text:
\section{Introduction}
\label{sec:intro}

The NLP research community has devoted significant efforts to support the safety and inclusiveness of online discussion forums by developing automatic systems to detect hurtful, derogatory or obscene utterances.
Most of these systems are based on supervised machine learning techniques, and require annotated data.
Several publicly available datasets have been created for the task \cite{mishra2019tackling,vidgen2020directions}.
However, due to the ambiguities in the task definition and complexities of data collection, cross-dataset generalizability remains a challenging and under-studied issue of online abuse detection.

Existing datasets differ in the considered types of offensive behaviour and annotation schemes, data sources and data collection methods.
There is no agreed-upon definition of harmful online behaviour yet.
Several terms have been used to refer to the general concept of harmful online behavior, including \textit{toxicity} \cite{hosseini2017deceiving}, \textit{hate speech} \cite{schmidt2017survey}, \textit{offensive} \cite{zampieri2019semeval} and \textit{abusive} language \cite{waseem2017understanding,vidgen2019challenges}.
Still, in practice, every dataset only focuses on a narrow range of sub-types of such behaviours and a single online platform \cite{jurgens2019just}.
For example, \citet{davidson2017automated} annotated tweets for three categories, \textit{Racist}, \textit{Offensive but not Racist} and \textit{Clean}, and \citet{nobata2016abusive} collected discussions from Yahoo! Finance news and applied a binary annotation scheme of \textit{Abusive} versus \textit{Clean}.
Further, since pure random sampling usually results in small proportions of offensive examples \cite{founta2018large}, various sampling techniques are often employed.
\citet{zampieri2019semeval} used words and phrases frequently found in offensive messages to search for potential abusive tweets. \citet{founta2018large} and \citet{razavi2010offensive} started from random sampling, then boosted the abusive part of the datasets using specific search procedures.
\citet{hosseinmardi2015analyzing} used snowballing to collect abusive posts on Instagram.
Due to this variability in category definitions and data collection techniques, a system trained on a particular dataset is prone to overfitting to the specific characteristics of that dataset.
As a result, although models tend to perform well in cross-validation evaluation on one dataset, the cross-dataset generalizability remains low \cite{van2018challenges,wiegand-etal-2019-detection}.

In this work, we investigate the impact of two types of biases originating from source data that can emerge in a cross-domain application of models: 1) task formulation bias (discrepancy in class definitions and annotation between the training and test sets) and 2) selection bias (discrepancy in the topic and class distributions between the training and test sets).
Further, we suggest topic-based dataset pruning as a method of mitigating selection bias to increase generalizability. This approach is different from domain adaptation techniques based on data selection \cite{ruder2017learning,liu2019reinforced} in that we apply an unsupervised topic modeling method for topic discovery without using the class labels. We show that some topics are more generalizable than others.
The topics that are specific to the training dataset lead to overfitting and, therefore, lower generalizability.
Excluding or down-sampling instances associated with such topics before the expensive annotation step can substantially reduce the annotation costs.

We focus on the Wikipedia Detox or \textit{Wiki}-dataset, (an extension of the dataset by \citet{wulczyn2017ex}), collected from English Wikipedia talk pages and annotated for toxicity. To explore the generalizability of the models trained on this dataset,
we create an out-of-domain test set comprising various types of abusive behaviours by combining two existing datasets, namely \textit{Waseem}-dataset \cite{waseem2016hateful} and \textit{Founta}-dataset \cite{founta2018large}, both collected from Twitter.

Our main contributions are as follows:
\begin{itemize}[leftmargin=*]

    \item We identify topics included in the \textit{Wiki}-dataset and manually examine keywords associated with the topics to heuristically determine topics' generalizability and their potential association with toxicity.

    \item We assess the generalizability of the task formulations by training a classifier to detect the \textit{Toxic} class in the \textit{Wiki}-dataset and testing it on an out-of-domain dataset comprising various types of offensive behaviours. We find that \textit{Wiki-Toxic} is most generalizable to \textit{Founta-Abusive} and least generalizable to \textit{Waseem-Sexism}.

    \item We show that re-sampling techniques result in a trade-off between the True Positive and True Negative rates on the out-of-domain test set. This trade-off is mainly governed by the ratio of toxic to normal instances and not the size of the dataset.

    \item  We investigate the impact of topic distribution on generalizability and show that general and identity-related topics are more generalizable than platform-specific topics.

    \item We show that excluding Wikipedia-specific data instances (54\% of the dataset) does not affect the results of in-domain classification, and improves both True Positive and True Negative rates on the out-of-domain test set, unlike re-sampling methods. Through unsupervised topic modeling, such topics can be identified and excluded before annotation. The pruned version of the Wikipedia dataset is available at \url{https://github.com/IsarNejad/cross_dataset_toxicity}.

\end{itemize}

\section{Biases Originating from Source Data}

We focus on two types of biases originated from source data: task formulation and selection bias.

\noindent \textbf{Task formulation bias:} In commercial applications, the definitions of offensive language heavily rely on community norms and context and, therefore, are imprecise, application-dependent, and constantly evolving  \cite{chandrasekharan2018internet}.
Similarly in NLP research, despite having clear overlaps, offensive class definitions vary significantly from one study to another.
For example, the \textit{Toxic} class in the \textit{Wiki}-dataset refers to
aggressive or disrespectful utterances that would likely make participants leave the discussion.
This definition of toxic language includes some aspects of racism, sexism and hateful behaviour.
Still, as highlighted by  \citet{vidgen2019challenges},  identity-based abuse is fundamentally different from general toxic behavior. Therefore,
the \textit{Toxic} class definition used in the \textit{Wiki}-dataset differs in its scope from the abuse-related categories as defined in the \textit{Waseem}-dataset and \textit{Founta}-dataset.
\citet{wiegand-etal-2019-detection} converted various category sets to binary (offensive vs. normal) and demonstrated that a system trained on one dataset can identify other forms of abuse to some extent.
We use the same methodology and examine different offensive categories in out-of-domain test sets to explore the deviation in a system's performance caused by the differences in the task definitions. \citet{fortuna-etal-2020-toxic} demonstrated that many different definitions are used for equivalent concepts, which makes most of the publicly available datasets incompatible. They suggested hierarchical multi-class annotation to formulate the online abuse detection task.

Regardless of the task formulation, abusive language can be divided into explicit and implicit \cite{waseem2017understanding}. Explicit abuse refers to utterances that include obscene and offensive expressions, such as \textit{stupid} or \textit{scum}, even though not all utterances that include obscene expressions are considered abusive in all contexts.
Implicit abuse refers to more subtle harmful behaviours, such as stereotyping and micro-aggression. Explicit abuse is usually easier to detect by human annotators and automatic systems. Also, explicit abuse is more transferable between datasets as it is part of many definitions of online abuse, including personal attacks, hate speech, and identity-based abuse. The exact definition of implicit abuse, on the other hand, can substantially vary between task formulations as it is much dependent on the context, the author and the receiver of an utterance \cite{wiegand-etal-2019-detection}.

\noindent \textbf{Selection bias:} Selection (or sampling) bias emerge when source data, on which the model is trained, is not representative of target data, on which the model is applied \cite{shah-etal-2020-predictive}.
We focus on two data characteristics affecting selection bias: topic distribution and class distribution.

In practice, every dataset covers a limited number of topics, and the \textbf{topic distributions}
depend on many factors, including the source of data, the search mechanism and the timing of the data collection.
For example, our source dataset, \textit{Wiki}-dataset, consists of Wikipedia talk pages dating from 2004--2015. On the other hand, one of the sources of our target dataset, \textit{Waseem}-dataset, consists of tweets collected using terms and references to specific entities that frequently occur in tweets expressing hate speech.
As a result of its sampling strategy, \textit{Waseem}-dataset includes many tweets on the topic of `women in sports'.
\citet{wiegand-etal-2019-detection} showed that different data sampling methods result in various distributions of topics, which affects the generalizability of trained classifiers, especially in the case of implicit abuse detection. Unlike explicit abuse, implicitly abusive behaviour comes in a variety of semantic and syntactic forms. To train a generalizable classifier, one requires a training dataset that covers a broad range of topics, each with a good representation of offensive examples.
We continue this line of work and investigate the impact of topic bias on cross-dataset generalizability by identifying and changing the distribution of topics in controlled experiments.

The amount of online abuse on mainstream platforms varies greatly but is always very low. \citet{founta2018large} found that abusive tweets form 0.1\% to 3\% of randomly collected datasets. \citet{vidgen2019much} showed that  depending on the platform the prevalence of abusive language can range between 0.001\%  and 8\%. Despite various data sampling strategies aimed at increasing the proportion of offensive instances, the \textbf{class imbalance} (the difference in class sizes) in available datasets is often severe.
When trained on highly imbalanced data, most statistical machine learning methods exhibit a bias towards the majority class, and their performance on a minority class, usually the class of interest, suffers.
A number of techniques have been proposed to address class imbalance in data, including data re-sampling, cost-sensitive learning, and neural network specific learning algorithms \cite{branco2016survey,haixiang2017learning,johnson2019survey}.
In practice, simple re-sampling techniques, such as down-sampling of over-represented classes, often improve the overall performance of the classifier \cite{johnson2019survey}. However, re-sampling techniques might lead to overfitting to one of the classes causing a trade-off between True Positive and True Negative rates. When aggregated in an averaged metric such as F-score, this trade-off is usually overlooked.

\section{Datasets}

\label{sec:task formulations}
We exploit three large-scale, publicly available English datasets frequently used for the task of online abuse detection.
Our main dataset, \textit{Wiki}-dataset \cite{wulczyn2017ex}, is used as a training set.
The out-of-domain test set is obtained by combining
the other two datasets, \textit{Founta}-dataset \cite{founta2018large} and \textit{Waseem}-dataset \cite{waseem2016hateful}.

\vspace{4pt}
\noindent \textbf{Training set}: The \textit{Wiki}-dataset includes 160K comments collected from English Wikipedia discussions and annotated for \textit{Toxic} and \textit{Normal}, through crowd-sourcing\footnote{\url{https://meta.wikimedia.org/wiki/Research:Detox/Data_Release}}. Every comment is annotated by 10 workers, and the final label is obtained through majority voting.
The class \textit{Toxic} comprises rude, hateful, aggressive, disrespectful or unreasonable comments that are likely to make a person leave a conversation\footnote{\url{https://github.com/ewulczyn/wiki-detox/blob/master/src/modeling/toxicity_question.png}}.
The dataset consists of randomly collected comments and comments made by users blocked for violating Wikipedia's policies to augment the proportion of toxic texts. This dataset contains  15,362 instances of \textit{Toxic} and 144,324 \textit{Normal} texts.

