Ying Zhang, Matthieu Petit Guillaume, Aurélien Krauth, Manel Labidi
CryptoGPT: a 7B model competing with GPT-4 in a specific task -- The Impact of Automatic Annotation and Strategic Fine-Tuning via QLoRAIn this article, we present a method aimed at refining a dedicated LLM of reasonable quality with limited resources in an industrial setting via CryptoGPT. It is an LLM designed for financial news analysis for the cryptocurrency market in real-time. This project was launched in an industrial context. This model allows not only for the classification of financial information but also for providing comprehensive analysis. We refined different LLMs of the same size such as Mistral-7B and LLama-7B using semi-automatic annotation and compared them with various LLMs such as GPT-3.5 and GPT-4. Our goal is to find a balance among several needs: 1. Protecting data (by avoiding their transfer to external servers), 2. Limiting annotation cost and time, 3. Controlling the model's size (to manage deployment costs), and 4. Maintaining better analysis quality.
Rug pulls in Solana have caused significant damage to users interacting with Decentralized Finance (DeFi). A rug pull occurs when developers exploit users' trust and drain liquidity from token pools on Decentralized Exchanges (DEXs), leaving users with worthless tokens. Although rug pulls in Ethereum and Binance Smart Chain (BSC) have gained attention recently, analysis of rug pulls in Solana remains largely under-explored. In this paper, we introduce SolRPDS (Solana Rug Pull Dataset), the first public rug pull dataset derived from Solana's transactions. We examine approximately four years of DeFi data (2021-2024) that covers suspected and confirmed tokens exhibiting rug pull patterns. The dataset, derived from 3.69 billion transactions, consists of 62,895 suspicious liquidity pools. The data is annotated for inactivity states, which is a key indicator, and includes several detailed liquidity activities such as additions, removals, and last interaction as well as other attributes such as inactivity periods and withdrawn token amounts, to help identify suspicious behavior. Our preliminary analysis reveals clear distinctions between legitimate and fraudulent liquidity pools and we found that 22,195 tokens in the dataset exhibit rug pull patterns during the examined period. SolRPDS can support a wide range of future research on rug pulls including the development of data-driven and heuristic-based solutions for real-time rug pull detection and mitigation.
Francesco Zola, Mikel Gorricho, Jon Ander Medina, Lander Segurola · 5 authors
Ransomware has become one of the most widespread threats, primarily due to its easy deployment and the accessibility to services that enable attackers to raise and obfuscate funds. This latter aspect has been significantly enhanced with the advent of cryptocurrencies, which, by fostering decentralisation and anonymity, have transformed this threat into a large-scale outbreak. However, recent reports indicate that a small group of individuals dominate the ransomware ecosystem and try to obfuscate their activity using multiple strains characterised by a short time to live. This scenario suggests that different strains could share mechanisms in ransom collection, fund movement, and money laundering operations. For this reason, this study aims to analyse the address-transaction graphs generated in the Bitcoin network by low-labelled ransomware families. Our goals are to identify payment spreading patterns for evaluating the evolution of ransomware families and to detect similarities among different strains that potentially can be controlled by the same attacker. Specifically, this latter task assigns an address behaviour to each node in the address-transaction graphs according to its dynamics. The distribution of the behaviours in each strain is finally used to evaluate the closeness among different ransomware families. Our findings show that although ransomware families can quickly establish connections with millions of addresses, numerous families require multiple-step analysis. Furthermore, the study demonstrates that the introduced behaviours can effectively be used to highlight similarities among different ransomware strains. The outcome shows that families are similar primarily due to behaviours usually associated with ransom collection and money laundering operations.+
Srisht Fateh Singh, Panagiotis Michalopoulos, Andreas Veneris
This paper introduces BAKUP, a smart contract insurance design for decentralized finance users to mitigate risks arising from platform vulnerabilities. While providing automated claim payout, BAKUP utilizes a modular structure to harmonize three key features: the platform's resilience against vulnerabilities, the flexibility of underwritten policies, and capital efficiency. An immutable core module performs capital accounting while ensuring robustness against external vulnerabilities, a customizable oracle module enables the underwriting of novel policies, and an optional and peripheral yield module allows users to independently manage additional yield. The implementation incorporates binary conditional tokens that are tradable on automated market maker (AMM)-based exchanges. Finally, the paper examines specific liquidity provision strategies for the conditional tokens, demonstrating that a conservative strategy and parameterization can effectively reduce the divergence loss of liquidity providers by more than 47 % compared to a naive strategy in the worst-case scenario.
