The pandemic period in education brought many challenges to all organizations. The activities of the higher educational institutions are being affected and the situation can last for a longer time. Under these circumstances, it is important to shift to online learning and improve educational processes through all organizational levels. The organizations had to assure appropriate distance or remote learning process by identifying their opportunities, meeting challenges, and identifying the sustainable quality factors for remote or distance learning. This study aimed to map and test the factors that influence online learning success in the pandemic situation in higher education in one of the European Union countries, Lithuania. Factors analyzed and presented in the paper are the quality of institutions and services, infrastructure and system quality, quality of courses and information, and online learning environment. Data were collected through surveys by distributing questionnaires and interviews. Authors are providing the main criteria for successful education based on administrative positions and design makers of the educational organizations. The article summarizes the interviews of 15 respondents from the three Lithuanian higher education institutions and how their informants met changes, opportunities, and identified quality factors addressed to the successful learning and teaching process during a pandemic period.

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.

Despite the fact that business process (BP) modeling has its long-lasting traditions in various areas of application, this discipline remains in the constant process of improvement and issue-solving. The possibilities of synergy among business process models and business vocabularies and rules are analyzed in this paper. We emphasize the existing gap between business process modeling and specification of business vocabularies and rules. Such a situation may lead to misunderstandings while reading and interpreting business models and also miscommunication issues within and among the organizations. Some of these issues could be resolved by realizing the integration of BP modeling standards with business vocabularies and rules. The paper presents some argumentation to back such statements. Later, basic principles of the approach for BPMN (Business Process Model and Notation) Business process model integration with SBVR (Semantics of Business Vocabulary and Business Rules) business vocabulary & rules are presented and briefly described in this paper.

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.INDEX TERMS Blockchain, model driven architecture, model-driven development, smart contracts, solidity, state machine, unified modeling language.

Lack of guidelines for implementing distance learning, lack of infrastructure, lack of competencies, and security-related problems were the challenges met during the pandemic. These challenges firstly fall on the administration of a higher education institution. To assist in solving the challenges of the pandemic for the administration of a higher education institution, the paper presents several models for the organization of the processes of distance learning. These models are as follows: a conceptual model of distance learning, a model of strategic planning of distance learning, a model of the assessment before the start of distance learning, a model of the preparation for distance learning, and a model of the process of distance learning and remote work. Student profile, lecturer profile, organizational environment, assessment, and planning of the infrastructure of information and communication technology (ICT), assessment and planning of the virtual learning environment, and assessment of distance learning competencies of participants of the study process are also considered. The developed models are based on five main processes of instructional design, i.e., analysis, design, development, implementation, and evaluation. The models provide guidelines for the administration of higher education institutions on the preparation and delivery of distance learning during the pandemic. The models were validated by 10 experts from different higher education institutions. The feasibility of the data collection instrument was determined by Cronbach’s alpha coefficient that is above 0.9.