Libraries tagged by analyse

AutoIt Obfuscator Web API interface can help you to protect your AutoIt script source code against analysis, reverse engineering and decompilation. AutoIt Obfuscator provides advanced AutoIt source code parsing based on AST trees, multiple advanced obfuscation strategies are available.

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PetaLog is a powerful log and exception aggregation tool designed specifically for Laravel projects. It seamlessly captures exceptions from your Laravel applications and centralizes them in a user-friendly interface, allowing developers to efficiently monitor, analyze, and resolve issues.

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Are you in need of a TYPO3 Extension that offers features such as system information reporting, statistical analysis of TYPO3 extensions, downloadable compatibility options, and more? TYPO3 Extensions Compatibility Report, tailored to meet your specific requirements!

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Dev tools for linting and analysing php projects.

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Use Debugbar in Production for performance analysis and fast debugging

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An extendable, XPath driven, static code analysis tool for Magento, built on the top of PHP-Parser library.

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Web page performance/seo/security/accessibility analysis, browser-less.

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Specification Analysis for PHP

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IDE and static analysis helper for the krakjoe/ilimit extension

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A Static Analyzer for PHP

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Extension for PHPStan to allow analysis of Magento 1 code.

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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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Entity Base is a content analysis plugin that uses the TextRazor Natural Language Processor to extract entities from post content.

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A framework for analysing objects in your application and finding related content.

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一个高性能、自动化的微信账单解析器，支持压缩包密码自动破解与账单数据智能提取，适用于账单分析、账单自动化入账、个人理财工具开发等场景 | A fast, automated Wechat bill parser that cracks compressed file passwords and extracts bill data. Perfect for bill analysis, automatic bookkeeping, and personal finance tools

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