\vspace{4pt}
\noindent \textbf{Out-of-Domain test set}: The toxic portion of our test set is composed of four types of offensive language: \textit{Abusive} and \textit{Hateful} from the \textit{Founta}-dataset, and \textit{Sexist} and \textit{Racist} from the  \textit{Waseem}-dataset. For the benign examples of our test set, we use the \textit{Normal} class of the \textit{Founta}-dataset.

The \textit{Founta}-dataset is a collection of 80K tweets crowd-annotated for four classes: \textit{Abusive}, \textit{Hateful}, \textit{Spam} and \textit{Normal}.
The data is randomly sampled and then boosted with tweets that are likely to belong to one or more of the minority classes by deploying an iterative data exploration technique.
The \textit{Abusive} class is defined as content with any strongly impolite, rude or hurtful language that shows a debasement of someone or something, or shows intense emotions.
The \textit{Hateful} class refers to tweets that express hatred towards a targeted individual or group, or are intended to be derogatory, to humiliate, or to insult members of a group, on the basis of attributes such as race, religion, ethnic origin, sexual orientation, disability, or gender.
\textit{Spam} refers to posts consisted of advertising/marketing, posts selling products of adult nature, links to
malicious websites, phishing attempts and other unwanted information, usually
sent repeatedly. Tweets that do not fall in any of the prior classes are labelled as \textit{Normal} \cite{founta2018large}.
We do not include the \textit{Spam} class in our test set as this category does not constitute offensive language, in general. The \textit{Founta}-dataset contains 27,150 of \textit{Abusive},  4,965 of \textit{Hateful} and 53,851 of \textit{Normal} instances.

The \textit{Waseem}-dataset includes 16K manually annotated tweets, labeled as \textit{Sexist}, \textit{Racist} or \textit{Neither}.
The corpus is collected by searching for common slurs and terms pertaining to minority groups as well as identifying tweeters that use these terms frequently.
A tweet is annotated as \textit{Racist} or \textit{Sexist} if it uses a racial or sexist slur, attacks, seeks to silence, unjustifiably criticizes or  misrepresents a minority or defends xenophobia or sexism. Tweets that do not fall in these two classes are labeled as \textit{Neither} \cite{waseem2016hateful}.
The \textit{Neither} class represents a mixture of benign and abusive (but not sexist or racist) instances, and, therefore, is excluded from our test set.
\textit{Waseem}-dataset contains 3,430 of \textit{Sexist} and 1,976 of \textit{Racist} tweets.

\iffalse
\begin{table}[t]
\small{
\centering
\begin{tabular}{lr}
\textbf{Dataset} &\textbf{Number of instances}\\
\hline
\textit{Wiki}-dataset &\\
$\ \ \ \ $ Personal attack & 15,362\\
$\ \ \ \ $ Normal  & 144,324 \\
\textit{Founta}-dataset & \\
$\ \ \ \ $ Abusive & 27,150\\
$\ \ \ \ $ Hateful & 4,965\\
$\ \ \ \ $ Normal  &  53,851\\
\textit{Waseem}-dataset & \\
$\ \ \ \ $ Sexist  & 3,430\\
$\ \ \ \ $ Racist  & 1,976\\
\hline

\end{tabular}
\caption{Number of instances per class }
\label{tab:data_statistics}
}
\end{table}
\fi

\setlength{\tabcolsep}{3pt}

\begin{table}[]
\small{
\centering
\begin{tabular}{ll}
\textbf{\small{Topics}} & \textbf{\small{Top words}} \\[2pt]
\hline
\textbf{\small{Category 1}} & \\
$\ \ $ \small{topic 0}  & \small{know, like, thank, think, want} \\
$\ \ $ \small{topic 1}  & \small{time, like, peopl, think, life} \\[2pt]
\textbf{\small{Category 2} }& \\
$\ \ $ \small{topic 2}  & \small{suck, year, school, c*ck, p*ssi} \\
$\ \ $ \small{topic 7}   & \small{english, countri, american, nation, german} \\
$\ \ $ \small{topic 8}  & \small{kill, die, jewish, islam, israel} \\
$\ \ $ \small{topic 9}  & \small{god, christian, cast, presid, japanes} \\
$\ \ $ \small{topic 12}  & \small{person, editor, attack, accuse, user} \\
$\ \ $ \small{topic 14}  & \small{f*ck, sh*t, *ss, stupid, bastard} \\
$\ \ $ \small{topic 16}  & \small{team, footbal, gay, match, station} \\[2pt]
\textbf{\small{Category 3}} & \\
$\ \ $ \small{topic 3}  & \small{redirect, talk, categori, film, episod}\\
$\ \ $ \small{topic 4}  & \small{page, wikipedia, edit, talk, articl}\\
$\ \ $ \small{topic 5}  & \small{sourc, claim, cite, wikipedia, publish}\\
$\ \ $ \small{topic 6}  & \small{link, list, page, inform, articl}\\
$\ \ $ \small{topic 10}  & \small{delet, articl, imag, tag, copyright}\\
$\ \ $ \small{topic 11}  & \small{univers, law, scienc, theori, definit}\\
$\ \ $ \small{topic 13}  & \small{page, discuss, review, talk, templat}\\
$\ \ $ \small{topic 15}  & \small{articl, section, discuss, refer, editor}\\
$\ \ $ \small{topic 17}  & \small{http, com, www, org, wiki}\\
$\ \ $ \small{topic 18}  & \small{edit, block, vandal, user, account}\\
$\ \ $ \small{topic 19}  & \small{style, align, color, background, border}\\[2pt]
\hline
\end{tabular}
\caption{Topics identified in the \textit{Wiki}-dataset. For each topic, five of ten top words that are most representative of the assigned category are shown.
}
\label{tab:categories}
}
\end{table}
\section{Topic Analysis of the \textit{Wiki}-dataset }
\label{sec:topic categories}

We start by exploring the content of the \textit{Wiki}-dataset through topic modeling. We train a topic model using the Online Latent Dirichlet Allocation (OLDA) algorithm \cite{HoffmanLDA} as implemented in the Gensim library \cite{rehurek_lrec} with the default parameters.
Latent Dirichlet Allocation (LDA) \cite{blei2003latent} is a Baysian probabilistic model of a collection of texts.
Each text is assumed to be generated from a multinomial distribution over a given number of topics, and each topic is represented as a multinomial distribution over the vocabulary.
We pre-process the texts by lemmatizing the words and removing the stop words.
To determine the optimal number of topics, we use a coherence measure that calculates the degree of semantic similarity among the top words \cite{roder2015exploring}. Top words are defined as the most probable words to be seen conditioned on a topic. We experimented with a range of topic numbers between 10 and 30 and obtained the maximal average coherence with 20 topics. Each topic is represented by 10 top words.
For simplicity, each text is assigned a single topic that has the highest probability.
The full list of topics and their top words are available in the Appendix.

We group the 20 extracted topics into three categories based on the coherency of the top words and their potential association with offensive language.
This is done through manual examination of the 10 top words in each topic.
Table \ref{tab:categories} shows five out of ten top words for each topic that are most representative of the assigned category.

\vspace{4pt}

\noindent\textbf{Category 1: incoherent or mixture of general topics}

The top words of two topics (topic 0 and topic 1) are general terms such as \textit{think}, \textit{want}, \textit{time}, and \textit{life}. This category forms 26\% of the dataset. Since these topics appear incoherent, their association with offensiveness cannot be judged heuristically. Looking at the toxicity annotations we observe that 47\% of the \textit{Toxic} comments belong to these topics.
These comments mostly convey personal insults, usually not tied to any identity group.
The frequently used abusive terms in these \textit{Toxic} comments include \textit{f*ck}, \textit{stupid}, \textit{idiot}, \textit{*ss}, etc.

 \vspace{4pt}

\noindent\textbf{Category 2: coherent, high association with offensive language}

Seven of the topics can be associated with offensive language; their top words represent profanity or are related to identity groups frequently subjected to abuse.
Topic 14 is the most explicitly offensive topic; nine out of ten top words are associated with insult and hatred.
97\% of the instances belonging to this topic are annotated as \textit{Toxic}, with 96\% of them containing explicitly toxic words.\footnote{Following \citet{wiegand-etal-2019-detection}, we estimate the proportion of explicitly offensive instances in a dataset as the proportion of abusive instances that contain at least one word from the lexicon of abusive words by \citet{wiegand-etal-2018-inducing}.} These are generic profanities with the word \textit{f*ck} being the most frequently used word.

The top words of the other six topics (topics 2, 7, 8, 9, 12, and 16) include either offensive words or terms related to identity groups based on gender, ethnicity, or religion. On average, 16\% of the comments assigned to these topics are labeled as \textit{Toxic}. We manually analyzed these comments, and found that each topic (except topic 12) tends to concentrate around a specific identity group.
Offensive comments in topic 2 mostly contain sexual slur and target female and homosexual users.
In topic 7, comments often contain racial and ethnicity based abuse.
Topic 8 contains physical threats, often targeting Muslims and Jewish folks (the words \textit{die} and \textit{kill} are the most frequently used content words in the offensive messages of this topic).
Comments in topic 9 involve many terms associated with Christianity (e.g., \textit{god}, \textit{christian}, \textit{Jesus}).
Topic 16 has the least amount of comments (0.3\% of the dataset), with the offensive messages mostly targeting gay people (the word \textit{gay} appears in 67\% of the offensive messages in this topic).
Topic 12 is comprised of personal attacks in the context of Wikipedia admin--contributor relations.
The most common offensive words in this topic include \textit{f*ck}, \textit{stupid}, \textit{troll}, \textit{ignorant}, \textit{hypocrite}, etc.
20\% of the whole dataset and 35\% of the comments labeled as \textit{Toxic} belong to this category.

 \vspace{4pt}

\noindent\textbf{Category 3: coherent, low association with offensive language}

The remaining eleven topics include top words specific to Wikipedia and not directly associated with offensive language. For example, keywords of topic 4 are terms such as \textit{page}, \textit{Wikipedia}, \textit{edit} and \textit{article}, and only 0.4\% of the 10,471 instances in this topic are labeled as \textit{Toxic}. These eleven topics comprise 54\% of the comments in the dataset and 18\% of the \textit{Toxic} comments.

\begin{table}[]
\small{
\centering
\begin{tabular}{lrrr}
\textbf{Dataset/Class}&\textbf{Cat.\#1}&\textbf{Cat.\#2}&\textbf{Cat.\#3}\\
\hline
Training Set &\\
$\ \ \ \ $ \textit{Wiki-Toxic} &48\% &34\% &18\%\\
$\ \ \ \ $ \textit{Wiki-Normal}  &24\% &18\% &58\% \\
Test Set & \\
$\ \ \ \ $ \textit{Founta-Abusive} &58\% &33\% &8\%\\
$\ \ \ \ $ \textit{Founta-Hateful} &54\% &37\% &9\%\\
$\ \ \ \ $ \textit{Waseem-Sexist}  &50\% &35\% &15\%\\
$\ \ \ \ $ \textit{Waseem-Racist}  &23\% &67\% & 10\%\\
$\ \ \ \ $ \textit{Founta-Normal}  & 51\%&28\% &21\% \\

\hline

\end{tabular}
\caption{Distribution of topic categories per class  }
\label{tab:topic distribution}
}
\end{table}

\section{Topic Distribution of the Test Set }
\label{sec:topic categories in test}
We apply the LDA topic model trained on the \textit{Wiki}-dataset as described in Section~\ref{sec:topic categories} to the Out-of-Domain test set.
As before, each textual instance is assigned a single topic that has the highest probability.
Table \ref{tab:topic distribution} summarizes the distribution of topics for all classes in the three datasets.

