Real-time media rich applications rely on live streams of rich and accurate meta-data describing the video content to provide personal user experiences. Unfortunately, the general amount of video meta-data today is often limited to titles, synopsis and a few keywords. A wildly used approach for extraction of meta-data from video is computer vision. It has been developed a number of different ...

In this paper, we present an algorithm for automatically detecting events in soccer videos using 3D convolutional neural networks. The algorithm uses a sliding window approach to scan over a given video to detect events such as goals, yellow/red cards, and player substitutions. We test the method on three different datasets from SoccerNet, the Swedish Allsvenskan, and the Norwegian Eliteserien. ...

Computer-aided detection, localization, and segmentation methods can help improve colonoscopy procedures. Even though many methods have been built to tackle automatic detection and segmentation of polyps, benchmarking of state-of-the-art methods still remains an open problem. This is due to the increasing number of researched computer vision methods that can be applied to polyp datasets. ...

Today, with the prevalence of many- and multi-core systems has it been sparked a new interest for programming models that permits developer to exploit their resources. This has sparked renewed interest in creating larger event-based systems, systems where stack ripping occurs and with an obfuscated control flow. Both increases the complexity of debugging errors. During the development of the ...

Live-streaming and particular live video-streaming are becoming increasingly popular as a multimedia-service. More and more types of content and events are live-streamed. Live video-streaming has shown to be a useful tool in the case of pandemic outbursts such as Covid-19. As people should use social distancing, live video-streaming can give people the ability to simulate conventional social ...

<p>Background: Reinforcement learning (RL) is a computational approach to understanding and automating goal-directed learning and decision-making. It is designed for problems which include a learning agent interacting with its environment to achieve a goal. For example, blood glucose (BG) control in diabetes mellitus (DM), where the learning agent and its environment are the controller and the body ...

We have attempted to reproduce the results in <i>Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs</i>, published in JAMA 2016; 316(22), using publicly available data sets. We re-implemented the main method in the original study since the source code is not available. The original study used non-public fundus images from EyePACS ...

In recent years the Web has come into its own as a social platform where health consumers are actively creating and consuming Web content. Moreover, as the Web matures, consumers are gaining access to personalized applications adapted to their health needs and interests. The creation of personalized Web applications relies on extracted information about the users and the content to personalize. The ...

This paper describes a data warehouse approach to environmentally friendly transportation nudging. Transportation makes up a significant part of global carbon emissions. These emissions impacts both the climate and the health of individuals. As such, efforts should be done to address transportation patterns and habits. In addition to the reduction of air pollution, making people more active ...

As modern computer hardware becomes more affordable, the application areas of Artificial Intelligence (AI) expand and become more feasible to implement efficiently. This aspect opens up opportunities where AI robots can be used to support and improve many areas of human lives. One such area is education, and this thesis project combines AI and education to evaluate whether the results from that union ...