR, Carlos Retired (Desert Adapt), El Paso, TX
Cranston, Ron Disability Advocacy (ADAPT of Texas)
Fernandez, Javier Advocate (Self), El Paso, TX
Fernandez, Ruben Advocate (Self), El Paso, TX
Flores, Timothy (Self)
Grajeda, Fransica Personal Attendant (Desert Adapt), El Paso, TX
Kafka, Bob Organizer (ADAPT of TX)
Lozanco, Frank Unemployed (Desert Adapt), El Paso, TX
Padilla, Elvia A. Advocate (Self), El Paso, TX
Peet, Nelson (ADAPT of Texas)
Rodrigues, Josue Advocate (Self), El Paso, TX
Saenz, Danny (Self), Austin, TX
Solis, Teresa (Desert Adapt), El Paso, TX
Storey, Brenda ADAPT (ADAPT), Round Rock, TX
Providing written testimony:
On:
Cranston, Catherine R. Personal Care Attendant (PACT / ADAPT), Austin, TX
McPhail, Jennifer Community organizer (ADAPT of Texas)
Reid, Susan H. (League of Women Voters of Texas), Austin, TX
SB 1 - Texas Ethics Commission
ON:
Ashley, Natalia Luna General Counsel (TX Ethics Commission), Austin
Delco, Wilhelmina Retired (Ethics Commission)
Reisman, David Executive Director (Texas Ethics Commission), Austin, TX
Registering, but not testifying:
On:
McElhaney, Michael L. State Employee (Texas Ethics Commission)
SB 1 - Texas Facilities Commission
ON:
Keel, Terry Executive Director (Texas Facilities Commission), Austin, TX
Registering, but not testifying:
On:
Arellano, Luis CFO - TFC (Resource Texas Facilities Commission), Austin, TX
Korn, Stanton Budget Director (Texas Facilities Commission), Austin
Lacy, Mike Deputy Executive Director Planning & Asset Mgmt. (Texas Facilities
Commission), Austin, TX
Ramirez, Jorge Deputy Executive Director (Texas Facilities Commission)
SB 1 - Texas Parks and Wildlife Department
FOR:
Kramer, Ken Director (also providing written testimony) (Lone Star Chapter, Sierra Club)
2
WITNESS LIST
Finance
March 3, 2011 10:30 AM - or upon adjournment
AGAINST:
Cain, Delmar Retired (also providing written testimony) (Boerne Chapter, NPSOT,
Myself, Volunteer), Boerne, TX
ON:
Atkins, Rick Legislative Chair TRAPS & Director of Parks & Rec. City of Round Rock
(also providing written testimony) (Present the TRAPS Position Paper)
Bristol, George President, Texas Coalition for Conservation (also providing written
testimony) (Texas Coalition for Conservation), Austin, TX
Brittain, David Retired from Chase Bank (San Jacinto Friends), Sugar Land, TX
DeVault, Jan President, Friends of the San Jacinto Battleground (also providing written
testimony) (Friends of the San Jacinto Battleground)
Dunn, Jeff Attorney at law (also providing written testimony) (Friends of San Jacinto
Battleground (as a volunteer)), Dallas, TX
Eeds, Tony Architect (Texas Motorized Trails Coalition), Dallas, TX
Haas, Jo Nell Community member (also providing written testimony) (Citizens of
Blanco), Blanco, TX
Hemker, Mike Parks & Recreation Director - City of Hutto (also providing written
testimony) (Texas Municipal Parks, Recreation & Tourism Association), Hutto, TX
Holland, Debbie Mayor Pro tem (also providing written testimony) (Hutto Tx), Hutto, TX
Honeycutt, Rufus Round Rock City Council (City of Round Rock)
Jones, Cecil Consulting (also providing written testimony) (Friends of San Jacinto
Battleground), Katy, TX
McCarty, Gene (Texas Parks & Wildlife Dept.)
Metzger, Luke Director (Environment Texas), Austin, TX
Park, Joey (also providing written testimony) (Texas Outdoor Partners)
Pollard, Ken Retired (also providing written testimony) (TX Buffalo Soldier Association
& Texans for State Parks), Austin, TX
Smith, Carol L. 4-H ATV Safety & OHV Education Program Director (also providing
written testimony) (OHV Community - Education)
Smith, Carter Executive director (Texas Parks and Wildlife), Austin, TX
Thomas, John Attorney (Self), Austin, TX
Westcott, Marcy Westcott Graphic Print (also providing written testimony) (Blanco
Chamber of Commerce), Blanco, TX
Registering, but not testifying:
Against:
Barron, Connie R.N., CPT (Citizens of Blanco, Texas)
Gosnell, Wayne U.S. Army, retired (Blanco Citizens Assn.), Blanco, TX
Mangan, Karen (Blanco Citizens' Association), Blanco, TX
On:
Boruef, Scott Deputy Exec. Director (TPWD)
Flores, Peter Colonel Game Warden (Texas Parks & Wildlife law enforcement Div.),
Austin, TX
3
WITNESS LIST
Finance
March 3, 2011 10:30 AM - or upon adjournment
Providing written testimony:
Against:
Ninaud, Christian (RideDualSport.com)
On:
Hardy, Richard Lobbyist (Motorcycle Manufactures / Dealers (MIC/TMDA)), Aus., TX
ON:
Beck, Tina LBB Staff (LBB)
Borgstedte, Amy LBB Staff (LBB)
Hernandez, Demetrio LBB Staff (LBB)
Lambert, Tom LBB Staff (LBB)
Prado, Elizabeth LBB Staff (LBB)
Scallion, Marva LBB Staff (LBB)
Registering, but not testifying:
On:
Parks, Ursula Assistant Director (LBB)
Smith, Zelma LBB Staff (LBB)
4


PMD provides several methods by which Rule violations can be suppressed. Follow these steps to help you determine which expression method works best for you:
-
Is the thing you need to suppress universally appealing to other users of PMD, or is it a false positive? Can you modify the Rule to support this specific suppression via a configuration property, or to fix the false positive? If you can do this, then please do so, and submit a patch back to the PMD project. Since PMD is built by users for users, your help would be greatly appreciated by everyone. If you cannot…
-
Can you use Annotations or the NOPMD marker to work around your particular issue on a case by case basis? If not…
-
Can a regular expression matching the violation message work around your particular issue? If not…
-
Can a XPath query on the violation node work around your particular issue? If not…
-
Your last and final option is to see the first point about changing the Rule, but you do not need to submit a patch back to the PMD project.
If you need to modify the Rule, see How to write a rule. Otherwise, the other suppression methods are explained in the following sections.
Annotations
When using Java 1.5 or later, you can use annotations to suppress PMD warnings, like this:
// This will suppress all the PMD warnings in this class
@SuppressWarnings("PMD")
public class Bar {
void bar() {
int foo;
}
}
When using Apex make sure to use single quotes instead of double quotes
// This will suppress all the PMD warnings in this class
@SuppressWarnings('PMD')
Or you can suppress one rule with an annotation like this:
// This will suppress UnusedLocalVariable warnings in this class
@SuppressWarnings("PMD.UnusedLocalVariable")
public class Bar {
void bar() {
int foo;
}
}
Multiple rules can be suppressed by providing multiple values, ie:
@SuppressWarnings({"PMD.UnusedLocalVariable", "PMD.UnusedPrivateMethod"})
For Apex, the syntax for this is slightly different:
@SuppressWarnings('PMD.UnusedLocalVariable, PMD.UnusedPrivateMethod')
PMD Java also obeys the JDK annotation @SuppressWarnings(“unused”), which will apply to all rules in the unused ruleset.
// This will suppress UnusedLocalVariable and UnusedPrivateMethod warnings in this class
@SuppressWarnings("unused")
public class Bar {
void bar() {
int foo;
}
private void foobar(){}
}
NOPMD comment
Alternatively, you can tell PMD to ignore a specific line by using the “NOPMD” marker in a comment, like this:
public class Bar {
// 'bar' is accessed by a native method, so we want to suppress warnings for it
private int bar; //NOPMD
}
You can use whatever text string you want to suppress warnings, by using the -suppressmarker
CLI option.
For example, here’s how to use TURN_OFF_WARNINGS
as the suppressor:
$ cat Foo.java
public class Foo {
void bar() {
int x = 2; // TURN_OFF_WARNINGS
}
}
$ ./run.sh pmd -d Foo.java -f text -R java-unusedcode -suppressmarker TURN_OFF_WARNINGS
No problems found!
UnusedLocalVariable rule violation suppressed by //NOPMD in /home/tom/pmd/pmd/bin/Foo.java
Note that PMD expects the //NOPMD marker to be on the same line as the violation. So, for
example, if you want to suppress an “empty if
statement” warning, you’ll need to place it on
the line containing the if
keyword, e.g.:
$ cat ~/tmp/Foo.java
public class Foo {
void bar() {
int x = 42;
if (x > 5) { // NOPMD
}
}
}
$ java net.sourceforge.pmd.PMD -d ~/tmp/Foo.java -f text -R java-basic
No problems found!
A message placed after the NOPMD marker will get placed in the report, e.g.:
public class Foo {
void bar() {
try {
bar();
} catch (FileNotFoundException e) {} // NOPMD - this surely will never happen
}
}
XPath and Regex message suppression
If a particular rule consistently reports a warning in a particular context, you can fall back to disabling the rule for all these contexts using one of two rule properties that are defined on every rule. Depending on what you’re after, you can suppress violations for specific messages using regular expressions, or specific nodes using an XPath expression.
The property violationSuppressRegex
This property defines a regular expression to match against the message of the violation. If the regular expression matches, then the violation will be suppressed.
For example, to suppress reporting specifically named parameters which are unused:
<rule ref="rulesets/java/unusedcode.xml/UnusedFormalParameter">
<properties>
<property name="violationSuppressRegex" value=".*'mySpecialParameterName'.*"/>
</properties>
</rule>
Note for message based suppression to work, you must know how to write a regular expression that matches the message of violations you wish to suppress. Regular expressions are explained in the JavaDoc for standard Java class java.util.regex.Pattern.
This technique of course relies on how the rule’s message is implemented. Some rules always report the same message, in which case this property is of no use.
The property violationSuppressXPath
This property defines an XPath query to be executed using the violation node as the starting point. If the XPath query matches anything, then the violation will be suppressed.
For example, to suppress reporting specifically typed parameters which are unused:
<rule ref="rulesets/java/unusedcode.xml/UnusedFormalParameter">
<properties>
<property name="violationSuppressXPath" value=".[typeIs('java.lang.String')]"/>
</properties>
</rule>
Note for XPath based suppression to work, you must know how to write an XPath query that matches the AST structure of the nodes of the violations you wish to suppress. XPath queries are explained in XPath Rule tutorial.


