text:
\section{Introduction}
Non-verbal communication is an essential component of the communication process, as it conveys a large portion of the message's meaning.
Thus, equipping digital representations of humans with non-verbal communication skills finds many applications in human-computer systems, from telehealth to entertainment to education.

Despite ongoing research in human-machine communication, the interaction with virtual humans does not feel similar to talking to a person.
We tackle the problem of unrealistic human-agent communication in virtual reality.
We aim for synthesizing and animating \emph{natural} non-verbal communication in the form of hand and arm gestures.
Our objective is to improve the interaction between humans and machines. Whenever people communicate, body language plays an important role. Hand and arm gestures are performed to support spoken language during communication and to convey meaning effectively. Moreover, speech and gestures reflect the personality and the intention of a person which is an important factor in virtual online communication. Gestures are idiosyncratic and contain information such as personal feelings, empathy, aggressivity, as well as authority and authenticity of a human \cite{KRAUSS1996389, gesturesEmotion2009}. The meaning of spoken language highly depends on body language because it has the power to signal irony and even reverse the sense of words pronounced. Therefore, the relationship between speech and gestures is of great importance when animating virtual humans.

In this paper, we propose a novel, data-driven approach to generating meaningful gestures.
We utilize the large amount of speaker video data available to train a model such that it learns the relationship by observing samples of speakers.
We adopt the state-of-the-art human pose estimation algorithms \cite{openpose1, videopose3d2019, hand3d2017} to extract the 2D pose from video and to project it into 3D space. Using our extraction framework, we create a 3D speech-gesture human pose dataset with two educational speakers and two comedians. Our resulting dataset is significantly larger than previous datasets \cite{JapaneseDataset, gestureRNN} that are recorded using motion capture. Moreover, our approach of creating 3D speech-gesture data can be applied to any speaker with exiting video material. In contrast to the dataset created by \cite{speech2gesture2019}, our dataset consists of human pose data in 3D space.
We design a Generative Adversarial Network (GAN) \cite{goodfellow2014generative} framework and apply deep learning to train a model that generates speaker-specific gestures from speech. We train our model on different speakers from our 3D speech-gesture dataset. Compared to the GAN framework of \cite{speech2gesture2019}, our architecture predicts 3D pose data. Moreover, we enforce constant bone length during the training of our GAN model to generate anatomically plausible gestures.
Once we have generated gestures, we post-process the human pose data further such that we animate smooth and plausible gestures on avatars in a virtual environment. To achieve that, we apply skeletal constraints and inverse kinematic computations.
We design a user study to evaluate the qualitative results of our trained gesture model.
In contrast to other recent approaches  \cite{speech2gesture2019, gesturesGAN, gestureGenerative} which perform the user study directly on the gesture output in human pose format, we animate the generated gestures on an avatar in a virtual environment. Hence, instead of comparing abstract skeletal representations of gestures, our study setting is natural and closely related to watching real humans communicate. Our user study is set up in a format that is inspired by the Turing test. We compare the gestures extracted from the original video to our generated gestures side-by-side and ask the participants of our study to identify the ``real'' gestures extracted from the video source. In a second experiment, we compare our generated gestures against gestures from an uncorrelated speech of the same speaker.
We show examples of speakers from two different domains and their original and generated gestures animated on a virtual character in \autoref{fig:teaser}.

We discuss related work in the area of gesture synthesizing in the next section. Then, we describe our approach, consisting of the 3D gesture dataset creation, the gesture-speech translation with the GAN, and the animation on a virtual character, in detail. We present the evaluation of our method that was done by conducting a user study in \autoref{sec:eval}.

\section{Prior Work}
Over the last three decades, many techniques were developed to generate gestures and body posture for a virtual human \cite{levine2009real,marsella2013virtual,lhommet2013gesture}. Typically, the animation generation is divided into two stages. First, the gesture and body pose to be generated is identified by analyzing a speech or text of the utterance. In the second stage, the gesture and body pose is generated and applied to the virtual human.

\subsection{Communication Theory}
Previously developed techniques often do not apply the communication theories and ignore the neurophysiological basis of gestures. Our insight is supported by the Gestural Theory \cite{hewes1973primate} according to which non-verbal communication is not secondary to the speech's semantics or prosodics.
Rather, speech and gestures are co-related and emerge together from the communicative intention of the thoughts forming in the speaker's mind. The Gestural Theory also poses that gesturing is not used only as a conscious way to illustrate speech, but rather is an integral part of the thought process that \emph{generates} the speech \cite{mcneill1992hand}. To further support that claim, various studies provided evidence that gestures are most effective when they are complementary rather than redundant with the semantics of the speech \cite{singer2005children}.  Other theories of communication, such as McNeill's Growth Point hypothesis also state that gestures and language emerge in a shared process from a communicative intent \cite{mcneill2008gesture}. In fact, new cognitive models explore the distribution of communicative content across output modalities rather than assuming speech-to-gesture causality \cite{kopp2013spreading}.

An important related dimension of virtual human non-verbal communication that is not addressed by previous animation synthesis techniques is the viewer's perspective. From the perspective of the viewer, the process of understanding a gesture performed by others (e.g. virtual human) is a combination of interpretations of auditory and visual signals rather than being a purely visual phenomenon. This is because humans are \emph{simultaneously} verbally \emph{and} visually-oriented \cite{verbalVisual1992}. The human brain expects to process visible body movement when we hear spoken words. If the speaker is not visible, humans recreate the missing visuals in their imagination. If the speaker's body language does not meet the expectation or, worse, clashes with the semantics of the utterance, the process of communication is negatively affected.

Research in the neurology of speech suggests that the brain's auditory processing of speech is based on the gestural production of sounds and the original vocal energy as referenced from the body, rather than being based on the acoustical attributes of the sound itself \cite{deacon1998symbolic}. This is because our brains first evolved to understand visible gesture and then re-adapted for vocal ``gestures'', to finally learn to process spoken words.
Based on these observations, we conclude that the traditional approach to the generation of gestures from speech does not take into account communication theories and neuroscience findings. This disconnect was previously acknowledged by Chiu \textit{et al}. , who wrote: ``It is believed that in human communication, the brain is co-planning the gesture and the utterance, so approaches that do not use future information about the planned utterance may be unlikely to match the sophistication of human gesture-speech coordination.''\cite{chiu2015predicting}.

\subsection{Gesture Generation}
An example of successful generation of beat gestures was presented by Levine \textit{et al}.  \cite{levine2009real}, who generated co-verbal gestures from speech by extracting \emph{prosodic} (tone and intonation) variations from the speech. The approach was unsuccessful in temporally matching the expressiveness of the speech with iconic or metaphoric gestures, as the information required to do so is not present in the prosodic properties of the speech. This concern was addressed by combining prosody with parsing of the spoken text \cite{marsella2013virtual}. Text can be analyzed for emotional content and rhetorical style, providing a rich -- although imposed by text semantics and thus lacking subtleties of subtext interpretation -- basis for iconic and metaphoric gesture synthesis. Finally, text-driven rule-based approaches \cite{lhommet2013gesture} create a sequence of gestures, but the rules must be coded manually. As a result, the gesture animations produced using rule-based systems tend to be time-consuming, repetitive, and lacking in variation.

Gesture synthesis, once a gesture to be generated is identified, often uses kinematic procedural techniques \cite{hartmann2005impl}. As an alternative, Kopp and Wachsmuth \cite{kopp2004synthesizing} presented a neurophysiologically-based approach to drive the trajectory of gesturing arm motions. Physics-based simulation has also been implemented, achieving better visual fidelity than kinematic-based approaches \cite{welbergen2010elckerlyc}. Procedural techniques enable full control of the motion, allowing to map the temporal dimension of the gesture to the temporal dimension of the speech. Unfortunately, they usually result in unnatural-looking hand movement and thus cause the uncanny valley effect \cite{seyama2007uncanny}.

To mitigate the uncanny valley effect, techniques based on motion capture data have seen increased use. The use of motion graphs \cite{kovar2008motion} allows concatenated segments of motion captures to create a sequence, such as in \cite{muller2005efficient, pietroszek2017real}. Chi \textit{et al}.  \cite{chi2000emote} use the effort and shape components of Laban Movement Analysis to provide an expressive parameterization of motion. Hartmann \textit{et al}.  \cite{hartmann2005implementing} use tension, continuity, and bias splines to control arm trajectories and provide expressive control through parameters for activation, spatial and temporal extent, fluidity, and repetition. Pietroszek \textit{et al}.  proposed synthesizing motion by comparing rough input with a large number of high-quality motion templates in real-time using a compute shader implementation of Dynamic Time Warping \cite{pietroszek2017real}. Project SAIBA that combines the efforts of several research groups developed ``behavior realizers'' \cite{vilhjalmsson2007behavior}. The project is based on animation engines capable of realizing commands written in the Behavior Markup Language \cite{vilhjalmsson2007behavior}. These systems emphasize control and use a combination of procedural techniques and motion captures, but they do not allow the generation of gestures in real-time from speech.

Machine learning data-driven approaches were also applied to predict the gestures that may be specific to a given person's gesturing style \cite{kipp2005gesture}. Recent work proposed applying deep learning to the mapping from text and prosody to gesture \cite{chiu2015predicting}. Prior work \cite{kopp2004synthesizing} also addressed modeling individual gesture styles through analyzing the relationship between extracted utterance information and a person's gesture. While earlier works based on this approach have focused on addressing the mapping relation between linguistic features and gestures \cite{kita1990temporal}, recent work \cite{kopp2013spreading} has also addressed how to use acoustic features to help gesture determination. Chiu \textit{et al}.  \cite{chiu2015predicting} proposed a data-driven model that builds upon prior work \cite{artieres2010neural} by combining the advantages of deep neural networks for mapping complex relationships with an undirected second-order linear chain for modeling the temporal coordination of speech and gestures.

