text:
\section{Introduction}

Disease is a leading factor that affects the growth and development of rice. Rice diseases are causing a large quantity of loss in grain yield. FAO (Food and Agriculture Organization, USA) estimates that plant diseases cost the global economy around \$220 billion annually and between 20 to 40 percent of global crop production is lost to diseases and pests \citep{agrios2005plant}. Identifying the plant diseases rapidly and precisely is critical to reduce the economic losses. However, disease diagnosis and treatment usually require special skills and equipment, which farmers often don't have, thus resulting in a delay of treatment while waiting for experts to diagnose and treat the disease or due to misdiagnosis. The rapid advancement in mobile device and digital camera technologies have made it highly convenient for people to obtain digital images of crop plants and organs with diseases.  Meanwhile, machine learning, deep learning, and computer vision technologies have made image processing and disease diagnosis become feasible as shown in many studies \citep{wang2019deep, lu2018dual, zhang2018medical}.
Pesticide treatments are used as the main methods to control corp diseases. However, the use of pesticides highly depends on diagnosis results, and unrestrained pesticides usage causes considerable environmental damage as well \citep{wu2017characterization}. Therefore, precise and timely diagnosis of the diseases is critical. Currently, the diagnosis of rice diseases is often performed manually, and the process can be extremely labor intense and time-consuming. To overcome the drawbacks, computer-aided diagnosis is applied as an important tool in detecting and classifying rice diseases.

The convolutional neural network (CNN) is a type of popular deep neural networks (DNNs) that has demonstrated high generalization performance in many image analysis studies. For instance, an attention based deep residual network is proposed to detect the type of infection in tomato leaves \citep{Tomato}. Performance of different deep learning based approaches is studied to classify skin diseases from colored digital photographs \citep{goceri2019skin, goceri4}. Neural network modeling associated with adaptive neuro-fuzzy inference system (ANFIS) is used to describe and predict performance of a horizontal ground-coupled heat pump (GCHP) system \citep{esen2008performance, esen2009artificial, esen2017modelling}. To obtain high performances from deep networks, appropriate loss functions, activation functions and batch sizes should be used \citep{Goceri, Goceri2}. Challenges and recent solutions for image segmentation in deep learning are well-explained \citep{Goceri3}. Even though DNN techniques have been developed rapidly, seldom has been implemented for rice disease detection. In other words, a proper model designated for rice disease classification is lacking. In this study, we focus on analyzing the extracted rice disease pattern from a deep learning model. An optimization-based attention mechanism is developed to help the deep neural networks pay more attention to the critical features. In addition, our model is designed for running on portable devices, such as mobile phones and single board computers, which is a significant improvement from other studies. To extract diagnostic features, a large amount of rice leaf images are typically required in training deep learning models. In this research, we propose an attention mechanism to enhance the DNN model learning process to guide the model focus more on extracting more informative features.

Visualizing and understanding how the neural networks learning are quite essential. In practice, there are many helpful approaches, for instance, intermediate activation visualization, convolution filter visualization, heat map and saliency map. The aim of the feature visualization is to reveal how the model represents the visual world from its perspective. Additionally, it helps us to understand how a pre-trained convolutional network extracts feature, and show intuitively desirable properties such as compositionality, increasing invariance and class discrimination as the layers being ascended. These visualization techniques can also be used to debug problems with the model to obtain better results \citep{zeiler2014visualizing}.

The contributions of our work presented in this article are listed as follows: (1) a novel CNN model structure applying attention-based depthwise separable convolution is proposed and tested; (2) the proposed ADSNN-BO model outperforms all the tested state-of-the-art models in classification accuracy and other performance measurements with regard to rice disease classification mission; (3) the performance of ADSNN-BO model is analyzed and compared with other deep learning models using filter visualization approach. The structure of this paper is organized as following. Section 2 provides an overview of deep learning models and implementations in rice diseases. Section 3 describes different learning architectures as well as modifications for rice diseases classification. Experimental results and related performance evaluation of the deep learning models are illustrated in Section 4. Approaches used to extract samples and other image processing steps are also discussed in Section 4. The research findings and future work are concluded in Section 5.

\section{Literature Review}

Multiple deep learning techniques have been developed and implemented with rice diseases detection over the past few years.

\begin{table}[!htp]
\centering
\caption{Publications related to rice diseases detection by machine learning and deep learning}
\label{table 1}
\resizebox{\textwidth}{!}{%
\begin{tabular}{ccccccc}
\hline
Year & Number of Diseases & Type of Disease                                                                                & Methods                 & Highest Accuracy ($\%$) & \multicolumn{1}{l}{ Filter Visualization} & Reference \\ \hline
2016  & 1                  & Bakanae disease                                                                                & SVM, GA                 & 87.90                & $\times$                                                  & \citet{chung2016detecting}  \\
2017  & 10                 & \begin{tabular}[c]{@{}c@{}}Rice diseases including rice blast, rice false smut, \\ rice brown spot, etc.\end{tabular}   & CNN
& 95.48                &                                                & \citet{lu2017identification}   \\
2018  & 9                  & \begin{tabular}[c]{@{}c@{}}Rice diseases including false smut, \\ brown plant hopper, etc.\end{tabular}                 & CNN              & 93.30                &                                              & \citet{rafeed2018identification}   \\
2019  & 1                  & Rice blast                                                                                & CNN, SVM, LBPH          & 95.83                & $\times$                                                & \citet{liang2019rice}   \\
2019  & 4                  & \begin{tabular}[c]{@{}c@{}}Rice diseases including rice blast, bacterial blight, \\ sheath rot, brown spot\end{tabular} & DNN$\_$JOA, ANN, DAE      & 97.00           & $\times$                                                  & \citet{ramesh2020recognition}   \\
2019  & 1                  & Rice blast                                                                                & RiceTalk                & 89.40                & $\times$                                                  & \citet{chen2019ricetalk}   \\
2019  & 1                  & Different levels of contamination of grain discoloration                                                                & InceptionV3             & 88.20                & $\times$                                                  & \citet{duong2019classification}   \\
2019  & 3                  & Rice blast, bacterial leaf blight, sheath blight                                                                        & AlexNet, CNN, SVM       & 91.37               & $\times$                                                  & \citet{shrivastava2019rice}  \\ \hline
\end{tabular
}
\end{table}

Liang et al. reported a novel rice diseases identification method based on deep CNN techniques \citep{lu2017identification}. They used a dataset of 500 natural images of diseased and healthy rice leaves and stems captured from rice experimental field with 10 classes of common rice diseases. Under the 10-fold cross-validation strategy, the proposed CNNs-based model achieves an accuracy of 95.48\%. Rahman et al. applied state-of-the-art large scale architectures, VGG16 and InceptionV3, and fine tuned for detecting and recognizing rice diseases and pests \citep{rafeed2018identification}. Their experimental results showed the effectiveness of these models with real datasets. Additionally, since large scale architectures were not suitable for mobile devices, a two-stage small CNN architecture was proposed and compared with the state-of-the-art memory efficient architectures MobileNet, NasNet Mobile and SqueezeNet. They achieved the desired accuracy of 93.3\% with a significantly reduced model size. Liang et al. presented a rice blast feature extraction and disease classification method based on deep CNN \citep{liang2019rice}. They also established a rice blast disease dataset with the assistance of plant protection experts, which can be combined with other rice disease images to build a content-rich dataset. They conducted comparative experiment and applied t-SNE approach and found that the high-level features extracted by CNN were more discriminative and representative than local binary patterns histograms (LBPH) and Haar-WT (wavelet transform). Their quantitative analysis results indicated that CNN and CNN with support vector machine (SVM) had almost the same performance, which was better than that of LBP + SVM and Haar-WT + SVM.

Ramesh and Vydeki utilized the image processing techniques to minimize the reliance on the farmers to protect the agricultural products \citep{ramesh2020recognition}. They proposed a recognition and classification of paddy leaf diseases using optimized DNN with Jaya Algorithm. Their images of rice plant leaves were directly captured from the farm field for healthy, bacterial blight, brown spot, sheath rot and blast diseases. In preprocessing, for the background removal the RGB images were converted into HSV images and based on the hue and saturation parts binary images were extracted to split the diseased and non-diseased part. Classification of diseases is carried out by using Optimized Deep Neural Network with Jaya Optimization Algorithm (DNN\_JOA). Their experimental results were evaluated and compared with ANN, DAE and DNN. They achieved high accuracy of 98.9\% for the blast affected, 95.78\% for the bacterial blight, 92\% for the sheath rot, 94\% for the brown spot and 90.57\% for the healthy leaf image. Chen et al.'s work studied rice blast detection and classification by using the Internet of Things (IoT) and artificial intelligence (AI) \citep{chen2019ricetalk}. Their research was developed to overcome the gap that existing AI and IoT studies detecting plant diseases by images or nonimage hyperspectral data requires manual operations to obtain the photographs or data for analysis. Besides, image detection typically is too late as rice blast may already spread to other plants. They explored the RiceTalk project that utilized nonimage IoT devices to detect rice blast based on an IoT platform for soil cultivation. Unlike the image-based plant disease detection approaches, their agriculture sensors generated nonimage data that can be automatically trained and analyzed by the AI mechanism in real time. Their approach reduced the platform management cost significantly to provide real-time training and predictions. In their implementation, the accuracy of the RiceTalk prediction on rice blast is 89.4\%.