This tutorial walks you through the steps of creating a Hello World web app example with Spring Boot and Thymeleaf


Profiles
New
Name
Client
Deluge
qBittorrent
ruTorrent
Transmission/Vuze/Azureus
URL
Username
Password
Show
Use for left-click on magnet links
Autostart torrents
Save
Test
Remove


404
Page Not Found
Sorry, but the page you are looking for has note been found. Try checking the URL for error, then hit the refresh button on your browser or try found something else in the app.


TABLE OF INTERACTIONS WITH
FOLINIC ACID
See also: folates
from the French ANSM drug interactions document of September 2016, p. 5
FOLINIC ACID RxNorm: 6313 ATC: B03BB B03BB01 B03BB51 |
FLUOROURACIL (AND, BY EXTRAPOLATION, OTHER FLUOROPYRIMIDINES) CLASS CODE: L01BC-001 |
Strengthening of the effects, both cytostatic and undesirable, of the fluorouracil. |
Take into account |


Topics for This Week:
- Driving Higher Power Circuits
- Introduction to NodeJS
- Connecting to external Software: Part 1
Project 1
Controlling a higher power load
Parts for this project:
- Solderless Breadboard
- 5 x Flexible Wire Jumpers
- 1 x DC Motor
- 1 x 9 volt Battery
- 1 x 9 volt Battery Clip
- 1 x irf520 Transistor
- Arduino Duo board
- USB Cable
Step 1
Step 2
Step 3
Step 4
Step 5
Step 6
The Transistor
Upload 'SerialMotorControl.ino'


Best Website Ever!!
This is my paragraph
This is second header
Pankaj is the
best
ever.Because he
belives in himself
...
You are learning how to develop any webpage.


Binary classification with TensorFlow.js
Train a model to predict a class.
Toggle Visor
build
Train & Test
Training status:
Loading data...
Testing status:
Not yet tested
Train New Model
Test Model
Load Model
Save Model
timeline
Predict
Square feet of living space:
House price ($):
Predict class


influence.feeder
Feeding interface for Influence class
-
class
InfluenceFeeder
[source]
Bases: object
-
reset
()[source]
reset dataset
-
test_indices
(indices)[source]
test data feeder
| Parameters: | indices (testing sample index) – |
| Returns: |
- x (feed input values)
- y (feed label values)
|
-
train_batch
(batch_size)[source]
training data feeder by batch sampling
| Parameters: | batch_size (batch size) – |
| Returns: |
- xs (feed input values)
- ys (feed label values)
|
-
train_one
(index)[source]
single training data feeder
| Parameters: | index (training sample index) – |
| Returns: |
- x (feed one input value)
- y (feed one label value)
|


Home
My Work
Contact Me
Manuel Savino
JavaScript, MongoDB, Express, React, Node.js
my
work
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Github
LET'S
TALK!
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Submit


This web has to able JavaScript!
Getting started with WebGl
Will get deeper when needed.
If does not support, this message will show up.


The Cayan Component Library for Delphi allows you to integrate with the Genius solutions. This includes communication with the different types of devices which Cayan provides for payment collection.
This component provides direct access to Cayan's Genius systems, and a single CED payment terminal. To support multiple terminals, you can use different instances of TCayanGenius, each connected to the same TCayan component for authorization.
This component is to define a device. It is not meant to actually perform transactions alone. Here are all the possible components which link up with this one:


Pulang Bareng
Exclusive Bodyguard
Event
Name Event
Vanue
Start Date
End Date
Guards
Price
Status
Payment Status
Coldplay Release Album
Bali
25/10/2016
26/10/2016
30
Rp 10.000.000,00
Confirmed
Paid


Interactive catalogue explorer. Click a yellow catalogue node to expand. The green node is the current cursor.


Columns
| Column | Type | Size | Nulls | Auto | Default | Children | Parents | Comments | ||
|---|---|---|---|---|---|---|---|---|---|---|
| artefact_group_member_id | INT UNSIGNED | 10 | null |
|
|
|||||
| edition_id | INT UNSIGNED | 10 | null |
|
|
|||||
| edition_editor_id | INT UNSIGNED | 10 | null |
|
|
Indexes
| Constraint Name | Type | Sort | Column(s) |
|---|---|---|---|
| PRIMARY | Primary key | Asc/Asc | artefact_group_member_id + edition_id |
| artefact_group_member_owner_to_edition | Performance | Asc | edition_id |
| artefact_group_member_owner_to_edition_editor | Performance | Asc | edition_editor_id |


Suggestion Form
Choose the name of the NGO
*
Childcare
Anti Dowry Force
ARV GROUP
Write your suggestion below:
*
Submit


"""
A Python version of the classic "bottles of beer on the wall" programming
example.
By Guido van Rossum, demystified after a version by Fredrik Lundh.
"""
import sys
n = 100
if sys.argv[1:]:
n = int(sys.argv[1])
def bottle(n):
if n == 0: return "no more bottles of beer"
if n == 1: return "one bottle of beer"
return str(n) + " bottles of beer"
for i in range(n, 0, -1):
print(bottle(i), "on the wall,")
print(bottle(i) + ".")
print("Take one down, pass it around,")
print(bottle(i-1), "on the wall.")


