Live Graph Lab: Towards Open, Dynamic and Real Transaction Graphs with NFT

Numerous studies have been conducted to investigate the properties of large-scale temporal graphs. Despite the ubiquity of these graphs in real-world scenarios, it's usually impractical for us to obtain the whole real-time graphs due to privacy concerns and technical limitations. In this paper, we introduce the concept of {\it Live Graph Lab} for temporal graphs, which enables open, dynamic and real transaction graphs from blockchains. Among them, Non-fungible tokens (NFTs) have become one of the most prominent parts of blockchain over the past several years. With more than \$40 billion market capitalization, this decentralized ecosystem produces massive, anonymous and real transaction activities, which naturally forms a complicated transaction network. However, there is limited understanding about the characteristics of this emerging NFT ecosystem from a temporal graph analysis perspective. To mitigate this gap, we instantiate a live graph with NFT transaction network and investigate its dynamics to provide new observations and insights. Specifically, through downloading and parsing the NFT transaction activities, we obtain a temporal graph with more than 4.5 million nodes and 124 million edges. Then, a series of measurements are presented to understand the properties of the NFT ecosystem. Through comparisons with social, citation, and web networks, our analyses give intriguing findings and point out potential directions for future exploration. Finally, we also study machine learning models in this live graph to enrich the current datasets and provide new opportunities for the graph community. The source codes and dataset are available at https://livegraphlab.github.io.Comment: Accepted by NeurIPS 2023, Datasets and Benchmarks Trac

BERT4ETH: A Pre-trained Transformer for Ethereum Fraud Detection

As various forms of fraud proliferate on Ethereum, it is imperative to safeguard against these malicious activities to protect susceptible users from being victimized. While current studies solely rely on graph-based fraud detection approaches, it is argued that they may not be well-suited for dealing with highly repetitive, skew-distributed and heterogeneous Ethereum transactions. To address these challenges, we propose BERT4ETH, a universal pre-trained Transformer encoder that serves as an account representation extractor for detecting various fraud behaviors on Ethereum. BERT4ETH features the superior modeling capability of Transformer to capture the dynamic sequential patterns inherent in Ethereum transactions, and addresses the challenges of pre-training a BERT model for Ethereum with three practical and effective strategies, namely repetitiveness reduction, skew alleviation and heterogeneity modeling. Our empirical evaluation demonstrates that BERT4ETH outperforms state-of-the-art methods with significant enhancements in terms of the phishing account detection and de-anonymization tasks. The code for BERT4ETH is available at: https://github.com/git-disl/BERT4ETH.Comment: the Web conference (WWW) 202

AI-powered Fraud Detection in Decentralized Finance: A Project Life Cycle Perspective

In recent years, blockchain technology has introduced decentralized finance (DeFi) as an alternative to traditional financial systems. DeFi aims to create a transparent and efficient financial ecosystem using smart contracts and emerging decentralized applications. However, the growing popularity of DeFi has made it a target for fraudulent activities, resulting in losses of billions of dollars due to various types of frauds. To address these issues, researchers have explored the potential of artificial intelligence (AI) approaches to detect such fraudulent activities. Yet, there is a lack of a systematic survey to organize and summarize those existing works and to identify the future research opportunities. In this survey, we provide a systematic taxonomy of various frauds in the DeFi ecosystem, categorized by the different stages of a DeFi project's life cycle: project development, introduction, growth, maturity, and decline. This taxonomy is based on our finding: many frauds have strong correlations in the stage of the DeFi project. According to the taxonomy, we review existing AI-powered detection methods, including statistical modeling, natural language processing and other machine learning techniques, etc. We find that fraud detection in different stages employs distinct types of methods and observe the commendable performance of tree-based and graph-related models in tackling fraud detection tasks. By analyzing the challenges and trends, we present the findings to provide proactive suggestion and guide future research in DeFi fraud detection. We believe that this survey is able to support researchers, practitioners, and regulators in establishing a secure and trustworthy DeFi ecosystem.Comment: 38 pages, update reference