This paper presents a theoretical extension of the DeTEcT framework proposed by Sadykhov et al., DeTEcT, where a formal analysis framework was introduced for modelling wealth distribution in token economies. DeTEcT is a framework for analysing economic activity, simulating macroeconomic scenarios, and algorithmically setting policies in token economies. This paper proposes four ways of parametrizing the framework, where dynamic vs static parametrization is considered along with the probabilistic vs non-probabilistic. Using these parametrization techniques, we demonstrate that by adding restrictions to the framework it is possible to derive the existing wealth distribution models from DeTEcT. In addition to exploring parametrization techniques, this paper studies how money supply in DeTEcT framework can be transformed to become dynamic, and how this change will affect the dynamics of wealth distribution. The motivation for studying dynamic money supply is that it enables DeTEcT to be applied to modelling token economies without maximum supply (i.e., Ethereum), and it adds constraints to the framework in the form of symmetries.
This research proposes a novel arbitrage approach in multivariate pair trading, termed the Optimal Trading Technique (OTT). We present a method for selectively forming a "bucket" of fiat currencies anchored to cryptocurrency for monitoring and exploiting trading opportunities simultaneously. To address quantitative conflicts from multiple trading signals, a novel bi-objective convex optimization formulation is designed to balance investor preferences between profitability and risk tolerance. We understand that cryptocurrencies carry significant financial risks. Therefore this process includes tunable parameters such as volatility penalties and action thresholds. In experiments conducted in the cryptocurrency market from 2020 to 2022, which encompassed a vigorous bull run followed by a bear run, the OTT achieved an annualized profit of 15.49%. Additionally, supplementary experiments detailed in the appendix extend the applicability of OTT to other major cryptocurrencies in the post-COVID period, validating the model's robustness and effectiveness in various market conditions. The arbitrage operation offers a new perspective on trading, without requiring external shorting or holding the intermediate during the arbitrage period. As a note of caution, this study acknowledges the high-risk nature of cryptocurrency investments, which can be subject to significant volatility and potential loss.
We have designed an innovative portfolio rebalancing mechanism termed the Cascading Waterfall Round Robin Mechanism. This algorithmic approach recommends an ideal size and number of trades for each asset during the periodic rebalancing process, factoring in the gas fee and slippage. The essence of the model we have created gives indications regarding whether trades should be made on individual assets depending on the uncertainty in the micro - asset level characteristics - and macro - aggregate market factors - environments. In the hyper-volatile crypto market, our approach to daily rebalancing will benefit from volatility. Price movements will cause our algorithm to buy assets that drop in prices and sell as they soar. In fact, the buying and selling happen only when certain boundaries are crossed in order to weed out any market noise and ensure sound trade execution. We have provided several numerical examples to illustrate the steps - including the calculation of several intermediate variables - of our rebalancing mechanism. The Algorithm we have developed can be easily applied outside blockchain to investment funds across all asset classes at any trading frequency and rebalancing duration. Shakespeare As A Crypto Trader: To Trade Or Not To Trade, that is the Question, Whether an Optimizer can Yield the Answer, Against the Spikes and Crashes of Markets Gone Wild, To Quench One's Thirst before Liquidity Runs Dry, Or Wait till the Tide of Momentum turns Mild.
Shiying Zhang, Jun Li, Long Shi, Ming Ding · 7 authors
As an emerging technology that enables seamless integration between the physical and virtual worlds, the Metaverse has great potential to be deployed in the industrial production field with the development of extended reality (XR) and next-generation communication networks. This deployment, called the Industrial Metaverse, is used for product design, production operations, industrial quality inspection, and product testing. However, there lacks of in-depth understanding of the enabling technologies associated with the Industrial Metaverse. This encompasses both the precise industrial scenarios targeted by each technology and the potential migration of technologies developed in other domains to the industrial sector. Driven by this issue, in this article, we conduct a comprehensive survey of the state-of-the-art literature on the Industrial Metaverse. Specifically, we first analyze the advantages of the Metaverse for industrial production. Then, we review a collection of key enabling technologies of the Industrial Metaverse, including blockchain (BC), digital twin (DT), 6G, XR, and artificial intelligence (AI), and analyze how these technologies can support different aspects of industrial production. Subsequently, we present numerous formidable challenges encountered within the Industrial Metaverse, including confidentiality and security concerns, resource limitations, and interoperability constraints. Furthermore, we investigate the extant solutions devised to address them. Finally, we briefly outline several open issues and future research directions of the Industrial Metaverse.
