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text:
\section{Introduction}

With the exponential growth of the Internet of Things (IoT), billions of new wireless devices are being deployed across the world every year \cite{statista2020}. The sheer number of devices available means that security systems that authenticate these devices should become cheaper, more secure and more robust. While traditional cryptography-based authentication systems have been the mainstay of wireless authentication, the unique requirements of IoT devices call for alternative methods that are sensitive to their computation and power constraints.

Passive Physical Layer Authentication (passive PLA) has been proposed as a low-overhead authentication method that requires little to no work on the part of the transmitter \cite{wang_wireless_2016}. Here, the authenticator uses channel state information and fingerprints due to hardware impairments to identify transmitters. Recently, research on passive PLA that uses deep learning techniques has been gaining momentum. Most such techniques process raw IQ samples from transmitters to extract features that are used to build classifiers. Since deep learning based classifiers tend to extract better features, these approaches have been shown to outperform others which use handcrafted features, reaching markedly higher accuracies \cite{riyaz_deep_2018}. However, there is an inherent vulnerability of deep learning-based classifiers to so-called \emph{adversarial examples}. For example, the existence of targeted adversarial examples have been pointed out \cite{szegedy2013}: given a valid input $x$, a classifier $C$ and a target $t$, it is possible to find an $x'=x+r$ such that $C(x') = t$ and $||r||$ is minimized. In this light, it is critical that deep learning-based PLA systems be analyzed for their vulnerabilities.

Radio fingerprints are usually considered hard to reproduce or replay because the replaying device suffers from its own impairments which disturb the features in the RF fingerprint. As such, naive replay attacks have limited success; only very recently has this problem been approached in more methodical ways. In \cite{shi2019generative}, Generative Adversarial Networks are used to train a spoofing device. While the method shows promising results, it relies on being able to place an adversarial receiver near the authenticating receiver, and only considers the case when there is one authorized transmitter. Furthermore, it is only verified through simulations where little information is given about how transmitter fingerprints were simulated. In \cite{restuccia2020}, this problem was investigated in a variety of angles, considering targeted and untargeted adversarial attacks, where the adversary tries either to make signals from a target transmitter $T_i$ be recognized as another specified transmitter $T_j$ (targeted) or be recognized as any transmitter other than $T_i$ (untargeted). They showed that spoofing can be done at a high accuracy both when the full gradients and the activations of the classifier are known; and only the activations of the final layer are known.  We identify the availability of both these as being practically unrealistic---the most we can expect from the authenticator is a binary feedback such as ACK or NACK denoting its decision.  Although such 1-bit feedback is technically compatible with their approach, the efficacy of the proposed method was not not evaluated in that regard. Additionally, their approach relies on signals from authorized transmitters being available to the adversary, and was only verified through training on offline data.

Inspired by this past work, we explore an adversarial attack that at most expects a binary feedback from the authenticator; is able to achieve high fooling rates in realistic channel conditions under a wide range of signal-to-noise ratios (SNRs); and attacks in real-time through online training. In this paper, we formulate this problem as a reinforcement learning problem and propose the use of policy gradient methods to perform spoofing of transmitters in a wireless network. Our results show that by distorting IQ samples of an adversarial transmitter---constrained to a maximum distortion level---before transmission, it is possible to fool a deep-learning based authenticator with high success rates even at low SNR and even when the only information available about the authenticator is a binary feedback received from it.

\section{System Model}

\begin{figure}[t]
    \begin{center}
    \vspace*{18px}
    \includegraphics[width=0.49\textwidth]{figures/system_new.pdf}
    \end{center}
    \caption{System model considered in this project}%
    \label{fig:system_model}%
\end{figure}

We consider a wireless environment in which there are $|\mT|$ transmitters $\mT=\{T_1, T_2, \dots, T_{|\mT|}\}$ which are authorized to transmit to a single receiver $R$. $R$ is equipped with a pre-trained neural network-based authenticator $D$ that uses raw IQ samples of the received signals to perform a binary authentication decision at the physical layer, denoting whether the signal under consideration is from an authorized transmitter or not. There is an adversarial transmitter $T_A$ that wants to gain access to $R$, and it tries to do this by impersonating one of the authorized transmitters in $\mT$. $T_A$ employs a generator $G$ whose purpose is to distort the complex IQ samples of input discrete time signal $z(t)$ at $T_A$ such that after it is transmitted, it will be classified as authenticated at $D$. We assume that $D$ sends its authentication decision for each signal received from any transmitter, back to the transmitter. $T_A$ is also aware of the modulation being used by transmitters in $\mT$. This is visualized in Fig. \ref{fig:system_model}.

In a wireless communication system, there are three main sources of non-linearities that are imparted on the intended transmitted signal: if $x(t)$ is the signal at the beginning of the transmitter chain, the signal at the end of the receiver chain will be of the form $y(t)=f_R(f_C(f_T(x(t))))$, where $f_R$, $f_C$ and $f_T$ are non-linearities introduced by the receiver hardware, channel and transmitter hardware respectively. Since the channel is variable, $f_C$ is not a good a fingerprint and since there is only one receiver ($R$), $f_R$ will effectively be invariable across all transmitters. Therefore $D$ will be discriminating transmitters based on $f_T$.

We will try to spoof one of $\mT$ by only using the feedback from $R$. If we assume that $T_A$ could be placed reasonably close to transmitters in $\mT$, $f_C$ could be assumed to have the same variability for $T_A$ as well as for $\mT$. Such an assumption is justifiable when transmitters in $\mT$ are close together and are reasonably far away from $R$. This ensures that $D$ will discriminate signals from both $T_A$ and $\mT$ based on $f_T$.

\section{Proposed Solution}

We model this problem as a Markov Decision Process (MDP). An MDP is characterized by an \emph{agent} and an \emph{environment} that interact at each discrete time step $t$, whereby the agent selects an action $a_t$ according to a policy $\pi$ that takes into account the environment's state $s_t$. In response to $a_t$, the agent receives a numerical reward $r_t$ from the environment and transitions to the next state $s_{t+1}$ \cite{sutton_reinforcement_2018}. We model $G$, represented with a neural network parameterized by $\theta$ (weights and biases), as the policy $\pi$ of the agent. The action $a_t \in \mathbb{R}^2$ such that $a_t \sim G_{\theta}(s_t)$, is the distorted value of $s_t$, and $r_t$ is a binary feedback received from $D$, which is part of the environment. The state $s_t$ can be represented in a number of ways, and will be discussed later in this section.

Assume the agent has collected a trajectory $\tau$ of length $\Gamma$ defined as a sequence of states, actions, and rewards, $\{s_1, a_1, r_1, s_2, a_2, r_2,\dots, s_{\Gamma}\}$. Now, the goal is to tune the parameters $\theta$ of $G$ under the following optimization problem:
\begin{align}
    \begin{array}{l}
         \text{maximize}_{\theta}~\mathbb{E}_{\tau}[J\vert G_{\theta}]  \\
         \text{where}~J= \sum_{t=1}^{\Gamma} \gamma^t r_t(s_t,a_t,s_{t+1})
    \end{array}
    \label{eq:optimization_problem}
\end{align}

Here $J$ is a metric of the the policy's performance, which is simply the cumulative reward of $\tau$ discounted by factor $\gamma$. To solve this, we can use a policy gradient method: we repeatedly estimate the gradient of the expected value of $J$ with respect to $\theta$ and use that to update $\theta$. To estimate the gradients, we will use a \emph{score function gradient estimator}.
With the introduction of a baseline $b(s)$ to reduce variance, an estimate $\hat{g}$ for $\nabla_{\theta}\mathbb{E}_{\tau}[J]$ is \cite{schulman}
\begin{align}
\resizebox{.49 \textwidth}{!}
{
    $\nabla_{\theta}\mathbb{E}_{\tau}[J(\tau)]\approx\hat{g}=\sum_{t=1}^{\Gamma} \nabla_\theta \log G_{\theta}(a_t|s_t) \left( \sum_{t'=t}^{\Gamma} r_{t'}\gamma^{t'-t} - b(s_t) \right)$
    \label{eq:gradient}
}
\end{align}
Now, the policy update can be done with any gradient ascent algorithm (e.g. $\theta \leftarrow \theta+\epsilon \hat{g}$). This can be repeated for a number of iterations, with a trajectory collected for each iteration, until $G$ converges to a satisfactory state. This algorithm also allows for $b(s)$ to be trained along with $\theta$ \cite{schulman}.

There are practical considerations when designing $r_t$ and $s_t$. For example, depending on the main type of distortion that the impersonator tries to mimic, different definitions of the state $s_t$ can be used.
\begin{enumerate}
    \item The state is a vector $s_t = \left[\text{Re}\{z(t)\}, \text{Im}\{z(t)\}\right]$ containing the real and imaginary part of the most recent IQ sample of the signal $z(t)$. This is applicable when the distortion over each sample is independent of the other samples. For example, the distortion imparted by the power amplifier in the RF chain will have this property \cite{hanna2019deep}.
    \item $s_t$ is $\{\left[\text{Re}\{z(t)\}, \text{Im}\{z(t)\}\right], H_{t-1}\}$, where $H_{t-1}$ is the hidden state of $G(s_{t-1})$, when it is modeled as a recurrent neural network. This state can in theory apply to any type of non-linearity.
\end{enumerate}
Irrespective of the particular definition of $s_t$, $a_t \sim G(s_t)$ is expected to reflect the distorted value of the most recent IQ sample; hence, $a_t \in \mathbb{R}^2$.

