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Perl Weekly
Issue #187 - 2015-02-23 - The FOSDEM after-effect
latest | archive | by Neil Bowers
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Hi,
We're definitely getting more blog posts about Perl 6 since FOSDEM. I wonder if that's going to continue for the rest of the year, or if it's still the after-effect from FOSDEM...
More than anything, participants in the Pull Request challenge seem to want to fix bugs. Put your buggy dists on github! :-)
Editor #3, Neil
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Sponsors
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CPAN News
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Hacking with Perl and CPAN
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Today's bit of black perl
by Aristotle Pagaltzis (ARISTOTLE)
Aristotle presents a small perl script which illustrates a way that the unwary, and those new to Perl, can be surprised when playing around with subroutine arguments.
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Always make immutable (unless you have a good reason not to)
You often read about the need to call make_immutable on your Moose classes, but do you know what the impact can be if you don't? kd benchmarked one of his webs after before and after, and found that adding make_immutable made it 5 times faster.
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Analysing CPAN Testers' Reports
by E. Choroba (CHOROBA)
For his February assignment, E decided to look into his dist's CPAN Testers failures. He describes his approach and how it led in his pull request. The comments on his post point out some other tools that can help this sort of investigation.
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Misc
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CPAN dists not on github
by Neil Bowers (NEILB)
There are several hundred people taking part in the pull request challenge who want to fix your bugs. All you need to do is put your distribution on github, and make sure the repo is listed in the metadata.
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Perl 6
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Profiling Perl 6 code on IRC
by Moritz Lenz (MORITZ)
Moritz presents the IRC bot camelia, which will run Perl 6 code snippets seen in the #perl6 IRC channel, and give feedback on them. I have to admit that my first thought was wondering what naughtiness might be possible, but it's pretty cool!
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Grants
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RFC: Limiting grant duration
by Makoto Nozaki
Nozaki-san has proposed a change to TPF rules for grants, aimed at defining a deadline for all grant applications, beyond which projects would be deemed to have failed. If you have an opinion, please add a comment.
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Perl Maven Tutorials
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Conferences and Meetups
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You know, you could get the Perl Weekly right in your mailbox. Every Week. Free of charge!
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jsjws: Mobile Top
The
jsjws
is a pure JavaScript library for JSON Web Sign(JWS) and JSON Web Token(JWT) and has
the broardest algorithm support
than others.
demo
JWT signer/verifier
documents
API reference
Go to PC top page.
© 2013
@kjur


{% extends '_layouts/layout_3-3.html' %} {% block page_title %}Proof of address{{serviceName}}{% endblock %} {% block pageTitle %}Proof of address{% endblock %} {% block content %}
There are a number of different document types you can use, including:


David T. wrote:Never got any feedback on whether or not Karl wanted this sent anywhere or not so as of now my models sit "collecting dust" on my hard drive.
David T. wrote:Never got any feedback on whether or not Karl wanted this sent anywhere or not so as of now my models sit "collecting dust" on my hard drive.
David T. wrote:So on that note, what format do you want them in and where can I send them?
Return to Modeling and Textures
Users browsing this forum: No registered users and 1 guest


[1]
accurate navigation; Doesn't always give street names.
[2]
Great features; Slow satellite reception


This visualisation lays a multi-access key over a taxonomic knowledge-base. Taxa, represented by rectangles, are ranked in the column according to how well they match any characters states you specify, with the best overall matches appearing at the top. This visualisation is particularly useful if you want to explore how character states you specify match against each of the taxa because every score for each taxon/character combination is displayed (as described next).
As you move the mouse pointer over the name of a taxon a tooltip is shown with an image of the taxon (only if at least one picture for a taxon has been specified in the knowledge-base). If you find these distracting, you can turn them off by right-clicking the mouse in the visualisation and selecting 'remove taxon image tooltips'. You can turn them back on again by repeating and selecting the 'display taxon image tooltips' item.
Each time you specify a state value(s) for a character, the matching score for that character, for each taxon, is displayed in a circle in the rectangle. Scores can range between -1 and 1. By default the unweighted scores are displayed, but you can change this to display the weighted scores by right-clicking your mouse, with the mouse cursor anywhere within the visualisation, to invoke context-menu that allows you to switch between weighted and unweighted scores. For more information on how scores are calculated and what weighting does, see a later section. On the left-hand side of each taxon rectangle is a small coloured rectangle indicating the overall weighted matching score for the taxon.
The background colour of the circles containing the character/taxon scores changes to give a quick visual reference for the scores: blue indicates the best overall matching score for that character and vermilion the worst, whilst a score of zero is coloured pale yellow. Intermediate scores are shown with intermediate colours. The same is true of the colour of the rectangles with the overall matching scores for the taxon.
If you click on any of the circles showing the taxon/character scores, a window appears containing more details of the value of the character in the knowledge-base for this taxon, the weighting and strictness (if applicable) applying to this character, the state value(s) you have specified and both the weighted and unweighted scores.
If at least one picture for a taxon has been specified in the knowledge-base then a small camera icon will be visible on the left of the rectangle representing that taxon. Clicking this will open an 'image group' for this taxon as a separate window. (See 'Working with image groups' below.)
As with all visualisations in this tool, important functionality is often made available from a context menu which can be invoked by right-clicking the mouse when the pointer is anywhere within the visualisation. The first option is Get URL for single-column key. Selecting this menu item builds a URL that can be used to reproduce the page as you have configured it. The remaining functions available from the context menu for this visualisation have been described elsewhere in the relevant sections.


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PU2CLR Si4735 Arduino Library 2.1.4
Arduino Library for Si47XX Devices - By Ricardo Lima Caratti
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Functions | |
| si47x_status | SI4735::getInterruptStatus () |
| Updates bits 6:0 of the status byte. | |
| void | SI4735::setGpioCtl (uint8_t GPO1OEN, uint8_t GPO2OEN, uint8_t GPO3OEN) |
| Enables output for GPO1, 2, and 3. | |
| void | SI4735::setGpio (uint8_t GPO1LEVEL, uint8_t GPO2LEVEL, uint8_t GPO3LEVEL) |
| Sets the output level (high or low) for GPO1, 2, and 3. | |
| void | SI4735::setGpioIen (uint8_t STCIEN, uint8_t RSQIEN, uint8_t ERRIEN, uint8_t CTSIEN, uint8_t STCREP, uint8_t RSQREP) |
| Configures the sources for the GPO2/INT interrupt pin. | |
| int16_t | SI4735::getDeviceI2CAddress (uint8_t resetPin) |
| I2C bus address setup. | |
| void | SI4735::setDeviceI2CAddress (uint8_t senPin) |
| Sets the I2C Bus Address. | |
| void | SI4735::setDeviceOtherI2CAddress (uint8_t i2cAddr) |
| Sets the other I2C Bus Address (for Si470X) | |
|
protected |
Updates bits 6:0 of the status byte.
This command should be called after any command that sets the STCINT or RSQINT bits.
When polling this command should be periodically called to monitor the STATUS byte, and when using interrupts, this command should be called after the interrupt is set to update the STATUS byte.
The CTS bit (and optional interrupt) is set when it is safe to send the next command.
References SI4735::waitToSend().
| void SI4735::setGpioCtl | ( | uint8_t | GPO1OEN, |
| uint8_t | GPO2OEN, | ||
| uint8_t | GPO3OEN | ||
| ) |
Enables output for GPO1, 2, and 3.
GPO1, 2, and 3 can be configured for output (Hi-Z or active drive) by setting the GPO1OEN, GPO2OEN, and GPO3OEN bit.
The state (high or low) of GPO1, 2, and 3 is set with the GPIO_SET command.
To avoid excessive current consumption due to oscillation, GPO pins should not be left in a high impedance state.
| GPIO Output Enable | value 0 | value 1 |
|---|---|---|
| GPO1OEN | Output Disabled (Hi-Z) (default) | Output Enabled |
| GPO2OEN | Output Disabled (Hi-Z) (default) | Output Enabled |
| GPO3OEN | Output Disabled (Hi-Z) (default) | Output Enabled |
| GPO1OEN | |
| GPO2OEN | |
| GPO3OEN |
References SI4735::waitToSend().
| void SI4735::setGpio | ( | uint8_t | GPO1LEVEL, |
| uint8_t | GPO2LEVEL, | ||
| uint8_t | GPO3LEVEL | ||
| ) |
Sets the output level (high or low) for GPO1, 2, and 3.
GPO1, 2, and 3 can be configured for output by setting the GPO1OEN, GPO2OEN, and GPO3OEN bit in the GPIO_CTL command.
To avoid excessive current consumption due to oscillation, GPO pins should not be left in a high impedance state.
To avoid excessive current consumption due to oscillation, GPO pins should not be left in a high impedance state.
| GPIO Output Enable | value 0 | value 1 |
|---|---|---|
| GPO1LEVEL | Output low (default) | Output high |
| GPO2LEVEL | Output low (default) | Output high |
| GPO3LEVEL | Output low (default) | Output high |
| GPO1LEVEL | |
| GPO2LEVEL | |
| GPO3LEVEL |
References SI4735::waitToSend().
| void SI4735::setGpioIen | ( | uint8_t | STCIEN, |
| uint8_t | RSQIEN, | ||
| uint8_t | ERRIEN, | ||
| uint8_t | CTSIEN, | ||
| uint8_t | STCREP, | ||
| uint8_t | RSQREP | ||
| ) |
Configures the sources for the GPO2/INT interrupt pin.
Valid sources are the lower 8 bits of the STATUS byte, including CTS, ERR, RSQINT, and STCINT bits.
The corresponding bit is set before the interrupt occurs. The CTS bit (and optional interrupt) is set when it is safe to send the next command.
The CTS interrupt enable (CTSIEN) can be set with this property and the POWER_UP command.
The state of the CTSIEN bit set during the POWER_UP command can be read by reading this property and modified by writing this property.
| STCIEN | Seek/Tune Complete Interrupt Enable (0 or 1). |
| RSQIEN | RSQ Interrupt Enable (0 or 1). |
| ERRIEN | ERR Interrupt Enable (0 or 1). |
| CTSIEN | CTS Interrupt Enable (0 or 1). |
| STCREP | STC Interrupt Repeat (0 or 1). |
| RSQREP | RSQ Interrupt Repeat(0 or 1). |
| int16_t SI4735::getDeviceI2CAddress | ( | uint8_t | resetPin | ) |
I2C bus address setup.
Scans for two possible addresses for the Si47XX (0x11 or 0x63).
This function also sets the system to use the found I2C bus address of Si47XX.
The default I2C address is 0x11. So, you do not need to use this function if you are using an SI4735 and the
SEN PIN is configured to ground (GND) or you are using the SI4732 and tnhe SEN PIN is configured to Vcc.
Use this function if you do not know how the SEN pin is configured.
| uint8_t | resetPin MCU Mater (Arduino) reset pin |
References SI4735::reset().
| void SI4735::setDeviceI2CAddress | ( | uint8_t | senPin | ) |
Sets the I2C Bus Address.
The parameter senPin can be 0 or 1 (is not the I2C bus address).
It refers to the SEN pin setup of your schematic (eletronic circuit).
If are using an SI4735 and SEN pin is connected to the ground, call this function with senPin = 0; else senPin = 1.
If are using an SI4732 and SEN pin is connected to the Vcc, call this function with senPin = 0; else senPin = 1.
Consider using the getDeviceI2CAddress function instead.
| senPin | 0 - SI4735 device: when the pin SEN (16 on SSOP version or pin 6 on QFN version) is set to low (GND - 0V); 1 - Si4735 device: when the pin SEN (16 on SSOP version or pin 6 on QFN version) is set to high (+3.3V). If you are using an SI4732 device, reverse the above logic (1 - GND or 0 - +3.3V). |
| void SI4735::setDeviceOtherI2CAddress | ( | uint8_t | i2cAddr | ) |
Sets the other I2C Bus Address (for Si470X)
You can set another I2C address different of 0x11 and 0x63
It can be useful if another device made by Silicon Labs uses another address setup
| uint8_t | i2cAddr (example 0x10) |


|
IdTool
0.4.2
A tool for inspecting and manipulating SQRL identities.
|
| CBlockConnector | A helper class for connecting GUI representations of IdentityBlock instances to their underlying data source |
| CBlockDesignerDialog | A UI dialog window for designing S4 identity storage blocks |
| CCryptUtil | A collection of static utility functions mostly dealing with SQRL-related cryptographic operations |
| CIdentityBlock | Represents a single identity block within the SQRL's S4 identity storage format |
| CIdentityBlockItem | Represents a single item/setting within an IdentityBlock |
| CIdentityClipboard | A class for enabling copy-pasting of identity blocks |
| CIdentityModel | Represents a SQRL identity stored in the S4-format |
| CIdentityParser | A class for parsing identity information stored in the S4 format |
| CIdentitySettingsDialog | A UI dialog for editing user-defined settings of a SQRL identity |
| CIdentityTab | Represents a tab widget visualizing a single identity |
| CItemConnector | A helper class for connecting GUI representations of IdentityBlockItem instances to their underlying data source |
| CItemDataTypeInfo | |
| CItemEditorDialog | A UI dialog window for editing individual block items/settings |
| CMainWindow | The app's main application window |
| CTabManager | A class for managing multiple loaded identities in a tabbed interface |
| CUiBuilder | A class for dynamically visualizing a SQRL identity within the application's MainWindow GUI |


