The reason behind IPv6 adoption rate dramatical drop in China according to Google measurements?

Google has an IPv6 measurement page that reports that their numbers report on the percentage of users that access Google over IPv6.

According to the report by Jan 2020 0.3% of users in China used IPv6 to access Google

However, looking at this metric in dynamic we see the substantial drop starting from June 2019. enter image description here

I failed to find any solid news that may cause such behavior. I have two hypotheses in mind.

  1. Also as it is a percentage metric, they can adjust their calculation on the total internet penetration rate in China.
  2. Previously open discussions between netizens took place on Google Plus groups. In April 2019, Google shut down Google Plus. Technical discussions continue on Chinese-language blogs, forums, and groups. For obvious reasons, discussions must be hosted outside China, and posters must register under pseudonyms. So probably that caused the shift from Google services but I hardly believe that it may cause such plummet.

how to exploit user which I own according to bloodhound?

During a pentest, I succeeded to compromize a user with low privileges. As this is a domain user, I decided to run BloodHound based on its credentials to check whether there is a path from this user to domain admins.

I have the own privilege on another active directory account, which mean I should be able to change its attributes, such as userPassword, which I did, using ldp.exe. Unfortunately, Active Directory does not seem to allow authentication based on this LDAP attribute. So I needed to find something else.

I would like to use the MMC Active Directory Users and Computers but i need to be logged in as a domain member, which I can’t as I would need to integrate my PC to the domain (I prefer not to do so).

which tool can I use to act/impersonate this user I am supposed to own ?

Many thanks;

Sorting array of strings (with repetitions) according to a given ordering

We get two arrays:

ordering = ["one", "two", "three"] 

and

input = ["zero", "one", "two", "two", "three", "three", "three", "four"]; 

We want to find the array output so that

output = ["one", "two", "two", "three", "three", "three", "zero", "four"] // or output = ["one", "two", "two", "three", "three", "three", "four", "zero"] 

The strings (with possible repetitions) should be sorted as in the ordering array. Not found/contained strings should be put at the end of the new array and their order doesn’t matter.

The $ n^{2}$ solution is obvious, can we do better? The memory doesn’t matter and it doesn’t have to be an in-place algorithm.

Calculating match % and ranking according to that

I’m creating a website like where users will answer some yes/no questions set by me, up to them how many of those questions they want to answer. After a user submits his answer(s), he will be shown top 5 matches along with their match percentages. If two users have 10 common questions and their answers match for 8 of those questions then their match % will be 80%.

I can make this but my concern is about efficiency. A way of making this: If a user wants to see his top matches then match % (or match ratio) will be calculated for him vs every other user in the system. This will be stored in a temporary array. Array is sorted. Top 5 matches from the array are displayed.

Any less resource intensive way to calculate and show top matches?

Why no DPDA can accept Palindrome? (according to this proof)

This proof is from the book “Introduction to Languages and the Theory of Computation” by John C. Martin.

My question is from the pink part at the second page:

It follows in particular that no sequence of moves can cause M to empty its stack.

and further, it talks about $ y_x$ that is my second question. I can’t understand what $ y_x$ is.

I’ll appreciate it if someone please describe me the whole proof.

First page of the proof Second page of the proof

Decomposition of graph to subgraphs according to parallel edges

I am supposed to calculate all-pair shortest path lengths of a graph. However, I first need the graph to be decomposed/expanded to a simple one based on the presence of parallel edges.

If N parallel edges exist between any two vertices A and B, I need to create N replicas of both vertices. Each replica of A will be connected to one and only one replica of B, and vice versa. In addition, all replicas of a vertex must be fully connected to each other.

As an example:-

 A === B 

will become

 A1 ----- B1 |        | A2 ----- B2  

Does this formulation match any well-defined graph theory problem? I am trying to come up with an algorithm that can make use of a GPU’s speed, since the graphs I am dealing with can become huge, and I am trying to do it by manipulating the adjacency matrix.

How does Shelyn feel about Philters of Love, according to the lore?

One of my players off-handedly mentioned the possibility of acquiring a Philter of Love, which has the following description:

Philter of Love

This potent preparation causes a creature who drinks it to fall madly in love with the first creature he or she perceives after consuming it. The drinker’s attitude toward that creature becomes helpful. If a romantic attraction is possible toward the first person viewed, the drinker falls in love with that person. Otherwise, the drinker’s love is a platonic adoration. The effects of the philter are permanent unless removed by a break enchantment, dispel magic, limited wish, miracle, remove curse, or wish.

Another player is a cleric of Shelyn, the goddess of art, beauty, love, and music. In the unlikely event that the PCs follow up on the idea of getting a Philter of Love, would Shelyn be displeased if her cleric went along with it?

Some sources describe her as the goddess of love “in all its forms”, which sort of suggests that she might be okay with it. On the other hand, the fact that a Philter of Love can be undone by magic (and Remove Curse at that) makes me think that it doesn’t count as “real love” as far as Shelyn is concerned.

Are there any sources that show Shelyn having feelings one way or another about relationships built on magical influence, such as Philters of Love, charm person, etc.? How she feels about relationships built on dishonesty (e.g., a poor street rat claiming to be a noble to woo a princess) might also be relevant.

Relabel to front, what does topological sort according to admissible network mean?

In the CLRS book it says that “relabel to front” algorithm, which solves the maximum-flow problem, maintains a list of topologically sorted vertices in the admissible network and that vertices with zero excess flow are moved to the front of the list.

I do not fully understand what is the meaning of it. I would imagine that the vertices are sorted according to the number of admissible edges incident on it. But then how would moving vertices with no excess flow to front affects the sorting order in this case. Also how come the list is already sorted when it is initialized with random order of the vertices?

Edit:

Just realized that topological sorting of vertices in the admissible network means that for every admissible edge (u,v) in the admissible network the vertex u appears before v in the list.

This does not answer my last two parts of my question though, how is that the list is already sorted when initialized and what effect does moving zero-excess-flow vertices to front affect the order. Thanks.