Observe that Category 3 is the least represented category of topics across all classes, except for the \textit{Normal} class in the \textit{Wiki}-dataset. Specifically, there is a significant deviation in the topic distribution between the \textit{Wiki-Normal} and the \textit{Founta-Normal} classes. This deviation can be explained by the difference in data sources.
Normal conversations on Twitter are more likely to be about general concepts covered in Category 1 or identity-related topics covered in Category 2 than the specific topics such as \textit{writing} and \textit{editing} in Category 3. Other than \textit{Waseem-Racist}, which has 67\% overlap with Category 2, all types of offensive behaviour in the three datasets have more overlap with the general topics (Category 1) than identity-related topics (Category 2).
For example, for the \textit{Waseem-Sexist}, 50\% of instances fall under Category 1, 35\% under Category 2 and 15\% under Category 3.
Topic 1, which is a mixture of general topics, is the dominant topic among the \textit{Waseem-Sexist} tweets.
Out of the topics in Category 2, most of the sexist tweets are matched to topic 2 (focused on sexism and homophobia) and topic 12 (general personal insults).

\section{Generalizability of the Model Trained on the \textit{Wiki}-dataset}
\label{sec:test}

To explore how well the \textit{Toxic} class from the \textit{Wiki}-dataset generalizes to other types of offensive behaviour, we train a binary classifier (\textit{Toxic} vs. \textit{Normal}) on the \textit{Wiki}-dataset (combining the train, development and test sets) and test it on the Out-of-Domain Test set. This classifier is expected to predict a positive (\textit{Toxic}) label for the instances of classes \textit{Founta-Abusive}, \textit{Founta-Hateful}, \textit{Waseem-Sexism} and \textit{Waseem-Racism}, and a negative (\textit{Normal}) label for the tweets in the \textit{Founta-Normal} class. We fine-tune a BERT-based classifier \cite{devlin2018bert} with a linear prediction layer, the batch size of 16 and the learning rate of $2 \times 10^{-5}$ for 2 epochs.

\vspace{1.5mm}
\noindent \textbf{Evaluation metrics: }
In order to investigate the trade-off between the True Positive and True Negative rates, in the following experiments we report accuracy per test class. Accuracy per class is calculated as the rate of correctly identified instances within a class. Accuracy over the toxic classes (\textit{Founta-Abusive}, \textit{Founta-Hateful}, \textit{Waseem-Sexism} and \textit{Waseem-Racism}) indicates the True Positive rate, while accuracy of the normal class (\textit{Founta-Normal}) measures the True Negative rate.
Note that given the sizes of the positive and negative test classes, all other common metrics, such as various kinds of averaged F1-scores, can be calculated from the accuracies per class. In addition, we report macro-averaged F-score, to show the overall impact of the proposed method.

\begin{table}[]
\small{
\centering
\begin{tabular}{lrrrr}
& \multicolumn{4}{c}{\textbf{\small{Test Subset}}}\\
\cline{2-5}
\textbf{Dataset/Class}&\textbf{All}&\textbf{Cat.\#1}&\textbf{Cat.\#2}&\textbf{Cat.\#3} \\
\hline
\textit{Out-of-Domain} - Toxic & \\
$\ \ \ \ $ \textit{Founta-Abusive} &0.94 &0.94 &\textbf{0.96}&0.91\\
$\ \ \ \ $ \textit{Founta-Hateful} &0.62 &\textbf{0.65} &0.62&0.43\\
$\ \ \ \ $ \textit{Waseem-Sexist}  &0.26 &\textbf{0.29} &0.26&0.17\\
$\ \ \ \ $ \textit{Waseem-Racist}  &0.35 &\textbf{0.37} & 0.36&0.20\\

\textit{Out-of-domain} - Normal & \\
$\ \ \ \ $ \textit{Founta-Normal}  & 0.96&0.95 &0.97&\textbf{0.99} \\
\hline

\end{tabular}
\caption{Accuracy per test class and topic category for a classifier trained on \textit{Wiki}-dataset. Best results in each row are in bold.}
\label{tab:accuracies}
}
\end{table}

\vspace{1.5mm}
\noindent \textbf{Results:} The overall performance of the classifier on the Out-of-Domain test set is quite high: macro-averaged $F_{1} = 0.90$.
However, when the test set is broken down into the 20 topics of the \textit{Wiki}-dataset and the accuracy is measured within the topics, the results vary greatly. For example, for the instances that fall under topic 14, the explicitly offensive topic, the F1-score is 0.99. For topic 15, a Wikipedia-specific topic, the F1-score is 0.80.
Table~\ref{tab:accuracies} shows the overall accuracies for each test class as well as the accuracies for each topic category (described in Section \ref{sec:topic categories}) within each class.

For the class\textit{ Founta-Abusive}, the classifier
achieves 94\% accuracy.
12\% of the \textit{Founta-Abusive} tweets fall under the explicitly offensive topic (topic 14), and those tweets are classified with a 100\% accuracy. The accuracy score is highest on Category 2 and lowest on Category 3.
For the \textit{Founta-Hateful} class, the classifier recognizes 62\% of the tweets correctly. The accuracy score is highest on Category 1 and lowest on Category 3. 8\% of the \textit{Founta-Hateful} tweets fall under the explicitly offensive topic (topic 14), and are classified with a 99\% accuracy.
For the \textit{Founta-Normal} class, the classifier recognizes 96\% of the tweets correctly. Unlike the \textit{Founta-Abusive} and \textit{Founta-Hateful} class, for the \textit{Founta-Normal} class, the highest accuracy is achieved on Category 3. 0.1\% of the \textit{Founta-Normal} tweets fall under the explicitly offensive topic, and only 26\% of them are classified correctly.

The accuracy of the classifier on the \textit{Waseem-Sexist} and \textit{Waseem-Racist} classes is 0.26 and 0.35, respectively.
This indicates that the \textit{Wiki}-dataset, annotated for toxicity, is not well suited for detecting sexist or racist tweets. This observation could be explained by the fact that none of the coherent topics extracted from the \textit{Wiki}-dataset is associated strongly with sexism or racism. Nevertheless, the tweets that fall under the explicit abuse topic (topic 14) are recognized with a 100\% accuracy.
Topic 8, which contains abuse mostly directed towards Jewish and Muslim people, is the most dominant topic in the \textit{Racist} class (32\% of the class)
and the accuracy score on this topic is the highest,
after the explicitly offensive topic.
The \textit{Racist} class overlaps the least with Category 3 (see Table \ref{tab:topic distribution}), and the lowest accuracy score is obtained on this category. The definitions of the \textit{Toxic} and \textit{Racist} classes overlap mostly in general and identity-related abuse, therefore higher accuracy scores are obtained in Categories 1 and 2. Similar to \textit{Racist} tweets, \textit{Sexist} tweets have the least overlap and the lowest accuracy score on Category 3. The accuracy score is the highest on the explicitly offensive topic (100\%) and varies substantially across other topics.

\subsection{Discussion}

The generalizability of the classifier trained on the \textit{Wiki}-dataset is affected by at least two factors: task formulation and topic distributions.

\noindent \textbf{The impact of task formulation:}
From task formulations described in Section \ref{sec:task formulations}, observe that the \textit{Wiki}-dataset defines the class  \textit{Toxic} in a general way. The class \textit{Founta-Abusive} is also a general formulation of offensive behaviour. The similarity of these two definitions is reflected clearly in our results. The classifier trained on the \textit{Wiki}-dataset reaches 96\% accuracy  on the \textit{Founta-Abusive} class. Unlike the \textit{Founta-Abusive} class, the other three labels included in our analysis formulate a specific type of harassment against certain targets. Our topic analysis of the \textit{Wiki}-dataset reveals that this dataset includes profanity and hateful content directed towards minority groups but the dataset is extremely unbalanced in covering these topics. Therefore, not only is the number of useful examples for learning these classes small, but the classification models do not learn these classes effectively because of the skewness of the training dataset. This observation is in line with the fact that the trained classifier detects some of the \textit{Waseem-Racist}, \textit{Waseem-Sexist} and \textit{Founta-Hateful} tweets correctly, but overall performs poorly on these classes.

 \begin{figure}
\centering
\includegraphics[width= 7cm]{balance_effect.png}
\caption{The classifier's performance on various classes when trained on subsets of the \textit{Wiki}-dataset with specific class distributions.}
\label{fig:balance effect}
\end{figure}

\noindent \textbf{The impact of topic distribution:}
Our analysis shows that independent of the class labels, for all the abuse-related test classes, the trained classifier performs worst when test examples fall under Category 3. Intuitively, this means that the platform-specific topics with low association with offensive language are least generalizable in terms of learning offensive behaviour. Categories 1 and 2, which include a mixture of general and identity-related topics with high potential for offensiveness, have more commonalities across datasets.

\section{Impact of Data Size, Class and Topic Distribution on Generalizability}
\label{sec:quantitative}
Our goal is to measure the impact of various topics on generalization. However, modifying the topic distribution will impact the class distribution and data size. To control for this, we first analyze the impact of class distribution and  data size on the classifier's performance. Then, we study the effect of topic distribution by limiting the training data to different topic categories.

\noindent \textbf{Impact of class distribution:}
The class distribution in the \textit{Wiki}-dataset is fairly imbalanced; the ratio of the size of \textit{Wiki-Toxic} to \textit{Wiki-Normal} is 1:10.
Class imbalance can lead to poor predictive performance on minority classes, as most of the learning algorithms are developed with the assumption of the balanced class distribution. To investigate the impact of the class distribution on generalization, we keep all the \textit{Wiki-Toxic} instances and randomly sample the \textit{Wiki-Normal} class to build the training sets with various ratios of toxic to normal instances.

