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DANNY Murphy is convinced that Manchester United will qualify for the Champions League this season by finishing fourth in the league.
The former Liverpool midfielder made the declaration on talkSPORT this morning, after the Red Devils overcame a resolute Crystal Palace 2-1 in south London last night.
"Burnley at home, Stoke at home, Arsenal at home," Murphy said.
"All game United should have won comfortably – six points. Is their season that bad?
"OK they haven't played the free-flowing old school United way but they are progressing nicely.
"I really fancy them to be top four by the end of the season, I really do.
Victory over Palace meant United are now unbeaten in their last nine games – since suffering a shock 2-1 defeat to Fenerbahce in the Europa League on November 3.
It required a late goal from Zlatan Ibrahimovic to secure all three points at Selhurst Park, poking home a Paul Pogba assist in the 88th minute.
JOSE MOURINHO is back in good spirits and showed it by celebrating with his players on the pitch.
After the game, boss Mourinho said: "[The win] was very difficult but it was very deserved.
"In the first half we were in control, in the second half they had a little reaction and they got the bonus from that reaction with a good goal.
"After we went through the best period of the game which was 20 minutes of being almost unstoppable.
The result leaves United in sixth place, three points adrift of Tottenham and six behind arch-rivals Man City in fourth place.


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text:
\section{Introduction}
With the development of vehicular networks, vehicles can support the real-time vehicular applications such as speech recognition and multimedia application to facilitate people's life\cite{b1}.
In the vehicular networks, vehicles collect data from surroundings and train the models based on data to support the vehicular applications.
Due to the limited computing capability, vehicles usually transmit data to a cloud with powerful computing, storage and processing ability, which processes the data and sends back the computing results to the corresponding vehicles in the vehicular network. The cloud is usually far from the vehicles, and thus incur a large delay due to the long transmitting distance.
To solve this issue, edge-assisted vehicular networks are introduced to process the data at a road side unit (RSU) deployed at the edge in order to reduce the delay. However, vehicles are usually reluctant to share data with each other due to data privacy, thus the RSU is not able to collect enough data to train an accurate model for vehicular applications.

Federated learning (FL) can solve the above problem \cite{b2}. For the traditional FL, the RSU updates the global model for multiple rounds. For each round,  each vehicle first downloads the global model from the RSU, then trains its local model based on the downloaded global model parameters. After that, each vehicle  uploads its updated local model to the RSU to further update the global model through aggregation. The data privacy can be protected through sharing model parameters instead of data. In recent years, some works have studied FL in edge-assisted vehicular networks.
In \cite{b3}, Xiao \emph{et al.} considered the mobility of vehicles to jointly optimize the onboard computing capability, transmission power and local model accuracy to minimize the cost of the FL.
In \cite{b4}, Yuan \emph{et al.} proposed a bidirectional connection broad learning system to train the dataset stored in vehicles and then proposed a federated broad learning system algorithm to improve the aggregation efficiency.
In \cite{b5}, Zhou \emph{et al.} employed a FL in a two-layer vehicular network and designed a novel multi-layer heterogeneous model selection and aggregation scheme to achieve high accuracy. Moreover, they designed a context-aware learning mechanism to reduce the learning time of the object detection.

The traditional FL updates the global model synchronously, i.e., the RSU needs to wait for all vehicles to upload local models to update the global model in each round. However, vehicles may usually drive out of the coverage of the RSU before they update their local models. Therefore, the RSU cannot aggregate local models of the vehicles to update the global model, which reduces the accuracy of the global model. It is necessary to propose an asynchronous federated learning (AFL) to solve this problem, where the RSU updates the global model once receiving a local model from a vehicle and thus the global model can be updated in time without waiting for other vehicles to upload the local model.

Moreover, some factors may affect the accuracy of the global model. Specifically, the local model of a vehicle may be not accurate if the amount of data carried by the vehicle is small, which reduces the accuracy of the global model. In addition, each vehicle would experience the process including the global model downloading, local training and uploading the local model to update the global model. If the time duration of a vehicle from the global model downloading to the local model uploading is large, the RSU may update the global model based on the local models of other vehicles. In this case, the local model of the vehicle is stale and not accurate, which further reduce the accuracy of the global model. Hence the time for vehicles to download the global model, train the local model and upload the local model, which are referred to the downloading delay, local training delay and uploading delay, respectively, have a great impact on the accuracy of the global model. Different vehicles usually have different amount of data and computing capabilities, and the position of each vehicle changes in real time. The amount of data and computing capabilities of vehicles affect the local training delay. In addition, the time varying position affects the transmitting rate of vehicles, which incurs the frequent changes of the uploading delay for each vehicle. Therefore, it is necessary to consider the amount of data, computing capability and vehicle mobility in designing an AFL scheme in the edge-assisted vehicular networks to improve the accuracy of the global model, which motivates us to conduct this work.

In this paper, we jointly consider the amount of data, computing capability and vehicle mobility to design an AFL scheme to improve the accuracy of the global model in the edge-assisted vehicular networks. The rest of this paper is organized as follows. Section II presents the system model. Section III presents our scheme in detail. Section IV shows the simulation results. Section V concludes this paper.

\begin{figure}
\center
\includegraphics[scale=0.55]{systemmodel-eps-converted-to.pdf}
\caption{System model}
\label{fig1}
\end{figure}

\section{System Model}
As shown in Fig. \ref{fig1}, we consider an edge-assisted vehicular network architecture consisting of one RSU at the network edge and $K$ vehicles driving with a constant volocity in the coverage of the RSU. All vehicles are driving to the east. Different vehicles carries different amount of data and the computing capabilities of different vehicles are also different. The traversing time duration within the RSU's coverage is divided into discrete time slot. A three dimensional coordinate system is constructed to facilicate the formulation of vehicle position in real time, where the origin is set as the position of the bottom of the RSU, the direction of $x$ axis is set as east and the direction of $y$ axis is south, the direction of $z$ axis is set along the antennas of RSU which are perpendicular to both $x$ axis and $y$ axis.

The vehicular network adopts the AFL to update model for $M$ rounds. For the first round, the RSU initializes the global model based on the convolutional neural network (CNN) and each vehicle downloads the global model from the RSU. Then each vehicle adopts the global model to update the local model through training its local data based on CNN and uploads its local model after it finishes its local training/finishing its local training. The RSU updates the global model once it receives a local model from a vehicle to finish the first round. After that the vehicle downloads the global model from the RSU to continue to update its local model. The RSU would update the global model again when it receives another local model. The above procedures will be repeated in different rounds. When the global model is updated for $M$ rounds, the AFL is finished and the RSU obtains the final global model.

\section{Asynchronous Federated Learning Scheme for Edge-assisted Vehicular Networks}
In this section, we propose an AFL scheme to improve the accuracy of the global model in edge-assisted vehicular networks. In the scheme, each vehicle would  experience the process including global model downloading, local training and uploading the local model to update the global model. Next we will introduce the process for vehicle $i$ in detail.

\subsection{downloading the global model}\label{AA}
Vehicle $i$ first downloads the global model $w_{g}$ from the RSU after it updates the global model based on the local model of vehicle $i$. Particularly, at the beginning of the scheme, the global model is initialized by the RSU based on the CNN.

\subsection{local training}\label{AA}
After that vehicle $i$ employs the global model $w_{g}$ to update its local model through training. Specifically, the training process consists of $l$ iterations. Let $w_{j}^{i}$ be the local model parameter for vehicle $i$ in iteration $j$ $({j \in \left[ {1,l} \right]})$ which is updated at the end of the previous iteration. Note that $w_{j}^{i}$ is set as $w_{g}$ in iteration $1$. For each iteration $j$ $({j \in \left[ {1,l-1} \right]})$, vehicle $i$ first inputs each local data $a$ into the CNN with local model $w_{j}^{i}$ and outputs the predicted label of data $a$, denoted as $\hat{{y}_{a}}$.

Then vehicle $i$ adopts the cross-entropy loss function to calculate the loss of $w_{j}^{i}$, i.e.,

\begin{equation}
{{f}_{i}}\left( w_{j}^{i} \right)\text{=}-\sum\limits_{a=1}^{{{D}_{i}}}{{{y}_{a}}\log }\hat{{y}_{a}}\,,
\label{eq1}
\end{equation}
where ${{D}_{i}}$ is the number of data carried by vehicle $i$, $w_{j}^{i}$ is the local model for vehicle $i$ in iteration $j$ and ${{y}_{a}}$ is the label of data $a$.

Then the stochastic gradient descent algorithm is adopted to update the local model, i.e.,
\begin{equation}
w_{j+1}^{i}=w_{j}^{i}-\eta \nabla {{f}_{i}}\left( w_{j}^{i} \right),
\label{eq2}
\end{equation}
where $\nabla {{f}_{i}}\left( w_{j}^{i} \right)$ is the gradient of ${{f}_{i}}\left( w_{j}^{i} \right)$ and $\eta $ is the learning rate. Then vehicle $i$ set $w_{j+1}^{i}$ as the model of the CNN and starts the next iteration to update the local model. When the number of iterations reaches $l$, vehicle $i$ obtains the updated local model $w^{i}$.

Since the amount of data, computing capability and vehicle mobility need to be jointly considered, we will calculate the weights of its data, local training delay and uploading delay of local models, i.e., data weight $\beta _{d}^{i}$, local training delay weight $\beta _{l}^{i}$ and uploading delay $\beta _{u}^{i}\left( t \right)$, where $\beta _{d}^{i}$, $\beta _{l}^{i}$ and $\beta _{u}^{i}\left( t \right)$ belong to $\left( 0,1 \right)$. Note that since the download bandwidth is large, the downloading delay is much small and can be neglected here.

The accuracy of local model incrases as the amount of data increases, thus the data weight $\beta _{d}^{i}$ is calculated as

\begin{equation}
\beta _{d}^{i}=\frac{{{D}_{i}}}{D},
\label{eq3}
\end{equation}
where ${{D}_{i}}$ is the number of the local data carried by vehicle $i$, $D$ is the total number of the local data carried by all vehicles.

