layout: true
--- class: center, middle # Convolutional Neural Networks Guillaume Ligner - Côme Arvis --- # Fields of application We are going to find out about convolutional networks
What are they used for ?
-- Most importantly in **computer vision** .center[
] --- # Fields of application Computer vision: object detection .center[
] --- # Fields of application Computer vision: ImageNet classification .center[
] --- # Fields of application Computer vision .center[
] --- # Fields of application Computer vision: example of Mask-RCNN application for segmentation .center[
] --- # Fields of application Another few examples - Speech recognition & speech synthesis - Natural Language Processing - Any problem with a spatial (or sequential) structure --- # The Convolution Operation - In Deep Learning we usually talk about Convolution networks, layers, operations - Instead, most Deep Learning frameworks implement in practice a related function: the cross-correlation .left-column50[
] .right-column50[
] --- # The Convolution Operation - Discrete cross-correlation between two functions $f$ and $g$: $$ (f \star g) (x) = \sum\_{b-a = x} f(a) . g(b) = \sum\_{a} f(a) . g(x + a) $$ - 2D-convolutions (actually 2D cross-correlation): $$ (f \star g) (x, y) = \sum_n \sum_m f(n, m) . g(x + n, y + m) $$ $f$ is a convolution **kernel** or **filter** applied to the 2-d map $g$ (our image) --- # The Convolution Operation - Here is the computer vision point of view of the convolution operation $$ (k \star im) (x, y) = \sum\limits\_{n=0}^2 \sum\limits\_{m=0}^2 k(n, m) . im(x + n, y + m) $$ - Kernel $k$ here is of shape $(3,3)$ and image $im$ of shape $(6,6)$ .center[
] --- # The Convolution Operation A snapshot example of the operations performed - Element of the output tensor is formed by applying the kernel to the corresponding region in the input tensor - Output is the sum of the entrywise (Hadamart) product between kernel and region of the input .center[
] --- # Convolution in images - What we just saw is the convolution operations between a $2$d kernel and a $2$d image - An image is broken into several $2$d descriptions: $1$ per color - RGB image is a 3d tensor (3, H, W), meaning 3 color matrix $(H, W)$ - Example of $3$d convolution formula with $(4,4,3)$ kernel $$ (k \star im) (x, y, z) = \sum\limits\_{n=0}^{4-1} \sum\limits\_{n=0}^{4-1} \sum\limits\_{c=0}^{3-1} k(n, m, c) . im(x + n, y + m, z + c) $$ --- # Convolution in images - Feature map elements formed by applying a $3$d kernel to $3$d input regions - Convolution operations for all $(x, y)$ lead to a (H, W) matrix - Final output is a $(K, H, W)$ $3$d tensor: - One (H, W) matrix per kernel for all $K$ kernels - Sets of convolution operations performed by different kernels lead to different characteristics being extracted from the image - Some padding used, we will see about that later .center[
] --- # Motivations for CNNs - Convolution leverages three important ideas - **Sparse interactions**, **Parameter sharing**, **Equivariant representations** Sparse interactions (Local connectivity) - An output neuron depends only on a few local input neurons - This is accomplished by making the kernel smaller than the input .left-column50[
]
.right-column50.small80[ - $(Top)$ When $s$ is formed by convolution with a kernel of width $3$, only three outputs are affected by $x_3$ - $(Bottom)$ When $s$ is formed by matrix multiplication, connectivity is no longer sparse, so all of the outputs are affected by $x_3$ ] --- # Motivations for CNNs Parameter sharing - The same parameter is used in more than one function in a model - In a traditional neural net, each element of a weight matrix is used **exactly once** - In a conv' net, each kernel parameter is used at every location of the image (except boundary pixels)
.left-column40[
] .right-column60.small70[ - Black arrows indicate uses a parameter in an output - $(Top)$ The black arrows indicate uses of the **central element of a 3-element kernel** in a convolutional model - Parameter involved in **all output neurons $s_i$** - $(Bottom)$ **Single** black arrow indicates **single** use of the central element of the weight matrix in a fully connected model - Model has no parameter sharing, parameter involved in **output neuron $s_3$** only ] --- # Motivations for CNNs Equivariance of convolution to translation - To say a function is equivariant means that if the input changes, the output changes in the same way - Specifically, a function $f$ is equivariant to a function $g$ if
- $f(g(x)) = g(f (x))$
- $ f \circ g (x) = g \circ f (x)$ - In our context, saying that convolution is equavariant to translation means that: .center[ $Translation \circ Convolution(image) = Convolution \circ Translation(image)$ ] --- # Motivations for CNNs .small90[ Equivariance of convolution to translation - Let's analyze a concrete example: ] -- .center[
] --- # Motivations for CNNs .small90[ Equivariance of convolution to translation .center[
] ] -- .fsmall80[
We visually showed how: .center[$Translation \circ Convolution(image) = Convolution \circ Translation(image)$] ] --- # Multiple convolutions .center[
] - Red feature map $=$ result of all convolution operations - Output feature map represents red kernel trying to extract one type of characteristic from the image at all locations - .firebrick[**1**] kernel, .firebrick[**1**] set of convoltution operations, .firebrick[**1**] feature map, .firebrick[**1**] type of characteristic extracted, thus feature map output of shape (24,24,.firebrick[**1**]) and kernel params tensor of shape (5,5,3,.firebrick[**1**]) --- # Multiple convolutions .center[
] - Often we want to extract more than one type of characteristic --- # Multiple convolutions .center[
] - Often we want to extract more than one type of characteristic - Following this example we use another kernel of shape $(5,5,3)$ --- # Multiple convolutions .center[
] - Each kernel extracts a type of characteristic at all locations of the image and outputs $1$ more feature map --- # Multiple convolutions .center[
] - Final kernel params' is of shape $(5,5,3,4)$, final feature map is $(24,24,4)$ - $4$ stands for $4$ different kernels for $4$ sets of convolution operations and $4$ feature maps outputted, $1$ per kernel --- # MLP vs Convolutional Network codes **Fully Connected Network: MLP (Keras functional API)** ```python input_image = Input(shape=(28, 28, 1)) x = Flatten()(input_image) x = Dense(128, activation='relu')(x) x = Dense(10, activation='softmax')(x) mlp = Model(inputs=input_image, outputs=x) ``` **Convolutional Network (Keras functional API)** ```python input_image = Input(shape=(28, 28, 1)) *x = Conv2D(32, 5, activation='relu')(input_image) x = Flatten()(x) x = Dense(128, activation='relu')(x) x = Dense(10, activation='softmax')(x) convnet = Model(inputs=input_image, outputs=x) ``` .small80.center[ 2D spatial organization of features preserved untill `Flatten` ] --- # Strides .small80[ - Strides: increment step size for the convolution operator - For stride size $k$ kernel performs convolution operation, skips next $k-1$ pixels and performs another convolution operation and so on - There are $vertical$ and $horizontal$ stride sizes which may not be the same - Strides reduce the size of the output map - Stride of shape $(2,2)$ ($2$ $vertical$ and $2$ $horizontal$) reduce number of numerical outputted quantities by roughly $2 \times 2$ ] .center[
] .center.small[ Example with kernel size $3 \times 3$ and a stride of $2$ (image in blue) ] --- # Strides - Essentially used to reduce the number of activations and parameters in the network and reduce the output dimensionality - Allows to reduce the network number of parameters without diminishing the number of filters - Used when one does not want too much overlap between convolution operations --- # Padding [//]: # (Useful to keep output spatial dimension of feature maps constant across kernels of different shapes)
- Padding: artificially fill borders of image - Useful with strides and large kernels - Because of strides kernel might end up virtually out of the image - Usually: fill with 0s - Useful to preserve the dimensions of the input to the output .center[
] --- # Dealing with shapes **Kernel** or **Filter** shape $(F, F, C^i, C^o)$ .left-column40.small80[ - $F \times F$ kernel size, - $C^i$ input channels: e.g. of colors - $C^o$ output channels: number of feature maps Kernel number of parameters: $(F \times F \times C^i + 1) \times C^o$ - $1$ stands for the bias, 1 per kernel of shape $(F, F, C^i)$ - There is $1$ kernel per outputted feature map **Activations** or **Feature maps** shape: - Input $(W^i, H^i, C^i)$ - Output $(W^o, H^o, C^o)$ ] .right-column60[ .center[
] ] .small70.center[ $W^o = \frac{(W^i +2P) - F}{S} + 1$ $(P)$ being the amount of zero padding around borders, $(S)$ stride applied ] --- # Dealing with shapes Reminders about padding .left-column50[ - Convenient to pad around borders - Size of padding is a hyperparameter ] .left-column50[ - To control the output spatial size - To preserve shape of the input ] .center[
] - In example: $1d$ kernel of size $F = 3$, $1d$ input of size $W = 5$, zero-padding around borders $P = 1$ - Left: stride of $S = 1$, giving output of size $\frac{(5 + 2 \times 1) - 3}{1} + 1 = 5$ - Middle: stride of $S = 2$, giving output of size $\frac{(5 + 2 \times 1) - 3}{2} + 1 = 3$ - Right: No padding, output size is $3$: it is the number of kernels that horizontally fit across the image --- # Dealing with shapes **Constraints on strides** - This formula means that hyperparameters have mutual constraints - In $1$d convolution with zero padding example stride S = 3 was impossible - $\frac{(5 + 2 \times 1) - 3}{3} = 1.333\cdots$ i.e. not an integer, indicating that the kernels don’t "fit" neatly across the input **Going back to shapes** - $W^i +2P$ represents virtual width of image with padding - $(W^i +2P) - F$ represents space left on image after placing kernel over it - convolution operation followed by stride produces $1$ scalar on feature map - $(W^i +2P) - F$ must be a multiple of stride - $W^o = $ number of scalars produced during horizontal striding across image
- $W^o = \frac{(W^i +2P) - F}{S} + 1$ --- # Pooling - No parameters: typically **max** or **average** of $n \times n$ units - Using pooling we assume that each pool unit should be invariant to small translations of the input - Useful when the presence of a feature in the image matters and not where it is in the picture .center[
] --- # Hierarchical representation .center[
