text:
Bullet Resistant Vests
India has been fighting cross-border proxy wars, terrorism and insurgencies past three decades plus. The requirement of bullet proof jackets (BPJs) for every security forces fighter operating in counter insurgency and counter terrorist environment is the basic necessity. The fact that requisite BPJs in terms of numbers and quality have not been provided to our security forces is ironic, reflecting the callous and nonchalant attitude of the powers that be. It is not that the Maharashtra police was found sans BPJs during the 26/11 terrorist attacks. The Indian Army continues to remain deficient of modular BPJs almost a decade back, with the Defence Acquisition Council (DAC) having cleared the acquisition of 1,86,138 such BPJs in October 2009, despite private Indian companies exporting BPJs to over 100 countries including British, German, Spanish and French armies, plus police forces stretching from Japan in the East to the US in the West.
In 2014, former Defence Minister announced "emergent" procurement of quantity 50,000 modular BPJs for the Army, but these started trickling in last year in 2016 from Tata Advanced Materials Limited (TAML) and the last lot are to e delivered by March 30, 2017 - so much for promise of "emergent procurement" by the defence minister himself. The bigger case for acquisition of1.86 lakh jackets that were to be procured by year 2012, is still lingering but meanwhile the BPJs deficiency of the Army has gone beyond 3,50,000. A British company is presently marketing the PPSS Bullet Resistant Vest - CV2 with NIJ Level IIIA+ protection level that has following characteristics: highly advanced ultra-resilient European ballistic material Waterproof and breathable Cordura® 180 ground-breaking temperature controlling Outlast® space technology; protection against full contact shots Protection from Tokarev Ball 7.62 x 25mm and Makarov 9 x 18mm Protection against more aggressive handgun ammunition Protection against TASER®, and; weighing 1.66 kg. But what is more relevant here is that Amrita University, Coimbatore is offering equally and more advanced technology and the proposal titled "Development of Hyper Velocity Impact Resistant Ultra Light Weight High Temperature Resistant Thermoplastic Polymer-Carbon Fiber Composite" is lying with MoD. The firm claims that this bullet resistant vest has passed the most up to date National Institute of Justice Standard 0101.06, and offers protection from full contact shots, since many times professionals accidentally get shot by their own firearm from extremely close range.
Schematic representation of PEK/PEI-Carbon sandwich composit with silicone foam	for bullet proof vest
Amrita University sent the proposal to DRDO and the joint propel TDF-DRDO was then sent to MoD. Dr Shantanu Bhowmik, Head Research & Projects, Department of Aerospace Engineering, Amrita university, Coimbatore also wrote to then Defence Minister Manohar Parrikar that the proposal needs to be progressed expeditiously since similar research is ongoing by foreign companies like Boeing, EADS, Airbus, Lockheed Martin, Bombardier etc for developing high performance thermoplastic-carbon fibre composite for various applications in aviation and defence, and that Amrita University is in the forefront of this technology globally. He, however, pointed out that delay in implementing ths technology in India, would result in these foreign companies exploiting the Indian market.
What is the fate of the proposal sent to the MoD is unknown and whether it has died with the moving out of Manohar Parrikar is not known either, but this is a case that needs to be given due weightage by the PMO. If the claims of Amrita University are correct, which they should be since the joint proposal was sent through the DRDO, it must be examined on priority. These state-of-the-art bullet resistant vests are not required by the Army alone. We should not have only the Army pursuing these and the CAPF going in for imports when better material has been developed indigenously. Of course force individual force requirement would need to be met, like covering the neck, shoulder, grin etc, which should not be a problem. Dr Shantanu Bhowmik had assured then Defence Minister Manohar Parrikar that Amrita University is fully geared and prepared to work 24 x 7 on the project once approved. This is a case that needs to be examined with speed and executed under the 'Make in India' project forthwith since no foreign collaboration and transfer of technology (ToT) is involved. In fact, the venture without doubt has tremendous potential for exports, provided we can productionize these bullet resistant vests before the foreign firms catch up. The million dollar question is can the government act with speed and cut the red tape?


Description: One year young large 2 bedroom and a den south facing unit in The Crescendo building. Stainless steel appliances, Granite countertops, tiled kitchen back splash, electric fireplace are some of the features. Building has Exercise room, hot tub, sauna, steam room and a lounge. Steps away from all the restaurants and Cafes in the Newport Village. Very close to Library, parks and Beaches.


text:
cask "commandq" do
  version "3.0.0"
  sha256 "cd826add0aab404d231b42486902ae4e9f150b1ce7ff218fbca48c314c57e573"

  url "https://download.clickontyler.com/commandq/commandq20_#{version.no_dots}.zip",
      verified: "download.clickontyler.com/"
  name "CommandQ"
  desc "Never accidentally quit an app again"
  homepage "https://commandqapp.com/"

  livecheck do
    url "https://shine.clickontyler.com/appcast.php?id=43"
    strategy :sparkle, &:short_version
  end

  depends_on macos: ">= :sierra"

  app "CommandQ.app"
end

redpajama_set_name: RedPajamaGithub


text:
Star Wars Resistance Episode 18 Review: The Disappeared
Star Wars Resistance continues more table-setting plotting, but at least this episode sets up for the endgame.
By Kevin Johnson | February 25, 2019 |
Star Wars Resistance Episode 18
Star Wars is anything but subtle, but with so many people out there who fail to grasp the parallels, "The Disappeared" has Stormtroopers bursting into taverns and forcing them to display very ominous, very Nazi-like military propaganda on the counter.
It's red and glowing, depicting three "proud" First Order soldiers standing in a row, with the clarion call to others to join, to be a part of something bigger. You really can't get more clear than that. The only thing that Star Wars Resistance failed to do was introduce these kinds of visuals earlier, to really get the sense of the First Order's slowly overpowering, suffocating, dangerous presence.
The background stuff is finally starting become the foreground stuff. Things are getting ugly and complicated, and they are now elements that's driving the story and various character motivations. It's still very wonky (there's still three other Aces that have yet to do anything), but the germs of an overarching idea is starting to form.
No more goofy one-offs running a shop or Poe excursions that, shocker, reveal the First Order are bad hombres. We now see them actually being bad hombres to people we know and ostensibly care about, and the actual people on The Colossus are doing something instead of random fist-shaking.
I think that there's something to the arguments that are developing among Kaz, Tam, and Neeku that feel, if not realistic, then relevant. Kaz is antagonistic, obviously, but Tam is still stubbornly pro-First Order, even as their rights and freedom (and races) are being taken away. Neeku is still playing a wishy-washy centrist role, with no clear preference either way.
further Reading: How Star Wars Resistance Tells Unique Stories
It might be frustrating to see those latter two characters fail to be alarmed in any way by the aggressive behavior and tactics of the First Order, even after they've heard about what happened to Kel and Elia, and witness what they do to Hype and his ship, and how they treat the actual Captain of the The Colossus, and the fact that people are literally disappearing despite the facade of the First Order supposedly protecting people. But people refusing "to get it" is pretty clearly reflective of our contemporary environment, isn't it? Again, Star Wars is anything but subtle.
"The Disappeared" is still a kind of sloppy episode in how it tells its story, with some early focus on Hype as the races are shut down and they impound his ship, before he disappears. Then Aunt Z suddenly becomes a prominent character, loudly voicing her complaints and protests against the First Order's behavior and propaganda before she disappears (which makes me wish that we got see her developed a bit more, especially at the end when she says she has a friend off world that could help them).
Then we follow Kaz and Torra as they investigate what happened to Hype and Aunt Z, but this occurs in the middle of the episode. Yet we don't really get to following their investigation, which would properly build suspense; the episode instead cuts to Hype, Aunt Z, and two other "captured" people to show where they are and what's happening to them, mostly for the gag of Aunt Z climbing on top of Hype. It's not a smooth narrative transition, particularly since Aunt Z, as mentioned, is underdeveloped until now. But it does make the overall threat of the First Order feel more real, so… baby steps?
The episode ends with a small fight as Kaz, Torra, Hype, and Aunt Z battle the Stormtroopers, with the heroes' dumb luck winning out again. It's relatively silly, but the dynamic is interesting, with Hype inadvertently joining Aunt Z to meet with what I assume to be a part of the Resistance. It's unexpected for sure, and it would be bold if the show played down the role of the "Aces" for something grander.
At least there are stakes now, the conflicts directly on the surface, and the tension reaches its peak by ending with Kaz, Tam, Neeku and Yeager all captured by the First Order. I have no idea what's going to happen next, which is something you always want for your endgame. Star Wars Resistance just needs to tighten up its narratives considerably to really pull something out from its own wishy-washiness.
Keep up with Star Wars Resistance news and reviews here.
Listen to the latest Star Wars Blaster Canon podcast:
Subscribe: Apple Podcasts | Spotify | Stitcher | Acast | RSS
Kevin Johnson is a writer who loves cartoons and animation. He has written for The AVClub and Topless Robot, and has more content at his own blog here. You can also follow him on Twitter.
Tags: Star Wars ResistanceDisney XDStar Wars
More on Star Wars Resistance
Star Wars Resistance Season 2 Episode 18 Review: The Escape
January 26, 2020 | By Kevin Johnson
Star Wars Resistance Season 2 Episode 16 Review: No Place Safe
Star Wars Resistance Season 2 News, Reviews, and Episode Guide
January 26, 2020 | By John Saavedra

redpajama_set_name: RedPajamaCommonCrawl


text:
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text:
\section{Introduction}

The current coverage of the political landscape in the press and in social media has led to an unprecedented situation. Like never before, a statement in an interview, a press release, a blog note, or a tweet can spread almost instantaneously
and reach the public in no time.
This proliferation speed has left little time for double-checking claims against the facts, which has proven critical in politics, e.g.,~during the 2016 presidential campaign in the USA, which was arguably impacted by fake news in social media and by false claims.

\begin{figure}
\footnotesize
\centering
\includegraphics[width=230px]{fc_pipeline.pdf}
\caption{Information verification pipeline.}
\label{fig:pipeline}
\end{figure}

\noindent Investigative journalists and volunteers have been working hard trying to get to the root of a claim and to present solid evidence in favor or against it. Manual fact-checking has proven very time-consuming, and thus automatic methods have been proposed as a way to speed-up the process.
For instance, there has been  work on checking the factuality/credibility of a claim, of a news article, or of an information source~\cite{castillo2011information,ba2016vera, zubiaga2016analysing, ma2016detecting, Hardalov2016,RANLP2017:clickbait,RANLP2017:factchecking:external,RANLP2017:credibility:trolls}. However, less attention has been paid to other steps of the fact-checking pipeline, which is shown in Figure~\ref{fig:pipeline}.