Recurrent neural networks (RNNs) such as long short-term memory (LSTM) \cite{hochreiter1997lstm} or gated recurrent units (GRU) \cite{cho2014properties} have been at the forefront of human motion prediction. However, such deep neural networks based models are primarily deterministic. There have been attempts to modify RNN encoder-decoder frameworks to work as a combination of deterministic and probabilistic human motion prediction models \cite{fragkiadaki2015recurrent}. In recent advances, Jain \textit{et al}.  \cite{jain2016structural} have introduced several structures and frameworks with deep RNNs to produce state-of-the-art results in human motion prediction.
In contrast to RNN approaches, \cite{butepage2017deep} proposed a sparse autoencoder model to predict human motions in an unsupervised manner without recurrent units. Even though these models are good at human motion prediction, applying them to the generation of novel human actions has been a challenge.

Most recent approaches focus on the non-deterministic relationship between speech and gestures.
To overcome the problem of predicting the mean gesture, Alexanderson \textit{et al}.  \cite{gestureGenerative} introduce a probabilistic approach. They adapt the MoGlow framework \cite{henter2019moglow} which implements LSTM cells to generate gestures with realistic motion. Ferstl \textit{et al}.  \cite{gesturesGAN} use a GAN model in their non-deterministic approach. They train the adversary to generate realistic sub-features such as plausible dynamics and smooth motion.
In contrast, Ginosar \textit{et al}.  \cite{speech2gesture2019} implement a single adversary which ensures realistic motion. Compared to previous approaches, they use in-the-wild YouTube video data to train their speaker models. It allows them to generate models for speakers from different genres, \textit{i}.\textit{e}.  show business, education and religion. Furthermore, their dataset is much larger than previous datasets. For a given set of speakers, they process more than 20 hours of data. This allows the models to learn from a variety of different input gestures. One drawback of the work of Ginosar \textit{et al}.  compared cannot be used in applications that require virtual humans. Although previous authors \cite{speech2gesture2019, gesturesGAN, gestureGenerative} evaluate the results comprehensively, they conduct the human study using gestures in the skeletal human pose format which is different from observing humans in reality. Thus, for the evaluation of our generative approach, we animate the gestures on virtual characters which are visually more closely related to real human communication.

\section{Our Approach}

We model the correlation between body language and speech without imposing a causality between them.
We consider learning models that correlate the input and output rather than create a causal relationship between them. As a result, rather than considering discriminative models that model the conditional probability $p(y|x)$, which can be interpreted as a causal relationship from $x$ to $y$, we focus only on generative models that model the joint probability distribution $p(x,y)$, which can be interpreted as a co-relational relationship between x and y.

Translating our theoretical approach into the computation, we design a GAN architecture that predicts gestures given input speech. The goal of the network is to learn the co-relation, not causality, in a \emph{$<$speech, gesture$>$} pair. Once trained, the GAN generates the \emph{gesture} sequence given the \emph{speech} sequence. Our non-verbal communication animation model is therefore data-driven.
The GAN takes speech as input and predicts the gesture animation as output.

Our method is divided into three core components. We illustrate the structural overview in \autoref{fig:gesture-pipeline}.
The gestures in human pose format are provided by our first component 3D Human Pose Estimation. We estimate the human pose in each frame of input video sequences. Once the 2D human pose is estimated, it is projected into 3D space and handed over to the GAN.
The gesture generation GAN represents the main component. Our GAN takes two inputs during training, the raw audio of the speech and the gestures in human pose format. We extract the raw audio from our video dataset.
The third component animates the generated gestures on a virtual character. This component computes the rotation angles of body parts given pose data and enforces physically plausible animations.

\begin{figure*}
	\centering
	\includegraphics[width=\textwidth]{fig/pipeline/pipeline}
	\caption{Gesture pipeline. Our gesture generation pipeline takes videos of speakers as an input (top left) and produces animated gestures (top right). In between, three major components exist: 3D Human Pose Estimation (blue), Gesture Generation with a Generative Adversarial Network (red) and Pose Animation (green).
	\label{fig:gesture-pipeline}}
\end{figure*}

\subsection{3D Human Pose Estimation}
The amount and the quality of the training data determine the predictive power of our model. In the multimodal task of gesture generation, it is of utmost importance to have a large dataset that contains a wide variety of gestures. Unfortunately, existing motion capture datasets of gestures do not suffice, as they are neither large enough nor sufficiently labeled. Instead of relying on existing, insufficient datasets, we generate the training data from another readily-available large source of data: 2D videos of people performing lectures and speeches. Recent advancements in motion capture estimation from 2D video \cite{chen20163d,zhou2016sparseness,du2016marker} allow us to process many online videos of lectures and speeches and extract 3D body poses and gestures from them.

We extract the gestures from video data in the 3D human pose format. This efficient representation of gestures allows us to extract only the information required for gesture modeling. By extracting the human pose, we ignore image information such as background, color, and shape, which is irrelevant to our task.
We take the following precautions to ensure high quality of data, both before and after extracting body language from the video. In the process, we ensure that the extracted gestures are still temporally synchronized with audio. Before extracting motion from a video, we eliminate those fragments of the videos where the face, arms, or hands of the speaker are not entirely visible, \textit{e}.\textit{g}.  because it is being partially occluded by an object.

We approach the gesture extraction in two steps. In the first step, we extract the 2D Human Pose from the raw video using the OpenPose framework \cite{openpose1, openpose2}. In the second step, we project the 2D pose into 3D space for the large body parts and hands separately. For the 3D body pose estimation, we use the model implemented by Pavllo \textit{et al}.  \cite{videopose3d2019}. For the 3D hand pose estimation, we implement a model similar to Zimmerman \textit{et al}.  \cite{hand3d2017}. We create a continuous heatmap by applying Gaussian kernels at the estimated OpenPose 2D keypoint positions. We also add temporal smoothing to improve video processing.

As a result of the above process, we created a large dataset of motion captures that correspond to the gestures used by humans when speaking. Throughout the process, we preserved the time synchronization between the audio recording of the speech and the extracted motion corresponding to the speech. The extracted motion capture sequences serve as the $speech$ time series that is labeled by the $gesture$ time series during the supervised training of our network.

\subsection{Gesture Generation - Generative Adversarial Network}
We implement the Generative Adversarial Neural Network (GAN) \cite{goodfellow2014generative,mirza2014conditional,arjovsky2017wasserstein,zhang2017stackgan} framework that allows us to model the multimodal task of predicting gestures from speech. Inside the GAN framework, we locate the gesture generator $G$ and the motion discriminator $D$.

\paragraph{Gesture Generator} The objective of the gesture generator is twofold. First, the generator is trained to predict gestures close to the ground truth gestures extracted from the input video. Second, the main objective of the generator inside the GAN framework is to fool the discriminator. Consequently, the predicted gestures should have realistic motion.

We implement the UNet architecture introduced by \cite{unet} for our generator. The UNet models the relationship between the gestures encoded in human pose format and speech. Inside the UNet, the skip connections forward low-level prosodic features extracted from the input audio. These features are necessary to predict smaller beat gestures.
The UNet bottleneck extracts high-level features that contain information about long input sequences. This is used to predict the posture of the speaker.

We introduce the following loss function to train the parameters of our UNet model:
\begin{align} \label{eqn:gen}
\mathcal{L}_\text{Gen}(G) \ = \ &\mathbb{E}_{(\mathbf{s},\mathbf{p})}[ ||\mathbf{p} - G(\mathbf{s})||_1 ] \ + \\ &\lambda_\text{bone} \ \mathbb{E}_{(\mathbf{s})}[ ||B(G(\mathbf{\mathbf{s}}_t)) - B(G(\mathbf{s}_{t-1}))||_1 ]\notag ,
\end{align}
where vector $\mathbf{s}$ refers to the input speech and vector $\mathbf{p}$ refers to the pseudo ground truth body keypoints. The function $B$ returns the bone lengths in 3D space by computing the euclidean distance between pairs of keypoints at consecutive time steps $t$ and $t-1$.

The first term in \autoref{eqn:gen} ensures that model learns to predict pose positions $G(\mathbf{s})$ that are similar to the ground truth pose positions $\mathbf{p}$ extracted from the video.
The second term \autoref{eqn:gen} ensures that the predicted bone lengths stay constant over time. This term forces the model to learn about the anatomical constraint of constant bone length in the human skeleton. The hyperparameter $\lambda_\text{bone} \in (0,1)$ is used to weight the importance of constant bone length term with respect to the other terms within our objective function defined in \autoref{eqn:Whole}.
During training, we accept a higher loss from the first term to avoid regressing to the mean pose. The loss of the second term remains low.

\paragraph{Motion Discriminator}
The communication theory suggests that the same utterance can lead to different gestures. Hence, the regression loss between generated gestures and pseudo ground truth does not model the relation between speech and gestures as a whole. The problem is the regression towards the mean pose. For example, if the input data includes the same utterance twice where one time the pose moves into the direct opposite direction of the other time, the model learns to predict no motion. When considering the whole training dataset, this will result in predictions with less motion compared to the training dataset. To avoid this phenomenon, our discriminator inside the Gesture GAN architecture ensures that the motion of the generated gestures is similar to the motion extracted from video.

The discriminator receives a motion sequence, either predicted by the generator or obtained from the input video, as input. The motion is computed by taking the difference between consecutive pose vectors. We express this by introducing the function $M$
\begin{equation}
\label{eqn:motion}
M(\mathbf{v}) = \mathbf{v}_{t} - \mathbf{v}_{t-1} \ ,
\end{equation}
which computes the motion between two consecutive pose vectors $\mathbf{v}_{t}$ and $\mathbf{v}_{t-1}$.

The objective of the discriminator is to detect if a given gesture sequence is real or generated, while the generator tries to fool the discriminator. Consequently, we have created a two-player optimization game. As a result, the generator tries to predict gestures similar to the real gestures in terms of the motion. The discriminator tries to identify the predicted gestures as such, even as they get more realistic during training.

The complete GAN loss function including the discriminator $D$ is defined as
\begin{equation}
\label{eqn:gan}
\mathcal{L}_\text{GAN}(G, D) = \mathbb{E}_{(\mathbf{p})}[\log D(M(\mathbf{p})) ]+ \mathbb{E}_{(\mathbf{s})}[\log(1 - D(M(G(\mathbf{s}))))] \ .
\end{equation}
The objective of the discriminator is to maximize this function. Consequently, the term loss is only true concerning the generator. The discriminator is trained to output $D(\cdot) \rightarrow 1$ if input motion is real and $D(\cdot) \rightarrow 0$ if the input motion is generated.

