---
layout: page
title: "About"
description: "Chittara Elements"
header-img: "img/about_bg.JPG"
---
Chittara is a small Travel and Photographic family having vision of bringing innovative photography to the common people
and the comfort of Travelling to exotic places and enjoy the nature in its purest form to all.
At Chittara We are passionate about Travel and Photography. Browse through the gallery of places we visited
And if You would like to enjoy the nature in the raw form, we are the best choice.
We can arrange a trek in a way that would make you feel you are right amidst the nature.
Do You like Our Pictures? We are here to provide you the Best quality Photographic work amidst nature


RT @rubyconf: TGIF! And with it, the RubyConf 2016 CFP is open: http://cfp.rubyconf.org/events/rubyconf2016 Read the guidelines *carefully* & submit proposals…
8/8/2016, 5:59:30 PM
Favs: 0
Retweets: 0
link--- layout: tweet ---
RT @rubyconf: TGIF! And with it, the RubyConf 2016 CFP is open: http://cfp.rubyconf.org/events/rubyconf2016 Read the guidelines *carefully* & submit proposals…
8/8/2016, 5:59:30 PM
Favs: 0
Retweets: 0
link


Booth Content
Kombucha +1 irony fixie man bun, austin slow-carb lyft brooklyn green juice try-hard tumeric. Flannel literally asymmetrical, master cleanse you probably haven't heard of them ethical vice lo-fi letterpress cred locavore semiotics glossier cold-pressed. Gentrify kinfolk truffaut hot chicken swag organic, unicorn trust fund meggings mumblecore kogi slow-carb wolf gochujang. Chicharrones pour-over you probably haven't heard of them truffaut gentrify, whatever bushwick photo booth. Ramps polaroid poutine, church-key yr four loko truffaut mustache jean shorts scenester aesthetic live-edge squid kickstarter vinyl. Squid mixtape pop-up lomo activated charcoal iceland. Helvetica listicle snackwave pug, paleo succulents mlkshk 8-bit cold-pressed put a bird on it lo-fi cred pour-over DIY.
Pug wayfarers kombucha, squid pitchfork semiotics kale chips tumblr tousled post-ironic irony glossier mixtape. PBR&B readymade everyday carry copper mug typewriter art party. Meditation letterpress roof party, succulents dreamcatcher tumblr pickled. Chicharrones affogato man bun ethical, cold-pressed kitsch yuccie wayfarers tote bag marfa. Street art taxidermy gastropub, distillery kinfolk raclette 90's synth. Vice jianbing disrupt, health goth lumbersexual bespoke microdosing mixtape dreamcatcher butcher cred occupy. Slow-carb art party activated charcoal, single-origin coffee occupy edison bulb austin ugh DIY green juice butcher locavore lumbersexual.
Gentrify organic kale chips cliche. 8-bit normcore master cleanse keytar 3 wolf moon jianbing gentrify, viral succulents tousled. Tumeric asymmetrical meggings hashtag enamel pin occupy mixtape, salvia normcore mustache sustainable migas chambray.  Chia chartreuse austin, hammock single-origin coffee butcher sartorial yuccie.  Sriracha godard disrupt meggings quinoa dreamcatcher.
Gochujang pour-over YOLO meggings normcore glossier hella, green juice +1. Messenger bag man braid green juice waistcoat kickstarter, chillwave fingerstache marfa activated charcoal umami yr. Lyft four dollar toast stumptown taxidermy mustache, selvage pour-over plaid lo-fi man braid +1 vegan flexitarian mumblecore. Chillwave roof party direct trade, pour-over keytar meggings cornhole. Locavore poke cray raw denim, pickled whatever fap prism flannel. Meditation keytar pug whatever, hella authentic plaid vaporware man braid vegan vape. 3 wolf moon godard freegan stumptown irony.


My name is Samardeep I am from India,currently living in Upper Hutt (wellington) with my husband and 20months old baby boy. I moved to NZ 3 years ago.I have completed Masters in computer science in 2013 and after that did job as a computer Teacher in India. I have customer service experience in NZ. Web Development seems like a great opportunity to advance my skills and change my career.


•Display a list of all datasets included with installed R packages.
•Display information about selected objects in an R workspace file or dataset
•Read an R data frame from a workspace or dataset and create a Statistics dataset from it. It can also read a set of variables that can be converted into a data frame and create a Statistics dataset.
Typical usage starts with finding the dataset or workspace file; then finding the names and types of objects in it, and finally creating an SPSS dataset from a data frame or set of series.
R Workspace or Dataset Select the source to be used. It can be an R workspace file or a dataset distributed with an R package. If this command has already been run in this session, and it loaded a workspace or dataset that is still in memory, you can check the second box to use that as the source.
Datasets are associated with packages. To use a package dataset, enter the package and dataset names. Only installed packages can be selected. Datasets in base R are in the datasets package.
List all package datasets Check this box to produce a table listing all the datasets in the installed packages. The listing includes the package name, the dataset name, and a title. If the dataset name includes a parenthesized string, e. g., BJsales.lead (BJsales), use the text inside the parentheses to refer to the dataset when loading it.
Clear workspace after use Check this box to remove the workspace or data from memory after this command completes.
Task Choose whether to display information about the contents of the workspace or dataset or to create a Statistics dataset from an appropriate object.
Display contents information By default, information about all data frames is displayed. You can filter the names by entering one or more specific names in the Name Filter field or by entering a regular expression such as "x.*" to limit the information to matching names. This example would list all the objects whose name starts with x and satisfy the class filter.
You can select the object classes for the output by entering one or more class names in the Class Filter. Use "." to request all classes. The list of possible R classes is open ended, but common ones include "data.frame", "list", "matrix", and "function".
• Names in R are case sensitive.
Create Statistics Dataset Choose this button to create a Statistics dataset corresponding to an R data frame or a set of compatible series. Enter a data frame name in the Data Frame field or one or more series names in the Series to Combine field. Enter a name for the Statistics dataset in the Dataset Name field.
Instead of converting a data frame to a dataset, you can specify a list of variables in the R workspace or dataset. They will be combined into a data frame which is then imported. All the variables must have the same number of cases.
A reasonable effort is made to map R variable names to legal Statistics names, but some unusual names may cause the extraction to fail. If the Statistics name differs from the R name, the R name is used as the variable label.
• The dataset name must not already be in use.
This dialog generates syntax for the STATS GET R extension command. To display the syntax help, run the following command.
STATS GET R /HELP.
© Copyright IBM Corp. 2014


Plugin Introduction
In this page, you will see the lifecyle and event for Chimee plugin.
Create
load
Play
Pause
Focus
Destroy
Add plugin
remove plugin
video full screen
container full screen
wrapper full screen


