442 skills found · Page 10 of 15
MatinHQ / Flawless Pdf GeneratorFree, open-source HTML to PDF converter with perfect rendering. Built when paid converters failed with Persian fonts. Features: web fonts, RTL text, CSS gradients, PDF metadata, ATS optimization, scrollable single-page mode, batch processing. No watermarks, no limits, just flawless PDFs
aliasgharheidaricom / RUN Beyond The Metaphor An Efficient Optimization Algorithm Based On Runge Kutta MethodThe optimization field suffers from the metaphor-based “pseudo-novel” or “fancy” optimizers. Most of these cliché methods mimic animals' searching trends and possess a small contribution to the optimization process itself. Most of these cliché methods suffer from the locally efficient performance, biased verification methods on easy problems, and high similarity between their components' interactions. This study attempts to go beyond the traps of metaphors and introduce a novel metaphor-free population-based optimization based on the mathematical foundations and ideas of the Runge Kutta (RK) method widely well-known in mathematics. The proposed RUNge Kutta optimizer (RUN) was developed to deal with various types of optimization problems in the future. The RUN utilizes the logic of slope variations computed by the RK method as a promising and logical searching mechanism for global optimization. This search mechanism benefits from two active exploration and exploitation phases for exploring the promising regions in the feature space and constructive movement toward the global best solution. Furthermore, an enhanced solution quality (ESQ) mechanism is employed to avoid the local optimal solutions and increase convergence speed. The RUN algorithm's efficiency was evaluated by comparing with other metaheuristic algorithms in 50 mathematical test functions and four real-world engineering problems. The RUN provided very promising and competitive results, showing superior exploration and exploitation tendencies, fast convergence rate, and local optima avoidance. In optimizing the constrained engineering problems, the metaphor-free RUN demonstrated its suitable performance as well. The authors invite the community for extensive evaluations of this deep-rooted optimizer as a promising tool for real-world optimization. The source codes, supplementary materials, and guidance for the developed method will be publicly available at different hubs at http://aliasgharheidari.com/RUN.html.
Briechenstein12 / Jerusalem2020j2IL RepositorySearch documentation... Support Dashboard Card Payments Quickstart Securely collect card information from your customers and create a card payment. Supported cards Users in the United States can accept Visa Mastercard American Express Discover JCB Diners Club credit and debit cards. Stripe also supports a range of additional payment methods, depending on the country of your Stripe account. Accepting a card payment using Stripe is a two-step process, with a client-side and a server-side action: From your website running in the customer’s browser, Stripe securely collects your customer’s payment information and returns a representative token. This, along with any other form data, is then submitted by the browser to your server. Using the token, your server-side code makes an API request to create a charge and complete the payment. Tokenization ensures that no sensitive card data ever needs to touch your server so your integration can operate in a PCI compliant way. Step 1: Securely collecting payment information Checkout reference Complete information about available options and parameters is provided in the Checkout reference. The simplest way for you to securely collect and tokenize card information is with Checkout. It combines HTML, JavaScript, and CSS to create an embedded payment form. When your customer enters their payment information, the card details are validated and tokenized for your server-side code to use. To see Checkout in action, click the button below, filling in the resulting form with: Any random, syntactically valid email address (the more random, the better) One of Stripe’s test card numbers, such as 4242 4242 4242 4242 Any three-digit CVC code Any expiration date in the future To get started, add the following code to your payment page, making sure that the form submits to your own server-side code: <form action="your-server-side-code" method="POST"> <script src="https://checkout.stripe.com/checkout.js" class="stripe-button" data-key="pk_test_2DtHIU1N9li5GpmJjyxkQMHh" data-amount="999" data-name="Demo Site" data-description="Example charge" data-image="https://stripe.com/img/documentation/checkout/marketplace.png" data-locale="auto"> </script> </form> We’ve pre-filled the data-key attribute with your test publishable API key—only you can see this value. When you’re ready to go live with your payment form, you must replace the test key with your live key. Learn more about how the keys play into test and live modes. Although optional, we highly recommend also having Checkout collect the user’s ZIP code, as address and ZIP code verifications help reduce fraud. Add data-zip-code="true" to the above and enable declines on verification failures in your account settings. You can also set Checkout to collect the user’s full billing and shipping addresses (using the corresponding parameters). Requiring more than the minimum information lowers the possibility of a payment being declined or disputed in the future. Any fraudulent payments that you process are ultimately your responsibility, so requiring a little more than the minimum amount of information is an effective way to combat fraud. Radar, our modern suite of fraud protection tools, is only available to users who have implemented client-side tokenization. By doing so, it ensures that you can pass the