Figure \ref {fig:balance effect} shows the classifier's accuracy on the test classes when trained on subsets with different class distributions. Observe that with the increase of the \textit{Wiki-Normal} class size in the training dataset, the accuracy on all offensive test classes decreases while the accuracy on the \textit{Founta-Normal} class increases.
The classifier assigns more instances to the the \textit{Normal} class resulting in a lower True Positive (accuracy on the offensive classes) and a higher True Negative (accuracy on the \textit{Normal} class) rates. The drop in accuracy is significant for the  \textit{Waseem-Sexist}, \textit{Waseem-Racist} and \textit{Waseem-Hateful} classes and relatively minor for the \textit{Founta-Abusive} class.
Note that the impact of the class distribution is not reflected in the overall F1-score. The classifier trained on a balanced data subset (with class size ratio of 1:1) reaches 0.896 macro-averaged F1-score, which is very close to the F1-score of 0.899 resulted from training on the full dataset with the 1:10 class size ratio. However, in practice, the designers of such systems need to decide on the preferred class distribution depending on the distribution of classes in the test environment and the significance of the consequences of the False Positive and False Negative outcomes.

\begin{figure}
\centering
\includegraphics[width= 0.9\linewidth]{sample-size.png}
\caption{The classifier's average performance on various classes when trained on balanced subsets of the \textit{Wiki}-dataset of different sizes.}
\label{fig:size effect}
\end{figure}

\noindent \textbf{Impact of dataset size: } To investigate the impact of the size of the training set, we fix the class ratio at 1:1 and compare the classifier's performance when trained on data subsets of different sizes.
We randomly select subsets from the \textit{Wiki}-dataset with sizes of 10K (5K \textit{Toxic} and 5K \textit{Normal} instances) and 30K (15K \textit{Toxic} and 15K \textit{Normal} instances). Each experiment is repeated 5 times, and the averaged results are presented in Figure \ref{fig:size effect}. The height of the box shows the standard deviation of accuracies. Observe that the average accuracies remain unchanged when the dataset's size triples at the same class balance ratio. This finding contrasts with the general assumption that more training data results in a higher classification performance.

\begin{figure}[]
\centering
\begin{subfigure}{0.45\textwidth}
  \centering
  \includegraphics[width = 7cm]{topic_effect_3K_27K.png}
  \caption{toxic - normal : 3K - 27K }
  \label{fig:3K-3K}
\end{subfigure}
\begin{subfigure}{0.45\textwidth}
  \centering
  \includegraphics[width = 7cm]{topic_effect_3K_balanced.png}
  \caption{toxic - normal : 3K - 3K }
  \label{fig:3K-27K}
\end{subfigure}
\caption{The classifier's performance on various classes when trained on specific topic categories.}
\label{fig:topic effect}
\end{figure}

\noindent \textbf{Impact of topics: }In order to measure the impact of topics covered in the training dataset, we compare the classifier's performance when trained on only one of the three categories of topics described in Section \ref{sec:topic categories}. To control for the effect of class balance and dataset size, we run the experiments for two cases of toxic-to-normal ratios, 3K-3K and 3K-27K. Each experiment is repeated 5 times, and the average accuracy per class is reported in Figure \ref{fig:topic effect}.

For both cases of class size ratios, shown in Figures \ref{fig:3K-3K} and \ref{fig:3K-27K}, we notice that the classifier trained on instances belonging to Category 3 reaches higher accuracies  on the offensive classes, but a significantly lower accuracy on the \textit{Founta-Normal} class.
The benign part of Category 3 is overwhelmed by Wikipedia-specific examples. Therefore, utterances dissimilar to these topics are labelled as \textit{Toxic}, leading to a high accuracy on the toxic classes and a low accuracy on the \textit{Normal} class. This is an example of the negative impact of topic bias on the detection of offensive utterances.

In contrast, the classifiers trained on Categories 1 and 2 perform comparably across test classes. The classifier trained on Category 2 is slightly more effective in recognizing \textit{Founta-Hateful} utterances, especially when the training set is balanced. This observation can be explained by a better representation of identity-related hatred in Category 2.

\section{Removing Platform-Specific Instances from the Training Set }

We showed that a classifier trained on instances from Category 3 suffers a big loss in accuracy on the \textit{Normal} class.
Here, we investigate how the performance of a classifier trained on the full \textit{Wiki}-dataset changes when the Category 3 instances (all or the benign part only) are removed from the training set. Table~\ref{tab:exclude benign impact} shows the results. Observe that removing the domain-specific benign examples, referred to as `excl. C3 \textit{Normal}' in Table~\ref{tab:exclude benign impact}, improves the accuracies for all classes.
As demonstrated in the previous experiments, this improvement cannot be attributed to the changes in the class balance ratio or the size of the training set, as both these factors cause a trade-off between True Positive and True Negative rates. Removing the Wikipedia-specific topics from the \textit{Wiki}-dataset mitigates the topic bias and leads to this improvement.

Similarly, when all the instances of Category 3 are removed from the training set (`excl. C3 all' in Table~\ref{tab:exclude benign impact}), the accuracy does not suffer and actually slightly improves on all classes, except \textit{Waseem-Racist}. This is despite the fact that the training set has 58\% less instances in the \textit{Normal} class and 18\% less instances in the \textit{Toxic} class.
The overall macro-averaged F1-score on the full Out-of-Domain test set also slightly improves when the instances of Category 3 are excluded from the training data (Table~\ref{tab:F-score}).
Removing all the instances of Category 3 is particularly interesting since it can be done only with inspection of topics and without using the class labels.

To assess the impact of removing Wikipedia-specific examples on in-domain classification, we train a model on the training set of the \textit{Wiki}-dataset, with and without excluding Category 3 instances, and evaluate it on the full test set of the \textit{Wiki}-dataset. We observe that the in-domain performance does not suffer from removing Category 3 from the training data (Table \ref{tab:F-score}).

\setlength{\tabcolsep}{7pt}

\begin{table}[]
\small{
\centering
\begin{tabular}{lccc}
&\multicolumn{3}{c}{\textbf{\small{Training Set}}}\\
\cline{2-4}

\small\textbf{Dataset/Class}& \small\textbf{\textit{Wiki}} & \small\textbf{\textit{Wiki} excl.}& \small\textbf{\textit{Wiki} excl.}\\
&  & \small\textbf{C3 \textit{Normal}}& \small\textbf{C3 all}\\

\hline

Toxic & \\
$\ \ \ \ $ \textit{\small{Founta-Abusive}}  & 0.94 &0.96&0.95\\
$\ \ \ \ $ \textit{\small{Founta-Hateful}}  & 0.62 &0.67&0.65\\
$\ \ \ \ $ \textit{\small{Waseem-Sexist}}  & 0.26  &0.30 &0.28\\
$\ \ \ \ $ \textit{\small{Waseem-Racist}} & 0.35 &0.40 &0.32\\

Normal & \\
$\ \ \ \ $ \textit{\small{Founta-Normal}}  & 0.96 &0.97 &0.97\\

\hline
\end{tabular}
\caption{Accuracy per Out-of-Domain test class for a classifier trained on the \textit{Wiki}-dataset, and the \textit{Wiki}-dataset with Category 3 instances (\textit{Normal} only or all) excluded.}
\label{tab:exclude benign impact}
}
\end{table}

\section{Discussion }
In the task of online abuse detection, both False Positive and False Negative errors can lead to significant harm as one threatens the freedom of speech and ruins people's reputations, and the other ignores hurtful behaviour. Although balancing the class sizes has been traditionally exploited when dealing with imbalanced datasets, we showed that balanced class sizes may lead to high misclassification of normal utterances while improving the True Positive rates. This trade-off is not necessarily reflected in aggregated evaluation metrics such as F1-score but has important implications in real-life applications. We suggest evaluating each class (both positive and negative) separately taking into account the potential costs of different types of errors. Furthermore, our analysis reveals that for generalizability, the size of the dataset is not as important as the class and topic distributions.

We analyzed the impact of the topics included in the \textit{Wiki}-dataset and showed that mitigating the topic bias improves accuracy rates across all the out-of-domain positive and negative classes. Our results suggest that the sheer amount of normal comments included in the training datasets might not be necessary and can even be harmful for generalization if the topic distribution of normal topics is skewed. When the classifier is trained on Category~3 instances only (Figure \ref{fig:topic effect}), the \textit{Normal} class is attributed to the over-represented topics, leading to high misclassification of normal texts or high False Positive rates.

In general, when collecting new datasets, texts can be inspected through topic modeling using simple heuristics (e.g., keep topics related to demographic groups often subjected to abuse) in an attempt to balance the distribution of various topics and possibly sub-sample over-represented and less generalizable topics (e.g., high volumes of messages related to an incident with a celebrity figure happened during the data collection time) before the expensive annotation step.

\setlength{\tabcolsep}{8pt}

\begin{table}[]
\small{
\centering
\begin{tabular}{lccc}
&\multicolumn{3}{c}{\textbf{\small{Training Set}}}\\
\cline{2-4}

\small\textbf{Test Set}& \small\textbf{\textit{Wiki}} & \small\textbf{\textit{Wiki} excl.}& \small\textbf{\textit{Wiki} excl.}\\
&  & \small\textbf{C3 \textit{Normal}}& \small\textbf{C3 all}\\

\hline

\textit{Out-of-Domain} & 0.89 &0.91 &0.90\\
\textit{In-Domain} & 0.89&0.89&0.89\\

\hline
\end{tabular}
\caption{Macro-averaged F1-score for a classifier trained on portions of the \textit{Wiki}-dataset and evaluated on the in-domain and out-of-domain test sets. }
\label{tab:F-score}
}
\end{table}

\section{Conclusion }

Our work highlights the importance of heuristic scrutinizing of topics in collected datasets before performing a laborious and expensive annotation. We suggest that unsupervised topic modeling and manual assessment of extracted topics can be used to mitigate the topic bias.
In the case of the \textit{Wiki}-dataset, we showed that more than half of the dataset can be safely removed without affecting either the in-domain or the out-of-domain performance.
For future work, we recommend that topic analysis, augmentation of topics associated with offensive vocabulary and targeted demographics, and filtering of non-generalizable topics should be applied iteratively during data collection.

redpajama_set_name: RedPajamaArXiv


On my daddy's side of the family, the O'Brien's, my Irish history can be traced to the early 1800's. This family picture was taken about 1897 probably in Braidwood IL. My grandpa was Moses O'Brien, the dashing young man on the left holding the toy gun.
The characters Gemma and Ben, my heroine and hero from Under Fire: The Admiral, shared an experience they had a few months ago while visiting Ireland.
They were in the Irish countryside on a very dark, stormy night–really it was–in the middle of nowhere. They'd stopped at a local pub for dinner and were enjoying the food, pints, and conversation when the pub door slammed open. A soaking wet, obviously upset young man stood in the doorway. He rushed in babbling about a horrible experience.
He was settled into a chair and given a pint. The beer was half-gone before he could string words into sentences and answer the many questions. The young man explained he was backpacking through Ireland and on a deserted road when it began raining so hard he could hardly see a few feet ahead of him. Finally, a car came slowly towards him and stopped. Desperate for shelter and thinking he was being offered a ride, he got in and closed the door only to realize there was nobody behind the wheel. Even though the engine wasn't on, the car once again started moving. Ireland's many ghost stories rumbled through his brain and fear paralyzed him. That is until he looked at the road ahead and saw a curve looming. Gathering courage, he prepared to jump. Then, through the driver's window, a ghostly hand appeared out of gloom. In terror, he watched as the hand turned the wheel, guiding the car around the curve.
The lights of the pub appeared and gathering strength, he jumped out of the car and ran for it.
A silence enveloped the pub when everybody realized he was crying.


