text:
\section{Introduction}
\IEEEPARstart{W}{ith} the rapid development of network technology, the types and quantity of traffic data in cyberspace are increasing. Network traffic identification and classification is a crucial research task in the area of network management and security. It is the footstone of dynamic access control, network resources scheduling, content based billing, intrusion and malware detection etc. High efficient and accurate traffic classification is of great practical significance to provide service quality assurance, dynamic access control and abnormal network behaviors detection. With the widespread adoption of encryption techniques for internet, especially 5G and IoT applications, the growth of portion of encrypted traffic has dramatically posed a huge challenge for QoS, network management and security monitoring. Therefore, studies on encrypted traffic classification not only help to improve the fine-grained network resource allocation based on application, but also enhance security level of network and applications.

Traditionally, the evolution of encrypted traffic classification technology has gone through three stages: port matching, payload matching and flow statistical characteristics based classification methods. Port matching based method infers applications' types by assuming that most applications consistently use 'well known' TCP or UDP port numbers, however, the emergence of port camouflage, dynamic port, proprietary protocols with user-defined ports and tunneling technology makes these methods lose efficacy quickly. Payload matching based methods, namely, DPI (Deep Packet Inspection) technology cannot deal with encrypted traffic because of invisible packet content of encrypted traffic, in addition, it incurs high computational overhead and requires manual signatures maintenance~\cite{Finsterbusch2014,SDN-HGU,PhoneNumber}. As a result, in order to attempt to solve the aboved problems of encrypted traffic identification , flow-based methods emerged, which usually combine statistical or time series traffic features with Machine Learning (ML) algorithms,  such as naive bayes(NB), support vector machine(SVM), decision tree, Random Forest(RF), k-nearest neighbor(KNN) ~\cite{Pescape2008,Sun2010,Velan2015,Arndt2011}. Although classical machine learning approaches can solve many issues that port and payload based methods cannot solve, it still has some limitations, such as handcrafted traffic features driven by domain-expert, time-consuming, lack of ability of automation, rapidly outdated when compared to the evolution. Unlike most traditional ML algorithms, DL performs automatic feature extraction without human intervention, which undoubtedly makes it a highly desirable approach for traffic classification, especially encrypted traffic.  Recent research work has demonstrated the superiority of DL methods in traffic classification~\cite{MobileTC-2018}, such as MLP~\cite{Datanet}, CNN~\cite{deeppacket,Wang-1D-CNN,Wang2D-CNN,Seq2Img,HierarchicalTC}, SAE~\cite{blackhat}, LSTM~\cite{IoT-CNN-2017,HAST-IDS}.

However, due to the different popularity of various applications, the class imbalance problem of traffic samples often occurs when building traffic datasets. That is, the number of popular applications samples is much larger than others, which always leads to the misclassifying problems of minor applications and thereby incurs deterioration of classifier performance. Imbalanced class distribution of a dataset has posed a serious challenge to most ML based classifiers which assume a relatively balanced distribution~\cite{Japkowicz:2002}. Network traffic classification is no exception due to the imbalanced property of network traffic data~\cite{Vu2017,Vu2016}, especially encrypted traffic. Therefore, it is very crucial to address such challenges of imbalanced class distribution of traffic datasets for network traffic classification. However, there are very few studies focusing on traffic data augmentation for traffic classification to overcome the limitation of class imbalance.

In this paper, we proposed traffic data augmentation method called PacketCGAN using Conditional GAN, one of a genre of GAN, which can control the modes of the data to be generated. As a generative model, PacketCGAN exploits the benefit of CGAN to generate synthesized traffic samples by learning the characteristics of the original traffic data. The synthesized data is then combined with the original (viz. real) data to build the new traffic dataset and thereby keep balance between major and minor classes of the dataset. As a proof of concept, three classical DL models like CNN were adopted and designed to classify four types of encrypted traffic datasets augmented by ROS, SMOTE, vanilla GAN and PacketCGAN respectively using two public datasets: ISCX2012 and USTC-TFC2016. The experimental evaluation results demonstrate that DL based encrypted traffic classifier over our new dataset augmented by  PacketCGAN can achieve better performance than the other three in terms of encrypted traffic classification.

The rest of this paper is organized as follows. Section II introduces related works of traffic classification and some current methods for tackling with the problem of imbalanced class data. Section III describes the principles of GAN and CGAN. Section IV illustrates the methodology of PacketCGAN. The experimental results are provided and discussed in Section V. Section VI  concludes our work and presents some future works.

\section{Related works}\label{sec:related_works}
\subsection{ML and DL based approach of Traffic Classification}
Different from port and payload matching methods, ML based classification methods always use payload-independant parameters such as packet length, inter-arrival time and flow duration to circumvent the problems of encrypted content and user's privacy~\cite{2007legal}. Many work was carried out using ML algorithms during the last decades. In general, there are two learning strategies used: one is the supervised methods like decision tree, SVM and Naive Bayes, the other is unsupervised approaches like k-means and PCA~\cite{2008NguyenTC}. Nevertheless,  many drawbacks hindered ML based methods widely applied to traffic classification, such as handcrafted traffic features driven by domain-expert, time-consuming, unsuited to automation, rapidly outdated when compared to the evolution. Unlike most traditional ML algorithms, Deep Learning performs automatic feature extraction without human intervention, which undoubtedly makes it a highly desirable approach for traffic classification, especially encrypted traffic.  Recent research work has demonstrated the superiority of DL methods in traffic classification~\cite{MobileTC-2018,deeppacket,Datanet,Wang-1D-CNN,Wang2D-CNN,IoT-CNN-2017,HAST-IDS,Seq2Img,blackhat,TC-VAE,HierarchicalTC}. The workflow of DL based classfication usually consists of three steps. First, model inputs are defined and designed according to some principles, such as raw packets, PCAP files or flow statistics features. Second, models and algorithms are elaborately chosen according to models' characteristics and aim of the classifier. Finally, the DL classifier is trained to automatically extract the features of traffic.

\subsection{Traditional methods for handling imbalanced data}\label{subsec_imbalance}
In general, there are three methods for dealing with class imbalance problem: \emph{\textbf{Modifying the objective cost function, Sampling and Generating artificial data}}~\cite{Vu2016}. The approach of \emph{\textbf{modifying objective cost function}} alleviates the problem by means of weighting differently the data samples in minor and major classes, which gives higher score on the minor samples to penalize more intensely on miss-classifiying of the sample in the minor class. Sampling methods include two different ways of \emph{\textbf{under-sampling}} and \emph{\textbf{over-sampling}}, which is to reduce the size of major class by removing some major data samples and raise the ones in the minor class, respectively. Random under sampling (RUS) and Random over sampling (ROS) are two main methods of under-sampling and over-sampling~\cite{ROS2000}. RUS randomly removes some instances in major class, accordingly, ROS generates some copies of samples of the minor class. However, overfitting problem is always the main drawback of ROS due to generating same copies from the minor class. A classical method for generating artificial data is Synthetic Minority Over-sampling Techinique (SMOTE) in which minority samples are generated by synthetic samples rather than copies~\cite{SMOTE2002}.

\subsection{The application of GAN and other DL techniques in generating traffic data samples}
Due to the great success of GAN applying in images, computer vision and NLP etc., this innovative technique has been already applied to network security recently. A few current studies have shown that GAN has been applied in IDS and Malware detection to generate adversarial attacks to deceive and evade the detection systems~\cite{Lin2018IDSGANGA,Hu2017GAN} and thus effectively improve the performance of malware detection or IDS~\cite{Hu2017GAN,KimGAN,Lin2018IDSGANGA,Salem2018GAN,Knife-Fight}. Correspondingly, as for traffic classification, some researchers have introduced some approaches based on GAN to generate the traffic samples to overcome the imbalaced limitation of network data. In~\cite{Vu-GAN}, the authors proposed a novel method called auxiliary classifier GAN (AC-GAN) to generate synthesized traffic samples for balancing between the minor and major classes over a well-known traffic dataset NIMS. The AC-GAN took both a random noise and a class label as input in order to generate the samples of the input class label accordingly. The experimental results has shown that their proposed method achieved better performance compared to other methods like SMOTE. However, the NIMS dataset was only composed of  SSH and non-SSH two classes. In ~\cite{Hasibi2019AugmentationSF}, the authors proposed a novel data augmentation approach based on the use of Long Short Term Memory (LSTM) network to learn the traffic flow patterns and Kernel Density Estimation (KDE) for replicating the sequence of packets in a flow for classes with less population. The results have shown that this method can improve the performance of DL algorithms over augmented datasets.

\section{Generative Adversarial Networks}\label{gan}
\subsection{GAN}\label{AA}
As an unsupervised learning model, a classic GAN network consists of two parts, the generator $G$ and the discriminator $D$.
The role of the generator is to take random noise as input by learning the characteristic distribution of real data. The discriminator aims at determining whether the data is real or generated by $G$.
The generator $G$ simulates the feature distribution $P_g$ of the real data by the prior distribution $P_z(z)$. The input of the discriminator is the real and generated data, correspondingly, the output $D(x)$ indicates the probability of whether the input data is real or not~\cite{r11}. During the training process, $G$ and $D$ play a two-player mini-max game until $D$ can't judge whether the sample data is real, which means that the two networks reach the Nash Equilibrium. The objective function of GAN can be expressed by \eqref{GAN}:
\begin{equation}
	\begin{split}
		\label{GAN}
		\mathop {\min }\limits_{\rm{G}} \mathop {\max }\limits_D V(D,G) = \mathbb{E}_{x\sim{p_{data}}(x)}[\log D(x)] \\
		+ {\mathbb{E}_{z\sim{p_z}(z)}}[\log (1 - D(G(z)))]
	\end{split}
\end{equation}

\iffalse
\begin{figure}[htbp]
	\centerline{\includegraphics{GAN-20191017.png}}
	\caption{Network structure of GAN.}
	\label{fig_gan}
\end{figure}
\fi

In Equation \eqref{GAN}, $P_{data}(x)$ represents distribution of the real data. When training $D$, the goal is to optimize the probability of TRUE $D(G(z))$ as small as possible and the probability of TRUE $D\left(x\right)$ of the real data $x$ as much as possible. When training $G$, the goal is to make $D(G(z))$ as much as possible. From \eqref{GAN}, we can calculate the optimal discriminator as \eqref{eq1}. As can be seen from \eqref{eq1} below, when $P_{data}\left(x\right)=P_z\left(z\right)$, it means that $D$ cannot distinguish whether the sample is true or false, $D$ and $G$ reach the Nash Equilibrium, and the discriminator output is 0.5.

