# Copyright 2017-2022 John Snow Labs
#
# 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.
"""Contains classes for the ContextSpellChecker."""
from sparknlp.common import *
[docs]class ContextSpellCheckerApproach(AnnotatorApproach):
"""Trains a deep-learning based Noisy Channel Model Spell Algorithm.
Correction candidates are extracted combining context information and word
information.
For instantiated/pretrained models, see :class:`.ContextSpellCheckerModel`.
Spell Checking is a sequence to sequence mapping problem. Given an input
sequence, potentially containing a certain number of errors,
``ContextSpellChecker`` will rank correction sequences according to three
things:
#. Different correction candidates for each word — **word level**.
#. The surrounding text of each word, i.e. it’s context —
**sentence level**.
#. The relative cost of different correction candidates according to the
edit operations at the character level it requires — **subword level**.
For extended examples of usage, see the article
`Training a Contextual Spell Checker for Italian Language <https://towardsdatascience.com/training-a-contextual-spell-checker-for-italian-language-66dda528e4bf>`__,
the `Examples <https://github.com/JohnSnowLabs/spark-nlp/blob/master/examples/python/training/italian/Training_Context_Spell_Checker_Italian.ipynb>`__.
====================== ======================
Input Annotation types Output Annotation type
====================== ======================
``TOKEN`` ``TOKEN``
====================== ======================
Parameters
----------
languageModelClasses
Number of classes to use during factorization of the softmax output in
the LM.
wordMaxDistance
Maximum distance for the generated candidates for every word.
maxCandidates
Maximum number of candidates for every word.
caseStrategy
What case combinations to try when generating candidates, by default 2.
Possible values are:
- 0: All uppercase letters
- 1: First letter capitalized
- 2: All letters
errorThreshold
Threshold perplexity for a word to be considered as an error.
epochs
Number of epochs to train the language model.
batchSize
Batch size for the training in NLM.
initialRate
Initial learning rate for the LM.
finalRate
Final learning rate for the LM.
validationFraction
Percentage of datapoints to use for validation.
minCount
Min number of times a token should appear to be included in vocab.
compoundCount
Min number of times a compound word should appear to be included in
vocab.
classCount
Min number of times the word need to appear in corpus to not be
considered of a special class.
tradeoff
Tradeoff between the cost of a word error and a transition in the
language model.
weightedDistPath
The path to the file containing the weights for the levenshtein
distance.
maxWindowLen
Maximum size for the window used to remember history prior to every
correction.
configProtoBytes
ConfigProto from tensorflow, serialized into byte array.
maxSentLen
Maximum length for a sentence - internal use during training.
graphFolder
Folder path that contain external graph files.
References
----------
For an in-depth explanation of the module see the article
`Applying Context Aware Spell Checking in Spark NLP <https://medium.com/spark-nlp/applying-context-aware-spell-checking-in-spark-nlp-3c29c46963bc>`__.
Examples
--------
>>> import sparknlp
>>> from sparknlp.base import *
>>> from sparknlp.annotator import *
>>> from pyspark.ml import Pipeline
For this example, we use the first Sherlock Holmes book as the training dataset.
>>> documentAssembler = DocumentAssembler() \\
... .setInputCol("text") \\
... .setOutputCol("document")
>>> tokenizer = Tokenizer() \\
... .setInputCols("document") \\
... .setOutputCol("token")
>>> spellChecker = ContextSpellCheckerApproach() \\
... .setInputCols("token") \\
... .setOutputCol("corrected") \\
... .setWordMaxDistance(3) \\
... .setBatchSize(24) \\
... .setEpochs(8) \\
... .setLanguageModelClasses(1650) # dependant on vocabulary size
... # .addVocabClass("_NAME_", names) # Extra classes for correction could be added like this
>>> pipeline = Pipeline().setStages([
... documentAssembler,
... tokenizer,
... spellChecker
... ])
>>> path = "sherlockholmes.txt"
>>> dataset = spark.read.text(path) \\
... .toDF("text")
>>> pipelineModel = pipeline.fit(dataset)
See Also
--------
NorvigSweetingApproach, SymmetricDeleteApproach : For alternative approaches to spell checking
"""
name = "ContextSpellCheckerApproach"
inputAnnotatorTypes = [AnnotatorType.TOKEN]
outputAnnotatorType = AnnotatorType.TOKEN
languageModelClasses = Param(Params._dummy(),
"languageModelClasses",
"Number of classes to use during factorization of the softmax output in the LM.",
typeConverter=TypeConverters.toInt)
wordMaxDistance = Param(Params._dummy(),
"wordMaxDistance",
"Maximum distance for the generated candidates for every word.",
typeConverter=TypeConverters.toInt)
maxCandidates = Param(Params._dummy(),
"maxCandidates",
"Maximum number of candidates for every word.",
typeConverter=TypeConverters.toInt)
caseStrategy = Param(Params._dummy(),
"caseStrategy",
"What case combinations to try when generating candidates.",
typeConverter=TypeConverters.toInt)
errorThreshold = Param(Params._dummy(),
"errorThreshold",
"Threshold perplexity for a word to be considered as an error.",
typeConverter=TypeConverters.toFloat)
epochs = Param(Params._dummy(),
"epochs",
"Number of epochs to train the language model.",
typeConverter=TypeConverters.toInt)
batchSize = Param(Params._dummy(),
"batchSize",
"Batch size for the training in NLM.",
typeConverter=TypeConverters.toInt)
initialRate = Param(Params._dummy(),
"initialRate",
"Initial learning rate for the LM.",
typeConverter=TypeConverters.toFloat)
finalRate = Param(Params._dummy(),
"finalRate",
"Final learning rate for the LM.",
typeConverter=TypeConverters.toFloat)
validationFraction = Param(Params._dummy(),
"validationFraction",
"Percentage of datapoints to use for validation.",
typeConverter=TypeConverters.toFloat)
minCount = Param(Params._dummy(),
"minCount",
"Min number of times a token should appear to be included in vocab.",
typeConverter=TypeConverters.toFloat)
compoundCount = Param(Params._dummy(),
"compoundCount",
"Min number of times a compound word should appear to be included in vocab.",
typeConverter=TypeConverters.toInt)
classCount = Param(Params._dummy(),
"classCount",
"Min number of times the word need to appear in corpus to not be considered of a special class.",
typeConverter=TypeConverters.toFloat)
tradeoff = Param(Params._dummy(),
"tradeoff",
"Tradeoff between the cost of a word error and a transition in the language model.",
typeConverter=TypeConverters.toFloat)
weightedDistPath = Param(Params._dummy(),
"weightedDistPath",
"The path to the file containing the weights for the levenshtein distance.",
typeConverter=TypeConverters.toString)
maxWindowLen = Param(Params._dummy(),
"maxWindowLen",
"Maximum size for the window used to remember history prior to every correction.",
typeConverter=TypeConverters.toInt)
configProtoBytes = Param(Params._dummy(), "configProtoBytes",
"ConfigProto from tensorflow, serialized into byte array. Get with config_proto.SerializeToString()",
TypeConverters.toListInt)
maxSentLen = Param(Params._dummy(),
"maxSentLen",
"Maximum length of a sentence to be considered for training.",
typeConverter=TypeConverters.toInt)
graphFolder = Param(Params._dummy(),
"graphFolder",
"Folder path that contain external graph files.",
typeConverter=TypeConverters.toString)
[docs] def setLanguageModelClasses(self, count):
"""Sets number of classes to use during factorization of the softmax
output in the Language Model.
Parameters
----------
count : int
Number of classes
"""
return self._set(languageModelClasses=count)
[docs] def setWordMaxDistance(self, dist):
"""Sets maximum distance for the generated candidates for every word.
Parameters
----------
dist : int
Maximum distance for the generated candidates for every word
"""
return self._set(wordMaxDistance=dist)
[docs] def setMaxCandidates(self, candidates):
"""Sets maximum number of candidates for every word.
Parameters
----------
candidates : int
Maximum number of candidates for every word.
"""
return self._set(maxCandidates=candidates)
[docs] def setCaseStrategy(self, strategy):
"""Sets what case combinations to try when generating candidates.
Possible values are:
- 0: All uppercase letters
- 1: First letter capitalized
- 2: All letters
Parameters
----------
strategy : int
Case combinations to try when generating candidates
"""
return self._set(caseStrategy=strategy)
[docs] def setErrorThreshold(self, threshold):
"""Sets threshold perplexity for a word to be considered as an error.
Parameters
----------
threshold : float
Threshold perplexity for a word to be considered as an error
"""
return self._set(errorThreshold=threshold)
[docs] def setEpochs(self, count):
"""Sets number of epochs to train the language model.
Parameters
----------
count : int
Number of epochs
"""
return self._set(epochs=count)
[docs] def setBatchSize(self, size):
"""Sets batch size.
Parameters
----------
size : int
Batch size
"""
return self._set(batchSize=size)
[docs] def setInitialRate(self, rate):
"""Sets initial learning rate for the LM.
Parameters
----------
rate : float
Initial learning rate for the LM
"""
return self._set(initialRate=rate)
[docs] def setFinalRate(self, rate):
"""Sets final learning rate for the LM.
Parameters
----------
rate : float
Final learning rate for the LM
"""
return self._set(finalRate=rate)
[docs] def setValidationFraction(self, fraction):
"""Sets percentage of datapoints to use for validation.
Parameters
----------
fraction : float
Percentage of datapoints to use for validation
"""
return self._set(validationFraction=fraction)
[docs] def setMinCount(self, count):
"""Sets min number of times a token should appear to be included in
vocab.
Parameters
----------
count : float
Min number of times a token should appear to be included in vocab
"""
return self._set(minCount=count)
[docs] def setCompoundCount(self, count):
"""Sets min number of times a compound word should appear to be included
in vocab.
Parameters
----------
count : int
Min number of times a compound word should appear to be included in
vocab.
"""
return self._set(compoundCount=count)
[docs] def setClassCount(self, count):
"""Sets min number of times the word need to appear in corpus to not be
considered of a special class.
Parameters
----------
count : float
Min number of times the word need to appear in corpus to not be
considered of a special class.
"""
return self._set(classCount=count)
[docs] def setTradeoff(self, alpha):
"""Sets tradeoff between the cost of a word error and a transition in
the language model.
Parameters
----------
alpha : float
Tradeoff between the cost of a word error and a transition in the
language model
"""
return self._set(tradeoff=alpha)
[docs] def setWeightedDistPath(self, path):
"""Sets the path to the file containing the weights for the levenshtein
distance.
Parameters
----------
path : str
Path to the file containing the weights for the levenshtein
distance.
"""
return self._set(weightedDistPath=path)
[docs] def setMaxWindowLen(self, length):
"""Sets the maximum size for the window used to remember history prior
to every correction.
Parameters
----------
length : int
Maximum size for the window used to remember history prior to
every correction
"""
return self._set(maxWindowLen=length)
[docs] def setConfigProtoBytes(self, b):
"""Sets configProto from tensorflow, serialized into byte array.
Parameters
----------
b : List[int]
ConfigProto from tensorflow, serialized into byte array
"""
return self._set(configProtoBytes=b)
[docs] def setGraphFolder(self, path):
"""Sets folder path that contain external graph files.
Parameters
----------
path : str
Folder path that contain external graph files.
"""
return self._set(graphFolder=path)
[docs] def setMaxSentLen(self, sentlen):
"""Sets the maximum length of a sentence.
Parameters
----------
sentlen : int
Maximum length of a sentence
"""
return self._set(maxSentLen=sentlen)
[docs] def addVocabClass(self, label, vocab, userdist=3):
"""Adds a new class of words to correct, based on a vocabulary.
Parameters
----------
label : str
Name of the class
vocab : List[str]
Vocabulary as a list
userdist : int, optional
Maximal distance to the word, by default 3
"""
self._call_java('addVocabClass', label, vocab, userdist)
