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João Gabriel Lima jgabriellima

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jgabriellima / GoogleHackMasterList.txt
Created Apr 28, 2017 — forked from cmartinbaughman/GoogleHackMasterList.txt
The definitive super list for "Google Hacking".
View GoogleHackMasterList.txt
admin account info" filetype:log
!Host=*.* intext:enc_UserPassword=* ext:pcf
"# -FrontPage-" ext:pwd inurl:(service | authors | administrators | users) "# -FrontPage-" inurl:service.pwd
"AutoCreate=TRUE password=*"
"http://*:*@www” domainname
"index of/" "ws_ftp.ini" "parent directory"
"liveice configuration file" ext:cfg
"parent directory" +proftpdpasswd
Duclassified" "DUware All Rights reserved"
View keras VGG-Face

VGG-Face model for Keras

This is the Keras model of VGG-Face.

It has been obtained through the following method:

  • vgg-face-keras:directly convert the vgg-face matconvnet model to keras model
  • vgg-face-keras-fc:first convert vgg-face caffe model to mxnet model,and then convert it to keras model

Details about the network architecture can be found in the following paper:

jgabriellima /
Created Sep 16, 2017 — forked from GilLevi/
Emotion Recognition in the Wild via Convolutional Neural Networks and Mapped Binary Patterns

Gil Levi and Tal Hassner, Emotion Recognition in the Wild via Convolutional Neural Networks and Mapped Binary Patterns

Convolutional neural networks for emotion classification from facial images as described in the following work:

Gil Levi and Tal Hassner, Emotion Recognition in the Wild via Convolutional Neural Networks and Mapped Binary Patterns, Proc. ACM International Conference on Multimodal Interaction (ICMI), Seattle, Nov. 2015

Project page:

If you find our models useful, please add suitable reference to our paper in your work.

View gist:8c9d0d73ec2ff7b6a65efc36192fd1ed
var subset = dataFrame.skip(10).take(15);
View tabela_funcoes
Function | Description
startAt | todos os valores a partir de um índice específico
endAt | todos os valores terminando em um índice particular
after | todos os valores após um índice particular.
before | todos os valores antes de um índice particular.
between | todos os valores entre dois índices
View Politia_Privacidade_Mobilito
Privacy Policy
João Gabriel built the Mobilito app as a Free app. This SERVICE is provided by João Gabriel at no cost and is intended for use as is.
This page is used to inform website visitors regarding my policies with the collection, use, and disclosure of Personal Information if anyone decided to use my Service.
If you choose to use my Service, then you agree to the collection and use of information in relation to this policy. The Personal Information that I collect is used for providing and improving the Service. I will not use or share your information with anyone except as described in this Privacy Policy.
The terms used in this Privacy Policy have the same meanings as in our Terms and Conditions, which is accessible at Mobilito unless otherwise defined in this Privacy Policy.
View base_5_min_EUR_JPY
This file has been truncated, but you can view the full file.
USDJPY,2017-10-27 01:35:00,114.2020,114.2040,114.2000,114.2000,114.0367,114.0413,58.83,114.0094,114.2857,0.0323,114.1900
USDJPY,2017-10-27 01:30:00,114.1820,114.1820,114.1760,114.1810,114.0334,114.0377,56.72,114.0000,114.2780,0.0318,114.1890
USDJPY,2017-10-27 01:25:00,114.2140,114.2200,114.2110,114.2200,114.0304,114.0339,62.87,113.9907,114.2720,0.0327,114.2220
USDJPY,2017-10-27 01:20:00,114.1820,114.1960,114.1820,114.1900,114.0266,114.0297,59.75,113.9848,114.2556,0.0294,114.2550
USDJPY,2017-10-27 01:15:00,114.1880,114.2010,114.1880,114.1890,114.0233,114.0253,59.65,113.9779,114.2438,0.0278,114.1300
USDJPY,2017-10-27 01:10:00,114.2180,114.2240,114.2090,114.2220,114.0199,114.0212,64.82,113.9768,114.2294,0.0255,114.1580
USDJPY,2017-10-27 01:05:00,114.2400,114.2550,114.2320,114.2550,114.0158,114.0165,70.50,113.9873,114.2050,0.0188,114.0940
View Resultado_das_transações_DeepOptions
SIGNAL_100;100.00;25/10/17 21:00;100.00;CALL_FRXUSDJPY_100_1508964887_1508965247_S0P_0;1508965253
-;0.00;25/10/17 21:00;20.00;PUT_FRXUSDJPY_20_1508964887_1508965247_S0P_0;1508965253
NEURAL_NEUTRO;10.00;25/10/17 20:55;10.00;PUT_FRXUSDJPY_10_1508964577_1508964937_S0P_0;1508964941
-;0.00;25/10/17 20:50;10.00;PUT_FRXUSDJPY_10_1508964262_1508964622_S0P_0;1508964624
SIGNAL_100;100.00;25/10/17 18:50;100.00;PUT_FRXUSDJPY_100_1508957070_1508957430_S0P_0;1508957432
NEURAL;20.00;25/10/17 18:50;20.00;PUT_FRXUSDJPY_20_1508957070_1508957430_S0P_0;1508957432
NEURAL;20.00;25/10/17 18:50;20.00;PUT_FRXUSDJPY_20_1508957070_1508957430_S0P_0;1508957432
SIGNAL_100;100.00;25/10/17 18:50;100.00;PUT_FRXUSDJPY_100_1508957070_1508957430_S0P_0;1508957432
NEURAL_NEUTRO;10.00;25/10/17 18:40;10.00;PUT_FRXUSDJPY_10_1508956452_1508956812_S0P_0;1508956814
View flatten_array_js
function flatten(data){
return [].concat.apply([],[].concat.apply([],data))
var result = flatten([[1,2,[3]],4]);