Phishing scams represent a significant criminal activity on Ethereum, driving the need for effective detection methods. The methods based on graph neural networks(GNNs) make significant breakthroughs due to their ability to model complex transaction networks. However, existing approaches often overlook the heterogeneity of Ethereum’s transaction graph during neighbor nodes aggregation. These methods typically focus on low-order neighbors, disregarding high-order ones, which limits their overall performance. To this end, we propose the High- and Low-order Transaction Aggregation Graph Network(HLTAG), which separately aggregates high- and low-order features for more effective feature representation. Specifically, we utilize biased random walk to aggregate low-order neighbors. We employ path aggregation to handle high-order neighbors. To mitigate the influence of noise and redundant information from high-order neighbors, we introduce a combination of attention decay, node similarity, and path attention mechanism, which dynamically adjust the aggregation weights. Extensive experiments demonstrate that HLTAG (94.4% Recall and 89.3% AUC) outperforms the state-of-the-art approaches in detecting Ethereum phishing scams, and exhibits significant advantages in large-scale scenarios.

An innovative architecture called execute-order-validate (EOV) has been proposed by Hyperledger Fabric that enables concurrent processing of transactions. However, the architecture suffers from issues such as excessive invalid transactions and serialization limitations in scenarios with high transaction conflicts, which restrict its applicability in real-time and high-performance settings. To address the aforementioned limitations, we propose ParFabric to enhance the EOV architecture. Firstly, we analyze four essential characteristics required for the transaction reordering algorithm within this architecture. We propose a heuristic dynamic reordering algorithm to reduce the number of invalid transactions. This is achieved through real-time identification and early abortion of transactions based on weighted pre-ordering and the construction of a transaction conflict graph. Secondly, leveraging the transaction conflict graph, we introduce a novel optimal block packing strategy based on transaction dependencies. This strategy replaces the total transaction order with partial order, enabling parallel validation and commit at the block level, thereby leading to increased system throughput while reducing transaction latency. Experimental results indicate that, ParFabric demonstrates excellent performance in terms of vertical scaling of peers. Additionally, at the same infrastructure cost, ParFabric provides 2.2x and 1.6x higher throughput than FabricPlusPlus and FabricSharp in high-conflict scenarios.

Reconstructing 2D freehand Ultrasound (US) frames into 3D space without using a tracker has recently seen advances with deep learning. Predicting good frame-to-frame rigid transformations is often accepted as the learning objective, especially when the ground-truth labels from spatial tracking devices are inherently rigid transformations. Motivated by a) the observed nonrigid deformation due to soft tissue motion during scanning, and b) the highly sensitive prediction of rigid transformation, this study investigates the methods and their benefits in predicting nonrigid transformations for reconstructing 3D US. We propose a novel co-optimisation algorithm for simultaneously estimating rigid transformations among US frames, supervised by ground-truth from a tracker, and a nonrigid deformation, optimised by a regularised registration network. We show that these two objectives can be either optimised using meta-learning or combined by weighting. A fast scattered data interpolation is also developed for enabling frequent reconstruction and registration of non-parallel US frames, during training. With a new data set containing over 357,000 frames in 720 scans, acquired from 60 subjects, the experiments demonstrate that, due to an expanded thus easier-to-optimise solution space, the generalisation is improved with the added deformation estimation, with respect to the rigid ground-truth. The global pixel reconstruction error (assessing accumulative prediction) is lowered from 18.48 to 16.51 mm, compared with baseline rigid-transformation-predicting methods. Using manually identified landmarks, the proposed co-optimisation also shows potentials in compensating nonrigid tissue motion at inference, which is not measurable by tracker-provided ground-truth. The code and data used in this paper are made publicly available at https://github.com/QiLi111/NR-Rec-FUS .

Despite existing parallel chain systems improving blockchain throughput by allowing concurrent blocks to be appended, challenges such as the excessive number of waiting blocks before confirmation and the inconsistency between block generation order and global confirmation sequence still persist. To address these challenges, we propose CVchain, a parallel chain system with a cross-voting mechanism. Blocks from other subchains are incorporated into the consistency determination of the main chain, reducing the probability of confirmation errors. Our global sorting mechanism leverages both real-time height information and the implicit temporal order contained in voting to improve the accuracy of block ordering. Furthermore, our voting mechanism randomly splits the mining power of the system, preventing targeted attacks on the specific subchain and defending against liveness attacks. We prove the safety and liveness properties of CVchain. We demonstrated its performance with a prototype implementation and large-scale experiments involving 200 nodes across 10 cloud servers in a distributed network environment. The results indicate that CVchain achieves a latency reduction of approximately 32.3% at a confirmation error probability of 0.01 while maintaining throughput levels comparable to OHIE. Additionally, it provides enhanced transaction ordering services.

Sharding is an important technology that utilizes group parallelism to enhance the scalability and performance of blockchain. However, the existing solutions use a historical transaction-based approach to reallocate shards, which cannot handle temporary overload and incurs additional overhead during the reallocation process. To this end, this paper proposes LMChain, an efficient load-migratable beacon-based sharding blockchain system. The primary goal of LMChain is to eliminate reliance on historical transactions and achieve the high performance. Specifically, we redesign the state maintenance data structure in Beacon Shard to effectively manage all account states at the shard level. Then, we innovatively propose a load-migratable transaction processing protocol built upon the new data structure. To mitigate read-write conflicts during the selection of migration transactions, we adopt a novel graph partitioning scheme. We also adopt a relay-based method to handle cross-shard transactions and resolve inter-shard state read-write conflicts. We implement the LMChain prototype and conducted experiments in a real network environment comprising 17 cloud servers. Experimental results show that, compared with state-of-the-art solutions, LMChain effectively reduces the average transaction wait latency of overloaded transactions by 30% to 48% in different cases within 16 transaction shards, while improving throughput by 3% to 10%.