Built with reveal.js
Big data…
Two class problem: spam and not spam.
1Extract features
2Train a model using the training set
3Test using the test set
4Use in real world
5[Optional] Refine model over time
Focus on: feature extraction
Build a Python-based tokenizer
Token: the unit we operate on (a single 'word' separated by whitespace)
$P(x)$: The probability of random event $x$, range $[0,1]$
$P(x \mid y)$: The probability of random event $x$ given random event $y$
$P(x,y)$: The probability of random events $x$ and $y$
$P(x,y) = P(x) * P(y)$: For independent events $x$ and $y$
$\lvert X \rvert$: The number of elements in set $X$
Think of all words in a spam or not spam email as a giant unordered set independent from each other within each email.
For us, we have two $c$ values in $C$: spam and not spam:
and,
where each $w_i$ is a word occurring in a query document.
1. $P(\text{spam}) = \frac{\lvert \text{spam} \rvert}{\lvert \text{spam} \rvert + \lvert \text{not spam} \rvert}$
2. $P(\text{not spam}) = \frac{\lvert \text{not spam} \rvert}{\lvert \text{spam} \rvert + \lvert \text{not spam} \rvert}$
3. $P(w_i \mid \text{spam}) = \frac{\lvert \text{spam containing } w_i\rvert}{\lvert \text{spam} \rvert}$
4. $P(w_i \mid \text{not spam}) = \frac{\lvert \text{not spam containing } w_i \rvert}{\lvert \text{not spam} \rvert}$
#!/usr/bin/env python
import fileinput
import json
from sys import argv
from base64 import b64encode
USAGE = '''%s <list of file names>'''
if __name__ == '__main__':
if len(argv) < 2:
print USAGE % argv[0]
exit(1)
bag_of_words = []
for line in fileinput.input():
bag_of_words += [b64encode(token) for token in line.split()]
print json.dumps(bag_of_words)
cd /home/wolf/github
wget 'gs9671.sp.cs.cmu.edu/CSDMC2010_SPAM_DATASET.tar.gz'
tar xzf 'CSDMC2010_SPAM_DATASET.tar.gz'
find ham/*.eml -print0 | \
xargs -0 ./tokenize_files.py \
> /tmp/log.json


ReturnType
- The type of the object that will be retrieved from this request.public interface SOAP11ListObserver<ReturnType> extends SOAPListObserver<ReturnType,SOAP11Fault>
SOAP11Observer
to deal with ListRequest
s,
pre-specifying the SOAP11Fault as the fault type to use.onNewItem
onCompletion, onException
Copyright © 2016. All rights reserved.


This week's project included creating a module that would house different classes.
It was the first week that we were told we weren't allowed to create something that would interact with the user and it took all my self control to abide by that.
I was pretty proud of my code until I sent it to a friend who started pointing out some shortfalls. I wish I had time to keep working on this one but one must keep moving forward!
Check out the code here.


It looks like you just submitted the query to . Please answer the following questions about that search.


NoteFor version 5.0, new constructors for |
Services are units of sound-handling functionality that are
automatically available when an application program makes use of an
implementation of the Java Sound API. They consist of objects that
do the work of reading, writing, mixing, processing, and converting
audio and MIDI data. An implementation of the Java Sound API
generally supplies a basic set of services, but mechanisms are also
included in the API to support the development of new sound
services by third-party developers (or by the vendor of the
implementation itself). These new services can be "plugged into" an
existing installed implementation to expand its functionality
without requiring a new release. In the Java Sound API
architecture, third-party services are integrated into the system
in such a way that an application program's interface to them is
the same as the interface to the "built-in" services. In some
cases, application developers who use the
javax.sound.sampled
and javax.sound.midi
packages might not even be aware that they are employing
third-party services.
Examples of potential third-party, sampled-audio services include:
The javax.sound.sampled
and
javax.sound.midi
packages provide functionality to
application developers who wish to include sound services in their
application programs. These packages are for consumers of
sound services, providing interfaces to get information about,
control, and access audio and MIDI services. In addition, the Java
Sound API also supplies two packages that define abstract classes
to be used by providers of sound services: the
javax.sound.sampled.spi
and
javax.sound.midi.spi
packages.
Developers of new sound services implement concrete subclasses
of the appropriate classes in the SPI packages. These subclasses,
along with any additional classes required to support the new
service, are placed in a Java
Archive (JAR) archive file with a description of the included
service or services. When this JAR file is installed in the user's
CLASSPATH
, the runtime system automatically makes the
new service available, extending the functionality of the
Java platform's runtime system.
Once the new service is installed, it can be accessed just like
any previously installed service. Consumers of services can get
information about the new service, or obtain instances of the new
service class itself, by invoking methods of the
AudioSystem
and MidiSystem
classes (in
the javax.sound.sampled
and
javax.sound.midi
packages, respectively) to return
information about the new services, or to return instances of new
or existing service classes themselves. Application programs need
not—and should not—reference the classes in the SPI
packages (and their subclasses) directly to make use of the
installed services.
For example, suppose a hypothetical service provider called Acme
Software, Inc. is interested in supplying a package that allows
application programs to read a new format of sound file (but one
whose audio data is in a standard data format). The SPI class
AudioFileReader
can be subclassed into a class called,
say, AcmeAudioFileReader
. In the new subclass, Acme
would supply implementations of all the methods defined in
AudioFileReader
; in this case there are only two
methods (with argument variants), getAudioFileFormat
and getAudioInputStream
. Then when an application
program attempted to read a sound file that happened to be in
Acme's file format, it would invoke methods of the
AudioSystem
class in javax.sound.sampled
to access the file and information about it. The methods
AudioSystem.getAudioInputStream
and
AudioSystem.getAudioFileFormat
provide a standard API
to read audio streams; with the AcmeAudioFileReader
class installed, this interface is extended to support the new file
type transparently. Application developers don't need direct access
to the newly registered SPI classes: the AudioSystem
object methods pass the query on to the installed
AcmeAudioFileReader
class.
What's the point of having these "factory" classes? Why not permit the application developer to get access directly to newly provided services? That is a possible approach, but having all management and instantiation of services pass through gatekeeper system objects shields the application developer from having to know anything about the identity of installed services. Application developers just use services of value to them, perhaps without even realizing it. At the same time this architecture permits service providers to effectively manage the available resources in their packages.
Often the use of new sound services is transparent to the
application program. For example, imagine a situation where an
application developer wants to read in a stream of audio from a
file. Assuming that thePathName
identifies an audio
input file, the program does this:
File theInFile = new File(thePathName); AudioInputStream theInStream = AudioSystem.getAudioInputStream(theInFile);Behind the scenes, the
AudioSystem
determines what
installed service can read the file and asks it to supply the audio
data as an AudioInputStream
object. The developer
might not know or even care that the input audio file is in some
new file format (such as Acme's), supported by installed
third-party services. The program's first contact with the stream
is through the AudioSystem
object, and all its
subsequent access to the stream and its properties are through the
methods of AudioInputStream
. Both of these are
standard objects in the javax.sound.sampled
API; the
special handling that the new file format may require is completely
hidden.
Service providers supply their new services in specially formatted JAR files, which are to be installed in a directory on the user's system where the Java runtime will find them. JAR files are archive files, each containing sets of files that might be organized in hierarchical directory structures within the archive. Details about the preparation of the class files that go into these archives are discussed in Chapters 14 and 15, which describe the specifics of the audio and MIDI SPI packages; here we'll just give an overview of the process of JAR file creation.
The JAR file for a new service or services should contain a class file for each service supported in the JAR file. Following the Java platform's convention, each class file has the name of the newly defined class, which is a concrete subclass of one of the abstract service provider classes. The JAR file also must include any supporting classes required by the new service implementation. So that the new service or services can be located by the runtime system's service provider mechanism, the JAR file must also contain special files (described below) that map the SPI class names to the new subclasses being defined.
To continue from our example above, say Acme Software, Inc. is distributing a package of new sampled-audio services. Let's suppose this package consists of two new services:
AcmeAudioFileReader
class, which was mentioned
above, and which is a subclass of AudioFileReader
AudioFileWriter
called
AcmeAudioFileWriter
, which will write sound files in
Acme's new format/devel
—where we want to do the build, we create
subdirectories and put the new class files in them, organized in
such a manner as to give the desired pathname by which the new
classes will be referenced:
com/acme/AcmeAudioFileReader.class com/acme/AcmeAudioFileWriter.classIn addition, for each new SPI class being subclassed, we create a mapping file in a specially named directory
META-INF/services
. The name of the file is the name of
the SPI class being subclassed, and the file contains the names of
the new subclasses of that SPI abstract class.
We create the file
META-INF/services/javax.sound.sampled.spi.AudioFileReader
,
which consists of
# Providers of sound file-reading services # (a comment line begins with a pound sign) com.acme.AcmeAudioFileReader
and also the file
META-INF/services/javax.sound.sampled.spi.AudioFileWriter,
which consists of
# Providers of sound file-writing services com.acme.AcmeAudioFileWriter
Now we run jar
from any directory with the command
line:
jar cvf acme.jar -C /devel .The
-C
option causes jar
to switch to the
/devel
directory, instead of using the directory in
which the command is executed. The final period argument instructs
jar
to archive all the contents of that directory
(namely, /devel
), but not the directory itself.
This run will create the file acme.jar
with the
contents:
com/acme/AcmeAudioFileReader.class com/acme/AcmeAudioFileWriter.class META-INF/services/javax.sound.sampled.spi.AudioFileReader META-INF/services/javax.sound.sampled.spi.AudioFileWriter META-INF/Manifest.mfThe file
Manifest.mf,
which is generated by the
jar
utility itself, is a list of all the files
contained in the archive.
For end users (or system administrators) who wish to get access
to a new service through their application programs, installation
is simple. They place the provided JAR file in a directory in their
CLASSPATH.
Upon execution, the Java runtime will find
the referenced classes when needed.
It's not an error to install more than one provider for the same service. For example, two different service providers might supply support for reading the same type of sound file. In such a case, the system arbitrarily chooses one of the providers. Users who care which provider is chosen should install only the desired one.


