Libraries tagged by putEvent
orangecat/checkip
153 Downloads
Prevent crawlers and IPs reported from creating a session
nlac/nlsclientscript
7923 Downloads
Yii ClientScript extension for prevent reloading javascript and merging/minfying resources
msp/shield
37112 Downloads
Advanced Intrusion Prevention System for Magento2 - Member of MageSpecialist SecuritySuite
msp/nospam
46965 Downloads
Comment Spammers, Harvesters and Suspicious users access prevention for Magento2 - Member of MageSpecialist SecuritySuite
middlewares/honeypot
2563 Downloads
Middleware to implement a honeypot spam prevention
mediawiki/anti-spoof
4163 Downloads
The AntiSpoof extension is an extension for preventing confusable usernames from being created. It blocks the creation of accounts with mixed-script, confusing and similar usernames.
masugadesign/linkvault
10395 Downloads
Protect and track downloads on your site. Prevent and track leech attempts.
macropage/laravel-scheduler-watcher
293 Downloads
logs artisan commands run via scheduler to mysql with plenty of infos, prevent running command again in case of error, allows full monitoing of artisan commands
liveecommerce/security-advisories
3650 Downloads
Prevents installation of composer packages with known security vulnerabilities: no API, simply require it
lipemat/limit-logins
1143 Downloads
WordPress plugin to prevent brute force attacks
laravel-enso/versions
8283 Downloads
Prevents update conflicts using the pessimistic lock pattern in Laravel
laravel-enso/versioning
16813 Downloads
Prevents update conflicts using the optimistic lock pattern in Laravel
larasecure/ip-blocker
457 Downloads
Restrict access to the web by preventing IP Addresses
la-haute-societe/craft-restrict-asset-delete
7910 Downloads
Prevent accidental removal of used assets
inda-hr/php_sdk
876 Downloads
# 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.