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There's a dilemma before you get any further and it involves what you're thinking of as spontaneity versus something more considered. The first option involves ploughing purposefully on and not paying any attention to Jarvis' critical output other than to be aware of the Main Points whilst the second is to absorb the gist of those essays which might be pertinent to what's going on here. This particular problem seems important because of Night Office's use of rhyme and metre to say quite complex things. From what you remember, you disagree with the Jarvis perspective on formal constraints enhancing complex themes but it might be as well to get a bit more of the rationale.
The first is the argument that technique is the way art thinks. The second is the argument that art thinks historically, and that what it knows, when it thinks well, is natural-historical experience. So called 'form' becomes in Adorno's account a kind of inexplicit mimesis, a mimesis which is not of individual objects in the world, but of those features of natural-historical experience which are at once the most elusive and amongst the most important: of structural shifts in the texture of experience itself which are too painful, or too blissful, directly to be thematized. No art is about itself. So technique knows something about the world. Yet it knows it, Adorno suggests, just by the most obsessive, and perhaps even the most fetishistic and solipsistic, absorption in its own proper stuff. We can see how this might suggest a different line of enquiry from that pursued by Prynne. If technique is the way art thinks, and if self-absorption is, curiously, the way art notices others, then might this 'virtuoso incantation' be, not simply a screen or a cocoon or an anaesthetic, but a medium - a medium for thinking, and for thinking about historical experience, just when in the very act of apparently retreating from it?
You'd forgotten the force with which this is expressed but you find that you are less impressed with this thesis than you were a couiple of years ago. You have a number of prejudices with regard to Adorno and a few more pertaining to the Romantics but, in order to understand a model, you need to understand how the model "works". For example you can accept the Marxian model of how the economy works because the theory provides a reasonably detailed exlpanation of how capital functions. In order to accept the Jarvis view of prosody (as well as the Heidegger/Prynne view of poetry) you would need to know how rhyme and metre acquire this exalted and powerful position. It doesn't appear to you that this is provided so you remain sceptical. You are intrigued, in spite of yourself, as to form as "inexplicit mimesis".
necessities of fear, desire and pain.
Tonight one little tune won't leave my head.
sitting me upright by this sleepless bed.
I do not want the snow to turn to rain.
till I detect this pattering refrain.
over the river in the freezing rain.
You find this magnificent, you are brought into this chilly night and the sudden immensities, the dramas of this familiar but still very strange object and you recall your own recent experience of a 'son et lumiere' gig last winter at Durham Cathedral which wasn't that special but did re-emphasise for you the sense of vastness and too bigness described here. You're also thinking about monumentality as an expression of power which you usually frame in exclusively negative terms but here there's this sense of protection and justice: "to put down evil".
You also gather that there's some quite complicated but pleasing things been done with rhyme, 'slipped / slid', 'pavement / moment' , 'swerved / recovered / discovered', none of which would you have recognised had you not read the lines aloud. You're very keen on how things sound because it's the one test that is reliable when it comes to deciding if things 'work'. There are some lines in the recent work of Geoffrey Hill, for example that look okay on the page but sound abysmal when read out loud. You worry about the number of commas in the last line and whether 'pavement' is sufficiently poetic but you are impressed. It's also using ordinary language with none of the usual syntactical bends and spirals deployed by most complex contemporary verse.
What's less satisfactory is the cathedral as ship trope which is too tired and worn out for this kind of use even though it may be setting the frame for more complex observations or ideas later on. The sharpened bow doesn't make a lot of sense because it's not at all clear how it got to be sharpened, nor how having a sharp bow might be helpful in "putting down" evil. Of course you may be suffering from an unhealthy preoccupation with David Jones' "The Anathemata" and the extended church / ship metaphor. You also have more than a bit of a problem with the notion of evil but you are prepared to hold this in abeyance for the moment because you know that our poet is a committed positivist and has no truck whatsoever with the way that you 'read' the world.
You notice "adits" and dip into the OED which comes up with: " A roughly horizontal passage introduced into a mine for the purpose of access or drainage. Also in extended use; spec. a watercourse, esp. one that carries water away from a mine" and " A means of access or entry; an avenue. Also as a mass noun: access, approach" which is pleasantly wry. You know how infuriating it is to have an unidentified tune running through your head that you can't get rid of and at such times your head does feel riddled with contradictory adits. You resolve to try and introduce the term into the next weekly visit from your mental health professional.
You re-read the three stanzas together and puzzle over the rain / snow repeat (inversion?) because it's obviously an extension of the snow covering all the "determined blacks" referred to earlier. Snow that turns to rain will uncover those features that have just been covered, but in the second stanza a distant ridge (higher, more likely to be snowed upon) refuses to whiten so everywhere within sight may well be receiving this freezing rain. You scan a little way ahead to encounter the snow again but things still don't make sense.
You're not seeing thus far any indication of influence between the two but there is at least a similarity between a gesture and a nod of recognition. You're also aware that it might be a mistake to read and write about both of these at the same time because you do have this tendency to extrapolate themes / similarities that weren't anywhere near the poet's head at the time of composistion. You reflect on this for a moment and decide that you're enjoying the process of attending to both at once and nobody is twisting your arm and you don't have any rules that are set in stone and so the moment of anxious introspection passes.
Before moving on to the next (musical) part of the cathedral story, you recall something from "Why Rhyme Pleases" that tlks about Pope's rhymes being more effective because the rhyming words come from different parts of speech.You read back and notice that the most effective nd impressive stanza, the third, follows this tendency mor than the other two.


Lucy is so great with him!! she lets him rough house without worry, and she is very patient :) the puppy (not pictured) is pretty good about that too, but he is a little more energetic!
I hardly visit this community.
haha! I didn't put it in babyrodgers!


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Behind The Scenes on SaskTel's "Be Kind Online"


text:
Elon Musk sold a San Francisco mansion, which was his last remaining home, for $30 million
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Elon Musk had said he would sell his property portfolio to fund a Mars colony.
Elon Musk sold his final home, a California mansion, for $30 million, according to MLSListings.com.
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He told Twitter users in June that he lives in a $50,000 Texas home, which he rents from SpaceX.
Get the latest tech news & scoops — delivered daily to your inbox.
Tesla and SpaceX CEO Elon Musk has finally sold his last remaining home, after repeatedly saying he wanted to sell most of his possessions, including all his houses, to fund a colony on Mars.
The 47-acre property in Hillsborough in the San Francisco Bay Area sold on December 2 for $30 million, according to MLSListings.com. Mansion Global first reported on the sale.
The buyer was represented by John Bretthauer Real Estate Experts, per Realtor.com. Bretthauer told Insider than it was "an honor to work on this fascinating transaction," but said that the client didn't want their identity to be revealed.
Musk bought the house in 2017 for just over $23 million from Christian de Guigne IV, whose family had owned the property for 150 years, before listing it himself for $37.5 million in June 2021.
After failing to find a buyer for the property, Musk then listed it with Compass' Mary and Brent Gullixson for $31.99 million in October.
Musk had previously been criticized for keeping the Hillsborough property, despite vowing to sell all his houses. He said on Twitter in June that he had kept the 16,000-square-foot mansion to rent out "for events."
Musk called the property a "special place" and said he wanted to sell it to a large family.
Musk has sold his property portfolio to fund a Mars colony
Musk has said he wants to send 1 million people to Mars by 2050.
He announced last May that he planned to sell "almost all" of his belongings to fund the venture and that he "will own no house." Since then, the entrepreneur has sold his real-estate portfolio, which was once worth upward of $100 million. This includes selling three neighboring homes in the Bel-Air neighborhood of Los Angeles and an estate formerly belonging to the actor Gene Wilder.
"It's going to take a lot of resources to build a city on Mars," Musk told Mathias Döpfner, the CEO of Insider's parent company, Axel Springer, in an interview last December. "I want to be able to contribute as much as possible to the city on Mars. That means just a lot of capital."
Musk told Döpfner that he would have "basically almost no possessions with a monetary value," apart from stock in his companies.
"People will attack me and say, 'oh, he's got all these possessions. He's got all these houses.' OK, now I don't have them any more," he added.
Musk relocated to Texas from California during the pandemic. He said on Twitter in June that he lived in a property worth around $50,000 in Boca Chica, Texas, which he rented from his aerospace company SpaceX.
Musk reportedly lives in a tiny prefab home developed by Boxabl.
More: California Elon Musk San Francisco Mars
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redpajama_set_name: RedPajamaCommonCrawl


text:
Narodowy Uniwersytet Zachodni (es. Universidad Nacional del Oeste UNO) – argentyński uniwersytet w San Antonio de Padua.

Uniwersytet został założony w dniu 11 listopada 2009 roku. Kampus został oficjalnie otworzony 16 września 2011 roku - w 35 rocznicę Nocy ołówków.

Szkoła przyznaje tytuły naukowe w zakresie administracji, administracji publicznej, wychowania fizycznego, pielęgniarstwa i informatyki.

