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critics that I hope to please, those whose years count no more than six.
likes them, I shall be satisfied.
past the shed and past the barn and past the orchard to the wheat-field.
a sharp end; and little John walked along by the drag.
to the river. And when they came to the river, they stopped.
and wash dishes, and wash clothes.
slopped over the top of the barrel on to the drag, and on to the ground.
in at the big gate, and up to the kitchen door, and there they stopped.
was the barrel of water, all ready.
he said: "Gee up there, Buck; gee up there, Star."
through the gate into the corn-field.
until they had been all over the field.
barn and went to sleep; and Uncle John put the drag in the shed.
covered over, they went away and left it.
time to gather the corn, for it was ripe.
out the wide gate and along the road to the corn-field.
the ears of corn were in the pen.
away; and he put the cobs in the shed, to use in making fires.
out the wide gate, and into the road.
that takes care of the mill is called the Miller.
And Uncle John said: "I've got some corn to grind."
the mill, and the miller asked Uncle John how he wanted the corn ground.
ground into fine hominy, and some of it into meal.
put it into the bags again, and tied them up.
he put the fine hominy into the bags again and tied them up.
And he said: "Here's your meal, Deborah."
old oxen went into the barn and went to sleep.
the shed and past the barn and past the orchard to the wheat-field.
summer was over and it was beginning to get cold.
the yoke, and the old oxen went to their places and went to sleep.
the way to the field where they went to get water, but the other way.
cover that came down on top, and a screw and a long handle above.
both kegs were full of juice. And they call the juice cider.
or a mug could go under the spigot.
think I would like a drink of cider."
in he turned the spigot the other way and the cider stopped running.
Then he carried the cider up to his father, and his father drank it.
"Father, that's pretty good cider; you'd better have some."
down cellar again and get another pitcher of cider.
another gate where the bars were across, into the maple-sugar woods.
And in that field wheat grew.
slowly along past the barn and past the orchard to the wheat-field.
went all around the field and around until it was all ploughed.
that it lay in the soft ground, and then he went away and left it.
high as that all winter and the snow covered it.
at the top of each stem got yellow and brown and the wheat was ripe.
This was in the beginning of the summer.
lying there in the sun.
back to the barn and in at the great doors.
and the oxen, and the cart went in between, with the wheat in it.
places and went to sleep.
fastened to its end by a piece of leather so that it flapped about.
This was to beat the wheat with, and they called it a flail.
through. These broken pieces that blow away, they call chaff.
was enough to take some to market besides.
where the fields would be were all covered with trees.
necks and the bows up under, and he hooked great chains to the yokes.
the man piled them up and built a wall.
the oxen went into the barn and went to sleep.
wanted to and eat the grass in either field.
have fun, so he ran after him.
the wall into the next field.
back into the other field, he climbed over into the road.
off--he was going to take to the mill, to have them sawed into boards.
So, one morning, after that was all done, the man got out the oxen.
the road, pulling the sleds with the logs on them, for a long way.
big saw go, inside the building.
off. That piece was round on one side and flat on the other.
to give to the men for helping him.
into the barn and went to sleep.
shed and past the barn and past the orchard to the wheat-field.
went into the house another way, and kept out of Uncle Solomon's way.
But he didn't tip Uncle Sam into the bushes any more.
his clothes, and Aunt Deborah got up too, and they went down-stairs.
were put that were to go to market.
lantern, because it wasn't daylight yet.
started walking down the little track into the road and along the road.
not many people in the streets, for they had not had their breakfasts.
better let it go and get back."
on the truck, a part at a time, and he wheeled them into the market.
then they lifted up their heads and started along the streets.
country that led to the farm-house.
high seat, for he had been there a long time and he was very tired.
through the wheat-field to the maple-sugar woods.
enough was on the ground for the sled to go on.
they came to a little house beside the road, and there they stopped.
were some square iron pans.
they came where were some maple-sugar trees, and there they stopped.
they did until they had used up all their buckets.
orchard and past the barn to the shed.
and the old oxen went into the barn and went to sleep.
buckets--drip--drop--drip--drop--and the buckets were nearly half full.
back to the little house and emptied them into the big kettle.
until he had taken all the sap that had dripped.
was about right for maple-syrup.
bung-hole and set the keg in the corner.
taken all the sap that the trees ought to give.
way would be to put a rail fence across.
trees, but a different kind.
they cut the other trees into logs that were just long enough for posts.
was a rail. But the post logs they didn't split.
barn and past the shed and went in at the kitchen door.
across the wheat-field, and he took down the bars at the other side.
the trees down, because they were afraid he might get hurt.
dragged the sled along past the orchard and past the barn to the shed.
be set until the winter was over and the ground was soft.
over and the bows under and hooked the tongue of the cart to the yoke.
pounded the dirt down hard.
the oxen walked back to the field again and in beside the row of posts.
each post were four holes, and four rails went across.
the barn and went to sleep.
corner next to the cows was the milk-room.
eat the grass, and that was better.
and one of the legs was shorter than the other two, so that it sloped.
milk she had, the pail was about half full.
cows were eating their meal all the time they were being milked.
full of water, so that the cows could drink whenever they were thirsty.
stood by the gate, waiting for little John to come.
push, and the one that pushes harder is the leader.
go through the gate, the leader first, and the others following after.
and let them down, so that they could go through.
back to the farm-house to get his breakfast.
and they all walked over to the big tree and got in the cool shade.
chewed up fine. That is what they call chewing the cud.
go. And little John came running after.
cow-yard and went into the house.
into the house for their supper.
lay down and went to sleep.
and in this field was growing grass that would be made into hay.
then Uncle Solomon and Uncle John would go and help them cut it.
all the grass in the field.
the horn from the hook and blew it.
the sun shone on the grass and dried it, so that it was changing to hay.
so that it would dry better; and they left it until the afternoon.
the cart and took the pitchforks, and gave little John his little rake.
walked along beside one of the haycocks, and there they stopped.
enormous load, higher than the room. And little John raked after.
kept, and the top of the load was higher than the floor of the loft.
full as it would hold.
cows to eat all winter.
went into the barn and went to sleep.
pile it up in a great pile near the kitchen door.
without burning their hands, and then they could swing it back again.
weren't any matches then, so that it was hard to light the fire.
they liked to fly up into the trees, and sit on the branches to sleep.
and Uncle Solomon and Aunt Phyllis.
fire and get breakfast ready.
top of the pan again and left it.
cook any more, and she put them in a bowl and put them on the table.
down the horn and blew it.
breakfast, and it was very nice.
over it in the smoke.
and put in some more wood.
and they mixed them together just the right way, and made some dough.
bottom of some plates that were middle-sized deep.
the cores cut out, and the nice part of the apples cut up into slices.
in the top with a knife.
and they were three kinds.
over the top of the squash pies, but just a thin strip around the edge.
both the tables, for Uncle Solomon and Uncle John liked pies.
hotter, for it had cooled, baking so many pies.
it around and pushed it with her hands. That is what they call kneading.
getting cooler all the time, and the bread would not get burned.
whole week, and they would not bake any more until the next Saturday.
John: "John, let's go in swimming."
And little John said: "All right."
edge of the river, at the end of a stone wall.
on the bank. And when their shirts were dry, they put their clothes on.
they hadn't been away at all.
little Charles. "Charles, I want you to get me some eggs."
want to come very near, so he came only a little way.
And Aunt Deborah said: "Charles, I want you to come right here to me."
quite dry with his shirt.
And Aunt Deborah said: "Charles, you've been in swimming."
although they sometimes did things they ought not to do.
And Charles said, "Yes'm." Then he ran away quickly to find the eggs.
Then Aunt Deborah said: "John, come here to me."
that his hair was wet.
at his mother and grinned and said, "Yes'm."
And Aunt Deborah said, "You mustn't do that, John. You're too little.
in his boat." So little John ran off after little Charles.
to go out in the boat with me?"
So Uncle Solomon said, "Come on, boys."
because that might tip it over.
happened to harm them. And they swam around and had a fine time.
boat back to the place where he kept it.
all she gave to Uncle Solomon.
And they all ate their gingerbread, and thought it was very good indeed.
they like to get up in the trees and sleep out-of-doors.
like to lay eggs in places where eggs are already.
fight very hard, for they were not the kind that fight hard.
apple-trees in the orchard, but some of the hens roosted in other trees.
and picked the bugs and worms off the vines.
did not like to lay eggs in the hen-house.
chickens ran along with her.
she didn't have any chickens.
them to eat. And so they did until it began to get dark.
under their wings and went to sleep.
was a young man, and a long time before he had the farm.
they were going, and the things to eat, and the water they would drink.
the country where the monkeys live, and around the end of that country.
the ship would sail ahead.
buy to take back to Boston.
about to see what he could buy that was very nice.
were very fine and beautiful.
the teak-wood and the other things.
great anchor and hoisted the sails.
sailed in past the islands and into the harbour, and up to the wharf.
they came to the place where the men stayed that owned the _Industry_.
That place they call an office.
going to give this shawl to my daughter Lois."
Lois was Captain Jacob's wife.
farm-house, and that man had built the barn first and then the house.
them away to another place.
was a long way from that town, and it was not on the shore of the ocean.
time ago, more than one hundred years.
they had put great flat stones, so that the water ran over the stones.
And on top of the stones the water wasn't deep at all.
the cream comes to the top.
days until it was just right to make into butter.
enough, and was in one big lump.
some to take to market besides.
Uncle John had to get some bean-poles for the bean plants to climb up.
come up through the ground, and were tall enough to begin to climb.
and past the orchard to the wheat-field, and little John came after.
bottom, and they were about twice as tall as Uncle John.
thumb. These smooth trees without any branches they called poles.
field, and went along to the bean plants, and there they stopped.
road and in at the wide gate and up past the kitchen door to the shed.
and the oxen went into the barn.
ripe, Uncle John gathered them and took them in to Aunt Deborah.


