javascript – How to perform this subtraction several times? Subtract once and start adding

        

Balance

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usability – How to support a "Save draft" in an assisted approach when the user takes a long time to perform a step?

I have a simple user workflow, in which the user has to finish the process sequentially. But step 2 can take a day or more to be completed.

In the current user interface, if the user decides to finish the process later, he can click "save" to skip the process and he will be directed to the summary page. The user can save the entire process as a draft. When the user comes back to edit, there will be no wizard approach. They can modify each step by individually selecting the process steps in the summary.

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Is the approach intuitive? Are there any other templates for managing draft backups in a wizard approach?

dnd 5e – What can an invisible PC do to perform the "Help" action and stay invisible?

My wizard ally wants to become invisible and use the "Help" action to help attacks while remaining invisible. My DM ruled that a physical interaction with the enemy would constitute an action that breaks invisibility.

I thought I would use the same action on my invisible pet. What could they do especially not to break in an invisible way?

Perform a CSRF attack when the CSRF token is sent in the custom request header

I found that the web application uses a weak algorithm generate CSRF TokenThe CSRF token is sent in the header of the request

X-CSRF-TOKEN: "Token chain"

Since the query header is used, how to perform a CSRF attack to perform a sensible action in real time?

Custom headers can be sent using JS, but they are blocked due to CORS. I've seen few topics mentioning ActionScript in Flash that can be used to send custom headers. Does it work again? (Whereas chrome has stopped using Flash). Is there anyway I can perform the attack?

mysql – How to store API JSON data and perform searches efficiently

I display the documentation with the help of Swagger, but the documentation being long, I created a side menu and split the data into the database. By splitting it, it allows me to search specific areas and endpoints of the documentation and display it, and to only display what is selected instead of everything, every time.

There are different versions of the documentation, so some information overlaps. I'm not sure that the best way to store this, and currently it is separated by a version of the same documentation but there is a lot of redundancy because a lot of data overlap compared to previous versions.

Currently, the structure looks like this:

api_data table

id
tags
general_path
path_content
product_id
search (keywords to index)
overhead_id
version_id

api_data_overhead table

id
product_id
file_overhead

api_product_versions table

id
active
product_id
name

The file overload contains a lot of common information for each path to this documentation file. It is very long but does not need to be indexed or searched.

I am looking for a way to improve the design of this database as it develops and new documentation is added. I am concerned about performance when searching and downloading data. There are a lot of redundancies right now and I wonder if it can be better.

Programming Languages ​​- Can Anyone Perform My Computer Work? I am ready to $$

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Can you use GetSpectrumData () + FFTWindows to perform audio visualizations in Unity 2d?

I've watched online and the tutorials are focused on Unity 3D. With the problems, I'm not sure if I can use this function and FFTWindow to create a 2D audio viewer or let the audio affect my 2D objects.

Can be done in 2D?

Thank you.

security – Is it possible to perform an offline scan of malware from the device image created using a root prompt?

I have created an image of all of my device and all its partitions (mmcblk0.img). I want to perform an "offline" scan of my device on my computer.

Are there any known projects or ways to perform an analysis, analysis, or investigation of this image for spyware or malware? Maybe an emulator built for this purpose could help here?

python – Is there a way to make this code perform faster (deep learning)?

The purpose of this network is to see in the dark. I am a novice in deep learning and I have not had time to debug this network and test it. Although I would appreciate any advice.
Here is the code:

I used Tensorflow and sympy

class network(): 

def __init__(self, inputs, width, height, kernel_size=(5, 5)):
    super(fully_convolution, self).__init__()
    self.kernel_size = kernel_size
    self.batch_size = 256
    trial = 0



def elu(x, alpha=1):
    return np.where(x < 0, alpha * (np.exp(x) - 1), alpha*x) #-1+ae^x{x<0}, ax{x>0}





def squeeze_net(input, squeeze_depth, expand_depth, scope): 
    # fire module used for keeping the same map size and reducing the number of parameters which keeps the neurons from being heavily affected
    with tf.variable_scope(name, "squeeze", values=(inputs)):
        squeezed = tf.nn.conv2d(input, squeeze_depth, (1, 1), name="squeeze")
        x = tf.nn.conv2d(squeezed, expand_depth, (1, 1), name="1x1")
        y = tf.nn.conv2d(squeezed, expand_depth, (3, 3), name="3x3")
        return tf.concat((x, y), axis=0)



