## SEO Black Hat and distribution of malware by creating pages on lots of sites targeting the name of my site

My site made with WordPress is under SEO black hat attack. They’re creating many HTML pages using my site name such as following URLs with my site title & descriptions.

• https://aaa.example111.it/my-site-name.html
• https://bbb.example22222222.it/my-site-name.html
• https://ccccc.example333.it/my-site-name.html

If you click the links of Google search results, first it displays ‘checking your browser before accessing’, then redirect to the malware site zvideo-live.com. Please see the attached list (although they are in Japanese).

What’s happening is very similar to ‘Japanese keyword hack’, but the difference is my site has not hacked and they are using another domains for this. (I thoroughly checked my site and Google tools.) Actually, the users don’t have any problems as far as they click my site domain on Google search results but my site and site domain are very new and most of the search results occupy these phishy sites and it’s annoying.

I made a abuse report to Google and OVHcloud, the domain company, but the malicious pages with new domains are being added every day and it’s very hard to keep doing this.

Following are the list of the domains that hackers are using. (As far as I detect.)

acquariobeb.it areaformativaliceomiranda.it brandoleseconsulenza.it byogastudio.it calabriamediterranea.it cmtservicesrl.it computerassistancesas.it domusvenetia.it fabioviglionephotography.it flanweb.it gabriellaricciocoach.it geniusdomus.it gpad.it granfondovalledelnisi.it lamonicaservizi.it macellerialimonenicola.it onmiccatania.it orsiinchianti.it pizzapadellino-slap-torino.it retedinapoli.it ristorantelafollia.it studiobaldin.it teatrokoine.it triede20.it xtecna.it zancleartecontemporanea.it 

## Background

It is obvious that there are patterns to spell distribution across classes.

For instance, typically only druids and rangers have access to nature-themed spells. As another example, healing spells tend toward cleric and away from wizard, if that makes sense.

There are some surprises. For instance, Cure Wounds is available to bard, cleric, druid, paladin, and ranger; whereas Healing Word is available to only bard, cleric, and druid.

Another somewhat surprising example: Find Familiar is only available to wizard. Why is that? Certainly the general tropes of fantasy don’t suggest clerics would have familiars, but what about sorcerers and warlocks?

Clearly there are patterns. In the case of Cure Wounds and Healing Word, the pattern could be that only full casters get to heal at a distance; or, perhaps healing by touch is more thematically appropriate for paladins and rangers.

## The Question

Is there any published official material stating, as the title says, What the patterns are to spell distribution across classes?

Failing published official material, is there any published unofficial material on the question, or any third party analysis?

I’ve looked through the published materials, the internet generally, and particularly here on rpg, and haven’t found anything, but maybe I’m just not looking in the right place.

## distribution implement for RandomVariate

I have a scalar function of two random matrices of dimension $$n$$ which are in the Gaussian Unitary Ensemble $$f(x,y) \in \mathbb R, \quad x^{\dagger}=x \: \operatorname{and} \: y^{\dagger} = y, \:\operatorname{dim}(x)=\operatorname{dim}(y)=n$$ I would like to generate a sample of various of values of $$f(x,y)$$ for example, $$10^8$$ of them. To do this I use the following Mathematica program

RandomVariate[MatrixPropertyDistribution[f[x, y],                     {x \[Distributed] GaussianUnitaryMatrixDistribution[n],                      y \[Distributed] GaussianUnitaryMatrixDistribution[n]}],10^8] 

However, this program runs slow for large dimension $$n$$ due to the complicated form of $$f(x,y)$$. So I work in a basis that the matrix $$x$$ is diagonalized: $$x\rightarrow\operatorname{diag}(a_1,\dots,a_n), \: y \rightarrow U^\dagger y U,$$ where $$U$$ is the unitary matrix that diagonalizes $$x$$. The scalar function $$f$$ is invariant under this transformation.

