troposphere¶
About¶
troposphere - library to create AWS CloudFormation descriptions
The troposphere library allows for easier creation of the AWS CloudFormation JSON by writing Python code to describe the AWS resources. troposphere also includes some basic support for OpenStack resources via Heat.
To facilitate catching CloudFormation or JSON errors early the library has property and type checking built into the classes.
Installation¶
troposphere can be installed using the pip distribution system for Python by issuing:
$ pip install troposphere
To install troposphere with awacs (recommended soft dependency):
$ pip install troposphere[policy]
Alternatively, you can run use setup.py to install by cloning this repository and issuing:
$ python setup.py install # you may need sudo depending on your python installation
Examples¶
A simple example to create an instance would look like this:
>>> from troposphere import Ref, Template
>>> import troposphere.ec2 as ec2
>>> t = Template()
>>> instance = ec2.Instance("myinstance")
>>> instance.ImageId = "ami-951945d0"
>>> instance.InstanceType = "t1.micro"
>>> t.add_resource(instance)
<troposphere.ec2.Instance object at 0x101bf3390>
>>> print(t.to_json())
{
"Resources": {
"myinstance": {
"Properties": {
"ImageId": "ami-951945d0",
"InstanceType": "t1.micro"
},
"Type": "AWS::EC2::Instance"
}
}
}
A simple example to create an instance (YAML) would look like this:
>>> from troposphere import Ref, Template
>>> import troposphere.ec2 as ec2
>>> t = Template()
>>> instance = ec2.Instance("myinstance")
>>> instance.ImageId = "ami-951945d0"
>>> instance.InstanceType = "t1.micro"
>>> t.add_resource(instance)
<troposphere.ec2.Instance object at 0x101bf3390>
>>> print(t.to_yaml())
Resources:
myinstance:
Properties:
ImageId: ami-951945d0
InstanceType: t1.micro
Type: AWS::EC2::Instance
Alternatively, parameters can be used instead of properties:
>>> instance = ec2.Instance("myinstance", ImageId="ami-951945d0", InstanceType="t1.micro")
>>> t.add_resource(instance)
<troposphere.ec2.Instance object at 0x101bf3550>
And add_resource()
returns the object to make it easy to use with Ref()
:
>>> instance = t.add_resource(ec2.Instance("myinstance", ImageId="ami-951945d0", InstanceType="t1.micro"))
>>> Ref(instance)
<troposphere.Ref object at 0x101bf3490>
Examples of the error checking (full tracebacks removed for clarity):¶
Incorrect property being set on AWS resource:
>>> import troposphere.ec2 as ec2
>>> ec2.Instance("ec2instance", image="i-XXXX")
Traceback (most recent call last):
...
AttributeError: AWS::EC2::Instance object does not support attribute image
Incorrect type for AWS resource property:
>>> ec2.Instance("ec2instance", ImageId=1)
Traceback (most recent call last):
...
TypeError: ImageId is <type 'int'>, expected <type 'basestring'>
Missing required property for the AWS resource:
>>> from troposphere import Template
>>> import troposphere.ec2 as ec2
>>> t = Template()
>>> t.add_resource(ec2.Instance("ec2instance", InstanceType="m3.medium"))
<troposphere.ec2.Instance object at 0x109ee2e50>
>>> print(t.to_json())
Traceback (most recent call last):
...
ValueError: Resource ImageId required in type AWS::EC2::Instance
Currently supported AWS resource types¶
Currently supported OpenStack resource types¶
OS::Neutron::Firewall
OS::Neutron::FirewallPolicy
OS::Neutron::FirewallRule
OS::Neutron::FloatingIP
OS::Neutron::FloatingIPAssociation
OS::Neutron::HealthMonitor
OS::Neutron::Pool
OS::Neutron::LoadBalancer
OS::Neutron::Net
OS::Neutron::PoolMember
OS::Neutron::Port
OS::Neutron::SecurityGroup
OS::Nova::FloatingIP
OS::Nova::FloatingIPAssociation
OS::Nova::KeyPair
OS::Nova::Server
Todo:
Add additional validity checks
Duplicating a single instance sample would look like this¶
# Converted from EC2InstanceSample.template located at:
# http://aws.amazon.com/cloudformation/aws-cloudformation-templates/
from troposphere import Base64, FindInMap, GetAtt
from troposphere import Parameter, Output, Ref, Template
import troposphere.ec2 as ec2
template = Template()
keyname_param = template.add_parameter(Parameter(
"KeyName",
Description="Name of an existing EC2 KeyPair to enable SSH "
"access to the instance",
Type="String",
))
template.add_mapping('RegionMap', {
"us-east-1": {"AMI": "ami-7f418316"},
"us-west-1": {"AMI": "ami-951945d0"},
"us-west-2": {"AMI": "ami-16fd7026"},
"eu-west-1": {"AMI": "ami-24506250"},
"sa-east-1": {"AMI": "ami-3e3be423"},
"ap-southeast-1": {"AMI": "ami-74dda626"},
"ap-northeast-1": {"AMI": "ami-dcfa4edd"}
})
ec2_instance = template.add_resource(ec2.Instance(
"Ec2Instance",
ImageId=FindInMap("RegionMap", Ref("AWS::Region"), "AMI"),
InstanceType="t1.micro",
KeyName=Ref(keyname_param),
SecurityGroups=["default"],
UserData=Base64("80")
))
template.add_output([
Output(
"InstanceId",
Description="InstanceId of the newly created EC2 instance",
Value=Ref(ec2_instance),
),
Output(
"AZ",
Description="Availability Zone of the newly created EC2 instance",
Value=GetAtt(ec2_instance, "AvailabilityZone"),
),
Output(
"PublicIP",
Description="Public IP address of the newly created EC2 instance",
Value=GetAtt(ec2_instance, "PublicIp"),
),
Output(
"PrivateIP",
Description="Private IP address of the newly created EC2 instance",
Value=GetAtt(ec2_instance, "PrivateIp"),
),
Output(
"PublicDNS",
Description="Public DNSName of the newly created EC2 instance",
Value=GetAtt(ec2_instance, "PublicDnsName"),
),
Output(
"PrivateDNS",
Description="Private DNSName of the newly created EC2 instance",
Value=GetAtt(ec2_instance, "PrivateDnsName"),
),
])
print(template.to_json())
Community¶
We have a Google Group, cloudtools-dev, where you can ask questions and engage with the troposphere community. Issues and pull requests are always welcome!
Licensing¶
troposphere is licensed under the BSD 2-Clause license. See LICENSE for the troposphere full license text.
- Quick Start
- examples
- troposphere
- tests
- Changelog
- 2.5.2 (2019*09*29)
- 2.5.1 (2019*08*25)
- 2.5.0 (2019*07*28)
- 2.4.9 (2019*06*26)
- 2.4.8 (2019*06*23)
- 2.4.7 (2019*05*18)
- 2.4.6 (2019*03*20)
- 2.4.5 (2019*02*19)
- 2.4.4 (2019*02*13)
- 2.4.3 (2019*02*13)
- 2.4.2 (2019*02*02)
- 2.4.1 (2019*01*09)
- 2.4.0 (2019*01*06)
- 2.3.4 (2018*11*04)
- 2.3.3 (2018*09*05)
- 2.3.2 (2018*09*04)
- 2.3.1 (2018*07*01)
- 2.3.0 (2018*05*26)
- 2.2.2 (2018*05*23)
- 2.2.1 (2018*03*10)
- 2.2.0 (2018*01*29)
- 2.1.2 (2017*12*03)
- 2.1.1 (2017*11*26)
- 2.1.0 (2017*11*19)
- 2.0.2 (2017*10*23)
- 2.0.1 (2017*10*21)
- 2.0.0 (2017*10*07)
- 1.9.6 (2017*09*24)
- 1.9.5 (2017*07*26)
- 1.9.4 (2017*06*04)
- 1.9.3 (2017*04*13)
- 1.9.2 (2017*01*29)
- 1.9.1 (2017*01*03)
- 1.9.0 (2016*11*15)
- 1.8.2 (2016*10*08)
- 1.8.1 (2016*09*12)
- 1.8.0 (2016*08*15)
- 1.7.0 (2016*07*07)
- 1.6.0 (2016*05*04)
- 1.5.0 (2016*03*01)
- 1.4.0 (2016*01*01)
- 1.3.0 (2015*10*21)
- 1.2.2 (2015*09*15)
- 1.2.1 (2015*09*07)
- 1.2.0 (2015*09*04)
- 1.1.2 (2015*07*23)
- 1.1.1 (2015*07*12)
- 1.1.0 (2015*06*15)
- 1.0.0 (2015*05*11)
- 0.7.2 (2015*03*23)
- 0.7.1 (2015*01*11)
- 0.7.0 (2015*01*02)
- 0.6.2 (2014*10*09)
- 0.6.1 (2014*09*28)
- 0.6.0 (2014*08*26)
- 0.5.0 (2014*03*21)
- 0.4.0 (2014*02*19)
- 0.3.4 (2013*12*05)
- 0.3.3 (2013*10*04)
- 0.3.2 (2013*09*25)
- 0.3.1 (2013*09024)
- 0.3.0 (2013*08*07)
- 0.2.9 (2013*05*07)
- 0.2.8 (2013*04*24)
- 0.2.6 (2013*03*26)
- 0.2.5 (2013*03*25)
- 0.2.0 (2013*02*26)
- 0.1.2 (2013*02*21)
- Contributing
- Releasing
- Code of Conduct
- License


