PACKETS
Every webpage that you receive comes as a series of packets, and every e-mail you send leaves as a series of packets. Networks that ship data around in small packets are called packet switched networks.
On the Internet, the network breaks an e-mail message into parts of a certain size in bytes. These are the packets. Each packet carries the information that will help it get to its destination -- the sender's IP address, the intended receiver's IP address, something that tells the network how many packets this e-mail message has been broken into and the number of this particular packet. The packets carry the data in the protocols that the Internet uses: Transmission Control Protocol/Internet Protocol (TCP/IP). Each packet contains part of the body of your message. A typical packet contains perhaps 1,000 or 1,500 bytes.
Each packet is then sent off to its destination by the best available route -- a route that might be taken by all the other packets in the message or by none of the other packets in the message. This makes the network more efficient. First, the network can balance the load across various pieces of equipment on a millisecond-by-millisecond basis. Second, if there is a problem with one piece of equipment in the network while a message is being transferred, packets can be routed around the problem, ensuring the delivery of the entire message.
Let us understand Packets by considering IPv4 Packet Structure
(Image Credit: https://www.tutorialspoint.com/)
- Version: Version no. of Internet Protocol used
- IHL: Internet Header Length; Length of entire IP header
- DSCP: ifferentiated Services Code Point; this is Type of Service
- ECN: Explicit Congestion Notification; It carries information about the congestion seen in the route
- Total Length: Length of entire IP Packet (including IP header and IP Payload)
- Identification: If IP packet is fragmented during the transmission, all the fragments contain same identification number. to identify original IP packet they belong to
- Flags: As required by the network resources, if IP Packet is too large to handle, these ‘flags’ tells if they can be fragmented or not. In this 3-bit flag, the MSB is always set to ‘0’.
- Fragment Offset: This offset tells the exact position of the fragment in the original IP Packet
- Time to Live: To avoid looping in the network, every packet is sent with some TTL value set, which tells the network how many routers (hops) this packet can cross. At each hop, its value is decremented by one and when the value reaches zero, the packet is discarded
- Protocol: Tells the Network layer at the destination host, to which Protocol this packet belongs to, i.e. the next level Protocol. For example protocol number of ICMP is 1, TCP is 6 and UDP is 17.
- Header Checksum: This field is used to keep checksum value of entire header which is then used to check if the packet is received error-free
- Source Address: 32-bit address of the Sender (or source) of the packet
- Destination Address: 32-bit address of the Receiver (or destination) of the packet.
- Options: This is optional field, which is used if the value of IHL is greater than 5. These options may contain values for options such as Security, Record Route, Time Stamp, etc.


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Hi! I am Sonah S Tiju.
I am a student of Govt. Model Engineering College, Thrikkakara. I have taken electronics and communication branch and I am in 1st year. I am well versed in English, Hindi and Malayalam. I am an avid reader and enjoys reading in my leisure time. I am a very enthusiastic learner and grasps things easily. My strengths are that I am a team player and I mingle with people quiet easily. I am always open to suggestions and try to learn from my mistakes. I have learnt HTML and Python. I am good at writing content.
I enjoy participating in workshops and seminars. I am always ready to unertake any task assigned to me.


BioDare2 is a repository for circadian, biological data, providing a platform for data sharing and period analysis. The main features of BioDare2 are: fast period analysis, timeseries processing, attractive visualizations, simple data description and modern UI, all of it to assure the best user experience.
Period analysis is the strongest point of BioDare. BioDare gives you access to the best analysis methods: FFT NLLS, MESA, MFourFit, Enright Periodogram, Lomb-Scargle Periodogram, Spectrum Resampling and attractive visualizations.
You can contact us at: biodare (@) ed.ac.uk
You need a modern, up to date browser to use BioDare2 (we are testing it with Chrome).


Class p.p.f.PluginLoader:
Part of
p1tr.plugins.factoids
I'm responsible for loading plugins.
Function
__init__
Undocumented
Function
load_plugins
If no plug-ins are listed in my argument I load all plug-ins in the
def
__init__(self):
Undocumented
def
load_plugins(self, plugin_list=None):
If no plug-ins are listed in my argument I load all plug-ins in the plugin directory.


About
About
Hi, this is Habtom, a student at San Francisco State University and I love to play soccer and volleyball.


Type your text and get base64 encoded, or type your base64 and get the plain text. It's two way binded!
With ♥ by enbiso | Fork in Github


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Speakers
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Joining us from all around the world are over twenty fantastic
speakers, here to share their stories.
Schedule
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Enjoy three inspiring and action-packed days of talks, gatherings,
and all-around good times.
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Within the heart of downtown Chicago, The Chicago Theatre will provide
a beautiful conference venue.


The SQLs listed on the rigth pane are the top statements accumulated in the package cache as
measured by various metrics.
As a part of the initial diagnoses, the database administrator tries to determine the
part of the user workload impacted by the slowdown. Given the nature of this
problem, a suitable approach is to view the "top 10" statements, as these
statements are likely the statements involved in the problem.


The example demonstrate that tasks with high priority (such as feedback of user action) will always execute smoothly, even there're many tasks in the scheduler. But tasks with low priority (such as data analysis) may face the situation of starvation (never execute) if there're always some tasks have some higher priority.
Without Leopard
Leopard normal strategy
Leopard batch strategy


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Gene expression data¶
This page contains descriptions and examples to load gene expression data!
Fetch gene expression data¶
The ENGMA TOOLBOX provides microarray expression data obtained from the Allen Human Brain Atlas. Following the examples below, we can fetch microarray expression data.
Wanna know where we got those genes? 👖
The Allen Human Brain Atlas microarray expression data loaded as part of the ENIGMA TOOLBOX was originally
fetched from the abagen toolbox using the abagen.get_expression_data()
command, using data from all donors. We then re-organized the rows (corresponding to region labels) to match the order
used in ENIGMA-derived data matrices! For more flexibility, check our their toolbox!
Got NaNs? 🥛
Please note that two regions (right frontal pole and right temporal pole) in the Desikan-Killiany atlas were not matched to any tissue sample and thus are filled with NaN values in the data matrix.
Warning
The command fetch_ahba()
fetches a large (~24 MB) microarray dataset from the internet and may thus be
incredibly slow to load if you lack a good connection. But don’t you worry: you can download the
relevant file by typing this command in your terminal wget https://raw.githubusercontent.com/saratheriver/enigma-extra/master/ahba/allgenes.csv
and specifying its path in the fetch_ahba()
function as follows:fetch_ahba('/path/to/allgenes.csv')
>>> from enigmatoolbox.datasets import fetch_ahba
>>> # Fetch gene expression data (output of fetch_ahba() is a dataframe)
>>> df = fetch_ahba()
>>> # However, if you prefer to use numpy, you can also extract the data as follows:
>>> genex = df.iloc[:, 1:].to_numpy()
>>> # Obtain the region labels
>>> reglabels = df.iloc[:,0].to_list()
>>> # As well as the gene labels
>>> glabels = df.columns.values[1:].tolist()
%% Add the path to the ENIGMA TOOLBOX matlab folder
addpath(genpath('/path/to/ENIGMA/matlab/'));
%% Fetch gene expression data
% gx = matrix of gene expression data (82 x 15633)
% reglabels = name of cortical regions in same order as gx
% (82 x 1 cell array)
% genelabels = name of genes in same order as gx
% (1 x 15633 cell array)
[gx, reglabels, genelabels] = fetch_ahba();


Pyodide image should appear below
If streaming is working, you should see both progress bars rise at the same time.