Przypisy

Uczelnie w Argentynie

redpajama_set_name: RedPajamaWikipedia


A growing number of homebuilders and developers are using geothermal HVAC to differentiate their homes and neighborhoods in the tight new home market.
One of the keys to increasing consumer awareness of green or high performance homes is adding that information to Multiple Listing Service descriptions for homes in local markets.
In two months of spring and summer the home produced as much energy as it used.
Strict attention to detail helps the Proud Green Home of Louisville deliver a high level of indoor air quality and efficiency to the high performance home.
Water is just as precious a resource as energy in the Proud Green Home of Louisville.
The Proud Green Home of Louisville is a sustainable, high performance home that will earn certification from multiple green building organizations.
If you want to be at the top of the green building programs, you have to do the best of everything.
The overall mission of Norton Commons is to provide its residents with More Life Per Square Foot.
The Proud Green Home of Louisville was designed and constructed with healthy living in mind.
Good design pays for itself, and the proof can be seen in the Proud Green Home of Louisville.
This home reflects what what the builder has learned so far about creating living spaces focused on good health, well-being, energy-efficiency, water-efficiency, durability and social and environmental restoration."
One of the top goals of the Proud Green Home of Louisville was to create a highly energy efficient yet very livable home.
From the Tiny House trend to tighter thermal envelopes, the marketplace is taking a deeper interest in high performance, sustainable homes.
On a residential or commercial building site, time equals money, so building professionals are interested in shortening the process while maintaining quality.


text:
#ifndef __mdaParameter__
#define __mdaParameter__

#include "public.sdk/source/vst/vstparameters.h"
#include "pluginterfaces/base/ustring.h"
#include <list>
#include <algorithm>

namespace Steinberg {
namespace Vst {
namespace mda {

//-----------------------------------------------------------------------------
class BaseParameter : public Parameter
{
public:
	BaseParameter	(	const TChar* title,
						const TChar* units,
						int32 stepCount,
						ParamValue defaultValueNormalized,
						int32 flags,
						int32 tag,
						UnitID unitID = kRootUnitId);

	bool fromString (const TChar* string, ParamValue& valueNormalized) const;
	bool setNormalized (ParamValue v);

};

//-----------------------------------------------------------------------------
class IndexedParameter : public BaseParameter
{
public:
	IndexedParameter (	const TChar* title,
						const TChar* units,
						int32 stepCount,
						ParamValue defaultValueNormalized,
						int32 flags,
						int32 tag,
						UnitID unitID = kRootUnitId);

	ParamValue toPlain (ParamValue valueNormalized) const;
	ParamValue toNormalized (ParamValue plainValue) const;

	void toString (ParamValue valueNormalized, String128 string) const;
	bool fromString (const TChar* string, ParamValue& valueNormalized) const;

	void setIndexString (int32 index, const String128 str);
protected:
	~IndexedParameter ();
	String128* indexString;
};

//-----------------------------------------------------------------------------
class ScaledParameter : public BaseParameter
{
public:
	ScaledParameter (	const TChar* title,
						const TChar* units,
						int32 stepCount,
						ParamValue defaultValueNormalized,
						int32 flags,
						int32 tag,
						ParamValue minValue = 0.,
						ParamValue maxValue = 1.,
						bool printAsInteger = false,
						UnitID unitID = kRootUnitId);

	ParamValue toPlain (ParamValue valueNormalized) const;
	ParamValue toNormalized (ParamValue plainValue) const;

	void toString (ParamValue valueNormalized, String128 string) const;
	bool fromString (const TChar* string, ParamValue& valueNormalized) const;
protected:
	ParamValue minValue;
	ParamValue maxValue;
	bool printAsInteger;
};

}}} // namespaces

#endif

redpajama_set_name: RedPajamaGithub


LCL stands for Less Than Container Load; it is a term used for shipments that make use of partial container freight. If an entire container is used for a single shipment (FCL or Full Container Load), the volume of the cargo becomes extraneous since the client owns the whole container, while carrier would have a weight limitation for FCL. However, in a case where the consignment is small, requiring only partial container freight, a universal formula is applied for calculating LCL chargeable weight.


Valentine's Day is just around the corner and we've designed 10 Valentine photo and personalized cards for your kiddos to share with their classmates!
Gone are the days of giving those plain old ones from the drug store. Let's get creative with personalized Valentines! I know my boys keep theirs for months… and even put their favorite ones in their class box (a box of memories from each school year).
If your kiddos give one of these cards, they're sure to make their little friends feel special. Plus I'm sure Grandma and Grandpa would love one too!
I love the XOXO hugs and kisses card!
Wonder full….! I adore your XOXO hugs as well as smooches minute card!


text:
Q: ffmpeg command not executing properly after reformatting pc and installing the newest version I have a set of images from frame 1 to frame 1241 in frame step 10
and run the following in cmd:

ffmpeg -start_number  1 -framerate 25 -i
  C:\MATLAB-Source-Files/Result_Eye/result_%05d.jpg -vf
  framestep=10,setpts=N/FRAME_RATE/TB -c:v mpeg4
  C:\MATLAB-Source-Files/eye_result.mp4 -y

I intend to create an mp4 in frame step 10. It used to work before but now it seems to not cover the frames. I get a resulting mp4 but 0:00.
Here is the result of executing the command in cmd

redpajama_set_name: RedPajamaStackExchange


We're here to make it easy.
That's important to you because it means that we have fast, flexible underwriting.
We realize that it's easy to feel helpless in the mortgage process because most banks shuffle your file from one faceless department to the next. At Centennial you can deal directly with the people who process your loan. Regular people who take the time to explain what is going on and why.
And the whole process is fast. In fact, Centennial loans close in as little as two weeks. For whatever loan. For whatever reason. Centennial's business is helping you get the loan you need. As efficiently and effortlessly as possible.
For more information or for a free pre-approval, you can apply on-line and a mortgage professional will work with you to find a loan to fit your needs. And there is no obligation – so contact us now!
Centennial has great rates. Since we shop the market for you, we can offer the most competitive rates in the industry. So you won't lie awake at night wondering if you paid too much.
Centennial has a huge variety of loan products. We have loans with low down payments. Debt consolidation loans. Refinances with low or no equity. No-income verification loans. Fixed rates and adjustables (and loans in between). There's a great chance we have a loan for you.
At Centennial we're real people doing loans for real people. At most banks, the only person you speak with is your loan officer. Once your loan goes into their loan process it becomes "just another file." Faceless. Sterile. Your left to wonder what's going on. At Centennial, you'll speak with the people processing your loan. They'll keep you informed about your loans progress, in real people language instead of bank-speak. So you won't feel like you're in the dark. Better still, you'll feel like someone cares about you and your loan.Quick approvals, great rates, tons of loan programs and real people who actually care about you and your loan.
What more could you ask for in a mortgage company?
You've got a friend in the mortgage business.
For more information or a free pre-approval, you can apply online and a mortgage professional will work with you to find a loan to fit your needs. And there's no obligation – so contact us now!
The Home Equity Division has established itself as one of the leading mortgage brokers specializing in Equity loans.
Service, Pricing, Products, and Reputation are just a few reasons we are consistently chosen by our clientele. Our philosophy in lending is defined by our name.
It is our belief that our borrowers, who for one reason or another do not qualify under "conforming" guidelines are still credit-worthy borrowers. It is for this reason that we have designed a product menu which accommodates people with all types of credit and income situations from A to Z.
Centennial Mortgage is dedicated to the highest level of service at the most competitive rates in the industry. We have offices and loan originators who pride themselves in their relationship with you, the customer. Centennial Mortgage places millions of dollars each month in loans for our servicing area, which allows us to offer some of the lowest rates available.


I still miss Blueprint magazine. I own all nine issues, and I still refer back to them. Last month, Kirsten at Simply Grove blogged about a house tour originally featured in Blueprint, that is now available at Martha Stewart. So, I pulled out issue number three (the best issue, btw) and read the article. My favorite picture is the one above, I love that beach scene!
The oversize photo was take by the house's owner, Tosca Radigonda, at an Italian beach. Ms. Radigonda is a professional photographer, but a Google search leads me to the conclusion that this work isn't for sale.
All of my Google searching led me to the work of Massimo Vitali, who has made a name for himself with his own photographs of Italian beach scenes.
This photo is listed for 25,000 Euros at the gallery that represents him. I don't even want to convert that to US dollars. Actually, it would be cheaper for me to buy a fancy camera, fly to Italy, and have my photo enlarged. I find it so frustrating that a photograph can go for so much- was the negative destroyed? This is why people roll their eyes at high end art.


Seems these days you can swing a dead bat without hitting a creature of the night. Whether its the brooding Edward of Twilight fame or the brooding Bill of True Blood, vampires have never been hotter and the Blood Lust for these vamps has never been more intense.
The Blood Lust cocktail is an intense muddle of fresh berries and delicious berry liqueurs and based on our testing can be enjoyed safely in direct sunlight.
Muddle the berries together in the bottom of a cocktail shaker. Add the rest of the ingredients, shake, then strain into a chilled cocktail glass. Garnish with 2-3 maraschino cherries.
Muddle the strawberries, basil and simple syrup in the cocktail shaker. Add 3 cubes of ice, the rest of the ingredients and shake vigorously. Strain into a chilled martini glasses and garnish with a fresh strawberry.
Our Cinco de Mayo margarita posts continue. Next into the blender went the frozen strawberries… two drinks, lots of tequila and still all fingers intact.
Combine the frozen strawberries and sugar in the blender and pulse until they are chopped well. Next, stir the tequila, lime juice and triple sec in a separate glass. Gradually pour the mixture into the blender until the mixture is smooth. Pour the mixture into cocktail glasses rimmed with coarse salt. Garnish with a slice of lime.


text:
Crime & Fraud
SEC & Regulatory Bodies
Laws & Regulations Cybersecurity
What Is Biometrics?
Biometrics are a group of digital security methods that rely on biological or physiological attributes and that are used to prevent data breaches such as credit card hacks or unauthorized logins. Biometrics use criteria that are physically unique to an individual that can prove their identity, such as a fingerprint or voice pattern, rather than relying on passwords or PIN codes that can be more easily hacked or stolen.
Biometrics refers to digitally encoding physical attributes of a user to gain access to data or computer systems.
Fingerprints, facial recognition, and voice patterns are among some of the most widespread uses of biometrics today in both consumer and commercial use.
By using physical identifiers that are unique to individuals, biometrics seeks to make hacking or gaining unauthorized access to computer systems much more difficult.
Understanding Biometrics
One common method of employing biometrics is the use of fingerprints as identification. This system can be used in more high-tech or high-security situations, but it has more recently been adapted on the individual consumer level. For example, Apple was the first major phone manufacturer to implement a fingerprint login system, starting with its iPhone 5s, and other companies followed soon after. Other systems of biometrics include iris or retina scans and voice recognition software.
As the world starts relying more heavily on technology and sharing information electronically, data breaches are increasingly common. Well-known examples of corporations that have been targeted by hackers over the past few years include Target and Home Depot. Biometrics is one method of counteracting these breaches.
Biometrics and Protecting Financial Data
Since so many people rely on credit cards, online banking, and apps to transfer payments, it's essential to employ the use of techniques such as biometrics to prevent hacks, breaches, and fraud. Many banks and financial institutions use the technology inherent in newer phones by creating apps that require fingerprints to access data, and their individual biometrics systems become more advanced as the mobile technology develops. Customers can access secure banking information at the press of a finger.
More recently, companies have begun to invest in more varied forms of biometric technology for the mass market. Zoloz is in the process of developing systems that will allow participating companies to use facial recognition technology to secure customers' account information. Another company, Nymi, developed ECG-reading bracelets that detect a person's heartbeat, synchronize it with a digital device, and use that data to log in to a secure network through Bluetooth. Companies involved in funding this development include Mastercard and Relay Ventures.
The promise of using data unique to each person in order to keep information safe is an attractive idea to financial investors, since biometrics is synonymous with enhanced security. Biometrics is an evolving technology, and it holds promise for the future. However, if you are considering investing in a company and are taking into account its use of biometrics, be sure to research the exact technology in use.
Some forms of biometrics are more secure than others; some companies have more intelligent systems than others. It is worth looking into the company's history to see if the technology has ever been subject to controversy or even has been breached in the past. You should also take into account how well the particular biometric technology functions in the context of the company's operation.
Two-factor authentication (2FA) is a security system that requires two distinct forms of identification in order to access something.
Encryption is a means of securing digital data using an algorithm and a key.
Cyber and Privacy Insurance
Cyber and Privacy Insurance provide coverage from losses resulting from a data breach or loss of electronically-stored confidential information.
Cybersecurity refers to the measures taken to keep electronic information, as well as hardware and software, private and safe from damage or theft.
Credit Card Dump Definition
A credit card dump is a type of crime in which the criminal makes an unauthorized digital copy of a credit card.
Quick Response (QR) Code Definition
A quick response (QR) code is a type of barcode that stores information and can be read by a digital device, such as a cell phone.
Is It Safe to Send Money Through Facebook?
PayPal vs. Credit Card: Which Is Safer?
Interactive Brokers vs. E*TRADE
E*TRADE vs. TD Ameritrade
7 Ways to Protect Against Credit Card Hacks
Jobs With the Lowest Unemployment Rates


text:
Апостольский нунций в Румынии — дипломатический представитель Святого Престола в Румынии. Данный дипломатический пост занимает церковно-дипломатический представитель Ватикана в ранге посла. С 1998 года, апостольский нунций в Румынии является дуайеном дипломатического корпуса. Апостольская нунциатура в Румынии была учреждена на постоянной основе 16 июня 1920 года, дипломатические отношения прерваны в 1946 году, восстановлены 15 мая 1990 года.

