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How do companies fake the domain creation date to ICANN?

I was searching for “Curatic.com” domain in icann lookup & godaddy lookup 4 months back. This domain was available till that date. But today I found that this domain was Created on: 2009-10-21 14:32:57 UTC.( ie back-date registration).

It seems there is a loophole in the system & domain registrars are circumventing loopholes to register domain names from backdate to benefit by selling domains at premium price. There might be some beneficiaries in ICANN organization and therefore needs a serious investigation.

ICANN has a duty towards ensuring that registries and registrars comply with policies. As per ICANN policy & International laws Cybersquatting, Typo squatting, or trademark infringement etc. are illegal.

The current registrant does not have any relevant interests regarding the domain name. Domain Name Hijacking means using the Policy in bad faith to attempt to deprive of a domain name. Hence such domain needs to be freed & how back date registry/creation of domain name was done must be investigated?

  1. If the domain was not renewed then as per ICANN policy a new registration date is created on the day of registration.

  2. 4 months back if domain was available & didn’t show past record AND now the record is being shown means; registrar was able to circumvent the loop hole.

Similarly “sincerejobs.com” was available a week back & now it shows registered in back date. I have observed this phenomena multiple times and I am not mistaken.

How do persons/registrars fake the domain creation date to ICANN? What action can be taken for investigating registrars?

Performance issue in python- network creation based on the Euclidean distance with nested for loops is too slow

I want to create a network in which the links are formed based on a similarity metric defined as the Euclidean distance between the nodes. The distance is calculated using socio-demographic features of customers such as gender and age. The problem is the code takes 200 seconds to just create the network and as I am tuning my model and the code executes at least 100 times, the long execution time of this piece is making the whole code run slowly.

So, the nodes are in fact customers. I defined a class for them. They have two attributes gender (numerical; specified by number 0 or 1) and age (varies from 24 to 44) which are stored in a csv file. I have generated a sample csv file here :

#number of customers
ncons = 5000
gender = (random.randint(0, 1) for i in range(ncons))
age = (random.randint(22, 45) for i in range(ncons))
customer_df = pd.DataFrame(
    {'customer_gender': gender,
     'customer_age': age
customer_df.to_csv('customer_df.csv', mode = 'w', index=False)

The Euclidean distance delta_ik is of the form enter image description herefollowing. In the formula, n is the number of attributes (here n=2, age and gender). For customers i and k, S_f,i - S_f,k is the difference between attribute f = 1,2 which is divided by the maximum range of attribute f for all the customers (max d_f). So the distance is the distance in the values of socio-demographic attributes, not geographical positions.

Then I define the similarity metric H_ik which creates a number between 0 and 1 from delta_ik as follow:customer similarity. Finally, For customers i and k, I generate a random number rho between 0 and 1. If rho is smaller than H_ik, the nodes are connected.

So, the code that keeps delta_ik in a matrix and then uses that to generate the network looks as below:

import random
import pandas as pd
import time
import csv
import networkx as nx
import numpy as np
import math
#Read the csv file containing the part worth utilities of 184 consumers
def readCSVPWU():
    global headers
    global Attr
    Attr = ()
    with open('customer_df.csv') as csvfile:
        csvreader = csv.reader(csvfile,delimiter=',')
        headers = next(csvreader)  # skip the first row of the CSV file.
        #CSV header cells are string and should be turned to a float number.
        for i in range(len(headers)):   
            if headers(i).isnumeric():
                headers(i) = float(headers(i))
        for row in csvreader:
            AttrS = row
    #convert strings to float numbers
    Attr = ((float(j) for j in i) for i in Attr)
    #Return the CSV as a matrix with 17 columns and 184 rows 
    return Attr

#customer class
class Customer:
    def __init__(self, PWU = None, Ut = None):
        self.Ut = Ut
        self.PWU = Attr(random.randint(0,len(Attr)-1))  # Pick random row from survey utility data  

#Generate a network by connecting nodes based on their similarity metric
def Network_generation(cust_agent):
    start_time = time.time() # track execution time

    #we form links/connections between consumeragentsbasedontheirdegreeofsocio-demographic similarity.
    global ncons
    Gcons = nx.Graph()
    #add nodes
    (Gcons.add_node(i, data = cust_agent(i)) for i in range(ncons))
    #**********Compute the node to node distance
    #Initialize Deltaik with zero's
    Deltaik = ((0 for xi in range(ncons)) for yi in range(ncons)) 
    #For each attribute, find the maximum range of that attribute; for instance max age diff = max age - min age = 53-32=21
    maxdiff = ()
    allval = ()
    #the last two columns of Attr keep income and age data
    #Make a 2D numpy array to slice the last 2 columns (#THE ACTUAL CSV FILE HAS MORE THAN 2 COLUMNS)
    np_Attr = np.array(Attr)
    #Take the last two columns, income and age of the participants, respectively
    socio = np_Attr(:, (len(Attr(0))-2, len(Attr(0))-1))
    #convert numpy array to a list of list
    socio = socio.tolist()
    #Max diff for each attribute

    for f in range(len(socio(0))):
        for node1 in Gcons.nodes():
        #keep all values of an attribute to find the max range
        allval = ()