] --- # Pre-trained models - Training a model can take days if it has to extract concepts for lots of different classes from millions of images - Many models are trained on ImageNet, their weights are publicly available - Those architectures and weights have been trained on datasets and hardware not normally available to the general public --- # Transfer learning - Load a public model - Use the pre-trained weights by removing the layers used to address the specific problem (fully connected layers) → Stop at the last layer that extracts concepts (conv/pool layers) - Loaded network is used as a generic feature extractor - Add your own set of fully connected layers that lead to the answer to your task using the high-level representation of the image - Train the weights of the newly added layers on the new task --- # Data augmentation .left-column40[
] .-column60[
] - Object translations make the object invariant but the noise (background) changes - The model focuses on the invariant part to provide the answer - Distorsion and rotations of the object enrich the model learning with shapes that might not be in the training set but likely belong to the true distribution $(X, Y)$ --- # Data augmentation With Keras: ```python from keras.preprocessing.image import ImageDataGenerator image_gen = ImageDataGenerator( rescale=1. / 255, rotation_range=40, width_shift_range=0.2, height_shift_range=0.2, shear_range=0.2, zoom_range=0.2, horizontal_flip=True, channel_shift_range=9, fill_mode='nearest' ) train_flow = image_gen.flow_from_directory(train_folder) model.fit_generator(train_flow, train_flow.n) ``` --- # ImageNet: an image database .center[
] - Since 2010 **ImageNet Large Scale Visual Recognition Challenge** (**ILSVRC**) is the competition where research teams evaluate their algorithms on the “ImageNet” image database - Tasks involve classification, detection from images and video - They compete to achieve higher accuracy on several visual recognition tasks - In **2012** **AlexNet** improved previous best score by **36**% --- # Architectures: AlexNet .center[
] - Depth of the model was essential for its high performance
→ Computationally expensive, made feasible due to GPUs - **AlexNet** contained eight layers; the first **five** were **convolutional layers**, and the last **three** were **fully connected** layers - Marked start of an industry-wide artificial intelligence boom in usage of **deep convolutional networks** --- # Architectures: VGG-16 .center[
]
- VGG = Visual Geometry Group, an academic group at Oxford University - VGG is also the CNN architecture that secured $1^{st}$ and $2^{nd}$ spots in the localisation and classification tasks in ImageNet 2014 --- # Architectures: VGG-16 .center[
]
- The main contribution of VGG is to show that classification/localisation can be improved by increasing the depth of CNN in spite of using smaller kernels - CNNs prior to VGG used kernels $7\times7$ and $11\times11$ against $3\times3$ in VGG
$\rightarrow$ But they were not as deep as VGG - There are few variants of VGG, the deepest one has $19$ weight layers --- class: middle, center # VGG memory and parameters --- ```md Activation maps Parameters INPUT: [224x224x3] = 150K 0 CONV3-64: [224x224x64] = 3.2M (3x3x3)x64 = 1,728 CONV3-64: [224x224x64] = 3.2M (3x3x64)x64 = 36,864 POOL2: [112x112x64] = 800K 0 CONV3-128: [112x112x128] = 1.6M (3x3x64)x128 = 73,728 CONV3-128: [112x112x128] = 1.6M (3x3x128)x128 = 147,456 POOL2: [56x56x128] = 400K 0 CONV3-256: [56x56x256] = 800K (3x3x128)x256 = 294,912 CONV3-256: [56x56x256] = 800K (3x3x256)x256 = 589,824 CONV3-256: [56x56x256] = 800K (3x3x256)x256 = 589,824 POOL2: [28x28x256] = 200K 0 CONV3-512: [28x28x512] = 400K (3x3x256)x512 = 1,179,648 CONV3-512: [28x28x512] = 400K (3x3x512)x512 = 2,359,296 CONV3-512: [28x28x512] = 400K (3x3x512)x512 = 2,359,296 POOL2: [14x14x512] = 100K 0 CONV3-512: [14x14x512] = 100K (3x3x512)x512 = 2,359,296 CONV3-512: [14x14x512] = 100K (3x3x512)x512 = 2,359,296 CONV3-512: [14x14x512] = 100K (3x3x512)x512 = 2,359,296 POOL2: [7x7x512] = 25K 0 FC: [1x1x4096] = 4096 7x7x512x4096 = 102,760,448 FC: [1x1x4096] = 4096 4096x4096 = 16,777,216 FC: [1x1x1000] = 1000 4096x1000 = 4,096,000 TOTAL activations: 24M x 4 bytes ~= 93MB / image (x2 for backward) TOTAL parameters: 138M x 4 bytes ~= 552MB (x2 for plain SGD, x4 for Adam) ``` --- ```md Activation maps Parameters INPUT: [224x224x3] = 150K 0 *CONV3-64: [224x224x64] = 3.2M (3x3x3)x64 = 1,728 *CONV3-64: [224x224x64] = 3.2M (3x3x64)x64 = 36,864 POOL2: [112x112x64] = 800K 0 CONV3-128: [112x112x128] = 1.6M (3x3x64)x128 = 73,728 CONV3-128: [112x112x128] = 1.6M (3x3x128)x128 = 147,456 POOL2: [56x56x128] = 400K 0 CONV3-256: [56x56x256] = 800K (3x3x128)x256 = 294,912 CONV3-256: [56x56x256] = 800K (3x3x256)x256 = 589,824 CONV3-256: [56x56x256] = 800K (3x3x256)x256 = 589,824 POOL2: [28x28x256] = 200K 0 CONV3-512: [28x28x512] = 400K (3x3x256)x512 = 1,179,648 CONV3-512: [28x28x512] = 400K (3x3x512)x512 = 2,359,296 CONV3-512: [28x28x512] = 400K (3x3x512)x512 = 2,359,296 POOL2: [14x14x512] = 100K 0 CONV3-512: [14x14x512] = 100K (3x3x512)x512 = 2,359,296 CONV3-512: [14x14x512] = 100K (3x3x512)x512 = 2,359,296 CONV3-512: [14x14x512] = 100K (3x3x512)x512 = 2,359,296 POOL2: [7x7x512] = 25K 0 *FC: [1x1x4096] = 4096 7x7x512x4096 = 102,760,448 FC: [1x1x4096] = 4096 4096x4096 = 16,777,216 FC: [1x1x1000] = 1000 4096x1000 = 4,096,000 TOTAL activations: 24M x 4 bytes ~= 93MB / image (x2 for backward) TOTAL parameters: 138M x 4 bytes ~= 552MB (x2 for plain SGD, x4 for Adam) ``` --- # Residual Networks: ResNet .center[
] .center[
] .small80[ Two appoaches to ResNet - When network without ResNet skip connections goes deeper and deeper, training may go wrong
$\rightarrow$ no longer extracts anything more useful from current feature maps
$\rightarrow$ Network can neither stabilize current feature maps by learning **identity function** that is difficult to learn, thus training becomes worse - The vanishing gradient problem, the output of a shallow layer disappears through too many layers ahead in the network ] --- # Residual Networks: ResNet .center[
] .center[
] .center[ $1$ every $2$ activations is propagated $2$ layers forward through a "skip connection" ] .left-column40[
] .right-column60.small70[ - Input block: $x$ output: $x + F(x)$ - Every $n$ layers a residue is added to $x$. This residue, equal to $0$ or not, is learned through a convolutional block - A block learns the residual with respect to the identity - Block learns $F(x)$ which is the residual (difference) of the output $F(x) + x$ with respect to the identity $x$ ] --- # Residual Networks: ResNet .center[
] .center[
] .left-column50.small90[ **Identity** function approach
] .right-column50.small70[ - $a^{l+2} = g(z^{l+2} + a^{l})$; $g(x) = reLU(x)$ - $a^{l+2} = g(W^{l+2} \cdot a^{l+1} + b^{l+2} + a^{l})$ → if $W^{l+2} = 0$ and $b^{l+2} = 0$ then $a^{l+2} = g(a^{l}) = a^{l}$ → **$a^{l}$ copied to $a^{l+2}$** - Means that identity function can easily be learned - Somehow guaranteed that performance will either stabilize or improve ] --- # Residual Networks: ResNet .center[
] .center[
] .left-column50.small90[ **Vanishing gradient problem** approach
] .right-column50.small70[ - $a^{l+2} = g(z^{l+2} + a^{l})$; $g(x) = reLU(x)$ - $a^{l+2} = g(W^{l+2} \cdot a^{l+1} + b^{l+2} + a^{l})$ - $W$ and $b$ are **not** set to $0$ - Gradient issue is solved by propagating information forward and constructing an ensemble of shallow networks ] --- # State of the art Automated finding of right architectures .left-column100[- Reinforcement Learning ex: ENAS Efficient Neural Architecture Search]
.left-column40[
] .right-column60[ - An RL algorithm samples Deep Convolutional Network architectures - Rewards are the accuracies on validation set - Algorithms becomes better and better at selecting right architectures for the problem at hand ]
.left-column100[- Evolutionary algorithms] --- # Beyond Image Classification ### Object detection - We don't know in advance the number of objects in the image - Object detection relies on *object proposal* and *object classification* **Object proposal:** find regions of interest (RoIs) in the image **Object classification:** classify the object in these regions
**Object Detection = Object proposal + Object classification** $\rightarrow$ One example: **YOLO algorithm** --- # Beyond Image Classification ### Segmentation - Output a class map for each pixel (for example: cat vs background) - We predict a class for each pixel in image - Usually classes are: “pixel belongs to object of interest” or “pixel does **NOT** belong to object of interest” (background) $\rightarrow$ One example: **Mask-RCNN** --- # Beyond Image Classification .center[
] --- # YOLO - You Only Look Once .center[
] For each cell of the $S \times S$, predict: - $B$ **boxes** - $4$ values per box for the center ($x$ and $y$), width ($W$) and height ($H$) - $x$ and $y$ relative positions in the cell, H and W are normalized - $B$ **confidence scores** C ($1$ per box) - $k$ **probabilities**, $1$ proba per class. Each proba is the probability of this class being inside the cell conditioned on an object being in cell --- # YOLO - You Only Look Once .center[
] For each cell of the $S \times S$: - Confidence scores are {$C_1, ..., C_B$}, probabilities are {$p_1, ..., p_k$} - There are $B \times k$ $(C_i, p_j)$ pairs for each gridcell --- # YOLO - You Only Look Once .center[
] For each cell of the $S \times S$: - Considering pairs $(C_i, p_j)$ we keep cases where probability to **have an object in cell** is high AND **probability of classified object** is high - We define a thresold - Final detection (localisation + classif): pair gridcell and class $j$ where **$C_i \times p_j > thresold$** --- # Mask-RCNN .center[
] --- # Mask-RCNN - A Deep Neural Network architecture to address segmentation - It separates objects in images or videos - For a given image, it outputs bounding boxes, class and binary mask for each box .center[
] --- # Mask-RCNN - Stage 1: a CNN, refered to as the backbone network, extracts characteristics from the image. It outputs the image feature map - It is usually a ResNet or VGG, on its own or part of a FPN network. - Stage 2: the Region Proposal Network (RPN) takes in the feature map, predicts input regions that should have an object in them - Stage 3: RoI align algorithm selects a fixed size subpart of the feature map for each proposed region - It is used to predict a newly refined bounding box, the class and a $28\times28$ mask that is next extended to fit the proposed region


