VLDB 2026 Research / reviewers in the wild / expert
Xiaofei Luo
dblp:160/6200
· DBLP profile ↗
9ranked-venue papers
3as first author
7since 2021 · last 2026
—ORCID · conflict
Domains — the database's venue-derived domains; a paper can count in several
Computer networks · 4 · 1 first-author · 3 since 2021Software engineering, systems software and programming languages · 2 · 2 since 2021Artificial intelligence and machine learning · 1Systems, architecture and hardware · 1 · 1 first-author · 1 since 2021Applied, interdisciplinary, general and emerging computing · 1 · 1 first-author · 1 since 2021
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | Folium: Decoupling Transaction Execution from Consensus via Follower Nodes in a Blockchain
Xiaofei Luo, Huawei Huang, Baozhou Xie |
ICDCS | 1 |
| 2026 | Multifocal Optical-Resolution Photoacoustic Microscopy With a Masked Single-Element TransducerabstractOptical-resolution photoacoustic microscopy (OR-PAM) can visualize cellular-level wavelength-dependent optical absorption with high resolution and sensitivity. However, the imaging speed of OR-PAM has been limited by the laser repetition rate due to the point-by-point scanning of a focused laser beam. To overcome this limitation, we propose multifocal optical-resolution photoacoustic microscopy (MOR-PAM) with a single-element ultrasonic transducer, leveraging the diffractive optical element (DOE) and a custom-designed encoding acoustic mask. The DOE generates 8 focal spots of $3~\mu $ m diameter. The acoustic mask was designed to encode photoacoustic signals from different focal spots. MOR-PAM achieved an 8-fold increase in imaging speed compared to conventional OR-PAM with the same laser repetition rate. We demonstrated the MOR-PAM using a 266 nm laser at 10 KHz, providing solutions for rapid OR-PAM beyond the laser repetition rate in a cost-effective way. The proposed method can be applied to versatile OR-PAM configurations and enable new applications where high-speed imaging is critical. Xiaofei Luo, Rui Cao 0006, Yilin Luo 0001, Yushun Zeng, Yide Zhang, Manxiu Cui, Qifa Zhou, Geng Ku, Lihong V. Wang |
IEEE Trans. Medical Imaging | 1 |
| 2025 | DecoupleChain: A Two-Layer Blockchain Sharding System Enabling Frequent Shard Reconfiguration
Huawei Huang, Miaoyong Xu, Chenlin Wu, Xiaofei Luo, Jianru Lin, Zibin Zheng |
ICWS | 4 |
| 2025 | BrokerChain: A Blockchain Sharding Protocol by Exploiting Broker AccountsabstractState-of-the-art blockchain sharding solutions, such as Monoxide, can cause severely imbalanced distribution of transaction (TX) workloads across all blockchain shards due to the deployment policy of their accounts. Imbalanced TX distributions then producehot shards, in which the cross-shard TXs may experience an unlimited confirmation latency. Thus, how to address the hot-shard issue and how to reduce cross-shard TXs become significant challenges of blockchain sharding. Through reviewing the related studies, we find that a cross-shard TX protocol that can achieve workload balance among all shards and simultaneously reduce the quantity of cross-shard TXs is still absent from the literature. To this end, we propose BrokerChain, which is a cross-shard blockchain protocol dedicated to account-based state sharding. Essentially, BrokerChain exploits fine-grained state partition and account segmentation. We also elaborate on how BrokerChain handles cross-shard TXs through broker accounts. The security issues and other properties of BrokerChain are analyzed rigorously. Finally, we conduct comprehensive evaluations using an open-source blockchain sharding prototype namedBlockEmulator. The evaluation results show that BrokerChain outperforms other baselines in terms of transaction throughput, transaction confirmation latency, the queue size of the transaction pool, and workload balance. Huawei Huang, Zhaokang Yin, Qinde Chen, Xiaofei Luo, Guang Ye, Xiaowen Peng, Zibin Zheng, Song Guo 0001 |
IEEE Trans. Netw. | 5 |
