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Deepfakes, derived from "deep learning" and "fake," involve techniques that merge the face images of a target person with a video of a different source person. This process creates videos where the target person appears to be performing actions or speaking as the source person. In a broader...
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Chronic conditions are becoming a serious global and national health problem. Recommendation systems play an important role in supporting patients in managing their long-term health issues. They generally rely on expert rules or machine learning models to provide health advice. Recently,...
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Federated learning (FL) is an emerging machine learning paradium to enable distributed clients (e.g., mobile devices) to jointly train a machine learning model without pooling their raw data into a
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university was a major commitment, receiving a scholarship has reduced the financial burden significantly on both myself and my family. Having of the support of a scholarship has meant I have been able to make
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is experiencing financial hardship, a financial reward and a generous package of benefits allowing the student to boost their academic, personal and professional development. Total scholarship value Up
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will vary according to funds available. Selection criteria The scholarship is awarded on low income and scholarship proposal. The panel will assess artistic merit, artistic and professional development
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develop mechanisms to analyse the data and communicate insights to teachers, students and or decision makers. The following paper can serve as an illustrative example of this strand of research: “I Spent
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learning solver applied to various practical problems. In particular, the project will develop novel techniques for the incremental use of core-guided MaxSAT and CP solvers in the context of a series of
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of developing scalable solutions for privacy-preserving machine learning. This is done by both making the ML techniques more crypto-friendly as well as making the crypto building blocks more ML-friendly, so that
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achieve what neither a human being nor a machine can achieve on their own.The aim of this research is to develop cutting-edge Human-in-the-Loop Machine Learning algorithms that are able to avoid bias