codetime package¶
Subpackages¶
Submodules¶
codetime.admin module¶
codetime.apps module¶
codetime.models module¶
-
class
codetime.models.
TimeLog
(*args, **kwargs)¶ Bases:
django.db.models.base.Model
Description for Time Logging DB Model
- Variables
log_file_time_id – Primary key indexing each time log of a file of a user
api_token – unique token for each user
file_name – file being edited by user
file_extension – extension of the file (or file type)
detected_language – language in the file
log_date – date of when was the file logged
start_timestamp – start time recorded for activity on file
end_timestamp – end time recorded for activity on file
created_at – timestamp of when file was created
modified_at – timestamp of when file was modified last
-
exception
DoesNotExist
¶ Bases:
django.core.exceptions.ObjectDoesNotExist
-
exception
MultipleObjectsReturned
¶ Bases:
django.core.exceptions.MultipleObjectsReturned
-
api_token
¶ A wrapper for a deferred-loading field. When the value is read from this object the first time, the query is executed.
-
created_at
¶ A wrapper for a deferred-loading field. When the value is read from this object the first time, the query is executed.
-
detected_language
¶ A wrapper for a deferred-loading field. When the value is read from this object the first time, the query is executed.
-
end_timestamp
¶ A wrapper for a deferred-loading field. When the value is read from this object the first time, the query is executed.
-
file_extension
¶ A wrapper for a deferred-loading field. When the value is read from this object the first time, the query is executed.
-
file_name
¶ A wrapper for a deferred-loading field. When the value is read from this object the first time, the query is executed.
-
get_next_by_created_at
(*, field=<django.db.models.fields.DateTimeField: created_at>, is_next=True, **kwargs)¶
-
get_next_by_log_date
(*, field=<django.db.models.fields.DateField: log_date>, is_next=True, **kwargs)¶
-
get_next_by_modified_at
(*, field=<django.db.models.fields.DateTimeField: modified_at>, is_next=True, **kwargs)¶
-
get_previous_by_created_at
(*, field=<django.db.models.fields.DateTimeField: created_at>, is_next=False, **kwargs)¶
-
get_previous_by_log_date
(*, field=<django.db.models.fields.DateField: log_date>, is_next=False, **kwargs)¶
-
get_previous_by_modified_at
(*, field=<django.db.models.fields.DateTimeField: modified_at>, is_next=False, **kwargs)¶
-
log_date
¶ A wrapper for a deferred-loading field. When the value is read from this object the first time, the query is executed.
-
log_file_time_id
¶ A wrapper for a deferred-loading field. When the value is read from this object the first time, the query is executed.
-
modified_at
¶ A wrapper for a deferred-loading field. When the value is read from this object the first time, the query is executed.
-
objects
= <codetime.models.TimeLogManager object>¶
-
start_timestamp
¶ A wrapper for a deferred-loading field. When the value is read from this object the first time, the query is executed.
-
class
codetime.models.
TimeLogManager
¶ Bases:
django.db.models.manager.Manager
-
static
create_log
(time_log)¶ Create a new log for a file for a particular user
- Parameters
time_log (dict) – validated time log details from post request
- Returns
return status
- Return type
int
-
get_file_name_extension_wise_summary
(api_token)¶ Get fileextension wise log summary for a particular user
- Parameters
api_token (str) – unique token for each user
- Returns
list of all the file logs of the user
- Return type
list
-
get_time_logs
(api_token)¶ Get all the filelogs for a particular user
- Parameters
api_token (str) – unique token for each user
- Returns
list of all the file logs of the user
- Return type
list
-
get_time_spent_per_coding_language
(api_token)¶ Get the record for time spent on various codeing languages for a particular user
- Parameters
api_token (str) – unique token for each user
- Returns
list of time spent per coding language for the user
- Return type
list
-
get_time_spent_per_file
(api_token)¶ Get the record for time spent on on each of the file for a particular user
- Parameters
api_token (str) – unique token for each user
- Returns
list of time spent per file for the user
- Return type
list
-
get_weekday_count_summary
(api_token)¶ Get weekday’s summary for a particular user
- Parameters
api_token (str) – unique token for each user
- Returns
list of count of logs per day of the user
- Return type
list
-
static
-
class
codetime.models.
User
(*args, **kwargs)¶ Bases:
django.db.models.base.Model
Description for User DB Model
- Variables
log_user_id – Primary key indexing each user
username – unique username of the user
password – password of the user
api_token1 – unique token for each user stored for sublime activity
-
exception
DoesNotExist
¶ Bases:
django.core.exceptions.ObjectDoesNotExist
-
exception
MultipleObjectsReturned
¶ Bases:
django.core.exceptions.MultipleObjectsReturned
-
api_token
¶ A wrapper for a deferred-loading field. When the value is read from this object the first time, the query is executed.
-
log_user_id
¶ A wrapper for a deferred-loading field. When the value is read from this object the first time, the query is executed.
-
objects
= <codetime.models.UserManager object>¶
-
password
¶ A wrapper for a deferred-loading field. When the value is read from this object the first time, the query is executed.
-
username
¶ A wrapper for a deferred-loading field. When the value is read from this object the first time, the query is executed.
-
class
codetime.models.
UserManager
¶ Bases:
django.db.models.manager.Manager
APIs for the User Performs User actions (CRUD operations) for signup, login and updating user.
-
static
create_user
(user)¶ Create a new user.
- Parameters
user (dict) – validated user details from post request
- Returns
response status (0 for failure, 1 for success and 2 for DB error)
- Return type
int
-
get_user_from_api_token
(api_token)¶ Returns the user details using the unique API token of the user.
- Parameters
api_token (str) – unique apitoken of user
- Returns
dictionary response
- Return type
object
-
get_user_from_username
(username, password)¶ Returns the user details using the login credentails (basic auth) of the user.
- Parameters
username (str) – username of the user
password (str) – password of user
- Returns
dictionary response
- Return type
object
-
login
(username, password)¶ Login a user using basic auth
- Parameters
username (str) – username of the user
password (str) – password of user
- Returns
api_token is such user exists
- Return type
str
-
update_user
(user, api_token)¶ Update user information.
- Parameters
user (dict) – validated user details from post request
api_token (str) – unique apitoken of user
- Returns
response status (0 for success, 1 for no such user with api_token)
- Return type
int
-
static
codetime.request_handlers module¶
This modules has functions to handle all the supported commands for the codetime server’s APIs.
-
codetime.request_handlers.
get_invalid_request_param
(message)¶ Returns error response for invalid request parameters
- Parameters
error (str) – message
- Returns
error_response
- Return type
object
-
codetime.request_handlers.
get_invalid_user_credentials
(data=None)¶ Returns error response for invalid user credentials
- Parameters
data (str) – message
- Returns
response
- Return type
object
-
codetime.request_handlers.
get_missing_param_response
(data=None)¶ Returns error response for missing parameters
- Parameters
data (str) – message
- Returns
response
- Return type
object
-
codetime.request_handlers.
get_serializer_error_response
(error)¶ Returns error response from serializer
- Parameters
error (str) – message
- Returns
error_response
- Return type
object
-
codetime.request_handlers.
get_something_went_wrong_response
(data=None)¶ Returns error response for server related error
- Parameters
data (str) – message
- Returns
response
- Return type
object
-
codetime.request_handlers.
get_valid_output_response
(data)¶ Returns success message correct processing of post/get request
- Parameters
data (str) – message
- Returns
response
- Return type
object
-
codetime.request_handlers.
get_valid_post_response
(data)¶ Returns success message correct processing of post/get request
- Parameters
data (str) – message
- Returns
response
- Return type
object
-
codetime.request_handlers.
handle_get_file_logs
(request)¶ Handler for get request to obtain all records for a user.
- Parameters
GET request (HTTP) – request
- Returns
response
- Return type
object
-
codetime.request_handlers.
handle_log_file_post
(request)¶ Handler for post request made by logging files
- Parameters
POST request (HTTP) – request
- Returns
response
- Return type
object
-
codetime.request_handlers.
handle_summary_request
(request)¶ Handler for get request to summary records for a user.
- Parameters
GET request (HTTP) – request
- Returns
response
- Return type
object
-
codetime.request_handlers.
handle_user_get
(request)¶
-
codetime.request_handlers.
handle_user_post
(request)¶ Handler for post request made by a user to signup and login
- Parameters
POST request (HTTP) – message
- Returns
response
- Return type
object
codetime.serializers module¶
codetime.urls module¶
codetime.views module¶
-
class
codetime.views.
TimeLogSummaryView
(**kwargs)¶ Bases:
rest_framework.generics.ListAPIView
TimeLog Summary View
-
get
(request, *args, **kwargs)¶ TimeLogSummary get request
-