В настоящее время Апостольским нунцием в Румынии является архиепископ Мигель Маури Буэндиа, назначенный Папой Франциском 5 декабря 2015 года.

История
До установления дипломатических отношений между Румынией и Святым Престолом в 1920 году, римско-католические архиепископы Бухареста служили в качестве представителей Верховного понтифика перед румынскими властями. Апостольская нунциатура в Румынии была учреждена на постоянной основе 16 июня 1920 года, папой римским Бенедиктом XV, с резиденцией в столице Румынии — городе Бухаресте. 12 июня 1920 года первый посол Румынии при Святом Престоле, Димитрий Пенеску, вручил свои верительные грамоты в Ватикане.

С приходом коммунистического правительства в конце Второй мировой войны дипломатические отношения между Святым Престолом и Румынией были прерваны. Апостольский нунций Андреа Кассуло был объявлен «персоной нон грата» румынскими коммунистическии властями. Между 1947-1950 годами Апостольскую нунциатуру, в ранге временного поверенного в делах возглавлял епископ Джеральд Патрик О'Хара, до своей высылки в 1950 году. 7 июля 1950 года, Румынская Народная Республика в одностороннем порядке разорвала дипломатические отношения со Святым Престолом.

Дипломатические отношения были восстановлены 15 мая 1990 года после крушения коммунистического режима. Резиденцией апостольского нунция в Румынии является Бухарест — столица Румынии. Апостольский нунций в Румынии с 22 марта 2003 года, по совместительству, исполняет функции апостольского нунция в Молдавии.

Правительство Чорбя, с 1 января 1998 года, ратифицировала нормы международного права, по которым Апостольский нунций является дуайеном дипломатического корпуса, аккредитованного в стране, где он находится.

Апостольские нунции в Румынии
 Франческо Мармаджи — (1 сентября 1920 — 30 мая 1923 — назначен апостольским нунцием в Чехословакии);
 Анджело Мария Дольчи — (30 мая 1923 — 13 марта 1933, в отставке);
 Валерио Валери — (1 июля 1933 — 11 июля 1936 — назначен апостольским нунцием во Франции);
 Андреа Кассуло — (14 июня 1936 — 1947 — назначен апостольским делегатом в Турции);
 Джеральд О'Хара — (1947 — 1950) — временный поверенный в делах;
 Дипломатические отношения прерваны (1950 — 1990);
 Джон Буковски, S.V.D. — (18 августа 1990 — 24 декабря 1994 — назначен апостольским нунцием в России);
 Януш Болонек (23 января 1995 — 30 сентября 1998 — назначен официалом Государственного секретариата Ватикана);
 Жан-Клод Периссе (12 ноября 1998 — 15 октября 2007 — назначен апостольским нунцием в Германии);
 Франсиско Хавьер Лосано Себастьян — (10 декабря 2007 — 20 июля 2015, в отставке);
 Мигель Маури Буэндиа — (5 декабря 2015 — по настоящее время).

См. также
 Католицизм в Румынии.

Ссылки
 Апостольская нунциатура в Румынии на gcatholic.org.

Румыния

Католицизм в Румынии


text:
namespace Microsoft.Azure.Management.Network.Fluent.Models
{
    using Management.ResourceManager;
    using Management.ResourceManager.Fluent;
    using Management.ResourceManager.Fluent.Core;

    using Newtonsoft.Json;
    /// <summary>
    /// Defines values for ApplicationGatewaySslCipherSuite.
    /// </summary>
    /// <summary>
    /// Determine base value for a given allowed value if exists, else return
    /// the value itself
    /// </summary>
    [JsonConverter(typeof(Management.ResourceManager.Fluent.Core.ExpandableStringEnumConverter<ApplicationGatewaySslCipherSuite>))]
    public class ApplicationGatewaySslCipherSuite : Management.ResourceManager.Fluent.Core.ExpandableStringEnum<ApplicationGatewaySslCipherSuite>
    {
        public static readonly ApplicationGatewaySslCipherSuite TLSECDHERSAWITHAES256CBCSHA384 = Parse("TLS_ECDHE_RSA_WITH_AES_256_CBC_SHA384");
        public static readonly ApplicationGatewaySslCipherSuite TLSECDHERSAWITHAES128CBCSHA256 = Parse("TLS_ECDHE_RSA_WITH_AES_128_CBC_SHA256");
        public static readonly ApplicationGatewaySslCipherSuite TLSECDHERSAWITHAES256CBCSHA = Parse("TLS_ECDHE_RSA_WITH_AES_256_CBC_SHA");
        public static readonly ApplicationGatewaySslCipherSuite TLSECDHERSAWITHAES128CBCSHA = Parse("TLS_ECDHE_RSA_WITH_AES_128_CBC_SHA");
        public static readonly ApplicationGatewaySslCipherSuite TLSDHERSAWITHAES256GCMSHA384 = Parse("TLS_DHE_RSA_WITH_AES_256_GCM_SHA384");
        public static readonly ApplicationGatewaySslCipherSuite TLSDHERSAWITHAES128GCMSHA256 = Parse("TLS_DHE_RSA_WITH_AES_128_GCM_SHA256");
        public static readonly ApplicationGatewaySslCipherSuite TLSDHERSAWITHAES256CBCSHA = Parse("TLS_DHE_RSA_WITH_AES_256_CBC_SHA");
        public static readonly ApplicationGatewaySslCipherSuite TLSDHERSAWITHAES128CBCSHA = Parse("TLS_DHE_RSA_WITH_AES_128_CBC_SHA");
        public static readonly ApplicationGatewaySslCipherSuite TLSRSAWITHAES256GCMSHA384 = Parse("TLS_RSA_WITH_AES_256_GCM_SHA384");
        public static readonly ApplicationGatewaySslCipherSuite TLSRSAWITHAES128GCMSHA256 = Parse("TLS_RSA_WITH_AES_128_GCM_SHA256");
        public static readonly ApplicationGatewaySslCipherSuite TLSRSAWITHAES256CBCSHA256 = Parse("TLS_RSA_WITH_AES_256_CBC_SHA256");
        public static readonly ApplicationGatewaySslCipherSuite TLSRSAWITHAES128CBCSHA256 = Parse("TLS_RSA_WITH_AES_128_CBC_SHA256");
        public static readonly ApplicationGatewaySslCipherSuite TLSRSAWITHAES256CBCSHA = Parse("TLS_RSA_WITH_AES_256_CBC_SHA");
        public static readonly ApplicationGatewaySslCipherSuite TLSRSAWITHAES128CBCSHA = Parse("TLS_RSA_WITH_AES_128_CBC_SHA");
        public static readonly ApplicationGatewaySslCipherSuite TLSECDHEECDSAWITHAES256GCMSHA384 = Parse("TLS_ECDHE_ECDSA_WITH_AES_256_GCM_SHA384");
        public static readonly ApplicationGatewaySslCipherSuite TLSECDHEECDSAWITHAES128GCMSHA256 = Parse("TLS_ECDHE_ECDSA_WITH_AES_128_GCM_SHA256");
        public static readonly ApplicationGatewaySslCipherSuite TLSECDHEECDSAWITHAES256CBCSHA384 = Parse("TLS_ECDHE_ECDSA_WITH_AES_256_CBC_SHA384");
        public static readonly ApplicationGatewaySslCipherSuite TLSECDHEECDSAWITHAES128CBCSHA256 = Parse("TLS_ECDHE_ECDSA_WITH_AES_128_CBC_SHA256");
        public static readonly ApplicationGatewaySslCipherSuite TLSECDHEECDSAWITHAES256CBCSHA = Parse("TLS_ECDHE_ECDSA_WITH_AES_256_CBC_SHA");
        public static readonly ApplicationGatewaySslCipherSuite TLSECDHEECDSAWITHAES128CBCSHA = Parse("TLS_ECDHE_ECDSA_WITH_AES_128_CBC_SHA");
        public static readonly ApplicationGatewaySslCipherSuite TLSDHEDSSWITHAES256CBCSHA256 = Parse("TLS_DHE_DSS_WITH_AES_256_CBC_SHA256");
        public static readonly ApplicationGatewaySslCipherSuite TLSDHEDSSWITHAES128CBCSHA256 = Parse("TLS_DHE_DSS_WITH_AES_128_CBC_SHA256");
        public static readonly ApplicationGatewaySslCipherSuite TLSDHEDSSWITHAES256CBCSHA = Parse("TLS_DHE_DSS_WITH_AES_256_CBC_SHA");
        public static readonly ApplicationGatewaySslCipherSuite TLSDHEDSSWITHAES128CBCSHA = Parse("TLS_DHE_DSS_WITH_AES_128_CBC_SHA");
        public static readonly ApplicationGatewaySslCipherSuite TLSRSAWITH3DESEDECBCSHA = Parse("TLS_RSA_WITH_3DES_EDE_CBC_SHA");
    }
}


LSL: Impossible to enter "Number 9"
phone number mentioned in the game 555-6969.
Ticket imported from: #1751385. Ticket imported from: bugs/3309.
lsl1.004 (1.2 KB) - added by SF/vorph999 12 years ago.
Impossible to enter "Number 9" → LSL: Impossible to enter "Number 9"
What kind of computer / platform are you playing on?
Fixed in SVN. A silly bug that was introduced in a recent change of the keyboard handling in ScummVM. Thanks for reporting it!


