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Artificial intelligence (AI) is predicted to have profound effects on the future of video capsule endoscopy (VCE) technology. The potential lies in improving anomaly detection while reducing manual labour. Existing work demonstrates the promising benefits of AI-based computer-assisted diagnosis systems for VCE. They also show great potential for improvements to achieve even better results. Also, medical data is often sparse and unavailable to the research community, and qualified medical personnel rarely have time for the tedious labelling work. We present Kvasir-Capsule, a large VCE dataset collected from examinations at a Norwegian Hospital. Kvasir-Capsule consists of 117 videos which can be used to extract a total of 4,741,504 image frames. We have labelled and medically verified 47,238 frames with a bounding box around findings from 14 different classes. In addition to these labelled images, there are 4,694,266 unlabelled frames included in the dataset. The Kvasir-Capsule dataset can play a valuable role in developing better algorithms in order to reach true potential of VCE technology.

The transaction-rate bottleneck built into popular proof-of-work (PoW)-based cryptocurrencies, like Bitcoin and Ethereum, leads to fee markets where transactions are included according to a first-price auction for block space. Many attempts have been made to adjust and predict the fee volatility, but even well-formed transactions sometimes experience unexpected delays and evictions unless a substantial fee is offered. In this article, we propose a novel transaction inclusion model that describes the mechanisms and patterns governing miners decisions to include individual transactions in the Bitcoin system. Using this model we devise a Machine Learning (ML) approach to predict transaction inclusion. We evaluate our predictions method using historical observations of the Bitcoin network from a five month period that includes more than 30 million transactions and 120 million entries. We find that our ML model can predict fee volatility with an accuracy of up to 91%. Our findings enable Bitcoin users to improve their fee expenses and the approval time for their transactions. [ABSTRACT FROM AUTHOR]

Detecting events in videos is a complex task, and many different approaches, aimed at a large variety of use-cases, have been proposed in the literature. Most approaches, however, are unimodal and only consider the visual information in the videos. This paper presents and evaluates different approaches based on neural networks where we combine visual features with audio features to detect (spot) and classify events in soccer videos. We employ model fusion to combine different modalities such as video and audio, and test these combinations against different state-of-the-art models on the SoccerNet dataset. The results show that a multimodal approach is beneficial. We also analyze how the tolerance for delays in classification and spotting time, and the tolerance for prediction accuracy, influence the results. Our experiments show that using multiple modalities improves event detection performance for certain types of events. [ABSTRACT FROM AUTHOR]

Holisticmedical multimedia systems covering end-to-end functionality from data collection to aided diagnosis are highly needed, but rare. In many hospitals, the potential value of multimedia data collected through routine examinations is not recognized. Moreover, the availability of the data is limited, as the health care personnel may not have direct access to stored data. However, medical specialists interact with multimedia content daily through their everyday work and have an increasing interest in finding ways to use it to facilitate their work processes. In this article, we present a novel, holistic multimedia system aiming to tackle automatic analysis of video from gastrointestinal (GI) endoscopy. The proposed system comprises the whole pipeline, including data collection, processing, analysis, and visualization. It combines filters using machine learning, image recognition, and extraction of global and local image features. The novelty is primarily in this holistic approach and its real-time performance, where we automate a complete algorithmic GI screening process. We built the system in a modular way to make it easily extendable to analyze various abnormalities, and we made it efficient in order to run in real time. The conducted experimental evaluation proves that the detection and localization accuracy are comparable or even better than existing systems, but it is by far leading in terms of real-time performance and efficient resource consumption. [ABSTRACT FROM AUTHOR]

In this paper, we present initial results from our distributed edge systems research in the domain of sustainable harvesting of common good resources in the Arctic Ocean. Specifically, we are developing a digital platform for real-time privacy-preserving sustainability management in the domain of commercial fishery surveillance operations. This is in response to potentially privacy-infringing mandates from some governments to combat overfishing and other sustainability challenges. Our approach is to deploy sensory devices and distributed artificial intelligence algorithms on mobile, offshore fishing vessels and at mainland central control centers. To facilitate this, we need a novel data plane supporting efficient, available, secure, tamper-proof, and compliant data management in this weakly connected offshore environment. We have built our first prototype of Dorvu, a novel distributed file system in this context. Our devised architecture, the design trade-offs among conflicting properties, and our initial experiences are further detailed in this paper. [ABSTRACT FROM AUTHOR]

Article 17 of the General Data Protection Regulation (GDPR) contains a right for the data subject to obtain the erasure of personal data. The right to erasure in the GDPR gives, however, little clear guidance on how controllers processing personal data should erase the personal data to meet the requirements set out in Article 17. Machine Learning (ML) models that have been trained on personal data are downstream derivatives of the personal data used in the training data set of the ML process. A characteristic of ML is the non-deterministic nature of the learning process. The non-deterministic nature of ML poses significant difficulties in determining whether the personal data in the training data set affects the internal weights and adjusted parameters of the ML model. As a result, invoking the right to erasure in ML and to erase personal data from a ML model is a challenging task. This paper explores the complexities of enforcing and complying with the right to erasure in a ML context. It examines how novel developments in machine unlearning methods relate to Article 17 of the GDPR. Specifically, the paper delves into the intricacies of how personal data is processed in ML models and how the right to erasure could be implemented in such models. The paper also provides insights into how newly developed machine unlearning techniques could be applied to make ML models more GDPR compliant. The research aims to provide a functional understanding and contribute to a better comprehension of the applied challenges associated with the right to erasure in ML. [ABSTRACT FROM AUTHOR]