相关论文: Detection of Crowdsourcing Cryptocurrency Launderi…

On electronic game platforms, different payment transactions have different levels of risk. Risk is generally higher for digital goods in e-commerce. However, it differs based on product and its popularity, the offer type (packaged game,…

The entry of new technological infrastructures into the financial markets poses serious concerns about the misuse of the economic system for illicit purposes, such as money laundering and financing of terrorism. Although there are cases in…

Crowdsourcing systems often have crowd workers that perform unreliable work on the task they are assigned. In this paper, we propose the use of error-control codes and decoding algorithms to design crowdsourcing systems for reliable…

Anti-money laundering (AML) regulations mandate financial institutions to deploy AML systems based on a set of rules that, when triggered, form the basis of a suspicious alert to be assessed by human analysts. Reviewing these cases is a…

In financial markets, abnormal trading behaviors pose a serious challenge to market surveillance and risk management. What is worse, there is an increasing emergence of abnormal trading events that some experienced traders constitute a…

Detecting fraudulent activities in financial and e-commerce transaction networks is crucial. One effective method for this is Densest Subgraph Discovery (DSD). However, deploying DSD methods in production systems faces substantial…

DGCC protocol has been shown to achieve good performance on multi-core in-memory system. However, distributed transactions complicate the dependency resolution, and therefore, an effective transaction partitioning strategy is essential to…

Ethereum faces growing fraud threats. Current fraud detection methods, whether employing graph neural networks or sequence models, fail to consider the semantic information and similarity patterns within transactions. Moreover, these…

Frauds severely hurt many kinds of Internet businesses. Group-based fraud detection is a popular methodology to catch fraudsters who unavoidably exhibit synchronized behaviors. We combine both graph-based features (e.g. cluster density) and…

The development of clustering heuristics has demonstrated that Bitcoin is not completely anonymous. Currently, existing clustering heuristics only consider confirmed transactions recorded in the Bitcoin blockchain. However, unconfirmed…

With the ever-increasing popularity of blockchain applications, securing blockchain networks plays a critical role in these cyber systems. In this paper, we first study cyberattacks (e.g., flooding of transactions, brute pass) in blockchain…

Multifractality is a concept that helps compactly grasping the most essential features of the financial dynamics. In its fully developed form, this concept applies to essentially all mature financial markets and even to more liquid…

A main characteristic of crowdsourcing software development (CSD) is the complexity of tasks and skills required by workers to achieve successful software crowdsourcing. The tasks proposed to the crowd in CSD are checked to ensure they are…

Crowd-sensing has emerged as a powerful data retrieval model, enabling diverse applications by leveraging active user participation. However, data availability and privacy concerns pose significant challenges. Traditional methods like data…

When working with joint collections of confidential data from multiple sources, e.g., in cloud-based multi-party computation scenarios, the ownership relation between data providers and their inputs itself is confidential information.…

One of the defining features of Bitcoin and the thousands of cryptocurrencies that have been derived from it is a globally visible transaction ledger. While Bitcoin uses pseudonyms as a way to hide the identity of its participants, a long…

Privacy protection mechanisms are a fundamental aspect of security in cryptocurrency systems, particularly in decentralized networks such as Bitcoin. Although Bitcoin addresses are not directly associated with real-world identities, this…

Crowdsourcing platforms such as Amazon Mechanical Turk (MTurk) are important tools for researchers seeking to conduct studies with a broad, global participant base. Despite their popularity and demonstrated utility, we present evidence that…

In recent years, machine-learning methods have become increasingly important for the experiments at the Large Hadron Collider (LHC). They are utilised in everything from trigger systems to reconstruction and data analysis. The recent…

We propose a Multi-Task Learning (MTL) paradigm based deep neural network architecture, called MTCNet (Multi-Task Crowd Network) for crowd density and count estimation. Crowd count estimation is challenging due to the non-uniform scale…