Prior to MDW 4.5.12 and MDW 5.1.05, the class AdapterActivityBase
is an abstract
class that provides common base for all adapter activities,
and most activities extend this base class directly.
Since MDW 4.5.12 and MDW 5.1.05, we improved the adapter mechanism so that
the adapters can be directly used in connection pools, and support automatic
retries through adapter configuration, rather than a retry loop (a link from
it to itself) as typically used with old implementors.
In order to keep backward compatibility, we introduced a new
base class, PoolableAdapterBase
, to support the new functionalities.
Adapters defined on top of the new base class are called poolable adapters.
A poolable adapter can therefore be used in two ways:
MDW provides a set of common adapters for common protocols such as SOAP based MDW web services, Tibco BUS, JMS and MQ Series, as well as the adapter activity using connection pools, mentioned above. These adapters are highly configurable and hence can meet common needs in many cases. You may however encounter situations where further customization beyond configuration is needed. Here is a list of common reasons:
getRequestData
method to be described below.onSuccess
.
This method may also be needed to perform custom logics such as extracting
or translating the responses before persistence.invoke
.onFailure
.DATA
field for request data.
This can be achieved
by overriding the method getMetaData
.PoolableAdapterBase
directly.The next section describes such customizations in details.
A new poolable adapter should extend the abstract base class
com.centurylink.mdw.workflow.adapter.PoolableAdapterBase
.
The following describes the methods you must implement or may extend.
void init(Properties parameters)
.
This method is used by connection pools only.
It is invoked by the pool manager when a new
connection instance is created and added to a connection pool.
The following is the implementation of the MQ adapter:
public void init(Properties parameters) { qmgr_name = parameters.getProperty(PROP_QUEUE_MANAGER_NAME); queue_name = parameters.getProperty(PROP_REQUEST_QUEUE_NAME); replyQueueName = parameters.getProperty(PROP_REPLY_QUEUE_NAME); isSynchronous = "true".equalsIgnoreCase(parameters.getProperty(PROP_SYNCHRONOUS_RESPONSE)); }
void init()
.
This method is invoked by adapter activities only.
Typically the method should set configuration parameters from attributes
and their default values. Note that the invoke method should
not call getAttributeValue
to get configuration
parameters as the method is not available when used in a connection pool.
All activity attributes must be set as Java instance variables in this method.
The following is the implementation of the MQ adapter:
public void init() throws ConnectionException, AdapterException { qmgr_name = getAttribute(PROP_QUEUE_MANAGER_NAME, null, true); queue_name = getAttribute(PROP_REQUEST_QUEUE_NAME, null, true); replyQueueName = getAttribute(PROP_REPLY_QUEUE_NAME, null, true); isSynchronous = "true".equalsIgnoreCase(getAttributeValue(PROP_SYNCHRONOUS_RESPONSE)); }
Object openConnection()
.
This method is to be implemented to open a communication channel
with the external system. It is invoked by an adapter activity
when it opens a connection, right after the call to init()
.
When it is used with connection pools,
with one-connection-for-all style,
the method is invoked by the pool manager immediately after
the init()
call when a new connection instance is created.
With one-connection-per-call style, the method is invoked
right before a message is to be sent.
Its implementation typically remembers the object representing
the open connection in a Java instance variable.
The method can throw ConnectionException
or AdapterException
.
See the description for invoke
method for their differences.
The method should return an object identifying the open connection, and the
object will passed to invoke
and closeConnection
methods.
It is typical to return the adapter class instance itself.
The following is the implementation of the MQ adapter:
public Object openConnection() throws ConnectionException { try { qmgr = new MQQueueManager(qmgr_name); return this; } catch (Exception e) { throw new ConnectionException(ConnectionException.CONNECTION_DOWN, "Failed to open connection to MQ server", e); } }
void closeConnection(Object connection)
.
The method should close the connection with the external system opened by openConnection
.
The method is invoked by an adapter activity when it is closing a connection
after the message is sent.
When it is used with a connection pool,
with one-connection-for-all style,
the method is invoked by the pool manager when the connection is removed from the connection
pool.
With one-connection-per-call style, the method is invoked
right after a message is sent.
The only argument is passed in with object returned by openConnection
.
The following is the implementation of the MQ adapter:
public void closeConnection(Object connection) { try { qmgr.close(); } catch (Exception e) { } }
String invoke(Object connection, String message, int timeout, Map<String,String> meta_data)
.
You will need to implement this method to actually send a message.
It should also wait for responses for synchronous and certified messages.
The timeout value is in seconds. The implementation should allow a value less than 0
to use the global value specified in the configuration parameter timeout
,
or a hard-coded default value when the configuration parameter is absent.
In case of error detected, the method should}
ConnectionException.CONNECTION_DOWN
(41290) if the exception
is used to count for automatic shutdown after consecutively receiving such exceptions.
The single argument constructor sets this code by default.
If the adapter is used to support certified messages, the method should verify the responses it receives are correct acknowledgment.
The following is the implementation of the MQ adapter:
public String invoke(Object connection, String requestData, int timeout, Map<String,String> metainfo) throws AdapterException, ConnectionException { try { MQMessage msg = new MQMessage(); msg.writeString((String)requestData); int openQueueOptions = MQC.MQOO_INPUT_AS_Q_DEF|MQC.MQOO_OUTPUT|MQC.MQOO_BROWSE; MQPutMessageOptions putOptions = new MQPutMessageOptions(); putOptions.options = putOptions.options | MQC.MQPMO_SET_IDENTITY_CONTEXT; String correlationId = metainfo==null?null:metainfo.get(PROP_CORRELATION_ID); if (correlationId!=null) msg.correlationId = correlationId.getBytes(); if (replyQueueName!=null) msg.replyToQueueName = replyQueueName; MQQueue replyQueue; if (isSynchronous) { if (replyQueueName==null) replyQueueName = "REPLY_MODEL_QUEUE"; String dynamicQueueName = replyQueueName + "*"; replyQueue = qmgr.accessQueue(replyQueueName, openQueueOptions, null,dynamicQueueName,null); msg.replyToQueueName = replyQueue.name; } else replyQueue = null; qmgr.put(queue_name, msg); String result; if (replyQueue!=null) { MQGetMessageOptions getOptions = new MQGetMessageOptions(); getOptions.options = getOptions.options|MQC.MQGMO_WAIT; if (timeout<=0) timeout = defaultTimeout; getOptions.waitInterval = timeout*1000; MQMessage rmsg = new MQMessage(); rmsg.messageId = msg.messageId; replyQueue.get(rmsg, getOptions, 1024*1024); result = rmsg.readString(rmsg.getTotalMessageLength()); } else result = null; return result; } catch (Exception e) { throw new AdapterException(-1, "Exception in invoking JmsAdapter" , e); } }
boolean ping()
.
This method is only used by connection pools.
The method is for pinging the external system after the pool
is automatically shut down due to consecutive connection failures.
The method should return true when the connection with the
external system is up, and false otherwise.
The following is the implementation of the MQ adapter, which
cannot really implement this semantics, being a generic protocol adapter
and not knowing how to tell if an interfacing system is up:
public boolean ping() { return false; // need to implement this in application specific subclass }
String getLabel()
: The method is used
by the designer to obtain the label for the activity implementor.
You typically should override this method to return a unique and descriptive
name for the adapter. Here is the sample implementation for the MQ adapter:
@Override public String getLabel() { return "MQ Series Adapter"; }
boolean canBeSynchronous()
: you must implement this method. It should
return true if the adapter can be used synchronously (receiving a response), and false otherwise.
The following is the implementation of the MQ adapter:
protected boolean canBeSynchronous() { return true; }If the adapter can be used both synchronously and asynchronously, an option will be shown in the activity configuration to choose between the two. . And the method
isSynchronous
is the API to determine whether the adapter is used synchronously (an adapter can support
both mode but can only be configured to use one.
boolean canBeAsynchronous()
: you must implement this method. It should
return true if the adapter can be used asynchronously (sending a one way message without
expecting a reply), and false otherwise.
The following is the implementation of the MQ adapter:
protected boolean canBeAsynchronous() { return true; }
Map getMetaData()
: you may override this method
if there is meta data to be passed when calling the invoke
method. The super class implementation simply returns null.
The following is the implementation of the MQ adapter, which may optionally
pass in a correlation ID as meta data:
@Override protected Map<String,String> getMetaData() throws AdapterException, ConnectionException { HashMap<String,String> meta_data; try { String correlationId = this.getAttributeValueSmart(MqSeriesAdapter.PROP_CORRELATION_ID); if (correlationId!=null) { meta_data = new HashMap<String,String>(); meta_data.put(MqSeriesAdapter.PROP_CORRELATION_ID, correlationId); } else meta_data = null; } catch (PropertyException e) { super.logexception("failed to evaluate correlation ID", e); meta_data = null; } return meta_data; }
The above are all the methods that you typically implement or override, and the MQ adapter implements only these methods, which are shown as example above. The following methods may be overridden less commonly:
getRequestData()
: this method returns the request message. The default
implementation reads it from a variable configured in the designer. You can override it
to get the message from elsewhere.onSuccess(String response)
: the method is invoked when the external system
returns a successful response. The method may convert external-system-detected errors into failure
by throwing an exception here (ConnectionException for retriable errors and
AdapterException for non-retriable errors), although it is preferable to perform such interpretation
in the method invoke
, as the logic can be shared with connection pools.
Throwing an exception here
also triggers the method onFailure
to be called.
This method is also the place to translate external responses
into internal format and persist them somewhere, if these are needed.
The default implementation stores the message in a variable configured in the designer.
onFailure(int tryCount, int maxTries, Throwable errorCause)
:
The method is invoked on the failure of each try.
The outcome of the method must be one of the following:
AdapterConnectionPool.EXCEPTION_EXCEED_MAXTRIES
You can look at out-of-box poolable adapters as samples. We note that the out-of-box adapters typically do not throw exceptions when it receives any messages from external systems, even the messages may indicate some error conditions (e.g. an XML message that contains an error message). This is obviously due to the fact there is no generic way to identify what messages indicate errors. When you implement your application-specific adapters, which you may have knowledge as to what response messages indicate errors, you should throw exceptions as described above appropriately, so that the auto-retry mechanism can function properly.
The out-of-box implementor ConnectionPoolAdapter
is implemented as a subclass of PoolableAdapterBase
as well,
even though itself is not really a poolable adapter
and should be never be used by a connection pool.
This is because it shares many common functionalities as direct connection
adapter activities. As a good consequence, you can customize the connection pool
adapter just like other adapters, as described above. Some methods only
used by connection pools are not meant to be overridden, such as init(Properties)
,
and ping
. The methods that may have needs to be overridden include:
invoke
onSuccess
onFailure
getRequestData
getMetaData
This section is provided for those using older versions of MDW where poolable adapters are not available, or for any historical reasons you need still to use the old (non-poolable) adapters.
The following methods must be implemented by concrete subclasses.
isSynchronous
:
The method determines if the adapter itself is synchronous, i.e.
waiting for responses.
openConnection
:
The method should open a connection to the external system.
You may implement this through a connection pool you implement,
then this method is to get an instance from the pool.
closeConnection
:
The method is called to close a connection opened.
If you have implemented your connection pool, then
this method should return the connection instance
to the pool.
invoke
:
This method must be implemented to perform the
real invocation of external system service.
AdapterException
.
This exception can be set with a retryable indicator. When it is true,
the work flow engine will retry this adapter activity (double check???).
You can also set an error code here, which will be passed
to handleAdapterFailure
.
getLabel
: The subclasses should override this method to give
specific names displayed in the designer activity implementor paine.
getType
: The method should
return an XML specification for customizing the
attribute dialog. Follow this link
for the syntax.
getRequest
:
Override this method if need to translate data from variable
or need to get data elsewhere. The default
method assumes the data is in the variable REQUEST_VARIABLE
Also, if a translation is needed to convert data in internal
format to one required by external system, this is the place
for doing it.
The method should throw TranslationException
,
when the translation fails.
handleAdapterSuccess
:
The method is invoked when the external system interaction is a success.
(i.e. the external system responded something, even an error code).
The method may convert external-system-detected errors into failure
by throwing an Adapter exception here.
This method is also the place to translate external responses
into internal format and persist them somewhere, if these are
needed.
For one way communication protocols, you can leave the default
which does nothing.
The method should throw TranslationException
,
when the translation fails. Throwing non-retryable
exceptions will lead to handleAdapterFailure to be called.
handleAdapterFailure
:
The method is invoked when the requests failed to send
or the response is not received from external systems
(for protocols where responses are expected).
The default method throws a ActivityException
which will cause an ERROR transition.
You can invoke the following method to retry the call,
or set to a different return code to follow process definition
transitions.
setReturnCode(ActivityResultCodeConstant.RESULT_RETRY);
externalRequestToString
:
This method is used to serialize external system requests for
logging purpose.
externalResponseToString
:
This method is used to serialize external system responses for
logging purpose.
externalResponseFromString
:
This is used when the adapter is asynchronous but
the protocol is synchronous, where response data is
logged in adapter instance table. This method is invoked
to translate serialized data back to the actual data.
This method is also used to translate stubbed data
from strings to objects.
getStubResponse
:
This method returns stubbed response from external system.
The default method reads the stubbed response from
the attribute STUB_RESPONSE and this implementation is
probably good for most cases. One reason you may want
to override the method is to get more dynamic stub
responses (different responses based on request data).
doLogging
: This method
determines if external requests/responses should
be logged into adapter instance table.
The default method reads the attribute value
DO_LOGGING. If the attribute is missing, the default
is on for logging.
You may need to override externalRequestToString
and externalResponseToString
in order to log the
information properly.
execute
:
This method is invoked by the workflow engine to execute
the activity. The default implementation performs the function
by directly or indirectly calling other methods described here, so you
should rarely have need to override this method. You may
want to override it to perform additional implementation
specific functions prior and/or after the standard
executer. If you do override it, be sure to invoke
the superclass version.
getIconName
: you only need to override this method
if you would like to have a distinct icon in the designer,
instead of the default adapter icon.
MDW provides support for Basic Authentication in the following adapters and web services
The adapters below include configuration to set a user/password Basic Authentication combination that will be sent with each request
SoapWebServiceAdapter
DocumentWebServiceAdapter
RestfulServiceAdapter
For specific details on how to configure the above adapters for Basic Authentication, please refer to these adapters in the eclipse help.
The MDW servlets below support HTTP Basic Authentication for hosted web services.
SoapListenerServlet
RestfulServicesServlet
To enable these servlets to use Basic authentication, you should set the following property in your mdw.properties or com.centurylink.mdw.cfg configuration file.
mdw.http.listeners.auth.mode=Basic
After authentication is successful, the authenticated username will be available in the metaInfo property "AuthenticatedUser" or Listener.AUTHENTICATED_USER_HEADER