return self
[docs] def addRegexClass(self, label, regex, userdist=3):
"""Adds a new class of words to correct, based on regex.
Parameters
----------
label : str
Name of the class
regex : str
Regex to add
userdist : int, optional
Maximal distance to the word, by default 3
"""
self._call_java('addRegexClass', label, regex, userdist)
return self
@keyword_only
def __init__(self):
super(ContextSpellCheckerApproach, self). \
__init__(classname="com.johnsnowlabs.nlp.annotators.spell.context.ContextSpellCheckerApproach")
def _create_model(self, java_model):
return ContextSpellCheckerModel(java_model=java_model)
[docs]class ContextSpellCheckerModel(AnnotatorModel, HasEngine):
"""Implements a deep-learning based Noisy Channel Model Spell Algorithm.
Correction candidates are extracted combining context information and word
information.
Spell Checking is a sequence to sequence mapping problem. Given an input
sequence, potentially containing a certain number of errors,
``ContextSpellChecker`` will rank correction sequences according to three
things:
#. Different correction candidates for each word — **word level**.
#. The surrounding text of each word, i.e. it’s context —
**sentence level**.
#. The relative cost of different correction candidates according to the
edit operations at the character level it requires — **subword level**.
This is the instantiated model of the :class:`.ContextSpellCheckerApproach`.
For training your own model, please see the documentation of that class.
Pretrained models can be loaded with :meth:`.pretrained` of the companion
object:
>>> spellChecker = ContextSpellCheckerModel.pretrained() \\
... .setInputCols(["token"]) \\
... .setOutputCol("checked")
The default model is ``"spellcheck_dl"``, if no name is provided.
For available pretrained models please see the `Models Hub <https://sparknlp.org/models?task=Spell+Check>`__.
For extended examples of usage, see the `Examples <https://github.com/JohnSnowLabs/spark-nlp/blob/master/examples/python/training/italian/Training_Context_Spell_Checker_Italian.ipynb>`__.
====================== ======================
Input Annotation types Output Annotation type
====================== ======================
``TOKEN`` ``TOKEN``
====================== ======================
Parameters
----------
wordMaxDistance
Maximum distance for the generated candidates for every word.
maxCandidates
Maximum number of candidates for every word.
caseStrategy
What case combinations to try when generating candidates.
errorThreshold
Threshold perplexity for a word to be considered as an error.
tradeoff
Tradeoff between the cost of a word error and a transition in the
language model.
maxWindowLen
Maximum size for the window used to remember history prior to every
correction.
gamma
Controls the influence of individual word frequency in the decision.
correctSymbols
Whether to correct special symbols or skip spell checking for them
compareLowcase
If true will compare tokens in low case with vocabulary.
configProtoBytes
ConfigProto from tensorflow, serialized into byte array.
vocabFreq
Frequency words from the vocabulary.
idsVocab
Mapping of ids to vocabulary.
vocabIds
Mapping of vocabulary to ids.
classes
Classes the spell checker recognizes.
weights
Levenshtein weights.
useNewLines
When set to true new lines will be treated as any other character. When set to false correction is applied on paragraphs as defined by newline characters.
References
-------------
For an in-depth explanation of the module see the article `Applying Context
Aware Spell Checking in Spark NLP
<https://medium.com/spark-nlp/applying-context-aware-spell-checking-in-spark-nlp-3c29c46963bc>`__.
Examples
--------
>>> import sparknlp
>>> from sparknlp.base import *
>>> from sparknlp.annotator import *
>>> from pyspark.ml import Pipeline
>>> documentAssembler = DocumentAssembler() \\
... .setInputCol("text") \\
... .setOutputCol("doc")
>>> tokenizer = Tokenizer() \\
... .setInputCols(["doc"]) \\
... .setOutputCol("token")
>>> spellChecker = ContextSpellCheckerModel \\
... .pretrained() \\
... .setTradeoff(12.0) \\
... .setInputCols("token") \\
... .setOutputCol("checked")
>>> pipeline = Pipeline().setStages([
... documentAssembler,
... tokenizer,
... spellChecker
... ])
>>> data = spark.createDataFrame([["It was a cold , dreary day and the country was white with smow ."]]).toDF("text")
>>> result = pipeline.fit(data).transform(data)
>>> result.select("checked.result").show(truncate=False)
+--------------------------------------------------------------------------------+
|result |
+--------------------------------------------------------------------------------+
|[It, was, a, cold, ,, dreary, day, and, the, country, was, white, with, snow, .]|
+--------------------------------------------------------------------------------+
See Also
--------
NorvigSweetingModel, SymmetricDeleteModel: For alternative approaches to spell checking
"""
name = "ContextSpellCheckerModel"
inputAnnotatorTypes = [AnnotatorType.TOKEN]
outputAnnotatorType = AnnotatorType.TOKEN
wordMaxDistance = Param(Params._dummy(),
"wordMaxDistance",
"Maximum distance for the generated candidates for every word.",
typeConverter=TypeConverters.toInt)
maxCandidates = Param(Params._dummy(),
"maxCandidates",
"Maximum number of candidates for every word.",
typeConverter=TypeConverters.toInt)
caseStrategy = Param(Params._dummy(),
"caseStrategy",
"What case combinations to try when generating candidates.",
typeConverter=TypeConverters.toInt)
errorThreshold = Param(Params._dummy(),
"errorThreshold",
"Threshold perplexity for a word to be considered as an error.",
typeConverter=TypeConverters.toFloat)
tradeoff = Param(Params._dummy(),
"tradeoff",
"Tradeoff between the cost of a word error and a transition in the language model.",
typeConverter=TypeConverters.toFloat)
maxWindowLen = Param(Params._dummy(),
"maxWindowLen",
"Maximum size for the window used to remember history prior to every correction.",
typeConverter=TypeConverters.toInt)
gamma = Param(Params._dummy(),
"gamma",
"Controls the influence of individual word frequency in the decision.",
typeConverter=TypeConverters.toFloat)
correctSymbols = Param(Params._dummy(), "correctSymbols",
"Whether to correct special symbols or skip spell checking for them",
typeConverter=TypeConverters.toBoolean)
compareLowcase = Param(Params._dummy(), "compareLowcase", "If true will compare tokens in low case with vocabulary",
typeConverter=TypeConverters.toBoolean)
configProtoBytes = Param(Params._dummy(), "configProtoBytes",
"ConfigProto from tensorflow, serialized into byte array. Get with config_proto.SerializeToString()",
TypeConverters.toListInt)
vocabFreq = Param(
Params._dummy(),
"vocabFreq",
"Frequency words from the vocabulary.",
TypeConverters.identity,
)
idsVocab = Param(
Params._dummy(),
"idsVocab",
"Mapping of ids to vocabulary.",
TypeConverters.identity,
)
vocabIds = Param(
Params._dummy(),
"vocabIds",
"Mapping of vocabulary to ids.",
TypeConverters.identity,
)
classes = Param(
Params._dummy(),
"classes",
"Classes the spell checker recognizes.",
TypeConverters.identity,
)
[docs] def setWordMaxDistance(self, dist):
"""Sets maximum distance for the generated candidates for every word.
Parameters
----------
dist : int
Maximum distance for the generated candidates for every word.
"""
return self._set(wordMaxDistance=dist)
[docs] def setMaxCandidates(self, candidates):
"""Sets maximum number of candidates for every word.
Parameters
----------
candidates : int
Maximum number of candidates for every word.
"""
return self._set(maxCandidates=candidates)
[docs] def setCaseStrategy(self, strategy):
"""Sets what case combinations to try when generating candidates.
Parameters
----------
strategy : int
Case combinations to try when generating candidates.
"""
return self._set(caseStrategy=strategy)
[docs] def setErrorThreshold(self, threshold):
"""Sets threshold perplexity for a word to be considered as an error.
Parameters
----------
threshold : float
Threshold perplexity for a word to be considered as an error
"""
return self._set(errorThreshold=threshold)
[docs] def setTradeoff(self, alpha):
"""Sets tradeoff between the cost of a word error and a transition in the
language model.
Parameters
----------
alpha : float
Tradeoff between the cost of a word error and a transition in the
language model
"""
return self._set(tradeoff=alpha)
[docs] def setWeights(self, weights):
"""Sets weights of each word for Levenshtein distance.
Parameters
----------
weights : Dict[str, float]
Weights for Levenshtein distance as a mapping.
"""
self._call_java('setWeights', weights)
[docs] def setMaxWindowLen(self, length):
"""Sets the maximum size for the window used to remember history prior to
every correction.
Parameters
----------
length : int
Maximum size for the window used to remember history prior to
every correction
"""
return self._set(maxWindowLen=length)
[docs] def setGamma(self, g):
"""Sets the influence of individual word frequency in the decision.
Parameters
----------
g : float
Controls the influence of individual word frequency in the decision.
"""
return self._set(gamma=g)
[docs] def setConfigProtoBytes(self, b):
"""Sets configProto from tensorflow, serialized into byte array.
Parameters
----------
b : List[int]
ConfigProto from tensorflow, serialized into byte array
"""
return self._set(configProtoBytes=b)
[docs] def setVocabFreq(self, value: dict):
"""Sets frequency words from the vocabulary.
Parameters
----------
value : dict
Frequency words from the vocabulary.
"""
return self._set(vocabFreq=value)
[docs] def setIdsVocab(self, idsVocab: dict):
"""Sets mapping of ids to vocabulary.
Parameters
----------
idsVocab : dict
Mapping of ids to vocabulary.
"""
return self._set(idsVocab=idsVocab)
[docs] def setVocabIds(self, vocabIds: dict):
"""Sets mapping of vocabulary to ids.
Parameters
----------
vocabIds : dict
Mapping of vocabulary to ids.
"""
return self._set(vocabIds=vocabIds)
[docs] def setClasses(self, value):
"""Sets classes the spell checker recognizes.
Parameters
----------
value : list
Classes the spell checker recognizes.
"""
return self._set(classes=value)
[docs] def getWordClasses(self):
"""Gets the classes of words to be corrected.
Returns
-------
List[str]
Classes of words to be corrected
"""
it = self._call_java('getWordClasses').toIterator()
result = []
while (it.hasNext()):
result.append(it.next().toString())
return result
[docs] def updateRegexClass(self, label, regex):
"""Update existing class to correct, based on regex
Parameters
----------
label : str
Label of the class
regex : str
Regex to parse the class
"""
self._call_java('updateRegexClass', label, regex)
return self
[docs] def updateVocabClass(self, label, vocab, append=True):
"""Update existing class to correct, based on a vocabulary.
Parameters
----------
label : str
Label of the class
vocab : List[str]
Vocabulary as a list
append : bool, optional
Whether to append to the existing vocabulary, by default True
"""
self._call_java('updateVocabClass', label, vocab, append)
return self
[docs] def setCorrectSymbols(self, value):
"""Sets whether to correct special symbols or skip spell checking for
them.
Parameters
----------
value : bool
Whether to correct special symbols or skip spell checking for
them
"""
return self._set(correctSymbols=value)
[docs] def setCompareLowcase(self, value):
"""Sets whether to compare tokens in lower case with vocabulary.
Parameters
----------
value : bool
Whether to compare tokens in lower case with vocabulary.
"""
return self._set(compareLowcase=value)
def __init__(self, classname="com.johnsnowlabs.nlp.annotators.spell.context.ContextSpellCheckerModel",
java_model=None):
super(ContextSpellCheckerModel, self).__init__(
classname=classname,
java_model=java_model
)
@staticmethod
[docs] def pretrained(name="spellcheck_dl", lang="en", remote_loc=None):
"""Downloads and loads a pretrained model.
Parameters
----------
name : str, optional
Name of the pretrained model, by default "spellcheck_dl"
lang : str, optional
Language of the pretrained model, by default "en"
remote_loc : str, optional
Optional remote address of the resource, by default None. Will use
Spark NLPs repositories otherwise.
Returns
-------
ContextSpellCheckerModel
The restored model
"""
from sparknlp.pretrained import ResourceDownloader
return ResourceDownloader.downloadModel(ContextSpellCheckerModel, name, lang, remote_loc)