\paragraph{GAN Objective}
We train the parameters of our model by combining the loss functions shown in \autoref{eqn:gen} and \autoref{eqn:gan}.
The final objective function is defined as
\begin{equation} \label{eqn:Whole}
\min_G \max_D \mathcal{L}_\text{GAN}(G, D) + \mathcal{L}_\text{Gen}(G)	.
\end{equation}
The generator $G$ has the objective to minimize this function whereas the discriminator $D$ aims to maximize $\mathcal{L}_\text{GAN}$.

\subsection{Pose Animation}
Our Gesture GAN model produces 3D Human Pose sequences which we animate on virtual humans using rotation angles between bones and inverse kinematic computations. By connecting the keypoints predicted by our Gesture GAN, we create a skeletal representation of the human pose. We use this skeletal representation of the gestures to animate an avatar in a virtual environment.

One challenge when transferring the predicted 3D Human Pose into a 3D animation is the missing information about anatomical details and ambiguities. Since we animate the gestures on a virtual human of different size and shape compared to the original speaker, we omit the information about the bone length of the generated skeleton. Instead, we only consider the rotation between the bones.
When viewing the angles between two bones in the Euler angle representation, we obtain the pitch and yaw angles from the skeleton. However, we do not have any information about the roll angle. To tackle this problem, we approximate the roll rotation of different body parts using inverse kinematics.

Besides recovering the missing information about the roll angle, we also have to deal with the second problem introduced when converting the human pose representation to a virtual human animation, which is implausible motion. Although our Gesture GAN is trained to predict motion which is similar to real human motion, there exists no constraint which particularly enforces anatomically plausible motion. Hence, the predicted motion in some cases appears to be very artificial, especially when animated on a virtual human avatar. This problem is very distracting for the viewer of the animation because anatomically implausible motion is quickly noticed. To overcome this issue, we introduce motion constraints on the fingers. The motion constraints enforce that the fingers can only be bent in anatomically possible angles and directions. In addition to the motion constraints, we apply motion smoothing.

\subsection{Training the Network}
For the purpose of training our GAN, we generate a gesture dataset of over one hundred hours extracted from videos of four speakers from two domains. Specifically, we generated 72 hours of motion capture data for television show hosts John O. and Ellen D. as well as 64 hours of motion capture data for professor Jonathan G. and Shelly K. We encode the gestures in the efficient human pose format. By using this format, we ignore irrelevant information such as background and the shape of different body parts of the speaker.

We feed the extracted gestures in 3D human pose format to the discriminator inside our GAN architecture.
The input to our GAN generator is the Mel spectrogram of the audio directly extracted from the training videos of each speaker. We sample the audio data at a rate of 16,000 samples per second. In order to provide the generator with the temporal context, the input interval is four seconds long, which was found to be the best length empirically.
We evaluate the predictions of our Gesture GAN on our validation set using the Percent of Correct Keypoints (PCK) \cite{pck2013} metric with proximity radius $\alpha = 0.2$. The quantitative results are shown in \autoref{tab:quantitative-results}. We observe that John O. and Shelly K. who are in sitting position and therefore show less upper body movement achieve a higher PCK. In contrast, the PCK is lower for speakers that are standing (Ellen D.) and walking around (Jonathan G.).
We select the speakers John O. because of the high PCK and Jonathan G. because we want to examine both sitting and standing speakers for further experiments in our user study.

\begin{table}
	\renewcommand{\arraystretch}{1.2}
	\setlength{\tabcolsep}{0.6cm}
	\centering
	\begin{tabular}{l|l|l}
		 \textbf{Category} & \textbf{Speaker} & \textbf{PCK $\uparrow$} \\ \hline  \hline
		 \multirow{2}{*}{TV Show}& Ellen D. & 31.0 \\
		 & John O. &  \textbf{60.3} \\ \hline
		 \multirow{2}{*}{Education}& Shelly K. & 40.6 \\
		 & Jonathan G.& 23.6 \\
		\hline
	\end{tabular}
	\vspace{0.1cm}
	\caption{Quantitative evaluation of gestures. We evaluate the four speakers from the two categories TV Show and Education on our validation datasets. For comparison we use the Percent of Correct Keypoints (PCK) metric.
	}
	\label{tab:quantitative-results}
\end{table}

\section{Experimental Evaluation} \label{sec:eval}
In order to validate the perceptual quality of the synthesised animations, we performed a user study designed to answer the following questions:
\begin{enumerate}
    \item Can users detect difference between the synthesized gesture and the ground truth (original) gesture for a given speech fragment?
    \item Are synthesized co-verbal gestures perceived by users as correlated with a given speech fragment?
\end{enumerate}
To answer the above questions, we designed two experimental tasks.

\begin{figure}
	\centering
	\includegraphics[trim={80 0 0 0},clip=true, width=1.0\columnwidth]{fig/user-study/vr}
	\caption{Virtual scene. The scene shows the two virtual characters performing non-verbal communication next to each other. We use this setting for our side-by-side comparisons.}
	\label{fig:3dscene}
\end{figure}

In the first task, we present users with a series of side by side videos of two identical avatars performing different gestures for the same, 12-second long speech fragment that can be heard by the users. One of the videos shows the ground through gestures, as extracted from the video recording of the speaker and applied to the avatar. The other video shows the gestures synthesized by our system from the same speech fragment. The left-right position of the videos is randomized. The user is asked to decide which sequence of gestures is the ground truth gesture for the speech fragment.

In the second task, we also present the user with a series of videos of two identical avatars performing different gestures for 12-second long speech fragment. In this tasks gesture sequences for both avatars are synthesized. However, one of the gestures sequence, either right or left, is synthesized from the speech fragment heard by the participant, while the other avatar is performing gestures synthesized for a different, randomly selected speech fragment from the same speaker. The user is asked to decide which sequence of gestures is the ground truth gesture for the presented speech fragment.

In both tasks, the user can hear the speech and see the avatar performing the gestures, but the face of the gesturing avatar is hidden. We consider face expression and lips synchronization a confounding factor in our study, resulting from the uncanny valley effect.
Our experimental design is inspired by the original Turing test \cite{turing2004computing}.
Our system ``passed'' our test, if after a limited number of attempts, the human judges are unable to establish a statistically significant difference between the sequence of gestures generated by our system and the real gestural sequence.

\subsection{Implementation and Apparatus}

For the visualization of our generated gestures, we animate the UMA 2 Multipurpose Avatars using the Unity 3D game engine version 2019.3. Both animations of the side-by-side comparison run on two identical avatars, see \autoref{fig:3dscene}. The only difference between the avatars are the gestures that they perform. The avatars are placed against a neutral background, with a light source in front of them. The avatars are positioned at a distance of 1.5 meters next to each other, such that they do not occlude each other while gesturing. Both avatars face the camera at the same \ang{180} angle. The camera is positioned three meters in front of the avatars. The camera angle and shot size is adjusted such that it records the upper body of both avatars without showing the face.

While the avatars are performing the gestures in 3D space, the virtual camera records the animation in 2D at the resolution of $1000\times360$ at 30 frames per second. We record the avatars from the hip upwards to only capture the information relevant for gesture evaluation and to eliminate the face expression as a confounding factor. An example of our setting is shown in Figure \ref{fig:user-study}.

\subsection{Null Hypotheses}
For the first task, the null hypothesis is that the proposed gesture synthesis is perceptually indistinguishable from the ground truth.
For the second task, the null hypothesis is that the speech fragment is not correlated with the synthesised gesture.

\begin{figure}
    \centering
    \includegraphics[width=.98\columnwidth]{fig/user-study-gruber.png}
    \caption{User study. The participants in our study were asked to decide which animated gestures they perceived as more natural. The gestures were animated on two identically looking avatars, referred to as A and B.}
    \label{fig:user-study}
\end{figure}

\subsection{Participants}
For our study, we recruited 100 participants on Amazon Mechanical Turk (Mturk) platform. Each participant received a compensation of \$$3$. The requirement was that the participants are fluent in English. All participants finished the study. We did not collect any demographic data, or any identifiable data or information about participants' prior computer-related experience -- all collected data was anonymous at all time.

\subsection{Procedure}
The participants were first presented with the description of the study and asked to confirm their willingness to participate. Then, the participants were presented with three repetitions of task 1 and task 2 as a training. Next, the participants were presented with a series of twenty 12-second long videos for task 1 followed by a series of ten 12-seconds long videos for task 2. In each task, half of the videos were generated for the speech of John O., while the other half were generated from the speech of professor Jonathan G.

For each task, the participants were asked to select the video that feels as a more natural representation of gestures for the presented speech fragment. Participants were forced to select one option, before they could proceed to the next video. Also, we ensured that it was not possible to select the answer without watching the whole video. Additionally, it was not possible to watch the same video twice. Once completed, the participants were presented with a ``thank you'' page.