\begin{equation}
	\begin{split}
		\label{eq1}

		D(x)=\frac{P_{data}(x)}{P_{data}(x)+P_z(z)}
	\end{split}
\end{equation}

\subsection{CGAN}
In GAN, there is no control over modes of the data to be generated. The conditional GAN changes that by adding the constraint condition $y$ as an additional parameter to the generator $G$ and hopes that the corresponding images are generated. For example, in MNIST, the digit generated by GAN may be either any digit from 0-9 instead of specified one or always output the same digit. Fig.~\ref{fig_cgan} shows the network structure of CGAN.

\begin{figure}[htbp]
	\centerline{\includegraphics[width=8cm, height=6cm]{CGAN-20191017.pdf}}
	\caption{The network structure of CGAN.}
	\label{fig_cgan}
\end{figure}

The principle, structure and training process of CGAN are similar to GAN. The cost function is slightly different as is shown in \eqref{CGAN}:

\begin{equation}
	\begin{split}
		\label{CGAN}
		\mathop {\min }\limits_{\rm{G}} \mathop {\max }\limits_D V(D,G) = \mathbb{E}_{x\sim{p_{data}}(x)}[\log D(x|y)] \\
		+ {\mathbb{E}_{z\sim{p_z}(z)}}[\log (1 - D(G(z|y)))]
	\end{split}
\end{equation}

As shown in Fig.~\ref{fig_cgan}, CGAN's training process includes the following steps:
\begin{itemize}
	\item Sampling the real data to obtain $P_{data}(x)$, obtaining the label $y$ corresponding to the sampling data $P_{data}(x)$, feed $P_{data}(x)$ and $y$ into the discriminator $D$, then updating the parameters according to the output results;
	\item Generating random noise $P_z(z)$, which is then fed into generator $G$ together with label $y$ in the above step, and $G$ generates synthesized data.
	\item Feeding the synthesized data and the label $y$ generated in the above step into the discriminator $D$, and $G$ will optimize the parameters according to the output result of $D$.
	\item Repeat the above steps until $G$ and $D$ reach the Nash equilibrium.
\end{itemize}

\iffalse
\subsection{InfoGAN}
{\color{purple}In CGAN, the labels $y$ always explicitly are read and passed into the generator $G(z,y)$ and discriminator $D(x,y)$. However, in InfoGAN, the generator and discriminator are $G(z,c)$ and $D(x)$, which $c$ is the latent code representing the semantic features of the datapoints and the noise vector $z$ is the source of noise for the latent variables similar to CGAN. In InfoGAN, $c$ will be learned from $x$ in a DNN instead of initializing it explicitly with labels in CGAN. The codes are then made meaningful by maximizing the Mutual Information between the code and the Generator $G$ output. The objective function of the InfoGAN is as shown in Equation \eqref{InfoGAN}.  }

\begin{equation}
	\begin{split}
		\label{InfoGAN}
		\mathop {\min }\limits_{\rm{G}} \mathop {\max }\limits_D V_I(D,G) = V(D,G)-\lambda I(c;G(z,c))
	\end{split}
\end{equation}
with

\begin{equation}
	\begin{split}
		\label{V_InfoGAN}
		V_{GAN}(D,G) = \mathbb{E}_{x\sim{p_{data}}(x)}[\log D(x)] \\
		+ {\mathbb{E}_{z\sim{p_z}(z)}}[\log (1 - D(G(z,c)))]
	\end{split}
\end{equation}

{\color{purple}To compute $I(c,G(z,c))$, it is necessary to approximate $p(c|x)$ with a function $Q(c|x)$(Variation Maximization), which is as follows:}

\begin{equation}
	\begin{split}
		\label{Variation_Maximization}
		\mathbb{E}_{x\sim{G(z,c)},c\sim{p(c|x)}}[\log p(c|x)] \\
		= \mathbb{E}_{x\sim{G(z,c)},c\sim{p(c|x)}}[\log Q(c,x)] + \\ \mathbb{E}_{x\sim{G(z,c)},c\sim{p(c|x)}}[\log \frac{p(c|x)}{Q(c,x)}] \\
		=  \mathbb{E}_{x\sim{G(z,c)},c\sim{p(c|x)}}[\log Q(c,x)] + \\
		\mathbb{E}_{x\sim{G(z,c)},c\sim{p(c|x)}}[{D_{KL}(p(c|x))}||{Q(c,x)}]\\
		\geq  \mathbb{E}_{x\sim{G(z,c)},c\sim{p(c|x)}}[\log Q(c,x)]
	\end{split}
\end{equation}

\fi

\section{The Methodology of PacketCGAN}\label{algorithm}
\subsection{The Workflow of PacketCGAN Based Encrypted Traffic Classification}
The workflow of PacketCGAN based encrypted traffic classification is shown in Fig.~\ref{fig:workflow}, in which there are three phases: packet data preprocessing, class balancing using PacketCGAN and traffic classifier training.

\begin{figure}[ht!]
	\centering\includegraphics[width=3.3 in]{PacketCGAN-WorkFlow-ICC2020.pdf}
	\caption{The workflow of PacketCGAN.}\label{fig:workflow}
\end{figure}

\subsubsection{Phase 1}\label{data_preprocess}
Generally speaking, packet data preprocessing is the fundermental task of DL modelling. As we all know, the captured network traffic data is often saved in PCAP or PCAPNG~\cite{pcap} format, in which  packet bytes of traffic data are saved in hexadecimal. The bytes converted to decimal range from 0 to 255, which resemble the pixel range of the single-channel grayscale image. Apparently, traffic classification can refer to DL methods of image recognition. However, packets from PCAP files can not directly be used for model training and need to be pre-processed. Two public datasets used in this paper are ISCX2012~\cite{ISCX} and USTC-TFC2016~\cite{Wang2D-CNN} as shown in (A) of Fig.~\ref{fig:workflow}, which are both composed of PCAP files. We will describe the two datasets in Section.~\ref{exp:dataset}.

Packet data preprocessing consists of three steps as follows:
\begin{itemize}
	\item File header removal and packet filtering as shown in (B) of Fig.~\ref{fig:workflow}. The first 24 bytes of the PCAP file header need to be removed which only contains file information and does not help with traffic classification. Filtering useless packets in PCAP file for classification is also needed, such as APR and DHCP packets etc., which is irrelevant to specific traffic types.
	\item Truncation or zero padding as shown in (C) of Fig.~\ref{fig:workflow}. During the training of DL model, the input of which needs to be converted into a vector or matrix, which requires the length of the data input to be fixed. Nevertheless, the length of captured packet in dataset tends to be different, so it is necessary to truncate the long packets or padding those short ones based on a fixed and predefined length value~\cite{r8}, which is 1480 in this paper.
	\item Data normalization as shown in (D) of Fig.~\ref{fig:workflow}. Normalizing the pre-processed traffic data to the range [0,1] can improve the accuracy and enhance convergence speed of the model training.
\end{itemize}

After all above mentioned operations, each processed packet will be formated to a Packet Byte Vector (PBV)~\cite{Datanet} which can be stored in csv format.

\subsubsection{Phase 2}
Data balancing is the most critical problem in this phase. It is essential to read the vectors of PBV from csv files to build the matrix of PBM as the input of PacketCGAN as shown in (E) of Fig.~\ref{fig:workflow}, which will be illustrated in detail in Section.~\ref{algorithm_description}. Synthesized packet samples will be generated by PacketCGAN in PBM format and then combined with the samples of original dataset to build the new balanced traffic dataset as shown in (E) and (F) of Fig.~\ref{fig:workflow}.

\subsubsection{Phase 3: Traffic Classifier Training}
In this paper, we adopted five DL traffic classifiers  to evaluate the performance of datasets augmented by PacketCGAN, which are MLP/CNN/SAE from ~\cite{Datanet} and 1D-CNN/2D-CNN from~\cite{Wang-1D-CNN,Wang2D-CNN}, respectively. The new balanced traffic datasets will always be devided into three parts: training, testing and validation datasets. Finally, classifier related performance metrics will be evaluated to indirectly verify the quality of datasets generated by different data augmentation methods as shown in (H,I,J) of Fig.~\ref{fig:workflow}.

\subsection{The Model Architecture and Algorithm Description of PacketCGAN}\label{algorithm_description}
\subsubsection{The Description of Model Architecture}
The model of our proposed PacketCGAN is shown in Fig.~\ref{fig:pcgan_model}, in which both $G$ and $D$ adopted MLP as the basic architecture. Random noise $P_z(z)$ as the input of model with Normal Distribution are N*100-dimentional vectors, which will be fed into $G$ with $G(z)$ distribution. Different from vanilla GAN, the label of traffic/applications types with one-hot encoding will also be fed into our PacketCGAN as conditional $c$. The synthesized samples as the output of $G$ are N*1480 vectors, which will be compose of the matrix of PBM and fed into $D$ with $D(x)$ distribution eventually. Meanwhile, PBM from original unbalanced datasets with $P_{data}(x)$ distribution will be fed into $D$ with the same 1480-dimentional to compete to reach Nash equilibrium with fine-tune training.

\begin{figure}[ht!]
	\centering\includegraphics[width=3.3 in]{PCGAN_architecture.pdf}
	\caption{The Architecture of PacketCGAN.}\label{fig:pcgan_model}
\end{figure}

\subsubsection{The Description of Model Algorithm}\label{model_algorithm}
In general, PacketCGAN's training mainly includes the following three steps: the training of discriminator $D$, generator $G$ and fine-tune optimization. During the training of $D$ in step 1, one needs to fix the parameters of $G$ while training $D$. We adopt the simple MLP to build the $D$ model, in which there are three layers: the input layer, a hidden layer and the output layer.  The data for the input layer is derived from the N*1480 matrix of  PBM aforementioned in Section~\ref{data_preprocess} with the distribution of $P_{data}(x)$. The hidden layer consists of 128 neurons with the notation of $D_{h1}$.

\begin{equation}
	\begin{split}
		\label{d_input}
		D_{h1}=ReLU(input_D\cdot W^D_1+b^D_1)
	\end{split}
\end{equation}

The $input_D$ in  \eqref{d_input} is composed of the real data $x$ with $P_{data}(x)$ and the traffic/applications types label $y$ in one-hot encoding, such as 0 for SSH, 1 for YouTube. The hidden layer $D_{h1}$ adopts $ReLU$ as the activation function. The output layer has only one neuron referring to real or fake sample as following Equation:~\ref{d_output}:

\begin{equation}
	\begin{split}
		\label{d_output}
		D_{out}=D_{h1}\cdot W^D_2+b^D_2
	\end{split}
\end{equation}

\iffalse
\begin{figure*}[htbp]
	\centering
	\subfigure[Data Preprocessing]{
		\begin{minipage}[t]{0.33\linewidth}
			\centering
			\includegraphics[width=1.6in]{preprocesse.png}
			\label{preprocesse}
		\end{minipage}%
	}%
	\subfigure[PacketCGAN Model Training]{
		\begin{minipage}[t]{0.33\linewidth}
			\centering
			\includegraphics[width=1.6in]{cgan_train.png}
			\label{cgan_train}
		\end{minipage}%
	}%
	\subfigure[Unbalanced Dataset Processing]{
		\begin{minipage}[t]{0.33\linewidth}
			\centering
			\includegraphics[width=1.6in]{unba.png}
			\label{unba}
		\end{minipage}%
	}%
	\caption{The methodology of FlowGAN}
	\label{methodology}
\end{figure*}
\fi