If the local training delay is large, the local model would be stale and thus not accurate. Hence, the local training delay weight $\beta _{l}^{i}$ is calculated as

\begin{equation}
\beta _{l}^{i}={{\zeta }^{C_{l}^{i}}},
\label{eq4}
\end{equation}
where $C_{l}^{i}$ is the local training delay of vehicle $i$ and $\zeta$ is a parameter which belongs to $\left( 0,1 \right)$ to make $\beta _{l}^{i}$ decreases as the local training delay increases, here $C_{l}^{i}$ is calculated as
\begin{equation}
C_{l}^{i}=\frac{{{D}_{i}}\text{ }\!\! \cdot \! \! C_y}{{{\delta }_{i}}},
\label{eq5}
\end{equation}
where $C_y$ is the CPU cycles to train a data and ${{\delta }_{i}}$ is the CPU cycle frequencies allocated for vehicle $i$.

Similar with the local training delay weight, the uploading delay weight at time slot $t$ is calculated as

\begin{equation}
\beta _{u}^{i}\left( t \right)={{\gamma }^{C_{u}^{i}\left( t \right)}},
\label{eq6}
\end{equation}
where $C_{u}^{i}(t)$ is the uploading delay of vehicle $i$ at time slot $t$ and $\gamma$ is a parameter which belongs to $\left( 0,1 \right)$ to make $\beta _{u}^{i}\left( t \right)$ decreases as the uploading delay decreases. $C_{u}^{i}(t)$ can be calculated as
\begin{equation}
C_{u}^{i}\left( t \right)=\frac{\left| {{w}} \right|}{r_u^i\left( t \right)},
\label{eq7}
\end{equation}
where the size of the local model of each vehicle keeps the same and is denoted as $\left| {{w}} \right|$, and ${r_u^i}\left( t \right)$ is the transmission rate of vehicle $i$ at time slot $t$.

According to Shannon's theorem, the transmission rate of vehicle $i$ at time slot $t$ is calculated as
\begin{equation}
r_u^i\left( t \right)=B{{\log }_{2}}\left( 1+\frac{p_{m}\text{ }\!\!\cdot\!\!\text{ }{{h}^{i}}\text{ }\!\!\cdot\!\!\text{ }{{\left( {{d}^{i}}\left( t \right) \right)}^{-\alpha }}}{{{\sigma }^{2}}} \right),
\label{eq8}
\end{equation}
where $B$ is the bandwidth, $p_{m}$ is the transmit power of each vehicle, ${{h}^{i}}$ is the channel gain of vehicle $i$, $\alpha$ is the path loss exponent and ${{\sigma }^{2}}$ is the noise power, ${{d}^{i}}\left( t \right)$ is the distance between vehicle $i$ and the RSU at time slot $t$. The height of the RSU is $H$, thus the position of the the RSU's antennas ${{P}_{R}}$ is $\left( 0,0,H \right)$. In this case, ${{d}^{i}}\left( t \right)$ is calculated as
\begin{equation}
{{d}^{i}}\left( t \right)=\left\| {{P}^{i}}\left( t \right)-{{P}_{R}} \right\|,
\label{eq13}
\end{equation}
where ${{P}^{i}}\left( t \right)$ is the position of vehicle $i$ at time slot $t$ and is expressed as $\left( d_{x}^{i}\left( t \right),d_{y}^i,0 \right)$,  $d_{x}^{i}\left( t \right)$ and ${d_{y}^{i}}$ are the distances between vehicle $i$ and the antennas of RSU along $x$ axis and $y$ axis at slot $t$, respectively, where ${d_{y}^{i}}$ is a fixed $d_{y}$ for each vehicle $i$. Since each vehicle driving at a constant speed ${v}$, $d_{x}^{i}(t)$ is calculated as
\begin{equation}
{d_{x}^{i}\left( t \right)}=d_{x}^{i}(0)+{{v}}\cdot t,
\label{eq12}
\end{equation}
where $d_{x}^{i}(0)$ is the position of vehicle $i$ at the initial time slot, which is known by vehicle $i$ based on the global positioning system (GPS). Thus given $d_{x}^{i}(0)$, vehicle $i$ can calculate the uploading delay weight at each time slot according to Eqs. \eqref{eq6}-\eqref{eq12}.

According to the above weights, the weighted local model $w_{w}^{{{i}}}$ is calculated as
\begin{equation}
w_{w}^{{{i}}}=w^{i}*\beta _{d}^{i}*\beta _{l}^{i}*\beta _{u}^{i}.
\label{eq9}
\end{equation}

\subsection{uploading local model and aggregation}
After vehicle $i$ uploads its weighted local model to the RSU, the RSU can update the global model through aggregation. The global model in the $(r+1)th$ updating can be expressed as

\begin{equation}
{{w}_{r+1}}=\beta {{w}_{r}}+\left( 1-\beta  \right)w_{w}^{{{i}}},
\label{eq10}
\end{equation}
where ${{w}_{r}}$ is the global model in round $r$, $\beta $ is the aggregation proportion that belongs to $\left( 0,1 \right)$.
When the number of rounds reaches $M$, the RSU finishes updating its global model and achieves the final global model.

\section{Simulation Results}
In this section, we conduct simulation experiments to verify the effectiveness of the proposed scheme. The scenario has been described as the system model. We will first introduce the relevant simulation settings and evaluate the performance of our scheme.

\subsection{Simulation Setup}

\begin{table}\footnotesize
\caption{Values of parameters}
\label{tab1}
\centering
\begin{tabular}{|c|c|c|c|}
\hline
\textbf{Parameter} &\textbf{Value} &\textbf{Parameter} &\textbf{Value}\\
\hline
$K$ & 10 & $v$ & $20m/s$ \\
\hline
$H$ & $10m$ & $d_{y}$ & $10m$ \\
\hline
$C_y$ & ${{10}^{5}}cycles$ & $\left| {{w}} \right|$ & $5000bits$ \\
\hline
$B$ & ${{10}^{5}}HZ$ & $p_{m}$ & $0.1w$ \\
\hline
$\alpha$ & 2 & ${{\sigma }^{2}}$ & ${{10}^{-11}}mw$ \\
\hline
$\beta$ & 0.5 & $\zeta$ & 0.9 \\
\hline
$\gamma$ & $0.9$ & $ $ &  \\
\hline
\end{tabular}
\end{table}

The simulation tool is Python 3.9. ${\delta_{i}}$ is set as $1.5\cdot \left( i+ 5 \right)\cdot {{10}^{8}}$ cycles/s. We employ the orthogonal frequency division multiplexing (OFDM) technology and Rayleigh fading channel in this simulation, thus $h_i$ is a stochastic value for each vehicle $i$ based on the autoregressive model \cite{b9}. The data carried by each vehicle are the images randomly selected from 60000 images in the MNIST dataset, where vehicle $i$ carries $\left( 2250+3750i \right)$ images. The detailed simulation parameters are listed in Table \ref{tab1}.

\subsection{Performance Evaluation}
The accuracy (i.e., denoted as acc) and loss of the global model are adopted as the metrics in our experiments, where the loss is calculated according to Eq. \eqref{eq1} and the accuracy reflects the classification performance of the global model obtained by our scheme and is defined as
\begin{equation}
acc=\frac{\text{the number of correctly classified data}}{\text{the number of total data}}\times 100\%
.
\label{eq11}
\end{equation}

Next, we evaluate the metrics of the global model in our scheme by comparing with the traditional FL scheme, where each result is obtained by averaging the results of three experiments.
\begin{figure}
\center
\includegraphics[scale=0.55]{acc1-eps-converted-to.pdf}
\caption{Accuracy in different rounds}
\label{fig2}
\end{figure}

Fig. \ref{fig2} shows the accuracy of the global model in different rounds.  We can observe that the accuracy of our scheme first increases as the number of rounds increases and then becomes relatively stable.
This is because that the global model keeps to be updated by more local models when the number of rounds increases. The accuracy of the global model is improved. When the RSU receives sufficient local models for global model updating, the accuracy of the global model becomes stable.

\begin{figure}
\center
\includegraphics[scale=0.55]{loss3-eps-converted-to.pdf}
\caption{Loss in different rounds}
\label{fig3}
\end{figure}

Fig. \ref{fig3} investigates the loss of the global model for each round in our scheme. We can see that the loss decreases as the number of rounds increases. This is becasue that the accuracy increases  as the number of rounds increases.

\begin{figure}
\center
\includegraphics[scale=0.55]{beta1-eps-converted-to.pdf}
\caption{Accuracy under different beta}
\label{fig4}
\end{figure}

\begin{table}
\centering
\caption{\label{tab2}the acc and loss under different scheme.}
\begin{tabular}{l|l|l}
index & our sheme & FL \\\hline
acc & 97.59$\%$ & 94.74$\%$ \\
loss & 0.26 & 0.85
\end{tabular}
\end{table}

Table \ref{tab2} compares the accuracy and loss of the two schemes. We can see that the acc and loss of our scheme outperforms the FL scheme. This is because our scheme jointly consider the amount of data, computing capability and vehicle mobility to aggregate the global model asynchronously.

Fig. \ref{fig4} shows the model accuracy of our scheme under different $\beta$ when the number of rounds is $10$. We can see that our scheme keeps a relatively high accuracy when $\beta$ is smaller than 0.5. In addition, the accuracy decreases as the value of $\beta$ increases when $\beta$ is larger than 0.5. This is because that according to Eq. \eqref{eq10}, the weight of the local model is small when $\beta$ is larger than 0.5. In this case, the global model tends to be updated according to the previous global model, which decreases the accuracy. Particularly, when $\beta$ is 0.9, the accuracy is decreased significantly. This is because that the weight of the local model is much small, thus the update of the global model majorly depends on the previous global model, which decreases the accuracy significantly.

\section{Conclusions}
In this paper, we have considered the amount of data, computing capability and vehicle mobility and proposed an AFL scheme to improve the accuracy of global model. We have conducted extensive simulations to demonstrate the performance of the proposed scheme. The conclusions are summarized as follows:
\begin{itemize}
	\item Our scheme considers the amount of data, computing capability and mobility of vehicles to improve the accuracy and thus outperforms the FL scheme.

	\item The aggregate proportion $\beta$ affects the accuracy of the global model and the desireable accuracy is achieved when $\beta$ is relatively small.
\end{itemize}

\section{Introduction}
With the development of vehicular networks, vehicles can support the real-time vehicular applications such as speech recognition and multimedia application to facilitate people's life\cite{b1}.
In the vehicular networks, vehicles collect data from surroundings and train the models based on data to support the vehicular applications.
Due to the limited computing capability, vehicles usually transmit data to a cloud with powerful computing, storage and processing ability, which processes the data and sends back the computing results to the corresponding vehicles in the vehicular network. The cloud is usually far from the vehicles, and thus incur a large delay due to the long transmitting distance.
To solve this issue, edge-assisted vehicular networks are introduced to process the data at a road side unit (RSU) deployed at the edge in order to reduce the delay. However, vehicles are usually reluctant to share data with each other due to data privacy, thus the RSU is not able to collect enough data to train an accurate model for vehicular applications.