Money is a leading cause of divorce and marriage counseling. How can couples avoid letting money ruin their love?
The answer – communication. When you say, "I do," you unite more than just your families – your finances are now intertwined as well. While there is no 'one size fits all' approach to managing finances, a relationship benefits from open and honest conversations on debt, savings goals and budgeting plans.
Over the past 1,000 years, the world hasn't changed.
So began the story from our new-to-us Certified Financial Planner® practitioner, Dave Stevens. Dave has been a friend to one or more of us for more than a decade and he joins our team with open arms, and over 30 years of investment experience. I hope you enjoy his story half as much as we do.


Hi everyone! I have been really slacking on the blog this week because I'm in St. Louis preparing for our big move in May. We signed a lease, Bob had some job interviews, and I've been getting in some quality family time (and shopping!) I have been busy ever minute, but it's been a lot of fun.
My mom, my sister, and I decided that this year, instead of dyeing Easter eggs we would decorate sugar cookies! After seeing several posts about Easter cookies, I couldn't resist trying them out. I love the technique of using toothpicks to drag out lines or dots into beautiful patterns. We had so much fun playing around with different designs!
Each time I've used Royal icing (here and here), I used a different recipe for the icing and a different sugar cookie recipe (see a great sugar cookie comparison post here). I keep thinking I'll find one that is better than the last time, but really they all seem very similar to me. I'll provide links to the recipes I used this time below, but know that these techniques work on any cookie, and I'm sure any royal icing recipe.
The one thing I did differently this time was to use only one consistency icing per color and to use it for both outlining and flooding, which were done at the same time, rather than what I've done in the past: outline, wait for it to dry, then flood. When using the same color for outlining and flooding, the outline blends in nicely. Of course this might not work for the Monkey Face Cookies where having a distinct outline is essential, but for these cookies it worked very well.
This time, I drew inspiration from a number of different sources. I first saw this post on the King Arthur flour blog about these fun techniques for Easter cookies. Then I saw a guest post on Bake at 350 from Sweetopia, which led me to other posts at Sweetopia. Each of these blogs have some great tips for working with royal icing.
For those of you who celebrate Easter, you still have time to try these out before Sunday - so get to it! :) Or see the sidebar for some other great Easter treats.
Have a great Easter, Passover, or a nice relaxing weekend!
My mom's creation - remind you of anyone?
These are so cute! I'm baking with my little siblings tomorrow. Thanks for the inspiration!
These are absolutely beautiful! I want you to send me some for my kids. They look amazing!
Have fun preparing for your move!
so cute! glad you're blogging again - we missed you! but i'm sooooo glad you're having fun and getting to relax a bit!
when do I get to come over and eat these?
These are so pretty! I'm royal icing impaired, I'm afraid, although after Andrew saw these, he's begging to make sugar cookies. I think we'll give it another try.
My brother and his family live in St. Louis, Webster Groves, actually. We haven't visited lately, but I remember the parks were especially lovely.
These are absolutely gorgeous - beautiful work!
How adorable! I wouldn't be able to eat them because they are just so cute :) I love how you played with the toothpicks too! Good luck with everything and Happy Easter Meghan!
I'm thinking Hoops and Yoyo. :) Love the chick with bunny ears. These are incredibly creative and beautiful. Happy Easter!
These are adorable!! I would love if you would post these on my facebook page please - such cute ideas!!
I would much rather decorate cookies than dye Easter eggs too. You did such a stunning job--wow, they look picture perfect. Hope you had a good Easter!


text:
Castro Emphasizes Immigration, Education In Frisco
By Galilee Abdullah / KERA News • Jul 1, 2019
Julian Castro waves at a town hall event in Arizona in June.
Democratic presidential candidate Julian Castro says he wants to boost legal immigration in the U.S. -- and that immigrants play an important role in the country.
Castro on immigration
Castro spoke Monday in Frisco to the Frisco Democratic Club. He said that immigration is just one issue that he's working to address during his campaign.
"It's not just about one issue, all of these things are connected, they all go together," Castro said. "The ability of our children to get a good education, making sure that we do something about common sense gun reform, so that our children can learn in school better without having an anxiety in some places about what might happen."
Castro on the issues at hand
Castro also appeared in Fort Worth Monday evening. The Fort Worth event was hosted by United Fort Worth, a nonpartisan grassroots coalition that aims to get presidential candidates to meet marginalized groups where they are.
Group co-founder Daniel Garcia Rodriguez says he does not want people of color and young adults used simply for political votes.
"I think in order to get communities of color invested in voting, candidates, parties, groups have to invest in the community and go to them and say, 'Hey, I know you feel like this system isn't working for you,' but we're gonna make sure that we make this system be yours," Rodriguez said.
The 1st Democratic Primary Debate In 100 Words (And 7 Video Clips)
By Jessica Taylor • Jun 27, 2019
Massachusetts Sen. Elizabeth Warren controlled the debate early with a progressive policy pitch. She and New York City Mayor Bill de Blasio said they would eliminate private insurance in favor of "Medicare-for-all." Former Housing and Urban Development Secretary Julián Castro was aggressive on immigration, backing decriminalizing illegal border crossings.
Texas Campaign Workers Of Color Face Harassment, Discrimination In The Field
By Alex Samuels / The Texas Tribune • Jun 26, 2019
Leslie Boorhem-Stephenson / The Texas Tribune
While campaigning for a City Council hopeful earlier this year, Arash Farasat was assigned to knock on doors in an affluent Dallas neighborhood where most of the residents didn't look like him.

redpajama_set_name: RedPajamaCommonCrawl


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text:
\section{Introduction}

Automatic spelling correction has been an important  natural language processing component of any input text interface of today \cite{jurafsky2014speech}.  While spelling errors in common writing could be regarded as
innocuous omissions,  affecting opinions about the writer's competence at worst, those of the malicious kind can potentially be highly offensive, since they are intended to deceive an automated mechanism--be it a spam filter for emails or a profanity detector for social media.
For those applications that rely on automatic detection of specific keywords to enable real-time control of the cyber-environment, detecting and correcting spelling errors is of primary importance.

Perspective \cite{perspective}, is one such application that helps reduce abusive content online by detecting profane language. It has been pointed out that Perspective's otherwise  good performance in detecting toxic comments  is not robust to spelling errors \cite{hosseini2017deceiving}.
Consider the sentence, ``My thoughts are that people should stop being stupid.'' which was assigned a perceived toxicity score of 86\% by Perspective.  When the word ``stupid'' was misspelled as ``stup*d'' in the same sentence, its toxicity score reduces to $8\%$. This marked reduction induced by the deceptive spelling error on the keyword  `stupid'  reflects the importance of accurate spelling correction for optimal toxicity detection. Had the spelling error been detected and corrected \textit{before} scoring for toxicity, the score would not have lowered.

Likewise, deliberate typographic errors are common in phishing scams and spam emails. For instance, ``Please pya any fees you may owe to our company'' where \emph{pya} is clearly a spelling error,  included to deceive spam filters that are sensitive to keywords.  In both these instances, spelling correction as a preprocessing step of the messages are critical for a robust performance of the  target system.
Spelling correction in the preprocessing stage in malicious settings constitutes the focus of this study.

In this paper, we empirically demonstrate the effect of spelling errors in a malicious setting by adding synthetic misspellings to sensitive words in the context of two applications--profanity detection and spam detection. We propose an \textit{unsupervised} embedding-based algorithm to correct the targeted misspelled words. Earlier approaches to spelling correction primarily depend on the edit distance to find  words morphologically similar to corrections. More recently, spell checkers have been improved with the addition of contextual information (e.g., n-gram modeling in  \cite{wint2018non}), often with intensive computation and memory requirements (to obtain and store N-gram statistics).  A recently studied neural network-based spell checker  learns the misspelling pattern from annotated train data in a supervised way, and was found to be sensitive to data domains and dependent on human annotations \cite{ghosh2017neural}.
Our approach to make  the correction procedure context-aware involves harnessing the geometric properties of word embeddings. It is light-weight in comparison, unsupervised, and can be adapted to a variety of data domains including Twitter data and spam data, since domain information can be well captured by tuning embeddings on domain-specific data \cite{taghipour2015semi}.

We first demonstrate the effect of spelling errors in a malicious setting in the context of two applications--profanity detection and spam detection.
Then we propose an unsupervised  algorithm to correct the targeted spelling errors. Besides performing well on the correction of synthetic spelling errors, the algorithm also performs well on  spell checking of real data, and shows that it can improve hate speech detection using Twitter data.

\section{Related Work}

\textbf{Spelling correction}. Automatic spelling correction in non-malicious settings has  had a long research track as an important component of text processing and normalization. Early works  have used edit distance to find morphologically similar corrections \cite{ristad1998learning}, noisy channel model for misspellings \cite{jurafsky2014speech}, and iterative search to improve corrections of distant spelling errors \cite{gubanov2014improved}. Word contexts have been shown to be improve the robustness of spell checkers with n-gram language model as one approach to incorporate contextual information  \cite{hassan2013social, farra2014generalized}. Other ways of incorporating contextual information  include n-gram statistics capturing the cohesiveness of a candidate word with the given context \cite{wint2018non}.

\noindent\textbf{Effect of malicious misspellings}. Misspellings are commonly seen in online social platforms such as Twitter and Facebook, and recent studies have drawn attention to the fact that online users deliberately introduce spelling errors to thwart bullying detection \cite{power2018detecting} or to challenge to moderators of online communities \cite{papegnies2017impact}. This is because  simple ways of filtering terms of profanity  are rendered inadequate in the presence of spelling errors. Likewise, email spam filters  are often obfuscated by misspelled sensitive words  \cite{zhong2014deobfuscation} because word-based spam filters tend to make false  decisions in the presence of misspellings \cite{saxena2015spamizer}, and so are word-based cyberbullying detection systems \cite{agrawal2018deep}. Misspelling is also seen in web attacks where a phishing site has a domain name as a misspelling of a legitimate website \cite{howard2008web}.