--- layout: post title: 10 30 Day Challenge Ideas category: 10ideas ---
One of my favorite TED talks is Try something new for 30 days by Matt Cutts. It's a very short talk, and he ends it by saying the following:
So here's my question to you: What are you waiting for? I guarantee you the next 30 days are going to pass whether you like it or not, so why not think about something you have always wanted to try and give it a shot! For the next 30 days.
My plan is to do 12 30 day challenges that will make me a better programmer.


|
UMI3D Unity SDK
2.5.b.221125
A UMI3D sdk implementation using Unity
|
Classes | |
| class | ConnectionDto |
| DTO describing a base connection message to connect to a media. More... | |
| class | ForceLogoutDto |
| DTO describing a forced logout order. More... | |
| class | ForgeSocketBase |
| Forge server configuration and adresses. More... | |
| class | FormConnectionAnswerDto |
| DTO describing a connection to a media with a form answer. More... | |
| class | GateDto |
| DTO describing to use to help a masterserver to guide a user through a gate. More... | |
| class | IdentityDto |
| DTO describing an identity, a set of identifying info of the user. More... | |
| class | JoinDto |
| DTO describing user configuration when joining an environment. More... | |
| class | PendingTransactionDto |
| DTO describing the indication that a user should wait for Transactions or Requests to come before finishing the scene loading More... | |
| class | PlayerCountDto |
| DTO describing the number of users in an environment at a given time. More... | |
| class | PrivateIdentityDto |
| DTO describing idnetifiers that are known only from the user and the media. More... | |
| class | PublicIdentityDto |
| DTO describing a publicly visible identity. All browsers can see this identity. More... | |
| class | RedirectionDto |
| DTO describing the order to a user to disconnect from an environment and connect to an other. More... | |
| class | RegisterIdentityDto |
| DTO describing an identity sent to the UMI3D server. More... | |
| class | StatusDto |
| DTO describing the current status of the user from the server point of view. More... | |
| class | StatusRequestDto |
| DTO describing a request from the server to know the current status of the browser's client. More... | |
| class | TokenDto |
| DTO to describe an update of a token. More... | |
| class | UMI3DAuthenticator |
| Check that the client-server connection is valid and authorized. More... | |
| class | UMI3DCollaborationEnvironmentDto |
| class | UMI3DCollaborationNetworkingModule |
| Helper module to serialize objects related to the Collaboration module. More... | |
| class | UMI3DForgeSocketBase |
| Forge server configuration and UMI3D data communication. More... | |
| class | UserConnectionAnswerDto |
| DTO describing the browser answer to connection requirements. More... | |
| class | UserConnectionDto |
| DTO describing user requirements for a connection. More... | |
| class | UserDto |
| DTO describing a user as a collaborative entity. More... | |
| class | VoiceDataDto |
| DTO describing a vocal message sent through the UMI3D server. More... | |
| class | VoiceDto |
| DTO describing a vocal server configuration. More... | |
Enumerations | |
| enum | DataChannelTypes |
| Exclusive channel for each data type. More... | |
Exclusive channel for each data type.


Modal Headline
Any kind of modal content can go here.
Promo molecules are like other messages, except that these are shown by default on page load (instead of triggered by some user action) and may be dismissed by the user.
Messages convey important information related to the state of your app or the device and often request feedback.
Messages are fixed to the top of the window by default and can be triggered by any means necessary, and most messages will dismiss themselves – unless they require the user to confirm an action – after 10 seconds, or the user can dismiss them using the close icon.
If you’d rather have your messages on the bottom of the screen, simply add a .bottom
class to the parent <div class="messages">
.
An error message provides contextual information displayed when an unexpected condition occurs.
A success message provides assurances to the user that an intended action is successful.
A confirmation message provides the user an opportunity to confirm a requested action.
A modal is a dialog box or popup window that is displayed on top of the current page.
A modal is a dialog box or popup window that is displayed on top of the current page.
Push Down messages literally push the rest of the page down the screen once the window is done loading.


| SL.no | Driver ID | Driver Name | Driver Phone | Customer Id | Customer Name | Customer Phone | Package | Delivery Address |
|---|---|---|---|---|---|---|---|---|
| {{i+1}} | {{diver.driver_uuid}} | {{diver.driver_name}} | {{diver.phone}} | {{dive.customer_uuid}} | {{dive.name}} | {{dive.phone}} | {{driver.type_of_packages}} | {{driver.delivery_address.address}} |


To use Shoo, you need a set of public/private keys.
Fill up the following form and click on Generate Keys.
 Always keep this safely and never share with anyone.
Your Public Key. You may give this to others for encrypting and sending messages to you.


Extension Tools are collection of advanced individual tool that helps you analyze and recover your QR code according to the problems that being encountered in your work.
You can access these tools by clicking Tools in main menu.
QRazyBox provides following extension tools :


Get away from me! I don't want anyone to think we are together!