Although a reward $r_t$ is required for each $a_t$, a \emph{signal} transmitted by $T_A$ is usually a sequence of $N_s$ complex symbols (effectively a $N_s \times 2$ real vector) and hence a reward cannot immediately be obtained for each $a_t (t<\Gamma)$. So we set $\Gamma=N_s$ (i.e. the trajectory length is $N_s$) and transmit $A=\{a_1[0]+ja_1[1],a_2[0]+ja_2[1],\dots,a_{\Gamma}[0]+ja_{\Gamma}[1]\}$ as the signal to get the feedback $\hat{D}(A)=D(f_R(f_C(f_T(A)))$. To estimate $r_t$ from $\hat{D}(A)$, a Monte-Carlo search can be performed from $s_t$ until $s_{\Gamma}$, using a roll-out policy $G_{\beta}$ \cite{yu2017seqgan}.
Specifically, at time $t$, if we have $\{s_1, a_1, r_1, s_2, a_2, r_2,\dots, s_t, a_t, r_t\} \sim G_{\theta}$, the rest of the trajectory is sampled from $G_{\beta}$ as $\{s_{t+1}, a_{t+1}, r_{t+1},\dots, s_{\Gamma}, a_{\Gamma}, r_{\Gamma}\} \sim G_{\beta}$ to produce a simulated $A_t$. Then for any $1 \leq t \leq \Gamma$, the reward $r_t$ can be written as
\begin{align}
r_t=\begin{cases}
 \frac{1}{M} \sum_{m=1}^{M} \hat{D}(A_t^{m}) & t<\Gamma \\
 \hat{D}(A) & t=\Gamma\\
\end{cases}
\label{eq:r_t}
\end{align}
where for $t<\Gamma$, we have averaged over $M$ Monte-Carlo searches due to the stochasticity of $G_{\beta}$.  In this approach $G_{\beta}$ is periodically updated to be the same as $G_{\theta}$; however, considering the large number of MC searches expected to be run, we can use a faster (and maybe less accurate) function approximator for $G_{\beta}$ \cite{silver_mastering_2016}.

We now add several optimizations to the method proposed above. First, to encourage exploration, we introduce entropy-regularization \cite{ahmed2019}: the agent gets a bonus reward at each time step proportional to $H(G_{\theta}(\cdot|s_t))$, the entropy of the policy at that timestep. i.e. $J$ in \eqref{eq:optimization_problem} changes to
\begin{align}
J= \sum_{t=1}^{\Gamma} \gamma^t \left[r_t(s_t,a_t,s_{t+1})+ \beta H(G_{\theta}(\cdot|s_t))\right]
\end{align}
where $\beta$ is the entropy coefficient (higher $\beta \implies$ more exploration). Also in practice, the components of a symbol cannot be distorted arbitrarily, as the decodability of the signal at the receiver side must be ensured. To integrate this constraint, we impose an action space limitation (clipping):
\begin{align}
a_t = \min(s_t+\epsilon|s_t|, \max(G(s_t), s_t-\epsilon|s_t|))
\end{align}
This effectively means that the maximum distortion level allowed is $\epsilon\%$ relative to the input state.

Now we present an algorithm summarizing the proposed method above, depicted as Algorithm \ref{alg:cooperative}. Note that $G_{\theta}$ is initially trained by using Mean Squared Error (MSE) loss on a set of signals $\mathcal{S}_A$ captured from $T_A$, such that at the beginning there is no distortion ($G_{\theta}$ acts as an autoencoder). $G_{\beta}$ is initialized to $G_{\theta}$ and updated to $G_{\theta}$ periodically, after training $G_{\theta}$ for $g_{\text{steps}}$  iterations. This process is repeated for $K$ steps.

\begin{algorithm}
\begin{small}
\SetAlgoLined
\SetKwInOut{Input}{Input}
\SetKwInOut{Output}{Output}
\SetKwInOut{Initialization}{Initialization}
\Input{Generator policy $G_{\theta}$ roll-out policy $G_{\beta}$, Discriminator $D$, set of signals $\mathcal{S}_A$ from $T_A$\;
}
\Output  {$G_{\theta}$\;}
Initialize $G_{\theta}, G_{\beta}$\;
Pretrain $G_{\theta}$ using MSE on $\mathcal{S}_A$\;
$\beta \leftarrow \theta$\;
\For {$i={1,\dots,K}$}
{
\For {$g \in [1,g_{\text{steps}}]$}
 {
Generate a sequence $\{s_1,a_1,s_2,a_2,\dots,s_{\Gamma},a_{\Gamma}\} \sim G_{\theta}$\;
\For {$t={1,\dots,\Gamma}$}
{
Calculate $r_t$ using \eqref{eq:r_t}\;
}
Calculate $\nabla_{\theta}\mathbb{E}_{\tau}[J]$ using \eqref{eq:gradient}\;
Update $G_{\theta}$ using gradient descent\;
 }
$\beta \leftarrow \theta$\;
}
\end{small}
\caption{Generative adversarial attack with a cooperative $R$}
\label{alg:cooperative}
\end{algorithm}

\section{Experimental Evaluation}

This section is divided into three sections: Section \ref{sec:setup} details the simulation environment and the hardware testbed used for the evaluation, as well as the choices for different parameters; Section \ref{sec:nn_arch} presents the neural network architectures used for $D$ and $G$; and Section \ref{sec:results} describes four experiments conducted and the results obtained.

\subsection{Setup and parameters}
\label{sec:setup}
The proposed method was first evaluated on a simulated wireless environment written in Python. Power amplifier non-linearities $f_T(\cdot)$ are modeled by the Volterra Series, $f_T(z_t)=z_t(1 + \psi_0|z_t|^2 + \psi_1|z_t|^4)$, where $\psi_0$ and $\psi_1$ are coefficients unique to each transmitter, generated to follow a non-linear curve. Every transmitted packet of data consisted of completely random bits. We use QPSK modulation and root-raised-cosine (RRC) pulse shaping with 0.2 excess bandwidth.

Two channel models were investigated. The first one is a simple additive-white-gaussian-noise (AWGN) channel. The second one is a dynamic channel, which includes a set of more realistic impairments including timing errors, frequency errors, fading, inter-symbol interference and noise. The timing error is simulated by interpolating the signal by a factor of 32, choosing a random offset, and then downsampling. The frequency error is obtained by multiplying the signal with a complex exponential whose frequency  is selected from a Gaussian distribution with zero mean and 1 kHz standard deviation. For fading and inter-symbol interference, a three tap channel was used along with a Rayleigh coefficient of scale 0.5.

The state definition 2 in  Section 3 was used ($s_t$ is $\{\left[\text{Re}\{z(t)\}, \text{Im}\{z(t)\}\right], H_{t-1}\}$). The discount factor $\gamma$ was set to 1 (undiscounted). The gradient ascent on $\theta$ was done with the Adam optimizer, with the default configuration provided in the Keras API for Tensorflow, except that the learning rate was annealed starting from 0.001 to ensure convergence. We do not use any baseline function to train the generator, as entropy regularization and clipping already allow us to train the generator successfully. $\Gamma=N_s=256$, $\beta=1000$ and values for SNR, $\epsilon$, and $|\mT|$ were changed in different tests.

Finally, to test our attack on real hardware, we created a testbed consisting of 8 Analog Devices ADALM Pluto Software Defined Radios (SDRs); for convenience of operation, all were connected to a single computer. 6 SDRs were designated as authorized transmitters, 1 as an unauthorized transmitter and the other as the receiver, as shown in Fig. \ref{fig:fr_sdr_setup}. The Python module \texttt{pyadi-iio} was used to interface with the SDRs.
\subsection{\label{sec:nn_arch}Neural Network Architectures of $G$, $D$}

\begin{figure}[!t]
\begin{minipage}{.25\textwidth}
\begin{figure}[H]
    \includegraphics[width=.8\linewidth]{figures/residual_block.pdf}
    \caption{{Residual block $[f]$}}%
    \label{fig:residual_block}%
\end{figure}
\begin{figure}[H]
    \includegraphics[width=.8\linewidth]{figures/generator_architecture.pdf}
    \caption{{Architecture of $G$}}%
    \label{fig:generator_arch}%
\end{figure}
\end{minipage}%
\begin{minipage}{.25\textwidth}
\begin{figure}[H]
    \includegraphics[width=.8\linewidth]{figures/discriminator_feature_extractor.pdf}
    \caption{{Discriminator classifier $[y]$}}%
    \label{fig:discriminator_classifier}%
\end{figure}
\begin{figure}[H]
    \includegraphics[width=.8\linewidth]{figures/simple_channel_discriminator_new.pdf}
    \caption{{Input for a simple channel}}%
    \label{fig:input_simple_channel}%
\end{figure}
\end{minipage}%
\begin{figure}[H]
    \begin{center}
    \includegraphics[width=1\linewidth]{figures/dynamic_channel_discriminator_new.pdf}
    \end{center}
    \caption{{Input formats for discriminator architecture}}%
    \label{fig:input_dynamic_channel}%
\end{figure}
\end{figure}

We used a binary discriminator architecture for $D$, which has been shown to perform well in \cite{hanna_spawc_2020} for similar transmitter fingerprinting based classifications. It consists of a feature extractor consisting of a series of residual blocks with different numbers of filters, and a classifier block; the architecture of each type of block is shown in Fig. \ref{fig:discriminator_classifier}. Note that it produces a scalar output through a sigmoid activation; when providing binary feedback, this was thresholded at 0.5 to get a binary value (1 if greater than 0.5 and 0 otherwise). L2 regularization was used in the dense layers with weights of either 0.001 or 0.002 to avoid overfitting.

When using a simple channel, the IQ samples of the raw signal was passed to the discriminator classifier in Fig. \ref{fig:discriminator_classifier} without any pre-processing (each signal being a $(256 \times 2)$ vector). However, when using the dynamic channel model, this approach yielded poor discriminators with high fooling rates to begin with. So we first calculated the Discrete Fourier Transform of the raw signal, took the magnitude of the result, and reshaped it into a 2D signal of $(128 \times 2)$ before feeding to the discriminator. This pre-processing stage was chosen as it has been shown to produce superior results in similar transmitter fingerprinting based classifications \cite{hanna2019deep}. Fig. \ref{fig:input_simple_channel} and Fig. \ref{fig:input_dynamic_channel} depict this pictorially.

The architecture of $G_{\theta}$ (and $G_{\beta}$) is shown in Fig. \ref{fig:generator_arch}. Following the input, an LSTM layer with an output dimensionality of 100 was used, with the default configuration provided in Keras. Its outputs were modeled as the mean and the diagonal covariance of a two dimensional Gaussian distribution (one dimension each for the complex and imaginary part of the IQ samples), which was sampled to obtain the action, and to find the action probability (when calculating gradients) and entropy.

\subsection{Results}
\label{sec:results}
In this section, we report results of four experiments; Experiment 1-3 are conducted on the simulated environment and Experiment 4 is conducted on the hardware testbed.