-
sym.h
- External header file
-
symInt.h
- Internal header file
-
sym.c
-
-
symAssignMdd.c
- routines to build mdd for an assignment
-
symBitvec.c
- provides utilities to build transition relation for bit vector operations
-
symExprMdd.c
- routines to build mdd for boolean expressions
-
symIntf.c
- Tcl interface to image computation
-
symMddArray.c
- routines to manage an array of MDDs
-
symMV.c
- Builds the MDDs for the multi-valued expressions.
-
symUtil.c
- utilities provided by sym package
sym.h
External header file
By: Sriram K. Rajamani
See Alsooptional
symInt.h
Internal header file
By: Sriram K. Rajamani
sym.c
By: Sriram K. Rajamani
-
Sym_ModulePerformBFS()
- do invariant checking
-
Sym_DebugTracePrint()
- print debug trace for an invariant
-
Sym_ModuleBuildTransitionRelation()
- build symbolic transtion relation for a module
-
Sym_ModuleBuildInitialRegion()
- build mdd for initial region of module
-
Sym_AtomBuildTransitionRelation()
- build transition relation for an atom
-
Sym_AtomBuildInitialRegion()
- build initial region for an atom
-
SymModuleSearch()
- do reachability + invariant checking
-
MddGroupVariables()
- Group all bdd vars corresponding to mdd vars initMddId to
initMddId + (blockSize-1) in a block which will not be split in reordering.
symAssignMdd.c
routines to build mdd for an assignment
By: Sriram K. Rajamani
An assignment has an LHS var and an RHS expression.
Assignments can be classified based on the type of LHS.
This file has routines to build MDDs corresponding to assignments.
(1) If the LHS is boolean, MDDs are created separately for
LHS and RHS, and are then xnored.
(2) If the LHS is bitvector or multivalued specialized functions
(implemented on symMV.c or symBitVector.c) are called.
See AlsoSymExprMdd.c
-
Sym_AssignmentBuildMdd()
- build MDD for an assignment
-
SymAssignmentBuildMddStandard()
- build MDD for a standard asssignment
-
SymAssignmentBuildMddBoolean()
- Build MDD for boolean assignment with LHS and RHS
of type boolean
symBitvec.c
provides utilities to build transition relation for bit vector operations
By: Sriram K. Rajamani
optional
See Alsooptional
-
SymBitVectorBuildAssignmentMdd()
- build MDD for bit vector assignment
-
SymBitVectorBuildBooleanExpressionMdd()
- build MDD for a boolean expression of bit vectors
-
SymBitVectorBuildMddArray()
- build MDD array for bit vector expression
-
SymBitVectorBuildMddArrayForVar()
- return and mdd array for a bitvector variable
symExprMdd.c
routines to build mdd for boolean expressions
By: Sriram K. Rajamani
This file containes routies that are used to build MDDs
corresponding to boolean expressions. Note that the
boolean expressions can have multi valued variables
e.g: (x < 5) & (y > x + 5)
See AlsoSymAssignMdd.c
-
Sym_ExprBuildMdd()
- build MDD for an expression
-
SymExprBuildExprConstMdd()
- build MDD for an expression-constant equality
symIntf.c
Tcl interface to image computation
By: Sriram Rajamani
-
Sym_Init()
- Initilization function for sym package
-
Sym_Reinit()
- Re-initialization function for sym package
-
Sym_End()
- Termination function for sym package
-
Sym_RegionManagerReadRegionTable()
- Read region manager's region table
-
Sym_RegionManagerReadCounter()
- Read region manager's region counter
-
Sym_RegionManagerIncrementCounter()
- Increment region manager's region counter
-
Sym_RegionAlloc()
- Allocate a region struct
-
Sym_RegionFree()
- required
-
Sym_RegionManagerAlloc()
- Allocate a region manager
-
Sym_RegionManagerFree()
- required
-
Sym_RegionManagerReadSymInfo()
- Find the symInfo for the given module
-
Sym_RegionManagerAddModuleToSymInfo()
- Add a module and symInfo pair to the moduleToSymInfo table
-
Sym_RegionManagerDeleteModuleToSymInfo()
- Delete a module-syminfo pair from moduleToSymInfo table
-
SymWriteOrder()
- Write variable order for symInfo into a file
-
SymReadOrder()
- Read variable order for module from a file
-
CommandComputeReachSet()
- driver for building reached set
-
CommandComputeInitSet()
- driver for building initial state
-
CommandBuildTrans()
- driver for building transition relation
-
CommandComputePostSet()
- compute the post set of a given set
-
CommandComputeUnion()
- compute the union of two regions
-
CommandPrintRegion()
- Print the given region
-
CommandDynamicVarOrdering()
- Implements the dynamic_var_ordering command.
-
CommandWriteOrder()
- Write the current ordering of the module into a file
-
CommandReadOrder()
- Order the MDD variables of a module as specified by a file
-
VarBuildBddIdArray()
- Gets the levels of the BDD variables corresponding to the MDD
variable of a node.
-
VarOrdersCompareBddIdArray()
- Used to sort an array of nodes in ascending order of lowest BDD
level.
-
IntegersCompare()
- Used to sort an array of integers in ascending order.
-
StringConvertToDynOrderType()
- Converts a string to a dynamic ordering method type.
-
DynOrderTypeConvertToString()
- Converts a dynamic ordering method type to a string.
-
ModuleTotalOrderVariables()
- Convert an ordering of a subset of variables to a total
ordering
-
VarOrderIsInArray()
- check if a variable is in a varOrderArray
-
ModuleAssignMddIdsToVariables()
- Create MDD variables according to the specified total ordering
symMddArray.c
routines to manage an array of MDDs
By: Sriram K. Rajamani
optional
See Alsooptional
-
SymMddArrayObtainConstant()
- Obtain constant MDD array
-
SymMddArrayOrMdd()
- Door "or" on all components of MDD array with given mdd
-
SymMddArrayAndMdd()
- Does "and" on all components of MDD array with given mdd
-
SymMddArrayImpliesMddArray()
- do "implies" on two MDD arrays
-
SymMddArrayOrMddArray()
- do "or" on two MDD arrays
-
SymMddArrayAndMddArray()
- do "and" on two MDD arrays
-
SymMddArrayXnorMddArray()
- do "xnor" on two MDD arrays
-
SymMddArrayXorMddArray()
- do "xor" on two MDD arrays
-
SymMddArrayPlusMddArray()
- do "add" on two MDD arrays
-
SymMddArrayMinusMddArray()
- do "subtract" on two MDD arrays
-
SymMddArrayNot()
- do "not" on MDD array
-
SymMddArrayAndComponents()
- do "and" of all MDD array components
-
SymMddArrayCompareEqual()
- do equality check on two MDD arrays
-
SymMddArrayRedAnd()
- do Reduction And of an Array
-
SymMddArrayRedOr()
- do Reduction Or of an Array
-
SymMddArrayRedXor()
- do Reduction Or of an Array
-
SymMddArrayCompareGreater()
- do greater than check on two MDD arrays
-
SymMddArrayCompareLesser()
- do lesser than check on two MDD arrays
-
SymMddArrayCompareGreaterEqual()
- do greater than or equal check on two MDD arrays
-
SymMddArrayCompareLesserEqual()
- do lesser than or equal check on two MDD arrays
-
SymMddArrayFree()
- free MDD array
-
SymMddArrayPrint()
- print MDD array
symMV.c
Builds the MDDs for the multi-valued expressions.
By: Shaz Qadeer
optional
See Alsooptional
-
SymExprBuildMvf()
- Given a parse tree for a multivalued expression compute its mvf
-
SymMVBuildBooleanExpressionMdd()
- Given a boolean expression involving multivalued
variables, build mdd for it
-
SymMVBuildAssignmentMdd()
- Build MDD for multi valued assignment
symUtil.c
utilities provided by sym package
By: Sriram K. Rajamani
This file contains utilities that are used both by other files
of the sym package, as well as by users of sym package
See Alsosym.c
-
Sym_SymInfoAlloc()
- Allocate and return a symInfo struct
-
SymRangeReadDummyId()
- Utility to access a dummyId for a range
-
Sym_SymInfoFree()
- Utility to free symInfo
-
Sym_MddPrintCubes()
- Utility to dump mdds
-
Sym_MddPrintCubesImplSpec()
- Utility to dump mdds
-
Sym_MddPrintStateForErrorTrace()
- Utility to generate a state string for Tcl error
trace display
-
Sym_MddPrintRawCubes()
- Utility to dump mdds - in terms of raw mdd ids
-
Sym_ModuleMatchMddIds()
- match mdd ids of two modules
-
Sym_SymInfoBuildFlatTrans()
- Utility to build a flat transition relation
-
Sym_SymInfoReadVarToUnprimedId()
- read the varToUnprimedId st_table
-
Sym_SymInfoReadVarToPrimedId()
- read the varToPrimedId st_table
-
Sym_SymInfoReadUnprimedIdArray()
- read the unprimedIdArray array
-
Sym_SymInfoReadPrimedIdArray()
- read the primedIdArray array
-
Sym_SymInfoReadQuantifyIdArray()
- read the quantifyIdArray array
-
Sym_SymInfoReadImgInfo()
- read the image info from symInfo
-
Sym_SymInfoReadIdToVar()
- read the idToVar table from symInfo
-
Sym_SymInfoSetImgInfo()
- read the image info from symInfo
-
Sym_SymInfoReadConjuncts()
- read the conjuncts from symInfo
-
Sym_SymInfoReadManager()
- read the mdd manager from symInfo
-
Sym_SymInfoReadConjunctForAtom()
- read the conjunct (transtion relation) for the given atom
-
Sym_SymInfoSetConjunctForAtom()
- set the conjunct (transtion relation) for the given atom
-
Sym_SymInfoLookupPrimedVariableId()
- lookup primed mdd id for a variable
-
Sym_SymInfoLookupUnprimedVariableId()
- lookup unprimed mdd id for a variable
-
Sym_MddPickMinterm()
- utility to pick a minterm form a MDD
-
Sym_AtomsComputePartialImage()
- Compute partial image with respect to a set of atoms
-
Sym_AtomsBuildPartialInitialRegion()
- build mdd for partial initial region
-
SymIsUnprimedId()
- does this id belong to a primed or unprimed variable
-
SymCreateMddVariable()
- utility to create an MDD variable
-
SymGetNvals()
- utility to get the number of possible values of a multi
valued variable
-
SymMddInTrueSupport()
- Check if the given variable is in the true support of
the given mdd
-
SymGetTopMddId()
- get top mdd id
-
SymGetMddLiteral()
- utility to get and MDD literal
-
SymGetConstant()
- utility to get a constant value from an expression
-
SymIsNumEnumRangeConstant()
- utility to check if a given expression is a num or enum or range
constant
-
SymGetVariableId()
- utility to get the mdd variable id corresponding to the expression
-
SymGetVariableIdConstantAndPlus()
- utility to get the components of an expression of the
form v + c or v - c
-
SymIsMultiValuedVariable()
- is the given expression a multi-valued variable?
-
SymGetArrayElementVar()
- required
-
SymGetArrayId()
- required
-
SymGetArrayNvals()
- required
-
SymInfoReadModule()
- required
-
SymGetRelExprMdd()
- required
-
Sym_StateInMdd()
- Check if a state is in the mdd.
-
Sym_StateInMddWithResidueMdd()
- Check if a state is in the mdd.
Last updated on 1031104 18h39