Analyzing stories from Hacker News using Natural Language Processing (NLP).
Hacker News: Natural Language Processing
Analyzing stories from Hacker News using Natural Language Processing (NLP).
Natural Language Processing (NLP) is the study of enabling computers to understand human languages. This field may involve teaching computers to automatically score essays, infer grammatical rules, or determine the emotions associated with text.
In this project we will be analyzing stories from Hacker News using NLP. We will be predicting the number of upvotes the articles received, based on their headlines. Because upvotes are an indicator of popularity, we’ll discover which types of articles tend to be the most popular.
1. About Hacker News
Hacker News is a community where users can submit articles, and other users can upvote those articles. The articles with the most upvotes make it to the front page, where they’re more visible to the community.
2. The Data
Our data set was collected using the Hacker News API to scrape the data and consists of submissions users made to Hacker News from 2006 to 2015.
3000
rows have been sampled from the data randomly, and all of the unnecessary columns have been removed. Our data now only has four columns:
| Columns | Description |
|---|---|
| submission_time | When the article was submitted |
| upvotes | The number of upvotes the article received |
| url | The base URL of the article |
| headlines | The article’s headline |
import numpy as np
import pandas as pd
submissions = pd.read_csv("sel_hn_stories.csv")
submissions.columns = ["submission_time", "upvotes", "url", "headline"]
submissions = submissions.dropna()
submissions.head()
| submission_time | upvotes | url | headline | |
|---|---|---|---|---|
| 0 | 2010-02-17T16:57:59Z | 1 | blog.jonasbandi.net | Software: Sadly we did adopt from the construc... |
| 1 | 2014-02-04T02:36:30Z | 1 | blogs.wsj.com | Google’s Stock Split Means More Control for L... |
| 2 | 2011-10-26T07:11:29Z | 1 | threatpost.com | SSL DOS attack tool released exploiting negoti... |
| 3 | 2011-04-03T15:43:44Z | 67 | algorithm.com.au | Immutability and Blocks Lambdas and Closures |
| 4 | 2013-01-13T16:49:20Z | 1 | winmacsofts.com | Comment optimiser la vitesse de Wordpress? |
3. Tokenizing Headlines
Our goal is to train a linear regression algorithm that predicts the number of upvotes a headline would receive. To do this, we will need to convert each headline into a numerical representation and will utilize the bag of words
model. A bag of words model represents each piece of text as a numerical vector.
The first step in creating a bag of words model is tokenization. In tokenization, all we are doing is splitting each sentence into a list of individual words, or tokens. The split occurs on the space character (" "
).
tokenized_headlines = []
for item in submissions["headline"]:
tokenized_headlines.append(item.split(" "))
4. Preprocessing Tokens to Increase Accuracy
We will process our tokens to make our predictions more accurate. We will need to convert variations of the same word, for example: China
, China.
, china
, so that they’re consistent.
clean_tokenized = []
punctuation = [",", ":", ";", ".", "'", '"', "’", "?", "/", "-", "+", "&", "(", ")"]
# Loop through each item in tokenized_headlines, which is a list of lists
for item in tokenized_headlines:
tokens = []
for token in item:
# Convert each individual token to lowercase
token = token.lower()
# Remove all of the items in the punctuation list from each individual token
for punc in punctuation:
token = token.replace(punc, "")
tokens.append(token)
# Append the clean list to clean_tokenized
clean_tokenized.append(tokens)
Clean_tokenized
should now be a list of lists. Each list should contain the preprocessed tokens associated with the headline
in the corresponding position of the submissions
DataFrame.
5. Assemble a Matrix of Unique Words
Now that we have our tokens, we can begin converting the sentences to their numerical representations.
First, we’ll retrieve all of the unique words from all of the headlines. Then, we’ll create a matrix, and assign those words as the column headers. We’ll initialize all of the values in the matrix to 0
.
unique_tokens = []
single_tokens = []
for tokens in clean_tokenized:
for token in tokens:
if token not in single_tokens:
single_tokens.append(token)
elif token in single_tokens and token not in unique_tokens:
unique_tokens.append(token)
counts = pd.DataFrame(0, index=np.arange(len(clean_tokenized)), columns=unique_tokens)
6. Counting Token Occurances
Now that we have a matrix where all our values are 0
, we need to fill in the correct counts for each cell. This involves going through each set of tokens, and incrementing the column counters in the appropriate row.
When we’re finished, we will have a row vector for each headline that tells us how many times each token occured in that headline.
To accomplish this, we can loop through each list of tokens in clean_tokenized
, then loop through each token in the list and increment the proper cell.
# We've already loaded in clean_tokenized and counts
for i, item in enumerate(clean_tokenized):
for token in item:
if token in unique_tokens:
counts.iloc[i][token] += 1
7. Removing Columns to Increase Accuracy
We have over 2000
columns in our matrix. This can make it very hard for a linear regression model to make good predictions. Too many columns will cause the model to fit to noise instead of the signal in the data.
There are two kinds of features that will reduce prediction accuracy.
-
Features that occur only a few times will cause overfitting, because the model doesn’t have enough information to accurately decide whether they’re important. These features will probably correlate differently with upvotes in the test set and the training set.
-
Features that occur too many times can also cause issues. These are words like
and
andto
, which occur in nearly every headline. These words don’t add any information, because they don’t necessarily correlate with upvotes. These types of words are sometimes calledstopwords
.
To reduce the number of features and enable the linear regression model to make better predictions, we’ll remove any words that occur fewer than 5
times or more than 100
times.
# We've already loaded in clean_tokenized and counts
word_counts = counts.sum(axis=0)
counts = counts.loc[:,(word_counts >= 5) & (word_counts <= 100)]
8. Splitting the Data into Train and Test Sets
Now we need to split the data into two sets so that we can evaluate our algorithm effectively. We’ll train our algorithm on a training set, then test its performance on a test set.
The [train_test_split()
function from scikit-learn will help us accomplish this.
We’ll pass in .2
for the test_size
parameter to randomly select 20%
of the rows for our test set, and 80%
for our training set.
X_train
and X_test
contain the predictors, and y_train
and y_test
contain the value we’re trying to predict (upvotes).
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(counts, submissions["upvotes"], test_size=0.2, random_state=1)
9. Making Predictions
Now that we have a training set and a test set, let’s train a model and make test predictions.
First we’ll initialize the model using the LinearRegression
class. Then, we’ll use the fit()
method on the model to train with X_train
and y_train
. Finally, we’ll make predictions with X_test
import warnings
warnings.filterwarnings(action="ignore", module="scipy", message="^internal gelsd")
from sklearn.linear_model import LinearRegression
clf = LinearRegression()
clf.fit(X_train, y_train)
predictions = clf.predict(X_test)
When we make predictions with a linear regression model, the model assigns coefficients to each column. Essentially, the model is determining which words correlate with more upvotes, and which with less.
By finding these correlations, the model will be able to predict which headlines will be highly upvoted in the future. While the algorithm won’t have a high level of understanding of the text, linear regression can generate surprisingly good results.
10. Calculating Prediction Error
Now that we have predictions, we can calculate our prediction error. We will use Mean Squared (MSE), which is a common error metric.
With MSE, we subtract the predictions from the actual values, square the results, and find the mean. Because the errors are squared, MSE penalizes errors further away from the actual value more than those close to the actual value. We want to use MSE because we’d like all of our predictions to be relatively close to the actual values.
mse = sum((predictions - y_test) ** 2) / len(predictions)
mse
2652.6082512522839
Our MSE is 2652
, which is a fairly large value. There’s no hard and fast rule about what a “good” error rate is, because it depends on the problem we’re solving and our error tolerance.
In this case, the mean number of upvotes is 10
, and the standard deviation is 39.5
. If we take the square root of our MSE to calculate error in terms of upvotes, we get 46.7
. This means that our average error is 46.7
upvotes away from the true value. This is higher than the standard deviation, so our predictions are often far off-base.
11. Recommendations
We can take several steps to reduce the error and explore natural language processing further:
1. Collect more data.
- There are many features in natural language processing. Using more data will ensure that the model will find more occurrences of the same features in the test and training sets, which will help the model make better predictions
2. Add “meta” features like headline length and average word length
3. Use a random forest, or another more powerful machine learning technique
4. Explore different thresholds for removing extraneous columns


{% extends "accounts/account_form.html" %} {% load i18n static mezzanine_tags social_auth_filter %} {% block breadcrumb_menu %}{% endblock %} {% block body_id %}account signup{% endblock %} {% block main_content %} {# If user is logged in, notify them that they are already logged in #} {% if request.user.is_authenticated %}
{% trans "You're already logged in. If you'd like to create a new account, you'll need to log out first." %}
{% trans "After signing up, your account will be reviewed for approval. You'll receive an email when your account is activated." %}
{# If the account needs email verification, tell the user #} {% elif settings.ACCOUNTS_VERIFICATION_REQUIRED %}{% trans "After signing up, you'll receive an email with a link you need to click in order to activate your account." %}
{% endif %}


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Simplifies idiomatic setup of a Node.js environment for running Alterior applications.
You must still ensure you enable experimentalDecorators
and emitDecoratorMetadata
in your tsconfig.json to properly run Alterior applications.
Within your entry point (usually main.ts
):
import '@alterior/platform-nodejs'; // must be first!
import { Application } from '@alterior/runtime';
// other imports here...
Application.bootstrap(MyModule);
Important: Make sure import '@alterior/platform-nodejs';
is the first line in your entrypoint (main.ts
).
This does a number of things for you:
zone.js
as early as possible to
ensure all code properly runs within the root Zone.reflect-metadata
as early as possible to
ensure all subsequently loaded code has Typescript
reflection metadata emittedsource-map-support/register
as early as possible
to ensure any stack traces are shown using the available
source maps (this avoids seeing compiled JS files in stack traces, instead showing the original source file locations).dotenv/config
as early as possible to ensure that
any dotenv
files are properly accounted for within process.env
.fetch()
available globally (when not already provided). Does this with globalThis.fetch = require('node-fetch')
Generated using TypeDoc