{% extends "main.html" %}
{% block tabs %}
{{ super() }}
Explore and test your systems to discover their weaknesses.The Chaos Engineering toolkit for Developers
Declare and store your Chaos Engineering experiments as JSON/YAML files so you can collaborate and orchestrate them as any other piece of code.
The Chaos Toolkit is extensible at will for any system through its Open API..
The Chaos Toolkit loves automation and can be embedded in your favourite CI/CD chain.
The Chaos Toolkit is Open Source with an Apache 2 license. No lock-in and powered by its community


{# By default, this just includes the django template defined from the view/form. Override this template to change that #} {% if html_template_name %} {% include html_template_name %} {% else %}
{% include template_name %}


You need Node
> 0.10.20
You can check with
node --version
If you do not have Node, you can install it by going to http://nodejs.org/ and clicking 'Install'.

Then head over to basic-post-grunt
To pull down the npm dependencies, run
npm install
You can see all tasks and check your setup with
grunt --help
Development mode with auto reload
grunt dev
Create a version for release
grunt release
Serve Content
grunt serve
cssmin
to the package.jsoncssmin: {
options: {
report: 'min'
},
css: {
files: {
'<%= distDir %>/main.css': '<%= distDir %>/main.css'
}
}
},
release
taskIf you are having problems:
The final GruntFile.js
is called gruntFile.js.final
Setup
cd mobile-example
gem install compass # if this fails, pair up and install ruby later :)
npm install
Running
Create a build of the project
grunt build
Serve the files
grunt serve


Abstract
Vecto is a simplified interface to the powerful CL-VECTORS vector rasterization library. It presents a function-oriented interface similar to CL-PDF, but the results can be saved to a PNG instead of a PDF file. Since Vecto and all supporting libraries are written completely in Common Lisp, without depending on external non-Lisp libraries, it should work in any Common Lisp environment. Vecto is available under a BSD-like license. The current version is 1.4.3, released on August 26, 2009.
Vecto is used by Easystreet and Movie Charts.
The canonical location for Vecto is http://www.xach.com/lisp/vecto/.
Download shortcut:
Vecto is a library that provides a simple interface to the the CL-VECTORS vector drawing library. It supports drawing on a canvas and saving the results to a PNG file.
Vecto depends on the following libraries:
The easiest way to install Vecto and all its dependencies is with ASDF-Install.
Vecto's function interface is similar to the PDF vector description and painting interface: you create images by describing vector paths, then using stroke or fill operations to paint to the canvas.
Vecto's color system uses red, green, blue, and alpha color components for drawing. The results can be be saved to a PNG with an alpha channel.
Vecto's coordinate system starts at the lower-left corner of the image, and increases rightwards along the X axis and upwards along the Y axis.
All measurements are in pixels.
PDF is a feature-rich system. Vecto supports a small subset of PDF-style operations. In particular, it does not support:
Other limitations:
Related libraries:
All examples are available in doc/examples.lisp in the Vecto distribution. That file starts with:
(defpackage #:vecto-examples (:use #:cl #:vecto)) (in-package #:vecto-examples)
(defun radiant-lambda (file) (with-canvas (:width 90 :height 90) (let ((font (get-font "times.ttf")) (step (/ pi 7))) (set-font font 40) (translate 45 45) (draw-centered-string 0 -10 #(#x3BB)) (set-rgb-stroke 1 0 0) (centered-circle-path 0 0 35) (stroke) (set-rgba-stroke 0 0 1.0 0.5) (set-line-width 4) (dotimes (i 14) (with-graphics-state (rotate (* i step)) (move-to 30 0) (line-to 40 0) (stroke))) (save-png file))))
(defun feedlike-icon (file) (with-canvas (:width 100 :height 100) (set-rgb-fill 1.0 0.65 0.3) (rounded-rectangle 0 0 100 100 10 10) (fill-path) (set-rgb-fill 1.0 1.0 1.0) (centered-circle-path 20 20 10) (fill-path) (flet ((quarter-circle (x y radius) (move-to (+ x radius) y) (arc x y radius 0 (/ pi 2)))) (set-rgb-stroke 1.0 1.0 1.0) (set-line-width 15) (quarter-circle 20 20 30) (stroke) (quarter-circle 20 20 60) (stroke)) (rounded-rectangle 5 5 90 90 7 7) (set-gradient-fill 50 90 1.0 1.0 1.0 0.7 50 20 1.0 1.0 1.0 0.0) (set-line-width 2) (set-rgba-stroke 1.0 1.0 1.0 0.1) (fill-and-stroke) (save-png file)))
(defun star-clipping (file) (with-canvas (:width 200 :height 200) (let ((size 100) (angle 0) (step (* 2 (/ (* pi 2) 5)))) (translate size size) (move-to 0 size) (dotimes (i 5) (setf angle (+ angle step)) (line-to (* (sin angle) size) (* (cos angle) size))) (even-odd-clip-path) (end-path-no-op) (flet ((circle (distance) (set-rgba-fill distance 0 0 (- 1.0 distance)) (centered-circle-path 0 0 (* size distance)) (fill-path))) (loop for i downfrom 1.0 by 0.05 repeat 20 do (circle i))) (save-png file))))
The following symbols are exported from the VECTO package.
Canvases
[Macro]
with-canvas (&key width height)
&body body
Evaluates body with a canvas established with the specified dimensions as the target for drawing commands. The canvas is initially completely clear (all pixels have 0 alpha).
[Function]
clear-canvas => |
Completely fills the canvas with the current fill color. Any marks on the canvas are cleared.
[Function]
save-png file => truename
Writes the contents of the canvas as the PNG file, and returns the truename of file.
[Function]
save-png-stream stream => |
Writes the contents of the canvas as a PNG to stream, which must accept (unsigned-byte 8) data.
The graphics state stores several parameters used for graphic operations.
[Macro]
with-graphics-state &body body
Evaluates the forms of body with a copy of the current graphics state. Any modifications to the state are undone at the end of the form.
[Functions]
set-rgba-fill r g b alpha => |
set-rgb-fill r g b => |
Sets the fill color. r, g, b, and alpha should be in the range of 0.0 to 1.0.set-rgb-fill is the same as set-rgba-fill with an implicit alpha value of 1.0.
The fill color is used for CLEAR-CANVAS, FILL-PATH, EVEN-ODD-FILL, FILL-AND-STROKE, EVEN-ODD-FILL-AND-STROKE, and DRAW-STRING.
[Function]
set-gradient-fill
x0 y0 r0 g0 b0 a0
x1 y1 r1 g1 b1 a1
&key (extend-start t)
(extend-end t)
(domain-function 'linear-domain)
Set the fill color source to an axial gradient. The start point is x0,y0 and the start color is r0,g0,b0,a0. The end point is x1, y1 and the end color is r1,g1,b1,a1.Two domain functions are available:
- LINEAR-DOMAIN, the default, makes a transition from the start color to the end color along the axis between the start and end points
- BILINEAR-DOMAIN makes a transition from the start color to the end color from the start point to the midpoint, then back to the start color from the midpoint to the end point
(defun gradient-example (file) (with-canvas (:width 200 :height 50) (set-gradient-fill 25 0 1 0 0 1 175 0 1 0 0 0) (rectangle 0 0 200 50) (fill-path) (save-png file)))(defun gradient-bilinear-example (file) (with-canvas (:width 200 :height 50) (set-gradient-fill 25 0 1 0 0 1 175 0 1 0 0 0 :domain-function 'bilinear-domain) (rectangle 0 0 200 50) (fill-path) (save-png file)))
[Functions]
set-rgba-stroke r g b alpha => |
set-rgb-stroke r g b => |
Sets the stroke color. r, g, b, and alpha should be in the range of 0.0 to 1.0.set-rgb-stroke is the same as set-rgba-stroke with an implicit alpha value of 1.0.
The stroke color is used for STROKE, FILL-AND-STROKE, and EVEN-ODD-FILL-AND-STROKE.
[Function]
set-line-cap style => |
Sets the line cap style to style, which must be one of :BUTT, :SQUARE, or :ROUND. The initial value is :BUTT.
:BUTT :SQUARE :ROUND
[Function]
set-line-join style => |
Sets the line join style to style, which must be one of :MITER, :BEVEL, or :ROUND. The initial value is :MITER.
:MITER :BEVEL :ROUND
[Function]
set-line-width width => |
Sets the line width for strokes to width.
[Function]
set-dash-pattern dash-vector phase => |
Sets the dash pattern according to dash-vector and phase.dash-vector should be a vector of numbers denoting on and off patterns for a stroke. An empty dash-vector is the same as having no dash pattern at all.
phase is how far along the dash pattern to proceed before applying the pattern to the current stroke.
Appearance Dash Vector and Phase #() 0 #(30 30) 0 #(30 30) 15 #(10 20 10 40) 0 #(10 20 10 40) 13 #(30 30) 0, :ROUND line caps
[Function]
translate x y => |
Offsets the coordinate system by x units horizontally and y units vertically.
[Function]
rotate radians => |
Rotates the coordinate system by radians.
[Function]
scale sx sy => |
Scales the coordinate system by sx horizontally and sy vertically.
[Function]
skew ax ay => |
Skews the X axis of the coordinate system by ax radians and the Y axis by ay radians.
[Function]
clip-path => |
Defines a clipping path based on the current path. It is not applied immediately, but is created after after the painting is done in the next call to one of FILL-PATH, EVEN-ODD-FILL, FILL-AND-STROKE, EVEN-ODD-FILL-AND-STROKE, or END-PATH-NO-OP.The clipping path initially covers the entire canvas; no clipping is done. Subsequent calls to CLIP-PATH set the clipping path to the intersection of the established clipping path and the new clipping path, and all drawing will be done within the outline of the clipping path.
The outline of the clipping path is defined with the nonzero winding rule, as with FILL-PATH.
There is no way to enlarge the clipping path. However, the clipping path is part of the graphics state, so changes may be localized by using WITH-GRAPHICS-STATE.
A filled red rectangle, not clipped The same rectangle drawn with a circle clipping path in effect Clipped to a rounded rectangle clipping path Clipped to the intersection of the circle and rounded rectangle clipping paths
[Function]
even-odd-clip-path => |
Like CLIP-PATH, but uses the even/odd fill rule to determine the outline of the clipping path.
Paths are used to create lines for stroking or outlines for filling. Paths consist of straight lines and curves. Paths consist of one or more subpaths.
[Function]
move-to x y => |
Starts a new subpath at (x,y). move-to must be the first step of constructing a subpath.
[Function]
line-to x y => |
Appends a straight line ending at (x,y) to the current subpath.
[Function]
curve-to
cx1 cy1
cx2 cy2
x y => |
Appends a cubic Bézier curve ending at (x,y) and with control points (cx1,cy1) and (cx2,cy2) to the current subpath.
[Function]
quadratic-to
cx cy
x y => |
Appends a quadratic Bézier curve ending at (x,y) and with the control point (cx,cy) to the current subpath.
[Function]
arc x y radius angle1 angle2
=> |
Appends an arc of a circle to the current path. The center of the arc is at (x,y). The arc begins at point at a distance radius from the center and with an angle angle1 radians from the positive x axis, and ends at the point with angle2 radians. If angle2 is less than angle1, it is increased by increasing multiples of 2π until it is greater than or equal to angle1.
radius, angle1, angle2 If there is a current point, a straight line is added from the current point to the start point of the arc. Otherwise, a new path is started.
(defun pie-wedge (file) (with-canvas (:width 80 :height 60) (let ((x 0) (y 0) (radius 70) (angle1 (* (/ pi 180) 15)) (angle2 (* (/ pi 180) 45))) (translate 5 5) (set-rgb-fill 1 1 1) (move-to 0 0) (arc x y radius angle1 angle2) (fill-and-stroke) (save-png file))))
[Function]
arcn x y radius angle1 angle2
=> |
Like ARC, but draws the arc clockwise instead of counterclockwise. If angle2 is greater than angle1, it is decreased by increasing multiples of 2π until it is less than or equal to angle1.(defun wiper (file) (with-canvas (:width 70 :height 70) (let ((x 0) (y 0) (r1 40) (r2 60) (angle1 0) (angle2 (* (/ pi 180) 90))) (translate 5 5) (set-rgba-fill 1 1 1 0.75) (arc x y r1 angle1 angle2) (arcn x y r2 angle2 angle1) (fill-and-stroke) (save-png file))))
[Function]
close-subpath => |
Closes the current subpath. If the current point is not the same as the starting point for the subpath, appends a straight line from the current point to the starting point of the current subpath.Subpaths with start and end points that coincidentally overlap are not the same as closed subpaths. The distinction is important when stroking:
Open subpath Closed subpath If the subpath is not closed, the start and points of the subpath will be drawn with the current line cap style. If the path is closed, the start and endpoints will be treated as joined and drawn with the line join style.
[Function]
stroke-to-paths => |
Sets the current active paths to the paths that would result from outlining a STROKE operation.
[Function]
rectangle x y width height => |
Creates a rectangular subpath with the given width and height that has its lower-left corner at (x,y). It is effectively the same as:(move-to x y) (line-to (+ x width) y) (line-to (+ x width) (+ y height)) (line-to x (+ y height)) (close-subpath)
[Function]
rounded-rectangle x y
width height rx ry => |
Like RECTANGLE, but rounds the corners of the rectangle paths by the x radius rx and the y radius ry.
[Function]
centered-ellipse-path
x y
rx ry
Adds a closed subpath that outlines an ellipse centered at (x,y) with an X radius of rx and a Y radius of ry.
[Function]
centered-circle-path x y radius => |
Adds a closed subpath that outlines a circle centered at (x,y) with a radius of radius. It is effectively the same as:(centered-ellipse-path x y radius radius)
After a path is defined, filling, stroking, or both will use the path to apply color to the canvas. After a path has been filled or stroked, it is no longer active; it effectively disappears.
[Function]
fill-path => |
Fills the current path with the fill color or gradient. If the path has not been explicitly closed with CLOSE-SUBPATH, it is implicitly closed before filling. The non-zero winding rule is used to determine what areas are considered inside the path.
[Function]
even-odd-fill => |
The same as FILL-PATH, but uses the even/odd rule to determine what areas are considered inside the path.
[Function]
stroke => |
Strokes the current path. The line width, stroke color, line join style, line cap style, and dash pattern and phase determine how the stroked path will appear on the canvas.
[Function]
fill-and-stroke => |
Fills the current path, then strokes it.
[Function]
even-odd-fill-and-stroke => |
Fills the current path using the even/odd rule, then strokes it.
[Function]
end-path-no-op => |
Ends the current path without painting anything. If a clipping path has been specified with CLIP-PATH or EVEN-ODD-CLIP-PATH, it will be created by end-path-no-op.
Vecto can draw text to a canvas. It loads glyph shapes from TrueType font files with ZPB-TTF.
[Function]
get-font font-file => font-loader
Creates and returns a ZPB-TTF font loader object from font-file. Any font loader created this way will automatically be closed at the end of its enclosing WITH-CANVAS form.
[Function]
set-font font-loader size => |
Sets the active font to the font associated with font-loader, scaled to size units per line.The first argument can be any ZPB-TTF font loader; it need not be created via GET-FONT. However, only font loaders created via GET-FONT will be automatically closed at the end of WITH-CANVAS.
[Function]
set-character-spacing spacing => |
Sets the scale of the spacing used between characters when drawing text with DRAW-STRING and related functions.Normally, the character spacing for drawing text is taken directly from a font's advance-width and kerning metrics. SET-CHARACTER-SPACING can be used to adjust this spacing; for example, a character spacing of 2.0d0 would double the normal space between characters, and 0.5d0 would halve it.
[Special variable]
*default-character-spacing*
The default scale for character spacing when drawing text with DRAW-STRING and related functions. Initial value is 1.0d0.Changes to this variable affect the next WITH-CANVAS form. To affect the spacing within the current WITH-CANVAS form, use SET-CHARACTER-SPACING.
[Function]
draw-string x y string => |
Draws string on the canvas with the active font. The glyph origin of the first character in the string is positioned at x and the baseline of the string is positioned at y. The text is filled with the current fill color.The string may be a specialized vector of characters (a true CL string) or a vector containing characters, Unicode code-points, or both. For example, #(#\L #\a #\m #\b #\d #\a #\= #x3BB) is a valid argument for DRAW-STRING.
The horizontal space between characters is determined by the advance-width and kerning metrics of the font, then multiplied by the current character spacing. At the default character spacing of 1.0d0, the spacing is not adjusted at all. SET-CHARACTER-SPACING can be used to increase or decrease the character spacing.
[Function]
string-paths x y string => |
Like DRAW-STRING, but instead of drawing the text, adds the subpaths that make up the string glyphs to the graphics state. This can be used to stroke, fill, or clip based on the outlines of a string.
[Function]
draw-centered-string x y string => |
Draws string on the canvas with the active font. The horizontal center of the string is positioned at x and the baseline of the string is positioned at y.
[Function]
centered-string-paths x y string => |
Like DRAW-CENTERED-STRING, but adds subpaths instead of painting. See also STRING-PATHS.
[Function]
string-bounding-box string size loader
=> #(xmin ymin xmax ymax)
Calculates the bounding box of string for font-loader at size.
[Constant]
+kappa+ => 0.5522847498307936d0.
This constant is useful to draw portions of a circle.
Many thanks to Ben Deane for permission to adapt code from his curve library for drawing arcs.
Ryan Davis helped fix my adaptation of the arc drawing.
Feedback
If you have any questions, comments, bug reports, or other feedback regarding Vecto, please email Zach Beane.


