Definitions:
- XAI (Explainable AI): A set of processes and methods that allow human users to understand and trust the results and output created by machine learning algorithms. Explainable AI aims to make the decision-making process of AI models transparent and interpretable, ensuring that users can understand how and why certain decisions are made.
- XSS (Cross-Site Scripting): A type of security vulnerability typically found in web applications. XSS enables attackers to inject malicious scripts into web pages viewed by other users. This can result in the theft of sensitive data, session hijacking, or other malicious activities. Mitigating XSS involves validating and sanitising user inputs and outputs.
- XML (eXtensible Markup Language): A markup language that defines a set of rules for encoding documents in a format that is both human-readable and machine-readable. XML is used to store and transport data, and is widely used in web services, data interchange, and configuration files. It allows for the creation of custom tags to describe the structure and content of data.
- XPath: A language used to navigate through elements and attributes in an XML document. XPath enables the selection of nodes from an XML document, allowing for the extraction and manipulation of data. It is commonly used in web scraping, data transformation, and querying XML databases.
- XQuery: A query language designed for querying and transforming XML data. XQuery provides a powerful and flexible way to extract and manipulate data from XML documents. It is used in various applications, including data integration, web services, and content management systems.
- XSLT (eXtensible Stylesheet Language Transformations): A language used to transform XML documents into other formats, such as HTML, text, or other XML structures. XSLT enables the definition of rules for transforming the structure and content of XML data, making it a useful tool for data conversion and presentation.
- XGBoost (Extreme Gradient Boosting): An optimised gradient boosting library designed for efficiency, flexibility, and portability. XGBoost is widely used in machine learning for tasks such as classification, regression, and ranking. It is known for its high performance and scalability, making it suitable for large-scale data processing and real-time applications.
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