\subsection {Results}
We illustrate the quantitative results for both speakers in \autoref{fig:bar-chart}. We separate the two tasks for each speaker.

\begin{figure}
	\centering
	\includegraphics[width=\columnwidth]{fig/user-study/bar_chart_with_std_noTitle.png}
	\caption{Quantitative results. The four bars refer to the four different tasks the participants completed during the user study for the speakers John O. and Jonathan G. The height of the bar represents the rate at which the participants selected the gestures predicted by our model. The vertical intervals at the top of each bar indicate the standard deviation across the participants.}
	\label{fig:bar-chart}
\end{figure}

For task 1, determining which gestures are generated and which are ground truth, the participants mistook synthesized gestures as ground truth $52.5\%$ of the time on average ($\sigma^2 = 1.0\%$). The difference between selecting ground truth and synthesized gestures were not significant. The results for individual speakers were not significant either, with participants selecting synthesized gesture over the ground truth, in average, $51.2\%$ of the time for John O. ($\sigma^2 = 1.4\%$), and $53.8\%$ of the time for professor Jonathan G. ($\sigma^2 = 0.6\%$).

\begin{figure*}[ht]
	\newcommand{\cropimgGt}[1]{\includegraphics[ width=.25\textwidth]{fig/gruber/#1-crop}}
	\newcommand{\cropimg}[1]{\includegraphics[ width=.25\textwidth]{fig/gruber/#1cut}}
	\centering
	\cropimg{1}\cropimg{3}\cropimg{2}\cropimg{4}\\
	\cropimgGt{1}\cropimgGt{3}\cropimgGt{2}\cropimgGt{4}
	\caption{Qualitative results. We compare the predicted gestures animated on a virtual character (top) and the original video (bottom) of speaker
	Jonathan G.
	Overall, the motion and the beat gestures are very similar. Whenever
	Jonathan G.
	lifts his hand, the same motion is predicted by our model. In column two and three we show that sometimes only one or the other hand is lifted in the prediction. In column four, we show that metaphoric gesture ``intersection''
	is not predicted by our model. \label{fig:quality}}
\end{figure*}

For task 2, determining which gesture was generated for a given speech fragment, the participants chose the gesture sequence that was indeed generated for the given speech fragment in average $65\%$ of the time ($\sigma^2=3.55\%$). Interestingly, the participants were significantly more likely to point at a correct gesture sequence in case of gestures generated for John O., selecting it correctly $71.2\%$ of the time, while correctly identifying the gesture sequence, on average, $58.8\%$ of the time for gestures generated for professor Jonathan G. In both cases considered individually, the experiment showed significant difference between the gestures generated for the heard fragment of speech and gestures generated for some other fragment of speech from the same speaker.

\subsection{Discussion}
Based on the above results, we conclude that our system generates realistic body language, since the participants were unable to determine whether the gesture animations were generated or captured. It is important to note that our study does not attempt to establish that our gesture synthesis creates gesture sequences that are perceptually superior to the ground truth. Instead, we attempt to establish that, despite a relatively large and diverse sample, the participants are unable to reject the null hypothesis for task 1, while at the same time being able to reject the null hypothesis for task 2. In other words, the closer our system is to ground truth, the more difficult it is to reject the null hypothesis for task 1.

Rejecting the null hypothesis in task 1 would mean that our system generates gestures that are perceptually inferior as compared to the ground truth. Not rejecting the hypothesis, however, does not mean we have proven the equivalence between synthesized and extracted gestures -- it is not possible to do so. However, not rejecting the null hypothesis shows that a difference, if any, between the synthesized gestures and ground truth gestures must be small.

The inability to reject the null hypothesis for task 1 could be a result of either the difference between the two groups being too small for our sample size to be significant \emph{or} it could be a result of the participants not paying attention to the study and selecting right or left videos at random. However, the rejection of the null hypothesis for task 2 serves as a validation for the claim that our generated gestures are indistinguishable from the real gestures, because finding significant difference in task 2 suggests,
that the participants were indeed paying attention to the presented videos and were not selecting answers A or B at random.

We also took a closer look at each specific user's decision in judging the generated vs ground truth gesture animations. The analysis revealed that participants were fooled by our gesture prediction in those gesture sequences, in which the ground truth does not include an iconic gestures. Whenever the speaker performs an iconic gesture, subjects were able to identify the ground truth. This is an interesting result, since it suggests that the fact that our system does not model the speech semantics and therefore does not explicitly generate iconic gestures might provide our users with a winning strategy: searching for iconic gestures in the speech and identifying those. It remains an open question whether or not sufficiently large training dataset would result in the system encoding correlation between semantics of speech and iconic gestures. We give an insight into the qualitative results of speaker Jonathan G.
in \autoref{fig:quality}.

Another qualitative observation is the difference in the correct recognition of gestures generated for a given speech fragment between John O. and Jonathan G. gestures. We observe that ground truth gestures of John O., a comedian, are more pronounced and exaggerated. Possibly, the gestures performed by John O., an experienced actor, may be better correlated with his speech than are the gestures of professor Jonathan G. If that is true, it may be possible to create a model that actually generates better gestures for a given speaker than the speaker's own gestures.

One aspect that needs to be considered when interpreting our results is that we animated real and generated gestures on virtual humans which cannot be directly compared to a real human communication which is more complex. In real human communication, the human brain uses biological motion detection when watching living beings \cite{bioMotion2013}. Biological and nonbiological motion is detected differently as shown by Hiris \cite{hiris2007detection}. As a consequence, watching gestures of a speaker on video and animated gestures on avatars are judged differently by humans.

\section{Conclusion}
Generation of nonverbal communication, including gestures, is a missing cornerstone in creating virtual humans that communicate as efficiently as humans. Human-agent communication is used broadly in online learning environments, virtual assistance, and communication tools.

In this work, we predict the gestures from speech and animate them on virtual characters in a 3D environment. We implemented a novel 3D human pose estimation pipeline, which allows us to train speaker-specific gesture generation models from in-the-wild video data. Utilizing large-scale video datasets allows our model to learn from a variety of different gestures.
We implement the non-deterministic relationship between speech and gestures using a GAN model.

The results of our user study show that the gestures predicted by our model are natural and correspond to speech. 51.2\% and 53.8\% of participants selected that our generated gestures seem more natural than the original gestures for speakers John O. and Jonathan G., respectively. Furthermore, when comparing speech correlated and uncorrelated gestures from the same speaker, participants identified the speech correlated gestured correctly in 71.2\% of times for speaker John O. and 58.8\% for speaker Jonathan G. The results of both experiments indicate that our method can be applied successfully to speakers from different genres.

In future work, we will develop a model that synthesizes gesture animations given a speech stream of an arbitrary speaker. Therefore, data from multiple speakers needs to be combined such that a universal speech-gesture model can learn about how gesturing styles depend on the voice and prosody of humans.

\acknowledgments{
We want to thank Graz University of Technology and American University for maintaining a successful collaboration and supporting research stays abroad.
This collaboration was endorsed by the Marshall Plan Foundation.}

\bibliographystyle{abbrv-doi}

redpajama_set_name: RedPajamaArXiv


text:
Encouragement Communications
Travelogue & Podcast
Quest for Joy Podcast
TruePublic
JuniFelix.com
Limitless Pathways
Intimate Faith · Eternal Strength
Hi, I'm Juni!
ADVENTURER, SPEAKER, TECHNOLOGIST - BEHAVIOR DESIGNER, FUTURIST, RADIO HOST, GAMER, SCI-FI GEEK, C.S. LEWIS INSTITUTE FELLOW
"You can never miss your chance. Your situation is not your destiny. Take the next step, believe again and be blessed."
Life is an Adventure & Kindness is a Superpower
Through my ongoing adventures, I've learned that Kindness is a Superpower, compassion can heal, and the hope that's found through faith in Jesus Christ leads to limitless pathways and endless possibilities.
While you're here, feel free to check out my Travelogue for some of my most recent discoveries. Tech Adventures where you'll find some of my favorite Tech Initiatives & Encouragement Communications, a place to find resources for you or a loved one who's hurting and in need of renewed hope.
Also, scroll down to Around the World where I share some of favorite memories from my ongoing global adventures.
You might know me from the more than 15 years of radio! Most recently Chicago's own 'Karl and June Mornings!'
Radio is amazing! So much wisdom from thousands of interviews with people from all over the world and from all walks of life! So much inspiration from millions of listeners and hundreds of guests. I've learned so much from you. What an adventure! I'm thankful!
"Joy is an identity feature. It's comprehensive, completing and eternal. "
Touring Santo Domingo in the Dominican Republic with Moody Radio & Compassion International.
Top Left:Me and Mael. Top Right: Kiddos at the Compassion Student Center. Bottom Left: With Wilena and her son Antony. This young woman is a Warrior! After surviving heart surgery with no anesthesia and the loss of her first child, she is out encouraging others to never give up! Wow!
Johnny Cakes!
The Dominican Republic is a place filled with miracles! There I met so many inspiring people. People who wake up every day with one question on their hearts: "Who can I be a blessing to today in Jesus Name?" My favorite quote was from a man named Mael. He lives in a steel shack sells a homemade treat called 'Johnny Cakes.' He told me, "No man is so poor that he cannot give, and no man is so rich that he does not have need. If I have two plantains, one is for my neighbor." Now that will preach!
Click here if you'd like to to find out how you can speak hope into the life of a child in poverty in the Dominican Republic through Compassion International.
Visiting friends at WindFM in Ulaanbaatar, Mongolia!
One of the most inspiring stories I've ever heard was from a new friend, Battsetseg in Mongolia. After hearing a radio program about the love of Jesus, she and her husband decided to create a library for orphans. She sold his paintings on the streets for money and inspired her community so much that even the homeless residents were helping out. After meeting Battsetseg, I knew I wanted to join her in this mission to share love with these children. Now the library has grown exponentially. They have a building and uniforms and hope is overflowing every day! Battsetseg says "My dream has come true!"
Theovision International
A man imprisoned for years to pay the debts of his employer, singing praises to God for goodness and grace. Barefoot messengers on bicycles traveling from community to community to share hope that never runs out. Villagers who gather in Churches with no walls and worms falling from thatched roofs to listen to the Bible in their own language, thankful for the love and peace of God.
Trying out one of the new water pumps! One of many freshwater projects made possible through partnerships with Theovision International. This pump is shared by several neighboring villages.
Beautiful people with unrelenting faith. Reminders all around that when God gives a vision, nothing can stop it from coming to pass.
"I know Lord, that You alone are God, You can do all things and no purpose of Yours can be thwarted or withheld." - Job 42:2 - The Bible
"To see the faces of the people when they hear the hope of the Gospel in their own language, and hear the stories of lives changed for good, it's a Light that shines through the darkness." - Theo Asare
Every day, Theo Asare - Founder of Theovision International and his team works to share the transforming message of the Gospel in West Africa. They also offer ongoing training in sound engineering, radio, television, training, AV production, and health care services, including the newly opened Tree of Life Clinic in Accra.
Tech Adventures
With great power comes great responsibility." - Uncle Ben ( Spiderman)
As a Technologist specializing in Behavior Design, it's my joy to help Tech Teams build and implement sound strategies for ongoing success. As tech designers we have great influence on a global scale. This is why we must make sure that the products & services we create are a force for good in the world. As one of my mentors, Dr. BJ Fogg, creator of Behavior Design and Captology says, I believe 'We can leverage persuasive technology to create more harmony among disparate cultures and countries.' Here are a few of my favorite tech initiatives, including my 1st App consulting project - TruePublic!
Download today in the App Store or Google Play! WWW.TruePublic.com
In January 2018, TruePublic made '50 Startups to Watch" list.
TruePublic is a way to bring people together. Across the United States, we are becoming increasingly divided into our own groups. It's a fun, safe place to bridge the gaps between groups of people who otherwise don't connect.
Our hope is that as we begin to better understand each other, we'll be able to better connect with one another. What do you believe? Where do you fit? Who agrees with you? Do you stand with the bold majority? Or the fearless minority? The opinions we're sharing and the answers we're gathering each day are bringing us to a new understanding of ourselves and others across the USA. Join the conversation by downloading today!
A Wise Rat
Are you the kind of person who avoids confrontation? At TruePublic, these are the kinds of conversations we love having with you. It's good to spend a few seconds keeping it real even when things get uncomfortable. That's one of the reasons why we built TruePublic. We've created a place where people are coming together, one vote at a time to see how our opinions connect to build our lives, our experiences and our culture.
As we've examined thousands of opinions shared by family, friends, and fellow citizens of the USA, we're able to see clearly how even our contrasting points of view are helpful. The data is proof that the often reported divisions are not as wide as we believed and this tension could make us stronger than we ever imagined, if we're all willing to do our part.
*https://www.yahoo.com/movies/the-teenage-mutant-ninja-turtles-confront-the-190540370.html
A wise rat named Splinter once said to his oldest Ninja Turtle son, Leonardo, that when it comes to unity and strength for the team, "You shouldn't want them all to think the same, it's their different points of view that make the team strong. A good leader understands this. A good brother accepts it."*
So would you say that you're the kind of person who avoids confrontation or can you appreciate the beauty that's born of diversity? Do you realize the importance of every opinion you hold and preference you have?
Without your opinion, we have an incomplete picture of humanity and your value to our story is immeasurable. So don't hold back, join the conversation today and Vote Happy!
Chicago violence is continually in the headlines. The stories that aren't often told are of the heroes who work 24/7 to reach young people who are directly affected by this heartbreaking, dream shattering violence. We Are Chicago was created by a team of heroes to offer a first-person view of what it's like for these families through this one of a kind 'first person narrative-driven adventure game using real stories. You'll explore your relationships to find out the truth and uncover what really matters.'
'We Are Chicago is a strikingly authentic game for PC/Mac/Linux that strives to portray the harsh realities associated with growing up on the south and west side of Chicago. Beyond the game itself, we are using the game's voice and a portion of the proceeds from the game to help raise awareness and support two non-profit groups, All Stars Project of Chicago and Reclaim Our Kids. who have a mission to help curb violence and provide positive and creative opportunities to kids living on Chicago's south and west side.'
Above all, the hope is that We Are Chicago 'will create a deeper understanding, motivate and inspire change, and cultivate a larger conversation surrounding the issues of violence and income disparity.'
Get your copy today and please tell a friend!
Sweet Beautiful Girl
The Gift of Sorrow