TEAM MAFIA ON TRACK 2
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THE ONE AND ONLY
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logs
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loss angels


Prince Andrew’s Accuser Ready to Testify to FBI – Report
Woman claims she was recruited by Ghislaine Maxwell
by SputnikThe accusations, which the alleged sex slave first floated in 2007, may add weight to the ongoing FBI investigation into Jeffrey Epstein, a one-time friend of Prince Andrew’s.
The Prince has already faced allegations of rape in this case but vehemently denied any wrongdoing.
A woman who has accused Prince Andrew of sexual misconduct at Jeffrey Epstein’s home is ready to talk to American prosecutors looking into Jeffrey Epstein’s case, The Mirror reports.
The plot to surveil the Trump campaign went all the way to “the top” of the Obama administration, the president says.
Johanna Sjoberg, 39, claims that Jeffrey Epstein’s alleged madam, Ghislaine Maxwell, had recruited her to provide sexual massages to the late financier.
Sjoberg also alleged that Andrew groped her breast when she sat on his lap at Epstein’s New York home in 2001. She was 21 at the time.
“As a victim of Epstein, Sjoberg’s testimony over how she was recruited and abused is vital to the FBI,” an anonymous source told The Mirror.
“Andrew has had serious allegations made against him and agents are determined that a person’s standing in life does not make them immune from being questioned.”

Joe Schildhorn/Patrick McMullan via Getty Images
One of the alleged “sex slaves,” Victoria Roberts-Giuffre, claims that she was forced to have sex with Prince Andrew in the early 2000s. The 59-year-old royal, who is known to have been friends with Epstein, denies the allegations.
In a bid to clear his public image, he has recently given a lengthy interview on those charges, which many deemed inconclusive and even damaging.
The interview addressed the claims by Roberts-Giuffre but did not focus on Sjoberg’s accusations, which she first made in 2007.
It has been reported that the FBI and the US Department of Justice are discussing whether to interview Prince Andrew on his connections with Epstein. The Duke of York would be “happy to cooperate” with the FBI, a source close to the royal told The Sunday Times.
Organization pledges to “arrest and purge” corporate leaders who resist.
By the way, overhaul your body’s cellular engine with our fan-favorite DNA Force Plus!


Every user has a different
multipath response
User 2
User 1
User 1
User 2
Time Impulse
Time Impulse
Transmitter
Freq Response
Freq Response


This is inside a webview tag
Say with GoogleTTS API and iframe
Say with GoogleTTS API and iframe through a proxy php script
Say with GoogleTTS API and HTML5 audio tag
Say with GoogleTTS API and HTML5 audio tag through a proxy php script
Say with WebspeechAPI
Create Normal iframe


OBEventPolling1
- maxEvents : Integer
Maximum number of events to be returned. A value of zero indicates the ASPSP should not return events even if available
- returnImmediately : Boolean
Indicates whether an ASPSP should return a response immediately or provide a long poll
- setErrs : Map
An object that encapsulates all negative acknowledgements transmitted by the TPP


x
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Please select at least one Administrative unit, period and data.
Administrative units
all
Regions
disticts
Periods
Yearly
quarterly
monthly
Data
Category
period
administrative units
Table
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Export to Excel


@(scoreboard: Scoreboard)
Beach,Date,Reading,Prediction,SwimStatus
@for(beachRanking <- scoreboard.beachRankings){
@for(ecoliMeas <- beachRanking.ecoliMeasurements){
@beachRanking.beachUrl,@ecoliMeas.getDate(),@ecoliMeas.getReading(),@ecoliMeas.getPrediction(),@ecoliMeas.getMaxSwimStatus()
}
}


amber$ cd main/CSS/\[CSS\]\ position:absolute\ doesn\'t\ work\!
[CSS] position:absolute doesn't work!
When I set position: absolute;, I found that that element isn't placed
related to its parent, it is placed related to the screen instead. So I
searched online, it is said that the statement will work when its parent
set position to relative.
Here is the demo:


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{% extends "base.html" %} {% block title %}
Medieval catalogues > UNIVERSITY AND COLLEGE LIBRARIES OF CAMBRIDGE > Donor documents > Gift of Thomas Anlaby, after 1431; Pembroke Hall
{% endif %}15 identified entries found.
All data was derived from the List of Identifications by Professor Richard Sharpe.
{% if not printing %}
A key to codes used in the List is available (opens in new tab).{% endif %}


Options and Status Status: testing... Options Capture webcam: Capture audio: Capture video + audio: Capture entire screen:


BPM WHY?
Gain or lose...
Organize or repeat...
See how to make software again soft :
---> BPM Reloaded
---> BPM Why?
Tell us and to the world Your NEEDS or VIEWPOINTS :
......