Recent advances in hydrothermal carbonisation:from tailored carbon materials and biochemicals to applications and bioenergy

Introduced in the literature in 1913 by Bergius, who at the time was studying biomass coalification, hydrothermal carbonisation, as many other technologies based on renewables, was forgotten during the "industrial revolution". It was rediscovered back in 2005, on the one hand, to follow the trend set by Bergius of biomass to coal conversion for decentralised energy generation, and on the other hand as a novel green method to prepare advanced carbon materials and chemicals from biomass in water, at mild temperature, for energy storage and conversion and environmental protection. In this review, we will present an overview on the latest trends in hydrothermal carbonisation including biomass to bioenergy conversion, upgrading of hydrothermal carbons to fuels over heterogeneous catalysts, advanced carbon materials and their applications in batteries, electrocatalysis and heterogeneous catalysis and finally an analysis of the chemicals in the liquid phase as well as a new family of fluorescent nanomaterials formed at the interface between the liquid and solid phases, known as hydrothermal carbon nanodots

Separation microkernel security studies and its formal verification related work

A separation kernel provides temporal and spatial separation among applications or partitions. This type of kernels ensure that there are no unwanted channels for information flows between partitions. XtratuM, an open source separation microkernel, is implemented based on ARINC 653 standard for safety-critical system. In order to guarantee that it is free of bugs and is following security policies, completely formal verification on XtratuM is conducted by Securify team. During reasoning about information flow described in ARINC 653, some covert channels are found by Securify Team. In this paper, a successful demonstration on the existence of covert channel in XtratuM is provided, followed by improvement suggestions on fixing the covert channel. With the objective of modeling and hence formally verifying XtratuM, in-depth analysis of its source code and verification related work are discussed in this report.Bachelor of Engineering (Computer Engineering

Explicitly Modeling Stress Softening and Thermal Recovery for Rubber-like Materials

Rubber-like materials exhibit stress softening when subject to loading–unloading cycles, i.e., the Mullins effect. However, this phenomenon can be recovered after annealing the previously stretched sample under a stress-free state. The aim of this paper is to establish a constitutive model with thermodynamic consistency to account for the stress softening and thermal recovery. Towards this goal, (i) an explicit form of Helmholtz free energy can be found such that the restrictions from thermodynamic law can be satisfied; (ii) a compressible, multi-axial strain-energy function considering energy dissipation is proposed by introducing specific invariants; (iii) a unified shape function based on the symmetry property of the test data in a one-dimensional case with stress softening and thermal recovery is provided by introducing a weight variant; (iv) it is proven that the new potential can automatically reduce to the one-dimensional case, i.e., uniaxial tension, equal biaxial, or plane strain; (v) numerical results for model validation are exactly matched with classical experimental data

Explicitly Modeling Stress Softening and Thermal Recovery for Rubber-like Materials

Rubber-like materials exhibit stress softening when subject to loading–unloading cycles, i.e., the Mullins effect. However, this phenomenon can be recovered after annealing the previously stretched sample under a stress-free state. The aim of this paper is to establish a constitutive model with thermodynamic consistency to account for the stress softening and thermal recovery. Towards this goal, (i) an explicit form of Helmholtz free energy can be found such that the restrictions from thermodynamic law can be satisfied; (ii) a compressible, multi-axial strain-energy function considering energy dissipation is proposed by introducing specific invariants; (iii) a unified shape function based on the symmetry property of the test data in a one-dimensional case with stress softening and thermal recovery is provided by introducing a weight variant; (iv) it is proven that the new potential can automatically reduce to the one-dimensional case, i.e., uniaxial tension, equal biaxial, or plane strain; (v) numerical results for model validation are exactly matched with classical experimental data

Accelerating exact constrained shortest paths on GPUs

Ministry of Education, Singapore under its Academic Research Funding Tier 2The source code, data, and/or other artifacts have been made available at https://github.com/xtra-computing/vine.</p

Revisiting Hash Join on Graphics Processors: A Decade Later

10.1109/ICDEW.2019.00008IEEE 35th International Conference on Data Engineering Workshops (ICDEW)234-24