Libraries tagged by axon

This package contains the Underscore javascript library, along a Mouf installer file to add Underscore easily in your Mouf project.

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This package contains the SyntaxHighlighter javascript library, along a Mouf installer file to add SyntaxHighlighter easily in your Mouf project.

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This package contains the JQuery UI library, along a Mouf installer file to add JQuery UI easily in your Mouf project.

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This package contains the jQuery FileTree library, along a Mouf installer file to add JQuery FileTree easily in your Mouf project.

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This package contains the JQuery library, along a Mouf installer file to add JQuery easily in your Mouf project.

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This package contains the History.js library, along a Mouf installer file to add History.js easily in your Mouf project.

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This TYPO3 extension allows you to randomize the content of database tables, either during a database export or directly in the database. The extension is useful for anonymizing data on or for development systems. The random values are generated with the fakerphp/faker package.

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This repository offers a PHP-based solution for declension of nouns by grammatical cases along with numerals. It ensures correct noun forms based on case (nominative, genitive, etc.) and the numeral used, handling the specific rules of declension in supported languages.

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Composer compatible stand alone port of Fuel's Cli class.

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🔏 Stand-alone DKIM class to sign your emails with a 2048 bit private key hashed with SHA-256 algorithm.

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Stand alone port of Laravel's tap method.

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# Introduction **INDA (INtelligent Data Analysis)** is an [Intervieweb](https://www.intervieweb.it/hrm/) AI solution provided as a RESTful API. The INDA pricing model is *credits-based*, which means that a certain number of credits is associated to each API request. Hence, users have to purchase a certain amount of credits (established according to their needs) which will be reduced at each API call. INDA accepts and processes a user's request only if their credits quota is grater than - or, at least, equal to - the number of credits required by that request. To obtain further details on the pricing, please visit our [site](https://inda.ai) or contact us. INDA HR embraces a wide range of functionalities to manage the main elements of a recruitment process: + [**candidate**](https://api.inda.ai/hr/docs/v2/#tag/Resume-Management) (hereafter also referred to as **resume** or **applicant**), or rather a person looking for a job; + [**job advertisement**](https://api.inda.ai/hr/docs/v2/#tag/JobAd-Management) (hereafter also referred to as **job ad**), which is a document that collects all the main information and details about a job vacancy; + [**application**](https://api.inda.ai/hr/docs/v2/#tag/Application-Management), that binds candidates to job ads; it is generated whenever a candidate applies for a job. Each of them has a specific set of methods that grants users the ability to create, read, update and delete the relative documents, plus some special features based on AI approaches (such as *document parsing* or *semantic search*). They can be explored in their respective sections. Data about the listed document types can be enriched by connecting them to other INDA supported entities, such as [**companies**](https://api.inda.ai/hr/docs/v2/#tag/Company-Management) and [**universities**](https://api.inda.ai/hr/docs/v2/#tag/Universities), so that recruiters may get a better and more detailed idea on the candidates' experiences and acquired skills. All the functionalities mentioned above are meant to help recruiters during the talent acquisition process, by exploiting the power of AI systems. Among the advantages a recruiter has by using this kind of systems, tackling the bias problem is surely one of the most relevant. Bias in recruitment is a serious issue that affect both recruiters and candidates, since it may cause wrong hiring decisions. As we care a lot about this problem, we are constantly working on reduce the bias in original data so that INDA results may be as fair as possible. As of now, in order to tackle the bias issue, INDA automatically ignores specific fields (such as name, gender, age and nationality) during the initial processing of each candidate data. Furthermore, we decided to let users collect data of various types, including personal or sensitive details, but we do not allow their usage if it is different from statistical purposes; our aim is to discourage recruiters from focusing on candidates' personal information, and to put their attention on the candidate's skills and abilities. We want to help recruiters to prevent any kind of bias while searching for the most valuable candidates they really need. The following documentation is addressed both to developers, in order to provide all technical details for INDA integration, and to managers, to guide them in the exploration of the implementation possibilities. The host of the API is [https://api.inda.ai/hr/v2/](https://api.inda.ai/hr/v2/). We recommend to check the API version and build (displayed near the documentation title). You can contact us at [email protected] in case of problems, suggestions, or particular needs. The search panel on the left can be used to navigate through the documentation and provides an overview of the API structure. On the right, you can find (*i*) the url of the method, (*ii*) an example of request body (if present), and (*iii*) an example of response for each response code. Finally, in the central section of each API method, you can find (*i*) a general description of the purpose of the method, (*ii*) details on parameters and request body schema (if present), and (*iii*) details on response schema, error models, and error codes.

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Simple library for sending reliable messages among apps

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Lightweight boolean container with stand-alone converter

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Base classes and interfaces for implementing the Use Case pattern along with entities and entity emitting iterators.

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