Francesca Di Giuseppe, Joe McNorton, Anna Lombardi, Fredrik Wetterhall
Abstract Recent advancements in machine learning (ML) have expanded the potential use across scientific applications, including weather and hazard forecasting. The ability of these methods to extract information from diverse and novel data types enables the transition from forecasting fire weather, to predicting actual fire activity. In this study we demonstrate that this shift is feasible also within an operational context. Traditional methods of fire forecasts tend to over predict high fire danger, particularly in fuel limited biomes, often resulting in false alarms. By using data on fuel characteristics, ignitions and observed fire activity, data-driven predictions reduce the false-alarm rate of high-danger forecasts, enhancing their accuracy. This is made possible by high quality global datasets of fuel evolution and fire detection. We find that the quality of input data is more important when improving forecasts than the complexity of the ML architecture. While the focus on ML advancements is often justified, our findings highlight the importance of investing in high-quality data and, where necessary create it through physical models. Neglecting this aspect would undermine the potential gains from ML-based approaches, emphasizing that data quality is essential to achieve meaningful progress in fire activity forecasting.
This article examines the regulatory approaches of Hong Kong and the United Kingdom (UK) towards cryptocurrencies , highlighting their distinct regulatory philosophies and frameworks. Hong Kong has adopted a comprehensive and proactive regulatory approach, creating a dual-licensing regime for virtual asset trading platforms covering security and non-security tokens and tailoring the existing licensing framework under the Securities and Futures Ordinance to risks in managing and distributing portfolios that invest in virtual assets. This measured approach prioritises investor protection, market integrity, and financial crime prevention while fostering an innovation-friendly environment. Conversely, the UK has taken a conservative stance, integrating cryptocurrency regulation into existing financial systems and prioritising stability, consumer protection, and control over speculative risks. The UK’s framework emphasises Anti-Money Laundering and Counter-Terrorism Financing compliance, registration for crypto-related activities, and restrictions on high-risk products for retail investors. Through comparative analysis, this article illustrates how both jurisdictions balance regulatory oversight with financial innovation and how their regulatory strategies reflect their economic and financial priorities. The findings suggest that Hong Kong’s flexible, forward-looking approach, characterised by dedicated licensing, proactive regulation, and tailored investor protection, positions it as an agile player in the evolving crypto landscape. In contrast, the UK’s framework leans heavily on stability and consumer safeguards. Ultimately, Hong Kong emerges as a rising Asian crypto hub, embracing growth and innovation, while the UK focuses on reinforcing its regulatory defences. This comparison sheds light on how regional priorities shape cryptocurrency regulation, offering insights into the broader global regulatory landscape.
The article deals with the theoretical basics of digital asset valuation and substantiates the need for their integration into modern financial analysis and corporate finance. It concludes that traditional methods—discounted cash flow (DCF), the capital asset pricing model (CAPM), and comparative multiple analysis—have proven effective in valuing stocks, bonds, and other traditional instruments, but are limited in the digital economy. Cryptocurrencies, utility tokens, digital rights, and non-fungible tokens (NFTs) possess unique features: intangible nature, lack of guaranteed cash flows, high price volatility, dependence on network effects, and decentralization. The article looks into the latest adaptation of valuation methods, including network metrics (market capitalization to transaction volume ratio (NVT), Metcalfe's law), modified fee discounting models, and scenario-based venture approaches. It also explores the using the MV=PQ equation for tokenomics analysis and the determining of a "price floor" by means of mining or staking cost. Particular attention is paid to the role of Big Data and on-chain analytics, which enable applying open blockchain data on transactions and users' activity, as well as using artificial intelligence and machine learning algorithms for price forecasting, identifying fundamental value, and separating it from speculative factors. It emphasizes the need to expand the conceptual framework, to consider legal definitions, and develop specialized models for various token types (utility, security, stablecoins, NFTs) that take into account the technological characteristics of networks, incentive economics, and behavioral factors. It is concluded that integrating digital assets into financial valuation theory requires an interdisciplinary approach that compraises finance, network economics, legal regulation, data-driven analytics, and tokenomics engineering design.