---
layout: page
title: Cracked Glass Effect Using an HTML5 Canvas
group: canvas
tags : [javascript, html5, canvas, effect, algorithm]
blog: /2012/10/09/using-html5-canvas-to-create-cracked-glass-effect/
---
{% include JB/setup %}
This demo contains an image and HTML5 Canvas element positioned over the image
which contains the actual cracks. The image is unaffected. You can hide the image
to see the canvas overlay. Click the "Add Cracks" to draw the whole crack effect.
Click the "Add Single Crack" to see what the crackedLineSegment() function does
for one cracked line. Also, take a
look at my update version
which I think does a better job rendering the effect.


[CHAPTER 315
INTERSTATE AGREEMENT ON QUALIFICATIONS
OF EDUCATIONAL PERSONNEL]
Section
315-1 Enactment of agreement
315-2 Official for State
315-3 Contracts under agreement


Map for sort fields (local to remote).
Object, default = {}
By default, if you click on the column, it will add column field to the sortData array to send to the server side for sorting.
However, sometimes column field names are not the same as sort names. The .sortMap allows to map sort
fields differently.
You can defined it during object creation:
Or any time during the run-time:


willOS
kernel Directory Reference
Directory dependency graph for kernel:
Files
file
console.h
[code]
file
kmain.h
[code]
file
kshell.h
[code]
file
panic.h
[code]
src
kernel
Generated by
1.8.17


vcsh
Version Control System for the home directory using Git repositories.
More information: home page
- Initialize an (empty) repository:
vcsh init repository_name
- Clone a repository into a custom directory name:
vcsh clone git_url repository_name
- List all managed repositories:
vcsh list
- Execute a Git command on a managed repository:
vcsh repository_name git_command
- Push/pull all managed repositories to/from remotes:
vcsh push|pull
- Write a custom `.gitignore` file for a managed repository:
vcsh write-gitignore repository_name


Just then another visitor entered the drawing room: Prince Andrew Bolkónski, the little princess’ husband. He was a very handsome young man, of medium height, with firm, clearcut features.  It was evident that he not only knew everyone in the drawing room, but had found them to be so tiresome that it wearied him to look at or listen to them. And among all these faces that he found so tedious, none seemed to bore him so much as that of his pretty wife.
“You are off to the war, Prince?” said Anna Pávlovna.
“General Kutúzov,” said Bolkónski, speaking French and stressing the last syllable of the general’s name like a Frenchman, “has been pleased to take me as an aide-de-camp....”
“And Lise, your wife?”
“She will go to the country.”
“Are you not ashamed to deprive us of your charming wife?”
“André,” said his wife, addressing her husband in the same coquettish manner in which she spoke to other men, “the vicomte has been telling us such a tale about Mademoiselle George and Buonaparte!”
 Pierre, who from the moment Prince Andrew entered the room had watched him with glad, affectionate eyes, now came up and took his arm. Before he looked round Prince Andrew frowned again, expressing his annoyance with whoever was touching his arm, but when he saw Pierre’s beaming face he gave him an unexpectedly kind and pleasant smile.
“There now!... So you, too, are in the great world?” said he to Pierre.


