text:
\section{Evaluation of Representative Models}
One of the objectives of this work is to build resources and bias measurement models in dialog scenarios.
Hence, we present the evaluation of social bias risks of three representative dialog systems and one popular language model using both the developed automatic classifier and human evaluation.

\subsection{Evaluated Models}
We evaluate the following public Chinese pre-trained dialog systems and a language model.
\begin{itemize}[align = left, wide = 1pt, itemsep=1pt, parsep=2pt,topsep = 2pt ]

\item \textsc{CDial-GPT}~\cite{wang2020chinese} trains a dialog model with 104M parameters on a cleaned Chinese dialog dataset \textit{LCCC} (12M dialog sessions).
\item \textsc{EVA}~\cite{coai2021eva} is the largest Chinese open-source pre-trained dialog model (2.8B parameters) trained on WDC-Dialog corpus with 1.4B context-response pairs.
\item \textsc{EVA2.0}~\cite{coai2022eva2} has the same model structure with \textsc{EVA}. But it is trained on a 60B dialog dataset cleaned for context-response relevance, fluency, and entertainment tendency.
\item \textsc{CPM}~\cite{zhang2021cpm} is a Chinese pre-trained language model using 100GB of training data with 2.6B parameters.
We follow \citeauthor{zhang2021cpm} to condition the language model on chit-chat scenarios with conversational prompts.
\end{itemize}
For these evaluated models, we use the $262$ contexts from our test set as input and generate ten responses for each context with different random seeds.
We then evaluate the context-response pairs using the best-performing \textsc{Multi-task} classifier (see \S~\ref{sec:clf}).
Also, we randomly sampled $100$ test cases with different contexts for each model and manually labeled the portion of \textit{Biased} responses.

\input{Figures/eval}
\subsection{Results}
We present the automatic and human evaluation results in Figure~\ref{fig:eval}.
The ratios of \textit{Biased, Neutral}, and \textit{Anti-Bias} responses of each generative model are shown as different colored bars, while the human evaluation results are presented as magenta dots.

In general, the classifier and human evaluation results show similar trends, which justifies the reliability of the classifier.
All of these generative models show a non-negligible
\add{tendency to bias to varying degrees}.
We then analyze their performances in detail.

\textsc{EVA} and \textsc{CDial-GPT} generate relatively fewer biased responses compared to the other two models, yet they also tend to generate more irrelevant responses.
In human evaluation, we find that they both tend to avoid the discussion and generate trivial responses.
For example, \textsc{CDial-GPT} answer $13$ out of $100$ sample contexts with \textit{``I don't know.''}, and such responses will be labeled as \textit{Irrelevant} (to bias) by the classifier.

Both \textsc{CPM} and \textsc{EVA2.0} have higher bias response ratios, and their responses relevance is also higher.
\textsc{CPM} also generates trivial responses like \textit{``Alright.''} or \textit{``haha.''}. We find that a large portion of its responses is still quite offensive towards the discussed groups, which results in the second-high bias level.
Benefiting from the data relevance filtering strategy, \textsc{EVA2.0} seldom generates trivial responses and usually provides informative sentences.
Meanwhile, it also suffers most from generating \textit{Biased} statements.

Altogether, we find that dialog safety w.r.t. bias and response relevance of existing models are contrasting. A more capable system that can generate highly relevant responses might trigger unsafe responses more easily.
Therefore, we contend that it is not enough to build a dialog system by only focusing on common quality factors, such as response relevance and consistency, without constraints on more influential safety factors such as bias, offensiveness, and many others.
Serving as a direct interface to users, dialog systems can greatly harm the user experience and even endanger society by conveying biased opinions.
However, current research rarely takes the bias issue into consideration.
There is an urgent need to minimize such risks for developing and deploying more reliable systems.

\section*{\add{Ethical Considerations}}
In this work, we propose a pioneering resource and a novel benchmark for Chinese dialog social bias detection. However, we acknowledge the following limitations in our work that may lead to ethical issues.
\paragraph{\add{Data Collection Issues}}
Firstly, we ensure that the collected data is \textbf{legal to use} according to the Zhihu terms\footnote{\url{https://www.zhihu.com/term/zhihu-terms}}:``\textit{Information posted by users through Zhihu is public information, and other third parties can access the information posted by users through Zhihu.}''
Secondly, we ensure that the research subject in this work is not human. This work does not need \textbf{ethics approval}, in the region of where it is conducted.
Lastly, we use two methods to ensure the data does not contain any \textbf{private information}: 1) we did not include any account information during the data collecting procedure to keep anonymous; 2) we cleaned the potential private information such as emails, id numbers, etc. to further ensure privacy.

\paragraph{Data Coverage}
Though widely explored the Chinese social media before devising the scope of data crawling, we are mindful that this work has limited coverage of existing social bias.
There may be a bunch of un-discussed social biases on uncovered social groups in the proposed dataset.
Consequently, the detectors trained on this dataset may have unpredictable behavior on data related to such groups.

\paragraph{Potential Mis-annotation}
Recently work revealed that bias underlying the annotation process can be enlarged by the system \cite{sap2021annotators}.
To avoid such annotation biases, we designed a strict annotation process and hire annotators with various demographics.
However, we also acknowledge that there may be a portion of stealthy misleading annotations in this dataset.
\add{We are aware that asking annotators to specify the reason why some utterances are biased can reduce mis-annotation~\cite{sap2020social}, yet it also requires high annotation costs.
We consider this direction as our future work.}
Additionally, though we manage to ensure diversity of annotators, this work still requires native Chinese speakers for annotation.
All the annotators are from the People's Republic of China with similar cultural backgrounds.
The understanding of biases may inevitably have some differences among populations and cultures~\cite{schmidt2017survey,Ung2022saferdialogues}.

\paragraph{Potential Misuse}
The proposed dataset aims to facilitate research in detecting and migrating social bias in dialogue systems.
We realize that it can also be misused in malicious scenarios such as creating more biased dialog systems.
We appeal for more socially responsible research in this field and believe that this work provides more value than risks for studying social bias in dialog systems.

\section*{Limitations}
In the above Ethical Consideration section, we claim that this work may have limitations in data coverage, potential mis-annotation, and potential misuse.
Apart from these ethical issues, we are also mindful that this work may have the following limitations.

\paragraph{Lack of Reliable Baseline}
As a pioneer work in dialog social bias measurement, this work lacks well-aligned prior research and reliable baselines to compare with.
We devise the first conceptual bias identifying framework \textsc{Dial-Bias Framework}\xspace based on the previous research in the field of social bias in the general NLP field and the emerging topic of dialog safety.
The \textsc{CDial-Bias Dataset}\xspace is also the first well-annotated dataset in Chinese dialog social bias, therefore, we only compared our work with off-the-shelf APIs.

\paragraph{Unbalanced label distribution}
We are mindful that the proposed dataset is unbalanced in label distribution.
Specifically, the \textit{Anti-Bias} class merely takes up $1.6\%$ in the total dataset.
However, we claim that this imbalance indeed reflects the distribution in a real online community.
We hope this work can shed light on this imbalance problem and also call for special considerations for the minority \textit{Anti-Bias} data towards building more socially responsible dialog systems.

\subsection{Data Source}
We crawl and build conversational data related to social bias from a Chinese question-and-reply website Zhihu \footnote{\url{www.zhihu.com}}.
Each data entry is a two-turn conversation in the form of a question-reply pair.
To collect content related to social bias, we restrict the scope of data crawling by searching a list of representative and most widely discussed keywords (in Appendix ~\ref{sec:data_subtopic}) under four common social bias categories (i.e. topics) including \textit{Race}, \textit{Gender}, \textit{Region}, and \textit{Occupation}.
\add{Note that to ensure the data coverage is not restricted to the listed groups, we also include some umbrella words like \textit{Regional Discrimination}, \textit{Discrimination against men}, etc. Therefore the dataset contains more groups than pre-defined.}

\input{Tables/data-v2}
\subsection{Human Annotation}
We devise our human annotation guideline based on the proposed \textsc{Dial-Bias Framework}.
Given each data entry, the annotator is asked to answer four sequential questions and get four labels as illustrated in Figure \ref{fig:frame_example}.
We provide the annotation interface and detailed questions in Appendix \ref{sec:anno_ui}.

We employ crowd-sourcing workers and report their detailed demographics in Appendix \ref{sec:annotator}.
Each data entry is labeled by at least three annotators.
To avoid missing any data that may potentially offend certain groups, we adopt the \textit{Biased} label as long as one annotator fires an alarm and keep all the specified targeted groups.
For other labels, we reserve the most voted ones.

We measure the Inter Annotator Agreement by Krippendorf's alpha $k$.
Compared with related resources \cite{sun2021safety}, \textit{context-sensitivity} and \textit{data type} labels have acceptable $k$ scores ($45.89$, $53.96$).
The \textit{bias attitude} label achieves $74.7$ $k$ score, which indicates that the proposed framework effectively reduced the ambiguity in the bias identification process.
For the \textit{targeted group} label, annotators give the same answer for 90.41\% data.
We present the detailed annotation statistics for
the proposed dataset in Table~\ref{tab:anno_stat}.

\section{Social Bias Measurements}
\label{sec:Exp}
The \textsc{Dial-Bias Framework}\xspace and the \textsc{CDial-Bias Dataset}\xspace aim to nurture more research to identify social bias in dialog systems.
With these resources, we study the following research questions:
\begin{enumerate}[label=  \textbf{RQ\arabic*}:, align = left, wide = 1pt, itemsep=2pt, parsep=2pt,topsep = 2pt ]
\item \textit{How to perform fine-grained dialog bias measurement with auxiliary labels?}
\item \textit{How does context influence the bias measurement task?}
\item \textit{How do different bias topics correlate to each other? }
\end{enumerate}

\subsection{Problem Definition}
We define the fine-grained dialog bias measurement task as follows.
Given a two-turn dialog $d_i$ including a context $c_i$ and a response $r_i$, we aim to predict the bias label $y_{bias}$ of $r_i$, in the categorisation of: $0$-\textit{Irrelevant}, $1$-\textit{Anti-bias}, $2$-\textit{Neutral}, and $3$-\textit{Biased}.

Specially, each response has four auxiliary labels, including three annotated via \textsc{Dial-Bias Framework}\xspace: a two-way context-sensitivity label $y_{ctx}$ ( $0$-\textit{Context-Independent} and $1$-\textit{Context-Sensitive}), a three-way data type label $y_{dt}$ ($0$-\textit{Irrelevant}, $1$-\textit{Bias-Discussing}, and $2$-\textit{Bias-Expressing}), and a targeted group label $y_{group}$, and one topic label $y_{tpc}$ ($0$-\textit{Race}, $1$-\textit{Gender}, $2$-\textit{Region}, and $3$-\textit{Occupation}) assigned through the data collection procedure.
To simulate the real scenario, all these auxiliary labels are unavailable during the test phase.