For Experiment 1, we used a set of 10 authorized transmitters $|T|=10$ and a maximum distortion level of $\epsilon=0.2$. Then for five SNR values $\{5,10,15,20,25\}$ we evaluated the \emph{fooling rate} of $D$ at convergence for both channel models. The results are shown in Fig. \ref{fig:fr_snr}. The dashed lines show the initial fooling rate; for the simple channel, it starts at around 7\% for low SNR and decreases to near 0\% for higher SNRs. For the dynamic channel, the initial fooling rates are higher but still less than 10\% for even moderately high SNRs. This shows that the discriminator performs excellently at the beginning (except for the case of 5 dB SNR for the dynamic channel). It is clear that even at really low SNR, significant increases in fooling rate can be achieved, with near 100\% fooling rates being achieved at and above 20 dB SNR for both types of channels. Although slightly higher fooling rates are achieved for the dynamic channel at certain SNRs, this should be put in perspective with the higher initial fooling rates of the discriminator in the dynamic channel---in fact, the simple channel gives a higher relative improvement. Fig. \ref{fig:fr_steps} denotes the convergence time corresponding to Fig. \ref{fig:fr_snr}, measured by the number of gradient descent updates of $G$ (number of iterations of the inner-loop of Algorithm \ref{alg:cooperative}). As expected, we see that algorithm converges faster for higher SNRs, except for the jump from 5 dB to 10 dB. This is due to the fooling rate gain at 5 dB being much smaller than at 10 dB, and hence the algorithm achieving that smaller gain in a less number of iterations.
\begin{figure}[t]
    \subfloat[{Fooling rate for different levels of SNR}]{\label{fig:fr_snr}\includegraphics[width=1\linewidth]{figures/fr_snr_binary_only_new}}

    \subfloat[{Convergence time for different levels of SNR}]{\label{fig:fr_steps}\includegraphics[width=1\linewidth]{figures/steps_snr}}
    \caption{{Fooling rate and corresponding convergence time for different levels of SNR for two channel models}}%
    \label{fig:fr_steps_snr}%
\end{figure}
For a practical perspective, consider the simple channel at 20 dB SNR with convergence time of roughly 100 iterations. Since each iteration requires 256 feedbacks, we need 25600 feedbacks in total from $D$. Although this might seem excessive, assuming the system environment stays fairly static, we can space out the attack say, over 24 hours to reduce suspicion. This means that we only need a feedback every $\approx 3.4~s$, which is several orders of magnitude larger than a typical packet transmission time (e.g. 1 ms)---if we do not desire a near 100\% fooling rate, this time-interval can be greatly increased.

In Experiment 2, we wish to evaluate the effect of the maximum distortion level $\epsilon$ on the fooling rate---specifically, we seek justification for our intuition that allowing $G$ more freedom for distortion should allow it to reach higher fooling rates. Fig. \ref{fig:snr_eps} shows the results obtained when the fooling rate was evaluated for $\epsilon \in \{0.1,0.2,0.3,0.4\}$, and for the same $\mT$ and $\beta$ as in Experiment 1, but only for the case of a simple channel. As expected, we see that a higher $\epsilon$ most certainly allows a higher fooling rate to be achieved and that a sufficiently high $\epsilon$ allows for near 100\% fooling rates. This means that by limiting $\epsilon$, we can still launch a successful attack, while keeping the amount of distortion imparted on the transmitted signals at a controlled level.
\begin{figure}[t]
    \begin{center}
    \includegraphics[width=1\linewidth]{figures/snr_eps_25.pdf}
    \end{center}
    \caption{{Effect of changing $\epsilon$ on the fooling rate at different SNRs}}%
    \label{fig:snr_eps}%
\end{figure}

\begin{figure}[t]
    \centering
    \subfloat[{SDR testbed with $|\mT|=6$}]{\label{fig:fr_sdr_setup}\includegraphics[width=0.38\linewidth]{figures/sdr_setup_3.png}}\hfill
    \subfloat[{Effect of changing $\epsilon$ on the fooling rate}]{\label{fig:fr_sdr_results}\includegraphics[width=0.6\linewidth]{figures/sdr_eps_fr_small}}
    \caption{{Experiment conducted to evaluate the effect of changing $\epsilon$ on the fooling rate. The testbed consisted of 8 ADALM Pluto SDRs.}}%
    \label{fig:fr_sdr}%
\end{figure}

We seek to understand a fundamental property of our algorithm in Experiment 3: is $G$ learning adversarial noise, or is it somehow learning to replicate the fingerprint of one of the transmitters in $\mT$? Unlike images where we may answer this problem with a visual inspection, we try to find an answer to this question numerically with the following experiment: first $G$ is allowed to converge on a particular instance of $D$, and the signals coming through that $G$ are tested on several other realizations of $D$---of different neural network architectures---trained to discriminate the same set of authorized transmitters $\mT$. If $G$ had actually learned to replicate RF fingerprints, it should achieve similar fooling rates irrespective of the particular $D$ it is being tested upon. To test our hypothesis, we trained 6 different realizations of $D$, $\mathcal{D}=\{\text{disc}_1, \text{disc}_2, \text{dclass}_1, \text{dclass}_2, \text{ova}_1, \text{ova}_2\}$; three different architectures \textit{disc}, \textit{dclass} and \textit{ova} were used and two instances each were created from each architecture. \textit{disc} is the binary discriminator architecture described in Section \ref{sec:nn_arch}. \textit{dclass} and \textit{ova} are two additional architectures defined and tested in \cite{hanna_spawc_2020} for RF fingerprinting, both sharing the same feature extractor given in Fig. \ref{fig:discriminator_classifier} and only differing in the classifier blocks used. \textit{dclass} consists of  a multi-class classifier having $|\mT|+1$ outputs; the first $|\mT|$ outputs correspond to the authorized transmitters and the last one corresponds to outliers. \textit{ova} has a single feature extractor shared across $|\mT|$ copies of the binary classifier block in Fig. \ref{fig:discriminator_classifier}, with the $i$-th such block denoting whether the signal is from the $i$-th transmitter or not. Table \ref{tab:fr_table} denotes the fooling rates observed when $G$ was allowed to attack $D=\text{disc}_1$ and signals from $T_A$  were tested on each discriminator in $\mathcal{D}$. This was then repeated with $D=\text{disc}_2$. It is clear that while a $G$ trained on either $\text{disc}_1$ or $\text{disc}_2$ could be used to attack the other with practically the same level of effectiveness, the effectiveness drops significantly when used against other architectures (albeit a significant increase in fooling rate). This confirms the hypothesis we set out to test; that is, $G$ learns to produce adversarial examples and does not learn actual RF fingerprints in $\mT$.

\begin{table}[H]
\setlength{\arrayrulewidth}{0.7pt}
\begin{tabular}{|c|c|c|c|c|c|c|}
\hline
\multirowcell{2}{$G$ is train- \\ed on} & \multicolumn{6}{c|}{$G$ is tested on} \\ \cline{2-7}
                                 & $\text{disc}_1$ & $\text{disc}_2$ & $\text{dclass}_1$ & $\text{dclass}_2$ & $\text{ova}_1$ & $\text{ova}_2$    \\ \hline
$\text{disc}_1$                           &      \cellcolor{green!25}0.999 &	\cellcolor{green!25}0.999 &	\cellcolor{yellow!25}0.514 & \cellcolor{yellow!25}0.552 & \cellcolor{yellow!25}0.548 &	\cellcolor{red!25}0.449 \\ \hline
$\text{disc}_2$                           &    \cellcolor{green!25}1 &	\cellcolor{green!25}1 &	\cellcolor{red!25}0.276	& \cellcolor{yellow!25}0.893 &	\cellcolor{red!25}0.217 &	\cellcolor{red!25}0.359  \\ \hline
\end{tabular}
\caption{Effectiveness of a $G$ trained on $D=\text{disc}_1$ and $D=\text{disc}_2$ on some $D$s of different architectures}
\label{tab:fr_table}
\end{table}

For Experiment 4,  a binary discriminator was trained offline from a dataset captured on the SDR testbed; each transmitter took turns repeatedly transmitting the same predefined sequence of 256 IQ samples to the receiver, and the signals received at $R$ were collected for each transmitter. Then the impersonator started transmitting (authorized transmitters were inactive), and it was allowed to modify its  IQ samples before transmission according to Algorithm \ref{alg:cooperative}, using the feedback from the receiver. Note that the SDRs were simply used for over-the-air transmission and reception---all other operations such as training the attacker and calculating authentication decisions were done inside the computer. The results obtained, given in Fig. \ref{fig:fr_sdr_results}, closely resembles the trend suggested in the moderate SNR region in Fig. \ref{fig:snr_eps}. This means that our experimental results are consistent with the simulation results.

\section{Conclusion and Future Work}
In this paper, we evaluated the feasibility of using policy gradient methods to penetrate a physical layer wireless authentication system which uses a passive deep-learning based classifier. We introduced an algorithm that adds carefully learned perturbations to the IQ samples transmitted by an adversarial transmitter to fool the authenticator into classifying it as an authorized transmitter. Experiments on a simulated wireless environment and an SDR testbed revealed that it is possible to fool the authenticator at extremely high fooling rates, using surprisingly little information---namely, a binary feedback from the authenticator indicating its decision and the modulation and pulse shaping used by the authorized transmitters. We also  showed that by limiting $\epsilon$, the distortion level of the impersonator signals could be kept low while still reaching a high fooling rate. Furthermore, we provided empirical evidence that our approach in fact produces adversarial examples and does not replicate the RF fingerprints of the transmitters.

While we only considered untargeted attacks, the possibility of launching targeted attacks with this method---where we try to impersonate a particulay transmitter in $\mT$---still remains. In a future work, we expect to present an algorithm for the case when $R$ is non-cooperative (it does not provide feedback endlessly), where an adversarial receiver is used instead to aid the impersonator. We also wish to evaluate the possible defenses that can be put in place against these types of attacks, both proactively and reactively.