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Provides a complete overview of user's Account
Provides Complete Information about certificates awarded on completion of courses
Displays a List of courses currently enrolled
User can see a list of courses that the company will offer in different months
Displays all the skills currently possesed by the trainee with a grades in each of them
This section helps the trainee take assessments anywhere on the go
We started with our research work and after deciding all the features we drafted a layout for our app.
We started developing our frontend module by developing one feature at a time.
Designing of logos and icons. We also started linking our frontend to the backend module.
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Frontend Developer
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public static interface CellSetListener.CellSetChange
| Modifier and Type | Method and Description |
|---|---|
List<CellSetListener.AxisChange> |
getAxisChanges()
Returns a list of axis changes, or null if server cannot provide
detailed changes.
|
List<CellSetListener.CellChange> |
getCellChanges()
Returns a list of cells that have changed, or null if the server
cannot provide detailed changes.
|
CellSet |
getCellSet()
Returns the cell set affected by this change.
|
CellSet getCellSet()
List<CellSetListener.CellChange> getCellChanges()
The server is always at liberty to provide a CellSetChange
without a detailed list of changes, even if
CellSetListener.Granularity.COARSE
was specified when the listener was
attached. Here are some typical reasons:
List<CellSetListener.AxisChange> getAxisChanges()
The reasons why this method returns null are similar to the
reasons why getCellChanges()
returns null.


In this project, you'll use generative adversarial networks to generate new images of faces.
You'll be using two datasets in this project:
Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.
If you're using FloydHub, set data_dir
to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".
data_dir = './data'
# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper
helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
show_n_images = 25
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images
.
show_n_images = 25
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

You'll build the components necessary to build a GANs by implementing the following functions below:
model_inputs
discriminator
generator
model_loss
model_opt
train
This will check to make sure you have the correct version of TensorFlow and access to a GPU
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf
# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer. You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))
# Check for a GPU
if not tf.test.gpu_device_name():
warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
Implement the model_inputs
function to create TF Placeholders for the Neural Network. It should create the following placeholders:
image_width
, image_height
, and image_channels
.z_dim
.Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)
import problem_unittests as tests
def model_inputs(image_width, image_height, image_channels, z_dim):
"""
Create the model inputs
:param image_width: The input image width
:param image_height: The input image height
:param image_channels: The number of image channels
:param z_dim: The dimension of Z
:return: Tuple of (tensor of real input images, tensor of z data, learning rate)
"""
# TODO: Implement Function
i_input = tf.placeholder(tf.float32, (None, image_width, image_height, image_channels))
z_input = tf.placeholder(tf.float32, (None, z_dim))
lr = tf.placeholder(tf.float32, name="learning_rate")
return i_input, z_input, lr
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
Implement discriminator
to create a discriminator neural network that discriminates on images
. This function should be able to reuse the variables in the neural network. Use tf.variable_scope
with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the discriminator, tensor logits of the discriminator).
def discriminator(images, reuse=False):
"""
Create the discriminator network
:param images: Tensor of input image(s)
:param reuse: Boolean if the weights should be reused
:return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
"""
# TODO: Implement Function
# similar to previous execise
alpha = 0.3
with tf.variable_scope('discriminator', reuse=reuse):
# Input layer is 28x28x3
x2 = tf.layers.conv2d(images, 128, 5, strides=2, padding='same')
bn2 = tf.layers.batch_normalization(x2, training=True)
relu2 = tf.maximum(alpha * bn2, bn2)
# 14x14x128
x3 = tf.layers.conv2d(relu2, 256, 5, strides=2, padding='same')
bn3 = tf.layers.batch_normalization(x3, training=True)
relu3 = tf.maximum(alpha * bn3, bn3)
# 7x7x256
# Flatten it
flat = tf.reshape(relu3, (-1, 7*7*256))
logits = tf.layers.dense(flat, 1)
out = tf.sigmoid(logits)
return out, logits
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Implement generator
to generate an image using z
. This function should be able to reuse the variables in the neural network. Use tf.variable_scope
with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim
images.
def generator(z, out_channel_dim, is_train=True):
"""
Create the generator network
:param z: Input z
:param out_channel_dim: The number of channels in the output image
:param is_train: Boolean if generator is being used for training
:return: The tensor output of the generator
"""
# similar to previous execise, but different image size, one layer less
alpha = 0.2
with tf.variable_scope('generator', reuse=not is_train):
# First fully connected layer
x1 = tf.layers.dense(z, 7*7*512)
# Reshape it to start the convolutional stack
x1 = tf.reshape(x1, (-1, 7, 7, 512))
x1 = tf.layers.batch_normalization(x1, training=is_train)
x1 = tf.maximum(alpha * x1, x1)
# 7x7x256 now
x2 = tf.layers.conv2d_transpose(x1, 256, 5, strides=1, padding='same')
x2 = tf.layers.batch_normalization(x2, training=is_train)
x2 = tf.maximum(alpha * x2, x2)
# 14x14x256 now
x3 = tf.layers.conv2d_transpose(x2, 128, 5, strides=2, padding='same')
x3 = tf.layers.batch_normalization(x3, training=is_train)
x3 = tf.maximum(alpha * x3, x3)
# 14x14x128 now
# Output layer
logits = tf.layers.conv2d_transpose(x3, out_channel_dim, 5, strides=2, padding='same')
# 28x28x3 now
out = tf.tanh(logits)
return out
return None
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Implement model_loss
to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:
discriminator(images, reuse=False)
generator(z, out_channel_dim, is_train=True)
def model_loss(input_real, input_z, out_channel_dim):
"""
Get the loss for the discriminator and generator
:param input_real: Images from the real dataset
:param input_z: Z input
:param out_channel_dim: The number of channels in the output image
:return: A tuple of (discriminator loss, generator loss)
"""
# TODO: Implement Function
output_dim = out_channel_dim
g_model = generator(input_z, output_dim)
d_model_real, d_logits_real = discriminator(input_real)
d_model_fake, d_logits_fake = discriminator(g_model, reuse=True)
d_loss_real = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real, labels=tf.ones_like(d_model_real)))
d_loss_fake = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.zeros_like(d_model_fake)))
g_loss = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.ones_like(d_model_fake)))
d_loss = d_loss_real + d_loss_fake
return d_loss, g_loss
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Implement model_opt
to create the optimization operations for the GANs. Use tf.trainable_variables
to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).
def model_opt(d_loss, g_loss, learning_rate, beta1):
"""
Get optimization operations
:param d_loss: Discriminator loss Tensor
:param g_loss: Generator loss Tensor
:param learning_rate: Learning Rate Placeholder
:param beta1: The exponential decay rate for the 1st moment in the optimizer
:return: A tuple of (discriminator training operation, generator training operation)
"""
t_vars = tf.trainable_variables()
d_vars = [var for var in t_vars if var.name.startswith('discriminator')]
g_vars = [var for var in t_vars if var.name.startswith('generator')]
with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
d_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars)
g_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(g_loss, var_list=g_vars)
return d_train_opt, g_train_opt
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np
def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
"""
Show example output for the generator
:param sess: TensorFlow session
:param n_images: Number of Images to display
:param input_z: Input Z Tensor
:param out_channel_dim: The number of channels in the output image
:param image_mode: The mode to use for images ("RGB" or "L")
"""
cmap = None if image_mode == 'RGB' else 'gray'
z_dim = input_z.get_shape().as_list()[-1]
example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])
samples = sess.run(
generator(input_z, out_channel_dim, False),
feed_dict={input_z: example_z})
images_grid = helper.images_square_grid(samples, image_mode)
pyplot.imshow(images_grid, cmap=cmap)
pyplot.show()
Implement train
to build and train the GANs. Use the following functions you implemented:
model_inputs(image_width, image_height, image_channels, z_dim)
model_loss(input_real, input_z, out_channel_dim)
model_opt(d_loss, g_loss, learning_rate, beta1)
Use the show_generator_output
to show generator
output while you train. Running show_generator_output
for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator
output every 100 batches.
import matplotlib.pyplot as plt
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
"""
Train the GAN
:param epoch_count: Number of epochs
:param batch_size: Batch Size
:param z_dim: Z dimension
:param learning_rate: Learning Rate
:param beta1: The exponential decay rate for the 1st moment in the optimizer
:param get_batches: Function to get batches
:param data_shape: Shape of the data
:param data_image_mode: The image mode to use for images ("RGB" or "L")
"""
steps = 0
print_every = 10
show_every = 50
# extract image format
image_height = data_shape[1]
image_width = data_shape[2]
image_channels = 1
if data_image_mode == "RGB":
image_channels = 3
losses = []
# rest straight forward from last execise:
input_i, input_z, lr = model_inputs(image_width, image_height, image_channels, z_dim)
d_loss, g_loss = model_loss(input_i, input_z, image_channels)
d_opt, g_opt = model_opt(d_loss, g_loss, lr, beta1)
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
for epoch_i in range(epoch_count):
for batch_images in get_batches(batch_size):
# scaling up to [-1,1] range
batch_images *= 2
steps += 1
batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))
# Run optimizers
_ = sess.run(d_opt, feed_dict={input_i: batch_images, input_z: batch_z, lr: learning_rate})
_ = sess.run(g_opt, feed_dict={input_z: batch_z, input_i: batch_images, lr: learning_rate*0.8})
if steps % print_every == 0:
# At the end of each epoch, get the losses and print them out
train_loss_d = d_loss.eval({input_z: batch_z, input_i: batch_images})
train_loss_g = g_loss.eval({input_z: batch_z})
print("Epoch {}/{}...".format(epoch_i+1, epochs),
"d_loss: {:.4f}...".format(train_loss_d),
"g_loss: {:.4f}".format(train_loss_g))
# Save losses to view after training
losses.append((train_loss_d, train_loss_g))
if steps % show_every == 0:
show_generator_output(sess, 16, input_z, image_channels, data_image_mode)
fig, ax = plt.subplots()
losses = np.array(losses)
plt.plot(losses.T[0], label='Discriminator')
plt.plot(losses.T[1], label='Generator')
plt.title("Training Losses")
plt.legend()
Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.
batch_size = 64
z_dim = 80
learning_rate = 0.0002
beta1 = 0.5
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2
mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
mnist_dataset.shape, mnist_dataset.image_mode)
Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.
batch_size = 32
z_dim = 512
learning_rate = 0.0006
beta1 = 0.4
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1
celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
celeba_dataset.shape, celeba_dataset.image_mode)
When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.


