martinisoft

# Rails 3 jQuery Install Rakefile
# by Aaron Kalin
# Compiled from http://www.railsinside.com/tips/451-howto-unobtrusive-javascript-with-rails-3.html
# 
# Note: this assumes you use git, if not then use the optional usage
# 
# Usage: rake install_query
# 
# Optional usage: rake install_jquery[nogit]
# 

johnthethird

###
------> HTTPClientWithCache <------

This class is a wrapper around the standard Titanium.Network.HTTPClient(), but it adds a
few nice features:

  * A cache backed by a SQLite database. All HTTPClientWithCache instances use the same database table, with
    the primary cache key being a hash of the full URL (and any data parameters in a POST)
  * The cache is automatically pruned before each query
  * A retry mechanism, so that you can retry a particular query a number of times before failing. 

jdiamond

var mongoose = require('mongoose');
var Q = require('q');

mongoose.Promise.prototype.then = function(fulfilled, rejected) {
    var deferred = Q.defer();

    this.addCallback(deferred.resolve);
    this.addErrback(deferred.reject);

    return deferred.promise.then(fulfilled, rejected);

chuckg

Splunk Recipes for Heroku

A collection of Splunk recipes for Heroku logs. Instructions for setting up Splunk Storm with Heroku can be found here. For the vast majority of these recipes you'll need to have enabled the Heroku labs feature, log-runtime-metrics, for your application.

joyrexus

Demo of multipart form/file uploading with hapi.js.

Usage

npm install
npm run setup
npm run server

Then ...

jstrimpel

// app/server/synchers/base.js
define(['lazoSyncher'], function (LazoSyncher) {

    'use strict';
    
    // set up mongo connection here; not sure of best practices for establishing connections
    // https://www.npmjs.org/package/mongodb
    
    return LazoSyncer.extend({

GilLevi

Age and Gender Classification using Convolutional Neural Networks

name: Age Classification CNN

caffemodel: age_net.caffemodel

caffemodel_url: https://github.com/GilLevi/AgeGenderDeepLearning/raw/master/models/age_net.caffemodel

GilLevi

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: http://www.openu.ac.il/home/hassner/projects/cnn_emotions/

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

ishay2b

name	caffemodel	caffemodel_url	license	sha1	caffe_commit
Vanilla CNN Model	vanillaCNN.caffemodel	https://github.com/ishay2b/VanillaCNN/blob/master/ZOO/vanillaCNN.caffemodel?raw=true	unrestricted	b5e34ce75d078025e07452cb47e65d198fe27912	9c9f94e18a8909580a6b94c44dbb1e46f0ee8eb8

Implementation of the Vanilla CNN described in the paper: Yue Wu and Tal Hassner, "Facial Landmark Detection with Tweaked Convolutional Neural Networks", arXiv preprint arXiv:1511.04031, 12 Nov. 2015. See project page for more information about this project.

simme

//
//  debounce-throttle.swift
//
//  Created by Simon Ljungberg on 19/12/16.
//  License: MIT
//

import Foundation

extension TimeInterval {