\section*{Acknowledgments}

We wish to thank Samer Hanna (UCLA) for help in implementing the wireless system of the simulation environment.

redpajama_set_name: RedPajamaArXiv


The Marauders are some of the most beloved and controversial characters in the "Harry Potter" series. Episode 19 of "Reading, Writing, Rowling" takes an in-depth look at these troublemakers.
Ezra Miller feels that the big reveal about his character in "Fantastic Beasts: The Crimes of Grindelwald" is typical of J.K. Rowling's style, but is it really comparable to how she wove mysteries in "Harry Potter"?
"Fantastic Beasts and Where to Find Them" gave us a lot to look forward to in the sequel, but "Fantastic Beasts: The Crimes of Grindelwald" failed to deliver on a number of fronts.
MuggleNet was at the London premiere of "Fantastic Beasts: The Crimes of Grindelwald" – here are some of our highlights from the blue carpet!
Join host Katy McDaniel and expert guest John Granger as they speak to Brett Fish of the University of Tennessee – Knoxville about how J.K. Rowling uses techniques from detective fiction and ring composition to create compelling and ultimately satisfying mysteries.


text:
Thousands in Arkansas without power following Laura's stop in the natural state
At its peak, Entergy Arkansas had over 47,000 customers without power around 5 p.m. on Thursday.
LITTLE ROCK, Ark. — Even though the sun was out for most of the day in central Arkansas on Friday, many were still left in the dark after Hurricane Laura blew through the state.
More than 27,000 people were still without power around 5 p.m. on Friday, over 24,000 of those were Entergy Arkansas customers.
RELATED: Tropical Storm Laura moving through Arkansas after making landfall
Little Rock citizen Craig Berry was one of the thousands left in the dark following Laura's stop in the natural state.
"Trying to save some ice cream and some other perishable items I have in my refrigerator and freezer last night with no success," he said.
The storm knocked out Berry's power at 3 p.m. on Thursday.
"I thought, it'll just be a few hours and it'll be done and it'll be alright. And here we are, right now, still no power," he said.
David Lewis, Entergy spokesman, said as soon as the sun came up on Friday morning, Entergy Arkansas's storm team of about 1,100 people were out on the streets cutting down trees and repairing power lines.
"They aren't all lineman. Some of them are support personnel, some of them are tree trimmers, some are supervisors, safety specialists," he said.
According to Lewis, at its peak, over 47,000 customers were without power around 5 p.m. on Thursday.
Union County in Southwest Arkansas got hit the worst.
"We had something like 9,000 customers out down there. They really got slammed because they're on the southern border and they took the first hit as the storm came up from the south," he said.
While Laura developed on the coast, Entergy Arkansas brought in a couple hundred contractors for additional resources.
Part of the team was working while the storm made landfall, Lewis said, assessing certain pockets of damage to route the power around.
"We got about 6,000 back on that way, but you can get only so far with that maneuver and then you have to go in and actually do the repairs," he said.
Those repairs will continue into the weekend while neighbors, like Berry, remain thankful the historical storm didn't leave more damage behind.
"I'm glad that it's over and ready to start the new day," he said.
Lewis said Entergy's goal is to have everyone in Arkansas back with power by Sunday night.
RELATED: Arkansas reaches over 54,000 power outages as Laura moves inland
You can check progress on restorations across the state by going to Entergy's website.
To report a power outage or if you see a line on the ground, call 1-800-9-OUTAGE so Entergy crews can get to it as soon as possible.
Tags: weather
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Next Arkansas County sheriff resigns after being recorded using racial slurs

redpajama_set_name: RedPajamaCommonCrawl


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The National Geospatial-Intelligence Agency (NGA) awarded GeoNorth Information Systems (GNIS) a five-year, $15 million contract for persistent surveillance services of the Arctic region.
Lockheed Martin will provide a scalable geospatial processing platform to enable the surveillance project.
GNIS will leverage Lockheed Martin's Rosetta technology, which includes a versatile and highly automated set of commercial and civil image processing tools that scale and adapt to deliver precision geospatial intelligence products to the NGA.
GNIS, a wholly owned subsidiary of the Tatitlek Corporation, an Alaska Native Village Corporation, will work with Lockheed Martin and the University of Alaska Fairbanks' (UAF) Alaska Satellite Facility (ASF), under the banner of the Arctic GeoData Cooperative. GNIS and the partners will build, improve, monitor and maintain terrain elevation models of the Arctic region.


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In the News (#989)
Posted in Current Events, Miscellaneous, News, Perception, Tyranny, tagged agency denial, bogus studies, California, Canada, censorship, China, Colorado, cops, drugs, Facebook, fantasy, Florida, harm reduction, Harm Reduction updates, hysteria, internet, Islam, Kansas, King of the Hill, License To Rape, Little Boxes, Missouri, North Carolina, Not for Any Reason Whatsoever, politicians, porn, Portugal, pregnancy, propaganda, Property of the State, psychology, Pyrrhic Victory, Quiet Genocide, racism, rape, restaurants, scams, semen, sex toys, slavery, South Carolina, sporting events, Stalkers in Blue, streetwalkers, Stupor Bowl, surveillance, The Crumbling Dam, The Missing Word, The Prudish Giant, The Pygmalion Fallacy, They Still Don't Get It, tits, To Molest and Rape, United Kingdom on November 16, 2019| 2 Comments »
The only way for an individual to ensure he or she is not subject to collection of biometric information when traveling…is to refrain from traveling. – US DHS
License to Rape
Police states define the bodies of all citizens as "crime scenes" which can be violated by "authorities" at will:
A North Carolina [pig molested a young woman using the pretext of]…a body search…K.E Van Althius [groped]…the unidentified woman's breasts [and vulva]…"[any] normal person, especially a woman, would not want to be touched [that way] by a man," [said] the woman's lawyer, Karen Griffin…
They Still Don't Get It
It's fascinating to watch fanatics parroting nonsense they clearly neither understand nor believe:
In the shadow of [Kansas] city, there lies a seedy underbelly [of cops predating on people suffering from] addiction [or] mental illness [or using bizarre and meaningless phrases like] moral degradation [to justify police violence, public shaming and even rape]…For the past 18 years [the same sociopath]…has been the top cop of the group. During that time, he [has been responsible for the destruction of thousands of lives by scheming to victimize them]…with…fierce [perverts]…target[ing human beings, in recent years using the excuse of] human trafficking [hysteria. Sows get off on roleplaying porcine fantasies of sex workers, arresting any man who gets near or even]…just driv[es by]…The Vice [gang] also targets [women for the "crime" of trying to make a living. Sometimes for a change of pace they]…raid…adult book store and pornographic movie theater[s and accuse men of having sexual urges]…which is against the law…[the pigs also vomited up a lot of "broken windows" propaganda, which the reporters got on their knees and eagerly lapped up]…
The Missing Word
Only ten years for this horror, while people renting rooms to sex workers get life sentences:
A South Carolina restauranteur was sentenced to [a short term in] prison after admitting he used violence, threats, and intimidation to force a black man to work more than 100 hours a week with no pay…Judge R. Bryan Harwell sentenced…Bobby Paul Edwards to [a mere] 10 years in federal prison after he…[admitted to enslaving] Chris Smith, an intellectually disabled black man…In 1996, when Smith was only 12 years old, he accepted a job at J&J Cafeteria…Six years later, Edwards took over as manager and stopped paying Smith. Over the next 17 years, Edwards would torture, imprison and withhold pay from Smith, claiming that his pay was kept in an "account" that was inaccessible to Smith…[who was forced to] work…18 hours a day, six days a week. On Sundays, he only had to work 11 hours…he never had a work break or a day off [and was] forced to live in a "cockroach-infested" apartment behind the business, Smith [said] that Edwards' family never tried to intervene…When Smith's family would try to check on him, Edwards would lock Smith in the kitchen or even in the freezer. On the rare occasion that Smith indicated displeasure or tried to escape, he was [verbaly abused,] hit in the head with a frying pan, burned with hot tongs, [and] beaten with belt-buckles…Customers reported that they sometimes heard Smith being beaten and screaming for his life [but none of them could be bothered to do anything until]…Geneane Caines, who was friends with the Edwards family, was [told by]…her daughter, who worked at the eatery…how Edwards abused Smith…Edwards was sentenced to 10 years…[and] ordered to pay [a mere] $272,952.96 in restitution [for 17 years of slavery]…
The judge actually believes $3.08/hour, no overtime or punitive damages, was sufficient compensation to Edwards' victim. Smith should fucking own that place.
Property of the State
Pregnant women are uniquely vulnerable to state coercion:
A 25-year-old woman named Chelsea Cheyenne Becker was arrested and charged with first-degree murder in Hanford, California, after delivering a stillborn baby in September. [Prosecutors claim] the stillborn had toxic levels of methamphetamine in its body…cases like this are part of a burgeoning, disturbing pattern, in which the rights of pregnant women are directly pitted against those of the "unborn child," a term that encompasses fertilized eggs, embryos, and fetuses. It's a direct result of the so-called "fetal personhood" movement, which seeks to legally redefine a fetus as a person as a means of overturning Roe v. Wade…there have been cases, already, of women being charged for taking prescription medication while pregnant — one woman was reportedly charged for taking a Valium…
Harm Reduction (#627)
Alas, politicians, being exceptionally stupid creatures, cannot understand that the same principles hold true for all prohibition:
With more than 5,500 people dying as a result of drug use across the United Kingdom last year…activists, health professionals and law enforcement agencies agree that change…in the nation's drugs policies [is vital]…a…cross-party group of MPs want the possession of drugs for personal use to be decriminalised across the whole of the UK to tackle the drug-death crisis. They are also calling on Westminster to support safe consumption rooms in Scotland…the committee's…report highlights the example of Portugal, where decriminalisation was introduced in 2001…[and] death rates and HIV incidences have…plummeted…
Stalkers in Blue
Another sexual predator selects a traumatized woman as his prey:
A Calgary [cop] has been fired after he tried to start an unwanted and "predatory" relationship with a young woman who had just tried to kill herself, sending her up to a hundred text messages in four days while she lay recovering in hospital and dropping by her home uninvited…Dave Pizzolato…is only the third [cop] in more than two decades to be terminated from CPS…"Ms. M"…is [emotionally immature]…and was victimized for most of her childhood…she…showed [Pizzolato's texts] to hospital staff who were so concerned that they contacted the police…Yet, Pizzolato continued to message…and then…turned up at her home…on the day she was released from hospital…she later told investigators she felt pressured…and "scared about what he might do to her" if she [refused his advances]…Pizzolato [had a history of doing] the same thing with other women…and [had been warned to stop] just two weeks earlier…
The Pygmalion Fallacy (#824)
SAVE THE TOASTERS!!!!
…[James Issac Short] claimed he was trying to prevent sex trafficking when he tried to "rescue" mannequins in the window of an adult store [in Lake Ozark, Missouri]. "He said that the mannequins were telling him that they needed to be let out or something," explains Mont Hardcastle…manager of [a bar]…across the street…Mi Dream Angels…has several displays in its windows, including mannequins wearing different styles of lingerie. Hardcastle says he watched Short try the door several times before Short jumped onto a bench and kicked the glass window until it shattered…Hardcastle says Short was halfway into the store when he pulled him out…Two women then came over and sat on Short's legs until police arrived…Short caused well over $1,000 in damage, destroying a window, lighting, and a security camera…
The Prudish Giant (#896)
Siouxsie Q on Facebook, censorship and new sex-worker-friendly social media:
Facebook has gone so far as to explicitly outline its supremely staunch position on the use of "suggestive emojis" to solicit sexual communication…cartoon eggplants and peaches…are [now banned when]…"used alongside an implicit or indirect ask for nude imagery, sex or sexual partners, or sex chat conversations"…[for] sex workers…remaining visible has been a challenge due to a massive chilling of online speech related to all forms of sex, sexuality, and sexual health…In response, the adult industry has focused on creating and investing in its own platforms that allow users to share content and build community. Adult Video News…recently launched a social network that has similar functionality to Twitter, but also provides a way for performers to charge money for access to their explicit content. Similar sites like FanCentro and OnlyFans are thriving, and even the tube site monolith, PornHub, is now primarily a social interface…We may be entering an era where the Internet is no longer an interconnected web of information, but rather a landscape of private fiefdoms empowered to exclude those they deem unworthy, or worse, a law enforcement mechanism used to deter and punish — definitely not the 1996-vision of the internet's potential…
It's good to at least some mainstream publications buying articles from sex workers.
Pyrrhic Victory (#897)
While naive civil libertarians celebrate toothless local laws, DHS is building a giant biometric database:
The US Department of Homeland Security…expects to have face, fingerprint, and iris scans of at least 259 million people in its biometrics database by 2022, according to a recent presentation from the agency's Office of Procurement Operations…The agency is transitioning from a legacy system called IDENT to a cloud-based system (hosted by Amazon Web Services) known as Homeland Advanced Recognition Technology, or HART. The biometrics collection maintained by DHS is the world's second-largest, behind only India's…data kept by DHS is shared with other US agencies, state and local law enforcement, as well as foreign governments…Biometrics "make it possible to confirm the identity of travelers at any point in their travel," Kevin McAleenan, US president Donald Trump's recently-departed acting DHS secretary, told congress last year. The criteria used by US Customs and Border Protection…to screen out specific travelers as suspicious…was determined in conjunction with Palantir…and…could be tracking travelers "from the moment they begin their internet travel research"…
Stupor Bowl (#940)
Virtually the only people who still profess to believe this nonsense are cops and politicians…and, apparently, the Associated Press:
Major events…such as the Super Bowl have become hubs for sex trafficking [in the sexual fantasies of] Miami officials…[blah blah] children as young as 12 [blah blah]…"sexual exploitation of our children"…[blah blah] a "creepy guy" poster bearing the words: "Buy Sex. Be Exposed"…[blah blah] phone number…[blah blah] Florida…ranks third in the nation. About 40% of victims are children and most of the rest are young adults between 18 and 23 years old…[blah blah] sold for sex up to 20 times a day. A trafficker can make between $150,000 and $200,000 a year for each person they sell for sex…
One would think they'd be tired of wanking to this same fantasy for over a decade, but I guess child rape on an impossible scale is a deep-rooted fetish for busybodies.
Quiet Genocide
Just in case you doubted this was about total genocide:
The Chinese government assigns [male Communist Party members] men as "relatives" to monitor the families of [caged] Muslim Uighur men…in order to "promote ethnic unity"…the [program, sold with the Orwellian slogan] "Pair up and Become Family"…involves Uighurs [being forced to quarter] Communist officials, who [subject the family to weeks of propaganda and force the wives]…to [submit to rape by] their paired male "relatives"…Roughly one million Uighurs are [caged] in [concentration] camps that the government calls "free hospital treatment for the masses with sick thinking," from which accounts of…political indoctrination and torture have emerged…[while the] U.S. [fascist establishment has] been mostly silent…
Little Boxes (#979)
The puritans are learning that they are indeed subject to federal courts:
A Colorado woman was awarded a $50,000 settlement after she was [illegally] charged with indecent exposure for playing frisbee topless. Efrosini "Effie" Krokos [was victimized by a neighborhood busybody who called the pigs]…on September 26…[fortunately for them, Krokos and her fiance are white, so] Greg Harris of the Loveland Police Department [did not murder or tase either of them, but he is so stupid and poorly-trained he imagined]…Loveland [was not subject to federal courts]…Attorney David Lane…wrote a letter to the city…proposing a settlement instead of…a federal lawsuit. The city [wisely] agreed and paid Krokos…making it the first damages claim to a woman wrongfully charged for being topless in the country…
To Molest and Rape (#985)
Cops show you what they are every day; why don't you believe them?
…police moved [child-raping cop] Dennis Perkins to a state prison [to protect him from being lynched after bureaucrats]…informed parents…[that his accomplice wife, who is an elementary school teacher, may have intentionally fed] more than 50 children…small cakes that possibly contained [Perkins'] semen…he…has been tested and showed no signs of having any STDs…someone getting an STD in this manner would be rare, but not impossible…Perkins…was booked with [69 crimes ranging from rape to child porn]…