def count_sketch(img, x):
    a, b = img.shape
    c = np.zeros((a, x))
    hash_indices = np.random.choice(x, b, replace=True) # memory table that convert one data to another
    rand_sign = np.random.choice(2, b, replace=True) * 2 - 1 # generate random samples
    matrix_a = img * rand_sign.reshape(1, b) # flip the signs of 50% columns of A
    for i in range(x):
        index = (hash_indices == i)
        c(:, i) = np.sum(matrix_a(:, index), 1)
    return c


def bilinear(x1, x2, output_size):
    p1 = count_sketch(x1, output_size)
    p2 = count_sketch(x2, output_size)
    pc1 = tf.complex(p1, tf.zeros_like(p1))
    pc2 = tf.complex(p2, tf.zeros_like(p2))

    conved = tf.batch_ifft(tf.batch_fft(pc1) * tf.batch_fft(pc2))
    return tf.real(conved)


def deconv_network(layer1, layer2, channel, pool_size=2):
    filter = np.array((None, None), (None, None), dtype=np.int64)
    layer = tf.nn.conv2d_transpose(layer1, filter, tf.shape(layer2), strides=(pool_size, pool_size))
    bilinear = bilinear((layer, layer2), 3)
    bilinear.set_shape((50, 50))
    return bilinear



def bayes_prob(layer):
    with tf.compact.v1.name_scope(“bayesian_prob”, values=(layer)):
        model = tf.keras.Sequential((
            tfp.layers.DenseFlipout(512, activation=tf.nn.relu),
            tfp.layers.DenseFlipout(10),
        ))

    logits = model(features)
    neg_log_likelihood = tf.nn.softmax_cross_entropy_with_logits(
            labels=labels, logits=logits)
    kl = sum(model.losses)
    loss = neg_log_likelihood + kl
    train_op = tf.train.AdamOptimizer().minimize(loss)
    return model



def refine_net(x1, x2, num_hidden):    
    n = int(num_hidden)
    image = bilinear(x1, x2, 50)
    x = tf.nn.conv2d(image, n*2, pool_size=2, padding=“valid”, activation_function=tf.nn.ReLU)
    y = bayes_prob(x)

   #______________________*light_enhancement*_________________________


    image = tf.image.resize_nearest_neighbor(y, (50, 50))
    dark = squeeze_net(image, n, n*2, scope=“dark”)
    bright = tf.nn.conv2d(dark, n, 3, stride = (3, 3), activation=tf.nn.ReLU, padding='same')
    bright2 = tf.nn.conv2d(n=n*2, 3, stride = (3, 3), activation=tf.nn.ReLU, padding='same')(bright)
    bright3 = tf.nn.conv2d(n=n*2, 3, stride = (3, 3), activation=tf.nn.ReLU, padding='same')(bright2)

    conv_resize = tf.image.resize_nearest_neighbor(bright3, (50, 50))

    layer1 = tf.nn.conv2d(n*2, 3, stride(1, 1), activation=tf.nn.ReLU, padding='same')(conv_resize)
    layer2 = deconv_network(layer1, bright3, n*2)
    layer3 = tf.nn.conv2d(n*2, 3, stride(1, 1), activation=tf.nn.ReLU, padding='same')(conv_resize)
    layer4 = deconv_network(layer3, bright2, n*2)




    output_size = tf.image.resize_nearest_neighbor(layer4, (tf.shape(y)(1), tf.shape(y)(2)))

    light_image = bilinear(output_size, y, 50)
    enhancement = Model(input=dark, output=light_image)

    n = int(num_hidden)



   #_____________________*deblurring*________________________

    blur_net = squeeze_net(y, n, n*2, scope="blur")
    deblur_net = tf.nn.conv2d(blur_pixel_net, n, strides, pool_size=2, padding="same", tf.nn.ReLU)
    deblur_squeeze = tf.squeeze(deblur_net, (10, 10), scope="depixel")
    deblur_layer = deconv_network(deblur_squeeze, deblur_net, n=n*2, pool_size=3)
    deblur_layer2 = deconv_network(deblur_layer, deblur_net, n*2, pool_size=3)

    deblur_output = deconv_network(deblur_layer2, deblur_net, n*2, pool_size=3)

    deblur = Model(input=blur_net, output=deblur_output)

    n = int(number_hidden)

    #_______________________*denoising*________________________



    # Encoder: Uses activation techniques; Sigmoid for accuracy

    bias = tf.Variable(tf.random_normal(n))
    encode = tf.nn.sigmoid(tf.add(tf.matmul(y, weight(model))))


