This platform combines Federated Machine Learning and Privacy Preserving Machine Learning disciplines to provide scalable and robust privacy over different privacy scenarios commonly found in data-driven industrial applications. The platform provides a set of configurable “Privacy Operation Modes” (POMs) in which the platform operates and a library with a full set of machine learning algorithms efficiently implemented on a federated manner under the assumptions of each POM.
(Health) care analytics refers to the use of collected data to provide organisations or patients with actionable insights. These insights are developed through analytical disciplines to drive fact-based decision making. In turn, these decisions improve planning, management, measurement and learning. Our CareAnalytic in this context is defined as a contextually-aware procedure or algorithm which can detect and react to patterns in current or historic data available to the systems.
We have used Natural Language Processing (NLP) techniques in tools aimed to support and improve the software development and software quality processes for Java and C/C++ languages. The use of complex models has increased performance in many common NLP tasks, such as named entity recognition, text classification, summarisation and translation among others. Besides, transfer learning has also become an interesting option when not much labelled data is available and knowledge learnt from one problem can be applied to a new but related task. In this context, our two NLP-based source code analysis tools - namely Variable Misuse and Code Summarisation - have been conceived by and for software developers.
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