Federated learning (FL) can solve the above problem \cite{b2}. For the traditional FL, the RSU updates the global model for multiple rounds. For each round,  each vehicle first downloads the global model from the RSU, then trains its local model based on the downloaded global model parameters. After that, each vehicle  uploads its updated local model to the RSU to further update the global model through aggregation. The data privacy can be protected through sharing model parameters instead of data. In recent years, some works have studied FL in edge-assisted vehicular networks.
In \cite{b3}, Xiao \emph{et al.} considered the mobility of vehicles to jointly optimize the onboard computing capability, transmission power and local model accuracy to minimize the cost of the FL.
In \cite{b4}, Yuan \emph{et al.} proposed a bidirectional connection broad learning system to train the dataset stored in vehicles and then proposed a federated broad learning system algorithm to improve the aggregation efficiency.
In \cite{b5}, Zhou \emph{et al.} employed a FL in a two-layer vehicular network and designed a novel multi-layer heterogeneous model selection and aggregation scheme to achieve high accuracy. Moreover, they designed a context-aware learning mechanism to reduce the learning time of the object detection.

The traditional FL updates the global model synchronously, i.e., the RSU needs to wait for all vehicles to upload local models to update the global model in each round. However, vehicles may usually drive out of the coverage of the RSU before they update their local models. Therefore, the RSU cannot aggregate local models of the vehicles to update the global model, which reduces the accuracy of the global model. It is necessary to propose an asynchronous federated learning (AFL) to solve this problem, where the RSU updates the global model once receiving a local model from a vehicle and thus the global model can be updated in time without waiting for other vehicles to upload the local model.

Moreover, some factors may affect the accuracy of the global model. Specifically, the local model of a vehicle may be not accurate if the amount of data carried by the vehicle is small, which reduces the accuracy of the global model. In addition, each vehicle would experience the process including the global model downloading, local training and uploading the local model to update the global model. If the time duration of a vehicle from the global model downloading to the local model uploading is large, the RSU may update the global model based on the local models of other vehicles. In this case, the local model of the vehicle is stale and not accurate, which further reduce the accuracy of the global model. Hence the time for vehicles to download the global model, train the local model and upload the local model, which are referred to the downloading delay, local training delay and uploading delay, respectively, have a great impact on the accuracy of the global model. Different vehicles usually have different amount of data and computing capabilities, and the position of each vehicle changes in real time. The amount of data and computing capabilities of vehicles affect the local training delay. In addition, the time varying position affects the transmitting rate of vehicles, which incurs the frequent changes of the uploading delay for each vehicle. Therefore, it is necessary to consider the amount of data, computing capability and vehicle mobility in designing an AFL scheme in the edge-assisted vehicular networks to improve the accuracy of the global model, which motivates us to conduct this work.

In this paper, we jointly consider the amount of data, computing capability and vehicle mobility to design an AFL scheme to improve the accuracy of the global model in the edge-assisted vehicular networks. The rest of this paper is organized as follows. Section II presents the system model. Section III presents our scheme in detail. Section IV shows the simulation results. Section V concludes this paper.

\begin{figure}
\center
\includegraphics[scale=0.55]{systemmodel-eps-converted-to.pdf}
\caption{System model}
\label{fig1}
\end{figure}

\section{System Model}
As shown in Fig. \ref{fig1}, we consider an edge-assisted vehicular network architecture consisting of one RSU at the network edge and $K$ vehicles driving with a constant volocity in the coverage of the RSU. All vehicles are driving to the east. Different vehicles carries different amount of data and the computing capabilities of different vehicles are also different. The traversing time duration within the RSU's coverage is divided into discrete time slot. A three dimensional coordinate system is constructed to facilicate the formulation of vehicle position in real time, where the origin is set as the position of the bottom of the RSU, the direction of $x$ axis is set as east and the direction of $y$ axis is south, the direction of $z$ axis is set along the antennas of RSU which are perpendicular to both $x$ axis and $y$ axis.

The vehicular network adopts the AFL to update model for $M$ rounds. For the first round, the RSU initializes the global model based on the convolutional neural network (CNN) and each vehicle downloads the global model from the RSU. Then each vehicle adopts the global model to update the local model through training its local data based on CNN and uploads its local model after it finishes its local training/finishing its local training. The RSU updates the global model once it receives a local model from a vehicle to finish the first round. After that the vehicle downloads the global model from the RSU to continue to update its local model. The RSU would update the global model again when it receives another local model. The above procedures will be repeated in different rounds. When the global model is updated for $M$ rounds, the AFL is finished and the RSU obtains the final global model.

\section{Asynchronous Federated Learning Scheme for Edge-assisted Vehicular Networks}
In this section, we propose an AFL scheme to improve the accuracy of the global model in edge-assisted vehicular networks. In the scheme, each vehicle would  experience the process including global model downloading, local training and uploading the local model to update the global model. Next we will introduce the process for vehicle $i$ in detail.

\subsection{downloading the global model}\label{AA}
Vehicle $i$ first downloads the global model $w_{g}$ from the RSU after it updates the global model based on the local model of vehicle $i$. Particularly, at the beginning of the scheme, the global model is initialized by the RSU based on the CNN.

\subsection{local training}\label{AA}
After that vehicle $i$ employs the global model $w_{g}$ to update its local model through training. Specifically, the training process consists of $l$ iterations. Let $w_{j}^{i}$ be the local model parameter for vehicle $i$ in iteration $j$ $({j \in \left[ {1,l} \right]})$ which is updated at the end of the previous iteration. Note that $w_{j}^{i}$ is set as $w_{g}$ in iteration $1$. For each iteration $j$ $({j \in \left[ {1,l-1} \right]})$, vehicle $i$ first inputs each local data $a$ into the CNN with local model $w_{j}^{i}$ and outputs the predicted label of data $a$, denoted as $\hat{{y}_{a}}$.

Then vehicle $i$ adopts the cross-entropy loss function to calculate the loss of $w_{j}^{i}$, i.e.,

\begin{equation}
{{f}_{i}}\left( w_{j}^{i} \right)\text{=}-\sum\limits_{a=1}^{{{D}_{i}}}{{{y}_{a}}\log }\hat{{y}_{a}}\,,
\label{eq1}
\end{equation}
where ${{D}_{i}}$ is the number of data carried by vehicle $i$, $w_{j}^{i}$ is the local model for vehicle $i$ in iteration $j$ and ${{y}_{a}}$ is the label of data $a$.

Then the stochastic gradient descent algorithm is adopted to update the local model, i.e.,
\begin{equation}
w_{j+1}^{i}=w_{j}^{i}-\eta \nabla {{f}_{i}}\left( w_{j}^{i} \right),
\label{eq2}
\end{equation}
where $\nabla {{f}_{i}}\left( w_{j}^{i} \right)$ is the gradient of ${{f}_{i}}\left( w_{j}^{i} \right)$ and $\eta $ is the learning rate. Then vehicle $i$ set $w_{j+1}^{i}$ as the model of the CNN and starts the next iteration to update the local model. When the number of iterations reaches $l$, vehicle $i$ obtains the updated local model $w^{i}$.

Since the amount of data, computing capability and vehicle mobility need to be jointly considered, we will calculate the weights of its data, local training delay and uploading delay of local models, i.e., data weight $\beta _{d}^{i}$, local training delay weight $\beta _{l}^{i}$ and uploading delay $\beta _{u}^{i}\left( t \right)$, where $\beta _{d}^{i}$, $\beta _{l}^{i}$ and $\beta _{u}^{i}\left( t \right)$ belong to $\left( 0,1 \right)$. Note that since the download bandwidth is large, the downloading delay is much small and can be neglected here.

The accuracy of local model incrases as the amount of data increases, thus the data weight $\beta _{d}^{i}$ is calculated as

\begin{equation}
\beta _{d}^{i}=\frac{{{D}_{i}}}{D},
\label{eq3}
\end{equation}
where ${{D}_{i}}$ is the number of the local data carried by vehicle $i$, $D$ is the total number of the local data carried by all vehicles.

If the local training delay is large, the local model would be stale and thus not accurate. Hence, the local training delay weight $\beta _{l}^{i}$ is calculated as

\begin{equation}
\beta _{l}^{i}={{\zeta }^{C_{l}^{i}}},
\label{eq4}
\end{equation}
where $C_{l}^{i}$ is the local training delay of vehicle $i$ and $\zeta$ is a parameter which belongs to $\left( 0,1 \right)$ to make $\beta _{l}^{i}$ decreases as the local training delay increases, here $C_{l}^{i}$ is calculated as
\begin{equation}
C_{l}^{i}=\frac{{{D}_{i}}\text{ }\!\! \cdot \! \! C_y}{{{\delta }_{i}}},
\label{eq5}
\end{equation}
where $C_y$ is the CPU cycles to train a data and ${{\delta }_{i}}$ is the CPU cycle frequencies allocated for vehicle $i$.

Similar with the local training delay weight, the uploading delay weight at time slot $t$ is calculated as

\begin{equation}
\beta _{u}^{i}\left( t \right)={{\gamma }^{C_{u}^{i}\left( t \right)}},
\label{eq6}
\end{equation}
where $C_{u}^{i}(t)$ is the uploading delay of vehicle $i$ at time slot $t$ and $\gamma$ is a parameter which belongs to $\left( 0,1 \right)$ to make $\beta _{u}^{i}\left( t \right)$ decreases as the uploading delay decreases. $C_{u}^{i}(t)$ can be calculated as
\begin{equation}
C_{u}^{i}\left( t \right)=\frac{\left| {{w}} \right|}{r_u^i\left( t \right)},
\label{eq7}
\end{equation}
where the size of the local model of each vehicle keeps the same and is denoted as $\left| {{w}} \right|$, and ${r_u^i}\left( t \right)$ is the transmission rate of vehicle $i$ at time slot $t$.