Since machine learning techniques are commonly applied before deep learning becomes popular. Rehman et al. reviewed statistical machine learning technologies with machine vision systems in agriculture \citep{rehman2019current}. Two types of statistical machine learning techniques, supervised and unsupervised learning, have been concentrated on. Their paper comprehensively surveyed current application of statistical machine learning techniques in machine vision systems, such as Naive Bayes (NB), discriminant analysis (DA), k-nearest neighbour (kNN), support vector machine (SVM), etc. They analyzed each technique potential for specific application and represents an overview of instructive examples in different agricultural areas. Suggestions of specific statistical machine learning technique for specific purpose and limitations of each technique were also given. Duong-Trung et al. implemented the idea of transfer learning to classify grain discoloration disease of rice \citep{duong2019classification}. The discoloration was considered as a potential risk to the rice-producing countries. Transfer learning is a method of learning in a new prediction task with the transferred knowledge from a related task that has already been learned. By utilizing InceptionV3 model of CNNs and transferred weights from ImageNet to train their collected data, the classification accuracy of 88.2\% was achieved. Shrivastava et al. employed transfer learning of deep CNN to classify rice plant diseases \citep{shrivastava2019rice}. Their proposed model was able to classify rice diseases with classification accuracy of 91.37\% for 80\%- 20\% training-testing partition. They adjusted the benchmarking of the model making it more appropriate.

Chung et al. proposed an approach to discriminate infected and healthy seedlings nondestructively at the age of 3 weeks using machine vision \citep{chung2016detecting}. Their work concentrated on detecting bakanae disease, which is a seed-borne disease of rice. Infected plants can yield empty panicles or perish. The images of infected and control seedlings were acquired using flatbed scanners to quantify their morphological and color traits. Support vector machine (SVM) classifiers were employed for distinction. A genetic algorithm was also mentioned for selecting essential traits and optimal model parameters for the classifiers. Their proposed approach distinguished infected and healthy seedlings with an accuracy of 87.9\% and a positive predictive value of 91.8\%.

Feature analysis is important in understanding the performance of the deep learning model being used. \citep{lu2017identification} implemented feature maps to visualize the corresponding feature visualization of rice disease image patch. \citep{rafeed2018identification} showed the first convolution activation outputs of the CNN model. The activations retained almost all of the information in the input image, and the intermediate outputs for different classes are also visually different. Since feature analysis is seldom discussed in previous researches, this paper will concentrate more on feature visualization and understanding. More sophisticated deep learning models should rather be understood as an attempt to both search for the minimum necessary ingredients for recognition with CNNs \citep{springenberg2014striving}. The connection between the gradient-based CNN visualisation methods and deconvolutional networks can be established via saliency maps \citep{simonyan2013deep}.

Advanced optimization method is important for improving deep learning model performance. Similarly, the loss function used in a deep network affects performance of the model. Therefore, cross-entropy, Tversky similarity function or combination of them has been applied, such as in \citep{goceri2020deep}, in the literature to solve classification problems. Bayesian Optimization provides an efficient technique based on Bayes Theorem to guide an optimization search problem. The algorithm functions by building a probabilistic model of the objective function which is searched efficiently with an acquisition function. The candidate samples are determined by being evaluated on the objective function \citep{snoek2012practical}.

In summary, a list of related literature has been presented in Table \ref{table 1}. Although deep learning techniques have been used in rice disease diagnosis, studies on models that can easily fit to the design requirements for mobile are still very limited. Also, advanced methods need to be applied to tune hyper-parameters in deep learning models. Insightful model performance evaluation is still missing. Research in analyzing feature characteristics within different models is also not available at the current stage. To search for an optimized mobile device friendly model for rice disease detection, we propose a new model (ADSNN-BO) and compare it with several DNNs, and provide intermediate activations visualization as well as feature patterns visualization of various convolutional layers. Potential methods to evaluate useful convolution filters are also discussed in this paper.

\section{Attention-based Depthwise Separable Neural Network with Bayesian Optimization}

\subsection{Attention-based Depthwise Separable Deep Neural Network}
MobileNet deep learning model embedded with attention augmented layer and Bayesian optimization method is mainly focused on. We here illustrated the new ADSNN-BO model we proposed. Besides, the comparison of other deep learning models, such as VGG16, DenseNet, ResNet, InceptionV3, and Xception model are discussed to classify rice disease images. We have considered inserting attention convolutional layer into original deep learning models. Bayesian optimization is applied when exploring the influence of parameters in a deep learning model. Although different optimization approaches, such as Sobolev gradient, have been used in some recent works \citep{goceri2019diagnosis} to increase generalization performance of deep networks, they cause high computational costs. Therefore, Bayesian optimization has been implemented in this work.

CNN is a multi-layer neural network with a learning architecture which is usually consisted of two parts, including a feature extractor and a trainable classifier. In practice, a CNN learns the values of filters by itself during the training process. More precisely, a convolution layer attempts to learn filters in a three-dimensional space, with two spatial dimensions and a channel dimension. Therefore, a single convolution kernel is conducted with simultaneously mapping cross-channel and spatial correlations.

MobileNet was proposed as an efficient CNN architecture to achieve very small, low latency models that can easily fit to the design requirements for mobile and embedded vision applications \citep{howard2017mobilenets, gocceri2020impact}. The MobileNet model is based on a streamlined architecture using depthwise separable convolutions to build DNNs with light weights. Compared with a standard convolution, a depthwise separable convolution consists of a depthwise convolution and a 1 $\times$ 1 pointwise convolution. The conventional convolution combines inputs into a new set of outputs within one step. The depthwise separable convolution splits this into two layers, a separate layer for filtering and another for combining. To illustrate, a conventional convolutional layer takes $D_{F} \times D_{F} \times M$ as an input and produces a $D_{G} \times D_{G} \times N$ output feature map, where $D_{F}$ is the spatial width and height of an input feature map, M is the number of input channels, $D_{G}$ is the spatial width and height of an output feature map, and N is the number of output channels. The conventional convolutional layer is represented by convolution kernel $K$ of size $D_{K} \times D_{K} \times M \times N$ where $D_{K}$ is the spatial dimension of the kernel.

From the computational cost perspective, standard convolutions have the computational cost of,

\begin{equation} \label{eq1}
\begin{aligned}
D_{K} \cdot D_{K} \cdot M \cdot N \cdot D_{F} \cdot D_{F}
\end{aligned}
\end{equation}

MobileNet architecture addresses each of these terms and their interactions. Depthwise separable convolutions is used to break the interaction between the number of output channels and the size of the kernel. In this way, the model will learn information within one particular channel and interactions between the channels separately. Depthwise separable convolution involves two kinds of layers: 1) depthwise convolutions and 2) pointwise convolutions. The Depthwise convolution refers to a single filter applied for each input channel. Pointwise convolution refers to a simple 1 $\times$ 1 convolution which is used to establish a linear combination of the output of the depthwise layer. Depthwise convolution has a computational cost of,

\begin{equation} \label{eq2}
\begin{aligned}
D_{K} \cdot D_{K} \cdot M \cdot D_{F} \cdot D_{F}
\end{aligned}
\end{equation}

Depthwise convolution performances well inside each particular channel, and pointwise convolution is then employed to address the lack of the relations between channels. Thus, an additional layer that creates a linear combination of the output of depthwise convolution via 1 $\times$ 1 convolution is added to generate new features. Overall, depthwise separable convolution has a computational cost of,

\begin{equation} \label{eq3}
\begin{aligned}
D_{K} \cdot D_{K} \cdot M \cdot D_{F} \cdot D_{F} + M \cdot N \cdot D_{F} \cdot D_{F}
\end{aligned}
\end{equation}

which refers to the summation of the depthwise and pointwise convolutions.

By presenting convolution as a two-step process with filtering and combining. A reduction in computational cost can be achieved as,

\begin{equation} \label{eq4}
\begin{aligned}
\frac{D_{K} \cdot D_{K} \cdot M \cdot D_{F} \cdot D_{F} + M \cdot N \cdot D_{F} \cdot D_{F}}{D_{K} \cdot D_{K} \cdot M \cdot N \cdot D_{F} \cdot D_{F}}
\end{aligned}
\end{equation}

which equals to $\frac{1}{N} + \frac{1}{D_{K}^{2}}$. Therefore, by utilizing depthwise separable convolution module, MobileNet can effectively reduce the model size and computation complexity.

Similar to MobileNet, the methodology behind the Inception module is to make the training process easier and more efficient by clearly sectioning it into a series of operations that independently focuses on cross-channel and spatial correlations. For the InceptionV3 model, Wojna et al. explored some ways of factorizing convolutions in various settings, especially to increase the computational efficiency of the solution \citep{szegedy2016rethinking}. Since Inception networks are fully-convolutional, each weight corresponds to one particular multiplication per activation. Thus, any reduction in computational cost will lead to reduced number of parameters.

Chollet proposed a CNN architecture fully based on depthwise separable convolution layers called Xception. As an improvement regarding to the Inception model, the hypothesis was made that the mapping of cross-channels and spatial correlations in the feature maps can be entirely disengaged \citep{chollet2017xception}. The original Xception model possesses 36 convolutional layers forming the feature extraction base. Compared with the Inception module, it is noted that there are two minor differences. One is the order of the operations. In Xception module, depthwise separable convolutions as usually are implemented to perform first channel-wise spatial convolution and then perform 1x1 convolution, however the Inception model performs the 1x1 convolution first. The other is that the presence or absence of a non-linearity after the first operation. In Inception module, both operations are followed by a ReLU non-linearity, whereas for Xception, depthwise separable convolutions are usually implemented without non-linearities.