The model uses the Adam optimization algorithm as the optimizer to update weights $W^D_1$, $W^D_2$ and bias $b^D_1$,  $b^D_2$. The overall training process of the discriminator is shown in Algorithm.~\ref{algorithm:discriminator}, in which $X_{\tau}=\begin{pmatrix}
x_{11}&\cdots&x_{1k}\\
\vdots&\ddots&\vdots\\
x_{m1}&\cdots&x_{mk}
\end{pmatrix}$
,$y_{\tau}=\begin{pmatrix}
y_{11}\\
\vdots\\
y_{m1}
\end{pmatrix}$
,k is 1480, which is the data dimension. $m$ is mini\_batch referring to a group of packets from dataset.

\begin{algorithm}[htbp]
	\caption{The training of Discriminator of PacketCGAN}  \label{algorithm:discriminator}
	\begin{algorithmic}[1]
		\REQUIRE real data $X_{\tau}$ and label $y_{\tau}$ from Section ~\ref{model_algorithm}, $\tau$ is the number of iterations
		\ENSURE the probability of the output data being real or false
		\STATE Initializing the relevant parameters, e represents the training cycle: epoches.
		\FOR {$\tau$ in $e$ }
		\FOR {each batch of $m$ input data}
		\STATE concatenate {\tiny } $X_\tau$ with $y_\tau$:
		\STATE Compute the output using Equation.~(\ref{d_input});
		\STATE Compute the output using Equation.~(\ref{d_output});
		\STATE Output discriminating results according to Equation.~(\ref{d_output});
		\STATE Optimize the loss function
		\STATE Update weights and bias;
		\ENDFOR \\
		\ENDFOR \\
	\end{algorithmic}
\end{algorithm}

Step 2 is to train the generator $G$. The parameters of $D$ need to be fixed like step 1. The input layer of $G$ consists of 115 neurons, which includes stochastic noise $P_z(z)$ with 100 neurons and sample label $y$ with 15 neurons of applications types. The hidden layer also has 128 neurons to update the weight and bias of the input data:

\begin{equation}
	\begin{split}
		\label{g_input}
		G_{h1}=ReLU(input_G\cdot W^G_1+b^G_1)
	\end{split}
\end{equation}

The 'input' in \eqref{g_output} is made up of random noise $P_z(z)$ and the sample label $y$ with $ReLu$ as the activation function. The output layer contains 1480 neurons with the same dimension of the $P_{data}(x)$. The computed result will be activated by the sigmoid function for discrimination of real or fake.

\begin{equation}
	\begin{split}
		\label{g_output}
		G\_out=sigmoid(G_{h1}\cdot W^D_2+b^D_2)
	\end{split}
\end{equation}

The optimization function is the same as the discriminator. The generator training algorithm is shown in Algorithm.~\ref{algorithm:generator}. The sample label $y$ in Algorithm.~\ref{algorithm:generator} should be consistent with $y$ in Algorithm.~\ref{algorithm:discriminator}.
$Z_{\tau}=\begin{pmatrix}
z_{11}&\cdots&z_{1n}\\
\vdots&\ddots&\vdots\\
z_{m1}&\cdots&z_{mn}
\end{pmatrix}$,$n=100$.

\begin{algorithm}[htbp]
	\caption{The training of Generator of PacketCGAN}  \label{algorithm:generator}
	\begin{algorithmic}[1]
		\REQUIRE random noise $Z_{\tau}$, and label $y_{\tau}$, $\tau$ is the number of iterations
		\ENSURE synthesized data $g_{\tau}$
		\STATE Set the relevant initialization parameters, $e$ represents the training cycle: epoches.
		\FOR {$\tau$ in $e$ }
		\FOR {each batch of $m$ input data}
		\STATE concatenant $Z_\tau$ with $y_\tau$:
		\STATE Compute the output using Equation.~(\ref{g_input});
		\STATE Compute the output using Equation.~(\ref{g_output});
		\STATE Output generation data according to Equation.~(\ref{g_output});
		\STATE Optimal the loss function
		\STATE Update weights and bias;
		\ENDFOR \\
		\ENDFOR \\
	\end{algorithmic}
\end{algorithm}

\iffalse
\begin{figure}[htbp]
	\centerline{\includegraphics[width=8cm, height=8cm]{cnn.png}}
	\caption{Model architecture of CNN.}
	\label{cnn}
\end{figure}
\fi

\section{Evaluation and Experimental results}\label{exp:results}
\subsection {Experimental Settings}\label{exp:setting}
\subsubsection{Dataset for Evaluation}\label{exp:dataset}
Two public datasets used in this paper are ISCX2012~\cite{ISCX} and USTC-TFC2016~\cite{Wang2D-CNN}, which are both composed of PCAP files. The full name of 'ISCX2012' dataset is 'ISCX VPN\-nonVPN traffic dataset', which contains many encryption applications or protocols such as HTTPS, SFTP, Facebook, Hangouts, etc. In addition to these applications encapsulated in regular session, some VPN tunnel's applications were also captured in this dataset. 15 applications were chosen to build the original dataset as shown in Table~\ref{tab:Desc_Samples_ISCX}. Apparently, the original chosen dataset happens to the problem of class imbalance, in which the majority class such as Netflix accounting for 25.13\%, while the minor class ICQ account for only 2.05\%. 10 applications were chosen from USTC-TFC2016 as shown in Table~\ref{tab:Desc_Samples_USTC}. Balanced dataset made by different generative methods are all 10000 samples per applications.

\begin{table}[htbp]
	\centering
	\fontsize{6.5}{8}\selectfont
	\begin{threeparttable}
		\caption{Description of the chosen datasets from ISCX.}  \label{tab:Desc_Samples_ISCX}
		\begin{tabular}{l|c|cc|cc|}
			\toprule
			\multirow{2}{*}{\textbf{Application}}&
			\multirow{2}{*}{\textbf{Security}}&
			\multicolumn{2}{c}{\textbf{Unbalanced dataset}}&\multicolumn{2}{|c}{\textbf{Balanced  dataset}} \cr
			\cmidrule(lr){3-4} \cmidrule(lr){5-6}
			&\textbf{Protocol} & \textbf{Quantity} & \textbf{Percentage} & \textbf{Quantity} & \textbf{Percentage}\cr

			\midrule
			AIM				&HTTPS	 	   &4869&2.356\%	  &10000&6.67\%\cr
			Email-Client&SSL			  &4417&2.137\%		 &10000&6.67\%\cr
			Facebook	&HTTPS	 	   &5527&2.674\%	  &10000&6.67\%\cr
			Gmail		   &HTTPS		  &7329&3.546\%		 &10000&6.67\%\cr
			Hangout		&HTTPS	 	   &7587&3.671\%	  &10000&6.67\%\cr
			ICQ				&HTTPS	 	   &4243&2.053\%	  &10000&6.67\%\cr
			Netflix			&HTTPS		   &51932&25.126\%	&10000&6.67\%\cr
			SCP				&SSH	  		 &15390&7.446\%	   &10000&6.67\%\cr
			SFTP			&SSH	 		 &4729&2.287\%	    &10000&6.67\%\cr
			Skype		   &proprietary	 &4607&2.229\%	    &10000&6.67\%\cr
			Spotify		   &proprietary	 &14442&6.987\%	   &10000&6.67\%\cr
			torTwitter	  &proprietary	&14654&7.089\%	  &10000&6.67\%\cr
			Vimeo		  &HTTPS		  &18755&9.074\%	&10000&6.67\%\cr
			voipbuster	&proprietary   &35469&17.161\%	&10000&6.67\%\cr
			Youtube		&HTTPS			 &12738&6.163\%	   &10000&6.67\%\cr
			\midrule

			TOTAL&  &{\bf 206688}&{\bf 100\%}&{\bf 150000}&{\bf 100\%}
		\end{tabular}
	\end{threeparttable}
\end{table}

\begin{table}[htbp]
	\centering
	\fontsize{6.5}{8}\selectfont
	\begin{threeparttable}
		\caption{Description of the chosen datasets from USTC-TFC2016.}  \label{tab:Desc_Samples_USTC}
		\begin{tabular}{l|c|cc|cc|}
			\toprule
			\multirow{2}{*}{\textbf{Application}}&
			\multirow{2}{*}{\textbf{Security}}&
			\multicolumn{2}{c}{\textbf{Unbalanced dataset}}&\multicolumn{2}{|c}{\textbf{Balanced  dataset}} \cr
			\cmidrule(lr){3-4} \cmidrule(lr){5-6}
			&\textbf{Protocol} & \textbf{Quantity} & \textbf{Percentage} & \textbf{Quantity} & \textbf{Percentage}\cr

			\midrule
			BitTorrent				&SSL	 	   &7535&8.10\%	  &10000&10.00\%\cr
			Facetime&SSL			  &2990&3.22\%		 &10000&10.00\%\cr
			FTP		   &FTP		  &11506&12.37\%		 &10000&10.00\%\cr
			Gmail		&HTTPS	 	   &11477&12.34\%	  &10000&10.00\%\cr
			MySQL				&MySQL	 	   &11385&12.24\%	  &10000&10.00\%\cr
			Outlook			&SSL		   &7467&8.03\%	&10000&10.00\%\cr
			Skype		   &proprietary	 &6028&6.48\%	    &10000&10.00\%\cr
			SMB		   &SMB	 &11543&12.41\%	   &10000&10.00\%\cr
			Weibo	  &HTTPS	&11510&12.38\%	  &10000&10.00\%\cr
			WorldOfWarcraft		  &SSL		  &11559&12.43\%	&10000&10.00\%\cr
			\midrule

			TOTAL&  &{\bf 93000}&{\bf 100\%}&{\bf 100000}&{\bf 100\%}
		\end{tabular}
	\end{threeparttable}
\end{table}

\subsubsection{Configurations of the Computing Platform}\label{exp:config}
The experimental environmental parameters of this paper are shown in Table~\ref{tab:parameters}. The performance evaluations are conducted using a Dell R730 server with an Intel I7-7600U CPU 2.8 GHz, 16 GB RAM and an external GPU (Nvidia GeForce GTX 1050TI). The software platform for deep learning is built on Keras library with Tensorflow (GPU-based version 1.13.1) as the back-end support.

\subsubsection{Description of deep learning based network traffic classifier}\label{exp:classifier}
To verify the feasibility and evaluate the performance of the PacketCGAN algorithm, we adopted three classical DL models to classify the traffic over the datasets synthesized by different generative methods, which is MLP, CNN and SAE, respectively. The detail of these three models can be find in our previous works~\cite{Datanet}.