Installing node.js on Windows and Ubuntu
To get the latest Apache conrdova, one need to make sure that node.js (and npm) exists.
Installing node on Windows
Download the latest node.js from http://nodejs.org/
Follow the below steps after opening the downloaded setup file:
Let the installer compute required space.
So, if we have required space:
Select destination to install node
Select which node components to install - this may be useful for server configuration
Installing node on Ubuntu
There are various ways to install node on Ubuntu, one of them is compiling and installing
Use the below commands one-line-after-the-other
$ sudo apt-get install build-essential
$ wget http://nodejs.org/dist/v0.8.16/node-v0.8.16.tar.gz
$ tar -xzf node-v0.8.16.tar.gz
$ cd node-v0.8.16/
$ ./configure
$ make
$ sudo make install
To verify the installation:
$ node -v
v0.8.16
$ npm -v
1.1.69
Yes, that’s it. Done with the task!
Written on January 1, 2016


8.4. The Ingredients section¶
The $ingredients
section contains a section for global ingredient keywords and then a section for each ingredient type.
Each ingredient type in the recipe should have a subsection denoted by begin <ingredient type>
.
Example $ingredients
section:
$ingredients
begin ingredients_global
keyword1 k1value1
end
begin ingredient_type1
keyword1 k1value2
keyword2 k2value1
end
begin ingredient_type2
keyword2 k2value2
end
$end
Program-specific keywords such as VASP INCAR keywords are included in these sections. All other keywords are prefaced with mast_
.
If there are no changes from the ingredients_global section, just add an empty subsection for that ingredient type:
begin ingredient_type
end
For a specific ingredient type, if a keyword is not specified in that ingredient type’s subsection but is specified in the ingredients_global subsection, then, the value for that keyword will be taken from ingredients_global.
- In the example above,
ingredient_type2
would inheritkeyword1 k1value1
from ingredients_global.
8.4.1. Program-specific keywords¶
VASP keywords such as IBRION
, ISIF
, LCHARG
, LWAVE
, and so on, can be specified under each ingredient type in the $ingredients
section of the input file.
Such program-specific keywords are only allowed if they are listed in the program-specific file located in the <MAST installation directory>/MAST/ingredients/programkeys/
folder, for example, <MAST installation directory>/MAST/ingredients/programkeys/vasp_allowed_keywords.py
.
These program-specific keywords will be turned into uppercase keywords. The values will not change case, and should be given in the case required by the program. For example, lwave False
will be translated into LWAVE False
in the VASP INCAR file.
One exception for VASP keywords is the IMAGES
keyword, which signals a nudged elastic band run, and should instead be set in the $neb
section of the input file.
For VASP ingredients, please include
lcharg False
lwave False
in your ingredient global keywords in order to avoid writing the large VASP files CHGCAR and WAVECAR, unless you really need these files.
8.4.2. Special MAST keywords¶
Any keyword that starts with mast_
is considered a special keyword utilized by MAST and will not be written into the VASP INCAR file or any custom input file.
<IP_ADDRESS>. Submission script keywords¶
The following queue submission keywords are platform-dependent and are used along to create the submission script (see Installation).
mast_exec: The command used in the submission script to execute the program. Note that this is a specific command rather than the class of program, given in mast_program
, and it should include any MPI commands.
mast_exec //opt/mpiexec/bin/mpiexec ~/bin/vasp_5.2
mast_nodes: The number of nodes requested.
mast_ppn: The number of processors per node requested.
mast_queue: The queue requested.
mast_walltime: The walltime requested, in whole number of hours
mast_memory: The memory per processor requested.
<IP_ADDRESS>. MAST control flow keywords¶
mast_program: Specify which program to run (vasp
, vasp_neb
, or None
for a generic program, are currently supported)
mast_program vasp
- This keyword must be in lowercase
mast_frozen_seconds: A number of seconds before a job is considered frozen, if its output file has not been updated within this amount of time. If not set, 21000 seconds is used.
mast_auto_correct: Specify whether mast should automatically correct errors.
- The default is True, so if this keyword is set to True, or if this keyword is not specified at all, then MAST will attempt to find errors, automatically correct the errors, and resubmit the ingredient.
- If set to False, MAST will attempt to find errors, then write them into a
MAST_ERROR
file in the recipe folder, logging both the error-containing ingredient and the nature of the error, but not taking any corrective actions. The recipe will be skipped in all subsequent MAST runs until theMAST_ERROR
file is manually deleted by the user.
<IP_ADDRESS>. VASP-specific keywords¶
mast_kpoints: Specify k-point instructions in the form of kpoints along lattice vectors a, b, and c, and then a designation M for Monkhorst-Pack or G for Gamma-centered.
mast_kpoints = 3x3x3 G
- Either this keyword or
mast_kpoint_density
is required for VASP calculations.
mast_kpoint_density: A number for the desired kpoint mesh density.
- Only works with
mast_write_method
ofwrite_singlerun_automesh
- Either this keyword or
mast_kpoints
is required for VASP calculations.
mast_pp_setup: Specify which pseudopotential goes to which element:
mast_pp_setup La=La Mn=Mn_pv O=O_s
mast_xc: Specify an exchange correlation functional; for VASP, follow the conventions of pymatgen (e.g. pw91, pbe)
- This keyword is required for VASP calculations.
mast_multiplyencut: Specify a number with which to multiply the maximum ENCUT value of the pseudopotentials. Volume relaxations in VASP often take 1.5; otherwise 1.25 is sufficient.
- Default is 1.5
- If
encut
is given as a program keyword, then that value will be used andmast_multiplyencut
should have no effect
mast_setmagmom: Specify a string to use for setting the initial magnetic moment. A short string will result in multipliers. For example, mast_setmagmom 1 5 1
will produce 2*1 2*5 8*1
for a 12-atom unit cell with 2A, 2B, and 8C atoms. A string of the number of atoms in the POSCAR
file will be printed as entered, for example, mast_setmagmom 1 -1 1 -1 1 -1 1 -1
.
mast_charge: Specify the charge on the system (total system)
- -1 charge means the ADDITION of one electron. For example, O2- has two more electrons than O neutral.
- A positive charge is the REMOVAL of electrons. For example, Na+ with a +1 charge has one FEWER electron than Na neutral.
mast_coordinates: For a non-NEB calculation, allows you to specify a single POSCAR-type of CIF structure file which corresponds to the relaxed fractional coordinates at which you would like to start this ingredient. ONLY the coordinates are used. The lattice parameters and elements are given by the $structure section of the input file. The coordinates must be fractional coordinates.
mast_coordinates POSCAR_initialize
For an NEB calculation, use a comma-delimited list of poscar files corresponding to the correct number of images. Put no spaces between the file names. Example for an NEB with 3 intermediate images:
mast_coordinates POSCAR_im1,POSCAR_im2,POSCAR_im3
The structure files must be found in the directory from which the input file is being submitted when initially inputting the input file (e.g. the directory you are in when you run
mast -i test.inp
); once theinput.inp
file is created in the recipe directory, it will store a full path back to these poscar-type files.This keyword cannot be used with programs other than VASP, cartesian coordinates, and special ingredients like inducedefect-type ingredients, whose write or run methods are different.
<IP_ADDRESS>. Structure manipulation keywords¶
mast_strain: Specify three numbers for multiplying the lattice parameters a, b, and c. Only works with mast_run_method
of run_strain
mast_strain 1.01 1.03 0.98
This example will stretch the lattice along lattice vector a by 1%, stretch the lattice along lattice vector b by 3%, and compress the lattice along lattice vector c by 2%
<IP_ADDRESS>. mast_xxx_method keywords¶
The following keywords have individual sections:
mast_write_method: Specifies what the ingredient should write out before running (e.g., create the INCAR)
mast_ready_method: Specifies how MAST can tell if the ingredient is ready to run (often, in addition to writing its own files, an ingredient must also wait for data from its parent ingredient(s)).
mast_run_method: Specifies what MAST should do to run the ingredient (e.g. submit a submission script to a queue, or perform some other action)
mast_complete_method: Specifies how MAST can tell if the ingredient is considered complete
mast_update_children_method: Specifies what information an ingredient passes on to its children, and how it does so.
Specific available values for each keyword are given in the accompanying sections, and require no arguments, e.g.:
mast_write_method write_singlerun
They depend on having an appropriate program set in mast_program
.
However, you may choose to specify arguments where available, e.g.:
mast_complete_method file_has_string myoutput "End of Execution"
You may also choose to specify multiple methods. These methods will be performed in the order listed. For mast_ready_method
or mast_complete_method
, all methods listed must return True in order for the ingredient to be considered ready or complete, respectively.
Use a semicolon to separate out the methods:
mast_complete_method file_has_string myoutput "End of Execution"; file_exists Parsed_Structures
In the example above, the file “myoutput” must exist and contain the phrase “End of Execution”, and the file “Parsed_Structures” must exist, in order for the ingredient to be considered complete.
Update-children methods will always get the child name appended as the end of the argument string. For example,
mast_update_children_method copy_file EndStructure BeginStructure
will copy the file EndStructure of the parent ingredient folder to a new file BeginStructure in the child ingredient folder. There is no separate argument denoting the child ingredient.
All arguments are passed as strings. Arguments in quotation marks are kept together.
Some common open-ended methods are:
file_exists <filename>
file_has_string <filename> <string>
copy_file <filename> <copy_to_filename>
softlink_file <filename> <softlink_to_filename>
copy_fullpath_file <full path file name> <copy_to_filename>: This method is for copying some system file like //home/user/some_template, not an ingredient-specific file
write_ingred_input_file <filename> <allowed file> <uppercase keywords> <delimiter>: The allowed file specifies an allowed keywords file name in
<MAST installation directory>/MAST/ingredients/programkeys
.Use “all” to put any non-mast keywords into the input file.
Use 1 to uppercase all keywords, or 0 to leave them as entered.
Leave off the delimiter argument in order to use a single space.
Examples:
write_ingred_input_file input.txt all 0 = write_ingred_input_file input.txt phon_allowed_keys.py 1
no_setup: Does nothing. Useful when you want to specifically specify doing nothing.
no_update: Does nothing (but, does accept the child name it is given). Useful when you want to specify doing nothing for a child update step.
run_command: <command string, including all arguments>: This method allows you to run a python script.
The python script may take in only string-based arguments
Please stick to common text characters.
Example:
mast_run_method run_command "//home/user/myscripts/my_custom_parsing.py 25 0.01"
In the example above, the numbers 25 and 0.01 will actually be passed into sys.argv as a string.
This method is intended to allow you to run short custom scripts of your own creation, particularly for
mast_write_method
when setting up your ingredient.- For long or complex execution steps where you want the output tracked separately, do not use this method. Instead, do the following in order to get your script submitted to the queue:
# Use
write_submit_script
in yourmast_write_method
, along with any other write methods # Usemast_run_method run_singlerun
# Put your script in themast_exec
keyword
<IP_ADDRESS>. mast_write_method keyword values¶
write_singlerun
- Write files for a single generic run.
- Programs supported: vasp
write_singlerun_automesh
- Write files for a single generic run.
- Programs supported: vasp
- Requires the
mast_kpoint_density
ingredient keyword
write_neb
- Write an NEB ingredient. This method writes interpolated images to the appropriate folders, creating 00/01/…/0N directories and uses linear interpolation between images.
- Programs supported: vasp
write_pathfinder_neb
Write an NEB ingredient using a charge-density-based pathfinding method.
Programs supported: vasp
This method takes the argument of an ingredient name for the ingredient from which to take the charge density.
- The ingredient name must be fully specified, e.g. no <S>, <N>, etc. tags.
- The ingredient must have a CHGCAR file written. A gamma-point calculation is sufficient for this purpose.
- The ingredient should have both endpoints removed. For example, for vacancy migration, the ingredient should have neither a vacancy at the initial position, nor a vacancy at the final position. However, it should have all other non-migrating defects that are common to both the initial and final state.
This method also takes a second argument as a number of additional sites from the bottom of the sorted POSCAR for which to find the path, if the moving atoms(s) are not interstitials; e.g. a vacancy-mediated moving atom must be sorted to the end, and the second argument would therefore be 1.
This method only works with
use_structure_index True
in the$structure
section of the input file.
write_neb_subfolders
- Write static runs for an NEB, starting from a previous NEB, into image subfolders 01 to 0(N-1).
- Programs supported: vasp
write_phonon_single
- Write files for a phonon run.
- Programs supported: vasp
write_phonon_multiple
- Write a phonon run, where the frequency calculation for each atom and each direction is a separate run, using selective dynamics. CHGCAR and WAVECAR must have been given to the ingredient previously; these files will be softlinked into each subfolder.
- Programs supported: vasp
<IP_ADDRESS>. mast_ready_method keyword values¶
ready_singlerun
- Checks that a single run is ready to run
- Programs supported: vasp (either NEB or regular VASP run), phon
ready_defect
- Checks that the ingredient has a structure file
- Programs supported: vasp
ready_neb_subfolders
- Checks that each 01/…/0(N-1) subfolder is ready to run as its own separate calculation, following the ready_singlerun criteria for each folder
- This method is used for NEB static calculations rather than NEB calculations themselves.
ready_subfolders
* Checks that each subfolder is ready to run, following the ready_singlerun criteria.
* Generic
* This method is used for calculations whose write method includes subfolders, and where each subfolder is a calculation, as in write_phonon_multiple
.
<IP_ADDRESS>. mast_run_method keyword values¶
run_defect
- Create a defect in the structure; not submitted to queue
- Generic
- Requires the
$defects
section in the input file (see The Defects section).
run_singlerun
- Submit a run to the queue.
- Generic
run_neb_subfolders
- Run each 01/…/0(N-1) subfolder as run_singlerun
- Generic
run_subfolders
- Run each subfolder as run_singlerun
- Generic
run_strain
- Strain the structure; not submitted to queue
- Generic
- Requires the
mast_strain
ingredient keyword
run_scale
- Scale the structure (e.g. a 2-atom unit cell scaled by 2 becomes a 16-atom supercell)
- Requires the
$scaling
subsection in the input file (see The Structure section). - Must not be run on the starting ingredient.
<IP_ADDRESS>. mast_complete_method keyword values¶
complete_singlerun
Check if run is complete
Programs supported: vasp
Note that for VASP, the phrase
reached required accuracy
is checked for, as well as aUser time
in seconds. The exceptions are:- NSW of 0, NSW of -1, or NSW not specified in the ingredients section keywords is taken as a static calculation, and .EDIFF is reached. is checked instead of .reached required accuracy.
- IBRION of -1 is taken as a static calculation, and .EDIFF is reached. is checked instead of .reached required accuracy.
- IBRION of 0 is taken as an MD calculation, and only user time is checked
- IBRION of 5, 6, 7, or 8 is taken as a phonon calculation, and only user time is checked
complete_neb_subfolders
- Check if all NEB subfolders 01/…/0(N-1) are complete, according to complete_singlerun criteria.
- This method is not for checking the completion of NEBs! An NEB ingredient should have
mast_program vasp_neb
andmast_complete_method complete_singlerun
. - An NEB static calculation, or a static calculation for each image, would use this keyword as
mast_complete_method complete_neb_subfolders
but havemast_program vasp
instead of vasp_neb.
complete_subfolders
- Check if all subfolders are complete, according to complete_singlerun criteria.
- Generic
complete_structure
- Check if run has an output structure file written
- Programs supported: vasp (looks for CONTCAR)
<IP_ADDRESS>. mast_update_children_method keyword values¶
give_structure
- Forward the relaxed structure
- Programs supported: vasp (CONTCAR to POSCAR)
give_structure_and_energy_to_neb
- Forward the relaxed structure and energy files
- Programs supported: vasp (CONTCAR to POSCAR, and copy over OSZICAR)
give_neb_structures_to_neb
- Give NEB output images structures as the starting point image input structures in another NEB
- Programs supported: vasp (01/…/0(N-1) CONTCAR files will be the child NEB ingredient.s starting 01/…/0(N-1) POSCAR files.
give_saddle_structure
- Forward the highest-energy structure of all subfolder structures
- Programs supported: vasp
<IP_ADDRESS>. Example Ingredients section¶
Here is an example ingredients section:
$ingredients
begin ingredients_global
mast_program vasp
mast_nodes 1
mast_multiplyencut 1.5
mast_ppn 1
mast_queue default
mast_exec mpiexec //home/mayeshiba/bin/vasp.5.3.3_vtst_static
mast_kpoints 2x2x2 M
mast_xc PW91
isif 2
ibrion 2
nsw 191
ismear 1
sigma 0.2
lwave False
lcharg False
prec Accurate
mast_program vasp
mast_write_method write_singlerun
mast_ready_method ready_singlerun
mast_run_method run_singlerun
mast_complete_method complete_singlerun
mast_update_children_method give_structure
end
begin volrelax_to_singlerun
isif 3
end
begin singlerun_to_phonon
ibrion -1
nsw 0
mast_update_children_method give_structure_and_restart_files
mast_multiplyencut 1.25
lwave True
lcharge True
end
begin inducedefect
mast_write_method no_setup
mast_ready_method ready_defect
mast_run_method run_defect
mast_complete_method complete_structure
end
begin singlerun_vac1
mast_coordinates POSCAR_vac1
end
begin singlerun_vac2
mast_coordinates POSCAR_vac2
end
begin singlerun_to_neb
ibrion -1
nsw 0
mast_update_children_method give_structure_and_energy_to_neb
lwave True
lcharge True
end
begin neb_to_neb_vac1-vac2
mast_coordinates POSCAR_nebim1,POSCAR_nebim2,POSCAR_nebim3
mast_write_method write_neb
mast_update_children_method give_neb_structures_to_neb
mast_nodes 3
mast_kpoints 1x1x1 G
ibrion 1
potim 0.5
images 3
lclimb True
spring -5
end
begin neb_to_neb_vac1-vac3
mast_coordinates POSCAR_nebim1_set2,POSCAR_nebim2_set2,POSCAR_nebim3_set2
mast_write_method write_neb
mast_update_children_method give_neb_structures_to_neb
mast_nodes 3
mast_kpoints 1x1x1 G
ibrion 1
potim 0.5
images 3
lclimb True
spring -5
end
begin neb_to_nebstat
mast_write_method write_neb
mast_update_children_method give_neb_structures_to_neb
mast_nodes 3
ibrion 1
potim 0.5
images 3
lclimb True
spring -5
end
begin nebstat_to_nebphonon
ibrion -1
nsw 0
mast_write_method write_neb_subfolders
mast_ready_method ready_neb_subfolders
mast_run_method run_neb_subfolders
mast_complete_method complete_neb_subfolders
mast_update_children_method give_saddle_structure
end
begin phonon_to_phononparse
mast_write_method write_phonon_multiple
mast_ready_method ready_subfolders
mast_run_method run_subfolders
mast_complete_method complete_subfolders
mast_update_children_method give_phonon_multiple_forces_and_displacements
ibrion 5
nfree 2
potim 0.01
istart 1
icharg 1
end
$end