Now $$\{a_1,\dots,a_n\}$$ is in a multinormal distribution wherein the covariance matrix is the identity matrix. At the same time, $$y$$ is still in the Gaussian Unitary Ensemble. So I would like to generate a sample of values of $$f$$ in terms these new random variable. But I find it is hard to implement the distribution. I tried

distx = Array[x, n] \[Distributed] MultinormalDistribution[ConstantArray[0, n], DiagonalMatrix[ConstantArray[1, n]]];  disty = y \[Distributed] GaussianUnitaryMatrixDistribution[n];  distribution = MatrixPropertyDistribution[f[x, y], Join[distx, {disty}]];  sample = RandomVariate[distribution,10^8]; 

However, I got the following message from Mathematica

Is there any way to solve this problem so that I can obtain the values from the random variables? Thanks very much.

## Alternative approaches to Iframes for content distribution via json api

I am currently working on a project that uses iframes to distribute content to customers. Going ahead we would like to switch to a json based rest api to deliver the content. Api access would need a token to which specific content could be exposed and traffic limits set.

To replace the frontend appearance of the iframe I am thinking about writing a reusable bundle using a lightweight react alternative like preactjs. But this would mean exposing the raw api and the specific token to the end user. Simply routing user requests via the customers server would conceal the token but still allow raw api access to the enduser.

What would be a good architecture for such a use case?

Are there server side rendered solutions that can easily be implemented across a variety of backend frameworks, without rewriting everything for each customer that is?

## Probability distribution with random paremeters

Is it possible to set something like this? A probability distribution with random parameters

p = BetaDistribution[1, 1] count = BinomialDistribution[10, p]  (* and to calculate stuff like: *) Expectation[count] Probability[count == 3 \[Conditioned] p > 1/2] $$$$ 

## Sampling from the uniform distribution

Is there an efficient classical algorithm that generates samples from the uniform distribution? Would such an algorithm exist for any distribution that has an analytic description?

## How can I calculate the distribution of 3d6, keep and rerolling any 1s and 2s, once?

The way my DM wants to try is roll 3d6 and reroll any 1’s or 2’s once. If you roll a 2 and the reroll ends up being a 1 you have to take the 1.

## Median of distribution with memory constraint

I want to approximate the median of a given distribution $$D$$ that I can sample from.

A simple algorithm for this, using $$n$$ samples, is:

samples = [D.sample() for i in range(n)] # generate n samples from D sort(samples) return samples[n/2] `

However, I am looking for an algorithm that requires less than $$O(n)$$ space.

## Ideas

I have looked into these algorithms:

• Median of medians: Needs $$O(n)$$ space, so it does not work for me.
• Randomized median: It seems like this could be easily generalized to an algorithm that uses $$O(n^{3/4})$$ space.

Are there any other algorithms that use less then $$O(n)$$ space that could solve my problem? In particular, I was thinking there may be an algorithm that uses $$O(m)$$ space by generating batches of samples from $$D$$ of size $$m$$

## Details

• Ideally, I am looking for a reference to an algorithm that also includes analysis (success probability, expected runtime, etc).
• Actually, I need an algorithm to estimate $$D$$‘s $$p$$-th percentile for a given $$p$$, but I am hoping most median-finding algorithms can be generalized to that.

# What does this expression mean?

## Normal distribution with condition

I am reading a research paper and found the following expression (Eq.28 in the paper below).

It means a Gaussian distribution, but the mean component seems conditional probability-like expression $$\it{\bf{s}}_t | \it{\bf{m}}_{b, t, m}^{(j)}$$. I have never seen this expression before and cannot find any info about it.

The variables $$\it{\bf{s}}_t$$ and $$\it{\bf{m}}_{b, t, m}^{(j)}$$ are both vectors and $$\bf{\Sigma}_{b}$$ is a covariance matrix.

Does anybody have an idea of what this expression means?

## Original paper where the expression is.

The original paper can be found here: https://eprints.soton.ac.uk/437941/1/08340823.pdf

Consider there is a city with $$n$$ residents who are in need of internet and there are $$m$$ internet providers in the city. Here in the city every resident needs internet and every resident knows what providers are available to him. Formally let resident $$i$$ has list of providers $$a_i$$. Also each provider has a maximum number of connections he can give, that is, provider $$i$$ can have at max $$k_i$$ connections. Find the optimal way of providing internet so that the number of residents having internet is maximum.