The process starts when a document is made public. First, an intrinsic analysis is carried out in which check-worthy text fragments are identified.
Then, other documents that might support or rebut a claim in the document are retrieved from various sources. Finally, by comparing a claim against the retrieved evidence, a system can determine whether the claim is likely true or likely false. For instance, \citet{10.1371journal.pone.0128193} do this on the basis of a knowledge graph derived from Wikipedia. The outcome could then be presented to a human expert for final judgment.\footnote{As of present, fully automatic methods for fact checking still lag behind in terms of quality, and thus also of credibility in the eyes of the users, compared to what high-quality manual checking by reputable sources can achieve, which means that a final double-checking by a human expert is needed.}

\noindent In this paper, we focus on the first step: predicting check-worthiness of claims. Our contributions can be summarized as follows:
\begin{enumerate}
\item \emph{New dataset:} We build a new dataset of manually-annotated claims, extracted from the 2016 US presidential and vice-presidential debates, which we gathered from nine reputable sources such as CNN, NPR, and PolitiFact, and which we release to the research community.
\item \emph{Modeling the context:}  We develop a novel approach for automatically predicting which claims should be prioritized for fact-checking, based on a rich input representation. In particular, we model not only the textual content, but also the context: how the target claim relates to the current segment, to neighboring segments and sentences, and to the debate as a whole, and also how the opponents and the public react to it.
\item {State-of-the-art results:} We achieve state-of-the-art results, outperforming a strong rivaling system by a margin, while also demonstrating that this improvement is due primarily to our modeling of the context.
\end{enumerate}
We model the problem as a  ranking task, and we train both Support Vector Machines (SVM) and Feed-forward Neural Networks (FNN) obtaining state-of-the-art results. We also analyze the relevance of the specific feature groups and we show that modeling the context yields a significant boost in performance. Finally, we also analyze
whether we can learn to predict which facts are check-worthy with respect to each of the individual media sources, thus capturing their biases.
It is worth noting that while trained on political debates, many features of our model can be potentially applied to other kinds of information sources, e.g., interviews and news.

The rest of the paper is organized as follows: Section~\ref{sec:related} discusses related work. Section~\ref{sec:data} describes the process of gathering and annotating our political debates dataset. Section~\ref{sec:model} presents our supervised approach to predicting fact-checking worthiness, including the explanation of the model and the information sources we use. Section~\ref{sec:evaluation} presents the evaluation setup and discusses the results.
Section~\ref{sec:discuss} provides further analysis.
Finally, Section~\ref{sec:conclusion} presents the conclusions and outlines some possible directions for future research.

\section{Related Work}
\label{sec:related}

The previous work that is most relevant to our work here is that of \cite{Hassan:15}, who developed the \emph{ClaimBuster} system, which assigns each sentence in a document a score, i.e., a number between 0 and 1 showing how worthy it is of fact-checking. The system is trained on their own dataset of about eight thousand debate sentences (1,673 of them check-worthy), annotated by students, university professors, and journalists. Unfortunately, this dataset is not publicly available and it contains sentences without context as about 60\% of the original sentences had to be thrown away due to lack of agreement.

In contrast, we develop a new publicly-available dataset,\footnote{The dataset and the source code are available in GitHub: \url{https://github.com/pgencheva/claim-rank}}  based on manual annotations of political debates by nine highly-reputed fact-checking sources, where sentences are annotated in the context of the entire debate. This allows us to explore a novel approach, which focuses on the context.

Note also that the \emph{ClaimBuster} dataset is annotated following guidelines from \cite{Hassan:15} rather than a real fact-checking website; yet, it was evaluated against CNN and PolitiFact \cite{Hassan2016ComparingAF}. In contrast, we train and evaluate directly on annotations from fact-checking websites, and thus we learn to fit them better.

Beyond the document context, it has been proposed to mine check-worthy claims on the Web. For example, \citet{ennals2010disputed} searched for linguistic cues of disagreement between the author of a statement and what is believed, e.g., ``\emph{falsely claimed that X}''.
The claims matching the patterns go through a statistical classifier, which marks the text of the claim. This procedure can be used to acquire a dataset of disputed claims from the Web.

Given a set of disputed claims, \cite{ennals2010highlighting} approached the task as locating new claims on the Web that entail the ones that have already been collected. Thus, the task can be conformed as recognizing textual entailment, which is analyzed in detail in \cite{dagan2009recognizing}.

Finally, \citet{le2016towards} argued that the top terms in claim vs.\ non-claim sentences are highly overlapping, which is a problem for bag-of-words approaches. Thus, they used a Convolutional Neural Network, where each word is represented by its embedding and each named entity is replaced by its tag, e.g., \emph{person}, \emph{organization}, \emph{location}.

\begin{table}[tbh]
\small
\centering
\begin{tabular}{lrrrrr}
  \bf Medium & \bf 1st & \bf 2nd & \bf VP & \bf 3rd & \bf Total\\
 \hline
  ABC News& 35 & 50 & 29 & 28 & 142 \\
  Chicago Tribune & 30 & 29 & 31 & 38 & 128\\
  CNN & 46 & 30 & 37 & 60 & 173 \\
  FactCheck.org & 15 & 45 & 47 & 60 & 167 \\
  NPR & 99 & 92 & 91 & 89 & 371 \\
  PolitiFact & 74 & 62 & 60 & 57 & 253 \\
  The Guardian & 27 & 39 & 54 & 72 & 192 \\
  The New York Times & 26 & 25 & 46 & 52 & 149 \\
  The Washington Post & 26 & 19 & 33 & 17	 & 95 \\
 \hline
  \bf Total annotations & 378 & 391 & 428 & 473 &  1,670 \\
  \bf
  Annotated sentences & 218 & 235 & 183 & 244 & 880 \\
\end{tabular}
\caption{Number of annotations in each medium for the 1st, 2nd and 3rd presidential and the vice-presidential debates.}
\label{table:per:medium}
\end{table}

\section{The CW-USPD-2016 dataset on US Presidential Debates}
\label{sec:data}

We created a new dataset called CW-USPD-2016 (check-worthiness in the US presidential debates 2016) for finding check-worthy claims in context.
In particular, we used four transcripts of the 2016 US election: one vice-presidential and three presidential debates. For each debate, we used the publicly-available manual analysis about it from nine reputable fact-checking sources, as shown in Table~\ref{table:per:medium}. This could include not just a statement about factuality, but any free text that journalists decided to add, e.g., links to biographies or behavioral analysis of the opponents and moderators. We converted this to binary annotation about whether a particular sentence was annotated for factuality by a given source.
Whenever one or more annotations were about part of a sentence, we selected the entire sentence, and when an annotation spanned over multiple sentences, we selected each of them.

Ultimately, we ended up with a dataset of four debates, with a total of 5,415 sentences.
The agreement between the sources was low as Table~\ref{table:agreement} shows: only one sentence was selected by all nine sources, 57 sentences by at least five, 197 by at least three, 388 by at least two, and 880 by at least one. The reason for this is that the different media aimed at annotating sentences according to their own editorial line, rather than trying to be exhaustive in any way. This suggests that the task of predicting which sentence would contain check-worthy claims will be challenging. Thus, below we focus on a ranking task rather than on absolute predictions. Moreover, we predict which sentence would be selected ({\em i})~ by at least one of the media, or ({\em ii})~ by a specific medium.

\begin{table}[tbh]
\small
\centering
  \begin{tabular}{crr}
    \bf Agreement & \bf Number of & \bf Cumulative \\
    \bf Level & \bf Sentences & \bf Sum \\
    \hline
    9 & 1 & 1 \\
    8 & 6 & 7 \\
    7 & 5 & 12 \\
    6 & 19 & 31 \\
    5 & 26 & 57 \\
    4 & 40 & 97 \\
    3 & 100 & 197 \\
    2 & 191 & 388 \\
	\bf 1 & \bf 492 & \bf 880 \\
    \hline
    \multicolumn{3}{l}{\bf Total number of sentences: 5,415}\\
  \end{tabular}
\caption{Agreement between the media represented as the number of sentences that $n$ out of nine providers identified as check-worthy.}
\label{table:agreement}
\end{table}

\noindent Note that the investigative journalists did not select the check-worthy claims in isolation. Our analysis shows that these include claims that were highly disputed during the debate, that were relevant to the topic introduced by the moderator, etc. We will make use of these contextual dependencies below, which is something that was not previously tried in related work.

\section{Modeling Check-Worthiness}
\label{sec:model}

We developed a rich input representation in order to model and to learn the \emph{check-worthiness} concept. The feature types we implemented operate at the sentence- (S) and at the context-level (C), in either case targeting \emph{segments} by the same speaker.\footnote{We define a \emph{segment} as a maximal set of consecutive sentences by the same speaker without intervention by another speaker or by the moderator.}
The context features are novel and a contribution of this study. We also implemented a set of core features to compare to the state of the art. All of them are described below.

\subsection{Sentence-Level Features}

\textbf{ClaimBuster-based} (\emph{1,045 S features}; core):
First, in order to be able to compare our model and features directly to the previous state of the art, we re-implemented, to the best of our ability, the sentence-level features of \emph{ClaimBuster} as described in ~\cite{Hassan:15}, namely TF.IDF-weighted bag of words (998 features), part-of-speech tags (25 features), named entities as recognized by \emph{Alchemy API}\footnote{\url{http://www.ibm.com/watson/alchemy-api.html}}
(20 features), sentiment score from Alchemy API (1~feature), and number of tokens in the target sentence (1 feature).

\noindent Apart from providing means of comparison to the state of the art, these features also make a solid contribution to the final system we build for check-worthiness estimation.
However, note that we did not have access to the training data of ClaimBuster, which is not publicly available, and we thus train on our dataset (described above).