According to Shannon's theorem, the transmission rate of vehicle $i$ at time slot $t$ is calculated as
\begin{equation}
r_u^i\left( t \right)=B{{\log }_{2}}\left( 1+\frac{p_{m}\text{ }\!\!\cdot\!\!\text{ }{{h}^{i}}\text{ }\!\!\cdot\!\!\text{ }{{\left( {{d}^{i}}\left( t \right) \right)}^{-\alpha }}}{{{\sigma }^{2}}} \right),
\label{eq8}
\end{equation}
where $B$ is the bandwidth, $p_{m}$ is the transmit power of each vehicle, ${{h}^{i}}$ is the channel gain of vehicle $i$, $\alpha$ is the path loss exponent and ${{\sigma }^{2}}$ is the noise power, ${{d}^{i}}\left( t \right)$ is the distance between vehicle $i$ and the RSU at time slot $t$. The height of the RSU is $H$, thus the position of the the RSU's antennas ${{P}_{R}}$ is $\left( 0,0,H \right)$. In this case, ${{d}^{i}}\left( t \right)$ is calculated as
\begin{equation}
{{d}^{i}}\left( t \right)=\left\| {{P}^{i}}\left( t \right)-{{P}_{R}} \right\|,
\label{eq13}
\end{equation}
where ${{P}^{i}}\left( t \right)$ is the position of vehicle $i$ at time slot $t$ and is expressed as $\left( d_{x}^{i}\left( t \right),d_{y}^i,0 \right)$,  $d_{x}^{i}\left( t \right)$ and ${d_{y}^{i}}$ are the distances between vehicle $i$ and the antennas of RSU along $x$ axis and $y$ axis at slot $t$, respectively, where ${d_{y}^{i}}$ is a fixed $d_{y}$ for each vehicle $i$. Since each vehicle driving at a constant speed ${v}$, $d_{x}^{i}(t)$ is calculated as
\begin{equation}
{d_{x}^{i}\left( t \right)}=d_{x}^{i}(0)+{{v}}\cdot t,
\label{eq12}
\end{equation}
where $d_{x}^{i}(0)$ is the position of vehicle $i$ at the initial time slot, which is known by vehicle $i$ based on the global positioning system (GPS). Thus given $d_{x}^{i}(0)$, vehicle $i$ can calculate the uploading delay weight at each time slot according to Eqs. \eqref{eq6}-\eqref{eq12}.

According to the above weights, the weighted local model $w_{w}^{{{i}}}$ is calculated as
\begin{equation}
w_{w}^{{{i}}}=w^{i}*\beta _{d}^{i}*\beta _{l}^{i}*\beta _{u}^{i}.
\label{eq9}
\end{equation}

\subsection{uploading local model and aggregation}
After vehicle $i$ uploads its weighted local model to the RSU, the RSU can update the global model through aggregation. The global model in the $(r+1)th$ updating can be expressed as

\begin{equation}
{{w}_{r+1}}=\beta {{w}_{r}}+\left( 1-\beta  \right)w_{w}^{{{i}}},
\label{eq10}
\end{equation}
where ${{w}_{r}}$ is the global model in round $r$, $\beta $ is the aggregation proportion that belongs to $\left( 0,1 \right)$.
When the number of rounds reaches $M$, the RSU finishes updating its global model and achieves the final global model.

\section{Simulation Results}
In this section, we conduct simulation experiments to verify the effectiveness of the proposed scheme. The scenario has been described as the system model. We will first introduce the relevant simulation settings and evaluate the performance of our scheme.

\subsection{Simulation Setup}

\begin{table}\footnotesize
\caption{Values of parameters}
\label{tab1}
\centering
\begin{tabular}{|c|c|c|c|}
\hline
\textbf{Parameter} &\textbf{Value} &\textbf{Parameter} &\textbf{Value}\\
\hline
$K$ & 10 & $v$ & $20m/s$ \\
\hline
$H$ & $10m$ & $d_{y}$ & $10m$ \\
\hline
$C_y$ & ${{10}^{5}}cycles$ & $\left| {{w}} \right|$ & $5000bits$ \\
\hline
$B$ & ${{10}^{5}}HZ$ & $p_{m}$ & $0.1w$ \\
\hline
$\alpha$ & 2 & ${{\sigma }^{2}}$ & ${{10}^{-11}}mw$ \\
\hline
$\beta$ & 0.5 & $\zeta$ & 0.9 \\
\hline
$\gamma$ & $0.9$ & $ $ &  \\
\hline
\end{tabular}
\end{table}

The simulation tool is Python 3.9. ${\delta_{i}}$ is set as $1.5\cdot \left( i+ 5 \right)\cdot {{10}^{8}}$ cycles/s. We employ the orthogonal frequency division multiplexing (OFDM) technology and Rayleigh fading channel in this simulation, thus $h_i$ is a stochastic value for each vehicle $i$ based on the autoregressive model \cite{b9}. The data carried by each vehicle are the images randomly selected from 60000 images in the MNIST dataset, where vehicle $i$ carries $\left( 2250+3750i \right)$ images. The detailed simulation parameters are listed in Table \ref{tab1}.

\subsection{Performance Evaluation}
The accuracy (i.e., denoted as acc) and loss of the global model are adopted as the metrics in our experiments, where the loss is calculated according to Eq. \eqref{eq1} and the accuracy reflects the classification performance of the global model obtained by our scheme and is defined as
\begin{equation}
acc=\frac{\text{the number of correctly classified data}}{\text{the number of total data}}\times 100\%
.
\label{eq11}
\end{equation}

Next, we evaluate the metrics of the global model in our scheme by comparing with the traditional FL scheme, where each result is obtained by averaging the results of three experiments.
\begin{figure}
\center
\includegraphics[scale=0.55]{acc1-eps-converted-to.pdf}
\caption{Accuracy in different rounds}
\label{fig2}
\end{figure}

Fig. \ref{fig2} shows the accuracy of the global model in different rounds.  We can observe that the accuracy of our scheme first increases as the number of rounds increases and then becomes relatively stable.
This is because that the global model keeps to be updated by more local models when the number of rounds increases. The accuracy of the global model is improved. When the RSU receives sufficient local models for global model updating, the accuracy of the global model becomes stable.

\begin{figure}
\center
\includegraphics[scale=0.55]{loss3-eps-converted-to.pdf}
\caption{Loss in different rounds}
\label{fig3}
\end{figure}

Fig. \ref{fig3} investigates the loss of the global model for each round in our scheme. We can see that the loss decreases as the number of rounds increases. This is becasue that the accuracy increases  as the number of rounds increases.

\begin{figure}
\center
\includegraphics[scale=0.55]{beta1-eps-converted-to.pdf}
\caption{Accuracy under different beta}
\label{fig4}
\end{figure}

\begin{table}
\centering
\caption{\label{tab2}the acc and loss under different scheme.}
\begin{tabular}{l|l|l}
index & our sheme & FL \\\hline
acc & 97.59$\%$ & 94.74$\%$ \\
loss & 0.26 & 0.85
\end{tabular}
\end{table}

Table \ref{tab2} compares the accuracy and loss of the two schemes. We can see that the acc and loss of our scheme outperforms the FL scheme. This is because our scheme jointly consider the amount of data, computing capability and vehicle mobility to aggregate the global model asynchronously.

Fig. \ref{fig4} shows the model accuracy of our scheme under different $\beta$ when the number of rounds is $10$. We can see that our scheme keeps a relatively high accuracy when $\beta$ is smaller than 0.5. In addition, the accuracy decreases as the value of $\beta$ increases when $\beta$ is larger than 0.5. This is because that according to Eq. \eqref{eq10}, the weight of the local model is small when $\beta$ is larger than 0.5. In this case, the global model tends to be updated according to the previous global model, which decreases the accuracy. Particularly, when $\beta$ is 0.9, the accuracy is decreased significantly. This is because that the weight of the local model is much small, thus the update of the global model majorly depends on the previous global model, which decreases the accuracy significantly.

\section{Conclusions}
In this paper, we have considered the amount of data, computing capability and vehicle mobility and proposed an AFL scheme to improve the accuracy of global model. We have conducted extensive simulations to demonstrate the performance of the proposed scheme. The conclusions are summarized as follows:
\begin{itemize}
	\item Our scheme considers the amount of data, computing capability and mobility of vehicles to improve the accuracy and thus outperforms the FL scheme.

	\item The aggregate proportion $\beta$ affects the accuracy of the global model and the desireable accuracy is achieved when $\beta$ is relatively small.
\end{itemize}

redpajama_set_name: RedPajamaArXiv


text:
Intercontinental Exchange Statement on Trayport Acquisition
theice.com – LONDON- Intercontinental Exchange (NYSE:ICE), a leading operator of global exchanges and clearing houses and provider of data and listings services, today provided further information to investors on its acquisition of Trayport following the UK's Competition and Markets Authority's (CMA) announcement of its provisional findings on August 16, 2016.
The CMA's provisional findings identified areas of concern. ICE does not agree with the findings which do not align with ICE's vision for continuing to operate Trayport as an open and autonomous software provider. This was ICE's original statement of intention and reflects how the business has operated since the acquisition was completed in December 2015.
The findings are provisional and the final CMA decision is not expected to be known until mid-October. ICE now has an opportunity to address the CMA's concerns and demonstrate how they do not reflect the manner in which Trayport will operate as a subsidiary of ICE.
Trayport will continue to operate as it does today in European utilities markets and will ensure that brokers, exchanges and clearing houses are all treated fairly and reasonably and are not discriminated against, including with respect to pricing, access and support.
In addition, Trayport will continue to invest in and enhance its existing services in response to customer and market demand. It will operate as a separate, independent business within ICE and will have a dedicated senior management team and defined processes to ensure customer feedback is received and given appropriate consideration.
Given its provisional findings, the CMA has started a consultation on potential remedies if its final decision is that ICE's ownership of Trayport could give rise to an adverse impact on competition. As is customary with a CMA phase 2 review, the list of potential remedies includes the divestment of Trayport along with alternative protective measures. We do not believe that divestment is necessary, appropriate or in the best interests of Trayport's customers. ICE is committed to retaining ownership of Trayport and is willing to memorialize its intentions with regard to Trayport's future operation with formal CMA remedies.
Intertrust Acquires Viteos
Firm can now offer advanced fintech solutions to clients.
06.20.2019 By John D'Antona , Editor, Traders Magazine
TRADING UP: Citi Adds Hugon, Gerbier; Russo Exits Goldman
The weekly recap of hires, job moves and promotions around Wall Street.
ITRS Snags OP5 to Expand Analytics
OP5 will remain and operate as a separate brand and entity.
IHS Markit Completes Acquisition of Ipreo
Ipreo expands IHS Markit business and customer base.
FIS Acquires Piedmont Investment
The transaction is expected to close in the third quarter of 2018.