| Package | Description |
|---|---|
| org.apache.taverna.activities.dataflow | |
| org.apache.taverna.activities.stringconstant |
Contains the activity classes required in the execution of Beanshell scripts.
|
| org.apache.taverna.annotation |
Entities within the workflow object model may be marked as annotated.
|
| org.apache.taverna.facade |
Facade interfaces to represent a workflow instance within the enactor.
|
| org.apache.taverna.facade.impl | |
| org.apache.taverna.platform.execution.api | |
| org.apache.taverna.platform.execution.impl.local | |
| org.apache.taverna.provenance.item | |
| org.apache.taverna.workflowmodel |
Defines classes and interfaces for workflow level entities and events.
|
| org.apache.taverna.workflowmodel.impl |
Implementation package for workflow entities
|
| org.apache.taverna.workflowmodel.processor.activity |
Provides definitions for a single Activity to be contained within a
Processor.
|
| org.apache.taverna.workflowmodel.processor.activity.impl |
Contains specific implementations of the Activity Ports, together with an implementation of ActivityPortBuilder which is responsible for building them.
|
| org.apache.taverna.workflowmodel.processor.dispatch |
Definition and support classes for the Dispatch Stack.
|
| org.apache.taverna.workflowmodel.processor.dispatch.impl |
Implementation package for the dispatch stack interfaces.
|
| org.apache.taverna.workflowmodel.processor.dispatch.layers |
Contains implementations of DispatchLayer defined by the core Taverna 2
specification.
|
| org.apache.taverna.workflowmodel.processor.iteration |
Object model and enactment logic for the iteration strategy component of
a Processor
|
| org.apache.taverna.workflowmodel.processor.iteration.impl |
Implementation logic for the iteration system.
|
| org.apache.taverna.workflowmodel.utils |
| Class and Description |
|---|
| Configurable
Interface for workflow items that can be configured from a bean.
|
| Dataflow
Top level definition object for a dataflow workflow.
|
| Edits
Defines the set of all available edit actions over a workflow model.
|
| WorkflowItem
An item that forms a structural part of a Workflow.
|
| Class and Description |
|---|
| Configurable
Interface for workflow items that can be configured from a bean.
|
| Edits
Defines the set of all available edit actions over a workflow model.
|
| WorkflowItem
An item that forms a structural part of a Workflow.
|
| Class and Description |
|---|
| Edit
The workflow object model exposed by this API is read only.
|
| Class and Description |
|---|
| ControlBoundary
Denotes that the associated type creates a boundary of control within the
dataflow.
|
| Dataflow
Top level definition object for a dataflow workflow.
|
| Class and Description |
|---|
| Dataflow
Top level definition object for a dataflow workflow.
|
| InvalidDataflowException
Thrown if attempting to use a workflow that is not
valid . |
| Class and Description |
|---|
| Dataflow
Top level definition object for a dataflow workflow.
|
| Class and Description |
|---|
| Dataflow
Top level definition object for a dataflow workflow.
|
| EditException
Superclass of all exceptions thrown when altering the workflow model through
the edit manager.
|
| Edits
Defines the set of all available edit actions over a workflow model.
|
| Processor
A single node within the dataflow digraph, the Processor is the basic
functional unit within a Taverna workflow.
|
| Class and Description |
|---|
| Dataflow
Top level definition object for a dataflow workflow.
|
| Class and Description |
|---|
| AbstractPort
Port definition with depth and name
|
| Condition
Defines the base interface for a condition which must be satisfied before a
processor can commence invocation.
|
| Configurable
Interface for workflow items that can be configured from a bean.
|
| ConfigurationException
Thrown when attempting to configure a
Configurable with an invalid
configuration. |
| ControlBoundary
Denotes that the associated type creates a boundary of control within the
dataflow.
|
| Dataflow
Top level definition object for a dataflow workflow.
|
| DataflowInputPort
An input port on a Dataflow contains a nested output port within it.
|
| DataflowOutputPort
Output port of a DataFlow, exposes an internal EventHandlingInputPort into
which the internal workflow logic pushes data to be exposed outside the
workflow boundary.
|
| DataflowPort
Defines that the implementing port belongs to a Dataflow
|
| DataflowValidationReport
Contains a validation report from a dataflow validation check.
|
| Datalink
A single point to point data link from an instance of
EventForwardingOutputPort to an instance of EventHandlingInputPort
|
| Edit
The workflow object model exposed by this API is read only.
|
| EditException
Superclass of all exceptions thrown when altering the workflow model through
the edit manager.
|
| EventForwardingOutputPort
An extension of OutputPort defining a set of target EventReceivingInputPorts
to which internally generated events will be relayed.
|
| EventHandlingInputPort
Input port capable of receiving and reacting to workflow events.
|
| FailureTransmitter
Used to message interested parties when a top level failure occurs within a
Dataflow |
| FilteringInputPort
A filtering input port is one capable of filtering events to only pass
through data events at a certain depth.
|
| InputPort
Marker interface denoting that the instance is an input port.
|
| InvalidDataflowException
Thrown if attempting to use a workflow that is not
valid . |
| Merge
Allows multiple outputs to be routed to a single input within the dataflow.
|
| MergeInputPort
Input port on a Merge object
|
| MergePort |
| NamedWorkflowEntity
Entities existing directly within a workflow such as Processors, Merge
operators and other potential future extensions exist within a naming scheme.
|
| OrderedPair
A simple generic class to hold a pair of same type objects.
|
| OutputPort
Port representing the output of an activity, processor or workflow.
|
| Port
Named port which receives events from some other entity and handles them
appropriately.
|
| Processor
A single node within the dataflow digraph, the Processor is the basic
functional unit within a Taverna workflow.
|
| ProcessorInputPort
Input port on a Processor, is both a filtering input port and a processor
port
|
| ProcessorOutputPort
Input port on a Processor, is both an event forwarding output port and a
processor port
|
| ProcessorPort |
| TokenProcessingEntity
Superinterface for all classes within the workflow model which consume and
emit workflow data tokens.
|
| WorkflowItem
An item that forms a structural part of a Workflow.
|
| Class and Description |
|---|
| AbstractOutputPort
Simple implementation of OutputPort, extends AbstractPort and adds the
granular depth bean getter.
|
| AbstractPort
Port definition with depth and name
|
| Condition
Defines the base interface for a condition which must be satisfied before a
processor can commence invocation.
|
| Configurable
Interface for workflow items that can be configured from a bean.
|
| Dataflow
Top level definition object for a dataflow workflow.
|
| DataflowInputPort
An input port on a Dataflow contains a nested output port within it.
|
| DataflowOutputPort
Output port of a DataFlow, exposes an internal EventHandlingInputPort into
which the internal workflow logic pushes data to be exposed outside the
workflow boundary.
|
| DataflowPort
Defines that the implementing port belongs to a Dataflow
|
| DataflowValidationReport
Contains a validation report from a dataflow validation check.
|
| Datalink
A single point to point data link from an instance of
EventForwardingOutputPort to an instance of EventHandlingInputPort
|
| Edit
The workflow object model exposed by this API is read only.
|
| EditException
Superclass of all exceptions thrown when altering the workflow model through
the edit manager.
|
| Edits
Defines the set of all available edit actions over a workflow model.
|
| EventForwardingOutputPort
An extension of OutputPort defining a set of target EventReceivingInputPorts
to which internally generated events will be relayed.
|
| EventHandlingInputPort
Input port capable of receiving and reacting to workflow events.
|
| FailureTransmitter
Used to message interested parties when a top level failure occurs within a
Dataflow |
| FilteringInputPort
A filtering input port is one capable of filtering events to only pass
through data events at a certain depth.
|
| InputPort
Marker interface denoting that the instance is an input port.
|
| InvalidDataflowException
Thrown if attempting to use a workflow that is not
valid . |
| Merge
Allows multiple outputs to be routed to a single input within the dataflow.
|
| MergeInputPort
Input port on a Merge object
|
| NamedWorkflowEntity
Entities existing directly within a workflow such as Processors, Merge
operators and other potential future extensions exist within a naming scheme.
|
| NamingException
Potentially thrown when an edit fails due to naming of entities created or
modified by the edit.
|
| OrderedPair
A simple generic class to hold a pair of same type objects.
|
| OutputPort
Port representing the output of an activity, processor or workflow.
|
| Port
Named port which receives events from some other entity and handles them
appropriately.
|
| Processor
A single node within the dataflow digraph, the Processor is the basic
functional unit within a Taverna workflow.
|
| ProcessorFinishedEvent
An event saying that a processor with a given owning process has finished with execution
(that includes the whole dispatch stack - iterations of the processor and all).
|
| ProcessorInputPort
Input port on a Processor, is both a filtering input port and a processor
port
|
| ProcessorOutputPort
Input port on a Processor, is both an event forwarding output port and a
processor port
|
| TokenProcessingEntity
Superinterface for all classes within the workflow model which consume and
emit workflow data tokens.
|
| WorkflowItem
An item that forms a structural part of a Workflow.
|
| Class and Description |
|---|
| Configurable
Interface for workflow items that can be configured from a bean.
|
| ConfigurationException
Thrown when attempting to configure a
Configurable with an invalid
configuration. |
| Dataflow
Top level definition object for a dataflow workflow.
|
| Edits
Defines the set of all available edit actions over a workflow model.
|
| InputPort
Marker interface denoting that the instance is an input port.
|
| OutputPort
Port representing the output of an activity, processor or workflow.
|
| Port
Named port which receives events from some other entity and handles them
appropriately.
|
| WorkflowItem
An item that forms a structural part of a Workflow.
|
| Class and Description |
|---|
| AbstractOutputPort
Simple implementation of OutputPort, extends AbstractPort and adds the
granular depth bean getter.
|
| AbstractPort
Port definition with depth and name
|
| InputPort
Marker interface denoting that the instance is an input port.
|
| OutputPort
Port representing the output of an activity, processor or workflow.
|
| Port
Named port which receives events from some other entity and handles them
appropriately.
|
| WorkflowItem
An item that forms a structural part of a Workflow.
|
| Class and Description |
|---|
| Configurable
Interface for workflow items that can be configured from a bean.
|
| Processor
A single node within the dataflow digraph, the Processor is the basic
functional unit within a Taverna workflow.
|
| WorkflowItem
An item that forms a structural part of a Workflow.
|
| Class and Description |
|---|
| Configurable
Interface for workflow items that can be configured from a bean.
|
| Edit
The workflow object model exposed by this API is read only.
|
| EditException
Superclass of all exceptions thrown when altering the workflow model through
the edit manager.
|
| WorkflowItem
An item that forms a structural part of a Workflow.
|
| Class and Description |
|---|
| Configurable
Interface for workflow items that can be configured from a bean.
|
| ConfigurationException
Thrown when attempting to configure a
Configurable with an invalid
configuration. |
| ControlBoundary
Denotes that the associated type creates a boundary of control within the
dataflow.
|
| Processor
A single node within the dataflow digraph, the Processor is the basic
functional unit within a Taverna workflow.
|
| WorkflowItem
An item that forms a structural part of a Workflow.
|
| Class and Description |
|---|
| WorkflowItem
An item that forms a structural part of a Workflow.
|
| Class and Description |
|---|
| WorkflowItem
An item that forms a structural part of a Workflow.
|
| WorkflowStructureException
Thrown predominantly at runtime under circumstances that suggest an
inconsistancy in the workflow model.
|
| Class and Description |
|---|
| Dataflow
Top level definition object for a dataflow workflow.
|
| Datalink
A single point to point data link from an instance of
EventForwardingOutputPort to an instance of EventHandlingInputPort
|
| Edit
The workflow object model exposed by this API is read only.
|
| Edits
Defines the set of all available edit actions over a workflow model.
|
| EventForwardingOutputPort
An extension of OutputPort defining a set of target EventReceivingInputPorts
to which internally generated events will be relayed.
|
| EventHandlingInputPort
Input port capable of receiving and reacting to workflow events.
|
| InputPort
Marker interface denoting that the instance is an input port.
|
| Merge
Allows multiple outputs to be routed to a single input within the dataflow.
|
| NamedWorkflowEntity
Entities existing directly within a workflow such as Processors, Merge
operators and other potential future extensions exist within a naming scheme.
|
| OutputPort
Port representing the output of an activity, processor or workflow.
|
| Port
Named port which receives events from some other entity and handles them
appropriately.
|
| Processor
A single node within the dataflow digraph, the Processor is the basic
functional unit within a Taverna workflow.
|
| ProcessorInputPort
Input port on a Processor, is both a filtering input port and a processor
port
|
| ProcessorOutputPort
Input port on a Processor, is both an event forwarding output port and a
processor port
|
| TokenProcessingEntity
Superinterface for all classes within the workflow model which consume and
emit workflow data tokens.
|
Copyright © 2015–2016 The Apache Software Foundation. All rights reserved.