This study provides empirical evidence that cryptocurrency market movements are influenced by sentiment extracted from social media. Using a high frequency dataset covering four major cryptocurrencies (Bitcoin, Ether, Litecoin, and Ripple) from October 2017 to September 2021, we apply state-of-the-art natural language processing techniques on tweets from influential Twitter accounts. We classify sentiment into positive, negative, and neutral categories and analyze its effects on log returns, liquidity, and price jumps by examining market reactions around tweet occurrences. Our findings show that tweets significantly impact trading volume and liquidity: neutral sentiment tweets enhance liquidity consistently, negative sentiments prompt immediate volatility spikes, and positive sentiments exert a delayed yet lasting influence on the market. This highlights the critical role of social media sentiment in influencing intraday market dynamics and extends the research on sentiment-driven market efficiency.
Advances in digital technology - particularly Web3’s pseudonymity and decentralized naming systems, combined with information flows’ anonymity, accessibility, and cross-border nature - enable terrorist organizations to recruit members and perpetrate discrete socially dangerous acts. Conventional reactive counterterrorism measures prove inadequate against rapid illicit content dissemination that leaves detectable digital traces. This study explores artificial intelligence’s (AI) counter-criminal potential on machine learning and predictive analytics for proactively identifying and preventing terrorist activity through behavioral indicators and digital footprints that facilitate a strategic shift to proactive security paradigms. The research develops a multimodal analytical framework integrating natural language processing, computer vision, audio analysis, and social network analysis, detailing the complete machine learning pipeline from data preprocessing to model deployment. It examines the “RED-Alert” system as practical implementation and proposes a novel “Threshold Adaptive Intervention” (PORA) module utilizing graph neural networks and time-series analysis for digital risk assessment. Machine learning excels at threat detection and digital evidence generating, necessitating reevaluation of internet service providers’ (ISP) liability - particularly collective digital inaction. A differentiated liability framework accounts for providers’ technical influence while treating AI-derived risk indicators as ancillary tools for establishing individual culpability. Machine learning and predictive analytics enable a strategic shift to proactive counterterrorism.
Open access
Terrorism, Counterterrorism, and Political Violence
This article explores the impacts of digital transformations and new technologies in industrial sector (particularly through the Fourth Industrial Revolution) on optimizing production processes. Characterized by key technologies such as the Internet of Things (IoT), big data analytics, artificial intelligence (AI), blockchains, and advanced robotics, Industry 4.0 has significantly shaped modern manufacturing management. IoT enables autonomous communications between machines and equipment, providing real-time insights into production parameters and enabling predictive maintenance, and big data plays a vital role by analyzing the large volumes of data that are generated by these devices, thus supporting informed management decisions. AI and machine learning help automate complex tasks, optimize production schedules, and improve product quality through real-time adjustments. Blockchain enables decentralized and secure data recording, which is particularly useful in supply-chain management. Advanced robotics increases production speed and accuracy, thus reducing labor costs and mitigating any risks that are associated with hazardous tasks. Integrating these technologies requires strategic planning, including identifying key challenges, conducting pilot projects, integrating with existing IT and OT systems, and managing organizational change. Measuring the effectiveness of Industry 4.0 implementation should involve well-defined key performance indicators (KPIs) and return-on-investment (ROI) analysis. The primary challenges that are associated with adopting Industry 4.0 include the alignment of technology with specific business needs, employee resistance to change, and hidden costs of implementation. In summary, industrial transformation offers opportunities for companies to optimize production processes, reduce costs, and increase competitiveness in the global marketplace. However, a careful approach is necessary to maximize efficiency, foster innovation, and secure long-term success in an increasingly digitalized world.
The consensus protocol is essential in practically every blockchain application. Most of these existing blockchain consensus protocols need massive computationalcapabilities, substantial energy consumption, and dependency on monetary stakes. These shortcomings in the mainstream consensus approach lead to their unsuitability for low-resource applications like IoT. As a result of this work, a lightweight consensus process referred as Delegated Proof of Accessibility(DPoAC) is implemented and evaluated. DPoAC makes use of Shamir secret sharing, Proof of Stake (PoS) with random selection, and the Inter-PlanetaryFile System (IPFS). The DPoAC operation is composed of four modules: secret generation and distribution, retrieval of secret shares, block creation andverification, and block rewards and penalty. A detailed description of DPoAC has been provided and implemented in JavaScript and experimental resultsdemonstrate that our solution meets the necessary performance and security requirements for a lightweight scalable protocol for IoT systems.