necessary data required for our machine-learning fraud prevention models to make more accurate predictions. The amount provided in the Checkout form code is only shown to the user. It does not set the amount that the customer will be charged—you must also specify an amount when making a charge request. As you build your integration, make sure that your payment form and server-side code use the same amount to avoid confusion. An alternative to the blue button demonstrated above is to implement a custom Checkout integration. The custom approach allows you to use any HTML element or JavaScript event to open Checkout, as well as be able to specify dynamic arguments, such as custom amounts. Stripe.js and Elements If you’d prefer to have complete control over the look and fel of your payment form, you can make use of Stripe.js and Elements, our pre-built UI components. Refer to our Elements quickstart to learn more. Mobile SDKs Using our native mobile libraries for iOS and Android, Stripe can collect your customer’s payment information from within your mobile app and create a token for your server-side code to use. Step 2: Creating a charge to complete the payment Once a token is created, your server-side code makes an API request to create a one-time charge. This request contains the token, currency, amount to charge, and any additional information you may want to pass (e.g., metadata). curl Ruby Python PHP Java Node Go .NET curl https://api.stripe.com/v1/charges \ -u sk_test_fyzWf8eDyljIob76fMVSwIsi: \ -d amount=999 \ -d currency=usd \ -d description="Example charge" \ -d source=tok_6Pk6W3hFiGB7dyNavdvyrFkM These requests expect the ID of the Token (e.g., tok_KPte7942xySKBKyrBu11yEpf) to be provided as the value of the source parameter. Tokens can only be used once, and within a few minutes of creation. Using this approach, your customers need to re-enter their payment details each time they make a purchase. You can also save card details with Stripe for later use. Using this method, returning customers can quickly make a payment without providing their card details again. Next steps Congrats! You can now accept card payments with Stripe using Checkout. You may now want to check out these resources: Creating charges Getting paid Managing your Stripe account Supported payment methods Saving cards Questions? We're always happy to help with code or other questions you might have! Search our documentation, contact support, or connect with our sales team. You can also chat live with other developers in #stripe on freenode. Was this page helpful? Yes No
marcgarnica13 / Ml Interpretability European FootballUnderstanding gender differences in professional European football through Machine Learning interpretability and match actions data. This repository contains the full data pipeline implemented for the study *Understanding gender differences in professional European football through Machine Learning interpretability and match actions data*. We evaluated European male, and female football players' main differential features in-match actions data under the assumption of finding significant differences and established patterns between genders. A methodology for unbiased feature extraction and objective analysis is presented based on data integration and machine learning explainability algorithms. Female (1511) and male (2700) data points were collected from event data categorized by game period and player position. Each data point included the main tactical variables supported by research and industry to evaluate and classify football styles and performance. We set up a supervised classification pipeline to predict the gender of each player by looking at their actions in the game. The comparison methodology did not include any qualitative enrichment or subjective analysis to prevent biased data enhancement or gender-related processing. The pipeline had three representative binary classification models; A logic-based Decision Trees, a probabilistic Logistic Regression and a multilevel perceptron Neural Network. Each model tried to draw the differences between male and female data points, and we extracted the results using machine learning explainability methods to understand the underlying mechanics of the models implemented. A good model predicting accuracy was consistent across the different models deployed. ## Installation Install the required python packages ``` pip install -r requirements.txt ``` To handle heterogeneity and performance efficiently, we use PySpark from [Apache Spark](https://spark.apache.org/). PySpark enables an end-user API for Spark jobs. You might want to check how to set up a local or remote Spark cluster in [their documentation](https://spark.apache.org/docs/latest/api/python/index.html). ## Repository structure This repository is organized as follows: - Preprocessed data from the two different data streams is collecting in [the data folder](data/). For the Opta files, it contains the event-based metrics computed from each match of the 2017 Women's Championship and a single file calculating the event-based metrics from the 2016 Men's Championship published [here](https://figshare.com/collections/Soccer_match_event_dataset/4415000/5). Even though we cannot publish the original data source, the two python scripts implemented to homogenize and integrate both data streams into event-based metrics are included in [the data gathering folder](data_gathering/) folder contains the graphical images and media used for the report. - The [data cleaning folder](data_cleaning/) contains descriptor scripts for both data streams and [the final integration](data_cleaning/merger.py) - [Classification](classification/) contains all the Jupyter notebooks for each model present in the experiment as well as some persistent models for testing.
