Continuously generated volumes of health data make healthcare a data-intensive domain. This data needs to be collected, stored, and shared among different healthcare actors for various purposes, such as reporting, analysis, collaborative research, and personalized healthcare services. However, the existing data storage and exchange solutions in the healthcare domain exhibit several challenges related to e.g., data security, patient privacy, and interoperability. Recently, the industry and research community turned its focus to the possible use of blockchain technology to solve some of these challenges in the healthcare domain. The blockchain technology along with the support from smart contracts is considered a salient facilitator for secure and efficient health data sharing. This is due to its unique features, such as decentralization, trustlessness, immutability, traceability, and transparency. In this paper, we provide a comprehensive survey of the state-of-the-art efforts that envision the use of blockchain-based solutions in the healthcare domain. To this end, we introduce a systematic framework for classifying and analyzing such systems. The framework consists of classification in several dimensions: interactions between healthcare entities, functional components of healthcare storage systems, challenges in the healthcare domain that can be overcome by using the blockchain technology, and benefits for healthcare storage systems derived from the fundamental features of the technology. When analyzing over 40 systems and solutions proposed in the state-of-the-art, we perform their rigorous placement by identifying the exact scope of each solution and mapping it to the above taxonomies of interactions, functional components, challenges, and benefits. We additionally provide an extensive discussion of compliance with privacy-related regulations of General Data Protection Regulation (GDPR) in EU, and Health Insurance Portability and Accountability Act (HIPAA). Following the results of the analysis, we have outlined a number of important research gaps and future directions yet to be addressed.

In maritime traffic surveillance, detecting illegal activities, such as illegal fishing or transshipment of illicit products is a crucial task of the coastal administration. In the open sea, one has to rely on Automatic Identification System (AIS) message transmitted by on-board transponders, which are captured by surveillance satellites. However, insincere vessels often intentionally shut down their AIS transponders to hide illegal activities. In the open sea, it is very challenging to differentiate intentional AIS shutdowns from missing reception due to protocol limitations, bad weather conditions or restricting satellite positions. This paper presents a novel approach for the detection of abnormal AIS missing reception based on self-supervised deep learning techniques and transformer models. Using historical data, the trained model predicts if a message should be received in the upcoming minute or not. Afterwards, the model reports on detected anomalies by comparing the prediction with what actually happens. Our method can process AIS messages in real-time, in particular, more than 500 Millions AIS messages per month, corresponding to the trajectories of more than 60 000 ships. The method is evaluated on 1-year of real-world data coming from four Norwegian surveillance satellites. Using related research results, we validated our method by rediscovering already detected intentional AIS shutdowns.

The availability of automatic identification system (AIS) data for tracking vessels has paved the way for improvements in maritime safety and efficiency. However, one of the main challenges in using AIS data is often the low quality of the data. Practically, AIS-based trajectory data of vessels are available at irregular time intervals; consequently, large temporal gaps often exist in the historical AIS data. Meanwhile, certain tasks such as waypoint detection using historical data, which involves finding locations along the trajectory where the vessel changes its course (and possibly speed, acceleration, etc.), require AIS messages with a high temporal resolution. High-resolution AIS data are especially required for waypoint detection in critical areas where vessels maneuver carefully because of, e.g., narrow pathways or the presence of islands. One possible solution to address the problem of insufficient AIS data in vessel trajectories is interpolation. In this paper, we address the problem of detecting waypoints in a single representative trajectory with insufficient data using various interpolation-based methods. To this end, a two-step approach is proposed, in which the trajectories are first interpolated, and then the waypoint detection method is applied to the merged trajectory containing both interpolated and observed AIS messages. The numerical results demonstrate the effectiveness of exploiting various interpolation methods for waypoint detection. Moreover, the results of the numerical experiments show that the proposed methodology is effective for waypoint detection in envisaged settings with insufficient data, and outperforms the competing algorithm.

Summary: Continuous integration, at its core, includes a set of practices that aim to prevent and reduce the cost of software integration issues by merging working software copies often. Regression testing is considered a good practice in software development with continuous integration, which ensures that code changes are not negatively affecting software functionality. As, nowadays, software development is carried out iteratively, with small code increments continuously developed and regression tested, it is of critical importance that continuous regression testing is time efficient. However, in practice, regression testing is often long lasting and faces scalability problems as software grows larger or as software changes are made more frequently. One contributing factor to these issues is test redundancy, which causes the same software functionality being tested multiple times across a test suite. In large‐scale software, especially highly configurable software, redundancy in continuous regression testing can significantly grow the size of test suites and negatively affect the cost effectiveness of continuous integration. This paper presents a practical learning algorithm for optimizing continuous integration testing by reducing ineffective test redundancy in regression suites. The novelty of the algorithm lies in learning and predicting the fault‐detection effectiveness of continuous integration tests using historical test records and combining this information with coverage‐based redundancy metrics. The goal is to identify ineffective redundancy, which is maximally reduced in the resulting regression test suite, thus reducing test time and improving the performance of continuous integration. We apply and evaluate the algorithm in two industrial projects of continuous integration. The results show that the proposed algorithm can improve the efficiency of continuous integration practice in terms of decreasing test execution time by 38% on average compared to the industry practice of our case study and by 40% on average compared to the retest‐all approach. The results further demonstrate no significant reduction in fault‐detection effectiveness of continuous regression testing. This suggests that the proposed algorithm contributes to the state of the practice in the continuous integration development and testing of highly configurable systems.