--- layout: post title: JOpt Simple 2.4.1 released published: 1 category: - Programming ---
JOptSimple is my Java library for parsing command line options. Release 2.4.1 fixes a bug with the new help screen generation changes. Thanks to Hans Dockter for reporting it. As usual, download instructions are available at the project's website.


We present TACO, a simple yet effective temporal contrastive learning framework that simultaneously learns state and action representations. This framework is flexible and could be integrated into both online and offline visual RL algorithms with minimal changes to the architecture and hyperparameter tuning efforts.
Abstract
Despite recent progress in reinforcement learning (RL) from raw pixel data, sample inefficiency continues to present a substantial obstacle. Prior works have attempted to address this challenge by creating self-supervised auxiliary tasks, aiming to enrich the agent's learned representations with control-relevant information for future state prediction. However, these objectives are often insufficient to learn representations that can represent the optimal policy or value function, and they often consider tasks with small, abstract discrete action spaces and thus overlook the importance of action representation learning in continuous control. In this paper, we introduce TACO: Temporal Action-driven COntrastive Learning, a simple yet powerful temporal contrastive learning approach that facilitates the concurrent acquisition of latent state and action representations for agents. TACO simultaneously learns a state and an action representation by optimizing the mutual information between representations of current states paired with action sequences and representations of the corresponding future states. Theoretically, TACO can be shown to learn state and action representations that encompass sufficient information for control, thereby improving sample efficiency. For online RL, TACO achieves 40% performance boost after one million environment interaction steps on average across nine challenging visual continuous control tasks from Deepmind Control Suite. In addition, we show that TACO can also serve as a plug-and-play module adding to existing offline visual RL methods to establish the new state-of-the-art performance for offline visual RL across offline datasets with varying quality.
Online Visual RL
Offline Visual RL
TACO could also easily combine with existing strong offline RL algorithms such as CQL and and TD3+BC to further improve their performance. (The average normalized score here is the average score across 8 offline datasets on 4 selected challenging tasks from Deepmind Control Suite.)


@*
* Copyright 2023 HM Revenue & Customs
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*@
@import config.TrackingConsentConfig
@()(implicit request: Request[_], flash: Flash, user: Option[CachedData], feedbackUrl: String, trackingConsentConfig: TrackingConsentConfig, messages: Messages)
@main_template(title = "Contact us") {
@* Telephone: 0207 276 6969
*@
@*
You can call us from 9am to 5:30pm, Monday to Friday, excluding Bank Holidays.
*@
If you have made an application, then please email us from the email account that you used to register to help us deal with your query effectively.
Email: faststream.applicationsupport@@cabinetoffice.gov.uk
}


This island is a popular destination being so close to mainland Greece. It has good ferry connections and an airport. The town of Skiathos is a lively, but pleasant and relaxed sort of place. There is a new harbour here, and an anchorage close by.
On approach from the south, it is advisable to keep the little island of N. Dhaskalo to port. On-going work to extend the outer breakwater means the area to the west of N. Dhaskalo should be avoided.
In the harbour, moor bows/stern-to on the northern part of the quay (the extreme north is reserved for day trip boats and the pontoon in the north for charter boats). It might be difficult to find space, especially at weekends, when the charter fleet is in. Both the shelter and the holding are good. It is advisable to call ahead on VHF channel 77 (Skaithos marina) to ask for a berth.
Water and electricity is now available on the quay here (new 2015). Contact the harbourmaster for connection. Fuel can be delivered by mini-tanker.
The wash in the harbour caused by the frequent arrival/departure of the ferries could make the anchorage at the head of the bay, a more attractive proposition. Keep clear of the prohibited area under the flight path to the airport. The holding here, in 4 - 6m of sand, is very good. Care is needed to avoid the many moorings here.
As with many ports in this area, take care if the wind is from the south as this port can be uncomfortable, possible untenable.
Yachts are hauled out here and there are some repair facilities, chandlers and hardware stores.
Last updated June 2015.


Rickshaw puller murdered following argument, 1 held
Accused stabbed victim multiple times for using abusive words
by Staff ReporterA 40-year-old rickshaw puller was allegedly stabbed to death during an argument in outer Delhi’s Nangloi area on Friday night. The accused has been arrested, the police said.
DCP (Outer) A. Koan said that the victim Kanhaiya, a rickshaw puller hailing from Rajasthan, was murdered by Neeraj alias Guthli (22).
The police said that around 10.50 p.m., a PCR call was received at Nangloi police station informing that a person was lying injured and unconscious in Swarna Park. The police rushed to the spot but by then the man was shifted to Sanjay Gandhi Hospital where he was declared brought dead.
The police said that a case under Section 302 (Punishment for murder) of the Indian Penal Code was registered and an investigation was taken up.
During investigation, it was revealed that Kanhaiya was stabbed by Neeraj after the two had an argument. “Teams were formed to nab him and he was arrested. He is a resident of Swarna Park area,” the officer said.
During interrogation, Neeraj allegedly told the police that he stabbed the victim multiple times following an argument over the victim using abusive words.


A externally changeable button component
Enter the button name:
Causing data change
Here we have an input field which collects the name value for the button and
passes
it to the the
vbutton
component using
props
.
The
parent
usually does not know about the internals of the
child
.


Registration
Username:
Password:
Confirm Password:
Email:
Mobile Number:
Programming Languages:
Java
C++
.Net
Register


{% set nav_bg_color = 'bg-red-400' %} {% extends 'conftheme/2021/parts/base.html'%} {%set nav_text_color = 'text-white'%} {% block head %}
{% if not prev_talk == None %} <- {% endif %} Talk / {{ len_(talks) }} {% if not next_talk == None %} -> {% endif %}
{{ talk.summary }}
Description
{{ talk.description }}
Notes
{{ talk.notes }}


