Libraries tagged by expiration

Validates popular debit and credit cards numbers against regular expressions and Luhn algorithm. Also validates the CVC and the expiration date

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Plugins enables the exchange of a Microsoft access token for an application access token. The service assumes the user's front end will handle the authentication flow to acquire and manage the expiration of the Microsoft access token. When the framework app's token expires, user must re-exchange a valid Microsoft access token for an updated app access token. There are no app refresh tokens or mechanisms.

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Validates popular debit and credit cards numbers against regular expressions and Luhn algorithm. Also validates the CVC and the expiration date

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The project is a lightweight caching solution that improves performance by storing and retrieving data efficiently. It offers a file-based storage approach with customizable expiration times, automatic removal of expired data, and ensures the cache folder is accessible and writable. Integrating this caching mechanism enhances application speed and reduces processing time.

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Set expiration date for database records, like future deleted at columns

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Validate credit card number, expiration date, cvc

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Validate credit card number, expiration date, cvc

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Override of Symfony PSR6 cache RedisAdapter, retrieving expiration on an existing item.

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React-PHP based priority queue key-value storage for expiration time based object eviction.

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Generate a url with an expiration date and signature to prevent unauthorized access

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Checks to see that the given array of websites are providing the 200 responce header as expected and can also check the SSL expriation date of the websites

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nmap is a PHP wrapper for Nmap, a free security scanner for network exploration.

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Objects that allow you to programmatically connect to Apache Drill.

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nmap is a PHP wrapper for Nmap, a free security scanner for network exploration. This is (mostly) a fork of willdurand/nmap with PHP 7 and vimeo/psalm static analysis improvements.

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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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