imlucas / Gulp Juiceprocess html files through juice to inline CSS
GaurangPohankar / Python Google Places ExtractionPython script to scrape the data from the google places with reviews , website , name , total reviews , phone number etc . and stores it into CSV file . The simple GUI is created with tkinter to ease the process . It is simple automation process with the selenium and beautiful soap for processing the HTML content . Processing file are the stepping stones to get here.
kemingy / PlaneA text processing tool including tag(HTML, URL, Email) extraction and removing, punctuation normalization, simple segmentation, and so on.
filiph / SsiA tiny pre-processor that supports including files in other files. Think of it as "C macros for HTML and Markdown files".
bottd / Vite Plugin NorgA Vite plugin to enable processing of .norg files with support for Svelte, React, and HTML
wyfang / Wyfang.github.ioA personal website with html+css that adapts to all devices and supports automatic night mode.Beautiful icon color patch loading process for bad network environments. A small piece of social platform friend code and supports the mouse to display the QR code.
ruanyangry / Gromacs Free Energy CalculationThis repository contained python code used to do organic free energy calculation. Reference: http://www.bevanlab.biochem.vt.edu/Pages/Personal/justin/gmx-tutorials/free_energy/index.html; https://github.com/ruanyangry/gromacs-lammps-process-simulation.
MSNP1381 / Advanced Raptor RagAdvanced RAG + Raptor: A sophisticated document processing and retrieval system combining hierarchical document clustering with advanced query processing. Features HTML-to-markdown conversion, recursive document clustering, query expansion, cross-encoder re-ranking, and contextual response generation using LangChain, Vertex AI, PostgreSQL/pgvector,
jashwanth / Remote Code PublisherRemote-Code-Publisher Purpose: A Code Repository is a Program responsible for managing source code resources, e.g., files and documents. A fully developed Repository will support file persistance, managment of versions, and the acquisition and publication of source and document files. A Remote Repository adds the capability to access the Repository's functionality over a communication channel, e.g., interprocess communication, inter-network communication, and communication across the internet. In this project we will focus on the publication functionality of a Remote Repository. We will develop a remote code publisher, local client, and communication channel that supports client access to the publisher from any internet enabled processor. The communication channel will use sockets and support an HTTP like message structure. The channel will support: HTTP style request/response transactions One-way communication, allowing asynchronous messaging between any two endpoints that are capable of listening for connection requests and connecting to a remote listener. Transmission of byte streams that are set up with one or more negotiation messages followed by transmission of a stream of bytes of specified stream size2. The Remote Code Publisher will: Support publishing web pages that are small wrappers around C++ source code files, just as we did in Project #3. Accept source code text files, sent from a local client. Support building dependency relationships between code files saved in specific repository folders, based on the functionality you provided in Project #2 and used in Project #3. Support HTML file creation for all the files in a specified repository folder1, including linking information that displays dependency relationships, and supports and navigation based on dependency relationships. Delete stored files, as requested by a local client. Clients of the Remote Code Publisher will provide a Graphical User Interface (GUI) with means to: Upload one or more source code text files to the Remote Publisher, specifying a category with which those files are associated1. Display file categories, based on the directory structure supported by the Repository. Display all the files in any category. Display all of the files in any category that have no parents. Display the web page for any file in that file list by clicking within a GUI control. This implies that the client will download the appropriate webpages, scripts, and style sheets and display, by starting a browser with a file cited on the command line2. On starting, will download style sheet and JavaScript files from the Repository. Note that your client does not need to supply the functionality to display web pages. It simply starts a browser to do that. Browsers will accept a file name, which probably includes a relative path