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Securty - Don't depend on Package Scope.
Package-level access is not secure enough for security. Java packages are not closed (meaning that new elements can be added to them, even at program runtime). As a result, an attacker can potentially introduce a new class inside your packages and use this new class to access the things you thought you hide.
Tip: Do not rely on package-level access. Make your class, method, field to have least access as possible.
REFERENCE
Statically Scanning Java Code: Finding Security Vulnerabilities.
John Viega, Gary McGraw, Tom Mutdosch, and Edward W. Felten
IEEE SOFTWARE September/October 2000


|
BTK
0.3dev.0
Open-source library to visualize/process biomechanical data
|
TODO. More...
Public Types | |
| typedef SharedPtr< const ModelCalibrationFilter > | ConstPointer |
| typedef SharedPtr < ModelCalibrationFilter > | Pointer |
| Public Types inherited from btk::ProcessObject | |
| typedef SharedPtr< const ProcessObject > | ConstPointer |
| typedef SharedPtr< ProcessObject > | Pointer |
| Public Types inherited from btk::Object | |
| typedef SharedPtr< const Object > | ConstPointer |
| typedef SharedPtr< Object > | Pointer |
Public Member Functions | |
| BTK_MODELS_EXPORT void | ClearModelOption () |
| CalibrationProcedure::Pointer | GetCalibrationProcedure () |
| bool | GetDeleteIncompleteSegmentDefinition () const |
| Acquisition::Pointer | GetInputAcquisition () |
| LandmarksTranslator::Pointer | GetInputLandmarksTranslator () |
| Model::Pointer | GetInputModel () |
| Model::Pointer | GetOutput () |
| BTK_MODELS_EXPORT void | SetCalibrationProcedure (CalibrationProcedure::Pointer builder) |
| BTK_MODELS_EXPORT void | SetDeleteIncompleteSegmentDefinition (bool activation=false) |
| void | SetInputAcquisition (Acquisition::Pointer input) |
| void | SetInputLandmarksTranslator (LandmarksTranslator::Pointer input) |
| void | SetInputModel (Model::Pointer input) |
| BTK_MODELS_EXPORT void | SetModelOption (int id, double o) |
| BTK_MODELS_EXPORT void | SetModelOptions (const std::map< int, double > &options) |
| Public Member Functions inherited from btk::ProcessObject | |
| int | GetInputNumber () const |
| int | GetOutputNumber () const |
| int | GetValidInputNumber () const |
| void | ResetState () |
| void | Update () |
| Public Member Functions inherited from btk::Object | |
| unsigned long int | GetTimestamp () const |
Static Public Member Functions | |
| static Pointer | New () |
Protected Member Functions | |
| virtual BTK_MODELS_EXPORT void | GenerateData () |
| Model::Pointer | GetOutput (int idx) |
| virtual BTK_MODELS_EXPORT DataObject::Pointer | MakeOutput (int idx) |
| BTK_MODELS_EXPORT | ModelCalibrationFilter () |
| Protected Member Functions inherited from btk::ProcessObject | |
| int | GetInputIndex (DataObject::Pointer input) |
| DataObject::Pointer | GetNthInput (int idx) |
| DataObject::ConstPointer | GetNthInput (int idx) const |
| DataObject::Pointer | GetNthOutput (int idx) |
| DataObject::ConstPointer | GetNthOutput (int idx) const |
| int | GetOutputIndex (DataObject::Pointer output) |
| bool | IsModified () const |
| void | Modified () |
| ProcessObject () | |
| void | SetInputNumber (int num) |
| virtual void | SetNthInput (int idx, DataObject::Pointer input) |
| virtual void | SetNthOutput (int idx, DataObject::Pointer output) |
| void | SetOutputNumber (int num) |
| virtual | ~ProcessObject () |
| Protected Member Functions inherited from btk::Object | |
| Object () | |
| Object (const Object &toCopy) | |
| virtual | ~Object () |
Additional Inherited Members | |
| Protected Attributes inherited from btk::Object | |
| unsigned long int | m_Timestamp |
TODO.
Smart pointer associated with a const ModelCalibrationFilter object.
Smart pointer associated with a ModelCalibrationFilter object.
|
protected |
Constructor.
| void btk::ModelCalibrationFilter::ClearModelOption | ( | ) |
Removes all the options.
|
protectedvirtual |
|
inline |
Gets the procedure used to calibrate the model.
|
inline |
Returns the state of the option to delete or not the segment which cannot be well defined due to missing marker(s).
If this option is not activated, then a mis-defined segment will throw an error and will abort the calibration.
|
inline |
Gets the output created with this process.
|
inlineprotected |
Returns the output at the index idx.
|
protectedvirtual |
Creates a Model:Pointer object and return it as a DataObject::Pointer.
Implements btk::ProcessObject.
|
inlinestatic |
Creates a smart pointer associated with a ModelCalibrationFilter object.
| void btk::ModelCalibrationFilter::SetCalibrationProcedure | ( | CalibrationProcedure::Pointer | procedure | ) |
Sets the calibration procedure.
| void btk::ModelCalibrationFilter::SetDeleteIncompleteSegmentDefinition | ( | bool | activation = false | ) |
Sets the state of the option to delete or not the segment which cannot be well defined due to missing marker(s). If this option is not activated, then a mis-defined segment will throw an error and will abort the calibration.
|
inline |
Sets the translator between the acquisition's landmark and these used in the model.
|
inline |
Sets the model used to determine the segment to calibrate.
| void btk::ModelCalibrationFilter::SetModelOptions | ( | const std::map< int, double > & | options | ) |
Sets model's options.


Raise
Congratulations !
You successfully added your fundraising offer
Contract {{offer.uuid}}
Your Wallet address:
Now you can follow your fundraising offer on: Manage


Movie Recommendation
Watched Movie:
Submit
Output
Processing...
{% for i in result %}
{{i[0]}}
{% endfor %}


Loading...
Forward Algorithm
Mute
Pause
Credits
Author:
Wenli Zhang
See
arclands
for license information.
Web Audio library used:
jWebAudio
.
Sound effects downloaded from:
www.freesfx.co.uk
,
www.pacdv.com
and
www.mediacollege.com
.
Textures downloaded from:
www.photoshoproadmap.com
and
www.lovetextures.com
.


| Home | Trees | Indices | Help |
|
|---|
|
|
The root of the proton exception hierarchy. All proton exception classes derive from this exception.
| Instance Methods | |
|
Inherited from Inherited from Inherited from |
| Properties | |
|
Inherited from Inherited from |
| Home | Trees | Indices | Help |
|
|---|
| Generated by Epydoc 3.0.1 on Tue Dec 13 17:33:02 2016 | http://epydoc.sourceforge.net |


Output:
"handle": "grewal",
"warning": "test mode using privkey for -pubkey, only for testing. there is no point using quantum secure signing if you are using a privkey with a known secp256k1 pubkey!!",
"pkaddr": "PDbbtPWt1BgXyD4eK2fHdKPhDHjRTQvsUY",
"skaddr": "Sfq7SiNT2CWFrgTHNFruAve1rbHYVskmmr",
"hex": "0400008 .... 28f2218de78ade",
"result": "success"
}