The PathCapabilities interface provides a way to programmatically query the operations offered under a given path by an instance of FileSystem, FileContext or other implementing class.
public interface PathCapabilities { boolean hasPathCapability(Path path, String capability) throws IOException; }
There are a number of goals here:
Capabilities are defined as strings and split into “Common Capabilites” and non-standard ones for a specific store.
The common capabilities are all defined under the prefix fs.capability.
Consult the javadocs for org.apache.hadoop.fs.CommonPathCapabilities for these.
Individual filesystems MAY offer their own set of capabilities which can be probed for. These MUST begin with fs. + the filesystem scheme + .capability. For example fs.s3a.capability.select.sql;
Probe for the instance offering a specific capability under the given path.
if fs_supports_the_feature(path, capability): return True else: return False
Return: True, iff the specific capability is available.
A filesystem instance MUST NOT return True for any capability unless it is known to be supported by that specific instance. As a result, if a caller probes for a capability then it can assume that the specific feature/semantics are available.
If the probe returns False then it can mean one of:
This predicate is intended to be low cost. If it requires remote calls other than path/link resolution, it SHOULD conclude that the availability of the feature is unknown and return False.
The predicate MUST also be side-effect free.
Validity of paths There is no requirement that the existence of the path must be checked; the parameter exists so that any filesystem which relays operations to other filesystems (e.g viewfs) can resolve and relay it to the nested filesystem. Consider the call to be relatively lightweight.
Because of this, it may be that while the filesystem declares that it supports a capability under a path, the actual invocation of the operation may fail for other reasons.
As an example, while a filesystem may support append() under a path, if invoked on a directory, the call may fail.
That is for a path root = new Path("/"): the capabilities call may succeed
fs.hasCapabilities(root, "fs.capability.append") == true
But a subsequent call to the operation on that specific path may fail, because the root path is a directory:
fs.append(root)
Similarly, there is no checking that the caller has the permission to perform a specific operation: just because a feature is available on that path does not mean that the caller can execute the operation.
The hasCapabilities(path, capability) probe is therefore declaring that the operation will not be rejected as unsupported, not that a specific invocation will be permitted on that path by the caller.
Duration of availability
As the state of a remote store changes,so may path capabilities. This may be due to changes in the local state of the fileystem (e.g. symbolic links or mount points changing), or changes in its functionality (e.g. a feature becoming availaible/unavailable due to operational changes, system upgrades, etc.)
Capabilities which must be invoked to determine availablity
Some operations may be known by the client connector, and believed to be available, but may actually fail when invoked due to the state and permissons of the remote store —state which is cannot be determined except by attempting side-effecting operations.
A key example of this is symbolic links and the local filesystem. The filesystem declares that it supports this unless symbolic links are explicitly disabled —when invoked they may actually fail.
Implementors MUST NOT return true for any capability which is not guaranteed to be supported. To return true indicates that the implementation/deployment of the filesystem does, to the best of the knowledge of the filesystem client, offer the desired operations and semantics queried for.
For performance reasons, implementations SHOULD NOT check the path for existence, unless it needs to resolve symbolic links in parts of the path to determine whether a feature is present. This is required of FileContext and viewfs.
Individual filesystems MUST NOT unilaterally define new fs.capability-prefixed capabilities. Instead they MUST do one of the following:


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{% extends 'html/base.html'%} {% block content %} Bethwel Bethwel's Content About Project About Bethwel {% for idx, question, answer in todays_content %} Question: {{question}} Answer: {{answer}} {% endfor %} {% endblock %}
Bethwel's Content
Question: {{question}}
Answer: {{answer}}


Submitted by calypso_rae on Thu, 29/01/2015 - 11:45
During the last couple of years, several forms of my single-phase "Mk2" surplus power diverter have appeared on this forum. This code can be run on various standard platforms such as the Arduino Uno or the emonTx. More recently, I have developed my own PCB-based hardware which is available via my website at www.mk2pvrouter.co.uk
I have also developed a PCB which allows full monitoring of energy flow within a 3-phase environment. In a 3-phase environment, the user is generally charged for their net consumption across all three phases. By having just one processor which knows about the flow of energy on all of the phases, any surplus power can be accurately assessed and diverted for some useful on-site purpose.
3-phase diverter code of this type has been successfully running in the field for some months. From today, that code is available via the Downloads page of my website, and a copy is attached to this post.
This code is intended to be run on my 3-phase PCB, details of which can be found on the 3-phase page of my website. With a separate dual-secondary mains transformer for each phase, this PCB avoids the need for any extra adapters. A circuit diagram for this PCB is attached.
This 3-phase code could also be run on any Arduino-type platform which provides access to all six of the analog ports on the Atmega 328 processor. Because the emonTx only has a single sensor for AC voltage, this code is not suitable for that platform.
My 3-phase diverter code has its roots in various single-phase "Mk2" sketches. The 6-way sampling sequence is an extension of the 3-way one that's in Mk2i_rev6 where an extra analog channel was used to measure the amount of current that has been diverted. The ability to control multiple loads has been taken from Mk2i_rev5 including the single-threshold AF algorithm which is well suited to a multi-load environment. The RF datalogging mechanism is similar to that in Mk2i_rev6. Average power and Vrms data for each phase is regularly sent to the Serial monitor, but is only transmitted by RF if the literal RF_PRESENT is included.
This code is based on the premise that 3-phase users are charged according to their net use of power across all three phases. As with its single-phase equivalent, the 3-phase diverter relies on there being a small range (aka "sweet-zone") where energy can move to and fro across the supply meter without penalty.
Diversion of Surplus 3-phase Power
Submitted by calypso_rae on Thu, 29/01/2015 - 11:45During the last couple of years, several forms of my single-phase "Mk2" surplus power diverter have appeared on this forum. This code can be run on various standard platforms such as the Arduino Uno or the emonTx. More recently, I have developed my own PCB-based hardware which is available via my website at www.mk2pvrouter.co.uk
I have also developed a PCB which allows full monitoring of energy flow within a 3-phase environment. In a 3-phase environment, the user is generally charged for their net consumption across all three phases. By having just one processor which knows about the flow of energy on all of the phases, any surplus power can be accurately assessed and diverted for some useful on-site purpose.
3-phase diverter code of this type has been successfully running in the field for some months. From today, that code is available via the Downloads page of my website, and a copy is attached to this post.
This code is intended to be run on my 3-phase PCB, details of which can be found on the 3-phase page of my website. With a separate dual-secondary mains transformer for each phase, this PCB avoids the need for any extra adapters. A circuit diagram for this PCB is attached.
This 3-phase code could also be run on any Arduino-type platform which provides access to all six of the analog ports on the Atmega 328 processor. Because the emonTx only has a single sensor for AC voltage, this code is not suitable for that platform.
My 3-phase diverter code has its roots in various single-phase "Mk2" sketches. The 6-way sampling sequence is an extension of the 3-way one that's in Mk2i_rev6 where an extra analog channel was used to measure the amount of current that has been diverted. The ability to control multiple loads has been taken from Mk2i_rev5 including the single-threshold AF algorithm which is well suited to a multi-load environment. The RF datalogging mechanism is similar to that in Mk2i_rev6. Average power and Vrms data for each phase is regularly sent to the Serial monitor, but is only transmitted by RF if the literal RF_PRESENT is included.
This code is based on the premise that 3-phase users are charged according to their net use of power across all three phases. As with its single-phase equivalent, the 3-phase diverter relies on there being a small range (aka "sweet-zone") where energy can move to and fro across the supply meter without penalty.


