The Recurrent Reinforcement Learning Crypto Agent

We demonstrate a novel application of online transfer learning for a digital assets trading agent. This agent uses a powerful feature space representation in the form of an echo state network, the output of which is made available to a direct, recurrent reinforcement learning agent. The agent learns to trade the XBTUSD (Bitcoin versus US Dollars) perpetual swap derivatives contract on BitMEX on an intraday basis. By learning from the multiple sources of impact on the quadratic risk-adjusted utility that it seeks to maximise, the agent avoids excessive over-trading, captures a funding profit, and can predict the market’s direction. Overall, our crypto agent realises a total return of 350%, net of transaction costs, over roughly five years, 71% of which is down to funding profit. The annualised information ratio that it achieves is 1.46

Manipulation of the Bitcoin market: an agent-based study

Fraudulent actions of a trader or a group of traders can cause substantial disturbance to the market, both directly influencing the price of an asset or indirectly by misinforming other market participants. Such behavior can be a source of systemic risk and increasing distrust for the market participants, consequences that call for viable countermeasures. Building on the foundations provided by the extant literature, this study aims to design an agent-based market model capable of reproducing the behavior of the Bitcoin market during the time of an alleged Bitcoin price manipulation that occurred between 2017 and early 2018. The model includes the mechanisms of a limit order book market and several agents associated with different trading strategies, including a fraudulent agent, initialized from empirical data and who performs market manipulation. The model is validated with respect to the Bitcoin price as well as the amount of Bitcoins obtained by the fraudulent agent and the traded volume. Simulation results provide a satisfactory fit to historical data. Several price dips and volume anomalies are explained by the actions of the fraudulent trader, completing the known body of evidence extracted from blockchain activity. The model suggests that the presence of the fraudulent agent was essential to obtain Bitcoin price development in the given time period; without this agent, it would have been very unlikely that the price had reached the heights as it did in late 2017. The insights gained from the model, especially the connection between liquidity and manipulation efficiency, unfold a discussion on how to prevent illicit behavior

Tracking the Temporal-Evolution of Supernova Bubbles in Numerical Simulations

The study of low-dimensional, noisy manifolds embedded in a higher dimensional space has been extremely useful in many applications, from the chemical analysis of multi-phase flows to simulations of galactic mergers. Building a probabilistic model of the manifolds has helped in describing their essential properties and how they vary in space. However, when the manifold is evolving through time, a joint spatio-temporal modelling is needed, in order to fully comprehend its nature. We propose a first-order Markovian process that propagates the spatial probabilistic model of a manifold at fixed time, to its adjacent temporal stages. The proposed methodology is demonstrated using a particle simulation of an interacting dwarf galaxy to describe the evolution of a cavity generated by a Supernov

The Metaverse: Survey, Trends, Novel Pipeline Ecosystem & Future Directions

The Metaverse offers a second world beyond reality, where boundaries are non-existent, and possibilities are endless through engagement and immersive experiences using the virtual reality (VR) technology. Many disciplines can benefit from the advancement of the Metaverse when accurately developed, including the fields of technology, gaming, education, art, and culture. Nevertheless, developing the Metaverse environment to its full potential is an ambiguous task that needs proper guidance and directions. Existing surveys on the Metaverse focus only on a specific aspect and discipline of the Metaverse and lack a holistic view of the entire process. To this end, a more holistic, multi-disciplinary, in-depth, and academic and industry-oriented review is required to provide a thorough study of the Metaverse development pipeline. To address these issues, we present in this survey a novel multi-layered pipeline ecosystem composed of (1) the Metaverse computing, networking, communications and hardware infrastructure, (2) environment digitization, and (3) user interactions. For every layer, we discuss the components that detail the steps of its development. Also, for each of these components, we examine the impact of a set of enabling technologies and empowering domains (e.g., Artificial Intelligence, Security & Privacy, Blockchain, Business, Ethics, and Social) on its advancement. In addition, we explain the importance of these technologies to support decentralization, interoperability, user experiences, interactions, and monetization. Our presented study highlights the existing challenges for each component, followed by research directions and potential solutions. To the best of our knowledge, this survey is the most comprehensive and allows users, scholars, and entrepreneurs to get an in-depth understanding of the Metaverse ecosystem to find their opportunities and potentials for contribution

Social trust-based blockchain-enabled social media news verification system

In the current digital era, information exchanges can be done easily through the Internet and social media. However, the actual truth of the news on social media platforms is hard to prove, and social media platforms are susceptible to the spreading of hoaxes. As a remedy, Blockchain technology can be used to ensure the reliability of shared information and can create a trusted communications environment. In this study, we propose a social media news spreading model by adapting an epidemic methodology and a scale-free network. A Blockchain-based news verification system is implemented to identify the credibility of the news and its sources. The effectiveness of the model is investigated by utilizing agent-based modelling using NetLogo software. In the simulations, fake news with a truth level of 20% are assigned a low News Credibility Indicator (NCI ± -0.637) value for all of the different network dimensions. Moreover, the Producer Reputation Credit is also decreased (PRC ± 0.213) so that the trust factor value is reduced. Our epidemic approach for news verification has also been implemented using Ethereum Smart Contract and several tools such as React with Solidity, IPFS, Web3.js, and Metamask. By showing the measurements of the credibility indicator and reputation credit to the user during the news dissemination process, this proposed smart contract can effectively limit user behaviour in spreading fake news and improve the content quality on social medi

Social trust-based blockchain-enabled social media news verification system

In the current digital era, information exchanges can be done easily through the Internet and social media. However, the actual truth of the news on social media platforms is hard to prove, and social media platforms are susceptible to the spreading of hoaxes. As a remedy, Blockchain technology can be used to ensure the reliability of shared information and can create a trusted communications environment. In this study, we propose a social media news spreading model by adapting an epidemic methodology and a scale-free network. A Blockchain-based news verification system is implemented to identify the credibility of the news and its sources. The effectiveness of the model is investigated by utilizing agent-based modelling using NetLogo software. In the simulations, fake news with a truth level of 20% are assigned a low News Credibility Indicator (NCI ± -0.637) value for all of the different network dimensions. Moreover, the Producer Reputation Credit is also decreased (PRC ± 0.213) so that the trust factor value is reduced. Our epidemic approach for news verification has also been implemented using Ethereum Smart Contract and several tools such as React with Solidity, IPFS, Web3.js, and Metamask. By showing the measurements of the credibility indicator and reputation credit to the user during the news dissemination process, this proposed smart contract can effectively limit user behaviour in spreading fake news and improve the content quality on social medi