youtube – Why does my live stream have more Peak Concurrent Views than Unique Views?

I’m looking at the analytics for a live stream I ran in the past to get an idea of how many people watched it. I see some confusing data in the report.

According to the report, on the day of the stream, I had 21 Unique Viewers. However, my Peak Concurrent Viewers stat says that at one point, my stream had 33 simultaneous viewers.

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It seems to me that Uniqie Viewers should be at least 33 since I had that many viewers at one specific moment in time. It might be possible that some viewers were watching on multiple devices simultaneously, but I doubt that explains what 12 of the 33 viewers were doing.

How do I make sense of this apparent discrepancy in my analytics report?

untagged – Where can i turn the unique hash result of SHA256 to bitcoin?

i watched on youtube from 3Brown1Blue channel about “how bitcoin works”, it said what makes bitcoin valued is come from the unique hash result of SHA256. for example if someone put string to an argumen function SHA256 like SHA256("Hello World!") and consider the result of hash 000006d40bf420404a011733cfb7b190d62c65bf0bcda32b57b277d9ad9f146e, there’s 5 zeros at first which we can say unique result. Then that’s result make bitcoin valuable.

Now what if i found another unique result hash of SHA256 with just SHA256 encryptor online manually? How to add it to blockchain so my identity is approved by blockchain watcher that i have it value and how i get the reward from it?

formulas – Need to count unique occurrences past 365 days from date of service google sheets

Assuming that account names are in sheet Visits column A2:A, and dates are in B2:K, try Insert > New sheet and this formula in cell A1 of the new sheet:

=arrayformula( 
  query( 
    flatten( 
      if( 
        Visits!B2:K >= today() - 365, 
        Visits!B2:K, 
        iferror(1/0) 
      ) 
    ), 
    "select Col1, count(Col1) 
     where Col1 is not null 
     group by Col1 
     label Col1 'date' ", 
    0 
  ) 
)

Format column A as Format > Number > Date.

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sharepoint online – Delete unique permissions of a File or Folder in a document library

I have a document library which has 3 folders in it and inside each folder there is a document (docx, xls, ppt).

My objective is to iterate every file inside my document library and check if It has unique permissions and If it does it should remove them.

For the folder part I already have this code working properly which fetches the root folder and prints if it has unique permissions.

$siteUrl = "https://**.sharepoint.com/sites/**"
Connect-PnPOnline –Url $siteURL –Credentials **
$context = Get-PnPContext 
$ListName="List name"

$list = Get-PnPList $ListName
$folders = $list.RootFolder.Folders
$context.Load($folders);
$context.ExecuteQuery();

foreach($folder in $folders)
{
  if($folder.ItemCount -gt 0)
  {    
    $f = Get-PnPFolder -Url $folder.ServerRelativeUrl -Includes ListItemAllFields.RoleAssignments, ListItemAllFields.HasUniqueRoleAssignments

    $context.Load($f);
    $context.ExecuteQuery();
    Write-Host $f.ServerRelativeUrl -> $f.ListItemAllFields.HasUniqueRoleAssignments
  }
}   

For the document / item part I’m struggling a bit because I cant seem to put this to work.
It always gives me that the item is not part of a list.

ERROR

So my questions is:

How do I check if a item / document has unique permissions and how to remove them?

Will making HTTP cookies unique to a given website make cookies aligned with the strictest privacy guidelines?

As far as I know, the only privacy problem with cookies is that in general, the owners of website Y could read what a visitor has searched for or had done in website X.

The privacy problem with cookies is that they can be used to track a user over multiple sites and specific web pages. And based on these information a profile can be created about the interests of the user – which allow targeted ads and similar.

… so other websites won’t be able to access it

Other websites cannot access the cookies or a site. Its the cookies these web sites itself set, i.e. cookies from Facebook when a like button is included in any page, cookies from Google if Google Analytics is used in a page etc.

This also means that your approach of unique cookies will not help, since it does not addresses the actual problem.

passwords – How can I use a unique salt for each user

I know there are various questions that seem similar, for instance, this one. However, it does not answer my question.

I’m creating a signup/login system (with node.js to be particular), and I’m trying to hash the user’s password (with bcrypt), as well as use aes-256 for the rest of the user information. I’ve been told that I should use a different salt for each user.

My problem is when a user creates an account, his password is hashed with a special salt and stored inside a database along with other user information. When the user logs in, I hash this password with the salt I assigned it when he signed up. However, how do I know which salt to use out of all the salts in my database?

There’s also this option out there (comparing a plain text to a hash):

bcrypt.compare(myPlaintextPassword, hash, function(err, result) {
    // result == true
});

But how do I know which hash (of all the other hashes in the database) to compare to the user input? Also, if bcrypt could check if it matches without a salt, couldn’t anyone do that?

I believe there must be a better implementation of this… is there?

python – Finding unique top sums from multiple lists

My question arises from this post on MSE where I have provided an answer to solve the question :

There are multiple lists given. The number of lists is arbitrary.
Each list contains numbers and is sorted descendingly.
We shall take exactly $1$ element from each list and calculate the sum of elements.How would you go about finding the top $N$ sums?

Here the top sums need not to be different, just the indices of them. I wanted to write an algorithm to find the top $N$ unique sums, namely where the sum of elements is different. What I have done is using the same approach described in the linked post. The problem is that for some inputs there are lots of duplicate sums, so the research for the unique ones gets slower and slower. I post the implementation in python

import time

def top_solutions_2(N,lists):

    N_best = ()
    k, len_k_lists = len(lists), (len(x) for x in lists)
    init_sol = (sum(x(0) for x in lists),tuple(0 for x in range(k)))
    comp_list, new_vals = ((init_sol)), ()
    seen = {init_sol(1)}

    for _ in range(N) :

        curr_best = (float('-inf'))
        for x in comp_list :
            if x and x(-1)(0) > curr_best(0) : curr_best = x(-1)

        N_best.append(curr_best)

        inds = ()

        for arr in comp_list : 

            while arr :
                
                comp_val = arr.pop()
                
                if curr_best(0) > comp_val(0) : arr.append(comp_val); break
                
                inds.append(comp_val(1))

        comp_list.append(())
        
        for ind in inds :
            
            for x in range(k) :

                if len_k_lists(x) > ind(x)+1 : r = tuple(c if i != x else c+1 for i,c in enumerate(ind))
                else : continue

                if r not in seen :
                    curr_sum = curr_best(0)+lists(x)(r(x))-lists(x)(r(x)-1)
                    comp_list(-1).append((curr_sum,r))
                    seen.add(r)                
                
        comp_list(-1).sort()

    return N_best
        
for N in range(10,60,10) :

    lists = ( (23,5,3,2,1),
              (19,9,8,7,0),
              (17,12,4,2,1),
              (15,13,11,9,2),
              (21,17,13,9,4),
              (16,13,12,11,1),
              (27,23,21,18,4),
              (31,25,24,12,1),
              (27,22,14,7,3),
              (9,8,7,6,5))

    a = time.time()
    top_solutions_2(N,lists)
    b = time.time()
    print("Top {} in {} sec".format(N,b-a))

where the output is

Top 10 in 0.0 sec
Top 20 in 0.07787561416625977 sec
Top 30 in 0.5308513641357422 sec
Top 40 in 2.2048890590667725 sec
Top 50 in 7.203002452850342 sec

How can a more efficient algorithm with a lower complexity and/or a lower running time be made?And also, how can my approach be improved in efficiency?

Thank you in advance