\noindent\textbf{Approaches to correct malicious misspellings}. Given the malicious intent of misspellings in the context of the Internet, recent studies have proposed correction strategies specifically for malicious misspellings. Levenshtein edit distance is commonly used in spell checking of detection systems. Spam filters \cite{zhong2014deobfuscation} and cyberbullying detection systems \cite{wint2017spell} rely on the idea of edit distance to recognize camouflaged words by  capturing the pattern of misspellings. For example, \cite{rojas2013revealing} studied an edit distance function designed to reveal instances where spammers had interspersed black-listed words with non-alphabetic symbols. Lexical and context statistics, such as  word frequency, have also been used to make corrections in social texts
\cite{baziotis-pelekis-doulkeridis:2017:SemEval2,jurafskyspeech}. {Existing approaches have the problem of domain specificity, since their lexical statistics are obtained from a certain domain which might differ from the domain of target applications.}

Our study considers the spelling correction in a malicious setting where errors are not random, but are carefully  introduced.
Our context-aware approach to spelling correction  relies on the geometry of word embeddings, {which has the advantage of efficient domain adaptation. As we will show in Section~\ref{subsec:corr_context}, the embedding can be easily tuned on a small corpus from the target domain to capture domain knowledge.}

\section{Methods}
\label{sec:addError}
We study malicious spelling correction for three target applications--toxicity detection using the Perspective API, email spam detection, and hate speech detection on Twitter data. We work with synthetic spelling errors in the first two applications, and study the real misspellings in the third.

For the toxicity detection task, we use the Perspective dataset \cite{perspective}, which provides a set of comments collected from the Internet with human annotated scores of toxicity. Using the Perspective API we obtained the toxicity score for $2767$ comments in the  dataset.

The spam dataset consists of $960$ emails from the Ling-Spam dataset \footnote{\url{http://csmining.org/index.php/ling-spam-datasets.html}}. We randomly split the data into $700$ train emails and $260$ test emails. Both the train and test sets were balanced with respect to the positive and negative classes. The most frequent $2500$ words in the training data were selected, and we counted the occurrence of each word in the emails. The number of occurrences of these frequent words were used as a $2500$-dimension feature vector for each email. We first used these features to train a Naive Bayes classifier. It achieved an accuracy of 98\% in spam detection.

As for the Twitter data,  a total of 16K user posts were collected from Twitter, a social communication platform and used in  used in \cite{waseem2016hateful}. Of these tweets,  $1937$ were labeled as being racist,  $3117$ were labeled as being sexist, and the rest were neither racist nor sexist. The task of hate speech detection is to classify these tweets into one of the three  categories {\textit{racist, sexist, neither}}. We randomly split the tweets into train and test data, and trained a neural-network based hate speech detection system on the training data (described later in Section 5.3).

\subsection{Characterization}
Here we summarize our assumptions on the characteristics of the  malicious spelling errors. \\
(1) Malicious errors are usually made on the sensitive keywords in order to obfuscate the true intentions and deceive detection systems that rely on keywords \cite{weinberger2009feature}. \\
(2) The error yields a word that is similar to the original word in surface form. The misspelled words would be words that humans can easily understand (thus the erroneous words still convey the intended meaning, while being in disguise).  Their similarity is reflected in the small edit distance between the erroneous and the correct word \cite{howardspelling}. The errors often involve character-level operations as shown in Table \ref{tab:errorExample} \cite{liu2011insertion}. \\
(3) With edit distance as the similarity criterion, it is often the case that the word with an error is similar to multiple valid words, making correction a challenge in real applications. For example, both \emph{stud} and \emph{stupid} can be thought of as the correct form of the erroneous word \emph{stupd}. In such cases, spelling correction relies on the context in which the word occurs.

\subsection{Effect  of malicious spelling errors}
\label{sec:adv_error}
We quantify the effect of spelling errors by showing that a simple mechanism of injecting malicious spelling errors greatly degrades the performance of toxicity detection and spam detection. Toward this, we first describe  the general mechanism to generate synthetic errors and then study their impact on toxicity and spam detection. We point out that owing to the absence of a dataset with intended malicious errors, we had to generate them synthetically.

{\bf Spelling error generation.}
We first choose the ``sensitive'' words that  the detection algorithm relies (the mechanism will be discussed separately for each application we consider). We then replace these words with their erroneous forms (which may not be real words). Given the characteristics of malicious errors mentioned in Section 3.1, our assumption is that as a result of these errors the altered words will appear similar to the original ones. As illustrated in Table \ref{tab:errorExample}, we consider four basic character-level operations to change the sensitive words:  insertion,  permutation, replacement, and removal. {In our experiments we  perform these operations on randomly picked characters of the sensitive word.} For permutation, we exchange this character with the next one.
Each operation in Table~ \ref{tab:errorExample} increases the edit distance by 1, and we generate malicious misspellings of the sensitive word that are at most  2  edit distance from the correct word.
\begin{table}[htbp!]
\centering
\caption{Common spelling errors}
\label{tab:errorExample}
\resizebox{0.45\textwidth}{!}{
\begin{tabular}{|c|c|c|c|c|c|}
\hline
Type & insertion &  permutation & replacement & removal \\ \hline
Error & idio.t &  moeny & chanse & stupd \\ \hline
Correction & idiot &  money & chance & stupid \\ \hline
\end{tabular}}
\end{table}

Finally, we obtain revised sentences by replacing the sensitive words in the original sentences with their erroneous counterparts. Next, we introduce our mechanism for selecting the ``sensitive'' words for each application.

\textbf{Perspective toxicity detection}.
We select $2767$ clean comments from a total of $2969$ comments in the Perspective data, with the selection criteria  given below.
\begin{itemize}
\item The most toxic word in a comment should contain more than two characters. A word that is too short is not likely to be a meaningful toxic word, and can have too many candidate corrections in the dictionary.
\item The most toxic word in a comment should appear at least 100 times in the dataset, since rare words tend to be misspellings in online texts.
\item The most toxic word in a comment should be a content word, i.e., it should not belong in the list of function words such as
``must'', ``ought'', ``shall'' and``should''.
Function words are not toxic and so were excluded from our experiments.
\end{itemize}

For each comment, its predicted toxicity score lies in the range of $[0,1]$. The higher the score, the more toxic the sentence is. For each sentence, we chose the most toxic word to be that word, without which the toxicity score reduced the most. We then added malicious errors, as described above, to this word. We note that {for $2380$ toxic comments with toxicity scores higher than 0.5,  spelling errors brought down their predicted toxicity by $32\%$.}
Section \ref{sec:exp} provides the details of the  degradation in toxicity detection.

\textbf{Spam detection}. A Naive Bayes spam detection model provides the likelihood of each word given the spam and non-spam classes. For  a given word, the difference between these probabilities  reflects how important that word is in spam detection. We sorted all the words in decreasing order of this difference in probabilities, and  picked the most spam-indicative words. We then added malicious errors to these words in test spam emails. For a spam filtering system which relies heavily on the counts of spam-indicative words, the errors can easily disguise spams as non-spams. This is seen in the test accuracy dropping from $98\%$ on the original test data to $72\%$ on the revised test data.

We note that for the Perspective data, we added errors to only one word per sentence, but did not have such limits for the spam data. Also, we do not limit the number of corrections during the spell check process.

\section{Spelling Correction}
\label{sec:model}

We have shown that malicious errors degrade the performance of toxicity detection and spam detection. In this section, we introduce our unsupervised algorithm on non-word error correction based on the relevant context.

Our correction method proceeds in two stages. In the first stage of {\bf candidate enumeration}, the algorithm detects the spelling error and proposes candidates that are valid words and similar in surface form to the misspelled word. In the next stage,  {\bf correction via context},  the best candidate is chosen based on its context.

\subsection{Candidate Enumeration}
As in the case of non-word spelling correction \cite{jurafsky2014speech}, we check whether an erroneous word is valid or not using a vocabulary of valid words. For toxicity detection and spam detection, the vocabulary consists of words that occur more than $100$ times in English Wikipedia corpus \footnote{available at: \url{http://www.cs.upc.edu/~nlp/wikicorpus/}}
For hate speech detection, the vocabulary consists of standard words from  Wikipedia and the list of internet slang words as detailed in Section~\ref{sec:exp_cyberbullying}, since slang words frequently occur in tweets.
For an invalid word, we  find candidate words with the smallest Damerau-Levenshtein edit distance \cite{damerau1964technique,levenshtein1966binary}. This distance between two strings is defined as the minimum number of unit operations required to transform a string into another, where the unit operations include character addition, deletion, transposition and replacement. We note that because there are potentially multiple candidates having the smallest distance to the misspelled word, the output of this stage is a set of candidate corrected words.

\subsection{Correction via Context}
\label{subsec:corr_context}
After enumerating all possible candidates, we  choose the one that fits the best in the context.  We propose a word-embedding-based method to match the context with the candidate.

\noindent\textbf{Pretrained embedding}.
We use word2vec CBOW model to train embeddings \cite{NIPS2013_5021}.
The Perspective and Twitter data  have an informal style, while email data consists of relatively formal expressions. Word embeddings are known to be domain-specific \cite{nguyen2014employing} and naturally  domain-specific corpora are used to train word embeddings for use in a given domain. We note that in the absence of a large enough representative corpus  to train domain-specific high-quality embeddings for this study, we reconcile with word embeddings  trained on a large WikiCorpus \cite{polyglot:2013:ACL-CoNLL} to capture the general lexical semantics and further tuned on a domain-specific corpus, such as that of the Perspective dataset, the spam emails data and the Twitter data.
 This step allows us to combine domain information in trained embeddings.

\noindent\textbf{Error Correction}.
Word vectors permit us to decide the fit of a word in a given context by considering the geometry of the context words in relation to the given word.
Firstly, the embedding of the compositional semantics of phrases or sentences can be approximated by a linear combination of the embeddings of their constituent words \cite{salehi2015word}. Let $v_{w}$ be the embedding of the token $w$. Take the phrase ``lunch box'' as an example, it holds that
\begin{align*}
v_{\text{hate\_group}} \approx \alpha v_{\text{hate}} + \beta v_{\text{group}},
\end{align*}
where $\alpha$ and $\beta$ are real-valued coefficients.