This page displays measures for the network Semantic Network [Tactics x Tactics].
Network images have nodes colored by group and sized by betweenness centrality.
Tactics-Level Measures
These measures were run on the specified network, and produced a vector of values, one value for each Tactics node.
Input network: Semantic Network [Tactics x Tactics]
Measure Min Mean Max Std.Dev Centrality, Authority 0 0.046 0.503 0.094 Centrality, Authority [Unscaled] 0 0.033 0.356 0.067 Centrality, Betweenness 0 0.010 0.219 0.033 Centrality, Betweenness [Unscaled] 0 19.940 426.983 63.808 Centrality, Bonacich Power 0 0.002 0.017 0.003 Centrality, Bonacich Power [Unscaled] 0 0.093 1 0.182 Centrality, Closeness 0.002 0.003 0.004 0.001 Centrality, Closeness [Unscaled] 2.756e-05 5.054e-05 6.645e-05 1.898e-05 Centrality, Contribution 0 0.048 0.504 0.093 Centrality, Contribution [Unscaled] 0 0.034 0.356 0.066 Centrality, Eccentricity 0 3.063 6 2.536 Centrality, Ego Betweenness 0 0.153 1 0.300 Centrality, Eigenvector 0 0.047 0.503 0.094 Centrality, Eigenvector [Unscaled] 0 0.033 0.356 0.066 Centrality, Exponential Rank 0.925 0.932 1 0.013 Centrality, Exponential Rank [Unscaled] 0.016 0.016 0.017 2.131e-04 Centrality, Hub 0 0.046 0.503 0.094 Centrality, Hub [Unscaled] 0 0.033 0.356 0.067 Centrality, In-Closeness 4.340e-04 4.781e-04 5.084e-04 3.638e-05 Centrality, In-Closeness [Unscaled] 6.889e-06 7.590e-06 8.070e-06 5.775e-07 Centrality, In-Degree 0 0.006 0.069 0.012 Centrality, In-Degree [Unscaled] 0 3.281 39 6.648 Centrality, In-Inverse Closeness 0 0.129 0.358 0.111 Centrality, In-Inverse Closeness [Unscaled] 0 8.152 22.583 7.021 Centrality, Inverse Closeness 0 0.129 0.358 0.111 Centrality, Inverse Closeness [Unscaled] 0 8.152 22.583 7.021 Centrality, Katz 0 0.061 0.655 0.119 Centrality, Katz [Unscaled] 0 0.093 1 0.182 Centrality, Out-Degree 0 0.006 0.069 0.012 Centrality, Out-Degree [Unscaled] 0 3.281 39 6.648 Centrality, PageRank 7.226e-04 0.016 0.172 0.029 Centrality, Radiality 0 0.348 0.586 0.287 Centrality, Radiality [Unscaled] 0 12,601.031 21,240 10,387.082 Centrality, Total-Degree 0 0.006 0.069 0.012 Centrality, Total-Degree [Unscaled] 0 3.281 39 6.648 Overall Top-Ranked Tactics
This chart shows the Tactics nodes that are top-ranked overall using the node-level measures listed below. The value shown for a node is the average of its measure values, each of which is first converted into a z-score. The final scores are then normalized to be between zero and one.
There are 64 nodes in the nodeset.
Overall Score Measure List
Centrality, Authority [Semantic Network [Tactics x Tactics]]
Centrality, Betweenness [Semantic Network [Tactics x Tactics]]
Centrality, Bonacich Power [Semantic Network [Tactics x Tactics]]
Centrality, Closeness [Semantic Network [Tactics x Tactics]]
Centrality, Contribution [Semantic Network [Tactics x Tactics]]
Centrality, Eccentricity [Semantic Network [Tactics x Tactics]]
Centrality, Ego Betweenness [Semantic Network [Tactics x Tactics]]
Centrality, Eigenvector [Semantic Network [Tactics x Tactics]]
Centrality, Exponential Rank [Semantic Network [Tactics x Tactics]]
Centrality, Hub [Semantic Network [Tactics x Tactics]]
Centrality, In-Closeness [Semantic Network [Tactics x Tactics]]
Centrality, In-Degree [Semantic Network [Tactics x Tactics]]
Centrality, In-Inverse Closeness [Semantic Network [Tactics x Tactics]]
Centrality, Inverse Closeness [Semantic Network [Tactics x Tactics]]
Centrality, Katz [Semantic Network [Tactics x Tactics]]
Centrality, Out-Degree [Semantic Network [Tactics x Tactics]]
Centrality, PageRank [Semantic Network [Tactics x Tactics]]
Centrality, Radiality [Semantic Network [Tactics x Tactics]]
Centrality, Total-Degree [Semantic Network [Tactics x Tactics]]
Centrality, Authority
A node is authority-central to the extent that its in-links are from nodes that have many out-links. Individuals or organizations that act as authorities are receiving information from a wide range of others each of whom sends information to a large number of others.
If the node of interest has a higher than normal value (greater than 1 standard deviation(s) above the mean) the row is colored red. The row is green if the node is within 1 standard deviation of the mean. Finally, the row is colored blue if the node has a lower than normal value (less than one standard deviation(s) below the mean).
Input network(s): Semantic Network [Tactics x Tactics]
Rank Tactics Value Unscaled 1 phishing 0.503 0.356 2 techniques 0.452 0.319 3 spear_phishing 0.284 0.201 4 tactics 0.247 0.175 5 scripting 0.130 0.092 6 fingerprinting 0.126 0.089 7 exfiltration 0.121 0.086 8 defacement 0.097 0.069 9 interception 0.085 0.060 10 sql_injection 0.083 0.059 11 brute_force 0.065 0.046 12 jamming 0.063 0.045 13 pharming 0.063 0.044 14 host_discovery 0.056 0.040 15 network_sniffing 0.056 0.040 16 code_injection 0.046 0.032 17 eavesdropping 0.042 0.030 18 fuzzing 0.041 0.029 19 port_scanning 0.039 0.028 20 browser_fingerprinting 0.036 0.025 21 credential_access 0.033 0.023 22 masquerading 0.031 0.022 23 pretexting 0.031 0.022 24 targeted_malware 0.031 0.022 25 touching 0.031 0.022 26 attending 0.028 0.020 27 flooding 0.028 0.020 28 reverse_engineering 0.028 0.020 29 sudo 0.028 0.020 30 protocol_analysis 0.013 0.009 31 command_injection 0.010 0.007 32 audio_capture 0.008 0.006 33 defense_evasion 0.006 0.005 34 privilege_escalation 0.006 0.005 35 data_destruction 0.006 0.004 36 session_hijacking 0.006 0.004 37 path_traversal 0.003 0.002 38 process_hollowing 0.003 0.002 39 access 0 0 40 analysis 0 0 41 attack 0 0 42 attacks 0 0 43 automated_collection 0 0 44 blockage 0 0 45 bootkit 0 0 46 cache_poisoning 0 0 47 change 0 0 48 cleared 0 0 49 cryptanalysis 0 0 50 develop 0 0 51 dominated 0 0 52 dumpster_diving 0 0 53 excavation 0 0 54 fault_injection 0 0 55 icmp_flood 0 0 56 landside 0 0 57 privilege_abuse 0 0 58 process_injection 0 0 59 remembering 0 0 60 sharing 0 0 61 to-libc 0 0 62 transformed 0 0 63 udp_flood 0 0 64 violations 0 0
Value statistics Min: 0 Mean: 0.046 Lower quartile: 0 Max: 0.503 Std.dev: 0.094 Median: 0.007 Upper quartile: 0.044
Unscaled value statistics Min: 0 Mean: 0.033 Lower quartile: 0 Max: 0.356 Std.dev: 0.067 Median: 0.005 Upper quartile: 0.031 Centrality, Betweenness
The Betweenness Centrality of node v in a network is defined as: across all node pairs that have a shortest path containing v, the percentage that pass through v. When the data is weighted, the higher the weight the more value the link has. Individuals or organizations that are potentially influential are positioned to broker connections between groups and to bring to bear the influence of one group on another or serve as a gatekeeper between groups. This agent occurs on many of the shortest paths between other agents. The scientific name of this measure is betweenness centrality and it is calculated on agent by agent matrices.
If the node of interest has a higher than normal value (greater than 1 standard deviation(s) above the mean) the row is colored red. The row is green if the node is within 1 standard deviation of the mean. Finally, the row is colored blue if the node has a lower than normal value (less than one standard deviation(s) below the mean).
Input network(s): Semantic Network [Tactics x Tactics]
Rank Tactics Value Unscaled 1 techniques 0.219 426.983 2 phishing 0.110 215.567 3 code_injection 0.081 158.500 4 exfiltration 0.062 121.683 5 defacement 0.042 82.567 6 credential_access 0.029 55.883 7 fingerprinting 0.026 50.650 8 sql_injection 0.022 42.550 9 scripting 0.018 36 10 fuzzing 0.014 27.200 11 brute_force 0.011 21.400 12 browser_fingerprinting 0.010 19.433 13 spear_phishing 0.005 9.200 14 interception 0.002 4.333 15 eavesdropping 0.002 3.700 16 command_injection 1.280e-04 0.250 17 jamming 1.280e-04 0.250 18 access 0 0 19 analysis 0 0 20 attack 0 0 21 attacks 0 0 22 attending 0 0 23 audio_capture 0 0 24 automated_collection 0 0 25 blockage 0 0 26 bootkit 0 0 27 cache_poisoning 0 0 28 change 0 0 29 cleared 0 0 30 cryptanalysis 0 0 31 data_destruction 0 0 32 defense_evasion 0 0 33 develop 0 0 34 dominated 0 0 35 dumpster_diving 0 0 36 excavation 0 0 37 fault_injection 0 0 38 flooding 0 0 39 host_discovery 0 0 40 icmp_flood 0 0 41 landside 0 0 42 masquerading 0 0 43 network_sniffing 0 0 44 path_traversal 0 0 45 pharming 0 0 46 port_scanning 0 0 47 pretexting 0 0 48 privilege_abuse 0 0 49 privilege_escalation 0 0 50 process_hollowing 0 0 51 process_injection 0 0 52 protocol_analysis 0 0 53 remembering 0 0 54 reverse_engineering 0 0 55 session_hijacking 0 0 56 sharing 0 0 57 sudo 0 0 58 tactics 0 0 59 targeted_malware 0 0 60 to-libc 0 0 61 touching 0 0 62 transformed 0 0 63 udp_flood 0 0 64 violations 0 0
Value statistics Min: 0 Mean: 0.010 Lower quartile: 0 Max: 0.219 Std.dev: 0.033 Median: 0 Upper quartile: 1.280e-04
Unscaled value statistics Min: 0 Mean: 19.940 Lower quartile: 0 Max: 426.983 Std.dev: 63.808 Median: 0 Upper quartile: 0.250 Centrality, Bonacich Power
This computes the centrality of each entity based on the centrality of its neighbors. Beta should be chosen such that its absolute value is less than the reciprocal of the largest eigenvalue of N.
If the node of interest has a higher than normal value (greater than 1 standard deviation(s) above the mean) the row is colored red. The row is green if the node is within 1 standard deviation of the mean. Finally, the row is colored blue if the node has a lower than normal value (less than one standard deviation(s) below the mean).
Input network(s): Semantic Network [Tactics x Tactics]
Rank Tactics Value Unscaled 1 techniques 0.017 1 2 phishing 0.016 0.953 3 spear_phishing 0.007 0.403 4 tactics 0.006 0.389 5 scripting 0.004 0.226 6 fingerprinting 0.004 0.225 7 code_injection 0.003 0.199 8 exfiltration 0.003 0.190 9 brute_force 0.003 0.183 10 defacement 0.003 0.180 11 sql_injection 0.003 0.174 12 interception 0.003 0.170 13 jamming 0.003 0.165 14 fuzzing 0.003 0.164 15 protocol_analysis 0.002 0.103 16 credential_access 0.002 0.100 17 host_discovery 0.001 0.084 18 network_sniffing 0.001 0.084 19 eavesdropping 0.001 0.083 20 command_injection 0.001 0.083 21 pharming 0.001 0.082 22 browser_fingerprinting 0.001 0.065 23 port_scanning 0.001 0.063 24 defense_evasion 9.821e-04 0.059 25 privilege_escalation 9.821e-04 0.059 26 attending 7.049e-04 0.042 27 flooding 7.049e-04 0.042 28 reverse_engineering 7.049e-04 0.042 29 sudo 7.049e-04 0.042 30 masquerading 6.847e-04 0.041 31 pretexting 6.847e-04 0.041 32 targeted_malware 6.847e-04 0.041 33 touching 6.847e-04 0.041 34 audio_capture 3.751e-04 0.022 35 path_traversal 3.637e-04 0.022 36 process_hollowing 3.637e-04 0.022 37 data_destruction 3.554e-04 0.021 38 session_hijacking 3.554e-04 0.021 39 icmp_flood 2.862e-04 0.017 40 udp_flood 2.862e-04 0.017 41 access 0 0 42 analysis 0 0 43 attack 0 0 44 attacks 0 0 45 automated_collection 0 0 46 blockage 0 0 47 bootkit 0 0 48 cache_poisoning 0 0 49 change 0 0 50 cleared 0 0 51 cryptanalysis 0 0 52 develop 0 0 53 dominated 0 0 54 dumpster_diving 0 0 55 excavation 0 0 56 fault_injection 0 0 57 landside 0 0 58 privilege_abuse 0 0 59 process_injection 0 0 60 remembering 0 0 61 sharing 0 0 62 to-libc 0 0 63 transformed 0 0 64 violations 0 0
Value statistics Min: 0 Mean: 0.002 Lower quartile: 0 Max: 0.017 Std.dev: 0.003 Median: 6.847e-04 Upper quartile: 0.002
Unscaled value statistics Min: 0 Mean: 0.093 Lower quartile: 0 Max: 1 Std.dev: 0.182 Median: 0.041 Upper quartile: 0.092 Centrality, Closeness
The closeness of a node to the other nodes in a network (also called out-closeness). Loosely, Closeness is the inverse of the sum of distances in the network from a node to all other nodes.
If the node of interest has a higher than normal value (greater than 1 standard deviation(s) above the mean) the row is colored red. The row is green if the node is within 1 standard deviation of the mean. Finally, the row is colored blue if the node has a lower than normal value (less than one standard deviation(s) below the mean).
Input network(s): Semantic Network [Tactics x Tactics]