The adoption of the Pao Tang digital wallet in Thailand, promoted under the Khon la Krueng (50-50 Co-Payment) Scheme, illustrates Thailand's receptiveness to digital financial instruments, amassing over 40 million users in just three years during the COVID-19 social distancing era. Nevertheless, acceptance of this platform does not confirm a broad understanding of cryptocurrencies and Web 3.0 technologies in the region. Through a mix of documentary research, online surveys and a targeted interview with the Pao Tang app's founder, this study evaluates the factors behind the Pao Tang platform's success and contrasts it with digital practices in Switzerland. Preliminary outcomes reveal a pronounced knowledge gap in Thailand regarding decentralized technologies. With regulatory frameworks for Web 3.0 and digital currencies still nascent, this research underscores the need for further exploration, serving as a blueprint for shaping strategies, policies, and awareness campaigns in both countries.
In the distributed systems landscape, Blockchain has catalyzed the rise of cryptocurrencies, merging enhanced security and decentralization with significant investment opportunities. Despite their potential, current research on cryptocurrency trend forecasting often falls short by simplistically merging sentiment data without fully considering the nuanced interplay between financial market dynamics and external sentiment influences. This paper presents a novel Dual Attention Mechanism (DAM) for forecasting cryptocurrency trends using multimodal time-series data. Our approach, which integrates critical cryptocurrency metrics with sentiment data from news and social media analyzed through CryptoBERT, addresses the inherent volatility and prediction challenges in cryptocurrency markets. By combining elements of distributed systems, natural language processing, and financial forecasting, our method outperforms conventional models like LSTM and Transformer by up to 20\% in prediction accuracy. This advancement deepens the understanding of distributed systems and has practical implications in financial markets, benefiting stakeholders in cryptocurrency and blockchain technologies. Moreover, our enhanced forecasting approach can significantly support decentralized science (DeSci) by facilitating strategic planning and the efficient adoption of blockchain technologies, improving operational efficiency and financial risk management in the rapidly evolving digital asset domain, thus ensuring optimal resource allocation.
Xintong Wu, Wanling Deng, Yutong Quan, Lin William Cong · 5 authors
Trust mechanisms diverge between centralized and decentralized exchanges, representing distinct sociotechnical governance paradigms. However, quantifying trust dynamics and their redistribution between these architectures remains empirically challenging, limiting understanding of how institutional shocks affect market behavior. The FTX collapse offers a natural experiment to bridge this gap. Through an interdisciplinary approach combining causal inference and computational text analysis, we find significant price declines and capital reallocation from centralized to decentralized exchanges following the event. While sentiment metrics showed no sharp discontinuities, topic modeling and network analysis of Discord communities reveal that seasonal holiday discourse obscured underlying trust concerns in centralized exchange forums. These findings underscore the fragility of institutional trust architectures and demonstrate how mixed methods can illuminate behavioral patterns during systemic crises, offering insights for exchange risk management and regulatory assessment.
Marcin Wątorek, Paweł Szydło, Jarosław Kwapień, Stanisław Drożdż
The non-fungible token (NFT) market emerges as a recent trading innovation leveraging blockchain technology, mirroring the dynamics of the cryptocurrency market. The current study is based on the capitalization changes and transaction volumes across a large number of token collections on the Ethereum platform. In order to deepen the understanding of the market dynamics, the collection-collection dependencies are examined by using the multivariate formalism of detrended correlation coefficient and correlation matrix. It appears that correlation strength is lower here than that observed in previously studied markets. Consequently, the eigenvalue spectra of the correlation matrix more closely follow the Marchenko-Pastur distribution, still, some departures indicating the existence of correlations remain. The comparison of results obtained from the correlation matrix built from the Pearson coefficients and, independently, from the detrended cross-correlation coefficients suggests that the global correlations in the NFT market arise from higher frequency fluctuations. Corresponding minimal spanning trees (MSTs) for capitalization variability exhibit a scale-free character while, for the number of transactions, they are somewhat more decentralized.