This is a very elegant and well-written piece of code, but it's not perfect. What might we do differently? I might rearrange matchhere to deal with $ before *. Although it makes no difference here, it feels a bit more natural, and a good rule is to do easy cases before difficult ones.
In general, however, the order of tests is critical. For instance, in this test from matchstar:
} while (*text != '\0' && (*text++ == c || c == '.'));
we must advance over one more character of the text string no matter what, so the increment in text++ must always be performed.
This code is careful about termination conditions. Generally, the success of a match is determined by whether the regular expression runs out at the same time as the text does. If they do run out together, that indicates a match; if one runs out before the other, there is no match. This is perhaps most obvious in a line like:
if (regexp[0] == '$' && regexp[1] == '\0') return *text == '\0';
but subtle termination conditions show up in other cases as well.
The version of matchstar that implements leftmost longest matching begins by identifying a maximal sequence of occurrences of the input character c. Then it uses matchhere to try to extend the match to the rest of the pattern and the rest of the text. Each failure reduces the number of cs by one and tries again, including the case of zero occurrences:
/* matchstar: leftmost longest search for c*regexp */ int matchstar(int c, char *regexp, char *text) { char *t; for (t = text; *t != '\0' && (*t == c || c == '.'); t++) ; do { /* * matches zero or more */ if (matchhere(regexp, t)) return 1; } while (t-- > text); return 0; }
Consider the regular expression (.*), which matches arbitrary text within parentheses. Given the target text:
for (t = text; *t != '\0' && (*t == c || c == '.'); t++)
a longest match from the beginning will identify the entire parenthesized expression, while a shortest match will stop at the first right parenthesis. (Of course a longest match beginning from the second left parenthesis will extend to the end of the text.)