Cheryl Swope, author of "Simply Classical, A Beautiful Education for Any Child, Mother and Home Educator," joins Andy and Sarah to talk about chapter 4 in Emmanuel Press's new book He Restores My Soul, where she talks about motherhood and mental illness, including her story about becoming a parent, experiences parenting children with special needs, and what she has learned through this journey in life. Read all of the author interviews at kfuo.org/tag/he-restores-my-soul and find book details at emmanuelpress.us. The book releases September 28!


text:
Shinning Star Media Inc. Apps – Privacy Policy
Shinning Star Media Inc., its subsidiaries and its affiliates ("Company" or "we") respect the privacy of the users (including children) of their mobile applications ("User" or "you") and are committed to protect the Users' information. We believe that you have a right to know our practices regarding the information we may collect and use when you use our Apps (defined below).
IMPORTANT - Shinning Star Media Inc. operates mobile apps which are oriented for children under the age of thirteen (13) (collectively, "Kids Apps") as well as mobile apps which are not targeted to children under the age of thirteen (13) (collectively, "Apps for Everyone"). "Kids Apps" and "Apps for Everyone" shall collectively be referred to as "Apps". Shinning Star Media Inc.'s privacy practices and principles that pertain to Kids Apps are described in Chapters A and C below, our Kids' Privacy Policy. Shinning Star Media Inc.'s privacy practices and principles that pertain to Apps for Everyone are described in Chapters B and C below, our Apps for Everyone Privacy Policy.
Note that the "Kids Privacy Policy" (Chapter A below) and the "Apps for Everyone Privacy Policy" (Chapter B below), and each together with Chapter C detailed below, shall be referred to collectively herein as "this Privacy Policy".
Capitalized terms which are not defined herein, shall have the meaning ascribed to them in our terms of use available at: ("TOU"), into which this Privacy Policy is incorporated by reference.
1. Your consent (important, please read carefully!)
BY INSTALLING THE APPS ON YOUR MOBILE DEVICE, ENTERING INTO, CONNECTING TO, ACCESSING AND/OR USING THE APPS, YOU AGREE TO THE TERMS AND CONDITIONS SET FORTH IN THIS PRIVACY POLICY, INCLUDING TO THE POSSIBLE COLLECTION AND PROCESSING OF YOUR PERSONAL INFORMATION. PLEASE NOTE: IF YOU OR, AS APPLICABLE, YOUR LEGAL GUARDIAN, DISAGREE TO ANY TERM PROVIDED HEREIN, YOU MUST NOT INSTALL, ACCESS AND/OR USE THE APPS AND YOU ARE REQUESTED TO PROMPTLY ERASE ALL Shinning Star Media INC. APPS FROM YOUR MOBILE DEVICE.
If you are under the age of eighteen (18) you represent that your legal guardian has reviewed and agreed to this Privacy Policy.
Chapter A: Kids Privacy Policy
This Kids Privacy Policy is designated to provide you, the legal guardian of a child under the age of 13 years old, with guidance regarding our privacy policies and principles with respect to collecting, using and disclosing personal information regarding children under the age of thirteen (13) ("Kids") that are using our Kids Apps.
We do not knowingly collect any personal information from Kids other than as expressly specified in Sections 3.2 and 3.3 below.
We hereby prohibit any User under the age of 13 from actively and directly providing us with any personal information other than as specified in Section 3.3 below. If you are a legal guardian of a child under the age of 13 and you believe that your child has provided us with any personal information, please contact us at nancybrown339@gmail.com and we will delete such information from our servers.
Shinning Star Media Inc. may collect and maintain the information described in Section 3, directly or through certain products or services, solely for the purpose of supporting Shinning Star Media Inc.'s internal operations as permitted under applicable laws and rules (as described in greater detail below).
Shinning Star Media Inc.'s third party providers of certain products or services operating on our behalf, such as advertising networks, analytics services etc. ("Third Parties") may passively collect and maintain information about Users. Such Third Parties are carefully selected by Shinning Star Media Inc. to preserve compliance with applicable privacy laws, and we confirm that they are compliant with the US Children's Online Privacy Protection Act of 1998 and the rules promulgated by the Federal Trade Commission (FTC) under that Act, including but not limited to the Children's Online Privacy Protection Rule 16 C.F.R. Part 312) ("COPPA") in performance of their services for Shinning Star Media Inc.. Any question about Third Parties' privacy practices and policies should be directed to our Privacy Policy Administrator by e-mail to: nancybrown339@gmail.com. We will forward your inquiry to the applicable Third Party and make an effort to provide you with such Third Party's response within a reasonable timeframe.
2. Offline Use
Shinning Star Media Inc. allows an offline use (without Internet connection and without GPS enablement) of some features of its Kids Apps. Offline use of Kids Apps will preclude the collection by Shinning Star Media Inc. and/or Third Parties of any information pertaining to you, as the legal guardian of a Kid, and/or your use (including, as applicable, information pertaining to your Kid and his/her use) of the Kids Apps.
3. Online Use
There are three types of data and information which may be collected from Users of our Kids Apps:
3.1 Non-personal Information: Shinning Star Media Inc. and its Third Parties may passively collect non-identifiable and anonymous information as detailed below ("Kids Non-personal Information"). To put it simply, with respect to such Kids Non-personal Information, Shinning Star Media Inc. and the Third Parties are not aware of the identity of the User from whom Kids Non-personal Information has been collected. Kids Non-personal Information collected by either Shinning Star Media Inc. or Third Parties may include technical information such as: type of operating system (e.g. Android, iOS, etc.), type of browser (e.g. Explorer, Firefox, Chrome, Safari, etc.), screen resolution (e.g. 800×600, 1024×768, etc.), User session ID, time zone, session start / stop time, source of traffic (e.g. which ad units, sites or applications, and campaigns a User came from), language (e.g. English), device type (e.g. Galaxy, HTC, iPhone, etc.), data regarding User behavior within our Kids Apps (e.g. advertisements impressions, User's response to advertisements (including advertisements viewed or clicked), User's in-app purchases, User's gameplay (e.g. level reached, frequency and time spent in the Kids App and in each level, game progress, fail rate, mini game(s) played, virtual currency (if any) collected or earned, tools and in-app functionalities used, screens accessed, User retention, performance errors and problems, Kids Apps' stability, crashes, functioning, etc.), and other predefined events and behavior relating thereto (e.g. if User viewed a specific advertisement and other game play events, time of the event, internet connectivity (Wi-Fi, 3G), device time zone, etc.), other Shinning Star Media Inc. Apps installed, User age-related information (meaning whether a User passes an age-gate, if included to prevent access to certain features to Users who do not pass the age-gate to protect Kids privacy), randomly generated User ID which may not be matched back to the User, and geo-location – only at a country level. It is hereby clarified that no street name or city/town name is collected.
3.2 Kids' Persistent Device Identifier and No Other Kids personal identifiable information:
Shinning Star Media Inc. and Third Parties may also passively collect a persistent device identifier from Kids, such as IP address unique device identifier, advertising identifier (when available on a device) etc. ("Kids' Persistent Device Identifier"). Shinning Star Media Inc. does not collect and does not permit Third Parties to collect from Kids any information that may personally identify an individual (e.g.name, address, e-mail) (other than the Kids' Persistent Device Identifier which is collected for the limited purpose of supporting the internal operations of the Kids Apps) and any other personally identifiable information of a private and/or sensitive nature about the Kids. We note that Shinning Star Media Inc. may collect, use and provide access to the User's advertising identifier in accordance with the applicable platform provider's policies.
Shinning Star Media Inc. does not link any additional personally identifiable information to the Kids' Persistent Device Identifier, even from other services.
3.3 Information gathered through the Kids Apps "Ask a Question" or "Support" type of a feature:
If a Kid e-mails a question to Shinning Star Media Inc., using the "Ask a Question" or "Support" type of a feature which may be available within the Kids Apps, Shinning Star Media Inc. may respond to such question by using the Kid's e-mail address. Promptly after responding (or, at Shinning Star Media Inc.'s discretion, not responding) to such question, Shinning Star Media Inc. will delete the Kid's e-mail address.
Purpose of the collection: The information abovementioned is collected and processed solely for the purpose of supporting Shinning Star Media Inc.'s internal operations. This means the following:
a. providing and improving Kids Apps as well as ensuring proper functioning of the Apps and services;
b. maintaining or analyzing the functioning of the Kids Apps and services;
c. authenticating Users (e.g. verifying the User's identity when interacts with advertisements or acquires in-app purchases);
d. personalizing content and enhancing User experience;
e. performing network communications;
f. protecting the security or integrity of Users, Kids Apps and services;
g. serving contextual advertisements;
h. capping the frequency of advertising;
i. ensuring legal or regulatory compliance;
j. fulfilling a User's request under Section 3.3.
The collection and/or processing in connection with these purposes will performed as permitted under applicable laws and rules.
How the Information about our Users of Kids Apps is being collected? The abovementioned Kids Non-personal Information and Kids' Persistent Device Identifier are collected either by Shinning Star Media Inc. or by Third Parties through the Users' use of the Kids Apps, including, without limitation, through Third Parties' SDK embedded in our Kids Apps. In addition, Kids Non-personal Information and the Kids' Persistent Device Identifier may also be stored locally on the Users' device via generally accepted industry technologies used within the Kids Apps, such as "cookies-like technology" and local storage (see Section 19 below for further details with respect to Cookies and Local Storage).
If Third Parties whose SDK is installed in the Kids Apps collect Kids Non-personal Information, Kids' Persistent Device Identifier and/or any other information directly from Kids, such collection and processing is governed by such Third Parties' privacy policies and not by this Privacy Policy. However, all Third Parties are carefully selected to preserve compliance with applicable privacy laws, and we confirm that they are compliant with COPPA in performance of their services for Shinning Star Media Inc..
Shinning Star Media Inc. does not collect or process any financial or payment information, any government identification numbers, any non-public phonebook or contact information, any microphone and camera sensor data, or any sensitive device data concerning Kids.
4. Sharing Kids information with third parties
We may share the Kids' Persistent Device Identifier only in limited cases, including the following:
4.1 to satisfy any applicable law, regulation, legal process, subpoena or governmental request;
4.2 to enforce this Kids Privacy Policy or the Terms of Use, including investigation of potential violations thereof;
4.3 to detect, prevent, or otherwise address fraud, security or technical issues;
4.4 to protect the rights, property, or personal safety of Shinning Star Media Inc., its Users, suppliers, licensors, and/or licensees, or the general public;
4.5 when Shinning Star Media Inc. is undergoing any change in control, including by means of merger, acquisition or purchase of all or substantially all of the assets of Company, in which case it may also be disclosed, transferred and/or assigned; and/or
4.6 pursuant to the approval of a Kid's legal guardian, in order to supply certain services that such legal guardian or the User of the Kids Apps may have requested from the Company;
For avoidance of doubt, Shinning Star Media Inc. may transfer and disclose Kids Non-personal Information to third parties at its discretion.
We accept advertisements from Third Party ad networks which may be displayed in our Kids Apps. These Third Parties ad networks are carefully selected to preserve compliance with applicable privacy laws, and we confirm that they are compliant with COPPA in performance of their services for Shinning Star Media Inc.. Note that if the User of the Kids Apps clicks on any of these advertisements, the User will leave our Kids Apps and go to the advertisers' site the User selected, and the advertisers may use cookies and other web-tracking technologies to collect Kids Non-personal Information and/or Kids personally identifiable information. We recommend that you, as the legal guardian of a Kid, review the terms of use and privacy policy for any advertiser with whom you and/or the User of the Kids Apps are interacting before doing so. Their privacy policy, not ours, will apply to any of those interactions.
In addition, Shinning Star Media Inc. may, at its discretion, advertise other Shinning Star Media Inc. Apps within the Kids Apps. Such advertisements are not directed towards specific Users but are rather broadly posted onto random Shinning Star Media Inc. Apps.
6. Additional Terms and Conditions
See further terms and conditions which also apply to Users of the Kids Apps in Chapter C below: "Terms and Conditions Common to Both Kids Privacy Policy and Apps for Everyone Privacy Policy".
Chapter B: Apps for Everyone Privacy Policy
This Apps for Everyone Privacy Policy is designated to provide you with guidance regarding our privacy policies and principles with respect to collecting, using and disclosing personal information regarding Users who are using our Apps for Everyone.
7. Which information may be collected from Users of our Apps for Everyone?
There are two types of data and information which may be collected from Users of our Apps for Everyone:
7.1 Non-personal Information. Non-identifiable and anonymous information ("Non-personal Information") may be collected, stored and/or processed by us and/or our third party providers of certain products and services, such as ad networks, analytics, etc. ("Third Party(ies)"). Shinning Star Media Inc. and Third Parties do not know the identity of the User from whom Non-personal Information has been collected. Non-personal Information is any information which is available to Shinning Star Media Inc. and/or Third Parties through use by Users of the Apps for Everyone. Non-personal Information which may be collected may include, for example, aggregate data, technical information, statistical information and behavioral information, and may contain, inter alia, device type, type and version of operating system, network provider, browser identifiers, type of browser, screen resolution, session information, User's click stream on the Apps for Everyone, SDK version, API key, time spent playing, browser cookies, levels achieved, application version, in-app purchases and data regarding receipt validation, network status, local and time zone, clicks on advertisements Third Parties display on the Apps for Everyone, certain information about the advertisements reviewed by the User, the activities of the User in the Apps for Everyone (including without limitation any information relating to the User's activity in connection with the advertising displayed therein), what tools in the Apps for Everyone the User uses, etc., as well as the categories of data mentioned in Section 3.1 above.
