Related papers: Contrastive Learning for Efficient Transaction Val…
Cryptocurrency networks such as Bitcoin have emerged as a distributed alternative to traditional centralized financial transaction networks. However, there are major challenges in scaling up the throughput of such networks. Lightning…
Sharding is a way to address scalability problem in blockchain technologies. Ethereum, a prominent blockchain technology, has included sharding in its roadmap to increase its throughput. The plan is also to include multiple execution…
Current Ethereum fraud detection methods rely on context-independent, numerical transaction sequences, failing to capture semantic of account transactions. Furthermore, the pervasive homogeneity in Ethereum transaction records renders it…
Blockchain sharding is a promising approach to solving the dilemma between decentralisation and high performance (transaction throughput) for blockchain. The main challenge of Blockchain sharding systems is how to reach a decision on a…
Sharding is a technique to speed up transaction processing in blockchains, where the $n$ processing nodes in the blockchain are divided into $s$ disjoint groups (shards) that can process transactions in parallel. We study dynamic scheduling…
Distributed ledger technology such as blockchain is considered essential for supporting large numbers of micro-transactions in the Machine Economy, which is envisioned to involve billions of connected heterogeneous and decentralized…
Our work focuses on the design of a scalable permissionless blockchain in the proof-of-stake setting. In particular, we use a distributed hash table as a building block to set up randomized shards, and then leverage the sharded architecture…
Traditional machine learning algorithms use data from databases that are mutable, and therefore the data cannot be fully trusted. Also, the machine learning process is difficult to automate. This paper proposes building a trustable machine…
In this work, we perform a comprehensive study of the machine learning (ML) methods for the purpose of characterising the quantum set of correlations. As our main focus is on assessing the usefulness and effectiveness of the ML approach, we…
Contrastive learning allows us to flexibly define powerful losses by contrasting positive pairs from sets of negative samples. Recently, the principle has also been used to learn cross-modal embeddings for video and text, yet without…
Traditional blockchains cannot achieve the same transaction throughput as Web2, so their use cases are limited. Therefore, state sharding has been proposed to improve transaction throughput by dividing the blockchain network and managing…
Although blockchain, the supporting technology of various cryptocurrencies, has offered a potentially effective framework for numerous decentralized trust management systems, its performance is still sub-optimal in real-world networks. With…
Blockchain Sharding is a blockchain performance enhancement approach. By splitting a blockchain into several parallel-run committees (shards), it helps increase transaction throughput, reduce computational resources required, and increase…
Heterogeneous graph neural networks (HGNNs) have demonstrated their superiority in exploiting auxiliary information for recommendation tasks. However, graphs constructed using meta-paths in HGNNs are usually too dense and contain a large…
This thesis proposes techniques aiming to make blockchain technologies and smart contract platforms practical by improving their scalability, latency, and privacy. This thesis starts by presenting the design and implementation of…
The emerging parallel chain protocols represent a breakthrough to address the scalability of blockchain. By composing multiple parallel chain instances, the whole systems' throughput can approach the network capacity. How to coordinate…
Blockchain-enabled Federated Learning (BFL) enables mobile devices to collaboratively train neural network models required by a Machine Learning Model Owner (MLMO) while keeping data on the mobile devices. Then, the model updates are stored…
Accurate forecasting of Bitcoin (BTC) has always been a challenge because decentralized markets are non-linear, highly volatile, and have temporal irregularities. Existing deep learning models often struggle with interpretability and…
DAG-based consensus has attracted significant interest due to its high throughput in asynchronous network settings. However, existing protocols such as DAG-rider (Keidar et al., PODC 2021) and ``Narwhal and Tusk'' (Danezis et al., Eurosys…
USDT, a stablecoin pegged to dollar, has become a preferred choice for money laundering due to its stability, anonymity, and ease of use. Notably, a new form of money laundering on stablecoins -- we refer to as crowdsourcing laundering --…