\paragraph{Classifiers}
For all the experimented classifiers, we adopt the pre-trained \texttt{Bert-Base-Chinese}\footnote{\url{https://huggingface.co/bert-base-chinese}} model to encode the input and Fully Connected~(FC) layer(s) for label prediction.\footnote{Training details are attached in Appendix~\ref{sec:parameters}.}

\subsection{RQ1: Utilizing Rich Annotations}
\label{sec:RQ1}
Firstly, we explore that except for facilitating the annotation process, can the auxiliary labels ($y_{ctx}$,  $y_{dt}$, and $y_{tpc}$) be utilized to boost the performance of the bias measurement task?
Note that the targeted group label is not included here as it is written in free texts and is not suitable for a classifier to predict. The utilization of this feature will be left as future work.

\subsubsection{Methods}
\label{sec:clf}

To investigate this problem, we devise below three methods.
These methods all take $c_i$ and $r_i$ (with a [SEP] token) as input but vary in model structures.
\vspace{-2mm}
\paragraph{\textsc{Vanilla}}
The \textsc{Vanilla} model simply adopts one FC layer as the classification head and predicts the bias label $\tilde{y}_{bias}$ without using auxiliary labels. The following two methods utilize auxiliary labels in different manners.

\paragraph{\textsc{Mixture-of Experts (MoE)}}
It builds $24$ experts with $24$ FC layers for data with different auxiliary label combinations ($2$ context-sensitivities, $3$ data types, and $4$ topics) in a mixture-of-expert manner~\cite{Saeed2014MoE}.
To aggregate the final prediction $\tilde{y}_{bias}$ from these $24$ experts in a soft manner, a linear layer is applied with output size $24$, and its input is the concatenation of outputs of three additional classifiers predicting auxiliary labels: context-sensitivity $\tilde{y}_{ctx}$, data type $\tilde{y}_{dt}$, and topic $\tilde{y}_{tpc}$, respectively.
We provide supervised learning for these four labels during the training procedure.

\vspace{-2mm}
\paragraph{\textsc{Multi-task}}
As $\tilde{y}_{bias}$ is based on predictions of the three auxiliary labels, the \textsc{MoE} model may suffer from error propagation.
Therefore, we adopt a more straightforward multi-task learning model for this task.
This model adopt four parallel FC layers to predict $\tilde{y}_{ctx}$,  $\tilde{y}_{dt}$, $\tilde{y}_{tpc}$, and $\tilde{y}_{bias}$, and optimise them with equal weight.

\vspace{-2mm}
\paragraph{Off-the-shelf APIs}
To the best of our knowledge, there is a lack of Chinese bias resources that align well with this task.
Therefore, we compare the following two APIs that correlate with certain categories.

\textbf{BD-Cens}, the Baidu text censor API\footnote{\url{https://ai.baidu.com/tech/textcensoring}} flags the toxic online texts.
We record the flagged texts as \textit{Biased} and report the F1 score of this category.

\textbf{BD-Dial}, the Baidu dialog emotion detection API\footnote{\url{https://ai.baidu.com/tech/nlp_apply/emotion_detection}} that categorizes dialog data into positive, neutral, and negative sentiments, which can roughly match with the three implied bias attitudes (class 1, 2 and 3).
We test it on bias-related data and report the F1 scores on these three categories.

\add{\noindent\textbf{\textsc{Random}} A random classifier is also adopted for comparison, which randomly samples a label subject to the label distribution.}

\subsubsection{Results}
We report F1 scores on each bias category and the overall weighted F1 score (weighted by class sizes) in Table~\ref{tab:NewRQ1}.
Firstly, the three proposed bias classifiers trained on the \textsc{CDial-Bias Dataset} largely outperform existing APIs ({BD-Cens/Dial}) \add{and \textsc{Random}} by achieving much higher F1 scores on the \textit{Biased} category.
We \add{assert} that general APIs do not align well with the fine-grained dialog bias measurement task.
Secondly, we compare the performances between the \textsc{Vanilla} model and the other two classifiers.
Results show that the \textsc{Multi-task} model achieves the highest weighted F1 score ($63.90$) and performs best in the \textit{Biased} category ($59.87$).
The \textsc{MoE} model also slightly outperforms the \textsc{Vanilla} model.
We conclude that auxiliary labels can assist in completing the bias measurement task.

We further analyze the performance of the auxiliary classifiers.
The accuracy of $\tilde{y}_{ctx}$,  $\tilde{y}_{dt}$, and $\tilde{y}_{tpc}$ are $69.69 / 66.73 / 99.96$ for \textsc{MoE}
and $68.24 / 67.08 / 99.75$ for \textsc{Multi-task}.
The low accuracy scores of $\tilde{y}_{ctx}$ and  $\tilde{y}_{dt}$ may hinder the performances of both \textsc{MoE} and \textsc{Multi-task}, and there are still room for improvements.

\begin{table}[tb!]
\centering
\small
\setlength{\tabcolsep}{5pt}
    \begin{tabular}{l|c|cccc}
    \toprule
   Model &  W F1 & Irr. & Anti. & Neu. & Biased \\ \midrule

   BD-Cens    & - & - &- & - & 13.9 \\%\hline
   BD-Dial    & - & - &4.00 & 68.72 & 11.93\\\midrule
   \textsc{\add{Random}} & 35.15 & 43.95 & 0.00 & 31.75 & 26.97 \\\midrule
   \textsc{Vanilla} & 63.07 & 72.93 & 35.29 & 55.64 & 57.22 \\
   \textsc{MoE} & 63.37 & 73.51 & 27.69 & 54.56 & 57.75 \\
   \textsc{Multi-task} & \textbf{63.90} & 73.67 & 31.88 & 55.25 &\textbf{ 59.87} \\

    \bottomrule
    \end{tabular}
    \caption{Weighted F1 scores (W F1) and F1 scores on each category of the APIs and models.}
    \label{tab:NewRQ1}
\vspace{-3mm}
\end{table}
\subsection{RQ2: Influence of Context}
\label{sec:RQ2}
In this subsection, we investigate how context influences the bias measurement task in the dialog scenario.
Specifically, we study two sub-questions:
\textit{1. Is it beneficial to include context information?
2. Is it essential to distinguish \textit{Context-Independent} and \textit{Context-Sensitive} cases?}

\subsubsection{Methods}
We split the training set into two parts: \textit{Context-Independent} data $CI(c, r)$ and \textit{Context-Sensitive} data $CS(c, r)$, where $(c, r)$ represents the context and response for each data entry accordingly.
We answer above research questions by conducting \textsc{Vanilla} classifier on the following four settings of training data.
\begin{enumerate}[align = left, wide = 1pt, itemsep=1pt, parsep=2pt,topsep = 2pt ]
    \item $CI(c, r)$ and $CS(c, r)$, a \textsc{Full data} model trained on all the data, same as the \textsc{Vanilla} model in \S~\ref{sec:RQ1}.
    \item $CI(r)$ and $CS(r)$, a \textsc{w/o\ ctx} model trained on responses only to study the influence of context.
    \item $CI(r)$, a \textsc{CI-only} model trained on the responses of \textit{Context-Independent} data only.
    \item $CS(c, r)$, a \textsc{CS-only} model trained on \textit{Context-Sensitive} data only.
\end{enumerate}
For evaluation, we ensure the input, with or without the context, is consistent with the training phase.

\begin{table}[t!]
\centering
\small
\setlength{\tabcolsep}{5pt}
    \begin{tabular}{l|l|cc|c}
    \toprule
    \multirow{2}{*}{Model}
    & \multirow{2}{*}{Training Data}
    & \multicolumn{3}{c}{Test set split} \\ \cmidrule{3-5}
    && CI & CS & Overall \\ \midrule
\textsc{Full data} & $CI(c, r)$, $CS(c, r)$ &  67.79 & 55.58 & \textbf{63.07}  \\
\textsc{w/o\ ctx} &  $CI(r)$, $CS(r)$ & 70.43 & 53.34 & 63.00  \\ \midrule
\textsc{CI-only} & $CI(r)$ & \textbf{71.12} & 45.56 & 59.77 \\
\textsc{CS-only} & $CS(c, r)$ & 59.23 & \textbf{56.41} & 57.88  \\
    \bottomrule
    \end{tabular}
    \caption{Weighted F1 scores on three test set splits.}
    \label{tab:NewRQ2}
\vspace{-3mm}
\end{table}

\subsubsection{Results}
We report the weighted F1 scores on the two test sets (CI, CS) and on the Overall set in Table \ref{tab:NewRQ2}.
We observe all the models perform much better on CI than on CS, which indicates that context-sensitive bias is more challenging to identify.

We then compare \textsc{Full data} and \textsc{w/o ctx}.
They have comparable overall performance, and \textsc{w/o ctx} performs better on CI and worse on CS.
This observation indicates that dropping the context greatly degrades the model's ability on classifying context-sensitive data.
However, adding context information may introduce noises for context-independent data.

Next, we compare results of \textsc{CI-only} and \textsc{CS-only}.
Both of them achieve the best performances on their corresponding test sets (CI - $71.12$, CS - $56.41$).
Also, they have the lowest F1 scores on the other split of data.
Thus, we contend that there is a big gap between these two scenarios, and solving them requires different considerations.

\subsection{RQ3: Correlation among different topics}
\label{sec:RQ3}
The proposed dataset covers four topics and the previous models are trained on all the topics.
In this subsection, we investigate\add{:} \textit{is multi-topic training beneficial, and what are the correlations among these topics?}

\subsubsection{Methods}
We compare classifiers under three settings.
\vspace{-2mm}
\paragraph{\textsc{Multi-topic}} The model is trained on all the topics, the same as the \textsc{Vanilla} model in \S~\ref{sec:RQ1}.
\vspace{-2mm}
\paragraph{\textsc{Leave-one-out}} For a certain topic, we conduct the leave-one-out experiment by training on data under the other three topics.
\vspace{-2mm}
\paragraph{\textsc{Topic-specific}} We model each topic separately by training on topic-specific data.

\input{Figures/RQ3}

\subsubsection{Results}
We present the weighted F1 scores of the above three settings on test sets of different topics in Figure~\ref{fig:NewRQ3}.
Results show that the \textsc{Multi-topic} model largely outperforms the other two settings on all four topics.
This result shows that these topics share some common features and benefit from the multi-topic joint training.