7.2 Personal Information. Individually identifiable information ("Personal Information"). This information may identify an individual and/or may be of a private and/or sensitive nature.
Facebook Login feature: A User who wishes to enjoy certain features which may be available on our Apps may open an Account by using the "Log-in with Facebook" feature. Additionally, if a User uses the "Log in with Facebook" feature (which functionality is described in the TOU) then Facebook provides us with access to (i) certain information about such User as is stored in the User's Facebook account, namely, the User's public profile (i.e., User's name, profile picture, email address, gender and other public information) and his/her list of friends and/or any other information which is detailed and displayed to the User in the notice which appears during the "Log in with Facebook" process. Please read carefully such notice in order to understand what information is available to Shinning Star Media Inc. via Facebook and (ii) information available in the Facebook Page Insights of the applicable App for Everyone. We may collect and/or process the identifier number of the User's Facebook account Facebook access token, User's Facebook profile name, a link to the User's Facebook profile picture, User's e-mail and the User's Facebook friends who installed the applicable App for Everyone, to the extent legally permissible. Please remember that the manner in which Facebook uses, stores and discloses your information is governed solely by its policies and Shinning Star Media Inc. shall have no liability or responsibility for the privacy practices or other actions of Facebook that may take place within the Apps for Everyone. If the User uses the "Log in with Facebook" feature, then such User agrees that we may share his/her name and/or profile picture and/or his/her game data (such as moves, progress in our games and scores) with respect to the applicable Apps for Everyone, with Users who have also used the "Log in with Facebook" feature in connection with the applicable App for Everyone or with the User's Facebook contacts. If you do not agree to these practices please do not use the "Log in with Facebook" feature in any of our Apps for Everyone.
Opening an Account: When Users open an Account, they are required to provide their full name, e-mail address and to choose and confirm a password, birthday, gender, location (country). The User may be able (but is not required) to provide, the following details: links to other profiles of the User (e.g., Facebook page, Pinterest, YouTube, his/her own website, etc.). Please note that with respect to certain Apps, once User Generated Content created within an App is posted by a User within the App or in his/her Social Network or Platform, the details in his App profile will be visible to other Users of that App or Social Network or Platform users.
User Generated Content: Personal Information which is being gathered consists of any personal details provided consciously and voluntarily by the User within the scope of their User Generated Content. If you publish your contact information in any User Generated Content or as part of your App account, you may receive unsolicited messages from other Users of the Apps or the public. We, therefore, encourage you only to post information that you are sure you want to be accessible to anyone. All User Generated Content must comply with Shinning Star Media Inc.'s Terms of Use.
In addition, Shinning Star Media Inc. may collect and/or process, directly or through Third Parties, the following Personal Information:
the User's e-mail address which may be collected within the framework of the "Ask a Question" type of feature which may be available on the Apps;
the User's personal identifiers (such as, advertising identifiers (when available on a device), device identifiers, for example the IP address, carrier user ID, Media Access Control (MAC) address or social network ID). We note that Shinning Star Media Inc. may collect, use and provide access to the User's advertising identifier in accordance with the applicable platform provider's policies. We are also able to identify when a User installs an App for Everyone as a result of advertisements and/or campaigns that we launch on other services, including in Social Networks or Platforms (e.g. on Facebook or otherwise) and the source-ad which led to such installation and when a User views or clicks on such advertisements;
information in connection with User's activities within the Apps for Everyone including use of the services therein, including notifications, invite data, monetization events, game progress, accomplished stages, how long User spent in the game, inventory items, currency, etc.;
geo location data from GPS-enabled device (to the extent that the User explicitly allowed it) provided by the devices' interaction with the Apps for Everyone or association with your IP address. In addition, Third Parties may track and collect information regarding your on-line activities over time and across different online platforms (such as, websites, applications, etc.). For more information regarding your ability to cease such tracking and collection, please see below Section 20; and/or
identifier numbers of User's account on Google Play Games or Apple Game Center, if enabled by the User in Apps for Everyone.
For avoidance of doubt, any Non-personal Information connected or linked to or associated with any Personal Information shall be deemed as Personal Information as long as such connection, linkage or association exists.
We do not collect any Personal Information from you or related to you without your approval, which is obtained, inter alia, through your acceptance of the TOU and the Privacy Policy.
Shinning Star Media Inc. does not collect or process any financial or payment information, any government identification numbers, any non-public phonebook or contact information, any microphone and camera sensor data, or any sensitive device data concerning Users.
8. How the Information about Our Users is being collected?
There are three main methods:
8.1 Non-personal Information may be collected through your use of the Apps for Everyone. In other words, when you are using an App for Everyone, Shinning Star Media Inc. and Third Parties are aware of it and may gather and collect, including without limitation, through such Third Parties' SDK embedded in our Apps for Everyone, the information relating to such usage. In addition, local Non-personal Information may also be stored on your device via generally accepted industry technologies used within the Apps for Everyone, such as "cookies" and local storage (see Section 19 below for further details with respect to Cookies and Local Storage).
8.2 Personal Information which you provide Shinning Star Media Inc. directly and/or through Third Parties via your installation and/or use of the Apps for Everyone or behavior with respect to advertisements thereof (such as your advertising identifier, persistent identifier and/or your geo location data), inter alia, through such Third Parties' SDK embedded in our Apps for Everyone. In addition, local Personal Information may also be stored on your device via generally accepted industry technologies used within the Apps for Everyone, such as "cookies" and local storage (see Section 19 below for further details with respect to Cookies and Local Storage).
8.3 We collect Personal Information which you provide us voluntarily. We collect Information which the User voluntary provides Shinning Star Media Inc. through the Apps for Everyone "Ask a Question" type of feature, when he/she opens an Account or when he/she makes his/her User Generated Content available to others. In addition, we collect Personal information which you voluntarily provide when you use the "Log in with Facebook" feature (as further detailed above).
9. What are the Purposes of the Collection of Information?
Non-personal Information is collected in order to:
Use it for statistical, analytical and research purposes and for customization, development and improvement of our Apps for Everyone (for example, analyzing and fixing of problems, frequency capping (limiting the number of times a specific advertisement is presented to the same mobile device), developing new offerings, providing better content, simplifying and improving the User experience including based on preferences shown by Users usage within the Apps for Everyone).
Measure traffic and usage on the Apps for Everyone.
Promote our Apps.
Personal Information is collected in order to:
Personalize and enhance the Users experience while using the Apps for Everyone.
Improve the Apps for Everyone.
Provide the Users with personalized content and information, including customized advertisements, interest-based advertisements, targeted advertisements, etc.
Enable certain ad serving features, such as, frequency capping, etc.
Enable certain functionalities available within the Apps, for example in-app purchases.
Ensure proper functioning of the Apps and services, respond to questions or requests which were provided using the "Ask a Question" feature.
Enable the User to use the Game Center Features, the Social Features and the features which are available while using the "Log in with Facebook" feature.
Provide Users with updated information regarding our products and services, including without limitations with respect to competitions which we may hold from time to time.
To allow Users to participate in contests or other online game events and to publicly display achievements or statistical data (such as scores, rankings and achievements) that is generated through such User's participation.
Authentication purposes including to verify the User's identity when he/she signs in to the App or his/her Account; verify clicks and/or installs, verifying User's identity for the purpose of dealing with inappropriate interactions of Users and/or fraudulent use of our Apps.
Measure reach and effectiveness of advertisements campaigns, to offer targeted advertising, to personalize experience by showing advertisements for products and services that are more likely to appeal to the User (i.e. behavioral advertising).
Send push notifications to the User's device after the User has agreed to accept push notifications from Shinning Star Media Inc. or Third Parties. Users can deactivate push notification by changing the settings within the App and/or changing the notification settings in accordance with the instructions of the operating system running on the Users' device. Users may also opt out by clicking the "unsubscribe" option contained in the message.
Any other purpose explained at the time collection.
10. Sharing Information with Third Parties
We may share Personal Information only in the following cases:
10.1 to satisfy any applicable law, regulation, legal process, subpoena or governmental request;
10.2 to enforce this Privacy Policy and/or the TOU and other Shinning Star Media Inc. terms or policies, including investigation of potential violations thereof;
10.3 to detect, prevent, or otherwise address fraud, security or technical issues;
10.4 to respond to claims that any content published on the Apps for Everyone violates any right of a third-party;
10.5 to protect the rights, property, or personal safety of the Company, its Users, suppliers, licensors, and/or licensees, or the general public;
10.6 when the Company is undergoing any change in control, including by means of merger, acquisition or purchase of all or substantially all of the assets of Company, in which case it may also be disclosed, transferred and/or assigned;
10.7 pursuant to your explicit approval prior to the disclosure;
10.8 as part of a competition, receiving rewards or otherwise interact with our social media channels.
For avoidance of doubt, Shinning Star Media Inc. may transfer and disclose Non-personal Information to third parties at its discretion.
Please note that some of the Apps for Everyone include a sharing feature which allows Users to share their User Generated Content with others. User Generated Content may include Personal Information. YOU ACKNOWLEDGE AND AGREE THAT YOU ARE SOLELY RESPONSIBLE FOR SHARING YOUR PERSONAL INFORMATION WITH OTHERS AND THAT YOU ARE DOING SO AT YOUR OWN RISK.
11. Advertisements
We accept advertisements, in various formats (such as banners, interstitials, rewarded videos, etc.) from third parties ad networks which may be displayed in our Apps for Everyone. These third parties ad networks may collect and use, inter alia (i) information about your visits to Apps for Everyone in connection with such marketing, sales and advertising activities; and (ii) geographic tracking and carrier network preferences; (iii) information, such as age, gender and logged from device to ensure that appropriate advertising is presented within the App and calculate or control the number of views of an ad, and/or deliver advertisements relating to User's interests, and measure the effectiveness of advertisements campaigns. The delivery of advertisements to you may be based on IP address, device identifiers and other Personal Information gathered during your use of the Apps for Everyone. Note that third parties ad networks which are referred to in relation to the Apps for Everyone may include third parties service providers, such as Facebook and other ad networks. Note that if you click on any of these advertisements, the advertisers may use cookies and other web-tracking technologies (such as tracking pixel agent or visitor identification technology, etc.) on your device to collect data regarding advertisement performance, your interaction with such advertisements and our Apps for Everyone and your interests (which may include, non-personal and/or personal information (such as, device and network information, unique identifiers, gender, age and geo-location) about you) in order to serve you advertisements, including targeted advertisements, and for the legitimate business interests of such Third Parties ad networks. We recommend that you review the terms of use and privacy policy of any third party advertisers with whom you are interacting before doing so. Their privacy policy, not ours, will apply to any of those interactions.
In addition, Shinning Star Media Inc. may, at its discretion, advertise other Shinning Star Media Inc. Apps within the Apps for Everyone. Such advertisements are not directed towards specific Users but are rather broadly posted onto random Shinning Star Media Inc. Apps.
12. Using Game Center Features and Social Features
Our Apps for Everyone may enable using certain Game Center Features and may include Social Features (defined in our TOU). The Game Center Features and the Social Features are operated or allow for social integration with certain third party social networks or third party platforms ("Social Network" or "Platform").
The Game Center Features and Social Networks and Platforms are created and maintained by third parties who are not affiliated with and/or controlled by Shinning Star Media Inc.. If you enable this integration, your use of the Game Center Features and/or the Social Features is subject to the terms of use and privacy policies of the applicable third party provider of the Social Network's or Platform (for example, Apple Game Center which terms are available at: https://www.apple.com/legal/internet-services/itunes/gamecenter/us/terms.html; Google Play Games which terms are available at: https://play.google.com/store/apps/details?id=com.google.android.play.games; and Amazon GameCircle feature which terms are available at: https://www.amazon.com/gp/help/customer/display.html?nodeId=201371550). You should ensure that you read their terms of use and privacy policies to understand how they treat your information and in order to understand the methods for changing the privacy or sharing settings on such services. If you do not agree to the practices described in such terms you should not allow our Apps for Everyone to interact with such Social Networks or Platforms, however you may find that you are not able to enjoy all the features that may be made available by our Apps for Everyone. You understand that where you allow interaction with any third party Social Network or Platform, we may share data about you with your contacts (and vice versa) in order that you may enjoy social elements. This data may include, without limitation, your scores and/or progress in our Apps for Everyone, your name, your social media profile picture and any test that you may upload.