from pyredis import Client
client = Client(host="localhost")
client.ping()
Bulk Mode can be used to import large amounts of data in a short time. With bulk mode enabled sending requests and fetching results is separated from each other. Which will save many network round trips, improving query performance.
All executed commands will return None.
There is a threshold, which defaults to 5000 requests, after which the results of the previous requests are fetched into a list. If you are not interested in the results, this can be disabled by calling bulk_start with the parameter keep_results=False.
Fetching results, when the threshold is reached is a transparent operation. The client will only notice that the execution of the the request triggering the threshold will take a little longer.
Calling bulk_stop() will fetch all remaining results, and return a list with fetched results. This list can also contain exceptions from failed commands.
from pyredis import Client
client = Client(host="localhost")
client.bulk_start()
client.set('key1', 'value1)
client.set('key1', 'value1)
client.set('key1', 'value1)
client.bulk_stop()
[b'OK', b'OK', b'OK']
from pyredis import Pool
pool = Pool(host="localhost")
client = pool.aquire()
client.ping()
b'PONG'
pool.release(client)
from pyredis import ClusterPool
pool = ClusterPool(seeds=[('seed1', 6379), ('seed2', 6379), ('seed3', 6379)])
client = pool.aquire()
client.ping(shard_key='test')
b'PONG'
pool.release(client)
from pyredis import SentinelPool
pool = SentinelPool(sentinels=[('sentinel1', 26379), ('sentinel2', 26379), ('sentinel3', 26379)])
client = pool.aquire()
client.ping()
b'PONG'
pool.release(client)
from pyredis import Client, PubSubClient
client = Client(host='localhost')
subscribe = PubSubClient(host='localhost')
subscribe.subscribe('/blub')
subscribe.get()
[b'subscribe', b'/blub', 1]
client.publish('/blub', 'test')
1
subscribe.get()
[b'message', b'/blub', b'test']


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Nested Routes
/parent
/parent/foo
/parent/bar
/baz
/parent/qux
/parent/quy
/parent/zap
/parent/zap/1
{ params: { zapId: 2 }} (relative params)


history
Ratan Naval Tata (born 28 December 1937) is an Indian industrialist, philanthropist, and a former chairman of Tata Sons. who was later adopted by Ratanji Tata, son of Jamsetji Tata, the founder of Tata Group. He is an alumnus of the Cornell University College of Architecture and Harvard Business School through the Advanced Management Program that he completed in 1975. He joined his company in 1961 when he used to work on the shop floor of Tata Steel, and was the apparent successor to J. R. D. Tata upon the latter's retirement in 1991.
vision
To be globally significant in each of our chosen businesses by 2025. To be the most reliable global network for customers and suppliers, that delivers value through products and services. To be a responsible value creator for all our stakeholders.
- list item
- list item
- list item
goals
In his interview to Rose, Tata said he plans to focus on rural development, water conservation and child nutrition after he steps down as chairman of Tata Sons. “My most visible goal is to do something in nutrition to children in India, and pregnant mothers," Tata said. Because that would change the mental and physical health of our population.