Rank Tactics Value Unscaled 1 techniques 0.004 6.645e-05 2 code_injection 0.004 6.638e-05 3 exfiltration 0.004 6.637e-05 4 defacement 0.004 6.636e-05 5 fuzzing 0.004 6.635e-05 6 browser_fingerprinting 0.004 6.635e-05 7 phishing 0.004 6.633e-05 8 credential_access 0.004 6.632e-05 9 sql_injection 0.004 6.632e-05 10 fingerprinting 0.004 6.631e-05 11 spear_phishing 0.004 6.631e-05 12 attending 0.004 6.630e-05 13 flooding 0.004 6.630e-05 14 reverse_engineering 0.004 6.630e-05 15 sudo 0.004 6.630e-05 16 brute_force 0.004 6.625e-05 17 scripting 0.004 6.624e-05 18 command_injection 0.004 6.623e-05 19 jamming 0.004 6.623e-05 20 protocol_analysis 0.004 6.623e-05 21 interception 0.004 6.622e-05 22 path_traversal 0.004 6.622e-05 23 process_hollowing 0.004 6.622e-05 24 port_scanning 0.004 6.622e-05 25 defense_evasion 0.004 6.621e-05 26 privilege_escalation 0.004 6.621e-05 27 data_destruction 0.004 6.620e-05 28 session_hijacking 0.004 6.620e-05 29 eavesdropping 0.004 6.619e-05 30 masquerading 0.004 6.617e-05 31 pretexting 0.004 6.617e-05 32 targeted_malware 0.004 6.617e-05 33 touching 0.004 6.617e-05 34 tactics 0.004 6.615e-05 35 host_discovery 0.004 6.614e-05 36 network_sniffing 0.004 6.614e-05 37 audio_capture 0.004 6.608e-05 38 pharming 0.004 6.602e-05 39 icmp_flood 0.002 2.800e-05 40 udp_flood 0.002 2.800e-05 41 access 0.002 2.756e-05 42 analysis 0.002 2.756e-05 43 attack 0.002 2.756e-05 44 attacks 0.002 2.756e-05 45 automated_collection 0.002 2.756e-05 46 blockage 0.002 2.756e-05 47 bootkit 0.002 2.756e-05 48 cache_poisoning 0.002 2.756e-05 49 change 0.002 2.756e-05 50 cleared 0.002 2.756e-05 51 cryptanalysis 0.002 2.756e-05 52 develop 0.002 2.756e-05 53 dominated 0.002 2.756e-05 54 dumpster_diving 0.002 2.756e-05 55 excavation 0.002 2.756e-05 56 fault_injection 0.002 2.756e-05 57 landside 0.002 2.756e-05 58 privilege_abuse 0.002 2.756e-05 59 process_injection 0.002 2.756e-05 60 remembering 0.002 2.756e-05 61 sharing 0.002 2.756e-05 62 to-libc 0.002 2.756e-05 63 transformed 0.002 2.756e-05 64 violations 0.002 2.756e-05
Value statistics Min: 0.002 Mean: 0.003 Lower quartile: 0.002 Max: 0.004 Std.dev: 0.001 Median: 0.004 Upper quartile: 0.004
Unscaled value statistics Min: 2.756e-05 Mean: 5.054e-05 Lower quartile: 2.756e-05 Max: 6.645e-05 Std.dev: 1.898e-05 Median: 6.617e-05 Upper quartile: 6.624e-05 Centrality, Contribution
This computes Eigenvector Centrality on a transformation of the input network. Link values are transformed to be proportional to the dissimilarity of the nodes they connect. The intuition is that a link between two nodes with the same neighbors is not an important link since neither node gains new neighbors by the connection. Specifically, each link is weighted by the inverse of the Jaccard similarity of its nodes.
If the node of interest has a higher than normal value (greater than 1 standard deviation(s) above the mean) the row is colored red. The row is green if the node is within 1 standard deviation of the mean. Finally, the row is colored blue if the node has a lower than normal value (less than one standard deviation(s) below the mean).
Input network(s): Semantic Network [Tactics x Tactics]
Rank Tactics Value Unscaled 1 phishing 0.504 0.356 2 techniques 0.451 0.319 3 spear_phishing 0.281 0.199 4 tactics 0.234 0.165 5 scripting 0.133 0.094 6 fingerprinting 0.128 0.091 7 exfiltration 0.116 0.082 8 defacement 0.104 0.074 9 interception 0.091 0.065 10 sql_injection 0.082 0.058 11 pharming 0.070 0.050 12 brute_force 0.065 0.046 13 jamming 0.064 0.045 14 host_discovery 0.063 0.044 15 network_sniffing 0.063 0.044 16 eavesdropping 0.048 0.034 17 code_injection 0.043 0.031 18 port_scanning 0.042 0.030 19 browser_fingerprinting 0.038 0.027 20 fuzzing 0.038 0.027 21 masquerading 0.035 0.025 22 pretexting 0.035 0.025 23 targeted_malware 0.035 0.025 24 touching 0.035 0.025 25 credential_access 0.034 0.024 26 attending 0.031 0.022 27 flooding 0.031 0.022 28 reverse_engineering 0.031 0.022 29 sudo 0.031 0.022 30 icmp_flood 0.031 0.022 31 udp_flood 0.031 0.022 32 audio_capture 0.009 0.007 33 protocol_analysis 0.009 0.006 34 data_destruction 0.007 0.005 35 session_hijacking 0.007 0.005 36 command_injection 0.007 0.005 37 defense_evasion 0.005 0.004 38 privilege_escalation 0.005 0.004 39 path_traversal 0.003 0.002 40 process_hollowing 0.003 0.002 41 access 0 0 42 analysis 0 0 43 attack 0 0 44 attacks 0 0 45 automated_collection 0 0 46 blockage 0 0 47 bootkit 0 0 48 cache_poisoning 0 0 49 change 0 0 50 cleared 0 0 51 cryptanalysis 0 0 52 develop 0 0 53 dominated 0 0 54 dumpster_diving 0 0 55 excavation 0 0 56 fault_injection 0 0 57 landside 0 0 58 privilege_abuse 0 0 59 process_injection 0 0 60 remembering 0 0 61 sharing 0 0 62 to-libc 0 0 63 transformed 0 0 64 violations 0 0
Value statistics Min: 0 Mean: 0.048 Lower quartile: 0 Max: 0.504 Std.dev: 0.093 Median: 0.009 Upper quartile: 0.045
Unscaled value statistics Min: 0 Mean: 0.034 Lower quartile: 0 Max: 0.356 Std.dev: 0.066 Median: 0.006 Upper quartile: 0.032 Centrality, Eccentricity
The Eccentricity Centrality of a node is the normalized inverse of its maximum distance to any other node. It is highest when the distance to all other nodes is small.
If the node of interest has a higher than normal value (greater than 1 standard deviation(s) above the mean) the row is colored red. The row is green if the node is within 1 standard deviation of the mean. Finally, the row is colored blue if the node has a lower than normal value (less than one standard deviation(s) below the mean).
Input network(s): Semantic Network [Tactics x Tactics]
Rank Tactics Value 1 brute_force 6 2 command_injection 6 3 defense_evasion 6 4 host_discovery 6 5 jamming 6 6 network_sniffing 6 7 path_traversal 6 8 pharming 6 9 privilege_escalation 6 10 process_hollowing 6 11 protocol_analysis 6 12 tactics 6 13 attending 5 14 audio_capture 5 15 code_injection 5 16 credential_access 5 17 data_destruction 5 18 eavesdropping 5 19 flooding 5 20 fuzzing 5 21 interception 5 22 masquerading 5 23 port_scanning 5 24 pretexting 5 25 reverse_engineering 5 26 session_hijacking 5 27 spear_phishing 5 28 sudo 5 29 targeted_malware 5 30 touching 5 31 browser_fingerprinting 4 32 defacement 4 33 exfiltration 4 34 fingerprinting 4 35 phishing 4 36 scripting 4 37 sql_injection 4 38 techniques 4 39 icmp_flood 1 40 udp_flood 1 41 access 0 42 analysis 0 43 attack 0 44 attacks 0 45 automated_collection 0 46 blockage 0 47 bootkit 0 48 cache_poisoning 0 49 change 0 50 cleared 0 51 cryptanalysis 0 52 develop 0 53 dominated 0 54 dumpster_diving 0 55 excavation 0 56 fault_injection 0 57 landside 0 58 privilege_abuse 0 59 process_injection 0 60 remembering 0 61 sharing 0 62 to-libc 0 63 transformed 0 64 violations 0
Unscaled value statistics Min: 0 Mean: 3.063 Lower quartile: 0 Max: 6 Std.dev: 2.536 Median: 4 Upper quartile: 5 Centrality, Ego Betweenness
The Ego (Network) Betweenness Centrality of node in a network is its betweenness score within its own ego network. The ego network contains the following: the node itself, its immediate neighbor nodes, and all links between them.
If the node of interest has a higher than normal value (greater than 1 standard deviation(s) above the mean) the row is colored red. The row is green if the node is within 1 standard deviation of the mean. Finally, the row is colored blue if the node has a lower than normal value (less than one standard deviation(s) below the mean).
Input network(s): Semantic Network [Tactics x Tactics]
Rank Tactics Value 1 browser_fingerprinting 1 2 eavesdropping 1 3 defacement 0.950 4 scripting 0.833 5 techniques 0.820 6 phishing 0.815 7 fingerprinting 0.700 8 code_injection 0.692 9 interception 0.583 10 sql_injection 0.528 11 exfiltration 0.500 12 credential_access 0.417 13 spear_phishing 0.417 14 brute_force 0.333 15 fuzzing 0.144 16 command_injection 0.042 17 jamming 0.025 18 access 0 19 analysis 0 20 attack 0 21 attacks 0 22 attending 0 23 audio_capture 0 24 automated_collection 0 25 blockage 0 26 bootkit 0 27 cache_poisoning 0 28 change 0 29 cleared 0 30 cryptanalysis 0 31 data_destruction 0 32 defense_evasion 0 33 develop 0 34 dominated 0 35 dumpster_diving 0 36 excavation 0 37 fault_injection 0 38 flooding 0 39 host_discovery 0 40 icmp_flood 0 41 landside 0 42 masquerading 0 43 network_sniffing 0 44 path_traversal 0 45 pharming 0 46 port_scanning 0 47 pretexting 0 48 privilege_abuse 0 49 privilege_escalation 0 50 process_hollowing 0 51 process_injection 0 52 protocol_analysis 0 53 remembering 0 54 reverse_engineering 0 55 session_hijacking 0 56 sharing 0 57 sudo 0 58 tactics 0 59 targeted_malware 0 60 to-libc 0 61 touching 0 62 transformed 0 63 udp_flood 0 64 violations 0
Unscaled value statistics Min: 0 Mean: 0.153 Lower quartile: 0 Max: 1 Std.dev: 0.300 Median: 0 Upper quartile: 0.033 Centrality, Eigenvector
Calculates the principal eigenvector of the network. A node is central to the extent that its neighbors are central.
If the node of interest has a higher than normal value (greater than 1 standard deviation(s) above the mean) the row is colored red. The row is green if the node is within 1 standard deviation of the mean. Finally, the row is colored blue if the node has a lower than normal value (less than one standard deviation(s) below the mean).
Input network(s): Semantic Network [Tactics x Tactics]
Rank Tactics Value Unscaled 1 phishing 0.503 0.356 2 techniques 0.452 0.319 3 spear_phishing 0.284 0.201 4 tactics 0.247 0.175 5 scripting 0.130 0.092 6 fingerprinting 0.126 0.089 7 exfiltration 0.121 0.086 8 defacement 0.097 0.069 9 interception 0.085 0.060 10 sql_injection 0.083 0.059 11 brute_force 0.065 0.046 12 jamming 0.063 0.045 13 pharming 0.063 0.044 14 host_discovery 0.056 0.040 15 network_sniffing 0.056 0.040 16 code_injection 0.046 0.032 17 eavesdropping 0.042 0.030 18 fuzzing 0.041 0.029 19 port_scanning 0.039 0.028 20 browser_fingerprinting 0.036 0.025 21 credential_access 0.033 0.023 22 masquerading 0.031 0.022 23 pretexting 0.031 0.022 24 targeted_malware 0.031 0.022 25 touching 0.031 0.022 26 icmp_flood 0.031 0.022 27 udp_flood 0.031 0.022 28 attending 0.028 0.020 29 flooding 0.028 0.020 30 reverse_engineering 0.028 0.020 31 sudo 0.028 0.020 32 protocol_analysis 0.013 0.009 33 command_injection 0.010 0.007 34 audio_capture 0.008 0.006 35 defense_evasion 0.006 0.005 36 privilege_escalation 0.006 0.005 37 data_destruction 0.006 0.004 38 session_hijacking 0.006 0.004 39 path_traversal 0.003 0.002 40 process_hollowing 0.003 0.002 41 access 0 0 42 analysis 0 0 43 attack 0 0 44 attacks 0 0 45 automated_collection 0 0 46 blockage 0 0 47 bootkit 0 0 48 cache_poisoning 0 0 49 change 0 0 50 cleared 0 0 51 cryptanalysis 0 0 52 develop 0 0 53 dominated 0 0 54 dumpster_diving 0 0 55 excavation 0 0 56 fault_injection 0 0 57 landside 0 0 58 privilege_abuse 0 0 59 process_injection 0 0 60 remembering 0 0 61 sharing 0 0 62 to-libc 0 0 63 transformed 0 0 64 violations 0 0
Value statistics Min: 0 Mean: 0.047 Lower quartile: 0 Max: 0.503 Std.dev: 0.094 Median: 0.011 Upper quartile: 0.044
Unscaled value statistics Min: 0 Mean: 0.033 Lower quartile: 0 Max: 0.356 Std.dev: 0.066 Median: 0.008 Upper quartile: 0.031 Centrality, Exponential Rank
Computes the Exponential Ranking Centrality that defines the centrality of a node as its trustworthiness, which is based on the degree to which other nodes trust it. The link values define initial trust between nodes: positive values indicate trust and negative values distrust (negative link values can be used). This measure is similar to eigenvector centrality.
If the node of interest has a higher than normal value (greater than 1 standard deviation(s) above the mean) the row is colored red. The row is green if the node is within 1 standard deviation of the mean. Finally, the row is colored blue if the node has a lower than normal value (less than one standard deviation(s) below the mean).
Input network(s): Semantic Network [Tactics x Tactics]
Rank Tactics Value Unscaled 1 techniques 1 0.017 2 phishing 0.991 0.017 3 tactics 0.948 0.016 4 spear_phishing 0.947 0.016 5 code_injection 0.942 0.016 6 fingerprinting 0.939 0.016 7 scripting 0.939 0.016 8 brute_force 0.938 0.016 9 fuzzing 0.938 0.016 10 exfiltration 0.937 0.016 11 defacement 0.937 0.016 12 sql_injection 0.937 0.016 13 jamming 0.937 0.016 14 interception 0.937 0.016 15 protocol_analysis 0.935 0.016 16 credential_access 0.933 0.016 17 command_injection 0.933 0.016 18 eavesdropping 0.931 0.016 19 defense_evasion 0.931 0.016 20 privilege_escalation 0.931 0.016 21 host_discovery 0.929 0.016 22 network_sniffing 0.929 0.016 23 pharming 0.929 0.016 24 browser_fingerprinting 0.929 0.016 25 port_scanning 0.929 0.016 26 attending 0.927 0.016 27 flooding 0.927 0.016 28 reverse_engineering 0.927 0.016 29 sudo 0.927 0.016 30 masquerading 0.927 0.016 31 pretexting 0.927 0.016 32 targeted_malware 0.927 0.016 33 touching 0.927 0.016 34 path_traversal 0.927 0.016 35 process_hollowing 0.927 0.016 36 audio_capture 0.927 0.016 37 data_destruction 0.927 0.016 38 session_hijacking 0.927 0.016 39 icmp_flood 0.927 0.016 40 udp_flood 0.927 0.016 41 access 0.925 0.016 42 analysis 0.925 0.016 43 attack 0.925 0.016 44 attacks 0.925 0.016 45 automated_collection 0.925 0.016 46 blockage 0.925 0.016 47 bootkit 0.925 0.016 48 cache_poisoning 0.925 0.016 49 change 0.925 0.016 50 cleared 0.925 0.016 51 cryptanalysis 0.925 0.016 52 develop 0.925 0.016 53 dominated 0.925 0.016 54 dumpster_diving 0.925 0.016 55 excavation 0.925 0.016 56 fault_injection 0.925 0.016 57 landside 0.925 0.016 58 privilege_abuse 0.925 0.016 59 process_injection 0.925 0.016 60 remembering 0.925 0.016 61 sharing 0.925 0.016 62 to-libc 0.925 0.016 63 transformed 0.925 0.016 64 violations 0.925 0.016