\iffalse
we set up an unbalanced data set, a random oversampling balanced data set, and a PacketCGAN balanced data set for comparison. The classification of the three data sets was verified by the CNN convolutional neural network, the most basic deep learning model. As shown in Fig.~\ref{cnn}, we design a simple CNN model for encrypted traffic identification. The model uses 1 input layer, 1480 neurons; 3 convolution layers and pooling layer, composed of 128 convolution kernels, activated by ReLU; a fully connected layer of 128 neurons and an output layer, 15 neurons, and the activation function is Softmax for classification.
\fi

\linespread{1.5}
\begin{table}[htbp]
	\caption{Experimental Environment Parameters}
	\begin{center}
		\begin{tabular}{c c}
			\hline
			\textbf{Category}&{\textbf{Parameters}} \\
			\hline

			GPU & Nvidia GPU(GeForce GTX 1050Ti)  \\

			Operating System & Win 10 \\
			Deep learning platform & TensorFlow 1.13.1 + Keras 1.0.7\\
			CUDA Version & 9.0\\
			CuDNN Version & 7.6.0\\
			\hline
		\end{tabular}
		\label{tab:parameters}
	\end{center}
\end{table}

\subsubsection{Performance Metrics for Classification}

The performance metrics used for evaluations of network traffic classifiers are \emph{Precision}, \emph{Recall} and \emph{$F_1$ score}.
\begin{itemize}

	\item \textbf{\emph{Precision}}: precision $r_{p}$ is the ratio of \emph{true positives} $n_T^P$ over the sum of $n_T^P$ and \emph{false positives} $n_F^P$. In the proposed classification methods, precision is the percentage of packets that are properly attributed to the targeted application.
	\begin{equation}
		r_p = \frac{n_T^P}{n_T^P+n_F^P}.
	\end{equation}

	\item \textbf{\emph{Recall}}: recall $r_c$ is the ratio of $n_T^P$ over the sum of $n_T^P$ and \emph{false negatives} $n_F^N$ or the percentage of packets in an application class that are correctly identified.
	\begin{equation}
		r_c = \frac{n_T^P}{n_T^P+n_F^N}.
	\end{equation}

	\item \textbf{\emph{$F_1$-score}}: the $F_1$ score $r_f$ is a widely-used metric in information retrieval and classification that considers both precision and recall as follows:
	\begin{equation}
		r_f = \frac{2 r_p \cdot r_c}{r_p + r_c}.
	\end{equation}

\end{itemize}

\subsection {Data Augmenting using PacketCGAN}
\subsubsection {Data Augmenting Methods handling the problem of class imbalance}
In this paper, four methods were used to address the problem of the unbalanced dataset for the purpose of comparism. The first one is random oversampling method (ROS) mentioned in Section~\ref{subsec_imbalance} . The essence of this method is to randomly copy some samples of minor class to supplement the dataset. Since the simple method is only a copy of the original data, it will lead to some wrong features be learned by the model easily and even over-fitting. The second method is SMOTE in which minority samples are generated by synthetic samples rather than copies. The third one is vanilla GAN with a generator and discriminator based on MLP. The last method is to generate minor sample data using our PacketCGAN generator. The dataset balanced by different methods is described in ~\ref{exp:dataset}.

\subsubsection {The Architecture of PacketCGAN}
In our experiments, a fully connected MLP network was adopted to design generators and discriminators, as shown in Table~\ref{tab:generator} and ~\ref{tab:discriminator}. The generator's input is a 100-dimensional vector generated by random Gaussian noise with additional 15 applications types for ISCX2012 or 10 for USTC-TFC2016 datasets. The next three hidden layers have 128, 256, 512 neurons, and 1480 output neurons with additional 15 for ISCX2012 or 10 for USTC-TFC2016 datasets. Therefore, input of discriminator network is 1495 or 1490 dimension vectors mixed by real traffic data or generated data. Three hidden layers and output layers are both activated by LeakyReLU.

\linespread{1.5}
\begin{table}[htbp]
	\caption{The Architecture of Generator Network of PacketCGAN}
	\fontsize{6.5}{8}\selectfont
	\begin{center}
		\begin{tabular}{c| c| c}
			\hline
			\textbf{Layer(type)}&{\textbf{Output Shape (ISCX)}} &{\textbf{Output Shape (USTC)}} \\
			\hline

			dense\_1(Dense) 	& 115 	& 110  \\

			LeakyReLU 			& 115  	& 110   \\
			dense\_2(Dense) 	& 256 	& 256	\\
			LeakyReLU 			& 256	& 256    \\
			dense\_3(Dense) 	& 512 	& 512   \\
			LeakyReLU 			& 512	& 512   \\
			dense\_4(Dense)		 & 1495 & 1490	\\
			LeakyReLU 			& 1495		& 1490 \\

			\hline
		\end{tabular}
		\label{tab:generator}
	\end{center}
\end{table}

\linespread{1.5}
\begin{table}[htbp]
	\caption{The Architecture of Discriminator Network of PacketCGAN}
	\fontsize{6.5}{8}\selectfont
	\begin{center}
		\begin{tabular}{c| c| c}
			\hline
			\textbf{Layer(type)}&{\textbf{Output Shape (ISCX)}} &{\textbf{Output Shape (USTC)}} \\
			\hline

			dense\_6(Dense) 	& 1495 	& 1490  \\

			LeakyReLU 			& 1495  	& 1490   \\
			Dropout 				& 1495  	& 1490   \\
			dense\_7(Dense) 	& 512 	& 512	\\
			LeakyReLU 			& 512	& 512    \\
			Dropout 				& 512  	& 512   \\
			dense\_8(Dense) 	& 256 	& 256   \\
			LeakyReLU 			& 256	& 256   \\
			Dropout 				& 256  	& 256   \\
			dense\_9(Dense)		 & 128 	& 128	\\
			LeakyReLU 			& 128		& 128 \\
			Dropout 				& 128  	& 128   \\
			dense\_10(Dense) 	& 1  	& 1   \\

			\hline
		\end{tabular}
		\label{tab:discriminator}
	\end{center}
\end{table}

\subsubsection {The Training of PacketCGAN}
Table~\ref{training_parameters} shows the parameters of the optimizer, loss function, epoches, and mini\_batch used in the deep training model training process.

\linespread{1.5}
\begin{table}[htbp]
	\fontsize{6.5}{8}
	\caption{Training Parameters of Packet CGAN}
	\begin{center}
		\begin{tabular}{c c c }
			\hline
			\textbf{Training Parameters}&{\textbf{Generator}}&{\textbf{Discriminator}} \\
			\hline

			Optimizer & Adma & Adma   \\

			Loss Function & Cross-Entropy & Cross-Entropy  \\
			Epoches & 200000 & 200000 \\
			Mini\_batch & 64 & 64 \\
			\hline
		\end{tabular}
		\label{training_parameters}
	\end{center}
\end{table}

Figure~\ref{cgan_loss} shows the trend of loss of generator and discriminator during the PacketCGAN training process. It can be seen that PacketCGAN does not improve the unstability characteristics of GAN. The loss of $G$ and $D$ always fluctuates within a range instead of convergence. Nevertheless, it is a big improvement that only one CGAN model needs to be trained when using PacketCGAN compared with vanilla GAN. One can generate any samples with the additional input of the applications types as conditional $c$, while vanilla GAN can only generate one type of samples at a time, that means we can control the output of the PacketCGAN generator so that we can easily generate specified minor classes samples needed to be augmented to handle the problem of class balance.

\begin{figure}[htbp]
	\centering
	\subfigure[d\_loss for ISCX2012]{
		\begin{minipage}[t]{0.5\linewidth}
			\centering
			\includegraphics[width=1.6in]{dloss_ISCX.png}
		\end{minipage}%
	}%
	\subfigure[g\_loss for ISCX2012]{
		\begin{minipage}[t]{0.5\linewidth}
			\centering
			\includegraphics[width=1.7in]{gloss_ISCX.png}
		\end{minipage}%
	}%
	\\
	\centering
	\subfigure[d\_loss for USTC-TFC2016]{
		\begin{minipage}[t]{0.5\linewidth}
			\centering
			\includegraphics[width=1.6in]{dloss_USTC.png}
		\end{minipage}%
	}%
	\subfigure[g\_loss for USTC-TFC2016]{
		\begin{minipage}[t]{0.5\linewidth}
			\centering
			\includegraphics[width=1.7in]{gloss_USTC.png}
		\end{minipage}%
	}%

	\caption{Loss of PacketCGAN}
	\label{cgan_loss}
\end{figure}

Fig.~\ref{confusion_matrices_ISCX} and ~\ref{confusion_matrices_USTC} shows the Confusion Matrices of the CNN-based encrypted traffic identification model based on 8 datasets augmented by four different generative methods, which are ROS/SMOTE/GAN/CGAN over ISCX2012 and USTC-TFC2016, respectively. The elements on the diagonal of Confusion Matrix refer to the correct ones of classification and all the others are mis-classified. It can be clearly seen that the false positive rate of minor class in Fig~\ref{cgan_cm_iscx} and ~\ref{cgan_cm_ustc} is lower than ROS as shown in  Fig~\ref{ros_cm_iscx} and ~\ref{ros_cm_ustc}, SMOTE as shown in  Fig~\ref{smote_cm_iscx} and ~\ref{smote_cm_ustc} and GAN as shown in  Fig~\ref{gan_cm_iscx} and ~\ref{gan_cm_ustc}.

\begin{figure*}[htb]
	\centering
	\subfigure[ROS]{
		\begin{minipage}[t]{0.25\linewidth}
			\centering
			\includegraphics[width=1.7in]{CNNBase_on_istc_REPEAT.png}
			\label{ros_cm_iscx}
		\end{minipage}%
	}%
	\subfigure[SMOTE]{
		\begin{minipage}[t]{0.25\linewidth}
			\centering
			\includegraphics[width=1.7in]{CNNBase_on_istc_SMOTE.png}
			\label{smote_cm_iscx}
		\end{minipage}%
	}%
	\subfigure[GAN]{
		\begin{minipage}[t]{0.25\linewidth}
			\centering
			\includegraphics[width=1.7in]{CNNBase_on_istc_GAN.png}
			\label{gan_cm_iscx}
		\end{minipage}%
	}%
	\subfigure[PacketCGAN]{
		\begin{minipage}[t]{0.25\linewidth}
			\centering
			\includegraphics[width=1.7in]{CNNBase_on_istc_CGAN.png}
			\label{cgan_cm_iscx}
		\end{minipage}%
	}%
	\caption{Confusion Matrices of CNN-based encrypted traffic identification method on ISCX2012 dataset}
	\label{confusion_matrices_ISCX}