By using the Social Features and/or the Game Center Features you hereby agree and understand that the applicable Social Networks or Platforms may collect certain Personal Information, such as your IP address, Non-personal Information and may set cookies and/or other web tracking technologies to enable the functionality of the Social Features and/or the Game Center Features. The use of such features enables the sharing of information with your friends or the public, depending on the settings you establish with the third party that provides the Social Features and/or the Game Center Features. You hereby understand and agree that when you share content via Social Networks or Platforms that your content may become public and any person may re-post-it.
13. Using a feature which enables purchase of Merchandise
Some of our Apps for Everyone may enable the User to purchase certain Merchandise which are offered for sale or otherwise distributed by third party Vendors ("Vendors") (as such terms are defined in our TOU). The Vendors are responsible for the Merchandise and the Merchandise Operations (as defined in our TOU). The Vendors may collect Personal Information from Users who wish to purchase Merchandise, which is required in order to enable the purchase transaction (for example, the User's address in order to deliver him/her the Merchandise, the User's payment information in order to enable the billing and the processing of payment via various payment methods, etc.). Shinning Star Media Inc. will not collect, use or have any access to this information. This information will be collected, maintained, shared, transferred and/or used in accordance with the applicable Vendors privacy policy and terms of use. When buying an item of Merchandise, you are subject to and you agree to the applicable Vendor's privacy policy and terms of use, and you are responsible for reading such terms in full before purchasing any such items of Merchandise in order to understand how the Vendors treat your information. If you do not agree to the practices described in such terms you should not acquire Merchandise.
14. Additional Terms and Conditions
See further terms and conditions which also apply to Users of the Apps for Everyone in Chapter C below: "Terms and Conditions Common to both Kids Privacy Policy and Apps for Everyone Privacy Policy".
Chapter C: Terms and Conditions Common to both Kids Privacy Policy and Apps for Everyone Privacy Policy
15. Third Party Providers
While using the Apps, we may be using Third Parties, who may, inter alia, collect, store and/or process the information detailed herein. Shinning Star Media Inc. uses commercially reasonable efforts to engage with Third Parties that post a privacy policy governing their collection, retention, processing and use of non-personal and personal information. For further information regarding the collection, processing and usage of your information by the Third Parties referred to in relation to the Kids Apps, please review their privacy policy practices. Note that Third Parties referred to in relation to Apps for Everyone may include third party service providers, such as providers of hosting, advertisements and analytics services, such as (a) Google Analytics, which privacy policy is located at www.google.com/policies/privacy/partners/ and https://www.google.com/intl/en/analytics/privacyoverview.html; (b) Google Analytics for Display Advertisers, which privacy practices is located at https://support.google.com/analytics/answer/2700409?hl=en&topic=2611283. Shinning Star Media Inc. may use the following Google Analytics Advertising Features: Google Display Network Impression Reporting, DoubleClick Platform integrations and/or Google Analytics Demographics and Interest Reporting; (c) Amazon cloud services which privacy policy can be found at: https://aws.amazon.com/privacy/; (d) Unity Technologies ApS' services, which privacy policy practices can be found at: https://unity3d.com/legal/privacy-policy; and (e) iAd Network which privacy practices is located at www.apple.com/privacy.
16. The User's compliance with Certain Additional Terms and Conditions
A User's access to and use of the Apps (including use of Promotional Codes defined in TOU) may be subject to certain third party terms and conditions and privacy policies, including but not limited to those of application stores, mobile software platforms, Third Parties and payment providers. Shinning Star Media Inc. does not exercise control of such third parties and is not responsible for their privacy practices and their use of the Users' personal information. You hereby consent to the collection and use of your Personal Information by such third parties in accordance with their privacy policies.
17. Links to Third Party sites
Certain links provided in the Apps through advertisements or otherwise permit Users to leave our Apps and enter non-Shinning Star Media Inc. sites or services. These linked sites and services are not under the control of Shinning Star Media Inc.. Most of such linked sites and services provide legal documents, including terms of use and privacy policy, governing the use thereof. It is always advisable to read such documents carefully before using those sites and services, inter alia, in order to know what kind of information they are collecting.
18. Cookies & Local Storage
When you access or use the Apps, we and/or our Third Parties may use industry-wide technologies such as "cookies", web beacons, tags and local storage (or other similar technologies), which store certain local information on your device ("Local Storage"), to identify a User's device and 'remember' things about the User which may enable, inter alia, automatic activation of certain features (for example, if a User muted the sound within the App at a certain stage, this preference will be stored in the cache on the device and maintained the next time the User accesses the App on his device), monitoring of use of the Apps (counting Users who visit certain pages, use certain tools, viewed certain advertisements), frequency capping (for example, if we promote another App, this ensures that we do not advertise the same App more than a set number of times), to improve the quality of our services, measure ad performance, provide relevant advertisements etc. and making the User's App experience and usage simpler, more relevant, convenient and effortless. Such information is locally stored in the User's device. Shinning Star Media Inc. and/or Third Parties may access such information. Third Parties may use both session cookies (which expire once you close the App) and persistent cookies (which stay on the User's device until he/she deletes them). Such Local Storage used by the Apps may store non-personal information (such as the different pages viewed by a User within the App) as well as User persistent identifier and geo-location data, which will be collected in accordance with the terms specified herein. It is easy to prohibit and/or delete the Local Storage, inter alia, via uninstalling the App from your mobile device and/or through the Settings option of your device. Most browsers and platforms will allow you to erase cookies from a device, block acceptance of cookies, or receive a warning before a cookie is stored. In order to erase or disable the Local Storage option you may use the settings option of your device or according to the specific instructions provided by the technology provider and/or Third Parties privacy policies and terms of use. However, if you block or erase cookies, or change the settings of your device, your App experience may be affected and may be limited.
19. "Do Not Track" and Opt-out of Certain Third Parties' Interest-Based Services
A User may prevent or limit targeted advertising from device settings which may vary from device to device. For example, if you are using an iOS device with iOS 7 or a newer version, this can be done from settings/privacy/advertising by resetting advertising identifier or limiting ad tracking setting. If you are using Google Android device with Android 2.3 or a later version, you can find the ad identifier settings in the app drawer under Google Settings> Ads. Please note that activation of any applicable "Do-not-track" settings on the User's mobile device or any other device-level setting opt outs for targeted advertising, (to the extent that such options exist) may not cease all tracking activities by Shinning Star Media Inc.'s third parties' service providers.
If you would like to cease the tracking by third parties of your on-line activities over time and across different online platforms (such as, websites, applications, etc.) and opt out of the use of your information for interest-based and targeted advertising and/or other services by certain of our third party service providers, please visit the privacy practices of such third parties and follow the instructions specified therein, including without limitation, if you wish to opt out from Flurry Analytics tracking and personalized advertisements/recommendations from Flurry, please use the following link: https://www.flurry.com/user-opt-out.html. If you wish to opt out of certain Unity Technologies ApS' personalized advertisements, please view Unity's policies at: https://unity3d.com/legal/cookie-policy#cookies. We also note that Users can opt-out of the Google Analytics Advertising Features, including through ads settings, ad settings for mobile apps, or any other available means (for example, the NAI's consumer opt-out) by going to the User's Google account settings on the User's device and changing the User's preferences in accordance with the instructions of the device's manufacturer and the operating system running on your device.
20. International Data Transfer
We may transfer (and store) information collected about you, including Personal Information, to affiliated entities, or to other third party service providers across borders and from your country or jurisdiction to other countries or jurisdictions around the world (all solely for legitimate business purposes). Please note that we may transfer such information to a country and jurisdiction that does not have the same data protection laws as your jurisdiction, and you consent to such transfer of information. We will take steps reasonably necessary to ensure the protection of your Personal Information (such as engaging with host providers which are privacy-shield certified or offer an equivalent level of protection). We will centralize all privacy-related decisions in (and the Personal Information may be accessed from) countries that are declared as offering an adequate level of protection of personal data by the European Commission.
21. Direct Marketing
You hereby agree that we may use the contact details you provided us to inform you regarding competitions we may hold, our new products and/or services, and to send to you other material. You may withdraw your consent by changing the settings within the App, and/or by clicking the 'unsubscribe' option or alternatively following the instructions for removing yourself from the mailing list which are contained in the message which was sent to you.
22. California Privacy Rights
To the extent applicable, (i) California Civil Code Section 1798.83 may permit our Users who are California residents to request certain information regarding our disclosure of Personal Information to third parties for their direct marketing purposes. To make such a request please send an email to nancybrown339@gmail.com. Shinning Star Media Inc. is only required to respond to one request per user each calendar year; and (ii) if you are a California resident under the age of 18 and a User, California Business and Professions Code Section 22581 may permit you to remove content or Personal Information you have publicly posted. If you wish to remove such content or Personal Information and you specify which content or Personal Information you wish to be removed, we will do so in accordance with applicable law.
Please be aware that after removal you will not be able to restore removed content. In addition, such removal does not ensure complete or comprehensive removal of the content or Personal Information you have posted and that there may be circumstances in which the law does not require us to enable removal of content.
23. Deletion and Modifications of Information
You or, as applicable, your legal guardian, may request, by sending us an email to nancybrown339@gmail.com, the following:
23.1 to receive access to, amend, and/or delete your Personal Information and/or, as applicable, Kids' Persistent Device Identifier, that we may hold, and/or that we cease collection or use of your Personal Information; and/or
23.2 that we request Third Parties who collected through the Apps the User's Personal Information and/or, as applicable, the Kids' Persistent Device Identifier, to do the same. Shinning Star Media Inc. will use reasonable efforts to ensure that such Third Parties do so.
We note that some of these actions may be done directly through the settings of your Account.
After you have stopped using the Apps and/or following the termination of your Account, we will retain User's Personal Information, and, as applicable, Kids' Persistent Device Identifier, collected through the use of the App for a reasonable period, as is necessary to fulfill the purposes for which it was collected, as those are specified above, including to comply with our legal or business requirements, to resolve disputes, prevent fraud, to enforce these Terms and our agreements and/or to enable Users to reinstate their Account, all as permitted under applicable privacy laws.
Personal Information that a User has shared using our Apps for Everyone via any Social Network or third party Platform cannot be amended or deleted by Shinning Star Media Inc., where such information is in the control of that Social Network or third party Platform. We note, however, that you can modify data that a Social Network or Platform shares with us, or stop our Apps interacting with that Social Network or Platform by adjusting your settings with that third party provider. However, should you choose to block such interaction, you may not be able to enjoy all the features of our Apps. Aggregate and/or anonymous information may remain on our servers indefinitely.
24. Security
Security of your information is very important to us. We take reasonable measures to maintain the security and integrity of our Apps and User information and prevent unauthorized access to it or use thereof through generally accepted industry standard technologies and internal procedures. Please note, however, that there are inherent risks in transmission of information over the Internet or other methods of electronic storage and we cannot guarantee that unauthorized access or use will never occur. Shinning Star Media INC. SHALL NOT BE RESPONSIBLE OR LIABLE FOR UNAUTHORIZED DISCLOSURE OR ACCESS, HACKING, OR OTHER SECURITY INTRUSIONS OR THE THEFT, ALTERATION, DELETION, CORRUPTION, DESTRUCTION, DAMAGE, OR LOSS OF ANY DATA OR INFORMATION INCLUDED IN THE USER GENERATED CONTENT.
The terms of this Privacy Policy will govern the use of the Apps and any information collected therein. The Company reserves the right to change this policy at any time, so please re-visit this page frequently to check for any changes. In case of any material change, we will make reasonable efforts to post a clear notice on the Apps and/or our website. Such material changes will take effect seven (7) days after such notice was provided on our App and/or Site, whichever is the earlier. Otherwise, all other Changes to this Privacy Policy are effective as of the stated "Last Revised" and your continued use of the Apps on or after the Last Revised date will constitute acceptance of, and agreement to be bound by, those changes.
26. Got any Questions?
If you have any questions (or comments) concerning this Privacy Policy, you are most welcome to send us an email to the following address: nancybrown339@gmail.com and we will make an effort to reply within a reasonable timeframe.