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Donation of {{value}} BTC Received. Click here for a mega boost of $1,000,000,000
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--- layout: page title: Filmmaking permalink: /filmmaking/ ---
When we work together it is a truly collaborative process. I take the time to get to know you and your organization, and to really understand your story so that I can sell it to the world.
As a communications professional and filmmaker what I bring to the table is a heightened knowledge of marketing trends. My ear is constantly to the ground for the latest analytics on what videos are being shared and for the most effective means of converting views into sales. I want to make the strongest marketing material for you, in addition to making something beautiful and innovative.
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Have some test footage of your product which you’ve shot but never used? I've worked with clients such as Tesco and LCA Films to turn several hours of unused footage into a scintillating 3-4 mins promotional video.
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I may have a slight problem with chargering my phone, just ask Zafir. I created a protable charger to solve this problem.
This was pretty awesome because it worked, but kind of a bummer since I burned some hair...heh heh
On the second week of Street Code, we started The Independent Intelligent Agency aka IIA (catchy right).  We may or may not be official spies but if your in the market, contact us ;) info on page


ToxoDB offers a new search,
Identify Genes based on Post-Translational Modifications,
which returns genes whose protein products were experimentally determined to be post-translationally modified. This search is categorized under Search for Genes, Proteomics, Post-Translational Modification.


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| 2008 - now |
Senior team coach and board member
Tempo Titans Prague
Team leading, running the club and event organizating
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| 2012 |
Web developer
3sixty s.r.o.
Coding of latte templates in jQuery and HTML and developing CMS 360(build on nette)
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| 2004 – 2009 | Grammar school Přípotoční High school with sport-oriented program |
| 2009 – 2010 | Charles University Faculty of math and physics – IT Major |
| 2010 - now | Czech Technical University in Prague Faculty of IT – IT Major |
| English | Professional working proficiency |
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| Spanish | Elementary proficiency |
| C/C++ | Intermediate |
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| PHP - OOP | Advanced (frameworks Nette and Symfony) |
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| latte, twig | Intermediate |
| GIT | Intermediate |
| SVN | Beginner |
| MySQL / Oracle SQL | Intermediate |
| Linux - bash | Advanced |
| Windows | Imtermediate |
| Cisco | Beginner |
| Photoshop / Gimp | Advanced |
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In a nutshell, I am a computational linguist who believes in hard work, discipline and devotion.
Look for everything business-related (work and my education endeavors) in my CV (click).
I also have a blog where I share some personal thoughts/experiences on many things, check it out as well: blog (click)
Image Captioning, Image Paragraph Generation, Grounding, Natural Language Generation, Natural Language Processing, Language and Vision.
uni[lastname][AT]gmail[DOT]com


Oral statement to Parliament
The Secretary of State for Education provides an update on progress in Birmingham schools.
With permission, Mr Speaker, I should like to update the House on progress in implementing the recommendations contained in Peter Clarke’s report into Birmingham’s schools.
The government accepted every one of Peter Clarke’s recommendations, and I am today placing a document in the libraries of both Houses outlining the progress that has been made on each one. I am pleased to report that since I last updated the House in July, all of the recommendations have either been implemented or are on track and that - as a result - I am confident that if the events we witnessed in Birmingham were repeated again today they would be identified and dealt with more quickly and in a far more effective way.
However, let me be clear that there is no room for complacency - both in the specific case of Birmingham, and more generally. We must always remain vigilant. There is no more important responsibility than keeping children safe and giving them the chance of a first-class education that prepares them for life in modern Britain.
That is why I am determined that we should not only act when and where we receive information of concern, we should also build resilience into the system to ensure it is more able to withstand attempts to undermine or subvert it.
We are addressing both concerns.
On the specific issue of Birmingham, a significant amount has been achieved. The job is not done - the problems we encountered in Birmingham arose over a number of years and will not be resolved overnight - but we have already made considerable progress.
We have acted quickly in the schools most affected by the issues in Peter Clarke’s report. New trustees are in place at all the academies, led by outstanding and dedicated headteachers able to tackle the troubling legacy of previous trustees. I am enormously grateful for their work.
There is good progress at 2 of the 3 Park View academies. More needs to be done at Park View Academy itself, where the significant number of suspended staff has hampered progress, but through the regional schools commissioners’ network my department has been supporting the trustees to find new staff, and will consider all reasonable requests for additional funding if and where it can help.
In respect of Oldknow Academy, the trustees have voted to bring in ARK, a well-established multi-academy sponsor with the capacity and capability to turn the academy around. I am confident that this arrangement will deliver the right results for the children at that school.
I am also pleased to report that yesterday the Park View educational trustees announced that Golden Hillock will also join the ARK network. This decision is a significant step along the road to ensuring that its pupils get the best possible chance to fulfil their potential.
In April 2014, Ofsted judged that Saltley School and Specialist Science College required special measures. The governing body resigned in June and an interim executive board was appointed. Since 1 September, Washwood Heath Academy - which was praised by Ofsted last year for its “strong lead on issues of religious extremism” - has been responsible for leadership in Saltley. It is now sponsoring Saltley’s application to convert to academy status. This will happen on 1 March.
Peter Clarke recommended that my department should consider the case for taking formal action against individual governors, or teachers who may have breached the teacher standards. The 2 academy trusts at the centre of concerns have already suspended a significant number of staff, pending disciplinary hearings.
I can also confirm that the National College for Teaching and Leadership ( NCTL ) is investigating a number of teachers, and officials are considering formal action against other individuals involved. We must take prompt and decisive action, but must also follow established and fair processes. I expect to see further progress in this area very shortly, but it is important to note that all cases will be judged against the strengthened advice I have issued to the NCTL .
Some key people involved in Birmingham schools were also leading figures in the Association of Muslim Schools UK ( AMSUK ), which performs some statutory functions in state-funded Muslim schools. These individuals have been removed from their positions, and AMSUK now has a new constitution recognising the importance of member schools upholding and promoting fundamental British values. The department will continue to monitor closely how AMSUK implements that constitution, to be sure it is suitable for its statutory role.
So, Mr Speaker, we have acted swiftly to turn around the academies mentioned in Peter Clarke’s report, but his investigation also recommended that Birmingham city council should review systems, processes and policies for supporting maintained schools in the city.
For this reason I appointed Sir Mike Tomlinson as Education Commissioner for Birmingham in September last year to work with the council and oversee the necessary reforms. I would like to thank Sir Mike and his deputy, Colin Diamond, for their invaluable work to date.
The House will also be aware that Sir Bob Kerslake published his review of governance in Birmingham city council in December. This showed the scale of the challenge. I am pleased that Sir Bob recommended that both Sir Mike and Lord Warner, who continues as Children’s Social Care Commissioner in Birmingham until March, should be ex-officio members of an improvement panel that will oversee much needed reform.
The council now has an education services improvement plan, which I welcome. But they have much more to do to put that into practice. And schools need to be confident that any concerns they raise with the council will be tackled quickly and effectively.
I met the leader of the council earlier this month, with the cabinet member for children’s services and senior council officers. I told them I was concerned that reform is too slow, and that I want to see much stronger leadership. If the council do not take urgent steps to improve their leadership capacity, I am prepared to make use of the powers available to me to issue a statutory direction to the council. I will continue to keep this under review.
I can tell the House that I have extended Sir Mike Tomlinson’s appointment to March 2016 to oversee the council’s delivery of the plan they have developed.
Mr Speaker, as I said earlier we need not only to act on individual cases but to build greater resilience into the system too.
As Peter Clarke recommended, I have increased my department’s capacity and expertise in counter-extremism - dramatically expanding the Due Diligence and Counter Extremism Group in the DfE and placing it under the leadership of a full-time director. We will make further changes within the department in response to my Permanent Secretary’s report, which was laid before the House on Friday 16 January - for example establishing a counter-extremism steering group that ensures the whole department recognises and acts upon its responsibilities in this area.
Since Peter Clarke’s report was published, my department has strengthened the process for converting to academy status or joining a multi-academy trust. New checks are now done on prospective trustees. Regional schools commissioners decide convertor applications using local intelligence, with help from local headteacher boards. Academies must also now publish registers of trustees’ interests and inform the Education Funding Agency ( EFA ) of changes to trustees. We are consulting on similar requirements on registers of interest for governors of maintained schools.
We have made important changes and clarifications to the Governors’ Handbook , with clear expectations about skills and capacity, and the information governors need to provide. We are also responding to recommendations on Prevent training, by changing statutory safeguarding guidance.
I want school staff and the public to feel confident about reporting safeguarding concerns, including extremism, to the department. I intend to extend the scope for extending legal protections to school staff making ‘whistleblowing’ allegations. Ofsted is also reviewing its arrangements for handling complaints, to make sure these adequately capture extremism concerns.
On 25 November, Ofsted published an advice note on no-notice inspection, having indicated already that they would broaden their criteria for considering when to conduct them. There were 35 unannounced inspections in the autumn term. While the chief inspector has confirmed that Ofsted will not routinely inspect schools without notice, it does so where concerns arise, such as in a number of inspections in Tower Hamlets in October. This has already proven to be an effective approach.
Other important changes over the last 6 months, including to the inspection handbook for publicly funded schools, respond partly to lessons from Birmingham. The handbook makes clear that inspectors should assess how schools keep children safe from the risks of extremism and radicalisation, and how they promote the fundamental British values of democracy, the rule of law, individual liberty and mutual respect and tolerance of those with different faiths and beliefs.
Mr Speaker, I want to be clear that it is right that all schools of whatever type should promote these values. They lie at the heart of our country and society. They help to open young people’s minds, making them into citizens who respect difference, who welcome disagreement and who challenge intolerance. They are the attributes that have in this century and the last made our country one of the greatest forces for good. They are the values that bind us together. The values that mean that despite the many differences in our nation, we can find a way to move forward together.
These values unite rather than divide. And so I say again that no school should be exempt from promoting them, just as no school should be exempt from promoting rigorous academic standards. It is not one rule for some and another for the rest, but a fair and transparent system that has the best interests of children at its heart.
Every school should be promoting fundamental British values, not just because they act as a bulwark against extremism, but because it is the right thing to do.
Mr Speaker, this government will not tolerate extremism of any kind. It turns one against another. It warps minds. It causes harm and division in communities. And it can ultimately lead to support for terrorism.
The battle against it begins at school where young people learn to be active, resilient and tolerant citizens, ready to seize the rich opportunities of modern Britain. That is why I am proud that no government has done more to tackle extremism in schools than this government, and we shall continue to do so in the years to come.
I commend this statement to the House.
Published: 29 January 2015