    for node1 in Gcons.nodes():
        for node2 in Gcons.nodes():
            tempdelta = 0
            #for each feature (attribute)
            for f in range(len(socio(0))):
                Deltaik(node1)(node2) = (Gcons.nodes(node1)('data').PWU(-2:)(f)-Gcons.nodes(node2)('data').PWU(-2:)(f))
                #max difference
                insidepar = (Deltaik(node1)(node2) / maxdiff(f))**2
                tempdelta += insidepar
            Deltaik(node1)(node2) = math.sqrt(tempdelta)
    #Find maximum of a matrix
    maxdel = max(map(max, Deltaik))
    #Find the homopholic weight
    import copy
    Hik = copy.deepcopy(Deltaik)
    for i in range(len(Deltaik)):
        for j in range(len(Deltaik(0))):
            Hik(i)(j) =1 - (Deltaik(i)(j)/maxdel)
    #Define a dataframe to save Hik
    dfHik = pd.DataFrame(columns = list(range(ncons) ),index = list(range(ncons) ))
    temp_h = ()
    #For every consumer pair $i$ and $k$, a random number $rho$ from a uniform distribution $U(0,1)$ is drawn and compared with $H_{i,k}$ . The two consumers are connected in the social network if $rho$ is smaller than $H_{i,k}$~cite{wolf2015changing}.
# THE MOST TIME CONSUMING PART ********************
    for node1 in Gcons.nodes():
        for node2 in Gcons.nodes():
            #Add Hik to the dataframe
            rho = np.random.uniform(0,1,1)
            if node1 != node2:
                if rho < Hik(node1)(node2):
                    Gcons.add_edge(node1, node2)
        #Row idd for consumer idd keeps homophily with every other consumer
        dfHik.loc(node1) = temp_h
        temp_h = ()
    # nx.draw(Gcons, with_labels=True)            
    print("Simulation time: %.3f seconds" % (time.time() - start_time))

    return Gcons     
#number of customers
ncons = 5000
gender = (random.randint(0, 1) for i in range(ncons))
age = (random.randint(22, 39) for i in range(ncons))
customer_df = pd.DataFrame(
    {'customer_gender': gender,
     'customer_age': age
customer_df.to_csv('customer_df.csv', mode = 'w', index=False)
customer_agent = dict(enumerate((Customer(PWU = (), Ut = ()) for ij in range(ncons)))) # Ut=()
G = Network_generation(customer_agent)

I realized that there are two nested loops that are more time consuming than others, but I am not sure how to write them more efficiently. I would tremendously appreciate if you could please give me some advice on the ways to decrease the elapsed time.

Thank you so much

i18n l10n – At creation time, why are shortcut titles translated, but menu link titles are not?

The idiom that the community points to for creating menu links usually looks like this (from DrupalTestslanguageFunctionalLanguageSwitchingTest::testLanguageSessionSwitchLinks()):

// Add a link to the homepage.
$link = MenuLinkContent::create((
  'title' => 'Home',
  'menu_name' => 'main',
  'bundle' => 'menu_link_content',
  'link' => (('uri' => 'entity:user/2')),

Note that the title is not run through t().

But, yet, the idiom for shortcut links (from standard_install()) is:

// Populate the default shortcut set.
$shortcut = Shortcut::create((
  'shortcut_set' => 'default',
  'title' => t('Add content'),
  'weight' => -20,
  'link' => ('uri' => 'internal:/node/add'),

Note that the title is run through t().

Why are shortcut titles run through translation at creation time, but menu link titles are not? Aren’t shortcuts a type of menu link? Shouldn’t they be handled the same way?

flash – HDR creation on flashes without exposure information

There is a challenge to shoot HDR of a subject in a studio, but the problem is that the subject is illuminated with flashes that flash momentarily in complete darkness. Because of this, it is not possible to change the exposure on the camera. Therefore, it was decided to change the flash power between shots for HDR. But we faced the problem that the software for creating HDR from a series of images requires information about the exposure of the camera. In our case, we only changed the flash power, and the camera settings remain unchanged. Therefore, it is not possible to calculate HDR.

As a result, the question arises, is it possible to calculate HDR in some software without information about the exposure of the images? Or does it all make no sense, and is this information required? And if necessary, then there are suggestions on how you can shoot HDR in such conditions?

performance of creation to create explosives

Assuming a level 10 creation bard uses performance of creation to create a 200gp explosive that is any size up to large. How big would the explosive be, would it be blackpowder-based, and how much damage would it do?

Profile creation only !

How to prevent GSA SER from systematically creating a profile on websites, i want it to publish some posts not to create profiles ! If you can help me I think I have an incorrect setting…

❓ASK – How can I Earn Money through my Content Creation? | NewProxyLists

I am a content creator, creating videos on Youtube and creating songs and distributing them onto Spotify, however, I’m having a hard time getting money from these ventures. I’m trying to get my social media up and running too and expanding myself to get noticed, however, it seems impossible. Does anybody have any tips on how I can get more popular and earn money through my content creation?

Content creation for Hosting website

Hi everyone!
I’ve faced a problem of finding a competent copywriter who is proficient in the creation of powerful content in Hosting topic…. | Read the rest of https://www.webhostingtalk.com/showthread.php?t=1838242&goto=newpost

complexity theory – The concept of the creation of a trapdoor in NP-complete or NP-hard problems

I am reading the book An Introduction to Mathematical Cryptography. In its chapter 7, there is the following statement:

In real world scenarios, cryptosystems based on NP-hard or NP-complete problems tend to rely on particular subclass of problems either to achieve efficiency or to allow the creation of a trapdoor.


Is is possible to explain what does it mean the phrase “to allow the creation of a trapdoor“?

Thanks for any help