to display a web page from the local directory. You could also start IIS web server and provide an appropriate URL to the browser on startup. Either approach is acceptable. If you use IIS, you won't have to download files, but you are obligated to show that you can do that. Requirements: Your Remote Repository: (2) Shall use Visual Studio 2015 and its C++ Windows console projects, as provided in the ECS computer labs. You must also use Windows Presentation Foundation (WPF) to provide a required client Graphical User Interface (GUI). (1) Shall use the C++ standard library's streams for all console I/O and new and delete for all heap-based memory management. (3) Shall provide a Repository program that provides functionality to publish, as linked web pages, the contents of a set of C++ source code files. (4) Shall, for the publishing process, satisfy the requirements of CodePublisher developed in Project #3. (4) Shall provide a Client program that can upload files3, and view Repository contents, as described in the Purpose section, above. (3) Shall provide a message-passing communication system, based on Sockets, used to access the Repository's functionality from another process or machine. (2) The communication system shall provide support for passing HTTP style messages using either synchronous request/response or asynchronous one-way messaging. (1) The communication system shall also support sending and receiving streams of bytes6. Streams will be established with an initial exchange of messages. (5) Shall include an automated unit test suite that demonstrates you meet all the requirements of this project4 including the transmission of files. (5 point bonus) Shall optionally use a lazy download strategy, that, when presented with a name of a source code web page, will download that file and all the files it links to. This allows you to demonstrate your project using local webpages instead of downloading the entire contents of the Code Publisher for demonstration. (5 point bonus) Shall optionally have the publisher accept a path, on the commandline, to a virtual directory on the server. Then support browsing directly from the server by supplying a url to that path when you start a browser. This works only if you setup IIS on your machine and make the path a virtual directory. The TAs will do that on the grading machines. Categories are the names of folders in which the Repository stores its source code and web files. You may define Categories in any way that seems sensible. For example, they could simply be the namespace(s) for the uploaded files, or a Client supplied name. You will find a demonstration of how to programmatically start an application here. The stream capablity is intended to send files, which could be either text or binary format. Stream size will be the file size. Transmitting and receiving byte streams will be used to send and receive files in either text or binary format. This is in addition to the construction tests you include as part of every package you submit. Project 3 statement: Purpose: A Code Repository is a Program responsible for managing source code resources, e.g., files and documents. A fully developed Repository will support file persistance, managment of versions, and the acquisition and publication of source and document files. This project focuses on just the publishing functionality of a repository. In this project we will develop means to display source code files as web pages with embedded child links. Each link refers to a code file that the displayed code file depends on. There are several things you need to know in order to complete this project: Each file to be published is a C++ source file. Our publisher will generate, for each of these, an HTML file, with most of the contents drawn from the code file. The pages we will generate have only static content, with the exception of some embedded JavaScript and styling, so we won't need a web server. We will need to preserve the white space structure of the displayed source code. That can be done embedding all the code between the tags <pre> and </pre> or by using the CSS white-space property with value "pre" to style a div with all the code in its contents. Any markup characters in the code text will have to be escaped, e.g., replace < with < and > with >. File dependencies are displayed in the web page with embedded links, which are implemented in HTML5 with anchor elements: <a href="[url of referenced html page]">source code file name</a> For each class, we will, optionally, implement outlining, similar to the visual studio outlining feature. To do that we will use the CSS display property, with values: normal or none, to control whether