To overcome the limitations of trading off accuracy to reduce size and latency, we propose a new model based on depthwise separable neural network embedded with augmented attention mechanism. We take the architecture of MobileNetV1 as the bare-bones of the model, and apply attention layers between the last convolution block and the average pooling layer. We keep the original MobileNet structure as well as the hyperparameters settings. The detailed architecture is shown in Figure \ref{Figure 1}. Each convolution block is specified in the figure. In addition, the outputs of corresponding convolution operations are indicated.

\begin{figure}[!h]
\captionsetup[subfigure]{justification=centering}
  \centering
  \includegraphics[width=0.8\textwidth]{images/"Model2".pdf}
  \caption{CNN architecture of the proposed model}
  \label{Figure 1}
\end{figure}

Neural network models are generally referred to as being opaque. This means that they usually fail to explain the reason why a specific decision or prediction was made. CNNs are designed to work with image data, and their structure and function suggest that should be less inscrutable than other types of neural networks. Specifically, the models are comprised of small linear filters and the result of applying filters called activation maps or feature maps. Both filters and feature maps can be visualized. An efficient way to inspect the filters learned by convolutional networks is to display the visual pattern that each filter is meant to respond to \citep{francois2017deep}. This can be conducted with gradient ascent in input space: applying gradient descent to the value of the input image of a convolutional network so that the response of a specific filter is maximized. The procedure usually starts from a blank input image. The resulting image will be one that the chosen filter is maximally responsive to. These filter visualizations reveal a lot about how convolutional layers learn. Each layer in a convolutional network learns a collection of filters so that their inputs can be expressed as a combination of the filters. Further visualization techiques such as heatmap can be followed up to explore and evaluate the model.

\subsection{Augmented Attention Mechanism}

Attention mechanism in deep learning has gathered widespread attention as a computational module for modeling sequences because of its ability to apprehend long distance interactions within a model. Our attention augmented networks do not rely on pre-training of the fully convolutional counterparts and employ self-attention along the entire architecture. Additionally, the architecture is enhanced with the representational power of self-attention over images by extending relative self-attention to two dimensional inputs\citep{bello2019attention}.

For a self-attention procedure, given an input tensor of shape $(H, W, F_{in})$, we flatten it to a matrix $X \in R ^ {HW \times F_{in}}$ and perform multi-head attention. The output of the self-attention mechanism for a single head $h$ can be formulated as:

\begin{equation} \label{eq5}
\begin{aligned}
O_{h}=Softmax(\frac{(XW_{q})(XW_{k})^{T}}{\sqrt{d_{k}^{h}}})(XW_{v})\end{aligned}
\end{equation}

where $W_{q}, W_{k} \in R^{F_{in}\times d_{k}^{h}}$ and $W_{v} \in R^{F_{in}\times d_{v}^{h}}$
are learned linear transformations that map the input $X$ to queries $Q = XW_{q}$, keys $K = XW_{k}$ and values $V = XW_{v}$. $N_{h}$ and $d_{k}$ respectively refer to the number of heads, the depth of values and the depth of queries and keys in multihead-attention. The outputs of all heads are then concatenated and projected again as follows:

\begin{equation} \label{eq6}
\begin{aligned}
M(X)=Concat[O_{1}, ..., O_{Nh}]W^{O}\end{aligned}
\end{equation}

where $W^{O} \in d_{v}\times d_{v}$ is a learned linear transformation. $M(X)$ is then reshaped into a tensor of shape $(H, W, d_{v})$ to match the original spatial dimensions. It is noted that multi-head attention incurs a complexity of $O((HW)^2d_{k})$ and a memory cost of $O((HW)^2N_{h})$ as it requires to store attention maps for each head.

With an original convolution operator with kernel size $k$, $F_{in}$ input filters and $F_{out}$ output filters. The corresponding attention augmented convolution can be expressed as:

\begin{equation} \label{eq7}
\begin{aligned}
AConv(X)=Concat[Conv(X), M(X)]\end{aligned}
\end{equation}

We employed the augmented convolution layers directly after conventional convolutions which are followed by average pooling computation. Similarly to the traditional convolution, the utilized attention augmented convolution is equivalent to translation and can smoothly work on inputs of different spatial dimensions. Effects on attention parameters such as the number of filters and the number of attention layers implemented are explored using Bayesian optimization approach.

\subsection{Bayesian Optimization for Hyperparameter Tuning}

Bayesian optimization works by constructing a posterior distribution of functions which is gaussian process that best describes the function to optimize. As the number of observations grows, the posterior distribution improves, and the algorithm becomes more certain of which regions in parameter space are worth exploring and computing.

\begin{algorithm}[!h]
\SetAlgoLined
 Place a Gaussian process prior on $f$.\\
 Observe $f$ at $n_{0}$ points according to an initial space-filling experimental design.\\
 Set $n = n_{0}$.\\
 \While{$n \leq N$}{
  Update the posterior probability distribution on $f$ using all available data\;
  Let $x_{n}$ be a maximizer of the acquisition function over $x$, where the acquisition function is computed using the current posterior distribution\;
  Observe $y_{n} = f(x_{n})$\;
  Increment $n$\;
  }
  Return a solution: either the point evaluated with the largest $f(x)$, or the point with the largest posterior mean.\
 \caption{Bayesian Optimization Algorithm}
 \label{tab:algorithm}
\end{algorithm}

\begin{figure}[!h]
\captionsetup[subfigure]{justification=centering}
  \centering
  \includegraphics[width=0.95\textwidth]{images/"pre".pdf}
  \caption{Samples of rice leaf throughout image preprocessing: (a) original samples; (b) images after applying morphology; (c) cropped and position re-adjusted samples}
  \label{Figure 2}
\end{figure}

Bayesian optimization method consists of two major components: one is a Bayesian statistical model to model the objective function, and the other is an acquisition function to decide where to sample next \citep{frazier2018tutorial}. In other words, presenting sampling points in the search space is achieved by acquisition function. The function will trade off exploitation and exploration. Exploitation refers to sample where the Bayesian statistical model predicts a high objective score, and exploration means sampling at locations where the prediction uncertainty is high. Both contribute to high acquisition function values and the aim is to maximize the acquisition function to determine the next sampling point. After evaluating the objective based on an initial space-filling experimental design, often containing points chosen uniformly at random, they are used iteratively to allocate the remainder of a budget of $N$ function evaluations, which is shown in Algorithm \ref{tab:algorithm}. The time complexity for deep learning is $O(w \cdot m \cdot e)$, where $w$ refers to the number of weights, $m$ refers to the number of learning samples, and $e$ refers to the running epochs. For Bayesian optimization, the time complexity is $O(n^{3})$, where n is the number of observations.

In this research, we conduct hyperparameters tuning with regard to the embedded augmented attention mechanism. Particularly, the number of filters that are employed in each attention layer is optimized. For hyperparameter tuning with Bayesian optimization, we construct an objective function that takes the number of filters in each attention layer as input and returns a test accuracy score. Throughout the optimization procedure, the best combination of the filter number for each attention layer can be determined.

\begin{table}[!h]
\centering
\caption{Detailed category information of the rice disease dataset}
\label{tab:dataset}
\begin{tabular}{cc}
\hline
Category          & Number of samples \\ \hline
Brown spot        & 523               \\
Rice hispa damage & 565               \\
Leaf blast        & 779               \\
Healthy           & 503               \\
Total             & 2370              \\ \hline
\end{tabular}
\end{table}

\section{Experiments}

\subsection{Data Preparation}

In this paper, we focus on three specific types of rice diseases, which are brown spot, rice hispa damage, and rice leaf blast. The typical manual diagnosis process is based on visualization symptom of each disease. Brown spots are round to oval, dark-brown lesions with yellow halo. As lesions enlarge, they remain round, with center area necrotic, gray and the lesion margin reddish-brown to dark brown. Rice hispa damage scrapes the upper surface of leaf blades leaving only the lower epidermis. The disease also tunnels through the leaf tissues. When damage is severe, plants become less vigorous. The rice hispa damage can be directly detected by visualizing the bug on the leaf. For rice leaf blast, lesions varying from small round, dark spots to oval spots with narrow reddish-brown margins and gray or white center. Spots become elongated, diamond-shaped or linear with wit pointed ends and gray dead areas in the center surrounded by narrow reddish-brown. We conducted our study on a dataset with 2370 rice leaf samples in total, including all the three classes as well as the healthy rice samples \citep{HuyDo2019}. The detailed class information is provided in Table \ref{tab:dataset}. 100 samples are selected in each category for training and testing. Additional image preprocessing methods including morphology, edge detection and cropping are involved in sample preprocessing.