The performances of \textsc{Leave-one-out} and \textsc{Topic-specific} differ among topics, which reflects different topic correlations.
For \textit{Gender} bias, \textsc{Leave-one-out} outperforms the \textsc{Topic-specific} model.
We believe that in the dataset and real scenario, \textit{Gender} bias is a general topic and frequently appears with other topics~\cite{Maronikolakis2022analyzing}, e.g.,  bias on housewives (which is also \textit{Occupational} bias), bias on colored women (which is also \textit{Racial} bias), etc..
Contrarily, \textit{Regional} biases are not essentially correlated with other topic scenarios, thus needing specific data to perform the task.
For \textit{Occupational} and \textit{Racial} bias, these two settings have similar F1 scores (less than $0.4$ differences). These two topics overlap with other topics at a medium level.

In summary, our experiments w.r.t. the three RQs reveal that the dialog bias measurement needs multi-dimensional analysis, and considering auxiliary annotations, including context-sensitivity, data type, and topics, is crucial for the task of dialog bias detection.
As exploratory and pioneer efforts on this task, we call for more studies on the proposed benchmark for building safer and more reliable dialog systems.

\section{Social Bias Measurements}
\label{sec:Exp}
The \textsc{Dial-Bias Framework} and the \textsc{CDial-Bias Dataset} aim to nurture more research to eliminate social bias in dialog systems.
With these resources, we study following research questions:
\begin{enumerate}[label=  \textbf{RQ\arabic*}:, align = left, wide = 1pt, itemsep=2pt, parsep=2pt,topsep = 2pt ]
\item \textit{Is it beneficial to model different bias topics separately in order to detect and measure them?}
\item \textit{How does context influence the bias measurement task?}
\item \textit{Is it essential to differentiate between the bias-expressing and bias-discussing data?}
\end{enumerate}

\subsection{Experiment Setup}

We propose two tasks at different bias alert levels to detect bias:
\begin{itemize}[align = left, wide = 1pt, itemsep=0pt, parsep=2pt,topsep = 2pt ]
\item  \textbf{Task 1 -- Bias Relevance Prediction (\textsc{B-Rel})}: it predicts a binary label of whether a given data entry is conveying any opinion w.r.t certain populations or bias phenomenons or not, i.e., Bias-Related and Irrelevant.
\item \textbf{Task 2 -- Fine-grained Bias Attitude Prediction (\textsc{B-Att})}: it is a three-way classification task to predict the response into one of three classes: Anti-Bias, Neutral, and Biased.
\end{itemize}

For both tasks, we consider two settings of bias measurement:
\begin{itemize}[align = left, wide = 1pt, itemsep=0pt, parsep=2pt,topsep = 2pt ]
\item \textbf{Utterance bias measurement}: it is performed on the  \textsc{CDial-Bias Utt} dataset and takes a single response as input.
\item \textbf{Context bias measurement}: it is performed on the  \textsc{CDial-Bias Ctx} dataset, and it takes both the context and response as input to a detector with a separator [SEP] as well as different speaker ids to differentiate the context and response.
\end{itemize}
\textsc{CDial-Bias Utt} and \textsc{CDial-Bias Ctx} datasets are split into the training, validation, and test sets in the ratio of 8:1:1.
All the performances are reported by the weighted F1 score aggregated over all classes (two classes for Task 1 and three classes for Task 2).

\subsection{Bias Classifier}
To build detectors for the two bias measurement tasks in two settings (utterance and context biases), we use the pre-trained Bert-Base-Chinese\footnote{\url{https://huggingface.co/bert-base-chinese}} model with a Fully Connected Layer for classification.\footnote{Training details are attached in Appendix~\ref{sec:parameters}.}
We consider two types of models to deal with data from different topics:
\begin{itemize}[align = left, wide = 1pt, itemsep=0pt, parsep=2pt,topsep = 2pt ]
\item{\textbf{Topic-specific (TS)}.}
In this case, we train four topic-specific bias detectors w.r.t. the four data of topics (i.e., \textit{Race}, \textit{Gender}, \textit{Region}, and \textit{Occupation}) separately.
These models are then tested on the corresponding test set of the topic.
For the \textit{Overall} result considering a \textit{combined test set} across all the topics, we report \textit{TS} as weighted average of the topic-specific classifier performances, and the weights are proportional to the test data size of this topic. This result ideally serves as an upper bound of using topic-specific detectors.
We also provide $\textit{TS}_\textit{\scriptsize Avg}$ on the combined test set by averaging the logits of these four models as the results.

\input{Tables/RQ1}

\item{\textbf{Combined (Comb.)}}.
We then ignore the information of topics, that is we simply train a single \textit{topic-agnostic} classifier (\textit{Comb.}) using the combined training data from all four topics.
For this model, we present performance details on the combined (Overall) test set as well as on each topic test subset.

\end{itemize}

\paragraph{Baselines}
To the best of our knowledge, there is a lack of Chinese bias datasets and hence for Task 1 (\textsc{B-Rel}), we compare the \textit{Gender} bias detector to \textbf{SWSR}~\cite{JIANG2022SWSR}, a Chinese sexism dataset containing 8k online comments.\footnote{We reproduce the classifier using SWSR data and it has comparable performance as reported in the original paper.}
We also compare with \textbf{BD-Sens}, the Baidu text sensor API\footnote{\url{https://ai.baidu.com/tech/textcensoring}} aiming at filtering the sensitive, abusing, and toxic online texts, which is similar to this task.
For Task 2 (\textsc{B-Att} task), we compare with two popular APIs: \textbf{BD-Sent} Baidu sentiment analysis API and
 \textbf{BD-Dial} Baidu dialog emotion detection API.\footnote{\url{https://ai.baidu.com/tech/nlp\_apply/emotion\_detection}}

\subsection{Results and Discussions}

\paragraph{Overall Results}
Several observations can be noted from Table~\ref{tab:RQ1}.

First, the proposed bias detectors (\textit{Comb./TS}) trained on the \textsc{CDial-Bias Dataset} largely outperforms existing APIs (\textit{BD-Sens/Sent/Dial}) and bias detectors (\textit{SWSR)} in different bias measurement settings and tasks.
Only \textit{BD-Dial} achieves similar performance compared to \textit{Comb./TS} of the \textit{B-Att} Task on the \textsc{CDial-Bias-Ctx}.
We believe that general APIs do not aligned well with the characteristics of the dialog bias measurements, and \textit{SWSR} is more specialized for explicit offensiveness, rather than for subtle bias measurements.
This observation shows that current available resources cannot handle the proposed bias measurement task, while the proposed classifiers can do better.

Second, we find that the task of context bias measurement is generally more challenging than  utterance bias measurement.
The performances of most models in two bias measurement tasks (\textsc{B-Rel, B-Att}) and data topics are lower on the \textsc{CDial-Bias-Utt} compared to on the \textsc{CDial-Bias-Ctx}.
Nevertheless, context bias measurement is unique and indispensable for dialog scenarios.
Therefore, we call for further explorations along this important direction.

Next, we seek to address the three research questions stated above.

\paragraph{RQ1: Modelling Different Bias Topics.}
We compare the performance of training topic-specific bias detectors (\textit{TS}) with the single classifier trained with data from all four topics (\textit{Comb.}).
In Table~\ref{tab:RQ1}, we observe that \textit{TS} outperforms \textit{Comb.} on $15$ out of all $20$ settings across two different bias measurement tasks and two datasets, and the margin of improvement is more evident on the context bias dataset.
This reveals that even though utterances may have some common features among different topics, considering topic-specific features offers benefits to the development of  dialog bias measurement models.

\paragraph{RQ2 : Influence of context. }
We study the influence of context using the \textsc{CDial-Bias-Ctx} by considering three settings.
\begin{itemize}[align = left, wide = 1pt, itemsep=1pt, parsep=2pt,topsep = 2pt ]
\item \textbf{With Context (WithCon)}: it concatenates the context and response as input feeding to bias detection models.
\item \textbf{No Context~(NoCon)}: an ablation setup that only provides responses to the model.
\item \textbf{Ensemble}: ensembling the predictions from model without context ($y_{\textit{\scriptsize NoCon}}$) and with context ($y_{\textit{\scriptsize WithCon}}$).
The ensemble output $y_{\textit{\scriptsize ens}}$ of \textit{Ens.} is $y_{\textit{\scriptsize ens}} = \gamma \times y_{\textit{\scriptsize NoCon}} + ( 1 - \gamma ) \times y_{\textit{\scriptsize WithCon}}$,
leveraged by a hyper-parameter $\gamma \in [0,1]$.
\end{itemize}
For both tasks, we report the F1 scores on the combined test set of each setting.

From Table~\ref{tab:RQ2}, we observe that in most cases classifiers with context (\textit{WithCon}) can outperform those without context (\textit{NonCon}).
Furthermore, the ensemble methods (Ens.) further achieve the best overall performance, showing some complementary of both \textit{WithCon} and \textit{NonCon} classifiers.
These results demonstrate that \textit{adding context information and performing ensemble are both beneficial for context-sensitive bias measurement tasks.}

\input{Tables/RQ2}

\input{Tables/RQ3}
\paragraph{RQ3 : Differentiation between
bias-expressing and bias-discussing data.}

Finally, we study the importance of differentiating between bias-expressing (BE) and bias-discussing (BD) data mentioned in Section~\ref{sec:frame} for the Bias Attitude Prediction task (RQ3), using on both the \textsc{Cdial-Bias-Utt} and the \textsc{Cdial-Bias-Ctx}.
For each dataset, we split the bias-related data into two subsets, BE and BD, and then we train \textbf{Data-type-Specific (DS)} classifiers over each subset.
In Table~\ref{tab:RQ3}, we compare the performances with the combined classifier (\textbf{Comb.}) trained on the whole dataset that is the same as the  \textit{Comb.} model in Table~\ref{tab:RQ1}.

We see that \textit{DS} outperforms \textit{Comb.} regarding the \textit{Overall} performance on the combined test set on both datasets.
Moreover, the performance of \textit{DS} on the utterance Bias-Discussing test set is higher than the \textit{Comb.} classifier by $13.0\%$.
These two models have comparable performance on the other metrics.
These results suggest that \textit{distinguishing these two types of data can boost the overall performance due to their different expression characteristics}, hence we call for further study on considering data type differences for bias measurement tasks.