This enables us to represent the context as a linear space of the component words. Another property is that semantically relevant words lie close in the embedding space. If a word fits the the context, it should be close to the context space, i.e., the normalized Euclidean distance from the word embedding to the context space should be small \cite{gong2017geometry}. We quantify the inconsistency between the word and its context by the distance between the word embedding and the span of the context words.

For a misspelled word in a sentence, all words occurring within a window of size $p$ are considered to be its context words. Let the context $T_{p}$ be the set of words within distance $p$ from $w_{0}$:
$$T_{p}=\{w_{-p}, w_{-p+1},\ldots, w_{0}, \ldots, w_{p-1}, w_{p}\},$$
where $w_{0}$ is the misspelled word.
Let $C$ be the set of candidate replacements of $w_{0}$.
Let $v_{i}$ be the word embedding of context word $w_{i}$. We denote by $\text{dist}(\text{c}, T_{p})$,  the distance between a candidate $c$ and the linear span of the words in its context $T_p$, termed as the candidate-context distance  of a candidate,
defined as the normalized distance between the word embedding of the candidate, $v_c$  and its linear approximation obtained by the context vectors:
\begin{align}
\text{dist}(c, T_{p}) = \min\limits_{\{a_{i}\}}\frac{1}{\lVert v_{c} \rVert_{2}} \lVert \sum\limits_{\substack{i=-p,\\ i\not=0}}^{p}a_{i}v_{i}-v_{c}\rVert_{2}^{2}.
\end{align}
This is a quadratic minimization problem for which we can find a closed-form solution

Instead of fixing one context window size, we consider multiple window sizes and weigh the distances obtained using different window sizes. The context words that are closer to the misspelled word are more informative in candidate selection. In the sentence ``the stu*pid and stubborn administrators'', the word \emph{stubborn} suggests that the misspelled word should be a negative personality adjective, and the word \emph{administrator}  that it should be an adjective for people. Thus, the closest context word \emph{stubborn} provides more relevant information than the distant word \emph{administrator}.

We thus weigh the distance by the inverse of the context window size, i.e., the weight $\frac{1}{p}$ for the window size $p$. Suppose that $T$ is the full context, and the candidate-context distance is defined below:
\begin{align}
\text{dist}(c, T) = \sum\limits_{p=1}^{P}\frac{1}{p}\cdot\text{dist}(c, T_{p}),
\end{align}
where $P=4$ in our experiments.

Given the context $T$, the suggested correction $c^{*}$ is the candidate with the smallest distance to the linear space of context words, i.e.,
\begin{align}
c^{*}=\arg\min\limits_{c\in C} \text{dist}(c, T).
\end{align}

\begin{table}[tbp!]
\centering
\caption{Correction Accuracy on Perspective and Spam}
\label{tab:acc}
\resizebox{0.48\textwidth}{!}{
\begin{tabular}{|c|c|c|c|c|}
\hline
Dataset & Our system & PyEnchant & Ekphrasis & Google \\ \hline
Perspective & {\bf 0.840} & 0.476 & 0.713 & 0.323 \\ \hline
Spam & {\bf 0.786} & 0.618 & 0.639 & 0.526 \\ \hline
\end{tabular}}
\end{table}

\begin{table*}[htb!]
\centering
\caption{Correction on perspective data}
\label{tab:perspective_example}
\resizebox{\textwidth}{!}{
\begin{tabular}{|l|l|l|l|}
\hline
original sentence & the {\bf stupid} and stubborn administrators & anti American {\bf hate} groups & you're a biased {\bf fuck} \\ \hline
revised sentence & the {\bf stu*pid} and stubborn administrators & anti American {\bf ahte} groups & you're a biased {\bf fucdk} \\ \hline
Our correction & the {\bf stupid} and stubborn administrators & anti American {\bf hate} groups & you're a biased {\bf fuck} \\ \hline
PyEnchant & the {\bf sch*pid} and stubborn administrators & anti American {\bf hate} groups & you're a biased {\bf Fuchs} \\ \hline
Ekphrasis & the {\bf stupid} and stubborn administrators & anti American {\bf ate} groups & youre a biased {\bf fuck} \\ \hline
Google & the {\bf stupid} and stubborn administrators & anti American {\bf ahte} groups & you're a biased {\bf fucdk} \\ \hline
\end{tabular}}
\end{table*}

\section{Experiments}
\label{sec:exp}
We evaluated our spelling correction approach in three settings: toxicity and spam detection with synthetic misspellings, and hate speech detection with real spelling errors. Even though some recent works using neural networks \cite{ghosh2017neural} are available, they require ground truth corrections for supervised training. For our unsupervised approach, we compare its performance with that of three strong unsupervised baselines below, which are generic off-the-shelf tools used in many NLP systems such as AbiWord \cite{abiword} and Google search engine \cite{google_search}. \\
(1) Pyenchant: a generic spell checking library of multiple correction algorithms \cite{enchant}. We use the MySpell library in this work.\\
(2) Ekphrasis: a spelling correction and text normalization tool for texts from social networks \cite{baziotis-pelekis-doulkeridis:2017:SemEval2}.\\
(3) Google spell checker: a Google search engine-based spell check  \cite{google}. We chose it for its ability to undertake context-sensitive correction on the basis of user search history.

\subsection{Toxicity Detection}
\label{sec:exp_toxicity}
\begin{figure}[htbp!]
\centering
\includegraphics[width=0.45\textwidth]{perspective_plot.png}
\caption{Toxicity scores by different approaches}
\label{fig:perpective_plot}
\end{figure}

We took $2380$ sentences from Perspective dataset whose toxicity scores were higher than $0.5$ and added synthetic errors maliciously to these sentences as described in Section \ref{sec:addError}. Some example Perspective sentences, revised sentences and corrections given by different algorithms are shown in Table~\ref{tab:perspective_example}. The correction accuracy achieved by different approaches is reported in Table~\ref{tab:acc}.

We divided toxic sentences into $5$ bins according to their original toxicities. In Fig.~\ref{fig:perpective_plot}, we report the average toxicity of the original, revised and corrected sentences in each bin. We see that the malicious errors drastically reduce the sentences' toxicities. The original average toxicity in the first bin is 0.55, whereas the revised toxicity is only 0.33 (a drop by $40\%$). This shows that  toxicity detection is very sensitive to spelling errors.

From the same figure we see that our proposed error correction  results in  toxicity scores closer to the original ones when  compared with the  baselines, validating the effectiveness of our approach. We note that in some cases our approach might result in a slightly higher toxicity score than the original one,  because of the pre-existing misspellings in the original sentences. For example, original sentences ``you are a \emph{fagget}'', ``\emph{fukkin} goof's track record'' and ``I suspect that closedmouth is \emph{g*y}'' have crude misspellings, which are used as Internet slang. Our approach  corrects these pre-existing misspellings in addition to the errors we add later, resulting in higher toxicity scores of corrected sentences. We perform corrections of all spelling errors, recovering more toxic words than we added maliciously.

\subsection{Spam Detection}
\label{sec:exp_spam}
\begin{table*}[htbp!]
\centering
\caption{Correction examples on spam data}
\label{tab:spam_example}
\resizebox{\textwidth}{!}{
\begin{tabular}{|l|l|l|l|}
\hline
Original sentence & \begin{tabular}[c]{@{}l@{}}it really be a \textbf{great} oppotunity to make relatively \\ easy \textbf{money} , with little \textbf{cost} to you .\end{tabular} & \begin{tabular}[c]{@{}l@{}}we have quit our \textbf{jobs} , and will soon buy a home on \\ the beach and live off the interest on our \textbf{money} .\end{tabular} \\ \hline
Revised sentence & \begin{tabular}[c]{@{}l@{}}it really be a \textbf{grfat} opportunity to make relatively \\ easy \textbf{fmoney}, with little \textbf{cosgt} to you.\end{tabular} & \begin{tabular}[c]{@{}l@{}}we have quit our \textbf{tobs}, and will soon buy a home on \\ the beach and live off the interest on our \textbf{jmoney}.\end{tabular} \\ \hline
Our correction & \begin{tabular}[c]{@{}l@{}}it really be a \textbf{great} opportunity to make relatively \\ easy \textbf{money}, with little \textbf{cost} to you.\end{tabular} & \begin{tabular}[c]{@{}l@{}}we have quit our \textbf{jobs}, and will soon buy a home on \\the beach and live off the interest on our \textbf{money}.\end{tabular} \\ \hline
PyEnchant & \begin{tabular}[c]{@{}l@{}}it really be a \textbf{graft} opportunity to make relatively \\ easy \textbf{fmoney}, with little \textbf{cost} to you.\end{tabular} & \begin{tabular}[c]{@{}l@{}}we have quit our \textbf{bots}, and will soon buy a home on \\the beach and live off the interest on our \textbf{money}.\end{tabular} \\ \hline
Ekphrasis & \begin{tabular}[c]{@{}l@{}}it really be a \textbf{great} opportunity to make relatively \\ easy \textbf{money}, with little \textbf{cost} to you.\end{tabular} & \begin{tabular}[c]{@{}l@{}}we have quit our \textbf{tobs}, and will soon buy a home on \\ the beach and live off the interest on our \textbf{money}.\end{tabular} \\ \hline
Google & \begin{tabular}[c]{@{}l@{}}it really be a \textbf{great} opportunity to make relatively \\ easy \textbf{fmoney}, with little \textbf{cosgt} to you.\end{tabular} & \begin{tabular}[c]{@{}l@{}}we have quit our \textbf{jobs}, and will soon buy a home on \\the beach and live off the interest on our \textbf{jmoney}.\end{tabular} \\ \hline
\end{tabular}}
\end{table*}

\begin{figure}[htbp!]
\centering
\includegraphics[width=0.4\textwidth]{spam_plot.png}
\caption{Spam detection accuracy on test emails}
\label{fig:spam_plot}
\end{figure}

We next evaluate our spelling correction on the spam data. Synthetic malicious errors were added to spam-indicative words in spam mails based on our misspelling generation mechanism.

Some example spams are shown in Table~\ref{tab:spam_example}. Sensitive words such as ``money'' which are indicative of spams are highlighted, and the corrections given by different approaches are shown. Table \ref{tab:acc} shows the spelling correction accuracy achieved by our algorithm and the baselines for the spam data.