\textbf{Sentiment} (\emph{2 S features}): Some sentences are highly negative, which can signal the presence of an interesting claim to check, as the two example sentences below show (from the 1st and the 2nd presidential debates):

\vspace{3pt}
\begin{footnotesize}
\begin{tabular}{rp{55mm}}
Trump:	& Murders are up.	\\
Clinton:	&  Bullying is up.	\\
\\
\end{tabular}
\end{footnotesize}
We used the NRC sentiment lexicon~\cite{Mohammad13} as a source of words and $n$-grams with positive/negative sentiment, and we counted the number of positive and of negative words in the target sentence. These features are different from those in the \emph{CB features} above, where these lexicons were not used.

\textbf{Named entities (NE)} (\emph{1 S feature}): Sentences that contain named entity mentions are more likely to contain a claim that is worth fact-checking as they discuss particular people, organizations, and locations.
Thus, we have a feature that counts the number of named entity mentions in the target sentence; we use the \emph{NLTK toolkit} for named entity recognition~\cite{Loper02nltk:the}.
Unlike the \emph{CB features} above, here we only have one feature; we also use a different toolkit for named entity recognition.

\textbf{Linguistic features} (\emph{9 S features}): We count the number of words in each sentence that belong to each of the following lexicons: Language Bias lexicon \cite{Recasens+al:13a}, Opinion Negative and Positive Words \cite{Liu:2005:OOA:1060745.1060797}, Factives and Assertive Predicates \cite{hooper1974assertive}, Hedges \cite{hyland1998hedging}, Implicatives \cite{karttunen1971implicative}, and Strong and Weak subjective words. Some examples are shown in Table~\ref{table:linguistic}.

\begin{table}[htb]
\footnotesize
\centering
\begin{tabular}{ll}
\textbf{Feature Name} & \multicolumn{1}{c}{\textbf{Examples}} \\
\hline
Bias & capture, create, demand, follow\\
Negatives & abnormal, bankrupt, cheat, conflicts\\
Positives & accurate, achievements, affirm\\
Factives & realize, know, discover, learn\\
Assertives & think, believe, imagine, guarantee\\
Hedges & approximately, estimate, essentially\\
Implicatives & cause, manage, hesitate, neglect \\
Strong-subj & admire, afraid, agreeably, apologist\\
Weak-subj & abandon, adaptive, champ, consume\\
\hline
\end{tabular}
\caption{Linguistic features and examples.}
\label{table:linguistic}
\end{table}

\textbf{Tense} (\emph{1 S feature}): Most of the check-worthy claims mention past events. In order to detect when the speaker is making a reference to the past or s/he is talking about his/her future vision and plans, we include a feature with three values---indicating whether the text is in past, present, or future tense. The feature is extracted from the verbal expressions, using POS  tags and a list of auxiliary verbs and phrases such as \emph{will}, \emph{have to}, etc.

\textbf{Length} (\emph{1 S feature}): Shorter sentences are generally less likely to contain a check-worthy claim.\footnote{One notable exception are short sentences with negations, e.g.,~\emph{Wrong.}, \emph{Nonsense.}, etc.}
Thus, we have a feature for the length of the sentence in terms of characters.
Note that this feature was not part of the \emph{CB features}, as there length was modeled in terms of tokens, but here we do so using characters.

\subsection{Contextual Features}

\textbf{Position} (\emph{3 C features}): A sentence on the boundaries of a speaker's segment could contain a reaction to another statement or could provoke a reaction,
which in turn could signal a check-worthy claim.
Thus, we added information about the position of the target sentence in its segment: whether it is first/last, as well as its reciprocal rank in the list of sentences in that segment.

\textbf{Segment sizes} (\emph{3 C features}): The size of the segment belonging to one speaker might indicate whether the target sentence is part of a long speech, makes a short comment or is in the middle of a discussion with lots of interruptions. The size of the previous and of the next segments is also important in modeling the dialogue flow. Thus, we include three features with the sizes of the previous, the current and the next segments.

\textbf{Metadata} (\emph{8 C features}): Check-worthy claims often contain mutual accusations between the opponents, as the following example shows (from the 2nd presidential debate):

\begin{footnotesize}
\begin{tabular}{rp{55mm}}
Trump:	& \textbf{Hillary Clinton} attacked those same women and attacked them viciously.	\\
Clinton:	&  They're doing it to try to influence the election for \textbf{Donald Trump}.	\\
\end{tabular}
\end{footnotesize}

Thus, we use a feature that indicates whether the target sentence mentions the name of the opponent, whether the speaker is the moderator, and also who is speaking (3 features). We further use three binary features, indicating whether the target sentence is followed by
a system message:
\emph{applause}, \emph{laugh}, or \emph{cross-talk}.\medskip

\subsection{Mixed Features}

The feature groups in this subsection contain a mixture of sentence- and of contextual-level features. For example, if we use a discourse parser to parse the target sentence only, any features we extract from the parse would be sentence-level. However, if we parse an entire segment, we would also have contextual features.

\textbf{Topics} (\emph{300+3 S+C features}):
Some topics are more likely to be associated with check-worthy claims, and thus we have features modeling the topics in the target sentence as well as in the surrounding context.
We trained a Latent Dirichlet Allocation (LDA) topic model~\cite{blei2003latent} on all political speeches and debates in \emph{The American Presidency Project}\footnote{\url{http://www.presidency.ucsb.edu/debates.php}}
using all US presidential debates in the 2007--2016 period\footnote{\url{https://github.com/paigecm/2016-campaign}}.
We had 300 topics, and we used the distribution over the topics as a representation for the target sentence. We further modeled the context using cosines with such representations for the previous, the current, and the next segment.

\textbf{Embeddings} (\emph{300+3 S+C features}): We also modeled semantics using word embeddings.
We used the pre-trained 300-dimensional Google News word embeddings by~\citet{DBLP:journals/corr/MikolovLS13} to compute an average embedding vector for the target sentence, and we used the 300 coordinates of that vector.
We also modeled the context as the cosine between that vector and the vectors for three segments: the previous, the current, and the following one.

\textbf{Discourse} (\emph{2+18 S+C features}):
We saw above that contradiction can signal the presence of check-worthy claims, and contradiction can be expressed by a discourse relation such as \textsc{Contrast}. As other discourse relations  such as \textsc{Background}, \textsc{Cause}, and \textsc{Elaboration} can also be useful, we used a discourse parser~\cite{jotycodra} to parse the entire segment, and we focused on the relationship between the target sentence and the other sentences in its segment; this gave rise to 18 contextual indicator features. We further analyzed the internal structure of the target sentence ---how many nuclei and how many satellites it contains---, which gave rise to two sentence-level features.

\textbf{Contradictions} (\emph{1+4 S+C features}): Many claims selected for fact-checking contain contradictions to what has been said earlier, as in the example below (from the 3rd presidential debate):

\vspace{3pt}
\begin{footnotesize}
\begin{tabular}{rp{55mm}}
Clinton:	& [\ldots] about a potential nuclear competition in Asia, you said, you know, go ahead, enjoy yourselves, folks.	\\
Trump:		& \textbf{I didn't say} nuclear. \\
\\
\end{tabular}
\end{footnotesize}
We model this by counting the negations in the target sentence as found in a dictionary of negation cues such as \textit{not}, \textit{didn't}, and \textit{never}.
We further model the context as the number of such cues in the two neighboring sentences from the same segment and the two neighboring segments.

\textbf{Similarity to known positive/negative examples (kNN)} (\emph{2+1 S+C features}): We used three more features inspired by $k$-nearest neighbor (kNN) classification. The first one (sentence-level) uses the maximum over the training sentences of the number of matching words between the testing and the training sentence, which is further multiplied by $-1$ if the latter was not check-worthy. We also used another version of the feature, where we multiplied it by $0$ if the speakers were different (contextual). A third version took as a training set all claims checked by \emph{PolitiFact} (excluding the target sentence).

\section{Experiments and Evaluation}
\label{sec:evaluation}

In this section, we describe our evaluation setup
and the obtained results.

\subsection{Experimental Setting}
\label{subsec:setting}
We experimented with two learning algorithms. The first one is an SVM classifier with an RBF kernel.\footnote{The RBF kernel worked better than a linear kernel.}  The second one is a deep feed-forward neural network (FNN) with two hidden layers (with 200 and 50 neurons, respectively) and a softmax output unit for the binary classification. We used ReLU \cite{pmlr-v15-glorot11a} as the activation function and we trained the network with Stochastic Gradient Descent \cite{lecun1998gradient}.

The models were trained to classify sentences as positive if \emph{one or more media} had fact-checked a claim inside the target sentence, and negative otherwise. We then used the classifier scores to rank the sentences with respect to \emph{check-worthiness}.\footnote{We also tried using ordinal regression, and SVM-perf, an instantiation of SVM-struct, to directly optimize precision, but none of them yielded improvements.}

\noindent We tuned the parameters and we evaluated the performance using 4-fold cross-validation, using each of the four debates in turn for testing while training on the remaining three ones.

\begin{table}[t]
\small
\centering
\begin{tabular}{l@{}p{0.5cm}p{0.69cm}p{0.5cm}p{0.6cm}p{0.6cm}p{0.6cm}}
    \bf System& \bf MAP & \bf R-Pr & \bf P@5 & \bf P@10 & \bf P@20 & \bf P@50 \\ \hline
    \multicolumn{6}{l}{\bf \emph{Baselines}}\\
     Random      & .164 & .007 & .200 & .125 & .138 & .160\\
     TF.IDF	 & .314 & .333& .550 & .475 & .413 & .360\\
     CB Platform & .317 & .349 & .500 & .550 & .488 & .405\\
     SVM$_{CBfeat}$ & .360 & .393 & .400 & .425 & .525 & .495\\
     FNN$_{CBfeat}$ & .357 & .379 & .500 & .550 & .550 & .510\\\hline
    \multicolumn{6}{l}{\bf \emph{Systems (using all features)}}\\
     SVM$_{All}$     & .395 & .406 & .650 & \bf .725 & .588 & .565\\
     FNN$_{All}$ 	 & \bf .427 & \bf .432 &  \bf .800 & \bf .725 & \bf .713 & \bf .600\\
    \hline
  \end{tabular}
\caption{Evaluation results: our full systems (SVM$_{All}$ and FNN$_{All}$) vs. a number of baselines: random, TF.IDF-based, \emph{ClaimBuster} from the platform, and our two reimplementations thereof.}
\label{table:combination}
\end{table}

For evaluation, we used ranking measures such as \emph{Precision at $k$} ($P@k$) and \emph{Mean Average Precision} (MAP). As Table~\ref{table:per:medium} shows, most media rarely check more than 50 claims per debate. \emph{NPR} and \emph{PolitiFact} are notable exceptions, the former going up to 99; yet, on average there are two claims per sentence, which means that there is no need to fact-check more than 50 sentences even for them. Thus, we report $P@k$ for $k \in \{5, 10, 20, 50\}$.\footnote{Note that while the difference between the P@k metrics (especially between 5 and 10) can be in terms of a few sentences, the deviation between them can seem large}

MAP is the mean of the Average Precision across the four debates. The average precision for a  debate is computed as follows:

 \begin{equation}
 \hbox{AvPrec} = \frac{\sum_{k=1}^{n} (P(k)\times rel(k))}
 	  {\hbox{number of relevant utterances}}
 \end{equation}
where $n$ is the number of sentences to rank in the debate, $P(k)$ is the precision at $k$ and $rel(k)=1$ if the utterance at position $k$ is check-worthy, and it is 0 otherwise.

