This thesis examines whether cryptocurrencies can function as diversification or risk-reducing assets relative to the Swedish equity market during periods of financial stress. Using daily data for Bitcoin, Ethereum and Ripple from 2018 to 2024, their dynamic relationship with the OMX30 index is analyzed. To provide a broader benchmark, gold, the German DAX index, and the U.S. S&P 500 index are included as comparison assets. Periods of financial stress are identified as episodes in which the OMX30 declines by at least 10 percent from a recent peak. Time-varying correlations are estimated using a Dynamic Conditional Correlation GARCH (DCC-GARCH) model, allowing the analysis of how interasset relationships evolve over time. In addition, hedge effectiveness measures are employed to assess the cryptocurrencies practical ability to reduce portfolio risk.The results show that Bitcoin, Ethereum and Ripple exhibit weak but positive correlations with the Swedish equity market, implying that they may serve as diversifiers but not ashedges or safe-havens. During periods of financial stress, correlations tend to increase rather than decrease, indicating limited protective properties. Hedge effectiveness estimates further suggest that the risk-reducing capacity of cryptocurrencies is unstable and generally weak. Incontrast, gold displays more consistent negative correlations and superior hedging performance. Overall, the findings suggest that cryptocurrencies offer limited diversification benefits for Swedish investors and should not be considered reliable risk-mitigating assets during market stress.
Sai Srikanth Madugula, jose Luis de la Rosa Esteva, Daya Shankar
This paper presents an integrated framework for decentralized invoice-backed loan underwriting combining interpretable machine learning, dynamic pricing algorithms, and on-chain trust infrastructure. We develop and validate SHAP-explainable ML models for real-time default probability assessment, design a Reverse Kelly AMM smart contract for optimal risk-adjusted loan pricing, integrate ERC-725 identity and on-chain reputation scoring with an automated insurance reserve, and deploy the system on Ethereum testnet with end-to-end functional and security testing. Stress testing across simulated default and fraud scenarios demonstrates the model achieves AUC-ROC of 0.89 on validation data, maintains LP yields of 12ā18% under normal conditions while containing non-performing loan ratios below 3% under adverse scenarios, and sustains reserve solvency across 95th percentile stress events. The framework addresses critical gaps in DeFi lending by bridging regulatory interpretability requirements with decentralized credit assessment, demonstrating both technical feasibility and economic viability for permissionless SME financing at scale.
Saha Reno, Koushik Roy, G M Abdullah Al Kafi, Khandakar Md Shafin
ABSTRACT The simultaneous achievement of scalability, security and decentralisation remains an open problem for distributed ledger technologies. This paper introduces InternxtChain, a novel framework leveraging Internxt's decentralised storage infrastructure with zeroāknowledge proofs (ZKPs) and sharded proofāofāstorage (SPoS) consensus. Specifically, erasureācoded sharding ensures data availability and fault tolerance by splitting files into encoded fragments distributed across nodes; BLSā381 aggregated signatures enable efficient consensus by compressing multiple signatures into a single short proof; and zkāSNARK audits provide tamperāevident storage verification without revealing user data. InternxtChain addresses this challenge through three synergistic mechanisms: (i) erasureācoded sharding with (6,3) ReedāSolomon encoding, (ii) zkāSNARKs for storage auditability and (iii) an SPoS consensus based on BLSā381 aggregated signatures. Experimental evaluation on a testbed of 2048 nodes across 16 geographic regions shows that InternxtChain processes 2800 transactions per second (TPS) with a median latency of 420 ms, while maintaining 99.9% data integrity under up to 30% Byzantine nodes. These results establish a practical path toward harmonising Web3 principles with realāworld throughput, cost and General Data Protection Regulation (GDPR) auditability requirements.