#include <WaveletMath.h>
|
| static void | Decompose (const std::vector< double > *data, const Wavelet *wavelet, std::vector< double > *approx_coeffs, std::vector< double > *details_coeffs, DyadicMode dyadic_mode=DyadicMode::Odd, PaddingMode padding_mode=PaddingMode::Zeroes) |
| |
| static void | Reconstruct (const std::vector< double > *coeffs, const std::vector< double > *reconstruction_coeffs, std::vector< double > *data, const Wavelet *wavelet, size_t data_size, DyadicMode dyadic_mode=DyadicMode::Odd, PaddingMode padding_mode=PaddingMode::Zeroes) |
| |
| static void | DyadicUpsample (const std::vector< double > *data, std::vector< double > *data_upsampled, DyadicMode dyadic_mode=DyadicMode::Odd) |
| |
| static void | DyadicDownsample (const std::vector< double > *data, std::vector< double > *data_downsampled, DyadicMode dyadic_mode=DyadicMode::Odd) |
| |
| static void | Convolve (const std::vector< double > *data, const std::vector< double > *coeffs, std::vector< double > *result) |
| |
| static void | Pad (const std::vector< double > *data, std::vector< double > *extended_data, size_t pad_left, size_t pad_right, PaddingMode padding_mode=PaddingMode::Zeroes) |
| |
A container for static methods useful to compute wavelet math functions. This is not meant to be a complete wavelet solution, it exists to allow the narrow set of wavelet math functions required to compute wavelet packet trees.
- See also
- WaveletPacketTree
-
StationaryWaveletPacketTree
◆ Convolve()
| void WaveletMath::Convolve |
( |
const std::vector< double > * |
data, |
|
|
const std::vector< double > * |
coeffs, |
|
|
std::vector< double > * |
result |
|
) |
| |
|
static |
Perform a convolution.
- Parameters
-
| data | The input signal to convolve. |
| coeffs | The filter coefficients. |
| result | The destination for our convolved signal data. Any values in the vector will be overwritten. |
◆ Decompose()
| void WaveletMath::Decompose |
( |
const std::vector< double > * |
data, |
|
|
const Wavelet * |
wavelet, |
|
|
std::vector< double > * |
approx_coeffs, |
|
|
std::vector< double > * |
details_coeffs, |
|
|
DyadicMode |
dyadic_mode = DyadicMode::Odd , |
|
|
PaddingMode |
padding_mode = PaddingMode::Zeroes |
|
) |
| |
|
static |
Decompose a signal into approximation and details coefficients.
Uses wavelet to get the decomposition highpass and lowpass filters.
- Parameters
-
| data | The signal data we wish to decompose. |
| wavelet | Wavelet we will use to get the highpass and lowpass decomposition filters. |
| approx_coeffs | Destination approximation coefficients. Any existing contents will be overwritten. |
| details_coeffs | Destination details coefficients. Any existing contents will be overwritten. |
| dyadic_mode | Mode we should use when dyadically downsampling. (default: Odd) |
| padding_mode | Padding mode we should use when padding the signal data. (Default: Zeroes) |
◆ DyadicDownsample()
| void WaveletMath::DyadicDownsample |
( |
const std::vector< double > * |
data, |
|
|
std::vector< double > * |
data_downsampled, |
|
|
DyadicMode |
dyadic_mode = DyadicMode::Odd |
|
) |
| |
|
static |
Dyadically downsample a data signal.
Half of the original values are included in the downsampled data. The dyadic mode controls which elements from the original data are taken into the downsampled data.
If dyadic mode is Even, every even index value in data is copied into the downsampled data. Odd index values are left out.
If dyadic mode is Odd, every odd index value in data is copied into the downsampled data. Even index values are left out.
The downsampled data has size half the original data's size. Even dyadic mode rounds up, Odd dyadic mode rounds down in case original data's size is not even.
- Parameters
-
| data | The original data signal we want to downsample. |
| data_downsampled | The downsampled data will be written to this vector. Existing contents will be erased. |
| dyadic_mode | Mode we should use to dyadically downsample. (default: Odd) |
- See also
- DyadicMode
◆ DyadicUpsample()
| void WaveletMath::DyadicUpsample |
( |
const std::vector< double > * |
data, |
|
|
std::vector< double > * |
data_upsampled, |
|
|
DyadicMode |
dyadic_mode = DyadicMode::Odd |
|
) |
| |
|
static |
Dyadically upsample a data signal.
All of the original values from data are included in the upsampled data but there are zeroes separating each value. The dyadic mode determines which indices in the upsampled vector contain zeroes.
If dyadic mode is Even, every even index in the upsampled data will contain a zero.
If dyadic mode is Odd, every odd index in the upsampled data will contain a zero.
The upsampled data has size double the original data's size plus one in even dyadic mode or minus one in odd dyadic mode.
- Parameters
-
| data | The original data signal we want to upsample. |
| data_upsampled | The upsampled data will be written to this vector. Existing contents will be erased. |
| dyadic_mode | Mode we should use to dyadically upsample. (default: Odd) |
- See also
- DyadicMode
◆ Pad()
| void WaveletMath::Pad |
( |
const std::vector< double > * |
data, |
|
|
std::vector< double > * |
extended_data, |
|
|
size_t |
pad_left, |
|
|
size_t |
pad_right, |
|
|
PaddingMode |
padding_mode = PaddingMode::Zeroes |
|
) |
| |
|
static |
Pad a vector by inserting elements on the right and left.
If padding_mode is Zeroes, the inserted elements are all zero.
If padding_mode is Symmetric, the inserted elements are the values closest to the beginning or end but flipped symmetrically around either the first or last element. If the requested pad length is longer than data size, the last or first element of data is used for all the elements which would overflow data.
For example, [1,2,3] symmetrically padded by 3 on the left and right produces [3,3,2,1,2,3,2,1,1]
- Parameters
-
| data | The original data we want to pad. |
| extended_data | The destination for our padded data. It will have length equal to pad_left + pad_right + data->size(). Any existing values will be overwritten. |
| pad_left | The number of elements to pad on the left of data. Can be zero. |
| pad_right | The number of elements to pad on the right of data. Can be zero. |
| padding_mode | Padding mode we should use to insert padding elements. |
- See also
- PaddingMode
◆ Reconstruct()
| void WaveletMath::Reconstruct |
( |
const std::vector< double > * |
coeffs, |
|
|
const std::vector< double > * |
reconstruction_coeffs, |
|
|
std::vector< double > * |
data, |
|
|
const Wavelet * |
wavelet, |
|
|
size_t |
data_size, |
|
|
DyadicMode |
dyadic_mode = DyadicMode::Odd , |
|
|
PaddingMode |
padding_mode = PaddingMode::Zeroes |
|
) |
| |
|
static |
Reconstruct a signal from approximation or details coefficients.
- Parameters
-
| coeffs | Either the approximation or details coefficients produced during a decomposition. |
| reconstruction_coeffs | Should be a lowpass or highpass reconstruction filter with size equal to the length of wavelet. |
| data | Destination vector for the reconstructed signal. Any existing contents will be erased. |
| wavelet | Wavelet we used to do the decomposition. |
| data_size | Size of the reconstructed signal. |
| dyadic_mode | Mode we should use when dyadically upsampling. (default: Odd) |
| padding_mode | Padding mode we should use when padding the coefficient data. (Default: Zeroes) |
The documentation for this class was generated from the following files:


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Manuel Stoilov
About |
Contact |
Get in touch
At the moment, the best way to get in touch with me is via my email:
manuel.stoilov.bg@gmail.com