\begin{table}[!h]
\centering
\caption{The performance comparison of different deep learning models}
\label{tab:performance}
\resizebox{\textwidth}{!}{%
\begin{tabular}{cccccc}
\hline
Model                   & Precision (SD) (\%)  & Recall (SD) (\%)     & F-1 Score (SD) (\%)  & Test Accuracy (SD) (\%) & Training Time (SD) (min) \\ \hline
VGG16                   & 22.3 (17.08)        & 62.5 (26.30)          & 35.5 (2.38)          & 67.12 (5.84)            & \textbf{2.161 (0.03)}   \\
ResNet50                & 62.8 (25.24)         & 76.8 (14.04)         & 64.0 (12.98)         & 79.32 (6.12)            & 5.164 (1.23)            \\
DenseNet121             & 84.0 (12.67)         & 76.0 (16.31)         & 78.2 (7.98)          & 88.27 (4.77)            & 9.354 (0.48)            \\
MobileNet               & 85.6 (12.95)         & \textbf{90.2 (9.28)} & 86.8 (6.22)          & 91.36 (2.82)            & 3.325 (0.41)             \\
Inception V3            & 70.8 (18.58)         & 64.8 (18.19)         & 65.2 (13.77)         & 83.20 (5.08)            & 5.616 (0.38)             \\
Xception                & 88.2 (9.68)          & 80.4 (11.06)         & 84.2 (10.76)         & 90.38 (2.74)            & 7.968 (0.80)             \\
\textbf{ADSNN-BO} & \textbf{92.6 (8.17)} & 87.4 (8.26)          & \textbf{89.6 (4.62)} & \textbf{94.65 (2.07)}   & 3.368 (0.50)            \\ \hline
\end{tabular}%
}
\end{table}

Image preprocessing procedure is performed to address a few limitations of the dataset. Specifically, the white background occupies much image space in each image, and rice leaf samples are collected with different angles. Therefore, we first applied Otsu's thresholding approach to detect the leaf and cropped out the white background. Subsequently, we adjusted all the samples horizontally aligned. This procedure is described in Figure \ref{Figure 2}. Step 1 includes Otsu's threshold and opening morphology that would detect the shape of the rice leaf. Step 2 refers to removing the unrelated background with the detected object and readjust the image position horizontally. In this way, useless information in the original samples is maximally excluded, which is beneficial for the learning performance. All the rice leaf images are then resized to 299$\times$299 pixels to fix the deep learning model input size. The influence of different input size are also discussed in classification performance section.

\begin{table}[]
\centering
\caption{Classification performance for each class with different models in training procedure}
\label{tab:each_class}
\resizebox{0.65\textwidth}{!}{%
\begin{tabular}{ccccc}
\hline
\multirow{2}{*}{Model} & \multicolumn{4}{c}{Classification category}                                                        \\
                       & Brown Spot              & Healthy                & Rice hispa damage             & Leaf blast             \\ \hline
VGG16                  & 0.712 (0.414)           & 0.629 (0.488)          & 0.600 (0.548)            & 0.563 (0.519)          \\
ResNet50               & 0.764 (0.173)           & 0.632 (0.314)          & 0.533 (0.232)          & 0.359 (0.278)          \\
DenseNet121            & 0.684 (0.282)           & 0.770 (0.275)           & 0.743 (0.167)          & 0.784 (0.190)           \\
MobileNet              & 0.825(0.197)            & 0.937 (0.048)          & \textbf{0.931 (0.062)} & 0.704 (0.243)          \\
InceptionV3            & 0.647 (0.185)           & 0.887 (0.097)          & 0.563 (0.272)          & 0.540 (0.306)           \\
Xception               & 0.856 (0.110)            & 0.954 (0.026)          & 0.824 (0.110)           & 0.702 (0.294)          \\
\textbf{ADSNN-BO}              & \textbf{0.980 (0.029)} & \textbf{0.967 (0.075)} & 0.911 (0.097)          & \textbf{0.937 (0.062)} \\ \hline
\end{tabular}%
}
\end{table}

\begin{figure}[!h]
\captionsetup[subfigure]{justification=centering}
  \centering
  \begin{minipage}[b]{0.47\textwidth}
    \includegraphics[width=\textwidth]{images/"acc".pdf}
    \subcaption{Test accuracy}
  \end{minipage}
  \hfill
  \begin{minipage}[b]{0.47\textwidth}
    \includegraphics[width=\textwidth]{images/"f1".pdf}
    \subcaption{F-1 score}
  \end{minipage}
  \begin{minipage}[b]{0.47\textwidth}
    \includegraphics[width=\textwidth]{images/"precision".pdf}
    \subcaption{Precision}
  \end{minipage}
  \begin{minipage}[b]{0.47\textwidth}
    \includegraphics[width=\textwidth]{images/"recall".pdf}
    \subcaption{Recall}
  \end{minipage}
  \begin{minipage}[b]{0.47\textwidth}
    \includegraphics[width=\textwidth]{images/"time".pdf}
    \subcaption{Training time}
  \end{minipage}
  \caption{Performance comparison of different deep leaning models}
  \label{Figure 3}
\end{figure}

\begin{figure}[!h]
\captionsetup[subfigure]{justification=centering}
  \centering
  \begin{minipage}[b]{0.47\textwidth}
    \includegraphics[width=\textwidth]{images/"acc_ec1".pdf}
    \subcaption{Accuracy for different models}
  \end{minipage}
  \begin{minipage}[b]{0.47\textwidth}
    \includegraphics[width=\textwidth]{images/"acc_ec2".pdf}
    \subcaption{Accuracy for different categories}
  \end{minipage}
      \begin{minipage}[b]{0.47\textwidth}
    \includegraphics[width=\textwidth]{images/"f1_ec1".pdf}
    \subcaption{F-1 score for different models}
  \end{minipage}
  \begin{minipage}[b]{0.47\textwidth}
    \includegraphics[width=\textwidth]{images/"f1_ec2".pdf}
    \subcaption{F-1 score for different categories}
  \end{minipage}
  \caption{Performance comparison of the top three accuracy models for different classes classification}
  \label{Figure 4}
\end{figure}

\subsection{Performance Measurement}

The proposed ADSNN-BO model is applied to rice disease classification problem. In our experiment, we use dataset consisting of 400 images that are labeled 4 classes of disease categories. The samples are resized to 224 $\times$ 224 as the inputs of the model. Different original deep learning models are tested, including VGG16, ResNet50, DenseNet121, MobileNetV1, Inception V3, and Xception model. We follow the original architecture and test each CNN model with five fold cross-validation. Pre-trained ImageNet weights are utilized to achieve better performance. For each training set, we further split it into training and validation set with the ratio of 0.7 and 0.3. The number of running epoch is set to be 100. All experiments are performed using TensorFlow under the computational specification of 64-bit Windows 10, with Intel i9-9900
processor (3.10GHz), 32GB random-access memory (RAM), and NVIDIA GeForce RTX 2080.

\begin{equation} \label{eq8}
\begin{aligned}
p_{i} =\frac{n_{ii}}{\sum _{j=1}^{M}n_{ji}}\end{aligned}
\end{equation}

\begin{equation} \label{eq9}
\begin{aligned}
r_{i} =\frac{n_{ii}}{\sum _{j=1}^{M}n_{ij}}\end{aligned}
\end{equation}

\begin{equation} \label{eq10}
\begin{aligned}
f_{i} =\frac{2r_{i}p_{i}}{r_{i}+p_{i}}\end{aligned}
\end{equation}

To measure the model classification performance, a multi-class confusion matrix is introduced \citep{wang2019deep}. Given a problem with M classes, a $M \times M$ matrix is constructed, where $n_{ij}$ denotes the number of samples with the actual label $i$ that are classified as the predicted label $j$. Precision, recall, and F1-score of each class can be calculated based on the returned confusion matrix. Those measurement equations are indicated in Eq.[\ref{eq8}]-[\ref{eq10}].
Although Matthews correlation coefficient is identified to perform better for binary classification, it is not suitable for our multi-class classification tasks\citep{chicco2020advantages}. The experimental results evaluated based on the performance measurement are shown in Table \ref{tab:performance}. The table reveals the mean value and standard deviation of each measurement based on five fold cross-validation results. The bold characters indicate the best performance among the tested deep learning models.

According to the comparisons of the model performance, our proposed ADSNN-BO model achieves the highest test accuracy along with the highest precision and F-1 score. The original MobileNet model attains the highest recall value. The training time of the proposed model also prevails most of the models, which is close to the original MobileNet. ADSNN-BO increased the precision of 8.18\%, F-1 score of 3.23\%, and the accuracy of 3.6\% compared with the original MobileNet model. Furthermore, we conduct performance comparisons of the top three accuracy models, which are proposed ADSNN-BO, MobileNet, and Xception. Boxplots of accuracy and F-1 score are shown in Figure \ref{Figure 4}. In general, for each type of rice disease, ADSNN-BO outperforms other deep learning models. According to the comparisons of different category performance within one model, it can be considered that leaf blast is the most difficult disease to detect and classify. Further explanation will be discussed through filter visualization.