Make sure you take note or a screen shot of your keys as once you close this window will not be able to get the account private key(s)
Owner PK: {{ owner_pkey }}
Owner PUB: {{ owner_key }}
Active PK: {{ active_pkey }}
Active PUB: {{ active_key }}


Classes
Methods
append(name, value)
Appends a value to a list-valued property. If the property does not currently exist,
it will be created as a list of one element. If the property does exist and doesn't
currently have a list value, the append will be ignored.
Parameters:
| Name | Type | Description |
|---|---|---|
name |
string | the People Analytics property that should have it's value appended to |
value |
object | the new value that will appear at the end of the property's list |
clearCharges()
Permanently clear the whole transaction history for the identified people profile.
deleteUser()
Permanently deletes the identified user's record from People Analytics.
Calling deleteUser deletes an entire record completely. Any future calls to People Analytics using the same distinct id will create and store new values.
increment(prop, by)
Add the given amount to an existing property on the identified user. If the user does not already
have the associated property, the amount will be added to zero. To reduce a property,
provide a negative number for the value.
Parameters:
| Name | Type | Description |
|---|---|---|
prop |
string | the People Analytics property that should have its value changed |
by |
number | the amount to be added to the current value of the named property |
remove(name, value)
Remove value from a list-valued property only if they are already present in the list.
If the property does not currently exist, the remove will be ignored.
If the property exists and is not list-valued, the remove will be ignored.
Parameters:
| Name | Type | Description |
|---|---|---|
name |
string | the People Analytics property that should have it's value removed from |
value |
object | the value that will be removed from the property's list |
set(prop, to)
Sets a single property with the given name and value for this user.
The given name and value will be assigned to the user in Mixpanel People Analytics,
possibly overwriting an existing property with the same name.
Parameters:
| Name | Type | Description |
|---|---|---|
prop |
string | The name of the Mixpanel property. This must be a String, for example "Zip Code" |
to |
object | The value of the Mixpanel property. For "Zip Code", this value might be the String "90210" |
setOnce(prop, to)
Works just like set(), except it will not overwrite existing property values. This is useful for properties like "First login date".
Parameters:
| Name | Type | Description |
|---|---|---|
prop |
string | The name of the Mixpanel property. This must be a String, for example "Zip Code" |
to |
object | The value of the Mixpanel property. For "Zip Code", this value might be the String "90210" |
trackCharge(charge, properties)
Track a revenue transaction for the identified people profile.
Parameters:
| Name | Type | Description |
|---|---|---|
charge |
number | the amount of money exchanged. Positive amounts represent purchases or income from the customer, negative amounts represent refunds or payments to the customer. |
properties |
object | an optional collection of properties to associate with this transaction. |
union(name, value)
Adds values to a list-valued property only if they are not already present in the list.
If the property does not currently exist, it will be created with the given list as it's value.
If the property exists and is not list-valued, the union will be ignored.
Parameters:
| Name | Type | Description |
|---|---|---|
name |
string | name of the list-valued property to set or modify |
value |
array | an array of values to add to the property value if not already present |
unset(name)
permanently removes the property with the given name from the user's profile
Parameters:
| Name | Type | Description |
|---|---|---|
name |
string | name of a property to unset |


Generalized Linear Mixed Model (GLMM) for Binary Randomized Response Data. Includes Cauchit, Compl. Log-Log, Logistic, and Probit link functions for Bernoulli Distributed RR data. RR Designs: Warner, Forced Response, Unrelated Question, Kuk, Crosswise, and Triangular.
| Version: | 0.2.0 |
| Depends: | lme4, methods |
| Imports: | lattice, stats, utils, grDevices |
| Published: | 2016-08-09 |
| Author: | Jean-Paul Fox [aut], Konrad Klotzke [aut], Duco Veen [aut] |
| Maintainer: | Konrad Klotzke <omd.bms.utwente.stats at gmail.com> |
| License: | GPL-2 | GPL-3 |
| NeedsCompilation: | no |
| CRAN checks: | GLMMRR results |
| Reference manual: | GLMMRR.pdf |
| Package source: | GLMMRR_0.2.0.tar.gz |
| Windows binaries: | r-devel: GLMMRR_0.2.0.zip, r-release: GLMMRR_0.2.0.zip, r-oldrel: GLMMRR_0.2.0.zip |
| OS X binaries: | r-release: GLMMRR_0.2.0.tgz, r-oldrel: GLMMRR_0.2.0.tgz |
| Old sources: | GLMMRR archive |
Please use the canonical form https://CRAN.R-project.org/package=GLMMRR to link to this page.


| 2023-02-28 12.36 EET |
Report received.
Hello,
I found XSS vulnerability in “reaction on comment” functionality. An attacker with user privilege on board can insert JavaScript code instead of reaction.
Name: Alexander Starikov at Jet Infosystems (https://jetinfosystems.com/)
Twitter: @alexstar_46
Bug type: Stored XSS
Severity: Medium
CVSS (optional): AV:N/AC:L/PR:L/UI:R/S:C/C:L/I:L/A:N (5.4)
CWSS (optional): CWE-79: Improper Neutralization of Input During WebPage Generation ('Cross-site Scripting')
Tested on:
- Self-hosted Wekan, latest version - v6.76 (last commit:8a0899a819cef5a2c779edbe16a3edddb7450d84)
- Firefox verison 110.0
Steps to reproduce (screenshots in the attachment):
1) Add a comment in card:
2) Add any reaction on comment and intercept this request in Proxy (I use Burp Suite).
Replace the default “reactionCodepoint” value on payload: <img src=1 onerror=alert()>:
Impact: An attacker can execute JavaScript code in the browsers of users who open card with “malicious reaction”. For example, attacker can steal Meteor.loginToken or change page content for phishing.
If you have any questions I will be happy to answer them.
|


graph
modifyModel
draw
save
properties
label
create Label
modify Label
delete Label
Relation
create Relation
modify Relation
delete Relation
properties
create property
delete property


Attention My Dear Good Friend, I have Paid for the delivery fee for your Cheque Draft.but the manager of Financial Trust Bank Plc Benin told me that before the check will get to you that it will expire.So i told him to cash $850,000.00 all the necessary arrangement of delivering the $850,000.00 in cash was made with CAJA COURIER DELIVERY COMPANY Below is the needed information to enable them deliver your fund to you immediately. the only fee you have to pay them is $95 usd that they will use to obtain insurance certificate and claims of affadvite that will prove that the parcel that consist total sum of $850.000.00 belong to you be rest assure that all other fees have been paid by me. CAJA COURIER COMPANY DIRECTOR REV DR AUGUSTINE CHINEDU. EMAIL=========<EMAIL_ADDRESS>CONTACT NUMBER<PHONE_NUMBER>038 1.YOUR FULL NAME 2.YOUR HOME ADDRESS. 3.YOUR CURRENT HOME TELEPHONE NUMBER. 4.YOUR CURRENT OFFICE TELEPHONE. 5.A COPY OF YOUR PICTURE 6.COMPANY REGISTRATION NO EG58945 7.CODE NMBER 0140479 Please make sure you send this needed info¢s to the Director general of Caja Courier Company Dr Augustine Chinedu with the address given to you. Note ;The Caja COURIER COMPANY LTD don't know the contents of the Box. I registered it as a Box of family valuable belongs. They don't know it contents fund. this is to avoid them delaying with the Box. don't let them know that is money that is in that Box. You can as well call him immediately you received this email with the number given to you. I am waiting for your urgent response. Thanks and Remain Blessed. SECRETARY Benjamine.


section-feedback card
Light Bootstrap Dashboard Feedback
Thanks! Awesome design. You are making bootstrap community better.
Light Bootstrap Dashboard Feedback
Thanks! Awesome design. You are making bootstrap community better.
Light Bootstrap Dashboard Feedback
Thanks! Awesome design. You are making bootstrap community better.


Hey! Before you read on, click the play button to listen to some relaxing music!
"All the world’s a stage, And all the men and women merely players; They have their exits and their entrances; And one man in his time plays many parts." - Shakespeare
Hi there! I'm a UC Berkeley grad aspiring to become a software engineer. I majored in Statistics while in UCSB and then in Applied Mathematics when I transferred to UC Berkeley, but the few Computer Science courses I took as prerequisites peaked my interest in the world of programming. Such interests led me to delve even further and to take the core CS classes such as those in Data Structures and Algorithms. I've had the pleasure of working on numerous amazing course projects such as the Enigma Machine Project, in which I reproduced the Enigma machine that served a key role in WWII (except my enigma machine was fully virtual, with the moving pieces of the actual enigma machine implemented in their full functionality as objects).
I'm also eager to learn new skills and technologies that help individuals and companies create wonderful content and products. That's why I'm currently studying the in's-and-out's of web development. I've always had a bit of an artistic inclination ever since I was a wee lad, and always wanted to nurture and grow that aspect of myself. It would seem that I've found the perfect blend between my love of art and coding in the form of web development!
I'm also a hobbyist game developer! I recently used my knowledge of Python to make a Galaga clone game (which I named "Infinitum", because Latin is awesome) with Pygame! Besides that, I've dabbled with Unity to create some very simple platformers and puzzle games.


Our aim is to track every government and corporate financial transaction across the world and present it in useful and engaging forms for everyone from a school-child to a data geek.
We're an open community that anyone is invited to join! We're looking for researchers, coders and citizen activists to join us and to help gather and understand financial data.