Altogether, the experiments above w.r.t. the three research questions suggest that the dialog bias measurement needs multi-dimensional analysis and considering \textit{bias topic}, \textit{context-sensitivity}, and \textit{data type} can improve the ability of for bias detectors.
Note that some aspects introduced in the \textsc{Dial-Bias Framework} are not covered in this section due to limited space, and we call for more explorations on the proposed benchmarks.

\section{Appendix}
\input{Tables/Type_Examples}
\begin{figure}[!th]
    \centering
    \includegraphics[width=\linewidth]{Figures/interface.drawio.png}
    \caption{Annotation User Interface.}
    \label{fig:annotation_ui}
\end{figure}
\input{Figures/groups}

\subsection{Data Type Taxonomy}
\label{sec:data_type}

We present the data type (\textit{Bias-Discussing}, \textit{Bias-Expressing}, \textit{Irrelevant}) examples and judging criteria in Table~\ref{tab:type}.
The examples D-1 and D-2 discuss discrimination towards Asians and racial minorities, while E-1 and E-2 express biased opinions towards certain groups of people.

\subsection{Dataset Construction Details}
\paragraph{Details of Subtopics}

\label{sec:data_subtopic}
We list the detailed sub-topics used as keywords in the data collection stage and hints at the annotation stage to identify targeted groups in Table \ref{tab:Raw}.
\add{Additionally, we present the distribution of Top 15 targeted groups in Figure~\ref{fig:group_freq}.}

\paragraph{Annotation Procedure}
\label{sec:annotator}

We employ twenty-six Chinese crowd-sourcing workers who are native Chinese speakers with ages ranging from 20 to 50, equally distributed genders, various occupations, and from different regions all over China.
The annotators have acknowledged the use of annotated data sets and are paid an average annotation salary.
We present our annotation interface in Figure \ref{fig:annotation_ui}.
For each data entry, the annotator is required to answer the following four questions sequentially.
\input{Tables/parameters_new}
\input{Tables/Dataset_raw}

\begin{itemize}  [itemsep=-1pt,topsep=0pt,leftmargin=12pt]
\item Q1: The annotator decides whether the context is needed to determine whether the utterance is bias-related.
If yes, then the context (question) will be shown to the annotator, and this entry would be regarded as \textbf{context-sensitive} data.
\item Q2: The annotator needs to judge the \textbf{data type} of the given utterance (potentially paired with its context if the answer to Q1 is ``yes''), whether it is (1) expressing bias towards a certain group, (2) discussing a bias phenomenon, or (3) irrelevant to bias.
\item Q3: If the utterance is relevant to bias determined by Q2, the annotator needs to further specify the \textbf{referenced group} of mentioned by the utterance.
\item Q4: Finally, judge the \textbf{implicated attitude} of the utterance in three classes, including (1) anti-bias, (2) neutral, and (3) biased.

\end{itemize}
\label{sec:anno_ui}

\iffalse
\paragraph{Disagreement Resolution}
\label{sec:anno_conflict}
Due to the complexity of the bias detection task,
there is a certain portion of data that annotators disagree with.
The original agreement rates on the four dimensions are:   context-sensitivity -- 61.8\%, data type -- 76.8\%, and targeted group -- 97.3\%, and bias attitude -- 54.8\%.
To make the best use of collected and annotated data, we devise four prioritized rules to resolve conflicting labels:
\begin{enumerate} [itemsep=-1pt,topsep=0pt,leftmargin=12pt]
    \item \textbf{Context sensitivity disagreement.} The context-level label is prioritized to provide more contextual information for better understanding in conversation scenarios.
    \item \textbf{Data type disagreement.} Bias-expressing label is  prioritized. We assume that even if a piece of data is discussing the bias phenomenon, as long as it is annotated as \textit{bias-expressing}, then the annotator probably spotted the subtle or unconscious bias within the text.
    \item \textbf{Targeted group disagreement.} In this case, we keep all the group labels as an utterance might be biased towards multiple groups, and trusting a single annotation might be incomplete.
    \item \textbf{Implied attitude disagreement.}  We set the priority rule to choose labels as ``bias''  > ``anti-bias''> ``neutral opinion''. The intuition of this order is to improve the recall rate of detection in the relatively more crucial ``bias'' class.
\end{enumerate}
\fi

\subsection{Training Details}
\label{sec:parameters}

We fine-tune the BERT model and the fully connected output layer(s) with weighted cross-entropy.
We optimize the hyper-parameters, including dropout rate, learning rate, and batch size for each experiment setting on the validation set with the maximum training epochs set to 30.
We adopt the early-stopping mechanism when the weighted F1 score of all classes does not improve for three consecutive epochs to avoid over-fitting.
The search ranges of each parameters in the classifiers mentioned in Section~\ref{sec:Exp} are listed below:
\begin{enumerate} [itemsep=-1pt,topsep=0pt,leftmargin=12pt]
    \item Dropout rate ($d$): [0.3, 0.4, 0.5]
    \item Learning rate ($lr$): [ 5e-5, 3e-5, 1e-5, 5e-6]
    \item Batch size ($B$):  [32, 64, 128]
\end{enumerate}

We use grid search to find the best hyper-parameters and their configurations in different experiments are provided in
Table~\ref{tab:best-para}.
We also present the standard variance $std$ of the model performances over all the hyper-parameters combinations within the search range.
Note that we report the models on different test set splits in \S~\ref{sec:Exp} for detailed analyses.
Here we calculate $std$ of the weighted F1 scores on the test set that aligns to the training set only for clarity.
For instance, we only report $std$ of F1 scores on the $CI$ test set for \textsc{CI-only} model (refer to \S~\ref{tab:NewRQ2}.
Additionally, we report the weighted F1 score on the validation set for all the best performing configurations, which can correspond to the results on the test set in Table~\ref{tab:NewRQ1}, \ref{tab:NewRQ2}, and \ref{fig:NewRQ3} in \S~\ref{sec:Exp}.

We use 2 NVIDIA V100 GPUs in total for all of our experiments, and the training time for the above models ranges from 20 minutes to one hour.

\iffalse
\subsection{\textcolor{red}{Needs modification} Examples of Evaluating Representative Models}
\label{sec:eval}

We provide several responses labeled as \textit{Irrelevant} to bias by the utterance-level \textsc{B-Rel} detectors but detected as Bias-Related by our context-sensitive bias detectors in Table \ref{tab:eval_example}.
We could see that some of these responses are irrelevant at the first glance. However, they may show strongly biased opinions and have great negative impacts on the involved populations.
For example, answering ``Yes, I think so'' or ``It's true'' for the first example (``Is homosexuality really disgusting?'') is indeed very relevant and undesirable.
Thus a context-sensitive bias detector is essential for dialog safety.

We also cherry-picked several examples in Table \ref{tab:eval_example_2}.
Given the same question, the models show very different attitudes in their responses.
EVA responds with anti-bias opinions to these two questions, which is more socially responsible.
CDial-GPT gives safe and neutral responses.
While CPM generates biased responses which are likely to hurt the interlocutor.

# Race, Region, Occ, Gender
# Comb, LOO, TS
a = [62.39, 61.57, 57.76, 67.07]
b = [61.51, 57.220000000000006, 55.730000000000004, 63.05]
c = [61.120000000000005, 59.04, 56.11, 61.34]
We report the models on different test set splits in \S~\ref{sec:Exp} for detailed analyses.
For example, in Table~\ref{tab:NewRQ2}, we present the models' results on $CS$, $CI$, and the Overall test set.
Here we only provide the $mean$ and standard variance $d$ of its results on the most representative set (also listed in the table).
Specifically, for models studied in \S~\ref{sec:RQ1} and \S~\ref{sec:RQ2}, we present the statistics on the Overall test set.
While for models in \S~\ref{sec:RQ3}, we list the results on the corresponding topic test set.

\fi

\section{Introduction}
\input{Sections/Introduction}
\section{\textsc{Dial-Bias Framework}}
\input{Sections/Frame}
\section{Dataset Collection}
\input{Sections/Dataset}
\input{Sections/Experiments_New}
\input{Sections/Experiments_evaluation}
\section{Related Work}
\input{Sections/Review}
\section{Conclusion}
\input{Sections/Conclusion}
\input{Sections/Ethics}

\section{Pipeline to Tackle Bias in Dialog Systems }
\MF{to discuss}
We first propose an promising framework to tackle dialog social biases (or dialog safety in general) in Figure \ref{figure:safety_overview}.
Given an user input, an unsafety detector is deployed to identify unsafe input whose response are provided by selecting from a pool of pre-defined backup safe responses \footnote{handcrafted rules or a learnable function can be deployed for selecting a safe response.} to guide safe conversation.
A safe user input is passed to a response generation model, whose output is again passed by an unsafety detector (w.r.t both utterance and context) to prevent unsafe generations to be conveyed to users.
With the framework above, \textbf{unsafety detection} is a important problem towards build safe dialog systems.

\begin{figure}[!t]
\centering
\includegraphics[width=0.5\textwidth]{Figures/dialog_safety_overview.PNG}
\caption{}
\label{figure:safety_overview}
\vspace{-0.1in}
\end{figure}
\section{Introduction}

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\begin{table}
\centering
\begin{tabular}{lc}
\hline
\textbf{Command} & \textbf{Output}\\
\hline
\verb|{\"a}| & {\"a} \\
\verb|{\^e}| & {\^e} \\
\verb|{\`i}| & {\`i} \\
\verb|{\.I}| & {\.I} \\
\verb|{\o}| & {\o} \\
\verb|{\'u}| & {\'u}  \\
\verb|{\aa}| & {\aa}  \\\hline
\end{tabular}
\begin{tabular}{lc}
\hline
\textbf{Command} & \textbf{Output}\\
\hline
\verb|{\c c}| & {\c c} \\
\verb|{\u g}| & {\u g} \\
\verb|{\l}| & {\l} \\
\verb|{\~n}| & {\~n} \\
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\hline
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\label{tab:accents}
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\begin{table*}
\centering
\begin{tabular}{lll}
\hline
\textbf{Output} & \textbf{natbib command} & \textbf{Old ACL-style command}\\
\hline
\citep{Gusfield:97} & \verb|\citep| & \verb|\cite| \\
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\caption{\label{citation-guide}
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The style is based on the natbib package and supports all natbib citation commands.
It also supports commands defined in previous ACL style files for compatibility.
}
\end{table*}

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\nocite{Ando2005,andrew2007scalable,rasooli-tetrault-2015}

