Blockchain technology enables the creation of a decentralized environment where transactions and data are not under the control of any third party organization. Any transaction ever completed is recorded in a public ledger in a verifiable and permanent way. Based on blockchain technology, we propose a global higher education credit platform, named EduCTX. This platform is based on the concept of the European Credit Transfer and Accumulation System (ECTS). It constitutes a globally trusted, decentralized higher education credit and grading system that can offer a globally unified viewpoint for students and higher education institutions (HEIs), as well as for other potential stakeholders such as companies, institutions and organizations. As a proof of concept, we present a prototype implementation of the environment, based on the open-source Ark Blockchain Platform. Based on a globally distributed peer-topeer network, EduCTX will process, manage and control ECTX tokens, which represent credits that students gain for completed courses such as ECTS. HEIs are the peers of the blockchain network. The platform is a first step towards a more transparent and technologically advanced form of higher education systems. The EduCTX platform represents the basis of the EduCTX initiative which anticipates that various HEIs would join forces in order to create a globally efficient, simplified and ubiquitous environment in order to avoid language and administrative barriers. Therefore we invite and encourage HEIs to join the EduCTX initiative and the EduCTX blockchain network.

Wearable devices have recently received considerable interest due to their great promise for a plethora of applications. Increased research efforts are oriented towards a non-invasive monitoring of human health as well as activity parameters. A wide range of wearable sensors are being developed for real-time non-invasive monitoring. This paper provides a comprehensive review of sensors used in wrist-wearable devices, methods used for the visualization of parameters measured as well as methods used for intelligent analysis of data obtained from wrist-wearable devices. In line with this, the main features of commercial wrist-wearable devices are presented. As a result of this review, a taxonomy of sensors, functionalities, and methods used in non-invasive wrist-wearable devices was assembled.

Self-Sovereign Identity is an emerging, user-centric, decentralized identity approach utilizing some form of decentralized technology. It provides a means for digital identification without reliance on any external authority, enabling entities to control their identity and data flow during digital interactions while enhancing security and privacy. With the rise of blockchain technology, Self-Sovereign Identity is gathering momentum in academia and industry while the number of research papers increases rapidly. However, Self-Sovereign Identity is still a young unstructured field in its early stages of research. Thus, a systematic mapping methodology was adopted to provide a coarse-grained overview of decentralized and Self-Sovereign Identity and structure the research area by identifying, analyzing, and classifying the research papers according to predefined parameters, which is to say according to their contribution, application domain, IT field, research type, research method, and place of publication. Furthermore, the nature and scope of the research were determined, while existing research topics, gained insights into trends, demographics, challenges, gaps, and opportunities for future research were also presented. The results suggest that validation research and solution proposals prevail, addressing decentralized identity in a general matter. Papers mainly propose systems/solutions, architectures, and frameworks, focusing on authentication, security, privacy, and trust, while there are hardly any studies researching usability, user experience, patterns, and good practices.

The basis of blockchain-related data, stored in distributed ledgers, are digitally signed transactions. Data can be stored on the blockchain ledger only after a digital signing process is performed by a user with a blockchain-based digital identity. However, this process is time-consuming and not user-friendly, which is one of the reasons blockchain technology is not fully accepted. In this paper, we propose a machine learning-based method, which introduces automated signing of blockchain transactions, while including also a personalized identification of anomalous transactions. In order to evaluate the proposed method, an experiment and analysis were performed on data from the Ethereum public main network. The analysis shows promising results and paves the road for a possible future integration of such a method in dedicated digital signing software for blockchain transactions.