This is an encrypted pastebin service! You can only load a paste with the paste ID and encryption key! Features include:
- AES-128 Encryption
- Server-Side Entropy Generation
- Modern Python3 Support
This is an encrypted pastebin service! You can only load a paste with the paste ID and encryption key! Features include:


| About Dailyrotten |
|
Feedback
If you like this page, hate it, or would like to suggest changes,
<EMAIL_ADDRESS>is listening. This page
is experimental and somewhat different from what we usually offer, so please
do send your feedback.
Adding Stories
If you are a rotten newshound and you would like to suggest a story,
visit our submit page to
send us the URL. Stories that are accepted will have your name credited.
Posting Comments
In order to post comments to stories, you will have to
create an account.
Accounts will generally be validated within 24 hours, and of
course will be disabled if there is abuse.
Viewership
On the first full day this page went live, 100,000 people visited this Rotten
News page. Of course that was the day after the WTC attacks, so that number is
somewhat distorted. Right now this page gets between 50,000 and 75,000 people
every day.
Linking
We encourage everyone to link to this page; if you like it, others probably
will as well.
Other Rotten
Rotten
Gaping Maw
Celebrity Morgue
Mugshots
Rotten Store
Rate My Poo
Rate My Kitten
Penny Postcards
Phallic
Boners
Home
Make Dailyrotten your start page
|
|
|
October 31, 2001
Hacker Jailed for Sewage Attacks
The Register | Submitted by: thbb
"An Australian man was today sent to prison for two years after he was found
guilty of hacking into the Maroochy Shire, Queensland computerised waste
management system and caused millions of litres of raw sewage to spill out into
local parks, rivers and even the grounds of a Hyatt Regency hotel."
|
|
Read article...
|
|
Information Lockdown
The Nation | Submitted by: Soylent
The U.S. government wants to remove the policy of open information in favor
of a "born secret" standard, where any information that the public wants must
be pried out through legal means. Even if the information wanted has
nothing to do with terrorism: "Ashcroft reverses this presumption,
instead calling on agencies to withhold information whenever the law
permits: You can be assured that the Department of Justice will defend your
decisions." This new atmosphere is reminiscent of East Germany, frankly.
|
|
Read article...
|
|
Move along now
Knight Ridder | Submitted by: thbb
Apparently we are letting highly suspicious persons free because of
blatant incompetence. Six middle eastern men were stopped in the midwest with
photographs and/or plans for nuclear reactors and the Trans-Alaskan Pipeline,
but were let go after their visas were in order.
|
|
Read article...
|
|
ANTHRAX brings out the best
Lincoln Journal-Star | Submitted by: Coal
"He's been flipped off, scowled at, talked about and judged. The worst, he
said, was a woman with a carload of kids who saw his license plate.
|
|
Read article...
|
|
Explanation of Tunguska
BBC | Submitted by: Soylent
"A flash fire burned thousands of trees near the impact site. An atmospheric
shock wave circled the Earth twice. And, for two days afterwards, there was so
much fine dust in the atmosphere that newspapers could be read at night by
scattered light in the streets of London, 10,000 km (6,213 miles) away."
|
|
Read article...
|
|
Just shoot me
Associated Press | Submitted by: Soylent
Teenagers with nothing to do? "Kenneth Stabler, 16, of Mount Vision, was shot
three times, Christopher Ruke, 16, of Morris was shot twice, and James Fisher,
17, of Morris, was shot once, according to police." Why were they shot?
They took turns shooting each other with a .22 because it's apparently fun.
|
|
Read article...
|
|
Choccolocco Monster
Anniston Star | Submitted by: Soylent
"When the shots rang out, Neal Williamson was standing on the side of
Choccolocco Road, draped in a white sheet and holding a cow skull above his
head. He was doing a little dance, which, when combined with the outfit, made
him the Choccolocco Monster, the chimera that haunted these backwoods for
days more than three decades ago."
|
|
Read article...
|
|
Texas protected by Luftwaffe
Dallas Morning News | Submitted by: Tuff
"The four-page ad in Texas Monthly shows the officer in crisp, gilt-edge dress
blues against a backdrop of the American stars and stripes. But the uniform is
of the Luftwaffe, complete with military insignias and a name-tag bearing the
German flag."
|
|
Read article...
|
|
|
|
|


| OLD line(s): 45
<xsd:attribute name="referenceDocumentName" type="StringType" fixed="IRS8865ScheduleK1 IRS8865ScheduleO IRS8865ScheduleP MultipleCategory1FilersStatement IRS3468 IRS4255 IRS4684 IRS4797 IRS5884 IRS6478 IRS6781 IRS6765 IRS8271 IRS8586 IRS8824 IRS8825 IRS8833 IRS8858 IRS4562 BalanceSheetsAgreeStatement ControlledForeignPartnershipReportingStatement" /> | NEW line(s): 45
<xsd:attribute name="referenceDocumentName" type="StringType" fixed="IRS8865ScheduleK1 IRS8865ScheduleO IRS8865ScheduleP MultipleCategory1FilersStatement IRS3115 IRS3468 IRS4684 IRS4797 IRS6781 IRS8586 IRS8594 IRS8824 IRS8825 IRS8833 IRS8858 IRS4562 IRS8883 IRS8886 BalanceSheetsAgreeStatement BinaryAttachment ControlledForeignPartnershipReportingStatement" /> |
| OLD line(s): 686
<!-- Partnership's Total Receipts for Yax Year < $250,000 AND Total Assets at End of Tax Year < $600,000 ? --> | NEW line(s): 686
<!-- Partnership's Total Receipts for Yax Year < $250,000 AND Total Assets at End of Tax Year < $1,000,000 ? --> |
| OLD line(s): 690
<Description>The partnership's total receipts for the year were less than $250,000 and the value of the partnership's total assets at the end of the tax year was less than $600,000</Description> | NEW line(s): 690
<Description>The partnership's total receipts for the year were less than $250,000 and the value of the partnership's total assets at the end of the tax year was less than $1,000,000</Description> |


Nice Ticks
min:
max:
ticks:
(Results may not match exactly but close, default is 4)
generate
nice
native
(probably don't want)
Source in Github


InfluxDB is a time series database designed to handle high write and query loads. It is an integral component of the TICK stack. InfluxDB is meant to be used as a backing store for any use case involving large amounts of timestamped data, including DevOps monitoring, application metrics, IoT sensor data, and real-time analytics.
Key features
Here are some of the features that InfluxDB currently supports that make it a great choice for working with time series data.
- Custom high performance datastore written specifically for time series data. The TSM engine allows for high ingest speed and data compression
- Written entirely in Go. It compiles into a single binary with no external dependencies.
- Simple, high performing write and query HTTP APIs.
- Plugins support for other data ingestion protocols such as Graphite, collectd, and OpenTSDB.
- Expressive SQL-like query language tailored to easily query aggregated data.
- Tags allow series to be indexed for fast and efficient queries.
- Retention policies efficiently auto-expire stale data.
- Continuous queries automatically compute aggregate data to make frequent queries more efficient.
The open source edition of InfluxDB runs on a single node. If you require high availability to eliminate a single point of failure, consider the InfluxDB Enterprise Edition.


--- layout: post status: publish published: true title: Podcast 4.36 - The Cubs, a team only a mother could love. author: display_name: Corey Fineran login: Corey email<EMAIL_ADDRESS>url: '' author_login: Corey author_email<EMAIL_ADDRESS>wordpress_id: 7815 wordpress_url: http://ivyenvy.com/?p=7815 date: '2012-09-23 21:01:10 +0000' date_gmt: '2012-09-24 02:01:10 +0000' categories: - Podcast tags: - NL Central - Reds - Josh Vitters - Quad Cities - Brett Jackson - Peoria - Midwest League - Colorado - Kane County - Dr. Who - Ballpark Food comments: [] ---
Andy, Corey, Kurt and Jeremy begin to wrap up the 2012 season. We talk about the unexpected performance Soriano exhibited this season. We predict his future with the Cubs. Brett Jackson and Josh Vitters continue to struggle and while there's an entire off-season to figure out what the long-term plans with these two are, it's worth mentioning that highly touted prospects that don't work out is a very real possibility when building an organization from the bottom up.
The Midwest League puzzle pieces are falling into place with the Cubs officially moving to Kane County and the Cardinals moving to Peoria. As residents of towns with Midwest League teams, we discuss the impact of these moves.
The post-season picture is becoming clear. The Reds have won the NL Central. We take a look at some players that wore Cubs uniforms this season, but have been dealt to playoff contenders and how the teams and those players are doing.
We look at some listener submitted questions and topics, which include Dr. Who, ballpark food and the upcoming Cubs series with Colorado.
We've given away some copies of The Essential Games of the Chicago Cubs DVD Box Set with the photo caption contest. Thank you to A&E Home Entertainment and MLB Productions for these sets. If you would like to purchase a copy of the box set, they're available at the MLB Shop.


Gradient Boosting Regression
Gradient boosting is a machine learning technique for regression problems, which produces a prediction model in the form of an ensemble of weak prediction models, typically decision trees.
Lasso Regression
In statistics and machine learning, lasso (least absolute shrinkage and selection operator) is a regression analysis method that performs both variable selection and regularization in order to enhance the prediction accuracy and interpretability
of the statistical model it produces.
Linear Regression
In statistics, linear regression is a linear approach for modelling the relationship between a scalar dependent variable y and one or more explanatory variables denoted X. The case of one explanatory variable is called simple linear
regression. For more than one explanatory variable, the process is called multiple linear regression.
Neural Network
Neural networks are computing systems inspired by the biological neural networks that constitute animal brains. Such systems learn tasks by considering examples, generally without task-specific programming.
Support Vector Machine
In machine learning, support vector machines are supervised learning models with associated learning algorithms that analyze data used for classification and regression analysis.
K-Nearest Neighbors
In pattern recognition, the k-nearest neighbors algorithm is a non-parametric method used for classification. In both cases, the input consists of the k closest training examples in the feature space. In k-NN classification, the output
is a class membership. An object is classified by a majority vote of its neighbors.
Logistic Regression
In statistics, logistic regression, or logit model, is a regression model where the dependent variable is categorical. In binary logistic regression, output can take only two values, "0" and "1". Cases where the dependent variable has
more than two outcome categories may be analysed in multinomial logistic regression.
Neural Network
Neural networks are computing systems inspired by the biological neural networks that constitute animal brains. Such systems learn tasks by considering examples, generally without task-specific programming.
Random Forests
Random forests are an ensemble learning method for classification, that operate by constructing a multitude of decision trees at training time and outputting the class that is the mode of the classes.
Support Vector Machine
In machine learning, support vector machines are supervised learning models with associated learning algorithms that analyze data used for classification and regression analysis.