	\centering
	\subfigure[ROS]{
		\begin{minipage}[t]{0.25\linewidth}
			\centering
			\includegraphics[width=1.7in]{CNNBase_on_ustc_REPEAT.png}
			\label{ros_cm_ustc}
		\end{minipage}%
	}%
	\subfigure[SMOTE]{
		\begin{minipage}[t]{0.25\linewidth}
			\centering
			\includegraphics[width=1.7in]{CNNBase_on_ustc_SMOTE.png}
			\label{smote_cm_ustc}
		\end{minipage}%
	}%
	\subfigure[GAN]{
		\begin{minipage}[t]{0.25\linewidth}
			\centering
			\includegraphics[width=1.7in]{CNNBase_on_ustc_GAN.png}
			\label{gan_cm_ustc}
		\end{minipage}%
	}%
	\subfigure[PacketCGAN]{
		\begin{minipage}[t]{0.25\linewidth}
			\centering
			\includegraphics[width=1.7in]{CNNBase_on_ustc_CGAN.png}
			\label{cgan_cm_ustc}
		\end{minipage}%
	}%
	\caption{Confusion Matrices of CNN-based encrypted traffic identification method on USTC-TFC2016}
	\label{confusion_matrices_USTC}
\end{figure*}

Fig~\ref{performance} shows the performance metrics of 8 datasets more clearly. As we can see from Fig~\ref{performance} that PacketCGAN greatly outperforms the other three methods for the minor class like gmail, facebook.

\begin{figure*}[htb]
	\centering
	\subfigure[ROS]{
		\begin{minipage}[t]{0.25\linewidth}
			\centering
			\includegraphics[width=1.7in]{ISCX_ROS_BAR.png}
		\end{minipage}%
	}%
	\subfigure[SMOTE]{
		\begin{minipage}[t]{0.25\linewidth}
			\centering
			\includegraphics[width=1.7in]{ISCX_SMOTE_BAR.png}
		\end{minipage}%
	}%
	\subfigure[GAN]{
		\begin{minipage}[t]{0.25\linewidth}
			\centering
			\includegraphics[width=1.7in]{ISCX_GAN_BAR.png}
		\end{minipage}%
	}%
	\subfigure[PacketCGAN]{
		\begin{minipage}[t]{0.25\linewidth}
			\centering
			\includegraphics[width=1.7in]{ISCX_CGAN_BAR.png}
		\end{minipage}%
	}%

	\caption{Performance metrics of DL-based encrypted traffic identification method on ISCX}

	\centering
	\subfigure[ROS]{
		\begin{minipage}[t]{0.25\linewidth}
			\centering
			\includegraphics[width=1.7in]{2DCNNBase_on_ustc_oversample.png}
		\end{minipage}%
	}%
	\subfigure[SMOTE]{
		\begin{minipage}[t]{0.25\linewidth}
			\centering
			\includegraphics[width=1.7in]{2DCNNBase_on_ustc_smote.png}
		\end{minipage}%
	}%
	\subfigure[GAN]{
		\begin{minipage}[t]{0.25\linewidth}
			\centering
			\includegraphics[width=1.7in]{2DCNNBase_on_ustc_GAN.png}
		\end{minipage}%
	}%
	\subfigure[PacketCGAN]{
		\begin{minipage}[t]{0.25\linewidth}
			\centering
			\includegraphics[width=1.7in]{CNNBase_on_ustc_CGAN_BAR.png}
		\end{minipage}%
	}%
	\caption{Performance metrics of DL-based encrypted traffic identification method on USTC-TFC2016}
	\label{performance}
\end{figure*}

From Table ~\ref{tab:Desc_Samples}, we can see the detail of the evaluation performance metrics of classification over the datasets augmented by different methods more clearly. For simplicity, only the performance metrics of ISCX2012 datasets are presented here as an example. The performance statistics of classification based on all four methods are shown in Table ~\ref{table:performance_classifier}, apparently, our proposed PacketCGAN has achieved better performance than the other three methods.

\begin{table*}[htb]
	\centering
	\fontsize{5.5}{8}\selectfont
	\begin{threeparttable}
		\caption{Description of Experimental results on different generative methods for ISCX2012}  \label{tab:Desc_Samples}
		\begin{tabular}{l|ccc|ccc|ccc|ccc|ccc|}
			\toprule
			\multirow{2}{*}{\textbf{Application}}&
			\multicolumn{3}{c}{\textbf{Unbalanced dataset}}&\multicolumn{3}{|c}{\textbf{ROS  dataset}}&\multicolumn{3}{|c}{\textbf{PacketCGAN  dataset}} &\multicolumn{3}{|c}{\textbf{GAN  dataset}}
			&\multicolumn{3}{|c}{\textbf{PacketCGAN  dataset}}
			\cr
			\cmidrule(lr){2-4} \cmidrule(lr){5-7}  \cmidrule(lr){8-10}  \cmidrule(lr){11-13}  \cmidrule(lr){14-16}
			&\textbf{Precision} & \textbf{Recall} & \textbf{F1-Score} &\textbf{Precision} & \textbf{Recall} & \textbf{F1-Score}&\textbf{Precision} & \textbf{Recall} & \textbf{F1-Score}&\textbf{Precision} & \textbf{Recall} & \textbf{F1-Score}&\textbf{Precision} & \textbf{Recall} & \textbf{F1-Score}\cr

			\midrule
			AIM			 & 0.8823&0.9027&0.8924  	& 0.9757&0.9557&0.9656 &	0.9532&0.9386&0.9642  & 0.9989&0.9989&0.9989 &	0.9792&0.9875&0.9833\cr
			Email-Client & 0.9949&0.9893&0.9921 & 0.9985&0.9866&0.9925 &	0.9541&0.9627&0.9558  & 0.9821&0.9584&0.9701 &	0.9792&0.9875&0.9833\cr
			Facebook	 & 0.9287&0.913&0.9208 & 0.9808&0.9571&0.9688 &	0.9531&0.9639&0.9579   	   & 0.9469&0.9667&0.9567 &	0.9792&0.9875&0.9833\cr
			Gmail		 & 0.9803&0.988&0.9841 & 0.9939&0.9919&0.9929 &	0.9426&0.9578&0.9512 	      & 0.9417&0.9436&0.9427 &	0.9792&0.9875&0.9833\cr
			Hangout		 & 0.9421&0.9827&0.962 & 0.9712&0.9881&0.9796 &	0.9578&0.9621&0.9601       & 0.9669&0.9881&0.9774 &	0.9792&0.9875&0.9833\cr
			ICQ			 & 0.9778&0.9225&0.9494 & 0.9343&0.9807&0.9569 &	0.9731&0.9639&0.9681     & 0.9731&0.9778&0.9754 &	0.9792&0.9875&0.9833\cr
			Netflix		 & 0.9999&0.9965&0.9982 & 0.9983&0.9978&0.998 &	0.9781&0.9651&0.9688         & 0.998&0.9998&0.9998 &	0.9792&0.9875&0.9833\cr
			SCP			 & 0.9997&0.9989&0.9993 & 0.9343&0.9982&0.9991 &	1&0.9679&0.9736  	       & 0.9891&0.9872&0.9882 &	0.9792&0.9875&0.9833\cr
			SFTP		 & 0.9963&0.9942&0.9952 & 0.9995&0.998&0.9988 &	0.9841&0.9837&0.9826  	   & 0.9833&0.9652&0.9742 &	0.9792&0.9875&0.9833\cr
			Skype		 & 0.9473&0.992&0.9692 & 0.996&0.996&0.996 &	0.9881&0.9871&0.9873  	      & 0.9787&0.9787&0.9787 &	0.9792&0.9875&0.9833\cr
			Spotify		 & 0.995&0.9935&0.9942 & 0.9952&0.9975&0.9964 &	0.9731&0.9652&0.9663  	   & 0.9768&0.9671&0.9719 &	0.9792&0.9875&0.9833\cr
			torTwitter & 0.9343&0.9993&0.9997 & 0.9343&0.9992&0.9996 &	0.9831&0.9846&0.9841  	 & 0.9908&0.9998&0.9954 &	0.9792&0.9875&0.9833\cr
			Vimeo		 & 0.9973&0.9951&0.9962 & 0.9948&0.9973&0.9961 &	0.9656&0.9631&0.9642  & 0.9718&0.9564&0.9641 &	0.9792&0.9875&0.9833\cr
			voipbuster & 0.9991&0.9965&0.9978 & 0.9343&0.9909&0.9954 &	0.9639&0.9745&0.9721     & 0.9887&0.9826&0.9856 &	0.9792&0.9875&0.9833\cr
			Youtube		 & 0.9975&0.9986&0.998 & 0.9997&0.9982&0.999 &	0.968&0.9662&0.9731  		& 0.9613&0.9807&0.9709 &	0.9792&0.9875&0.9833\cr
			\midrule

			Average&{\bf 0.9759}&{\bf 0.9775}&{\bf 0.9766}&{\bf 0.9892}&{\bf 0.9889}&{\bf 0.9891}&{\bf 0.9751}&{\bf 0.9789}&{\bf 0.9771}&{\bf 0.9766}&{\bf 0.9767}&{\bf 0.9766}&{\bf 0.9936}&{\bf 0.9958}&{\bf 0.9947}
		\end{tabular}
	\end{threeparttable}
\end{table*}

\begin{table}[htb]
	\centering
	\fontsize{6.5}{8}
	\caption{Classification Performance Statistics.}\label{table:performance_classifier}
	\begin{tabular}{|l|c|c|c|c|
		\hline
		data augmenting methods & Accuracy & Precision & Recall & F1-Score \\
		\hline
		\textbf{unbalanced dataset}  & 0.9797 & 0.9759 & 0.9775 &  0.9766 \\
		\hline
		\textbf{ROS dataset}   & 0.9889 & 0.9892 & 0.9889& 0.9891 \\
		\hline
		\textbf{SMOTE dataset}   & 0.9769 & 0.9751 & 0.9789& 0.971 \\
		\hline
		\textbf{GAN dataset}   & 0.9766 & 0.9766 & 0.9767 & 0.9766 \\
		\hline
		\textbf{PacketCGAN dataset}   & 0.9951 & 0.9936 & 0.9958  & 0.9947 \\
		\hline
	\end{tabular}
\end{table}

\section{Conclusion and Future work}\label{conclusion}
In this paper, we proposed a CGAN-based traffic data augmenting method called PacketCGAN to solve the class imbalance problem in the dataset. After the specified sample data is generated by the generator of PacketCGAN, the synthesized data is combined with the original data to build  a new balanced dataset. We use MLP/CNN/SAE models to verify the classification performance over different datasets. The experimental results show that the balanced dataset augmented by FlowCGAN can achieve better performance than the others including ROS, SMOTE and GAN. In the future, we will further study other types of GAN applications in traffic classification and  try to solve the problems of unstable training and mode collapse.

