Informatica

Neural networks (NNs) are well established and widely used in time series forecasting due to their frequent dominance over other linear and nonlinear models. Thus, this paper does not question their appropriateness in forecasting cryptocurrency prices; rather, it compares the most commonly used NNs, i.e. feedforward neural networks (FFNNs), long short-term memory (LSTM) and convolutional neural networks (CNNs). This paper contributes to the existing literature by defining the appropriate NN structure comparable across different NN architectures, which yields the optimal NN model for Bitcoin return forecasting. Moreover, by incorporating turbulent events such as COVID and war, this paper emerges as a stress test for NNs. Finally, inputs are carefully selected, mostly covering macroeconomic and market variables, as well as different attractiveness measures, the importance of which in cryptocurrency forecasting is tested. The main results indicate that all NNs perform the best in an environment of bullish market, where CNNs stand out as the optimal models for continuous dataset, and LSTMs emerge as optimal in direction forecasting. In the downturn periods, CNNs stand out as the best models. Additionally, Tweets, as an attractiveness measure, enabled the models to attain superior performance.

The article presents the tax declaration scheme using blockchain confidential transactions based on the modified ElGamal encryption providing additively-homomorphic property. Transactions are based on the unspent transactions output (UTxO) paradigm allowing to effectively represent digital asset of cryptocurrencies in e-wallets and to perform financial operations. The main actors around transaction are specified, include money senders, receivers, transaction creator, Audit Authority (AA) and Net of users. A general transaction model with M inputs and N outputs is created, providing transaction amount confidentiality and verifiability for all actors with different levels of available information.

Blockchain is gaining traction for improving the security of healthcare applications, however, it does not become a silver bullet as various security threats are observed in blockchain-based applications. Moreover, when performing the security risk management (SRM) of blockchain-based applications, there are conceptual ambiguities and semantic gaps that hinder from treating the security threats effectively. To address these issues, we present a blockchain-based healthcare security ontology (HealthOnt) that offers coherent and formal information models to treat security threats of traditional and blockchain-based applications. We evaluate the ontology by performing the SRM of a back-pain patient’s healthcare application case. The results show that HealthOnt can support the iterative process of SRM and can be continually updated when new security threats, vulnerabilities, or countermeasures emerge. In addition, the HealthOnt may assist in the modelling and analysis of real-world situations while addressing important security concerns from the perspective of stakeholders. This work can help blockchain developers, practitioners, and other associated stakeholders to develop secure blockchain-based healthcare applications in the early stages.

Voting systems are as useful as people are willing to use them. Although many electronic election schemes have been proposed through the years, and some real case scenarios have been tested, people still do not trust electronic voting. Voting is not only about technological challenges but also about credibility, therefore, we propose a voting system focused on trust. We introduce political parties as active partners in the elections as a mechanism to encourage more traditional electors to participate. The system we propose here preserves elector’s privacy, it operates publicly through a blockchain and it is auditable by third parties.

Blockchain is a decentralized database, which can protect the safety of trade and avoid double payment. Due to the widespread attention of researchers, the studies of this field have increased sharply in recent years. It is meaningful to reveal the development level and trends based on this literature. This paper adopts bibliometric methods to study the collaboration characteristics from the levels of author, institution and country. Furthermore, several kinds of collaboration networks and their centrality analysis are also presented, which not only display the development level and collaboration degree but also the evolution of author collaboration modes in different phases.