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redpajama_set_name: RedPajamaArXiv


text:
Home›Email›Syrian war monitor says 465,000 killed in six years of fighting
EmailHightlightsMiddle EastReadRecommendedWorld News
Syrian war monitor says 465,000 killed in six years of fighting
A graveyard is pictured at night in Aleppo, Syria December 11, 2015. Picture taken December 11, 2015. REUTERS/Ammar Abdullah
The Syrian Observatory for Human Rights, a Britain-based war monitor, said on Monday there are so far about 465,000 people killed and missing in Syria\'s civil war.
The war began six years ago on Wednesday with protests against President Bashar al-Assad\'s government. It has since dragged in global and regional powers, allowed Islamic State to grab huge tracts of territory and caused the biggest refugee crisis since the second world war.
The Observatory said it had documented the deaths of more than 321,000 people since the start of the war and more than 145,000 others had been reported as missing.
Among those killed are more than 96,000 civilians, said the Observatory, which has used a network of contacts across the country to maintain a count of casualties since near the start of the conflict.
It said government forces and their allies had killed more than 83,500 civilians, including more than 27,500 in air strikes and 14,600 under torture in prison.
Rebel shelling had killed more than 7,000 civilians, the Observatory said.
The Islamic State jihadist group has killed more than 3,700 civilians, air strikes by the U.S.-led coalition have killed 920 civilians and Turkey, which is backing rebels in northern Syria, has killed more than 500 civilians, it added.
Syria\'s government and Russia both deny targeting civilians or using torture or extrajudicial killings. Most rebel groups and Turkey also deny targeting civilians. The U.S.-led coalition says it tries hard to avoid civilian casualties and always investigates reports that it has done so.
South Korean court throws president out of office, two die in protest
Winter Storm Stella hits US Northeast, NY dodges worst
Police collect nearly 800 bodies from Ecuador's virus epicenter
Latvia to ease coronavirus restrictions for public gatherings from May 12
Radiation readings spike at Fukushima nuclear plant

redpajama_set_name: RedPajamaCommonCrawl


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Since my last writing I have experienced a number of seminal events. The astrological and solar alignments as they affect me personally have been intense and deep, and continue to open new levels of awareness as each old shell or layer of personality crumbles. Many of the clients I facilitate via astrology and counselling are ever more aware of how they can no longer relate to their reality the way they used to. Even though anxiety levels are up across the board, so are the incidences of spontaneous bliss and sudden insights and understandings. 2012 is living up to all previous musings about it, while at the same time leaving the outcomes hidden and undefined. One thing for sure, whatever happens next will be unpredictable, no matter whose prophecies or predictions you have previously relied upon.
Changes continue to bubble up, some of them gentle, and some not so gentle. I feel like I am on the inside of a pressure cooker, and at some point the lid will be unlatched, and I'll explode into some new form...of formlessness. Not all of these sensations are accompanied by the deep emotions and letting go that I experienced in the wake of my kundalini awakening between 1994 and 1998. No, this is quite different, and more dynamically keyed towards a more self-actualized state of being. As such, I feel that I am to come out more fully into the world to share the vision I have of Spirit, and not seek for myself in any more inside books, or methodology, or other teachers and healers.
The very function and nature of human relationships is changing for me, and I feel that you may also sense the need to change the perceptions about our reasons for relating to others. I feel that it is still essential to honor the deep presence of Spirit in others, as well as all of Creation, but without the projections we have all been habitually accustomed to. It will become more important to delve deeper into the relating with one's own Spirit, and the Absoluteness that it represents, and cease using relationships as a substitute for this most primary of all relationships. I remember too many times in which I have betrayed my sovereignty for acknowledgment, security, adoration, comfort, agreement, compromise, acceptance and what I thought was love. The external can and does reflect these experiences back to us, but have we ever questioned what keeps us all looking for ourselves 'out there'? The common responses are; 'my ego', or unresolved childhood issues with one parent or another, or perhaps a past life issue, or the original 'fall' and loss of innocence. There are mega-billion dollar industries designed to encourage and support us in the search and acquisition of relationship nirvana, and yet it still eludes us. Have you ever wondered why? Even though the consumer culture has created a narcissistic obsession with the love of things, it has also excelled in reducing the relationship, or rather the search for the 'perfect' relationship, to one of the most important acquisitions we can aspire to. This may be the ultimate narcissistic fantasy, the narcotic of self-validation through The Other. As Neo learned in The Matrix, just as there is no spoon, there is no 'other'.
One of the revelations that came about the other morning over breakfast with my partner Lynn was the role of magnetism in the realm of relationships. This also has to do with a more controversial subject I will be dealing with more in depth soon, which is about the less than benign influence of certain objects affecting the planetary magnetics. However, consider for a moment the primary symbol of romantic love in mostly modern times - Earth's moon.
Since the mirror game most consistently happens when we're 'non-conscious' (it is a very lunar-oriented psychological condition) we seldom are in a place of observing it in action. However, the suffering and pain of gaining and losing love in relationship after relationship creates a flicker of awareness, a certain awakening that all is not as real as it was described in the romance novels, films and Hallmark greeting cards. What happened, we wonder, to true love?
The Moon reflects light, but is not the Light. It is a dead orb. It may hold secrets that we have scant knowledge of, knowledge that may be the key to understanding the human condition. Why then have we been so easy to adopt it as a symbol of romantic love? We look for our mirror then fail to see ourselves in it as we really are. We become enraptured with our image of the other person, and lose ourselves in them. The only thing that changes is the degree with which we allow ourselves to slip into the dreamlike trance.
The mirror game is going to become more intense, because as we hear and feel the call to transcend that this energy of passing through the plane of the Galactic Center demands of us, then we will become less and less hypnotized by the old patterns, the old projections and the sense of need that used to drive us to look elsewhere for love. It will also dissolve the obsessive pattern of self-reduction that occurs when we are abused by others and then explain it all away as 'being our own damn fault'. This is not always the case, and constantly re-identifying demonstrated destructive attitudes and behaviours as 'our own' may actually add to the dysfunctional mirror game. This is not love, but self-abuse, which perpetuates the cycle, a cycle that cannot be healed through psychoanalysis.
We are being called upon to rise into a new understanding of Love, a new demonstration of lovingness. This demonstration is based on self-responsibility. Yes, some of the reflecting we receive from others is reflecting the patterns that we call 'our stuff'. But to identify with it is to become it, and that doesn't serve anyone, and it certainly can't be called Love.
Ultimately, we are Love. We are the Light. Even in this mutant paradigm, the dance of duality, the pristine emanations of the Divine Matrix are available to be accessed, enjoyed and celebrated. It is to be found in Nature, in the arts, and in the beauty of seeing it in the eyes of another, whether human or animal. It is our right to say, "I belong here, and I create here." However, there is no reflection that is pure enough to bring this truth to us directly, except from and through a deep desire to discover it from within ourselves and to dwell in it continually. The truth is in our own hearts. We are immortal and infused in Infinite Love, for in it we live and move and have our being. It is this that is the truth of our existence. There are still many layers that appear to keep us from knowing this core truth in ourselves, but it is inevitable that to each one that so desires it, they will realize it.
"If I shall speak with every human and Angelic language and have no love in me, I shall be clanging brass or a noise-making cymbal. And if I have prophecy, and I know all mysteries and all knowledge and if I have all faith so that I may remove mountains, and I have no love in me, I would be nothing. And if I should feed everything that I have to the poor, and if I hand over my body to be burned up and I have no love in me, I gain nothing.
Love is patient and sweet; love does not envy; love is not upset neither puffed up. Love does not commit what is shameful, neither does it seek its own; it is not provoked, neither does it entertain evil thoughts, rejoices not in evil, but rejoices in the truth, endures all things, believes all things, hopes all, bears all.
Love never fails; for prophecies shall cease, tongues shall be silenced and knowledge will be nothing; for we know partially and we prophesy partially, but when perfection shall come, then that which is partial shall be nothing. When I was a child, I was speaking as a child, I was led as a child, I was thinking as a child, but when I became a man, I ceased these childish things. Now we see as in a mirror, in an allegory, but then face-to-face. Now I know partially, but then I shall know as I am known. For there are these three things that endure: Faith, Hope and Love, but the greatest of these is Love."
How long do moments last?
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I've just finished reading a book entitled "You Are Not a Gadget" by Jaron Lanier, and I highly recommend it. Yes, a real book. Not an e-book, or a Kindle book, or a downloadable .PDF book. A physical book made with physical paper, from physical trees (or recycled physical cardboard) and physical ink. Quaint, huh? It's not exactly an enjoyable read, but it may be an important read. It's about creativity, the Internet, social networking, and the question of personhood. Are we in charge of our technology, or is our technology now shaping us in ways we never imagined. And, is it healthy? Do those who are at the cutting edge of VR (virtual reality) and AI (artificial intelligence) sufficiently aware to be entrusted with the future of mankind? These are the sort of big questions the book poses, and, it was written by a Silicon Valley insider that has been at the forefront of the developing technological revolution known as the World Wide Web, and who is paradoxically one of its harshest critics.
Here are a couple of quotes to ponder on: "If you want to know what's really going on in a society or ideology, follow the money. If money is flowing to advertising instead of musicians, journalists and artists, then society is more concerned with manipulation than truth or beauty. If content is worthless, then people will start to become empty-headed and contentless", and "Humans are free. We can commit suicide for the benefit of a Singularity (Web 2.0). We can engineer our genes to better support an imaginary hive mind. We can make culture and journalism into second-rate activities and spend centuries remixing the detritus of the 1960's and other eras before individual creativity went out of fashion. Or we can believe in ourselves. By chance, it might turn out we are real", and finally; "The deep meaning of personhood is being reduced to an illusion of bits."
My recent personal experiences with social networking and my forays into designing websites have brought me to a spiritual and intellectual impasse. When I am spending time in nature, or conversing with someone else while in their physical presence, compared with the experience of the immediacy of online sound-bites or artificial landscapes of Facebook, I am becoming more and more aware of a feeling of discontentedness with the noosphere and the hive mind, the proposed Singularity of digital self-images. I feel less like a person than I used to!
It is interesting to look at the language of Cyberspace for the clues. We're so adept at naming things for what they are, then ignoring the consequences. In the past, the word 'web' was applied to the technology of spiders, or to denote a trap of some kind. Now we assume it describes a social and spiritual mechanism that frees humanity from many perceived constraints and limits. Perhaps the promise of the Singularity is a betrayal, and we would do well to heed the warnings of someone like Jaron Lanier. Trust your instincts, develop your discernment and delve into your Self. I suppose what I'm favouring here is organic realism versus digital copies of realism. Even though we have become all too familiar with Matrix-like themes about the nature of 'organic' models in the multiverses and our infantile understandings about the nature of Consciousness, we would do well to appreciate a natural flesh and blood humanity versus a cloned cyber-copy. I have no love of being inserted into any virtual reality, where my sense of self is embedded in an avatar. I say this not because I am a hater of technology, but because I love, and a chip of silicon circuitry does not.
I encourage you to consider that the promise of the web could easily be turned into a tyranny of sorts. Reducing ourselves to some common digital denominator for the sake of group consciousness/consensus can become such a tyranny. Individuation of Consciousness is the anti-thesis to what the progenitors of Singularity envision, and I celebrate that our spiritual potential is worth elevating above hive mindedness. An current example would be the real people occupying Wall Street, versus Tweeting about it on the Net. The street and the coffee shop will be where the r-evolution is played out, not in cyberspace. Welcome to the Real.