\begin{figure}[!h]
\captionsetup[subfigure]{justification=centering}
  \centering
  \includegraphics[width=0.8\textwidth]{images/"activations".pdf}
  \caption{Activation maps of different layers for each type of disease}
  \label{Figure 5}
\end{figure}

\begin{algorithm}[!h]
\SetAlgoLined
 Start from a gray image with some noise.\\
 Build a loss function that maximizes the activation of the $n$th filter of the layer under consideration.\\
 Compute the gradient of the input picture with regard to this loss.\\
 Normalize the gradient.\\
 Return the loss and gradients given the input picture.\\
  Run gradient ascent for $i$ steps.\\
  Utility function to convert a tensor into a valid image to get the output.\\
 \caption{Convnet Filter Visualization Algorithm}
 \label{tab:algorithm2}
\end{algorithm}

\begin{figure}[!h]
\captionsetup[subfigure]{justification=centering}
  \centering
  \begin{minipage}[b]{\textwidth}
    \includegraphics[width=\textwidth]{images/"act1".png}
    \subcaption{Brown spot}
  \end{minipage}
  \begin{minipage}[b]{\textwidth}
    \includegraphics[width=\textwidth]{images/"act2".png}
    \subcaption{Rice hispa damage}
  \end{minipage}
  \begin{minipage}[b]{\textwidth}
    \includegraphics[width=\textwidth]{images/"act3".png}
    \subcaption{Leaf blast}
  \end{minipage}
  \caption{Activation maps of one single layer (70th) in the model for each type of disease}
  \label{Figure 6}
\end{figure}

\begin{figure}[!h]
\captionsetup[subfigure]{justification=centering}
  \begin{minipage}[b]{\textwidth}
  \centering
    \includegraphics[width=0.85\textwidth]{images/"filter_a".pdf}
    \subcaption{Original MobileNet model}
  \end{minipage}
  \\
  \begin{minipage}[b]{\textwidth}
    \centering
    \includegraphics[width=0.85\textwidth]{images/"filter_b".pdf}
    \subcaption{ADSNN-BO model}
  \end{minipage}
  \caption{Filters visualization comparison of different convolutional layer (2nd, 8th, 12th, 13th depthwise layer) for the three rice diseases}
  \label{Figure 7}
\end{figure}

\subsection{Feature Evaluation}

Features that deep learning models extract are evaluated based on two major approaches: (1) activation map, and (2) filters visualization. Activation map, considered as the most straight-forward visualization technique, will provide a visual representation of the activation numbers within different layers of the neural network. In order to generate a series of activation maps throughout running the model, we first load and fix the weights of entire network with the best performed model. Then an original rice image sample is input into the model for testing. After each layer's operation, the output will tell what type of input maximally activates the layer. A series of selected activation maps are generated for our proposed ADSNN-BO model in Figure \ref{Figure 5}. The figures reveal how the model deals with different rice diseases. For example, it is shown that the network captures and extracts the spot pattern successfully when detecting brown spot disease. In addition, activation maps of all the filters within one layer are presented in Figure \ref{Figure 6}. According to the results, it is apparent that most of the filters are getting activated with regard to the pattern for each disease, and various types of patterns are activated for different disease categories. The blank convolution outputs mean that the pattern encoded by the filters were not found in the input image. These patterns must be complex shapes most likely that are not present in the input image.

\begin{figure}[!h]
\captionsetup[subfigure]{justification=centering}
  \begin{minipage}[b]{\textwidth}
  \centering
    \includegraphics[width=0.2\textwidth]{images/"cla_1_o".png}
    \includegraphics[width=0.2\textwidth]{images/"cla_2_o".png}
    \includegraphics[width=0.2\textwidth]{images/"cla_3_o".png}
    \subcaption{Original MobileNet model}
  \end{minipage}
  \\
  \begin{minipage}[b]{\textwidth}
    \centering
    \includegraphics[width=0.2\textwidth]{images/"cla_1".png}
    \includegraphics[width=0.2\textwidth]{images/"cla_2".png}
    \includegraphics[width=0.2\textwidth]{images/"cla_3".png}
    \subcaption{ADSNN-BO model}
  \end{minipage}
  \caption{Visualizations of the last convolutional layer for each type of disease, brown spot, rice hispa damage, leaf blast, for original MobileNet and ADSNN-BO model, respectively.}
  \label{Figure 8}
\end{figure}

Different from activation maps, filter visualization of the model is able to show the filters which are the weights that a model learns. Here, we compare the original MobilNet with our proposed ADSNN-BO model using filters visualization. This visualization procedure starts from a random image, repeatedly apply the gradient ascent, and convert the resulting input image back to a displayable form. The detailed algorithm is shown in Algorithm \ref{tab:algorithm2}. The comparison result is shown in Figure \ref{Figure 7} - \ref{Figure 8}. Figure \ref{Figure 7} indicates the filters of different convolutional layers addressing three rice diseases in two models. In this figure, we can conclude that top layers are usually informative to show the learning ability of a model. Because the lower portion of the model constructure often captures general pattern and information of an image which is meaningless. The filters belonged to top layers are commonly comprehensible and able to evaluate a model's performance. Additionally, we can state that for one typical disease such as brown spot, both models are responsive to the spot pattern. While the filters in ADSNN-BO model are more recognizable and possess less noise compared with the original MobileNet. Visualizations of the last layer for different disease are shown in Figure \ref{Figure 8}. ADSNN-BO model outperforms original MobileNet when detecting brown spot pattern as the classification accuracy results indicate in Table \ref{tab:algorithm}. The results of other disease categories are similar according to the visualization.

\section{Conclusions}

In this paper, an overview of deep learning models and implementations in rice diseases is provided. Six existing deep learning models have been implemented and compared with each other for rice disease classification problem. Pre-trained ImageNet weights are used to provide better results for all CNN architectures. A new model named ADSNN-OB has been proposed,tested, and discussed. The details of data preparation steps are introduced. Various experiments are conducted which show that our proposed ADSNN-OB model outperforms other deep learning models in various scenarios. Feature evaluation including activation map and filters visualization is also implemented to illustrate the performance of the model. The results show that a new model which outperforms all tested state-of-the-art models and fits well with the mobile device environment is created.

In the next step, we plan to explore more on our proposed ADSNN-OB model with different optimization methods as well as hyperparameter tuning to improve the performance and make the procedure more effectively. Also, since the dataset we used is different from those in the earlier literature, we tend to apply the model on different public datasets. Integrating location, weather and soil information along with the given rice disease image can be interesting to investigate for a more comprehensive and automated rice disease detection and classification mechanism.

\bibliographystyle{apalike}\biboptions{authoryear}

redpajama_set_name: RedPajamaArXiv


text:
Escobaria zilziana är en kaktusväxtart som först beskrevs av Boed., och fick sitt nu gällande namn av Curt Backeberg. Escobaria zilziana ingår i släktet Escobaria och familjen kaktusväxter. Inga underarter finns listade i Catalogue of Life.

Källor

Kaktusväxter
zilziana

redpajama_set_name: RedPajamaWikipedia


$14,900 | 2 Beds | 1 Baths (1 Full) | 728 Sq. Ft.
Opportunity city!! This cute 2 bedroom/1 bathroom home in the heart of Lansford is ready for a new owner! Property has updated electricity, a steam boiler, and hardwood floors! Property needs paint, minor drywall work, and an updated bathroom and you are ready to go! Owner is a licensed PA Realtor! Owner will consider owner financing and is ready to sell! Bring us an offer! WBA 19-1810 Directions to property: From 209 Take a right on Pardee and a Left onto Ridge. Home is on the left.
642 E Ridge 1IUF66JQHXYN, Lansford, PA 18232, U.S.A.
From 209 Take a right on Pardee and a Left onto Ridge. Home is on the left.
Opportunity city!! This cute 2 bedroom/1 bathroom home in the heart of Lansford is ready for a new owner! Property has updated electricity, a steam boiler, and hardwood floors! Property needs paint, minor drywall work, and an updated bathroom and you are ready to go! Owner is a licensed PA Realtor! Owner will consider owner financing and is ready to sell! Bring us an offer!


Dana Laake and her special guest Dr. Rob Ivker will discuss the holistic Sinus Survival program for treating, preventing, and curing chronic and fungal sinusitis, allergies, and colds.
Dr. Rob Ivker developed the Sinus Survival program to treat allergy problems while addressing the root cause of the symptoms. He has made tremendous breakthroughs over the years to help his patients, readers and internet audience around the world. He is the best-selling author of Sinus Survival: The Holistic Medical Treatment for Allergies, Colds and Sinusitus, and a leading authority on integrative medical treatment for acute, chronic and fungal sinusitis.
Host Dana Laake and her special guest Dr. Rob Ivker will be discussing proactive tips for surviving the sinus season and a new online tutorial and self-educational solution for sinus sufferers.
Dr. Robert S. Ivker has been a family physician since 1972. For the past 18 years, he has been practicing holistic medicine with an emphasis on the treatment and prevention of chronic disease. Dr. Ivker is the author of "Sinus Survival: The Holistic Medical Treatment for Sinusitis, Allergies, and Colds." He is the director of Fully Alive Medicine, in Boulder, Colorado and director of the Sinus Survival Respiratory Healer Network.
Host Dr. Kevin Passero and his special guest Dr. Rob Ivker will be discussing allergies and hay fever relief.
Dr. Rob Ivker developed the Sinus Survival Program to treat allergy problems while addressing the root cause of the symptoms. He has made tremendous breakthroughs over the years to help his patients, readers and internet audience around the world.
Dr. Robert S. Ivker is the best-selling author of Sinus Survival: The Holistic Medical Treatment for Allergies, Colds and Sinusitus, and a leading authority on integrative medical treatment for acute, chronic and fungal sinusitis.
Host Dana Laake and her special guest Dr. Robert S. Ivker will be discussing the "Sinus Survival Quick Fix," a natural approach for sinus sufferers.


