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of affective computing. The project aims to develop reproducible machine learning approaches for automatic emotion recognition in music, with stronger theoretical grounding, transparent model
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of affective computing. The project aims to develop reproducible machine learning approaches for automatic emotion recognition in music, with stronger theoretical grounding, transparent model
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increase in data volume, it is necessary to adopt parallelism techniques to achieve reasonable query response time. The main focus will be on parallel query processing, which is the main driver for Big Data
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investments from “tech giants” like Microsoft, Google, Meta, and Apple, mixed reality displays have rapidly evolved from limited, heavy, and computer-tethered devices to standalone, lightweight alternatives
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will commence with, in parallel, the survey for collecting the data and a comparison of machine learning methods on artificial pseudo-randomly generated data. The world will see all too many changes
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will commence with, in parallel, the survey for collecting the data and a comparison of machine learning methods on artificial pseudo-randomly generated data. The world will see all too many changes
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truck. Computing the location and time of these connections remains an unsolved computational challenge. This project will create new algorithms for calculating cost-effective and time-efficient routes
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truck. Computing the location and time of these connections remains an unsolved computational challenge. This project will create new algorithms for calculating cost-effective and time-efficient routes
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NMT models where sufficient parallel corpora are not available (eg [1,2]). This project investigates methods to enable high performing NMT in low-resource scenarios. [1] Neural Machine Translation for
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NMT models where sufficient parallel corpora are not available (eg [1,2]). This project investigates methods to enable high performing NMT in low-resource scenarios. [1] Neural Machine Translation for