redpajama_set_name: RedPajamaCommonCrawl


text:
Criminal Minds: Preview of Episode 4.13 "Bloodline"
From CBS' official press release:
The BAU team tracks a family of suspects working together to abduct young women, on Criminal Minds, Wednesday, January 21.
Emmy Award nominated actor Tim Matheson (The West Wing) directs.
The BAU team becomes involved in the case of a family working together to abduct young women, on Criminal Minds, Wednesday, January 21 (9:00-10:00pm ET/PT) on the CBS Television Network. Emmy award nominated actor Tim Matheson (The West Wing) directs the episode.
Criminal Minds stars Joe Mantegna, Thomas Gibson, Paget Brewster, Shemar Moore, Matthew Gray Gubler, AJ Cook, and Kirsten Vangsness.
Source: CBS


Oléo Pazzo offers the flavors of the Mediterranean in the heart of the Hungarian capital. Our restaurant also features a hearty, American-style breakfast buffet; fuel up for a busy day of work or exploring Budapest city center.
Cosy, yet elegant restaurant which has a wine rack full of spledid bottles. Fausti di Vora has always been at his best runnin what is one of the very best restaurants in town.
The old style cafe was reborn in 2006, in art deco style according to the designs of the internationally renowned architect David Collins. The daily special is always special and most of the ingredients comes from Paris.
Thia eatery, which was a meeting place for students in the '80s and a Mafia style bar in the '90s, became the culinary success story in 2007 and it is still going strong. They use the finest ingredients money can buy and top kitchen techniques.
Situated in Rozsadomb's nouveau riche area, this Japanese restaurant is authentic in design and food. If you want to try it all, you can go for the kaiseki-ryori, a sort of degustation menu originally for posh tea ceremonies.
Located at the Buda facing end of the Tunnel just beyond the Chain Bridge, this lounge style restaurant probably has the best selection of French wine in town. The cuisine is a sort of Central Europe meets Mediterranean style with a Hungarian touch.
The cuisine is Hungarian or Central European, also quite conservative, but in an elegant way. Goose liver, goulash, schnitzel, all the local specialties done much better than most places. Wine list with a wide selection of Hungarian wines.
When three townships Pest, Buda and Obuda merged into one in 1873, the drive of novelty and parvenu building birthed the very first underground railway in Europe, paved avenues, erected grand palaces and last, but not least, a restaurant was founded.
We prepare the classic meals of the Hungarian cuisine, like the popular goulash soup, the little bit spicy vegetable ragout "Lecsó" and the city's popular "Budapest style beefsteak with goose liver".
Just north of the Parliament, there is this tiny, no nonsense restaurant called Biarritz. Simple, elegant white tables, minimalist table ware, cuisine provencal with some local favourites. Pretty busy at lunchtime.
Both tourists and downtown business people can be seen here. The kitchen is thoughtful, with complex flavours. Cyrano provides the most magnificent place for this peeping pleasure as it has an unencumbered view of Vaci utca.


Cambridge: Starters, Movers, Flyers, KET for school /adults, PET, FCE (First Certificate in English), CAE (Certificate in Advanced English), CPE (Certificate of Proficiency in English).
EOI (Marco Común europeo), A1, A2, B1, B2, C1, C2.
TOEFL (Test of English as a Foreign Languages).
TOEIC (Test of English in International Communication).


Unless one is involved in art or design world making a visit to an art gallery at times may be overwhelming. This feeling is due to lack of knowledge by some people on what to see and where in particular to see it. There are some people who give up easily after visiting one or two art galleries reason being either they don't get it or they feel at times intimidated by the setting. Below are important tips one should take into consideration before visiting an art gallery.
Before visiting an art gallery it's important to carry out a detailed research on the locally available galleries and the kind of art they are involved in. Availability of many art galleries call for a lengthy and timely research on those galleries to be able to ascertain the best art gallery. Doing research will help one gather important information that he or she should understand about a given art gallery. To make your visit to an art gallery enjoyable, one has to do his or her homework probably so as to make the right choice of the art gallery to visit.
Its important for one to come up with a schedule and time he or she want to visit while at the art gallery. Some art galleries are small in size that one can cover the whole place just by spending few hours, other are large and big in size that one cannot cover all the places just within a mere single day. A good plan will help you avoid wasting time on visiting places that are not so important leaving the most important sector. Using your time at the art gallery effectively one needs a good time schedule before visiting the place.
One should consider drafting some questions that he or she will use in asking the gallery staff more about the art that he or she does not understand well. One may feel at times intimidated or embarrassed when asking random questions but with the help of a well drafted samples of questions will help out. It is also important to plan on going with a friend or family member who has a different taste in art than yourself. Different taste from a different person will make you want to have that different taste also about art.
Before visiting an art gallery it's important to understand all the rules and regulations that guide one when he or she is in the art gallery store. Some galleries do have strict rules and regulations concerning taking of pictures inside the gallery store. To avoid trouble at the art gallery store it's important for one to know the rules first. Also finally one should consider making a right dress choice to avoid felling uncomfortable while on a tour at the art gallery.


text:
List of Water Ski World Championships champions.

Results

List of Ski Racing World champions

List of Cable Ski World champions - Men & Women

See also
 Water skiing
 List of Water Skiing European Champions
 List of Water Skiing Under-21 European Champions
 List of Water Skiing Under-17 European Champions

References

Male professional water skiers
Water Ski World Championships
Water Ski World Championships

redpajama_set_name: RedPajamaWikipedia


This home is fully furnished with a Queen bed in one bedroom and a Daybed with a trundle (two twins) in the second bedroom.
The kitchen is complete with a stove/oven, refrigerator, dishwasher, coffee maker, blender, toaster, pots/pans, dishes/utensils, some spices and paper products. A stack washer/dryer is provided.
There is a covered carport and ample parking. The backyard is fenced. This rental is animal friendly...as long as your animal is friendly!


Audacity for novices and Adobe Audition CC for more advanced users.
Once you've learned the ropes of basic audio editing and you're ready to step up your game (that is, take your podcast to a larger audience), we recommend Adobe Audition CC, a powerful platform with an exhaustive range of tools to smooth out and sweeten the sound quality of recorded tracks. Its noise-reduction features in particular are top-notch, and its EQ tools go a step beyond those of the other software we evaluated for this guide. Audition is easy to use, with a clean user interface and a wide range of templates and presets, but it also offers a high level of customization. At $20 per month, or $240 per year, it isn't cheap, but it is the best podcast recording and editing software in that price range. If sound quality is your main concern, Audition is well worth the investment.
Using the above criteria, we narrowed our list from the dozens of software titles we considered to six: Adobe's Audition CC, Apple's GarageBand, Audacity, Avid's Pro Tools First, Cockos's Reaper, and Steinberg's Cubase Elements 9. The cost for these packages ranges from free up to $240 (for an annual license), though most are priced under $100, since spending more than that takes you into the territory of professional DAW software.
Another advantage of Audacity is its comprehensive manual, which clearly details all aspects of the program and is available for downloading or viewing online. The Audacity Wiki features useful tips and tutorials, and if you still have questions, you'll likely find answers on the Audacity forum, which has an active community.
Avid Pro Tools First is another powerful option, and one that I really liked using due to its uncluttered interface and sophisticated tools for editing and processing audio files. It's also free—a remarkable thing given that more-advanced versions of Pro Tools are considered the industry standard for music and audio production. But by default Pro Tools First limits you to three projects at any one time, and you need to store them in the cloud, on Avid's servers. Unless you are willing to pay $5 per month for the expanded plan, you can't save projects to your computer's hard drive. And if you decide to upgrade to a paid version of Pro Tools, the cost is a steep jump up from either version of Pro Tools First.