| 2025 | BlockEmulator: An Emulator Enabling to Test Blockchain Sharding ProtocolsabstractNumerous blockchain simulators have been proposed to allow researchers to simulate mainstream blockchains. However, we have not yet found a testbed that enables researchers to develop and evaluate their new consensus algorithms or new protocols for blockchain sharding systems. To fill this gap, we developed BlockEmulator, which is designed as an experimental platform, particularly for emulating blockchain sharding mechanisms. BlockEmulator adopts a lightweight blockchain architecture so developers can only focus on implementing their new protocols or mechanisms. Using layered modules and useful programming interfaces offered by BlockEmulator, researchers can implement a new protocol with minimum effort. Through experiments, we test various functionalities of BlockEmulator in two steps. First, we prove the correctness of the emulation results yielded by BlockEmulator by comparing the theoretical analysis with the observed experiment results. Second, other experimental results demonstrate that BlockEmulator can facilitate measuring a series of metrics, including throughput, transaction confirmation latency, cross-shard transaction ratio, the queuing status of transaction pools, workload distribution across blockchain shards, etc. We have made BlockEmulator open-source in Github. Huawei Huang, Guang Ye, Qinglin Yang, Qinde Chen, Zhaokang Yin, Xiaofei Luo, Jianru Lin, Taotao Li, Zibin Zheng |
IEEE Trans. Serv. Comput. | 6 |
| 2022 | Learning-Based Off-Chain Transaction Scheduling in Prioritized Payment Channel NetworksabstractPayment channel network (PCN) is one of the promising solutions for scalable blockchains since it shows great potential in improving blockchain network throughput. However, the growing number of transactions and the payment-channel sharing of concurrent transactions can lead to channel congestion. Although many studies have proposed different solutions to solve this problem, they ignore a fact that applications may have different transaction rate requirements at different times. In this paper, we propose a priority-aware PCN to meet the requirements of those transactions. Senders in priority-aware PCNs can specify the priority of their transactions by paying a corresponding forwarding fee on each hop along the transaction path. However, capacity competition occurs on the shared hops. Moreover, we propose a multi-agent DQN-based priority assignment algorithm to address the competition issue and design a PCN simulator for performance evaluation. Simulation results show that our solution can guarantee a high throughput of transactions and assign priorities appropriately to balance the transaction rate and forwarding fee cost. The experimental results demonstrate that the priority scheduling scheme can achieve higher transaction throughput and success ratio than other scheduling methods in a congested PCN environment. Xiaofei Luo, Peng Li 0017 |
IEEE J. Sel. Areas Commun. | 1 |
| 2021 | Cooperation of Mobile Devices for Fast Inference of Deep Learning Applications
Qinglin Yang, Xiaofei Luo, Peng Li 0017, Toshiaki Miyazaki, Wenfeng Shen, Weiqin Tong |
Mob. Networks Appl. | 2 |
| 2020 | Privacy-preserving Payment Channel Networks using Trusted Execution EnvironmentabstractPayment channel networks (PCN) have demonstrated its significant advantages in improving the scalability of blockchain. However, the existing work of PCN leads to serious privacy leakage problem that intermediate nodes along a payment path can collude to obtain the payment amounts and payment receivers. To address this problem, we propose to move PCN-related modules into the Trusted Execution Environment (TEE) commonly available on modern CPUs, so that adversaries cannot access the critical payment information protected by TEE, even though they compromise the software (e.g., blockchain clients or operating system) outside of TEE. An additional challenge is that adversaries can still infer payment receivers by observing the pattern of message transmissions among nodes. To hide payment receivers, we further propose to send redundant transactions to pseudo receivers to confuse adversaries. A fast algorithm with provable approximation ratio has been proposed to maximize the level of privacy protection under the constraint of communication overhead. Both experiments on a small-scale testbed and large-scale simulations are conducted to evaluate our proposal. The results show that our proposed solution outperforms existing work significantly. Peng Li 0017, Xiaofei Luo, Toshiaki Miyazaki, Song Guo 0001 |
ICC | 2 |
| 2016 | A Deep Convolutional Neural Network for segmenting and classifying epithelial and stromal regions in histopathological images
Jun Xu 0005, Xiaofei Luo, Guanhao Wang, Hannah Gilmore, Anant Madabhushi |
Neurocomputing | 2 |