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This NTTP has been rescinded. Instead, a community-governed NyzoScript will be implemented. This script will enable manual scoring modifications to promote cycle stability and, when needed, swiftly remove verifiers that are behaving irresponsibly.
Nyzo is a constantly evolving democracy. New verifiers are added every two cycles, and verifiers that do not meet the expectations of the cycle are removed.
While a verifier-removal mechanism is necessary for ensuring the health of the cycle, its use should be minimized. Flaws in the consensus process have resulted in large numbers of verifiers being removed from the cycle over short periods of time. Also, there is potential that an attacker could try to gain influence in the cycle by forcing many other verifiers out of the cycle.
There are currently two situations in which a verifier will be removed from the cycle. If a verifier's block is missing, the verifier is skipped and removed after a short delay. If a verifier consistently fails to vote in the consensus process, its block will be rejected.
The second case, forced removal even when a block is available, is deactivated when any verifier has left in the last cycle. The first case, though, does not adjust due to removals.
NTTP-2: we propose increasing the time to wait for a verifier's block if other verifiers have recently left the cycle. If a single verifier has left in the last cycle, the removal delay time will increase from 80 seconds to 10 minutes. With two losses, the delay time will increase to 30 minutes. With three losses, the delay time will increase to an hour. Further losses will not increase the delay time beyond one hour. When a cycle completes with no losses, the delay time will return to 80 seconds.
This would result in hour-long stalls between verifier removals if several verifiers have recently left the cycle. This would cause the frozen edge to fall far behind the open edge, and it would delay the processing of some transactions.
This would also put a much lower limit on the number of verifiers that could be removed in any time period. It would likely allow verifier operators sufficient time to recover their verifiers from a bug or attack that might otherwise cause those verifiers to leave the cycle.
NTTP-2 was approved by the cycle in a cycle transaction from ⛵ Argo 746 (c34a6f1942cb7ec1-0d2a440b3e116041-d05df2746ebe7b41-802340a1495e7af5) to ⛵ Argo 752 (7cd4b6c2bd8316e4-40b0a6affb2c78dc-b5d7fdaa17aae8c6-a9bd45ac10017f9d).
This transaction was not successfully incorporated into the blockchain, so it will be stored as on-chain metadata. A link to view the data will be posted here.
The price for this NTTP is currently under consideration.
The implementation time for this NTTP is currently under consideration.


Hyperlinks
- jsonary.js - the core Jsonary library
- basic.jsonary.js - a default set of renderers, that look similar to plain JSON
- basic.jsonary.css - the stylesheet used by the above renderers
Defining a hyperlink
One of the many wonders of JSON Schemas is their ability to define hyperlinks on the data that they describe.
These are defined using the links
keyword in schemas. Links are specified as an array of Link Description Objects, like in this example schema:
{ "title": "Schema with links", "links": [ { "href": "home.json", "rel": "home" }, { "href": "page2.json", "rel": "next" } ] }
"Well OK," you say, "but the URIs are fixed! You'll need a different schema for every bit of data!".
That's a good point. Luckily for us, JSON Schema has a way around this. The URI specified in the href
property is a template, which is filled with values from the instance data.
For example, let's look at this data: (data-list.json)
[ { "id": 12345, "content": "foo" }, { "id": 99999, "content": "bar" } ]
We would like to define a link on each of the items in that array, pointing to comments on that item. The URI of the link should be "http://example.com/items/{id}/comments
", where {id}
is replaced by the "id" property from the data.
Actually, that is exactly the syntax we need to use. So let's take this schema: (schema-list.json)
{ "title": "Adaptive links", "items": { "title": "Sub-schema", "links": [ { "href": "http://example.com/items/{id}/comments", "rel": "comments" } ] } }
And let's render our item:click to run
Hopefully, next to the schema titles annotating the data, you should see some links, labelled "comments" according to the rel
value.
If you select these links, you should see a popup containing the URI of the link that you clicked. The URIs are different for the two items, due to their different values for "id
".
Submitting data
Hyperlinks in JSON Schema can define data to be submitted with the link. The following properties are all optional:
schema
- specifies the form the data should take, as a JSON Schema.method
- the method (GET/POST/etc.) with which the link should be accessed. Defaults to "GET".encType
- the encoding method for the data.
Any of these options can be omitted.
For example, here is a link definition that defines some data: (schema-link.json)
{ "title": "Example schema", "links": [ { "href": "mirror.php", "rel": "example", "method": "POST", "enctype": "application/x-www-form-urlencoded", "schema": { "type": "object", "properties": { "num": {"type": "integer"}, "str": {"type": "string"} }, "required": ["num", "str"], "additionalProperties": {"type": "boolean"} } } ] }
Let's take a look at what an instance described by that schema might look like:
Note: this example uses PHP, and therefore will not work if you are viewing this on GitHub.
When you click the link, a prompt should appear in which you can edit some data to submit. The response (from mirror.php
) will be shown below:
When you submit the link data, you should (hopefully) see the data returned by mirror.php
, which includes the POST data.
You may notice that the POST data is formatted exactly like a query string in a URL. This is the encoding defined by the media type application/x-www-form-urlencoded
, and it is the default encoding when using HTML forms.
Submitting JSON data
When using the application/x-www-form-urlencoding
encoding, all the data is bundled up into a query string, and therefore loses all type information. The number 2
is indistinguisable from the string "2"
.
Fortunately, this is not the only encoding we can use. We can use any media type, including application/json
: (schema-link-json.json)
{ "title": "Example schema", "links": [ { "href": "mirror.php?some=parameters&because=why_not", "rel": "example", "method": "POST", "encType": "application/json", "schema": { "type": "object", "properties": { "num": {"type": "integer"}, "str": {"type": "string"} }, "required": ["num", "str"], "additionalProperties": {"type": "boolean"} } } ] }
This is actually the default value of encType
for POST links in JSON Schema (if you are making GET requests, then only application/x-www-form-urlencoded
is available). Let's look at what happens when we use that link:
Note: this example uses PHP, and therefore will not work if you are viewing this on GitHub.
The response from mirror.php
will be shown below:
As you can hopefully see, the data being sent as the POST body is now encoded as JSON.
Please note: submitting JSON data as above is not the same as submitting JSON data as a string value in an HTML form. Submitting JSON data as an input with name "jsonData
" would end up POSTing something like this:
jsonData=%7B%22num%22%3A0%2C%22str%22%3A%22value%22%7D


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7
8
9
10
The
New
York
copyright
reserved
botanical
Garden
INSTITUTE
OF
JAMAICA
PLANTS
OF
JAMAICA,
B.W.
I.
Survey
of
vegetation
on
bauxite
and
related
areas
Parish:
St.
Elizabeth
Dipholis
salicifolia
A/
DC/
Tree
of
151;
fruit
"black
when
mature.
Along
the
old
road
from
Retirement
to
Mountainside.
G.lProItor
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Legend -
Level of confidence that token is an accurately-transcribed word
extremely low
very low
low
undetermined
medium
high
very high


An interactive installation celebrating the connective power of the sun and breakthrough LED technology.
Category: Interactive installation
Team: Nik Hafermass, Ivan Cruz, Blossom Liu, Maddie Ma, Danny Grey, Chris Taylor
Year: 2019
An international tech client wanted to to communicate the power of their revolutionary LED technology — light that embodies the sun. Our team concepted an interactive installation that lets visitors experience the power of pure light on a massive scale.
This project was a first for ArtCenter: a student team collaborating with Triad Berlin and a real-world client. We created small-scale prototypes and VR sketches to iterate quickly and discovered that simple forms best communicated the power of the light. Through its simple form, The Blade shows off the power of these lights in an immediate, immerse manner.
To celebrate the end of our trip, we threw a party. We designed audio-reactive visuals for the DJs and multi-media installations for guests. I designed the typeface, Platz, used throughout.