Hackers
Simply apply to have your resume reviewed by 20+ up and coming startups in Vancouver
Simply apply to have your resume reviewed by 20+ up and coming startups in Vancouver
- Never write a CV again and have your work speak for itself.
- Have your resume reviewed by Co-founders and HR Managers of 20+ top startups in Vancouver.
- Work for startups with jobs that you are interested in
- Absolutely FREE for hackers.
Startups
The best way to hire
top hackers for your startup.- Obtain a list of top candidates for your company in your inbox every month
- Instant branding in the communit of top hackers in Vancouver and Seattle
- No overpriced recruiter fees. Just pay a simple monthly fee.
- No middlemen. Communicate directly with applicants from day one.
Why HackerBatch?
01
High Standards
Instantly get matched with top hackers and companies that are pre-screened and interviewed. We don't just let anyone join our hiring pool.
02
No Recruiting Agencies
HackerBatch isn't a recruiting agency. Unlike traditional recruiters, we filter applicants into 'batches' by job type, experience and technical expertise before providing them to companies.
03
Easy to Use
HackerBatch makes applying to startups as easy as buying tickets to your favourite concert.
04
Direct Contact
HackerBatch directly connects hackers & startups through our simple, powerful & elegant matching platform.


@(message: String) @Main("anastasia") {
There’s “hot” hot and there’s “red-hot-and-I-know-it” hot. Consider 21-year old LA-based surfer girl Anastasia Ashley the latter. Anastasia, who plans to surf all the women’s WQS she can this year, has her own signature shoe line with Airwalk and is getting to work on an as of yet untitled film project. In between shoes, films, and shredding, Anastasia has been spending a lot of time with Hulk Hogan’s son, Nick, whom she calls her best friend and her biggest fan. The pair were recently stalked by the paparazzi at the Sundance Film Festival and their photo appeared in Star Magazine with the fitting title, “Nick’s New Surfer Babe.” Selfishly, we want Anastasia to make the World Championship Tour just to see who wears a smaller bikini—her or Alana Blanchard.