text:
Home•Employer
Employeradmin2019-12-11T17:45:51-05:00
Hiring Foreign Workers
Employers hiring foreign workers must first determine if they need to apply for a Labour Market Impact Assessment (LMIA). It is a document that will show that there is a need for a foreign worker to fill the job and that no Canadian worker can do the job. Employer when applying for a LMIA will need to demonstrate that:
efforts have been made to recruit and/or train willing and available Canadians/permanent residents;
that the wages being offered are consistent with the prevailing wage rate paid to Canadians in the same occupation in the region;
that the working conditions for the occupation meet the current provincial labour market standards; and
any potential benefits that the hiring of the foreign worker may have on the Canadian labour market (e.g., creation of new jobs, transfer of skills and knowledge, etc.).
The government further has established additional requirements to determine if a job offer is "genuine":
Whether the employment is being made by an employer that is actively involved in the field which the job offer is being made;
Whether the offer is consistent with the employer's labour needs;
Whether the employer can reasonably fulfill the terms of the job offer; and
Whether the employer, or recruiter acting on behalf of an employer, has previously complied with provincial and federal laws regulating employment or recruiting of workers.
Temporary Foreign Workers Requiring LMIA:
The LMIA process has differentiation between "high-wage" and "low-wage" foreign workers. Temporary foreign workers whose salary is below the provincial/territorial median hourly wage are considered under the low-wage stream. Temporary foreign workers whose salary is equal to or above the median wage are considered high-wage stream.
Prevailing wages are determined by job requirements and duties of the associated job title defined in the National Occupational Classification (NOC). After employers have determined whether they are hiring a TFW for a low-wage position or a high-wage position, they must ensure that they meet all of the requirements for that stream such as meeting the prevailing wage requirement.
Foreign Workers under High-wage Stream:
The Stream for High-wage Positions allows employers to hire temporary foreign workers (TFW) for full-time positions (minimum of 30 hours of work/week) where the wage being offered is at or above the provincial/territorial median hourly wage where the job is located.
Employers under this stream must complete a 'transition plan' that will explain how they intend to permanently fill the job being held by the temporary foreign worker. Transition plans exist to ensure the employer intends to reduce their reliance on temporary foreign workers. However, some employers maybe exempted on the transition plan if they will hire TFWs for positions which have a limited duration of between:
1 and 120 days; and
more than 120 days to a maximum of 2 years (e.g. non-recurring project-based positions).
These positions are:
time-limited;
not going to exist after the TFW leaves; and
not jobs where employers could be expected to transition the position to a Canadian or permanent resident worker.
Further, employers under this stream are required to keep more detailed records during the foreign worker's stay in Canada.
There are certain LMIA applications that will be processed faster. Foreign workers in skilled trades, high paid workers with salaries in the top 10% of Canadian earnings, and workers coming for 120 days or less will all receive LMIA decisions in 10 business days.
Foreign Workers under Low-wage Stream:
The Stream for Low-wage Positions allows employers to hire temporary foreign workers (TFW) for full-time positions (minimum of 30 hours of work/week) where the wage being offered is below the provincial/territorial median hourly wage where the job is located.
Canadian employers hiring low-wage workers do not need to submit a transition plan when applying for a Labour Market Impact Assessment (LMIA). However, they are subject to a cap that limits the number of low-wage temporary foreign workers a given business can employ. Canadian employers with more than 10 employees will be restricted to a maximum 10% cap on low-wage temporary foreign workers.
Employers in the accommodation and food service sector as well as the retail trade sector will no longer be allowed to apply for LMIAs for jobs in 10 lower-skill occupations. This will include all regions across Canada with unemployment rate of 6% or higher.
LMIA Priority 10 Day Processing
There are certain LMIA applications that will be processed faster. This condition serves to help expedite the applications for the highest paid workers or the most specialized positions or critical employees to help them come to Canada. If the LMIA application will fall with any of the following criteria, the LMIA will qualify for 10 days expedited processing:
Highly paid workers with wages which are in the top 10 percent of wages paid in the province or territory to which they are applying.
Short term workers with employment is equal to or less than 120 calendar days. However, the salary must also be considered a high wage (equal to or greater than the provincial or territorial median).
Skilled trades workers with designated positions from the list from Employment and Social Development Canada. These must also have high wage salaries.
Positions exempt from LMIA
Foreign workers who need a Work Permit, but do not require a LMIA include:
Post-doctoral fellows;
Research award recipients;
Eminent individuals, for example leaders in various fields;
Guest lecturers;
Visiting professors;
Citizens of the U.S. and Mexico appointed as professors under the university, college and seminary levels of the North American Free Trade Agreement (NAFTA);
Citizens of Chile appointed as professors under the Canada Chile Free Trade Agreement (CCFTA).

redpajama_set_name: RedPajamaCommonCrawl


A Super Mario Plush by Global Holdings is a marvellous toy doll. Among the best characteristics for this doll is the officially licensed super mario product by global holdings. Other highlights include things like limited availability and new and sealed inside retail packaging. The Mario doll dimensions are 10"H x 8"L x 8"W. To see the great price I found, click on the shopping cart link on this site.
This Super Mario Bowser plush toy is an official, licensed item made by Global Holdings. This item measures approximately 10 inches along the longest dimension. Global Holdings Super Mario plush toys are high-quality, well-made, and safe products. The UPC for this item is 895221014109.
Dimensions: Height: 10" Length: 8" Width: 8"
Package Dim.: Height: 5.6" Length: 10.2" Depth: 6.9"


text:
By Dimitra Lavrakas
Senior Voice Travel Correspondent
Vancouver is just a short hop away
Neighborhoods to explore, lots of entertainment choices -- and beer
Tourism Vancouver
A stunning shot of Vancouver from the air.
Alaskans will find much familiar in Vancouver. On the shores of the Pacific Ocean, with a backdrop of the North Shore Mountains, the tail end of the Coast Mountains, it could almost be Anchorage or Juneau. But there's a difference.
Home to close to 640,000 people, it's near the population count of 739,000 of all Alaska. But think of it – that means in the 1,111 square miles of the city you're among the number of people that inhabit Alaska's 663,300 square miles. Tight squeeze.
Cosmopolitan with lots to do
And they keep all those folk amused with festivals, world-class entertainment, excellent restaurants and, of course, beer.
Should you want to celebrate Valentine's Day in the city there's a David Bowie celebration on Feb. 16 that features alumni band members Mike Garson, Earl Slick, Gerry Leonard and Carmine Rojas, along with world-class vocalists such as Bernard Fowler and Corey Glover, among others, and drummer Lee John performing a mix of Bowie's hits. Info at http://www.commodoreballroom.com/view-all/2019/2/16/bowie-celebration.
Or maybe attend a comedy show, Howie Mandel and Friends, for a memorable night of laughs on Valentine's Day at the Orpheum Theater. Go to http://www.eventbrite.ca/e/howie-mandel-friends-tickets-53156341047.
Events and entertainment options are available on the TourismVancouver site at http://www.tourismvancouver.com as well as guides to the unique parts of the city – its neighborhoods.
An international enclave with a distinct experience
First of all, Vancouver has a Chinatown, which means you'll find excellent Chinese food if you've hungered for some in whatever far-flung part of Alaska you live in.
One of the largest Chinatowns in North America, its brightly colored Millennium Gate welcomes you into this unique neighborhood.
In 2002, Canadian Prime Minister Jean Chrétien inaugurated the towering entrance to highlight this distinct part of the city. At the top, the inscription in Chinese reads, "Remember the past and look forward to the future."
The first wave of Chinese immigrants came between 1886 and1920, settling around Carrall and Pender streets. By 1890, the area was home to more than 1,000 Chinese residents who brought their traditions with them, like the three Chinese opera theatres, the first built in the 1890s.
Today, the area bustles with tourists and locals enjoying the many places to dine out, a lively arts scene and teashops.
Take a ferry ride
Like Alaska, Vancouver has a series of islands accessible by ferry. This is a chance to see the city from a different viewpoint, as you would, say, from a boat ride to Bainbridge Island off Seattle. From the Aquatic Center in Vancouver it's a three-minute trip to Granville Island. Go to https://granvilleislandferries.bc.ca/plan-your-trip/ for the schedule and fares. But for seniors it's a mere $2.25 to $4 – and that's in Canadian dollars, now at $.76 in U.S. dollars!
The island, completed in 1916, from material dredged from False Creek, was once a mud flat. It became home to the warehouses, mills, factories and shops that serviced local industries. One chain-making factory left behind a massive chain link in 2009 and can be seen inside the parking garage on Old Bridge Street.
Once on the island, amble toward the Public Market Vendor Booths in the Railspur District, featuring artists' booths that offer hand-painted silks, yarn, blacksmith art, a bakery, pie shop, beadwork, photography, fused and stained glass, handmade body care, jams and jellies and a broom maker too.
Across from the public market on the island is the Net Loft, a building full of interesting retail stores with authentic First Nations art and gifts, block-printed and painted textiles, or ceramic pieces. There's even an artisan who repairs and restores antique musical instruments.
Enjoy the tranquility of this island before returning to the city.
Vancouver proper
Once back in the city, if you still have stamina there's some attractions not to be missed.
Located in the downtown Harbor Center at 555 W. Hastings Street, the 430-foot-high Vancouver Lookout gives you a birds' eye view of the city and beyond. Senior tickets are $15.25 CDN or $11.55 USD.
There's an aquarium, the VanDusen Botanical Garden, Dr. Sun Yat-Sen Classical Chinese Garden, the Vancouver Art Gallery, Science World, and a gondola ride up Grouse Mountain with an even more expansive view of the area.
The Millennium Gate on Pender Street in Chinatown neighborhood beckons visitors.
Or you could drink beer. A fun and informative craft beer adventure takes you to three breweries in three hours with each located in a different brewery district and neighborhood of Vancouver. It's $79.99 plus tax – but again that's only $ 60.56 US. Go to https://vancouverbrewerytours.com for details.
Packages make it affordable
Thanks to the AARP travel site, packages that include flights, car and hotel make it very affordable for a stay from Feb. 12 to 15.
In downtown Vancouver you can book the Sandman Suites for $457 per person or the Pan Pacific with magnificent views of the harbor for $560 per person. The most expensive is the Fairmont Vancouver at $667 per person.
Car rental will cost from $39 to $50 depending on what type of car you prefer to drive.
Hotel and car rental prices are in U.S. currency.
The best thing is that even though Canada is still attached at the hip to Britain, at least you don't have to drive on the left side of the road.
Back to your roots: Travel to reconnect
Rivers offer a different kind of cruise
Fall in Fairbanks: Good fun and few crowds
An All-American road, on water
Traveling safe (and smart) in 2019
Student load debt is a...Alan M. Schlein