John C. Kolesar, Shan Ali, Timos Antonopoulos, Ružica Piskač
Zero-knowledge (ZK) protocols enable software developers to provide proofs of their programs’ correctness to other parties without revealing the programs themselves. Regular expressions are pervasive in real-world software, and zero-knowledge protocols have been developed in the past for the problem of checking whether an individual string appears in the language of a regular expression, but no existing protocol addresses the more complex PSPACE-complete problem of proving that two regular expressions are equivalent. We introduce Crêpe , the first ZK protocol for encoding regular expression equivalence proofs and also the first ZK protocol to target a PSPACE-complete problem. Crêpe uses a custom calculus of proof rules based on regular expression derivatives and coinduction, and we introduce a sound and complete algorithm for generating proofs in our format. We test Crêpe on a suite of hundreds of regular expression equivalence proofs. Crêpe can validate large proofs in only a few seconds each.
Angela Hemesath, William M. Tian, Bryce W. Polascik, Suzanna Joseph · 10 authors
Purpose:. To combine the perspectives of health and commercialization experts on the ethical and regulatory needs for non-fungible token (NFT) implementation in healthcare.Design:. PerspectiveMethods:. For a multidisciplinary perspective by an interdisciplinary group, current event articles and research articles were interpreted and assessed.Results:. Health data has become fragmented and disorganized, resulting in poor accessibility, increased administrative costs, and integrity vulnerability. Healthcare is uniquely suited to adopt blockchain and NFT technology as potential solutions. The incorporation of blockchain technology may offer multiple improvements in data-sharing through consensus, tokenization, and decentralization. However, the current regulatory infrastructure to support blockchain is poorly defined.Conclusions:. Healthcare NFTs would revolutionize patient control over their health data and promote more ethical transparency of data ownership while also reducing administrative security costs. However, blockchain poses unprecedented requirements of healthcare regulation within the unique realms of patient privacy and data ownership. Large-scale implementation of blockchain cannot be achieved without regulatory collaboration.
Open access
Pharmaceutical Economics and Policy
Health Systems, Economic Evaluations, Quality of Life
The Internet of Things (IoT) is a disruptive technology that underpins Industry 5.0 by integrating various service technologies to enable intelligent connectivity among smart objects. These technologies enhance the convergence of Information Technology (IT), Operational Technology (OT), Core Technology (CT), and Data Technology (DT) networks, improving automation and decision-making capabilities. While cloud computing has become a mainstream technology across multiple domains, it struggles to efficiently manage the massive volume of OT data generated by IoT devices due to high latency, data transfer costs, limited resilience, and insufficient context awareness. Fog computing has emerged as a viable solution, extending cloud capabilities to the edge through a distributed peer-to-peer (P2P) network, enabling decentralized data processing and management. However, IoT networks still face critical challenges, including connectivity, heterogeneity, scalability, interoperability, security, and real-time decision-making constraints. Security is a key challenge in IoT implementations, including secure data communication, IoT edge and fog device identity, end-to-end authentication, and secure storage. This paper presents an efficient blockchain-based framework that creates a secure end-to-end communication cooperative flow IoT network. The framework utilizes a hybrid blockchain network that collaborates to offer a collaborative flow of end-to-end secure communication from end devices to cloud storage. The fog servers will maintain a private blockchain as a next-generation public key infrastructure to identify and authenticate the IoT's edge devices. The consortium blockchain will be maintained in the cloud and integrated with the permission blockchain system. This system ensures secure cloud storage, authorization, efficient key exchange, and remote protection (encryption) of all sensitive information. To improve the synchronization and block generation, reduce overhead, and ensure scalable IoT network operation, we proposed the threshold signature-based Proof of Stake and Validation (PoSV) consensus. Additionally, lightweight authentication protects resource-constrained IoT nodes using an aggregate signature, ensuring security and performance in real-time scenarios. The proposed system is implemented, and its performance is evaluated using key metrics such as cryptographic processing overhead, consensus efficiency, block acceptance time, and transaction delay. The findings show that threshold signature-based Proof of Stake and Validation (PoSV) consensus, reduces the computational burden of individual signature verification, which results in an optimized transaction latency of 80-150 ms, compared to the previous 100-200 ms without Non-PoSV. Additionally, aggregating multiple signatures from different authentication events reduces signing time by 1.98 ms compared to the individual signature time of 2.72 ms and the overhead of verifying multiple individual transactions is 2.87 ms is significantly reduced to1.46 ms along with authentication delay ranges between 95-180 ms. Hence, the proposed framework improves over existing approaches regarding linear computing complexity, increased cryptographic methods, and a more efficient consensus process.