Andrew is well known for his subtle and minimalistic design approach and is always curious about new concepts in garden and landscape architecture.
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I have a 10 year old Jenn Air Gas Range/ Electric stove model JDS9860ACP. YJenn- Air Range/ Stove/ Oven Timer. Failure codes, problems, and resolutions for Jenn- Air timer model. F4: intermittent F4 fault - even with new oven sensor. Appliance RepairJenn- Air Fault Codes. Jenn- Air Fault Codes. Jenn Air Range / Stove / Oven Fault Codes: Failure Code:. F4: Shorted or open oven temperature sensor ( RTD). JENN- AIR - Range/ Stove/ Oven Fault Codes F7= Function key shorted or stuck button. Replace touch pad/ clock assembly ( also called the ERC) or attempt to unstick button. Find solutions to fix your Jenn air oven error code.
Jenn- Air Appliance Error Codes. F4: Shorted or open oven temperature sensor. I need your help with Jenn Air oven error codes. That' s not a surprise given how often they error out and break down. That doesn' t help me today. How do I check an oven temperature sensor? While the sensor probe itself is most often the cause of an F3 or F4 fault code, it is not a certainty. An F1 error code on a Jenn- Air range model SVE47100W indicates that. How Do I Reset a Whirlpool Oven If an Error Code Shows. sell Jenn- Air ovens.
Jenn- Air W30400W wall oven with a F5 error. for this wall oven, the F5 error code pops up when the. com/ wp/ / 05/ 16/ jenn- air- svd48600- range- power. Hi: I have a Jenn- Air SVE47100B Oven Range that has a stuck broiler ( won' t shut off) and a F2 code. I was using the Bake function last nite when the oven suddenly started beeping- - I noticed the broiler was stuck on and wouldn' t shut off. Maytag/ Jenn- Air 83 Failure Codes,. Jenn- Air: D156: 83: F4: Oven works for period. Oven will start and then shut off with fault code F0,. When trying to turn on the oven / broiler or self clean the F3 code comes on what might the problem be thanks John Related Models Jenn- Air FCE30500W RANGE- S/ C F/ S. Jenn- Air ERC Fault Codes.
( whereby the timer is not displaying the correct fault code). F3 or F4: Open or shorted oven temperature sensor:. My Jenn- air Electric Stove Oven is giving me an F3 code. my jenn air oven is about 15 years. jenn aire ww2780b shows a fault code f3 flashing and constantly. Find Jenn- Air Parts. Range/ Stove/ Oven Fault Codes. Range/ Stove 24" Built- In Self- Clean Wall Oven Fault Codes. Find Kenmore Parts. jenn air w131w wall oven f1 error code; jenn air w131w wall oven fault f5;. what does it mean when my jenn air oven, w131w shows a F4 code?
Electronic Range Fault Codes; Troubleshooting. which stands for runaway temperature or an F3 or F4 code,. Code 4d21 on my Jenn Air. I have Jenn Air Wall Oven with error code 4d21 while. I have Jenn Air Wall Oven with error code 4d21 while preheating. JENN AIR FAULT CODES. Jenn Air Range / Stove / Oven Fault Codes. F3 or F4: Shorted or open oven temperature sensor ( RTD) Check sensor harness and harness. JENN AIR Range/ Stove/ Oven Fault Codes. Failure Code Condition Check/ Repair F1 Defective touch pad or membrane. Replace touch pad or membrane ( this problem is usually the touch pad). F2 code on Jenn air double oven I have a jenn air double oven. I cleaned the top oven and after it was done I get a - Jenn- Air JJW8230D Electric Double Oven question.
THE FIRST STEP in diagnosing ANY oven error code is to attempt to reset. and F4 indicates a shorted sensing circuit. Jenn- Air Oven & Stove Repair;. I started getting F3 error code intermittently on our 8- year old Jenn- Air gas range; also once or twice I got an F1 error. Yesterday I replaced the oven temperature probe,. Jenn Air range/ stove/ oven displaying fault code f4? Use our DIY troubleshooting & videos. Then, get the parts you need fast. Return any part for 365 days. What does the F 1 fault code indicate on my Jenn- Air oven model U276W? What does code f5 mean on my Sears Kenmore built in wall oven mean?
Discover the details of Jenn- Air® appliances,. Now you can use your Jenn- Air Connected Wall Oven with voice- control devices and advance your kitchen experience. Range Fault Codes. Fault codes can and do vary from manufacturer to manufacturer, even between models from the same manufacturer these codes may well be different.
Home / Repair Services / Electronic Range Fault Codes / Jenn- Air Fault Codes. Older Style Jenn- Air Models. Open or shorted oven temperature sensor.
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Jenn Air Error Codes and JennAir Fault Codes. JENN AIR - Range/ Stove/ Oven Fault Codes. F3 or F4 Oven temperature sensor is open or shortened.
F4 error code on Jenn- Air W30400BC expressions built in oven When trying to preheat oven an error code of f4 flashes - Jenn- Air JJW8527C Expressions question. Model Number: JES9800AAB Brand: Jenn- Air Age: More than 10 years My Jenn- Air stove has been intermittently flashing a F1- 07 default code for several days now. The top burners are functional, but when the code is flashing, the oven itself will not work.


text:
Minimalism and Beyond
Mnuchin Gallery, New York, NY
September 14 – October 21, 2017
Press Release Works
MINIMALISM & BEYOND
Mnuchin Gallery will open its fall season with Minimalism & Beyond, an exhibition exploring Minimalism and its legacy in contemporary art from the second half of the twentieth century through the present day. Opening September 13 and on view through October 18, 2017, it will be accompanied by a fully illustrated catalogue authored by Pac Pobric.
The exhibition will present Minimalist masterworks of the 1960s alongside painting and sculpture of the following decades that both elaborate upon and disrupt Minimalism's formal language to a diverse range of ends, from the playful and the personal to the subversive and the political. Taking as its starting point iconic works by Carl Andre, Dan Flavin, Donald Judd, Agnes Martin, Robert Ryman, and Frank Stella, the exhibition continues with Postminimalist figures such as Eva Hesse, Robert Morris, On Kawara, and Bruce Nauman. It carries on with works from the 1980s and 90s by artists including Robert Gober, Felix Gonzalez-Torres, David Hammons, Jeff Koons, Cady Noland, and Christopher Wool. The exhibition culminates with contemporary examples by Carol Bove, Wade Guyton, Rashid Johnson, Glenn Ligon, and Rudolf Stingel, artists who employ the supposedly "neutral" aesthetic tropes of Minimalism to explore the politics of race, class, and technology.
At its outset Minimalism arose as a reaction to the exuberant individualism of Abstract Expressionism, turning away from its manifestation of the psychological state of the artist. Early practitioners retained the abstract component of their predecessor's work while using basic geometric forms to hone in on the characteristics of the medium. Donald Judd's idea of the Specific Object, neither painting nor sculpture, encompassed Minimalism's attempt to reduce artworks to their unique essence. Rejecting any attempts to find narrative meaning in his work, Frank Stella famously told Bruce Glaser in 1966, "What you see is what you see," coining a phrase which became a framework for understanding early Minimalist art. Stella strove for an abstraction predicated on the nature of paintings. In Creede I and Creede II, both 1961, Stella's signature bands replicate the shape created by the canvas on its stretcher while emphasizing its flatness.
Moving into the 1980s artists continued to explore Minimalist ideas about specificity and economy of form, but pared down their work with the intent of maximizing suggested meaning rather than removing it. The seemingly effortless suspension of basketballs in Jeff Koons' Two Ball Total Equilibrium Tank (Spalding Dr. J Silver Series, 1985) belies the complex calibration that keeps them afloat. The basketballs symbolize the spectacle of professional athletics, the elusive dream of court stardom that inspires monumental determination and struggle for so many only to be achieved by a small few. On Kawara's monochromatic date paintings each record a discrete unit of time, the day they were created. Evoking at once the universal drumbeat of time marching on and the personal temporality of the quotidian, the formal austerity of each painting elucidates, rather than eliminates, the possibilities beyond it.
By the beginning of the 21st century, artists had broken open Minimalist techniques in service of questioning the societal structures and cultural tropes that define our time. Glenn Ligon's Untitled (Stranger in the Village #17), 2000 is printed with text from James Baldwin's essay "Stranger in the Village" about being the first African American to visit a Swiss town. The overwhelming darkness of the painting renders the text barely legible, a metaphor for the racial dynamics of visibility/invisibility and the capacity of language to address these dynamics. Rudolf Stingel's Untitled, 2004 considers the classic dichotomy between "decorative" and "fine" art. Its opulent gold sheen and decadent pattern play into perceptions of art world excess and the ever-increasing threshold of record auction prices.
The elimination of the artist's hand that began in the 1960s comes full circle with Wade Guyton, who replaces human touch with that of a machine. For his fire paintings, as in Untitled, 2006, Guyton uses an inkjet printer to "paint" a found image on linen, sublimating his control within the variability of technological error. Guyton highlights the narrowing interface between traditional image-making and the growing insertion of digital devices and languages into every facet of life. Neither "painting" nor sculpture, Guyton proves the persistent relevance of Judd's Specific Object, foreshadowing its applicability to the future of art-making as the digital hand becomes inseparable from the human.