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Руско-украинската война (, ), наричана също руската въоръжена агресия срещу Украйна, или войната за независимост на Украйна (), се характеризира с пряко и непряко използване на въоръжена сила от Руската федерация срещу суверенитета и териториалната цялост на Украйна. Явните компоненти на въоръжената агресия на Русия срещу Украйна, която започва през 2014 г., са:

 Анексиране на Крим от Русия през февруари-март 2014 г. (с последващото начало на временната окупация на полуострова от Русия на 20 февруари 2014 г.)
 Войната в Източна Украйна (Донбас) от април 2014 г., която започва със създаването на така наречените Донецка и Луганска "народни републики"
 Руското нападение над Украйна на 24 февруари 2022 г., която започва след продължително военно натрупване и признаването от Русия на терористичните квази-държави "ДНР" и "ЛНР" като държавни образувания.

Към януари 2021 г., според ООН, общият брой на жертвите във войната е 13 100 – 13 300 души. Това включва 3375 цивилни жертви, приблизително 4150 украински военни жертви и приблизително 5700 проруски бойци. Почти 1,8 милиона души са разселени вътрешно. Русия окупира повече от 7% от територията на Украйна.

На 27 януари 2015 г. Върховната Рада на Украйна обявява Руската федерация за агресор.

Предпоставки

Основните предпоставки за конфронтацията между Русия и Украйна, която ескалира във въоръжена агресия на Русия срещу Украйна, са системната криза на руската държавност, трансформирана от руските политици в опити за възстановяване на Руската империя, разширяване на контрола върху част от света и запазване на световната хегемония, загубена с разпадането на СССР и икономическия упадък.

На 21 ноември 2013 г. в Киев започват антиправителствени протести (Евромайдан), в т.ч. митинги, демонстрации, студентски стачки, в отговор на решението на Кабинета на министрите на Украйна да спре подготовката за подписване на споразумението за асоцииране между Украйна и Европейския съюз. Тези протести са насочени най-вече към поддържане на законно установения геополитически курс на Украйна към Европа, което означава не само по-нататъшно освобождаване на Украйна от руския контрол, но и икономически, политически и военни гаранции в дългосрочен план за Украйна.

Смяната на властта в Украйна през февруари 2014 г., наречена по-късно Революция на достойнството, води до отстраняването на президента Виктор Янукович от власт и бягството му в Русия, възстановяването на конституцията на Украйна от 2004 г., отмяната на диктаторските закони и свикването на предсрочни президентски избори. Смяната на правителството драстично намалява влиянието на Русия в Украйна.

Революцията на достойнството е претекстът, използван от Русия за отключване на агресията, но не и причината за войната. Има дори версии, че руските специални служби целенасочено са работили за радикализиране на протестите за унищожаване на украинската държавност като цяло и все пак са използвали момента на временно отсъствие на президента и други висши служители, включително правоохранителните органи, за да превземат Крим.

Подготовка на Русия за агресия и компоненти на агресията

Информационна война
Основната предпоставка за конфликта е все по-враждебната политика на подвластната на идеологията на Владимир Путин (т.нар. путинизъм) Руска федерация спрямо Украйна. Тази политика, с оглед на съдържанието на руската пропаганда в медиите, става отявлено враждебна, трансформирайки се от антиукраинска във военна пропаганда. Агресивността на руската политика спрямо Украйна довежда до първите жертви от украинска страна още по време на режима на Янукович. За да противодейства на влиянието на руската пропаганда, през лятото на 2014 г. Държавният комитет за телевизия и радио на Украйна започва да се бори срещу разпространението на сепаратистки и антидържавни материали в медиите.

Правна война
Един от аспектите на подготовката на Русия за агресия е правната конфронтация между Русия и Украйна. Правната подкрепа на конфронтацията от страна на Русия е извършена на най-високо законодателно ниво чрез приемане на определени резолюции по въпроси, които позволяват най-ефективно, от гледна точка на руската политика, да помогнат за решаването на проблема за възстановяване на контрола върху Украйна. Сред тези въпроси най-развити са правният статут на Крим, нивото на сътрудничество на Украйна с НАТО и статутът на руския език в Украйна.

Икономическа война
Предпоставка за началото на въоръжената агресия срещу Украйна е икономическото обезкръвяване на Украйна, осъществено чрез пряк икономически натиск чрез търговски и тарифни конфронтации, блокиране на украинската търговия, блокиране на доставките на газ и др. В същото време то тези процеси протичат при липса на ефективна опозиция или дори пряка подкрепа от страна на украинските политици. Като пример подписването от Юлия Тимошенко на изключително неблагоприятно за Украйна газово споразумение е представено като значимо политическо постижение на ръководения от нея кабинет.

Икономическата война срещу Украйна е насочена преди всичко срещу икономиките на Източна и Южна Украйна, които поради определени особености се намират в положение на зависимост от Руската федерация, по-специално като получатели на суровини и енергия от Русия и като пазар за значителен дял от собствените си продукти.