Hadoop KMS is a cryptographic key management server based on Hadoop's KeyProvider API.
It provides a client and a server components which communicate over HTTP using a REST API.
The client is a KeyProvider implementation interacts with the KMS using the KMS HTTP REST API.
KMS and its client have built-in security and they support HTTP SPNEGO Kerberos authentication and HTTPS secure transport.
KMS is a Java web-application and it runs using a pre-configured Tomcat bundled with the Hadoop distribution.
The KMS client KeyProvider uses the kms scheme, and the embedded URL must be the URL of the KMS. For example, for a KMS running on http://localhost:16000/kms, the KeyProvider URI is kms://http@localhost:16000/kms. And, for a KMS running on https://localhost:16000/kms, the KeyProvider URI is kms://https@localhost:16000/kms
Configure the KMS backing KeyProvider properties in the etc/hadoop/kms-site.xml configuration file:
<property> <name>hadoop.kms.key.provider.uri</name><EMAIL_ADDRESS></property> <property> <name>hadoop.security.keystore.java-keystore-provider.password-file</name> <value>kms.keystore.password</value> </property>
The password file is looked up in the Hadoop's configuration directory via the classpath.
NOTE: You need to restart the KMS for the configuration changes to take effect.
KMS caches keys for short period of time to avoid excessive hits to the underlying key provider.
The Cache is enabled by default (can be dissabled by setting the hadoop.kms.cache.enable boolean property to false)
The cache is used with the following 3 methods only, getCurrentKey() and getKeyVersion() and getMetadata().
For the getCurrentKey() method, cached entries are kept for a maximum of 30000 millisecond regardless the number of times the key is being access (to avoid stale keys to be considered current).
For the getKeyVersion() method, cached entries are kept with a default inactivity timeout of 600000 milliseconds (10 mins). This time out is configurable via the following property in the etc/hadoop/kms-site.xml configuration file:
<property> <name>hadoop.kms.cache.enable</name> <value>true</value> </property> <property> <name>hadoop.kms.cache.timeout.ms</name> <value>600000</value> </property> <property> <name>hadoop.kms.current.key.cache.timeout.ms</name> <value>30000</value> </property>
Audit logs are aggregated for API accesses to the GET_KEY_VERSION, GET_CURRENT_KEY, DECRYPT_EEK, GENERATE_EEK operations.
Entries are grouped by the (user,key,operation) combined key for a configurable aggregation interval after which the number of accesses to the specified end-point by the user for a given key is flushed to the audit log.
The Aggregation interval is configured via the property :
<property> <name>hadoop.kms.aggregation.delay.ms</name> <value>10000</value> </property>
To start/stop KMS use KMS's bin/kms.sh script. For example:
hadoop-2.6.5 $ sbin/kms.sh start
NOTE: Invoking the script without any parameters list all possible parameters (start, stop, run, etc.). The kms.sh script is a wrapper for Tomcat's catalina.sh script that sets the environment variables and Java System properties required to run KMS.
To configure the embedded Tomcat go to the share/hadoop/kms/tomcat/conf.
KMS pre-configures the HTTP and Admin ports in Tomcat's server.xml to 16000 and 16001.
Tomcat logs are also preconfigured to go to Hadoop's logs/ directory.
The following environment variables (which can be set in KMS's etc/hadoop/kms-env.sh script) can be used to alter those values:
NOTE: You need to restart the KMS for the configuration changes to take effect.
Configure the Kerberos etc/krb5.conf file with the information of your KDC server.
Create a service principal and its keytab for the KMS, it must be an HTTP service principal.
Configure KMS etc/hadoop/kms-site.xml with the correct security values, for example:
<property> <name>hadoop.kms.authentication.type</name> <value>kerberos</value> </property> <property> <name>hadoop.kms.authentication.kerberos.keytab</name> <value>${user.home}/kms.keytab</value> </property> <property> <name>hadoop.kms.authentication.kerberos.principal</name> <value>HTTP/localhost</value> </property> <property> <name>hadoop.kms.authentication.kerberos.name.rules</name> <value>DEFAULT</value> </property>
NOTE: You need to restart the KMS for the configuration changes to take effect.
Each proxyuser must be configured in etc/hadoop/kms-site.xml using the following properties:
<property> <name>hadoop.kms.proxyuser.#USER#.users</name> <value>*</value> </property> <property> <name>hadoop.kms.proxyuser.#USER#.groups</name> <value>*</value> </property> <property> <name>hadoop.kms.proxyuser.#USER#.hosts</name> <value>*</value> </property>
#USER# is the username of the proxyuser to configure.
The users property indicates the users that can be impersonated.
The groups property indicates the groups users being impersonated must belong to.
At least one of the users or groups properties must be defined. If both are specified, then the configured proxyuser will be able to impersonate and user in the users list and any user belonging to one of the groups in the groups list.
The hosts property indicates from which host the proxyuser can make impersonation requests.
If users, groups or hosts has a *, it means there are no restrictions for the proxyuser regarding users, groups or hosts.
To configure KMS to work over HTTPS the following 2 properties must be set in the etc/hadoop/kms_env.sh script (shown with default values):
In the KMS tomcat/conf directory, replace the server.xml file with the provided ssl-server.xml file.
You need to create an SSL certificate for the KMS. As the kms Unix user, using the Java keytool command to create the SSL certificate:
$ keytool -genkey -alias tomcat -keyalg RSA
You will be asked a series of questions in an interactive prompt. It will create the keystore file, which will be named .keystore and located in the kms user home directory.
The password you enter for "keystore password" must match the value of the KMS_SSL_KEYSTORE_PASS environment variable set in the kms-env.sh script in the configuration directory.
The answer to "What is your first and last name?" (i.e. "CN") must be the hostname of the machine where the KMS will be running.
NOTE: You need to restart the KMS for the configuration changes to take effect.
KMS ACLs configuration are defined in the KMS etc/hadoop/kms-acls.xml configuration file. This file is hot-reloaded when it changes.
KMS supports both fine grained access control as well as blacklist for kms operations via a set ACL configuration properties.
A user accessing KMS is first checked for inclusion in the Access Control List for the requested operation and then checked for exclusion in the Black list for the operation before access is granted.
<property> <name>hadoop.kms.acl.CREATE</name> <value>*</value> <description> ACL for create-key operations. If the user does is not in the GET ACL, the key material is not returned as part of the response. </description> </property> <property> <name>hadoop.kms.blacklist.CREATE</name> <value>hdfs,foo</value> <description> Blacklist for create-key operations. If the user does is in the Blacklist, the key material is not returned as part of the response. </description> </property> <property> <name>hadoop.kms.acl.DELETE</name> <value>*</value> <description> ACL for delete-key operations. </description> </property> <property> <name>hadoop.kms.blacklist.DELETE</name> <value>hdfs,foo</value> <description> Blacklist for delete-key operations. </description> </property> <property> <name>hadoop.kms.acl.ROLLOVER</name> <value>*</value> <description> ACL for rollover-key operations. If the user does is not in the GET ACL, the key material is not returned as part of the response. </description> </property> <property> <name>hadoop.kms.blacklist.ROLLOVER</name> <value>hdfs,foo</value> <description> Blacklist for rollover-key operations. </description> </property> <property> <name>hadoop.kms.acl.GET</name> <value>*</value> <description> ACL for get-key-version and get-current-key operations. </description> </property> <property> <name>hadoop.kms.blacklist.GET</name> <value>hdfs,foo</value> <description> ACL for get-key-version and get-current-key operations. </description> </property> <property> <name>hadoop.kms.acl.GET_KEYS</name> <value>*</value> <description> ACL for get-keys operation. </description> </property> <property> <name>hadoop.kms.blacklist.GET_KEYS</name> <value>hdfs,foo</value> <description> Blacklist for get-keys operation. </description> </property> <property> <name>hadoop.kms.acl.GET_METADATA</name> <value>*</value> <description> ACL for get-key-metadata and get-keys-metadata operations. </description> </property> <property> <name>hadoop.kms.blacklist.GET_METADATA</name> <value>hdfs,foo</value> <description> Blacklist for get-key-metadata and get-keys-metadata operations. </description> </property> <property> <name>hadoop.kms.acl.SET_KEY_MATERIAL</name> <value>*</value> <description> Complimentary ACL for CREATE and ROLLOVER operation to allow the client to provide the key material when creating or rolling a key. </description> </property> <property> <name>hadoop.kms.blacklist.SET_KEY_MATERIAL</name> <value>hdfs,foo</value> <description> Complimentary Blacklist for CREATE and ROLLOVER operation to allow the client to provide the key material when creating or rolling a key. </description> </property> <property> <name>hadoop.kms.acl.GENERATE_EEK</name> <value>*</value> <description> ACL for generateEncryptedKey CryptoExtension operations </description> </property> <property> <name>hadoop.kms.blacklist.GENERATE_EEK</name> <value>hdfs,foo</value> <description> Blacklist for generateEncryptedKey CryptoExtension operations </description> </property> <property> <name>hadoop.kms.acl.DECRYPT_EEK</name> <value>*</value> <description> ACL for decrypt EncryptedKey CryptoExtension operations </description> </property> </configuration> <property> <name>hadoop.kms.blacklist.DECRYPT_EEK</name> <value>hdfs,foo</value> <description> Blacklist for decrypt EncryptedKey CryptoExtension operations </description> </property> </configuration>
KMS supports access control for all non-read operations at the Key level. All Key Access operations are classified as :
These can be defined in the KMS etc/hadoop/kms-acls.xml as follows