\section*{Acknowledgment}
This work was supported by the National Science Foundation of China (61972211)

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M'enard, R., S. E. Cohn, L.-P. Chang, and P. M. Lyster. 2000. "Assimilation of stratospheric chemical tracer observations using a Kalman filter. Part I: Formulation." Monthly Weather Review, 128 (8): 2654--2671.
Tippett, M. K., S. E. Cohn, R. Todling, and D. Marchesin. 2000. "Low-dimensional representation of error covariance." Tellus, 52A (5): 533-553.
Riishojgaard, L., S. E. Cohn, Y. Li, and R. M'enard. 1998. "The use of spline interpolation in semi-Lagrangian transport models." Monthly weather review, 126 (7): 2008--2016.
Cohn, S. E., A. Da Silva, J. Guo, M. Sienkiewicz, and D. Lamich. 1998. "Assessing the Effects of Data Selection with the DAO Physical-Space Statistical Analysis System*." Monthly Weather Review, 126 (11): 2913--2926.
Cohn, S. E. 1997. "An introduction to estimation theory." JOURNAL-METEOROLOGICAL SOCIETY OF JAPAN SERIES 2, 75: 147--178.
Kalnay, E., D. L. Anderson, A. F. Bennett, et al. A. J. Busalacchi, S. E. Cohn, P. Courtier, J. Derber, A. C. Lorenc, D. Parrish, and T. Schlatter. 1997. "Data assimilation in the ocean and in the atmosphere: what should be next?" Journal of the Meteorological Society of Japan, 75 (1B): 489--496.
Lyster, P., S. E. Cohn, R. Menard, et al. L. Chang, S. Lin, and R. Olsen. 1997. "Parallel implementation of a Kalman filter for constituent data assimilation." Monthly weather review, 125 (7): 1674--1686.
Cohn, S. E., and R. Todling. 1996. "Approximate data assimilation schemes for stable and unstable dynamics." JOURNAL-METEOROLOGICAL SOCIETY OF JAPAN SERIES 2, 74: 63--75.
Cohn, S. E. 1993. "Dynamics of short-term univariate forecast error covariances." Monthly weather review, 121 (11): 3123--3149.
Cohn, S. E., and D. F. Parrish. 1991. "The behavior of forecast error covariances for a Kalman filter in two dimensions." Monthly Weather Review, 119 (8): 1757--1785.
Cohn, S. E., D. Dee, D. Marchesin, E. Isaacson, and G. Zwas. 1985. "A fully implicit scheme for the barotropic primitive equations." Monthly weather review, 113 (4): 436--448.


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Home » World News » Doctor accused of raping ex-patient at the Espy has charges struck out
Doctor accused of raping ex-patient at the Espy has charges struck out
A Melbourne doctor who had been accused of the rape and sexual assault of a former patient at the Hotel Esplanade in St Kilda has had his charges struck out.
The doctor, who cannot be named for legal reasons, watched a remote hearing at Melbourne Magistrates Court on Friday as prosecutor Ashleigh Harrold applied for all charges to be withdrawn. Ms Harrold did not say why the charges were to be withdrawn.
Deputy chief magistrate Susan Wakeling struck out the charges and ordered Victoria Police to pay the doctor's legal costs.
Police charged the doctor in May on summons with two counts of rape and three of sexual assault following a 14-month investigation by the Bayside sexual offences and child abuse investigation team. Detectives seized CCTV footage as part of their investigation.
Police had alleged the incident occurred in a cubicle for disabled patrons on March 29 last year.
The doctor's lawyers previously said their client denied the allegations.
A source familiar with the police investigation previously said it was to be alleged that the doctor was socialising at the St Kilda landmark when he met the woman, who he had previously treated at a clinic in a bayside suburb.
The Australian Health Practitioner Regulation Agency suspended the doctor's registration as a medical practitioner in July last year.
The agency has been contacted for comment on the doctor's suspension.
The Age is unable to name the doctor after his legal team this year obtained a suppression order by claiming the public identification of his client would cause significant psychological harm to the doctor and his wife, and damage his professional standing and earning capacity.
If you or anyone you know needs support, you can contact the National Sexual Assault, Domestic and Family Violence Counselling Service on 1800RESPECT (1800 737 732), Lifeline 131 114, or Beyond Blue 1300 224 636.
The most important news, analysis and insights delivered to your inbox at the start and end of each day. Sign up here.

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text:
Art UK	Art Foundation
The Virgin and Child Enthroned with Eight Saints (triptych)
Nardo di Cione	(c.1320–1365/1366)
Photo credit: The Samuel Courtauld Trust, The Courtauld Gallery, London
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This small triptych is believed to have been created in Florence by one of the most prominent painters of the mid-fourteenth century, Nardo di Cione. The author of fresco cycles in churches throughout the city, Nardo also produced altarpieces and domestic devotional objects, such as this triptych. These became highly collectible in the nineteenth century. As a result, damaged works were repaired and overpainted, as is the case here.
The frame structure dates from the mid-nineteenth century, although it follows general Renaissance principles. While the central panel has been heavily repainted, the side panels still display Nardo's characteristic style and sensitive rendering of human emotion.
Read more on The Courtauld Gallery
14th C
tempera on panel
H 64.3 x W 14.6 cm;
H 64.6 x W 15 cm
P.1966.GP.300
Acquisition method
bequeathed by Mark Gambier-Parry, 1966
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Somerset House, Strand, London, Greater London WC2R 0RN England
This venue is open to the public. Not all artworks are on display. If you want to see a particular artwork, please contact the venue.
Artist or maker
Nardo di Cione
c.1320–1365/1366
The Coronation of the Virgin Victoria and Albert Museum
The Virgin and Child Enthroned with Eight Saints The Courtauld Gallery
Saint John the Baptist with Saint John the Evangelist (?) and Saint James The National Gallery, London
Virgin and Child Enthroned with Eight Saints The Courtauld Gallery
Crucifixion with Virgin of the Annunciation The Courtauld Gallery
Nativity with Angel of the Annunciation The Courtauld Gallery
The Crucifixion with the Virgin Mary and St John The Ashmolean Museum of Art and Archaeology


text:
Tag archive for 'Oscar Isaac'
By Brandon Jones On Saturday, July 22nd, 2017
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Adam Driver praises 'The Last Jedi' as 'remarkable' and creates 'new rules' in Universe
Adam Driver goes into full hype mode when talking about this year's Star Wars: The Last Jedi, the eighth film in the core Star Wars franchise. He has raved about the script, about director Rian Johnson, More...
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Writer/Director Rian Johnson has not said much about his upcoming film Star Wars Episode 8, but as part of a 2017 preview, Johnson talked briefly with USA Today and teased the plot for Star Wars Episode 8. "I More...
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Fans are eager to learn more about Star Wars 8, the follow-up to Star Wars: The Force Awakens, and while appearing on the Larry King show, Adam Driver alluded to plot points which will explain Kylo Ren drift More...
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Steven Spielberg will be arriving this summer with The BFG, arriving in cinemas in the next few weeks, before embarking on directing the big screen take on Ernest Cline's novel, Ready Player One. After that fans More...
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'X-Men Apocalypse' fall short of previous films, but sets up new cast, adventure
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Chris Claremont discusses death threats for killing Jean Grey
Comic book fans know his name as Chris Claremont wrote The Uncanny X-Men and many spin-off titles between 1975 and 1991. Inside Empire's X-Men: Apocalypse cover story Claremont discussed key storylines More...
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New 'X-Men Apocalypse' trailer reveals mutants at war
Fox has released a new trailer for X-Men: Apocalypse, revealing a lot of new footage. The new film centers on the first and most powerful mutant from Marvel's X-Men universe, an immortal named Apocalypse More...
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New 'X-Men Apocalypse' poster showcases Four Horsemen
Fox unveiled a new poster for X-Men: Apocalypse focusing on the key four henchmen of Oscar Isaac's titular villain: Storm (Alexandra Shipp), Psylocke (Olivia Munn), Magneto (Michael Fassbender) and Archangel More...
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'Star Wars The Force Awakens' arrives on DVD, Blu-Ray on April Fool's Day
Star Wars: The Force Awakens shattered box office records upon its debut to become the cinematic event of a generation with over $926 million domestic and $2.05 billion worldwide. As announced this morning by Chairman More...
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Laredo police arrest man accused of stealing acquaintance's car
César Rodriguez, LMTonline.com / Laredo Morning Times
Jan. 8, 2019 Updated: Jan. 8, 2019 10:19 a.m.
Ricardo Campos, 34, was charged with unauthorized use of a vehicle.
Webb County Sheriff's OfficeShow MoreShow Less
Keep scrolling to see some of the most bizarre arrests that occurred in Laredo in 2018.
Justin Castañeda, 24, was charged with possession of a controlled substance, possession of marijuana and terroristic threat against a public servant. Police said they received several calls from people at North Central Park that a man was offering them drugs. Police identified the individual as Castañeda.
Rosemary Centeno, 21, was charged with assault, four counts of assault on a public servant, resisting arrest and terroristic against a public servant after allegedly threatening an officer. A policeman said Centeno threatened to find his family and "cut my head off."
Police said Adriana Marcelina Pellum, 38, fired a gun in September to scare away two men who went to her home to recover a Dallas Cowboys blanket that belonged to their friend.
CourtesyShow MoreShow Less
Carlos Davalos, 26, is accused of stealing laundry detergent from a local Family Dollar store at knifepoint, according to police.
Alejandro Gonzalez, 54, was arrested for slapping a 10-year-old boy on the head while passing him on a sidewalk, police said.
Melizza Medina, 23, is accused of allegedly biting a man and breaking a glass object over his head after he receives call from another female, police said.
Videos of Miguel Francisco Fernandez, 30, who was seen evading Laredo authorities in August went viral on Facebook. In one of the videos, Fernandez is seen leading police on a pursuit before coming to a stop and appearing to surrender. As an officer approaches his vehicle, he then drives away and is pursued for a while longer before becoming surrounded by police vehicles. He was charged with evading arrest with a motor vehicle.
Police said a female employee at Coyote Drive-Thru, 3401 Santa Maria Ave, was dragged by a vehicle as she held on to beer during a theft.
A car went up in flames after crashing near a gas pump in north Laredo in August. As the vehicle became engulfed in flames, the driver refused to exit the vehicle, a video of the crash indicates. When authorities arrived at the gas station, he then jumped out of the vehicle and fled the scene. No charges have been brought against the individual.
Cesar Martinez/Laredo Morning TimesShow MoreShow Less
Priscilla Villanueva, 17, was charged with aggravated assault with a deadly weapon after allegedly hitting a man with a glass bottle upon seeing him with another female.
Jeanette Lopez, 24, was charged with aggravated assault against a security officer after she allegedly attacked a TKO bouncer with a beer bottle.
Alberto Alvarez, 21, was charged with aggravated assault with a firearm for allegedly pistol-whipping a person who was urinating.
22of45Alberto Aaron Juarez, 20, was charged with two counts of deadly conduct, criminal mischief and unlawful carrying of a weapon after allegedly firing a gun at vehicles. Police said he was angry that a woman had humiliated him after he declared his love for her.Webb County Sheriff's OfficeShow MoreShow Less
Reymundo Hernandez, 34, was charged with robbery after allegedly stealing probiotic supplements from a local CVS.
Jennifer Lynn Castillo, 28, was charged with theft after allegedly stealing $150 worth of brisket from a local grocery store.
Officers found a Minecraft TNT novelty pillow after responding to reports of a suspicious package at the H-E-B on Calton Road and San Dario Avenue.
Steven Hernandez Show MoreShow Less
Javier Solis Jr., 36, was charged with making a false report to police after telling police someone stole his Cadillac from a strip club. Police later confirmed the Cadillac was involved in a crash.
29of45Djose Ike Hernandez, 24, was charged with aggravated assault with a deadly weapon after he pulled out a firearm during a road rage incident. Laredo police said the weapon turned out to be an air sport gun.Webb County Sheriff's OfficeShow MoreShow Less
Javier Pedroza, 25, and Yanira Vasquez, 27, were charged with robbery after allegedly carjacking a man in central Laredo. The male complainant told police that the woman pulled him out of his vehicle and injected him with a dark brown liquid before stealing his car.
Casimiro Tovar-Rivera, 64, and Maria Adriana Solis-Lopez, 57, were arrested after allegedly stealing an iPhone while operating their ice cream van.
Laredo Police Department Show MoreShow Less
Christian April Resendez, 26, was arrested after allegedly stealing a 6-foot teddy bear from a local Wal-Mart.
35of45John Velasquez, 25, was charged with robbery after allegedly robbing a woman of her purse after they met at a club.Webb County Sheriff's OfficeShow MoreShow Less
37of45Arturo Zavala, 25, was charged with aggravated assault with a knife. His sister told police he pointed a knife at her chest after she confronted him about stealing meat from the refrigerator.Webb County Sheriff's OfficeShow MoreShow Less
38of45A man told police that he was pistol-whipped several times while celebrating a birthday party in Colonia Los Obispos. Bill Hutchinson / The ChronicleShow MoreShow Less
40of45Jose Fernandez, 32, was charged with indecent exposure after officers responded to reports of a naked man walking out of a local park.Webb County Sheriff's Office /Courtesy photoShow MoreShow Less
Santiago Aguilar, 44, and Karla Alvarado, 40, were charged with theft after allegedly stealing 26 televisions valued at $250 each from hotel.
Webb County Sheriffs OfficeShow MoreShow Less
Jose Manuel Rodriguez, 30, and Yaritza Rivera, 24, were charged with public lewdness after allegedly having sex in the parking lot of a baseball field in central Laredo.
Jessica Carlson, 30, was charged with abandoning a child with intent to return, resisting arrest and aggravated assault on a peace officer.
A man featured as one of Laredo Crime Stoppers' most wanted has been arrested for allegedly driving off in a vehicle that belonged to an acquaintance.
Ricardo Campos, 34, was served with an arrest warrant on Friday that charged him with unauthorized use of a vehicle. He remained behind bars at the Webb County Jail as of Monday afternoon, according to custody records.
READ ALSO: Over $24K in rewards given to Laredo Crime Stoppers' tipsters in 2018
The case dates back to Nov. 29. At about 6:45 a.m. that day, officers were dispatched to a stolen vehicle report in the 5300 block of San Dario Avenue. A man told police that he had given a ride to an acquaintance he had met that night.
When he exited his Nissan Altima to withdraw money from an ATM in the 5300 block of San Dario, the acquaintance, Campos, got impatient, jumped over to the driver's seat and drove off, according to police.
LPD said the owner later recovered the vehicle. It had broken windows, other damages and missing items, police said.
César Rodriguez
César G. Rodriguez is a reporter for the Laredo Morning Times.