Руската православна църква във война
Значителна част от рускоезичното население в Източна Украйна, силното влияние на руските медии и руската църква, и хибридният характер на руската война срещу Украйна водят до прякото и непряко участие на Руската православна църква във въоръжената агресия срещу Украйна. Елемент от косвеното участие на РПЦ в агресията срещу Украйна е посланието на Московския патриарх Кирил до Константинополския патриарх Вартоломей, в което патриарх Кирил заявява, че след Майдана "схизматиците" (т.е. Украинска православна църква на Киевската патриаршия) и униатите "открито призовават за изкореняване на православието" на територията на Украйна и "с началото на военните действия униати и разколници, получавайки оръжие, под прикритието на антитерористична операция, започват да извършват пряка агресия срещу духовенството на каноничната украинска православна църква на изток".

Освен косвеното участие на църквата са записани и свидетелства за нейното пряко участие в агресията. По-специално е установено, че някои терористични групи в Русия са съсредоточени в църкви, а някои "свети отци" са пряко замесени във военни действия срещу Украйна.

Хронология

"Законодателна подкрепа" на агресията

На 1 март 2014 г. Съветът на федерацията на Руската федерация подкрепя искането на руския президент Владимир Путин за разрешение за използване на въоръжените сили на Руската федерация на територията на Украйна. Във връзка с руската агресия Съветът за национална сигурност и отбрана на Украйна решава да приведе въоръжените сили на Украйна в пълна бойна готовност и разработва "подробен план за действие в случай на пряка военна агресия от страна на Руската федерация".

Окупация на Крим (22 февруари – 18 март 2014 г.)
На 23 февруари 2014 г. започват да се провеждат проруски и проукраински митинги. В проукраинските митинги вземат участие голям брой кримски татари, водени от Рефат Чубаров – лидера на общественото сдружение и основна неправителствена структура на коренната общност на кримските татари Меджлис. На 27 февруари неидентифицирани въоръжени мъже, за които се предполага, че са бивши бойци от разпуснатото на 25 февруари специално подразделение Беркут, участвали в потушаването на протестите в Киев, и руски специални части, завземат и блокират Върховната Рада на Крим и други административни сгради, летищата в Симферопол и Севастопол, средства за масова информация и др. По тяхно желание в парламента на Крим идват някои депутати, които гласуват за референдум за разширяване на автономията на Крим на 25 май 2014 г. – в деня на президентските избори в Украйна. Наличието на кворум обаче е под въпрос, тъй като медиите не са допуснати до заседанието. Датата на референдума е променена два пъти: най-напред на 30 март, а скоро след това – на 16 март. Променена е и формулировката на въпроса: вместо за разширяване автономията, имащите право на глас трябва да заявят позиция относно присъединяването на Крим към Русия. Но тъй като Украйна е унитарна държава, въпросът за отделянето на региона може да бъде решен само на национален референдум. Имайки предвид това, още преди референдума лидерите на ЕС, САЩ и много други го обявяват за незаконен, а резултатите от него – за невалиден.

През нощта на 27 февруари въоръжени неизвестни лица превземат сградите на Върховната Рада и правителството на Автономна република Крим и поставят руски знамена върху сградите. Според народния депутат Сергей Куницин административните сгради са завзети от професионално обучени хора, въоръжени с автомати, картечници, гранатомети и др.. Медиите инфомрират, че центърът на Симферопол е бил блокиран от служители на реда, които вдигнали тревога и ограничили движението на градския транспорт.

Проруски действия в източната и южната част на Украйна

През март и април 2014 г. се провеждат редица акции с участието на руските специални служби в източните, централните и южните райони на Украйна, чиито участници са предимно за отделяне на южната и източната част на Украйна и присъединяване на тези региони към Руската федерация, провеждането на референдум за федерално устройство на Украйна, предоставяне на руския език статут на втори държавен език, и срещу новото украинско правителство. В контраст с тези действия в източните и южните райони проукраинските сили провеждат акции в подкрепа на териториалната цялост на Украйна и нейната държавна система.

Окупацията на Крим от Руската федерация и проявите на украинофобия на проруските протестиращи в южната и източната част на Украйна предизвикват голямо възмущение сред населението на Украйна, което за няколко дни премина в масови протести.

Конфронтации в Донецк и Луганск
По време на демонстрациите проруски активисти се опитват да превземат държавни институции (заграбени са сградите на областните държавни администрации в Донецк и Луганск). Избухнват сблъсъци между участници в проруски и проукраински митинги, най-мащабният от които се провежда в Донецки по време на които десетки са ранени и поне един убит.

Конфронтации в Харков
По време на демонстрациите проруски активисти се опитват да превземат областната държавна администрация в Харков. Избухват сблъсъци между участници в проруски и проукраински митинги.

Конфронтации в Одеса
На 2 май 2014 г. избухват сблъсъци, които прерастват в масови безредици в град Одеса. Избухват улични боеве между проукраински граждани, ултраси на футболните клубове "Черноморец Одеса" и "Металист Харков", от една страна, и проруски сепаратистки активисти, от друга. Конфронтациите започват от историческия център на града по улиците "Грецкая", "Дерибасовская" и "Грецкая", а по-късно се превръщат в преследване на проруски активисти през по-голямата част от центъра на града до Куликово поле, където са дадени най-много жертви в следствие на опожаряването на сепаратисткия щаб в Дома на профсъюзите.

В резултат на сблъсъка 48 души умират – шестима умират от огнестрелни рани, а останалите – по време на пожар в синдикална сграда; 247 са ранени.

Антитерористична операция

Началото на антитерористичната операция (13 април-20 юни 2014 г.)

Едностранно прекратяване на огъня (20 – 30 юни 2014 г.)

Възобновяване на антитерористичната операция

Ходът на конфронтацията през 2016 – 2020

Инцидент в Керченския проток
На 25 ноември 2018 г. се състои битката при Керченския проток, в която бронираните артилерийски катери "Бердянск" и "Никопол" и рейдовият влекач на Военноморските сили на Украйна "Яни Капу" извършват планиран преход от пристанище Одеса до пристанище Мариупол в Азовско море. "Яни Капу" е ударен от руския граничен кораб "Дон". След това, когато напуснаха 12-милната зона, лодки на ФСБ на Руската федерация откриват огън по корабна група на ВМС на Въоръжените сили на Украйна.

Ескалация на руската агресия

Инвазия (2022)

На 21 февруари 2022 г. самопровъзгласилите се Донецка народна република и Луганска народна република са признати за независими държавни образувания с указ на Владимир Путин, на територията на Украйна е изпратена редовна армия на Въоръжените сили на Руската федерация и е подписан Договора за приятелство, сътрудничество и взаимопомощ с ДНР и ЛНР.

Вечерта на 23 февруари 2022 г. лидерите на ДНР и ЛНР Денис Пушилин и Леонид Пасичник се обръщат към Владимир Путин за военна помощ.

На 24 февруари 2022 г. около 5 ч. сутринта Владимир Путин обявява война на Украйна в нарушение на член 2 на Хартата на ООН. В същото време руските войски започват интензивен обстрел на части на украинските въоръжени сили на изток, прекосяват североизточната граница и изстрелват ракети и бомби по летища и оръжейни складове в почти цяла Украйна. Върховната Рада единодушно одобрява въвеждането на военно положение. Сирени за въздушни нападения се чуват в цяла Украйна през по-голямата част от деня. Информационно-комуникационната инфраструктура в Украйна понася удари в резултат на кибератаки и бомбардировки от руската армия. Няколко украински града и съоръжения са окупирани, включително атомната електроцентрала АЕЦ Чернобил. Въпреки това, според представител на американската отбрана, руската армия "среща по-голяма съпротива от очакваното" (особено в посока Киев).

Геноцид над украинците

На 14 април 2022 г. Върховната Рада на Украйна признава за геноцид на украинския народ действията, предприети от Руската федерация по време на последната фаза на въоръжената агресия на Руската федерация срещу Украйна, започнала на 24 февруари 2022 г. Посочва се, че актовете на геноцид в действията на Русия се проявяват по-специално в извършването на масови зверства в градовете Буча, Бородянка, Гостомел, Ирпин и др.

Източници

Вижте още
 Хронология на руското нападение над Украйна (2022)
 Клане в Буча
 Ракетен удар по железопътната гара в Крематорск

Външни препратки
 Актуализирана онлайн карта на войната на Liveuamap
 Най-кратката история на руско-украинската война: откъде и как започна конфликтът в Донбас
 Виртуален музей на руската агресия
 Русия нападна Украйна (24 февруари 2022 г.): хронология на основните събития

Справка
 Международен въоръжен конфликт в   // Rulac: Върховенството на закона във въоръжени конфликти
 Конфликт в   // Съвет за външни отношения

Журналистика и анализ
Владимир Хорбулин, Резюме на втората годишнина от руската агресия срещу Украйна // Украински кризисен медиен център, 20 февруари 2016 г.

Видео

Руско-украинска война

redpajama_set_name: RedPajamaWikipedia


text:
title: How To Avoid Spam Mail
layout: post
---

So, you are setting up a new email account for yourself, your mom, brother, sister, friend, etc… What is the best advice you can give them? *DON'T GIVE IT OUT*.

![](/uploads/versions/email-spam-600---x----600-300x---.jpg)

Everybody knows what its like and everybody hates it: spam. How can you avoid it?

1. Never, ever, give your email out to someone you do not absolutely trust. If the company is not reputable, then just don't cough it up. It's that simple.

2. Encode your email address on your website/s. Do not use the traditional:

<a href="mailto:myemailaddress@me.com">Email Me</a>

Instead, use a decimal converter [(here)](https://www.branah.com/ascii-converter) to translate your regular email address into the following:

<a href="10997105108116111581091211011099710510897100100114101115115641091014699111109">Email Me</a>

You need to encode the html which is written for your website if you are putting your email on a website. Why is this a big deal? It's a big deal because "bots" are computer programs that run automated tasks. There are millions of "bots" that comb the interenet for - you guessed it - *email addresses*. So, don't put yours out there - that includes putting your email address on your Facebook profile.