R/mvn.R
mvnram.Rd
Generates an \(n \times k\) multivariate data matrix
or a list of \(n \times k\) multivariate data matrices of length R
from the multivariate normal distribution
$$
\mathbf{X} \sim \mathcal{N}_{k}
\left( \boldsymbol{\mu}, \boldsymbol{\Sigma} \right) .
%(\#eq:dist-X-mvn)
$$
The model-implied matrices used to generate data
is derived from the Reticular Action Model (RAM) Matrices.
mvnram( n, mu = NULL, M = NULL, A, S, F, I, tol = 1e-06, empirical = FALSE, df = FALSE, varnames = NULL, R = NULL, par = FALSE, ncores = NULL, mc = TRUE, lb = FALSE, cl_eval = FALSE, cl_export = FALSE, cl_expr, cl_vars )
| n | Integer. Sample size. |
|---|---|
| mu | Numeric vector. Location parameter mean vector \(\boldsymbol{\mu}\) of length \(k\). |
| M | Numeric vector. Mean structure. Vector of means and intercepts. |
| A | \(\mathbf{A}\) matrix. Asymmetric paths (single-headed arrows), such as regression coefficients and factor loadings. |
| S | \(\mathbf{S}\) matrix. Symmetric paths (double-headed arrows), such as variances and covariances. |
| F | \(\mathbf{F}\) matrix. Filter matrix used to select the observed variables. |
| I | \(\mathbf{I}\) matrix. Identity matrix. |
| tol | Numeric.
Tolerance (relative to largest variance)
for numerical lack of positive-definiteness in |
| empirical | Logical.
If |
| df | Logical.
If |
| varnames | Character string.
Optional column names with the same length as |
| R | Integer.
Number of Monte Carlo replications. If |
| par | Logical.
If |
| ncores | Integer.
Number of cores to use if |
| mc | Logical.
If |
| lb | Logical.
If |
| cl_eval | Logical.
Execute |
| cl_export | Logical.
Execute |
| cl_expr | Expression.
Expression passed to |
| cl_vars | Character vector.
Names of objects to pass to |
If R = NULL
or R = 1
, returns an \(n \times k\)
multivariate normal data matrix or data frame .
If R
is an integer greater than 1, (e.g., R = 10
)
returns a list of length R
of \(n \times k\)
multivariate normal data matrix or data frame.
The multivariate normal distribution has two parameters,
namely the \(k \times 1\) mean vector
mu
\(\left( \boldsymbol{\mu} \right)\)
and the \(k \times k\) variance-covariance matrix
Sigma
\(\left( \boldsymbol{\Sigma} \right)\).
The mean vector mu
can be supplied directly using the mu
argument.
It can also be derived using M
.
Note that the argument mu
takes precedence over M
.
If mu
is not provided, it is computed using M
with the jeksterslabRsem::ram_mutheta()
function.
If both mu
and M
are not provided,
mu
is set to a vector of zeroes with the appropriate length.
The variance-covariance matrix Sigma
is derived
from the RAM matrices A
, S
, F
, and I
.
mu
and Sigma
are then used by the mvn()
function
to generate multivariate normal data.
Options for explicit parallelism are provided when R > 1
especially when R
is large. See par
and suceeding arguments.
McArdle, J. J. (2013). The development of the RAM rules for latent variable structural equation modeling. In A. Maydeu-Olivares & J. J. McArdle (Eds.), Contemporary Psychometrics: A festschrift for Roderick P. McDonald (pp. 225--273). Lawrence Erlbaum Associates.
McArdle, J. J., & McDonald, R. P. (1984). Some algebraic properties of the Reticular Action Model for moment structures. British Journal of Mathematical and Statistical Psychology, 37 (2), 234--251.
jeksterslabRsem::ram_Sigmatheta()
, and jeksterslabRsem::ram_mutheta()
for more information on the Reticular Action Model (RAM)
and mvn()
for multivariate normal data generation.


{% extends "base.html" %} {% block stylesheets %} {% endblock %} {% block content %}
You can now reset the password for your account and log in. Please enter in a new password below.
Please provide the email address associated with your account below.


An internal CSS is used to define a style for a single HTML page.
Other than external stylesheet, adding style every inline element becomes hard to manage and also sometimes style is repeated.
Inline CSS is added into style element defined n head element. The syntax is as followed:
tagname { property1: value1; property2: value2; }

Internal CSS styled text line

Shift
key + alt
key


| Trees | Indices | Toggle frames |
|---|
Audio details.
An instance of this class is provided by sources with audio tracks. You should not modify the fields, as they are used internally to describe the format of data provided by the source.
| __init__(self, channels, sample_size, sample_rate) | |
| __eq__(self, other) | |
| __ne__(self, other) | |
| __repr__(self) |
| Trees | Indices | Toggle frames |
|---|
| Generated by Epydoc 3.0beta1 on Thu Dec 31 17:58:19 2009 | http://epydoc.sourceforge.net |


Mycroft’s Precise Wake Word Spotter supports multiple different wake words that you can choose to activate your Mycroft device. To ensure Precise wake words are trained to work well with everyone, audio samples are subjected to classification process we call "tagging". The audio samples are provided by users who have agreed to share their data with Mycroft for this purpose.
Wake word tagging enables the Mycroft community to contribute to improving Precise. There are multiple characteristics of a sample that help the training process. The audible presence of the wake word, the level of background noise and the perceived gender of the speaker are examples of these characteristics.
To start tagging audio samples, select a wake word below. A screen containing an audio sample and a question will appear. Play the audio and answer the question to the best of your ability. If you are unsure how to answer skip to the next sample.
Thank you for your contribution to Mycroft!


trash
?
Logic Gate Thing
Drag elements onto the page
Drag between connectors to join elements.
Outputs can have more than one connection, inputs can only have one.
Connections can be detached from the input end.
Toggle inputs on and off
Unwanted elements can be dragged into the trash
let me play...


See: Description
| Interface | Description |
|---|---|
| MEJsonConstants |
Contains constants required for the conversion between the COBRAme JSON
schema and SBMLme schema.
|
| Class | Description |
|---|---|
| MEJsonCOBRAme |
Represents JSON schema of COBRAme models for SBML to JSON conversion.
|
| MEJsonMetabolite |
Represents the JSON format of a species in a COBRAme model.
|
| MEJsonMetaboliteType |
Represents the species type in a COBRAme model.
|
| MEJsonMetaboliteTypeAttributes |
Represents attributes of different metabolite types in the COBRAme JSON
schema.
|
| MEJsonProcessData |
Represents a process data object in a COBRAme JSON schema.
|
| MEJsonProcessDataType |
Represents the different types of process data in a COBRAme JSON schema.
|
| MEJsonProcessDataTypeAttributes |
Represents the attributes of process data objects in the COBRAme JSON schema.
|
| MEJsonReaction |
Represents the JSON schema of a single reaction entry in the list of
reactions in a COBRAme JSON file.
|
| MEJsonReactionType |
Represents the different types of reactions in a COBRAme JSON schema.
|
| MEJsonReactionTypeAttributes |
Represents the JSON schema for the inner attributes of the different reaction
types.
|
| MEJsonToSBML |
Contains methods for converting a COBRAme JSON file into a SBML model.
|
| MESBMLToJson |
Contains methods for converting a SBMLme model to a COBRAme JSON file.
|
| SBMLmeConverter |
Contains the main method for the bidirectional conversion between an SBMLme
and a COBRAme model via the command line.
|


Travis Greene Declares Ghana As The Country With The Best Women And Best Jollof (+Video)
by vanessa nordziAmerican gospel singer and pastor, Travis Montorius Greene has declared Ghana as the country with the most beautiful women and best jollof.
According to Travis Greene Ghana has the best women. Since Travis is married to a Ghanaian, he must be speaking from experience.
Travis Greene was in Ghana for the annual “Heaven on Earth Conference” organized by the Empowerment Worship Centre, under the leadership of Prophet Gideon Danso.
This year’s event, which is themed “Thy Will be Done” saw live ministrations from Tim Godfrey of “Nara Ekele Mo” fame and EWC Live.
During his ministration Travis Greene stated that he is always excited to be in Ghana as they serve the best jollof and best women.
“There is no place in the world like Ghana, Ghana has the best Jollof rice and the best women because I married one” he stated.
Travis Greene began his music career in 2007 with the release of ‘The More’ via Greenelight Records. He has since released two studio albums, ‘Stretching Out’ in 2010, ‘The Hill’ in 2015, and an extended play, ‘Intentional’ in 2015.
Watch video below;
http://content.jwplatform.com/players/KWxWXRra-eqk5XVg5.html


The Intertidal Extents Model (ITEM v1.0) product is a national scale gridded dataset characterising the spatial extents of the exposed intertidal zone, at intervals of the observed tidal range. It demonstrates the potential of the Australian Geoscience Data Cube to use satellite imagery to model the continental scale extent and elevation profile of the intertidal zone. The current version utilises 201,964 Landsat observations for Australian coastal regions (excluding off-shore Territories) between 1987 and 2015 (inclusive).
The model can assist in understanding the extents and relative elevation profile of the intertidal zone, delineating exposed areas at differing tidal heights and stages. Examples of where this knowledge can contribute include environmental monitoring applications for migratory bird species, habitat mapping in coastal regions, hydrodynamic modelling, and geomorphological studies of features in the intertidal zone.
Through attribution of the Lowest Observed Tide (LOT) and Highest Observed Tide (HOT) values modelled for each cell (contained in the Coastal Cells layer), the Relative Extents Model can be used to derive contours and an elevation profile of the observed intertidal zone extent on a cell by cell basis. The ability to create an elevation model relative to Mean Sea Level can then enable the data to be integrated as a complimentary data source, linking shallow water bathymetry and terrestrial elevation models.
The Confidence Model layer is designed to highlight regions where changes over the time series do not correspond to the modelled tidal information. This layer acts as a quality assessment of the Relative Extents Model, and can also be used as an indicator of coastal change and/or instability.


