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

A Survey of Imbalanced Learning on Graphs: Problems, Techniques, and Future Directions

Graphs represent interconnected structures prevalent in a myriad of real-world scenarios. Effective graph analytics, such as graph learning methods, enables users to gain profound insights from graph data, underpinning various tasks including node classification and link prediction. However, these methods often suffer from data imbalance, a common issue in graph data where certain segments possess abundant data while others are scarce, thereby leading to biased learning outcomes. This necessitates the emerging field of imbalanced learning on graphs, which aims to correct these data distribution skews for more accurate and representative learning outcomes. In this survey, we embark on a comprehensive review of the literature on imbalanced learning on graphs. We begin by providing a definitive understanding of the concept and related terminologies, establishing a strong foundational understanding for readers. Following this, we propose two comprehensive taxonomies: (1) the problem taxonomy, which describes the forms of imbalance we consider, the associated tasks, and potential solutions; (2) the technique taxonomy, which details key strategies for addressing these imbalances, and aids readers in their method selection process. Finally, we suggest prospective future directions for both problems and techniques within the sphere of imbalanced learning on graphs, fostering further innovation in this critical area.Comment: The collection of awesome literature on imbalanced learning on graphs: https://github.com/Xtra-Computing/Awesome-Literature-ILoG

Improving Run Time in Three-Dimensional Reservoir Hydrodynamics and Water Quality Modeling

Source: ICHE Conference Archive - https://mdi-de.baw.de/icheArchiv

Highly concentrated KTFSI: Glyme electrolytes for K/bilayered-V2O5 batteries

Highly concentrated glyme-based electrolytes are friendly to a series of negative electrodes for potassium-based batteries, including potassium metal. However, their compatibility with positive electrodes has been rarely explored. In this work, the influence of the molar fraction of potassium bis(trifluoromethanesulfonyl)imide dissolved in glyme on the cycling ability of K/bilayered-V2O5 batteries has been investigated. At high salt concentration, the interaction between K+ ions with the glyme is strengthened, leading to a limited number of free glyme molecules. Therefore, the anodic decomposition of the electrolyte solvent, as well as the dissolution of the Al current collectors, is effectively suppressed, resulting in the improved cycling ability of the K/bilayered-V2O5 cells. In these cells, the positive electrode active material exhibits reversible capacities of 93 and 57 mAh g−1 at specific current densities of 50 and 1000 mA g−1, respectively. After 200 charge-discharge cycles at 500 mA g−1, the cell retains 94 % of the initial capacity. The promising rate performance and capacity retention demonstrate the importance of proper electrolyte engineering for the K/bilayered-V2O5 batteries, and the good compatibility of highly concentrated glyme-based electrolytes with positive electrode materials for potassium batteries. © 2020 The Authors. Published by Wiley-VCH Verlag GmbH & Co. KGaA

Highly Concentrated KTFSI : Glyme Electrolytes for K/Bilayered‐V₂O₅ Batteries

Highly concentrated glyme‐based electrolytes are friendly to a series of negative electrodes for potassium‐based batteries, including potassium metal. However, their compatibility with positive electrodes has been rarely explored. In this work, the influence of the molar fraction of potassium bis(trifluoromethanesulfonyl)imide dissolved in glyme on the cycling ability of K/bilayered‐V2O5 batteries has been investigated. At high salt concentration, the interaction between K+ ions with the glyme is strengthened, leading to a limited number of free glyme molecules. Therefore, the anodic decomposition of the electrolyte solvent, as well as the dissolution of the Al current collectors, is effectively suppressed, resulting in the improved cycling ability of the K/bilayered‐V2O5 cells. In these cells, the positive electrode active material exhibits reversible capacities of 93 and 57 mAh g−1 at specific current densities of 50 and 1000 mA g−1, respectively. After 200 charge‐discharge cycles at 500 mA g−1, the cell retains 94 % of the initial capacity. The promising rate performance and capacity retention demonstrate the importance of proper electrolyte engineering for the K/bilayered‐V2O5 batteries, and the good compatibility of highly concentrated glyme‐based electrolytes with positive electrode materials for potassium batteries

CGraph : a correlations-aware approach for efficient concurrent iterative graph processing

With the fast growing of iterative graph analysis applications, the graph processing platform has to efficiently handle massive Concurrent iterative Graph Processing (CGP) jobs. Although it has been extensively studied to optimize the execution of a single job, existing solutions face high ratio of data access cost to computation for the CGP jobs due to significant cache interference and memory wall, which incurs low throughput. In this work, we observed that there are strong spatial and temporal correlations among the data accesses issued by different CGP jobs because these concurrently running jobs usually need to repeatedly traverse the shared graph structure for the iterative processing of each vertex. Based on this observation, this paper proposes a correlations-aware execution model, together with a core-subgraph based scheduling algorithm, to enable these CGP jobs to efficiently share the graph structure data in cache/memory and their accesses by fully exploiting such correlations. It is able to achieve the efficient execution of the CGP jobs by effectively reducing their average ratio of data access cost to computation and therefore delivers a much higher throughput. In order to demonstrate the efficiency of the proposed approaches, a system called CGraph is developed and extensive experiments have been conducted. The experimental results show that CGraph improves the throughput of the CGP jobs by up to 2.31 times in comparison with the existing solutions