    # Decoder

    decode = tf.nn.sigmoid(tf.add(tf.matmul(encode, weight(model))))


    if trial == 0:
        encode = tf.nn.sigmoid(tf.add(tf.matmul(y, weights)))
        weights = tf.Variable(tf.math.abs(tf.random_normal(n, n_=2*n)))
        decode = tf.nn.sigmoid(tf.add(tf.matmul(encode, weights)))
        weights = tf.Variable(tf.math.abs(tf.random_normal(n_, n)))
        decode = tf.nn.sigmoid(tf.add(tf.matmul(decode, weights)))


    autoencoder = Model(input=encode, output=decode)



    #----------------------------------------------------------



    #uses parameter sharing and breaks down into certain tasks        
    dropout = layers.Dropout(rate=0.12, noise_shape=(batch_size, 1, features), seed=None)
    merge = bilinear(deblur, decoder, n)
    merge = bilinear(merge, enhancement, n)
    deconv_output = deconv_network(merge, y, n*2, n*4, pool_size=5)
    upsampling_output =  tf.nn.upsampling2d(pool_size=(3, 3), interpolation='bilinear')(deconv_output)

    global_step = tf.Variable(0, trainable = False)

    loss_light = enhancement.compile(loss='sparse_categorical_crossentropy', 
                                     metrics=('accuracy'), optimizer=Adam(learning_rate=1e-3, decay=0.99))
    loss_deblur = deblur.compile(loss='sparse_categorical_crossentropy', 
                                     metrics=('accuracy'), optimizer=Adam(learning_rate=1e-3, decay=0.99))
    input = tf.placeholder(tf.float32, (None, None, None, 3), name='input')
    loss_noise = tf.reduce_mean(tf.pow(input - decode, 2))


    lr = tf.train.exponential_decay(1e-3, global_step, 100, 0.96)
    optimizer_light = tf.train.AdamOptimizer(lr, name='AdamOptimizer')
    train_op_light = optimizer_light.minimize(loss_light, global_step=global_step)

    optimizer_deblur = tf.train.AdamOptimizer(lr, name='AdamOptimizer')
    train_op_deblur = optimizer_deblur.minimize(loss_deblur, global_step=global_step)

    optimizer_noise = tf.train.AdamOptimizer(lr, name='AdamOptimizer')
    train_op_noise = optimizer_noise.minimize(loss_noise, global_step=global_step)

    return upsampling_output


#Makes Completely Dark Image Contain Less Noise & Black Level:Encode Check For Filter/ Neuron Size
def model(input=self.input):

    Conv1 = tf.nn.conv2d(input, 16, 2, strides=(1,1), padding='same', activation=elu)
    Conv1_ = tf.nn.conv2d(Conv1, 16, 2, strides=(1,1), padding='same', activation=elu)
    Pool1 = MaxPooling2D(pool_size = kernel_size)(Conv1_)
    Conv2 = tf.nn.conv2d(Pool1, 32, 3, strides=(1,1), padding='same', activation=elu)
    Conv2_ = tf.nn.conv2d(Conv2, 32, 3, strides=(1,1), padding='same', activation=elu )
    Pool2 = MaxPooling2D(pool_size = kernel_size)(Conv2_)
    Conv3 = tf.nn.conv2d(Pool2, 96, 3, strides=(1,1), padding='same', activation=elu)
    Conv3_ = tf.nn.conv2d(Conv3, 96, 3, strides=(1,1), padding='same', activation=elu )
    Pool3 = MaxPooling2D(pool_size = kernel_size)(Conv3_)
    Refine = refine_net(Pool2, Pool3, 8)
    Conv4 = tf.nn.conv2d(Refine, 128, 3, strides=(1,1), padding='same', activation=elu)
    Conv4_ = tf.nn.conv2d(Conv4, 128, 3, strides=(1,1), padding='same', activation=elu )
    Pool4 = MaxPooling2D(pool_size = kernel_size)(Conv4_)
    Refine2 = refine_net(Pool3, Pool4, 16)
    Conv5 = tf.nn.conv2d(Refine2, 256, 3, strides=(1,1), padding='same', activation=elu)
    Conv5_ = tf.nn.conv2d(Conv5, 256, 3, strides=(1,1), padding='same', activation=elu)
    Pool5 = MaxPooling2D(pool_size=kernel_size)(Conv5_)
    output = refine_net(Pool4, Pool5, 32)

    return output

Really perform the HETZNER server auction?

Hello.

When you go to

[h = 3] Server auction – Hetzner Online GmbH [… | Read the rest of https://www.webhostingtalk.com/showthread.php?t=1787166&goto=newpost