For all keys for which a key access has not been explicitly configured, It is possible to configure a default key access control for a subset of the operation types.
If no ACL is configured for a specific key AND no default ACL is configured for the requested operation, then access will be DENIED.
NOTE: The default ACL does not support ALL operation qualifier.
<property> <name>key.acl.testKey1.MANAGEMENT</name> <value>*</value> <description> ACL for create-key, deleteKey and rolloverNewVersion operations. </description> </property> <property> <name>key.acl.testKey2.GENERATE_EEK</name> <value>*</value> <description> ACL for generateEncryptedKey operations. </description> </property> <property> <name>key.acl.testKey3.DECRYPT_EEK</name> <value>*</value> <description> ACL for decryptEncryptedKey operations. </description> </property> <property> <name>key.acl.testKey4.READ</name> <value>*</value> <description> ACL for getKeyVersion, getKeyVersions, getMetadata, getKeysMetadata, getCurrentKey operations </description> </property> <property> <name>key.acl.testKey5.ALL</name> <value>*</value> <description> ACL for ALL operations. </description> </property> <property> <name>default.key.acl.MANAGEMENT</name> <value>user1,user2</value> <description> default ACL for MANAGEMENT operations for all keys that are not explicitly defined. </description> </property> <property> <name>default.key.acl.GENERATE_EEK</name> <value>user1,user2</value> <description> default ACL for GENERATE_EEK operations for all keys that are not explicitly defined. </description> </property> <property> <name>default.key.acl.DECRYPT_EEK</name> <value>user1,user2</value> <description> default ACL for DECRYPT_EEK operations for all keys that are not explicitly defined. </description> </property> <property> <name>default.key.acl.READ</name> <value>user1,user2</value> <description> default ACL for READ operations for all keys that are not explicitly defined. </description> </property>
KMS delegation token secret manager can be configured with the following properties:
<property> <name>hadoop.kms.authentication.delegation-token.update-interval.sec</name> <value>86400</value> <description> How often the master key is rotated, in seconds. Default value 1 day. </description> </property> <property> <name>hadoop.kms.authentication.delegation-token.max-lifetime.sec</name> <value>604800</value> <description> Maximum lifetime of a delagation token, in seconds. Default value 7 days. </description> </property> <property> <name>hadoop.kms.authentication.delegation-token.renew-interval.sec</name> <value>86400</value> <description> Renewal interval of a delagation token, in seconds. Default value 1 day. </description> </property> <property> <name>hadoop.kms.authentication.delegation-token.removal-scan-interval.sec</name> <value>3600</value> <description> Scan interval to remove expired delegation tokens. </description> </property>
KMS supports multiple KMS instances behind a load-balancer or VIP for scalability and for HA purposes.
When using multiple KMS instances behind a load-balancer or VIP, requests from the same user may be handled by different KMS instances.
KMS instances behind a load-balancer or VIP must be specially configured to work properly as a single logical service.
When KMS instances are behind a load-balancer or VIP, clients will use the hostname of the VIP. For Kerberos SPNEGO authentication, the hostname of the URL is used to construct the Kerberos service name of the server, HTTP/#HOSTNAME#. This means that all KMS instances must have a Kerberos service name with the load-balancer or VIP hostname.
In order to be able to access directly a specific KMS instance, the KMS instance must also have Keberos service name with its own hostname. This is required for monitoring and admin purposes.
Both Kerberos service principal credentials (for the load-balancer/VIP hostname and for the actual KMS instance hostname) must be in the keytab file configured for authentication. And the principal name specified in the configuration must be '*'. For example:
<property> <name>hadoop.kms.authentication.kerberos.principal</name> <value>*</value> </property>
NOTE: If using HTTPS, the SSL certificate used by the KMS instance must be configured to support multiple hostnames (see Java 7 keytool SAN extension support for details on how to do this).
KMS uses Hadoop Authentication for HTTP authentication. Hadoop Authentication issues a signed HTTP Cookie once the client has authenticated successfully. This HTTP Cookie has an expiration time, after which it will trigger a new authentication sequence. This is done to avoid triggering the authentication on every HTTP request of a client.
A KMS instance must verify the HTTP Cookie signatures signed by other KMS instances. To do this all KMS instances must share the signing secret.
This secret sharing can be done using a Zookeeper service which is configured in KMS with the following properties in the kms-site.xml:
<property> <name>hadoop.kms.authentication.signer.secret.provider</name> <value>zookeeper</value> <description> Indicates how the secret to sign the authentication cookies will be stored. Options are 'random' (default), 'string' and 'zookeeper'. If using a setup with multiple KMS instances, 'zookeeper' should be used. </description> </property> <property> <name>hadoop.kms.authentication.signer.secret.provider.zookeeper.path</name> <value>/hadoop-kms/hadoop-auth-signature-secret</value> <description> The Zookeeper ZNode path where the KMS instances will store and retrieve the secret from. </description> </property> <property> <name>hadoop.kms.authentication.signer.secret.provider.zookeeper.connection.string</name> <value>#HOSTNAME#:#PORT#,...</value> <description> The Zookeeper connection string, a list of hostnames and port comma separated. </description> </property> <property> <name>hadoop.kms.authentication.signer.secret.provider.zookeeper.auth.type</name> <value>kerberos</value> <description> The Zookeeper authentication type, 'none' or 'sasl' (Kerberos). </description> </property> <property> <name>hadoop.kms.authentication.signer.secret.provider.zookeeper.kerberos.keytab</name> <value>/etc/hadoop/conf/kms.keytab</value> <description> The absolute path for the Kerberos keytab with the credentials to connect to Zookeeper. </description> </property> <property> <name>hadoop.kms.authentication.signer.secret.provider.zookeeper.kerberos.principal</name> <value>kms/#HOSTNAME#</value> <description> The Kerberos service principal used to connect to Zookeeper. </description> </property>
REQUEST:
POST http://HOST:PORT/kms/v1/keys Content-Type: application/json { "name" : "<key-name>", "cipher" : "<cipher>", "length" : <length>, //int "material" : "<material>", //base64 "description" : "<description>" }
RESPONSE:
201 CREATED LOCATION: http://HOST:PORT/kms/v1/key/<key-name> Content-Type: application/json { "name" : "versionName", "material" : "<material>", //base64, not present without GET ACL }
REQUEST:
POST http://HOST:PORT/kms/v1/key/<key-name> Content-Type: application/json { "material" : "<material>", }
RESPONSE:
200 OK Content-Type: application/json { "name" : "versionName", "material" : "<material>", //base64, not present without GET ACL }
REQUEST:
GET http://HOST:PORT/kms/v1/key/<key-name>/_metadata
RESPONSE:
200 OK Content-Type: application/json { "name" : "<key-name>", "cipher" : "<cipher>", "length" : <length>, //int "description" : "<description>", "created" : <millis-epoc>, //long "versions" : <versions> //int }
REQUEST:
GET http://HOST:PORT/kms/v1/key/<key-name>/_currentversion
RESPONSE:
200 OK Content-Type: application/json { "name" : "versionName", "material" : "<material>", //base64 }
REQUEST:
GET http://HOST:PORT/kms/v1/key/<key-name>/_eek?eek_op=generate&num_keys=<number-of-keys-to-generate>
RESPONSE:
200 OK Content-Type: application/json [ { "versionName" : "encryptionVersionName", "iv" : "<iv>", //base64 "encryptedKeyVersion" : { "versionName" : "EEK", "material" : "<material>", //base64 } }, { "versionName" : "encryptionVersionName", "iv" : "<iv>", //base64 "encryptedKeyVersion" : { "versionName" : "EEK", "material" : "<material>", //base64 } }, ... ]
REQUEST:
POST http://HOST:PORT/kms/v1/keyversion/<version-name>/_eek?ee_op=decrypt Content-Type: application/json { "name" : "<key-name>", "iv" : "<iv>", //base64 "material" : "<material>", //base64 }
RESPONSE:
200 OK Content-Type: application/json { "name" : "EK", "material" : "<material>", //base64 }
REQUEST:
GET http://HOST:PORT/kms/v1/keyversion/<version-name>
RESPONSE:
200 OK Content-Type: application/json { "name" : "versionName", "material" : "<material>", //base64 }
REQUEST:
GET http://HOST:PORT/kms/v1/key/<key-name>/_versions
RESPONSE:
200 OK Content-Type: application/json [ { "name" : "versionName", "material" : "<material>", //base64 }, { "name" : "versionName", "material" : "<material>", //base64 }, ... ]
REQUEST:
GET http://HOST:PORT/kms/v1/keys/names
RESPONSE:
200 OK Content-Type: application/json [ "<key-name>", "<key-name>", ... ]
GET http://HOST:PORT/kms/v1/keys/metadata?key=<key-name>&key=<key-name>,...
RESPONSE:
200 OK Content-Type: application/json [ { "name" : "<key-name>", "cipher" : "<cipher>", "length" : <length>, //int "description" : "<description>", "created" : <millis-epoc>, //long "versions" : <versions> //int }, { "name" : "<key-name>", "cipher" : "<cipher>", "length" : <length>, //int "description" : "<description>", "created" : <millis-epoc>, //long "versions" : <versions> //int }, ... ]
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When working with Spry data sets, you can create master and detail dynamic regions to display more detail about your data. One region on the page (the master), drives the display of the data in another region on the page (the detail).
Typically, the master region displays an abbreviated form of a set of records from the data set, and the detail region displays more information about a selected record. Because the detail region depends on the master region, any time the data in the master region changes, the data in the detail region changes as well.
In the following example, a master dynamic region displays general data from the dsSpecials data set, and a detail dynamic region displays more detail about the row of data that’s been selected in the master region:
 . . .     . . .

