arXiv subjects

Hongping Wang

Publications and source records attributed to Hongping Wang.

9 recordsLinked to original sources

arXiv2026

Mitigating Error Propagation in Chain-of-Thought: A Tree-of-Thought Framework for Smart Contract Repair

Smart contracts power blockchain applications such as DeFi and NFTs. However, once deployed, they cannot be modified. Even minor bugs can result in significant financial losses. Current AI-based repair methods rely on linear reasoning, which leads to the accumulation of errors and unreliable patches. Our method combines document parsing, static analysis, and Tree of Thoughts reasoning. We first convert audit reports into structured data. Then we use Slither to locate the exact vulnerable code. Our three-step framework explores multiple repair paths simultaneously, evaluates options, and eliminates poor choices. Finally, we verify patches through compilation and manual checks. We test our method on 50 real vulnerabilities from Code4Rena. Our method achieves a 62% single success rate and an 84% top-3 success rate, outperforming ContractTinker by 12 and 6 percentage points, respectively. We also increase the proportion of fully effective patches to 44%, while reducing defective patches from 38% to 22% and invalid patches from 10% to 4%. This approach overcomes the limitations of linear reasoning and makes smart contract repair more accurate and practical.

cs.CR↗

arXiv2026

From Viral to Void: Multi-Dimensional Behavioral and Contractual Analysis for Rug Pull Identification

As the blockchain and decentralized finance (DeFi) ecosystems continue to expand and mature, rug pull scams involving meme coins are occurring with increasing frequency, posing a threat to the security of investors' assets and the healthy development of the industry. Rug Pull scams are characterized by extremely low deployment costs, covert execution, rapid fund transfers, and high detection difficulty. Traditional manual reviews or fixed rules struggle to meet real-time early warning requirements, and existing detection methods generally suffer from issues such as a single feature dimension, inadequate handling of class imbalance, and weak model generalization and interpretability. To address these shortcomings, this paper focuses on the detection of Ethereum-based rug pull scams. First, we clarify their definitions, types, and harm mechanisms, and construct a multi-dimensional feature system based on dimensions such as malicious smart contract design, on-chain transaction anomalies, liquidity manipulation, and social media disclosures. Next, using the "Second Uncle Coin"(token symbol: BOBU) case as an example, we reconstruct the attack process and derive quantitative detection metrics. Subsequently, a risk detection model based on a Multi-Layer Perceptron (MLP) is designed. We employ a combined strategy of SMOTE oversampling and Focal Loss to address the issue of sample imbalance, dynamically search for optimal thresholds to balance precision and recall, and incorporate gradient pruning and early stopping to enhance training stability. Experiments show that the model achieves an accuracy of 0.927, an F1 score of 0.787, and an AUC-ROC of 0.952 on the test set, outperforming traditional methods. Finally, a visualizable web-based detection system is developed using the Flask framework, enabling batch risk assessment, high-risk ranking display, and result export functions.

cs.CR↗

arXiv2026

Fingerprint-Driven Automation: Coupling Reconnaissance with POC Verification

In the field of network security confrontation, reconnaissance is the first and most critical step. Accurate, efficient, and comprehensive reconnaissance can help network security workers more fully understand the target's current state, identify potential weaknesses, and formulate a targeted attack strategy. However, there are some problems in the existing tools on the market, such as low accuracy of the collected information, lack of concealment, time-consuming, low or high integration of tools, which makes them difficult to start using. With the continuous development of technology, systems need to continuously upgrade their technologies and strategies, and expand new functions. The scalability of tool functions is also one of the factors that need to be taken into consideration. These problems in the practice of network security have a serious impact on user experience and work efficiency. In view of this, a more user-friendly, more automated reconnaissance and vulnerability verification tool is designed. This paper proposes to develop a highly automated reconnaissance and vulnerability verification tool. This paper presents the tool functions and conducts the basic design. Finally, we implement and test all the functions specifically to verify the feasibility of the tool functions and highlight the advantages of this tool in terms of automation, retrieval, and use in network security operations. Through the test of the online security simulation range, the results show that this tool can complete the automatic reconnaissance and in-depth data processing of specific targets, and conduct automatic vulnerability detection and verification.

cs.CR↗

arXiv2025

Distributed force element decomposition with method of fundamental solutions

Quantifying the contribution of vortex structures to pressure stress is useful for designing flow control strategies to mitigate low or drag. The traditional force-element method focuses on the contribution of vortex structures to the resultant force. However, the contribution of vortex structures to the distributed force can not be identified. To address this problem, a distributed force element method is proposed. This method projects the Navier-Stokes equation to a divergence-free space and obtain a Poisson equation for pressure. The Green's function is employed to express pressure stress on the solid boundary as a combination of volume and surface integrals. Contributions to the distributed source are divided into acceleration surface elements, vorticity surface elements, convection volume elements. The method of fundamental solutions and singular value decomposition are used to efficiently solve the Green's functions. The distributed force element method is validated using laminar flows around a stationary circular cylinder and oscillating circular cylinder, and three-dimensional laminar and turbulent flows around a sphere. In laminar flow over a circular cylinder, fluctuation of forces mainly originate from volume sources, while in flow over an oscillating circular cylinder, inertial and volume source terms may cancel out, suppressing lift fluctuation. In flows over a sphere, a strong negative volume source in the boundary layer on the sphere creates low-pressure regions, while pressure in the separated shear layer is close to zero.