Canada Fun Run is coming up this weekend - Sunday 20 June - scroll down for last post for more info about this one.
NSW bike challenge - are you logging all your bike rides? See earlier blog for this one too.
Couch Potato? - who can help it when the World Cup Soccer is on!
Be SMART in your goal setting.
Think about your priorities, what do you really want to achieve in life? Be Specific.
How do you know that you're heading in the right direction? How will you Measure your progress?
Can you really Achieve it? If it seems impossible consider the steps you need to make it possible.
Really? Is it a realistic goal?
When are you going to achieve your goals? Set a Timeframe.


Jose Rizal (1861-1896)Philippine National Hero. Born on 19 June 1861. Doctor, Poet, Writer, Educator, Linguist, Inventor.
Authored novels Noli Me Tangere (1887) and El Filibusterismo (1891) which bared colonial oppression of the Filipinos. He advocated nonviolent struggle for freedom but was charged with inciting dissent.
The living soul of the 1896 Philippine Revolution, he was executed on 30 December 1896.
His martyrdom is inspired a sense of nationhood that eventually led to the proclamation of Philippine Independence Day in 1898.
Installed in commemoration of Jose Rizal's 150th birth anniversary.


text:
from django import forms
from django.forms import Textarea, Select
from django.utils.translation import ugettext_lazy as _

from .models import FinancialAidApplication, FinancialAidMessage, \
    FinancialAidReviewData, FinancialAidEmailTemplate, Receipt

class FinancialAidApplicationForm(forms.ModelForm):
    class Meta:
        model = FinancialAidApplication
        exclude = ["timestamp", "user", "status"]
        widgets = {
            'travel_plans': Textarea(
                attrs={'cols': 80, 'rows': 10,
                       'class': 'fullwidth-textarea',
                       'maxlength': 1024}),
            'what_you_do': Textarea(
                attrs={'cols': 80, 'rows': 10,
                       'class': 'fullwidth-textarea',
                       'maxlength': 500}),
            'involvement': Textarea(
                attrs={'cols': 80, 'rows': 10,
                       'class': 'fullwidth-textarea',
                       'maxlength': 1024}),
            'what_you_want': Textarea(
                attrs={'cols': 80, 'rows': 10,
                       'class': 'fullwidth-textarea',
                       'maxlength': 500}),
            'portfolios': Textarea(
                attrs={'cols': 80, 'rows': 3,
                       'class': 'fullwidth-textarea',
                       'maxlength': 500}),
            'use_of_python': Textarea(
                attrs={'cols': 80, 'rows': 10,
                       'class': 'fullwidth-textarea',
                       'maxlength': 500}),
        }

class FinancialAidReviewForm(forms.ModelForm):

    class Meta:
        model = FinancialAidReviewData
        fields = ['status', 'amount', 'grant_letter_sent', 'cash_check',
                  'notes', 'disbursement_notes',
                  'promo_code']
        widgets = {
            'notes': Textarea(
                attrs={'cols': 80, 'rows': 5,
                       'class': 'fullwidth-textarea'}),
            'travel_preferred_disbursement': Textarea(
                attrs={'cols': 80, 'rows': 5,
                       'class': 'fullwidth-textarea'}),
            'grant_letter_sent': Select(
                choices=(
                    (False, _("No")),
                    (True, _("Yes")),
                )
            ),
        }

class MessageForm(forms.ModelForm):
    class Meta:
        model = FinancialAidMessage
        fields = [
            "message"
        ]
        widgets = {
            'message': Textarea(attrs={'class': 'fullwidth-textarea'}),
        }

class ReviewerMessageForm(forms.ModelForm):
    class Meta:
        model = FinancialAidMessage
        fields = [
            "visible",
            "message"
        ]
        widgets = {
            'message': Textarea(attrs={'class': 'fullwidth-textarea'}),
        }

class BulkEmailForm(forms.Form):
    subject = forms.CharField()
    template = forms.ModelChoiceField(
        queryset=FinancialAidEmailTemplate.objects.all(),
        empty_label=u"Pick a bulk mail template to use",
    )

class ReceiptForm(forms.ModelForm):
    class Meta:
        model = Receipt
        fields = ["description", "amount", "receipt_image"]

redpajama_set_name: RedPajamaGithub


In cross-continental projects, nothing goes according to plan. Xin Liu, responsible for the international rollout of vehicle services in China, explains why this is the case.
For which project are you working?
I look after the rollout and integration of vehicle services for Audi in China. We differentiate between "infotainment" and vehicle-related services. Infotainment services include, for example, Tencent myCar, weather information, news or the point-of-interest search.
The vehicle-related services, on the other hand, tend to run in the background, such as synchronizing traffic data.
China is the largest market for German cars. But it is not only the vehicle itself that is important to the Chinese. They want to be entertained more than any other nation – not only on mobile devices, but also in the car itself. In China, a large part of communication and trade already takes place directly via the vehicle. Who in Europe can imagine managing their tickets directly via the infotainment system in the vehicle?
The Chinese market offers enormous potential: 1.4 billion people – that speaks for itself.
What are the challenges of such a constellation?
It is interesting that we were constantly confronted with something new. As we quickly realized, the Chinese market follows its own rules: it is extremely fast-moving – we have to pick up on trends quickly in order to meet the wishes of end customers. In China, for example, it is customary for points of interest not to be sought by customers themselves, but rather announced and sent by a concierge – or shared by friends via WeChat. A further complication is that the Chinese legal situation prohibits the processing of data outside China. So an in-country backend server had to be set up.
As a product owner, I am primarily responsible for communicating the requirements of our Chinese customer to my team in an understandable manner. I am also the communication interface between our Chinese customer and the customer in Germany. I helped to set up our office in China – from job interviews and coordination of local employees to their integration into the teams in Germany.
Which of your skills were most needed to be successful in this market environment?
On the one hand, my knowledge of the Chinese language of course, but more importantly, my knowledge of Chinese culture helped me. So I could understand the requirements of the market well – and also read between the lines. But the most important thing was my technical knowledge, which I was able to acquire as an systems architect on the German market.
With more than 15 years of experience in the international automotive industry, Valtech Mobility has the technical understanding and knowledge of the specifics of the respective markets. In addition, our many years of experience in the European market enable us to assess the organisational costs very well and to support our customers in this respect as well. In markets as fast-moving as the Chinese market, our agile approach allows us to respond quickly to market needs.


text:
namespace llvm {
namespace codeview {

using llvm::support::ulittle16_t;
using llvm::support::ulittle32_t;

/// Data in the the SUBSEC_FRAMEDATA subection.
struct FrameData {
  ulittle32_t RvaStart;
  ulittle32_t CodeSize;
  ulittle32_t LocalSize;
  ulittle32_t ParamsSize;
  ulittle32_t MaxStackSize;
  ulittle32_t FrameFunc;
  ulittle16_t PrologSize;
  ulittle16_t SavedRegsSize;
  ulittle32_t Flags;
  enum : uint32_t {
    HasSEH = 1 << 0,
    HasEH = 1 << 1,
    IsFunctionStart = 1 << 2,
  };
};

} // namespace codeview
} // namespace llvm

#endif // LLVM_READOBJ_CODEVIEW_H


text:
My Vote for Mother of the Year
The Story of the Woman with 14 Orphans
Dorothy Bwalya was a Franciscan Sister in Zambia for 18 years. Internal politics drove her away and when she quit, her family disowned and shunned her. Years later, her siblings began to die of AIDS and other causes and Dorothy began to take in the orphans.
With very little income as a teacher at a vocational school, she has tried to provide for them, but with 14 now, many of them with health problems, it has become impossible.
I am going to write her story in the days leading up to Mother's Day hoping that some of you out there will recognize her hero Mother status and help me raise money for her to rent a house with more than 2 rooms and eventually become an accredited orphanage.
No AIDS Test For Mothers Without Fathers
No AIDS test without absentee father
Peggy's family lives on a huge plantation in Zambia in substandard housing, no transportation and under feudal and oppressive rules of the owners and managers. For three years she and her younger brother were unable to go to school because they could not afford the bus fare. For eight months the AACDP provided a tutor to help Peggy catch up to her grade level.
Then she became pregnant.
The father of her child, a young man living on the farm, refuses to acknowledge any responsibility, not surprisingly. But worse than this, he will not accompany her to be tested for HIV/AIDS. And the doctors will not test her without his presence. So she can't find out her baby's status which, if positive, can be benefit from treatment before, during and after birth.
AIDS awareness has become widely advertised in Zambia.
Zambia is talking about HIV/AIDS
But terrible, life-threatening stigmas are widespread and the disease continues wipe out the middle generations, leaving children in the care of their grandparents. Retrovirals are available to a small percentage on the infected, despite their global availability and lowered costs. So a young man refuses to get tested and a young woman cannot discover her own status. Whatever the reasoning behind this system, who suffers? The mother and, ultimately, the baby.
Jack Mwanapapa -Simplicity, Beauty & HIV/AIDS
Sounds of Jack Mwanapapa
I came to Jack Mwanapapa in late January of this year to visit the family of my friend and assistant in Lusaka, Sydney Mwamba. It is, by far, the most beautiful place I have been in this rural area of southern Zambia., Nearing our destination we passed two small boys driving an oxcart and crossed a lovely winding river as we entered the village in our taxi. There are no gas powered vehicles here and no electricity.
Small flower gardens and tall shade trees intersperse the little round mud houses that cluster about a central open space where meetings are held and the children play soccer. I was taken to see their orchards of banana and citrus, mango and papaya and followed a twisting path flanked by small fields of corn and pumpkin, cucumbers, peanuts and peppers. The ambient sounds were peepers, cicadas, birdsong and the children's voices in the distance.
Though not wealthy, these folks have everything they need to feed, house and clothe themselves. They have a system of volunteers who care for those with AIDS in their community, visiting and feeding them daily, and riding them by bicycle into Livingstone, a very long trip on dirt roads, once a week.