We also measure the recall at the $R$-th position of returned sentences for each debate. $R$ is the number of relevant documents for that debate and the metric is known as $R$-Precision ($R$-Pr).

\renewcommand{\tabcolsep}{2pt}
\begin{table}[t]
\small
\centering
\begin{tabular}{c@{}l@{}rrrrrr}
 \bf S or C & \bf Feat. Group & \bf MAP & \bf R-Pr & \bf P@5 & \bf P@10 & \bf P@20 & \bf P@50 \\ \hline
 S+C & Embeddings	& .357 & .380 & .450 & .525 & .488 & .495 \\
 S+C & $k$NN		& .313 & .322 & .800 & .725 & .612 & .445 \\
S & Linguistic	& .308 & .333& .450 & .450 & .463 & .430 \\
 S & Sentiment	& .260 & .277 &.550 & .400 & .288 & .315 \\
 C & Metadata	& .256 & .268 &.350 & .300 & .388 & .370 \\
 S & Length		& .254 & .350&.350 & .375 & .400 & .340 \\
 S & NEs	& .236 & .251 & .250 & .275 & .313 & .280 \\
 S+C & Contradiction	& .222 & .222 &.400 & .275 & .288 & .260 \\
C & Segment size	& .217 & .231 &.100 & .150 & .150 & .245 \\
C & Position	& .212 & .230 &.100 & .075 & .175 & .230 \\
S+C & Discourse	& .205 & .206&.200 & .300 & .325 & .255 \\
S+C & Topics		& .180 &.178& .000 & .000 & .013 & .085 \\
   \hline
  \end{tabular}
\caption{Performance of each feature group in isolation, when using the FNN system.}
\label{table:features}
\end{table}

\subsection{Evaluation Results}
\label{subsec:results}

Table~\ref{table:combination} shows the performance of our models when using all features described in Section~\ref{sec:model}: see the SVM$_{All}$ and the FNN$_{All}$ rows.
In order to put the numbers in perspective, we also show the results for five increasingly competitive baselines.

\noindent First, there is a random baseline, followed by an SVM classifier based on a bag-of-words representation with TF.IDF weights learned on the training data. Then come three versions of the \emph{ClaimBuster} system:
CB-Platform uses scores from the online demo,\footnote{\url{http://idir-server2.uta.edu/claimbuster/demo}} which we accessed on December 20, 2016, and SVM$_{CBfeat}$ and FNN$_{CBfeat}$ are our re-implementations, trained on our dataset.

We can see that all systems perform well above the random baseline. The three versions of ClaimBuster also outperform the TF.IDF baseline on most measures.
Moreover, our reimplementation of \emph{ClaimBuster} performs better than the online platform in terms of MAP.
This is expected as their system is trained on a different dataset and it may suffer from testing on slightly out-of-domain data. At the same time, this is reassuring for our implementation of the features, and allows for a more realistic comparison to the \emph{ClaimBuster} system.

More importantly, both the SVM and the FNN versions of our system consistently outperform all three versions of \emph{ClaimBuster} on all measures.
This means that the extra information coded in our model, mainly more linguistic, structural, and contextual features, has an important contribution to the overall performance.

We can further see that the neural network model, FNN$_{All}$, clearly outperforms the SVM model: consistently on all measures. As an example, with the precision values achieved by FNN$_{All}$, the system would rank on average 4 positive examples in the list of its top-5 choices, and also 14-15 in the top-20 list. Considering the recall at the first $R$ sentences, we will be able to encounter 43\% of the total number of check-worthy sentences.  This is quite remarkable given the difficulty of the task.

\begin{table}[tbh]
\small
\centering
\begin{tabular}{l@{ }rrrrrr}
  \bf System & \bf MAP & \bf R-Pr & \bf P@5 & \bf P@10 & \bf P@20 & \bf P@50 \\ \hline
  All 	                & .427 &.432& .800 & .725 & .713 & .600\\
  All, no contextual    & .385 & .390 & .550 & .500 & .550 & .540\\
\hline
  Only contextual & .317 & .404 & .725 & .563 & .465 & .465\\
  \it CB Platform & \it .317  & \it .349& \it .500 & \it .550 & \it .488 & \it .405\\
\hline
  \end{tabular}
\caption{Impact of the contextual features on the overall performance (FNN system).}
\label{table:context}
\end{table}

\subsection{Individual Feature Types}
\label{subsec:individual}

Table~\ref{table:features} shows the performance of the individual feature groups, which we have described in Section~\ref{sec:model} above, when training using our FNN model, ordered by their decreasing MAP score. We can see that \emph{embeddings} perform best, with MAP of .357 and P@50 of .495. This shows that modeling semantics and the similarity of a sentence against its context is quite important.

Then come the \emph{kNN} group with MAP of .313 and P@50 of .455. The high performance of this group of features reveals the frequent occurrence of statements that resemble already fact-checked claims. In the case of false claims, this can be seen as an illustration of the essence of our post-truth era, where lies are repeated continuously, in the hope to make them sound true \cite{davies2016age}.

Then follow two sentence-level features, \emph{linguistic features} and \emph{sentiment}, with MAP of .308 and .260, and P@50 of .430 and .315, respectively; this is on par with previous work, which has focused primarily on similar sentence-level features.

Then we see the group of contextual features \emph{Metadata} with MAP=.256, and P@50=.370, followed by two sentence-level features: \emph{length} and \emph{named entities}, with MAP of .254 and .236, and P@50 of .340 and .280, respectively.

At the bottom of the table we find  \emph{position}, a general contextual feature with MAP of .212 and P@50 of .230, followed by \emph{discourse} and \emph{topics}.

\section{Discussion}
\label{sec:discuss}

In this section, we present some in-depth analysis and further discussion.

\subsection{Error Analysis}

We performed error analysis of the decisions made by the Neural Network that uses all available features. Below we present some examples of False Positives (FP) and False Negatives (FN):

\begin{footnotesize}
\begin{tabular}{ll@{}r@{}p{55mm}}
1 &FP & Clinton: & He actually was sued twice by the Justice  Department.\\
2& FP& Clinton: & Five million people lost their homes.\\
3&FP& Clinton: & There's no doubt now that Russia has used cyber attacks against all kinds of organizations in our country, and I am deeply concerned about this.\\
4& FP& Trump: & Your husband signed NAFTA, which was one of the worst things that ever happened to the manufacturing industry.\\
5& FN & Trump: & This is one of the worst deals ever made by any country in history.\\
6& FN & Trump: & Well, nobody was pressing it, nobody was caring much about it.\\
7& FN & Trump: & So Ford is leaving.\\
8& FN & Trump: & It was taken away from her.
\end{tabular}
\end{footnotesize}

The list of false negatives contains sentences that belong to a whole group of annotations and some of them are not check-worthy on their own, e.g., the eighth example. Some of the false negatives, though, need to be fact-checked and our model missed them, e.g., the sixth and the seventh examples. Note also that the fourth and the fifth sentences make the same statement, but they use different wording. On the one hand, the annotators should have labeled both sentences in the same way, and on the other hand, our model should have also labeled them consistently.

Regarding the false positive examples above, we can see that they could also be potentially interesting for fact-checking as they make some questionable statements. We can conclude that at least some of the false positives of our ranking system could make good candidates for credibility verification, and we demonstrate that the system has successfully extracted common patterns for check-worthiness. Thus, the top-$n$ list will contain mostly sentences that should be fact-checked. Given the discrepancies and the disagreement between the annotations, further cleaning of the dataset might be needed in order to double-check for potentially missing important check-worthy sentences.

\subsection{Effect of Context Modeling}
\label{subsec:context}

Table~\ref{table:context} shows the results when using all features vs. excluding the contextual features vs. using the contextual features only. We can see that the contextual features have a major impact on performance: excluding them yields major drop for all measures, e.g., MAP drops from .427 to .385, and P@5 drops from .800 to .550. The last two rows in the table show that using contextual features only performs about the same as \emph{CB Platform} (which uses no contextual features at all).