This is a good example of how internet security is a combination of human discipline - don't give your email out to just anyone - and good software - encoding email addresses on websites.

redpajama_set_name: RedPajamaGithub


West Yorkshire Police has begun a new campaign to highlight the issues of forced marriage and honour-based abuse as a conference is held in Leeds.
It coincides with the third annual national day of remembrance for victims of "honour" killings, which takes place today.
July 14 was the birthday of Shafilea Ahmed, originally from Bradford, who was killed by her parents in Warrington in 2003 after suffering honour-based violence and an attempted forced marriage.
Assistant Chief Constable Catherine Hankinson said: "Tackling forced marriage and honour-based abuse is a priority for West Yorkshire Police. There are specialist safeguarding units in every district of West Yorkshire with professionals who understand the issues and know how to help.
"There is no honour in any form of abuse. We take a victim-led approach to deal with these challenging issues which respects the views of victims and witnesses, provides the necessary support, confidentiality and protection from harm."
In June 2014 it became a crime to force someone to marry against their will, with those found guilty facing a prison sentence of up to seven years.
Police can also apply for Forced Marriage Protection Orders to safeguard victims or potential victims.
The orders place legally binding conditions on those involved in trying to force another person to marry, with a breach resulting in a prison sentence of up to five years.
Meanwhile, national charity Karma Nirvana is inviting police, Leeds City Council, Leeds Community Healthcare NHS Trust and other agencies to affirm their commitment to leading the way nationally when it comes to tackling the issue.
Among those set to sign a pledge at the charity's conference today is Chief Superintendent Paul Money, Leeds District Police Commander.
"Hosting the national day of remembrance event in Leeds allows us to really throw the spotlight on this issue, and remind everyone, locally and nationally, that these abhorrent crimes still take place in our communities," he said.
"We must collectively continue to do all that we can to tackle issues of forced marriage and honour-based abuse. Everyone has a right to choose and to be safe."
Mark Burns-Williamson, West Yorkshire Police and Crime Commissioner, said he was very willing to sign the pledge to ensure victims get the protection they deserve.
He said: "Often the victims of honour-based abuse can feel totally isolated from their own families and find it very difficult to come forward for a number of complex reasons.
Anyone wishing to report concerns about forced marriage or honour-based violence, whether for themselves or someone they know, can talk to the police via 101 or call 999 in an emergency.
People can also contact other agencies for support, such as Karma Nirvana on 0800 5999 249 and Childline on 0800 1111.
Visit www.westyorkshire.police.uk/forcedmarriage for more information about the new forced marriage legislation and support available for victims.


After months of anticipation, Chez B finally puts out his mixtape – "Sun Day". The super skilled Simple Sound Xclusive music producer releases this six track mixtape featuring Alpha Romeo, Young Daddy Zemus, Alpha Jr, Camfella, Jonny Cee, Kabamba, Bra B, Young Dee, Stevo Rap Guru, Ben Da'Future and more.
All song were produced, mixed and mastered by Chez B.


Initial unemployment insurance (UI) claims for the week ending January 14, 2012 fell by nearly 125,000 over the previous week. At 521,613 claims under state programs, the week was also down about 28,000 claims from the same period in 2011. For the week ending January 7, 2012, the total number of UI benefits claims numbered over 4 million and was down about 85,000 claims from the prior week.
States offering extended unemployment benefits, as of the week ending December 31, 2011 include Alabama, California, Colorado, Connecticut, Delaware, the District of Columbia, Florida, Georgia, Idaho, Illinois, Indiana, Kansas, Kentucky, Maine, Maryland, Massachusetts, Michigan, Minnesota, Missouri, Nevada, New Jersey, New Mexico, New York, North Carolina, Ohio, Oregon, Pennsylvania, Rhode Island, South Carolina, Tennessee, Texas, Washington, West Virginia, and Wisconsin.
Former federal civilian employees seeking initial UI benefits jumped by over 1000 to 2,651 for the week ending January 7. Additionally, initial UI claims for newly discharged veterans rose to 2,578, up by 683 over the previous week. The total number of former federal civilian employees claiming UI benefits numbered 31,866 during the week ending December 31, 2011, climbing by over 2,000 from a week earlier. Claims from newly discharged veterans increased slightly to 42,291, up 194 from the preceding week.
Overall, state programs reported over 3 million persons seeking Emergency Unemployment Compensation for the week ending December 31, up over 100,000 claims from the prior week but down by about 700,000 over the year. States with the highest unemployment rates for the week ending December 31 were Alaska (6.9 percent), Connecticut (6.6 percent), Oregon (5.0 percent), Wisconsin (4.9 percent), Pennsylvania (4.7 percent), Idaho (4.5 percent), Rhode Island (4.5 percent), Montana (4.3 percent), New Jersey (4.2 percent), Arkansas (4.0 percent), Illinois (4.0 percent), and Washington (4.0 percent).


This book chronicles Steven Spielberg's life as part of the "Who Is?" series for Penguin Books. A high point of this project was that out of 80 final illustrations I received zero corrections. Of course it's equal parts hard work and blind luck when things like that happen. But it was very gratifying all the same.
One of the many challenges of this project was to represent iconic images from Steven Spielberg's career, without infringing on the copyrights of the actual images. Therefore I had to create new images that felt like the originals.


text:
Insurgency in Northeast India: The Chinese Link
02 Feb, 2015 · 4826
Wasbir Hussain explains why insurgency cannot be controlled unless the flow of small arms to the region is checked
Wasbir Hussain
External Affairs Minister Sushma Swaraj has just ended a four-day visit to China where she discussed "bilateral, regional and global issues of concern" for both countries. The range of discussions with her Chinese counterpart Wang Yi, that stretched to over two hours, were rather extensive: finalising the transit issue for Indian pilgrims to Kailash Manasarovar through Sikkim to the border question, to defence contacts between the two neighbours, trade and commerce, and possibly river waters, in view of the concerns in India over the massive damming of the Yarlung Tsangpo (Brahmaputra). What is not known, however, is whether Sushma Swaraj or the new Foreign Secretary, S Jaishankar, an expert China hand who spent four years in Beijing as India's Ambassador there, raised the issue of official Chinese arms manufacturing companies regularly selling small arms (man-portable lethal weapons like AK series rifles, light and sub-machine guns, grenades etc) to insurgents in Northeast India. China, in fact, holds the key to the availability of weapons and ammunition among the terror groups in Northeast India that is actually keeping insurgency alive in this far-eastern frontier.
One has heard the Modi Government at the Centre talking of a 'zero tolerance policy' on terror, something that has not been clearly articulated as yet. Going by New Delhi's diktat to the security establishment in Assam to go all out against the insurgents indulging in violence, in the wake of the 23 December 2014 massacre of around 80 Adivasis in the state by rebels of the National Democratic Front of Bodoland (Songbijit faction), one can assume that the Centre now is in favour of tough action to neutralise trigger-happy rebels. The approach seems to have yielded good results because from 23 December 2014 to 31 January 31, 2015, security forces engaged in stepped-up counter-insurgency operations against the NDFB (Songbijit) have arrested nearly 140 cadres, killed a top commander, and recovered nearly two dozen rifles, including sophisticated German HK 33 and US-make M 16 rifles and a range of AK series ones, most likely made in China. Close to 2,000 rounds of ammunition have been seized.
There is every reason to believe that unless the flow of small arms to the region is checked, insurgency cannot be eliminated or controlled in Northeast India. Any new anti-terror policy that New Delhi may formulate in the coming days would have to take this fact into consideration. It is here that the China factor will come into play, something that the Modi Government will have to confront.
In fact, if one looks at the charge-sheet filed by the National Investigating Agency (NIA) on 26 March 2011 against Anthony Shimray, chief arms procurer of the Isak-Muivah faction of the National Socialist Council of Nagaland (NSCN-IM), it becomes clear that the insurgent group was actively buying weapons from Chinese companies. The FIR lists out the plan in detail and specifically says that Shimray, accompanied by a representative of another rebel group, the National Democratic Front of Bodoland (NDFB), visited the Norinco headquarters in Beijing. Norinco or the China North Industries Corporation, is one of China's largest State-owned weapons manufacturers. Bangkok-based NSCN-IM rebels paid USD 500,000 to Norinco and bought 1,800 weapons that landed at Bangladesh's Cox Bazar in 1996 and were transported onwards to Northeast India, to NSCN-IM and NDFB camps. Half of these weapons, of course, were seized by Bangladeshi security forces while being off-loaded.
Around 2007, NSCN-IM faced desertion from its ranks with people going away with weapons. That was the time the outfit again decided to buy 1,000 weapons, mainly AK series rifles, light machine guns, sub-machine guns, pistols, rocket-propelled grenades etc. NSCN-IM approached another Chinese arms manufacturing company, TCL, and paid USD 1,00,000. The money was paid through a Thai arms dealer Wuthikorn Naruenartwanich alias Willy. The deal did not materialise due to the 'disturbed situation' in Bangladesh where the consignment was meant to be delivered. The NIA has electronic receipt of the payment.
Reports attributed to the Ministry of Home Affairs (MHA) said that a definite money trail exists as payment to the Chinese firm was made through normal banking channels via a leading private bank's branch in an African country. NSCN (I-M), according to the MHA, has parked its funds in bank accounts across several African nations. The NIA is bent on pursuing the Anthony Shimray arms procurement case to its logical end and has received a shot in the arm with the arrest in 2013 of Wuthikorn Naruenartwanich. His extradition to India was cleared by a criminal court in Thailand but Willy has since moved a higher court there and is awaiting its verdict on the matter of his extradition. What is clear is the Chinese link in weapons supply to rebels in Northeast India.
Bangladesh and Myanmar have been the key transit routes through which small arms made in China reaches the Northeast. The main conduits in Myanmar are the Karen National Union (KNU) and the Kachin Independence Army (KIA). These two ethnic insurgent groups have acted as the interlocking chain for the illegal weapons flow from Yunnan in China via Myanmar to Northeast India, but the most effective illegal weapons trader in Myanmar is another armed ethnic group, the United Wa State Army (UWSA).
The UWSA is the military wing of the United Wa State Party (UWSP) founded in 1989 with members of the Wa National Council (WNC), which represent the Wa ethnic group and former members of the Communist Party of Burma (CPB). The UWSA's biggest source of revenue is its involvement in the illegal small arms network across South and Southeast Asia. It manufactures Chinese weapons with an "informal franchise" procured from Chinese ordnance factories. The main motive is to sell these weapons for huge profit to armed groups in Northeast India.
A security situation in the Northeast that remains under control is vital to the pursuance of India's Look East Policy. Therefore, New Delhi will have to devise a strategy to neutralise insurgency in the Northeast, and that strategy will have to factor in the flow of small arms to these groups. The ability to chock this flow right at the source of its origin could well hold the key.
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We were reminded last night of how blessed we are. We have a marriage that has not only endured, but thrived for 28 years. We love each other more today than the day we married. We are one another's best friend. We have a marriage built on the foundation Jesus Christ and He alone is our strength. When this grandbaby is born and we hold that precious life (yes, we will both be sobbing!) we will share an amazing experience. We will know we have fought for our marriage, we have fought to raise godly children in an ungodly world and we will see a new heritage begin.
The greatest gift we can leave our children and grandchildren is the legacy of Jesus Christ. Oh, we can't wait to begin loving those grandbabies for the Lord! Mia & G Pa are ready to lay that sweet baby in that crib! September can't come soon enough!!!
To see the latest ultra-sound pictures, go to Aunt Paige's blog.
seriously mom.... tears you cant write touching posts like this one and not expect a pregnant girl to cry. love you guys so much!
You are too cute!! Love you!
Although, after reading this I asked Nathan why we don't have weekly date night?!? I think we will work on that!