physics.flu-dyn↗

arXiv2025

Lagrangian-Eulerian learning of flow field and trajectories with TrajectoryFlowNet

Predicting particle transport in complex flows is traditionally achieved by solving the Navier-Stokes equations. While various numerical and experimental methods exist, they typically require deep physical insights and incur high computational costs. Machine learning offers an alternative by learning predictive patterns directly from data, avoiding explicit physical modeling. However, purely data-driven approaches often lack interpretability, physical consistency, and generalizability in sparse data regimes. To this end, we propose TrajectoryFlowNet, a Lagrangian-Eulerian physics-informed neural network architecture, for fluid flow velocimetry and imaging via learning to predict spatiotemporal flow fields and long-range particle trajectories. The salient features of our model include its ability to handle complex flow patterns with irregular boundaries, predict the full-field flows, image the long-range flow trajectory of any arbitrary particle, and ensure physical consistency in predictions based only on very scarce measurement of flow trajectories. We validate TrajectoryFlowNet via both numerical examples (e.g., lid-driven cavity flow and complex cylinder flow) and experimental test cases (e.g., aortic and ventricle blood flows) across diverse flow scenarios. The results demonstrate our model's effectiveness in capturing intricate particle-laden flow dynamics, enabling long-range tracking of particles and accurate construction of flow fields in real-world applications.

physics.flu-dyn↗

arXiv2020

Effects of space sizes on the dispersion of cough-generated droplets from a walking person

The dispersion of viral droplets plays a key role in the transmission of COVID-19. In this work, we analyze the dispersion of cough-generated droplets in the wake of a walking person for different space sizes. The air flow is simulated by solving the Reynolds-Averaged Navier-Stokes equations, and the droplets are modelled as passive Lagrangian particles. Simulation results show that the cloud of droplets locates around and below the waist height of the manikin after two seconds from coughing, which indicates that kids walking behind an infectious patient are exposed to higher transmission risk than adults. More importantly, two distinct droplet dispersion modes occupying significantly different contamination regions are discovered. A slight change of space size is found being able to trigger the transition of dispersion modes even though the flow patterns are still similar. This shows the importance of accurately simulating the air flow in predicting the dispersion of viral droplets and implies the necessity to set different safe-distancing guidelines for different environments.

physics.flu-dyn↗

arXiv2020

The motion of respiratory droplets produced by coughing

Coronavirus disease 2019 (COVID-19) has become a global pandemic infectious respiratory disease with high mortality and infectiousness. This paper investigates respiratory droplet transmission, which is critical to understanding, modeling and controlling epidemics. In the present work, we implemented flow visualization, particle image velocimetry (PIV) and particle shadow tracking velocimetry (PSTV) to measure the velocity of the airflow and droplets involved in coughing and then constructed a physical model considering the evaporation effect to predict the motion of droplets under different weather conditions. The experimental results indicate that the convection velocity of cough airflow presents the relationship $t^{-0.7}$ with time; hence, the distance from the cougher increases by $t^{0.3}$ in the range of our measurement domain. Substituting these experimental results into the physical model reveals that the small droplets (initial diameter $D \leq$ 100 $\mu$m) evaporate to droplet nuclei and that the large droplets with $D \geq$ 500 $\mu$m and initial velocity $u_0 \geq$ 5 m/s travel more than 2 m. Winter conditions of low temperature and high relative humidity can cause more droplets to settle to the ground, which may be a possible driver of a second pandemic wave in the autumn and winter seasons.

physics.flu-dyn↗

arXiv2019

Post-processing techniques of 4D flow MRI: velocity and wall shear stress

As the original velocity field obtained from four-dimensional (4D) flow magnetic resonance imaging (MRI) contains considerable amount of noises and errors, the available Divergence-free smoothing (DFS) method can be used to process the 4D flow MRI data for reducing noises, eliminating errors, fixing missing data and eventually providing the smoothed flow field. However, the traditional DFS does not have the ability to deal with the flow in the near wall region of vessel, especially for satisfying the no-slip boundary condition. In this study, therefore, an improved DFS method with specific near wall treatment is introduced for processing with 4D flow MRI inner flow with curved wall boundary as the blood flows. On the other hand, due to the coarse resolution of 4D flow MRI, velocity gradients in the near wall region are normally underestimated. As a result, a special wall function is required for accurately computing wall shear stress (WSS).

cs.CE↗

arXiv2019

Particle reconstruction of volumetric particle image velocimetry with strategy of machine learning

Three-dimensional particle reconstruction with limited two-dimensional projections is an under-determined inverse problem that the exact solution is often difficult to be obtained. In general, approximate solutions can be obtained by iterative optimization methods. In the current work, a practical particle reconstruction method based on a convolutional neural network (CNN) with geometry-informed features is proposed. The proposed technique can refine the particle reconstruction from a very coarse initial guess of particle distribution generated by any traditional algebraic reconstruction technique (ART) based methods. Compared with available ART-based algorithms, the novel technique makes significant improvements in terms of reconstruction quality, {robustness to noises}, and at least an order of magnitude faster in the offline stage.

eess.IV↗