text:
Home / Daily Dose / Unemployment Rate Falls in 42 States Monthly in December
Unemployment Rate Falls in 42 States Monthly in December
in Daily Dose, Featured, Market Studies, News January 27, 2015	1,238 Views
The latest improvements in regional and state employment statistics across the United States are keeping in line with the latest drop in the nation's unemployment rate (down to 5.6 percent in December) – and in line with analysts' assessments of consistently improving economic and labor conditions.
The unemployment rate declined in 42 states and the District of Columbia month-over-month in December, staying in line with the falling U.S. unemployment rate, according to the U.S. Bureau of Labor Statistics (BLS) Regional and State Employment and Unemployment report for December 2014 released Tuesday. The unemployment rate increased in only four states from November to December, while four states experienced no change in unemployment rate month-over-month.
Mississippi had the highest unemployment rate among states in December, with 7.2 percent (the District of Columbia had an unemployment rate of 7.3 percent for the month). The lowest unemployment rate among states for the month was in North Dakota, at 2.8 percent; 19 states had a "significantly lower" unemployment rate than the nationwide average for the month of 5.6 percent. Ten states plus the District Columbia had a significantly higher rate than the national average, while 21 states reported an unemployment rate "not appreciably different" from the U.S. rate.
Nineteen states experienced significant month-over-month declines in unemployment in December), led by Delaware (0.6 percent) and Michigan and North Carolina (0.4 percent each). Thirty states experienced significant unemployment rate declines year-over-year, led by Illinois (2.7 percentage points), and Rhode Island (2.5 percentage points), with the only significant increase occurring in Louisiana (1.3 percentage points).
Idaho and Vermont, at 0.4 percent each, experienced the largest month-over-month declines in employment in December.
Meanwhile, payrolls increased from November to December in 41 states and declined in only nine, plus the District of Columbia. Texas had the largest month-over-month job gain with 45,700, followed by New York (30,400) and Illinois (17,100). Minnesota experienced the largest month-over-month decline in jobs with 5,200.
Year-over-year, payrolls increased in all 50 states and the District of Columbia in December. The largest increases occurred in North Dakota (5.4 percent), Texas (4.0), and Utah (3.9 percent).
Bureau of Labor Statistics Employment Rate Jobs Unemployment Rate	2015-01-27
Brian Honea
Tagged with: Bureau of Labor Statistics Employment Rate Jobs Unemployment Rate
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About Author: Brian Honea
Brian Honea's writing and editing career spans nearly two decades across many forms of media. He served as sports editor for two suburban newspaper chains in the DFW area and has freelanced for such publications as the Yahoo! Contributor Network, Dallas Home Improvement magazine, and the Dallas Morning News. He has written four non-fiction sports books, the latest of which, The Life of Coach Chuck Curtis, was published by the TCU Press in December 2014. A lifelong Texan, Brian received his master's degree from Amberton University in Garland.
Foreclosure Activity Drops to All-Time Low
Generation Gaps: How Housing Priorities Vary Among Cohorts
Parking, outdoor space, and pet-friendly homes top the priority list for consumers looking to buy and relocate. Here's what else a new report reveals.


You know Fall is upon us once you start smelling apples on the wind. We collect the drops from our trees and feed them to our pigs, keep the good ones for eating or cidering, maybe even sell a few. The pigs probably eat better than we do, always gorging on whatever leftovers or imperfects we don't sell or eat.
They have become a large part of our farm, providing us with a freezer full of meat come winter in addition to a cleared semi-tilled plot of land to grow on next year. We are raising 7 Large Black pigs, a heritage breed from England with notably big floppy ears for covering up and protecting their eyes while they forage. If you are interested in purchasing a half or whole pig to feed yourself and your family send us an email and we will give you the details.
The watermelon radish ain't your average salad radish. It has a much longer growing period, forming a tougher outer layer but what it protects is a beautiful delight. Green skin gives way to pinkish red flesh, we halve or quarter them then slice thinly and simmer in apple cider vinegar, salt, and maple syrup. They accompany any Asian style dish or salad if cooked in this way. Enjoy!


I wanted to respond to the post regarding substance abuse and how it can be detrimental to other forms of abuse. What I want to discuss is when the nurses are abusing substances. Several years ago, while working on a med surg floor, I received report from the nurse coming off the shift who was, based on my assessment, was under the influence. The nurse had pin point pupils, had slurred speech and a bandaid over an area on left hand with fresh blood noted. Upon the initial assessments of my pts, all that had the nurse I had received report from where rating their pain greater than 6/10. Upon review of pyxis machine it was determined that all pts had medications every six hours as ordered but all state they did not receive meds through the night. I handled this situation by meeting with my supervisor and instructing her on talking with the nurse prior to her going home to see if she saw the manifestations I did. We as nurses need to perform assessments on each other to insure we are being pt advocates, as well as team players with other nurses if they need assistance with any substance abuse.


The Tommy Hilfiger Group, which is wholly owned by PVH Corp. (NYSE: PVH), is pleased to announce that Mexican Pop-Rock duo Jesse & Joy wore Tommy Hilfiger on the red carpet and to perform onstage during the 2013 Latin Recording Academy Person Of The Year honoring Miguel Bose at Mandalay Bay Convention Center in Las Vegas.
Jesse of Jesse & Joy was wardrobed in FW13 runway collection signature glen plaid cotton button-down with white collar/attached red knit cardigan front and black dress pant all by Tommy Hilfiger.
Joy of Jesse & Joy was wardrobed in burgundy coated denim skinny jeans by Tommy Hilfiger.
These looks and similar styles are currently available at Tommy Hilfiger Flagship stores.


text:
package org.eclipse.bpel.ui.util;

import java.util.Collections;
import java.util.List;

import org.eclipse.wst.wsdl.Definition;
import org.eclipse.wst.wsdl.Fault;
import org.eclipse.wst.wsdl.Input;
import org.eclipse.wst.wsdl.Message;
import org.eclipse.wst.wsdl.Operation;
import org.eclipse.wst.wsdl.Output;
import org.eclipse.wst.wsdl.Part;
import org.eclipse.wst.wsdl.PortType;
import org.eclipse.wst.wsdl.util.WSDLConstants;

public class PartMappingUtil {

	public static List<Part> getPartsForPartMapping(Operation operation, int type, String faultName) {

		Definition definition = operation.getEnclosingDefinition();
		PortType portType = (PortType) operation.eContainer();

		// if our operation is a proxy or it does not live in a resource we cannot do anything about
		// it
		if (portType == null || definition == null) {
			return Collections.emptyList();
		}

		Message message = getMessage(operation, type, faultName);

		// if we don't have a message we cannot have parts
		if (message == null) {
			return Collections.emptyList();
		}

		return getPartsForPartMapping(operation, message, type);
	}

	// TODO MI: find better constants for input, output, fault
	private static Message getMessage(Operation operation, int type, String faultName) {

		Message message = null;

		if (type == WSDLConstants.INPUT) {
			Input input = operation.getEInput();
			if (input != null) {
				message = input.getEMessage();
			}
		} else if (type == WSDLConstants.OUTPUT) {
			Output output = operation.getEOutput();
			if (output != null) {
				message = output.getEMessage();
			}
		} else if (type == WSDLConstants.FAULT) {
			Fault fault = (Fault) operation.getFault(faultName);
			if (fault != null) {
				message = fault.getEMessage();
			}
		}

		return message;
	}

	private static List<Part> getPartsForPartMapping(Operation operation, Message message, int type) {

		List<Part> parts = message.getEParts();

		// TODO Do we have any restrictions on the parts we can use for part-mapping? (WSDL
		// style? doc-lit-wrapped? single-part?) If yes - implement them here.

		return parts;
	}

}

redpajama_set_name: RedPajamaGithub


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In lieu of a topic more focused on technical subject matter or practical application, I decided to take a post and detail some of my experiences in writing a book. It's not actually published yet, but I do have an agreement established with an excellent publisher and I should be turning in the edited manuscript come next month. It's been quite the long haul, but I think being able to hold a copy of it in my hands will make it all worth it. So, here is the process as I've learned it from my own perspective.
Writing a book is a nice idea to mentally toy with, but if you resolve to actually commit to doing it, be serious about it. The process of writing a book generally takes months upon months of work. In my case, I'm trying to get it done while holding down a full-time job, helping to maintain a household, and (really trying to) have a semi-balanced family life.
To be quite blunt, writing this book been a black hole for what little free time I had before I started writing it. When I'm not working or helping to wrangle the kids, I'm writing. While I try to intersperse some more recreational activities into my schedule, it's difficult to do with a deadline looming. So, bottom line, don't commit to the idea if you aren't willing to follow through on it.
While the purpose of technical books is mainly to propagate knowledge to an interested audience and produce profit for authors and publishing companies in the process, the reasons behind the task of writing technical books have a bit more variation to them. Whether you're going through a publisher or self-publishing, it's important to know why you want to write a book before you commit to doing so.
One of the bigger mistakes you can make is publishing a book and expecting it to produce a big pay-off. There's very little money in technical books. Their sales are generally lackluster at best, even when the author is fairly well-known.
By and large, the reason to write a technical book is for credentials. It's an accomplishment that looks impressive to industry peers and prospective employers, and rightfully so for the amount of work and the meager monetary benefits involved. Draw upon the experience of others to get a sense of what you're getting into before you commit to it.
Nailing down what you want to write a book about isn't as easy as it sounds, but trying to proceed without one is only going to result in frustration and wasted time. Coming up with a general topic is easy, but specific subtopics takes some deeper thought.
Books generally have organization to them, like chapter and section subdivisions and so on, which make the book easier to consume in portions and grok in general. So, it wouldn't be in particularly good form to have your hypothetical work come across to prospective publishers or readers as you putting brain dumps on a page.
Even if you plan to self-publish, the preliminary documentation that publishers generally require from potential authors is a useful tool to help you tighten up your initial idea, so start there.
Write a synopsis of the book. Think of this as what's likely to go on the back cover of a published copy. Its job is to present an elevator pitch to prospective readers by summarizing the book's premise and why they should read it.
Make it concise (a few hundred words) but informative. Only hit the high points of what you intend to cover in the book itself. This together with the market analysis are what publishers are likely to look at first, so make them both good.
Draft a chapter outline. Include titles and bullet point lists of subtopics. Try to limit each point to a sentence or two summarizing what areas the chapter will cover. This doesn't necessarily have to be set in stone once you actually start writing or get a publishing deal (though in the latter case it depends on the terms of the deal).
Put some hard thought into what information you'll present and how you'll organize it. Your goal is to minimize the level of difficulty involved in readers consuming it. Try to establish a logical progression of high-level concepts.
Prepare a market analysis. This doesn't have to be as fancy as it probably sounds. Look around popular online bookstores (or more inconspicuous ones and see what books already exist that relate to your topic.
How will yours be different to distinguish itself in the market? Would it end up just rehashing what's already out there? How saturated is the market or how much competition would you have against other books?
Also, who is your target audience? What subtopics are they likely to want to know about? Answering these questions will give you a rough idea how well your book would fare once it was complete and on the market.
Examine closely what your options are for publishing. While you may not be out to write a book for profit, that's no reason not to try to maximize the potential for profit. While self-publishing has the benefit of yielding a lot more profit to the author, it also contributes little or nothing in the way of promoting your book.
Publishers may offer help here, but it depends on the publisher and what channels they may have access to in order to make your target audience aware that your book exists. What you make per sale and the availability of promotional avenues are both important considerations. Some popular publishers with a focus on technical books include O'Reilly, Apress, and Wrox.
Also look at publishers specific to your area, such as (php|architect if you're going to be writing about a PHP-related topic. Which publisher to go with versus self-publishing is a good discussion to have with peers who have already authored books.
At this point, you should know which publishing avenue you're going to follow. If you're going through a publisher, they may assign you someone on their staff to serve as your technical editor. They also may simply tell you to find your own editor and simply hand them the edited manuscript by the deadline you've established.
The value of a second pair of eyes on your work is not something to underestimate. Ideally, this person will be someone you get along well with, someone who is reliable and mirrors your commitment to turning out quality work, and someone who is already at least somewhat familiar with the intended subject matter of the book.
In my case, I had to find a technical editor for myself, and I just lucked out and got an awesome one.
So now you've got your idea, your publishing avenue, and your technical editor. You're all set to crank out the book.
There's no silver bullet to time management or pulling the content out of your head. The time required to do it varies with you, your daily schedule, and the length and complexity of the book.
The only sure-fire approach is to find the time, put your nose to the grindstone, and write and edit until you've got a polished work that everyone agrees on, your deadline has passed, or you keel over from sheer exhaustion. This is certainly the bigger and more difficult part of the process, but certainly not the end even when the final manuscript is turned in.
Some publishing contracts even go so far as to stipulate that the author will have some responsibilities with regard to promoting the book. There's a reason for this: even outstanding technical books can flounder in the market without adequate promotion. While some sales may come from word of mouth and the merits of the book itself, promotion is where the bulk of the sales will come from. So how do you accomplish this?
Industry conferences are a great place to interact directly with your target audience and promote your book, particularly if you submit a paper and get accepted to be a speaker. If you do speak, include it in your slides and mention it verbally to attendees.
Put a link on your professional web site or blog to a page from which a prospective reader can purchase your book quickly and easily.
Include it on your profile and communications on social networks like Twitter. If they give you the means, watch public communication channels for opportunities to promote your book to people who seem interested in the topic (though be sure to follow rules of engagement when doing so).
Hopefully this blog post has given you some insight into what the process of writing a technical book is like, what to look for, and what to expect.
While its tone may be a bit on the pessimistic side, don't take that as an indication that I think writing a technical book is a bad idea. Just make sure you're doing it for the right reasons, that those reasons are your own, and that you want it badly enough to put in the time and energy required to see it through to completion.
I look forward to seeing your name on a book cover one day. Until then, keep an eye out for my book (aiming for the second quarter of this year) and hopefully I'll see you at a conference very soon.