Value statistics Min: 0.925 Mean: 0.932 Lower quartile: 0.925 Max: 1 Std.dev: 0.013 Median: 0.927 Upper quartile: 0.933
Unscaled value statistics Min: 0.016 Mean: 0.016 Lower quartile: 0.016 Max: 0.017 Std.dev: 2.131e-04 Median: 0.016 Upper quartile: 0.016 Centrality, Hub
A node is hub-central to the extent that its out-links are to nodes that have many in-links. Individuals or organizations that act as hubs are sending information to a wide range of others each of whom has many others reporting to them. Technically, an agent is hub-central if its out-links are to agents that have many other agents sending links to them. The scientific name of this measure is hub centrality and it is calculated on agent by agent matrices.
If the node of interest has a higher than normal value (greater than 1 standard deviation(s) above the mean) the row is colored red. The row is green if the node is within 1 standard deviation of the mean. Finally, the row is colored blue if the node has a lower than normal value (less than one standard deviation(s) below the mean).
Input network(s): Semantic Network [Tactics x Tactics]
Rank Tactics Value Unscaled 1 phishing 0.503 0.356 2 techniques 0.452 0.319 3 spear_phishing 0.284 0.201 4 tactics 0.247 0.175 5 scripting 0.130 0.092 6 fingerprinting 0.126 0.089 7 exfiltration 0.121 0.086 8 defacement 0.097 0.069 9 interception 0.085 0.060 10 sql_injection 0.083 0.059 11 brute_force 0.065 0.046 12 jamming 0.063 0.045 13 pharming 0.063 0.044 14 host_discovery 0.056 0.040 15 network_sniffing 0.056 0.040 16 code_injection 0.046 0.032 17 eavesdropping 0.042 0.030 18 fuzzing 0.041 0.029 19 port_scanning 0.039 0.028 20 browser_fingerprinting 0.036 0.025 21 credential_access 0.033 0.023 22 masquerading 0.031 0.022 23 pretexting 0.031 0.022 24 targeted_malware 0.031 0.022 25 touching 0.031 0.022 26 attending 0.028 0.020 27 flooding 0.028 0.020 28 reverse_engineering 0.028 0.020 29 sudo 0.028 0.020 30 protocol_analysis 0.013 0.009 31 command_injection 0.010 0.007 32 audio_capture 0.008 0.006 33 defense_evasion 0.006 0.005 34 privilege_escalation 0.006 0.005 35 data_destruction 0.006 0.004 36 session_hijacking 0.006 0.004 37 path_traversal 0.003 0.002 38 process_hollowing 0.003 0.002 39 access 0 0 40 analysis 0 0 41 attack 0 0 42 attacks 0 0 43 automated_collection 0 0 44 blockage 0 0 45 bootkit 0 0 46 cache_poisoning 0 0 47 change 0 0 48 cleared 0 0 49 cryptanalysis 0 0 50 develop 0 0 51 dominated 0 0 52 dumpster_diving 0 0 53 excavation 0 0 54 fault_injection 0 0 55 icmp_flood 0 0 56 landside 0 0 57 privilege_abuse 0 0 58 process_injection 0 0 59 remembering 0 0 60 sharing 0 0 61 to-libc 0 0 62 transformed 0 0 63 udp_flood 0 0 64 violations 0 0
Value statistics Min: 0 Mean: 0.046 Lower quartile: 0 Max: 0.503 Std.dev: 0.094 Median: 0.007 Upper quartile: 0.044
Unscaled value statistics Min: 0 Mean: 0.033 Lower quartile: 0 Max: 0.356 Std.dev: 0.067 Median: 0.005 Upper quartile: 0.031 Centrality, In-Closeness
The closeness of all other nodes to a node in the network. Loosely, In-Closeness Centrality is the inverse of the sum of distances in the network to a node and from all other nodes.
If the node of interest has a higher than normal value (greater than 1 standard deviation(s) above the mean) the row is colored red. The row is green if the node is within 1 standard deviation of the mean. Finally, the row is colored blue if the node has a lower than normal value (less than one standard deviation(s) below the mean).
Input network(s): Semantic Network [Tactics x Tactics]
Rank Tactics Value Unscaled 1 techniques 5.084e-04 8.070e-06 2 code_injection 5.084e-04 8.069e-06 3 exfiltration 5.083e-04 8.069e-06 4 defacement 5.083e-04 8.069e-06 5 fuzzing 5.083e-04 8.069e-06 6 browser_fingerprinting 5.083e-04 8.069e-06 7 phishing 5.083e-04 8.068e-06 8 credential_access 5.083e-04 8.068e-06 9 sql_injection 5.083e-04 8.068e-06 10 fingerprinting 5.083e-04 8.068e-06 11 spear_phishing 5.083e-04 8.068e-06 12 attending 5.083e-04 8.068e-06 13 flooding 5.083e-04 8.068e-06 14 reverse_engineering 5.083e-04 8.068e-06 15 sudo 5.083e-04 8.068e-06 16 brute_force 5.082e-04 8.067e-06 17 scripting 5.082e-04 8.067e-06 18 command_injection 5.082e-04 8.067e-06 19 jamming 5.082e-04 8.067e-06 20 protocol_analysis 5.082e-04 8.067e-06 21 interception 5.082e-04 8.067e-06 22 path_traversal 5.082e-04 8.067e-06 23 process_hollowing 5.082e-04 8.067e-06 24 port_scanning 5.082e-04 8.067e-06 25 defense_evasion 5.082e-04 8.067e-06 26 privilege_escalation 5.082e-04 8.067e-06 27 data_destruction 5.082e-04 8.066e-06 28 session_hijacking 5.082e-04 8.066e-06 29 eavesdropping 5.082e-04 8.066e-06 30 masquerading 5.082e-04 8.066e-06 31 pretexting 5.082e-04 8.066e-06 32 targeted_malware 5.082e-04 8.066e-06 33 touching 5.082e-04 8.066e-06 34 tactics 5.081e-04 8.066e-06 35 host_discovery 5.081e-04 8.066e-06 36 network_sniffing 5.081e-04 8.066e-06 37 audio_capture 5.081e-04 8.065e-06 38 pharming 5.080e-04 8.064e-06 39 icmp_flood 4.358e-04 6.917e-06 40 udp_flood 4.358e-04 6.917e-06 41 access 4.340e-04 6.889e-06 42 analysis 4.340e-04 6.889e-06 43 attack 4.340e-04 6.889e-06 44 attacks 4.340e-04 6.889e-06 45 automated_collection 4.340e-04 6.889e-06 46 blockage 4.340e-04 6.889e-06 47 bootkit 4.340e-04 6.889e-06 48 cache_poisoning 4.340e-04 6.889e-06 49 change 4.340e-04 6.889e-06 50 cleared 4.340e-04 6.889e-06 51 cryptanalysis 4.340e-04 6.889e-06 52 develop 4.340e-04 6.889e-06 53 dominated 4.340e-04 6.889e-06 54 dumpster_diving 4.340e-04 6.889e-06 55 excavation 4.340e-04 6.889e-06 56 fault_injection 4.340e-04 6.889e-06 57 landside 4.340e-04 6.889e-06 58 privilege_abuse 4.340e-04 6.889e-06 59 process_injection 4.340e-04 6.889e-06 60 remembering 4.340e-04 6.889e-06 61 sharing 4.340e-04 6.889e-06 62 to-libc 4.340e-04 6.889e-06 63 transformed 4.340e-04 6.889e-06 64 violations 4.340e-04 6.889e-06
Value statistics Min: 4.340e-04 Mean: 4.781e-04 Lower quartile: 4.340e-04 Max: 5.084e-04 Std.dev: 3.638e-05 Median: 5.082e-04 Upper quartile: 5.082e-04
Unscaled value statistics Min: 6.889e-06 Mean: 7.590e-06 Lower quartile: 6.889e-06 Max: 8.070e-06 Std.dev: 5.775e-07 Median: 8.066e-06 Upper quartile: 8.067e-06 Centrality, In-Degree
The number of links directed into a node normalized by the maximum number of such links. This measure is also called Column Degree Centrality because it is computed by taking the sum of the column values in the input network.
If the node of interest has a higher than normal value (greater than 1 standard deviation(s) above the mean) the row is colored red. The row is green if the node is within 1 standard deviation of the mean. Finally, the row is colored blue if the node has a lower than normal value (less than one standard deviation(s) below the mean).
Input network(s): Semantic Network [Tactics x Tactics]
Rank Tactics Value Unscaled 1 techniques 0.069 39 2 phishing 0.060 34 3 tactics 0.021 12 4 spear_phishing 0.019 11 5 code_injection 0.016 9 6 brute_force 0.012 7 7 fingerprinting 0.012 7 8 fuzzing 0.012 7 9 scripting 0.012 7 10 defacement 0.011 6 11 exfiltration 0.011 6 12 interception 0.011 6 13 jamming 0.011 6 14 sql_injection 0.011 6 15 protocol_analysis 0.009 5 16 command_injection 0.007 4 17 credential_access 0.007 4 18 defense_evasion 0.005 3 19 eavesdropping 0.005 3 20 privilege_escalation 0.005 3 21 browser_fingerprinting 0.004 2 22 host_discovery 0.004 2 23 network_sniffing 0.004 2 24 pharming 0.004 2 25 port_scanning 0.004 2 26 attending 0.002 1 27 audio_capture 0.002 1 28 data_destruction 0.002 1 29 flooding 0.002 1 30 icmp_flood 0.002 1 31 masquerading 0.002 1 32 path_traversal 0.002 1 33 pretexting 0.002 1 34 process_hollowing 0.002 1 35 reverse_engineering 0.002 1 36 session_hijacking 0.002 1 37 sudo 0.002 1 38 targeted_malware 0.002 1 39 touching 0.002 1 40 udp_flood 0.002 1 41 access 0 0 42 analysis 0 0 43 attack 0 0 44 attacks 0 0 45 automated_collection 0 0 46 blockage 0 0 47 bootkit 0 0 48 cache_poisoning 0 0 49 change 0 0 50 cleared 0 0 51 cryptanalysis 0 0 52 develop 0 0 53 dominated 0 0 54 dumpster_diving 0 0 55 excavation 0 0 56 fault_injection 0 0 57 landside 0 0 58 privilege_abuse 0 0 59 process_injection 0 0 60 remembering 0 0 61 sharing 0 0 62 to-libc 0 0 63 transformed 0 0 64 violations 0 0
Value statistics Min: 0 Mean: 0.006 Lower quartile: 0 Max: 0.069 Std.dev: 0.012 Median: 0.002 Upper quartile: 0.007
Unscaled value statistics Min: 0 Mean: 3.281 Lower quartile: 0 Max: 39 Std.dev: 6.648 Median: 1 Upper quartile: 4 Centrality, In-Inverse Closeness
The average closeness of a node to the other nodes in a network considering only paths emanating out from a node. Inverse Closeness is the sum of the inverse distances between a node and all other nodes.
If the node of interest has a higher than normal value (greater than 1 standard deviation(s) above the mean) the row is colored red. The row is green if the node is within 1 standard deviation of the mean. Finally, the row is colored blue if the node has a lower than normal value (less than one standard deviation(s) below the mean).
Input network(s): Semantic Network [Tactics x Tactics]
Rank Tactics Value Unscaled 1 techniques 0.358 22.583 2 code_injection 0.313 19.700 3 phishing 0.282 17.750 4 fuzzing 0.277 17.450 5 defacement 0.265 16.667 6 exfiltration 0.261 16.417 7 credential_access 0.253 15.950 8 browser_fingerprinting 0.249 15.667 9 fingerprinting 0.247 15.583 10 brute_force 0.240 15.117 11 sql_injection 0.239 15.083 12 spear_phishing 0.236 14.867 13 jamming 0.224 14.117 14 command_injection 0.224 14.117 15 attending 0.221 13.950 16 flooding 0.221 13.950 17 reverse_engineering 0.221 13.950 18 sudo 0.221 13.950 19 protocol_analysis 0.216 13.617 20 defense_evasion 0.206 12.983 21 privilege_escalation 0.206 12.983 22 port_scanning 0.205 12.917 23 path_traversal 0.200 12.617 24 process_hollowing 0.200 12.617 25 interception 0.199 12.567 26 scripting 0.194 12.250 27 eavesdropping 0.191 12.033 28 masquerading 0.186 11.700 29 pretexting 0.186 11.700 30 targeted_malware 0.186 11.700 31 touching 0.186 11.700 32 data_destruction 0.184 11.567 33 session_hijacking 0.184 11.567 34 tactics 0.162 10.233 35 network_sniffing 0.158 9.983 36 host_discovery 0.158 9.983 37 audio_capture 0.151 9.500 38 pharming 0.137 8.650 39 icmp_flood 0.016 1 40 udp_flood 0.016 1 41 access 0 0 42 analysis 0 0 43 attack 0 0 44 attacks 0 0 45 automated_collection 0 0 46 blockage 0 0 47 bootkit 0 0 48 cache_poisoning 0 0 49 change 0 0 50 cleared 0 0 51 cryptanalysis 0 0 52 develop 0 0 53 dominated 0 0 54 dumpster_diving 0 0 55 excavation 0 0 56 fault_injection 0 0 57 landside 0 0 58 privilege_abuse 0 0 59 process_injection 0 0 60 remembering 0 0 61 sharing 0 0 62 to-libc 0 0 63 transformed 0 0 64 violations 0 0
Value statistics Min: 0 Mean: 0.129 Lower quartile: 0 Max: 0.358 Std.dev: 0.111 Median: 0.184 Upper quartile: 0.221
Unscaled value statistics Min: 0 Mean: 8.152 Lower quartile: 0 Max: 22.583 Std.dev: 7.021 Median: 11.567 Upper quartile: 13.950 Centrality, Inverse Closeness
The average closeness of a node to the other nodes in a network (also called out-inverse closeness centrality). Inverse Closeness is the average inverse distance from a node and to all other nodes.
If the node of interest has a higher than normal value (greater than 1 standard deviation(s) above the mean) the row is colored red. The row is green if the node is within 1 standard deviation of the mean. Finally, the row is colored blue if the node has a lower than normal value (less than one standard deviation(s) below the mean).
Input network(s): Semantic Network [Tactics x Tactics]
Rank Tactics Value Unscaled 1 techniques 0.358 22.583 2 code_injection 0.313 19.700 3 phishing 0.282 17.750 4 fuzzing 0.277 17.450 5 defacement 0.265 16.667 6 exfiltration 0.261 16.417 7 credential_access 0.253 15.950 8 browser_fingerprinting 0.249 15.667 9 fingerprinting 0.247 15.583 10 brute_force 0.240 15.117 11 sql_injection 0.239 15.083 12 spear_phishing 0.236 14.867 13 command_injection 0.224 14.117 14 jamming 0.224 14.117 15 attending 0.221 13.950 16 flooding 0.221 13.950 17 reverse_engineering 0.221 13.950 18 sudo 0.221 13.950 19 protocol_analysis 0.216 13.617 20 defense_evasion 0.206 12.983 21 privilege_escalation 0.206 12.983 22 port_scanning 0.205 12.917 23 path_traversal 0.200 12.617 24 process_hollowing 0.200 12.617 25 interception 0.199 12.567 26 scripting 0.194 12.250 27 eavesdropping 0.191 12.033 28 masquerading 0.186 11.700 29 pretexting 0.186 11.700 30 targeted_malware 0.186 11.700 31 touching 0.186 11.700 32 data_destruction 0.184 11.567 33 session_hijacking 0.184 11.567 34 tactics 0.162 10.233 35 host_discovery 0.158 9.983 36 network_sniffing 0.158 9.983 37 audio_capture 0.151 9.500 38 pharming 0.137 8.650 39 icmp_flood 0.016 1 40 udp_flood 0.016 1 41 access 0 0 42 analysis 0 0 43 attack 0 0 44 attacks 0 0 45 automated_collection 0 0 46 blockage 0 0 47 bootkit 0 0 48 cache_poisoning 0 0 49 change 0 0 50 cleared 0 0 51 cryptanalysis 0 0 52 develop 0 0 53 dominated 0 0 54 dumpster_diving 0 0 55 excavation 0 0 56 fault_injection 0 0 57 landside 0 0 58 privilege_abuse 0 0 59 process_injection 0 0 60 remembering 0 0 61 sharing 0 0 62 to-libc 0 0 63 transformed 0 0 64 violations 0 0