\renewcommand{\tabcolsep}{2pt}
\begin{table}[h!]
\centering
\footnotesize
\begin{tabular}{l@{}rrrrrr}
    \bf System & \bf MAP & \bf R-Pr & \bf P@5 & \bf P@10 & \bf P@20 & \bf P@50 \\
    \hline
   \\
    \multicolumn{7}{@{}c}{ \bf   PolitiFact (PF)} \\
    \hline
    CB Platform & .154 &.213 & .200 & .300 & .238 & .210\\
    \bf NN (train on PF) & .218 & .274 & .450 & .325 & .300 & .270 \\
    \bf NN (train on all) & .213 & .246 & .400 & .350 & .375 & .290 \\
    \hline
    \\
    \multicolumn{7}{@{}c}{\bf NPR} \\
    \hline
   	CB Platform & .144 & .186 &.200 & .225 & .225 & .180\\
    \bf NN (train on NPR) & .193 & .216& .550 & .475 & .350 & .255 \\
    \bf NN (train on all)& .208 & .250 &.500 & .450 & .375 & .255 \\
    \hline
    \\
     \multicolumn{7}{@{}c}{\bf The New York Times (NYT)} \\
     \hline
   	CB Platform & .103 & .250 & .250 & .163 & .135 & .135\\
    \bf NN (train on NYT) & .136 &.178& .250 & .225 & .188 & .135 \\
    \bf NN (train on all)& .136 &.169& .150 & .200 & .163 & .160\\
    \hline
    \\
    \multicolumn{7}{@{}c}{\bf The Guardian (TG)} \\
    \hline
   	CB Platform & .084 &.128& .100 & .100 & .125 & .140\\
    \bf NN (train on TG) & .121&.156 & .250 & .225 & .200 & .155 \\
    \bf NN (train on all)& .128&.185 & .100 & .150 & .188 & .165 \\
    \hline
    \\
     \multicolumn{7}{@{}c}{\bf FactCheck (FC)} \\
     \hline
   	CB Platform & .081&.213 & .150 & .125 & .100 & .115\\
    \bf NN (train on FC) & .081&.098 & .050 & .125 & .088 & .085 \\
    \bf NN (train on all)& .115&.149 & .100 & .125 & .125 & .140\\
    \hline
    \\
    \multicolumn{7}{@{}c}{ \bf   CNN} \\
    \hline
    CB Platform & .082 &.096& .150 & .125 & .088 & .085\\
    \bf NN (train on CNN) & .079&.076 & .100 & .100 & .100 & .090 \\
    \bf NN (train on all)& .095&.087 & .000 & .075 & .088 & .100 \\
    \hline
    \\
    \multicolumn{7}{@{}c}{ \bf   Chicago Tribune (CT)} \\
    \hline
    CB Platform & .053 &.032& .050 & .050 & .038 & .065\\
    \bf NN (train on CT) & .087&.118 & .150 & .150 & .175 & .105 \\
    \bf NN (train on all)& .092&.098 & .150 & .075 & .100 & .090 \\
    \hline
    \\
    \multicolumn{7}{@{}c}{ \bf   ABC} \\
    \hline
    CB Platform & .065 &.066& .150 & .125 & .088 & .080\\
    \bf NN (train on ABC) & .059&.068 & .050 & .050 & .100 & .060 \\
    \bf NN (train on all)& .088&.090 & .150 & .150 & .113 & .100 \\
    \hline
    \\
    \multicolumn{7}{@{}c}{ \bf   Washington Post (WP)} \\
    \hline
    CB Platform & .048 &.056& .050 & .075 & .050 & .045\\
    \bf NN (train on WP) & .102&.098 & .200 & .175 & .113 & .080 \\
    \bf NN (train on all)& .076&.751 & .200 & .100 & .075 & .080 \\
    \hline
  \end{tabular}
\caption{Training on the target medium vs. training on all media when testing with respect to a particular medium (using the FNN system).}
\label{table:per:medium:results}
 \end{table}

\subsection{Mimicking Each Particular Source}
\label{subsec:media}

In the experiments above, we have been trying to predict whether a sentence is check-worthy in general, i.e., with respect to at least one source; this is how we trained and this is how we evaluated our models. Here, we want to evaluate how well our models perform at finding sentences that contain claims that would be judged as worthy for fact-checking with respect to each of the individual sources. The purpose is to see to what extent we can make our system potentially useful for a particular medium.

\noindent Another interesting question is whether we should use our generic system or we should retrain with respect to the target medium.
Table~\ref{table:per:medium:results} shows the results for such a comparison, and it further compares to \emph{CB Platform}. We can see that for all nine media, our model outperforms \emph{CB Platform} in terms of MAP and P@50; this is also true for the other measures in most cases.

Moreover, we can see that training on all media is generally preferable to training on the target medium only, which shows that they do follow some common principles for selecting what is check-worthy; this means that a general system could serve journalists in all nine, and possibly other, media.\footnote{One exception is Washington Post, where our system performs better when trained on that medium only.} Overall, our model works best on PolitiFact, which is a reputable source for fact checking, as this is their primary expertise. We also do well on NPR, NYT, Guardian, and FactCheck, which is quite encouraging.

\section{Conclusions and Future Work}
\label{sec:conclusion}

We have developed a novel approach for automatically finding check-worthy claims in political debates, which is an understudied problem, despite its importance. Unlike previous work, which has looked primarily at sentences in isolation, here we have focused on the context: relationship between the target statement and the larger context of the debate, interaction between the opponents, and reaction by the moderator and by the public.

Our models have achieved state-of-the-art results, outperforming a strong rivaling system by a margin, while also confirming the importance of the contextual information.
We further compiled, and we are making freely available, a new dataset of manually-annotated claims,
extracted from the 2016 US presidential and vice-presidential debates,
which we gathered from nine reputable sources including FactCheck, PolitiFact, CNN, NYT, WP, and NPR.

In future work, we plan to extend our dataset with additional debates, e.g., from other elections, but also with interviews and general discussions.
We would also like to experiment with distant supervision, which would allow us to gather more training data, thus facilitating deep learning.
We further plan to extend our system with finding claims at the sub-sentence level, as well as with automatic fact-checking of the identified claims.

\section*{Acknowledgments}
This research was performed by the Arabic Language Technologies group at Qatar Computing Research Institute, HBKU\@, within the Interactive sYstems for Answer Search project ({\sc Iyas}).