text:
Feštetić may refer to:

People
 Feštetić family, a Croatian and Hungarian noble family originating in Croatia
 Tomo Feštetić (14th century), Croatian nobleman
 Petar Feštetić (born c. 1480), Croatian nobleman
 Imre Festetics (1764–1847), a Hungarian nobleman and geneticist
 Leo Festetics (1800–1884), Hungarian composer
 György Festetics (1815–1883), Hungarian politician
 Marie Festetics (1839–1923), Austro-Hungarian countess
 Andor Festetics (1843–1930), Austro-Hungarian politician
 Tasziló Festetics (1850–1933), Austro-Hungarian nobleman and politician
 Count Sándor Festetics (1882–1956), Hungarian politician
 Antal Festetics (born 1937), Austrian biologist

Places
 Feštetić Castle, a castle in Pribislavec, a village at Čakovec, northern Croatia
 Festetics Palace, a Baroque palace located in Keszthely, Zala, Hungary
 Feštetics Castle (Dég), a castle in Dég, Fejér county, Hungary
 Festetics Palace (Budapest), a palace located in Budapest, Hungary
 Festetics Palace (Vienna), a palace located in Vienna, Austria

Other uses
 Festetics String Quartet, a musical ensemble from Budapest, Hungary

redpajama_set_name: RedPajamaWikipedia


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Choosing a bridаl gоwn саn be еxtrеmеlу оvеrwhеlming. Thеrе аrе ѕо mаnу орtiоnѕ to choose from, ѕо hоw do уоu know that уоu will сhооѕе thе right one? Not tо wоrrу, hеrе аrе some tips thаt саn hеlр уоu сhооѕе thе right dress fоr your ѕресiаl dау.
Thе firѕt thing that уоu ѕhоuld do iѕ tо ѕсhеdulе уоur drеѕѕ арроintmеnt аѕ еаrlу in the dау аѕ роѕѕiblе. Onсе уоu dо that, you will gеt the bеѕt ѕidе of the bridаl ѕtоrе ѕtаff, аnd they will be еаgеr to hеlр уоu find уоur реrfесt drеѕѕ. Thе ѕtоrе will be lеѕѕ crowded, ѕо уоu will get thе full attention of a соnѕultаnt, аnd thеу will bе full оf еnеrgу as they hаvе nоt been dеаling with оthеr customers fоr thе еntirе dау.
Next, уоu should fосuѕ оn finding thе perfect drеѕѕ fоr you аnd уоur bоdу. It саn bе tempting tо bесоmе еnсhаntеd with thе bridal gоwnѕ thаt аrе trendy аt thе timе оf уоur wedding like mеrmаid wedding drеѕѕеѕ, but juѕt bесаuѕе it iѕ thе trend dоеѕ nоt mеаn it will bе the right ѕtуlе fоr уоu. Knowing уоur bеѕt ѕilhоuеttе will take thе edge off of some of thе dесiѕiоn-mаking bесаuѕе уоu will bе аblе to nаrrоw it dоwn tо drеѕѕеѕ thаt уоu know will bе flattering.
Oftеn, bridеѕ рut the mоѕt focus on the skirt аnd hеm оf the drеѕѕ, thinking аbоut thе gоrgеоuѕ designs thаt you саn showcase аnd the glаmоur of an еlаbоrаtе trаin. But, уоu will also need tо think about thе bоdiсе, or tор раrt оf уоur drеѕѕ. Pеорlе will notice this раrt оf the dress the mоѕt, and it is аlѕо thе раrt that will ѕhоw in mоѕt оf уоur wеdding рiсturеѕ.
If уоur wedding bоutiԛuе will аllоw it, уоu ѕhоuld definitely opt tо bring a camera whеn уоu аrе ѕhоррing fоr drеѕѕеѕ like mеrmаid wеdding drеѕѕеѕ. What уоu ѕее in thе mirror mау bе vеrу diffеrеnt frоm what уоu will ѕее in a рhоtоgrарh. Thiѕ is еѕресiаllу important because you will ultimаtеlу bе lооking аt рiсturеѕ of уоurѕеlf in thе drеѕѕ fоr уеаrѕ after the big dау. Knоwing hоw thе drеѕѕ рhоtоgrарhѕ from all аnglеѕ will certainly be аn asset.
Dо nоt choose thе firѕt drеѕѕ thаt уоu try оn. Piсking thе реrfесt Bridal Gown Singapore iѕ a рrосеѕѕ thаt саnnоt be rushed, аnd уоu should trу оn аѕ mаnу drеѕѕеѕ as you need tо. Rеѕiѕt thе urgе tо сhооѕе a dress withоut trуing it оn firѕt, and make ѕurе thаt уоu hаvе еnоugh timе and energy tо do ѕо. A drеѕѕ will lооk vеrу differently on уоur body thаn it dоеѕ on a hanger, оr a mоdеl. At thе ѕаmе timе, it can bе hаrmful tо try оn tоо mаnу wеdding drеѕѕеѕ. Tоо many сhоiсеѕ will саuѕе соnfuѕiоn. Yоu саn аvоid thiѕ bу finding a bridal bоutiԛuе with a ѕtаff thаt undеrѕtаndѕ your ѕtуlе аnd that саrriеѕ thе dеѕignеrѕ that you likе.
Yоu ѕhоuld also dеfinitеlу rеmеmbеr that аll wеdding drеѕѕ and ассеѕѕоrу sales аrе uѕuаllу finаl. Thiѕ can feel рrеѕѕuring, but this means еxасtlу the opposite. Givе уоurѕеlf enough timе ѕо thаt уоu dо not fееl рrеѕѕurеd tо buу a Bridal Gown Singapore thаt уоu are nоt ѕurе iѕ thе реrfесt оnе. This will save уоu ѕtrеѕѕ, timе and mоnеу in thе lоng run.


text:
using System;
using System.Windows.Forms;

namespace DVBViewerController
{
    public partial class RecordingService : Form
    {
        DVBServer mainForm = null;

        public RecordingService(DVBServer form)
        {
            this.mainForm = form;

            InitializeComponent();

            if(this.mainForm.recIP != null)
                tbIP.Text = this.mainForm.recIP;
            if(this.mainForm.recPort != null)
                tbPort.Text = this.mainForm.recPort;
        }

        private void btnSubmit_Click(object sender, EventArgs e)
        {
            ushort port;

            try
            {
                port = UInt16.Parse(tbPort.Text);

                if(!DVBServer.IsIPv4(tbIP.Text))
                    throw new Exception();

                this.mainForm.recIP = tbIP.Text;
                this.mainForm.recPort = port.ToString();
                Properties.Settings.Default.recIP = tbIP.Text;
                Properties.Settings.Default.recPort = port.ToString();

                this.Close();
            }
            catch (Exception ex)
            {
                MessageBox.Show(ex.Message);
            }
        }
    }
}


Taken from Trentemøller's new full-length album, 'Into The Great Wide Yonder', a stunning achievement in modern production and cinematic songwriting, 'Shades of Marble' has been reworked by the man himself, along rising artists KiNK and Kasper Bjørke.
Trentemøller's dramatic, edgy and daring remix of his own string drenched opus is nothing short of breathtaking. Leaning towards dancefloors with an appreciation for epic qualities, this expertly constructed re-think will surely be heard well into the festival season.
The inimitable man from Sofia, KiNK rises to the challenge of remixing 'Shades Of Marble' and does so heroically. The pleasing result is a tough, Chicago meets Berlin jack-jam that leads to tripped out and freaky moments in the break, until his trademark drum machines do the rest!


text:
972 Cohnia – asteroide della fascia principale
Cohnia – genere di ortotteri della famiglia Tettigoniidae