Fig.~\ref{fig:spam_plot} shows the spam detection accuracy on the original, revised and corrected test emails. Again we see that malicious errors resulted in a large accuracy drop,  the accuracy was restored after spelling correction, and the increase in accuracy using our approach was the maximal among those compared.

\subsection{Real Misspellings in Tweet Hate Speech Detection}
\label{sec:exp_cyberbullying}
\begin{table}[htbp!]
\centering
\caption{Example of tweets and hate speech categories}
\label{tab:tweet_example}
\resizebox{\linewidth}{!}{
\begin{tabular}{|c|l|l|}
\hline
\begin{tabular}[c]{@{}c@{}}Category\end{tabular} & \begin{tabular}[c]{@{}c@{}} Example sentence\end{tabular} \\ \hline
Racism & \begin{tabular}[l]{@{}l@{}}\#isis \#islam pc puzzle: converting to a religion \\ of peace leading to violence? http://t.co/tbjusaemuh \end{tabular} \\ \hline
Sexism & \begin{tabular}[l]{@{}l@{}}Real question is do feminist liberal bigots \\understand that different rules for men/women \\is sexism\end{tabular} \\ \hline
Neither & The mother and son team are sooooo nice !!! \\ \hline
\end{tabular}}
\end{table}

We have shown that synthetic misspellings are able to deceive the toxicity detector and the spam detector, and reported the performance of spell checkers on the synthetic data. We also experimented with data containing real spelling errors collected from Twitter, where instances of hate speech contain user-generated spelling errors. The correction performance of spell checkers are now compared on the real misspellings.

\begin{table*}[htbp!]
\centering
\caption{Hate speech detection results with different spelling corrections}
\label{tab:cyberbullying_detection}
\begin{tabular}{|c|c|c|c|c|c|c|}
\hline
Category & Metric & Original & Our system & PyEnchant & Ekphrasis & Google \\ \hline
\multirow{3}{*}{racist} & Precision & 0.630 & \textbf{0.640} & 0.630 & 0.630 & 0.569 \\ \cline{2-7}
 & Recall & 0.617 & 0.681 & 0.617 & 0.617 & \textbf{0.702} \\ \cline{2-7}
 & F1 score & 0.623 & \textbf{0.660} & 0.623 & 0.623 & 0.628 \\ \hline
\multirow{3}{*}{sexist} & Precision & 0.641 & 0.630 & 0.629 & 0.629 & \textbf{0.649} \\ \cline{2-7}
 & Recall & 0.775 & 0.767 & \textbf{0.790} & 0.790 & 0.759 \\ \cline{2-7}
 & F1 score & 0.701 & 0.692 & \textbf{0.701} & \textbf{0.701} & 0.700 \\ \hline
\multirow{3}{*}{\begin{tabular}[c]{@{}c@{}}Macro average over \\ all categories\end{tabular}} & Precision & 0.721 & \textbf{0.721} & 0.718 & 0.718 & 0.703 \\ \cline{2-7}
 & Recall & 0.741 & 0.757 & 0.743 & 0.743 & \textbf{0.759} \\ \cline{2-7}
 & F1 score & 0.727 & \textbf{0.737} & 0.727 & 0.727 & 0.727 \\ \hline
\end{tabular}
\end{table*}

\textbf{Tweet normalization}. Some example tweets of racist and sexist nature are shown in Table~\ref
{tab:tweet_example}. Tweets are notoriously noisy and unstructured given the frequent occurrences of hashtags, URLs, reserved words and emojis. These non-standard tokens greatly increase the vocabulary size of tweets while also injecting noise to classification tasks. We use Tweet Preprocessor, a tweet preprocessing tool which can replace aforementioned tokens with a special set of tokens. For example, one tweet is ``\#isis \#islam pc puzzle: converting to a religion of peace leading to violence ?,http://t.co/tbjusaemuh http://t.co/g4xoh...'', which becomes ``\$HASHTAG\$ \$HASHTAG\$ pc puzzle: converting to a religion of peace leading to violence? \$URL\$ \$URL\$...'' after preprocessing. This preprocessing stage can clean texts without dealing with misspellings. Tweets are preprocessed right before they are fed into the neural network.

\textbf{Hate speech detection}. The state-of-the-art system for hate speech detection is a Bidirectional Long Short Term Memory (BLSTM) network with attention mechanism \cite{agrawal2018deep}. The model takes a sequence of words in a tweet, obtains word embeddings in the embedding layer, and passes them to bidirectional recurrent layers to generate a dense representation of the input tweet. The feedforward layer takes the tweet vector and predicts its probability distribution over all three classes. The class with the highest likelihood is chosen as the category of the input tweet. In our experiment, we use a BLSTM model for the hate speech detection task.

\textbf{Vocabulary construction}. Firstly we build a vocabulary list using both a standard dictionary and the frequent words (frequency higher than 5) in the training data. This list will serve as a reference for spelling correction; words outside the list will be taken as spelling errors and replaced with legal words from the vocabulary. The reason for collecting words from the training data is to include the Internet slangs in tweets which may not exist in the standard dictionary. Some examples are ``tmr'' (for \emph{tomorrow}), ``fwd'' (for \emph{forward}) and ``lol'' (for \emph{laughing out loudly}). Some previous works proposed to replace these slang terms with their standard forms \cite{gupta2017tweet,modupe2017semi}, which require either expert knowledge or human annotations. We argue that because these Internet slangs change rapidly we should understand them in a data-driven manner instead of standardizing them based on human knowledge. Since the representation of these slangs and their use in hate speech  will be learned by the neural network from the train data, we add these slangs to our vocabulary as legal words.

\textbf{Spelling correction}. Misspellings are another source of text noise which cannot be handled in the tweet normalization stage. Some misspellings are user-created to deceive the online detection system. For example, the swear word ``fucking'' has a lot of variants in tweets such as ``fckn'', ``f*ckin'', ``f**king'', ``fckin'', ``fuckin'', ``fking'',``fkin'' and ``fkn''. When a new variant of a swear word arises in the test data, the model takes it as an out-of-vocabulary word, and is unable to match it with any learned pattern. The purpose of spelling correction is to map these new variants to words that are known to the model. As discussed in Section~\ref{sec:model}, we enumerate the candidates of a misspelled word, and choose the candidate which best fits with the context as its correction.

\textbf{Results}. We  do a random 80:20 train-test split of the Twitter dataset. A  detection system was trained on the normalized train data (without spelling correction) using the state-of-the-art BLSTM model. There were $3218$ test tweets, $571$ of which had misspellings.
We applied the different spelling correction approaches to these $571$ tweets, and the corrected tweets were then cleaned as described in the tweet normalization stage. Processed tweets were input to the trained detection system.
We compared the hate speech detection results on the $571$ test tweets to evaluate the effect of spelling correction  on this downstream application. As shown in Table~\ref{tab:cyberbullying_detection}, We report results on the original test data, and also on the test data which are corrected by our approach, PyEnchant, Ekphrasis, and google spell checker.
We report precision, recall and F1-score of racist and sexist classification respectively and the macro-averages (to evaluate the overall performance)

The best performance of each metric is highlighted in the table. Compared with the classification performance on the test data with spelling errors for the racist category, our approach improves the precision  by $1$\%, the recall by $6.4$\%, and the F1 score by $3.7$\% absolute  points. Both PyEnchant and Ekphrasis spell checkers improve the recall of sexist category, but decrease the precision, so their corrected forms achieve  F1 scores similar to the original test data. Google spell checker also gives similar F1 score on both racist and sexist classes. Our approach outperforms the other baselines in terms of F1 score for the racist class and the macro F1 score.

For sexism-related tweets, our approach does not improve the results compared to  the original test data. Taking a closer look at the nature of the sexist tweets we notice that they often contain some abbreviations which might be taken as misspellings, and that their contextual information is insufficient to decide the appropriate corrections. For example, in the sexist tweet ``I'm sorry but if you watch women ufc fights kys''.  Our approach replaces \emph{kys} with \emph{keys}, and the trained neural network misclassified the corrected tweet. Another sexist-related tweet is ``these nsw promo girls think way too highly of themselves'', where \emph{nsw} is incorrectly replaced with \emph{new} by our approach.

\section{Conclusion}
In this study, we showed how malicious spelling errors can deceive profanity- and spam detectors. To deal with these malicious misspellings, we proposed a context-sensitive spelling corrector based on word embeddings. Our spell checker is light-weight, unsupervised and can be easily incorporated into downstream applications. It achieved a  favorable spelling correction performance when compared with general purpose spell-checking tools such as PyEnchant, Ekphrasis and Google spell checkers on both synthetic and real misspellings from different datasets.