Open access
Blockchain Technology Applications and Security
IoT and Edge/Fog Computing
Advanced Steganography and Watermarking Techniques
Abstract Anti-money laundering has been an issue in our society from the beginning of time. It simply refers to certain regulations and laws set by the government to uncover illegal money, which is passed as legal income. Now, with the emergence of cryptocurrency, it ensures pseudonymity for users. Cryptocurrency is a type of currency that is not authorized by the government and does not exist physically but only on paper. This provides a better platform for criminals for their illicit transactions. New algorithms have been proposed to detect illicit transactions. Machine learning and deep learning algorithms give us hope in identifying these anomalies in transactions. We have selected the Elliptic Bitcoin Dataset. This data set is a graph data set generated from an anonymous blockchain. Each transaction is mapped to real entities with two categories: licit and illicit. Some of them are not labeled. We have run different algorithms for predicting illicit transactions like Logistic Regression, Long Short Term Memory, Support Vector Machine, Random Forest, and a variation of Graph Neural Networks, which is called Graph Convolution Network (GCN). GCN is of special interest in our case. Different evaluation parameters such as accuracy, ROC and F1 score are analyzed for different models. Our experimental results show that the proposed GCN model gives the accuracy $$98.5\%$$ , the AUC 0.9444 and the RMSE 0.1123, which concludes that our GCN is better than the existing models, in particular with the model proposed in Weber et al. (Anti-money laundering in bitcoin: experimenting with graph convolutional networks for financial forensics, 2019. http://arxiv.org/abs/1908.02591 ).
ABSTRACT Ensuring the security and privacy of sensitive health data in Internet of Things (IoT)‐based healthcare systems (HCS) is a critical challenge. This paper proposes a robust security framework by integrating blockchain mechanisms and deep learning (DL) approaches to enhance security and data privacy. The proposed framework leverages the Ethereum blockchain with zero knowledge proof (ZKP) to ensure data integrity and confidentiality, while the interplanetary file system (IPFS) provides secure and efficient data storage. Additionally, a novel At‐GAN‐BiLSTM model is introduced for intrusion detection by combining the attention mechanism, generative adversarial networks (GAN) and bidirectional long short‐term memory (Bi‐LSTM) to improve detection accuracy and also help to enhance model robustness. The proposed model is evaluated by two different benchmark datasets, namely CICIDS‐2018 (D1) and ToN‐IoT (D2), achieving accuracies of 99.9% and 99.1%, respectively. Comparative investigation shows that the proposed approach reduces false alarm rates (FAR) and performs better than current models in identifying impersonation, insider, and man‐in‐the‐middle (MITM) attacks. By integrating blockchain and DL, the proposed framework significantly enhances intrusion detection, data security, and overall system resilience, addressing key vulnerabilities in IoT‐based healthcare security.
The rapid advancement of Large Language Models (LLMs) has catalyzed the development of multi-agent systems, where multiple LLM-based agents collaborate to solve complex tasks. However, existing systems predominantly rely on centralized coordination, which introduces scalability bottlenecks, limits adaptability, and creates single points of failure. Additionally, concerns over privacy and proprietary knowledge sharing hinder cross-organizational collaboration, leading to siloed expertise. To address these challenges, we propose AgentNet, a decentralized, Retrieval-Augmented Generation (RAG)-based framework that enables LLM-based agents to autonomously evolve their capabilities and collaborate efficiently in a Directed Acyclic Graph (DAG)-structured network. Unlike traditional multi-agent systems that depend on static role assignments or centralized control, AgentNet allows agents to specialize dynamically, adjust their connectivity, and route tasks without relying on predefined workflows. AgentNet’s core design is built upon several key innovations: (1) Fully Decentralized Paradigm: Removing the central orchestrator, allowing agents to coordinate and specialize autonomously, fostering fault tolerance and emergent collective intelligence. (2) Dynamically Evolving Graph Topology: Real-time adaptation of agent connections based on task demands, ensuring scalability and resilience. (3) Adaptive Learning for Expertise Refinement: A retrieval-based memory system that enables agents to continuously update and refine their specialized skills. By eliminating centralized control, AgentNet enhances fault tolerance, promotes scalable specialization, and enables privacy-preserving collaboration across organizations. Through decentralized coordination and minimal data exchange, agents can leverage diverse knowledge sources while safeguarding sensitive information. Experimental results demonstrate that AgentNet outperforms traditional centralized multi-agent systems, significantly improving efficiency, adaptability, and scalability in dynamic environments, making it a promising foundation for next-generation autonomous, privacy-respecting multi-agent ecosystems.