the contents of a div are visible or not. The Code Publisher will be embedded in a mock Repository with almost no functionality except to support publishing of source code as web pages. Specifically you are not expected to provide support for: package checkin or checkout versioning You are expected to support: Dependency analysis of the C++ source code files you will publish, using the analyzer you developed in Project #2. The ability to specify, on the command line, files to be published, by providing command line arguments for path and file patterns. The ability to display any file cited on the command line, by starting a process that runs a browser of your choice, naming the specification of the file you want to display. Note that the CodePublisher project creates a code generator. Its inputs are C++ code and its outputs are HTML code. Requirements: Your CodePublisher Project: (1) Shall use Visual Studio 2015 and its C++ Windows console projects, as provided in the ECS computer labs. (2) Shall use the C++ standard library's streams for all console I/O and new and delete for all heap-based memory management1. (4) Shall provide a Publisher program that provides for creation of web pages each of which captures the content of a single C++ source code file, e.g., *.h or *.cpp. (10) Shall, optionally2 provide the facility to expand or collapse class bodies, methods, and global functions using JavaScript and CSS properties. (2) Shall provide a CSS style sheet that the Publisher uses to style its generated pages and (if you are implementing the previous optional requirement) a JavaScript file that provides functionality to hide and unhide sections of code for outlining, using mouse clicks. (2) Shall embed in each web page's <head> section links to the style sheet and JavaScript file. (4) Shall embedd HTML5 links to dependent files with a label, at the top of the web page. Publisher shall use functionality from your Project #2 to discover package dependencies within the published set of source files. (2) Shall develop command line processing to define the files to publish by specifying path and file patterns. (3) Shall demonstrate the CodePublisher functionality by publishing all the important packages in your Project #3. (5) Shall include an automated unit test suite that demonstrates you meet all the requirements of this project2. That means that you are not allowed to use any of the C language I/0, e.g., printf, scanf, etc, nor the C memory management, e.g., calloc, malloc, or free. This optional requirement will take a significant amount of work to complete successfully. You should get everything else working before attempting this additional effort. This is in addition to the construction tests you include as part of every package you submit.
mthd98 / Project Algorithm For A Dog Identification AppProject Overview Welcome to the Convolutional Neural Networks (CNN) project in the AI Nanodegree! In this project, you will learn how to build a pipeline that can be used within a web or mobile app to process real-world, user-supplied images. Given an image of a dog, your algorithm will identify an estimate of the canine’s breed. If supplied an image of a human, the code will identify the resembling dog breed. Sample Output Along with exploring state-of-the-art CNN models for classification, you will make important design decisions about the user experience for your app. Our goal is that by completing this lab, you understand the challenges involved in piecing together a series of models designed to perform various tasks in a data processing pipeline. Each model has its strengths and weaknesses, and engineering a real-world application often involves solving many problems without a perfect answer. Your imperfect solution will nonetheless create a fun user experience! Project Instructions Instructions Clone the repository and navigate to the downloaded folder. git clone https://github.com/udacity/dog-project.git cd dog-project Download the dog dataset. Unzip the folder and place it in the repo, at location path/to/dog-project/dogImages. Download the human dataset. Unzip the folder and place it in the repo, at location path/to/dog-project/lfw. If you are using a Windows machine, you are encouraged to use 7zip to extract the folder. Download the VGG-16 bottleneck features for the dog dataset. Place it in the repo, at location path/to/dog-project/bottleneck_features. (Optional) If you plan to install TensorFlow with GPU support on your local machine, follow the guide to install the necessary NVIDIA software on your system. If you are using an EC2 GPU instance, you can skip this step. (Optional) If you are running the project on your local