Value statistics Min: 0 Mean: 0.129 Lower quartile: 0 Max: 0.358 Std.dev: 0.111 Median: 0.184 Upper quartile: 0.221
Unscaled value statistics Min: 0 Mean: 8.152 Lower quartile: 0 Max: 22.583 Std.dev: 7.021 Median: 11.567 Upper quartile: 13.950 Centrality, Katz
This computes the centrality of each entity based on the centrality of its neighbors. Alpha should be chosen such that its absolute value is less than the reciprocal of the largest eigenvalue of N.
If the node of interest has a higher than normal value (greater than 1 standard deviation(s) above the mean) the row is colored red. The row is green if the node is within 1 standard deviation of the mean. Finally, the row is colored blue if the node has a lower than normal value (less than one standard deviation(s) below the mean).
Input network(s): Semantic Network [Tactics x Tactics]
Rank Tactics Value Unscaled 1 techniques 0.655 1 2 phishing 0.623 0.953 3 spear_phishing 0.264 0.403 4 tactics 0.254 0.389 5 scripting 0.148 0.226 6 fingerprinting 0.148 0.225 7 code_injection 0.130 0.199 8 exfiltration 0.124 0.190 9 brute_force 0.120 0.183 10 defacement 0.118 0.180 11 sql_injection 0.114 0.174 12 interception 0.111 0.170 13 jamming 0.108 0.165 14 fuzzing 0.107 0.164 15 protocol_analysis 0.068 0.103 16 credential_access 0.065 0.100 17 host_discovery 0.055 0.084 18 network_sniffing 0.055 0.084 19 eavesdropping 0.054 0.083 20 command_injection 0.054 0.083 21 pharming 0.054 0.082 22 browser_fingerprinting 0.042 0.065 23 port_scanning 0.042 0.063 24 defense_evasion 0.038 0.059 25 privilege_escalation 0.038 0.059 26 attending 0.028 0.042 27 flooding 0.028 0.042 28 reverse_engineering 0.028 0.042 29 sudo 0.028 0.042 30 masquerading 0.027 0.041 31 pretexting 0.027 0.041 32 targeted_malware 0.027 0.041 33 touching 0.027 0.041 34 audio_capture 0.015 0.022 35 path_traversal 0.014 0.022 36 process_hollowing 0.014 0.022 37 data_destruction 0.014 0.021 38 session_hijacking 0.014 0.021 39 icmp_flood 0.011 0.017 40 udp_flood 0.011 0.017 41 access 0 0 42 analysis 0 0 43 attack 0 0 44 attacks 0 0 45 automated_collection 0 0 46 blockage 0 0 47 bootkit 0 0 48 cache_poisoning 0 0 49 change 0 0 50 cleared 0 0 51 cryptanalysis 0 0 52 develop 0 0 53 dominated 0 0 54 dumpster_diving 0 0 55 excavation 0 0 56 fault_injection 0 0 57 landside 0 0 58 privilege_abuse 0 0 59 process_injection 0 0 60 remembering 0 0 61 sharing 0 0 62 to-libc 0 0 63 transformed 0 0 64 violations 0 0
Value statistics Min: 0 Mean: 0.061 Lower quartile: 0 Max: 0.655 Std.dev: 0.119 Median: 0.027 Upper quartile: 0.060
Unscaled value statistics Min: 0 Mean: 0.093 Lower quartile: 0 Max: 1 Std.dev: 0.182 Median: 0.041 Upper quartile: 0.092 Centrality, Out-Degree
For any node, e.g. an individual or a resource, the out-links are the connections that the node of interest has to other nodes. For example, imagine an agent by knowledge network where the number of out-links an agent would have is the number of pieces of knowledge it is connected to. The scientific name of this measure is out-degree and it can be calculated on any network.
If the node of interest has a higher than normal value (greater than 1 standard deviation(s) above the mean) the row is colored red. The row is green if the node is within 1 standard deviation of the mean. Finally, the row is colored blue if the node has a lower than normal value (less than one standard deviation(s) below the mean).
Input network(s): Semantic Network [Tactics x Tactics]
Rank Tactics Value Unscaled 1 techniques 0.069 39 2 phishing 0.060 34 3 tactics 0.021 12 4 spear_phishing 0.019 11 5 code_injection 0.016 9 6 brute_force 0.012 7 7 fingerprinting 0.012 7 8 fuzzing 0.012 7 9 scripting 0.012 7 10 defacement 0.011 6 11 exfiltration 0.011 6 12 interception 0.011 6 13 jamming 0.011 6 14 sql_injection 0.011 6 15 protocol_analysis 0.009 5 16 command_injection 0.007 4 17 credential_access 0.007 4 18 defense_evasion 0.005 3 19 eavesdropping 0.005 3 20 privilege_escalation 0.005 3 21 browser_fingerprinting 0.004 2 22 host_discovery 0.004 2 23 network_sniffing 0.004 2 24 pharming 0.004 2 25 port_scanning 0.004 2 26 attending 0.002 1 27 audio_capture 0.002 1 28 data_destruction 0.002 1 29 flooding 0.002 1 30 icmp_flood 0.002 1 31 masquerading 0.002 1 32 path_traversal 0.002 1 33 pretexting 0.002 1 34 process_hollowing 0.002 1 35 reverse_engineering 0.002 1 36 session_hijacking 0.002 1 37 sudo 0.002 1 38 targeted_malware 0.002 1 39 touching 0.002 1 40 udp_flood 0.002 1 41 access 0 0 42 analysis 0 0 43 attack 0 0 44 attacks 0 0 45 automated_collection 0 0 46 blockage 0 0 47 bootkit 0 0 48 cache_poisoning 0 0 49 change 0 0 50 cleared 0 0 51 cryptanalysis 0 0 52 develop 0 0 53 dominated 0 0 54 dumpster_diving 0 0 55 excavation 0 0 56 fault_injection 0 0 57 landside 0 0 58 privilege_abuse 0 0 59 process_injection 0 0 60 remembering 0 0 61 sharing 0 0 62 to-libc 0 0 63 transformed 0 0 64 violations 0 0
Value statistics Min: 0 Mean: 0.006 Lower quartile: 0 Max: 0.069 Std.dev: 0.012 Median: 0.002 Upper quartile: 0.007
Unscaled value statistics Min: 0 Mean: 3.281 Lower quartile: 0 Max: 39 Std.dev: 6.648 Median: 1 Upper quartile: 4 Centrality, PageRank
Calculates the importance of a node based on the importance of its in-coming neighbors. The input network links are normalized and interpreted as the probability of a transition from node i to node j. The PageRank of a node can be interpreted as the fraction of times a node would be visitied when traversing the network according to the network of probabilities.
If the node of interest has a higher than normal value (greater than 1 standard deviation(s) above the mean) the row is colored red. The row is green if the node is within 1 standard deviation of the mean. Finally, the row is colored blue if the node has a lower than normal value (less than one standard deviation(s) below the mean).
Input network(s): Semantic Network [Tactics x Tactics]
Rank Tactics Value 1 techniques 0.172 2 phishing 0.148 3 tactics 0.051 4 spear_phishing 0.047 5 code_injection 0.041 6 brute_force 0.032 7 fuzzing 0.032 8 scripting 0.031 9 fingerprinting 0.031 10 defacement 0.028 11 jamming 0.027 12 interception 0.027 13 sql_injection 0.027 14 exfiltration 0.026 15 icmp_flood 0.024 16 udp_flood 0.024 17 protocol_analysis 0.023 18 credential_access 0.019 19 command_injection 0.018 20 defense_evasion 0.014 21 privilege_escalation 0.014 22 eavesdropping 0.014 23 browser_fingerprinting 0.009 24 host_discovery 0.009 25 network_sniffing 0.009 26 port_scanning 0.009 27 pharming 0.009 28 data_destruction 0.005 29 session_hijacking 0.005 30 path_traversal 0.005 31 process_hollowing 0.005 32 audio_capture 0.005 33 attending 0.005 34 flooding 0.005 35 reverse_engineering 0.005 36 sudo 0.005 37 masquerading 0.005 38 pretexting 0.005 39 targeted_malware 0.005 40 touching 0.005 41 access 7.226e-04 42 analysis 7.226e-04 43 attack 7.226e-04 44 attacks 7.226e-04 45 automated_collection 7.226e-04 46 blockage 7.226e-04 47 bootkit 7.226e-04 48 cache_poisoning 7.226e-04 49 change 7.226e-04 50 cleared 7.226e-04 51 cryptanalysis 7.226e-04 52 develop 7.226e-04 53 dominated 7.226e-04 54 dumpster_diving 7.226e-04 55 excavation 7.226e-04 56 fault_injection 7.226e-04 57 landside 7.226e-04 58 privilege_abuse 7.226e-04 59 process_injection 7.226e-04 60 remembering 7.226e-04 61 sharing 7.226e-04 62 to-libc 7.226e-04 63 transformed 7.226e-04 64 violations 7.226e-04
Unscaled value statistics Min: 7.226e-04 Mean: 0.016 Lower quartile: 7.226e-04 Max: 0.172 Std.dev: 0.029 Median: 0.005 Upper quartile: 0.024 Centrality, Radiality
The Radiality Centrality of a node is the normalized sum of its closeness to all other nodes. The closeness of a node u to another node v is the network diameter minus the shortest path distance from u to v.
If the node of interest has a higher than normal value (greater than 1 standard deviation(s) above the mean) the row is colored red. The row is green if the node is within 1 standard deviation of the mean. Finally, the row is colored blue if the node has a lower than normal value (less than one standard deviation(s) below the mean).
Input network(s): Semantic Network [Tactics x Tactics]
Rank Tactics Value Unscaled 1 techniques 0.586 21,240 2 code_injection 0.586 21,223 3 exfiltration 0.586 21,221 4 defacement 0.586 21,218 5 fuzzing 0.586 21,217 6 browser_fingerprinting 0.586 21,216 7 phishing 0.586 21,212 8 credential_access 0.586 21,210 9 sql_injection 0.586 21,210 10 fingerprinting 0.585 21,208 11 spear_phishing 0.585 21,208 12 attending 0.585 21,204 13 flooding 0.585 21,204 14 reverse_engineering 0.585 21,204 15 sudo 0.585 21,204 16 brute_force 0.585 21,194 17 scripting 0.585 21,191 18 command_injection 0.585 21,190 19 jamming 0.585 21,190 20 protocol_analysis 0.585 21,189 21 interception 0.585 21,187 22 path_traversal 0.585 21,187 23 process_hollowing 0.585 21,187 24 port_scanning 0.585 21,186 25 defense_evasion 0.585 21,184 26 privilege_escalation 0.585 21,184 27 data_destruction 0.585 21,182 28 session_hijacking 0.585 21,182 29 eavesdropping 0.585 21,179 30 masquerading 0.585 21,176 31 pretexting 0.585 21,176 32 targeted_malware 0.585 21,176 33 touching 0.585 21,176 34 tactics 0.584 21,170 35 host_discovery 0.584 21,168 36 network_sniffing 0.584 21,168 37 audio_capture 0.584 21,155 38 pharming 0.584 21,140 39 icmp_flood 0.016 575 40 udp_flood 0.016 575 41 access 0 0 42 analysis 0 0 43 attack 0 0 44 attacks 0 0 45 automated_collection 0 0 46 blockage 0 0 47 bootkit 0 0 48 cache_poisoning 0 0 49 change 0 0 50 cleared 0 0 51 cryptanalysis 0 0 52 develop 0 0 53 dominated 0 0 54 dumpster_diving 0 0 55 excavation 0 0 56 fault_injection 0 0 57 landside 0 0 58 privilege_abuse 0 0 59 process_injection 0 0 60 remembering 0 0 61 sharing 0 0 62 to-libc 0 0 63 transformed 0 0 64 violations 0 0
Value statistics Min: 0 Mean: 0.348 Lower quartile: 0 Max: 0.586 Std.dev: 0.287 Median: 0.585 Upper quartile: 0.585
Unscaled value statistics Min: 0 Mean: 12,601.031 Lower quartile: 0 Max: 21,240 Std.dev: 10,387.082 Median: 21,176 Upper quartile: 21,192.500 Centrality, Total-Degree
The Total-Degree Centrality of a node is the normalized sum of its In-Degree and Out-Degree.
If the node of interest has a higher than normal value (greater than 1 standard deviation(s) above the mean) the row is colored red. The row is green if the node is within 1 standard deviation of the mean. Finally, the row is colored blue if the node has a lower than normal value (less than one standard deviation(s) below the mean).
Input network(s): Semantic Network [Tactics x Tactics]
Rank Tactics Value Unscaled 1 techniques 0.069 39 2 phishing 0.060 34 3 tactics 0.021 12 4 spear_phishing 0.019 11 5 code_injection 0.016 9 6 brute_force 0.012 7 7 fingerprinting 0.012 7 8 fuzzing 0.012 7 9 scripting 0.012 7 10 defacement 0.011 6 11 exfiltration 0.011 6 12 interception 0.011 6 13 jamming 0.011 6 14 sql_injection 0.011 6 15 protocol_analysis 0.009 5 16 command_injection 0.007 4 17 credential_access 0.007 4 18 defense_evasion 0.005 3 19 eavesdropping 0.005 3 20 privilege_escalation 0.005 3 21 browser_fingerprinting 0.004 2 22 host_discovery 0.004 2 23 network_sniffing 0.004 2 24 pharming 0.004 2 25 port_scanning 0.004 2 26 attending 0.002 1 27 audio_capture 0.002 1 28 data_destruction 0.002 1 29 flooding 0.002 1 30 icmp_flood 0.002 1 31 masquerading 0.002 1 32 path_traversal 0.002 1 33 pretexting 0.002 1 34 process_hollowing 0.002 1 35 reverse_engineering 0.002 1 36 session_hijacking 0.002 1 37 sudo 0.002 1 38 targeted_malware 0.002 1 39 touching 0.002 1 40 udp_flood 0.002 1 41 access 0 0 42 analysis 0 0 43 attack 0 0 44 attacks 0 0 45 automated_collection 0 0 46 blockage 0 0 47 bootkit 0 0 48 cache_poisoning 0 0 49 change 0 0 50 cleared 0 0 51 cryptanalysis 0 0 52 develop 0 0 53 dominated 0 0 54 dumpster_diving 0 0 55 excavation 0 0 56 fault_injection 0 0 57 landside 0 0 58 privilege_abuse 0 0 59 process_injection 0 0 60 remembering 0 0 61 sharing 0 0 62 to-libc 0 0 63 transformed 0 0 64 violations 0 0
Value statistics Min: 0 Mean: 0.006 Lower quartile: 0 Max: 0.069 Std.dev: 0.012 Median: 0.002 Upper quartile: 0.007
Unscaled value statistics Min: 0 Mean: 3.281 Lower quartile: 0 Max: 39 Std.dev: 6.648 Median: 1 Upper quartile: 4