|
||||||||||
| PREV NEXT | FRAMES NO FRAMES |
| Classes in org.encog.ml used by org.encog.app.analyst | |
|---|---|
| MLMethod
This interface is the base for all Encog Machine Learning methods. |
| Classes in org.encog.ml used by org.encog.app.analyst.commands | |
|---|---|
| MLMethod
This interface is the base for all Encog Machine Learning methods. |
| Classes in org.encog.ml used by org.encog.app.analyst.csv | |
|---|---|
| MLMethod
This interface is the base for all Encog Machine Learning methods. |
|
| MLRegression
Defines a Machine Learning Method that supports regression. |
| Classes in org.encog.ml used by org.encog.app.generate | |
|---|---|
| MLMethod
This interface is the base for all Encog Machine Learning methods. |
| Classes in org.encog.ml used by org.encog.ca.universe.basic | |
|---|---|
| BasicML
A class that provides basic property functionality for the MLProperties interface. |
|
| MLMethod
This interface is the base for all Encog Machine Learning methods. |
|
| MLProperties
Defines a Machine Learning Method that holds properties. |
| Classes in org.encog.ml used by org.encog.ensemble | |
|---|---|
| MLClassification
This interface defines a MLMethod that is used for classification. |
|
| MLInput
Defines a MLMethod that accepts input. |
|
| MLInputOutput
This is a convenience interface that combines MLInput and MLOutput. |
|
| MLMethod
This interface is the base for all Encog Machine Learning methods. |
|
| MLOutput
Defines a MLMethod that produces output. |
|
| MLRegression
Defines a Machine Learning Method that supports regression. |
| Classes in org.encog.ml used by org.encog.ensemble.ml.mlp.factory | |
|---|---|
| MLMethod
This interface is the base for all Encog Machine Learning methods. |
| Classes in org.encog.ml used by org.encog.ensemble.training | |
|---|---|
| MLMethod
This interface is the base for all Encog Machine Learning methods. |
| Classes in org.encog.ml used by org.encog.mathutil.randomize | |
|---|---|
| MLMethod
This interface is the base for all Encog Machine Learning methods. |
| Classes in org.encog.ml used by org.encog.ml | |
|---|---|
| MLCluster
Defines a cluster. |
|
| MLInput
Defines a MLMethod that accepts input. |
|
| MLInputOutput
This is a convenience interface that combines MLInput and MLOutput. |
|
| MLMethod
This interface is the base for all Encog Machine Learning methods. |
|
| MLOutput
Defines a MLMethod that produces output. |
|
| MLProperties
Defines a Machine Learning Method that holds properties. |
|
| MLRegression
Defines a Machine Learning Method that supports regression. |
|
| TrainingImplementationType
Specifies the type of training that an object provides. |
| Classes in org.encog.ml used by org.encog.ml.bayesian | |
|---|---|
| BasicML
A class that provides basic property functionality for the MLProperties interface. |
|
| MLClassification
This interface defines a MLMethod that is used for classification. |
|
| MLError
Defines Machine Learning Method that can calculate an error based on a data set. |
|
| MLInput
Defines a MLMethod that accepts input. |
|
| MLInputOutput
This is a convenience interface that combines MLInput and MLOutput. |
|
| MLMethod
This interface is the base for all Encog Machine Learning methods. |
|
| MLOutput
Defines a MLMethod that produces output. |
|
| MLProperties
Defines a Machine Learning Method that holds properties. |
|
| MLResettable
Defines a Machine Learning Method that can be reset to an untrained starting point. |
| Classes in org.encog.ml used by org.encog.ml.bayesian.training | |
|---|---|
| MLMethod
This interface is the base for all Encog Machine Learning methods. |
| Classes in org.encog.ml used by org.encog.ml.ea.codec | |
|---|---|
| MLMethod
This interface is the base for all Encog Machine Learning methods. |
| Classes in org.encog.ml used by org.encog.ml.ea.genome | |
|---|---|
| MLMethod
This interface is the base for all Encog Machine Learning methods. |
| Classes in org.encog.ml used by org.encog.ml.ea.population | |
|---|---|
| BasicML
A class that provides basic property functionality for the MLProperties interface. |
|
| MLMethod
This interface is the base for all Encog Machine Learning methods. |
|
| MLProperties
Defines a Machine Learning Method that holds properties. |
| Classes in org.encog.ml used by org.encog.ml.ea.score | |
|---|---|
| CalculateScore
Used by simulated annealing and genetic algorithms to calculate the score for a machine learnign method. |
|
| MLMethod
This interface is the base for all Encog Machine Learning methods. |
| Classes in org.encog.ml used by org.encog.ml.ea.score.parallel | |
|---|---|
| CalculateScore
Used by simulated annealing and genetic algorithms to calculate the score for a machine learnign method. |
| Classes in org.encog.ml used by org.encog.ml.ea.train | |
|---|---|
| CalculateScore
Used by simulated annealing and genetic algorithms to calculate the score for a machine learnign method. |
| Classes in org.encog.ml used by org.encog.ml.ea.train.basic | |
|---|---|
| CalculateScore
Used by simulated annealing and genetic algorithms to calculate the score for a machine learnign method. |
|
| MLMethod
This interface is the base for all Encog Machine Learning methods. |
|
| TrainingImplementationType
Specifies the type of training that an object provides. |
| Classes in org.encog.ml used by org.encog.ml.factory | |
|---|---|
| MLMethod
This interface is the base for all Encog Machine Learning methods. |
| Classes in org.encog.ml used by org.encog.ml.factory.method | |
|---|---|
| MLMethod
This interface is the base for all Encog Machine Learning methods. |
| Classes in org.encog.ml used by org.encog.ml.factory.train | |
|---|---|
| MLMethod
This interface is the base for all Encog Machine Learning methods. |
| Classes in org.encog.ml used by org.encog.ml.fitness | |
|---|---|
| CalculateScore
Used by simulated annealing and genetic algorithms to calculate the score for a machine learnign method. |
|
| MLMethod
This interface is the base for all Encog Machine Learning methods. |
| Classes in org.encog.ml used by org.encog.ml.fitting.gaussian | |
|---|---|
| MLInput
Defines a MLMethod that accepts input. |
|
| MLInputOutput
This is a convenience interface that combines MLInput and MLOutput. |
|
| MLMethod
This interface is the base for all Encog Machine Learning methods. |
|
| MLOutput
Defines a MLMethod that produces output. |
|
| MLRegression
Defines a Machine Learning Method that supports regression. |
| Classes in org.encog.ml used by org.encog.ml.fitting.linear | |
|---|---|
| MLError
Defines Machine Learning Method that can calculate an error based on a data set. |
|
| MLInput
Defines a MLMethod that accepts input. |
|
| MLInputOutput
This is a convenience interface that combines MLInput and MLOutput. |
|
| MLMethod
This interface is the base for all Encog Machine Learning methods. |
|
| MLOutput
Defines a MLMethod that produces output. |
|
| MLRegression
Defines a Machine Learning Method that supports regression. |
| Classes in org.encog.ml used by org.encog.ml.genetic | |
|---|---|
| CalculateScore
Used by simulated annealing and genetic algorithms to calculate the score for a machine learnign method. |
|
| MethodFactory
|
|
| MLEncodable
Defines a Machine Learning Method that can be encoded to a double array. |
|
| MLMethod
This interface is the base for all Encog Machine Learning methods. |
| Classes in org.encog.ml used by org.encog.ml.genetic.genome | |
|---|---|
| MLMethod
This interface is the base for all Encog Machine Learning methods. |
| Classes in org.encog.ml used by org.encog.ml.hmm | |
|---|---|
| BasicML
A class that provides basic property functionality for the MLProperties interface. |
|
| MLMethod
This interface is the base for all Encog Machine Learning methods. |
|
| MLProperties
Defines a Machine Learning Method that holds properties. |
|
| MLStateSequence
A state sequence ML method, for example a Hidden Markov Model. |
| Classes in org.encog.ml used by org.encog.ml.hmm.train.bw | |
|---|---|
| MLMethod
This interface is the base for all Encog Machine Learning methods. |
|
| TrainingImplementationType
Specifies the type of training that an object provides. |
| Classes in org.encog.ml used by org.encog.ml.hmm.train.kmeans | |
|---|---|
| MLMethod
This interface is the base for all Encog Machine Learning methods. |
|
| TrainingImplementationType
Specifies the type of training that an object provides. |
| Classes in org.encog.ml used by org.encog.ml.kmeans | |
|---|---|
| MLCluster
Defines a cluster. |
|
| MLClustering
A machine learning method that is used to break data into clusters. |
|
| MLMethod
This interface is the base for all Encog Machine Learning methods. |
| Classes in org.encog.ml used by org.encog.ml.prg | |
|---|---|
| MLError
Defines Machine Learning Method that can calculate an error based on a data set. |
|
| MLInput
Defines a MLMethod that accepts input. |
|
| MLInputOutput
This is a convenience interface that combines MLInput and MLOutput. |
|
| MLMethod
This interface is the base for all Encog Machine Learning methods. |
|
| MLOutput
Defines a MLMethod that produces output. |
|
| MLRegression
Defines a Machine Learning Method that supports regression. |
| Classes in org.encog.ml used by org.encog.ml.prg.generator | |
|---|---|
| CalculateScore
Used by simulated annealing and genetic algorithms to calculate the score for a machine learnign method. |
| Classes in org.encog.ml used by org.encog.ml.prg.train | |
|---|---|
| BasicML
A class that provides basic property functionality for the MLProperties interface. |
|
| CalculateScore
Used by simulated annealing and genetic algorithms to calculate the score for a machine learnign method. |
|
| MLInput
Defines a MLMethod that accepts input. |
|
| MLInputOutput
This is a convenience interface that combines MLInput and MLOutput. |
|
| MLMethod
This interface is the base for all Encog Machine Learning methods. |
|
| MLOutput
Defines a MLMethod that produces output. |
|
| MLProperties
Defines a Machine Learning Method that holds properties. |
|
| MLRegression
Defines a Machine Learning Method that supports regression. |
| Classes in org.encog.ml used by org.encog.ml.svm | |
|---|---|
| BasicML
A class that provides basic property functionality for the MLProperties interface. |
|
| MLClassification
This interface defines a MLMethod that is used for classification. |
|
| MLError
Defines Machine Learning Method that can calculate an error based on a data set. |
|
| MLInput
Defines a MLMethod that accepts input. |
|
| MLInputOutput
This is a convenience interface that combines MLInput and MLOutput. |
|
| MLMethod
This interface is the base for all Encog Machine Learning methods. |
|
| MLOutput
Defines a MLMethod that produces output. |
|
| MLProperties
Defines a Machine Learning Method that holds properties. |
|
| MLRegression
Defines a Machine Learning Method that supports regression. |
| Classes in org.encog.ml used by org.encog.ml.svm.training | |
|---|---|
| MLMethod
This interface is the base for all Encog Machine Learning methods. |
| Classes in org.encog.ml used by org.encog.ml.train | |
|---|---|
| MLMethod
This interface is the base for all Encog Machine Learning methods. |
|
| TrainingImplementationType
Specifies the type of training that an object provides. |
| Classes in org.encog.ml used by org.encog.neural.art | |
|---|---|
| BasicML
A class that provides basic property functionality for the MLProperties interface. |
|
| MLClassification
This interface defines a MLMethod that is used for classification. |
|
| MLInput
Defines a MLMethod that accepts input. |
|
| MLInputOutput
This is a convenience interface that combines MLInput and MLOutput. |
|
| MLMethod
This interface is the base for all Encog Machine Learning methods. |
|
| MLOutput
Defines a MLMethod that produces output. |
|
| MLProperties
Defines a Machine Learning Method that holds properties. |
|
| MLResettable
Defines a Machine Learning Method that can be reset to an untrained starting point. |
| Classes in org.encog.ml used by org.encog.neural.bam | |
|---|---|
| BasicML
A class that provides basic property functionality for the MLProperties interface. |
|
| MLMethod
This interface is the base for all Encog Machine Learning methods. |
|
| MLProperties
Defines a Machine Learning Method that holds properties. |
| Classes in org.encog.ml used by org.encog.neural.cpn | |
|---|---|
| BasicML
A class that provides basic property functionality for the MLProperties interface. |
|
| MLError
Defines Machine Learning Method that can calculate an error based on a data set. |
|
| MLInput
Defines a MLMethod that accepts input. |
|
| MLInputOutput
This is a convenience interface that combines MLInput and MLOutput. |
|
| MLMethod
This interface is the base for all Encog Machine Learning methods. |
|
| MLOutput
Defines a MLMethod that produces output. |
|
| MLProperties
Defines a Machine Learning Method that holds properties. |
|
| MLRegression
Defines a Machine Learning Method that supports regression. |
|
| MLResettable
Defines a Machine Learning Method that can be reset to an untrained starting point. |
| Classes in org.encog.ml used by org.encog.neural.cpn.training | |
|---|---|
| MLMethod
This interface is the base for all Encog Machine Learning methods. |
| Classes in org.encog.ml used by org.encog.neural.freeform | |
|---|---|
| BasicML
A class that provides basic property functionality for the MLProperties interface. |
|
| MLClassification
This interface defines a MLMethod that is used for classification. |
|
| MLContext
Defines a MLMethod that can hold context. |
|
| MLEncodable
Defines a Machine Learning Method that can be encoded to a double array. |
|
| MLError
Defines Machine Learning Method that can calculate an error based on a data set. |
|
| MLInput
Defines a MLMethod that accepts input. |
|
| MLInputOutput
This is a convenience interface that combines MLInput and MLOutput. |
|
| MLMethod
This interface is the base for all Encog Machine Learning methods. |
|
| MLOutput
Defines a MLMethod that produces output. |
|
| MLProperties
Defines a Machine Learning Method that holds properties. |
|
| MLRegression
Defines a Machine Learning Method that supports regression. |
|
| MLResettable
Defines a Machine Learning Method that can be reset to an untrained starting point. |
| Classes in org.encog.ml used by org.encog.neural.freeform.training | |
|---|---|
| MLMethod
This interface is the base for all Encog Machine Learning methods. |
|
| TrainingImplementationType
Specifies the type of training that an object provides. |
| Classes in org.encog.ml used by org.encog.neural.hyperneat | |
|---|---|
| MLMethod
This interface is the base for all Encog Machine Learning methods. |
| Classes in org.encog.ml used by org.encog.neural.neat | |
|---|---|
| BasicML
A class that provides basic property functionality for the MLProperties interface. |
|
| CalculateScore
Used by simulated annealing and genetic algorithms to calculate the score for a machine learnign method. |
|
| MLError
Defines Machine Learning Method that can calculate an error based on a data set. |
|
| MLInput
Defines a MLMethod that accepts input. |
|
| MLInputOutput
This is a convenience interface that combines MLInput and MLOutput. |
|
| MLMethod
This interface is the base for all Encog Machine Learning methods. |
|
| MLOutput
Defines a MLMethod that produces output. |
|
| MLProperties
Defines a Machine Learning Method that holds properties. |
|
| MLRegression
Defines a Machine Learning Method that supports regression. |
| Classes in org.encog.ml used by org.encog.neural.neat.training | |
|---|---|
| MLMethod
This interface is the base for all Encog Machine Learning methods. |
| Classes in org.encog.ml used by org.encog.neural.networks | |
|---|---|
| BasicML
A class that provides basic property functionality for the MLProperties interface. |
|
| MLClassification
This interface defines a MLMethod that is used for classification. |
|
| MLContext
Defines a MLMethod that can hold context. |
|
| MLEncodable
Defines a Machine Learning Method that can be encoded to a double array. |
|
| MLError
Defines Machine Learning Method that can calculate an error based on a data set. |
|
| MLFactory
This interface defines the fact that a class, or object, is having the ability to generate an Encog factory code from the objects instanciated state. |
|
| MLInput
Defines a MLMethod that accepts input. |
|
| MLInputOutput
This is a convenience interface that combines MLInput and MLOutput. |
|
| MLMethod
This interface is the base for all Encog Machine Learning methods. |
|
| MLOutput
Defines a MLMethod that produces output. |
|
| MLProperties
Defines a Machine Learning Method that holds properties. |
|
| MLRegression
Defines a Machine Learning Method that supports regression. |
|
| MLResettable
Defines a Machine Learning Method that can be reset to an untrained starting point. |
| Classes in org.encog.ml used by org.encog.neural.networks.structure | |
|---|---|
| MLMethod
This interface is the base for all Encog Machine Learning methods. |
| Classes in org.encog.ml used by org.encog.neural.networks.training | |
|---|---|
| CalculateScore
Used by simulated annealing and genetic algorithms to calculate the score for a machine learnign method. |
|
| MLMethod
This interface is the base for all Encog Machine Learning methods. |
| Classes in org.encog.ml used by org.encog.neural.networks.training.anneal | |
|---|---|
| CalculateScore
Used by simulated annealing and genetic algorithms to calculate the score for a machine learnign method. |
|
| MLEncodable
Defines a Machine Learning Method that can be encoded to a double array. |
| Classes in org.encog.ml used by org.encog.neural.networks.training.cross | |
|---|---|
| MLMethod
This interface is the base for all Encog Machine Learning methods. |
| Classes in org.encog.ml used by org.encog.neural.networks.training.lma | |
|---|---|
| MLMethod
This interface is the base for all Encog Machine Learning methods. |
| Classes in org.encog.ml used by org.encog.neural.networks.training.nm | |
|---|---|
| MLMethod
This interface is the base for all Encog Machine Learning methods. |
| Classes in org.encog.ml used by org.encog.neural.networks.training.pnn | |
|---|---|
| MLMethod
This interface is the base for all Encog Machine Learning methods. |
| Classes in org.encog.ml used by org.encog.neural.networks.training.propagation | |
|---|---|
| MLMethod
This interface is the base for all Encog Machine Learning methods. |
| Classes in org.encog.ml used by org.encog.neural.networks.training.pso | |
|---|---|
| CalculateScore
Used by simulated annealing and genetic algorithms to calculate the score for a machine learnign method. |
|
| MLMethod
This interface is the base for all Encog Machine Learning methods. |
| Classes in org.encog.ml used by org.encog.neural.networks.training.simple | |
|---|---|
| MLMethod
This interface is the base for all Encog Machine Learning methods. |
| Classes in org.encog.ml used by org.encog.neural.pattern | |
|---|---|
| MLMethod
This interface is the base for all Encog Machine Learning methods. |
| Classes in org.encog.ml used by org.encog.neural.pnn | |
|---|---|
| BasicML
A class that provides basic property functionality for the MLProperties interface. |
|
| MLClassification
This interface defines a MLMethod that is used for classification. |
|
| MLError
Defines Machine Learning Method that can calculate an error based on a data set. |
|
| MLInput
Defines a MLMethod that accepts input. |
|
| MLInputOutput
This is a convenience interface that combines MLInput and MLOutput. |
|
| MLMethod
This interface is the base for all Encog Machine Learning methods. |
|
| MLOutput
Defines a MLMethod that produces output. |
|
| MLProperties
Defines a Machine Learning Method that holds properties. |
|
| MLRegression
Defines a Machine Learning Method that supports regression. |
| Classes in org.encog.ml used by org.encog.neural.rbf | |
|---|---|
| BasicML
A class that provides basic property functionality for the MLProperties interface. |
|
| MLError
Defines Machine Learning Method that can calculate an error based on a data set. |
|
| MLInput
Defines a MLMethod that accepts input. |
|
| MLInputOutput
This is a convenience interface that combines MLInput and MLOutput. |
|
| MLMethod
This interface is the base for all Encog Machine Learning methods. |
|
| MLOutput
Defines a MLMethod that produces output. |
|
| MLProperties
Defines a Machine Learning Method that holds properties. |
|
| MLRegression
Defines a Machine Learning Method that supports regression. |
| Classes in org.encog.ml used by org.encog.neural.som | |
|---|---|
| BasicML
A class that provides basic property functionality for the MLProperties interface. |
|
| MLClassification
This interface defines a MLMethod that is used for classification. |
|
| MLError
Defines Machine Learning Method that can calculate an error based on a data set. |
|
| MLInput
Defines a MLMethod that accepts input. |
|
| MLInputOutput
This is a convenience interface that combines MLInput and MLOutput. |
|
| MLMethod
This interface is the base for all Encog Machine Learning methods. |
|
| MLOutput
Defines a MLMethod that produces output. |
|
| MLProperties
Defines a Machine Learning Method that holds properties. |
|
| MLResettable
Defines a Machine Learning Method that can be reset to an untrained starting point. |
| Classes in org.encog.ml used by org.encog.neural.som.training.basic | |
|---|---|
| MLMethod
This interface is the base for all Encog Machine Learning methods. |
| Classes in org.encog.ml used by org.encog.neural.som.training.clustercopy | |
|---|---|
| MLMethod
This interface is the base for all Encog Machine Learning methods. |
| Classes in org.encog.ml used by org.encog.neural.thermal | |
|---|---|
| BasicML
A class that provides basic property functionality for the MLProperties interface. |
|
| MLAutoAssocation
Defines a MLMethod that can handle autoassocation. |
|
| MLInput
Defines a MLMethod that accepts input. |
|
| MLInputOutput
This is a convenience interface that combines MLInput and MLOutput. |
|
| MLMethod
This interface is the base for all Encog Machine Learning methods. |
|
| MLOutput
Defines a MLMethod that produces output. |
|
| MLProperties
Defines a Machine Learning Method that holds properties. |
|
| MLRegression
Defines a Machine Learning Method that supports regression. |
|
| MLResettable
Defines a Machine Learning Method that can be reset to an untrained starting point. |
| Classes in org.encog.ml used by org.encog.plugin | |
|---|---|
| MLMethod
This interface is the base for all Encog Machine Learning methods. |
| Classes in org.encog.ml used by org.encog.plugin.system | |
|---|---|
| MLMethod
This interface is the base for all Encog Machine Learning methods. |
| Classes in org.encog.ml used by org.encog.util.error | |
|---|---|
| MLRegression
Defines a Machine Learning Method that supports regression. |
| Classes in org.encog.ml used by org.encog.util.simple | |
|---|---|
| MLClassification
This interface defines a MLMethod that is used for classification. |
|
| MLMethod
This interface is the base for all Encog Machine Learning methods. |
|
| MLRegression
Defines a Machine Learning Method that supports regression. |
| Classes in org.encog.ml used by org.encog.util.validate | |
|---|---|
| MLMethod
This interface is the base for all Encog Machine Learning methods. |
|
||||||||||
| PREV NEXT | FRAMES NO FRAMES |


{# # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. -#} {% extends "admin/base.html" %} {% block title %}Malware Checks{% endblock %} {% block breadcrumb %}
| Check Name | State | Type | Last Modified | Description |
|---|---|---|---|---|
| {{ check.name }} | {{ check.state.value }} | {{ check.check_type.value }} | {{ check.created }} | {{ check.short_description }} |
| No checks! |


use Red::AST::Operator; use Red::AST::Unary; #| Base role to infix operators unit role Red::AST::Infix does Red::AST::Operator; has Red::AST $.left is required; has Red::AST $.right is required; has Bool $.bind-left = False; has Bool $.bind-right = False; #method gist { "$!left.gist() $.op $!right.gist()" } proto method new($left, $right, |) {*} multi method new($left, $right, Bool() :$bind-left = False, Bool() :$bind-right = False, Str() :$cast!) { ::?CLASS.new: $left ~~ ::("Red::Column") ?? $left.cast($cast) !! $left, $right ~~ ::("Red::Column") ?? $right.cast($cast) !! $right, :$bind-left, :$bind-right } multi method new($left, $right, Bool :$bind-left = False, Bool :$bind-right = False) { ::?CLASS.Mu::new: :$left, :$right, :$bind-left, :$bind-right } method args { $!left, $!right } method not { Red::AST::Not.new: self } method find-column-name { ($_ with $.left.find-column-name), ($_ with $.right.find-column-name) }


Next: Fully recoverable hard failure, Previous: Library-recoverable hard failure, Up: Failure [Contents][Index]
An ancestry-recoverable hard failure is a type of partially recoverable hard failure. An ancestry-recoverable failure allows a superset of the application behaviors allowed by a library-recoverable hard failure. More precisely, let the “failure object” be the object that detected the ancestry-recoverable hard failure. After an ancestry-recoverable hard failure, the following behaviors are no longer application behaviors:
Recall that any use of a behavior that is not an application behavior is an irrecoverable failure.
The application behaviors remaining after a ancestry-recoverable hard failure are the following:
Note that all Libmarpa destructors remain available after an ancestry-recoverable failure. An application will often want to destroy the failure object and all of its descendants, in order to clear memory of problematic objects.
As an example,
users calling marpa_g_precompute()
will often want to treat a MARPA_EVENT_EARLEY_ITEM_THRESHOLD
event
as if it were an ancestry-recoverable hard failure.
See marpa_g_precompute().
Library-recoverable failure is a special case of ancestry-recoverable failure. When the failure object is a grammar, ancestry-recoverable failure is synonymous with library-recoverable failure.


--- post_id: /presentation-in-milano-variations-on-the-theme-of-openess date: 2010-03-18 12:16 return_url: '2009-06-20-presentation-in-milano-variations-on-the-theme-of-openess.html' name: 'Gus Heckenberg' email<EMAIL_ADDRESS>comment: '
Thank you to those who offered advice.  Big Fish Games Fan.
' ---


Class: JSUtils.Hash
| Defined in: | hash/Hash.coffee |
Overview
This is an implementation of a dictionary/hash that does not convert its keys into Strings.
Keys can therefore actually by anything!
This class is not built for performance but for easy usage (without hashing anything).
o the lookup is O(n)
since the keys are stored in an array.
Class Method Summary
- . (JSUtils.Hash) fromObject(obj) Creates a new Hash from a given JavaScript object.
Instance Method Summary
- # (Object) toObject(toString = function(key) { return "" + key; }) Converts the hash into a native JavaScript Object.
- # (JSUtils.Hash) clone() Clones the hash.
- # (JSUtils.Hash) invert() Inverts the hash.
-
#
(JSUtils.Hash)
_findKeyIdx(key)
Private
Return the index of the given key (or
-1
). - # (JSUtils.Hash) put(key, val) Add a new key-value pair or overwrite an existing one.
- # (mixed) get(key) Retrieve the value for the specified key.
- # (Array) items() Returns a list of all key-value pairs.
- # (Boolean) has(key) Indicates whether the hash has the specified key.
- # (Boolean) hasKey() Alias for JSUtils.Hash#has.
- # (Number) size() Returns the number of entries in the hash.
- # (Array) getKeys(clone = true) Returns all keys of the hash.
- # (Array) getValues(clone = true) Returns all values of the hash.
- # (Array) getKeysForValue(value, equality = function(a, b) { return a === b; }) Returns a list of keys that have val (or anything equal as specified in 'eqFunc') as value.
- # (JSUtils.Hash) empty() Remove all entries from the hash.
-
#
(JSUtils.Hash)
remove(key)
Remove the entry with key
key
from the hash. - # (JSUtils.Hash) each(callback, order) Iterates all entries in the hash.
Class Method Details
.
(JSUtils.Hash)
fromObject(obj)
Creates a new Hash from a given JavaScript object.
Constructor Details
#
(JSUtils.Hash)
constructor(obj, defaultVal, equality = function(a, b) {
return a === b;
})
Instance Method Details
#
(Object)
toObject(toString = function(key) {
return "" + key;
})
Converts the hash into a native JavaScript Object. Note that the keys will be stringified. You can use the optional parameter for customizing the stringification.
#
(JSUtils.Hash)
clone()
Clones the hash.
Note that neither keys nor values are deeply cloned:
I.e. cloning {[1] -> 1}
and pushing an element to the key would modify both hashes.
#
(JSUtils.Hash)
invert()
Inverts the hash. The inverted hash has the keys of the original hash as values and vice versa.
#
(JSUtils.Hash)
_findKeyIdx(key)
Private
Return the index of the given key (or -1
).
#
(JSUtils.Hash)
put(key, val)
Add a new key-value pair or overwrite an existing one.
#
(mixed)
get(key)
Retrieve the value for the specified key.
If not found defaultVal
(defined in the constructor) will be returned.
If defaultVal
is a function the return value of it - called with the key parameter - will be returned instaed.
#
(Array)
items()
Returns a list of all key-value pairs. Each pair is an Array with the 1st element being the key, the 2nd being the value.
#
(Boolean)
has(key)
Indicates whether the hash has the specified key.
#
(Boolean)
hasKey()
Alias for JSUtils.Hash#has.
#
(Number)
size()
Returns the number of entries in the hash.
#
(Array)
getKeys(clone = true)
Returns all keys of the hash.
#
(Array)
getValues(clone = true)
Returns all values of the hash.
#
(Array)
getKeysForValue(value, equality = function(a, b) {
return a === b;
})
Returns a list of keys that have val (or anything equal as specified in 'eqFunc') as value.
#
(JSUtils.Hash)
empty()
Remove all entries from the hash.
#
(JSUtils.Hash)
remove(key)
Remove the entry with key key
from the hash.
#
(JSUtils.Hash)
each(callback, order)
Iterates all entries in the hash.


PART II. RATES AND ADMINISTRATION
§431:10C-201 Motor vehicle insurance rates generally. Except as expressly provided in this part, all premium rates for motor vehicle insurance shall comply with the provisions of the rating law contained in article 14. [L 1987, c 347, pt of §2]


22 ISIS militants surrender to Afghan Special Forces in Nangarhar: MoD
by Khaama PressA group of 22 militants of ISIS Khurasan, the offshoot of IS terrorist group in Afghanistan, surrendered to Afghan Special Forces in Nangarhar province.
According to a statement released by Ministry of Defense (MoD), the militants surrendered to Special Forces in Achin district of Nangarhar.
The statement further added that the militants also handed over 18 Ak-47 assault rifles to Special Forces.
This comes as a group of 10 ISIS militants surrendered to Special Forces in Achin district a day earlier, days after more than 600 militants and family members of the group surrendered to security forces in Nangarhar.
The terror group started its operations in Achin district of Nangarhar nearly five years ago and were attempting to turn the province into their regional operational hub.
However, the security forces backed by coalition forces suppressed the terror group before they could expand their foothold in Nangarhar and other parts of the country.
Author
-
The Khaama Press News Agency is the leading and largest English news service for Afghanistan with over 3 million hits a month. Independent authors/columnists and experts are welcomed to contribute stories, opinions and editorials. Send stories to news@khaama.com.


Implement a Queue data structure that exhibits FIFO ordering, has an
enqueue
method to add elements to the Queue, a
dequeue
method to remove the next element from the Queue, and
a peek
method that returns the next Queue element but does
not remove that element from the Queue (in other words, the
peek
method should return the same thing as the
dequeue
method, except peek
doesn’t modify the
contents of the Queue).
You are allowed to use up to two arrays except you are limited to array methods that you would find on a Stack data structure, i.e. adding to the end of the array and removing the most recently added element. You may also index into the arrays.
Analyze the time and space complexity of your solution.


