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zbyshak
zbyshak commented Nov 9, 2020

Problem in MISP/app/Lib/Export/NidsExport.php /
causes there are two rows in beggening of all rules regarding email. Bug is in row 161

case 'email':
$this->emailSrcRule($ruleFormat, $item['Attribute'], $sid);
$this->emailDstRule($ruleFormat, $item['Attribute'], $sid);
break;
this causes inserting both source and destinati

T: enhancement good first issue
sebix
sebix commented Jul 4, 2022

Changelog of version 2.0.0:

dns.resolver.resolve() has been added, allowing control of whether search lists are used. dns.resolver.query() is retained for backwards compatibility, but deprecated. The default for search list behavior can be set at in the resolver object with the use_search_by_default parameter. The default is False.

dns.resolver.query() is used here:

https://github.com/c

bug component: bots component: core good first issue
myungjoo
myungjoo commented Apr 2, 2022

We can get the code from https://twitter.com/rafaelcaricio/status/1509856495927808038?cxt=HBwWzMC-2fiei_QpAAAA&cn=ZmxleGlibGVfcmVjcw%3D%3D&refsrc=email and write a few subplugins that can provide an example pipeline with nnstreamer in the form of

... ! tensor_converter ! tensor_filter fw=custom ${some pre-proc code from vosk} ! tensor_filter ${NN model} ! tensor_decoder ${some post-proc
good first issue backlog
DaProfiler

DaProfiler is an OSINT tool allowing you to collect certain information about yourself in order to rectify by rgpd requests the traces you may have left on the net. DaProfiler is indeed able to recover: Addresses, Social media accounts, e-mail addresses, mobile / landline number, jobs. On a specified subject in a limited time. DaProfiler is designed for Educational Purposes only, We accept no responsibility for the use you make of it.

  • Updated Jul 8, 2022
  • Python
kparichay
kparichay commented Dec 20, 2021

Gradients currently use initializer::ZEROS which initializes the gradient for the first iteration.
However, gradients are not initialized to zero for the next iterations and the layer is expected to initialize the gradients to zero if expected.

To maintain this consistency, gradients should be initialized for initializer::NONE.

good first issue

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