Posts

  • Oct 22, 2022

    scout

    Laravel Scout provides a driver based solution to searching your Eloquent models.

    Laravel Scout provides a simple, driver-based solution for adding full-text search to your Eloquent models. Once Scout is installed and configured, it will automatically sync your model changes to your search indexes. Currently, Scout supports:

    Tags: #php • algolia • laravel

  • Oct 22, 2022

    cnn-svm

    An Architecture Combining Convolutional Neural Network (CNN) and Linear Support Vector Machine (SVM) for Image Classification

    Convolutional neural networks (CNNs) are similar to “ordinary” neural networks in the sense that they are made up of hidden layers consisting of neurons with “learnable” parameters. These neurons receive inputs, performs a dot product, and then follows it with a non-linearity. The whole network expresses the mapping between raw image pixels and their class scores. Conventionally, the Softmax function is the classifier used at the last layer of this network. However, there have been studies (Alalshekmubarak and Smith, 2013; Agarap, 2017; Tang, 2013) conducted to challenge this norm. The cited studies introduce the usage of linear support vector machine (SVM) in an artificial neural network architecture. This project is yet another take on the subject, and is inspired by (Tang, 2013). Empirical data has shown that the CNN-SVM model was able to achieve a test accuracy of ~99.04% using the MNIST dataset (LeCun, Cortes, and Burges, 2010). On the other hand, the CNN-Softmax was able to achieve a test accuracy of ~99.23% using the same dataset. Both models were also tested on the recently-published Fashion-MNIST dataset (Xiao, Rasul, and Vollgraf, 2017), which is suppose to be a more difficult image classification dataset than MNIST (Zalandoresearch, 2017). This proved to be the case as CNN-SVM reached a test accuracy of ~90.72%, while the CNN-Softmax reached a test accuracy of ~91.86%. The said results may be improved if data preprocessing techniques were employed on the datasets, and if the base CNN model was a relatively more sophisticated than the one used in this study.

    Tags: #python • artificial-intelligence • artificial-neural-networks

  • Oct 21, 2022

    MiDaS

    Code for robust monocular depth estimation described in “Ranftl et. al., Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfer, TPAMI 2022”

    This repository contains code to compute depth from a single image. It accompanies our paper:

    Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfer
    René Ranftl, Katrin Lasinger, David Hafner, Konrad Schindler, Vladlen Koltun

    and our preprint:

    Vision Transformers for Dense Prediction
    René Ranftl, Alexey Bochkovskiy, Vladlen Koltun

    MiDaS was trained on 10 datasets (ReDWeb, DIML, Movies, MegaDepth, WSVD, TartanAir, HRWSI, ApolloScape, BlendedMVS, IRS) with multi-objective optimization. The original model that was trained on 5 datasets (MIX 5 in the paper) can be found here.

    Tags: #java • monocular-depth-estimation • single-image-depth-prediction

  • Oct 21, 2022

    Secured-Preference-Store

    A cryptography library and a SharedPreferences wrapper for Android that encrypts the content with 256 bit AES encryption. The Encryption key is securely stored in device’s KeyStore.

    A SharedPreferences wrapper for Android that encrypts the content with 256 bit AES encryption. The Encryption key is securely stored in device’s KeyStore. You can also use the EncryptionManager class to encrypt & decrypt data out of the box.

    Tags: #java • encryption • encrypted-store

  • Oct 21, 2022

    rally

    Macrobenchmarking framework for Elasticsearch

    You want to benchmark Elasticsearch? Then Rally is for you. It can help you with the following tasks:

    • Setup and teardown of an Elasticsearch cluster for benchmarking
    • Management of benchmark data and specifications even across Elasticsearch versions
    • Running benchmarks and recording results
    • Finding performance problems by attaching so-called telemetry devices
    • Comparing performance results

    We have also put considerable effort in Rally to ensure that benchmarking data are reproducible.

    Tags: #python • elasticsearch

  • Oct 20, 2022

    ISO-3166-Countries-with-Regional-Codes

    ISO 3166-1 country lists merged with their UN Geoscheme regional codes in ready-to-use JSON, XML, CSV data sets

    These lists are the result of merging data from two sources, the Wikipedia ISO 3166-1 article for alpha and numeric country codes, and the UN Statistics site for countries’ regional, and sub-regional codes. In addition to countries, it includes dependent territories.

    The International Organization for Standardization (ISO) site provides partial data (capitalised and sometimes stripped of non-latin ornamentation), but sells the complete data set as a Microsoft Access 2003 database. Other sites give you the numeric and character codes, but there appeared to be no sites that included the associated UN-maintained regional codes in their data sets. I scraped data from the above two websites that is all publicly available already to produce some ready-to-use complete data sets that will hopefully save someone some time who had similar needs.

    Tags: #ruby • region-codes • countries

  • Oct 20, 2022

    danbooru

    A taggable image board written in Rails.

    Run this to start a basic Danbooru instance:

    curl -sSL https://raw.githubusercontent.com/danbooru/danbooru/master/bin/danbooru | sh

    This will install Docker Compose and use it to start Danbooru. When it’s done, Danbooru will be running at http://localhost:3000.

    Alternatively, if you already have Docker Compose installed, you can just do:

    wget https://raw.githubusercontent.com/danbooru/danbooru/master/docker-compose.yaml
docker-compose up

    Tags: #ruby • rails • booru

  • Oct 19, 2022

    m

    A Test::Unit runner that can run tests by line number.

    This project uses Appraisal to test against different versions of dependencies.

    To install all scenarios (appraisals):

    bundle install
bundle exec appraisal install

    Tags: #ruby

  • Oct 19, 2022

    mp4box.js

    JavaScript version of GPAC’s MP4Box tool

    Similar to MP4Box -info file.mp4, MP4Box.js can provide general information about the file (duration, number and types of tracks …). For that, create an MP4Box ISOFile object, set the onReady callback and provide data in the form of ArrayBuffer objects. MP4Box.js supports progressive parsing. You can provide small buffers at a time, the callback will be called when the ‘moov’ box is parsed.

    var MP4Box = require('mp4box'); // Or whatever import method you prefer.
var mp4boxfile = MP4Box.createFile();
mp4boxfile.onError = function(e) {};
mp4boxfile.onReady = function(info) {};
mp4boxfile.appendBuffer(data);
mp4boxfile.appendBuffer(data);
mp4boxfile.appendBuffer(data);
...
mp4boxfile.flush();

    Tags: #javascript

  • Oct 19, 2022

    acmephp

    Let’s Encrypt/ACME Command Line client written in PHP

    Acme PHP provides several major improvements over the default clients:

    • Acme PHP comes by nature as a single binary file: a single download and you are ready to start working ;
    • Acme PHP is based on a configuration file instead command line arguments. Thus, the configuration is much more expressive and the same setup is used at every renewal ;
    • Acme PHP is very extensible it to create the certificate files structure you need for your webserver. It brings several default formatters to create classical file structures (nginx, nginx-proxy, haproxy, etc.) but you can very easily create your own if you need to ;
    • Acme PHP follows a strict BC policy preventing errors in your scripts or CRON even if you update it (see the Backward Compatibility policy of Acme PHP for more informations) ;

    Tags: #php

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