text:
Boehner moves GOP closer to shutdown fight over immigration
The new Congress has only just begun and Republican leaders are already debating how to handle a looming shutdown fight over immigration.
Speaker of the House John Boehner, R-Ohio, holds his weekly press conference in the Capitol on Jan. 8, 2015 in Washington, D.C.
Jan. 8, 2015, 7:14 PM UTC / Updated Jan. 8, 2015, 8:48 PM UTC
By Benjy Sarlin
Funding for the Department of Homeland Security expires Feb. 27 and conservatives want any new legislation extending the department's cash line to include language stopping President Obama's plan to protect millions of undocumented immigrants from deportation. The White House indicated last month that it would veto measures blocking the president's executive action, which would lead to a partial government shutdown if Congress doesn't back down.
Speaker John Boehner headed off a larger shutdown in December by passing legislation funding the government through September, except for DHS, which handles immigration enforcement, among other things. At the time, he argued Republicans would be in a better position to win a fight once the new, majority-GOP Senate was sworn in. On Thursday, he assured members that his plan to challenge Obama has not changed, even in the wake of a terrorist attack on Paris that's put national security issues back on the front page.
"I said we would fight it tooth and nail when we had the majority and I meant it," Boehner said in a weekly press briefing.
The immigration dispute is designed almost perfectly to divide the GOP. Senate Majority Leader Mitch McConnell has said he wants to keep the party from appearing "scary" and House Republican leaders have indicated they want to be known for more than constant high-stakes confrontations with the White House. A number of Republicans, although increasingly marginalized, have urged the party to focus on passing immigration reform instead of demanding more deportations. But Boehner, who put down a significant rebellion from right-leaning members to win his post this week, is under intense pressure to confront Obama with every tool at his disposal.
RELATED: Congress reelects Boehner as speaker on first day of new session
Reading the tea leaves on how far Republicans are willing to go to stop Obama can be difficult, in part because all sides insist they're against shutting down the department and leaders have yet to put forward a formal plan. Proponents of attaching the immigration language argue it's Obama's fault if DHS goes dark because he and Democrats refused to accede to their legitimate demands.
"I don't believe that the funding of the department is, in fact, at risk," Boehner said Thursday. "What is at risk is the rule of law and the sanctity of America's Constitution."
It's clear from public statements, however, that some Republicans are uneasy with using Homeland Security funding as a bargaining chip in the immigration fight, especially after this week's deadly attack on a satirical newspaper in Paris. If things come to a head, they could potentially push leadership towards splitting the two issues across separate measures, which would all but guarantee a defeat on immigration but avoid a politically explosive shutdown.
"I don't believe that the funding of the department is, in fact, at risk. What is at risk is the rule of law and the sanctity of America's Constitution."'
"Whatever we do on that, as far as immigration, cannot in any way be allowed to interfere with our counterterrorism methods," Congressman Peter King, chairman of the House Homeland Security Subcommittee on Counterterrorism and Intelligence, said on Fox News Wednesday. "I mean, the juxtaposition would be terrible: a terrorist slaughter in Paris, and the U.S. cuts back on Homeland Security funding."
Senator Lindsey Graham of South Carolina, a prominent hawk, warned Republicans the same day that they should tread carefully.
"I hope that we could challenge the executive action of the president in a mature fashion," Graham told CNN. "I've never been for shutting down Homeland Security."
Asked whether the Paris attack affected his calculus, Boehner took issue with the notion Republicans were threatening to defund the agency.
"The issue isn't about funding the Department of Homeland security," he said. "Members of Congress support funding the department, but we cannot continue to allow the president to go around the Congress, to go around the law and take unilateral action."
House Homeland Security Committee Chairman Michael McCaul said on Wednesday that Republican leaders were still working out a strategy to keep DHS running while separately maintaining the fight against President Obama's executive actions on immigration.
"We want to stop this executive action, but I think the responsible individuals like myself have no desire to shut down this department," McCaul said on CNN Wednesday night. "It's too important to the national security interest of the United States."
McConnell agreed Wednesday that the department must remain open.
"It's an important piece of legislation and we'll decide in February how to handle it, but at the end of the day we're going to fund the department, obviously," McConnell told reporters.
RELATED: Eight tough issues for Congress to tackle in 2015
Republicans have some leeway here in that much of Homeland Security's staff would likely be deemed "essential" in the case of a shutdown, meaning they would continue working regardless. But Homeland Security Secretary Jeh Johnson warned on Wednesday that Congress' decision to fund the department with a short continuing resolution rather than a longer-term bill is already having negative effects.
"We cannot continue through the course of the year to function on a continuing resolution," Johnson told reporters. "That poses real risk to homeland security."
Amanda Sakuma contributed to this report.


text:
Just 14.2% of health professionals feel prepared to tackle COVID-19, survey reveals
The interviewers found that 91.25% of community health agents and endemic disease control agents are afraid of catching the disease. Among nursing professionals and doctors, the numbers are 84.31% and 77.68%, respectively.
The Bureaucracy Studies Center (NEB) at Fundação Getulio Vargas' Sao Paulo School of Business Administration (FGV EAESP) recently carried out a survey called "The COVID-19 Pandemic and Public Health Professionals in Brazil." Among other results, it was found that just 14.2% of interviewed health sector professionals feel prepared to deal with COVID-19. Most of them (64.97%) said they are unprepared, while the remainder declined to answer the question.
The survey also found that health professionals in Brazil's North and Northeast regions feel the most helpless. Breaking down professionals by groups, community health agents and endemic disease control agents feel the least prepared. Just 7.61% of them feel ready to tackle the crisis. The figure for nursing professionals – just 20.09% – is also worryingly low.
According to the survey, more than 55% of health professionals know a confirmed or suspected COVID-19 patient. As a result, fear is a common feeling among these people, regardless of where they live or their level of medical care. The interviewers found that 91.25% of community health agents and endemic disease control agents are afraid of catching the disease. Among nursing professionals and doctors, the numbers are 84.31% and 77.68%, respectively.
"This survey shows that the professionals on whom we depend the most to tackle the pandemic are in a state of extreme vulnerability. There are shortages of personal protective equipment, there isn't enough information and support from the government, and most people do not feel well placed to deal with the crisis. This puts these professionals in a very fragile situation, as they need to be on the front line, but they are afraid that they may get sick and become a vector of infection," says Gabriela Lotta, NEB's coordinator and FGV EAESP professor.
Personal protective equipment and training
According to the survey, just 32% of professionals said they had been given personal protective equipment. Among community health agents and endemic disease control agents, the figure was a mere 19.65%.
"Working without the proper PPE creates a very high risk of infection, for both professionals and patients. It also makes professionals feel more insecure and feeds hostility among patients," Lotta explains.
With regard to governmental support, more than half of the interviewees said they feel unsupported by the government. The number is 67% when it specifically refers to the federal government and 51% in the case of state governments. Regarding support from managers, 71.82% of professionals said they haven't perceived any backing of this kind. Just 21.91% said they have received training, and most of them were physicians.
"The way professionals are being exposed without any support, equipment and information is very serious. It is as if they had been sent to war blindfolded and unarmed. If the state cannot look after its own workers, how can we expect it to look after the population? We are reliant on acts of heroism," Lotta says.
Relationship with patients
The survey also looked at how the crisis has altered work processes and interactions between professionals and patients. Three out of four interviewees said that the crisis has altered their routines, in terms of work flows, procedures, different priorities and new technologies, among other changes.
Furthermore, 88% of professionals said that the crisis has altered the way they interact with patients. The biggest impact is related to physical distancing.
"These issues are very important for health, especially primary care, in which everyday contact and physical touch are key to building bonds with families. One community health agent said it is terrible not to be able to take hold of a patient's hand and say that everything will be OK. So, the crisis has major impacts that go beyond COVID-19, affecting the way professionals interact with patients," Lotta says.
The online survey covered 1,456 public health professionals, at all levels of care and in all the country's regions, between April 15 and May 1, 2020.
In terms of gender, 79% of the participants were women, 19.6% were men and fewer than 1% preferred not to say. Regarding length of career, 64.84% of the professionals have been working in their respective field for more than 10 years and 65% have long-standing ties with the location where they work or they were born in the region.
Superior Electoral Court acknowledges FGV's contribution to fight against disinformation and fake news in 2020 elections
Master's in Public Policy and Government now accepting applications for 2021 selection
Book on post-pandemic international order to be published by London-based Routledge
FGV Energy and ANP present guide to offshore decommissioning
International study evaluates how banks helped long-standing clients during COVID-19 crisis
IPC-S slows down in second week of January
FGV to represent Brazil in Venture Capital Investment Competition for second consecutive time


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Hana Machi – by SAKI&Bitches @ Sway Gallery
Hana Machi – by SAKI&Bitches
PRIVATE VIEW: 13 Sep 18-20
In a celebration of heritage and the power of a woman's sexuality, 'Hana Machi' is an intimate exhibition by Japanese-born, London-based painter, SAKI&Bitches.
SAKI&Bitches is Japanese Street artist/painter/illustrator currently based in London.
She has a background in the fashion industry, having worked as a make-up artist for seven years in Tokyo, New York and London before switching from faces to the canvas.
As a self-taught artist, SAKI's work/style grow from doodles on the back of scrap pieces of paper to full-scale portraits.
She's playing around with multiple mediums over the years; paint, pencils, stencils, woodcraft and some sculpting.
Her work is unintentionally controversial and erotically sweet, it's plain twisted.
Her glamorous paintings keep flirting around London!
Website: www.sakiandb.com
instagram: @sakiandbitches
An art exhibition in support of National Ugly Mugs (NUM).
Part of the art sales will be donated to NUM.
Their mission: ending violence against sex workers
Categories: Art & Culture Charity Exhibitions
Brave Blossoms: the History of Rugby in Japan @ World Rugby Museum, Twickenham Stadium
Nov 17 2018 – Aug 31 2019 all-day
Explore the full history of the birth and evolution of rugby union in Japan, from 1866 when Yokohama became the site of the oldest rugby club in Asia, to being awarded Rugby World Cup 2019.
This special exhibition, produced in partnership with England Rugby Travel and Kanto District Transport Bureau, charts the evolution of rugby alongside the development of modern Japan. How 'the father of Japanese rugby', Ginosuke Tanaka, brought the sport to Keio University and how a series of sporting pioneers, including members of the Japanese Royal Family, allowed the sport to flourish. It will examine how Japanese corporations have nurtured the game at a domestic level allowing Japanese rugby to make significant impacts at international level, and culminating in the famous victory the Brave Blossoms achieved over the Springboks at Rugby World Cup 2015 and the awarding of Rugby World Cup 2019 to Japan.
World Rugby Museum adult ticket £12.50. Concessions available.
Categories: Exhibitions Sports