machine (and not using AWS), create (and activate) a new environment. Linux (to install with GPU support, change requirements/dog-linux.yml to requirements/dog-linux-gpu.yml): conda env create -f requirements/dog-linux.yml source activate dog-project Mac (to install with GPU support, change requirements/dog-mac.yml to requirements/dog-mac-gpu.yml): conda env create -f requirements/dog-mac.yml source activate dog-project NOTE: Some Mac users may need to install a different version of OpenCV conda install --channel https://conda.anaconda.org/menpo opencv3 Windows (to install with GPU support, change requirements/dog-windows.yml to requirements/dog-windows-gpu.yml): conda env create -f requirements/dog-windows.yml activate dog-project (Optional) If you are running the project on your local machine (and not using AWS) and Step 6 throws errors, try this alternative step to create your environment. Linux or Mac (to install with GPU support, change requirements/requirements.txt to requirements/requirements-gpu.txt): conda create --name dog-project python=3.5 source activate dog-project pip install -r requirements/requirements.txt NOTE: Some Mac users may need to install a different version of OpenCV conda install --channel https://conda.anaconda.org/menpo opencv3 Windows (to install with GPU support, change requirements/requirements.txt to requirements/requirements-gpu.txt): conda create --name dog-project python=3.5 activate dog-project pip install -r requirements/requirements.txt (Optional) If you are using AWS, install Tensorflow. sudo python3 -m pip install -r requirements/requirements-gpu.txt Switch Keras backend to TensorFlow. Linux or Mac: KERAS_BACKEND=tensorflow python -c "from keras import backend" Windows: set KERAS_BACKEND=tensorflow python -c "from keras import backend" (Optional) If you are running the project on your local machine (and not using AWS), create an IPython kernel for the dog-project environment. python -m ipykernel install --user --name dog-project --display-name "dog-project" Open the notebook. jupyter notebook dog_app.ipynb (Optional) If you are running the project on your local machine (and not using AWS), before running code, change the kernel to match the dog-project environment by using the drop-down menu (Kernel > Change kernel > dog-project). Then, follow the instructions in the notebook. NOTE: While some code has already been implemented to get you started, you will need to implement additional functionality to successfully answer all of the questions included in the notebook. Unless requested, do not modify code that has already been included. Evaluation Your project will be reviewed by a Udacity reviewer against the CNN project rubric. Review this rubric thoroughly, and self-evaluate your project before submission. All criteria found in the rubric must meet specifications for you to pass. Project Submission When you are ready to submit your project, collect the following files and compress them into a single archive for upload: The dog_app.ipynb file with fully functional code, all code cells executed and displaying output, and all questions answered. An HTML or PDF export of the project notebook with the name report.html or report.pdf. Any additional images used for the project that were not supplied to you for the project. Please do not include the project data sets in the dogImages/ or lfw/ folders. Likewise, please do not include the bottleneck_features/ folder.
hashicorp / Terraform Provider JdcloudTerraform JDcloud provider. Please note: This Terraform provider is archived per our provider archiving process: https://terraform.io/docs/internals/archiving.html
liamfiddler / Eleventy Plugin TransformdomA plugin to process & change the generated HTML output of your Eleventy site.
BBN-E / AdeptAdept framework for information extraction (IE), natural language processing (NLP) and human language technology (HLT). For more information, see http://www.darpa.mil/opencatalog/DEFT.html.
xrenaa / SBU Kinect Dataset Processa python code to pre-process of SBU Kinect Interaction Dataset: https://www3.cs.stonybrook.edu/~kyun/research/kinect_interaction/index.html
narottamandeep2003 / Grocery StoreA full-stack grocery store project entails creating an interactive user interface using React, HTML, CSS, and JavaScript for browsing products, searching, sorting, login, registration, and checkout functionalities. On the backend, Node.js with Express.js is employed to set up routes for user authentication, product management, and order processing
anusthan12 / QR Code GeneratorA QR CODE GENERATOR MADE USING USING HTML,CSS AND JAVASCRIPT HAVE VISUAL DELITE VISUAL EXPERIENCE AND SMOOTH PROCESS USING API SERVER .