text:
Amazon Echo Show 10 (3rd Generation)
byWill Greenwald Sep 30, 2020
Smart displays are usually stationary screens. They have microphones and speakers, and you can talk to them and they answer back, but they otherwise just stay still. Their screens point in one direction, and if you want them to point somewhere else, you have to move them yourself. That isn't the case with the third-generation $249.99 Amazon Echo Show 10, coming out later this year, which features a motorized screen with a camera that can follow you as you move around the room.
The third-gen Echo Show 10 features a 10.1-inch, 1,200-by-800-pixel screen mounted on a cylindrical speaker base, with a motorized "neck" that lets the screen pivot left and right. When using it, the screen will turn to face you wherever you are in the room. This means you can wander freely as you take a video call, or move around in the kitchen while following a recipe.
The motorized base works in conjunction with a new 13-megapixel camera built into the screen, for smart framing during video calls. While mechanically panning, the camera can also digitally pan and zoom to keep your face in the center of the frame.
Amazon has slowly been expanding the video call capabilities of its devices, so you can use the Echo Show to make calls to other Show devices, the Amazon Alexa app, and Skype, with support for Zoom coming soon.
The pivoting camera on the screen enables a new home security function with Alexa Guard. The Echo Show can periodically scan the room it's in and let you know if it detects anyone while you're away. You can also access the camera directly from the Alexa app or another Echo Show device, turning the smart display into a home security camera of sorts (though without recording capabilities).
The speaker base features two one-inch tweeters and a three-inch woofer, the same drivers used in the new Amazon Echo smart speaker. It also includes a built-in smart home hub that supports Zigbee, Bluetooth Low Energy (BLE), and Amazon Sidewalk, just like the new Echo.
The Echo Show 10 also incorporates Amazon's AZ1 Neural Edge processor, a chip designed for machine learning. According to the company, it enables a new speech recognition system that makes Alexa more responsive through edge processing, relying on onboard power in addition to Amazon's servers.
While the third-generation Echo Show 10 is slightly more expensive than the previous previous model, the addition of a motorized base and more powerful speakers seems worth it for the additional $20. We'll let you know for sure when we get one in for testing.
About Will Greenwald
Will Greenwald has been covering consumer technology for a decade, and has served on the editorial staffs of CNET.com, Sound & Vision, and Maximum PC. His work and analysis has been seen in GamePro, Tested.com, Geek.com, and several other publications. He currently covers consumer electronics in the PC Labs as the in-house home entertainment expert, reviewing TVs, media hubs, speakers, headphones, and gaming accessories. Will is also an ISF Level II-certified TV calibrator, which ensures the thoroughness and accuracy of all PCMag TV reviews.
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Share this Story: Ecological civil disobedience set for dialogue
Ecological civil disobedience set for dialogue
Douglas Todd
Civil disobedience, in this case to protect the ecosphere, is in the news these days in B.C. Its ethics are the topic of a discussion set for the evening of Friday, Sept. 21, at the Vancouver Public Library.
A host of well-known Canadian environmentalists plan to organize what they have called the "largest act of civil disobedience" in the country's history to protest the Northern Gateway pipeline.
Ecological civil disobedience set for dialogue Back to video
Here's an excerpt from a story in the Sept. 12th Vancouver Sun:
"Greenpeace Canada says a mass sit-in planned for the front lawn of the B.C. legislature in Victoria on Oct. 22 is backed by more than 80 leaders from the business, First Nations, environmental, labour and academic communities across Canada. The group says those supporters include environmentalist David Suzuki, former Canadian UN ambassador Stephen Lewis, Council of Canadians chair Maude Barlow, lawyer Clayton Ruby, author Naomi Klein and economist Mark Jaccard. Barlow says the protest is aimed at showing that attempts to gut Canada's environmental laws and put a price tag on the B.C. coast can't been done without a public response.
The Northern Gateway Project is a controversial plan to build a pipeline from the Alberta oil sands to the West Coast of Canada. (Handout photo: Enbridge)
Meanwhile, Kevin Washbrook, a director of Voters Taking Action on Climate Change, is one of the organizers of the public dialogue on civil disobedience at 7:30 p.m. in the VPL's Alice McKay room.
Presumably, the discussion will focus on the similarities and differences between contemporary environmental civil disobedience and the non-violent resistance made famous by Gandhi and Martin Luther King Jr., whose followers often put life and limb on the line in the name of freedom and justice.
Washbrook writes:
I was one of 13 people arrested in White Rock this past May for blocking the BNSF railway for 24 hrs to stop the export of US coal to Roberts Bank coal port. Those arrested included academics, public figures, retired labour leaders, film makers and musicians. The action has sparked an ongoing dialogue about the legitimacy of civil disobedience as an ethical response to the climate crisis. The people who have joined in this dialogue are not what I would consider committed social activists — they are academics and other family and job oriented professionals who are deeply troubled by the way things are going and feel the need to step out of their everyday lives to take action. Some of the 13 arrested in White Rock are planning a public discussion on Sept 21 at the VPL downtown. We are inviting like-minded people to join us for an open discussion about engaging in civil disobedience as a last ditch effort to hold on to a stable and secure climate for our children and grandchildren. The event isn't meant to plan an action, but rather to give concerned citizens a chance to discuss their motivations and worries, and to consider what is involved, legally, financially and practically, in making a commitment to this kind of action.
Police dogs attack peaceful Birmingham demonstrators during civil rights movement.
RELATED: Vancouver eco-theologian advises Dalai Lama
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SFBT is an approach in counselling, psychotherapy and social work that aims to help clients to generate solutions. Its ideas have also been used in clinical supervision, coaching, education and management. It is an approach that is client-centred, strength-based, goal-focused, contextual and usually brief.
The Canadian Council of Professional Certification (CCPC) was established for the purpose of recognizing the accomplishments of professionals working in their specific disciplines. Certification is granted and a professional designation is awarded when applicants have met the criteria set out by each discipline's board of examiners.
In 1975, CCPC was granted a Federal Charter by letters of patent under the provisions of Part ll of the Canada Corporations Act (www.ccpcprofessionals.com).


text:
va ser un tirador d'esgrima belga que va competir durant la dècada de 1920. Va prendre part en tres edicions dels Jocs Olímpics, el 1920, 1924 i 1928.

El 1920, als Jocs d'Anvers disputà dues proves del programa d'esgrima. Fou quart en la prova de sabre per equips i desè en la d'espasa individual. Quatre anys més tard, als Jocs de París, disputà tres proves del programa d'esgrima. En la competició d'espasa individual guanyà la medalla d'or i en la d'espasa per equips la de plata. En la competició de sabre per equips quedà eliminat en sèries. La tercera i darrera participació en uns Jocs fou el 1928 a Amsterdam. Allà disputà dues proves del programa d'esgrima. En la competició d'espasa per equips fou quart i en la d'espasa individual sisè.

El 1930, a Lieja, es proclamà campió del món d'espasa per equips.