redpajama_set_name: RedPajamaArXiv


text:
Congress breaks impasse on bill to slap sanctions on Russia, Iran and North Korea
House Majority Leader Kevin McCarthy (R-Calif.) left, speaks with reporters during the Speaker's weekly news conference on June 8 on Capitol Hill. House Speaker Paul D. Ryan (R-Wis.) is to the right. (Cliff Owen/AP)
A weeks-long impasse over imposing new financial sanctions on Iran and Russia broke late Friday, with the House preparing to vote next week on a measure that would prevent President Trump from lifting measures against Moscow
House leaders agreed to vote on an expanded version of the bill after incorporating a roster of sanctions aimed at freezing North Korea's nuclear program and draining the government of revenue it uses to fund it. The measures against Pyongyang, which passed the House 419 to 1 as a stand-alone bill earlier this year, were inserted at the request of House Republican leaders.
While some details have yet to be finalized, congressional aides said, the bill is set for a vote Tuesday, according to a schedule circulated Saturday by House Majority Leader Kevin McCarthy (R-Calif.). It will proceed under special expedited procedures for noncontroversial bills expected to pass with a two-thirds majority.
The legislation, however, has hardly had a smooth ride to this point.
An initial Senate bill — which slapped new sanctions on Iran in response to its ballistic missile testing and on Russia for its meddling in the 2016 U.S. presidential election — passed in June on a 98-to-2 vote. Critically, the sanctions could only be lifted with congressional approval — a departure from the flexibility presidents are traditionally given to conduct foreign policy.
But the bill hit an immediate procedural snag due to claims it ran afoul of the constitutional requirement that revenue bills originate in the House. The roadblock arose as Trump administration officials stepped up a lobbying campaign against the bill, prompting Democrats to accuse House GOP leaders of stalling on Trump's behalf.
New obstacles emerged earlier this month: House Democrats objected to Senate changes to the bill that could freeze out the House minority's ability to block sanctions relief, and the oil-and-gas industry raised fears that U.S. companies could be frozen out of projects with Russian partners.
Last week, McCarthy added another twist by proposing the North Korea sanctions to the Senate bill. That again raised hackles around the Capitol at the prospect that the legislation could be complicated further. But according multiple congressional aides, negotiations continued behind the scenes this week, with McCarthy and House Minority Whip Steny H. Hoyer (D-Md.) working to strike a compromise, along with Sens. Bob Corker (R-Tenn.) and Benjamin L. Cardin (D-Md.), the leaders of the Senate Foreign Relations Committees.
The version of the bill posted on a House website just before midnight Friday includes changes aimed at addressing the energy industry concerns as well as some White House objections. But despite pressure from lobbyists and the Trump administration, negotiators maintained unprecedented oversight authority for Congress.
"The legislation ensures that both the Majority and Minority are able to exercise our oversight role over the Administration's implementation of sanctions," Hoyer said in a statement. "Other changes made to the bill will ensure effective and unified implementation among partners and make provisions more workable."
Senate Minority Leader Charles E. Schumer (D-N.Y.) also issued a statement Saturday praising the breakthrough and calling for the bill's swift passage. "
"Given the many transgressions of Russia, and President Trump's seeming inability to deal with them, a strong sanctions bill such as the one Democrats and Republicans have just agreed to is essential," he said.
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The Woodlands Clinic106 Vision Park BlvdShenandoah, TX 77384
Dr. Jones's Background
Dr. Jones, MD is a practicing Internist in Shenandoah, TX. Dr. Jones graduated from Louisiana State University School of Medicine New Orleans in 1994 and has been in practice for 25 years. She completed a residency at Baylor College of Medicine. Dr. Jones also specializes in Hospitalist. She currently practices at The Woodlands Clinic and is affiliated with Methodist Willowbrook Hospital. Dr. Jones accepts multiple insurance plans including Aetna, Humana, and Medicare.
Dr. Jones, MD is a practicing Internist in Shenandoah, TX. Dr. Jones graduated from Louisiana State University School of Medicine New Orleans in 1994 and has been in practice for 25 years. She compl...Dr. Jones, MD is a practicing Internist in Shenandoah, TX. Dr. Jones graduated from Louisiana State University School of Medicine New Orleans in 1994 and has been in practice for 25 years. She completed a residency at Baylor College of Medicine. Dr. Jones also specializes in Hospitalist. She currently practices at The Woodlands Clinic and is affiliated with Methodist Willowbrook Hospital. Dr. Jones accepts multiple insurance plans including Aetna, Humana, and Medicare. More...Less
Methodist Willowbrook Hospital
Louisiana State University School of Medicine New Orleans
Learn More About Internal Medicine
Women and Heart Disease: 3 Days Can Change Your Life!
It's never too late to make that big change and win the battle against heart disease. Ada was in her eighties and had received eight stents and two pacemakers when she joined the Ornish Lifestyl......Read More
Q: What are some tips for a someone who is having trouble sleeping at night?
Q: How can I prevent colds and flu?
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Q: How do cold and flu affect the body?
Q: How long does chickenpox usually last?
106 Vision Park BlvdShenandoah, TX 77384
Dr. Rajnikant Kushwaha
150 Pine Forest Dr Ste 604 Shenandoah, TX 77384
Dr. Thimos Paschalis
17450 St Lukes Way Ste 290 Conroe, TX 77384
Dr. Tony Nguyen
Dr. Sunitha Alexander
Dr. Shilpa Vaidya
3115 College Park Dr Ste 112 Conroe, TX 77384


2) If you like lists, Cigar Journal (formerly European Cigar Cult Journal) has released its Finest Cigars of 2011. The magazine says the 25-cigar list was compiled from its reviews during the year, with only the best chosen for inclusion. The top three include the Padrón Family Reserve No. 85 Maduro, San Lotano Oval Robusto, and Master by Carlos Toraño.
3) Inside the Industry: The highly anticipated Litto Gomez Small Batch No. 4 from La Flor Dominicana is now hitting retail shops. This year's blend is a Dominican puro with a pelo de oro wrapper. Only 240 boxes of the cigars were made, and each smoke will sell for $19. In addition, La Flor is releasing a Cameroon-wrapped Chisel and the Factory Press IV.
4) Around the Blogs: Cigar Fan examines the Arturo Fuente Sun Grown Magnum R52. Smoking Stogie reveals the top five cigars of 2011. Cigar Explorer reviews the Trinidad Short Robusto T LE 2010. Tiki Bar names the Four Kicks the best cigar of the year.
5) Deal of the Week: To help you ring in the New Year, this special deal gets you 10 cigars for under $30. Inlcuded are smokes from La Aurora, Don Pepin Garcia, La Gloria Cubana, Perdomo, Toraño, Punch, and more. Or you can double-up for a total $50.


art supplies paint brush art supplies paints.We offer the best wholesale price, quality guarantee, professional e-business service and fast shipping . You will be satisfied with the shopping experience in our store. Look for long term businss with you.
This is a portable compact fountain pen, soft brush pen.
It is a good companion for painting, whether you are beginners, painting enthusiasts or professional person.
Children can practice calligraphy with it.
Water injection: remove the nib, fill the dropper with enough water.
Moisture adjustment: Initially, squeeze the pen nib before you use, it will effluent and you can wipe with a paper towel, adjust the water contents.
How to change color: When you need to change color, wipe the nib with a paper towel, you can clear out the color and then stain another.
How to preserve: When not in use, clean up the water, close the lid.


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HomeFind a JobSenior SAP Production & Plant Maintenance Analyst
Senior SAP Production & Plant Maintenance Analyst
Join Amcor and you can be part of the team that makes the packaging of the future better for people and our planet.
Here at Amcor, we work together every day to make a positive impact on the lives of millions of people across the globe by providing packaging for essential products, including food, beverages, pharmaceutical, medical, home and personal-care.
We are sustainability leaders in our industry. We are the first global packaging company to commit to make all our products recyclable or reusable by 2025.
Our products are vital to support the lives of millions of people across the globe every day.
We are always looking for talented and passionate individuals who are motivated to make a difference. Working at Amcor means you will have a unique opportunity to be a part of an organisation that is committed to innovating and driving new solutions to create more sustainable packaging solutions.
To find out more about our commitment to sustainability and about Amcor, visit I I III
At Amcor Rigid Packaging (ARP), our teams are focused on developing and making plastic bottles and jars that create a unique experience and are better for the environment. Our bottles and jars are primarily made from PET (polyethylene terephthalate), which has rapidly become the world's preferred packaging material. PET is lightweight, shatterproof, recloseable, resealable, reusable and infinitely recyclable*.
With approximately 6,000 people across about 50 manufacturing operations in 11 countries and an annual revenue of US$2.9 Billion, ARP provides rigid packaging solutions to the beverage, pharmaceutical, food, and home and personal care segments.
Learn more about the many benefits of PET bottles
*with existing technologies including chemical recycling
Amcor Rigid Packaging
REMOTE or Manchester, MI
Provide support for Amcor's SAP processes by analyzing demand planning, warehouse management, materials management, and production planning needs, making recommendation for changes to the SAP and computer systems, and then expertly manipulating the standard configuration SAP packaged software and third-party applications to perform customizations as necessary to meet Amcor's business needs.
Essential Responsibilities and Duties:
Provide day-to-day support for Supply Chain and Operations internal customers for SAP PP, PM, and MM
Provide SAP application and process expertise related to BOM, Routings, Production Orders, Work Orders, Production Costing and Production Reporting
Knowledge of integration points between PP and the other SAP modules (MM, PM)
Experience with multi-country and/or multi-business model implementations
Work with minimal supervision with various levels of Amcor's Business
Assist in establishing best practices for Amcor's business processes. Leverage experience and expertise to suggest improvements to current processes
Lead SAP analysis, implementation and customization projects to fulfill business requests for new functionalities and major enhancements.
Perform SAP configuration activities, write functional specifications, test/validate functionality and follow change management processes
Create user documentation including Work Instructions for the internal customers
Deliver end user training
Education: Bachelor's degree or foreign equivalent in Computer Science, Engineering, or equivalent experience
10 years as an SAP Analyst, SAP Consultant, or in a closely related occupation involving SAP ECC
10 years working with SAP MM/PP/PM
Experience with Logility or other 3rd party logistics systems and their integration to SAP is a plus
4 years working with SAP LSMW and other data migration tools
4 years using at Make to Stock and Make to Order in process manufacturing and Preventative Maintenance and Repairs processes
1 year using Production Planning
Experience performing at least 2 full-cycle SAP implementations or large-scale ERP implementations for manufacturing companies, with configuration responsibilities including at least two of the following SAP functionalities: PP, PM, MM, IM, WM.
Travel 10%
Amcor Leadership Framework Competencies
Core Competencies:
Learning on the Fly
Interpersonal Savvy
3-5 Applicable ALF Competencies:
Functional/Technical Skills
Directing Others
Relationships:
External customers
Contractors and Consultants
Co-workers in IT and Business
Internal and External Auditors
ADA Requirements:
Distant Vision
Near Vision
Hearing/ Listening
Talking/Speaking
Hand/Finger Dexterity
Pushing/Pulling
Lifting (amount 50+ pounds)
Carrying (amount 50+ pounds)
Driving- Forklift
Lifting (amount < 50 pounds)
Carrying (amount < 50 pounds)
Driving - Automobile/truck/van
Other Physical Requirements: N/A
Work Environment:Office environment
Amcor is a thriving S&P 500 organisation listed both on the NYSE (AMCR) and ASX (AMC) with US$13 billion in sales. We have a proud history dating back to the 1860's and come a long way from a single paper mill based in Melbourne to becoming the global leading packaging company. Today, Amcor is an international organisation empowering its 50,000 employees in over 40 countries and across 250 sites.
At Amcor we believe in talent through diversity and promote equal opportunity in the workplace and recognise that discrimination and harassment of co-workers based on race, creed, disability, gender, marital or maternity status, religious or political beliefs, age or sexual orientation is unlawful.
#LI-VA123
We are proud to be an Equal Opportunity/Affirmative Action Employer, making decisions without regard to race, color, religion, creed, sex, sexual orientation, gender identity, marital status, national origin, age, veteran status, disability, or any other protected class.


