Vessel navigation is influenced by various factors, such as dynamic environmental factors that change over time or static features such as vessel type or depth of the ocean. These dynamic and static navigational factors impose limitations on vessels, such as long waiting times in regions outside the actual ports, and we call these waiting regions gateway ports. Identifying gateway ports and their associated features such as congestion and available utilities can enhance vessel navigation by planning on fuel optimization or saving time in cargo operation. In this paper, we propose a novel temporal graph neural network (TGNN) based port classification method to enable vessels to discover gateway ports efficiently, thus optimizing their operations. The proposed method processes vessel trajectory data to build dynamic graphs capturing spatio-temporal dependencies between a set of static and dynamic navigational features in the data, and it is evaluated in terms of port classification accuracy on a real-world data set collected from ten vessels operating in Halifax, NS, Canada. The experimental results indicate that our TGNN-based port classification method provides an f-score of 95% in classifying ports.

The continuous generation of large volume of medical data from different sources, such as patient monitoring, clinical trials management, and processing payments and reimbursement claims, makes healthcare a data-intensive domain. This data needs to be shared among different medical facilities for various purposes, such as in-depth data analysis and collaborative research, to achieve innovative advances in the medical treatment procedures and drug developments, and providing personalised healthcare services to the patients. However, the existing data exchange solutions in healthcare domain exhibits several challenges, like data security, patient privacy, data owner consent management, interoperability, and chain-of-custody. Recently, the industry and research community turned its focus on the possible use of blockchain technology to solve one or more of the above challenges in healthcare domain. The blockchain technology along with the support from smart contracts is considered as an adequate solution for secure and efficient medical data sharing. It is due to its unique features, such as decentralization, trustlessness, immutability, traceability, and security. In this paper, we provide a comprehensive survey on the state-of-the-art efforts that envision the usage of blockchain-based solutions to improve one or more aspects in healthcare domain.
To this end, first we categorize the existing works based on a set of key challenge(s) that they address in healthcare by using blockchain technology. The particular challenges that we consider for the categorization of the surveyed articles are as follows: (i) security and privacy, (ii) interoperability, (iii) compliance management, (iv) medical records management, and (v) chain-of-custody. Second, we discuss the benefits and limitations that the surveyed solutions exhibit when incorporating blockchain in healthcare. We also investigate the causal relationships among these challenges to demonstrate how various factors causing these challenges are connected with each other. Third, we identify the practical issues that blockchain-driven healthcare must overcome to become a success, and we suggest the additional future research required to address the identified issues. Our survey shows that there is an exponential increase in the number of efforts towards the use blockchain and smart contracts to improve various functionalities in several healthcare services such as clinical trials, medical data management, and drug supply chain management. However, most of these efforts are still in their initial phases, and a lot of work still remains before their integration in practical healthcare scenarios. This work is currently in-progress, and it is planned to be performed in an accepted research-based innovation project by Research Council of Norway called “SMARTMED – Secure and accountable sharing of medical records using smart contracts and blockchain”.

What is gratifying to a software engineering researcher? Three of many possible answers to this question are (a) the intellectual exercise in developing/disseminating approaches that address emerging and existing challenges, (b) recognition from impact in a community of researchers and (c) widespread use of novel ideas, including software, in the society at large leading to enhancement of human ability and job creation. A culmination of these sources requires an organizational effort. This article presents a detailed account of a research-based innovation centre, Certus, to facilitate such a culmination for software engineering researchers. Certus has established a body of knowledge, methods and tools for the validation and verification of software systems in the Norwegian private and public sector. Certus works in close cooperation with five founding user partners and is hosted by the Simula Research Laboratory. We present the organizational structure of Certus and describe how Certus’s life and health is planned and evaluated on a regular basis as a research-based innovation centre. We expound two successful collaborations, with (a) the private sector entity Cisco systems, Norway and (b) the public sector entity the Norwegian Customs and Excise. We hope that this document will serve as a basis to encourage national/international funding schemes to create call for proposals for long-term research-based innovation centres. This, we believe, is one way to justify use of tax payers resources in creating a win–win situation for the triple helix: government, researchers and industry.

Testing data-intensive systems is paramount to increase our reliance on information in e-governance, scientific/ medical research, and social networks. Common practice to test these systems is by using a live production database. This testing approach is space and time inefficient and lacks clarity about what test cases or scenarios are covered. In this paper, we leverage classification tree modelling to specify desired test cases as data interactions between a set of fields across multiple tables of an existing database. Our methodology and tool, DEPICT, uses test case specifications in classification tree models to (a) automatically derive a spanning tree representing a relationship between any set of fields for any given database schema (b) generates queries to create an efficient inner join between related tables in the spanning tree (c) extract records from various tables that satisfy data interactions in the classification tree model (d) discovers holes or unsatisfied test cases in the test databases. We perform experiments to show that our approach is fast and scalable to extract test databases. Our experiments are based on selecting test databases from 8000 declarations for 60,000 items from the Norwegian Customs and Excise information system TVINN.