If you are tired of the noise a rotary makes or the inconvience of having to turn it on and off just to use a machine, you should look at this!
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{% extends "base.html" %} {% block title %}500 (Internal Server Error){% endblock %} {% block heading %}500 — Internal Server Error{% endblock %} {% block content %}
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Step in, step back and get your rocks off in our New York inspired bar and pizzeria. Their selection of unique handmade pizzas cater for every taste and their huge choice of beers from around the globe wash them down nicely.
If cocktails are more your tipple then chill out and indulge in a few retro classics followed by some tantalizingly tasty modern creations. And if you're into more active pleasures, they have live Premiership football every week throughout the season and dj sets that will get your mojo back into the groove.
Budget Rating ££
Bakchich is quite simply a little piece of Lebanon that sits on the chic and quirky Bold Street in Liverpool. This most beautiful and lovingly crafted restaurant is, from start to finish an experience and flavour of the Mediterranean.
A brand spanking new restaurant, bar and dynamic space in Liverpool’s upcoming Bold Street zone. Pushing the boundaries with their creative take on true Italian classics, they offer a range of different options for your dining pleasure. Sit back and relax with a hot slice of pizza and a cold beer at Crust.
Mowgli is all about how Indians eat at home and on their streets. She was born to feed the raw need Indians have for tiffins full of fresh bright intense flavour. Mowgli is not about the intimate, hushed dining experience. It is about the smash and grab zing of healthy, light, virtuosic herbs and spices.
Budget Rating £££
Budget Rating ££
Budget Rating ££
Budget Rating £££
Budget Rating ££
Budget Rating ££


Building permit service suspended
Software upgrading work on
by Staff ReporterThe Kozhikode Municipal Corporation has temporarily suspended the service of approving new building plans to facilitate a smooth sailing into the upgraded ‘Suvega’ software.
The software, developed by the Bengaluru-based E-governance Foundation, is being upgraded to suit the new building policy that came into existence on November 8. The upgradation will also be carried out by the foundation.
The corporation is clearing the plans submitted before midnight on November 7, based on the old building policy.
All files will be cleared before the upgraded software, that approves the plans online, will be in place.
The process is expected to take around a month.
Initially, the software will be used for residential building permits while those for commercial building permits will be considered in the second phase, corporation sources said.


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George Harrison once told Elton John to 'stop putting marching powder up nose'
Elton John has recalled the time George Harrison told him to ‘stop putting marching powder up his nose’.
The 72-year-old has recently addressed his former cocaine addiction in his new autobiography, Me: Elton John.
And when talking to Chris Evans on his Virgin Radio Breakfast Show, the father-of-two confessed that the infamous Beatles star had advised him to put down the Class A drug.
Chatting to the host, the Honky Cat hitmaker dished up some advice to struggling musicians, before turning his attention to the late singer.
‘You’ve got to play live,’ the Rocketman insisted: ‘If you want to have a career, record sales will go up and down and people are so fickle.
‘Now, when you’re young, they’ll drop you like a five-pound note on the floor and you’ve got to be able to play live and believe in yourself, but play live.’
Chris pressed: ‘Okay. And the best piece of advice George Harrison gave you.’
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Pausing for a moment, Elton laughed: ‘Stop putting that marching powder up your nose.’
Chuckling along with the star, Chris quizzed Elton over his sobriety as he asked: ‘How long’s it been now? How long’s it been?’
‘Twenty-nine years,’ the living legend proudly replied as he joked: ‘The nose is still here!’
This isn’t the first time he’s spoken about his substance abuse, as Elton wrote about it in his recent memoir.
Speaking of a period in mid-1983 when he was filming his I’m Still Standing music video, he recalled being told he’d smashed up his entire hotel suite. Only he didn’t remember a thing.
‘Everything else was on its side, or upside down, or in pieces. Sitting among the splinters was a cowboy hat that [personal assistant] Bob liked to wear. It was completely flat, like Yosemite Sam’s after Bugs Bunny drops an anvil on his head,’ he wrote of his PA waking up him to survey the damage.
‘”F***ing hell,” I said. “What happened?” There was a long pause. “Elton,” he said eventually. “You happened”.’
Elton revealed the shoot in Cannes kicked off at 4am, before wrapping at sunset with the crooner going back to his room before the night schedule began. It was there he bumped into Duran Duran’s Simon Le Bon and joined him for a drink at the bar.
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After downing ‘either six or eight vodka martinis in the space of an hour, and a couple of lines of coke’, Elton returned to set where he ‘started rolling around on the floor naked’ before punching his then-manager John Reid.
Got a showbiz story?
If you've got a story, video or pictures get in touch with the Metro.co.uk Entertainment team by emailing us<EMAIL_ADDRESS>calling 020 3615 2145 or by visiting our Submit Stuff page - we'd love to hear from you.


---
layout: pagetop-layout
title: OpenVidu Support
active: support
top-title: OPENVIDU SUPPORT
top-subtitle: How to get help if you have problems developing OpenVidu based apps
custom_css:
- custom-social-layout
---
Problems with deployment? Doubts on how to add a feature to your videoconference
service? If you have problems deploying OpenVidu or implementing your service ask for help in the
forum. If you have any doubt
about how OpenVidu will behave in certain circumstances, ask in the forum. If you want to
suggest a new feature,
present it in the forum. If OpenVidu is not behaving as described in the docs, maybe there is a bug. Please review
bugs opened as issues in
GitHub repository to see if your problem has been reported. If not, create a new issue
including all the information
asked in the template.Technical Support
https://openvidu.discourse.group/
Public Forum for Help
https://github.com/OpenVidu/openvidu/issues
GitHub Issues for Bugs
OpenVidu team provides commercial support. Contact us for details
OpenVidu team provides commercial support to help companies and individuals with OpenVidu. We provide consultancy, prioritizing bug fixes or new features, custom app development, etc.
If you have questions about OpenVidu technical capabilities in CE, Pro or Enterprise editions please use the public forum so others can benefit in shared knowledge.
If you have purchased an OpenVidu commercial license and you have any technical problem, please use the public forum. In case you have any reason to maintain your issue private, you can write us to the email above.