an open mind and a willingness to participate!
or it can be learned by exploring ourselves from within. This training takes the latter approach.
will explore different leadership theories and examine what makes a great leader.
dealing with different personalities – emotional and intellectual.
combined with experience will improve your business savvy.
deal with the generation gap at work will help you become a better manager or co-worker.
met in a satisfactorily manner.
give participants a six-step process that they can use to modify and resolve conflict of any size.
customer complaints in a customer service role.


text:
Q: JavaFX TreeTableView custom TreeCell with controls I'm new to JavaFX 8 and tried to implement a TreeTableView with some jfxtras controls in my second and third column. Therefore I set up the cell factories of those columns with custom TreeCells e.g. like this:
col3.setCellFactory(new Callback<TreeTableColumn<Object, String>, TreeTableCell<Object, String>>() {
        @Override
        public TreeTableCell<Object, String> call(TreeTableColumn<Object, String> param) {
            TreeTableCell<Object, String> cell = new TreeTableCell<Object, String>() {
                private ColorPicker colorPicker = new ColorPicker();
                @Override
                protected void updateItem(String t, boolean bln) {
                    super.updateItem(t, bln);
                    setGraphic(colorPicker);
                }
            };
            return cell;
        }
    });

Now I can see the ColorPickers and can use them, but the column somehow does not react on expanding or collapsing the nodes of column 1 (which shows String information out of POJOs). So e.g. if I collapse the whole tree, the third column still shows all ColorPickers.
So what else is necessary to get the columns 'synced'?
Thank you!!

A: When the item displayed by the cell changes, the updateItem(...) method is invoked. If the cell is empty (e.g. because the user collapsed cells above), the second parameter will be true; you need to check for this and unset the graphic.
So:
col3.setCellFactory(new Callback<TreeTableColumn<Object, String>, TreeTableCell<Object, String>>() {
        @Override
        public TreeTableCell<Object, String> call(TreeTableColumn<Object, String> param) {
            TreeTableCell<Object, String> cell = new TreeTableCell<Object, String>() {
                private ColorPicker colorPicker = new ColorPicker();
                @Override
                protected void updateItem(String item, boolean empty) {
                    super.updateItem(item, empty);
                    if (empty) {
                        setGraphic(null);
                    } else {
                        setGraphic(colorPicker);
                    }
                }
            };
            return cell;
        }
    });

redpajama_set_name: RedPajamaStackExchange


Rigorous fire safety testing for cable and duct systems.
When builders install electrical cable systems and grease and ventilation duct assemblies, they must ensure that these systems comply with code-prescribed safety standards. Fire-resistive wrapping systems provide hourly fire-resistive ratings ensuring that cables and duct systems will remain functional during fire exposure and prevent fire from spreading.
For electrical cables with fire-resistant jacketing, UL tests their functionality during exposure to fire by applying voltage and current to the cable and monitoring their electrical and visual performance. When cable jacketing is not designed to provide fire resistance, cables are placed within a fire-resistive barrier or installed within an hourly rated fire-resistive assembly.
For grease duct enclosure assemblies, UL experts evaluate their ability to contain an internal fire or repel an external fire during predetermined test exposures according to standard requirements.
UL ratings help builders specify cables and duct assemblies that comply with rigorous fire safety standards.