We argue that the current POW based consensus algorithm of the Bitcoin network suffers from a fundamental economic discrepancy between the real world transaction (txn) costs incurred by miners and the wealth that is being transacted. Put simply, whether one transacts 1 satoshi or 1 bitcoin, the same amount of electricity is needed when including this txn into a block. The notorious Bitcoin blockchain problems such as its high energy usage per txn or its scalability issues are, either partially or fully, mere consequences of this fundamental economic inconsistency. We propose making the computational cost of securing the txns proportional to the wealth being transferred, at least temporarily. First, we present a simple incentive based model of Bitcoin's security. Then, guided by this model, we augment each txn by two parameters, one controlling the time spent securing this txn and the second determining the fraction of the network used to accomplish this. The current Bitcoin txns are naturally embedded into this parametrized space. Then we introduce a sequence of hierarchical block structures (HBSs) containing these parametrized txns. The first of those HBSs exploits only a single degree of freedom of the extended txn, namely the time investment, but it allows already for txns with a variable level of trust together with aligned network fees and energy usage. In principle, the last HBS should scale to tens of thousands timely txns per second while preserving what the previous HBSs achieved. We also propose a simple homotopy based transition mechanism which enables us to relatively safely and continuously introduce new HBSs into the existing blockchain. Our approach is constructive and as rigorous as possible and we attempt to analyze all aspects of these developments, al least at a conceptual level. The process is supported by evaluation on recent transaction data.
Kostas Kryptos Chalkias, Angelos Kostis, Ali Alnuaimi, Peter Knez · 8 authors
In the contemporary era, biodiversity conservation emerges as a paramount challenge, necessitating innovative approaches to monitoring, preserving, and enhancing the natural world. This paper explores the integration of blockchain technology in biodiversity conservation, offering a novel perspective on how digital resilience can be built within ecological contexts. Blockchain, with its decentralized and immutable ledger and tokenization affordances, presents a groundbreaking solution for the accurate monitoring and tracking of environmental assets, thereby addressing the critical need for transparency and trust in conservation efforts. Unlike previous more theoretical approaches, by addressing the research question of how blockchain supports digital resilience in biodiversity conservation, this study presents a grounded framework that justifies which blockchain features are essential to decipher specific data contribution and data leveraging processes in an effort to protect our planet's biodiversity, while boosting potential economic benefits for all actors involved, from local farmers, to hardware vendors and artificial intelligence experts, to investors and regular users, volunteers and donors.
This research paper introduces innovative approaches for multivariate time series forecasting based on different variations of the combined regression strategy. We use specific data preprocessing techniques which makes a radical change in the behaviour of prediction. We compare the performance of the model based on two types of hyper-parameter tuning Bayesian optimisation (BO) and Usual Grid search. Our proposed methodologies outperform all state-of-the-art comparative models. We illustrate the methodologies through eight time series datasets from three categories: cryptocurrency, stock index, and short-term load forecasting.
Applying advanced digital technologies such as artificial intelligence (AI), blockchain (BLC), bigdata analytics (BDA) and digital twin (DT)/simulations to enhance supply chain resilience (SCRes) has been widely discussed in light of the global pandemic, regional conflicts, and the technology revolution such as Industry 4.0 and 5.0. Previous studies are limited at the conceptual level as the proactive SCRes measure with a standalone fashion. The intelligent recommendation system (IRS) obtains the capabilities for enhancing SCRes as a reactive digital measure. However, the utilization of the IRS as the SCRes enhancement tool is neglected, investigation on implementing the IRS for the SC disruption response is yet to come. To close these gaps, a data-driven supply chain disruption response IRS baseline framework was proposed by this research as an initial SCRes reactive solution. To guarantee the reliability of the proposed IRS as a stable, secure, and resilient decision support system, blockchain technology is integrated into the baseline architecture. The BLC-IRS framework is demonstrated with user prototype and industrial case to present its executable functions. A system dynamics (SD) simulation model is adopted to validate the BLC-IRS framework, the simulation results indicated that our proposed BLC-IRS can be implemented as an effective a SC disruption response measure. Our developed BLC-IRS contributes an executable SCRes digital solution with synthetic technologies as a reactive SCRes measure, enabled users to mitigate the firm and partial network level disruption in an agile and safe manner.