Virtually Disposable: A Theory and History of Digital Trash historicizes and critically analyzes the conditions under which the cultural category of trash does and does not come into play in historical and contemporary digital environments, from the American 1980s to the present. Understanding the determination of real-world objects as trash as a reflexive, cross-cultural aspect of the maintenance of physical space, this dissertation evaluates the criteria according to which modern users and web platforms perform the same calculus in their management of digital files, from the machine-readable forms of data collected by corporate entities to artifacts as potentially personally meaningful as a photo stored on oneās cell phone. Chapter 1 traces the development of delete functions on business-oriented personal computers, posing the graphical representation of delete in trash can-styled icons on user-facing interfaces as a design response to the limited storage affordances of local machines in the 1980s. This chapter locates the trash can icon as part of the same genealogy of digital waste-management strategies as commercial content-moderation of the Web 2.0 era (ca. 2006ā) but advances their respective models of digital disposability in terms of ādata custodiansā (local, determined by storage) and ācontent janitorsā (non-local, informed by hygiene). Chapter 2 thinks about the contemporary management of user-generated content by platforms in terms of archive, arguing that the cloud has ushered in a post-storage moment. The tacit valuation of user-generated content as data under platform capitalism may account for its seemingly indefinite maintenance online, but the same conditions of preservation and display on the user profile provoke new anxieties in the user that act upon their online self-representation in digital space, resulting in curated, digitally hygienic archives that are neither truly personal nor all that personally revealing. Chapter 3 then assesses the extent to which the value reflexively assigned to user-generated content (with a particular interest in digital images, in this case) under platform capitalism may reliably translate into extra-digital systems of value. This chapter catalogs various failed attempts on the part of relatively empowered cultural institutions such as the museum and art world to confer value on born-digital visual art and reads them in conversation with similarly doomed efforts to stabilize digital images as financial commodities, most notably in the form of non-fungible tokens (NFTs). Although digital media may appear virtual, digital culture of our post-cloud, post-Web 2.0 moment relies upon material infrastructure, the ongoing support of which depends upon the consumption of rare-earth minerals, energy, and water and which results in the generation of toxic e-waste. Virtually Disposable builds upon research located at the intersection of critical discard studies and environmental media studies by questioning the implicit determinations of value that inform and have informed the personal and corporate maintenance and disposal of digital files both today and historically. Analyzing the logics according to which a digitally mediated thing becomes disposable, as well as attending to the functional suspension of trash as a cultural category under the cloud and financial imperatives of platform capitalism, this dissertation accounts for a variable in the equation seldom examined in existing studies of the flows of e-waste alone: that the physical machines that afford file-storage reach their breaking points in no small part due to the deluge of value-unclear files they are now made to store and process.
Carlos NĆŗ Nez-GĆ³mez, VĆctor Garcia-Font, Helena RifĆ -Pous, Muhammad Asad
The submitted work contains the following highlights: ā¢ We detail NxGenT, a decentralized reputation system for B5G and 6G networks. ā¢ We propose a three-phase reputation mechanism based on smart contracts. ā¢ We implement NxGenT and release its source code as open-source software. ā¢ We analyze NxGenTās design and resilience against relevant trust attacks. ā¢ We evaluate NxGenTās functionality and performance through experimental analysis. The evolution towards Beyond 5G (B5G) and 6G networks presents highly heterogeneous and dynamic scenarios in which numerous entities, including network operators, service providers and end users, interact in environments of mutual trust. However, the open nature of these networks poses significant challenges regarding security and trust, as traditional centralized mechanisms may prove inadequate or insufficient in such scenarios. In this context, decentralized trust systems are positioned as a promising solution to assess the reliability of entities participating in B5G and 6G networks, thereby enhancing decision-making processes and resilience of these environments. This paper introduces NxGenT , a decentralized reputation system based on blockchain and smart contracts for B5G/6G networks that guarantees the immutability and transparency of the collected evidence on entitiesā performance and behavior, while decentralizing and automating the reputation mechanism. NxGenT is a decentralized, trustless system in which entities establish and verify compliance with Service Level Agreements (SLA) and provide feedback or subjective opinions about the entities they interact with in order to compute and assign reputation scores. To evaluate the proposal, we implement a local B5G testbed that deploys the primary components of this type of network, along with a second cloud-based testbed to analyze scalability in networks of different sizes. Finally, we contextualize NxGenT within the 6GENABLERS project as a representative use case of the proposed trust system, thus demonstrating its applicability in real scenarios.
Graph neural networks (GNNs) have shown notable success in identifying security vulnerabilities within Ethereum smart contracts by capturing structural relationships encoded in control- and data-flow graphs. Despite their eff... | Find, read and cite all the research you need on Tech Science Press
The rise of Bitcoin has revolutionized the financial landscape, but it has also opened the door to a new era of criminal activities. Criminals take advantage of the anonymity provided by Bitcoin to conduct illicit transactions and engage in fraudulent activities. To address this issue, this paper proposes a detection model using Graph Neural Networks (GNNs) to detect fraudulent activities in the complex financial systems of Bitcoin. From the GNNs, we use EvolveGCN and EvolveGGCN to compare between them and find a powerful model that can investigate the network construction of financial transactions and capture patterns and anomalies that traditional methods may miss. In the literature, there have been a limited number of studies on Bitcoin fraud detection using GNNs, especially EvolveGGCN. Therefore, in this paper, we focus on the detection of fraud in the Bitcoin network using EvolveGCN and EvolveGGCN. In addition, we used a more recent dataset called Elliptic++, which is an extension of the Elliptic Dataset. The dataset provides valuable information on the behavior and patterns of fraudulent actions in the Bitcoin network. The results show that EvolveGGCN outperforms other models in terms of precision, recall, F1 score, and micro-F1 score. With an F1-score of 0.90 and micro-F1 of 0.93 for detecting illicit transactions in the early time steps.