The protection of personal privacy has become a paramount issue in the field of data science, with its significance continuously rising. Differential privacy technology has garnered significant attention for its effectiveness in preserving individual privacy. However, the implementation of differential privacy relies on a degree of trust in the entities or individuals executing the algorithms. This paper proposes an innovative solution: a verifiable differential privacy mechanism based on zero-knowledge proofs. This approach integrates differential privacy with zero-knowledge proof technology to not only verify the correctness of the differential privacy techniques but also enhance the transparency and reliability of the algorithms. Additionally, we have designed a publicly verifiable data release scheme that integrates commitment mechanisms and range proofs, ensuring that the range of published data noise does not exceed predetermined thresholds, thereby ensuring the utility of the data. Compared to other verifiable differential privacy solutions, our approach is unique in that it does not rely on the number of participants but is solely dependent on the precision of the data. This means that our computational cost will not increase with the addition of more participants. Finally, we conducted a performance evaluation of the solution, which only took 700ms to complete a single verification. On average, there was a 6% reduction in expectation and a 40% reduction in variance, demonstrating the enhancement of its data utility and the feasibility and effectiveness in practical applications.
Blockchain and Distributed Ledger Technology (DLT) have emerged at once to transform various industries through revolutionary innovations that secure transactions and develop smart contracts as well as detect and prevent fraud. Blockchain technology serves the purpose of this study to better secure digital transactions and render them more transparent while also achieving greater efficiency. Blockchain protects records from tampering because of its decentralized structure and unalterable properties so organizations achieve reduced risk of fraud and unauthorized changes. Smart contracts act as automated self-executing agreements which perform predefined rules to minimize transaction needs of intermediaries thus reducing operational costs The research addresses implementation barriers of blockchain adoption including the challenges related to scalability and regulatory challenges in addition to energy consumption issues. This paper presents investigative research about blockchain and DLT using case examples to show their capability for generating economic innovation while promoting digital integrity in modern digital markets
Purpose Non-fungible tokens (NFTs) are reshaping art markets and gaining strong stakeholder interest. While research has examined their applications in art ecosystems, their role in advancing Web3D markets remains unclear. Design/methodology/approach A systematic literature review was conducted to investigate the impact of NFTs on the Web3D market and its impact on stakeholders, analysing 89 systematically selected articles. Findings The results of the study show that NFTs in the Web3D context can enhance privacy and trust through blockchain technology and protect intellectual property and ownership rights while influencing market dynamics, behaviour and investment strategies. Originality/value As the Web3D ecosystem grows, ongoing research and collaboration are critical to developing strategies that ensure sustainability, transparency and innovation in digital arts. This study is the first step in exploring these dynamics. Highlights
This paper examines the hesitancy of Australian musicians towards embracing the music non-fungible token (NFT) as a commodity. Drawing on the concepts of cultural autonomy and the digital disruptive sublime, the study argues that the overtly economic nature of NFTs challenges the ideology of creative independence in the hegemonic ‘indie’ music scene. Through interviews with nine Australian musicians who participated in our project, the research finds a cautious curiosity towards the technology, with technical barriers and a perceived cultural disconnect between the NFT ‘community’ and traditional music scenes contributing to hesitation. The paper concludes that attempts to engineer disruption in the music industry through web3/blockchain technology have thus far failed to attract sustained interest from musicians, as the cultural norms and practices associated with NFTs do not align with the values of the existing indie music ecosystem. The findings highlight the difficulties in planning and engineering cultural change within the music industry.
This study explores the optimized application of combining blockchain (Blockchain) and artificial intelligence (AI) in the intelligent risk control of decentralized finance (DeFi). Although the decentralization and transparency of DeFi have driven financial innovation, they have also introduced risks related to market manipulation, smart contract vulnerabilities, and liquidity. Traditional centralized risk control approaches struggle to adapt. This research proposes a blockchain+AI-based intelligent risk control framework. Blockchain’s tamper-resistance enhances transaction security, while AI’s intelligent learning capabilities improve risk identification. Experimental results show that this model outperforms traditional solutions in detection accuracy (94.1%), false alarm rate (2.1%), and detection latency (180ms), and it remains robust under high market volatility. The findings suggest that combining blockchain and AI can effectively strengthen DeFi risk control, enhance system transparency and security, and provide theoretical and practical directions for future intelligent and automated risk management.
