In today’s society we are constantly fed information about catastrophic or sad events through media. While it is important to know about these events, it should be equally important to also see all the good things that ...

Large and diverse data is crucial to train object detection systems properly and achieve satisfactory prediction performance. However, in some areas, such as ma rine science, gathering sufficient data is challenging and ...

Convolutional Neural Networks (CNN) have shown successful results in the recent years, especially within the area of image analysis. The idea of learning to predict the result of a crash simulation using machine learning ...

Modern video games offer substantial amounts of customization options. Manually testing the visual compatibility of all options is time-consuming and error-prone. Together with Ghost Games, we present a method of learning ...

Traceability has become a very important part of the software development lifecycle and therefor a lot of research has been done and is being done about it. Our aim with this paper is to explore and evaluate the effect ...

The frequent elements problem involves processing a stream of elements and finding all elements that occur more than a given fraction of the time. A relaxed version of this problem is the -approximate elements problem ...

This thesis is written in collaboration with the Swedish Public Employment Service and aims to investigate methods and techniques to automatically extract metadata from unstructured texts. The Swedish Public Employment ...

In this thesis, the effects of incorporating unstructured data—images in the wild—in contextual multi-armed bandits are investigated, when used within a recommender system setting, which focuses on picture-based content ...

This thesis aims to answer whether it is possible to produce one or more baselines based on naturalistic driving data collected over a period of 8 months. The baseline is based on variables extracted from the drivers ...

In the field of medical science datasets are often highly imbalanced, where rare datapoints are of high importance. This study aims to explore the usage of synthetic datasets to improve the classification of echocardiogram ...