ACTION JACKSON A film review by Andrew Hicks Copyright 1997 Andrew Hicks
(1988) *1/2 (out of four)
Toward the bottom of the '80s action movie barrel lies ACTION JACKSON, the only movie in Hollywood history to show Sharon Stone and Vanity topless within a span of ten minutes. This Carl "Apollo Creed" Weathers vehicle features the traditional cop vs. evil establishment crook, and relies on all the '80s trappings, from the token heroin addict who needs a fix to the shouting superior officer. Somewhere in between come the explosions and boobs, although there is a curious lack of exploding boobs.
Weathers is Action Jackson, a Detroit cop known for all sorts of crazy vigilante techniques. Speaking of one past criminal apprehension, Jackson's superior yells, "You tore his arm off!" Jackson replies, "He had a spare." Jackson has been busted down to a desk job because of past problems with auto manufacturer Dellaplane (Craig T. Nelson) and now has to act as department liaison to a dinner honoring Nelson. After hearing what Jackson thinks of him, Stone says, "I take it you're not one of Dellaplane's friends." "Not unless they changed the definition," Jackson glowers. And of course it turns out Stone is Mrs. Dellaplane. Faux pas, Jackson...
Not everything is happy in Motown; people are being killed and Dellaplane has evil plans for the AWA. It's all up to Jackson to stop it, and his only lead lies in Vanity (not his own). Wherever she left off with Prince in terms of exchanging sexual favors for career advancement she picks up in ACTION JACKSON. She plays the heroin addict and chanteuse in Dellaplane's nightclub. After singing one particularly sultry number for him, she saunters over and complains, "I expected a standing ovation."  Prince wouldn't even let out an innuendo that lame and he's the king of the horndogs... or at least the prince.
It's a testament to the '80s that Sharon Stone is killed off within the first 30 minutes but Vanity survives the whole movie. ACTION JACKSON is another variation on the "unlikely partners" buddy action flick, with Jackson lugging junkie Vanity around. This leads to some of the worst paired acting of the me decade, and with the dialogue they're given, there's not much room for improvement. My favorite is when Vanity, feeling the effects of drug withdrawal, says, "I feel like my teeth are hollow, my gums are made of dry rubber and someone's trying to start a bonfire in the back of my bloody head." Jackson's response is, "I think I felt that way once. They called it love."
You'll understand when I say watch this at your own risk.
--
Visit the Andrew Hicks: Movie Critic at LARGE homepage at http://www.missouri.edu/~c667778/movies.html
Serving The World For Nearly 1/25th of a Century!
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


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Our product and research:
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The relevant stakeholders of Flikr are the Ministry of Education, schools, school staff, students and parents. While the needs of each stakeholder varies slightly, Flikr is able to meet all these requirements. The needs of the Ministry of Education is a digital solution which can be implemented across the province to support young children for feelings of loneliness, anxiety and isolation. The product was specifically created with the needs of the Ministry of Education in mind. The app is accessible and the interface encourages young children to use it. Ontario schools have very similar needs to the Ministry as the Ministry of Education oversees the schools. Flikr allows schools to easily create apps for all its students. Teachers and other school staff need a product that will help them with teaching and taking care of the students. Flikr fulfills both needs with their mentorship and their mental health features. Students are arguably the most important stakeholder because they are the target demographic. Many students can benefit from a mentorship program because it allows them to ask a student who has recently been through similar experiences. Flikr allows students to interact with each other in a safe environment. Parents are another important stakeholder. They need a product that will make themselves feel comfortable with their kids using on a daily basis, as well as something that will assist them with helping students navigate themselves through their middle school years. This product addresses these needs through the mentorship program and the strict privacy policies.
1.3 Research and Literature
Gaps in Research In comparison with the many chat apps on the market, Flikr is unique because it fills many gaps that are not present in other apps. To begin, Flikr’s accessibility makes it stand out. Many apps that are currently on the market require you to either pay upfront or have hidden payment features presented once the user is already using the app. With funding from the Ministry of Education, Flikr does not need to collect any money from its users, making this app accessible to anyone who has a mobile device. The second gap we will fill is safety. According to an article published, “Young people highlighted that mental health apps must be safe to use in terms of confidentiality, cyber-bullying and stigma.” (Kenny, Dooley, & Fitzgerald, 2014) Flikr greatly values our users’ safety so we fill this gap by allowing everyone to stay anonymous if they wish. In addition, Flikr keeps track of past messages by flagging certain keywords that may signify any type of intimidation. By having this app be safe-guarded by the ministry, it ensures that only students in Ontario have access to it. The final gap, and perhaps the one that makes Flikr stand out the most, is our mentorship program. This feature is unique to this product because this app is not only for students that are dealing with mental health issues but as well as a platform where students can connect with their peers for other reasons. They can use this feature for help with schoolwork and student guidance, among others. This will aid with the need of social inclusion and loneliness as mentioned in the client statement.
2.0 Detailed Design
2.1 Proposed Design
Flikr is an all new creative concept aimed to target loneliness and anxiety predominantly in children ages 11-13 during these challenging times. With Flikr, users are able to join anonymously with a code given by the school system to ensure that all its users are truly students in Ontario. Once joined, students are able to join groups for their schools, classes, etc. in order to make new support systems. However, what really allows Flikr to stand out is it’s focus on it’s mentorship program. Children ages 11-13 will be paired up with a student 1-2 years older than them that attends the school they are in currently or a school they will be moving into. With this, younger students will have a friend they can look up to for help and older students will earn badges that can be transferred into volunteer hours. Flikr’s main goal is the mental well-being of all its users which is why there is a page dedicated to seeking alternative resources and help. It also includes features such as flagging/reporting people/comments anonymously. It’s intuitive algorithm also searches for potentially triggering keywords that will block the message. Flikr allows children, parents, teachers and schools to rest with a peaceful mind knowing that all its users have a safe platform to make new friendships and alleviate these feelings of loneliness and anxiety that many experience today.
2.3 Effectiveness of Design
This app is designed primarily for two functions; to reduce mental health problems in students as well as connecting students with their peers. Effectiveness of apps can be measured primarily by it’s usage, accessibility, and quality of content. The more people that use this app, the more effective it will be. “Mobile technologies can provide almost constant support to users and allow interventions to be administered in users’ natural environments, thus ensuring a high level of ecological validity....Adolescents use their phones on a daily basis and are likely to find them appealing as a familiar intervention medium.” (Kenny, Dooley, & Fitzgerald, 2014) The popularity of phones in adolescents means that a high user usage is promising especially when schools are encouraging the use of this app and create incentives for its use (volunteer hours) The goal of this app is to not only allow students to make new friends and stay connected as well as have a helping hand from an older student, but to provide an excellent user experience in-app as well. Many other health apps have trouble with this. “Many patient end users stop using a health app two weeks after download. Clinician end user adherence is influenced by familiarity with technology and app match to the clinician's therapeutic expertise.” (Torous et al., 2019) Flikr shouldn’t have this problem as it’s not a professional-client service, but rather a client-client platform. Another reason why many users delete these apps is due to a clunky user interface and experience. We provide a clean, straight to the point interface where nothing is confusing and everything is intuitive. Flikr will be the first option for students to turn to whenever they want to seek help. If they cannot get the help they need from peers, we have many external resources listed on an easily accessible page. According to the Medical Journal of Australia, “Young people may seek help through talking to their family and friends, with family being more important for younger adolescents, and friends and partners becoming more influential later on.” (Rickwood, Deane, & Wilson, 2007) The mentorship program in this app is the perfect example of the app's effectiveness. Students will feel that they will have someone they can rely on for help, decreasing their overall stress. The combination of this app’s promising high usage, easy accessibility and its mentorship program all work in conjunction with each other to make a very effective product.
3.0 Conclusions
Flikr has incredible potential because of its unique safety features, mentorship program and accessibility. With the implementation of this app in the school system, children ages 11-13 will be able to form new friendships and mentors all within a safe environment. This app will alleviate feelings of loneliness and anxiety with the main goal of improving mental health in adolescents.
4.0 Sources
Torous, J., Nicholas, J., Larsen, M. E., Firth, J., & Christensen, H. (2018). Clinical review of user engagement with mental health smartphone apps: Evidence, theory and improvements. Evidence Based Mental Health, 21(3), 116-119. doi:10.1136/eb-2018-102891 Kenny, R., Dooley, B., & Fitzgerald, A. (2014). Developing mental health mobile apps: Exploring adolescents’ perspectives. Health Informatics Journal, 22(2), 265-275. doi:10.1177/1460458214555041 Torous, J., Andersson, G., Bertagnoli, A., Christensen, H., Cuijpers, P., Firth, J., . . . Arean, P. A. (2019). Towards a consensus around standards for smartphone apps and digital mental health. World Psychiatry, 18(1), 97-98. doi:10.1002/wps.20592 Rickwood, D. J., Deane, F. P., & Wilson, C. J. (2007). When and how do young people seek professional help for mental health problems? Medical Journal of Australia, 187(S7). doi:10.5694/j.1326-5377.2007.tb01334.x


Search Jobs
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Project 2
Instructions
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Press X to translate the mesh in the positive X direction,
C for negative X.
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Press Y to translate the mesh in the positive Y direction,
U for negative Y.
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Press Z to translate the mesh in the positive Z direction,
for negative Z.
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Press R to rotate the mesh around the X direction in its
current position, T for the reverse direction.
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Press F to rotate the mesh around the Y direction in its
current position, G for the reverse direction.
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Press H to rotate the mesh around the Z direction in its
current position, J for the reverse direction.
- Press B to toggle mesh pulsing.
- Press N to toggle drawing normal vectors.


{% extends 'base.html' %} {% comment %} # Copyright 2017 Lockheed Martin Corporation # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # {% endcomment %} {% load bootstrap3 %} {% block title %}Missions{% endblock %} {% block extra_nav_bar_content_right %}
| Mission Name | Mission # | Business Area | Actions | |
|---|---|---|---|---|
| {% bootstrap_icon "edit" %} Edit | {{ m.mission_name }} | {{ m.mission_number }} | {{ m.business_area }} |
{% bootstrap_icon "stats" %} Test Cases {% bootstrap_icon "hdd" %} Mission Hosts {% bootstrap_icon "file" %} Generate Report {% bootstrap_icon "gift" %} Generate Data Package |


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Ploror688.github.io
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Dr. Norman Borlaug
The man who saved billion lives
Dr. Norman Borlaug, third from the left, trains biologists in Mexico on how to increase wheat yields - part of his life-long war on hunger.
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