CLICK ON CAROLINE. Dear Caroline Film Reviews: http://www.geocities.com/Hollywood/Studio/7066
The Replacement Killers
Dear Caroline --
For fans of Hong Kong action star Chow Yun-Fat, the greatest anticipation is for his first English words in a Hollywood film: "My condolences."
In a premiere showing, the crowd cheered. All hail the king! Chow, who attended the premiere, said he was so nervous his first day on the set, that he was unable to get the words out. It didn't help that he was without a gun in each hand.
For those who haven't heard of Chow, he is one of the biggest names in Asian cinema rivaled only by Jackie Chan. Chow made his debut in director John Woo's "A Better Tomorrow" (1986), which became the top-grossing film in Hong Kong history. Since Britain relinquished its sovereignty over Hong Kong last year to China, it was only a matter of time before Chow crashed our shores.
Like "A Better Tomorrow," he's done it with a bang in a slick, action thriller, "The Replacement Killers," directed by first-timer Antoine Fuqua and co-produced by Woo and his long-time partner Terence Chang. Woo made his Hollywood debut with "Hard Target" (1993) and more recently helmed "Face/Off" last summer.
Chow stars with Mira Sorvino as two toughies on the run from the Chinese mob in L.A. Sorvino is an un-Romylike loser, who helps forge a passport for assassin John Lee (Chow) after Lee becomes a marked man for refusing an assignment. Sorvino, the girlfriend of gun-happy director Quentin Tarantino, is the toughest bad girl since "La Femme Nikita." Tarantino, a long-time admirer of Chow, used HK Chow films like "City on Fire" and "A Better Tomorrow III" as the inspiration for "Reservoir Dogs."
"The Replacement Killers" features an impressive debut by Fuqua, shot by cinematographer Peter Lyons Collister and includes a thrilling score by Harry Gregson-Williams. Ken Sanzel wrote the script, rewritten especially for Chow.
Fuqua was wise in trying not to copy Woo's over-the-top directing style, given the fact that the master actionmaker stood nearby as a producer. Woo and Chang were wise to let Fuqua develop his own visionary mayhem.
Fuqua's influence is from the Bruckheimer School of Film, named after the producer of "Beverly Hills Cop" and "Top Gun" where visuals are everything and plot comes later. Early disciples from commercial TV included the Scott brothers, Ridley and Tony, and Adrian Lyne ("Flashdance"). The current generation of former music video directors consists of Michael Bay ("Bad Boys," "The Rock"), Simon West ("Con Air") and Alex Proyas ("The Crow," "Dark City"). The fast-paced action is furious, shots last milliseconds before edits, the camera is constantly moving and the lighting is always in contrast between dark shadows and warm blue hues, usually with dry ice from somewhere filling up the screen for atmospheric reasons only.
Fuqua gives "The Replacement Killers" a slick, quick-edit look, especially during the opening shootout in a nightclub. The scene concludes with Fuqua's version of the famous mirror shot from Robert Clouse's "Enter the Dragon" with Bruce Lee. It's an example of why Fuqua's debut stands out. With Collister's assistance, he has created his own visual style, while also adding a few tributes from Woo's masterpiece, "The Killer," such as slow-motion walks, the sliding gunslinger and using a church or temple as a headquarters for the hero signifying peaceful tranquility in a violent world.
Given Chow's limitations due to learning English, Fuqua sticks with what Chow knows best. Minimal dialogue with plenty of hail Mary gunfire. A shootout at a car wash is on par with some of HK films' best shootout scenes such as the opening teahouse massacre in Woo's "Hard-Boiled" or the concluding raid on a warehouse in Ringo Lam's "City on Fire."
"The Replacement Killers," the title refers to the assassins brought in to kill Lee, goes into motion early on when Detective Stan Zedkov (Michael Rooker) breaks up a drug shipment involving the son of crime boss Terence Wei (Kenneth Tsang). After Wei's son is killed by Zedkov, Wei seeks revenge. Jurgen Prochnow stars as Wei's second in command.
The film will take your breath away, that is when you have time to come up for air between all the blazing gunfire.
Rating: *** 1/2 (Three-and-a-half stars)
Thinking of you, Geo. M. Wilcox
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


Wayanad is an Indian district in the north-east of Kerala state with administrative headquarters at the municipality of Kalpetta. It is set high on the Western Ghats with altitudes ranging from 700 to 2100 meters.The district was formed on 1 November 1980 as the 12th district in Kerala by carving out areas from Kozhikode and Kannur districts. About 885.92 sq.km of area of the district is under forest.Wayanad has three municipal towns—Kalpetta, Mananthavady and Sulthan Bathery. There are many indigenous tribals in this area.In 2006 the Ministry of Panchayati Raj named Wayanad one of the country's 250 most backward districts out of a total of 640.
Kuruvadweep or Kuruva Island is a 950-acre (3.8 km2) protected river delta. It comprises three densely wooded uninhabited islands and a few submergible satellite islands, which lies on the banks of the tributaries of Kabini River in the Wayanad district, Kerala, India.It is uninhabited island, which is home to rare species of birds, orchids, herbal plants etc.These islands consists of dense and evergreen forest that is uninhabited and hence home to rare kinds of flora and fauna: endemic species of birds, orchids and herbs namely.
Read MoreEdakkal Caves also Edakal, are two natural caves at Edakkal, in Wayanad district of Kerala. The caves are situated 1,200 m (3,900 ft) above sea level on Ambukutty Mala, on the Mysore Plateau, in the Western Ghats.The Edakkal caves are believed to be camping shelters of the Neolithic community (the south Indian Neolithic culture zone). The cave walls contain a collection of Neolithic rock engravings and images (which were incised over a period of time). The major part of images on the cave walls may belong to late Neolithic period (i. e., first millennium BC).
Read MoreThamarassery Churam is a mountain pass in Kerala. "Churam" is the Malayalam word for mountain pass. Hence, it is more prominently called Wayanad Churam. The British used to transport spices from Wayanad to Beypore Port, Kozhikode via the Kuttiadi route. Between 1700 and 1750, they searched for a new route which could reduce the distance. For which, they sought the help of Karinthandan Moopan (chief), a tribal leader from the Paniya tribe in Wayanad.
Read More


Implements a thin wrapper around a subset of classes and methods of SLFJ.
The primary classes of this package are {@link com.pixelmed.slf4j.LoggerFactory LoggerFactory} and {@link com.pixelmed.slf4j.Logger Logger}.
Typical usage is as follows:
import com.pixelmed.slf4j.Logger; import com.pixelmed.slf4j.LoggerFactory; public class MyClass { private static final Logger slf4jlogger = LoggerFactory.getLogger(MyClass.class); public static void main(String arg[]) { try { if (arg.length > 0) { slf4jlogger.error("Too many arguments - expected 0 got {}",arg.length); } } catch (Exception e) { slf4jlogger.info("Not really expecting this",e); } } }
Properties that affect the behavior of the log output even when no SLF4J implementation is provided are as follows:


[Erowid Note: While it may be that the substances described below are MDA and MDMA, Ecstasy tablets are notoriously impure or mis-represented, often containing chemicals other than those they are presumed to contain. This report is marked as an MDA report, but readers should be aware that this is more of a general Ecstasy report.]
This is going to be a little (scratch that, a LOT) long, but if a description of my experiences with pills over the last three months might be helpful to others, then it's worth writing them out.
I was first introduced to pills in Mid-September of this year. prior to this, I had never experimented with anything harder than pot, so my first experience was absolutely amazing. I was introduced first to MDA by a friend of mine who has a lot more experience with a wide variety of drugs than I do. I came home one night from a party pissed off and depressed, and ran into him outside. We decided to head back out, but before we could leave, the subject of MDA (White Apples, in this case) came up, and I suggested we take those instead of going back out to a bar.
It was incredible. We took the pills (just one for me), smoked a couple of bowls, and went out walking.  I had no trouble walking straight, I was talking completely coherently, and exhibiting no sign that I was high. My friend and I went downtown and sat by the waterfront for hours, talking. It was an incredible period of self-exploration for both of us, one that I will never forget.
We both talked about ourselves, deeper conversations than we'd ever had before. We realized things that had been bothering us in our respective lives, and realized how great we both actually are. It wasn't that the drug had given us confidence, so much, it's that it had opened us up to seeing thngs as they were. Any insecurities were wiped away. The best part was that this feeling lasted long after the come down (Which, from MDA, was almost non-existant). I'm still carrying with me now, two months later, the feeling of well-being from the first few times I took the drug.
My friend and I took MDA a few more times together, with similar results. At that point, it seemed impossible to have a bad trip on MDA. Whatever my mindstate when I took it, as soon as it came on it would elevate and open me, and I would have a great time.
All good things must come to an end, however. Eventually, the supply of white apples, which are not usually common in my city, ran out. We decided to try MDMA, see how similar it was to MDA. My friend's dealer had gotten in some Versaces and we took those a few times. They were never really as enjoyable as the MDA. We had a couple of good trips, usually with another friend of ours, but a couple of bad ones too. The general impression both of us had was that MDMA was a) speedier and b) less completely wholesome. While on MDA, there was simply no room in my head for bad thoughts. Everything seemed great. on MDMA, it was more like 95% good, 5% bad, and it was possible to slip into a spiral of bad thoughts.
That was what happened on my last roll, about three weeks ago. My friend and I each took a couple of versaces. The night before, we had also taken a couple, and gone our separate ways. I had gone out dancing with my now-girlfriend (at the time she was torn between myself and her old boyfriend at home) and had one of the best nights in recent memory. I used sips of her beer to control the speediness (my friend usually used small tokes.. if uncontrolled, both of us have found that the extra speediness can induce minor panic-attack type reactions and general feelings of negativity) and we both had a great time.
The next day, my friend and I started out together, but he soon had to leave to visit his girlfriend. The one thing that was different about that night also was that I had smoked up while waiting for the E to come on, something I had stopped doing a month before. I found that when I didn't, the effect of the E (or MDA, depending on which we were using) was cleaner and more unpolluted.
This night went bad from the beginning. I couldn't think straight. I had the desire to talk that I was familiar with, but I couldn't follow the thread of a conversation. I would head off on random tangents and then become obsessed about returning to the original topic. I could feel, at the edge of the high, this shadow hanging over me, and I was fighting it all night. Once my friend left, I went to see the girl from the night before, hoping she could cheer me up.
It didn't work. She wass nearly ready for bed, and while I laid with her for a while to try to calm myself down, I eventually made myself leave so as not to irritate or frighten her. I walked over to visit another friend, and repeated the same process. Tried to sit and talk with her, couldn't hold a conversation, irritated her and myself. All the while the depression was building. I felt out of place. I felt useless.
I started to question why I was putting so much work into this relationship with the first girl, why I thought there was even the slightest chance of ending up with her. I walked around the city alone for three hours trying to pull myself out of this spiral. I tried music, I tried happy thoughts, happy memories. Nothing worked. All the while, as I sank deeper, I was driven on by this terrible mental and physical energy. I knew if I went home I'd just lay awake tossing and turning for hours.
Finally it burned itself out and I went home. I woke up the next day feeling alright, not too much of a hangover, but with absolutely no desire to ever roll again.
As far as long term effects go, I'll address the positive first and then the negative.
I feel I've taken a lot from my experiences. I wpould probably, were I to do it again, stick to MDA. That, for me, was an incredible experience in self-discovery. I changed as a person as a result of the drug and the long walks and conversations my friend and I had. Additionally, my friend and I really bonded over the drug. We shared some of our deepest personal fears and insecurities, and worked together to overcome them. I feel, and I know he feels the same, that we have developed the kind of friendship that could last over many years and many miles. MDA certainly played a part in this.
Also, I feel MDA was instrumental in bringing me together with my girlfriend. It changed me in fundamental ways, made me more receptive to her feelings, and allowed me to see what she needed me to do, what she actually wanted. There have been times in our relationship, before we were actually dating, that I should, by rights, have had no idea what to do, yet I did exactly the right thing. I am left with a choice between God, Fate, and MDA, and I choose the drug. Thanks in no small part to MDA, I am completely in love with a girl who is completely in love with me. We are unbelievably happy together, and I am becoming increasingly convinced that I have been lucky enough to meet the one girl out there who is perfect for me.
On the other hand, there have been negative effects too. Both my friend and I recently developed health problems. While I have not exhibited (THANKFULLY) any of the mental problems I have seen associated with frequent Ecstacy use, I have developed recently a mysterious glandular swelling under my jaw and a sore throat, coupled with exhaustion, chills, and a fever. I am currently taking Penicillin and praying it responds. It baffled the doctor I went to see about it, and if the antibiotic doesn't fix it, I'll end up in the hospital.
My friend also developed, after having been off of E for a week or so, a massive throat infection, which spread to the nearby lymph nodes. His has responded well to antibiotics, but was nightmarish to see when at its worst. I cannot help but wonder if our period of frequent use of MDMA and MDA (His was much longer than mine, it is worth noting... perhaps as long as six months of weekly or twice weekly use, at times VERY heavy) has in some way permanently affected our immune systems. It's a frightening thought.
I continue to believe that MDA in particular, but also MDMA, is a worthwhile drug. The best piece of advice I think I could give is one I read in another report on this site: 'When you get the message, hang up'. I feel that I've taken what I can from the drug, and so I see no need to do it again. Unless you're taking it to get fucked up, my recommendation would be to be aware of the same thing. When you lose the feeling that you're taking something new away from each new experience, stop taking it.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


The class allowing the callee to retrieve the attributes of an incoming call invitation.
More...
#include <IAgoraRtmCallManager.h>
The class allowing the callee to retrieve the attributes of an incoming call invitation.
◆ ~IRemoteCallInvitation() [1/3]
| virtual agora::rtm::IRemoteCallInvitation::~IRemoteCallInvitation |
( |
| ) |
|
|
protectedvirtual |
◆ ~IRemoteCallInvitation() [2/3]
| virtual agora::rtm::IRemoteCallInvitation::~IRemoteCallInvitation |
( |
| ) |
|
|
protectedvirtual |
◆ ~IRemoteCallInvitation() [3/3]
| virtual agora::rtm::IRemoteCallInvitation::~IRemoteCallInvitation |
( |
| ) |
|
|
protectedvirtual |
◆ getCallerId() [1/3]
| virtual const char* agora::rtm::IRemoteCallInvitation::getCallerId |
( |
| ) |
const |
|
pure virtual |
Allows the callee to get the User ID of the caller.
◆ getCallerId() [2/3]
| virtual const char* agora::rtm::IRemoteCallInvitation::getCallerId |
( |
| ) |
const |
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pure virtual |
Allows the callee to get the User ID of the caller.
◆ getCallerId() [3/3]
| virtual const char* agora::rtm::IRemoteCallInvitation::getCallerId |
( |
| ) |
const |
|
pure virtual |
Allows the callee to get the User ID of the caller.
◆ getChannelId() [1/3]
| virtual const char* agora::rtm::IRemoteCallInvitation::getChannelId |
( |
| ) |
const |
|
pure virtual |
◆ getChannelId() [2/3]
| virtual const char* agora::rtm::IRemoteCallInvitation::getChannelId |
( |
| ) |
const |
|
pure virtual |
◆ getChannelId() [3/3]
| virtual const char* agora::rtm::IRemoteCallInvitation::getChannelId |
( |
| ) |
const |
|
pure virtual |
◆ getContent() [1/3]
| virtual const char* agora::rtm::IRemoteCallInvitation::getContent |
( |
| ) |
const |
|
pure virtual |
Allows the callee to get the call invitation content set by the caller.
- Note
- The caller sets the call invitation content using the setContent method.
◆ getContent() [2/3]
| virtual const char* agora::rtm::IRemoteCallInvitation::getContent |
( |
| ) |
const |
|
pure virtual |
Allows the callee to get the call invitation content set by the caller.
- Note
- The caller sets the call invitation content using the setContent method.
◆ getContent() [3/3]
| virtual const char* agora::rtm::IRemoteCallInvitation::getContent |
( |
| ) |
const |
|
pure virtual |
Allows the callee to get the call invitation content set by the caller.
- Note
- The caller sets the call invitation content using the setContent method.
◆ getResponse() [1/3]
| virtual const char* agora::rtm::IRemoteCallInvitation::getResponse |
( |
| ) |
const |
|
pure virtual |
Allows the callee to get his/her response to the incoming call invitation.
- Note
- The callee sets a response to the incoming call invitation using the setResponse method.
◆ getResponse() [2/3]
| virtual const char* agora::rtm::IRemoteCallInvitation::getResponse |
( |
| ) |
const |
|
pure virtual |
Allows the callee to get his/her response to the incoming call invitation.
- Note
- The callee sets a response to the incoming call invitation using the setResponse method.
◆ getResponse() [3/3]
| virtual const char* agora::rtm::IRemoteCallInvitation::getResponse |
( |
| ) |
const |
|
pure virtual |
Allows the callee to get his/her response to the incoming call invitation.
- Note
- The callee sets a response to the incoming call invitation using the setResponse method.
◆ getState() [1/3]
Allows the callee to get the state of the incoming call invitation.
- Returns
- The state of the incoming call invitation See: REMOTE_INVITATION_STATE.
◆ getState() [2/3]
Allows the callee to get the state of the incoming call invitation.
- Returns
- The state of the incoming call invitation See: REMOTE_INVITATION_STATE.
◆ getState() [3/3]
Allows the callee to get the state of the incoming call invitation.
- Returns
- The state of the incoming call invitation See: REMOTE_INVITATION_STATE.
◆ release() [1/3]
| virtual void agora::rtm::IRemoteCallInvitation::release |
( |
| ) |
|
|
pure virtual |
◆ release() [2/3]
| virtual void agora::rtm::IRemoteCallInvitation::release |
( |
| ) |
|
|
pure virtual |
◆ release() [3/3]
| virtual void agora::rtm::IRemoteCallInvitation::release |
( |
| ) |
|
|
pure virtual |
◆ setResponse() [1/3]
| virtual void agora::rtm::IRemoteCallInvitation::setResponse |
( |
const char * |
response | ) |
|
|
pure virtual |
Allows the callee to set a response to the call invitation.
- Parameters
-
| response | The callee's response to the call invitation. The response must not exceed 8 KB in length if encoded in UTF-8. |
◆ setResponse() [2/3]
| virtual void agora::rtm::IRemoteCallInvitation::setResponse |
( |
const char * |
response | ) |
|
|
pure virtual |
Allows the callee to set a response to the call invitation.
- Parameters
-
| response | The callee's response to the call invitation. The response must not exceed 8 KB in length if encoded in UTF-8. |
◆ setResponse() [3/3]
| virtual void agora::rtm::IRemoteCallInvitation::setResponse |
( |
const char * |
response | ) |
|
|
pure virtual |
Allows the callee to set a response to the call invitation.
- Parameters
-
| response | The callee's response to the call invitation. The response must not exceed 8 KB in length if encoded in UTF-8. |
The documentation for this class was generated from the following file:


NOTE: For this example to work you have to run it in a web server context. This is a simple example showing a resource histogram which makes it easy to detect overallocation. You can change task start/end dates and assignments (by clicking resource column and selecting in the editor) to view how these changes affect the resource histogram.
Note that the js for the example code is not minified so it is readable. See histogram.js.


private fun makeAdapter(): ArrayAdapter<String>
Returns an ArrayAdapter that can be used to provide the list and layout of suggestions.