Dump extensive information about the contents of the input Field to an HTML file, and write a simple summary to a string output (user interface parameter.)
module PrintField< src_file="printfld.c"> { PrintFieldParams &PrintFieldParams <NEportLevels={2,1}>; string filename <NEportLevels={1,1}> => PrintFieldParams.filename; int gridout <NEportLevels={1,1}> => PrintFieldParams.gridout; int cellout <NEportLevels={1,1}> => PrintFieldParams.cellout; int xformout <NEportLevels={1,1}> => PrintFieldParams.xformout; int nodesout <NEportLevels={1,1}> => PrintFieldParams.nodesout; int htmlout <NEportLevels={1,1}> => PrintFieldParams.htmlout; int allout <NEportLevels={1,1}> => PrintFieldParams.allout; int trigger <NEportLevels={1,1}> => PrintFieldParams.trigger; string message <NEportLevels={1,1}> => PrintFieldParams.message; string notes <NEportLevels={1,1}> => PrintFieldParams.notes; Mesh+Node_Data &in <NEportLevels={2,0}>; omethod+req print_fld( filename+read+notify+req, gridout+read+notify+req, cellout+read+notify+req, xformout+read+notify+req, nodesout+read+notify+req, htmlout+read+notify+req, allout+read+notify+req, trigger+read+notify+req, message+write, notes+write, in+read+notify+req ) = "print_field"; };
PrintField accepts any type of Field for input and dumps extensive selectable information about the Grid, Cell, Node, Xform and Point data to a plain text or HTML formatted file. Additionally a summary of the information is written to a string parameter, which is displayed by the UI macro.
&in
Reference to a field containing Mesh+Node_Data.
&PrintFieldParams
Reference to the parameter block, which contains real instances of the information toggles and message output strings.
filename
Contains the filename and path of the HTML file to write the contents of the field to.
gridout
Toggle to select if the Grid information in the field should be included in the HTML file dump.
cellout
Toggle to select if the Cell information in the field should be included in the HTML file dump.
xformout
Toggle to select if the Xform information in the field should be included in the HTML file dump.
nodesout
Toggle to select if the Nodes information in the field should be included in the HTML file dump.
allout
Toggle to select if all the Point information in the field should be included in the HTML file dump.
htmlout
Toggle to select if output contains HTML formatting. If turned off, the file only contains a purely textual dump of the same information.
trigger
Used to trigger the actual printing (dumping) of the information to the selected file.
&PrintFieldParams
Reference to the parameter block, which contains real instances of the information toggles and message output strings.
message
Used to hold status of the printing.
notes
When HTML file is dumped, this multi-line string is filled with a simple summary of the field contents.
The low-level PrintField module is used in the User Macro print_field. Note that there is no Functional Macro. Additionally, the User Macro also uses the PrintFieldParams parameter block group object, as does the PrintFieldUI UI Macro.
An example application PrintFieldEg is provided, which uses the AVS/Express sample data file hydrogen.fld, to display or dump the contents of that field.
xp_comm_proj/printfld/pfldmods.v contains the PrintFieldParams group and the PrintField module V definitions.
The low-level PrintFieldMods library containing the module PrintField does not specify a process. By default the express process will be used.
Note that if "Print All Point" is selected then the output file may become very large!
The filename specified as the output HTML file may only be written to once; any attempt to do so will result in an error message in the error dialog window.
Phil Chen Connie Farb
Paul G. Lever
PrintFieldParams, PrintFieldUI and print_field
print_field in AVS/Express version 3.4.