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Canadian Dividend All-Stars Expected To Announce Dividend Increases - Week Of Nov. 25
by Mat LitalienSummary
- Canadian Dividend All-Stars are companies that have raised dividends for at least five consecutive years.
- Boyd Group Income Fund came through with an as expected raise.
- Magellan Aerospace raise was significantly lower than expected and its prospects aren't enticing.
It is the calm before the storm. Although there are no Canadian Dividend All-Stars expected to raise dividends, the majority of banks begin to report next week and we should see several dividend increases. It has been a couple of weeks since the last update, and I figured it best to get everyone caught up before the banks start to report.
Of note, all figures are in Canadian dollars unless otherwise noted.
Last Week – Results
The last couple of weeks unfolded with little fanfare. The lone All-Star expected to announce a dividend increase - Boyd Group Income Fund (BYD.CA)[TSX:BYD.UN] - didn’t disappoint. Likewise, I received some clarification on Magellan Aerospace (OTCPK:MALJF)[TSX:MAL], which did in fact announce a raise in the week it was expected.
| EST DGR | EST Increase | ACTUAL DGR | ACTUAL Increase | NEW DIV | |
| Boyed Group | 2-3.3% | $0.001-$0.0015 | 2.22% | $0.001 | $0.046 |
| Magellan Aerospace | 15.00% | $0.10 | 5.00% | $0.005 | $0.105 |
Let’s start with Boyd Group Income Fund which raised inline with expectations. As discussed previously, Boyd isn’t going to impress anyone with its 0.28% yield which is the lowest among all Canadian Dividend All-Stars. The company raised dividends by $0.001 per share or 2.2% for a new monthly dividend of $0.046 per share.
What it lacks in yield however, it more than makes up in capital appreciation. To put its performance into perspective, have a peak at its 10-year chart as compared to the some of the hottest stocks of the past decade:
(Source:Ycharts)
No, you eyes are not deceiving you - Boyd Group has outperformed the likes of Amazon (AMZN), Apple (AAPL) and Netflix (NFLX). Who said consumer cyclical stocks were boring?
I think it is safe to say that shareholders are in great hands and that the company as effectively deployed its cash. Management has done a phenomenal job of turning Boyd Group into a leading industry consolidator.
Next, let’s take a deeper look at Magellan Aerospace. I searched high and low for any information on Magellan’s dividend announcement and came up blank. Turns out, it was announced after the market close on Friday which was after I had submitted my article for the week.
The company announced a 5% dividend increase, which was much lower than expected. The company had a history of raising dividends in the high-teens, and last year it raised by 17%. I thought I was being conservative with my 15% estimate considering it had a low payout ratio (25%).
It was however, a disappointing third quarter. Revenue jumped by only 4%, and EPS dropped by 15.6% which missed estimates by 4 cents. The market didn’t react too kindly to earnings as its share price dropped by 3.6% the day after it released earnings.
Outside of an uptick in revenu, it was red across the board. The reduced profit was a result of “lower production volumes on certain programs, production inefficiencies and higher manufacturing costs”. That isn’t exactly a sentence that instills confidence.
At the time of writing, Magellan is trading at what looks to be an attractive 11.80 times earnings. However, earnings are only expected to grow in the low single-digits on average through 2022. As you can see in looking at the F.A.S.T. Graph below, Magellan has been trading inline with its historical P/E ratio since about 2013. It dipped below the average in 2016-17, but considering it was reporting negative earnings growth, this isn’t all that surprising.
Likewise, the company doesn’t exactly have a stellar reputation of meeting estimates. Take a look at this chart:
(Source – Thomson Reuters)
Taking all this into account, the company appears to be fairly valued. Although the company is one of the few TSX-listed aerospace companies, don’t expect out-sized returns. Over the past five years, its share price has averaged low, single digit returns. It doesn’t look any better over the next couple of years.
If you would like to receive updates for any of my upcoming articles, please click the "Follow" text at the top of this page next to my profile.
Disclosure: I am/we are long AAPL. I wrote this article myself, and it expresses my own opinions. I am not receiving compensation for it (other than from Seeking Alpha). I have no business relationship with any company whose stock is mentioned in this article.
Editor's Note: This article discusses one or more securities that do not trade on a major U.S. exchange. Please be aware of the risks associated with these stocks.


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About The Books Reseller
Books reseller application is where the used books can be reselled.It has only communication between the customer and buyer.Customer have to upload a book picture with valuable price and author name.
CUSTOMER
Customer has to upload a book with a picture
BUYER
Buyer has to give request to the customer
Books Out For Sale
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Preparation time
30 mins to 1 hour
Cooking time
30 mins to 1 hour
Serves
Serves 12
Dietary
These speciality mince pies are perfect for those who suffer from wheat, gluten and nut allergies, and can be dairy, soya and egg-free.
Mix all the dried fruits with the spices and suet if you are using it.
Put the apple, orange and lemon pieces in a food processor and purée.
Add this mixture to the dried fruit along with the brandy or whisky.
Mix well, cover and set aside for anywhere between two hours and 24 hours. Alternatively, you can place in sterilized jars and store for later use.
To make the pastry, mix the flour and sieve into the bowl of a food processor.
Cut in the butter or margarine and blend until it is forms crumbs.
Add 8-10 tablespoons cold water. Mix to a dough and blend until it forms a ball.
Remove from the processor and chill for 30 minutes.
Pre-heat the oven to 180C/350F/Gas 4. Grease and flour your mince pie tins.
Remove the pastry from the fridge, and roll out to a thickness of about 5mm. Cut the pastry in circles with a cutter slightly larger than the size of each pie, and line your mince pie tins with it, pushing the pastry up the edges.
Spoon the mixture into each pie then rollout lids and top each pie, wetting the edges of the dough and pressing it together with your fingers.
If you are using the egg, beat it in a bowl and brush the lid of each pie generously.
Bake for around 20 minutes or until the pies are lightly browned and the pastry cooked.
Remove from the oven and sprinkle with sugar if desired. Cool slightly before carefully lifting them out of the tins (running a sharp knife round the edges to loosen them helps) and cooling on a rack.
When cold store in an airtight tin. Serve warm or cold.


--- layout: post title: "[iPhone] 画面サイズ" categories: - blog tags: - Blog - "徒然" status: publish type: post published: true meta: {} author: login: sonson email<EMAIL_ADDRESS>display_name: sonson first_name: '' last_name: '' ---


Bacon ipsum dolor amet capicola strip steak pancetta pastrami cow pork belly drumstick leberkas alcatra meatball chuck ground round pork loin. Corned beef short loin prosciutto turducken, ground round drumstick brisket. Pork belly tri-tip flank porchetta. Corned beef prosciutto turkey chuck. Kielbasa tail cow salami leberkas, boudin corned beef hamburger rump cupim ham hock. Biltong porchetta pancetta, ham hock leberkas shoulder ribeye flank jerky turkey.
Fatback swine flank jerky leberkas. Leberkas ground round sausage, porchetta chicken tri-tip turkey pork chop meatball sirloin. Pancetta bacon ground round sausage tri-tip turkey. Filet mignon hamburger kielbasa short loin, chuck cow prosciutto pastrami fatback. Turducken pork chop rump ribeye tenderloin, ham hock frankfurter alcatra shankle.
Jowl swine strip steak kevin ribeye ball tip beef ribs. T-bone sirloin alcatra, spare ribs salami boudin hamburger pancetta. Pork loin spare ribs ham hock cupim picanha turkey doner short ribs strip steak. T-bone pork loin ham, biltong cow filet mignon chuck. Capicola turducken prosciutto, flank pastrami pork brisket venison. Kevin capicola pork loin ground round picanha short loin sausage porchetta short ribs ribeye andouille jowl prosciutto filet mignon flank.
Corned beef prosciutto salami short ribs, cupim doner leberkas spare ribs porchetta drumstick beef alcatra filet mignon. Meatloaf andouille beef ribs turducken flank, tenderloin meatball ribeye ham hock t-bone. Meatball porchetta ham doner beef tenderloin. Beef pastrami fatback prosciutto meatball drumstick pancetta swine rump. Capicola prosciutto chicken boudin. Sausage turducken frankfurter, alcatra kevin fatback beef picanha bresaola boudin tenderloin hamburger cow pancetta.
Cow filet mignon meatloaf shoulder pig, salami turducken venison drumstick. Kevin boudin cupim, bresaola pastrami jerky rump ribeye jowl leberkas. Ball tip pork belly meatball ham, short loin bacon biltong pancetta prosciutto tail pig fatback corned beef tenderloin. Alcatra corned beef meatloaf turkey pork chop doner picanha short ribs brisket beef chuck landjaeger bresaola.
 Ground round capicola filet mignon tongue brisket, shank turducken hamburger tenderloin tri-tip picanha.  Bresaola shank filet mignon prosciutto landjaeger meatball spare ribs. Kielbasa brisket fatback cupim, cow salami meatloaf pork belly hamburger venison beef swine tail corned beef. Pork belly drumstick shoulder spare ribs, rump boudin pork chop t-bone venison picanha sirloin pork loin corned beef leberkas. Bacon drumstick kielbasa leberkas flank.