See Peppa Pig this Half Term! Free!
Peppa Pig will be taking a break from jumping in muddy puddles, to pop along to Affinity Lancashire on Saturday 16th & Sunday 17th February!
Come along and see your favourite, cheeky piggy during story time sessions, led by a narrator, at 11am, 12noon, 1.30pm, 2.30pm and 3.30pm. There'll be fun games too… don't forget to bring your camera!
Families are being advised to arrive early to avoid disappointment.
For the latest details, check out our Facebook page.


About EDC - The Entertainment Design Co.
The Entertainment Design Co. is one of the world's most formidable luxury invitation houses, recognized for its bespoke and superior handcrafted personalized stationery for the most discerning clientele from across the globe. Founded by Kapil and Sandy Khurana, EDC believes in the philosophy of crafting exquisite and desirable works of personalized art. At a time when the concept of couture invitations was a distant thought in India, today every piece of EDC carries an emboss of innovation in design, sophistication, craftsmanship and the finest materials. 15 years on, EDC continues to push the envelope and redefine personalised invitations and stationery, with its impeccable aesthetics and superior execution.


How many times have you mumbled your way through afternoon prayer or made sure to look bored in religion classes? How many times have you refrained from singing or even responding at Mass because it's 'not cool'. Why is this? Why do we feel the need to pretend we are too good for prayer or too cool to worship our God in front of our peers?
You want to know what's cool, singing to the best of your God-given abilities at Mass because it is the best way to praise the Lord, genuflecting with both knees when you enter into adoration as a sign of respect and getting up early once a week to attend Friday morning liturgy. These are not hard things. If you are at school, if you are given the opportunity to praise the Lord, why not use it? I'm only asking for participation. I understand singing may not be your thing or you may not be good at it, but I'm not asking you to join choir. The Lord doesn't care how good of a singer you are, so why should you?
Junior Haley Cummings poses for a picture at St. Pius X for her First Communion. Photo Submitted.
Perhaps you've been up all night studying for a big test so it may be hard to get up early to go to Mass, but what if you used that time to pray for yourself and all those taking that test? Great opportunities, such as this, may not always be available to you, so take advantage of them while you can. Juniors come back from Kairos with their faith overflowing only to have it slowly drained by people judging them and making them feel like their faith has no place in their friendships. We should support Kairos attendees and encourage the 'kai-high', not put them down because they are now 'too churchy'.
Our teachers and administrators have challenged us this Lenten season to give up complaining. Why spend time complaining about your issues when you can fix them! We can all do with a little less complaining in our own lives, whether we are the ones saying it or hearing it. Will you be the one to complain about this no complaining rule or will you be the one who realizes what this Lenten season is all about? Students should be encouraging those who want to complain less, not knocking on them for being a 'goody two shoes' that always follows the rules.
I'll ask again, why do we feel the need to hide our faith or put others down for theirs? We go to a Catholic school, whose goal is to develop the total person in a Catholic environment. When did we lose sight of what that really means? I challenge you, Saints. This Lenten season, speak up a little louder, sing out a little stronger, complain a little less and get up a little earlier on Fridays. If we spend more time building up others in their faith, we may just find that ours has been strengthened too. Let us come together, support one another and show others we aren't afraid to speak up for our faith or support our friends in theirs.


It's Fall! For me, the baker, the best time for exploring all the possibilities with my favorite (or nearly favorite) root vegetable. CARROTS!
OK, who doesn't love carrot cake? Yeah…but all that rich icing is always a big challenge to me. Personally, the best part of the cake….is the CAKE!
So, onto the ultimate search for a carrot cake that is so very delicious it doesn't have to hide under a mountain of sweet dairy!
I'm not a vegan, but I'm almost a vegetarian, and just don't do very well with a lot of dairy, so when reading this recipe there sprang a mountain of possibilities.
The most important was discovered AFTER baking this loaf. It's incredibly moist, amazingly dense, and the toasted sesame seeds add a savory flavor that sets off the gingery cake. YUM-MY!
My thanks to Jessica Koslow for this sensational recipe. Typically, I usually put a bit of my own spin on recipes, but this one deserves to live on it's own.
Thanks also to Food 52 at food52.com food blog for sharing.
Preheat oven to 350ºF. Brush the inside surfaces of an 8 1/2″ x 4 1/2″ loaf pan with a little oil. Dust with flour, tapping out any excess.
In a small bowl, combine the flour, baking powder and cinnamon.
In a large bowl, whisk together the granulated sugar, brown sugar, applesauce, almond milk, ginger, vanilla and salt. Using a rubber spatula, fold in the flour mixture, followed by the carrots and finally, the oil.
Scrape the batter into the prepared pan and sprinkle the top with the sesame seeds. You want the seeds to completely cover the top of the batter. Bake until the middle of the loaf has puffed and a toothpick inserted into the center comes out clean, about 1 hour 10 minutes.
Instead of using oil and flour to prepare your pan, you may want to cut parchment to fit the pan. There will be two pieces, one for the long side of the pan, the other for the width of the pan. Make sure the paper is cut long enough to cover the ends or sides and bottom of pan along with 2 or 3″ more for drape over the sides. Once you fit the paper in the pan, spray with cooking spray to coat the paper. You'll find that when cooled, the cake will lift out of the pan seamlessly. Then, gently peel away the paper and you have a perfect cake.
Don't be afraid of the fresh ginger. Use the smallest side of your grater to really grate down finely. I love fresh ginger and used more than the recipe called for….it's all about taste!!!
Although the recipe calls for baking 1 hour 10 minutes, I found that my cake required 1 hour 25 minutes. Remember that fresh carrots contain moisture and when really fresh are really full of liquid. Be prepared to bake a bit longer than expected.


The liver is the largest internal organ in the body. It is also known as primary liver cancer or hepatoma and hepatocellular carcinoma is a cancer arising under the liver. The liver performs various vital affairs. It processes and places many of the nutrients absorbed from the bowel. It also obliges some of the clotting parts needed to stop oozing from a slash or trauma. Hemangioma( he-man-ge-O-muh) is the most common type of harmles liver tumor. It begin in blood vessels and Hepatic adenomas are benign tumors that start from the central type of liver cells( hepatocytes ). Women have a much higher chance of having one of these tumors if they make oral contraceptive pill, although this is rare. Stopping the pills can cause the tumor to cringe. Hepatic adenomascancer begins in numerous distinguishes throughout the liver and is not confined to a single tumor. This is most often considered to be in parties with liver cirrhosis and is the most common structure seen in the United States.
Hepatic tumors also called Liver cancer. Hepatic tumors is are tumors or emergences on or in the liver. There are many forms of liver tumors premary is Malignant. Malignant, primary liver cancer is hepatocellular carcinoma( likewise mentioned hepatoma, which is a misnomer ). These emergences can be harmles or malignant( cancerous ). They may be discovered on medical imaging( even for a different conclude than the cancer itself ). Most of the time when cancer is found in the liver, it did not getting started but spread to the liver from a cancer that began somewhere else in the body. These tumors are listed after the place where they embarked( primary site) and are farther was regarded as metastatic. Evidences can include a glob or pain on the right side of your abdomen and yellowing of the surface. Nonetheless, you are not able to have indications and the cancer may not be found until it is advanced.
Some investigates believe that cancer begins with damage to DNA information materials that contains the instructions for every substance process in your organization, including the rate of cellular proliferation. DNA damage makes changes in these instructions. Liver cancer too comes as metastatic cancer, which happens when tumors from other parts of the body spread( metastasize) to the liver. In the liver cancer some cells begin to grow exceptionally. One result is that cadres may begin to grow out of sovereignty and eventually constitute a tumor a mass of malevolent cells. In the United States, most cancer found in the liver spread there after originating abroad. Rather than being called liver cancer, this kind of cancer in the liver is referred after the organ in which it began such as metastatic colon cancer in cancer that starts in the colon and spreads to the liver.
Hepatitis B virus( HBV) illnes is the main causes of liver cancer. Liver cancer relates to( correlates with) the frequency of chronic hepatitis B virus infection. Analyzes in swine also have provided that hepatitis B virus can cause liver cancer. For precedent, liver cancer develops in other mammals that are naturally infected with hepatitis B virus-related viruses. Ultimately, by infecting transgenic mice with certain parts of the hepatitis B virus, scientists induced liver cancer to develop in mouse that do not generally develop liver cancer.
Symptoms of liver cancer Liver cancer is very common larges countries around the world wide. Generally is discovered at a very advanced stagecoach of cancer canker for several reasonableness. In additive, cases from these regions actually have most aggressive liver cancer disease. live cancer tumor generally contacts an advanced stage and effects manifestations more rapidly. Abdominal pain is the most common symptom of liver cancer and usually shows a very large tumor or pervasive involvement of the liver and other symptom is weight loss. These indications are less common in beings with liver cancer in the U.S.an additionally symptoms is ascites( abdominal fluid and swelling ), jaundice( yellow shade of the surface ), or muscle wasting.
The treatment of liver cancer is overall surrounding of individual patients. First treatment if liver cancer is Chemotherapy. Chemotherapy is a strong stimulants to kill cancer cells. Chemotherapy is anot effective not some cases of liver cancer but type of chemotherapy is known as chemoembolization plays a key role in management for HCC. chemoembolization makes many of the same side effect as other means of chemotherapy, including abdominal sorenes, nausea and upchuck. Second care is surgery. Surgery is good care for localized resectable cancer is generally an operation known as surgical resection. In some instances, the area of the liver where the cancer is observed can be completely removed. Alcohol injection has been shown to improve survival in people with small hepatocellular tumors. It may also be used to help reduce manifestations in cases of metastatic liver cancer. The most common side effect is leaking of alcohol onto the liver or into the abdominal cavity.
Juliet Cohen writes commodities for health physician. She likewise writes essays for dip medication and cancer treatment.