| Evaluation Item | Points Earned |
Possible points |
|---|---|---|
| Project successfully runs. Netbeans project zipped and submitted to D2L dropbox. Student's Name appears in the Window Frame when program is run and in screen capture image for both Part2 and Part3 and in screen capture. | |
5 |
| Output of Part2 shows results of entering several names into the TextFields — more than one name is shown in the TextArea | |
5 |
| Names are formatted properly in output in Part2 | |
5 |
| For Part3, interface contains 4 buttons, for moving left, right, up and down | |
5 |
| Screen capture for Part3 shows the message in the lower right quadrant of the screen | |
5 |
| Total Points ====> | |
25 |


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Un paese quasi perfetto
Un paese quasi perfetto is a movie starring Fabio Volo, Silvio Orlando, and Nando Paone. Pietramezzana, a remote village in the Lucan Dolomites, is likely to disappear. Its inhabitants, led by the volcanic Dominic do not give up...


kaazing
Businessmodel of Kaazing
Customer Segments
Kaazing has a mass market business model, with no significant differentiation between customers. The company targets its offerings at companies across industries that want to simply the development of web and mobile applications and rely less on complex web architecture.
Value Proposition
Kaazing offers three primary value propositions: performance, risk reduction, and brand/status.
The company’s WebSocket solution improves performance by minimizing the drag on connectivity for services. Specifically, it connects cloud applications to on-premise services without application code changes. Its network architecture can scale to support hundreds of applications at a time.
The company’s Enterprise Shield solution reduces risk by enabling access to a trusted network while maintaining security behind the firewall. Administrators typically need to open a port to the outside world to allow access, creating the risk of hacks. The solution allows them to close their inbound ports while still being able to initiate connections.
The company has established a strong brand due to its success. It pioneered the innovative HTML5 WebSocket standard. Its customers include one of the world’s top trading and energy-producing firms, one of the world’s largest five commercial airlines, one of the three leading U.S. transportation companies, and one of the top global retail brands. Specific customers include Allstate Insurance, HSBC, FedEx, United Airlines, BMC Software, JPMC, Union Pacific, Banque Populaire, and XPO.
Channels
Kaazing’s main channel is its direct sales team, through which it acquires most customers; its website is also used for this purpose. The company promotes its offering through the site, its social media pages, and attendance at various industry conferences.
Customer Relationships
Kaazing’s customer relationship is primarily of a self-service, automated nature. Customers utilize the service through the main platform while having limited interaction with employees. The company’s website features several self-help resources, including “how-to” articles, tutorials, white papers, case studies, data sheets, demos, developer documentation, and answers to frequently asked questions.
Despite this orientation, there is a personal assistance element in the form of e-mail support. The company also offers phone support, the extent of which varies by subscription level. Customers with a “K2” level subscription can receive guidance 12 hours a day, five days a week (Monday to Friday), while those with a “K1” level subscription can receive support 24/7.
Key Activities
Kaazing’s business model entails maintaining and updating its platform for its customers.
Key Partners
Kaazing operates a partner program that provides training, solutions, and other support needed for third parties to serve their customers effectively with its product. The program has two categories:
Technology Partner – Includes OEM, Certified Technology Partner, and Referral partnerships. Kaazing works with companies that build enterprise web and mobile solutions using its technology (specifically WebSockets and its Kaazing Gateway). Specific partners include Apica, BMC Software, Informatica, ITRS, Neotys, and Tibco.
System Integration Partner – Includes Certified Implementation Partner and Reseller partnerships. The company works with third parties by providing them with necessary tools and resources to help them ensure a successful deployment of its platform for customers. Specific partners include Cognizant, DataArt, Ekito, Ener4Soft, Hartford Consulting Group, JDK, Kentor, StormMQ, Tavant Technologies, and Zenika.
Key Resources
Kaazing’s main resource is its proprietary software platform. It also depends on its human resources in the form of technology staff, particularly its skilled application server professionals. Other important employees include customer service workers who can provide up to 24/7 support. Lastly, as a startup it has relied heavily on funding from outside parties, raising $37.02 million from four investors as of April 2013.
Cost Structure
Kaazing has a cost-driven structure, aiming to minimize expenses through significant automation and low-price value propositions. Its biggest cost driver is likely research and development, a fixed expense. Other major drivers are in the areas of customer support/operations, administration, and sales/marketing, all fixed expenses.
Revenue Streams
Kaazing has one revenue stream: the subscription fees it charges for access to its platform. Its pricing is not publicly available – sales staff must be contacted for specifics.


Downward Diaper? Baby Yoga is the Newest Fitness Craze With New Parents
Downward diaper? This baby yoga class helps parents bond with their infants and get a workout, too!
Custom poses help these tiny yogis not only stretch their muscles but also stretch their brains.
Certain poses help activate different parts of the brain, helping babies reach milestones sooner.
"Baby yoga is a nice way for mom and baby or dad and baby to interact, communicate, play and just be together," said Patti Ideran, a pediatric occupational therapist at Northwestern Medicine in St. Charles, Illinois.


This page is about my hobbies. When I get some spare time my activities include watching movies, listening to music, cooking/baking and gardening.
Gardening
Gardening gives me immense pleasure. When I saw seed sown by me growing into beautiful plants and blooming flowers, it gives me a great sense of fulfillment. I enjoy tendering my plants and watering my garden every day. It gives me satisfaction and helps in keeping myself active and busy.
Listening to Music
Music gives me the needed refuge whenever I feel like my head is spinning and I just want to shut out everything around me. The good thing about music is that there is music for every occasion and feeling. Whenever I feel depressed, I listen to sad songs. They guide me and keep me company during those trying moments. Whenever I’m happy, I have a collection of party songs that I dance to for hours to amplify the good feeling. The beauty of music is that it can be incorporated with other hobbies.
Watching Movies
Watching movie is my best way to spend my leisure time. It can refresh my mind. If I felt sad, watching a cartoon or comedy film can entertain myself. Watching movie in the cinema is more interesting. I used to do that with my family or my friends. I have watched nearly 200 movies so far altogether with different languages.


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There are several different views available in NeoMem - the views on the left side of the screen let you navigate among all the objects in the file, while the views on the right side show information about the currently selected item. Different views may be available on the right side depending on the class of the selected object.
Each object has a Text View associated with it, where you can enter text with a simple word processor. Each object may also have a Contents View which shows the contents of the selected object, and a Properties View which shows the properties associated with the selected object.
You can switch among the views by clicking on the tab containing its name, by pressing Alt+PageDown or Alt+PageUp to cycle through the visible views, or by pressing the Alt key plus the letter underlined in the tab.
For more information, click on a topic below:


Female workers in a Japanese shop are encouraged to wear period badges
A store in Japan has introduced an unusual badge system for its female workers.
In an effort to take on the stigma around menstruation in the country, a shop in Osaka is encouraging female team members to wear a special badge to signify that they are on their period.
The paper badge features a manga cartoon character, known as Seiri-chan, sometimes referred to as ‘Little Miss Period’, and is said to symbolise a woman’s menstrual cycle, according to WWD Japan.
The initiative has been launched at the Michi Kake store, which sells female sexual and menstrual products, but women are in no way forced to wear the badges and can decline to do so if they want.
‘In Japan, there is a tendency for women’s sexuality and periods to be subjects “not to be mentioned”,’ store manager Takahiro Imazu told WWD Japan.
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‘Not all customers will react positively to [this shop], but the fem tech boom is growing, and the values of young people are changing with it.
‘I might be going a little far in saying this, but I am excited for it to become a shop for solutions for women’s sensitive needs, and to be an asset to the marketplace.’
The store was launched in partnership with the period prediction app, Luna, and customers can explore four sections, with each representing a stage of a woman’s menstrual cycles.
For instance, the blue area is for women who are on their period at that exact time, while the glittering period is for when you have finished your menstrual cycle for the month.
There’s also the turbulence period – for when you’re feeling a bit vexed – and finally, the gloomy period, which represents the time right before your period kicks in.
If you’re around Japan, swing by the shop to pick up some menstrual goodies or take a positive period selfie with a life-sized version of Seiri-chan.


By the end of this module, you should understand how to create conditional and repetitive control flow in a program. Conditional execution is accomplished by the if statement, allowing selective execution of code. Iteration is accomplished using the while or for statements, allowing repeated executuion of code.


{% extends "password_reset/base.html" %} {% load i18n %} {% block title %}{% trans "Password recovery sent" %}{% endblock %} {% block content %}
{% blocktrans with ago=timestamp|timesince %}An email was sent to {{ email }} {{ ago }} ago. Use the link in it to set a new password.{% endblocktrans %}
{% endblock %}