You need the Growl.framework framework. With or without installer included. You can place it whereever you want (e.g. inside the application bundle) as long as you can get a folderitem for it.
Your application must have a bundle ID! Else it won't register.
Download the growl framework on http://growl.info/


AWS IoT TopicRule SnsAction
Sns
is a property of the Actions
property that describes an
action that publishes data to an SNS topic.
Syntax
JSON
{ "MessageFormat":
String
, "RoleArn":String
, "TargetArn":String
}
YAML
MessageFormat:
String
RoleArn:String
TargetArn:String
Properties
MessageFormat
-
The format of the published message. Amazon SNS uses this setting to determine whether it should parse the payload and extract the platform-specific bits from the payload.
For more information, see Appendix: Message and JSON Formats in the Amazon Simple Notification Service Developer Guide.
Required: No
Type: String


Description: A diminutive magical goblet casts a protective shield from the central city, CityNameGoesHere. Human and demon forces can only attack cities that are within the neutral zone between the goblet shield and the demonic influence barrier. When either side captures a city, it gains influence within the neutral zone. The demonic influence is weaker for cities closer to the central city. The neutral zone barriers are set a fixed distance away from the closest captured city of the opposite side.
This is a fan pre-production prototype of something vaguely related to the game MASSIVE CHALICE. I am not affiliated with MASSIVE CHALICE except as a backer.
Select a city by clicking one of the small circles in the map to the left.


An implementation of turtle graphics <http://en.wikipedia.org/wiki/Turtle_graphics>. Turtle graphics comes from Papert's language Logo and has been used to teach concepts of computer programming.
| Version: | 1.0-8 |
| Depends: | R (≥ 3.0), grid |
| Suggests: | knitr, digest |
| Published: | 2018-02-14 |
| Author: | Anna Cena [aut], Marek Gagolewski [aut], Barbara Zogala-Siudem [aut, cre], Marcin Kosinski [aut], Natalia Potocka [aut] |
| Maintainer: | Barbara Zogala-Siudem <zogala at rexamine.com> |
| BugReports: | https://github.com/gagolews/TurtleGraphics/issues |
| License: | GPL (≥ 3) |
| URL: | http://www.gagolewski.com/software/TurtleGraphics/ |
| NeedsCompilation: | no |
| Materials: | NEWS |
| CRAN checks: | TurtleGraphics results |
| Reference manual: | TurtleGraphics.pdf |
| Vignettes: |
A guide to the TurtleGraphics package |
| Package source: | TurtleGraphics_1.0-8.tar.gz |
| Windows binaries: | r-devel: TurtleGraphics_1.0-8.zip, r-release: TurtleGraphics_1.0-8.zip, r-oldrel: TurtleGraphics_1.0-8.zip |
| OS X binaries: | r-release: TurtleGraphics_1.0-8.tgz, r-oldrel: TurtleGraphics_1.0-8.tgz |
| Old sources: | TurtleGraphics archive |
| Reverse imports: | mazealls |
Please use the canonical form https://CRAN.R-project.org/package=TurtleGraphics to link to this page.


--- title: "Sundance" date: "2017-07-09" categories: - summer subtitle: "free outdoor movies" layout: "events" event-image: "sundance.jpg" event-dates: "TBD" event-location: "Red Butte Garden" button-type: "" tickets-link: "" member-sale-date: "N/A" public-sale-date: "N/A" member-cost: "Free" public-cost: "Free" event-notes: "free outdoor movies" tickets-button: "" published: false ---
Photo Credit: Fred Hayes
FREE outdoor community screenings in the Red Butte Garden Amphitheatre. Pack a blanket and a picnic, low-back chairs, and bring your friends to enjoy Sundance Film Festival favorites under the stars. It can get chilly at night, so come prepared.
Gates open at 7:30PM and movies begin at dusk. Please enter through the Amphitheatre gates. All films go on rain or shine (pending lightening). All screenings are Free, no ticket needed.
THANK YOU FOR MAKING THE 2016 FILM SERIES A SUCCESS!
Check back next spring for the 2017 movie lineup.
www.sundance.org/utah
Step into the world of the Sundance Institute Artist Programs.
These events are FREE and open to the public.


Backpack Cali
Want a chance to get away from it all? Do you prefer to see nature at your own pace? Respond to the call of the great outdoors with our Backpack Cal tour packages. Whether it's hiking beneath the towering Redwoods, walking the ridges of the beautiful Channel Islands, or testing yourself against the harshest environments nature has to offer, we have a tour for your abilities and interests.
If you're looking to just take in the incredible natural beauty of California at your own pace, try our Big Sur Retreat, a stress-free retreat into one of America's most beautiful forest regions.
Our Backpack Cal Tours offer a range of physical activity, from easy to difficult. Please check the difficulty rating before booking a tour to ensure that your experience is the best possible for your current fitness level. Tours with a rating of Moderate or higher require a signed waiver before booking.
 The Big Sur Retreat takes you to the most majestic part of the Pacific Coast and show you secret trails and spectacular scenery. This is one of our most flexible tours, with enough options to satisfy the casual hiker to the hard core experience junkie.
Optional 4 day tour available | Rating: Medium
Channel Islands Excursion
1 day $150
The chain known as the Channel Islands offer some of the most diverse and unique landscape on the Pacific coast. No motor vehicles are allowed on the islands, which makes this relaxing day trip hiking package the best and most interesting way to visit.
Rating: Easy
The Death Valley Survivor's Trek
2 days $250
Hot stuff? Need more of a challenge? Take this tour to the hottest place in North America: Death Valley. Due to extreme temperatures (120 degrees and higher) in the summer months, this tour is only offered November through April. Are you up to the challenge?
Rating: Difficult
In the Steps of John Muir
3 days $600
Follow in the steps on John Muir, famous naturalist and founder of the Sierra Club, and walk the same trails he helped blaze in and around Yosemite National Park.
Rating: Difficult
The Mt. Whitney Climbers Tour
4 days $650
Climb to the sky! The Mt. Whitney Climbers Tour takes you to the top of this 14,000 ft. of mountain in 4 days- our longest and most strenuous backpacking tour. Explore California will set you up with a trail permit, a two night camping pass, and an expert guide.
Rating: Difficult