 Item	 Description	 Price

 {item}	 {description}	 {price}

 Ingredients	 Calories

 {ingredients}	 {calories}

 . . .
In the example, the first div tag contains the id and spry:region attributes that create a container for the master dynamic region:

The first table-row tag of the master region contains a spry:setrow attribute that sets the value of the current row in the data set when the user clicks the row.

The second div tag contains the attributes that create a container for the detail dynamic region:

Every Spry data set maintains the notion of a current row. By default, the current row is set to the first row in the data set. A spry:detailregion works in exactly the same way as a spry:region except that when the data set’s current row changes, the detail region updates automatically.
The data references in the detail region ({ingredients} and {calories}) display data from the data set’s current row. When a user clicks a row in the master region table, the spry:setrow attribute changes the current row in the data set to the row the user selected.
The {ds_RowID} data reference is a built-in part of the Spry framework that points to an automatically generated unique ID for each row in the data set. (See Data reference options.) When the user selects a row in the master region table, the spry:setrow attribute supplies the unique ID to the setCurrentRow method, which sets the current row in the data set.
Whenever the data set is modified, all dynamic regions bound to that data set regenerate themselves and display the updated data. Because the detail region, like the master region, is an observer of the dsSpecials data set, it also changes as a result of the modification, and displays data related to the row the user selected (the new current row).
The difference between a spry:region and a spry:detailregion is that the spry:detailregion specifically listens for CurrentRowChange notifications (in addition to DataChanged notifications) from the data set, and updates itself when it receives one. Normal spry:regions, on the other hand, ignore the CurrentRowChange notification, and only update when they receive a DataChanged notification from the data set.


Certain things can be difficult when trying to achieve pure isomorphism. This page showcases these solved issues.
Don't make a second API request when the data was retrieved from the server already. This produce "flicker-free" responses. Reload this page and pay attention to how the list below does not flicker.
Server rendered SVGs are very effecient. They are also flicker free! But having a large set of SVGs stored in an angular service penalizes users by making them download a large app bundle. In order to fix this, we provide a server and client variant of the SVG service. Initial rendered SVGs come from the server and client routed SVGs are an async fetch.


Without your support, we would not have been able to make Radical Networks 2017 as great as it was.

But first, let's get connected!
Your device has been configured with a web server and wireless access point named:
WHO'Z A GOOD BOI
Your device is configured with the default password for the `parrot` user as:
toor
You should change this immediately! Learn how to here.
There are a few ways you can connect to your Raspberry Pi - By plugging in an external monitor, keyboard and mouse, via SSH in the command line, or through an FTP client like Filezilla or Transmit. Read on...
WHO'Z A GOOD BOIwireless network
$ ssh<EMAIL_ADDRESS>toor
WHO'Z A GOOD BOIwireless network
toorfor the
parrotuser
$ ssh<EMAIL_ADDRESS>toor
After you've connected to your RPi, the first thing to do is to change the default password to something secure.
$ sudo passwd parrot
$ <enter a new password>
$ <enter it again>
$ sudo passwd parrot
$ <enter a new password>
$ <enter it again>
Changing your wireless network name is easy and even fun!
$ sudo nano /etc/ssid.config
$ sudo reboot
$ sudo nano /etc/ssid.config
$ sudo reboot
Your WiFi network is open by default, but you can set a password to secure it by adding a few lines to a configuration script.
$ sudo nano /etc/hostapd/hostapd.conf
$ sudo reboot
$ sudo nano /etc/hostapd/hostapd.conf
$ sudo reboot
Your hostname is currently radical-network. This is essentially the name of your device, which is automatically resolved to an IP address either in the browser window or in the command line. You can also think of it as the domain name associated with your offline webserver.
If you want to change this name to something else, it's easy. Follow these steps:
$ sudo nano /etc/hostname
$ sudo nano /etc/hosts
<IP_ADDRESS> radical-network
$ sudo reboot
$ ssh parrot@<your new hostname>.local
$ sudo nano /etc/hostname
$ sudo nano /etc/hosts
<IP_ADDRESS> radical-network
$ sudo reboot
$ ssh parrot@<your new hostname>.local
Your Raspberry Pi has Parrot OS installed on it. From wikipedia:
“Parrot Security OS (or ParrotSec) is a Linux distribution based on Debian with a focus on computer security. It is designed for penetration testing, vulnerability assessment and mitigation, computer forensics and anonymous web browsing.”
Parrot OS comes bundled with an array of network monitoring tools for you to explore. Anonymize yourself online, inspect WiFi traffic and more with:
From the Parrot OS desktop, find these application in Applications > Parrot > Information Gathering and Applications > Parrot > Sniffing & Spoofing
Keep up to date with what's happening with Radical Networks!
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