Inverse functions, in the most general sense, are functions that "reverse" each other. For example, if \(f\) takes \(a\) to \(b\), then the inverse, \(f^{-1}\), must take \(b\) to \(a\).
Let \(f\) be a function whose domain is the set \(F\), and whose
codomain is the set \(Y\). Then \(f\) is invertible if there exists a
function g with domain \(F\) and image \(X\), with the property:
\(f(x)=y\,\,\Leftrightarrow \,\,g(y)=x\).
If \(f\) is invertible, the function \(g\) is unique, which means that
there is exactly one function \(g\) satisfying this property (no more,
no less). That function \(g\) is then called the inverse of \(f\), and
is usually denoted as \(f^{-1}\)
A function says that for every x, there is exactly one y. That is, y values can be duplicated but x values can not be repeated. If the function has an inverse that is also a function, then there can only be one y for every x. A one-to-one function, is a function in which for every x there is exactly one y and for every y, there is exactly one x. A one-to-one function has an inverse that is also a function.
There are functions which have inverses that are not functions. There
are also inverses for relations. For the most part, we disregard these,
and deal only with functions whose inverses are also functions. If the
inverse of a function is also a function, then the inverse relation must
pass a vertical line test.
Since all the x-coordinates and y-coordinates are switched when finding
the inverse, saying that the inverse must pass a vertical line test is
the same as saying the original function must pass a horizontal line
test. If a function passes both the vertical line test (so that it is a
function in the first place) and the horizontal line test (so that its
inverse is a function), then the function is one-to-one and has an
inverse function.
Uniqueness If an inverse function exists for a given function \(f\), then it is unique.This follows since the inverse function must be the converse relation, which is completely determined by \(f\).
Symmetry : There is a symmetry between a function and its inverse. Specifically, if \(f\) is an invertible function with domain \(X\) and range \(Y\), then its inverse \(f^{-1}\) has domain \(Y\) and range \(X\), and the inverse of \(f^{-1}\) is the original function f.
Self-Inverses Single-variable calculus is primarily concerned with functions that map real numbers to real numbers. Such functions are often defined through formulas, such as: \(f(x)=(2x+8)^{3}\)
Even if a function f is not one-to-one, it may be possible to define a
partial inverse of f by restricting the domain.
\(f(x)=x^{2}\) is not one-to-one, since \(x2 = (−x)2\). However, the
function becomes one-to-one if we restrict to the domain \(x ≥ 0\), in
which case \(f^{-1}(y)={\sqrt {y}}.\)


Base type: System.Entity
Summary: This entity presents details of the request.
| Name | Type | Notes |
|---|---|---|
| ID | System.Int64 | The unique integer representation of request details. |
| SiteID | System.Int32 | The unique integer representation of request target site. |
| RequestID | System.Int64 | The unique integer representation of target request. |
| Verb | System.String | The requested action, for example, a GET method. |
| ClientIP | System.String | The IP address of the client that made the request. |
| ElapsedTime | System.Int64 | The time that request took. |
| URLQuery | System.String | The parameters of the request url. |
| RequestDate | System.DateTime | The date on which the request occurred. |
| Status | System.Int32 | The status of request, based on ElapsedTime and some predefined thresholds. |
| DetailsUrl | System.String | URL to site details page. Used in alerting. |
| DisplayName | System.String | Display name for request details. Used in alerting. |
| DisplayName | System.String | |
| Description | System.String | |
| InstanceType | System.Type | |
| Uri | System.String | |
| InstanceSiteId | System.Int32 | Default='0'. |
| Request | Orion.APM.IIS.Request | Defined by relationship Orion.APM.IIS.RequestHostsRequestDetails (System.Hosting) |


Popular Areas in new apple notebook launch date
MacBook Pro 2012: everything you need to know | News | TechRadar
Jun 12, 2012 . Page 1 of 2MacBook Pro 2012 release date, news and specs . Apple has updated its new MacBook Pro with the latest Intel Core processors, .
http://www.techradar.com/us/news/mobile-computing/laptops/macbook-pro-2012-everything-you-need-to-know-1084023
Apple's MacBook Pro, MacBook Air to retain current designs in ...
Dec 28, 2012 . For 2013, Apple's thin-and-light is said to be switching to a new processor . Rumor: Apple planning to launch $799 MacBook Air in Q3 2012 · Apple . Briefly : 'Jobs' biopic release date delayed ~16 hours ago; article_thumb .
http://appleinsider.com/articles/12/12/28/apples-macbook-pro-macbook-air-to-retain-current-designs-in-reported-june-refresh
Apple Mac: Mac OS, Mac laptop, Mac desktop - CNET.com
Along with the new hardware, the company demoed features from the next version of Mac OS . Bookmark this page to find the most up-to-date Mac coverage: software and system . Apple plans Q3 launch for MacBook Air with Retina -- report .
http://www.cnet.com/apple-mac.html
Mac Buyer's Guide: Know When to Buy Your Mac, iPod or iPhone
Feb 13 2013: Apple Updates Retina MacBook Pro with New Processors, Drops Pricing on . Mar 05 2013: Apple Launches $1099 21.5-Inch iMac With 3.3 GHz .
http://buyersguide.macrumors.com/
Apple MacBook Pro with Retina Display Review - Watch CNET's ...
Jun 11, 2012 . Review Date: 6/11/12 . Apple MacBook Air (13-inch, Summer 2012) . The release of a brand-new Apple laptop design is rare, and always .
http://www.cnet.com/laptops/apple-macbook-pro-with/4505-3121_7-35331572.html


The Siege of Jerusalem survives in nine manuscript copies. Of these, six are complete or nearly complete, two are substantial fragments (P and V), and one (Ex) is a binding fragment containing a portion of one column of text on either side. Each of these is presented here with full transcriptions linked to images of all extant manuscript leaves containing versions of the poem. Detailed instructions for interacting with the transcriptions and images may be found in the How to Use section. Each transcription is accompanied by a description that covers dating, codicology, history, scribal practices, and dialect and contains a bibliography of previous descriptions and literature concerning the manuscript. Clicking on the name of a manuscript will take you directly to the transcription of that text and also provide a link to the description.
©The Society for Early English and Norse Electronic Texts, 2012. Use of this site is subject to the following terms and conditions.


HEX
Welcome to HEX, try and connect your color from one side to the other. VR requires specific browser build:
Moz VR
.
AI
VR
Restart
Hide/Show