Preparation time
1-2 hours
Cooking time
1 to 2 hours
Serves
Serves 4-6
For an extra special Sunday lunch serve up a classic beef Wellington, James Martin even shows you how to make the puff pastry from scratch.
Tip the flour and salt into a large bowl, add the cubes of butter and rub the butter into the flour using your fingertips, until the mixture resembles breadcrumbs.
Gradually stir in 150ml/5fl oz cold water, until the mixture comes together as a soft dough (you may not need all the water). Use your hands to pat the dough into a 2cm/¾in thick rectangular block.
Roll out the dough onto a lightly floured work surface to a rectangle measuring about 30x20cm/12x8in. Sandwich the remaining block of butter between two pieces of greaseproof paper. Using a rolling pin, roll or bash it out to a rectangle measuring about 15x20cm/6x8in.
Place the flattened butter into the centre of the rolled pastry. Fold one edge of the pastry over the butter to half-cover it, then fold the other edge of the pastry over the butter to cover the other half, so that the two edges of pastry meet. Pinch together the top and bottom edges of the pastry to seal the butter inside.
Fold the pastry in half lengthways, then turn the pastry 90 degrees to the right (a quarter-turn). Roll out the pastry again to a rectangle measuring about 30x20cm/12x8in.
Fold the pastry as before, bringing each edge into the centre so that the edges meet, then folding in half lengthways, before giving the pastry a quarter-turn.
Repeat the rolling and folding process once more, then wrap the pastry in cling film and chill in the fridge for 1 hour.
For the beef Wellington, preheat the oven to 100C/80C Fan/Gas ½.
Heat the oil in frying pan over a high heat. Quickly fry the beef fillet, turning it every few seconds, until browned on all sides.
Cut 4 large pieces of aluminium foil big enough to roll the beef fillet in, and place them on top of each other. Place the beef fillet in the centre and wrap it very tightly to form a cylinder, twisting the ends to secure. Place the wrapped beef fillet on a baking tray and roast for 1 hour. Remove the beef from the oven, set aside until cooled to room temperature, then chill in the fridge until completely cold.
Meanwhile, heat a knob of butter in a frying pan over a medium heat. When the butter is foaming, add the chopped mushrooms and tarragon and fry for 1-2 minutes, or until all of the liquid has evaporated and the mushrooms have softened. Transfer the mushrooms to a bowl and wipe the pan with a piece of kitchen roll.
Add a little more butter to the pan and fry the spinach for 1 minute, or until just starting to wilt. Remove the pan from the heat and continue to stir the spinach until completely wilted. Transfer to a bowl and set aside.
Next, make the pancakes. Place the 2 whole eggs and the flour in a bowl and whisk together until well combined. Gradually whisk in the milk until the mixture forms a smooth batter that is thick enough to coat the back of a spoon.
Heat a knob of butter in a frying over a high heat. When the butter is foaming and starting to smoke, add a ladleful of the pancake batter and swirl the pan to evenly coat the base. Fry for 1-2 minutes, or until the underside of the pancake is golden-brown. Flip the pancake over and cook for a further minute, or until the pancake is cooked through and golden-brown all over. Slide the pancake out onto a plate lined with greaseproof paper, then cover it with another layer of greaseproof paper. Repeat the process with the remaining butter and pancake batter.
Place the chicken liver pâté in a bowl and beat until smooth and soft.
Increase the oven temperature to 230C/210C Fan/Gas 8.
Roll out the puff pastry on a lightly floured work surface to a rectangle measuring 40x50cm/16x20in with a thickness of 2-3mm (it needs to be large enough to wrap the beef fillet).
 Trim the overlapping edges of the pancakes using a sharp knife so they sit in one even layer.
Spread the softened pâté in an even layer all over the pancakes. Arrange a layer of wilted spinach on top. Spread the cooked mushrooms on top of the spinach. Season with salt and pepper. Finally, peel the foil from the beef fillet and place it in the centre.
Whisk the egg yolks in a bowl, then brush them around the pastry border. Tightly roll the beef fillet in the pastry, ensuring the seal sits at the bottom of the Wellington. Tuck the ends underneath the Wellington without trimming them. Transfer the Wellington to a baking tray lined with baking paper and brush all over with the remaining beaten egg yolks. If desired, decorate with pastry trimmings.
Bake the beef Wellington for 20-25 minutes (for medium beef), or until the pastry has risen and is golden-brown. Leave to rest before serving.
Meanwhile for the Madeira jus, heat a saucepan, once hot add the butter, once melted add the shallot and cook for 2-3 minutes. Deglaze with the Madeira and then add the stock. Cook until reduced by half.
For the ceps, heat a large griddle pan until hot. Drizzle the oil over the mushrooms and place on the griddle pan. Cook on each side for 1-2 minutes.
To serve, carve the beef Wellington into thick slices and place each into the centre of a serving plate. Drizzle the Madeira sauce around the edge of the plates and garnish with the ceps.


Network science is an academic and applied field that studies complex networks in domains such as telecommunications, computer networks, biological, cognitive and semantic networks, and social networks. The field draws on theories and methods from various fields, including graph theory (from mathematics), statistical mechanics (from physics), information spread and diffusion (from both epidemiology and communications), and social structure (from sociology), to only name a few. Recently, because of the broad availability of datasets in highly applied domains like social media, corporate filings and transportation, network science and research areas like AI and Big Data have started to converge in exciting ways, leading to interdisciplinary collaborations and scientific verification (or debunking) of theories previously believed to be quantitatively untestable.
AI, Networks and Society is a collection of projects in our group that seeks to use various sources of data to learn more about society using empirically rigorous, data-driven methodologies. Datasets used in our investigations include social media data (primarily, Twitter), news data, structured data (such as tables and graphs), economic data and election data.


BREAKING: Court nullifies creation of new Emirates in Kano
by https://www.facebook.com/CheerfulpraiseKolade Adeyemi, Kano
A Kano High Court sitting in Kano on Thursday nullified the four newly Emirates created in the state by Governor Abdullahi Ganduje.
Justice Usman Naába in his judgment averred that the creation of the Emirates and appointment of four first Emirs was illegal.
Details shortly…


Presets
Lincoln Douglas
Affirmative Constructive
Negative Constructive
First Affirmative Rebuttal
Negative Rebuttal
Second Affirmative Rebuttal
Public Forum
Constructive
Rebuttal
Open Timer


This is my first post so please go easy on me!
I have installed a 50kW Solar PV system on a large industrial factory and the client wishes to have a display in his reception to show consumption, generation, export and CO2 savings if possible, with the consumption and generation figures the most important. We have tried to use some of the domestic/small commercial systems available on the market, but all seem to have a max CT clamp size of approx 15-20mm window, while the incoming mains has buss bars of 40mm x 4mm, so the clamps wont physically fit them.
Because of the large incoming mains the DNO meter is CT metered at 800:5 ratio, and we have even tried to fit the monitoring devices ct clamps onto the DNO CT's and transform up but this wasnt successful, and also took apart the devices small ct clamps and counted the windings to be 2560 turns, in an attempt to find a large window clamp with similar number of turns.
Can someone please advise me there is a system available on the market or help on designing a system, where the consumption of a large supply and a PV system can be monitored and reported onto a programme and displayed on a screen.
Any advise is greatly welcomed....
thanks
Re: 3 phase monitoring for PV and Consumption
Take a look at MartinR's "Full Fat 3-phase Monitor". You won't be able to use our standard CTs, but Magnelab do some fairly big ones, 3" x 5" aperture is the biggest - OK, they are designed to give 1/3rd of the output voltage we require but by derating a large one, you could put those directly on your bars (we'd need to check with Magnelab first - I don't have the full data for all of their range!). Then for the display, you could have a web browser. In between, you would have an emonBase to feed Ethernet to your own server running emonCMS and feeding the display - and logging all the data.
On the downside, there's probably a steep learning curve for you, you need to build and box the units, and you need to install emonCMS on a suitable machine and learn your way around it, especially if you want to add to it (but your client's IT Dept might help there). The upside is there's nothing proprietary and hidden, full data is available.
I'll go into more detail if what I propose looks promising.
Re: 3 phase monitoring for PV and Consumption
I have some larger current transformers from this UK manufacturer on my 'self-imposed' export meter for my 21.84kWp solar PV system.
http://www.currenttransformers.co.uk/split-core/clip-together-range-shor...
Re: 3 phase monitoring for PV and Consumption
That particular range won't do as the aperture is not large enough, but they do have others. The snag is all appear to be 5 A secondaries, so they would need the burden resistor mounting off-board both from the current viewpoint (I think our jack sockets are not rated to 5 A) and heat (5 W dissipation). However, it will be worth an enquiry. Or a visit - they're only 5 miles up the road from me.


Rules
Prelims(Classic Sudoku)
Sudoku is played over a 9x9 grid, divided to 3x3 sub grids called "regions":
Sudoku begins with some of the grid cells already filled with numbers:
The object of Sudoku is to fill the other empty cells with numbers between 1 and 9 (1 number only in each cell) according the following guidelines:
1. Number can appear only once on each row.
2. Number can appear only once on each column.
3. Number can appear only once on each region.
A summary of these guidelines would be, that a number should appear only once on each row, column and a region.
Finals(Clock Sudoku)
1. A circular Sudoku puzzle containing ring shaped regions and pairs of wedges. Each ‘region’ in this puzzle is a “full” Sudoku region in the sense that it contains all the numbers from 1-12. There are 6 “rings”, nested from the inside to the outside and 12 “wedges” of 6 cells that must be considered in pairs.
2. Fill in with the integers 1- 12 such that each of the six rings has no repeated entries; each same-colored adjacent pair of “wedges” has no repeated entries and each antipodal pair of wedges has no repeated entries.(An “Antipodal” pair of wedges is a set of 2 wedges that are opposite or “across” from each other on the circle)
Levels
Prelims
I. Beginner
II. Intermediate
III. Advanced
Finals
IV. Expert
Time Limit: 60 minutes ( For I,II and III)
Time Limit: 45 minutes (For IV)
Time Limit: 45 minutes (For IV)
Prize
Certificate Policy
1. Certificate of Excellence will be provided to the participants who qualify for the FINALS.
2. Certificate of Participation will be provided to all the participants taking part in the event.
3. The teams which get disqualified due to disobeying any of the rules provided will not be entertained with any kind of certification.
Prize money worth: will be updated soon


By {{blog.createdByFullName}} {{blog.createdByFullName}} on {{blog.date}}
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Research and production environment for simulations. Housed under the Institute for Simulations and Trainging in UCF.
Aircraft ground support training simulators.
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