redpajama_set_name: RedPajamaArXiv


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Hello Everyone! I'd like to start the last post for the year by wishing everyone a very Merry Christmas! I hope everyone had a splendid eve celebrating, feasting, and continuing tradition with family and loved ones. What are some of your Christmas traditions? I'd love to know in the comments below! We had a simple and quiet Noche Buena at home, and of course watched some Christmas classics such as the Home Alone series. We're not a family who gives gifts during the season of Christmas because we treat each other throughout the year – be in paying for the entire meal when we eat out as a family, or paying for the tickets and snacks when we go to the cinema together; gifts that allow us to spend time together as a family rather than materialistic things.
That aside, I mentioned in my last post that Christmas or Simbang Gabi would not be complete without this famous breakfast kakanin that is sold alongside Bibingka just outside the church. But, as all the other kakanins out there, you don't have to wait until the Christmas season to roll around as you can find Puto Bumbong every day of the year at your local market, various street stalls, and in many restaurants and cafés nationwide.
Puto Bumbong is a type of Filipino steamed rice cake that is traditionally made from a special variety of heirloom sticky (glutinous) rice known as pirurutong which has a distinct purple colour to it. Food colouring is not necessary for this. It is soaked in salted water and then dried overnight. It is then ground in a grinder made of solid stone before it is stuffed into a bamboo tube known as bumbong ng kawayan. It is then steamed until steam rises out of the bamboo tubes, placed onto a pre-cut banana leaf, and topped with margarine (or butter), grated coconut, and muscovado sugar to enhance its flavours.
And so for my last breakfast recipe of the year, I will share two ways in how you can make Puto Bumbong at home, with and without the bamboo tubes – depending if you have bamboo tubes readily available or not to be able to tackle this recipe. Before we dive in, be sure to check out the original recipe that I followed over on Panlasang Pinoy.
*Be sure to allot yourself 2 days before you plan on tackling this recipe, as the rice needs to soak.
Combine all the different types of rice in a large mixing bowl together with the room temperature water. Set aside and leave to soak for at least 2 days.
Drain and place the soaked rice in a large food processor. Pulse and grind until the rice becomes very fine (takes about 8 to 10 minutes to achieve this consistency). If you only have a small food processor handy, then work the rice in batches.
Fill each bumbong (bamboo tube) with the powdered rice mixture, making sure not to compress the rice to allow the steam to pass easily.
Prepare the steamer with enough water for steaming. Once the water has been brought to a boil, arrange each bamboo tube on the steamer. Cook until steam starts coming out of the tubes, about 8 to 10 minutes.
Remove the tubes from the steamer, and carefully remove the puto bumbong from the tube and place over a piece of banana leaf.
Spread with butter or margarine (I chose to leave this out of my serving) and top with freshly grated coconut and muscovado sugar. Serve, share, and enjoy with a hot cuppa!
Add about 2 cups of water to the powered rice mixture and mix together to form a dough. You may need less or more water, depending, so it's best to add the water in gradually. Knead until smooth.
Pinch off about 2 tablespoons of the dough and, using your hands, make a ball and then roll into a log, about 4 to 5 inches in length. Alternatively you can place the dough into a piping bag and using piping tip #807, pipe the dough onto a heat-proof plate greased with a bit of butter or margarine.
Place the plate into a prepared steamer with a muslin-covered lid to prevent any droplets of water dripping onto the puto bumbong mixture. Steam for about 8 – 10 minutes.
Remove from the steamer and place onto a banana leaf. Top with your preferred toppings and enjoy!
I usually take the first month of the beginning of the year off to properly plan for the upcoming year ahead and to get a head start on experimenting in the kitchen for recipes to share with you guys. But since I haven't been working since August of this year (still continuing to look for a job as I write this), I've had time to plan ahead and I'm excited to share with you what I have in store for the upcoming year! You'll just have to wait until next week to find out the theme for the year ahead – which technically is in another week I just realised!
So for now, I would like to wish all my family, friends, and followers a Happy & Prosperous New Year! May the New Year bring you happiness, health, wealth, and peace!
Hello Everyone! Only 13 more days to Christmas!
Before I start, I'm going to make this post short a sweet. I've had a busy day of designing to meet a 9-hour deadline so I'm pretty much mentally drained at this point – apologies in advance.
Anyway, in my previous post that I shared last week, I talked about how a much-loved part of the Simbang Gabi tradition during the Christmas season amongst Filipinos is the various local delicacies served just outside of the churches. Last week I shared all about Suman, and tonight I will be sharing a favourite with you, Sapin-Sapin.
Sapin-Sapin is a Filipino sticky rice cake that is made from glutinous rice and coconut milk that is traditionally composed of layers with different colours and flavour profiles that compliment each other. Sapin-Sapin can be made of 4, 3 or 2 layers, or even enjoyed just on its own single slab. The most common flavours are coconut, ube, and jackfruit. It is then topped with a toasted residue of coconut milk known as latik.
Latik: Pour the coconut milk into a small saucepan and bring to a boil over medium-high heat. Continuously stir until most of the liquid evaporates. This will take about 12 to 15 minutes per cup of coconut milk.
When the texture of the milk turns gelatinous, lower the heat and continue to stir. By this time the oils should start separating from the milk. Keep stirring until brownish residues are formed.
Turn the heat off and place the latik on a small plate lined with a paper towl to soak up the excess oil. Set aside. At this point you can store the latik in a container and in the fridge for up to a week or use it immediately to top various rice cakes.
Sapin-Sapin: Combine the glutinous rice flour and sugar in a large mixing bowl. Then, pour in the condensed milk, coconut milk, and vanilla extract, mixing well until the texture of the mixture is smooth.
Divide the mixture into 3 equal parts into smaller mixing bowls.
Add the mashed purple yam, ube extract, and violet food colouring into one of the mixtures. Stir thoroughly and then set aside.
Shred the jackfruit (without the seeds) in a food processor. Add the shredded jackfruit into another mixture along with the yellow food colouring. Set aside.
Leave the last mixture as it is.
Grease a 9in round baking pan by brushing a bit of coconut oil and pour in the plain coconut mixture into the pan. Make sure that the mixture settles. Cover the baking pan with cheesecloth and then steam for about 12 to 16 minutes.
Once done, remove the baking pan and then pour over the ube mixture. Use a spatula to spread it evenly on top of the coconut mixture. Remove excess water from the cheesecloth by squeezing it. Place it back on top of the baking pan, and into the steamer to steam for another 12 to 16 minutes.
Repeat step 10 again for the jackfruit mixture and then steam for a further 15 to 20 minutes. If you think your mixture is still a tad bit runny, steam for a further 5 minutes. Remove of the steamer and set aside.
Serve: Place a clean banana leaf over a wide serving plate and brush a bit of coconut oil over the leaf.
Gently run the side of the baking pan using a spatula brushed with coconut oil. Turn the baking pan over onto the banana leaf and let the cooked sapin-sapin fall out of the pan on its own. Therefore make sure that the colour that you want on top is the bottom layer in the pan when being cooked.
Brush some coconut oil on top of the sapin-sapin and sprinkle generously with latik.
Serve for breakfast, merienda, or dessert with a hot cup of coffee. Share and enjoy!
Unfortunately, most commercial sapin-sapin delights that you find in large supermarket chains omit the use of natural flavours such as the ube and jackfruit to reduce costs. In fact, if you see, red is also often used in the making of sapin-sapin. When I was researching the flavours, I found out that the red layer actually has no flavouring to it, just the plain coconut from the initial mixture.
Before I end tonight's post, what are some of your favourite traditional Christmas treats? I'd love to hear about the different food traditions from around the world! Comment down below!
Hello Everyone! Just 20 more days until Christmas, which means that Simbang Gabi is just around the corner! Simbang Gabi is a well-loved holiday tradition amongst Filipino Catholic devotees who attend mass at 4am. It is a series of 9 Novena Masses that commences on the morning of December 16 and culminates with the Misa de Gallo on Christmas Eve.
Apart from the Mass itself, another much loved part of the Simbang Gabi tradition that many look forward to are the local delicacies served just outside of the churches. You'll find many vendors selling local Christmas favourites such as Suman, Puto Bumbong, Bibingka, Sapin-Sapin, and plenty more! Nowadays, these yummy treats, also known as kakanins, can be found in many establishments nationwide all year around so you don't have to wait for Christmas to have your fill of them.
For the last month of the year, I will be sharing recipes for these traditional Filipino Christmas breakfast delights with you. And I'm going to kick it off with a popular favourite – Suman 3 ways!
Glutinous Rice (Malagkit) is a type of rice that is famous throughout Asia for its culinary use, especially in many variations of sweets. Here in the Philippines, one of the MANY most loved ways to cook glutinous rice is to wrap it in a banana leaf and then steamed. It is very easy to make, however time consuming. Filipinos take pride in doing it and many households have their own secret way to making this sweet and sticky rice delicacy.
In its simplest and most basic form is the Suman Malagkit, or translated, Sticky Rice Roll in a Banana Leaf.
Malagkit Mixture: In a large bowl, soak the glutinous rice for 2 to 3 hours to soften, and strain when ready to cook.
Place the strained glutinous rice in a large pot together with the coconut milk and salt. Bring to a brisking boil.
Once boiling, immediately turn the heat down to low until the rice and coconut milk mixture comes down to a slow simmer. Give it a good stir and then simmer for about 20 to 30 minutes. Stir occasionally to keep the rice from sticking to the bottom of the pot.
Turn the heat off, and leave the mixture for a further 15 to 30 minutes, covered, to allow the rice to finish cooking and cool down.
Wrapping: While waiting for the rice to finishing cooking, prepare the banana leaves by quickly passing them over an open flame to make the leaves soft and pliable so that they are easier to work with when wrapping. This method also releases the natural aroma of the leaves.
Cut the leaves into equal sizes, depending on the size of the suman roll that you want to make.
Scoop about 2 to 3 heaping tablespoons of the rice onto the prepared banana leaf and shape into a log, leaving about 1 inch on the sides. Roll the banana leaf tightly around the rice to form a log and fold both edges in to seal.
Repeat until all of the rice has been wrapped, yields about 20 pieces.
Cook: Stack the suman rolls in a steamer and steam over boiling water for about 30 to 60 minutes. Make sure that they are tender before removing them from the steamer.
Serve: Allow to cool slightly before unwrapping. Serve with fresh grated coconut, brown sugar, or my absolute favourite – with sweet ripe mangoes! They can also be enjoyed on its own without any accompaniments.
This is basically the same as Suman Malagkit, but is treated with lihiya (lye water) which gives the suman its yellowish colour, a delicacy from the southern part of Luzon, especially in the Batangas Region.
It is also known as Sumang Magkayakap because of how it is served – two rice cakes tied together appearing as if they are embracing/hugging (magkayakap) each other.
Rice Mixture: In a large bowl, soak the glutinous rice for 2 to 3 hours to soften. Strain the water and then mix in the lye water together with the drained rice until it turns yellow in colour.
Wrapping: Prepare the banana leaves by quickly passing them over an open flame to make the leaves soft and pliable so that they are easier to work with when wrapping. Cut the leaves into equal sizes, depending on the size of the suman roll that you want to make.
Scoop about 3 heaping tablespoons of the rice onto the prepared banana leaf and shape into a log, leaving about 1 inch on the sides. Roll the banana leaf tightly around the rice to form a log and fold both edges in to seal.
Take two of the wrapped rice rolls, with the folded sides facing each other, and tie them together using the cotton string.
Repeat until all of the rice has been wrapped, yields about 24 pieces or 12 pairs of Sumang Magkayakap.
Cook: Arrange the suman rolls in a large cooking pot. Pour enough water (room temperature) to cover them. Bring to a boil over low heat for about 2 hours or until done.
Serve: Allow to cool slightly before unwrapping. Serve with fresh grated coconut or a sweet latik sauce.
A native delicacy known simply as Ibos (or also spelt as Ibus) by Ilonggos that is wrapped in palm or buri leaves. This is the counterpart of banana leaves, which is commonly used in many other suman recipes. They are mostly grouped and sold in bundles be it on the streets, in markets, mall stalls, and even restaurants.
The challenge with Suman sa Ibos is not the in cooking processing, but in preparing the 'container'. You will need to swirl the palm leaves over a mold and locked properly to make individual containers to hold the glutinous rice mixture.
Preparation: In a large bowl, soak the glutinous rice for 2 to 3 hours to soften.
While waiting for the rice, prepare the ibos (palm leaf) container/wrapper. The leaves should be about 1.5 inches in width. Fold the bottom edge of the palm leaf into a triangle. Swirl the leaf in an overlapping manner to create a cylindrical mold. To secure the tube, you can lock the leaf using a small piece of wooden pick. Prepare about 20 containers.
Once the rice is done, drain the excess water and mix the coconut milk and salt in thoroughly. Set aside to stand for about 10 minutes.
Fill the ibos container 3/4 of the way with the glutinous rice mixture. Seal the container by using the strips of the palm leaves.
Cook: Arrange the suman containers in a large cooking pot. Pour enough water (room temperature) to cover them. Bring to a boil over low heat for about 2 hours or until done.
Serve: Allow to cool slightly before unwrapping.Best paired with muscovado sugar or regular white sugar. Also best eaten with ripe mangoes or native tsokolate/tablea.
And there you have it! 3 ways in which you can make Suman! Of course there are many others way in which you can make suman that is native to the province of which they originate from. Two favourites of mine, which I have not included in this post, are Tupig native to Pangasinan and Ilocos Norte, and Suman Pinipig from Bulacan. These two are a little more complicated than the basic ones that I've shared in this post. You can find Suman Pinipig in many mall stalls across Metro Manila, however I still have yet to find Tupig around here.
With these rice cakes, you don't necessarily have to eat it the moment you finish cooking it. It does however; definitely taste better when it's not chilled. If you need to keep it in the fridge and indulge in them the day after, you can easily steam them again for 10 minutes before serving.