Utilizing graph analytics and learning has proven to be an effective method for exploring aspects of crypto economics such as network effects, decentralization, tokenomics, and fraud detection. However, the majority of existing research predominantly focuses on leading cryptocurrencies, namely Bitcoin (BTC) and Ethereum (ETH), overlooking the vast diversity among the more than 10,000 cryptocurrency projects. This oversight may result in skewed insights. In our paper, we aim to broaden the scope of investigation to encompass the entire spectrum of cryptocurrencies, examining various coins across their entire life cycles. Furthermore, we intend to pioneer advanced methodologies, including graph transfer learning and the innovative concept of "graph of graphs". By extending our research beyond the confines of BTC and ETH, our goal is to enhance the depth of our understanding of crypto economics and to advance the development of more intricate graph-based techniques.
Inwon Kang, Maruf Ahmed Mridul, Abraham Sanders, Yao Ma · 7 authors
Cryptocurrency is a fast-moving space, with a continuous influx of new projects every year. However, an increasing number of incidents in the space, such as hacks and security breaches, threaten the growth of the community and the development of technology. This dynamic and often tumultuous landscape is vividly mirrored and shaped by discussions within Crypto Twitter, a key digital arena where investors, enthusiasts, and skeptics converge, revealing real-time sentiments and trends through social media interactions. We present our analysis on a Twitter dataset collected during a formative period of the cryptocurrency landscape. We collected 40 million tweets using cryptocurrency-related keywords and performed a nuanced analysis that involved grouping the tweets by semantic similarity and constructing a tweet and user network. We used sentence-level embeddings and autoencoders to create K-means clusters of tweets and identified six groups of tweets and their topics to examine different cryptocurrency-related interests and the change in sentiment over time. Moreover, we discovered sentiment indicators that point to real-life incidents in the crypto world, such as the FTX incident of November 2022. We also constructed and analyzed different networks of tweets and users in our dataset by considering the reply and quote relationships and analyzed the largest components of each network. Our networks reveal a structure of bot activity in Crypto Twitter and suggest that they can be detected and handled using a network-based approach. Our work sheds light on the potential of social media signals to detect and understand crypto events, benefiting investors, regulators, and curious observers alike, as well as the potential for bot detection in Crypto Twitter using a network-based approach.
Stylianos Kampakis, Melody Yuan, Oritsebawo Paul Ikpobe, Linas Stankevicius
In the evolving domain of cryptocurrency markets, accurate token valuation remains a critical aspect influencing investment decisions and policy development. Whilst the prevailing equation of exchange pricing model offers a quantitative valuation approach based on the interplay between token price, transaction volume, supply, and either velocity or holding time, it exhibits intrinsic shortcomings. Specifically, the model may not consistently delineate the relationship between average token velocity and holding time. This paper aims to refine this equation, enhancing the depth of insight into token valuation methodologies.
The increasing significance of sustainability considerations within both public spheres (such as policies and regulations) and private sectors (including voluntary commitments by major multinational corporations) underscores the imperative to harness cutting-edge technological advancements. This is essential to ensure that the momentum of this trend translates into tangible outcomes, thwarting phenomena like greenwashing and upholding high standards of integrity, all while expediting progress through automation. This paper focuses specifically on carbon markets, which, after enduring years of confusion and controversy, may finally be on the brink of converging toward internationally recognized minimum standards. Beginning with an introduction to fundamental concepts pertaining to carbon markets and Distributed Ledger Technologies (DLTs), the paper proceeds to dissect the challenges and opportunities within this burgeoning field. Its primary contribution lies in offering a comprehensive overview of recent developments across various initiatives (such as ICVCM, IETA/WorldBank/CAD Trust, IEEE/ISO) and providing a layered analysis of the entire ecosystem. This framework aids in understanding and prioritising future endeavours. Ultimately, the paper furnishes a set of recommendations aimed at bolstering scalability and fostering widespread adoption of best practices within international markets.
