Posts
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PyPattyrn
A simple library for implementing common design patterns.
from pypattyrn.creational.singleton import Singleton class DummyClass(object, metaclass=Singleton): # DummyClass is now a Singleton! ...PyPattyrn is a python package aiming to make it easier and faster to implement design patterns into your own projects.
Design patterns by nature cannot be directly translated into code as they are just a description of how to solve a particular problem. However, many of the common design patterns have boilerplate code that is common throughout all implementations of the pattern. This package captures that common code and makes it easy to use so that you dont have to write it yourself in all your projects. ___
Tags: #python • design-patterns • library
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josh.js
Toolkit for building a bash-like shell in the browser, including full readline support
Josh allows developers to build their own command line interface to any sites. It supports full CLI Readline in the browser like TAB completion, emacs-style line editing, killring and history with reverse search. When you are tired of clicking your way through a hierachy tree, Josh will come in handy. It will helps you browse or navigate text files quickly and minimal the using of mouse click.
Tags: #javascript
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otroslogviewer
Log viewer focused on developers work
Useful software for analysing applications logs and traces.
Tags: #java
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PyGrid
A Peer-to-peer Platform for Secure, Privacy-preserving, Decentralized Data Science
PyGrid platform is composed by three different components.
- Network - A Flask-based application used to manage, monitor, control, and route instructions to various PyGrid Domains.
- Domain - A Flask-based application used to store private data and models for federated learning, as well as to issue instructions to various PyGrid Workers.
- Worker - An emphemeral instance, managed by a PyGrid Domain, that is used to compute data.
Tags: #python • peer-to-peer • pygrid
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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
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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
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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 Koltunand our preprint:
Vision Transformers for Dense Prediction
René Ranftl, Alexey Bochkovskiy, Vladlen KoltunMiDaS 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 5in the paper) can be found here.Tags: #java • monocular-depth-estimation • single-image-depth-prediction
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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
SharedPreferenceswrapper 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 theEncryptionManagerclass to encrypt & decrypt data out of the box.Tags: #java • encryption • encrypted-store
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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
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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
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