Envato is a startup based out of Australia with people around the world and sites serving pages every second. We started in a living room in 2006 and have been steadily working to build our company into a world-class contender. Our background is creative, we love open source, we believe that work is about way more than just making money, and we're totally committed to making products that are awesome!
Visit the Envato Website


Man (57) due before Belfast Magistrates Court next month charged with drug offences
A 57-year-old man is expected to appear before Belfast Magistrates Court next month charged with drug offences.
The man, who is due in court on December 12, has been charged with possession of a Class B drug and possession of a Class B drug with intent to supply.
It is understood the charges relate to an incident in east Belfast last night when a car was stopped and searched on the Beersbridge Road.
A man was arrested after a quantity of Class B drugs - with an estimated street value of £60,000 - was recovered from the vehicle.
A subsequent search of a property in the Dundonald area was carried out and a quantity of cash was recovered.
The operation was conducted in relation a police investigation into the criminal activities of the East Belfast UVF.


COVID -19 Cases in
Active Cases
Total Cases
Critical Cases
Total Deaths
Recovered Cases
Total Testes Done


Student Bio
Eunji Russ
I am a self-employed artist who focus on theme of self-preservation and the simulacrum of nature
Contact Info
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<EMAIL_ADDRESS>Github:
eunjiruss
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2 Kings 4:30
And the mother of the child said, As the LORD liveth, and as thy soul liveth, I will not leave thee. And he arose, and followed her.


To obtain a position in a growing reputable company where I can contribute my experience and help the company and myself improve.
Working currently as Full Stack Developer at Cognizant Technology Solutions.
CGPA: 7.18
92.25%
93%
It is an restaurant website, where a user can browse through fooditems, add to the cart and order the food..
It is a site where user can browse through movies and it's details,An user can also add a movie to his watchlist list.
It is a site where user can search for cricket players , An user can also add a player to his favorite list and view his stats .
Apart from being a developer, I enjoy my time being outdoors and riding .
When forced indoors, I follow a number of sci-fi and fantasy genre movies and television shows, and I spend a large amount of my free time exploring the latest technology advancements in the world.


{{> metas}}
Use this document as a way to quickly start any new project. All you get is this text and a mostly barebones HTML document.
{{name}}


{% extends "base.html" %} {% block nav %}
Git-a-thing is a git-based distributed object design sharing platform. Behind those buzzwords, we simply intend to build a website for sharing DIY designs easily, while being based on the git version control system and thus letting power-users use all the git advanced features.


The following is a list of frequently asked questions when
creating rotations.
- What's a All divisions similar division switch style?
- The rotation is created for the first division,
and then copied team for team, sail for sail, to the other
divisions.
- What's Franny division-switch style?
- The rotation is created for the first division specified,
and then offset by a fixed amount for the subsequent
divisions. The offset amount equals the total number of boats
divided by the number of divisions.


3 held for duping finance firm of ₹20 lakh
One of the accused is an employee of the company
by Staff ReporterThree men have been arrested for allegedly duping a finance company of ₹20 lakh. One of the accused is an employee of the firm, the police said on Sunday.
Sunil Kumar (25) was working as a marketing agent at Leyland Finance Limited. Harish Dhingra (40) worked as a share broker, and Mohammad Sualeheen (28) is a dealer of old tyres in Jama Masjid, they said.
According to the police, a complaint was received stating that Sunil and his associates had fabricated documents of customers and obtained loans from the company for himself.
After getting the loans, the accused procured 28 scooters on ‘hire-purchase’ basis, said Deputy Commissioner of Police (South-East) Chinmoy Biswal.
A case was registered in this regard at Okhla Industrial Area police station.
During interrogation, Sunil disclosed the names of his associates, who were later nabbed by the police.
It was revealed that Harish was the mastermind behind the fraud and lured marketing agents of finance companies, who worked in automobile showrooms, and facilitated loans for prospective buyers, said the DCP.
Six vehicles have been recovered from their possession, he said.


Generic Selectors API Specification
SlickSpec is a framework independant specification for selectors engines.
In this first release all the various separate specs have been combined into a single spec runner. Some of the frameworks specifically choose not to support some selectors that other frameworks fully support. In our next SlickSpec release we plan to separate out these selectors and have both a strict standards-only set and a common extenstions set of specs.
Here we list the historical results from various browsers so that you can quickly see previous results without having to run the specs yourself.
To run SlickSpec on your own selector engine all you need is a link to the source and a simple bootstrap script.
The bootstrap script maps the generic methods that the specs use to the specific selector engine's methods.
See the Slick.js bootstrap as an example bootstrap/slick.slickspec.js.
:first
SlickSpec—Copyright © MooTools 2009—MIT License


class Solution: def findMaxValueOfEquation(self, points: List[List[int]], k: int) -> int: q = [] sol = -float('inf') for i, (x, y) in enumerate(points): while q and abs(q[0][1] - x) > k: heapq.heappop(q) if q: yx2, x2, i2 = q[0] sol = max(sol, (-yx2)+y+x) heapq.heappush(q, (x-y, x, i)) return sol


| 002:248 Khan | : |
And their Prophet (Samuel) said to them: Verily! The sign of His Kingdom is that there shall come to you At-Tabut (a wooden box), wherein is Sakinah (peace and reassurance) from your Lord and a remnant of that which Musa (Moses) and Harun (Aaron) left behind, carried by the angels. Verily, in this is a sign for you if you are indeed believers. |
| 002:248 Maulana | : |
And their prophet said to them: Surely the sign of his kingdom is that there shall come to you the heart in which there is tranquillity from your Lord and the best of what the followers of Moses and the followers of Aaron have left, the angels bearing it. Surely there is a sign in this for you if you are believers. |
| 002:248 Pickthal | : |
And their Prophet said unto them: Lo! the token of his kingdom is that there shall come unto you the ark wherein is peace of reassurance from your Lord, and a remnant of that which the house of Moses and the house of Aaron left behind, the angels bearing it. Lo! herein shall be a token for you if (in truth) ye are believers. |
| 002:248 Rashad | : |
Their prophet said to them, "The sign of his kingship is that the Ark of the Covenant will be restored to you, bringing assurances from your Lord, and relics left by the people of Moses and the people of Aaron. It will be carried by the angels. This should be a convincing sign for you, if you are really believers." |
| 002:248 Sarwar | : |
Their Prophet further told them, "As the evidence of his authority, he will bring to you the Ark which will be a comfort to you from your Lord and a legacy of the household of Moses and Aaron. It will be carried by the angels. This is the evidence for you if you have faith." |
| 002:248 Shakir | : |
And the prophet said to them: Surely the sign of His kingdom is, that there shall come to you the chest in which there is tranquillity from your Lord and residue of the relics of what the children of Musa and the children of Haroun have left, the angels bearing it; most surely there is a sign in this for those who believe. |
| 002:248 Sherali | : |
And their Prophet said to them, 'The sign of his sovereignty is that there shall be given you a heart wherein there will be tranquility from your Lord and a legacy of good left by the family of Moses and the family of Aaron - the angels bearing it. Surely, in this there is a sign for you if you are believers.' |
| 002:248 Yusufali | : |
And (further) their Prophet said to them: "A Sign of his authority is that there shall come to you the Ark of the covenant, with (an assurance) therein of security from your Lord, and the relics left by the family of Moses and the family of Aaron, carried by angels. In this is a symbol for you if ye indeed have faith." |
Concepts/Themes Covered: الجهاد > الجهاد في سبيل الإسلام > الدعوة إلى الجهاد Jihad> The Call to Jihad>The Call for the Holy War القصص والتاريخ > موسى > التابوت The Stories and the History>Moses>Tabut(Wooden Box) القصص والتاريخ > موسى > قوم موسى The Stories and the History>Moses>People of Moses القصص والتاريخ > موسى > هارون The Stories and the History>Moses>Aaron
This tool uses eight English parallel translations for the Holy Quran imported from Mr. Clay Smith. You can also visit Qurany Concepts for browsing the Quran themes or topics. If you want to search the Holy Quran for keywords, please visit Qurany Keywords.


Try opening me in a private browser or check for cookies
Canvas fingerprinting is one of a number of browser fingerprinting techniques for tracking online users that allow websites to identify and track visitors using the HTML5 canvas element instead of browser cookies or other similar means.
Canvas fingerprinting works by exploiting the HTML5 canvas element.
When a user visits a page, the fingerprinting script first draws text with the font and size of its choice and adds background colors.
Next, the script calls Canvas API’s ToDataURL method to get the canvas pixel data in dataURL format, which is basically a Base64 encoded representation of the binary pixel data.
Finally, the script takes the hash of the text-encoded pixel data, which serves as the fingerprint ...
At the image format level – web browsers use different image processing engines, image export options, compression level, the final images may get different checksum even if they are pixel-identical.
At the system level – operating systems have different fonts, they use different algorithms and settings for anti-aliasing and sub-pixel rendering (browserleaks).
Every computer and browser engine renders a slightly diffrent canvas output when drawing the image on screen due to the slight discrepencies in both the soft and hardware.
Essentially this allows us to track a user without cookies or any other persisted method.
We use the unique user's computer to generate a fingerprint on page load.


src/graph_populator.py M 103:4 GraphPopulator.construct_version_query - E (32) 1 blocks (classes, functions, methods) analyzed. Average complexity: E (32.0)