text:
As part of its mission, the purpose of the BUE is to help build strong technical and business skills that attract inward investment to Egypt. To achieve this, the BUE has a vision that its learning and teaching should produce skilled and capable graduates to serve the local, regional and international community.
This can only happen by providing an academic environment that encourages and draws together research, learning, and teaching and engages with the social, professional and business communities, to underpin high quality academic provision, and advance industry and the professions.
This is achieved via close and intimate collaboration and understanding between the university, the business community and society.
Establish grounds for successful industrial collaboration and provide multiple avenues for collaboration, including recruiting academic staff from industry, inviting industrial personnel to the University for guest lectures and involving them in student research projects.Establish a technological centre through collaboration with industrial organisations to provide an industrial environment where local and international industry professionals, teaching and research staff will interact and collaborate on joint projects.Offer a wide choice of approved internship facilities that prepare graduates for employment.
Propose solutions to industrial technical problems in Egyptian organisations.
Help ensure BUE programmes equip students with the knowledge and skills employers need.
CCCS
Centre for Egypt-Africa
Centre for Egypt and the Middle East Studies
Centre for Teaching & Learning
Science & Innovation Park
Technology Innovation and Commercialization Office
CCCS – Centre for Community and Consulting Services is an educational training centre that was founded in 2008 in the British University in Egypt. The services provided by the Centre are based on the needs of industry and the community at large, within the areas of specialisation offered by the BUE (currently Business Administration, Computer Science, Economics, Engineering, Languages and Political Science).Specially designed training programmes for individuals clients.
The aim of the centre is to help strengthen the Egyptian economy by introducing firms and organisations to the latest developments in management and technology, through consultancy, research and training. It also aims at developing individuals and organizations to prepare them to the dynamic business environment, through offering a number of courses and sessions that aims at fulfilling the needs of the trainees.
Become part of the community development and the educational quality offered to develop the workforce in Egypt
To deliver courses and sessions that aim at meeting the needs of the trainees through quality education supported by the British Educational System.
Train and develop the workforce in Egypt, targeting individuals and organizations.
Training Meet the needs of individuals and organizations who aim at personal and professional development.
Offer certified courses in the English Language Training.
Specifically CCCS, offers
Specially designed (bespoke) training programmes for individual clients.
Regularly scheduled short courses and seminars.
Regularly Scheduled Professional Development Programmes.
Research and Consultancy activities.
Problem solving & special studies.
Dr.Tamer Mohamed
Professor of Industrial Engineering
Egypt/Africa Center was established by the British University in Egypt (BUE) December 2014. It was inaugurated by Honorable. Catherine Samba Panza, President of the Central African Republic, who accepted to be the Honorary President of the Centre.
Egypt/Africa Centre is an independent think-tank specializing in African affairs aiming at enhancing and strengthening relations between Egypt and other African sisterly Countries as well as promoting the understanding of the key geo-political and developmental challenges and opportunities existing the African Continent.
Egypt/Africa Centre aims at generating a vision for the region's growth and prosperity.
It was founded by Ambassador/ Mona Omar – Former Assistant Minister of Foreign Affairs for Africa.
Ambassador Aly El-Hefny Assumes His Functions As The New CHAIRMAN of The CENTER As of February 12, 2018.
The Center looks forward to assist and cooperate with the Egyptian Government, Business Sector and African Countries and their Embassies in Cairo to achieve mutual interests of both sides.
The Center looks forward to assist and cooperate with all other African countries, the business sector and think tanks all over the world specializing in African Affairs to create a peaceful prosperous, developed continent for all of us.
Enhance the strength of Egypt soft power
Providing insights and solutions designed to strengthen political and. economic relations between Egypt and African Countries.
Achieve and enhance the Egyptian interests in Africa.
The Mechanisms:
The Centre provides support for the Egyptian Government through its policy papers which assist high officials in making appropriate decisions towards other African Countries.
Provides Egyptian Businessmen, interested in Africa, with a database on investment and trade opportunities available in other African Countries.
The center organizes workshops, forums, local and international conferences, seminars, round tables and group discussions on important events and emerging issues happening in the continent of Africa.
Deepen awareness of African Issues, interests, challenges and Dynamics of the Egyptian Africa Relation within the British University in Egypt.
The center contributed in organizing the visit of the President of Central African Republic in Egypt in December, 2014.
The center organized "Parliamentarian Women in Africa and The Arab World" Conference, in the presence of the President of the Arab Parliament, At the Arab League States HEADQUARTERS In Cairo, to promote Arab-African cooperation in the parliamentary field.
The center organized a cultural event showing the South African documentary on "Nelson Mandela in Cairo "at the British University in Egypt, in the presence of the movie cast and many African ambassadors in Egypt led by the South Africa Ambassador.
The center initiated series of visits to the Egyptian industrial cities for the African Ambassadors accredited to Egypt starting with 10th of Ramadan Industrial City.
The center helped African Countries in particular (Ethiopia, Guinea, and Nigeria,) to get scholarships at the British University in Egypt.
The center organized a conference to define for the Egyptian Businessmen the benefits of the African Economic Blocs' Summit held in SHARM EL -SHEIKH.
The Center cooperates with "Cairo Center for Conflict Resolution & Peacekeeping in Africa" in the field of training of African troops.
The Center organized a conference on "Egypt in the African Heart" The conference aimed at achieving the desired partnership between Egyptian Businessmen and their counterparts in the rest of the continent and to opening new horizons to strengthen Egypt's presence in Africa. It also aimed at establishing a partner relationship, in different sectors among all states, as well as the presentation of successful experiences in the continent.
Address: British University in Egypt "BUE" – El Sherouk City – Suez Desert Road – Cairo - Egypt
Hotline: 19283 or (+202) 26890000 – Extension: 1059
Mobile: (+2) 01001582297
Fax: (+202) 26300010/20
Email address: Aly.ELHefny@bue.edu.eg or Ali.metwaly@bue.edu.eg .
/ Egypt-Africa News
The African Universities Union (AUU)
Webinar On "Post Covid 19 Era: aspects, global, challenges and Multidisciplinary opportunities"
A seminar on Egypt and Africa: Issues and Interactions
Welcome to the Centre For Egypt and Middle East Studies at the British University in Egypt (CEMES). The mission of the Centre covers the ME region but focuses primarily on Egypt Studies, projects, and cooperation with relevant organizations including in the investment, business and industrial fields in Egypt. To this end, the CEMES regularly organizes workshops, scholarly conferences and lecture series.
About CEMES
Our Center is geared towards expanding BUE's international connectivity with many circles around the globe. Highlighting BUE's role as a cultural beacon, interaction with governmental and non- governmental circles, civil society, community services, inviting International leaders to speak from our podium, are all relevant activities we perform in synergy and cooperation with all faculties. Expanding our activities to include the new library by offering our recorded activities to become a part of BUE's digital library allowing our student to interact with guests and pose questions. The recorded outcome of such activities prepared by the students can become a strong educational tool and a primary source of knowledge.
The center has concluded several workshops on a variety of issues such as on developments in the Ukraine and has hosted an international day for Foreign Embassies in Egypt to expand our policy of increasing enrollment for foreign students at BUE, the centre hosted the Director of the Hungarian institute , Speaker such as Miguel Moratinos Former Minister of Spain., a key delegation from the UK Department from the UK Parliament..etc
The center also aims to target discipline and practical research topics and paradigms and introduce our student to exchange of knowledge and training with training programs such as the one we had with the League of Arab states recently whose five students from BUE attended, Comesa, Human Rights, Disarmament…etc. E learning is an additional outsourcing method we apply.
Our most recent activity has been concluded with the full cooperation of the BAEPS Department in inviting the President of the ME Institute in Washington DC to visit, lecture our student, and prepare an agreement of cooperation that shall include full cooperation with the Washington based institute in the areas of a joint journal, research, exchange visits by our students and faculty, joint conferences and website cooperation.
Ambassador Dr. Mahmoud Karem
Advisor to the President of the British University in Egypt (BUE) for International Affairs, and Head of the Centre for Egypt and Middle East Studies at the University.
Bio of Ambassador Dr. Mahmoud Karem
Email: mahmoud.karem@bue.edu.eg
drkarem@hotmail.com
Mahsheed Shoukry
Office Director
Email: mahsheed.shoukry@bue.edu.eg
Address: El Sherouk City, Suez Desert Road, Cairo 11837 – P.O. Box 43
The Centre is located within the BUE Campus, Building C
Fax: +202 26300010 / 20
Website: http://www.bue.edu.eg/index.php/cmes-home
/ BUE Homepage News, Centre for Egypt & Middle East Studies
Welcoming Mr Kenichiro Matsubayashi; Deputy Chief of Mission of Embassy of Japan
BUE Webinar On "Post Covid 19 Era: Aspects, Global, Challenges and Multidisciplinary Opportunities", Wednesday, September 16, 2020
Ambassador Sir Geoffrey Adams Delivers a Lecture on Diplomacy & Conflict
Leslie Croxford
Senior Vice-President for Learning and Teaching
Teaching and Learning. We at BUE are committed to providing our students not merely with degrees but also with the skills to put what they have learned to use in their careers. This enables them, whenever they change employment, to apply these skills to new areas. This is a key part of the University's Strategic Plan and it naturally depends on the nature and quality of our teaching.
BUE ensures that teaching is fit for this purpose by aligning it with the most enlightened current international developments in teaching and learning. They seek to make possible, as a core component of the Student Experience, teaching that is communicative, project based and student centred.
The Centre for Teaching and Learning supports this important objective with numerous resources. You are invited to review and use them. We will also appreciate your suggestions as to how the continuing usefulness of the Centre can be enhanced.
The mission of the Centre for Teaching and Learning (CTL) at the BUE is to contribute to the student experience for helping staff to develop their knowledge, skills andperformance, to enhance the quality of teaching to improve learner outcomes. This will be done through planned learning opportunities that support the University's strategic objectives.
"Every teacher needs to improve, not because they are not good enough, but because they can be even better." Dylan Wiliam
Passport to Effective Teaching for Fresh Graduate Teachers
Programme overview Certified certificate Participants to receive certificates on 2nd May 2018 Schedule – February to April, 2017-2018
CTL Resources
Teaching Enhancement
Active Learning Teaching and Learning Resources Active Learning 2 Flipped learning
Open Education Resources 20 tips and resources for using learning technology in higher education
Teaching Excellence Framework (TEF) Free Online Courses
Staff Development Days
Staff Development Day – 7th September, 2016 Staff Development Day – 13th September, 2017
Teaching & Learning Guidance and Strategies
Certified certificate
Participants to receive certificates on 2nd May 2018
Schedule – February to April, 2017-2018
On Tuesday, January 19, 201 BUE signed an agreement with TusPark Holdings, China to establish Tus-BUE Science and Innovation Park (SIP) as the first park in Egypt and the region to offer world-class environment for academics, businesses and entrepreneurs to actively engage in innovation activities, share knowledge and create opportunities to drive entrepreneurship. With building area over 20,000 m2 Tus-BUE SIP is built over BUE premises at El-Sherouk City to provide a combination of high quality office spaces, facilities and business-support services tailored to science and technology businesses' needs to enable their growth and success in today's tough economy. Business support is provided in the form of unlimited access to the BUE research and lab facilities, network facilitation, access to finance, marketing, mentoring and coaching, training and entrepreneurship support. Accommodation is provided with flexible terms especially for startups to enable appropriate access to facilities and allow potential expansion as they grow. The park also enjoys excellent communication links with nearby industrial cities including 10th of Ramadan, Badr, and El-Obour City and is less than 20 km away from Cairo Airport and other universities like AUC, GUC, UFE, etc. Aiming to be the top university-linked science and innovation park in Egypt and the region, SIP with an initial budget of 80 million EGP is expected to open its first phase by summer 2017 and will focus on technological areas such as electronic industry, gas and petrochemical industry, new and renewable energy, textile, and pharmaceuticals.
Address Science and Innovation Park
The British University in Egypt (BUE)
El Sherouk City, Cairo – Suez Desert Road
Postal No. 11837 – P.O. Box 43
Hotline: 19(BUE) – 19283
Tel: (+202) 26890000 Ext: 1056
Email: SIP@bue.edu.eg
About TICO
TICO's Vision:
The vision of the Technology Innovation and Commercialization office (TICO) at the BUE is to close the innovation cycle in order to bridge the gap between research and industrial and societal needs. Furthermore, TICO BUE Office aims to support innovation and technology in the areas that would develop the business of the existing firms and establish strong and stable small and medium projects.
TICO's Mission:
The goal of the Technology Innovation and Commercialization office (TICO) at the BUE is to establish sustainable mechanisms to the development of science and technology solutions that are research-based, innovative and sustainable, and to maximize their economic and social impact.
TICO office aims to achieve its vision and mission through its 3 offices; Technology Transfer Office (TTO), Technology Innovation Support Center (TISC), and Grants and International Collaboration Office (GICO).
TICO's Main Website
Main Office: TICO Office at the BUE,
Building D, Third Floor, Office 306
Islam Mohamed , Office Coordinator
islam.orban@bue.edu.eg
Sarah Nasser , Office Coordinator
Sarah.Nasser@bue.edu.eg
The Business Incubator at the British University in Egypt is a dynamic program that prepares today's entrepreneurs with strong functional business knowledge and understanding and equips them with the needed skills to accelerate their business! We offer a tailored technical and business program for your start-up.
BUE Business Incubator believes that entrepreneurship is one of the most positive forces in Egypt to generate sustainable economic growth and prosperity.
BI Vision:
Our vision is to bring innovation to every corner in Egypt.
Business Incubator's Main Website
The Science & Innovation Park Buildings 7
AL SHEROUK CITY
Suez Desert Road
POSTAL NO. 11837, P.O. BOX 43, EGYPT
B.INC@BUE.EDU.EG
Tel: +2 02 26300293
info@bue.edu.eg
El Sherouk City , Suez Desert Road , Cairo 11837 - P.O. Box 43
Tweets by BUE_University
Copyright © 2020 BUE , All rights reserved.