text:
August 24–26, 2021
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ABENS - Brazilian Association of Solar Energy The Brazilian Association of Solar Energy (ABENS) is a non-for-profit, nongovernmental organization developed to foster and support the research, development, and application of all forms and technologies of solar energy in Brazil. ABENS works in agreement with the goals and principles of the International Solar Energy Society (ISES), representing its Brazilian associates on ISES board of directors.
ABGD - Brazilian Distributed Generation Association Association of companies formed by solution providers, EPC's, integrators, distributors, manufacturers that have in common the direct or indirect performance in the distributed generation from renewable sources. The ABGD is predisposed to represent and defend the interests of its associates with government agencies, class entities, regulators, players in the sector and work towards the inclusion of the different sectors of society in order to actively benefit from Distributed Generation, Incorporating the concepts of sustainability, financial return, legal certainty and energy efficiency.
ABNT - Brazilian Association of Technical Standards ABNT is the National Standardization Forum, in recognition of Brazilian society since it was created in September 28, 1940; and confirmed by the Federal Government through legal instruments. ABNT is responsible for Brazilian Standards development (NBR) which are aimed at various sectors. ABNT participates in the Regional Standardization by Pan American Commission on Technical Standards (COPANT) and by Mercosur Association for Standardization (AMN); in the International Standardization by International Organization for Standardization (ISO) and by International Electrotechnical Commission (IEC), influencing the standard´s content and seeking to ensure competitive conditions for Brazilian products and services, in addition to exercising their social role. Furthermore, ABNT is also a conformity assessment body accredited by Inmetro for certification of products, systems and environmental programs, such as the eco-label and verification of greenhouse gas. www.abnt.org.br
ABRACEEL The Brazilian Association of Energy Traders (ABRACEEL) defends the right of free choice of the power supplier, the so-called light and natural gas bill portability, by the consumers. Founded in the year 2000, the association currently counts with 93 associated companies, which combined commercialize 85% of the electricity. Its main purpose is to work together with the society in general, opinion makers and government bodies, and to encourage a free competitive market as an instrument of efficiency in both the electric energy and the natural gas segments.
ABRASOL The Brazilian Association of Solar Thermal Energy (ABRASO) unites the entire value chain of the solar thermal sector, a step forward for the new possibilities that renewable energy foretells for the future.
ACERA - The Chilean Association for Renewable Energies and Storage The Chilean Association for Renewable Energies and Storage, ACERA AG., gathers about 130 members including developers, generators and suppliers of products and services, national and foreign, in the whole value chain of the industry of non-conventional renewable energy (NCRE). ACERA seeks the protection of the environment and sustainable development for Chile, through the promotion of renewable energies and energy storage, their best complement. Its mission is to promote a regulatory framework that allows NCREs and energy storage to compete on equal terms with traditional sources. To achieve this, it is working on three strategic objectives: Promote the generation of Non-Conventional Renewable Energies; contribute permanently to the development of a regulatory framework to encourage the production of NCRE, and finally, to promote the installation of NCRE plants that not only comply with but exceed participation goals established for these energies in the national legislation.
Argentinean-German Chamber of Industry and Commerce The Argentinean-German Chamber of Industry and Commerce (AHK Argentina) is a non-governmental, autonomous and non-profit organization. Its main objective is the strengthening of bilateral cooperation aiming at the social and economic growth of both countries. Given its expertise in various strategic areas (such as: sustainable development, ethics and compliance, education and training, business and technology and innovation as well), AHK Argentina is an active agent in the reinforcing of Argentinean-German relations.
Câmara de Comércio e Indústria Brasil-Alemanha no Rio Grande do Sul The promotion of bilateral economic relations between Brazil or Rio Grande do Sul and Germany are at the center of the goals of our Chamber of Commerce Abroad. The AHK Rio Grande do Sul is instrumental in promoting South Brazil in Germany as well as in Europe, thereby promoting both trade and investment on both sides of the Atlantic and facilitating the market entry of its members, partners and customers.
Brazilian Trade and Investment Promotion Agency (Apex-Brasil) The Brazilian Trade and Investment Promotion Agency (Apex-Brasil) works to promote Brazilian products and services abroad, and to attract foreign investment to strategic sectors of the Brazilian economy, including Renewable Energy.
BSW-Solar – German Solar Association With about 800 member companies, Bundesverband Solarwirtschaft e.V. (German Solar Association) is the interest group of the German solar energy industry. Forming a strong community of companies, BSW-Solar acts as an informant and intermediary between business and the political and public sectors. It represents the common commercial interests of businesses within the solar energy value chain. Our objective is to establish solar energy as a permanent pillar of a global energy industry.
CCIBAC The mission of the "Câmara de Comércio e Indústria Brasil-Alemanha do Ceará" (CCIBAC) is to promote bilateral trade relations between Ceará and Germany and to create incentives for the development of economic, scientific and cultural relations. It is a civil society, independent, not-for-profit association of private law. CCIBAC was founded in 2016 by former Honorary Consul Dieter Gerding and a group of entrepreneurs operating in the Brazilian market and having the necessary experience to offer qualified services specifically for German investors. CCIBAC enables German and Brazilian companies to expand their networks and find new business partners.
CELA – Clean Energy Latin America CELA Clean Energy Latin America is a financial advisory and strategy consulting firm supporting companies and investors in the renewable energy sector Latin America. CELA has advised clients in the sale and/or acquisition (M&A) of more than 1,300 MWp of solar PV projects, secured project financing for more than R$ 1.5 billion, successfully supported clients in obtaining PPAs in the regulated and in the free market (financial modelling and due diligence) and developed business plans for some of the main players in the DG market in Brazil. In 2019 the company launched Re.value, a software that analyzes the financial viability of solar DG projects of all sizes and business models in Brazil.
Freiburg Green City The Business Network for Environmental and Renewable Energy Industries in Freiburg and the Region
ICZ – Instituto de Metais Não Ferrosos ICZ has the vision to lead and support the sectors of metals zinc, nickel and lead in the development and dissemination of their applications and sustainable use in Brazil through the alignment of past experiences to new marketing concepts, providing its members the assistance and access to new technologies and products, providing a greater competitiveness for the whole production chain. Currently the ICZ operates heavily in dissemination of technology of hot-dip galvanizing as protection against corrosion in the steels used in the structures of anchorages for photovoltaic panels.
SolarPower Europe SolarPower Europe's aim is to ensure that more energy is generated by solar than any other energy source by 2030 and to lead our members to make solar the core of a smart, sustainable and inclusive energy system.
Litro De Luz We are an international organization operating in more than 20 countries. In Brazil, we are present in the five regions and we have directly impacted more than 10 thousand people. We bring light to residents of local communities who do not have access to electricity or who live without light in their homes. We use a simple, economical and ecologically sustainable technology, consisting of plastic bottles, solar panels and LED lamps.
The Brazilian Women`s Network in Solar Energy The Brazilian Women`s Network in Solar Energy (MESol, from the Portuguese abbreviation) aims to connect, support and empower women from the solar and renewable energy sector in Brazil through events, workshops, discussion panels, reports and mentorship programs. MESol was founded in 2019 as a result from the 1st Meeting of Women in Solar Energy which took place at the Solar Energy Research Laboratory from Universidade Federal de Santa Catarina (Fotovoltaica-UFSC). MESol activities can be followed at its Facebeook page, Instagram and Linkedin.
VDMA - The Mechanical Engineering Industry Association The Mechanical Engineering Industry Association (VDMA) represents more than 3,200 member companies in the SME-dominated mechanical and systems engineering industry in Germany and Europe.
Global Media Partners
Premium Media Partners
Intersolar North America
Intersolar Mexico
Intersolar Middle East
Intersolar India
Intersolar Summits


