WFN | Windows Firewall Notifier extends the default Windows | Firewall library

by wokhansoft C# Version: v2.5-beta License: GPL-3.0

X-Ray Key Features Code Snippets Community Discussions(2)Vulnerabilities Install Support

kandi X-RAY | WFN Summary

WFN is a C# library typically used in Security, Firewall applications. WFN has no bugs, it has no vulnerabilities, it has a Strong Copyleft License and it has low support. You can download it from GitHub.

WFN started as a hobby around 2010 and is an "extension" to the embedded Windows firewall, offering real time connections monitoring, connections map, bandwidth usage monitoring... Its main feature being the Notifier alert itself, which tells you about outgoing connections attempts and allows you to allow or block them, either permanently or temporarily. It has been made open source a few years ago. Please read the documentation about the features and limitation of WFN.

Support

Quality

Security

License

Reuse

Support

WFN has a low active ecosystem.

It has 417 star(s) with 65 fork(s). There are 40 watchers for this library.

It had no major release in the last 12 months.

There are 39 open issues and 71 have been closed. On average issues are closed in 251 days. There are 1 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of WFN is v2.5-beta

Quality

WFN has 0 bugs and 0 code smells.

Security

WFN has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

WFN code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

WFN is licensed under the GPL-3.0 License. This license is Strong Copyleft.

Strong Copyleft licenses enforce sharing, and you can use them when creating open source projects.

Reuse

WFN releases are available to install and integrate.

WFN saves you 18 person hours of effort in developing the same functionality from scratch.

It has 48 lines of code, 0 functions and 105 files.

It has low code complexity. Code complexity directly impacts maintainability of the code.

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WFN Key Features

No Key Features are available at this moment for WFN.

WFN Examples and Code Snippets

No Code Snippets are available at this moment for WFN.

Community Discussions

Trending Discussions on WFN

Excel custom formula not working as expected

Neural Net (from scratch, no tensorflow) gives incorrect answers even after hours of training

QUESTION

Excel custom formula not working as expected

Asked 2021-Apr-19 at 08:00

Am basically pretty new to creating custom excel formula's. I have the following custom excel formula:

...

ANSWER

Answered 2021-Apr-19 at 08:00

You're not referencing your cells properly. Range K77:P77 is an object so must be Set.
As you've written it I think (without Option Explicit) it will treat K77 as a variable and P77 as an undefined function (at least that's the errors I got).

The function assumes the ranges are on the same sheet as the function is entered.

Source https://stackoverflow.com/questions/67156512

QUESTION

Neural Net (from scratch, no tensorflow) gives incorrect answers even after hours of training

Asked 2020-Nov-05 at 22:18

my neural network is split into 2 files, the class and the one that actually creates/runs it
I believe the problem lays in the class file.
The purpose of the NN is to be an OR gate ([1, 1] = 1, [1, 0] = 1, [0, 1] = 1, [0, 0] = 0) while [1, 1],[1, 0], and [0, 1] output 1 (as they should) [0, 0] outputs .5 when I would expect something much lower

...

ANSWER

Answered 2020-Nov-05 at 22:18

Basic logic gates can be a pain on ass on NN.

I did not spot amnything wrong in your code, but by the nature of your problem and what you describe, I will bet the problem is in the training data. When you have a disbalance between the positives and negatives in a data, an NN can be biased towards the positive cases and generate false-posivtives. If your training data have equal numbers of each input, your negative cases are 25%.

There are two ways to correct this, one is changing your training set, putting more cases of [0,0] OR you can give more emphasis to the false-positive error, like multipling this error by an constant before use it to adjust the nodes weights.

Source https://stackoverflow.com/questions/64701065

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install WFN

You can download it from GitHub.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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