Unfortunately we do not yet have a bio of this speaker. We'll soon update this.
I have more than 7 years experience in document generation projects. For example I was part of a team, which developed a text editor with included script interpreter based on Java Swing (Styled Editor Kit).
Unfortunately we do not yet have a bio of this speaker. We'll soon update this.
Research fellow, University of Perugia
Research fellow at the Computer Science Department of Perugia University, on Open Data and FLOSS impact on regional digital policies. He discussed his master degree's thesis on the use case of LibreOffice's adoption in the Umbria Region, and worked in the LibreUmbria project Team at Consorzio SIR Umbria (www.libreumbria.it). He is a member of the executive board at Umbria Region FLOSS Competence Center (CCOS) and a cofounder and board member of LibreItalia (www.libreitalia.it).
Andras Timar started to work on Hungarian localization of OpenOffice.org in 2002. He joined the LibreOffice project on the first day and started to work on localization related issues, including tools and infrastructure. He worked full time as a paid developer with the SUSE LibreOffice Team from April, 2011 to August, 2013, and he has been contracted by Collabora Ltd. since then. His LibreOffice related activities included coordination of localizations, release engineering, hacking on Windows installer, hacking on localization tools, general bugfixing, and mentoring students.
Bjoern Michaelsen is a currently employed by Canoncial Ltd. and is responsible for the packaging for LibreOffice on Ubuntu. He joined the company and LibreOffice development in February 2011, coming from Oracle (and before that Sun), where he was working on the same codebase for a few years in the Writer/Framework area. Even since long before that he is an open source enthusiast and did start with minor contributions to a range of open source projects.
Caolán McNamara is an all-around hero; he has been involved in reverse engineering and writing filters for Microsoft file formats from the very beginning of Free Software support for them. He has contributed extensively to LibreOffice fixing issues from graphics subsystems, through to the Writer core. After working for Sun‘s StarDivision, he now works for RedHat full time leading their LibreOffice team.
Charles-H. Schulz (The “H” letter standing for his second name “Henri”) is a French technologist, Free Software and Open Standards advocate. One of the founders and former directors of the Document Foundation he regularly contributes to the LibreOffice social media and marketing activities.
I'm one of the few people employed by TDF, Release Engineer, Infrastructure Administrator. Been with the LibreOffice project since it's creation (and before that the OOo project)
Cor Nouws (The Netherlands, 1959) originally is naval architect, and with studies MBA and Java Software Engineering he entered the world of business and software. After working as consultant in larger projects with Microsoft Office, he was caught by open source software. In 2004 he started his company Nou&Off, supplying various support for OpenOffice.org. Nowadays Nou&Off is the leading Dutch LibreOffice / OpenOffice.org consulting firm. As a logic combination with commercial services, Cor contributes to the OpenOffice.org community. He helps in the fields of marketing, QA and localization. In 2008 he was chosen as a member of the community council. In 2010 he became one of the founding members of The Document Foundation where he continues to contribute to various aspects of the LibreOffice project. Cor Nouws is married and father of three children. He has been vegan since many years, and loves good food, nature, people, music, cycling, running, (ice and in-line) skating and sailing.
Sofware engineer at Red Hat for 6 years, working first on OpenOffice.org, then on LibreOffice after it's creation. author of or contributor to several import filter libraries. Co-founder of the Document Liberation Project. Fedora maintainer of LibreOffice and many related libraries.
IT analyst and graduate degree in Computer Networking. Working at "Universidade Estadual Paulista" (UNESP) on IT Department since 2003 and exclusively with the use of free software in critical servers, since 2005, plus support for OpenOffice / LibreOffice / LibreOffice suite. I have taught courses in units LibreOffice, participated in the discussions that led to the creation of the document establishing the use of free software in UNESP, starting with the migration of the current office suite to LibreOffice. I'm a Linux user since 1998, working in the community where I entered as a disseminator of culture and values of FOSS. Member of the Brazilian Community LibreOffice, colaborating with manual translates. And eventually DJ.
QA Architect, Synerzip Softech Pvt Ltd
Unfortunately we do not yet have a bio of this speaker. We'll soon update this.
A LibreOffice hacker since the early days at Star Division, later Sun Microsystems, one other company and now Red Hat, Inc. Main areas of expertise are the Calc spreadsheet core, formula compiler and interpreter, number formatter/scanner, the i18n framework and locale data. Ardent supporter of Free Software, knowledge spreader, cyclist and sailor. Known on the net as erAck.
I am a Scottish open source developer. I work for open source consultancy Itomig, where I focus on improvements to LibreOffice.
Florian Effenberger has been a free software enthusiast for over ten years. He is one of the founders of The Document Foundation, the charitable entity behind LibreOffice. Next to his activities in marketing and public relations, he is a frequent contributor to a variety of professional magazines worldwide. Additionally, his focus is on planning and running Linux-based infrastructure solutions, especially web and mail servers.
He is a 19 year old student from Linz, Austria, currentlystudying Business Information Systems at the local university. He has been involved in the LibreOffice project shortly after the start and found his field of interest in the QA part of the community.
Software engineer at SUSE, working on SUSE Linux Enterprise. Christian, married with Susan and having 3 wonderful children, Patrick, Miriam and Nathanael. Has been involved with LibreOffice since its birt and with OpenOffice.org before. Former Chair of the TDF Membership Committee and currently Member of its Board of Directors. Co-founder of the Document Liberation Project and author or co-author of several import filters for LibreOffice.
Italo Vignoli is a TDF founders and former director. He is now n charge of media relations, communication strategy and coordination, d certification.
Software Engineer, Igalia
Jacobo obtained a degree in Computer Science from the University of Coruña in Spain. Shortly thereafter he joined Igalia, a Computer Engineering enterprise specializing in Free Software development and consultancy, of which he is now a partner. Over the past seven years, Jacobo has worked with a variety of software stacks ranging from web applications to desktop and mobile technologies, and collaborated with teams and communities from around the world. More recently, Jacobo joined the LibreOffice community where he has been enhancing and fixing bugs in the ATK accessibility backend used by free desktop assistive technologies, as well as improving the interoperability capabilities of the suite.
Jan / Kendy is with LibreOffice since the very first day, but his knowledge of our codebase goes back to 2003 when he joined OpenOffice.org. He started developing office software even earlier, in 1998-9 he programmed the drawing part of KTTV, a Linux word processor and vector drawing program for lecture notes. Over the years, he has contributed to many areas, including the KDE integration, x86-64 porting, build/SCM related problems, DOCX export, and user interface in general. Kendy proudly works for Collabora Productivity.
Unfortunately we do not yet have a bio of this speaker. We'll soon update this.
Software engineer at Collabora
Principal software engineer at Collabora Productivity, and LibreOffice hacker with emphasis on Calc.
Managing Director of .riess applications
Lothar K. Becker is managing partner of .riess applications gmbh (riess-app.de). He is co-founder of topics- and expert platform F-O-X. For over 10 years, Lothar is a management consultant for the use of open source on the desktop and in particular of free office suites. He himself is still much at Customer's sites for consulting and implementation projects. After receiving his diploma in computer science and economics, he held senior positions within two major international software and service provider. He is married and lives in Karlsruhe and Zurich.
Luboš Luňák is a LibreOffice developer currently working for Collabora Productivity, focusing mainly on Writer and its MSOffice import/export filters.
LibreOffice contributor (http://cgit.freedesktop.org/libreoffice/core/log/?qt=author&q=nemeth), and (ex) lead programmer of Free Software Competence Center, Hungary
Marina Latini is an Italian community member of TDF that focuses her activities on migration from MS Office, localization and recently bug triage. She took some lessons for the LibreUmbria Project. She is a member of LibreItalia community and believes in the importance of using and promoting open standards and open formats. Actually she works in the LibreOffice division of Studio Storti Srl, working on migration, helpdesk and bug triage.
Markus is a Libreoffice hacker working mostly on Calc and charts. When he is not hacking calc he is implementing new, crazy testing ideas for the Libreoffice project. Apart from his work on Libreoffice he maintains the Libreoffice cppunit version and works from time to time on orcus, ixion and mdds. Outside of the FOSS world he is in his last year studying mathematics at a university.
Unfortunately we do not yet have a bio of this speaker. We'll soon update this.
Unfortunately we do not yet have a bio of this speaker. We'll soon update this.
Michael Stahl joined the Sun Hamburg OpenOffice.org Writer team in 2007, and struggling with various parts of OOo since then; nowadays continuing the work of cleaning up messes in LibreOffice, employed by Red Hat, Inc.
Involved with OpenOffice.org since 2006 where I started as a packager for Frugalware Linux. Then I completed two Google Summer of Code projects to improve the RTF import/export filter of Writer. Hacking on LibreOffice Writer for Collabora.
Miloš Šrámek is a programmer and researcher in the area of medical image processing. However, Open source is, his passion, occupying most of his limited spare time. Some time ago he co-founded the Society for Open Information Technologies, a not-for-profit organization based in Slovakia, that promotes the ideas of open source and free software, open data, and other kinds of openness. Miloš has been taking care of LibreOffice localization to Slovak since the OOo times.
Senior LibreOffice consultant, main LibreOffice translator for Brazilian Portuguese, Former Board of Directors Member. Graduated Electronic Engineer in 1982, MSc in Digital Signal Processing in 1985 and MBA in Oil & Gas industry in 2001. He initiated his career as Associate Researcher for digital signal processing in the IBM Scientific Center in Brasilia, Brazil, for the Oil & Gas industry in seismic data processing and high performance computing. Later he moved to marketing and sales for the same industry all over Latin America. In 1998 he joined Oracle Brazil in sales for the Oil & Gas industry and later as Alliance relationship manager for large hardware manufacturers as well as with the Oracle Academic Initiative. Since 2002 and on his own, he participated actively in FLOSS projects notably in OpenOffice.org as Community Council member and as one of the translators for Brazilian Portuguese. He is now senior consultant in LibreOffice technology for large corporations on migration projects.
I am a software engineer at CloudOn, and over the last year, I have been leading our growing team of software engineers, working on a the bridging layer, providing LibreOffice functionality to our iOS mobile app (based on the work Tor began). I would like to have the opportunity to represent CloudOn, and talk about the work we have done over the last year, with respect to LibreOffice on iOS.
QA Engineer, The Document Foundation
Free Software evangelist and hacker, web developer, and LibreOffice Community Outreach Herald. Robinson is a volunteer in LibreOffice, splitting his time between Community Outreach and QA. He has shepherded improvements to project infrastructure including Etherpad, MediaWiki, ownCloud, Bugzilla, and the BSA, represented LibreOffice at SCALE, OSCON, and Fossetcon, and has given talks at the LibreOffice Annual Conference, LinuxFest Northwest, Libreplanet, and Texas LinuxFest. Robinson has spent time working on interactive training programs for doctors and first responders at the Interactive Media Lab at the Geisel School of Medicine, and consulted on projects including Metadata Games, a Free/Open-Source game suite for libraries, archives, and other institutions. When he's not speaking about LibreOffice or hacking on code, you may find him tinkering in his basement, growing beans and hops in his garden, brewing beer in his kitchen, roasting something over the fire, or floating around a lake somewhere.
Product Manager at CloudOn with eng background and a attention to UX, working in CloudOn from its early days. Responsible of the Mobile content authoring tool and leading the transition plan from Microsoft Office to Libra Office.
Sonia Montegiove is ICT specialist and trainer at Provincia di Perugia. She's contributor for many ICT magazine like Girl Geek Life and Tech Economy. She worked in the LibreUmbria projetc Team and she is co-founder of LibreItalia.
Stanislav Horáček is a satisfied long-term OOo/LibreOffice user from the Czech Republic who found out that contributing to this project could be a nice way how to relax. He has been participating in LibreOffice localization to Czech for a few years and tries to explore also other areas like updating the Help.
From the original Hamburg StarOffice crowd, now a full-time LibreOffice guy at Red Hat.
Sr. Software Engineer, Synerzip Softech Pvt Ltd
I have about 3 years of experience in OOXML format. I have been working on LibreOffice since last 8 months. I have contributed in 3 areas of Libreoffice viz. Development, Testing and Documentation. About 23 commits of mine have been merged to LO master. I have logged several bugs on libreoffice bugzilla.
Svante works as freelancer on Document Formats. He is the submitter of the actual change-tracking proposal of the OASIS ODF Advanced Collaboration Subcomittee, where he recently become the chair. Aside standardization of ODF he is an active committer of the Apache ODF Toolkit (Incubating). For twelve years he had been working for Sun Microsystems in the field of standardization, most recently as a freelancer. Among other things, he was .. * Responsible for the XML transformations of the OpenOffice.org * Development Lead of the Java based ODF Toolkit * Co-author on the ODF 1.2 Metadata specification of the Open Document Format * Representatives from Sun in the W3C Compound Document Format * Representatives from Sun / Oracle in DIN ISO SC34 * Responsible for collaboration in the browser-based Web ODF office suite * Creator of the XHTML transformation, e.g. used for the OASIS ODF specification
I'm a young man from Hungary who just finished his computer science studies in this year and continues programming inside Collabora Productivity. I use and develop LibreOffice for about two years. I've done more small things on different segments of the office suite. I improved localization infrastructure with killing SDF file format, implemented character border in Writer as a GSoC 2013 project and now working on integration of 3D model support into Impress.
Student
Tobias Mueller is a lead member of the GNOME Bugsquad for the last years and thus responsible for managing the bug database as well as the team around it. He is involved in deciding on effective policies for the bug database, actually dealing with the bugs in the database and recruiting new members who will help triaging. He also got elected to the Board of Directors in 2012 where he serves the GNOME Foundation to achieve their goals of creating and distributing great Free Software products. Besides being a Free Software and GNOME lover, Tobias is involved in the German security research community around the Chaos Computer Club. Topics of interest include Platform- and System-Security, Cryptography and Security Protocols.
Software engineer from Maribor, Slovenia. Worked in insurance software field for 7 years. Gained experience in information system, software design and XP methodologies. Since 2012 worked on LibreOffice as volunteer and now working for Collabora Productivity.
Software engineer from Maribor, Slovenia. Worked in insurance software field for 7 years. Gained experience in information system, software design and XP methodologies. Since 2012 worked on LibreOffice as volunteer and now working for Collabora Productivity.
Tor Lillqvist is a cheerful old chap who works on LibreOffice for Collabora Productivity. He lives in Helsinki with his wife, daughter and dog and is an atheist. He enjoys the occasional craft beer and loves Swiss trains and chocolate.
Graduated in Technology and pos-graduated in Computers Networks. Working at Engineering University, UNESP - "Universidade Estadual Paulista", Campus Ilha Solteira, São Paulo - Brazil, since 1988. In 1999, started to use GNU/Linux on servers and personal desktop. In 2007, initiated the planning to work only with free software in UNESP. Organizer of Free Software Forum of UNESP, with the proposal to disseminate, promote and sensibilize the university community to work with free software and ODF standard. Trainer of LibreOffice and Ubuntu GNU/Linux in many units of UNESP. Member of Brazilian LibreOffice Community and TDF member. Collaborator of LibreOffice Magazine project and support volunteer at libreoffice lists and askbot..