Referències

Medallistes belgues als Jocs Olímpics d'estiu de 1924
Esportistes belgues als Jocs Olímpics d'estiu de 1920
Esportistes belgues als Jocs Olímpics d'estiu de 1928
Tiradors d'esgrima belgues
Esportistes de la Regió de Brussel·les-Capital
Persones d'Ixelles
Morts a Brussel·les
Naixements del 1893

redpajama_set_name: RedPajamaWikipedia


text:
← Evangelical Leader Prepares Way For Putin
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Two Christian Camps
Posted on June 7, 2020 by Royal Rosamond Press
I warned you!
https://www.nbcnews.com/think/opinion/trump-s-stunt-st-john-s-result-american-churches-bowing-ncna1223746
On Monday when Donald Trump raised overhead a Bible – the Sword of the Spirit, to believers – he unwittingly cleaved his loyal Christian supporters into two camps.
His most ardent evangelical supporters saw it as a blow against evil and described his walk from the White House to St John's Episcopal church, over ground violently cleared of protesters, as a "Jericho walk".
The Rev Johnnie Moore, president of the Congress of Christian Leaders, described Trump in shepherd-like terms on Twitter:
"I will never forget seeing @POTUS @realDonaldTrump slowly & in-total-command walk from the @WhiteHouse across Lafayette Square to St. John's Church defying those who aim to derail our national healing by spreading fear, hate & anarchy. After just saying, 'I will keep you safe.'"
But evangelicals are not monolithic: some saw the gesture as cynical, a ploy by a president whose decisions, both private and public, do not align with biblical principles.
"I guess it's a sort of Rorschach test, then," said Dallas pastor Robert Jeffress, who is one of Trump's most important defenders among the faithful. "You see what you expect to see."
But that's not true, Trump's emerging evangelical critics say: an objective measure is contained in the very book Trump wielded.
"Blessed are the peacemakers! Blessed are the merciful! It's right there in the Sermon on the Mount," said John Fea, a professor of American history at Messiah College. "Just read Jesus."
Trump's photo opportunity required police to attack and push away protesters against police brutality. He walked surrounded by key civilian and military advisers, some of whom later said they were caught unaware by the stunt and the violence that preceded it. Some evangelical leaders said they felt similarly aghast, watching the event unfold.
"Pelting people with rubber bullets and spraying them with teargas for peacefully protesting is morally wrong," said Russell Moore, president of the Ethics & Religious Liberty Commission of the Southern Baptist Convention. "What we need right now is moral leadership – from all of us, in the churches, in the police departments, in the courts, and in the White House. The Bible tells us so. So do our own consciences."
The day's events left Moore "alarmed", he said.
The staunchest of evangelicals, 90-year-old televangelist Pat Robertson, split from Trump on Tuesday.
He told his television viewers of the president: "He said, 'I'm ready to send in military troops if the nation's governors don't act to quell the violence that has rocked American cities.' A matter of fact, he spoke of them as being jerks. You just don't do that, Mr President. It isn't cool!"
It could be far worse than uncool, politically.
Trump can't afford to lose evangelicals, even by the handful. A record 81% of white evangelicals voted for him in 2016, and he only narrowly won the presidency, sometimes by just a few thousand votes in crucial areas. His gesture with the Bible outside St John's was meant to shore up that support, reminding his base of a tacit agreement.
"It's a contract between him and evangelical voters," Fea, the professor, who is a Christian, said. In exchange for their ballots, he said, Trump has packed the courts, including the supreme court, with conservative judges who agree with them on social issues.
So while evangelicals lifted Trump to power by voting together, they may prove his undoing if a contingent breaks away. In which case his campaign might shudder to hear of evangelical believers like Anthony Kidd in Daphne, Alabama.
During the week Kidd works at a salvage yard, and on weekends he does audio work during church services. He's conservative.
"The past few years he has done things that are good for Christians, I'll grant that," he said. But when he saw Trump lift the Bible outside St John's, he said, "It made me want to throw up a little bit."
Fea said it was unclear what happens next: whether evangelicals will stay by Trump, or make a significant split. But whatever happens, he said, is unlikely to be peaceful.
"Here's a good rule of thumb," he said. "Looking back through history, whenever you see someone in authority using the Bible to justify law and order, it ends badly."
Americans have had enough …
… and are marching for justice in unprecedented numbers. In small towns and big cities across the country, thousands of people are giving voice to the grief and anger that generations of black Americans have suffered at the hands of the criminal justice system. Young and old, black and white, family and friends have joined together to say: enough.
The unconscionable examples of racism over the last weeks and months come as America's communities of color have been hit hardest by the coronavirus and catastrophic job losses. This is a perfect storm hitting black Americans. Meanwhile, the political leadership suggests that "when the looting starts, the shooting starts". The president who promised to end the "American carnage" is in danger of making it worse.
At a time like this, an independent news organisation that fights for truth and holds power to account is not just optional. It is essential. Because we believe every one of us deserves equal access to fact-based news and analysis, we've decided to keep Guardian journalism free for all readers, regardless of where they live or what they can afford to pay. This is made possible thanks to the support we receive from readers across America in all 50 states.
after the area was cleared of protesters on June 1, 2020.Brendan Smialowski / AFP – Getty Images
June 3, 2020, 2:36 PM PDT
By William H. Lamar IV, pastor, Metropolitan African Methodist Episcopal Church in Washington, D.C.
No one should have been surprised by President Donald Trump's co-opting of the symbols of American Christianity — a church, the Bible — to speak Monday to the white evangelical base that made his election possible. Every American president since George Washington has sworn his oath upon the Bible. Every American presidential candidate who wants to be taken seriously must proclaim loyalty to some church or another. Every presidential speech — whether to press for civil rights legislation or gain public support for a mendacious war — must end with "God bless America." Candidates and winners alike are expected to participate in inaugural prayer gatherings and worship services, including those at the church where I pastor and with the denomination to which I belong.
From the founding of this nation, the church and the Holy Scriptures have been used by the state with the church's permission to dislocate people, rain violence upon them and declare the sovereignty of American empire and white male flesh.
From St. John's Episcopal Church to pandemic response, Trump co-opts religion to woo the religious right
Trump's contrived excursion from the Rose Garden to St. John's Episcopal Church, was then, far more than a photo op: It was the latest in a long line of acts that wed the church to the state in ways that evidence the conundrum of faith that has always been present, but is now more pronounced. When Trump dislocated the protesters from Lafayette Square, he actually cleared them from a space designed to commemorate the violence and victory of the Revolutionary War; he stood in front of a church whose history is rife with complicity in such settler colonial violence. Episcopal Bishops Michael Curry and Mariann Budde rightly decried his actions, but there are no clean hands in our faith.
The American church sold itself long ago to perpetuate the myth of America's divine destiny for the paltry price of access to power and respectability. From the days of the Roman emperor Constantine (who ended the persecution of Christians in order to shore up his power) until this moment, politicians have known that the surest way to quiet a revolution is to subsume it under the guise of allyship. In its insatiable desire for proximity to power, the church has fallen prey to this seduction, repeatedly bowing down to the twin golden statues of capitalism and empire.
Christianity in the age of Trump has lost its way. Here's how to get back on track.
Grover Norquist, founder of Americans for Tax Reform, famously quipped, "I'm not in favor of abolishing the government. I just want to shrink it down to the size where we can drown it in the bathtub." This propensity to atomize institutions and ideas intended for the commonweal is clear in American politics, yet artfully obscured in American Christianity. Shored up by white evangelicalism and its propaganda machine, the American Christianity that informs the popular imagination takes the grand gospel — which envisions a new heaven and a new earth committed foremost to human flourishing and care for the earth — and drowns it in the bathtub of individual piety and soul salvation.
Jesus' politics as a revolutionary, then, have been subsumed by the glory of his personal resurrection. That kind of mendacity, this theological malpractice, has relegated freedom from oppression to the great "by and by," and unashamedly acted in the service of empire and personal fulfillment.
Don't be confused about why white evangelicals love Trump. It's about race.
The challenge before American Christianity now is to find the courage to disentangle itself from America's imperialist impulses — and among the first steps toward this courage is in truth telling. I am disinterested in the outrage of my colleagues, whether feigned or heartfelt, around Trump's use of the Bible and the church to further both his political ambitions and American imperial violence, domestically and internationally. It is intrinsically disingenuous to be outraged by a visual act while remaining silent about the ways American Christianity makes violence and oppression more palatable, especially when you've silently watched other rhetorical acrobatics, informed by the truth that you can sell most Americans almost anything that is wrapped in God-language and stamped with the imprimatur of the church.
The murderous conquest of Native peoples upon this continent was so languaged and so wrapped. The stealing of Africans and the violent extraction of labor from them was so languaged and so wrapped. The subjugation of women was (and is) so languaged and so wrapped. The vitriolic exclusion of non-heteronormative persons is so languaged and so wrapped. The dehumanization of immigrants is so languaged and so wrapped.
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It is time for all churches to unequivocally declare that they will no longer be theological marionettes whose strings are pulled by kleptocratic capitalists and imperialists. We must build power to topple regimes that work against the flourishing of humans and the earth. God's wind and fire seek to create something new in us and in the world.
But will we abandon the mendacity of the American myth and embrace the reign of God?
We want to hear what you THINK. Please submit a letter to the editor.
If American Christianity is to embrace God's earthly reign and not empire, the church must be committed foremost to structural change. The power we must build is to exert unrelenting pressure such that all human beings — especially those whom Jesus referred to as "the least of these" — have full access to quality health care and education, and can earn a living wage and afford safe housing. The reign of God is incomplete unless the prison industrial complex is abolished, fair tax structures are enacted, universal job training and employment opportunities are available equitably to all humans, our economy is one based on the value of what we produce with our labor rather than what we simply extract from others', retirement benefits are available to all seniors that allow a good quality of life and the earth is revered as God's creation.
Are we bold enough to do that? That remains the question.


With almost 200 million French-speakers all over the world, French is an official language in 29 countries. It is also one of the working languages of the United Nations, alongside Arabic, Chinese, English, Russian and Spanish. French is the preferred language used for the deliberations of the Court of Justice of the European Union. Almost half of French native speakers live in Europe, with the other half distributed over Africa and the Arab world, even reaching as far as Asia (i.e., in Vietnam there are about 400,000 French speakers). French is also one of the two official languages of Canada and it is the main language used in the province of Québec.
French is an Indo-European language and part of the Romance family, along with Spanish and Italian. English and French share many grammatical features and contain many cognates. Phonetically distinctive French sounds are the nasal vowels and the uvular r. Written French uses the Roman alphabet. French spelling is closer to the pronunciation than is English spelling.
The French Language Program at CLIC offers a two-year academic program of studies focused on the development of interactional competence and intercultural knowledge. The courses offered through CLIC guide students to understand the French speaking culture and society through our focus on language awareness and critical language analysis.
300 and 400 level classes in French are offered in the Department of Classical and European Studies.
French is one of the options that fulfills the language requirements for the Linguistics major offered by the Rice Linguistics Department.