A research team led by Behnam Sharif, Alberta Bone and Joint Health Institute's postdoctoral fellow, and Deborah Marshall, ABJHI's research director, has been awarded a seed grant to conduct research groundwork that could lead to new and better ways to manage obese patients with osteoarthritis – a fast-growing problem in Alberta.
The grant was awarded by the Diabetes, Obesity and Nutrition Strategic Clinical Network (DON SCN). It will be used to gather evidence on effective strategies for managing obesity in people with OA and to select the strategies that would be most effective with Alberta's population. An estimated one in eight adults in Alberta has OA.
"This work will give us the foundation we need for the next stage, which will be a proposal to develop a 'what-if' computer model that could demonstrate how the selected obesity management strategies would perform when used on individual patients," Dr. Sharif said.
Obese people are up to four times more likely than non-obese people to develop OA, the leading cause of hip and knee replacements. Obesity and OA together present complex health and health care challenges, including associated chronic conditions such as diabetes and heart disease and elevated risk of infection and subsequent surgery to repair problems with a hip or knee joint implant.
The computer model would use discrete-event simulation (DES) to simulate a treatment strategy and immediately observe its predicted impact on patient outcomes and health system costs. DES is a mathematics-based method of analyzing complex systems using computer applications to simulate the effects of a change in one component on the entire system.
Dr. Marshall has already designed a DES model for patients who have OA in the hip or knee. That work was funded by a grant from the Canadian Institutes of Health Research. The work funded by the DON SCN's seed grant and the research proposal that will follow would extend the use of the model into the challenging area of obesity and OA.
The researchers will use data on thousands of hip and knee replacements in Alberta since 2012 to help design different obesity management strategies for simulation. They will have access to ABJHI's hip and knee replacement data repository, which is unique in Canada for capturing end-to-end data on patient experience and outcomes. They will also have data from Alberta Health Services and from Alberta Health.


Sometimes, being a fan means navigating a tricky line between wanting to know everything about a star and trying to respect their privacy. Since the Star Wars actor welcomed his first child in April, no one can blame you if you're wondering where the photos of Oscar Isaac's son are, or if they even exist. Want the good news first? There was a tweet containing a video of Isaac's girlfriend Elvira Lind cradling their newborn baby earlier this year, where she explains why she couldn't be at the Tribeca Film Festival. The bad news? Well, there don't appear to be any other images of the child available. But if you're a fan, you'll surely respect Isaac's right to privacy and acknowledge this as a good and healthy thing.
The couple has been publicly— or as public as possible when you're with the notoriously private Isaac — dating since the 2016 Golden Globes, according to Us Weekly, when Lind was his date to the event. According to her own website, Lind is a "a Danish director from Copenhagen based in New York" who directed the documentary Bobbi Jene. Perhaps Lind's skills in visuals abetted them — the pair managed to conceal the pregnancy until the month of the birth, with JustJared reporting in April that Isaac's girlfriend showed her pregnancy bump "for the very first time" at the PS122 Gala.
Presumably Isaac is aware that photos of his son would only increase press focus on his first child, and as such, probably the lack of photos is out of protectiveness. This seems fair. After all, while every first born tends to be the apple of their father's eye, this baby seems to mean everything to the actor.
In a July 2017 New York Times interview, Isaac confessed that his son, Eugene, is named after his mother, Eugenia, who passed away this past February, and that the baby has his mother's lips and hands. In the same interview, he confided that while playing the titular role in a stage production of Hamlet, he thought about his son during the "to be or not to be" speech, which he argues is about finding a reason to live rather than dying. "You have a child," he told the paper, "and you must — you must for their sake — you must say yes to life."
But while Isaac might be among the most eloquent stars when it comes to expressing the joy of having a baby, he isn't the only Hollywood parent who seems staunchly anti-photos when it comes to his child. According to the BBC, when pictures of George Clooney' newborn twins were taken by a French magazine Voici (who Clooney claimed scaled the fence surrounding his property and climbed a tree presumably to reach the windows of his home) without permission, the actor promised to prosecute.
Similarly, according to The Guardian, while speaking in favor of a bill to protect children from paparazzi, Jennifer Garner said in August 2013, "I chose a public life and understand that this means sacrifices in terms of privacy for our jobs." She continued, "In my case this means that I am sometimes photographed. However my three children are private citizens and more than that—at one, 4 and 7 years old—they're just little kids."
If Isaac ever needed tips on how to keep his child's life entirely private, he should probably go talk to Gilmore Girls actor Alexis Bledel. According to Elite Daily, nobody even knew that Bledel was pregnant until after she'd given birth. The same source reports that basic details, like the child's name and date of birth, have also never been revealed.
So, no, there are no photos of Eugene Isaac yet, but if you're a genuine fan of his dad, you'll be OK with that. Admiring Oscar Isaac means accepting the importance he places on privacy. Given the fact that Eugene is very much a child, the lack of photos is understandable.


Bomgar Remote Support is Now BeyondTrust Remote Support!
We are excited to share a new brand identity that aims to make Remote Support more accessible and useful to our users. In this release, you will have a first look at some of the design considerations that went into evolving the user experience and incorporating new brand elements.


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The Bridge School > About the School
The Bridge School is a private, co-educational childhood learning center modeled on standards of developmentally appropriate educational practices as set by the National Association for the Education of Young Children (NAEYC), USA. Its Preschool sits on its own 1300-square meter tree-lined property on 29 Cairo Street and 32 Canberra Street in the safe community of BF Northwest in BF Homes, Paranaque City. Its Grade School, likewise sits on two properties on both sides of the Preschool – 25 Canberra Street and 50 Cairo Street.
At The Bridge School, our mission is to keep alive a child's natural enthusiasm for learning, to appropriately channel and challenge it, and to cultivate in each child a genuine desire and passion to continue learning. In so doing, we envision children who will grow up with a strong sense of who they are and what they can contribute to the world.
The Bridge School's Preschool was founded in 2001 and Grade School in 2008 by Tere Tabuena-del Rosario, Katrina Tabuena-del Rosario, Michelle Tabuena-Filart, and Denise Tabuena-Lumbad. The school provides an exceptional learning environment for preschool and grade school children. Education at TBS is experiential, interdisciplinary, and collaborative, rooted on Christian principles and on an understanding of Filipino culture and values. Our commitment is to the whole child – heart, mind, body, and spirit.
Class Size/Ratio
At The Bridge School, we maintain low teacher-to-child ratios in all of our classes. In the Nursery classes (2.0 – 2.11 years old) and Younger Preschool classes (3.0 – 3.11 years old), we have a 1:5 teacher-child ratio in each class. In the Older Preschool (4.0-4.11 years old) classes, we have a 1:6 teacher-child ratio in the class. In the Kindergarten classes (5.0 – 5.11 years old), we have a 1:8 teacher-child ratio in the class. All with a maximum of 16 students in each class. All of the Grade School classes maintain a 1:14 teacher-student ratio with a maximum of 22 students in each class.


text:
Leucandra prava är en svampdjursart som först beskrevs av Breitfuss 1898.  Leucandra prava ingår i släktet Leucandra och familjen Grantiidae.

Artens utbredningsområde är Portugal. Inga underarter finns listade i Catalogue of Life.

Källor

Kalksvampar
prava

redpajama_set_name: RedPajamaWikipedia


Important note: It's always a good idea to look for firmware updates for your Amcrest camera. Keeping your firmware up-to-date ensures that the camera has the latest patches and fixes which will keep it working well.
If you have the iOS or Android app, you can fully setup the Amcrest camera.
Step #1: Plug the ethernet cable from the router to the camera.
Step #2: Make sure your mobile device is on WiFi. Tip: Most Amcrest cameras work on the 2.4GHz band. Many networks have both bands (2.4 and 5GHz). Make sure you are on the 2.4GHz WiFi network if you are setting up a camera that supports 2.4GHz only.
Step #3: Click the plus (+) sign to add a Amcrest camera. Follow the onscreen instructions.
If all goes well you should be able to setup your camera, assign a password (if it's brand new out of the box) and setup the camera on WiFi if capable.
If you are setting up the camera on the web you first need to setup the password.
Step #1. Configure the password for your camera.
Tip: if you have already setup your camera with a password other than the default then you can skip to step #2.
Plug your camera into one of the ports on the back of your router with a network cable. Most routers have ports labelled 1-4 on the back that the camera can be plugged into. Your camera may have come packaged with a network cable. If it did not, you will need to purchase one seperately.
Once the camera is plugged in, it will be auto assigned an IP address. To find the address, download the Amcrest IP Config Tool from here. Once you install the tool, run it to find the IP Address of the camera. The default username and password for Amcrest cameras are "admin" for both.
Using the IP address that was found with the Config Tool, log into the camera interface to change your default password. Once logged in, navigate to Setup (top right) > System > Manage Users. This may vary, but should be the path for most Amcrest Cameras. Check your camera documentation if you find that this path is not applicable.
Step #2. First Login to your account. You should see your "Timeline". Start the "Add Camera" Wizard. Click on "Add Camera" and pick "Amcrest". Enter a name for your camera and the camera serial number.
Tip: You can find your serial number on the bottom sticker of the camera. Alternatively, use the above mentioned Amcrest IP Config Tool.
Step #3 Once you click on next, you'll have to add in your camera's username and password. Click "Next" when you are done.
Step #4. This is where you can also change whether or not you'd like notifications, Image Appearance, Quality and IR Mode (optional) Once you're done, click on "Finish".
Now the setup wizard will configure your chosen settings and close.