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Home Culture The past comes alive at Good Hope's Alabama Bicentennial Festival
The past comes alive at Good Hope's Alabama Bicentennial Festival
By: Christy Perry
The late Elwin Robinson (portrayed by Preston Seymore) poses for a photo with his wife, Sue Robinson, at Saturday's There's Good Hope in Alabama Festival. (Christy Perry for The Cullman Tribune)
GOOD HOPE, Ala. – The rich history of Good Hope was celebrated Saturday at the There's Good Hope in Alabama Festival. The students and faculty of Good Hope High School (GHHS) worked all year to make sure the festival was a success. The event was one of many celebrations taking place across Cullman County and the state this year commemorating the Alabama 200 Bicentennial.
A statement from the school explained, "The purpose of the project is to bring the school and the community together in order to forge relationships for the present and future as well as to recognize and exhibit the reverence to the history, people and entities of Good Hope's past. Our faculty, students, town leaders and community will work side-by-side to produce this project. This will serve as a lifelong guide for the students to understand the importance of community history and volunteerism. The school will work to instill pride and accomplishment in the students as they work to see There's Good Hope in Alabama to maturation. We will forever hold our bit of history as participants in the 200 Alabama Bicentennial."
The festival schedule had to be rearranged and altered a bit due to the stormy weather Saturday morning, but all the planned events were held. The Raider Run 5K was postponed to the afternoon and the cemetery tours were held in the school's band room as it was determined the cemetery grounds were too wet for heavy foot traffic.
Festival director Anita Whitlock and the students transformed the school's gymnasium into the Good Hope museum featuring the many people, places and events that shaped the city. Artwork by the students of Good Hope was also displayed throughout the museum. Younger children were treated to a bouncy house in the gym and the GHHS cheerleaders offered face painting. The corn hole tournament was held in the gym's balcony.
A craft show was held in the hallways of the high school and the SGA hosted a Kat Daddy's BBQ lunch in the school's cafeteria.
The gym also served as the main stage for several special events held throughout the day. The opening ceremony featured Bailey Swann performing "The Star Spangled Banner." Swann also took the stage a few more times during the festival. Bethany Bynum also performed.
Two special ceremonies took place: Rep. Corey Harbison, R-Good Hope was honored for his contributions and leadership in Good Hope, and David Bice was recognized for his 37 years of service as the school's ag teacher. The men each received special appreciation awards. Bice will be retiring at the end of this school year.
Bice said that he would miss the kids, then added, "I got a few things I'm looking at doing after I retire. I'm not just going to sit still."
History came alive in the band room as Good Hope students/actors waited to perform for the cemetery tours. Each student learned of the lives and stories of former Good Hope residents who are buried in the Good Hope Cemetery. The students wore costumes depicting the time period and personalities of the people they portrayed. Making the project more memorable, surviving family members of many of those portrayed attended the performances.
A special performance took place Friday evening for the students' family members, as well as the family members of those the students chose to portray.
Bynum played young Wanda Coker, who was tragically killed in 1948 when she was struck by a car as she was on her way to school.
Lawton Farr played Charles Christopher Reyer, Jr.
Staying in character, Farr explained, "I was a military Confederate soldier in the war and I died in the war. I had four little girls and right before I left we had them. I was 31 when I died."
He added, "This was Mrs. Whitlock's idea, but we all kind of pitched in to help. We did one last night for the family members with a VIP reception."
As The Tribune sat with the actors prior to their first Saturday tour, the personal commitment the students demonstrated to the people they would portray was apparent. They were eager to share everything they had learned.
Cullman County Schools Superintendent Dr. Shane Barnette, whose son Noah portrayed James Melvin (JM) Sanford, who owned "The Rock Store," said of the performance, "They did a great job. When I was a kid, probably 6 or 7 years old, we went to church in Good Hope and we always stopped at that Rock Store on the way to church. We'd go in there and get a snack or something, usually a pack of Certs. These kids will never forget this. It will be embedded in their minds forever."
Elwin Robinson and Fred and Ethel Robinson were three of the individuals portrayed by the students. Their performance was attended by Sue Robinson. Elwin Robinson was her husband and Fred and Ethel Robinson were her parents. She enjoyed meeting the young actors portraying her late family members and praised them for a job well done. Thanks to the actors, four generations of the Robinson family were represented Saturday as they posed for many memorable photos.
Whitlock was pleased with the day's festivities, saying, "We have had about 200-300 people sign our guest book, and everyone has done a great job."
Christy Perry
christy@cullmantribune.com
There's Good Hope in Alabama
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Q: How do I output the unique lines from a grep pattern match while ignoring other parts of the line? Suppose I have a file called names.txt, which contains a list of names and corresponding emails of people in the following format:
FName1 LName1 <random1@test.com>
FName2 LName2 <random2@test2.com>
FName3 LName3 <random3@test3.com>
FName4 <random1@some.com>
FName5 MName1 LName4 <something@test34.com>
FName1 LName1 <random01@asdf.com>
...

What I am trying to accomplish is to output all of the unique people, case-insensitively, based on name (i.e. ignoring email) from names.txt. So, the output would look like the following:
FName1 LName1
FName2 LName2
FName3 LName3
FName4
FName5 MName1 LName4

Note that the same name can appear multiple times in the file and names can be a mix of lower case characters, upper case characters, numbers, etc. Examples of names that can appear would be: "JoHn sMitH JOnes", "StEve", or "RoB3rt Fro5t"
What I am struggling with is how to just output the unique names without the associated email. I am able to use grep to match the patterns I am looking to output by using the following:
grep -i "^[A-Za-z0-9]*[ ]*[A-Za-z0-9]*[ ]*[A-Za-z0-9]*" names.txt

However, I am unsure how to use these pattern results and output the unique names while leaving out the email part of each line. Is there some way I can use these grep results and send them as standard input into some other command? Any feedback or suggestion would be greatly appreciated.

A: With awk:
awk '
  {
    sub(/<.*/, "") # remove email address
    $1 = $1 # remove leading and trailing blanks, squeeze all sequences of blanks
            # into one space
    if (!seen[tolower($0)]++) print # print if not seen before
  }' < names.txt

A: You can go the other way around. Ignore the emails:
cut -d'<' -f1 names.txt | sort -fu

That cuts the line at the < separator, and prints the first field, sorts (ignoring the case -f) and then keeps just the first one of names that only differ in their case (also ignoring the case with -u).

redpajama_set_name: RedPajamaStackExchange


text:
Zoológico Bosque Guarani Municipal é um exemplo de recuperação de uma área que, antes degradada, foi transformada em um espaço de lazer, educação ambiental e turismo. Foi inaugurado em 9 de junho de 1996.

História
Sendo o 3º maior zoológico do Paraná, possui um total de 20 recintos, que são percorridos através de trilhas, onde estão expostos animais resgatados do tráfico, como macacos, onças, tucanos, araras, papagaios, emas, garças, sabiás, cisnes, gralhas, entre outros.

O local possui intensa vegetação, com cerca de 963 árvores nativas e três lagos, onde foi aproveitada uma nascente de água. Há ainda um playground, uma lanchonete e um anfiteatro.

Acervo de animais
O zoológico atualmente abriga cerca de 150 espécies de animais silvestres, sendo 49 espécies de aves, com um total de 223 exemplares; 03 espécies de répteis, com um total de 16 exemplares; 05 espécies de mamíferos, entre eles o bugio, sagui-de-tufos-pretos e o mico-leão-de-cara-dourada, com um total de 30 exemplares.

Localização e horário de funcionamento
O Zoológico Bosque Guarani fica localizado na Rua Tarobá, nº 875, Centro de Foz do Iguaçu. O local abre todos os dias: de terça a domingo das 9h às 17 horas, e nas segundas-feiras das 12h às 17 horas. A entrada é gratuita.

Zoologicos
Atrações turísticas de Foz do Iguaçu
Fundações no Paraná em 1996
Áreas verdes de Foz do Iguaçu


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text:
New In Store: Brian Jonestown Massacre, Goat, Hookworms & More
|By Jack Palfrey
Here's a round-up of the latest non-Fuzz Club vinyl added to our store this month…
Earlier this month, Swedish psych monoliths Goat released, 'Let it Burn', their first offering since 2016's 'Requiem' LP. The track is an enchanting, fuzzed-out workout that sees the masked troupe amp up their lysergic psychedelic motifs tenfold to create a truly mesmerising piece of work that could be their finest to date. The 7" is up in our store ahead of its official release May 25th. On top of that, there's also a new 12" single from the ever-prolific Brian Jonestown Massacre – 'Hold That Thought' and the B-side 'Drained' offering a taste of the TWO albums Anton is going to be putting out this year!
Leeds noisenik's Hookworms are having quite the year, aren't they? Their new album, 'Negative Space', has left the band with pretty remarkable praise across the board for its refreshing synth-driven psych pop sound, delivered with the same motorik pulse the band have been busy honing throughout their career. However, for those fancying a trip down memory lane, Gringo Records have recently reissued the band's self-titled debut which offers a darker exercise in the droning psych-rock that made the band so revered in the first place. You can pick it up below.
As well as the Goat 7", some more gems from our friends at Rocket Recordings include the new Bonnacons of Doom LP and the new long-player from Salford industrialists Gnod which, going off the first single 'Donovan's Daughters', see's the mythic collective at their most incendiary, volatile and forward-thinking. Not that you'd expect anything else from them, right?
Gnod Goat Hookworms New In Store The Brian Jonestown Massacre