Introducing the Distinguished Rogues—wickedly irresistible lords and the enchanting ladies of the ton who capture their hearts. Bestselling regency romance author, Heather Boyd, brings readers the first three novels in this series in this special edition boxed set.
Broken (Book 2): Unrepentant rogue Giles Wexham, Earl of Daventry, is dedicated to avoiding needy women, and marriage. During his summer retreat, he becomes reluctant nursemaid to Lillian Winter, his former betrothed. Shy, fragile Lilly tempts him but to claim her Giles must expose the dangers her past—before it's too late.
Charity (Book 3): Darling of the ton Oscar, Lord Carrington, has thoroughly lost his way but knows what he needs to be happy. However, righting past wrongs isn't easy and he'll have to risk scandal to make everything right again for the woman he loved and lost.
Enjoyable reading good romances with a bit of a twist in the storyline, I am glad I purchased the three novels together so I did not have to chase them up.
Amazing stories couldn't put it down!


DVD, The Art of the Tactical Carbine, 2nd Edition, Volume 1.
All of our offer comes with The Art of the Tactical Carbine : 2nd Edition : Magpul Dynamics along with the products of the most extremely famous companies (the accepted leader sector leaders in addition to lesser known manufacturers) encouraging the customers can use with definitely not less qualitative products for a reasonable value.
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You just have to pick the best unit with supply across the AMERICA and around the universe. In our purchase you can easily pick out and buy the coffee quality The Art of the Tactical Carbine : 2nd Edition : Magpul Dynamics on the net!


All Kia approved parts and accessories fitted prior to, or at time of, delivery of a new Kia vehicle will be covered by the same warranty applying to that vehicle. If installed after retail sale, accessories are covered for the remainder of the warranty or 12 months, whichever is the greater.
Choose from the current range to customise your Kia with genuine accessories, exclusively available through McRae Kia. To see the full range of accessories available for your Kia Vehicle and Light Truck click on the link below and open the specification brochure.
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An arduino library, written to drive popular HUB12 based single color P10 LED display panels. It uses TimerOne and SPI library of arduino.
Supports dual line display, i.e. one panel divided into two.
Supports dynamic screen refresh. You can program to display dynamic content on the panels.
Static and scrolling text mode. Can switch from one to another at run time.
Supports only text based content right now.
Unzip the LedP10 archive inside any directory on your machine.
Read the readme Install file.
Copy LedP10 folder into /Arduino/libraries of your arduino installation.
Open arduino IDE. (You need to reopen it after installing the LedP10 library).
You will find a few example program to demonstrate the usage of our library.
Build your own project by modifying these examples in the way you want.
* This example displays an integer counter value.
* first argument can be an interger value or a pointer to character string or a string in double quotes.
* second argument is to select font, for which rwo values i.e. 0 and 1 are supported.
/*To display two counters of height 8 pixels.
First function-showmsg_single_scroll is for scroling of single message on P10 panel with 4 arguments which are message, no. of times of message scrolling, speed and font.
for message, pointer to a character string can be passed. For continuously running the message, INF can be passed in second argument, speed can be in range of 0-30, and font can be 0 or 1 as two type of fonts are currently supported for full screen messages.
For second function-showmsg_double_scroll, there would be 7 arguments, 2 for messages, two for no. of times of message scrolling, two for speed, and last one for font.
/*This example is to illustrate the use of the functon <setbrightness(uint8_t brightness)>, this function takes one argument of type uint8_t, it's value can be from 0 to 255, 255 being highest brightness and 0 is lowest.
this function can be called anytime after or before calling any other function of LedP10 library.
In this example brightness reduces when counter 'num1' reaches value of 50.
Comment below to report any issues and bugs related to this Arduino library.
This entry was posted in 8051, ADC Interfacing, Arduino, ARM7, Display, Electronics, General Interest, General Know How, Hello World, Interfacing Tutorial, LED Display, Lonely Night Projects, Make, Open Source Hardware, P10 LED Panel, Physical Computing, Repair Tricks, SPI, Tutorials and tagged arduino library for p10 led display, Arduino library for P10 led panels display, P10 Arduino code, P10 led interfacing arduino, P10 led module arduino, P10 led panel arduino, P10 Library for arduino on April 30, 2017 by Devesh Samaiya.


text:
OCR00198 - Bubble Bobble "This Is Not a True Ending (Asphyxiation)"
By Ginnsu, June 5, 2002 in ReMix Comments/Reviews
Ginnsu
Alex Kidd (+200)
Bubble Bobble 'This Is Not a True Ending (Asphyxiation)'
Personally, I think this mix is great especially that part that starts at about 0:54. Throughout the rest of the mix, the distortion makes it sound like really heavy guitars or something. And the best part about this mix is that if you don't like this one, there is even another version with the same title without the Asphyxiation. Awesome work, Children of the Monkey Machine, keep it up.
Xaleph
Mudkip (+150)
genre: industrial
This is a kick butt song =D, especially if you're in the mood for industrial music. It's a must get for industrial artists, for the good industrial style I'd give it a 10/10 =D. For your choice in instruments, 10/10. For the way the overall piece flows a 9/10 (too short, and something didn't seem to click really well). Over all you get a 9.66/10
~dj carbunk1e
Domukaz
Octorok (+25)
Awesome. I can almost picture some little demon hopping around, lobbing orbs of energy at passers-by. The tune sounds sinister, even though one can just barely tell it was supposed to be cheerful. Love the instrumentation and the little ambient interlude in the middle of the song. Definitely one of Cotmm's more accessible works, because of its beat.
KarethGamer
By far the best bubble bobble remix, I loved It. As with the last review, I can picture the little demon. favorite part was the backwards notes by far.
SixthFlyingMan
Eggplant Wizard (+50)
I've never played the game, probably never will. CotMM has done an absolutely EXCELLENT job here; I can't really picture a little demon hopping around, but what I do picture is a rather sizeable demon breaking bones and spraying blood. The only dubious choice they made, in my opinion, is removing their wall of sound for about 14 seconds at 0:53. That and their choice of instrument for that section. Thank you, CotMM, for another great piece.
DarkCecil13
Bad Dude (+400)
Heck yes, one of my favorite old ReMixes by CotMM. Industrial strength bubble bobble, pretty much. This owns you.
OceansAndrew
Unsung Heroes Director
it's quite possible that COTMM has a serious case of ADD; it seems that none of his mixes end up past the 2 minute point. This mix alllllllmost makes it, but falls just a little short. Maybe next time!
The effects on this are very cool and have a grimy industrial feel to them, but honestly, the bubble bobble theme makes me want to asphyxiate (ReMix) myself. The reversed notes and minor key make things better, but still, i'd put the source in my top 10 of worst songs ever.
StandingInMotion
You know what you guys did to this piece? You beat the crap out of it, ripped it up, threw it in a mud puddle, and stomped it to death... and I don't think I could enjoy it any more! I LOVE what you guys did to this piece! I think it's cool how the bright, peppy mood of the original Bubble Bobble contrasts sharply with the grimy, hateful mood of this piece.
This is really one of the only times negative words can describe such an awesome piece.
DragonAvenger
Judge, Unsung Heroes Asst. Director
Not something I should be listening to late at night. Weird stuff going on here. The panning on the 'reverse' section was pretty cool, I found that to be the highlight of the track. Aside from that, this is a little too weird for me.
Dafydd
Mascot Bio Project Co-Editor & Director
This isn't very pretty. It's a nice idea, but I really miss the original melody, it's like the melody in here is the counter melody from the original... and the distortion makes everything very messy.
Moderator, Maverick Rising/Serious Monkey Business Director
Whoa this mix starts more brashly than I expected.
It just is way too short though - there could be a lot more variation to keep things interesting .
Rexy
Judge, Project Chaos Asst. Director
Coming to the knowledge that this requires hearing the original "This is Not a True Ending" first to understand the direction, my thoughts may be taken in a slightly different direction.
So this feels so much fuller than the original track, and though this remix-of-a-remix does feel dirty and overloaded with distortion it can indeed be seen as a trademark of CotMM's more conceptualist style providing you feel more open to it. Arrangement-wise, I think it was good to take the familiar theme into a minor key, though outside from the fundamentals there doesn't seem to be much change. On a plus note, I thought the reversed glockenspiel at 0:57 was pretty cool; it may be playing with concepts, but I'm glad that even back in the site's origins there were people hoping to jump outside of the box.
It's a shame we don't see CotMM much these days, but I'm sure that he may poke his head out one of these days and provide a surprise mix somewhere. Your influence is being missed D:
TillyFun
Pac-Man (+500)
Really cool
If it didn't work as well as it did, I'd swear it was a cacophony... but it works really well. XD
Especially love the bit at 0:50-1:10, though the whole track is smashing! ^__^
Also reminds me of Ultima for some reason. (not a bad thing!
Necrox
Too distorted and very static, but better than the previous mix, partly because it has more arrangement. Still far too short. The reversing is cool, but sounds lonely.
Crulex
Ryu Hayabusa (+2300)
Okay, yes, this is better to me and a step in the right direction from where the original mix left off. Still has some issues with the distortion being a bit too fuzzy (by a tick, though, it didn't bug me that much, but it is noticeable), and it's still pretty short with a quick ending, but I enjoyed this more than the original mix. With a more evil industrial sound and the cool reverse notes and panning effect that is really awesome with headphones as the sound goes in one bud and out the other, this is much better to me arrangement-wise.