Upgradeable smart contracts (USCs) have been widely adopted to enable modifying deployed smart contracts. While USCs bring great flexibility to developers, improper usage might introduce new security issues, potentially allowing attackers to hijack USCs and their users. In this paper, we conduct a large-scale measurement study to characterize USCs and their security implications in the wild. We summarize six commonly used USC patterns and develop a tool, USCDetector, to identify USCs without needing source code. Particularly, USCDetector collects various information such as bytecode and transaction information to construct upgrade chains for USCs and disclose potentially vulnerable ones. We evaluate USCDetector using verified smart contracts (i.e., with source code) as ground truth and show that USCDetector can achieve high accuracy with a precision of 96.26%. We then use USCDetector to conduct a large-scale study on Ethereum, covering a total of 60,251,064 smart contracts. USCDetecor constructs 10,218 upgrade chains and discloses multiple real-world USCs with potential security issues.
In this paper, we studyhow to optimize existing non-fungible token (NFT) incentives. Upon exploring a large number of NFT-related standards and real-world projects, we uncover an unexpected finding: current NFT incentive mechanisms, often organized in an isolated and one-time-use fashion, tend to overlook their potential for scalable organizational structures. To address this, we propose, analyze, and implement a novelreference incentivemodel, inherently structured as a directed acyclic graph (DAG)-based NFT network. Leveraging the Stackelberg game framework and deep reinforcement learning (DRL), this model aims to maximize connections (or references) between NFTs, enabling isolated NFTs to expand their networks and accumulate rewards from subsequent or subscribed ones. Through both theoretical and practical analyses, we demonstrate the optimal utility of the proposed model.
Renan Lima Baima, Iván Abellán Álvarez, Ivan Pavić, Emanuela Podda
In response to the European Commission's aim of cutting carbon emissions by 2050, there is a growing need for cutting-edge solutions to promote low-carbon energy consumption in public infrastructures. This paper introduces a Proof of Concept (PoC) that integrates the transparency and immutability of blockchain and the Internet of Things (IoT) to enhance energy efficiency in tangible government-held public assets, focusing on curbing carbon emissions. Our system design utilizes a forecasting and optimization framework, inscribing the scheduled operations of heat pumps on a public sector blockchain. Registering usage metrics on the blockchain facilitates the verification of energy conservation, allows transparency in public energy consumption, and augments public awareness of energy usage patterns. The system fine-tunes the operations of electric heat pumps, prioritizing their use during low-carbon emission periods in power systems occurring during high renewable energy generations. Adaptive temperature configuration and schedules enable energy management in public venues, but blockchains' processing power and latency may represent bottlenecks setting scalability limits. However, the proof-of-concept weakness and other barriers are surpassed by the public sector blockchain advantages, leading to future research and tech innovations to fully exploit the synergies of blockchain and IoT in harnessing sustainable, low-carbon energy in the public domain.
Renan Lima Baima, Loïck Chovet, Eduard Hartwich, Abhishek Bera · 7 authors
In the new space economy, space agencies, large enterprises, and start-ups aim to launch space multi-robot systems (MRS) for various in-situ resource utilization (ISRU) purposes, such as mapping, soil evaluation, and utility provisioning. However, these stakeholders' competing economic interests may hinder effective collaboration on a centralized digital platform. To address this issue, neutral and transparent infrastructures could facilitate coordination and value exchange among heterogeneous space MRS. While related work has expressed legitimate concerns about the technical challenges associated with blockchain use in space, we argue that weighing its potential economic benefits against its drawbacks is necessary. This paper presents a novel architectural framework and a comprehensive set of requirements for integrating blockchain technology in MRS, aiming to enhance coordination and data integrity in space exploration missions. We explored distributed ledger technology (DLT) to design a non-proprietary architecture for heterogeneous MRS and validated the prototype in a simulated lunar environment. The analyses of our implementation suggest global ISRU efficiency improvements for map exploration, compared to a corresponding group of individually acting robots, and that fostering a coopetitive environment may provide additional revenue opportunities for stakeholders.