Ujun Jeong, Lynnette Hui Xian Ng, Kathleen M. Carley, Huan Liu
Decentralized online social networks have evolved from experimental stages to operating at unprecedented scale, with broader adoption and more active use than ever before. Platforms like Mastodon, Bluesky, Hive, and Nostr have seen notable growth, particularly following the wave of user migration after Twitter's acquisition in October 2022. As new platforms build upon earlier decentralization architectures and explore novel configurations, it becomes increasingly important to understand how these foundations shape both the direction and limitations of decentralization. Prior literature primarily focuses on specific architectures, resulting in fragmented views that overlook how different social networks encounter similar challenges and complement one another. This paper fills that gap by presenting a comprehensive view of the current decentralized online social network landscape. We examine four major architectures: federated, peer-to-peer, blockchain, and hybrid, tracing their evolution and evaluating how they support core social networking functions. By linking these architectural aspects to real-world cases, our work provides a foundation for understanding the societal implications of decentralized social platforms.
Francisco Gomes Figueira, Martin Derka, Ching Lun Chiu, Jan Gorzny
A rollup network is a type of popular "Layer 2" scaling solution for general purpose "Layer 1" blockchains like Ethereum. Rollups networks separate execution of transactions from other aspects like consensus, processing transactions off of the Layer 1, and posting the data onto the underlying layer for security. While rollups offer significant scalability advantages, they often rely on centralized operators for transaction ordering and inclusion, which also introduces potential risks. If the operator fails to build rollup blocks or propose new state roots to the underlying Layer 1, users may lose access to digital assets on the rollup. An escape hatch allows users to bypass the failing operator and withdraw assets directly on the Layer 1. We propose using a time-based trigger, Merkle proofs, and new resolver contracts to implement a practical escape hatch for these networks. The use of novel resolver contracts allow user owned assets to be located in the Layer 2 state root, including those owned by smart contracts, in order to allow users to escape them. This design ensures safe and verifiable escape of assets, including ETH, ERC-20 and ERC-721 tokens, and more, from the Layer 2.
Smart contracts are fundamental pillars of the blockchain, playing a crucial role in facilitating various business transactions. However, these smart contracts are vulnerable to exploitable bugs that can lead to substantial monetary losses. A recent study reveals that over 80% of these exploitable bugs, which are primarily functional bugs, can evade the detection of current tools. Automatically identifying functional bugs in smart contracts presents challenges from multiple perspectives. The primary issue is the significant gap between understanding the high-level logic of the business model and checking the low-level implementations in smart contracts. Furthermore, identifying deeply rooted functional bugs in smart contracts requires the automated generation of effective detection oracles based on various bug features.To address these challenges, we design and implement PromFuzz, an automated and scalable system to detect functional bugs in smart contracts. In PromFuzz, we first propose a novel Large Language Model (LLM)-driven analysis framework, which leverages a dual-agent prompt engineering strategy to pinpoint potentially vulnerable functions for further scrutiny. We then implement a dual-stage coupling approach, which focuses on generating invariant checkers that leverage logic information extracted from potentially vulnerable functions. Finally, we design a bug-oriented fuzzing engine, which maps the logical information from the high-level business model to the low-level smart contract implementations, and performs the bug-oriented fuzzing on targeted functions. We evaluate PromFuzz from 4 perspectives on 5 ground-truth datasets and compare it with multiple state-of-the-art methods. The results show that PromFuzz achieves 86.96% recall and 93.02% F1-score in detecting functional bugs, marking at least a 50% improvement in both metrics over state-of-the-art methods. Moreover, we perform an in-depth analysis on 10 real-world DeFi projects and detect 30 zero-day bugs. Our further case studies, the risky first deposit bug and the AMM price oracle manipulation bug on real-world DeFi projects, demonstrate the serious risks of the exploitable functional bugs in smart contracts. Up to now, 24 zero-day bugs have been assigned CVE IDs. Our discoveries have safeguarded assets totaling $18.2 billion from potential monetary losses.