text:
Q: Is not making /n need a Regular Expression I'm splitting some text so that I can find any link when I share something in my site, but it doesn't recognize /n and it shares all in the same line. Can you help me with a Regular Expression that fixes this?
here's my code:
function urlify(text) {
    var urlRegex = /https?:\/\/(www\.)?[-a-zA-Z0-9@:%._\+~#=]{2,256}\.[a-z]{2,6}\b([-a-zA-Z0-9@:%_\+.~#?&//=]*)/gi;

    return urlRegex.exec(text);
}

function parseLinha(linha) {
    return linha.split(/[\s,]+/)
        .map((x, i) => urlify(x) ? <a key={i} href={x.trim()} target='_blank' rel='nofollow'>{x} </a> : x + " ");
}

What I write: http://imgur.com/J2cC8PS
The problem: http://imgur.com/RjO9DZ0

redpajama_set_name: RedPajamaStackExchange


HELP NEEDED Time and stigma keep too many women with postpartum mood disorders from asking for help. About half of the mothers in a small study reported that they were currently experiencing symptoms of a mood disorder.
Words can't describe the pandemonium that follows a child's birth, but I'll try anyway. After my first daughter was born, I felt like a giant had picked up my life, shaken it hard, martini-style, and returned it to the ground. The familiar objects in my life were all still there, but nothing seemed to be the same.
The day we came home from the hospital as a family of three, my husband and I plunged headfirst into profound elation and profound exhaustion, often changing by the minute. We worried. We snipped at each other. We marveled at this new, beautiful person. The experience, as new parents the world over know, was intense.
The first week home, my body took a bruising. I was recovering from the wildness that is childbirth. I was insanely thirsty and hungry. I was struggling to both breastfeed and pump every two hours, in an effort to boost my milk supply. And against this backdrop, my levels of estrogen and progesterone, after climbing to great heights during pregnancy, had fallen off a cliff.
Massive reconfigurations were taking place, both in life and in my body. And at times, I felt like the whole thing could go south at any point. After talking to other new mothers, I now realize that almost everyone has a version of this same story. Childbirth and caring for a newborn is really, really hard, in many different ways.
That fraught time can be particularly dangerous for postpartum mood disorders such as depression and anxiety. Unsurprisingly, most women experience mood disturbances in the aftermath of a baby. For the majority, symptoms are mild and ease up with time. But for an estimated 10 to 15 percent of women in industrialized countries and 20 to 40 percent of women in developing countries, symptoms of depression will be troublesome and persistent. And these estimates account for only depression — not anxiety, OCD or other disorders postpartum women sometimes experience.
As a clinical psychologist, Betty-Shannon Prevatt of North Carolina State University in Raleigh saw firsthand how hard the transition to motherhood was for many women. She set out to study why women with postpartum mood disorders often don't get the help they need.
Along with her colleague Sarah Desmarais, Prevatt surveyed 211 women who had given birth in the previous three years. The researchers asked the women about potential symptoms of mood disorders, whether they had received treatment and, if not, factors that may have kept them from doing so.
I found the results, published August 1 in Maternal and Child Health Journal, shocking. At the time of the survey, 51 percent of the women felt they currently met the criteria for a postpartum mood disorder. That self-report isn't the same as a diagnosis from a doctor, nor is it indicative of women's rate overall. But still, the number is high. "I was absolutely surprised," Prevatt says. The number was especially notable because these are women who would presumably have a good shot at getting help — they are primarily white, married, well-educated and middle class.
The follow-up number is even more worrisome: Twenty percent of the women who self-reported that they were struggling didn't report their struggles to their health care provider. The two biggest roadblocks to getting help were time constraints (no shock there) and stigma.
A new mother can have trouble finding time to take a shower, let alone to make a doctor's appointment, call insurance companies, find someone to watch the baby and all the other tasks that go into seeking help. Paid maternity leave policies might help alleviate some of this pressure for women who need to go back to work quickly, the authors write. Strong social support can help, too.
Overcoming stigma is another huge challenge. "Women fear judgment that they are not a good mother … and often feel embarrassed," Prevatt says.
That has to change. Women ought to be able to seek the help they need without fear or shame. There's a push among some providers to use a universal screening tool, to ask every postpartum woman about her mental health. But these new results hint that even a universal screen wouldn't catch women who are ashamed of their illness. For providers to better catch that population, women need to know that they're experiencing something that's quite common, and often treatable. "The more we can normalize the wide range of emotions that follow childbirth, the easier it will be for women to disclose how they are truly feeling," Prevatt says.
The postpartum time can be grueling, even for people lucky enough to not have to deal with a mood disorder. The best we can do is to try and take care of new moms who are giving their all to take care of their baby.


Good day! I am David Alberto Jiménez Noguera, passionate about tech, sport and more.
Always pushing to get tired, sweaty: camping, cycling, running, exploring.
Learning languages, people's culture and history, travelling.
That is where I live, amazing beer, beautiful streets and parks and coffee.
This is my attempt at their tutorial.
Duolingo will open their Hindi course in 4/1/18!
Currently injured, struggling with pain in the Achilles heel tendon.
This thing is amazing, measures your heart rate from the wrist and even your deep sleep.
Almost ten years in the university sector. Worked with CMSs, business systems such as timetables and grades.
Currently working in the EFC application, that uses angularjs and typescript to produce forms for credit cards applications and such things.


text:
<!DOCTYPE html>

<title>depositOFwords</title>
<link rel="shortcut icon" href="img/logo.png" type="image/x-png"/>
<meta charset="utf-8">
<link rel="icon" href="../img.head.png" type="png">

    <link rel="stylesheet" href="css/style.css"
    type="text/css" href="img/logo.png">

<audio src="audio/Mix1.mp3" controls preload></audio>

Tempus frangit

Neil Gailman

nota a side

menu a side

menu a side

texto inicial

link leva home,text
 (TEXTO LEVA ao teste <a href="menu.html" class="pagina" )

nota

menu

menu

@G

@H

youtube

home

lol kg:

https://www.twitch.tv/

lik SC

Links SC

LINK SC

background que combina tanto com cores quentes como escuras:

background com imagens pequenas para balancear icones com estilo liso e simples d+
que combina tanto com cores quentes como escuras:

Tempus frangitnota bottonS/Lmenumenu


For PINKaholic Fashion Shoppe's next collection which will be out tomorrow! Watch out for it by following them on Facebook or Instagram.
One of my most favorite yet simplest shoots to date! K-pop has been taking over my life, and you can evidently see how I pretty much transformed Ericka to look a bit Korean here. I swear, it's all in the (photoshopped) brows. It also helps that she is a real singer with a perfect S-line body enough to give Goo Hara's proportions a run for their money!


text:
Q: How to set "from address" in microsoft flow I am trying to set the from address in microsoft flow to take the address of another user. But I get the error as
You are not authorized to send mail on behalf of the specified sending account.
 clientRequestId:
How do we solve this?

A: Yes, I meet the same issue.
MS said "The Outlook connector will be able to send emails only using those email accounts that your credentials have access to. You cannot use the connector to send a mail on behalf of some other user - for obvious security reasons. This behavior and so the error you get are by design."
You can vote here to let product team know our requirement:https://powerusers.microsoft.com/t5/Flow-Ideas/Create-flow-using-quot-Send-as-quot-by-person-s-email-in-quot/idi-p/70316

A: The person who you are trying to send an email on behalf of must give you permission. "On Behalf Permissions"

A: You have the ability to keep the account performing the connection to Outlook the same. This may be desired if you do not have extra Office 365 licenses lying around.
From there, you can keep the "Send As" address that you would like (i.e. a Distribution List), and then just grant permission to the workflow account (delegating) to allow it to send emails.
See more:
You are not authorized to send mail on behalf of the specified sending account


text:
import * as Analyzer from "../analyzers"
import * as Core from "../core"
import { TsChildDecoratorTransformer } from "./ts-child-decorator"

export class TsDecorator extends Core.TransformerBase {
    constructor(private parserType: Analyzer.ParserType) {
        super()
    }

    @Core.Call.when(Core.SyntaxKind.ExpressionStatement)
    transform(node:any, parent: Core.ParentMetaData | Core.ClassMetaData) {
        let analyzer = <Analyzer.DecoratorAnalyzer>Analyzer
            .get(this.parserType, Analyzer.AnalyzerType.Decorator, node)
        let clazz:Core.ClassMetaData;
        if(parent.type == "Class") clazz = parent;
        else if(!parent.children) return;
        else
            clazz = <Core.ClassMetaData>parent.children.filter(x => x.name == analyzer.getClassName())[0]
        if (analyzer.isMethodDecorator()) {
            this.transformMethod(node, clazz, analyzer)
        }
        if(analyzer.isPropertyDecorator()){
            this.transformProperty(node, clazz, analyzer)
        }
        else if (analyzer.isClassDecorator()) {
            this.transformClass(node, clazz, analyzer)
        }
    }

    private transformProperty(node:any, clazz: Core.ClassMetaData, analyzer: Analyzer.DecoratorAnalyzer) {
        let methodName = analyzer.getMethodName();
        if (clazz) {
            //property is special, because it only appears only on run time,
            //so here we add it manually
            let property:Core.PropertyMetaData = {
                type: "Property",
                name: analyzer.getMethodName(),
                analysis: Core.AnalysisType.Valid,
                decorators: [],
                location: {
                    end: 0, start: 0
                }
            }
            if(!clazz.properties) clazz.properties = []
            clazz.properties.push(property)
            this.traverse(analyzer.getChildren(), property, [
                new TsChildDecoratorTransformer(this.parserType)
            ])
        }
    }

    private transformMethod(node:any, clazz: Core.ClassMetaData, analyzer: Analyzer.DecoratorAnalyzer) {
        let methodName = analyzer.getMethodName();
        if (clazz && clazz.methods) {
            let method = clazz.methods.filter(x => x.name == methodName)[0]
            this.traverse(analyzer.getChildren(), method, [
                new TsChildDecoratorTransformer(this.parserType)
            ])
        }
    }

    private transformClass(node:any, clazz: Core.ClassMetaData, analyzer: Analyzer.DecoratorAnalyzer) {
        if (clazz) {
            this.traverse(analyzer.getChildren(), clazz, [
                new TsChildDecoratorTransformer(this.parserType)
            ])
        }
    }
}