For the development of blockchain smart contracts, a structured approach based on the principles of the Model Driven Architecture can be beneficial and facilitate the implementation of smart contracts. This paper presents such an approach, which, in combination with Unified Modeling Language (UML) Class and State machine diagrams, allows the smart contract structure and behavior logic to be modeled in several abstraction layers. This paper delves into details on how the model-to-model transformations from the specified Blockchain Platform Independent Model (PIM) with specified state-like behavior can be used to produce a Solidity Platform Specific Model (PSM). Subsequently, we elaborate on how the Solidity PSM is used for Solidity smart contract code generation by employing model-to-text transformations. The paper also demonstrates the process of our proposed transformations and code generation using smart contract code examples from Solidity documentation. Based on the examples, a Blockchain PIM is specified and transformed to Solidity PSM, which is then used for Solidity smart contract code generation. The generated smart contract code is then compiled, deployed on the Ethereum blockchain JavaScript virtual machine, and compared to the original smart contract code in terms of Solidity code metrics, similarity scores, and execution costs. The evaluation results indicate that our approach could be successfully used to model and later generate smart contract code.

Federated learning is a branch of machine learning where a shared model is created in a decentralized and privacy-preserving fashion, but existing approaches using blockchain are limited by tailored models. We consider the possibility to extend a set of supported models by introducing the oracle service and exploring the usability of blockchain-based architecture. The investigated architecture combines an oracle service with a Hyperledger Fabric chaincode. We compared two logistic regression implementations in Go language—a pure chaincode and an oracle service—at various data (2–32 k instances) and network (3–13 peers) sizes. Experiments were run to assess the performance of blockchain-based model inference using 2D synthetic and EEG eye state datasets for a supervised machine learning detection task. The benchmarking results showed that the impact on performance is acceptable with the median overhead of oracle service reaching 2–4%, depending on the dimensionality of the dataset. The overhead tends to diminish at large dataset sizes with the runtime depending on the network size linearly, where additional peers increased the runtime by 6.3 and 6.6 s for 2D and EEG datasets, respectively. Demonstrated negligible difference between implementations justifies the flexible choice of model in the blockchain-based federated learning and other machine learning applications.

Blockchain smart contracts can support the decentralisation of business processes, but due to smart contracts’ specifics, their development is a complicated process. Introducing model-driven development principles in smart contract development can facilitate requirement specification, design, and implementation activities. This paper presents a model-driven development method MDAsmartCD (Model-Driven Architecture-based Smart Contract Development) to alleviate smart contract development by supporting the complete MDA life cycle, covering the definition of Computation-Independent Model, Platform-Independent Model, and two instances of Platform-Specific Models. In MDAsmartCD, model transformations (model-to-model and model-to-text) are used to produce smart contract code in the Hyperledger Fabric platform Go and the Ethereum platform Solidity programming languages. The method application was demonstrated by implementing the smart contract for the hackathon solution and executing the generated Solidity and Go smart contracts in the workflow of issuing certificates for hackathon participants. During the execution of the workflow, both deployed smart contracts behaved identically and recorded analogous results in respective blockchain data storages. This demonstrated that the MDAsmartCD method enables the generation of compilable and executable smart contract code, ready for deployment on a blockchain platform.

Currently, the integration of immersive virtual reality applications into learning management systems is limited, which disadvantages kinesthetic learners, as the majority of learning material is mostly written or visual in nature. This research presents a method on how to transform existing conventional learning material into immersive virtual reality and integrate it into learning management systems, demonstrating it with a pilot study and an immersive virtual reality application prototype. The prototype application uses WebXR technology and provides integration into the Moodle learning management system using the Sharable Content Object Reference Model standard. The developed learning object was evaluated using the System Usability Scale, Web-based Learning Tool evaluation scale and the igroup Presence Questionnaire. The results of the pilot study revealed that the learning object performed above average in all tested fields and is preferable to established non-IVR solutions.