Well, I'm simply a terrible programmer. Who only knows Python. But is trying to learn other stuff.
So, as someone with no HTML, CSS, or JS experiece whatsoever, I was inspired to do just that.


Target Language (TL):
French
German
Spanish
Execute:
GO!
SL Sentence:
TL Sentence:
Source:
POS
dependencies
NER
frames
Alignments:
alignment
Target PoS:
project
predict
Target Dependencies:
project
predict
Target NER:
project
Target SRL:
project
predict
S:
T:


TickedID: 409788
Name:
Parker Seidel
No Active Support
Email:
<EMAIL_ADDRESS>Response Time:
72hours, 60hours left
Last Action:
Never been locked
Link to public post
Status:
Not answered


Now we've put the previous document inside an iframe again so the rootScroller is nested 2 levels deep! Neat-o!
How about cross-origin?


[CHAPTER 232E]
TAX REVIEW COMMISSION
Section
232E-1 Establishment of the commission
232E-2 Term
232E-3 Duties


--- layout: post title: "Composite as a monoid" description: "When can you use the Composite design pattern? When the return types of your methods form monoids." date: 2018-03-12 9:39 UTC tags: [Software Design, Design Patterns] image: "/content/binary/composite-as-subset-of-monoid.png" image_alt: "Composite shown as a subset of the set of monoids." --- {% include JB/setup %}
{{ page.description }}
This article is part of a series of articles about design patterns and their universal abstraction counterparts.
The Composite design pattern is a powerful way to structure code, but not all objects are composable. When is an object composable? This article explores that question.
In short, Composites are monoids.
Not all monoids are Composites, but as far as I can tell, all Composites are monoids.
First, I'll use various software design isomorphisms to put Composite in a canonical form. From unit isomorphisms, function isomorphisms, and argument list isomorphisms, we know that we can represent any method as a method or function that takes a single argument, and returns a single output value. From abstract class isomorphism we know that we can represent an abstract class with interfaces. Thus, you can represent the interface for a Composite like this:
public interface IInterface1 { Out1 Op1(In1 arg); Out2 Op2(In2 arg); Out3 Op3(In3 arg); // More operations... }
In order to create a Composite, we must be able to take an arbitrary number of implementations and make them look like a single object.
You have a set of implementations of IInterface1
. In order to create a Composite, you loop over all of those implementations in order to produce an aggregated result. Imagine that you have to implement a CompositeInterface1
class that composes imps
, an IReadOnlyCollection<IInterface1>
. In order to implement Op1
, you'd have to write code like this:
public Out1 Op1(In1 arg) { foreach (var imp in this.imps) { var out1 = imp.Op1(arg); // Somehow combine this out1 value with previous values } // Return combined Out1 value }
This implies that we have an ordered, finite sequence of implementations: imp1, imp2, imp3, .... In C#, we could represent such a sequence with the type IReadOnlyCollection<IInterface1>
. Somehow, we need to turn that collection into a single IInterface1
value. In other words, we need a translation of the type IReadOnlyCollection<IInterface1> -> IInterface1
.
If we look to Haskell for inspiration for a moment, let's replace IReadOnlyCollection<T>
with Haskell's built-in linked list. This means that we need a function of the type [IInterface1] -> IInterface1
, or, more generally, [a] -> a
. This function exists for all a
as long as a
forms a monoid; it's called mconcat
:
mconcat :: Monoid a => [a] -> a
We also know from a previous article that a collection of monoids can be reduced to a single monoid. Notice how the above outline of a composite implementation of Op1
looks similar to the Accumulate
method shown in the linked article. If IInterface1
can form a monoid, then you can make a Composite.
When can an object (like IInterface1
) form a monoid?
From object isomorphisms we know that we can decompose an object with n members to n static methods. This means that instead of analysing all of IInterface1
, we can consider the properties of each method in isolation. The properties of an object is the consolidation of the properties of all the methods.
Recall, still from object isomorphisms, that we can represent an object as a tuple of functions. Moreover, if you have a tuple of monoids, then the tuple also forms monoid!
In order to make an object a monoid, then, you have to make each method a monoid. When is a method a monoid? A method is a monoid when its return type forms a monoid.
That's it. An interface like IInterface1
is a monoid when Out1
, Out2
, Out3
, and so on, form monoids. If that's the case, you can make a Composite.
From unit isomorphism, we know that we can represent C#'s and Java's void keywords with methods returning unit, and unit is a monoid. All methods that return void
can be part of a Composite, but we already knew that Commands are composable. If you search for examples of the Composite design pattern, you'll find more than one variation involving drawing shapes on a digital canvas, with the central operation being a Draw
method with a void
return type.
Another example could be calculation of the price of a shopping basket. If you have an interface method of the type decimal Calculate(Basket basket)
, you could have several implementations:
Boolean values also form at least two monoids (any and all), so any method you have that returns a Boolean value can be used in a Composite. You could, for example, have a list of criteria for granting a loan. Each such business rule returns true
if it evaluates that the loan should be granted, and false
otherwise. If you have more than one business rule, you can create a Composite that returns true
only if all the individual rules return true
.
If you have a method that returns a string, then that is also a candidate for inclusion in a Composite, if string concatenation makes sense in the domain in question.
You probably find it fairly mundane that you can create a Composite if all the methods involved return numbers, strings, Booleans, or nothing. The result generalises, however, to all monoids, including more complex types, including methods that return other interfaces that themselves form monoids, and so on recursively.
The result, then, is that you can make a Composite when all methods in your interface have monoidal return types. If only a single method has a return type that isn't a monoid, you can't aggregate that value, and you can't make a Composite.
Your interface can have as many methods you like, but they must all be monoids. Even one rogue method will prevent you from being able to create a Composite. This is another argument for Role Interfaces. The smaller an interface is, the more likely it is that you can make a Composite out of it. If you follow that line of reasoning to its ultimate conclusion, you'll design your interfaces with a single member each.
There can be some exceptions to the rule that all return values must be monoids. If you have at least one implementation of your interface, then a semigroup may be enough. Recall that monoids accumulate like this:
public static Foo Accumulate(IReadOnlyCollection<Foo> foos) { var acc = Identity; foreach (var f in foos) acc = acc.Op(f); return acc; }
You only need Identity
in order to start the accumulation, and to have something to return in case you have no implementations. If you have at least one implementation, you don't need the identity, and then a semigroup is enough to accumulate. Consider the bounding box example. If you have a method that returns BoundingBox
values, you can still make a Composite out of such an interface, as long as you have at least one implementation. There's no 'identity' bounding box, but it makes intuitive sense that you can still compose bounding boxes into bigger bounding boxes.
Haskell formalises the rule for semigroups:
sconcat :: Semigroup a => Data.List.NonEmpty.NonEmpty a -> a
The sconcat
function reduces any non-empty list of any semigroup a
to a single a
value.
If you have a non-empty list of implementations, then perhaps you don't even need a semigroup. Perhaps any magma will work. Be aware, however, that the lack of associativity will cause the order of implementations to matter.
Technically, you may be able to program a Composite from a magma, but I'd suggest caution. The monoid and semigroup laws are intuitive. A magma without those properties may not form an intuitive Composite. While it may compile, it may have surprising, or counter-intuitive, behaviour. I'd favour sticking to monoids or semigroups.
When is an object-oriented design composable? Composition could mean more than one thing, but this article has focused exclusively on the Composite design pattern. When can you use the Composite pattern? When all method return types are monoids.


Indian Institute of Technology, Madras has been at the forefront of India's technology revolution and aims at producing Potential Leaders of Tomorrow. E-Cell (Entrepreneurship Cell) is a student body of IIT Madras aimed to imbue the spirit of entrepreneurship among the student and faculty community of various colleges across India, to take on entrepreneurial challenges and assist them in their efforts to launch and run business ventures. Our institute supports student-led entrepreneurship and is evident by the fact that 4 of the top 5 Student Startups are from IIT Madras campus in the list by Economic Times with Ather Energy as the winner of the ET Startup Award 2016.
E-Summit is the annual entrepreneurship fest of IIT Madras. E-Summit 2017 - Launching Leaders, scheduled on April 7-9, promises to be an amazing learning experience for entrepreneurs with its exciting and exclusive line-up of events.
One of the highlights of the E-Summit is the Startup Bootcamp. The motive of organizing Bootcamp is to provide a platform for very early stage startup teams to get mentored by professionals and industry experts to get their prototypes or ideas in best shape!
Registration and application from submission deadline: 21 February 2017
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This package contains classes that can be used to implement a proxying Radius server using TinyRadius.
In addition to the abstract methods from RadiusServer, you have to override the method getProxyServer() from RadiusProxy in order to determine which packets shall be proxied and to which proxy server they will be sent.