At the one end of the spectrum, locally installed applications allow users to work offline and at their convenience but do not support collaboration among multiple users or devices. At the other end, cloud-based applications offer collaboration and data synchronization but require an uninterrupted internet connection. To address these limitations, Local-First software was introduced to merge the ...

This master’s thesis evaluates the scalability and cost-effectiveness of the AWS cloud platform used to collect and utilize data generated by the 87 digitally equipped trams. The SL-18 Cloud Platform was developed before the trams arrived, and resource configuration estimates were made to handle the data generated by the trams. However, with a few trams currently operational, it is crucial to evaluate ...

Secure multiparty computation (SMC) is a technique that allows multiple parties to jointly compute a function while keeping their inputs private. This technique has gained significant attention due to its potential applications in various fields, including privacy-preserving healthcare, politics and finance. SMC involves a set of protocols that enable parties to achieve secure computation and ...

This study investigated the potential of self-reported wellness data from a player monitoring system and its predictive power of individual match performance among a female professional football player cohort. Using longitudinal data collected from the Pm Reporter Pro mobile application and corresponding individual performance scores (InStat Index), the study investigated if pre-match perceived ...

New digital technologies like activity trackers, nudge concepts, and approaches can inspire and improve personal health. There is increasing interest in employing such devices to monitor people’s health and well-being. These devices can continually gather and examine health-related information from people and groups in their familiar surroundings. Context-aware nudges can assist people in self-managing ...

The arctic tundra is considered the terrestrial biome expected to be most impacted by climate change, with temperatures projected to increase as much as 10 °C by the turn of the century. The Climate-ecological Observatory for Arctic Tundra (COAT) project monitors the climate and ecosystems using several sensor types. We report on results from projects that automate image annotations from two of the ...

Segmenting medical images accurately and reliably is crucial for disease diagnosis and treatment. Due to the wide assortment of objects’ sizes, shapes, and scanning modalities, it has become more challenging. Many convolutional neural networks (CNN) have recently been designed for segmentation tasks and achieved great success. This paper presents an optimized deep learning solution using DeepLabv3+ ...

Patients with diabetes and health personnel do not have an optimal way of interacting. Health personnel must use multiple ICT systems, such as third-party companies’ services, to access health-related data from diverse vendors’ CGM platforms and Electronic Health Record (EHR). Furthermore, other health-related data like physical activities, quality of life or well-being is often discussed but ...

Thumbnail selection is a very important aspect of online sport video presentation, as thumbnails capture the essence of important events, engage viewers, and make video clips attractive to watch. Traditional solutions in the soccer domain for presenting highlight clips of important events such as goals, substitutions, and cards rely on the manual or static selection of thumbnails. However, such ...

Semantic image segmentation is the process of labeling each pixel of an image with its corresponding class. An encoder-decoder based approach, like U-Net and its variants, is a popular strategy for solving medical image segmentation tasks. To improve the performance of U-Net on various segmentation tasks, we propose a novel architecture called DoubleU-Net, which is a combination of two U-Net ...