text:
Анто́нова Еле́на Анато́льевна:
 Антонова, Елена Анатольевна (род. 1972) — украинская легкоатлетка (метание диска).
 Антонова, Елена Анатольевна (род. 1974) — российская синхронистка.

redpajama_set_name: RedPajamaWikipedia


text:
Emirates stops flights to major Australian cities 9:53 pm
Prime Minister presents land titles to 33 residents 9:47 pm
Youth elected to National Police Youth Club Movement executive trained 9:31 pm
Students, parents benefit from Flow's virtual online learning webinars
KINGSTON, Jamaica — More than 170 parents and students from institutions across the island benefitted from Flow's annual Mission Week training webinars to help them better understand the various online learning platforms.
Flow's Mission Week was observed from November 16 to 20 this year.
"We're aware that many students and parents are challenged in their ability to navigate the required online learning space. We saw the need, particularly during this time, and therefore rolled out these online learning sessions which empowered them and will further their progress," Director of Communications and Stakeholder Engagement at Flow, Kayon Mitchell said.
"Additionally, we were able to share with the students and parents some safety tips to promote their responsible use of online technology," she continued.
The company said the participating institutions were Mountain View Primary, Mona Heights Primary, New Providence Primary, Louise Bennett-Coverly Primary, St Andrew Primary and St Albans Primary in the Corporate Area. Other participating institutions were Servite Primary in St Ann, Sandy Bay Primary in Westmoreland, Balaclava Primary in St Elizabeth, Barracks Road Primary and Corinaldi Primary in St James, Aintree Basic School and the Tower of Praise Ministries in St Catherine.
Davia Samms-England, a teacher at Barracks Road Primary, who participated in one of the webinars, was very grateful for the content shared with the students.
"I found it very informative, fun, interactive and presented at the children's level. I really liked the part where the children were exposed to tips on how to be safe online as this will benefit them as they continue to use the various online sites," she said.
Cecelia Robinson, a parent and president of the St Richards Primary Parent Teachers Association was equally appreciative of the sessions.
"The use of videos kept the children engaged and helped to enhance their learning experience. I'm particularly happy because there are some parents who get frustrated because of the challenges they have in understanding the online learning platforms. The instructor was also easy to work with and I wish the sessions would continue," she added.
Mission Week is a company-wide project led by Flow's parent company, Liberty Latin America.
Prime Minister presents land titles to 33 residents
Youth elected to National Police Youth Club Movement executive trained


BLUE Sky Private Real Estate plans to push the boundaries of design with one of Australia's first sky pools at its latest development in Brisbane.
The Blue Sky Alternative Investments (ASX:BLA) division's residential project Florence features two towers connected by a pool suspended 25m in the air.
Located at Flora Street in Greenslopes, Florence comprises 107 apartments across eight storeys and is targeted at the owner-occupier market.
Blue Sky enlisted AG Architects, interior designers Merge Interiors and landscapers Form LA to tap into a new generation of city dwellers who seek locality over house and land.
Blue Sky Private Real Estate investment manager Edan Norris says demand for luxury three-bedroom apartments was on the rise in Brisbane.
"Demand for quality owner-occupier stock has risen in the last six months, driven by the strong local housing market and mature couples seeking to downsize from their family homes," Norris says.
"These empty nesters demand very well thought-out apartments. Separation between bedrooms, larger floor plans and well-considered kitchen and storage are in demand.
"Florence's most ground-breaking feature is the roof top swimming pool and its deck which bridges the two buildings, offering residents spectacular city views while swimming laps."
AG Architects director Adam Garton says Florence and its eye-catching pool would help define Brisbane's south skyline.
"Florence residents will enjoy features such as a cinema, landscaped gardens, bicycle storage and an outdoor kitchen with lounge facilities - these new living spaces replace the traditional backyard without needing to spend weekends on upkeep," Garton says.
One bedroom apartments with one car park start at $379,000, two bedroom, two bath and one car park start at $469,000 and three bedroom, two bathroom and two car parks start at $655,000.
Construction will commence in June 2016 and is due to be completed by September 2017.


text:
https://okcthunderwire.usatoday.com/2020/03/16/report-nba-players-wont-be-drug-tested-during-season-stoppage-due-to-coronavirus/
Report: NBA players won't be drug tested during the coronavirus season stoppage
Photo: Orlando Ramirez/USA TODAY Sports
Logan Newman
March 16, 2020 3:45 pm CT
[jwplayer 9EoGTR2m-z6KDnl0B]
The NBA will not issue drug tests to players during the NBA stoppage in play due to the coronavirus, according to Yahoo Sports' Chris Haynes.
The collective bargaining agreement states players are subject to "six random, unannounced urine drug tests during each season and off-season."
Marijuana, cocaine, steroids and performance-enhancing drugs are among the list of banned drugs.
Players who use drugs of abuse are subject to suspensions of at least two years. Performance-enhancing drugs result in a suspension of 25 games for the first violation, 55 for the second and up to two years for a third.
This season, Phoenix Suns center Deandre Ayton, Atlanta Hawks big John Collins and Brooklyn Nets wing Wilson Chandler served 25-game suspensions for violations of the drug policy.
The stoppage of drug testing is temporary, according to Haynes.
For at least the next month, and likely longer, players will not be given tests by the league.
ESPN's Adrian Wojnarowski reported that owners and executives are preparing for a three-month stoppage in action as the best-case scenario.
The Centers for Disease Control and Prevention recommended against any gathering of 50 people or more for the next two months.
How the coronavirus suspension could impact the OKC Thunder
Gallinari: Coronavirus a 'worldwide problem bigger than basketball'


How fun! Love what you made and what you received! The blogging community has so many wonderful crafting exchanges.
WOW…I love both of the pin keeps! I'm sure Michelle was thrilled with her little Amish doll, and how creative to mount it on a jar that she can use for storage! Great job! Thank you again for joining the swap.


Outlook Nebraska, Inc. (ONI), a nonprofit organization providing employment and other opportunities for people who are blind and visually impaired, has promoted Mark Plutschak from Human Resource Manager to Director of Human Resources.
Plutschak began his ONI career in 2005. He became the Human Resource Facilitator in 2007 and the Human Resource Manager in 2009. He has an Associate's degree in Pre-Social Work at Iowa Western Community College and a dual Bachelor's degree in Human Services and Psychology from Buena Vista University.
Plutschak is a current member of the Leadership Omaha Class 36. He is always looking for another learning opportunity and enjoys sharing the success stories of ONI associates with the public.


be posted in each of the home rooms.
help cover the cost of the pictures.
cents when the Hilltopper is delivered.
will be prepared by Marilyn Behymer.
Herbert James will be copy editor.
Mr. Willis receives M. S.
pictures are now on display in a showcase on the ground floor.
for the floor show of the dance.
Hauser, Miss Mabel Goddard, Mr. C. M.
were printed rules for the care of books.
show new books exhibited by L. S.


text:
Stephie: "I am signing because I want change and I want to be free from this corrupt government of a murderer m7 [Museveni].
Because of the thousands of innocent Ugandans that were murdered before and after the elections 2021.
Because of the innocent Ugandans that are abducted by the regime each day – among them is my Best friend."
Stephen: "I desperately need freedom in my country. Rule of law and constitutionalism must be respected and most importantly the right to life."
Denis: "I need Justice in Uganda. Free all political prisoners."
Paul: "Uganda isn't safe for Ugandans any more apart from those in power."
Maria: "I am tired of seeing my country bleeding."
Mabid: "Because l need every one to have their freedom."
Kizito: "Innocent Ugandan lives matter."
Eunice: "There's a silent genocide in Uganda and human rights violation by Museveni, Muhoozi and the security operatives #UgandaIsBleeding."
Lenon: "Because we need the world to come and join us as we cry for help. Their is a silent genocide by Gen. Museveni on all supporters of Bobi Wine. And we need the world to join us."
Mutyaba: "Am signing because am a Ugandan living in Uganda where the abuse of Human Rights is on everyday menu."
Jovia: "Lives of Ugandan's should be valued and whoever takes someone's life should be held accountable. Security organisations aren't judges to pass judgements of death to Ugandans without fair hearing. This should stop."
Please support them and other Ugandans by SIGNING and SHARING this PETITION.


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Micronclean's SureGuard disposable garments are the first defence in protecting the environment from airborne particulate and your operators from the environment. SureGuard's micro porous fabric is manufactured from polypropylene with a polyethylene laminate, affording high levels of cleanliness with a superior tensile strength.
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