The-Machine | A 2D box pushing game with some Minecraft Redstone mechanics | Game Engine library

Possible word subsequences of certain maximum length means it considers every word with the 7 words before and 7 after the word?

As I understand it from the documentation, your description is not quite right. Since every possible sequence up to 15 words in length is evaluated, this would include a word with 7 words before and after it, but also that word with 5 words before and 3 after it, etc. (i.e. every possible N-gram between len(1) and len(15). Initial probabilities are derived, overlapping strings are compared and any overlaps with lower probabilities are discarded so that the final candidates are non-overlapping.

How can the neural net generate a score when its a binary classification entity and non-entity? Or do they mean the probability score for entity?

According to the Google AI Blog, "for each candidate the scoring neural net assigns a value (between 0 and 1) based on whether it represents a valid entity." So that would be a probability.

How to train a binary NER? Like any other NER except replace all entities to type 'entity' and then generate negative samples for non-entity?

Yes but, because this is a perceptron model, many binary classifiers will be trained and each will function as neuron in the model. It is important to note that the classifier only classifies entity/non-entity, not what type of entity it is. The post also discusses automatically generating negative samples by taking a positive sample (marked by a start token and end token in a string) and deliberately including the token before or after that entity. This technique would greatly increase the size of the training data.

How can this model be fast, as they claim, when it processes every word in the text plus 7 words before and after said word?

The computational cost of taking relatively small string (len 15) and fitting it to a model is small. The computational cost of dividing a longer string into substrings of this length is also quite small. Even if the text is 5000 words long (which would be huge for a query of this sort), that's only about 600,000 n-grams to evaluate, and most of those will have very low entity scores. As I understand it, the most significant computational cost of these approaches is training the model. This is where the "hashed charactergram embedding" technique discussed in the post is utilized.

You can see the machine SID on your computer by running Sysinternals PsGetSid with no parameters

so i simply look under debugger how PsGetSid do this.

it get SID from POLICY_ACCOUNT_DOMAIN_INFO - DomainSid : Pointer to the SID of the account domain

code can be next

By default SCCM programs are executed with the SYSTEM account of the current computer.

If this is a program from the package/program model what you have to do to change this is in the properties of the program go to "Environment" select "program can run: Only when a user is logged on" and "Run with user's rights" possibly also go to "Advanced" and select "When this program is assigned to a computer: Run once for every user who logs on"

If it's an application type you have to go to the properties for the Deployment Type and to "User Experience" and there change "Installation Behavior" to "Install for User".

This would be the SCCM internal method to do what you want. It of course also means you lose all admin rights and accesses as the context is now the logged on user's. Access to the userprofile should be no problem (the better environment variable would be $env:appdata btw) but you will also need readaccess to $dirFiles for every user.

A different approach (if this has only to be done once for all the computers) would be keeping the admin rights and instead of using the environment variable get all users with something like "gci C:\users" (minus public profile) and then with the admin replace all users files at once.