Manfred Musigmann, Christine Spiekers, Jacob Stake, Burak Han Akkurt · 8 authors
Abstract In our study, we aim to predict the antibody serostatus of patients with suspected autoimmune encephalitis (AE) using machine learning based on pre-contrast T2-weighted MR images acquired at symptom onset. A confirmation of seropositivity is of great importance for a reliable diagnosis in suspected AE cases. The cohort used in our study comprises 98 patients diagnosed with AE. 57 of these patients had previously tested positive for autoantibodies associated with AE. In contrast, no antibodies were detected in the remaining 41 patients. A manual bilateral segmentation of the hippocampus was performed using the open-source software 3D Slicer on T2-weighted MR-images. Subsequently, 107 Radiomics features were extracted from each T2-weighted MR image utilizing the open source PyRadiomics software package. Our study cohort was randomly divided into training and independent test data. Five conventional machine learning algorithms and a neural network were tested regarding their ability to differentiate between seropositive and seronegative patients. All performance values were determined based on independent test data. Our final model includes six features and is based on a Lasso regression. Using independent test data, this model yields a mean AUC of 0.950, a mean accuracy of 0.892, a mean sensitivity of 0.892 and a mean specificity of 0.891 in predicting antibody serostatus in patients with suspected AE. Our results show that Radiomics-based machine learning is a very promising method for predicting serostatus of suspected AE patients and can thus help to confirm the diagnosis. In the future, such methods could facilitate and accelerate the diagnosis of AE even before the results of specific laboratory tests are available, allowing patients to benefit more quickly from a reliable treatment strategy.
In order to solve the problem of low security of high network information sharing, the architecture and implementation of agent-based information sharing and pushing system are proposed. Firstly, the information node registration algorithm is introduced to draw the organization network connected to the public network into an orderly block network and extract the abnormal information of network nodes; Secondly, a complete network identity security threat intelligence is generated through effective scheduling and transmission of network identity security threat intelligence; Finally, the distributed ledger technology is introduced to match it with IP network, and the private chain and data are extracted by constructing data structure on the information chain to realize the generation and operation of intelligence information sharing model. The experimental results show that the model designed in this paper can protect the privacy information that all network users need to protect in the process of sharing information resources, and its privacy protection intensity exceeds 0.950. Conclusion: The proposed sharing model can realize the safe sharing of user intelligence information in practical application, and the user’s private intelligence information will not be leaked during the sharing process, which reduces the threat degree of network security.
The paper explores the complex issue of accounting and taxation related to digital means of payment, with a particular focus on cryptocurrency. The growing importance of the topic is evident from two main factors: firstly, the rapid increase in cryptocurrency trading volumes worldwide, and secondly, the insufficient technical capabilities of tax authorities to effectively monitor and regulate such transactions. Despite the growing global interest in cryptocurrencies, the taxation and accounting of these digital assets remain a significant challenge, primarily due to the absence of universally recognized and established approaches to their regulation. The study highlights a critical gap in understanding the nature of cryptocurrency. It is unclear whether cryptocurrency should be considered a currency, a commodity, or a form of payment or exchange. This ambiguity contributes to the challenges faced in both legal enforcement and taxation. Without a clear legal definition or status for cryptocurrencies, it becomes extremely difficult to implement consistent taxation policies that can be applied universally. The paper emphasizes that the current regulatory framework for cryptocurrency transactions is fragmented. While a range of legal and regulatory acts exists, they fail to provide a cohesive, standardized approach to governing these digital currencies. In addition to addressing these theoretical issues, the paper systematically analyzes the experiences of various countries in the field of cryptocurrency tax regulation. This comparison reveals certain global trends in the taxation of digital currencies, showcasing both successful models and ongoing challenges. The study also delves into the specific characteristics of cryptocurrency taxation in Ukraine, drawing attention to the unique challenges faced by the country in aligning its tax policies with global standards. The paper identifies several key problems in the taxation of cryptocurrency transactions, such as the lack of comprehensive tax guidelines, the difficulty of tracking transactions, and the challenges in categorizing cryptocurrency for tax purposes. It also discusses the potential future developments in cryptocurrency taxation, both in Ukraine and internationally. The study assesses the prospects of creating a more effective and unified tax system for digital currencies, emphasizing the importance of international collaboration and the need for updated legal frameworks to address the growing role of cryptocurrencies in the global economy.