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Breaking| Jul 19, 2020, 04:39pm EDT |
Trump Refuses To Commit To Accepting Election Results
Andrew SolenderForbes Staff
I write about politics and the Biden transition.
Updated Jul 20, 2020, 02:55pm EDT
During an interview with Fox News anchor Chris Wallace that aired Sunday, President Trump refused to commit to accepting the results of the 2020 election, slammed mail-in voting and called polls showing him trailing former Vice President Joe Biden "fake"
WASHINGTON, DC - JULY 15: President Donald J. Trump walks from the Oval Office to board marine One ... [+] and depart from the South Lawn at the White House on Wednesday, July 15, 2020 in Washington, DC. (Photo by Jabin Botsford/The Washington Post via Getty Images)
The Washington Post via Getty Images
Presented with a Fox News poll showing him trailing Biden by 8 points, Trump told Wallace, "first of all, I'm not losing, because those are fake polls."
Wallace asked Trump "can you give a direct answer that you will accept the election?"
"I have to see," Trump said of accepting the result in November. "I have to see. No, I'm not going to just say yes. I'm not going to say no, and I didn't last time either."
The comments echo Trump saying at a 2016 presidential debate "I will tell you at the time. I will keep you in suspense," about whether he would accept the results of that election, which he followed up with the statement, "I will totally accept the results of this great and historic presidential election if I win."
Trump has repeatedly dismissed polls showing him down by wide margins as "fake" and "suppression polls," instead citing anecdotal metrics like boaters and motorcyclists with Trump flags as evidence of enthusiasm.
Trump told Wallace that he is not a good loser, and reiterated his conspiratorial and mostly unsubstantiated claim that "mail-in voting is going to rig the election."
Trump campaign communications director Tim Murtaugh doubled down on Trump's comments on mail-in voting in a statement to Forbes, accusing Democrats of attempting to "eliminate protections for election integrity" and declaring "we don't know what kind of shenanigans Democrats will try leading up to November."
Big Number
63%. The Fox News poll found broad support for mail-in voting, with 63% of registered voters saying they favor allowing all U.S. citizens to vote by mail, while just 28% said they oppose such a measure.
Since seizing the nomination in April, Biden has frequently raised alarms about foul play on Trump's part. In April, Biden predicted that Trump will "try to kick back the election somehow, come up with some rationale why it can't be held" and collude with Russia. Biden made similar claims in June, telling the Daily Show's Trevor Noah "this president is going to try to steal this election." During a virtual fundraiser on Friday, Biden told a group of lawyers that he has begun receiving national security briefings and that "the Russians are still engaged in trying to delegitimize our electoral process. Fact."
In response to Trump's comments to Wallace, the Biden campaign's Rapid Response Director Andrew Bates said "the American people will decide this election. And the United States government is perfectly capable of escorting trespassers out of the White House."
News Peg
Trump's comments come as concerns are mounting that the 2020 result may not be decisive on election night–or even in the weeks after the election. New York is still counting ballots from its primary, which took place three weeks ago, with some close races still uncalled. Additionally, in California, more than 100,000 mail-in ballots went uncounted, leading to concerns about possible voter suppression that build on already-widespread concerns among Democrats that Republicans will suppress voters in red states.
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Andrew Solender
I am a news reporter covering politics and the Biden transition. I have previously worked for MSNBC and Chronogram Magazine. I attended Vassar College and the London
I am a news reporter covering politics and the Biden transition. I have previously worked for MSNBC and Chronogram Magazine. I attended Vassar College and the London School of Economics.


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LLB (HONS.) UNIVERSITY OF LONDON
BACHELOR OF LAW (LLB HONORS) – UNIVERSITY OF LONDON INTERNATIONAL PROGRAMMES
The University of London, founded in 1836, is one of the world's leading university, internationally recognized for its high academic standards. The University of London established the University of London International Programmes, a distance and flexible learning programme, in 1858 to allow students around the world to study for a top UK qualification. The reputation of the University of London is based on the outstanding teaching and research of its renowned Colleges and specialist institutes. The Colleges are responsible for the academic direction, including syllabus, learning resources, academic support, and assessment of all courses offered by the University of London International Programmes. The International Programmes now has over 54,000 students worldwide, including more than 2,400 in Pakistan.
The University of London International Programme enables you to study for a prestigious University of London qualification wherever you are in the world. University of London's LLB (Hons) degree is internationally recognized and open doors to lucrative careers in law, business, politics, education, IT and other industries. The high reputation of its LLB (Hons) has been maintained because International Programme students are marked to the same high standards as campus-based students. Upon graduation you will join a distinguished group of lawyers, solicitors, barristers and judges from around the world who have obtained their law degree through the University of London, including Nelson Mandela.
The University of London Undergraduate Laws Programme is governed academically by the 6 University of London's Colleges, collectively known as the Laws Consortium. The 6 Colleges are (1) Birbeck, (2) King's College London, (3) The London School of Economics and Political Science (LSE), (4) Queen Mary, (5) SOAS, and (6) UCL.
The University of London's law degree is recognized as a Qualifying Law Degree and the completion of the LLB (Hons) degree allows you to qualify as a Solicitor or a Barrister. The University of London's LLB (Hons) degree is also recognized by the Pakistan Bar Council and allows you to qualify as an Advocate in Pakistan. When you graduate, you are awarded a University of London degree with the University of London's crest and Vice-Chancellor's signature, indicating that you were registered with the University of London.
LLB Programme Regulations 2017-18
Standard Entry Route - Qualifying Law Degree
Graduate Entry Route - Qualifying Law Degree
Four compulsory moduels Three compulsory moduels
Legal system and method Criminal law
Criminal law Contract law
Contract law Public law
Public law -
Three compulsory modules Three compulsory modules
Tort law Tort law
Equity and Trust Equity and Trust
EU law EU law
One optional module chosen from: -
Commercial law -
Family law -
Administrative law -
International protection of human rights -
Two compulsory modules Two compulsory modules
Jurisprudence and legal theory Jurisprudence and legal theory
Property law Property law
Two optional modules chosen from: One optional modules chosen from:
Civil and criminal procedure Civil and criminal procedure
Company law Company law
Conflict of laws Conflict of laws
Criminolgy Criminolgy
Dissertation Dissertation
Evidence Evidence
Labour law Labour law
Introduction to Islamic law Introduction to Islamic law
Public international law Public international law
Common law reasoning and institutions: This introduction to the English legal system seeks to convey what is distinctive about the common law approach as a legal methodology. The course is vital in initiating students into the process of legal research and the understanding of legal reasoning.
Criminal law: This course examines general principles of criminal liability, a range of fatal and non-fatal offences against the person and selected offences against property. Attempts to commit offences, secondary liability and defences also form part of the curriculum.
Elements of the law of contract: This course places its emphasis on understanding the key underlying principles of English law, covering the core topics, including formation of contracts, capacity to contract and privity, performance and breach of contract and remedies for breach of contract.
Public law: Focusing upon constitutional issues and reform, students will analyse key issues governing the relation between citizens and the state, including sovereignty and the division of powers between legislature, executive and administration.
Jurisprudence and legal theory: Jurisprudence poses the fundamental questions about the nature of law, its place in society and how a legal system operates as a system of rules and as a social institution engaging with ideals of justice and often conflicting moral codes.
Law of Tort: The law of tort concerns the civil liability for the wrongful infliction of injury by one person upon another. Negligence is a key topic and other topics include the duty of care; nuisance; trespass; defamation; occupiers and employers' liability.
Equity and Trusts: Part of equity law, the law of trusts deals with the rules and principles governing the creation and operation of trusts (a particular method of holding property) including express trusts, trusts arising as a matter of law and breach of trust.
Property Law: Property law centres on the concept of the nature of the various interests that can exist in land, the principles governing the creation, transfer and extinction of these interests and the extent to which those interests are enforceable against third parties.
Commercial law: Commercial law is concerned with obligations between parties to commercial transactions and the relationship with rules of personal property. Emphasis is placed on both knowledge of principles and the ability to apply the rules of law to achieve practical solutions to practical problems. Students will become familiar with a range of issues including: ownership of or title to goods; transfers of title and its effect on third parties; passing of property between buyer and seller.
Company law: A vital course for anyone intending to operate in a commercial field, the syllabus centres on the way the law regulates companies and the facilities that company law offers, such as limited liability and transferability of shares, as well as corresponding burdens such as duties of disclosure and compliance with statutory procedures.
EU law [EU law is a required course by the professional bodies in England and Wales for the LLB as a Qualifying Law Degree]: Appealing to students who enjoy public law or who have an interest in public affairs, politics, economics or international relations, the course examines EU institutions and law making powers as well as the key questions of the impact of EU law on national law and its overall consequences for business enterprises and individuals.
Evidence: The law of evidence governs what evidence may be presented and contested in the courtroom, techniques for eliciting evidence, and the role of the lawyers, jury and judge in an adversarial system. Highly relevant to actual day-to-day legal practice, this course will appeal particularly to students intending to become courtroom lawyers.
Intellectual property: The law of intellectual property rights seeks a difficult balance between rewarding the right owner of a piece of intellectual property, and the needs of society to gain access to scientific, technological or cultural benefits. It includes copyright, patent and trade mark law with particular emphasis on English law.
Introduction to Islamic law: The course offers an overview of Islamic law covering its religious historical and contemporary dimensions. The course addresses first the religious and historical foundations of Islamic law before going on to address its application in contemporary jurisdictions.
International protection of human rights: This course concerns protection afforded to individuals and seeks to develop students' understanding of the fundamental concepts, principles, theories and philosophies underpinning international human rights, as well as an understanding of the mechanisms installing/enforcing and monitoring these rights.
Public international law: Public international law concerns legal relations between states but also deals with the role of the UN and other international organisations and, in the fields of human rights and international criminal law, the rights and duties of individuals.
Labour law: Of appeal to anyone interested in industrial relations, this course relates both to individuals in their workplace and the operation of the labour market in general. The course covers not only individual matters (such as employment contracts, unfair dismissal, redundancy, sex and race discrimination) but also 'collective' labour law – including trade union 'recognition', the law relating to trade disputes and the protections given to workers who choose to join a trade union.